LPTP_Analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "<a href=\"https://colab.research.google.com/github/jvictorferreira3301/LPTP_Analyzer/blob/main/LPTP_Analyzer.ipynb\" target=\"_parent\">\n",
    "        <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" style=\"height: 27px; margin-right: 10px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Estatística da Sincronização de Tempo em Redes Locais Usando LinuxPTP\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A sincronização precisa de tempo em redes de computadores é fundamental para diversas aplicações, incluindo redes industriais, telecomunicações e data centers. Neste trabalho, utilizamos o [LinuxPTP](https://linuxptp.sourceforge.net/), uma implementação do [Precision Time Protocol (PTP)](https://endruntechnologies.com/pdf/PTP-1588.pdf), para simular e analisar a sincronização de tempo entre diferentes nós de uma rede. O LinuxPTP oferece uma maneira robusta de sincronizar relógios em diferentes dispositivos numa LAN, permitindo atingir precisões na ordem de microssegundos ou até nanosegundos, dependendo das condições da rede e do hardware disponível.\n",
    "\n",
    "A análise deste trabalho é feita utilizando **Estatística Descritiva e Inferencial**, com a aplicação de **testes de hipóteses** para avaliar o comportamento da sincronização em diferentes cenários. Para isso, configuramos nós em uma rede local que atuam como mestres e escravos PTP, e coletamos dados de _**Time offset**_ durante a sincronização.\n",
    "\n",
    "Os principais objetivos deste trabalho incluem:\n",
    "\n",
    "- **$I.$ Testar _Timestamping_ em _Software_ vs. _Hardware_**: Comparar a eficácia da sincronização utilizando **timestamping em software** e **timestamping em hardware**.\n",
    "\n",
    "- **$II.$ Comparar Topologias de Sincronização**: Verificar a eficácia de diferentes configurações, como topologias **mestre-escravo** simples e aquelas que envolvem **_Transparent Clocks_** ou **_Boundary Clocks_**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision Time Protocol (PTP)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <div style=\"margin-right: 20px;\">\n",
    "        <p>O Precision Time Protocol (PTP) é um protocolo de rede utilizado para sincronizar relógios em sistemas de computadores distribuídos. Ele é definido pelo padrão IEEE 1588 e é amplamente utilizado em aplicações que requerem alta precisão na sincronização de tempo, como redes de telecomunicações, sistemas de automação industrial e data centers.</p>\n",
    "        <h4> Mestre e Escravo:</h4>\n",
    "   <p>- O PTP opera em uma arquitetura mestre-escravo, onde um dispositivo mestre (Master) distribui o tempo para um ou mais dispositivos escravos (Slaves).\n",
    "   <p>- O mestre é responsável por enviar mensagens de sincronização para os escravos.</p>\n",
    "        <h4> Mensagens de Sincronização:</h4>\n",
    "   <p>- Sync: O mestre envia uma mensagem de sincronização (Sync) contendo a hora atual.\n",
    "   <p>- Follow_Up: Opcionalmente, o mestre pode enviar uma mensagem de acompanhamento (Follow_Up) com informações adicionais de tempo.\n",
    "   <p>- Delay_Request: O escravo envia uma mensagem de solicitação de atraso (Delay_Request) para o mestre.\n",
    "   <p>- Delay_Response: O mestre responde com uma mensagem de resposta de atraso (Delay_Response) contendo o tempo de recepção da solicitação.\n",
    "    </div>\n",
    "    <figure style=\"text-align: center;\">\n",
    "        <img src=\"./assets/PTP.png\" width=\"auto\" height=\"auto\">\n",
    "        <figcaption>Fig. 1 - Diagrama lúdico do PTP.</figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linux PTP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "O [LinuxPTP](https://linuxptp.sourceforge.net/) é uma implementação do Precision Time Protocol (PTP), conforme definido pelo padrão [**IEEE 1588**](https://endruntechnologies.com/pdf/PTP-1588.pdf). O LinuxPTP é utilizado para sincronizar relógios de uma rede local (LAN) em terminais linux.\n",
    "\n",
    "Componentes Principais:\n",
    "\n",
    "- `ptp4l`: O daemon principal do LinuxPTP, responsável por implementar o protocolo PTP e gerenciar a sincronização de tempo entre os dispositivos.\n",
    "- `phc2sys`: Uma ferramenta auxiliar que sincroniza o relógio de hardware do PTP (PHC) com o relógio do sistema, garantindo que ambos permaneçam alinhados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; justify-content: center;\">\n",
    "    <figure style=\"margin-right: 10px; text-align: center;\">\n",
    "        <img src=\"./assets/log.png\" width=\"auto\" height=\"auto\" style=\"display: flex; margin:0\">\n",
    "        <figcaption>Fig. 2 - Exemplo de log do linux PTP. </figcaption>\n",
    "    </figure>\n",
    "    <figure style=\"text-align: center;\">\n",
    "        <img src=\"./assets/HS.png\" width=\"auto\" height=\"auto\" tyle=\"display: flex; margin:10px\">\n",
    "        <figcaption> Fig. 3 - Diagrama do LinuxPTP.</figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ambiente de testes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Para este estudo, utilizamos 3 computadores cuja as especificações são descritas na tabela abaixo:\n",
    "\n",
    "| Especificação      | PC 1 (jv3301)                | PC 2 (Vostro-3470)               | PC 3 (lasse100)                      |\n",
    "|--------------------|---------------------------------|--------------------------------------------|-----------------------------------------|\n",
    "| OS                 | Ubuntu 22.04.5 LTS        | Ubuntu 22.04.4 LTS                   | Ubuntu 18.04.4 LTS                |\n",
    "| Kernel             | 6.8.0-45-generic                | 6.5.0-35-generic                           | 4.15.0-213-generic                      |\n",
    "| NIC                 | Intel X550-T2 PCI-E X4         | Intel X550-T2 PCI-E X4                     | Intel X550-T2 PCI-E X4      |\n",
    "| CPU                | Intel i5-7600 (4) 4.1GHz     | Intel i5-9400 (6) 4.1GHz                | Intel i7-5930K (12) 3.7GHz          |\n",
    "| GPU                | Intel HD Graphics 630           | Intel HD Graphics 630  | AMD Radeon HD 7770/8760                 |\n",
    "| Memory             | 8GB                 | 16GB                           | 64GB                        |\n",
    "\n",
    "**Tabela 1** - Especificações dos computadores utilizados nos testes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $I$. Software Timestamping Vs. Hardware Timestamping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Para a primeira análise inferencial vamos comparar o sincronismo utilizando **Software Timestamping** Vs. **Hardware Timestamping**. \n",
    "\n",
    "<!-- - **Software timestamping** é o processo de marcar o tempo de eventos diretamente no software. Isso geralmente é feito no nível da aplicação ou do sistema operacional. A precisão do timestamp depende da carga do sistema e da latência do software, o que pode introduzir variações e atrasos.\n",
    "\n",
    "- **Hardware timestamping**, por outro lado, envolve a marcação de tempo diretamente no hardware de rede, como em placas de rede (NICs). Isso permite uma precisão muito maior, pois os timestamps são gerados no momento exato em que os pacotes são enviados ou recebidos, minimizando a latência e a variação introduzida pelo software. -->\n",
    "\n",
    "<!-- Nas imagens abaixo podemos ver onde cada tipo de timestamping atua na camada de pilhas e a topologia usada para essa primeira comparação: -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Característica         | Software Timestamp                  | Hardware Timestamp                  |\n",
    "|------------------------|-------------------------------------|-------------------------------------|\n",
    "| **Precisão**           | Menos preciso                       | Muito preciso                       |\n",
    "| **Latência**           | Alta (introduzida pelo SO e driver) | Baixa (registrada diretamente pelo hardware) |\n",
    "| **Jitter**             | Alto                                | Baixo                               |\n",
    "| **Custo**              | Baixo                               | Alto                                |\n",
    "| **Complexidade**       | Baixa                               | Alta                                |\n",
    "| **Uso Comum**          | Aplicações não críticas             | Aplicações críticas                 |\n",
    "\n",
    "**Tabela 2** - Comparação entre timestamps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Nas imagens abaixo podemos ver onde cada tipo de timestamping atua na camada de pilhas e a topologia usada para essa primeira comparação:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; justify-content: center;\">\n",
    "    <figure style=\"margin-right: 10px; text-align: center;\">\n",
    "        <img src=\"./assets/ptp-hardware-software-based-timestamp.png\" width=\"500\" height=\"480\" style=\"display: flex; margin:0\">\n",
    "        <figcaption>Fig. 4 - Hardware Vs. Software Timestamping. <a href=\"https://networklessons.com/cisco/ccnp-encor-350-401/introduction-to-precision-time-protocol-ptp#:~:text=PTP%20works%20best%20when%20your%20hardware%20supports%20it.%20A%20NIC%20with%20PTP%20support%20has%20its%20own%20onboard%20clock%2C%20which%20is%20used%20to%20timestamp%20messages.%20You%20can%20run%20PTP%20in%20software%20without%20hardware%20support%2C%20but%20your%20OS%20will%20have%20to%20take%20care%20of%20timestamping.%20This%20negatively%20affects%20delay.\">Fonte</a></figcaption>\n",
    "    </figure>\n",
    "    <figure style=\"text-align: center;\">\n",
    "        <img src=\"./assets/topo1.png\" width=\"500\" height=\"480\" tyle=\"display: flex; margin:10px\">\n",
    "        <figcaption>Fig. 5 -Topologia do teste 1.</figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A segunda imagem ilustra uma topologia de rede ponto a ponto, composta por dois computadores: PC 3 (nó \"_Master_\") e PC 1 (nó \"_Slave_\"). Esses dispositivos estão conectados diretamente por um link físico, representado pela linha azul, sem outros dispositivos de rede intermediários. Ambos os PCs possuem placas de interface de rede Intel X550-T2 PCI-E X4, que são usadas para estabelecer a comunicação direta.\n",
    "\n",
    "Para realizar os testes de sincronização de tempo, configuramos o PC 3 como o mestre executando o comando `ptp4l`, enquanto o PC 1 foi definido como escravo também utilizando `ptp4l`. No lado do _slave_, o log da execução foi redirecionado para um arquivo de texto, registrando dados por um período de 30 minutos. Fizemos esses mesmos passos testando ambas as formas de timestamping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo, realizamos o carregamento e processamento dos logs de PTP para extrair informações de time offset e frequency offset, a fim de armazenar esses dados em DataFrames para posterior análise e visualização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from scipy.stats import ttest_ind, shapiro, mannwhitneyu, probplot, levene\n",
    "from src.log_processing import process_log_file\n",
    "from src.plotting import plot_graphs, plot_acf_log, plot_frequency_graphs\n",
    "\n",
    "\n",
    "# Padroes de time e freq\n",
    "offset_pattern = r'ptp4l\\[(\\d+\\.\\d+)\\]: master offset\\s+(-?\\d+)'\n",
    "freq_pattern = r'ptp4l\\[(\\d+\\.\\d+)\\]: master offset\\s+-?\\d+ s2 freq\\s+(-?\\d+)'\n",
    "\n",
    "log_files1 = ['logs/Soft.txt', 'logs/Hard.txt'] # Arquivos de texto contendo o log extraido do ptp4l.\n",
    "\n",
    "# Soft.txt -> log da sincronização usando software timestamping.\n",
    "# Hard.txt -> log da sincronização usando hardware timestamping.\n",
    "\n",
    "dataframes1 = {}\n",
    "\n",
    "for log_file in log_files1:\n",
    "    df = process_log_file(log_file, offset_pattern, freq_pattern)\n",
    "    df = df.drop(columns=['frequency_offset'])\n",
    "    dataframes1[log_file] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dados a leitura e o processamento, agora podemos visualizar os dados de Software e Hardware timestamping "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 - Dados do sincronismo usando software timestamping\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1797.00</td>\n",
       "      <td>1797.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>898.06</td>\n",
       "      <td>-2325.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>518.93</td>\n",
       "      <td>16514.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-87897.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>449.03</td>\n",
       "      <td>-7507.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>898.06</td>\n",
       "      <td>-527.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1347.09</td>\n",
       "      <td>4962.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1796.12</td>\n",
       "      <td>73373.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seconds  time_offset\n",
       "count  1797.00      1797.00\n",
       "mean    898.06     -2325.72\n",
       "std     518.93     16514.02\n",
       "min       0.00    -87897.00\n",
       "25%     449.03     -7507.00\n",
       "50%     898.06      -527.00\n",
       "75%    1347.09      4962.00\n",
       "max    1796.12     73373.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s_log = dataframes1['logs/Soft.txt']# dataframe da sincronização usando software timestamping.\n",
    "df_s_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 - Dados do sincronismo usando hardware timestamping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1822.00</td>\n",
       "      <td>1822.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>910.56</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>526.15</td>\n",
       "      <td>453.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-12749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>455.28</td>\n",
       "      <td>-132.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>910.56</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1365.84</td>\n",
       "      <td>125.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1821.13</td>\n",
       "      <td>11990.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       seconds  time_offset\n",
       "count  1822.00      1822.00\n",
       "mean    910.56         0.78\n",
       "std     526.15       453.69\n",
       "min       0.00    -12749.00\n",
       "25%     455.28      -132.00\n",
       "50%     910.56         0.00\n",
       "75%    1365.84       125.75\n",
       "max    1821.13     11990.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_h_log = dataframes1['logs/Hard.txt']# dataframe da sincronização usando hardware timestamping.\n",
    "df_h_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 - Filtragem e Tratamento de Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- **Remoção de Outliers**: Durante a sincronização, os primeiros momentos geralmente são instáveis, e as medidas podem variar muito, gerando **outliers** (valores que fogem muito do padrão). Esses valores iniciais podem atrapalhar a análise dos dados porque são menos precisos e não refletem o comportamento normal.\n",
    "\n",
    "    Para lidar com isso, usamos um método chamado **Intervalo Interquartil (IQR)**. Esse método ajuda a identificar e remover esses valores que estão muito fora do padrão, focando apenas nos dados mais representativos. Com isso, garantimos que nossa análise seja mais precisa e que os dados sejam uma boa representação do que realmente aconteceu durante a sincronização.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Subamostragem**: Em séries temporais, como os dados de **offset** coletados no experimento de sincronização de tempo com **LinuxPTP**, é comum que um valor influencie o próximo. Isso se chama **autocorrelação**, e significa que os valores próximos estão conectados, ou seja, o valor atual depende do valor anterior. No entanto, muitos testes estatísticos assumem que cada valor é independente dos outros.\n",
    "\n",
    "    Quando existe autocorrelação, esses testes podem não funcionar corretamente, porque a variância (a medida de quanto os valores variam) pode ser subestimada. Isso aumenta a chance de erros nos resultados e pode fazer com que os testes não sejam válidos.\n",
    "\n",
    "    Para resolver isso, usamos a **subamostragem**, que é basicamente escolher apenas algumas partes dos dados originais. Em nosso caso, pegamos uma observação a cada duas. Isso diminui a conexão entre os valores e faz com que os dados se comportem de forma mais independente, como os testes precisam.\n",
    "\n",
    "    Dessa forma, a subamostragem nos ajuda a garantir que os testes estatísticos que usamos sejam mais confiáveis e que os resultados que obtemos sejam mais precisos.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo podemos ver a significância da autocorrelação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_acf_log(df_h_log, 'time_offset', 'ACF - Hard Log', lags=50) \n",
    "#plot_acf_log(df_s_log, 'time_offset', 'ACF - Soft Log', lags=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remover outliers com IQR\n",
    "def remove_outliers(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    # Filtra o DataFrame diretamente utilizando o método between\n",
    "    return df[df[column].between(Q1 - 1.5 * IQR, Q3 + 1.5 * IQR)]\n",
    "\n",
    "# Selecionar os DataFrames\n",
    "df_h_log = dataframes1['logs/Hard.txt']\n",
    "df_s_log = dataframes1['logs/Soft.txt']\n",
    "\n",
    "# Realizar a subamostragem \n",
    "df_h_log = df_h_log.iloc[::2]\n",
    "df_s_log = df_s_log.iloc[::2]\n",
    "\n",
    "# Aplicar a remoção de outliers\n",
    "df_h_log = remove_outliers(df_h_log, 'time_offset')\n",
    "df_s_log = remove_outliers(df_s_log, 'time_offset')\n",
    "\n",
    "# Atualizar os DataFrames no dicionário\n",
    "dataframes1['logs/Hard.txt'] = df_h_log\n",
    "dataframes1['logs/Soft.txt'] = df_s_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_h_log = df_h_log[df_h_log['seconds'] <= 1796] #bota os dfs no range do menor\n",
    "\n",
    "df_s_log = df_s_log[df_s_log['seconds'] <= 1796] #bota os dfs no range do menor\n",
    "\n",
    "dataframes1['logs/Hard.txt'] = df_h_log\n",
    "dataframes1['logs/Soft.txt'] = df_s_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo obtemos os datasets ja filtrados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>792.00</td>\n",
       "      <td>792.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>870.48</td>\n",
       "      <td>-43.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>501.05</td>\n",
       "      <td>9596.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.00</td>\n",
       "      <td>-24677.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>437.53</td>\n",
       "      <td>-4597.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>863.06</td>\n",
       "      <td>529.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1304.59</td>\n",
       "      <td>4813.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1794.12</td>\n",
       "      <td>23706.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seconds  time_offset\n",
       "count   792.00       792.00\n",
       "mean    870.48       -43.18\n",
       "std     501.05      9596.10\n",
       "min      18.00    -24677.00\n",
       "25%     437.53     -4597.75\n",
       "50%     863.06       529.50\n",
       "75%    1304.59      4813.75\n",
       "max    1794.12     23706.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Dados do sincronismo usando software timestamping filtrados e subamostrados\n",
    "df_s_log = dataframes1['logs/Soft.txt']\n",
    "df_s_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.00</td>\n",
       "      <td>891.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>902.75</td>\n",
       "      <td>-5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>516.68</td>\n",
       "      <td>167.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.00</td>\n",
       "      <td>-422.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>459.03</td>\n",
       "      <td>-134.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>904.06</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1349.09</td>\n",
       "      <td>118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1794.12</td>\n",
       "      <td>475.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seconds  time_offset\n",
       "count   891.00       891.00\n",
       "mean    902.75        -5.35\n",
       "std     516.68       167.61\n",
       "min       4.00      -422.00\n",
       "25%     459.03      -134.50\n",
       "50%     904.06         0.00\n",
       "75%    1349.09       118.00\n",
       "max    1794.12       475.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Dados do sincronismo usando hardware timestamping filtrados e subamostrados\n",
    "df_h_log = dataframes1['logs/Hard.txt']\n",
    "df_h_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 - Comparação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com os dados já filtrados podemos comparar as duas sincronizações por meio de gráficos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fu/VreP7C5/HyL1/G7sfvjvq96n3+BqRQGIdxaERjvptBCMkhDDonhBAHkvH0rEBt69ry2JL8I5ZkrJy1UvfepmWbtL+dyvciUd5+CCGkUBCdUip1u9QB0Ieduw06N3LQTw7CuOPHoa+rD0997ynmEhJCSAnBUQEhhDiQTKRFqVLPuhAHCh/P+lj7e/Urq7F63mrpcjKUaGnvR0IIKSSMTikAmlNWzJVSS7fdlu+pKIqCUx44BVWDq7DhrQ147YbXsmswIYSQgoGiFCGEOCCKUqVediaKTWtfW4u+rj4kk0nM++08AEBdQ+rJuVOmFJ1ShBBSOMicUjJRSivf8+iUAoBBOw/CCXeeAAB49Q+vYuPSjRm2lhBCSCHBUQEhhDhAp1QasZSxr6sPaxesxWdzPsPaBWsRrYziqN8fBSAtXq17Yx0e/9bjaFnTolsPnVKEEFI4yJxSsvK9TJ1SKhPOnoDxZ4xHoi+Bp773lG5WV0IIIcUJRSlCCHGAolQaY1neZ3M/01xSky6cpA1S1KflDx7+IJY/uRxPf+9p3ee8hOASQgjJL56dUhle4xVFwYl3n4iakTXY+uFWLLhpQWYNJoQQUjBwVEAIIQ5QlEpjFKXeuPkNbHhrA8oHlOOIXx6hPR03LhdbGdP9m+V7hBBSOHjNlMqkfE+lZngNjvpdynW7bsG6jNdDCCGkMChzXoQQQkobnSilUJQCUtlRrWvTJRsHX3owakbUYNun2wCYM6V0uVyguEcIIYWE1Cm1y2AAQPuWdvR29qK8uly7R2RavqdSPbQagPneQYqfJjShAhWBbiOGmPNChJCcQVGKEEIcoFMqjTrgGDRmEJLJJNrWtQEADrvyMADpp+NGp5RxYMHyPUIIKRxUp5QoSlUNqULFwAr0bO9B65pW1O9Vn1XQuYiaO5iI28/kSoqPWZiVk+1UV1Wjvr4+J9sihNhDUYoQQhzg7HtpVLEpUhbB1+/4OpbctwTH33689lRbFZvUgYmG4WE3g84JIaRwUJ1SYvmeoigY3DgYW97fgpY1Lajfqz7roHNt3f0PgMTJNUhpMBmTMRzDXS/fjGbMwzzMmDED48ePd/25+vp6NDQ0ZNJEQojPUJQihBAHxCe1LN9L7QslqmD86eMx/nR9B1AdiJjK95JJJJNyx9n2jdux7O/LMPHHE1EzoiaophNCCMkQmVMKSJXwbXl/i5YrlW3QuYqaO8jyvdJjHMahEY2ul9+ADZiHeRg/fjwmTpwYXMMIIYFBUYoQQhygUyqN6JSSoTmlJOV74hNv8fOPfuNRbFq2CZ889wl++PoP/W4yIYSQLJE5pQCgrjE146oqSvkRdA6kH1ywfI8QQoqfggn1uOGGGzBp0iQMHDgQI0aMwGmnnYaVK1fqlkkmk7jmmmswevRoVFdX4+ijj8aHH36oW6a7uxsXX3wx6uvrUVNTg1NOOQXr16/XLdPc3Ixp06ahrq4OdXV1mDZtGlpaWnTLrF27FieffDJqampQX1+PSy65BD09PYF8d0JIftGJUiX+0NZRlCqXl+8lE0mde0qcfW/Tsk0AgHVvcJYlQggJI3ZOKQBoXZOa+MKvoHO1xJvle9ZwbEQIKRYKRpSaP38+fvazn2HRokWYO3cu+vr6MGXKFLS3t2vL3HzzzfjTn/6EO++8E2+99RZGjRqFr33ta9i+fbu2zGWXXYannnoK//73v7FgwQLs2LEDJ510EuLx9GDp7LPPxrJlyzB79mzMnj0by5Ytw7Rp07T34/E4TjzxRLS3t2PBggX497//jSeffBJXXHFFbnYGISSnsHwgTTZOqXhP+jrLTClCCCkcVFHK6JQa3DgYAMzle1k6pVi+5wzHRoSQYqFgyvdmz56t+/ff//53jBgxAkuWLMGRRx6JZDKJO+64A7/+9a9x+umnAwD+8Y9/YOTIkXj00UdxwQUXoLW1FQ888AD++c9/4rjjjgMAzJgxA2PHjsVLL72E448/HsuXL8fs2bOxaNEiHHzwwQCA+++/H4ceeihWrlyJPffcE3PmzMFHH32EdevWYfTo0QCA2267Dd///vdx3XXXYdCgQTncM4SQoBE7xWIuUiniJEpZZUohqXdPiU4pQggh4UYt3zM5pQyilN9B5yzfs4ZjI0JIsVCwo4LW1pRNeOjQoQCAVatWYdOmTZgyZYq2TGVlJY466ii88cYbAIAlS5agt7dXt8zo0aOxzz77aMssXLgQdXV12kUXAA455BDU1dXpltlnn320iy4AHH/88eju7saSJUuk7e3u7kZbW5vu/4SQwoBPatOoAwQnp1QyrndGGcv3Sl3cI4SQQsLKKVW3SypTasfGHejr7vMt6Fwr3+P91zUcGxFCCpWCFKWSySQuv/xyHHHEEdhnn30AAJs2pTJJRo4cqVt25MiR2nubNm1CRUUFhgwZYrvMiBEjTNscMWKEbhnjdoYMGYKKigptGSM33HCDVoddV1eHsWPHev3ahJA8ocu0KPH+sdtMKQDobuvW/jaW73GgQQghhYOVU2pA/QCUD0gJVa1rW30POmemlDs4NiKEFDIFU74nctFFF+G9997DggULTO8Zp2tPJpOOU7gbl5Etn8kyIldffTUuv/xy7d9tbW28+BJSIFBASeM2UwrQi1KJvgRFKUIIKVCsnFKKomBw42Bs/WgrWte0ak6pbMv31BJvlu+5o5jGRk1oQgUqbNsnEkMMALB8+XLttfr6ejQ0NLheByEkvxScKHXxxRfjv//9L1599VWMGTNGe33UqFEAUkr9TjvtpL2+ZcsWTbkfNWoUenp60NzcrHsisGXLFhx22GHaMps3bzZtd+vWrbr1LF68WPd+c3Mzent7TU8JVCorK1FZWZnJVyaE5BlmSqVxmykFmJ1SPTvSs/A0rWzCE2c+gdNnnB5QSwkhhPiFlVMKSJXwbf1oK1pWt6TvEX45pfgAw5FiGxvNwixX31tEgYKpU6dq/66uqsaKlSsoTBFSIBRM+V4ymcRFF12EmTNn4n//+x923XVX3fu77rorRo0ahblz52qv9fT0YP78+dpF9cADD0R5eblumY0bN+KDDz7Qljn00EPR2tqKN998U1tm8eLFaG1t1S3zwQcfYOPGjdoyc+bMQWVlJQ488ED/vzwhJK+wU5wm0/I9AOhq7tL9+6MnPsK7/3zX5xYSQgjxGyunFKAPO/ct6DzK8j0ninVsNBmTcbrH/30X38X5/f87Haejs6sTsVjM03YJIfmjYJxSP/vZz/Doo4/imWeewcCBA7X65Lq6OlRXV0NRFFx22WW4/vrrsccee2CPPfbA9ddfjwEDBuDss8/Wlv3Rj36EK664AsOGDcPQoUNx5ZVXYsKECdqME+PHj8fXv/51nHfeebj33nsBAOeffz5OOukk7LnnngCAKVOm4Mtf/jKmTZuGW265Bdu2bcOVV16J8847j7NLEFKEUJRKo4pS6oDBiPp0GzCLUp3Nnablu1q6AAUln9VFCCFhxs4ppYpSzZ83a9fybIPO1fI93n+tKdax0TiMQyMafdpLhJBCoGBEqbvvvhsAcPTRR+te//vf/47vf//7AICrrroKnZ2duPDCC9Hc3IyDDz4Yc+bMwcCBA7Xlb7/9dpSVleHMM89EZ2cnjj32WDz00EOIRtNPdB555BFccskl2kwUp5xyCu68807t/Wg0iueeew4XXnghDj/8cFRXV+Pss8/GrbfeGtC3J4TkE12nuMT7x05OKUVRECmPINGbQFer3hnV1dIl/UykLKLlkBBCCAkfdk4pdQa+bZ9u017zq3yPmVLWcGxECCkWCkaUcpPjoigKrrnmGlxzzTWWy1RVVWH69OmYPn265TJDhw7FjBkzbLfV0NCAZ5991rFNhJDCh53iNE6ilPpeojfhWL5nXJ4QQkj4SCaTrpxS2z5Ji1Is3wsejo0IIcVCwWRKEUJIvmDQeRo3opQ6GHFTvue0LkIIIfkl3hPXXMJlVdailOiGZdA5IYQQt3AkQAghDrBTnMaNKKU+Se+Idehe79xmIUpFeSsihJCwopbuAfLyvZoRNSaxyq9MKTqVCSGk+OFIgBBCHGCmVBo3olT1kGoAwI4NO3SvdzbRKUUIIYWGWroHBYhWmsvyFEXRcqWAVOmdosgnw3CLVr7Hh0KEEFL0cCRACCEOsFOcRs33sBOSqoZUAQDavmjTvd6xtUO2uOVMfoQQQvKP6pQqqyqzFJvUEj4g+zwpQCjfY6YUIYQUPQUTdE4IIfmCmVJpvDiltm/Yrnu9ZU2LdHk6pQghJLyoTilZ6Z6K6JTKNk8KYPleKdOEJlSgIuPPxxADACxfvtyX9tTX16OhocGXdRFC5FCUIoQQB/ikNo0bUapqcMopZRKlVrVIl6coRQgh4UVzSklm3lMJzClFp3LJMQuzsl6HAgVTp071oTVAdVU1VqxcQWGKkAChKEUIIQ6wU5xGE6VswsnV8r3e9l5X66QoRQgh4aWvO12+Z4UoSvnhlNIypfhQqOSYjMkYjuFZraMKVahFbdZtiSGGmV0zEYvFKEoREiAUpUhJ8votr6OipgKTLpyU76aQAoBB52lcOaX6RSmV8gHl6O2wFqg4+x4hhISD3s5e/O0rf8PYw8fipHtOAuDuuj94l8Ha3344pdT7Ah8KlR7jMA6NaMx3MwghOYQjAVJydDR14KWrXsLzP3se3du7890cUgCwU5zGS6aUSu0o+6eVdEoRQkg4WPnMSmz5YAuW3LtEe02b4MLmAYLOKeXDNV0t32OmFCGEFD8cCZCSQx1UA0Dr2tY8toQUCgw6T+MlU0qlZmSN5bKKolCUIoSQkCC7x6nCkN1MqbWjahGtSDmkWL5HCCHECxwJkJJD7OC0rG7JX0NIwUCnVJpMyvfolCKEkMJA5oZy45RSIgrqGlIz8PkZdA7wYRAhhBQ7HAmQkkO0glOUIm7QlQ+UeN/Y7/K95TOXI7Yi5k/jCCGEZIXohlLFIPXBjCgUyVBL+PxwSokCGN1ShBBS3FCUIiWHzillMUU9ISJ0SqXxu3xv3evrbEPQCSHBE++JY/H0xRSIie7arvaX3JTvAUDdLj46pURxjPdgQggpaihKkZKDTiniFWZKpcm2fI+leoSEj4V/WojZl8zGXePvyndTSJ4RHUrxnjgAd+V7gOCU8jHoHGDYOSGEFDtl+W4AIbmGTiniFT6lTaMNTryU741Mi1K7HrsrPnvxs2AaRwjJiHWvr8t3E0hIEB1Kfd19KB9Q7topNWS3IQCA8gHlWbeD5XulSxOaUIGKfDcDABAD3aOE5AKKUqTkEJ+4dbV05bElpFCgKJVGdUrZDU4qBlZAiSjafhPL98ZNGUdRihBCQoroUPLqlNrz1D0x8byJ2PvMvX1tB+/BpcUszMp3E3RUV1Wjvr4+380gpKihKEVKDvGJW28ns2yIM7oOcYn3jd2U7ymKgqrBVejc1gkAqKqrwm7H7Ya2L9qw79R9MeeKOTlpKyHEJfYGGFJCiPe7eE8cc6+aizdueQOAs1OqoqYCJ993si/tELfF8r3SYjImYziG53y7zWjGPMzDjBkzMH78eO31+vp6NDQ05Lw9hJQSFKVIySF2bvo6+/LYElIosHQgjRtRCkjlSqmiVFl1GabNnYZkIqk9ebeio6kD0fIoKgdV+tNgQgghrhHvd32dfZogBTjPvucndEqVLuMwDo1ozPl2N2AD5mEexo8fj4kTJ+Z8+4SUMkycJSUHnVLEKww6T+NWlBJzpdR8ESWiOA5qbqm/BTfW3ZhlKwkhhGSC+ODuzj3v1L3nVL7nJzpRig+GCCGkqKEoRUoOscMV744H9gSOT/aKB/6Wabw4pVTKq9Oht26ftJe6+EcIIfnATgByKt/zE0VJP8Rg+R4hhBQ3FKVIyaEOqlX6uvwv4Wte1Yxbht+Ceb+f5/u6Se5hplQa16LU4LQoVVadrhR3O6ihEEiI/yT6EoHc80jxYOwjieTSKQWkH2LwfkAIIcUNRSlSchifAgZRwjfvN/PQua0Tr/7hVd/XTXIPO8RpvDqlIuURRMuj2uuKQlGKkHzw4RMf4tZRt+KOxjvQ26G/77k9L0nxY+dKyqVTStwey/cIIaS4oShFSg5jh4th58QJZkql8ZopJZbueYGiFCH+svjPi9HZ1In2ze1o+6It380hIcVOAKJTihBCSBBQlCIlRy6cUpxeu7hgnkUar04pNeTcKxyEEOIv4syXJuGB9yzST5icUqoIxnswIYQUN2XOixBSXBg7N8YyBkKMUCBJ4zVTSsyT8gL3OSHZk0wmtdI8MSvINMjn6Ub6sQ06dzlRhV+wfK80aUITKlCR8+3GEMv5NgkhKShKkZLD2LkJonyP+RzFBYPO06iDWacyDq18j04pQvLCf3/8X6yZvwY/efcnKB9QrhOlOMgnVti5kli+R3LBLMzK27arq6pRX1+ft+0TUqpQlCIlh8kpxfI94gA7xGncOqWG7z0cADDsS8My2g73OSHZ8c4D7wBIBZzvf+7+9k4p3rNIP7ZOKZbvkRwwGZMxHMMD3UYzmjEP8zBjxgyMHz9ee72+vh4NDQ2BbpsQYoaiFCk5cuGUIsUFg87TuBWlRuw9Ahd/ejEGjh6Y0XYoShHiE/2nEp1SxA10SpF8Mw7j0IjGQLexARswD/Mwfvx4TJw4MdBtEUKcoShFSo5cOKVYvldcsEOcxq0oBQBDxw3NeDvc54T4iyhEGe+DvGcRlTA5pZgpRQghpQFn3yMlB51SxCvMlErjRZTKBopShPgLnVLEDZx9jxBCSK6hKEVKDrFjDjBTijjDAVyKZDKp7QuKUoQUFraZUoT0Y+wjibB8jxBCSBBQlCIlh7Ezztn3iBPMlEoh7oegpwbnIIQQf9GJUjbCA8+90sa2fC/g675peyzfI4SQkoCiFCk5jJ2bQJxSpKjgIK0fcTcEPDbhPifEX6zK9579ybNY8fSK9HJ0UZU0YSrfo1OKEEJKA4pSpOTIhVOK5Xu5p6ulCy1rWgJZNzvEKUSXWNBuQO5zQvxBPW9l5XvJRBJL7l2iW97ORUWKHztXUq7L95gpRQghpQFn3yMlw/rF6zHnijkYvvdw3et0ShUHNw25CQBw+ReXY+Dogb6um0Hn/dApRUjBInNKxXvjtsuR0iOUTimW75UUTWhCBSoC3UYMMQDA8uXLHZetr69HQ0NDoO0hpNShKEVKhgcPexDJRBLrXl+ne51OqeLii7e+wF6n7uXrOimQmKFTipDCQhQb1L/jPWZRigJAaRMmp5SWKcX7QUkxC7Nysh0FCqZOneq4XHVVNVasXEFhipAAoShFSgarTk0QTikGnecP477/4LEPMGjMIDQcnnlnQhzMlXTQeQ6/OwchhPiL1CklEaXolCptwuSUYvleaTIZkzEcw50XzJIqVKEWtbbLxBDDzK6ZiMViFKUICRCKUqTkCcQpRfKGODtQ2/o2PPmdJ1HXUIfL1lyW8TopkPTD8j1CCpJkMqlzwKiD/ESvebBPUaq0CdXseyzfK0nGYRwa0ZjvZhBCcgiDzknJE+82PynOGhql8oew7zubOwEAXa1dWa2SmVIpGHROSGFiHNTTKUWssHMlsXyPEEJIEFCUIiVPKZdjFSOiWKIOuLLt0LJD3E9InVKfvvgpXr/5dZ7LhFhgFJq0TCkGnRMDtk6pPAWds3yPEEKKG5bvkZKHgkORIfSZ1dIUP0UpCh8pwuSUeuTrjwAARu43Ersfv3tQTSKkYDEKTbZOKQoAJU2YnFLq9thPI4SQ4oZOqZAT74mjfUt7vptR1ATR2WHQef4QMy+0AVeWPzE7xCnCHnTeuqY1gJYQUuAkzUIDM6WIFXa/f86dUlFmShFCSClAUSrk/HWfv+LWkbeieVVzvptSfKh9K/Z1igpd+V6vT+V77BCnCGn5HiGlRrwnjoeOegjP/ew5V8ubyvf6/81MKWJEvd/VjKgxvZfzTCmW7xFCSElAUSrkbPtkGwDg41kf57klxUe0PAogoMEvjVI5RefgEfZ9IJlSJayVMOickHDw8XMfY82ra/D2X992XDbeE8cH//pA9xqDzokVqgB08KUHm97LtVOK5XuEEFIaMFOqQMh1R6AUiJRHEO+Js3yvCLD6DYPIlCpp6JQiJBRs/2K79vfGpRsxct+RiJTJnzO++sdXdcsDDDon1qiCpazfKZbH5wJ1e3QrlxZNaEIFKvLdDABADLF8N4GQkoCiVIFg1dkkmROtiKK3vZeD3yJA/A2l5XtZZiEx6DwFnVKE5IcXLnkBNSNqcORvjgQAXdbkfQfeh/1/sD9OffBU7TXxXDUKUgCdUsQa9diQ9TtzXr6nZkrxflBSzMKsfDdBhwIF3d3d+W4GIUUNRakCIdcdgVKA5XvFg/gUVRZ0TqdUANApRUhOaF7VjDenvwkAOPwXhyNaHjVNgLLs78t0opRTmbFd0DldKaWNemzI+p35Kt9jplRpMRmTMRzD890MAEAzmjEP81BZWZnvphBS1FCUKhBYvuc/6lPAUna+FAs6AUM4VbQBl5+z75Xy4ZLD705RipAUomOls6kTtaNq0b7ZflZep/sanVLECrvyvXwFnVMoLS3GYRwa0ZjvZgAANmAD5mFevptBSNFD+02BQKeU/0TKgwvQZKZUbrEs3xMGXNmIj3xKm4Lle4TkHvFc64h1AIDJKWXE6fxhphSxIkxOKZbvEUJIaUClo0CgU8obreta8exPn8XW5Vstl2H5XvFg5ZQSB1zZ/M7MlOqHQeeE5BzxXGjfmhKjnEQpJ1cjnVLEijA6pfhgiBBCihuKUgUCnVLeePz0x7HkniX428F/s1wmSKcUyS1ih9XSKZXN78z+MAA6pQjJB+K50LHV2im1ePpi7Rx1Es81pxRFKWLA1imV49n31DbwfkAIIcUNlY4QoytJolPKExve3gAA6NneY7mMlinF8r2CR3euCJ1mMcTXL6cU6SfgQ/zdh97Fjs07gt0IIQWAKDC1b21HMplEd5t5JqjZl8zGiqdWpD7jcM1S3TCyoHO6UkobVZSU9TvzVr7HTClCCClqKEqFGPFpJZ1SHnHRb1LL9wIJb6YmlVMsy/dEF0AWvzODzvvJ4Xdf9tAyPHj4g54+U9KllaRo0TmlYh3o7ei1XHbrR/0l6y5n36NTihhRBSBZv5Ple4QQQoKASkeI0YlSZfypvODGqeRUvicLgCXhxEo0CiJTqpTJZfkeADR/1hz4NggJO8byPZlLSkXtNzgGnfcx6JzIUQWgMDilWL5HCCGlAZWOECN2DFm+5xEPTilZZ+eZHz2DW0fciu0btme2eZbv5RRdELnwt1+ZUuJT2pJ24+Qw6Fyl6ZMmfPHWF7nZGCFhRDjvOrZ2oLvVWpRSr3NO1ykGnRMrNKeU5GFovpxSLN8jhJDipizfDSDW6ESpHIdLFjqKoiDpUL9g55Ra9uAyAMDb976N3vZejD9jPMYeOtb3dhJ/EDus4mBMlymVhZjEp7Qpcu2UAoA7v3QnAODyLy7HwNEDc7JNQsKEcfa9rtYuy2U1Ad1l+Z40U4qiVEkjBp1fsOwCPHrio9j+ReoBXd4ypXgPLima0IQKVOS7GQCAGGIAgOXLl2uv1dfXo6GhIV9NIqQooSgVYihKZYEXp5RBrBBdMUvvX4odG3dg4W0L8fvk733dPvGPnJbvsW+cNWc9fRZe+f0r2PzuZtefaVnTIhWlStq5RkoC8frTua3T1inltnzPzilFV0ppo/7+SlTBqP1G4ZjrjsEz338GADOlSG6YhVn5boIOBQqmTp2q/bu6qhorVq6gMEWIj1CUCjG6p5XsI3oim0wpcartjliHvw0jgaAr3xNECr/K9/iUth+fdsNep+6FvU7dC/+c8k98PvdzV58pH1AeaJsICSvGa5qdU8pt+R6DzokVolNK/C+Q+weknH2vNJmMyRiO4fluhkYVqlCLWgAp59TMrpmIxWIUpQjxEYpSIUbsGNIN4D9WmVKqTR1IdcZk5Q0kXFg5mXS/nU+z75Xyuah9d5/GJSfffzL+3PhnV8tGK6LyNtHFRooc8RhP9CbsM6XUwbvDuaA5pRh0TgyITilAny3FoHOSC8ZhHBrRmO9mEEJyCIPOQ4yus8j7sTdc9JusnFJtX7Rpf2cqQIhOrVIWMXKFVRA5nVI+o2pSeQjyt3pSzvOLFD2GkmTbTCm3s+/RKUUsMDqlRCGK5XuEEEKCgKJUiKFTKnPcDJrdOKV8EQP50wWOG6dUVqIUSwcA+O+U8iJuWQ2UKRiSYsc4o6ifs+8x6JwYMTmlovlzSjHonBBCSgOKUiGGmVLBolnSDfvWD6eUOGhnZyp4cpopxZ8zL04pqyflPL9IsWMq32uzCTqP++CUiifQtr5Ndy8kpYPa99QypYTyvXw5pfhgiBBCihuKUiGGTqksyKJ8b8eGHel/ZKpJieV7HDQHjqvZ97I4h/gb9qPuBr80KQ/rsSzf429Dihyd0N5r75TS+g0uM6VkTqnejl7ce8C9uPeAe+maKkFUwVJ1KYnuqHxlSrF8jxBCihsGnYcYOqUyJ5vZ93o7e7W//RADKSgGTy6dUqX8e+bzu9MpRUoVY/meH7PvaUHnEqfU9i+2azPP9rT3oKquynObSeGiHhuqQyoUs+/xOk8IIUUNnVIhhk6pLHDRb7LKlPJFDGT5Xk7RuWiCyJTib5jC56BzL+tRf2Pjb0HBkBQ7mcy+51i+12ddvrdjY9ot3NfV56mtpPAxBp2zfI8QQkjQ0CkVYuiUypxsnFJ+lCuwfC+36IQJg6sg/YY/6y/lc9HvoHMvJPoSeOuvb+Hlq1/G1DlTMebgMf2Nyn1bCMkpotDe55Ap5bJ8T8uU6pU4pTamJ/uId5vfJ8WNMeg8DOV77EeVFk1oQgUq8t0MKTHEAADLly+3Xa6+vh4NDQ25aBIhRQFFqRBDp1SwaE6ppLUo5ct+508XOJble73+lO8xz0JPvoLOn//Z8wCAJ7/7JC79/FIAHKyQ4sd4jPfs6HFcNpvyvR2b6JQqZUxOqWgInFK8zpcUszAr302wRYGCqVOn2i5TXVWNFStXUJgixCUUpUKMLoCU92NvZBF0zvK9wsMy6LzIMqVe/tXL6Grtwol3nZifBoQk6Fx0b/D8IsWOKfewo9diSQ/le/3CgyzonOV7pY3RKSWW7+XaKUVRqjSZjMkYjuH5boYlVahCLWot348hhpldMxGLxShKEeISilIhRufY4Q3ZE26cHK4ypXyALrfgsRKNiilTKplIYsGNC4Ak8NVrv4oB9QNy34aQBJ33dacHyiytJMWO8bzrabd2SmnnicvZ92ROKfEeSFGq9DA6pUQhKl9OKbqVS4txGIdGNOa7GYSQHMKg8xDD8r1gYaZU8aDrsIpOKbF8L4tzKAy/YTKR1L6bbCCZm0ak/pOPoHOrgbJVnhghxYKX8r3lTy7Hoj8vcrzeaZlSDtcSilKlh8kpFc2jU4qz7xFCSElAUSrEMOg8C9yU76mWdMO+9cUpxfK9nGKZKeWifG/+H+bjkW88Ig38lX42Tz+nbgYun918rtuQx6BzN+V7PNdIUWI4rNXjv6xabnZ/8bIXHc8FzSllc90DKEqVInaz76nOpVzB8j1CCCkNKEqFGDqlMidM5XsUFIPHSjRyU773yu9fwacvfIqPn/3Y3frzhFWAe24bkfqPb0HnHlYjuuGsro1h+J0I8Rur47q8utzmQ/br1JxSDrPrUZQqPexm38tb0Hmc13ZCCClmKEqFGDqlssCvoHMf4EA5eNw4pRwHaTa/u9ghzpdAHAanlEaenVK61+mUIkWO1XFt5ZSy+4z2fv/5JOazyaAoVXqo9xfVIZXX8j06pQghpCSgKBVi6JQKlmhFgE4p0bjD3y5wdIKFVaaUQ6dWdc5J1x+CDrFOlJLMmJWTNvh8LDs5rnb+ys7a31ZBtxSlSLFjdd7ZOaXcZkqpotOEsyfgkMsPMS3nJFqR4sOufC/XTil1e7y2E0JIcUNRKsToSnR4P/ZEvsv3WFKUW7LJlFJRnXNO6y/lTCnfy/ccOOBHB2DclHEArL8zRSlS7GTilHI7+54qSh352yOx7zn7mpajU6r0sCvfo1OKEEJIENj0aEi+oVMqCzwEnRv3re9OKXamAifTTCnxt7dzSoViYGbhAMtpE/wOOndYjxJV0rMvsXyPlCiZZEo5nQvqfU7NlIpWRqXCfCiufSRnJJNJ7dhRXUpiuDkzpUguaEITKlCR72ZkTAyxfDeBkIKDolSIYaZU5rhxcmiiVBBOqRA4a0oJK2FC55SSCLvib23nlGrf2m67nlwQhvK9XDulItGINggylu+9ePmLOP5Px1OUIsWPxWFdVmWTKeWxfK+sqkwqzFOUKi1E8UdzSgmiVM5n34vSKVWKzMKsfDcha6qrqlFfX5/vZhBSMFCUCjF0SmWBF6eUobPjxxM5lu/lFln5XiKecBQsRNHKyinV19WH7tZuv5qaMaEo31Pxa/I9B3HL6JRSIoq2HxbdvgiHX3U4XYmk6Mko6NziPrbrsbti1curkIwnkehLXyPLKstQMdDsTKAoVVqILlw1d1O8TrN8j+SCyZiM4Rie72a4phnNmId5mDFjBsaPHw8AqK+vR0NDQ55bRkjhQFEqxNAplTlunBxWnR2W7xUeOhdN/+42uolkv4O4jJVTqn1Lu/T1XKNzgOW5fC+nTqmytFMqWhlFX2d6kNy+tR2L/7w43T6ea6QIyaR8TzfzaD+nP3o6BtQPwKqXVyERT+gEJzqlCKC/J8qOh5w7pShKlSTjMA6NaMx3M1yzARswD/Mwfvx4TJw4Md/NIaQgoSgVYuiUygIX/aYgRSmdU4q/XeDInFIm4UbyM4gDN3GGIRGTKMWgc/9wkSmlle/1JVBWWaYTpR477TE0f96cbh7PNVKEyI5rJapoThYZMuF6xN4j0BHrSK0zntQJTtFKa6coKR3E48aupD1XqP00q9lXCSGEFAf5v+MQS+iUChbNhm7Yt3RKFR6yDC83Tik3jqMdm3dk1Ta/EAem+cqU8j3o3IFINKIr3zNm6IiCFMBzjRQnsuM6Wh61LaWSOaUiZXrnYV93X/p1iwBrilKlhXrcKJH0AwGxrNNOCA0CtQ28thNCSHFDp1SIoVMqc/Jdvqf7vfjTBY7UKWUYlDllSln9Tu2b9U6pUASd59kp5Vf5nptMKTHo3OnJPQcupBiRilIVUduZ0GSilDGjTQw5t1xPd35KhUl+UB94iNfa6iHV+OaMbyJSFrEtGQ0Clu8RQkhpQFEqxDhNZ09scBN0bvEEjk6pwkMX6tv/p9EFJZ19z4XjKDROqRBkSmnkySnlNAkBzzVSlEgO60h5xPY8lF3bREdUIp7QBCer0j2ATqkw0tfVh96OXlQPrfZ93eq9xZgnte85+/q+LTdQlCKEkNKA5XshhuV7wZKzTCl2pgLHD6eUlQOKmVJCG/wOOneTKVWWzpRyyhXhuUaKjR2bduDjWR+bXncqo5KW7xlEXplT6ox/n4Gdv7IzDvv5YQAoSoWRP+/6Z9w87GZ0NHX4vm71uMl1mZ4VWj/Nh1mRCSGEhBeKUiGG5XuZ46l8LxmwU4q/XeAEmSllLN/LG0Lz85UplWtBTomky40S8YTjuUlRihQbd+55J959+F3T67KZ0UQsM6UEp5RMlNrnrH3w48U/Rv1e9QAoSoWRHZtS7t31C9f7vm5Z+V4+0URUXtsJIaSoYfleiKFTKgvczL4n6ewkk0k6pQqQUsuUylf5nt9B507icSSaHkSzfI+UIt1t3dLXHZ1SkmuEKVOqP+i8rNLcFVSFKopS4UK8/wThZrIq38sXLN8rTZrQhApUOC8YEmKIAQCWL19uu1x9fT0aGhpy0SRCCg6KUiFGFEfiPXEkk0n/ymaKHC9OKSSh7VvfOj5Ji79JIMicUqZBmeR3cOM4MpXv5YkwlO/5HXTuhDiIplOKkDQZle+JTqk+uVNKhaJUOBGD5wMRpcJavsdre0kxC7Py3QTPKFAwdepU22Wqq6qxYuUKClOESKAoFWLEAdgLF7+ANfPX4NtPfDuPLSogPASdA0gNthX/Bvt0SuUWMWtI3d9+ZUqFMeg8b+V7Kn5pUg7riUTTU9gn42kXY6Q8It0HPNdIqRApj9g+8HDKlHIKOldfoygVLno7erW/gxCOQle+F0kfr6R0mIzJGI7h+W6GJ6pQhVrUWr4fQwwzu2YiFotRlCJEAkWpEGMUSD76z0d5aklxojmlkBrMKhHFPwcKZ9/LKbLyPVeZUpKBm0iiL4GOWCpMtnZUbSrLI19B58n8O6V8Dzp3QIkqOmeHOjApqypDT2+PuX0810iJkK1TyiroXIVOqXDS25kWpYKYBTVs5XtWsyST4mYcxqERjfluBiEkh4TjUQiRknc3RAHjqXwPgpBhM9j3kiWkc0ox6DxwpOV7RqeU5HfQlfhJfqaOWIfmohtQP8CHlmZOGDKl/Bbk3GRKycr3ZBk4AAcupHRwEg1k/QdjOaytKFVJUSqMiE6pIB5OqMcNy/cIIYTkEopSIcaqw9HR1IGmT5py3JoCw0PQOZDu8Nh28rz0ieiUCpwdm3doeU/SoPPe7Mv31PUPqB+QDggOQdB5vp1SQTyhB4C9z9xb92+jU0o9r2SDaIDnGikuurfLQ84Bn5xSNkHnWtksz6lQIYpSThM/ZIJ63IStfC+I70oIISQ8hOOuQ6RYDTxvqb8Fd37pTmz5cAsW/2UxWte15rhlxYGYKaV2vO06Pl4658yUCpa+7j7cNuo23DryViT6Evrfrf9PN+V7Tm5ENU+qdmRt3icZCEWmlN9B54bV7HX6XjjiV0do/xadUrqAX0kGDsBzjRQPq+atwo2DbrR830k0sBKl3Dql1HOT51S46OtMO9eCeDgRtvI9OqUIIaQ0oCgVYpw6HA8e9iBmXzobfzv4bzlqUeHguXzPhVPKU6eIs+8FSsfWDu3v7u3dcqeUx6Bz2e/UvjnllKoZWWO7XE4Qtpu32fdUAtTnxPNSiSqaY0MUpeiUIsXOS794yfZ9mVNqwPABGDRmEAB5ia8SUXQTB6iilEzkFWenJeEhV+V7oXFKRSlKEUJIKRCOu45LXn31VZx88skYPXo0FEXB008/rXs/mUzimmuuwejRo1FdXY2jjz4aH374oW6Z7u5uXHzxxaivr0dNTQ1OOeUUrF+/XrdMc3Mzpk2bhrq6OtTV1WHatGloaWnRLbN27VqcfPLJqKmpQX19PS655BL09JiDd7PBqcPR3Zay9u/YGI7ZwUKFm/K9HIlS7Ez5jy70uzchz5QyDsokP4NTNpNavlczoiZQIcYNYciU8jvo3LgeRVF052Ukmi43EgVEZkqRYkc8D2TInCxVdVVa9p3RTalEFSiKoguOtnNK0aESToIWpdTrLDOlCoNSGxcRQoqXghKl2tvbsd9+++HOO++Uvn/zzTfjT3/6E+6880689dZbGDVqFL72ta9h+/bt2jKXXXYZnnrqKfz73//GggULsGPHDpx00kmIx9MDnrPPPhvLli3D7NmzMXv2bCxbtgzTpk3T3o/H4zjxxBPR3t6OBQsW4N///jeefPJJXHHFFb5+X06BmzniYNcqA0jMlNJKvnwSpRh0Hiyia6avq8+VU2rTu5tM55RTplRPe6pDVTGwwna5XBCm8r1cOqXU81TNvwFYvkeKH0dRqiJqvhYp6Swo4/VPFaPE+55aCiYVpRSKAWFEJ0oF0EcMa/ke+8NySm1cRAgpXuSPm0PKCSecgBNOOEH6XjKZxB133IFf//rXOP300wEA//jHPzBy5Eg8+uijuOCCC9Da2ooHHngA//znP3HccccBAGbMmIGxY8fipZdewvHHH4/ly5dj9uzZWLRoEQ4++GAAwP33349DDz0UK1euxJ577ok5c+bgo48+wrp16zB69GgAwG233Ybvf//7uO666zBo0CBfvm+hdwZ3bN6BltUtGHPwmNxvXNSbEkm9AKUuEqBTSieSFPjvGEZEgcIoSlllSr3yu1ew/YvtOOmek7TXnMQdNasqEo2EK1Mq30HnfmHcpQosnVLLn1yuvW5VvgeOW0iRIGYeypCKUrARpfpfF9eriu525Xu8f4WL3s7SKt8TnX3ETLGOi5rQhApUOC9YQMQQAwAsX77cYUn/qK+vR0NDQ862R0g2FJQoZceqVauwadMmTJkyRXutsrISRx11FN544w1ccMEFWLJkCXp7e3XLjB49Gvvssw/eeOMNHH/88Vi4cCHq6uq0Cy8AHHLIIairq8Mbb7yBPffcEwsXLsQ+++yjXXgB4Pjjj0d3dzeWLFmCr371q758p0K/Cd826jYAwA/f+CHGHjo2p9vWOaXiSUDy0E8WdM7yvcJAnKa8r6tP9xTVyikFAEvuXaITpcRltry/Bf/94X9x9B+Oxj5n7QMg/XRWJmrmGnEAmo/yvfWL1+OZ7z8DwMegcwlWmVIiVqUlPNdIseB0zYmUR0zXOEVRLEUpdX3ienvbUwKHbfkenb6hguV7xC2FPC6ahVlZfPPwokDB1KlTc7a96qpqrFi5gsIUKQiKRpTatGkTAGDkyJG610eOHIk1a9Zoy1RUVGDIkCGmZdTPb9q0CSNGjDCtf8SIEbpljNsZMmQIKioqtGWMdHd3o7s7Pb1zW1ub43eSzQRXiB3EVf9blXNRSsSqMxOoUyppdu4Q/zCKUq4ypSSIyzz30+cAAE9+58m0KNV/POhcCxa/58alG/HJC5/gsCsPQ0esAzUjanwtgci3U+qBQx5I/8OvyfdcZEoZB+dKVLHcrxy4kGJBVr5XVl2mldxFK6LmyRySSc3hsvT+pbr3ZE4pTZSSZLRRDAgnoihlN1twpoS1fI/HoXfCPi4CrMdGkzEZwzHc1fcsJKpQhVrU5mRbMcQws2smYrEYRSlSEBSNKKViHOQkk0nHp/rGZWTLZ7KMyA033IBrr73Wth2m9UluwoV4Y85L2ZOhfE+6iChKJZ1FKU+ZBnRKBYqdKKX+LXNKGXFaRivfK4s4CjH3HXgfgJTj6sPHPsROB+6E898+37ENbglFplQ/OXNKRRRTGVMkGpG6pwCea6R4kJXv1TXUoWllU+p9i/Iqq3NDlinVsyNVvicth1Un3+M5FSpUURIojfI9TZQKQIArFcI6LgKsx0bjMA6NaLRtIyGkuAjHXccHRo0aBQAmRX7Lli2aej9q1Cj09PSgubnZdpnNmzeb1r9161bdMsbtNDc3o7e31/SkQOXqq69Ga2ur9v9169Y5fidZZzDvU8EXCOJN0FJMUmDqeDPoPHsSfQls+WBLoN/bVpRSBUYXwo3TMrLyPafv9eFjqZltNi7Z6Lh9L+TbKaXDL03KIVNKDDpXiZRRlCLFj8wpNXiXwel/SA51sXzPiMwp1bY+5UqoGVljvX2eUqEi8PK93pCV70XplMqUsI+LgMzGRoSQ4qRoRKldd90Vo0aNwty5c7XXenp6MH/+fBx22GEAgAMPPBDl5eW6ZTZu3IgPPvhAW+bQQw9Fa2sr3nzzTW2ZxYsXo7W1VbfMBx98gI0b04POOXPmoLKyEgceeKC0fZWVlRg0aJDu/05InVJ8WuQOF04pwGwNt+vkrV2wFm/f87Y7saWEnVJPfe8p3D3hbiy6Y1Fg2zCJUuJ5oZbv+emUClvQeZ6dUkFiFXSuvW+RMwWU3rlGiheZKDWgfoD2d8fWDqlg5CRKiSJv8+epgahO7DJsn+dUuMhVplTonFI8Dj0T9nERkNnYiBBSnBRU+d6OHTvw6aefav9etWoVli1bhqFDh6KhoQGXXXYZrr/+euyxxx7YY489cP3112PAgAE4++yzAQB1dXX40Y9+hCuuuALDhg3D0KFDceWVV2LChAnarBPjx4/H17/+dZx33nm49957AQDnn38+TjrpJOy5554AgClTpuDLX/4ypk2bhltuuQXbtm3DlVdeifPOO8/XC6rM4ZN3h0Qm5KN6zxh0brGMElGQjCddiVKPn/44AGDwroOx+/G7225f55Qqsc7UB//6AADw+o2v49D/OzSQbbhxSnnNlJIhDTrP188pbDff1wG/BDqnTCmZAEWnFCkFpDPGCq/t2LwDg3Y29zessoC0oHNFSd2Tk+nyvbpd6szLKxQDwohOlPISKeAS9YEHM6UKg1IbFxFCipeCEqXefvtt3QwOl19+OQDg3HPPxUMPPYSrrroKnZ2duPDCC9Hc3IyDDz4Yc+bMwcCBA7XP3H777SgrK8OZZ56Jzs5OHHvssXjooYcQjaZvwI888gguueQSbTaKU045BXfeeaf2fjQaxXPPPYcLL7wQhx9+OKqrq3H22Wfj1ltv9fX7+lm+t33jdgzcaaDzggEQJoeJDsXc8Xazf7d9sg04Xv5evDee6syJmyzRvlS+yvfU/e3GTeTklNKCzl1kSgWN+B39mn0v3hvHR098hMajGzFwtIfrQ472hSzonJlSpBSQOaXE19o3t5tFKcXZKQWkziHx2iY79zn7XjgJOlNKvbeEzSkVhABXDJTauIgQUrwUlCh19NFH23aQFEXBNddcg2uuucZymaqqKkyfPh3Tp0+3XGbo0KGYMWOGbVsaGhrw7LPPOrY5G/wSpRbctAAv//JlHHvjsTjiF0f40TRvhGgwb8SYm+Fm/8oGCwCw5L4lmH3pbHz32e+WtFMqF7hySrko33MSrsTyPeP6c41T+V5Xaxc+e/EzfOmkL6F8QLmrdS68bSFevvplDKgfgJ9v/bnrtgQmNPc7OLR/RiVB52URKGXy7fNcI8WCLOhcURTUjqrFjk07MPaIsejr6NMvkHQOOgf6XVP9Hx00ZpB8W3SohJJcle+FJVNKPTZ5HMoptXERIaR4CcejECJFdhN2M9A28vIvX9b9txQQb9JWT9jEUiEvTikrke3ZC55FX1cfHj/j8ZLOlNII8Gu7cUq5Kt9zckoJ5XthcvzJjtM3bn0D/znrP1j6t6Wm96z4+NmPAQAdsQ5vjQkq6Bz6c1fmlAKsB94le66RokP68CMC/Hjxj3HcTcdhyi1TUD2s2ryIG6eU8LesdC/VgNR/eE6FC1GUCiJjlOV7hBBC8kFBOaVKDb9EqVJE55yxK98zilIuLOJO4kRfV19Jz76XC4yilPi7Ze2UEuOjJE6pfCEeRzLBbcfGHQCA9i3tOWtTIBidUoZBdiKeoChFih6r8r26hjocftXhAICjfn8UYiti+Hzu5/0LWJddieKueD2rGysXpTj7Xjjp7czN7HthK9/jJD+lRROaUIGKfDejoIkhlu8mEOIJilIhRnYTLkRRKi8OE9E4Y9OZycQpZVW+pxLvjtMpFTCiKNXb2etrplR5dbr0TT0ewhB07uSUUveJp+yNDL9LkEHnRqeUURBM9FGUIsWPNOjccL4MGDYA0+ZMw7XKtdprbpxS4rrLBsi7gXSohJN4d/qeFYQolejpd0qFpHyPx2FpMguz8t2EoqC6qhr19fX5bgYhrqAoFWKKximVD03KhVNKm4UI/opSgMEdxb6U7/iVKSVzHPV29GLBjQtw2JWHpZ1SDkHnuQhhdcqU0kSp/mO4Z0cPlv1jGcZ/c7y3EHM3BHlOG5xSxsE5RSlSCjgFnVvhJlNK97fF8px9L5yI97UgnVKhKd+L8jgsRSZjMoZjeL6b4SvNaMY8zMOMGTMwfvz4nGyzvr4eDQ0NOdkWIdlCUSrEFI0olQeMopRVCZ1xhqFsMqX0DZC3pZQo5Nn3Xr76ZUQroprY5BR0Lj69Dgq3TilVSHvh0hew7MFlWPSnRbjks0t8bYtv7kfjahRzphSdUqQUkZYMuzjtrMqurJxSlqIUZ98LJX3dwux7ATwMUe+boSvf47W9pBiHcWhEY76b4SsbsAHzMA/jx4/HxIkT890cQkJHOO46REqxiFL5KN8zBZ3L+jOyTKkAnFLsTPmPrVMq4d4ptf2L7ZbvfT73c03g0QWdS35OcaAQGMJ2ZQ4vo1Pqk2c/AQA0f95svcpMB5y5ckpFzE6pZDxJUYoUPRk7pSzy76wypRxFKZ5ToSJnTqmQle/lwo1MCCEkf1CUCjGym3AhilL5wI1TKtPZ91yJbKJxp1SfNAf4teNdcd3fYm6YVr7nMPte2/o2bH5vs+X7OzbvkDqlHvvmY3j95tf17QmRU0q7bgQoHOUqUwowD5rplCKlQKailNX9JlOnFMvPw0XgolRPuMr31Hsvr+2EEFLcUJQKMcXilMp7plQ8aZ8rBW+ilJvvQ6dUsLgp33M6Vz55/hPb99s3t2vHgzFT6qVfvKRvTw6cUm4zpTR3l4sBbCFgdH4kE0lLN0jJCsCk6HATdC5736psWRSf3DildLOQ8rwKDeIDkCBmpGP5HiGEkHwQjrsOkVI0olQ+MGY65bp8TyKSEP9wE3Sudq5lgzsA+OQ5e1Fqx+YduvI9O3LtlHJTvmcl3OhXmmFj/NK7JJlSxjbJ9j2dUqTYkYpSLu49Vg5R8XrgySkFnldhouSCzilKEUJISUBRKsRIRSmHkqQwkpdMKWP5nmRfiuV7Wjh2FqKU2NHvbe+VtoX4gxenVFmleT6Hvq4+fP7S57bbSMaTOoHH7jjOiVNKzElzEXSeTamPE0Ge06byPYm4FpQope7D7u3deOioh7D4L4uzWh8hfuJKlLJ4cOXVKUVRKpzkrHwvZJlSQbjCCCGEhAeKUiFGdhMuSKdUnsv3EvGEdfmej5lSYie+s7lT2pZSIqez79lkSkUrzZ3rNa+tQW9HLwaOHoiR+4603E7L6hYA4XNK2ZXvacdwkOedX5PvSTKljE4p2aA5CFFq6d+W4rrq6/DeI+9h8V8WY82razD70tkZr4+QbJAOwl2cd4ke+T1MvIa5ckqJ52Zp3sJCScnNvhelU4oQQkoBs4WAhAaW73lj07ubsHbBWhz0k4NMmU5SgUQo32tZ04JoRTTjTKlkIqkTCrqau9LvMY/Dd9w4pdRBneyJb+vaVgDAqANG2c7At+3TbQBsclfUNuQ6U8pF0HmQmVK5dErlqnxv1nmzAABPTX0Kh//y8IzXQ4gfSN29dEqVNMmkvp9RSuV7QOr758N5T3JPE5pQgYp8N8NXYogBAJYvX57nlvhDfX09Ghoa8t0MUkRQlAoxFKW88eJlL2L1K6sxct+RroLOFUXRBKbHT38cALD/D/Z33I5sYCCKJADQ1SKIUomk7YxhRUuA4xg3mVLqa7LyL7Uz77ZEIRKN2LoUSi5Tyi9cZErlsnyPkLCQsSjlIlNKJ1BRlCoYjA7ZZF8AQefi5B4hwHgcOrmWSXEwC7Py3YRAUKBg6tSp+W6GL1RXVWPFyhUUpohvUJQKMbkWpT568iPM+808fOuxb9mWNHklV0+2VCGou63bXdA5zJ38ZX9fBgCoGVmD9s3t8s9Ivo9RlOpu69b+fvrcp/HCRS/g4k8uRs2IGqevQVwg7u/ezl65U0oVpSSda6tZ9axw6ggbf/9AEL6im0ypQijfk2F0SsmchhSliptkMomWVS0YvOvgknVGyMr33OwLN7PvuSnf4+x74cPoyA3CKeV2co9coROl4kkgHAYuEjCTMRnDMTzfzfCdKlShFrX5bkbWxBDDzK6ZiMViFKWIb1CUCjG5FqWe+NYTAIAnv/skLvzwQv9WnKO+jVq2ZAw2two6F8v3jDQe1YgPH/9Q+p7xM8lE0l6USKZEqqUPLMXkqyc7fAsiI5lM4n+//h9G7jsS+3xnH93+NjrhTE4pJ1HKBbKgc7GUIOfle4aBZ6IvoQ0m1O9WCOV70vUYTlXZIJuiVHHz8q9exus3vo5jrj+mZK+ZGTmlFOs+gk6IispL+ay2xfMqHBh/20wzpdq3tuOVa17BxB9PxE4H7KR7z85hnA/EdvA4LB3GYRwa0ZjvZhBCckg47jpEiqzDkYvyvd6OXueFHMjHk1V1UG4UoRLxhGV7ZJ38yrpKjNh3hOV2xM/856z/YPoe09G5rdNyee1zJfrE3w8+f+lzLLhhAZ787pMA9M6kZDKpP1cMTinZE19RlHLzu0jXIYglOQ86NzwhF/eHl0ypjM/TgA5lRVFMbZKVI1GUKm5ev/F1AMD/fvW/PLckf8ju/47ndBKo26VO+pZXpxRFqfBhEqUydEo9/7Pn8fZf38Z9E+8zvZeLTEIv8DgkhJDSgKJUiMlXppQfnRGx7bkSY7ROfBLmoHOLTCnZd204osHeQSN85MPHP0Tz58345PlPMm53seKnMNm+RV9KaXSmGZ1x4n91g7H+31sTpcrdle/Jyvx0uVY5dkoZj2mjcwwwi6cPHv6gb7M1+XZOy1ZDpxQhlu5eJ4678ThU1JoDgi2dUi5m3+N5FQ6MDz8yFaW2frjV8r0wl+8FMdsgIYSQcEBRKsTIOoJWeRHZ8Nmcz7Dh7Q3pF3zoi+SjE2vllLIKOociH1zvcuQu9oPu/lWJHUJXHbhw9PGKAp0olTQINnble/2/QSble0Z6O9OOwlw7pQC9g0jnlJKU7334+IdY98Y6bH53c8CtzBLFLGaO2n+UabHARSmOwUmeybR8r3poNb75z2+a3srGKcXzIRwYH0rKcsfcEK20DmYKW/kenVKEEFIahOOuQ6TIbsB+OzJa17VixvEzcP+k+7XX/HBB6Nqe60yppDlTym3QOdAvStl0/tVBc/f2dJi5G3Gj5Mr3Auo/JvoSOkHQ+Hubgs6jEqdUr/fyPeNyfZ05dkoZxBpxH7gt3zNdUzL9jfwySrnIlKoeWo0rN1+pe41OKVLsZBp0DsjPfd2Me26cUhQDQodf5XtlldZxsqEr34vyOCSEkFKAolSIkXVK/S7fa1vfZnqtUMv3RKeUcfY9t+V7VYOrsNPEnewH3f2r6m5Ni1KuOofh6OMVPDIBSDxX7JxSpvK9AnZKdcQ6tL+dnFLaOnwqqQzqnJZlSgEwzVoZuCgl08qSSaxftN5Vfhwh2ZKRU0pdTuLc1YnzXmffoxgQCvyafc/WKRXi8j0eh4QQUrxQlAoxuSjfk3VIfXlCloe+g9Xse5ZB55LZ96a9NA3RiqjtoFtdd3dbWpQSXTMkWEwzHRrK90xOKSdRyuKnFo8N2XmSz0wpAPhz45/x+BmPo6+rzzFTKv2mT40JcLxSN1Ye1OxmIB3koOXzlz7HA4c+gLv3vTuQ9Xe1dOGFS17AF29+Ecj6SWGRiSil3rdkIrpnp5SYKZWHiUuImVw4pUJdvpdhuSIhhJDwE467DpGSi6DzaLnkiVmBZkppHTRjxpCFUwqA7ruOPWwsRh84OvWyi/K9rtYu7TU3MxaWXPmej4j7zhRybizXtHNKKe6dUpFyg7PAGHQuCJFWTik/B3OyY3j5zOV4669vuXZK+dW+wI5lBTjgRwfgK5d8Bd+d9V3dW6f87RQAwNHXHp0XUeqzFz8DAGz/Ynsg63/xihfx5vQ38beD/xbI+klhIQ11dpp8L2ktSIuiriunlLAeOlTCgUmUyjD4284pFbryPSV97+VxSAghxYv14xKSd3IhSgXllMpHppQu6Fycfc8i6NxYvlc+oFx4025Dqf+I5XtuRKlSK98L6um6ySkFe6eUbjAmcUpZCSzR8qgmNjmV71k6pZLw73e32J3vPPAORkwYof3bNlPKr9/Ez2NZge67RcujOOHPJ5gW2//7++NLJ38JA4YNwOpXVktXFeSgZdCYQdrfib6E69JPt4Q+hJ7kFN/L9zw6pdTt2T7UITnFr9n3bJ1SISvfA/qPQ6sJa0hR0oQmVMA8iygJBzHE8t0EUoRQlAox2YpSq+atcl5e0u8o1Ewpz0HnhvK98pq0KGXrlJKU79EpFSyimOJUvudLppRidkrZBp1LhDK1DX49cbbqkG/9aCs2LduUXs6mfC/s5Q9O58iAYQMA5Kd8r3pYtfZ32/o2DG4c7Ov6g5hZlRQu0qBzl+V70qBzr5lSQLp/EO7LRsngV/leIc2+B1CUKkVmYVa+m0AcqK6qRn19fb6bQYoIilIhRmbNditKxXvjePiYh50XlEUt+T37Xo7wGnQO6DvvFTXppzK2mVIZlu+VHAEdAjIBSHeueMiUipZHpcJspCyiK22VddDFdlj9/n6eB3br2rFph/a3XflevNdw/XDZPKPDyk+B1Src3I7ARSlhNUv/thT7fGcfnUjQsqbFf1EqwwEmKU6s3L1ucMyUsvjbtD2W74UKY/8v04cMhVS+Bwj37QzLFUnhMRmTMRzD890MAqAZzZiHeZgxYwbGjx+vvV5fX4+GhoY8towUGxSlQkoyKXf3uBWl3D51l3Y2/ciUykMwquaUMnTUrILOjeV7ZQOE08Fj+R6Dzt2z9vW1GDRmEAbvMtj1Z7LKlBIHaP2rcSrfG7H3CLRvbdf+LQtEF8v3etp6pO0OOlNK2/729Pa1jrtkceN1wW37TIOfoMYrLtdrVVoSxOB51nmzsPa1tdj54J2111rXtPq+nXyLUn1dfXj0pEex23G74YhfHpHXthD/y/d07igP5XtWbSG5x6/Z98TyPWMpchjL9yLRCOKI8zgsIcZhHBrRmO9mEAAbsAHzMA/jx4/HxIkT890cUsSEx59L9Fjce12LUi6fKMmWK/RMKeN3snVKKRZOqQDK9/iUD9j8/mb8/Yi/48+Nf854HSYBMKn/LVw5pXrl5XtKVMGXv/VlfPs/39Y5pWQddLEdomtO1zQ/nVI2ApJOlOofqEidlkanlNttG76Hr6WoGaxKOkEDfNzfhja9+/C7eP5nz2v/blnd4s92BPItSr37z3ex6uVVePnql/PaDpIim3uztHyvzHv5nnqec/a9cKD2/8qqUqKSH+V74sMVQMhiDKFTiqIUIYQULxSlQorVzdetA8ptZyWbEgGv6w0aK6eUZRaBXaZUAOV7zIwBNi7dmPU6etpTAoz423Vs7dD+Vn9r15lSwk89+qDR+PYT38bQcUN1mVKRqNlRJXbmRdecSK7K93p2pEUprYxVUtqR6TFoGiDnebwydI+h0tdl+2jDkg2Ye9VcNH3c5H4DDj/b9g3+z8CXqWDoFyxBDhfZuJil5XvRiPxvOqUKBlWUUidlyViUqkiLUsaHPOq1PmyZUgCPQ0IIKWZYvhdSrFw1VrN8JZNJ3aA5m/I9351SOepH2DmlrLKzspl9TyzZciVKlVhmjOzpekVt2o2WaQi4uq/LqsrQ29GLZDKJzqZO03YzCToXzyFHp5RQRqhzagnkSpTq3p7evuaUkhxvGWdKBRiQrigKkv0NcSuIi65GEeM+2vLBFtx/0P0AgK6WLpx838lZtDRNEK7HvF8fON4LFZkEndstl5FTimJAqFBn3ysfUI7ObZ2ZX4eEn1N8uCLGRoSpfE87DkM+UQchhJDMCc+jEKLDqhNoVb5nXN61UyqLjq/teo2z3wWMmCtk/O4ZBZ27KN/z6pTKtxMiDIiilCikeEF9sltWndLUe3b06HOmDOV7YudaJkrphBDhT51TSjJwy3n5nsdMKdk1wC+nVBhmkvzKJV8xvWbcR82fN2t/dzXLfyMpDl8viMFR3kUpEioyemDU/7ZMUMhElOLse+HCL6eUeGyJfRfdrMlhKt+LUhwlhJBih6JUSPFavmcK986mfM+PzoholMpBHoVOBHMZdJ5t+Z4oBNApJUGyy8UBUFeLB5FAQC3fU3M1jKVztk4pxd4pJWKafc8m6DwX5Xt2A0NR4LNyDAJZDGKCDDpXLP52YMqtUzDltim614z7WxTxjdkp2RDE4Cjb68O6N9bhuQufsxRISWHh9+x7DDovfPwSpcR7g/hwRbzOs3yPEEJILgnPXYfoyNYp5daVE9jsezku3xM7U26DzhVF0X1XsXzPVpjrX5W4j90MeHOVKdW9vRtbPtiSk215RexEWwk5UoSfQxUAy6vL5csanVIRG6dUuV5sEgd9Vs4CFbWUIhFPaJlOxgFePjKlvJTvuRWMw+iUipZHcejlh+peM+4jsdzZ0wyZDrslEKdUlteHBw9/EG/f/TZe+uVLPrWI5JNQBJ1TDAgV6vVM7atkeh3SOaX6+y4ta1rw1LSntNdDWb7H45AQQooWZkqFlJyV7+Ug6DwXTimxAy8LOrcaZFqV79kJc1qZoDCIDJNT6q7xd2H7F9tx7ivnovGoxpxs0y3iPhCdUp+/9DmG7TkMdWPr5B8UMzDUTKlq+eXL6JQSj2enTCkRo7PAeF6oAo/omKsZUaMLwS6k8j1jLp3ttv2cfE/8fbK89pSyU0ol9lEso89xhrVwkc0DI2n5XiZB55x9L1QEUb6nivX/Oes/+GLxF9rroSrfU+/bnMG4ZGhCEyogz44kuSWGzPoUhHiFolRIsXoCZiVKGW/Wbjsr2TyNtSPnmVIZOKWyLd8T93GYMqW2f5ESRZbPXB5uUaq/zGjV/1bhn1/7JwDg98nfO66jtz0ddC7F4JQSUX9v9bewy5TSiSWSQZ4q8KjfI1oZRWVdpX5mNj+r9yTfp6yqDH1dfRk7pfQbgOWgt1ACZm1FKS+zyzlcAsMcdF6MZcK9Hb1495/vYs+T98TA0QPz3ZycoJ5zR1x9BBbcsAAAnVKlTiCZUv1ivdFdHabyPbUtPA5Lh1mYle8mEIHqqmrU19fnuxmkyKEoFVKydkplMfteIZbviYNEY0etr6vPVdB5NuV7bgbtOR8s5mC/dzZ34uNnP8Zep+2FyoGV+s1Lnq7LnFKrX1ntvCEP5XtGp5RsPTqnlJUQFTEM3AyHhLoOdea9ykGVKKvUX1KDdkqV15TrQ96RPhZlx6TpuiCepjazIeasfC/L1dqJUr6W74XYKVWMotScK+fg7bvfxqLbF+GiFRfluzk5wW72UCeYKVWciLPvAf46pYzXdJbvkXwyGZMxHMPz3YyCphnNmId5mDFjBsaPH5/Vuurr69HQ0OBTywiRQ1EqpOS1fM8Pp5QgSOQk6FwYgBsH453NndI2KIqi64h5Lt/z2CHMVaZULnn89Mex+pXV2PusvfGtf3/LcXlxH6iZUq6OD1n5no1TSpyNUSc6eSnfi8gHcSrqd1G/R1VdFaKVUd0yvopSkv1UUVOBzqZOfbsydErZtTVnQedZYhKlujN0SjltJ8TOsaISpfqPjQ8f/xAA0LSyKY+NyS2aKFVunqjBCvV9zr5XnKj9v7IBqXufF8dm69pWrF2wFnufubfUKWXs94WxfC/M113iL+MwDo1ozHczCpoN2IB5mIfx48dj4sSJ+W4OIY5QlAopnkWpTGffk9zk/S7fy7lTytBR69zW6b18z2YfaOV7HkWmohos9qO6nD587EOzKCXZ5dJMKRfHh/ibauV7hkwpJaKkSjWThgwxB1HKsnxPDEiXBZ33Czxq+V5lXaVJKAvcKTXA7BbLNFMqEU8giqjleyJBOaVCkynl0IwwP7HPuLQwvF/J5AYsBdTfUZwFNKvyPVFYF35rOqUKB6fyvUQ8ASWiSK+jf9n9L0j0JkxxBpqD1PCRUIpSPA4JIaRoCU/RONFhNbAoyKDzHGdKGYW2ruYu6wGX1ex7dvugf12yfWwVvg3k3ikVxnBamSjlpp26J7sW5XuacJS0PubU39XKKWVZvicJOjc6pfJVvmdaLp4S5mTXELvZ92ydUjkKOs8W38r3HAhz4G4xid/qsSE63koFqVPKQShQz2eZs9NKfKIoVTho5XvV5tn3ejt6MX2P6XjiW0+YPpeIJ7T71aZlmxydUlbCVr5Q7+08DgkhpHihKBVSLJ1SFuU3psGYy1DtXGRK5Xr2PbdOKUVRdAM4r+V7sn1slXMEFNdgMVNkQedukIlSRleSOIASl5d1rq0ypfQrNK9bRP39xUypQMv3JOvSHbOGZT3PvuelfC8ofM6UUqdQB1LHjetrUR4ypfzCj+tM2ATtUrx2yjKlrM6PQWMGAQD2PHXP1GIOQefi70tRqnDQyvf6H36Jv8snL3yCllUtWD5zuelzseXp2bOGfWkYIJxO6sytVg9kwgCPQ0IIKX5YvhdSrG6+VoPDsGVK6QZ1uSjfE76v8bvble+JZSGey/c8OqVyNfueSpiedKqI+6y7JSXmuDk+xN+vpz3ViZaV76nr0/3ewm4w5oGJpTHGZU3HgFXQ+fa0KGUKHfezEy1Zlax8D0hdJzxnStkIT4GW7/l5mBq+gtFZGu+OW2eRedlMiLNN/GhbMp6EUhaC60cImpAv1N/RTfneeW+dh89f/hxf/taXU8tJyo11r7kt3+s/z8MmUpYqWvme8PArmUxCURRLFz0ArF+8Pr18Qu+ibVndkvpDvPeFKOQcEMruQ+xQJYQQkh10SoUUr4NZ483arSglu8n7nSnlR4d23cJ1WHLfEst12Qadb5MHnQP6kh5d599N+Z7EdRImp1S+BxJOs+/17Ohxvy4vTqlk0lrUNQiKpkwpyfqsUH9/9XtU1FaEonwPSH0/V04pMfotX0HnPmJXvge4z5VyOnfC/MQ+4xm5XJZyktzgpXyvdlQt9j1nX+3641S+R6dUYaI6P8V7n/agxcYFu/Wjrdrfib6E7vds/qwZQLidUurxzOOQEEKKF4pSIcXrzdfklHKZX1QomVIPHvYgnr3gWXw25zPp+5mW71nNyGXrlLKZfS9MmVJhROdo6/+dXGVKCaKIuh8t86AMTind8WwQFN1mSpnWg7TrSC1/qBhYgWhVbsv3rJxSib6E1DGT6ex7QTqldPvcj2uPcDyZRCmXM/A5uY3C/MS+ZXULXrn2lazWEZbvF0a3Z66QBp273B+OQecOyxrfoxgQDozle0D6WiVe64znr/hevDeu+z23fboNgPNss/mExyEhhBQ/LN8LKV5LMMJWvhfU7Hux5THsfvzu5u2JooXhu3c1d8n3p2Izq5OdUSppkyllIRLI2lX0SHa5rMzSTUdTXEbd78YSA1dOKYOgaMqUEksYHAaAMqeUOjOgtj0f3WpenFJWpRxGYVRcp50QEWTQud8kE0nt2DAGZLsNO3c6Jv0eHPl9bZh/zXxMvnoyohXy2RSdCE15YoiPs6DJJOhcW05SfqUT4N3+vOpqQnI4lDrS8j1JxmWiN6ETloz9I/H6tX3D9pRYXwDlexSlSocmNKEC8sxM4o4YUllyy5ebc+byRX19PRoaGvLdDBJSKEqFFM9OqXiGolRAg4+ggs6t9os4oDZ+p2QiaRmqbVXOk+nse3ble7nOlArjQMIu+8sOnXiiupwMT3MtM6XE9WRTvmeRKaU6pSoHVmozCsranS2y88gq6FwM+BYxHoO688ZD+V5gmVI+rDbRlx6QZeyUyrEoZfV7ZUMinkAUmYlSYXFKlTJegs6NyMQrUWhwe0+mGBAuVJHdqXwv3qPPztM5yXsTpt+z+fPmUJfvacdhWMRyEjizMCvfTSgKFCiYOnVqvpuhUV1VjRUrV1CYIlIoSoWUbMv3spl9z48OqK7T62M/wmqwpHsS2L9MWXWZVqLX2dRp+oyiKJbOCafyvWQyKS3HM86+pmt7qTmlJEhFKY9B5+rnTE9zVU3K6JSyCTr3Ur5nRCvf25Eu3zPmXOWrfM/oEFIxOaXELDYP5Xthpru1G2UjUr9DpplSTt/X78GR1e+VDZ7b6DJfLJcoiqLPPyoPV1lRkHgJOjfilCnl9p5MUSpcSMv3+n8bUdg2XvfsnFIAEFsR090nQ1e+F+VxWGpMxmQMx/B8N6PgqUIValGb72YASDm3ZnbNRCwWoyhFpFCUCilZZ0plUb7niyjlc6aUyvuPvI9xU8Zh1H6jdK/LnFKKoqB6aDV6O3rRvrVduj7LQapD+Z7sO5VVldk6SJgp5ZNTqs+bU0qaKWVVvidbnwXq76nOvpePoHOrmeSsnDfGfe72PA0y6NzvTKnObZ2oGVEDwDw4C2v5nmnWxv4ZtbIhGyExTI4E0d3mx8yJhYLv5XtRedC57Xo4+16o0EQpiQtKvLa5FqUUAElg/aL1umOL5Xsk34zDODSiMd/NIITkkHA9DiEaXgcUmc6+lxNRyscO7eZ3N+Pe/e81b08y+54SUTB096EAgLWvrTWvTLEepNp2/pNygamsusx2sM7Z9ywypVy0Uzy+tUwpYxC5RaaUbFYxTZQyOi/EXA23Qec70uV7Rqdc0KKUlXPErVPKruzVajkg3AHUndvSrkjjfvCrfM9v55hRRPRDFPKcSyicJ6Fxxinp8lhA7xoqdtTfQHQ4ZRV0TqdUwaNeJ2SZUuJstkZRynj/VD/TcETKrbDu9XV6USqs5Xs8DgkhpGihKBVSnG6+xoDjTJ1SssGHHwOSoILOrdBlJqjlXREFe31zLwDAx7M+Nn3GroNv914ykZTPvOfglMp5plQICbJ8Tzb7nhJRpAKVVaaU7vdz6JdrQefb00HnQZbvyfaTcZCudt7dZkq5Ld8L0inld6ZUZ7MgSmVYvpdvp5Qf12CvIrhM2A8D3W3d2t+ldA1VjzG/yveYKVX42JXv9bQLopQxO9Bw3zWKUhuWbNAJ+KEr3+s/DkMjlhNCCPGdcN15iIajKDXAQZRyWSpWaE4py+1JMqWgAONPH5/ZCh3K96Qz79mEnAO5d0rl3c1i+Nm7Wruw9cOt2r+9OKVkQedunFJKRJFm5VhlSsnWl35B/091HVr53sDcl+8ZnVKqWO06U8pt+Z7hvbwfWzbonFIOQecfPv4hnr/4edNgx0mUCTpTyo/1Z+O2DZMIoROlLGaVLEayKd+T3b84+17ho4lSwn1GvVb07ug1LWdcBugPOu//95BxQ1A+oByJ3gTat6QjDsJWvqeKZGG6LhFCCPGX0gloKDA8i1KZzr4XkCil68Tm2Ckllu8NGjMIVYOrTLOiOeFYvmfhlLIt38txplS+y/eM3PmlO3UdX+2Y9eiUUgVBU0i5EIZq5ZRCMrVfLDOlbMr3jJiCzgN2SklFKWEfRCuimqvC6LxRsZt9z07ECPIJdRCZUiqqYyxSHkGiN2Eq1/3PWf8BAIw9dKzu9Vw7pYwOLj8EbM/lezJhP88oimISpfzI2yoEZEHnrmffk+wfOwHecj10SoUKVbyOVka1e5vUKWVTvic6pSLRCKKVUZNYz/I9QgghuYZOqZDiNKAwTgWfcdC5ZDu+5JkEFHRuuT1DkCeQ7sgMHD3Q/AGHPlcm5XuR8oh90HmJz74nClKAR6dUXC8sAZKgczfle4mkbl1eZt8z/raJ3gSSyWReM6XEAWtZVZn2fSyDzjOcfS/Q8j2f2frRViy6YxE6mzu1wVlVXRUA60wp40QIfmVKxXviOlHFCuMyvpTvZeOU8ngPiPfE8fa9b2PbZ9s8fc7VukUhNRmu0sIgycopJSGjoHOKAaFCdEoZf5vedndOqc/nfo61r6cyNpWogmiFOactrOV7pXLuE0JIKUKnVEjJNlPKKnujp70HSkTRSs0KvXyvq7UL0fKo1PGhiggDRw/E1o+2Sj9vhV3nP5lMSl1PTh25nOehhKj/JhuYZzr7nkpG5XtJvaBozJTSrc/BjRHvjadEjv71V9RWmIOY/YyUkpxH4oC1rKpMc4tZle+ZMqVciseBBp37nCm19L6lAIDV81Zrg7OK2gp0xDoQ740j3hu3zxKDi/I9l9fIO/e8Ey2rW3DVtqtQPaRa915fVx8+f+lzNH61Ed2t+vPDz6DzhX9aiNiKGE669yR7sT0Lp9TivyzG3J/PBQD8Pvn7DFprgWK+TsR74hm5fgoNTZQq80mUyiboPGSu21JFvZ5FK6ImUcpt0HnTx03a30pEkU4eELbyPYqjhBBS/FCUCilON98jfnkEnr3gWXTEOqTLWw34b6i9AdGKKH7d+WtzaZPLbbshF0HnPe09uGnwTYiUR3D2s2enNyeU7wFyp5Q6ODvopwfh7bvfxjHXH2NYwGbDFuV7SkQJ1ex7+UYcyLR90WZ6P9OgcxVT0Lk6YDM6pQyz7xlFKdfle8ZMqd5EemYwJVVSaxws59oppS7j1inl1h0TZqfUqQ+dioW3LcQuR+2Ct+58S3t95X9XYvCugwGkg4G727px2063YaeJO2HanGnassbf2rF8z6Vo1LK6BUBqdqsvnfQl3XsvXPoClt63FONPH4/dT9hd956fTqk5V8wBAOx37n5oOLzBcXnA+3G7fuF6z+1b/JfF6NzWiaOvOVr3ulEAMe7reE/cVL5ebIjCui7oPAsxOKOgc4ViQFgQy+5EUUo9b23L9yz6HkpEkc7gGrryvSiPw1KjCU2oQIXzgqRgiCEGAFi+fHmeWxIc9fX1aGiw7mcReyhKhRSnm++gsYNwxaYrcNded2Hbp9tMgxg7ASTeE0dfVx/KB5QXtFNq2yepUpFEb0I6+546cK4dXWv+cP97J0w/AZN+NgnDvzxc/7ZD+Z7U9aTYfy7XmVJhom2djSjlAmmektGZpmpSSftMKZMoZYFTxzzRl0jnSdVUSDv4vnaiJbtL55SqLtMcUq6dUm7L9wIMOs82U2r/c/fH/ufuj2UPLTO9p+4H1Rn66QuforOpE5/P/VzfhqhHUcrj7ypbXnV0LZ+5HGMOG6N7z/PMeZJrbDKe1L1udUyIy8v+dkPl4EpPywPA7EtnAwD2+e4+qN+zXrptRVFM97ZSCDsX3Sy6a0oWp11WTimKAXlHPO6jlVGTUGPnlLI6n62cUmEt3wtL1h0JnlmYle8mkABQoGDq1Kn5bkZgVFdVY8XKFRSmMoSiVEhx6gRGy6OIRCOWnUYnAUSbhUxyk/fjxm90pwSC0EEXv69WvmeXKdVPJBrBiL1HmFftVL5n5ZSyIddOqbyXXAibb1tvLUp5nX1Pxap8zylTyq58TyeKOPTL471x3cx7gLkzn8ug86q6KnQ0pZyTrjOlMizfCyNVQ6pMr5mmULf4iqa8MIfv63V/OB3jxvJWzyHlkt8u0ZcwDWTtcBt6L6NqsHnf2yHuj45YB7CnvB1Q5E6pYqavqw937XWX9u+MZs2TIF6bKuvciYjiNZXkF/GaLivfEzOl7ByxIkpEnikVtvI99RxgplTpMBmTMRzDnRckBUUVqlALiVGgCIghhpldMxGLxShKZQhFqZDiNChQn55a2ZqdBBB1/YVQvudGtBA7bF7K9yxxKt+TiH6y8r1T/34qPnn+E3z0xEe5z5QKEXailJvjQ3Y+WJXvuc2UUqKK+TgQ/rnrV3fFsgeXpd8yLpuElgVUOTA1yLMr39v83mbMvnQ2jrnuGIw9TD/bmxucyvcqB1WiqzU1y6Rbp5RbISLQ8j2f1iUTRtTyZlm5l3hdMZXv+ZQp5Xb5bIPOZdf7RDyhC3YXp5GX4dY1J0Pc970dvc7ldcLqjeHzpplkS8wpJTpeAP05ns2DBvHadMQvj8AXi7/AhHMm2H9I1aTolMo74nEfKRMeSMazc0oVQvmeeuyWWgRCKTMO49CIxnw3gxCSQyhKhRSnTqB6k7aalcRRlOrLnSiVrWPHqj2iSCB2wmRB516xdUpZzL6nRMwix6j9R2HclHH46ImPct6hyve06bpMKZdOqR2bd2Dze5ux23G76drvpnzPcvY98fgTyve0QZrFbppw9gREK6IYPWm0xTcEOps7AaSCtAHYlu898o1HsP2L7Xjw8AczCoN2CjqvrKtEZEP/7Htd/s6+F2jQuW7FmX9UVoaiopbviYjCnVHgdBKQPT+xd1jcGHTuuXzPYhbVvk7hOHDYt9nMvldWle5KdMQ6UNdQZ7u8eCwbRSmxHYqilJxTyniey0SDTBCP8eoh1fj+K993/gzL90KDFnJeGYWiKGanVIf17Hu2TqkCKN+jKEUIIcVPuO48RMNN+R5g3Wl0GlRpgoDFYCZbfHVKWe0LYZAlDjDdOKWcBmi2mVJJeaaUNOhcSQ8qjBkvQZP38j0B20wpoZl/2e0vmDFlBpY/qQ9C9BJ0bnRKiUHKYh6YLE9KV8oXUbD3mXtjyK5D+l8wf6+u5pQzSSvfs3FKbf9iu3kFHlDXtc939gEATDxvoskppW5fdQ4aZ+nMdPa9IJ1SfglcdkKyJpoIi4jOAlP5nsPgJ2inlNdrsFUZtjhQdXR/ZTH7nri/VHea7bYS1qKUyfVbYk4p4/fzrXwvgxkLOfteeFD7OGq5nSocJRNJU6RA0ydNut/MLuhcWr5HpxQhhJAcQ1EqpDg6pfqFDrFjIuLolOrNoVMqy/VJhbNkUjeQFJ0hWnlWf8eqdpS5fjnr8j2ZU0qyTkVRdIOBUu1UuXVKqQPUT174RLesl6Bzo1Oqfq96XPzJxdq2jE6pbEQRo1PKWCIltls8DjYu3Yg3bnsDm9/b7Hpb6rrGHDYGv2z9JU669yTdOivrKjWhTh3AqO1SEZ1SonhnbKuRrpYu3b+Dckr5NbuYEa2cTPiK4mxVspkV7XAj2uhy9YwzyiWTum2anFI+lO8l40md4OMlJ8uzKNbroyglbttDplRnc2dRlEgbjz3fyvcycL9w9r3woDmlKswPJI3HzPxr5mPe7+Zp//ZcvhfSTKlS7T8RQkgpQFEqpDgNCoxOKS+z74nvByVKGXN8slqVrI1x/aBOVr6nvu+UpSLDsXzPIlNKllEkDipKdQY+mSiVTCQtjzXjfvQSdG7KlEK6Iy+WXkqdAzZ9cZlg0rktJUqpmVI7TdwJu31tt3S7k3JR6r4D78PcK+fiiTOfsN6gAe07KQoqB1WmBE+xfG9QpTbwVJ1SRlFKHLQb96nVNSe2MqbNlKYRwkwpW6dUf9C5+J17tguilOGrO7lx3FwjrUoj/33av/G3g/+mWzbroHPJ8tk4pbzcA1655hW88vtXtH+3b213/Iy4fl2JIST3MhdOqe0btuPmoTfj7gl3u2lyqDEKa36V72XllKIolXe0SRv6+zNi308mxr72x9e0v60E6Ug0wvI9QgghoSBcdx6i4dYpZdVpTPbZf95OlPJl9j0fy/esSlN0AxuboHMpWZbvyRwusvI9o1Mqp0/ys9jv3W3dePOuN7F9YxYlZ/3b79nRY3LaqCTiCVftlA2ojU9zdeUMBlFKnEXKLlPKq1NHFaXKa8u17UybMw07HbiT1har9gLAtk+3ud9Y/6rE41ocUFTVVWnfSXNK1RicUn3Wbhira86bd77pvo3Zks2U9zYDKVWUEs+/53/2vPa3Uegwnqfq76niRjSyugaufGYlNry1QfeaGlCv4nXwJb1G9iXQ29mr+7dtezMo34v3xDH/2vm617w6pXSONUM7kHTnlFKdlU0rmxy3HXaMDy78KN876KcHZSVKcfa9/KP2cTSnlDDJjeNsyww6J4QQEnIoSoUU10HnascknsRzFz6HRXcsAuA+U0o2+MjkqeiGJRvwxm1vSGf1C8opJb5uF3Quw0l8sOuUffDoB3jx/1509xkFusyGQslDee6nz+GFi17Aw8c8nPW6ZC4plURfQn58GCe6y6J8T3xPdErZBWNLscmUUp1S2qISsVg2KEzG5aH5MkzfCTA5pdTrgSpGmILXbdwwVud9zfAa02t+lu/lJFOq310gZs+teXWN9rcpGNgwyDvgRwfo/u3mGqkrh+s/xq0+F8Tse7ko35Ots2OrsyhlN/uecUZIV5lSRSSaGL9ftuV7e5+1N07864mZNYaz74UGu/I9p36FbdC5JFPKVyesD1CUIoSQ4oez74UUr0Hn695Yh7fvfhsAcMhlh+S8fO/+g+4HkCoXOuiCg/zNlLJwc4kDJ3GwacyUAlIdOU+CkE2nbPsGuXtIVr6nzpKjbt9qVrSwseKZFQCA2IpY1utyEqVkA0pX5Xsug86N76llW6p7Rrctj51xLejcUCYnFaUsnDxdrV0YMGyA47akopQhU0r9d++O1EDfOOuceF1wKpMCUvtrwHBJ24KafC+gTCn1PdFRKeLklLLLCrNC5pSyGhyGrXzPrSgmW6fozrL8nMtMqWQiWXKz7xmPvaxdK1ncflm+Fx7E2fcAgyiV4WyhVrPvhU3kpShVejShCRWocF6QkJAQQ2q8tHz5cocl/aG+vh4NDQ052VauoCgVUryW74kzScV74nnLlNry/pbUOpLmAVmmWM0QaOWUkpXv1Y6qReva1vQKsijfs/6QZL1qrlV1WUqU6iwMUcrPTmlGTiljc1w4pcRSE5MoJQT27ti0AwAwcKf+WRmzKd9Tg84HOotSVqJJV7M3UUpsr2n2vf59ol4PVOFNxc4NY9zH8//ffCy9fykO+ulBprb4GnQecKbU1DlTse71dQD04rWIk1PK6DhzFXQucaVZDQ5721PCTNWQKnQ1d3l3SvUvXz6gHMP2HIZN72zKzinl8h4gFTI9ljbaZUol40lXTqlimh3OthQrg6+Zzf28GEWp1rWtGDRmUOhK1Jwwzr6n/TZxefme6ICym31PVr4XtvOJolTpMQuz8t0EQjyjQMHUqVNzsq3qqmqsWLmiqIQpilIhxWkAod6k1UGo2MHqaulynj1KFaUkA4i+zj48f/HzGLnvSBx43oGe2i2z+wdRvmcs6xAdEMagcwAYMWGETpTKpnzPy2fU7ZRVlaG7tbtgnFJO9Hb2uhYm2r5wcEpJMO5L2flgCjpXhScbpxSS0HKyZLMy2uEm6NzYNqfyPcA8s52RRF8CkbKIdh5Zle+JmVKaKFVlEKX6rIUH479f+d0rAFIzOZkIajwXQKZUwxENWL9oPQBrl42TU8q4bq9OKfW3c7quV9X1i1JeM6VUd2hU0dqaiCd0go+XTCm3Ti0rhxaQmmGyrqEOA+rNgiudUtbYuV4yuZdmc/8Vr6nFwHsz3sNT057CvtP2xTcf/ma+m+MJY/memKEoOyfKB5Rj5jkz0b6lHU0fy7PW6JQiYWUyJmM4hue7GYR4ogpVqIW3sUUmxBDDzK6ZiMViFKVI8NgNepRoukxMHaCKHfuuFudBjZ1TqiPWgbfufAsAPItS0imks+zgWOVe6ZxS3fZOqZPvOxl/2vlP7jeaiVHKYvY9IC0OFIooZTcISfQlcNuo21wLd6oLpKyqzPT9rcr33GRKWZXv2WVKAcCOjSmnVO1OqRtHzsr3LEQp1W0lo3lVM+6ecDcO+ulB5u8Es1NK3SeqKGUs31P3jRJRTOeVlRARdDh/0JlSkWhEe8+qfM/4um3YNNyJNjLnkdPnygeUu16/rD2RsojWVqNTymmdxiwnN1g5pTa8vQH3T7ofkbIIftv7Wyx/ajlaVrfg0P87NLWMjShl3G+uMqWKCN+/H8v3NNRQ/vf++Z6volT71nb0dfWhbmydb+sEUg89qodWA7Cefc+ufO/9R9+3Xb8SURCpoFOKhI9xGIdGNOa7GYSQHMKg85Bi1wkUB6Ja+Z4wvXm2opQfBJ0plYzrn6BLg86FQerA0QNx7ivnplfgVL6XqVPKqEn1D7hVccBN3opfZNWxtAkibt/aju62bkeHj4o64K8cVGl6z235HiSHs1X5ntQpJQgfaiaYKkq5RnJIqE4pN+V7lplSzdb78dU/vIre9l4svHWhc6aURfneEVcfoYkdQPr8cDv7npvML78IIlMqUhbR9ovb8j1Tro9h3clEEpve3YT1i9dbtkeaKWVzXS6rKksPvjIs34tEI1pbE30JT+V7fjmlkokkVr+yWmsDADx++uOYc/kcbFy6UVtGRRWtrdZpFAi7t3fjqWlP4bUbXhM+5Kq5BYHv5XvZOKWKTJQKSmy5dcStuKPhDnRu6/RtG0sfWIqbh92MpX9bCkAy+17/b5OIJ6THjJs+DJ1ShBBCwgJFqZBi1wkUS3aMzgggJUq5nX3PbWezZ0cP7tnvHsz9xVz7BfNUvicNOs9mgJvBZ61m3wMKzyklcsOgG9D8eXPGn1cH/FailIxMgs7tZt8TfxtNlFLL97LIlFIxOaUkjsFMyvdkbhuxjeK5VV5TLi3fO/b6Y3HFxivS67Q49z0NPP3UpILMlFL6HYxR+8G1cVBnckoZBMV4Txz37n8vHjjkAd21V0QWHG4nDJVVl+lcTl7Q3KGG8r1MnVLZZEol4glTlpmKmucmHrc97fr9Z3JGGe5lC29diPdmvIf//ep/6faGzNmRDX6X7/nhlAqbSBEmxHv6zcNuxtyfO/SRXDLrx6lMnVnnpf5rCjoXrmmyY8bqfiNilSkVNihKEUJI8RP+u1GJYjeAkDqlhIFRZ3Ona6eU49Pz/sHJOw++g83vbcYbN79hu7w2YBabn2WH1o+gc13b4EJ88Kl8T8yUAtyLUpvf24wXLn0B7VvbvTdEJRujlDD4ScaTeOPW9O9u5TaR0dvZqzkhZKJUMp7MuHzPi1NKXJ8qSmlB5zbb1b1lc8xYZUqJ3y2T8j2Z20Y8rmtG1GDfqfvigB8dgOoh1SaRWhUHxG2r54epfM+DKBWUUyobZE40Y/aeFcbyPSenlIiVqCjuT7sMP5Xy6nKdy8kL6vKiUyoZT+qcmV6cUtnMvpeMJ3VlozohRfLQwijqOTmlWla3uGpboeJ3+V5Wgp3k9yJ6jOf/wtsW6idV8Ql1nVWDqwAYyvckx4zdNUtcRuaUCpvIS1GKEEKKH0+ZUitXrsS//vUvvPbaa1i9ejU6OjowfPhwHHDAATj++ONxxhlnoLLSPPAk3rF1SgkDTLVj0r09PaV4JuV7e3xjD3z64qfSUNmyqjLd+m3JoVNKbKss6NzWvh5E+Z5iLt8TZ98DzDNNWXHPfvcAANrWteGsmWd5bkvWGHa52Mm2yuWRcdtOt6G7NXXsZFO+5yro3G72PRunlB8CSzaZUnble7IyWPG7KIqCb/4znY1iFXQuDlCsBBJPJWM+alLZZHrp1iM5Z2UTQchI9NjnFtmJWkanTzKZhKLoM7vcPAQoqy7TuZy0z8YTjqKaVr4nlCoanVKO9wSbmRndfEb8rOiUkl3z7Mr3MsqQErXb/v1fqHD2vQAJ4GvIHiosfWApvnrtV31Zf9WQlAi1Zv4aAKmJGwD9byMt33NxDigRRTdLn0bIfu6wi1IcGxFCSPa4ckq98847+NrXvob99tsPr776KiZNmoTLLrsM/+///T9MnToVyWQSv/71rzF69GjcdNNN6O52KWAQS9yW7xkzZIDMRKmdDtoJF628yLScKkC4dfhIg86z7EdYDXycgs6tBCI3ZFq+Z3JKRTJzSqlsWrbJczv8wCgU6UQpD99BFaQAm/I9F5lFXoLOxRB8WaaU2iZZppTt727zlptMKasn13ble8aZyMR1yxBnZALSWWaiIKZlSnks31MDd8OKbP+q39vJNWAUPozXT7t9rooqib4E/j7575gxZYbOrSeuz61TSl1uzWtrcMPAG7Dw9oW27RfL98R19HX0mZZxWgeQnVMqEU/onFJq7hogvz84OaW8lrN5LX0MG3bf1zibpit8KN8Lm3MmTMgeKmz/YntW6+xuS983q4dWo6+rT5tBdJejdgEg/DZxefmem/xKq/K9sP3eahvDJkpxbEQIIf7hqodz2mmn4ec//zkee+wxDB061HK5hQsX4vbbb8dtt92GX/3qV741shTxHHRuFKXsnrbCPEiKRCPSp/Gq2ONaiMhXppRD0LmpmQ6iU8ZB5xbbUQdpXkWpTNrhCzZOKS/leyJG4QbIbvY9T0Hnhv0YKYtgwLAB5m1luLtz6ZSya6NxG6pjRfz+Vq4dJ1FKfKIeyqBzmVPKZfmeSZQyXD9HTBhh+VnVjbR+8XqsXbAWQModpBN51H3uMej8zelvoq+zD3Mun4O+rj4cftXh0u8ilu9ZOqUchKZMMqWsJqEQB7qiKKXeC+xEKS9OqWQiCSWq6O4xXa1daP6sGaMnjS5Ix5Ts3n3M9cdg0zubMO74cZ7Xl1XQuewhUyETwOEgc0oZ3X9e2fbZNu3vZDyJ9YvXI94dR+2oWgz70jAA+gcQst/HOEGJDAadZwfHRoQQ4h+uRKlPPvkEFRXmAaWRQw89FIceeih6euTBr8Q94qDa2OHQBZ37NPueElGkgzpfnFLCnx8+8SE2Lt2IY68/1vWAwU2mlK58zyLo3FOZUIaZUpble/1PuL3Ovpc3UcqA6HjyUr4n4iXo3IjUKWVTvmcSJg27sWZkjdRFlSmuRCkLYUR8Km5EJu7aiq0GR5BWvqekHDTirJWm2fccHCa6Mg8/D0uf1iXbv+r+8OqUMh5vg3YehJ++/1MoUQV//fJfde+p5Xuiq7Gvu0/qlHJdvte/vDoABYD//ep/2PkrO2PXY3ZFy+oWDG4cDEVR8MVbX6BlTQuA/vI9YQAnXm/E33fRHYvQua0TX/3DV6XvZ1O+l4jrhWZRlNLOd+F9473FKVPK+J7xd//75L8jtjyGM/59BvY5ax+nrxA6ZCLc5KsnZ75CP5xSxSJKBfA1ZA8V3AhCdjR/1qz93dncqZXuNR7dqN2vdOV7kvuom4dHBeOUCqkoxbFRcDShCRVw3reElCIxxAAAy5cvD2T99fX1aGhoCGTddrgSpZwuui0tLRg8eLDr5YkzYkaIsZPq6JRqdp59T33fSZRSt+02C0lzSiXNg2kA+M+Z/wGQykX40olfslyNOoNgzfAaV5lSboLOsynfO+/t8/DPr/3T1tViF3QerUr9ZoXilJKWxSSSUCKKZWfXKcvFS6aUqXxPMkg2le+pgqgLp5Q2857Ddt28JwuL9TKYsxMqpbPv2ZXvGZxSYhlVpCyCeDye8ex7usFLUIel35lSavmew3nkJrdoxD4jTPlRQNoVsXHpRu21vq4+aaaUU/me5iTqX844i11XcxfenP4mZl86G8fdfBz2PGVP/O0rf9PeN5bviYPjZy94FoPGDsLuX98dL/7fiwCAfafti2F7pIQvY46VG9w8MBBFKfXaoXug0NWnu3YYj0NVlJI9oJENUmPLU5219x95vzBFKYd7t1eyckpx9j1HpE6pLEUpMcOzu7Ubq/63CkC6dA9I/zaJeCLjY6ZYM6WSiSRWzlqJnSftjIGjJROa+ATHRsExC7Py3QRCQo0CBVOnTg1k3dVV1VixckXOhSnPAQU33XQTGhsbcdZZqfDlM888E08++SRGjRqF559/Hvvtt5/vjSxF1M63TJTSBZ1LHABdLV2W05SrmJxSUQtRymP5nmxgIRvstm+2nlUumUzipiE3AQCu3nG1ZYmIrnyvO9jyvfo963HYzw/TTUNu/pD1axmX72Xh4snqaafkox1NHagZXmP5HWYcPwN1DXWWqxRFqWhFFPGeeCDle7qgc9UpY9iPupn3shRYROHH2BaZW8aI3QDGKejctF2jU0oQNSLRCOKIh658z6912WVKeS3fs0K2HlWo2vDWBu21vq7MnFLqdUxdznjMJJNJzL50NgDgpatewuDGwab2ieV7RgH50W88il93/lr7t+jS89MpJX73jqYO7W91PxuP60RvQju+rMr3KgZW6BybgNx5pWJ0LxYKTqX3XsnK5SQpxyd6ZA+qutu60bmtM+McPuM9VnRKqciCzgc3DvY0O6VV+V6hO6WWPbQM//3Rf1FWVaa73gUJx0b+MhmTMRzD890MQkJLFapQC/kD9myIIYaZXTMRi8XCL0rde++9mDFjBgBg7ty5mDt3Ll544QU8/vjj+PnPf445c+b43shSxDioFpGV74l0t3W7F6UEV5FsW17L96SdWEn/RtbpiffGsXreaow+aLT2Wtu6NlcDH11JmRq9Y+GkcYWkDC+THCr1M1rQuVvHmc06g6S7rdvSuaO23ap87/O5n9uuWxSlyqrKNFHKTQfYU9C5C6dUzaga+YbsdrfFe0Y3i7isG1HK7piQilI2x6FRNBGDkY15RV5n39MNXgI6LIPKlPJavudlG6pTqiOWFl/6uvSZUvOvmY+BOw3EmEPGWK67vLpc+4xVBlVnU9qVMfbwsageoh/0Gp1STgHIVuHmrjOlPDql1GuHcf19XX2aKGUVdF45qNIkStk5RApVlPLbKVUzwuJa54JiK98LQmyRTVTxxZtf4OZhN+PS1Zdi8C6DHdexfeN2LLhxAQ76yUEYPn64ZX9LFKG18zyRPs/r96rHifeciEe+/oirtluV7xW6U+rTFz4F4P0hYDZwbOQv4zAOjWjMdzMIITnEsyi1ceNGjB07FgDw7LPP4swzz8SUKVPQ2NiIgw8+2PcGliqaU0ryZF5WvicS747rMqZkuM2UUp+0uw23ljqlXHYE5/12Hl6/6XWMnpQWpcqqy6QlM2I2jlX7quqqbBpq3xZZXpGTQKREFHNnzpAp5bmTlENNKplM4vaG21MhzZJBiNr2TIPOdU6pyn5XRBaz75nODfUjSZhdRYb9KDqlxG1lIoo4OaV6O3oR741n5pTyOvueTfmeOojxwynlKwFmSqmvOTml3Oakyfa9en0Sf9++rj7TNp+94Fmcv+R8y3WXVZVpgpFRnFJZ8+oa7e/BjYNNgmikTO+UkrluxGuQ+L0zmX3PzQMDp/I9tU3q9cHKKWWXSSe7xxSsKOVSIHXiO898B0vuW4Ipt07JeB2cfc8Zu5L+FU+vwCGXHuK4jplnz8TqV1Zj2d+X4eq2q637CWIspnCPUY+ZaEUUZZXuu/VW5Xth+701UcqlizAf7efYiBBCssO+py5hyJAhWLduHQBg9uzZOO644wCkbgLxuL9P+EoZcYpvI+KTLdlgq6uly3GAaRSlItGI66BzN09NnZxSstfevudtAPoymEg0YjntuKVTqp+qIQZRSuzQObmeZNlQHoUscT3q4DGX5Xten3Y2f96M7tZuy0GRdixkGHReXiMIJBG9QGLCRfmeVdB5NplSdlj9FuUDbESpZBJ/3vXPuGnwTdLsEcB9ppTmAMwg6BxId+y1oPMsMqUCm9UsxJlSgPx6rDqlxGP5patewub3N5uWdR10blG+JzqFEr0J07UxEo04OqVEZ544S5jOKeWyfE/qlErYZEr1xLFu4To8fsbjus9YubeA9EC0cqCNKCU5dgtVlFK/76j9R+GytZdlvJ49T9kTZz97dnZOqWKbfS8ArK7rgPtjcP2i9QDSE9ZY9RN0D1Ai6fNcPWYi5RHLWV5lRKIRzr7nExwbEUJIdnh2Sp1++uk4++yzsccee6CpqQknnHACAGDZsmXYfffdfW9gqSJmShnRZUpJBltihocVRreEElGkApcWdC50khJ9CWvXhOBWUZFmQkmeZMm2bzXdsSlTSjKoNIlSXjDsVmOIeaQsgkkXTULL5y1Y+d+V2jKmthqdUiEu31v3+jrb97N1SonOtYraCrRvbnddvicbzFuV78mcUkYRpXYnQZQS38pgd8vK98Sn2O1bUvlpOzbu0C0TrYwi3h3XOaW627rx5p1vYu8z98bQ3Ydm7ZTSZUoZOvZhmX0vF5lSTuV7bp/Ay9oqc0p9/tLn+Pwlczmr3T4uqy7TCUrGdQLmWUaN54USVXS/s5NTSnSh+uWUMpXvNenL9x487EHbNnlySvXKxTtAL8gWEqqQ2PjVRtSNtc7oywn9lxOKUnq6WroQKY+goqbC1inlVpQy3gPdOKXU/pJYvhctjzpe63SrsyrfCxmFIEpxbEQIIdnh+W50++2346KLLsKXv/xlzJ07F7W1qcHdxo0bceGFF/rewFIlm/I9Y+6GDK/le0ZRygrX5Xuyki3J9q1EKaNTSiaUmPJWxAGlV9eTon9t2JeG4eu3fx2Dxg7SL2Mx+16uyveysa2vfX2t7fteQ++NVNZV4ox/n4HTHj5Ne3rvZ/me3ex7xv04oH6A9y9g8Vu4DTo3ojo/RKFy9v/Nxv9+/T/cs989ps+7EqWi1uV7mgvHRfme7DjSXXcCckr5nSmlDtD8Kt+TbUcVFd24i+zEnvLqctPgyygqidc5WUlopCztlLKalUuc2cvSKZVFppRd+d7q/62Wrkc8B0yZUm7K92TtCPEA1g71Nw+sXNYDnH3PTM+OHtw05CbcMvwWAPrzyUhFjUu3nmH/enJKJTJ3ShVr0Hk+4NiIEEKyw/OjxPLyclx55ZWm1y+77DI/2kP6cRt0HqnI7CmXW1FKVr5n2zGQBDy77dBKRalk0vppvJgp5dEplW35nqwsTIkoSCpyp5QqDtiVaknb4dUplUVfcusHW23fz7Z8L1oR1aZoX3r/UgCwdkoJX7t7e7enoHM3TimrmeQyEUVsnVI2QkXFwAp0xDpSge/xBCLRiDbLkiZ0yGaxtGmiySllF3RuU74nFQHFJ+p+alI+rUs7R4Wm+x10DpgHbLLyPcvPOjilnMr3jE4pWfme5qAQynpEROeSOCGGX7Pv2QWdfzbnM+l6bJ1S/cJaxSDzAN8qEN7qtUJAPRbD4GBh+Z6ZrctT90k1e9HuXHH7G8oy1qS4yJTyLErJxM+Q/dyFIEpxbEQIIdmRkb/9448/xiuvvIItW7YgkdDfJH73u9/50rBSx84pJXY6ZC4NlcpBlbopv0VMolRUPvteUE4p2WtenVLi4EXWpqrB1plSTkiDzkXxQt1Xkk6i7nNZOqW8iiTZDB6c2pZt+Z4YwCp2Mu3a/PKvXsaCGxZI3zM5pYTZiKSuIkGw0HXcsyzfs3NK2Z0rYmlHX2cfKmorTMeQzr2SdHZKmTKlBMHMGHRuKt9zmCkwDM4NJ5SIovtefmdKATAN2LyIUraZUlXO5XtiO63K99R1fDzrY7RvbTdtRyzvFsv3dNdTl+V7Xp1SVue6XaaU5pSyyZSStdfvWexyhViKlW+KbfY9X8QWYR193fJJQbRF3WazGdYR75IfuzKnlOiIjJRHHF2huvVZlO/RKZUZHBsRQkjmeBal7r//fvz0pz9FfX09Ro0aZXIZ8MLrD2onW/bUSxyYSKej76dqcJWzKBVPD3TtnFJi7o0rp5TYqXEZdG4pSrmYdlxGNuV7pvcN5Xtqx8/USZR8DshClPLolMqmM+k0iNMEykydUpXpQZaTKKXuVytBCpAIh3ble9ALFl6eJhvXD6RC21Uxws4p5XbK+t6OXpMoZRzcZ12+Zwg6Nw7knYKurdxl2eLpvHQgEo3oQmXV7+w0UMtmxrOe9h5L8dyI3bVTV75n4ZQSBeFEb8Jcvic4pTa8vQEyOmJpUUos3wvMKdVkHQStYueU0oLOJeV76vlVjOV7oXBKcfY9E+K+6OuyF6Vci7sZZEqJD2G0ks/yDJxSDDr3BY6N/KUJTahAYU5WQUghE0MMALB8+XLd6/X19WhoaAh0255FqT/+8Y+47rrr8Itf/CKI9pB+7Mr3xIGEnVOqanAVWte2St9bMXMF3nngHU20csqU0j1R9+qUcvmUVfZdbTOlHAZO+Sjfs8yUqs4s6BwK8MkLn6BubB1G7DPCeXlRC/Q4kHAKe1Y7yplmSomihqNTyoU44aV8D0j9FkmYRalMyvcqaiq089Bu9j27cyVSFkFZVRn6uvo0p4jY3luG36Jzl2nfyaaNtuV7hkwpu/I9R6dUMJFSWWO8hmnisY/le0Z623uzchapiEHnVmVpjuV7QqaUFVble35mSonrcnO9yDZTSlq+5zK8PmyEMVOqaJxSHln3xjpsfn8zDjz/wHTfJu5elHK937LMlNI5pTyKUnRK+QPHRv4yC7Py3QRCShYFCqZOnap7rbqqGitWrghUmPIsSjU3N+Pb3/52EG0hAnble6JryW6GITtRZtOyTbp/i0/YReI9cSSTScvBC2DowEgypbIt31O3N2r/UYhWRvHF4i8yckplXL6nVuo5le/JxIIsnVJbPtiCR7/xKADg98nfOy4fpFNKHRDnsnzPDlP5nvBU38opZfXZ9EI2GxTeq6it0GbVkzqlFJeiVHW/KNVhFqWMszqp+93unLcr3zNlShkG/zs27sCWD7dgxN4j5KJUUEHnTueQl1UZvn+unFJuB0uOQedRg5vNzinVZ3ZKibPvWSE6pXIx+54b3GRKldeYxd+iLN/rCU/5njQjsoDxen988PDUTJGLbl+EI39zJPaduq/uWI13x23PFbeOQyNu+glibqHbTKkJZ0/A+4++r1sHM6X8gWMjf5mMyRiO4fluBiElSRWqUIv0LOUxxDCzayZisVi4RKlvf/vbmDNnDn7yk58E0R7Sj/rkWDYAFQcSTuV7bpGWniElRMS74/oBi7FjIGpSsmDUbMv3JGHsxqfxMmy/v4fyPZkrSnNgGEUPw3rV/aE62ryKUp6dVUmLv13g2imVRdC5iujakZbvuShblM2QCMDSKaWbTtsqU8ol4gBZ6lZUp1K3C8GNRlA+oBxdzV3a72z3vdVZnuymGdc5wCJ6gcI0s5vh/Fl0+yIsun0RLv70YqkrRTepQqE4pVwGnWfjqult73U9WDIeD0o0XVIqZkoZy/eilVHEu+O6cy/eax4QWz1cEBGdUpaz77nNwnGRKeUGN5lSMpFG/d3clu/FVsRQUVuBQWMGmd4LC6LrJd9w9r0UTSub8NS0p/Clk76kO1Z9c0oZkPYTDJcwTcA2lO/ZXetOf+R0xHvj+OiJj1Kr5Ox7vsGxkb+Mwzg0ojHfzSCE5BDPotTuu++O3/72t1i0aBEmTJiA8nL9gOySSy7xrXGljDpwkE1d76V8zy2y0jMg9STQ+MTZ2DHQDYxcOqWs2mBEJ0pFFZ2TwGv5nhcXhrSkSyZUGez0pm30/1MVZDIVdNySlVPKwS2iujQydUpJM6UsBrCOv5ViPl48OaUsnibbbVd8T5zq2y5TyrbUNaqkZ2WUOKWMqKW2MseIiihIRCujUnefVfmeyhdvfoHGoxtNrwfl3PA7U0q37qhZUPabnvYe1yKOcbA5dNxQNH3cBCD1u5iEw/7/llWVpa7FBqdUJuV7YtD55nc34+lzn8ZhVx0WGqeU8bPqgFv2vbyU77Vvacdd4+8C4M51mi9EgSHfFFv5XrZOzPceeQ+1o9JPj/3KlDKing+R8kj6+DdGA2RYvqd7cBGVl++FTYT0LErlof0cGxFCSHZ4FqXuu+8+1NbWYv78+Zg/f77uPUVReOH1CbXEomZEjek9q/K9itoKXZmdXfmeEatBW193n2ngY+wYiJ0yreOUNL8vCiaeZt8Tw9gFJ4FdZzBSHpFm/ZjaafW+zUx6gEX5ns1njCHTgZHF6h3L97LNlCr3kCnlgHRf97+25f0tWpaaMVNK+zsqf90tTk4pV5lS0fQxKsuUMqK6qdw6pYxlGcZj0O5YzGXQuZ9YOaWMYtWJd5+I6qHV+M9Z/8l6mz073JfviS4LQH+N7mzu1M4Ro9hSXl2O7tZu/ex7kqBzUbi3QnRKbX5vMza/txkrnl6hu5ZlmykVRPme7HvZle8ZRanYypinNuULbfa9MGRKyZzPBUy2DqDV/1uNvb65l/bvvi5z/0i3vQz3m3qdKK8uR3dv/2Q1xuddgigl5pA5iVLGB2l0SvkDx0aEEJIdnkWpVatWBdEOYkAVpQYMlzilRFFKcGmU15Qj3hO3DYa1wurperw7bn5ybSNKSTMoktav6drg4JQSS1OS8aTtU8iKmgpL15LpbxmyeKgMyve0PKqos0jhB9l0Jh3L97LMlJI5lWRuj9TCqTwt23VZlEoCwILrF5i26cYp5TpTyienlPpZzSnlQuyxE6XE89g42DAGnVueP0l5u3VP1P3UpHKQKWXK2qoqw95n7o3ymnL866R/aa+rZV1t69tcb7O7tdt1fpGxHDdaEcWEcybgsxc/w/jTx2PhbQsBpM9F0SkF6N2MiT5zCXMynnR2SgmZUtp3aOvWHVdZzb7nciZCEV3QueGz6neWfS+72fe6t3fj0ZMexR7f2AOTLpzkqT35RP2+YSrfKxZRKlv6uvt0Aqpj+V6WmVJl1WXpyWgsnFKJeEJX4uokShsfpDFTyh84NiKEkOzwLEqR3KCWWDiJUqJLo3xAOXo7erUOit3g1YidU8o0RbdRlBI6XtLZ9/qFEqeSPllnSgw69+qUMuKpfE/YH1pbHcr3oFh3HHPVqcpm8OA0sFbLRv0oQXRySq3+32q8fuPrlp+XznRo456y2n5qIeFPl8eIW6eU3T6NlKWdUm4ypdS22pXs6sr3LJxSWtC5x7KTgnRKReVOKfU6YhQna0fVomFyAxbdvsj1NhN9CZ1D1Q6jUypaEcU3//lNJPoSqQFluUE47NWLUsbtGq8nXa1djoNSsXxPtz7hN7cT/OO9cSy6fRF2+9pu0kF3JuV7ukwpi/K9SDSiK2cC7Mv3Vj6zEgDwyXOfpESpkA20rQhl+V7InDP5ItGXyGmmlO5ab3wIEzU7pdyU7xkf1IRB/HSiEEQpQggh2eHqbnTjjTeio0PekTWyePFiPPfcc1k1ighOKSFTSu1M7P/D/bXXdE6pAeW6Gc4qB3pwSlkMhuM9Hp1SstdkTimXbcg0U8ppYOZYvid5X1b+ZeXEMX5Gc6lkmDHhmizGDk5OqTdueQOvXPtKxk4pEaUs7SSSHRdfvPmF/edlx6vkJ5eKizDkamRZvidmZRm3+/Zf37ZcRyQaMWdKObhcKmoqbIUrN+V7WqaUzfnjNPuen06pIDOltNValfUZBnBKRLGdPMK84tR/xJI4O4ylr9GKVO6Xum/V9qhipuaUkrQp3hs3/YZdLV2Og1Kr81dcl92x8dZf38JLv3gJ9028T142l0H53oLrF2Dlf1Mikp1T6tLPL8XZz52NXY7cJbUtJ9dfARKmoPNim30vW2Ey0ZvQufpkD+10y2eZKSWe924ypaLlLN/LJRwbEUKIf7jq9Xz00UdoaGjAT3/6U7zwwgvYunWr9l5fXx/ee+89/PWvf8Vhhx2G73znOxg0KLwz2xQCib6ENh18zfB0ptQZ/z4D337i2zjhLydor4lP0MsHlOsGohUD3TulrAZzbsr3HIPO1UypLMv3PDmlZB2zDMv31E6crHzP5J6yKN/LmVMqab+P7T7npm3zr5mfcaaUiLo/3nngHWz7bJvnz0vL9xycUuJA21K0tDkurILOLdvngBJVPGVKAc7uR1HUMg5qtSfr/fvBdjAlE6UK0CmlvW5R1idzUNll0RlRS6St3EdGZOV7un/bZEoZSfQmdDOxAilRyknYtEI3+57NtXXzss3p5XxySgHA42c8Ll2nWL49aMwg7PGNPdKOsl5rp5SpXSEbaFsRRqeUW9G12En0JRzL98741xna34E6pSLp67lWvldhP/uecT1W5Xthm53Sa/8pV+c6x0aEEOIfrh4JP/zww3jvvfdw11134ZxzzkFrayui0SgqKyu1pwQHHHAAzj//fJx77rmorHTv0CFmOpvTHcDqYdXa35WDKrH78bvrljWW74muDT/K9+Ld5qfxVoMGQCjfk4SaO5XvuQk6d5sp5fS00Anp/nAo35OWlKmClkEQCIwMV+/kkhLxo3xP3Q8tq1oy+nwkGvFcvicef1ble27ROVdcCqxGItEIlIrUci9c9AL2P3f/rEUpP5xSyaTchRiYcyPATClNFDaIT7ZOKUmpnBWVgyrR3drtetBuKt8z5n6VycUWmVNqx6YdeOmql3SvdTU7l+9Z4Xb2PXEf++WUAtLuQ6vP6gRXD64/GclkMrTCqigw5Bv1evTuP97FLkftggN+cECeW5Rf4r1xx/K9mpE12POUPbHyvyt9yZRSMd3v+s+Hdx9+F7HlqRB/P8r3Rk8ajVP/fmpG7Q6KsDqlODYKjiY0oQLuxzCEkOCIIXWPWb58ufZafX09GhoafN2O6973vvvui3vvvRf33HMP3nvvPaxevRqdnZ2or6/H/vvvj/r6el8bVsqopXtVg6t0gxbZgDXo8r2+bnOnK9GXwIpnVqDp4yYc/vPDMws6d9uGpP5Juc4pZVe+J+mY6QSkoMr3wuSU8oDboGYg86Bzka6Wrqw+7zY/ypUoJS5vd1xIREnjel2tR10mqmiOSAD4+LmPHT/nKEqJmVIOQee2mVI5LN/zE89OqSxFqaq6KrSta0PnNpflew5OKVOmlCHo3IlsnFIidtdWcf1+OqWGjhua+rzFZ2XHtltRKplI6q45yYRzIHy+CFP5nng9+u8P/1vyopQbp1S0PKrLe/K8jXhCE6XdOKVUQUrdtuegc8N94pznz9HFRoQBbebYfue8m4c+uYJjo2CYhVn5bgIhRECBgqlTp2r/rq6qxoqVK3wVpjwHnSuKgv322w/77befb40oZP7617/illtuwcaNG7H33nvjjjvuwOTJk7Napy5PysFF4Ff5nt3se7Kg88dOewwAMOaQMRi2xzDT55yCzqXuEkkbjEHnaodr45KN0hB4FUenlJPD3UHgkJbv2QhZmsMrkQzsKX0ykdTtVy8CVa6dUgdfcjC2fboNm97ZlNHnnWbf0y3Xj3gcW2ZKZfCzuHX9GYmURTBq4ih8/OzHAFKChdPnxCwrq3WqOAWde81CCap8z891ZZspFYlGdMK+E5V1KeHftSglyZSStcvolLILtxeJ98QzdkqJ2DqlLM4p8bVMBuNDdhsCwJtTypi9ZcUj33gEn734mfbvZCIJ5N+IJCWM5XuAe2G0mDFlSnX1mQTRaEVUNzOeE+sXr9f9+z9n/kf7202mlEikPJJybEcUV+eguqzTevONeC4k4glEI/k/N4xwbJTGj3HRZEzGcAwPqIWEEK9UoQq1qAWQck7N7JqJWCyWX1GKpHnsscdw2WWX4a9//SsOP/xw3HvvvTjhhBO0OvNMEUUppwGzXflekE4plZbVLbr8AVl+lPQ1L+V7QtC5OjB554F37L+PTGTz0teSLSsr3zM4pUwDbINTCgBev/l1HPDDA3R5YdmyfeN23L3P3fjSSV/K6PNenFJ+ZEo1HNGAC5ZegDv3uhNNK5s8fz4Tp5Qo2GXS8bZy2o09bKyrtpiWiSo4+JKD8eofXnX9uWwypVyXPCWdM6UCc0pluV7TAMsgCquo+8J4nVAiijS43oqquioAmWdKRSr07bLKlHIrCOx12l7+OKXs8vqEfZmJU2rQ2EFoW9dmel39jlYDeXG7xmPZafAvClJAuIO71e+UbQm6H4jnk5estWLFjVMqUh7RPYSyo+2LNjxwyAO615bPTJdHuHFKifRsT2XMKVFrUUp2H4uURbTjLoyilHguqDOVknDi17hoHMahEY3BNZQQEjry3+spYP70pz/hRz/6EX784x9j/PjxuOOOOzB27FjcfffdWa1XzScxOaUcyveUiKJ7yp9NppT4xN448BAHrB/P+hh/2e0v6TdlpXo2r9m1Qf2MGHTu1gWQs/I9Q6aUlXtHHCi+/MuX8cS3n7DdvlfeuPUNdG7rxLsPv5vR5704pdSZ4vwg086lNL/LpVBlXsjibwcu/+Jy/HjxjzFywsiMthuJRjBg2ADsceIeAOCqLMFT+Z7BhSPmmm37dBs+ee4T6Tp2bNqBZ77/jOn1wILO/RwDWazLdfleVPHmlOoPOs84U8rCKeVm9j2Rfb6zD07+28k49e+n+iJmuC3fs3RK2Xy+fi95OYtTWalMcM00UyrbWdiCRBOlwlC+JzqlvMxKGVK8uIdlx2GiT++Ukk0EEy1PO6Wcjsvmz5pt37c7BmR9oarBKZHc7hpg5bDS3g9hWatRlCLhJahxESGk+Ml/r6dA6enpwZIlSzBlyhTd61OmTMEbb7xhWr67uxttbW26/1uhOqWqh1U7CinikzRFUTIv3zN0VNT1iKKQitgp+OiJj3TvactKysj8CDp322EKIujc1ex7pg/J27Nm/pqs2uc3XpxSmeRB7XbcbtLXMw3ztcvvMi3nZb0uM6WgAANHD8TOX9nZun1O2zLkkvkiSrkMOp++x3SseHqFdB1zfz4XTR+b3Wu5GCT7Xtaqnn9BBZ17Ld8zZkoZc78yzJSqHlaNiT+aiKrBVb6U79nm9Tk4pfo6+9C+td3y8/Xj06LUzl/ZGV/+9pcBOItSUqeUh9n3ROiUcolwOpaaU0omuMqCzo3LiTPgOR1nTtdUu/6f8V4x5tAxmlNaPHZM1w7JJdYpuzTfUJQqDLyOiwBvYyNCSHETgl5PYRKLxRCPxzFypN4lMXLkSGzaZM7JueGGG1BXV6f9f+xYc8mPimWmlOwJl2EWsUxn3zMOZNQBrSwfxK5TYJcfJSvpc0IUxSLRSFZOKU+OGOF97QlxBuV7VuVDdlg9zfUaYu5l4KXO+ORuxe4XHbr7UPzfuv/DOS+cI30/U6FDJk5m6pTKRAhx+oybjrN6TPgpSon7xTLo3EXOiYygyvdyMQuaZaZU1CxKuS3fUyJKWpQS3K12WXdunVJuZt/TtUX43f1wOmQy+97R1x4NIHUteevOtyw/P3D0QN26VMHak1PKY/meEYpS7tCV77nMNSsWLJ1SLsr3vGRK2WLTZzFe04745RHS65rxniG73uqcUiEUpXTXHIpSocXruAjwNjYihBQ3nns9P/zhD7F9+3bT6+3t7fjhD3/oS6MKCeMN3irE+uqrr0Zra6v2/3Xr1lmuUxzgeAlhFsv3yqrLPAkhpkFbeToTQRZ0boXrTCnJE3bZeo1B566dUg7f3bF8T9gfO03cyfQZWfkeFFi6d7wMMCwHTF7HUR6W91K+5wUlomDQmEGW39/X8j2HoHPrlVn8nQU97T2OyxgHEIl4IlCnlFLWP1DKsFOve5oelJAU1Gpdlu95CTpXooopU6p2VC2u2HAFBo0dJP2MU9C5MVNKdTA6OaXE75GNU0oV2dxmSqli9v4/3B/7f39/23V/465v4OJPL9Z9XokorrPOdE4pC0eZWyhKuaPoMqUsfvZPZ3+Kh456CNs+3ZZeVNZHkQWdS8r3rDKlkskk3r7nbWxculH7vBUTzp7gySkliuni+WCcHEN2j9GVZodRlFLSfb+wilIcG6VxOy4CvI2NCCHFjedezz/+8Q90dppLFTo7O/Hwww/70qhCoL6+HtFo1KT+b9myxfSUAAAqKysxaNAg3f+t8DL7ng7BKaWGnLvtYFiW70lCa207BRJXVM+OHsz73TxsXLIxvZhkUCANzbUIOnfCKVPKCXHZMYeMSb0muqIMpVfGv43r8eJesBqU2Q6kJG95cVZ5Kd/zgtPxl1X5XoavZYwH8ap3h3PuViDle5KBu/G9jEWpoILOA3RdOQWdG68Tw/Ya5s0pNUhfvhcpiyBSZi1smcr3XGZKOblUdEJPFk6pnSelylHdZkqp3ycSjThud8whYzB03FDTNdMkSmXglPKaKUVRyh3i+VQUopQFj5zwCNa8ugYzp87UXpPmpcmcUnGJU0rI7xNZ8fQKPPfT53Dfgfdpn5cRrYzimzO+ae+UMpxv4jVHdD6b7hkFWL4HmM95W/JwenNs5H1cBHgbGxFCihvX4RltbW1IJlPT2W/fvh1VVVXae/F4HM8//zxGjBgRSCPDSEVFBQ488EDMnTsX3/zmN7XX586di1NPPTWrdesypRxEDxExU0rNk3I9NbChg6N2UsRMJxU7S7rMFbVp2SZsWqa/SVl1+GTrEzOlsirfE/FQvqeKUtJSSsNrVrPvKYr7aZqt9q/XgZSX5YN0StmRcfmeLFTeh6BzvxxAPTtcOKUk5XtOx6XxqbdpnS4ypTyHQqufF0s8AnJKBbZel06pcV8b5zpTKhKNaM4iddYrdX1WYqtxEGoSDq0ypRzK98Tvkc0+HNSQGhD0dvTiybOfRMvqFnzv5e9h/aL12HnSzqiordBdg9VyRCXqfG1Wv4Ou1FBxL0rJMqU08Y7le4FQbEHnTrRvTmehya6TpkypTrOoFK1IB50bj8vY8pju31aiVFlVWarP4MEpJV63RFHKKJA7le/5kUkXBJGyCOLd8dA5pTg2ShPkuIgQUvy47mUMHjxYu0l+6UvmaecVRcG1117ra+PCzuWXX45p06bhoIMOwqGHHor77rsPa9euxU9+8pOs1is6pbyW76lP+dWnY9k6pTLOlHJw6Rg7fIv/stgkXAHm2feyCjr3ID4oioKxh49FR6wDu399d9NnNEHB5ex7AGynaRaxdEp5zJTy8rSwEJ1Svs2+p/uAzVseZm90I0qZnFJxZ6eUk2Ciy5SyEKVkwogbUTLT38qJfGZKGfdXwxENiFZEMfzLw7H1o62O66yo0bsQnESpTDOlnJxSfmWuqO355Pn0zIzP/eQ5vPvwu9j1mF3xvZe/pztH1Jk43Til1GPXD6eUsczRc/me12tpDgmTKCXup2J2SqmI39fN7HvG8xnon33PIujc6MK0dEqpziU7p5RN+Z7dctLyvfKAXLA+4skplUM4NtIT1LiIEFL8uBal5s2bh2QyiWOOOQZPPvkkhg4dqr1XUVGBXXbZBaNHjw6kkWHlrLPOQlNTE/7whz9g48aN2GefffD8889jl112yWq9aj7JgPoBuhuw4wBbST8V87N8L5NMKTj0G8R1bl2+FbMvnW25Pt+Czj3y/fnf1xxOQGble8Y2iYP/OVfOwddu+ZppUB42p1TtTrU45LJD8NIvXtJei1ZE3YWjOxx+2WRKZfqaaZlMRBGHj7jKlIrqhRE35XtO+0uXLWRw4ajbMbatrLIMPb3O7c3JwCWg9bop3xtz6Bht4P3T93+KrR9txZNnP4mjrzla3tSoYhqoa6KUxQBRFXFUnDKl3M6+p8t5ykJglgkP7z78LgBg1f9WAdCL5n0dqUG1K6eUS1HK6vonnX2vyMr3dPe7EIhSogBT6EHn8d64s6NOOCykbu5effleb7tZlBKDzo3HpfE8ljmtAOG6YPMgzXi+WZUMm+4psvI9YXu5eEiQCWEVpTg20uPXuKgJTaiA+8maCCG5I4aY80IZ4FqUOuqoowAAq1atQkNDQ2hvXLnmwgsvxIUXXujb+hJ9CXQ1dwFIiVKindxqn088byLeefAdHHblYXjrrtSsR2L5nhvsgs5NTik7V4UkU0q6mPC++B1ly2USdC6dnc2D6wyQ2NidyvcMdnvjNozrW3jbQux6zK7Y4xt76F7PJFNK9uTfU6aUg8B0+FWHY/3C9Vjx9AoAqc61G1EqqPK9SDTiX/meuLzddc3DqlyV76luHbF8zwEnt5J4jFm5cIx5V9HKKLDDcdPBle/l4Fbipnyv8auN6eUjCkbsMwI/fe+n1uuMKKZySq/le24zpbyU72VTiqsGt9shDgq9OKVUUcMx6NxLplSvvZBlRVhFKfF7hEGUEkXUTK/VYSDeG8efG/+MHRvtL3RuJmPRiVIdcqeUVdC5KEolE0lrp1T/dcGuP5GpU0qG+tuGNU8KCK8oxbGRGT/GRbMwy6fWEEKCoLqqGvX19b6u03NIwC677ILXXnsN9957Lz7//HM88cQT2HnnnfHPf/4Tu+66K4444ghfG1hqqIG5AFA9pBrtWwTBxuJed9K9J+GEv5yAsqqyjMv3jILJ4MbB2PTOJmn5nt2TeFmmlHQ5ocPnJLaIQedZOaWyzA6Slu8ZnVJ2TzYlbVJLNUX8ckplUr5XUVthKaioQidg3QE2ksvyPdn5EdTse07Hj+wJumkdBrddIp5wdHw47S9ZiZOKeswaf183s81FyiKeRd1M8L1D3786N06pxqMbPa06Eo1Ylu9lHHRulSnloWwzG6eUGtxuXLd6XL778Lu69auDcj+dUq4ypYp09j3xe4RNlApzyaMTzZ83Y/sG88xoRsTvaJV7KT6MkYlSSkTRXdPjvXFsXLoRow8crbsu9Hb2OopSdv0JU6aU1XXcxcMb9V5RLKJUPo5Vjo38ZTImYziG57sZhLimGc2Yh3mYMWMGxo8fn+/mBE59fT0aGhp8XadnUerJJ5/EtGnTcM4552Dp0qXo7u4GAGzfvh3XX389nn/+eV8bWGqoolTV4CrTQNCqw6AoitbhV/+bqVPqrKfOwicvfIK9z9wbK55aIQ06t3PIuM2UEjt8tqKUIeg8q0ypLJGW7xl+H9snm7K2S16yFCa8alIZlO9VDqp0JUq5ETKA4JxS+Zh9z4soUzOyRje9uAxZ+Z6T48PRKeUi6NxYvudGYIyURfTHv4/iUZBPlrXZL41O0P59Xz6gHLsctQuSiSQajvB2c1WiZqeU+luK+37IuCGoqKnA5vc2S6eP17VLcAAlE0ntnHecfc8np5RMlCqvLteuCU+f+zQG7jxQe0/N1HHjlFKPMz9n33v77rdxxC+PKJryvTCLUk4l+YWGdJp64bCwOkacnFKA/pr+wsUvYMm9S3DwpQdj7GFj059ttxalNOeSB6eUlXA9+qDRWL9wffpzNkHnYQ05B9LX1Hh3MPmX2cKxkb+Mwzg0ojHfzSDENRuwAfMwD+PHj8fEiRPz3ZyCxPMd6I9//CPuuece3H///SgvT3eUDzvsMCxdutTXxpUifd2pTorWwfDo7tnzlD0x+qDRmHD2hNRn3Ja79Xdw9jptL5x878na9pNxc/mebdlWBuV7jqKU4JRyi6xDn7XTw6l8zxgo6sIpJftNfcuU8lK+1+9+qBpiLt9R2yhOLe12hrIgnVJungC7+Z29BJi75bR/nOa8XWPQuaRU1oiTiOemfM/klHLxW0bKDOWSQelIQTmwjNOnqzPBKQrOnXcufvDqDzwPyCLRiKtMqcavNmL0JHmmiF2mlChQ5CpTSiZKGY+57V+kHSdenFKaQBjVX4fd5kPJMqUA4F+n/Mt7SU84NalQi1JhFfIyRfZ9dEHnFseh+BtZ9YXETKkl9y4BACz+82Ld+nvae7JzShlnTLZ4uHDs9cfiiF8dgQveucC0TuP2wuyUUq+Bah85bHBsRAgh2eG517Ny5UoceeSRptcHDRqElpYWP9pU0qhPuWVPytx0GHY6YCec99Z52O3Y3Uyft8OUuSJkIhhFElunVAble3buEOPse26fkgXxxM9r+Z5TppRsGSCzTCnpIMvDGEI97qoGW2fKqOH5gI/le34GnUuOdasn2foPZrB9h/Nq6LihOPbGY7V/1+1Sh6P/cDROvv9k7TXjMSRzJRrx4pSyDDrPoHxPiUrKJQsI47knikmZfi+3s+9FohHL380uU8qLKKW7fmehHchEKbtSVC+ZUip+zr4HAJvf3Vw8mVKiKBUC14pYbhrWfeYG2TkuFaUS7volKmJfqHpYNcZNGQcAlplSujy29l7p7H1AZplSVtfxitoKHHvdsRi1/yjp54DCKN/TRCkLIS/fcGxECCHZ4bnXs9NOO+HTTz81vb5gwQLstttuvjSqlFGfcruZEtgNmQad63JuPDilkskkOmId+PCxD223J3b47IQmY9C5qxnfEFCmlEP5nnHmGqtp6HXrDNIp5WF5db8aB9kAtP2WUfmew372M1NKdqx3t3V7XHGG70kQB5Xl1eU46rdHYeeDd06/r+YaqQMYyUyXRjxlSrl0SrkRGJ3y0rIiB+Mgk6sgw+POuE6roHPx/FAiintRSshKEh1PXoLOJ543EYPGDnLxDczIRCm7a67olMrkXpNxppThWlps5XtiLlE+KTWnlK58z8UxJZ4bl39xOc6ZfQ4Aff9Jt/q4S6eUpP/ndL+TuWhl1x278r0wHHNWhF2U4tiIEEKyw7ModcEFF+DSSy/F4sWLoSgKNmzYgEceeQRXXnmlr7PQlSpqp1SbmSvL0qJMnl6Ln5O5N+wyS5KJJN75+zuO2xM7hHadDN0U2dGIa+u2UubwvX0q3zM5pcTFHez2Vu2wdEp5DO/MpHzPbrAulu/55ZTKOFNKNruiZFs9bc6z4GV0jrkpC5QcG6LDI5PyPSdnmS5TyiLo3Oh8cRV0Hi2QoHPjx9UqW+O56IOopopNsqyjSIUww1xUsTzOrYTDRG/m5XvVQ6px2ZrL3H0JA5V1ZlHKDp1Tyq0r18dMKfXvYgs6D0PpHlA8opRM7Hcs33PxfUXhOFoRNZWompxSQjt6O5yDzt3O5hutjErPP+l9utDL99yIUnk4VDk2IoSQ7PAcdH7VVVehtbUVX/3qV9HV1YUjjzwSlZWVuPLKK3HRRRcF0caSwli+Z5dZ5Aa3nxFLs8TPZZIp5cadIgovVhZ2wBx07rp8z6UryQuy8j3T72NXvhekU0r21TIo37MTicRjxLdMKR/L92T7oHu7R6eU3TY9ilfiIFptr7h/AynfCyhTSokG55Qq1LJAVYgpH1COnu2pferVKWU83ywzpRyEQ6NIm+k+lTml7FDLuzzl/QnHqEyUsrr+WTmlqoZUFV35XlhEqUOvOBTr3lgHILz7zA0y0dLJKeWpfM/CJW28nuv6Pe29iHfJ+zNeZ9+zuj64dUpp5XsezuNcE3anFMdGhBCSHRn1fK677jrEYjG8+eabWLRoEbZu3Yr/9//+n99tK0mM5XvZuhPcilID6gfo/q0OADyX7wkD6xETRlguJ3b43DqllGiWmVIBl+8ZS8pMQec5zpTyVL4nHHdWx0wQs+9lFXQOOIq23a0uRKksS2QtVys5XuycUrJzzYiX8j23mVJuy/dy4ZTKRSmfXwP+QWNSJXJiyasmSgnldpFoxFJ8dZMpFSmPOAfcZ/CdZMeS8eGEE6JTyi3GWRxNU71baAE6kVf4u3pIddGV74VFlBp/+ngceuWhAMK7z9zgVpRyE3SuW6/6MMdw/FtmSgn9np72Hu2B3Ljjx6F6WLX2nvQ6b7g26kQpiwcLbtYDsHzPLzg2IoSQzPHslFIZMGAADjroILS1teGll17CnnvuifHjx/vZtpLE1CnNkVPK2AkW3Ruegs6TSe072LlgxA6fa1Eqorgu33Ps1OegfM9vp5RXS7qX8j3RKVVWXSYNNw5V+Z4s6NyPTCnbjVr8bYHREQJYOKWi1ueaES9B524yzQD35XuFKEQNbhxses3tsWvF1BenYuGfFuKke08CAF2ulCZKVWWXKZWMJ9PnZJm1qKV9zuvMgWXyYHK78/Hk+0/G8xc9r3swoGUgeXBYZJwpJRy/4nldWVfp+Tz3WgqdK8ImSgGpSRuAIhSlZKKTR6eUmAEmYpUpJbbjye8+iRH7pB7c7X3m3uiIdaCzqROAxUQ3Nk4pq2ua7NpeEplSefwaHBv5QxOaUAFJxikhISWGGABg+fLlWa+rvr4eDQ0NWa+n0PAsSp155pk48sgjcdFFF6GzsxOTJk3CqlWrkEwm8e9//xtnnHFGEO0sGWxn38vS3ePpc+pA2WP5njiwtutYe8mU0gWdh7l8T7Evcco6U8rroMDD4qJTqqxKLkrpyvdC4pRSFEUbYGZ8rGd5jlmu1yFTSss78lC+5+iYkQhh2ntWopSb8j0HF6Bf+LneA358ACb/arLpdbelp1aMmzJOm2UL0Dul1HNcJ0pFFddOBvE3Uq+LkbKIo0jhVcSIlEekQpbdeqKVUZRXl0uvwZ6cUoayVtEdBrgLOu+IdaTfSJoH/9HKqOMEGmEkjKKUeH0qVFw7pYTXvHxfu0xO3frF63sS2PL+FgCp64V4b5CV75mcUsJ55KV8ryQypfIAx0b+Mguz8t0EQjyjQMHUqVOzXk91VTVWrFxRcsKU5975q6++il//+tcAgKeeegqJRAItLS34xz/+gT/+8Y+88GaJqXxP7CQEVL5nnEEK0NvPjQNlp0wpsezEcjEbp9RZT5+Fdx54Bx/P+tgUdJ7V7HsCvpXvGYQH22DSAph9L1IeMQ2g1TbqyvfcDuwddnPWmVIWTsLJv56M9x99H6f94zRvbbJ5z3OmlESU0p0TSf17roLOs3FKWQgHrsr3DJlSvpY5BiRwnXL/KdLXsxWljJQPMDulyqvTrykRRXfuiBh/T/F80IlSDmKk1ywYq/POTlyKVkQtjxU/nVJW1z9xG6qjBEjtJ6PoUFZVRlHKJwpRlEomk1h420KM3G8kxn1tXGDleyrG41+cUVXE6tguqyrTneOyoPOMnFKSa924KeOw4PoFutek/c2QEfagc46N/GUyJmM4hue7GYR4ogpVqEVtVuuIIYaZXTMRi8UoSjnR2tqKoUNTdu7Zs2fjjDPOwIABA3DiiSfi5z//ue8NLDXEkg0AOSnfqxpcZfm5TDKl3JTv2WVKNRzRgHcfeldbnxh0nlX5XraDaln5niTcVPt3LjOlZMs7lKgsfWApNry9ASfedaJ23EUroroBtUgQ5XtZZ0pZvLbv1H1xzB+PcffZLI8Fy0UkQefiOSGKrUB/yVaW5XuybapYDXTd/JbG2dUCCyfPwZjIrcvPLU7le5FoxDKryTRDovAbqXkzrpxSHsv3TJMyAIBif75GK6KW+y7jTCkv5XvCNg657BAsuXcJAKCvu890zXQSu9+++20c+ZsjUTsqu86j31CU8ofP5nyGuT+fCwD4ffL3gQWdq1iV7+m2oVjf28uqy/QzSkomurHNlDKclyfceQLm/XYeTv37qaZtNR7ViB+89gMM3X2oaXteryO5JOxOKY6N/GUcxqERjfluBiEkh3i+A40dOxYLFy5Ee3s7Zs+ejSlTpgAAmpubUVVlFjeIN4wuo2wHgm46GdVDqk2v2eXcOGVKqR0vW6dUIonWta149Y+vYvv67aY2y9wjSlTB4F0GO34fsf1+EsTsezIsnVI2IpP0PUMHu6u1S/f2rB/PwpJ7luDjZz/WHHoyp5T6PcSBdbTSOhBd99GAMqU0l5HF+WEn3tiJh0GX74nfV8xKU//t6JRyyhYSBzaGc9/qnHAj0jgd21mR44fz2WZKGZEGnRsypdw6pcTjI9vyvWkvTcNOB+6EM588U/oZt6Kl2Far8yrT2fegwLUoJW6jfq96nDvvXAD9TinDNdPpuvLWXW/hH1/9h+s25wqKUv7QsqpF9+/AnVJW5XvCOpSIYuuUkpXv2d2P7ILOv/Kzr+Cqpquw08SdpJ9tOKJBJ8gWnVMqD3BsRAgh2eH5kfFll12Gc845B7W1tdhll11w9NFHA0hZVydMmOB3+0oOY/men+4eK+ycUrJMKdVVI0Uo33MKOv/7kX9H65pW03uRsrQoJeaFKBEFx99+PJLJJAYMH4A3//Km5fqdSuVyUb7nJlNK1vH1zSklLP/QUQ9h3evrcOmqS03hzx2xjrRTqj9TSr+i1H/KqsugRBQkE8mUkOHGLeTklMqwfE89T6ychHailK1Q67K0z83xIw06L3MQpSS//fC9h2Prh1sBuHBK2QTvZ+OUUqJKTgYsgTmwBHwv35M5par1mVKWTinD7ynu477OtCilKAqUqGJ5bZBdW3Y7djec//b5AIBvPf4tlFeX418n/6v/A5IZw9yIUhbHih9OqWQ8mXqoYSVKGY4/dbayeHfcJDq4ua7EVsRctzlXhFmU8uIcyjuG0yHoTCnTAwCJkKdEFGk7AOvyPbt7jrhN2Xnp5VrKTKns4diIEEKyw1XPp62tTfv7wgsvxKJFi/Dggw9iwYIFiERSq9htt93wxz/+MZhWlhC2Qec5LN/TZUplWL5n17FOxBNSQQrQD4CNs+/VjKjBGY+eoQsalhFIp96hfA+Kfhk3TilZx9e3TCnhqe+619cBAD547APz9voS9k6pfhQl7fgoqyrzxSmVafmeVBgVNuXFKRVY+Z6wHW2mPeF4ER2AQOp3N/72l629DNVDHaYKF7dpc70Y9qVh0s+4nX0vMEdZDoQokVxkShmdUpWDXIpSiqJd+8XyPcBeaHG63u397b1110xFMYuMTusoqyyzPFb8yJQC+oUpidPWlNeH9HErK98Lk6jjhTCLUoXklDJmCkkf/mRRvmc83q3y+8R1KBFrUbmsSl++J3VKeSjf80ohzb4X73KXK5oLODYihBD/cHUnGzJkCDZu3IgRI0bgmGOOwcyZM3HggQfqljnxxBMDaWCpYeqUenRnGHHzBHvX43Y1vabLlDJ0pNYuWGu9smS6A2g3gLazxotOKWPQuYqXwGeNLF1nsvI9O2eK6cmmpE2yjq/lvvE6+Z5Dp1trQ1/C3iklfI2K2gp0t3bnvXxPm4pbFHmE/WYrStkMoG3LJTyef3b5TgBMswYaBeBoZRR1Y+t0r3nZX8ZtjtpvFAbUD9DPXAb3s+8FVr6n21CWH3fxG+U6U8pL+Z66jkRvQueUEv8rw8113vh5q3BmK4J2SgGp81o93qPlUe26JDtn1bZkUr4XVkIpSlnMJFdI+F2+Z5yh1k2mlF35Xnl1ub58T+KUzyTo3C3FVr7nlKfpFxwbEUKIf7jq+dTW1qKpqQkA8Morr6C31zxdPPEH0+x7ATulTph+Ar5y0VfMnxMyEbx0RnVOKYdMKStMmVJx/eAdcB5YygZJQZTvGd1TtrPvSdok6/haWfxtfwfZWzJNStJZS8TdOaWAdK5UWaVLp5TDfs7YKSXZR+JrmWZKuSXT2fdE3Jbvia9lOvhX/73bcbuZlnNdvhdU0HmOx0F+Z0qJTlPZ7Ht2Qeeya6R67Tc6peyup26cSsaZXDPJlMrUKVVZl/7+RrFW/F6iKCW+LjvuNedE9/9v787jpKbv/4G/ZmZ3ZvZmYRaWBZaFBWQ5dcEDdEVUDi+8FQUErVqrqEi1Fo9W/X3V2qq1ar1axVas2AOtaEVQV7zQIoInKFoQFBF2XW72zu+PJdlkJskkmWQmmXk9Hw8fsjOZJJO5Pnnn/X5/2mK+66yWBaeaK4NSHsyUiv6dU/29UAsQGcyUipmhNjpzSq2nlM+n2W8zK5ylyLg0lCkVMD4eikfKlHKgF6dd3Fi+x3MjIiL7GPolO/744zF+/HhUVVUBAE4//XQEg+pXfl9//XX79i4DRZfvJZzdEydwoBaQin6cVpBEjSB0NkaPV76nxefXKN+TDZgsZUolKk75XnQ2iZFMqWSV7+lpb+3MhvNn+RX9cKKJGR92ZUqZOnn0QTppUOspZTUoFbMNK/epUOspJScd8ziz71nt5aJ2gjHk7CH4dKGyhNNIYNAf8CcnUyoJ7C7fK+hZIP1bfC2NZEr5/D7VYIv4PSFmSomfEfn3R58j+0gludH3GZWMnlKDTx+MHz76Aac/dbp0W3RwTCtTSn672ntZ70TcTUEdMxiUcobdPaViglIamVLR5Xvi36HCEPbX75fuy87NRk5EpUzbYKaUPKBlBXtKWcNzIyIi+xganS9YsAB/+ctf8PXXX2P58uUYOnQocnNznd63jBQ9KLUzu8cM+UmGGAAYeNJA/PDRD9j17S6thxnPlIozs028TCkvlO/pXdkUmSnfS6TRuZ721nbF+y5mwC173sF8e3tKme1FIx4bqaxHtm9SoCrOemNOoK2UyBpYzFKmVLvyyjpgbhYoueiG9gAw+LTBMbcZOQGO18Q/EY5lYGmwOyiV37NzFivV8r2Ar+PzEtWoXOs7TPzebNrVBKDzhFMewD3sysNwzK3H4Knjn+p4jIWp3LUypc5ceCb+feG/ccytx+DVX7yq2F+zmVIHTTkI5y46V3O70YG59tbOvmry56v2/PQy3li+Z5+MCkoduJAjCALq1mo3wo8XlJL35JQvI37+g3nBmKBUbrfOMbVaT1G9nlIFvQuQiHQr30sWnhs5px71CEI9wEeUzurgvklYksXQ6LylpQWXXXYZAOCDDz7AXXfdhS5duji5XxlLXkYFQDU7xwyrgwz5SYYYAMgKZWHYecPw7u/e1X6g0dn34gxwHekpJV+/E+V7vjiz76lsU7V8TytTSi/zSe3pGCzpUwSlAn70GdsHHz/1sepmIlURbKzdiK6VXe3pKWXi5EsRlFI5yegxogeKyouQ1yPP8FTaZpgNnsTtKWW0fM/kyeCs5bOwc9NOlI4sjd0nvw9Xb7waj1U/hv0/dpwUqZ3wj5w5Eu2t7fjk6U86n4t7z1dMsbunlCJTSm32vQMBvVBBCI07GqXbNYNSWepBKUXmkN+neB5mgxjibH6K2w68D4edOwxDzhyCXd/tiglKmc2UUnvfR2cQiv+JFzTsyJRi+Z59MikoJf4+Lr9tOZbfslxzfUYzpRTle7LZ9+R96ICO7wtxNknAfKZUl75dNPfVCC81OndTUIrnRs5ZjMWp3gWilMkJ5yASiaR6N5LOdKPzZM+UlGnkDacB/StlRlg+AZc9TgyUycvqtMhPrPWuVsctSfLL1tduT6ZUwu9dtfK96GwYnWwstaCS6tVaK5lSBvtHqYku36u+pBptLW1YctWSmGUn/34yDr/ycEQGR0z3VVJjtUeSWvleIBjAVV9fFT87S6+nlI1fb/EypYrKixT3xcx0eeAhZsv3+h7dV/f+Ln27YMy1Y/D6Da9r7lt+aT4iVREpKBU9+56tAaoklwXa3VOqoKwzKCUeS/lJq/geDxYEFUGp3Ij6FXXxu79xZ8eyUlAqqseSYnY6C71g9D4n/iw/gnnKq9RWMqXUtqH2ufBn+dHW3Gaqp5TYj0ptJk43BXXMYFDKJlG7ajZTSi8gBSh7xgGx70+15vDy8j1F/6hQAP6AX5EpZaSnlHybRX2LdPc3nrTLlErSW5XnRs6pQQ1KUJLq3aA004AG1KIWCxYskMpu3SgSiaC8vDzVu5F0hoJSYjO/7t27Y/ny5Wzm5yC7Z9+zo3xPmuksEHt1PZogCMYypUyU74kDOTOzzcQ9SbOrfE+nEX3M66WWpWSih5Ad5Xvy8gTptjZB8Rr7A34cfuXhqkGpQDCAyOCO6H0qMqVEaieiPp/PcCma8gbDu2DqMYqMENn7cdqSadj87mYMOWuI4r7onlLH3XGcdLvd4mWhhApDugFXLw/A7S7fy+uRJ/1bDDoptnHgUIUKO5t9j/3FWAw7d5jq+qRMqZ36mVLxytt0+fTLWAFlc3IgNlMqOy9bmoHMTKaU0aCU/PlpfZ9nhbPQ3NIcczvL9+zjyaCUjLylQPTtRm4Tidl8gPWeUvLyPZH4+ZYHqY1kSsnvSzRTStyelTLgZHFjphTPjZxTiUpUoCLVu0FpZgu2oBa1qKqqQnV1dap3h6KYbnQuCAKb+TkounzPDZlSUv8ejca8coqeUjoDayvle2Yanas+70QTpeKV7/n1Z99TzZSSBRxWz1+NHRt3oKRK4+qQ2XMCvcQq2Xaje0oZZei9FS82qBM87HV4L/Q6vBf+e/9/Y7YnBdFsDtTqrs/kprQypQZMGoABkwbE3CfPMpz6wlQMOnkQAOuNzvXIP8dqn+mYoFRA/72diGT3lFKbgTAR8uDJ3u17ASgzKcT3avPuzuDJuJvHSf3Zokk9peIEpRSZRBaCGPGC6IHsgCITKTooVTqyFJvf7Wi2bjlTytcZlAJiszZFWr87WaEsxXGV77sXMShlP/nvm5xe+Z6arHAWWva1SP+WM9JTCj6oZkqJ/1aU7xnIlBe/H4DOrFurWL5nDc+NiIjsw0bnLhNdvqcYiFgYE9rRU0oKlAX88TOQhM6Bl17gKN6JttTk2WKjc9WToURPfk2W7xk59vJB6wsXvQAAOPzqw+Mua4TQLmDzis1SZpOc/Pi3t7Xrz5io8TRsyZTSCXL6s/woPbizL5JaUMpK6Zcd5Xt29JSKvk8egI0cFFF8BuwWHeCIFioMxWYGejxT6uqNV2PbJ9sw8KSBjm1j3/Z9AJQnrW3NHd+fOzftlG7TCkgBsT2lsnI71hWdOWQkk0iLWk8ptfd+MK+z5DC6fK/ssDIpKGUqU0rlc6EISqmU72k9P80eV8yUso3ng1JtJoJSOuRBqej3nVZ/tuieUuLf8p5SUqaUvNG5gUx5+YQziWZ/FvcrBgAU9ilMaD1OcmNQiudGRET2MfRLlpOTw2Z+SRLT6DxBdmdKGekpZffse2rlgHF7SsVLQ3egfC9mdhyL5Xt7tu5R3b7eIFotC2vLB1vwxNgnlP0mDiymyJRqUTY6N8xIolS8/k46J9N6jePlM+2p3W91m7qPMxm8itdTSiQec3lwUL68E+V7isCARvmefPAfkwXoFAc30aVvl4TLXLSIpT29Du8FQPndp1Zqqkezp5TDmVJqgvmdQSl/tl9xMt7rsF5x12WmfA+AZvmeZqaUxsm4m4I6ZjAoZQ+t8nTFMhaCUtK/Q3HK97R6Sh3YD7XyPXmmVMv+juCX3m9O33EdvQOjy2yt6FndEz9d81MU9y9OeF1OMRWUStI1E54bERHZx/TIp7a2Fl26dEFdXR3q6+ud2KeMpjsoTVH5ntToPGCgfE8QVMsvosXNlBIHwoIgZRrIA1FxZ9BSe9oOlO9Fn2AlUr4namuKDbgAnQPc1sZWPDXxKbx7t84siDI7v9kZc5t8sKxVMhOP05lS0UFQ1UCpHeV7DmUARc8yFm9/FFmB8tkvVU6oEt63OKVRquV7Li7tkKRoF6/86kqc/OjJOGz2YR27IQ+gHvj+Gv9/4wEAZz17lu66ojOlxBNYReZQoj2lVB6j9t6XZ3T4A37F926PET2kf+/cHPsdA6gHPI0GpYxkSmn9DsiPTdeBXTHqp6NUl3MbBqXsJw/2y5kN9stn1PRn+3UvOsTrKaWWKSV/L4uffb3fpvIjy3Hx+xfjqq+uMvU8tJSOLEWoIPEAl1Pc2OhcjudGRESJMTXy2bFjB6644gpEIhH06NED3bt3RyQSwezZs7Fjxw6HdjGzxJTvJfjjajko5essRVNkShkp3zPS6NxET6nWpo5BiDwoFS+TLG6GToLle9IJXXS5nl6AQ62FhcpxEJ9vzLIHglqr56/G/5b9D8uuW2Zot9XElO/pnAxpHSs7glJ6ZUy6QSkby/eUd+o9UPZPkzMPmi3fUzxXJ3pKmWx0Hl2+5xQvlgUCHeUvoy4dpRokEYNSNTfU4NofrsXQc4bqrstIT6mEZ9/z6QdnRdGfT0Wj85xslB7SUV7b/zj1Pl1q73u1YK08KKVWSmwmU6qgV4Hi2Bx909HodlA31ce7DYNS9jPVU0qH/L0WyA4oPnPR70+1nlJas+/J/y1q3tPRJy1edm6vw3ppzuKZbuRBKaMzCycLz42IiBJnuBD9xx9/xJgxY/Ddd99h2rRpUmO/tWvX4sknn8Rrr72Gd999F8XF7k3/9QIxAKQWdLFywnb0zUdjw+sb0GdsH6n/h1H+gB/tre3SSZWd5XtbV2/VXY98IKyWKRXvWKhOIZ5o03iVmfaibzO7DXGQKh9kxcuUUmvsa5Q0+55Go3O1k1utAaAdQakuFV1w5PVH4p273lF/bHTQT29bRsv34jR4NrYSA4sY7SklK/WQTshl719HekrFyeIKFYZijr1XA0apJn1/+nzI654XZ+nYTCnxpFXRQ8rvi9sXTI/PF/tdrvb6ysuMgKgLA1l+/GTFT9C4oxH5PfLVtxOnfE98j8Ur3zPSUyqvRx5O+dMp6H14byz7RWfA3jNZfmBQyjayXbVUvudDzEUkeVDKn+2HP8uvuGCnePiB96t8uz6feqaU/DN1yE8OwdpFa3HIRYd07ofs8ZlM/llva26Lny2fJDw3IiKyh+Fv9dtuuw3BYBBff/01evToEXPfxIkTcdttt+H3v/+97TuZSewelFYcU4Ff1P8CTbub8IeKPwAATn70ZHy68FOMvXas7mN9AR/QCkXAwkj5ntToPIEZkOIFpYw+3k5GyvcUy0cNIvXK9+QDZM1MqfbYZTvv1Nvz2OUUmVIa2QnxGBokG1jk+N8cjx8++gFfLflK+VCdTClT+xBFt2RQZ31mtxWTaRRnOaFNfaZJJ3pKWSnfk7+Wtl6ptpDt5iXi95dR4S5hxd9aPaUU369WJsEwkF0VnSklPxH0Z3WU82kFpMT91LvNaPme3ux78n8fdMpBqo/1WlDKl+We/VVr2u12WpnAcnpBKXmpnSgmKKUT2Bf/ln/25ZlS8mCvfD1T/jwFJz18kqHZ9zKN0xdqrOK5kTPqUY8gtDPpiayoQ12qd4F0GA5KPf/883j00UdjvnQBoLS0FL/97W9x2WWX8Ys3QWL/JnFQYsfJX07XHMXgqLh/MWa+PjPu46S+CCbK94xmShndtiIopTHTkt7jlTfK/ulE+Z7PZ/oEWwo0yQbAWj0TdINSJikypZr1G507Wb7XuaD6Y9Wy08ysw9A+JZg1pyWhnlIOl+/Fa3QeLAjGBNUy/Uq9VWaDUoW9lTNgSUGpqHI9+d9WfieMfA7kGR2A8jtYLYAdKgx19sNR2wbUMwhVg1JxSkyBqECBbPnoLCsrPbdSgZlS9pAHoaxkSsULSkWX70W/P8X3mzzrWd7oXP65iv6MKILNzJSSKCb/cNF7kedGzliMxaneBUpTOeEcRCKxs5JT6hkOSn3//fcYOlS7F8awYcOwdat+SRbFF12+l52j33vAKHmWkdETGGlgJc4IGPDHH9wLsNQ0O5qRTKkTHjgBWz/aitV/Xq35eMVtTpTv6TQ6NzP7nnzQrFW+J82cZ0OgUj6oa2tus3QyJH/uY64dgw//9KHUB0dtGd11qQy4jWRKWRm0W+4pZZLZnlLy94DiqrDDmVJq+5admx27/5l9TmSZ2dn3CnoVKP7WnH1P/rcNGYOq5XtRmVLyx6h9V1z+2eX428l/ww8f/SDtZ7S4mVJtxmff0wqSRWcCan3+2tvaXRWwcmNQSq0/kttplacrlokTlIoWLurMYDSaKSXPepYHuhStCOLMQNv5h+ZiGcFMUCqZPad4buSMGtSgBCWp3g1ygQY0oBa1WLBgAaqqqhJeXyQSQXl5uQ17RnYzHJSKRCLYuHEjevfurXr/hg0b0K2bN5qJuln0oDSYH8S5z50LQRB0m0LHIx+8G53NSzVTykRPKTPldlrb1gtKiTNdGQ1KJUq1fE8vkyc6JqVTvifPhtHKrNDLlDI7CJNvr7WpVT+QqHEo5c934u8mYsT0EXj04Ec1l9GjGXDy6S9j5mT86F8djTdvexOT75+suQ7d9ZnMglOcbOicY0YHf6Mf63RPKfm/Sw8pxfQl0zt6DvmU7/dkXKlPx2yA8qPNDX60MqWie0plhbJwyMWHoHlXM4r6FpnbKZ+xz2b0b46iubPKd0Vh70KMvGAklv58qbSfMZvWCUq1tbRZnn1PUbIX9VjNoFQLg1LxeD1Tymr5XrTc7p0NxWMypTR6SsVkSqn0DNR9/zFTSuLWTCmeGzmjEpWoQEWqd4NcYAu2oBa1qKqqQnV1dap3hxxkOCg1efJk3HjjjVi2bBmCQeVAtampCTfffDMmT56s8WgyKrp8DwAGnzY44fXKAzqGg1LiwOrAPvkCBsr3BFlQyq6eUk0Wekqp7acD5Xt62SRmZt9TlO9Z6ClldJCm1uhcnillahavqEXVXu+Ey/cMZhtprUNu/K3jUTOvJnbGLofK90xnSskyapwu39Mqjep7dF+pGXdMT6zMPicy7eqNV+Pb977F0LP1Z9uLVthLo3xPpQ/YlD9NsbRvPp/Kd7nK6ztg8gD894H/dm7XQEldvAb/RntKGcqUCqpnSkU/Vmtf25rbVGfwSxUGpewh/860Wr4XLa+kc5ICK5lS4r4A8T8j0n0ZHoiSc2tQiudGRET2MDwau/XWWzF69GgMHDgQV1xxBQYP7giUfP7553jooYfQ1NSEp556yrEdzRR6s+8lQj7ITSRTKm6jc/kMYiYH1iNmjMDQc4cqtu2qRudqWVFRmTxWZ99TZC5p9ZQStINSiTQ6b2tqs9boPOoYqz22+7DultYl3hZ9zCffPxlLrlqCCXdPOHCjbHkDA/i4J6B6iVIJNDpPpHyv12G9sLF2Y0x/n0REnxTNrJ2JT575BONvG6+6zzHvbafOCdLoHKxL3y7o0reL6cfFlO/lqfSUsuH7Ld7EDAAw8MSBOP+l81EytKOMIl75XvTtavupOJn3aQeljATA/EGNkr2ofdA6XvLsRDdgUMoeRjKl9IL9ap+F3Ih2plT0uEitp1R7m/qEIroXgkxm56YztwaleG5ERGQPw0Gp3r17Y8WKFbj88ssxb9486QTZ5/NhwoQJePDBB9GnTx/HdjRTqGVK2UE+yDIalIouKzJSvgdBNrA2GVib8ucpUuBJLShlZgpgR3pKxSnfi16nqdn3ZJlLLftaVLdvR6ZU9HaBjqu5eo3OtcQMxKNe7+N+cxyqLzaWamukpxR8wOFXHo7h5w1XnCAkKtGsOS2GG50feC9tX7tddfkznj4Db//mbRz6s0PN76fWvkVl3ZQfVY6KYyqidixqHz1wUpRXkoft2B5/QRczkimVcFDKp/JZ11jlwBMHdi4Sp3wPiP++N5opJZ+BzkimlCI7ymD5ntkm9E5jUMoe8t83uzKlcks6f3OsZEopJoEx+ttgtLQ8E8ivicTrs5jEtyrPjYiI7GEqb71fv354+eWX0dDQgPXr1wMABgwYgK5duzqyc5koGYNSq5lSemUQIkVPKZOBNbWUdkOZUj7EDELinbQlo3wv5iRPp3xPftW2Za9zQSlxwBTdwyrRRufRjy09pBRHXX+U4XVple+p9ZSSB6TsbgRrtKeU2fI9vWCfuJz8dZd/Fgp6FuCEP5wQd3tmGCrDigrCeqGn1JTHp+D5mc9j7HVjbdqj5AvmBxHuEkbjjkYA2rPvJczCKoycTJsp3xP3QR6UEr+bomfQU2O4fE+np5SbMChlD/nvm7x5vpxuUErl/ZbbTZkppRckFh8fHRxTK98z2lPKCxcFnCTNbiy4773IcyMiosRZaqZQXFyMww47zO59IThXvqe2jXhiekoZKd8TOgdeZp+D2smK0BY/KCWf1UZ1XVHrtMps+Z6RTClx8KwYvGoMuKTbdYJb8UiZWfLZ95radBudd6noor6yqOOpOIlMoNRNflvcEjg7BuoODfwN9w2JU+LkBEPBBQ/OvlfcvxgXvnVhqncjYb2P6I2vlnwFwJlMqbzueTGfUUOB1oD295sobqaUygm/avmevHG5yZ5S0QE8rcczUyo+TwalHGh0ntM1R/q3P1t5gS76Pa32eEX5HjOlLPEH/IrvCLfhuRERkXXuGfkQAOfK9+S6DTI2E0hMTykDjc7l5XtmnoNa/yBAedKgFZRSO+GIe1JvV/meXtDEwDak2feMZK+p9IOS7jI4SFMLgmk1Op/15iwMPn0wTvvLaarr0suUMjsboGb5nt7shgbWYSezWVlGe0qpvVed6Imm2KZK02y9fYjONrF1ym35qnjeBQDoP7G/9G+t2fesmPbyNPQ5sg/OfObM2DsNrNJIsNRUppS43niNzjWCNJpBqaj3N3tKWefFoJQT5Xv5pfmyB+sHltQ+J0K7eqZU33F9NfeDmVJKXnwvEhGRMe6ZdoYAODsoveSDS/Dj+h/R+wj1qWujWekpZbXRudaVRnnTb71MKUO3JdqoWa18LzpoolfiZbB8T4sd5Xvi+0vRWF3eU0r2mvWt6Yu+NdoDZt2glNlBo1oSlC9+ppQd5XtOXY02fDXc4PvXTnpX+aXbfcpleKU+eYadOwxL5y5FXvc86XvPSCAxngGTB2DA5AGq9xl5fYP5wbjLmOkpJX4nBkIdz7G1sdVUo3NFUEqWHRVd+ueVnlJCq3bGaqp4MRCQcKZU1O/6iQ+diIKyzgkImvc26wZf1d5vQptybHTl+iux+d3NGDF9hLH94PevJ9+LZE096hFE/N8bSn91qEv1LlCSMCjlMk6W75WNKkPZqDLDy4uDLnlPKSPle1KmlInZ8qLXqxqUCmkEpWSDw6ozq7Bjww70OqyXyoKGd0d9O/HK93z6s+8ZLd/TYmv5njxTyuLsezU31OAfZ/9DmjFR/p418nzkNE9effrL2H712OD6zPaUMhuUcpqRUjDd8j2Hzgl44tWhoKwAV319FQKhQMwMdUBq3jMA0Pfovhg2dRi6DdbOto2XKSX/rhe/E8NFYQBA084m1fLvRMr39Hohui0oxUwpe0T3lEokU+rQ2Ydi9E9HK+5v3t2snIkyevyi8n6TB8f8AT+6DuiKrgPi9BxippSCF9+LZM1iLE71LpCL5IRzEIlEUr0b5DAGpVwmGeV7RkkldPJMKRONzs0E1uJlSun1s5IPIM/55zkQBCH+ya1T5XtmM6XaTGRKCTZkSrXFZkopGp2byMAYctYQzPlmDgp7d8wWJj+RMtpMX2Rk9j3HyveMPszkCYLhnlJ2NK02Se+EShRTvseAUVIV9y9W/K3ok+REUMpgSapq6Z9MvPeW2r6HuoQAAI07GlXL98w2Oo/OsmKjc+u8GAiQ//4kWr6ntlzzHguZUhrle3qYKaXkxfciWVODGpSgJNW74WkNaEAtarFgwQJUVVWlencSEolEUF5enurdIIcxKOUybhqUiicUUk8pI+V7bYIUfDHzHOJlSullXcU8VmPwlvCgzkj5nsbyADD4jMHY+MZGxW1SSZ6JTKmEZt9TyZTSKt8zoqi8SPq3/CTSSJBNTrPReZyeUm4u3/NMppTWCZL80AZ8lo9vXMwGMMTQa5YAu977VnpKiZlSjTsbVcv3jGRKyb9/ovtveaV8z02//yKvBAK2r92OZdctw7hfjVP8vpkt33v9ptcV72G1xzbvaTbfU0pevmf04g+/GxW88l6kxFWiEhWoSPVueNoWbEEtalFVVYXq6upU7w5RXO4Z+RCA5My+Z1RMppTOLEYieeNYs43O1f5u3R8/KJWswZpagCQ68KAXRDn08kMx9YWpitv2fL8HL1zyAr5979u427elp5RYLiiffa+5zfQVXDXy52s2U0q1p5TJTCmnmQ1eJdJTymlGpiTXe29T8tnR6FyXTau00lMq3OVA+d6OJtXZ90xnSkWX73mt0bnDs2+a4ZVAwN9O/BvWv7Qefz78z8YypTQuBL11+1vYtXmX6nKDThkEoOO3nJlSySdNvmPyohcREbkfM6VcxlXle1E9pYyU78nLIews39MLShk+QUs0UUqlfC+m55FO+Z4/4MdBpxykuO3rpV8DAFb/eXXc7UtBKZXeVIk0Om9r6jwxs+sKvemeUhrle2Z6StlSvmfjuF+xvzrrTXmmlIGeUv6An1fqU8wNPaWMiJspJbtf/C4LFR0o39vZKH03KWbfM9tTKiqrTOvxzJSKzyuBgB0bd0j/VmRKWegpJSd/3uc+dy721e1Dfo983cxFzZ5SZns3MlNKwSsBUiIiMo9BKZex0o/JKdGZUkYanVvNlLKzfM8xKgEQ3fIxuweRYp9zm8v35I+162TIjvI9+KMCgWqBKxvK9zTXF3Onxr+1FpefmOi8PKnIijBSChYdhJUfG7XAqB2YDaDNSONvU6KrjW069qYypQ4QM6XkPaUMZUrJs8eyfKq362ZKuS0odeB7MxV95rR4MRCQ6Ox7iuVkv5X+gB/5PfKlf4sMl++ZzIRjppSS+LmI99o59ftERETOSX3kgxSk8j0XXCm10lPKsUwpjZn3ACArx1hsNdFBnenyPZsHkXb2lNIKGtkVIDGbKaVavueL31PKDlZeJ7Oz79mxnJ0MNTpPwrEn4xzPlLJplXEzpVQ+O4rZ92zoKRUd0PJKo3Np5kE3le8ZDAS4ifz3zUqjc8VyGr9lySjfY6aUkhcDpEREZIx7Rj4EwGXlewcGAE27mjr+DsQv35NnSiWr0fk5/zwHBb0KcPqC0/U3Yuegzhf1f8SWm9k9iLS1p5TGQDtlmVJWZ9+zo3xPY30xd5lcv/w9rXfl1jPle8nAEy9NTveUSlamlEJUT6ntn29Hy94WAPbNvucP+DUfz0yp+LwYCIjOlFL7vbNSvienF9iPW75n4fuUmVLefC8SEZExLN9zEUEQcNjsw9DW0oZgfjDVu6OavRRvMCVdefZpBzgqJ1Xi61e+jrstwFhQqmx0GeZ+O1d3v+wgDyxI5Xs6QRPbM6UE7aCUXnmYYrF4mVJ2BaXMNjpX2ayhnlKKB5jbpNrjDL9mRsr3jGZKpeAEVHGV32D5HqWW07Pv2SVeppQasaeUnJHnqxWUig5oeaXRuSszpTwYCFCUp7skUwpC5++ilUbnDNh7871IRETGMCjlIj6fDyfcf0Kqd0Oilr2kNbgfMWMEPn7qY0X/Ka0T/G4HdcPuLbux7ZNt2ts68NiW/QeumuvNvmdQwkEi2ThIKt+LKnFychCpliklCAJ8Pp/hQVrL/hZ8+PiH0qyG0ew62bWr0Xm8TKlUXT02VL5n9MTDA5lSydpHZgNoU5Skubh8z0yZoRhoFzOlFOsx0ENLEZSSL2909j1mSsUlHTuh8/fG7RSZUjY2OpdTvCf9OvfJ12W2PYPdmcAex6BU5qhHPYJI/cV5L6tDHQBg7dq1Ca8rEomgvLw84fUQ6WFQijSpNe/UGixH95/yZ2nP1uXz+3D6X0/Ho4c82nmbRqaUODOcHUGpRCkGQho9kJwcRErbb1fe5gsYD0p98e8v8MW/v9C8P1WZUloNkeP2NbLheBsOJJpcvZt7ShnapmyRmJMsnhMknZEeS4lIZfleqDA2U8qu8j29iyluC0q5OVMKQMfr5YHYiDyQZGejczn5e1Kr/UA08f1mpdG5F4670wwHpfj75HmLsTjVu5AWfPBh+vTpCa8nJ5yDdV+sY2CKHMWgFGkyVb534GYxUyp6ti7Fenw+lB5cioveuQhPHPlEx8PjDOqyQja8VRMc1Cma6qoEyWIGosnIlGoXgIB9Vw7tCpCYnj5cLd5kpKdUnHVY3bYdj1G8p102+56Cxr7ple85NrsRT7w0Od1TKlmNztWo9VA02+hcr3wvXuaKW7g6UwoHLoJ4YNIDeRBKq3zP6G+UkVL3mPI9jdeveU+z7v0xmCmlIAWlTGRieyW7j5RqUIMSlKR6NzwvjDDykZ/QOupQh0WNi1BXV8egFDmKQSnSpDbQMpMpFW/wqtfXJvqxdmdKWRmk5HTNwfj/Nx7wdc4Wpegz5fDse2LwQBGAUrvNIr1Aoll2le/F6yll9zHWW5/pbRk970hFppTsuWgFmFJRvkfaHJ99zyamMqX01hM1g54azUwpg+V7bps63u2ZUl4pm5L//sgzpab+eyrWzF+Ddc+vSzhTSu99Hu99z0wpayyV73kku4+UKlGJClSkejeIKIkYlCJNZnpKiScNUk8pnfI9cUChN7uXE0EpOwIYR990tPb6kzX7niDE3mbDyYJdpXuAhdn3NErzkjH7npWBv6GeUgYCP0BqAgzh4jDKDi1De0s78nuoX0WT73+yTpJ5RVubkSBNIoacNcSW9ZjJlJJ/Lo7+1dF487Y3pb/l2U5GMqXkyxttdO62IIsXMqW8QKunVH7PfOk9I7QL+O+D/0VBWYH+ujR+y/QuqsX7vmSmlDXicTXzPhQEAT5GpYiIXI9BKdKk1lNKa7Al3S50/q01iBIHFIorjUnOlLJNVPNzJzOlNMv3YFNQys7Ag9nd0Srfi9dTKs46bOVQwDFVmVIXv3ex7vY5+567OJkpde5z52LQyYNsWZeVnlIAMP7W8VjzxBrs+nZXx3oSyZTKMpgp5bIgCzOl7CEPJMnL9/xZnX0xt326DR8+9mHcdTmSKWXwAhADUUqWM6WIiMj1GJQiTWqBIiPTxwPuzJSS748jZRvRzzdZPaXgvkwps4zMvqfZXD7hjdu8PjU6L0+qAj5xy2tTUb7HczBNTvaUGnzaYNvWlUgwUzPIZLKnVHRAS2s/3BZkkX4bXRQE9mJQSh5I2rt9L/Zs3QMAyOueJz2ffdv2GVqXoUwpgz2lRIaDjg5mXnuR0aCU28pyiYgoPvdcjiPXUbv6F698T6TXU0pt4B03UyrkjvI93fVH90CyO1NKUAlKqdxmVSpPhDRn3zMRGHH89XUoCy76eR121WG2rTsRekFjXn1OPrszpRz7vMi/A02U7wHKYJLp2fc0HuulTCmpfM9FPcO8GJSSl++tf2k9hHYB3Yd1R2Gvws7nY/AQO5EpZfS31tEelR5kJVOKASoiIm9gUIo0xWQv6cxiFH17W3Nb3PK9pGdKyddvV0NvnUbnzJQyQaN8L+4Jrg1XklPdTFb+vEZeMBIn/OGE5O+EGvlhcVHmRqaSgi4+d5+gmiq5jaIIJmWby5RS/J5EBfC8EpRi+Z495NlNW1ZuAQAMPGkgAPPvyREXjFC9Xf6dGP16xXv9mClljdGglOL70RtvWSKijOeekQ+5TvQVJt3yvajb927bG7dkIuk9pRwe1MVcLdU4cbzso8vQe0xv0+tP56CU1UypZM6+p1zQvm3KPwd2ZATaJRXle24OtqSa+Pl0UxaNKhOZUtEUgags7d8HkeJ3QfYVmBXOkvbFn6XdC9FtQRY2OreBT5kpJeoxskfH3Qeej5HvmgvfvhCHXHiI+mb0GvrHWTUzpaxhphQRUfpiTynS1N6iHNgZanR+gNAuxD0RSGWmlG2iGp0bubLZY0QPjPvVODx9wtOWtqUYkKndZlEqr85r9pQykXVhedBu9GEJlGYanX1PflKearrle5R0YpDG7a+FqUypqI+F1gx6RjKl5N+B4S5hHHn9kcjOzTZUSu4Wbs+UMjurair4A37VkrusUMdw12j5ni/gQ/mR5drb0Smn9fk6Lqhovb8MXwBippSCeJxNvQ/d9REng+pRjyCCqd4NAlCHulTvAmUIBqVIU1tLm+JvMz2lRNl52WjZ26K4zVJPKRuCUsnoOWT0yqb8+WbnZqNlX4vmsiJpgCvE3ub1TCmtJuZxs3XSrHxPfiKeaor3crIyN3jipUl8b6RTppReTykjs+/Jl4le1/G/OT7ufrgtKOXKTCnZ94DbjpcaX8CnmiklZqFKgQ2VZRTriTNe0Mv0FrejGZQyGHRkppQSG51njsVYnOpdIJmccA4ikUiqd4PSHINSpKmtOSoopTOLkdagP68kDzv27lDcpla+59lMKbnoQ6AzhpQ/v2BB0FRQSjHddZoEpbTK9xIpBbK0H3qBxESCVwZn30tpYDCKXkCQg/7k80r5nm09pQzMvie/Xe870CtBKTdmSgEd31FCm+C646XGH/CrZtKIYwgxkNnW1BazjFzcoJVe+Z54f2v8x+pvROPfGUo8bizfS381qEEJSlK9G0nXgAbUohYLFixAVVVVqndHEolEUF6unTlKZAcGpUhTdPmez6/d6FwzKNU9Dzs27lDcpla+F68ngy29dhwY1CkanfuMB1HkxzGYH8TeH/bG35YYgJKVJkiz79kw8Erp7Hta5Xsmeko5Xr6nsd1EKZ6jmzIkUlC+x2wAbeJ3oG2BS6cOdQJlrlqNy418LnSDUnH6G7qFGzOlgANZPy4OSil+h7UypQ68t8TAZ2ujRsTIIL3Z98T726Ae+GKmlDVWekqxfM+bKlGJClSkejeSbgu2oBa1qKqqQnV1dap3hyipGJQiTdGZUoB2oEVrkJVbkhtzWzqV7xX3L9bchtHyvVBByNC2xIG3fMCdLplSWrPvxc26sPtKst46HDonSEVDcSN094sD/aQr7l+MEdNHoNtB3VK9K7qCebI+IPHezlHvI60Z9wydxOtlI2plSqn0Hkol12ZKWQkGJJF8rBKvp5T4Hks0KKU3+x6g/Z6b/sp049/zzJRSYKNzIqL0xaAUaYruKdXW3GZ49j1RXklezG3p1Og8XBTGnE1zOmd7kjNRvmeE4+V7qWx0bnH2PVu2bSVYaeOuxLvinjJJLp2M3iYp+Xw+nP7U6anejbgKygpw7O3HIisnSwoEGCUv37M1U8oj5XtuzpQC3He8RPKglFsypbRew8qJlYa3wUwpJWZKERGlLwalSFN0plRrY6vh2femvTwNgH6mlF6jUEeCUg6N6Yr6FKluQ28QGV2+Z4QUlJIPuO2cfS+VPaU0yvfiHU+7B+2Ge0qZZHj2PRdlSCg+gzwfIhNqbqgxtJxeo3Oznwu978B4M8G6BTOlrJH3hzLaU6plf/w+jnri9pSyI5DPTCkFw+9Dd75NiYhIh7tGPuQq0T2l2pqMZUr1ObIPBkweAKCjp1Q01Z5SviQEpZLAaDNsK+V7UgBK3lMqTcr3LGdK2TFot7COTOspFf18nSqJYDZAZhh23jAAwKE/O1Rxu6KnlE55txpmSjnH9UEp2QU0QRB0Z98Tf+eS0VMqmtlAFTOllFi+R0SUvhiUIk3R5Xutja2GekrJgxuDTxsMACjoVdC58IExgt7AO3o7ZstAVNeZjEGdwUwp+fPLzs82tGq1TCk7g1K2nAjZERiS3ZbITF6OMrsrBvvduOk5MlOKnHLG02fgxv03oktFF8XtWuV7RgLmhb0LNe/zSlCKmVLWtDZ1BpjaW9tVe0rZXr4ne09G95YENAJV2SZfV2ZKKUjvQzO94Nz5liUioigs3yNN0ZlSgWDA0Ox78hOLrgO64uqNVyOnaw5+U/gbAOqZUnrrE7dtq2QM8HS2IT+OhhudOxyUsiNTyh/wx51KW00qZ98zvA6H3jPxykBSJVlX6Y1mF1L68Pl8qn34rDQ6n/rCVHyz/BsMnzZce3seCUoxU8oaefme0KaeKWV3o/PGnY3Sv/se3TfmfrXX0OxvLDOllJgpRUSUvhiUIk3yTKmDTj0Iw6YO0zxpVMxEEzXw6tK3i+JvtZ5S0et1vKeUQ+MUK7PvGW10vvzW5Rh0yiBFv4yGDQ3Y9M6mmACiFXZcnfdnWQtKqQ0co3tKxc3rtDNLK95DTJ4gsKcUUXxaPaX0gjQHnXIQDjrlIN31aj3ebUEWZkpZIy/fa29t1+0pJV40kweyrPjuve+kf6u1KVALhMov2BnCTCkF8XPBRufprx71CMLY2Did1KEOALB27VpH1h+JRFBeXu7IuokSxaAUaZIP9KY+PxUAVAd7QNRV7Tgp6lKmlInyPTuCUsku3zM8+57BRucA8KfRf0LJ0BLp76eOf8rM3umyI1PK6hX+6Kb6gIWeUnbQe80cev94onwvWdtkNkBG0+oplWiQxguZUvLANTOlzIku39Obfc+uTKnD5xyOTW9vwsEXHqx6v9p7lplSiTH6PmR2lPctxuJU70LK+ODD9OnTHVl3TjgH675Yx8AUuRKDUqRJLftGa3CvlykVzRWNzpMRnzI4+57hRucHbP9su+V90mNX+Z4ValetfX5f3J5SSS3fUzzI0qbib99NJ6PyRCkXBcsofWn1lEr0c+GJoJSsTw4zpcyRX9Roa2lTzY4RG52bzlbSMOTMIZj95eyYvmgi1Z5SZn9jmSmlwPK9zFGDGpSgJP6CaSiMMPKRb/t661CHRY2LUFdXx6AUuRKDUqRJNXtF46Rdq6eUGtXyPZ31AZ0DyoQkOxCll3VjoXzPaXYERJKeKaV4gKVNGyd/ac0ErwSgckKlscVdFPzRCxqzJIKcoFW+l2iQRuvxbgqyyLOQXRWchjuDUl+++CVqb67FaX89TXlRQ2MXxYCQ6WbjOroN7KZ5n2pPKZPbZqaUkqX3oXvesmRCJSpRgYpU7wYRJZG7LseRq5j54bdUvqfTs8bxTCmnGAxcWC3fM8vMcbMlU8riOuTlFyKfT9lTyrHyPQevRs/9bi4ueO0CDDhhgKHl3ZQh4aYAGdlv1KWjAAC9Du+V4j3pJL+gIf8sMFMqtdwYlHrmlGewdc1W/PPcf6pe1JALBAPS77FdmVLxMFPKfkbfh/KxFzOliIi8wV0jH3KVnK45hpdNtHwvZn0++4NSSb/SqLO5RMr3zDDzGtoRlDp41sEAzJ/otjerl4rqZutEL2/D66sbSLQwS1xBzwL0O7af4X1zUyCIjc7T28ATB+KKdVdg1vJZqd4ViaKnlI2ZUl4ISnkiU6rNPcdL1Ly7WfWihpw801rvotmxtx+L3mN627Jf7CllP/aUIiJKXwxKkabpr0xH7yN648K3Loy7rOIEQmPQl1uSCwAYePLA2Mcnu6eUQyzNvudgplS4OGx4WTuuzo//f+Nx7nPnYvoSc00aVTOlonpKqXE6EOXkdlXX66aglM572c5BP08gUidyUARZIfdU8SvK92TfkcyUSi03ZkqJAsGAoUwp6d8amVKBUAA1N9QgOyfblv2yffY9ko6p1oQ7qtz3liUiIhXuGY2S65SNLsNPVvzE0LKK8j2Nq4E/++Rn+Pa9bzHo5EFx1+fVoJSl2fdkPaUCoUDCU1XLhbuYCErZkCmVFcrC4NMGm36cZqNzM32k7C7lSwFXZUiwdISSrPvQ7tK/5YHQhDOlND5XbgqyKDKlXBScBjqPv5uOl8if7VfN4Oo2qBvqv6wHoBw/aF00E4955aRKbHh9Q8Kvgd09pfgdzEbnRETpjEEpsoV8AKZ1NTC/Rz4Gn2osYOHZoJSMbjaNbJwkL987bPZh8AV8GHjiQPzlmL8kvA9WSzCTTavRuZnAiOXsJaMPS0KQxk0no7pZfxznkwMGnDAAJz50IkpHlgLo+E4S2oTEM6U0vhvcFGSRB1ZcFZyG+zOl1DJnelb3lIJS8mxArfGJ+B454pojEO4SRr/j+iW0X3aU71maXCONiZ8LNjonIko/DEqRLYxkSumK0+jcTSUmeoxe2Wxr6QzCyMv3/Nl+HH/n8bbtT05xcntKWaVZvhcnSGN3+V6qB/5uK9uR8HyIksDn8+HQnx3a+be/Iyjl1OfCTUEWKbDiS/33UDTXB6VaY4NSheWFimVE8TKlAtkBaRKARNhRvsdMKSXD70PZ3cyUIiLyBpeeAZHXGOkpZXV9gIcypQxe2QwXdZbVyYNSdg/6TfWUSmFQSrN8L17AKInle8kIXrkpU0rOyZNkt52Ak3uInwenMofcFGQRM6XcGJh2dVAqO6BavlfQs6BzmVD8nlJ2f/eqlu8xUyohbHRORJS+vJF+Qq5nZvY9Q+vzaFDK6JXNnK45mLZkGrLCWYrnlsqglNvK9+Azd6LgePleoo8xslqXle1IXLpblN78AT/a0JZRmVJu/A5wdVBKo3wvv2e+YhmR5kUzmw+72m8Xe0olxkqmFMv3vKke9QjCuUmAMlEd6lK9C0S6GJQiW8hPGkzPMKNC3vwb8E5QSi5eQGXApAExt9k96Pd6+V68TCi7rx7rri8JJwW9Du3l/EYs4FV6SgVmSrmDm4NSWo3O80vVg1LJypRiTyn7WcmUYtaUNy3G4lTvQlrKCecgEomkejeIVDEoRbZINFMqesAVPWucHSWBetuzb8WJbSNTy/fUeoYZmn1P8QBr27ZyNdru98+cb+Zgzw97EBnszsFCUXmR4m8O9CkZxN8VZkqllpuDUlqZUvLyPflvm9bvnO0XOFR+u8x+vzNTSomZUpmjBjUoQUmqdyPpGtCAWtRiwYIFqKqqsn39kUgE5eXltq+XyA4MSpEtFD2lbAhuyGekA7xzlTDhQaTNAyhTmVIpvEJ/6vxT8Y+z/4HqS6uxdO5SAOZ7Sjn9HnHyBKGovCgm8OMGF759IfZt34euA7qmelcoAzFTyh3cFpSSB8W1Gp3LM6XkPQvjNTq3i/w9O2L6CGTnZePY/zvW5Eo0/p2hpPehSmacHDOlvK8SlahARap3I+m2YAtqUYuqqipUV1eneneIksp9ox/yJPlA2tIJRJzZ97zIDZlS8r4a8aQyU6r7sO64Yu0VGDZ1mHRbsmbfY4mEtvIjyzH4tMHS36HCjmDxwBMHpmqXKIOIpVZ2lIRfsfYK9DpcWR7rliALwEwpM+RBJq1G5/IWAC37WhTLq3Kwp1TfY/ri5EdOlr4/Da/DRTPDuoGlTCkiIvIEZkqRLRTley680hvNsatnCV7ZtHO/Rv9sNMpGl0l/9zqsF7r064LPnv1MdXk3nAzJB/LJKt+zIlNPEOZ8Mwe7vt2F7sO6p3pXKAOMvW4stq7eaktZa2RwBH3G9sF3738n3eaWIAvATCkzWvbLgkwa5Xvy72j58snKlFJcqLO6bmZKKYhjFFOz77njLUtERHEwKEW2UAQTbApu+LP9aG+JHWy6WaJXNu0a9I/66Sic9NBJyht9wEkPnaQZlEplppRIcfyiG52rPkD9sVa3qbs9nhQg3CUc0++NyCljrx1r6/qig/5uCbIAnfvihosD0dwWlGrdL5sYwxe/nMtIppSTPaWsBhqZKaVkJVOK5XtERN6Q+rNQSgvyQZcdjc4BIDs3O6F9Mrs9+zdi/iG2DfpVti20C7pXbN0QlIoOMin216mXzEpgi+cHRN4T9fXqliAL0Fm+x0yp+OSZT0KboNpTSrH8vuRnSsmDi7asm785lmbfY6YUEZE3uG/0Q57kRPleMC8YfyG3ccnse2rbjhuUcsHJUPT+xTuGyZydiFetidKLW4IsQGe2DzOl4pNnSrW3tauW72ktn4qeUnaU7/E3x9r7kJlSRETekPqzUEoLdjc6B4DsPOcypZySaJAk0zOl9AbeTs2+x2m3iTKDm8v3vJApFS/4kyzRmVLxyvfampM/+17CYyLwtykaG50TEaUv9pQiW9jRPyGaJzOlZNyWKQXEDo79WX6p9MEVV+jd3NjVzftGRKbFC2YkkycypVxyvBSZUq3xM6XkUtFTiplS9mD5XuaoRz2C8PY5gBV1qEv1LhClDINSZAtF/wQLg+pk95RyTIKBi6LyInt2QzYIPunhk1B7cy2mPD4lZnAcCAakoJQrMqXkA/no94RaopQdV5I58CfKDOwpZYn42+CW4yXPlGpva9fsKXXaX0/D8zOfx1nPniXd5qWeUsyUUjKcscdG5563GItTvQspkxPOQSSS+IyzRF7DoBTZQtHo3KZBtReDUlYHkdOWTMPaRWtx5HVH2rQjnf8cfdlojPrpKPh8PrS1tCkWC4QCUhNYVwSlzAaF7A4o6ayCPaWIvM3N5XtuzpQSfxuifz9SJTpTSiuDa+SMkRh6zlBkhTqHuj6fD76AL/YxDvaUsjwm4gUTBWZKZY4a1KAEJaneDcMa0IBa1GLBggWoqqpKaF2RSATl5eU27RmRdzAoRbZINFNKbUB48IUH43+v/g8lQx34YXLTTG4ABkwagAGTBti3G1HbFv+OyZSSlTK44gq9TqaZU4NyS+vl+QGR9zBTyhIxuyjeLHfJEjP7XlTmjPwCizwgJQpkB9Da1qq4zdGeUsyUsoV4TM30lGKmlDdVohIVqEj1bhi2BVtQi1pUVVWhuro61btD5EkMSpEtnMiUGnbeMBT2KUSP4T1sWZ9CMsYpqRxEamxbrXxP5IpMKZODd7sH7bwaTZQ53BSU8kKmVHuLxnAsnwAAWdVJREFUO4JS0bPvRWc9yX/X1Piz/UCj8jbbe0oleqEOYKZUFEuZUkRE5AmpPws16Pbbb8fYsWORm5uLLl26qC6zadMmnHLKKcjLy0MkEsFVV12F5uZmxTKffPIJxo0bh5ycHPTq1Qu33XZbzA/Y8uXLMWrUKITDYfTv3x+PPPJIzLb+9a9/YciQIQiFQhgyZAiee+45256r22WFY2OZtgzAotfp86FvTV+Eu4RtWV8yuKXES7PRuc+nGOi6LiiVwDGz/FijfcB4gkDkaW4u3xOzfezO2LGDFJRyYaZUe2tsTymtvlEitWbntveUsqHROTOllCzNvueej7gjeG5EROki9WehBjU3N+Pss8/Gz372M9X729racNJJJ2Hv3r14++23sXDhQvzrX//Cz3/+c2mZXbt2YcKECSgrK8PKlSvxwAMP4O6778a9994rLbNhwwaceOKJqKmpwerVq3HDDTfgqquuwr/+9S9pmRUrVuDcc8/FjBkz8NFHH2HGjBk455xz8P777zt3AFzkyq+uxNn/OBsHzzpYui3R/gnxrmx6hltmaNPrjSR7reTH3RVX6M0GflK1yy44VESUGDcFpcRsHzeW74lBHNf0lGrszJRSK9/TmmFPpBq0svk73Y7yPf7OKFnJlEr3rCmeGxFRuvBM+d6tt94KAHjyySdV71+6dCk+//xzbN68GWVlZQCAe+65B7NmzcLtt9+OwsJCPP3002hsbMSTTz6JUCiEYcOG4csvv8S9996LuXPnwufz4ZFHHkF5eTnuu+8+AEBVVRU++OAD3H333TjzzDMBAPfddx8mTJiAefPmAQDmzZuH5cuX47777sMzzzzj7IFwgcJehRhy1hB89cpX0m2Kq4ImghsT7p6AVY+uwvjbxtu6j27gxkwpoOO1Ek+AXJcp5de5MhzvcFod9xvMbmN2FJHHeaCnlCsuDkRxW6aUfD/UGp27IVNKfsnXaqDRLZnXbsFMqVg8NyKidJH6s1CbrFixAsOGDZO+dAFg0qRJaGpqwqpVq6Rlxo0bh1AopFhmy5Yt2Lhxo7TMxIkTFeueNGkSPvjgA7S0tOgu8+6772ruX1NTE3bt2qX4z+vkgySrPaXG/nwsrvzyShSUFdi6b3E5NL5zTbq9hUwpN1yhT3n5ntPbIqKUcXP5npszpaRG5y7pKSUPQqn2lIqXKaVyAcbu73TbM6X4k8NMKQt4bkREXuG+0Y9FW7duRY8eyobYxcXFCAaD2Lp1q+Yy4t/xlmltbUVdXZ3uMuI61Nx5550oKiqS/uvTp4+FZ+kuWlktbrzSmzQu6TsUL1NK5LZMKb3jp/qc7B5vGuwpRUTeU/0T5axIbgpKMVMqvi9f+hLv/eE9Rbme0CaY7imldr/be0rxQogsKNVmIlMqw/HciIi8IqXle7fccouUeqpl5cqVGD16tKH1qf1oC4Kg+8MuXkWxYxm9QcO8efMwd+5c6e9du3Z5/8tXI4Dgxiu9KZGkMWRWTpZiNiJAfxDs5qCUbvle3Adb3KaVgT/PD4g8p2x0GeZumYvtn23HUxOeclVQyguZUqnuKfXMyR0lQAMmD5Bua29tj+0pFadHpWomlc3f6XbPvsffHIuz77nnI24Yz436oB71CCKo+Ti3qUNdqneByPNSGpSaPXs2pk6dqrtMRUWFoXWVlpbGNNNraGhAS0uLFLkvLS2Nidhv27YNAOIuk5WVhW7duukuE32FQC4UCilSY9OB1pVAN17pTRbFj3OSZlHqWtkV2z7dFrUj2svLT3rc1uhcNyikdlcSM9N41ZrI+wp6FuDH9T8CYKaUUW7JlBLt3LRT+rda+d6wqcN0H6+aKWXzdzozpewnfjbM9JTyYvkez42AxVhs5Om5Sk44B5FIJNW7QeRZKQ1KRSIR2z7AY8aMwe23347vv/8ePXv2BNDR4C8UCmHUqFHSMjfccAOam5sRDAalZcrKyqQv+DFjxmDxYuWX4dKlSzF69GhkZ2dLyyxbtgzXXHONYpmxY8fa8ly8Qqt/khsyblImBeV7xf2Lccxtx+Dd376Lb9/7Nu623ZwplZKrwbwaTZRRDDdMTiJxX9yYKSVmFrmlp5Q8Y0ueKXXMrcegZGgJDppykO7j1TKl7A5eWO2zqcDfJoVMyZTiuRFQgxqUoMT045zQgAbUohYLFixAVVWV5nKRSATl5eVJ3DOi9OKZ2fc2bdqEH3/8EZs2bUJbWxvWrFkDABgwYADy8/MxceJEDBkyBDNmzMDvfvc7/Pjjj7j22mtxySWXoLCwEABw/vnn49Zbb8WsWbNwww03YP369bjjjjvwq1/9SjqJv+yyy/Dggw9i7ty5uOSSS7BixQo8/vjjipkjrr76ahx99NG46667cOqpp+Lf//43Xn31Vbz99ttJPy6ppLgSKAuC9BiufVUk3TnR6Ly4shgNXzdo3u/P9qPq9CpsXb1VCkoZbXQuv2LshqCU3pXhuEG+ZA7aeYJA5FmuDEodyPZhplR88uCY0CZIxy6/NB9DzhwS9/GBkEpQKl6fIpOYKWU/K7PveTFTyox0PTeqRCUqUJHw8bHDFmxBLWpRVVWF6urq+A8gIks8E5T61a9+hb/85S/S34cccggAoLa2FscccwwCgQBeeuklXH755TjyyCORk5OD888/H3fffbf0mKKiIixbtgxXXHEFRo8ejeLiYsydO1dRz9yvXz/85z//wTXXXIM//vGPKCsrw/333y9NeQoAY8eOxcKFC3HTTTfh5ptvRmVlJZ599lkcfvjhSTgSLhJ1Fe+Xu36J1v2tCHcJp2yX3MSuQWTVmVUYMW0E9v+4H38Z/5eY+8WrvlpBwpj9cvHse4mwerwND/x5TkCUFtwYlBKzfdz4PeyWnlIiRaZUW7sULDMa0Avmxfaqie5LlShFTynOvmcLN35uU43nRkSULjwTlHryySfx5JNP6i5TXl6OF198UXeZ4cOH480339RdZty4cfjwww91lznrrLNw1lln6S6T9mTjAp/fh1BBCKGC9OqbZZoDg8iyUWXoMaIHvv/we9X7pWwng9t2dfmeXKpL+fQW41Vr+/BQUpK58eSWmVL65K+VPFNq5zc7sfObjh5TRgN62XnZset3MlPK4mvKTCmlTCnfM4PnRkSULjwTlCJ344Cpg52DyMs+ugzf/fc7DDm7oxxBK2gkBqUMZ0oF1INSbjsZMn38rO6+lUCUuw4VEZngxqCUmzOlpJ5SKQxKyTOZtDK23JQpJX8dmSllD5bvERGlLwalyDoOmPQleEx6jOiBHiM6+3NpBaXEwJJi4KuXKeVTD0q5LlMqmtpzkmfrOV2+R0RpwY1BKU9kSqWw0bl821r7YfQ3LOmZUuwpZQvpcxvntcqkTCkionTh8rNQ8grLVwLTjYOz72kGpdR6Shl8PVwdlHJb+V4KZlYkIvu5MSjl5kwpMRs3pZlSsm1r7Uci5XtO9pTi7Hv2YKYUEVH6ct/ohzyJJ+kdnCzx0rqCLpXvGbyqKh+keb7RuR2DdiuP49udyLPcGJRippQ+I0Epw+V7+bHle8yUcj9xjGKqpxQREXkCy/fIHhwvxUhlppTe6yEf0Lk6UyqK6vG0oXwv7jZsXD8RpZ4bg1JuzpRyRU8pGzOlkt5TymqgkZlSClYypVi+5031qEcQsZ/TVKhDXap3gSgjMChFlvEqngoHB5F2NTqXD9LSqtF5ErfD9zuRd7kxKOWFTCmtBuPJYCQgZvTYJaWnVICZUnazMvses6a8aTEWp3oXFHLCOYhEIqneDaK0xqAU2cJrPaWcGuA5OYiMlylV2LtQtiPa69Es33NZppS8yTuA+EE+Ow63t97G6YHnDJRkbgxKiZk6rgxKuaynlBajv2HJyJSyo3yPmVJKzJTKHDWoQQlKbF1nAxpQi1osWLAAVVVVph4biURQXl5u6/4QkRKDUmQPDphiJSlTSgwsRQZ3XsUxmiklnmzorT/Zrqu7Ds27m5HXPc/U4ywHAY02MOcJAlFacGNQSszUcWP5ntt6SmlJpNG53ZlS8n2x+poyU0qJmVKZoxKVqECFrevcgi2oRS2qqqpQXV1t67qJKHEMSpEtOGA6IAWz74mBpW6Dukm37avbp7ke+YBOvk63nAzldstFbrfcmNvd9B5z074QkTluDEpJmVIuzDp2W08pLYYbnTNTypMsZUoREZEnuOMslDzPjQPpVHBy9r145XvZuZ1Xf+vWaTdmlF85VFzNdUmmlGVWx/0GXzMGoojSgyuDUgeCLm78HvZKTyk3ZUqxp5T9xOMYL4CoyI5yz0eciIh0uG/0Q97E8VKHFGZKyTXuaNRekcYgzY0nQ0lh5WXi+53Is9wYlBK/s8Ndwinek1hp11MqP8mZUpx9zxZWMqVYvkdE5A0ZehZKduNVPBV2Z0ppXAUWM6UA4PQFp6OgrACT7p2kuR6tfgtubLArV1xZrHu/He9Bwz2liMiz3BiU2l+3HwCQG4ktXU41N/SUMpKllUj5nu2ZUjaU73FcpSS+vmZ6SjFTiojIG9hTiqzjVbwYTqbbaw245ZlSI6aNwIhpI3TXoxjQyZueuzRT6oLXLsDGNzZi5AUj9Rd0uHxP8zGUGB5KSjKjZUDJJPYBdGNQyis9pRIp33OS5X6NDmZeexF7ShERpS8GpcgW7Cl1gIOBOq1BqTj7nmHymFS7en8pN+l3bD/0O7ZfqnfD0X5hRJQ8bsyUkoJSJe4LSnmlp1QimVJ2U1ygsiNTir85nH2PiCiNMShFtuBVvFjJCtTJy/eMkA/oFE3PXZopZZTl9yCvRhNlFKNlQMnk5kypdOsplexMKTtm3+Nvk8Vgsns+4mRCPeoRhL3B4zpoTwBERKnHoBSRjVIxW45ao3M9Wv0WvB6UsuVKMsv3iNKeFCQQOr4PU/15FgTB3UEpF/SUsrN8z3R2cYKs9mtkppQSG51njsVY7Mh6ffChqanJkXUTUWIYlCKyUwr6bJnNlNIasGVqCabhE1L2UCNKC4rvOgEp/zw37WqSgi653dwXlPJKTymjwR+fz4ecrjnY/+P+RHfL2PaYKWULK+V7zJTyphrUoAQltq6zAQ2oRS1CoZCt6yUiezAoRZkpCeM7r2VKeZ3js+8RUVqQBwmEdiHlAXkxSyqYH0RW2H3DMq/0lDLTF3Hud3PRsr8Fv+3620R2yxD2lLKHFJSKN1NiGo1rMlUlKlGBClvXuQVbUItaW9dJRPZx3+iHKBkcGrSkYhCZUE8pF/VUSZjV422hZI/BKyLvig5Kpdq+7e4t3QPSr6cUAGSFs5IWAOTse/Zgo3MiovTl8SYylEocJKlIwSDSbH8MrUbnXmTH/vNqNFFmcV1QysX9pADv9JSy2rvJacyUsocY3DPTU4pZU0RE3sCgFJFTHBxEhovD0r/Nlu+l64DN8fI99pQiSgtuC0o17epovBsqdGevE6/0lLKckeQwO8pDeRGw8zjGKyNlphQRkfe48xecyKOcLvE66ZGTMOy8YRh+/nDpNtPle2k0YLPlSrKFx/EEgci73BaUam/rCLi4dQZUV/SUMpCl5apMKbtng3XRU0sV8fX9cf2P+PKlL7UXTNMLb0RE6cydIyAipzk1wHN4EDn6p6Nx5t/OVGRHZXKmlO1BNb1EKQaiiNKC24JSYuNmVwVVZKTyPbdnSrk0qGcV+xgqlR5cirzueQCA1Y+v1lzO6xfbiIgyUXr9ghOlWLIGkfKTKrOZUnJuOCGzi9Xjbek14/kBkWe5LSglBlzcWn4mXfgQOrO6ks3L5XuWMVNKIZgXxPF3HQ8AaNnbor2gfIJhBqiIiDyBs+8ROcXBQaT8pMp0ppSM1wdsvHpMRGa5LijlkfI9oCM4lIrgj9candvd45C/dR2y87IBAC37tYNSinFN6j/eZEE96hFE0NZ11qEOALB27Vpb16slEomgvLw8KdsiSgcMSpF1HCPFStLse/J1m519T8HjAzZbgmpGr0Zzem6itJCMoJQgCPjbiX9DsCCIs/9+tv6yLi/fk2fjtre0Aynox85MqZTthatk53YEpVr3t2ovxEwpz1uMxY6s1wcfpk+f7si6o+WEc7Dui3UMTBEZxKAUkY2SNYUzy/dSjCcIRJ4l/5526jtwx8Yd+GrJVwA6sjqyc7I1l5UypVwaVInOlEoF9pTijw4A6XPUso+ZUumsBjUoQYnt6w0jjHzk277eaHWow6LGRairq2NQisggBqWIHJKsnlKJlO8FC+xNj/YiowN/nhQQpQ+f3wehXUhKYD7eNtyeKSUP9jg5A19rUytWPrQSAyYPQElVCV6Z+wp8AR8m/m6iY+V7voBPOv6uw0ypGFk5HacteuV7DER5XyUqUYGKVO8GESVRel1WIkq1ZA0iZetOJFNq9GWj0X9Cf5zwwAk27JRHWXjNGKAi8jYxsO9UUEqe9dTeoh9QcXumlM/vk46Xk5lS7979LpbOXYqHhjyE/Q378d7v38OKu1dgX/0+x8r33HrMAWZKqREzpfTK9+SZUizfIyLyBmZKEdkoFbPvJZQplRfEjKUz7Nil9Mer1kRpw+mglDxrJ152kdszpYCO35m2pra4AbZEfLvi284/ZC9L064mY5lSfmuZUq7F35wYYk8pw5lSjEkREXmCey8REXmdB3pKEa9GE2Uip4NS8vXGzZRqdffse0DnviWrp5SYPQYAzbubVbd78fsX49g7jk1oOwMmDwAAdKnoktB6nMDfplhS+Z5OTyk5ZkoREXkDM6WI7JTk2fd8AZ+lq8OUGJ4gEHlbMoNS8TKlxACMm7N2AtkBtKDF0Z5ScvIgVOPORtWgVHZuNor7Fye0nSmPT0HvI3pj2HnDElqPI9z7dkgZsXyvvaUd7W3tquWXbHROROQ9DEqRZTwxj5Xs2feYJWUDgyUSfL8TpQ/xO9RoxoVZiqBUs7HyPTf3N8rKyQJ26PfysZO8+XjjjkbVYJjP70s4uyynOAdH/uLIhNahyoafi2SNJ7xEzJQCOt6LwXyVyVoYiCIi8hz3joCIHJSMAIOTGUziuhPpJ0WxDL8veIJA5G0HPsMPDX0obtDIClPlex7IlAoVhgB09HdKBkWm1A71TClfwOfaQN6wc4chXBzGkLOGWF9JkjKvvUTMlAK0+0qx0TkRkfcwU4oykmMDlWQNIg+smplSibMSiOIJApG3yQNFDf9rQGRwxNb1yzN9jDY6d2uABXBnUMof8KNkSElS9sescJcwrtt2XUKBRv7OxPL5fQiEAmhratPOcmSjc8+rRz2CUMmC84g61KV6F4g8h0EpIhslu3yPmVI24zkAUUZobewsQ8sK2z8UStdMqcadjUnZnrzR+e4tuzUzpboO6IoZr85AXve8pOyXGQk3rnfv2yGlsnOy0dbUpllKykwp71uMxanehYTlhHMQidh7sYMonTEoRRnJsSuQyWp0zp5S9jH4mrG/B1F6cuLE1VSjcw/MvhcuCgNITabUO795R3UZMbOs/3H9k7JPycZMKXVifzOt8j1mSnlfDWpQAnuzIBvQgFrUYsGCBaiqqrJ13WoikQjKy8sd3w5RumBQisgpTmZKHRisBoIMSiXKysCfJwtE6UMtCydRZjKlWL4Xy8hr4ubMMluk+dOzKju3o6+UVvkes6O8rxKVqECFrevcgi2oRS2qqqpQXV1t67qJKHHuHQEReZA8WJGMTCmW79lM7yXjCYIjGOCjVHMiKCUvP4ubKeWB8r1gYUd/F0eDUrJYgrwnlxY3B/HswO9GdWKzc82ZIOXvIwaoiIg8Ib1/0clZHC/F8mn82+7NsHzPPlZeJ773idIGM6Xic2WmlIMz3LpCmj89q7JyOoo8jMy+x/I9IiJvcO8IiMjjmCmVBDYPOHllmijzOB2USodMKTEo1byrOSnbY/kef4+0MFOKiCj9sKcUkY2S1gz7wLqtZEoVlBVg95bdKCovsnmnvMnoa5as0kwiSi5mSsXnZKbU1o+2YssHWxQBBHn5oxY3Hy9b8GdGlZQpZaSnFGNSRESewKAUkZ2SPPuelUypC16/AO/85h0cdcNRdu9W8qVq0M6TBaK04UhQqi29Zt8Tg1KNOxttX/ejBz8acxszpXjxQ4vU6NzI7HtEROQJDEqRZRwwxeHSnlKRgyI4df6pdu9Satgx+DQaSOTb3REsr6BUc0umlJuDLG7sKcVMqcwUr3xP/pvC3xciIm9gUIos4499rKTNvndg3YEgG52nAgOyROnDLT2l3BxkCReFAdgflNLKvDIy+56bg3h24O+MuniNzhUXqzhM9aR61COIoK3rrEMdAGDt2rW6y0UiEZSXl9u6bSKKj0EpIjslefa9jG90bgMrPaWIKH0wUyo+pzKldm3epXq7+JoEQgG0NakH9dwcxLOFe98OKZUV7jh1aW3UaHQuw4un3rQYix1Zrw8+TJ8+XXeZnHAO1n2xjoEpoiRjUIos40m6PiePT0FZAQCgsE+hY9vIGFb6gPGtT5Q2nAhKyRt1p0OmlBSU2mlvUGrn5p2qt4uvSXZutmZQys1BPDtwjKVOfN2NZNMxU8qbalCDEpTYvt4wwshHvub9dajDosZFqKurY1CKKMkYlKLM5NBYL1mz7w06eRBmLZ+F0kNKndsIKSWpiT0RJRczpeILF3eU77U2tqJlf4vU1ydRmplSbZ1BqcaGzhK/YH4QzXuaAWTA93CaPz2rxAkB1GZoZGZUeqhEJSpQkerdIKIkcu9lOSIvSuLse32P7otQQcixbWSKZAUSicidUt5TyiOz74nl4vu277NtvUYypRT7UdTxm+fmAJ5d0j7oZpEUlFL73EbFpBikIiLyBveOgIg8iAEOb2P5HlFmOOHBE6R/pzpTygvlez6fD3nd8wAAe7fvtW298XpKRQelxIbrbj5WtuHvjCrxtVf73MYEoRiTIiLyhAz4VSfHcMCki1c5PcJoHCpJMysSkfMOu+Iw9DuuHwCHglKyfjdtzfqZUl4o3wOAvJIDQaltiQelXr/5dax6bBX2/qC+LvGYBPOUM3AxU4rETCnVnlLMlCIi8iQGpciyIWcNAQAU9S1K8Z64SJJm3yOH8DVLOp54UarolgElyFT5ngcypQAgtyQXQOLle1s/2oq3/u8tvPjTFzWzrpgpBcXvEYMrnfQ+tzxORETexEbnZFmvQ3vhyvVXSjPBUVQ2jZ8n215gqWSPLy2R5yUrKJUOjc4BWaZUguV78hn8tAJc4muSlaMcpjJTisTX3khPKZbvERF5A4NSlJCuA7qmehdciwNK7+FrRpQ5mClljl2ZUvKg/u4tu1UXEY9JIBhQ3B7ukpmZUtRJr3wvOlOKmVNERN6QAb/qREnEbBrvMfiasacUUXpxMigln64+bqPzA9t3e/aPE43OtY69NCNhVPCJmVJkZvY9ZkoREXkDM6WIbMTAhfdYep340hJ5nlsypcSMD3F/3ErKlNqWWKaUke9cMZAXfUxCBQeCUiyPz1hmZt8T/25vbcf6/6xH7zG9pTJUcq961COIYPwFbVaHuqRvk4g6MChF5BSOmT1H92SJrydRWnFLTymvlO/Z1VPKyPEWA3nRQalAqKOcz+3HipwjfW7bDGRKHfDlS1/i2dOexYgZI3D6X093cO/IDouxOGXbzgnnIBKJpGz7RJmKQSkiO8kCF8yU8ggriVJ8bYk8zy1BKa80Orerp1RrU2vcZdqaOoJS0cdE7DHl9mNFztFrdB7TQ+rAn2Lvsob/NTi6b2SPGtSgBCWWH9+ABtSiFgsWLEBVVZWpx0YiEZSXl1veNhFZw6AUkY0UwQqOmb2HrxlRxnA0KNWWfo3O7eop1dasfzzky8RkSgWZKZXp9BqdR2dKiUGq1saOQOj+H/c7um9kj0pUogIVlh+/BVtQi1pUVVWhurravh0jIsfwV53IIcym8QajfcAYcCRKL8yUMkcs32ve3Syd5FthR1DK7ceKnKP3udXKlBIz7xiUIiJyJwaliOzE2fcyAgOORN7nlkbnWjPNuU2oKAR/dsc+JpItJQYIdJdpVi/fywp1JPi7/ViRc+SNzt/53TtYPX91550GMqViAldERJRyLN8jshFn3/Mgo4FEvpxEacUtmVJS+Z7LZ9/z+XzIjeRiz/d7sG/7PhT1KbK0HmZKUSLE98S3732Lr5d+DQA45MJDAKhkSh0g9jFrb2lHy94WBPOTP7MbERFpc/cIiMhrmCnlaYYDiXxtiTzPyaCUfGaweJlSXinfA+yZgc9IUEoMImgGpfzuP1bkDPE90bSrKf7CB2JU8nJTlvAREbkPg1KUkZKRxcRMKW8w+joxC44ovbguU8oDJWlis/NEZuAzMvtee7P6MWGjc1IL3kqfN43yPXnJKINSRETuw191ykhO9RRgM2yP42tGlDHc0lPKS5lSuSW5AIC92+zLlMrOy9ZcJjpTqmRoCbLCWehZ3dPy9snb1Mpcxc+wVqNzeSCUQSkiIvdhTykiO8ljUsym8QYrLxNfWiLPY6aUeVJQysbyvVBhCC17W1SXiQ7UdenbBdduuzbjegJxPNFJNSjV1o4AAtqZUo3MlPKSetQjCOuf8TrUAQDWrl2ruD0SiaC8vDyhfSMiZzAoRRkpKQM8jiEdZ0fGm9GyPM7YQ5ReHA1KtXV+X3y/+nu8cPELGPercSgqj20OLm7fC5lSYk+pRMr3omffCxWGsOf7PcplNDKlACBUELK8bfI+teCtZqbUAcyU8pbFWJzwOnzwYfr06YrbcsI5WPfFOgamiFyIQSkiG7HvkMcZ7XPO15bI85KVKSW0CVj9+Grs3rIb0/4zLXbZAwEst8++B9jTUyomU0olyKQXlKLMple+F50pxUbn3lSDGpSgJKF1hBFGPvKlv+tQh0WNi1BXV8egFJELMShFZCfGKpLKluAQy/eIMpJ4ciu02p8FKQ9KiX786kfVZT1ZvmdjT6lQYWxQSpp9zwPHhJLLTE8pNjr3pkpUogIVqd4NIkoi/toTOYWBC8fZXVLHDCiizOFkppQYaJJTK90DvNXoXCzfs7unlNYy/iw/xvx8DADg2NuPtbxNSh9qn5O4mVIs3yMicjVmShHZiEEN77EyYyJfZyLvS1b5nkgrKOXFTKlEyvfkAQIgflBqwm8nYNSlo9B1YFfL26T0oZYpJQZ2tTKlWL5HRORu7h8BEXkJZ99LKh5jIrIq2UGp7Lxs9WU9mCnVtKspJrhkVHuz8ngHC2Nn2RLLrXwBH3x+H7oN6sbvewJgsqfUASzfIyJyNwaliJzC8bPjbCnfMxpIlG+Kry2R5yU7KBVdtiYSt++FTKlwl7B03PbVWcuWMlu+R5z9Vc7U7Htqjc7rGZQiInIb/toT2YhXcjMDX2ci73M0KNUWG0Rob4ndjjx45YUAjM/vQ24ksWbnZsv3iOTMZEpJ5XvsKUVE5Gr8tSeyE2MVnmOlpxQReV8yMqWqL63GQVMOAqCeKSVviO6F8j0g8b5SMZlSBTpBKQ9kj1FymZl9TwxSsXyPiMjd2OicyEbMoPEgK33A+DLbh8eSUiQZQalAdgD9ju+HL174Qj1TSpZR5ZUATKIz8EUHpbLCsUNRZkqRFjOz76k1Om9tbEXL/hZk56j3eKPUq0c9gojtNZeIOtTZuj4isheDUkQOYYAqffG1JfK+ZASlfH4fAsEAgPTJlMrr3hGUspwp1aQ8DmrPm0Ep0qKaKdWmkSl1QHTJ6P4f9yO7F4NSbrUYix1Zb044B5FIxJF1E1FiGJSizOTU2N8b5xQkY7R8j41midKLk0Ep8STZ5/chkH0gKNUSG5TyYqaUWL5ntadUdHBOLcggBhG8Eqij5DE1+57Q8dstz5QCOoJShb0KHdpDSlQNalCCEsuPb0ADalGLBQsWoKqqSro9EomgvLzcjl0kIpsxKEVkI2bQZAi+zPZhrI9SJCmZUgEf/NkHtqNSvifftlcCMFJQyqbyPdXZ1A4cK2ZKUTS92feiCYLQcd+B35m87nnYu20v+0q5XCUqUYEKy4/fgi2oRS2qqqpQXV1t344RkWP4a0/kFG+cX5CVnlJE5HluK9/zSqaU2FPKavledCmVXuCJQSmKZrbRubxcNL9nPgA2Oycichv+2hPZiTENbzPa55zBKyLPS1pQykj5nq9jWS9IuKeULDjX77h+uhliXgnUUfKYKd+LLt0r6FkAgEEpIiK34a89kY0YrPAeS68ZX2Yiz3M0KNVmLlPKS8EXu8r3pv57Kqa/Mp2ZUmSKWhBT/Lyp9X4UM/P8WX7pvcugFBGRu/DXnsghDFB5D18zoszhZFBKzM7wB/y6PaWk4JVH+kkBneV7iTY6L+xT2HF8dAJyDEpRNLONzsXPYiAUQE7XHAAMShERuQ1/7Yns5J3zChL5NP6t9xAGr4g8z6mg1Gs3voZVj64CEJUppVK+5+VMqaadTarZX/GIPX7E46IXePJSsI6SQ6/ReXSmlCAI0vstK5zFoBQRkUt5ZxRE5AEMVniP4deMs8QRpRWnglJv3/G29G9FTymVAI4XM6VyinOk/d1XZ76vlHgcxKCUbk8pZkpRFNOZUgfK97JCnUGpxh8bndxFIiIyKSvVO0CUtrxzjkEHGA5Q8bUl8jwny/dEPr9Pt3xP3LaXgi8+vw+5kVzs/WEv9m7fi4KyAlOPF4NSWaGOIahuTykPZZBRcpiZfU8QBLTsawEAZOUwU8or6lGPIIKWH1+HOgDA2rVr7dqluCKRCMrLy5O2PaJ0w6AUkZ0YrPAeK33OmRFH5Hniya1aWZ1V+xuUJ7u+QPo1Ogc6+krt/WGv6b5SgiBImStS+Z7Ocw8VhqzvZBoR+3iR+iyV4udILaO5eU8zACBUEGJQyiMWY3HC6/DBh+nTp9uwN8bkhHOw7ot1DEwRWcSgFJGNGKxIMrtL6vjyEWUMJzKlGv7XoPhbUb6nEvySZ3F4idhXat92c+V7QpsgfW8bKd/L65HZwZgznj4D3773LarOqEr1rriaVqYUhM6gVDA/yKCUR9SgBiUoSWgdYYSRj3yb9khfHeqwqHER6urqGJQisshboyAimzgWPGJQw3Pk7wWW76UAjyWliF5ZnVWqQSmdTKnm3QeyODyWESTNwLfdXKaU/BgEQvqNzn0BH3KKcyzuYXoYfv5wDD9/eKp3w/W0ekoJgsCglAdVohIVqEj1bhBREnkrX5zIJjFX0xzArKkkSNEh5mtL5H16GUxWqQWl9IJfTbuaAHgvKJXb3VqmlFi6B8Qv38vrnqdaqkUUzWymVPOeZkszRxIRkTMYlCKyEYMVSWZHbNGn8e/oTSUhkElEyeNEptTu73YrtxHw6wa/pKBUgbeCUnZkSokZUlqZUvk9klN6Q94nBqXUSnHlQalQUUj6nWe2FBGRezAoRRkpKeV7jE+lL762RJ4nBouEdgFCuz1B58YG5VTz8vK99pb2mOB2026PZkqJPaW2mcuUEoNSgVBA+h3W6imV6f2kyLj21o7PlhjkFQmCIJXIZudnwx/wI9wlDIBBKSIiN2FQisghzJryBks9pYjI88RMKcC+Er6Y2fdk5XtAbCaHeBIdLLQ+/XkqWM6UajoQlDoQqAOYKUWJE9oEvHDRC5h/1PyoO5SZUkBnAFgMCBMRUep5Iii1ceNG/OQnP0G/fv2Qk5ODyspK/PrXv0Zzc7NiuU2bNuGUU05BXl4eIpEIrrrqqphlPvnkE4wbNw45OTno1asXbrvttpgrl8uXL8eoUaMQDofRv39/PPLIIzH79K9//QtDhgxBKBTCkCFD8Nxzz9n/xMlzGNTwIKPZbbKvCb7ORN4nD4bYVcKnlykFxDY792r5ntXZ96RMKXlQSqOnlNi3iiie9tZ2rHlyTczt8kbn4mdMDE6Jt3sVz42IKJ14Yva9devWob29HY8++igGDBiATz/9FJdccgn27t2Lu+++GwDQ1taGk046CSUlJXj77bdRX1+PmTNnQhAEPPDAAwCAXbt2YcKECRg/fjxWrlyJL7/8ErNmzUJeXh5+/vOfAwA2bNiAE088EZdccgkWLFiAd955B5dffjlKSkpw5plnAgBWrFiBc889F//v//0/nH766Xjuuedwzjnn4O2338bhhx+emoNE7sBYRWbg60zkeWL5HmBfplTjjqigVMCn2E508Murjc7zuhvLlBIEAe2t7Z19tQ4EpbJCncNPZkpRotR6SQFQzZRKl6AUz42IKJ14Iig1efJkTJ48Wfq7f//++OKLL/Dwww9LX7xLly7F559/js2bN6OsrAwAcM8992DWrFm4/fbbUVhYiKeffhqNjY148sknEQqFMGzYMHz55Ze49957MXfuXPh8PjzyyCMoLy/HfffdBwCoqqrCBx98gLvvvlv64r3vvvswYcIEzJs3DwAwb948LF++HPfddx+eeeaZJB4ZMuvgCw/GmvlrUHNTjfMbY+DCE1i+R5SZ9MrqrIpXvhedKSX2u/FcUOpA+V5jQyPaWtoUgTe5hVMW4pu3vsGcjXMQ7hJWzZTS6imVG2GmFBmj9/lN16BUOp8b1aMeQXinpLkOdaneBSLP80RQSs3OnTvRtWtX6e8VK1Zg2LBh0pcuAEyaNAlNTU1YtWoVxo8fjxUrVmDcuHEIhUKKZebNm4eNGzeiX79+WLFiBSZOnKjY1qRJk/D444+jpaUF2dnZWLFiBa655pqYZcQva3KvKY9PwaTfT0K4KOzI+hnU8DiDLx9fZyLv8/l88AV8ENoER8v3fD4f/Fl+tLe2x2RkST2lCrxzAgYAOV1z4PP7ILQL2Fe3DwU9C1SX+/LFLwEA6/69DgfPPBitTa0AjPWU8lqgjlJHKyglL99Lt6CUmnQ5N1qMxaYfk2o54RxEIpFU7waRZ3kyKPX111/jgQcewD333CPdtnXrVvTo0UOxXHFxMYLBILZu3SotU1FRoVhGfMzWrVvRr18/1fX06NEDra2tqKurQ8+ePTWXEbejpqmpCU1NnU0Vd+3aZfwJk218Pp9jAamODTi3anKIldeMrzNRWghkB9Da1mpL+V5rYytaG1sVt/n8HV8W/uyOoFS6lO/5/D7kdMvBvu37sG+7dlBKWv5AIN9MTymvBeooddrbdMr3dmdGUCqdzo1qUIMSlBh85s5qQANqUYsFCxagqqpKc7lIJILy8vIk7hlReklpUOqWW27BrbfeqrvMypUrMXr0aOnvLVu2YPLkyTj77LNx8cUXK5ZVy14QBEG3PEds5GfHMnrZE3feeWfc50rphdk03sPXjCiz+LP9QKM9jc6jS/eAzoBLIBhA6/5W7UbnHgtKAR19pfZt32dsBr4DX61SUCoUv3zPi8eEUiOdMqV4bgRUohIVqNB8XDJtwRbUohZVVVWorq5O9e4Qpa2UBqVmz56NqVOn6i4jj95v2bIF48ePx5gxY/DYY48plistLcX777+vuK2hoQEtLS1S5L60tDQmYr9t2zYAiLtMVlYWunXrprtM9BUCuXnz5mHu3LnS37t27UKfPn00lydvYlDDe6y8ZnydidKD1IDbhkyp6NI9oDNTSr4deQ8mqaeUx2bfAzr6Sm3HdkMz8EmZUk0qmVJa5XsePCaUGtEZihJ5o/MCbwSleG7EcyOiTJTSoFQkEjFcf/vdd99h/PjxGDVqFObPnw+/XzmIGTNmDG6//XZ8//336NmzJ4COBn+hUAijRo2SlrnhhhvQ3NyMYDAoLVNWViZ9wY8ZMwaLFytrmZcuXYrRo0cjOztbWmbZsmWK2umlS5di7NixmvsfCoUU9dpE5EKMNRFlFLEJuVOZUvLyPQCoW1eHx494HIf85BBMvm+ypzOlcks6GpHv3WYgU+oAlu+RE/QCTF7LlOK5ERFlIvWRgMts2bIFxxxzDPr06YO7774b27dvx9atWxUR+YkTJ2LIkCGYMWMGVq9ejddeew3XXnstLrnkEhQWFgIAzj//fIRCIcyaNQuffvopnnvuOdxxxx3S7BIAcNlll+Gbb77B3LlzsXbtWjzxxBN4/PHHce2110rbuvrqq7F06VLcddddWLduHe666y68+uqrmDNnTlKPCxHZQBaIYgYUUWYRs3Qcz5Q6EIR57ZevoXlPM97/Q0f2QloEpTTK9xR9fqLK97JCnddEmSlFiWrZ06J6u175ntZjvILnRkSUTjzR6Hzp0qX46quv8NVXX6F3796K+8Sa5kAggJdeegmXX345jjzySOTk5OD888+XpkUFgKKiIixbtgxXXHEFRo8ejeLiYsydO1eROtqvXz/85z//wTXXXIM//vGPKCsrw/333y9NeQoAY8eOxcKFC3HTTTfh5ptvRmVlJZ599lkcfvjhDh8J8hTGNzzBaCBK/K4hovQhltHZkSnVuEMlKBVQlu/t3LxTuu/Hr39Ey74W+Pw+5EZyE95+suWV5AGAZvme/JiK37Nqs+9p9ZQSgwdE8TTvVc96atnbAqG947fbK5lSRvHciIjSiSeCUrNmzcKsWbPiLldeXo4XX3xRd5nhw4fjzTff1F1m3Lhx+PDDD3WXOeuss3DWWWfF3Sci8hAGEokyilhWZ0emVNPujqynYH5QOuGNzpQSeyoBwKfPfAoA6H98f09mSuV11w9KyZu6r35iNeADXry0Y4xmpHxPK4OK6KApB+GLF77AwBMHYv1/1msGmMRAsc/vS7ugFM+NiCideCIoRUTkGJbvEWUsKVNKY/YuM1r2dpQDhYvDMUEpMfgl98nfPgEADDtvWMLbToV45XvyQN+G1zZgw2sbpL8Vs+/5+b1L5pz73LnYV7cPq+ev1g9KHSipDRWFpN93sVeZ14NSRETphEEpIocwwOE8ltQRUSLsbHQulhDlFOdg1+ZdAGIzpeTq1tYhEApg8OmDE952Kojle1qNzvWOafTxKD2kFFtXb9VYmkjJ5/chr3uelE0XL1MqXBSWbkuXTKl0Vo96BOGO8t061KV6F4gyAoNSREQivTgi419EaUfMlLKjfE88yQ0Xd54Ai6VpWWH14dagkwYpTpi9RMyU0izf0zmm0UGpPkf2YVCKTBM/X3GDUl0YlPKSxVgcf6EkygnnGJ4RkYisYVCKiDyL2WhElAg7M6XE8r2c4hzpNjFTSswqilZ+dHnC200VsafU/h/3o721PaYHlG6mVEgZlKr+STVWPrjS/p2ktGY4U4pBKU+pQQ1KUOLoNhrQgFrUYsGCBaiqqtJdNhKJoLzcu9/VRF7AoBSRzQrKCrB7y270GNkj1buS9mwp35OtgkEuoswintTakSkl7yklEoNSYlZRNK0MKi/I6ZrTkV0qAPvq9yG/R77ifjOZUqUHl+KSlZfgT4f+yYldpTQlfn7Fz160/Q37AXT0lBIxKOV+lahEBSoc3cYWbEEtalFVVYXq6mpHt0VE8Xl3NETkUldvvBrtLe3Izs1O9a6QWYxJEWUUqdG5HT2lDpzk5nRVyZTqrp4ppdZryiv8AT9yu+ViX90+7N22NyYoZaanFACUjS6DL+CD0MZaaTJGCio3qwdA9TKlWhtbbZnggIiIEsf5dolsFsgOMCCVJLZkNjEQRZSxxPI9W3pK7Y3tKeUL6GdKeTkoBej3lTKTKSViQIrMiC4ZjSYGRtUypYDOzywREaUWg1JE5Fl2z76nF+TiTH9E6cfOTCnVnlK+9M2UAmQz8G2PnYFP75hmhdQT9Y+/63gAwFHzjrJh7yjdiUHfeOSZUoFgoLMXFYNSRESuwPI9IiIiyki2ZkqplO+1t3UEZrQanXs+KHUg2GZXptTY68ZiyNlD0KWiiy37R+lNK1PK5/dBaO+8kCQPSvl8PgTzg2jc0ajZi4qIiJKLmVJElNnkCVAs5SPKKFKmlA29ZdTK98T1pmumlFi+p5YppdXnR4/P50Nxv2JOOkGGaAWlomd3DBeFFX+z2TkRkbswKEVEdABPhJKPx5xSScyUcqp8T+yRlO49pfZuM1e+19rY6tg+UebQDEpFfa7kmVKALCjF8j0iIldg+R4REaUMe3VRKomZUnaW78mbKouZUrnd0jMoJZYlmi3fa9nHsilKnFZQKiuUhSY0SX/LP5NAZ1CK5XvuVI96BBGMv2AC6lDn6PqJyBwGpYiIREzaIcoovqyOD32imVKCIEhZF/LZvcSglD/Lj7JDy1C3rg7NuzuzMzwflNLpKcVMKXJawplSLN9zpcVYnJTt+OBDU1NT/AWJyHEMShERHcBSMqLMYlemVFtzm1SqF8yLDUoBwEVvX4S25jb8ruR3UlDG60Ep3Z5SeplS+5mhQonT+vywfM/balCDEpQ4uo0GNKAWtQiFQvEXJiLHMShFREREGcmunlLyjIvsvGzp3+Lse0DHiXIgGOjYZmPnbV4mlu+Z7SnV79h+ju0TZY6skPppjNFG5yzfc6dKVKICFY5uYwu2oBa1jm6DiIxjUIqISKSXKMXWR0Rpx65MKfHkNhAMSOsEYjM05NsUl/cyMVNq/4/70d7WDn+gs5xK65iet/g8DDxpYFL2j9JbdPBJuj3qcxXdUyo7vyNwzPI9IiJ3YFCKiIiIMpKYKbXn+z0JrWd/w34AnVlSpz55KrZ8sAWDThqkuU0gDYJS3XI7gvkCsL9+v9RjClDPlCquLMagk2OPCZEVWWH10xh5BlV2brYiEAywfI+IyG3UOwQSEWUg9pQiyiziyeonT3+C9x9439I6vl/9PZ49/VkAQNfKrgCAg2cejBMfOBE+f+x3SjplSvmz/MjpmgMgtq9UdKZUUXkRLnzrwqTtG6U/zfK9oH62Isv3iIjchUEpIiKRTkxKEFi/R5Ru5FlLS65aYvrxH/31Izwx9gns2LADxf2LMeWJKXEfIw9UeT0oBXT2lYqegS86U6r60moU9CxI2n5R+tPKlDIclNrDoBQRkRuwfI+IiIgyktaU8vG0NbdhyTVL8MFDHwAABp44EKcvOB05xTlxHysPcKdDUCq3JBdYF9vsPDpTKrqEiihRWj2l5J/r6H5SAMv3iIjchkEpIqIDWL5HlFmsBEp2fbcL/zj7H/h2xbcAgHG3jMO4m8epluqpkiVdpkNQSpqBL7p8r1kZlJJnpRHZQat8T571rJcpxaAUEZE7MChFREREGclsoOSbN7/BP875B/b+sBfhLmGcvuB01WbmRqVD9lBu944Z+OKV76XDcyV30SrfkwsXsXzPa+pRjyCCjm6jDnUAgLVr19qyvkgkgvLyclvWRZSJGJQiooym6BXFRCmijGK0v5MgCHj/D+9j6bVLIbQJ6DGiB85ZdI7U2NwM+XeO4ewqF1PLlGptbI0p32OmFNlN8zMr+1kPddEu32va0+TEblGCFmNxUrbjgw/Tp0+3ZV054Rys+2IdA1NEFjEoRUR0AMv3ko/HnFKpsaFR+rdW0KR5bzMWX7wYny78FAAw/PzhOOVPpyA7N9vaRtNszoTcko5MqQ2vbsDHCz5GpCqCJ8Y+EVO+x0wpspvP74M/2x+TlddtcDd8vfRrAJx9z4tqUIMSlDi+nTDCyEd+wuupQx0WNS5CXV0dg1JEFjEoRUQZjUERoswlz+5p2duC9rZ2+AOdwan69fX4+xl/x7ZPt8Gf5cfEeybisCsPS+h7I91m8hQzpeq/rMdzM57TXI6ZUuSErHAWmls6ekNdsvISFPcvxop7V0j365XvNe9hTyk3qkQlKlCR6t0goiRiUIqIMprh8r30Oo8kIgAt+5SZEo07GpHbrSPz54vFX+C5Gc+haWcT8kvzcfY/zkb5UTZcBU+z75K87nmGlkuHpu7kPlmhLDTv7ggu5UZykdM1R/FbntMtdkZMNjonInIXXrYiIjqAWVNEmaVmXg3ye3aWbzQ2NKK9rR2v3/w6Fk5ZiKadTehzZB9c+uGl9gSkkH6ZUmL5Xjws3yMnBEKd76usnI5r7eVHliM7NxuDTxuMIWcOiXmMVL63j+V7RERuwEwpIvKu9Dq3I6Ik6zqgK+Z+Nxf3ld+HXd/uwo9f/4iXr3wZXy35CgBw2JWHYeLdE+3N8kmz7y2xfC8elu+RE+STBYiz8Q2YPADz9szTvNAkBqWIiMgdGJQiIhIxUSrpKo6twOZ3N/OElVLG5/MhXBzGrm934e9n/B0t+1qQlZOFUx47BSOmj7B9e+mWKaVWHqWGmVLkCNnHSQxKAfqZz9k52R2/9+n1USQi8iwGpYjIuxhE8ryjbzwahb0KUTmpMtW7Qhksp7gjsNKyrwXF/YtxzqJzUDqyNMV75Q1Gg00MPJPTjGY0+vw+BPOCaNzTGH9hIiJyHEcIRORdNl/lZE+p5MsKZ2H0ZaNR3K841btCGazroK4AgAEnDMAlH1zibEAqQ7MzmClFTpBnHpr5DQ8WsISPiMgtmClFRCTSGc+mW8kNEXWadO8kjJwxEuVHlSt61DghU79LmClFjrD4cWJfKSIi92BQioi8i4lNRGSDUEEIfY/um5yNpWFMKq9HHvb+sFd3GWZKkROsBnkZlHKvetQjCO+8PnWoS/UuEHkeg1JE5F12n9yl4ckiEblLOmZK/eTdn+Avx/4FO7/ZCQAo6luEPVv3oK2pTVqGmVLkCGZKpZ3FWJzqXTAtJ5yDSCSS6t0g8iwGpYgoo3Ub1A09q3siXByGL8DUKyJyWPrFpFDcvxjjfjUOL/zkBQBAUZ8iXPbRZfjg4Q/w2rzXADBTityFQSn3qkENSlCS6t1Q1YAG1KIWCxYsQFVVlXR7JBJBeXl5CveMyNsYlCKijOYP+HHJyksAn36T1MqJHbPDdR3QNVm7RkRpKB0zpQDlSX5ejzyEi8IYcMIAKSjFTClyAsv30k8lKlGBilTvhqot2IJa1KKqqgrV1dWp3h2itMGgFBFlPCONjfN75OP6HdcjOzc7CXtERGkrPWNSMUEpAPBndQaimClFjmD5HhGR5zEoRURkULgonOpdICJyJflJfn5pPgBlIIqZUuQmDEoREbkHRwhERERESTLxnokAgMPnHJ7iPbGXIijVoyMoJe/Tx0wpcgLL94iIvI+ZUkRERERJUn1xNQZMHoCCXgWp3hVbqZXvyUurmClFbsKgFBGRezAoRURERJREhb0LU70LtlPLlJILBJkpRQ5gTykiIs/jZSsiIiIiSohappS8tIrle+SEQ2cfCgAYcMIAU4/rO64vJtw1wYldIiIik5gplSLiQG3Xrl0p3hMi7wr2DqJxTSMAfpaIiFJJaBcQ7BVEW1MbUNDxnewr8qERHd/Re/bvga8x/kynRGYcfOXB6FrdFWWjy0yNA3L75WLQ+YOA6633pSJ7ia/DVmxN8Z5oq0c9AGDPnj0cdxIZIH5O4n3P+gR+E6fEt99+iz59+qR6N4iIiIiIMtbmzZvRu3fvVO9Gxvvf//6HysrKVO8GETkg3vcsg1Ip0t7eji1btqCgoAA+H68cWrFr1y706dMHmzdvRmFh+vXnSCYeS3vwONqHx9IePI724bG0B4+jfXgsEyMIAnbv3o2ysjL4/exokmo7duxAcXExNm3ahKKiolTvTtrj90dyZerxNvo9y/K9FPH7/bwqY5PCwsKM+nA7icfSHjyO9uGxtAePo314LO3B42gfHkvrGPxwD/GEtaioiO/nJOL3R3Jl4vE28j3LywJERERERERERJR0DEoREREREREREVHSMShFnhUKhfDrX/8aoVAo1bvieTyW9uBxtA+PpT14HO3DY2kPHkf78FhSOuH7Obl4vJOLx1sfG50TEREREREREVHSMVOKiIiIiIiIiIiSjkEpIiIiIiIiIiJKOgaliIiIiIiIiIgo6RiUIle58847ceihh6KgoADdu3fHaaedhi+++EKxjCAIuOWWW1BWVoacnBwcc8wx+OyzzxTLNDU14corr0QkEkFeXh6mTJmCb7/9NplPxVXuvPNO+Hw+zJkzR7qNx9G47777DtOnT0e3bt2Qm5uLgw8+GKtWrZLu57GMr7W1FTfddBP69euHnJwc9O/fH7fddhva29ulZXgc1b355ps45ZRTUFZWBp/Ph+eff15xv13HraGhATNmzEBRURGKioowY8YM7Nixw+Fnlzx6x7GlpQXXX389hg8fjry8PJSVleGCCy7Ali1bFOvgcewQ7z0p99Of/hQ+nw/33Xef4nYeS2PHce3atZgyZQqKiopQUFCAI444Aps2bZLu53GkdPDQQw+hX79+CIfDGDVqFN56661U71JasOu8iqyxev6ViRiUIldZvnw5rrjiCrz33ntYtmwZWltbMXHiROzdu1da5re//S3uvfdePPjgg1i5ciVKS0sxYcIE7N69W1pmzpw5eO6557Bw4UK8/fbb2LNnD04++WS0tbWl4mml1MqVK/HYY49hxIgRitt5HI1paGjAkUceiezsbLz88sv4/PPPcc8996BLly7SMjyW8d1111145JFH8OCDD2Lt2rX47W9/i9/97nd44IEHpGV4HNXt3bsXI0eOxIMPPqh6v13H7fzzz8eaNWuwZMkSLFmyBGvWrMGMGTMcf37Joncc9+3bhw8//BA333wzPvzwQyxatAhffvklpkyZoliOx7FDvPek6Pnnn8f777+PsrKymPt4LOMfx6+//hpHHXUUBg8ejDfeeAMfffQRbr75ZoTDYWkZHkfyumeffRZz5szBjTfeiNWrV6OmpgYnnHCCIvhK1th1XkXmJXL+lZEEIhfbtm2bAEBYvny5IAiC0N7eLpSWlgq/+c1vpGUaGxuFoqIi4ZFHHhEEQRB27NghZGdnCwsXLpSW+e677wS/3y8sWbIkuU8gxXbv3i0MHDhQWLZsmTBu3Djh6quvFgSBx9GM66+/XjjqqKM07+exNOakk04SLrroIsVtZ5xxhjB9+nRBEHgcjQIgPPfcc9Lfdh23zz//XAAgvPfee9IyK1asEAAI69atc/hZJV/0cVTz3//+VwAgfPPNN4Ig8Dhq0TqW3377rdCrVy/h008/Ffr27Sv8/ve/l+7jsYyldhzPPfdc6TtSDY8jpYPDDjtMuOyyyxS3DR48WPjlL3+Zoj1KX1bOq8i8RM6/MhUzpcjVdu7cCQDo2rUrAGDDhg3YunUrJk6cKC0TCoUwbtw4vPvuuwCAVatWoaWlRbFMWVkZhg0bJi2TKa644gqcdNJJOP744xW38zga98ILL2D06NE4++yz0b17dxxyyCH405/+JN3PY2nMUUcdhddeew1ffvklAOCjjz7C22+/jRNPPBEAj6NVdh23FStWoKioCIcffri0zBFHHIGioqKMPbY7d+6Ez+eTsiJ5HI1rb2/HjBkzcN1112Ho0KEx9/NYxtfe3o6XXnoJgwYNwqRJk9C9e3ccfvjhihI/HkfyuubmZqxatUrxHgaAiRMn8v3pACvnVWReIudfmYpBKXItQRAwd+5cHHXUURg2bBgAYOvWrQCAHj16KJbt0aOHdN/WrVsRDAZRXFysuUwmWLhwIT788EPceeedMffxOBr3v//9Dw8//DAGDhyIV155BZdddhmuuuoq/PWvfwXAY2nU9ddfj/POOw+DBw9GdnY2DjnkEMyZMwfnnXceAB5Hq+w6blu3bkX37t1j1t+9e/eMPLaNjY345S9/ifPPPx+FhYUAeBzNuOuuu5CVlYWrrrpK9X4ey/i2bduGPXv24De/+Q0mT56MpUuX4vTTT8cZZ5yB5cuXA+BxJO+rq6tDW1ub7m8Y2cPqeRWZk+j5V6bKSvUOEGmZPXs2Pv74Y7z99tsx9/l8PsXfgiDE3BbNyDLpYvPmzbj66quxdOlSRe+JaDyO8bW3t2P06NG44447AACHHHIIPvvsMzz88MO44IILpOV4LPU9++yzWLBgAf72t79h6NChWLNmDebMmYOysjLMnDlTWo7H0Ro7jpva8pl4bFtaWjB16lS0t7fjoYceirs8j6PSqlWr8Ic//AEffvih6efMY9lJnATi1FNPxTXXXAMAOPjgg/Huu+/ikUcewbhx4zQfy+NIXmPlN4zMsfu8imI5ef6V7pgpRa505ZVX4oUXXkBtbS169+4t3V5aWgoAMdHkbdu2SVHn0tJSNDc3o6GhQXOZdLdq1Sps27YNo0aNQlZWFrKysrB8+XLcf//9yMrKko4Dj2N8PXv2xJAhQxS3VVVVSQ04+Z405rrrrsMvf/lLTJ06FcOHD8eMGTNwzTXXSFeSeBytseu4lZaW4ocffohZ//bt2zPq2La0tOCcc87Bhg0bsGzZMilLCuBxNOqtt97Ctm3bUF5eLv3+fPPNN/j5z3+OiooKADyWRkQiEWRlZcX9/eFxJC+LRCIIBAK6v2GUuETOq8g4O86/MhWDUuQqgiBg9uzZWLRoEV5//XX069dPcX+/fv1QWlqKZcuWSbc1Nzdj+fLlGDt2LABg1KhRyM7OVizz/fff49NPP5WWSXfHHXccPvnkE6xZs0b6b/To0Zg2bRrWrFmD/v378zgadOSRR8ZMn/vll1+ib9++APieNGrfvn3w+5U/OYFAQMoG4HG0xq7jNmbMGOzcuRP//e9/pWXef/997Ny5M2OOrRiQWr9+PV599VV069ZNcT+PozEzZszAxx9/rPj9KSsrw3XXXYdXXnkFAI+lEcFgEIceeqju7w+PI3ldMBjEqFGjFO9hAFi2bBnfnzaw47yKjLPj/CtjJa+nOlF8P/vZz4SioiLhjTfeEL7//nvpv3379knL/OY3vxGKioqERYsWCZ988olw3nnnCT179hR27dolLXPZZZcJvXv3Fl599VXhww8/FI499lhh5MiRQmtrayqelivIZ38QBB5Ho/773/8KWVlZwu233y6sX79eePrpp4Xc3FxhwYIF0jI8lvHNnDlT6NWrl/Diiy8KGzZsEBYtWiREIhHhF7/4hbQMj6O63bt3C6tXrxZWr14tABDuvfdeYfXq1dKscHYdt8mTJwsjRowQVqxYIaxYsUIYPny4cPLJJyf9+TpF7zi2tLQIU6ZMEXr37i2sWbNG8fvT1NQkrYPHsUO892S06Nn3BIHHUhDiH8dFixYJ2dnZwmOPPSasX79eeOCBB4RAICC89dZb0jp4HMnrFi5cKGRnZwuPP/648Pnnnwtz5swR8vLyhI0bN6Z61zzPrvMqss7K+VcmYlCKXAWA6n/z58+Xlmlvbxd+/etfC6WlpUIoFBKOPvpo4ZNPPlGsZ//+/cLs2bOFrl27Cjk5OcLJJ58sbNq0KcnPxl2ivxR5HI1bvHixMGzYMCEUCgmDBw8WHnvsMcX9PJbx7dq1S7j66quF8vJyIRwOC/379xduvPFGxQk/j6O62tpa1e/FmTNnCoJg33Grr68Xpk2bJhQUFAgFBQXCtGnThIaGhiQ9S+fpHccNGzZo/v7U1tZK6+Bx7BDvPRlNLSjFY2nsOD7++OPCgAEDhHA4LIwcOVJ4/vnnFevgcaR08Mc//lHo27evEAwGherqamH58uWp3qW0YNd5FVln5fwrE/kEQRCcysIiIiIiIiIiIiJSw55SRERERERERESUdAxKERERERERERFR0jEoRUREREREREREScegFBERERERERERJR2DUkRERERERERElHQMShERERERERERUdIxKEVEREREREREREnHoBQRERERERERESUdg1JERJQyPp8Pzz//fKp3I65E9/OWW27BwQcfbNv+mHXzzTfj0ksvNbTstddei6uuusrhPSIiIiIiYlCKiIgcMmvWLPh8vpj/Jk+enOpds5Xac5T/N2vWLFx77bV47bXXUrJ/P/zwA/7whz/ghhtuMLT8L37xC8yfPx8bNmxweM+IiIjICZs3b8ZPfvITlJWVIRgMom/fvrj66qtRX1+f6l0jipGV6h0gIqL0NXnyZMyfP19xWygUStHeOOP777+X/v3ss8/iV7/6Fb744gvptpycHOTn5yM/Pz8Vu4fHH38cY8aMQUVFhaHlu3fvjokTJ+KRRx7BXXfd5ezOERERka3+97//YcyYMRg0aBCeeeYZ9OvXD5999hmuu+46vPzyy3jvvffQtWvXpO9Xc3MzgsGg4ra2tjb4fD74/cyVyWR89YmIyDGhUAilpaWK/4qLizWXv/766zFo0CDk5uaif//+uPnmm9HS0iLdL5bBPfroo+jTpw9yc3Nx9tlnY8eOHdIyb7zxBg477DDk5eWhS5cuOPLII/HNN99I9y9evBijRo1COBxG//79ceutt6K1tVW6f/369Tj66KMRDocxZMgQLFu2TPc5yp9bUVERfD5fzG3R5XuzZs3CaaedhjvuuAM9evRAly5dpP247rrr0LVrV/Tu3RtPPPGEYlvfffcdzj33XBQXF6Nbt2449dRTsXHjRt39W7hwIaZMmaK47Z///CeGDx+OnJwcdOvWDccffzz27t0r3T9lyhQ888wzuuslIiIi97niiisQDAaxdOlSjBs3DuXl5TjhhBPw6quv4rvvvsONN94IAGhqasIvfvEL9OnTB6FQCAMHDsTjjz8ureezzz7DSSedhMLCQhQUFKCmpgZff/01AOCYY47BnDlzFNs97bTTMGvWLOnviooK/N///R9mzZqFoqIiXHLJJXjyySfRpUsXvPjiixgyZAhCoRC++eYbNDc34xe/+AV69eqFvLw8HH744XjjjTekdYmPe+WVV1BVVYX8/HxMnjxZcWEQAJ544gkMHToUoVAIPXv2xOzZs6X7du7ciUsvvRTdu3dHYWEhjj32WHz00UfS/R999BHGjx+PgoICFBYWYtSoUfjggw8SfTnIAAaliIjINQoKCvDkk0/i888/xx/+8Af86U9/wu9//3vFMl999RX+/ve/Y/HixViyZAnWrFmDK664AgDQ2tqK0047DePGjcPHH3+MFStW4NJLL4XP5wMAvPLKK5g+fTquuuoqfP7553j00Ufx5JNP4vbbbwcAtLe344wzzkAgEMB7772HRx55BNdff70jz/X111/Hli1b8Oabb+Lee+/FLbfcgpNPPhnFxcV4//33cdlll+Gyyy7D5s2bAQD79u3D+PHjkZ+fjzfffBNvv/22NChrbm5W3UZDQwM+/fRTjB49Wrrt+++/x3nnnYeLLroIa9euxRtvvIEzzjgDgiBIyxx22GHYvHmzIphHRERE7vbjjz/ilVdeweWXX46cnBzFfaWlpZg2bRqeffZZCIKACy64AAsXLsT999+PtWvX4pFHHpGyur/77jvpAt3rr7+OVatW4aKLLlJcxDPid7/7HYYNG4ZVq1bh5ptvBtAxnrnzzjvx5z//GZ999hm6d++OCy+8EO+88w4WLlyIjz/+GGeffTYmT56M9evXS+vat28f7r77bjz11FN48803sWnTJlx77bXS/Q8//DCuuOIKXHrppfjkk0/wwgsvYMCAAQAAQRBw0kknYevWrfjPf/6DVatWobq6Gscddxx+/PFHAMC0adPQu3dvrFy5EqtWrcIvf/lLZGdnm38RyDyBiIjIATNnzhQCgYCQl5en+O+2226TlgEgPPfcc5rr+O1vfyuMGjVK+vvXv/61EAgEhM2bN0u3vfzyy4Lf7xe+//57ob6+XgAgvPHGG6rrq6mpEe644w7FbU899ZTQs2dPQRAE4ZVXXlFdf7z9FM2fP18oKiqKuf3Xv/61MHLkSOnvmTNnCn379hXa2tqk2w466CChpqZG+ru1tVXIy8sTnnnmGUEQBOHxxx8XDjroIKG9vV1apqmpScjJyRFeeeUV1f1ZvXq1AEDYtGmTdNuqVasEAMLGjRs1n8fOnTt1jyMRERG5z3vvvac7Zrn33nsFAML7778vABCWLVumuty8efOEfv36Cc3Nzar3jxs3Trj66qsVt5166qnCzJkzpb/79u0rnHbaaYpl5s+fLwAQ1qxZI9321VdfCT6fT/juu+8Uyx533HHCvHnzFI/76quvpPv/+Mc/Cj169JD+LisrE2688UbV/X3ttdeEwsJCobGxUXF7ZWWl8OijjwqCIAgFBQXCk08+qfp4chZ7ShERkWPGjx+Phx9+WHGbXh+Df/7zn7jvvvvw1VdfYc+ePWhtbUVhYaFimfLycvTu3Vv6e8yYMWhvb8cXX3yBcePGYdasWZg0aRImTJiA448/Hueccw569uwJAFi1ahVWrlwpZUYBHf0MGhsbsW/fPqxdu1Z1/U4YOnSooodCjx49MGzYMOnvQCCAbt26Ydu2bdK+f/XVVygoKFCsp7GxUUqnj7Z//34AQDgclm4bOXIkjjvuOAwfPhyTJk3CxIkTcdZZZynKKsWrq/v27UvwWRIREZFbCAeyojds2IBAIIBx48apLrdmzRrU1NQknCkkz9QWBYNBjBgxQvr7ww8/hCAIGDRokGK5pqYmdOvWTfo7NzcXlZWV0t89e/aUxkjbtm3Dli1bcNxxx6nux6pVq7Bnzx7F+oCOcZI4hpo7dy4uvvhiPPXUUzj++ONx9tlnK7ZHzmFQioiIHJOXlyelTsfz3nvvYerUqbj11lsxadIkFBUVYeHChbjnnnt0HyeW5on/nz9/Pq666iosWbIEzz77LG666SYsW7YMRxxxBNrb23HrrbfijDPOiFlPOBxWlLBFr99u0QM9n8+nelt7ezuAjtLCUaNG4emnn45ZV0lJieo2IpEIgI4yPnGZQCCAZcuW4d1338XSpUvxwAMP4MYbb8T777+Pfv36AYCUyq61XiIiInKfAQMGwOfz4fPPP8dpp50Wc/+6detQXFyM3Nxc3fVEl/5F8/v9MWMmeQ9QUV5enuq65WOr9vZ2BAIBrFq1CoFAQLGsfJIYtTGSuA/x9re9vR09e/ZU9KkSdenSBUBH39Lzzz8fL730El5++WX8+te/xsKFC3H66afrrpsSx55SRETkCu+88w769u2LG2+8EaNHj8bAgQNVexpt2rQJW7Zskf5esWIF/H6/4grbIYccgnnz5uHdd9/FsGHD8Le//Q0AUF1djS+++AIDBgyI+c/v92PIkCGq63eD6upqrF+/Ht27d4/Z96KiItXHVFZWorCwEJ9//rnidp/PhyOPPBK33norVq9ejWAwiOeee066/9NPP0V2djaGDh3q6HMiIiIi+3Tr1g0TJkzAQw89JGVLi7Zu3Yqnn34a5557LoYPH4729nYsX75cdT0jRozAW2+9pRpoAjouWsmbjLe1teHTTz+1tM+HHHII2trasG3btpjxTWlpqaF1FBQUoKKiAq+99prq/dXV1di6dSuysrJitiFewAOAQYMG4ZprrsHSpUtxxhlnxMwgTc5gUIqIiBzT1NSErVu3Kv6rq6tTXXbAgAHYtGkTFi5ciK+//hr333+/IlAiCofDmDlzJj766CO89dZbuOqqq3DOOeegtLQUGzZswLx587BixQp88803WLp0Kb788ktUVVUBAH71q1/hr3/9K2655RZ89tlnWLt2rZRNBQDHH388DjroIFxwwQXS+sVZalJt2rRpiEQiOPXUU/HWW29hw4YNWL58Oa6++mp8++23qo/x+/04/vjj8fbbb0u3vf/++7jjjjvwwQcfYNOmTVi0aBG2b98uHSMAeOutt1BTUxP3yiMRERG5y4MPPoimpiZMmjQJb775JjZv3owlS5ZgwoQJ6NWrF26//XZUVFRg5syZuOiii/D8889jw4YNeOONN/D3v/8dADB79mzs2rULU6dOxQcffID169fjqaeewhdffAEAOPbYY/HSSy/hpZdewrp163D55ZcrZkI2Y9CgQZg2bRouuOACLFq0CBs2bMDKlStx11134T//+Y/h9dxyyy245557cP/992P9+vX48MMP8cADDwDoGN+NGTMGp512Gl555RVs3LgR7777Lm666SZ88MEH2L9/P2bPno033ngD33zzDd555x2sXLlSMTYi5zAoRUREjlmyZAl69uyp+O+oo45SXfbUU0/FNddcg9mzZ+Pggw/Gu+++K83UIjdgwACcccYZOPHEEzFx4kQMGzYMDz30EICOfgPr1q3DmWeeiUGDBuHSSy/F7Nmz8dOf/hQAMGnSJLz44otYtmwZDj30UBxxxBG499570bdvXwAdQZznnnsOTU1NOOyww3DxxRcr+k+lUm5uLt58802Ul5fjjDPOQFVVFS666CLs378/pu+W3KWXXoqFCxdKZYCFhYV48803ceKJJ2LQoEG46aabcM899+CEE06QHvPMM8/gkksucfw5ERERkb0GDhyIDz74AJWVlTj33HNRWVmJSy+9FOPHj8eKFSuk3p4PP/wwzjrrLFx++eUYPHgwLrnkEuzduxdAR8bV66+/jj179mDcuHEYNWoU/vSnP0kldBdddBFmzpyJCy64AOPGjUO/fv0wfvx4y/s8f/58XHDBBfj5z3+Ogw46CFOmTMH777+PPn36GF7HzJkzcd999+Ghhx7C0KFDcfLJJ0uz9/l8PvznP//B0UcfjYsuugiDBg3C1KlTsXHjRvTo0QOBQAD19fW44IILMGjQIJxzzjk44YQTcOutt1p+TmScT1BroEFERORCt9xyC55//nmsWbMm1bviGYIg4IgjjsCcOXNw3nnnxV3+pZdewnXXXYePP/4YWVlsPUlEREREzmGmFBERURrz+Xx47LHH0Nraamj5vXv3Yv78+QxIEREREZHjOOIkIiJKcyNHjsTIkSMNLXvOOec4vDdERERERB1YvkdEREREREREREnH8j0iIiIiIiIiIko6BqWIiIiIiIiIiCjpGJQiIiIiIiIiIqKkY1CKiIiIiIiIiIiSjkEpIiIiIiIiIiJKOgaliIiIiIiIiIgo6RiUIiIiIiIiIiKipGNQioiIiIiIiIiIko5BKSIiIiIiIiIiSrr/Dw95DvlumyN0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(log_files1, dataframes1, 'Offset (ns)', 'offset_results', 'purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 - Análise inferencial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualmente ja podemos vislumbrar a superioridade de precisão do Hardware Timestamping, para confirmar essa impressão realizaremos a seguir o teste de hipótese, mas antes iremos verificar se os dados em questão seguem uma distribuição nomral para definir qual teste é adequado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hardware timestamping - Shapiro-Wilk test:\n",
      "Statistic: 0.9912949077237317, p-value: 4.102929811016252e-05\n",
      "\n",
      "Software timestamping - Shapiro-Wilk test:\n",
      "Statistic: 0.9736366556811694, p-value: 9.217426467880161e-11\n",
      "\n",
      "Os dados de hardware timestamping não são normalmente distribuídos.\n",
      "Os dados de software timestamping não são normalmente distribuídos.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offsets_hardware = df_h_log['time_offset']\n",
    "offsets_software = df_s_log['time_offset']\n",
    "\n",
    "shapiro_hardware = shapiro(offsets_hardware)\n",
    "shapiro_software = shapiro(offsets_software)\n",
    "\n",
    "print(\"Hardware timestamping - Shapiro-Wilk test:\")\n",
    "print(f\"Statistic: {shapiro_hardware.statistic}, p-value: {shapiro_hardware.pvalue}\")\n",
    "\n",
    "print(\"\\nSoftware timestamping - Shapiro-Wilk test:\")\n",
    "print(f\"Statistic: {shapiro_software.statistic}, p-value: {shapiro_software.pvalue}\")\n",
    "\n",
    "alpha = 0.05\n",
    "if shapiro_hardware.pvalue < alpha:\n",
    "    print(\"\\nOs dados de hardware timestamping não são normalmente distribuídos.\")\n",
    "else:\n",
    "    print(\"\\nOs dados de hardware timestamping são normalmente distribuídos.\")\n",
    "\n",
    "if shapiro_software.pvalue < alpha:\n",
    "    print(\"Os dados de software timestamping não são normalmente distribuídos.\")\n",
    "else:\n",
    "    print(\"Os dados de software timestamping são normalmente distribuídos.\")\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "probplot(offsets_hardware, dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot - Hardware Timestamping')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "probplot(offsets_software, dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot - Software Timestamping')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como os dados não seguem uma distribuição normal optammos por usar o teste não parametrico de _Mann-Whitney U_ com as seguintes hipóteses:\n",
    "\n",
    "- Hipótese Nula (H0): As duas amostras vêm da mesma distribuição ou têm medianas iguais.\n",
    "\n",
    "- Hipótese Alternativa (H1): Existe uma diferença significativa entre as duas amostras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-statistic: 325445.0\n",
      "P-value: 0.005915235017812091\n",
      "Rejeitamos a hipótese nula: Há uma diferença significativa na eficácia da sincronização entre hardware e software timestamping.\n"
     ]
    }
   ],
   "source": [
    "u_stat, p_value = mannwhitneyu(offsets_hardware, offsets_software, alternative='two-sided')\n",
    "\n",
    "print(f\"U-statistic: {u_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"Rejeitamos a hipótese nula: Há uma diferença significativa na eficácia da sincronização entre hardware e software timestamping.\")\n",
    "else:\n",
    "    print(\"Falhamos em rejeitar a hipótese nula: Não há evidência suficiente para afirmar que há uma diferença significativa na eficácia da sincronização entre hardware e software timestamping.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusões\n",
    "\n",
    "Os resultados do teste inferencial confirmaram as observações descritivas, indicando uma diferença significativa na eficácia da sincronização entre hardware e software timestamping. O hardware timestamping demonstrou ser mais eficaz e preciso.\n",
    "\n",
    "Em aplicações onde a precisão e a estabilidade são críticas, o hardware timestamping é claramente a escolha mais adequada. No entanto, para aplicações onde o custo e a flexibilidade são mais importantes, o software timestamping pode ser uma solução viável."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4442/4006604356.py:18: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='Timestamping', y='time_offset', data=df_combined, ax=axes[1], palette='Set3')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_s_log['Timestamping'] = 'Software'\n",
    "df_h_log['Timestamping'] = 'Hardware'\n",
    "\n",
    "# unir DataFrames\n",
    "df_combined = pd.concat([df_s_log, df_h_log])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(18, 8))\n",
    "\n",
    "# Densidade\n",
    "for timestamping in df_combined['Timestamping'].unique():\n",
    "    subset = df_combined[df_combined['Timestamping'] == timestamping]\n",
    "    sns.kdeplot(subset['time_offset'], label=timestamping, fill=True, ax=axes[0])\n",
    "axes[0].set_title('Distribuição dos Offsets por Método de Timestamping')\n",
    "axes[0].set_xlabel('Offset (ns)')\n",
    "axes[0].set_ylabel('Densidade')\n",
    "axes[0].legend(title='Timestamping')\n",
    "\n",
    "sns.boxplot(x='Timestamping', y='time_offset', data=df_combined, ax=axes[1], palette='Set3')\n",
    "axes[1].set_title('Boxplot dos Offsets por Método de Timestamping')\n",
    "axes[1].set_xlabel('Timestamping')\n",
    "axes[1].set_ylabel('Offset (ns)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_acf_log(df_h_log, 'time_offset', 'ACF - Hard Log', lags=50) \n",
    "# plot_acf_log(df_s_log, 'time_offset', 'ACF - Soft Log', lags=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $II$. Topologias de Sincronização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <div style=\"margin-right: 20px;\">\n",
    "        <p>Para o segundo conjunto de análises vamos comparar o sincronismo em 2 configurações exploradas ao longo dessa seção. Basicamente serão utilizados os tipos de <i>relógios</i> mostrados na figura ao lado. Abaixo vemos a importação dos logs com os dados de interesse.</p>\n",
    "    </div>\n",
    "    <figure style=\"text-align: center;\">\n",
    "        <img src=\"./assets/tcbcoc.jpg\" width=\"auto\" height=\"auto\">\n",
    "        <figcaption>Fig. 6 - Diferentes tipos de <i>clock</i> PTP. <a href=\"https://www.cisco.com/c/en/us/td/docs/dcn/aci/apic/5x/system-management-configuration/cisco-apic-system-management-configuration-guide-51x/m-precision-time-protocol.html\">Fonte</a></figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_files2 = ['logs/TC.txt', 'logs/Switch.txt'] # Arquivos de texto contendo o log extraido do ptp4l.\n",
    "\n",
    "# TC.txt -> Arquivo de texto contendo os logs utilizando transparent clock\n",
    "# Switch.txt -> Arquivo de texto contendo os logs utilizando um switch entre os nós\n",
    "\n",
    "dataframes2 = {}\n",
    "\n",
    "for log_file in log_files2:\n",
    "    dataframes2[log_file] = process_log_file(log_file, offset_pattern, freq_pattern)\n",
    "\n",
    "df_TC_log = dataframes2['logs/TC.txt']\n",
    "df_Switch_log = dataframes2['logs/Switch.txt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abaixo realizamos a remoção de outliers pelo motivo explicado na seção anterior. Como podemos ver nas ACFs, a autocorrelação nos dados não é significativa então não foi necessária a subamostragem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_acf_log(df_TC_log, 'time_offset', 'ACF - TC Log', lags=50) \n",
    "# plot_acf_log(df_Switch_log, 'time_offset', 'ACF - Switch Log', lags=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_TC_log = dataframes2['logs/TC.txt']\n",
    "df_Switch_log = dataframes2['logs/Switch.txt']\n",
    "\n",
    "df_TC_log = remove_outliers(df_TC_log, 'time_offset')\n",
    "df_Switch_log = remove_outliers(df_Switch_log, 'time_offset')\n",
    "\n",
    "dataframes2['logs/TC.txt'] = df_TC_log\n",
    "dataframes2['logs/Switch.txt'] = df_Switch_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Switch_log = df_Switch_log[df_Switch_log['seconds'] <= 1052] #bota os dfs no range do menor\n",
    "\n",
    "df_TC_log = df_TC_log[df_TC_log['seconds'] <= 1052] #bota os dfs no range do menor\n",
    "\n",
    "dataframes2['logs/TC.txt'] = df_TC_log\n",
    "dataframes2['logs/Switch.txt'] = df_Switch_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 - Topologias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com os dados importados e filtrados podemos começar a descrição dos cenários:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.1 - **PC 3 (PTP _Master_) --> Switch PTP Unaware --> PC 1 (PTP _Slave_):**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A topologia mostrada na fig. 7 é uma configuração simples de sincronização PTP (Precision Time Protocol) onde um _Master_ e um _Slave_ estão conectados através de um switch Ethernet comum (PTP-unaware). O _Master_ envia mensagens de sincronização para o Slave, que ajusta seu relógio com base nelas. O switch utilizado não compensa os atrasos dos pacotes, o que pode introduzir variabilidade nos offsets e limitar a precisão da sincronização de tempo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center;\">\n",
    "        <img src=\"./assets/topo2.png\" width=\"640\" height=\"480\">\n",
    "        <figcaption>Fig. 7 - Topologia Switch do teste 2.</figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "      <th>frequency_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1052.00</td>\n",
       "      <td>1052.00</td>\n",
       "      <td>1051.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>525.63</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-14926.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>303.91</td>\n",
       "      <td>247.87</td>\n",
       "      <td>241.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-644.00</td>\n",
       "      <td>-15657.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>262.82</td>\n",
       "      <td>-172.75</td>\n",
       "      <td>-15089.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>525.64</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>-14921.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>788.45</td>\n",
       "      <td>183.00</td>\n",
       "      <td>-14766.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1051.26</td>\n",
       "      <td>614.00</td>\n",
       "      <td>-14266.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seconds  time_offset  frequency_offset\n",
       "count  1052.00      1052.00           1051.00\n",
       "mean    525.63         1.00         -14926.09\n",
       "std     303.91       247.87            241.60\n",
       "min       0.00      -644.00         -15657.00\n",
       "25%     262.82      -172.75         -15089.50\n",
       "50%     525.64        -6.00         -14921.00\n",
       "75%     788.45       183.00         -14766.00\n",
       "max    1051.26       614.00         -14266.00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Switch_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.2 - **PC 3 (PTP Master) --> PC 2 (_Transparent clock_) --> PC 1 (PTP _Slave_)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A topologia mostrada na Fig. 8 é uma configuração PTP (Precision Time Protocol) com um _Transparent clock_ (TC) entre o Master e o _Slave_. O Master envia mensagens de sincronização através do TC, que mede o tempo que os pacotes levam para atravessar o dispositivo e adiciona essa informação ao campo de correção dos pacotes. O Slave então ajusta seu relógio com base nessas informações, compensando os atrasos introduzidos pelo TC. Esta configuração melhora a precisão da sincronização ao garantir que os atrasos ao longo do caminho são contabilizados, reduzindo a variabilidade nos offsets e aumentando a estabilidade temporal da rede."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center;\">\n",
    "        <img src=\"./assets/tc.png\" width=\"740\" height=\"480\">\n",
    "        <figcaption>Fig. 8 - Topologia TC do teste 2.</figcaption>\n",
    "    </figure>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>time_offset</th>\n",
       "      <th>frequency_offset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1162.00</td>\n",
       "      <td>1162.00</td>\n",
       "      <td>1162.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>527.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-3077.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>303.08</td>\n",
       "      <td>12.89</td>\n",
       "      <td>12.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.70</td>\n",
       "      <td>-36.00</td>\n",
       "      <td>-3115.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>265.75</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>-3086.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>527.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3076.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>790.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>-3069.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1051.31</td>\n",
       "      <td>35.00</td>\n",
       "      <td>-3038.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       seconds  time_offset  frequency_offset\n",
       "count  1162.00      1162.00           1162.00\n",
       "mean    527.41         0.03          -3077.03\n",
       "std     303.08        12.89             12.78\n",
       "min       2.70       -36.00          -3115.00\n",
       "25%     265.75        -9.00          -3086.00\n",
       "50%     527.01         0.00          -3076.00\n",
       "75%     790.06         8.75          -3069.00\n",
       "max    1051.31        35.00          -3038.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TC_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Comparacão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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n7g9315EdR7Rmyhqde8W5Huvy3S3fad3UwurWuQ/PVZ/n+njc53td3itclwPHFJkYKUna+etO7fptl7rc3cXolWX+nTAzDxaLMk91NP/freBEgbIPZyuiZoTl8j8+dv02bPpxk5wFTq/9urbP3a7tc7frpnk3qV73epKsfXbMlZ7mA0HetmWrJ6/WiZwTlum8vjidTh3ZfkSrp6xWx9s76s2Wb0qS7j14r9ff8sy9mVrxzgq1G1G4Pdm9ZLeSuiTpr2l/SU6p6VVNS3xcN/PraK7a8vczeODPA0YgJbnCzkteu0TL3limPz8vnG658YfCSmB3c/W8o3nKO5Zn7G/k5+b7PLCWtS/Lr55f73Z+V8MXD7cEU+aKqLyjeVo9ebUkV0+6Vxq+omaDmlmmHZcnX1VMnwz4RLuX7NbFr16s3KxczR43W/V61NPVn16tiJoRcjgcytidoZXvrlT7ke2L/R3YMW+HJZTa9dsufdjnQzkCHHok/xFrOP///z2Rc0Lvnv+uUlcXVs+ZD3LMfXiuEfT9a+2/jMtTV6eqRuPC6bZFn+tvz/+mhv0bqtZ5JQ9wvx7ytfG52DR9k5o7Sx9KLXtjmUdVflHm34mM3RmqdV4tS+DsDskKThTo+JHjPg/EFD15R1ZqlmW7cCL7hNdQylngVN7RPKUfTVduVm65hlKMlc4cvsKqqVOnaurUqaf0sW+99VbddtttJS94ijB1ERXFr1Bq/fr1qlu3rgYNGqTLLrtM7du3NxrOnThxQuvXr9eiRYv00UcfKSUlRR988MEpXenTyeLnFyuhdYKq1qqqmi1qWqpZcjJylJmSqQPrD1iO+Ban6A56ysoUJV+Q7HqsiYt18K+DGvi2dVA1Y/QMy9Fscx8Q85E+dzBkrqaQCnuETO4+WbsX79bAtweq9Y2t/T6Lyb4/9mnm2MKzjHz3z++KWdo383QDc4XI9nnb9dV1Xylta5pu//t2VT/H1TT6tSavKTczV8fTj6vPM56DstIoGkr5CuSy07L13fDv1PrG1h4DRrOQyJBig7gFTy2w9OXwNf3FPCh0V9lkpmTq4F8HjfdNkhY+uVANejWwDMjN1xdlDq9ys3I9jgj//e3f+uyKz3T+2PO1Z1nh2bZ8BVg7fy0MEXcsKOwtVFw5fXFTfo4dPKZq9aoZPTKWTFxihFLmQDVzb6Z+vP1H429zZdDhTYe9Vjgc3X9U+bn5HtNS3+38rtGAe8vMLR5TOEsKVs2DiP3r9ntdJ3PPkEUTFun3VwqnMLh3iN2VYN0e7GaZOnb0wFHFnhurXb/t0qIJi9TtwW7FTql05jstgcvx9OOW4Kzo5yO4qinQ2+A5ffjgBs/phWbmAPVE9gnjvTuy/YiOHTqmtC3ez8pXnIzdGZpxxwx1GN1BDXo10LI3lmnBEwu04IkFHkHXxKSJysnI0QMZD1jCyYMbDhrN3g+sP6ABb1j7EVkOJBzNVVZqluX98xa4+GLejjwX95zu2HSHYs6JMapoki9M9j6ILNIAf3L3yUrfka4N33pOSTMHUpbbF8NcCfV+N1dVS9WEqmo9tLUO/HXACJSLKm7qX0mVUtOGTtO6T9dp5B8jFd+ysLrDPKBf9uYyNR/UXBE1I7xuc7fN3lYYSu32HkqZn/vupbs179F5lvv49uZvJbkO4Fz/w/UezcXNHA6HpvScoiPbj1im1x3ZfsRrOPPNTd9o6y9bLb180ral6eDfB/X5Va4z/z2Q/oB2/rpTSV2SjCb6vphDE3PVR9FpWtvnbVeNxjWMoNGt6PvgDoB8nRHT6XRq+/ztxt/Zh7KNUOrdLq6Q5O69dyuiZoRlW565N9MIpUraT3i387uWUM9ceXg8/bhWTlpp/J22JU2/Pv2rEUpt/mmzVry9QgPfHugRbrrlZuVqx4Idqt+rvmX6mzdFz67p/j1zH3xYPXm18fuyfd52vZDwgno83kPdH+muLwZ9od1LdmvHvB26ae5Nvh+jyBTQ3Utd9+0scKrgRIHXYGzfmn2WQEqyHuQ4su2I8X/ze/z1DV/rl/u9n6F4+dvLNeehOZrz0By/KgjNQWVxVr23Sttmb9Pl71/u0evT/Ftstnryam35eYsun3y5ZXvr/uyYQyn3Z/bTKz7VpumbdMemO4wThWybu02xTWIVmRjpUcHq3sa75R3LK/FgZd6xPI9q6JPBWAnlYdKkSZo0yftvsh3CqoTp7w1/E0zBdn6FUh988IH++OMPvf7667rhhhuUnp6uwMBAhYaG6tgx1+C2bdu2uvXWW3XTTTcpNPTkqlYqm2lDp0mSwuPCNeDNwqMeOZk5+vzqz3V482Fd9911anJpE53IOaGCEwVG+LRl5hZlH85Wm2Ft5HA4PEKpPUv3KPmCZDkLnEbw0+bmNh7r8GHfD43/Z6Vkae4jc7V11lY1G+S7ssHNXSnlDqt+uO0HVxNt075mblauQqqGGNN4zEevPuhV/A+rv/1GzEe8j+w4YvzfHHit/Xituj/SXblZucbOzf61rgHkvMfmadW7q3TDjBsUXTdaAUEBCg4P1rFDxxReI1zOAteZzPav26+G/RuqxXUtjPsturN4PO243mr9lpK6JRnTIyXXmc3+nva3/p72t8ZljdNfX/+l4LBg/fb8b4ptEqvez/bWyv+tLPGI29z/zFW7W9sZlV/mHVC3lxu8bGlM7R4cv37u68rJyFHza61HMqf0nFLsY0qSnK4dMXNA4C2UcvdaWvLiEsvAJyczx2uj+k3TC8MR8w50cb2rinuNjh44avnbvfNbUvWe+fF89WXLSs3SnIfn6LdnrVVaKStStPjFxUrqkmRp4uyWk5mj/Lx85efkW6bhrf9qvX4e87Plu+b+TEpSbkbhjq75M/bX19amtO6B5zc3fSNJWjR+keX6QxsPKalzkr4Y9IUy92aWOIgoWimVk55jCc6Kvv7mnXxzKOxm/n4eTz+u357/TcHhweo4uqPe7/a+JUBL35VuadL7XOxzxa6r+zHXfbpO7Ue2V1Zqlj7s86GxHn9/87cedT5q+dyagyOpMAA9vPmwJQT56vqvLMtlpWYpspbpM216jdzhkVnu0VwteXmJEtslqm634nfSigZ9i55ZZDmRQGZKptdQKiAwwBJouafWFHcyA8s6ljCYKjoYl1wVHK2HttbK/630covC+929ZLe1F+D//0SZB8fegjt3r8K3Wr2lmAYxGvDmAJ3T9xxLSDtj9AwtfmGx7tp6l9f+OObvu7nqMivFFRyunbrWUlW79KWlPqeI7/tjn+Y/MV+XvXOZa529/C5lH842qsbMParM3435/52vtR+t1aAvBhnBtvn7nrY5TaveX2VZfvHzi1W7U20NmzfM8ngF+QVaMnGJki9MVmIHa4BuDt/N39Xt87ZrSs8pCgwJ1H9y/uP1uboZU7O8hIv5efma8x/rdvDQxkNa/tZyBYYGGpXLW2ZtUasbWlm2yeYQwZ+eZc/FPqf7Dt+nsJgwy2/dL/f/4vU3YmLSRLUf1d5oPRAQHKABbwxQeGy4dizYoa9v+Fr9X+mvplc21fe3fq91U9ep24Pd1OupXpb7Wf/leoVUDTEabhft05STmWP57QsOD/bobTXv0XlK6ppkBFfb520v9rkW/S03ByNp29Isv5/uz7c50Hdzb6udTqcy9hQGskW/a+aw1mzfmsLtcUF+gT7s86FCqoboum+vO6mG/u5p9nUvrOtX9aFUGAwndUtS7Y6FTdXd+3Dm35tF4xdp3dR1xn7Eyv+tVO+ne2vHwh364KIPFBgaqAePPlhiL0J/qp7yjuVZtjsnWynFWAlngoKCAq1du1YHDxZ/ILKiML3xzOV3T6lWrVrp7bff1ltvvaU//vhD27dvV3Z2tmJjY9WmTRvFxnrvO3G6euONN/Tcc88pJSVFzZs310svvaQLLrig5BsW49iBY5ajibmZuTq82XV625XvrFSjixvp/Qve18G/Diqsepgydhc2a3bmO3XeLed5hFLuHSFzFZHXhsKmsXrm3kwtfWmpJOnXLb+WuN4FeQUeDdNXvL3CEtq8UOsF3bPvHv3075+0ctJKRSdH66qPrlLdbnUtVTfemHcegsI8P3Jp29J07OAxy064ryafaz9eq7QtaWoxpHDdNs/YrJ/H/mxMOfrl/l+0Z9kehVQN0fljztdPd/2kjnd0VLNBzTTrXteZzNZMWWM5y1TBiQLLwG39l+sluQYy5lBqz5LCqqGno562DJr2LN2jIzuOaMf84s9C5pa6OlXn9DlHkveKoiPbjlgCntzMXKWsSjEGAe6G1KXhLHBq25xtlgFZSQNa82cjNzPXI5Ra88Eao6eRe73dipv+U9zjHjtwzNor6v9DKW9hSdHbuZk/T2arJ6/22SfIHcZ5k5ORo++Gf6e1H6/VJa9fouoNq6tavWrGWcvMU94O/n3Qcjs38+tetFlz3tE8HTt0zGd12fe3fK8/Pvij2ObTZs6C4iulcjJztPWXrarTuY5CIkIsr8mhjZ6B3tF9RzXvsXkKCA5QaFSoFj7p6sezb80+j2bgbzR7o9h1K9rkOu9YnuY+PFdrPlijPz78Q+Fx4V7fa/MULl+DkqLvbdEKrbStaT5DKW82Td9khK4lnQFx/RfrLZelLE+xvOa+pg85Ah3SSfR59rW9dDvw5wHNf3y+pbLh+GHX9q64AWratjRtnrHZOvB1ul578+A4LztPOZk52rt8r+Jbxnt8HtK2pumjfh/plqW3GP3t3I5sc53Ywtv2ICcjR9+N+E71etTzqJRyT60zK6kpvblKw1tQZ57W7AhwGNPkp/ScortTXBVD8x6ZJ0l6+7y3FRgaaAQ/xvPZYT1Rx+LnF0ty/T6Yey85Ahxa+8la4zdpXNY4y/0se72wabp5sOyetmT+LGUfztbeFXs9piu5tzfeqjyzUrM8gvlFExZ5TItyB/DmbWtWSpZ+GfeLQiJC1PIGz36U3vz5+Z9qf1t7SyVR0e+LW8buDCOQci+3/sv16np/V/36tGuf5vOrPtejzkeNysFfn/7VEkodO3jMCFPdgVjRKqac9BzLb3hQlSCP91NyNcn21+FNh7XmwzX6e9rf6nRXJ/35aeEBjoN/HVR868LA3B3sent/3NuN+Y/PtwRM/gYn5pNqpKxMMVpD5Gbl+nUW4JIc2X5EO3/dqZj6MYpMjPS5bTOHfDNGz7Bc536ORacPm/chfn3mVzW/trnmP+46g11+Tr7mPTqvxMpl8+u06JlFPpcp755SZ9JY6VSMk3D6y8nN0cCBA0tesILYMb2RKYwVo9SNzh0Oh1q3bq3WrSvuFNkn67PPPtOYMWP0xhtvqGvXrnr77bd18cUXG6W3J8O882Ae7BzZdkRrp641+qcU3QH/fsT3qlKtitdKKcn6o11S413LUTU/qpTyc/Mtgz03y6lys1yBiLvUPn1Hut6/4H31mtDL43ZFvdb4NeP/7kHRHx/9oaikKCW0SdArDV6RJMU0iCnxvg5tPKRDGw95HFU3BwLuKU3HDhzTT3e5mg7//urvWvXuKstt3MGT5Hp/fn/V+9mC3j7vbXW+u7MWPLHAMlj3VrXjbyAluY64pqxIUYvrW3hMqfTmyPYjRiN4qfizIfmy6t1VHq/D8reWa+P3GzX4q8FKaJPgEWaYd/7c1UJrPlijBf9doKs/uVoz7rTuaJo/q8VVSvk6wiu5KqXMYad7yuCBvw74uomHYwePadMMz+ltZW2cb66imf6v6cUsaW32ah78Fvd9zMnM8VqpY7ZjwQ7Ft/Ic9HuTfzzf8j7u/X2vlr1WOND946M/tOKtFYpvFa+Ra0aWeCanDd9uMCrIWg1tZVxu7gXnL3dY75a+M92YRlS0Asota1+WJYhyVxMVNeu+WfrHT/9QcLhrCl9I1RDL65C2NU11uxZu5/2p9HBb9sYyzR43W/l5+ep6X1f1eKyHcd1vz/2mLTO3WJZP25ZmnIFUKqx+yM3KlSPAYaxjQGCA8lXY26+0Sjpr3Mp3Vnr0enEPCL31dHIz9ycye6nuS6pWr5rx94njJzSl55QSPwvuEyAUte+PfR5nppRkbJNX/W+VmlxeeOIJX2G3tzDVLCLeVZl69MBRvdXqLY/rzc2vzaGUs8Cp5+OfV90LCj83znynCpyeB4iyUrMsVYJm7rOEuu/zmxu/Mf4uLqTPO5an4+nH9f4F71uqstzB/WdXfaYd83dYAg/JFea81fotrweyzH3v3NxTzcyOHzmuxS8utkwB3PLzFuOgSNEphL5k7MowpseWiVNGIOUP8+Ns+XmLWlzXwiOIzMnIsVSAHT9y3OdZAB0BDuM3/0TOCeXn5is0MlTH0497TBNzv69F+zEe3HDQmIomuV4Tp9PpdRvk3la4wxi34s5qJ0nPJzyvaz69xhI2f3/L98b/sw9ne4RS2+Z6Vhe+0fwNxTSIUct/tFSLa1uo4IT14OWvT/+qX5/+VdUbVteoP0d5/T0vyC/w2a/O/ByLW0aSZb9HKjxBQXHMAZOvs+u+0+EdSwhZnmd6ruxjpVM5TgJOhh3TG0NDQvXV119VyoqsyhyonZVn33vxxRc1fPhw3XLLLZKkl156ST///LPefPNNTZgw4aTu21za7Q5EJNfgpOgZ6or6YtAXHjt4GbszlLEnw/Kj7e0Ir5mv/hG+ZKdle92hKBq6eAuuZo/z/mPvS25WrjZO32hMeTQPiEqz3uZKFH8VPQq24Rtrv5YF/13g9Xapq1I17R/TSv14JXFPz1r7yVq/jlx6POdyasa+9GVXVd2yN5bp0kmX6vtbv7ccFTfLzczVlJ5TjM/ye10LmykndkjU3mV7LYOrrNQs5WXnadOPm5Sfk696PeopJDJEv7/2u+VIeFE75u2wTBfLPpwtZ4HT60DKl6P7juqTSz7xe/lTxVelVFF/f/13iU1ipZJ7O7mZp7hKhaeid1vx1gpJrkBg5687SwzrzOvuDsslz4DJH0Xfx+3ztiuuaZzPoElyNUQ3b6d8Vc3tXLhTvz3/m7o/4jqLXdEzHh7866ByMnL0/YjvlZOZU6oTOph7psx/fL62/LxFrYa2UsP+Db027c9Jz7H0Dds0fZPWfrJWG77doODwYDW8uKEuf+9yV6XU/xsfMd7v9TE/TnG8hTVHth1xfVfLOJPHPH3vwPoDZQon3XYv3e1xlraizO+3uaKpNHIzcrVl5hZ91O8jr9ebXydvFWQ7F+60/O3ts3M87bjHmSj9UdJU5+VvLbcEUpL0xTVfqNmgZsbBEPNBMTdfAfbBvzy3I962AUtfWurR78hcpTvvsXk+19tszQdr9Purv3sEMJGJkX5XfxZl3mco2tvKPL150/RNanFdC8/pexk5lmneWSlZXiulJOt7/Vart3Ts4DFd+MiFmnXvLLX/l3/T2LIPZ3ucYfHrG75Wg96ePUez07K15GXPE06U5Oi+o5rSc4pa31QYhpg/A9mHslUtuZolZPvgIs8WDAfWH9CB9Qe08YeNykrN0s4FO7Xhe88ed4c3H9aaD9Z4Pai48n8ri91HnPfoPLW7rV2JoVRZfD/ie3W5t0uxLSyKvtfmNgRnu1M5TgJOd7m5uad1tVhxKnNPsLMulMrNzdWKFSv0wAMPWC7v27evfvvN+5nAcnJylJNTuDOTkeG7wsM8Jce8k5l3NK/YyhA3bztnE+tMtPxtHqx740/VjVn2oWyvU2GKHn2feY/vqU3+cuY7LafxLc2A0Kzo1KfSqN6wug5vPlyqqpvy1Oe5PsaUDcnVj8SfU08X1zS8LGq2qOlRlbJ3xV6PSiqzoweOeg1XQyJDvDaiPbL9iH7+989a8faKUq3bxh82WgYVBScKdHT/Ua8DKV9K6v1hF/MgzNeARyocREXER1imHobVCLNUcBR3H5bHzczxuwpo84zNpWryWlJFSkl+uPUHy9/TR00vMWSdeqn1jDfFhfy7ft2lXYt3ado/pnmE3YsmLNLBvw/6dVZJXxLaJCh1dap2L9ltOUNjSdZ8sMb4f96xPP311V+WJtlS8Sco8KWkQb23KdaHNx/Wm63e9Hn2rpKYf4fMFXhlsW/NvmIbkEuyBCMlTRkvqtuD3bRo/CKtnrzaONubN+awtSyVqG6lqb5zK+5AS96xPK/h719f/+XRm85fRStwzJK6Jhnfr6KBVFHeDlaVZrnYprHKycgpU5PpV855pfAPp2t/IjstW+s+XWcJQ/746A+1H9VeS16yhjwZuzO0ddZWy9/+cG//fh7zsyRZgufi/Pr0r5apw5LrxAXe+qAdTzvuMb2yNMy/GWZZ+7J0IudEqfa93M/Tl9+e+81SReg2fWTxFcWSq+q4uIMRZbXvj32aNnSa6vWs5/dtip785Gx1qsdJwOnO19kXK4Py7Almd/+usy6UOnjwoPLz8xUfby1zj4+PV2qq952vCRMm6PHHHz/px/Znyo1Z0cGom3lqkDebZ2wu1eMcPXDU6yCz6A90SY/rr7LsRPt6Lcoi5pwY1w5+Kbc5Fzx0gRLaJFib/pZBcvdkj8tKmn4jlT3AK6rRgEZqPri51kxZY7n86P6jlkGzN76ml4XFhHkN1gryCkodSJnFNIhRYGigDv51UBt/2FimM7hVtJyMHB3eclhzHprj19SVvi/01cp3VhrVDzENYsr02c/NyvV7cLx93vZynbpgFnNOjMf75g5YjcGv6aN9/r/P19JXlpZYbVJcf7GQyBB9cc0XPsOakwmkqjeqrmHzh2n2Q7NPOowpL2WpNIhKivLoW1cq5bjPtuLtFSVvJ0yPV9rtQI1GZQve7OQtcHcfOMg7lqfM3eXz++tW3G/OFZOv0HfDv7OcSbWoOufXUX5e/klVyElSRFyEgsODT/rMZ5Lr5CDpO9O9fjbf6/Kex2VFT4JgB3MFvZu3A41lqUA1cwdsRfedPrnkE8U1j/PrPsbsGKOXkl/yef21067VZ1d+ZrRVKIvNMzb7fTIcsyaXN/F6dtKiXqr7UhnW6uxWkeMkACenPHuCPfroo3rsscfK5b78cdaFUm5FS/OdTqfPhq/jxo3T2LFjjb8zMjKUlJRU6sf094iiW5d7uig6OVo7F+7UjgU7jOqgX5/xv6+CP5z5Ti1/03vvENs5XM1Gi55hpl6Pejq04ZDPfjOS68w5VWKqlBieVW9YXVt+Luz9UrPl/1cMlTDIimsep2bXlHw2w+LEt45XYvtEVU2o6hlQFDkt/Kly/ffXy+FweFRoZKVklXmAUSWmikcoVa9nPaO5atVaVdXk8ibGlLHi1DqvlnHWsSaXN1FkYqRm3TtLS19eetIDl6GzhurDPv43rS2NtsPb6tCGQ9q5yDrFJyc9Rz+O+tGj35AvSV2StPG7wiqxmPoxRi+64lSJqWI5u15uZq6lv1xxSppafDKqRPuuAky+MFk1W9a0fC6aXtVUvcb30lNhTxmXhUSG+Gxs2/za5h5nS9w+b7vXIK9odWB4XLjPqaq+RNWJUmhUqC559RLFNY3zeRr0smozrI1aDW1V4llNzcrSp+eKKVd4nbZzJqqaULVc769h/4ba/FPpDv5Uq1+t2ACwaOVSZO1I1T6/tvav26+ZY2ca/bDsEJUUpXP6nVNsKFX7/No6vOnkghNJCq8ZbvRWK42qtap69BYra8WNI9DV362k5tklCYkM0YnjJ8pU7ViU+7fTl4DggGIfx13hdu3X1yq6brRerv+ycZ2/lebRdaPV5d4uXqcmS1LDixsa0/Z9GbFshKrWqupR8e/mK5CKTo5W3a51tfaTtV6v96e6XCq/g3lno4oYJwHwze5eV3b31PLejbMY//znP5WZ6TnoP3r0qP75z3+Wy0qdSrGxsQoMDPRI+/fv3+9xVMAtNDRUUVFRln+nWqt/tFKnuzqp5fUtNeCNARq1bpS6PditxNuNWj+qTI9Xmp2xgZMGKuackpuSl0V4jXCvjVPDY8M1YtkIXf/D9Qqq4pmlnnvlubp1xa0epfFu5p4tRde9SrUqqn5O9aI38RBV5+Tf93/8/A85HA6v/VNi6p+a17T/y/3V+sbWuuzdyzRmxxhjp6Jor519a/f5PX2hqODwYI+dxOQLCyvCajSuoX4v9vNoCOuNuRFsTIMYtR3eVlWqVdH+dftPqvfE6A2j/W7K60txg9vOd3dWn+f7eFxecKJAW2b5F0gFVQlStXrVLAPQag2q+XXbuGbWo99H9x/VX1+6gsdL3rjEr/sojfC4cK/fxaICQwN9XhdWPUwXv3yx5bKQqiEKqhKk0OjCz0q3cd63fU2vamr5nLn5qizr+WRPy9+J7RI9lgkILv5n0Xz2yQ6jOig8LrzY5UsjIDhA3R/rbksAUb9nfXV9oKvH5Y4Ah84fe365P15S1yRVifFvIOlL0W2WP4LCgvwawF7+/uV+3V+jAY10/Q/Xq8cTPfxeh7oX1C3170eValUUHFYY1vg6m6hbdF3P6Y9j94z1smTxv2VRdaIUFFrya+YOZ09WRM0Iy1l5hy8e7rGMt8uu//563TT3Jj2c97C63NvFuHzQF4PUdnhb429zbyVfgsOCLY37O43ppPDY0n+vQ6qG+B2A1u9VX6M3jlZs07Kdjc3f37Kw6mGqVq+azhtxnsd1/my/za+LWcP+DRUUGqTrv7++2Nsntk9UVO0o1e1WfH8T8z6aJN2x6Q5d9fFVqtXO+6CotNuS2Kaxqt+rvs/rq8RU0YilI0p1n8WpzGOlyjJOAs42AQEBatmypc477zxb/tkdSpW6UmrKlCl6+umnFRlp/UHMzs7WBx98oPfe8yyRPp2EhISoXbt2mjVrlq688krj8lmzZunyy/3bKS1v/V7qZ5mz3+iSRrrywys9litacRBVJ8ojRIg9t3xON3vFB1dYzgzkFtMgRu1GtFN8q3i9e/67Jd5PjcY1FBwRrNY3ttbP/y6+L4Hk6qcTGhnqMT0jNCpUQVWC1HhAY7W+qbXHVI+2/2zreu4+mvbGNok1miubQw9Jik6KVniN8BLL5aNqn/yPrPuIcPfHuluaxUquI+NF+9+MWDZCYdXDrL0ziuhyX5die0/Et4pXpzs7eVxuHvRL8qhOK0n3R7sbR/ed+U6PQUzNljWN/0fVjlJwWLDO6XeOz9OAG8vWLXydIxMjFRYTptY3tTYaspdUURYYEui1H0yNxjVKHbrdufVOfdz/Y2NqQt0L6qrxwMZa+c5Kj4qo0MhQj538Go1ruG7r58HaqKQoORwOSyjhz1kpJddOt7niyd13JyopSucNP08/jvJe0VO0giOkaohfFWntbmuntR+tLTEoNB9ZLTqVJKx6mAJDAtWgdwNjurA7eAirHmY0Jq7ZoqblNu7pf02uaGIZuBdlnjrYamgrNbmsieV6b2eeq5ZcrdhtQWg16/emSnSVUldbSd4rHe4/fL9CqoZ4TE1sclkTJfdIVsauDMuZRktSvVH1YqtZml3dzHJGs4j4CN2x8Q7tXbFXS160Pk5J91WSm+bepPzcfO35fY/2/bGvxD413jS+tLHWTV1X7DKNBjSyNCwOjw33K5Sq1a6WImpGeEylanxpY7X6Ryt9ee2XklxnAg0IDFD3h7srMjFSiyYsKnE6Ydt/tvXY3kuuz1+f5/to5ljPfo1VqlUpVQiffGGy/vjoD8tlvoKLKtWq+NwW9n+5v7FMcaLqRBU7Zcvb/onk2obWbFnTOLNjRM0Iy/Y6oW2Cx228HcQJrxFuhMrn//t8bf1lq5pd00zNrmmmplc39dkb8V/r/qUN322wnGgjODxYVROqGo3kO97eUe1va6/Xm77u9T5im8Z6nW4ZEhGimHNijKr4AW8O8Hqm1nP6naNrPrtGVaKrKKZBTLG9Er31fnQ/f38qw6KSXL+nYTXCPK4b8uOQEqslo5O993q7/gdXGOWtl6SbeR9gyPQhWvX+KrUe2lqBoYHKO5an52s+b1yf2D7R6OkWGBKowGDXwYzIxEivFdz+Vkq5BVUJ8lnlU7NFTd04+0blVyl7H7miKvNY6XQcJwH+CA4K1nPPP6fY2PIZC3sTGxuruDj/pkCfiseujA3M/eV3KJWR4Tp1rdPpVGZmpqpUKfxByM/P148//qiaNWsWcw+nj7Fjx2ro0KFq3769OnfurEmTJmnnzp0aOXLkSd2v+WwmpdHpzk7qdEcnbZ29Vc4Cp87pc47X5cxHEyXvFQi+fnSvnXat0nemG/0MHIEOtbi2hdqNbKcN323Q4ucXW5b3FW65j4zW6VRHt/x+i/7X0fspvt0GfTFI8a1cR1aKhlJXfnSlvrnpG0vvmIiaEV7PRGc+Iuttp9UdBphf/7jmcTrw5wEFhgQqomaEEUpVq1dNAUEBxmmya7aqqaZXNdXOX3cqLCbM5462P0cmWw5p6bPcXJIxgE5onaBR60fpjWZvFHv/1RtVd51evJggpqQGwb6qC9rd6jrrTVSdKK18Z6VxeUyDmGLPhJjUNUnD5g1TQFCAEUoV5BdYjqhG1YmyVH5F1nE9N38qQCLiCpdxvybm9zwyMbLYKZrNrmmmpK5JXqdVeTvtfHFi6sfooqcuMvqIhUaFqvWNrdX6xtZKXZ2qt9u+bblv86ngHYEOJbZPLFWvDXe1Q9X4wqPt/oZScU29/0jW7VZXgSGB6v9yfx3ZfkQ5mTla9T/XgK3H4z2U2CHRcpbCyNqROrTBus79X+mvfWv2KbJ2pBY+uVAJbRN0/pjz5cx3atGERX4/v/4v9TfOvNl8cHO1vKGlJOsAwwilYsKMsMz82aoSU8UIpeJbxSu8RmFFQ91udY2wsEq1KmrYv6GWve7q+9T57s4e28f6vetr04/WMy5FJ0cboZR5O2E8fpHBkDmIHLlmpN7t/K7HmT4lVwh85QdX6tPLXX3Z6l9UX/vW7FNWapaCqgQpsUOi8dyLVqDEt45X53931q/P+p66XaVaFUufoIiaEer/cn/Le3v5+5fr25u/Vb+X+klybV/MGvZvqNCoUK/bjE53dtKMO2b4fPx2t7Urti9UYLBroFm/Z30lX5hshFJBVYIsU3g63N5B5155rj7sXTjNtmpCVRXkF6j307315+d/Gr8Z7Ue1N8INt7jmcZZQqvuj3X0OYM3BSURchG5bdZuy07L1ySWfGMFgcHiwmg9uboRS5s/QecPP0741+/T7q8U3uk7skOi1j9klr1+i9iPbK65pnD6++GPLdb2f6a25D8/1eZ/n3Xqe/vrqLyPkTe7hGUpJUoM+DSzNvCXPz5ckndP3HPV4oofqdHIFQEUPWhQVVbv4SqmI+AjjtW07vK0REoVEhCgkovDzFREXYQnBg0L92y01v6eRtSJ128rbjL/N71He0TzL72dsk1hV/3d1JV+QrN1Ld2vWPbN05YdXFh70kCvI8TUl9tYVtyoqKcoSqBjrXiVIA98eqHc6vKOazWuq/cj2CggK0JKJS4z9jwseukAXPXmRcZuSqs6zUrN055Y7PQ5M+bOv2fuZ3sbBTPN2UpJuWXqLanesrYFvD9QPt/3gcVv3ds1X5Zf7t87XPqckXfrOpcb/Q6NCdf5dhRWYIREhlimYcc3jjFAqOKLwQIOvxw+LCfOYjt/7md5KXZ3qNbjOz833etCy38R+On+Ma73Kozn3mTJWOlXjJOBUyjuRpzFjxpzSx7C7z9LZxO9Qqlq1anI4HHI4HGrcuLHH9Q6Ho9I0ubv22mt16NAhPfHEE0pJSVGLFi30448/KjnZcxpIaQRHFPYkePDogzqw/oC+vPZLVatXTdvmeJ5dxc3hcEgO+Qyj3Mz9YiTX4Pz676+3BBu+JHZIVNVahT/u1RtW11UfXyXJewN2X41hzYP62h1ql/i45iCt9zO99cv9vygoLEhXfXyVml7ZVE0ua6I1U9YYg52ImhFegwPzzq+3dXOXcpt31K784Eqt/WStGg1opO+Gf2dcHtc0TqFRoZaBbY1GNXTX1rsUEBRg6WVjeS4llLq3uK6Frvr4Km2bu01ZKVleBwMBQYWhRVzTOEUnRxtHO6OTo3XTvJs0pceUwuf1/zuUza5upvVfuiqMzFUl/qyXeQfPLKFNgm748QZJrrN3uQdycc3j1GtCLx3efFhBYUFqfWNrLX9rueb+xzVAajmkpeV5SK4pauf0K/z81mpXyzKdxB00XTDuAm2dtVUth7TUvEfneV0v8xFddyjlDjYlV3BTXCjV8oaWanRJIzUa0Egv13vZcl1ppv9Uq1/NeDw380Ct6KAtpGqIpfImNDJUDfo08BpSFu3/5OZ+LPNr4Gta590pd2v2g7O1+v3VkqQaTbx/Z93TH9zVcl//42vjuu6PdJfkmh7nDpeqJlT1CKUiakbosv9dJkk6f8z5qlKtihwOh7o90E1VqlXRL/f/4vWxHYEOy0Cg2TXNtOaDNapWv5oufbtwwGL+DLs/r2HVC18D8/c/vEa4UZkSe26sZRBbrV41I5RqNKCRZWqdeyAcGhWqnIwcJbZPVMfRHeXMd6rO+XX0/gXvS5IlWIxpEOMRKpqn70nWU9HHNY/TkB+H6Lt/fmcJdu9Pu98YRA94a4AWP79YF796sSITIxUY7KrsM08bLLoNdL8mxVUHNBvUzBIuu39T3M83rlmc2gxro3OvONe4H3P1bYfRHYzBsrcDAyVNlxnwxgCtmLTCGPy3GdbG55nuAgIDNGT6EOVk5Gj+4/MtZ56LaRCjBr0a6B8z/6EfbvtBA98aqPoX1deJnBMKiXBN7cw76gr9Ot3ZyRJKNRrQyLJtuvjVi9X2n219nrHSHIaF1QhTYHCgIhMjVf+i+sa6e/Q7KjKwNX82fVUZ1mhUQ4c2FX6OOt/TWdvnbFeroa0kuapE3JoNaqZL37lUVaKrqPfTvfW/Tt4P/Jx7+bkKCArQ8jeWq/WNrVXrPO9l9ld+cKVWT1mt2Q8Uno3OW+AUVj3MCKQkeZ0Kb67ui4iPKDaUSmyfaFS4xLcu3H47C5w6duiYZbniKjMved019bhoQFzS1MG4ZnE6sP6Amg1upvVfFVbnBgQFKCAoQHW71VVS1yS1H9leIREhlr5JQaFBlvf9/H+frxqNayi+VbzxOl8x5QrNf2K+rvn0Gr3T4R1JrhMOVEuupru23mXc/rxbzlNC2wS90961jHm7Jkk1mxcfUHS+p7PXAxM1W9R0bRec0q7fvPcENFeYmQ/s9Hyyp2p3dO2/tbu1nbbN3qY/P7f25XPvuyW0TlBy92RF1Y4q9oCbZD1z7LD5wyyfJ2+ik6KNUMpcwW7+nfb1m12lWhU16N3AEng2u6aZxz6Xm69tQHlOv5bOnLHSqRon4exldz+kU6Wyr//pzO9Qau7cuXI6nbrooov01VdfqXp10w9ISIiSk5OVmOjZn+N0NWrUKI0aVbb+S770fb6vpv9rujrc3kHB4cFKbJ+oO7fcqYITBVrw1AKlb09XaLVQLX1pacl35oX5qHaza5qp1dBWimsap3/8/A991O8joz/Ipe9cqu9HfG+5bdWEqkYII1l/6M07s8OXDFdUnShXnyUv0zXcJdVFtRvZTlF1oozgolq9amo2qJklQOp6X1d1vc/awyQ0MtSyUxARH+F1J8Q8eDC/Do0HNlZMw5jC52A6eFjrvFrG5eZpMgFBAZZBsDvwKC6waNi/ofH/8/99vtcpNO7Qb8TvI5SxO0Pb5mzzuYPkVqNxDSOUajygsZIvTNbFr16sGXfM0DWfX2Ms1/qm1kYoVadLHbUb2U5fXOOq3ikplPIniBnywxA97nDtKGUfzlbzwc0t1zfs39B4b70NTp35ToVGhqrt8Lb648M/1P3R7l6nC0QmRmr036MlyWcoZQ4F3EdJ45rGGQOi1je11u7Fu30+F3ffiGrJ1Tym8pjvu36v+urxWA8jjCi6niN+d/WXcE9/kGQ5wm8e0AcEB1juW3K97q2GttKKt1do9xLr+g6dNVSLJixSrwm9dOzgMePMUO6BgDloMT++WUR8hGXKpa9KvqJTX7xN0zSHfkUHTZL1M2YOZUKjQtX1vq7aMX+HNv24SYEhgWo8sLFxhs2AwABd9NRFmtJjijrc3kFBVYI0dOZQj/s3BzLuwVydznWM8DU0KlRXf3q1Nn6/Ub3G99LrTV9XrXa1jNfplqW3aMlLS9T3xb6KSopSTkaOej7R0zKIcgc7N865UYsmLFLvp3srMDhQXe4p7EcjSbU71Taa0nurRvUIhkzbnIDAANXrXk93brlT71/wvnYu2qkGfRpYbtP+tvZqf1t7y10EhlgfJyAwwBJwuG9fNEwwN2pvO7ytJZQa8MYABQQF6MqPrtS6T9apz3N9vK7/Xdvv0rGDxyz9tbxtM0qaLuMIcDWLdgdGl79/uY4dPKaNP2xUQhvP6tZGlzSSJC0cv9Byufvzfk6fc3TX1rsK1ynItU7u+5dc20+3avWr6ZrPrrGcCKTt8LZyOBwKqhKk2p1qa8/SPWo8sLE2/uA6kUDVhKrGNFfzb1uDPg18hlJFK0PM70nNFjU9vuuS6/1NvjBZB/86qGr1qqnvc30t15u3qfm5+ca2pXbH2hr81WB9fvXnkqzvd2hUqHo/3VvNBzVX8oXJHmfTu/DhC43n2O3+bpZQytvJB4pW3nh7z8KqhykoNEh52XmqVq9ascFQ3W51jco58/bD6XSq2TXNtPq91er2YDdF1YmyvKeWdWiboA6jOkgqPFukm7ept2Y3L7xZ+/7Yp+TuyXI4HJbw2LgPh8PYpnsEwaZpwY4Ah9qPtH5n3RWzZu7va9Hvj/l1Kvr72XJIS6+VSpLrBC+d/93Z63XOAqf+ucjVm2jPsj1a/uZy4wCFt8eq37Own1JsE2slvLdgxv38A4ICNGzeMEnyGUrdvPBmLX15qdre0lYf93dV/PlTWW6ugrKEUqbfWV9N8I0Dkab3NTw23OdZJU8cP+G1qsu8DSkPZ9JY6VSMk3D2cvdDOpOnn+Hk+B1Kde/uOqK+bds21a1bt9iS3bPVuVecq6ZXN/UY1AUEBajHoz0kSTt/3WmEUtUbVVebm9v4ff/NBzdXVkqWki9MthxZPafvObr34L3G4553y3k698pz9UKtF4wwJiAwwPLjbt5pSuqc5GpeXj/GcmRr1LpRejL0Scs6FN2x6/pAV238zjVQPPj3Qc2VK7jo8XgPjx02X8yVKFXjq3oM0CRrhZG5t1OXe7tYGh37KmkfOMlVou4uJzefKt6fnSd3/wRJ6vtCX13w0AXKSsnSn5//qQX/XSCpcCc5qk6UoupEafdS38GJW4/He2jb7G2KqhOlpK6uM5V0uL2DWt/U2lKtYG6GGhIRoga9GpR4327+NDOVpOTuydoxf4fa/rOtx3W1zqslR6BDznynpULOPV3K3Vh24FsD1feFvsagx93fqV7Pen6vr3nw6/4sBFUJ0rXTrlX2oWy1GtpK00d69uno8UQPNejVwBLoFDcVpGpCVa/NV3tN6KV2t7UzwhfzVLrco4VH9C0DMi8fO/d0vjb/bOMxUE1sl6jBXw6W5NoZd/cNadDb9b6aQyJfQbDD4bCEtdWSqxnBXZthbbRl1ha1vrG1krpYz4BTNdFzOsS5V5yr2h1rq2armpbptG7F9W2SpMveu0yr3l2ltv9sq6oJVY2A0xHoUL3u9XTfofuKrbQxf7/d4d55I87Tgv8uUGh0qEIjQ9Xi2hZqcW0LSa4GzuZtWO2OtXX1J1dLknqN71W43qYqQfdAx/zam438Y6Q2/7RZne7opJ2Ldmr73O1qclkTo8+MW9Hn4W2wK0nXfHaNVk9ZrfNu8Www7I/IxEijSsvdmLpomHDJ65do6UtLVat9LUvFxbXTrtW5V5wrSWpyaRM1udTaS8usWnI1VUuuZrnMWzDpTw8XcyglSZdPvlwrJq1Q66G+fwtqtqhpeY29Nez2xbwf0mpoK4VEWKsVzd//G2ffqOVvLVejSxoZoVSjgY3U6a5ORlWkW2KHwt9XdzDp7k/WbLD1DKzm96Rez3rGd919hj53lVCv8b0UnRytVje08nge5kC76NnIzFOeWw1tZfT6cn8v6vWo51oP0+eyzT/bqOcT1qb+ZiFRnqFjQb51mqojwKFOYzpZDqI5HA7d/tftklzbJW8VdW7m3yzz9qNa/WpqdHEjS/VgUVd+eKXmPjLX0nx+0OeD9Pk1n6tGoxrqcl8Xr7czC6seZrw2vnpNmvWa0Eupq1ONpumWdgkl3N79O+grKDO/TkWfc0jVEN22+jbtmL/DaK/g1uTyJl73hyQpP6+w8qd2h9qWMwp7eyxHgEP/WvcvbZu9TU2vampZrmjls2TtB1WSut3qqm63upYeaEXbTXhjbm5ubr5v3rb7DKX+/3tn3t6ERIaoz/N99M2N36jNzW0sZ7Q8kXPC6/tYUqVaaTFWwumqoiuVzvR+SDh5pW50npycrIULF+rtt9/W1q1b9cUXX6h27dr68MMPVb9+fXXrVvIZ4s40Fz5yoZpc2qTYho9udbvW1bAFw1T9nOqlPhNYQGCAOo/1ftSsaL+A8BrhHk10zT/uRR+73Yh2HvfpbWeoaOjTe0Jv9Z7QW5J1x6s0p3iuc34dXfbuZdrw3Qa1uK6F0Wi2yeVN1PKGlvrrq78sAztHgEMXv3qxUtekeoQKvqqCGl3cSP/e+W+v1/mz02DpFeRwKLxGuMJrhCuuWZwRShXdqS9pIC+5AsFbfr9FYdXDLD0aiu7sm3vqHDt0zBKI5GV7HmV2V88Fhwd7TDfy5dpp12rP73uMYMTM4XBozI4xykrNshzRvP6H67Xn9z2qf5HrKGxAUIBlkHbnljt1ZPsRr2c5+8fP/9CC/y6QI8BhOfV440sbq83NbVS7k3V6aOMBnqXwZh1v7+gxmO56f1f99fVfanF9C6/PyZtuD1i3YeaBhrvxtmTdkTd/L9wVLu5KkJK2Cw6HQ8OXDNeu33YZr31UnSiNWDbCGFS4qzyKVn6Z3/uw6mEa/ttwBYUFFbuj3eOxHjq676jaDGtjXBZUJUi3LL1FkjTrvlketykp2KwaX1UXPHiBx+Xuz7S3kMOynJcz3kUnRWvU+lHGdBszf5vcmkMJX9NY3eJbxiu+pStAGDpzqLbN3aakLkla+JS1ksfjtfDR2iUyMVIXjPN8TfwVXTfaI5QyP4fBXw1W06uaqvkgV1WjORwry1nqzILDg5V8YbLle2l+zc+98lz9Pe1vo5rW3UC+6DYvvEZ4ia9Bv4n9lLIixXiu/oZS7s/M4K8G68/P/zQq3swDXfN3NyQiRF3udi1z7TfXat0n63TBuAu8VvuYp0tl7XVNL7plyS1KXZNqbOvczO9JvZ71jGmwne/urM53dzamz4ZVD/Pr81A0lErqkqROYzoptkmsWg1tpYCgAGUfyvb4jpu3Z+ZKEzd3c+7Glzb2er35QJdb7wm91bB/Q00bOk3HDhzTOX3Psfy2e3vtWg1tpXo96lk+C0FhQbphxg1a9e4q9XrKFRoX9x1u9Y9WavUPa3iX2D5RY7aP8XmbkxXbJNZSmVf0N78413x2jX68/Ud1vKOj1+vNVVjeDpQktE5QQusENbummQ5vOazJF06WVHwwVHSb6G3Zor/9NZvX9PrbYN5eDJs/TEtfWar+L/X3+di+mPcZ/e0NZizvZQq35PoszHlojsfy7u+5eeqnw+E6CDJmxxgd2X7EEkoFhwV7fR9Ls69aGoyV4E1FBkOEQjjdlTqU+uqrrzR06FDdcMMNWrlypXJyXIO0zMxMjR8/Xj/+6P0MT2eyGo1reN2h8yX5goqZk23+8S3p9LxuV3xwheb+Z67H2aC8Me94lTQANHM4HGr7z7ZGhU5Mgxjde+BehdUIk8PhMAZdZh1He9/5G/DWAE0dOFXdH+3u12N7G2y7e1H4te6mQU/RwM6fM5hJ3k9LX5R5Bzk4PNjyuEWnY41YNkKJ7RM1Lmuc67ZejoJ6ExYTpob9Gvq8Pqp2lMcZCKtEVym2F5q7asybc/qeo3P6um67d/lefXbVZ+r9dG8FBAbo8vf8O8NLeGy4Mf3G2wCpdsfars+Sl1DE3Xx92Pxhmtx9siRZ+q5Zlk2MVObeTCNocnN/VsynHb/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7uyM6Ohp379419tSIdGJu7AkQEZFpCwwMRHx8vEabQqEw0mwaxp07d6T/37lzJ95//33cuHFDarOyskLz5s3RvHlzY0wPW7ZswYABA9CuXbs69Xd0dMSIESOwceNGfPTRRw07OSIiItLJ999/jwEDBsDLyws7duyAh4cHMjMzMX/+fBw6dAhnzpxBy5YtDT6v8vJyWFhYaLRVVlZCJpPBzIznv1DdMFKIiEgnCoUCzs7OGi97e/uX9l+4cCG8vLxgbW2N9u3bY+nSpfjll1+k/VWXwW3atAlubm6wtrbG+PHj8eDBA6lPcnIy+vXrBxsbG7Ro0QIDBw5EXl6etH/fvn3w8fGBpaUl2rdvj2XLlqGiokLan52djSFDhsDS0hJdu3ZFUlJSjWt8cW1KpRIymaxa268v3wsPD0dQUBBWrFgBJycntGjRQprH/Pnz0bJlS7Rp0waff/65xs8qLCzExIkTYW9vDwcHB4wbNw63bt2qcX4JCQkYO3asRtvXX3+N7t27w8rKCg4ODvD398eTJ0+k/WPHjsWOHTtqHJeIiIiMb+bMmbCwsMCRI0fg6+uLtm3bYuTIkTh69CgKCwsRExMDACgrK8OCBQvg5uYGhUKBjh07YsuWLdI4mZmZGD16NOzs7GBra4vBgwfju+++AwC89dZbmD17tsbPDQoKQnh4uLTdrl07fPjhhwgPD4dSqcS0adOwdetWtGjRAvv370fXrl2hUCiQl5eH8vJyLFiwAK1bt4aNjQ3efPNNJCcnS2NVvS8xMRFdunRB8+bNERgYqPFFIAB8/vnn6NatGxQKBVxcXBAVFSXtKykpQWRkJBwdHWFnZ4ehQ4fi0qVL0v5Lly7Bz88Ptra2sLOzg4+PD86fP6/rr4P0jEUpIiIyKFtbW2zduhVZWVn4+OOPsXnzZvzrX//S6JOTk4OvvvoK+/btw+HDh5GRkYGZM2cCACoqKhAUFARfX19cvnwZaWlpiIyMhEwmAwAkJiZiypQpmDVrFrKysrBp0yZs3boVy5cvBwCo1WoEBwdDLpfjzJkz2LhxIxYuXNggaz1+/Dhu376NlJQUrFmzBnFxcRgzZgzs7e2Rnp6O6dOnY/r06SgoKAAAlJaWws/PD82bN0dKSgpOnTolJWnl5eVaf8b9+/dx9epV9O3bV2q7c+cOJk+ejIiICFy7dg3JyckIDg6GEELq069fPxQUFGgU84iIiKhxuXfvHhITEzFjxgxYWVlp7HN2dkZISAh27twJIQRCQ0ORkJCAdevW4dq1a9i4caN0FndhYaH0hdzx48ehUqkQERGh8aVdXfzjH/+At7c3VCoVli5dCuB5/rJy5Ur85z//QWZmJhwdHfGnP/0Jp0+fRkJCAi5fvozx48cjMDAQ2dnZ0lilpaX45z//iW3btiElJQX5+fmYN2+etH/Dhg2YOXMmIiMjceXKFezduxeenp4AACEERo8ejaKiIhw8eBAqlQp9+vTBsGHDcO/ePQBASEgI2rRpg3PnzkGlUmHRokVo1qzZq/8SqGEJIiKiegoLCxNyuVzY2NhovD744AOpDwCxe/ful47x97//Xfj4+EjbsbGxQi6Xi4KCAqnt0KFDwszMTNy5c0fcvXtXABDJyclaxxs8eLBYsWKFRtu2bduEi4uLEEKIxMRErePXNs8q8fHxQqlUVmuPjY0VPXv2lLbDwsKEu7u7qKyslNo6deokBg8eLG1XVFQIGxsbsWPHDiGEEFu2bBGdOnUSarVa6lNWViasrKxEYmKi1vlcvHhRABD5+flSm0qlEgDErVu3XrqOkpKSGv8ciYiIyPjOnDlTY46yZs0aAUCkp6cLACIpKUlrv8WLFwsPDw9RXl6udb+vr6+Ijo7WaBs3bpwICwuTtt3d3UVQUJBGn/j4eAFAZGRkSG05OTlCJpOJwsJCjb7Dhg0Tixcv1nhfTk6OtP/TTz8VTk5O0rarq6uIiYnROt9jx44JOzs78ezZM432Dh06iE2bNgkhhLC1tRVbt27V+n5qPHhPKSIi0omfnx82bNig0VbTfQ2+/vprrF27Fjk5OXj8+DEqKipgZ2en0adt27Zo06aNtD1gwACo1WrcuHEDvr6+CA8PR0BAAIYPHw5/f39MmDABLi4uAACVSoVz585JZ0YBz+9v8OzZM5SWluLatWtax28I3bp107ingpOTE7y9vaVtuVwOBwcHFBcXS3PPycmBra2txjjPnj2TTq//tadPnwIALC0tpbaePXti2LBh6N69OwICAjBixAi8/fbbGpdVVn3bWlpaquMqiYiIyFjE/58FnZubC7lcDl9fX639MjIyMHjwYJ3PFHrxzOwqFhYW6NGjh7R94cIFCCHg5eWl0a+srAwODg7StrW1NTp06CBtu7i4SDlRcXExbt++jWHDhmmdh0qlwuPHjzXGA57nRVU509y5c/HOO+9g27Zt8Pf3x/jx4zV+HjUOLEoREZFObGxspFOpa3PmzBlMmjQJy5YtQ0BAAJRKJRISErB69eoa31d1aV7Vf+Pj4zFr1iwcPnwYO3fuxJIlS5CUlIT+/ftDrVZj2bJlCA4OrjaOpaWlxiVsvx5f336d+MlkMq1tarUawPNLC318fLB9+/ZqY/3mN7/R+jNatWoF4PllfFV95HI5kpKSkJqaiiNHjuCTTz5BTEwM0tPT4eHhAQDSqe0vG5eIiIiMz9PTEzKZDFlZWQgKCqq2//r167C3t4e1tXWN4/z60r9fMzMzq5YjvXjPzyo2NjZax34xl1Kr1ZDL5VCpVJDL5Rp9X3wojLacqGoOtc1XrVbDxcVF4z5VVVq0aAHg+X1K//jHP+LAgQM4dOgQYmNjkZCQgN///vc1jk2GxXtKERGRwZw+fRru7u6IiYlB37590bFjR633NMrPz8ft27el7bS0NJiZmWl849a7d28sXrwYqamp8Pb2xpdffgkA6NOnD27cuAFPT89qLzMzM3Tt2lXr+I1Bnz59kJ2dDUdHx2pzVyqVWt/ToUMH2NnZISsrS6NdJpNh4MCBWLZsGS5evAgLCwvs3r1b2n/16lU0a9YM3bp1a9A1ERERUf05ODhg+PDhWL9+vXR2dJWioiJs374dEydORPfu3aFWq3HixAmt4/To0QMnT57UWmgCnn9J9eJNxisrK3H16tV6zbl3796orKxEcXFxtXzG2dm5TmPY2tqiXbt2OHbsmNb9ffr0QVFREczNzav9jKov7ADAy8sLc+bMwZEjRxAcHFztidFkfCxKERGRTsrKylBUVKTx+vnnn7X29fT0RH5+PhISEvDdd99h3bp1GoWSKpaWlggLC8OlS5dw8uRJzJo1CxMmTICzszNyc3OxePFipKWlIS8vD0eOHMHNmzfRpUsXAMD777+P//73v4iLi0NmZiauXbsmnU0FAP7+/ujUqRNCQ0Ol8aueWmNsISEhaNWqFcaNG4eTJ08iNzcXJ06cQHR0NH744Qet7zEzM4O/vz9OnToltaWnp2PFihU4f/488vPzsWvXLvz000/SnxEAnDx5EoMHD671m0giIiIyrn//+98oKytDQEAAUlJSUFBQgMOHD2P48OFo3bo1li9fjnbt2iEsLAwRERHYs2cPcnNzkZycjK+++goAEBUVhYcPH2LSpEk4f/48srOzsW3bNty4cQMAMHToUBw4cAAHDhzA9evXMWPGDI0nH78KLy8vhISEIDQ0FLt27UJubi7OnTuHjz76CAcPHqzzOHFxcVi9ejXWrVuH7OxsXLhwAZ988gmA5/ncgAEDEBQUhMTERNy6dQupqalYsmQJzp8/j6dPnyIqKgrJycnIy8vD6dOnce7cOY1ciBoHFqWIiEgnhw8fhouLi8Zr0KBBWvuOGzcOc+bMQVRUFHr16oXU1FTpyS0v8vT0RHBwMEaNGoURI0bA29sb69evB/D8/gPXr1/HH/7wB3h5eSEyMhJRUVH4y1/+AgAICAjA/v37kZSUhDfeeAP9+/fHmjVr4O7uDuB5EWf37t0oKytDv3798M4772jcf8qYrK2tkZKSgrZt2yI4OBhdunRBREQEnj59Wu2+Wy+KjIxEQkKCdBmgnZ0dUlJSMGrUKHh5eWHJkiVYvXo1Ro4cKb1nx44dmDZtWoOviYiIiHTTsWNHnD9/Hh06dMDEiRPRoUMHREZGws/PD2lpadK9PDds2IC3334bM2bMQOfOnTFt2jQ8efIEwPMzro4fP47Hjx/D19cXPj4+2Lx5s3QJXUREBMLCwhAaGgpfX194eHjAz8+v3nOOj49HaGgo/va3v6FTp04YO3Ys0tPT4ebmVucxwsLCsHbtWqxfvx7dunXDmDFjpKf3yWQyHDx4EEOGDEFERAS8vLwwadIk3Lp1C05OTpDL5bh79y5CQ0Ph5eWFCRMmYOTIkVi2bFm910QNQya03VyDiIjISOLi4rBnzx5kZGQYeyomQwiB/v37Y/bs2Zg8eXKt/Q8cOID58+fj8uXLMDfn7SWJiIiIyDh4phQREZGJk8lk+Oyzz1BRUVGn/k+ePEF8fDwLUkRERERkVMxGiYiIXgM9e/ZEz54969R3woQJDTwbIiIiIqLa8fI9IiIiIiIiIiIyOF6+R0REREREREREBseiFBERERERERERGRyLUkREREREREREZHAsShERERERERERkcGxKEVERERERERERAbHohQRERERERERERkci1JERERERERERGRwLEoREREREREREZHBsShFREREREREREQG939BTrRjxotd4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(log_files2, dataframes2, 'Offset (ns)', 'offset_results', 'purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_graphs(log_files2, dataframes2, 'Offset (ns)', 'offset_results', 'green','logs/TC.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Análise inferencial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualmente ja podemos evidenciar a superioridade de precisão da topologia com _transparent clock_, para confirmar essa impressão realizaremos a seguir o teste de hipótese, mas antes iremos verificar se os dados em questão seguem uma distribuição nomral para definir qual teste é adequado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TC - Shapiro-Wilk test:\n",
      "Statistic: 0.9966650367173112, p-value: 0.014142485402610008\n",
      "\n",
      "Switch - Shapiro-Wilk test:\n",
      "Statistic: 0.993693402328911, p-value: 0.00019951218503077347\n",
      "\n",
      "Os dados de TC não são normalmente distribuídos.\n",
      "Os dados de Switch não são normalmente distribuídos.\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiIiIiIiIm6nUEpERERERERERNxOoZSIiIiIiIiIiLidQikREREREREREXE7hVIiIiIiIiIiIuJ2CqVERERERERERMTtFEqJiIiIiIiIiIjbKZQSERERERERERG3UyglIiIiIiIiIiJup1BKRERERERERETcTqGUiIiIiIiIiIi4nUIpERERERERERFxO4VSIiIiIl7iypUr/N///R/Vq1cnMDCQG2+8kZEjR5Kenm4dY7FYeO2116hYsSKBgYFERkby66+/2twnJSWFQYMGUbZsWUqUKEGHDh04cuSIu38cERER8XIKpURERES8xJgxY/j444+ZNGkSv//+O2PHjuWdd95h4sSJ1jFjx45l3LhxTJo0ie3btxMREUFMTAznz5+3jomNjWXRokXMmTOHzZs3c+HCBdq1a4fZbC6IH0tERES8lMlisVgKughPkpSURGhoKImJiYSEhBR0OSIiIuJGhX0e0K5dO8LDw/nss8+sx7p06UJQUBAzZ87EYrFQsWJFYmNjef755wFjVVR4eDhjxozhySefJDExkXLlyjFz5kwefPBBAI4dO0blypVZsWIFLVu2tKuWwv67FBERkfyzdx6glVIiIiJSuFy5UtAVeKz777+fdevW8eeffwLwv//9j82bN9OmTRsA9u/fz4kTJ2jRooX1moCAAJo2bcqWLVsA2LFjB2lpaTZjKlasSJ06daxjspOSkkJSUpLNS0RERDyYB8ypFEqJiIhI4XHsGNx5JyxcWNCVeKTnn3+enj17csstt1CsWDHq1atHbGwsPXv2BODEiRMAhIeH21wXHh5uPXfixAn8/f0pXbp0jmOyM3r0aEJDQ62vypUrO/NHExEREWfatw/q1IE1awq0DIVSIiIiUjgcOQKRkfDrr/D885CaWtAVeZy5c+fy5ZdfMmvWLH788UemT5/Ou+++y/Tp023GmUwmm/cWiyXLsWvlNWbEiBEkJiZaX4cPH87/DyIiIiKu89dfxpxq924YPhwKsGdk0QL7ziIiIiL2OnQIoqKMT/WqVYO1a8Hfv6Cr8jjDhg3jhRdeoEePHgDUrVuXgwcPMnr0aPr06UNERARgrIaqUKGC9bqTJ09aV09FRESQmprK2bNnbVZLnTx5kkaNGuX4vQMCAggICHDFjyUiIiLO8uef0KwZHD0Kt9wCK1eCn1+BlaOVUiIiIuLZDhyApk2NQOrGG2HDBiOYkiwuXbpEkSK20zs/Pz/S09MBqF69OhEREcTHx1vPp6amsnHjRmvgVL9+fYoVK2Yz5vjx4+zatSvXUEpEREQ83B9/GCukjh6FW2815lSZPqQqCFopJSIiIp5r3z5jhdShQ1CzJqxfD+pVlKP27dvz1ltvUaVKFW677TZ27tzJuHHjePTRRwHjsb3Y2FhGjRpFrVq1qFWrFqNGjSIoKIhevXoBEBoaSr9+/Rg6dChhYWGUKVOG5557jrp169K8efOC/PFEREQkv377zVgh9fffULeuseq8fPmCrkqhlIiIiHiov/4yJk+HD8NNNxmB1A03FHRVHm3ixIm8/PLL9O/fn5MnT1KxYkWefPJJXnnlFeuY4cOHk5ycTP/+/Tl79iz33HMPa9asITg42Dpm/PjxFC1alO7du5OcnEx0dDTTpk3DrwCX94uIiEg+7dplzKlOnYI77jACqbJlC7oqAEwWi8VS0EV4kqSkJEJDQ0lMTCQkJKSgyxEREfFN1/Y7WL/eLcvLNQ9wHv0uRUREPMD//gfNm8M//8Bdd0F8PJQp4/Jva+88oND2lBo9erR1CXoGi8XCa6+9RsWKFQkMDCQyMpJff/214IoUERERx3lgvwMRERGRQmfnTuNDvn/+gQYNjBVSbgikHFEoQ6nt27czZcoUbr/9dpvjY8eOZdy4cUyaNInt27cTERFBTEwM58+fL6BKRURExCG//WYEUsePG/0OEhLg313hRERERMROP/xgBFJnzsA99xgrpDLtquspCl0odeHCBXr37s3UqVNttim2WCxMmDCBl156ic6dO1OnTh2mT5/OpUuXmDVrVgFWLCIiInb55RcjkPr7b7jzTuORPQ9owCkiIiJSqHz/vfHI3rlz0KgRrFkDpUoVdFXZKnSh1IABA2jbtm2W3V/279/PiRMnaNGihfVYQEAATZs2ZcuWLTneLyUlhaSkJJuXiIiIuNn//mfssnfqlNHvYN06j2nAKSIiIlJobNkCMTGQmAhNmsCqVeDBvR0L1e57c+bM4ccff2T79u1Zzp04cQKA8GuW+IeHh3Pw4MEc7zl69Ghef/115xYqIiIi9vvxR2PydOYMNGwIq1d75PJyEREREY+2eTO0bg0XLhirz7/+GkqUKOiqclVoVkodPnyYwYMH8+WXX1K8ePEcx5lMJpv3Fosly7HMRowYQWJiovV1+PBhp9UsIiIiefjhB4iONgKpe+/12H4HIiIiIh5t40Zo1coIpKKjYflyjw+koBCtlNqxYwcnT56kfv361mNms5lvvvmGSZMmsXv3bsBYMVUh0w49J0+ezLJ6KrOAgAACAgJcV7iIiIhk7/vvoWVLY3l5o0awcqVHLy8XERER8Ujr10O7dpCcDC1awOLFEBhY0FXZpdCslIqOjuaXX37hp59+sr4aNGhA7969+emnn7jxxhuJiIggPj7eek1qaiobN26kUaNGBVi5iIiIZFHI+h2IiIiIeKT4eGjb1gikWrWCJUsKTSAFhWilVHBwMHXq1LE5VqJECcLCwqzHY2NjGTVqFLVq1aJWrVqMGjWKoKAgevXqVRAli4iISHYKYb8DEREREY+zahV06gQpKUYwtXAhFLInwQpNKGWP4cOHk5ycTP/+/Tl79iz33HMPa9asITg4uKBLExERETD6HbRtCxcvGv0Oli6FoKCCrkpERESkcFmxAh54AFJToWNHmDu30AVSACaLxWIp6CI8SVJSEqGhoSQmJhKixwhEREScpxD0O9A8wHn0uxQREXGRZcugSxdIS4POnWH2bPD3L+iqbNg7Dyg0PaVERESkECvk/Q5EREREPMKiRVcDqW7dYM4cjwukHKFQSkRERFxr1Spo3x4uXzaCqcWLoXjxgq5KREREpHBZsAC6dzcCqR49YNYsKFasoKu6LgqlRERExHVWrDD6HKSkGP8WwgacIiIiIgVu7lwjiLpyBR56CGbOhKKFv024QikRERFxjWXLjB1hUlONfgfz5imQEhEREXHUrFnQqxeYzdCnD0yb5hWBFCiUEhEREVfwsn4HIiIiIgVixgx4+GFIT4d+/eDzz8HPr6CrchqFUiIiIuJcXtjvQERERMTtvvgC+vY1AqknnoApU6CId8U43vXTiIiISMHy0n4HIiIiIm41dSo8+ihYLNC/P0ye7HWBFCiUEhEREWfx4n4HIiIiIm7z8cfGyiiAZ56BSZO8MpAChVIiIiLiDF7e70BERETELSZNgqefNr5+9lmYMAFMpgItyZUUSomIiMj1+fzzq/0OnnzSK/sdiIiIiLjchAkwaJDx9fDh8N57Xh1IgUIpERERuR5TphgroywWGDDAa/sdiIiIiLjUu+8aK6MAXnwR3n7b6wMpUCglIiIi+TV5srEyCmDwYJg40ScmTyIiIiJO9fbbMGyY8fUrr8Cbb/rMnEqhlIiIiDhu4kRjJxiAIUNg/HifmTyJiIiIOM2bb8KIEcbXr79uvHxoTqVQSkRERBwzfryxEwwY/Q7efdenJk8iIiIi181igddeg5dfNt6/9ZaxSsrHKJQSERER+737rrEyCnyq34GIiIiI01gsRgD1+uvG+zFjjHmVD1IoJSIiIvbx4X4HIiIiIk5hsRgB1JtvGu/fe89Yee6jihZ0ASIiIlIIvPnm1eXlr79eYMvLzWbYtAmOH4cKFaBJE/DzK5BSRERERBxjsVxtfQDw/vtXWyL4KIVSIiIikjOL5WrTTTD6HRTQ8vK4OGOTvyNHrh6rVMmYz3XuXCAliYiIiNjHYjFaIEyYYLyfNAkGDCjQkjyBHt8TERGR7HlQv4O4OOja1TaQAjh61DgeF1cgZYmIiIjkzWIxVkRlBFIff6xA6l8KpURERCQrD+p3YDYbK6QslqznMo7FxhrjRERERDxKeroRQE2aZPTi/PRTePLJgq7KYyiUEhEREVsZ/Q7eftt4//77V3fcKwCbNmVdIZWZxQKHDxvjRERERDxGejo89RRMnmwEUp9/Dv36FXRVHkU9pUREROQqF/Q7uN7m5MePO3eciIiIiMuZzfD44/DFF1CkCEyfDg89VNBVeRyFUiIiImLI6HcwaZLx/uOPr3t5uTOak1eo4NxxIiIiIi5lNsOjj8KMGUYg9eWX0LNnQVflkfT4noiIiLik34GzmpM3aWIEWSZT9udNJqhc2RgnIiIiUqCuXIFHHjECKT8/mD1bgVQuFEqJiIj4Ohf0O3Bmc3I/P2NlFWQNpjLeT5jg2COBIiIiIk535YrxiN6sWVC0KMydC927F3RVHk2hlIiIiC8zm+Gxx2DqVGN5+YwZ0Lfvdd/W2c3JO3eGBQvghhtsj1eqZBy391FAEREREZdISzNWRM2dC8WKGROULl0KuiqPp55SIiIivspshv/+F2bOdHq/A1c0J+/cGTp2vL6m6SIiIiJOl5oKPXrAokXg7w8LF0K7dgVdVaGgUEpERMQXXbkCffoYy8sz+h1062YzJLdd8/LaUc9Vzcn9/CAy0rFrRERERFwmJcWYQy1bBgEBRjDVunVBV1VoKJQSERHxNWlpRr+DefOu9ju45vm33HbNg7x31MtoTn70aPZ9pUwm47yak4uIiEihdfmy8YjeihVQvDgsWQItWhR0VYWKekqJiIj4kox+B/PmXe13kE0gldOueV26GK+8dtRTc3IRERHxasnJ0KmTEUgFBsLXXyuQygeFUiIiIr4iNdXYAWbhQqPfQVyc0aQpE3t2zctOdjvqqTm5iIiIeKVLl6BDB1i9GoKCjGAqOrqgqyqU9PieiIiIL7Cz30Feu+blJvOOehl9n9ScXERERLzKxYvQvj0kJECJErByZaHsR5BXf1B3USglIiLi7TL1O7AUL87PI5fw27kWVNhwdQ6VMSn57bfr/3bX7qin5uQiIiLiFS5cgLZt4ZtvIDjYCKQaNy7oqhyWW+9Qd69kVyglIiLizZKT4YEHYPVqrvgH8lDJZcwdfnV5eViY8e/p0877lo7uqCciIiLi8c6fN1aZf/sthIQYj+7de29BV+WwjN6h17ZlyOgP6u4WC+opJSIi4q0y9Tu4EhBETOoK5v5j2+/g9GnnBVImE1SuXChXsIuIiIjkLDERWrY0AqlSpWDtWrcHUmYzbNgAs2cb/2b08HT0Hnn1Ds3cH9QdFEqJiIh4o4sXoV07WLsWS4kSPBiyig1EXtctM++ipx31RERExCecO2fsqrd1K5QubQRSDRu6tYS4OKhWDaKioFcv499q1a7uemyvvHqHZu4P6i4KpURERLzNhQvQpo3RgDM4mJ2jVxN36vqXL1WqZGzct3ChdtTzZEePHuWhhx4iLCyMoKAg7rzzTnbs2GE9b7FYeO2116hYsSKBgYFERkby66+/2twjJSWFQYMGUbZsWUqUKEGHDh04kt8O+CIiIoXVmTPQvDls22b0PFi/HurXd2sJGY/bXft/wxmP2zkSTF3b9/N6xzmDekqJiIh4k2z6Hezen//l5f/3f3DrrVl3ZdGOep7p7NmzNG7cmKioKFauXEn58uXZu3cvpUqVso4ZO3Ys48aNY9q0adx00028+eabxMTEsHv3boKDgwGIjY1l2bJlzJkzh7CwMIYOHUq7du3YsWMHfvoPWkREfMHp0xATAzt3QtmysG4d3H67079Nbrvg5fW4nclkPG7XsaN98zB7+366sz+oyWLJ7sfzXUlJSYSGhpKYmEhISEhBlyMiImK/xEQjkNq61eh3sGYNNGzIhg3GMu/8SEjwrZ3zCvs84IUXXuDbb79lUw7r7i0WCxUrViQ2Npbnn38eMFZFhYeHM2bMGJ588kkSExMpV64cM2fO5MEHHwTg2LFjVK5cmRUrVtCyZUu7ainsv0sREfFhp04ZK6R+/hnKlzcCqTp1nP5t8toFz945nL3zNbPZeOzv6NHsgy6Tyfj++/df/4eN9s4D9PieiIiIN8il30GTJsYE49o+ULlR0/LCaenSpTRo0IBu3bpRvnx56tWrx9SpU63n9+/fz4kTJ2jRooX1WEBAAE2bNmXLli0A7Nixg7S0NJsxFStWpE6dOtYx2UlJSSEpKcnmJSIiUuicPAnNmhmBVESEkQy5KJDK67E8Zz9u5+dnBF7gOf1BFUqJiIgUdnn0O8htApIdNS0vvPbt28fkyZOpVasWq1ev5qmnnuKZZ55hxowZAJw4cQKA8PBwm+vCw8Ot506cOIG/vz+lS5fOcUx2Ro8eTWhoqPVVuXJlZ/5oIiIirnfihLE0adcu4xm2DRugdm2nfxt7d8ErX96++znyuF3nzkYfUE/pD6pQSkREpDA7fdoIpHbsMPodrF8Pd96ZZVhOE5CwMOOVmZqWF17p6encddddjBo1inr16vHkk0/y+OOPM3nyZJtxpmvSSYvFkuXYtfIaM2LECBITE62vw4cP5/8HERERcbdjx4xn4H77zZgwbdwIN9983bc1m41sa/Zs49+MHlL27IIHua92z+/K9s6d4cAB47G/WbOMf/fvL5i5nxqdi4iIFFYO9jvo3Dn7BuWgpuXeokKFCtx66602x2rXrs3ChQsBiIiIAIzVUBUyfax68uRJ6+qpiIgIUlNTOXv2rM1qqZMnT9KoUaMcv3dAQAABAQFO+1lERETc5uhRY4XUnj1GypOQADVqXPdtc+oZ1bWrfdefPGmsdu/a1QigMq+sut6V7X5+ntE3VKGUiIhIIZLx6dqZP07SYkw0JQ/sIjk0gnmPrqfo/2pzwz/QqBFs2WKETBnLvk+evBo4ZTcB8YRJiVy/xo0bs3v3bptjf/75J1WrVgWgevXqREREEB8fT7169QBITU1l48aNjBkzBoD69etTrFgx4uPj6d69OwDHjx9n165djB071o0/jYiIiBscPmwEUnv3QtWqRiBVvfp13zajZ9S1j+gdPWoESfaoUMGYoy1YkH24NWFC4V/ZrlBKRESkkMj4tC3tyAnWEU1JfuMYFYhKTODPt68uL/fzM8Kr7GTe0UW8z7PPPkujRo0YNWoU3bt3Z9u2bUyZMoUpU6YAxmN7sbGxjBo1ilq1alGrVi1GjRpFUFAQvXr1AiA0NJR+/foxdOhQwsLCKFOmDM899xx169alefPmBfnjiYiIONeBA0ZT8/37jSAqIcEIpvKQ8SFhTivPy5fPu2eUnx+kp+e+C17GfXNa7e4NK9sVSomIiBQCGZ+2RViOsYFm3MJuDlOJZqznL2rZjM0pkIKrO7qoZ5R3atiwIYsWLWLEiBGMHDmS6tWrM2HCBHr37m0dM3z4cJKTk+nfvz9nz57lnnvuYc2aNQQHB1vHjB8/nqJFi9K9e3eSk5OJjo5m2rRp+HnD7FdERARg3z5jhdShQ1CzptGX045NOrJ7JC+jP+fp0/Z/+4z5mr2P5XnK43bOZrJYssvlfFdSUhKhoaEkJiYSEhJS0OWIiIhgNkO1amA5coT1NOMm9nCQKkSRwH5udPh+GZ++7d/vHZ+wOZPmAc6j36WIiHisv/4yVkgdPgw33WQEUtfuBpONnB7Jy6/YWOODwswBV+XK3vFYnr3zAK2UEhER8XCbNoHpyCE2EEUN9rGfakSRwEGq5et+GTu6bNrknZ+4iYiIiOTozz+NQOroUbjlFiOQyrT5R07M5pwfycuvjh3h3Xe987E8eymUEhER8XBJPx9gI1FU5wB7uZFmrOcQefc7yMvx404oTkRERKSw+OMPI5A6fhxuvdUIpP7dfTYvmzbZrmi6Hpl7RnnrY3n2UiglIiLiyfbto8WoKIpziD3UpBnrOULe/Q7sYceHgiIiIiLe4bffjEDq77+hbl1Yu/bqNsV2cNaHeTn1jPJVRQq6ABEREcnBX39BZCTF/z7Ebm4ikg1OCaRMJqNfQcaOLiIiIiJebdcuYznS33/DHXcYK6QcCKTMZuNSZ6hUSRvOZKaVUiIiIp4oU7+D37mFZqznBNe/tEmfzomIiIhP+d//oHlz+OcfuOsuiI+HMmXyvMxsNh7ZW7IEvvoKTp1y/FubTEb/9GnT4ORJ3+wZlReFUiIiIp4mU7+D3UVvJfLKek5iX78DMCY6GdsMX6tSJe/Y0UVEREQkTzt3GoHUmTPQoAGsWQOlS2c7NCOEOn4c9uyBqVOvr4dUxgeB778P0dH5v4+3UyglIiLiSTL1O7hwY12a7FvLKXJfXv7ii1CunPG64QZo1Ai2bDEmVRkr0/XpnIiIiPiUH36AmBg4dw7uuQdWrYJSpbIdGhdn7KyX3xAqLMz49/Tpq8f0QaB9FEqJiIh4CPP/dmGObIb/uVOcqngHY/+zllP7yuZ5XZ060LOn7TFf3sVFREREfNy2bdCiBSQmGp/WrVwJISHZDo2Lg65dwWLJ37caPx4GDTK+zlhppQ8C7adQSkRExAOsG/c/7hzWnLD0f9jBXcQci+fstLz7HYB20RMRERGx2roVWrWCpCS4/35YsQKCg7MdmpoKTz2V/0AKIDz8avikDwUdp933RERECti6d3dy59BmhKX/w3Ya0Jy1nCXvQEq76ImIiIhksnmzsUIqKQmaNjVWSOUQSMXFGW0P8tPAPDN9OHh9tFJKRESkAJm//4H6w2MoxTm+4x5asYpESuV5nXbRExEREcnkm2+gTRu4eNHoz7l0KZQoke3Q631kD4y5WKVK+nDwemmllIiISEHZtg1LdHNKWc7xLY1owRq7AimAsmVhwQI1zxQREREhIQFatzYCqZgYWLYsx0DKbDaaml9vIAX6cNAZFEqJiIgUhK1bISaGohcT2cT9tGIV58m+AWd2xo9XICUiIiLC2rXQti1cumT0klqyBIKCchy+aVP+d9nLUKmSPhx0Fj2+JyIi4iZmM2zYAPtmbOaROa0JSL3AvipNaX3oay5S0qF73XCDa2oUERERKTRWr4ZOneDyZSOYWrAAihfPdqjZbARSY8bk71uVKwe9e0PHjtpZz5kUSomIiLhBXBw88QTcevobVtCGAC6yjmZ0OLSUS2S/vDw76l8gIiIigrGrXufOkJICHTrAvHkQEGA9nRFCHT0K69YZC6jOnLH/9mXLwqBBUKuW0cxcQZRrKJQSERFxsbg46NIFoljPMtpTgkusIYZOLCaZnJeX50T9C0RERMSnLVtmdCpPTYUHHoA5c8DfHzDCqLfegvffdyyEyqxcOeMRv39vKS6knlIiIiIuZDbDM89Ac+JZTltKcImVtKIDSx0OpCpXVv8CERER8XGLFhmf9qWmQrduMHcu+PtjNsPIkVCqFLz6av4DKYCPP1Yg5S5aKSUiIuJCmzZBnaOrWEwnipPC17SlCwtJJSDvi4EBA+C++4weUlo2LiIiIj5twQLo2ROuXCH9wR5senwmR+YVZd06mD8fLly4/m8RG6sPAN1JoZSIiIiTZDQyX78eDh0yej+V3rqCJTxAAKkspiMPMtfuQAqgcWNj7iUiIiLi0+bONTqNm80c+s9D/GfzFxyc6/xIo2NHp99ScqFQSkRExAkyGpmfPn31WDuWsZAu+JPGQjrTk9mk4dha8AoVnFyoiIiISGEzaxY8/DCkp3Mwsg81NnyGGecuH9dmMgVDPaVERESuU0Yj88yBVCcWWQOpeXSjB3McDqQ0MRIRERGfN2OGNZBKf7QfTfZ87pJACrSZTEFQKCUiInIdMhqZZ9aFBcyjO/6kMZse9GIWVyjm8L3ff18TIxEREfFhX3wBfftCejo88QTf9J7C4aPOjzEqVdJmMgVFj++JiIhch02b4OjRq++7M5ev6E1RzMzkIf7LF5gd/L/bsDCYMkUTIxEREfFhU6cavREA+veHiRNZPMR5gVRICDz6qNFDSpvJFByFUiIiItfh+PGrX/dkFjN5GD/SmUYf+vEZ6XksL3/qKahVy3j0r0gRiIw0XpoYiYiIiM/6+GN4+mkA0gc9wzcPTCAu1sTEidd/6zJlYPBgeOklzbc8gUIpERERB2XeZe/7741jDzODL/gvfqTzKf14gilY7HhK/sEHjRBKRERERIBJk2DQIAC23vssLb94j/MTTfm+XYkS0K0bNG8ON9ygVVGeRqGUiIiIA7LbZa8vX/AZ/SiChU94gqeZbFcgpUbmIiIiIplMmADPPgvAOL9hDP1uDJC/QKpkSRg2TCuiPJ1CKRERETtl7LKX2WNMZSpGv4MP6c8gJtoVSIEamYuIiIiAsQp934D3qPXJcwCMYgQvmd8iP4FUcDA895zCqMJCoZSIiIgdsttl70k+5mOMfgfv8wyxTMCeyZMamYuIiIgYFiyAXx4aw+spLwAwkpd5ldfJTyBVrhwcOQL+/k4uUlxGoZSIiIgdrt1lbwCTmITR72AczzKU98hp8tSzp/FJXZUq0KyZGpmLiIiIAAwfDsXeeYu3+D8AXuF13uCVfN/v448VSBU2CqVERESykbmZ+YEDsH//1XODmcAEjH4HYxnG8+Te76B9eyOYEhERERHD/PkQ+M7rvM5rALzEm4zipXzdq0gRmDtXq9ALI4VSIiIi18iumXmGIbzHe2Tqd0De/Q4qVHBBkSIiIiKFlPmKhYN9XuV13gDged5mLM/n+36zZkHXrs6qTtxJoZSIiEgm2TUzzzCcMYzBsX4H2mFPREREJBOLhS1RL/Fc8mgAhvIu4xia79sNGwYPPuis4sTdFEqJiIj8K7tm5hleJH/9DrTDnoiIiMi/rRESLBx/5HkeOv4OYLRE+IDB+bpfuXLw4YfQrZszqxR3s2/PahERER9wbTNzg4VXeN0aSL3Em3YFUmFhsHChehuIiIiILFgAZUpb+DlmiDWQGsAkhwOpoCDjA8SEBDh+XIGUN9BKKRERkX8dP37tEQsjeYWXeRMwHt97h+E5Xt+4MTRtqh32RERERDIMGwbvvmvhA55hEJMAeJKPmcKTdt/j3nvhzTc1v/JGCqVERMRnZOyot3YtbN8Oly7B5ctQvDgEBsLZs5lHWxjFi4zgbcBocD6eIbneP2OyJCIiIuLrzGbo0QMWLkjnQwbSn8mkY+JxpvI5/ey6R0gIfPqpVkR5M4VSIiLiE3LbUS8rC2MZzjDeBeAZ3mciOTSb+pcamouIiIgY4uLgkUfg0sV0PuYpnmAq6Zh4lM+ZTl+77vHuuxAbq5VR3k6hlIiIeL3cdtTLysI4hvAsEwCj38FHDMjzKjU0FxEREbk67yqCmU95nEf5AjNF6MN0vuIhu+5RqZICKV+hRuciIuLVcttRLyuj30FGIPUkH+cZSKmhuYiIiIjBbIbHHjMCqc951BpIPcSXdgdSoA/7fIlWSomIiFfLfke9rEykM8mOfgf16sGtt0LVqmpoLiIiIpLBbIY77oCks1eYQR96M4sr+NGLWcynu133CA6GadP0YZ8vUSglIiJex2yGdetg+nSjoXleTNjf76BxY5g40bn1ioiIiBRWZjO88YbxMqVf4UseogdzSaMoPZhDHPb1UOjeHWbN0od9vkahlIiIeJW4OOjTBy5csG98EcxMdaDfQY0aTipUREREpJBbsAAeftjYzbgoacyiF91YQCrF6MZ8ltIxz3tohz3fpp5SIiLiNTIaazoSSDnS78DPD/r3d1KxIiIiIoXY8OFGkHT5MhQjlbk8SDcWkII/nYnLM5C65x5YuxbOnFEg5cu0UkpERLyC2QyDBtk/3o8rTHew38GQIeDvf52FioiIiBRyc+fCO+8YX/uTwny60YFlXCaAzsSxkja5Xj90KLz7rhsKFY+nUEpERLzCpk1w7Jh9Y/1wrN9BkSLG5GnsWCcVKyIiIlJIzZ0LPXoYXwdwmYV0oS0rSKY4nVjMGlrmev2cOfDgg24oVAoFhVIiIuIVjh+3b1xO/Q5uvRUiIowl6MWLg8kEJUtCkybGCiytkBIRERFfN2zY1RVOxUkmjs60ZhWXCKQDS1lH81yvnz1bgZTYUiglIiJeoWzZvMcUI5U59KAzi0jBny4sZDntAPjwQ4iMdG2NIiIiIoXV0KEwbpzxdSCXWEwnWhDPRYJox9dsICrX6zt0uLrCSiSDGp2LiEihN3w4tGiR+xh/UlhAVzqziMsE8ACLrIFUpUrGiigRbzN69GhMJhOxsbHWYxaLhddee42KFSsSGBhIZGQkv/76q811KSkpDBo0iLJly1KiRAk6dOjAkSNH3Fy9iIh4ArPZaESeEUgFcZFltKcF8VygBK1ZaVcgtWSJG4qVQkehlIiIFGrDh19ttJmTAC4TR2c6sIxkitORJTYNON9/39hZT8SbbN++nSlTpnD77bfbHB87dizjxo1j0qRJbN++nYiICGJiYjh//rx1TGxsLIsWLWLOnDls3ryZCxcu0K5dO8xms7t/DBERKUBxcRAaCgsWGO9LcIHltCWa9ZynJC1ZzSb+k+P1/v5GDykFUpIThVIiIlJopabmvXNLcZJZxAO0ZQWXCKQ9y6wNOMPCYOFC6NzZDcWKuNGFCxfo3bs3U6dOpXTp0tbjFouFCRMm8NJLL9G5c2fq1KnD9OnTuXTpErNmzQIgMTGRzz77jPfee4/mzZtTr149vvzyS3755RfWrl1bUD+SiIi42YIF0KULXLxovC/JeVbSmkg2kkgILVjDFhrneH337nDpknpISe4USomISKH10UdgseR8PpBLLKEjrVnFRYJoy3LO3tWcESNg7Vr4+28FUuKdBgwYQNu2bWne3Lbh7P79+zlx4gQtMj3vGhAQQNOmTdmyZQsAO3bsIC0tzWZMxYoVqVOnjnVMdlJSUkhKSrJ5iYhI4TRnjvHIXoZgklhNS5qwmXOEEkM833FfjtfPnm3s0qeV6JIXNToXEZFCa+/enM9l9DtoRgIXKEEbVrCJ/zDrOejZ0301irjbnDlz+PHHH9m+fXuWcydOnAAgPDzc5nh4eDgHDx60jvH397dZYZUxJuP67IwePZrXX3/9essXEZECYjbDhg3w1FPw119Xj4dyjtW05B62cYbSxBDPj9TP8T5Dh6qhudiv0KyUGj16NA0bNiQ4OJjy5cvTqVMndu/ebTPGnsadIiJSOGU8qteoEVStary+/DL7sSW4wAra0IwEkgi26XdQoYIbixZxs8OHDzN48GC+/PJLihcvnuM4k8lk895isWQ5dq28xowYMYLExETr6/Dhw44VLyIiBSYuDsLDoXlz20CqNGdYS3PuYRv/EEY063INpIYNy7u1gkhmhSaU2rhxIwMGDOC7774jPj6eK1eu0KJFCy5mPOCKfY07RUSk8Bk+HIoXNyY6W7fCoUPG69y5rGNLcp5VtKIp32Tpd6Bd9sTb7dixg5MnT1K/fn2KFi1K0aJF2bhxIx988AFFixa1rpC6dsXTyZMnreciIiJITU3l7NmzOY7JTkBAACEhITYvERHxfHFxRu+o06dtj5fhNGtpTgN2cIqyNGM9P1Ev23v4+Rn9o8aOdUPB4lUKTSi1atUq+vbty2233cYdd9zBF198waFDh9ixYwdgX+NOEREpfDJ218utd1SGEBJZTUvu51vOUormrOV77rWe1y574u2io6P55Zdf+Omnn6yvBg0a0Lt3b3766SduvPFGIiIiiI+Pt16TmprKxo0badSoEQD169enWLFiNmOOHz/Orl27rGNERMQ7mM3Qq1fW42U5xXqacRc7+ZvyRJHAL9yedeC/5syBwEAXFipeq9D2lEpMTASgTJkyQN6NO5988skCqVNERPLPnt31MuTW7yA4GKZNU1Nz8X7BwcHUqVPH5liJEiUICwuzHo+NjWXUqFHUqlWLWrVqMWrUKIKCguj1718loaGh9OvXj6FDhxIWFkaZMmV47rnnqFu3bpbG6SIiUrj17AkpKbbHynGSdURTl10cJ4JmrOcPaud4j2HDoGtXFxcqXqtQhlIWi4UhQ4Zw//33WydY9jTuzE5KSgopmf5bqJ1iREQ8R16762UozRnW0IIG7OAfwmjOWv7HnYDRrHPSJK2QEskwfPhwkpOT6d+/P2fPnuWee+5hzZo1BAcHW8eMHz+eokWL0r17d5KTk4mOjmbatGn46b9IIiJeY9YsmD/f9lg4J1hHNLfxG8eoQBQJ/MnN2V5fvDjMmGG7S5+IowplKDVw4EB+/vlnNm/enOWco407tVOMiIjnSE2FDz6AhQuNnfXs+ZygDKeJJ4a72MkpyhLNOpvl5UWLKpAS37Zhwwab9yaTiddee43XXnstx2uKFy/OxIkTmThxomuLExGRAtGhAyxbZnusAsdYTzNuYTdHuIEoEviLWtle360bzJ6tOZZcv0LTUyrDoEGDWLp0KQkJCVSqVMl6PCIiAsi9cWd2tFOMiIhnyNzM/Lvv4NSprMvJr2VPv4MaNVxYtIiIiEghkppqbPxybSBVkaNsIJJb2M0hKtOUjTkGUkOHwrx5CqTEOQpNKGWxWBg4cCBxcXGsX7+e6tWr25yvXr16no07s6OdYkRECp4jzcwzlOMk62nGHfzMcSKIZAO/YttLx88P+vd3crEiIiIihUhGj86KFSEgAI4etT1ficNspCk3sYcDVKUpG9lH1k/1/PyMMMrefp8i9ig0j+8NGDCAWbNmsWTJEoKDg60rokJDQwkMDMRkMuXZuFNERDyPI83MM9jb72DIEPD3d1KhIiIiIoXMsGG5z7OqcJAEoriR/eyjOlEkcIiqWcZ17WrssKfVUeJshSaUmjx5MgCRkZE2x7/44gv69u0L2Ne4U0REPIu9zcwz2NPvwGSC556DsWOdXKyIiIhIIZCaCvXqwW+/5TymGvtJIIpqHOQvahBFAkeobDMmPBwOHdKHfOI6hSaUstjxF4s9jTtFRMSz7N1r/9iKHCWBKG5iD4eoTBQJ7KMGZcpASIixLP2BB+CZZzR5EhEREd9jNkOPHrBgQe7jbmQvCURRhcP8SS2iSOAYN2QZp0BKXK3QhFIiIuKdqmZdIZ6tShwmgShqspcDVCWKBA5g9BdcuBCuWUgrIiIi4lPmzYOePSE9PfdxNdlDAlFU4ii/cwvNWM8JKmQZ99xzCqTE9QpNo3MREfE+w4cbvQ7yUoWDbKQpNdnLPqrTlI3WQKpSJWjSxMWFioiIiHigjN6cpUvDgw/mHUjdxG420pRKHOVXbiWKhGwDqY4djU1oRFxNK6VERKRAZOy4l5e8+h28/76aboqIiIjvGToUxo2zf3xtfmM9zYjgb36hDtGs4xTlbcaYTDB7thFwibiDQikREXG71FT7Aqnc+h0EB8O0adC5s2trFREREfE0d98N27fbP/42drGOaMI5yU/cQXPWcpqyNmNuuslojK4P+8Sd9PieiIi43Ucf5T2mJnvYSFOqcJjfuYUOIRspdesN9OoFa9bA2bMKpERERMT3DBniWCBVl59JIIpwTvIj9YhmXZZA6tlnYfduBVLiflopJSIibpGaChMmGKub9uzJfexN7CaBKCpynF+5lWjWMf7jCHr2dEelIiIiIp4pORnGj7d//J3sZC3NCeMM22lAC9ZwjtLW8//5D8THq6G5FBytlBIREZcbPhwCAuD55+H33+HKlZzH1uY3NtKUihznF+oQRQJ/E0GFrD04RURERHxGXByUKGH/+LvYwTqiCeMM33M3McRbA6n//AdSUmDjRgVSUrAUSomIiEvZ29AcjH4HG4gkgr/5iTuIIoFTlNcOeyIiIuLT5s2DLl3AYrFvfEO2sY5oynCWLdxHC9aQSCmKFTPupTBKPIVCKRERcRl7G5oD3M7/SCCK8pxiB3fRjPXWfgfaYU9ERER8kdkMXbs6thvevWwlnhhKkcgm7qclq0kilJdfNh7/69bNdfWKOEo9pURExGXsaWgOOfc7CAyEL79UQ3MRERHxPXFx0KMHpKXZf01jNrOS1gRzgQ00pR1f07prSebM0Qd84pkUSomIiMvs3Zv3mPr8QDwxlOYc33EPrVhFIqUAmDpVgZSIiIj4nrg443E9RzThG1bQhpJcZB3NeP6WpZz5Xwk9piceTaGUiIi4RHIy/PBD7mMaso01tKAUiXxLI1qzkvOEWM/fcIOLixQRERHxMGYzDu84HEkCX9OOElziu+AYGu1fzA9hQa4pUMSJ1FNKREScrlMnCAqC777Lecy1/Q5ascomkFJzcxEREfE1ZjOUK2f05bRXNGtZTltKcIk1fq1oeGwJgQqkpJBQKCUiIk7VqRMsWZL7mMZsZg0tCCWJDTSlNSu5QLDNGDU3FxEREV8SFwdFi8LZs/Zf04LVLKM9QSTzNW259OUi/EoGuq5IESdTKCUiIk6TnJx3INWEb1hFK4K5wDqa0ZblXKSk9XxgICxcqF5SIiIi4jvmzXO8h1RrVrCEjgRymSV04MqchXTqUdw1BYq4iHpKiYiI0wwblvv5zP0O1hBDJxaTTBBly8IddxjXN2+uFVIiIiLiO555BiZOdOyadixjAV0JIJXlAQ/Q7uwc/ALV0VwKH4VSIiJy3VJTYdw4+PTTnMdEs5aldCCIZFbSis7EcRljefkHHzje0FNERESkMEtNhbAwuHDBses6sph5dMefNNaW6krbk7OgWDHXFCniYnp8T0RErsvw4RAQACNGQEpK9mOu7XfwAIusgRRAhQpuKlZERETEA2TMnxwNpDqzkPl0w580Dtzbg+anZiuQkkJNK6VERCTfhg+Hd97JfUxrVhBHZ4qTwhI60J15pBJgPa9d9kRERMRXmM3QvbvR1NxR3ZjHLHpRFDMHGvem2oZpRmd0kUJMK6VERCRfUlPzDqTasYxFPEBxUojjAbox3yaQAu2yJyIiIr5hwQJjUVN+AqmezGI2PSmKmfWVH6HaxukKpMQrKJQSEZF8+eij3M93ZDEL6UIAqcynKw8ylzSuNuDULnsiIiLiK4YPh27dwGJx/NqHmMlMHsaPdL6v8yjN9n+uT/TEayiUEhGRfNm7N+dzmfsdzKYHPZnNFYx+B6VKwapVcP68AikRERHxfvPn5726PCd9mMZ0+uBHOuZHH+ee/01VICVeRaGUiIg4JDkZHnss5532ujGPuTxIMa7wJb15mJmYM7Uw7N0bWrbUfEpERES8n9kMPXrk79p+fMrnPEoRLPD00/hN/RiK6E948S56CFVEROzWqRMsWZLz+Z7Msi4vn84jPMrnpGObPuX3k0IRERGRwqZSJUhPd/y6J/iET3jKeDNokNGE02RybnEiHkAxq4iI2CWvQCpzv4PPeDTbQKpjR6OXlIiIiIi3a9AATpxw/Lr+fHg1kHr2WQVS4tUUSomISJ6Sk3MPpPryhbXfwSc8weNMzTaQWrzYtXWKiIiIeILZs2HHDsevG8wEPmSg8WbYMHjvPQVS4tUUSomISJ6GDcv53GNM5Yt/+x18SH+eZjKWTP/30qYNXLqkQEpERER8g9kMvXo5ft0Q3mMCzxpvRoyAMWMUSInXUyglIiI5Mpvh669h2rTszz/Jx0zlCQDe5xkGMskmkAJ46CE9siciIiK+4/77Hb9mOGN4j+eMNy+/DG+9pUBKfIJCKRERyVZcnBEmtW8PFy9mPT+ASXzM0wCM41limQBknTxVqODaOkVEREQ8xdy58N13jl3zIm8xhheMN6+/DiNHKpASn6FQSkREsoiLgy5dIC0t+/ODmcAkBgEwlmEM5T2yC6RuuAGaNHFhoSIiIiIewmyGvn0du+YVXuct/s948+ab8MorTq9LxJMplBIRERtmMwwYkPP5zP0ORjGC5xlDdoEUwAcfgJ9ftqdEREREvErjxnD5sr2jLbzOK7zOa8bbt9+Gl15yUWUinqtoQRcgIiKeZdOmnLcvHs4Y6/LykbzMq7xOdoGUv7+x60znzi4sVERERMRDhIZCUpK9oy28xUu8yGjj7bvvwtChripNxKNppZSIiNg4fjz745n7HbzC67zKSK4NpOrVgxUrjN32FEiJiIiILyhe3LFAagzPXw2kJkxQICU+TSulRETE6sIF45G7a73C69bl5S/xJqPIury8UydYtMi19YmIiIh4kqpVISXF3tEW3mMoQxhvvJ00KfeeCSI+QKGUiIgAcPfdsH37tUctvM6rvMIbADzP24zl+WyvnznTtfWJiIiIeJI77oBDh+wdbeF9BvMME423kyfDU0+5qjSRQkOhlIiI5BhIZe53MJR3GUf2y8sbNoSSJV1bo4iIiIgnSE2FkBD7V0iZSGcSA+nPZNIxkT55CkWfesy1RYoUEuopJSLi4y5cyD6QytzvYDATcg2ktm1zbY0iIiIinmD4cAgIcCyQmszT1kBqTvPPFEiJZKKVUiIiPu7hh689YtvvYACT+Iis/Q6qVIFff9UKKREREfENw4fDO+/YP95EOlN5nH58jpkiDA6ZxqT4LBMvEZ+mlVIiIj7GbIYlS+DGG8FkgsWLM581+h1kBFJPMTnbQArgllsUSImIiIhvSE11LJAqgpnPedQaSA0tN5NJiQqkRK6lUEpExIfExRlLzjt1gv37bc+ZSOdDBvAME0nHxGNM5RNybsBZq5ZraxURERHxFBER9o/14wrT6UNfpnMFP7YOnMWEk71cV5xIIabH90REfERcHHTpkv25jH4HTzKFdEz04zOm8d9c7+fIp4UiIiIihVWNGnD2rH1j/bjCTB6mJ3NIoyhXvpzD/b1zmICJiFZKiYj4ArMZ+vfP/lxGv4MnmYKZIvRhep6BVMeOEBjogkJF5LqMHj2ahg0bEhwcTPny5enUqRO7d++2GWOxWHjttdeoWLEigYGBREZG8uuvv9qMSUlJYdCgQZQtW5YSJUrQoUMHjhw54s4fRUTEI3z1FezbZ9/YoqQxi170ZA6pFOOdhvMJVCAlkiuFUiIiPmDTJvj776zHi2DmC/5r7XfwMDP5ktz7HXTseG0fKhHxFBs3bmTAgAF89913xMfHc+XKFVq0aMHFixetY8aOHcu4ceOYNGkS27dvJyIigpiYGM6fP28dExsby6JFi5gzZw6bN2/mwoULtGvXDrPZXBA/lohIgTCb4aGH7BtbjFTm8iDdmU8K/rxQayEvbuvk0vpEvIHJYrFYCroIT5KUlERoaCiJiYmEhIQUdDkiIk4xezb0uqaVQUa/g97M4gp+9GIW8+me7fVFikC/fvD++1ohJd7N2+YBp06donz58mzcuJH//Oc/WCwWKlasSGxsLM8//zxgrIoKDw9nzJgxPPnkkyQmJlKuXDlmzpzJgw8+CMCxY8eoXLkyK1asoGXLlnZ9b2/7XYqI7+ncGRYtynucPynMpxsdWMZlAvhuWByRY9u4vkARD2bvPEArpUREvFhqKowaBU8+aXvcjyt8yUP0ZhZpFOVB5uYYSAF06ABTpiiQEilsEhMTAShTpgwA+/fv58SJE7Ro0cI6JiAggKZNm7JlyxYAduzYQVpams2YihUrUqdOHesYERFvl5pqXyAVwGXi6EwHlpFMcfyWLVEgJeIANToXEfFSw4dn34w8o99BNxaQSjG6MZ+ldMz1XjNnuqhIEXEZi8XCkCFDuP/++6lTpw4AJ06cACA8PNxmbHh4OAcPHrSO8ff3p3Tp0lnGZFyfnZSUFFJSUqzvk5KSnPJziIgUhODgvMcUJ5k4OtOaVVwikO9GLKVZu+auL07Ei2illIiIF8opkMrod9CNBaTgT2fi8gykGjaEkiVdVKiIuMzAgQP5+eefmT17dpZzJpPJ5r3FYsly7Fp5jRk9ejShoaHWV+XKlfNXuIhIAatWzVgplZtALrGEjrRmFRcJYujNy2k2SoGUiKMUSomIeJnU1OwDKX9SWEBXOrOIywTwAItYTrtc79WwIWzb5qJCRcRlBg0axNKlS0lISKBSpUrW4xEREQBZVjydPHnSunoqIiKC1NRUzl6z/3nmMdkZMWIEiYmJ1tfhw4ed9eOIiLhNbCz8u3A0R0FcZBntaUE8FyjB6/euZPIfUW6pT8TbKJQSEfEyH32U9di1/Q46soSVZN/vIDAQ2rSB8+cVSIkUNhaLhYEDBxIXF8f69eupXr26zfnq1asTERFBfHy89VhqaiobN26kUaNGANSvX59ixYrZjDl+/Di7du2yjslOQEAAISEhNi8RkcIkOdnY1CU3JbjActoSzXrOU5LeYasZu/U/7ilQxAupp5SIiBc5cwZef9322LX9DjqwlHVkv7y8RQtYvdoNhYqISwwYMIBZs2axZMkSgoODrSuiQkNDCQwMxGQyERsby6hRo6hVqxa1atVi1KhRBAUF0evfLTpDQ0Pp168fQ4cOJSwsjDJlyvDcc89Rt25dmjfXoyki4p3i4qBLl9zHlOQ8K2hDEzaTSAitWMXGY/e5p0ARL6VQSkTES0REwN9/2x4L5BKL6UQL4rlIEO34mg3kvLy8Vi0XFykiLjV58mQAIiMjbY5/8cUX9O3bF4Dhw4eTnJxM//79OXv2LPfccw9r1qwhOFNX3/Hjx1O0aFG6d+9OcnIy0dHRTJs2DT8/P3f9KCIibmNPIBVMEqtoRSO2co5QWrCGyp3vxt/fPTWKeCuTxWKxFHQRniQpKYnQ0FASExO17FxECo3sAqkgLrKUDkSznguUoA0r2ETuy8svXTIe3xPxVZoHOI9+lyJSGJjNEBBg/JuTUM6xilbcy/ecoTQxxLPTVJ+0NFBWL5I9e+cBDveUOnz4MEeOHLG+37ZtG7GxsUyZMiV/lYqIyHU5cyZrIHVtv4OWrM4zkOrYUYGUiLNoviQiUjhUrpx7IFWKs8QTw718z2nKEM06fqQ+8+YpkBJxBodDqV69epGQkAAYO7fExMSwbds2XnzxRUaOHOn0AkVEJHdNm9q+L8l5VtKaSDaSSAgtWMMWGud6j44dYfFi19Uo4ms0XxIR8XwdOsDx4zmfL8Np1hFNQ37gFGVpxnp+oh6xsdC1q9vKFPFqDodSu3bt4u677wZg3rx51KlThy1btjBr1iymTZvm7PpERCQPx45d/TqYJFbTkiZs5hyhxBDPd2TfgLNYMXjsMeORPQVSIs6l+ZKIiGdLToZly3I+H8Y/rCOau9jJScoRRQI/cwe1asH48e6rU8TbORxKpaWlERAQAMDatWvp0KEDALfccgvHc4uZRUTEaZKToW9f8Pc3Ht8Do9/BGlrQiK2coTTRrGM7d+d4j7FjYepUPbIn4gqaL4mIeC6zGYKCcj5fjpOspxl38j9OEE4kG/iVOphM8Pvv7qtTxBc4HErddtttfPzxx2zatIn4+HhatWoFwLFjxwgLC3N6gSIiYqtTJ2MiNX06pKUZx3Lqd5ATPz/o39899Yr4Is2XREQ8U1wcFM1lD/ry/E0CUdzOLxyjApFs4HduBWDBAvWREnE2h0OpMWPG8MknnxAZGUnPnj254447AFi6dKl1mbqIiLhGp06wZIntsZz6HeRmyBC0hbGIC2m+JCLieeLioEuXnM9HcJwNRHIbv3GEG2jKRnZzCwCzZkHnzm4qVMSHmCwWi8XRi8xmM0lJSZQuXdp67MCBAwQFBVG+fHmnFuhu2r5YRDxVcnLWpeZh/MNamnMn/+Mk5WjGen6lTq73GTbMeHRPRLJy5jzAm+dL9tCcSkQ8idmc+wqpihxlPc24mT85RGWiSGAfNQBo1y73/lMikpW98wCHV0oBWCwWduzYwSeffML58+cB8Pf3Jyi3B3NFROS6DBtm+z6nfgfZKVoURo2ClBQFUiLuovmSiIjnyG2FeCUOs5Gm3MyfHKAqTdloDaRq1FAgJeJKuWTF2Tt48CCtWrXi0KFDpKSkEBMTQ3BwMGPHjuXy5ct8/PHHrqhTRMRnJSfD008bPaQyhHOCdURzG79xjAo0Y711eXl2KlSAESPcUKyIAJoviYh4En9/SE/P/lwVDpJAFDeyn31UJ4oEDlEVMOZPf/3lxkJFfJDDK6UGDx5MgwYNOHv2LIGZtmx64IEHWLdunVOLExHxdZmbmmeowLEc+x3kpEoV19YpIrY0XxIR8Qxly17dGOZa1djPRppyI/v5ixo0ZaM1kAI4cMA9NYr4ModXSm3evJlvv/0W/2vWP1atWpWjR486rTAREV+XXVPzihwlgShuYk+Wfge5Wb7cNTWKSPY0XxIRKXjPPAOnT2d/7kb2kkAUVTjMn9QiigSOcYP1/LBh2hRGxB0cXimVnp6O2WzOcvzIkSMEBwc7pSgREV+XnJw1kMrod3ATe7L0O8hNjRoQGuqiQkUkW5oviYgUrNRUmDgx+3M12cNGmlKFw/zOLTRlY5ZASj04RdzD4VAqJiaGCRMmWN+bTCYuXLjAq6++Sps2bZxZm4iIz7q2qXkVDrKRptRkL/uoTlM2coDqed6nRg31QhApCJoviYgUrHr1sj9+E7vZSFMqcZRfuZUoEjhBBev58+cVSIm4k8OP740fP56oqChuvfVWLl++TK9evdizZw9ly5Zl9uzZrqhRRMTn7Nlz9etq7CeBKKpxkL+oQRQJHKFyjteaTHD33bB6tVZIiRQUzZdERArO4MHw229Zj9fmN9bTjAj+5hfqEM06TlHeen7YMChZ0o2FiojjoVTFihX56aefmD17Nj/++CPp6en069eP3r172zTyFBERx5jNxiN7Tz8NJ08ax/Lqd5CdceMgNtb19YpIzjRfEhEpGB07wtKlWY/fxi7WEU04J/mJO2jOWk5T1nr+uee0QkqkIJgsFouloIvwJElJSYSGhpKYmEhISEhBlyMiPiIuDrp1s92uuCZ7SCCKShzld26hGettlpdnx88PLl1SY06R/NI8wHn0uxQRd3vuOXjvvazH6/Iz64imHP/wI/WIIZ4zhFnPz5oFPXu6sVARH2DvPMDhlVIzZszI9fwjjzzi6C1FRHxaXBx06WJ77CZ2k0AUFTnOr9xKNOv4m4g87zVkiAIpEU+g+ZKIiHulpmYfSN3JTtbSnDDOsJ0GtGAN5yhtPR8ZqUBKpCA5vFKqdOnSNu/T0tK4dOkS/v7+BAUFcebMGacW6G76VE9E3MlshooVrz6uB3n3O8iJdooRuX7Omgd4+3zJHppTiYg7lSkDZ8/aHruLHcQTQxnO8j1305LVJFLKZkxKij7QE3EFe+cBDu++d/bsWZvXhQsX2L17N/fff78ad4qIOGjTJttA6jZ2kUAUEfzNT9xBFAm5BlLFisGbbxoTKgVSIp5D8yUREfepVi1rINWQbawjmjKcZQv30YI1WQKp2FgFUiIFzWk9pX744Qceeugh/vjjD2fcrsDoUz0RcYfkZKOh+cyZV/tI5dXvIDstWhi77ImIc7h6HuAt8yV7aE4lIu5QvjycOmV77B6+YzUtCSWJTdxPG1ZwgWCbMdWrw759bixUxMe4rKdUTvz8/Dh27Jizbici4rU6dTJ22cssr34HOalVyzU1iohraL4kIuI81aplDaQa8S0raU0I59nIf2jLci5S0mZMyZIKpEQ8hcOh1NJr9te0WCwcP36cSZMm0bhxY6cVJiLijbILpOzpd5CTd95xeoki4gSaL4mIuFb16nDwoO2xJnzDCtpQkousJ4r2LOMSJWzG+PnB+fNuLFREcuVwKNWpUyeb9yaTiXLlytGsWTPey267AxERAYxH9q4NpBqyjTW0oBSJbOE+WrOSJELtul/HjhAY6IJCReS6ab4kIuI6HTrAgQO2x5qygeW0pQSXiKc5HVlCMkE2Y0wmuHLFfXWKSN4cDqXSM5qfiIiIQ4YNs31vT7+DnHTsCIsXO79GEXEOzZdERFwjORmWLbM91ox1LKM9QSSzipY8wCIuk/WTu8uX3VSkiNjN4d33REQkf/bsufp1I75lDS0IJYmN/IfWrMwzkAoJgcceg0uXFEiJiIiIbwqyXfxEDGv4mnYEkcxy2tCJxdkGUtppT8Qz2bVSasiQIXbfcNy4cfkuRkTE25w5A/fdB3/+efWYPf0OrtW4MWze7OJiReS6aL4kIuJa1wZSrVjJIh6gOCkspT3dmE8qAVmuu/FGGD/eTUWKiEPsCqV27txp181MJtN1FSMi4k0iIuDvv22PRZLA17SjBJdYQwydWJyl30F2li93UZEi4jSaL4mIuE5QkPHoXoZ2LGMBXQkglTgeoAdzSCPrUqhy5WDvXjcWKiIOsSuUSkhIcHUdIiJeJbtAKpq1LKUDQSSzklZ0Ji7b5eXXqlEDQu3rfS4iBUjzJRER1yhWzLZBeUcWM4/u+JPGfLrSi1lcoViW66pWzdoQXUQ8i3pKiYg42ZkzWQOpFqy2NuD8mrY5NuC8Vo0a8NdfLipURERExMNdG0h1ZiHz6YY/acymBz2ZnW0gVb68AimRwsDh3fcAtm/fzvz58zl06BCpqak25+Li4pxSmIhIYdW0qe371qwgjs4UJ4UldKA787Ltd5DZ3XfDmjVaISVSmGm+JCJyffz8IPNmpt2Yxyx6URQzX9KbvkzDnM2ftCYTHDvmxkJFJN8cXik1Z84cGjduzG+//caiRYtIS0vjt99+Y/369YTqrycR8UHJyfDII1CkiDEJ2rXr6rl2LLM24IzjgRwbcGbWogV8/70CKZHCTPMlEZH8S0425lSZA6mezGI2PSmKmek8Qh+mZxtIASxYYARaIuL5HA6lRo0axfjx4/n666/x9/fn/fff5/fff6d79+5UqVLFFTWKiHisTp2MxpszZ4LFYnuuI4tZSBcCSGU+XXmQudk24LxWrVquqVVE3EfzJRERx6WmQu3aWXfZe4iZzORh/EjnMx7lUT4nnexTp/nzoXNnNxQrIk7hcCi1d+9e2rZtC0BAQAAXL17EZDLx7LPPMmXKFKcXKCLiqTp1giVLsj9nb7+D7LzzjvNqFJGCofmSiIhjhg+HgAD44w/b432YxnT64Ec6U3icx5maYyD15ZfQtasbihURp3E4lCpTpgznz58H4IYbbmDXv8+pnDt3jkuXLjm3OhERD5WcnHMg1Y15zOVBinGFL+nNw8zMcXn5tTp2hMC8+5+LiIfTfElExH7Dh2f/oVw/PuVzHqUIFj7iaZ7iYyw5/AnboAH07u3iQkXE6RxudN6kSRPi4+OpW7cu3bt3Z/Dgwaxfv574+Hiio6NdUaOIiMcZNiz74z2ZZV1ePp1Hcl1efq2OHWHxYufVKCIFR/MlERH7pKZmH0g9wSd8wlMAfMAgBvM+YMr2HvXrw/btLixSRFzG7lDqp59+4s4772TSpElcvnwZgBEjRlCsWDE2b95M586defnll11WqIiIJ9mzJ+uxh5jJNPpa+x08wZQ8A6mgIOjRAyZN0gopEW+g+ZKIiGPq1ct6rD8f8iEDARhPLEMYR06B1MCBMHGiCwsUEZcyWSzXtubNXpEiRahXrx6PPfYYvXr18tqdY5KSkggNDSUxMZGQkJCCLkdEPEhqKrz9Nrz1lvF1Zn2YZl1ePoXHc11eDlCnDvzyi4sLFhGHXe88wFfmS/bQnEpE8jJ3rvHhXGbP8D7vEwvAOzzHcMaSUyDVoUPO7RREpGDZOw+wu6fUt99+y1133cULL7xAhQoVeOihh0hISHBKsSIini6j+earr2YNpBzpd5Bh40YXFisiBUbzJRER+5jNWQOpZxlnDaRG80KugVRsrAIpEW9gdyh13333MXXqVE6cOMHkyZM5cuQIzZs3p0aNGrz11lscOXLElXWKiBSYnJpvgtHv4FMepwgWPmAQA/gwz0AqPBzKlHFBoSJS4DRfEhGxT9FrGskMYyzjGArAG/wfLzKKnAKpZ5+F8eNdXKCIuIXdj+9lZ+/evXzxxRfMmDGD48ePExMTw4oVK5xZn9tpqbmIZJaaaqyQyo4j/Q4yhIfDiRNOLlJEnMYV8wBvnC/ZQ3MqEclJkSKQ+a/QEYxiFC8B8CqvMZJXc7x22DAYO9bVFYrI9XL643vZqVGjBi+88AIvvfQSISEhrF69+npuJyLicT76KPvjz/C+NZB6h+dyDaRMJrjlFjh9WoGUiC/SfElE5Ko2bWwDqZcZaQ2k/o83cgykbrkFUlIUSIl4m3yHUhs3bqRPnz5EREQwfPhwOnfuzLfffuvM2kRECsSFC9C2rREmPfts1vOO9Dto0QLS0+H33/XInogv0nxJROSq5GRYuTLjnYXXecUaQr3AaN7i/7K97vPPjbmUv7976hQR9yma95CrDh8+zLRp05g2bRr79++nUaNGTJw4ke7du1OiRAlX1Sgi4jZ33w3bt+d8fhhjGcvzgNHv4BVGktsje7VqOblAEfF4mi+JiGTv6hM8Ft7k/3iJUQA8xzu8x3PZXhMWBv/9r3vqExH3szuUiomJISEhgXLlyvHII4/w6KOPcvPNN7uyNhERt8orkHKk30GGnBqki4h30nxJRCR71arBlSsAFt7mBZ7HeA4vlvHWFejZOXbMHdWJSEGxO5QKDAxk4cKFtGvXDj8/P1fWJCLidhcu5B5IvcxIawj1Em9aw6ncdOwIgYHOqlBECgPNl0REsho8GA4eBLDwHkMZgrF13kAmWnt0ZmfYMD2yJ+Lt7A6lli5d6so6REQK1MMP53TGwuu8yiu8AcDzvG19fC83HTvC4sVOK09ECgnNl0REbM2aBR98AGDhfQbzDBMBeIrJfMJTOV6nXfZEfINDPaVERLzV3r3ZHbXwFi/xIqMBGMq7jGNojvcICoIePWDSJK2QEhEREWnfHr7+GkykM4mB9Gcy6Zh4gil8xmM5XrdsGbRr58ZCRaTA5Hv3PU/20UcfUb16dYoXL079+vXZtGlTQZckIh7mzBm4+WZjhz2TCX755doRFsbwvDWQGsyEXAOpTp3g4kX47DMFUiLiPTSnEpH8ql79aiA1maetgVQ/Pss1kPL3h9at3VioiBQorwul5s6dS2xsLC+99BI7d+6kSZMmtG7dmkOHDhV0aSLiISIijJ1c/vwzpxFGv4PhGF3KBzCJDxic6z1nznRujSIiBU1zKhHJr8BAOHDACKSm8jhPMgUzRejDdKaR+1Z6M2eCWvKJ+A6TxWKxFHQRznTPPfdw1113MXnyZOux2rVr06lTJ0aPHp3n9UlJSYSGhpKYmEjI1T1LRcRLRETA33/nNsKxfgcADRvCtm3Oq1FECo7mAVdpTiUi+VGypLF6vAhmPqMffZmOmSI8zExm0yvXa9u3B7XmE/EO9s4D7Oop5UjTzg4dOtg91tlSU1PZsWMHL7zwgs3xFi1asGXLlgKqSkQ8xZkzuQdSjvY7AAVSInJVYZkv2UNzKhHJj7AwI5Dy4wrT6MtDfMUV/OjNV8zjwVyvrVVLgZSIL7IrlOrUqZPNe5PJROYFViaTyfq12Wx2TmX58M8//2A2mwkPD7c5Hh4ezokTJ7K9JiUlhZSUFOv7pKQkl9YoIgWnadOcz2X0O3iSKdZ+BzktL/f3h+homDfP+DRQRAQKz3zJHppTiYijQkLg/HkjkJrJw/RkDmkUpQdziKNLrteaTPD7724qVEQ8il09pdLT062vNWvWcOedd7Jy5UrOnTtHYmIiK1as4K677mLVqlWurtcumSd9ABaLJcuxDKNHjyY0NNT6qly5sjtKFBE3ydzQfNeu7Mc40u9g4EBISYEVKxRIiYitwjZfsofmVCJij7vuMgKpoqQxi170ZA6pFKMb8/MMpAAWLFAfKRFfZddKqcxiY2P5+OOPuf/++63HWrZsSVBQEE888QS/F2DEXbZsWfz8/LJ8gnfy5Mksn/RlGDFiBEOGDLG+T0pK0iRKxEvk3T/K8X4HNWo4uUgR8UqePF+yh+ZUImKv2bNh504oRipz6EFnFpGCP11ZwNe0z/P6hQuhc2c3FCoiHsnh3ff27t1LaGholuOhoaEcOHDAGTXlm7+/P/Xr1yc+Pt7meHx8PI0aNcr2moCAAEJCQmxeIlL42RNI+XGF6fShL9O5gh+9mJVrIFWkCPTv7+RCRcQrefJ8yR6aU4mIPcxm6NUL/ElhPt3ozCIuE8ADLMozkCpSBK5cUSAl4uscDqUaNmxIbGwsx48ftx47ceIEQ4cO5e6773ZqcfkxZMgQPv30Uz7//HN+//13nn32WQ4dOsRTT+W+e5aIeI+8GprD1X4HD/EVaRTlQebm2YBz6FCjn5SISF48fb5kD82pRCQvEREQwGUW0oWOLOUyAXRkCStpk+t1ZcoYgZYe2RMRhx/f+/zzz3nggQeoWrUqVapUAeDQoUPcdNNNLF682Nn1OezBBx/k9OnTjBw5kuPHj1OnTh1WrFhB1apVC7o0EXGT3Bqag9Hv4Ct60535pFKM7sxjCZ1yvWbYMBg71nk1ioh38/T5kj00pxKR3JQsCVcuXmYRD9CaVSRTnPYsYx3Nc73ujjvgp5/cU6OIeD6TJfO2MHayWCzEx8fzxx9/YLFYuPXWW2nevHmOjS8Lk6SkJEJDQ0lMTNSyc5FCKizMWC2VHUf6HZQoAS+8AMOHa4WUiK9w5jzAm+dL9tCcSsR7+fmBf3oyS+hIC+K5SBDtWUYCzXK9rlo12L/fPTWKSMGydx6Qr1Aqw+XLlwkICPCqyZUmUCKFj9kMS5bAU0/BqVM5j/MnhXl0ty4v70xcluXlmiyJ+DZXzAO8cb5kD82pRLxTkSJQ3HKJZbQnmvVcoARtWc435L5UvXp12LfPTUWKSIGzdx7gcE+p9PR03njjDW644QZKlizJ/n//env55Zf57LPP8l+xiEg+xMUZq5i6dMk9kLK338G2bS4sVkR8huZLIuKNqlaFIMsFltOWaNZznpK0YlWegVTdugqkRCR7DodSb775JtOmTWPs2LH4Z3qepW7dunz66adOLU5EJDdxcUYYlZ6e+7gAjH4H7VhOMsVpx9esoWWWcaGhUK6ci4oVEZ+i+ZKIeJuvvoIzh86zgjZEsYEkgmnBGr7l/lyv8/ODn392U5EiUug4HErNmDGDKVOm0Lt3b/wybZdw++2388cffzi1OBGRnJjN8PTTeY8rTjJL6UBrVnGRINqyPNsGnKGhcO6c8+sUEd+k+ZKIeBOzGZ5+KIlVtOI/bOIcocQQz3fcl+e158+7oUARKbQcDqWOHj1KzZo1sxxPT08nLS3NKUWJiORl0yY4eTL3MUFc5Gva0YJ4LlCC1qzM0oCzcmXjPgqkRMSZNF8SEW9SqeQ51tCCxmzhDKVpzlq2cU+e17VtC4GBbihQRAoth0Op2267jU2bNmU5Pn/+fOrVq+eUokREcpOYCP365T6mBLb9Dlqymk38x2bMrFlw6JAe2RMR59N8SUS8Rd3KZ1l6OYZ7+Z7TlCGadeygQZ7XhYbC11+7oUARKdSKOnrBq6++ysMPP8zRo0dJT08nLi6O3bt3M2PGDL7W/+qIiIvVrAl79+Y+piRGv4MmbCaREFqxKtvl5RUquKhIEfF5mi+JSGGXmgo3lz3NwvMx3MVOTlGW5qzlZ+7I89p69eDHH91QpIgUeg6vlGrfvj1z585lxYoVmEwmXnnlFX7//XeWLVtGTEyMK2oUEQHsC6SCSWI1LWnC5lz7HdxwAzRp4qJCRcTnab4kIoXZM89AxYB/WHQ+mrvYyUnKEUWCXYHU9OkKpETEfg6tlLpy5QpvvfUWjz76KBs3bnRVTSIiWSQm5h1IhXKOVbTiXr7nDKWJIZ4fqZ/t2A8+MHaDERFxNs2XRKSwSk42HrsrlXaS9TTndn7hBOE0Yz2/c2ue1996KzzyiBsKFRGv4dBKqaJFi/LOO+9gNptdVY+ISLbats39fCnOEo9tv4PsAqkiRWDhQujc2UWFiojP03xJRAqj9u0hKAhKp/1NAlHczi8cowKRbLArkALYudPFRYqI13H48b3mzZuzYcMGF5QiIpKzQ4dyPleG06wjmob8wCnK0oz1/IRtI+GKFWHJEqM/ggIpEXE1zZdEpDCJiDCakkdwnA1Echu/cYQbaMpGdnOLXfcYMgT8/V1cqIh4HYcbnbdu3ZoRI0awa9cu6tevT4kSJWzOd+jQwWnFiYhcuAAPPgiHD2d/Pox/WEtz7uR/nKQczVjPr9Sxnq9TB375xU3Fioj8S/MlESks2reHv/+GihxlPc24mT85RGWiSGAfNey6R/368N57Li5URLySyWKxWBy5oEiRnBdXmUymQr9UPSkpidDQUBITEwkJCSnockR82t13w/btOZ8vx0nW5tHv4PRpKFPGxYWKiNdw1jzA2+dL9tCcSsTzJScbj+xV4jAJRFGTvRygKlEkcIDqdt2jalU4cMC1dYpI4WPvPMDhlVLp6enXVZiIiD3yCqTK8zfracZt/MYxKtCM9VmWl4eHK5ASkYKh+ZKIFAZBQVCFgyQQxY3sZx/VacZ6DlLNruuDgxVIicj1cbinVGaXL192Vh0iIlYXLuQeSNnT7yA8HE6ccHGhIiJ20HxJRDyN2QwmE1RjPxtpyo3s5y9qEMkGuwOpsmUhKcm1dYqI93M4lDKbzbzxxhvccMMNlCxZkn379gHw8ssv89lnnzm9QBHxPQ8/nPO5ihxlA5HU5g8OUZmmbOQvalnPV65sPLKnQEpECpLmSyLiqWbPhqJF4Ub2soFIqnGQP6lFJBs4TBW77jFoEJw65eJCRcQnOBxKvfXWW0ybNo2xY8fin2l7hbp16/Lpp586tTgR8T1nzsCKFdmfq8RhNtKUm/mTA1SlKRuzNOAcM0aP7IlIwdN8SUQ80d13Q69eUJM9bCCSqhziD24mkg0cpVKe11esCCkp8MEHbihWRHyCw6HUjBkzmDJlCr1798bPz896/Pbbb+ePP/5wanEi4lsiIiAsDFJTs56rwkE20pSa7GUf1YlkQ7YNOCtUcEOhIiJ50HxJRDxNx45Ge4Sb2M0GIqnMEX6jNpFs4DgV87z+/Hk4ehQy5ewiItfN4UbnR48epWbNmlmOp6enk5aW5pSiRMT3REQY2xFnpxr7SSCKahzkL2rQjPXZLi+vWBGaNHFxoSIidtB8SUQ8SXIyLF0Kt/A762lGBU7wC3WIZh2nKJ/n9VeuQKZ8XUTEaRxeKXXbbbexadOmLMfnz59PvXr1nFKUiPiWM2dyDqQc6XcwcaImTCLiGTRfEhFPEhwMt/IrG4ikAif4H7fTjPV2BVILF2p+JSKu4/BKqVdffZWHH36Yo0ePkp6eTlxcHLt372bGjBl8/fXXrqhRRLxc06bZH6/JHtbTjMoc4Q9uphnrs11ebjLBggXQubOLCxURsZPmSyLiKcLC4Fbzz6wjmnL8w07upDlrOUNYntfOnav5lYi4lsMrpdq3b8/cuXNZsWIFJpOJV155hd9//51ly5YRExPjihpFxMsdO5b1mD39DgICYNEiSEvThElEPIvmSyLiCcqVg8pnfmI9zSjHP/xAfaJZZ1cgNWQIdO/uhiJFxKc5vFIKoGXLlrRs2dLZtYiIj0lNhXffhbNnbY/X5jfWEZ1nv4O334ZOndxTq4iIozRfEpGCVK4cVPlnB/HEUIazfM/dtGQ1iZTK89qOHeG991xfo4hIvkIpEZHrNXw4vPNO1uO3sYt1RBPOSX7iDpqzltOUzTLOzw/693dDoSIiIiKFTNmycOPpbayhBaVIZAv30ZqVJBGa57Vz5sCDD7qhSBER7AylSpcujclksuuGZ86cua6CRMT75RRI1eVqv4MfqUcM8TkuLx8yRFsSi4hn0XxJRDxByZJQ5+J3rKYloSSxiftpwwouEJzntZcuQWCgG4oUEfmXXaHUhAkTrF+fPn2aN998k5YtW3LfffcBsHXrVlavXs3LL7/skiJFxHukpmYfSN3JTtbSnDDOsJ0GtGAN5yid7T2GDYOxY11cqIiIgzRfEpGCVrQo3GP+lpW0JoTzbOQ/tGU5FymZ57XDhimQEhH3M1ksFosjF3Tp0oWoqCgGDhxoc3zSpEmsXbuWxYsXO7M+t0tKSiI0NJTExERCQkIKuhwRrzNhAjz7rO2xu8i734HJBG+8YUyYtEJKRFzFWfMAb58v2UNzKhH3KlEC6l/6hhW0oSQXWU8U7VnGJUrkeW1sLIwf7/oaRcR32DsPcDiUKlmyJD/99BM1a9a0Ob5nzx7q1avHhQsX8lexh9AESsT5UlONlU1jxsC1/xPREPv6HZQpA6dPu6lgEfFZzpoHePt8yR6aU4m4h9lsrHBqlLaB5bSlBJeIpzkdWUIyQXle37YtfP21GwoVEZ9i7zygiKM3DgsLY9GiRVmOL168mLCwvLcWFRHfMnw4BATAyy9nDaTu4TviiaEUiWziflqyOscGnBUruqFYEREn0XxJRNxh9mzjkb0maetYQRtKcIlVtKQDS+0KpG68UYGUiBQsh3ffe/311+nXrx8bNmyw9kj47rvvWLVqFZ9++qnTCxSRwiunhuYAjXCs38HGjS4qUkTEBTRfEhFXa9AAduyAGNawhI4EcpnltKELC0mheJ7XV6sGe/e6vk4Rkdw4HEr17duX2rVr88EHHxAXF4fFYuHWW2/l22+/5Z577nFFjSJSCOXU0BygCY71OwgPNx7fExEpLDRfEhFXuXDBmBelpUErVrKIByhOCktpTzfmk0pAnveoXh327XNDsSIieXAolEpLS+OJJ57g5Zdf5quvvnJVTSLiBT76KPvjTXGs30F4OJw44aIiRURcQPMlEXEFsxluuQX++st435avWUgXAkhlEZ14kLmkkfduMPXqwY8/urhYERE7OdRTqlixYtn2RxARuVZ2y8GbYX+/g2LFjMbmCqREpLDRfElEnG3BAqN3VEYg1YElxNGZAFJZQBe6M8+uQKpdOwVSIuJZHG50/sADD/jENsYikj+pqfDmm/DJJ7bHY1jD17QjiGSW04ZOLOYygTnep21bPbInIoWX5ksi4izDh0O3blffP0AcC+iKP2nM4UF6MpsrFMvzPgMHwrJlLixURCQfHO4pVbNmTd544w22bNlC/fr1KVHCtg/MM88847TiRKRwyamxeX76Hcyc6aIiRUTcQPMlEXGG+fNt51Zdmc9selIUM1/Riz5Mx2zHn3QNGsDEiS4sVEQkn0wWi8XiyAXVq1fP+WYmE/sKece8pKQkQkNDSUxMJCQkpKDLESk0cgqk8tPvoGFD2LbNRYWKiOTCWfMAb58v2UNzKpHrYzYbj+xl6MFsZvIwRTEzg4f5L1+Qjl+e9+nQAZYscWGhIiLZsHce4PBKqf37919XYSLifXLaaa8DS5hPN/xJYwFd7FperkBKRLyB5ksicr2Cg69+3ZsvmU4f/Ejnc/7L40y1K5C6dAkCc+6WICJS4BzuKZXhn3/+4fTp086sRUQKqex22nOk34G/P7RuDefPK5ASEe+i+ZKI5EfZspCcbHz9CNOZwSP4kc5UHuMxPs0zkCpTBiwWBVIi4vkcCqXOnTvHgAEDKFu2LOHh4ZQvX56yZcsycOBAzp0756ISRcQTJSfDo49CUBA8+6ztua7MZx7dKcYVvqIXD/FljoFUixaQkgIrVkDJkm4oXETExTRfEpHrUa2asQMxwKN8xhf8lyJYmMxTPMknWPL4E65evavXi4h4Orsf3ztz5gz33XcfR48epXfv3tSuXRuLxcLvv//OtGnTWLduHVu2bKF06dKurFdEPECnTjn3JnC030GtWq6pUUSkIGi+JCLXo1o1OHjQ+PpxpjCFJwGYyECe4QPAlOv1zz4L48a5tkYREWeyO5QaOXIk/v7+7N27l/Dw8CznWrRowciRIxk/frzTixQRz5FbIJWffgfZ9aISESmsNF8SkfwqXx5OnTK+7s+HfMhAAMYTyxDGkVsg1asXfPGF0RJBRKQwsfvxvcWLF/Puu+9mmWABREREMHbsWBYtWuTU4kTEsyQn5xxI9WGatd/BFB63q99Bx47qdSAi3kXzJRHJj7vuuhpIPcP71kDqHZ7LM5C6dAm++kqBlIgUTnaHUsePH+e2227L8XydOnU4ceKEU4oSEc80bFj2x/vxKZ/zKEWw8BFP8xQf59nvoGNHWLzY+TWKiBQkzZdExFGDBsHOncbXzzKO94kFYDQvMJyx5BZIDRumD/hEpHCzO5QqW7YsBw4cyPH8/v37CQsLc0ZNIuKh9uzJeuwJPuFTHqcIFj5gEAP4MMdAymQymqNfuqRASkS8k+ZLIuKIDh1g0iTj62GMZRxDAXiD/+NFRpFbIDVkCIwd64YiRURcyO5QqlWrVrz00kukpqZmOZeSksLLL79Mq1atnFqciHiG1FR4+23YvNn2eH8+5BOeAox+B4N5n9wmT/37w2ef6RM9EfFemi+JiL06dIBly4yvRzCKsTwPwKu8xiu8QW5zqmeegffec0ORIiIuZrJYLBZ7Bh45coQGDRoQEBDAgAEDuOWWWwD47bff+Oijj0hJSeGHH36gcuXKLi3Y1ZKSkggNDSUxMZGQkJCCLkekwA0fnn0z8md437q8/B2ey3N5ORgrpBRIiYgnu955gK/Ml+yhOZVIzoYMgYz9Dl5mJCN5FYD/4w3e4v9yvbZBA9i+3dUViohcH3vnAXbvvlepUiW2bt1K//79GTFiBBlZlslkIiYmhkmTJvnEBEvEl+QUSD3LOOvy8tG8kOfyclBTcxHxDZoviUhe5s/PCKQsvM6r/66KghcYzRheyPXau+5SICUi3sXux/cAqlevzsqVK/nnn3/47rvv+O677zh16hSrVq2iZs2arqpRRApAamr2gZSj/Q5ATc1FxLcU1HzpwIED9OvXj+rVqxMYGEiNGjV49dVXszxKeOjQIdq3b0+JEiUoW7YszzzzTJYxv/zyC02bNiUwMJAbbriBkSNHYufiehHJRWoqdO8OYOFN/s8aSD3HO3YFUjt2uL5GERF3snulVGalS5fm7rvvdnYtIuJBPvoo67ERjGIULwFGv4OMpebZCQkxJl0ffKAVUiLim9w9X/rjjz9IT0/nk08+oWbNmuzatYvHH3+cixcv8u677wJgNptp27Yt5cqVY/PmzZw+fZo+ffpgsViYOHEiYCy3j4mJISoqiu3bt/Pnn3/St29fSpQowdChQ93284h4m7g46NIFwMLbvMDzGF3KYxlvbYmQk3r1FEiJiHeyu6eUr1D/A/F1ycnG1sQzZkBa2tXjjvQ7GDgQ/v3bRkSkUPG2ecA777zD5MmT2bdvHwArV66kXbt2HD58mIoVKwIwZ84c+vbty8mTJwkJCWHy5MmMGDGCv//+m4CAAADefvttJk6cyJEjRzCZcl8dm8Hbfpci1yNzIPUuzzGUcQAMZCIfMjDXa6tWhVw29RQR8Uj2zgMcenxPRLxbp04QFGTskHc1kLLwOq9YA6kXGJ1nA84aNVxapoiI2CkxMZEyZcpY32/dupU6depYAymAli1bkpKSwo5/l2Fs3bqVpk2bWgOpjDHHjh3jgP4yFnGY2Xw1kJpArDWQepqP8gykgoMVSImId1MoJSKAEUgtWXLtUcf7Hfj5Qf/+LilRREQcsHfvXiZOnMhTTz1lPXbixAnCw8NtxpUuXRp/f39OnDiR45iM9xljspOSkkJSUpLNS0SgTBkwkc4kBjKYDwB4nCl8zNO5Xle2LOi/RiLi7RRKiQjJydkHUm/zAi8xCjD6HbzHc3nea8gQ8Pd3fo0iIr7qtddew2Qy5fr64YcfbK45duwYrVq1olu3bjz22GM257J7/M5isdgcv3ZM5l0EczJ69GhCQ0OtL+0yKALly8P5pHQ+oj8D+Ih0TDzKZ3zK47leV64cnDrlpiJFRApQvhqdi4h3GTbs2iOO9zvIuM/Ysc6vT0TElw0cOJAePXrkOqZatWrWr48dO0ZUVBT33XcfU6ZMsRkXERHB999/b3Ps7NmzpKWlWVdDRUREZFkRdfLkSYAsK6gyGzFiBEOGDLG+T0pKUjAlPq1ePfjnVDpTeILH+Ix0TPRlGjN5JM9rjx93Q4EiIh5AoZSIsGdP5ndGv4OM5eVP81Guy8uLFYPXX4ehQ7VCSkTEFcqWLUvZsmXtGnv06FGioqKoX78+X3zxBUWK2C6Kv++++3jrrbc4fvw4FSpUAGDNmjUEBARQv35965gXX3yR1NRU/P/9H/Y1a9ZQsWJFm/DrWgEBATZ9qER8WYMG8PNPZj7jMf7LNMwU4RFmMIveeV47d67RDkFExBfo8T0RH3bhArRvD2vXGu/z0+/giSdgxAgFUiIiBe3YsWNERkZSuXJl3n33XU6dOsWJEydsVj21aNGCW2+9lYcffpidO3eybt06nnvuOR5//HHrzji9evUiICCAvn37smvXLhYtWsSoUaMYMmSI3TvvifiyIUNg5w4z0+jLf5nGFfzoxSy7AqmOHaF7dzcUKSLiIbRSSsRH3X03bN9+9b0Jo9/BU3xCOiYe41O+4NE87/POOy4sUkRE7LZmzRr++usv/vrrLypVqmRzLqMnlJ+fH8uXL6d///40btyYwMBAevXqxbvvvmsdGxoaSnx8PAMGDKBBgwaULl2aIUOG2DyaJyLZS02FD8ZfYSaP0IvZpFGUnsxmIV3zvHboUMj0X0UREZ9gsmTMUgQw+h+EhoaSmJho/cRQxNtkF0jlp99Bx46weLHr6hQRcTfNA5xHv0vxNWYzlPBPY0Z6b7oznzSK0p15LOaBPK89dw5CQ11fo4iIu9g7D9DjeyI+5sIF20CqCGY+51Ee4zPMFOEhvlQgJSIiIuKAuDgILJrKV+k96M58UilGFxbaFUh17KhASkR8l0IpER/z8MNXv/bjCtPpQ1+mW/sdzKZXjteGhMDjj8OlSwqkRERERMAIpHp2SWE+3ehCHJcJoBOLWUaHPK9t315zKhHxbQqlRHxEcjI89hgsWWK89+MKM3mYh/iKNIryIHOZx4O53uPjj2HKFAgMdEPBIiIiIh7ObIan/3uZhXShI0u5TAAdWcJK2uR57cCBsHSpG4oUEfFganQu4gM6dboaRgEUJY2v6G1dXt6deSyhU573+Xf3cBEREREB7rj5MtOSHqA1q0imOO1Zxjqa53ldgwYwcaIbChQR8XAKpUS83LWBVDFSmUMPOrOIFPzpygK+pn2e97nhBmjSxHV1ioiIiBQmEaHJzEjqSAviuUgQ7VlGAs3yvO7OO237e4qI+DKFUiJeLDnZNpDyJ4V5dLcuL+9MnF3LywE++AD8/FxUqIiIiEghYTbD7TUv8VVSe6JZzwVK0JblfEPTPK8NCYGdO91QpIhIIaGeUiJebNiwq18HkL9+B35+sHAhdO7swkJFRERECoEFCyC06AUmHWhLNOs5T0lascquQArgzBkXFygiUsgolBLxYnv2GP8GcJlFPEA7lpNMcdrxNWtomeu15cvDsmWQkqJASkRERGTYMPhvt/OsoA1RbCCJYFqwhm+5367rFy7UqnMRkWvp8T0RL5ScDIMHwzffQHGSWYLj/Q7mzoXISNfXKiIiIuLphg6FqeOSWEVrGrOFc4TSktVs4x67rp87Vx/yiYhkR6GUiJfJ3Ng8kEssw/F+B2pqLiIiImIYPBimfZDIGlpyL99zllLEEM8OGth1/aBB0L27i4sUESmkFEqJeJHMgVQJLrCM9kSxgfOUpDUr7V5erqbmIiIi4utSU6F6dbh07CzxtORutnOaMsQQz07ususe4eHGvEpERLKnnlIiXiLzTnslyV+/A39/NTUXERER32Y2Q9euEBAAycfOsJbm3M12/iGMZqy3O5CqXx9OnHBxsSIihZxWSol4iYyd9oJJYqUD/Q6KFIHbb4dRo6BFC62QEhEREd+1YAE8+CCkp0MY/xBPDPX4iZOUI5p17KKuXfc5eRLKlXNxsSIiXkChlEghltHQfNky45O4EBJZ7WC/g9tug5073VSwiIiIiAdKTTU+nNu40XhfllOsI5rb+YUThNOM9fzOrXbdq2NHBVIiIvZSKCVSSGXuHwVQirOszke/gxo1XFejiIiIiKcbPhzeeefq+/L8zTqiqcOvHKMCzVjPbm6x614dO8Lixa6pU0TEGymUEimErg2kSnOGeGKoz4/8QxjRrONn7rDrXjNnuqZGEREREU93bSAVwXHW04za/MFRKhJFAnu4Kc/7lCxpPLIXGOjCYkVEvJAanYsUMpkbmoPR72Ad0dTnR05SjigS7A6kGjY0JlEiIiIiviY11TaQqshRNhBJbf7gMJVoyka7Aqlq1eD8eQVSIiL5oZVSIoVMRkNzuL5+Bw0bwrZtLipSRERExMNlbmFwA0dIIIpa/MVBqhBFAvu5Mc97VK8O+/a5sEgRES+nlVIihcyePca/5fmbBKK4nV84RgUi2WBXIHXnncaneQqkRERExFc1aABHjhhfV+EgG2lKLf5iH9Vpyka7AqnBgxVIiYhcL62UEilEzGYoVsy238ERbiCKBP6ill33GD9ej+yJiIiI7xoyBHbsML6uxn4SiKIaB/mLGjRjPYepkuv1pUsbux77+7uhWBERL6eVUiKFRFycESbtXH6138EhKtOUjXYHUpUqQZMmLi5URERExEOlphof0AHcyF42EEk1DvIntYhkQ56B1JdfwpkzCqRERJxFK6VECoG4OOjSBSpxmASiqMleDlCVKBI4QHW77/P+++Dn58JCRURERDxYixbGvzXZw3qaUZkj/MHNNGM9x6mY43W1asHvv2seJSLibFopJeLhzGYYOPBqv4Oa7GUf1Ylkg92BVGAgLFwInTu7uFgRERERD9WhA2zcCDexmw1EUpkj/EZtItmQayAVGwt//qlASkTEFbRSSsTDbdoEAccd73cQEgL16/9/e3ceHlV9PX78PQQTIptgkEVAXKqV4gp+KaKyKYuAQRAULIsLlipugFq0Ci6IFXDjV6zWFtwVZZGCYJBNKIKCoogKqFB2UUCCqAmG+f1xm0BClgkkmSzv1/PMk5k7d27OXLCcnnvu+cBdd8HFF5tISZKksiktDS64AJYsgd/yBXNpTW22sZJGtGEO33Fcjp995RXo2bMIg5WkMsailFTM/PxzsJrLjBmwezec8OvXzKc1J7CBNfyG1sxlM3VzPcYZZ8CnnxZRwJIkScXUm2/CVVcFhamGrGIuranJdj7hTC7mXb6nRo6fHTLEgpQkFTaLUlIx0qULvPXWgdensJZZ+Zh3kO7kkwsvRkmSpJLg9tvhiSeC52fwKXNoQw2+52PO5mLeZSfH5vjZ7t1h1KiiiVOSyjKLUlIxkbUgdSqrmUtrjmcLn3M6rZnLt9SK6Fgvvlg4MUqSJJUE550Hy5YFz89iBe9yMQnsYBmNaUsSu6ie42fj4+HVV4soUEkq4xx0LhUDP/+cuSD1W75gPi05ni2spBEtmR9xQeq886BSpUIKVJIkqRhLS4Pf//5AQeocPmIurUlgB0v5Py7m3VwLUgATJjiLU5KKikUpqRi4444Dzxuyivm0pDbb+IQzac3cXAdwHuy88+CDDwopSEmSpGLszTchNhaWLg1eN+FD5tCG6uzifX5PW5LYzTG5HuOyy6BHj8KPVZIUsCglFQNr1wY/z+BT5tOSmmznY86mNXNzHcB5sOuvtyAlSZLKpjvuCOZA7d8fvG7KEt7lYqrxA4toTjveIZmquR6jc+fMneuSpMJnUUqKgrQ0SEqCK6+EE06AefOCeQdzaU0NvmcZjWnDnFwHcGb1u98VYsCSJEnF1O23w+jRB143YzFJtKUqySzgItoziz1UyfUYt9wC06YVcqCSpEM46FwqYpMnQ9++8OOPB7adw0e8y8VUZxdL+T/a8U6e7eUHi4mBG28s+FglSZKKs8TEzMWkC1jI21xKZX5kLq3ozL/5iYq5HuOyy+DJJws5UElStuyUkorQ5MnQrVvmgtThzDvIatCgYIaCJElSWTFkSOaCVAvmM4v2VOZHZnMxnZieZ0Fq8GBv2ZOkaLJTSioiaWlw882ZtzVlCe/Qjqoks4jmXMrbebaXZ3XHHfDoowUYqCRJUjGXmgpjxhx43Zo5/JvOHM3PvENbujCVX4jP8fMNG8LHH3tRT5KizaKUVEQWLoQtWw68bsZiZtGeKuxhARfRkRnspVKux6hQIUie6tYNbgG87TaTKUmSVPa0bXvg+SUk8RaJxPMLb9OBrkwmhQo5fvbll6FXryIIUpKUJ4tSUhHZuPHA88OZd9C2LbzzTiEHKUmSVMxddhksWBA8b89MpnA5FUjh33TiCt4klbgcPzt4sAUpSSpOnCklFYE774Q+fYLnhzPvAOA3vynkICVJkoq5zp3h3/8OnndkOlPpQgVSmEIXujEp14LUHXdkXqVPkhR9dkpJhezOO2HUqOD5wfMOZtGOy5mS67yDg6UfQ5IkqSzq1AlmzAieX8ZbvEF3YtnHm3SjJ6/yK0dl+7nmzWHuXEceSFJxZFFKKkSpqQeuyB0872AGl9KNSbnOOzhYYiLER1a7kiRJKlVSU+Gkk2Dz5uD15Uzmda7kKH7lNa6kNy/mWJC67DJX15Ok4szb96RCNG4chMPBvINpXEY8vzCNznkO4DxYYiJMnVq4cUqSJBVHd9wBcXEHClJX8AYT6cFR/MrL9OIPvJRjQeqKKyxISVJxZ6eUVIi+/jqYdzCJbsSRyhS6cCWvs4+c+8ePOgoaNIA2beCxx+yQkiRJZVOXLpmLSlfxKi/Sm/Kk8QK9uYbx7Ccm289WqgSvvVY0cUqSDl+J6JRav3491113HSeeeCLx8fGcfPLJDBs2jNTU1Ez7bdiwgc6dO1OxYkUSEhK45ZZbDtlHKgrpt+398vpbTKYrcaTyJt3owcRcC1IAjz4Ka9bA009bkJIkSWXT669nLkhdzUu8xB8oTxr/4ppcC1IA48dDTM5vS5KKiRLRKfXll1+yf/9+nnnmGU455RQ+++wz+vfvz969exn9v4E9aWlpdOzYkRo1arBo0SJ27NhB3759CYfDjB07NsrfQGXJnXcGBaku4cjnHaSLiYEbbyyiQCVJkoqh1FTo1evA6z48z3iuoRxh/sH1/JFnCOdybf2OO4Jb9yRJxV+JKEq1b9+e9u3bZ7w+6aSTWL16NU8//XRGUSopKYnPP/+cjRs3UqdOHQDGjBlDv379GDFiBFWqVIlK7Cpb0lfau4I3eJWelCeNl+lFX54nLYL/3AYNcmUYSZJUNqWlwf33w4MPHth2Lf/kH/SnHGGeZgA38bdcC1KvvQZXXlkEwUqSCkSJuH0vO7t376Z69eoZr99//30aNWqUUZACaNeuHSkpKSxfvjwaIaqMSb9l7ypezShIvUBv+vBCngWpUCi4qvfoo0UUrCRJUjEyeXIwB+rgglR/nuWfXE85woxlIDcyLteC1MSJFqQkqaQpEZ1SWX399deMHTuWMWPGZGzbtm0bNWvWzLRftWrViI2NZdu2bTkeKyUlhZSUlIzXycnJBR+wyoRx46BX+CWepy8x7OdfXEN//pHrvAOAZs1g/nw7pCRJUtmUXTHpT4xjHDcB8AS3cjuPA6FsP1++fDCDqmvXQg5UklTgotopNXz4cEKhUK6PZcuWZfrMli1baN++Pd27d+f666/P9F4odOg/VOFwONvt6UaOHEnVqlUzHvXq1SuYL6dSL70zqnlz+N3v4Kt7n+cF+hDDfv7B9VzPc3kWpAAqV7YgJUmSyqbBgw8tSN3MUxkFqdEMzrUgdeqp8MsvFqQkqaSKalFq4MCBfPHFF7k+GjVqlLH/li1baNWqFc2aNePZZ5/NdKxatWod0hG1a9cu9u3bd0gH1cGGDh3K7t27Mx4bN24s2C+pUunOO6FCheCWu8WL4fef/5OnfrwmY95BXgM4D/ab3xRysJKkMiclJYWzzz6bUCjEihUrMr0XyWrFK1eupEWLFsTHx3P88cfzwAMPEA6Hi/AbqDRLS4M5c4Ic6LHHMr93G4/zFLcC8Ah3cQejyKkgddttsHq1q+xJUkkW1dv3EhISSEhIiGjfzZs306pVKxo3bsz48eMpVy7z/+Fv1qwZI0aMYOvWrdSuXRsIhp/HxcXRuHHjHI8bFxdHXFzc4X8JlTnpw8zT9edZnuWPAIxlILfwFDklT9k5+FiSJBWEO++8kzp16vDJJ59k2h7JasXJyclccskltGrVig8//JA1a9bQr18/KlasyODBg6PxdVSKvPkmXHst7Nlz6HtDGMUo7gTgIe7hXh4kp5zq5Zczr9AnSSqZSsRMqS1bttCyZUvq16/P6NGj+e677zLeq1WrFgBt27alYcOG9O7dm1GjRrFz506GDBlC//79XXlPBSb9lr10+Zl3kJ3ERIiPL+AgJUll2syZM0lKSmLSpEnMnDkz03uRrFb88ssv88svvzBhwgTi4uJo1KgRa9as4bHHHmPQoEG5jkWQcpP1wt7B/sxIRnI3AMMZxv0MI6ecqnNnC1KSVFqUiNX3kpKS+Oqrr5g7dy5169aldu3aGY90MTExzJgxgwoVKtC8eXN69OhBly5dGH1wBUE6QuPGQfrdC/mZd5CdxESYOrXgY5QklV3ffvst/fv358UXX+Too48+5P1IVit+//33adGiRaZO8nbt2rFlyxbWr19f6N9BpdPrr+dckPoLD2YUpO7lAe5nODnlVE2awLRphROjJKnolYiiVL9+/QiHw9k+Dla/fn2mT5/OTz/9xI4dOxg7dqy35qlAff118DM/8w4OVrUqDBgAP/1kQUqSVLDC4TD9+vVjwIABNGnSJNt9IlmtOLt90l/ntaJxcnJypocE8MYbcNVV2b0TZjjDeJD7ABjKwzzEvTke5/bb4cMPCydGSVJ0lIiilBQtqanBAM7ERGjcOLgyN4RRPM4gIJh3MJSRRNohNXw4PP20t+xJkiIX6WrFY8eOJTk5maFDh+Z6vEhWK866T/qFQFc0Vn5Nngw9emT3TpgHuZdhPADAHTzKI2T/d7dhQ0hJOXQouiSp5CsRM6WkaLjzThgzBvbvP7BtKA/zMPcAec87yComBm68sRAClSSVagMHDuSq7NtMMjRo0ICHHnqIJUuWHNIl3qRJE66++mqef/55atWqxdKlSzO9n3W14uxWNN6+fTtAnisaDxo0KON1cnKyhakyLjUV+vTJ7p0wj/Bn7uJRAG7nMZ7g9myPcfvtFqMkqTSzKCVlI7tBnPfyAA8wDAhmH4zgL/k65qBBEBtbUBFKksqKSFcrfuqpp3jooYcyXm/ZsoV27drx+uuv07RpUyCy1YqbNWvG3XffTWpqKrH/+4crKSmJOnXq0KBBgxx/vysaCyAtDebPD+ZwTplyYBbnAWFGM4TBBJWmm3mK/8fNhxznqKOCFfa6dy/0kCVJUWRRSsoiNTXokDogzP0M4z4eBILVYf7KnyM+XigEQ4bAo48WbJySJB2sfv36mV5XqlQJgJNPPpm6desCka1W3KtXL+6//3769evH3Xffzdq1a3n44Ye57777XHlPOUpLgwcfhL/+FX75Jae9wjzBbdzKU0CwivHf+dMhezVrBgsXBl3mkqTSzaKUlMW4cQffshfmIf7CPTwMBPOkxjAk189XrQrVqkGdOnD55XDLLXZISZKKh/TVim+88UaaN29OfHw8vXr1yrRacdWqVZk9ezY33XQTTZo0oVq1agwaNCjTrXnSwd58M7hN7+efc94nxH7GcjM3MQ6A/jzLc/Q/ZL/Bg8HFsyWp7LAoJf1Perv5Sy+lb8k87+A2HudJbsvzOL17w9ixhRWlJEmRadCgwSErFcOB1Ypzc8YZZ/Dee+8VVmgqJdLSoFcvmDgx9/1C7GccNzKAZ9hPiOt5jvFcm2mf3r3huee8kCdJZY1FKYlgZZgbboAdO9K3ZJ53MJCx/I2BER3r5JMLJ0ZJkqRoSr+AN3ducHvd4sXBttyE2M+z3MD1/JP9hOjHBF4k8/Rzu6MkqeyyKKUyb/Jk6Nbt4C2RzTvIjivsSZKk0ujNN+G66yA5OfLPlCON57iea5hAGuXowwu8wtWZ9hky5NDFZSRJZYdFKZVpaWnBzKd0kc47yIkr7EmSpNIgLS3ohtq8Obitbv78/H2+HGlMoB+9eYlfieFqXmYiV2ba5+WXg9v/JElll0UplWnpyRZENu8gJ+XKBa3nrrAnSZJKusmT4dZbYdOmw/t8DL/yAn3oxavsozw9eZVJXJFpn8REC1KSJItSKuO2bg1+RjLv4GAnnxyssle3Llx0Edx8sx1SkiSp5Js8Ga64ArKZkR+R8uzjZa6mB2+wj/L0YCJTuTzTPs6QkiSlsyilMid9SOe770JSUmTzDrJ67jlo2bJIwpUkSSoSaWlBh9ThFqSOIpVX6Uk3JpPKUVzBm/yby4Bg7uYf/gDPPuuFPEnSARalVKZkXWUvknkHWdWtCxdeWATBSpIkFaGFCw//lr2jSGUiPejCW6QQS1cm8zYdqVAB7roL7r03KExJknQwi1IqM7KushfJvIPsPPmkSZUkSSp90sca5FcsKbzJFXRmOr8QRxemsvv37Xn3oaCz3LxJkpQTi1IqE7KushfJvIOsKleGCROga9fCjVWSJCka3nor/5+J4xcm05VLmcnPVKB77DSue+kSuncv+PgkSaWPRSmVCQevspfbvIOcPPooDBrklT5JklQ6vfEGvP56/j5TgZ+ZShfakcRPxPNGn+m89a/W5kuSpIhZlFKplD7MfO5cWL8e1q0Ltuc07yAvdetakJIkSaVTWhpcf33+PhPPT8w5+jKa/TSHXytUJG7GDPq2blE4AUqSSi2LUip1sg4zT5fdvIN3aB/RMWvXLoRAJUmSioGHHoLk5Mj3r1V5L5/U78Rxq+ZDpUqUf/ttV4GRJB2WctEOQCpI6cPMsxak4viFKVxOZ6bzMxXozL8jLki52p4kSSqt3ngD7r8/8v2ffGgPW87qEBSkKleGd94xUZIkHTY7pVRqZB1mni7rvINOTGcerSM+rqvtSZKk0mjyZOjRI/L9Tzw2mZtndCD0/mKoUgWSkqBp08ILUJJU6tkppVLj4GHm6eL5iel0oh1J/EhFOjAz4oLUscfCpEmutidJkkqftDS49dbI96/Cbj6s3jYoSB1zDMyZY0FKknTE7JRSqbF1a+bXFfmRf9OZVsxnD5XowEz+wwU5fr5ZMzjpJDjhBGjdGlq2tENKkiSVTgsXwqZNke17DLv4sHo7jl37IVSvDrNnw7nnFm6AkqQywaKUSqSsq+uFQrB//4H3K7GHGXTkIhaSTGXa8Q5LaJbrMR9+OChESZIklXZZL+blpBo7mRtzCafs/ChoI58zB846q3CDkySVGRalVOLktLpeusokM5MONGcxP1CVdrzDB+TeXu4wc0mSVJZEsrLwsXzPbC7h7LQVUKNGUJA644xCj02SVHZYlFKJkr66Xk6qsJt3aMfvWcoujuESZrOcJnke12HmkiSpLDn//GBWeXJy9u8n8B1zaMOZrISaNYP29IYNizZISVKp56BzlRg5ra6X7hh2MZtL+D1L2UF12jAnz4KUw8wlSVJZ8+abQeNTTgWp4/iWebQKClK1awczEyxISZIKgZ1SKjGyW10vXTV2MptLaMxHfM+xtGEOn3LovIM2baBWLYeZS5KksictDa6+Gl5/Ped9arGVubTmdL4kXKcOoXnz4NRTiy5ISVKZYlFKxVr6QPP582Hlyuz3SZ93cA4r2E4N2jCHz8h+3sF110HPnoUWriRJUrGTlgYjRsCjj8LevTnvV4fNzKU1p7GGjdRly6PzaHrqKUUXqCSpzLEopWIrr4HmkHnewTZq0pq5fEHO7eWRDPWUJEkqLSLJpwCOZxPzaMVv+Ir/Up9WzGNEuZPyWCpGkqQjY1FKxVJeA80hmHcwhzY0YhVbqE1r5rKa3+a4vyvsSZKksiSSfAqgHhuYRytO5hvW0YBWzOO/NPBiniSp0FmUUrGT10BzyDzvYDN1aMU81pL7vANX2JMkSWVFWhrcemve+53AeubRihNZz9ecRCvmsZH61KvnxTxJUuFz9T0VO7kNNIdg3sF8WnI6X7KRurRgQa4FKVfYkyRJZc3ChbBpU+77nMg3LKAFJ7KetZxCCxawkfqEQvDEE17MkyQVPjulVOxs3Zrze9nNO1jHSQBcdhlccAHs3BkkYfXru8KeJEkqW9LSgoLUG2/kvt/JfMU8WlGPTazmVFoxj63U4dhj4dlnvZgnSSoaFqUUVemJ0+bN8N13QVfT0qXZ75vTvIN0t98eFKAkSZLKosmTg1v28uqQ+g1rmEcrjmcLn3M6bZjDNmpz5ZXw8stezJMkFR2LUoqaSBMnyHneQTqHmEuSpLJs8mS44goIh3Pf7zS+ZB6tqM02PuN3tGEOv1SpycTnoHv3oolVkqR0FqUUFZEmThDMO5hHK05gA2s5hVbMYzN1M+3jEHNJklRWpQ81zyuvasgq5tCGWnzLp5xBpwpzuGloDe65xzxKkhQdFqVU5CJNnCDneQfpnHsgSZLKuhEj8u48b8RK5tCG4/iOjzmbS5jNk88lcPXVRROjJEnZsSilIhfJajAAp7KaubQ+ZN7B1VfDiScG86McYi5JksqyyZNh2LDc9zmLFbzLxSSwg+WcyyXMZhfVOf74oolRkqScWJRSgUofXL51Kxx3XLBt+/YDz7dtg3feyfs4v+UL5tKa2mxjJY24mHfZTk0AOnaEnj0L6QtIkiQVQ9nlWFu2wM035/65c/iId7mY6uziA86jHe/wA9WoV895nJKk6LMopQKTn8HluWnIKubSmpps5xPO5GLe5XtqZLxfu/YRBipJklSCHG6O1YQPSaIt1fiB9/k97ZlFMlUJheCJJ+w2lyRFX7loB6DSIX1w+ZEWpM7gU+bTkpps52POpjVzMwpSoRBe1ZMkSWVGWho88AB065b/HKspS3iXi6nGDyyiOe14h2Sqcuyx8OabzuOUJBUPdkrpiOVncHluDp53sIzGtCWJXVTPtI9X9SRJUlkweTLccgts3pz/zzZjMbNoTxX2sICL6MgM4qpX4v5bcaU9SVKxYlFKRyzSweW5OXjewVL+j3a8w26OyXi/Xr2gIOVVPUmSVJqlpQWr6eU1vDwnF7CQt7mUyvzIXFrRt9q/eeuNii4OI0kqlixK6Yht3Xpkn88672B8j1mM7VSVHTugRg04/vjglj0TKUmSVJodSXcUQAvmM4OOVOQnZnMxXXiLF587mjZtCjZOSZIKikUpHZaDV4DZsuXwj9OUJbxDO6qSzCKacylvM+1PVWjZssBClSRJKvbS53Me7jiE1szh33TmaH7mHdrSP2EqLz4Tb5e5JKlYsyilfCuoVfayzjvoxAyq1avkIHNJklSmHOl8zktI4i0SiecX3qYDf0yYzNebKxAbW7BxSpJU0CxKKV+O9CpeuqzzDjrzb34OVeT5J7xNT5IklV4Hd5vXrg3nnw/jxh3+xb52zGIqXahACv+mE915k1eeibMgJUkqESxKKWIFtcpe1nkHibxFQr2jHWQuSZJKtey6zWNighzrcFzKDCbTlThSmUoig+tO5JUnY82nJEklhkUpRexwVtkbPRrq1IHjjgteh9+dQ4sxnTlq38/897S27LhrKm+fGO8gc0mSVKrl1G1+OAWpYcPg8phpNLr/CmLS9rHhvG5Ue/hV1rQ6ynxKklSiWJQqxbK2h2ct/OT0fnZt5YsXw6RJ+Y+hTh3o2fN/L5KS4IlE2PcLdOjACZMnc0KFCgXyXSVJkoqrguo2r1sXnnwSuoamQI8ekPYr9OhB/Zdeov5RRxVMsJIkFSGLUqVUdu3hGYlM15zf79kTXn214NrKa9f+35NZs6BLF0hJgU6d4M03IS7u8A4qSZJUghxOt3lW998P99wDMZPfCBK2tLTg5wsvQHlTeklSyeS/YKVQTu3hmzcH24cMCW6ry/r+pk0watShxzucglQoFBS5LrwQmDEjqISlpkJiIkyciNM3JUlSaZfefX443ebp6tXjwNzN116DP/whOHDv3jB+vPMPJEklmkWpUia39vD0bY89duTt47kJhYKfTzwBMTOmBZWwffugW7egDcv2ckmSVMpl15WeX48/Djff/L+608svQ58+sH8/9OsHzz1nQUqSVOKVi3YAKliRtIcf7q14kapbN7g7r2toSlCI2rcvmHtgQUqSpEI3Y8YMmjZtSnx8PAkJCXTNshTbhg0b6Ny5MxUrViQhIYFbbrmF1NTUTPusXLmSFi1aEB8fz/HHH88DDzxAuDCvaJUy6V3rh1uQCoWCDqmMgtTzzwedUfv3w/XXwz//aUFKklQq2ClVymzdWrS/b+DAYFQUwPbtBw1Md96BJElFbtKkSfTv35+HH36Y1q1bEw6HWblyZcb7aWlpdOzYkRo1arBo0SJ27NhB3759CYfDjB07FoDk5GQuueQSWrVqxYcffsiaNWvo168fFStWZPDgwdH6aiXGkQ41z9RxHgP8619BISochj/+EcaNg3JeV5YklQ5WCUqZjMHiRaRbN2jZMstG5x1IklTkfv31V2699VZGjRrFddddl7H9tNNOy3ielJTE559/zsaNG6lTpw4AY8aMoV+/fowYMYIqVarw8ssv88svvzBhwgTi4uJo1KgRa9as4bHHHmPQoEGE0qsmylZ+h5pnXVCmbt2DZkg9+2xQiAK46SYYO/ZA1UqSpFLAyyylzIUXBslMbvlKTMyR5zPpbeUXXpjljZdegquvDrKrfv0sSEmSVEQ++ugjNm/eTLly5TjnnHOoXbs2HTp0YNWqVRn7vP/++zRq1CijIAXQrl07UlJSWL58ecY+LVq0IO6gVXLbtWvHli1bWL9+fY6/PyUlheTk5EyPsiItDebPDyYVzJkT2WcGDoR58+Cnn4Kfr7wS/Fy37n8FqXHjDhSkbr3VgpQkqVSyKFXKxMTAk08Gz7PmLaFQ8Bg0KPv3I3VIW3m6558/MIDTeQeSJBWpb775BoDhw4fzl7/8henTp1OtWjVatGjBzp07Adi2bRs1a9bM9Llq1aoRGxvLtm3bctwn/XX6PtkZOXIkVatWzXjUq1evwL5bcTZ5MjRoAK1aQa9e8NBDkX0uvds8Njb42bNn8DMmBnjqqaAzCmDw4GDiuQUpSVIpZFGqFOraNRg0fvzxmbenDyB/9NHs369XD+64I9jvYFnrShmDzA+em/rPf8I11wTzDgYMgGeecd6BJEkFYPjw4YRCoVwfy5YtY//+/QDcc889dOvWjcaNGzN+/HhCoRBvvPFGxvGyu/0uHA5n2p51n/Qh57ndujd06FB2796d8di4ceMRfe+S4HAGmufYbZ7u8ceDziiAu+6CUaMsSEmSSi1nSpVSXbtCYmIw12Dr1oMGkMfk/f7IkZm3n38+LF6c/XGAzPMOBg4Mru6ZPEmSVCAGDhzIVVddles+DRo0YM+ePQA0bNgwY3tcXBwnnXQSGzZsAKBWrVosXbo002d37drFvn37MrqhatWqdUhH1Pbt2wEO6aA6WFxcXKZb/kq7wxlonmO3ebpRo+DOO4Pn99wDDz5oTiVJKtUsSpViMTHZDCGP4P3stud4nHHjDrSX33qr7eWSJBWwhIQEEhIS8tyvcePGxMXFsXr1ai644AIA9u3bx/r16znhhBMAaNasGSNGjGDr1q3U/t/qKElJScTFxdG4ceOMfe6++25SU1OJjY3N2KdOnTo0aNCgEL5hyZCWlvmiXVpa/jqkIMsQ86xGjoS77w6eDxsWPMypJEmlnEUpHb6nnjrQXj54sO3lkiRFUZUqVRgwYADDhg2jXr16nHDCCYwaNQqA7t27A9C2bVsaNmxI7969GTVqFDt37mTIkCH079+fKlWqANCrVy/uv/9++vXrx913383atWt5+OGHue+++0rdyntZC02HdIP/z+TJQcpzcBGqevXIfsdf/gING+Z+fB58EO67L3j+wANw7735/i6SJJVEFqV0eB5//MDE9LvuCq7ulbJEVZKkkmbUqFGUL1+e3r178/PPP9O0aVPmzp1LtWrVAIiJiWHGjBnceOONNG/enPj4eHr16sXo0aMzjlG1alVmz57NTTfdRJMmTahWrRqDBg1iUPq/+6VEdoWmunWDBWMO7mRKnxuV9Ta9/82Oz1ObNrl0nIfDMHx4UIgCePhhGDo0wm8gSVLJFwqH83MnfOmXnJxM1apV2b17d8YVQ2XhvANJUillHlBwivO5zKnQlJ7OpC/okpYWrKyX39v00o9Vty6sW5dDd1Q4HHREjRgRvH700WDFGUmSSoFI8wCXR1P+jBx5oCA1bJgFKUmSVKLkNqA8fdtttx24te9wC1KQy0DzcDjoiEovSD32mAUpSVKZ5O17ipzzDiRJUpRFOgcqJ3kVmsJh2LjxwO+IRPXqmW/ny3WgeTgcFKDGjAleP/UU3HxzpOFLklSqWJRS3px3IEmSioFI50DlJtJCU3rRKxITJwaFsTwLZeEw3H57EDDA3/4GN94Y2S+RJKkUsiil3DnvQJIkFQM5zYHavDnYnj4HKi+RFprSi0t16wa/I7vb/dLnRrVsGUG3VjgcdET97W/B62eegRtuiCwYSZJKKWdKKWfOO5AkScVAfuZA5SW90JTTSMxQCOrVO9DtlN7UlHX/POdGHWz//qAj6m9/Cz74z39akJIkCYtSykn6vIO//jV4/dRTQbu5JElSEcvPHKi85LfQ1LVr0IV1/PGZ961bN8LurP374Y9/hL//PfgF48fDtdfmHagkSWWARSkdKn3eQfoAzr/9zQGckiQpavIzByoS+S00de0K69fDvHnwyivBz3XrIihIpaXBddfBc89BuXLwwgvQt29kQUqSVAY4U6oEOtJVZ3LlvANJklTM5GcOVKS6doXExMhzqpiYYHZUxNLS4Jpr4MUXgw+/9BJcdVU+DiBJUulnUaqEKYhVZ3K0fz/cdNOB9vLnnrO9XJIkRV2kA8cvvDB/x813oSlSv/4adES98krwS159Fbp3L4RfJElSyebteyVI+qozWWcqpK86M3nyERzceQeSJKmYKrCB40Vh3z64+uqgIFW+PEycaEFKkqQcWJQqIQpy1ZlsD37wvIMXX3TegSRJKlaOeOB4UUhNDW7RmzgRjjoKJk0qJoFJklQ8efteCZGfVWfyPe+gX79gzkFMDLz8Mlx55RFGK0mSVPDyOweqSKWmQo8e8NZbEBsbtLB37BjtqCRJKtYsSpUQBb3qDBDMO+jTJ5hzUL588POKKw4rPkmSpKJQaHOgjkRKSpBDTZ8OcXEwdSq0bx/tqCRJKvYsShWRI10xr8BXnUmfd/DGGwfmHVx+eeQBSZIkCX75JWjhmjkTKlSAadPgkkuiHZUkSSWCM6WKwOTJ0KABtGoFvXoFPxs0yN9g8vRVZ7IO90wXCkG9ehGuOpM+7+CNNw7MO7AgJUmSlD8//xzcTzhzJsTHw4wZFqQkScoHi1KFrKBWzCuwVWfS5x1MnhzMO5gyBS67LLIgJEmSFPjpJ+jcGZKSoGLFoDDVunW0o5IkqUSxKFWICnrFvCNedSYlBbp1CwZwxsUFPx3AKUmSlD979wY51Jw5UKlSUJBq0SLaUUmSVOI4U6oQFcaKeYe96ozzDiRJko7cnj1BQWrhQqhcGWbNgvPPj3ZUkiSVSBalClGhrJjHYaw68/PP0KVL0F4eHx+sDGN7uSRJUv4kJ8Oll8J//gNVqgS5VdOm0Y5KkqQSy6JUISrwFfMOx08/BTOj5swJ5h3MmGF7uSRJUn7t3g3t28OSJXDMMUFB6rzzoh2VJEklmjOlClGBrph3OJx3IEmSdOR++CEYe7BkCVSrFuRWFqQkSTpiFqUKUYGtmHc49uyBDh1g/vxg3sE77xRi9UuSJKmU2rkTLr4YPvwQjj0W5s6Fc8+NdlSSJJUKFqUK2RGvmHc4kpODgtTChcG8g9mzHcApSZKUXzt2QJs2sHw51KgB8+bB2WdHOypJkkoNZ0oVgcNeMe9wOO9AkiTpyH33XdAh9emncNxxQYfU734X7agkSSpVLEoVkXyvmHc4fvgB2rYN2surVYN337W9XJIkKb++/TbokFq1CmrVCgpSp58e7agkSSp1LEqVFjt3BgWp5cuDeQfvvmt7uSRJUn5t2watW8MXX0CdOkFB6rTToh2VJEmlkkWp0mDHjqC9fMWKYN7BnDlwxhnRjkqSJKlk2bIlKEitXh0MAJ03D045JdpRSZJUalmUKum++y5oL1+5EmrWDApSzjuQJEnKn02bgoLU2rVQv35QkDrppGhHJUlSqWZRqiQ7eN5B7dpBe/lvfxvtqCRJkkqWDRugVSv45hto0CAoSDVoEO2oJEkq9SxKlVRbtwZX8778Mph3MG8enHpqtKOSJEkqWdavDwpS69cHnVHz5gWdUpIkqdCVi3YAOgybNwdL+X35ZTDvYMECC1KSJEn59c030KJFUJA65ZQgp7IgJUlSkbFTqqTZtCm4mvfVV847kCRJOlxffRXkVJs2BRf35s0Lus8lSVKRsVOqJNmwIbia99VXwZyDBQssSEmSJOXXmjVBTrVpE5x+Osyfb0FKkqQosChVUqxfHyRP33wTFKIWLHAApyRJUn59+WWQU23ZEqxYPG9esGCMJEkqchalSgLnHUiSJB25VauCuZzbtsEZZwQFqZo1ox2VJElllkWp4u6rr4KC1IYNwbyDBQuC4eaSJEmK3MqVwQypb7+Fs8+GuXOhRo1oRyVJUplW4opSKSkpnH322YRCIVasWJHpvQ0bNtC5c2cqVqxIQkICt9xyC6mpqdEJtCA470CSJOnIffJJUJD67js491yYMwcSEqIdlSRJZV6JW33vzjvvpE6dOnzyySeZtqelpdGxY0dq1KjBokWL2LFjB3379iUcDjN27NgoRXsEvvwySJ62bQvmHcyZY3u5JElSfn30EVxyCezcCeedB++8A9WqRTsqSZJECeuUmjlzJklJSYwePfqQ95KSkvj888956aWXOOecc7j44osZM2YM//jHP0hOTo5CtEfAeQeSJElHbtkyaNMmKEj9/vcwe7YFKUmSipESU5T69ttv6d+/Py+++CJHH330Ie+///77NGrUiDoH3d7Wrl07UlJSWL58eY7HTUlJITk5OdMjqpx3IEmSdOSWLoWLL4YffoDzzw86pKpWjXZUkiTpICWiKBUOh+nXrx8DBgygSZMm2e6zbds2ambpJqpWrRqxsbFs27Ytx2OPHDmSqlWrZjzq1atXoLHni/MOJEmSjtzixcEte7t3w4UXwqxZUKVKtKOSJElZRLUoNXz4cEKhUK6PZcuWMXbsWJKTkxk6dGiuxwuFQodsC4fD2W5PN3ToUHbv3p3x2Lhx4xF/r8Py0UfQujXs2BHMO3j3XahePTqxSJIklVSLFkG7drBnTzAOYeZMqFw52lFJkqRsRHXQ+cCBA7nqqqty3adBgwY89NBDLFmyhLi4uEzvNWnShKuvvprnn3+eWrVqsXTp0kzv79q1i3379h3SQXWwuLi4Q45b5JYtC67m/fBDMO9g1izbyyVJkvJrwQLo2BH27g1mSU2bBtmMfZAkScVDVItSCQkJJERwe9pTTz3FQw89lPF6y5YttGvXjtdff52mTZsC0KxZM0aMGMHWrVupXbs2EAw/j4uLo3HjxoXzBQrC0qXB1bzdu4N5BzNn2l4uSZKUX3PnQqdO8PPP0LYtTJ0K8fHRjkqSJOUiqkWpSNWvXz/T60qVKgFw8sknU7duXQDatm1Lw4YN6d27N6NGjWLnzp0MGTKE/v37U6W4FnkWL4b27YP28gsvhBkzbC+XJEnKr9mz4bLL4JdfgtxqyhSoUCHaUUmSpDyUiEHnkYiJiWHGjBlUqFCB5s2b06NHD7p06cLo0aOjHVr2Fi503oEkSdKRmjULOncOClKdOgUdUhakJEkqEUpEp1RWDRo0IBwOH7K9fv36TJ8+PQoR5dP8+cG8g59+CpYqfust5x1IkiTl14wZ0LUrpKZCYiJMnAixsdGOSpIkRajUdEqVGHPmwKWXBgWptm0dwClJkgrMmjVrSExMJCEhgSpVqtC8eXPmzZuXaZ8NGzbQuXNnKlasSEJCArfccgupqamZ9lm5ciUtWrQgPj6e448/ngceeCDbC4JRNW0aXH55UJDq1g3eeMOClCRJJYxFqaKUlHRgAGeHDkGHlAM4JUlSAenYsSO//vorc+fOZfny5Zx99tl06tSJbdu2AZCWlkbHjh3Zu3cvixYt4rXXXmPSpEkMHjw44xjJyclccskl1KlThw8//JCxY8cyevRoHnvssWh9rUNNmRIUovbtgx494NVX4aijoh2VJEnKp1C42F32iq7k5GSqVq3K7t27C3ZA+iefQNOmkJISFKbefBPi4gru+JIk6YgVWh5QBL7//ntq1KjBe++9x4UXXgjAnj17qFKlCu+++y5t2rRh5syZdOrUiY0bN1KnTh0AXnvtNfr168f27dupUqUKTz/9NEOHDuXbb78l7n+5yiOPPMLYsWPZtGkToVAoongK7VwuWhTM40xLg5494YUXoHyJnEghSVKpFWkeYKdUUWnUCLp3D+YdTJpkQUqSJBWoY489ltNPP50XXniBvXv38uuvv/LMM89Qs2ZNGjduDMD7779Po0aNMgpSAO3atSMlJYXly5dn7NOiRYuMglT6Plu2bGH9+vU5/v6UlBSSk5MzPQrF//1fcIGvd2948UULUpIklWD+K15UYmJgwgTYv9/2ckmSVOBCoRCzZ88mMTGRypUrU65cOWrWrMmsWbM45phjANi2bRs1a9bM9Llq1aoRGxubcYvftm3baNCgQaZ90j+zbds2TjzxxGx//8iRI7n//vsL9ktlJzY2GGgeExM8JElSiWWnVFGKibEgJUmS8mX48OGEQqFcH8uWLSMcDnPjjTdy3HHHsXDhQj744AMSExPp1KkTW7duzThedrffhcPhTNuz7pM+7SG3W/eGDh3K7t27Mx4bN2480q+es9hYC1KSJJUCdkpJkiQVYwMHDuSqq67KdZ8GDRowd+5cpk+fzq5duzJmN4wbN47Zs2fz/PPP8+c//5latWqxdOnSTJ/dtWsX+/bty+iGqlWrVkbXVLrt27cDHNJldbC4uLhMt/xJkiTlxaKUJElSMZaQkEBCQkKe+/30008AlCuXuRG+XLly7N+/H4BmzZoxYsQItm7dSu3atQFISkoiLi4uY+5Us2bNuPvuu0lNTSU2NjZjnzp16hxyW58kSdKR8PY9SZKkUqBZs2ZUq1aNvn378sknn7BmzRruuOMO1q1bR8eOHQFo27YtDRs2pHfv3nz88cfMmTOHIUOG0L9//4zuql69ehEXF0e/fv347LPPmDJlCg8//DCDBg2KeOU9SZKkSFiUkiRJKgUSEhKYNWsWP/74I61bt6ZJkyYsWrSIt956i7POOguAmJgYZsyYQYUKFWjevDk9evSgS5cujB49OuM4VatWZfbs2WzatIkmTZpw4403MmjQIAYNGhStryZJkkqpUDh9cqUASE5OpmrVquzevTvjiqEkSSobzAMKjudSkqSyK9I8wE4pSZIkSZIkFTmLUpIkSZIkSSpyFqUkSZIkSZJU5CxKSZIkSZIkqchZlJIkSZIkSVKRsyglSZIkSZKkImdRSpIkSZIkSUXOopQkSZIkSZKKnEUpSZIkSZIkFTmLUpIkSZIkSSpyFqUkSZIkSZJU5CxKSZIkSZIkqchZlJIkSZIkSVKRsyglSZIkSZKkIlc+2gEUN+FwGIDk5OQoRyJJkopa+r//6fmADp85lSRJZVekOZVFqSz27NkDQL169aIciSRJipY9e/ZQtWrVaIdRoplTSZKkvHKqUNhLgZns37+fLVu2ULlyZUKhULTDOURycjL16tVj48aNVKlSJdrhFEueo9x5fnLn+cmd5yd3np/clYTzEw6H2bNnD3Xq1KFcOaccHInimlOVhL+H0eY5ypvnKG+eo7x5jnLn+clbcT5HkeZUdkplUa5cOerWrRvtMPJUpUqVYveXrrjxHOXO85M7z0/uPD+58/zkrrifHzukCkZxz6mK+9/D4sBzlDfPUd48R3nzHOXO85O34nqOIsmpvAQoSZIkSZKkImdRSpIkSZIkSUXOolQJExcXx7Bhw4iLi4t2KMWW5yh3np/ceX5y5/nJnecnd54fFQf+Pcyb5yhvnqO8eY7y5jnKnecnb6XhHDnoXJIkSZIkSUXOTilJkiRJkiQVOYtSkiRJkiRJKnIWpSRJkiRJklTkLEqVcJdddhn169enQoUK1K5dm969e7Nly5Zoh1UsrF+/nuuuu44TTzyR+Ph4Tj75ZIYNG0Zqamq0Qys2RowYwfnnn8/RRx/NMcccE+1wom7cuHGceOKJVKhQgcaNG7Nw4cJoh1RsvPfee3Tu3Jk6deoQCoWYOnVqtEMqVkaOHMl5551H5cqVOe644+jSpQurV6+OdljFxtNPP82ZZ55JlSpVqFKlCs2aNWPmzJnRDksCzKVyYy4VGfOpQ5lT5c68KnfmVXkrTbmVRakSrlWrVkycOJHVq1czadIkvv76a6644opoh1UsfPnll+zfv59nnnmGVatW8fjjj/P3v/+du+++O9qhFRupqal0796dP/3pT9EOJepef/11brvtNu655x4+/vhjLrzwQjp06MCGDRuiHVqxsHfvXs466yz+3//7f9EOpVhasGABN910E0uWLGH27Nn8+uuvtG3blr1790Y7tGKhbt26PPLIIyxbtoxly5bRunVrEhMTWbVqVbRDk8ylcmEuFRnzqczMqfJmXpU786q8labcytX3Splp06bRpUsXUlJSOOqoo6IdTrEzatQonn76ab755ptoh1KsTJgwgdtuu40ffvgh2qFETdOmTTn33HN5+umnM7adfvrpdOnShZEjR0YxsuInFAoxZcoUunTpEu1Qiq3vvvuO4447jgULFnDRRRdFO5xiqXr16owaNYrrrrsu2qFImZhL5c5cKmfmUwFzqvwxr8qbeVVkSmpuZadUKbJz505efvllzj//fJOoHOzevZvq1atHOwwVM6mpqSxfvpy2bdtm2t62bVsWL14cpahUku3evRvA/73JRlpaGq+99hp79+6lWbNm0Q5HysRcKm/mUsqNOZUKg3lV7kp6bmVRqhS46667qFixIsceeywbNmzgrbfeinZIxdLXX3/N2LFjGTBgQLRDUTHz/fffk5aWRs2aNTNtr1mzJtu2bYtSVCqpwuEwgwYN4oILLqBRo0bRDqfYWLlyJZUqVSIuLo4BAwYwZcoUGjZsGO2wJMBcKlLmUsqLOZUKmnlVzkpLbmVRqhgaPnw4oVAo18eyZcsy9r/jjjv4+OOPSUpKIiYmhj59+lCa78rM7/kB2LJlC+3bt6d79+5cf/31UYq8aBzO+VEgFApleh0Ohw/ZJuVl4MCBfPrpp7z66qvRDqVYOe2001ixYgVLlizhT3/6E3379uXzzz+PdlgqpcylcmculTfzqSNjTqWCYl6Vs9KSW5WPdgA61MCBA7nqqqty3adBgwYZzxMSEkhISODUU0/l9NNPp169eixZsqREtu5FIr/nZ8uWLbRq1YpmzZrx7LPPFnJ00Zff86Pgv6GYmJhDruBt3779kCt9Um5uvvlmpk2bxnvvvUfdunWjHU6xEhsbyymnnAJAkyZN+PDDD3nyySd55plnohyZSiNzqdyZS+XNfOrwmFOpIJlX5a605FYWpYqh9MTocKRf1UtJSSnIkIqV/JyfzZs306pVKxo3bsz48eMpV670Nwceyd+fsio2NpbGjRsze/ZsLr/88ozts2fPJjExMYqRqaQIh8PcfPPNTJkyhfnz53PiiSdGO6RiLxwOl+p/qxRd5lK5M5fKm/nU4TGnUkEwrzo8JTW3sihVgn3wwQd88MEHXHDBBVSrVo1vvvmG++67j5NPPrnUXtnLjy1bttCyZUvq16/P6NGj+e677zLeq1WrVhQjKz42bNjAzp072bBhA2lpaaxYsQKAU045hUqVKkU3uCI2aNAgevfuTZMmTTKuBG/YsMG5Gf/z448/8tVXX2W8XrduHStWrKB69erUr18/ipEVDzfddBOvvPIKb731FpUrV864Qly1alXi4+OjHF303X333XTo0IF69eqxZ88eXnvtNebPn8+sWbOiHZrKOHOp3JlLRcZ8KjNzqryZV+XOvCpvpSq3CqvE+vTTT8OtWrUKV69ePRwXFxdu0KBBeMCAAeFNmzZFO7RiYfz48WEg24cCffv2zfb8zJs3L9qhRcXf/va38AknnBCOjY0Nn3vuueEFCxZEO6RiY968edn+Xenbt2+0QysWcvrfmvHjx0c7tGLh2muvzfhvq0aNGuE2bdqEk5KSoh2WZC6VB3OpyJhPHcqcKnfmVbkzr8pbacqtQuFwKZ7iKEmSJEmSpGKpbNwULkmSJEmSpGLFopQkSZIkSZKKnEUpSZIkSZIkFTmLUpIkSZIkSSpyFqUkSZIkSZJU5CxKSZIkSZIkqchZlJIkSZIkSVKRsyglSZIkSZKkImdRStIh1q9fTygUYsWKFdEOJV8aNGjAE088UWDHa9myJbfddluBHS8aQqEQU6dOBUrun6skSSVVSf2315zqUOZUUuGwKCWVMaFQKNdHv379oh1iniZMmMAxxxxzyPYPP/yQG264oegDKgaGDx/O2Weffcj2rVu30qFDh6IPSJKkUs6cqnQyp5KKVvloByCpaG3dujXj+euvv859993H6tWrM7bFx8eza9euaIRGWloaoVCIcuUOr15eo0aNAo6o5KtVq1a0Q5AkqVQypypbzKmkwmGnlFTG1KpVK+NRtWpVQqHQIdvSffPNN7Rq1Yqjjz6as846i/fffz/TsRYvXsxFF11EfHw89erV45ZbbmHv3r0Z7+/atYs+ffpQrVo1jj76aDp06MDatWsz3k+/Ojd9+nQaNmxIXFwc//3vf0lNTeXOO+/k+OOPp2LFijRt2pT58+cDMH/+fK655hp2796dcSVy+PDhwKGt5j/88AM33HADNWvWpEKFCjRq1Ijp06cDsGPHDnr27EndunU5+uijOeOMM3j11VfzfT4feeQRatasSeXKlbnuuuv485//nOnqWnbt6l26dMl09fSll16iSZMmVK5cmVq1atGrVy+2b9+e8f78+fMJhULMmTOHJk2acPTRR3P++ednJL4TJkzg/vvv55NPPsk4JxMmTAAyt5pn5/PPP+fSSy+lUqVK1KxZk969e/P9999nvP/mm29yxhlnEB8fz7HHHsvFF1+c6c9YkqSyypzKnOpg5lTS4bEoJSlH99xzD0OGDGHFihWceuqp9OzZk19//RWAlStX0q5dO7p27cqnn37K66+/zqJFixg4cGDG5/v168eyZcuYNm0a77//PuFwmEsvvZR9+/Zl7PPTTz8xcuRInnvuOVatWsVxxx3HNddcw3/+8x9ee+01Pv30U7p370779u1Zu3Yt559/Pk888QRVqlRh69atbN26lSFDhhwS+/79++nQoQOLFy/mpZde4vPPP+eRRx4hJiYGgF9++YXGjRszffp0PvvsM2644QZ69+7N0qVLIz4/EydOZNiwYYwYMYJly5ZRu3Ztxo0bl+/znJqayoMPPsgnn3zC1KlTWbduXbYt//fccw9jxoxh2bJllC9fnmuvvRaAK6+8ksGDB/O73/0u45xceeWVef7erVu30qJFC84++2yWLVvGrFmz+Pbbb+nRo0fG+z179uTaa6/liy++YP78+XTt2pVwOJzv7yhJUllmTpU7cyqpDAtLKrPGjx8frlq16iHb161bFwbCzz33XMa2VatWhYHwF198EQ6Hw+HevXuHb7jhhkyfW7hwYbhcuXLhn3/+ObxmzZowEP7Pf/6T8f73338fjo+PD0+cODHj9wPhFStWZOzz1VdfhUOhUHjz5s2Zjt2mTZvw0KFDc437hBNOCD/++OPhcDgcfuedd8LlypULr169OuLzcemll4YHDx6c8bpFixbhW2+9Ncf9mzVrFh4wYECmbU2bNg2fddZZuR4jMTEx3Ldv3xyP+8EHH4SB8J49e8LhcDg8b968MBB+9913M/aZMWNGGAj//PPP4XA4HB42bFim35sOCE+ZMiUcDh/4c/3444/D4XA4fO+994bbtm2baf+NGzeGgfDq1avDy5cvDwPh9evX5xirJEkyp8rKnMqcSoqUnVKScnTmmWdmPK9duzZARgv08uXLmTBhApUqVcp4tGvXjv3797Nu3Tq++OILypcvT9OmTTOOceyxx3LaaafxxRdfZGyLjY3N9Hs++ugjwuEwp556aqZjL1iwgK+//jri2FesWEHdunU59dRTs30/LS2NESNGcOaZZ3LsscdSqVIlkpKS2LBhQ8S/44svvqBZs2aZtmV9HYmPP/6YxMRETjjhBCpXrkzLli0BDokltz+Pw7F8+XLmzZuX6Tz/9re/BeDrr7/mrLPOok2bNpxxxhl0796df/zjH1GbjSFJUklmTpU7cyqp7HLQuaQcHXXUURnPQ6EQELRwp//84x//yC233HLI5+rXr8+aNWuyPWY4HM44FgRDQA9+vX//fmJiYli+fHlGW3i6SpUqRRx7fHx8ru+PGTOGxx9/nCeeeIIzzjiDihUrctttt5Gamhrx74hEuXLlDmnNPrjVfu/evbRt25a2bdvy0ksvUaNGDTZs2EC7du0OiSW3P4/DsX//fjp37sxf//rXQ96rXbs2MTExzJ49m8WLF5OUlMTYsWO55557WLp0KSeeeOJh/15Jksoac6ojZ04llU4WpSQdlnPPPZdVq1ZxyimnZPt+w4YN+fXXX1m6dCnnn38+EAzCXLNmDaeffnqOxz3nnHNIS0tj+/btXHjhhdnuExsbS1paWq7xnXnmmWzatIk1a9Zke2Vv4cKFJCYm8oc//AEIkom1a9fmGltWp59+OkuWLKFPnz4Z25YsWZJpnxo1amRanSctLY3PPvuMVq1aAfDll1/y/fff88gjj1CvXj0Ali1bFnEM6SI5J1mde+65TJo0iQYNGlC+fPb/HIRCIZo3b07z5s257777OOGEE5gyZQqDBg3Kd4ySJOlQ5lTmVFJZ5u17kg7LXXfdxfvvv89NN93EihUrWLt2LdOmTePmm28G4De/+Q2JiYn079+fRYsW8cknn/CHP/yB448/nsTExByPe+qpp3L11VfTp08fJk+ezLp16/jwww/561//yttvvw0EK8L8+OOPzJkzh++//56ffvrpkOO0aNGCiy66iG7dujF79mzWrVvHzJkzmTVrFgCnnHJKxhWrL774gj/+8Y9s27YtX+fg1ltv5V//+hf/+te/WLNmDcOGDWPVqlWZ9mndujUzZsxgxowZfPnll9x444388MMPGe/Xr1+f2NhYxo4dyzfffMO0adN48MEH8xVH+jlZt24dK1as4PvvvyclJSXPz9x0003s3LmTnj178sEHH/DNN9+QlJTEtddeS1paGkuXLuXhhx9m2bJlbNiwgcmTJ/Pdd9/lK8mUJEm5M6cyp5LKMotSkg7LmWeeyYIFC1i7di0XXngh55xzDvfee2/GffkA48ePp3HjxnTq1IlmzZoRDod5++23M7VMZ2f8+PH06dOHwYMHc9ppp3HZZZexdOnSjKte559/PgMGDODKK6+kRo0aPProo9keZ9KkSZx33nn07NmThg0bcuedd2Zc+br33ns599xzadeuHS1btqRWrVp06dIlX+fgyiuv5L777uOuu+6icePG/Pe//+VPf/pTpn2uvfZa+vbtS58+fWjRogUnnnhixhU9CK76TZgwgTfeeIOGDRvyyCOPMHr06HzFAdCtWzfat29Pq1atqFGjRkRLMdepU4f//Oc/pKWl0a5dOxo1asStt95K1apVKVeuHFWqVOG9997j0ksv5dRTT+Uvf/kLY8aMoUOHDvmOT5IkZc+cypxKKstC4aw35kqSDtvw4cOZOnUqK1asiHYokiRJJZY5lVQ22CklSZIkSZKkImdRSpIkSZIkSUXO2/ckSZIkSZJU5OyUkiRJkiRJUpGzKCVJkiRJkqQiZ1FKkiRJkiRJRc6ilCRJkiRJkoqcRSlJkiRJkiQVOYtSkiRJkiRJKnIWpSRJkiRJklTkLEpJkiRJkiSpyFmUkiRJkiRJUpH7/zBS6UfGOJ30AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offset_tc = df_TC_log['time_offset']\n",
    "offset_switch = df_Switch_log['time_offset']\n",
    "\n",
    "shapiro_tc = shapiro(offset_tc)\n",
    "shapiro_switch = shapiro(offset_switch)\n",
    "\n",
    "print(\"TC - Shapiro-Wilk test:\")\n",
    "print(f\"Statistic: {shapiro_tc.statistic}, p-value: {shapiro_tc.pvalue}\\n\")\n",
    "\n",
    "print(\"Switch - Shapiro-Wilk test:\")\n",
    "print(f\"Statistic: {shapiro_switch.statistic}, p-value: {shapiro_switch.pvalue}\\n\")\n",
    "\n",
    "alpha = 0.05\n",
    "\n",
    "if shapiro_tc.pvalue < alpha:\n",
    "    print(\"Os dados de TC não são normalmente distribuídos.\")\n",
    "else:\n",
    "    print(\"Os dados de TC são normalmente distribuídos.\")\n",
    "\n",
    "if shapiro_switch.pvalue < alpha:\n",
    "    print(\"Os dados de Switch não são normalmente distribuídos.\")\n",
    "else:\n",
    "    print(\"Os dados de Switch são normalmente distribuídos.\")\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "probplot(offset_tc, dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot - TC')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "probplot(offset_switch, dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot - Switch ')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como os dados não seguem uma distribuição normal optammos por usar o teste não parametrico de _Mann-Whitney U_ com as seguintes hipóteses:\n",
    "\n",
    "- Hipótese Nula (H0): As duas amostras vêm da mesma distribuição ou têm medianas iguais.\n",
    "\n",
    "- Hipótese Alternativa (H1): Existe uma diferença significativa entre as duas amostras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-statistic: 624478.5\n",
      "P-value: 0.3771330723180305\n",
      "Falhamos em rejeitar a hipótese nula: Não há evidência suficiente para afirmar que há uma diferença significativa na precisão da sincronização entre a topologia com Transparent Clock e a com Switch Unaware.\n"
     ]
    }
   ],
   "source": [
    "u_stat, p_value = mannwhitneyu(offset_tc, offset_switch, alternative='two-sided')\n",
    "\n",
    "print(f\"U-statistic: {u_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"Rejeitamos a hipótese nula: Há uma diferença significativa na precisão da sincronização entre a topologia com Transparent Clock e a com Switch Unaware.\")\n",
    "else:\n",
    "    print(\"Falhamos em rejeitar a hipótese nula: Não há evidência suficiente para afirmar que há uma diferença significativa na precisão da sincronização entre a topologia com Transparent Clock e a com Switch Unaware.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vimos, os gráficos mostram uma diferença clara: o _Transparent clock_ aparenta ter uma precisão maior, com offsets mais estáveis e concentrados em torno de zero, enquanto o Switch Unaware mostra offsets dispersos e variáveis. No entanto, estatisticamente, não podemos corroborar essa afirmação. Essa inconsistência entre a análise descritiva, através dos gráficos, e a análise inferencial, através dos testes, pode ocorrer por várias razões:\n",
    "\n",
    "- **Tamanho da Amostra:** Se o tamanho da amostra for pequeno, a estatística inferencial pode não ter poder suficiente para detectar uma diferença estatisticamente significativa, mesmo que visualmente ou descritivamente ela pareça evidente. Quanto menor a amostra, maior a variabilidade aleatória e maior o erro padrão, o que dificulta a detecção de diferenças. Uma amostra maior tende a proporcionar mais precisão aos testes inferenciais e a reduzir a discrepância entre o que vemos visualmente e o que os testes indicam.\n",
    "\n",
    "- **Variabilidade Dentro dos Grupos:** Quando há uma alta variabilidade dentro dos grupos (por exemplo, dentro dos offsets de cada topologia), essa dispersão interna pode mascarar as diferenças entre as médias dos grupos. Nesse caso, mesmo que visualmente uma topologia pareça mais estável, a inferência estatística pode falhar em encontrar uma diferença significativa se a variabilidade interna for alta. Por isso, alguns testes assumem homocedasticidade (igualdade de variâncias).\n",
    "\n",
    "Por essa razão, decidimos complementar a análise inferencial utilizando o teste de Levene para comparar a variabilidade das topologias. Além disso, uma topologia pode ter uma média de offset próxima a zero, como é o caso das últimas duas testadas, mas se a variação for alta, a sincronização não será precisa. Uma variância maior é indicativa de que a topologia introduz mais variabilidade (jitter) nos offsets, o que compromete a precisão da sincronização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levene Test Statistic: 2132.473901500096\n",
      "P-value: 0.0\n",
      "Rejeitamos a hipótese nula: As variâncias dos offsets são significativamente diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Realizar o Teste de Levene\n",
    "stat, p_value = levene(offset_tc, offset_switch)\n",
    "\n",
    "print(f\"Levene Test Statistic: {stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"Rejeitamos a hipótese nula: As variâncias dos offsets são significativamente diferentes.\")\n",
    "else:\n",
    "    print(\"Falhamos em rejeitar a hipótese nula: Não há evidência suficiente para afirmar que as variâncias dos offsets são diferentes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusões\n",
    "\n",
    "O __Teste de Levene__ revelou uma heterocedasticidade significativa entre os dois grupos de dados, confirmando que as variâncias dos offsets entre as topologias de Transparent Clock e Switch Unaware são substancialmente diferentes. Isso não só corrobora a análise visual, mas também aponta para importantes implicações técnicas na escolha e análise das topologias de sincronização de tempo.\n",
    "\n",
    "Nos gráficos, a topologia com Switch Unaware apresenta uma variabilidade elevada nos offsets, o que se traduz em uma dispersão maior em torno da média, com valores de offset que flutuam amplamente entre valores positivos e negativos. Isso é evidenciado pelos altos valores de desvio padrão e pela amplitude dos valores dos offsets. Essa variabilidade interna pode ser causada por fatores como:\n",
    "\n",
    "- **Latência Variável:** O switch unaware, diferente do transparent clock, não ajusta nem compensa os atrasos que os pacotes PTP (Precision Time Protocol) sofrem ao atravessar o dispositivo, resultando em latências inconsistentes que introduzem flutuações nos offsets.\n",
    "\n",
    "- **Congestionamento de Rede:** A ausência de mecanismos para medir e corrigir os tempos de propagação nos switches pode fazer com que variações na carga da rede (por exemplo, congestão ou mudanças na rota dos pacotes) afetem a precisão da sincronização, aumentando a variabilidade dos offsets.\n",
    "\n",
    "A latência variável e o congestionamento de rede têm um impacto significativo na precisão da sincronização na topologia com Switch Unaware. A topologia com Transparent Clock, por outro lado, é mais robusta a essas variabilidades, proporcionando uma sincronização mais consistente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4442/2242320215.py:21: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='Topology', y='time_offset', data=df_combined, palette='Set2', ax=axes[1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coluna para identificar a topologia\n",
    "df_TC_log['Topology'] = 'Transparent Clock'\n",
    "df_Switch_log['Topology'] = 'Switch Unaware'\n",
    "\n",
    "# juntar dfs\n",
    "df_combined = pd.concat([df_TC_log, df_Switch_log])\n",
    "\n",
    "# plots lado a lado\n",
    "fig, axes = plt.subplots(1, 2, figsize=(18, 8))\n",
    "\n",
    "# Densidade\n",
    "for topology in df_combined['Topology'].unique():\n",
    "    subset = df_combined[df_combined['Topology'] == topology]\n",
    "    sns.kdeplot(subset['time_offset'], label=topology, fill=True, ax=axes[0])\n",
    "axes[0].set_title('Distribuição dos Offsets por Topologia')\n",
    "axes[0].set_xlabel('Offset (ns)')\n",
    "axes[0].set_ylabel('Densidade')\n",
    "axes[0].legend(title='Topologia')\n",
    "\n",
    "# Boxplot\n",
    "sns.boxplot(x='Topology', y='time_offset', data=df_combined, palette='Set2', ax=axes[1])\n",
    "axes[1].set_title('Boxplot dos Offsets por Topologia')\n",
    "axes[1].set_xlabel('Topologia')\n",
    "axes[1].set_ylabel('Offset (ns)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf_log(df_TC_log, 'time_offset', 'ACF - TC Log', lags=50) \n",
    "plot_acf_log(df_Switch_log, 'time_offset', 'ACF - Switch Log', lags=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referências\n",
    "\n",
    "LinuxPTP. (n.d.). Retrieved from https://linuxptp.sourceforge.net/\n",
    "\n",
    "EndRun Technologies. (n.d.). Precision Time Protocol (PTP). Retrieved from https://endruntechnologies.com/pdf/PTP-1588.pdf\n",
    "\n",
    "Cisco Systems. (n.d.). Cisco PTP Guide. Retrieved from https://www.cisco.com/c/en/us/td/docs/dcn/aci/apic/5x/system-management-configuration/cisco-apic-system-management-configuration-guide-51x/m-precision-time-protocol.html\n",
    "\n",
    "Rouwet, W. (2022). Synchronization in open radio access networks. In W. Rouwet (Ed.), Open Radio Access Network (O-RAN) Systems Architecture and Design (pp. 265-307). Academic Press. https://doi.org/10.1016/B978-0-323-91923-4.00005-7\n",
    "\n",
    "Cochran, R., Marinescu, C., & Riesch, C. (2011). Synchronizing the linux system time to a ptp hardware clock. In 2011 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control and Communication (pp. 87–92). IEEE. https://doi.org/10.1109/ISPCS.2011.6070148"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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