dcn_traffic_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "'''\n",
        "Panagiotis Christakakis\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3osbg8fma7Cn",
        "outputId": "09f86eaf-e152-443d-bac6-b35298ee9313"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: dpkt in /usr/local/lib/python3.10/dist-packages (1.9.8)\n"
          ]
        }
      ],
      "source": [
        "# Install libraries needed\n",
        "!pip install dpkt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-2BMsGhQflHZ"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import dpkt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "import socket"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "O39xVlMpXmH0"
      },
      "outputs": [],
      "source": [
        "filename='trace.pcap'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaUz1V1FOQVs",
        "outputId": "613d66b2-6c73-40d2-f035-50e2b8b680d0"
      },
      "outputs": [],
      "source": [
        "# Define variables for counting protocols\n",
        "counter = 0\n",
        "tcp_counter = 0\n",
        "udp_counter = 0\n",
        "icmp_counter = 0 \n",
        "arp_counter = 0\n",
        "\n",
        "# Define list to store packet lengths\n",
        "total_packet_lengths = []\n",
        "\n",
        "# Define Dictionaries to store flow sizes for TCP & UDP packets\n",
        "tcp_flows = {}  \n",
        "udp_flows = {}\n",
        "\n",
        "# Define Dictionaries to store grouped flows based on Source-Destination IPs and Ports for TCP & UDP packets\n",
        "tcp_flows_dur = {}\n",
        "udp_flows_dur = {}\n",
        "\n",
        "# Define lists to store flow durations for TCP and UDP packets\n",
        "tcp_durations = []\n",
        "udp_durations = []\n",
        "\n",
        "\n",
        "# Iterate through the contents of .pcap file\n",
        "for timestamp, pkt in dpkt.pcap.Reader(open(filename,'rb')):\n",
        "\n",
        "    # Unpack the Ethernet frame\n",
        "    eth = dpkt.ethernet.Ethernet(pkt)\n",
        "    counter += 1\n",
        "\n",
        "    # ARP packet\n",
        "    if isinstance(eth.data, dpkt.arp.ARP):\n",
        "      arp = eth.data\n",
        "      arp_counter += 1\n",
        "      total_packet_lengths.append(len(arp))\n",
        "\n",
        "    # Make sure the Ethernet data contains an IP packet (IPv4 and IPv6)\n",
        "    if (isinstance(eth.data, dpkt.ip.IP) or isinstance(eth.data, dpkt.ip6.IP6)):\n",
        "\n",
        "      # Grab the data within the Ethernet frame (the IP packet)\n",
        "      ip = eth.data\n",
        "\n",
        "      # Later we'll need to use Source-Destination IPs and Ports \n",
        "      # so it's needed to obtain them for both ipv4 and ipv6\n",
        "      \n",
        "      #IPv4\n",
        "      try:\n",
        "        src_ip = socket.inet_ntop(socket.AF_INET, ip.src)\n",
        "      except ValueError:\n",
        "        src_ip = socket.inet_ntop(socket.AF_INET6, ip.src)\n",
        "\n",
        "      #IPv6\n",
        "      try:\n",
        "        dst_ip = socket.inet_ntop(socket.AF_INET, ip.dst)\n",
        "      except ValueError:\n",
        "        dst_ip = socket.inet_ntop(socket.AF_INET6, ip.dst)\n",
        "\n",
        "      # Needed for the CDF of Package Size\n",
        "      # Add the packet length to the list\n",
        "      total_packet_lengths.append(len(ip))\n",
        "\n",
        "      # TCP packet\n",
        "      if isinstance(ip.data, dpkt.tcp.TCP):\n",
        "          tcp = ip.data\n",
        "          tcp_counter += 1\n",
        "          \n",
        "          # Needed for the CDF of Flow Sizes\n",
        "          # Fill the dictionary with lenghts of the packets based on grouped\n",
        "          # flow keys that contain Source-Destination IPs and Ports for TCP packets\n",
        "          flow_size = len(pkt)\n",
        "          flow = (ip.src, ip.dst, ip.data.sport, ip.data.dport)\n",
        "          if flow in tcp_flows:\n",
        "              tcp_flows[flow] += flow_size\n",
        "          else:\n",
        "              tcp_flows[flow] = flow_size\n",
        "          \n",
        "          # Needed for the CDF of Flow Durations\n",
        "          # Create a grouped flow key based on Source-Destination IPs and Ports for TCP\n",
        "          src_port = tcp.sport\n",
        "          dst_port = tcp.dport\n",
        "          flow_key = (src_ip, dst_ip, src_port, dst_port, 'TCP')\n",
        "\n",
        "          # Check if we have already seen this flow\n",
        "          if flow_key not in tcp_flows_dur:\n",
        "              # If not, add it to the dictionary with it's \n",
        "              # starting timestamp and ending timestamp set to 0\n",
        "              tcp_flows_dur[flow_key] = [timestamp, 0]\n",
        "          else:\n",
        "              # If yes, add the last timestamp to the flow\n",
        "              tcp_flows_dur[flow_key][1] = timestamp\n",
        "\n",
        "      # UDP packet\n",
        "      elif isinstance(ip.data, dpkt.udp.UDP):\n",
        "          udp = ip.data\n",
        "          udp_counter += 1\n",
        "\n",
        "          # Needed for the CDF of Flow Sizes\n",
        "          # Fill the dictionary with lenghts of the packets based on grouped\n",
        "          # flow keys that contain Source-Destination IPs and Ports for UDP packets\n",
        "          flow_size = len(pkt)\n",
        "          flow = (ip.src, ip.dst, ip.data.sport, ip.data.dport)\n",
        "          if flow in udp_flows:\n",
        "              udp_flows[flow] += flow_size\n",
        "          else:\n",
        "              udp_flows[flow] = flow_size\n",
        "\n",
        "          # Needed for the CDF of Flow Durations\n",
        "          # Create a grouped flow key based on Source-Destination IPs and Ports for UDP\n",
        "          src_port = udp.sport\n",
        "          dst_port = udp.dport\n",
        "          flow_key = (src_ip, dst_ip, src_port, dst_port, 'UDP')\n",
        "\n",
        "          # Check if we have already seen this flow\n",
        "          if flow_key not in udp_flows_dur:\n",
        "              # If not, add it to the dictionary with it's \n",
        "              # starting timestamp and ending timestamp set to 0\n",
        "              udp_flows_dur[flow_key] = [timestamp, 0]\n",
        "          else:\n",
        "              # If yes, add the last timestamp to the flow\n",
        "              udp_flows_dur[flow_key][1] = timestamp\n",
        "\n",
        "      # ICMP packet\n",
        "      elif isinstance(ip.data, dpkt.icmp.ICMP):\n",
        "          icmp = ip.data\n",
        "          icmp_counter += 1\n",
        "\n",
        "# For debugging reasons\n",
        "# print(\"Total number of packets in the pcap file: \", counter)\n",
        "# print(\"Total number of TCP packets: \", tcp_counter)\n",
        "# print(\"Total number of UDP packets: \", udp_counter)\n",
        "# print(\"Total number of ICMP packets: \", icmp_counter)\n",
        "# print(\"Total number of ARP packets: \", arp_counter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7XdZEw3bzj-J",
        "outputId": "f3a10218-379b-4493-8860-04551a6ce2d0"
      },
      "outputs": [
        {
          "data": {
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47oiIiIwj6WipAQMGYMCAATXef+HChQgPD8cXX3wBAGjevDl27dqFOXPmICYmpq7KNHspKSn62ytWrMC0adNw9uxZ/TYnJyesXbsWjz32GGJiYvDrr78iMjIS6enpWLlyJaZOnYoVK1bo9//333/RsmVLXL16Fa+++ioGDBiAM2fOwM3NrT7fFhERWZiivGzkpF6E3KcpfFydJKvDooaC7927F9HR0QbbYmJi8Prrr1f5nJKSEpSUlOjv5+bm1lV5kvHz89PfdnV1hUwmM9hWWFiI0aNH44EHHsCff/6p3x4eHo6oqKgKExZ6enrCz88Pfn5++Pzzz9G9e3fs37+/QQdIIiK6s1N71qHj3pdxwaYRfN49JFkdFhVuUlNT4evra7DN19cXubm5KCoqgkqlqvCcWbNmYebMmbV+TUEQUKSRZnizylZhklFIGzduREZGBt58881KH6+uRab8M1Wr1XddBxERWa/k7CLE741DRwApdmFoJGEtFhVuamPKlCmIjY3V38/NzUVwcHCNn1+k0aLFNGn69Jx6LwYOdnf/FZ0/fx4A0KxZM6Oel52djffffx9OTk7o0qXLXddBRETWa8epqxgsbAZkgH97aVv6LSrc+Pn5IS0tzWBbWloaXFxcKm21AQClUgmlUlkf5ZktQRCM2r9bt26Qy+UoKChAREQEVqxYUaHFjIiI6Fanjv2HobJcFMid0ei+0ZLWYlHhpmvXrli/fr3Bts2bN6Nr16519poqWwVOvSdNAlXZKkxynCZNmgAAzpw5U6PPasWKFWjRogU8PT3ZiZiIiGpEyL4CAFC7hMFRIe0yPJKGm/z8fFy4cEF/PyEhAUeOHIGHhwdCQkIwZcoUJCcnY/HixQCAl156CV9//TXefPNNPPvss9iyZQt+++03rFu3rs5qlMlkJjk1JKV+/frBy8sLn376qUGH4nLZ2dkGISY4OBiRkZH1WCEREVk6H911AIDgGihxJRLPc3Pw4EG0b98e7du3BwDExsaiffv2mDZtGgBxiHNSUpJ+//DwcKxbtw6bN29G27Zt8cUXX+CHH37gKJ47cHR0xA8//IB169bh4Ycfxr///ovExEQcPHgQb775Jl566SWpSyQiIguWU6SBfeE1AIDGSfpwI2mTRJ8+fartD1LZ7MN9+vThpHK1MGjQIOzZswezZs3CsGHD9B2r77vvPnzwwQdSl0dERBZs08lUBMgyAAAeAdK3/Fv2+RaqYNSoURg1alSlj3Xq1Al//PFHlc8NCwszuvMxERHRlaxC9JZlAQBs3Ws+IrmucOFMIiIiqjWtTsCSfZdhj7L50OwcpC0IDDdERER0Fy6k5+NGoQY2KJvwVi7tSCmA4YaIiIjuQkJGAQDA0aasW4Nc+h4vDDdERERUa+Xhxl2WJ25QuUlXTBmGGyIiIqo1dakO9iiBo7ZsYWrXIGkLAsMNERER3QW1VosAWaZ4x84ZsHeVtiAw3BAREdFdUJfq4I6yU1KOXtIWU4bhhoiIiGpNoxVgA514R+I1pcox3BAREVGtqbU6OMiKxTs2SmmLKcNwQ0RERLVWqtUhQpYi3vGIkLaYMgw3VqJPnz54/fXXK2xftGiRfsXvGTNmQCaTQSaTwcbGBl5eXujVqxfmzp2LkpKSCscr39fe3h4tWrTAN998Uw/vhIiILEF+SSnOpuZhy5nrCC8PN56NpS2qjPQz7VC9atmyJf7991/odDpkZmZi27Zt+OCDD7BkyRJs27YNzs7O+n3HjBmD9957D4WFhVi8eDHGjRsHd3d3DB06VMJ3QEREUkrNKcbDX+9Cet7NP4q9bMuGgTv7SVSVIbbcNDA2Njbw8/NDQEAAWrdujVdeeQXbt2/HiRMn8Mknnxjs6+DgAD8/P0RERGDGjBlo3Lgx/vrrL4kqJyIiqeWXlKLHJ1v0wcZVZYuWAS5o4lwWdMxktBRbbu5EEABNoTSvbesAyGR1/jLNmjXDgAEDsGrVKnzwwQdV7qdSqaBWq+u8HiIiMk+7zl9HqU5cZmHh0x3Rv1VZS83XJUAhAAdP6Yq7BcPNnWgKgY8CpHntt68Bdo718lLNmjXDpk2bKn1Mq9Vi2bJlOHbsGF544YV6qYeIiMxLqVaH/+1KAAAMiwq5GWwAoLRIvLatn99Zd8JwQwAAQRAgu62V6JtvvsEPP/wAtVoNhUKBCRMmYOzYsRJVSERE9S0jvwR/H72Gy5mFWLQnEQCgslVgTM/bRkXpyua5qYezDTXBcHMntg5iC4pUr11DLi4uyMnJqbA9Ozsbrq53ngr79OnTCA8PN9g2fPhwvPPOO1CpVPD394dczi5aREQNydTVJ/DPiVSDbePva4Rwr9taaISycCNX1FNl1WO4uROZrN5ODd2Npk2bVnpaKT4+Hk2aNKn2uWfOnMGGDRswZcoUg+2urq5o1KiRSeskIiLLcfF6PgDgkfaB6BjqjkhvJ0SFe1TcUdCK1zLz+COY4cZKjB07Fl9//TVeffVVPP/881AqlVi3bh2WLVuGv//+W79faWkpUlNTKwwFb9euHSZNmiThOyAiInOTliuOghrbJxJNfJ2r3rG85UbGlhsyoYiICOzYsQPvvPMOoqOjoVar0axZM6xcuRL9+/fX73fy5En4+/tDoVDA1dUVLVq0wJQpUzB27FgoleYxbTYREZmHIo3YIuOovENc0LHlhupI586dqxzxBIgzFM+YMaNGx9q2bZtpiiIiIotVqhVbZGwVd+gobGZ9bswjYhEREZFZ0eoElE1pA9s7DSjRn5Yyj1hhHlUQERGRWdGUtdoAgK3NHeJC+WS3tqo6rKjmGG6IiIioAvWt4aa601JaDaArLduR4YaIiIjMVGGJ2ElYIZfBTlFNXFAX3LxtJjMUM9xUQhAEqUuwOPzMiIisS36J2BrjaKeoMIO9AU3Z0gsyBaCwrYfK7ozh5ha2tuKXUlgo0UKZFqx8QU2Fwjx6yhMR0d0pLVtSwa6m/W3sHLn8gjlSKBRwc3NDeno6AMDBwaH6tEoAAJ1Oh+vXr8PBwQE2NvyRIiKyBqVasUXe5k4jpcysMzHAcFOBn5+4yml5wKGakcvlCAkJYRgkIrIS5R2Kbe40x02pOIsxbMxnIliGm9vIZDL4+/vDx8cHGo1G6nIshp2dHRfWJCKyIuUtN7bVdSYGzG7pBYDhpkoKhYL9R4iIqMEqn53YRn6HlhszW3oBYIdiIiIiqoSmbHpimzu23DDcEBERkQWw1HWlAIYbIiIiqoRGP1qKp6WIiIjICpTPc3PnDsXl4YYtN0RERGTGajxaSl0+iZ9DHVdUcxwtRURERHolpVqcT8vHxpOpAGowz406X7y2c6rjymqO4YaIiIgAAHsuZGDc0njcKLw5z1uY5x0Ww9SKy+/Axr4OKzMOww0REVEDdiWrEKvik5GQkY/VR64BABzsFGgV4Ir2IW4Yf1+j6g9ghgsnM9wQERE1UOuOpWDyqmPIKy7Vb3O2t8HeKX3hpDQyIpjR8jsMN0RERA3Q9nPXMW5pPADAzcEWL/SKQLinIzqFeRgZbNhyQ0RERBIrKdXi7VXHAQDhXo74+5UexrfUmDEOBSciImpgXlt2BMnZRVDIZVjyXJe7CzZm2OeG4YaIiKgBKdZosaFsmPcXT7RFkLuJ5qcxoz43DDdEREQNSLFGq7/9YBv/uz9g+dpSYLghIiIiCRSVhRtbhezOsw/XRGmxeG2ruvtjmQjDDRERUQNSpBbDjb2tidaCKl9+geGGiIiIpFDecqMyVbgpn6FYoTTN8UyA4YaIiKgBKe9z42BnonCjK5sAUGFrmuOZAMMNERFRA5KUJZ5GcrAz0bw25eFGbqKwZALWM2MPERERVapUq8OaI9dw+MoN/HNcHAbeNtjNRAcv61BsRqelGG6IiIis2Laz6Xjz92NIzyvRb/NwtMNzPcJM8wIleeK1vYtpjmcCDDdERERWal7ceXyx+Zz+fpsgVzzXIxx9mvjA1cFEfWTUBeK1GY2WYrghIiKyMoIg4EBClj7YKOQyrBnXHa0CXU3/YrqySQHl5tOhmOGGiIjIyny84Qy+3X5Jfz9+6v1wVdVR+BDKw435RArJR0vNnz8fYWFhsLe3R1RUFA4cOFDt/nPnzkXTpk2hUqkQHByMCRMmoLi4uJ6qJSIiMm/pucUGwWbR6M51F2yAW0ZLmU+4kbSSFStWIDY2FgsXLkRUVBTmzp2LmJgYnD17Fj4+PhX2X7p0KSZPnowff/wR3bp1w7lz5zBq1CjIZDLMnj1bgndARERkXt5YeRQA4OuixK637jPNEgvVMcOh4JK23MyePRtjxozB6NGj0aJFCyxcuBAODg748ccfK91/z5496N69O4YNG4awsDD069cPQ4cOvWNrDxERUUMwf+sF7DyfAZkMWPJcVN0HG+CWPjfm03IjWbhRq9U4dOgQoqOjbxYjlyM6Ohp79+6t9DndunXDoUOH9GHm0qVLWL9+PR544IEqX6ekpAS5ubkGFyIiImvz4bpT+GzjWQDAvU190MTXuX5emKelbsrIyIBWq4Wvr6/Bdl9fX5w5c6bS5wwbNgwZGRno0aMHBEFAaWkpXnrpJbz99ttVvs6sWbMwc+ZMk9ZORERkTv5LzML3OxMAAL2aeGPB0x3q78XNMNxI3qHYGNu2bcNHH32Eb775BvHx8Vi1ahXWrVuH999/v8rnTJkyBTk5OfrLlStX6rFiIiKiurfyoPi7bUArPyx+tguUNvXY/8UM+9xIFrO8vLygUCiQlpZmsD0tLQ1+fn6VPmfq1Kl45pln8PzzzwMAWrdujYKCArzwwgt45513IJdXzGpKpRJKpflMCU1ERGRqZ1PFWYIHtgmo/xcvLZv5WGFX/69dBclabuzs7NCxY0fExcXpt+l0OsTFxaFr166VPqewsLBCgFEoxKQoCELdFUtERGTGkrOLAABhXg71/+JqcSFO2DnV/2tXQdITZLGxsRg5ciQ6deqELl26YO7cuSgoKMDo0aMBACNGjEBgYCBmzZoFAHjooYcwe/ZstG/fHlFRUbhw4QKmTp2Khx56SB9yiIiIGorTKblYvDcRGflqAECAqwRLIKjzxWs7x/p/7SpIGm6GDBmC69evY9q0aUhNTUW7du2wYcMGfSfjpKQkg5aad999FzKZDO+++y6Sk5Ph7e2Nhx56CB9++KFUb4GIiEgyry47jPPpYrgY2MYf7o4SnBrSlLfcSNBqVAWZ0MDO5+Tm5sLV1RU5OTlwcTGfFUyJiIiModMJaDr1H2i0At4f1BLDokKhkMvquQgt8J6HeHvSJcDRs85eypjf3xY1WoqIiIhEGfkl0GgFyGXA0C4h9R9sgJsrggNmdVqK4YaIiMgCXcsR11X0dbGHTX3MRFyZ8nAjkwM25jMymeGGiIjIAiVliX1d/F3tpStCc8tIKZkELUdVYLghIiKyQGuPXgMAtAlyk66I8pYbW/PpTAww3BAREVmchIwCbDolToLbJdxDukLKw40Z9bcBGG6IiIgszs7z1wEAQe4q9Gvhe4e965CmPNyw5YaIiIjuwr+n0wEAj3cMkq4zMXDLaSm23BAREdFdOJ8mriXVo5GXtIXol15guCEiIqJaSsosREpOMWzkMjTzl3gyWv3SCzwtRURERLV0LDkbANAy0BVOSklXUTIcCm5GGG6IiIgsSHpuCQDA19kMJs3TiKuRw0bCuXYqwXBDRERkQQ5dvgEAaBvsJm0hAKDViNcKCRbsrAbDDRERkYUoVJfi4nWxn4ukMxOX05WHG1tp67iNxCfriIiIqCZOXsvBM/87gKwCNeQyoHWgq9Ql3Wy5kZtXnGDLDRERkQVYvOcysgrU8HC0w/xhHdDY11nqkm45LWVeLTcMN0RERBbgyg1xZNK7DzbHgNb+EldTpvy0lNzCw01BQUFd1EFERETVyC4Ug4SHoxl13i1Vi9c2ZjBy6xZGhxtfX188++yz2LVrV13UQ0RERJUo0mgBAI5Sz21zq9KyoeC2KmnruI3R4eaXX35BVlYW7rvvPjRp0gQff/wxrl27Vhe1EREREQCdTkBWgdhK4mCnkLiaW2iKxWtLb7kZPHgwVq9ejeTkZLz00ktYunQpQkNDMXDgQKxatQqlpaV1UScREVGD9c+JVOQUaeBsb4PGPmbQkbhcaXm4MYNh6beodYdib29vxMbG4tixY5g9ezb+/fdfPP744wgICMC0adNQWFhoyjqJiIgaHEEQMPPvkxi3NB4A0KuJN+xszGgsUKk4W7K5hZtan7hLS0vDzz//jEWLFuHy5ct4/PHH8dxzz+Hq1av45JNPsG/fPmzatMmUtRIREVmtE8k5+P3QVWQVqJFdpEF2oRrHruboH+8S7oHX+jaWsMJKlK8tZWZ9bowON6tWrcJPP/2EjRs3okWLFnj55Zfx9NNPw83NTb9Pt27d0Lx5c1PWSUREZJVOXcvFlFXHcPSWIHO7mJa++PaZTvVYVQ2py0ZQm9nCmUaHm9GjR+Opp57C7t270blz50r3CQgIwDvvvHPXxREREVkjrU5A3Ok0nE/Px2cbz+q3R3g74pF2gQhwU8HNwbbsYocIL0cJq62GflVwB2nruI3R4SYlJQUODtW/CZVKhenTp9e6KCIiImtUqtUhq0CNd1afwOZTafrtCrkMa8Z1RytzWFLBGGbaodjocOPs7IyUlBT4+PgYbM/MzISPjw+0Wq3JiiMiIrJ0W86k4eN/ziAjX40bhWoIws3HejTyQlS4B6IiPC0v2AA3OxSb2argRocb4dZv5RYlJSWwszOvN0dERCSlD9aewg+7Egy2yWWAp5MST3YKwqSYZhJVZiKWPlrqq6++AgDIZDL88MMPcHK62XlIq9Vix44daNbMwr8kIiIiE0nOLjIINl8+1Q7dG3nB3cEOCrlMwspMRKe7ubaUmU3iV+NwM2fOHABiy83ChQuhUNycIdHOzg5hYWFYuHCh6SskIiKyQH/GXwUA+LvaY+eb98JGYUbz05iCtuTmbUs9LZWQIKbPe++9F6tWrYK7u3udFUVERGTp/ku8AQAY2S3M+oINABSXDV2XyS1/KPjWrVvrog4iIiKrUaguxd5LmQCAvs187rC3hSrIEK9VHoDcvMJbjcJNbGws3n//fTg6OiI2NrbafWfPnm2SwoiIiCzVjnMZUJfqEOyhQiMf82rVMJlCMbzBwVPaOipRo3Bz+PBhaDQa/e2qyGRW0EGKiIjoLpSUavH+2lMAgL7NfK33d2N5uHH0kraOStQo3Nx6KoqnpYiIiCp3ObMAT367F2m5JbCzkWN09zCpS6o7+pYbD2nrqIR5nSQjIiKyUHnFGrz8azzScsVRRF8PbY9QTzNdNsEU9OHGQltuHn300RofcNWqVbUuhoiIyJKUlGrx6YazOJ6cgwMJWQDEpRR+e/EedAw1vxYNkyrvUGypfW5cXS1wSmgiIqI6lJ5XjJl/n8K6Yyn6bbYKGWY+3Mr6gw0AFIlD3aEyv6lhahRufvrpp7qug4iIyCJk5pdg3pYLWP5fEoo1OgDAPREeeLN/M0R4OcLNwbwmtKsz5YtmmtmK4EAt5rkhIiJqSHKKNLieV4KsAjWyCtRYsP0ijl7JBgA083NGv5Z+eKl3BBzsGtivVDNdERyoYbjp0KED4uLi4O7ujvbt21c7rC0+Pt5kxREREdWXK1mF2HUhA9mFGmQXqZFTqMGZ1Dwcu5oNXSVrRo/qFoZpA1tAbg3rRNWGftFM81pXCqhhuBk0aBCUSrH4wYMH12U9RERE9aJYo8XWM+lIzCxEUlYhVh9ORpFGW+m+dgo5At1VcHewhYejEt0beWJUtzDrncOmJiy95Wb69OmV3iYiIrI0Wp2AX/dfxldxF5CRX2LwWOtAVzTxdYabgy3cVLZwc7BFiwCXhtFB2Fj6cGOhLTeVOXjwIE6fPg0AaNGiBTp27GiyooiIiOrKi0sO4d/TaQCAQDcVosI9EOSuQhM/Zwxo5Q9FQz3NZCz9aSkLbbm51dWrVzF06FDs3r0bbm5uAIDs7Gx069YNy5cvR1BQkKlrJCIiumtanYDpf53QB5tpA1vgma6hsLXGFbvrg77lRiVtHZUw+ht9/vnnodFocPr0aWRlZSErKwunT5+GTqfD888/Xxc1EhER1YogCMjML8Hei5l4dflh/LIvCQDw6n2N8GyPcAabu2HpHYpvtX37duzZswdNmzbVb2vatCnmzZuHnj17mrQ4IiKi2riSVYjPN53FjnPXcaNQY/DY5AHN8FLvSIkqsxI6HVCULd5WOktaSmWMDjfBwcH6FcJvpdVqERAQYJKiiIiIauvktRwM/2E/sstCjUwm9q1pG+SGfi198XBb/q66a/lpgLYEkCkAV/PrjmJ0uPnss8/wyiuvYP78+ejUqRMAsXPxa6+9hs8//9zkBRIREdXUxev5ePCrXQAApY0cXz7VDn2a+sDeViFxZVam4Lp47eAJyM3vs61RuHF3dzcYy19QUICoqCjY2IhPLy0thY2NDZ599lnOg0NERPUqp0iDjSdT8eu+yzh6NUe/fdOEXta9KreUCssWzXT0lraOKtQo3MydO7eOyyAiIqqdkT8ewJGy5RAAINhDhfcGtWKwqUvlK4I7mt+K4EANw83IkSPrug4iIiKjnbqWqw82o7qFYUyvCAS6md/QZKtTflrK0UfaOqpwV6t8FRcXQ61WG2xzcXG5q4KIiIhq6nx6HgCgbZArZjzcUuJqGpD8dPHaTE9LGT3Av6CgAOPHj4ePjw8cHR3h7u5ucCEiIqovx8v62LQIcJW4kgbGzE9LGR1u3nzzTWzZsgULFiyAUqnEDz/8gJkzZyIgIACLFy+uixqJiIgqdSAxCwDQJojhpl5pCsVrOydp66iC0ael/v77byxevBh9+vTB6NGj0bNnTzRq1AihoaH49ddfMXz48Lqok4iIqIKMPHGW3Ob+7BJRr3Rl893J76p3S50xuuUmKysLERERAMT+NVlZYmru0aMHduzYYdrqiIiIqpFXXAoAcLE3z1+yVksrfu5Q2EpbRxWMDjcRERFISEgAADRr1gy//fYbALFFp3whTWPMnz8fYWFhsLe3R1RUFA4cOFDt/tnZ2Rg3bhz8/f2hVCrRpEkTrF+/3ujXJSIiy6cVBACAjZxrRNWrwkzxWmmeLWZG/zSMHj0aR48eBQBMnjwZ8+fPh729PSZMmIBJkyYZdawVK1YgNjYW06dPR3x8PNq2bYuYmBikp6dXur9arcb999+PxMRE/P777zh79iy+//57BAYGGvs2iIjICujKws0t88xSfcgXV1Y3x6UXgFr0uZkwYYL+dnR0NE6fPo34+Hg0atQIbdq0MepYs2fPxpgxYzB69GgAwMKFC7Fu3Tr8+OOPmDx5coX9f/zxR2RlZWHPnj2wtRWbwsLCwox9C0REZCXKsg3kcqabelW+Iritec4pdNfteGFhYXj00UeNDjZqtRqHDh1CdHT0zWLkckRHR2Pv3r2VPuevv/5C165dMW7cOPj6+qJVq1b46KOPoNVqq3ydkpIS5ObmGlyIiMg66MMNs039Ki0Sr23spa2jCrUKN3FxcRg4cCAiIyMRGRmJgQMH4t9//zXqGBkZGdBqtfD19TXY7uvri9TU1Eqfc+nSJfz+++/QarVYv349pk6dii+++AIffPBBla8za9YsuLq66i/BwcFG1UlEROar/LSUnOel6ld5y42NUto6qmB0uPnmm2/Qv39/ODs747XXXsNrr70GFxcXPPDAA5g/f35d1Kin0+ng4+OD7777Dh07dsSQIUPwzjvvYOHChVU+Z8qUKcjJydFfrly5Uqc1EhFR/WGfGwkIAlBaLN62Mc/TUkb3ufnoo48wZ84cjB8/Xr/t1VdfRffu3fHRRx9h3LhxNTqOl5cXFAoF0tLSDLanpaXBz8+v0uf4+/vD1tYWCsXN5dWbN2+O1NRUqNVq2NnZVXiOUqmEUmmeyZKIiO6Oruy0lAxMN/WmvNUGsJ6Wm+zsbPTv37/C9n79+iEnJ6eSZ1TOzs4OHTt2RFxcnH6bTqdDXFwcunbtWulzunfvjgsXLkCn0+m3nTt3Dv7+/pUGGyIisl5CeYcbsM9NvSpvtQGsp8/Nww8/jD///LPC9jVr1mDgwIFGHSs2Nhbff/89fv75Z5w+fRpjx45FQUGBfvTUiBEjMGXKFP3+Y8eORVZWFl577TWcO3cO69atM6q1iIiIrMct2YZ9bupTebiRyc12Er8anZb66quv9LdbtGiBDz/8ENu2bdO3sOzbtw+7d+/GG2+8YdSLDxkyBNevX8e0adOQmpqKdu3aYcOGDfpOxklJSZDfMjFTcHAwNm7ciAkTJqBNmzYIDAzEa6+9hrfeesuo1yUiIsunM2i5YbipN/r+NvZm29lJJtzarleF8PDwmh1MJsOlS5fuuqi6lJubC1dXV+Tk5MDFxTxnViQiojtTl+rQ5N1/AADHZvSDi715tiJYnfQzwDdRgModeCux3l7WmN/fNWq5KV9ugYiIyFyw5UYi5bMTO3hKW0c17moSP0EQUIOGHyIiIpMz7HMjXR0NTvZl8dotVNo6qlGrcLN48WK0bt0aKpUKKpUKbdq0wZIlS0xdGxERUZVubbnhUPB6VL5oppNv9ftJyOh5bmbPno2pU6di/Pjx6N69OwBg165deOmll5CRkWGw9hQREVFdMQg3zDb1pyBDvLZ3lbaOahgdbubNm4cFCxZgxIgR+m0PP/wwWrZsiRkzZjDcEBFRvbi1UwT73NSjG4nitWekpGVUx+jTUikpKejWrVuF7d26dUNKSopJiiIiIroTnY6T+ElCV7ZYtZnOcQPUItw0atQIv/32W4XtK1asQOPGjU1SFBERUXV0OgHrj4uLLNvbymGjuKvxMWQMoSzcyBTV7ycho09LzZw5E0OGDMGOHTv0fW52796NuLi4SkMPERHR3RAEAb/sT0Lc6TRk5quRmV+CjAI11KXiUjyPtA+SuMIGprzlRm5F4eaxxx7DgQMHMHv2bKxevRqAuHjlgQMH0L59e1PXR0REDUyxRot/T6fh90NXcfVGEa5kFaKkVFdhPyelDR5q64+pA5tLUGUDptOI13KjI0S9MaoyjUaDF198EVOnTsUvv/xSVzUREVEDdTY1D8N/2I+M/BKD7fa2cozpGYF2wW7wdFLC09EOfq72sOXpqPqXW9a/1lqGgtva2uKPP/7A1KlT66oeIiJqgNYcScbXWy7gfHo+AMDZ3gZPdAxG3+Y+8He1R4CbCva25nsapMEQBCDnqnjbLVjaWqphdJvS4MGDsXr1ag75JiKiWjl2NRt/Hk5GdqEGOUUaXLyej8uZhfrHfV2U+PX5e9DIx0nCKqlShZlAaREAGeBivn2djA43jRs3xnvvvYfdu3ejY8eOcHR0NHj81VdfNVlxRERkPXKLNdh+9jom/X4UxZqKfWhaBbrg+xGd4OdiDxnnrTFPxTnitdIZsLGTtpZq1GhV8FtVt0I4VwUnIqLKLN6biBl/nUT51DRdwjwQ3cIHripbuKrs4KqyRfsQN556MndpJ4EF3QBHb2DShXp9aZOvCn4rrhBOREQ1oS7V4UxqLi6k52PampMAAFuFDKO7h+P16MZwsDPf0TZUhdJi8dpGJW0dd2DUT9a+ffvw999/Q61Wo2/fvujfv39d1UVERBYqLbcYC7dfxD/HU5GaW6zfbqeQ4/C0++GoZKixWEXZ4rXSWdIy7qTGP2G///47hgwZApVKBVtbW8yePRuffPIJJk6cWJf1ERGRBSnWaPH8zwdxPFnsm6GyVaCZvzOC3B0wunsYg42ly70mXrv4S1vHHdT4p2zWrFkYM2YM5s+fD4VCgVmzZuGjjz5iuCEiIr05/57TB5v3B7XEIx2C4MRAYz3yyua4cTbvcFPj2Y/Onj2LiRMnQqEQO3u98cYbyMvLQ3p6ep0VR0REluOXfZfx7XZxUMmkmKZ4pmsYg4210bfcBEpbxx3UONwUFhYa9E62s7ODvb098vPz66QwIiKyHDmFGsz8W+w0/EBrP7zUO1LiiqhOWNtpKQD44Ycf4OR0c1Kl0tJSLFq0CF5eXvptnOeGiKjhyS5SQ6MV4GCnwPxhHThPjbXKs4yWmxqHm5CQEHz//fcG2/z8/LBkyRL9fZlMxnBDRNQAlU/Kp7JVMNhYs/KWGzPvc1PjcJOYmFiHZRARkSUr0mgBgJPwWTNNsbj8AgC4BEhbyx1wOVUiIrpriRkFAACVHcON1SofKWVjD6jcpa3lDhhuiIjoruQUahD72xEAQKiHg7TFUN25dRi4mZ96ZLghIqJaS8gowKMLdkMnAB6OdpjUv6nUJVFdsZBh4EAt1pYiIiICgE82nMGCbRf192c/2RbN/LggsdXKThKvzXwYOMBwQ0RENVSq1SGrQI0jV7Lx+6Gr2HQqDQAQ6e2It/o3Q5+mPhJXSHVGqwHifxZvB3SQtpYaqFW4uXjxIn766SdcvHgRX375JXx8fPDPP/8gJCQELVu2NHWNREQkobTcYjz/80GcuJYDQTB87Ol7QvDew60gl5t3Hwy6S0eXAzcSAQcvoONIqau5I6P73Gzfvh2tW7fG/v37sWrVKv0MxUePHsX06dNNXiAREUlrxX9XcDxZDDZyGeDlpES/Fr74Y2xXfDC4NYNNQ3BokXjd7RXAzlHSUmrC6JabyZMn44MPPkBsbCycnW8ueX7ffffh66+/NmlxRERUfwRBwPZz1/HP8VRkF6mRW1SK3GINTl7LBQCM7ROJif2aQsEw0/CkHBWvWzwsbR01ZHS4OX78OJYuXVphu4+PDzIyMkxSFBER1R9BELDlTDq+3noBh5OyK93Hxd4GL/SMYLBpsMrOR9qopC2jhowON25ubkhJSUF4eLjB9sOHDyMw0PyHhxERNWTFGi1yijT6S0JGARbtTsSpFLF1Rmkjx6MdgtAywAUuKls429vAxd4Wkd6OcHOwk7h6opoxOtw89dRTeOutt7By5UrIZDLodDrs3r0bEydOxIgRI+qiRiIiMtKxq9nYfykLcWfSkJmv1oeZklJdpfs72CnwTNdQPN8jAt7OynqulszawR8BXSkgkwO2Vtpy89FHH2HcuHEIDg6GVqtFixYtoNVqMWzYMLz77rt1USMREdXAjQI1vow7j0V7EqvdTy4DXFS2cC27RIV74OU+jeDuyJYZuo22FNj4jni72yuAyk3ScmpKJgi3D+yrmaSkJJw4cQL5+flo3749GjdubOra6kRubi5cXV2Rk5MDFxdONkVElu10Si5OJOfgen4JFmy9iLySUv1jtgoZ3urfDC38XW6GGQdbONnZcIQT1UxBJvBZhHj73euAjXQB2Jjf30a33OzatQs9evRASEgIQkJCal0kERHVXmJGAZb/dwULt1802C6XAe882AL3NvVGhLeTRNWR1biRIF47B0gabIxldLi57777EBgYiKFDh+Lpp59GixYt6qIuIiICkJ5bjOTsIuSXlCK/uBSZBWqsO5aCvZcy9fs08nFC2yA3+LgoEd3cFx1DzXvFZrIgWZfEa89IaeswktHh5tq1a1i+fDmWLVuGjz/+GG3atMHw4cMxdOhQBAUF1UWNREQNysXr+dh0Mg2L9iQgLbekyv26RngiuoUvRnYNhY2C6yBTHVAXiNdKy+rGYXS48fLywvjx4zF+/HgkJCRg6dKl+PnnnzFlyhT06tULW7ZsqYs6iYisXqlWh293XMLnm84aLHMQ6KaCs71N2cUWLvY2eLJzMLpFeklXLJEZu6uFM8PDwzF58mS0bdsWU6dOxfbt201VFxFRg6DVCfjy33P4/dBVpOYWQ3dLqHk9ujEebO2Pxr7OVR+AqE6V/UDKLKsDeq3Dze7du/Hrr7/i999/R3FxMQYNGoRZs2aZsjYiIquWU6TB8z//h/8Sb+i3qWwVeL5nOF7t2xi2PNVEUqvdgGrJGR1upkyZguXLl+PatWu4//778eWXX2LQoEFwcHCoi/qIiKyCTifgUkY+Np1Kw4W0fGQXabDlTDoAwN5Wjtf6NsFjHQPh5ajkMG0yIw2k5WbHjh2YNGkSnnzySXh58XwvEVF1rt4oxKz1Z7Dj3HWDOWhuNfvJdnigtX89V0ZUA/qWGysPN7t3766LOoiIrE6huhQ9Ptmqv29vK0ebQDf0bOwFHxclXFV2CPV0QHN/yxqJQmTuahRu/vrrLwwYMAC2trb466+/qt334YctYzl0IiJT0mh1yCnSILuwfFFKNT7556z+8c8eb4PB7QPZj4YsR2EW8N8P4m0nH2lrMVKNll+Qy+VITU2Fj48P5PKq/2HKZDJotVqTFmhqXH6BiEwlLbcYry47jJPXcpFfxSknAHi2ezimPcQJT8mC3LgMLBksTuLn7A+8sA1w9pO0JJMvv6DT6Sq9TUTUEGXkl2DFf1fw99FrOJOaZ/CYi70NXB1s4aayg5uDLR5o7Y8nOwVLVCmRkXRa4NphYP0kMdi4hgDDV0oebIxldJ+bxYsXY8iQIVAqlQbb1Wo1li9fjhEjRpisOCIicyMIAkb87wBOpeQCAJyUNvj52S6I8HKEi8oWCo50Ikt0aRsQvxi4uAUoKpuaQOkKjF4PuFleODd6VXCFQoGUlBT4+Bief8vMzISPjw9PSxGRVdLpBKw6nIx5W87jcmYh5DLgjX5N8WBrf4R5OUpdHlHtJewAFg8ChLIzM0pXILIP0O1VIKiTpKXdqk5XBRcEAbJKxrtfvXoVrq6uxh6OiMisZeaXYNeFDHy64SySs4v028f2icS4extJWBnRXTq3EdgzD0jcKd5vdD/QayIQ2AlQ3NUCBpKrcfXt27eHTCaDTCZD3759YWNz86larRYJCQno379/nRRJRFRftDoBiZkF+PdUGg4nZWPLmXSotTf7Gg5uF4DJA5rDz9VewiqJ7lL6aWDpk+JtmQJoMQgYOAdQuUlalqnUONwMHjwYAHDkyBHExMTAyclJ/5idnR3CwsLw2GOPmbxAIqK6dCE9H78fuopzaXlIzCzA1awigzADAM38nNGzsRf6NvfFPRGeElVKZEI5yeK1Wwgw+h/ANUjaekysxuFm+vTpAICwsDAMGTIE9vb8q4WILNuOc9cx4scDFbbb28rRJsgN9zXzQVS4B9qHuEtQHVEdUpeN8nMNtrpgA9Siz83IkSProg4ionqj0erw677LmPH3Kf22Dx9phTBPR4R4OCDATcVRT2TdSsrCjdI6V5w3OtxotVrMmTMHv/32G5KSkqBWqw0ez8rKMllxRESmdjY1DxNXHsXx5BwAgEIuw5Jnu6BbI66VRw1IwXXx2s6p+v0slNHzgM+cOROzZ8/GkCFDkJOTg9jYWDz66KOQy+WYMWNGrYqYP38+wsLCYG9vj6ioKBw4ULGZuDLLly+HTCbT9wciIqqOVifg2UX/4XhyDmzkMrwe3Rj73+7LYEMNz5Fl4nVQZ2nrqCNGh5tff/0V33//Pd544w3Y2Nhg6NCh+OGHHzBt2jTs27fP6AJWrFiB2NhYTJ8+HfHx8Wjbti1iYmKQnp5e7fMSExMxceJE9OzZ0+jXJKKGafOpVCRnF8HdwRabJvTC69FN4OWkvPMTiayJIACZF8TbLaxzPUijw01qaipat24NAHByckJOjti0O3DgQKxbt87oAmbPno0xY8Zg9OjRaNGiBRYuXAgHBwf8+OOPVT5Hq9Vi+PDhmDlzJiIiIox+TSJqmPYniKfNB7YJQIS3dTbHE91RaQmAsvl7eVpKFBQUhJSUFABAZGQkNm3aBAD477//KizJcCdqtRqHDh1CdHT0zYLkckRHR2Pv3r1VPu+9996Dj48PnnvuOWPLJ6IGLCNf7CMY6ukgcSVEEtIU3rxta53/FozuUPzII48gLi4OUVFReOWVV/D000/jf//7H5KSkjBhwgSjjpWRkQGtVgtfX1+D7b6+vjhz5kylz9m1axf+97//4ciRIzV6jZKSEpSUlOjv5+bmGlUjEVmPIrW4crej0rJnXyW6K+XhRmFn8TMRV8Xod/Xxxx/rbw8ZMgQhISHYu3cvGjdujIceesikxd0uLy8PzzzzDL7//nt4edWsA+CsWbMwc+bMOq2LiCyDRis2xdtwmDc1ZJqyZURsVdLWUYfuOrJ17doVXbt2rdVzvby8oFAokJaWZrA9LS0Nfn4Vl1e/ePEiEhMTDUKUTifOJGpjY4OzZ88iMjLS4DlTpkxBbGys/n5ubi6Cgy1vhVMiunulZf9f2NkYfUaeyHqUt9xY6SkpoIbh5q+//qrxAR9+uOY9r+3s7NCxY0fExcXph3PrdDrExcVh/PjxFfZv1qwZjh8/brDt3XffRV5eHr788stKQ4tSqTS6LxARWaebLTcMN9SAseVGVNN5ZGQyGbRarVEFxMbGYuTIkejUqRO6dOmCuXPnoqCgAKNHjwYAjBgxAoGBgZg1axbs7e3RqlUrg+e7ubkBQIXtRES3Ky1bM8pGwdNS1ICpC8RrW0dp66hDNQo35ad+6sKQIUNw/fp1TJs2DampqWjXrh02bNig72SclJQEOf/KIqJaEAQBJaU6FJSUolCtRUGJ+MeXLcMNNWTpp8Vre1dp66hDMkEQBKmLqE+5ublwdXVFTk4OXFxcpC6HiGpBqxOQmFmAIrUWhWotCtWlKFJrkZBZgCtZhcgtLkXC9QKcSql8dOSS57qgZ2Pveq6aSGLqAmD/t8DWDwFdKdD9deB+yxlwY8zvb6M7FL/33nvVPj5t2jRjD0lEVGM6nYDhP+zDvkvGrWOnslXAUalAuJcjV/mmhufwL8Dm6UBhhni/2UCg5xvS1lSHjG65ad++vcF9jUaDhIQE2NjYIDIyEvHx8SYt0NTYckNkWbIK1FgVfxU7zmcg+UYhLl4v0D/mpLSBt7MSKlsFHOwU8HSyQ3N/F7jY28LZ3gYuKlt0CnWHm4MdV/mmhu0DP6C0CHDwAu5/D2g3DJBZ1r+JOm25OXz4cKUvOGrUKDzyyCPGHo6ICIDY2XfpgSRczixEbpEGOUUaXLiej4SMAtz+J5itQobXo5tg3L2NpCmWyJLotGKwAYCXdgIuAdLWUw9MMjWhi4sLZs6ciYceegjPPPOMKQ5JRFaiVKtDYmYB8opLUVCixe6LGbhRoEZydhGK1FoUacTLpVtaZG7XzM8ZbYJcERXuiS7hHvB3tYeNggMNiKpVWgKknwLOlK37aKMCnHyrf46VMNm8yzk5OfpFNImoYdPpBPx97Bq+3X4JSVmFyC8prfFzI7wd8ViHILjY2yDYwwGtAl25cjdRTR37Dbi0HUg9Ko6K0t3yb6/7a4BcIV1t9cjocPPVV18Z3BcEASkpKViyZAkGDBhgssKIyLIcv5qDDSdTkJpTgj/irxo8ZqeQw9tZCSelDRyVCmh1Ap7pGgYnpQL2tgqobBVQ2Sng6aREoJv1TixGVKcubQdWjTHcpnIH/NoAjfoCXStOjmutjA43c+bMMbgvl8vh7e2NkSNHYsqUKSYrjIgshyCII5hyiw1baPxd7TH9oRa4t5kPlDYN4y9GIknkXAX+fk287R4G9PsA8G8LuAZbXMdhUzA63CQkJNRFHURkwab/dVIfbAa3C0DbYDdEN/dFsIf1rl1DZFbi3gduJIhhZvQ/DaLTcHWsc61zIqpXu86Lc2cEuqkw+8l2kHPYNVH9un5GvL5/ZoMPNkAtwk1xcTHmzZuHrVu3Ij09vcLSDOY+zw0Rmd7VbHGY6Y+jOjPYENW3/OvA9bPibUcfaWsxE0aHm+eeew6bNm3C448/ji5dukDWAM/lEdFNW86kQV0q/pHjqrKVuBqiBkQQgEM/iaekSosAz8ZAUGepqzILRoebtWvXYv369ejevXtd1ENEFmZn2SmpYA8VfF04ZJuo3pxdD6ydIN72bgY8uQSwtZe2JjNhdLgJDAyEs7NzXdRCRBZCo9Xh6o0iLNx2EXFn0gEAo7uFsyWXqD4U5wL7vgG2zRLvO/kBL+0GFOxGW87oT+KLL77AW2+9hYULFyI0NLQuaiIiM6DR6rDivys4nJSNgpJSFGm0KNZosT+h8gUrWwW61nOFRA3Uny+KrTaAGGwe/orB5jZGfxqdOnVCcXExIiIi4ODgAFtbw3PsWVnGrdRLROZn8d5ETFtz8o77BbqpMPyeEMS09EOkt1M9VEbUgKkLgE1TbwabgXOAdk8DNnbS1mWGjA43Q4cORXJyMj766CP4+vqyGZrIwpVqddh3KQs5RRpkF6mx4r8rOHb15lIqncPc8XC7QNjbyKGyE2cTDnRXobGPM1faJqovhVnAL48B18pGJPd+C+j0rLQ1mTGjw82ePXuwd+9etG3bti7qIaJ69Nt/V/DmH8cqfey5HuF4PboxnO05AopIcts+FoONygN44icgoo/UFZk1o8NNs2bNUFRUVBe1EFEdW3nwCtYfT8G17GJcyylC3i3LJdjZyNGjkReclDboGOqOEV1D2TJLZC5yytZru/dtBpsaMDrcfPzxx3jjjTfw4YcfonXr1hX63Li4uJisOCKqnZxCDfZeykRKThHyi0uRnF2EAwlZuJRRUGFfLyclVrx4D/vMEJkzoWzCXBtOt1ATRoeb/v37AwD69u1rsF0QBMhkMmi1WtNURkQ1dvF6PlYevIq/j15DSakWGfnqSveTyQAfZyU+fqwNgt1V8HdVwVHJURZEZk8o+90qk0tbh4Uw+n+1rVu31kUdRFRLm0+lYczigxW2K+Qy9G/lBxd7Gzjb26JjqDvuCfeEqwP70BBZnPKWG5lC2joshNHhpnfv3nVRBxHV0uojyQAABzsFukV6Irq5LzqGuiPS24nrPBFZC324YctNTRgdbnbs2FHt47169ap1MURkvEvXxX40c4a0Q0xLP4mrIaI6oeNpKWMYHW769OlTYdutIyrY54aofiw7kISv4s4jJacYANDUl8uiEFktddlgADtHaeuwEEZHwBs3bhhc0tPTsWHDBnTu3BmbNm2qixqJ6DbqUh2mrDquDzYdQtwQ4uEgcVVEVCcu/AuknxZvq9ylrcVCGN1y4+pacf2Y+++/H3Z2doiNjcWhQ4dMUhgRVU4QBPSdvU1/f/fk+xDgas85aYiskboAWDka0BQAwfcAQZ2lrsgimGwMqK+vL86ePWuqwxFRFZKzi3AlS5xIc3C7AAS6qSSuiIjqhCAAF+KAklzx/si/uEBmDRn9KR07ZjhVuyAISElJwccff4x27dqZqi4iqsLBxBsAgOb+Lpj7VHuJqyEikxEEIHEXkLADSD4IJMcDxdniY5F9OYGfEYwON+3atYNMJoMgCAbb77nnHvz4448mK4yIKrf+eAoAoHukp8SVEJHJ5KYA62JvrvhdTqEEgrsAD3wmTV0Wyuhwk5CQYHBfLpfD29sb9vb2JiuKiCr37urj2HQqDQDQJdxD4mqIyCRyrgKLBgI3EgC5LdDqUTHQBHYEfFsBCk68aSyjw01oaGhd1EFE1UjMKMDqI8n4ZV8SACAq3AM9GntJXBURGa04B8i8AGRcAK7sE09BZV4QH7N3BUZvAHxbSFujFahxuNmyZQvGjx+Pffv2VVgcMycnB926dcPChQvRs2dPkxdJ1FCl5hQj9rcj2HMxU7+tia8TVrzYVcKqiKhamiIgK0EMLZkXgKyLQOZF8XbB9UqeIAP82wLR0xlsTKTG4Wbu3LkYM2ZMpat+u7q64sUXX8Ts2bMZbohM4ERyDl5bfhgXr99cxdvBToFx9zbCU52DJayMiCo4uRpI3FkWZi4BOVcACFXv7+QLeDYC/FoD4b2A0G6cv8bEahxujh49ik8++aTKx/v164fPP//cJEURNUSCIOB6fgkSMwrx5Ld7DR4b2MYfXz3VnmtFEZkTrQY4/Auw9vWKjyldAc9IMcR4Niq7HQl4RAL2FRsJyLRqHG7S0tJga1t1pyYbGxtcv15ZcxsR3S4psxBJWYUoVJfiyo0izNtyHkVqLUpKdQb7vXF/E7zQOwJKG64ETCQpnU5skUk/DVw/DSQfAi5tvzkHDQAMmi8GGY9IwNEL4MSakqlxuAkMDMSJEyfQqFGjSh8/duwY/P39TVYYkTUp1mhxLi0P/9uVgBPJOQanm24X6KZCIx8nTB7QDM39+RcekWQEAdj9JXD6L+D6WUCdX3EfBy+gUV+gxwTAp3n910iVqnG4eeCBBzB16lT079+/wrDvoqIiTJ8+HQMHDjR5gUSWLiWnCE8s3IurN4oMtgd7qODlpISDnQKhno6ICvfAA639Yavgqr9EZuHoMuDf6Tfvy20B76aAdzOx429EH8C/PSDnv1lzIxNun42vCmlpaejQoQMUCgXGjx+Ppk2bAgDOnDmD+fPnQ6vVIj4+Hr6+vnVa8N3Kzc2Fq6srcnJyKu0cTWQqlzML8Ed8MpbuT0JGfgkAoFukJ7ydlXi5TyM09eMq3kRmbdlQcVK9FoOBe98GPCI454yEjPn9XeOWG19fX+zZswdjx47FlClT9DMUy2QyxMTEYP78+WYfbIjqQ7FGi75fbEdy9s2WGncHW/zyfBRaBlRceJaIzJROK143vl9ssSGLYdQkfqGhoVi/fj1u3LiBCxcuQBAENG7cGO7uHMJGpNMJWHM0GRNWHNVv6xjqjrZBbnj9/sZwsedffESWpUYnNsgM1Wp5UXd3d3TuzGXXiQDgRoEay/5Lwqr4ZFxIv9nh8IPBrfD0PZzRm4iovnHtdKK7cDjpBh5dsAflPdfsbOQY0ikY4+5tBD9XrrdGZB04pNvSMNwQ1UJOkQb3fr4NWQVq/bbXoxtjWFQIfJwZaoisQs3G25AZYrghMpJOJ+DJhXv1waZNkCvmDGmHSG8niSsjIiKA4YbIKBtOpGDampNIzxOHdn/6WBs8ybWeiKwbZxq2OAw3RNXQaHX4Mz4Z289dx9UbhTh6NQcAoJDL8Mw9oXiiU5DEFRJR3eFpKUvFcENUhSK1FtGzDeerKbfzzXsR4KaSoCoiqn9subE0DDdEVfh1/2V9sBnRNRQ9GnnBRWWLCG9HdhomagjYodhiMdwQVaH8FFRjHye8N6iVxNUQEVFNMdwQ3eL41RzEJ93A+fQ8/H30GgDgpd6REldFRJJih2KLw3BDDdaJ5BysO56CrHw1cos1OHY1p0L/GielDR5s4y9RhUQkLZ6WslQMN9QgfbbxDOZvvVjl42P7RKKxjxPuifCEva2iHisjIqK7xXBDVksQBOQWlyKnUIOcIg3+OpqM5Owi3CjQYO+lTABAgKs9hkWFwEVlC2d7G4R5OqJtkBvkcjZDE1E5/n9gaRhuyGpk5Jdg94UMnE7Jw8lrOTh1LReZtyyPcDtPRztsf/Ne2Crk9VglEVkMjpayWAw3ZHEEQUBmgRrJN4qQnF2EqzcKsftCJvZezIRaq6uwv8pWATcHW/i42MPF3gaPdQiCq4MtWge6MtgQ0Z2xQ7HFYbghi5CRX4ItZ9Lx6/4knE3NRbGmYogBgGAPFbqEeaJ9iBvaBLmisY8zVHbsM0NEtcGWG0tlFuFm/vz5+Oyzz5Camoq2bdti3rx56NKlS6X7fv/991i8eDFOnDgBAOjYsSM++uijKvcny3T1RiG2nknHPydScSE9X7+W0618XZQIcndAoJsKwR4qNPJxwsA2AWyNISJq4CQPNytWrEBsbCwWLlyIqKgozJ07FzExMTh79ix8fHwq7L9t2zYMHToU3bp1g729PT755BP069cPJ0+eRGBgoATvgEwhq0CNzadSkZ5bgjOpedhwMhVaneFfTY18nNAhxA33t/BDryZeUNqwRYaI6gNPS1kamSBI22MqKioKnTt3xtdffw0A0Ol0CA4OxiuvvILJkyff8flarRbu7u74+uuvMWLEiDvun5ubC1dXV+Tk5MDFxeWu66faySvW4I9DVxF3Jh3xl2+gQK2tsE+EtyMebO2PtkFuuCfSE05KybM4EVk7dQFw7TBw5QBw4DsgLwV49AegzRNSV9bgGfP7W9LfFmq1GocOHcKUKVP02+RyOaKjo7F3794aHaOwsBAajQYeHh51VSaZkE4n4N01J7B0f1KFx4I9VOga4QkPRyU6hrqjbzMfDskmorojCMCNBODKf8DVA2KgSTsJCLf9seUWLE19VGuShpuMjAxotVr4+voabPf19cWZM2dqdIy33noLAQEBiI6OrvTxkpISlJTc7K+Rm5tb+4Kp1vKKNfjneCq+jDuvnwXYwU6B53tGINLbEVHhnvBz5WKURFRPtBpg2VDgwuaKj7kEAkGdxUtEb8Cvdf3XR3fFotv5P/74Yyxfvhzbtm2DvX3lvxhnzZqFmTNn1nNlpNMJSMktxn8JWfgj/ip2ns8wePyl3pGY2K8JbNj5l4iksP/bm8EmqIsYZII7i7dd2X/T0kkabry8vKBQKJCWlmawPS0tDX5+ftU+9/PPP8fHH3+Mf//9F23atKlyvylTpiA2NlZ/Pzc3F8HBbGKsK4cuZ+Gf46lYdTgZWbdNoOestMEDrf3xZOdgdAx1l6hCImqwCrOA+MXAxS1AwnZxW5cXgAc+k7YuMjlJw42dnR06duyIuLg4DB48GIDYoTguLg7jx4+v8nmffvopPvzwQ2zcuBGdOnWq9jWUSiWUSqUpy6ZKXM4swLQ1J7H93HWD7aGe4lDtMT0j0KepN2ScDIuIpKDTAUsGAylHb25zCwXaDpWsJKo7kp+Wio2NxciRI9GpUyd06dIFc+fORUFBAUaPHg0AGDFiBAIDAzFr1iwAwCeffIJp06Zh6dKlCAsLQ2pqKgDAyckJTk5Okr2PhupadhF+3pOIn/cm6ifWC3RT4bGOQRjZNRSeTgyWRCQhrQZIPy2ehioPNv0+BJr0BzwjOfuwlZI83AwZMgTXr1/HtGnTkJqainbt2mHDhg36TsZJSUmQy2/2y1iwYAHUajUef/xxg+NMnz4dM2bMqM/SG7wzqbl4cuFe5BaXAgA6hLjhzf7NEBXuwRYaIqp/Oh2QeV4cyp0cD1yLB1KPA6XFN/fpMALoVvWZAbIOks9zU984z41pLN2fhLf/PA4AcHewxaxHW6NfCz8O3Sai+iUIYh+a//4HJOwA1HkV91G6AAHtgMBOwD1jAaeKE8SS+bOYeW7IMv15+Ko+2ADAihe7oomvs4QVEVGDJAjAzw8BiTtvbrNRAf5tgcAOQEB7IKAD4BEByDkysyFhuCGjCIKA73YkABCXQ1j7Sg/Y23IZBCKqZzotkJ10M9h0fl485eTTElDwV1tDx58AMsr646k4nSJOhLhodGcGGyIyLU0xUJgBFGYCBWXX+elA7jUgN7ns+pq4LMKtMwn3+wCwVUlXN5kVhhuqsdScYoxbGg8AGNQuAEHuDhJXREQWKe0kcPJPIPuKGF70YSYT0BTU/DgyBeDsBzQbyGBDBhhuqEaOXc3G6J/+098f2ydSwmqIyCJoisW1mzIvAJkXxeuUI+IIpurIbQEHT/Hi6Ak4eotLIrgEAi4BZdf+gKMPT0FRpfhTQXekLtXh2UUHkVmgRpC7Ct890wnN/DjSjIjK5FwVW2MyLwJZZSEm8xKQcwVAJQNy5bZA0/7i6CVHr7Ig4yUGGQdPcXQTp5Ogu8BwQ1XKKdRgzdFk/LQ7ERn5JbBVyLDypa7wd2XzLxGV2bcQ2DgFEHSVP650FSfL84wEPBuJl4h7xSBDVEcYbqiC/xKz8OW/57HvUiZKdeJfXSpbBT59vA2DDRHdlH8d2Pi2GGzcw8XVsz0bGQYZB0+2wlC9Y7ghPXWpDm/+fhSrj1zTb2vi64ShXULwaPsguDrYSlgdEZmdwgxxxJKNCnj1MEMMmQ2GGwIgLqXQf+7NibDsFHIsHROFjqHuXEqBqKEryRfnk8m5Kvajyb4i3s6+LD5uzz4yZF4YbggFJaV4btFB/f2hXYLxweDWUHApBaKGSacV55S5kShednx+M8hUJqJPPRVGVDMMNw3cumMpeH3FYWi0Yt+af17rieb+HAlFZLV0WqDgetlEeKlA3jUgN0WcFC83WZz1NzsJ0JUaPs/JDwjuDLgGA65BNy8uQVyricwOw00DlVOkwRu/HcG/p9MBiItfTh3YgsGGyJxpNUDRDfFSmAUU5wAleeJikSXll/yy61xAnV9xuzqv6pFNt1LYAW4hgHsY4N0M6PaKOGEekQVguGmArmQVYvSi/3AhPR8AMKpbGCYPaMalFIjMQe41YM/XQH6qGGCKbgBFWUDhjcpXvK4VGeDkK06E5xxQdu0vTpBXHmic/QE5/08gy8Rw08D8sPMSPtt4FiWlOvi6KPHRI63Rt7mv1GURUbm1scC5f6rZQQbYuwIOHuK10lmc9E7pDNg5ld2/7aLf7gIoncQJ8zizL1kx/nQ3EFeyCjHp96PYdykLAODmYIs/X+6OADfOW0NkVq4eEK/bDgPCe4khRuUBqNxvBhq2qBBVi+GmAcgp0uDJb/ciJacYAPBAaz98PbQD5BwNRWRe1AU3O/L2mAB4N5G2HiILxXBj5YrUWjz/839IySmGs70NXuodiRd6RTDYENUHnQ4oySnrO5N9s/9Meafg/HRxhFJOsjh/THH2zefK5FJVTWTxGG6sWF6xBo8v2IuzaWInxKXP34PWQa4SV0VkxZL2Aec3Awk7gMzzYqCpbOHI6tg5A2E9AI/wuqiQqEFguLFiM/8+hbNpeZDJgBkPtWSwIapL5zYBS5+o/DE7J7HPjMrtZv8Zlbu47pJroDh3jEugeNue/06J7hbDjZVasjcRvx+6CpkM+HFUZ9zblJNsEdWp8hl8PSKBnrFAQHtxVJLKDbBRSloaUUPDcGOFtpxJw9Q1JwEAz3YPZ7Ahqg9ajXjt3xZo/7S0tRA1cAw3VqZIrcXbq04AAKKb++KdB5pLXBGRFdNqgP3fAgnbgfObxG32nOWbSGoMN1Zm0Z5EpOYWI9BNha+HteeoKKK6kH4auLQd2DsfyEm6uV3pCrQdKl1dRASA4cbq/HMiBQAw7t5GXE6ByNQKMoF/pwGHfzHc7t8OGDgH8GsNKGwlKY2IbmK4sSKCIOjXi+oS7iFxNURWpjAL+OYeoEBcbBaRfYHQrkBIVyC0OyBjKymRuWC4sSJFGi0K1VoAgL+rvcTVEFmZG4lisLFzAp7+Awi5R+qKiKgKnALTipy8lgtA/ANSxVNSRCZWNhmfyp3BhsjMMdxYiYKSUrz8azwAIMTDgR2JiYiowWK4sRKL9iTiel4J/Fzs8ctzUVKXQ2R99Kso8A8HInPHcGMFrt4oxMJtFwEA4+9rhGAPB4krIrJizDZEZo/hxgp8uuEs8kpK0TrQFUO7hEhdDpGVMnIBTCKSDMONhbuSVYi/jl4DALw/uBUU7GtDVMf4b4zI3DHcWLiVB68AANqHuKFdsJu0xRBZM4EtN0SWguHGggmCgI0n0wCAp6OI6lxZuOFkfURmj+HGQmUVqDFm8UGcTcuDQi5DnybeUpdERERkFjhDsQW5UaDGtnPp2HU+E/+eTkNOkQY2chmmP9QCPi6ckZioTulPS7HlhsjcMdyYuaNXsvHLvsuIT7qBi9cLDB4L9lBh4dMd0TLAVaLqiBognpYiMnsMN2bs31NpeOmXQyjV3ezI2NTXGX2aeqN7Iy/cE+EJOxueWSSqH+xQTGQpGG7MUE6hBt9sv4Bvt18CALTwd8HEmCZoE+QGLyelxNURNXRsuSEydww3ZqRUq8PqI9cwfc0JFJSt7t2vhS8+e7wtXB1sJa6OqIHjUHAii8FwYwYupOfjt4NXsGx/EvJKSgEAEd6OeHtAc0S38JW4OqIGTBCA7MvAtSPAoUXiNva5ITJ7DDcSKtXq8FXceXyz7aK+X42LvQ2e6BSMSTFNYW+rkLhCogYqLxVI3AXEzQSykwwfa/6wNDURUY0x3EhEEAS8+ccxrIpPBgD0bOyF4VGh6NfCF3IuoUBUv0rygNNrgTNrgSsHgIL0m4/JbQDfVoB/W6DZg0CTGOnqJKIaYbiRyMqDV7EqPhkKuQyfP9EGg9sFQsbmbqL6p9MCC7oZttDI5IBnYyCiD3DfO4A9p1sgsiQMN/WsWKPFnH/P6UdCTYhujEfaB0lcFZGVupEInFoDFN0AirKB4hygONvwdmHmzf07jATaDQf8WgF2jpKUTER3j+GmHm04kYLpf51EWm4JAGBE11CM7dNI4qqIrIxOJ55Wyr4CLBtiGF6qonQB+n0AdBxZ9/URUZ1juKkHmfklmP7XSaw9lgIACHC1x7SHWqJ/Kz+JKyOyUIIgtrzkpQDXz4ijmVKPiS01OVcBrdpw/4h7gZB7xNNL9m6Ays3wtoMXYGNX3++CiOoIw00dyivW4M/Dyfjy3/PILFBDIZfhpd4ReOW+xhwJRVQZQQA0RWIH3+IcIOeK2BcmO0m8nXtNDDS5KUBpUdXHkckBZ3/AIwLo+QYQeW/9vQcikhzDTR25kJ6Pp77bi4x88S/Ipr7O+PyJtmgdxI6J1MAIws0WlutngYxz4qmikjygJL/sOg9Ql10Lupof294VcA8DAtoD/u0AryaAaxDgEgAoOPElUUPFcFMHijVavLD4IDLy1Qh0U2FMz3AMiwrlOlBU93JTxOAgaAFdqdj/RFd6y32teDG4XyoGCoP72pv7Vvb8avcpP5ZGbHG5fhYoyTXyjcgApTPgEgi4hZRdgsX7zv6Aiz/g5AfYOdTJx0hElo3hpg58veUCLmUUwMdZiTXju3M9KGsmCLeEhVuvdfW3vTBLPGWTehxI2iv1J1I5mUI8ReTdVLw4+wN2TmKAUZZd2znfvG/rCMj5xwAR1Q7DjYml5BTh2x0XAQDvDWrJYGNOtBoxCGgKgNKSmxdt+e3iKrapxWut2nCf479J/Y4qp3QVhzHLFeJFphAnoqv0vo3YP8XgvuLmvvr7NmLYMLhfg+M7+wPezQDPSMCG/xaIqH4w3JjYt9svQaMV0CXMA/1b+UtdjvXJuSqOiMlLBTSFZYFDLV7feinfVpQFZJwXO6IWZdV/vTL5zbCgv5bfdl8hBocK+1W1/bbnK13EUzauwUBAOyCwY/2/TyIiM8JwY2KbT6UBAF7qEyFxJVagVA3kJgNn1wOZF4H0UyY47SITWzUUdoCNvdiaUH5RlN+2r2LbLc8p327vJo7EqazlQybnIotERBJguDGhklItruWIw1PbBLlJW4ylKMwC4t4TO54W54gdT4tzxduVDvWVAR7hgHOAGFJs7MSgobAru33LxUYp9uvwaiy2ajh6Aw4eYvAgIiKrxXBjQleyiiAIgKOdAp6OZjYhmFYjTkFfmHnLJcvwuujGzZEzEMRrQSi76G7brru5/Y773r7tlu1516qvW24jDvMN6wm4BgKNosWhv0RERFUwi3Azf/58fPbZZ0hNTUXbtm0xb948dOnSpcr9V65cialTpyIxMRGNGzfGJ598ggceeKAeK67c5cwCAECIp2PdLoKpKRL7kJz9R+z4qtWUdYJVi9cJOwBnv1vWzrkBlOTUXT2m4BoCDPhY7D9i7wrYl10rXdjSQkRERpE83KxYsQKxsbFYuHAhoqKiMHfuXMTExODs2bPw8fGpsP+ePXswdOhQzJo1CwMHDsTSpUsxePBgxMfHo1WrVhK8g5sOJ2UDAJr7O9fsCepCID+tbCG/G7dcsg2vb3+8tPjOx844W8lGGaByBxw8yy4eZZey+yp38XQOZDf7i8jKb8tv217ZNtlt22q4r429eOqI/VOIiMgEZIIgCFIWEBUVhc6dO+Prr78GAOh0OgQHB+OVV17B5MmTK+w/ZMgQFBQUYO3atfpt99xzD9q1a4eFCxfe8fVyc3Ph6uqKnJwcuLi4mO6NlKoxZsE/uHQtDW/dF4x+kaqylYezxeu8FCD9tHg6Rp0vhpQbibV/PVlZh1XXILFDq0Jp2P8Egjhjq73LzfBi78pWECIiskjG/P6WtOVGrVbj0KFDmDJlin6bXC5HdHQ09u6tfFTM3r17ERsba7AtJiYGq1evrnT/kpISlJSU6O/n5ho7U2rNHNy9Cd9njgCUAHaXXWrKOUBsNVG53XZddrG/7b7KXZzsjC0dREREFUgabjIyMqDVauHr62uw3dfXF2fOnKn0OampqZXun5qaWun+s2bNwsyZM01TcDWCfL2ggQJquQMcnVzFWVb1qw+XXSudAZWHOFOr0lkcvePdpM5rIyIiakgk73NT16ZMmWLQ0pObm4vg4GCTv45f0ygkjL+CEA8HQM4WFSIiIqlIGm68vLygUCiQlpZmsD0tLQ1+fn6VPsfPz8+o/ZVKJZTKepj2XSZDuJdj3b8OERERVUvSlens7OzQsWNHxMXF6bfpdDrExcWha9eulT6na9euBvsDwObNm6vcn4iIiBoWyU9LxcbGYuTIkejUqRO6dOmCuXPnoqCgAKNHjwYAjBgxAoGBgZg1axYA4LXXXkPv3r3xxRdf4MEHH8Ty5ctx8OBBfPfdd1K+DSIiIjITkoebIUOG4Pr165g2bRpSU1PRrl07bNiwQd9pOCkpCXL5zQambt26YenSpXj33Xfx9ttvo3Hjxli9erXkc9wQERGReZB8npv6Vmfz3BAREVGdMeb3t6R9boiIiIhMjeGGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNwQERGRVWG4ISIiIqvCcENERERWRfLlF+pb+YTMubm5EldCRERENVX+e7smCys0uHCTl5cHAAgODpa4EiIiIjJWXl4eXF1dq92nwa0tpdPpcO3aNTg7O0Mmk5n02Lm5uQgODsaVK1e4bpVE+B1Ij9+BeeD3ID1+B6YlCALy8vIQEBBgsKB2ZRpcy41cLkdQUFCdvoaLiwt/kCXG70B6/A7MA78H6fE7MJ07tdiUY4diIiIisioMN0RERGRVGG5MSKlUYvr06VAqlVKX0mDxO5AevwPzwO9BevwOpNPgOhQTERGRdWPLDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNyYyPz58xEWFgZ7e3tERUXhwIEDUpdktWbMmAGZTGZwadasmf7x4uJijBs3Dp6ennBycsJjjz2GtLQ0CSu2Djt27MBDDz2EgIAAyGQyrF692uBxQRAwbdo0+Pv7Q6VSITo6GufPnzfYJysrC8OHD4eLiwvc3Nzw3HPPIT8/vx7fhWW703cwatSoCv82+vfvb7APv4O7M2vWLHTu3BnOzs7w8fHB4MGDcfbsWYN9avJ/UFJSEh588EE4ODjAx8cHkyZNQmlpaX2+FavGcGMCK1asQGxsLKZPn474+Hi0bdsWMTExSE9Pl7o0q9WyZUukpKToL7t27dI/NmHCBPz9999YuXIltm/fjmvXruHRRx+VsFrrUFBQgLZt22L+/PmVPv7pp5/iq6++wsKFC7F//344OjoiJiYGxcXF+n2GDx+OkydPYvPmzVi7di127NiBF154ob7egsW703cAAP379zf4t7Fs2TKDx/kd3J3t27dj3Lhx2LdvHzZv3gyNRoN+/fqhoKBAv8+d/g/SarV48MEHoVarsWfPHvz8889YtGgRpk2bJsVbsk4C3bUuXboI48aN09/XarVCQECAMGvWLAmrsl7Tp08X2rZtW+lj2dnZgq2trbBy5Ur9ttOnTwsAhL1799ZThdYPgPDnn3/q7+t0OsHPz0/47LPP9Nuys7MFpVIpLFu2TBAEQTh16pQAQPjvv//0+/zzzz+CTCYTkpOT6612a3H7dyAIgjBy5Ehh0KBBVT6H34HppaenCwCE7du3C4JQs/+D1q9fL8jlciE1NVW/z4IFCwQXFxehpKSkft+AlWLLzV1Sq9U4dOgQoqOj9dvkcjmio6Oxd+9eCSuzbufPn0dAQAAiIiIwfPhwJCUlAQAOHToEjUZj8H00a9YMISEh/D7qUEJCAlJTUw0+d1dXV0RFRek/971798LNzQ2dOnXS7xMdHQ25XI79+/fXe83Watu2bfDx8UHTpk0xduxYZGZm6h/jd2B6OTk5AAAPDw8ANfs/aO/evWjdujV8fX31+8TExCA3NxcnT56sx+qtF8PNXcrIyIBWqzX4IQUAX19fpKamSlSVdYuKisKiRYuwYcMGLFiwAAkJCejZsyfy8vKQmpoKOzs7uLm5GTyH30fdKv9sq/t3kJqaCh8fH4PHbWxs4OHhwe/GRPr374/FixcjLi4On3zyCbZv344BAwZAq9UC4HdgajqdDq+//jq6d++OVq1aAUCN/g9KTU2t9N9K+WN09xrcquBk+QYMGKC/3aZNG0RFRSE0NBS//fYbVCqVhJURSeupp57S327dujXatGmDyMhIbNu2DX379pWwMus0btw4nDhxwqDPH5kHttzcJS8vLygUigo94dPS0uDn5ydRVQ2Lm5sbmjRpggsXLsDPzw9qtRrZ2dkG+/D7qFvln211/w78/PwqdLIvLS1FVlYWv5s6EhERAS8vL1y4cAEAvwNTGj9+PNauXYutW7ciKChIv70m/wf5+flV+m+l/DG6eww3d8nOzg4dO3ZEXFycfptOp0NcXBy6du0qYWUNR35+Pi5evAh/f3907NgRtra2Bt/H2bNnkZSUxO+jDoWHh8PPz8/gc8/NzcX+/fv1n3vXrl2RnZ2NQ4cO6ffZsmULdDodoqKi6r3mhuDq1avIzMyEv78/AH4HpiAIAsaPH48///wTW7ZsQXh4uMHjNfk/qGvXrjh+/LhB0Ny8eTNcXFzQokWL+nkj1k7qHs3WYPny5YJSqRQWLVoknDp1SnjhhRcENzc3g57wZDpvvPGGsG3bNiEhIUHYvXu3EB0dLXh5eQnp6emCIAjCSy+9JISEhAhbtmwRDh48KHTt2lXo2rWrxFVbvry8POHw4cPC4cOHBQDC7NmzhcOHDwuXL18WBEEQPv74Y8HNzU1Ys2aNcOzYMWHQoEFCeHi4UFRUpD9G//79hfbt2wv79+8Xdu3aJTRu3FgYOnSoVG/J4lT3HeTl5QkTJ04U9u7dKyQkJAj//vuv0KFDB6Fx48ZCcXGx/hj8Du7O2LFjBVdXV2Hbtm1CSkqK/lJYWKjf507/B5WWlgqtWrUS+vXrJxw5ckTYsGGD4O3tLUyZMkWKt2SVGG5MZN68eUJISIhgZ2cndOnSRdi3b5/UJVmtIUOGCP7+/oKdnZ0QGBgoDBkyRLhw4YL+8aKiIuHll18W3N3dBQcHB+GRRx4RUlJSJKzYOmzdulUAUOEycuRIQRDE4eBTp04VfH19BaVSKfTt21c4e/aswTEyMzOFoUOHCk5OToKLi4swevRoIS8vT4J3Y5mq+w4KCwuFfv36Cd7e3oKtra0QGhoqjBkzpsIfWfwO7k5lnz8A4aefftLvU5P/gxITE4UBAwYIKpVK8PLyEt544w1Bo9HU87uxXjJBEIT6bi0iIiIiqivsc0NERERWheGGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNwQERGRVWG4ISIiIqvCcENkBvr06YPXX39d6jLMwrZt2yCTySosPFgXMjMz4ePjg8TExHp/bXNjzM/gU089hS+++KJuCyK6Cww3RPVg1KhRkMlkFS7lqzXXtxkzZuhrsLGxgZeXF3r16oW5c+eipKSk3uqo7Bdqt27dkJKSAldX1zp//Q8//BCDBg1CWFhYvb+2JXv33Xfx4YcfIicnR+pSiCrFcENUT/r374+UlBSDy+0rCtenli1bIiUlBUlJSdi6dSueeOIJzJo1C926dUNeXt5dHVuj0dT6uXZ2dvDz84NMJrurGu6ksLAQ//vf//Dcc8/V+2tbulatWiEyMhK//PKL1KUQVYrhhqieKJVK+Pn5GVwUCkWl+964cQMjRoyAu7s7HBwcMGDAAJw/fx4AIAgCvL298fvvv+v3b9euHfz9/fX3d+3aBaVSicLCwirrsbGxgZ+fHwICAtC6dWu88sor2L59O06cOIFPPvlEv59MJsPq1asNnuvm5oZFixYBABITEyGTybBixQr07t0b9vb2+PXXX5GZmYmhQ4ciMDAQDg4OaN26NZYtW6Y/xqhRo7B9+3Z8+eWX+lakxMTESk8N/fHHH2jZsiWUSiXCwsIqnBIJCwvDRx99hGeffRbOzs4ICQnBd999V+V7B4D169dDqVTinnvu0W+7/bUXLVoENzc3bNy4Ec2bN4eTk5M+pFblxo0bGD58OLy9vaFSqdC4cWP89NNP+sevXLmCJ598Em5ubvDw8MCgQYP0p8XK/fjjj/r36+/vj/Hjx+sfS0pKwqBBg+Dk5AQXFxc8+eSTSEtL0z8+Y8YMtGvXDkuWLEFYWBhcXV3x1FNPGQTWgoICjBgxAk5OTvD396/0FNM333yDxo0bw97eHr6+vnj88ccNHn/ooYewfPnyaj9jIqkw3BCZoVGjRuHgwYP466+/sHfvXgiCgAceeAAajQYymQy9evXCtm3bAIi/TE+fPo2ioiKcOXMGALB9+3Z07twZDg4ORr1us2bNMGDAAKxatcromidPnozXXnsNp0+fRkxMDIqLi9GxY0esW7cOJ06cwAsvvIBnnnkGBw4cAAB8+eWX6Nq1K8aMGaNvyQoODq5w3EOHDuHJJ5/EU089hePHj2PGjBmYOnWqPlyV++KLL9CpUyccPnwYL7/8MsaOHYuzZ89WWe/OnTvRsWPHO76vwsJCfP7551iyZAl27NiBpKQkTJw4scr9p06dilOnTuGff/7B6dOnsWDBAnh5eQEQW7RiYmLg7OyMnTt3Yvfu3frApFarAQALFizAuHHj8MILL+D48eP466+/0KhRIwCATqfDoEGDkJWVhe3bt2Pz5s24dOkShgwZYlDDxYsXsXr1aqxduxZr167F9u3b8fHHH+sfnzRpErZv3441a9Zg06ZN2LZtG+Lj4/WPHzx4EK+++iree+89nD17Fhs2bECvXr0MXqNLly44cOBAvZ7GJKoxaRclJ2oYRo4cKSgUCsHR0VF/efzxx/WP9+7dW3jttdcEQRCEc+fOCQCE3bt36x/PyMgQVCqV8NtvvwmCIAhfffWV0LJlS0EQBGH16tVCVFSUMGjQIGHBggWCIAhCdHS08Pbbb1dZz/Tp04W2bdtW+thbb70lqFQq/X0Awp9//mmwj6urq/DTTz8JgiAICQkJAgBh7ty5d/wcHnzwQeGNN96o9H2X27p1qwBAuHHjhiAIgjBs2DDh/vvvN9hn0qRJQosWLfT3Q0NDhaefflp/X6fTCT4+PvrPozKDBg0Snn322Wpf+6effhIACBcuXNDvM3/+fMHX17fK4z700EPC6NGjK31syZIlQtOmTQWdTqffVlJSIqhUKmHjxo2CIAhCQECA8M4771T6/E2bNgkKhUJISkrSbzt58qQAQDhw4IAgCOJ36+DgIOTm5ur3mTRpkhAVFSUIgiDk5eUJdnZ2+p8lQRCEzMxMQaVS6b+LP/74Q3BxcTE4xu2OHj0qABASExOr3IdIKmy5Iaon9957L44cOaK/fPXVV5Xud/r0adjY2CAqKkq/zdPTE02bNsXp06cBAL1798apU6dw/fp1bN++HX369EGfPn2wbds2aDQa7NmzB3369KlVnYIg1KrPSadOnQzua7VavP/++2jdujU8PDzg5OSEjRs3Iikpyajjnj59Gt27dzfY1r17d5w/fx5arVa/rU2bNvrbMpkMfn5+SE9Pr/K4RUVFsLe3v+PrOzg4IDIyUn/f39+/2uOOHTsWy5cvR7t27fDmm29iz549+seOHj2KCxcuwNnZGU5OTnBycoKHhweKi4tx8eJFpKen49q1a+jbt2+lxz59+jSCg4MNWrhatGgBNzc3/c8GIJ6mc3Z2rrTmixcvQq1WG/x8eXh4oGnTpvr7999/P0JDQxEREYFnnnkGv/76a4VTnCqVCgCqPfVJJBWGG6J64ujoiEaNGukvt/aRMVZ5YNi+fbtBuNm+fTv+++8/aDQadOvWrVbHPn36tEFHZ5lMBkEQDPaprMOwo6Ojwf3PPvsMX375Jd566y1s3boVR44cQUxMjP70i6nZ2toa3JfJZNDpdFXu7+XlhRs3btTquLd/HrcaMGAALl++jAkTJuiDSvlprPz8fHTs2NEg5B45cgTnzp3DsGHD9IHhbhn7WdzO2dkZ8fHxWLZsGfz9/TFt2jS0bdvWoB9UVlYWAMDb29skNROZEsMNkZlp3rw5SktLsX//fv22zMxMnD17Fi1atAAg/rLq2bMn1qxZg5MnT6JHjx5o06YNSkpK8O2336JTp04VwkZNnDlzBhs2bMBjjz2m3+bt7W3Qgfb8+fM1+mt99+7dGDRoEJ5++mm0bdsWEREROHfunME+dnZ2Bq0vlWnevDl2795d4dhNmjSpskN2TbRv3x6nTp2q9fOr4+3tjZEjR+KXX37B3Llz9Z2bO3TogPPnz8PHx8cg6DZq1Aiurq5wdnZGWFgY4uLiKj1u8+bNceXKFVy5ckW/7dSpU8jOztb/bNxJZGQkbG1tDX6+bty4UeG7sbGxQXR0ND799FMcO3YMiYmJ2LJli/7xEydOICgoSN+fiMicMNwQmZnGjRtj0KBBGDNmDHbt2oWjR4/i6aefRmBgIAYNGqTfr0+fPli2bBnatWsHJycnyOVy9OrVC7/++it69+59x9cpLS1Famoqrl27huPHj2PevHno3bs32rVrh0mTJun3u++++/D111/j8OHDOHjwIF566aUKLQNVvY/Nmzdjz549OH36NF588UWDUT2AePpk//79SExMREZGRqWtC2+88Qbi4uLw/vvv49y5c/j555/x9ddfV9uptyZiYmJw8uTJGrXeGGPatGlYs2YNLly4gJMnT2Lt2rVo3rw5AGD48OHw8vLCoEGDsHPnTiQkJGDbtm149dVXcfXqVQDiaKcvvvgCX331Fc6fP4/4+HjMmzcPABAdHY3WrVtj+PDhiI+Px4EDBzBixAj07t27wmnBqjg5OeG5557DpEmTsGXLFpw4cQKjRo2CXH7z18HatWvx1Vdf4ciRI7h8+TIWL14MnU5ncOpq586d6Nevn6k+NiKTYrghMkM//fQTOnbsiIEDB6Jr164QBAHr1683CBW9e/eGVqs16FvTp0+fCtuqcvLkSfj7+yMkJAR9+vTBb7/9hilTpmDnzp1wcnLS7/fFF18gODgYPXv2xLBhwzBx4sQajcJ699130aFDB8TExKBPnz7w8/PD4MGDDfaZOHEiFAoFWrRoAW9v70r743To0AG//fYbli9fjlatWmHatGl47733MGrUqDvWUJ3WrVvrj21KdnZ2mDJlCtq0aYNevXpBoVDoh0w7ODhgx44dCAkJwaOPPormzZvjueeeQ3FxMVxcXAAAI0eOxNy5c/HNN9+gZcuWGDhwoH4aAJlMhjVr1sDd3R29evVCdHQ0IiIisGLFCqNq/Oyzz9CzZ0889NBDiI6ORo8ePQxGjrm5uWHVqlW477770Lx5cyxcuBDLli1Dy5YtAQDFxcVYvXo1xowZY4qPjMjkZEJ1J4+JiKzYunXrMGnSJJw4ccKg5YKqt2DBAvz555/YtGmT1KUQVcpG6gKIiKTy4IMP4vz580hOTq50jh2qnK2trf5UGZE5YssNERERWRW2wxIREZFVYbghIiIiq8JwQ0RERFaF4YaIiIisCsMNERERWRWGGyIiIrIqDDdERERkVRhuiIiIyKow3BAREZFV+T9mqEckcQr5TQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "1st Plot\n",
        "'''\n",
        "\n",
        "# Because some flows appear only once the tcp_flows_dur dictionary may have\n",
        "# only one timestamp (i.e. [1261068567.644630000, 0]). Thats why it's mandatory\n",
        "# to have two checking conditions in order not to get negative results. Then we\n",
        "# substract the ending and starting timestamps and convert them into seconds.\n",
        "# We do that for both TCP and UDP dictionaries. \n",
        "for flow_key in tcp_flows_dur:\n",
        "    if tcp_flows_dur[flow_key][1] != 0:\n",
        "      duration = (dt.datetime.utcfromtimestamp(tcp_flows_dur[flow_key][1]) - dt.datetime.utcfromtimestamp(tcp_flows_dur[flow_key][0])).total_seconds()\n",
        "    else:\n",
        "      duration = (dt.datetime.utcfromtimestamp(tcp_flows_dur[flow_key][0]) - dt.datetime.utcfromtimestamp(tcp_flows_dur[flow_key][0])).total_seconds()\n",
        "    tcp_durations.append(duration)\n",
        "\n",
        "for flow_key in udp_flows_dur:\n",
        "    if udp_flows_dur[flow_key][1] != 0:\n",
        "      duration = (dt.datetime.utcfromtimestamp(udp_flows_dur[flow_key][1]) - dt.datetime.utcfromtimestamp(udp_flows_dur[flow_key][0])).total_seconds()\n",
        "    else:\n",
        "      duration = (dt.datetime.utcfromtimestamp(udp_flows_dur[flow_key][0]) - dt.datetime.utcfromtimestamp(udp_flows_dur[flow_key][0])).total_seconds()\n",
        "    udp_durations.append(duration)\n",
        "\n",
        "# Compute the CDF for TCP flows\n",
        "tcp_durations.sort()\n",
        "tcp_cdf_dur = np.cumsum(tcp_durations) / sum(tcp_durations)\n",
        "\n",
        "# Compute the CDF for UDP flows\n",
        "udp_durations.sort()\n",
        "udp_cdf_dur = np.cumsum(udp_durations) / sum(udp_durations)\n",
        "\n",
        "# Plot the CDFs\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(tcp_durations, tcp_cdf_dur, label='TCP')\n",
        "ax.plot(udp_durations, udp_cdf_dur, label='UDP')\n",
        "ax.legend()\n",
        "ax.set_title('CDF of Flow Durations')\n",
        "ax.set_xlabel('Flow Duration (in seconds)')\n",
        "ax.set_ylabel('Cumulative Probability')\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('FlowDurations-CDF.png', bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Comments: The above diagram shows that TCP flows follow a linear increase as the seconds pass, while UDP flows appear to follow an exponential increase from a certain point onwards. More specifically, the linear increase in the duration of TCP flows is expected, since TCP is a reliable protocol that guarantees data delivery. As a result, the duration of TCP flows will increase as data is transmitted over the link.\n",
        "\n",
        "On the other hand, the exponential increase in the duration of UDP flows suggests that there are a large number of UDP flows with long lifetimes in the trace file. Unlike TCP, UDP is an unreliable protocol that does not guarantee data delivery. Therefore, UDP flows can have a wide range of durations and some flows may remain active for a long time. The sharp increase in the duration of UDP flows after 180 seconds suggests that 0.8 of the cumulative probability of UDP flows have a longer lifetime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UBjA7bAQtw_X",
        "outputId": "5bfbc86c-9880-467f-8547-365e901cb4ed"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "2nd Plot\n",
        "'''\n",
        "\n",
        "# Compute the CDF for TCP flows\n",
        "tcp_sizes = list(tcp_flows.values())\n",
        "tcp_sizes.sort()\n",
        "tcp_cdf = np.cumsum(tcp_sizes) / sum(tcp_sizes)\n",
        "\n",
        "# Compute the CDF for UDP flows\n",
        "udp_sizes = list(udp_flows.values())\n",
        "udp_sizes.sort()\n",
        "udp_cdf = np.cumsum(udp_sizes) / sum(udp_sizes)\n",
        "\n",
        "# Plot the CDFs\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(tcp_sizes, tcp_cdf, label='TCP')\n",
        "ax.plot(udp_sizes, udp_cdf, label='UDP')\n",
        "ax.legend()\n",
        "ax.set_title('CDF of Flow Sizes')\n",
        "ax.set_xlabel('Flow size (in bytes)')\n",
        "ax.set_ylabel('Cumulative Probability')\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('FlowSize-CDF.png', bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Comments: By plotting this CDF, we can see the percentage of network traffic consisting of small, medium and large flows for TCP and UDP protocols separately. More specifically for UDP packets, there is a sharp increase from 0 to 200,000 bytes representing 0.6 of the cumulative probability, which suggests that the majority of UDP flows are relatively small in size, which is common in many network traffic scenarios. This could be due to small control messages or data packets used for real-time applications such as VoIP. After 200,000 bytes to 2,000,000 bytes, there is a steady increase in the sizes of UDP flows.\n",
        "\n",
        "The sharp increase in TCP flows from 0 to 250,000 bytes representing 0.6 of the cumulative probability suggests that a significant number of TCP flows are small in size. This indicates that half or more of the number of TCP flows are starting and ending quickly and carrying only a small amount of data, as is the case for UDP.\n",
        "\n",
        "After 250,000 bytes, there is a steady increase in the size of TCP streams, indicating that there are TCP streams carrying large volumes of data. In fact, these far outnumber UDP flows by far, reaching 7,000,000 bytes, which could indicate relatively larger file-data transfers, video streams and application usage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dZrIAuhhf3Rc",
        "outputId": "9388bd82-8d32-444f-b9a5-e473e1f161ed"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "3rd Plot\n",
        "'''\n",
        "\n",
        "# Convert and sort the list of packet lengths \n",
        "# to a NumPy array for easier manipulation\n",
        "total_packet_lengths = np.array(total_packet_lengths)\n",
        "total_sorted_lengths = np.sort(total_packet_lengths)\n",
        "\n",
        "# Calculate the CDF\n",
        "yvals = np.arange(len(total_sorted_lengths)) / float(len(total_sorted_lengths))\n",
        "\n",
        "# Plot the CDF\n",
        "plt.plot(total_sorted_lengths, yvals)\n",
        "plt.title('CDF of Packet Size')\n",
        "plt.xlabel('Packet Size (in bytes)')\n",
        "plt.ylabel('Cumulative Probability')\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('PacketSize-CDF.png', bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Comments: the above diagram shows that 50% of the trace.pcap packets are up to ~55 bytes in size, while the other 50% are close to 80 bytes and possibly more. Also, it is important to note that in the interval 55 to ~78 bytes the cumulative probability has almost no increase which means that few packets take on these sizes. Overall, the CDF plot of packet sizes gives us information about their distribution in the flow set, which can help us understand the network traffic and identify potential problems or anomalies.\n",
        "\n",
        "In the code the counting of protocols was done with several control commands. In the dpkt documentation and on the web in general, all available implementations included the if isinstance(eth.data, dpkt.ip.IP) check which checks if the given packet is of type IPv4. This got me interested to see if there were IPv6 packets in the trace.pcap file and after checking in Wireshark I saw that there were 16 UDP and 2 ICMP packets, which means that we would have missed them in the previous graphs if we didn't use if isinstance(eth.data, dpkt.ip.IP) or isinstance(eth.data, dpkt.ip6.IP6) as a check."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "y01FXG5RY5Fc",
        "outputId": "ae92fbe9-2749-49d2-cb5b-6d33a705839a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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27ZPeTAukp6cDAP79918A0Li7zsDAQOM00OXLlxEfH1/oB/nz9RdFwfrd3d3V2o2MjODq6ipNL6Bt3+vq+f1bEKwK9u+VK1cghMCXX36JL7/8Uusy7t69q/FhWcDAwAA9e/bEL7/8gpycHCiVSmzduhVPnz5VC1MAsGrVKixcuBCXLl3C06dPpfZq1appLFdb2/MK26/As9PA0dHRxb74uU+fPvjpp5/w4YcfYtKkSWjbti169OiBXr16FetC8cJo266Cuz+XLFmC69evq13v99/TZ3IVnJKrU6eOzst40XtMAQMDA7VT58CzY+Xk5AQLCwu19oLT9c//DTzv3r17yM7OLvRYq1Qq3Lx5E56enrh69Sp69uz5wuX9+++/qFGjhkbtRann66+/RnBwMJydneHj44NOnTph4MCBaiGPXj2GKXplGjduXOjzZVQqFRQKBf744w+td/g8f53F8//3XFQqlQp2dnZYt26d1unPh5TC7jYS/7kotTBpaWnw8/ODpaUlZsyYATc3NxgbGyMuLg6ff/65TiMLKpUKXl5eWLRokdbpzs7OxV5mcem677V52f4t2EcTJkxAQECA1r4ve8RD3759sWzZMvzxxx8IDAzExo0b4eHhAW9vb6nP2rVrERISgsDAQEycOBF2dnbQ19fHnDlz1G5eKFCS+wAofOTl+RsVTExMcOjQIezfvx+///47oqKisGHDBrRp0wZ79uyRfXectu2aPXs2vvzySwwePBgzZ85ExYoVoaenh7Fjx76S0TE5XvQeU+C/I23lUVBQEFq0aIFt27Zhz549mD9/PubNm4etW7eiY8eOpV3eW4NhikqFm5sbhBCoVq0aatasqfMyTpw4gadPnxb6rB83NzfExsaiWbNmJfaBWNgH4YEDB3D//n1s3boVLVu2lNr/e/ciAOlupytXrqB169ZSe15eHhITE9Vua3dzc8O5c+fQtm3bIp/6eJmC9SckJKj932tubi6uX78Of39/nZctt8aCegwNDXWuo2XLlnB0dMSGDRvQvHlz7Nu3D1OmTFHrs3nzZri6umLr1q1qNRfc/aiL/+7X5126dAm2trbSqJS1tbXGnZOA9hEIPT09tG3bFm3btsWiRYswe/ZsTJkyBfv375d1rAqzefNmtG7dGitWrFBrT0tLg62tbYmtp+BC7vPnz7+WZ6D9V9WqVREbG4tHjx6pjU4VnIovOJaA9t/pSpUqwdTUtNBjraenJ/2PjpubG86fP//Sev766y+oVCq14KetHm0cHR3xySef4JNPPsHdu3fRoEEDzJo1i2HqNSq/cZ3eaD169IC+vj6mT5+uMeojhMD9+/dfuoyePXsiNTUVixcv1phWsMygoCDk5+dj5syZGn3y8vK0fqC9TMEH4vPzFowS/Hd7cnNzsWTJErV+DRs2hI2NDZYvX468vDypfd26dRqnE4OCgnD79m0sX75co47Hjx8jKyur2PX7+/vDyMgI3333nVqtK1asQHp6Ojp37lzsZRYobN8UlZ2dHVq1aoVly5YhKSlJY3rBM4xeRE9PD7169cLOnTuxZs0a5OXlaZzi03asTpw4gePHj+tUN/DsA61evXpYtWqV2vafP38ee/bsQadOnaQ2Nzc3pKen46+//pLakpKSsG3bNrVlPnjwQGM9BXcavuzRGLrS19fX+JvctGnTC69V00X79u1hYWGBOXPm4MmTJ2rTijISLEenTp2Qn5+v8d7xzTffQKFQqIUQMzMzrX/r7du3x2+//ab2qIeUlBT88ssvaN68OSwtLQE8e586d+6cxrEF/m87O3XqhOTkZLVr/fLy8vD999/D3Nwcfn5+WrcjPz9funyggJ2dHZycnF7Z7wdpx5EpKhVubm746quvMHnyZOmxABYWFrh+/Tq2bduGYcOGqT1VWZuBAwdi9erVGD9+PE6ePIkWLVogKysLsbGx+OSTT9CtWzf4+fnho48+wpw5c3D27Fm0b98ehoaGuHz5MjZt2oRvv/0WvXr1Klbt9erVg76+PubNm4f09HQolUq0adMGTZs2hbW1NYKDgzF69GgoFAqsWbNG44PByMgIYWFhGDVqFNq0aYOgoCAkJiYiMjISbm5uav8nPGDAAGzcuBEff/wx9u/fj2bNmiE/Px+XLl3Cxo0bER0dXeyv6qhUqRImT56M6dOno0OHDnjvvfeQkJCAJUuWoFGjRi98sOPL+Pj4AABGjx6NgIAA6OvrS48jKKqIiAg0b94cXl5eGDp0KFxdXZGSkoLjx4/j1q1bas87KkyfPn3w/fffIzQ0FF5eXtK1JwW6dOmCrVu3onv37ujcuTOuX7+OpUuXonbt2sjMzCxWvf81f/58dOzYEU2aNMGQIUOkRyNYWVmpPeOrb9+++Pzzz9G9e3eMHj0a2dnZ+OGHH1CzZk21i7xnzJiBQ4cOoXPnzqhatSru3r2LJUuWoEqVKmjevLnOdb5Ily5dMGPGDAwaNAhNmzbF33//jXXr1pX4NTiWlpb45ptv8OGHH6JRo0Z4//33YW1tjXPnziE7O7tIz1jSVdeuXdG6dWtMmTIFiYmJ8Pb2xp49e/Dbb79h7Nix0qgZ8Ox3OjY2FosWLYKTkxOqVasGX19ffPXVV9IzwD755BMYGBhg2bJlyMnJwddffy3NP3HiRGzevBm9e/fG4MGD4ePjgwcPHmDHjh1YunQpvL29MWzYMCxbtgwhISE4ffo0XFxcsHnzZhw9ehTh4eEa13YVePToEapUqYJevXrB29sb5ubmiI2NxZ9//omFCxe+sv1HWrz+GwipvCu4bfnPP/98ad8tW7aI5s2bCzMzM2FmZiY8PDzEiBEjREJCgtTHz89PeHp6ap0/OztbTJkyRVSrVk0YGhoKBwcH0atXL7XblYUQ4scffxQ+Pj7CxMREWFhYCC8vL/HZZ5+JO3fuSH2qVq0qOnfurLEOPz8/tccVCCHE8uXLhaurq9DX11d7TMLRo0fFu+++K0xMTISTk5P47LPPpNv1n3+UwnfffSeqVq0qlEqlaNy4sTh69Kjw8fERHTp0UOuXm5sr5s2bJzw9PYVSqRTW1tbCx8dHTJ8+XaSnp79w/2p7NEKBxYsXCw8PD2FoaCjs7e3F8OHDxcOHDzW2vbB9r01eXp4YNWqUqFSpklAoFNJt/oXdLi+E5q3eQghx9epVMXDgQOHg4CAMDQ1F5cqVRZcuXcTmzZuLVIdKpRLOzs5ab38vmD579mxp/9evX1/s2rVL45EFL6pb26MRhBAiNjZWNGvWTJiYmAhLS0vRtWtXcfHiRY359+zZI+rUqSOMjIyEu7u7WLt2rcajEfbu3Su6desmnJychJGRkXBychL9+vUT//zzT5H2gxAvfjTCpk2bNPo/efJEfPrpp8LR0VGYmJiIZs2aiePHj2v9O4CMRyMU2LFjh2jatKm0vxo3bix+/fXXF25TUd9jgoODhZmZmdZpjx49EuPGjRNOTk7C0NBQ1KhRQ8yfP1/jsQyXLl0SLVu2FCYmJgKA2mMS4uLiREBAgDA3NxempqaidevW4tixYxrrun//vhg5cqSoXLmyMDIyElWqVBHBwcEiNTVV6pOSkiIGDRokbG1thZGRkfDy8tL43RJC/e8lJydHTJw4UXh7ewsLCwthZmYmvL29xZIlS164X6jkKYR4xeOpRFQkKpUKlSpVQo8ePbSe1iMiojcTr5kiKgVPnjzROP23evVqPHjwQOPrZIiI6M3GkSmiUnDgwAGMGzcOvXv3ho2NDeLi4rBixQrUqlULp0+fLrHnOhER0avHC9CJSoGLiwucnZ3x3Xff4cGDB6hYsSIGDhyIuXPnMkgREZUxHJkiIiIikoHXTBERERHJwDBFREREJAOvmdKRSqXCnTt3YGFhUWJf80FERESvlhACjx49gpOTU4l9byPDlI7u3LnzWr5kloiIiErezZs3UaVKlRJZFsOUjgoe73/z5k3pO5iIiIjozZaRkQFnZ+dCv6ZHFwxTOio4tWdpackwRUREVMaU5CU6vACdiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZDEq7gLKuTmg09JSmr219iXM7v7Z1ERER0ctxZIqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhqlyJCIiAi4uLjA2Noavry9Onjz5wv6bNm2Ch4cHjI2N4eXlhd27d6tNDwkJgUKhUHt16NBBrc8///yDbt26wdbWFpaWlmjevDn279+v1uf5ZSgUCqxfv75kNpqIiKiUMUyVExs2bMD48eMRGhqKuLg4eHt7IyAgAHfv3tXa/9ixY+jXrx+GDBmCM2fOIDAwEIGBgTh//rxavw4dOiApKUl6/frrr2rTu3Tpgry8POzbtw+nT5+Gt7c3unTpguTkZLV+K1euVFtOYGBgiW4/ERFRaVEIIURpF1EWZWRkwMrKCs5jN0JPafra1ps4t7PWdl9fXzRq1AiLFy8GAKhUKjg7O2PUqFGYNGmSRv8+ffogKysLu3btktreffdd1KtXD0uXLgXwbGQqLS0N27dv17rO1NRUVKpUCYcOHUKLFi0AAI8ePYKlpSViYmLg7+8P4NnI1LZt2xigiIio1BV8fqenp8PS0rJElsmRqXIgNzcXp0+flsILAOjp6cHf3x/Hjx/XOs/x48fV+gNAQECARv8DBw7Azs4O7u7uGD58OO7fvy9Ns7Gxgbu7O1avXo2srCzk5eVh2bJlsLOzg4+Pj9pyRowYAVtbWzRu3Bg///wzmOGJiKi8MCjtAki+1NRU5Ofnw97eXq3d3t4ely5d0jpPcnKy1v7/PT3XoUMH9OjRA9WqVcPVq1fxxRdfoGPHjjh+/Dj09fWhUCgQGxuLwMBAWFhYQE9PD3Z2doiKioK1tbW0nBkzZqBNmzYwNTXFnj178MknnyAzMxOjR48uwb1ARERUOspsmIqMjMTYsWORlpZW2qWUW3379pX+7eXlhbp168LNzQ0HDhxA27ZtIYTAiBEjYGdnh8OHD8PExAQ//fQTunbtij///BOOjo4AgC+//FJaTv369ZGVlYX58+czTBERUblQ6qf5bt68icGDB8PJyQlGRkaoWrUqxowZo3Y6ycXFBeHh4aVX5BvO1tYW+vr6SElJUWtPSUmBg4OD1nkcHByK1R8AXF1dYWtriytXrgAA9u3bh127dmH9+vVo1qwZGjRogCVLlsDExASrVq0qdDm+vr64desWcnJyirqJREREb6xSDVPXrl1Dw4YNcfnyZfz666+4cuUKli5dir1796JJkyZ48ODBa6/p6dOnr32dchkZGcHHxwd79+6V2lQqlbQftWnSpIlafwCIiYkptD8A3Lp1C/fv35dGnLKzswE8uz7rv/T09KBSqQpdztmzZ2FtbQ2lUvniDSMiIioDSjVMjRgxAkZGRtizZw/8/PzwzjvvoGPHjoiNjcXt27cxZcoUtGrVCv/++y/GjRsnPaPov6Kjo1GrVi2Ym5tLt/H/108//YRatWrB2NgYHh4eWLJkiTQtMTERCoUCGzZsgJ+fH4yNjbFu3brXsu0lbfz48Vi+fDlWrVqF+Ph4DB8+HFlZWRg0aBAAYODAgZg8ebLUf8yYMYiKisLChQtx6dIlhIWF4dSpUxg5ciQAIDMzExMnTsT//vc/JCYmYu/evejWrRuqV6+OgIAAAM8CmbW1NYKDg3Hu3Dn8888/mDhxIq5fv47OnZ/ddbhz50789NNPOH/+PK5cuYIffvgBs2fPxqhRo17zHiIiIno1Su2aqQcPHiA6OhqzZs2CiYmJ2jQHBwf0798fGzZswOXLl1GvXj0MGzYMQ4cOVeuXnZ2NBQsWYM2aNdDT08MHH3yACRMmSIFo3bp1mDZtGhYvXoz69evjzJkzGDp0KMzMzBAcHCwtZ9KkSVi4cCHq168PY2NjrfXm5OSonZbKyMgoqV1RIvr06YN79+5h2rRpSE5ORr169RAVFSVdZH7jxg21EaSmTZvil19+wdSpU/HFF1+gRo0a2L59O+rUqQMA0NfXx19//YVVq1YhLS0NTk5OaN++PWbOnCmNKNna2iIqKgpTpkxBmzZt8PTpU3h6euK3336Dt7c3AMDQ0BAREREYN24chBCoXr06Fi1apHEsiYiIyqpSC1OXL1+GEAK1atXSOr1WrVp4+PAh8vPzoa+vDwsLC43reZ4+fYqlS5fCzc0NADBy5EjMmDFDmh4aGoqFCxeiR48eAIBq1arh4sWLWLZsmVqYGjt2rNSnMHPmzMH06dN12tbXZeTIkdLI0vMOHDig0da7d2/07t1ba38TExNER0e/dJ0NGzZ8Yb8OHTpoPDWdiIioPCn1C9DlPG/I1NRUClIA4OjoKD3xOysrC1evXsWQIUNgbm4uvb766itcvXpVbTkNGzZ86bomT56M9PR06XXz5k2d6yYiIqLyo9RGpqpXrw6FQoH4+Hh0795dY3p8fDysra1RqVKlQpdhaGio9rNCoZDCWWZmJgBg+fLl8PX1Veunr6+v9rOZmdlL61UqlbxgmoiIiDSU2siUjY0N2rVrhyVLluDx48dq05KTk7Fu3Tr06dMHCoUCRkZGyM/PL9by7e3t4eTkhGvXrqF69epqr2rVqpXkphAREdFbrFRP8y1evBg5OTkICAjAoUOHcPPmTURFRaFdu3aoXLkyZs2aBeDZc6YOHTqE27dvIzU1tcjLnz59OubMmYPvvvsO//zzD/7++2+sXLkSixYtelWbRERERG+ZUg1TNWrUwKlTp+Dq6oqgoCC4ublh2LBhaN26NY4fP46KFSsCePZ1JImJiXBzc3vhab/nffjhh/jpp5+wcuVKeHl5wc/PD5GRkRyZIiIiohKjEPzGWZ0UfOu089iN0FOavrb1Js7t/NrWRUREVN4UfH6np6fD0tKyRJZZ6nfzEREREZVlDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAalXUBZd356ACwtLUu7DCIiIiolHJkiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIiksGgtAso6+qERkNPafpKlp04t/MrWS4RERGVHI5MEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwVYZFRETAxcUFxsbG8PX1xcmTJ1/Yf9OmTfDw8ICxsTG8vLywe/fuQvt+/PHHUCgUCA8PV2uPi4tDu3btUKFCBdjY2GDYsGHIzMxU66NQKDRe69ev13k7iYiI3mQMU2XUhg0bMH78eISGhiIuLg7e3t4ICAjA3bt3tfY/duwY+vXrhyFDhuDMmTMIDAxEYGAgzp8/r9F327Zt+N///gcnJye19jt37sDf3x/Vq1fHiRMnEBUVhQsXLiAkJERjGStXrkRSUpL0CgwMLInNJiIieuMwTJVRixYtwtChQzFo0CDUrl0bS5cuhampKX7++Wet/b/99lt06NABEydORK1atTBz5kw0aNAAixcvVut3+/ZtjBo1CuvWrYOhoaHatF27dsHQ0BARERFwd3dHo0aNsHTpUmzZsgVXrlxR61uhQgU4ODhIL2Nj45LdAURERG8IhqkyKDc3F6dPn4a/v7/UpqenB39/fxw/flzrPMePH1frDwABAQFq/VUqFQYMGICJEyfC09NTYxk5OTkwMjKCnt7//dqYmJgAAI4cOaLWd8SIEbC1tUXjxo3x888/QwhR/A0lIiIqAximyqDU1FTk5+fD3t5erd3e3h7Jycla50lOTn5p/3nz5sHAwACjR4/Wuow2bdogOTkZ8+fPR25uLh4+fIhJkyYBAJKSkqR+M2bMwMaNGxETE4OePXvik08+wffff6/TthIREb3pDEq7AHoznD59Gt9++y3i4uKgUCi09vH09MSqVaswfvx4TJ48Gfr6+hg9ejTs7e3VRqu+/PJL6d/169dHVlYW5s+fX2hIIyIiKsvK5MjU8ePHoa+vj86dO6u1JyYmqt1BVrFiRfj5+eHw4cNq/cLCwqQ+BgYGcHFxwbhx4zTuSntT2draQl9fHykpKWrtKSkpcHBw0DqPg4PDC/sfPnwYd+/exTvvvAMDAwMYGBjg33//xaeffgoXFxdpnvfffx/Jycm4ffs27t+/j7CwMNy7dw+urq6F1uvr64tbt24hJydHxy0mIiJ6c5XJMLVixQqMGjUKhw4dwp07dzSmx8bGIikpCYcOHYKTkxO6dOmiESQ8PT2RlJSExMREzJs3Dz/++CM+/fTT17UJshgZGcHHxwd79+6V2lQqFfbu3YsmTZponadJkyZq/QEgJiZG6j9gwAD89ddfOHv2rPRycnLCxIkTER0drbE8e3t7mJubY8OGDTA2Nka7du0Krffs2bOwtraGUqnUZXOJiIjeaGXuNF9mZiY2bNiAU6dOITk5GZGRkfjiiy/U+tjY2Eh3kX3xxRdYv349Tpw4gffee0/qY2BgII3K9OnTB3v37sWOHTuwbNmy17o9uho/fjyCg4PRsGFDNG7cGOHh4cjKysKgQYMAAAMHDkTlypUxZ84cAMCYMWPg5+eHhQsXonPnzli/fj1OnTqFH3/8EcCzfWZjY6O2DkNDQzg4OMDd3V1qW7x4MZo2bQpzc3PExMRg4sSJmDt3LipUqAAA2LlzJ1JSUvDuu+/C2NgYMTExmD17NiZMmPAa9goREdHrV+bC1MaNG+Hh4QF3d3d88MEHGDt2LCZPnqz1Op/Hjx9j9erVAJ6N5ryIiYkJcnNzC52ek5OjdpoqIyNDxy0oGX369MG9e/cwbdo0JCcno169eoiKipIuMr9x44badUxNmzbFL7/8gqlTp+KLL75AjRo1sH37dtSpU6dY6z158iRCQ0ORmZkJDw8PLFu2DAMGDJCmFzw6Ydy4cRBCoHr16tJjHIiIiMojhShj96w3a9YMQUFBGDNmDPLy8uDo6IhNmzahVatWSExMRLVq1WBiYgI9PT1kZ2dDCAEfHx8cP35cem5SWFgYtm/fjrNnzwJ4dvF1hw4d0KpVK2zatEnresPCwjB9+nSNduexG6GnNH0l25o4t/PLOxEREVGRZWRkwMrKCunp6bC0tCyRZZapa6YSEhJw8uRJ9OvXD8CzU3V9+vTBihUr1Ppt2LABZ86cwZYtW1C9enVERkZqPIDy77//hrm5OUxMTNC4cWM0adJE4wGW/zV58mSkp6dLr5s3b5b8BhIREVGZU6ZO861YsQJ5eXlqX3MihIBSqVQLQs7OzqhRowZq1KiBvLw8dO/eHefPn1e7ANrd3R07duyAgYEBnJycXnoaUKlU8gJqIiIi0lBmRqby8vKwevVqLFy4UO2Os3PnzsHJyQm//vqr1vl69eoFAwMDLFmyRK3dyMgI1atXh4uLy0uDFBEREVFhykyY2rVrFx4+fIghQ4agTp06aq+ePXtqnOoroFAoMHr0aMydOxfZ2dmvuWoiIiIq78pMmFqxYgX8/f1hZWWlMa1nz544depUoXfYBQcH4+nTpy+8JoqIiIhIF2Xubr43RcHdALybj4iIqOx46+/mIyIiInrTMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMhiUdgFl3fnpAbC0tCztMoiIiKiUcGSKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAYDXWa6efMmFAoFqlSpAgA4efIkfvnlF9SuXRvDhg0r0QLfdHVCo6GnNC1y/8S5nV9hNURERPS66TQy9f7772P//v0AgOTkZLRr1w4nT57ElClTMGPGjBItkIiIiOhNplOYOn/+PBo3bgwA2LhxI+rUqYNjx45h3bp1iIyMLMn6iIiIiN5oOoWpp0+fQqlUAgBiY2Px3nvvAQA8PDyQlJRUctURERERveF0ClOenp5YunQpDh8+jJiYGHTo0AEAcOfOHdjY2JRogURERERvMp3C1Lx587Bs2TK0atUK/fr1g7e3NwBgx44d0uk/IiIioreBTnfztWrVCqmpqcjIyIC1tbXUPmzYMJiaFv3ONiIiIqKyTqcwBQD6+vpqQQoAXFxc5NZDREREVKYUOUzVr18fCoWiSH3j4uJ0LoiIiIioLClymAoMDHyFZRARERGVTUUOU6Ghoa+yDiIiIqIySedrpgDg9OnTiI+PB/DscQn169cvkaKIiIiIygqdwtTdu3fRt29fHDhwABUqVAAApKWloXXr1li/fj0qVapUkjUSERERvbF0es7UqFGj8OjRI1y4cAEPHjzAgwcPcP78eWRkZGD06NElXSMRERHRG0unkamoqCjExsaiVq1aUlvt2rURERGB9u3bl1hxRERERG86nUamVCoVDA0NNdoNDQ2hUqlkF0VERERUVugUptq0aYMxY8bgzp07Utvt27cxbtw4tG3btsSKIyIiInrT6RSmFi9ejIyMDLi4uMDNzQ1ubm6oVq0aMjIy8P3335d0jURERERvLJ3ClLOzM+Li4vD7779j7NixGDt2LHbv3o24uDhUqVKlpGt8a0RERMDFxQXGxsbw9fXFyZMnX9h/06ZN8PDwgLGxMby8vLB7925p2tOnT/H555/Dy8sLZmZmcHJywsCBA9VGEwv8/vvv8PX1hYmJCaytrdUe0Hru3Dn069cPzs7OMDExQa1atfDtt9+W2DYTERGVdTo/Z0qhUKBdu3Zo165dSdbz1tqwYQPGjx+PpUuXwtfXF+Hh4QgICEBCQgLs7Ow0+h87dgz9+vXDnDlz0KVLF/zyyy8IDAxEXFwc6tSpg+zsbMTFxeHLL7+Et7c3Hj58iDFjxuC9997DqVOnpOVs2bIFQ4cOxezZs9GmTRvk5eXh/Pnz0vTTp0/Dzs4Oa9euhbOzM44dO4Zhw4ZBX18fI0eOfC37hoiI6E2mEEKI4s40evRoVK9eXeMxCIsXL8aVK1cQHh5eUvW9sTIyMmBlZQXnsRuhpzQt8nyJcztrbff19UWjRo2wePFiAM8u8nd2dsaoUaMwadIkjf59+vRBVlYWdu3aJbW9++67qFevHpYuXap1HX/++ScaN26Mf//9F++88w7y8vLg4uKC6dOnY8iQIUXehhEjRiA+Ph779u0r8jxERERvgoLP7/T0dFhaWpbIMnU6zbdlyxY0a9ZMo71p06bYvHmz7KLeNrm5uTh9+jT8/f2lNj09Pfj7++P48eNa5zl+/LhafwAICAgotD8ApKenQ6FQSA9ajYuLw+3bt6Gnp4f69evD0dERHTt2VBuZKmw5FStWLOLWERERlW86han79+/DyspKo93S0hKpqamyi3rbpKamIj8/H/b29mrt9vb2SE5O1jpPcnJysfo/efIEn3/+Ofr16ycl8WvXrgEAwsLCMHXqVOzatQvW1tZo1aoVHjx4oHU5x44dw4YNGzBs2LBibSMREVF5pVOYql69OqKiojTa//jjD7i6usouikrW06dPERQUBCEEfvjhB6m94JlgU6ZMQc+ePeHj44OVK1dCoVBg06ZNGss5f/48unXrhtDQUD6clYiI6P/TKUyNHz8en332GUJDQ3Hw4EEcPHgQ06ZNw6RJkzBu3DhZBYWEhKjdTZacnIxRo0bB1dUVSqUSzs7O6Nq1K/bu3Sv1cXFxgUKhwPr16zWW5+npCYVCgcjISI3+CoUCZmZmaNCggdbw8LrY2tpCX18fKSkpau0pKSlwcHDQOo+Dg0OR+hcEqX///RcxMTFq54cdHR0BPHt6fQGlUglXV1fcuHFDbTkXL15E27ZtMWzYMEydOrX4G0lERFRO6RSmBg8ejIULF2LFihVo3bo1WrdujbVr1+KHH37A0KFDS6y4xMRE+Pj4YN++fZg/fz7+/vtvREVFoXXr1hgxYoRaX2dnZ6xcuVKt7X//+x+Sk5NhZmamsewZM2YgKSkJZ86cQaNGjdCnTx8cO3asxGovDiMjI/j4+KgFRJVKhb1796JJkyZa52nSpIlafwCIiYlR618QpC5fvozY2FjY2Nio9ffx8YFSqURCQoLaPImJiahatarUduHCBbRu3RrBwcGYNWuWrG0lIiIqb3R+NMLw4cMxfPhw3Lt3DyYmJjA3Ny/JugAAn3zyCRQKBU6ePKkWiDw9PTF48GC1vv3798c333yDmzdvwtnZGQDw888/o3///li9erXGsi0sLODg4AAHBwdERERg7dq12LlzJ5o2bVri21EU48ePR3BwMBo2bIjGjRsjPDwcWVlZGDRoEABg4MCBqFy5MubMmQMAGDNmDPz8/LBw4UJ07twZ69evx6lTp/Djjz8CeBaKevXqhbi4OOzatQv5+fnS9VQVK1aEkZERLC0t8fHHHyM0NBTOzs6oWrUq5s+fDwDo3bs3gGen9tq0aYOAgACMHz9eWoa+vj4qVar0WvcRERHRm0jnMAUA9+7dk0Y1PDw8YGtrWyJFAcCDBw8QFRWFWbNmaR1ZKrgjrYC9vT0CAgKwatUqTJ06FdnZ2diwYQMOHjyoNUz9l4GBAQwNDZGbm1ton5ycHOTk5Eg/Z2RkFG+DXqJPnz64d+8epk2bhuTkZNSrVw9RUVHSReY3btyAnt7/DSQ2bdoUv/zyC6ZOnYovvvgCNWrUwPbt21GnTh0Az77eZ8eOHQCAevXqqa1r//79aNWqFQBg/vz5MDAwwIABA/D48WP4+vpi3759sLa2BgBs3rwZ9+7dw9q1a7F27VppGVWrVkViYmKJ7gMiIqKySKfTfFlZWRg8eDAcHR3RsmVLtGzZEo6OjhgyZAiys7NLpLArV65ACAEPD48izzN48GBERkZCCIHNmzfDzc1NI0g8Lzc3F3PmzEF6ejratGlTaL85c+bAyspKehWMfpWkkSNH4t9//0VOTg5OnDgBX19fadqBAwfUrvsCno0eJSQkICcnB+fPn0enTp2kaS4uLhBCaH0VBCng2ZdTL1iwACkpKcjIyEBMTAw8PT2l6WFhYVqXwSBFRET0jM4XoB88eBA7d+5EWloa0tLS8Ntvv+HgwYP49NNPS6QwHZ4lis6dOyMzMxOHDh3Czz//rHEq8L8+//xzmJubw9TUFPPmzcPcuXPRubP2B2oCwOTJk5Geni69bt68Wez6iIiIqPzR6TTfli1bsHnzZrURjk6dOsHExARBQUFqt9/rqkaNGlAoFLh06VKR5yk4XRUaGooTJ05g27ZthfadOHEiQkJCYG5uDnt7eygUihcuW6lUQqlUFrkWIiIiejvoNDKVnZ2t8cBIALCzsyux03wVK1ZEQEAAIiIikJWVpTE9LS1N63yDBw/GwYMH0a1bN+m6H21sbW1RvXp1ODg4vDRIERERERVGpzDVpEkThIaG4smTJ1Lb48ePMX369EJv5ddFREQE8vPz0bhxY2zZsgWXL19GfHw8vvvuu0LXU6tWLaSmpmo8JoGIiIjoVdDpNF94eDg6dOiAKlWqwNvbGwBw7tw5GBsbIzo6usSKc3V1RVxcHGbNmoVPP/0USUlJqFSpEnx8fF54KvH55ykRERERvSoKocuV3nh2qm/dunXSNU21atVC//79YWJiUqIFvqkKvnXaeexG6ClNizxf4tzCL3InIiKiV6vg8zs9PV3tW0Hk0Glk6tChQ2jatKnG087z8vJw6NAhtGzZskSKIyIiInrT6XTNVOvWrfHgwQON9vT0dLRu3Vp2UURERERlhU5hSgih9Q64+/fva31aOREREVF5VazTfD169AAAKBQKhISEqD13KT8/H3/99VepfbcdERERUWkoVpiysrIC8GxkysLCQu1icyMjI7z77rsa11ERERERlWfFClMFz25ycXHBhAkTeEqPiIiI3no63c0XGhoKALh37x4SEhIAAO7u7qhUqVLJVUZERERUBuj8dTKDBw+Go6MjWrZsiZYtW8LJyQlDhgwpsa+TISIiIioLdApT48aNw8GDB7Fz506kpaUhLS0Nv/32Gw4ePIhPP/20pGskIiIiemPpdJpvy5Yt2Lx5M1q1aiW1derUCSYmJggKCnrhV70QERERlSc6n+azt7fXaLezs+NpPiIiInqr6BSmmjRpgtDQUDx58kRqe/z4MaZPn44mTZqUWHFEREREbzqdTvOFh4ejQ4cOqFKlCry9vQEA586dg7GxMaKjo0u0QCIiIqI3mU5hysvLC5cvX8a6detw6dIlAEC/fv3Qv39/tQd5EhEREZV3xQ5TT58+hYeHB3bt2sWnnRMREdFbr9jXTBkaGqpdK0VERET0NtPpAvQRI0Zg3rx5yMvLK+l6iIiIiMoUna6Z+vPPP7F3717s2bMHXl5eGt/Rt3Xr1hIpjoiIiOhNp1OYqlChAnr27FnStRARERGVOcUKUyqVCvPnz8c///yD3NxctGnTBmFhYbyDj4iIiN5axbpmatasWfjiiy9gbm6OypUr47vvvsOIESNeVW1EREREbzyFEEIUtXONGjUwYcIEfPTRRwCA2NhYdO7cGY8fP4aenk7XspdZGRkZsLKyQnp6OiwtLUu7HCIiIiqCV/H5XawEdOPGDXTq1En62d/fHwqFAnfu3CmRYoiIiIjKmmKFqby8PBgbG6u1GRoa4unTpyVaFBEREVFZUawL0IUQCAkJgVKplNqePHmCjz/+WO3xCHw0AhEREb0tihWmgoODNdo++OCDEiuGiIiIqKwpVphauXLlq6qDiIiIqEx6u27BIyIiIiphDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCRDsR7aSZrqhEZDT2la2mW8MolzO5d2CURERG80jkwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFskRERMDFxQXGxsbw9fXFyZMnC+174cIF9OzZEy4uLlAoFAgPD9foc+jQIXTt2hVOTk5QKBTYvn271mXFx8fjvffeg5WVFczMzNCoUSPcuHFDmv7RRx/Bzc0NJiYmqFSpErp164ZLly7J3VwiIiINDFOksw0bNmD8+PEIDQ1FXFwcvL29ERAQgLt372rtn52dDVdXV8ydOxcODg5a+2RlZcHb2xsRERGFrvfq1ato3rw5PDw8cODAAfz111/48ssvYWxsLPXx8fHBypUrER8fj+joaAgh0L59e+Tn58vbaCIioucohBCitIsoizIyMmBlZQXnsRuhpzQt7XJemcS5nQud5uvri0aNGmHx4sUAAJVKBWdnZ4waNQqTJk164XJdXFwwduxYjB07ttA+CoUC27ZtQ2BgoFp73759YWhoiDVr1hR5O/766y94e3vjypUrcHNzK/J8RERUvhR8fqenp8PS0rJElsmRKdJJbm4uTp8+DX9/f6lNT08P/v7+OH78+Ctbr0qlwu+//46aNWsiICAAdnZ28PX1LfR0IPBstGvlypWoVq0anJ2dX1ltRET0dmKYIp2kpqYiPz8f9vb2au329vZITk5+Zeu9e/cuMjMzMXfuXHTo0AF79uxB9+7d0aNHDxw8eFCt75IlS2Bubg5zc3P88ccfiImJgZGR0SurjYiI3k4MU1SmqFQqAEC3bt0wbtw41KtXD5MmTUKXLl2wdOlStb79+/fHmTNncPDgQdSsWRNBQUF48uRJaZRNRETlWJkIU61atdJ6bU1kZCQqVKgAAAgLC4NCoYBCoYCBgQFsbW3RsmVLhIeHIycnR2N5BX2NjY1Ru3ZtLFmy5DVsSflha2sLfX19pKSkqLWnpKQUenF5Sa3XwMAAtWvXVmuvVauW2t18AGBlZYUaNWqgZcuW2Lx5My5duoRt27a9stqIiOjtVCbCVFF5enoiKSkJN27cwP79+9G7d2/MmTMHTZs2xaNHj9T6Dh06FElJSbh48SKCgoIwYsQI/Prrr6VUedljZGQEHx8f7N27V2pTqVTYu3cvmjRp8krX26hRIyQkJKi1//PPP6hatWqh8wkhIITQCNZERERyGZR2ASXJwMBAGhVxcnKCl5cX2rVrB29vb8ybNw9fffWV1NfU1FTqGxYWhl9++QU7duxAv379SqX2smj8+PEIDg5Gw4YN0bhxY4SHhyMrKwuDBg0CAAwcOBCVK1fGnDlzADy7aP3ixYvSv2/fvo2zZ8/C3Nwc1atXBwBkZmbiypUr0jquX7+Os2fPomLFinjnnXcAABMnTkSfPn3QsmVLtG7dGlFRUdi5cycOHDgAALh27Ro2bNiA9u3bo1KlSrh16xbmzp0LExMTdOrU6XXtHiIiekuUqzCljYeHBzp27IitW7eqhannmZiYIDc3t9DpOTk5aqMaGRkZJVpnWdSnTx/cu3cP06ZNQ3JyMurVq4eoqCjpovQbN25AT+//Bj/v3LmD+vXrSz8vWLAACxYsgJ+fnxSETp06hdatW0t9xo8fDwAIDg5GZGQkAKB79+5YunQp5syZg9GjR8Pd3R1btmxB8+bNAQDGxsY4fPgwwsPD8fDhQ9jb26Nly5Y4duwY7OzsXuUuISKit1C5D1PAs0C1Z88erdPy8/Px66+/4q+//sKwYcMKXcacOXMwffr0V1VimTVy5EiMHDlS67SCgFTAxcUFL3usWatWrV7aBwAGDx6MwYMHa53m5OSE3bt3v3QZREREJaFcXTNVGCEEFAqFWlvBbfMmJiYYOnQoxo0bh+HDhxe6jMmTJyM9PV163bx581WXTURERGVAmRiZsrS0RHp6ukZ7WloarKysXjp/fHw8qlWrptbWv39/TJkyBSYmJnB0dFQ7HaWNUqmEUqksXuFERERU7pWJkSl3d3fExcVptMfFxaFmzZovnPfSpUuIiopCz5491dqtrKxQvXp1VK5c+aVBioiIiKgwZWJkavjw4Vi8eDFGjx6NDz/8EEqlEr///jt+/fVX7Ny5U+qXl5eH5ORkqFQq3L9/HwcOHMBXX32FevXqYeLEiaW4BURERFRelYkw5erqikOHDmHKlCnw9/dHbm4uPDw8sGnTJnTo0EHqd+HCBTg6OkJfXx9WVlaoXbs2Jk+ejOHDh/MUHREREb0SClGUW6dIQ8G3TjuP3Qg9pWlpl/PKJM7tXNolEBERlZiCz+/09HRYWlqWyDJ5sRARERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwGpV1AWXd+egAsLS1LuwwiIiIqJRyZIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpLBoLQLKOvqhEZDT2la2mUQEZGOEud2Lu0SqIzjyBQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREQvEBERARcXFxgbG8PX1xcnT54stG+rVq2gUCg0Xp07d9ba/+OPP4ZCoUB4eLhae1xcHNq1a4cKFSrAxsYGw4YNQ2Zmpsb8kZGRqFu3LoyNjWFnZ4cRI0bI2lbSDcMUERFRITZs2IDx48cjNDQUcXFx8Pb2RkBAAO7evau1/9atW5GUlCS9zp8/D319ffTu3Vuj77Zt2/C///0PTk5Oau137tyBv78/qlevjhMnTiAqKgoXLlxASEiIWr9FixZhypQpmDRpEi5cuIDY2FgEBASU2LZT0RmUdgFERERvqkWLFmHo0KEYNGgQAGDp0qX4/fff8fPPP2PSpEka/StWrKj28/r162FqaqoRpm7fvo1Ro0YhOjpaY9Rq165dMDQ0REREBPT09KT11q1bF1euXEH16tXx8OFDTJ06FTt37kTbtm2leevWrVsi203Fw5EpIiIiLXJzc3H69Gn4+/tLbXp6evD398fx48eLtIwVK1agb9++MDMzk9pUKhUGDBiAiRMnwtPTU2OenJwcGBkZSUEKAExMTAAAR44cAQDExMRApVLh9u3bqFWrFqpUqYKgoCDcvHlTp20leRimiIiItEhNTUV+fj7s7e3V2u3t7ZGcnPzS+U+ePInz58/jww8/VGufN28eDAwMMHr0aK3ztWnTBsnJyZg/fz5yc3Px8OFDaRQsKSkJAHDt2jWoVCrMnj0b4eHh2Lx5Mx48eIB27dohNzdXl80lGRimiIiIXoEVK1bAy8sLjRs3ltpOnz6Nb7/9FpGRkVAoFFrn8/T0xKpVq7Bw4UKYmprCwcEB1apVg729vTRapVKp8PTpU3z33XcICAjAu+++i19//RWXL1/G/v37X8v20f8pU2FK2x0S/32FhYUBAM6cOYPevXvD3t4exsbGqFGjBoYOHYp//vkHAJCYmKg2n42NDdq3b48zZ86U4tYREdGbxNbWFvr6+khJSVFrT0lJgYODwwvnzcrKwvr16zFkyBC19sOHD+Pu3bt45513YGBgAAMDA/z777/49NNP4eLiIvV7//33kZycjNu3b+P+/fsICwvDvXv34OrqCgBwdHQEANSuXVuap1KlSrC1tcWNGzfkbDbpoEyFqf/eIREeHg5LS0u1tgkTJmDXrl149913kZOTg3Xr1iE+Ph5r166FlZUVvvzyS7XlxcbGIikpCdHR0cjMzETHjh2RlpZWOhtHRERvFCMjI/j4+GDv3r1Sm0qlwt69e9GkSZMXzrtp0ybk5OTggw8+UGsfMGAA/vrrL5w9e1Z6OTk5YeLEiYiOjtZYjr29PczNzbFhwwYYGxujXbt2AIBmzZoBABISEqS+Dx48QGpqKqpWrarzNpNuytTdfP/9PwErKysoFAq1tuzsbAwaNAidOnXCtm3bpPZq1arB19dXIyjZ2NjAwcEBDg4OWLBgAZo1a4YTJ07w1lIiIgIAjB8/HsHBwWjYsCEaN26M8PBwZGVlSXf3DRw4EJUrV8acOXPU5luxYgUCAwNhY2Oj1m5jY6PRZmhoCAcHB7i7u0ttixcvRtOmTWFubo6YmBhMnDgRc+fORYUKFQAANWvWRLdu3TBmzBj8+OOPsLS0xOTJk+Hh4YHWrVu/gj1BL1KmwtTLREdHIzU1FZ999pnW6QW/hNoU3ClR2IV7OTk5yMnJkX7OyMjQvVAiIioT+vTpg3v37mHatGlITk5GvXr1EBUVJV2UfuPGDbW77oBno0VHjhzBnj17dF7vyZMnERoaiszMTHh4eGDZsmUYMGCAWp/Vq1dj3Lhx6Ny5M/T09ODn54eoqCgYGhrqvF7STbkKU5cvXwYAeHh4FGu+tLQ0zJw5E+bm5moXCv7XnDlzMH36dNk1EhFR2TJy5EiMHDlS67QDBw5otLm7u0MIUeTlJyYmarStXr36pfNZWlpixYoVWLFiRZHXRa9Gmbpm6mWK88sLQBpCtba2xrlz57BhwwaNW2ALTJ48Genp6dKLz/IgIiIioJyNTNWsWRMAcOnSpZdeHAg8+5qA2rVrw8bG5oWnAAFAqVRCqVSWRJlERERUjpSrkan27dvD1tYWX3/9tdbpz1+A7uzsDDc3t5cGKSIiIqLClKswZWZmhp9++gm///473nvvPcTGxiIxMRGnTp3CZ599ho8//ri0SyQiIqJyplyFKQDo1q0bjh07BkNDQ7z//vvw8PBAv379kJ6ejq+++qq0yyMiIqJyRiGKe9U2AXj2aAQrKys4j90IPaVpaZdDREQ6SpzbubRLoNeo4PM7PT0dlpaWJbLMcjcyRURERPQ6MUwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRERGRDAxTRERERDIwTBERERHJwDBFREREJAPDFBEREZEMDFNEREREMhiUdgFl3fnpAbC0tCztMoiIiKiUcGSKiIiISAaGKSIiIiIZGKaIiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGRgmCIiIiKSgWGKiIiISAaGKSIiIiIZGKaIiIiIZDAo7QLKKiEEACAjI6OUKyEiIqKiKvjcLvgcLwkMUzq6f/8+AMDZ2bmUKyEiIqLiun//PqysrEpkWQxTOqpYsSIA4MaNGyV2MEg3GRkZcHZ2xs2bN2FpaVna5bzVeCzeHDwWbw4eizdLeno63nnnHelzvCQwTOlIT+/Z5WZWVlb843hDWFpa8li8IXgs3hw8Fm8OHos3S8HneIksq8SWRERERPQWYpgiIiIikoFhSkdKpRKhoaFQKpWlXcpbj8fizcFj8ebgsXhz8Fi8WV7F8VCIkrw3kIiIiOgtw5EpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhqkXiIiIgIuLC4yNjeHr64uTJ0++sP+mTZvg4eEBY2NjeHl5Yffu3a+p0vKvOMdi+fLlaNGiBaytrWFtbQ1/f/+XHjsquuL+XRRYv349FAoFAgMDX22Bb5HiHou0tDSMGDECjo6OUCqVqFmzJt+nSkhxj0V4eDjc3d1hYmICZ2dnjBs3Dk+ePHlN1ZZfhw4dQteuXeHk5ASFQoHt27e/dJ4DBw6gQYMGUCqVqF69OiIjI4u/YkFarV+/XhgZGYmff/5ZXLhwQQwdOlRUqFBBpKSkaO1/9OhRoa+vL77++mtx8eJFMXXqVGFoaCj+/vvv11x5+VPcY/H++++LiIgIcebMGREfHy9CQkKElZWVuHXr1muuvPwp7rEocP36dVG5cmXRokUL0a1bt9dTbDlX3GORk5MjGjZsKDp16iSOHDkirl+/Lg4cOCDOnj37misvf4p7LNatWyeUSqVYt26duH79uoiOjhaOjo5i3Lhxr7ny8mf37t1iypQpYuvWrQKA2LZt2wv7X7t2TZiamorx48eLixcviu+//17o6+uLqKioYq2XYaoQjRs3FiNGjJB+zs/PF05OTmLOnDla+wcFBYnOnTurtfn6+oqPPvroldb5NijusXheXl6esLCwEKtWrXpVJb41dDkWeXl5omnTpuKnn34SwcHBDFMlpLjH4ocffhCurq4iNzf3dZX41ijusRgxYoRo06aNWtv48eNFs2bNXmmdb5uihKnPPvtMeHp6qrX16dNHBAQEFGtdPM2nRW5uLk6fPg1/f3+pTU9PD/7+/jh+/LjWeY4fP67WHwACAgIK7U9Fo8uxeF52djaePn1aol9q+TbS9VjMmDEDdnZ2GDJkyOso862gy7HYsWMHmjRpghEjRsDe3h516tTB7NmzkZ+f/7rKLpd0ORZNmzbF6dOnpVOB165dw+7du9GpU6fXUjP9n5L67OYXHWuRmpqK/Px82Nvbq7Xb29vj0qVLWudJTk7W2j85OfmV1fk20OVYPO/zzz+Hk5OTxh8MFY8ux+LIkSNYsWIFzp49+xoqfHvociyuXbuGffv2oX///ti9ezeuXLmCTz75BE+fPkVoaOjrKLtc0uVYvP/++0hNTUXz5s0hhEBeXh4+/vhjfPHFF6+jZPqPwj67MzIy8PjxY5iYmBRpORyZonJt7ty5WL9+PbZt2wZjY+PSLuet8ujRIwwYMADLly+Hra1taZfz1lOpVLCzs8OPP/4IHx8f9OnTB1OmTMHSpUtLu7S3zoEDBzB79mwsWbIEcXFx2Lp1K37//XfMnDmztEsjHXFkSgtbW1vo6+sjJSVFrT0lJQUODg5a53FwcChWfyoaXY5FgQULFmDu3LmIjY1F3bp1X2WZb4XiHourV68iMTERXbt2ldpUKhUAwMDAAAkJCXBzc3u1RZdTuvxdODo6wtDQEPr6+lJbrVq1kJycjNzcXBgZGb3SmssrXY7Fl19+iQEDBuDDDz8EAHh5eSErKwvDhg3DlClToKfHcY7XpbDPbktLyyKPSgEcmdLKyMgIPj4+2Lt3r9SmUqmwd+9eNGnSROs8TZo0UesPADExMYX2p6LR5VgAwNdff42ZM2ciKioKDRs2fB2llnvFPRYeHh74+++/cfbsWen13nvvoXXr1jh79iycnZ1fZ/nlii5/F82aNcOVK1ekQAsA//zzDxwdHRmkZNDlWGRnZ2sEpoKQK/h1ua9ViX12F+/a+LfH+vXrhVKpFJGRkeLixYti2LBhokKFCiI5OVkIIcSAAQPEpEmTpP5Hjx4VBgYGYsGCBSI+Pl6Ehoby0QglpLjHYu7cucLIyEhs3rxZJCUlSa9Hjx6V1iaUG8U9Fs/j3Xwlp7jH4saNG8LCwkKMHDlSJCQkiF27dgk7Ozvx1VdfldYmlBvFPRahoaHCwsJC/Prrr+LatWtiz549ws3NTQQFBZXWJpQbjx49EmfOnBFnzpwRAMSiRYvEmTNnxL///iuEEGLSpEliwIABUv+CRyNMnDhRxMfHi4iICD4aoaR9//334p133hFGRkaicePG4n//+580zc/PTwQHB6v137hxo6hZs6YwMjISnp6e4vfff3/NFZdfxTkWVatWFQA0XqGhoa+/8HKouH8X/8UwVbKKeyyOHTsmfH19hVKpFK6urmLWrFkiLy/vNVddPhXnWDx9+lSEhYUJNzc3YWxsLJydncUnn3wiHj58+PoLL2f279+v9f2/YP8HBwcLPz8/jXnq1asnjIyMhKurq1i5cmWx16sQgmOKRERERLriNVNEREREMjBMEREREcnAMEVEREQkA8MUERERkQwMU0REREQyMEwRERERycAwRURERCQDwxQRvRbZ2dno2bMnLC0toVAokJaWprXNxcUF4eHhpV0uEVGRMUwRkRqFQvHCV1hYmE7LXbVqFQ4fPoxjx44hKSkJVlZWWtv+/PNPDBs2TOf6XVxcpFrNzMzQoEEDbNq0SeflvS4HDhyQAiURlS0GpV0AEb1ZkpKSpH9v2LAB06ZNQ0JCgtRmbm4u/VsIgfz8fBgYvPyt5OrVq6hVqxbq1KnzwrZKlSrJ3QTMmDEDQ4cORUZGBhYuXIg+ffqgcuXKaNq0abGXlZubyy8CJqIX4sgUEalxcHCQXlZWVlAoFNLPly5dgoWFBf744w/4+PhAqVTiyJEjuHr1Krp16wZ7e3uYm5ujUaNGiI2NlZbZqlUrLFy4EIcOHYJCoUCrVq20tgHQOM2XlpaGjz76CPb29jA2NkadOnWwa9euF26DhYUFHBwcULNmTURERMDExAQ7d+4EANy8eRNBQUGoUKECKlasiG7duiExMVGaNyQkBIGBgZg1axacnJzg7u4OALh16xb69euHihUrwszMDA0bNsSJEyek+X777Tc0aNAAxsbGcHV1xfTp05GXlydNVygU+Omnn9C9e3eYmpqiRo0a2LFjBwAgMTERrVu3BgBYW1tDoVAgJCQEABAVFYXmzZujQoUKsLGxQZcuXXD16lW17T127Bjq1asHY2NjNGzYENu3b4dCocDZs2elPufPn0fHjh1hbm4Oe3t7DBgwAKmpqdL0zZs3w8vLCyYmJrCxsYG/vz+ysrJeuJ+J6BmGKSIqtkmTJmHu3LmIj49H3bp1kZmZiU6dOmHv3r04c+YMOnTogK5du+LGjRsAgK1bt2Lo0KFo0qQJkpKSsHXrVq1tz1OpVOjYsSOOHj2KtWvX4uLFi5g7dy709fWLXKuBgQEMDQ2Rm5uLp0+fIiAgABYWFjh8+DCOHj0Kc3NzdOjQAbm5udI8e/fuRUJCAmJiYrBr1y5kZmbCz88Pt2/fxo4dO3Du3Dl89tlnUKlUAIDDhw9j4MCBGDNmDC5evIhly5YhMjISs2bNUqtl+vTpCAoKwl9//YVOnTqhf//+ePDgAZydnbFlyxYAQEJCApKSkvDtt98CALKysjB+/HicOnUKe/fuhZ6eHrp37y6tOyMjA127doWXlxfi4uIwc+ZMfP7552rrTUtLQ5s2bVC/fn2cOnUKUVFRSElJQVBQEIBno5H9+vXD4MGDER8fjwMHDqBHjx7gV7cSFZG872cmovJs5cqVwsrKSvq54BvZt2/f/tJ5PT09xffffy/9PGbMGI1va9fWVrVqVfHNN98IIYSIjo4Wenp6IiEhocg1/3f+nJwcMXv2bAFA7Nq1S6xZs0a4u7sLlUol9c/JyREmJiYiOjpaCPHsW+Xt7e1FTk6O1GfZsmXCwsJC3L9/X+s627ZtK2bPnq3WtmbNGuHo6Cj9DEBMnTpV+jkzM1MAEH/88YcQ4v/27cOHD1+4fffu3RMAxN9//y2EEOKHH34QNjY24vHjx1Kf5cuXCwDizJkzQgghZs6cKdq3b6+2nJs3bwoAIiEhQZw+fVoAEImJiS9cNxFpx5EpIiq2hg0bqv2cmZmJCRMmoFatWqhQoQLMzc0RHx8vjUzp6uzZs6hSpQpq1qxZrPk+//xzmJubw9TUFPPmzcPcuXPRuXNnnDt3DleuXIGFhQXMzc1hbm6OihUr4smTJ2qnzry8vNSukzp79izq16+PihUral3fuXPnMGPGDGmZ5ubmGDp0KJKSkpCdnS31q1u3rvRvMzMzWFpa4u7duy/clsuXL6Nfv35wdXWFpaUlXFxcAEDatwkJCahbty6MjY2leRo3bqxR3/79+9Xq8/DwAPDsujVvb2+0bdsWXl5e6N27N5YvX46HDx++sC4i+j+8AJ2Iis3MzEzt5wkTJiAmJgYLFixA9erVYWJigl69eqmdOtOFiYmJTvNNnDgRISEh0vVBCoUCwLPQ5+Pjg3Xr1mnM898L35/fvpfVkZmZienTp6NHjx4a0/4bcgwNDdWmKRQK6XRdYbp27YqqVati+fLlcHJygkqlQp06dYq1bzMzM9G1a1fMmzdPY5qjoyP09fURExODY8eOYc+ePfj+++8xZcoUnDhxAtWqVSvyeojeVgxTRCTb0aNHERISgu7duwN49uH934u6dVW3bl3cunUL//zzT7FGp2xtbVG9enWN9gYNGmDDhg2ws7ODpaVlser46aef8ODBA62jUw0aNEBCQoLWdRZVwUhYfn6+1Hb//n0kJCRg+fLlaNGiBQDgyJEjavO5u7tj7dq1yMnJgVKpBAD8+eefGvVt2bIFLi4uhd55qVAo0KxZMzRr1gzTpk1D1apVsW3bNowfP17nbSJ6W/A0HxHJVqNGDWzduhVnz57FuXPn8P777790xKUo/Pz80LJlS/Ts2RMxMTG4fv06/vjjD0RFRem0vP79+8PW1hbdunXD4cOHcf36dRw4cACjR4/GrVu3Cp2vX79+cHBwQGBgII4ePYpr165hy5YtOH78OABg2rRpWL16NaZPn44LFy4gPj4e69evx9SpU4tcW9WqVaFQKLBr1y7cu3cPmZmZsLa2ho2NDX788UdcuXIF+/bt0wg3Bft62LBhiI+PR3R0NBYsWAAA0ojciBEj8ODBA/Tr1w9//vknrl69iujoaAwaNAj5+fk4ceIEZs+ejVOnTuHGjRvYunUr7t27h1q1ahV3FxO9lRimiEi2RYsWwdraGk2bNkXXrl0REBCABg0alMiyt2zZgkaNGqFfv36oXbs2PvvsM7XRm+IwNTXFoUOH8M4776BHjx6oVasWhgwZgidPnrxwpMrIyAh79uyBnZ0dOnXqBC8vL7W7CgMCArBr1y7s2bMHjRo1wrvvvotvvvkGVatWLXJtlStXxvTp0zFp0iTY29tj5MiR0NPTw/r163H69GnUqVMH48aNw/z589Xms7S0xM6dO3H27FnUq1cPU6ZMwbRp0wD83ylGJycnHD16FPn5+Wjfvj28vLwwduxYVKhQAXp6erC0tMShQ4fQqVMn1KxZE1OnTsXChQvRsWPH4u5ioreSQgje+0pEVJ6sW7cOgwYNQnp6us7XnRFR0fGaKSKiMm716tVwdXVF5cqVce7cOXz++ecICgpikCJ6TRimiIjKuOTkZEybNg3JyclwdHRE7969NR4YSkSvDk/zEREREcnAC9CJiIiIZGCYIiIiIpKBYYqIiIhIBoYpIiIiIhkYpoiIiIhkYJgiIiIikoFhioiIiEgGhikiIiIiGRimiIiIiGT4f/SwNZBf3gLDAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "4th Plot\n",
        "'''\n",
        "\n",
        "# Packet counters as percentages\n",
        "tcp_percent = tcp_counter / counter\n",
        "udp_percent = udp_counter / counter\n",
        "icmp_percent = icmp_counter / counter\n",
        "arp_percent = arp_counter / counter\n",
        "other_counter = counter - (tcp_counter + udp_counter + icmp_counter + arp_counter)\n",
        "other_percent = other_counter / counter\n",
        "\n",
        "# Put the percentages in a list\n",
        "percentages = [tcp_percent, udp_percent, icmp_percent, arp_percent, other_percent]\n",
        "\n",
        "# Define the labels for the bar plot\n",
        "labels = ['TCP', 'UDP', 'ICMP', 'ARP', 'Other']\n",
        "\n",
        "# Create the bar plot\n",
        "plt.barh(labels, percentages)\n",
        "\n",
        "# Add a title and labels to the plot\n",
        "plt.title('Percentage for the various Traffic Protocols')\n",
        "plt.xlabel('Traffic Percentages')\n",
        "plt.ylabel('Protocols')\n",
        "plt.xlim([0, 1])\n",
        "\n",
        "# Add text next to each bar\n",
        "for i, v in enumerate(percentages):\n",
        "    plt.text(v + 0.01, i, str(round(v, 4)))\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('Traffic-Percentages.png', bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Comments: Based on the above percentages, it appears that TCP is the most dominant protocol in network traffic, accounting for almost 75% of all traffic. UDP is the second most used protocol with 11.63%, while ARP and ICMP protocols are less used, accounting for 4.95% and 2.62% of traffic respectively. The remaining 5.85% of traffic is attributed to other protocols.\n",
        "\n",
        "This information can be useful in understanding the nature of the network traffic recorded in the trace.pcap file. For example, a high percentage of TCP traffic indicates that the network may be used primarily for data transfer or communication between network devices. The presence of ARP and ICMP protocols may indicate network maintenance or troubleshooting activities, since one is used for IP address to MAC address mappings and the other for error reporting and mainly diagnostic purposes. Such a representation of the distribution of protocols can help us in identifying potential network problems as well as perhaps optimizing network performance."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}