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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vPWnv6QeirHc"
+ },
+ "source": [
+ "# Frequency Synthesizer with Programmable Sweep\n",
+ "\n",
+ "For a better experience, please open in Colab:\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "```\n",
+ "Submission for IEEE SSCS Open-Source Ecosystem “Code-a-Chip” Travel Grant Awards at VLSI'24\n",
+ "Adithya Sunil Edakkadan, April 2024\n",
+ "SPDX-License-Identifier: Apache-2.0\n",
+ "```\n",
+ "\n",
+ "|Name|Affiliation|IEEE Member|SSCS Member|\n",
+ "|:--:|:----------:|:----------:|:----------:|\n",
+ "|Adithya Sunil Edakkadan
Email ID: adithyasunil26@gmail.com|IIIT, Hyderabad |Yes|No|\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "\n",
+ "With the growth of applications such as quantum sensing which have a wide array of applications in material science, mesoscopic physics and life science, there is an increasing requirement for frequency synthesizers with programmable sweep-step size.\n",
+ "\n",
+ "### NV Magnetometry\n",
+ "\n",
+ "Considering the example of nitrogen vacancy magnetometry, Nitrogen-Vacancy (NV) centre in diamond behaves as an isolated electronic spin system that can be used in quantum sensors [1]. When a vacancy replaces the adjacent carbon pair in a diamond lattice, the nitrogen atom and the vacancy form an NV centre. The NV defect has its ground level in a spin triplet state whose sub-levels are split in energy into a singlet ($m_s=0$) and a doublet of degenerate levels ($m_s=\\pm1$) separated by 2.87 GHz [2]. When an external magnetic field is applied on the NV ground state spin triplet, a Zeeman shift of energy $\\gamma_eB_z$ is induced, where $B_z$ represents the magnetic field component along the NV symmetry axis.\n",
+ "\n",
+ "![ODMR](https://drive.google.com/uc?export=view&id=1gAIWnlV9TrA38i8EY8ILpdNn9xnHf7S6)\n",
+ "\n",
+ "As shown in figure, optically detected magnetic resonance (ODMR) technique can be used in NV-based sensing to measure static or slow varying $|\\vec{B_z}|$ [1], [2]. In ODMR, NV electron spin transitions are excited by a microwave signal ($f_{RF}$) near 2.87 GHz and diamond is irradiated with a green light, which produces a red light proportional to $|\\vec{B_z}|$ and having photon frequency $\\Delta f_p$, which is detected using a photo-diode [1].\n",
+ "\n",
+ "$$\\Delta f_p = f_+ - f_- = 2\\gamma_e|\\vec{B_z}| $$\n",
+ "\n",
+ "$\\gamma_e$ is gyromagnetic ratio (28 GHz/T) and $f_+$ and $f_-$ are the transition frequencies from the singlet level to the doublet levels.\n",
+ "Usually, NV-ODMR is detected with lock-in technique for which $f_{RF}$ is frequency modulated ($f_m$) while using an external source [1]-[4]. The sensitivity of measured $|\\vec{B_z}|$ can be improved with reduced $f_m$, which results into lower $\\Delta f_p$. Moreover, overall power can also be reduced by having on-chip frequency sweep than using the external frequency modulator.\n",
+ "\n",
+ "\n",
+ "### This Work\n",
+ "\n",
+ "Towards the goal on minimziing sweep step size and power consumption, in this work, we present - 1) design of a microwave signal generator with a programmable sweep-step size, 2) it's implementation in the open-source SKY130 PDK technology and 3) post-layout simulation results to validate the MWG design.\n",
+ "\n",
+ "\n",
+ "### Architecture overview\n",
+ "\n",
+ "![PLL](https://drive.google.com/uc?export=view&id=162hQaotxAB3fMqotJTYGZN8dV3hQnN3e)\n",
+ "\n",
+ "\n",
+ "As shown in figure, a phase-locked loop (PLL) based fractional-N frequency synthesizer has been presented in this work, which contains a crystal oscillator (XO) to generate reference signal ($f_{ref}$), phase/frequency detector (PFD), a charge pump (CP), a loop filter (LPF), a voltage controlled oscillator (VCO) and a programmable divider.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0ID0EN3ujqsY"
+ },
+ "source": [
+ "# Install Dependancies\n",
+ "\n",
+ "This project is designed in the `SKY130` process node. The following tools have been used for designing and simulating the circuits:\n",
+ "1. Ngspice\n",
+ "1. Magic\n",
+ "\n",
+ "The following libraries have been used for processing and visualizing data in the notebook:\n",
+ "1. Numpy\n",
+ "1. Pandas\n",
+ "1. Matplotlib\n",
+ "1. Gdstk\n",
+ "1. CairoSVG"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FYNUSJPOKXCh"
+ },
+ "source": [
+ "Installing SKY130, Magic, Gdstk and CairoSVG through `mamba` package manager"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "CY1x1XkajUem",
+ "outputId": "aaec170f-f2df-4b3f-f828-f0e7d7ffc987"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Overwriting environment.yml\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%writefile environment.yml\n",
+ "channels:\n",
+ " - litex-hub\n",
+ " - conda-forge\n",
+ "dependencies:\n",
+ " - open_pdks.sky130a\n",
+ " - magic\n",
+ " - gdstk\n",
+ " - cairosvg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dOkw1lOdiaaI",
+ "outputId": "7577630c-e4d8-48ea-a6c6-41d11bfb8667"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: condacolab in /usr/local/lib/python3.10/site-packages (0.1.9)\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+ "\u001b[0m✨🍰✨ Everything looks OK!\n",
+ "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n",
+ "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/linux-64 ⣾ \n",
+ "conda-forge/noarch ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 No change\n",
+ "[+] 0.2s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/noarch 1%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/noarch 49%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/noarch 72%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/noarch 95%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \n",
+ "conda-forge/noarch 100%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/noarch \n",
+ "[+] 0.7s\n",
+ "litex-hub/linux-64 ⣾ \n",
+ "litex-hub/noarch ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glitex-hub/linux-64 No change\n",
+ "litex-hub/noarch No change\n",
+ "\u001b[?25h\n",
+ "\n",
+ "Looking for: ['open_pdks.sky130a', 'magic', 'gdstk', 'cairosvg']\n",
+ "\n",
+ "\n",
+ "\n",
+ " Pinned packages:\n",
+ "\n",
+ " - python 3.10.*\n",
+ " - python_abi 3.10.* *cp310*\n",
+ " - cuda-version 12.*\n",
+ "\n",
+ "\n",
+ "Transaction\n",
+ "\n",
+ " Prefix: /usr/local\n",
+ "\n",
+ " All requested packages already installed\n",
+ "\n",
+ "\u001b[?25l\u001b[2K\u001b[0G\u001b[?25hPreparing transaction: - \b\bdone\n",
+ "Verifying transaction: | \b\bdone\n",
+ "Executing transaction: - \b\bdone\n",
+ "#\n",
+ "# To activate this environment, use\n",
+ "#\n",
+ "# $ conda activate base\n",
+ "#\n",
+ "# To deactivate an active environment, use\n",
+ "#\n",
+ "# $ conda deactivate\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "CONDA_PREFIX = os.environ.get('CONDA_PREFIX', None)\n",
+ "if not CONDA_PREFIX:\n",
+ " !python -m pip install condacolab\n",
+ " import condacolab\n",
+ " condacolab.install()\n",
+ "\n",
+ "!mamba env update -n base -f environment.yml"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n6OXIkWjLVV6"
+ },
+ "source": [
+ "Insalling Ngspice using `apt-get` package manager"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FSb6j8pzK9e-",
+ "outputId": "9835d593-cddf-45f4-a7dd-376dfa89915d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Reading package lists... Done\n",
+ "Building dependency tree... Done\n",
+ "Reading state information... Done\n",
+ "ngspice is already the newest version (36+ds-1).\n",
+ "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!sudo apt-get install ngspice"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eTHeARTjLd42"
+ },
+ "source": [
+ "Importing required libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eIF-O1YGj3HF"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import gdstk\n",
+ "import cairosvg\n",
+ "from IPython.display import Image,clear_output\n",
+ "\n",
+ "CONDA_PREFIX = os.environ.get('CONDA_PREFIX', None)\n",
+ "if not CONDA_PREFIX:\n",
+ " import condacolab\n",
+ " CONDA_PREFIX = condacolab.PREFIX"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e0By5XD3LlzO"
+ },
+ "source": [
+ "Cloning the repository containing the custom layouts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Jyzv_kSjnXLR",
+ "outputId": "e11a9e5f-2d10-405c-b6c6-e19454ba7743"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Cloning into '2.87GHz-MWG-SKY130'...\n",
+ "remote: Enumerating objects: 134, done.\u001b[K\n",
+ "remote: Counting objects: 100% (134/134), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (103/103), done.\u001b[K\n",
+ "remote: Total 134 (delta 53), reused 106 (delta 30), pack-reused 0\u001b[K\n",
+ "Receiving objects: 100% (134/134), 1.60 MiB | 5.65 MiB/s, done.\n",
+ "Resolving deltas: 100% (53/53), done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!rm -rf sim gds images 2.87GHz-MWG-SKY130\n",
+ "!mkdir sim gds images\n",
+ "!git clone https://github.com/adithyasunil26/2.87GHz-MWG-SKY130.git"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VcjEtvlmpcSO"
+ },
+ "source": [
+ "## Functions for reading and plotting output data from simulations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "y6xumEVEN-Hk"
+ },
+ "source": [
+ "These functions allow for reading data exporting from ngspice and plotting them."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NglsQrgObit7"
+ },
+ "outputs": [],
+ "source": [
+ "def read_op_file(filename, columns):\n",
+ " with open(filename, \"r\") as f:\n",
+ " op = f.read().split('\\n')\n",
+ " op_filt = []\n",
+ "\n",
+ " for i in op[2:-1]:\n",
+ " # print(i)\n",
+ " if i[0] not in ['-','\\x0c','I']:\n",
+ " op_filt.append(i.split('\\t')[:-1])\n",
+ " return pd.DataFrame(op_filt,columns=['s','t']+columns)\n",
+ "\n",
+ "def plot_from_op(df):\n",
+ " for i in df.columns[2:]:\n",
+ " j = [abs(float(k)) for k in df[i]]\n",
+ " plt.plot(j, label=i)\n",
+ " plt.legend()\n",
+ " plt.ylabel(\"Voltage (V)\")\n",
+ " plt.show()\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ETjfuCf_3Csh"
+ },
+ "source": [
+ "## Function for adding signal generation, simulation and control statements to generated `spice` netlists"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XUbprxFiOeM-"
+ },
+ "source": [
+ "This function allows for generation of spice netlists for simulation by adding the given signals, simulation commands and control commands to the extracted netlist from magic."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yeq4nhvPkS0E"
+ },
+ "outputs": [],
+ "source": [
+ "def mod_extracted_net(path, signals, sim, control):\n",
+ "\n",
+ " with open(path, \"r\") as f:\n",
+ " op = f.read()\n",
+ "\n",
+ " signals = signals+'\\n'\n",
+ " sim = sim+'\\n'\n",
+ " control = control+'\\n'\n",
+ "\n",
+ " dec = f\"\"\"* Simulation\n",
+ "\n",
+ ".lib \"{CONDA_PREFIX}/share/pdk/sky130A/libs.tech/ngspice/sky130.lib.spice\" tt\n",
+ "\n",
+ ".param SUPPLY = 1.8\n",
+ ".global vdd gnd\n",
+ "\n",
+ "Vdd vdd gnd 'SUPPLY'\n",
+ "{signals}\n",
+ "\"\"\"\n",
+ "\n",
+ " commands = f\"\"\"\n",
+ "*Simulation Command\n",
+ "{sim}\n",
+ "\n",
+ "* ngspice control statements\n",
+ ".control\n",
+ "run\n",
+ "\n",
+ "{control}\n",
+ "\n",
+ ".endc\n",
+ ".end\n",
+ "\"\"\"\n",
+ "\n",
+ " op = '\\n'.join(op.split('\\n')[2:-3]).split('.subckt')\n",
+ "\n",
+ " with open(path.split('.')[0]+'_sim.spice', \"w\") as f:\n",
+ " f.write(dec+'\\n'+'.subckt'.join(op[:-1])+'\\n'.join(op[-1].split('\\n')[1:])+'\\n'+commands)\n",
+ "\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GECMyZVK3q4O"
+ },
+ "source": [
+ "## Function for reading `meas` outputs from ngspice log"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QwyU-DEdOscG"
+ },
+ "source": [
+ "This function is used for reading the output of the `meas` command from the ngspice simulation output."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oe-tW8yi3qHu"
+ },
+ "outputs": [],
+ "source": [
+ "def read_meas_from_spice_out(path):\n",
+ " with open(path, \"r\") as f:\n",
+ " op = f.read()\n",
+ " op = op.split('Measurements for Transient Analysis')[1].split('\\n')\n",
+ " op = [i for i in op if i!=''][:-1]\n",
+ " meas_out = []\n",
+ " for i in op:\n",
+ " meas_out.append(i.split('=')[1].split(' ')[-2])\n",
+ " return meas_out"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-M3GVjdrpYNF"
+ },
+ "source": [
+ "# Sample Inverter simulation\n",
+ "\n",
+ "In order to test the idea of running `spice` netlists through the notebook and parsing and plotting the outputs, a simple inverter netlist has been created and simulated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fqf6_5mHPDSl"
+ },
+ "source": [
+ "Creating spice netlist for a an inverter circuit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Bc1kdGnybclH",
+ "outputId": "cf72171b-2309-46d5-b2b9-cbc3c8fa8f6b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Writing sim/inv.spice\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%writefile sim/inv.spice\n",
+ "* Transient simulation\n",
+ "\n",
+ ".lib \"/usr/local/share/pdk/sky130A/libs.tech/ngspice/sky130.lib.spice\" tt\n",
+ "\n",
+ ".param SUPPLY = 1.8\n",
+ ".global vdd gnd\n",
+ "\n",
+ "Vdd vdd gnd 'SUPPLY'\n",
+ "va a gnd pulse 1.8 0 0ns 10ps 10ps 10ns 20ns\n",
+ "\n",
+ "X1 out a gnd gnd sky130_fd_pr__nfet_01v8 ad=0p pd=0u as=0p ps=0u w=900000u l=150000u\n",
+ "X2 out a vdd vdd sky130_fd_pr__pfet_01v8 ad=0p pd=0u as=0p ps=0u w=1.8e+06u l=150000u\n",
+ "\n",
+ "*Simulation Command\n",
+ ".tran 1ns 50ns\n",
+ "\n",
+ "* ngspice control statements\n",
+ ".control\n",
+ "\n",
+ "run\n",
+ "print v(a) v(out) > inv_plot_data.txt\n",
+ "quit\n",
+ "\n",
+ ".endc\n",
+ "\n",
+ ".end\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dhlNuK_sH-ag"
+ },
+ "source": [
+ "Simulating created netlist using ngspice and plotting the input and output signals of the inverter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 430
+ },
+ "id": "MqVcD1bkbhXM",
+ "outputId": "a8a4d7f3-2578-47b4-ea75-768ddc17d9e0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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EjOMYqAeQSRVn3yU1DMUNqQ0631EeRQYIgOKGuAfRWDFAqYGjujfsu6SGobghtYGZ2S+QHSAGuCxFHMZ031WFOfsuqV0obkhtoA4Q7QKzX4DODXEHyRiw5wPluai4CVGYE0JxQ6qfVBLY9Z7yXMTaBzj7Je5gzwdAOqlk7zWPF3sPhTkhFDekBti3EUgOAP56YMQksfcwboG4Af2SVKE9pfRQ3BBCcUNqAG2AmA54BLs8BwjiBswEE6uw7xJCcUNqALOZUgAHCOIOzMaKAey7hIDihtQCZrNNAMbcEOeR5SL7LgtQEkJxQ6qbYgeIEGNuiMN0bVYEijcAjDpU/H2MFyOE4oZUOT2dQHQvIHmAMUeIv4/WPnEaVZSPORzw+sXfR9eREIobUuWoA8SoQwF/WPx9tPaJ0xTjOAIU5oSA4oZ89DLwl28Bu9Y63RJ70IKJTQRkAoMHCO6uTJygmEwpgOKGEAA+pxtQTfz+tc04/bDRGD+izummiCHLwJ//H9C1BXjzYeCErwJnLFAKhu16D/joJaBrK9A6GRh9KDByCpDoV87v2qzcPBvbgaZxymO8V1kGiuxQdiRuGAs0timP8V6gZxfQ26l8Rv0YoHFsjtcGlI0tG8YC9aOBeB/Qu0v5SQ4oxxrGAvWjgHhU91pMOdYwRve+3cCGJcq/1eTstzsVQjMAyGm8/N7HSPsq5P+0zAR9XpwwsRUBX5nnSekU8NebgH0bgIOOA8afCLTPUPpS9zalD6YTSl8Oj1AG/ES/4sQNRADIQKABCDYA/jrltXiv0m8AZQPKQD3gCwPJfuW44WsSEKgr/D5/neIgJjKvJaLZ9+lfS0SB7W8p7zPZd3fHAxgDoCeyHys/2FX6ta5SRtQFcExHCyTR+kFWEd0PPDMPgAR0nACMP0Fxlnt3K/22Zwfg8WX7rj+s3GtjEeXR41f6baAB8AWz/Tbep7wv0KD0NW8g+1oimnmtXvda5j2JfsDj1b3Pn+O1zPs8fiDRp9x7E/2A16fUDwvUDXktqrT90LnlvbY6KG4sYsnaXfju4jVoqfPj7ouOwRmHCew87TR7NyhCBQDkFPDGr4B3n1A6ad9uZ9tmNWZSaQF89dH38KjsgU9K44bf/wN7MMKmhlU+8844BDfMnVbeL33lNmDlQ8rzj14u73eXFQkYe6Tw2dF4Etf+aSMe9QBd+/fjK4vetLFtlc9/ffkEnDGtjPfqdAp46krgwxeV39f9pXzfXW46Tqa4qQYOb2/EjPHN+Oe2bnxl0Ur8vzOm4No5h8LrKfOswAwb/q48Tj4DOOVa4Pmbs/vY+MLAxFOA0dOAAx8De9cD+z9Sjo84GGiZoMSl9GacmshORdk3tilOTqBecVR6OhVXJlCvODWN7cpMpHe38lrfbmXG0JBxcfxhoG9P5rU9mdfGKD++ENC3V/nc6F5lxqC9Fsy8tlt5LdCQcXnGAGOnAxM/YerSbNrXj16E0YI+nNDmxXZfs7XXvgro6k9g874o3tnWXd4vXv93RdwAwMnzgIFuYNsbSh8NNin9r+kgZXY60AX0H1DcmkCd8nqoSQkwj/VmZ7b+jOMSbFA+V5u5RrOv+TPuXSIq8Fq/0pf9dcr3AtkZbXJA6cva++Thr6nvO/RMICTe9/b0xLA7EQKCQLN3ADPGs9/mYnvXAPb2xvDOtu7yipuXf6YIG18Y+MR8Zelx20qgZydQN1Lpt03jADmt9NvofqUvBRuVfhBsVLbjiPcq/TcVyzougXrltVimT6fi2eP+usz7+nSvNWQdQ/W1RFR5TXVj/GFFkKnvSydyvNar9N9Br9WZEuV2QHFjEeNH1OGJq2fhx8++j9+9thn3vLgRb2/tws8vOgYjG4JONy83GzNLNlM/CxxyBnD1cmDjUqVzdpykCAY96bRSAr7cNq4D9MeT6JUUcfOf/+dQYPxxTjfJdaz4cB8ufug1bN0fLd+XHvgYePoq5fnxVwJn/jT7WiphLquoConGU+iVlcD5JkTxzLxTauLv1Sz3vbQRd/xtHbaUs++u/xuw7Hbl+bn3AEdfmH2NfddyGFBsIUGfFz86bzp+8aVjEPZ78b8b9uLse5Zj1eb9TjdtOLFeYPOryvOpn1EevX7gsDOBSZ8cLmwAZeuCGrhRyrKMaCKFnswgwYyp3HS0Ktdne1c/UukyBF0nBoAnLlPcmIOOA85cOPh1Dg6KuEGm38opZdZPhjF+hHKNth4ok7jZvykryk+4arCwAdh3bYDixgY+f8xBeOaaUzB5dD06IwO46Fev4TfLN0F2U9bNpmWK/dhysBIoTDRiyTRkGdlBglknOWlvDsPnkZBIydgVGbD/C1ctAnb+U7HvL/xtbgFe4/THU4giiDQykxD23Zx0tCpLhdvK5dwsvVVZPh1/AjD3p8bnk5KhuLGJQ8c24s/XnIp/ObodybSMHz27Ft9ZvMbpZmXRL0nVgBtjhmg8BQCavc8BIjdej4SDMjPgstj7e9cpj8dfCTSPt//7KpBoPAkZHvRL7LuFmJARNzsjA4glU/Z/4Z71yuNpNwO+gP3fRyhu7KQh6MO9F8/ED845Ah4JeOT1LViy1gWpmbIMbHhBea4uSRGNaDypPEqZQFAOEHnpyJQ9KEvcTU+n8tg0zv7vqlD6E8pAPeBR+y6XVHMxsj6AsN8LWQZ2dJXBdezZqTyy75YNihubkSQJXz5lEq765GQAwHcXv4vu/oSzjdq7HujeAniDprOIaoH+OAcIUdS4m60HyhDbEdmhPDa22/9dFUpU67uZrC8K85xIkpTtu3YL88QA0J+Ju2xss/e7iAbFTZm4fs6hmDSqHrsiMfzsr+872xg1BXziKdk0VaLRlxkgYl51gKC4yUdZYxdU54YDRF76YorrGPfWKwfYd/OiuY52BxX3ZvqtN6gUtiNlgeKmTIT8Xvzsi0ql0Uff2IpXN+51rjEbdPE2ZBjqslTcpw4QnP3mQx0gbI+5SSWzhSVp7edFdR3Zd41RhbntfVdbTm1nfGMZobgpIydNHol/PflgAMBNT7+jDaJlRZ8CPoXxNrlQB4iEj9a+EeoAYfvst2+3UthM8gJ1o+z9rgommom5SfrZd43Iuo42L6lyOdURKG7KzE1nTcO45hC27u/H029tL38Dtr+pVJJsmQCMPKT8318BqHELHCCMUbNOdkViGEjYmHWiBmQ2tin1lkhOVGGe8nNJ1YiOctW64XKqI/AuUWYagj5cNnsiAOD5NZ3lb0BPJlurdTIt0jyoA0Sa4saQEXV+1Ae8AIBtdgYVR1Rxw9lvIVQ3OO3nzuBGTBhZrmUpte9yObWcUNw4wFnTFQW/4qN9ONAXL++X9+1RHmnt50UbIAKZAWKAs998KFknZVia0js3JC+q65gOUtwYocaLdUUT6BmwMYOVfdcRKG4c4OCR9Ti8vQmptFz+ujfRTCBz/ejyfm8FocYtyAEOECKUJWNKs/bp3BRCdR1BcWNIfdCH1nqloN5WO+Nu2HcdgeLGIVT35q9rdpb3i1Xnpn5keb+3glAHCCnUpBxg3EJBypIxpRVB4wBRCNW5kYKZvkvXsSAd5aiwzb7rCBQ3DvG5oxRxs3zjXkTstESH0rdPeaRzk5foMHHTo1R1JjnJFkOzc/bLmBsRVNfRE9b1XZIXzXW0a0lVlhkv5hAUNw4xZUwjpoxpQCIl48X3d5fvixlzY4gqbrzqAMHdlQsyoSwxN8w4EaE/Ey/mo7gRQosXs8u5ifUAiT7lOftuWaG4cRB1aeq5d8u4NMWYG0PUgGJ/qAHg7sqG2D5AALpaIcw4KURfTBHmvjouqYqQrVJs0+RFFeXBZiBQb893kJxQ3DjImRlx88r6PVrZdNvpU8UNnZt8qM5NOOgDgpwBGzE+E7cQGUiiO2rDEmuiHxjoUp5z9lsQdePMQF2mzD/7bUEm2F2luEcV5ey35YbixkGOaG/ChNY6xJJpvLxuj/1fmOgH4r3Kc4qbvKgBxfUBny7rpNvBFrmbuoAPoxqCAGxamlJnv74wEGq2/vOrCNV1DNZnrhPjxQqixottOxCFbMd14nKqY1DcOIgkSTgrE1j8XDmyplTXxuPPOhJkGOoAEQ54gRCdGxFs3WFZn23CwpN5SadlDCTSAIBgQ0vmYAJIDjjXKJczriUMjwQMJNLY0xuz/gu0vsvl1HJDceMwnzl8LADgjU377f8yfbwNB4m8qMtSdQEv64UIYms6ODOlhOjXbX8RbtBNXth38+L3etDebKMwj7CAn1NQ3DjMYW3K4LmnJ2ZPvIIeLd6GNW4KoQ4SFDfiaM6NnctSHCAKoopyAAj5/QCLUAphaykDCnPHoLhxmMaQH+3NIQDAxj0234T6mCklghZQ7PdR3AiipYPbMUBwV2Uh+rV+64XHI+n6LjOmCqFlTNniOrI6sVNQ3LiAKWOUDRo37u6194tY40aI/pzLUhwgCsEBwnmiCSVWrC6zkSnjxcTosDNjin3XMShuXIAqbjbsslncsMaNIbIsawHFirhhGXsRspVe+5FOW5x1wmUpITTHURU3dB2FsG1JNZ3mppkOQnHjAqaOUW5CG/fY7dww5saIWDINdWwOM+ZGmPbmELweCfGUDVknaq0QZpwUZFAJAyDbdynMC6K6jtusLuTXv1/JVgMobhyA4sYFlM25YcyNIfqgzLoAi/iJ4vN60Bz2AwC6rAyMl2U6N4KohUDp3JhD3Rnc8oQO1bWpHw14/dZ+NjGE4sYFTM2Im+1d/fZWKmbMjSHqklTA54F3UFAmBwgjVHHT3W/hIDHQDSQyywWMWyjIoCw/gPFigqj9tieWRMrKJVWmgTsKxY0LGFEfwKgGZfbw0Z4++76IMTeGDAomBihuTNAUUpZDIlaKG9W1CbUA/rB1n1uFRIf1XbqOIjSFs65Kz4CVfVcVN1xOdQKKG5dwyOjM0tRuG29EjLkxJJovboEDhCFNdjg3rBMiTDagWO27FDci+L0eTRBa23e5nOokFDcuYepYVdzYFHcT78va+3Ru8jI840QdILi3lBG2LEsx20SYfjXLz0/X0Sz29F3WZ3ISihuXoGVM2SVuVNfGGwQCDfZ8RxXQP7RWCAcIYVTnJmKHtc9MKUOYCl48TaFM3+23MOZRdW6aKG6cgOLGJdheyI/7SgkR1VV5BTC4EBp3Vy6IPbNfWvuiDI+5YUCxKPa6jhQ3TkBx4xLUjKnN+/owoNsAzzIYbyNE3gEineTuygbYMkBw6wVhhgfDq8Kc4sYIW+LFmC3lKBQ3LmF0YxCNIR/SMvDxPhsypljjRojsAJEJyvTXA8g4XbT3C2KrtU9xY0g0MTSgmMtSojSFM5l+Vi2pphLZ0hvMlnIEihuXIEmS5t7YUsyPNW6EGBa34PFwkBBEdW5sSQWnuDFEDSiu595SprHcdezdDUAGPD6gjm65E1DcuAg1qNiWjCkt5obiphCD9pVSYeyCEM1WBxTLMtCripux1nxmFdMXY0BxsVgublRR3jBWmSCRssOr7iLUoOIP7RA3ffuUR4qbggxzbgDu0SOIau1bNkAk+pVYJwAINVvzmVVMNDFkSVXtt6k4kLR4v68qI7ukalHfVSdC7LeOQXHjIqaMtbGQn7osxZibgmgBxX5f9iBnwEJYPvtN6HZp9tdZ85lVTP9Q11Ff8oHCvCC29V32W8dwVNwsW7YM55xzDsaNGwdJkrB48eKC57/88suQJGnYT2dnZ3kabDNqzM2mvX1IptLWfri6LMWYm4IMGyAAVnoVRB0govEUElb033gmsN4XBjzewueSHPFi3qzA4ZJqQSyPF4tnxE2g3prPI6ZxVNz09fVhxowZuO+++0y9b926ddi5c6f2M2bMGJtaWF7GNYcR9nuRSMnYvD9q/AYzMFtKCM25CeaKuaG4KURjKLtHjyWDhDr7DXD2K8KwVHCAfVeQbAFKizL9EhlhTnHjGD7jU+zjrLPOwllnnWX6fWPGjEFLS4v1DXIYj0fClDENeHd7Nzbu7tX2myoZWWadG0GG7awMMKBYEK9HQmPQh55YEt39CYxsCJb2gapz4+cAIULuJdUmpZgcxU1BLF+W0vouhblTVGTMzTHHHIP29nZ85jOfwT/+8Q+nm2Mpbc0hAMCeHgsDAON9QLJfeU7npiDZCsVDBgiAA4QAls6A1QGCzo0h6bSsCfOcwfDsuwXRixvZikrkcbqOTlNR4qa9vR0PPPAAnnrqKTz11FPo6OjA6aefjrfeeivve2KxGCKRyKAfNzOyPgAA2N8Xt+5D1XgbX5g2qQHDKhQDdG5MYGmlVwZlCjOQzFY157KUedRMv1Ra1u4BJZGg6+g0ji5LmeWwww7DYYcdpv0+e/ZsfPjhh7j77rvxu9/9Lud7Fi5ciFtvvbVcTSyZVjvEDeNthMkdUMwBQpRmK9PB44xbEEU/IGv7ogEU5oKE/V74vRISKRnd/QnUB0scGhlQ7DgV5dzk4sQTT8TGjRvzvr5gwQJ0d3drP1u3bi1j68yjipt9togbxtsYUbDODcWNIZbWC0lwgBClX7fhq8ej2xiX+0sJIUmS1netcR25pOo0FeXc5GL16tVob89fmj0YDCIYLDGwsYxknRsLY25Y40aY6NC9pQDAH1YeuXGmIZYGZsa5LCVKXy7HEQD8Sgwfi/gZ0xz2Y19f3BphzmB4x3FU3PT29g5yXTZt2oTVq1ejtbUVEyZMwIIFC7B9+3b89re/BQD8/Oc/x6RJk3DkkUdiYGAAv/71r/Hiiy/i73//u1P/BMvJihsL9+dhjRshZFnOvf2CV/k/QdJCN61KsXQLhnimUjdnv4bkdBwBwJuZ2FHcGGJpvBgDih3HUXHz5ptv4owzztB+nz9/PgDg8ssvx6JFi7Bz505s2bJFez0ej+Ob3/wmtm/fjrq6Ohx99NF44YUXBn1GpTOyXrkZWevccF8pEWLJNNKZRIlBg4RPHSDo3BjRZGUxNC2gmLNfI3LWuAEAX0aYpyjMjWAwfHXhqLg5/fTTC6bdLVq0aNDvN954I2688UabW+UsrQ3ZgGJZliFJksE7BKC4EaJfF5RZ58/h3HCAMMSWZSnOfg3JOjdDbuleCnNRmm0pY2BRrTJimooPKK42WuuUgTSRktETs6haJmNuhFA3Hgx4PfB5dX8aPlr7omTL2FvQd1nlVRhtOdWfx7nhkqohlmb6sbq241DcuIxwwKulcu7vteiGNNClPIZarPm8KkVLAw8OHSAyQZkpihsjLN0ZPM5lKVHyLkupzg37riGWZvqx7zoOxY0L0YKKoxaJm0SmOjFnEQXJlq8fOkBw9iuKpctSnP0KkzegmK6jMJZunslgeMehuHEhI9W4G6ucGwa3CWE4QHD2a4gt2VKc/RqSc080QNd3KcyNsEWY857rGBQ3LsTyKsWqc8M/tIL056pxA9C5MYHe2k+nS9yjhwHFwmRLGOQLKKYwN8KybKl0KhvAzXgxx6C4cSGWVynWxE3Yms+rUoytfWacGKEOEGkZ6I2XGFTM2a8wOfdEA3QBxRQ3RljmOqr9FqC4cRCKGxeiZkxZVuuGg4QQeau8qrNfOaXMykheQn4vgj7ltlJy7ALTaYWJxhhQXCqWLUupjiOkbDICKTsUNy4kW+vGgrXfZBxIZ2bQdG4KYlgIDeAMWADL7H0GFAujljEYVueGqeDCZJdUS3QctWDiesCKOmWkKChuXMhIK/eX0lukdG4Koi1L+YcOELrZF2fAhlg3A1b352G/NSLnbvYAyxiYQO23/YkU4sl08R9Ep9wVUNy4kFZtCwYLZltqvI3kBbz+0j+visk7QHh8ADIzMM6ADbGkkF86zV3BTZA35oYBxcI0hnya0VKSMGcgvCuguHEhlgYU6wcIWqQFyTtASBLTwU3QFFKcr5JibpL92eecARuSdR25t1SxeDwSGoKZvltKUHGCsWJugOLGhViaCs5MKWGycQve4S9yBiyMJVkncS6nmiF/GQP2WzNYsqQa57KUG6C4cSGquInGUxhIlJidQ3EjjDpA1A8dIACm1JrAkoDihC7exsPblBHRhLIEOLyMAfutGdSg4tLEjercUNw4ialdwbu6uvCnP/0J//u//4vNmzcjGo1i9OjRmDlzJubOnYvZs2fb1c6aoinkg98rIZGSsb8vjnEtJQgTBrcJoxZCK+jccFnKEM5+yw/3lrIGS7Zg0IQ5Y8WcRGhKtGPHDnz1q19Fe3s7fvzjH6O/vx/HHHMMPv3pT2P8+PF46aWX8JnPfAZHHHEEHn/8cbvbXPVIkoQRdRYtTWnihs6NEXljbgBdIT/GLhhhyQDB2a8povlcRzVbKp1UgrRJQazpuwwodgNCzs3MmTNx+eWXY9WqVTjiiCNyntPf34/Fixfj5z//ObZu3Ypvfetblja01mitD2B3T6z0oGI6N8Lknf0CDCg2gSXWPme/wqTTsra3VN5lKUDpux5Ocgphya72zPJzBULiZu3atRg5cmTBc8LhMC6++GJcfPHF2LdvnyWNq2Varap1w5gbYfrieQqhAdxfygSWxNxw9ivMQDIFObONV95lKUCJu+F9oCDZYPgSyhjEKczdgNCy1MiRI/Hss88iLWhrGgkhYkxW3JRa5ZWbZoqSt84NwP2lTGDJAMHZrzDqkhSQIxVcX9uK6eCGaPFiUS6pVjrCaQjnnXceOjo68J3vfAcbN260s00EFlYp5rKUMHlrhQBZ54YDhCGWWPuc/QqjLqeG/B54PENqWUmSLh2cwtwIazL9eM91A8LiZtOmTfja176Gxx57DIcddhhOO+00/O53v0N/f7/xm4lpLKtSzGUpYYRibphSa4gl2VLcV0qYaL4aNyoMhhfGmhpNqnNDYe4kwuKmo6MDt9xyCz788EO88MILmDhxIr7+9a+jvb0dV199NVauXGlnO2sOdfPMfb0l3pC0GTDFTSFkWdaK+OUcJLhHjzDqABFPpouv06RuPsjZryFaCYNcjiOgcx3Zd42gc1M9FFUd64wzzsB///d/Y+fOnbjjjjvw7rvv4uSTT8aMGTOsbl/N0mpZKjhjbkSIp9JIpZWozNx1bhhQLEp9wAd1daTolNo4Y25EKeg4AllhTtfREGuK+LHvuoGSSn82Njbi05/+NM444wy0tLRg7dq1VrWr5tECiqNclioH/bqgTKaCl4bHI2kz4KLtfc5+hSlYnwng/lImsLSIH8WNoxQlbvr7+/Hb3/4Wp59+OqZOnYrHHnsM8+fPx8cff2xx82qXkQ1WF/HjIFEIdYAIeD3we3P8WdC5MUXJcTeMWxCm4J5oAPeXMoHab3tiSaQzTq5ptFAA3nOdxNT2C6+99hoefvhhPPHEE4jH4/jiF7+IF154AWeccYZd7atZVOemK5pAMpWGL9eAK4Lq3DAwsyBaplTe2S8zTsxQsr3PVHBh1BIGOfdEA7i/lAnUTD9ZBnoGkmiu8xu8Iwes0eQKhMXNEUccgXXr1mHmzJlYuHAh/u///b9obm62s201TUvYD0lS/sgORBMY3Rg0flMu6NwIYRi3wKBMU2Tt/SJr3XD2K4yhMOf+UsIEfV6E/B4MJNKIDCSKEzesru0KhMXNnDlz8OijjzJouEz4vB40h/3oiiawvy9egrhhzI0IfYU2zQSYTmsSLkuVD+OYGy5LmaE57MdAIobu/gQ6ivkABhS7AmFxc88999jZDpKD1voAuqIJ7OuLAWgs7kO4caYQwhknnP0KUXIhPzqOwkS1ytoGdW4YUCxEU8iPXZFYcX03lczeIyhuHEUokOPMM8/Ea6+9ZnheT08PbrvtNtx3330lN4xkqxQfKGULBg4SQmizX3+eAYIBxaZoKjXrhHELwggvS9G5EaKkjCl1SQrgPddhhJybCy64AOeffz6am5txzjnn4Pjjj8e4ceMQCoVw4MABrF27FsuXL8dzzz2Hs88+G3fccYfd7a4JLNk8k8tSQqi7KoeMrH06N0KoAcU9xe4vpaXTNljUoupFLZSYt4gfU8FN0RhShsWi+q4qyiVP9p5BHEFI3Fx55ZW49NJL8eSTT+Lxxx/Hgw8+iO7ubgCAJEk44ogjMHfuXKxcuRKHH364rQ2uJdQtGPaVkg6uOTe0SAsRS2bEjS+PmellxokZgpnrOJAstkIxHUdRYgllQ+OQP1/fpXNjhlBGJBbVd/X3W0kqfC6xFeGYm2AwiEsvvRSXXnopAKC7uxv9/f0YOXIk/P4iIsqJIa31ynUtqdYNnRshBrQBgkGZVqBeR3XgNQ13VhZGHYTz910KczOU1HcZCO8aTNW50dPc3MxUcJsp2blJJbNWNMVNQVTnJpjXueGylBnU6xgrZvabTgNJVZRzkDBCHYTZd62hpL7LDV9dQ0nbLxB7yQYUFylukrod22nvF0QbIPJZ+0wFN0VQnf0mi5j9qgMEwEFCAPUaB310Ha0gK26KcW7UDV8pyp2G4sbFZAOKixxQ1bgFSAxuM0Cz9o0GCM5+hVBjl4raFVwvbnx0HI1Qr3HemBumgptCi7kppu8yy881UNy4mIZM1L5aYM40+jRwBrcVxNC5YSq4KUpybuK6Cq8e3qKMMHRuGFBsipKcG5becA28c7gYNbVTv2O1KRhMLIywtU/nRojSrH0GE5vBMF6MqeCmCDKguCooStx0dXXh17/+NRYsWID9+/cDAN566y1s377d0sbVOpaJGw4ShsSMrH3Ofk1RkrXP2a8pBjTX0ci54aavIpRUxoAbvroG09lS77zzDubMmYPm5mZ8/PHHuOqqq9Da2oqnn34aW7ZswW9/+1s72lmTqFsB9CdSkGUZktmlJQ4Swhg7N0ynNYM1zg0HCBGEnRv2XSFKc254z3ULpp2b+fPn48tf/jI2bNiAUCikHf/c5z6HZcuWWdq4WketlpuWi13/5bKUKMYDBPeWMoMmbujc2I56bzB0HbksJURJqeBqthSFueOYFjcrV67E1772tWHHDzroIHR2dlrSKKKgL6denL2vBmZykDDCsIgfA4pNka3yWsLslwOEEOq9Ib/rmBHmdG6EyC6pMqC4kjEtboLBICKRyLDj69evx+jRoy1pFFHwez3we5WlqGgxcTd0boQxdm4YUGwG9TrGk2nIsmzuzQkuS4kiy3J2STVvKjgDis1QmnPDVHC3YFrcnHvuufjhD3+IRELZMVWSJGzZsgU33XQTzj//fMsbWOuos4j+kux9ihsjDAcI1bmR00rlZ1IQfXCr6SXVOB1HURIpGap2ZCq4NZSWCs4NX92CaXFz5513ore3F2PGjEF/fz9OO+00TJkyBY2NjfjJT35iRxtrGi2ouCTnhoOEEVohNKNUcIBZJwLoHTDTgZmc/Qqjz+jJX8SPzo0ZLCnix3uu45jOlmpubsaSJUuwfPlyvPPOO+jt7cWxxx6LOXPm2NG+midcknNDcSOKsXOjEzccJAzxez3weiSk0nLG3jexuW5CV8SPFEQvHANeozIGFOUisEZTdVD0xpmnnnoqTj31VCvbQnIQDij/RcU5N5xFiJLdfDCfte8DJI+yLEV7X4igz4NoPFXEshSdG1H0sWJ5S0VwXzRTqPeAkpalKMwdx7S4ueeee3IelyQJoVAIU6ZMwSc/+Ul4vXkGCWKKcMZJYECxvWh7S+VzbgAl6yQRZVCxIKq4MW3vM6BYGMMsP4DB8CZR7wHcW6qyMS1u7r77buzZswfRaBQjRowAABw4cAB1dXVoaGjA7t27MXnyZLz00kvo6OiwvMG1RjhQyvqvOouguDHC0LkBlKDiRJQzYEGUATdRvHPD2a8hhll+AAOKTWLJjvYU5o5jOqD4pz/9KU444QRs2LAB+/btw759+7B+/XqcdNJJ+MUvfoEtW7agra0N119/vR3trTnCfkV/lubccBZRCCWdVmCQ4AzYFEWn1DJuQRjDWDGAAcUmKamMQZzLUm7BtHPz3e9+F0899RQOOeQQ7diUKVPwH//xHzj//PPx0Ucf4fbbb2dauEWEA1YEFNO5KUQiJSOtptMWsvc5AzaF6oKZLobGWDFhDLP8APZbk4SGlDEouOQ3FApz12Daudm5cyeSyeF1PpLJpFaheNy4cejp6Sm9dQR13IDQdvTOQmHnhnv0mEGNXSjeueHs1wgx5yYjbuQUazQJUHQZg1QCSCv133jPdR7T4uaMM87A1772Nbz99tvasbfffhtf//rX8alPfQoA8O6772LSpEnWtbKGUZ2baLyImxJ3BRdCv7YuFLvAZSkhtKwTOje2IRwrpsK+a4jPI8GTSTwzJcxVUQ5QmLsA0+LmN7/5DVpbW3HcccchGAwiGAzi+OOPR2trK37zm98AABoaGnDnnXda3thaRFuWipeyzwmXpQqR3ZunQDotwJRak6huwoBp50YNymSVVyNiQll++gKUFDdGSJJU3P5S6v1W8g4WlMQRTMfctLW1YcmSJfjggw+wfv16AMBhhx2Gww47TDvnjDPOsK6FNU62iF8Jzg1nwAXRrP1Crg3AgGKTFO3caDsrs98aIeTceHQ1mhhULES2RpMZ50YnygtNkkhZKLqI37Rp0zBt2jQr20JyoImbkor40bkphDZAGAUOcmdwUwT9RVZ65bKUMEJZfpKkLKkm++ncCKKIRZNlDCjKXUVR4mbbtm3485//jC1btiAeH3yjv+uuuyxpGFEoLVuKg4QIQgX8ADo3JlEHXFPB8OlUdpsAxi0YIlTED1CC4ZP9dG4EKaqQH++3rsK0uFm6dCnOPfdcTJ48GR988AGmT5+Ojz/+GLIs49hjj7WjjTWN6tywQrF9CFn7gM654R49IoSKKYamDhAABwkBhJwbgPtLmaSoLRhYndhVmA4oXrBgAb71rW/h3XffRSgUwlNPPYWtW7fitNNOwwUXXGBHG2uaumIrFKfT2RsZC0oVRHiAYECxKYoq4qcOEJAoygUwHS/GvitEsJgyBtxXylWYFjfvv/8+LrvsMgCAz+dDf38/Ghoa8MMf/hC33Xab5Q2sdUKBIp2bZH/2OQeJgohb+yHlkctSQhRVxE+/rxSDMg3RiviJxoux7woRKqbvxrn1gpswLW7q6+u1OJv29nZ8+OGH2mt79+61rmUEgD5byuzmgzpxow7KJCfi1j4Dis1QVBE/rXw9rX0RxJ2bzD2AAcVClOTccFnKFZiOuTn55JOxfPlyHH744fjc5z6Hb37zm3j33Xfx9NNP4+STT7ajjTWNtixl1rlRBwlfGPCY1rA1BVPB7aGoVHDGLZhCONOP+0uZQltSNdV3uSzlJkyLm7vuugu9vUrK26233ore3l48/vjjmDp1KjOlbEALKC7WueGSlCExs9Y+Z79CaNlSpgKKOUCYYcB0QDH7rgjZJdVi6txQmLsB0+Jm8uTJ2vP6+no88MADljaIDCZbodisuGFaoijmgzI5QIigZUtxgLANOjf2UFSNpgSXVN2E6fWKyZMnY9++fcOOd3V1DRI+xBrCunTalLp1tQh0boTJihsj54bLUmbIZksVkQrOoEwhmApuD6WlgnPbEDdgWtx8/PHHSKWGz8RisRi2b99u6rOWLVuGc845B+PGjYMkSVi8eLHhe15++WUce+yxCAaDmDJlChYtWmTqOysN1bkBzBaU4qaZomQzToycGwYUmyFYTCE0xi2YQjzTj66jGYoqQJmg6+gmhJel/vznP2vP//a3v6G5uVn7PZVKYenSpZg4caKpL+/r68OMGTPwla98BV/84hcNz9+0aRPOPvtsXH311fjDH/6ApUuX4qtf/Sra29sxd+5cU99dKYR0bkJ/IoX6oOB/GZelhNGcG6aCW0pxs19mnJjBdI0mLksJUVQBSnX7Bd5zXYGwuDnvvPMAKDumXn755YNe8/v9mDhxoumdwM866yycddZZwuc/8MADmDRpkvY9hx9+OJYvX4677767asWNxyMh5PdgIJE2F3fDZSlhYgmmgttBqKi4BYpyMwjHizGg2BQlFaDkkqorEBY36bTyRzRp0iSsXLkSo0aNsq1R+VixYgXmzJkz6NjcuXNx3XXX5X1PLBZDLJb9g45EInY1zzbqAj4MJOLmat0wuE2YgQRTwe0gmwpOUW4XA5owZ0CxlWSXVE0IczWeiXXFXIHpmJtNmzY5ImwAoLOzE2PHjh10bOzYsYhEIujv78/5noULF6K5uVn76ejoKEdTLaWo/aU4SAgTSzIV3A6KCihWry0HCCHUa2sYL0bnxhShYpZUte1ueM91A0LOzT333CP8gd/4xjeKbowdLFiwAPPnz9d+j0QiFSdw1BuXuWUp1d7nH5oRTAW3h2zcgol+m6QoN4Nwpp+Pm76aQUsFN+U60rlxE0Li5u677xb6MEmSbBU3bW1t2LVr16Bju3btQlNTE8Lh3DfDYDCIYDBoW5vKQV1A+W8qKluKy1KGCFv7TAU3RTbjxEzMjTpAVPbfbLnQ+q6oc8NlKSG0In6mnJvMPZfixhUIiZtNmzbZ3Q4hZs2aheeee27QsSVLlmDWrFkOtag8lLYsRXFjRDZbijsrW0lR+/NocQt0bkTILksxFdxKQsU4N+q19VPcuIGSNh2SZRmybKKw3BB6e3uxevVqrF69GoAiolavXo0tW7YAUJaU1B3IAeDqq6/GRx99hBtvvBEffPAB/vM//xNPPPEErr/++lL+Ga5Hq1JcTM0FihtDxK19OjdmUOMWEilZvABlks6NKLIsI256XzQKcxGKKmOQoHPjJooSN7/97W9x1FFHIRwOIxwO4+ijj8bvfvc705/z5ptvYubMmZg5cyYAYP78+Zg5cyZuueUWAMDOnTs1oQMomVp/+ctfsGTJEsyYMQN33nknfv3rX1dtGriKtjN4PCn+JgYUC6PVCjG09pkKbgb99RR2bxiUKYx+4GUquLUUVcSPwfCuoqiNM7/3ve/hmmuuwSmnnAIAWL58Oa6++mrs3bvXlIty+umnF3R+clUfPv300/H222+bbXZFU1eMc6NVeuUgYYRW5ZXOjaXonbBYIo26gMCbGJQpjH7HauNlKaaCm0G9nnFR50aWGQzvMkyLm3vvvRf333//oOWic889F0ceeSR+8IMfVP0SkROEtM0zi7BIuSxliLhzw9mvGbweCX6vhERKFrf3WStEGLXfeiTA55EKn8y9pUxhuoxBOgnImXO5pOoKTC9L7dy5E7Nnzx52fPbs2di5c6cljSKD0QKKE1yWsoOYcBE/1rkxSzZ2weyyFMWNEfpYMUkyEDcMKDaF6WD4hK7OGoPhXYFpcTNlyhQ88cQTw44//vjjmDp1qiWNIoNRl6UGiqlzw1LghmQ3zjSxt1QJgfS1hOl0cAZlCiO84SuQjRfjspQQ6hK1cL/VO2J0blyB6WWpW2+9FRdddBGWLVumxdz84x//wNKlS3OKHlI6IVYothXx/Xl0QSOpRNbJIXkxXciPQZnCCGf5AdnrSedGCNPOjX451chFI2VB2LlZs2YNAOD888/H66+/jlGjRmHx4sVYvHgxRo0ahTfeeANf+MIXbGtoLVNUQDHFjRCyLJtPBQcYVCyI6dgFBmUKIxwrBjCg2CRBs2UMGAjvOoSdm6OPPhonnHACvvrVr+JLX/oSfv/739vZLqJDjbkxV6GYG2eKoB90hffnAZR0cLrPhgTMptRqzg0vrhHCWX4Ag+FNEhpSxkCtEp8XBsK7DmHn5pVXXsGRRx6Jb37zm2hvb8eXv/xl/O///q+dbSMZ1CJ+XJaynsG1QgwGCY8H8GRucsw6EUJblhKJXZBlXcwN+60RdG7sI+DViRuRvstAeNchLG4+8YlP4OGHH8bOnTtx7733YtOmTTjttNNw6KGH4rbbbkNnZ6ed7axptCJ+orNfWWaFYkHUAUKSAL9XYK2c+0uZwtSyVCoOILMEQOfGEOEsP4Cp4CbxeT1aer1Q36Vz4zpMZ0vV19fjiiuuwCuvvIL169fjggsuwH333YcJEybg3HPPtaONNY+2/YKoc6O/gdG5KUhMZ+0bptMCunRwzoBFCJpZUmW/NcVAUjDLD+C+aEUQMtN3GXPjOkraW2rKlCn49re/je9+97tobGzEX/7yF6vaRXSYDijW11ygc1MQU9Y+MDgdnBgSMuPcqAMEpMGZaSQnppwbVtc2jSnXkTuCuw7TqeAqy5Ytw8MPP4ynnnoKHo8HF154Ia688kor20YyhPwmnRt1ScobBDwCs7oaZsDMAAFwfymTBM2kgjOd1hSmUsH1AcWyzOsrQFbciPRd7gjuNkyJmx07dmDRokVYtGgRNm7ciNmzZ+Oee+7BhRdeiPp6Fouzi7BpccNgYlFiZqx9gDNgk5gq4segTFOoyyWmAoohK1sFeP32NaxKyC5LibiODIR3G8Li5qyzzsILL7yAUaNG4bLLLsNXvvIVHHbYYXa2jWRQ0xCFl6XiTAMXxZS1DzCl1iSmZr+sTmyKopwbQOm7FDeGBIpxbhgI7xqExY3f78cf//hH/Mu//Au8Xi51lBPVuUmmZcSTae2PLi90boQxNUAA3F/KJNkKxSLODasTm0GLFzMTcwMo1znYYFOrqoegmTIGLD7pOoTFzZ///Gc720EKoGZLAYp7YyxumAYuiqn9eQCmgpskaKaIH4MyTaEV8RNZUvV4AckLyCn2XUHUYPgBIddRjRejc+MWSsqWIuXB75XgzdRcEIq7oXMjjHnnhim1ZsjuCm7CuWHMjRCmnBuAO4ObxJxzo4ob3nPdAsVNBSBJEurMFPJTxU2Azo0RRQ8QnP0KoTpiQgMEgzJNocWLCZcxUPsuhbkI5lLBGQzvNihuKoSQmUJ+XJYSxpS1D+hSwSluRAiasfaTtPbNMJA0sbcUwGB4k5gq4scKxa6D4qZCyG7BkDQ+mctSwhTv3HD2K0JR1j77rRAxM6ngAPeXMokp54YVil0HxU2FoFUpjov8oamp4BwkjDBt7XOPHlNoy1IMyrQc0/FidG5MYa6IH8WN26C4qRBUizQaN+PccFnKiAHNuTGbCs7ZrwhaQDGDMi3HdKafj8LcDKaK+DHmxnVQ3FQIpvaX4rKUMOaDMrm3lBmKm/3SuRHBvHPDZSkzsABlZUNxUyGETe1Qy4BiUYoeIOjcCGGuiB9jbsyQ7bsmhTmXpYQoqowBxY1roLipENRsqaipOjcUN0YUbe3TuRHCVBE/BmWaIpYwuy8anRszqPcEUwUoKcxdA8VNhWCuzo3q3PAPzYjinRuKGxHMzX5p7ZvBtHPDgGJTmKtzw72l3AbFTYUQNlPnJs5lKVFY5dVestlSrFBsNVrfNZ0Kzr4rgqkyBixA6ToobioEU+KGAcXCmC/ix2UpM6jOjdiyFJ0bM2h9l6ngtmCqjAGdG9dBcVMhhItalqJzY4R554YBxWYIFuPcUNwIYd65obgxg7kyBpxQug2KmwpBEzd0biyl6IwTOjdCqK5CKi0jmTIYJDhACJNKy0ikZABFbPrKgGIhzKWCMxjebVDcVAjm6txknJtAvY0tqg6K31uKA4QIeldhwMi9YYViYfQDrnCmH5elTCFcxE+WWaHYhVDcVAjZCsV0bqyEu4LbS8Cbva4xI2HOCsXC6JdK9Ne4IEwFN4Wwc5OKA1BcNAbDuweKmwqhLuADwArFVpOtUMygTDvweCRt8DWMu2GFYmHUa+nzSPCJihv2XVMIlzHQb2dB58Y1UNxUCOGAYEEpWdZtnMmAYiOKDyjmACFKULQYGisUC6NeS+F+C+j6LveWEkG4iJ+6nAopu2xNHIfipkII+xXnxnBZKhUH5MxMg4OEITGmgtuO8AyYMTfCqNdSuN8Cur7LZSkRxJ0bXQkDSbK5VUQUipsKQbjOjRpMDNC5EcB8tpRq7XOAEEW40muShdBEMe04AkwFN4lwGQMWn3QlFDcVgnCdGzXexuMDvH6bW1XZpNIy4qkixQ2dG2GE7X0OEsIMmI0VA5gKbhJ9GYNEoTIGrE7sSihuKoQ6YedGDSZmGrgRcd2MjKng9iFk7zOd1hRFOTcMKDaFvoxBwb7L6sSuhOKmQgjpnBtZlvOfyE0zhdGneNK5sQ/N3i/k3OgHXIobQ0xn+QFMBTeJcBkDFp90JRQ3FYLq3AAGRaWYBi6Meh29xaTTpuJAWqAsO9Hs/YJF/NQBAqC4EWCAzo3t6MsYFO67dG7cCMVNhaBfNikYdxNnGrgoqnMTKiadFuAMWBAh50bNlJI8jBUTwHSWH6ALKGYquChifZcxN26E4qZC8HokBDKDcDSezH8inRthtEwpUwOEzlXg0pQQQtlS+urETKc1xHSWH5CNF6MoF0YoXkyrz0TH0U1Q3FQQ6tJUwawTxtwIU1QhNH2RLgYVC6G6C2Lihta+CMUV8csMvlyWEsacMKe4cRMUNxVEdmdwkZgbLksZUVQhNElXhZTOjRDqAFFQlLM6sSmK6rsMKDaNUBkD7gjuSihuKgi1kJ/QslSA4sYILePEzOwXYGCmSYSsfVYnNgVTwcuDqWUpihtXQXFTQQgV8tOWpShujCjK2gc4AzZJSKv0KuDcMChTCK2In49F/OxErIwBY27cCMVNBZFdlmLMjRUUFVAM6GIXmHUigjb7LVTCgDE3ptAy/fxFxIux3wojVMYgodtbirgGipsKQl2WGig0A6a4EaYoax/I3sQSHCREyAZlCqTTst8Kkc2WMiHMVTc3FQfSBpXOCQCTBSgpblwFxU0Fod7IGFBsDUVZ+0D22uoLz5G8aNlSBZ0bDhBm0JZUzTg3+mUTujdCiGVLUZi7EYqbCkIscp9/aKIUZe0D2UEiQXEjgjoAF3Qck7T2zaBlS5lKBdfdE9h3hVCFeeFMP1YodiMUNxWEGnMjtizFjTONKMraB7LCkQOEENrsV8S5YVCmEEXtLeXx6JZUoza0qvoQcm5YodiVUNxUENlZBPeWsoLsAGHWucksS3GAEEKoiB+DMk1RdLwYhbkpxFLB6dy4EYqbCkJsWYoBxaIMaHtL0bmxE1NF/ChuhChqbykg6y6w7woREgoo5oTSjVDcVBBC679x1rkRpWjnhgOEKcztz8MBQgQ6N+XBXAFKCnM3QXFTQQiJGy5LCcMBojyIpYKzzo0Zio8X45KqGYT6Ll1HV0JxU0GIxdzQuRFloFhrXxM3HCBECIr0W1YoNoU6wTGf6Udhbgahey4rFLsSipsKQr2RFd5+gc6NKMU7N+rslwOECOZmv3RuRCg50491boQImto6hOLGTVDcVBBaKXARcRNgKrgRpQ8QFDciZPeWYpafVWS3DinWuaHrKIJQGQPG3LgSipsKQt1+ofAfGrOlRKG1Xx6CIqKcFYpNofVdZvrZSkikthiD4V0JxU0FETKq9JpKAOmE8px/aIawiF95COqcG1mWc5/ECsWmKN65YUCxGcQKUHJJ1Y1Q3FQQIW1vqTziRn/DYkCxIVlxwwHCTlTxKMtAIpVP3LBCsSjJVBqptHIdmelnL4ap4LLMYHiXQnFTQQSNLFL1hiV5AG+gTK2qXGLashSdGzvRL/sZ9l06N4YM6AZa80X8uC+aGYJGhVNVUQ7QuXEZFDcVRNgoLVGfBi5JZWpV5aLOxgJmZ78s4meKgFcnbowGCYobQ/TVcvXXVghm+plCixfLJ8r1SQUMBXAVFDcVhOH2C8w4MUXPQBIA0BD0mXsjnRtTSJKkCfO8sQuMuRGmN6b027qAFx6PyUkM+64pDCeUqiiXPIDH5H2E2ArFTQVhWKFYEzeMtzFClmVE+pXg6+Y6v7k3c4AwTV0m068vnsx9QoKF0ETpVvtt2GS/BRgvZhK130Zj+fqtbkdwuuWuwhXi5r777sPEiRMRCoVw0kkn4Y033sh77qJFiyBJ0qCfUKg2boiquEmkZC2gcBCsTizMQCKNeEqZjTWFzDo3HCDMopYxiOYLhteWpeg6GqGKm6ZQMeImc69kET8hNHGTSOXO9GN1YtfiuLh5/PHHMX/+fHz/+9/HW2+9hRkzZmDu3LnYvXt33vc0NTVh586d2s/mzZvL2GLnCOuCB3O6N3HWuBElMqAMEF6PVPyyFAcIYdRBIm+mn7YsxaBMIyL9iotA58Z+VFEuy3kyplid2LU4Lm7uuusuXHXVVbjiiitwxBFH4IEHHkBdXR0efvjhvO+RJAltbW3az9ixY8vYYufQp33mFDd0boTJzn59kMzayfoqr/nqtpBBhAOKgMzp3KTTQCquPKcwN0Tru0WJGy6pmqEukJ345Oy7rE7sWhwVN/F4HKtWrcKcOXO0Yx6PB3PmzMGKFSvyvq+3txcHH3wwOjo68PnPfx7vvfde3nNjsRgikcign0rF45G0zJ6c+0sxoFgYSwYIgO6NIHV+dVkqR+yC/hrSuTEk23eLCGDl9gum8OruuQX7LsWN63BU3OzduxepVGqY8zJ27Fh0dnbmfM9hhx2Ghx9+GM888wx+//vfI51OY/bs2di2bVvO8xcuXIjm5mbtp6Ojw/J/RzkJ+dSMqRwWKbdeECZSSlCmPi6EM2AhCi5LDRI37LtGqEuqpS1LUZSLItR3GXPjOhxfljLLrFmzcNlll+GYY47BaaedhqeffhqjR4/Gr371q5znL1iwAN3d3drP1q1by9xia1HXgHMvSzFbSpSSMk68vmyRRM6AhSgYUKwOEB6fcm1JQUrquyziZ5qs61ig71KUuw5H7ySjRo2C1+vFrl27Bh3ftWsX2trahD7D7/dj5syZ2LhxY87Xg8EggsHqsbrVjKlYrqJS2o7gFDdGlJRxAig3s1ScM2BB6jOxCwWXU2ntC1FathQDis1SFywQL6bF3FTPGFMtOOrcBAIBHHfccVi6dKl2LJ1OY+nSpZg1a5bQZ6RSKbz77rtob2+3q5muIru/VKFlKYobI9SMk6JibgDGLpgk69zkiltgdWIzlLSkyoBi02jLUolcfZdxjm7FcQ94/vz5uPzyy3H88cfjxBNPxM9//nP09fXhiiuuAABcdtllOOigg7Bw4UIAwA9/+EOcfPLJmDJlCrq6unDHHXdg8+bN+OpXv+rkP6NsFKxSzJgbYUqy9gEOEibRivjFcln7dG7MUJq4yUx8kv1Kph8LzxkSLrgspQpzOjduw3Fxc9FFF2HPnj245ZZb0NnZiWOOOQbPP/+8FmS8ZcsWeDxZg+nAgQO46qqr0NnZiREjRuC4447Dq6++iiOOOMKpf0JZCRXaPJPiRhg1KLOojBOA9r5JCgZlsjqxKSIDJbiO+mucHOC9QoC6QvFi+grFxFU4Lm4A4JprrsE111yT87WXX3550O9333037r777jK0yp2ECu11woBiYSxzbpgKLoRW5yaX48h0WlOUFlA8JNOP4sYQtdZNzi0Y6Ny4lorLlqp11GUp1rkpDeuWpejciJB1blgrpBRkWWamX5kJ67ZgGAZjblwLxU2FoWVLFYy5qS9jiyqTSKnZUoy5MUVBa1+rFcIBwohoPKXtK1f8kir7rhmEllQpzF0HxU2FES60MzidG2FKCsoEOECYpGBQJtNphVFdG79XGrTXnCl87LtmEKrRRHHjOihuKozCMTcMKBalpO0XAAYUm0SNW8hd5ZXZUqLoa9yY3hNNhcLcFHX+AnVuWKHYtVDcVBjBQjE3cda5ESGZSqMvc6Mq2rnRKr0yoFiEbNwC69yUQsmOI0BhbhLGi1UmFDcVhlrEj8tSxaOm0gLKruBFwQHCFPXBQnELar/lAGFEyY4jQOfGJHVBkSVV9l23QXFTYWT3lmKF4mJRB4j6gBc+b5F/AhwgTFHY2ledG4pyIywVN0n2XRGyFYoZDF9JUNxUGNqu4IX2luIfWkGssfaZCm4GfVBmOpPto6HF3DCg2AjVdbSm71LciBAWiblh33UdFDcVhhZQPPQPLZ0CUpkZcICp4IWwZvarlrFnzI0I6uwXyCHMVeeGotyQbI2bEuqvUtyYghWKKxOKmwoj7/YL+hsVB4mCWCNu1IBiDhAi6NOWhw0SCTo3opRcnwlgvJhJCgcUs0KxW6G4qTDypoLrb1QMbiuIuq8UM07Kh6dnB6b5OwHkCCrWrH2KciOsXVKlMBehpWc9WhHRMiwHwQrFroXipsLIuyu4PpiYO/0WpOStFwAOEGaQZeA3n8GfvAvQgOhw54ZxC8JY0ndZxE+c/ZtwyNNn4deB/8hTo4nOjVuhuKkkNryAI1+/CQ2IDo/cZzCxMN209stL3x4gsh1hxNAu7Ud0qL2fYMaJKEwFLzOd70KS05gi7UA0noQsDwmGT9B1dCsUN5XEyz/F6I+exumefyKWb1mKaeCGRPotyDhhET9xDnysPR0pRQosS3E51QhrllSZ6SdMpu82SVF45SRiySH3XVYodi0UN5WCLAN7NwIAWqVIjmUpOjeiRCzJOFGdG85+DdGJmxHoKbAsxQHCCGuWVNl3hdH13Rb0DBbm6XQ2Q5V913VQ3FQK0X1ArBsAMAK9BcQNnRsjrLX2Ofs15MBm7Wmr1IPosL7L2a8o1iypZq4zyxgY01Wg7+qvH8WN66C4qRT2bdSetki96E+kBq//cllKGGutfc5+DdHNflsRGZ5SS+dGiFgypWVJMtOvTOj7rtQzuO9S3LgaiptKYd+H2tMRUg/SMpBI6cRNnDuCi2KttR9VlgxJfoYMEH0xpoIXgxorJklAY7F7ogEU5qKk00DXFu3X1qFLqmq/9fgAbwn/H8QWKG4qhf1ZcdOKHgBDCvklKG5EiVhZxA8ykIqX3qhqRmftj5B6hmf6MShTCNVxbAj64PGUUO6Bzo0YPTsH/W2PkPKIG4pyV0JxUykMWZYChmzBwJgbIWRZtmh/Ht115iCRn2Qc6N6m/arMfvOkgtPaL4gljiOgc24Yc1MQneMIACMxJNMvwfpMbobiplLY95H2tFUVN/p0cGZLCdEbSyKV2bixpEHC61fsaID2fiG6twLILtu1Dp39yrJu40yKm0JYJm5YxE+MIeJmuHPDe66bobipBGR50LKU5tzkWpbippkFUV2bgNeDoK/E7s+UWmPUAUJSrnXr0Do3sR4gnXFyQs3lbVuFYcm+UgAz/URRl1MzfXekFEGf3nWMHlAe2W9dCcVNJdCzc9CNqAH98CM5OB2czo0Q3dFsvI1U6jYVWiE/ipu8qOJm9OEAMstSMd0A0btLeQw2AcGG8ratwrBkXymAAcWiDOm7I4bWuelV9kpDY3t520WEoLipBNRMqZaDtVlEC3qHrP8yoFiEbisK+KlwkDBGHSAOOhYAEJISSMb6sq/37FQeG8aWt10ViHUxN8z0E2JI322VegcvS6l9t5F9141Q3FQC6pLUqKlAqAWAsjQ1oC8Fzjo3QqgZJyVlSqkw68QY1dofeyRSngAAwB/bl329R539tpW5YZWHuqTaVKowZ6afGGrxyYOOA6Auqepcxx46N26G4qYSUJ2b1kOAulYAikXKZSnzWDb7BejciKDOfkdMRCKo9F1/7ED2dW32ywHCCHVJ1TLnBqAwz0c8ml12yoibEUOXVCnMXQ3FTSWgipuRU4BwRtxIQ7ZgoHMjhGVxCwCdGxF04iYZUvpucJC44QAhimXCnJl+xqjF+4LNyn0XQFBKIhXryZ5DYe5qKG4qAXVZauRkzblpGSpuYkoGFcVNYSzLOAG4R48R/QeAAWU/NLRMQDo8EgAQTnZlz6G1L4w9S6oUNzlRl1NHTAACdUh4lL91b//+7DkU5q6G4sbtpFPA/k3K89ZDss4NegbXueEfmhD2LEvRucmJGrNQPwYI1EPOCPO6nOKG/dYISzZ8VWGmX2F0jiMAxAIjAADegYy4SaezmX4U5q6E4sbtdG8DUjHA4weaO3I7N+k0LVJBrBU3nP0WZMgAIdUpzk1Dqjt7jtZvKW6MYLxYGRnSdxNBRdz4YxlxE92Xqc8kKeKduA6KG7ejLkm1TlI2Zwsrf2Qj0Jt1bqL7gHQCgMRBwgDLMk4ADhBGaNb+wQAAT8NoAEBjulvZ0V6W6dyYwNolVcaLFUR1HVuUvqvFi8Uz8WKqKK8fzU0zXQrFjdvRZ0oBmnPTqt+AsGeH8tgwRgkWJHmhc1NGhsx+fY2jACiF/AYSaSUeRy1h30BxU4h0WkZPzII90VQozAuj9d1JAIB0JhwgnOhSjlOUux6KG7ejZUplxE1YXZbSpYJHMuKGS1KGdFs5+2XcQmHUASIz+/U3Kva9skdPMhuzEGoGAgyEL0TPQFKrt2eN65i53kn23WHIsk7cKH1XrlOEuRYvxjAA10Nx43a0ZanJyqNW56YXseQQcdM0rsyNqzwiVgZl0tovjGrtZ5wbb70Sc6PsDJ7iAGECNVMq5Pcg6POW/oF+CvO8RPcBiT4AkhLniGy8WH0yEy9G58b1UNy4HX2NG0Dn3OhibtRBguLGEAZllol0KlsrJCNukBkgWqWIsqTKAUIYS/stwEy/QqiuTdM4TQR6MsK8Ka2KGwpzt0Nx42ZSiWxQ5sjBMTct6EW/Wi2Ty1JCDCRSiGW2rLDGueEAkZfIDiXI3ePPiu56xdpvQR+iA3HdvlIUN0ZYupwKMF6sEEOWUwHA36QsqTbJESUYXksD575SboXixs10bVHSDX0hoDEzQGScG5+UhicRUY5xWUoI1dqXJKAxaGG2FIv4DUcV5S0dgCezjJLJ9PNIMuI9++jcmMDSytoAXcdCDAmEBwB/oyrMexBPsfRGJUBx42Z2vK08jpoKeDL/Vf4Qkl7lxqTt0cM/NCH0qbQej1T6B3KAyE+OAQJeP3qlegBAsncPqxObwPJlKR/7bl5y9N1gk1LGYKQUQX+cS6qVAMWNm1n/N+XxkE8NOpwItgAAAvHM+q/m3BxUpoZVJt39Fta4ARhQXIi9G5THTCqtSo+nBQCQ7qNzYwZLt14AKMwLsW+j8tia7bu+TKZfi9SHaDTK6sQVAMWNW0mngI0vKM+nzh30UiojboKJLmVPqVhmeaqJf2iFoLVfRnatUR7HHjnocJ+vOfNkLx1HE1gfUExhnhNZBna9pzzX993wCKShuL3J3R8AchqQPEoRP+JKKG7cyvZVQP9+ZVfajhMHvZTKVMsMJ7uzA0SgEQg2lruVFcWBaByAHQMExc0wOt9VHtuOHnQ46msBAHj66dyY4UCUzk1Z6NqsTBa9AWDUodnjHi8iaAAASKr4aRibjScjroPixq1s+LvyOOVTw6oOyyElMDOcjACR7cpBBhMbsv2AciMf1xy25gNZxC83vbsztr0EjD1i0EsD/hYAQLjnY2XPNEAZJEhB1L57UEvImg/UdrRn3x2EKspHTxt23414mgAAvj1rlQPst66G4satqPE2Uz877CUpkw5en+oGImqNG1r7Rmw9oFjwHa0WVcPl7Dc36gAx8hAgUD/opXhmd+URPZmYnPCI7EBL8qL13RFW9V26jjnpzCynth017KUej7KkGtz/vnKAy6muhuLGjUR2Ap3vKM+nfGb46xlx05Duye4r1Ujnxogt+1VxY5Fzo49bUGvjE128zfRhL8UzS6qj+jLihgOEIem0jG37FRFCYW4z2nLqcHHT520BANQdWKcc4HKqq6G4cSMblyiP444FGoYHrKll7JvkCORu1rgRZas6QFg2+80MEHJKKbhIFArMftV4sYbkfuUABwhDdvfEEE+l4fVIaG+2almKAcU52ZURNzmEeTQTDB+K7VUOUJi7GoobN6IuSR06N+fL3gZF3IxAD9JaGjj/0AqRSKWxs1sRNxOsnv0CjF3QU2D2mw6PHHyAA4Qh6pLUuJYQfF6Lbtmac8MClBr9XdktQ9qGi5uBzJKqBoW5q6G4cRvJOPDRy8rzHPE2QLZa5gipF3KEy1Ii7OwaQFoGgj4PRjcGrflQb0BJBwVo76skBoC965XnOcSNur+UBgcIQ7butzjeBtAV8aNzo6FmQTV3aNW09cSGiRsKczdDceM2trwKxHuB+jFA+zE5T/Gpe/RIvZC49YIQarzN+BFhSJIF1YkBZR8H2vuD2fO+skwXbs1585fqh4gb7itlyBY7xA1jboazK/9yKgAkg0PFDbOl3AzFjdtYn0kBn/qZ7JYLQ6lT/shGIgJPdI9yjOKmIKq1b9mSlAoHicHo421yiEjP0BgyOjeGaLFiVgXCA+y3uVCTOHLE2wBAMhMvpkHnxtVQ3LiJ/gPA6j8ozw89M/95mc0zQ1ICEmRl5+W6UWVoYOWiWfu2iRvGLgAoGG8DAMG6JsRlXeEzDhCGWF7CAKDjmAtNmOcWN+mwTtxIXt5zXQ7FjZtY9h/AQBcw5ghg2tn5zws1I6n/r2tsy+/yEAA2WfsAYxeGYmDt1wV92I+m7AE6N4bYIszV2kLM9FNIJYHdmfo1efqurBczvOe6Hv7vuIUDHwNvPKg8/8yPCpf1liT0SLqtFrgkZcjWAxbXCVGhvZ9FlrOz3zzWftjvwwFZ13dZ5bUgsWQKnRHFFbQ25kb3WRTmwL4NSsXsQAPQMjHnKVL9EHFDXA3FjVtY+kMgFQcmnw5M+bTh6b0e3QBBa9+QbVYX8FOhvZ+lawsQ61aWSfX78uioC3ixXxU3dSMBX6CMDaw8dnQNQJaBsN+LUQ0WXitm+g2mU1ffJo8jEwg1ICZntmRgILzrobhxA9tXAWueAiApro1ANk+vR2ft07kpSF8siX19yqaZdG5sRB0gxkzLK1rqAl7shyJuZM5+DdmqE+WWZfkBzPQbihYrlttxBJQl1X2Zvkvnxv1Q3DiNLAN//57yfMaXgPajC5+fIeptzv5CcVMQNSCzOexHU8iiXZVVVHHDIn66bRdyxywAQFjn3KTrOUAYYVusGKDb+JXB8EaxYoDSd7UlVbrlrofixmne/h2w+R/KjeZT3xV+W79PJ274h1YQNZXW8jRwgM6NHoNMKQCoC/iwW1ZKGSQaKMqNsCVTSoWbZ2bRlqXy9936oA+75Rbll+bx9reJlITP6QbUNLvfB567UXl+2k2m/mAG/HRuRNlqV7wNoBM3tPZFrH2vR8Ji6VNoSfbiX2Z8DZTlhVE3zBw/gn3XNnp2AX17lBikMYfnPS3s9+KHyQuw3X8wLj38nDI2kBQDnRuniEeBJ7+sLGcc8inglOtMvT1GcSOMNvu1w9rn7Fdh9/tA12bA4wPaCi+tRgMj8ZPkpehpmFietlUwthWfBOg6qqz/q/I45kggkP861wW8eFeejNvSlwLBhjI1jhQLxY1T/PVGYM8HSirsFx40XTMhoS8FzmWpgthWwA9gET+Vfz6qPE6dC4RbCp5aF1AM42g8ZXOjKp8ttvbdzGfWerzYPx9THo/6PwVPU/ttP/ttRUBx4wRv/0GJtYEEnP9rYGhJegGSgRYAQNTXAvgs2giySsmWr7cjKJPWPtIp4J0nlOczvmR4ejig1HCKxpN2tqri6RlIoCuqFNizR9yoAcU1LG72fwRsWaEsSR19UcFT1X6bTMuIJ9PlaB0pAYqbcvPmfwHPzFOen3YjMOmTRX1MT+NkAMDO8BSrWlaVyLKsW5ayM26hhgeIj14GenYqOykfOtfw9LrMIMEZcGFUUd5aH0BD0IbwSKaCZ12byacDTYUdcLXfAuy7lQDFTbmQZWDZHcCz1wGQgeOuUIKIiyTaPAVzYz/Db8ffalkTq5F9fXFE4ylIEnCQneKmZyeQrtHZnLokNf3/CLmIYb/q3HCAKEQ2DdyGfgtk+25kpz2f73bS6WzfnfF/DU/3ez3we5VaQ9EEXUe3Q3FTDhL9wF9vAl78sfL7J28A/uXuwlssGBDye7FOnoAD6XqLGlmdqPE2bU0hBH3FX++8qNkVm14BHrkQ6Ntr/Xe4mYEI8P6zyvMZFwu9hc6NGNsyjuN4O5akAGD0NOVx2e3A378LJOP2fI9b2bJCqaodaCy8l58OCvPKwRXi5r777sPEiRMRCoVw0kkn4Y033ih4/pNPPolp06YhFArhqKOOwnPPPVemlpokGQPeeAj4xTHAG79Sjp15m1LPpsRqoyG/8l/Xn+AfWSG0PaXsyJQCgImnKkLVFwI2LgHuPwXY8ELtuDhrn1ECUkcdChx0rNBbsgHFnP0WYqudBfwAYPb/A078mvL81XuBh+cCe9bZ811u5J+PKI9Hfr5glpQeBhVXDo6Lm8cffxzz58/H97//fbz11luYMWMG5s6di927d+c8/9VXX8XFF1+MK6+8Em+//TbOO+88nHfeeVizZk2ZW14AWVYCLO89DnjuW0BvJ9DcAVz4O+Dkqy35ilDGhRiguCmIOkCMt6PGjcrxXwGuehEYdZjyf/2H84G7pgGL/x1Y8zSw78Pq3XlZs/UvFhbsWkAx+25BVGFuSxo4oCwhfu524EuPAKEWYMdbwH0nAr88Efjbd4CNS4Hu7dUp1ONR4L1nlOcCS1IqdQE6N5WC40X87rrrLlx11VW44oorAAAPPPAA/vKXv+Dhhx/GzTffPOz8X/ziFzjzzDNxww03AAB+9KMfYcmSJfjlL3+JBx54oKxtz8mutYqg2fwP5feGNuCT3wKOvczSrKZQxh6NJarwxmMhqrixbYBQGXsk8G8vAUtuAVY/CvTuAlb/QfkBlGyM5vFA00FKrIMvrGSraI+hzGaGEgDJ4BGZ5yh8LjD4uUpOEZLjmNF5yYFMP5cMM0301KsDRIwDRCG22Fl8Us+0s4GrlwN/ma8Imr3rlJ8Vv1Re94WAloOB+tHZvuoPD370qtuaGPVdSdeFBM7VM6w/lvD63vVAvEf5d02YJXSZAKAuqPTdPrqOrsdRcROPx7Fq1SosWLBAO+bxeDBnzhysWLEi53tWrFiB+fPnDzo2d+5cLF68OOf5sVgMsVhM+z0SiZTe8Jxf1AO8/DPgtfsBOaUMWp/8FjBrXjZwz0LCAcV0+2hvH279n/cs//xq4R8fKjEwtln7egL1wNl3AnN/Cmx+Fdj4AvDRK8C+jcrSTdcW5afamPRJoPkg4dPDGWv/pXW7OUgUwNZ9pYbS0gFc8iTQ3wV89JKytLrlVaW/JgeygqfamPElUzXG6vxK3/3dis1Ytn6PXa2qCsaPqMOVp05y7PsdFTd79+5FKpXC2LFjBx0fO3YsPvjgg5zv6ezszHl+Z2dnzvMXLlyIW28tQ0bRB3/JznSm/Qtw5kKgZYJtXzeyXnGB9vbG8F//+Ni276kWpowpY0VRXxA45AzlB1CWKXs6gQMfK8tWiQFF7Ax9TCWUcyEPeUSOY0Nfy3VO5pgeecjvpb7uDQCnfKPg5RjKqAZlx/D3dkTw3g6bJhtVQsjvwbgWm50bPeEW4MgvKD8AkEoC3VuBA5uA/gO5+26iH0irIjVfP9U9Arpjhd4zlBzHSjkv1Ayc/O95L0UuRjUqfffFD3KHTZAsx05oqV1xUw4WLFgwyOmJRCLo6Oiw/ouOulCxdI++EJj6Ges/fwhHj2/Gwi8epWVUkPxMaK3D0eObjU+0C0lSamgY1NGoFS48oQOptIzIQJXGIVnIyZNHIuBzMDTS6wNaJyk/BDefeTimjmlEshrjkCymrKI8B46Km1GjRsHr9WLXrl2Dju/atQttbW0539PW1mbq/GAwiGCwDBV8PR7g/Ifs/54MkiTh4hPtc4YIsYumkB9fO+0Qp5tBiGkmjKzD9Z851OlmEAEczZYKBAI47rjjsHTpUu1YOp3G0qVLMWtW7iCvWbNmDTofAJYsWZL3fEIIIYTUFo4vS82fPx+XX345jj/+eJx44on4+c9/jr6+Pi176rLLLsNBBx2EhQsXAgCuvfZanHbaabjzzjtx9tln47HHHsObb76JBx980Ml/BiGEEEJcguPi5qKLLsKePXtwyy23oLOzE8cccwyef/55LWh4y5Yt8Oii2WfPno1HHnkE3/3ud/Htb38bU6dOxeLFizF9+nSn/gmEEEIIcRGSLOcMN69aIpEImpub0d3djaamJqebQwghhBABzIzfjlcoJoQQQgixEoobQgghhFQVFDeEEEIIqSoobgghhBBSVVDcEEIIIaSqoLghhBBCSFVBcUMIIYSQqoLihhBCCCFVBcUNIYQQQqoKx7dfKDdqQeZIJOJwSwghhBAiijpui2ysUHPipqenBwDQ0dHhcEsIIYQQYpaenh40NzcXPKfm9pZKp9PYsWMHGhsbIUmSpZ8diUTQ0dGBrVu3ct8qHbwuueF1yQ2vy3B4TXLD65Kbar0usiyjp6cH48aNG7Shdi5qzrnxeDwYP368rd/R1NRUVR3KKnhdcsPrkhtel+HwmuSG1yU31XhdjBwbFQYUE0IIIaSqoLghhBBCSFVBcWMhwWAQ3//+9xEMBp1uiqvgdckNr0tueF2Gw2uSG16X3PC61GBAMSGEEEKqGzo3hBBCCKkqKG4IIYQQUlVQ3BBCCCGkqqC4IYQQQkhVQXFjEffddx8mTpyIUCiEk046CW+88YbTTSorCxcuxAknnIDGxkaMGTMG5513HtatWzfonIGBAcybNw8jR45EQ0MDzj//fOzatcuhFjvDz372M0iShOuuu047VqvXZfv27bj00ksxcuRIhMNhHHXUUXjzzTe112VZxi233IL29naEw2HMmTMHGzZscLDF9pNKpfC9730PkyZNQjgcxiGHHIIf/ehHg/bSqYXrsmzZMpxzzjkYN24cJEnC4sWLB70ucg3279+PSy65BE1NTWhpacGVV16J3t7eMv4rrKfQdUkkErjppptw1FFHob6+HuPGjcNll12GHTt2DPqMarwuuaC4sYDHH38c8+fPx/e//3289dZbmDFjBubOnYvdu3c73bSy8corr2DevHl47bXXsGTJEiQSCXz2s59FX1+fds7111+P//mf/8GTTz6JV155BTt27MAXv/hFB1tdXlauXIlf/epXOProowcdr8XrcuDAAZxyyinw+/3461//irVr1+LOO+/EiBEjtHNuv/123HPPPXjggQfw+uuvo76+HnPnzsXAwICDLbeX2267Dffffz9++ctf4v3338dtt92G22+/Hffee692Ti1cl76+PsyYMQP33XdfztdFrsEll1yC9957D0uWLMGzzz6LZcuW4d/+7d/K9U+whULXJRqN4q233sL3vvc9vPXWW3j66aexbt06nHvuuYPOq8brkhOZlMyJJ54oz5s3T/s9lUrJ48aNkxcuXOhgq5xl9+7dMgD5lVdekWVZlru6umS/3y8/+eST2jnvv/++DEBesWKFU80sGz09PfLUqVPlJUuWyKeddpp87bXXyrJcu9flpptukk899dS8r6fTabmtrU2+4447tGNdXV1yMBiUH3300XI00RHOPvts+Stf+cqgY1/84hflSy65RJbl2rwuAOQ//elP2u8i12Dt2rUyAHnlypXaOX/9619lSZLk7du3l63tdjL0uuTijTfekAHImzdvlmW5Nq6LCp2bEonH41i1ahXmzJmjHfN4PJgzZw5WrFjhYMucpbu7GwDQ2toKAFi1ahUSicSg6zRt2jRMmDChJq7TvHnzcPbZZw/69wO1e13+/Oc/4/jjj8cFF1yAMWPGYObMmXjooYe01zdt2oTOzs5B16W5uRknnXRSVV+X2bNnY+nSpVi/fj0A4J///CeWL1+Os846C0DtXhc9ItdgxYoVaGlpwfHHH6+dM2fOHHg8Hrz++utlb7NTdHd3Q5IktLS0AKit61JzG2dazd69e5FKpTB27NhBx8eOHYsPPvjAoVY5SzqdxnXXXYdTTjkF06dPBwB0dnYiEAhof2QqY8eORWdnpwOtLB+PPfYY3nrrLaxcuXLYa7V6XT766CPcf//9mD9/Pr797W9j5cqV+MY3voFAIIDLL79c+7fn+ruq5uty8803IxKJYNq0afB6vUilUvjJT36CSy65BABq9rroEbkGnZ2dGDNmzKDXfT4fWltba+Y6DQwM4KabbsLFF1+sbZ5ZS9eF4oZYzrx587BmzRosX77c6aY4ztatW3HttddiyZIlCIVCTjfHNaTTaRx//PH46U9/CgCYOXMm1qxZgwceeACXX365w61zjieeeAJ/+MMf8Mgjj+DII4/E6tWrcd1112HcuHE1fV2IORKJBC688ELIsoz777/f6eY4ApelSmTUqFHwer3Dslt27dqFtrY2h1rlHNdccw2effZZvPTSSxg/frx2vK2tDfF4HF1dXYPOr/brtGrVKuzevRvHHnssfD4ffD4fXnnlFdxzzz3w+XwYO3ZsTV6X9vZ2HHHEEYOOHX744diyZQsAaP/2Wvu7uuGGG3DzzTfjS1/6Eo466ij867/+K66//nosXLgQQO1eFz0i16CtrW1YQkcymcT+/fur/jqpwmbz5s1YsmSJ5toAtXVdKG5KJBAI4LjjjsPSpUu1Y+l0GkuXLsWsWbMcbFl5kWUZ11xzDf70pz/hxRdfxKRJkwa9ftxxx8Hv9w+6TuvWrcOWLVuq+jp9+tOfxrvvvovVq1drP8cffzwuueQS7XktXpdTTjllWKmA9evX4+CDDwYATJo0CW1tbYOuSyQSweuvv17V1yUajcLjGXxb9nq9SKfTAGr3uugRuQazZs1CV1cXVq1apZ3z4osvIp1O46STTip7m8uFKmw2bNiAF154ASNHjhz0ek1dF6cjmquBxx57TA4Gg/KiRYvktWvXyv/2b/8mt7S0yJ2dnU43rWx8/etfl5ubm+WXX35Z3rlzp/YTjUa1c66++mp5woQJ8osvvii/+eab8qxZs+RZs2Y52Gpn0GdLyXJtXpc33nhD9vl88k9+8hN5w4YN8h/+8Ae5rq5O/v3vf6+d87Of/UxuaWmRn3nmGfmdd96RP//5z8uTJk2S+/v7HWy5vVx++eXyQQcdJD/77LPypk2b5KeffloeNWqUfOONN2rn1MJ16enpkd9++2357bfflgHId911l/z2229rWT8i1+DMM8+UZ86cKb/++uvy8uXL5alTp8oXX3yxU/8kSyh0XeLxuHzuuefK48ePl1evXj3oPhyLxbTPqMbrkguKG4u499575QkTJsiBQEA+8cQT5ddee83pJpUVADl//uu//ks7p7+/X/73f/93ecSIEXJdXZ38hS98Qd65c6dzjXaIoeKmVq/L//zP/8jTp0+Xg8GgPG3aNPnBBx8c9Ho6nZa/973vyWPHjpWDwaD86U9/Wl63bp1DrS0PkUhEvvbaa+UJEybIoVBInjx5svyd73xn0OBUC9flpZdeynk/ufzyy2VZFrsG+/btky+++GK5oaFBbmpqkq+44gq5p6fHgX+NdRS6Lps2bcp7H37ppZe0z6jG65ILSZZ1pS8JIYQQQiocxtwQQgghpKqguCGEEEJIVUFxQwghhJCqguKGEEIIIVUFxQ0hhBBCqgqKG0IIIYRUFRQ3hBBCCKkqKG4IIYQQUlVQ3BBCCCGkqqC4IYQQQkhVQXFDCCGEkKqC4oYQQgghVcX/B33sdkxt0hD3AAAAAElFTkSuQmCC\n",
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+ "