IAC 2024 Space Communications and Quantum Symposium

This repository contains resources related to the 75th International Astronautical Congress (IAC) 2024 symposium focused on Space Communications and Quantum Technologies.

Presentation Information

Title: Advancing Free-Space Optical Communication System Architecture: Performance Analysis of Diverse Optical Ground Station Network Configurations

Authors:

• Mr. Connor Casey: University of Massachusetts Amherst, United States
• Mr. Eugene Rotherham: University College London (UCL), United Kingdom
• Ms. Eva Fernandez Rodriguez: Netherlands Organisation for Applied Scientific Research (TNO), The Netherlands
• Ms. Karen Wendy Vidaurre Torrez: Kyushu Institute of Technology, Japan
• Mr. Maren Mashor: National Space Research and Development Agency (NASRDA), Nigeria
• Mr. Isaac Pike: University College London (UCL), United Kingdom

About the Presentation: This presentation explores the frontier of space-based optical and quantum communications. It provides an in-depth analysis of various configurations of optical ground station networks to enhance communication systems in space.

Repository Structure

The repository is organized into two main folders:

1. src: Contains the source code for each component of the model.
2. data: Stores input data and output results.

Source Code (src)

The src folder contains subfolders for each component of the model:

1. satellite_passes
2. turbulence
3. cloud_cover
4. data_integrator
5. dynamic_analysis

Each subfolder contains the scripts necessary for that particular component of the model.

Data (data)

The data folder is divided into two subfolders:

1. input: Contains the satelliteParameters.txt file with essential parameters for the model.
2. output: Stores the results from each component of the model in separate subfolders:
   • satellite_passes
   • turbulence
   • cloud_cover
   • data_integrator
   • dynamic_analysis

Data Flow and Execution Order

The model components are executed in the following order:

1. satellite_passes
2. turbulence
3. cloud_cover
4. data_integrator
5. dynamic_analysis

Key Points:

• Each component in the src folder reads data from the satelliteParameters.txt file in the data/input folder.
• The satellite_passes component also uses a .tle (Two-Line Element) file for satellite orbit data.
• The cloud_cover component interacts with an API gateway (EUMETSAT) for retrieving cloud cover data.
• The data_integrator component pulls data from the satellite_passes, turbulence, and cloud_cover components.
• The dynamic_analysis component uses data from both the satelliteParameters.txt file and the data_integrator component.

Software Architecture

This diagram illustrates the repository structure, execution order, and data flow between components.

Usage

Reproducing the Simulation Environment Using Docker

To ensure a consistent and reproducible environment, we've containerized the simulation using Docker. Follow the steps below to set up and run the simulation environment.

Prerequisites:

• Install Docker on your system.
• Ensure Docker is running.

Steps to Pull and Run the Docker Container:

1. Pull the Docker image from Docker Hub:

   docker pull cocasey/fso-simulation:1.0.1

2. Run the container interactively:

   docker run -it cocasey/fso-simulation:1.0.1

   This command will start the container and open an interactive shell.

3. Navigate to the project directory inside the container:

   cd /path/to/project

4. Set up the environment variables:

   Create a .env file in the project root directory inside the container and add your EUMETSAT API keys:

   echo "CONSUMER_KEY=your_consumer_key_here" >> .env
   echo "CONSUMER_SECRET=your_consumer_secret_here" >> .env

5. Add your TLE file:

   Place your .tle file (e.g., terra.tle) into the appropriate directory inside the container:

   cp /host/path/to/yourfile.tle /container/path/to/satellite_passes/

   (You may need to mount a volume or use docker cp to transfer files from the host to the container.)

6. Fill in necessary parameters:

   Edit the satelliteParameters.txt file located in the data/input folder to include your specific parameters.

7. Run the simulation script:

   Execute the run_simulation.zsh script located in the scripts folder from the main directory:

   cd scripts
   ./scripts/run_simulation.zsh

   Ensure the script has execute permissions. If not, you can make it executable with:

   chmod +x run_simulation.zsh

8. Check the output:

   The results will be stored in the data/output/dynamic_analysis folder within the container.

Exiting the Container:

• To exit the interactive session, type exit or press Ctrl+D.

Optional: Saving Your Work

• If you need to save data generated within the container to your host machine, consider using Docker volumes or the docker cp command.

Additional Notes:

• Ensure that any changes made inside the container are saved or exported if needed, as they will not persist after the container is stopped unless volumes are used.

Traditional Setup (Without Docker)

If you prefer to run the simulation without Docker, follow these steps:

1. Install Dependencies:

   • Python (version 3.9 or higher)

   • Use pip to install required Python packages:

     pip install -r requirements.txt

2. Prepare the Environment:

   • Add your TLE file (e.g., terra.tle) into the same folder as the satellite_passes component.

   • Create a .env file in the project root directory and add your EUMETSAT API keys:

     CONSUMER_KEY=your_consumer_key_here
     CONSUMER_SECRET=your_consumer_secret_here
     

   • Fill in all necessary parameters in the satelliteParameters.txt file located in the data/input folder.

3. Execute the Simulation Script:

   Run the run_simulation.zsh script located in the scripts folder from the main directory:

   cd scripts
   ./scripts/run_simulation.zsh

   Ensure the script has execute permissions. If not, you can make it executable with:

   chmod +x run_simulation.zsh

4. Check the Output:

   Check the data/output/dynamic_analysis folder for results from each component.

Dependencies

• Satellite TLE data file (.tle)
• Access to the EUMETSAT API gateway for cloud cover data
• Docker (if using the Docker setup)
• Python (version 3.9 or higher)
• Required Python packages listed in requirements.txt

Repository Contents:

• Paper: Link to Published Paper
• Datasets: Link to Datasets
• Data Analysis: Link to Notebook

Contact Information:

For inquiries or collaborations related to this presentation, please reach out to Connor Casey via the provided email addresse in AUTHORS.txt

We appreciate your interest in our work!

License

This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Summary of License Terms:

• Attribution (BY): You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• NonCommercial (NC): You may not use the material for commercial purposes.
• NoDerivatives (ND): If you remix, transform, or build upon the material, you may not distribute the modified material.

For full license details, please refer to the Creative Commons BY-NC-ND 4.0 License.