[](https://github.com/MALES-project/SpeckleCn2Profiler/actions/workflows/test.yaml)
[](https://males-project.github.io/SpeckleCn2Profiler/)
[](https://pypi.org/project/speckcn2/)
[](https://pypi.org/project/speckcn2/)
[](https://fairsoftwarechecklist.net/v0.2?f=31&a=30110&i=21202&r=132)
[](https://doi.org/10.5281/zenodo.11447920)
[](https://research-software-directory.org/software/speckcn2)
<!---
-->
# SpeckleCn2Profiler:
### Improving Satellite Communications with SCIDAR and Machine Learning
## Overview
Optical satellite communications is a growing research field with bright commercial perspectives. One of the challenges for optical links through the atmosphere is turbulence, which is also apparent by the twinkling of stars. The reduction of the quality can be calculated, but it needs the turbulence strength over the path the optical beam is running. Estimation of the turbulence strength is done at astronomic sites, but not at rural or urban sites. To be able to do this, a simple instrument is required. We want to propose to use a single star Scintillation Detection and Ranging (SCIDAR), which is an instrument that can estimate the turbulence strength, based on the observation of a single star. In this setting, reliable signal processing of the received images of the star is most challenging. We propose to solve this by Machine Learning.
## Repository Contents
This repository contains the workflow to implement and train machine learning models for turbulence strength estimation from SCIDAR data. Extensive **[Documentation](https://males-project.github.io/SpeckleCn2Profiler/)** is available to explain the methodology, algorithms used, and guidelines for using the code.
## Getting Started
To get started with the project, follow these steps:
- **Prerequisites:**
In order to correctly install `speckcn2` you need `python3.9` or higher. If you don't have it installed, you can download it from the [official website](https://www.python.org/downloads/). You will also need the header files that are required to compile Python extensions and are contained in `python3-dev`. On Ubuntu, you can install them with:
```bash
apt-get install python-dev
```
- **Install the package:**
```bash
python -m pip install speckcn2
```
- **Or: Clone the repository:**
```bash
git clone https://github.com/MALES-project/SpeckleCn2Profiler.git
cd SpeckleCn2Profiler
git submodule init
git submodule update
pip install .
```
## Usage
To use the package, you run the commands such as:
```console
python <mycode.py> <path_to_config.yml>
```
where `<mycode.py>` is the name of the script that trains/uses the `speckcn2` model and `<path_to_config.yml>` is the path to the configuration file.
[Here](https://males-project.github.io/SpeckleCn2Profiler/example) you can find a typical example run and an explanation of all the main configuration parameter. In the [example submodule](https://github.com/MALES-project/examples_speckcn2/) you can find multiple examples and multiple configuration to take inspiration from.
## What can we predict?
A machine learning model trained using `speckcn2` can predict:
##### 1. Instantaneous turbulence strength
Given a speckle pattern, the model can predict the instantaneous turbulence strength and also provide an uncertainty estimate if more patterns are available.
##### 2. Parameters estimation
The model can also estimate important parameters that are useful for the analysis of the speckle pattern. At the moment we support:
* Fried parameter `r0`
* Isoplanatic angle `θ0`
* Rytov Index `σ`
We also provide histograms of the estimated parameters and the error of the estimation.
## Contribution Guidelines
We welcome contributions to improve and expand the capabilities of this project. If you have ideas, bug fixes, or enhancements, please submit a pull request.
Check out our [Contributing Guidelines](CONTRIBUTING.md#Getting-started-with-development) to get started with development.
## Generative-AI Disclaimer
Parts of the code have been generated and/or refined using GitHub Copilot. All AI-output has been verified for correctness, accuracy and completeness, revised where needed, and approved by the author(s).
## How to cite
Please consider citing this software that is published in Zenodo under the DOI [10.5281/zenodo.11447920](https://zenodo.org/records/11447920).
## License
This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
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