Space Debris Data Acquisition and Data Processing

We are developing machine learning software aimed at predicting space debris collisions and enhancing mission planning. The initial phase of this project requires a meticulous approach to data preparation, which is critical for the success of our subsequent modeling efforts. The challenge lies in acquiring as much relevant data as possible for accurate predictions. Project Scope: 1. Preparation Stage: The freelancer will be responsible for identifying and researching all available sources of space debris data, both free and paid. This includes a comprehensive evaluation of data providers to ensure the data's relevance and quality. The output at this stage will be an Excel table summarizing the research, which will include provider names, links to data or registration, pricing (if it is a paid source), additional requirements from the provider, and descriptions of the data. We anticipate identifying up to 10 data providers. 2. Evaluation and Registration: We will evaluate the providers, register for their services, and handle payments if the data is not free. We will then provide the freelancer with access for further data acquisition. 3. Real-Time Data Acquisition: The freelancer will create a convenient method to acquire real-time data for prediction purposes, likely through API acquisition. Space debris data should be available in real time, providing time series information to understand the trajectory, size, movement, volume, and velocity of particular orbital debris objects. 4. Data Structuring and Preprocessing: Once data is obtained from various providers, the freelancer will need to structure and preprocess this data for further modeling. Project Deliverables: 1. An Excel table of data providers. 2. A Python script with a pipeline for data acquisition and preprocessing. This project does not include the development of machine learning models; the focus is strictly on the groundwork of data preparation.