Libre Space Foundation

Mentors: Student: ayush_bansal

<p>This project aims to make a tool to use results of Betsi and anomaly detectors to generate customisable and rich analysis Reports and make interactive graphs that can be exported in multiple formats.</p>

Mentors: Student: Yash Gondhalekar

<p>With the increasing number of satellites launched into space, orbital congestion is escalating ever-more rapidly. This eventually increases the probability of a variety of space events where some might be catastrophic while others serve as a source of beneficial information of scientific interest. As a result, it would be advantageous to detect such contingencies. The potentially harmful ones like orbital collisions can be prevented, and others would allow us to extract meaningful insights about the satellite dynamics. This would ultimately assist in devising optimal orbital design and maintenance strategies.</p> <p>The proposed project aims to add event detectors by implementing raw orbital algorithms allowing for increased efficiency and enhanced functionality and configuring them in high-level API. This would ensure swift processing of computationally intensive algorithms while simultaneously allowing to render an easy-to-use API to the user. Consequently, it would allow us to promptly recognize various intricate orbital mechanisms and formulate effective policies based on the outcomes.</p>

Mentors: Student: Michail Raptakis

<p>Improve the TX capabilities of SatNOGS using Gnu Radio and C++</p>

Mentors: Student: Adithya Venkateswaran

<p>The Polaris project used in-spacecraft data to learn and predict its behavior. The project aimed at adding external sources of data, such as orbit propagation, solar events, magnetic events, and other elements of space weather, to better predict the behavior of the spacecraft in any space environment.</p> <p>This was achieved by creating modules to collect data from various sources, converting it to a time series and learning features from the converted data. Storage of the data in a DBMS (InfluxDB) was also added.</p> <p>An auto-encoder based technique to detect anomalies in the space weather was also added. This module implements this research paper: <a href="https://arxiv.org/abs/1801.05394" target="_blank">https://arxiv.org/abs/1801.05394</a></p> <p>Improvements to the visualization module to make it more user friendly and interactive was also done. Ability to train on any satellite telemetry (with/without normalization to SI units) was also added.</p>

Mentors: Student: Ravi Charan Nudurupati

<p>While observing a satellite from the ground station, it could lead to more than one satellite being visible from the ground station at a given time due to the increase in the number of the deployed satellites. These satellites may transmit with the same or near frequencies to the ground station which often leads to interference in the observed results.</p> <p>The basic aim of the project is to develop a Python module that enables the users to determine when and over which area the interference occurs. This module would be used by SatNOGS as an internal or an external tool to let the observers know which other satellites are expected to be found in the results of their observations</p>