Paper ID: 2202.06617

An Application of Online Learning to Spacecraft Memory Dump Optimization

Tommaso Cesari, Jonathan Pergoli, Michele Maestrini, Pierluigi Di Lizia

In this paper, we present a real-world application of online learning with expert advice to the field of Space Operations, testing our theory on real-life data coming from the Copernicus Sentinel-6 satellite. We show that in Spacecraft Memory Dump Optimization, a lightweight Follow-The-Leader algorithm leads to an increase in performance of over $60\%$ when compared to traditional techniques.

Submitted: Feb 14, 2022