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An Application of Online Learning to Spacecraft Memory Dump Optimization

Machine Learning 2022-09-27 v2

Abstract

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%60\% when compared to traditional techniques.

Keywords

Cite

@article{arxiv.2202.06617,
  title  = {An Application of Online Learning to Spacecraft Memory Dump Optimization},
  author = {Tommaso Cesari and Jonathan Pergoli and Michele Maestrini and Pierluigi Di Lizia},
  journal= {arXiv preprint arXiv:2202.06617},
  year   = {2022}
}
R2 v1 2026-06-24T09:34:56.974Z