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EIRSAT-1 Test Campaign and Flight Dataset for Anomaly Detection

<p><span>We have curated a unique dataset derived from EIRSAT-1, Ireland's inaugural domestically produced satellite, as a testing and validation resource for these ML models and the future development cycle of AI-enabled small satellites. This dataset consists of a training set developed during ground testing and containing artificial anomalies induced to train satellite operators, a validation dataset containing real anomalies encountered during the qualification campaign, and an early flight test dataset collected since the satellite was launched on December 1<sup>st</sup>, 2023. This paper presents an in-depth analysis of the efficacy of these ML techniques when applied to the EIRSAT-1 dataset, offering insights into their potential to revolutionize the domain of satellite operations through enhanced autonomy and responsiveness. This study not only showcases the capabilities of these ML techniques in an operational environment but also sets the stage for future research and development in autonomous satellite systems.</span></p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0

Dataset details

Repository
zenodo
Published
June 6, 2024
License
cc-by-4.0
DOI
10.5281/zenodo.11518557

Scored with release 0.9.0

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