文章基本信息

标题：Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft
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作者：Matej Petković ; Luke Lucas ; Jurica Levatić 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-8
DOI：10.1038/s41597-022-01336-z
语种：English
出版社：Nature Publishing Group
摘要：We present six datasets containing telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars operated by the European Space Agency. The data consisting of context data and thermal power consumption measurements, capture the status of the spacecraft over three Martian years, sampled at six diferent time resolutions that range from 1 min to 60 min . From a data analysis point-of- view, these data are challenging even for the more sophisticated state-of-the-art artifcial intelligence methods . In particular, given the heterogeneity, complexity, and magnitude of the data, they can be employed in a variety of scenarios and analyzed through the prism of diferent machine learning tasks, such as multi-target regression, learning from data streams, anomaly detection, clustering, etc . Analyzing MEX’s telemetry data is critical for aiding very important decisions regarding the spacecraft’s status and operation, extracting novel knowledge, and monitoring the spacecraft’s health, but the data can also be used to benchmark artifcial intelligence methods designed for a variety of tasks .