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dc.contributor.authorPetković, Matej
dc.contributor.authorLucas, Luke
dc.contributor.authorLevatić, Jurica
dc.contributor.authorBreskvar, Martin
dc.contributor.authorStepišnik, Tomaž
dc.contributor.authorKostovska, Ana
dc.contributor.authorPanov, Panče
dc.contributor.authorOsojnik, Aljaž
dc.contributor.authorBoumghar, Redouane
dc.contributor.authorMartínez-Heras, José A
dc.contributor.authorGodfrey, James
dc.contributor.authorDonati, Alessandro
dc.contributor.authorDžeroski, Sašo
dc.contributor.authorSimidjievski, Nikola
dc.contributor.authorŽenko, Bernard
dc.contributor.authorKocev, Dragi
dc.date.accessioned2022-06-29T19:47:34Z
dc.date.available2022-06-29T19:47:34Z
dc.date.issued2022-05-24
dc.identifier.issn2052-4463
dc.identifier.other35610234
dc.identifier.otherPMC9130140
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338543
dc.description.abstractWe 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 different 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 artificial 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 different 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 artificial intelligence methods designed for a variety of tasks.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2052-4463
dc.sourcenlmid: 101640192
dc.titleMachine-learning ready data on the thermal power consumption of the Mars Express Spacecraft.
dc.typeArticle
dc.date.updated2022-06-29T19:47:33Z
prism.issueIdentifier1
prism.publicationNameSci Data
prism.volume9
dc.identifier.doi10.17863/CAM.85956
dcterms.dateAccepted2022-04-12
rioxxterms.versionofrecord10.1038/s41597-022-01336-z
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBreskvar, Martin [0000-0002-9079-3993]
dc.contributor.orcidKostovska, Ana [0000-0002-5983-7169]
dc.contributor.orcidPanov, Panče [0000-0002-7685-9140]
dc.contributor.orcidDžeroski, Sašo [0000-0003-2363-712X]
dc.contributor.orcidŽenko, Bernard [0000-0002-4133-7641]
dc.identifier.eissn2052-4463
cam.issuedOnline2022-05-24


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International