{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:01:01Z","timestamp":1760058061696,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"QoS-HPC-IoT Laboratory","award":["TKP2021-NKTA"],"award-info":[{"award-number":["TKP2021-NKTA"]}]},{"name":"University of Debrecen","award":["TKP2021-NKTA"],"award-info":[{"award-number":["TKP2021-NKTA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>NASA\u2019s Juno mission, involving a pioneering spacecraft the size of a basketball court, has been instrumental in observing Jupiter\u2019s atmosphere and surface from orbit since it reached the intended orbit. Over its first decade of operation, Juno has provided unprecedented insights into the solar system\u2019s origins through advanced remote sensing and technological innovations. This study focuses on change detection in terms of Juno\u2019s trajectory, leveraging cutting-edge data computing techniques to analyze its orbital dynamics. Utilizing 3D position and velocity time series data from NASA, spanning 11 years and 5 months (August 2011 to January 2023), with 5.5 million samples at 1 min accuracy, we examine the spacecraft\u2019s trajectory modifications. The instantaneous average acceleration, jerk, and snap are computed as approximations of the first, second, and third derivatives of velocity, respectively. The Hilbert transform is employed to visualize the spectral properties of Juno\u2019s non-stationary 3D movement, enabling the detection of extreme events caused by varying forces. Two unsupervised machine learning algorithms, DBSCAN and OPTICS, are applied to cluster the sampling events in two 3D state spaces: (velocity, acceleration, jerk) and (acceleration, jerk, snap). Our results demonstrate that the OPTICS algorithm outperformed DBSCAN in terms of the outlier detection accuracy across all three operational phases (OP1, OP2, and OP3), achieving accuracies of 99.3%, 99.1%, and 98.9%, respectively. In contrast, DBSCAN yielded accuracies of 98.8%, 98.2%, and 97.4%. These findings highlight OPTICS as a more effective method for identifying outliers in elliptical orbit data, albeit with higher computational resource requirements and longer processing times. This study underscores the significance of advanced machine learning techniques in enhancing our understanding of complex orbital dynamics and their implications for planetary exploration.<\/jats:p>","DOI":"10.3390\/fi17030125","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T16:39:03Z","timestamp":1741711143000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advanced Trajectory Analysis of NASA\u2019s Juno Mission Using Unsupervised Machine Learning: Insights into Jupiter\u2019s Orbital Dynamics"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8782-9721","authenticated-orcid":false,"given":"Ashraf","family":"ALDabbas","sequence":"first","affiliation":[{"name":"Intelligent Systems Department, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaid","family":"Mustafa","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-6497","authenticated-orcid":false,"given":"Zoltan","family":"Gal","sequence":"additional","affiliation":[{"name":"Department of Information Technology Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","unstructured":"Yamaguchi, T., Mimasu, Y., Tsuda, Y., Funase, R., Sawada, H., Mori, O., Morimoto, M.Y., Takeuchi, H., and Yoshikawa, M. 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