## 5 Conclusions  
Three years of TOPEX data (i.e., between 1993 and 1995) and Envisat data (i.e., between 2003 and 2005) were studied in this work, proving that the maneuvers of a satellite can be detected by analyzing the variance between the propagated semimajor axis or inclination obtained using SGP4 and the observed semimajor axis or inclination derived using TLE datasets. During this three-year period TOPEX had a total of six in-plane maneuvers while Envisat had a total of nine out-of-plane maneuvers and all the maneuvers were detected successfully. Maneuvers that are even in the range of cm/s could be detected using this technique if the parameters in the algorithm are calibrated properly. In the case studies of TOPEX and Envisat, two types of maneuvers (including fine control maneuvers, which are characterized by in-plane energy maneuvers and orbit control maneuvers, which are characterized by out-of-plane inclination maneuvers) were detected with a detection rate of 100%, even though the last part of the TOPEX data had substantial noise. Along with TOPEX and Envisat, the TLE data of TDRS-3, whose maneuver history is not available, were evaluated using the same method. A total of 19 maneuvers were performed during this period and out of the 19 maneuvers, 18 maneuvers were detected successfully. This was because of an error in the TLE data at that point. The efficiency rate of maneuver detection for TOPEX and Envisat was 100%, while for TDRS-3, which did not have any maneuver data, it was 94.73%. The data quality, density, and maneuver geometry for each satellite vary. Therefore, no one set of defined parameters could work on all satellites to detect their maneuvers under all conditions. The window length and detection threshold fed into the algorithm differ for every satellite. However, by analyzing a satellite's semimajor axis or inclination, both the parameters can be optimized in a manner that can predict the maneuvers. The following are available online at [[https://www.mdpi.com/article/10.3390/app112110181/s1](https://www.mdpi.com/article/10.3390/app112110181/s1)]([https://www.mdpi.com/article/10.3390/app112110181/s1](https://www.mdpi.com/article/10.3390/app112110181/s1)), Figure S1: Maneuver history of TOPEX, Vertical green line represents the maneuvers. Figure S2: Baseline Case, Vertical green line represents the maneuvers and the red circle represents potential maneuvers detected from this method. Figure S3: Case 1: Increase in maneuver detection threshold, Vertical green line represents the maneuvers and the red circle represents potential maneuvers detected from this method. Figure S4: Case 2: Decrease in maneuver detection threshold, Vertical green line represents the maneuvers and the red circle represents potential maneuvers detected from this method. Figure S5: Case 3: Window decreased by a factor of three relative to baseline case with detection limit 0.5-sigma, Vertical green line represents the maneuvers and the red circle represents potential maneuvers detected from this method. Figure S6: Case 4: Window increased by a factor of four relative to baseline case with detection limit 0.5-sigma, Vertical green line represents the maneuvers and the red circle represents potential maneuvers detected from this method. Conceptualization, A.M.; data curation, A.M.; formal analysis, A.M.; funding acquisition, A.M. and H.-C.W. ; investigation, A.M.; methodology, A.M.; project administration, A.M. and H.-C.W. ; resources, A.M.; software, A.M.; supervision, A.M. and H.-C.W. ; validation, A.M.; writing--original draft, A.M.; writing--review and editing, A.M. and H.-C.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the Ministry of Science and Technology, The Republic of China, under the Grants MOST 108-2823-8-194-002, 109-2622-8-194-001-TEI, and 109-2622-8-194-007.