## 5 Conclusions  
This work presented a performance evaluation of the handover using three variants of the TOPSIS algorithm, namely SD-TOPSIS, Entropy-TOPSIS, and Importance-TOPSIS, in the context of dynamic LEO satellite environments. To ensure a realistic analysis, real-world data from \"Starlink-on-the-Road-Dataset\", were used to assess the robustness and sensitivity of these algorithms during LEO satellite handovers. The simulation results showed that the SD-TOPSIS algorithm consistently outperforms the other algorithms under study in terms of robustness, as it exhibits the lowest sensitivity and standard deviation of scores across the four scenarios in response to small variations in the criteria values. Moreover, the SD-TOPSIS algorithm reduces sensitivity compared to both Entropy-TOPSIS and Importance-TOPSIS for all the four scenarios, making it particularly well-suited for handover in satellite communication systems where criteria such as upload speed, download speed, ping, and packet loss vary rapidly, due to the mobility of LEO satellites. Future work will explore hybrid algorithms that combine SD-TOPSIS with advanced methods such as machine learning. This integration could enhance adaptability and enable real-time decision-making, addressing the dynamic nature of LEO satellite networks while improving their handover performance and resilience. Conceptualization, P.B.N. and M.M.T. ; Methodology, P.B.N. and A.K.L. ; Software, P.B.N. ; Validation, A.K.L., M.M.T. and P.S.N. ; Formal analysis, P.B.N. ; Resources, A.K.L. and V.P. ; Data curation, P.B.N. ; Writing--original draft preparation, P.B.N. ; Writing--review and editing, M.M.T., M.-L.L.B., P.S.N. and V.P. ; Visualization, P.B.N. ; Supervision, A.K.L. and M.M.T. ; Project administration, A.K.L. and M.M.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This work presented a performance evaluation of the handover using three variants of the TOPSIS algorithm, namely SD-TOPSIS, Entropy-TOPSIS, and Importance-TOPSIS, in the context of dynamic LEO satellite environments. To ensure a realistic analysis, real-world data from \"Starlink-on-the-Road-Dataset\", were used to assess the robustness and sensitivity of these algorithms during LEO satellite handovers. The simulation results showed that the SD-TOPSIS algorithm consistently outperforms the other algorithms under study in terms of robustness, as it exhibits the lowest sensitivity and standard deviation of scores across the four scenarios in response to small variations in the criteria values. Moreover, the SD-TOPSIS algorithm reduces sensitivity compared to both Entropy-TOPSIS and Importance-TOPSIS for all the four scenarios, making it particularly well-suited for handover in satellite communication systems where criteria such as upload speed, download speed, ping, and packet loss vary rapidly, due to the mobility of LEO satellites. Future work will explore hybrid algorithms that combine SD-TOPSIS with advanced methods such as machine learning. This integration could enhance adaptability and enable real-time decision-making, addressing the dynamic nature of LEO satellite networks while improving their handover performance and resilience. Conceptualization, P.B.N. and M.M.T. ; Methodology, P.B.N. and A.K.L. ; Software, P.B.N. ; Validation, A.K.L., M.M.T. and P.S.N. ; Formal analysis, P.B.N. ; Resources, A.K.L. and V.P. ; Data curation, P.B.N. ; Writing--original draft preparation, P.B.N. ; Writing--review and editing, M.M.T., M.-L.L.B., P.S.N. and V.P. ; Visualization, P.B.N. ; Supervision, A.K.L. and M.M.T. ; Project administration, A.K.L. and M.M.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This work presented a performance evaluation of the handover using three variants of the TOPSIS algorithm, namely SD-TOPSIS, Entropy-TOPSIS, and Importance-TOPSIS, in the context of dynamic LEO satellite environments.