## 1 Introduction  
Satellite networks, particularly those based on Low Earth Orbit (LEO) satellite constellations, are revolutionizing global Internet access by enabling low latency and high-speed services through satellite links. Major initiatives such as SpaceX Starlink, OneWeb, and Amazon Kuiper are deploying thousands of LEO satellites to provide coverage to remote and underserved regions [1, 2]. These initiatives are transforming the way the world connects, offering the potential for seamless Internet access even in the most isolated areas. These satellites, operating between 300 and 1500 km in altitude, demonstrate better performance than Geostationary (GEO) ones in terms of latency and throughput. By reducing the required time for data to travel to and from Earth, LEO satellites significantly improve the quality of communication and enable real-time applications that require low latency, such as video calls, online gaming, and autonomous vehicle communication. However, their proximity to Earth presents complex challenges, mainly in handover management, which is crucial to maintaining continuous and optimal connectivity [2; 3]. Handover management in LEO networks is particularly challenging due to the rapid movement of satellites, which require constant relay switches between each other to avoid service interruptions [4; 5]. These satellites move at speeds of up to 7.5 km/s, meaning that handover decisions must be made in real-time to avoid drops in connectivity. Since performance parameters such as upload speed, download speed, ping, and packet loss vary in real-time, traditional handover management techniques exhibit critical limitations. Instead, multi-criteria decision-making handovers are required, such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which has proven useful and has been widely adopted for handover management in terrestrial cellular networks [6]. In this work, we explore the extension of TOPSIS to LEO satellite networks, where decision criteria are highly dynamic. This choice does not exclude other existing techniques but rather aims to assess the applicability of TOPSIS in a context where handover conditions differ significantly from traditional cellular networks. However, while its application in terrestrial networks is well established, its use in satellite networks, particularly in dynamic LEO constellations, remains largely unexplored. This work provides a study in this direction by evaluating the effectiveness of TOPSIS in a heterogeneous LEO satellite network. Considering multiple factors simultaneously, TOPSIS offers a comprehensive approach to managing handovers in environments where traditional metrics fall short [6; 7; 8; 9]. Among the variants of the TOPSIS algorithm, Standard Deviation TOPSIS (SDTOPSIS), Entropy-TOPSIS, and Importance-TOPSIS stand out for their ability to handle multiple performance criteria simultaneously [10]. Each of these variants introduces unique methods for assigning weights to different performance criteria, offering different strengths depending on the specific needs of the network. However, to the best of our knowledge, the sensitivity and robustness of these variants in response to rapid fluctuations in performance criteria remain underexplored in LEO satellite networks. Given the rapidly changing nature of satellite positions, it is essential to explore how these algorithms perform when faced with sudden changes in satellite performance parameters. We also note that, to the best of our knowledge, SD-TOPSIS has never been used in the context of handover management in LEO satellite networks. This study provides a comprehensive sensitivity analysis of these three TOPSIS variants in the context of LEO satellite networks. By focusing on the sensitivity to variations in key criteria such as upload speed, download speed, ping, and packet loss, this study aims to fill a gap in understanding how these algorithms can be adapted to the dynamic environment of LEO satellites.