## 2 Related Work  
Handover process in LEO satellite networks represents a major challenge due to the dynamic nature of satellite movements, varying communication links, and changing traffic conditions. Several techniques, including multi-criteria decision algorithms, have recently been proposed to address this challenge, with TOPSIS being one of the most commonly used methods for ranking alternatives based on multiple performance criteria. Handover management in heterogeneous cellular networks is addressed in [10] using Entropy-TOPSIS and SD-TOPSIS. The methodology used helped to reduce the number of handovers, improve the average user throughput, and decrease radio link failures, with SD-TOPSIS performing better than Entropy-TOPSIS. However, these methods were validated and limited to relatively stable cellular networks in terrestrial environments over a long period. They may not yield the same results in the unstable environments of LEO satellite networks, where criteria vary rapidly due to satellite mobility. Authors in [6] focused on vertical handover in heterogeneous 4G cellular networks, using Multiple Attribute Decision-Making (MADM) algorithms combined with a utility function to determine the best access network based on Quality of Service (QoS) criteria. The proposed approach calculates performance for each network according to traffic classes and uses the utility function to reflect user preferences, improving key parameters such as the reversal phenomenon, the ping-pong effect, and handoff failures. However, while this study demonstrates the effectiveness of this approach in cellular networks, it does not account for the rapid variations in performance criteria typical of LEO satellite networks, such as satellite mobility and fluctuating link quality. A sensitivity analysis considering these dynamic factors is crucial for adapting this approach to satellite environments. Network selection in heterogeneous wireless networks was addressed in [12] through two Multi-Attribute Decision-Making (MADM) methods: Multiple Analytic Network Process (M-ANP) for weighting criteria, and TOPSIS for ranking alternatives. The aim was to allow user devices to continuously choose the most appropriate access network during communication. Simulation results demonstrated the effectiveness of the proposed approach in reducing the reversal and ping-pong phenomena. While this approach proved effective in heterogeneous terrestrial networks, a sensitivity analysis considering these variations is necessary for adapting the approach to satellite networks, where factors like signal strength, bandwidth, and latency fluctuate rapidly due to satellite movement. The inclusion of TOPSIS, as mentioned in the study, could be expanded to incorporate these dynamic variations and provide a more robust solution in the context of satellite handovers. In [13], the authors propose TOPSIS-based Subset Replica (TS-REPLICA), an algorithm based on Entropy-TOPSIS to optimize replica placement in Hadoop Distributed File System (HDFS). By employing a multi-attribute matrix reflecting node performance and load, combined with entropy-based weighting, the algorithm significantly enhances performance by balancing loads in a Spark cluster. This method is limited to relatively stable terrestrial settings and does not assess the robustness of algorithms against rapid variations in performance criteria as is the case for LEO satellite networks. A two-step handover decision and access strategy in heterogeneous GEO/LEO networks was proposed in [7]. An essential component of this approach is the use of the Importance-TOPSIS variant to rank LEO satellites when a user chooses to connect to a LEO network. Importance-TOPSIS has been compared with Entropy-TOPSIS and has proven to be the superior method in reducing the number of handovers and decreasing the probability of forced connection termination while improving the overall system throughput. Despite this, it requires further refinement.