## 1 Introduction
Therefore, the decision-making process for satellite handover is intricate and demanding [15]. When formulating the handover strategy, one can categorize the criteria for handover satellite-ground links based on factors such as the elevation angle and service time of the candidate satellites mentioned earlier, and subsequently propose various handover schemes; distinct choices corresponding to diverse optimization objectives. The grid user terminals continuously select service satellites using the proposed handover strategy to ensure continuous communication. Current satellite handover algorithms are categorized into single-attribute handover and multi-attribute  
Figure 1: LEO satellite communication-assisted grid scenario figure. handover according to decision-making. Single-attribute handover algorithms make satellite handover decisions based on a single attribute, whereas multi-attribute handover algorithms consider multiple attributes simultaneously. Currently, the research on single-attribute handover algorithms is relatively mature due to their ease of implementation and flexibility in selecting different handover algorithms for varying scenarios. In [16; 17], a handover strategy based on the maximum elevation angle is proposed, in which the user terminal prioritizes the satellite with the greatest elevation angle among the candidate satellites. Due to atmospheric and geographic factors affecting the channel, the maximum elevation angle of the satellites may not accurately reflect the actual link quality. In [18; 19; 20], a strategy based on the maximum received signal strength was proposed, where user terminals prioritize satellites with the highest received signal strength, but which overlooks other handover factors, such as the candidate satellite load conditions, potentially leading to an increased handover frequency. In [21; 22], a handover strategy based on the longest remaining service time was proposed. The user terminal prioritizes satellites with the longest remaining service time among the candidates, effectively reducing the frequency of the handover. However, longer service times may indicate smaller satellite elevation angles, particularly in poorer channel conditions, which hinder effective communication. [PERSON] et al. [23] proposed a handover strategy based on the maximum load of satellites to mitigate service quality degradation resulting from satellite overload. However, this strategy proves ineffective in scenarios with higher user density. While these single-attribute-based interstellar handover algorithms are simpler, they possess shortcomings and struggle to meet user QoS requirements in actual satellite communication scenarios. To comprehensively assess the impact of various handover factors on interstellar handover, the current research emphasizes multi-attribute-based interstellar handover strategies. In [24], a handover strategy was proposed based on integrated weighting of the service quality, incorporating three variables: the service time, elevation angle, and number of idle channels. [PERSON] et al. [25] proposed a multi-attribute decision-making handover strategy that comprehensively considers three influencing factors: the received signal strength, remaining service time, and satellite idle channels. Given that the relationship between satellites and users can be modeled as a graph, numerous studies have utilized graph theory. [PERSON] et al. [26] proposed a satellite handover framework based on graph theory. Here, the satellite handover problem can be transformed into finding the shortest path in a weighted directed graph, and the shortest path algorithm is then employed to derive the user's optimal inter-satellite handover strategy. In consideration of the time-varying characteristics of satellite topology caused by satellite motion, [PERSON] et al. [27] proposed a real-time inter-satellite handover prediction framework based on the time-evolving graph (TEG) and a shortest path dynamic updating algorithm.