## 1 Introduction  
Satellite communication has the advantages of long communication distance, wide coverage, and no geographical restrictions, and plays an irreplaceable role in current and future communication systems [1]. Satellite networks operate in a wireless environment that is highly susceptible to solar activity, cosmic particle radiation, noise interference, etc., resulting in a bit error rate ranging from \(10^{-8}\) to \(10^{-5}\). Moreover, the Earth's atmosphere, ionosphere, and ice crystal rainfall layer also affect satellite-to-ground links, leading to higher bit error rates, especially in military scenarios where human interference can cause even higher rates exceeding \(10^{-5}\). In traditional TCP, packet loss is considered as an indication of network congestion by default. Therefore, the congestion avoidance mechanism needs to be enabled, which reduces the congestion window. However, in satellite networks, apart from packet losses caused by congestion, it is crucial not to ignore packet losses caused by channel errors. If network congestion is the default, the congestion window will be reduced, resulting in inefficient utilization of network bandwidth and compromising network reliability. In light of these issues, extensive research has been conducted by numerous scholars [2, 3]. From a measurement acquisition perspective, packet loss differentiation can be categorized into explicit and implicit methods. Explicit loss differentiation primarily relies on the active queue management mechanism of routing nodes to determine the cache queue occupancy and provide feedback to the sender. This approach is more direct and accurate but requires support from intermediate nodes. Implicit loss differentiation mainly utilizes end-to-end round-trip time (RTT) [3], relative one-way trip time (ROTT) [4; 5], packet inter-arrival time (IAT) [6], loss number [5], and other decision mechanisms to identify types of packet loss [7; 8; 9; 10]. It does not require the support of nodes other than the sender and receiver, and can be implemented with minimal changes in the network. Implicit loss differentiation can further be divided into threshold-based and learning algorithm-based loss differentiation methods. Packet loss differentiation based on threshold determination involves utilizing statistical analysis and calculation of feature parameter values, such as RTT, ROTT, IAT, etc., to characterize the network state. By comparing these feature parameters with predefined thresholds, the type of packet loss can be determined. Although this algorithm has low complexity, it heavily relies on experiential threshold settings. Additionally, relying solely on a single feature cannot achieve high determination accuracy and defining the relationship between multiple feature thresholds is challenging, which ultimately affects the accuracy of packet loss differentiation. In machine learning-based packet loss discrimination algorithms, decisions are made using models that learn correlations between features. Among them, decision trees (DTs), random forests (RFs), artificial neural networks (ANNs), K-nearest neighbors (KNNs), naive Bayes (NB), and other single classification models have become the mainstream methods for packet loss differentiation. However, the accuracy of differentiation in these single classification models is significantly affected by the number and features of samples, and their accuracy may not reach optimal levels. Ensemble learning is a classification method that combines results from multiple individual classifiers to obtain a final result, typically achieving better classification performance than using a single classifier alone while also offering greater diversity compared to homogeneous integration methods. Based on literature research findings, existing loss differentiation algorithms lack feature selection methods, individual classifier selection strategies, and effective ensemble strategy for accurate packet loss differentiation.