## 3 Loss Differentiation Feature Selection for LEO Satellite Network
### Loss Differentiation Features for LEO Satellite Network  
The ROTT is defined as the time difference between sending a packet at the sender and receiving it at the receiver. Accurate time synchronization of end-to-end satellite nodes is required to obtain this parameter. In congested areas, routers experience an increase in ROTT due to a large number of packets being queued in the cache for forwarding. When incoming packets exceed the cache capacity, packet loss occurs as they cannot enter the cache. However, wireless loss exhibits a random state and does not significantly affect the ROTT. Therefore, analyzing ROTT is crucial for identifying the causes of packet loss in satellite networks. Table 1 presents features related to end-to-end ROTT.  
The IAT refers to the duration between two adjacent packets arriving at the receiver and plays a vital role in Biaz and mBiaz schemes. These schemes consider that if packet IAT falls within a certain range, bit errors are likely causing packet loss. If a packet arrives much earlier than expected, previous packets may have been discarded from buffer memory; if it arrives much later than anticipated, queue delay might have increased in buffers. In either case, network congestion can be attributed as the cause of packet loss. Table 2 outlines features regarding IAT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Number** & **Symbol** & **Interpretation** \\ \hline \(F_{1}\) & ROTT & The one-way trip time of the last normal \\  & & received packet \\ \(F_{2}\) & ROTT\({}_{\text{min}}\) & Minimum end-to-end ROTT from the last packet \\  & & loss to the present packet \\ \(F_{3}\) & ROTT\({}_{\text{mean}}\) & Average end-to-end ROTT from the last packet \\  & & loss to the present packet \\ \(F_{4}\) & ROTT\({}_{\text{dev}}\) & End-to-end ROTT deviation from the last packet \\  & & loss to the present packet \\ \(F_{5}\) & \(\frac{\text{ROTT}}{\text{ROTT}_{\text{mean}}}\) & The ratio of the ROTT of the last normal received \\  & & packet to the average ROTT \\ \(F_{6}\) & \(\frac{\text{ROTT}}{\text{ROTT}_{\text{min}}}\) & The ratio of the ROTT of the last normal received \\  & & packet to the minimum ROTT \\ \(F_{7}\) & \(\frac{\text{ROTT}_{\text{min}}}{\text{ROTT}_{\text{mean}}}\) & The ratio of the minimum ROTT to the average ROTT \\  & & the ratio of the ROTT to the difference between \\ \(F_{8}\) & \(\frac{\text{ROTT}}{\text{ROTT}_{\text{mean}}-\text{ROTT}_{\text{dev}}}\) & the mean and deviation of the last normal \\  & & received packet \\ \hline \hline \end{tabular}
\end{table}
Table 1: The features regarding the end-to-end ROTT.  
Furthermore, the concept of consecutive lost packets is introduced in Table 3.  
In machine learning applications, data classification accuracy and efficiency heavily rely on feature selection methods. Filtering, encapsulation, and embedding are commonly used techniques for feature selection. The filtering method is independent of the classifier, but it scores the features based on the difference or correlation between sample data and selects features by setting a scoring threshold or specifying the number of features. Consequently, the feature subset can be determined prior to classification, demonstrating excellent adaptability. In this study, we combine Relief and mutual information methods for feature selection.