## 3 Results  
### Experimental Conditions and Parameters  
Satellite motion was predicted using TLE data in conjunction with the SGP4 model for three representative LEO constellations--Iridium, Orbcomm, and Starlink--encompassing a total of 6251 satellites. This method provides accurate short-term orbital predictions.  
Doppler measurement noise is modeled as zero-mean Gaussian white noise with a fixed variance of 100 Hz\({}^{2}\), a widely adopted and simplified assumption in signal processing. To simulate realistic observations, Doppler shifts are generated by superimposing the theoretical values with the noise.  
The downlink coverage of each constellation was represented by specific depression angle ranges: 40\({}^{\circ}\)-90\({}^{\circ}\) for Starlink, 15\({}^{\circ}\)-90\({}^{\circ}\) for Iridium, and 20\({}^{\circ}\)-90\({}^{\circ}\) for Orbcomm. A minimum elevation angle of 30\({}^{\circ}\) is set for the receiver antenna to ensure visibility within the defined coverage constraints.  
The signal power constraint in Equation (6) is expressed as \(g(x)-sensity\geq 0\), where the receiver sensitivity (\(sensity\)) is set to \(-140\) dBW. Considering the actual Iridium signal transmission intervals and the required processing length for satellite signals, the remaining observable time is denoted by \(h(x)>90 ms\).  
The UAV flight scenario is illustrated in Figure 2, where the trajectories of visible satellites are shown. A total of 2185 epochs, with a 30 s interval, were selected for the experiments. Initially, 20 Starlink satellites and 1 Orbcomm satellite were observed. After 228 s, an Iridium satellite entered the observation horizon. During the UAV flight in the scenario with 6251 satellites, an average of 22 LEO satellites were observed per second. The simulation was conducted on a computer with the following configuration: CPU: i7-12700F; Memory: DDR4-3200, 12 GB. Details of the simulation scenario and parameter settings are summarized in Table 3.  
To evaluate the performance of the dynamic satellite selection algorithm in a UAV dynamic scenario, the proposed NSWOA satellite selection algorithm is compared with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) [37], Particle Swarm Optimization (PSO) [38], and Gray Wolf Optimization (GWO) [39] algorithms under identical conditions. Among them, PSO and GWO share a similar population-based framework with NSWOA. PSO and GWO are computationally efficient with few parameters, making them suitable for embedded or edge devices. In contrast, NSGA-II, though more complex, provides high-quality solutions through its non-dominated sorting mechanism and serves as a performance benchmark. These three algorithms collectively represent a balance between optimization quality and computational efficiency. For each algorithm, the initial satellite subset \(x\) consists of 200 search agents, and the number of iterations \(T\) is set to 100.  
The parameters of the NSWOA are provided in Table 4. In comparison, NSGA-II has a crossover probability of \(pC=0.7\) and a mutation probability of \(pM=0.3\). The  
\begin{table}
\begin{tabular}{c c c c} \hline
**LEO Constellation** & **Starlink** & **Orbcomm** & **Iridium** \\ \hline Beam Coverage Range & 40\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) & 15\({}^{\circ}\)–90\({}^{\circ}\) & 20\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) \\ Minimum Elevation Angle & & 30\({}^{\circ}\)–90\({}^{\circ}\) & \\ Total Flight Time & & 65,550 s & \\ Observation Density & & 22 satellites per second & \\ \hline \end{tabular}
\end{table}
Table 3: Dynamic scenario parameters.  
PSO algorithm follows the NSWOA parameter settings, with a social learning factor of \(c_{1}=1.5\), a personal learning factor of \(c_{2}=1.5\), and an inertia weight defined as \(w=w_{max}-t(w_{max}-w_{min})/T\), where the maximum inertia weight is \(w_{max}=0.8\) and the minimum inertia weight is \(w_{min}=0.4\). The GWO algorithm uses the same parameter settings as the NSWOA.