## 4 Evaluation and Discussion
### Performance Evaluation  
The relationships between \(Q\) and \(\alpha\) for two cases are drawn in Figure 4. The first case is for UAVs that are distributed in a line, and the second case is for UAVs that are distributed randomly. In Figure 4, the vertical coordinate \(Q\) is the adjustable performance indicator. The larger the \(Q\) value is, the better the exhibited system performance. In addition, the horizontal coordinate \(\alpha\) is the adjustable factor. It can be adjusted according to different service requirements. To be specific, for services with higher reliability requirements, the \(\alpha\) value should be set higher. For services with higher effectiveness requirements, it should be set lower. For comparison, the curves of the proposed algorithm as well as two referenced ones, including the maximum capacity and maximum service time, are draw in Figure 4. To validate the performance of the proposed model for different services, the adjustable parameter \(\alpha\) is set from 0 to 1, and the interval is 0.1. From glancing at Figure 4a,b, it can be seen that the curve of the proposed algorithm is above the two referenced ones, proving that better overall system performance can be achieved by using the proposed algorithm no matter whether the UAVs are distributed in a line or randomly. For the proposed algorithm, its curve first drops and then rises, but the \(Q\) value overall remains at a high level, proving that the proposed algorithm can be adapted to different scenarios. For the referenced algorithm based on the maximum capacity, the quality value increases linearly with \(\alpha\). This phenomenon illustrates that the maximum capacity algorithm has better performance for services with high effectiveness requirements, but when the system reliability requirement increases, its performance gradually deteriorates. On the contrary, the quality value decreases linearly for the maximum service time algorithm. It shows that such an algorithm is suitable for services with high reliability requirements. Then, in order to intuitively display the backhaul link results of different algorithms, as an example, Figure 5 depicts the associated pattern of the UAVs and the satellites for the proposed algorithm and two referenced ones. Herein, the adjustable parameter is set to \(\alpha=0.5\) to simulate services that requires both effectiveness and reliability. Generally speaking, it is better to make the number of UAVs connected to each satellite as even as possible. This has two advantages: One is that the overall load of each satellite is more balanced. Such a method avoids the situation wherein some satellites are overloaded and some satellites are idle. The second is that once a satellite with a heavy load fails, the services provided by the UAVs connected to this satellite will be greatly affected and interrupted. Moreover, the overhead caused during the service switching process will also increase. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Item** & **Configuration** \\ \hline \multirow{5}{*}{Link budget parameters} & Transmitting power (\(P_{i}\)) & 30 dBm \\  & Bandwidth (\(W\)) & 250 Mbps \\  & Noise–power spectral density (\(n_{0}\)) & \(10^{-12}\) W/Hz \\  & Transmitting antenna gain (\(G_{t}\)) & 36.7 dBi \\  & Receiving antenna gain (\(G_{r}\)) & 32.8 dBi \\  & Wavelength (\(\lambda\)) & 2.5 cm \\ \hline \multirow{5}{*}{Constellation configuration} & Orbital height & 600 km \\  & Satellite moving speed & 7.56 km/s \\ \cline{1-1}  & Number of UAVs (N) & 11 \\ \cline{1-1}  & Number of satellites (\(M\)) & 4 \\ \cline{1-1}  & Maximum number of UAVs per satellite (\(P_{max}\)) & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The simulation configuration. In Figure 4(a), the number of UAVs associated with the four satellites using the proposed algorithm is 2, 3, 3, and 3. Meanwhile, the number of UAVs is 3, 2, 2, and 4 and 0, 2, 2, and 7 for the referenced algorithms based on the maximum capacity and the maximum service time, respectively.