## 4 Case Study
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Node**} & **Description of the** & \multirow{2}{*}{**Prediction Model**} & \multirow{2}{*}{**Prediction Model and Parameter**} \\  & **Prediction Model** & & \\ \hline \(\alpha\)-chain & & & \\ airborne data & degradation model for & \(Y=e^{(a_{1}+a_{2}(\frac{1}{-\frac{1}{28}}))}\cdot\left(e^{a_{3}+a_{4}*\frac{T} {28}}-1\right)+Y\) \\  & power MOSFET & \(Ea=0.616,A=7.79\times 10^{5},Y_{0}=5,L=8,\sigma=0.08\) \\  & & \((a_{1}=-8.0363,a_{2}=-5529.6,a_{3}=-0.019,a_{4}=-0.8395)\) \\ \hline \(\alpha\)-chain & Wiener process, & \\ repeater satellite & Arrhenius model & \(Ea=0.616,A=7.79\times 10^{5},Y_{0}=5,L=8,\sigma=0.08\) \\ \hline \(\alpha\)-chain & & \\ ground data & exponential distribution & \(R_{3}(t)=\exp\left[-\left(\frac{t}{2600}\right)\right]\) \\  & & \\ \hline \(\mathrm{A}\)-chain & & \\ airborne data & degradation model for & \\  & power MOSFET model & \(a_{1}=-7.1342,a_{2}=-5391.4,a_{3}=-0.022,a_{4}=-0.8411\) \\  & & \\ \hline \(\mathrm{A}\)-chain & & \\ ground data & exponential distribution & \(R_{5}(t)=\exp\left[-\left(\frac{t}{500}\right)\right]\) \\  & & \\ \hline \(\mathrm{B}\)-chain airborne & Combined & \\ data terminal & acceleration model & \(a_{1}=-7.8220,a_{2}=-5419.2,a_{3}=-0.023,a_{4}=-0.8317\) \\ \hline \(\mathrm{B}\)-chain & exponential distribution & \(R_{7}(t)=\exp\left[-\left(\frac{t}{500}\right)\right]\) \\  & & \\ repeater satellite & Arrhenius model & \(Ea=0.64,A=2.115\times 10^{6},Y_{0}=33,L=36.5,\sigma=0.178\) \\ \hline \(\mathrm{\beta}\)-chain ground & exponential distribution & \(R_{s}(t)=\exp\left[-\left(\frac{t}{500}\right)\right]\) \\  & & \\ \hline \(\mathrm{C}\)-chain airborne & degradation model for & \\ data terminal & power MOSFET & \(a_{1}=-8.2334,a_{2}=-5219.5,a_{3}=-0.014,a_{4}=-0.7991\) \\ \hline \(\mathrm{C}\)-chain & & \\ ground data & exponential distribution & \(R_{9}=\exp\left[-\left(\frac{t}{6000}\right)\right]\) \\ terminal & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Root node of solar-powered UAV data-link prediction model. Figure 6: The mode of solar-powered UAV data-link. non-line-of-sight uplink, \(\alpha\)-chain non-line-of-sight downlink, A-chain of the line-of-sight uplink, A-chain of the line-of-sight downlink, C-chain of the line-of-sight uplink, C-chain of the line-of-sight downlink and B-chain of the line-of-sight uplink, respectively. Accordingly, we constructed the BN as show in Figure 7, the leaf node \(X_{50}\) respect the health state of solar-powered UAV data-link. Considering the change of communication distance (The solar-powered UAV climbed to an altitude of 8500 m at sunrise and descended to an altitude of 1200 m at night.). Given this diurnal variation of the flying altitudes of the UAV, the communication distances of the line-of-sight chains A, B, C and the non-line-of-sight chain \(\alpha\) and \(\beta\) UAV-repeater satellite links are affected by the vertical height and the horizontal distance. The simulation of the communication distance is shown in Figure 8.  
According to the communication distance and fading margin (6\(\sim\)10 dB) requirements of each channel, we combined the communication interruption rate simulation curves using the Barnett-Vignant formula and bit error rate to get the health status of each channel. Figure 9 shows the curve \(P_{Error\_n}\) (except \(P_{Error\_1}\), \(P_{Error\_2}\), \(P_{Error\_7}\), \(P_{Error\_8}\)) as a function of time. For \(P_{Error\_1}\) (\(\alpha\)-chain ground-satellite uplink), \(P_{Error\_2}\) (\(\alpha\)-chain ground-satellite downlink) and \(P_{Error\_7}\) (\(\beta\)-chain ground-satellite uplink), \(P_{Error\_8}\) (\(\beta\)-chain ground-satellite downlink), the distance from the repeater satellite to the ground data terminal can be considered a fixed value because the distance between the repeater satellite and the ground data terminal was much larger than the relative motion distance between the repeater satellite and the ground data terminal.