## 4 Numerical Results  
In this section, to evaluate the performance of the proposed algorithm, we constructed the simulation system according to a hydropower plant with a complex communication environment, as illustrated in Figure 1. Specifically, 2000 sensors are used to collect data from hydropower equipment, and 4000 devices are used to receive instructions from the UAV via the covert communication channel in the hydropower plant. We conduct simulations on Matlab2017a and present the numerical results for the Intelligent UAV-assisted CCS with NOMA, which is applied in the hydropower IoT. To evaluate the performance of the proposed system, we implement finite length of transmission message block-Time Division Multiple Access (FLTMB-TDMA) [34], and infinite length of transmission block as the benchmarks [35]. Furthermore, Algorithm 1 is designed based on Successive Convex Approximation (SCA) and Dinkelbach to solve the formulated optimization problem. The parameters are listed in Table 1.  
Figure 2 shows the UAV trajectories of the proposed scheme, infinite length of transmission message block NOMA (IFLTMB-NOMA), and finite length of transmission message block NOMA (FLTMB-TDMA) with periods \(T=45\) s.  
From Figure 2, we can observe that when the flight period is small, the trajectories of the UAV adopting three schemes are much closer. There is also a small difference among the three schemes, the IFLTMB-NOMA scheme is more sensitive to the eavesdropper than the FLTMB-TDMA scheme when the UAV flies close to U1. When FLTMB-TDMA is adopted, the UAV flies closer to the user in the presence of [PERSON]. In the TDMA scheme, due to the limited flight time, the time slots are first allocated to the closest user of the UAV to achieve a better channel quality and thus improve the data rate. In this way, the trajectory of the UAV with the TDMA scheme is closer to [PERSON] than that of the NOMA scheme. We also observe that the UAV using the NOMA scheme is farther from [PERSON], which ensures the covertness of the covert communication. Figure 3 plots the covert rate of the proposed UAV aided with NOMA versus iterative numbers. In general, the global optimum solution can usually be obtained. We observe that the proposed SGPA algorithm can converge quickly with different parameter settings. When the number of SGPA iterations reaches \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline \(T\) & Number of time slots & 30 \\ \(L\) & Duration of time slot & 1 s \\ \(H\) & Altitude of UAV & 50 m \\ \(V_{max}\) & Maximum speed of UAV & 20 m/s \\ \(d_{t}\) & Duration of the time slot & 1 s \\ \(p\) & Transmission power & 20 dB \\ \(\sigma^{2}\) & Power of noise & 60 dB \\ \(\epsilon\) & Covertness measurement & \(10^{-5}\) \\ \(\delta\) & Detection error probability of UAV & \(10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 2: The trajectories of UAV for different accessing schemes, i.e., FLTMB-NOMA, FLTMB-TDMA, and IFLTMB-NOMA. rate of the proposed CCS remains almost constant with the increase in iteration numbers. In addition, according to [33], the proposed algorithm converges polynomial approximation at a locally optimal point. Figure 4 shows the covert rate of the proposed UAV-assisted CCS with NOMA by applying the joint optimization method on the flight altitude of the UAV and the transmit power versus the flying altitude of the UAV. From Figure 4, we can observe that the covert rate first obtains a slow increase as \(H\) increases and then decreases rapidly when the altitude of the UAV is larger than 73 m.  
Figure 4: The covert rate of UAV-assisted CCS with NOMA under joint optimization of different UAV flight altitudes with different detection error rates. Figure 3: Achieved covert rate of the proposed UAV-assisted CCS with NOMA versus the number of iterations for different detection error rates.