## 5 Performance Evaluation
### Simulation Setup  
We accomplished the simulation on a laptop with an AMD Ryzen 7 4800H with Radeon Graphics running at 2.90 GHz. The laptop had 16 GB of RAM and was running on the Windows 10 operating system. We envision a SAGIN-Cloud system where the coverage of the space layer includes both the air layer and the entire MECN. The following parameters are randomly generated within a certain range, with mean values generated according to a normal distribution.  
_(1) Channel State Information (CSI)_  
For information transportation within ECNs, different frequency bands were borrowed between the different layers, resulting in different bandwidths and transmission speeds. Currently, the C-band is one of the most commonly used frequency bands for satellite operations, with the Ka-band being a latecomer. This paper assumes that the data transmission between BSs and UAVs occupies the C-band, while the Ka-band is occupied between UAVs and LEO satellites. The bandwidth of the C-band is 20 MHz, while the available bandwidth of the Ka-band can reach up to 3500 MHz. As for the signal frequency range, the C-band and Ka-band are 3.4-8 GHz and 26.5-36 GHz, respectively.  
(2) ECNs Information  
The horizontal and vertical coordinates of the BSs on the ground and the UAVs in the air are within \((-1,1)\) km, respectively, while vertically, they are all 0. In contrast, the UAVs are located at altitudes of (0.075, 0.15) km. LEO satellites are located in space and they are at distances of (780, 800) km from the Earth's surface. LEO satellites are located in space and they are (780, 800) km away from the surface. They are not too space-constrained, so they are deployed in a square area with four points as vertices: \((-200,\,-200)\), \((-200,\,200)\), \((200,\,-200)\), and \((200,\,200)\).  
BSs, UAVs, and LEO satellites possess CPU process speeds of \(2*10^{9}\) cycles/s, \(3*10^{8}\) cycles/s, and \(5*10^{9}\) cycles/s, respectively.  
(3) Comparison Algorithm  
First, we propose using the most basic comparison algorithm, namely the Brute-Force algorithm.  
* The Brute-Force algorithm does not take into account any uncertainty or potential computational overflow of tasks to the extent that it may eventually result in the inability to obtain an optimal solution.  
Second, we compare two traditional algorithms that take parameter uncertainty into account: the RO algorithm and SO algorithm.  
* In RO, only the uncertainty of computational resources of ECNs is considered and their possible worst-case scenarios are experimentally selected to ensure their robustness.
* In SO, the fuzzy set is constructed based on the historical data of the computational resources of the given ECNs; thus, we obtain their means and variances.  
Third, there is another algorithm closest to our proposed one, which is the DRO algorithm; this is for obtaining the optimal solution.
* In DRO [5], the mean value in the range of the uncertainty set is obtained, and its optimal robust result is obtained by the min-max theory, which guarantees both robustness and practicality. However, there is also a drawback, namely that it is an uncertainty set constructed from historical data, which does not guarantee the stability of the uncertainty set and does not consider its risk.  
Finally, we propose the RaDROO algorithm, on the basis of the traditional DRO algorithm.  
* In RaDROO, in addition to what the DRO considers, it complements certain deficiencies that it possesses. It considers CVaR, choosing only the \(\alpha-tail\) part as the benchmark, while using \(\lambda\) as a weight with the original part. That is to say, it selectively aggravates the proportion of the worse part of the results within the final result in order to guarantee its risk resistance.