## 4 Experimental Evaluation
### Experimental Setup
#### 4.1.2 Datasets  
The experiments were conducted using the MNIST [36] dataset, the EuroSAT dataset [33], UC Merced Land Use Dataset [37], and RSI-CB dataset [38].  
The MNIST dataset contains handwritten digit images from 0 to 9. It is a standard benchmark dataset widely used to evaluate the performance of various algorithms. It comprises 10 categories, with each image being 28 \(\times\) 28 pixels.  
The EuroSAT dataset is a remote sensing dataset based on satellite imagery, containing 10 different land use types (such as agriculture, forest, water bodies, etc.). Each image is 64 \(\times\) 64 pixels.  
The UC Merced Land Use dataset contains high-resolution remote sensing images commonly used for land use classification and remote sensing image analysis. It includes 21 categories, such as agricultural land, urban areas, forests, lakes, roads, etc. The dataset contains 2100 images, with 100 images per category. Each image is 256 \(\times\) 256 pixels.  
The RSI-CB (Remote Sensing Image Classification Benchmark) dataset is a large-scale remote sensing image classification benchmark dataset constructed based on crowdsourced data. This dataset aims to provide better training and testing resources for deep learning in the field of remote sensing image classification. The RSI-CB dataset contains two subsets, RSI-CB256 and RSI-CB128. RSI-CB256 contains 35 categories with approximately 24,000 images, while RSI-CB128 contains 45 categories with 36,707 images of 128 \(\times\) 128 pixels. This paper conducts validation based on RSI-CB128.  
While these datasets are standard benchmarks in machine learning, they were specifically selected to simulate real-world AI applications in SAGIN environments. The MNIST dataset, though a general image classification benchmark, represents the type of lightweight classification tasks that might be performed by resource-constrained devices at various SAGIN layers, such as object recognition by UAVs or pattern detection on satellite imagery. More directly relevant to SAGIN applications, the EuroSAT, RSI-CB and UC Merced Land Use datasets represent actual remote sensing tasks performed by satellites and aerial platforms in real-world scenarios. These datasets simulate critical SAGIN-dependent AI applications mentioned earlier (line 131), including disaster monitoring, environmental surveillance, and precision agriculture, where satellite imagery analysis must be conducted across distributed nodes with privacy concerns. The heterogeneous nature of these datasets also allows us to evaluate our framework's performance on tasks with varying complexity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value/Range** & **Description** \\ \hline \multirow{6}{*}{Node Mobility Pattern} & \multirow{6}{*}{Varied} & LEO: Circular orbit with 90-min period \\  & & MEO: Circular orbit with 6-h period \\  & & GEO: Fixed relative to Earth \\  & & UAV: Random waypoint model at 100–300 m \\  & & HAPS: Fixed position at 20 km altitude \\ \hline \multirow{6}{*}{Connection Duration(min)} & \multirow{6}{*}{5—Continuous} & LEO-Ground: 5-15 min \\  & & MEO-Ground: 20–40 min \\  & & GEO–Ground: Continuous \\  & & UAV–Ground: 10–30 min \\  & & HAPS–Ground: Continuous \\ \hline \multirow{6}{*}{Topology Change Frequency} & \multirow{2}{*}{Varied} & LEO visibility changes: Every 5–15 min \\  & & UAV connectivity changes: Every 10–30 min \\  & & Ground device mobility: Every 30–60 min \\ \hline \multirow{6}{*}{Disconnection Probability (per hour)} & \multirow{6}{*}{0–50\%} & Space nodes: 10–50\% \\  & & Air nodes: 5–30\% \\ \cline{1-1}  & & Ground nodes: 0–10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamic topology parameters.  
and data distributions, mirroring the diverse AI workloads in SAGIN environments where computation must be coordinated across space, air, and ground layers while preserving data privacy.