## 3 Construction of the Experimental Dataset  
The success of deep learning models often requires a large amount of training data to achieve good performance. However, the number of spatial datasets available for training deep learning models is currently very limited, especially in the field of satellite imagery. This is because satellite images are often considered sensitive data, and genuine satellite image resources are scarce. To address this issue, we have created the TSC-Dataset, which aims to provide more available resources for the field of remote sensing. This will enable researchers to develop and test new algorithms more effectively, especially in achieving breakthroughs in the recognition of typical satellite components. This contribution is of great significance for advancing the development and application of remote sensing technology. The TSC-Dataset is sourced from four main parts: (1) 2351 valid satellite images filtered from the Satellite-Dataset [44]; (2) 976 satellite images of 92 satellites were collected from various perspectives and orbital positions using the STK 11.6 (Satellite Tool Kit 11.6) software; (3) 273 satellite images obtained through web scraping; (4) 200 real satellite images gathered from organizations such as NASA, the ESA, and the NSSDC. Some of these images are shown in Figure 8.  
In the process of collecting satellite images using STK software, to minimize the differences between simulated images and real-scene images as much as possible, this paper considered the specific locations of satellites and the lighting environment around them. By sampling satellites at high, medium, and low orbital positions, different imaging effects were obtained, thereby better simulating real scenes. Figure 6: RepConv structure: (**a**) structure during training and (**b**) validation during structure. Figure 7: RepHead structure. Figure 9 shows a comparison between the real image and the simulated image of the Artemis satellite. In this comparison, the various components of the Artemis satellite are very similar in terms of outline, shape, color, and texture, with the only difference being in brightness. It can be seen that the simulated image reasonably replicates the details and characteristics of the real image. The final dataset includes 3800 satellite images, comprising real images, simulated images, and synthetic images, with a diverse range of satellite types to ensure dataset diversity. These images were annotated at the pixel level using labeling 1.8.6 software. This paper categorizes the satellites into three typical parts, namely the solar panel, the main body, and the antenna (as shown in Figure 10). The annotation results are directly saved as XML files to facilitate the unification of the dataset into the PASCAL VOC format. This paper will use 200 real satellite images collected from institutions such as NASA for the final testing experiments to verify the generalization capability of the model. The remaining 3600 images will be used for training and evaluating the model. These images will be divided into training, validation, and test sets in a ratio of 7:2:1, resulting in 2520 images for the training set, 720 images for the validation set, and 360 images for the test set. Figure 8: Partial satellite images from the TSC-Dataset: (**a**) sourced from the Satellite-Dataset; (**b**) sourced from STK software; (**c**) obtained via web scraping; (**d**) sourced from NASA. Figure 9: Comparison between the real image (**a**) and simulated image (**b**). The TSC-Dataset covers various types of satellite components, including the antenna, the solar panel, and the main body. These components come from different types of satellites, such as communication satellites, Earth observation satellites, and navigation satellites. As shown in Figure 11, these satellites have similar visual characteristics. For example, the outlines of the solar panels are mostly rectangular, with colors primarily in blue or black. The outlines of the antennas are mostly circular, and their fronts are usually white.