# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 1 Introduction  
With the advancement of space technology and increasing human activity in space, numerous satellites with diverse purposes are continuously being launched into the geosynchronous orbit. This surge in satellite deployment has led to an alarming proliferation of space debris and defunct satellites encircling the Earth. Among this debris, defunct satellites act as non-cooperative targets and pose a significant threat to the safety of operational satellites. Therefore, it is both urgent and imperative to achieve the on-orbit capture of these non-functional satellites to mitigate safety risks for existing active satellites [1; 2; 3]. A crucial preliminary step towards this goal involves conducting a comprehensive survey of the space environment to detect key components of satellites. In recent years, optical imaging devices have gained widespread usage, significantly enhancing the observational capabilities of satellites towards surrounding objects. Consequently, optical-based object detection methods have garnered substantial research attention and practical significance [4]. Common satellite parts, such as solar panels, satellite bodies, and radar transmitters, are notably of varying sizes and geometries, which makes their optical characteristics easily discernible. In addition to optical imaging, the terahertz (THz) band also plays an important role in the context of space surveillance. The terahertz wave occupies the electromagnetic spectrum between microwaves and infrared light, providing unique capabilities to remote sensing devices. Theoretically, radiation can penetrate various materials, including clothing, plastics, and non-metallic objects, making it potentially useful for detecting components hidden or embedded within satellites or debris. Additionally, terahertz imaging systemscan operate in harsh weather conditions and provide continuous monitoring capabilities, even in challenging environments [5]. In recent years, with the continuous advancement of deep learning, neural networks have emerged as the predominant approach in the field of object detection [6]. The application in the aerospace domain holds the potential to expedite the intelligent evolution of space situational awareness [7; 8; 9]. However, the formidable cost and complexity associated with space data collection have led to the generation of most space target datasets through model simulations, overlooking the intricacies of real-space imaging [10]. In the space environment, targets are situated amidst cluttered backgrounds and are subject to various influences such as cosmic rays [11], solar storms [12], and thermal noise from electronic devices [13]. Given that neural networks rely on data, the absence of a substantial corpus of genuine on-orbit images renders current detection methods challenging to apply to real on-orbit capture tasks [14; 15]. Consequently, it is imperative to establish a unified satellite dataset for key component detection that accurately simulates space capture scenarios [16]. In real-world scenarios, imaging effects are constrained by three crucial factors, namely illuminance, motion states, and occlusions. Firstly, in terms of illuminance, the continuous variation in incident sunlight and other sources of stray light might result in phenomena such as overexposure and low illumination, which directly impact the quality of captured images. Secondly, motion states introduce additional complexity due to the continuous rotation of malfunctioning satellites during scene capture, which usually results in a diverse range of apparent information being recorded. Meanwhile, when capturing images from distant scenes, key components often appear as small-sized objects that require finer-grained detection abilities [17]. Furthermore, when the motion state of the observing satellite changes, certain key components obstruct others as the observing satellite orbits around the malfunctioning target satellite.