In the context of an increasingly complex and congested space debris environment, the development of high-precision long-term orbit prediction models has become a core technology for space situational awareness and space traffic management. Traditional orbit prediction methods often struggle to achieve an ideal balance between accuracy and computational efficiency. While deep learning-based orbit prediction approaches have demonstrated promising potential, they commonly face challenges related to high model complexity. This paper analyzes the long-term orbital prediction problem from the perspective of time series forecasting. Specifically, a dataset framework is first constructed, based on real high-precision orbital data, which incorporates temporal periodic features. Then, a novel model combining the Kolmogorov-Arnold Network (KAN) structure with a Gated Recurrent Unit (GRU) is proposed, named KiGRU, with the goal of enhancing the performance of orbital prediction. Experimental results show that the proposed KiGRU model outperforms existing methods in terms of prediction accuracy and achieves a good balance between model complexity and performance, making it more suitable for practical applications. Furthermore, this study demonstrates the effectiveness of integrating the KAN structure into traditional deep learning models to enhance their performance.

As space activities expand, the quantity of space debris also increases, posing significant risks to spacecraft and infrastructure. Space situational awareness (SSA) is essential for avoiding collisions and limiting the generation of extra debris. Accurate and efficient detection of space objects plays a critical role in achieving this goal. Our research focuses on the development of detection algorithms that are both precise and quick, taking into account the real-time and safety of spacecraft operations in orbit. For the first time, we take a fully Convolutional Neural Networks (ConvNets) to run the query-based end-to-end object detection for SSA. We further compare its performance with the newest YOLOv9 algorithm. This is an innovative attempt at SSA. First of all, it does not require predefined a priori anchor boxes or complex post-processing strategies such as Non-Maximum Suppression (NMS), and can directly achieve end-to-end target detection. Secondly, the fully ConvNets are selected as the basic framework, which not only retains the advantages of self-attention mechanism, but also greatly improves the computing efficiency. These methods show outstanding performance on the challenging SPARK data set. The fully ConvNets approach achieves end-to-end detection by utilizing the query attention mechanism, excluding the need for complicated post-processing in traditional object detection methods and with higher efficiency. YOLOv9 involves an enhanced feature pyramid fusion and a more powerful detection head, potentially resulting in higher precision. Following that, we will thoroughly assess the speed, accuracy, and trade-offs of the two algorithms using actual data sets in order to deliver an efficient and dependable solution for detecting targets in aerospace sensing missions.

Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions. However, the content these models hallucinate is necessarily inauthentic, since they are unaware of the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. Project page: https://realfill.github.io.