The recent advancements in the field of satellite and space technology have demonstrated significant progress in addressing critical challenges related to security, image processing, and system reliability. Innovations in ransomware infection vectors for satellites have introduced novel exploit paths, highlighting vulnerabilities in non-terrestrial networks that require immediate attention. In the realm of image processing, models leveraging physical-prior-knowledge and multi-task learning have shown remarkable improvements in nighttime depth estimation and underwater image enhancement, respectively. These approaches not only enhance the quality of images but also improve the accuracy of object detection tasks, which is crucial for various applications. Additionally, the integration of deep learning with physical models has led to advancements in deblurring and low-light image enhancement, addressing common issues in nighttime photography. On the system reliability front, fault-tolerant flight software designs for small satellites have been proposed, ensuring efficient parallel processing and system reliability. Furthermore, the use of multi-task and transfer learning in satellite imagery masking has significantly improved accuracy and computational efficiency, benefiting remote sensing applications. Lastly, deep learning models incorporating vision transformers have shown promise in satellite object detection, enhancing space safety and sustainability. These developments collectively underscore the field's movement towards more robust, efficient, and secure systems.