In recent years, global automotive industries are going through a significant revolution from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) for CO₂ emission reduction. Very similarly, the aviation industry is developing towards more electric aircraft (MEA) in response to the reduction in global CO₂ emission. To promote this technology revolution and performance advancement, plenty of electronic devices with high heat flux are implemented on board automobiles and aircraft. To cope with the thermal challenges of electronics, in addition to developing wide bandgap (WBG) semiconductors with satisfactory electric and thermal performance, providing proper thermal management solutions may be a much more cost-effective way at present. This paper provides an overview of the thermal management technologies for electronics used in automobiles and aircraft. Meanwhile, the active methods include forced air cooling, indirect contact cold plate cooling, direct contact baseplate cooling, jet impingement, spray cooling, and so on. The passive methods include the use of various heat pipes and PCMs. The features, thermal performance, and development tendency of these active and passive thermal management technologies are reviewed in detail. Moreover, the environmental influences introduced by vibrations, shock, acceleration, and so on, on the thermal performance and reliability of the TMS are specially emphasized and discussed in detail, which are usually neglected in normal operating conditions. Eventually, the possible future directions are discussed, aiming to serve as a reference guide for engineers and promote the advancement of the next-generation electronics TMS in automobile and aircraft applications.

Leveraging space-based optical platforms for space debris and defunct spacecraft detection presents several advantages, including a wide detection range, immunity to cloud cover, and the ability to maintain continuous surveillance on space targets. As a result, it has become an essential approach for accomplishing tasks related to space situational awareness. However, the prediction of the orbits of space objects is crucial for the success of such missions, and current technologies face challenges related to accuracy, reliability, and practical efficiency. These challenges limit the performance of space-based optical space situational awareness systems. To drive progress in this field and establish a more effective and reliable space situational awareness system based on space optical platforms, this paper conducts a retrospective overview of research advancements in this area. It explores the research landscape of orbit determination methods, encompassing orbit association methods, initial orbit determination methods, and precise orbit determination methods, providing insights from international perspectives. The article concludes by highlighting key research areas, challenges, and future trends in current space situational awareness systems and orbit determination methods.

We propose a high accurate quantitative phase imaging (QPI) method by using transport of intensity equation (TIE) and wavelet transform (WT). TIE provides a simple and fast method for QPI, but its accuracy is always limited due to the nonlinear error and noise problems caused by the defocus distance. To improve the accuracy of the phase recovered by TIE, WT is introduced to combine with TIE under several defocus distances. With the help of the multiresolution characteristics of the WT, effective information can be extracted from the phases retrieved with TIE at different defocus distances. As a result, a more accurate phase can be obtained by fusing this effective information. Moreover, to extend the applicability of our method, the problems of phase discrepancy and phase singularity in TIE are discussed and solved with an iterative WT-TIE algorithm. Numerical simulations and experiments with various types of phase maps are presented to comprehensively demonstrate the accuracy and effectiveness of our method.

Measuring the pose of non-cooperative targets in space is a critical supporting technology for cleaning up space debris and recovering items. However, most existing methods are simulation experiments conducted in good lighting environments and tend to show poor performance in dark lighting environments. A target pose measurement method based on binocular vision is proposed, which is suitable for dark lighting environments. First, the traditional features from accelerated segment test algorithm are improved to reduce the influence of illumination on the performance of feature point extraction under various postures. The point feature and line feature are combined to extract image features more easily in a dark lighting environment while retaining the high accuracy of the pose measurement algorithm based on point features. Second, the normalized cross-correlation coefficient matching method is combined with the epipolar constraint to narrow the search range of the matching points from the two-dimensional plane to the epipolar line, which substantially improves the matching efficiency and accuracy of the matching algorithm. Finally, post-processing through feature matching is performed to reduce the probability of mismatches. Simulation and physical experiment results show that our method can stably extract features and obtain high-precision target pose information in well-illuminated as well as dark lighting environments, making it suitable for high-precision target pose measurement under insufficient illumination.

Accurate detection of non-cooperative spacecraft based on optical sensor data is essential for critical space tasks, such as on-orbit servicing, rendezvous and docking, and debris removal. Traditional object detection methods struggle in the challenging space environment, which includes extreme variations in lighting, occlusions, differences in image scale. To address this problem, this paper proposes a high-precision, deep-learning-based, domain-adaptive detection method specifically tailored for non-cooperative spacecraft. The proposed algorithm focuses on two key elements: dataset creation and network structure design. First, we develop a spacecraft image generation algorithm using CycleGAN, facilitating seamless conversion between synthetic and real spacecraft images to bridge domain differences. Second, we combine a domain-adversarial neural network with YOLOv5 to create a robust detection model based on multi-scale domain adaptation. This approach enhances the YOLOv5 network’s ability to learn domain-invariant features from both synthetic and real spacecraft images. The effectiveness of our high-precision domain-adaptive detection method is verified through extensive experimentation. This method enables several novel and significant space applications, such as space rendezvous and docking and on-orbit servicing.

Phase unwrapping is a challenging task due to the presence of noise in the wrapped phase map. This paper describes, to the best of our knowledge, a novel technique, termed as noisy pixels identifying criteria, for phase unwrapping to minimize fake phase jump errors caused by noise and phase residues to a minimum. Its characteristic lies in fully leveraging the information contained in fringe patterns that can accurately identify noise-contaminated pixels of the wrapped phase map by constructing a phase-shifting version of the wrapped phase map. Based on it, a locally selective adaptive filtering strategy is introduced where noise filtering is applied exclusively to the identified noisy pixels, thereby avoiding the blurring and detail loss commonly caused by global noise filtering. We must note that the window size of our filter can be dynamically adjusted based on the number of non-noisy pixels in the local neighborhood, and is automatically expanded when insufficient valid pixels are present. Moreover, we developed an explicit filtering termination criterion that can ensure both stability and efficiency of the filtering process when iteratively applying the proposed method to the noisy wrapped phase map. Finally, with the aid of the proposed noisy pixels identifying criteria and iterative adaptive filtering, the denoised wrapped phase is unwrapped correctly by the transport of intensity equation (TIE) based phase unwrapping algorithm. Both simulation and experimental results confirm that the proposed method achieves superior phase reconstruction accuracy and robustness, even under complex and noisy measurement conditions.