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Due to the absence of communication and coordination with external spacecraft, non-cooperative spacecraft present challenges for the servicing spacecraft in acquiring information about their pose and location. The accurate segmentation of non-cooperative spacecraft components in images is a crucial step in autonomously sensing the pose of non-cooperative spacecraft. This paper presents a novel overlay accelerator of DeepLab Convolutional Neural Networks (CNNs) for spacecraft image segmentation on a FPGA. First, several software–hardware co-design aspects are investigated: (1) A CNNs-domain COD instruction set (Control, Operation, Data Transfer) is presented based on a Load–Store architecture to enable the implementation of accelerator overlays. (2) An RTL-based prototype accelerator is developed for the COD instruction set. The accelerator incorporates dedicated units for instruction decoding and dispatch, scheduling, memory management, and operation execution. (3) A compiler is designed that leverages tiling and operation fusion techniques to optimize the execution of CNNs, generating binary instructions for the optimized operations. Our accelerator is implemented on a Xilinx Virtex-7 XC7VX690T FPGA at 200 MHz. Experiments demonstrate that with INT16 quantization our accelerator achieves an accuracy (mIoU) of 77.84%, experiencing only a 0.2% degradation compared to that of the original fully precision model, in accelerating the segmentation model of DeepLabv3+ ResNet18 on the spacecraft component images (SCIs) dataset. The accelerator boasts a performance of 184.19 GOPS/s and a computational efficiency (Runtime Throughput/Theoretical Roof Throughput) of 88.72%. Compared to previous work, our accelerator improves performance by 1.5× and computational efficiency by 43.93%, all while consuming similar hardware resources. Additionally, in terms of instruction encoding, our instructions reduce the size by 1.5× to 49× when compiling the same model compared to previous work.

To assemble spacecraft automatically and precisely, it is vital to measure the relative spatial pose (position and orientation) of the assembly features of the spacecraft components before assembly. For large-scale spacecraft components, the global measurement method is mainly utilized to guide assembly control, and its accuracy and efficiency have ultimately failed to meet requirements. To address this issue, a novel measurement method is proposed. Since the goal is to measure the relative spatial pose of the assembly features of the spacecraft components, the proposed method measures it directly to ensure the consistency of the measurement and assembly coordinate system. This method has the advantage of high precision because it can reduce the influence of structural parameter errors and is not limited by the scale of the spacecraft components. In addition, it requires only one offline calibration, which significantly improves the efficiency of online measurement and assembly. Taking the control moment gyroscope (CMG) assembly task as an example, a measurement system and its corresponding calibration device are designed and developed. After calibration by the calibration device, the measurement system is mounted on the assembly features of the CMG to measure the relative spatial pose between the assembly features of the CMG and the assembly features of the mounted base (MB). Finally, six assembly experiments are completed according to the measurement results. The experimental results show that this method has high accuracy and can guide the robot to achieve high assembly accuracy, satisfying the assembly requirements of typical spacecraft components.

Spacecraft component detection is essential for space missions, such as for rendezvous and on-orbit assembly. Traditional intelligent detection algorithms suffer from drawbacks related to high computational burden, and are not applicable for on-board use. This paper proposes a convolutional neural network (CNN)-based lightweight algorithm for spacecraft component detection. A lightweight approach based on the Ghost module and channel compression is first presented to decrease the amount of processing and data storage required by the detection algorithm. To improve feature extraction, we analyze the characteristics of spacecraft imagery, and multi-head self-attention is used. In addition, a weighted bidirectional feature pyramid network is incorporated into the algorithm to increase precision. Numerical simulations show that the proposed method can drastically reduce the computational overhead while still guaranteeing good detection precision.

Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points.

Thermal vision cameras detect differences in temperature by sensing infrared wavelengths. If a coating could be developed that showed dynamic tuning of the effective temperature, it might be possible to hide objects from infrared sensing. Xu et al. started with a basic Bragg reflector made up of multiple layers of alternating materials with varying refractive index. The authors designed structures that were wavy to begin with so that they could be flattened out by electrical activation. This changed the infrared reflectivity and, thus, the effective temperature of the object observed in its infrared profile. Science , this issue p. 1495 Wrinkled multilayer structures underpin electrically actuated infrared-reflecting coating and display systems. Materials and systems that statically reflect radiation in the infrared region of the electromagnetic spectrum underpin the performance of many entrenched technologies, including building insulation, energy-conserving windows, spacecraft components, electronics shielding, container packaging, protective clothing, and camouflage platforms. The development of their adaptive variants, in which the infrared-reflecting properties dynamically change in response to external stimuli, has emerged as an important unmet scientific challenge. By drawing inspiration from cephalopod skin, we developed adaptive infrared-reflecting platforms that feature a simple actuation mechanism, low working temperature, tunable spectral range, weak angular dependence, fast response, stability to repeated cycling, amenability to patterning and multiplexing, autonomous operation, robust mechanical properties, and straightforward manufacturability. Our findings may open opportunities for infrared camouflage and other technologies that regulate infrared radiation.

The components with soft nature in the metal halide perovskite absorber usually generate lead (Pb)⁰ and iodine (I)⁰ defects during device fabrication and operation. These defects serve as not only recombination centers to deteriorate device efficiency but also degradation initiators to hamper device lifetimes. We show that the europium ion pair Eu3+-Eu2+ acts as the “redox shuttle” that selectively oxidized Pb⁰ and reduced I⁰ defects simultaneously in a cyclical transition. The resultant device achieves a power conversion efficiency (PCE) of 21.52% (certified 20.52%) with substantially improved long-term durability. The devices retained 92% and 89% of the peak PCE under 1-sun continuous illumination or heating at 85°C for 1500 hours and 91% of the original stable PCE after maximum power point tracking for 500 hours, respectively.

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

This study examines how a spacecraft reacts to constant body-fixed torques and a gyrostatic moment (GM), as well as the impact of energy dissipation. The spacecraft model being studied includes a spherical slug near the center of mass covered by a viscid layer. The problem’s difficulty lies in solving its governing equations of motion (EOMs), which are derived through Euler nonlinear equations. Understanding the behavior of this model can offer insights into how spacecraft respond to external torques, aiding in the development of more efficient and stable systems for aerospace and robotics applications. The research delves into the relationship between energy dissipation and GM on the spacecraft motion in three different scenarios involving constant torques around three various axes. Detailed analysis, as well as novel solution and simulation results, are presented for different energy dissipation possibilities. The influence of manipulating the value of the GM and the viscosity of the layer has been approached. These findings are crucial for comprehending, maintaining, and controlling the motion of spacecraft influenced by external forces in space. The study promises to have a significant impact on the aerospace industry, particularly in the design and operation of spaceships and satellites, by enhancing our knowledge of rotational motion and celestial bodies’ behavior. A comprehensive report will be produced to elucidate the complexities of rotational and orbital motion discovered during this research.

Space exploration requires the use of suitable materials to protect astronauts and structures from the hazardous effects of radiation, in particular, ionizing radiation, which is ubiquitous in the hostile space environment. In this scenario, polymer-based materials and composites play a crucial role in achieving effective radiation shielding while providing low-weight and tailored mechanical properties to spacecraft components. This work provides an overview of the latest developments and challenges in polymer-based materials designed for radiation-shielding applications in space. Recent advances in terms of both experimental and numerical studies are discussed. Different approaches to enhancing the radiation-shielding performance are reported, such as integrating various types of nanofillers within polymer matrices and optimizing the materials design. Furthermore, this review explores the challenges in developing multifunctional materials that are able to provide radiation protection. By summarizing the state-of-the-art research and identifying emerging trends, this review aims to contribute to the ongoing efforts to identify polymer materials and composites that are most useful to protect human health and spacecraft performance in the harsh radiation conditions that are typically found during missions in space.

This review article provides an exhaustive survey on experimental investigations regarding the thermal stability assessment of polymers and polymer-based composites intended for applications in the aeronautical and space fields. This review aims to: (1) come up with a systematic and critical overview of the state-of-the-art knowledge and research on the thermal stability of various polymers and composites, such as polyimides, epoxy composites, and carbon-filled composites; (2) identify the key factors, mechanisms, methods, and challenges that affect the thermal stability of polymers and composites, such as the temperature, radiation, oxygen, and degradation; (3) highlight the current and potential applications, benefits, limitations, and opportunities of polymers and composites with high thermal stability, such as thermal control, structural reinforcement, protection, and energy conversion; (4) give a glimpse of future research directions by providing indications for improving the thermal stability of polymers and composites, such as novel materials, hybrid composites, smart materials, and advanced processing methods. In this context, thermal analysis plays a crucial role in the development of polyimide-based materials for the radiation shielding of space solar cells or spacecraft components. The main strategies that have been explored to improve the processability, optical transparency, and radiation resistance of polyimide-based materials without compromising their thermal stability are highlighted. The combination of different types of polyimides, such as linear and hyperbranched, as well as the incorporation of bulky pendant groups, are reported as routes for improving the mechanical behavior and optical transparency while retaining the thermal stability and radiation shielding properties. Furthermore, the thermal stability of polymer/carbon nanocomposites is discussed with particular reference to the role of the filler in radiation monitoring systems and electromagnetic interference shielding in the space environment. Finally, the thermal stability of epoxy-based composites and how it is influenced by the type and content of epoxy resin, curing agent, degree of cross-linking, and the addition of fillers or modifiers are critically reviewed. Some studies have reported that incorporating mesoporous silica micro-filler or microencapsulated phase change materials (MPCM) into epoxy resin can enhance its thermal stability and mechanical properties. The mesoporous silica composite exhibited the highest glass transition temperature and activation energy for thermal degradation among all the epoxy-silica nano/micro-composites. Indeed, an average activation energy value of 148.86 kJ/mol was recorded for the thermal degradation of unfilled epoxy resin. The maximum activation energy range was instead recorded for composites loaded with mesoporous microsilica. The EMC-5p50 sample showed the highest mean value of 217.6 kJ/mol. This remarkable enhancement was ascribed to the polymer invading the silica pores and forging formidable interfacial bonds.