A fractionated spacecraft is an innovative application of a distributive space system. To fully understand the impact of various uncertainties on its development, launch and in-orbit operation, we use the stochastic missioncycle cost to comprehensively evaluate the survivability, flexibility, reliability and economy of the ways of dividing the various modules of the different configurations of fractionated spacecraft. We systematically describe its concept and then analyze its evaluation and optimal design method that exists during recent years and propose the stochastic missioncycle cost for comprehensive evaluation. We also establish the models of the costs such as module development, launch and deployment and the impacts of their uncertainties respectively. Finally, we carry out the Monte Carlo simulation of the complete missioncycle costs of various configurations of the fractionated spacecraft under various uncertainties and give and compare the probability density distribution and statistical characteristics of its stochastic missioncycle cost, using the two strategies of timing module replacement and non-timing module replacement. The simulation results verify the effectiveness of the comprehensive evaluation method and show that our evaluation method can comprehensively evaluate the adaptability of the fractionated spacecraft under different technical and mission conditions.

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.

In this paper, we investigate the multiple attribute decision making problems in which the attribute weights are usually correlative, attribute values take the form of linguistic variables, some new methods are developed. Then a new aggregation operator called induced generalized 2-tuple linguistic choquet ordered averaging (IG-2TCOA) operator is proposed, and some desirable properties of the IG-2TCOA operator are studied, such as idempotent, commutative, monotonic and bounded. An IG-2TCOA operators-based approach is developed to solve the MADM problems. Finally, an illustrative example for evaluating the performance of the mechanical products is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Various plants have the ability to follow the sun with their flowers or leaves via a mechanism known as heliotropism, which is powered by pressure gradients between neighboring motor cells. Adapting this bio-inspired mechanism, in this paper we present a novel origami-inspired pneumatic solar tracking system for a picosatellite named NPU-PhoneSat that is capable of solar tracking without altering the attitude of the NPU-PhoneSat. We give an overview of the system design and address the theoretical problem of modeling the origami-inspired pneumatic solar tracking system. The theoretical results are compared with the experimental data, demonstrating the validity of the proposed analytical model. Such understanding of soft solar trackers will allow their performance to be predicted, thus enabling their wide utilization in enhancing energy supply.

With the development of deep learning and super-resolution reconstruction, the performance of single-image super-resolution (SISR) has improved significantly. However, most of them cannot strike a great balance between computational cost and performance, which prevents them from being deployed on edge devices. Additionally, feature fusion and channel mixing are not considered in most lightweight networks, leading to the limited performance of these networks. To solve such problems, we propose a lightweight pyramid feature fusion network (PFFN), which mainly contains the pyramid spatial-adaptive feature extraction module (PSAFEM) and the enhanced channel fusion module (ECFM). They can extract global-to-local feature, build long-range dependence with small parameters increment and realize channel and spatial feature fusion. Finally, some state-of-the-art methods are utilized to compare with our network. Extensive experimental results indicate that our PFFN outperforms these methods in parameters, flops and performance.

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance.