Remote sensing satellites (RSS) are highly complex and customized from a common product family. It makes the traditional model-based system engineering (MBSE) method, which lacks architecture-level reusability, difficult to apply to their architecture design. Considering RSS has a relatively fixed common architecture from which the various design solutions are customized specific to different missions, the model-based product line engineering (MBPLE) methodology can be leveraged. In this paper, based on the analysis of the current RSS development process in practice and the main issues of current MBPLE methods, an RSS-specific MBPLE approach is proposed. Firstly, the RSS domain terminology is consolidated into the RSS-MBPLE SysML profile to support the construction of models in the design process. Then, the two key steps, i.e., architecture configuration and standalone product selection are efficiently conducted following the MBPLE principles supported by plugins of the mainstream SysML platform. Finally, a typical RSS control subsystem is illustrated as the case study to demonstrate the effectiveness of the proposed method. The results show that the proposed approach improves architecture-level reusability and design automation compared with traditional methodology, thereby reducing manual clone-and-modify efforts and enhancing the efficiency of RSS architecture design.

To address the attitude control problem under the uncertainty, external disturbance, and actuator failure, a predefined-time fault-tolerant control method based on a predefined time disturbance observer is proposed. First, the dynamics model of hypersonic morphing vehicle (HMV) is established, and the control system is designed as an outer-loop attitude angle control loop and an inner-loop angular rate control loop considering the actuator failure problem. Secondly, a predefined-time disturbance observer is designed to estimate the comprehensive disturbances, and compensate in the control law. By integrating back-stepping control with predefined-time theory, a predefined-time attitude tracking control method is proposed, enabling the convergence time of the attitude tracking error to be designed through a simple parameter. Rigorous Lyapunov function analysis has demonstrated that the attitude tracking error can converge to an arbitrarily small neighborhood around the origin within a predefined time, and all signals in the closed-loop system are bounded. Finally, comparative simulations validate the effectiveness of the proposed method.

The implementation of timely monitoring and preventive maintenance plays a fundamental role to ensure the reliable operation of complex systems. Condition-based maintenance strategy offers an effective means to leverage system remaining life information, enabling the application of targeted measures to reduce maintenance costs and elevate overall operational efficiency. This study delves into a performance degradation system affected by external random shocks, utilizing the Wiener process model to characterize the continuous degradation process. Within this framework, two distinct condition-based monitoring schemes are proposed: one is the real-time condition monitoring and the other is the dynamic periodic monitoring. Through the optimization of maintenance strategies for each scheme based on the long-term average cost, the study aims to optimize the preventive maintenance threshold for system failure. The Monte Carlo simulation algorithm is adopted to solve the optimization problem. Finally, a comprehensive numerical example is provided to validate the efficiency of both the models and the proposed maintenance strategies.

Integrating prognostics and health management (PHM) with the existing maintenance support system of systems plays an important role in implementing reliability-centered maintenance (RCM). However, the increasing complexity and integration of civil aircraft systems pose challenges for conventional document-based systems engineering (DBSE) practice. Aiming at the specific problems of poor modeling degree, weak traceability between problem and solution domains, and insufficient integration in civil aircraft PHM development, a model-based systems engineering (MBSE) approach is adopted to overcome the limitations of DBSE method. This paper proposes a structured integrated modeling method to facilitate PHM functional integration with other aircraft systems. An MBSE modeling method based on traceable requirements, functional, logical, and physical models, is applied in the integration process. Additionally, a multi-viewpoint analysis method within the Department of Defense Architecture Framework (DoDAF) is introduced to illustrate the modeling elements and processes from a multi-dimensional perspective. The modeling logic and architecture are subsequently presented, followed by examples of requirements, functional flows, and resource flows models using the systems modeling language (SysML). Finally, a preliminary logic simulation verification is conducted as a case study of typical PHM functional integration with maintenance support. The case study results demonstrate that the proposed method enhances information traceability and consistency, which can offer theoretical support and technical reference for the development of maintenance support system.

The rapid growth in satellite and aerial remote sensing platforms has created a growing need for distributed remote sensing scene classification. Conventional centralized scene classification methods, which involve transmitting remote sensing data to a ground station for processing, encounter limitations in both transmission efficiency and data privacy. Federated learning (FL) has emerged as a promising approach by enabling terminals to collaboratively train models without exchanging raw data. However, the non-independent and identically distributed (Non-IID) nature of remote sensing data significantly impedes FL performance. To address these challenges, a federated framework with feature distillation (FD) (FedFD) is proposed for FL-based remote sensing scene classification. Specifically, FedFD facilitates collaborative training by aggregating model parameters from multiple terminals to the cloud, thereby optimizing a global model. To further alleviate the impact of Non-IID data, an innovative partial feature-sharing strategy based on FD is designed, which divides features into globally shared essential features and locally maintained supplementary features. Moreover, to cope with object and scene scale variation, the squeeze and excitation module and the pyramid pooling module are incorporated into the scene classification network to enhance multiscale feature extraction. Extensive experiments on the Northwestern Polytechical University Remote Sensing Image Scene Classification 45 (NWPU-RESISC45) dataset and University of California, Merced Land Use (UC-Merced) dataset, under varying numbers of terminals and Non-IID levels, validate the effectiveness and scalability of FedFD, and demonstrate its superior performance in FL-based remote sensing scene classification.

Existing weapon portfolio selection methods do not sufficiently support specific combat tasks, with uncertainty in the decision information. Therefore, a combat task–oriented weapon portfolio selection method that adapts weapon capabilities to combat tasks is proposed. The approach is based on specific combat tasks and weapon background, using fuzzy interval values to describe indicators and the applicability of a weapon portfolio. In addition, an interval entropy weighting method is applied to obtain weight information of indicators. Meanwhile, we define the similarity measure of fuzzy interval values and use the interval fuzzy collaborative filtering algorithm to calculate the fitness of the residual weapons. Furthermore, the interval fuzzy set clustering algorithm clusters the tasks to inform the decision of weapon portfolio. Finally, we verify the method’s feasibility and advancement by comparing actual combat tasks as examples with the traditional methods. The contributions of this paper include improvements to the accuracy and reliability of decision-making from the perspective of adapting weapon capability to combat tasks. At the same time, this paper accounts for the method’s shortcomings by considering the hesitancy and ambiguity of the indicator data.

This research proposes an odd-even dimension RUNge Kutta algorithm (ODRUN) to solve global optimization and a well-known NP-hard problem in inventory management. The rpoposed algorithm integrates odd-even dimensional, fourth-order Runge-Kutta method, and neighbor search strategies. This hybrid approach significantly improves population diversity, avoids local optima, and enhances convergence accuracy. To validate the performance of the proposed algorithm, a widely recognized benchmark function suit from CEC2022 is first employed. Results confirm that ODRUN achieves an overall effectiveness ratio of 66.67% across three statistical indicators (best, mean, and standard deviation) for 12 benchmark functions. The test shows this algorithm is ranked first compared to seven state-of-the-art metaheuristic algorithms. Furthermore, ODRUN is applied to the joint replenishment problem with imperfect items and trade credit. Numerical examples from 600 randomly generated large-scale instances highlight that the algorithm’s performance remains unaffected by an increase in problem scale. The significant cost savings brought by the ODRUN algorithm, with the maximum improvement ratio in average cost and best-found total cost ranging from 14.81% to 19.5%, are achieved in comparison to other algorithms. In conclusion, ODRUN is an effective and robust tool for complex optimization problems.

A millimeter-wave (mm-Wave) dual circularly polarized (CP) antenna in gap waveguide (GWG) technology with high port isolation is proposed in this paper. It is consisted of a simplified orthomode transducer (OMT) and an improved multi-section hexagonal waveguide CP horn antenna. The OMT is composed of two metal layers without the traditional septum or iris, which makes the structure simpler. The CP horn antenna can be easily integrated with the OMT without mode conversion. The principle analysis as well as the simulated and measured results of the proposed antenna are given in this paper. The simulated and measured results agree very well with each other. The port isolation of more than 27 dB over bandwidth of 26.5−31 GHz (|S₁₁|< −15 dB) is achieved with relative bandwidth of 15.7%. The axial ratio (AR) lower than 2.5 dB for both left-hand and right-hand CP (LHCP and RHCP) are achieved over the bandwidth. The proposed antenna is a candidate for mm-Wave satellite communications or beyond fifth-generation (5G) communications applications.

Validation for simulation models often confronts challenges with small samples due to the costs of time and money. To address this issue, this paper presents a validation method for small-sample dynamic outputs based on Gaussian process regression (GPR) models. Firstly, a validation framework based on Bayes statistics is proposed, shifting the focus from merely analyzing validation data to a more comprehensive analysis of posterior distributions. Subsequently, the posterior distributions of both the simulation outputs and the reference data are separately captured through segmented GPR. Then, the consistency of these posterior distributions is evaluated in terms of the central tendency and the distribution range. This consistency serves as a quantitative measure of the simulation model’s credibility, expressed as a value ranging from 0 to 1, where a value closer to 1 indicates higher credibility. Finally, the effectiveness of this validation method is demonstrated through a numerical example and an application example, highlighting its capability in uncertainty description and adaptability to small samples.

Accurately sensing the channel state of heterogeneous networks is key to matching users’ diverse service communication demands with the channel state, and is an effective way to improve the utilization efficiency of network resource. However, existing channel state perception methods are not suitable for heterogeneous network, and their perception performance is easily affected by interference uncertainty. In order to achieve channel state perception of heterogeneous networks, this paper adopts a centralized collaborative perception model, where each node obtains local channel state perception results based on statistical pulse parameters at the physical layer. In order to reduce the impact of interference on perception performance, this paper uses the Jousselme distance to quantify the degree of difference among nodes caused by interference. Using the average credibility as a threshold, nodes in the sensing area are classified. On this basis, the local perception results of each node are performed classification-based correction to improve the accuracy and reliability of channel state perception. Simulation results indicate that the proposed method has good adaptability for channel state perception in complex electromagnetic environments. The perception results can accurately reflect the actual channel state, which is conducive to improving the network throughput.