To address the energy consumption issues caused by task lengths in task scheduling on heterogeneous multi-core systems, this paper proposes an adaptive parameterized improved simulated annealing algorithm based on the directed acyclic graph task model. The algorithm employs feedback from acceptance rates to dynamically adjust the temperature and neighborhood size of the simulated annealing process. Additionally, it introduces a security mechanism to enhance convergence speed and global search capabilities. Compared against classical simulated annealing and standard heuristic algorithms, the proposed algorithm achieves reductions exceeding 54% in convergence generations, 50% in task slots, and 10% in scheduling time, providing a direction for low-power task scheduling.

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.

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.

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.

To predict the ranking of the country’s innovation capability in the world in real-time, this study designs a two-phased prediction model based on the pairwise comparison. Data from the global innovation index (GII) reports are employed in this study. Countries with different income levels have shown different development inertias, the two-phased prediction model is thus proposed. In the first phase, the GII data from the previous year are applied to predict the ranking of innovation capability for high-income countries. In the second phase, more years of historical data are adopted to predict the innovation ranking for other countries. The global innovation rankings for all countries and economies are thus obtained. Experiments have proved that the model requires only a few indicators to get accurate results. The model provides real-time decision support for decision-makers to formulate innovative development policies.

We propose a deep-learning-assisted strapdown inertial navigation system (SINS)/refraction celestial navigation system (RCNS) integrated navigation method to control the adverse effects of atmospheric density errors on the accuracy of stellar refraction navigation and enhance the reliability of SINS/RCNS integrated navigation for aerospace vehicles. This method utilizes satellite navigation data and a long short-term memory network to establish a mapping relationship between the navigation moments, refraction angles, and the apparent height errors. Using deep learning algorithm to address complex time-series prediction problems, thereby compensates the impact of atmospheric density deviations on star sensor measurements. Simulation experiments of vehicle navigation in scenarios with atmospheric density errors are conducted using this method. The results show that the deep learning scheme can effectively resist the adverse effects of atmospheric density errors on navigation, demonstrating strong reliability.

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.

Lyapunov-based model predictive control (LMPC) is an effective approach for trajectory tracking because of its well-guaranteed and easy-to-implement stability. However, traditional LMPC utilizes pre-designed auxiliary controllers to estimate the domain of attraction (DOA) and construct stability constraints, which inevitably reduces its stable domain and degrades tracking performance. For this problem, this paper proposes a relaxed LMPC (RLMPC) which is designed independently of auxiliary controllers. The control Lyapunov function (CLF) is firstly introduced to decouple the DOA and auxiliary control, alleviating the conservatism in traditional LMPC. Subsequently, a multi-resolution sampling-based search algorithm is developed to estimate the DOA, where the state space is partitioned into hyper-rectangles. A verification condition is derived to extend the verification validity of sampling points to all states within hyper-rectangles, thereby reducing DOA estimation error. Based on the auxiliary-controller-independent DOA (ACI-DOA) and CLF, stability constraints are formulated to ensure stability for RLMPC, while relaxing the stable domain of RLMPC to the entire ACI-DOA. Furthermore, a convergence rate adaptive adjustment technology is developed to enhance the convergence rate while balancing it with control effort. Through numerical simulations involving asteroid orbiting missions, the proposed method is found to significantly expand the stable domain and improve tracking performance.

To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios, this paper proposes a multidimensional intention fusion-based inference method. Firstly, the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’ theorem. Secondly, the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions, and the corresponding likelihood probability is obtained by quantifying the cost of different intentions, which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving no-fly zones. Finally, a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints. The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods.

Resolving conflict and achieving consensus among social groups with diverse opinions becomes a critical issue in today’s extensively connected society. Despite the ubiquitous heterogeneity of connection or contact patterns, the study of how topological characteristics of network structure affect opinion convergence is still insufficient. Based on Deffuant and colleagues’ bounded confidence model and the transformable network structure between random network and typical complex network types, including small-world network and scale-free network, we analyze the critical factors affecting continuous opinion convergence. We find that the network density plays a crucial role in the aggregated process of opinions in the social group, followed by the modularized level and the average shortest path length of the social network. However, the structural features have little impact on the consensus phase transition threshold. The further simulation experiments under real networks can be well understood based on the interplay of these three main factors. These findings confirm the paramount importance of creating a high-frequency and widely communicated atmosphere to mitigate conflict and efficiently reach consensus.

This paper investigates the six degree-of-freedom (6DOF) relative kinodynamic motion planning problem for spacecraft close approach operations, wherein a controlled chaser spacecraft is required to approach a noncooperative space target at a close range under both dynamic constraints and motion constraints. An enhanced version of the bidirectional rapidly-exploring random tree* (BiRRT*) algorithm based on flight zoning (FZ-BiRRT*) is proposed to generate safe, feasible, and near-optimal relative motion trajectories. In the proposed algorithm, the space surrounding the space target is zoned in a spherical coordinate system based on the collision probability so that specific designs can be made for different phases of the approaching. Subsequently, based on the flight zone, dynamic constraints, and experiential knowledge, a series of modifications are made to the classic BiRRT* algorithm, and a postprocessing step is designed to accelerate convergence and promote search efficiency. Furthermore, a general regression neural network is introduced to fit a smooth and applicable final motion trajectory. Finally, the feasibility of the generated motion trajectory and the superiority of the proposed algorithm is demonstrated by means of numerical simulations

To overcome the limitations of traditional force aggregation methods, this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer (SATC-ALO) and the self-organizing map (SOM) network. The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences, adaptive tent chaos search, tournament selection, and logistic chaos sequences. Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network. The model is resolved using SATC-ALO and SOM independently, with experiments demonstrating that SOM achieves fast and accurate grouping, while SATC-ALO offers higher precision but requires longer computational runtime, making it more suitable for hybrid approaches. Both methods are validated as practical solutions for force aggregation tasks.

Agile imaging satellites play important roles in gathering Earth surface information due to their expansive field-of-view, high-resolution observations, and flexibility in observation timing. Genetic algorithm (GA) has been one of the most commonly utilized meta-heuristic algorithms for solving multiple agile satellites (MAS) scheduling problems due to its stable and good performance. Nonetheless, the iterative convergence speed of GA is slow owing to the blindness of the preliminary search. To address this issue, this paper proposes an efficient meta-heuristic MAS scheduling method integrated with a neural network (NN). In the proposed method, an NN is applied to learn the historical scheduling experience and generate effective initial solutions, thereby improving the convergence speed of GA for MAS scheduling. Experimental results demonstrate that the proposed NN-guided GA (NN-GA) accelerates the convergence of GA by approximately 60% and exhibits higher efficiency for MAS scheduling compared to existing state-of-the-art methods.

In order to provide low Earth orbit (LEO) satellite network survivability with limited resources, and effectively respond to the risk of on orbit operation, a survivable routing algorithm based on multi-agent reinforcement learning is proposed. The reinforcement survivable routing establishes the reward function from the perspective of network utility, and describes the improvement of survivability in the failure scenario as a joint optimization problem, which solves the online routing decision-making problems after LEO satellite network failure. Each cognitive user agent and satellite agent can independently determine the routing strategy for communication missions according to the time slot topology, node survival status, storage space and other environment information of the satellite network. The simulation results show that compared to the weighted semi-distributed routing algorithm (WSDRA) and Fibonacci multipath load balancing (FMLB) algorithm, the proposed multi-agent double deep Q-learning network (MADDQN) reinforcement survivable routing algorithm can adapt to satellite failure and topology changes, and improve network utility by 14% and 9% respectively in human attack scenario, and by 17% and 13% respectively in random failure scenario.

This paper proposes a unified dynamic modelling method for the rigid-flexible coupled robots that have variable topology. Furthermore, a global simulation method covering all the phases during the transformation from one topology to the another one, is also put forward. The proposed modelling and simulation method can be applied for both single and multi-robots transformation for vast on-orbit manipulation missions. First, a configuration parameter is presented that contains vital information on the topology configuration. An autonomous generation method of topology configuration description matrix for multi-arm spacecraft is also proposed. Secondly, a unified recursive kinematic modelling method and a general rigid-flexible coupling dynamic modelling methodology based on spatial operator algebra are described using configuration parameters. Then, a global simulation method that can be applied in both single and multi-robots operation scenario is presented to deal with abrupt changes in the state of motion. Finally, several topology simulations for rigid-flexible coupling multi-arm space robots are presented to verify the proposed method. These simulation results show that the methods can deal with several different topology scenarios while avoiding constraint violation problems.

The dramatic rise in vehicle numbers and the development of autonomous driving technology have underscored the importance of accurate environment perception to prevent traffic accidents. Due to its distinctive advantages, millimeter-wave radar has emerged as the dominant type of vehicle-mounted sensor. However, frequency band for automotive radar is restricted to 76−81 GHz, resulting in insufficient spectrum resources because of the high-resolution requirement. The rapid increase of radars in the actual traffic environment will inevitably lead to mutual interference between radars, posing a potential threat to public transportation. To address this problem, this paper investigates the mutual interference of automotive frequency modulated continuous wave (FMCW) radars, and mitigates the mutual interference utilizing a low-rank tensor model. By stacking the short-time Fourier transform (STFT) matrix of all chirps in a single channel as tensors, a tensor robust principal component analysis (TRPCA) algorithm is employed to separate the target signal and the interference. Mutual interference of multiple input multiple output (MIMO) radar could be suppressed by applying TRPCA in all channels. Both simulations and numerical experiments demonstrate that the proposed method achieves higher signal to interference plus noise ratio (SINR) and lower root mean square error (RMSE) compared with other methods, thereby proving its superior interference suppression capability in different practical scenarios.

To address the significant overhead of the beam training in millimeter wave (mmWave) wireless communications, we propose a balanced multimodal fusion network with bi-directional enhancement in the previous work. It can predict the optimal mmWave beam according to the mmWave wide beam and sub-6 GHz channel state information. Utilizing the interaction and complementarity between different modes can improve the accuracy and robustness of predictions compared to the single mode (mmWave only or sub-6 GHz only). However, the feature extraction of one modality can potentially impact other modalities via parameter backpropagation. Although multimodal models perform better than unimodal models, they may be underutilized. To solve the problem, the adaptive multimodal gradient (AMMG) optimization method is designed to update the gradient of per feature extraction branch adaptively in this paper. Furthermore, a multimodal bi-directional enhancement (MBdE) block is proposed to combine features of other modalities, enhancing the complementarity of mmWave modality and sub-6 GHz modality. As revealed by the numerical results, it is evident that the proposed scheme delivers more precise outcomes with lower overhead compared to conventional and modern deep learning methods.

This paper addresses the problem of designing distributed cooperative guidance laws for multiple missiles in three-dimensional (3D) space, considering the constraint of simultaneous impact and fully intermittent communication. The problem is structured using a 3D nonlinear mathematical model along with a topology network for interaction. By employing the relative distance and velocity leading angle as coordination variables, a novel finite-time cooperative guidance law is developed through an event-triggered strategy, which eliminates the need for time-to-go estimation. The law’s convergence is demonstrated through finite-time Lyapunov stability theory. Additionally, the guidance approach also eliminates Zeno behavior, which refers to infinite communication instances within a finite time interval, ensuring fully intermittent communication among the missiles. To prevent singularity caused by non-zero velocity leading angles in cooperative guidance, the proportional navigation guidance (PNG) law is applied when coordination variables reach consensus and missiles are nearing the target, which can guarantee that all missiles strike the target simultaneously. Simulation results verify that the developed law achieves impact time correction within a finite duration while maintaining fully intermittent communication throughout the process.

With the continuous improvement of 5G network infrastructure, the informatization process in the industrial sector is accelerating at an unprecedented pace. The market demand for gateways integrating 5G networks with multi-access edge computing (MEC) technology is growing increasingly strong. There is also a rising requirement for these gateways to possess capabilities such as high reliability, large bandwidth, fast processing speed, and localized data processing. This paper, in response to the practical management needs of photovoltaic power stations and industrial fields, proposes the design of a 5G artificial intelligence (AI) edge computing intelligent gateway with dual 5G modules. The design is based on the i.MX8M Plus processor and integrates dual 5G module design. It supports multiple communication modes, including 5G, Wi-Fi, Ethernet, and serial ports. The software incorporates edge computing scripts, various communication protocols, and a lightweight AI model, enabling real-time data acquisition and processing at the edge. It supports for several industrial standard protocols ultra-low latency acquisition, and intelligent operation features like remote over-the-air upgrade, secure shell secure operation, and configuration hot updates. The system was tested and validated in a typical photovoltaic application scenario. The results demonstrate that, compared with the traditional single-module Internet of Things gateways, the dual 5G AI edge gateway proposed in this paper reduces end-to-end latency by more than 70%, improves data reliability tenfold, enhances bandwidth by 100%–200%, extends coverage capability by 50%, effectively alleviates the bandwidth pressure on the core network, and enhances the overall reliability and security of the system.

Imagery modeling for microwave engineering generally requires a large number of training samples to generate images. In this paper, a variational autoencoder (VAE) with the multilayer perceptron (MLP) is proposed for efficient imagery modeling of electromagnetic behaviors of the microwave antenna and component. The input of VAE is the randomly generated binary images of a physical structure, and VAE is an unsupervised learning scheme. The training of MLP needs the labeled samples of the latent representation of VAE and the electromagnetic responses. Compared with supervised imagery modeling, VAE generates a large number of low-cost images applied to the image processing section. The implementation of unsupervised learning eliminates the need for labeled samples from full-wave simulations, resulting in significant time saving. The proposed model is confirmed with two examples of a pixel antenna and a microstrip/coplanar waveguide ultrawideband filter, and the results show its improvement in accuracy and efficiency.

A wideband low-profile magneto-electric (ME) dipole antenna is proposed in this paper. In order to reduce the profile of ME-dipole antenna, a planar wideband balun with metallic holes connection is proposed to replace the formal feeding structures. Four resonant modes are excited in the ME-dipole for wide impedance matching bandwidth. By combing the wideband balun with multi-mode ME-dipole, an impedance bandwidth of 81.3% (2.7−6.4 GHz) for voltage standing wave ratio (VSWR) less than 2.5 is achieved. This antenna structure is designed based on the multi-layer printed circuit board (PCB) technology, with the advantage of low cost and easy to fabricate. The overall size of the fabricated antenna is (0.5×0.5×0.061)λ_L (λ_L is the wavelength of low frequency). The radiation patterns of this antenna are measured with an average gain of 5.3 dB in each resonant modes. In addition, the average radiation efficiency of this antenna is more than 75%, and the cross polarization is lower than −20 dB over the whole frequency band.

Unmanned platforms, performing various tasks in urban environments, have garnered significant attention. This paper primarily focuses on analyzing and modeling the influence of various urban environmental factors on the unmanned platforms’ core competencies, which include motion, perception, operation, and communication abilities. Initially, the paper delves into the urban environment’s geographical, meteorological, electromagnetic, and temporal factors, identifying and categorizing the typical elements into static and dynamic components. It further models the primary urban elements and elucidates their interrelationships. Then, the paper categorizes the types of unmanned platforms that are suitable for urban task execution. It identifies six fundamental components essential for describing the capabilities of these platforms: the perception, computing, motion, power, operation, and communication systems. Subsequently, the paper constructs an impact model by analyzing how typical urban environmental factors affect these six components. A unified analysis framework is proposed to assess the urban environment’s impact on the capabilities of unmanned platforms. Finally, the effectiveness of this framework is validated through path-planning tasks in various typical urban scenarios.

Previous studies have paid limited attention to the critical role of unmanned aerial vehicle (UAV) altitude variation in determining three-dimensional (3D) search efficiency. To address this gap, this paper introduces a deep reinforcement learning framework integrated with adaptive matrix optimization in multi-UAV target search. The proposed framework explicitply models altitude variations’ impacts on observation range and detection accuracy. To balance flight safety, coverage, and detection precision, deep Q-network (DQN)-altitude matrix optimization (AMO) discretizes 3D trajectories into horizontal paths and vertical altitude decisions, effectively reducing the problem dimensionality. Furthermore, a curriculum learning approach is employed to decompose the search process into phased sub-tasks, each with customized decision rules and reward mechanisms. This hierarchical strategy accelerates agent learning and enhances performance in complex scenarios. Comprehensive experimental evaluations in simulated environments demonstrate that the proposed DQN-AMO outperforms benchmark methods in both robustness and generalization.

The survivability of the infrastructure network, especially the combat system, is a core index to reflect the merits of the system. The cascade effect caused by the failure of a single or a few nodes is the focus of the research on the resistance. Many scholars have studied this problem and put forward some useful cascading failure models. Considering node resilience, a cascade failure model is proposed in which nodes have three states: normal, overload and failure. In addition, the conditions for the failure of connected edges are given. The model fully considers the redundancy design of the real system, and can objectively reflect the performance of the system to deal with cascade failure. We study the effect of node resilience on network cascade failure in both typical network and real network. Experimental results show that the proposed model can reduce the scale of cascade failures with higher cost utilization compared with the classical model that is widely used, especially when the network capacity is small.

The missiles typically require multiple stages tests to improve hit accuracy. The existing estimation methods seldom consider the tests characteristics of multiple stages growth of missile hit accuracy, which bring difficulties to accurately estimate the missile hit accuracy. Considering the different degrees of damage caused by missile hitting the target in different areas, the Dirichlet distribution is selected as the prior distribution of hit accuracy parameters based on the Bayesian method. The sequence constraint relationship between the hit accuracy parameters of each stage test is established, and the Bayesian estimation model of hit accuracy based on multiple stages growth tests is constructed. The Markov chain-Monte Carlo method combined with Gibbs sampling is used to overcome the problem of solving the posterior high-dimensional integral of the model to complete the model solution. The example shows that this method can fuse multiple stages growth tests information compared with the existing single stage test method, which can provide reference for the estimation of missile hit accuracy during the period of research and development.

Non-terrestrial network (NTN) possesses ubiquitous coverage and access capability, which needs high-reliability and high-throughput transmission supported by the advanced channel coding. In this paper, a cooperative transmission and optimization scheme is proposed based on polar codes in NTN. Firstly, the transmission system model is designed. Then the polar coded cooperation method among the cooperative satellite nodes (CSN) and destination is provided in Rayleigh slow fading channel. Moreover, to improve the overall error probability performance and throughput efficiency, the key coding rate and power allocation of CSN are jointly optimized using multiobjective reinforcement learning algorithm. Finally, simulation and analysis demonstrate the validity of our approach and show the error-correcting performance and throughput advantages of the proposed cooperative transmission scheme in NTN.

Threat assessment is vital for air defense of warship, as it significantly improves decision making. Aiming at the problem that the uncertainty and combat intention of the target is not considered in the process of threat assessment for air defense of warship, a threat assessment method based on the variable weights intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is proposed. Considering the inherent attributes and spatial situation of air targets, the threat assessment index system is established. Aiming at the uncertainty problem in the process of threat assessment, the method of transforming indexes into intuitionistic fuzzy sets (IFSs) is proposed. The objective weights and subjective weights are calculated based on intuitionistic fuzzy entropy (IFE) and combat intention, respectively. By minimizing the distance to the objectivity and subjectivity simultaneously, the calculation method of comprehensive variable weights based on the least square is proposed. An example show that the proposed method can give accurate threat degree and ranking of targets. Compared with other algorithms, it is shown that the proposed method can fully exploit the influence of the inherent attributes and spatial situation and give more reasonable results according to the target combat intention.

This paper proposes a hybrid differential evolution and ant colony optimization (DE-ACO) algorithm for multi-unmanned aerial vehicle (UAV) task allocation and path planning, addressing dynamic task allocation in heterogeneous UAV clusters. The algorithm integrates task order constraints, uniform target point weights, and path planning to avoid terrain obstacles and radar threats, ensuring efficient resource utilization. Simulations demonstrate balanced task allocation, rapid convergence, and robustness under UAV failure and communication range reduction scenarios. Compared to a baseline ant colony optimization approach, the hybrid DE-ACO method offers enhanced flexibility, making it a promising solution for real-world multi-UAV missions.

The radar cross section (RCS) scale transformation of non-metallic targets is mainly limited by the frequency dispersion relationship of material, making it difficult to be implemented in actual measurements. If the scale dependences of scattering centers can be found to avoid the influence of frequency, it is expected to provide a new solution for the scale measurement of RCS of non-metallic targets. Therefore, based on the high-frequency asymptotic method, this paper first derives the scale dependences of the specular reflection mechanism and the edge diffraction mechanism for three common specular reflection structures and two edge diffraction structures. Then, referring to the expression of the frequency dependent factor in the geometric theory of diffraction (GTD) model, the scale dependent factors of the above scattering mechanisms are analogously proposed. The scale dependent factors are extended to thin-layer coated structures and arbitrary multiple scattering cases. Simulation experiments verified the correctness of the scale dependent factors. Finally, this paper presents an extended expression form of the GTD model, laying a theoretical foundation for the subsequent RCS scale transformation of non-metallic targets based on scattering centers.

This paper proposes a two-dimensional (2D) angular super-resolution framework for sparse arrays under the single snapshot condition. The azimuth-elevation 2D angular super-resolution model is established, which shows the relationship between the 2D angular super-resolution image and the signal. Using this model, the 2D angular super-resolution problem is transformed into the beyond linear optimization problem. In order to efficiently and accurately address this optimization problem, we propose the sparse multi-layer iterative (SMLI) algorithm based on beyond linear signal processing (BLiSP) theory. During the layered iterative process, the solution region is continuously narrowed. The constructed nonlinear weighting matrix and sparse constraint coefficient enhance the ability to differentiate between the effect of signal and noise in solving the beyond linear optimization problem. Additionally, the nonlinear weighting matrix can be adaptively updated during the solving process, ensuring high-performance angular super-resolution results under different signal-to-noise ratios (SNRs). Experiments confirm the proposed method’s effectiveness and robustness.

Lightweight convolutional neural networks (CNNs) have simple structures but struggle to comprehensively and accurately extract important semantic information from images. While attention mechanisms can enhance CNNs by learning distinctive representations, most existing spatial and hybrid attention methods focus on local regions with extensive parameters, making them unsuitable for lightweight CNNs. In this paper, we propose a self-attention mechanism tailored for lightweight networks, namely the brief self-attention module (BSAM). BSAM consists of the brief spatial attention (BSA) and advanced channel attention blocks. Unlike conventional self-attention methods with many parameters, our BSA block improves the performance of lightweight networks by effectively learning global semantic representations. Moreover, BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training, maintaining the network’s lightweight and mobile characteristics. We validate the effectiveness of the proposed method on image classification tasks using the Food-101, Caltech-256, and Mini-ImageNet datasets.

Flight mission segments are essential components of aero-engine flight mission profiles. Current recognition methods rely on manual processes, leading to inefficiencies and inaccuracies in aero-engine load analysis. To solve this problem, this paper presents an automatic method for recognizing flight mission segments using bidirectional recurrent neural network (Bi-RNN). Initially, the decomposition of flight mission profiles, manual division of mission segments, labeling of mission segment, and the establishment of maneuver sequence table are introduced, respectively. Secondly, the automatic recognition models are constructed using bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU). By collecting measured load data from a third-generation aero-engine, including 50 low maneuvering and 50 complex maneuvering flight mission profiles, the dataset is set utilizing K-fold cross-validation and the sliding window method. The optimal parameters for the models are determined through training, and their recognition performance is assessed. The results indicate that the Bi-RNN model achieves over 80% accuracy in recognizing flight mission segments in both low and complex maneuvering flight mission profiles. As a result, the flight mission segment automatic recognition method presented in this study allows for more efficient and precise aero-engine load analysis, which is of great significance for aero-engine life evaluation research.

Aerial gravity measurements in polar regions require higher accuracy and stability from satellite navigation systems. The performance of conventional Global Positioning System (GPS) significantly deteriorates as one moves from lower latitudes to polar areas. First, a regional comparative analysis of BeiDou Navigation Satellite System (BDS) and GPS positioning performance in polar regions is conducted. Results indicate that in polar environments, BDS demonstrates certain advantages regarding satellite availability and geometric distribution. Based on this analysis, a method for extracting airborne gravity anomalies using BeiDou signals in polar regions is proposed and improved. The process begins by optimizing BeiDou observation data using an altitude angle weighting method combined with precise ephemeris data. An error correction model is constructed to enhance system adaptability in the polar environment. Following this, high-precision position and acceleration estimation is achieved using the precise point positioning (PPP) technique, integrated with inertial navigation information, to extract aerial gravity anomalies in polar regions accurately. Results from Antarctic aerial gravity measurement experiments indicate that the BDS-based model improves accuracy by 30% compared to GPS under the same conditions.

A communication and navigation constellation which can cover the whole Cislunar space is useful for deep space exploration. In our previous work, we proposed a configuration of navigation constellations arranged in special long-period orbits of Cislunar space. In order to keep the navigation satellites strictly on the accurate nominal orbit, a terminal sliding mode control (TSMC) strategy based on the disturbance observer is proposed. Since the states of navigation satellites cannot be observed directly, a linear extended state observer (LESO) is designed to recover the state as well as estimate solar gravity to assist in spacecraft orbit determination. Then through the TSMC strategy, high-precision control of the spacecraft on special long-period orbits has been achieved. The stability conditions of the observer are deduced, and the stability of the system is proven using Lyapunov stability theory. The simulation results show that during the entire mission cycle of 76 days, the maximum position error is 259.95 m and the minimum is 1.41 m, and the maximum value of the velocity error is 0.08 m/s and the minimum velocity error is 6.15×10⁻⁶ m/s. The total velocity increment throughout the entire mission cycle is 30.96 m/s.

On-orbit refueling for low-Earth orbit (LEO) mega-constellations requires solving complex access sequence problems. The increasing number of satellites complicates finding optimal solutions. To tackle this, we introduce a data-driven enhanced ant colony optimization (DDEACO) method in this paper. DDEACO uses a two-step approach: initially, it trains a deep Q-network (DQN) using the $\Delta V$ transfer cost as a reward and employs gated recurrent units (GRUs) to learn Q values for selecting the optimal next satellite member for visitation, capturing the sequences’ temporal dependencies. Then, it integrates the pre-trained GRU with traditional ant colony optimization (ACO) to offer new heuristic paths, improving ACO’s exploration and efficiency. The effectiveness of DDEACO is demonstrated through numerical experiments on systems tool kit (STK)-generated datasets with 30 satellites and 200 satellites, showing its significant performance.

With the transformation of user demands in knowledge economy era, traditional knowledge service operation paradigm and implementation countermeasures cannot meet the demand of users. The previous research on knowledge service system is rich in theoretical content, but most of the research only stays on the shallow analysis of the concept and features. There is lack of information available to the construction of knowledge service system framework in details, especially in the academic area. This paper first contributes to the existing body of knowledge service solution, and then combines general knowledge service solution with integrated definition function (IDEF0) models to produce a knowledge service system framework. These IDEF0 models depict the hierarchy within knowledge service purposes and reveal strategies of meeting user demands in academic research. Finally, a prototype system in knowledge service system shows that the proposed framework can provide effective knowledge service for users, which will help to promote quality and satisfaction in knowledge service process.

This paper investigates a specific operational amplifier to overcome the limitations of traditional accelerated degradation testing, particularly issues related to limited data availability and inaccurate modeling. A particle swarm optimization algorithm is employed to design an optimal testing strategy that achieves a balance between model precision, reliability evaluation confidence, and cost-effectiveness. Utilizing the degradation data derived from experimental procedures, a time-scale function is incorporated into the Wiener process to model the nonlinear degradation behavior of the operational amplifier, accounting for the substantial randomness in environmental influences and the uncertainty associated with actual usage conditions. Furthermore, this study proposes a multivariate performance degradation modeling approach utilizing Copula functions to address product failures arising from competing performance parameters. Compared to conventional multivariate normal models, the proposed approach offers distinct advantages in accurately capturing complex nonlinear dependencies among performance parameters, streamlining the modeling process, and improving both flexibility and applicability. In the presented case study, an optimized reliability testing procedure is applied to a specific operational amplifier, followed by degradation modeling and reliability evaluation utilizing the proposed methodology. The results demonstrate that this integrated approach achieves 14.2% cost reduction, providing engineers with a practical framework for designing cost-effective testing programs without compromising assessment accuracy. The proposed methodology not only advances degradation modeling techniques but also offers direct economic benefits for industrial reliability testing applications.