Abstract:The aerodynamic environment in low-altitude regions is characterised by complex flow field structures, diverse disturbance sources, and significant coupling effects. These factors have a significant impact on the aerodynamic performance and flight safety of UAV (unmanned aerial vehicle), making them a key focus of research in the field of low-altitude UAV aerodynamics. Three typical scenarios were systematically reviewed: complex low-altitude wind fields, spatially constrained environments, and multi-UAV environments. The fundamental characteristics and modelling approaches of complex low-altitude wind fields were systematically outlined, and their effects on UAV and key research methodologies were summarised. Spatially constrained environments were categorised based on disturbance mechanisms and constraint types, and a comprehensive summary of the aerodynamic impacts on UAV within such environments was provided. The aerodynamic characteristics of UAV in multi-UAV environments were summarised, and the aerodynamic coupling mechanisms and research methodologies for cooperative and non-formation flight scenarios were outlined. Building upon this foundation, the core issues and key challenges currently facing aerodynamic research on UAV in low-altitude environments were further refined, and future research priorities in this field were outlined.

Abstract:To address the critical need for autonomous navigation of low-altitude UAVs (unmanned aerial vehicles) in GNSS (global navigation satellite system)-denied environments, a comprehensive survey was presented on absolute visual localization techniques based on a "retrieval-matching-pose estimation" framework. Key challenges inherent to low-altitude UAV observations—including significant imaging disparities, scale variations, and object occlusions—were analyzed, thereby elucidating the advantages of this hierarchical framework for large-scale, long-endurance localization tasks. Subsequently, the technological evolution and state-of-the-art advancements across three core components (cross-view image retrieval, pixel-level feature matching, and UAV pose estimation) were systematically reviewed, tracing the progression from traditional handcrafted features to deep learning paradigms. Finally, considering the deployment requirements of onboard edge computing platforms, the limitations of existing technologies were discussed, and promising future research directions were outlined. This survey is intended to serve as a valuable reference for both research and practical applications in absolute visual localization for low-altitude UAVs.

Abstract:Recent advances in information technology and new energy systems have introduced increasingly stringent requirements for regulating energy transport within materials. Conventional material-design paradigms are limited by inherent trade-offs among optical, thermal, and electrical transport properties, creating an urgent need for a new paradigm to fundamentally decouple and reconstruct material functionalities. Recent progress on nanoporous aerogels as an enabling platform was systematically summarized, emphasizing how hierarchical structural design and cross-scale assembly of building units allow precise control of diverse energy-carrier transport. Based on this theoretical framework, advanced applications in photo thermal electrical energy conversion were highlighted, with particular emphasis on research progress and performance optimization pathways of aerogels for photothermal, photoelectric, thermoelectric, and integrated photo thermal electric systems. Finally, future research directions including AI-driven inverse design and synergistic regulation of multiple energy carriers were outlooked, providing new perspectives for the on-demand development of next-generation high-performance photo-thermal-electrical conversion materials.

Abstract:With the increasing number of space activities, HVI (hypervelocity impacts) caused by space debris and micrometeoroids have become a major threat to the safety of spacecraft in orbit. Such collisions not only result in mechanical damage but also generate plasma, whose electromagnetic effects pose severe risks to highly integrated spacecraft electronic systems. A systematic review of plasma physical effects induced by hypervelocity impacts was provided.The review covered the mechanisms of plasma generation, kinetic characteristics, electromagnetic radiation, and induced discharge, encompassing both theoretical and experimental progress. Special emphasis was placed on the introduction of condensed-phase products (dust grains) in hypervelocity impacts and the resulting dusty plasma effects. This review aims to offer researchers in the field a comprehensive literature summary and to highlight key scientific questions and future research directions. Ultimately, it seeks to provide theoretical support for enhancing the survivability of spacecraft in orbit and for developing next-generation electromagnetic protection technologies.

Abstract:During the high-speed flight of aircraft, raindrop impact erosion causes significant damage to surface materials, thereby seriously affecting flight performance and structural safety. This paper is aimed to systematically review the mechanical mechanism, key influencing factors, and progress in corresponding protection technologies of high-speed raindrop impact erosion. In terms of experiments, current studies mainly rely on devices such as wind tunnels, rocket sleds, and rotating arms to construct simulated environments of real rain fields. Meanwhile, high-speed photography is used to record the dynamic erosion process, and laser velocimetry is employed to obtain raindrop impact velocity, thus achieving multi-dimensional observation of erosion behavior. In terms of numerical simulation, it was focused on introducing mainstream simulation techniques such as the finite element method and the smoothed particle hydrodynamics method. Additionally, an overview was provided of existing research methods and outcomes, which primarily involve establishing liquid-solid coupling models to analyze the propagation patterns of stress waves and the dynamic response characteristics of materials during impact processes. The research results show that raindrop impact velocity, impact angle, and mechanical properties of materials are the key factors affecting the erosion rate. Based on the actual flight envelope parameters of aircraft, targeted optimization design of rain erosion resistance performance can be carried out, which can provide a theoretical basis and technical support for improving the service safety of aircraft in harsh meteorological environments.

Abstract:To predict the boundary-layer transition location over a flat plate across varying Mach numbers, an efficient method was developed for small-sample settings. Flow-field disturbance datasets across multiple Mach numbers were generated using the nonlinear parabolized stability equations, with Ma=0.01 designated as the source domain and Ma=0.1, 0.2, 0.4, 0.8, 1.6 as target domains. The influence of Mach number variations on transition patterns was systematically analyzed. A convolutional neural network model was employed to map flow field patterns to transition locations, incorporating a transfer learning strategy with progressive unfreezing and layer-wise learning rates. Results demonstrate that transfer learning significantly outperforms direct training: for Ma≤0.4, only 1/10 of the target domain samples are required to achieve a mean absolute error below 2.04% of the average ground-truth value; for Ma≥0.8, a progressive domain adaptation strategy controls the error within 6.19%. The approach enhances transition prediction under small-sample conditions and provides a reliable technical pathway for cross-condition flow modeling.

Abstract:Currently, countries around the world are generally unable to defend effectively against high-speed vehicles, and related basic research is still in its infancy. Accelerating the development of high-speed target defense technology is crucial for maintaining aerospace security. Given the problems present in the near-space defense confrontation under this background, such as a narrow defensive posture, significant speed disadvantages, and limited single-missile defense capability, this paper reviews the current development status of the defense guidance law for high-speed near-space vehicles. It analyzes the deficiencies of the existing guidance law research from perspectives such as complex offensive and defensivescenarios,cooperative guidance mechanisms, and real environmentconstraints. It also foresees the key development directions of future defense guidance laws for high-speed vehicles, aiming to offer references for the construction of future defense systems for high-speed vehicles and the frontier basic research in the field of precision guidance.

Abstract:It was presented that a review of model predictive control and its applications in aircraft systems. Starting from representative mission scenarios and key technical challenges, it clarifies the design requirements of aircraft control systems. In response to the design needs of different classes of vehicles, the review surveys and synthesizes a coherent framework for model predictive control. It traces the origins and development of model predictive control and summarizes its theoretical foundations, with particular attention to robust model predictive control, Lyapunov model predictive control, switched model predictive control, and explicit model predictive control, thereby delineating the principal advances reported in recent years. Building on this framework, the paper examines applications of model predictive control to quadrotors, helicopters, fixed-wing aircraft, and high-speed aircraft. Finally, it outlines future research directions for model predictive control in aerospace control and offers concluding remarks.

Abstract:The aviation and aerospace transition zone, spanning altitudes between 50~250 km, constitutes a strategic arena for hypersonic weapon penetration and electronic warfare operations, serving as a critical battlefield that significantly impacts operational effectiveness. AI (artificial intelligence) is profoundly empowering the regions information warfare systems, driving their evolution toward dynamic and intelligent capabilities. Key AI technologies and applications across the entire “perception-fusion-prediction-countermeasure” chain are systematically reviewed: relying on deep learning for efficient inversion of environmental parameters; utilizing intelligent fusion to construct digital twins of battlefield environments; enhancing forecast accuracy through physical information; and developing autonomous learning and game-theoretic decision-making capabilities to support precise cognition and counter-interference.The core challenges facing AI-enabled information warfare include environmental perception uncertainty, weak model interpretability, difficulties in cross-domain transfer, and restricted data acquisition. Finally, the outlook for future development is presented, emphasizing that AI is evolving from a technical tool into a core driving force.

Abstract:A thermal concentrator is a thermal functional device based on transformation thermotics, effective-medium theory, and scattering-cancellation principles. By tailoring the spatial distribution of thermal conductivity or geometric configurations, it efficiently concentrates large-scale heat flux into localized regions, enabling precise control of both steady-state and transient heat transport. With advances in materials science and manufacturing technologies, research on the thermal concentrator is moving from theoretical models toward engineering implementation, and it shows application potential in microelectronic cooling, thermoelectric energy harvesting, energy heating, and thermal therapy. The physical mechanisms, structural designs, and implementation pathways of the thermal concentrator were systematically reviewed, summarized its development and representative works, compared the applicability and performance characteristics of different theoretical frameworks and configurations, and analyzed its technical advantages and engineering feasibility in typical application scenarios. Finally, future trends of the thermal concentrator were discussed, including extensions to complex geometries, multiscale systems, emerging energy platforms, and extreme thermal environments.

Abstract:Absolute gravity measurement based on laser interferometry is the main means to establish a gravity measurement reference, and it is also the gravity reference instrument used currently. In recent years, along with the development of quantum absolute gravity measurement techniques, new opportunities have been presented for establishing a gravity reference with higher accuracy. With the support of the National Development and Reform Commission, Huazhong University of Science and Technology had established the “PGMF(precision gravity measurement facility)” as a major national scientific and technological infrastructure. A key component of this facility′s construction is the establishment of a micro-Gal level absolute gravity measurement reference station. This station provides a standardized reference for gravity measurement instruments and data, serving as a foundation for achieving high-precision measurements and applications in the gravitational field. High-precision gravity measurement instruments are the core equipment for PGMF to achieve a gravity measurement reference. For this purpose, PGMF independently developed a reference quantum absolute gravimeter suitable for station measurement and a miniaturized quantum absolute gravimeter for reference extension. Furthermore, a gravity comparison field and a background physical environment monitoring system were established. Ultimately, a micro-Gal level gravity measurement reference station was established.

Abstract:Fiber-based supercontinuum offer broad spectral bandwidth, high brightness, and excellent spatial coherence, showing great promise in applications such as electro-optical countermeasures, gas sensing, and optical coherence tomography. To address the diverse performance requirements of supercontinuum imposed by different application scenarios, this paper reviewed recent advances in three key directions of fiber-based supercontinuum: power scaling, long wave extension, and low noise research. The technical approaches tailored to enhance each specific performance metric were summarized, and an outlook on future developments was provided, aiming to serve as a reference for the development and application of high-performance supercontinuum.

Abstract:DA (data assimilation) is a crucial technical method for improving the accuracy of atmospheric chemical forecasts by integrating the results of atmospheric chemistry models with multi-source observational data, reducing uncertainties in model input data. Centering on DA techniques for atmospheric chemistry models, the transformation process of initial field assimilation for pollutant gases and aerosols from single state variables to multi-state variables was systematically reviewed. Meanwhile, the important progress of pollutant emission source assimilation inversion using ensemble methods and four-dimensional variational methods was focused on the improvement of emission source accuracy, optimization of spatiotemporal resolution, and enhancement of pollutant concentration prediction performance. With the explosive growth of observational data, a core challenge in the current field lies in fully leveraging high-resolution geospatial and remote sensing data for atmospheric chemical DA. The deep integration of DA with artificial intelligence algorithms represents a key research direction to break through this bottleneck and significantly enhance the accuracy of atmospheric composition analysis and forecasting.

Abstract:Swarms of maritime USV(unmanned surface vehicle), as a core technology driving the development of marine intelligence, demonstrate significant application value in military reconnaissance, environmental monitoring, maritime search and rescue, and related fields. However, the inherent characteristics of the marine environment, including highly dynamic conditions, environmental uncertainties, and communication constraints, pose formidable challenges to achieving high-performance swarm control in maritime USV. To address these challenges, recent research advances in this field were systematically reviewed. The characteristics of the marine environment were described, the domestic and international developments in USV were summarized, and the core control requirements and key challenges in complex marine scenarios were analyzed. Furthermore, a comprehensive survey of three representative swarm control methodologies was presented: trajectory-based guidance control, path-based guidance control, and target-based guidance control. Finally, promising research directions and future development trends in maritime USV swarm control are discussed and proposed.

Abstract:Due to the limitations of human intelligence and artificial intelligence, developing hybrid augmented intelligence based on human-machine collaboration is one of the main research directions for the new generation of artificial intelligence, and the design of collaborative control algorithms is the core issue in achieving such intelligence. Therefore, a review of the current research status of human-machine collaborative enhanced intelligent control systems was provided in this article. Based on the black box characteristics of human behavior, the human behavior modeling methods of human-in-the-loop control systems were systematically sorted out and the advantages, disadvantages, and applicability of various modeling methods were analyzed. For the implementation of human-machine collaborative augmented intelligent control, the control design methods of machines collaborating with humans under different control theory frameworks were elaborated in detail. The scalability of human-machine collaborative control technology in the field of multi-agent systems and the evaluation methods of hybrid intelligence in the human-machine collaborative control systems were investigated and discussed. In addition, the application scenarios of human-machine collaborative augmented intelligent control methods in medical, industrial, military and other fields were presented. The prospects for human-machine collaborative augmented intelligent control research with the support of new technologies such as large models and embodied learning were presented.

Abstract:High-performance interconnection networks are among the key factors determining the scalability of supercomputing and intelligent computing systems. Topology serves as the core of scalability-oriented interconnection network design. The design of topology must not only address macro-level requirements from applications and hardware-software systems, but also consider multiple constraints such as router chip port count, number of virtual channels, and packaging density. Significant topological structures from both academia and industry were systematically analyzed and summarized, and a detailed exposition of representative novel topologies was provided. The design challenges of adaptive routing in high-radix networks were examined, the performance and cost of typical topologies were compared, and recommendations for topology selection were discussed. Furthermore, it preliminarily explored future challenges and trends in topology design, including developing cost-effective network topologies tailored to the characteristics of intelligent computing applications, coordinating topology design with building power supply constraints, and integrating and co-designing intra-and inter-supernode network topologies.

Abstract:In the current landscape of large-scale model training, the contradiction between the exponential growth of model parameters and the slow increase in GPU memory capacity has become increasingly prominent. Among memory optimization technologies, recomputation and computational offloading reduce GPU memory overhead by trading time for space. The development trends of recomputation and computational offloading were analyzed in this article. Then, the hardware bandwidth bottlenecks and software ecosystem adaptation challenges faced by memory optimization were analyzed, with a focus on the heterogeneous architecture characteristics of domestic artificial intelligence platforms. It also delved into the memory optimization technologies for large model training on domestic platforms such as MT-3000, with the aim of providing technical references for large model training on domestic platforms.

Abstract:In the era of big data intelligence, knowledge has become the fundamental driving force for technological development. Big data knowledge fusion, which constructs a unified knowledge system by associating multi-source fragmented data, serves as a key support for enhancing knowledge completeness and improving cognitive intelligence. Based on basic paradigms and classic methods, the research status of three key links， knowledge representation learning, knowledge alignment matching, and knowledge conflict resolution， was systematically summarized. Meanwhile, new progress in the context of big data was deeply explored, covering frontier directions such as temporal knowledge fusion, cross-modal knowledge fusion, and large model knowledge fusion. Furthermore, future development trends were prospected, and potential research issues, including the fusion of symbolic and parametric knowledge as well as cross-modal temporal knowledge fusion, were highlighted. Through a panoramic review of basic links and emerging paradigms, important reference and guidance are provided for theoretical expansion and technological evolution in the field of big data knowledge fusion.

Abstract:In the fields of intelligent manufacturing, aerospace, and robotics, control systems often operate under unknown dynamics. This significantly limits the effectiveness of traditional model-based control methods. RL(reinforcement learning), as a data-driven intelligent control approach, enables policy learning and optimization through interaction with the environment, showing great potential for solving optimal control problems in such model-unknown scenarios. This survey focuses on the issue of unknown dynamic models in continuous-time systems and reviews the development of general RL algorithms and their application in model-known scenarios through industrial examples and theoretical analysis methods. It also summarizes representative methods for model-unknown scenarios, such as model-based RL, off-policy integral RL, and Q-learning approaches. The survey introduces Lyapunov-based theoretical analysis tools and important assumptions. It discusses cutting-edge topics such as RL under partial observability using large language models, safe RL, and stability and robustness enhanced RL, while highlighting the challenges faced by existing methods.

Abstract:RRAM (resistive random-access memory) has emerged as a promising non-volatile memory technology due to its simple device structure, low power consumption, fast switching speed, and excellent scalability, addressing the data movement bottleneck in traditional compute-memory separation architectures. However, challenges in switching uniformity, cycling endurance, and integration reliability hinder its widespread adoption. This review systematically examined recent advances in RRAM, covering mechanism analysis, performance modulation, process integration technologies, and innovative applications. Starting from resistive switching mechanisms, key approaches based on process optimization and electrical programming strategies were summarized to enhance device uniformity and reliability. At the integration level, recent advances in CMOS（complementary metal-oxide-semiconductor） compatibility at advanced technology nodes and high-density 3D(three-dimensional) integration of RRAM were systematically reviewed. In terms of applications, the development trends of RRAM in high-energy-efficiency in-memory computing, neuromorphic computing, intelligent sensing, and secure chips were analyzed in detail. Towards the future, synergetic cross-scale innovation spanning mechanism, material, and architectural levels were emphasized, supporting the strategic goals of the integrated development of intelligent computing and information technologies.

Abstract:DFRC (dual-function radar-communication) is proposed to overcome spectrum conflicts, hardware redundancy, and electromagnetic compatibility bottlenecks inherent in traditional separated architectures through hardware resource sharing and isomorphic signal waveform design, whereby the integrated operational effectiveness and battlefield survivability of platforms are significantly enhanced. The evolution of DFRC technology from its conceptual inception, through architectural advancements, to system implementation was systematically reviewed. The DFRC waveform design methodologies based on mainstream signal schemes were analyzed emphatically, including linear frequency modulation, orthogonal frequency division multiplexing, and orthogonal time frequency space. Furthermore, various sensing-centric waveform design criteria were explored in depth, such as beampattern matching, Cramér-Rao bound minimization, information-theoretical design, and so on. The engineering roadmap from software-defined radio compatibility verification and airborne multimodal waveform fusion to multi-node and multi-domain cooperation was summarized, clearly illustrating the theoretical-to-practical transition of DFRC. Through presenting the complete technical evolution of the DFRC system from the conventional system to multiple-input multiple-output system, and then to the prototype demonstrations, this overview provides systematic theoretical guidance and practical references for future research and development of DFRC systems.

Abstract:In recent years, generative artificial intelligence is progressively introduced into the field of radio spectrum cognition due to its powerful capabilities in data distribution fitting, data generation, and data completion. Compared to conventional approaches relying on physical modeling, mathematical interpolation, and discriminative artificial intelligence techniques, generative AI has significantly enhanced the accuracy of radio spectrum cognition. This paper systematically reviewed the research progress of generative artificial intelligence in radio spectrum cognition, with a focused analysis on the technical principles, application scenarios, and representative works of different generative paradigms. The challenges faced by generative AI in spectrum cognition were further discussed, including scarce training data, limited generalization in unknown scenarios, and insufficient model interpretability. In the future, by cross-modal knowledge fusion, physics-informed embedding, and the establishment of a trustworthy assessment framework, generative artificial intelligence is expected to advance radio spectrum cognition toward high precision, robust generalization, and enhanced interpretability, thereby effectively supporting the efficient utilization of spectrum resources.

Abstract:Foundation models have become a focus in radar remote sensing intelligent interpretation due to their provision of universal and generalizable solutions. Significant progress has been achieved in both theoretical and applied aspects of radar remote sensing foundation models, making it imperative to systematically summarize current research advancements. In order to further advance the research on radar remote sensing foundation models, the concept, key technologies, and evaluation methods of foundation models were expounded. Besides, current research progress and application performance were reviewed, with representative approaches and typical instances summarized. In conclusion, discussions and future directions were highlighted from four perspectives: model architecture design, interpretability research, lightweight methods, and security assessment.

Abstract:Solid-state pulsed power driver is essential for realizing compact and high-repetition-rate operation of high-power microwave systems. This paper reviewed the domestic and international research status of solid-state pulsed power driver, focusing on the investigation and review of three mainstream modular superimposed technology routes: solid-state Marx pulse generator, solid-state linear transformer driver, and solid-state stacked Blumlein line pulsed power driver. From the perspective of switches, the current technical challenges were mainly concentrated on three aspects: the constraining factors of switch operating characteristics and frequency enhancement, the energy loss and thermal management, as well as the driving and control. Meanwhile, the future development trend of high-repetition-rate solid-state pulsed power drivers was also discussed, providing a reference and basis for the research and exploration of technical routes related to such driver.