Spaceborne slotted waveguide array antennas (SWAAs) face severe electromagnetic performance degradation due to harsh orbital thermal environments. This degradation stems from microscopic structural warping-which perturbs the coupled slot transimpedance matrix-and channel-level T/R module drift. To restore the distorted radiation characteristics, this paper establishes a rigorous active electromechanical-thermal coupling model (ETCM) along with a two-stage digital compensation framework. Unlike conventional arrays, slot excitations in the corporate-fed SWAA are implicitly coupled; thus, a sequential mesh-transfer procedure is developed to extract the dynamic transimpedance matrix via 3D full-wave calculation. For aperture reconfiguration, Stage I utilizes Least Squares Estimation for continuous compensation, while Stage II applies a deterministic threshold and a multi-objective genetic algorithm to simultaneously suppress the maximum sidelobe level (MSLL) and minimize beam pointing deviations. Quantitative full-wave verification under extreme thermal scenarios ([Formula: see text] to [Formula: see text]) demonstrates exceptional alignment, with absolute tracking discrepancies of MSLL and beam pointing errors tightly bounded within 0.66 dB and [Formula: see text], respectively. This paradigm successfully bridges multi-field modeling and finite-bit beam control for spaceborne radar applications.

Vibration energy harvesting is a promising solution for powering battery-free IoT systems; however, the instability of ambient vibrations presents significant challenges, such as limited harvested energy, intermittent power supply, and poor adaptability to various applications. To address these challenges, this paper proposes ViPSN2.0, a modular and reconfigurable IoT platform that supports multiple vibration energy harvesters (piezoelectric, electromagnetic, and triboelectric) and accommodates sensing tasks with varying application requirements through standardized hot-swappable interfaces. ViPSN 2.0 incorporates an energy-indication power management framework tailored to various application demands, including light-duty discrete sampling, heavy-duty high-power sensing, and complex-duty streaming tasks, thereby effectively managing fluctuating energy availability. The platform's versatility and robustness are validated through three representative applications: ViPSN-Beacon, enabling ultra-low-power wireless beacon transmission from a single transient fingertip press; ViPSN-LoRa, supporting high-power, long-range wireless communication powered by wave vibrations in actual marine environments; and ViPSN-Cam, enabling intermittent image capture and wireless transfer. Experimental results demonstrate that ViPSN 2.0 can reliably meet a wide range of requirements in practical battery-free IoT deployments under energy-constrained conditions.

The escalating power density in Active Phased Array Radar has made the thermal management of Transmitter and Receiver (T/R) modules a critical bottleneck for radar performance. To address the thermal resistance of traditional cold plates, this study investigates an innovative embedded cooling strategy utilizing micro-pyramid arrays and advanced nanofluids. Thermal performance was evaluated using maximum temperature, maximum temperature difference and surface temperature standard deviation (ST). Higher pyramid density markedly enhances temperature uniformity, an effect that scales positively with the power load. Under a 100 W condition, the 8-circle micro-pyramids configuration (the densest structure with roughness Ra = 1.3) achieved a 22.58 K reduction in maximum temperature and a 22.5% improvement in temperature uniformity compared to the 2-circle structure, and outperformed the 4-circle structure by 16.98 K and 17.9%, respectively. Furthermore, a comparative analysis of nanofluids (Al2O3, CuO, graphene, and h-BN) is conducted and it is found that graphene nanofluid exhibits the best overall heat transfer enhancement because of its high thermal conductivity and moderate reduction in specific heat capacity. The thermal performance of the nanofluid is evaluated by comparing the maximum temperatures of the heat source at the 8-circle structure. The synergistic coupling of graphene nanofluid with the 8-circle array yields a remarkable 35.38% enhancement in temperature uniformity at 100 W. The enhancement mechanisms are mainly attributed to intrinsic thermophysical properties of the nanoparticles and convection caused by denser pyramid array. The aforementioned findings provide important guidance for the thermal management design of antenna and other high-density integrated electronic systems with embedded cold plate design demand.

Missile-borne active phased array antennas have been widely used in missile guidance for their beam agility, multifunctionality, and strong anti-interference capabilities. However, due to space constraints on the platform and difficulty in heat dissipation, the thermal power consumption of the antenna array can easily lead to excessive temperature, causing two primary issues: first, temperature-induced drift in T/R components, resulting in amplitude and phase errors in the feed current; second, temperature-dependent ripple voltage in the array’s secondary power supply, which exacerbates feed errors. Both issues degrade the electromagnetic performance of the array antenna. To mitigate these effects, this paper investigates feed errors and compensation methods in high-temperature environments. First, a synchronous Buck circuit ripple coefficient model is developed, and an electromagnetic–temperature coupling model is established, incorporating temperature-dependent feed current characteristics, and the law of electromagnetic performance changes is analyzed. On this basis, an electromagnetic performance compensation method based on a genetic algorithm is proposed to optimize the quantization compensation amount of the amplitude and phase of each element under the effect of high temperature.

The rapid advancement of high-performance computing has spurred growing demand for miniaturized, high-density, high-power, and highly reliable electronic packaging. Through-silicon via (TSV), as a pivotal technology enabling high-density integrated packaging, achieves vertical interconnection that reduces signal latency and power consumption while substantially improving system integration. However, inherent challenges persist due to coefficient of thermal expansion mismatches among heterogeneous materials in TSV and parasitic effects introduced by high-density TSV arrays, leading to critical concerns regarding thermomechanical reliability and signal integrity. This study focuses on TSV structures, investigating their thermomechanical reliability and electrical performance. First, the macro-micro model of 2.5D package structure was established to address cross-scale challenges based on Representative Volume Element (RVE) homogenization and sub-model technique. Then, an equivalent circuit model integrating transmission line network theory was developed and validated through full-wave electromagnetic simulations using S-parameter analysis to analyze signal transmission characteristics. Finally, by introducing an improved multi-objective grasshopper algorithm, the structural parameters of TSV are co-optimized using a genetic algorithm back propagation network (GA-BP) and an improved multi-objective grasshopper algorithm (IMOGOA) to enhance both thermomechanical reliability and electrical characteristics simultaneously. The proposed approach offers a practical and effective solution for improving the reliability and performance of high-density integrated packaging, providing valuable insights for future packaging design and optimization.

Optimal sensor placement (OSP) is critical to building a full-lifecycle health monitoring network for high-aspect-ratio unmanned aerial vehicle wings, as it directly affects the deformation sensing of the wing shape. To address this challenge, this paper proposes a novel sensor placement method for wings based on a chaotic strengthened aquila optimizer (CSAO) that integrates chaotic mapping and a nonlinear search strategy. Specifically, the proposed method introduces a uniform initialization strategy based on the piecewise chaotic map and a nonlinear criterion for switching between exploration and exploitation in the basic aquila optimizer (AO). These enhancements increase the diversity of the initial population and raise the probability of global search in later iterations, thereby accelerating convergence and strengthening global optimization capability. First, the performance of the CSAO is compared with that of other popular intelligent algorithms on 10 benchmark functions. The results show that the proposed method exhibits superior convergence speed, higher-quality solutions, stronger global search ability, and better robustness, making it suitable for OSP problems involving tens of thousands of candidate points. Next, the CSAO is applied to sensor placement on a wing-shaped plate. Compared with other OSP methods, the proposed method offers significant advantages in terms of sensor distribution, computational time, and hardware cost. Finally, experimental validation is conducted using a wing test platform equipped with fiber Bragg grating (FBG) strain sensors. The measurement results demonstrate that the reconstructed shape is in excellent agreement with the measured shape. Therefore, the proposed CSAO-based OSP method, combined with the FBG-based structural monitoring system, offers a promising solution for health monitoring of deformable structures in extreme environments.

To achieve high interconnect density in 2.5D packages, various microscale structures such as through-silicon vias (TSVs), microbumps, and redistribution layers (RDLs) are employed. These features typically exist at the micron scale, whereas other package components span millimeter to centimeter scales, resulting in a wide range of physical dimensions within the package. Although finite element analysis (FEA) has proven effective for evaluating the mechanical and thermal characteristics of 2.5D packages, the inherent multi-scale nature poses significant computational challenges and numerical convergence issues, severely hindering the design and analysis of increasingly dense packages. To address this problem, this paper proposes an efficient numerical homogenization method for the mechanical and thermal analysis of 2.5D packages. The method employs periodic boundary conditions (PBCs) based on the concept of referential statistical volume elements (rSVEs). In this approach, typical microstructures—including TSVs, microbumps, and RDL traces together with the surrounding matrix material—are treated as a homogeneous medium, and the equivalent material properties of the multi-scale structures are evaluated. These properties include the stiffness matrices (from which the equivalent Young’s modulus, shear modulus, and Poisson’s ratio can be derived), coefficients of thermal expansion, and thermal conductivity. Validation results demonstrate that the proposed method ensures continuity of displacement, stress, strain, and heat flux across opposite surface pairs of the rSVEs. Compared with experimental measurements and other existing homogenization techniques, the method accurately determines the equivalent material properties of complex multi-scale structures without being restricted to specific geometries, while significantly improving computational efficiency. Finally, the proposed numerical homogenization method is successfully applied to wafer warpage analysis during the manufacturing process and to thermal analysis under operating conditions. The results indicate that the method achieves high computational efficiency while maintaining accuracy in both mechanical and thermal analyses of 2.5D packages, thereby laying a solid foundation for the development of next-generation 2.5D package structures.

This study tackles the multimodal radio frequency interference (RFI) challenges encountered by the Qitai Radio Telescope by developing a comprehensive numerical model. The model integrates four critical interference types: coupling effects, fixed-position interference, moving-position interference, and white noise. These interferences exhibit dynamic variations in both temporal and spatial domains. Innovatively, this work introduces the principal component analysis (PCA) algorithm to beamforming for the first time, conducting a systematic performance comparison with the conventional subspace projection (SP) algorithm. The interference cancellation capability of PCA is rigorously evaluated under two scenarios: (i) complete spatial separation between signal of interest (SOI) and RFI, and (ii) partial or full spatial overlap between SOI and RFI distributions. Results demonstrate that PCA achieves a 30 dB S/N improvement over SP while maintaining the same robust coupling effects as SP. Crucially, PCA better handles overlapping SOI-RFI scenarios, in which traditional methods exhibit significant performance degradation. This research provides a foundational framework for future beamformer design, offering significant potential to advance RFI mitigation technologies and enhance the observational robustness of radio telescopes in complex electromagnetic environments.

Deep space exploration has an extremely high requirement for beam pointing accuracy of the large reflector antenna fed by a beam waveguide system. Beam pointing error compensation is of great importance when the reflector is deformed by inevitable external loads. This study introduces an innovative compensation method for such large beam waveguide reflector antennas utilizing an adjustable ellipsoidal mirror in the beam waveguide system. Initially, the effect of the ellipsoidal mirror’s position deviation on the beam waveguide system’s outgoing focus is analyzed. Subsequently, an equivalent single reflector system for the dual reflector system of the beam waveguide reflector antenna is considered, and the derivation of the focus position deviation for the single reflector caused by the position deviation of the beam waveguide system’s outgoing focus is derived. Then, an electromechanical coupling model (EMCM) is formulated, incorporating both the reflector deformation and the ellipsoidal mirror’s position error. Finally, with the assumption of adjustability of the ellipsoidal mirror, the study explores the beam pointing error compensation problem based on the EMCM in scenarios of reflector deformation. This paper presents a case study on a 35 m aperture beam waveguide reflector antenna. The results demonstrate that the proposed EMCM achieves high accuracy in calculating the main beam direction, thereby validating the effectiveness of the proposed beam pointing error compensation method for large antennas.

Copper pillar bump (CPB) technology, employed as a new generation interconnection in flip chip, has been widely used in the field of chip package. However, the mismatch due to the thermal coefficient difference between the materials can lead to misalignment and residual stress, affecting the reliability of the electronic device. This article has proposed an intelligent optimization framework to analyze and optimize the misalignment and residual stress of CPBs. Specifically, an indirect coupling method was utilized for finite element analysis (FEA) of the CPB, allowing for the determination of displacement and residual stress. Then, the grasshopper optimization algorithm-backpropagation neural network grasshopper optimization algorithm backpropagation (GOA-BP) has been used as a surrogate model to establish the relationship between the CPB structure/process parameters and misalignment as well as residual stress, which is convenient for the following optimization. Subsequently, the multiobjective grasshopper optimization algorithm (MOGOA) has been used together with the GOA-BP surrogate model for optimization analysis. The aim is to identify the most optimal structure and process parameters that minimize the misalignment and residual stress in CPBs. This study has provided a systematic and effective optimization strategy for flip-chip package, contributing to significant advancements in chip reliability.

Active phased array antenna is a typical multidisciplinary, intensive knowledge-based equipment, and the traditional "design-analysis-improvement-redesign" method has problems including low utilization of mature design resources and long design cycles, and it is difficult to meet the efficient design needs of antennas. Active phased array antenna has a mature architecture, with a large number of multidisciplinary parameters, engineering instances, and other knowledge, and the reuse of design-related knowledge is high, and it is suitable for building a knowledge base to reasonably utilize the existing design knowledge. Through in-depth analysis of the antenna's composition, multidisciplinary performance, and design steps, the antenna's design knowledge is classified into three types of knowledge: parameter, rule, and instance, and the active phased array antenna design knowledge base is constructed, and a hybrid reasoning method combining rule-based reasoning and instance-based reasoning is further proposed to carry out the rapid matching of antenna instances from the knowledge base, as well as the multidisciplinary adaptation of the antenna structure when oriented to the new index requirements. A hybrid reasoning method combining rule-based reasoning and instance-based reasoning is further proposed to perform fast matching of antenna instances from the knowledge base, as well as multidisciplinary retrofitting of the antenna structure when facing new index requirements, thus realizing the multidisciplinary active phased array antenna intelligent and fast design.

Due to the continuous enhancement of pointing accuracy, detection range, and other performance metrics, reflector antennas are evolving toward high-frequency bands, increased gain, and overall improved performance. In this context, the influence of structural uncertainties, such as antenna feed and main reflector accuracy, on performance has become notably prominent. The inherent interdependence between structure and electromagnetic properties accentuates the impact of structural and electromagnetic uncertainties on electrical properties like gain and pointing of reflector antennas. Addressing the challenge of analyzing the impact of uncertainty on antenna performance and ensuring optimal functionality, this paper derives the phase interval resulting from feed position deviations in the aperture plane. Additionally, it investigates the influence of feed direction uncertainty on the amplitude of the aperture plane and provides an expression for the pattern under main reflector shape uncertainty. Building upon these analyses, an electromechanical coupling model for the structural uncertainty of the reflector antenna is established, unveiling the influence mechanism of key structural uncertainty factors on electrical performance. To illustrate these concepts, a calculation example is presented using the 110 m antenna design model from Qitai. The discussion delves into the profound impact mechanisms of feed and main reflector uncertainties on antenna gain, side lobes, pointing, and other performance aspects.