Search Results (13,375)

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

PredRNN is a spatiotemporal prediction model based on ST-LSTM units, capable of simultaneously extracting spatiotemporal features from ionospheric Total Electron Content (TEC). However, its internal convolutional operations require large kernels to capture low-frequency features, which can easily lead to model over-parameterization and consequently limit its performance. Although some studies have employed wavelet transform convolution (WTConv) to improve feature extraction efficiency, the introduced noise interferes with effective feature representation. To address this, this paper proposes a denoising wavelet transform convolution (DWTConv) and constructs the DWTPred-Net model with it as the key component. To systematically validate the model’s performance, we compared it with mainstream models (C1PG, ConvLSTM, and ConvGRU) under different solar activity conditions. The results show that both $MAE$ and $RMSE$ of DWTPred-Net are greatly reduced under all test conditions. In high solar activity, DWTPred-Net reduces $RMSE$ by 13.81%, 6.19%, and 9.28% compared to the C1PG, ConvLSTM, and ConvGRU, respectively. In low solar activity, the advantage of DWTPred-Net becomes even more pronounced, with $RMSE$ reductions further increasing to 19.39%, 11.51%, and 16.10%, respectively. Furthermore, in additional tests across different latitudinal bands and during geomagnetic storm events, the model consistently demonstrates superior performance. These multi-perspective experimental results collectively indicate that DWTPred-Net possesses obvious advantages in improving TEC prediction accuracy. Full article

With the development of avionics systems towards high integration and high power density, the thermal management of electronic equipment in ATR chassis is facing severe challenges, and the extreme aviation environment further exacerbates the difficulty of heat dissipation. Traditional fixed control strategies suffer from problems such as energy consumption, redundancy, and local overheating, whereas single-model predictive control (MPC) is prone to local optimization. This paper proposes a thermal management optimization scheme based on the ACO-MPC hybrid framework: Firstly, a compact thermal model integrating aviation environmental parameters, such as high-altitude, low-pressure conditions and vibration impacts, is constructed. The balanced truncation method is adopted for model order reduction in this study. By retaining the key thermodynamic characteristics of the system, the original three-dimensional thermal model containing more than 800 nodes is simplified to 25 core nodes, which ensures simulation accuracy while improving computational efficiency; Secondly, the ACO-MPC hybrid framework is designed, which uses Ant Colony Optimization (ACO) for global optimization to provide optimized initial values for Model Predictive Control (MPC), breaking through the local optimization limitation of MPC and realizing the collaboration of “global optimization—dynamic control”; Finally, the effectiveness of the framework is verified in three typical aviation scenarios. The results show that compared with traditional methods, this framework has significantly improved heat dissipation efficiency, energy consumption control, and temperature stability, and has strong adaptability to environmental disturbances, which can be migrated to the ATR chassis of different specifications. Full article

The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly deep learning, has catalyzed a paradigm shift. This review provides a systematic, comprehensive, and forward-looking analysis of the radar signal deinterleaving landscape, critically bridging foundational techniques with the cognitive frontiers. Previous reviews often focused on specific technical branches or predated the deep learning revolution. In contrast, our work offers a holistic synthesis. It explicitly links the evolution of algorithms to the persistent challenges of the CME. We first establish a unified mathematical framework and systematically evaluate classical approaches, such as PRI-based search and clustering algorithms, elucidating their contributions and inherent limitations. The core of our review then pivots to the deep learning-driven era, meticulously dissecting the application paradigms, innovations, and performance of mainstream architectures, including Recurrent Neural Networks (RNNs), Transformers, Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). Furthermore, we venture into emerging frontiers, exploring the transformative potential of self-supervised learning, meta-learning, multi-station fusion, and the integration of Large Language Models (LLMs) for enhanced semantic reasoning. A critical assessment of the current dataset landscape is also provided, highlighting the crucial need for standardized benchmarks. Finally, this paper culminates in a comprehensive comparative analysis, identifying key open challenges such as open-set recognition, model interpretability, and real-time deployment. We conclude by offering in-depth insights and a roadmap for future research, aimed at steering the field towards end-to-end intelligent and autonomous deinterleaving systems. This review is intended to serve as a definitive reference and insightful guide for researchers, catalyzing future innovation in intelligent radar signal processing. Full article

The integration of mobile devices into aviation powering electronic flight bags, maintenance logs, and flight planning tools has created a critical and expanding cyber-attack surface. Security for these systems must be not only effective but also transparent, resource-efficient, and certifiable to meet stringent aviation safety standards. This paper presents AVI-SHIELD, a novel framework for developing high-assurance, on-device threat detection. Our methodology, grounded in the MITRE ATT&CK^® framework, models credible aviation-specific threats to generate the AviMal-TinyX dataset. We then design and optimize a set of compact, interpretable detection algorithms through quantization and pruning for deployment on resource-constrained hardware. Evaluation demonstrates that AVI-SHIELD achieves 97.2% detection accuracy on AviMal-TinyX while operating with strict resource efficiency (<1.5 MB model size, <35 ms inference time and <0.1 Joules per inference) on both Android and iOS. The framework provides crucial decision transparency through integrated, on-device analysis of detection results, adding a manageable overhead (~120 ms) only upon detection. Its successful deployment on both Android and iOS demonstrates that AVI-SHIELD can provide a uniform security posture across heterogeneous device fleets, a critical requirement for airline operations. This work provides a foundational approach for deploying certifiable, edge-based security that delivers the mandatory offline protection required for safety critical mobile aviation applications. Full article

Under long-duration and high-speed flight conditions, the combined effects of external aeroheating and internal heat dissipation pose complex and challenging thermal design issues for the electronic equipment cabin of flight vehicles. This study employs a partitioned modeling strategy. By comparing the complexity of heat transfer pathways, the contact surface between the thermal protection structure (TPS) and the skin is selected as the interface. A two-way thermal coupling analysis model is established to investigate heat flux transport characteristics and coupling mechanisms between internal and external thermal environments of the electronic equipment cabin. The results indicate that the external thermal environment affects the internal environment primarily through the consumption of heat sink capacity by aeroheating penetrating the TPS, and the coupling effect intensifies with flight speed and duration. The internal thermal environment influences the external thermal environment by suppressing the penetration of aeroheating, and the coupling strength shows high sensitivity to the total internal heat dissipation. Heat conduction accounts for over 70% of the total heat transfer within the electronic equipment cabin, underscoring the importance of optimizing conductive heat transfer in thermal design. Compared to the conventional serial design approach based on the one-way coupling model, the collaborative thermal design derived from the two-way coupling model can achieve lower redundancy, lighter weight, and higher reliability. This paper is expected to provide support for the accurate thermal response prediction and collaborative thermal design of high-speed flight vehicles. Full article

The FY-4B satellite, launched in June 2021 as China’s new-generation geostationary meteorological satellite, carries three identical High-Energy Particle Detectors (HEPDs) that enable multi-directional, wide-spectral measurements of energetic electrons. The three units are mounted in the zenith (−Z), flight (+X with a +Y offset of 30°), and anti-flight (−X with a −Y offset of 30°) directions, allowing simultaneous observations from nine look directions over a field of view close to 180° in the 0.4–4 MeV energy range (eight energy channels). This paper systematically presents the design principles of the HEPD electron detector, the ground calibration scheme, and preliminary in-orbit validation results. The probe employs a multi-layer silicon semiconductor telescope technique to achieve high-precision measurements of electron energy spectra, fluxes, and directional anisotropy in the 0.4–4 MeV range. Ground synchrotron calibration shows that the energy resolution is better than 16% for energies above 1 MeV, and the angular resolution is about 20°, providing a solid basis for subsequent quantitative inversion. During in-orbit operation, HEPD remains stable under both quiet conditions and strong geomagnetic storms: the measured electron fluxes, differential energy spectra, and directional distributions show good agreement with GOES-16 observations in the same energy bands during quiet periods and for the first time provide from geostationary orbit pitch-angle-resolved images of the minute-scale evolution of electron enhancement events. These results demonstrate that HEPD is capable of long-term monitoring of the geostationary radiation environment and can supply high-quality, continuous, and reliable data to support studies of radiation-belt particle dynamics, data assimilation in space weather models, and radiation warnings for satellites in orbit. Full article

This paper presents the reconstruction of signal paths acquired from a power electronics system for energy conversion and management. This reconstruction is performed using the Kalman filter (KF) for monitoring photovoltaic (PV) systems enabled for Internet of Things (IoT) systems. This proposal is motivated by the fact that the global energy transition towards renewable sources makes PV systems a crucial alternative. To guarantee the efficiency and stability of these systems, monitoring critical electrical parameters using IoT technology is essential. However, the measurements acquired are frequently corrupted by stochastic noise, which obscures the true behavior of the system and limits its accurate characterization. Based on this problem, the main objective of this work is explicitly defined as evaluating the effectiveness of the KF as a power-path reconstruction method capable of recovering accurate electrical trajectories from noisy measurements in IoT-monitored photovoltaic networks. To achieve this goal, the system is modeled as a discrete-time stochastic process and the KF is implemented as a real-time estimator of power flow behavior. The experiment was conducted using real-world generation and consumption data from a proprietary two-layer IoT platform: an Edge Layer (acquisition with ESP8266 and PZEM-004T-100A sensors) and a Cloud Layer (visualization on Things-Board). To validate the results, quantitative metrics including the mean squared error (MSE), statistical moments, and probability distributions were computed. The MSE values were found to be nearly zero across all reconstructed power-paths. The statistical moments exhibited near-perfect agreement with those of the actual power signals, approaching 100% correspondence. Additionally, the probability distributions were compared visually and assessed statistically using the Kolmogorov–Smirnov (KS) test. The resulting KS values were very low, confirming the high accuracy of the reconstruction for all power-paths. The proposed research concluded that the KF successfully reconstructed the power trajectories, demonstrating high agreement with the measured steady-state behavior. This study thus confirms that integrating Kalman filtering with IoT monitoring delivers a practically viable and statistically accurate method for power trajectory reconstruction, which is fundamental for enhancing the observability and reliability of photovoltaic energy systems. Full article

Analog electronics, i.e., circuits that process continuously varying signals, have quietly powered the backbone of agricultural automation long before the advent of modern digital technologies. Yet, the accelerating focus on digitalization, IoT, and AI in precision agriculture has largely overshadowed the enduring, indispensable role of analog components in sensing, signal conditioning, power conversion, and actuation. This paper provides a comprehensive state-of-the-art review of analog electronics applied to agricultural systems. It revisits historical milestones, from early electroculture and soil-moisture instrumentation to modern analog front-ends for biosensing and analog electronics for alternatives source of energy and weed control. Emphasis is placed on how analog electronics enable real-time, low-latency, and energy-efficient interfacing with the physical world, a necessity in farming contexts where ruggedness, simplicity, and autonomy prevail. By mapping the trajectory from electroculture experiments of the 18th-century to 21st-century transimpedance amplifiers, analog sensor nodes, and low-noise instrumentation amplifiers in agri-robots, this work argues that the true technological revolution in agriculture is not purely digital but lies in the symbiosis of analog physics and biological processes. Full article

The development of sustainable, water-based conductive coatings is essential for advancing environmentally responsible wearable and printed electronics. Achieving high electrical conductivity and wash durability remains a key challenge. This is largely dependent on the compatibility between the polymer matrix, the conductive filler and the substrate surface. In this study, a facile formulation strategy is proposed by directly integrating few-layer graphene (FLG, 2.5 wt%) into commercial bio-based thermoplastic polyurethanes (TPUs), combined with polyvinylpyrrolidone (PVP) as a dispersing agent. The investigation focuses on how the segmental architecture of four TPUs with different structure and hard–soft segments composition influences filler dispersion, mechanical integrity, and electrical behavior. Coatings were deposited onto flexible substrates, including textiles and paper, using a bar-coating process and were characterized in terms of morphology, thermal properties, electrical conductivity, and wash resistance. The results demonstrate that TPUs containing a higher presence of hard segments interact more effectively with hydrophobic surfaces, while TPUs with a higher contribution of soft segments improve adhesion to hydrophilic substrates and facilitate the formation of the percolation network, underling the role of TPU microstructure in controlling interfacial interactions and overall coating performance. The proposed comparative approach provides a sustainable pathway toward durable, high-performance, and washable electronic textiles and paper-based devices. Full article

This paper presents a comparative study of various artificial intelligence methods, including artificial neural networks (ANNs), recurrent neural networks (RNNs), k-nearest neighbors (KNN), random forests (RFs), and particle swarm optimization (PSO) techniques, to see which one can predict conducted electromagnetic interference (CEMI) better. The DC/DC converter simulations and experimental results demonstrated a high level of matching. According to the simulation results, the datasets were highlighted by varying key parameters related to the supply voltage, load current, switching frequency, duty cycle, component choice, PCB layout, filter capacitance, and gate resistance in a systematic way. During the assessment, each AI technique is checked regarding prediction accuracy, computational efficiency, and error rates using different metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). It is observed that KNN performs better than the other methods, giving only the lowest error in predictions and showing very fast computing speed. Furthermore, KNN gave the best results with R² above 0.97, MAE below 5.9 dBµV, and RMSE under 7.3 dBµV. This method worked better than others in all test cases. According to the measurements, the predicted and actual EMI levels match very well and show that the proposed method is strong and reliable. Further, basically, these results show that KNN has the same potential to work as an effective and efficient tool for predicting CEMI in power electronics. Its strong performance can further help in developing better and more reliable power systems for practical use, while the system itself provides valuable insights to engineers for electromagnetic compatibility design and compliance. Full article

The present work aims to synthesize polyamide 1212 (PA 1212) via direct solid-state polymerization (DSSP), starting from its solid salt precursor. The DSSP of aliphatic polyamide salts has been found to proceed through melt intermediates, in harmony with the well-documented solid-melt transition (SMT) mechanism. However, PA 1212 salt is anticipated to deviate from this model due to its strongly hydrophobic nature. The reaction was initially investigated at the microscale in a thermo-gravimetric analysis (TGA) chamber and then scaled up to the laboratory scale. The influence of reactor design, reaction temperature, and residence time was examined. DSSP products were characterized in terms of molecular size and morphological properties. At the same time, a novel protocol was developed for qualitatively monitoring the progress of polymerization via Fourier Transform Infrared Spectroscopy-Attenuated Total Refraction (FTIR-ATR) analysis. Emphasis was given on the resulting morphology examined via Scanning Electron Microscopy (SEM imaging). Although DSSP has been found to proceed through a quasi-SMT, significant differences are observed compared to the classical mechanism established in the literature. This paper reveals that the limited surface softening or agglomeration phenomena encountered are mostly associated with the hydrophobic structure of the PA 1212 salt. Full article

To achieve high-frequency inductance and impedance enhancement for effective electromagnetic interference (EMI) mitigation in power electronics, this paper presents an electromagnetic analysis and experimental study of laminated Mn-Zn toroidal ferrite cores. The electromagnetic field is analyzed using a 2D analytical solution based on a simplified Cartesian approximation. Although neglecting curvature, this approach enables efficient eigenfunction expansion and is rigorously validated against cylindrical finite difference (FDM) and 3D finite element (FEM) benchmarks. The results demonstrate that lamination effectively interrupts eddy current loops; notably, a four-layer structure increases the resonant frequency by approximately 2.8 times compared to a monolithic core. Experimental measurements confirm that this design significantly mitigates the skin effect and extends the stable frequency bandwidth. This study establishes a validated, computationally efficient methodology for optimizing core geometries to prevent impedance degradation. Full article

Shale pores and throats are key factors controlling the enrichment and development efficiency of shale oil and gas. However, the characteristics and formation mechanisms of shale pores and throats remain unclear. Taking the Permian continental shales in the Mahu Sag of the Junggar Basin as an example, this paper studies the formation mechanisms of pores and throats in shales of different lithofacies through a series of experiments, such as high-pressure mercury injection and scanning electron microscopy. The results show that the Permian continental shales in the Junggar Basin are mainly composed of five lithofacies: rich siliceous shale (RSS), calcareous–siliceous shale (CSS), argillaceous–siliceous shale (ASS), siliceous–calcareous shale (SCS), and mixed-composition shale (MCS). The pores in shale are dominated by intergranular and intragranular pores. The intergranular pores are mainly primary pores and secondary dissolution pores. The primary pores are mainly slit-like and polygonal, with diameters between 40 and 1000 nm. The secondary dissolution pores formed by dissolution are irregular with serrated edges, and their diameters range from 0.1 to 10 μm. The throats are mainly pore-constriction throats and knot-like throats, with few vessel-like throats, overall exhibiting characteristics of nanometer-scale width. The mineral composition has a significant influence on the development of pores and throats. Siliceous minerals promote the development of macropores, and carbonate minerals promote the development of mesopores. Clay minerals inhibit pore development. Diagenesis regulates the development of pores and throats through mechanical compaction, cementation, and dissolution. Compaction leads to a reduction in porosity, and cementation has varying effects on the preservation of pores and throats. Dissolution is the main factor for increased pores and throats. These findings provide a lithofacies-based geological framework for evaluating effective porosity, seepage capacity, and shale oil development potential in continental shale reservoirs. Full article

This paper presents accurate TD-DFT calculations for several mixed-ligand gold(III) complexes with ligands including Cl⁻, Br⁻, I⁻, OH⁻, and NH₃. The calculated results show excellent agreement with available experimental data. The spectral shapes are determined by charge transfer transitions, which are systematically influenced by the ligand’s position in the spectrochemical series. The main vertical electron transitions and the molecular orbitals involved are identified and discussed. Furthermore, the results indicate that the iodide-containing gold(III) complexes, [AuCl₂I₂]⁻ and [AuI(OH)₃]⁻, are viable candidates for practical synthesis. Full article