Search Results (327)

The emergence and widespread use of low-orbit satellite communication systems has become one of the triggers for the development of the Internet of Vehicles (IoV) technology. The main goal of this integration was to increase the level of vehicle safety not only in cities and their suburbs but especially in remote areas of the country. Despite its effectiveness, satellite IoV remains susceptible to attacks on the radio channel. One of the effective ways to counter such attacks is to use wireless transmission systems with the Frequency-Hopping Spread Spectrum (FHSS) method. The effectiveness of FHSS systems largely depends on the operation of the pseudorandom code generator (PRCG), which is used to calculate the new operating frequency code (number). This generator must have the following properties. Firstly, it must have high cryptographic resistance to guessing a new operating frequency number by an attacker. Secondly, since this generator will be located on board the spacecraft, it must have high fault tolerance. The conducted studies have shown that substitution–permutation network “Kuznechik” (SPNK) meets these requirements. To ensure the property of resilience to failures and malfunctions, it is proposed to implement SPNK in codes of redundant residual class systems in polynomials (RCSP) using the isomorphism of the Chinese Remainder Theorem in polynomials. RCSP codes are an effective means of eliminating computation errors caused by failures and malfunctions. The aim of this work is to increase the fault tolerance of PRCG based on SPNK transformation by using the developed error correction algorithm, which has lower hardware and time costs for implementation compared to the known ones. The comparative analysis showed that the developed algorithm for error correction in RCSP codes provides higher fault tolerance of PRCG compared with other redundancy methods. Unlike the “2 out of 3” method of duplication, the developed algorithm ensures the operational state of PRCG not only when the first failure occurs but also during the subsequent second one. In the event of a third failure, RCSP is able to correct 73% of errors in the informational residues of code combination, while the “2 out of 3” duplication method makes it possible to fend off the consequences of only the first failure. Full article

To address flight safety risks from rotor defects in rotorcraft drones operating in complex low-altitude environments, this study proposes a high-precision diagnostic model based on the Multimodal Data Input and Spatio-Temporal Feature Fusion Network (MDI-STFFNet). The model uses a dual-modality coupling mechanism that integrates vibration and air pressure signals, forming a “single-path temporal, dual-path representational” framework. The one-dimensional vibration signal and the five-channel pressure array are mapped into a texture space via phase space reconstruction and color-coded recurrence plots, followed by extraction of transient spatial features using a pre-trained ResNet-18 model. Parallel LSTM networks capture long-term temporal dependencies, while a parameter-free 1D max-pooling layer compresses redundant pressure data, reducing LSTM parameter growth. The CSW-FM module enables adaptive fusion across modal scales via shared-weight mapping and learnable query vectors that dynamically assign spatiotemporal weights. Experiments on a self-built dataset with seven defect types show that the model achieves 99.01% accuracy, improving by 4.46% and 1.98% over single-modality vibration and pressure inputs. Ablation studies confirm the benefits of spatiotemporal fusion and soft weighting in accuracy and robustness. The model provides a scalable, lightweight solution for UAV power system fault diagnosis under high-noise and varying conditions. Full article

This paper proposes a unified fault-tolerant batch authentication scheme for vehicular networks, designed to address key limitations in existing approaches, namely the segregation between in-vehicle and V2I authentication scenarios and the lack of fault tolerance in traditional batch authentication methods. Based on a hardware–software co-design philosophy, the scheme deeply integrates the security features of hardware such as Tamper-Proof Devices (TPDs) and Physical Unclonable Functions (PUFs) with the efficiency of cryptographic primitives like Aggregate Message Authentication Codes (MACs) and the Chinese Remainder Theorem (CRT). It establishes an end-to-end, integrated authentication framework spanning from in-vehicle electronic control units (ECUs) to external roadside units (RSUs), effectively meeting the diverse requirements for secure and efficient authentication among the three core entities involved in Internet of Vehicles (IoV) data collection: in-vehicle ECUs, vehicle gateways, and RSUs. Security analysis demonstrates that the proposed scheme fulfills the necessary security requirements. And extensive experimental results confirm its high efficiency and practical utility. Full article

Temperature anomaly is a common fault in power transformers; therefore, achieving a fast and accurate calculation of the transformer temperature field is of great significance. This paper primarily introduces the methodology and self-programmed calculation for realizing the temperature field analysis of a single-phase, two-limb transformer iron core. First, the finite element equation for the three-dimensional steady-state temperature field is derived to provide the basis for the self-programmed Finite Element Method (FEM) calculation. Subsequently, the Finite Element Method (FEM) calculation of the single-phase, two-limb transformer iron core temperature field was implemented using the self-programmed code, and the results were compared with the COMSOL calculation results. The comparison showed that the error at each node was within 0.5 K. Compared to COMSOL, the computation time was reduced by 46.89%, and the memory usage was reduced by 82.37%. Finally, a temperature rise test was designed for the single-phase, two-limb transformer. Compared with the experimental data, the maximum error is within 3 K, which further confirms the accuracy of the program. Full article

Main shaft bearings are among the critical rotating components of subsea drilling rigs, and their health status directly affects the efficiency and reliability of the drilling system. However, in the high-pressure liquid environment of the deep sea, with intense noise, the vibration signals of the bearings attenuate rapidly. As a result, fault-related features have a low signal-to-noise ratio (SNR), which poses a challenge for bearing health monitoring. In recent years, Deep Neural Network (DNN)-based fault diagnosis methods for subsea drilling rig bearings have become a research hotspot in the field due to their strong potential for deep fault feature mining. Nevertheless, their reliance on high-power-consumption computational resources restricts their widespread application in subsea monitoring scenarios. To address the above issues, this paper proposes a fault diagnosis method for the main-spindle bearings of subsea drilling rigs that combines population coding with an adaptive-threshold k-winner-take-all (k-WTA) mechanism. The method exploits the noise robustness of population coding and the sparse activation induced by the adaptive k-WTA mechanism, achieving a noise-robust and energy-efficient fault diagnosis scheme for the main-spindle bearings of subsea drilling rigs. The experimental results confirm the effectiveness of the proposed method. In accuracy and generalization experiments on the CWRU benchmark dataset, the proposed method achieves good diagnostic accuracy that is not inferior to other SOTA methods, indicating relatively strong generalization and robustness. On the Paderborn real-bearing benchmark dataset, the results highlight the importance of selecting features that are adapted to specific operating conditions. Additionally, in the noise robustness and energy efficiency experiments, the proposed method shows advantages in both noise resistance and energy efficiency. Full article

Large Language Models of the Transformer architecture display great promise in automated code error detection based on their strength in processing sequential data. Nevertheless, their efficacy could be further improved by addressing the inherent weakness in handling structural code dependencies. In response to this, we introduce a novel model that integrates the semantic comprehension power of RoBERTa with the structural learning strength of Graph Neural Networks. This model aims to detect the most common categories of programming faults in the form of runtime errors, index errors, and import/module errors. Experimental evaluation has demonstrated that the hybrid model, utilizing a proper fusion technique, outperforms other models in terms of accuracy and robustness. The introduced mechanism leads to numerical benefits, improving test accuracy by 1.75% over competitive baseline. Full article

Implementing fault-tolerant quantum computing necessitates the realization of logical non-Clifford gates, which requires the preparation of specific quantum states known as magic states. However, IBM’s heavy-hexagon structure, which has limited qubit connectivity, presents challenges in adapting quantum error correction codes such as the surface code. Several methods have been proposed to address these challenges by adapting the surface code to the heavy-hexagon architecture. In this study, we implement the magic state injection process within two distinct implementations of surface codes (standard and rotated methods) suitable for the heavy-hexagon structure and compare their logical error rates. Furthermore, we propose initialization methods to enhance the performance of magic state injection in the heavy-hexagon structure, thereby efficiently achieving logical non-Clifford gates with reduced error rates. Full article

This paper presents AF-TAD, which stands for Aircraft Fuel Transformer Anomaly Detection, a Transformer-based reconstruction model specifically designed for time-series anomaly detection in aircraft fuel systems. The model improves anomaly detection by encoding and fusing key features such as fuel mass, overload, and RPM. Additionally, it incorporates global information, a multi-scale time window compression technique, and a conditional coding layer that integrates flight-specific information to enhance time-series data analysis, adaptability, and detection accuracy. Experimental results demonstrate that AF-TAD significantly outperforms other state-of-the-art anomaly detection models in detecting interval anomalies, achieving superior performance in key metrics. An ablation study further confirms that critical components, such as multi-window encoding and global trend information, contribute to improving the model’s detection capabilities. Overall, AF-TAD not only demonstrates powerful anomaly detection capabilities in the complex domain of aircraft fuel systems, but also offers new technical methods for fault prediction and maintenance in this field. Additionally, it holds broad potential for application in other complex systems. Full article

The pursuit of scalable quantum computing is intrinsically limited by qubit decoherence, making robust quantum error correction (QEC) techniques crucial. As a leading solution, the topological surface code offers inherent protection against local noise. This study presents the first comprehensive implementation and quantitative characterization of a full surface code pipeline, which includes encompassing lattice construction, multi-round syndrome extraction, and MWPM decoding, using the high-level Qrisp programming framework. The entire pipeline was executed on IQM superconducting quantum processors to provide an empirical assessment under current noisy intermediate-scale quantum (NISQ) conditions. Our experimental data definitively show that the system operates significantly below the fault-tolerance threshold. Crucially, a quantitative resource analysis isolates and establishes the lack of native qubit reset on the hardware as the dominant architectural bottleneck. This constraint forces the physical qubit count to scale as $d^2+(d^2-1)T$, effectively preventing scaling to larger code distances (d) and execution times (T) on current devices. The work confirms Qrisp’s capability to support advanced QEC protocols, demonstrating that high-level abstraction can reduce implementation complexity by simplifying scheduling and mapping, thereby facilitating deeper experimental analysis of hardware limitations. Full article

In deep mining engineering, grouting operations, although designed for reinforcement, inevitably induce microfracturing and associated microseismicity. Investigating the characteristics of grouting-induced microfractures in fractured rock masses is crucial for evaluating the grouting process and its effectiveness. Using the Wutongzhuang Mine as a case study, this paper first establishes mechanical criteria covering three stages—fracture filling, coupled permeation, and fracturing propagation—to analyze the process characteristics of grouting-induced microfractures. It reveals the mechanisms by which grouting pressure, in situ stress, and rock mass strength control fracture initiation and propagation. Furthermore, a grouting simulation method based on the Particle Flow Code (PFC) is proposed and summarized, constructing a “pipe-domain” fluid network considering fluid–solid coupling, thereby achieving a refined numerical reproduction of the entire grouting process. Addressing the complex geological conditions of the mine, three typical grouting modes are simulated and analyzed: grouting under conventional geological conditions, grouting under densely fractured conditions, and grouting near fault structures. The simulation results unveil their core influencing factors and behavioral characteristics: under conventional conditions, microfractures exhibit a “three-stage” evolution with the grouting process; under densely fractured conditions, the density of pre-existing fractures dominates the formation of complex fracture networks; and finally, fault structures guide fracture propagation, causing microfractures to cluster nearby. Subsequently, the development trends of microfractures under different grouting effects are clarified: after effective reinforcement, the rock mass strength increases, and the scope and quantity of fractures induced by subsequent grouting significantly decrease. The behavioral patterns under these different grouting modes are effectively validated through field microseismic monitoring, confirming the intrinsic relationship between the spatio-temporal evolution of grouting-induced microfractures and geological structures/grouting techniques. Finally, laboratory tests are conducted using a self-developed experimental apparatus, selecting grouting pressure, pore water pressure in the rock mass, and matrix grain size as variables. The mapping relationships between these variables and microseismic waveform characteristics, amplitude, etc., under different schemes are obtained, providing a basis for inverting the microfracturing process and evaluating grouting effectiveness. The research results provide multi-faceted references for characterizing the stability of fractured rock masses via microseismic monitoring and for optimizing grouting effectiveness. Full article

Near-fault ground motions exhibit significant characteristics such as velocity pulses, rupture directivity, and strong vertical components, which pose serious threats to structural safety. However, near-fault ground motion records remain scarce and have not been adequately accounted for in current seismic design codes. This paper proposes a data-driven simulation method for non-stationary near-fault ground motions based on Stationary Wavelet Transform (SWT) combined with Hilbert’s instantaneous frequency estimation. First, to address the baseline drift issue commonly observed in measured seismic motions, a baseline correction technique combining the least squares method and the Iwan method is proposed to enhance the reliability of seismic time histories. Subsequently, statistical distributions of velocity pulses and vertical-to-horizontal (V/H) acceleration ratios, along with their relationships with fault distance and magnitude, are analyzed based on more than 900 ground motion records. The results show that these near-fault motions generally contain pronounced long-period components, which will have significant implications for the seismic response of long-period structures. Additionally, unidirectional pulses dominate in near-fault records. Among the 107 selected long-period pulse records, unidirectional pulses account for 69.2%. Based on this, seismic motions are decomposed using SWT, and stochastic reconstruction is performed, combined with multivariate response spectrum matching to optimize the generation of near-fault time histories consistent with the target spectrum. Compared with the results obtained without optimization, the proposed method reduces the mean square error by about 40% or more, demonstrating a clear improvement in accuracy and reliability. This method provides reliable seismic input support for seismic analysis and performance-based design of bridges in near-fault regions. Full article

Wind energy has become a cornerstone of sustainable electricity generation, yet the reliable integration of wind energy conversion systems (WECSs) into modern grids remains challenged by dynamic variations in wind speed and stringent fault ride-through (FRT) requirements. Among the available technologies, the Doubly Fed Induction Generator (DFIG) and the Permanent Magnet Synchronous Generator (PMSG) dominate commercial applications; however, a comprehensive comparative assessment under diverse grid and fault scenarios is still limited. This study addresses this gap by systematically evaluating the performance of DFIG- and PMSG-based WECSs across three operating stages: (i) normal operation at constant speed, (ii) variable wind speed operation, and (iii) grid fault conditions including single-line-to-ground, line-to-line, and three-phase faults. To enhance fault resilience, a DC-link Braking Chopper is integrated into both systems, ensuring a fair evaluation of transient stability and compliance with low-voltage ride-through (LVRT) requirements. The analysis, performed using MATLAB/Simulink, focuses on active and reactive power, rotor speed, pitch angle, and DC-link voltage dynamics. The results reveal that PMSG exhibits smoother transient responses and lower overshoot compared to DFIG. Under fault conditions, the DC-link Braking Chopper effectively suppresses voltage spikes in both systems, with DFIG achieving faster reactive power recovery in line with grid code requirements, while PMSG ensures more stable rotor dynamics with lower oscillations. The findings highlight the complementary strengths of both technologies and provide useful insights for selecting appropriate WECS configurations to improve grid integration and fault ride-through capability. Full article

Near-fault ground motions significantly threaten bridges due to their distinct features, which are often inadequately considered in current seismic codes based mainly on far-field records. This study analyzes 941 near-fault records to evaluate the effects of site class, pulse-like motions, and vertical components on the peak acceleration ratio and normalized response spectra. A finite element model of a typical simply supported girder bridge is developed to examine how these factors affect pier internal forces. Results show that the peak acceleration ratio increases with softer sites and exhibits large scatter in near-fault regions, indicating that the conventional vertical-to-horizontal ratio of 0.65 may significantly underestimate vertical seismic actions. Pulse motions shift and broaden response spectra, raising seismic demands for medium- to long-period structures. Additionally, pulse effects combined with soft sites cause coupled amplification of internal forces. This work offers a theoretical basis for seismic design and assessment of similar bridges. Full article

Buildings crossing active faults often suffer severe damage due to fault dislocation during direct-type urban earthquakes. This study employs physical model tests to systematically investigate the dynamic response mechanisms of the integrated “surface rupture zone–overburden–foundation–superstructure” system subjected to bedrock dislocation. A testing apparatus capable of simulating reverse faults with adjustable dip angles (45° and 70°) was developed. Using both sand and clay as representative overburden materials, the experiments simulated the processes of surface rupture evolution, foundation deformation, and structural response under varying fault dislocation magnitudes. Results indicate that the fault rupture pattern is governed by the bedrock dislocation magnitude, soil type, and fault dip angle. The failure process can be categorized into three distinct stages: initial rupture, rupture propagation, and rupture penetration. The severity and progression of structural damage are primarily determined by the building’s location relative to the fault trace. Structures located entirely on the hanging wall exhibited tilting angles that remained below the specified code limit throughout the dislocation process, demonstrating behavior dominated by rigid-body translation. In contrast, buildings crossing the fault exceeded this limit even at low dislocation levels, developing significant tilt and strain concentration due to differential foundation settlement. The most severe damage occurred in high-angle dip sand sites, where the maximum structural tilt reached 5.5°. This research elucidates the phased evolution of seismic damage in straddle-fault structures, providing experimental evidence and theoretical support for the seismic design of buildings in near-fault regions. The principal theoretical and methodological contributions are (1) developing a systematic “fault–soil–structure” testing methodology that reveals the propagation of fault dislocation through the system; (2) clarifying the distinct failure mechanisms between straddle-fault and hanging-wall structures, providing a quantitative basis for targeted seismic design; and (3) quantifying the controlling influence of fault dip angle and soil type combinations on structural damage severity, identifying high-angle dip sand sites as the most critical scenario. Full article

Root cause analysis (RCA) identifies the faults and vulnerabilities underlying software failures, informing better design and maintenance decisions. Earlier approaches typically framed RCA as a classification task, predicting coarse categories of root causes. With recent advances in large language models (LLMs), RCA can be treated as a generative task that produces natural language explanations of faults. We introduce RCEGen, a framework that leverages state-of-the-art open-source LLMs to generate root cause explanations (RCEs) directly from bug reports. Using 298 reports, we evaluated five LLMs in conjunction with human developers and LLM judges across three key aspects: correctness, clarity, and reasoning depth. Qwen2.5-Coder-Instruct achieved the strongest performance (correctness ≈ 0.89, clarity ≈ 0.88, reasoning ≈ 0.65, overall ≈ 0.79), and RCEs exhibited high semantic fidelity (CodeBERTScore ≈ 0.98) to developer-written references despite low lexical overlap. The results demonstrated that LLMs achieve high accuracy in root cause identification from bug report titles and descriptions, particularly when reports contained error logs and reproduction steps. Full article