Electronics

19 pages, 631 KB

Open AccessArticle

Proactive DoS and DDoS Attack Detection Through Behavior-Based Threat Intelligence

by Orieb Abualghanam, Malik Al-Essa, Wesam Almobaideen, Mohammad Qatawneh and Ahmad Sami Al-Shamayleh

Electronics 2026, 15(12), 2559; https://doi.org/10.3390/electronics15122559 (registering DOI) - 10 Jun 2026

Abstract

The rapid growth of cyberattacks necessitates the development of more sophisticated detection techniques. DoS and DDoS are well-known harmful attacks that affect organizations. This paper proposes a proactive, behavior-based DoS and DDoS detection framework that integrates threat intelligence and machine learning to analyze [...] Read more.

The rapid growth of cyberattacks necessitates the development of more sophisticated detection techniques. DoS and DDoS are well-known harmful attacks that affect organizations. This paper proposes a proactive, behavior-based DoS and DDoS detection framework that integrates threat intelligence and machine learning to analyze attack behavior and enhance early detection. XGBoost is used to train the proposed model and evaluate feature importance. The evaluation of the proposed model and the generated rules is conducted using three different datasets: CICIoT2023, BoT-IoT, and Edge-IIoT. Experimental results demonstrate high detection performance, achieving up to 99.98% accuracy and 99.89% F1-score, while maintaining low false positive rates across diverse datasets. Integrating threat intelligence into SIEM has been evaluated using two datasets, DDoS-AT-2022 and CIC-DDoS2019. The rule-based detection technique enhances detection rates and mitigates false positives. Moreover, the proposed framework enhances detection accuracy. Full article

(This article belongs to the Special Issue Enhancing Cybersecurity: Advanced Attack Detection and Defense Techniques)

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20 pages, 4307 KB

Open AccessArticle

ContextMental: A Sociocultural Benchmark for Arabic Mental Health Understanding

by Lama Ayash, Ashwag Alasmari and Hassan Alhuzali

Electronics 2026, 15(12), 2558; https://doi.org/10.3390/electronics15122558 (registering DOI) - 10 Jun 2026

Abstract

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural [...] Read more.

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural classification, transformer-based models, and, more recently, large language models. However, most systems treat text primarily as a clinical signal rather than examining the social and cultural contexts in which distress is expressed. Arabic NLP research remains even more limited, largely focusing on detecting clinical conditions while overlooking contextual factors that shape mental health questions. This work introduces ContextMental, a multi-label annotation schema and benchmark dataset for modeling sociocultural context in Arabic mental health questions. The dataset contains 2677 questions, including 552 instances with contextual labels, enabling fine-grained analysis of social, cultural, and religious dimensions. An AraBERT-based classification framework is further developed using imbalance-aware optimization, semi-supervised pseudo-labeling, and adaptive threshold calibration. Experimental results indicate that pseudo-label augmentation improves overall classification performance, suggesting that semi-supervised learning can support context-aware Arabic mental health classification. This study provides a context-aware annotation framework, a benchmark dataset, and an AraBERT-based baseline modeling pipeline for Arabic mental health NLP, thereby supporting future research on socially, culturally, and religiously grounded language technologies. Full article

(This article belongs to the Special Issue Low-Resource Languages in the Age of Large Language Models)

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19 pages, 5656 KB

Open AccessArticle

Deep Reinforcement-Learning-Optimized Adaptive EKF for Robust Utility Harmonic Impedance Estimation

by Zhirong Tang, Xin Wei, Zhaobin Wei, Fei Tan, Cong Tian, Ying Tang and Xuedou Xiong

Electronics 2026, 15(12), 2557; https://doi.org/10.3390/electronics15122557 (registering DOI) - 10 Jun 2026

Abstract

Accurate estimation of the utility harmonic impedance at the Point of Common Coupling (PCC) is critical for harmonic pollution management in industrial power grids. Existing non-invasive methods rely heavily on restrictive assumptions that are rarely satisfied in practice, and conventional filtering-based approaches suffer [...] Read more.

Accurate estimation of the utility harmonic impedance at the Point of Common Coupling (PCC) is critical for harmonic pollution management in industrial power grids. Existing non-invasive methods rely heavily on restrictive assumptions that are rarely satisfied in practice, and conventional filtering-based approaches suffer from accuracy degradation in dynamic scenarios due to fixed-rule updates of the noise covariance. This paper proposes a deep reinforcement learning (RL)-optimized adaptive extended Kalman filter (AEKF) method for robust harmonic impedance estimation. A state-space model is established without restrictive assumptions, and a deep Q-network (DQN) framework is designed to optimize noise covariance updates adaptively. Simulation results show that the method achieves reliable estimation under normal conditions. Although errors rise under strong noise, it remains stable and exhibits better noise robustness than conventional methods. Field measurements in actual power grid environments further verified the feasibility and application potential of the proposed method in field engineering. Full article

(This article belongs to the Special Issue Reinforcement Learning: Emerging Techniques and Future Prospects)

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32 pages, 3182 KB

Open AccessArticle

Random-Drift Nonlinear Wiener Modeling of Contact Resistance Degradation in Automotive Airbag Electrical Connectors

by Jiayin Zhou, Liqiang Zhong, Dongkang Wang, Wenqiang Zhao and Wenhua Chen

Electronics 2026, 15(12), 2556; https://doi.org/10.3390/electronics15122556 (registering DOI) - 9 Jun 2026

Abstract

The contact performance of automotive airbag electrical connectors directly affects the stable conduction of the initiator circuit, yet sufficient failure data are difficult to obtain for such long-life safety-critical components. This study develops a degradation model for connectors with stainless-steel pins, beryllium-bronze sockets, [...] Read more.

The contact performance of automotive airbag electrical connectors directly affects the stable conduction of the initiator circuit, yet sufficient failure data are difficult to obtain for such long-life safety-critical components. This study develops a degradation model for connectors with stainless-steel pins, beryllium-bronze sockets, and Ni/Au composite coatings, using the contact resistance increment as the degradation measure. Considering the accumulation of oxidation corrosion products under thermal stress, as well as the local film rupture and re-oxidation induced by fretting wear under combined temperature-vibration stress, a nonlinear time scale t^α is introduced to describe the nonlinear growth of contact resistance. A random-drift nonlinear Wiener process is then constructed: the diffusion term represents local fluctuations within each sample trajectory, while the random drift rate captures growth-rate differences among samples. Parameter estimation was performed using degradation data obtained from 160 °C high-temperature and 160 °C temperature-vibration accelerated degradation tests. The estimation results show that the stress-class-specific time-scale model better reflects the different degradation mechanisms than a common time-scale model, and that the temperature-vibration group exhibits higher resistance growth and stronger trajectory fluctuations. Model diagnostics support the description of the main increment distribution and sample-to-sample differences, while EDS and XPS results provide supplementary evidence for oxidation-related surface composition changes and coating-state evolution. Full article

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25 pages, 3394 KB

Open AccessArticle

Impact of Electric Water-Heater Control Granularity on Self-Consumption and Economic Performance of Residential Photovoltaic Systems

by Pavol Belany, Roman Budjac and Stanislav Kriz

Electronics 2026, 15(12), 2555; https://doi.org/10.3390/electronics15122555 (registering DOI) - 9 Jun 2026

Abstract

The growing penetration of residential photovoltaic systems increases the need for effective demand-side management strategies that improve on-site electricity utilization without battery storage. This study investigates the impact of different electric water heater control strategies on the energy and economic performance of a [...] Read more.

The growing penetration of residential photovoltaic systems increases the need for effective demand-side management strategies that improve on-site electricity utilization without battery storage. This study investigates the impact of different electric water heater control strategies on the energy and economic performance of a residential PV system. A simulation-based analysis was performed in the PV*SOL Premium environment using a 5.4 kWp household PV installation and an electric water heater as a flexible thermal load. Five operating modes with different levels of control granularity, ranging from uncontrolled operation to continuous power modulation, were evaluated under climatic conditions representative of Dunajská Streda, Slovakia. The analyzed indicators included the self-consumption ratio, self-sufficiency ratio, electricity import and export, and total variable electricity costs. Compared to the reference mode, continuous control increased the self-consumption ratio from 38.73% to 66.43% and reduced electricity export from 3340 kWh/year to 1830 kWh/year. Total variable electricity costs decreased by 31.86%, from €725.53 to €494.44 per year. The results confirm a saturation effect, where increasing control complexity provides only marginal additional benefits. Moderately complex multi-level control, therefore, represents an effective and economically attractive solution for residential PV systems without battery storage. Full article

(This article belongs to the Special Issue Advanced Microcontrollers, Intelligent Control, and Edge Computing in Industrial IoT Ecosystems)

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25 pages, 4010 KB

Open AccessArticle

A Technology-Centric Cyber Resilience Evaluation Framework Using MITRE D3FEND for Bridging the Policy Technology Gap in Financial and Enterprise Environments

by GwangHyun Ahn and Dongkyoo Shin

Electronics 2026, 15(12), 2554; https://doi.org/10.3390/electronics15122554 (registering DOI) - 9 Jun 2026

Abstract

Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study [...] Read more.

Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study proposes D3-CREF, a technology-centric cyber resilience evaluation framework that maps the MITRE D3FEND taxonomy to financial security domains and introduces a Normalized Resilience Index (NRI) aggregating four dimensions—Coverage, Maturity, Automation, and Timeliness—via a closed-form weighted geometric mean with AHP-elicited weights (consistency ratio CR = 0.04). All NRI indicators are anchored to MITRE ATT&CK techniques and exemplar CVE entries, enabling threat-informed measurement. The framework was validated through a three-round Delphi study with 50 experts (Kendall’s W = 0.78, p < 0.001; Cronbach’s α = 0.89; CVR 0.68–0.92) and a Cyber Range-based simulation. For three institutions with identical BoK scores (92/100), NRI yielded discriminative values of 0.83, 0.44, and 0.09 (CV = 0.68 vs. 0.00 for the baseline), confirming a shift from compliance-based to performance-driven assessment. Full article

(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)

29 pages, 3789 KB

Open AccessArticle

CUBAT-AKA-Collaborative UAV Batch Authentication and Tree-Based Key Agreement

by Changqing Sun, Jiawei Zhang and Xinghua Li

Electronics 2026, 15(12), 2553; https://doi.org/10.3390/electronics15122553 (registering DOI) - 9 Jun 2026

Abstract

As Flying Ad Hoc Networks (FANETs) are highly vulnerable to security threats such as identity spoofing, session replay and man-in-the-middle attacks in open-air channels, it is crucial to design an authentication key agreement (AKA) scheme to ensure the security of unmanned aerial vehicle [...] Read more.

As Flying Ad Hoc Networks (FANETs) are highly vulnerable to security threats such as identity spoofing, session replay and man-in-the-middle attacks in open-air channels, it is crucial to design an authentication key agreement (AKA) scheme to ensure the security of unmanned aerial vehicle (UAV) swarm networking within FANETs. However, existing AKA schemes for FANETs often struggle to balance authentication efficiency and high dynamism within UAV swarms whilst meeting necessary security requirements. To address the issue, this paper proposes CUBAT-AKA (Collaborative UAV Batch Authentication and Tree-based Key Agreement), a lightweight UAV swarm authentication and key agreement scheme based on batch verification and a binary tree structure. The scheme constructs a secure and lightweight three-party authentication mechanism based on aggregated verification and the Chinese Remainder Theorem (CRT). By offloading computational tasks to the authentication center and aggregating authentication responses in batches, it significantly improves the efficiency of UAV access authentication in large-scale FANET scenarios. To address the dynamic nature of UAVs frequently joining and leaving clusters in FANETs, an improved binary tree-based key agreement method has been designed, reducing key update overhead to a logarithmic level and enabling lightweight session key distribution and updates for UAV clusters. Security analysis demonstrates that, under the random oracle model, CUBAT-AKA is resistant to eavesdropping, replay, man-in-the-middle, impersonation and collusion attacks, whilst ensuring forward and backward security during member changes. Performance analysis indicates that this scheme offers significant advantages over comparable solutions in terms of both UAV cluster access authentication efficiency and dynamic key agreement overhead. Full article

(This article belongs to the Special Issue Cryptography and Computer Security, 2nd Edition)

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15 pages, 1311 KB

Open AccessArticle

Robustness Estimation in TEAM 35 Problem with Interacting Geometric and Current-Density Uncertainties

by Tamás Orosz

Electronics 2026, 15(12), 2552; https://doi.org/10.3390/electronics15122552 (registering DOI) - 9 Jun 2026

Abstract

This paper revisits Problem A of the TEAM 35 benchmark from the viewpoint of robustness estimation under manufacturing uncertainty. Rather than treating the original extremal-position-based sensitivity metric as the formulation to be improved, it is used only as a baseline for comparison with [...] Read more.

This paper revisits Problem A of the TEAM 35 benchmark from the viewpoint of robustness estimation under manufacturing uncertainty. Rather than treating the original extremal-position-based sensitivity metric as the formulation to be improved, it is used only as a baseline for comparison with other metrics. In this work, robustness is evaluated as the largest degradation of the nominal magnetic-field homogeneity objective observed over prescribed sets of admissible manufacturing perturbations. In addition to turn-position uncertainties, the present study also includes uncertainty in the excitation current density. While turn-position errors affect each turn individually, current-density uncertainty affects the error contributions of all turns simultaneously through a common term. This common-mode excitation uncertainty represents an extension of the original benchmark formulation and is one of the paper’s main focal points. Several Design of Experiments (DoE) methodologies, as well as search-based robustness estimation strategies, are compared in terms of error in estimated robustness and computational demand. The results show that the original extremal-position-based approximation can substantially underestimate the sampled robustness of the nominal field-homogeneity objective. Including current-density uncertainty further increases the discrepancy between the original metric and the sampled robustness estimates. Full article

(This article belongs to the Special Issue Robust Design Methodologies and Optimization of Electrical Machines and Devices)

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28 pages, 4842 KB

Open AccessArticle

Design and Implementation of an Autonomous Surgical Robotic Aspirator

by Eva Góngora-Rodríguez, Irene Rivas-Blanco, Álvaro Galán-Cuenca, Carmen López-Casado, Isabel García-Morales and Víctor F. Muñoz

Electronics 2026, 15(12), 2551; https://doi.org/10.3390/electronics15122551 (registering DOI) - 9 Jun 2026

Abstract

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of a supervised autonomous robotic aspirator for detecting and removing bleeding [...] Read more.

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of a supervised autonomous robotic aspirator for detecting and removing bleeding in an in vitro experimental model. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level action execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate high blood removal rates (above 80% in all cases) and high suction efficiency. The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased task autonomy and reduced need for continuous manual suction control during minimally invasive procedures. Full article

(This article belongs to the Special Issue Advances in AI, IoT and Smart Sensors for Digital Healthcare Applications, 2nd Edition)

17 pages, 16326 KB

Open AccessArticle

A 7-Bit 1.6 GS/s Hybrid Capacitive-to-Charge-Injection DAC-Based Flash-Assisted Time-Interleaved SAR ADC with Background Gain Calibration for Temperature Robustness

by Seung-Hyeon Lee, Yong-Seok Seo, Jee-Taeck Seo, Tae-Hyun Kim, Jeong-Hun Lee, Ryun-Yeong Kim and Kwang-Hyun Baek

Electronics 2026, 15(12), 2550; https://doi.org/10.3390/electronics15122550 (registering DOI) - 9 Jun 2026

Abstract

This paper presents a 7-bit 1.6 GS/s hybrid capacitive-to-charge-injection DAC (C-CIDAC)-based flash-assisted time-interleaved (FATI) successive-approximation-register (SAR) analog-to-digital converter (ADC) that improves the limited input range and temperature-induced gain variation in conventional CIDAC-based SAR ADCs. In the proposed architecture, a DAC voltage common-mode ( [...] Read more.

This paper presents a 7-bit 1.6 GS/s hybrid capacitive-to-charge-injection DAC (C-CIDAC)-based flash-assisted time-interleaved (FATI) successive-approximation-register (SAR) analog-to-digital converter (ADC) that improves the limited input range and temperature-induced gain variation in conventional CIDAC-based SAR ADCs. In the proposed architecture, a DAC voltage common-mode (

V_{CM}

) shift up to 48 LSBs is internally generated during the coarse conversion, enabling a rail-to-rail ADC input range while improving

V_{CM}

independence. In addition, a fully on-chip background gain-calibration scheme is introduced to compensate for the gain error between the CDAC and CIDAC caused by temperature variation. By taking advantage of the pulse-activation-based CIDAC operation scheme, the proposed calibration achieves robust gain tracking without any external bias control. The proposed four-channel FATI-SAR ADC was designed using a 65 nm CMOS process and occupies 13,628

μ

m

^{2}

, including the background calibration circuitry. The peak differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.60/−0.60 LSB and +0.72/−0.76 LSB at

- 40^{\circ} C

and

105^{\circ} C

, respectively. At Nyquist input, the simulated SNDR and SFDR are 41.52 dB and 53.36 dB, respectively. The ADC consumes 8.551 mW and achieves an FoM

_{W}

of 54.6 fJ/conversion step. Comprehensive post-layout simulation results show that the proposed FATI-SAR ADC operates at 1.6 GS/s and maintains an ENOB above 6.3 across a temperature range from

- 40^{\circ} C

to

105^{\circ} C

at Nyquist input. Full article

32 pages, 2179 KB

Open AccessReview

A Review of Energy Storage Economics, Load Forecasting, and Hybrid Control Strategies for AC Microgrids in Modern Power Systems

by Yaser Ibrahim Rashed Alshdaifat, Krishnamachar Prasad and Jeff Kilby

Electronics 2026, 15(12), 2549; https://doi.org/10.3390/electronics15122549 (registering DOI) - 9 Jun 2026

Abstract

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and [...] Read more.

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma. Full article

(This article belongs to the Special Issue Application of Microgrids in Power System)

17 pages, 3855 KB

Open AccessArticle

Learning Depth from Focus with Multi-Candidate Estimation and Proximal Refinement

by Muhammad Tariq Mahmood

Electronics 2026, 15(12), 2548; https://doi.org/10.3390/electronics15122548 (registering DOI) - 9 Jun 2026

Abstract

In this paper, we propose a novel Depth from Focus (DFF) framework that formulates depth estimation as an energy minimization problem and unrolls the corresponding iterative optimization into a trainable neural architecture. Given a focal stack, a deep feature extractor constructs a learned [...] Read more.

In this paper, we propose a novel Depth from Focus (DFF) framework that formulates depth estimation as an energy minimization problem and unrolls the corresponding iterative optimization into a trainable neural architecture. Given a focal stack, a deep feature extractor constructs a learned focus volume that encodes defocus and structural cues. Based on this representation, multiple candidate depth maps are generated using a plane-based probabilistic formulation, while an attention mechanism adaptively assigns pixel-wise confidence weights to each candidate. The depth estimation is performed through an iterative refinement process, where each stage corresponds to a learned proximal update implemented via lightweight conditional networks. These updates incorporate focus consistency, adaptive step sizes, and learned regularization priors, enabling effective integration of physical imaging constraints with data-driven modeling. A final refinement module further enhances prediction accuracy by fusing the refined depth, focus volume features, and candidate hypotheses to estimate residual corrections. The entire framework is trained end-to-end, ensuring coherent optimization across all components. Experimental results demonstrate that the proposed method achieves improved robustness and accuracy, particularly in low-texture and noisy regions, while preserving interpretability through its unfolding-based design. Full article

(This article belongs to the Special Issue Image/Video Processing and Computer Vision)

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17 pages, 2431 KB

Open AccessArticle

Local LLMs for Industrial Supervision and Control: An Edge AI Event-Driven Architecture for Proactive Operational Context Management in Real Industrial Environments

by Fernando Hidalgo-Castelo, Antonio Guerrero-González, Francisco García-Córdova, Francisco Lloret-Abrisqueta and Antonio Piñera-Marín

Electronics 2026, 15(12), 2547; https://doi.org/10.3390/electronics15122547 (registering DOI) - 9 Jun 2026

Abstract

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning [...] Read more.

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems—consuming 15–30 min per query. Previous work integrated local large language models (LLMs) into a five-layer cognitive architecture deployed in a precast concrete plant, reducing that time to 14–23 s through voice-based conversational queries; however, model inference accounted for 55.3% of total latency and the system remained reactive. This work incorporates the event-driven paradigm as a non-intrusive augmentation layer that keeps the operational context permanently updated, continuously monitoring the process and refreshing knowledge only when significant changes occur. The architecture is fully local, cloud-independent, graphics processing unit (GPU)-free, and containerized via Docker Compose. Experimental results demonstrate a 26–31% reduction in response times (means of 9.84 s, 11.23 s, and 16.47 s for simple, moderate, and complex queries), an 8.4 °C reduction in peak hardware temperature (from 79.6 °C to 71.2 °C), a 41.6% decrease in thermal variability, and an expansion of the safety margin before central processing unit (CPU) throttling from 5.4 °C to 13.8 °C. The system achieved 100% success rate and availability over 30 min of autonomous operation, validated in a real industrial environment. Full article

(This article belongs to the Special Issue Multimodal Intelligence and Digital–Physical Systems in the Industrial Internet of Things)

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34 pages, 738 KB

Open AccessArticle

A Quantum-Adjusted Risk Model for Enterprise Infrastructure Across Data In Transit, In Use, and At Rest

by Simas Krušniauskas, Šarūnas Grigaliūnas, Rasa Brūzgienė and Mert Cayir

Electronics 2026, 15(12), 2546; https://doi.org/10.3390/electronics15122546 (registering DOI) - 9 Jun 2026

Abstract

Enterprise infrastructure operators face a critical challenge in prioritizing post-quantum migration, as quantum-related risk is not uniformly distributed across data in transit, in use, and at rest. Existing assessments rely on system-level evaluations or protocol-specific analyses, which do not capture the heterogeneity of [...] Read more.

Enterprise infrastructure operators face a critical challenge in prioritizing post-quantum migration, as quantum-related risk is not uniformly distributed across data in transit, in use, and at rest. Existing assessments rely on system-level evaluations or protocol-specific analyses, which do not capture the heterogeneity of exposure across infrastructure layers. This paper extends the Quantum-Adjusted Risk Scoring (QARS) model introduced in into an evidence-based, layer-specific framework that evaluates in-transit, in-use, and at-rest data separately. QARS applies a unified five-factor scoring framework separately to each data state and introduces a quantum-vulnerability attenuation mechanism grounded in Grover-bounded residual security that prevents overstating urgency for non-Shor-vulnerable symmetric protection. Observable host-level evidence determines the binary and ratio descriptors used by the model, while the fixed affine mapping coefficients are treated as transparent semi-quantitative calibration parameters. These coefficients are documented separately and subjected to coefficient-level sensitivity analysis to evaluate whether the reported layer ordering depends on their nominal values. The model is demonstrated through an illustrative controlled experiment using real infrastructure observations. Strengthening storage protection reduces the aggregate system risk from 0.707 (high) to 0.414 (moderate), a 41.5% reduction. However, the maximum-layer score remains high (0.657), indicating that the transport layer continues to dominate migration urgency. Sensitivity analysis confirms that the dominance of the transport layer is stable under wide perturbations of the calibration parameters. These findings demonstrate that risk reduction in one layer does not eliminate overall exposure but shifts the dominant vulnerability. By distinguishing between overall system posture and the most critical remediation priority, QARS supports infrastructure operators in identifying high-risk components and planning structured, evidence-based post-quantum migration. Full article

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22 pages, 6277 KB

Open AccessArticle

Two-Stage Fault Diagnosis of Distribution Network Based on MS-CNN and Spatio-Temporal Dual Attention

by Ying Yang, Jinyi Huang, Hao Zhu, Zibin Cai and Weijia Zheng

Electronics 2026, 15(12), 2545; https://doi.org/10.3390/electronics15122545 (registering DOI) - 9 Jun 2026

Abstract

Aiming at the problem of weak fault features and difficult localization of adjacent nodes in distribution networks, we constructed a two-stage cascaded architecture to decouple the diagnosis task into fault classification and section location. The feature layer fuses MS-CNN, SimAM, and Transformer to [...] Read more.

Aiming at the problem of weak fault features and difficult localization of adjacent nodes in distribution networks, we constructed a two-stage cascaded architecture to decouple the diagnosis task into fault classification and section location. The feature layer fuses MS-CNN, SimAM, and Transformer to form a spatio-temporal dual attention mechanism that synchronously captures spatial saliency and global temporal logic. A prototype network is introduced at the fault location decision layer, and metric learning is used to solve the problem of feature aliasing of adjacent nodes. The experimental results show that the accuracy of fault classification and localization are 98.61% and 94.22%, respectively, and it exhibits graceful degradation under extremely low-SNR conditions, which verifies the effectiveness of the proposed strategy in the refined fault diagnosis of distribution networks. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid: 2nd Edition)

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16 pages, 5459 KB

Open AccessFeature PaperArticle

Experimental Evaluation of Spatial–Temporal Interference Mitigation in CRPA GNSS Receivers Under Jamming and Spoofing

by Furkan Karlitepe

Electronics 2026, 15(12), 2544; https://doi.org/10.3390/electronics15122544 (registering DOI) - 9 Jun 2026

Abstract

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative [...] Read more.

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

(This article belongs to the Special Issue INS/GNSS Integration Techniques for Autonomous Navigation Systems)

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14 pages, 4527 KB

Open AccessArticle

3D Coverage Shaping of an On-Glass 5G NR N78 Monopole Using Open/Short-Circuited Stubs

by Fei-Lung Wu, Jung-Sheng Liu, Chia-Mei Peng, Li-Wei Kao, Pei-Hsuan Ko and I-Fong Chen

Electronics 2026, 15(12), 2543; https://doi.org/10.3390/electronics15122543 (registering DOI) - 9 Jun 2026

Abstract

This paper presents a compact modified monopole antenna tailored for 5G NR on-glass automotive applications operating in the n78 band. The design overcomes 3D radiation pattern limitations inherent in conventional monopole and inverted-F antennas (IFAs). Unlike traditional structures where auxiliary branches serve impedance [...] Read more.

This paper presents a compact modified monopole antenna tailored for 5G NR on-glass automotive applications operating in the n78 band. The design overcomes 3D radiation pattern limitations inherent in conventional monopole and inverted-F antennas (IFAs). Unlike traditional structures where auxiliary branches serve impedance matching or grounding, this design integrates open- and short-circuited stubs with a coplanar waveguide (CPW) feed to eliminate discrete components. By utilizing a resonant mechanism distinct from IFAs, it enables precise control over the current distribution and phase on the radiator to achieve passive 3D beam shaping without active switches or arrays. This suppresses the inherent elevation null, enhancing upper-hemisphere radiation. A prototype operating from 3.3 to 3.6 GHz was fabricated on a flexible printed circuit (FPC) and verified on a glass substrate. This study focuses strictly on radiation characteristics at the antenna element level; to ensure a focused investigation on dielectric-antenna interactions, large-scale vehicle body scattering and full-scale vehicle integration are excluded from this scope. The results, including S-parameters, gain, total efficiency, and 3D patterns, demonstrate superior elevation coverage and comparable impedance performance under on-glass boundary conditions. The proposed methodology offers a high-feasibility, low-complexity, and cost-effective solution for passive 3D radiation control in on-glass 5G wireless links. Full article

(This article belongs to the Section Microwave and Wireless Communications)

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23 pages, 3304 KB

Open AccessArticle

Coordinated Control of Dynamic Zoning and Load Shedding for Enhancing Fault Recovery of High-Penetration Renewable Distribution Network

by Wenliang Yin, Yudun Li, Kuan Li and Maozeng Lu

Electronics 2026, 15(12), 2542; https://doi.org/10.3390/electronics15122542 (registering DOI) - 9 Jun 2026

Abstract

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical [...] Read more.

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical electrical interactions among nodes. To address these limitations, this paper innovatively proposes a hierarchical coordinated control framework for distribution network fault recovery, combining dynamic zoning and coordinated load shedding. The core novelty of this research lies in integrating the node electrical correlation degree into the load grading process to assist in coordinating dynamic network dynamic zoning. By comprehensively evaluating real-time power flow, the regulation capabilities of distributed resources, and intra-region electrical correlations, the proposed framework adaptively optimizes both the zoning structure and the load shedding sequence. Simulation results demonstrate that, compared with conventional static or uncoordinated methods, the proposed approach significantly minimizes load loss while improving grid recovery efficiency and voltage stability. Ultimately, this coordinated control strategy effectively enhances the resilience and operational safety of high-penetration renewable distribution networks, providing robust support for distribution network operations under a high proportion of renewable energy integration. Full article

(This article belongs to the Special Issue Stability, Control, and Resilience of Future Power Systems with High Penetration of Distributed Energy Resources)

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15 pages, 1266 KB

Open AccessArticle

A Modular FPGA-Based Smart Multi-Functional Display Architecture for Low-Power and Real-Time Avionics Systems

by Cemalettin Albayrak, Serkan Kurt and Mehmet Cemil Kazanbaş

Electronics 2026, 15(12), 2541; https://doi.org/10.3390/electronics15122541 (registering DOI) - 9 Jun 2026

Abstract

This paper presents a modular FPGA-based Smart Multi-Functional Display (SMFD) architecture designed for low-power and real-time avionics applications. Conventional SMFD systems are typically based on tightly coupled monolithic architectures, which limit scalability, maintainability, and subsystem flexibility while increasing system complexity and power consumption. [...] Read more.

This paper presents a modular FPGA-based Smart Multi-Functional Display (SMFD) architecture designed for low-power and real-time avionics applications. Conventional SMFD systems are typically based on tightly coupled monolithic architectures, which limit scalability, maintainability, and subsystem flexibility while increasing system complexity and power consumption. To address these limitations, the proposed architecture separates processing, display, and communication functions into independent hardware modules, enabling flexible system integration and subsystem-level optimization. It consists of four primary modules: an FPGA-based Programmable Logic Device (PLD) module for deterministic video processing and display timing control, an NXP i.MX8X CPU module for application-level management, a high-resolution LCD module, and a dedicated I/O module supporting avionics communication interfaces, including AFDX and RS422. The architecture combines FPGA-assisted real-time processing with power-aware task partitioning strategies to improve both timing predictability and energy efficiency. Experimental evaluation performed on the implemented hardware prototype demonstrates that the proposed architecture achieves approximately 40% reduction in power consumption compared to a conventional baseline configuration while maintaining real-time operational capability with an average processing latency of 12.7 ms. In addition, the FPGA-based implementation enables dynamic display reconfiguration with a measured switching time of approximately 235 ms. The results indicate that the proposed modular architecture provides an effective balance between power efficiency, real-time performance, scalability, and system flexibility for next-generation avionics display applications. Full article

(This article belongs to the Section Computer Science & Engineering)

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