Search Results (25)

Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently authenticate the classical channel, and it does not prevent implementation side channels. In this work, we introduce ModPhase-8 (QUEST), a proposed QKD modulation and adaptive-receiver architecture evaluated through analytical modeling and simulation. Instead of using only a few quantum signal types, our system uses eight carefully designed signal variations created by adjusting the phase between two very short light pulses. The eight phase states are organized into four phase bases, each containing two antipodal states that encode one binary raw-key value. The enlarged signal set diversifies the physical representation of the key bit and changes the state-discrimination problem faced by an eavesdropper, but it does not increase the raw-key payload beyond one bit per successfully sifted signal. On the receiving side, the system adaptively switches between two measurement techniques based on the prevailing channel conditions. This adaptive detection mechanism enhances reliability and helps maintain low error rates even when the communication channel is affected by noise. We provide an analytical security assessment under the stated collective-attack, source, channel, receiver, and trusted-device assumptions, supplemented by attack-specific analyses of intercept–resend, beam-splitting, source-side multi-photon leakage, and selected implementation-related vulnerabilities. Simulation studies were conducted to examine the physical-layer and post-processing behavior of the proposed protocol under explicitly stated channel, receiver, detector, and finite-sample values. Under the adopted simulation model, ModPhase-8 maintains low error rates in the low- and moderate-noise operating regimes and exhibits favorable receiver-level robustness across the investigated channel conditions. The reported rate values are model-based performance estimates rather than rigorously certified secret-key lower bounds. In particular, Qiskit simulation does not establish a composable security proof or an optimal bound on Eve’s information for the exact eight-state time-bin ensemble. A protocol-specific numerical security analysis incorporating the homodyne–heterodyne measurement operators, post-selection, reconciliation efficiency, finite-size effects, and Eve’s Holevo information remains necessary before definitive rate comparisons can be made. ModPhase-8 should therefore be interpreted as a practically motivated receiver and modulation framework whose security-rate performance remains subject to further protocol-specific analysis. Full article

Autonomous robotic navigation in dynamic environments depends on sensor-derived occupancy maps that are often degraded by occlusion, localization error, dynamic blockage, incomplete observation, and perception noise. These uncertainties can make a nominally short route unsafe after deployment, motivating conservative route selection for collision-aware path planning under sensor-derived map uncertainty. We formulate Conservative Robust Route Selection (CRRS) as a finite-scenario robust optimization and route-selection framework for autonomous robotic path planning under this uncertainty. CRRS constructs a heterogeneous portfolio of candidate routes, scores each route using nominal and plausible-world information only, and applies a validation-frozen conservative override rule that defaults to the scenario ensemble unless a candidate route satisfies predefined feasibility, risk, clearance, and cost-ratio guards. The evaluation protocol separates implementation auditing, candidate-pool expansion, validation-based selector design, frozen confirmation, public-benchmark validation, simulated sensor-model validation, and a controlled validation–held-out mismatch stress test. On the generated 30-domain MovingAI-format benchmark, candidate-pool expansion finds strict-safe-superior candidates in 113/150 matched groups, and the frozen selector reduces plausible collision from 0.1250 to 0.0807 and held-out collision from 0.3053 to 0.2937. On an official long-distance MovingAI subset with 260 queries and a minimum start-goal distance of 100 cells, CRRS reduces the held-out collision from 0.9648 for the scenario ensemble to 0.8822, with 141 wins, zero losses, and 119 ties. In an additional LiDAR/SLAM-inspired simulated sensor-model validation on 200 routed official-query problems, CRRS reduces the held-out collision from 0.4059 to 0.3768 relative to the scenario ensemble. A validation–held-out mismatch stress ablation isolates the conservative override rule: CRRS differs from CVaR-only on 73/260 problems, obtains a lower or equal held-out collision in every comparison, and avoids the 18 harmful held-out losses incurred by CVaR-only relative to the scenario ensemble. The resulting claim is deliberately scoped: CRRS improves aggregate route robustness over a strong scenario-ensemble default on the evaluated robotic path-planning benchmarks, while real-time deployment, live sensor integration with calibrated sensors, physical robot validation, family-level variation, and benchmark-specific uncertainty models remain limitations. Full article

Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can significantly affect the reliable transmission of cross-vehicle semantic features and lead to a degradation in detection performance at the receiver. Although existing semantic communication methods based on DeepJSCC can alleviate the cliff effect of traditional separated source–channel coding under low signal-to-noise ratio conditions, they typically rely on additional external autoencoder structures, which increase model complexity and the deployment burden on vehicular edge computing platforms. Meanwhile, under high compression ratios, these methods struggle to adequately preserve detection-related fine-grained information, such as object boundaries, spatial locations, and local structures. Motivated by these challenges, we develop Mamba-KGSC as a lightweight knowledge-guided semantic communication framework for robust V2V cooperative object detection. At the transmitter, Mamba-KGSC utilizes the internal time-scale parameters of the Mamba-YOLO-T backbone network to generate spatial semantic masks, realizing the sparse encoding and transmission of task-relevant features while avoiding the introduction of complex external codec networks. At the receiver, a multi-source knowledge base constraint verification module is constructed to refine the initial detection results by combining physical consistency screening with visual–physical spatial joint redundancy suppression, thereby suppressing physically inconsistent misdetections and repeated detections induced by channel noise. The experimental evaluation indicates that, under a 50% compression ratio, multiple SNR settings, and different channel models, the front-end semantic communication branch of Mamba-KGSC improves mAP@0.5:0.95 by an average of 1.90 percentage points over the DeepJSCC baseline. The multi-source knowledge base constraint verification module further reduces abnormal and duplicate candidate bounding boxes. Overall, Mamba-KGSC provides a balanced solution in terms of transmission cost, detection accuracy, model complexity, and physical consistency, offering a lightweight implementation scheme for robust V2V cooperative detection in challenging communication environments. Full article

This paper presents a decentralized adaptive generalized-minimum-variance (GMV) control framework for large-scale stochastic nonlinear systems composed of interconnected multi-input multi-output (MIMO) Hammerstein subsystems with unknown time-varying parameters. Each subsystem consists of a coupled multivariable static nonlinearity represented on a known invertible basis, followed by a matrix-polynomial dynamic block affected by colored noise and delayed input–output interconnections. The proposed scheme estimates only identifiable composite Hammerstein parameters through a decentralized recursive extended least-squares algorithm with forgetting, thereby avoiding the non-unique separation of nonlinear and linear gains. A constructive matrix Diophantine identity is established to derive an optimal multi-step predictor, leading to a GMV control law expressed as a multivariable polynomial equation in the current input. Sufficient conditions for real solvability, mean-square boundedness, and near-optimal adaptive tracking are provided using Hadamard–Lévy global-diffeomorphism, minimum-phase, small-gain, persistent-excitation, strict-positive-realness, and convex-projection arguments, and the implemented controller—inexact Newton solver with fallback and persistent dither—is itself covered by the analysis. The analysis further shows that delayed interconnections become measurable and can be exactly compensated, while robustness to basis under-modeling is explicitly quantified. Simulation results on an interconnected two-subsystem MIMO Hammerstein process with coupled cubic nonlinearities, colored noise, delayed interactions, and time-varying parameters—run in the forgetting-factor regime required by the theory, with measured persistent excitation and complete solver diagnostics—demonstrate operational-noise-floor tracking and a $2.3$-fold mean-RMSE reduction relative to the strongest linear-MIMO surrogate, while a channel-wise SISO Hammerstein design fails structurally and a feedback-linearization controller with exactly known nonlinearity offers no advantage. The study further demonstrates scalability on a chain of four subsystems with size-independent per-subsystem computational cost, validates a physically motivated interconnected coupled-tank network with progressive-valve nonlinearities, and confirms agreement between the observed stability limits and the predicted small-gain boundary. Full article

This revised manuscript presents a real-calendar-anchored SEIR simulation benchmark for comparing manifold-constrained Gaussian process inference (MAGI) and physics-informed neural networks (PINNs). The study is explicitly positioned as an empirical benchmarking and reproducibility contribution rather than a new epidemic model or a new inference algorithm. A deterministic proportional SEIR system defines the mechanistic truth, while municipal surveillance records motivate the calendar and observation context. We compare full observation of $E, I, R$ with a missing–exposed regime in which only $I, R$ are observed. A parametric bootstrap with independent log-normal measurement noise generates an expanded ensemble ($B = 80$); this ensemble supports bootstrap medians, interquartile ranges, outlier assessment, and sensitivity analysis under the declared measurement-error model. The revision clarifies the role of the PINN data–physics weight $\lambda$, the oracle MAGI hyperparameter stabilization used in the missing-E experiment, the distinction between MAP estimates and Bayesian posterior uncertainty, and the operational role of PELT changepoint-guided sparse sampling. The results support a balanced conclusion: MAGI is stable in the fully observed setting, whereas PINNs can be competitive under appropriate $\lambda$ choices; the missing–exposed case remains ill-posed and requires cautious interpretation. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

This work investigates a hardware-aware, circuit-level emulation of BB84-like circuit primitives on noisy intermediate-scale quantum (NISQ) processors. The motivation is to evaluate whether BB84-like basis sifting and intercept–resend-induced QBER behavior remain observable when selected BB84 operations are mapped to parallel single-qubit circuits on gate-based devices. The proposed mapping represents Alice’s preparation, optional Eve intercept–resend emulation, and Bob’s measurement as processor-internal circuit layers; it is therefore an on-chip emulation and not an end-to-end optical QKD implementation. Experiments combine real IBM superconducting processors with Qiskit, Cirq, and Azure/Q# simulator-based or noise-modeled evaluations. Baseline QBER was first calibrated for each backend, and intercept–resend experiments then produced a clear QBER separation from the no-eavesdropper condition. The observed sifted-bit utilization was close to the expected 50% BB84 basis-matching reference, while the constant-depth circuit structure supported scalable raw/sifted-bit generation before any classical post-processing. These observations are treated as implementation-level consistency checks and backend-dependent experimental metrics, rather than as new BB84 protocol-level results. Finite-shot uncertainty, calibration drift, and backend-specific noise are treated as limitations of the proposed QBER-based evaluation rule rather than as deployment-level security guarantees. Because the study does not implement a physical quantum channel, authenticated classical communication, error correction, privacy amplification, finite-key security analysis, or general QKD attack models, the reported metrics should be interpreted as raw/sifted-bit experimental metrics and QBER-based disturbance evaluation for BB84-like NISQ emulation, not as secure key rates, secure throughput, or practical QKD deployment results. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

This paper investigates the design and evaluation of compact convolutional neural networks (CNNs) for keyword spotting (KWS) and acoustic event detection under the stringent constraints of the TinyML paradigm. The research expands upon traditional binary classification approaches by addressing a multi-class acoustic scenario encompassing eight distinct categories: stop, no, go, yes, unknown, silence, noise_ambient, and noise_sudden. The primary objective is to evaluate the feasibility of deploying reliable acoustic detection systems on ultra-low-power microcontrollers for edge computing applications. To this end, five lightweight architectures were developed and benchmarked: AlexNet-Tiny, LeNet-Tiny, MobileNet-Tiny, VGG-Tiny, and CustomCNN-Tiny. The models were trained using Mel-spectrogram features and optimized through INT8 post-training quantization to facilitate embedded deployment. Hardware simulation was conducted targeting the XIAO nRF52840 Sense microcontroller (64 MHz, 256 KB RAM). Experimental results demonstrate that the Gold VGG-Tiny architecture achieves the highest classification accuracy ($89.81\%$), while Silver MobileNet-Tiny provides the superior operational efficiency with the lowest inference latency ($0.88$ ms) and minimal energy consumption ($14.4$ µJ). Furthermore, the Bronze CustomCNN-Tiny model achieves the most reduced memory footprint ($42.9$ KB), highlighting its suitability for memory-constrained environments. Statistical validation using Cohen’s Kappa, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC) confirms the robustness and reliability of the proposed models. The potential application of this system is motivated by acoustic monitoring for the early detection of high-risk situations, such as gender-based violence. Future work will focus on on-device physical validation and real-world deployment in wearable safety electronics. Full article

This study proposes a noise-robust unsupervised acoustic anomaly detection framework for early identification of abnormal operating conditions in rotating machinery under submarine-like environments with severe data scarcity. In such environments, underwater background noise and onboard interference sources significantly degrade signal quality, while limited computing resources constrain the deployment of high-complexity deep learning models. To address the lack of labeled fault data, the publicly available MIMII dataset was adopted as a proxy platform, and representative submarine interference sources were physically modeled, including colored background noise, structure-borne resonance, band-limited auxiliary noise, tonal components, and sensor noise. These components were combined and scaled to predefined SNR levels (−6 to 6 dB) to generate realistic noise-augmented data. Three unsupervised approaches were compared under edge deployment constraints: (i) Gaussian Mixture Model (GMM) with statistical MFCC features, (ii) statistical-feature-based Ensemble Autoencoder, and (iii) Conv1D-based Ensemble Autoencoder using 1-s log Mel-spectrogram segments. Performance was evaluated in terms of AUC, F1-score, and computational cost. Results show that GMM provides competitive detection performance with minimal computational burden, whereas Conv1D achieves superior accuracy when temporal fault patterns dominate, at the expense of higher complexity. The study provides practical design guidelines for acoustic anomaly detection under multi-noise and resource-constrained conditions. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\mathbf{\Theta }$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems. Full article

Short-term wind power prediction is pivotal for maintaining the stability of power grids characterized by high renewable energy penetration. However, wind power time series exhibit complex characteristics, including local turbulence-induced fluctuations and long-term temporal dependencies, which challenge traditional forecasting models. Furthermore, the performance of hybrid deep learning models is often compromised by the difficulty of tuning hyperparameters over non-convex optimization surfaces. To address these challenges, this study proposes a novel framework: CPO—BiTCN—BiGRU—Attention. Adopting a physically motivated “Filter–Memorize–Focus” strategy, the model first employs a Bidirectional Temporal Convolutional Network (BiTCN) with dilated causal convolutions to extract multi-scale local features and denoise raw data. Subsequently, a Bidirectional Gated Recurrent Unit (BiGRU) captures global temporal evolution, while an attention mechanism dynamically weights critical time steps corresponding to ramp events. To mitigate hyperparameter uncertainty, the Crowned Porcupine Optimization (CPO) algorithm is introduced to adaptively tune the network structure, balancing global exploration and local exploitation more effectively than traditional swarm algorithms. Experimental results obtained from real-world wind farm data in Xinjiang, China, demonstrate that the proposed model consistently outperforms State-of-the-Art benchmark models. Compared with the best competing methods, the proposed framework reduces MAE and MAPE by approximately 30–45%, while maintaining competitive RMSE performance, indicating improved average forecasting accuracy and robustness under varying operating conditions. The results confirm that the proposed architecture effectively decouples local noise from global trends, providing a robust and practical solution for short-term wind power forecasting in grid dispatching applications. Full article

High-resolution marine geomagnetic anomaly maps are a prerequisite for accurate geomagnetic matching navigation. However, existing compilations are sparse and of low resolution, and conventional interpolation techniques fail to capture fine-scale anomalies. Although deep learning models have achieved remarkable success in natural-image super-resolution, they have rarely been tailored to geomagnetic grid data; their lack of physically motivated constraints frequently introduces geologically implausible structures. To address these limitations, we propose a physics-constrained generative adversarial network (PC-GAN) for the super-resolution reconstruction of marine geomagnetic anomaly maps. Building upon the Super-Resolution Generative Adversarial Network (SRGAN) backbone, we incorporate physics-informed loss terms for spatial continuity and edge preservation into the training objective, thereby endowing the data-driven architecture with geological consistency while maintaining numerical accuracy. Experiments were conducted on the NOAA EMAG2_V3 dataset across four representative marine regions. Over the Philippine Sea Plate, PC-GAN reduces the root-mean-square error (RMSE) by 28.0% and increases the peak signal-to-noise ratio (PSNR) by 2.85 dB relative to bicubic interpolation, and lowers RMSE by 27.5% while raising PSNR by 2.79 dB compared with PSO-Kriging. Ablation studies corroborate that the physics-based modules make a statistically significant contribution to reconstruction quality. PC-GAN furnishes a robust tool for generating high-fidelity geomagnetic reference maps and holds promise for high-precision geomagnetic matching navigation and related applications. Full article

Digitally controlled DC–DC converters are vulnerable to sensor-side spoofing, motivating plant-level anomaly detection that respects the converter physics. We present a physics-informed LSTM (PI–LSTM) autoencoder for a 24→12 V buck converter. The model embeds discrete-time circuit equations as residual penalties and uses a fixed decision rule ($\tau =\mu +3\sigma$, $N=3$ consecutive samples). We study three voltage-sensing attacks (DC bias, fixed-sample delay, and narrowband noise) in MATLAB/Simulink. We then validate the detection path on a TMS320F28379 DSP. The detector attains F1 scores of 96.12%, 91.91%, and 97.50% for bias, delay, and noise (simulation); on hardware, it achieves 2.9–4.2 ms latency with an alarm-wise FPR of ≤1.2%. We also define a unified safety box for DC rail quality and regulation. In simulations, we evaluate a confusion index (CI) policy for safety-bounded performance adjustment. A operating point yields $\mathrm{CI}\approx 0.25$ while remaining within the safety limits. In hardware experiments without CI actuation, the $V_{r,pp}$ and IRR stayed within the limits, whereas the ±2% regulation window was occasionally exceeded under the delay attack (up to ≈2.8%). These results indicate that physics-informed detection is deployable on resource-constrained controllers with millisecond-scale latency and a low alarm-wise FPR, while the full hardware validation of CI-guided deception (safety-bounded performance adjustment) under the complete safety box is left to future work. Full article