Furfural is a pivotal indicator of the aging condition of transformer oil-paper insulation. Traditional analytical techniques such as liquid chromatography require sophisticated pretreatment and phase-separation procedures and are therefore not well suited to rapid oil-sample analysis. This study reports the direct analysis of furfural in complex oil mixtures using surface-enhanced Raman spectroscopy (SERS). The weak vibrational response of furfural in oil was enhanced using a Au-coated silicon nanowire substrate fabricated by metal-assisted chemical etching (MACE). The textured metal-coated surface enabled trace furfural at the μL/L level to be measured directly in the oil mixture without adsorption enrichment or chemical extraction, with the entire test process completed within 1 min. Peak deconvolution was used to extract the fitted area of the 1365 cm−1 band, and the relationship between this Raman response and furfural concentration yielded R2 = 0.9910. Temperature- and pressure-dependent measurements were also performed to examine their effects on the positions of the main Raman peaks. This work demonstrates a rapid approach for analyzing trace furfural in complex liquid mixtures and provides a basis for further spectroscopic studies of transformer oil-paper insulation aging.
Multi-frame fusion is standard for converting frame-by-frame license plate character detections into stable, reliable readings. Character Time-Series Matching (CTM), a leading approach, associates characters across frames using the Hungarian algorithm with a fixed Euclidean distance threshold and a translation-only motion model, reporting 96.7% accuracy on the UFPR-ALPR dataset. In this work, we demonstrate that this high performance is protocol-dependent: when ground-truth static plate crops and pre-segmented tracks are used, CTM performs strongly. However, in real-world scenarios involving moving cameras (such as drone-mounted cameras, helmet-mounted cameras, and mobile platforms) where inter-frame geometry changes dynamically, baseline multi-frame association frameworks that combine fixed spatial gates with unconstrained translation propagation fail. In these cases, temporal fusion provides no benefit and degrades plate recognition performance below the single-frame baseline. Indeed, under its own Intersection over Union (IoU) tracker, this literature method correctly reads only 15.1% of plates in traffic videos recorded with a real moving camera (86 human-verified tracks). To address this vulnerability, we propose Geo-CTM (Geometry-Constrained CTM), an association pipeline integrating height-scaled adaptive matching gates, inter-frame similarity estimation via Random Sample Consensus (RANSAC), transform-guided character coasting, and co-occurrence-constrained duplicate track elimination. Systematic motion-model ablation demonstrates that while the complete association pipeline provides the primary foundation for robustness (raising mean accuracy from 85.40% to over 91.5%), estimating a similarity transform (91.82%) delivers the most physically grounded and identifiable representation on planar plates without estimation degeneration. While our method performs comparably to CTM on ideal data when using the same detector and detections, it minimizes performance loss under geometric distortion conditions where CTM is inadequate. For instance, a statistically significant improvement is achieved under a 0 → 60° perspective change; in real traffic videos, with the tracker held fixed so that the fusion layer is the only variable, performance rises from 15.1% to 26.7% under the IoU tracker of the original system and from 16.3% to 29.1% under ByteTrack (+11.6 and +12.8 points; exact McNemar p=0.021 and p=0.013), whereas changing the tracker alone while holding the fusion layer fixed moves accuracy by only 1–2 points and is not statistically significant. Finally, our error taxonomy analysis demonstrates that on the undistorted benchmark all residual errors correspond to zero-evidence cases beyond the reach of decision-level fusion, while under dynamic perspective distortion errors are dominated by association misalignment, highlighting the specific development areas that future performance improvements must target.
Background: This study analyzed the time allocation characteristics of 24-h movement behaviors among firefighter cadets, clarified their independent associations with physical fitness performance, and identified key predictors of physical fitness using compositional data analysis. Methods: A total of 333 fire academy cadets (326 males, 7 females) from five specialty directions were recruited. ActiGraph GT3X-BT accelerometers were used to objectively measure 24-h movement behaviors over seven consecutive days. Isometric log-ratio (ILR) transformation was applied to compositional time-use data, and bidirectional stepwise regression based on AIC was used to identify key predictors. Robustness checks included bootstrap resampling, 10-fold cross-validation, leave-one-out sensitivity analysis, and influence diagnostics. Results: The optimal model (F = 14.46, p < 0.001, adjusted R2 = 0.260) identified weight (β = −0.42, p < 0.001), sex (β = −16.14, p < 0.001), age (β = 2.00, p < 0.01), height (β = 0.18, p < 0.05), and Fire Rescue Technology (Fire Facilities) specialty (β = −4.87, p < 0.001) as significant predictors. The sex association should be interpreted as exploratory given the very small female subsample (n = 7). All ILR-transformed movement components were removed by stepwise regression. The weekend model showed slightly higher explanatory power (adjusted R2 = 0.270) than the weekday model (adjusted R2 = 0.260). Conclusions: Demographic and physical characteristics, rather than 24-h movement composition, are the primary predictors of physical fitness performance in this highly structured training population. The sex-related finding is exploratory and sample-specific due to the small female subsample. These findings provide evidence-based guidance for physical fitness training program design in fire academies and similar structured training environments.
To apply distributed optical fiber strain sensing (DOFSS) to the monitoring of rainfall-induced landslide deformation of landslide-prone soil slopes, this study proposes a deployment scheme and installation technique for strain-sensing optical fiber cables in soil slopes based on existing engineering experience and validates them through laboratory-scale physical model tests. The results show that direct burial of optical fiber cables in boreholes is a suitable installation method. A 2.0 mm tight-buffered optical fiber cable anchored with 5.0 mm heat-shrink-tube anchors was adopted as the fixed sensing configuration in the laboratory tests. In the laboratory model tests, the locations of strain peaks measured by the optical fiber cables were spatially associated with the visibly deformed regions, indicating that the strain anomalies can be used to approximately locate internal strain-concentration zones. The slope toe was identified as the location where localized failure was first observed, and the second rapid increase in strain at the slope toe may be regarded as a potential precursor of accelerated localized deformation under the tested condition. Borehole spacing has a significant influence on monitoring accuracy and is recommended to be controlled within 20–48% of the horizontal length of the potentially unstable slope zone. The variations in volumetric water content, earth pressure, and wetting front migration at different locations of the slope are highly correlated with rainfall infiltration and the evolution of slope surface erosion.
Autonomous navigation and mooring in confined waters require a navigation solution that remains reliable when individual sensing channels are delayed, unavailable, or environmentally degraded. A risk- and integrity-aware architecture was developed for joint processing of RTK-GNSS, inertial and heading measurements, short-range radar, LiDAR, camera, AIS, ultrasonic ranging, propulsion feedback, environmental, and mooring-line tension data. Asynchronous time alignment is combined with sensor-quality assessment, innovation-based fault detection and isolation, covariance adaptation, active-set reconfiguration, protection-level monitoring, and risk-dependent allocation of authority between automation and the operator. The system was evaluated during a five-day campaign comprising 40 runs, four operational phases, 480 synchronized evaluation epochs, and 16 controlled single-sensor or combined sensor-degradation events. Under nominal conditions, horizontal-position and heading RMSE were 0.043 m and 0.176°, respectively. All 64 fault-active diagnostic records were identified; mean event-log detection and controlled-recovery latencies were 3.17 s and 6.34 s. Horizontal protection-level coverage was 99.3% for single-fault epochs and 100% for combined-fault epochs. One combined-fault docking run contained five consecutive aborted decision epochs, while no unsafe-autonomy event was recorded. The measurements therefore indicate bounded degradation of the navigation solution and conservative transfer of authority when sensing integrity decreases. Because all injected-fault runs were acquired under adverse weather whereas nominal runs were acquired under calm or moderate conditions, the condition-class RMSE differences reported below are descriptive and must not be interpreted as isolated causal effects of sensor faults.
Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications.
Wireless sensor networks (WSNs) play a critical role in cyber-physical applications such as industrial monitoring, environmental sensing, and critical infrastructure management. In these environments, security mechanisms must not only detect malicious activities but also explain how compromised measurements propagate through sensing, aggregation, and decision processes while operating under stringent resource constraints. Existing approaches typically address intrusion detection, trust management, provenance analysis, or blockchain-based integrity independently, providing limited support for integrated and explainable security. This paper presents PhyProvTrust-WSN, a physics-aware framework that combines physics-consistency validation, dynamic provenance graphs, evidence-based trust propagation, multi-factor risk fusion, and selective evidence anchoring to improve the transparency and auditability of secure sensor data aggregation. The framework models sensing, forwarding, aggregation, validation, and response events as a bounded provenance directed acyclic graph (DAG), enabling causal tracing of suspicious activities while maintaining low memory and communication overhead. A weighted risk fusion mechanism integrates anomaly evidence, domain-consistency assessment, trust evolution, and inherited provenance risk to support explainable security decisions. Rather than continuously recording all events, only high-risk or decision-relevant evidence hashes are anchored to a permissioned audit layer, reducing storage and communication costs. To avoid overclaiming, the proposed framework is evaluated using a hybrid methodology that combines attack-labeled WSN datasets, real sensor measurements for physics-consistency validation, and simulation-based overhead analysis. The results demonstrate that the integrated framework provides strong detection capability while improving explainability, supporting root-cause analysis, and maintaining bounded communication and storage overhead suitable for resource-constrained WSN deployments.
Radiation fields around collimated or shielded sources exhibit strong gradients whose accurate delineation is critical for worker protection and emergency response. Mobile robots can survey such fields, but they sample sparsely and irregularly along their trajectories, and it remains unclear which reconstruction method can be trusted, and where. Using a single dominant collimated source in a two-dimensional indoor setting, this study shows that the answer depends decisively on sampling geometry, and proposes a physics-guided Gaussian process (GP) that performs reliably under trajectory-constrained sampling. A tracked robot combining light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) with a γ dose-rate detector surveyed a collimated Cs-137 field in seven independent runs, and all methods were evaluated under both random hold-out (interpolation near visited locations) and spatial block cross-validation (extrapolation into unvisited regions); truth-referenced evaluation against a dense reference field is provided by Poisson-sampled simulations, while experimental accuracy is cross-validated on held-out measurements. Under uniform sampling, a multilayer perceptron (MLP) robustly outperformed GP variants (R2=0.95, stable across 18 seed combinations); under trajectory sampling, its advantage vanished at visited locations and reversed catastrophically in unvisited regions. The proposed physics-guided GP, which uses a fitted collimated-beam template as the GP mean with a Matérn 3/2 residual process, achieved the highest extrapolation R2 (median 0.61; best baseline 0.31), reduced the extrapolation error by 32–69% relative to all eight baselines, recovered interpretable source parameters, and provided predictive intervals with approximately calibrated region-level coverage (point-wise error ranking remains weak); a runtime fit-quality gate further renders the correctness of the embedded prior an observable quantity, so the method flags when its own assumptions fail. These results offer quantitative guidance for method selection in robotic radiation mapping under the as low as reasonably achievable (ALARA) principle.
Clock steering, the core time-frequency technology for high-precision single-satellite time reference generation in global navigation satellite systems, can effectively combine the excellent short-term stability of Oven-Controlled Crystal Oscillators (OCXOs)—whose top performance indicators have already surpassed many space-borne atomic clocks in recent years—with the superior long-term stability of atomic clocks, to obtain time signals with optimal full-range stability. This paper proposes a novel clock steering scheme that integrates an adaptive variational Bayesian Kalman filter and a Proportional-Integral-Derivative (PID) automatic controller: the filter constructs a separable variational approximation for the joint posterior distribution of clock states and measurement noise parameters, to achieve real-time adaptive estimation of noise at each timestamp, while the PID controller performs closed-loop fine adjustment on the output frequency. Comparative simulations with the classic Linear Quadratic Gaussian (LQG) control scheme verify that the proposed method can generate steered time signals with better stability performance in both short-term and long-term dimensions. This work further investigates the influence of measurement noise at different intensity levels on clock steering performance and conducts corresponding mechanism analysis supported by quantitative data. The proposed scheme and conclusions can provide a reliable reference for selecting appropriate clock steering strategies under different noise conditions.
Quantum communication is emerging as a foundation for next-generation networks, offering unprecedented capabilities in security, entanglement-based connectivity, and distributed computation. However, the classical Open Systems Interconnection (OSI) model, designed for deterministic, error-tolerant systems, is incompatible with quantum phenomena such as decoherence, probabilistic entanglement, and the no-cloning theorem. This paper surveys and redefines the OSI model for quantum networking in the context of 7G systems. We propose a Quantum-Converged OSI stack by extending the classical seven-layer model with two additional layers: (i) Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and (ii) Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. The survey synthesizes over 150 research works published between 2018 and 2025, classifying them by OSI layer, enabling technologies (e.g., Quantum Key Distribution, Quantum Error Correction, and Post-Quantum Cryptography), and application domains such as satellite quantum links, quantum IoT, and federated edge systems. We further provide a taxonomy of cross-layer enablers and discuss simulation tools, including NetSquid, QuNetSim, and QuISP. Finally, an evaluation framework with quantum-native metrics, such as entropy throughput, coherence latency, and entanglement fidelity, is introduced, along with open challenges for programmable stacks, digital twins, and AI-defined quantum agents. The specific and novel contribution of this work is a Quantum-Converged OSI stack that extends the classical seven-layer model with two additional layers: Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. Unlike prior technology-centric surveys, the proposed framework classifies over 150 research works by OSI layer, maps enabling technologies (QKD, QEC, PQC) and application domains (satellite quantum links, quantum IoT, federated edge systems) to their functional layers, and introduces a quantum-native evaluation framework based on entropy throughput, coherence latency, and entanglement fidelity. This layer-resolved synthesis, together with the formal definition of cross-layer quantum-native metrics, constitutes the principal novelty distinguishing this survey from existing quantum-networking reviews.
Accurate extraction of the spatial distribution of buildings from remote sensing imagery in complex urban environments is essential for urban planning and development. However, existing methods often suffer from high computational costs and insufficient building boundary recovery, making it difficult to achieve both efficient and accurate building extraction. To address these limitations, this study proposes LGAS-UNet, a lightweight network for building extraction. Based on the UNet architecture, LGAS-UNet replaces the original encoder with LSNet and incorporates a Global Context Aggregation Module (GCAM), Attention Gates (AGs), and Self-Calibrated Convolution (SCConv) modules into the encoder–decoder bridge, skip connections, and decoder feature-fusion units, respectively. These components enhance the global contextual representation of deep features, suppress irrelevant background responses during cross-level feature propagation, and improve feature fusion and boundary detail recovery during decoding. Experiments were conducted on the public WHU Building Dataset and a Zhengzhou building dataset constructed from satellite imagery. With only 6.12 M parameters and 3.64 G FLOPs, LGAS-UNet achieved an intersection over union (IoU) of 86.24%, an F1-score of 92.61%, and a boundary F1-score (BF-score) of 87.84% on the WHU dataset, achieving the best overall performance among the compared methods. On the Zhengzhou building dataset, LGAS-UNet achieved an IoU of 72.37%, an F1-score of 83.97%, and a BF-score of 74.09%, representing improvements of 1.70, 1.16, and 2.11 percentage points, respectively, over UNet. These results demonstrate that LGAS-UNet can efficiently and accurately extract buildings from remote sensing imagery in complex urban environments, providing a practical methodological reference for urban planning and management.
Acoustic emission (AE) sensing is widely used for structural health monitoring, but conventional piezoelectric sensors can be constrained in high-temperature and radiation environments. This study evaluates whether an Idaho National Laboratory-developed magnetostrictive AE sensor retains sufficient information for automated impact-source classification under a controlled room-temperature configuration. A total of 334 synchronized acquisitions from six impact classes were recorded at 20 MHz using three fixed, non-coincident sensor channels. Because sensor type was not independently varied from position, mounting, coupling, bandwidth, or propagation path, comparisons represent complete sensor-channel responses rather than isolated transduction mechanisms. Each waveform was represented by 16 time-domain, spectral, and band-power features. Random forest classifiers were evaluated for individual channels, 48-feature fusion, and equal-vote decision fusion using 30 repeated stratified holdout runs and stratified 10-fold cross-validation. The magnetostrictive channel achieved 95.35 ± 2.45% mean holdout accuracy, compared with 92.37 ± 2.94% and 96.31 ± 1.98% for the two commercial channels. Feature-level fusion achieved 97.12 ± 2.19% and 97.91 ± 3.18% under holdout and 10-fold cross-validation, respectively. Because class-specific blocks were split at the event level, these accuracies are within-campaign and within-specimen, may be optimistic, and do not establish block-independent or operational generalization; validation using randomized or interleaved repeated blocks, multiple specimens, position-controlled comparisons with swapped, rotated, or co-located sensor placements, and harsh-environment testing remains necessary.
Adverse weather degrades LiDAR semantic segmentation by altering return density, measured intensity, local geometry, and feature reliability. We study source-only domain generalization from clear-weather or synthetic training data to unseen real adverse-weather scenes. WeatherMamba is a geometry- and reliability-aware state-space framework that integrates multiscale neighborhood aggregation, reliability-conditioned feature refinement, and efficient long-range sequence modeling. Its central component, Weather-Guided Reliability Gating (WGRG), derives a continuous point-wise gate from conditionally normalized measured intensity, multiscale local density, and latent features. This gate bounds residual corrections without an externally supplied weather type, prior target-reflectance values, or deployment-time meteorological measurements. A controlled pseudo-weather strategy models signal perturbation and point-support loss separately during source-domain training. WeatherMamba achieves 37.4% mIoU on SemanticKITTI→SemanticSTF and 22.8% on SynLiDAR→SemanticSTF, ranking third overall while attaining the highest class IoU in five classes under each protocol. In the single-seed module ablation, the complete configuration improves the module-free baseline from 29.4% to 37.4%. Relative to PTv3 with Modules, WeatherMamba reduces parameters, FLOPs, latency, and peak allocated memory by 19.1%, 34.8%, 16.5%, and 25.2%, respectively, with a 0.4-point reduction in mIoU. These results indicate a competitive accuracy–efficiency trade-off under the evaluated protocols.
A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions.
Fibre-optic Fabry–Pérot (FP) multi-cavity sensors hold significant promise for multi-parameter measurements in extreme environments. However, precisely demodulating signals from each cavity within the composite interference spectrum remains a key bottleneck that limits measurement accuracy. To address this issue, this paper proposes a high-precision demodulation method that combines digital filtering with distortion region suppression. This method first uses digital bandpass filters to separate the interference signals from each sub-cavity from the composite spectrum. It then removes the spectral regions distorted by the filters, retaining only the stable central spectral band for interference order fitting and cavity length calculation. To comprehensively evaluate the method, we systematically compared the distortion characteristics of six finite impulse response (FIR) window-function filters and three infinite impulse response (IIR) filters during spectral separation, along with their impact on demodulation accuracy. Simulation results show that, within the 490–510 μm cavity-length range, FIR filtering combined with distortion suppression reduces the demodulation error to below 0.04 nm, approaching the theoretical limit. In contrast, the best-performing IIR filter still yields an error of 0.58 nm after the same processing. Furthermore, in simulations covering a wide range of 200–800 μm, the maximum demodulation error of this method is less than 0.53 nm, demonstrating its excellent robustness. In dual-cavity temperature sensing experiments, the partial-spectrum demodulation strategy with distortion suppression reduced the standard deviation of cavity-length fluctuations from 0.112 nm to 0.072 nm, and the maximum adjacent point jump from 2.158 nm to 0.464 nm, compared with full-spectrum demodulation. This significantly enhances the continuity and stability of demodulation. This study not only provides a high-precision, highly robust demodulation scheme for multi-cavity FP sensors but also offers clear theoretical and experimental grounds for selecting and optimising digital filters in practical engineering applications.
We present the vicarious calibration and validation of atmospheric correction for the airborne Watersat Imaging Spectrometer Experiment (WISE) sensor, deployed over the south-west shores of Anticosti Island (Gulf of Saint Lawrence). Coincident in situ water-leaving reflectance was acquired from the research vessel Coriolis II (HyperSAS, above-water radiometry) and from a jet-ski (underwater radiometry). The HyperSAS Hyperspectral Surface Acquisition System dataset was used for calibration, while the jet-ski dataset was split into 30% calibration and 70% validation. We evaluated Ratio and Empirical Line (EL) calibration approaches and investigated the influence of residual sun glint. Modeled vs. measured at-sensor reflectance residuals show a clear dependence on angular distance to the specular direction, demonstrating that geometry-dependent glint bias affects vicarious gain estimation. The Ratio approach performs adequately with a limited calibration range (∼50% accuracy at 500 nm) but degrades when applied to a broader matchup dataset. In contrast, the EL method provides more stable gains by accommodating both additive and multiplicative discrepancies (∼20% accuracy at 500 nm). These results highlight the importance of accounting for residual sun glint in airborne aquatic imaging spectroscopy (for calibration or other purposes) and support the EL with Standard Major Axis regression as the most robust calibration strategy for quantitative retrieval of water-leaving reflectance from WISE.
Instrumented rock-bolt monitoring requires strain records that retain integrity and asset context after local processing and wireless transmission. An Internet of Things (IoT) strain-sensing system with LoRa telemetry and asset metadata was evaluated on a cantilever fixture. The node converted tare-referenced bridge counts to apparent strain, applied temperature compensation using a lagged temperature term, evaluated alarms locally, and buffered summaries for LoRa transfer. Histories were assessed using sequence continuity and a 32-bit cyclic redundancy check (CRC-32). Ten monotonic loading runs showed linear responses to nominal screw advance (R2 > 0.9997); fitted slopes had a coefficient of variation of 0.55%. Across three fixed-setting tests spanning 5.1–8.0 °C, unchanged embedded compensation reduced the magnitude of fitted apparent thermal sensitivity by 89.1–95.5%. In all 45 operator-controlled alarm trials, trigger or non-trigger outcomes matched expectations recorded before dashboard inspection. Of 60 planned application-layer history transfers, 50 passed; the remaining ten comprised four failures, three interruptions, and three invalid or contaminated trials. Passed transfers included exact reconstruction of a 1024-record circular buffer. Retrieved metadata passed CRC-32 verification and matched the registered laboratory asset. These results characterize a one-asset laboratory workflow before packaged-bolt, underground, and multi-asset validation.
Sound source localization based on microphone arrays has attracted extensive research interest in the fields of speech communication, detection and tracking. However, conventional near-field beamforming typically assumes free-field propagation and ignores coherent ground reflections, which bias phase estimates at floor-mounted arrays and degrade localization accuracy. To address this, the total Green’s function is reformulated by embedding the image-source reflected path directly into the beamforming propagation model. Unlike conventional approaches that assume free-field propagation, this formulation enables phase compensation to jointly account for the direct and ground-reflected wavefronts. The in-phase superposition of signals is then realized through frequency-domain phase compensation, and the sound source position is estimated via the maximum likelihood criterion. Subsequently, a dual-mode operation mechanism of automatic broadband scanning and manual single-frequency analysis is implemented, with frequency-weighted wideband spatial spectrum processing for noise and aliasing suppression. Finally, a real-time sound source localization system based on a 4 × 4 MEMS array is designed. Experimental results show that sound source localization in a broad frequency band can be achieved at standoff distances of 0.1 m–0.3 m from the array plane, with a localization accuracy of less than 0.015 m. The proposed method demonstrates strong robustness against ground-reflection interference and has potential applications in acoustic monitoring, industrial fault diagnosis and other related areas.
Flexible flatfoot (pes planus) alters lower-limb biomechanics and plantar-pressure distribution, raising the risk of pain and injury. Laboratory gait analysis with optical motion capture and force plates is the reference standard but is costly, space-constrained, and ecologically limited. We present the design, fabrication, and validation of a low-cost, sustainable smart insole for Internet-of-Things (IoT) remote body-load monitoring. The device pairs a dual-layer natural-rubber body—a silica-filled sponge–rubber upper for comfort and a carbon-black-reinforced solid outsole for durability—with four load cells per insole at high-pressure plantar landmarks, read through a 24-bit amplifier by an ESP32 that calibrates and streams left/right load over Wi-Fi to the ThingSpeak cloud, with a wrist-worn OLED for real-time feedback. Against reference weights in 25 participants, the system measured total body weight with a mean absolute error of 2.94%, a maximum error of 4.18%, and an RMSE of 1.94 kg (Pearson r = 0.99); the residual was an almost purely systematic proportional bias (slope 0.966, R2 = 0.98) removable by a single in-sample scalar recalibration. In 30 adults (15 normal-arch; 15 flexible flatfoot), spatiotemporal gait parameters were compared while both groups wore the smart insole. Forward-progression parameters, including step length, stride length, and walking velocity, did not differ significantly between groups during comfortable walking (all p > 0.18). The flatfoot group showed a wider mediolateral base—greater stance width during standing (+14%, p = 0.008) and step width during comfortable walking (+23%, p = 0.040, uncorrected). After correction for multiple comparisons, only the reduction in fast-walking cadence remained statistically significant. A sustainably sourced, affordable smart insole can thus deliver clinically meaningful remote body-load monitoring. The findings also point to a dissociation: forward propulsion was comparable between the groups while the insole was worn, whereas the mediolateral base remained wider in flatfoot. Controlled trials pairing orthotic support with active gait retraining are therefore warranted.
To address the issues of subcarrier orthogonality loss and inter-carrier interference (ICI) caused by carrier frequency offset (CFO), this paper proposes and experimentally validates a frequency offset estimation (FOE)-assisted dual-domain Transformer equalizer within an advanced, high-capacity optical-heterodyne radio-over-fiber (RoF)–wireless orthogonal frequency division multiplexing (OFDM) transmission system. To rigorously test the algorithm’s robustness under extreme physical conditions, the experimental platform integrates offline 16-GBaud signal generation, optical I/Q modulation, dual-optical-tone transport over a single-mode-fiber RoF feeder, remote photonic heterodyne frequency conversion based on a uni-traveling-carrier photodiode (UTC-PD), 4.6 km free-space wireless transmission, and 160-GSa/s ultra-high-speed real-time sampling. In this system, the receiver front-end employs an FOE module to pre-compensate for the dominant global CFO-induced phase rotation; subsequently, a low-complexity, compact local-window Transformer is utilized to perform adaptive residual compensation for local data-dependent impairments—such as residual waveform distortion and residual ICI—in both the time and frequency domains (before and after the Fast Fourier Transform, or FFT). This synergistic architecture, combining a physical model-driven approach with a self-attention mechanism, effectively mitigates the adverse impact of global frequency offset on neural network convergence. Experimental results demonstrate that, under conditions of strictly aligned multiply accumulate (MAC) operation complexity, the dual-domain architecture achieves significantly superior performance—in terms of bit error rate (BER), error vector magnitude (EVM), and constellation quality—compared to traditional linear DSP methods and baseline networks such as DNNs, CNNs, and LSTMs. Operating in 16 GBaud QPSK mode with an input optical power of 0 dBm, the system achieves a BER of 1.89×10−4, representing performance improvements of approximately 5.98-fold and 1.92-fold over the standalone Transformer and FOE-assisted DNN schemes, respectively.
