Search Results (374)

Efficient laser cleaning of glazed ceramic tiles requires the ink layer to be removed without damaging the brittle glaze. We investigated the removal of acrylic ink using a 1064 nm, 10 ns Nd:YAG laser operating at 1 Hz. The lens-to-sample working distance L, pulse energy, and pulse number were varied, and the cleaned regions were evaluated by optical microscopy (OM), scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM–EDS), theoretical analysis, and COMSOL simulation. At L = 20 cm, the sample was close to the nominal focal plane, and the high local fluence removed the ink rapidly, but it also produced whitening, depressions, micro-pits, and glaze damage. Increasing L to 25–30 cm enlarged the measured spot diameter, lowered the average fluence, and widened the controllable cleaning range. Two low-damage conditions were identified at L = 30 cm: 30.80 J/cm² with 3 pulses and 41.00 J/cm² with 2 pulses. SEM–EDS showed that cleaning quality cannot be judged from exposed area or carbon content alone; morphology, preservation of the native glaze microstructure, the C/O ratio, and recovery of substrate-related elements must be considered together. Under the stated model assumptions, the calculated local temperature exceeded the acrylic decomposition temperature, and the thermoelastic stress reached tens to hundreds of MPa. These results make thermal decomposition and stress-assisted interfacial separation physically plausible. However, the present data do not separate thermoelastic stress from pressure-wave loading. Likewise, visible air breakdown and non-monotonic cleaning at high pulse energy only suggest possible plasma-related attenuation because plasma density and transmitted laser energy were not measured. The reported combinations should therefore be regarded as a system-specific process window rather than a universal optimum. Full article

Internal temperature distributions in black tea fermentation regulate enzymatic oxidation, heat accumulation and fermentation uniformity, but continuous three-dimensional measurements remain difficult in practical processing. We developed a CFD-prior-constrained physics-informed neural network (PINN + CFD) to reconstruct the three-dimensional thermal state of a 1.35 m × 0.96 m × 0.08 m fermentation bed under sparse sensing. Nine sensors at z = 0.04 m were used for training, and six held-out depth-wise sensors at z = 0.02 m and z = 0.06 m were reserved for depth-wise validation. The model integrated measured temperatures, transient heat-transfer physics, convective boundary conditions and a CFD-derived volumetric soft spatial prior, which guided spatial extrapolation rather than serving as ground-truth temperature data. Although the multilayer perceptron achieved the lowest fitting error at the instrumented z = 0.04 m plane, PINN + CFD showed better depth-wise extrapolation, with RMSEs of 0.128, 0.129 and 0.296 °C across the three stages. However, its advantage was stage- and validation-target-dependent: the baseline PINN was slightly better in part of the dynamic-stage validation, and standalone CFD had the lowest surface infrared error in the constant-temperature stage, indicating that PINN + CFD mainly improved spatial extrapolation rather than uniformly minimizing all error metrics. The inferred apparent process-level heat-source index Q_react(t) varied continuously, and its cumulative trajectory showed a descriptive association with cumulative polyphenol loss. These results indicate that PINN + CFD enables physically consistent thermal-state reconstruction within the tested sparsely instrumented black tea fermentation bed. Full article

Continuous stream-oriented data-plane processing in 5G/6G edge nodes increases the energy and latency cost of CPU-centered execution. This paper studies this boundary on an Intel Cyclone V SoC FPGA and proposes Hardware Accountability: a partitioning discipline in which Linux performs supervisory control while high-rate payload processing remains in programmable logic. The evaluation uses the Strumok stream cipher, adopted as the Ukrainian national standard DSTU 8845:2019, as a secure fronthaul/payload workload with XOR- and shift-dominated logic. On the evaluated USB 2.0/Cortex-A9/Linux path, the software-driven stream approaches saturation near 20 MSPS. In contrast, the RTL core reaches 9.6 Gbps at 150 MHz and occupies less than 6% of the available logic. Quartus Prime vectorless power analysis estimates 24.00 mW dynamic power for the RTL computational core, corresponding to approximately 2.5 pJ/bit. Control-plane measurements show P99 orchestration jitter below 1 ms under Spatial Isolation, conservative full context reloads near 1290 per second, and more than 7200 shadow-register context/state update operations per second. A design-space exploration then projects an 83.2 Gbps multi-core data path when external DDR traffic is avoided through internal stream aggregation and elastic buffering. Full article

The transition towards decarbonised energy systems, often characterised by high photovoltaic penetration, imposes crucial challenges for operational security, flexibility and grid resilience. In this landscape, meteorological-data reliability has emerged as a strategic pillar to mitigate systemic risks arising from forecasting uncertainty, including grid imbalances, electricity-market volatility, and structural asset safety during extreme weather. This study provides a comparative analysis of four forecasting providers, evaluating their accuracy across atmospheric and solar irradiance parameters through heterogeneous datasets spanning diverse climatic zones and seasons. The analysis is performed by employing a dual-source validation framework that benchmarks every forecast against independent, real-world references rather than the providers’ own model-derived observations: first, the atmospheric variables are compared with real surface-station measurements; second, plane-of-array irradiance is benchmarked directly against on-site sensors at operational photovoltaic plants. This dual-source approach isolates systematic model biases relative to real-world environmental conditions, yielding a provider-independent estimate of accuracy against the true atmospheric and irradiance state. This work therefore proposes not merely a comparative analysis but a validation methodology that addresses a specific limitation of existing forecast-comparison approaches, offering actionable insights to minimise financial and operational risks while fostering a secure, resilient, and sustainable energy infrastructure. Full article

In recent years, the rapid evolution of large artificial intelligence (AI) models has placed unprecedented demands on the computing power of data center servers, driving an explosive growth in data center computing requirements. The power consumption of core computing components, represented by GPUs, has surged dramatically. When facing extremely high power densities, the traditional 12 V single-voltage power delivery architecture exposes severe limitations, including increased transmission link losses, thermal management difficulties, and low system efficiency. To address these challenges, this paper proposes and designs a hierarchical 54 V/12 V dual-plane multi-phase DC power delivery architecture for high-computing-power AI servers. By conducting refined hierarchical identification of system loads, this architecture introduces a 54 V high-voltage DC power plane for high-power loads while retaining the 12 V power plane for conventional loads. Within each power plane, multi-phase interleaved parallel Buck converters integrated with Turbo-COT control strategies and high-density DrMOS are deployed. Experimental results demonstrate that this power architecture exhibits excellent electrical characteristics: under steady-state conditions, the peak-to-peak (PK-PK) ripple voltage fluctuation amplitude of the 54 V power plane under different loads is compressed to between ±0.22% and ±0.26%, while the PK-PK ripple voltage fluctuation amplitude of the 12V power plane under different loads reaches ±0.66% to ±0.68%; in dynamic load step (0–50% and 50–100%) tests, the PK-PK voltage fluctuations of the 54 V plane are ±1.42% and ±1.33%, whereas the PK-PK voltage fluctuations of the 12 V power plane are ±2.36% and ±1.83%. Furthermore, the peak conversion efficiency of the 54 V power plane approaches 97%, and the maximum efficiency of the 12 V power plane reaches 94%, showing a measurable efficiency improvement under the tested conditions. The hierarchical multi-phase power delivery technology comprehensively reduces power supply link losses and enhances power stability, providing an important theoretical basis and engineering reference for the design of next-generation high-density AI servers and the optimization of green, energy-saving networks in data centers. Full article

Superpoint Transformers use superpoints as the basic processing units, thereby significantly reducing the number of tokens processed by Transformers. However, they have been seldom employed in point cloud roof plane instance segmentation, and existing superpoint Transformers suffer from limited performance due to the use of low-quality superpoints. To address this challenge, we establish a set of criteria that high-quality superpoints for Transformers should satisfy and introduce a corresponding two-stage superpoint generation process. The superpoints generated by our method not only have accurate boundaries, but also exhibit consistent geometric sizes and shapes, which greatly benefit the feature learning of superpoint Transformers. To compensate for the limitations of deep learning features when the training set size is limited, we incorporate multidimensional handcrafted features into the model. Additionally, we design a decoder that combines a Kolmogorov–Arnold Network with a Transformer module to improve instance prediction and mask extraction. Finally, our network’s predictions are refined using traditional algorithm-based post-processing. For evaluation, we annotated a real-world dataset and corrected annotation errors in the existing RoofN3D dataset. Experimental results show that our method achieves state-of-the-art performance on our dataset, as well as both the original and corrected RoofN3D datasets. Our model also shows significant advantages over existing methods when handling data with low point density, large density variations, or low 3D point precision. Moreover, it is not sensitive to plane boundary annotations during training, significantly reducing the annotation burden. We will release our code, trained models, and datasets. Full article

Industrial digitalization is moving from Industry 4.0 toward Industry 5.0’s emphasis on resilience, human-centric operation, and sustainability. This shift is enabled by the convergence of Operational Technology and Information Technology, but this integration also broadens the exposure of industrial infrastructures to cyber threats targeting communication integrity and process continuity. Mitigating these risks requires network control that is both programmable and aware of each asset’s operational context. However, there is still a lack of operational interfaces that translate the semantics of industrial assets into programmable, runtime-enforceable network behavior. In this paper, following a Design Science Research methodology, we introduce an asset-aware, closed-loop network control abstraction in which the industrial network itself is modeled as a managed asset through Asset Administration Shells. Asset state, lifecycle phase, and operational intent are translated into network policies enforced at runtime on programmable data planes, while in-network telemetry is exposed at the asset level and correlated with operational metrics. We validate the abstraction on a hybrid testbed that combines virtualized components with industrial-grade hardware and virtualized 5G connectivity, through three security-oriented use cases: (i) asset-driven customization of forwarding policies; (ii) human-centric secure maintenance with controlled remote access over 5G; and (iii) anomaly detection and isolation based on cross-layer telemetry correlation. The results show that asset-level operations can drive programmable network enforcement and make network telemetry available at the asset layer. Finally, the work outlines a first step toward standardizing network-oriented asset submodels by separating control-plane operations from data-plane state and telemetry. Full article

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration. Full article

Reconstructing building models from urban SAR tomography (TomoSAR) point clouds is often constrained by limited resolution, low positioning accuracy in elevation, as well as data incompleteness and artifacts caused by microwave imaging mechanisms. These challenges seriously restrict the extraction of high-accuracy building models with structural details from TomoSAR point clouds. This paper proposes a refined urban building modeling method that effectively utilizes structural priors, including directionality, orthogonality, and potential symmetry. First, a piecewise fitting strategy integrated with density-based segmentation is employed to iteratively estimate the main directions of the buildings and capture finer geometric variations of complex façade footprints than simple-plane approximations. Second, a roof extraction algorithm combining an adaptive Doug-las–Peucker approach with symmetry evaluation and constraints is developed to regularize roof outlines and repair data defects. Crucially, to handle extreme cases where roof data are entirely missing, a novel building width estimation method based on building shadow analysis is proposed. Experiments conducted on the SARMV3D-1.0 and SARMV3D-3.0 point cloud datasets demonstrate that the proposed method significantly enhances reconstruction accuracy and geometric fidelity in urban regions compared to state-of-the-art approaches. Full article

The morphological and structural state of rough solid electrodes usually has a complex effect on the kinetics of an electrochemical process. In order to correctly distinguish the influence of different factors on the rate of an electrode reaction, it is necessary to first separate a purely geometric current rise caused by the surface area increase. At the same time, it is necessary to take into account that surface roughness itself often not only leads to a geometric rise in the electrode area, but also contributes to a change in the kinetic parameters of the electrochemical process. As a consequence, the conclusion regarding an electrocatalytic effect will be reasonable only if the roughness effect is correctly taken into account. The most difficult problem is to establish the role of roughness when experimental electrochemical data are obtained under mixed diffusion–kinetic control of the electrode process. However, the use of appropriate theoretical approaches is required to correctly determine the kinetic characteristics of the electrochemical stage, i.e., of the charge transfer stage. This paper establishes the influence of the morphology and structure of electrodeposited copper coatings on the kinetics of the cathodic reduction of nitrate ion, which occurs in a mixed diffusion–kinetic mode, using the theoretical model of chronoamperometry of an electrochemical process on a rough electrode developed earlier by the authors. Several Cu-electrodes with roughness and structure, the parameters of which vary widely enough, were obtained by cathodic deposition from sulfate solutions of different compositions. The integral (roughness factor) and local (average roughness) characteristics of the surface morphology were determined by methods of underpotential deposition and atomic force microscopy, respectively. Structural investigation of the electrodeposited coatings was carried out by X-ray diffraction to determine their crystallographic structure and average crystallite size. The methods of voltammetry and a rotating disk electrode revealed the mixed kinetics of the electroreduction of ${\mathrm{NO}}_3^{-}$ ions. The kinetic parameters of the charge transfer stage on the copper coatings with a roughness factor of f_r ≤ 3.5 are determined for the first time in this paper by treatment of the experimental current decay curves with the non-linear theoretical equation obtained by the authors for the chronoamperogram of the process on rough electrodes. It was found that the rate constant of the charge transfer stage and the exchange current density of the nitrate ion electroreduction increase by about 50%, with an increase in the average surface roughness from 25 to 120 nm. Considering that this effect is not caused by a purely geometric increase in the true surface area of the electrode, and that the average crystallite size is approximately the same (25 ± 2 nm) for all investigated coatings, it can be concluded that the electrocatalytic activity of copper increases in the reaction of the cathodic reduction of nitrate ions during the transition to copper electrodes with the higher average surface roughness. Taking into account XRD data, the role of the structural and morphological state in the kinetics of the electroreduction of nitrate ions has been established. The smoothest polycrystalline coating was found to be the least electrocatalytically active in this reaction. On the contrary, the roughest coatings with the most prominent plane (220) show the highest activity, which increases with increasing average roughness, possibly due to the growth of defects and excess energy of such curved surfaces. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article

Magnetic skyrmions have emerged as promising candidates for next-generation nanomagnetic devices owing to their stability, nanoscale size, and efficient manipulability. In this work, we demonstrate the deterministic creation of skyrmions using a single ultrafast laser pulse in a thin ferromagnetic film. Through micromagnetic simulations, we model the effect of a focused picosecond laser pulse on a Pt/Co-based multilayer with interfacial Dzyaloshinskii–Moriya interaction (DMI). We find that above a threshold laser fluence, or equivalently, a critical pulse duration, a stable 25 nm Néel-type skyrmion diameter is created at low temperature under a modest out-of-plane magnetic field. Our results demonstrate that skyrmions can be written deterministically by a single picosecond laser pulse, eliminating the need for multiple exposures or electrical stimuli. This work systematically identifies the ultrafast excitation and material-parameter ranges that enable stable solitary skyrmion nucleation in experimentally realistic magnetic multilayers. This can be a foundation for photonic-spintronic integration, enabling optical data writing and magnetic storage, offering a pathway toward ultrafast, energy-efficient, and contactless control of topological spin states for future memory and logic applications. Full article

In a previous paper, in-plane magnetoresistance results were reported on a thin strip-shaped foil sample of nanocrystalline (nc) Ni metal. These studies have now been complemented by a measurement of the temperature dependence of the resistivity as well as the field dependence of the resistivity and the Hall effect on the same sample at 3 K and 300 K in polar magnetic fields up to 140 kOe, i.e., with the magnetic field perpendicular to the strip plane. Due to the strong contribution of grain-boundary scattering in the nc state, the residual resistivity was about 11% of the room-temperature value. The polar magnetoresistance (PMR) showed similar behavior to the previously reported transverse magnetoresistance (TMR), yielding an anisotropic magnetoresistance (AMR) value in good agreement with the AMR previously deduced from the in-plane MR data. As to the Hall effect, the results for the ordinary (R_o) and the anomalous (R_s) Hall coefficient fitted rather well with the rather dispersed reported data of bulk Ni at both temperatures. However, a closer look at the R_s values for nc-Ni revealed that at 300 K it is larger and at 3 K it is smaller than the corresponding bulk Ni values obtained on samples with the same zero-field resistivity as our nc-Ni foil. These deviations may be attributed to the nanocrystalline state containing a large density of grain boundaries. Full article

IEC 61850 digital substations depend on communication services whose compromise can affect protection, supervision, and control. Existing work has advanced substation threat modeling, cyber-physical testbeds, and intrusion detection, but the relation between structured threat priority and operational observability remains under-characterized. This article examines that relation in a smart grid simulator (SGSim)-based IEC 61850 digital-substation environment. DFD-guided STRIDE analysis, CVSS v3.1 scoring, likelihood–impact prioritization, and ATT&CK for ICS mapping produce a 47-threat inventory. Three high-priority scenarios are then validated using packet-capture evidence and SCADA/HMI observations: a volumetric denial-of-service (DoS) attack against the IEC 60870-5-104 supervisory path, a TCP SYN flood targeting the same service endpoint, and a GOOSE false data injection (FDI) attack targeting event communication. The analysis distinguishes risk priority, operational observability, and operational consequence, and evaluates each attack across network, service, and operator planes. The results show that, in the studied environment, the validated high-priority attacks do not disclose their severity through a common visibility pattern. The volumetric DoS case is strongly visible and primarily compromises communication availability; the SYN flood weakens control recoverability while remaining weakly visible at the operator plane; and the GOOSE FDI case preserves communication continuity while falsifying the represented operational state. These findings indicate that visible disruption alone is insufficient for interpreting cyber-physical severity in the studied SGSim-based digital substation. Full article