Search Results (42,107)

This paper investigates the optimal control problem of orbital rendezvous for spacecraft in near-circular orbits with a low-thrust propulsion system. Two optimality criteria are considered: time-optimal and motor-time-optimal control. A linearized mathematical model of relative motion between the active and passive spacecraft is employed, which is formulated in dimensionless variables that characterize secular, periodic, and lateral motion components of the relative motion. By applying Pontryagin’s Maximum Principle, the equations governing the optimal relative motion of the spacecraft are derived. To address the discontinuities associated with the bang–bang switching function inherent in the motor-time-optimal problem, and the lack of a suitable initial guess, a homotopy method is adopted, in which the solution to the rendezvous time-optimal problem is used as an initial guess and is gradually deformed into the motor-time-optimal control. Considering the errors introduced by the linearization of the relative motion model, the obtained control law is validated via numerical simulations based on the original nonlinear dynamics of the system. Simulation results demonstrate that the proposed trajectory optimization methodology achieves high success rates and rapid convergence, providing valuable theoretical support and practical guidance for mission scenarios with similar trajectory design requirements. Full article

To address the problems of weak geometric features, low signal response amplitude, and insufficient spatial resolvability of near-surface defects in metal substrates, a high-resolution spatiotemporal-coded eddy-current array probe is proposed. The probe adopts an array topology with time-multiplexed excitation and adjacent differential reception, achieving a balance between high common-mode rejection ratio and high-density spatial sampling. First, a theoretical electromagnetic coupling model between the probe and the metal substrate is established, and finite-element simulations are conducted to investigate the evolution of the skin effect, eddy-current density distribution, and differential impedance response over an excitation frequency range of 1–10 MHz. Subsequently, a 64-channel M-DECA probe and an experimental testing platform are developed, and frequency-sweeping experiments are carried out under different excitation conditions. Experimental results indicate that, under a 50 kHz excitation frequency, the array eddy-current response achieves an optimal trade-off between signal amplitude and spatial geometric consistency. Furthermore, based on the pixel-to-physical coordinate mapping relationship, the lateral equivalent diameters of near-surface defects with different characteristic scales are quantitatively characterized, with relative errors of 6.35%, 4.29%, 3.98%, 3.50%, and 5.80%, respectively. Regression-based quantitative analysis reveals a power-law relationship between defect area and the amplitude of the differential eddy-current array response, with a coefficient of determination R² = 0.9034 for the bipolar peak-to-peak feature. The proposed M-DECA probe enables high-resolution imaging and quantitative characterization of near-surface defects in metal substrates, providing an effective solution for electromagnetic detection of near-surface, low-contrast defects. Full article

This study provides a comprehensive, long-term evaluation of inter-sensor radiometric calibration biases for the NOAA OMPS Nadir and CrIS instruments using four complementary validation methodologies implemented within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System. Overall, SDR data quality from the three OMPS Nadir instruments and three CrIS instruments aboard SNPP, NOAA-20, and NOAA-21 remains stable. The iSensor-RCBA portal has also proven to be a powerful diagnostic resource, enabling the detection of both new and previously unrecognized calibration issues and anomalies. Using the 32-day averaged difference method, we were the first to discover and identify the root cause of an inconsistency near 280 nm in inter-sensor radiometric biases between the SNPP and NOAA-20 OMPS NP instruments. The same method also revealed an unusual radiometric feature in NOAA-21 CrIS SDRs over the southern high latitudes during spring and summer. In addition, we derived decade-long degradation rates at 11 Metop-B GOME-2 wavelengths using an independent dataset—Simultaneous Nadir Overpass observations between SNPP OMPS and Metop-B GOME-2. Furthermore, iSensor-RCBA monitoring confirmed two geolocation anomalies in SNPP CrIS through a new approach involving SNO-based inter-sensor biases between GOES-16 ABI and SNPP CrIS. These cases demonstrate that iSensor-RCBA is not only a monitoring visualization tool but also a diagnostic tool that delivers unique, complementary insight into instrument performance, enabling early identification of radiometric and geolocation issues across JPSS and other satellite missions. Importantly, the analysis methods used in this study are broadly applicable to current and future missions, including JPSS-03, JPSS-04, and non-NOAA satellite systems. Full article

This work explores late-time gravitational collapse using timelike thin-shell methods in classical general relativity. A junction surface separates a regular de Sitter interior from a Schwarzschild or Schwarzschild–de Sitter exterior in a post-transient regime with fixed exterior mass M (ADM for ${\mathrm{\Lambda }}_{+}=0$), modelling a vacuum–energy core surrounded by an asymptotically classical spacetime. The configuration admits a natural thermodynamic interpretation based on a geometric area functional $S_{\mathrm{shell}}\propto R^2$ and Tolman redshift, both derived from classical junction conditions and used as an entropy-like coarse-grained quantity rather than a fundamental statistical entropy. Key results include (i) identification of a deceleration mechanism at the balance radius $R_{\mathrm{thr}}={(3M/{\mathrm{\Lambda }}_{-})}^{1/3}$ for linear surface equations of state $p=w\sigma$; (ii) classification of the allowable radial domain $V\left(R\right)\le 0$ for outward evolution; (iii) bounded curvature invariants throughout the shell-supported spacetime region; and (iv) a mass-scaled frequency bound $f_c{R}_S\le \xi /\left(3\sqrt{3}\pi \right)$ for persistent near-shell spectral modes. All predictions follow from standard Israel junction techniques and provide concrete observational tests. The framework offers an analytically tractable example of regular thin-shell collapse dynamics within classical general relativity, with implications for alternative compact object scenarios. Full article

Background/Objectives: Geometric morphometric analysis (GMA) is a statistical method that captures and quantifies shape variation. This study aimed to assess hard and soft tissue shape variations and changes following orthodontic treatment in Class III skeletal malocclusion using GMA. Methods: A retrospective study was conducted on 84 lateral cephalometric radiographs (pre-treatment and near-end treatment) of Class III patients aged 16–40 years (ANB < 2°). Thirty-five landmarks were digitized in Cartesian coordinates using MorphoJ software for shape analysis. Results: The sample included 62% females and 38% males, with a mean age of 24.7 ± 5.2 years. Vertical dimension variations (hypodivergent to hyperdivergent) contributed most to shape changes PC1 (23.35%), followed by anteroposterior variations PC2 (13.51%). Gender significantly influenced hard and soft tissue variation with 30.91%SS (F = 56.99, p < 0.0001). Males had significantly larger and longer ramus, body of the mandible, alveolar height, LAFH, TAFH and upper lip length. (PD: 0.026, p < 0.05). Significant shape changes were seen in the mandible (PD = 0.018, p < 0.05). SNB increased by 0.41° (from 81.73° ± 3.67°), and ANB improved by 0.46° but remained Class III (−0.33° ± 1.82°). Lower anterior facial height increased by 1.78 mm (p < 0.05). The lower incisors retroclined significantly (from 92° ± 8.56° to 87° ± 6.96°, p < 0.05), while the interincisal angle increased by 5.9°. Upper incisors remained procline (118° ± 11°, p > 0.05). Upper lip length increased by 0.4 mm (p < 0.05). Conclusions: Vertical and anteroposterior shape variations are notable within Class III malocclusion. Post-treatment changes in both hard and soft tissues indicate that orthodontic camouflage can enhance facial esthetics and skeletal balance. GMA provides objective quantification and visualization of these treatment-related craniofacial changes. Full article

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m² near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance. Full article

The establishment of a sustained human presence on the Moon is critically dependent on the ability to utilize local resources, primarily the production of oxygen for life support and propellant. The ROXY (Regolith to Oxygen and metals conversion) process is a molten salt electrolysis technology designed for this purpose. This paper presents an economic analysis of a ROXY pilot plant capable of producing over one ton of oxygen per year. We evaluate the economic viability by analyzing development, transportation, and operational costs against the potential revenue from selling oxygen and metals within a nascent lunar economy. A key aspect of this analysis is the perspective of an early customer in habitation life support systems preceding that of much higher propellant production demand. The analysis contextualizes this paradigm by recognizing that the primary economic driver for oxygen production is the larger future market for propellant; however, early life support demand may incentivize a paradigm-shift from Earth-based consumable resupply. Scenarios based on varying transportation costs and development timelines are evaluated to determine the internal rate of return (IRR) and time to break even (TTBE). The results indicate that the ROXY pilot plant is economically viable, particularly in near-term scenarios with higher transportation costs, achieving a positive IRR of up to 47.4% when both oxygen and metals are sold. The analysis identifies facility mass, driven by the robotics subsystem, as the primary factor for future cost-reduction efforts, concluding that ROXY is a technically and economically sound pathway toward sustainable lunar operations. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

Marine aerosol particles influence the climate, and interactions between ocean waves and coral reefs may impact aerosol size distributions in remote locations, such as the Great Barrier Reef. However, quantifying these processes has proven to be challenging. We tested whether marine aerosol size distributions and concentrations differ across four zones: background air outside the lagoon, above the reef crest, within the lagoon, and near the beach of Heron Island, approximately 85 km offshore. Using a modified DJI Matrice 600 hexacopter equipped with a miniaturised optical particle counter and custom inline gas dryer, we measured aerosols from 165 to 3000 nm across 64 drone flights during 16 sampling events in November 2024. Aerosol concentrations showed substantial day-to-day temporal variability, while spatial differences among reef zones were generally minor; on certain days, the maximum difference between background and near-island measurements reached approximately 25%. K-means clustering identified four dominant air mass transport patterns, and Hybrid Single-Particle Lagrangian Integrated Trajectory model analysis indicated that upwind conditions had a strong influence on aerosol loading. Vertical profiles revealed limited variability within the lowest 100 m. Mixing layer height, air parcel travel speed, and water depth along the final 12 h of trajectories were key drivers of aerosol variability. These results demonstrate the potential of drone-based measurements for characterising marine aerosols and provide a foundation for improving climate model representations of natural aerosol processes. Full article

Hot-fluid injection in thermal-enhanced oil recovery (thermal-EOR, TEOR) imposes temperature-driven volumetric strains that can substantially alter in situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic (thermo-hydro-mechanical) effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study aims to examine the influence of sustained hot-fluid injection on stress redistribution, hydraulic-fracture deformation, and fracture stability in thermal-EOR by accounting for coupled thermal, hydraulic, and mechanical interactions. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection, with temperature contrasts of ΔT = 0–100 °C and reduced pumping rate. Results show that the hydraulic-fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75–100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions adjacent to the fracture plane and with markedly elevated shear stresses that may promote natural-fracture activation. These findings show that temperature effects can directly influence injectivity, fracture containment, and the risk of unintended fracture or natural-fracture activation, underscoring the importance of temperature-aware geomechanical planning and injection-strategy design in field operations. Incorporating these effects into project design can help operators anticipate injectivity decline, improve fracture containment, and reduce geomechanical uncertainty during long-term hot-fluid injection. Full article

Background: Whole-body electromyostimulation (WB-EMS) is a training technology that enables the stimulation of all the main muscle groups with dedicated intensity, attracting many sportspeople and athletes of various disciplines. The aim of this systematic review and meta-analysis was to determine the effect of WB-EMS on maximum jump, sprint, and agility performance in exercising cohorts. Methods: Systematic literature research of five electronic databases up to March 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme and including interventional trials with at least one WB-EMS and one active or inactive control group that focus on maximum jump, sprint, and agility performance in sportspeople and athletes. Applying a random-effect model that includes the inverse heterogeneity model (IVhet), effects sizes (SMD), and calculates 95% confidence intervals (95%-CIs). Subgroup analyses addressed superimposed WB-EMS application vs. underlying voluntary exercise. Results: Twelve studies with 145 participants in the WB-EMS and 148 participants in the control group were included. Most trials on jumping (10 of 12) and all trials on sprinting and agility performance applied superimposed WB-EMS protocols compared with underlying voluntary exercise. We observed no significant positive effects of WB-EMS on maximum jump (12 studies, SMD: 0.34, 95%-CI: −0.35 to 1.03), sprint (8 studies, SMD: 0.07, 95%-CI: −0.66 to 0.80), and agility performance (5 studies, SMD: −0.11, 95%-CI: −1.28 to 1.06). Heterogeneity between the trial results was considerable (I² > 80%) in all cases. Conclusions: Superimposed WB-EMS compared to the underlying predominately near-maximum to maximum intensity voluntary exercise provides only limited additional effects on jumping, sprinting, and ability performance. Full article

The paper presents a comparative experimental study of heat-transfer behavior in three alloys considered candidate materials for nuclear reactors: the austenitic stainless steel 316L, Zircaloy-4 (currently used in CANDU reactors), and an ODS alloy with a ferritic matrix. The investigation was conducted across five temperature intervals, each sample being subjected to a thermal shock through short-term overheating to the upper limit of its respective interval. The variation of thermal diffusivity in the three alloys was determined as a function of both measurement temperature and applied thermal shock, and trends in heat-transfer behavior were compared across the five temperature ranges. The experimental results show that up to 400 °C, Zircaloy-4 exhibits the highest thermal diffusivity, followed by the ODS alloy, with the lowest values measured for 316L steel. At approximately 450 °C, the ratio between 316L and the ODS alloy reverses. Beyond this point, increasing the temperature up to 900 °C is accompanied by a continuous rise in thermal diffusivity for both 316L stainless steel and Zircaloy-4. In contrast, for the ODS steel, increasing temperature leads to a continuous decrease in thermal diffusivity, reaching a minimum near the Curie point. The novelty of the study lies in the comparative assessment of the influence of temperature on the heat-transfer process in three alloys relevant to nuclear energy, covering the operating temperature ranges of CANDU and ALFRED reactors, as well as potential accidental overheating up to 900 °C. A particular feature of the work is the prior application of a short-duration overheating step produced using solar energy. The results are relevant not only for nuclear reactors but also for other high-temperature applications in corrosive environments. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D²-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article

Rice yield prediction at high spatial resolution is essential to support precision management and sustainable intensification in irrigated systems. While many remote sensing studies provide yield estimates at the field scale, pixel-level predictions are required to characterize within-field variability. This study assesses the potential of multitemporal Sentinel-2 imagery and machine learning to estimate rice yield at pixel level in the Albufera rice area (Valencia, Spain). Yield data from combine harvester maps were collected for ‘JSendra’ and ‘Bomba’ Japonica varieties over five growing seasons (2020–2024) and linked to 10 m Sentinel-2 bands in the visible, near-infrared (NIR) and short-wave infrared (SWIR) regions. Random Forest (RF) and XGBoost (XGB) models were trained with 2020–2023 data and independently validated in 2024. XGB systematically outperformed RF, achieving at 110 and 130 DAS (days after showing), R² values of 0.74 and 0.85 and RMSE values of 0.63 and 0.28 t·ha⁻¹ for ‘JSendra’ and ‘Bomba’. Prediction accuracy increased as the season progressed, and models using all spectral bands clearly outperformed configurations based only on spectral indices, confirming the dominant contribution of NIR reflectance. Spatial error analysis revealed errors at field edges and headlands, while central pixels were more accurately predicted. Overall, the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability and support both end-of-season yield estimation and early season forecasting, enabling the identification of potentially low-yield zones to support targeted management decisions. Full article