Search Results (1,285)

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article

Stratospheric solar-powered unmanned aerial vehicles (UAVs), commonly operated as High-Altitude Pseudo-Satellites (HAPS), promise satellite-like persistence for Earth observation, communications and remote sensing, but their feasibility is governed by a tight coupling between solar energy availability and onboard energy demand. This study presents an energy-balance simulation framework that predicts the diurnal charge–discharge behaviour and endurance of solar-powered UAVs. The framework couples a physics-based environmental irradiance model—astronomical solar position, an air-mass and pressure-scaled broadband atmospheric transmission and an eccentricity-corrected extraterrestrial irradiance—with a wing-geometry photovoltaic collection model that reduces the airfoil camber, planform, dihedral and cell layout of a real wing to three scalar coefficients, replacing the flat-plate assumption common in solar-UAV sizing. The closed-form collection coefficient captures the full dependence of collected power on sun position and aircraft heading and admits an exact orbit-averaging result for circular loiter. The model is implemented as a reproducible, modular tool with single-day, annual and global analysis modes. It is validated against a ground-based photovoltaic charging campaign conducted on the as-built Aurora solar UAV demonstrator (5.6 m span, 8 kg) over three clear-sky days spanning a 90-day seasonal range: predicted and measured wing-collected power agree with a Pearson correlation of 0.998, a coefficient of determination of 0.993, an RMS error of 6.0% and a daily-energy agreement within 3.5%. A structured residual identifies an unmodelled photovoltaic temperature effect bounded at the 6% level. The framework provides HAPS designers and operators with a transparent, validated tool for feasibility screening, component selection and mission planning across latitude and season. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article

Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign. Full article

Accurate attitude estimation for highly dynamic spacecraft relies on robust fusion of star-tracker and inertial measurements. However, asynchronous sensing, motion blur in star images, and delayed star-tracker outputs can significantly degrade estimation accuracy and temporal consistency. To address these challenges, this paper proposes a dual-layer factor graph optimization framework for asynchronous star-tracker/IMU fusion under highly dynamic conditions. At the lower layer, high-rate IMU measurements are combined with motion-blurred star streak observations to construct a local factor graph over the exposure interval. The proposed local fusion process reconstructs discrete star-trail points, estimates angular velocity, and selects IMU-aligned representative observations for temporally consistent association of blurred star measurements. At the upper layer, delayed attitude constraints, propagated star-vector information, and inertial rotational constraints are jointly incorporated to refine the attitude trajectory. Simulation and semi-physical experimental results demonstrate that the proposed framework achieves higher estimation accuracy, stronger robustness, and better tolerance to delayed or intermittent star-tracker observations than the comparison methods, while maintaining practical computational efficiency for near-real-time onboard implementation. Full article

Reproducing SM4 (GB/T 32907-2016) hardware-accelerator results on open-source RISC-V platforms is difficult, because most published designs depend on proprietary FPGA toolchains. This paper contributes an asymmetric dual-channel SM4 architecture together with a fully reproducible open-source verification framework; physical on-board acceleration is not claimed and is left as future work. The architecture exploits two algorithmic symmetries of SM4—encryption and decryption differ only in round-key order, and the round transform T shares the byte-wise S-box $\tau$ with the key-expansion transform $T^{\prime }$—but maps them onto an asymmetric workload. Bulk encryption is throughput-bound, whereas key expansion runs once per session. Accordingly, a 32-stage fully unrolled encryption pipeline (one 128-bit block per cycle in steady state) is paired with a single round function reused iteratively for the key schedule, and encryption and decryption share one datapath via round-key reversal. Because the TH1520 SoC on LicheePi 4A does not expose the Xuantie C910 RoCC port, we verify the design in three reproducible tiers on the board itself: (T1) RTL co-simulation of an sm4_rocc wrapper passes 1040/1040 vectors for both the standalone datapath and the full system. (T2) A pure-C reference model passes 10/10 GB/T 32907-2016 vectors on the real C910 at a measured 291.9 Mbps. (T3) A Linux illegal-instruction trap-and-emulate prototype confirms ISA and OS-level semantics. Open-source synthesis (Yosys + SkyWater Sky130) gives a measured area of 133 kGE and a switching-dominated post-synthesis power estimate of ≈0.28 W at 100 MHz (≈22 pJ/bit, ≈46 Gbps/W). At 100 MHz the unrolled pipeline reaches an RTL simulation-equivalent steady-state throughput of 12.8 Gbps, about 43.9× the software baseline. Every reported number is reproducible with open-source tools only (Icarus Verilog, GTKWave, GCC, Yosys, Sky130 PDK). Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

To address the high deployment cost, insufficient local coverage, and limited timeliness of conventional wave-observation methods in onboard real-time applications, this study conducts a comparative investigation of centre-of-gravity-equivalent instantaneous wave-height estimation models based on three-degree-of-freedom ship motion responses under the framework of the wave buoy analogy (WBA). The heave, roll, and pitch responses of a 1:2 scaled Series 62 4667-1 planing craft model in regular head seas are used as inputs, while the synchronous instantaneous wave-height signal measured by a wave probe near the centre of gravity is used as the label. A unified protocol is established with consistent inputs, labels, window construction, data partitioning, and evaluation metrics. Six models, namely SVR, TCN, LSTM, CNN-LSTM, Transformer, and LSTM-MHA, are compared and validated using STAR-CCM+ numerical simulation data and towing-tank experimental data. The results indicate that, in the simulated case of H = 0.10 m and T = 1.5 s, LSTM-MHA achieves the highest estimation accuracy, with RMSE and R² values of 0.001231 and 0.997848, respectively, but it also has the largest model size and computational cost. In comparison, TCN achieves near-optimal accuracy with a smaller parameter count and lower inference latency, and shows stable performance across multiple conditions. The towing-tank experimental results further show that both LSTM-MHA and TCN clearly outperform the SVR baseline. Overall, accuracy in the simulation domain, robustness in the towing-tank experimental domain, and cross-domain generalisation capability are not fully consistent. Therefore, the selection of onboard instantaneous wave-height estimation models should jointly consider estimation error, model complexity, computational latency, window length, and practical deployment requirements. Full article

The evaluation of wearable robotic systems remains a challenge, particularly in real-world environments where laboratory-based methods are impractical. Existing approaches rely on external instrumentation, such as surface electromyography (sEMG) or motion capture, which are difficult to deploy continuously and do not directly measure key internal metrics such as joint loading or spinal forces. This work introduces a new paradigm for exoskeleton evaluation in which biomechanical assessment is embedded directly within the device’s sensing and computational architecture. We present the ExoMetrix system, a platform that integrates onboard sensing, real-time data acquisition, cloud-based processing, and user-facing analytics into a unified workflow for continuous evaluation of human–exoskeleton interaction. Sensor data from the device are streamed and processed using physics-based models. The resulting outputs are translated into estimates of internal biomechanical quantities, including joint torques, spinal compression and shear forces, and muscle loading. By enabling real-time feedback and longitudinal monitoring without external instrumentation, this approach transforms evaluation from an external, episodic process into an embedded and continuous capability, supporting safer and more scalable deployment of exoskeleton technologies. Full article

Trackless rubber-tired vehicles are the important auxiliary transportation equipment in coal mines. The main difficulty of their unmanned driving is that the underground environment information is complex but the onboard computing resources for perception and measurement are limited. To solve this conflict, this paper establishes a lightweight map dimension reduction framework to assist in path planning. Firstly, motivated by the idea of image convolution, the framework using the simplicity kernel is proposed for the high-resolution grid maps, which can reduce planning time while retaining the useful map information. Secondly, the reverse automata based on the greedy strategy are designed to get suitable machine-selected key points, which can solve the problem that some self-selected key points become impassable because of the dimension reduction. Moreover, a Bezier smoothing method based on slope interpolation is presented to avoid the collision between the smooth path and obstacle grid caused by the small number of path points planned on the reduced-dimension map. Finally, comparison experiments and downhole map experiment are carried out and discussed. The results show that using the proposed method to assist path planning can reduce time by 99.77% and reduce the number of redundant path points by 79.60%, and using the improved smoothing method from the framework can avoid collision risks caused by fewer path points. Full article

Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, a UAV-assisted Distributed Sliding Mode Control (DSMC) is proposed to robustly and safely implement path tracking for autonomous ground vehicles (AGVs). The proposed system utilizes an aero-sensor layer for enhanced perception, such as obstacle sensing, reference path preview, and look-ahead trajectory information, and it shares this information with the vehicle via wireless communication. The fundamental scheme, called DSMC, is based on a conventional Sliding Mode Control (SMC) technique and uses UAV preview-based feedback. This allows anticipation of control actions to enhance tracking performance and achieve more timely, smoother obstacle avoidance than baseline SMC. The proposed method is designed to overcome the limitations of traditional SMC strategies, such as chattering and poor responsiveness. The proposed method features continuous nonlinear approximation and damping mechanisms to reduce chattering and improve response characteristics, thereby enhancing stability and reducing oscillations. Strict safety enforcement through constraint is always achieved by keeping the vehicle and obstacles separated by a minimum distance only; that is, a minimum distance is always guaranteed: a Constraint Barrier Function (CBF)-based constraint is used. By combining UAV-assisted perception with DSMC and CBF the system can guarantee its formal safety in the presence of disturbances and sensing uncertainties while maintaining accurate trajectory tracking. Based on our simulation results, the proposed UAV-assisted DSMC method is shown to be significantly superior to conventional SMC and Model Predictive Controller (MPC) in terms of tracking accuracy, control smoothness, and adherence to the safety margin. Our simulation results demonstrate that the proposed method significantly outperforms conventional SMC and MPC control. Specifically, it achieves a 22.9% reduction in RMSE (0.135 m vs. 0.175 m) and 63% lower mean control effort, and it strictly maintains the minimum safety distance under both static and dynamic obstacles. The algorithm runs in real-time with an average execution time of 1.85 ms (>200 Hz), making it highly suitable for embedded deployment. These results highlight the effectiveness of combining UAV-assisted preview, adaptive robust control, and formal safety constraints for reliable autonomous navigation in complex environments. Full article

Introduction: Sharks are key species in marine ecosystems, and their conservation is a priority across European waters. However, several fisheries unintentionally capture sharks as bycatch, raising concerns about their post-capture survival. In the Gulf of Cadiz, demersal trawl fisheries frequently capture the catsharks Galeus atlanticus and G. melastomus. Objective: This study aimed to assess the short-term survival rates of these two species following trawl capture and to identify potential blood biochemical markers predictive of survival. Methodology: Fieldwork was conducted aboard an oceanographic research vessel over two spring seasons. Standarized demersal trawl hauls of 1-h duration were performed. Immediately after capture, individuals were transferred to onboard seawater tanks, where their recovery was monitored for 24 h. Blood samples were collected at two time points: immediately after capture and after the 24-h recovery period. Biochemical parameters associated with secondary stress responses were analyzed. Results: Survival rates were high for both species, reaching 88 ± 8% for G. atlanticus and 90 ± 4% for G. melastomus. Blood analyses indicated a clear physiological recovery in all surviving individuals after 24 h, evidenced by the normalization of stress-related parameters. Notably, interspecific differences were observed in certain biochemical markers after capture, including amino acids and lactate concentrations, suggesting species-specific responses to capture stress. Conclusions: These findings provide valuable insights into the resilience of demersal catsharks to trawl-induced stress and highlight the potential of blood biomarkers as a tool for predicting post-capture survival. The results support the development of evidence-based onboard handling protocols aimed at maximizing the survival of incidentally captured sharks. Such measures can contribute to more sustainable fisheries management and the conservation of vulnerable elasmobranch species in European waters. Full article

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

Combine harvesters are evolving from machines equipped with isolated monitoring devices into distributed sensing platforms for process supervision, machine diagnosis, and adaptive control. This review summarizes representative research on six major sensing tasks in combine harvesters: grain loss, grain breakage, cleaning load, feed rate, grain-bin state, and grain quality. The reviewed studies are compared within a unified engineering framework that considers sensing target, installation position, signal path, disturbance source, calibration transferability, field robustness, and control relevance. Rather than evaluating sensors only as individual devices, this review emphasizes the coupled design of transducers, structural anti-interference measures, sampling paths, signal processing, and field-oriented validation under vibration-dominated and dust-laden harvesting conditions. The analysis shows that loss-rate and feed-rate sensing are currently the most mature and control-relevant categories, whereas breakage-rate, grain-bin, and integrated quality sensing remain constrained by representative sampling, disturbance resistance, and cross-condition generalization. Future progress will depend on multi-sensor fusion, realistic benchmark protocols, crop-aware calibration transfer, and tighter integration among onboard sensing, machine control, and digital harvesting systems. By clarifying the engineering value of these sensing routes, the review also supports loss reduction, quality preservation, labor-saving operation, and more reliable adaptive control in commercial grain harvesting. Full article