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Efficient cooling systems are crucial for achieving high efficiency and power density in electric vehicle motors. To enhance motor cooling performance, a novel oil spray cooling system was developed, referred to as the centripetal-inclined oil spray (CIOS) cooling system. The CIOS cooling system features axial oil channels evenly distributed on the surface of the stator core, with each channel connected at both ends to stepped oil channels. This configuration allows for direct oil spraying towards the center at specific inclined angles without the need for additional components such as nozzles, oil spray rings, and oil spray tubes, which reduces costs, minimizes the risk of oil leakage, and enhances motor reliability. Electromagnetic and computational fluid dynamic simulations were conducted on the motor with the CIOS cooling system. The results indicated that the CIOS cooling system adversely impacted core losses and torque, while these effects were minimized after optimization, with losses increasing by up to 0.29% and torque decreasing by up to 0.45%. The CIOS cooling system achieved stable oil spraying, forming oil films on the end-winding with a maximum formation rate of 49.4% and an average thickness of 1.56 mm. Compared to the motor with oil spray rings, the motor with the CIOS cooling system exhibited lower temperatures across all components and more uniform cooling. Finally, the cooling performance of the CIOS cooling system was verified through experiments, and the results showed that the measured temperature closely matched the simulated results, with a maximum error of 5.9%. The findings in this study are expected to provide new insights for optimizing oil cooling systems in electric vehicle motors. Full article

The high-speed motion of a vehicle underwater induces cavitation, and the resulting cavity alters the surface pressure distribution and flow field characteristics. This study employs a numerical approach combining the $\mathrm{k}-\omega$ SST (Shear Stress Transport) turbulence model, the VOF (Volume of Fluid) multiphase flow model, the Schnerr–Sauer cavitation model, and the overlapping mesh technique. The numerical method is validated through the good agreement between simulation results and experimental data for both cavity shape and vehicle trajectory, with a maximum relative error of 6.1% in vertical displacement. The results indicate that during the launch-tube exit phase, with $\sigma =0.235$ and $\mathrm{Fr}=47.9$, the vehicle acceleration causes the pressure at its shoulder to drop below the saturated vapor pressure, initiating cavitation. Under transverse flow (intensity U = 0.016–0.05), the cavity becomes asymmetric. Specifically, the axial length and radial thickness on the back side are significantly larger than those on the face side, and this asymmetry intensifies with increasing transverse flow intensity. Furthermore, after exiting the launcher, the vehicle’s trajectory and attitude deflect towards the back side and the deflection amplitude increases, with horizontal displacement and attitude angle variation positively correlated with transverse flow intensity. Full article

The whole-angle hemispherical resonator gyroscope (WA-HRG) is critical to high-precision attitude control and navigational positioning, boasting significant deployment potential in both highly dynamic inertial navigation systems and industrial instrumentation. This paper presents a mechanistic analysis of quantization error inherent to the HRG’s hardware detection and driving circuits, focusing specifically on its impact on parameter calculation and driving control in whole-angle mode. Furthermore, a simulation platform was constructed to verify and elucidate the correlations between the effects of quantization error and key resonator parameters, such as the major axis amplitude and the standing wave azimuth. Compared to existing HRG error studies which frame quantization error as isolated circuit noise, this work uniquely uncovers the azimuth-modulated periodic behavior of quantization error within the WA-HRG. It also formalizes a quantitative relationship between quantization error and the resonator’s key parameters, laying a critical theoretical foundation for suppressing quantization error and enhancing accuracy in high-performance WA-HRGs. Full article

In the domain of marine remote sensing, the real-time monitoring of ocean waves is a research hotspot, which employs acquired X-band radar images to retrieve wave information. To enhance the accuracy of the classical spectrum method using the extracted signal-to-noise ratio (SNR) from an image sequence, data from the preferred analysis area around the upwind is required. Additionally, the accuracy requires further improvement in cases of low wind speed and swell. For shore-based radar, access to the preferred analysis area cannot be guaranteed in practice, which limits the measurement accuracy of the spectrum method. In this paper, a method using extracted SNRs and an optimized genetic algorithm back-propagation (GABP) neural network model is proposed to enhance the inversion accuracy of significant wave height. The extracted SNRs from multiple selected analysis regions, included angles, and wind speed are employed to construct a feature vector as the input parameter of the GABP neural network. Considering the not-completely linear relationship of wave height to the SNR derived from radar images, the GABP network model is used to fit the relationship. Compared with the classical SNR-based method, the correlation coefficient using the GABP neural network is improved by 0.14, and the root mean square error is reduced by 0.20 m. Full article

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms. Full article

In the process of ore drawing using a caving method under interburden conditions, the key to controlling ore dilution lies in the accurate prediction of boundary particle migration trajectories. To address the challenges of high computational costs and complex modeling in traditional numerical simulations, this study designs a dataset construction method. After calibrating parameters using the angle of repose, ore-drawing numerical simulation datasets with interburden (post-defined and pre-defined models) are established. Building upon this foundation, an improved Transformer model is proposed. The model enhances spatiotemporal representation through multi-layer feature fusion embedding, strengthens long-range dependency capture via a reinforced spatiotemporal attention backbone, improves local dynamic modeling capability through optimized decoding at the output stage, and integrates transfer learning to achieve continuous prediction of particle migration. Validation results demonstrate that the model accurately predicts the spatial distribution patterns and collective motion trends of particles, with prediction errors at critical nodes confined to within a single stage and an average estimation error of approximately 4% in interburden regions. The proposed approach effectively overcomes the timeliness bottleneck of traditional interburden ore-drawing simulations, enabling rapid and accurate prediction of boundary particle migration under interburden conditions. Full article

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may require large networks and struggle with extrapolation. In this paper, we propose a low-latency, polynomial-based IK solution for spherical-wrist robots. The method leverages spherical coordinates and low-degree polynomial fits for the first three (positional) joints, coupled with a closed-form analytical solver for the final three (wrist) joints. An iterative partial-derivative refinement adjusts the polynomial-based angle estimates using spherical-coordinate errors, ensuring near-zero final error without requiring a full Jacobian matrix. The method systematically enumerates up to eight valid IK solutions per target pose. Our experiments against Levenberg–Marquardt, damped least-squares, and an fmincon baseline show an approximate 8.1× speedup over fmincon while retaining higher accuracy and multi-branch coverage. Future extensions include enhancing robustness through uncertainty propagation, adapting the approach to non-spherical wrists, and developing criteria-based automatic solution-branch selection. Full article

In the Internet of Things (IoT), ultra-wideband (UWB) plays an essential role in localization and navigation. However, in indoor environments, UWB signals are often blocked by obstacles, leading to non-line-of-sight (NLOS) propagation. Thus, reliable line-of-sight (LOS)/NLOS identification is essential for reducing errors and enhancing the robustness of localization. This paper focuses on a single-anchor UWB configuration and proposes a temporal deep learning framework that jointly exploits two-way ranging (TWR) and angle-of-arrival (AOA) measurements for LOS/NLOS identification. At the core of the model is a temporal convolutional network (TCN) augmented with a self-attentive pooling mechanism, which enables the extraction of dynamic propagation patterns and temporal contextual information. Experimental evaluations on real-world measurement data show that the proposed method achieves an accuracy of 96.65% on the collected dataset and yields accuracies ranging from 88.72% to 93.56% across the three scenes, outperforming representative deep learning baselines. These results indicate that jointly exploiting geometric and temporal information in a single-anchor configuration is an effective approach for robust UWB indoor positioning. Full article

We present a high-precision endoscopic shape-sensing method using only two calibrated outer cores of a multicore fiber Bragg grating (MC-FBG) array. By leveraging the geometric relationship among two non-collinear outer cores and the central core, the method estimates curvature and bending angle without relying on multiple outer-core channels, thereby reducing complexity and error propagation. On canonical shapes, the proposed method achieves maximum relative reconstruction errors of 1.62% for a 2D circular arc and 2.81% for a 3D helix, with the corresponding RMSE values reported for completeness. In addition, representative endoscope-relevant configurations including the α-loop, reversed α-loop, and N-loop are accurately reconstructed, and temperature tests over 25–81 °C further verify stable reconstruction performance under thermal disturbances. This work provides a resource-efficient and high-fidelity solution for endoscopic shape sensing with strong potential for integration into next-generation image-guided and robot-assisted surgical systems. Full article

The Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) technique enables cislunar spacecraft to obtain accurate position and velocity information, allowing full state estimation of two vehicles using only inter-satellite range (ISR) measurements when both their dynamical states are unknown. However, its stand-alone use leads to significantly increased orbit determination errors when the orbital planes of the two spacecraft are nearly coplanar, and is characterized by long initial convergence times and slow recovery following dynamical disturbances. To mitigate these issues, this study introduces an integrated navigation method that augments inter-satellite range measurements with line-of-sight vector angles relative to background stars. Additionally, an enhanced Adaptive Robust Cubature Kalman Filter (ARCKF) incorporating a chi-square test-based adaptive forgetting factor (AFF-ARCKF) is developed. This algorithm performs adaptive estimation of both process and measurement noise covariance matrices, improving convergence speed and accuracy while effectively suppressing the influence of measurement outliers. Numerical simulations involving spacecraft in Earth–Moon L4 planar orbits and distant retrograde orbits (DRO) confirm that the proposed method significantly enhances system observability under near-coplanar conditions. Comparative evaluations demonstrate that AFF-ARCKF achieves faster convergence compared to the standard ARCKF. Further analysis examining the effects of initial state errors and varying initial forgetting factors clarifies the operational boundaries and practical applicability of the proposed algorithm. Full article

This study focused on determining the conditions leading to variance in the measurements of an antenna array capable of measuring the direction of electromagnetic waves. The payload of the study is a cross-array of antennas that is able to measure direction through array beamforming and angle of arrival (AOA) technology. Starting from the modeling of satellite kinematics (in terms of the satellite’s position and attitude combined with its relative position with respect to an electromagnetic wave emitter located on Earth’s surface), this study provides the mathematical fundamentals to identify potential cases that lead to divergence in the estimation variance for the position of a signal emitter. The numerical and analytical predictions, conducted through an evaluation of the Cramér–Rao lower bound (CRLB) metrics, were on the azimuth, elevation, and broadside angles through the generation of errors in the attitude with Monte Carlo simulations. Recent advancements in the miniaturization of electronics make these studies of particular interest for a new set of technological demonstrators equipped with payloads composed of antenna arrays. Applications of interest include Earth-scanning missions, with exemplary cases of search-and-rescue operations or the spectrum monitoring of jamming in the E1/L1 band for the GNSS. Full article

Axial spondyloarthritis (axSpA) is a chronic inflammatory disorder causing structural spinal damage and pathological thoracic kyphosis. Accurate quantification of spinal curvature is crucial for monitoring disease progression and guiding treatment. Conventional Cobb angle measurement on radiographs or DEXA images is widely used but is time-consuming and prone to inter-observer variability. This study evaluates an automated deep learning-based approach using a You Only Look Once (YOLO) model for vertebral detection on lateral morphometric DEXA scans and estimation of thoracic kyphosis angles. A dataset of 512 annotated DEXA images, including 182 from axSpA patients, was used to train and test the model. Kyphosis angles were computed by fitting a circle through detected vertebral centroids (Th4–Th12) and calculating the corresponding curvature angle. Model-predicted angles demonstrated strong agreement with physician-measured Cobb angles (r = 0.92, p < 0.001), low mean squared error (4.2°) and high sensitivity and specificity for detecting clinically significant kyphosis. Automated lateral DEXA morphometry provides a rapid, reproducible and clinically interpretable method for assessing thoracic kyphosis and bone density in axSpA, representing a practical tool for integrated structural and metabolic evaluation. Full article

This paper presents a compact wide bandwidth, wide beamwidth circularly polarized (CP) antenna for satellite communication (SatCom) mobile terminals. The radiator is based on a cavity-backed crossed dipole, while a commercial quadrature power-divider/phase-shifter IC replaces conventional quarter-wavelength phase-delay lines to suppress dispersion-induced phase errors and maintain stable CP performance over a broad frequency range. To broaden the beam, a tightly coupled arc-shaped parasitic strip encircles the tapered semicircular arms, and the cavity cross-section is reduced to enhance lateral radiation. In addition, the cavity sidewalls are electrically connected to the parasitic element to increase the effective electrical length, downshift the operating frequency, and enable miniaturization. A prototype was fabricated and measured. The measured impedance bandwidth (IMBW, |S11| < −10 dB) is 1.76–3.08 GHz, fully covered by the AR < 3 dB bandwidth. The peak gain remains above 2 dBic over 1.7–3.1 GHz, while the half-power beamwidth (HPBW) stays around 114–142° and the 3 dB axial-ratio beamwidth (ARBW, AR < 3 dB) is around 114–144° across the entire operating band. These results indicate that the proposed antenna is a promising candidate for integrated multi-band SatCom terminals requiring wide bandwidth operation and wide-angle coverage. Full article

This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised intelligence modules into the localization pipeline. A refined two-stage time-of-flight (TOF) estimation method is introduced, combining a minimum-norm algorithm with a probability-weighted refinement mechanism that improves ranging accuracy under non-line-of-sight (NLOS) conditions. Simultaneously, an adaptive parameter-tuned DBSCAN algorithm is applied to angle-of-arrival (AOA) sequences, enabling unsupervised spatio-temporal clustering for stable direction estimation without requiring prior labels or environmental calibration. These AI-enabled components allow the system to dynamically suppress multipath interference, eliminate positioning ambiguity, and maintain robustness across diverse indoor layouts. Comprehensive experiments conducted on the Widar2.0 dataset demonstrate that the proposed method achieves decimeter-level accuracy with an average localization error of 0.63 m, outperforming existing methods such as “Widar2.0” and “Dynamic-MUSIC” in both accuracy and efficiency. This intelligent and lightweight architecture is fully compatible with commodity Wi-Fi hardware and offers significant potential for real-time human tracking, smart building navigation, and other location-aware AI applications. Full article

A key challenge in automated marigold harvesting lies in the accurate identification of picking points under complex environmental conditions, such as dense shading and intense illumination. To tackle this problem, this research proposes a lightweight instance segmentation model combined with a harvest position estimation method. Based on the YOLOv11n-seg segmentation framework, we develop a lightweight PDS-YOLO model through two key improvements: (1) structural pruning of the base model to reduce its parameter count, (2) incorporation of a Channel-wise Distillation (CWD)-based feature distillation method to compensate for the accuracy loss caused by pruning. The resulting lightweight segmentation model achieves a size of only 1.3 MB (22.8% of the base model) and a computational cost of 5 GFLOPs (49.02% of the base model). At the same time, it maintains high segmentation performance, with a precision of 93.6% and a mean average precision (mAP) of 96.7% for marigold segmentation. Furthermore, the proposed model demonstrates enhanced robustness under challenging scenarios including strong lighting, cloudy weather, and occlusion, improving the recall rate by 1.1% over the base model. Based on the segmentation results, a method for estimating marigold harvest positions using 3D point clouds is proposed. Fitting and deflection angle experiments confirm that the fitting errors are constrained within 3–12 mm, which lies within an acceptable range for automated harvesting. These results validate the capability of the proposed approach to accurately locate marigold harvest positions under top-down viewing conditions. The lightweight segmentation network and harvest position estimation method presented in this work offer effective technical support for selective harvesting of marigolds. Full article