Search Results (97)

To gain a deeper understanding of the lower-atmospheric dynamic characteristics in the transition zone on the northeastern margin of the Tibetan Plateau, high-resolution wind profile data collected by a Doppler wind lidar (DWL) at Yinchuan Hedong International Airport from 2021 to 2023 were used to analyze the vertical structure, seasonal variations, and diurnal characteristics of the low-height wind field and wind shear in this region. The results indicate that (1) the data acquisition rate (DAR) below 1.5 km is generally high, exceeding 90% during most periods, and decreases monotonically with height; the 90% DAR contour height exhibits clear seasonal and diurnal variations, with the largest diurnal amplitude in summer and the smallest in winter. (2) The middle- and low-height wind fields are jointly modulated by topographic forcing and local circulations. Below 0.4–0.7 km, north–northeast and south–southwest winds prevail across all seasons, which is consistent with the blocking and splitting effects of the Helan Mountains. At 42 m, the wind direction shows a marked diurnal transition that may reflect the combined influence of the Helan Mountains’ bypass flow, mountain–plain circulation, and thermal contrasts between the Yellow River and surrounding desert/plain surfaces. (3) Horizontal wind speeds are predominantly concentrated below 6 m s⁻¹, and the development height of this low-wind-speed zone varies seasonally. The vertical velocity statistics show weak positive values in parts of the observed layer, but these signals are interpreted cautiously because vertical-velocity retrieval is subject to additional uncertainty. (4) The low-level wind shear intensity reaches its peak below 100 m and generally exhibits a U-shaped vertical distribution; severe wind shear below 100 m occurs most frequently from nighttime to early morning during May–October, whereas its occurrence frequency is lowest in winter. These findings provide observational evidence for aviation meteorological support and boundary-layer studies in semi-arid regions of Northwest China. Full article

Thoracopagus conjoined twins are the most common type of conjoined twinning and are frequently associated with shared cardiac anatomy, the principal determinant of prognosis and postnatal survival. Early prenatal diagnosis is essential for accurate counseling and timely pregnancy management. We report the diagnosis of thoracopagus conjoined twins with a shared cardiac structure in a 35-year-old primigravid woman at 8 weeks and 3 days of gestation. High-resolution transvaginal ultrasound demonstrated persistent thoracic fusion, fixed relative fetal position, a single yolk sac, a single umbilical cord, and absence of an intertwin membrane. Color Doppler confirmed a single sonographically detectable cardiac complex, while three-dimensional ultrasound enhanced spatial visualization and facilitated parental counseling. Differential diagnoses, including monochorionic twins with close apposition, TRAP sequence, body stalk anomaly, and other forms of conjoined twinning, were systematically excluded. Early first-trimester recognition of thoracopagus conjoined twins with shared cardiac anatomy is feasible using dynamic ultrasound and Doppler imaging. Establishing the diagnosis before the end of the first trimester enables accurate prognostic assessment, timely multidisciplinary counseling, and informed parental decision-making for this condition associated with an extremely poor prognosis. Full article

Reliable fault detection and isolation (FDI) for actuators and sensors in underactuated autonomous underwater vehicles (AUVs) is challenging because nonlinear hydrodynamic coupling redistributes fault signatures, and persistent ocean currents can trigger false alarms. This paper presents a reinforcement learning (RL)-augmented dual-layer FDI framework for actuator and sensor faults in underactuated AUVs. The actuator layer uses a robust adaptive propagated interval observer (RAPIO) that evaluates thruster and control-surface residuals against a calibrated dynamics-consistency tube. The sensor layer forms estimator-consistency residuals for Doppler velocity log (DVL), depth, and inertial measurement unit (IMU) measurements against a reference-separated finite-time extended state observer (FTESO). An offline-trained soft actor–critic (SAC) policy schedules bounded actuator uncertainty margins and sensor alarm thresholds according to operating confidence. The scheduled actuator error dynamics remain Metzler and Hurwitz, preserving positive interval propagation and center-error input-to-state stability (ISS) independent of policy convergence. A Schmitt-trigger alarm and signal-space disambiguation rule classify healthy, actuator-only, sensor-only, and simultaneous-fault conditions under explicit residual-separation and persistence conditions. Across 72 simultaneous-fault episodes over a $4\times 6$ uncertainty–current grid, the proposed method achieved $100\%$ detection coverage for both actuator and sensor faults with only five false-alarm events, retaining full coverage in the severe-current, high-uncertainty subset where the selected actuator and sensor baselines achieved only 88.9% and 70.4% detection, respectively, with more false alarms. These results indicate that the proposed bounded RL scheduler can deliver reliable, certifiable actuator and sensor fault diagnosis under significant operational uncertainty. Full article

Terrain-aided navigation (TAN) enables autonomous positioning through fusing prior terrain databases with real-time sensor measurements in GNSS-denied environments. Typical factors, including wide beam width and terrain elevation variations, introduce inaccuracies in elevation measurements, degrading the performance of classical elevation-based TAN methods. The SAR altimeter operates in nadir-looking mode to acquire range–Doppler projection images with inherent cross-track ambiguity for positioning based on image information, yet its accuracy is limited by single-feature and fixed-grid approaches. In this paper, we introduce an adaptive positioning framework for the SAR altimeter that combines XGBoost-based multi-feature fusion with Bayesian particle filtering. First, a fast DDM template generation algorithm is employed to improve computational efficiency. Then, an ensemble learning framework integrating complementary similarity features is introduced to achieve robust single-frame matching. Additionally, a Bayesian filtering-based dynamic grid construction method is developed to concentrate particles in high-probability regions, eliminating boundary truncation errors inherent to fixed approaches. The proposed method’s primary advantage is the reliable three-dimensional localization under extreme radar configurations, such as wide beam width and high-altitude maneuvering platforms. Experimental results based on both simulated and real data validate the method, demonstrating superior positioning performance under wide-beam conditions. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 satellite imagery, surface observations, and radar optical flow features to nowcast multiple severe convective events within the next 30 min. The model uses 2D-CNN for spatial extraction, ConvLSTM for temporal dynamics, and a weighted joint loss (Focal Loss and Dice Loss) to address class imbalance. Trained on 396 samples (positive-to-negative ratio 1:2.5) from 83 events at Guanghan Airport (2021–2024), incorporating optical flow features significantly boosted performance: macro-F1 increased from 0.719 to 0.792, and Threat Score (TS) from 0.567 to 0.705. Notably, false negatives for minority classes dropped sharply, with strong winds F1-score rising from 0.15 to 1.00. Ablation analysis showed optical flow as the top contributor (Mean Decrease in TS ≈ 0.5). Through multi-modal fusion and motion enhancement, this interpretable model provides high-precision nowcasting for airport severe convective weather, offering substantial value for aviation safety. Full article

Objectives: This scoping review synthesized the available evidence on bedside ultrasonography used to confirm short-term nasogastric tube (NGT) placement in adults. Methods: The review followed JBI Collaboration methodology. Searches were conducted in CINAHL, Embase, LILACS, PubMed, and Scopus, as well as in gray literature sources (Google Scholar and ProQuest Dissertation & Thesis Global). Primary studies and clinical guidelines addressing bedside ultrasonography for short-term NGT placement in adults (≥18 years) were eligible, with no limits on language or publication year. Data were extracted and narratively summarized with the I-AIM framework (Indication, Acquisition, Interpretation, and Decision-Making). Results: Twenty-nine studies met the inclusion criteria. Most were single-center observational studies performed in intensive care units or emergency departments. Ultrasound was primarily used for confirmation prior to enteral nutrition initiation, while gastric decompression was less frequently reported. Acquisition protocols varied, although supine positioning, convex abdominal probes, and linear cervical probes were most commonly described. The gastric antrum and esophagus were the principal anatomical landmarks, with interpretation based on direct tube visualization and dynamic fogging; color Doppler was occasionally used. Radiography remained the reference standard in most studies, and only a minority initiated feeding based solely on ultrasound findings. Reported facilitators included bedside feasibility, absence of radiation exposure, and timeliness. Barriers included operator dependency, limited visualization in patients with obesity or gas interposition, protocol heterogeneity, and the limited methodological robustness of available studies. Conclusions: Current evidence suggests that ultrasonography may represent a feasible, radiation-free bedside approach for confirmation of NGT placement. Evidence from selected studies suggests that, with structured training, healthcare professionals may achieve diagnostic accuracy in specific clinical settings, although further robust multicenter investigations are needed to confirm these findings. Full article

With the development of 6G communication technology, Space–Air–Ground Integrated Networks (SAGINs) have become critical infrastructure for global intelligent collaborative computing. However, federated learning deployment in SAGINs faces three severe challenges: the high dynamics of satellite orbital motion, node resource heterogeneity, and privacy vulnerabilities in data transmission. This paper proposes an adaptive compressed sensing differential privacy federated learning framework based on orbital spatiotemporal characteristics. First, we design orbital periodicity-driven time-varying sparse sensing matrices that dynamically adjust compression strategies according to satellite orbital positions, achieving intelligent communication efficiency optimization. Second, we propose an orbital predictability-based privacy budget temporal allocation mechanism and perform differential privacy noise injection in the compressed domain, establishing a compression–privacy joint optimization algorithm. Furthermore, we construct an energy–communication–privacy ternary collaborative mechanism that achieves multi-objective dynamic balance through model predictive control. Finally, we design reinforcement learning-based dynamic routing scheduling and hierarchical aggregation strategies to effectively handle the time-varying characteristics of network topology. Simulation experiments demonstrate that compared to existing methods, the proposed approach achieves 3–12% improvement in model accuracy and 30–50% enhancement in communication efficiency while maintaining differential privacy protection with dynamic privacy budget $\epsilon \in [0.1,10.0]$ and compression ratio $\rho \in [0.2,0.8]$. Unlike static compressed sensing approaches that ignore orbital periodicity, the proposed orbital-driven time-varying sensing matrices reduce reconstruction error by up to 19.4% compared to fixed-matrix baselines, validating the synergistic effectiveness of integrating orbital spatiotemporal characteristics with federated learning in 6G SAGIN deployments. The framework assumes reliable orbital propagation via SGP4/SDP4 models and does not account for Doppler frequency shifts or inter-satellite link handover delays; future extensions include scalability to mega-constellations and integration of quantum-resistant privacy mechanisms. Full article

The proliferation of Low Earth Orbit (LEO) satellite constellations, driven by the NewSpace economy and reduced launch costs, has opened new opportunities for positioning, navigation, and timing (PNT) applications. Compared to traditional GNSS systems operating in Medium Earth Orbit, LEO satellites offer several advantages: higher received signal power, better satellite geometry and visibility in urban environments, and greater Doppler dynamics—enabling approaches such as single-satellite and Doppler-based positioning. Although dedicated LEO-PNT constellations are still under development, existing commercial LEO satellites can already be leveraged for experimental positioning applications. This paper presents a portable, multi-constellation testbed built using commercial off-the-shelf (COTS) hardware and software-defined radio (SDR) technologies. The platform enables the synchronous acquisition and processing of LEO signals from Orbcomm, Iridium, and Starlink, allowing for the extraction of key positioning observables. A comprehensive measurement campaign is conducted across both indoor and outdoor environments to evaluate signal visibility and Doppler tracking performance. Results highlight the potential of opportunistic LEO-based positioning, particularly in challenging scenarios such as indoor environments where traditional GNSS solutions are unreliable. Full article

The escalating development of Maritime Autonomous Surface Ships (MASS) has imposed rigorous demands on the precision, continuity, and resilience of onboard integrated navigation systems. However, in complicated marine settings, data from the Global Navigation Satellite System (GNSS) and Doppler Velocity Log (DVL) are frequently corrupted by multipath effects and non-line-of-sight (NLOS) interference. These disturbances introduce anomalous observations that violate Gaussian noise assumptions, thereby severely deteriorating the robustness and estimation quality of traditional sliding-window factor graph optimization (SW-FGO). To mitigate this problem, this study introduces a novel integrated navigation strategy termed gradient-adaptive factor graph optimization (GA-FGO). By designing a gradient-adaptive robust objective function within the factor graph structure, the proposed method dynamically re-weights constraints from the inertial navigation system (INS), GNSS, and DVL. This mechanism adequately suppresses the influence of measurement outliers at the optimization level. Furthermore, a unified solution framework utilizing iterative reweighted least squares (IRLS) and the Gauss–Newton method is established to simultaneously perform adaptive weight updates and state estimation. Validation was based on offline field data benchmarked against the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and standard SW-FGO. The simulation results demonstrated that the GA-FGO algorithm achieves superior positioning accuracy and estimation stability under realistic measurement conditions. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

This study aims to demonstrate the feasibility of a hybrid measurement system that combines Laser Doppler Vibrometry (LDV) and conventional accelerometers for operational modal analysis (OMA) of civil engineering structures. The proposed approach addresses the limitations of traditional accelerometer-based systems, particularly for large-scale or inaccessible structures, by integrating non-contact LDV measurements with conventional sensor data. Experimental tests were conducted on a cantilever beam and a pedestrian laboratory footbridge to validate the hybrid system. The LDV was used to measure velocity at key points, while accelerometers provided complementary reference acceleration measurements. Reflective targets were employed to facilitate non-contact data collection, allowing for the subsequent reuse of these targets for repeated measurements. The velocity data from the LDV were differentiated to obtain acceleration and integrated to estimate displacement, enabling a direct combination with accelerometer data. ARTeMIS Modal software was utilized to process and analyze the collected data, successfully identifying the natural frequencies and vibration modes of both structures. The results demonstrate that the LDV–accelerometer hybrid system effectively captures the dynamic behavior of structures, offering a comprehensive solution for modal analysis without extensive sensor deployment. This approach provides significant advantages in scenarios where traditional methods are impractical, positioning the hybrid system as a promising tool for dynamic analysis and infrastructure monitoring of complex structures. Full article

This paper proposes a compressive-sensing (CS) acquisition algorithm for low-power, high-dynamic GNSS receivers based on low-dimensional time-domain measurements, a non-iterative compressive-domain direct-projection peak-search pipeline, and a coherence-optimized sensing-matrix design. Unlike most existing GNSS-CS acquisition approaches that rely on explicit sparse-recovery formulations (e.g., OMP/BP/LS-type iterative reconstruction) to identify the delay–Doppler support—often incurring substantial computational burden and acquisition latency—the proposed method performs peak detection directly in the compressive measurement domain and is supported by unified Gram-matrix optimization and perturbation/detection analyses. Specifically, the measurement Gram matrix is optimized on the symmetric positive-definite (SPD) manifold to obtain a diagonally dominant and well-conditioned structure with reduced inter-column correlation, thereby bounding reconstruction-induced perturbations and preserving the main correlation peak. Simulation results show that the proposed scheme retains the low online complexity characteristic of direct-projection baselines while achieving a 2–3 dB acquisition sensitivity gain, and it requires substantially fewer operations than iterative OMP-based CS acquisition schemes whose cost scales approximately linearly with the sparsity level K. The proposed framework enables robust, low-latency acquisition suitable for resource-constrained GNSS receivers in high-dynamic environments. Full article

This study was designed, for the first time, to fully characterize the corpus luteum’s (CL) dynamics, echotexture, and ovarian blood flow on the ipsilateral side of the CL during different stages of the follicular wave in spontaneously non-mated camels (Camelus dromedarius) and to correlate the CL’s size echotexture with Doppler parameters. Of 20 non-mated camels, only 7 exhibited spontaneous ovulation. B- and color-mode analyses of the CL were estimated; CL frozen image echotextures [CL echogenicity (CLE) and CL heterogeneity (CLH)] and ovarian artery (OV. A.) dynamics were recorded, and ultrasound scanning was performed. Blood sampling and progesterone (P4) levels were measured after ovulation. CL diameter and echotexture were elevated (p = 0.025 and p = 0.037) at the mid-maturation stage and compared to the early growth and late regression stages (1.03 ± 0.45/cm and 82.65 ± 2.87 for CLE and 33.65 ± 1.83 for CLH vs. 1.98 ± 0.88 cm; 66.52 ± 4.32 for CLE and 15.66 ± 0.25 for CLH vs. 1.02 ± 0.02 cm, 65.12 ± 2.66 for CLE, and 19.32 ± 1.33 for CLH), as those parameters are critical in the determination of CL activity. Ipsilateral OV. A. diameter increased (p = 0.021) in the mid-maturation and regression stages, with a significant elevation in Doppler velocities (p = 0.025) in the maturation stage, with a decline in Doppler indices (p = 0.013), while the contralateral side was not affected. Ipsilateral mean velocity (Vm; cm/s) and blood flow volume (BFV; mL/min) were increased in the mid-maturation stage (23.55 ± 0.66 cm/s and 25.62 ± 0.32 mL/min). CL diameter was positively correlated with the CL’s total colored area/pixels (r = 0.81; p = 0.001), total colored area % (r = 0.93; p = 0.001), and OV. A. velocities (r = 0.96; p = 0.001). In addition, there was a positive correlation between CLH and OV. A.BFV (r = 0.89; p = 0.001). After spontaneous ovulation, the CL increases in diameter and reaches its peak on day 12, with an elevation in the P4 level at day 10, and the total colored area of the CL continues to elevate until day 14. Ipsilateral OV. A. blood flow is elevated and linked to changes that occur in the CL’s total coloration %. Evaluating luteal function in camels presents several challenges due to the species’ unique reproductive physiology. Full article

Background/Objectives: We aimed to characterize the microvascular imaging (MI) to demonstrate in hepatic hemangiomas in routine practice and to quantify the impact of lesion depth on MI signal detectability, and—when present—describe the distribution of MI appearances. Methods: In this single-center, retrospective study from January 2021 to December 2023, we screened 91 patients with 121 focal hepatic lesions on ultrasound. Lesions without typical hemangioma enhancement on dynamic MRI or dynamic CT were excluded. Two radiologists independently assessed MI signals and patterns using the Jeon classification, blinded to clinical and CT/MRI data; inter-observer agreement was quantified with Cohen’s κ. Results: Of 121 screened lesions, 36 lacked typical enhancement and were excluded; 85 hemangiomas remained. A total of 13 were excluded for motion artifacts near the heart or pulsatile vessels, yielding 72 hemangiomas (61 patients) for analysis. No lesion showed flow on color or power Doppler. MI signals were detected in 68/72 hemangiomas (94.4%). Among signal-positive lesions (n = 68), the patterns were non-specific in 25.0% (17/68), nodular rim in 22.1% (15/68), strip rim in 17.6% (12/68), central dot-like in 16.2% (11/68), peripheral dot-like in 10.3% (7/68), and staining in 8.8% (6/68). Signal-negative lesions were deeper than signal-positive lesions (median depth: 85 mm vs. 41.5 mm; p < 0.05). The inter-observer agreement was very good (κ = 0.821, 95% CI 0.767–0.921). Conclusions: MI is a reproducible, contrast-free technique that demonstrates hemangioma vascularity with high detection rates, particularly in more superficial lesions. In this cohort, lesion depth rather than size was the primary determinant of MI signal detectability. MI should be considered complementary to CT/MRI and may be especially useful where contrast agents are unavailable or contraindicated. Full article