Search Results (219)

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

Background/Objectives: Laser Doppler flowmetry enables rapid and simple measurement of microcirculation. However, variations in probe configuration can influence signal acquisition, making it essential to understand each probe’s characteristics when selecting equipment for specific physiological assessments. Therefore, this study aimed to compare perfusion measurements obtained with single-fiber (VP1T) and multi-fiber (VP1T/7) probes and to evaluate the effects of normalization strategies. Methods: Nine healthy female volunteers were recruited. Probes were positioned on the palmar aspects of the index and middle fingers of both hands while participants underwent a standardized brachial artery occlusion protocol. Data are presented as mean ± standard error of the mean. Correlations were assessed using Pearson’s correlation coefficient. Coefficients of variation (CV) and intraclass correlation coefficients were calculated. Baseline normalization was applied to measurements. Statistical analyses were performed using Student’s t-test, with significance set at p < 0.05. Results: Analysis of the full protocol revealed significant positive correlations between probes, indicating consistent temporal perfusion patterns. The VP1T/7 probe yielded significantly higher perfusion values than the VP1T probe, although both exhibited similar CVs. Inter-probe reliability was good, and intra-probe reproducibility ranged from good to excellent, particularly for the VP1T/7 probe. During the reperfusion phase, significant differences were observed only for ipsilateral measurements obtained with the VP1T probe. Normalization effectively reduced variability, and significant differences during reperfusion were detected with both probes. Conclusions: Although the multi-fiber probe consistently recorded higher perfusion values, normalization was essential to reduce variability and to enhance the detection of microvascular reactivity parameters. Full article

Background/Objectives: We assessed the prevalence of impaired coronary flow velocity reserve (CFVR) and aimed to identify echocardiographic and clinical predictors of coronary microvascular dysfunction in adolescents after neonatal arterial switch operation (ASO). Methods: This single-center, cross-sectional study included patients that underwent neonatal ASO for simple D-transposition of the great arteries (D-TGA) during 1998–2013. All patients were evaluated by echocardiography with global left ventricular strain measurement (GLS) and cardiac catheterization, including coronary angiography. Coronary flow velocity reserve was assessed by transthoracic Doppler echocardiography in the left anterior descending artery (LAD) using adenosine induced hyperemia. Patients were stratified into two groups according to CFVR: group with impaired CFVR (<2.5) and group with normal CFVR (≥2.5). Spearman correlation was used to assess the relationship between CFVR and echocardiographic variables. Binary logistic regression was used to determine independent predictors of impaired CFVR. Results: Out of 48 patients included (median age 16 years, age range 13 to 23 years, 71% male), impaired CFVR was found in 21 patients (44%). These patients had decreased longitudinal tricuspid annular plane systolic excursion (TAPSE), greater Z scores for left ventricular end-systolic dimensions and higher mean pulmonary artery pressures (mPAP). CFVR showed modest but significant positive correlations with tricuspid annular plane systolic excursion (TAPSE). Left pulmonary artery branch stenosis, reduced TAPSE and mPAP ≥ 20 mmHg, were significantly associated with impaired CFVR, while decreased TAPSE remained independent predictor in multivariable analysis (odds ratio 5.6, 95% confidence interval 1.24–25.26, p = 0.025). Conclusions: Impaired CFVR appears to be frequently observed in adolescents after uncomplicated neonatal ASO for simple D-TGA. Importantly, impaired CFVR is associated with right ventricular dysfunction. Full article

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel–road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3–18 ms for representative 20–50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work. Full article

With the increasing demand for three-dimensional positioning in Synthetic Aperture Radar (SAR) systems, multi-view SAR technology is rapidly evolving. Airborne-distributed SAR systems, benefiting from multi-platform collaborative observation, flexible baseline configuration, and synchronous imaging, have become an ideal solution for realizing this technology. However, the flight paths of these platforms are not optimal, and the airborne navigation equipment also suffers from measurement errors, which severely deteriorates the multi-view SAR target positioning accuracy of the airborne-distributed platforms. Currently, research on this issue remains scarce. This paper is based on the multi-view normalized Range Doppler positioning model, introducing platform position errors to derive the Cramér-Rao Lower Bound (CRLB). A detailed positioning accuracy analysis is conducted for different flight paths and various sources of errors, demonstrating that platform position errors are a primary factor affecting target positioning accuracy. To address this, a target positioning method based on inter-platform ranging information is proposed, which imposes constraints on the position of the airborne-distributed platform using inter-platform ranging data, thereby reducing the dependence of target positioning accuracy on platform position errors and enhancing the robustness of three-dimensional positioning for multi-view SAR targets. The effectiveness of the proposed method is verified using measured data, which reduces the 3D positioning error of the target by nearly 60%. Full article

Wind-driven shear and vertical mixing in the upper meter of the ocean strongly regulate near-surface circulation and buoyant tracer transport, yet direct field observations immediately beneath the air–sea interface remain scarce. We present Lagrangian observations, equipped with an upward-looking Acoustic Doppler Current Profiler (ADCP), collected during 5–7 April 2022 in the Jeju Strait under wind stresses of 0.0006–0.19 Pa. Near-surface shear and turbulence metrics were resolved within the top surface layer (TSL), and a response-time analysis showed that upper-layer shear responded most promptly to wind variability, whereas deeper-layer shear and sea-state metrics adjusted more slowly. Wave-period variability exhibited the weakest coupling, indicating additional nonlocal influences. Reynolds-stress estimates showed that the along-wind momentum flux was predominantly negative, indicating net downward transfer of downwind momentum, while cross-direction fluxes were smaller on average and frequently reversed sign, consistent with intermittent lateral transfers associated with evolving wave–current interactions. Using an eddy-viscosity framework, we derived stress-based exponential-saturation parameterizations for depth-averaged shear and vertical diffusivity, with the diffusivity magnitude treated as sensitive to the assumed turbulent Prandtl number. The relationships are intended for event-scale conditions within the observed forcing range and provide field-constrained, implementation-ready formulations for near-surface transport and mixing models. Full article

Type 1 diabetes (T1D) increases the risk for cerebral microangiopathy. However, the association between gender and cerebral microcirculatory dysfunction in T1D remains undetermined. Therefore, we have conducted a comparative analysis of cerebral endothelial-mediated microcirculatory parameters between middle-aged Caucasian females and males with type 1 diabetes. The present study examined the nitric oxide-induced vasomotor reactivity of middle cerebral arteries (MCA using transcranial Doppler and L–arginine infusion (L–arg VMR)). The study compared L–arg VMR between 23 males and 26 females with type 1 diabetes without a history of overt cerebrovascular disease. Mean L–arg VMR and baseline MCA flow velocity (V rest) were higher in females than in males (20.1 ± 5.4 vs. 14.6 ± 7.1% p < 0.01 and 73, 54–106 vs. 60.7–77 cm/s p < 0.01, respectively). Males were older than females (39.7 [range: 31.3–55.7] vs. 36.5 [range: 25.0–45.5] years, p = 0.02) and were characterized by later T1D onset and higher insulin/24 h, triglyceride levels and body mass index (BMI). Higher L–arg VMR in females persisted when co-variated with patients’ age, age of onset, BMI, triglyceride level and V rest. Cerebral vasomotor reactivity to L–arginine showed greater efficacy in middle-aged females than in males with T1D, independent of age and disease course. The protective effect of the female gender on cerebral endothelium function has been demonstrated in type 1 diabetes. Full article

Compared with conventional monostatic radar systems, space-air bistatic frequency diverse array (FDA) radar exhibits superior anti-jamming capability and enhanced early-warning performance for weak and maneuvering targets. However, the complex bistatic configuration and the high velocity of spaceborne platforms introduce several challenges, including range migration (RM), Doppler spread (DS), and Doppler frequency migration (DFM). In particular, frequency offsets among FDA array elements exacerbate inter-channel Doppler mismatches, significantly reducing the coherent integration gain and consequently degrading detection performance. To address these issues, this article establishes a target echo model within a three-dimensional coordinate framework and provides an analysis of the different order terms. Subsequently, the SOKT is implemented to eliminate first- and second-order range migrations as well as the coupling induced by velocity ambiguity. Thereafter, the MDF is employed in the slow-time domain to compress Doppler spread and restore coherent gain. Simulation results verify that the SOKT-MDF approach effectively mitigates the effects of target velocity and acceleration, alleviates the Doppler spread (DS) problem, and significantly improves detection performance while maintaining low computational complexity. Full article

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

Background/Objectives: Ethnicity is associated with differences in cardiac structure and function in non-pregnant populations, but pregnancy-specific data—particularly for myocardial deformation—remain limited. We investigated whether ethnicity influences cardiac geometry, biventricular and biatrial mechanics, hemodynamics, and carotid vascular indices in healthy women during the third trimester of pregnancy. Methods: In this prospective, monocentric study, 80 healthy women with singleton third-trimester pregnancies were enrolled, including 40 Asian and 40 Caucasian women matched for age and body mass index. All participants underwent standardized clinical and laboratory evaluation, comprehensive transthoracic echocardiography with Doppler, speckle-tracking analysis of both ventricles and atria, and bilateral carotid ultrasonography. Logistic regression analyses were performed in Asian women to identify correlates of supranormal left ventricular ejection fraction (LVEF ≥ 70%) and enhanced left ventricular global longitudinal strain (LV-GLS > 20%). Results: Age and gestational age were similar between groups, whereas body surface area was lower in Asian women (1.65 ± 0.12 vs. 1.77 ± 0.15 m², p < 0.001). Asian women exhibited smaller left ventricular dimensions and volumes but higher LVEF (median 71.6% vs. 66.4%, p < 0.001). Heart rate and blood pressure were comparable, whereas stroke volume [45.5 ± 9.6 vs. 68.0 (48.9–110) mL, p < 0.001] and cardiac output (3.9 ± 0.9 vs. 4.9 ± 0.8 L/min, p < 0.001) were lower in Asian women, who also demonstrated higher total peripheral resistance and lower ventricular–arterial coupling (0.31 ± 0.09 vs. 0.37 ± 0.07, p = 0.001). Speckle-tracking echocardiography revealed higher LV-GLS (21.9 ± 1.9% vs. 20.5 ± 2.0%, p = 0.002), higher LV global circumferential strain, enhanced right ventricular longitudinal strain, and higher reservoir strain of both atria in Asian women. Carotid ultrasonography showed smaller common carotid diameter and cross-sectional area in Asian women (10.7 ± 2.5 vs. 13.7 ± 2.3 mm², p < 0.001). In regression analyses, supranormal LVEF was independently associated with smaller LV end-diastolic diameter (OR 0.39, 95% CI 0.16–0.97), while enhanced LV-GLS was independently associated with lower neutrophil-to-lymphocyte ratio (OR 0.04, 95% CI 0.00–0.87). Conclusions: Ethnicity is associated with multidimensional differences in cardiac geometry, myocardial mechanics, vascular load, and carotid structure in healthy third-trimester pregnancy. Ethnicity-aware interpretation and tailored reference ranges may improve the accuracy of echocardiographic assessment during late gestation. Full article

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions. Full article

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data in high-precision geometric applications, especially in scenarios where ground control points (GCPs)—traditionally used for calibration—are inaccessible or costly to acquire. To address this challenge, this study proposes a novel GCP-free high-precision geolocation method based on multi-view SAR image fusion, integrating outlier detection, weighted fusion, and refined estimation strategies. The method first establishes a positioning error correlation model for homologous point pairs in multi-view SAR images. Under the assumption of approximately equal positioning errors, initial systematic error estimates are obtained for all arbitrary dual-view combinations. It then identifies and removes outlier images with inconsistent systematic errors via coefficient of variation analysis, retaining a subset of multi-view images with stable calibration parameters. A weighted fusion strategy, tailored to the geometric error propagation model, is applied to the optimized subset to balance the influence of angular relationships on error estimation. Finally, the minimum norm least-squares method refines the fusion results to enhance consistency and accuracy. Validation experiments on both simulated and actual airborne SAR images demonstrate the method’s effectiveness. For actual measured data, the proposed method achieves an average positioning accuracy improvement of 84.78% compared with dual-view fusion methods, with meter-level precision. Ablation studies confirm that outlier removal and refined estimation contribute 82.42% and 22.75% to accuracy gains, respectively. These results indicate that the method fully leverages multi-view information to robustly estimate and compensate for 2D systematic errors (range and azimuth), enabling high-precision planar geolocation of airborne SAR images without GCPs. Full article

Owing to the intrinsic sparsity of GNSS signals in the correlation domain, compressed sensing (CS) is attractive for the rapid acquisition of high-dynamic GNSS signals. However, the compressed measurement-associated noise folding inherently amplifies the pre-measurement noise, leading to an inevitable degradation of acquisition performance. In this paper, a novel CS-based GNSS signal acquisition algorithm is, for the first time, proposed with the efficient suppression of the amplified measurement noise and low computational complexities. The offline developed code phase and frequency bin-compressed matrices in the correlation domain are utilized to obtain a real-time observed matrix, from which the correlation matrix of the GNSS signal is rapidly reconstructed via a denoised back-projection and a non-iterative shrinkage-thresholding (NIST) operation. A detailed theoretical analysis and extensive numerical explorations are undertaken for the algorithm computational complexity, the achievable acquisition performance, and the algorithm performance robustness to various Doppler frequencies. It is shown that, compared with the classic orthogonal matching pursuit (OMP) reconstruction, the NIST reconstruction gives rise to a 3.3 dB improvement in detection sensitivity with a computational complexity increase of <10%. Moreover, the NIST-reconstructed CS acquisition algorithm outperforms the conventional CS acquisition algorithm with frequency serial search (FSS) in terms of both the acquisition performance and the computational complexity. In addition, a variation in the detection sensitivity is observed as low as 1.3 dB over a Doppler frequency range from 100 kHz to 200 kHz. Full article

Synthetic Aperture Radar (SAR) onboard satellites provides high-resolution Earth imaging independent of weather conditions. SAR data are acquired by an aircraft or satellite and sent to a ground station to be processed. However, for novel applications requiring real-time analysis and decisions, onboard processing is necessary to escape the limited downlink bandwidth and latency. One such application is real-time target recognition, which has emerged as a decisive operation in areas such as defense and surveillance. In recent years, deep learning models have improved the accuracy of target recognition algorithms. However, these are based on optical image processing and are computation and memory expensive, which requires not only processing the SAR pulse data but also optimized models and architectures for efficient deployment in onboard computers. This paper presents a fast and accurate target recognition system directly on raw SAR data using a neural network model. This network receives and processes SAR echo data for fast processing, alleviating the computationally expensive DSP image generation algorithms such as Backprojection and RangeDoppler. Thus, this allows the use of simpler and faster models, while maintaining accuracy. The system was designed, optimized, and tested on low-cost embedded devices with low size, weight, and energy requirements (Khadas VIM3 and Raspberry Pi 5). Results demonstrate that the proposed solution achieves a target classification accuracy for the MSTAR dataset close to 100% in less than 1.5 ms and 5.5 W of power. Full article