Search Results (85)

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings and the demand for real-time processing, this paper proposes a YOLOv7-MTI recognition model that combines the attention mechanism and involution. By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. Involution helps the model adaptively adjust the weights of spatial positions through dynamic parameterized convolution kernels, strengthening the discrete strong scattering points specific to aircraft and suppressing the continuous scattering of the background, thereby alleviating the interference of complex backgrounds. Experiments on the SAR-AIRcraft-1.0 dataset, which includes seven categories such as A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and others, show that the mAP and mRecall of YOLOv7-MTI reach 93.51% and 96.45%, respectively, outperforming Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8. Compared with the basic YOLOv7, mAP is improved by 1.47%, mRecall by 1.64%, and FPS by 8.27%, achieving an effective balance between accuracy and speed, providing research ideas for SAR aircraft recognition. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version of our previous SARS-CoV-2 model formulated as input–output non-linear dynamical systems (IO-NLDS). Methods: This updated framework incorporates age-dependent contact patterns, immune waning, and new data sources, including seropositivity studies, hospital dynamics, variant trends, the effects of non-pharmaceutical interventions, and the dynamics of vaccination campaigns. Results: We analyze the dynamics of various datasets spanning the entire pandemic in Germany and its 16 federal states using this model. This analysis enables us to explore the regional heterogeneity of model parameters across Germany for the first time. We enhance our estimation methodology by introducing constraints on parameter variation among federal states to achieve this. This enables us to reliably estimate thousands of parameters based on hundreds of thousands of data points. Conclusions: Our approach is adaptable to other epidemic scenarios and even different domains, contributing to broader pandemic preparedness efforts. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

Global warming and intensified human activities increase flood disasters, causing annual casualties and economic losses. Mountain shadows are a major source of interference in floodwater extraction from SAR imagery, severely impacting the accuracy of water body detection. This study proposes an innovative approach based on the Inverted Exponential Shadow Removal Model (IESRM). This model can adaptively and dynamically adjust the slope threshold according to the terrain characteristics. It is easy to use, eliminating the need for manual parameter setting. The experimental results demonstrate the following: (1) Water body detection tests across diverse terrains (mountains, plains, and foothill plains) show robust results even in complex foothill regions, with an overall accuracy of 94.51% and a Kappa coefficient of 0.86. (2) A comparative analysis with the shadow formation mechanism method and the HAND (Height Above Nearest Drainage) method revealed that the inverted exponential function model achieved the highest accuracy, with an overall accuracy of 96.46% and a Kappa coefficient of 0.89. The IESRM provides an innovative solution for removing mountain shadows, enhancing SAR imagery-based flood monitoring in complex terrains. It offers timely and accurate data support for flood disaster management agencies. Full article

Background/Objectives: Since the emergence of the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the discovery of compounds with antiviral potential from medicinal plants has been extensively researched. This study aimed to investigate plant metabolites with in vitro inhibitory potential against SARS-CoV-2 targets, including 3CL_pro, PL_pro, Spike protein, and RdRp. Methods: A systematic review was conducted following PRISMA guidelines, with literature searches performed in six electronic databases (Scielo, ScienceDirect, Scopus, Springer, Web of Science, and PubMed) from January 2020 to February 2024. Computational analyses using SwissADME, pkCSM, ADMETlab, ProTox3, Toxtree, and DataWarrior were performed to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles as well as other medicinal chemistry parameters of these compounds. Results: A total of 330 plant-derived compounds with inhibitory activities against the proposed targets were identified, with compounds showing IC₅₀ values as low as 0.01 μM. Our findings suggest that several plant metabolites exhibit significant in vitro inhibition of SARS-CoV-2 targets; however, few molecules exhibit drug development viability without further adjustments. Additionally, after these evaluations, two phenolic acids, salvianic acid A and protocatechuic acid methyl ester, stood out for their potential as candidates for developing antiviral therapies, with IC₅₀ values of 2.15 μM against 3CL_pro and 3.76 μM against PL_pro; respectively; and satisfactory in silico drug-likeness and ADMET profiles. Conclusions: These results reinforce the importance of plant metabolites as potential targets for antiviral drug discovery. Full article

This study focuses on the investigation of the Tepehan landslide triggered by the 6 February 2023, Kahramanmaraş earthquake in Türkiye. The overall goal of this study is to understand the slope condition and simulate the failure considering pre- and post-event geometry. Topographic variations in the landslide area were analyzed using digital elevation models (DEMs) derived from the Sentinel-1 Synthetic Aperture Radar (SAR) satellite data and geospatial analysis. Slope stability analyses were conducted over a representative alignment, including assessments of soil structure, geological history, and field features. A limit equilibrium back-analysis was performed under both static and pseudo-static conditions, where an earthquake load coefficient was considered in the analyses. A total of five scenarios were evaluated to determine factors of safety (FoS) based on fully softened and residual strength parameters. The resulting critical slip surfaces from the simulations were compared with the geomorphometric analysis, necessitating the adjustment of the subsurface hard clay layer for residual conditions. The analyses revealed that the slope behaves as a delayed first-time landslide, with bedding planes acting as localized weak layers, reducing mobilized shear strength. This integrated remote sensing–geotechnical approach advances landslide hazard evaluation by enhancing the precision of slip surface identification and post-seismic slope behavior modeling, offering a valuable framework for similar post-disaster geohazard assessments. Full article

Background/Objectives: Asthma outcomes in children and adolescents largely depend on parental adherence to prescribed treatment plans. This study investigates how the COVID-19 pandemic influenced parental decision-making in managing their children’s asthma, regardless of whether the children were infected with SARS-CoV-2. Material and method: In this research, 146 children with asthma were analyzed based on the following data: demographic parameters (gender, age group, and residence), before and after measurements of FeNO and pulmonary function test parameters were performed to assess the evolution of asthma for infected and non-infected children, exacerbations, parents’ compliance with the treatment, changes in treatment steps performed by physicians, and the GINA asthma control levels. Results: The effect of parent self-management of doses was evident in the variation of FeNO and pulmonary function test parameters before and after COVID-19 disease, including children with asthma who did not contract the virus, in the decrease in well-controlled asthma cases, as well as in the number of exacerbations per year. A step-down in treatment doses was statistically associated with increased FeNO values (p < 0.0005), and decreased FEV1 values (p = 0.025). Higher values of FeNO were statistically significantly associated with a higher number of exacerbations per year (p < 0.0005). There was a statistically significant moderately strong association between the treatment steps evolution (decided by the attending physician) and parents’ self-management of doses in the attempt to assess the control of the disease of children with asthma (p = 0.019). Also, 80.95% of children for whom the parents performed a step-down in dose no longer presented well-controlled asthma, leading to a statistically significant association relative to the level of asthma control and doses adjustments (p < 0.0005). Conclusions: During epidemiological circumstances, a strong collaboration between the parents/caregivers/pediatric patients with asthma and attending physicians is essential to correctly assess the symptoms and to comply the asthma treatment with ICS and a bronchodilator in order to control the disease. Full article

Synthetic Aperture Radar (SAR) constellations have become a key technology for disaster monitoring, terrain mapping, and ocean surveillance due to their all-weather and high-resolution imaging capabilities. However, the design of large-scale SAR constellations faces multi-objective optimization challenges, including short revisit cycles, wide coverage, high-performance imaging, and cost-effectiveness. Traditional optimization methods, such as genetic algorithms, suffer from issues like parameter dependency, slow convergence, and the complexity of multi-objective trade-offs. To address these challenges, this paper proposes a hybrid optimization framework that integrates chaotic sequence initialization and fuzzy rule-based decision mechanisms to solve high-dimensional constellation design problems. The framework generates the initial population using chaotic mapping, adaptively adjusts crossover strategies through fuzzy logic, and achieves multi-objective optimization via a weighted objective function. The simulation results demonstrate that the proposed method outperforms traditional algorithms in optimization performance, convergence speed, and robustness. Specifically, the average fitness value of the proposed method across 20 independent runs improved by 40.47% and 35.48% compared to roulette wheel selection and tournament selection, respectively. Furthermore, parameter sensitivity analysis and robustness experiments confirm the stability and superiority of the proposed method under varying parameter configurations. This study provides an efficient and reliable solution for the orbital design of large-scale SAR constellations, offering significant engineering application value. Full article

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling induced by range cell migration (RCM), which results in high computational cost and limits their applicability to large-scale imaging scenarios. To address this challenge, the approximated observation-based sparse SAR imaging algorithm was developed, which decouples the range and azimuth directions, significantly reducing computational and temporal complexities to match the performance of conventional matched filtering algorithms. However, this method requires iterative processing and manual adjustment of parameters. In this paper, we propose a novel deep neural network-based sparse SAR imaging method, namely the Self-supervised Azimuth Ambiguity Suppression Network (SAAS-Net). Unlike traditional iterative algorithms, SAAS-Net directly learns the parameters from data, eliminating the need for manual tuning. This approach not only improves imaging quality but also accelerates the imaging process. Additionally, SAAS-Net retains the core advantage of sparse SAR imaging—azimuth ambiguity suppression in under-sampling conditions. The method introduces self-supervision to achieve orientation ambiguity suppression without altering the hardware architecture. Simulations and real data experiments using Gaofen-3 validate the effectiveness and superiority of the proposed approach. Full article

Artificial metasurfaces can rapidly modulate their electromagnetic scattering properties and the characteristics of echo signals, which can lead to different imaging features in synthetic aperture radar (SAR) imaging results. Based on this, for the first time, this paper proposes an approach for SAR feature reconfiguring based on periodic phase modulation with inter-pulse time bias. Considering the position and energy requirements of the expected reconfigured imaging target, this approach optimizes the metasurface modulation parameters via a dual algorithm collaborative optimization system, i.e., a modulation parameter generation algorithm (MPGA) and a parameter mapping matching algorithm (PMMA). Time-modulated metasurface targets can reconfigure imaging features of different targets at SAR reconnaissance moments under the guidance of optimized modulation parameters obtained using this approach. Compared with the previous single-point target research on the combination of SAR and metasurfaces, this method is expanded to include the combined analysis of multi-point targets and the reconfigurability of SAR features. Experiments have proved that the programmable reconfigurability of different target features (such as passenger plane targets and truck targets) can be achieved in SAR imaging results through dynamic adjustment of the modulation parameter set. The reconfigured imaging features maintain geometric consistency within the resolution error range, and the size and position of the target can be set as required. Full article

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. Since the BDS-3’s inter-satellite link (ISL) can enhance the orbit estimation of BDS-3 satellites, the aim of this study is to establish an a priori SRP model for the satellite body using 281-day ISL observations to reduce the systematic errors in the final orbits. The adjustable box wind (ABW) model is employed to refine the optical parameters for the satellite buses. The self-shadow effect caused by the search and rescue (SAR) antenna is considered. Satellite laser ranging (SLR), day-boundary discontinuity (DBD), and overlapping Allan deviation (OADEV) are utilized as indicators to assess the performance of the a priori model. With the a priori model developed by both ISL and ground observation, the slopes of SLR residual for the China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM) satellites decrease from −0.097 cm/deg and 0.067 cm/deg to −0.004 cm/deg and −0.009 cm/deg, respectively. The standard deviation decreased by 21.8% and 26.6%, respectively. There are slight enhancements in the average values of DBD and OADEV, and a reduced β-dependent variation is observed in the OADEV of the corresponding clock offset. We also found that considering the SAR antenna only slightly improves the orbit accuracy. These results demonstrate that an a priori model established for the BDS-3 MEO satellite body can reduce the systematic errors in orbits, and the parameters estimated using both ISL and ground observation are superior to those estimated using only ground observation. Full article

Background: Rheumatoid arthritis (RA) patients are at heightened risk of Coronavirus Disease—19 (COVID-19) complications due to immune dysregulation, chronic inflammation, and treatment with immunosuppressive therapies. This study aims to characterize the clinical and laboratory parameters of RA patients diagnosed with COVID-19, identify predictive risk factors for severe forms of this infection for RA patients, and determine if any RA immunosuppressive therapy is associated with worse COVID-19 outcomes. Methods: A retrospective observational case-control study included 86 cases (43 diagnosed with RA and 43 cases without any inflammatory or autoimmune disease) that suffered from SARS-CoV-2 in two Romanian hospitals between March 2020 and February 2024. Data on demographics, RA disease characteristics, COVID-19 severity, treatment regimens, and outcomes were analyzed. Results: RA patients exhibited a distinct symptom profile compared to non-RA controls, with higher incidences of neurological, musculoskeletal, and gastrointestinal symptoms, while the control group showed more respiratory and systemic manifestations. Severe COVID-19 is correlated with age and laboratory markers like erythrocyte sedimentation rate (ESR), leucocytes, neutrophils, neutrophil-to-lymphocyte ratio (NLR), aspartate aminotransferase (AST), serum creatinine, and urea. Additionally, RA treatments, particularly rituximab (RTX), were associated with more severe COVID-19 outcomes (but with no statistical significance), potentially due to the advanced disease stage and comorbidities in these patients. Post-infection, a significant number of RA patients experienced disease flares, necessitating adjustments in their treatment regimens. Conclusions: This study underscores the complex interplay between RA and COVID-19, highlighting significant clinical heterogeneity and the need for tailored management strategies. Limitations include sample size constraints, possible selection, and information bias, as well as the lack of adjustments for potential confounding variables that hinder the ability to formulate definitive conclusions. Future research plans to expand the research group size and further elucidate these relationships. Full article

Synthetic aperture radar (SAR) inter-pulse parameter agility technology involves dynamically adjusting parameters such as the pulse width, chirp rate, carrier frequency, and pulse repetition interval within a certain range; this effectively increases the complexity and uncertainty of radar waveforms, thereby countering active deceptive interference signals from multiple dimensions. With the development of active deceptive interference technology, single-parameter agility can no longer meet the requirements, making multi-parameter joint agility one of the main research directions. However, inter-pulse carrier frequency agility can cause azimuth Doppler chirp rate variation, making azimuth compression difficult and compensation computationally intensive, thus hindering imaging. Additionally, pulse repetition interval (PRI) agility leads to non-uniform azimuth sampling, severely deteriorating image quality. To address these issues, this paper proposes a multi-parameter agile SAR imaging scheme based on traditional frequency domain imaging algorithms. This scheme can handle joint agility of pulse width, chirp rate polarity, carrier frequency, and PRI, with relatively low computational complexity, making it feasible for engineering implementation. By inverting SAR images, the echoes with multi-parameter joint agility are obtained, and active deceptive interference signals are added for processing. The interference-suppressed imaging results verify the effectiveness of the proposed method. Furthermore, simulation results of point targets with multiple parameters under the proposed processing algorithm show that the peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) are improved by 12 dB and 10 dB, respectively, compared to the traditional fixed waveform scheme. Full article