Search Results (563)

The simulation of synthetic aperture radar (SAR) echo signals usually relies on complex hardware equipment and a large amount of scene data, which results in high costs and low efficiency. In order to simulate SAR echo signals of ship targets in the sea quickly and accurately in complex environments at a lower cost, this paper proposes a SAR echo simulation method based on model segmentation and electromagnetic scattering characteristic simulation. This method first implements the simulation of sea models under different sea conditions based on PM wave spectrum model and the Monte Carlo method, and segments them according to the requirements of simulation resolution. Then, it uses Python API 3.11 in Blender 4.5 to segment the ship model automatically and optimize the visible surface elements and mesh for each sub-model. Next, it uses Lua API in Feko to simulate the electromagnetic scattering characteristics of each sub-model of the sea and the ship target automatically, and obtains the required radar cross section (RCS) data of the ship target in the sea after processing. Finally, SAR echo simulation is realized through dual-channel technology. To further verify the simulation result, the chirp scaling (CS) algorithm is used for imaging processing. The results show that this method can realize SAR echo simulation of various ship targets under different sea conditions in a quick, accurate and cost-effective manner without the need for any hardware equipment. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Long-term stability of dam infrastructure is crucial for flood protection, water resource management, and drinking water supply. In many regions, the increasing impact of climate change and structural aging necessitates advanced monitoring approaches for embankment and gravity dams. PSI has emerged as a valuable technique for detecting surface deformation rates with millimeter precision. This study presents a comprehensive monitoring concept that combines satellite-based PSI analyses with the first operational use of ECRs at dam sites in North Rhine-Westphalia (NRW), Germany. Over a period of more than two years, ECRs were observed under real-world conditions using Sentinel-1 data. Compared to traditional passive reflectors, ECRs offer improved signal stability and a compact design, making them particularly suitable for confined or sensitive dam environments. The analysis of displacement time series confirms the suitability of ECRs for long-term deformation monitoring in complex dam settings. Intercomparison of two PSI time series demonstrated high internal consistency (correlation > 0.9, RMSE < 1 mm), while validation against in situ measurements confirmed millimeter-level agreement with RMSE values between 2 and 5 mm and correlations up to 0.7. In addition, a dedicated web-based platform was developed to provide processed ECR-based PSI results to dam operators, offering interactive visualizations, time-series access, and standardized downloads. This integration of advanced interferometric synthetic aperture radar (InSAR) methods, innovative hardware, and user-oriented service delivery marks a significant step toward operational dam monitoring using satellite remote sensing. Full article

Fully polarimetric SAR provides richer scattering information than single-polarisation imaging, but multichannel sparse image formation can be computationally and memory demanding, especially when channels are processed jointly. In our previous work, we introduced Orthogonal Matching Pursuit 2D Fully Polarimetric (OMP2D-FP), a greedy reconstruction algorithm that enforces a shared spatial support across polarimetric channels while exploiting a separable 2D formulation to avoid vectorisation and reduce computational burden and memory footprint relative to vectorised OMP-based formulations. In this paper, we extend its validation to real measurements and further develop its theoretical foundations by recasting the atom-selection step as a detection–estimation problem, thereby defining a cumulative objective function (COF) design space that enables the incorporation of disturbance statistics and prior knowledge into sparse recovery. Experiments on fully polarimetric SAR data of a T-72 tank over a wide range of aspect angles, SNR levels, and measurement percentages show that joint support selection improves reconstruction fidelity and polarimetric information preservation over independent per-channel processing, with particularly clear gains under challenging conditions. Preliminary applications of the COF framework (a whitening COF incorporating polarimetric clutter statistics and a mask-based COF incorporating spatial prior knowledge) yield encouraging results, motivating further systematic investigation of adaptive COF designs. Full article

Synthetic aperture radar (SAR) ship detection is severely limited by the artifacts caused by motion. Due to the complex six-degree-of-freedom (6-DOF) motion of ships, the ship imaging exhibits aberration phenomena including spatial blurring, discrete ghosting, and Lorentz linear blurring. Traditional detectors rely on the identification of static spatial features. When the phase coherence is disrupted, they tend to fail. To overcome this problem, we propose a multimodal fusion framework based on physical principles. This framework establishes a theoretical connection between the ship hydrodynamic response and imaging degradation through short, long, and ultra-long coherence processing intervals (CPI). The framework adopts a cascaded architecture: first, a lightweight YOLOv8 performs rapid global screening, followed by a signal backtracking mechanism that extracts high-fidelity time-frequency domain (TFD) and range instantaneous Doppler (RID) features from the original distance compressed data. In the second-level detection, these physical features are adaptively fused with spatial intensity through a YOLOv8 network integrated with the convolutional block attention module (CBAM) to reduce the false detection rate. The validation on high-fidelity simulations and real GF-3 datasets shows that this method consistently achieves an average precision (mAP) of over 95%, outperforming several widely used detectors, and demonstrates strong generalization ability in extreme imaging conditions, suitable for maritime detection scenarios. Full article

Salicylic acid (SA) is a key endogenous regulator involved in plant defense responses to biotic and abiotic stresses. The increasing resistance of pathogens to chemical plant protection products and growing environmental restrictions have intensified the search for alternative strategies to enhance plant health and stress tolerance. Among these strategies, the induction of natural defense mechanisms, in which SA plays a central signaling role, has gained particular attention. This review summarizes current knowledge on the role of SA in shaping plant resistance to environmental factors. The fundamental mechanisms of plant defense, including innate immunity, induced systemic resistance (ISR), and systemic acquired resistance (SAR), are discussed, with emphasis on the signaling function of SA and its interaction with other phytohormones, especially jasmonic acid and ethylene. The role of SA in regulating physiological processes associated with stress tolerance, such as antioxidant system activity, photosynthesis, plant growth, and senescence, is highlighted. The review of research results indicates that appropriately selected doses and timing of SA treatments can enhance resistance to selected pathogens and improve plant tolerance to adverse environmental conditions. However, treatment effectiveness depends on multiple factors, particularly SA concentration and plant–pathogen interactions. Salicylic acid is a promising component of integrated and sustainable plant protection strategies. Further research, especially under field conditions, is necessary to optimize its practical use and fully determine its potential in modern agriculture. Full article

Aims/hypothesis: Hypoxia and oxidative stress have been implicated in both metabolic syndrome and COVID-19-associated dysglycaemia, yet it remains unclear whether shared or distinct mechanisms underlie β-cell dysfunction across these conditions. We investigated hypoxia- and oxidative stress-related pathways in relation to β-cell function during acute COVID-19, post-COVID metabolic states, and COVID-negative metabolic syndrome. Methods: In this prospective observational study, 100 adults were stratified into three groups: active COVID-19 (n = 32), post-COVID with newly diagnosed carbohydrate metabolism disorders (n = 35), and COVID-negative individuals with metabolic syndrome (n = 33). Circulating markers of hypoxia (HIF-1α), oxidative stress (8-epi-prostaglandin F2α), and antioxidant response (NFE2L2) were measured alongside α- and β-cell functional markers, including C-peptide, proinsulin, glucagon, and derived indices of β-cell processing and secretory efficiency. Non-parametric statistical analyses were applied. Results: Circulating HIF-1α levels differed significantly across study groups (p < 0.001), with the highest concentrations observed during active COVID-19, intermediate levels in COVID-negative individuals with metabolic syndrome, and the lowest levels in the post-COVID group. In contrast, oxidative stress, assessed by 8-epi-prostaglandin F2α, differed significantly across groups (p < 0.001), increasing from acute COVID-19 to post-COVID and reaching the highest levels in metabolic syndrome; however, the difference between the post-COVID and metabolic syndrome groups did not remain significant after correction for multiple testing. NFE2L2 concentrations did not differ significantly between groups. Marked β-cell dysfunction was observed predominantly in COVID-negative individuals with metabolic syndrome, characterized by reduced C-peptide levels, elevated glucagon concentrations, increased proinsulin/C-peptide ratios, and reduced C-peptide/glucose ratios (all overall group comparisons p < 0.001). In contrast, β-cell secretory indices were relatively preserved during acute and post-COVID states despite pronounced alterations in hypoxia and oxidative stress markers. Conclusions/interpretation: Hypoxia- and oxidative stress-related pathways exhibit distinct, context-dependent patterns across acute COVID-19, post-COVID dysglycaemia, and metabolic syndrome. Acute COVID-19 is characterized by pronounced hypoxia signalling with relative preservation of β-cell function, whereas chronic metabolic syndrome is associated with sustained oxidative stress and impaired β-cell processing and secretory efficiency. These findings suggest that diabetes-related β-cell dysfunction is more closely associated with chronic oxidative and metabolic stress than with transient infection-related hypoxia during SARS-CoV-2 infection. Full article

Cellular processes rely heavily on protein phosphorylation, a mechanism essential for organismal physiology and pathology. The CMGC family comprises a large group of serine/threonine kinases defined by a conserved catalytic core and closely related kinase domains. While several CMGC members have been extensively studied, others, including the RCK and CDKL subfamilies, remain less studied. Here, we synthesize current knowledge of CMGC kinases, emphasizing their structural organization, mechanisms of activation, and roles in infection and disease. CMGC kinases such as CDKs and DYRKs are activated downstream of growth factor signaling to drive proliferative programs. In contrast, other CMGC members respond to cellular stress signals, including stress cytokines, and function during quiescence or adverse conditions to regulate antiproliferative and pro-survival pathways. Through these context-dependent activities, CMGCs govern fundamental cellular processes, including growth, metabolism, transcription, and genome integrity. Although individual CMGC kinases operate within distinct signaling cascades, substantial crosstalk exists among their pathways. Both DNA and RNA viruses exploit host CMGC networks to reprogram the intracellular environment and enhance replication. While CMGC–virus interactions are often proviral, specific CMGC-mediated antiviral responses have been described, notably in SARS-CoV-2 infection. Collectively, CMGC kinases occupy a central position in cellular homeostasis and disease. Full article

Land subsidence and coseismic deformation can interact in groundwater-stressed sedimentary basins, yet basin-scale identification of event-timed vertical offsets in InSAR products requires explicit control of referencing and processing effects. This study evaluates whether the 27 September 2021 Arkalochori earthquake (Mw 6.0; central Crete) produced detectable coseismic vertical offsets within the Messara Basin by applying a reproducible screening workflow to Copernicus European Ground Motion Service (EGMS) Level-3 Vertical time series, from two processing generations (EGMS 2015–2021 and EGMS 2018–2022). An event-centered step metric (stepEQ), defined as the difference between post-event and pre-event mean displacements over a fixed acquisition window, is evaluated across three fixed spatial masks (MESSARA, R15060, R8750) together with a dispersion-based precision proxy (σstep) and a cross-generation sensitivity diagnostic (ΔstepEQ). A supplementary 2 + 2 subset sensitivity analysis indicates that the adopted fixed 3 + 3 estimator is stable at the basin scale, with sensitivity concentrated mainly in threshold-adjacent cases. Results indicate that Arkalochori-related offsets are not expressed as a basin-wide step across Messara; instead, non-background responses form a spatially limited and coherent subset concentrated where the basin intersects the near-source footprint. In EGMS 2018–2022, the higher vertical offset class (C2; |stepEQ| > 40 mm) is exclusively subsidence-direction and is enriched toward the screening center (up to ~19% within the radii mask R8750 m) but remains sparse at the basin scale mask (MESSARA mask) (~1%). Step-dominated points co-locate with strongly subsiding mean vertical velocity regimes and are hosted almost entirely by post-Alpine basin deposits, indicating strong material and background-deformation conditioning of step detectability. Cross-generation comparison shows basin-scale stability of background behavior but localized near-source sensitivity, supporting use of ΔstepEQ as a Quality Control (QC) lens for threshold-adjacent interpretations. The workflow provides a transparent, transferable approach for prioritizing candidate coseismic-step locations in EGMS time series. Results are interpreted as screening-level evidence in the derived vertical signal using event timing, spatial coherence, and QC diagnostics. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

Neutrophils are among the early responders of the innate immune system and play a key role in host defense against viral infections. Beyond their classical antimicrobial functions, neutrophils can engage in a specialized defense mechanism by releasing web-like extracellular DNA known as neutrophil extracellular traps (NETs). These extracellular traps are a mesh-like network of chromatin DNA decorated with cellular components, including histones, proteases, and antimicrobial enzymes, that function to contain and limit the spread of pathogens. While NET formation contributes to antiviral immunity, accumulating evidence indicates that excessive or dysregulated NET formation can significantly contribute to immunopathology during viral infections. Thus, depending on the context and outcome, NET formation may be viewed as a double-edged sword. Therefore, understanding the regulatory mechanisms governing NET formation and its harmful effects is critical for developing therapeutic strategies that enhance antiviral defense while minimizing tissue damage. In this review, we provide a comprehensive overview of the molecular mechanisms that drive NET formation and clearance, with a particular focus on how viruses modulate these processes to influence disease outcome. We also discuss the pathways underlying NET formation and subsequent neutrophil cell death (NETosis), including canonical and non-canonical pathways, and highlight key signaling axes involving SYK, MAPKs, and NF-κB. Using SARS-CoV-2 and hepatitis B virus as representative models, we examine how different viral components trigger, exploit, or evade NET targeting and how persistent accumulation of NETs can contribute to hyperinflammation, progressive tissue injury, and post-viral syndromes. We further explore emerging evidence linking impaired NET clearance and neutrophil heterogeneity, particularly low-density neutrophils (LDNs), to chronic inflammation and post-viral sequelae such as long COVID and autoimmune hepatitis. Finally, we summarize current and emerging therapeutic strategies aimed at modulating NET formation or enhancing NET clearance. Altogether, this review underscores the dual nature of NETs in viral infections, highlighting their potential roles in antiviral defense and tissue injury, and provides a framework for the development of targeted interventions to limit virus-induced immunopathology. Full article

Extracting road networks from Synthetic Aperture Radar (SAR) data represents a core challenge in remote sensing scene analysis, particularly for applications in traffic monitoring and emergency management. The task is complicated by several inherent limitations: speckle noise degrades image quality, geometric distortions arise from the side-looking acquisition geometry, and roads often exhibit weak radiometric separation from surrounding terrain. Traditional processing pipelines and recent single-branch deep learning frameworks have shown insufficient performance when global contextual reasoning and fine-scale spatial detail must both be addressed. This work presents DINOv3-PEFT, a parameter-efficient dual-encoder network designed specifically for SAR road segmentation. The architecture employs two complementary processing streams tailored to SAR characteristics: one stream utilizes adapter-based fine-tuning applied to pre-trained DINOv3 weights (kept frozen), which captures long-distance spatial relationships crucial for maintaining network connectivity despite speckle corruption. The second stream, based on convolutional operations, focuses on extracting localized geometric features that preserve the narrow, elongated structure and sharp boundaries typical of road infrastructure. Feature fusion occurs through the Topological-Geometric Feature Integration (TGFI) Module, which synthesizes multi-scale representations hierarchically. This mechanism proves effective at bridging fragmented road segments and recovering geometric accuracy in scenarios with heavy shadow casting or signal interference. Performance evaluation on the GF-3 satellite dataset across four spatial resolutions (1 m, 3 m, 5 m, and 10 m) demonstrates the proposed method achieves an 82.61% F1-score, a 76.51% IoU, and a 98.08% overall accuracy, all averaged across the four resolutions. When benchmarked against six state-of-the-art methods, DINOv3-PEFT demonstrates substantial improvements in road class segmentation quality and topological connectivity preservation, supporting its robustness for operational SAR road mapping tasks. Full article

Synthetic aperture radar (SAR), which appeared in the early 1990s, refers to a technology that creates a virtual large aperture by receiving/combining signals from various locations while moving with a fixed antenna. Using SAR-based image acquisition technology, a reconnaissance satellite can obtain high-quality images regardless of the weather and day/night conditions. In this study, the qualification tests of a space-grade 5m-class deployable reflector antenna for satellites, which is the primary payload of a SAR-based satellite, were conducted. In order to ensure the electrical performance of the reflector antenna, an alignment verification test was performed using a laser tracker system during the assembly and integration process. Generally, the satellite experiences a considerable amount of structural load under the launch condition and is exposed to extremely low- and high-temperature thermal environments under the orbital condition. For the space mission, environmental tests should be conducted to verify the structural/thermal stability for the launch and orbital conditions. A deployment repeatability test was conducted to ensure that the deployment mechanism operated properly before/after each test. The qualification process and philosophy proposed in this work could be applied to the development of the space-grade deployable reflector antenna. Full article

The coronavirus disease 2019 (COVID-19) pandemic has been associated with a wide range of neurological complications, among which persistent cognitive impairment and memory deficits are increasingly recognized as key symptoms of the post-acute sequelae of SARS-CoV-2 infection (PASC or long COVID). Although clinical and epidemiological studies have documented these symptoms across diverse patient populations, the underlying neurobiological mechanisms remain incompletely understood. Growing evidence from human studies, neuropathological analyses, and experimental models indicates that neuroimmune and inflammatory processes plays a central role in COVID-19-associated cognitive dysfunction. As the brain’s resident immune cells, microglia are vital for synaptic health, neuroplasticity, and memory, yet these processes may be compromised after SARS-CoV-2 infection. Systemic inflammation, blood–brain barrier (BBB) disruption, endothelial injury, and cytokine signaling can induce sustained microglial activation and priming, leading to inflammasome activation, complement-mediated synaptic remodeling, oxidative stress, and impaired hippocampal neurogenesis. These processes collectively disrupt neural circuits involved in learning and memory and may underlie the persistent “brain fog” reported by COVID-19 survivors. This review synthesizes clinical, biomarker, neuroimaging, and mechanistic evidence linking SARS-CoV-2 infection to microglia-mediated neuroinflammation and memory impairment. In contrast to prior reviews that broadly describe neuroinflammation in COVID-19, we integrate multidimensional evidence into a microglia-centric immunovascular framework that highlights converging pathogenic pathways underlying cognitive symptoms. We further discuss emerging biomarkers of glial activation and evaluate current and prospective therapeutic strategies targeting microglial and neuroimmune pathways. Understanding the role of microglial dysregulation in post-COVID cognitive impairment may facilitate the development of targeted interventions to mitigate long-term neurological consequences of COVID-19. Full article

Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. Full article