Search Results (4,013)

Distributed acoustic sensing sends laser pulses along a fiber optic cable and analyzes the backscattered light to identify acoustic signals along the entire fiber. Liquid slugs were produced in a 427 m vertical test well using surface-controlled gas lift valves. To enhance DAS monitoring, pressure pulses were induced by multiple acoustic shots from a fluid level gun. Visualization of the responses through frequency band energy plots and unfiltered phase shift measurements permitted tracking slug movement and estimating parameters such as velocity, location, and body length. The results demonstrate that DAS stimulated with acoustic pulses can effectively track liquid slugs in real-time. We observe that relying solely on flow-induced noise in multiphase flow environments may not provide sufficient signal strength for slug detection. Applications include real-time detection of liquid slugs for improved well monitoring and flow management. Full article

Existing macroscopic finite element models for electron beam welding (EBW) typically assume isotropic material behavior, often failing to accurately predict residual stresses induced by strong crystallographic textures. To address this limitation, this study established a sequential dual-scale coupled numerical model bridging micro-texture to macro-mechanics by combining the crystal plasticity finite element method (CPFEM) with thermal-elastic-plastic theory. Representative volume elements (RVEs) incorporating α and β dual-phase characteristics were constructed based on electron backscatter diffraction (EBSD) data from the TA15 weld cross-section. Through simulated tensile and shear calculations on the RVEs, homogenized orthotropic stiffness matrices and Hill yield constitutive parameters were derived and mapped onto the macroscopic model. Simulation results indicate that the proposed model maintains the prediction error for molten pool morphology within 16.3%, while effectively correcting the stress overestimation inherent in isotropic models. Specifically, it adjusts the peak longitudinal residual stress at the weld center from 800 MPa to approximately 350 MPa, significantly reducing the anomalous “M-shaped” stress distribution. By successfully capturing shear stress components, this work provides a high-fidelity computational approach for predicting complex stress states in welded joints, offering critical insights for structural integrity assessment. Full article

The latest advances in remote sensing play a central role in providing Earth observation (EO) data for numerous applications in the scope of reaching environmentally sustainable goals. However, over tropical rainforests, optical imaging is often hindered by extensive cloud coverage, which means that analysis-ready images are mostly restricted to the dry season. In this study, we propose combining radar features extracted from short time series of Sentinel-1 Interferometric Wide Swath (IW) data with a deep learning-based classification scheme to continuously monitor the state of forests. The proposed methodology is based on the joint use of SAR backscatter and interferometric coherences at different temporal baselines to perform pixel-wise classification of land cover classes of interest. However, we show that for a sequence of Sentinel-1 time series, different land cover classes exhibit particular seasonal-dependent variations. Another challenge in performing short-term predictions stems from the fact that ground truths are usually available only on a yearly basis. To address these challenges, we propose a seasonal sampling of the training data, masked by potential deforestation, along with a classification based on a modified U-Net model. The classification results show that overall accuracies above 90% can be achieved throughout the whole year with the proposed method, emerging as a potential tool for mapping rainforests with unprecedented temporal resolution. Full article

Persistent cloud cover and frequent rainfall in subtropical regions throughout the year significantly limit the applicability of optical remote sensing for tree species identification, thereby constraining dynamic forest monitoring and precise management of forest resources. To address this challenge, this study proposes a tree species identification method that integrates multi-source remote sensing temporal features. By combining multi-temporal optical imagery from Sentinel-2 and dual-polarisation Synthetic Aperture Radar (SAR) data from Sentinel-1, we constructed a comprehensive feature set that incorporates spectral, structural, and phenological attributes, including various vegetation indices, backscatter coefficients, and polarimetric decomposition parameters. Through correlation analysis and assessment of temporal feature variability, five distinct integration strategies (T1-T5) were developed to classify six typical subtropical tree species: Pinus massoniana, Pinus elliottii, Acacia, Eucalyptus grandis, Mangrove, and Other hardwoods, using a random forest classifier. The results indicate that the multi-source feature fusion approach significantly outperforms single-source models, with the T5 strategy achieving the highest overall accuracy (OA) of 95.33% and a Kappa coefficient of 0.94. The red-edge vegetation indices and SAR polarimetric features were identified as major contributors to improving the classification accuracy of hardwood species. This study demonstrates that multi-source remote sensing data fusion can effectively mitigate the spatiotemporal constraints of optical imagery, providing a viable solution and technical framework for high-accuracy remote sensing classification in complex subtropical forest environments. Full article

Magnesium alloys’ poor corrosion resistance limits their applications as biodegradable bone repair materials. Alloying tailors Mg alloys’ microstructure and properties. The present study investigates the effect of 2 wt.% Ag addition on the microstructure and initial corrosion behavior of hot-rolled Mg-1Ca alloy. Mg-1Ca and Mg-1Ca-2Ag alloys were prepared by melting using Mg-2Ca and Mg-4Ag master alloys, followed by homogenization at 400 °C for 2 h, hot rolling, and stress-relief annealing at 400 °C for 6 h. The alloys were systematically characterized using field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD). Initial corrosion behavior was evaluated via 3 h immersion tests in simulated body fluid (SBF). Results reveal Ag’s high thermal diffusivity promotes segregation at tensile twin boundaries, forming Ag₃Mg nanoparticles. These nanoparticles hinder grain boundary migration and, with increased deformation, facilitate grain rotation and high-angle grain boundary formation, weakening texture. Internal stress accumulation near twin boundaries—driven by grain orientation variation and nanoparticles—induces ~86° rotation of {10–12} tensile twins around the c-axis, forming double twins. During corrosion, nanoparticles and double twins synergistically promote dense protective film formation, significantly reducing corrosion rates. Full article

Accurate estimation of forest Canopy Height Model (CHM) is essential for understanding ecosystem structure, biomass, and carbon dynamics. Machine learning (ML) and deep learning (DL) models have shown strong potential when trained using LiDAR-derived (Light Detection and Ranging) canopy heights as reference data. However, the influence of LiDAR point density on model performance remains poorly understood. This study systematically evaluates three modeling frameworks, Extreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN) and hybrid CNN–Transformer architectures, for predicting CHM across multiple regions in Spain, using LiDAR data with varying point densities (1–5 points/m²) from the Plan Nacional de Ortofotografía Aérea (PNOA). Models were trained using multiple sources of remote sensing data: Sentinel-2 spectral bands (visible and near-infrared bands at 10 m) and Sentinel-1 VH backscatter and topographic variables (slope and elevation). Model performance was assessed in terms of predictive accuracy, generalization capacity and sensitivity to LiDAR point density data. Results show that CNN-based approaches outperform classical ML across all datasets, and that integrating Transformer modules further improves performance particularly in areas with higher LiDAR density. The best results were achieved on the dataset with higher point density (5 points/m²), where the CNN + Transformer model reached a Mean Absolute Error (MAE) of 3.37 ± 0.1 m and an R² of 0.66 ± 0.02. Lower-density LiDAR datasets degraded the accuracy for all methods. These findings highlight that both model architecture and LiDAR point density critically influence CHM estimation. The results provide practical guidance for designing large-scale structural mapping workflows in regions with heterogeneous LiDAR coverage, helping to support forest management, carbon accounting and environmental decision-making. Full article

Rapid, objective, and accurate crop damage assessment is essential to minimizing farmers’ financial loss and enabling early settlement of crop insurance. The present study proposes a framework for assessing the impact of heavy rainfall-induced flood on Kharif rice over West Bengal under a satellite-based crop insurance program. Daily actual and normal rainfall data from India Meteorological Department (IMD) were used to study the pattern and magnitude of rainfall to confirm the heavy rainfall incidence over affected districts of West Bengal. The Sentinel-1-derived temporal VH backscatter validated using ground observations was utilized to identify the rice crop areas. The backscatter difference images between pre-and post-peril periods, along with threshold and histogram approaches, were adopted to discriminate the inundated areas from non-inundated areas. The information on the inundated rice crop was derived by intersecting the rice layer with the inundated area. The behavior of temporal backscatter profiles, i.e., pre- and post-peril periods, was analyzed to ascertain the impacts of flood-induced inundation on rice crop health. Further, the phenological information during floods was identified from transplanting periods in backscatter profiles, and damage severity was assessed by integrating inundation, the affected area, and rice crop stage information. The outcomes, validated with ~1500 crop loss surveys (CLSs), show that the framework is effective and useful for crop insurance. Full article

The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in the long term, future wireless systems must fundamentally prioritize energy-efficient and autonomous operation. Integrated sensing and communication (ISAC) is emerging as a key enabler for next-generation systems by jointly supporting sensing and communication through shared spectrum, hardware, and signal processing resources. In IoT systems, sensing of target parameters, e.g., range, angle, velocity and identity, etc., form the basis of autonomous and environment-aware applications. However, this integration increases overall power consumption due to the added coordination overhead and the workload placed on shared hardware components. To this end, backscatter communication provides a low-power alternative that enables passive data transmission through energy harvesting and sharply reduces the need for active radio circuits. However, the coexistence of sensing and backscatter functions introduces mutual interference, which often requires large multiple-input multiple-output (MIMO) arrays for effective mitigation. Furthermore, sensing performance depends heavily on line-of-sight conditions, while backscatter links operate only over short ranges. Although increasing array size or transmit power can extend coverage, it imposes substantial energy and hardware costs and undermines sustainability goals. To address these limitations, cell-free MIMO is emerging as a promising candidate technology for next-generation systems. Cell-free MIMO relies on a dense deployment of distributed access points that cooperate to serve devices across a wide area. This cooperation enables effective beamforming and interference management, providing spatial diversity comparable to large, centralized antenna arrays without incurring their associated hardware or power costs. They also enable aggregation of weak double-hop reflections, reduced effective-illumination distances, multi-view sensing, and robustness to blockage, which is invaluable to backscatter communication. This perspective article introduces the foundations, challenges, and architectural considerations of cell-free backscatter-aided integrated sensing and communication (CF-BISAC) systems. By leveraging the advantages of battery-less backscatter IoT devices and the distributed nature of cell-free MIMO, CF-ISABC aims to maximize sensing and communication performance under strict energy constraints, contributing toward energy-aware ISAC systems capable of supporting high-density, low-power wireless applications. Full article

The surface properties and electrical behavior of carbon-based materials can be effectively modified by energetic ion irradiation. In the present study, graphene oxide (GO) and cyclic olefin copolymer foils (COC, Topas 112 and 011, respectively) were irradiated with 1 MeV Au ions using a 3 MV Tandetron accelerator at fluences of 1 × 10¹⁴, 1 × 10¹⁵, and 2.5 × 10¹⁵ cm⁻². The irradiation induced systematic modifications in surface chemistry, morphology, wettability, and electrical properties. Composition changes were investigated using Rutherford backscattering spectrometry (RBS) and elastic recoil detection analysis (ERDA), while surface morphology and roughness were characterized by atomic force microscopy (AFM). This revealed a clear fluence-dependent evolution of nanoscale topography. The vibrational characteristics were assessed through Raman spectroscopy, and the chemical composition of the surface layers was analyzed by X-ray photoelectron spectroscopy (XPS). The surface wettability was evaluated by static contact angle measurements, and surface free energy was determined using the Owens–Wendt–Rabel–Kaelble (OWRK) method. These measurements showed a consistent decrease in water contact angle and an increase in surface free energy with increasing ion fluence in the COC substrates, whereas GO exhibited a distinct response. Electrical characterization demonstrated a pronounced fluence-dependent decrease in sheet resistivity in polymers. The results show that 1 MeV Au ion irradiation enables systematic and fluence-dependent modification of both surface and electrical properties. Full article

The AA7075 holds significant importance in the aerospace field. Understanding its microstructure evolution and constitutive relationships during warm deformation is crucial for optimizing forming processes. To this end, isothermal compression experiments were conducted at different temperatures and strain rates to analyze their flow stress behavior. The microstructure evolution was characterized using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). Microstructural analysis confirmed that dynamic recovery constitutes the predominant softening mechanism under warm forming conditions. The results indicate that flow stress is highly sensitive to deformation parameters, decreasing with increasing temperature and rising with increasing strain rate. To accurately describe the flow behavior, two distinct constitutive models were formulated: (1) a phenomenological Hensel–Spittel–Garofalo (HSG) model; (2) a novel hybrid machine-learning model that innovatively integrates the Harris Hawks Optimization (HHO) algorithm with an LSTM model. Both constitutive models demonstrate reasonable predictive accuracy. In comparison, the HHO-LSTM model demonstrated a superior ability to capture complex nonlinear relationships, achieving highly precise predictions of flow stress across the full range of deformation conditions tested in this work. The hybrid machine-learning model proposed in this study provides a highly accurate method for describing and predicting the flow behavior of the AA7075 during warm forming, offering a powerful predictive tool for engineering applications. Full article

With the ongoing miniaturization of solder joints in three-dimensional integrated electronic packaging, electromigration reliability has become a pressing concern. This study systematically examines the interfacial intermetallic compound (IMC) growth behavior of Cu/Sn-58Bi/Cu joint under electromigration (EM) with a symmetrical square-wave alternating current (AC). Electron backscatter diffraction (EBSD) was employed to perform statistical spatial analysis of Sn grain orientations within the joints to reveal the growth mechanism of interfacial IMC. Results demonstrate that the AC field markedly enhances the anisotropy of IMC growth in Cu/Sn-58Bi/Cu joints, exhibiting two phenomena: uniform growth on both sides and rapid growth (polar growth) on one side of the interfacial IMC. Among them, the IMC thickness difference characterization quantity Δ_IMC reached as high as 45.56% for the latter. This is attributed to the directional regulation of atomic migration rate by Sn grain orientation (the angle θ between the c-axis and the electron flow) and is further amplified by the altered atomic diffusion pathways imposed by the Bi phase distribution. Specifically, the Sn grains exhibit a pronounced preferential orientation mode along the current path (horizontal direction), with an orientation gradient of 0.915 μm⁻¹. The arrangement of Bi-rich phases alters the distribution of Sn grains in Cu/Sn-58Bi/Cu joints, thereby reshaping the internal electron transport pathways and significantly intensifying the orientation-dependent effect of IMC growth. Moreover, Sn grains adjacent to the Bi-rich phase boundaries (phase boundary grains) display a stronger tendency for c-axis orientation parallel to the current direction, exhibiting an average effective orientation parameter 1.948 times greater than that of bulk grains, which establishes a well-defined spatial orientation gradient. Full article

The microstructural and textural evolution in hot-rolled Fe–3.0 wt.% Si steel sheets was investigated by quasi in situ electron backscatter diffraction (EBSD) analysis. During recrystallization, the Goss texture intensity in the surface region remains essentially unchanged, whereas the α and α* textures are strengthened. In the center region, the α texture weakens, and the α* texture shows little variation, while the Goss texture becomes intensified. In the surface region, {112}<110> recrystallized grains nucleate by consuming deformed matrices with orientations near {114}<221> and {110}<112>. Recrystallized {114}<481> and {001}<210> grains consume deformed matrices near {114}<221> and Goss orientations, while Goss grains nucleate by consuming Goss-oriented deformed matrices. In the center region, {112}<110>, {114}<481>, and {001}<210> recrystallized grains nucleate and grow by consuming α and λ type deformed matrices, whereas Goss recrystallized grains preferentially consume deformed matrices with orientations of {111}<112>. Full article

Systematic radiometric anomalies, manifesting as non-physical range-direction oscillations, significantly compromise the quality of Miniature Radio Frequency (Mini-RF) S-band SAR imagery and its scientific application in the lunar south polar region. In this study, we analyzed 1262 scenes from the Mini-RF archive in south polar regions. By employing a statistical screening method based on fitting the relationship of backscattering signal and off-nadir angle, 377 scenes (29.9%) were identified as radiometrically anomalous scenes with systematic errors. To correct these errors, a physics-based radiometric correction framework has been proposed by reconstructing the effective antenna gain pattern (AGP) of Mini-RF. Referenced relationship between the backscattering signal and the local incidence angle was established using normal scenes. For each anomalous scene, a simulation-driven gradient descent optimization approach is developed to estimate the offset of the AGP. Subsequently, the derived offset is applied to realign the AGP of the anomalous scene, effectively compensating for the systematic range-direction oscillations and restoring the true backscatter intensity. Using the proposed method, systematic errors in anomalous scenes have been eliminated effectively, reducing the Root Mean Square Error (RMSE) relative to the reference radiometric curve from 2.11 to 1.21 and decreasing the image entropy from 2.83 to 2.29. By eliminating systematic banding artifacts, the proposed method has significantly improved the radiometric fidelity of Mini-RF data. Furthermore, a temporal periodicity was found in the gain offsets, suggesting dynamic instrument distortion driven by variations in the orbital thermal environment. Full article

We investigate time-resolved wave-packet transport in monolayer graphene patterned with asymmetric arrays of circular electrostatic scatterers. Using the Dirac continuum model with a split-operator scheme, we track how transmission evolves with scatterer radius and polarity sequence. To this end, we consider three potential configurations (Samples 1–3). The results reveal a geometry-controlled crossover from near-ballistic propagation at small radii to interference-dominated backscattering at large radii. Sample 1, where the potential exhibit two parallel lines of circles, each line sharing the same potential sign, preserves the highest transmission. Conversely, in Sample 3, where potential signs are intercalated between circles of the same line, the dwell time increases, which produces stronger confinement. As the radius increases, pronounced temporal oscillations emerge due to repeated internal reflections (similar to Fabry–Pérot interferometer), and the radius dependence of the saturated transmission probability exhibits anti-resonant dips that are tunable by geometry and potential magnitude. These behaviors establish simple design rules for graphene nanodevices: small-radius Sample 1 for high-throughput transport, Sample 2 (with inverted potential signs as compared to Sample 1) for broadband suppression, and Sample 3 for finely tunable, interference-based confinement. Full article

Accurate crop type mapping remains challenging in regions where persistent cloud cover limits the availability of optical imagery. Multi-temporal dual-polarization Sentinel-1 SAR data offer an all-weather alternative, yet existing approaches often underutilize polarization information and rely on single-scale temporal aggregation. This study proposes PTU-Net, a polarization–temporal U-Net designed specifically for pixel-wise crop segmentation from SAR time series. The model introduces a Polarization Channel Attention module to construct physically meaningful VV/VH combinations and adaptively enhance their contributions. It also incorporates a Multi-Scale Temporal Self-Attention mechanism to model pixel-level backscatter trajectories across multiple spatial resolutions. Using a 12-date Sentinel-1 stack over Kings County, California, and high-quality crop-type reference labels, the model was trained and evaluated under a spatially independent split. Results show that PTU-Net outperforms GRU, ConvLSTM, 3D U-Net, and U-Net–ConvLSTM baselines, achieving the highest overall accuracy and mean IoU among all tested models. Ablation studies confirm that both polarization enhancement and multi-scale temporal modeling contribute substantially to performance gains. These findings demonstrate that integrating polarization-aware feature construction with scale-adaptive temporal reasoning can substantially improve the effectiveness of SAR-based crop mapping, offering a promising direction for operational agricultural monitoring. Full article