Search Results (1,186)

Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments. Full article

This paper presents an open, modular sensor platform based on wireless energy and data transmission. The platform is powered by the carrier signal of a transceiver and transmits the measured sensor data using backscatter modulation. Through the use of modular ready-to-buy components, the sensor platform can be flexibly adapted to different applications and is therefore suitable for both building automation systems and industrial automation tasks. Energy storage, power management, and modulation are designed so that the overall energy demand of the platform is mainly determined by the sensor in use. The performance of the system was verified with a demonstrator measuring underfloor temperature and humidity. The demonstrator operates at a carrier frequency of 868 MHz, an output power of $27 \mathrm{dBm}$ EIRP at the transceiver antenna, and an antenna gain of 0 dBi at the receiver antenna. A transmission range of more than 3 m has been achieved. The platform provides an input sensitivity of $-15 \mathrm{dBm}$. Its open design enables a straightforward scaling from prototype to small- and medium-volume production. Full article

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) vertical profile is essential for understanding global biogeochemical processes; however, no dedicated retrieval models currently exist. This study developed a novel PON profile retrieval model using the eXtreme Gradient Boosting (XGBoost) algorithm, based on a comprehensive global dataset that includes in situ PON measurements, MODIS-Aqua bio-optical data, and 3D reanalysis physical data. The XGBoost-retrieved PON profiles were compared with those derived from Copernicus particulate backscattering coefficient ($b_{\mathit{bp}}$) profiles and were further used to estimate the euphotic-zone PON stocks through an optimally performing regression model. The results showed that the proposed model significantly outperformed models constructed without physical inputs, achieving $R^2$ of 0.83, RMSE of 1.49 $\mathrm{mg} {\mathrm{m}}^{-3}$ and MAPE of 18.07%. Compared to the $b_{\mathit{bp}}$-based profiles, the XGBoost-retrieved profiles exhibited higher accuracy. The model also provided reliable estimates of euphotic-zone PON stocks, with $R^2$ of 0.76, RMSE of 200.31 mg ${\mathrm{m}}^{-2}$ and MAPE of 15.09%. These findings demonstrate the potential of the proposed retrieval model for investigating oceanic nitrogen dynamics and biogeochemical cycles. Full article

Background: Practical, quantitative ultrasound-based tools for measuring hepatic steatosis are needed in everyday MASLD care. We evaluated a new multiparametric quantitative ultrasound (QUS) platform that integrates ultrasound-guided fat fraction (UGFF), attenuation coefficient (AC), backscatter coefficient (BSC), and signal-to-noise ratio (SNR), using Controlled Attenuation Parameter (CAP) as the reference and examining the effect of breathing. Methods: In a prospective single-center study, adult patients underwent same-day liver QUS and FibroScan. QUS measurements were performed during breath-hold and during normal breathing. Regions of interest were placed in right-lobe parenchyma 2 cm below the capsule, avoiding vessels. Primary outcomes were correlation with CAP and ROC performance at CAP cutoffs for S1 (≥230 dB/m), S2 (≥275 dB/m), and S3 (≥300 dB/m). Results: QUS was feasible in almost all examinations. UGFF, BSC, and SNR were consistent across breathing conditions, while AC was slightly higher during normal breathing. UGFF showed strong correlation with CAP and high accuracy for detecting steatosis. Across grades, AUCs were around 0.89–0.91, with cutoffs (UGFF ≈ 4% for ≥S1 and ≈11% for ≥S3). Conclusions: Multiparametric QUS provides reliable liver fat quantification that aligns closely with CAP and remains robust in practice whether patients hold their breath or breathe normally. These findings support UGFF as a practical, reliable point-of-care alternative for liver fat quantification that can be embedded in routine ultrasound in real time. Validation against MRI-PDFF or histology and multicenter studies will further define cutoffs and generalizability. Full article

Radar backscatter from large-scale scenarios plays a crucial role in remote sensing applications. However, due to the diversity and heterogeneity of the natural environment, traditional empirical methods which rely on simplified physics and a limited set of parameters, fail to adequately model land backscatter, thus exhibiting significant limitations. While purely data-driven deep learning (DL) methods offer flexibility, they often struggle to ensure physical consistency and effectively generalize to unseen scenarios. To address these issues, we propose a novel knowledge-aided (KA) DL-based method (called KADL) in this paper for predicting the radar backscatter from large-scale scenarios. The proposed KADL is implemented in three parts. First, based on multi-source remote sensing data, the dielectric properties of land surface, i.e., soil moisture and leaf area index (LAI) are incorporated as priori physical knowledge into the Multi-Feature Clutter Dataset (MFCD) to obtain initialized input. Second, a knowledge perception module (KPM) is introduced into the cascaded deep neural network (DNN) solver to exploit the representative features within the inputs. Third, an efficient knowledge-weighted fusion (KWF) strategy is developed to further enhance the discriminative features and simultaneously suppress the non-informative features. For better comparison, we refitted the specific empirical models based on the measured data and introduced an advanced nonhomogeneous terrain clutter model (termed ANTCM) derived from our previous work. Extensive experiments conducted on the measured data demonstrate that KADL achieves a root mean square error (RMSE) of 4.74 dB and a mean absolute percentage error (MAPE) of 8.7% on independent test data. Furthermore, KADL exhibits superior robustness, with a standard deviation of RMSE as low as 0.18 dB across multiple trials. All these results validate the superior accuracy, robustness, and generalization ability of KADL for large-scale backscatter prediction. Full article

The article describes a system for monitoring the vital parameters of evacuated individuals, integrating three key functionalities: pulse detection, verification of wristband contact with the skin, and motion recognition. For pulse detection, the system employs the MAX30102 optical sensor and a signal processing algorithm presented in the study. The algorithm is based on spectral analysis using the Fast Fourier Transform (FFT) and incorporates a nonparametric estimator of the probability density function (PDF) in the form of Kernel Density Estimation (KDE). This developed real-time algorithm enables reliable assessment of vital parameters of evacuated individuals. The wristband contact with the skin is verified by measuring the brightness of backscattered light and the temperature of the wrist. Motion detection is achieved using the MPU-9250 inertial module, which analyzes acceleration across three axes. This allows the system to distinguish between states of rest and physical activity, which is crucial for accurately interpreting vital parameters during evacuation. The experimental studies, which were performed on a representative group of individuals, confirmed the correctness of the developed algorithm. The system ensures reliable monitoring of vital parameters by combining precise pulse detection, skin contact verification, and motion analysis. The classifier achieves nearly 95% accuracy and an F₁-score of 0.9465, which indicates its high quality. This level of effectiveness can be considered fully satisfactory for evacuation monitoring systems. Full article

Ti-Nb alloys have been under active consideration for superelastic applications in biomedical devices due to their superior biocompatibility compared to NiTi. However, these alloys have been found to be highly sensitive to processing conditions, with many studies measuring different transformation temperatures for the same alloy composition. Several processing factors, including heat treatment times, temperatures and cooling rates, have been investigated. However, the effect of the rolling ratio on superelastic properties has not yet been systematically considered. In this study, samples of Ti-24Nb-4Zr-8Sn (wt%) with varied cold rolling reduction ratios were produced, and the superelastic properties were characterised. After the heat treatment, all samples were found to be predominantly in the metastable cubic β phase, with a small, non-varying volume fraction of the ω phase also present. Electron backscattered diffraction was utilised to measure the resulting texture and grain size in each sample, and these values were correlated to the superelastic properties. Full article

In this paper, we investigate the physical mechanics of vibration wave detection in distributed acoustic sensing (DAS) systems with the aim of enhancing the interpretation of the quantitative wavefield. We investigate the nonlinear relationship of DAS gauge length and pulse width on the seismic wave response, and the result is explained by the trigonometric relationship of backscattered Rayleigh wave phases. We further demonstrate the influence of spiral winding on DAS performance and also build phase response models for P-waves and S-waves in helically wound cables. These models suggest that the winding angle controls the measurement interval spacing and the angle of wave incidence. Additionally, integration of structural reinforcement improves the amplitude response characteristics and SNR. The experimentally inspired results show, using simulations and field tests, that the same vibration sources can give helically wound cables with larger winding angles the largest phase amplitudes, which would substantially exceed that of straight cables. SNR increased significantly (approximately 10% to 30%). The efficacy of the method was also checked using experiments for different vibration amplitudes and frequencies. Such results provide evidence for the design and installation of fiber-optic cables for use in practical engineering applications involving safety monitoring. Full article

This study investigates the spatiotemporal changes in land use and land cover (LULC) in the Kağıthane basin, Istanbul, a region experiencing rapid urban growth, to assess its environmental sustainability. Sentinel-1 and Sentinel-2 satellite images processed on the Google Earth Engine (GEE) platform were used for 2017, 2020, and 2023. Optical data from Sentinel-2, after atmospheric and geometric corrections, combined with co- and cross-polarized radar backscatter from Sentinel-1, supported land cover classification. Additionally, 14 spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Urban Index (UI), enhanced discrimination between classes. To estimate LULC projections for 2035, 2050, 2065, 2080, and 2095, the Modules for Land Use Change Evaluation (MOLUSCE) model was used, which integrates artificial neural networks with a cellular automata framework. Six driving variables, roads, streams, topographic parameters (elevation, slope, and aspect), and population density, were incorporated into multiple scenarios. Model performance was evaluated using overall accuracy, Kappa statistics, and confusion matrices, yielding strong results (91.88% accuracy; Kappa = 0.84). The simulations indicate a significant decline in forest cover and barren lands, while vegetation and built-up areas are projected to grow steadily, raising concerns about long-term urban sustainability. Water bodies are projected to remain relatively stable. Under these changes, future direct carbon emissions were estimated using carbon emission coefficients by land class. Indirect carbon emissions were estimated based on natural gas and electricity consumption data. Considering both direct and indirect emissions, the results indicate a decrease in carbon emissions from 2023 to 2035, followed by an increase of up to 13% between 2035 and 2095. These findings emphasize the importance of combining multi-sensor remote sensing data with spatially explicit modeling to accurately assess land use changes in rapidly urbanizing basins. The study emphasizes the critical need to adopt sustainability measures that address changes in carbon emissions and guide future urban planning towards a more sustainable path. Full article

Suspended sediment transport in large rivers is characterized by complex cross-sectional patterns. This study investigates the cross-sectional distribution of the suspended sediment concentration (SSC), based on 15 measurement campaigns at six stations along a 67 km reach of the middle Rhine in Germany. Utilizing a multi-method approach, we conducted turbidity and acoustic backscatter measurements, in situ particle size data, recorded water quality parameters such as electrical conductivity, and took 495 pump-based water samples over a period of 2.5 years. Statistical analysis of this comprehensive dataset shows that lateral differences have greater importance for the cross-sectional SSC distribution than vertical differences, suggesting that incomplete river mixing is of greater importance than vertical stratification for uncertainties in load calculations. We demonstrate that surface measurements are consistently representative for the whole water column and that applying the traditional Rouse equation for vertical extrapolation from surface measurements leads to large errors. We conclude that efficient monitoring programs should prioritize covering the lateral SSC distribution for more accurate load calculations and offer practical recommendations for improved SSC monitoring in similar conditions. Full article

The aim of the research was to analyse the impact of peening each of the beads on the properties of a butt joint made of S960QL steel welded with ceramic backing on a robotic workstation using the 135 (MAG) method, and to determine the impact of pneumatic needle peening on the stress level. This analysis was based on a comparison of three butt joints: in the as-welded state, with each weld bead peened and post-weld heat treatment—stress relief annealing—performed. High-frequency peening (90 Hz) of each weld was performed to reduce stresses in the welded joint by introducing tensile stresses into it. A Weld Line 10 pneumatic hammer from PITEC GmBH was used for this purpose. The test joints obtained were tested in accordance with the requirements of EN ISO 15614-1. In order to determine the state of residual stresses, stress measurements were carried out using the Barkhausen effect based on the testing procedure of the technology supplier, NNT. This meter measures the intensity of the Barkhausen effect using a standard probe (with a single core). In order to verify the stress measurement using the Barkhausen method, stress measurements were performed using the XRD sin 2ψ technique based on the X’Pert Stress Plus program, which contains a database of material constants necessary for calculations. Structural studies, including phase analysis and crystallographic grain orientation, were performed using the backscattered electron diffraction method with a high-resolution scanning electron microscope and an EBSD (Electron Backscatter Diffraction) detector, as well as EDAX OIM analysis software. In addition, X-ray diffraction testing was performed on a Panalytical X’Pert PRO device using filtered cobalt anode tube radiation (λ = 1.79021 A). Qualitative X-ray phase analysis of the tested materials was performed in a Bragg–Brentano system using an Xcelerator strip detector. The tests showed that the high-frequency peening of each bead did not cause negative results in the required tests during qualification of the S960QL plate-welding technology compared to the test plates in the as-welded and post-stress-relief heat treatment states. Interpass peening of the weld face and HAZ resulted in a reduction in residual stresses after welding at a distance of 15 mm from the joint axis compared to the stress measurement result for the sample in the as-welded condition. This allows for a positive assessment of peening in terms of reducing the crack initiator in the form of the concentration of tensile stresses in the area of the fusion line and HAZ. Full article

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource. Full article

This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering an enhanced capacity for monitoring faster-moving landslides. The methodology integrates multi-baseline interferometric analysis, utilizing backscattered signals from both point-like and distributed radar targets at full spatial resolution. The approach utilizes ten interferometric datasets acquired between 2017 and 2021 from both ascending and descending orbits. Each annual dataset is restricted to a six-month observation window to reduce temporal decorrelation effects. The HIA was implemented in a landslide-prone sector of the Dolomites, a UNESCO World Heritage Site located in the Eastern Italian Alps. Comparative evaluation against EGMS ground motion products demonstrates that the HIA significantly broadens the range of detectable slope instabilities, thus providing a valuable supplement to existing ground motion monitoring services and contributing meaningfully to landslide hazard assessment and risk reduction efforts. Full article

In this work, Mo particles were incorporated into a Cu matrix, with the hope of retaining the advantageous properties of Cu while improving its mechanical performance. Mechanical ball milling was employed to fabricate Cu-Mo composite powders with different Mo concentrations; the Mo particles were incorporated at mass fractions of 5%, 10%, 15%, and 20%, which were subsequently densified by spark plasma sintering (SPS) to achieve a high-density composite. Phase identification and microstructural analysis were performed using X-ray diffraction (XRD). Tensile strength, compressive strength, and Vickers hardness measurements were performed to evaluate the mechanical performance of the Cu-Mo composite. Microstructural characterization of the tensile specimen was conducted via electron backscatter diffraction (EBSD), energy dispersive X-ray spectroscopy (EDS), and field-emission scanning electron microscopy (FE-SEM). The results demonstrate a consistent decrease in grain size and a corresponding increase in density with higher Mo content in the composite. For Cu-15wt%Mo composite, the Vickers hardness is 135 HV, compressive strength is 300 MPa, and tensile strength is 371 MPa. Compared with pure Cu, they were increased by 74%, 115%, and 64%, respectively. The main strengthening mechanisms have been revealed. This research can offer a foundation and reference for designing and developing high-performance Cu-Mo composite. Full article

The increasing demand for sustainable and multifunctional materials in radiation shielding and optical applications has driven research toward utilizing natural and waste-derived reinforcements in polymer matrices. However, achieving effective attenuation performance across different radiation types using eco-friendly fillers remains a significant challenge. In this study, polyvinyl chloride (PVC)/Polystyrene (PSt) blend composites (1:1 weight ratio) were reinforced with powdered snail shell (SSP) as a biogenic additive, aiming to enhance their shielding and optical performance. Composites containing 5%, 10%, 20%, and 30% SSP (w/v) were fabricated and characterized. Key parameters including linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), mean free path (MFP), half-value layer (HVL), and effective atomic number (Zeff) were measured using a variable-energy X-ray source (13.37–59.54 keV) and ULEGe detector. Fast neutron shielding performance and theoretical values for build-up factor (EBF) and macroscopic neutron cross-sections were also calculated. The results showed a marked improvement in X-ray attenuation with increasing SSP content (SSP30 > SSP20 > SSP10 > SSP5), while neutron shielding declined due to the high oxygen content of SSP. Among the tested samples, the SSP30 composite exhibited the highest X-ray attenuation efficiency, whereas the SSP5 composition showed the greatest enhancement in optical reflectance and neutron absorption, indicating optimal performance in these respective tests. Additionally, 5% SSP incorporation improved optical reflectance by 12%, indicating enhanced photon backscattering at the material surface. This behavior contributes to improved gamma shielding efficiency by reducing photon penetration and enhancing surface-level attenuation. These findings highlight the potential of snail shell-based fillers as low-cost, sustainable reinforcements in multifunctional polymer composites. Full article