Search Results (3,822)

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

Operation time and variability in structural, thermal, and environmental loads are important factors affecting the operational safety of power plant structures. Although conventional testing techniques are usually used to assess the level of damage introduced to a structure due to prolonged service, most of them are destructive and time- and cost-intensive. Therefore, in this paper, a novel approach consisting of Rayleigh optic strain sensors for deformation monitoring under creep conditions is proposed. The suitability of this methodology was assessed during quasi-static loading tests at room temperature, as well as during a long-term creep test at 540 °C under constant stress of 130 MPa, which was performed on a specimen made of 13HMF power engineering steel. The sensor attached to the specimen’s surface was used to monitor strain evolution during 678 days of high-temperature exposure under creep conditions. It was confirmed that the methodology proposed could be successfully used to monitor strain changes under quasi-static and creep conditions, as an excellent agreement between the fiber optic strain sensors and conventional strain recorders was achieved. Full article

This study investigates the thermal stability of fine-grained structures achieved through different severe plastic deformation (SPD) and heat treatment paths. Bulk fine-grained Al-0.1Sc-0.1Zr (wt%) alloy was produced via equal channel angular pressing (ECAP) using routes Bc or C, with aging before or after the ECAP. Electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM) analyses demonstrate excellent thermal stability of all four specimens. They maintain mean grain sizes below 5 μm after a 10 h thermal test at 450 °C, attributed to the presence of nano Al₃(Sc,Zr) precipitates within the microstructures. Route Bc in the ECAP method forms more stable high-angle grain boundaries (HAGBs) than route C. Whether aging occurs before or after the ECAP, similar microstructural changes are observed after thermal testing, allowing fine-tuning of the microstructure depending on the application or subsequent processes. Full article

This work presents a proof of concept for a short-range high spectral resolution lidar (SR-HSRL) optimized for aerosol characterization in the first kilometer of the atmosphere. The system is based on a compact, high-repetition-rate diode-based fiber laser with a 300 MHz linewidth and 5 ns pulse duration, coupled with an iodine absorption cell. A central challenge in the instrument’s development was identifying a laser source that offered both sufficient spectral resolution for HSRL retrievals and nanosecond pulse durations for high spatiotemporal resolution, while also being compact, tunable, and cost-effective. To address this, we developed a methodology for complete spectral and temporal laser characterization. A two-day field campaign conducted in July 2024 in Tsukuba, Japan, validated the system’s performance. Despite the relatively broad laser linewidth, we successfully retrieved aerosol backscatter coefficient profiles from 50 to 1000 m, with a spatial resolution of 7.5 m and a temporal resolution of 6 s. The results demonstrate the feasibility of using SR-HSRL for detailed studies of aerosol layers, cloud interfaces, and aerosol–cloud interactions. Future developments will focus on extending the technique to ultra-short-range applications (<100 m) from ground-based and mobile platforms, to retrieve aerosol extinction coefficients and lidar ratios to improve the characterization of near-source aerosol properties and their radiative impacts. Full article

Optical frequency domain reflectometry (OFDR) is one of the key diagnostic tools for fiber optic components and circuits built on them. A low signal-to-noise ratio, resulting from the low intensity of backscattered signals, prevents the correct quantitative description of the medium parameters. Known methods of signal denoising, such as empirical mode decomposition, frequency filtering, and activation function dynamic averaging, make the signal smoother but introduce errors into its dynamic characteristics, changing the intensity of reflection peaks and distorting the backscattering level. We propose a method to reduce OFDR trace noise using elliptical arc fitting (EAF). The obtained results indicate that this algorithm efficiently processes both areas with and without contrasting back reflections, with zero distortion of Fresnel reflection peaks, and with zero attenuation error in regions without Fresnel reflections. At the same time, other methods distort reflection peaks by 14.2–42.6% and shift the correct level of Rayleigh scattering by 27.2–67.3%. Further work will be aimed at increasing the accuracy of the method and testing it with other types of data. Full article

Objectives: The FibroScan–aspartate transaminase (AST) score (FAST score) is a hybrid biomarker combining ultrasound and blood test data for identifying nonalcoholic steatohepatitis (NASH). This study aimed to assess the feasibility of using quantitative ultrasound (QUS) biomarkers related to hepatic steatosis for NASH detection and to compare their diagnostic performance with the FAST score. Methods: A total of 137 participants, comprising 71 individuals with NASH and 66 with non-NASH (including 49 normal controls), underwent FibroScan and ultrasound exams. QUS imaging features (Nakagami parameter m, homodyned-K parameter μ, entropy H, and attenuation coefficient α) were extracted from backscattered radiofrequency data. A weighted QUS parameter based on m, μ, H, and α was derived via linear discriminant analysis. NASH was diagnosed based on liver biopsy findings using the nonalcoholic fatty liver disease activity score (NAS). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared with the FAST score using the DeLong test. Separation metrics, including the complement of overlap coefficient, Bhattacharyya distance, Kullback–Leibler divergence, and silhouette score, were used to assess inter-group separability. Results: All QUS parameters were significantly elevated in NASH patients (p < 0.05). AUROC values for individual QUS features ranged from 0.82 to 0.91, with the weighted QUS parameter achieving 0.91. The FAST score had the highest AUROC (0.96), though differences with the weighted QUS and homodyned-K parameters were not statistically significant (p > 0.05). Separation metrics ranked the FAST score highest, closely followed by the weighted QUS parameter. Conclusions: QUS biomarkers can be repurposed for NASH detection, with the weighted QUS parameter offering diagnostic accuracy comparable to the FAST score and serving as a promising, blood-free alternative. Full article

In order to measure the depth profile of the heat-treated case-hardened layer of linear guides, this paper proposes a B-scan imaging and 3D visualization method for detecting the depth profile of the case-hardened layer of linear guides based on the ultrasonic transverse wave backscattering technology. Firstly, by analyzing the generation mechanism of ultrasonic transverse waves and their advantages in material detection, and combining the differences in metallographic structure and hardness properties between the case-hardened layer and the base material, an ultrasonic transverse wave backscattering model for the case-hardened layer of linear guides was established. Then, an ultrasonic transverse wave detection experiment for the GH20 linear guide was designed and carried out to obtain the A-scan signals of the case-hardened layer depth at different positions on the cross-section of the linear guide. Finally, the A-scan signals obtained from the detection were used to generate the B-scan image of the case-hardened layer depth profile, and the 3D visualization of the case-hardened layer of the linear guide was achieved using Python and VTK tools. The experimental results show that the error between the measurement results of ultrasonic transverse waves and those of the metallographic method is 0.063 mm, and the detection results are within the allowable error range. This research provides an efficient, intuitive, and reliable technical method for detecting the depth of the case-hardened layer of linear guides in the industrial field. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Complex concentrated alloys (CCAs) have attracted significant attention due to their potential to develop materials with enhanced properties, such as increased hardness and strength. These properties are strongly influenced by the chemical composition and the processing method used. Body-centred cubic (BCC) structures are known to have high hardness but low fracture toughness, whereas face-centred cubic (FCC) structures typically exhibit lower hardness but higher toughness. In this study, Al-Cr-Cu-Fe-Mn-Ni CCAs with three distinct compositions were produced using pressureless sintering. One set of samples was prepared with equiatomic composition (composition E), whereas the compositions of the other two sets were defined based on thermodynamic calculations to obtain sintered samples predominantly formed by BCC (composition B) or FCC (composition F) phases. The samples were characterized using X-ray diffraction, scanning and transmission electron microscopy, energy-dispersive X-ray spectroscopy, electron backscatter diffraction, density measurements, hardness measurements, and uniaxial compression tests. For all compositions, good agreement was obtained between the phases predicted by thermodynamic calculations and those experimentally detected. In addition, significant differences in the mechanical properties were observed between samples with each composition. The samples with composition B exhibited the highest hardness, but almost no ductility. In contrast, samples with composition F showed the lowest yield strength and hardness, but the highest ductility. Samples with composition E had intermediate values between those of samples B and F. These differences were attributed to differences in the proportions and properties of the BCC and FCC phases in each composition and demonstrate that the mechanical properties of Al-Cr-Cu-Fe-Mn-Ni CCAs can be tailored using compositions defined based on thermodynamic calculations. Full article

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms or missed detections, thereby significantly degrading recognition accuracy. To overcome this limitation, through comprehensive simulations and experimental trials, we propose a novel pulse-width comparison method that utilizes the characteristic broadening of echoes induced by soot particles. The method exploits the differences in pulse width between target and soot-induced echoes and achieves effective interference suppression. The target-detection accuracy increases from 71.25% to 93.75% in environments with different concentrations (0–350 mg/${\mathrm{m}}^3$), corresponding to a 31.58% relative improvement. This approach leverages pulse-width differences between soot-induced and target echoes to enhance anti-interference capability in dusty environments. Full article

Sixth-generation (6G) communication systems, with ultra-wide bands, energy-autonomous end nodes, and dense connectivity, challenge existing network designs. Optimizing time resources with energy harvesting, backscatter communication, and relays is essential to maximize the total bit rate in multi-user symbiotic radio networks (SRNs) with blocked direct paths. The literature lacks a unified optimization treatment that explicitly accounts for imperfect successive interference cancellation (SIC). This study addresses this gap by proposing the first optimization framework to maximize total bit rate for energy-harvesting TDMA/PD–NOMA-based multi-cluster and relay-assisted peer-assisted SR networks. The two-phase architecture defines a tractable constrained optimization problem that jointly adjusts cluster-specific time slots ($\mathit{\tau }$ and $\lambda$). Incorporating QoS, signal power, and reflection coefficient constraints, it provides a compact formulation and numerical solutions for both perfect and imperfect SIC. Detailed simulations performed under typical 6G power levels, bandwidths, and energy-harvesting efficiencies demonstrate graphically that imperfect SIC significantly limits total throughput due to residual interference, while perfect SIC completely eliminates this ceiling under the same conditions, providing a significant capacity advantage. Furthermore, the gap between the two scenarios rapidly closes with increasing relay time margin. The findings demonstrate that network capacity is primarily determined by the triad of base station output power, channel noise, and SIC accuracy, and that the proposed framework achieves strong performance across the explored parameter space. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local hydrodynamic characteristics and propagates errors to global features, thereby limiting the accuracy and stability of reconstructions. To resolve this limitation, this study proposes a physics-constrained deterministic wave reconstruction methodology. We introduce the Data-Anchored Projection model for the differential matrix, extracting hydrodynamic constraints directly from radar backscatter data. This approach achieves stable solutions for rank-deficient systems without artificial boundaries. The model’s performance was rigorously validated through both simulated and real-sea experiments. The simulation results demonstrate a minimum 13% accuracy improvement over conventional methods and high stability under various sea states and at different range resolutions. In a real-sea trial under sea states 3 to 5, reconstruction errors remained below 10%, with consistent stability observed across varying sea states. Full article

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article