Search Results (48)

This study presents a geostatistical simulation approach for fine-scale grain size mapping in tidal flats, which complements sparse field survey data with high-resolution optical satellite imagery and quantifies prediction uncertainty at unsampled locations. Within a multi-Gaussian regression kriging (MGRK) framework, a random forest (RF) regression model is used to estimate the trend component of grain size variability in Gaussian space. Residual components are estimated using kriging, and the trend and residual components are combined to construct conditional cumulative distribution functions for uncertainty modeling. Sequential Gaussian simulation based on the CCDFs generates alternative realizations of grain size, allowing for quantification of prediction uncertainty. The potential of this integrated approach was tested on the Baramarae tidal flat in Korea using KOMPSAT-2 imagery. Three spectral features, the green band, red band, and normalized difference water index (NDWI), explained 42.74% of the grain size variability, with NDWI identified as the most influential feature, contributing 40.8% compared with 31.7% for the red band and 27.5% for the green band. MGRK effectively captured local grain size variations, reducing the mean absolute error from 0.554 to 0.280 compared with univariate kriging based solely on field survey data, corresponding to an improvement of approximately 49.5%. The benefit of the proposed approach was validated by a reduction in prediction uncertainty, with the mean standard deviation decreasing from 0.743 in simulations based solely on field data to 0.280 in MGRK-based simulations. These findings indicate that the proposed geostatistical approach, integrating satellite-derived features, is a reliable method for fine-scale mapping of intertidal sediment grain size by providing both predictions and associated uncertainty estimates. Full article

This study proposes a CatBoost-enhanced hyperspectral modeling approach for accurate prediction of wood dyeing formulations. Using Pinus sylvestris var. mongolica veneer as the substrate, 306 samples with gradient dye concentrations were prepared, and their reflectance spectra (400–700 nm) were acquired. After noise reduction and sensitive band selection (400–450 nm, 550–600 nm, and 600–650 nm), spectral descriptors were extracted as model inputs. The CatBoost algorithm, optimized via k-fold cross-validation and grid search, outperformed XGBoost, random forest, and SVR in prediction accuracy, achieving MSE = 0.00271 and MAE = 0.0349. Scanning electron microscopy (SEM) revealed the correlation between dye particle distribution and spectral response, validating the model’s physical basis. This approach enables intelligent dye formulation control in industrial wood processing, reducing color deviation (ΔE < 1.75) and dye waste by approximately 25%. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

This investigation of hydroxyl and polysiloxane absorption peaks in elastic polymer composites reveals significant spectral shifts within the fingerprint region of FTIR spectra. Using poly(vinyl butyral) (PVB) as the base polymer and poly(vinyl acetate) (PVAc) and poly(vinyl alcohol) (PVA) as reference materials, solvent effects on polymer–solvent interactions were systematically analyzed. Among the tested alcohol solvents, PEG 400 induced the most pronounced spectral changes, with the C=O stretching band shifting from 1740 to 1732 cm⁻¹ and the O–H band significantly broadening and downshifting to around 3300 cm⁻¹, reflecting strong hydrogen-bonding interactions. Wavelet-based noise reduction effectively enhanced the signal-to-noise ratio, reducing the baseline standard deviation by over 90%. This study introduces a novel noise-enhanced FTIR recognition model that integrates baseline noise metrics to improve detection sensitivity. The model successfully uncovers subtle structural variations in polymer–solvent systems that are typically masked by conventional FTIR techniques, advancing materials analysis and providing a robust framework for future FTIR-based diagnostics and material characterization. Full article

Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. The model is trained on data from Italian earthquakes recorded between 1972 and 2016, linking ground motion recordings to MCS observations located within 3 km. The dataset has been enhanced with site-specific correction factors to better capture local amplification effects. Key input features include Arias Intensity, spectral accelerations at four representative periods (0.15 s, 0.4 s, 0.6 s, and 2 s), and site condition proxies, such as slope and Vs30. The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. To ensure transparency, Shapley Additive Explanations (SHAPs) are used to quantify the contribution of each feature. Arias Intensity emerges as the dominant predictor, followed by spectral ordinates in line with structural response mechanics. As damage severity increases, feature importance shifts from PGA to PGV, while site-specific variables (slope, Vs30) act as refiners rather than amplifiers of shaking. The proposed approach enables near real-time prediction of local damage scenarios and supports data-driven decision-making in seismic emergency management. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article

Seafloor topography data are fundamental for marine resource development, oceanographic research, and maritime rights protection. However, approximately 75% of the ocean remains unsurveyed for bathymetry. Sole reliance on shipborne measurements is insufficient for constructing a global bathymetric model within a short timeframe; consequently, satellite altimetry-based inversion techniques are essential for filling data gaps. Recent advancements have improved the variety and quality of satellite altimetry gravity data. To leverage the complementary advantages of multiparameter gravity data, we propose a 1D convolutional neural network based on a convolutional attention module, termed the Attention Channel 1D Convolutional Network (AC1D). Results of a case study of the Mariana Trench indicated that the AC1D grid predictions exhibited improved agreement with single-beam depth checkpoints, with standard deviation reductions of 6.32%, 20.79%, and 36.77% and root mean square error reductions of 7.11%, 22.82%, and 50.99% compared with those of parallel linked backpropagation, the gravity–geological method, and a convolutional neural network, respectively. The AC1D grid demonstrated enhanced stability in multibeam bathymetric validation metrics and exhibited better consistency with multibeam bathymetry data and the GEBCO2023 grid. Power spectral density analysis revealed that AC1D effectively captured rich topographic signals when predicting terrain features with wavelengths below 6.33 km. Full article

Laser-induced breakdown spectroscopy (LIBS) is widely used for online quantitative analysis in industries due to its rapid analysis and minimal damage. However, challenges like signal instability, matrix effects, and self-absorption hinder the measurement accuracy. Recent approaches, including the internal standard method and crater limitation method, aim to improve the stability but suffer from high computational demands or complexity. This study proposes a method to enhance LIBS stability by utilizing craters formed from laser ablation without external cavity assistance. It first improves the plasma temperature calculation reliability using multiple elemental spectral lines, after which electron density calculations are performed. By fitting plasma parameter curves based on laser pulse counts and using a laser confocal microscope for crater analysis, stable plasma conditions were found within crater areas of 0.400 mm² to 0.443 mm² and depths of 0.357 mm to 0.412 mm. Testing with elemental spectral lines of Ti II, K II, Ca I, and Fe I showed a significant reduction in the relative standard deviation (RSD) of the LIBS spectral line intensity, demonstrating an improved signal stability within specified crater dimensions. Full article

Overhead power lines are important components of power grids, and the status of transmission line equipment directly affects the safe and reliable operation of power grids. In order to guarantee the reliable operation of lines and efficient usage of the power grid, the tension of overhead power is an important parameter to be measured. The tension of power lines can be calculated from the modal frequency, but the measured acceleration data obtained from the accelerometer is severely contaminated with noises. In this paper, a multiscale-based peak detection (M-AMPD) algorithm is used to find possible modal frequencies in the power spectral density of acceleration data. To obtain a reliable noise-free signal, median absolute deviations with baseline correction (MAD-BS) algorithm are applied. An accurate estimation of modal frequencies used for tension estimation is obtained by iteration of the MAD-BS algorithm and reduction in frequency range technique. The iterative range reduction technique improves the accuracy of the estimated tension of overhead power lines. An accurate estimation of overhead power line tension can contribute to improving the reliability and efficiency of the power grid. The proposed algorithm is implemented in MATLAB R2020a and verified by comparison with measured data by a tensiometer. Full article

Objectives: This study aimed to study the inner retina functional and morphological impairment of retinal ganglion cells (RGCs) from specific macular rings and sectors to identify whether selective macular regions were more vulnerable than others within the 20 central degrees in patients with open-angle glaucoma (OAG). Methods: In total, 21 OAG patients [mean age 50.19 ± 7.86 years, Humphrey Field Analyzer (HFA) 24-2 mean deviation (MD) between −5.02 and −22.38 dB, HFA 10-2 MD between −3.07 and −17.38 dB], providing 21 eyes, were enrolled in this retrospective case–control study. And 20 age-similar healthy subjects, providing 20 eyes, served as controls. The multifocal photopic negative response (mfPhNR) response amplitude density (RAD) from concentric rings and macular sectors and ganglion cell layer thickness (GCL-T) assessed by Spectral Domain–Optical Coherence Tomography (SD-OCT) was measured. Mean RAD and GCL-T values were compared between OAG and control ones by ANOVA. In OAG eyes, the relationship between mfPhNR and SD-OCT data was examined by linear regression analysis, and Pearson’s correlation coefficients were computed. Results: In considering all rings and sectors, compared to the controls, the OAG group showed a significant (p < 0.01) reduction in mean mfPhNR RAD and in GCL-T values with the greatest reduction in the central area. In OAG eyes, a significant (p < 0.01) correlation between all mfPhNR RAD and GCL-T values, with significant (p < 0.01) correlation coefficients, were found. Conclusions: In OAG eyes, RGC dysfunction was detectable by abnormal mfPhNR responses in localized macular areas, mainly in the central one. Localized macular RGC dysfunction was linearly correlated with the GCL morphological changes. Full article

In this work, we focus on two important aspects of modern cosmology: reheating and Hubble constant tension within the framework of a unified cosmic theory, namely the quintessential inflation connecting the early inflationary era and late-time cosmic acceleration. In the context of reheating, we use instant preheating and gravitational reheating, two viable reheating mechanisms when the evolution of the universe is not affected by an oscillating regime. After obtaining the reheating temperature, we analyze the number of e-folds and establish its relationship with the reheating temperature. This allows us to connect, for different quintessential inflation models (in particular for models coming from super-symmetric theories such as $\alpha$-attractors), the reheating temperature with the spectral index of scalar perturbations, thereby enabling us to constrain its values. In the second part of this article, we explore various alternatives to address the $H_0$ tension. From our perspective, this tension suggests that the simple Λ-Cold Dark Matter model, used as the baseline by the Planck team, needs to be refined in order to reconcile its results with the late-time measurements of the Hubble constant. Initially, we establish that quintessential inflation alone cannot mitigate the Hubble tension by solely deviating from the concordance model at low redshifts. The introduction of a phantom fluid, capable of increasing the Hubble rate at the present time, becomes a crucial element in alleviating the Hubble tension, resulting in a deviation from the Λ-Cold Dark Matter model only at low redshifts. On a different note, by utilizing quintessential inflation as a source of early dark energy, thereby diminishing the physical size of the sound horizon close to the baryon–photon decoupling redshift, we observe a reduction in the Hubble tension. This alternative avenue, which has the same effect of a cosmological constant changing its scale close to the recombination, sheds light on the nuanced interplay between the quintessential inflation and the Hubble tension, offering a distinct perspective on addressing this cosmological challenge. Full article

The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal’s phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal’s noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm. Full article

Heavy metal pollution not only causes detrimental effects on the environment but also poses threats to human health; thus, it is crucial to monitor the heavy metal content in the soil. Hyperspectral technology, characterized by high spectral resolution, rapid response, and non-destructive detection, is widely employed in soil composition monitoring. This study aims to investigate the effects of dimensionality reduction methods on the performance of hyperspectral inversion. To this end, 56 soil samples were collected in Daye, with the corresponding hyperspectral data acquired by the advanced ASD Fieldspec4 instrument. We employed the linear dimensionality reduction method, i.e., the principal component analysis (PCA), and non-linear method in terms of kernel PCA (KPCA) with polynomial, radial basis function (RBF), and sigmoid kernels to reduce the dimensionalities of original spectral reflectance and that processed by first-derivative transformation (FDT). Building upon this foundation, we applied the Adaptive Boosting (AdaBoost) algorithm for inverting the soil copper (Cu) content. The performance of each inversion model was evaluated by evaluation indices in terms of the coefficient of determination (R²), root-mean-square error (RMSE), and residual prediction deviation (RPD). The results revealed that the KPCA with polynomial kernel function applied to the FDT-based spectra could yield the optimal inversion accuracy, with corresponding R², RMSE, and RPD being 0.86, 21.47 mg·kg⁻¹, and 2.72, respectively. This study demonstrates that applying the FDT with KPCA processing can significantly improve the accuracy of the hyperspectral inversion for soil Cu content, providing a potential approach for monitoring heavy metal pollution using hyperspectral technology. Full article

In measuring object rotational velocity using vortex beam, the incident light on a tilted object causes spectral broadening, which significantly interferes with the identification of the true rotational Doppler shift (RDS) peak. We employed a velocity decomposition method to analyze the relationship between the spectral extremum and the central frequency shift caused by the object tilt. Compared with the linear growth trend observed when calculating the object rotational velocity using the frequency peak with the maximum amplitude, the central frequency calculation method effectively reduced the deviation rate of the RDS and velocity measurement value from the true value, even at large tilt angles. This approach increased the maximum tilt angle for a 1% relative error from 0.221 to 0.287 rad, representing a 29.9% improvement. When the tilt angle was 0.7 rad, the velocity measurement deviation reduction rate can reach 5.85%. Our work provides crucial support for achieving high-precision rotational velocity measurement of tilted object. Full article