Search Results (2,766)

Inland lakes and reservoirs are critical components of global freshwater resources. However, traditional water level monitoring stations are costly to establish and maintain, particularly in remote areas. As an alternative, satellite altimetry has become a key tool for lake water level monitoring. Nevertheless, conventional radar altimetry techniques face accuracy limitations when monitoring small water bodies. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with a single-photon counting lidar system, offers enhanced precision and a smaller ground footprint, making it more suitable for small-scale water body monitoring. However, the water level data obtained from the ICESat-2 ATL13 inland water surface height product are limited in quantity, while the lake water level accuracy derived from the ATL08 product is relatively low. To overcome these challenges, this study proposes a Spatial Distribution-Based Hierarchical Clustering for Photon-Counting Laser altimeter (SD-HCPLA) for enhanced water level extraction, validated through experiments conducted at the Danjiangkou Reservoir. The proposed method first employs Landsat 8/9 imagery and the Normalized Difference Water Index (NDWI) to generate a water mask, which is then used to filter ATL03 photon data within the water body boundaries. Subsequently, a Minimum Spanning Tree (MST) is constructed by traversing all photon points, where the vertical distance between adjacent photons replaces the traditional Euclidean distance as the edge length, thereby facilitating the clustering and denoising of the point cloud data. The SD-HCPLA algorithm successfully obtained 41 days of valid water level data for the Danjiangkou Reservoir, achieving a correlation coefficient of 0.99 and an average error of 0.14 m. Compared with ATL08 and ATL13, the SD-HCPLA method yields higher data availability and improved accuracy in water level estimation. Furthermore, the proposed algorithm was applied to extract water level data for five lakes and reservoirs in Hubei Province from 2018 to 2023. The temporal variations and inter-correlations of water levels were analyzed, providing valuable insights for regional ecological environment monitoring and water resource management. Full article

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 × 10⁴ km². The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R² value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R² value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model’s ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment. Full article

This study explores the mechanical behaviour of intra-laminar hybrid flax/E-glass composites, focusing on the role of micro-scale irregularities in flax fibres. By employing computational micromechanics and Monte Carlo simulations, it analyses the influence of flax fibre geometry and elastic properties on the performance of hybrid and non-hybrid composites. A Non-Circular Fibre Distribution (NCFD) algorithm is introduced to generate microstructures with randomly distributed non-circular flax and circular E-glass fibres, which are then modelled using a 3D representative volume element (RVE) model developed in Python 2.7 and implemented with Abaqus/Standard. The RVE dimensions were specified as ten times the mean characteristic length of flax fibres (580 μm) for the width and length, while the thickness was defined as one-tenth the radius of the E-glass fibre. Results show that Monte Carlo simulations accurately estimate the effect of fibre variabilities on homogenised elastic constants when compared to measured values and Halpin-Tsai predictions, and they effectively evaluate the fibre/matrix interfacial stresses and von Mises matrix stresses. While these variabilities minimally affect the homogenised properties, they increase the presence of highly stressed regions, especially at the interface and matrix of flax/epoxy composites. Additionally, intra-laminar hybridisation further increases local stress in these critical areas. These findings improve our understanding of the relationship between the natural fibre shape and mechanical performance in flax/E-glass composites, providing valuable insights for designing and optimising advanced composite materials to avoid or delay damage, such as matrix cracking and splitting, under higher applied loads. Full article

The lifetime performance index is widely used in the manufacturing industry to assess the capability and effectiveness of production processes. A new overall lifetime performance index is proposed when multiple parts of products are produced in multiple dependent production lines. Each individual lifetime performance index for a single production line is connected to the overall lifetime performance index for multiple independent or dependent production lines. The overall lifetime performance index increases with the overall process yield. We analyze the maximum likelihood estimators for the individual lifetime performance indices using progressively type I interval-censored samples while the lifetime of the ith part of products follows a Gompertz distribution for either independent or dependent cases. To determine whether the overall lifetime performance index meets the desired target value, the maximum likelihood estimator for the individual index is utilized separately to conduct the testing procedures about the overall lifetime performance index for either independent or dependent cases. Power analysis of the multiple testing procedure is illustrated with figures, and key findings are summarized. A simulation study is conducted for the test powers. Lastly, a practical example involving products with two parts is presented to demonstrate the application of the proposed testing algorithm. Given the asymmetry of the lifetime distribution, this research aligns with the study of asymmetric probability distributions and their diverse applications across various fields. Full article

Pareto distributions are widely applied in various fields, such as economics, finance, and environmental studies. The modeling of real-world data has created a demand for the discretization of Pareto distributions. In this paper, we propose using mean squared error representative points (MSE-RPs) as the discrete representation of Pareto distributions. We demonstrate the uniqueness and existence of these representative points under certain parameter settings and provide a theoretical k-means algorithm for the computation of MSE-RPs for Pareto I and Pareto II distributions. Furthermore, to enhance the applicability of MSE-RPs, we employ three methodological approaches to estimate the MSE-RPs of Pareto distributions. By analyzing the estimation bias under different parameters and methods, we recommend estimating the distribution parameters first before estimating the MSE-RPS for Pareto I and Pareto II distributions. For Pareto III and Pareto IV distributions, we suggest using the $B_q$ quantiles for MSE-RP estimation. Building on this, we analyze the sources of estimation bias and propose an effective method for determining the number of MSE-RPs based on information gain truncation. Through simulations and real data studies, we demonstrate that the proposed methods for MSE-RP estimation are effective and can be used to fit the empirical distribution function of data accurately. Full article

This paper proposes a novel sparse array design and an efficient algorithm for two-dimensional direction-of-arrival (2D-DOA) estimation. By analyzing the hole distribution in coprime arrays and introducing supplementary elements, we design a Complementary Coprime Planar Array (CCPA) that strategically fills key holes in the virtual array. This design enhances the array’s continuous Degrees Of Freedom (DOFs) and virtual aperture, achieving improved performance in 2D-DOA estimation with fewer physical elements. The virtualization of the array further increases the available DOFs, while the hole-filling strategy ensures better spatial coverage and continuity. On the algorithmic side, we introduce a dimensionality-reduction root MUSIC algorithm tailored for uniform planar arrays after virtualization. By decomposing the two-dimensional spectral peak search into two one-dimensional polynomial root-finding problems, the proposed method significantly reduces computational complexity while maintaining high estimation accuracy. This approach effectively mitigates the challenges of 2D peak search, making it computationally efficient without sacrificing precision. Extensive simulations demonstrate the advantages of the proposed array and algorithm, including higher DOFs, reduced complexity, and superior estimation performance compared to existing methods. These results validate the effectiveness of the proposed framework in advancing sparse array design and signal processing for 2D-DOA estimation. Full article

To address the limitations of conventional electrochemical impedance spectroscopy (EIS) testing, we propose an efficient rapid EIS testing system. This system utilizes an AC pulse excitation signal combined with an “intelligent fast fourier transform (IFFT) optimization algorithm” to achieve rapid “one-to-many” impedance data measurements. This significantly enhances the speed, flexibility, and practicality of EIS testing. Furthermore, the conventional model-fitting approach for EIS data often struggles to resolve the issue of overlapping impedance arcs within a limited frequency range. To address this, the present study employs the Regularization Distributed Relaxation Time (RDRT) method to process EIS data obtained under AC pulse conditions. This approach avoids the workload and analytical uncertainties associated with assuming equivalent circuit models. Finally, the practical utility of the proposed testing system and the RDRT impedance analysis method is demonstrated through the estimation of battery state of health (SOH). In summary, the method proposed in this study not only addresses the issues associated with conventional EIS data acquisition and analysis but also broadens the methodologies and application scope of EIS impedance testing. This opens up new possibilities for its application in fields such as lithium-ion batteries (LIBs) energy storage. Full article

Better understanding mathematical and numerical models often requires investigating the impacts of inputs on the model outputs, as well as interactions. Quantifying such effects for models with non-independent input variables (NIVs) relies on conditional distributions of the outputs given every subset of inputs. In this paper, by firstly providing additional dependency models of NIVs, functional outputs are composed by dependency models (yielding equivalent representations of outputs) to derive distributions of outputs conditional on inputs. We then provide an algorithm for selecting the necessary and sufficient equivalent representations that allow for obtaining all the conditional distributions of outputs given every subset of inputs, and for assessing the main, total, and interaction effects (i.e., indices) of every subset of NIVs. Unbiased estimators of covariances of sensitivity functionals and consistent estimators of such indices are derived by distinguishing the case of the multivariate and/or functional outputs, including dynamic models. Finally, analytical results and numerical results are provided, including an illustration based on a dynamic model. Full article

Aulonemia queko Goudot (Poaceae, Bambusoideae) is a species of great cultural importance that has been used as a non-timber forest product in Andean forests for centuries. Despite inhabiting montane forests vulnerable to deforestation, its distribution has not been thoroughly assessed for conservation. This study analyzes its potential distribution at the regional scale (the four countries where it is distributed) and locally (in greater detail within Ecuador), using presence records and climatic and land-use data. Maxent was identified as the best algorithm, achieving high values of AUC, TSS, sensitivity, and specificity. At a regional level, A. queko is estimated to occupy approximately 264,540 km², mostly in Peru, with small areas in Bolivia. In Ecuador, the historical scenario showed the widest distribution, while the current–near-future scenario (20–40–SSP126) presented a more stable model. Temperature and rainfall represented critical factors in defining suitable habitats, as A. queko is highly sensitive to seasonal moisture availability. Land-use changes have reduced potential habitats by more than 35%, underscoring an intensified threat of habitat loss in these biodiversity-rich regions. However, projected climate changes pose an even greater impact, significantly reducing potential distribution. Our findings highlight the compelling effects of both climate-change-driven and human-driven land-use change on the future persistence of A. queko and emphasize the urgent need for targeted conservation strategies to protect its core habitats. Full article

Traditional direction of arrival (DOA) and polarization parameter estimation algorithms generally perform well under ideal conditions. However, their performance degrades significantly in practical scenarios due to mutual coupling effects among array elements. This work introduces an innovative method based on a distributed crossed dipole array to jointly estimate DOA and polarization parameters in the presence of mutual coupling effects. This work firstly eliminates the mutual coupling matrix (MCM) through subarray selection, without requiring prior knowledge of the array’s mutual coupling. The DOA is then estimated using an improved high-resolution algorithm, followed by accurate estimation of the polarization parameters through parameter matching. The results from simulations confirm that the new method significantly improves the estimation accuracy in complex mutual coupling environments, showing notable potential for practical applications and robust performance. Full article

Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, a structured Bayesian super-resolution forward-looking imaging algorithm for maneuvering platforms under an enhanced sparsity model is proposed. An enhanced sparsity model for maneuvering platforms is established to address the reconstruction problem, and a hierarchical Student-t (ST) prior is designed to model the distribution characteristics of the sparse imaging scene. To further leverage prior information about structural characteristics of the scatterings, coupled patterns among neighboring pixels are incorporated to construct a structured sparse prior. Finally, forward-looking imaging parameters are estimated using the expectation/maximization-based variational Bayesian inference. Numerical simulations validate the effectiveness of the proposed algorithm and the superiority over conventional methods based on pixel sparse assumptions in forward-looking scenes for maneuvering platforms. Full article

Data generated during the shutdown or start-up processes of wind turbines, particularly in complex wind conditions such as offshore environments, often accumulate in the low-wind-speed region, leading to reduced multivariate power estimation accuracy. Therefore, developing efficient filtering methods is crucial to improving data quality and model performance. This paper proposes a novel filtering method that integrates the control strategies of variable-speed, variable-pitch wind turbines, such as maximum-power point tracking (MPPT) and pitch angle control, with statistical distribution characteristics derived from supervisory control and data acquisition (SCADA). First, thresholds for pitch angle and rotor speed are determined based on SCADA data distribution, and the filtering effect is visualized. Subsequently, a sliding window technique is employed for the secondary confirmation of potential outliers, enabling further anomaly detection (AD). Finally, the performance of the power estimation model is validated using two wind turbine datasets and two machine learning algorithms, with results compared with and without filtering. The results demonstrate that the proposed filtering method significantly enhances the accuracy of multivariate power estimation, proving its effectiveness in improving data quality for wind turbines operating in diverse and complex environments. Full article

This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. A novel approach is proposed for day-ahead weather forecasting, leveraging real-time data extraction from reliable weather websites and applying clear sky modeling to estimate photovoltaic (PV) generation with high accuracy. By automating weather data acquisition, the methodology bridges the gap between weather predictions and practical energy management, providing utilities with a reliable tool for operating and integrating renewable energy. The optimization framework focuses on minimizing the utility bill by analyzing a distribution feeder representative of Australia’s energy infrastructure, incorporating time-of-use (TOU) and flat tariff systems across eight Australian states to simulate realistic energy costs. Furthermore, voltage constraints are applied within the optimization framework to maintain system stability and improve voltage profiles, ensuring both technical reliability and economic efficiency. The proposed framework delivers actionable insights for utility industries, enhancing the scheduling of battery energy storage systems (BESS) and facilitating the integration of renewable energy into the grid. Full article

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions of soil. It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting machine (GBM), and generalized linear model (GLM) were used to study the accurate prediction model of SOM in Tieling County, Tieling City, Liaoning Province, China. The models were trained by using 1554 surface soil samples and 19 auxiliary variables. Recursive feature elimination was used as a feature selection method to identify effective variables. The results showed that Normalized Difference Vegetation Index (NDVI) and elevation were key auxiliary variables. Based on 10-fold cross-validation, the RF model had the highest prediction accuracy. In terms of accuracy, the coefficient of determination of RF was 0.77, and the root mean square error was 2.85. The average soil organic matter content was 20.15 g/kg. The spatial distribution of SOM shows that higher content is concentrated in the east and west, while lower content is found in the middle. The SOM content of cultivated land was lower than that of forest land. Full article

This paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. The state estimation model is trained using historical measurement data from distribution networks with high observability. Measurements updated for low-observable distribution networks are supplemented by transferring samples from high-observable distribution networks using sample migration techniques, resulting in a state estimation model suitable for low-observable distribution networks. Test results demonstrate that the proposed algorithm outperforms traditional algorithms in both estimation accuracy and robustness aspects, such as the Weighted Least Squares (WLS) and Weighted Least Absolute Value (WLAV) methods. Furthermore, sample migration enhances the generalization ability of the state estimation model. Full article