bluuebunny/crossref_metadata_2025 huggingface dataset:
Research and Application Progress of Crude Oil Demulsification Technology
Authors: Tang Longhao , Wang Tingyi , Xu Yingbiao , He Xinyi , Yan Aobo , Zhang Zhongchi , Li Yongfei , Chen Gang
Published: October 2024

The extraction and collection of crude oil will result in the formation of numerous complex emulsions, which will not only decrease crude oil production, raise the cost of extraction and storage, and worsen pipeline equipment loss, but also seriously pollute the environment because the oil in the emulsion can fill soil pores, lower the soil’s permeability to air and water, and create an oil film on the water’s surface to prevent air–water contact. At present, a variety of demulsification technologies have been developed, such as physical, chemical, biological and other new emulsion breaking techniques, but due to the large content of colloid and asphaltene in many crude oils, resulting in the increased stability of their emulsions and oil–water interfacial tension, interfacial film, interfacial charge, crude oil viscosity, dispersion, and natural surfactants have an impact on the stability of crude oil emulsions. Therefore, the development of efficient, widely applicable, and environmentally friendly demulsification technologies for crude oil emulsions remains an important research direction in the field of crude oil development and application. This paper will start from the formation, classification and hazards of crude oil emulsion, and comprehensively summarize the development and application of demulsification technologies of crude oil emulsion. The demulsification mechanism of crude oil emulsion is further analyzed, and the problems of crude oil demulsification are pointed out, so as to provide a theoretical basis and technical support for the development and application of crude oil demulsification technology in the future.
Thermodynamic Model-Based Synthesis of Heat-Integrated Work Exchanger Networks
Authors: Amini-Rankouhi Aida , Siddiqui Abdurrafay , Huang Yinlun
Published: October 2024

Heat integration has been widely and successfully practiced for recovering thermal energy in process plants for decades. It is usually implemented through synthesizing heat exchanger networks (HENs). It is recognized that mechanical energy, another form of energy that involves pressure-driven transport of compressible fluids, can be recovered through synthesizing work exchanger networks (WENs). One type of WEN employs piston-type work exchangers, which demonstrates techno-economic attractiveness. A thermodynamic-model-based energy recovery targeting method was developed to predict the maximum amount of mechanical energy feasibly recoverable by piston-type work exchangers prior to WEN configuration generation. In this work, a heat-integrated WEN synthesis methodology embedded by the thermodynamic model is introduced, by which the maximum mechanical energy, together with thermal energy, can be cost-effectively recovered. The methodology is systematic and general, and its efficacy is demonstrated through two case studies that highlight how the proposed methodology leads to designs simpler than those reported by other researchers while also having a lower total annualized cost (TAC).
Mechanism Analysis of Low-Frequency Oscillation Caused by VSG from the Perspective of Vector Motion
Authors: Zhang Hongqiang , Zhou Yunpeng , He Wei , Hu Jiabing , Huang Wei , Li Wenyun , Zhai Suwei
Published: October 2024

Virtual synchronous generators (VSGs) have attracted widespread attention due to their advantage in supporting voltage and frequency of power systems. However, relevant studies have shown that a VSG has similar low-frequency oscillation as synchronous generators, which is more likely to occur under strong grid conditions. In this paper, the linearized mathematical model of a VSG is established by using small-signal analysis; based on this, the physical process of low-frequency oscillation of a VSG is explained from the perspective of vector motion. Firstly, the amplitude and phase motion of the current vector of a VSG under small disturbance are analyzed, then the mechanism of negative damping caused by terminal voltage control is revealed, and the reason why a VSG is more prone to instability under strong grid conditions is explained. Based on these, the influence of control and grid strength on the low-frequency oscillation of a VSG is analyzed. Studies show that the amplitude motion of the output current is the main cause of negative damping, and the oscillation can be suppressed by optimizing the value of key parameters.
Rapid Detection of Adulteration in Minced Lamb Meat Using Vis-NIR Reflectance Spectroscopy
Authors: Zuo Xiaojia , Li Yanlei , Chen Xinwen , Chen Li , Liu Chang
Published: October 2024

In view of the phenomenon that adulterated lamb with other animal-derived meats in the market could not be quickly identified, this study used visible near-infrared spectroscopy combined with chemometric methods to quickly identify and quantify lamb rolls adulterated with chicken, duck, and pork. The spectra of the visible–near-infrared band (350–1000 nm) and near-infrared band (1000–1700 nm) of 360 lamb samples, which were mixed with chicken, duck, pork, and 10% lamb oil separately in different increasing proportions, were collected. It was found that the qualitative models of heterogeneous meat (adulterated with chicken, duck, and pork) in lamb were constructed by the combination of first derivative and multiplicative scatter correction (MSC); the accuracy of the validation set reached 100%; the meantime accuracy of the cross-validation set reached 100% (pure lamb), 98.3% (adulterated with chicken), 98.7% (adulterated with duck), and 97.3% (adulterated with pork). Furthermore, the correlation coefficient (R2c) of the adulterated chicken, pork, and duck quantitative prediction models reached 0.972 (chicken), 0.981 (pork), and 0.985 (duck). In summary, the use of Vis NIR can identify lamb meat mixed with chicken, duck, and pork and can quantitatively predict the content of adulterated meat.
Religious Possession and Self-Repossession: The Black Nationalist Movements and the Anglophone Caribbean Ritual Plays in the 1960s–1970s
Authors: Li Xin , Chen Hongwei
Published: October 2024

Since achieving political independence in the 1960s, Anglophone Caribbean nations have faced the urgent task of exploring cultural independence. In the socio-cultural sphere, Black nationalism dominated, with Pan-Africanism and Rastafarianism exerting significant influence. In literary creation, writers and intellectuals sought to unearth local popular religions and folk traditions to produce literature that was distinctively Caribbean. In this quest, rituals—especially those involving religious possession—emerged as pivotal tools for writers to explore historical traditions and reflect on identity formation. Ritual plays, in particular, vividly represented these dynamics within the socio-cultural context. This paper examines the interaction between Black nationalist movements and ritual plays during this period, highlighting their significant role in shaping Caribbean identities. It reveals that ritual plays such as Dream on Monkey Mountain, Couvade, and An Echo in the Bone challenge Pan-Africanism promoted by Black nationalist movements. Instead, they employ ancestor possession rituals and elements from multiple religious rituals to construct a native Caribbean identity. These plays underscore the central role of Afro-Caribbean traditions while also highlighting the region’s diverse cultural heritage and the localized nature of Caribbean identity. Furthermore, they broaden the use of religious rituals in recalling and understanding traditions.
Enhancing Portfolio Decarbonization Through SensitivityVaR and Distorted Stochastic Dominance
Authors: Rohmawati Aniq , Neswan Oki , Puspita Dila , Syuhada Khreshna
Published: October 2024

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement’s goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance.
Engineering of a Bio-Inspired Tiltable Oscillating Fin Submersible Thruster
Authors: Liu Zihao , Li Duanling
Published: October 2024

Oscillating fins are devices designed to produce thrust through periodic undulating movements. However, these structures lack flexibility and often provide thrust in only one fixed direction. Observation and biological references suggest that the dorsal fin rays of seahorses can tilt longitudinally in the spine direction, changing the thrust direction. This study aims to analyze the dynamic effects of seahorse dorsal fin inclining and design a flexible bionic thruster based on this principle. Computational fluid dynamics analysis hypothesizes that fin inclination controls the net force direction in the vertical plane. A force sensor and pulley system test platform were constructed to examine the influences of wave features and the inclination angle on thrust in both vertical and horizontal directions, with discrete fin surfaces used to eliminate force interference. Force testing and snapshots indicate that wave velocity positively impacts net force magnitude, while fin inclination allows for control over force orientation. This tiltable oscillating fin thruster possesses more degrees of freedom, leading to better flexibility and providing controllable thrust orientation.
Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method
Authors: Najem Sami , Baghdadi Nicolas , Bazzi Hassan , Zribi Mehrez
Published: October 2024

The radar-backscattering coefficient (σ0) depends on surface characteristics and instrumental parameters (wavelength, polarization, and incidence angle). For Sentinel-1 (S1), with incidence angles ranging from 25° to 45°, σ0 for similar targets typically differs by a few dB depending on their localization in the S1 swath. Overcoming this angular dependence is crucial for the operational applications of radar data. In theory, σ0 follows a cosine function with an exponent “N” that represents the degree of dependence between σ0 and the incidence angle. In order to reduce the effect of the incidence angle on σ0, dynamic N normalizations based on vegetation descriptors, NDVI and SAR Ratio (VV/VH), were applied and then compared to the results obtained with temporally fixed N normalizations. N was estimated at each S1 date during the period of the study for three main summer crops: corn, soybean, and sunflower. Analysis shows that the angular dependence of the S1 σ0 is similar for all three crops. N varies from 3.0 for low NDVI values to 2.0 for high NDVI values (stage of maximal vegetation development) in the VV polarization and from 2.5 to 1.5 for the VH polarization. Furthermore, N fluctuates strongly during the periods before plant emergence and after harvesting, due to variations in the soil roughness. Finally, the results demonstrated that the dynamic normalization of σ0 significantly reduces its angular dependence compared to fixed N (N = 1 and N = 2), with SAR ratio-based normalization performing similarly to NDVI-based normalization.
Limb Temperature Observations in the Stratosphere and Mesosphere Derived from the OMPS Sensor
Authors: Da Costa Louro Pedro , Keckhut Philippe , Hauchecorne Alain , Meftah Mustapha , Jaross Glen , Mangin Antoine
Published: October 2024

Molecular scattering (Rayleigh scattering) has been extensively used from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we investigate how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. The OMPS consists of three instruments whose main purpose is to study the composition of the stratosphere. One of these, the Limb Profiler (LP), measures the radiance of the limb of the middle atmosphere (stratosphere and mesosphere, 12 to 90 km altitude) at wavelengths from 290 to 1020 nm. This new data set has been used with a New Simplified Radiative Transfer Model (NSRTM) to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars and the ERA5 and MSIS models. The results show that OMPS and the lidars are in agreement within a range of about 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. We investigated various sources of bias, such as different attenuation sources, which can produce errors of up to 120 K in the UV range, instrumental errors around 0.8 K and noise problems of up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturised instrument that could provide real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats with our NSRTM.
Enhancing Hyperspectral Anomaly Detection Algorithm Comparisons: Leveraging Dataset and Algorithm Characteristics
Authors: Wheeler Bradley J. , Karimi Hassan A.
Published: October 2024

Validating the contributions of new algorithms is a critical step in hyperspectral anomaly detection (HAD) research. Typically, validation involves comparing the performance of a proposed algorithm against other algorithms using a series of benchmark datasets. Despite the longstanding use of this comparison process, little attention has been paid to the characteristics of datasets and algorithms that ensure each algorithm has an equal opportunity of performing well. Characteristics of datasets and algorithms that inadvertently favor one algorithm can skew results, leading to misleading conclusions. To address this issue, this study introduces a feature-centric framework designed to assist in ensuring an unbiased comparison of HAD algorithms. The framework identifies significant correlations between datasets and algorithms by extracting distribution-related features from the datasets and statistically testing them against the algorithmic outcomes. The identified trends are then compared across datasets to ensure that all relevant trends are equally represented, thereby ensuring diversity and validating that no singular algorithm is afforded an inherent advantage. The framework was tested on five algorithms across 14 datasets. The results indicate that multiple measures of variance within the datasets are key drivers of diversity, and these measures accurately predicted algorithmic outcomes for 12 of the 14 datasets. This suggests that the identified trends effectively explain the algorithmic outcomes and highlights the importance of incorporating datasets with a diverse range of variances in comparisons of HAD algorithms.
Water Use Efficiency in Rice Under Alternative Wetting and Drying Technique Using Energy Balance Model with UAV Information and AquaCrop in Lambayeque, Peru
Authors: Ramos-Fernández Lia , Peña-Amaro Roxana , Huanuqueño-Murillo José , Quispe-Tito David , Maldonado-Huarhuachi Mayra , Heros-Aguilar Elizabeth , Flores del Pino Lisveth , Pino-Vargas Edwin , Quille-Mamani Javier , Torres-Rua Alfonso
Published: October 2024

In the context of global warming, rising air temperatures are increasing evapotranspiration (ETc) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ETc (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying (AWD5, AWD10, and AWD20) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ETc values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ETc was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management.
Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
Authors: Gutiérrez-Hernández Oliver , García Luis V.
Published: October 2024

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.
Progress in Remote Sensing of Heavy Metals in Water
Authors: Xu Xiaoling , Pan Jiayi , Zhang Hua , Lin Hui
Published: October 2024

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms.
Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling
Authors: Ghanati Bahareh , Serra-Sagristà Joan
Published: October 2024

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.
Multi-Year Hurricane Impacts Across an Urban-to-Industrial Forest Use Gradient
Authors: Topete-Pozas Carlos , Norman Steven P. , Christie William M.
Published: October 2024

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity.
Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning
Authors: Wang Longshuai , Ma Liwen , Wu Tao , Wu Jiaji , Luo Xiang
Published: October 2024

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.
Field Programmable Gate Array (FPGA) Implementation of Parallel Jacobi for Eigen-Decomposition in Direction of Arrival (DOA) Estimation Algorithm
Authors: Zhou Shuang , Zhou Li
Published: October 2024

The eigen-decomposition of a covariance matrix is a key step in the Direction of Arrival (DOA) estimation algorithms such as subspace classes. Eigen-decomposition using the parallel Jacobi algorithm implemented on FPGA offers excellent parallelism and real-time performance. Addressing the high complexity and resource consumption of the traditional parallel Jacobi algorithm implemented on FPGA, this study proposes an improved FPGA-based parallel Jacobi algorithm for eigen-decomposition. By analyzing the relationship between angle calculation and rotation during the Jacobi algorithm decomposition process, leveraging parallelism in the data processing, and based on the concepts of time-division multiplexing and parallel partition processing, this approach effectively reduces FPGA resource consumption. The improved parallel Jacobi algorithm is then applied to the classic DOA estimation algorithm, the MUSIC algorithm, and implemented on Xilinx’s Zynq FPGA. Experimental results demonstrate that this parallel approach can reduce resource consumption by approximately 75% compared to the traditional method but introduces little additional time consumption. The proposed method in this paper will solve the problem of great hardware consumption of eigen-decomposition based on FPGA in DOA applications.
On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera
Authors: Wu Hongyu , Chen Guanzhou , Bai Yang , Peng Ying , Ba Qianqian , Huang Shuai , Zhong Xing , Sun Haijiang , Zhang Lei , Feng Fuyu
Published: October 2024

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.
A High-Resolution Remote Sensing Road Extraction Method Based on the Coupling of Global Spatial Features and Fourier Domain Features
Authors: Yang Hui , Zhou Caili , Xing Xiaoyu , Wu Yongchuang , Wu Yanlan
Published: October 2024

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of roads effectively from a single spatial domain perspective. To address the aforementioned issues, this article proposes a road extraction method that couples global spatial learning with Fourier frequency domain learning. This method first utilizes a transformer to capture global road features and then applies Fourier transform to separate and enhance high-frequency and low-frequency information. Finally, it integrates spatial and frequency domain features to express road characteristics comprehensively and overcome the effects of intra-class differences and occlusions. Experimental results on HF, MS, and DeepGlobe road datasets show that our method can more comprehensively express road features compared with other deep learning models (e.g., Unet, D-Linknet, DeepLab-v3, DCSwin, SGCN) and extract road boundaries more accurately and coherently. The IOU accuracy of the extracted results also achieved 72.54%, 55.35%, and 71.87%.
Quantifying Annual Glacier Mass Change and Its Influence on the Runoff of the Tuotuo River
Authors: Liu Lin , Zhang Xueyu , Zhang Zhimin
Published: October 2024

Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this study, we employed an albedo-based model with a daily land surface albedo dataset to derive the annual glacier mass balance over the Tuotuo River Basin (TRB). During 2000–2022, an annual glacier mass balance range of −0.89 ± 0.08 to 0.11 ± 0.11 m w.e. was estimated. By comparing with river runoff records from the hydrometric station, the contribution of glacier mass change to river runoff was calculated to be 0.00–31.14% for the studied period, with a mean value of 9.97%. Moreover, we found that the mean contribution in drought years is 20.07%, which is approximately five times that in wet years (4.30%) and twice that in average years (9.49%). Therefore, our results verify that mountain glaciers act as a significant buffer against drought in the TRB, at least during the 2000–2022 period.
Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model
Authors: Hu Xiaolong , Shi Liangsheng , Lin Lin , Li Shenji , Deng Xianzhi , Li Jinmin , Bian Jiang , Su Chenye , Du Shuai , Wang Tinghan , Wang Yujie , Zhang Zhitao
Published: October 2024

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.
A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution
Authors: Guo Zhida , Cheng Lei , Chang Liwei , Li Shiqiong , Li Yuzhu
Published: October 2024

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an MAE of 2.03 °C and an RMSE of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes.
Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects
Authors: Bussolino Massimiliano , Civardi Gaia Letizia , Quirino Matteo , Bechini Michele , Lavagna Michèle
Published: October 2024

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.
Ozone Detector Based on Ultraviolet Observations on the Martian Surface
Authors: Viúdez-Moreiras Daniel , Saiz-Lopez Alfonso , Smith Michael D. , Apestigue Víctor , Arruego Ignacio , García Elisa , Jiménez Juan J. , Rodriguez-Manfredi José A. , Toledo Daniel , Wolff Mike , Zorzano María-Paz
Published: October 2024

Ozone plays a key role in both atmospheric chemistry and UV absorption in planetary atmospheres. On Mars, upper-tropospheric ozone has been widely characterized by space-based instruments. However, surface ozone remains poorly characterized, hindered by the limited sensitivity of orbiters to the lowest scale height of the atmosphere and challenges in delivering payloads to the surface of Mars, which have prevented, to date, the measurement of ozone from the surface of Mars. Systematic measurements from the Martian surface could advance our knowledge of the atmospheric chemistry and habitability potential of this planet. NASA’s Mars 2020 mission includes the first ozone detector deployed on the Martian surface, which is based on discrete photometric observations in the ultraviolet band, a simple technology that could obtain the first insights into total ozone abundance in preparation for more sophisticated measurement techniques. This paper describes the Mars 2020 ozone detector and its retrieval algorithm, including its performance under different sources of uncertainty and the potential application of the retrieval algorithm on other missions, such as NASA’s Mars Science Laboratory. Pre-landing simulations using the UVISMART radiative transfer model suggest that the retrieval is robust and that it can deal with common issues affecting surface operations in Martian missions, although the expected low ozone abundance and instrument uncertainties could challenge its characterization in tropical latitudes of the planet. Other space missions will potentially include sensors of similar technology.
Enhancing Algal Bloom Level Monitoring with CYGNSS and Sentinel-3 Data
Authors: Jia Yan , Xiao Zhiyu , Yang Liwen , Liu Quan , Jin Shuanggen , Lv Yan , Yan Qingyun
Published: October 2024

Algal blooms, resulting from the overgrowth of algal plankton in water bodies, pose significant environmental problems and necessitate effective remote sensing methods for monitoring. In recent years, Global Navigation Satellite System–Reflectometry (GNSS-R) has rapidly advanced and made notable contributions to many surface observation fields, providing new means for identifying algal blooms. Additionally, meteorological parameters such as temperature and wind speed, key factors in the occurrence of algal blooms, can aid in their identification. This paper utilized Cyclone GNSS (CYGNSS) data, Sentinel-3 OLCI data, and ECMWF Re-Analysis-5 meteorological data to retrieve Chlorophyll-a values. Machine learning algorithms were then employed to classify algal blooms for early warning based on Chlorophyll-a concentration. Experiments and validations were conducted from May 2023 to September 2023 in the Hongze Lake region of China. The results indicate that classification and early warning of algal blooms based on CYGNSS data produced reliable results. The ability of CYGNSS data to accurately reflect the severity of algal blooms opens new avenues for environmental monitoring and management.
Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3–14
Authors: Lund Rasmussen Charlotte , Hendry Danica , Thomas George , Beynon Amber , Stearne Sarah Michelle , Zabatiero Juliana , Davey Paul , Roslyng Larsen Jon , Rohl Andrew Lloyd , Straker Leon , Campbell Amity
Published: October 2024

Background: ActiMotus, a thigh-accelerometer-based software used for the classification of postures and movements (PaMs), has shown high accuracy among adults and school-aged children; however, its accuracy among younger children and potential differences between sexes are unknown. This study aimed to evaluate the accuracy of ActiMotus to measure PaMs among children between 3 and 14 years and to assess if this was influenced by the sex or age of children. Method: Forty-eight children attended a structured ~1-hour data collection session at a laboratory. Thigh acceleration was measured using a SENS accelerometer, which was classified into nine PaMs using the ActiMotus software. Human-coded video recordings of the session provided the ground truth. Results: Based on both F1 scores and balanced accuracy, the highest levels of accuracy were found for lying, sitting, and standing (63.2–88.2%). For walking and running, accuracy measures ranged from 48.0 to 85.8%. The lowest accuracy was observed for classifying stair climbing. We found a higher accuracy for stair climbing among girls compared to boys and for older compared to younger age groups for walking, running, and stair climbing. Conclusions: ActiMotus could accurately detect lying, sitting, and standing among children. The software could be improved for classifying walking, running, and stair climbing, particularly among younger children.
Electromyography Parameters to Discriminate Hand Osteoarthritis and Infer Their Functional Impact
Authors: Gracia-Ibáñez Verónica , Mohseni Mahdi , Kedgley Angela E. , Jarque-Bou Néstor J. , Granell Pablo , Vergara Margarita , Sancho-Bru Joaquín L.
Published: October 2024

Surface-electromyography (sEMG) allows investigators to detect differences in muscle activation due to hand pathologies. However, its use as a functional indicator and the challenges related to the required normalization have not been fully addressed. This study aimed to use forearm muscle sEMG signals to distinguish between healthy individuals and patients with hand osteoarthritis (HOA). sEMG data were collected from seven sensors on the forearms of twenty-one healthy women and twenty women with HOA during the Sollerman test. Amplitude-based parameters (median and range) were normalized using three methods: maximum signals during Sollerman tasks (MAX), during maximum voluntary contraction tasks (MVC), and during maximum effort grasping (GRASP). Waveform parameters (new-zero-crossing and enhanced-wavelength) were also considered. MVC and GRASP resulted in higher values in patients. Discriminant analysis showed the worst success rates in predicting HOA for amplitude-based parameters, requiring extra tasks for normalization (MVC or GRASP), while when using both amplitude (MAX) and waveform parameters and only Sollerman tasks, the success rate reached 90.2% Results show the importance of normalization methods, highlight the potential of waveform parameters as reliable pathology indicators, and suggest sEMG as a diagnostic tool. Additionally, the comparison of sEMG parameters allows the functional impact of suffering from HOA to be inferred.
An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving Virtual Interpolation Array
Authors: Wang Zhaobo , Zhang Jun , Guo Hui , Miao Yingjie
Published: October 2024

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.
Analysis of the Validity and Reliability of the Photo Finish® Smartphone App to Measure Sprint Time
Authors: Marco-Contreras Luis Alberto , Bataller-Cervero Ana Vanessa , Gutiérrez Héctor , Sánchez-Sabaté Jorge , Berzosa César
Published: October 2024

In athletic training and research, the evaluation of sprint speed is widely used, and its accurate measurement is especially demanding. High-cost photocells are the gold-standard system for sprint time assessment, although low-cost smartphone applications can be a suitable option. This study assesses the validity and reliability of an application to measure sprint time compared to photocells. Five physically active subjects completed six sprints of 10 m and 20 m at maximal speed and a 5 m go and return sprint to evaluate the validity of the Photo Finish® app (Version 2.30). To assess reliability, six trials of 5 m go and return sprints were measured by two smartphones. The validity results showed a mean bias of 0.012 s (95% CL: 0.000, 0.024) between the application and the photocells for the 10 m sprint, 0.007 s (95% CL: −0.007, 0.022) for the 20 m sprint and 0.005 s (95% CL: −0.005, 0.017) for the 5 m go and return test. The results also found R2 between both systems (R2= 0.9863, 0.990 and 0.958) for each distance (10 m, 20 m and 5 m go and return, respectively). As for reliability, the application showed outstanding consistency between two smartphones operating simultaneously (ICC 0.999; R2: 0.999). This study shows that the Photo Finish® app is an accurate and reliable tool to measure sprint time with an error of 0.09 s.