bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies
Authors: Liu Jianli , Xiao Jincheng , Ren Yafeng , Liu Fei , Yue Huanyin , Ye Huping , Li Yingcheng
Published: August 2024

The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning.
Analysis of the Effect of Tilted Corner Cube Reflector Arrays on Lunar Laser Ranging
Authors: Cao Jin , Tang Rufeng , Huang Kai , Li Zhulian , Yang Yongzhang , Huang Kai , Li Jintao , Li Yuqiang
Published: August 2024

This paper primarily investigates the effect of the tilt of corner cube reflector (CCR) arrays on lunar laser ranging (LLR). A mathematical model was established to study the random errors caused by the tilt of the CCR arrays. The study found that, ideally, when the laser ranging pulse width is 10 picoseconds or less, it is possible to distinguish from which specific corner cubes within the CCR array each peak in the echo signal originates. Consequently, partial data from the echo can be extracted for signal processing, significantly reducing random errors and improving the single-shot precision of LLR. The distance obtained by extracting part of the echo can be reduced to the center position of the array, thereby providing multiple higher-precision ranging results from each measurement. This not only improves the precision of LLR but also increases the data volume. A simulation experiment based on the 1.2 m laser ranging system at Yunnan Observatories was conducted. By extracting one peak for signal processing, the single-shot precision improved from 32.24 mm to 2.52 mm, validating the theoretical analysis results. Finally, an experimental laser ranging system based on a 53 cm binocular telescope system was established for ground experiments. The experimental results indicated that the echo signal could identify the tilt state of the CCR array. By extracting the peak returned by the central CCR for signal processing, the ranging precision was greatly improved. Through theoretical analyses, simulation experiments, and ground experiments, a solution to reduce the random errors caused by the tilt of the CCR array was provided. This offers an approach to enhance the single-shot precision of future LLR and provides a reference for upgrading ground-based equipment at future laser ranging stations.
Extraction of River Water Bodies Based on ICESat-2 Photon Classification
Authors: Ma Wenqiu , Liu Xiao , Zhao Xinglei
Published: August 2024

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.
A Contrastive Learning Based Multiview Scene Matching Method for UAV View Geo-Localization
Authors: He Qiyi , Xu Ao , Zhang Yifan , Ye Zhiwei , Zhou Wen , Xi Ruijie , Lin Qiao
Published: August 2024

Multi-view scene matching refers to the establishment of a mapping relationship between images captured from different perspectives, such as those taken by unmanned aerial vehicles (UAVs) and satellites. This technology is crucial for the geo-localization of UAV views. However, the geometric discrepancies between images from different perspectives, combined with the inherent computational constraints of UAVs, present significant challenges for matching UAV and satellite images. Additionally, the imbalance of positive and negative samples between drone and satellite images during model training can lead to instability. To address these challenges, this study proposes a novel and efficient cross-view geo-localization framework called MSM-Transformer. The framework employs the Dual Attention Vision Transformer (DaViT) as the core architecture for feature extraction, which significantly enhances the modeling capacity for global features and the contextual relevance of adjacent regions. The weight-sharing mechanism in MSM-Transformer effectively reduces model complexity, making it highly suitable for deployment on embedded devices such as UAVs and satellites. Furthermore, the framework introduces a contrastive learning-based Symmetric Decoupled Contrastive Learning (DCL) loss function, which effectively mitigates the issue of sample imbalance between satellite and UAV images. Experimental validation on the University-1652 dataset demonstrates that MSM-Transformer achieves outstanding performance, delivering optimal matching results with a minimal number of parameters.
Evaluation and Correction of GFS Water Vapor Products over United States Using GPS Data
Authors: Liu Hai-Lei , Zhou Xiao-Qing , Zhu Yu-Yang , Duan Min-Zheng , Chen Bing , Zhang Sheng-Lan
Published: August 2024

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV3h and PWV6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV3h product performed slightly better than PWV6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases.
How Can Seasonality Influence the Performance of Recent Microwave Satellite Soil Moisture Products?
Authors: Albano Raffaele , Lacava Teodosio , Mazzariello Arianna , Manfreda Salvatore , Adamowski Jan , Sole Aurelia
Published: August 2024

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network.
GVC-YOLO: A Lightweight Real-Time Detection Method for Cotton Aphid-Damaged Leaves Based on Edge Computing
Authors: Zhang Zhenyu , Yang Yunfan , Xu Xin , Liu Liangliang , Yue Jibo , Ding Ruifeng , Lu Yanhui , Liu Jie , Qiao Hongbo
Published: August 2024

Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing devices for real-time processing of cotton aphid-damaged leaves captured by field cameras holds significant practical research value for large-scale disease and pest control measures. The mainstream detection models are generally large in size, making it challenging to achieve real-time detection on edge computing devices with limited resources. In response to these challenges, we propose GVC-YOLO, a real-time detection method for cotton aphid-damaged leaves based on edge computing. Building upon YOLOv8n, lightweight GSConv and VoVGSCSP modules are employed to reconstruct the neck and backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. In the backbone network, we integrate the coordinate attention (CA) mechanism and the SimSPPF network to increase the model’s ability to extract features of cotton aphid-damaged leaves, balancing the accuracy loss of the model after becoming lightweight. The experimental results demonstrate that the size of the GVC-YOLO model is only 5.4 MB, a decrease of 14.3% compared with the baseline network, with a reduction of 16.7% in the number of parameters and 17.1% in floating-point operations (FLOPs). The mAP@0.5 and mAP@0.5:0.95 reach 97.9% and 90.3%, respectively. The GVC-YOLO model is optimized and accelerated by TensorRT and then deployed onto the embedded edge computing device Jetson Xavier NX for detecting cotton aphid damage video captured from the camera. Under FP16 quantization, the detection speed reaches 48 frames per second (FPS). In summary, the proposed GVC-YOLO model demonstrates good detection accuracy and speed, and its performance in detecting cotton aphid damage in edge computing scenarios meets practical application needs. This research provides a convenient and effective intelligent method for the large-scale detection and precise control of pests in cotton fields.
Closed-Form Method for Unified Far-Field and Near-Field Localization Based on TDOA and FDOA Measurements
Authors: Gong Weishuang , Song Xuan , Zhu Chunyu , Wang Qi , Li Yachao
Published: August 2024

When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB).
Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California
Authors: Horton Dustin , Johnson Joel T. , Baris Ismail , Jagdhuber Thomas , Bindlish Rajat , Park Jeonghwan , Al-Khaldi Mohammad M.
Published: August 2024

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.
Distributed Phased Multiple-Input Multiple-Output Radars for Early Warning: Observation Area Generation
Authors: Luo Dengsanlang , Wen Gongjian
Published: August 2024

This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems.
Photometric Characteristics of Lunar Soils: Results from Spectral Analysis of Chang’E-5 In Situ Data Using Legendre Phase Function
Authors: Wang Meizhu , Liu Dawei , Xu Rui , He Zhiping
Published: August 2024

China’s Chang’E-5 (CE-5) mission has successfully landed in the Northern Oceanus Procellarum of the Moon. Lunar mineralogical spectrometer (LMS), as one of the important payloads onboard CE-5 Lander–Ascender Combination, aims to study the physical and compositional properties of the landing area. This paper applies the Legendre phase function to correct the photometric effects on the LMS in situ spectra and reveal the photometric characteristic of the CE-5 landing area. LMS obtained the reflectance spectra in various geometric configurations by performing full-view scanning of the CE-5 landing area. By fitting these LMS spectral data, the parameters b=−0.29 and c=0.44 of the Legendre phase function were obtained. This indicates the strong forward scattering characteristic of the CE-5 landing area, which is similar to that of the Chang’E-4 (CE-4) landing area, and the side scattering is weaker than that of CE-4. In addition, we derived the FeO content of the landing area using the photometric-corrected LMS spectral data. Our results demonstrate that the estimated FeO content of the landing area is close to the laboratory measured data of the returned samples. The LMS in situ reflectance data will contribute to a better understanding of the physical and mineralogical properties of the CE-5 landing area.
Accuracy Assessment of Estimated River Water Surface Elevations from Landsat 8 and 9 Imagery among Twenty Water Indices
Authors: Pan Feifei
Published: August 2024

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu’s automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.
Comparative Analysis of Satellite-Based Precipitation Data across the CONUS and Hawaii: Identifying Optimal Satellite Performance
Authors: Bhattarai Saurav , Talchabhadel Rocky
Published: August 2024

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets’ performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies.
Advanced Semi-Automatic Approach for Identifying Damaged Surfaces in Cultural Heritage Sites: Integrating UAVs, Photogrammetry, and 3D Data Analysis
Authors: Caciora Tudor , Ilieș Alexandru , Herman Grigore Vasile , Berdenov Zharas , Safarov Bahodirhon , Bilalov Bahadur , Ilieș Dorina Camelia , Baias Ștefan , Hassan Thowayeb H.
Published: August 2024

The analysis and preservation of the cultural heritage sites are critical for maintaining their historical and architectural integrity, as they can be damaged by various factors, including climatic, geological, geomorphological, and human actions. Based on this, the present study proposes a semi-automatic and non-learning-based method for detecting degraded surfaces within cultural heritage sites by integrating UAV, photogrammetry, and 3D data analysis. A 20th-century fortification from Romania was chosen as the case study due to its physical characteristics and state of degradation, making it ideal for testing the methodology. Images were collected using UAV and terrestrial sensors and processed to create a detailed 3D point cloud of the site. The developed pipeline effectively identified degraded areas, including cracks and material loss, with high accuracy. The classification and segmentation algorithms, including K-means clustering, geometrical features, RANSAC, and FACETS, improved the detection of destructured areas. The combined use of these algorithms facilitated a detailed assessment of the structural condition. This integrated approach demonstrated that the algorithms have the potential to support each other in minimizing individual limitations and accurately identifying degraded surfaces. Even though some limitations were observed, such as the potential for the overestimation of false negatives and positives areas, the damaged surfaces were extracted with high precision. The methodology proved to be a practical and economical solution for cultural heritage monitoring and conservation, offering high accuracy and flexibility. One of the greatest advantages of the method is its ease of implementation, its execution speed, and the potential of using entirely open-source software. This approach can be easily adapted to various heritage sites, significantly contributing to their protection and valorization.
Monitoring the Nitrogen Nutrition Index Using Leaf-Based Hyperspectral Reflectance in Cut Chrysanthemums
Authors: Wu Yin , Lu Jingshan , Liu Huahao , Gou Tingyu , Chen Fadi , Fang Weimin , Chen Sumei , Zhao Shuang , Jiang Jiafu , Guan Zhiyong
Published: August 2024

Precise nitrogen supply is crucial for ensuring the quality of cut chrysanthemums (Chrysanthemum morifolium Ramat.). The nitrogen nutrition index (NNI) serves as an important indicator for diagnosing crop nitrogen (N) nutrition. Hyperspectral remote sensing (HRS) technology has been widely used in monitoring crop N status due to its rapid, accurate, and non-destructive capabilities. However, its application in estimating the NNI of cut chrysanthemums has received limited attention. Therefore, this study aimed to use HRS to accurately determine the cut chrysanthemum NNI, thereby providing valuable guidance for managing N fertilization. During several key growth stages, a hyperspectral spectroradiometer was used to capture hyperspectral reflectance data (350–2500 nm) from three leaf layers. Subsequently, cut chrysanthemum canopies were sampled for aboveground biomass (AGB) and plant nitrogen concentration (PNC). The collected AGB and PNC data were then utilized to fit the critical N (Nc) dilution curve of cut chrysanthemums using a Bayesian hierarchical model, enabling the calculation of the NNI. Finally, spectral indices and partial least squares regression (PLSR) were used to establish the NNI estimation model for cut chrysanthemums. The results showed that the Nc dilution curve of the cut chrysanthemums was Nc = 5.401 × AGB−0.468. The first leaf layer (L1) proved to be optimal for estimating cut chrysanthemum NNI. Additionally, a newly proposed two-band spectral index, DVI-L1 (R1105, R700), demonstrated moderate predictive capabilities for the NNI of cut chrysanthemums (R2 = 0.5309, RMSE = 0.3210). Compared with the spectral index-based NNI estimation model, PLSR-L1 showed the best performance in estimating the cut chrysanthemum NNI (R2 = 0.8177, RMSE = 0.2000). Our results highlight the rapid NNI prediction potential of HRS and its significance in facilitating precise N management in cut chrysanthemums.
Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft
Authors: Yi Jinhui , Ma Yuebo , Long Hongfeng , Zhu Zijian , Zhao Rujin
Published: August 2024

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor’s robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only.
Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring
Authors: Kong Fanhao , Zou Yang , Li Zhou , Deng Yulin
Published: August 2024

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions.
Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR–FDS Method
Authors: Wu Yudong , Cui Zhanhao , Yan Wang , Huang Haibo , Ding Weiping
Published: August 2024

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR–FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR–FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance.
A Dynamic Position Embedding-Based Model for Student Classroom Complete Meta-Action Recognition
Authors: Shou Zhaoyu , Yuan Xiaohu , Li Dongxu , Mo Jianwen , Zhang Huibing , Zhang Jingwei , Wu Ziyong
Published: August 2024

The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial–temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.
A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial–Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging
Authors: Tang Binghui , Wang Yuemin , Gong Ruqing , Zhou Fan
Published: August 2024

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial–temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.
Decoupled Cross-Modal Transformer for Referring Video Object Segmentation
Authors: Wu Ao , Wang Rong , Tan Quange , Song Zhenfeng
Published: August 2024

Referring video object segmentation (R-VOS) is a fundamental vision-language task which aims to segment the target referred by language expression in all video frames. Existing query-based R-VOS methods have conducted in-depth exploration of the interaction and alignment between visual and linguistic features but fail to transfer the information of the two modalities to the query vector with balanced intensities. Furthermore, most of the traditional approaches suffer from severe information loss in the process of multi-scale feature fusion, resulting in inaccurate segmentation. In this paper, we propose DCT, an end-to-end decoupled cross-modal transformer for referring video object segmentation, to better utilize multi-modal and multi-scale information. Specifically, we first design a Language-Guided Visual Enhancement Module (LGVE) to transmit discriminative linguistic information to visual features of all levels, performing an initial filtering of irrelevant background regions. Then, we propose a decoupled transformer decoder, using a set of object queries to gather entity-related information from both visual and linguistic features independently, mitigating the attention bias caused by feature size differences. Finally, the Cross-layer Feature Pyramid Network (CFPN) is introduced to preserve more visual details by establishing direct cross-layer communication. Extensive experiments have been carried out on A2D-Sentences, JHMDB-Sentences and Ref-Youtube-VOS. The results show that DCT achieves competitive segmentation accuracy compared with the state-of-the-art methods.
Isolated Hydatid Cyst: A Misleading Diagnosis
Authors: Chatar Achraf , Amoch Abdelaziz , Lakmichi Mohamed Amine , Dahami Zakaria , Sarf Ismail
Published: August 2024

Cystic hydatid disease is an endemic disease caused by the larval form of Echinococcus granulosis. It is especially evident in the liver, lungs, and kidney. Testicular hydatidosis is extremely rare. A case of a hydatid cyst of the testis is reported that was misdiagnosed clinically as a testicular hydrocele. Echinococcosis should be considered in the differential diagnosis of testicular masses especially in endemic countries. Treatment is mainly surgical and, with proper diagnosis and treatment, the prognosis is good.
The Neutrophil-to-Lymphocyte Ratio as a Biomarker in Metastatic Castrate-Sensitive Prostate Cancer Patients Treated with Abiraterone Acetate
Authors: Suartz Caio Vinícius , Roy Marie-Laurence , Toren Paul
Published: August 2024

Given its known prognostic role, we aimed to investigate the role of neutrophil–lymphocyte ratio (NLR) as a biomarker in metastatic castration-resistant prostate cancer (mCRPC) patients receiving ADT, either as monotherapy or in conjunction with abiraterone acetate (AA) and prednisone. This retrospective cohort study analyzed the LATITUDE study of men with high-risk mCSPC. Patients were assigned to receive either AA, prednisone, and androgen deprivation therapy (ADT) or placebo plus ADT. Using a previously established NLR threshold of 2.5, we evaluated if this could predict clinical response to abiraterone. At baseline, there were no significant differences in NLR values between the treatment groups. Of the known baseline prognostic factors, NLR was associated with albumin levels and Eastern Cooperative Oncology Group performance scores. Moreover, the number of bone metastases was higher in patients with NLR ≥ 2.5. On multivariable analysis, baseline NLR ≥ 2.5 did not predict overall survival, PSA progression-free, or metastasis-free survival. However, changes in PSA and NLR at six months indicated distinct survival patterns between the placebo and AA groups, suggesting the potential for their combined assessment as a prognostic tool. Baseline NLR was not an independent predictor factor for response to AA in the LATITUDE study, though NLR changes at 6 months may predict better survival beyond PSA values alone. Further research is required to better understand in which patients with advanced prostate cancer NLR changes may be a useful prognostic tool.
Intraoperative Online Telementoring—Redefining Surgical Education in the Philippines Amidst the Pandemic
Authors: Andutan Raul Carlo , Abalajon Mark Joseph , Arellano Oyayi Atienza , Nikolavsky Dmitriy , Venkatesan Krishnan , Blakely Stephen
Published: August 2024

The COVID-19 pandemic presented a major challenge to not only health care centers around the world but also to institutions with residency training programs [...]
Oral Dissolution Therapy of Uric Acid Stones: A Systematic Review
Authors: Mousavi Ava , Takele Rebecca , Limbrick Bree’ava , Thaker Karan N. , Scotland Kymora B.
Published: August 2024

Background/Objectives: Uric acid stones are the second most common type of kidney stones worldwide. Despite being cost-effective and widely available, pharmacological management for these stones is less utilized compared to surgical approaches. Therefore, we conducted a systematic review of the literature to assess the efficacy and shortcomings of oral dissolution therapy for uric acid stones. Methods: Embase, Scopus, PubMed, and Cochrane library databases were searched for English language studies on adults who were diagnosed with uric acid kidney stones and treated with oral dissolution therapy. Results: Twelve studies met our inclusion criteria. Potassium citrate was the primary agent used for dissolution in 10 of the studies and demonstrated a 50% or higher success rate for complete dissolution of uric acid stones. Treatment time for successful dissolution varied, with an average of 3 months minimum and an average of 2.78 years maximum. Overall, 11 of the studies recommended that oral dissolution therapy should be used for either treatment or prevention of uric acid stones. Conclusions: The available evidence demonstrates benefit of oral dissolution therapy for patients with uric acid stones. However, future rigorous studies are required to determine the optimal treatment regimen.
Focusing on the Trend in Africa. Comment on Black, P. Publishing Urologic Research from Low- and Middle-Income Countries. Soc. Int. Urol. J. 2024, 5, 225–226
Authors: Nedjim Saleh Abdelkerim , Djoua Yannick Joël , Mahamat Mahamat Ali , Kifle Anteneh Tadesse , Ziba Ouima Justin Dieudonné , Gebreselassie Kaleab Habtemichael , Biyouma Marcella D. C. , Khalid Abdullahi , Kimassoum Rimtebaye , Ouchemi Choua , Mohamed Dakir , Adil Debbagh , Ndoye Alain Khassim , Lezrek Mohamed , Aboutaieb Rachid
Published: August 2024

In a recently published Editorial, Black [...]
Continuous UV/H2O2 Process: A Sustainable Wastewater Treatment Approach for Enhancing the Biodegradability of Aqueous PVA
Authors: Parsa Zahra , Dhib Ramdhane , Mehrvar Mehrab
Published: August 2024

Implementing efficient and cost-effective wastewater treatment methods in wastewater treatment plants (WWTPs) is crucial for ensuring sustainable development in contemporary societies. This study explores the feasibility of a continuous UV/H2O2 tubular photoreactor as a pre-treatment to enhance the biodegradability of aqueous polyvinyl alcohol (PVA) solutions, known as a nonbiodegradable wastewater. Using a combination of a Box–Behnken design (BBD) and the response surface methodology (RSM), three main process variables, including the PVA feed concentration, the inlet H2O2 concentration, and the PVA feed flow rate, are studied within ranges of 500–1500 mg/L, 390–780 mg/L, and 50–150 mL/min, respectively. The results show significant interaction effects between the PVA feed and inlet H2O2 concentrations on the effluent BOD5/COD ratio. The optimal operating conditions are determined using the RSM, with a PVA feed concentration of 665 mg/L, an inlet H2O2 concentration of 390 mg/L, and a PVA feed flow rate of 59 mL/min. Operating at this point leads to an increase in the effluent BOD5/COD ratio from 0.15 to 0.53, which is validated experimentally with a ±5% error. Under these conditions, the effluent demonstrates an enhanced biodegradability, allowing for redirection to a subsequent biological post-treatment phase. This study demonstrates that using the UV/H2O2 process to enhance the biodegradability of an aqueous PVA solution is more economical than focusing on the complete removal of total organic carbon (TOC). Also, a comparison of these results with those of our previous study indicates that wastewater becomes more biodegradable by progressing the UV/H2O2 process due to the breakdown of polymer molecules, which reduces their molecular weight and makes them more consumable for biomass.
Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data
Authors: Tynchenko Yadviga , Kukartsev Vladislav , Tynchenko Vadim , Kukartseva Oksana , Panfilova Tatyana , Gladkov Alexey , Nguyen Van , Malashin Ivan
Published: August 2024

This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.