bluuebunny/crossref_metadata_2025 huggingface dataset:
Research Progress in the Analysis of Chemical Forms of Mercury in Traditional Chinese Medicine
Authors: Peng Congnan , Kang Liping , Yuan Xin , Qiao Jiaqi , Fan Yilin , Yao Li , Qi Kailin , Sun Yaxuan , Dai Xueling , Zhang Yuan , Huo Qing
Published: September 2023

A comprehensive evaluation of the chemical forms of heavy metals, rather than their total amount of elements, is the basis of the scientific and objective evaluation of safety in heavy metals. As mercury, the most toxic heavy metal in the environment (such as in water, soil, and air), accumulates in medicinal plants, chemical forms of mercury in medicinal plants and their preparation need to be clearly understood. This study aims to summarize the chemical status of mercury in bulk Chinese medicinal herbs and traditional Chinese medicine preparations and to discuss research methods for their analysis. Further, widespread high-performance liquid chromatography–inductively coupled plasma mass spectrometry, advanced synchrotron X-ray absorption fine structure spectroscopy, and X-ray fluorescence were used for the in situ analysis of the chemical state of mercury. The results were then analyzed to improve the analytical methods and evaluation standards for the chemical state of mercury in Chinese herbal medicines.
The Relationship between Rehydration Time and Tenderness at Different Rehydrating Water Temperatures in Freeze-Dried Pork Loin
Authors: Répás Zoltán , Prokisch József , Győri Zoltán , Sipos Péter
Published: September 2023

Meat tenderness is one of the key factors determining consumer satisfaction and food quality. Freeze drying is a critical aspect of food preservation. Foods produced this way can be stored for an exceedingly long time, but they are characterized by being very hard in texture and requiring rehydration before use. This study presents an investigation focusing on finding preparation parameters of freeze-dried meat that would enable rehydration when left in an environment below 50 °C for 10 min, qualities that are ideal for hikers or soldiers. The meat samples were cooked at both 100 °C and 120 °C, cooled, and freeze-dried. Optical analysis, conducted using a microscope, was conducted to assess tissue damage. A compact and uniform structure was observed in the samples cooked at 120 °C, whereas fractures and gaps were detected on the surfaces of those cooked at 100 °C. Various rehydration durations and temperatures were explored to attain the samples’ original tenderness, as measured using the Warner–Bratzler method, and identify the optimal rehydration conditions. Based on this investigation, it was determined that with proper preparation, the original tenderness can be restored at temperatures below 50 °C within a 10 min rehydration period. It was determined that the recommended rehydration temperature is 40 °C for a 10 min duration.
Spiritual but Not Religious? French Muslim Discourses on Spirituality
Authors: Belhaj Abdessamad
Published: September 2023

This article explores the possibility of a spiritual Islam in the West as a viable alternative to traditionalist and political Islam. It looks at the capabilities and limitations of two French Muslim voices, Abdennour Bidar and Éric Geoffroy, who are the most vocal Muslim intellectuals in favor of spiritual Islam in France. A careful examination of their writings reveals that post-modernism, French secularism, and religious freedom all support the spread of spiritual Islam. However, because of its elitism, overemphasis on individualism, and lack of formal religious institutions for knowledge and practice, spiritual Islam struggles. Overall, spiritual Islam flourishes in a secular society, but it is too intellectual to truly oppose political and conservative Islam.
Religiosity Moderates Goal Courage and Self-Worth in Collegiate Christian Athletes
Authors: Bounds Elizabeth M. , Nelson Jenae M. , Melton Karen K. , Glanzer Perry L. , Schnitker Sarah A.
Published: September 2023

Research shows that the effects of athletics on virtue development are mixed. Religion provides people with a meaning-making system, community, and practices that can promote the cultivation of virtues and possibly enhance the impact of athletics on virtue development. Yet, little empirical research has assessed moral outcomes when religion and sport interact. The present study examines intrinsic religiosity as a moderator of the effect of athletic involvement on virtue and self-worth outcomes in collegiate student-athletes. Participants (N = 1930) were religious college students in the United States, 22% (n = 415) competing in an intercollegiate varsity sport. Moderated regression analyses revealed, contrary to predictions, that religiosity did not significantly moderate trait courage, trait patience, or goal patience. Consistent with predictions, religiosity significantly moderated goal courage, approval self-worth (i.e., the extent to which people base their worth on approval from others), and moral self-worth (i.e., the extent to which people base their worth on their own virtuousness). Whereas religiosity was associated with higher goal courage for non-athletes, the association was stronger for athletes. Religiosity was not associated with levels of approval self-worth for non-athletes, but the more religious athletes were, the less important other people’s approval was for their sense of self. Whereas religiosity was associated with higher moral self-worth contingency for athletes, the association was stronger for non-athletes. Applications for practitioners and future directions for researchers are discussed.
Ecumenism as Hope: The Prophetic Role and the Eschatological Function of the Church
Authors: Sonea Cristian-Sebastian
Published: September 2023

The article is structured around one of the questions that has been at the forefront of controversies in the orthodox church in recent years: why is ecumenical dialogue necessary and why “must” we engage in it? The answer can be simple: because it is the will of the Saviour Jesus Christ, who prays to the Father “that they may all be one” (John 17:21). Nevertheless, some Christians reject or even condemn ecumenical dialogue. To explain the presence of the orthodox church in the ecumenical movement, I proposed the concept of ecumenism as hope. In this article, I also offered a synthesis of the relations between the orthodox church and the other Christian communities from the perspective of teleology. I then tried to show that the re-evaluation of eschatology could oppose the “mechanical paradigm” of the historical–critical method, because eschatology contains the element of “hope”. I concluded that “hope” in the eschatological sense could be understood as the eschatological inauguration of the unity of faith in the church.
Religious Commitment and Intent to Die by Suicide during the Pandemic
Authors: Mason Karen , Moore Melinda , Palmer Jerry , Yang Zihan
Published: September 2023

Suicide is the second leading cause of death in 10–34-year-olds in the U.S. It is vital to identify protective factors that promote resilience in a suicide crisis. Background: This study explored the contributions of religious commitment (RC) and religious service attendance to decreased suicide intent in 18–34-year-olds. Possible moderators were investigated, including church-based social support, pandemic-related faith struggles (PRFS), and moral objections to suicide. Methods: Participants completed an online survey reporting on RC, suicide intent, church-based social support, religious service attendance, PRFS, and moral objections to suicide. Results: In the convenience sample of 451 18–34-year-olds (M = 24.97; 47.23% female), religious participants reported significantly less suicide intent than non-religious participants. RC and moral objections to suicide were more strongly negatively correlated with suicide intent than religious service attendance, but religious service attendance was associated with lower suicide intent in a regression model. Almost four times more religious young adult participants reported PRFS than not, and PRFS was found to moderate the benefits of social support received in their faith communities. Conclusions: It is suggested that professional caregivers use religious service attendance as a straightforward way to assess a possible protective factor for suicidal religious young adults. Professional caregivers may also assess for moral objections to suicide, which may provide simple decision rules in a suicide crisis. The large number of religious young adults reporting PRFS in this study suggests the need for professional caregivers to assess for spiritual struggles, which may confer suicide risk. Because of the interplay of spiritual risks and protections, mental health providers who are unsure of how to address these in therapy may need to collaborate with and make referrals to faith leaders to increase protections and reduce risks in suicidal religious young adults.
Cyber Insurance Premium Setting for Multi-Site Companies under Risk Correlation
Authors: Mastroeni Loretta , Mazzoccoli Alessandro , Naldi Maurizio
Published: September 2023

Correlation in cyber risk represents an additional source of concern for utility and industrial infrastructures, where risks may be introduced by connected systems. A major means of reducing risk is to transfer it through insurance. In this paper, we consider a company which has peripheral branches in addition to its headquarters, where risk correlation is present between all of its sites and insurance is adopted to hedge against economic losses. We employ the expected utility principle (which leads to the well-known mean variance premium formula) to derive the insurance premium under risk correlation under several risk scenarios. Under a first-order approximation, a quasi-linear relationship between the premium and the two major risk factors (the number of branches and the risk correlation coefficient) is determined.
A Framework for the Construction of a Heritage Corridor System: A Case Study of the Shu Road in China
Authors: Yue Fengting , Li Xiaoqin , Huang Qian , Li Dan
Published: September 2023

Heritage corridors are methods to effectively protect and utilize linear cultural heritage based on the concept of regional conservation. The construction of a heritage corridor system is extremely important to preserve the natural environment of the heritage corridor area as well as the history and culture alongside. The majority of the research on the construction of heritage corridors heretofore focused on the generation of corridors, whereas studies on the classification of corridors are relatively limited, without a complete system for the construction of heritage corridors. Therefore, this paper aimed to (1) establish a comprehensive system for the construction of heritage corridors, (2) provide new ideas for the construction of heritage corridors, and (3) guide the scientific development of heritage corridors combining conservation and tourism. In the first place, the minimum cumulative resistance (MCR) model was applied to analyze the spatial structure of the study area and explore site selection of the heritage corridors; secondly, spatial syntax was used to measure the heritage corridors and determine the level of the heritage corridors; last but not least, the kernel density analysis was used to classify the types of heritage corridors. The present study shows that the heritage corridor system is built in a scientific approach, covering all aspects including construction, protection, and development.
An Evaluation of Simulated Cloud Microphysical Characteristics of Three Mei-Yu Rainfall Systems in Taiwan by a Cloud-Resolving Model Using Dual-Polarimetric Radar Observations
Authors: Wang Chung-Chieh , Chen Yu-Han , Lan Yu-Yao , Chang Wei-Yu
Published: September 2023

This study selected three heavy-rainfall events of different types in Taiwan’s Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model’s capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model’s performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable.
Orbital Uncertainty Propagation Based on Adaptive Gaussian Mixture Model under Generalized Equinoctial Orbital Elements
Authors: Xie Hui , Xue Tianru , Xu Wenjun , Liu Gaorui , Sun Haibin , Sun Shengli
Published: September 2023

The number of resident space objects (RSOs) has been steadily increasing over time, posing significant risks to the safe operation of on-orbit assets. The accurate prediction of potential collision events and implementation of effective and nonredundant avoidance maneuvers require the precise estimation of the orbit positions of objects of interest and propagation of their associated uncertainties. Previous research mainly focuses on striking a balance between accurate propagation and efficient computation. A recently proposed approach that integrates uncertainty propagation with different coordinate representations has the potential to achieve such a balance. This paper proposes combining the generalized equinoctial orbital elements (GEqOE) representation with an adaptive Gaussian mixture model (GMM) for uncertainty propagation. Specifically, we implement a reformulation for the orbital dynamics so that the underlying state and the moment feature of the GMM are propagated under the GEqOE coordinates. Starting from an initial Gaussian probability distribution function (PDF), the algorithm iteratively propagates the uncertainty distribution using a detection-splitting module. A differential entropy-based nonlinear detector and a splitting library are utilized to adjust the number of GMM components dynamically. Component splitting is triggered when a predefined threshold of differential entropy is violated, generating several GMM components. The final probability density function (PDF) is obtained by a weighted summation of the component distributions at the target time. Benefiting from the nonlinearity reduction caused by the GEqOE representation, the number of triggered events largely decreases, causing the necessary number of components to maintain uncertainty realism also to decrease, which enables the proposed approach to achieve good performance with much more efficiency. As demonstrated by the results of propagation in three scenarios with different degrees of complexity, compared with the Cartesian-based approach, the proposed approach achieves comparable accuracy to the Monte Carlo method while largely reducing the number of components generated during propagation. Our results confirm that a judicious choice of coordinate representation can significantly improve the performance of uncertainty propagation methods in terms of accuracy and computational efficiency.
Using Texture Statistics to Identify and Map Different Dune Types within the Rub’ al Khali
Authors: Almutlaq Fahad , Mulligan Kevin
Published: September 2023

Understanding the movement, direction, and shape of sand dunes can contribute to reducing their impact on infrastructure and the environment. The Rub’ al Khali desert has a distribution of dune types. This study aims to identify and map the different types of dunes within the Rub’ al Khali using a texture analysis method based on a digital elevation model (DEM). Statistical texture analysis methods (variance, skewness, and kurtosis) show three different textures of sand dune shapes, according to the geography of the dunes, using data contained in global DEMs. The analysis presented in this study focused on the use of DEMs to investigate the varied dune morphology within the Rub’ al Khali. The GMTED2010 and EarthEnv_DEM90 digital elevation models were compared. Spatial variability in dune height, spatial variability in dune texture, and profile graphs were created to examine dune surfaces in cross-section. The results provided six different dune types within the sand sea: giant compound linear dunes, simple linear dunes, simple transverse dunes, compound crescentic dunes (megabarchans), huge star dunes, and many transitional forms that defy classification. The results showed that the compound linear dune and simple linear dune were the dominant dune types, covering 41.61% and 31.7% of the total study area, respectively. The maps of variance, using either 10 × 10 and 30 × 30 focal blocks, produced a fairly sharp distinction in dune texture. It is hoped that future research in aeolian geomorphology will greatly benefit from these results, which could easily be expanded with the use of more sophisticated pattern recognition software, which clearly shows the value of using such an approach.
Research on UT1-UTC and LOD Prediction Algorithm Based on Denoised EAM Dataset
Authors: Li Xishun , Wu Yuanwei , Yao Dang , Liu Jia , Nan Kai , Ma Langming , Cheng Xuan , Yang Xuhai , Zhang Shougang
Published: September 2023

The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy. In this study, we propose a hybrid method to reduce the noise of the EAM data and improve the accuracy of UT1-UTC and LOD predictions. We use the EOP data to denoise the EAM data, and use Kalman filtering to denoise the 1–6 days forecast of EAM. Then, we use the denoised EAM dataset to improve the UT1-UTC and LOD prediction. The denoised EAM dataset improved the prediction of UT1-UTC within 10 days by 20%. In addition, we found that by introducing two additional periodic (23.9 days and 91.3 days) components for the least-squares fitting, the accuracy of UT1-UTC and LOD prediction in the range of 30–80 days is significantly improved. In more than 430 UT1-UTC and LOD prediction experiments conducted during 2021–2022, the improvements in the 1–6 days forecast were significant. For the 6th day, 30th day, and 60th day, the MAE of UT1-UTC was 0.1592, 2.9169, and 6.7857 ms, respectively, corresponding to improvements of 31.35, 12.60, and 12.93%, respectively, when compared to predictions of Bulletin A. The MAE of LOD predictions on the 1st day, 6th day, 30th day, and 90th day was 0.0255, 0.0432, 0.1694, and 0.2505 ms, respectively, which improved by 26.09, 14.29, 6.36, and 3.76% when compared with our second EOPPCC method.
Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition
Authors: Song Qing , Zhao Rong , Fu Haiqiang , Zhu Jianjun , Li Yi
Published: September 2023

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques
Authors: Kang Seokchan , Lee Jeongwon , Lee Jiyeong
Published: September 2023

Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data’s position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process.
Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery
Authors: Papadopoulou Eleni , Mallinis Giorgos , Siachalou Sofia , Koutsias Nikos , Thanopoulos Athanasios C. , Tsaklidis Georgios
Published: September 2023

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.
UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation
Authors: Wang Qi , Chen Xiaokai , Meng Huayi , Miao Huiling , Jiang Shiyu , Chang Qingrui
Published: September 2023

Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values.
Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data
Authors: Schauer Henri , Schlaffer Stefan , Bueechi Emanuel , Dorigo Wouter
Published: September 2023

Salt pans are unique wetland ecosystems. In the Austrian Seewinkel region, salt pans are in an increasingly vulnerable state due to groundwater drainage and heightened climatic pressures. It is crucial to model how seasonal and long-term hydrological and climatological variations affect the salt pan dynamics in Seewinkel, yet a comprehensive understanding of the driving processes is lacking. The goal of this study is to develop random forest machine learning models driven by hydrological and meteorological data that allow us to predict in early spring (March) of each year the inundation state in the subsequent summer and fall. We utilize Earth observation data from Landsat 5 (L5), 8 (L8), and 9 (L9) to derive the time series of the inundation state for 34 salt pans for the period 1984–2022. Furthermore, we demonstrate that the groundwater level observed in March is the strongest predictor of the salt pan inundation state in summer and fall. Utilizing local groundwater data yields a Matthews correlation coefficient of 0.59. Models using globally available meteorological data, either instead of or in addition to groundwater data, provide comparable results. This allows the global transfer of the approach to comparable ecosystems where no in situ data are available.
Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles
Authors: Lu Zihao , Sun Hao , Ji Kefeng , Kuang Gangyao
Published: September 2023

As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference. Recently, deep ensembles with various defensive strategies against adversarial samples have drawn much attention due to the increased diversity and reduced variance for their members. Aimed at the recognition task of remote sensing images (RSIs), this paper proposes to use a reactive-proactive ensemble defense framework to solve the security problem. In reactive defense, we fuse scoring functions of several classical detection algorithms with the hidden features and average output confidences from sub-models as a second fusion. In terms of proactive defense, we attempt two strategies, including enhancing the robustness of each sub-model and limiting the transferability among sub-models. In practical applications, the real-time RSIs are first input to the reactive defense part, which can detect and reject the adversarial RSIs. The accepted ones are then passed to robust recognition with a proactive defense. We conduct extensive experiments on three benchmark RSI datasets (i.e., UCM, AID, and FGSC-23). The experimental results show that the deep ensemble method of reactive and proactive defense performs very well in gradient-based attacks. The analysis of the applicable attack scenarios for each proactive ensemble defense is also helpful for this field. We also perform a case study with the whole framework in the black-box scenario, and the highest detection rate reaches 93.25%. Most of the adversarial RSIs can be rejected in advance or correctly recognized by the enhanced deep ensemble. This article is the first one to combine reactive and proactive defenses with a deep ensemble against adversarial attacks in the context of RSI recognition for DNN-based UAVs.
Parametric Test of the Sentinel 1A Persistent Scatterer- and Small Baseline Subset-Interferogram Synthetic Aperture Radar Processing Using the Stanford Method for Persistent Scatterers for Practical Landslide Monitoring
Authors: Bahti Farid Nur , Chung Chih-Chung , Lin Chun-Chen
Published: September 2023

The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring.
Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands
Authors: Mollenhauer Hannes , Borg Erik , Pflug Bringfried , Fichtelmann Bernd , Dahms Thorsten , Lorenz Sebastian , Mollenhauer Olaf , Lausch Angela , Bumberger Jan , Dietrich Peter
Published: September 2023

Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.
High-Precision GPU-Accelerated Simulation Algorithm for Targets under Non-Uniform Cluttered Backgrounds
Authors: Zhang Yongqiang , Zhou Jianxiong , Song Zhiyong , Zhou Kaixin
Published: September 2023

This article presents a high-precision airborne video synthetic aperture radar (SAR) raw echo simulation method aimed at addressing the issue of simulation accuracy in video SAR image generation. The proposed method employs separate techniques for simulating targets and ground clutter, utilizing pre-existing SAR images for clutter simulation and employing the shooting and bouncing rays (SBR) approach to generate target echoes. Additionally, the method accounts for target-generated shadows to enhance the realism of the simulation results. The fast simulation algorithm is implemented using the C++ programming language and the Accelerated Massive Parallelism (AMP) framework, providing a fusion technique for integrating clutter and target simulations. By combining the two types of simulated data to form the final SAR image, the method achieves efficient and accurate simulation technology. Experimental results demonstrate that this method not only improves computational speed but also ensures the accuracy and stability of the simulation outcomes. This research holds significant implications for the development of algorithms pertaining to video SAR target detection and tracking, providing robust support for practical applications.
Study of the Interseismic Deformation and Locking Depth along the Xidatan–Dongdatan Segment of the East Kunlun Fault Zone, Northeast Qinghai–Tibet Plateau, Based on Sentinel-1 Interferometry
Authors: Kang Shuai , Ji Lingyun , Zhu Liangyu , Liu Chuanjin , Zhang Wenting , Li Ning , Xu Jing , Jiang Fengyun
Published: September 2023

The East Kunlun fault zone (EKFZ), located northeast of the Qinghai–Tibet Plateau, has experienced several strong earthquakes of magnitude seven or above since 1900. It is one of the most active fault systems and is characterized by left-lateral strike-slip. However, the Xidatan–Dongdatan segment (XDS) of the East Kunlun fault zone (EKFZ) has had no earthquakes for many years, and the Kunlun Mountains MS 8.1 earthquake has a stress loading effect on this segment, so it is widely regarded as a high-risk earthquake gap. To this end, we collected the Sentinel-1 data of the XDS of the EKFZ from July 2014 to July 2019 and obtained the high-precision interseismic deformation field by the Interferometric Synthetic Aperture Radar (InSAR) technique to obtain the slip rate and locking depth of the XDS of the EKFZ, and the seismic potential of the segment was analyzed. The results are as follows: (1) The LOS deformation field of the XDS of the EKFZ was obtained using Sentinel-1 data of ascending and descending orbits, which indicated that the XDS of the EKFZ is dominated by horizontal motion. Combined with the interference results, it is shown that the strike-slip rate dominates the deformation information of the XDS of the EKFZ. The deep strike-slip rate of the fault is about 6 mm/yr, the deep dip-slip rate is about 2 mm/yr, and the slip-deficit rate on the fault surface is about 6 mm/yr; (2) Combined with the spiral dislocation theory model, the slip rate of the XDS to Xiugou Basin of the EKFZ has a gradually increasing trend, with an average slip rate of 9.6 ± 2.3 mm/yr and a locking depth of 29 ± 5 m; (3) The stress accumulation is about 483 ± 92 years in the XDS of the EKFZ, indicating that the cumulative elastic strain energy of the XDS can produce an MW 7.29 ± 0.1 earthquake in the future.
A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
Authors: Cui Zhuofan , Zhou Pengwei , Wang Xiaolong , Zhang Zilun , Li Yingxuan , Li Hongbo , Zhang Yu
Published: September 2023

Geo-localization has been widely applied as an important technique to get the longitude and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible interference and blocking of GPS signals, the method based on image retrieval, which is less likely to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives. In this paper, an image transformation technique is used to extract cross-view geo-localization information from UAVs and satellites. A single-stage training method in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples. The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes a better convergence. Moreover, a data augmentation method for satellite images is proposed to overcome the disproportionate numbers of image samples. On the benchmark University-1652, the proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1) and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.
RETRACTED: Forecasting the Landslide Blocking River Process and Cascading Dam Breach Flood Propagation by an Integrated Numerical Approach: A Reservoir Area Case Study
Authors: Yan Jianhua , Xing Xiansen , Li Xiaoshuang , Zhu Chun , Han Xudong , Zhao Yong , Chen Jianping
Published: September 2023

This paper aims to introduce a numerical technique for forecasting the hazard caused by the disaster chain of landslide blocking river-dam breach floods through an integration of the distinct element method (DEM) and a well-balanced finite volume type shallow water model (SFLOW). A toppling slope in a reservoir area, the southeastern Tibetan Plateau, was chosen for the study. Creep has been observed in the potential instability area, and a possible sliding surface was identified based on the data collected from adits and boreholes. Catastrophic rock avalanches may be triggered after reservoir impoundment, and the associated landslide disaster chain needed to be predicted. First, the landslide blocking river process was modeled by the DEM using the three-dimensional particle flow code (PFC 3D). The landslide duration, runout distance, and kinematic characteristics were obtained. In addition, the landslide dam and barrier lake were constructed. Then, the cascading dam breach flood propagation was simulated using the self-developed SFLOW. The flow velocity, inundation depth, and area were obtained. The hazard maps derived from the combined numerical technique provided a quantitative reference for risk mitigation. The influences of two involved parameters on the final hazard-affected area are discussed herein. It is expected that the presented model will be applied in more prediction cases.
Assessing the Vegetation Dynamics and Its Influencing Factors in Central Asia from 2001 to 2020
Authors: Gao Chao , Ren Xiaoli , Fan Lianlian , He Honglin , Zhang Li , Zhang Xinyu , Li Yun , Zeng Na , Chen Xiuzhi
Published: September 2023

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.
A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health
Authors: Abrahams Mishkah , Sibanda Mbulisi , Dube Timothy , Chimonyo Vimbayi G. P. , Mabhaudhi Tafadzwanashe
Published: September 2023

Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
Deployment of Remote Sensing Technologies for Effective Traffic Monitoring
Authors: Chen Tingting , Qi Jingwen , Xu Min , Zhang Liye , Guo Yu , Wang Shuaian
Published: September 2023

A rising volume of vessel traffic increases navigation density, which leads to an increasing risk of vessel collisions in navigation channels. Navigation safety issues have been widely studied with the aim of reducing such collisions. Intelligent navigation channels, which involve deploying remote-sensing radars on buoys, are an effective method of tackling vessel collisions. This paper investigates the problem of radar deployment in navigation channels, aiming to expand the radar coverage area and effectively detect vessel locations. A mixed-integer linear programming model is formulated to determine the optimal deployment of radars in navigation channels under a given budget, where radars with different coverage radii and different types of buoys are introduced. Then, sensitivity analyses involving the impacts of budgets, the coverage radii of the radars, the distance between adjacent discrete locations, and the distribution of the existing buoys on the radar deployment plan are conducted. The computational results indicate that the coverage ratio of the navigation channel can be improved by reasonably deploying the different types of radars on the existing and new buoys under a given budget.