bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Non-Covalent Bonds in the Deformation Process of Coal: An Experimental Study on Bituminous Coal
Authors: Liu Hewu , Hou Chenliang
Published: September 2022

The chemical structures of tectonically deformed coal are significantly altered by stress. However, the stress response of non-covalent bonds in deformation experiments and the role of non-covalent bonds in the deformation process of coal have not been studied yet. In this work, coals before and after simulative deformation experiments were systematically investigated to uncover the coal’s deformation mechanism and the variation of non-covalent bonds. The results indicate that differential stress and temperature can promote ductile deformation while confine pressure hinders the deformation process. Differential stress and temperature in the ranges of 100–150 MPa and 100–200 °C, respectively, are key transition conditions from brittle to ductile deformation for the selected bituminous coal. Furthermore, hydrogen bonds and π–π bonds crosslinking coal molecular networks determine the mechanical properties of the coal. The simulative deformation experiments indicate that, with an increase in the coal’s deformation intensity, hydrogen bonds and π–π bonds are inclined to be disrupted in the relaxation stage, which enhances the motion ability of the liberated molecular structures and reduces the brittleness of the coal. In the rearrangement stage, tighter and more ordered configurations are formed, accompanied by the formation of π–π bonds. Coals in the deformation experiments are inclined to undergo ductile deformation once sufficient non-covalent bonds are cleaved in the relaxation stage.
Optimization Design and Internal Flow Analysis of Prefabricated Barrel in Centrifugal Prefabricated Pumping Station with Double Pumps
Authors: Xie Chuanliu , Zhang Tao , Yuan Zhenyang , Feng Andong , Wu Liming
Published: September 2022

In order to improve the hydraulic performance of the centrifugal prefabricated pumping station and improve its internal flow pattern, this paper optimizes the geometric model of the centrifugal prefabricated pumping station based on the orthogonal optimization method. Through the subjective analysis method and range analysis method, it is concluded that the primary and secondary order affecting the hydraulic performance of the prefabricated pumping station is: center distance Y, pump spacing S, inlet radius R, suspension height Z, inlet height H, and the optimal parameter combination is pump spacing 550 mm (5.5 d), The suspension height is 300 mm (3.0 d), the center distance is 100 mm (1.0 d), the inlet height is 700 mm (7.0 d), and the inlet radius is 75 mm (0.75 d). The orthogonal optimization results show that under the design condition (Qd = 33.93 m3/h), the efficiency of the centrifugal prefabricated pumping station is 64.69%, which is increased by 0.70%, compared with the initial scheme. The head is 8.76 m, which is increased by 0.10 m, compared with the initial scheme. After optimization, the recirculation vortex at the water inlet of the prefabricated pumping station is smaller than that before optimization, the flow velocity uniformity in the prefabricated barrel is improved, and the flow field is more stable. The research results of this paper can provide theoretical guidance and engineering reference value for the same type of prefabricated pumping stations.
Pinch Analysis for Heat Integration of Pulverized Coke Chemical Looping Gasification Coupled with Coke-Oven Gas to Methanol and Ammonia
Authors: Zhao Yaxian , Zhao Yingjie , Huang Yi , Wang Jiancheng , Bao Weiren , Chang Liping , Shi Lijuan , Yi Qun
Published: September 2022

Methanol and ammonia are important chemical materials in the chemical industry. During the production of methanol and ammonia, a large amount of waste heat is released. The waste heat can be used to save energy and reduce CO2 emissions. In this study, pinch analysis is used to design the heat exchanger network (HEN) of pulverized coke (PC) chemical looping gasification coupled with coke-oven gas (COG) to methanol and ammonia (PCCLHG-CGTMA). The heat integration process is accomplished in two ways, as mentioned below. (1) The HENs in each of the three heat exchange units are designed individually; (2) the HENs of the three heat exchange units are treated as a whole and designed simultaneously. Compared to the HEN designed individually, when the HENs are designed as a whole, a total of 112.12 MW of hot and cold utilities are saved. In the HENs designed as a whole, the reduction in operating cost is sufficient to offset the increase in capital cost; the total annual cost (TAC) is reduced by 10.9%. These results reveal that the HENs designed as a whole have more scope for energy saving, which can be a reference for new HEN design and modification to realize more heat recovery and lower investment.
Protective Effect of Ultrasound-Processed Amazonian Sapota-do-Solimões (Quararibea cordata) Juice on Artemia salina Nauplii
Authors: da Silva Rhonyele Maciel , Miguel Thaiz Batista Azevedo Rangel , de Castro Miguel Emilio , Campelo Pedro Henrique , Fernandes Fabiano A. N. , Rodrigues Sueli
Published: September 2022

Juice processing by non-thermal technology has been extensively studied, aiming at microbial inactivation and quality improvement. However, the knowledge about the possible toxic effects that those technologies can produce in foodstuffs due to the production of reactive oxygen species is still unknown. In this study, sapota-do-Solimões juice processed by ultrasound (2, 6, and 10 min) was evaluated by a toxicity test and protective effect through stress biomarkers (catalase, superoxide dismutase, and lipid peroxidation) using Artemia salina nauplii. The non-thermal processed juice was nontoxic to A. salina. However, the juice fibers imparted some damage to the animal’s body. The ultrasound-processed juice (2 and 6 min) decreased the A. salina mortality to 30% compared to the control assay with H2O2 where mortality was 80% after 48 h of exposure. However, after 72 h of exposure, the A. salina was entirely degraded by H2O2-induced toxicity. Furthermore, the catalase and superoxide dismutase presented the highest activity after A. salina was exposed to the unprocessed juice. Thus, sapota-do-Solimões juice processed by the ultrasound could promote a protective effect on A. salina, revealing this technology’s potential to enhance juice features without toxicity.
A Comprehensive Review Regarding Condensation of Low-GWP Refrigerants for Some Major Alternatives of R-134a
Authors: Kumar Abhishek , Chen Miao-Ru , Hung Kuo-Shu , Liu Chung-Che , Wang Chi-Chuan
Published: September 2022

In this review, the condensation HTCs (heat transfer coefficients) and pressure drop characteristics of some major low-global-warming-potential (GWP) refrigerants alternative to R-134a such as R-1234ze(E), R-1234ze(Z), R-1234yf, R-513A, and R-450A are reviewed. The thermofluids’ characteristics inside/outside a tube, minichannel, microfin tube, and plate heat exchanger are examined. In addition, several other refrigerants attributed to low GWP are also included in the present review. The experimental/numerical/simulation results’ analysis reveals that condensation HTCs and pressure drop characteristics depend on several parameters such as thermodynamics and transport properties of the working fluid, mass flux of the refrigerants, heat flux, saturation temperature, vapor quality, flow patterns, flow conditions, orientation of the condensing geometry, and condensation geometry (shape, size, and smooth/enhanced).
On the Moving of Neutral Point for Mn Subject to Submerged Arc Welding under Various Heat Inputs: Case Study into CaF2-SiO2-Na2O-MnO Agglomerated Fluxes
Authors: Zhang Dan , Shao Guoyou , Zhang Jin , Liu Zhongqiu
Published: September 2022

Neutral Point indicates the flux formula where no transfer of alloying element between the flux and weld metal occurs. For the submerged arc welding process, Neutral Point is an essential definition for flux design and specification since it helps to identify the flux microalloying ability. The scientific hypothesis that the Neutral Point is only a function of the flux formula is considered as the basis of the Mitra kinetic model. Within this framework, by performing submerged arc welding with CaF2-SiO2-Na2O-MnO agglomerated fluxes under various heat inputs, the moving of Neutral Point has been captured, indicating the scientific hypothesis proposed in Mitra kinetic model may be revised under high heat input welding. Additionally, although some studies have incorporated the consideration of the gas-slag-metal equilibrium, only the effective equilibrium temperature of 2000 or 2100 °C is utilized, which may be insufficient to constrain Mn content in the weld metal. In this study, we have incorporated all possible effective equilibrium temperatures that may be attained in the submerged arc welding process to simulate the transfer behavior of Mn. Then, a novel thermodynamic approach is proposed to detect the moving direction of Neutral Point for Mn from both slag-metal and gas-slag-metal equilibrium considerations, which may pave a vital way for the flux design and the setting of welding parameters. The factors responsible for the deviation between real and predicted data are discussed. The mechanism responsible for the moving of Neutral Point regarding the Mn element is evaluated from the perspective of both slag-metal and gas-slag-metal equilibrium considerations.
Research on Open Circuit Fault Modeling and Fault Tolerant Control Strategy of Five-Phase Induction Motor
Authors: Xu Hao , Zhao Jinghong , Yang Lv , Chen Hansi , Luo Xiangyu , Zhang Shuheng
Published: September 2022

Five-phase induction motors have the advantages of high reliability and strong fault-tolerant performance, so it’s open circuit fault model and fault-tolerant control strategy are widely studied. Based on the normal operation of the five-phase induction motor, the mathematical model of the five-phase induction motor under the conditions of single-phase open circuits, adjacent two-phase open circuits, and non-adjacent two-phase open circuits are established by using the reduced order decoupling transformation. Based on the principle of constant magnetic potential, the relationship between magnetic potential and each phase current is analyzed by using the symmetrical component method (MSC). The fault-tolerant control strategy of a five-phase induction motor with the above three open-circuit faults is designed. Through simulation and prototype experiments, the phase current and speed conversion under three open-circuit faults are analyzed. The results show that after the open-circuit fault of a five-phase motor, the residual phase current is no longer balanced, the motor speed is decreased, and the vibration is increased significantly. After fault-tolerant control, the residual phase current is balanced, the rated speed can be reached, and the vibration of the motor is reduced. Thus, the validity and correctness of the fault-tolerant control strategy for a five-phase induction motor are verified.
Biological Profiling of Essential Oils and Hydrolates of Ocimum basilicum var. Genovese and var. Minimum Originated from Serbia
Authors: Šovljanski Olja , Saveljić Anja , Aćimović Milica , Šeregelj Vanja , Pezo Lato , Tomić Ana , Ćetković Gordana , Tešević Vele
Published: September 2022

The genus Ocimum has many species that are used to treat diverse kinds of illnesses and sicknesses from ancient times. One of them, Ocimum basilicum L., commonly known as basil, has a vital role due to its various medicinal goods. It is best known as a plant with pharmacological activities, but also as an antioxidant, antimicrobial, and larvicidal agent. Although it has been traditionally used in Serbia in traditional medicine for centuries as an insecticidal, antibacterial, and antifungal plant as well as a traditional culinary plant, none of the O. basilicum varieties have been commercialised until today. There are significant numbers of information across the world that oils and by-products are part of the global market, but no references to the essential oil composition of Serbian plants were found. Therefore, the objective of this work was to evaluate the antioxidant and antimicrobial potentials of essential oil and hydrolate of two different varieties: O. basilicum var. genovese and Ocimum. basilicum var. minimum originating from Serbia for further industrial production of antimicrobial- and/or antioxidant-valued products. The results of this study confirm that essential oils of O. basilicum var. genovese and var. minimum represent a significant source of bioactive compounds, especially linalool, with a high rate of biological activities. Similar behaviour is observed for hydrolates, which are the by-product of the essential oil distillation process and can be utilised as bioactive-rich waste in further investigation.
God’s Prime Directive: Non-Interference and Why There Is No (Viable) Free Will Defense
Authors: Johnson David Kyle
Published: September 2022

In a recent book and article, James Sterba has argued that there is no free will defense. It is the purpose of this article to show that, in the most technical sense, he is wrong. There is a version of the free will defense that can solve what Sterba (rightly) takes to be the most interesting and severe version of the logical problem of moral evil. However, I will also argue that, in effect (or, we might say, in practice), Sterba is correct. The only working version of the free will defense requires embracing a view that entails consequences theists traditionally have not and cannot accept. Consequently, the one and only free will solution is not viable. Unless some other solution can be found (Sterba argues there is none), the logical problem of evil, as Sterba understands it, either commits one to atheism, or a version of theism that practically all theists would regard as a heresy.
Gratitude to God: A Unique Construct Adding to Our Understanding of Religiousness and Gratitude
Authors: Park Crystal L. , Wilt Joshua A. , David Adam B.
Published: September 2022

In two national samples in the United States, we aimed to determine the extent to which GTG is distinct from both general gratitude and general religiousness, using statistical methods to determine (1) if GTG shows patterns of association with other variables distinct from general gratitude and religiousness, and (2) whether GTG predicts wellbeing above and beyond both general gratitude and religiousness. Online studies were conducted with 267 (Study 1) and 184 (Study 2) adults. Results across the two studies were consistent in demonstrating that GTG shows associations with relevant constructs that are distinct from both general religiousness and general gratitude. Further, GTG independently predicted aspects of psychological wellbeing, although findings were not consistent across all aspects. These findings indicate GTG is a unique construct warranting future research.
Airborne Coherent GNSS Reflectometry and Zenith Total Delay Estimation over Coastal Waters
Authors: Moreno Mario , Semmling Maximilian , Stienne Georges , Dalil Wafa , Hoque Mainul , Wickert Jens , Reboul Serge
Published: September 2022

High-precision GNSS (global navigation satellite e system) measurements can be used for remote sensing and nowadays play a significant role in atmospheric sounding (station data, radio occultation observations) and sea surface altimetry based on reflectometry. A limiting factor of high-precision reflectometry is the loss of coherent phase information due to sea-state-induced surface roughness. This work studies airborne reflectometry observations recorded over coastal waters to examine the sea-state influence on Doppler distribution and the coherent residual phase retrieval. From coherent observations, the possibility of zenith total delay inversion is also investigated, considering the hydrostatic mapping factor from the Vienna mapping function and an exponential vertical decay factor depending on height receiver changes. The experiment consists of multiple flights performed along the coast between the cities of Calais and Boulogne-sur-Mer, France, in July 2019. Reflected signals acquired in a right-handed circular polarization are processed through a model-aided software receiver and passed through a retracking module to obtain the Doppler and phase-corrected signal. Results from grazing angle observations (elevation < 15°) show a high sensitivity of Doppler spread with respect to sea state with correlations of 0.75 and 0.88 with significant wave height and wind speed, respectively. An empirical Doppler spread threshold of 0.5 Hz is established for coherent reflections supported by the residual phase observations obtained. Phase coherence occurs in 15% of the observations; however, the estimated zenith total delay for the best event corresponds to 2.44 m, which differs from the typical zenith total delay (2.3 m) of 5%.
Separation of the Temperature Effect on Structure Responses via LSTM—Particle Filter Method Considering Outlier from Remote Cloud Platforms
Authors: Qin Yang , Li Yingmin , Liu Gang
Published: September 2022

Structural health monitoring (SHM) has been widely applied in the field of Mechanical and Civil Engineering in recent years. It is very hard to detect damage, however, using the measured data directly from the remote cloud platform of on-site structure, owing to changing environmental conditions. At the same time, outlier data from the remote cloud platform often occurs due to the harsh environmental conditions, interferences in the wireless medium, and the usage of low-quality sensors, which can greatly reduce the accuracy of structural health monitoring. In this paper, a novel temperature compensation method based on a long-short term memory (LSTM) network and the particle filter (PF) is proposed to separate the temperature effect from long-term structural health monitoring data. This method takes LSTMs as the state equation of PF, which solves the problem whereby PF cannot accurately derive the state equation for complex structures. A feedback model using the probability distribution generated by PF is developed to filter the observed value, thus measurement outliers can be successfully reduced. A numerical simulation and the measured deflection data from an SHM system are utilized to verify the proposed method. Results from the numerical simulation show that the LSTM-PF method can satisfactorily compensate for the temperature effect even when the nonlinear temperature effect is considered. Moreover, outputs from the SHM system of a large-scale suspension bridge indicate the temperature effect can be compensated and outliers can be appropriately reduced at the same time using the measured deflection data.
A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images
Authors: Boulila Wadii , Khlifi Manel Khazri , Ammar Adel , Koubaa Anis , Benjdira Bilel , Farah Imed Riadh
Published: September 2022

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%.
Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites
Authors: Bu Jinwei , Yu Kegen , Park Hyuk , Huang Weimin , Han Shuai , Yan Qingyun , Qian Nijia , Lin Yiruo
Published: September 2022

Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.
Polar Aerosol Vertical Structures and Characteristics Observed with a High Spectral Resolution Lidar at the ARM NSA Observatory
Authors: Zhang Damao , Comstock Jennifer , Xie Hailing , Wang Zhien
Published: September 2022

Aerosol vertical distributions impact both the direct and indirect radiative effects of aerosols. High Spectra Resolution Lidar (HSRL) separates between atmospheric molecular signals and aerosol particle signals and therefore can provide reliable measurements of aerosol properties. Six years of HSRL measurements between 2014 and 2019 from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory at Utqiaġvik are used to statistically analyze Arctic aerosol vertical distributions. The annual cycle of aerosol vertical distributions in terms of aerosol particulate backscatter coefficient (βp), lidar scattering ratio (SR), and aerosol particulate depolarization ratio (δp) profiles at the wavelength of 532 nm shows that Arctic Haze events are prevalent in later winter and spring at the NSA site. Mineral dust is frequently presented in strong aerosol layers in the spring, fall, and winter seasons. Over the summer season, the NSA site has large aerosol loadings that are dominated by small spherical aerosol particles.
3D LiDAR Aided GNSS/INS Integration Fault Detection, Localization and Integrity Assessment in Urban Canyons
Authors: Wang Zhipeng , Li Bo , Dan Zhiqiang , Wang Hongxia , Fang Kun
Published: September 2022

The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced.
Historical and Future Changes in Extreme Climate Events and Their Effects on Vegetation on the Mongolian Plateau
Authors: Ren Jinyuan , Tong Siqin , Ying Hong , Mei Li , Bao Yuhai
Published: September 2022

Climate change has increased the frequency of extreme climate events, with different regions showing different sensitivities to these events. In this study, the full subset regression analysis, correlation analysis, and multiple linear regression analysis were used to analyze trends of extreme climate changes and their effects on vegetation on the Mongolian Plateau from both historical and future perspectives. The results showed significant increasing and decreasing trends in extreme warming and extreme cooling indices, respectively, over the past three decades. The extreme temperature indices and precipitation trends under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) were consistent with historical trends, and the rates at which temperature and precipitation increased were positively correlated with increasing radiation intensity. In comparison with historical changes, there were gradual increases in areas of regions with increasing temperature and precipitation and decreases in areas with decreasing precipitation. There was an overall increasing trend in the normalized difference vegetation index (NDVI) of the Mongolian Plateau, and the indices that had the greatest influence on the NDVI during the analysis of climate extremes were: (1) the number of days of heavy rainfall (R20); (2) the number of summer days (SU25) and; (3) high extreme daily minimum temperature (TNx). There was an increasing trend in the NDVI from 2021 to 2080, and the rate of the NDVI increase decreased with increasing radiation intensity. The rates of change in the NDVI under all three scenarios were lower than that of the historical period.
Extraction of Saline Soil Distributions Using Different Salinity Indices and Deep Neural Networks
Authors: Gu Qianyi , Han Yang , Xu Yaping , Ge Huitian , Li Xiaojie
Published: September 2022

Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U2-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions.
Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning
Authors: Morellos Antonios , Pantazi Xanthoula Eirini , Paraskevas Charalampos , Moshou Dimitrios
Published: September 2022

Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%.
A Multi-Indicator Evaluation Method for Spatial Distribution of Urban Emergency Shelters
Authors: Wang Xinxiang , Guan Minglei , Dong Chunlai , Wang Jingzhe , Fan Yong , Xin Fei , Lian Guoyun
Published: September 2022

Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters.
Joint Inversion of 3D Gravity and Magnetic Data under Undulating Terrain Based on Combined Hexahedral Grid
Authors: He Haoyuan , Li Tonglin , Zhang Rongzhe
Published: September 2022

As an effective underground imaging method, the joint inversion of the gravity and magnetic data has an important application in the comprehensive interpretation of mineral exploration, and unstructured modeling is the key to accurately solving its topographic problem. However, the traditional tetrahedral grid can only impose the gradient-based constraints approximately, owing to its poor arrangement regularity. To address the difficulty of applying a cross-gradient constraint in an unstructured grid, we propose a joint inversion based on a combined hexahedral grid, which regularly divides the shallow part into curved hexahedrons and the deep part into regular hexahedrons. Instead of a cross-gradient in the spatial sense, we construct a geometric sense “cross-gradient” for a structural constraint to reduce the influence of approximation. In addition, we further correct the traditional sensitivity-based weighting function according to element volume, to make it suitable for an unstructured grid. Model tests indicate that the new grid can impose the cross-gradient constraint more strongly, and the proposed correction can effectively solve the false anomaly caused by the element volume difference. Finally, we apply our method to the measured data from a mining area in Huzhong, Heilongjiang Province, China, and successfully invert out the specific location of a known skarn deposit, which further proves its practicability.
Numerical Imaging of the Seabed and Acoustic Flares with Topography and Velocity Variance
Authors: Cheong Snons , Yelisetti Subbarao , Chun Jong-Hwa
Published: September 2022

During seabed mapping, anomalous acoustic events in the seawater layer often degrade bathymetric quality. Acoustic-flare-like events that are induced by gas seepage occur accompanied by small seabed depressions known as pockmarks. In this study, we performed numerical imaging to verify whether acoustic flares are related to changes in the topography of the seabed. The input models assumed convex or concave upward structures which differ based on aperture size. The imaging study determined that acoustic flares appear because the sensing instrument measures the velocity changes in the water layer regardless of the seabed topography. Changes in the velocity of the seawater column can be caused by the concentration of suspended particles and the wave current above the seabed, but further quantification research is required.
Responses of Satellite Chlorophyll-a to the Extreme Sea Surface Temperatures over the Arabian and Omani Gulf
Authors: Hamdeno Manal , Nagy Hazem , Ibrahim Omneya , Mohamed Bayoumy
Published: September 2022

Extreme events such as Marine Heat Waves (MHWs) and Low Chlorophyll-a (LChl-a) in the ocean have devastating impacts on the marine environment, particularly when they occur simultaneously (i.e., the compound of MHWs and LChl-a events). In this study, we investigate the spatiotemporal variability of MHWs and LChl-a events in the Arabian and Omani Gulf. For this purpose, we used satellite-based high-resolution observations of SST (0.05° × 0.05°; from 1982 to 2020) and chlorophyll-a concentration data (0.04° × 0.04°; from 1998 to 2020). Hourly air temperature, wind, and heat flux components from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) were used to explain the link between these extreme events and atmospheric forcings. Moreover, our results revealed that the annual frequency of MHW and LChl-a is related to the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results revealed an average SST warming trend of about 0.44 ± 0.06 °C/decade and 0.32 ± 0.04 °C/decade for the Arabian Gulf (AG) and the Gulf of Oman (OG), respectively. This warming rate was accompanied by MHW frequency and duration trends of 0.97 events/decade and 2.3 days/decade, respectively, for the entire study region from 1982 to 2020. The highest annual MHW frequencies were recorded in 2010 (6 events) and 2020 (5 events) associated with LChl-a frequency values of 4 and 2, respectively. La Niña events in 1999, 2010, 2011, and 2020 were associated with higher frequencies of MHW and LChl-a. The positive phase of IOD coincides with high MHW frequency in 2018 and 2019. The longest compound MHW and LChl-a event with a duration of 42 days was recorded in 2020 at OG. This extreme compound event was associated with wind stress reduction. Our results provide initial insights into the spatiotemporal variability of the compound MHW and LChl-a events that occurred in the AG and OG.
Determining Ionospheric Drift and Anisotropy of Irregularities from LOFAR Core Measurements: Testing Hypotheses behind Estimation
Authors: Grzesiak Marcin , Pożoga Mariusz , Matyjasiak Barbara , Przepiórka Dorota , Beser Katarzyna , Tomasik Lukasz , Rothkaehl Hanna , Ciechowska Helena
Published: September 2022

We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: “Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements”, Signal Processing Symposium 2021 (SPSympo 2021).
Land Cover Classification for Polarimetric SAR Images Based on Vision Transformer
Authors: Wang Hongmiao , Xing Cheng , Yin Junjun , Yang Jian
Published: September 2022

Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning methods for land cover classification. In this paper, a vision Transformer (ViT)-based classification method is proposed. The ViT structure can extract features from the global range of images based on a self-attention block. The powerful feature representation capability of the model is equivalent to a flexible receptive field, which is suitable for PolSAR image classification at different resolutions. In addition, because of the lack of labeled data, the Mask Autoencoder method is used to pre-train the proposed model with unlabeled data. Experiments are carried out on the Flevoland dataset acquired by NASA/JPL AIRSAR and the Hainan dataset acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences. The experimental results on both datasets demonstrate the superiority of the proposed method.
The Effect of Drought on Vegetation Gross Primary Productivity under Different Vegetation Types across China from 2001 to 2020
Authors: Wu Xiaoping , Zhang Rongrong , Bento Virgílio A. , Leng Song , Qi Junyu , Zeng Jingyu , Wang Qianfeng
Published: September 2022

Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate.
Monitoring Asian Dust Storms from NOAA-20 CrIS Double CO2 Band Observations
Authors: Fang Chenggege , Han Yang , Weng Fuzhong
Published: September 2022

Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite hyperspectral infrared observations and propose a new methodology to monitor the storms. An SDS emission and scattering index (SESI) is based on the differential responses of infrared CO2 shortwave and longwave IR bands to the scattering and emission of sand and dust particles. For a severe dust storm process during 14–17 March 2021, the SESI calculated by the Cross-track Infrared Sounder (CrIS) observations shows very negative values in the dusty region and is consistent with the spatial distribution of dust identified from the true-color RGB imagery and the dust RGB imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 Satellite. The use of the SESI index in the near-surface layer allows for monitoring of the dust storm process and enables an effective classification between surface variations and dust weather events.
Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods
Authors: Shen Lanzhi , Gao Maofang , Yan Jingwen , Wang Qizhi , Shen Hua
Published: September 2022

SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value.