bluuebunny/crossref_metadata_2025 huggingface dataset:
Scale Factor Determination for the GRACE Follow-On Laser Ranging Interferometer Including Thermal Coupling
Authors: Misfeldt Malte , Müller Vitali , Müller Laura , Wegener Henry , Heinzel Gerhard
Published: January 2023

The GRACE follow-on satellites carry the very first interspacecraft Laser Ranging Interferometer (LRI). After more than four years in orbit, the LRI outperforms the sensitivity of the conventional Microwave Instrument (MWI). However, in the current data processing scheme, the LRI product still needs the MWI data to determine the unknown absolute laser frequency, representing the “ruler” for converting the raw phase measurements into a physical displacement in meters. In this paper, we derive formulas for precisely performing that conversion from the phase measurement into a range, accounting for a varying carrier frequency. Furthermore, the dominant errors due to knowledge uncertainty of the carrier frequency as well as uncorrected time biases are derived. In the second part, we address the dependency of the LRI on the MWI in the currently employed cross-calibration scheme and present three different models for the LRI laser frequency, two of which are largely independent of the MWI. Furthermore, we analyze the contribution of thermal variations on the scale factor estimates and the LRI-MWI residuals. A linear model called Thermal Coupling (TC) is derived, which significantly reduces the differences between LRI and MWI to a level where the MWI observations limit the comparison.
A Numerical Assessment and Prediction for Meeting the Demand for Agricultural Water and Sustainable Development in Irrigation Area
Authors: Zhang Qiying , Qian Hui , Xu Panpan , Liu Rui , Ke Xianmin , Furman Alex , Shang Jiatao
Published: January 2023

The demand for agricultural water is a growing problem in irrigated regions across the globe, particularly in arid and semi-arid regions. Changes in the level of groundwater in irrigation districts will affect the flow of surface water connected to the aquifer, which may damage the sustainability of water resources and ecosystems. In this study, a two-dimensional unsteady flow model based on MODFLOW was constructed and three scenarios were established to assess the demand for agricultural water in the Jiaokou Irrigation District. The results show that the groundwater in the study area is basically balanced. However, the supply of irrigation water for summer irrigation is insufficient. The results of the model prediction indicate that when groundwater is primarily used for irrigation (scenario 1), the maximum water level decrease is 25 m, which is beyond this limit (15 m). When the ratio of groundwater to surface water is 2:1 for irrigation (scenario 2), the largest decrease in water level is approximately 10 m. Scenario 3 is proposed based on the Hanjiang-to-Weihe River Valley Water Diversion Project to prevent the salinization of soil owing to the rise in water level, and its result shows that the maximum decrease and buried depth are approximately 5 m and above 3 m, respectively, indicating that the scenario is more reasonable and sustainable. These findings provide theoretical guidance to protect water resources and prevent water pollution and should serve as a reference for rationally allocating water resources in other irrigation districts in arid and semi-arid areas.
Framework for Geometric Information Extraction and Digital Modeling from LiDAR Data of Road Scenarios
Authors: Wang Yuchen , Wang Weicheng , Liu Jinzhou , Chen Tianheng , Wang Shuyi , Yu Bin , Qin Xiaochun
Published: January 2023

Road geometric information and a digital model based on light detection and ranging (LiDAR) can perform accurate geometric inventories and three-dimensional (3D) descriptions for as-built roads and infrastructures. However, unorganized point clouds and complex road scenarios would reduce the accuracy of geometric information extraction and digital modeling. There is a standardization need for information extraction and 3D model construction that integrates point cloud processing and digital modeling. This paper develops a framework from semantic segmentation to geometric information extraction and digital modeling based on LiDAR data. A semantic segmentation network is improved for the purpose of dividing the road surface and infrastructure. The road boundary and centerline are extracted by the alpha-shape and Voronoi diagram methods based on the semantic segmentation results. The road geometric information is obtained by a coordinate transformation matrix and the least square method. Subsequently, adaptive road components are constructed using Revit software. Thereafter, the road route, road entity model, and various infrastructure components are generated by the extracted geometric information through Dynamo and Revit software. Finally, a detailed digital model of the road scenario is developed. The Toronto-3D and Semantic3D datasets are utilized for analysis through training and testing. The overall accuracy (OA) of the proposed net for the two datasets is 95.3 and 95.0%, whereas the IoU of segmented road surfaces is 95.7 and 97.9%. This indicates that the proposed net could accomplish superior performance for semantic segmentation of point clouds. The mean absolute errors between the extracted and manually measured geometric information are marginal. This demonstrates the effectiveness and accuracy of the proposed extraction methods. Thus, the proposed framework could provide a reference for accurate extraction and modeling from LiDAR data.
The Design of the 1D CNN–GRU Network Based on the RCS for Classification of Multiclass Missiles
Authors: Kim A Ran , Kim Ha Seon , Kang Chang Ho , Kim Sun Young
Published: January 2023

For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D CNN–GRU network with strengths in feature extraction and time-series processing was considered. The 1D CNN–GRU was experimentally changed and designed to fit the RCS characteristics. The performance of the proposed 1D CNN–GRU was compared and analyzed using the 1D CNN and 1D CNN–LSTM. The designed 1D CNN–GRU had the best classification performance with a high accuracy of 99.50% in complex situations, such as with different missile shapes with the same trajectory and with the same missile shapes that had the same trajectory. In addition, to confirm the general target classification performance for the RCS, a new class was verified. The 1D CNN–GRU had the highest classification performance at 99.40%. Finally, as a result of comparing three networks by adding noise to compensate for the shortcomings of the RCS, the 1D CNN–GRU, which was optimized for both the data set used in this paper and the newly constructed data set, was the most robust to noise.
VIIRS Edition 1 Cloud Properties for CERES, Part 1: Algorithm Adjustments and Results
Authors: Minnis Patrick , Sun-Mack Sunny , Smith William L. , Trepte Qing Z. , Hong Gang , Chen Yan , Yost Christopher R. , Chang Fu-Lung , Smith Rita A. , Heck Patrick W. , Yang Ping
Published: January 2023

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions.
Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices
Authors: Roth Richard T. , Chen Kanru , Scott John R. , Jung Jinha , Yang Yang , Camberato James J. , Armstrong Shalamar D.
Published: January 2023

In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors.
Radar and Jammer Intelligent Game under Jamming Power Dynamic Allocation
Authors: Geng Jie , Jiu Bo , Li Kang , Zhao Yu , Liu Hongwei , Li Hailin
Published: January 2023

In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games.
Comprehensive Assessment of BDS-2 and BDS-3 Precise Orbits Based on B1I/B3I and B1C/B2a Frequencies from iGMAS
Authors: Zhang Zhetao , Zeng Ping , Wen Yuanlan , He Lina , He Xiufeng
Published: January 2023

The BeiDou Global Navigation Satellite System (BDS), including the second generation (BDS-2) and the third generation (BDS-3), has been widely used in areas of positioning, navigation, and timing (PNT). One of the essential prerequisites for accurate PNT service is the precise satellite orbits of multi-frequency and multi-constellation BDS-2 and BDS-3 satellites. As usual, the precise orbit products can be obtained from analysis centers (ACs) of the international GNSS Service (IGS). The precise orbits can also be downloaded from the international GNSS Monitoring and Assessment System (iGMAS). Compared with the IGS ACs, the iGMAS can provide featured services such as satellite orbits based on the new B1C/B2a BDS signals. Considering the indispensability of the new signals, the performance of all BDS precise orbits from iGMAS needs to be known. However, there is no comprehensive assessment of BDS-2 and BDS-3 precise orbits based on B1I/B3II and B1C/B2a frequencies from iGMAS, especially for the period after the BDS entered the stable operation stage. In this paper, BDS-2/BDS-3 final (ISC), rapid (ISR), and ultra-rapid (ISU) products based on B1I/B3I and B1C/B2a frequencies from iGMAS are all assessed comprehensively. Specifically, at first, the precise orbits from iGMAS are compared with the ones from the IGS ACs. Based on this, the satellite laser ranging inspects the precise orbits from iGMAS. Finally, the orbit errors are discussed systematically by considering the beta and elongation angles. Using one year of data, the orbit accuracy of geostationary orbit, inclined geosynchronous orbit, and medium earth orbit (MEO) satellites can almost reach meter to decimeter level, decimeter to sub-decimeter level, and centimeter level, respectively, where the ISC products are the best. The ISC, ISR, and ISU products based on B1I/B3I frequencies are generally better than the ones based on B1C/B2a frequencies. Additionally, according to the SLR data, the results show that the accuracy of precise orbits of BDS-3 is better than that of BDS-2. The mean values of orbit biases of BDS-3 MEO satellites are approximately 2.88 cm. In addition, the orbit errors are related to the beta angle and elongation angle to some extent, and the manufacturers may also have an influence on the orbit errors.
A Comparative Study of Different CNN Models and Transfer Learning Effect for Underwater Object Classification in Side-Scan Sonar Images
Authors: Du Xing , Sun Yongfu , Song Yupeng , Sun Huifeng , Yang Lei
Published: January 2023

With the development of deep learning techniques, convolutional neural networks (CNN) are increasingly being used in image recognition for marine surveys and underwater object classification. Automatic recognition of targets on side-scan sonar (SSS) images using CNN can improve recognition accuracy and efficiency. However, the vast selection of CNN models makes it challenging to select models for target recognition in SSS images. Therefore, this paper aims to compare different CNN models’ prediction accuracy and computational performance comprehensively. First, four traditional CNN models were applied to train and predict the same submarine SSS dataset using both the original model and models with transfer learning methods. Then, we examined and studied the prediction accuracy and computation performance of four CNN models. Results showed that transfer learning enhances the accuracy of all CNN models, with lesser improvements for AlexNet and VGG-16 and greater improvements for GoogleNet and ResNet101. GoogleNet has the highest prediction of accuracy (100% in the train dataset and 94.27% in the test dataset) and good computational difficulty. The findings of this work are useful for future model selection in target recognition in SSS images.
RiDOP: A Rotation-Invariant Detector with Simple Oriented Proposals in Remote Sensing Images
Authors: Wei Chongyang , Ni Weiping , Qin Yao , Wu Junzheng , Zhang Han , Liu Qiang , Cheng Kenan , Bian Hui
Published: January 2023

Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To model the variant orientations of oriented objects, general CNN-based methods usually adopt more parameters or well-designed modules, which are often complex and inefficient. To address this issue, the detector requires two key components to deal with: (i) generating oriented proposals in a light-weight network to achieve effective representation of arbitrarily oriented objects; (ii) extracting the rotation-invariant feature map in both spatial and orientation dimensions. In this paper, we propose a novel, lightweight rotated region proposal network to produce arbitrary-oriented proposals by sliding two vertexes only on adjacent sides and adopt a simple yet effective representation to describe oriented objects. This may decrease the complexity of modeling orientation information. Meanwhile, we adopt the rotation-equivariant backbone to generate the feature map with explicit orientation channel information and utilize the spatial and orientation modules to obtain completely rotation-invariant features in both dimensions. Without tricks, extensive experiments performed on three challenging datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016 demonstrate that our proposed method can reach state-of-the-art accuracy while reducing the model size by 40% in comparison with the previous best method.
Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China
Authors: Dai Cong , Li Weile , Lu Huiyan , Zhang Shuai
Published: January 2023

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.
Spatiotemporal Heterogeneity of Forest Fire Occurrence Based on Remote Sensing Data: An Analysis in Anhui, China
Authors: Zhang Xiao , Lan Meng , Ming Jinke , Zhu Jiping , Lo Siuming
Published: January 2023

A forest fire is a destructive disaster that is difficult to handle and rescue and can pose a significant threat to ecosystems, society, and humans. Since driving factors and their effects on forest fires change over time and space, exploring the spatiotemporal patterns of forest fire occurrence should be addressed. To better understand the patterns of forest fire occurrence and provide valuable insights for policy making, we employed the Geographically and Temporally Weighted Regression (GTWR) model to investigate the varying spatiotemporal correlations between driving factors (vegetation, topography, meteorology, social economy) and forest fires in Anhui province from 2012 to 2020. Then we identified the dominant factors and conducted the spatiotemporal distribution analysis. Moreover, we innovatively introduced nighttime light as a socioeconomic driving factor of forest fires since it can directly reflect more comprehensive information about the social economy than other socioeconomic factors commonly used in previous studies. This study applied remote sensing data since the historical statistic data were not detailed. Here, we obtained the following results. (1) There was a spatial autocorrelation of forest fires in Anhui from 2012 to 2020, with high-high aggregation of forest fires in eastern cities. (2) The GTWR model outperformed the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression model (GWR), implying the necessity of considering temporal heterogeneity in addition to spatial heterogeneity. (3) The relationships between driving factors and forest fires were spatially and temporally heterogeneous. (4) The forest fire occurrence was mainly dominated by socioeconomic factors, while the dominant role of vegetation, topography, and meteorology was relatively limited. It’s worth noting that nighttime light played the most extensive dominant role in forest fires of Anhui among all the driving factors in the years except 2015.
Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)
Authors: Mansour Shawky , Ghoneim Eman , El-Kersh Asmaa , Said Sayed , Abdelnaby Shimaa
Published: January 2023

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.
Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods
Authors: Fan Yiguang , Feng Haikuan , Yue Jibo , Liu Yang , Jin Xiuliang , Xu Xingang , Song Xiaoyu , Ma Yanpeng , Yang Guijun
Published: January 2023

The estimation of physicochemical crop parameters based on spectral indices depend strongly on planting year, cultivar, and growing period. Therefore, the efficient monitoring of crop growth and nitrogen (N) fertilizer treatment requires that we develop a generic spectral index that allows the rapid assessment of the plant nitrogen content (PNC) of crops and that is independent of year, cultivar, and growing period. Thus, to obtain the best indicator for estimating potato PNC, herein, we provide an in-depth comparative analysis of the use of hyperspectral single-band reflectance and two- and three-band spectral indices of arbitrary bands for estimating potato PNC over several years and for different cultivars and growth periods. Potato field trials under different N treatments were conducted over the years 2018 and 2019. An unmanned aerial vehicle hyperspectral remote sensing platform was used to acquire canopy reflectance data at several key potato growth periods, and six spectral transformation techniques and 12 arbitrary band combinations were constructed. From these, optimal single-, two-, and three-dimensional spectral indices were selected. Finally, each optimal spectral index was used to estimate potato PNC under different scenarios and the results were systematically evaluated based on a correlation analysis and univariate linear modeling. The results show that, although the spectral transformation technique strengthens the correlation between spectral information and potato PNC, the PNC estimation model constructed based on single-band reflectance is of limited accuracy and stability. In contrast, the optimal three-band spectral index TBI 5 (530,734,514) performs optimally, with coefficients of determination of 0.67 and 0.65, root mean square errors of 0.39 and 0.39, and normalized root mean square errors of 12.64% and 12.17% for the calibration and validation datasets, respectively. The results thus provide a reference for the rapid and efficient monitoring of PNC in large potato fields.
Observations of Ionospheric Clutter at Near Equatorial High Frequency Radar Stations
Authors: Cook Thomas M. , Terrill Eric J. , Garcia-Moreno Carlos , Merrifield Sophia T.
Published: January 2023

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean’s surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth’s magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement.
A Compact High-Order Finite-Difference Method with Optimized Coefficients for 2D Acoustic Wave Equation
Authors: Chen Liang , Huang Jianping , Fu Li-Yun , Peng Weiting , Song Cheng , Han Jiale
Published: January 2023

High-precision finite difference (FD) wavefield simulation is one of the key steps for the successful implementation of full-waveform inversion and reverse time migration. Most explicit FD schemes for solving seismic wave equations are not compact, which leads to difficulty and low efficiency in boundary condition treatment. Firstly, we review a family of tridiagonal compact FD (CFD) schemes of various orders and derive the corresponding optimization schemes by minimizing the error between the true and numerical wavenumber. Then, the optimized CFD (OCFD) schemes and a second-order central FD scheme are used to approximate the spatial and temporal derivatives of the 2D acoustic wave equation, respectively. The accuracy curves display that the CFD schemes are superior to the central FD schemes of the same order, and the OCFD schemes outperform the CFD schemes in certain wavenumber ranges. The dispersion analysis and a homogeneous model test indicate that increasing the upper limit of the integral function helps to reduce the spatial error but is not conducive to ensuring temporal accuracy. Furthermore, we examine the accuracy of the OCFD schemes in the wavefield modeling of complex structures using a Marmousi model. The results demonstrate that the OCFD4 schemes are capable of providing a more accurate wavefield than the CFD4 scheme when the upper limit of the integral function is 0.5π and 0.75π.
Development and Validation of a Measuring Instrument for the Improvement of University Guidance and Tutoring
Authors: Suárez-Lantarón Belén , Castillo-Reche Irina Sherezade , López-Medialdea Ana
Published: January 2023

Guidance and tutoring in university has become relevant in the last decade, associated with various issues. However, many of the actions undertaken by universities do not meet the needs of our students. Therefore, we consider it necessary to know the perception, use and satisfaction that university students have about the tutoring they receive, raising as an objective of this work the design of a tool with which to achieve this end. In the construction of this tool, different phases have been followed: (i) a review of previous works from which ideas for the design of the instrument are taken, (ii) a validation of its content with the collaboration of experts in the field and (iii) a administration of the designed scale to a representative sample, whose analysis has allowed to verify the validity of the construct as well as its reliability. As a final result, a questionnaire with forty items distributed in four dimensions is constructed: socio-demographic profile of the students, knowledge of the students about the Tutorial Action Plan, use and knowledge of the students about academic tutoring and attitude/satisfaction of the students about academic tutoring. As a conclusion, we point out the need to promote the participation of students and enhance communication with tutors, as they are key to achieve better results.
Validity of the Jordanian Version of the Life Snapshot Inventory
Authors: Aldahadha Basim
Published: January 2023

Functional Analytic Psychotherapy (FAP) and the Life Snapshot Inventory (LSI) agree with a contextual, behavioral, relational approach to psychotherapy in which therapists focus on what happens in a session between the client and therapist to shape the interpersonal behaviors, emotional awareness, and self-expression necessary for clients to create and maintain close relationships and to live meaningful lives. This study aimed to test the reliability and the convergent validity of the (LSI) as a self-report instrument to measure meaningful personal and social factors. It was created in Functional Analytic Psychotherapy for use in various areas of life (e.g., family, work, love, spirituality, sexuality, health, etc.). This study involved 393 Jordanian participants (M = 49 years) in the sample. The examined questionnaire was compared with the Depression, Anxiety and Stress Scale-21 Items (DASS-21) and the Rosenberg Self-Esteem Scale (RSES) to examine its validity. The results demonstrated a high internal consistency (α = 0.92) of the LSI, with a correlation ranging between 0.74 and 0.87, both of which were statistically significant. Further, the factorial analysis identified only one factor (45.31% variance). Additionally, the results showed that both the DASS-21 and treatment options had an effect on the total scores of the LSI and the RSES. The LSI was introduced as the predictor and the variable related to depression, anxiety, stress, and the RSES as the dependent variables. The scale in total explained −31% of the variance in depression, −30% of the variance in anxiety, −22% of the variance in stress, and 46% of the variance in RSES. However, the results did not show any effects on the variables of gender, employment, and qualification. The examined questionnaire would be a helpful measure for use in healthcare, diagnosis, and clinical contexts in Jordanian society.
The Impact of Medical Insurance Payment Policy Reform on Medical Cost and Medical Burden in China
Authors: Lu Qianhong , Gan Xiaoqing , Chen Zhensheng
Published: January 2023

Medical insurance that pays for the medical expenses of the insured is regarded as being maximally fair, but often leads to the excessive use of medical services, medical expenses rising too rapidly, and the waste of health resources. The deductible and reimbursement ratio defines the part of the cost to be borne by the patient to reduce moral hazard, preventing adverse selection and easing the burden of medical expenses. But will improving the deductible or reducing the reimbursement ratio reduce medical expenses and reduce the medical burden of the insured? By using panel data from 2011 to 2019, this paper examines the effect of the deductible and reimbursement ratio on reducing medical expenses. The following conclusions were obtained: First, the impact of the deductible on medical costs and the medical burden is significant and negative. Specifically, lowering the deductible can reduce medical costs and reduce the medical burden of patients. Second, the impact of the reimbursement ratio on medical expenses and the medical burden is significant and negative, that is, with all other factors remaining equal, an increase in reimbursement ratio will result in a reduction in medical expenses and medical burden. Third, the combined effect of the deductible and reimbursement ratio has a significant impact on both medical expenses and medical burdens. Specifically, changes in the deductible and reimbursement ratio in the same direction can reduce both the medical expenses and the burden on patients. Fourth, based on an analysis in which 31 provinces were divided into economically developed and less developed areas, it was found that the deductible and reimbursement ratio have a certain impact on medical expenses and medical burden; in relatively less developed areas, with increasing per capita GDP, the flow of patients in large hospitals will inevitably increase, and the overall medical expenses will rise.
Challenges and Opportunities in the Management of Electronic Waste and Its Impact on Human Health and Environment
Authors: Ghulam Salma Taqi , Abushammala Hatem
Published: January 2023

Electronic waste (e-waste) is the fastest-growing class of waste because of the remarkable demand for various electronic gadgets such as mobiles and laptops. Moreover, its improper disposal is life-threatening because it includes hundreds of different substances, many of which are toxic elements and pollutants that can leach to soil and surface and groundwater or be emitted into the air, causing a major negative impact on the environment and public health. As a result, studies on the sustainable management of e-waste have gained increasing attention from researchers globally in the last decade to explore practical strategies to reduce or utilize this special waste. This review aims to provide an in-depth understanding of the major aspects of e-waste, including its definition, composition, and the impact of its end-of-life disposal on human health and the environment, while also focusing on some practical sustainable solutions and strategies toward effective e-waste management. It will also discuss the production of electronics; global demand and the mining boom; and the pollution caused by mining. It will also highlight the importance of effective governmental regulations, with which electronics producers, e-waste generators, and recycling facilities should comply. The research perspectives and orientations highlighted within this review can help in providing guidelines for future research studies and in exploring opportunities for more effective management of e-waste toward a circular economy and sustainable development.
Prospects for Governance and Climate Change Resilience in Peatland Management in Indonesia
Authors: Atkinson Christopher L. , Alibašić Haris
Published: January 2023

Southeast Asia has the most significant tropical peat/peat carbon storage area in the world, with Indonesia being the primary location for much of it. Anthropogenic changes to peatlands have resulted in a threat to these endangered ecosystems; policies that have favored industrial and elite interests above those of local communities have resulted in severe consequences for the environment and public health, not only in Indonesia and its region, but for the world community in terms of contributions to climate change. Decentralization has been seen as a means of sharing authority and accountability with lower government levels and providing additional opportunities for shared governance. Still, there is reason to question the means of these approaches and the results of such efforts. The research question is: What can be learned about the administration’s role in leading stakeholder involvement from the case of Indonesian peatland management? The authors utilize outcome additionality as a framework connected to the resilience of peatlands.
Study on the Influence of Cultural Communication on the Development of the Visitor Economy
Authors: Chen Qiao , Mao Yan , Morrison Alastair M.
Published: January 2023

This study includes China and 197 countries from 1995 to 2019 as the research subjects. It considers trade in cultural products and inbound tourism as proxy variables of cultural communication and the visitor economy to analyze cultural communication’s impact on the visitor economy’s development and finds that cultural communication significantly promotes the visitor economy’s development. A robustness test of the benchmark regression results was conducted using the shrinktail treatment, substitute variable, and subsample selection methods. Cultural products were divided into three types, and the influence of trade in different cultural products on inbound tourism varies. Forty-nine categories had the largest promoting effect on inbound tourism, followed by thirty-seven categories and ninety-seven categories. There are significant differences in the influence of cultural product exports on inbound tourism in different cultural circles and continents. The influence effect within each circle was in the following order: Indian cultural circles (0.49), Islamic cultural circles (0.42), East Asian cultural circles (0.40), Western cultural circles (0.39), and Eastern European cultural circles (0.33). From the aspect continents, the export of cultural products to Europe significantly impacted China’s inbound tourism, followed by Africa, Asia, and the Americas. Finally, countermeasures and suggestions are proven to promote the visitor economy’s development. This study provides a reference for the cultural inheritance and development of the global tourism economy.
Spatial Evolution and Multi-Scenario Simulation of Rural “Production–Ecological–Living” Space: A Case Study for Beijing, China
Authors: Yu Huang , Du Shanshan , Zhang Jingqiu , Chen Jinglei
Published: January 2023

With the vigorous development of industrialization and urbanization, rural space faces many difficulties in achieving sustainable development, such as the spatial structure being unbalanced. To explore the sustainable development of rural space, this study evaluates the spatial evolution and multi-scenario simulation of rural space with reference to the three dominant functions of land use, namely “production–ecological–living” (PEL), based on the interpretation of land use remote sensing data in 2000, 2010, and 2020. The change characteristics and the formation mechanism of the territorial spatial pattern were quantitatively analyzed by gravity center transfer, territorial spatial transfer matrix, standard deviational ellipse, geo-detector, and artificial neural network CA simulation. The results showed that (1) from 2000 to 2020, the rural production space in Beijing continued to decrease, the rural living space continued to increase and then gradually contracted, and the rural ecological space as a whole also showed a downward trend. (2) During the study period, the formation and evolution of the ecological spatial pattern of rural production and living in Beijing were affected by both regional physical geographical factors and human, social, and economic factors, with significant differences; the interaction between two driving factors is stronger than that within a single factor, and the main mode of action is double factor enhancement and nonlinear enhancement. (3) Compared with the other three scenarios, the performance of Beijing’s rural space in 2030 under the harmonious development scenario is more compact and stable, the rural production space is basically guaranteed, the intensity of rural living space is enhanced, and the overall situation of rural ecological space is stable. Our results show that in the future decision-making of rural land and space development around a metropolis, we should fully consider the evolution law of rural spatial pattern, as well as the driving force of natural geographical factors and economic activity factors and discuss the optimization and overall management of rural production and living ecological space through multi-scenario spatial simulation.
A Methodological Framework to Foster Social Value Creation in Architectural Practice
Authors: Larsen Vibeke Grupe , Antoniucci Valentina , Tollin Nicola , Sattrup Peter Andreas , Jens Krister , Birkved Morten , Holmboe Tine , Marella Giuliano
Published: January 2023

The building industry is essential for a national transition towards a circular economy (CE) in Denmark. The Danish state subsidises the Danish affordable housing sector, which is the largest single sector within the Danish building industry, making the sector an essential driver for the transition. The social components of sustainability are considered crucial to ensuring the quality of the environmental and economic components of the CE. However, social value creation (SVC) has been neglected in building processes, and public investments are being used without the policymakers thoroughly assessing the CE’s socioeconomic efficiency and effectiveness. The sector therefore needs integrated methodologies to support comprehensive decision making on the CE during construction and renovation. SVC is an apparent field for architectural firms. Two surveys were conducted among business and sustainability managers of Danish architectural firms to identify the challenges and potentials regarding assessing sustainability and SVC in architects’ practices. The results of the surveys are described and analysed in this study. Several impact categories, indicators and tools are identified, discussed and summarised in a methodological framework that can support architects in decision making about SVC in constructing and renovating affordable housing. Further refinement of the framework to support dynamic and iterative decision-making is anticipated as future work.
Establishment of a Model and System for Secondary Fertilization of Nutrient Solution and Residual Liquid
Authors: Wang Xinzhong , Fang Weiquan , Zhao Zhongfeng
Published: January 2023

At present, the nutrient solution of soilless culture is mostly configured by simply using the standard fertilizer formula, lacking the precise matching technology of nutrient solutions based on nutrient elements. It is unable to change the formula configuration according to vegetable types, different growth stages and growth needs, especially in the secondary fertilizer reuse of nutrient solution reflux. In order to make precise secondary fertilization, a model and system for secondary fertilization of nutrient solution residual liquid were established in this paper. It can be used for secondary fertilization based on nutrient ions and reused after the sterilization of the residual liquid. A nutrient solution fertilizer system based on nutrient elements was designed. The nutrient solution fertilizer system based on the online detection of ions was determined with different element compounds as the fertilizer unit. Combined with the existing hydroponic water-soluble inorganic salts, the ion concentration and its proportioning quantitative model of the nutrient solution recovery solution were established. The experimental verification and result analysis of the fertilizer model were carried out to test the accuracy and practicability of the established model. The ion concentration error obtained from the mathematical model was established as 0.0093–0.5294 mg·L−1.The precise proportioning technology of nutrient solution based on nutrient elements can realize the precise and intelligent proportioning of nutrient elements in the nutrient solution of crops and can also make full use of the nutrient solution. It also improves the efficiency of greenhouse cultivation.