bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Process Parameters on Cold Spray Additive Manufacturing of Quasicrystalline Al93Fe3Cr2Ti2 Alloy
Authors: de Araujo Aylanna Priscila Marques , Carmelo Felipe B. Do M. , Rocha Erlifas M. , Kiminami Claudio S. , Gargarella Piter
Published: July 2023

Quasicrystalline Al93Fe3Cr2Ti2 (at.%) gas-atomized powders, which exhibit a metastable composite microstructure, were used to produce coatings by cold spray additive manufacturing processing (CSAM) using different processing parameters. The metastable composite microstructure provides the Al93Fe3Cr2Ti2 alloy with excellent mechanical properties. At the same time, the metastability of its microstructure, achieved by the high cooling rates of the gas atomization process, limits the processability of the Al93Fe3Cr2Ti2 powder. The purpose of this study was to investigate the effect of process parameters on the CSAM of quasicrystalline Al93Fe3Cr2Ti2 powder. The powder was sieved and classified to a size range of −75 µm. Using N2 carrier gas combined with different temperatures, pressures, nozzle apertures, and deposition substrate conditions, cold-sprayed coatings were produced. The porosity and thickness of the coatings were evaluated by image analyses. By SEM, XRD, DSC, and TEM, the microstructure was identified, and by Vickers microhardness, the mechanical properties of the coatings were investigated. Dense (≤0.50% porosity) and thick (~185.0 µm) coatings were obtained when the highest pressure (4.8 MPa), highest temperature (475 °C), and lowest nozzle aperture (A) were used in combination with an unblasted substrate. The SEM, XRD, and DSC data showed that the composite powder’s microstructure was retained in all coatings with no decomposition of the metastable i-phase into equilibrium crystalline phases. Supporting these microstructural results, all coatings presented a high and similar hardness of about 267 ± 8 HV. This study suggests that the CSAM process could, therefore. produce metastable quasicrystalline Al93Fe3Cr2Ti2 coatings with a composite microstructure and high hardness.
The Study of an Energy Management Strategy within a Microgrid with Photovoltaic Production of the <> Project in the City of Tangier and Integrating V2G Technology
Authors: Doudou Youssra , Cherkaoui Abdeljabbar , Ezziyyani Mostafa
Published: July 2023

The energy stored in an electric vehicle’s (EV’s) battery would be drawn and distributed to the electrical grid to better drive energy consumption within a microgrid that includes a renewable generator(s) managed by a specific energy management strategy. This concept, known as vehicle-to-grid technology (V2G), makes the energy stored in the electric vehicle’s battery more beneficial by discharging it to the public grid during periods of high demand. In this study, we will consider a vehicle system connected to a microgrid, which includes a photovoltaic generator reported from the <> project in the city of Tangier, and will elaborate on a strategy of the car connected to the V2G (vehicle to grid) network. This study proposes an optimal energy management strategy within the micro grid to charge the battery of a single electric vehicle during off-peak hours when energy costs are low, and to discharge them for driving purposes during peak hours when the energy cost is high. This is explored through a case study of two scenarios in the form of two different driving profiles of electric vehicles. The goal is to understand the behavior of the system in terms of energy redistribution from the V2G and from G2V (grid to the vehicle), and the contribution of the photovoltaic array reported by the <> photovoltaic generator in the city of Tangier. This study is conducted in a simplified standard Matlab microgrid with the necessary adaptation for the integration of this project generator into the grid, along with a judicious choice of an EV to ensure more explicit battery behavior in relation to our grid parameters. The simulation covers a single day (24 h). This strategy will manage the solar power, the load power, the state of charge of the EV battery, the time of day, and the driving scenario. Using two driving profiles, we will show the performance of the proposed energy management strategy. On the one hand, the V2G technology depends on the driving profile of the EV user is demonstrated. On the other hand, the 100%, 4.65% and 3.32% contributions of the 6 kW photovoltaic generator of the project <> in the city of Tangier are demonstrated. This contribution covers the load demand for charging of the battery of the electric vehicle that does not have the possibility to charge at work, as well as charging of the battery of the electric vehicle that can be charged at work.
Machine Learning-Based Prediction of Controlled Variables of APC Systems Using Time-Series Data in the Petrochemical Industry
Authors: Lee Minyeob , Yu Yoseb , Cheon Yewon , Baek Seungyun , Kim Youngmin , Kim Kyungmin , Jung Heechan , Lim Dohyeon , Byun Hyogeun , Lee Chaekyu , Jeong Jongpil
Published: July 2023

For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term forecasting with small data, where many systems are being applied to improve production efficiency. The problem considered in this study is the problem of predicting time-series Controlled Variables (CV) with machine learning, which is necessary to utilize an Advanced Process Control (APC) system in a petrochemical plant. In an APC system, the most important aspect is the prediction of the controlled variables and how the predicted values of the controlled variables should be modified to be in the user’s desired range. In this study, we focused on predicting the controlled variables. Specifically, we utilized various machine learning techniques to predict future controlled variables based on past controlled variables, Manipulated Variables (MV), and Disturbance Variables (DV). By using a time delay as a parameter and adjusting its value, you can analyze the relationship between past and future data and improve forecasting performance. Currently, the APC system is controlled through mathematical modeling and research, The time-series data of controlled variables, manipulated variables, and disturbance variables are predicted through machine learning models to compare performance and measure accuracy. It is becoming important to change from mathematical prediction models to data-based machine learning predictions. The R-Squared (R2) and Mean Absolute Percentage Error (MAPE) metric results of this study demonstrate the feasibility of introducing an APC system using machine learning models in petrochemical plants.
Dynamic Analysis of High-Water-Cut Tight Sandstone Gas Reservoirs and Study of Water Production Splitting of Gas Wells: A Case Study on the Western Sulige Gas Field, Ordos Basin, NW China
Authors: Meng Dewei , He Dongbo , Guo Zhi , Wang Guoting , Ji Guang , Tang Haifa , Zeng Qian
Published: July 2023

The western Sulige gas field is a new and key reserve area for the rolling development of the Sulige gas field in China. However, due to the complex gas–water relationship and the difficulty in identifying gas and water formation, the scale and benefit deployment of the gas field are seriously restricted. In particular, almost all of the wells in the area produce water, and no water measurements have been carried out for any single well, which leads to an unclear understanding of the dynamic characteristics of the production wells, thus affecting the productivity calculations of the gas wells and the overall regional productivity evaluation. Based on the testing data for a gas well, the impacts of the reservoir property parameters on the gas and water production were analyzed by combining the production performance and static geological characteristics. It was determined that the physical parameters K, Kh, and φSg had good positive correlations with gas production but not with water production; thus, effective prediction cannot be obtained for water production in gas well testing. After the analysis of the liquid-loading law, the gas wells were classified into three types: continuous liquid-carrying production, slight liquid-loading, and liquid-loading wells. In general, up to 96% of the gas wells were liquid-loaded. According to the different production performances exhibited in the different stages of the gas wells, five types of methods for diagnosing water production wells were proposed (gas testing, pilot production, gas–liquid two-phase measurement testing, liquid level detection, and production performance analysis), as well as the diagnostic criteria and corresponding solutions. To obtain real-time water production data for each well and investigate the change in the water–gas ratio (WGR) during the whole production process, a water production splitting method for gas wells based on three-dimensional geological modeling and numerical simulation combined with the constraints of the total water production of gas gathering stations was explored and established. The splitting results can be used to evaluate the water and gas productivity of gas wells and determine the best deliquification period. The gas well productivity when water production was considered was about 10% lower than that when water production was not considered. The best deliquification period was determined to be 125 days for wells with small water production, 20 days for wells with moderate water production, and 3 days for wells with serious water production. The results of this study could provide technical support for the scientific evaluation of gas well production indicators, reduction in development costs, and improvement in oil recovery.
A Comprehensive Overview of the Utilization of Recycled Waste Materials and Technologies in Asphalt Pavements: Towards Environmental and Sustainable Low-Carbon Roads
Authors: Yaro Nura Shehu Aliyu , Sutanto Muslich Hartadi , Baloo Lavania , Habib Noor Zainab , Usman Aliyu , Yousafzai Arsalaan Khan , Ahmad Abdulaziz , Birniwa Abdullahi Haruna , Jagaba Ahmad Hussaini , Noor Azmatullah
Published: July 2023

Given the prevailing concerns about greenhouse gas emissions, global warming, and the growing demand for renewable resources, the pavement industry, among others, is actively engaged in researching and exploring low-carbon materials and technologies. Despite the growing interest in low-carbon asphalt pavement, there is still a significant knowledge gap regarding the use of various waste materials and technologies to achieve this goal. This study aims to close this gap by conducting a systematic review and scientometric assessment of the existing studies on the use of waste materials and technologies for low-carbon asphalt pavement. The study spans the years 2008 to 2023, and the scientometric analysis was conducted using the VOS viewer application. The study identifies materials and technologies in this area by examining co-authored country studies, publication sources, and keyword co-occurrence. It should be noted that a limited number of waste materials that allow CO2 emissions reduction was analyzed in this study. However, other waste categories, such as bio-oils and polymers, which can provide positive either environmental or economic impacts on the production of paving materials, were not considered in the scope of the study. Based on the current review, it was found that integrating recycled waste materials like recycled asphalt pavement, biochar, or crumb rubber with alternative mixing technologies such as warm mix asphalt and cleaner energy can significantly reduce CO2 emissions. China and the United States were identified as key research contributors to the low-carbon pavement. Furthermore, biomass-based fuel and electric construction equipment lower carbon and greenhouse gas emissions by 36–90% and 67–95%, respectively. However, before various recycled waste materials and technologies can be widely used in the asphalt industry, various challenges need to be addressed, including cost concerns, performance and durability concerns, standardization and regulations, availability, integration with existing facilities, and insufficient field and long-term data. The review identified critical research gaps, such as the absence of a homogeneous and reliable standard method for low-carbon asphalt pavement, limited field performance data, and a life cycle assessment approach in analyzing the emission reduction effects. The reviews will aid in the paradigm shift to a more carbon-friendly pavement industry that uses recycled waste materials and technologies.
Production and Characterization of Kombucha Tea from Different Sources of Tea and Its Kinetic Modeling
Authors: Tarhan Kuzu Kubra , Aykut Gamze , Tek Serap , Yatmaz Ercan , Germec Mustafa , Yavuz Ibrahim , Turhan Irfan
Published: July 2023

This study aimed to investigate the fermentation performance, sugar consumption, pH changes, total phenolic compounds, and antioxidant activity produced using different tea extracts and sugar concentrations and the kinetic characteristics of Kombucha fermentation. Three independent sugar concentrations (10 g/L, 40 g/L, and 70 g/L) were used in the fermentation process. The results showed that the Kombucha culture consumed all sugar in the fermentation medium when the sugar concentration was below a certain threshold, but when the sugar concentration was high, not all substrate was consumed. Sugar consumption values ranged from 48.39 to 55.40 g/L and affected biomass formation, with higher sugar consumption resulting in increased biomass production. The pH decreased during fermentation due to the production of organic acids and microbial by-products, while total acidity increased. Total phenolic compounds increased during fermentation, with the highest concentrations observed in herbal Kombucha teas. Antioxidant activity varied, with some samples showing a decrease in DPPH scavenging ability. Kinetic characterization revealed the relationship between substrate depletion, sugar consumption, total acidity, and phenolic compound production. The results showed that sugar concentration influenced the fermentation kinetics and end-product characteristics of Kombucha tea. Overall, this study provides valuable insights into the fermentation process of Kombucha tea and its impact on various parameters, contributing to the understanding of the factors affecting its quality and health benefits.
Open Educational Resources (OERs) at European Higher Education Institutions in the Field of Library and Information Science during COVID-19 Pandemic
Authors: Mićunović Milijana , Rako Sabina , Feldvari Kristina
Published: July 2023

The purpose of this study is to map the practices regarding open educational resources’ (OERs) development and implementation at European higher education institutions (HEIs) in the field of library and information science (LIS) during the COVID-19 pandemic and to identify the challenges and obstacles to their full and optimal utilization, both during crisis situations and beyond. A systematic literature review and questionnaire-based survey yielded results from 56 European LIS schools/departments (n = 56). Statistical analysis was performed using the R programming language, and descriptive statistics were used to quantify the data sets. The results have shown that the COVID-19 pandemic served as an impetus for the adoption of OERs, particularly in the context of digital education (DE) and remote learning. However, there is still a lack of awareness of the many benefits and opportunities they provide to higher education, as evidenced by the fact than less than half LIS schools/departments used OERs. Certain issues were identified, such as the lack of institutional policies regarding OERs, inadequate peer-review of OERs, and, in most cases, the absence of monitoring and evaluation practices for OERs. The results and insights from this study can be used to improve all aspects of OERs’ implementation and thus accelerate their adoption, both with regard to LIS schools/departments and other fields. Further research into the topic through interviews and focus groups should provide a deeper understanding of opportunities, challenges and practices surrounding the adoption of OERs in the field of LIS education.
New Perspectives on the Quaternary Paleogeography of Coastal Ecuador and Its Relationships with Climate Change
Authors: Quiñónez-Macías María , Chunga Kervin , Toulkeridis Theofilos , Mora-Mendoza Alvaro , Constantine Angelo
Published: July 2023

Well-preserved Quaternary sedimentary sequences in the central coast of Ecuador have provided sufficient relevant information for paleogeographic reconstruction and climatic evolution, from stratigraphic, geochemical, and biological analysis. The Jaramijo canton site is one of the most remarkable results in the stratigraphic correlation of lithological units with delineation of a paleo sea-cliff of age 14C 43,245 ± 460 B.P. (belonging to the MIS-3). This MIS-3 is associated with a period of glaciation, but the data obtained, such as δ 18O, indicate paleo-temperature values of −1 to −1.5, which are interpreted in this study, indicate that the central coast of Ecuador has an interstadial phase (warm years in a glacial stage). Two more paleo-coastal cliffs have been mapped from orthophoto analysis, but these are younger. The sedimentary levels analyzed in this study include deposits that occurred in MIS 3 to MIS 1. Holocene transgression has modified the central coast of Ecuador and increased the level of coastal climate hazard by sea level rise. Indeed, paleo-coastlines have been evidenced from bathymetric data in the depth contours of −5.5 m and −7.6 m, at 440 and 650 m distances from the up-to-date coastline. For the Jaramijó site, the rate of cliff-erosion and wave-cut platforms are in the order of 1.1 to 2.4 m/yr. These cliff-erosion rates, with a moderate to high coastal vulnerability index, can be increased if we consider mathematical models with an estimated sea-level rise scenario to be, in 2100, about +1 to +1.4 m.
Laser-Induced Breakdown Spectroscopy and X-ray Fluorescence Analysis of Bronze Objects from the Late Bronze Age Baley Settlement, Bulgaria
Authors: Penkova Petya , Malcheva Galina , Grozeva Margarita , Hristova Tanya , Ivanov Georgy , Alexandrov Stefan , Blagoev Kiril , Tankova Vani , Mihailov Valentin
Published: July 2023

In the presented work, a total of 60 bronze artefacts from the prehistoric settlement of Baley, Bulgaria were analyzed by means of laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). The archaeological finds were excavated from three levels, with a time span from the 15th century BC to the first half of the 11th century BC. The obtained analytical information was used for quantitative estimation of the amount of tin, lead and arsenic, which determine the mechanical properties of the alloy and the manufacturing technology. Based on the estimated quantities of these elements, a chemometric statistical analysis (principal component analysis—PCA) was performed to classify and divide the samples into separate groups according to the production dating. The data obtained in this study can be used for comparison with the elemental content in deposits from other settlements of this period.
AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods
Authors: Xiong Lu , Manathunga Vajira , Luo Jiyao , Dennison Nicholas , Zhang Ruicheng , Xiang Zhenhai
Published: July 2023

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes.
Special Issue “Legged Robots into the Real World”
Authors: Zhou Chengxu
Published: July 2023

In the landscape of intelligent systems and robotics, legged robots stand out as a fascinating fusion of biological inspiration and engineered design [...]
A Study of the Relationships between Depths of Soil Constraints and Remote Sensing Data from Different Stages of the Growing Season
Authors: Ulfa Fathiyya , Orton Thomas G. , Dang Yash P. , Menzies Neal W.
Published: July 2023

The presence of salinity and sodicity in the root zone can limit root development and impact crop yield. Topsoil constraints are likely to have the greatest impact on crop growth early in the growing season, when plant roots are still shallow. Later in the growing season, subsoil constraints may have a greater impact as roots reach deeper into the soil. This study investigated whether different patterns of spatial variation in crop growth would be evident in remote sensing data captured from different stages of the growing season, with the aim of providing a means of indicating whether soil constraints in the topsoil and in the subsoil might be impacting crop growth. If a topsoil constraint is impacting growth, we might expect its effects to show through a negative correlation between the soil constraint and the early-season vegetation index. However, we would not expect to observe the impact of a subsoil constraint until later in the season (when roots have reached the constraint). To test the results from the analysis of remote sensing data, we used soil data from five fields from across Australia’s northern grains-growing region. We used these data to assess soil constraint severity and correlations between the soil constraints and enhanced vegetation index (EVI). The results of the study were inconclusive, and it was difficult to identify a dominant soil constraint with a clear relationship to crop growth. The soil data were also insufficient to draw conclusions about the depths of any dominant soil constraints. Furthermore, there was a lot of subjectivity in the interpretations of the correlations between remote sensing and soil data. The study also investigated the consistency of the spatial variation in EVI over multiple years, but the results were still inconclusive. In conclusion, this study highlights the challenges of using remote sensing data to diagnose soil constraints in agricultural settings. While remote sensing can provide useful insights into crop growth, interpreting these data and drawing meaningful conclusions about soil constraints requires further research and development.
Optical and SAR Image Registration Based on Pseudo-SAR Image Generation Strategy
Authors: Hu Canbin , Zhu Runze , Sun Xiaokun , Li Xinwei , Xiang Deliang
Published: July 2023

The registration of optical and SAR images has always been a challenging task due to the different imaging mechanisms of the corresponding sensors. To mitigate this difference, this paper proposes a registration algorithm based on a pseudo-SAR image generation strategy and an improved deep learning-based network. The method consists of two stages: a pseudo-SAR image generation strategy and an image registration network. In the pseudo-SAR image generation section, an improved Restormer network is used to convert optical images into pseudo-SAR images. An L2 loss function is adopted in the network, and the loss function fluctuates less at the optimal point, making it easier for the model to reach the fitting state. In the registration part, the ROEWA operator is used to construct the Harris scale space for pseudo-SAR and real SAR images, respectively, and each extreme point in the scale space is extracted and added to the keypoint set. The image patches around the keypoints are selected and fed into the network to obtain the feature descriptor. The pseudo-SAR and real SAR images are matched according to the descriptors, and outliers are removed by the RANSAC algorithm to obtain the final registration result. The proposed method is tested on a public dataset. The experimental analysis shows that the average value of NCM surpasses similar methods over 30%, and the average value of RMSE is lower than similar methods by more than 0.04. The results demonstrate that the proposed strategy is more robust than other state-of-the-art methods.
Sea Ice Detection by an Unsupervised Method Using Ku- and Ka-Band Radar Data at Low Incidence Angles: First Results
Authors: Panfilova Maria , Karaev Vladimir
Published: July 2023

This paper presents the first results of sea ice detection using the data of Ka- and Ku-band radars at low incidence angles. A classification method based on an unsupervised K-means approach is applied to the arrays of the data for the Arctic and Antarctic regions. Comparison with Advanced Microwave Scanning Radiometer 2 (AMSR-2) data was performed, and the dependence of classification performance was evaluated for incidence angles from 0° to 18.15°. This paper evaluates the classification accuracy of sea ice detection based on Ku-band, Ka-band, and their combination. Preliminary results indicate that the classification based solely on Ku-band data achieves the best performance.
Infrared and Visible Image Homography Estimation Based on Feature Correlation Transformers for Enhanced 6G Space–Air–Ground Integrated Network Perception
Authors: Wang Xingyi , Luo Yinhui , Fu Qiang , Rui Yun , Shu Chang , Wu Yuezhou , He Zhige , He Yuanqing
Published: July 2023

The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects.
A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model
Authors: Li Klin , Lu Yutong
Published: July 2023

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.
MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile
Authors: Estay Sergio A. , Chávez Roberto O. , Lastra José A. , Rocco Ronald , Gutiérrez Álvaro G. , Decuyper Mathieu
Published: July 2023

Outbreaks of the Ormiscodes amphimone moth are among the largest biotic disturbances in South America, defoliating vast areas of native Nothofagus pumilio forests in the Chilean and Argentinian Patagonia in the last decade. Using MODIS 16-day composites of the enhanced vegetation index and the new functions of the latest release of the “npphen” R-package, we identified new maximum records of continuously defoliated area in the Aysén region (Chilean Patagonia). This approach allowed us to detect 55,193 ha and 62,344 ha of extremely defoliated N. pumilio forest in 2019 and 2022, respectively, in an area locally known as “Mallín Grande”. Extreme defoliation was accounted for by means of negative EVI anomalies with values falling among 5% of the lowest EVI records of the reference period (2000–2010). These new 2019 and 2022 outbreaks in Mallín Grande were the largest reported insect outbreaks in South American Patagonia in this century.
Insuring Alpine Grasslands against Drought-Related Yield Losses Using Sentinel-2 Satellite Data
Authors: Castelli Mariapina , Peratoner Giovanni , Pasolli Luca , Molisse Giulia , Dovas Alexander , Sicher Gabriel , Crespi Alice , Rossi Mattia , Alasawedah Mohammad Hussein , Soini Evelyn , Monsorno Roberto , Notarnicola Claudia
Published: July 2023

This work estimates yield losses due to drought events in the mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between the leaf area index (LAI) and grassland yield, we exploit the LAI as a proxy for yield. We estimate the LAI by using the Sentinel-2 biophysical processor and compare different gap-filling methods, including time series interpolation and fusion with Sentinel-1 SAR data. We derive the grassland production index (GPI) as the growing season cumulate of the daily product between the LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as an anomaly of the GPI. The validation of the Sentinel-2 LAI with ground measurements showed an RMSE of 0.92 [m2 m−2] and an R2 of 0.81 over all the measurement sites. A comparison between the GPI and yield showed, on average, an R2 of 0.56 at the pixel scale and an R2 of 0.74 at the parcel scale. The developed prototype GPI index was used at the end of the growing season of the year 2022 to calculate the payments of an experimental insurance scheme which was proposed to a group of farmers in Trentino-South Tyrol.
PointCNT: A One-Stage Point Cloud Registration Approach Based on Complex Network Theory
Authors: Wu Xin , Wei Xiaolong , Xu Haojun , Li Caizhi , Hou Yuanhan , Yin Yizhen , He Weifeng
Published: July 2023

Inspired by the parallel visual pathway model of the human neural system, we propose an efficient and high-precision point cloud registration method based on complex network theory (PointCNT). A deep learning network (DNN) design method based on complex network theory is proposed, and a multipath feature extraction network, namely, Complex Kernel Point Convolution Neural Network (ComKP-CNN) for point clouds is designed based on the design method. Self-supervision is introduced to improve the feature extraction ability of the model. A feature embedding module is proposed to explicitly embed the transformation-variant coordinate information and transformation-invariant distance information into features. A feature fusion module is proposed to enable the source and template point clouds to perceive each other’s nonlocal features. Finally, a Multilayer Perceptron (MLP) with prominent fitting characteristics is utilized to estimate the transformation matrix. The experimental results show that the Registration Recall (RR) of PointCNT on ModelNet40 dataset reached 96.4%, significantly surpassing one-stage methods such as Feature-Metric Registration (FMR) and approaching two-stage methods such as Geometric Transformer (GeoTransformer). The computation speed is faster than two-stage methods, and the registration run time is 0.15 s. In addition, ComKP-CNN is universal and can improve the registration accuracy of other point cloud registration methods.
Assimilating GNSS TEC with an LETKF over Yunnan, China
Authors: Tang Jun , Zhang Shimeng , Yang Dengpan , Wu Xuequn
Published: July 2023

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.
Joint Design of Transmitting Waveform and Receiving Filter via Novel Riemannian Idea for DFRC System
Authors: Zhao Yinan , Zhao Zhongqing , Tong Fangqiu , Sun Ping , Feng Xiang , Zhao Zhanfeng
Published: July 2023

Recently, the problem of target detection in noisy environments for the Dual-Functional Radar Communication (DFRC) integration system has been a hot topic. In this paper, to suppress the noise and further enhance the target detection performance, a novel manifold Riemannian Improved Armijo Search Conjugate Gradient algorithm (RIASCG) framework has been proposed which jointly optimizes the integrated transmitting waveform and receiving filter. Therein, the reference waveform is first designed to achieve excellent pattern matching of radar beamforming. Furthermore, to ensure the quality of system information transmission, the energy of multi-user interference (MUI) of communication signals is incorporated as the constraint. Additionally, the typical similarity constraint is introduced to ensure the transmitting waveform with a good ambiguity function. Finally, simulation results demonstrate that the designed waveform not only enhances the system’s target detection performance in noisy environments but also achieves a relatively good multi-user communication ability when compared with other prevalent waveforms.
Mutual Coupling Reduction of a Multiple-Input Multiple-Output Antenna Using an Absorber Wall and a Combline Filter for V2X Communication
Authors: Fu Yuanxu , Shen Tao , Dou Jiangling
Published: July 2023

This paper presents an MIMO antenna for vehicle-to-everything (V2X) communication, which adopts two ways of combline filters and absorption wall decoupling. A combline filter and an absorption wall are used, respectively, for internal and external decoupling. The combline filter is incorporated between the ground of the two adjacent antennas, which reduces the mutual coupling between them. Additionally, the mutual coupling of radiation between adjacent antennas is significantly reduced by the absorber wall. These combline filters and absorber walls use the method of electromagnetic field distribution to explain the reduction in the mutual coupling between the adjacent antennas. The transmission coefficient and surface current distribution explain the effectiveness of the decoupling structure. When the frequency is between 3.8 and 4.8 GHz, the simulation and measurement results show that S11 is less than −10 dB, the bandwidth is 25% and the peak gain is 7.8 dBi. In addition, the proposed MIMO antenna has a high isolation between antenna units (>37 dB), and the envelop correlation coefficient (ECC) is less than 0.005.
BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring
Authors: Sun Ao , Zhang Qiuzhao , Gao Xingwang , Meng Xiaolin , Zhang Yunlong , Hancock Craig
Published: July 2023

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of “zero mean” and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5–7%.
Determination of Blast Vibration Safety Criteria for Buried Polyethylene Pipelines Adjacent to Blast Areas, Using Vibration Velocity and Strain Data
Authors: Tu Shengwu , Zhong Dongwang , Li Linna , Gong Xiangchao , Tao Haohao
Published: July 2023

In order to ensure the safe operation of buried polyethylene pipelines adjacent to blasting excavations, controlling the effects of blasting vibration loads on the pipelines is a key concern. Model tests on buried polyethylene pipelines under blasting loads were designed and implemented, the vibration velocity and dynamic strain response of the pipelines were obtained using a TC-4850 blast vibrometer and a UT-3408 dynamic strain tester, and the distribution characteristics of blast vibration velocity and dynamic strain were analyzed based on the experimental data. The results show that the blast load has the greatest effect on the circumferential strain of the polyethylene pipe, and the dynamic strain response is greatest at the section of the pipe nearest to the blast source. Pipe peak vibration velocity (PPVV), ground peak particle velocity (GPPV), and the peak dynamic strain of the pipe were highly positively correlated, which verifies the feasibility of using GPPV to characterize pipeline vibration and strain level. According to the failure criteria and relevant codes, combined with the analysis of experimental results, the safety threshold of additional circumferential stress on the pipeline is 1.52 MPa, and the safety control vibration speed of the ground surface is 21.6 cm/s.
Numerical Analysis of GNSS Signal Outage Effect on EOPs Solutions Using Tightly Coupled GNSS/IMU Integration: A Simulated Case Study in Sweden
Authors: Jouybari Arash , Bagherbandi Mohammad , Nilfouroushan Faramarz
Published: July 2023

The absence of a reliable Global Navigation Satellite System (GNSS) signal leads to degraded position robustness in standalone receivers. To address this issue, integrating GNSS with inertial measurement units (IMUs) can improve positioning accuracy. This article analyzes the performance of tightly coupled GNSS/IMU integration, specifically the forward Kalman filter and smoothing algorithm, using both single and network GNSS stations and the post-processed kinematic (PPK) method. Additionally, the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs) solutions is investigated. Results demonstrate that the smoothing algorithm enhances positioning uncertainty (RMSE) for north, east, and heading by approximately 17–43% (e.g., it improves north RMSE from 51 mm to a range of 42 mm, representing a 17% improvement). Orientation uncertainty is reduced by about 60% for roll, pitch, and heading. Moreover, the algorithm mitigates the effects of GNSS signal outage, improving position uncertainty by up to 95% and orientation uncertainty by up to 60% using the smoothing algorithm instead of the forward Kalman filter for signal outages up to 180 s.
An Interface Setup Optimization Method Using a Throughput Estimation Model for Concurrently Communicating Access Points in a Wireless Local Area Network
Authors: Akhter Fatema , Funabiki Nobuo , Htet Ei Ei , Wu Bin , Kong Dezheng , Fang Shihao
Published: July 2023

The IEEE 802.11 wireless local-area network (WLAN) has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining access points (APs). To address this problem, we studied the AP transmission power optimization method, which selects the maximum or minimum power supplied to each AP so that the average signal-to-interference ratio (SIR) among the concurrently communicating APs is maximized.However, this method requires measurements of receiving signal strength (RSS) under all the possible combinations of powers. It may need intolerable loads and time as the number of APs increases. It also only considers the use of channel bonding (CB), although non-CB sometimes achieves higher performance under high levels of interference. In this paper, we present an AP interface setup optimization method using the throughput estimation model for concurrently communicating APs. The proposed method selects CB or non-CB in addition to the maximum or minimum power for each AP. This model approach avoids expensive costs of RSS measurements under a number of combinations. To estimate the RSS at an AP from another AP or a host, the model needs the distance and the obstacles between them, such as walls. Then, by calculating the estimated RSS with the model and calculating the SIR from them, the AP interface setups for a lot of APs in a large-scale wireless network can be optimized on a computer in a very short time. For evaluation, we conducted extensive experiments using Raspberry Pi for APs and Linux PCs for hosts under 12 network topologies in three buildings at Okayama University, Japan, and Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. The results confirm that the proposed method selects the best AP interface setup with the highest total throughput in any topology.
High-Frequency Ultrasonic Spectroscopy of Structure Gradients in Injection-Molded PEEK Using a Focusing Transducer
Authors: Summa Jannik , Kurkowski Moritz , Jungmann Christian , Rabe Ute , Spoerer Yvonne , Stommel Markus , Herrmann Hans-Georg
Published: July 2023

For high-performance thermoplastic materials, material behavior results from the degree of crystallization and the distribution of crystalline phases. Due to the less stiff amorphous and the stiffer and anisotropic crystalline phases, the microstructural properties are inhomogeneous. Thus, imaging of the microstructure is an important tool to characterize the process-induced morphology and the resulting properties. Using focusing ultrasonic transducers with high frequency (25 MHz nominal center frequency) enables the imaging of specimens with high lateral resolution, while wave propagation is related to the elastic modulus, density and damping of the medium. The present work shows experimental results of high-frequency ultrasonic spectroscopy (HF-US) applied to injection-molded polyether-ether-ketone (PEEK) tensile specimens with different process-related morphologies. This work presents different analysis procedures, e.g., backwall echo, time of flight and Fourier-transformed time signals, facilitating the mapping of gradual mechanical properties and assigning them to different crystalline content and morphological zones.