bluuebunny/crossref_metadata_2025 huggingface dataset:
Research Progress of Elastomer Materials and Application of Elastomers in Drilling Fluid
Authors: Yang Lili , Ou Zhiting , Jiang Guancheng
Published: February 2023

An elastomer is a material that undergoes large deformation under force and quickly recovers its approximate initial shape and size after withdrawing the external force. Furthermore, an elastomer can heal itself and increase volume when in contact with certain liquids. They have been widely used as sealing elements and packers in different oil drilling and development operations. With the development of drilling fluids, elastomer materials have also been gradually used as drilling fluid additives in drilling engineering practices. According to the material type classification, elastomer materials can be divided into polyurethane elastomer, epoxy elastomer, nanocomposite elastomer, rubber elastomer, etc. According to the function classification, elastomers can be divided into self-healing elastomers, expansion elastomers, etc. This paper systematically introduces the research progress of elastomer materials based on material type classification and functional classification. Combined with the requirements for drilling fluid additives in drilling fluid application practice, the application prospects of elastomer materials in drilling fluid plugging, fluid loss reduction, and lubrication are discussed. Oil-absorbing expansion and water-absorbing expansion elastomer materials, such as polyurethane, can be used as lost circulation materials, and enter the downhole to absorb water or absorb oil to expand, forming an overall high-strength elastomer to plug the leakage channel. When graphene/nano-composite material is used as a fluid loss additive, flexibility and elasticity facilitate the elastomer particles to enter the pores of the filter cake under the action of differential pressure, block a part of the larger pores, and thus, reduce the water loss, while it would not greatly change the rheology of drilling fluid. As a lubricating material, elastic graphite can form a protective film on the borehole wall, smooth the borehole wall, behaving like a scaly film, so that the sliding friction between the metal surface of the drill pipe and the casing becomes the sliding friction between the graphite flakes, thereby reducing the friction of the drilling fluid. Self-healing elastomers can be healed after being damaged by external forces, making drilling fluid technology more intelligent. The research and application of elastomer materials in the field of drilling fluid will promote the ability of drilling fluid to cope with complex formation changes, which is of great significance in the engineering development of oil and gas wells.
Formation and Investigation of Physicochemical and Microbiological Properties of Biocomposite Films Containing Turmeric Extract Nano/Microcapsules
Authors: Stanisławska Natalia , Khachatryan Gohar , Khachatryan Karen , Krystyjan Magdalena , Makarewicz Małgorzata , Krzan Marcel
Published: February 2023

In the era of growing plastic consumption, food waste by consumers and overproduction caused by economic reasons, the global goal is to decrease these phenomena. Biocomposite films investigated in the past years are creating a promising future toward ecological, intelligent and active packaging. Due to their unique properties, they can be used in many areas of our life and reduce the constantly increasing pollution of our planet. The aim of our study was to obtain innovative and flexible biopolymer films based on sodium alginate and chitosan, as well as to develop methods for generating nanocapsules with turmeric extract in them. Bionanocomposites were analyzed using UV-VIS, FTIR, photoluminescence spectroscopy and SEM microscopy, while contact angles, surface free energy, particle size (DLS) and zeta potential were determined. The mechanical and colorimetric properties of the produced films were investigated, and the water content, solubility and water absorption were determined. Microbiological tests were carried out to analyze the influence of the produced films on the development of microorganisms. The results of the performed analyses allowed us to confirm the presence of curcumin nano- and microcapsules in the alginate–chitosan composite. Moreover, studies have shown that the structure of polysaccharides does not change during capsule manufacturing. The film with the highest concentration of the capsules showed better parameters in tests of solubility, water content, degree of swelling and mechanical properties. The obtained properties of the developed films allow them to be used as active and intelligent packaging materials, or as their parts.
Recent Progress of the Preparation and Application of Electrospun Porous Nanofibers
Authors: Wang Pu , Lv He , Cao Xianyang , Liu Yanan , Yu Deng-Guang
Published: February 2023

Electrospun porous nanofibers have gained a lot of interest recently in various fields because of their adjustable porous structure, high specific surface area, and large number of active sites, which can further enhance the performance of materials. This paper provides an overview of the common polymers, preparation, and applications of electrospun porous nanofibers. Firstly, the polymers commonly used to construct porous structures and the main pore-forming methods in porous nanofibers by electrospinning, namely the template method and phase separation method, are introduced. Secondly, recent applications of electrospun porous nanofibers in air purification, water treatment, energy storage, biomedicine, food packaging, sensor, sound and wave absorption, flame retardant, and heat insulation are reviewed. Finally, the challenges and possible research directions for the future study of electrospun porous nanofibers are discussed.
Impact of the Chemical Structure of Photoreactive Urethane (Meth)Acrylates with Various (Meth)Acrylate Groups and Built-In Diels–Alder Reaction Adducts on the UV-Curing Process and Self-Healing Properties
Authors: Bednarczyk Paulina , Mozelewska Karolina , Klebeko Joanna , Rokicka Joanna , Ossowicz-Rupniewska Paula
Published: February 2023

A series of UV-curable urethane (meth)acrylates were obtained by copolymerization of the Diels–Alder adduct (HODA), isophorone diisocyanate, PEG1000, and various hydroxy (meth)acrylates. The aim of the present work was to determine the influence of the chemical structure of the introduced (meth)acrylic groups, i.e., hydroxyethyl acrylate, hydroxypropyl acrylate, hydroxyethyl methacrylate, and hydroxypropyl methacrylate, on the UV-curing process and self-healing properties of cured coatings. The chemical structure of prepolymers was characterized by FTIR and NMR spectroscopy, whereas the UV-curing process was monitored in real time using FTIR and photo-DSC. In turn, the self-healing properties were characterized in relation to the thermally reversible mechanism, which was tested using the following methods: an FTIR spectroscope equipped with a heating attachment; DSC and TG apparatus; and an optical microscope equipped with a stage with programmable heating. The result of comprehensive research on the self-healing of photocurable coatings in the context of the presence of various photoreactive groups and the course of the curing process allows one to control the self-healing process by reducing the effective healing temperature. The self-healing properties, taken together with the fast UV curing of the coatings and excellent properties of cured coatings, make the material attractive for a variety of applications, in particular in cases where coatings are not repaired, e.g., for economic reasons or when it is not possible, such as in flexible electronic screens, car paint film, and aircraft interior finishes.
Non-Conjugated Copoly(Arylene Ether Ketone) for the Current-Collecting System of a Solar Cell with Indium Tin Oxide Electrode
Authors: Lachinov Alexey N. , Karamov Danfis D. , Galiev Azat F. , Salazkin Sergey N. , Shaposhnikova Vera V. , Kost Tatiana N. , Chebotareva Alla B.
Published: February 2023

The mechanism of charge carrier transport in the indium tin oxide (ITO)/polymer/Cu structure is studied, where thin films of copoly(arylene ether ketone) with cardo fluorene moieties are used. This copoly(arylene ether ketone) is non-conjugated polymer which has the properties of electronic switching from the insulating to the highly conductive state. The dependence on the polymer film thickness of such parameters as the potential barrier at the ITO/polymer interface, the concentration of charge carriers, and their mobility in the polymer is studied for the first time. The study of this system is of interest due to the proven potential of using the synthesized polymer in the contact system of a silicon solar cell with an ITO top layer. The parameters of charge carriers and ITO/polymer barrier are evaluated based on the analysis of current–voltage characteristics of ITO/polymer/Cu structure within the injection current models and the Schottky model. The thickness of the polymer layer varies from 50 nm to 2.1 µm. The concentration of intrinsic charge carriers increases when decreasing the polymer film thickness. The charge carrier mobility depends irregularly on the thickness, showing a maximum of 9.3 × 10−4 cm2/V s at 210 nm and a minimum of 4.7 × 10−11 cm2/V s at 50 nm. The conductivity of polymer films first increases with a decrease in thickness from 2.1 µm to 210 nm, but then begins to decrease upon transition to the globular structure of the films at smaller thicknesses. The dependence of the barrier height on polymer thickness has a minimum of 0.28 eV for films 100–210 nm thick. The influence of the supramolecular structure and surface charge field of thin polymer films on the transport of charge carriers is discussed.
Hybrid Filter and Genetic Algorithm-Based Feature Selection for Improving Cancer Classification in High-Dimensional Microarray Data
Authors: Ali Waleed , Saeed Faisal
Published: February 2023

The advancements in intelligent systems have contributed tremendously to the fields of bioinformatics, health, and medicine. Intelligent classification and prediction techniques have been used in studying microarray datasets, which store information about the ways used to express the genes, to assist greatly in diagnosing chronic diseases, such as cancer in its earlier stage, which is important and challenging. However, the high-dimensionality and noisy nature of the microarray data lead to slow performance and low cancer classification accuracy while using machine learning techniques. In this paper, a hybrid filter-genetic feature selection approach has been proposed to solve the high-dimensional microarray datasets problem which ultimately enhances the performance of cancer classification precision. First, the filter feature selection methods including information gain, information gain ratio, and Chi-squared are applied in this study to select the most significant features of cancerous microarray datasets. Then, a genetic algorithm has been employed to further optimize and enhance the selected features in order to improve the proposed method’s capability for cancer classification. To test the proficiency of the proposed scheme, four cancerous microarray datasets were used in the study—this primarily included breast, lung, central nervous system, and brain cancer datasets. The experimental results show that the proposed hybrid filter-genetic feature selection approach achieved better performance of several common machine learning methods in terms of Accuracy, Recall, Precision, and F-measure.
Research on Resilience Evaluation of Coal Industrial Chain and Supply Chain Based on Interval Type-2F-PT-TOPSIS
Authors: Wu Anbo , Sun Yue , Zhang Huiling , Sun Linhui , Wang Xinping , Li Boying
Published: February 2023

As unexpected events such as natural disasters, the COVID-19 pandemic, and overseas containment have caused inevitable shocks to the energy industrial chain and supply chain, the current global energy crisis is intensifying, and different countries and regions have adopted different strategies according to the characteristics of their own national resource endowments in order to cope with energy security. Maintaining the security of the coal industrial chain and supply chain is a prerequisite for energy security to be effectively ensured, considering the main position of coal in China’s energy. Therefore, in the face of multiple uncertain risk factors under today’s momentous changes, this paper constructs an industrial coal chain and supply chain resilience evaluation indicator system from the perspective of resilience, based on four representational capabilities of resilience, namely preparedness, absorptive capacity, recovery capacity, and adaptability, in order to profoundly understand and enhance the resilience of the coal industrial chain and supply chain. An integrated method combining Interval Type-2 Fuzzy Prospect Theory and Technique for Order Preference by Similarity to an Ideal Solution (Interval Type-2F-PT-TOPSIS) is proposed for evaluating the resilience level of the coal industrial chain and supply chain. In the case of Shaanxi Province in China, it was found that the worst level of resilience of the coal industrial chain and supply chain in Shaanxi Province was in 2018, and the best was in 2021. Finally, based on the evaluation results, recommendations are provided to the key nodes of the industrial chain and supply chain in Shaanxi Province with a view to improving their resilience levels to cope with uncertain risks.
Evidence for an Extreme Cooling Event Prior to the Laschamp Geomagnetic Excursion in Eifel Maar Sediments
Authors: Albert Johannes , Sirocko Frank
Published: February 2023

We present a timeseries of flood and slumping phases in central Europe for the past 65,000 years from event layers in sediment cores from infilled Eifel maar basins (Germany). Palynological, petrographic and organic carbon (chlorins) records are used to understand the precise timing of these events. Periods of increased flood activity seem to coincide with Heinrich stadials in marine sediment records, which are associated with cold and more arid climate conditions, indicating a vegetation response within the maars’ catchment areas. This multi-proxy correlation reveals prominent slumps at different maar sites during Greenland Stadial (GS) 12. The stratigraphy is based on sediment records from the Auel infilled maar and we thus call this event Auel Cold Event (ACE). Frozen and fractured sediment packages within the slump suggest deep frost or permafrost conditions for the region during the stadial. The results agree well with sediment archives and archeological sites across Europe that report severely cold and arid conditions for the stadial. This supports the assumption that GS12 was indeed one of the coldest periods of the last glacial cycle rather than the Heinrich stadials. Based on our age model, the ACE occurred at 43,500 yr b2k (years before the year 2000), which coincides with the initial weakening of Earth’s magnetic field strength prior to the Laschamp geomagnetic excursion.
Chihuahuan Desert Vegetation Development during the Past 10,000 Years According to Pollen and Sediment Data at Upper Arroyo, Saltillo, Mexico
Authors: Albert Bruce M.
Published: February 2023

Pollen and sediment data from a 10.5 m-deep alluvial exposure and a secondary tributary exposure at Upper Arroyo, a seasonal river, in Saltillo, Mexico, were examined with the aim of reconstructing the vegetation and environmental history during the Holocene as a whole. The role of climate change in Chihuahuan Desert flora development after 8800 BP was assessed, in addition to more local physiographic factors, such as erosion and accumulation, soil development and denudation, and hydrological entrenchment. Climate change appeared to have been a principal agent of vegetation change in the Early and Middle Holocene, with a periodic expansion of desert vegetation. A reduction in the environmental carrying capacities for mesophytic flora according to physiographic factors, such as soil erosion and channel entrenchment, was then identified after 2300 BP, also promoting azonal ecological niches for xerophytic vegetation in southern Coahuila, Mexico, that persist despite modern variations in precipitation.
Learning through Listening and Responding: Probing the Potential and Limits of Dialogue in Local and Online Environments
Authors: Welz Claudia , Ikonen Essi , Kristiansen Aslaug
Published: February 2023

This article explores an age-old form of dialogical learning, havruta, which has been employed by Jews throughout the centuries to study the Torah and the Talmud, and evaluates the experiment of extending havruta from a couple of fellow students (haverim) to an international, multi-religious group reading philosophical texts together, and transferring the learning process from the Jewish house of study (in Hebrew: beit ha-Midrash, in German: Lehrhaus) to an online environment. Methodologically, the experiences from the online havruta are brought into a theory-practice feedback loop and are discussed from various theoretical angles: (1) The first section introduces how havruta was conducted traditionally and how Franz Rosenzweig, who in 1920 founded the Frankfurt Lehrhaus and invited Martin Buber to offer lecture courses, advanced havruta. (2) The second section explains how Rosenzweig’s pedagogical principles as distilled from his writings on education are applied and modified in the above-mentioned contemporary online reading group. (3) The third section draws on Buber’s philosophy of dialogue, Juhani Pallasmaa’s architectural theory and Michel Chion’s film theory in order to investigate the epistemological and pedagogical significance of different modes of listening, asking, and responding, and the role of trust for dialogical learning in local and online learning communities.
Active Adaptation and Passive Dependence: A Comparison of Protestant and Buddhist Social Services in Contemporary China
Authors: Wu Xianhong , Gao Jianguo
Published: February 2023

China’s religious social services complement the statutory welfare. Clarifying the situations and characteristics of different types of religious social services is conducive to promoting their better integration into public welfare. With the help of existing policy texts, research documents, and website data, this paper employs the thematic framework analysis method to analyze texts and documents and uses NVivo12 and SPSS26 to analyze website data. We explore the similarities and differences between contemporary Chinese Protestant social services and Buddhist social services from the perspectives of the service program, service organizations, and service resources, starting from multiple dimensions such as vertical and horizontal, similarity and difference. The main findings include the following: (a) In terms of service programs, Protestant social services are more inclusive than Buddhist social services and more public in terms of participant selection, religious environment, and the use of spiritual methods. Protestant social services are more open in terms of service value and public expression, while Buddhist social services are more localized and are politically consistent with the government. (b) In terms of service organizations, Protestant social services and Buddhist social services are based on three main types of legal persons. Protestant social services were the first to register organizational legal persons. Protestant social organizations differentiated into special service institutions and have core organizations with strong mobilization capabilities (CCC/TSPM). There is little difference between Protestant and Buddhist social services in private non-enterprise units and foundations (transparency index, business scope). (c) In terms of service resources, both Protestant and Buddhist social services rely on donations. The sources of funds for Protestant social services are more international, diversified, and market-oriented. In terms of government resource acquisition, Protestant social services actively “adapt”, while Buddhist social services passively “rely”.
On-Orbit Vicarious Radiometric Calibration and Validation of ZY1-02E Thermal Infrared Sensor
Authors: Tang Hongzhao , Xie Junfeng , Dou Xianhui , Zhang Honggeng , Chen Wei
Published: February 2023

The ZY1-02E satellite carrying a thermal infrared sensor was successfully launched from the Taiyuan Satellite Launch Center on 26 December 2021. The quantitative characteristics of this thermal infrared camera, for use in supporting applications, were acquired as part of an absolute radiometric calibration campaign performed at the Ulansuhai Nur and Baotou calibration site (Inner Mongolia, July 2022). In this paper, we propose a novel on-orbit absolute radiometric calibration technique, based on multiple ground observations, that considers the radiometric characteristics of the ZY1-02E thermal infrared sensor. A variety of natural surface objects were selected as references, including bodies of water, bare soil, a desert in Kubuqi, and sand and vegetation at the Baotou calibration site. During satellite overpass, the 102F Fourier transform thermal infrared spectrometer and the SI-111 infrared temperature sensor were used to measure temperature and ground-leaving radiance for these surface profiles. Atmospheric water vapor, aerosol optical depth, and ozone concentration were simultaneously obtained from the CIMEL CE318 Sun photometer and the MICROTOP II ozonometer. Atmospheric profile information was acquired from radiosonde instruments carried by sounding balloons. Synchronous measurements of atmospheric parameters and ECMWF ERA5 reanalysis data were then combined and input to an atmospheric radiative transfer model (MODTRAN6.0) used to calculate apparent radiance. Calibration coefficients were determined from the measured apparent radiance and satellite-observed digital number (DN), for use in calculating the on-orbit observed radiance of typical surface objects. These values were then compared with the apparent radiance of each object, using radiative transfer calculations to evaluate the accuracy of on-orbit absolute radiometric calibration. The results show that the accuracy of this absolute radiometric calibration is better than 0.6 K. This approach allows the thermal infrared channel to be unrestricted by the limitations of spectrum matching between a satellite and field measurements, with strong applicability to various types of calibration sites.
Divergence Observation in a Mesoscale Eddy during Chla Bloom Revealed in Submesoscale Satellite Currents
Authors: Hong Tran Thi My , Park Young-Gyu , Choi Jun Myoung
Published: February 2023

Oceanic mesoscale eddies continuously regulate the horizontal and vertical transport of mass, heat, salt, carbon, and nutrients throughout the ocean system owing to their ubiquity, three-dimensionality, and long-term persistence. Although satellites have been the main platforms used to observe mesoscale eddies and chlorophyll-a (Chla) distributions, they cannot support submesoscale physical–biological interactions. Contemporary satellite observations of Eulerian velocity fields are unable to resolve submesoscale processes that govern vertical migration and mixing, which are crucial for controlling the nutrients and light for phytoplankton in the surface layer. We explored the physical–biological interaction between the anticyclonic mesoscale eddy and the Chla secondary bloom that occurred after the spring bloom in the East/Japan Sea using the Geostationary Ocean Color Imager (GOCI). The GOCI currents were generated using GOCI Chla data and were used to map streamlines, vorticity, and divergence to characterize the surface current near the eddy. In the early spring bloom period, the eddy interior showed Chla depletion as the eddy was trapped externally. We found that the second bloom period coincided with a higher divergence or upwelling period in the eddy core, and a sharp Chla peak was observed when wind-induced Ekman suction was pronounced. This study describes the first satellite observation of surface layer divergence inside an anticyclonic mesoscale eddy with internal Chla blooms, utilizing a submesoscale-permitting GOCI-based surface current.
Combination of Continuous Wavelet Transform and Successive Projection Algorithm for the Estimation of Winter Wheat Plant Nitrogen Concentration
Authors: Chen Xiaokai , Li Fenling , Chang Qingrui
Published: February 2023

Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR−1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT–Scale6–SPA–PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (Rc2) was 0.85. Root mean square error of the calibration set (RMSEc) was 0.27. The determination coefficient of the validation set (Rv2) was 0.84. Root mean square error of the validation set (RMSEv) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid and nondestructive monitoring of PNC and provides a new technical method for precision nitrogen management.
Editorial for Special Issue: “Remote Sensing Based Building Extraction II”
Authors: Tian Jiaojiao , Yan Qin , Awrangjeb Mohammad , Kallfelz (Sirmacek) Beril , Demir Nusret
Published: February 2023

Accurate building extraction from remotely sensed images is essential for topographic mapping, urban planning, disaster management, navigation, and many other applications [...]
Characterization of Operational Vibrations of Steel-Girder Highway Bridges via LiDAR
Authors: Trias-Blanco Adriana , Gong Jie , Moon Franklin
Published: February 2023

This research is motivated by the need for rapidly deployable technologies such as wireless, non-contact or remote sensing for evaluating bridges under operating conditions to minimize the data collection time, avoid the disruption of traffic and increase the inspector’s safety. The objective established for this research is to explore the use of remote sensing (e.g., Light Detection and Ranging (LiDAR)) for characterizing the structural vibration of bridges to support and improve bridge assessment practices. To satisfy this objective, a field study was performed on a 12-span steel stringer bridge in the Philadelphia region. This structure was subjected to extensive LiDAR scanning and conventional vibration data collection through the use of accelerometers for validation purposes. The analysis of the data collected in the field revealed LiDAR’s capability for detecting the structure’s vibration. The field data displayed an error for LiDAR vs. accelerometers of between 1.9 and 10 percent. Additionally, numerical modeling was performed on MATLAB to allow for a better understanding of the interaction between the scanner and the structure. The numerical model presents a vibrating plate to represent a simply supported single-span bridge and a terrestrial LiDAR sensor located under the plate which scans while it is vibrating constantly without attenuation. Finally, a set of recommendations were established for the use of LiDAR scanning to evaluate the structure’s frequency of vibration.
Ecological Security Patterns at Different Spatial Scales on the Loess Plateau
Authors: Lin Liangguo , Wei Xindong , Luo Pingping , Wang Shaini , Kong Dehao , Yang Jie
Published: February 2023

The study of ecological security patterns (ESPs) is of great significance for improving the value of ecosystem services and promoting both ecological protection and high-quality socio-economic development. As an important part of the “Loss Plateau-Sichuan-Yunnan Ecological Barrier” and “Northern Sand Control Belt” in the national security strategic pattern, there is an urgent need to study the ESPs on the Loess Plateau. Based on a remote sensing dataset, this study identified the ESPs at different spatial scales, and analyzed the similarities and differences of ecological sources, corridors, and key strategic points, so as to better inform the development and implantation of macro and micro ecological protection strategies. When taken as a whole unit, we identified 58 ecological sources (areas with higher levels of ecosystem services) on the Loess Plateau (total area of 57,948.48 km2), along with 134 corridors (total length of 14,094.32 km), 1325 pinch points (total area of 315.01 km2), and 2406 barrier points (total area of 382.50 km2). When splits into ecoregions, we identified 108 sources (total area of 67,892.51 km2), 226 corridors (total length of 13,403.49 km), 2801 pinch points (total area of 851.07 km2, and 3657 barrier points (total area of 800.70 km2). Human activities and land use types are the main factors influencing the number and spatial distribution of corridors, ecological pinch points, and barrier points. ESPs constructed at different spatial scales are broadly similar, but significant differences among details were identified. As such, when formulating ecological protection and restoration strategies, the spatial scale should be considered. Moreover, specific programs should be determined based on ESP characteristics to maximize the protection of biodiversity and ecosystem integrity from multiple perspectives and directions.
Extracting a Connected River Network from DEM by Incorporating Surface River Occurrence Data and Sentinel-2 Imagery in the Danjiangkou Reservoir Area
Authors: Lu Lijie , Wang Lihui , Yang Qichi , Zhao Pengcheng , Du Yun , Xiao Fei , Ling Feng
Published: February 2023

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation.
Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives—The Case of the Region of East Macedonia and Thrace, Greece
Authors: Chrysafis Irene , Damianidis Christos , Giannakopoulos Vasileios , Mitsopoulos Ioannis , Dokas Ioannis M. , Mallinis Giorgos
Published: February 2023

The sustainability of Mediterranean ecosystems, even if previously shaped by fire, is threatened by the diverse changes observed in the wildfire regime, in addition to the threat to human security and infrastructure losses. During the two previous years, destructive, extreme wildfire events have taken place in southern Europe, raising once again the demand for effective fire management based on updated and reliable information. Fuel-type mapping is a critical input needed for fire behavior modeling and fire management. This work aims to employ and evaluate multi-source earth observation data for accurate fuel type mapping in a regional context in north-eastern Greece. Three random forest classification models were developed based on Sentinel-2 spectral indices, topographic variables, and Sentinel-1 backscattering information. The explicit contribution of each dataset for fuel type mapping was explored using variable importance measures. The synergistic use of passive and active Sentinel data, along with topographic variables, slightly increased the fuel type classification accuracy (OA = 92.76%) compared to the Sentinel-2 spectral (OA = 81.39%) and spectral-topographic (OA = 91.92%) models. The proposed data fusion approach is, therefore, an alternative that should be considered for fuel type classification in a regional context, especially over diverse and heterogeneous landscapes.
Mapping Lantana camara and Leucaena leucocephala in Protected Areas of Pakistan: A Geo-Spatial Approach
Authors: Iqbal Iram M. , Balzter Heiko , Firdaus-e-Bareen  , Shabbir Asad
Published: February 2023

Invasive alien plants are considered as one of the major causes of loss of native biodiversity around the world. Remote sensing provides an opportunity to identify and map native and invasive species using accurate spectral information. The current study was aimed to evaluate PlanetScope (3 m) and Sentinel (10 m) datasets for mapping the distribution of native and invasive species in two protected areas in Pakistan, using machine learning (ML) algorithms. The multispectral data were analysed with the following four ML algorithms (classifiers)—random forest (RF), Gaussian mixture model (GMM), k-nearest neighbour (KNN), and support vector machine (SVM)—to classify two invasive species, Lantana camara L. (common lantana) and Leucaena leucocephala L. The (Ipil-ipil) Dzetsaka plugin of QGIS was used to map these species using all ML algorithms. RF, GMM, and SVM algorithms were more accurate at detecting both invasive species when using PlanetScope imagery rather than Sentinel. Random forest produced the highest accuracy of 64% using PlanetScope data. Lantana camara was the most dominating plant species with 23% cover, represented in all thematic maps. Leucaena leucocpehala was represented by 7% cover and was mainly distributed in the southern end of the Jindi Reserve Forest (Jhelum). It was not possible to discriminate native species Dodonea viscosa Jacq. (Snatha) using the SVM classifier for Sentinel data. Overall, the accuracy of PlanetScope was slightly better than Sentinel in term of species discrimination. These spectral findings provide a reliable estimation of the current distribution status of invasive species and would be helpful for land managers to prioritize invaded areas for their effective management.
Block-Scrambling-Based Encryption with Deep-Learning-Driven Remote Sensing Image Classification
Authors: Alsubaei Faisal S. , Alneil Amani A. , Mohamed Abdullah , Mustafa Hilal Anwer
Published: February 2023

Remote sensing is a long-distance measuring technology that obtains data about a phenomenon or an object. Remote sensing technology plays a crucial role in several domains, such as weather forecasts, resource surveys, disaster evaluation and environment protection. The application of remote-sensing images (RSIs) is extensive in some specific domains, such as national security and business secrets. Simple multimedia distribution techniques and the development of the Internet make the content security of RSIs a significant problem for both engineers and scientists. In this background, RSI classification using deep learning (DL) models becomes essential. Therefore, the current research article develops a block-scrambling-based encryption with privacy preserving optimal deep-learning-driven classification (BSBE-PPODLC) technique for the classification of RSIs. The presented BSBE-PPODLC technique follows a two-stage process, i.e., image encryption and classification. Initially, the RSI encryption process takes place based on a BSBE approach. In the second stage, the image classification process is performed, and it encompasses multiple phases, such as densely connected network (DenseNet) feature extraction, extreme gradient boosting (XGBoost) classifier and artificial gorilla troops optimizer (AGTO)-based hyperparameter tuning. The proposed BSBE-PPODLC technique was simulated using the RSI dataset, and the outcomes were assessed under different aspects. The outcomes confirmed that the presented BSBE-PPODLC approach accomplished improved performance compared to the existing models.
A Transfer-Based Framework for Underwater Target Detection from Hyperspectral Imagery
Authors: Li Zheyong , Li Jinghua , Zhang Pei , Zheng Lihui , Shen Yilong , Li Qi , Li Xin , Li Tong
Published: February 2023

The detection of underwater targets through hyperspectral imagery is a relatively novel topic as the assumption of target background independence is no longer valid, making it difficult to directly detect underwater targets using land target information. Meanwhile, deep-learning-based methods have faced challenges regarding the availability of training datasets, especially in underwater conditions. To solve these problems, a transfer-based framework is proposed in this paper, which exploits synthetic data to train deep-learning models and transfers them to real-world applications. However, the transfer becomes challenging due to the imparity in the distribution between real and synthetic data. To address this dilemma, the proposed framework, named the transfer-based underwater target detection framework (TUTDF), first divides the domains using the depth information, then trains models for different domains and develops an adaptive module to determine which model to use. Meanwhile, a spatial–spectral process is applied prior to detection, which is devoted to eliminating the adverse influence of background noise. Since there is no publicly available hyperspectral underwater target dataset, most of the existing methods only run on simulated data; therefore, we conducted expensive experiments to obtain datasets with accurate depths and use them for validation. Extensive experiments verify the effectiveness and efficiency of TUTDF in comparison with traditional methods.
Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia
Authors: Li Shaoyu , Wang Xiao Hua , Ma Yue , Yang Fanlin
Published: February 2023

Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.
A Lightweight Authentication MAC Protocol for CR-WSNs
Authors: Aloufi Bashayer , Alhakami Wajdi
Published: February 2023

Cognitive radio (CR) has emerged as one of the most investigated techniques in wireless networks. Research is ongoing in terms of this technology and its potential use. This technology relies on making full use of the unused spectrum to solve the problem of the spectrum shortage in wireless networks based on the excessive demand for spectrum use. While the wireless network technology node’s range of applications in various sectors may have security drawbacks and issues leading to deteriorating the network, combining it with CR technology might enhance the network performance and improve its security. In order to enhance the performance of the wireless sensor networks (WSNs), a lightweight authentication medium access control (MAC) protocol for CR-WSNs that is highly compatible with current WSNs is proposed. Burrows–Abadi–Needham (BAN) logic is used to prove that the proposed protocol achieves secure and mutual authentication. The automated verification of internet security protocols and applications (AVISPA) simulation is used to simulate the system security of the proposed protocol and to provide formal verification. The result clearly shows that the proposed protocol is SAFE under the on-the-fly model-checker (OFMC) backend, which means the proposed protocol is immune to passive and active attacks such as man-in-the-middle (MITM) attacks and replay attacks. The performance of the proposed protocol is evaluated and compared with related protocols in terms of the computational cost, which is 0.01184 s. The proposed protocol provides higher security, which makes it more suitable for the CR-WSN environment and ensures its resistance against different types of attacks.
Precision Landing Tests of Tethered Multicopter and VTOL UAV on Moving Landing Pad on a Lake
Authors: Kownacki Cezary , Ambroziak Leszek , Ciężkowski Maciej , Wolniakowski Adam , Romaniuk Sławomir , Bożko Arkadiusz , Ołdziej Daniel
Published: February 2023

Autonomous take-off and landing on a moving landing pad are extraordinarily complex and challenging functionalities of modern UAVs, especially if they must be performed in windy environments. The article presents research focused on achieving such functionalities for two kinds of UAVs, i.e., a tethered multicopter and VTOL. Both vehicles are supported by a landing pad navigation station, which communicates with their ROS-based onboard computer. The computer integrates navigational data from the UAV and the landing pad navigational station through the utilization of an extended Kalman filter, which is a typical approach in such applications. The novelty of the presented system is extending navigational data with data from the ultra wide band (UWB) system, and this makes it possible to achieve a landing accuracy of about 1 m. In the research, landing tests were carried out in real conditions on a lake for both UAVs. In the tests, a special mobile landing pad was built and based on a barge. The results show that the expected accuracy of 1 m is indeed achieved, and both UAVs are ready to be tested in real conditions on a ferry.
Scattered Train Bolt Point Cloud Segmentation Based on Hierarchical Multi-Scale Feature Learning
Authors: Zeng Ni , Li Jinlong , Zhang Yu , Gao Xiaorong , Luo Lin
Published: February 2023

In view of the difficulty of using raw 3D point clouds for component detection in the railway field, this paper designs a point cloud segmentation model based on deep learning together with a point cloud preprocessing mechanism. First, a special preprocessing algorithm is designed to resolve the problems of noise points, acquisition errors, and large data volume in the actual point cloud model of the bolt. The algorithm uses the point cloud adaptive weighted guided filtering for noise smoothing according to the noise characteristics. Then retaining the key points of the point cloud, this algorithm uses the octree to partition the point cloud and carries out iterative farthest point sampling in each partition for obtaining the standard point cloud model. The standard point cloud model is then subjected to hierarchical multi-scale feature extraction to obtain global features, which are combined with local features through a self-attention mechanism, while linear interpolation is used to further expand the perceptual field of local features of the model as a basis for segmentation, and finally the segmentation is completed. Experiments show that the proposed algorithm could deal with the scattered bolt point cloud well, realize the segmentation of train bolt and background, and could achieve high segmentation accuracy, which has important practical significance for train safety detection.