bluuebunny/crossref_metadata_2025 huggingface dataset:
Off-Stoichiometry Thiol–Enes Polymers Containing Silane Groups for Advanced Packaging Technologies
Authors: Puchnin Kirill , Ryazantsev Dmitriy , Grudtsov Vitaliy , Golubev Yaroslav , Kuznetsov Alexander
Published: May 2022

New modified off-stoichiometry thiol–enes polymers, called OSTE-MS polymers, were developed by introducing mercaptosilane into the polymer mixture. This modification made it possible to introduce silane groups into the polymer frame, due to which the polymer gained the ability to bond with silicon wafers without modification of the wafer surface by any adhesive. The optimal composition for creating 3D polymer structures on a chip was selected, which consists of a volume ratio of 6:6:1 of allyl monomer, mercapto monomer, and mercaptosilane, respectively. The hardness, shift force, tensile strength, Young’s modulus, optical transparency, glass transition temperature, thermal stability, and chemical resistance of the OSTE-MS polymer, and the viscosity for the prepolymer mixture were studied. On the basis of the OSTE-MS polymer, 3D polymer structures of the well type and microfluidic system on the silicon chips were obtained.
Correction: Weingart et al. Expanded Polycarbonate (EPC)—A New Generation of High-Temperature Engineering Bead Foams. Polymers 2020, 12, 2314
Authors: Weingart Nick , Raps Daniel , Kuhnigk Justus , Klein Andreas , Altstädt Volker
Published: May 2022

In the original publication [...]
Influence of the Carbon and Nitrogen Sources on Diabolican Production by the Marine Vibrio diabolicus Strain CNCM I-1629
Authors: Delbarre-Ladrat Christine , Sinquin Corinne , Marchand Laetitia , Bonnetot Sandrine , Zykwinska Agata , Verrez-Bagnis Véronique , Colliec-Jouault Sylvia
Published: May 2022

Recent advances in glycobiotechnology show that bacterial exopolysaccharides (EPS) presenting glycosaminoglycan (GAG)-like properties can provide a valuable source of bio-active macromolecules for industrial applications. The HE800 EPS, named diabolican, is a marine-derived anionic high-molecular-weight polysaccharide produced by Vibrio diabolicus CNCM I-1629 which displays original structural features close to those of hyaluronic acid. We investigated the impact of carbon and nitrogen substrates on both Vibrio diabolicus growth and diabolican production. Both substrates were screened by a one-factor-at-a-time method, and experimental designs were used to study the effect of glucose, mannitol, and ammonium acetate various concentrations. Results showed that the medium composition affected not only the bacterium growth and EPS yield, but also the EPS molecular weight (MW). EPS yields of 563 and 330 mg L−1 were obtained in the presence of 69.3 g L−1 glucose and 24.6 g L−1 mannitol, respectively, both for 116.6 mM ammonium acetate. MW was the highest, with 69.3 g L−1 glucose and 101.9 mM ammonium acetate (2.3 × 106 g mol−1). In parallel, the bacterial maximum specific growth rate was higher when both carbon and nitrogen substrate concentrations were low. This work paves the way for the optimization of marine exopolysaccharide production of great interest in the fields of human health and cosmetics.
Biomineralized Nano-Assemblies of Poly(Ethylene Glycol) Derivative with Lanthanide Ions as Ratiometric Fluorescence Sensors for Detection of Water and Fe3+ Ions
Authors: Chen Tong , Zhao Sanping
Published: May 2022

An effective strategy was developed to fabricate novel lanthanide ions–pyromellitic acid–methoxy poly(ethylene glycol) (Ln-PMA-MPEG) nano-assemblies. The amphiphilic partially esterified derivative (PMA-MPEG) of pyromellitic acid with methoxy poly(ethylene glycol) was designed and synthesized via the coupling reaction. Ln-PMA-MPEG nano-assemblies were rapidly fabricated using PMA-MPEG as a polymer ligand with Eu3+ ions or mixed Eu3+/Tb3+ ions through biomimetic mineralization in neutral aqueous systems. The size of the as-prepared materials could be designed in the range 80–200 nm with a uniform distribution. The materials were readily dispersed in various solvents and displayed visible color variations and different photoluminescent properties for solvent recognition. The mixed Eu/Tb-PMA-MPEG nanomaterials were investigated as ratiometric sensors for the detection of trace water in DMF and Fe3+ ions in aqueous solutions. The sensor materials can quantitatively detect trace water in DMF from 0% to 10% (v/v). The resultant materials also display a strong correlation between the double luminescence intensity ratios (ITb/IEu) and Fe3+ concentration, with a good linear detection concentration in the range of 0–0.24 mM and a limit of detection of 0.46 μM, and other metal ions did not interfere with the sensing mechanism for Fe3+ ions. The novel nano-assemblies have potential applications as ratiometric fluorescent nanosensors in the chemical industry as well as in biomedical fields.
Characteristic and Chondrogenic Differentiation Analysis of Hybrid Hydrogels Comprised of Hyaluronic Acid Methacryloyl (HAMA), Gelatin Methacryloyl (GelMA), and the Acrylate-Functionalized Nano-Silica Crosslinker
Authors: Nedunchezian Swathi , Wu Che-Wei , Wu Shung-Cheng , Chen Chung-Hwan , Chang Je-Ken , Wang Chih-Kuang
Published: May 2022

Developing a biomaterial suitable for adipose-derived stem cell (ADSCs)-laden scaffolds that can directly bond to cartilage tissue surfaces in tissue engineering has still been a significant challenge. The bioinspired hybrid hydrogel approaches based on hyaluronic acid methacryloyl (HAMA) and gelatin methacryloyl (GelMA) appear to have more promise. Herein, we report the cartilage tissue engineering application of a novel photocured hybrid hydrogel system comprising HAMA, GelMA, and 0~1.0% (w/v) acrylate-functionalized nano-silica (AFnSi) crosslinker, in addition to describing the preparation of related HAMA, GelMA, and AFnSi materials and confirming their related chemical evidence. The study also examines the physicochemical characteristics of these hybrid hydrogels, including swelling behavior, morphological conformation, mechanical properties, and biodegradation. To further investigate cell viability and chondrogenic differentiation, the hADSCs were loaded with a two-to-one ratio of the HAMA-GelMA (HG) hybrid hydrogel with 0~1.0% (w/v) AFnSi crosslinker to examine the process of optimal chondrogenic development. Results showed that the morphological microstructure, mechanical properties, and longer degradation time of the HG+0.5% (w/v) AFnSi hydrogel demonstrated the acellular novel matrix was optimal to support hADSCs differentiation. In other words, the in vitro experimental results showed that hADSCs laden in the photocured hybrid hydrogel of HG+0.5% (w/v) AFnSi not only significantly increased chondrogenic marker gene expressions such as SOX-9, aggrecan, and type II collagen expression compared to the HA and HG groups, but also enhanced the expression of sulfated glycosaminoglycan (sGAG) and type II collagen formation. We have concluded that the photocured hybrid hydrogel of HG+0.5% (w/v) AFnSi will provide a suitable environment for articular cartilage tissue engineering applications.
Hierarchical Exploration of Drying Patterns Formed in Drops Containing Lysozyme, PBS, and Liquid Crystals
Authors: Pal Anusuya , Gope Amalesh , Iannacchione Germano S.
Published: May 2022

Biological systems, by nature, are highly complex. These systems exhibit diverse hierarchical spatial and temporal features when driven far from equilibrium. The generated features are susceptible to the initial conditions that largely depend on vast parameter space. Extracting information on their properties and behavior thus becomes far too complex. This work seeks to examine the drying kinetics of the drops containing a globular protein (lysozyme (Lys)), phosphate buffer saline (PBS), and thermotropic liquid crystal (LCs). The drying evolution and the morphological crack patterns of these drops are examined using high-resolution microscopy, textural image analysis, and statistical methods. This study observes that the textural parameters can identify the (i) phase separation of the salts present in the PBS and (ii) the LCs’ birefringence during the drying evolution. This birefringence activities of the LCs slow down when the initial PBS concentration is increased from 0.25 to 1× despite using a fixed volume of LCs. To comprehend such a surprising effect, the combinations of (i) Lys+PBS and (ii) PBS+LCs are thoroughly examined. A phase diagram is established as a function of initial concentrations of Lys and PBS. The scanning electron microscopic images of Lys+PBS reveal that the tuning between lysozyme and salt concentrations in PBS plays a significant role in determining the morphological patterns. The Lys drops with and without LCs exhibit two distinct regions: the peripheral ring (“coffee-ring”) and the central ones. This phase-separated ring formation indicates that the film containing Lys and salts might have formed on top of these LCs in the central region, which reduces the optical response (birefringence) of LCs. A physical mechanism is proposed in this paper to anticipate the redistributions of LCs in a multi-component system such as Lys+PBS+LCs.
Quasi-Static Flexural Behavior of Epoxy-Matrix-Reinforced Crump Rubber Composites
Authors: Shahapurkar Kiran , Alblalaihid Khalid , Chenrayan Venkatesh , Alghtani Abdulaziz H. , Tirth Vineet , Algahtani Ali , Alarifi Ibrahim M. , Kiran M. C.
Published: May 2022

Waste tires have emerged as a severe environmental threat worldwide as they create a number of disposal and landfill burden issues. In the present study, environmental pollutant crump rubber derived from waste discarded tires was reinforced with epoxy resin and prepared by means of an open-mold casting method to assess its mechanical properties. The impact of crump rubber content (0, 10, 20 and 30 vol.%) on the mechanical behavior of the composites was assessed using three-point bending tests at a constant strain rate of 0.1 and 0.01 mm/s. The stress–strain profiles of the 0.01 mm/s specimens revealed higher strains to failure compared with the 0.1 mm/s tested specimens and all the specimens showed brittle failure. Irrespective of the strain rates, tests revealed a marginal increase in the strength values of the composites and a significant increase in the modulus of all the composites compared with neat epoxy specimens. The results suggest that crump rubber can be effectively used in utilitarian composites requiring good flexural modulus and strength properties. Crump rubber epoxy composites with 30 vol.% of crump rubber showed higher modulus and strength compared with neat epoxy and other composites owing to the toughening phase induced by the crump rubber particles. The failure and fracture features of the specimens were analyzed using scanning electron microscopy.
Flexible, Strong and Multifunctional Anf@Ag Nanocomposite Film for Human Physiology and Motion Monitoring
Authors: Long Haofan , Li Qing , Peng Shulan , Chen Shiqiang , Zhang Tonghua , Zhang Mingyuan , Li Minghua , Chen Lei
Published: May 2022

To expand the application range of flexible pressure sensors, endowing them with multifunction capabilities becomes extremely important. Herein, a flexible, strong and multifunctional nanocomposite film was prepared by introducing silver nanoparticles (Ag NPs) into aramid nanofiber (ANF) film using a simple two-step vacuum filtration method. When the Ag content was 27.6 vol%, the electrical resistance of the resulting ANF@Ag nanocomposite film was as low as 1.63 Ω/cm2, and the water contact angle of the nanocomposite film reached 153.9 ± 1°. Compared to the ANF film, the tensile strength of the nanocomposite film increased from 55 MPa to 66.3 MPa with an increase of 20.5%. After being applied to the human body, the nanocomposite film served as a pressure sensor that was able to recognize different stimuli for healthcare monitoring. Based on the advantages, it may become a potential candidate for electronic skin, intelligent wearable devices and medical detection equipment.
Economic Optimization Dispatch Model of a Micro-Network with a Solar-Assisted Compressed Air Energy Storage Hub, with Consideration of Its Operationally Feasible Region
Authors: Yang Libin , Zong Ming , Chen Xiaotao , Si Yang , Chen Laijun , Guo Yongqing , Mei Shengwei
Published: May 2022

Using a variety of renewable energy sources can significantly improve energy system flexibility and efficiency. Energy hubs, which have the function of generating, converting, and storing energy in various forms, are vital facilities in micro-energy networks (MENs). In this paper, we present a Solar-Assisted Compressed Air Energy Storage (SA-CAES) hub which can accommodate and flexibly supply multi-energy by being connected to a power distribution network (PDN) and a district heating network (DHN). We formulate economic dispatch models of the SA-CAES hub, the PDN, and the DHN, respectively. The economic dispatch model is formulated as a mixed-integer linear programming problem (MILP) that can be solved by commercial solvers. Further, the operationally feasible region of the SA-CAES hub is explored by thermodynamic analysis. The results indicate that the operation costs have been reduced by 4.5% in comparison with conventional MENs.
Silica Particles Derived from Natural Kaolinite for the Removal of Rhodamine B from Polluted Water
Authors: Celoria Giorgio , Miglio Vanessa , Paul Geo , Bisio Chiara , Golemme Giovanni , Boccaleri Enrico
Published: May 2022

This manuscript deals with the thermal and chemical modification of a natural kaolinite that shows excellent performance in the capture of a cationic organic pollutant from the aqueous phase. Kaolinite was calcined at 700 °C and treated with HCl to remove aluminium and to obtain a siliceous material. The structural changes and the physico-chemical properties of the materials at different stages of thermal and chemical modification were investigated with several techniques, including XRPD, MAS-NMR, SEM-EDX, FT-IR, and N2 physisorption at 77 K. The ability of the parent kaolinite and siliceous material to capture the organic dye, Rhodamine B, from the aqueous phase was investigated by means of UV-Vis spectroscopy. The siliceous material exhibited better adsorption capacity with respect to the parent kaolinite. Finally, the functional stability of the siliceous material was tested over three cycles of regeneration and adsorption.
Edible Insects’ Transformation for Feed and Food Uses: An Overview of Current Insights and Future Developments in the Field
Authors: El Hajj Rachelle , Mhemdi Houcine , Besombes Colette , Allaf Karim , Lefrançois Victor , Vorobiev Eugène
Published: May 2022

The integration of insect-derived extracts in feed and food products has become a field of growing interest in recent years. In this review, we collect different studies carried out on edible insects’ transformation processes and focus on the various treatment operations, extraction technologies, and solvents used in different processing steps. We include an overview of current insights into the different steps of the transformation process: insect reception, killing methods, pretreatments, storage, delipidation, protein extraction, as well as chitin and chitosan extraction. Finally, we reflect on the most important future challenges of this sector.
Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models
Authors: Fang Yuan , Xu Linlin , Wong Alexander , Clausi David A.
Published: May 2022

Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration estimation has been designed. First, instead of using single Landsat scenes, the utilization of multiple Landsat scenes of the same location over time is considered. Second, to generate a preferred feature set as input to a regression model, a number of feature extraction methods are motivated and compared. Third, to find a preferred regression model, a variety of approaches are implemented and compared for accuracy. In this research, 11 Landsat-8 images from 2013 to 2017 of Gulin County, Sichuan China, and 138 soil samples with lab-measured Cu concentrations collected from the area in 2015 are used. A variety a metrics under cross-validation are used for comparison. The results indicate that multi-temporal images increase accuracy compared to single Landsat images. The preferred feature extraction varies based on the regression model used; however, the best results are obtained using support vector regression and the original data. The final soil Cu map generated using the recommended data processing pipeline shows a consistent spatial pattern with a ground-truth land cover classification map. These results indicate that machine learning has the ability to perform large-scale soil heavy metal mapping from widely available satellite remote sensing images.
Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events
Authors: Bazzi Hassan , Baghdadi Nicolas , Charron François , Zribi Mehrez
Published: May 2022

Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition (Δt). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI ≤0.7 and NDVI >0.7). The main results showed that when the vegetation is moderately developed (NDVI ≤0.7), the C-band temporal variation remains sensitive to the soil moisture dynamics and the irrigation events could be detected. The C-VV signal decreases due to the drying out of the soil when the time difference between the S1 image and irrigation event increases. For well-developed vegetation cover (NDVI >0.7), the C-band sensitivity to irrigation events becomes dependent on the crop type. For well-developed Gramineae grass with longs stalks and seedheads, the C band shows no correlation with Δt due to the absence of the soil contribution in the backscattered signal, contrary to the legume grass type, where the C band shows a good correspondence between C-VV and Δt for NDVI > 0.7. In contrast, analysis of the L-band backscattering coefficient shows that the L band remains sensitive to the soil moisture regardless of the vegetation cover development and the vegetation characteristics, thus being more suitable for irrigation detection than the C band. The L-HH signal over Gramineae grass or legume grass types shows the same decreasing pattern with the increase in Δt, regardless of the NDVI-values, presenting a decrease in soil moisture with time and thus high sensitivity of the radar signal to soil parameters. Finally, the co-polarizations for both the C and L bands (L-HH and C-VV) tend to be more adequate for irrigation detection than the HV cross-polarization, as they show higher sensitivity to soil moisture values.
Multiscale Diagnosis of Mangrove Status in Data-Poor Context Using Very High Spatial Resolution Satellite Images: A Case Study in Pichavaram Mangrove Forest, Tamil Nadu, India
Authors: Ghosh Shuvankar , Proisy Christophe , Muthusankar Gowrappan , Hassenrück Christiane , Helfer Véronique , Mathevet Raphaël , Andrieu Julien , Balachandran Natesan , Narendran Rajendran
Published: May 2022

Highlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. This study presents the current situation of the Pichavaram mangrove area, a coastal region of Southeast India prone to both cyclones and reduced freshwater inflow. Based on the supervised classification and visual inspection of very high spatial resolution (VHSR) satellite images provided with a pixel size of <4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. We achieved a high mapping accuracy (>85%), which confirmed the potential of classification techniques applied to VHSR images in capturing changes in mangroves on a very fine scale. Our diagnosis reveals variable expansion rates in plantations made by the local authorities. We also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. Despite a lack of field data, VHSR images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile Pichavaram mangrove area upon which the coastal community is dependent.
The Seasonality of Surface Urban Heat Islands across Climates
Authors: Sismanidis Panagiotis , Bechtel Benjamin , Perry Mike , Ghent Darren
Published: May 2022

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates.
Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States
Authors: Rishmawi Khaldoun , Huang Chengquan , Schleeweis Karen , Zhan Xiwu
Published: May 2022

Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013–2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites.
Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China
Authors: Tian Lei , Tao Yu , Fu Wenxue , Li Tao , Ren Fang , Li Mingyang
Published: May 2022

Exploring the spatial distribution of land use/cover change (LUCC) and ecosystem carbon storage under future climate change scenarios can provide the scientific basis for optimizing land resource redistribution and formulating policies for sustainable socioeconomic development. We proposed a framework that integrates the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and tradeoffs (InVEST) model to assess the spatiotemporal dynamic changes in LUCC and ecosystem carbon storage in Guangdong based on shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios provided by the Coupled Model Intercomparison Project 6 (CMIP6). The future simulation results showed that the distribution patterns of LUCC were similar under SSP126 and SSP245 scenarios, but the artificial surface expanded more rapidly, and the increase in forest land slowed down under the SPP245 scenario. Conversely, under the SSP585 scenario, the sharply expanded artificial surface resulted in a continuous decrease in forest land. Under the three scenarios, population, elevation, temperature, and distance to water were the highest contributing driving factors for the growth of cultivated land, forest land, grassland, and artificial surface, respectively. By 2060, the carbon storage in terrestrial ecosystems increased from 240.89 Tg in 2020 to 247.16 Tg and 243.54 Tg under SSP126 and SSP245 scenarios, respectively, of which forest ecosystem carbon storage increased by 17.65 Tg and 15.34 Tg, respectively; while it decreased to 226.54 Tg under the SSP585 scenario, and the decreased carbon storage due to forest destruction accounted for 81.05% of the total decreased carbon storage. Overall, an important recommendation from this study is that ecosystem carbon storage can be increased by controlling population and economic growth, and balancing urban expansion and ecological conservation, as well as increasing forest land area.
Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning
Authors: Shi Hao , Sheng Qingqing , Wang Yupei , Yue Bingying , Chen Liang
Published: May 2022

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.
Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region
Authors: Wang Xue , Zhang Jiahua , Xun Lan , Wang Jingwen , Wu Zhenjiang , Henchiri Malak , Zhang Shichao , Zhang Sha , Bai Yun , Yang Shanshan , Li Shuaishuai , Yu Xiang
Published: May 2022

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.
Pre-Seismic Temporal Integrated Anomalies from Multiparametric Remote Sensing Data
Authors: Jiao Zhonghu , Shan Xinjian
Published: May 2022

Pre-seismic anomalies have the potential to indicate imminent strong earthquakes in the short to medium terms. However, an improved understanding of the statistical significance between anomalies and earthquakes is required to develop operational forecasting systems. We developed a temporal integrated anomaly (TIA) method to obtain the temporal trends of multiparametric anomalies derived from the Atmospheric Infrared Sounder (AIRS) product before earthquakes. A total of 169 global earthquakes that occurred from 2006 to 2020 and had magnitudes of ≥7.0 and focal depths of ≤70 km were used to test this new method in a retrospective manner. In addition, 169 synthetic earthquakes were randomly generated to demonstrate the suppression capacity of the TIA method for false alarms. We identified four different TIA trends according to the temporal characteristics of positive and negative TIAs. Long-term correlation analyses show that the recognition ability was 12.4–28.4% higher for true earthquakes than for synthetic earthquakes (i.e., higher than that of a random guess). Incorporating 2–5 kinds of TIAs offered the best chance of recognizing imminent shocks, highlighting the importance of multiparameter anomalies. Although the TIA trend characteristics before the earthquakes were not unique, we identified certain unexplained pre-seismic phenomena within the remote sensing data. The results provide new insight into the relationships between pre-seismic anomalies and earthquakes; moreover, the recognition ability of the proposed approach exceeds that of random guessing.
Characterization of Wildfire Smoke over Complex Terrain Using Satellite Observations, Ground-Based Observations, and Meteorological Models
Authors: Nakata Makiko , Sano Itaru , Mukai Sonoyo , Kokhanovsky Alexander
Published: May 2022

The severity of wildfires is increasing globally. In this study, we used data from the Global Change Observation Mission-Climate/Second-generation Global Imager (GCOM-C/SGLI) to characterize the biomass burning aerosols that are generated by large-scale wildfires. We used data from the September 2020 wildfires in western North America. The target area had a complex topography, comprising a basin among high mountains along a coastal region. The SGLI was essential for dealing with the complex topographical changes in terrain that we encountered, as it contains 19 polarization channels ranging from near ultraviolet (380 nm and 412 nm) to thermal infrared (red at 674 nm and near-infrared at 869 nm) and has a fine spatial resolution (1 km). The SGLI also proved to be efficient in the radiative transfer simulations of severe wildfires through the mutual use of polarization and radiance. We used a regional numerical model SCALE (Scalable Computing for Advanced Library and Environment) to account for variations in meteorological conditions and/or topography. Ground-based aerosol measurements in the target area were sourced from the National Aeronautics and Space Administration-Aerosol Robotic Network; currently, official satellite products typically do not provide the aerosol properties for very optically thick cases of wildfires. This paper used satellite observations, ground-based observations, and a meteorological model to define an algorithm for retrieving the aerosol properties caused by severe wildfire events.
Editorial for Special Issue: “New Insights into Ecosystem Monitoring Using Geospatial Techniques”
Authors: Agrillo Emiliano , Alessi Nicola , Álvarez-Martínez Jose Manuel , Casella Laura , Filipponi Federico , Lu Bing , Niculescu Simona , Šibíková Mária , Smith Kathryn E. L.
Published: May 2022

Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [...]
Differential Strategy-Based Multi-Level Dense Network for Pansharpening
Authors: Yin Junru , Qu Jiantao , Chen Qiqiang , Ju Ming , Yu Jun
Published: May 2022

Due to the discrepancy in spatial structure between multispectral (MS) and panchromatic (PAN) images, the general fusion scheme will lead to image error in the fused result. To solve this issue, a differential strategy-based multi-level dense network is proposed, and it regards the image pairs at different scales as the input of the network at different levels and is able to map the spatial information in PAN images to each band of MS images well by learning the differential information of different levels, which effectively solves the scale effect of remote sensing images. An improved dense network with the same hierarchical structure is used to obtain richer spatial features to enhance the spatial information of the fused result. Meanwhile, a hybrid loss strategy is used to constrain the network at different levels for obtaining better results. Qualitative and quantitative analyses show that the result has a uniform spectral distribution, a complete spatial structure, and optimal evaluation criteria, which fully demonstrate the superior performance of the proposed method.
Performance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment
Authors: Lubac Bertrand , Burvingt Olivier , Nicolae Lerma Alexandre , Sénéchal Nadia
Published: May 2022

Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.
Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China
Authors: Wang Hongwei , Qi Yuan , Zhang Juan , Zhang Jinlong , Yang Rui , Guo Junyu , Luo Dongliang , Wu Jichun , Zhou Shengming
Published: May 2022

The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10–30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km2 of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation.
Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany
Authors: Rubin Yoav , Rostkier-Edelstein Dorita , Chwala Christian , Alpert Pinhas
Published: May 2022

Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity at high resolution, where most of the humidity’s sinks and sources are found, is a challenging task using classical tools. A novel approach for measuring the humidity is based on commercial microwave links (CML), which provide a large part of the cellular networks backhaul. This study focuses on employing humidity measurements with high spatio–temporal resolution in Germany. One major goal is to assess the errors and the environmental influence by comparing the CML-derived humidity to in-situ humidity measurements at weather stations and reanalysis (COSMO-Rea6) products. The method of retrieving humidity from the CML has been improved as compared to previous studies due to the use of new data at high temporal resolution. The results show a similar correlation on average and generally good agreement between both the CML retrievals and the reanalysis, and 32 weather stations near Siegen, West Germany (CML—0.84, Rea6—0.85). Higher correlations are observed for CML-derived humidity during the daytime (0.85), especially between 9–17 LT (0.87) and a maximum at 12 LT (0.90). During the night, the correlations are lower on average (0.81), with a minimum at 3 LT (0.74). These results are discussed with attention to the diurnal boundary layer (BL) height variation which has a strong effect on the BL humidity temporal profile. Further metrics including root mean square errors, mean values and standard deviations, were also calculated.
Research on Trajectory Tracking Control of Inspection UAV Based on Real-Time Sensor Data
Authors: Yang Mingbo , Zhou Ziyang , You Xiangming
Published: May 2022

In power inspection, uncertainties, such as wind gusts in the working environment, affect the trajectory of the inspection UAV (unmanned aerial vehicle), and a sliding mode adaptive robust control algorithm is proposed in this paper to solve this problem. For the nonlinear and under-driven characteristics of the inspection UAV system, a double closed-loop control system which includes a position loop and attitude loop is designed. Lyapunov stability analysis is used to determine whether the designed system could finally achieve asymptotic stability. Sliding-mode PID control and a backstepping control algorithm are applied to analyze the superiority of the control algorithm proposed in this paper. A PX4 based experimental platform system is built and experimental tests were carried out under outdoor environment. The effectiveness and superiority of the control algorithm are proposed in this paper. The experimental results show that the sliding mode PID control can achieve good accuracy with smaller computing costs. For nonlinear interference, the sliding mode adaptive robust control strategy can achieve higher trajectory tracking accuracy.
A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models
Authors: Gopal Pranesh , Gesta Amandine , Mohebbi Abolfazl
Published: May 2022

Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artificial vision and tactile and surface electromyography (sEMG). If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities. However, despite the advancements in sensing technologies, as well as excellent mechanical capabilities of the prosthetic devices, their control is often limited and usually requires a long time for training and adaptation of the users. The myoelectric prostheses use signals from residual stump muscles to restore the function of the lost limbs seamlessly. However, the use of the sEMG signals in robotic as a user control signal is very complicated due to the presence of noise, and the need for heavy computational power. In this article, we developed motion intention classifiers for transradial (TR) amputees based on EMG data by implementing various machine learning and deep learning models. We benchmarked the performance of these classifiers based on overall generalization across various classes and we presented a systematic study on the impact of time domain features and pre-processing parameters on the performance of the classification models. Our results showed that Ensemble learning and deep learning algorithms outperformed other classical machine learning algorithms. Investigating the trend of varying sliding window on feature-based and non-feature-based classification model revealed interesting correlation with the level of amputation. The study also covered the analysis of performance of classifiers on amputation conditions since the history of amputation and conditions are different to each amputee. These results are vital for understanding the development of machine learning-based classifiers for assistive robotic applications.
Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method
Authors: Fu Jun , Lv Teng , Li Bao
Published: May 2022

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.
A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring
Authors: Rathnayaka Prabod , Mills Nishan , Burnett Donna , De Silva Daswin , Alahakoon Damminda , Gray Richard
Published: May 2022

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.