bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-Wide Identification and Characterization of Oil-Body-Membrane Proteins in Polyploid Crop Brassica napus
Authors: Zhao Wei , Liu Jun , Qian Lunwen , Guan Mei , Guan Chunyun
Published: August 2022

Oil-body-membrane proteins (OBMPs) are essential structural molecules of oil bodies and also versatile metabolic enzymes involved in multiple cellular processes such as lipid metabolism, hormone signaling and stress responses. However, the global landscape for OBMP genes in oil crops is still lacking. Here, we performed genome-wide identification and characterization of OBMP genes in polyploid crop Brassica napus. B. napus contains up to 88 BnaOBMP genes including 53 oleosins, 20 caleosins and 15 steroleosins. Both whole-genome and tandem duplications have contributed to the expansion of the BnaOBMP gene family. These BnaOBMP genes have extensive sequence polymorphisms, and some harbor strong selection signatures. Various cis-acting regulatory elements involved in plant growth, phytohormones and abiotic and biotic stress responses are detected in their promoters. BnaOBMPs exhibit differential expression at various developmental stages from diverse tissues. Importantly, some BnaOBMP genes display spatiotemporal patterns of seed-specific expression, which could be orchestrated by transcriptional factors such as EEL, GATA3, HAT2, SMZ, DOF5.6 and APL. Altogether, our data lay the foundations for studying the regulatory mechanism of the seed oil storage process and provide candidate genes and alleles for the genetic improvement and breeding of rapeseed with high seed oil content.
Bacterial Cellulose Membranes as Carriers for Nisin: Incorporation, Antimicrobial Activity, Cytotoxicity and Morphology
Authors: dos Santos Gabriela Ribeiro , Soeiro Victória Soares , Talarico Carolina Fernanda , Ataide Janaína Artem , Lopes André Moreni , Mazzola Priscila Gava , Oliveira Thais Jardim , Oliveira Junior José Martins , Grotto Denise , Jozala Angela F.
Published: August 2022

Based on the previous study, in which nisin and bacterial cellulose were utilized, this new experiment loads nisin into bacterial cellulose (N–BC) and evaluates the morphological characteristics, cytotoxicity, antimicrobial activity and stability of the developed system. The load efficiency of nisin in BC was evaluated by an agar diffusion assay, utilizing Lactobacillus sakei, and total proteins. After having found the ideal time and concentration for the loading process, the system stability was evaluated for 100 days at 4, 25 and 37 °C against Staphylococcus aureus and L. sakei. Thus, in this study, there is a system that proves to be efficient, once BC has enhanced the antimicrobial activity of nisin, acting as a selective barrier for other compounds present in the standard solution and protecting the peptide. After 4 h, with 45% of proteins, this activity was almost 2 log10 higher than that of the initial solution. Once the nisin solution was not pure, it is possible to suggest that the BC may have acted as a filter. This barrier enhanced the nisin activity and, as a consequence of the nisin loading, a stable N–BC system formed. The N–BC could create meaningful material for pharmaceutical and food applications.
Characterization and Multiscale Modeling of the Mechanical Properties for FDM-Printed Copper-Reinforced PLA Composites
Authors: Özen Arda , Ganzosch Gregor , Völlmecke Christina , Auhl Dietmar
Published: August 2022

Additive manufacturing is an emerging technology and provides high design flexibility to customers. Fused deposition modeling (FDM) is an economical and promising additive manufacturing method. Due to its many advantages, FDM received great attention in recent years, and comprehensive studies are being undertaken to investigate the properties of FDM-printed polymers and polymer composites. As a result of the manufacturing technology employed in FDM, inner structures are changed with different process parameters, and thus, anisotropic properties are observed. Moreover, composite filaments such as particle- or fiber-reinforced polymers already have anisotropy before FDM printing. In this study, we investigate the effect of different process parameters, namely layer thickness and raster width on FDM-printed copper-reinforced poly(lactic acid) (PLA). Mechanical characterizations with a high-resolution camera are carried out for analyzing the deformation behaviors. Optical microscopy characterizations are performed to observe the mesostructural changes with various process parameters. Scanning electron microscopy (SEM) and an energy-dispersive X-ray spectroscopy (EDS) analysis are conducted for investigating the microstructure, specifically, copper particles in the PLA matrix. A 2D digital image correlation code with a machine learning algorithm is applied to the optical characterization and SEM-EDS images. In this way, micro- and mesostructural features, as well as the porosity ratios of the specimens are investigated. We prepare the multiscale homogenization by finite element method (FEM) simulations to capture the material’s response, both on a microscale and a mesoscale. We determined that the mesostructure and, thereby, the mechanical properties are significantly changed with the aforementioned process parameters. A lower layer thickness and a greater raster width led to a higher elasticity modulus and ultimate tensile strength (UTS). The optical microscopy analysis verified this statement: Decreasing the layer thickness and increasing the raster width result in larger contact lines between adjacent layers and, hence, lower porosity on the mesoscale. Realistic CAD images were prepared regarding the mesostructural differences and porosity ratios. Ultimately, all these changes are accurately modeled with mesoscale and multiscale simulations. The simulation results are validated by laboratory experiments.
High-Voltage FDS of Thermally Aged XLPE Cable and Its Correlation with Physicochemical Properties
Authors: Wang Haoyue , Sun Maolun , Zhao Kaijie , Wang Xiaowei , Xu Qilong , Wang Wei , Li Chengrong
Published: August 2022

This paper aims to investigate the influence of thermal aging on a crosslinked polyethylene (XLPE) cable, and the relationships between the macroscopical high-voltage dielectric and the microscopical physicochemical properties are also elucidated. To better simulate thermal aging under working condition, the medium-voltage-level cable is subjected to accelerated inner thermal aging for different aging times. Then, high-voltage frequency domain spectroscopy (FDS) (cable sample) and analyses of microscopic physical and chemical properties (sampling from the cable), including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and elongation at the break (EAB), are conducted at different cable aging stages. The dielectric test results show that after a certain aging time, the high-voltage FDS curves of the cable have layered characteristics, and this phenomenon is more obvious as the aging degree increases. Moreover, the slope and the integral of the high-voltage FDS curves rise with aging time. The mechanism is deduced by the physicochemical results that thermo-oxidative aging results in increasing polar groups and dislocation defects in the crystal region, which leads to the above phenomenon. On the one hand, the appearance of polar groups increases the density of the dipole. On the other hand, the destruction of the crystal region increases the probability and amplitude of dipole reversal. In addition, the breaking of molecular bonds and the increase in the amorphous phase also reduce the rigidity of the XLPE molecular main chain. The above factors lead to obvious delamination and larger dielectric parameters of the thermally aged cable. Finally, according to the experimental results, an on-site diagnosis method of cable insulation thermal aging based on high-voltage FDS is discussed.
A Combined Rheological and Thermomechanical Analysis Approach for the Assessment of Pharmaceutical Polymer Blends
Authors: Isreb Mohammad , Chalkia Marianiki , Gough Timothy , Forbes Robert Thomas , Timmins Peter
Published: August 2022

The viscoelastic nature of polymeric formulations utilised in drug products imparts unique thermomechanical attributes during manufacturing and over the shelf life of the product. Nevertheless, it adds to the challenge of understanding the precise mechanistic behaviour of the product at the microscopic and macroscopic level during each step of the process. Current thermomechanical and rheological characterisation techniques are limited to assessing polymer performance to a single phase and are especially hindered when the polymers are undergoing thermomechanical transitions. Since pharmaceutical processing can occur at these transition conditions, this study successfully proposes a thermomechanical characterisation approach combining both mechanical and rheological data to construct a comprehensive profiling of polymeric materials spanning both glassy and rubbery phases. This approach has been used in this study to assess the mechanical and rheological behaviour of heterogenous polymer blends of hydroxypropyl cellulose (HPC) and hydroxypropyl methylcellulose (HPMC) over a shearing rate range of 0.1–100 s−1 and a temperature range of 30–200 °C. The results indicate that HPC and HPMC do not appear to interact when mixing and that their mixture exhibits the mechanistic properties of the two individual polymers in accordance with their ratio in the mixture. The ability to characterise the behaviour of the polymers and their mixtures before, throughout, and after the glassy to rubbery phase transition by application of the combined techniques provides a unique insight towards a quality-by-design approach to this and other polymer-based solid dosage forms, designed with the potential to accelerate their formulation process through obviating the need for multiple formulation trials.
A Novel Triple Crosslinking Strategy on Carbon Nanofiber Membranes as Flexible Electrodes for Lithium-Ion Batteries
Authors: Xu Hang , Hou Xinran , Gong Man , Yang Changshu , Luo Jinpeng , Chen Yuluo , Ma Lei , Zhou Lang , Yin Chuanqiang , Li Xiaomin
Published: August 2022

In order to solve the problem of low electrical conductivity of carbon nanofiber membranes, a novel triple crosslinking strategy, including pre-rolling, solvent and chemical imidization crosslinking, was proposed to prepare carbon nanofiber membranes with a chemical crosslinking structure (CNMs-CC) derived from electrospinning polyimide nanofiber membranes. The physical-chemical characteristics of CNMs-CC as freestanding anodes for lithium-ion batteries were investigated in detail, along with carbon nanofiber membranes without a crosslinking structure (CNMs) and carbon nanofiber membranes with a physical crosslinking structure (CNMs-PC) as references. Further investigation demonstrates that CNMs-CC exhibits excellent rate performance and long cycle stability, compared with CNMs and CNMs-PC. At 50 mA g−1, CNMs-CC delivers a reversible specific capacity of 495 mAh g−1. In particular, the specific capacity of CNMs-CC is still as high as 290.87 mAh g−1 and maintains 201.38 mAh g−1 after 1000 cycles at a high current density of 1 A g−1. The excellent electrochemical performance of the CNMs-CC is attributed to the unique crosslinking structure derived from the novel triple crosslinking strategy, which imparts fast electron transfer and ion diffusion kinetics, as well as a stable structure that withstands repeated impacts of ions during charging and discharging process. Therefore, CNMs-CC shows great potential to be the freestanding electrodes applied in the field of flexible lithium-ion batteries and supercapacitors owing to the optimized structure strategy and improved properties.
Recycling Waste Nonmetallic Printed Circuit Boards for Polyvinyl Chloride Composites
Authors: Moe Aung Kyaw , Chungprempree Jirasuta , Preechawong Jitima , Sapsrithong Pornsri , Nithitanakul Manit
Published: August 2022

To reduce environmental threats, such as land filling, incineration and soil pollution, which are associated with the improper waste management of waste printed circuit boards, the utilization of NMPCBs from waste PCBs as a filler in composites was pursued. Untreated and treated NMPCBs in varying ratios, 10–30 wt.%, were blended with PVC to produce NMPCB/PVC composites, using the melt-mixing method via an internal mixer, in order to solve the remaining NMPCB waste problem after the valuable metals in PCBs were recovered. The incorporation of the NMPCB with PVC resulted in an increase in the tensile modulus and the thermal stability of the resulting composites. Scanning electron microscopy (SEM) results indicated improved interfacial adhesion between the treated NMPCB and the PVC matrix. The FTIR results of the NMPCB treated with 3-glycidyloxypropyltrimethoxysilane (GPTMS) revealed the formation of Si-O-Si bonds. The densities of the composites were found to increase with an increase in the content of the treated NMPCB, and compatibility improved. The tensile properties of the treated NMPCB/PVC composites were higher than those of the untreated NMPCB/PVC composites, suggesting improved compatibility between the treated NMPCB and PVC. The PVC composite with 10 wt.% of the treated NMPCB showed the optimum tensile properties. It was observed that the tensile modulus of the treated NMPCB/PVC composite increased by 47.65% when compared to that of the neat PVC. The maximum thermal degradation temperature was 27 °C higher than that of the neat PVC. Dynamic mechanical analysis results also support the improved interfacial adhesion as a result of the improvement in the storage modulus at the glassy region, and the loss factor (tan δ) peak shifted to a higher temperature range than that of the PVC and the untreated NMPCB/PVC composite. These studies reveal that the NMPCB was successfully modified with 1 wt.% of GPTMS, which promoted the dispersion and interfacial adhesion in the PVC matrix, resulting in better tensile properties and better thermal stability of the PVC composite.
Effects of Fluorine-Based Modification on Triboelectric Properties of Cellulose
Authors: Zhu Qiuxiao , Wang Tingting , Sun Xiaoping , Wei Yuhe , Zhang Sheng , Wang Xuchong , Luo Lianxin
Published: August 2022

The hydroxyl groups on the cellulose macromolecular chain cause the cellulose surface to have strong reactivity. In this study, 1H, 1H, 2H, 2H-perfluorodecyltriethoxysilane (PDOTES) was used to modify cellulose to improve its triboelectric properties, and a triboelectric nanogenerator (TENG) was assembled. The introduction of fluorine groups reduced the surface potential of cellulose and turned it into a negative phase, which enhanced the ability to capture electrons. The electrical properties increased by 30% compared with unmodified cellulose. According to the principles of TENGs, a self-powered human-wearable device was designed using PDOTES-paper, which could detect movements of the human body, such as walking and running, and facilitated a practical method for the preparation of efficient wearable sensors.
Preparation and Performances of Polyether Polytriazole Elastomers Based on Click Chemistry
Authors: Cong Kun , Liu Zhenhui , Hu Fa , He Jiyu , Yang Rongjie
Published: August 2022

Since the polyurethane elastomer synthesis process is susceptible to moisture, polytriazole polyethylene oxide-tetrahydrofuran (PTPET) elastomer was used as a replacement owing to its mild production environment. In contrast to the conventional flask-synthesis method, the twin-screw reactor instrument could provide more meaningful data in the synthesis. In this study, PTPET elastomer was prepared by the MiniLab twin-screw reactor method for the first time, and the activation energy of the PTPET elastomer was calculated using the torque variation obtained from the MiniLab twin-screw reactor during the synthesis process at two different temperatures. The addition of flame retardants could endow the composites with more useful properties. The PTPET composites poly (phenylsilsesquioxane) (PTPET-PPSQ), octaphenyl polyhedral oligomeric silsesquioxane (PTPET-OPS) and PTPET-PhVPOSS (phenyl/vinyl polysilsesquioxane) were synthesized by using the MiniLab twin-screw reactor. The prepared PTPET elastomer and composites were fully characterized by FT-IR, TG, DSC, swelling test, mechanical test, SEM and combustion test. The characterization results show that the addition of the flame retardants has little influence on the original structure and properties of PTPET elastomer. The flame retardancy was characterized by the combustion test showing that all PTPET composites form a certain thickness of char layer during the burning process. These results indicate that the addition of flame retardants maintains the outstanding properties of PTPET elastomer and also endows the materials with a certain extent of flame retardancy; thus, it is believed to be a good engineering material that could be applied in many realms.
Three-Dimensional Printable Ball Joints with Variable Stiffness for Robotic Applications Based on Soft Pneumatic Elastomer Actuators
Authors: Guo Jin , Low Jin-Huat , Liu Jun , Li Yangfan , Liu Zhuangjian , Yeow Chen-Hua
Published: August 2022

This paper contributes to a new design of the three-dimensional printable robotic ball joints capable of creating the controllable stiffness linkage between two robot links through pneumatic actuation. The variable stiffness ball joint consists of a soft pneumatic elastomer actuator, a support platform, an inner ball and a socket. The ball joint structure, including the inner ball and the socket, is three-dimensionally printed using polyamide−12 (PA12) by selective laser sintering (SLS) technology as an integral mechanism without the requirement of assembly. The SLS technology can make the ball joint have the advantages of low weight, simple structure, easy to miniaturize and good MRI compatibility. The support platform is designed as a friction-based braking component to increase the stiffness of the ball joint while withstanding the external loads. The soft pneumatic elastomer actuator is responsible for providing the pushing force for the support platform, thereby modulating the frictional force between the inner ball, the socket and the support platform. The most remarkable feature of the proposed variable stiffness design is that the ball joint has ‘zero’ stiffness when no pressurized air is supplied. In the natural state, the inner ball can be freely rotated and twist inside the socket. The proposed ball joint can be quickly stiffened to lock the current position and orientation of the inner ball relative to the socket when the pressurized air is supplied to the soft pneumatic elastomer actuator. The relationship between the stiffness of the ball joint and the input air pressure is investigated in both rotating and twisting directions. The finite element analysis is conducted to optimize the design of the support platform. The stiffness tests are conducted, demonstrating that a significant stiffness enhancement, up to approximately 508.11 N·mm reaction torque in the rotational direction and 571.93 N·mm reaction torque in the twisting direction at the pressure of 400 kPa, can be obtained. Multiple ball joints can be easily assembled to form a variable stiffness structure, in which each ball joint has a relative position and an independent stiffness. Additionally, the degrees of freedom (DOF) of the ball joint can be readily restricted to build the single-DOF or two-DOFs variable stiffness joints for different robotic applications.
Insights on Development Aspects of Polymeric Nanocarriers: The Translation from Bench to Clinic
Authors: Tewari Akhilesh Kumar , Upadhyay Satish Chandra , Kumar Manish , Pathak Kamla , Kaushik Deepak , Verma Ravinder , Bhatt Shailendra , Massoud Ehab El Sayed , Rahman Md. Habibur , Cavalu Simona
Published: August 2022

Scientists are focusing immense attention on polymeric nanocarriers as a prominent delivery vehicle for several biomedical applications including diagnosis of diseases, delivery of therapeutic agents, peptides, proteins, genes, siRNA, and vaccines due to their exciting physicochemical characteristics which circumvent degradation of unstable drugs, reduce toxic side effects through controlled release, and improve bioavailability. Polymers-based nanocarriers offer numerous benefits for in vivo drug delivery such as biocompatibility, biodegradability, non-immunogenicity, active drug targeting via surface modification, and controlled release due to their pH—and thermosensitive characteristics. Despite their potential for medicinal use, regulatory approval has been achieved for just a few. In this review, we discuss the historical development of polymers starting from their initial design to their evolution as nanocarriers for therapeutic delivery of drugs, peptides, and genes. The review article also expresses the applications of polymeric nanocarriers in the pharmaceutical and medical industry with a special emphasis on oral, ocular, parenteral, and topical application of drugs, peptides, and genes over the last two decades. The review further examines the practical, regulatory, and clinical considerations of the polymeric nanocarriers, their safety issues, and directinos for future research.
Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas
Authors: Huang Chong , Xiao Chaoliang , Rong Lishan
Published: August 2022

Accurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban spaces or the corresponding POI data has not been fully considered in previous studies. In this study, we proposed a new scheme for urban-functional-area identification by combining POI data, OpenStreetMap (OSM) datasets, and high-resolution remote-sensing imagery. A function-intensity index that integrates the quantitative-density index and average-nearest-neighbor index (ANNI) of POIs was built for representing the urban function. The results show that the proposed function-intensity index can balance the impact of the spatial heterogeneity of each type of POI on determining the functional characteristics of the urban units. In Futian District, Shenzhen, China, the method was effective in distinguishing functional areas with fewer POI amounts but high ANNIs from those functional areas with dense POIs. The overall accuracy of the proposed method is about 11% higher than that of the method using the POI density only. This paper argues for considering both the quantitative density and spatial heterogeneity of POIs to improve urban-functional-area identification.
Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China
Authors: Gu Zhengnan , Zhang Zhen , Yang Junhua , Wang Leilei
Published: August 2022

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a−1, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province.
Design and Analysis of a New Deployer for the in Orbit Release of Multiple Stacked CubeSats
Authors: Zhao Yong , Yue Honghao , Mu Xingke , Yang Xiaoze , Yang Fei
Published: August 2022

More and more CubeSats cooperate to implement complex space exploration missions. In order to store and deploy more CubeSats in a rocket-launch mission, this paper presents a new CubeSat deployer with large-capacity storage. Different from the traditional one with the compression springs, the deployer with electromagnetic actuators is proposed to achieve the transportation and release. A new electromagnetic actuator with high thrust density was applied to adjust the release speeds of the CubeSats with different masses, and a new electromagnetic convey platform with attractive force was designed to transfer the stacked CubeSats to the release window. The equivalent magnetic circuit method was used to the establish electromagnetic force models. The simplified dynamic models of the transportation and release were built. The magnetic field, electromagnetic force, and motion characteristics were analyzed. The prototype was developed to verify the performance of the proposed configuration of the deployer with electromagnetic actuators. The experimental results show that stacked CubeSats can be transported smoothly even under constant external interference. The launcher achieved high thrust density and effectively adjusted the separation speed of the CubeSats.
A Hierarchical Information Extraction Method for Large-Scale Centralized Photovoltaic Power Plants Based on Multi-Source Remote Sensing Images
Authors: Ge Fan , Wang Guizhou , He Guojin , Zhou Dengji , Yin Ranyu , Tong Lianzi
Published: August 2022

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels.
Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar (SAR), Texture, and Shallow Learning Techniques
Authors: Lasko Kristofer
Published: August 2022

Multispectral imagery provides unprecedented information on Earth system processes: however, data gaps due to clouds and shadows are a major limitation. Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Water Index (NDWI) are two spectral indexes employed for monitoring vegetation phenology, land-cover change and more. Synthetic Aperture Radar (SAR) with its cloud-penetrating abilities can fill data gaps using coincident imagery. In this study, we evaluated C-band Sentinel-1, L-band Uninhabited Aerial Vehicle SAR (UAVSAR) and texture for gap filling using efficient machine learning regression algorithms across three seasons. Multiple models were evaluated including Support Vector Machine, Random Forest, Gradient Boosted Trees and an ensemble of models. The Gap filling ability of SAR was evaluated with Sentinel-2 imagery from the same date, 3 days and 8 days later than both SAR sensors in September. Sentinel-1 and Sentinel-2 imagery from winter and spring seasons were also evaluated. Because SAR imagery contains noise, we compared two robust de-noising methods and evaluated performance against a refined lee speckle filter. Mean Absolute Error (MAE) rates of the cloud gap-filling model were assessed across different dataset combinations and land covers. The results indicated de-noised Sentinel-1 SAR and UAVSAR with GLCM texture provided the highest predictive abilities with random forest R2 = 0.91 (±0.014), MAE = 0.078 (±0.003) (NDWI) and R2 = 0.868 (±0.015), MAE = 0.094 (±0.003) (NDVI) during September. The highest errors were observed across bare ground and forest, while the lowest errors were on herbaceous and woody wetland. Results on January and June imagery without UAVSAR were less strong at R2 = 0.60 (±0.036), MAE = 0.211 (±0.005) (NDVI), R2 = 0.61 (±0.043), MAE = 0.209 (±0.005) (NDWI) for January and R2 = 0.72 (±0.018), MAE = 0.142 (±0.004) (NDVI), R2 = 0.77 (±0.022), MAE = 0.125 (±0.004) (NDWI) for June. Ultimately, the results suggest de-noised C-band SAR with texture metrics can accurately predict NDVI and NDWI for gap-filling clouds during most seasons. These shallow machine learning models are rapidly trained and applied faster than intensive deep learning or time series methods.
Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Deep Learning Approach
Authors: Zhong Jing , Sun Jie , Lai Zulong , Song Yan
Published: August 2022

Accurate bathymetric data is crucial for marine and coastal ecosystems. A lot of studies have been carried out for nearshore bathymetry using satellite data. The approach adopted extensively in shallow water depths estimation has recently been one of empirical models. However, the linear empirical model is simple and only takes limited band information at each bathymetric point into consideration. It may be not suitable for complex environments. In this paper, a deep learning framework was proposed for nearshore bathymetry (DL-NB) from ICESat-2 LiDAR and Sentinel-2 Imagery datasets. The bathymetric points from the spaceborne ICESat-2 LiDAR were extracted instead of in situ measurements. By virtue of the two-dimensional convolutional neural network (2D CNN), DL-NB can make full use of the initial multi-spectral information of Sentinel-2 at each bathymetric point and its adjacent areas during the training. Based on the trained model, the bathymetric maps of several study areas were produced including the Appalachian Bay (AB), Virgin Islands (VI), and Cat Island (CI) of the United States. The performance of DL-NB was evaluated by empirical method, machine learning method and multilayer perceptron (MLP). The results indicate that the accuracy of the DL-NB is better than comparative methods can in nearshore bathymetry. After quantitative analysis, the RMSE of DL-NB could achieve 1.01 m, 1.80 m and 0.28 m in AB, VI and CI respectively. Given the same data conditions, the proposed method can be applied for high precise global scale and multitemporal nearshore bathymetric maps generation, which are beneficial to marine environmental change assessment and conservation.
ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images
Authors: Liu Bing , Gao Kuiliang , Yu Anzhu , Ding Lei , Qiu Chunping , Li Jia
Published: August 2022

Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES2FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES2FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples.
Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target
Authors: Zhou Fansen , Wang Yidi , Zheng Wei , Li Zhao , Wen Xin
Published: August 2022

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.
Effect of Number and Configuration of Participating Stations on Lightning Location outside the Network
Authors: Gu Jiaying , Zhang Qilin , Li Jie , Zhang Junchao , Zhou Jiahao , Dai Bingzhe , Sun Hao , Wang Yao , Wang Jialei , Zhong Yuqing , Li Qingda , Yang Jing
Published: August 2022

The effect of the number and configuration of participating stations on lightning location outside the network is herein studied by evaluating the deviation distance between the reference location and the locations determined by the ToA technique, using recorded data from the location network in Fujian. It was found that the deviation distance decreases with the increase of number of stations, changing from 0.07 to 424.7 km with an average of 35 km for five stations while being 0.03~21.6 km and 2.2 km, respectively, for eight stations. The spatial distribution of those locations outside the network seems to be on a straight line. When the number of stations was five, the station linear configuration led to a narrow and long intersection region, resulting in a large deviation distance. The more dispersed distribution of stations leads to the minimum deviation distance. The percentage of deviation distance less than specific location accuracy (LA) can provide references on network design. 7 stations are sufficient to locate the lightning near and inside the network. 8–9 stations are necessary for an LA of 1 km when the lightning is 200 to 300 km away from the center of the network. The network is not suitable for locating the lightning from each station more than 400 km on average.
HFENet: Hierarchical Feature Extraction Network for Accurate Landcover Classification
Authors: Wang Di , Yang Ronghao , Liu Hanhu , He Haiqing , Tan Junxiang , Li Shaoda , Qiao Yichun , Tang Kangqi , Wang Xiao
Published: August 2022

Landcover classification is an important application in remote sensing, but it is always a challenge to distinguish different features with similar characteristics or large-scale differences. Some deep learning networks, such as UperNet, PSPNet, and DANet, use pyramid pooling and attention mechanisms to improve their abilities in multi-scale features extraction. However, due to the neglect of low-level features contained in the underlying network and the information differences between feature maps, it is difficult to identify small-scale objects. Thus, we propose a novel image segmentation network, named HFENet, for mining multi-level semantic information. Like the UperNet, HFENet adopts a top-down horizontal connection architecture while includes two improved modules, the HFE and the MFF. According to the characteristics of different levels of semantic information, HFE module reconstructs the feature extraction part by introducing an attention mechanism and pyramid pooling module to fully mine semantic information. With the help of a channel attention mechanism, MFF module up-samples and re-weights the feature maps to fuse them and enhance the expression ability of multi-scale features. Ablation studies and comparative experiments between HFENet and seven state-of-the-art models (U-Net, DeepLabv3+, PSPNet, FCN, UperNet, DANet and SegNet) are conducted with a self-labeled GF-2 remote sensing image dataset (MZData) and two open datasets landcover.ai and WHU building dataset. The results show that HFENet on three datasets with six evaluation metrics (mIoU, FWIoU, PA, mP, mRecall and mF1) are better than the other models and the mIoU is improved 7.41–10.60% on MZData, 1.17–11.57% on WHU building dataset and 0.93–4.31% on landcover.ai. HFENet can perform better in the task of refining the semantic segmentation of remote sensing images.
Giant Aufeis—Unknown Glaciation in North-Eastern Eurasia According to Landsat Images 2013–2019
Authors: Makarieva Olga , Nesterova Nataliia , Shikhov Andrey , Zemlianskova Anastasiia , Luo Dongliang , Ostashov Andrey , Alexeev Vladimir
Published: August 2022

Based on the analysis of Landsat satellite images over the period of 2013–2019, the number (6683) and total area (4529 km2) of giant aufeis fields (area ≥ 0.1 km2) were estimated for the territory of North-Eastern Eurasia. The contribution of aufeis runoff to river streamflow in different seasons was calculated for 58 hydrological gauges (area 523–526,000 km2). The contribution of aufeis and glaciers to water balance is compared. The aufeis resources vary from 0.4 to 4.25 km3 (or 3.7–11 mm) for individual basins of large rivers. They are at least 10.6 km3 in total or 5 mm of water depth on average for the study area. Aufeis annual runoff varies from 0.3 to 29 mm (0.1–22%, average 3.8%), with the share in winter runoff amount about 6–712% (average 112%) and the spring freshet 0.2–43% (average 7.1%). In general, the aufeis runoff exceeds the glacial runoff. The dynamics of aufeis formation are directly related to winter runoff, whose changes are observed in different parts of the cryosphere. The presented results are relevant for studying the impact of climate change on the hydrological cycle and its components in the permafrost regions of the Northern Hemisphere.
Investigation of Cultural–Environmental Relationships for an Alternative Environmental Management Approach Using Planet Smallsat Constellations and Questionnaire Datasets
Authors: Kimijima Satomi , Nagai Masahiko , Sakakibara Masayuki , Jahja Mohamad
Published: August 2022

The values (i.e., importance) that humans place on ecosystems are critical for sustainable socioecological management. Recently, the value pluralism approach with instrumental, intrinsic, and relational values using multiple disciplines that integrate qualitative and quantitative methodologies has been encouraged. However, these values have received little attention in environmental management. This study explored the values placed on cultural practices among groups experiencing different land cover transformations (LCTs) under the rapid shrinkage of the Limboto Lake, Gorontalo Province, Indonesia, using questionnaires and time-series Landsat and PlanetScope smallsat constellations (SSCs). The time series of LCTs and questionnaire data were computed, visualized, and analyzed statistically using the chi-square test for comparing the two village groups. Results show SSCs enabled a detailed analysis due to high spatiotemporal resolutions in tropical regions. This observation would help in monitoring natural disasters (floods) caused by the decreased lake’s water storing capacity, agricultural damage, locality safety, and environmental protection in shorter cycles. Furthermore, we found that relational values originating from traditional beliefs and practices were the domain values in the land steady-type villages. Hence, integrating the cultural–environmental values of localities with prior spatiotemporal analysis into environmental management policy and implementation processes would be a high-potential alternative for environmental conservation.
Measuring Land Surface Deformation over Soft Clay Area Based on an FIPR SAR Interferometry Algorithm—A Case Study of Beijing Capital International Airport (China)
Authors: Xing Xuemin , Zhu Lingjie , Liu Bin , Peng Wei , Zhang Rui , Ma Xiaojun
Published: August 2022

Long-term settlement monitoring of infrastructure built in soft clay areas is of great importance. When using InSAR technology for soft clay settlement monitoring, deformation modeling is a key process. In most InSAR deformation modeling, each component of the total deformation is expressed directly with a fixed functional model in phase functions and assumed to occupy an equal weight. This causes equal weight assumption uncertainty and ignores the actual certain contribution of each phase component related to certain deformational factors. Moreover, the commonly used mathematical empirical models in traditional InSAR are not suitable for describing the nonlinear characteristics of the temporal settlement evolution for soft clay. To address these limitations, we propose an SAR interferometry algorithm, namely, FIPR (FastICA Poisson-curve reciprocal accumulation method), which separates the original InSAR signal based on FastICA to extract each deformation component, and then the models can each extract deformation components and estimate the unknown parameters based on a reciprocal accumulation method. Each independent component and the obtained deformation parameters are used to generate the final deformation time series. Both simulated and real data experiments were designed. The simulated experimental results indicated that the sICA (spatial independent component analysis) separated results were much closer to the original signals than those of the tICA (temporal independent component analysis), with their RMSE lower than 2 mm, and the sICA is thus more highly recommended. Beijing Capital International Airport in China was selected as the study area in the real data experiment. Using 24 high-resolution TerraSAR-X radar satellite images from January 2012 to February 2015, the time-series deformation was obtained, with the maximum cumulative subsidence of 126 mm. The modeling accuracy for the proposed model was estimated as ±2.6 mm, with an improvement of 36.6% compared to the EWA-LM (linear model with equal weight accumulation) algorithm and 16.1% compared to the EWA-PC (Poisson curve with equal weight accumulation) algorithm. The RMSE with external leveling measurements was estimated as ±1.0 mm, with 69.7% improvement compared to EWA-LM and 50% to EWA-PC. This indicated that FIPR can reduce the uncertainty of artificial assumptions in deformation modeling and improve the accuracy of deformation analysis for highways in soft clay areas, providing a reference for road maintenance and management.