bluuebunny/crossref_metadata_2025 huggingface dataset:
Physical and Mechanical Properties of Natural Leaf Fiber-Reinforced Epoxy Polyester Composites
Authors: Kumar Sanjeev , Prasad Lalta , Patel Vinay Kumar , Kumar Virendra , Kumar Anil , Yadav Anshul , Winczek Jerzy
Published: April 2021

In recent times, demand for light weight and high strength materials fabricated from natural fibres has increased tremendously. The use of natural fibres has rapidly increased due to their high availability, low density, and renewable capability over synthetic fibre. Natural leaf fibres are easy to extract from the plant (retting process is easy), which offers high stiffness, less energy consumption, less health risk, environment friendly, and better insulation property than the synthetic fibre-based composite. Natural leaf fibre composites have low machining wear with low cost and excellent performance in engineering applications, and hence established as superior reinforcing materials compared to other plant fibres. In this review, the physical and mechanical properties of different natural leaf fibre-based composites are addressed. The influences of fibre loading and fibre length on mechanical properties are discussed for different matrices-based composite materials. The surface modifications of natural fibre also play a crucial role in improving physical and mechanical properties regarding composite materials due to improved fibre/matrix adhesion. Additionally, the present review also deals with the effect of silane-treated leaf fibre-reinforced thermoset composite, which play an important role in enhancing the mechanical and physical properties of the composites.
A Cascading Mean-Field Approach to the Calculation of Magnetization Fields in Magnetoactive Elastomers
Authors: Romeis Dirk , Saphiannikova Marina
Published: April 2021

We consider magnetoactive elastomer samples based on the elastic matrix and magnetizable particle inclusions. The application of an external magnetic field to such composite samples causes the magnetization of particles, which start to interact with each other. This interaction is determined by the magnetization field, generated not only by the external magnetic field but also by the magnetic fields arising in the surroundings of interacting particles. Due to the scale invariance of magnetic interactions (O(r−3) in d=3 dimensions), a comprehensive description of the local as well as of the global effects requires a knowledge about the magnetization fields within individual particles and in mesoscopic portions of the composite material. Accordingly, any precise calculation becomes technically infeasible for a specimen comprising billions of particles arranged within macroscopic sample boundaries. Here, we show a way out of this problem by presenting a greatly simplified, but accurate approximation approach for the computation of magnetization fields in the composite samples. Based on the dipole model to magnetic interactions, we introduce the cascading mean-field description of the magnetization field by separating it into three contributions on the micro-, meso-, and macroscale. It is revealed that the contributions are nested into each other, as in the Matryoshka’s toy. Such a description accompanied by an appropriate linearization scheme allows for an efficient and transparent analysis of magnetoactive elastomers under rather general conditions.
Design, Manufacturing and Test of CFRP Front Hood Concepts for a Light-Weight Vehicle
Authors: Bere Paul , Dudescu Mircea , Neamțu Călin , Cocian Cătălin
Published: April 2021

Composite materials are very often used in the manufacture of lightweight parts in the automotive industry, manufacturing of cost-efficient elements implies proper technology combined with a structural optimization of the material structure. The paper presents the manufacturing process, experimental and numerical analyses of the mechanical behavior for two composite hoods with different design concepts and material layouts as body components of a small electric vehicle. The first model follows the black metal design and the second one is based on the composite design concept. Manufacturing steps and full details regarding the fabrication process are delivered in the paper. Static stiffness and strain values for lateral, longitudinal and torsional loading cases were investigated. The first composite hood is 254 times lighter than a similar steel hood and the second hood concept is 22% lighter than the first one. The improvement in terms of lateral stiffness for composite hoods about a similar steel hood is for the black metal design concept about 80% and 157% for the hood with a sandwich structure and modified backside frame. Transversal stiffness is few times higher for both composite hoods while the torsional stiffness has an increase of 62% compared to a similar steel hood.
Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil
Authors: Alshehawy Abdelrahman M. , Mansour Diaa-Eldin A. , Ghali Mohsen , Lehtonen Matti , Darwish Mohamed M. F.
Published: April 2021

Condition assessment of insulating oil is crucial for the reliable long-term operation of power equipment, especially power transformers. Under thermal aging, critical degradation in oil properties, including chemical, physical, and dielectric properties, occurs due to the generation of aging byproducts. Ultraviolet-visible (UV-Vis) spectroscopy was recently proposed for the condition assessment of mineral oil. However, this absorption technique may involve all electronic states of the investigated material which typically yield a broad spectrum, and thus cannot precisely reflect the electronic structure of aged oil samples. It also cannot be implemented as an online sensor of oil degradation. In this paper, photoluminescence (PL) spectroscopy is introduced, for the first time, for effective condition assessment of insulating oil. The PL technique involves emission processes that only occur between a narrow band of electronic states that are occupied by thermalized electrons and consequently yields a spectrum that is much narrower than that of the absorption spectrum. Aged oil samples with different aging extents were prepared in the laboratory using accelerated aging tests at 120 °C, under which 1 day of laboratory aging is equivalent to approximately 1 year of aging in the field. These aged samples were then tested using PL spectroscopy with a wavelength ranging from 150 nm to 1500 nm. Two main parameters were evaluated for quantitative analysis of PL spectra: The full width at half-maximum and the enclosed area under the PL spectra. These parameters were correlated to the aging extent. In conjunction with PL spectroscopy, the aged oil samples were tested for the dielectric dissipation factor as an indication of the number of aging byproducts. Interestingly, we find a correlation between the PL spectra and the dielectric dissipation factor. The results of PL spectroscopy were compared to those of UV-Vis spectroscopy for the same samples and the parameters extracted from PL spectra were compared to the aging b-products extracted from UV-Vis spectra. Finally, the corresponding physical mechanisms were discussed considering the obtained results and the spectral shift for each spectrum. It was proved that PL spectroscopy is a promising technique for the condition assessment of insulating oil when compared to conventional transformer oil assessment measuring techniques and even to other optical absorption techniques.
Analyzing Impacts of Interfacial Instabilities on the Sweeping Power of Newtonian Fluids to Immiscibly Displace Power-Law Materials
Authors: Esmaeilpour Morteza , Gholami Korzani Maziar
Published: April 2021

Injection of Newtonian fluids to displace pseudoplastic and dilatant fluids, governed by the power-law viscosity relationship, is common in many industrial processes. In these applications, changing the viscosity of the displaced fluid through velocity alteration can regulate interfacial instabilities, displacement efficiency, the thickness of the static wall layer, and the injected fluid’s tendency to move toward particular parts of the channel. The dynamic behavior of the fluid–fluid interface in the case of immiscibility is highly complicated and complex. In this study, a code was developed that utilizes a multi-component model of the lattice Boltzmann method to decrease the computational cost and accurately model these problems. Accordingly, a 2D inclined channel, filled with a stagnant incompressible Newtonian fluid in the initial section followed by a power-law material, was modeled for numerous scenarios. In conclusion, the results indicate that reducing the power-law index can regulate interfacial instabilities leading to dynamic deformation of static wall layers at the top and the bottom of the channel. However, it does not guarantee a reduction in the thickness of these layers, which is crucial to improve displacement efficiency. The impacts of the compatibility factor and power-law index variations on the filling pattern and finger structure were intensively evaluated.
Effect of Vertical Permeability Heterogeneity in Stratified Formation on Electricity Generation Performance of Enhanced Geothermal System
Authors: Zeng Yuchao , Sun Fangdi , Zhai Haizhen
Published: April 2021

Because geologic sedimentation and hydrofracturing processes are not homogeneous, the reservoirs of enhanced geothermal systems (EGSs) are also heterogeneous; this has a significant influence on the electricity generation performance of EGS. Presently, there are a lack of systematic and profound studies on the effect of vertical permeability heterogeneity in stratified formation on the electricity generation performance of EGS. In order to uncover the effect of vertical permeability heterogeneity on electricity generation performance of EGS, in this work we analyzed the influence of vertical permeability heterogeneity on electricity generation performance of EGS through a numerical method based on geological data at the Yangbajing geothermal field. The results indicate that when the average permeability of stratified formations is constant for a homogeneous reservoir, the system attains maximum water production rate, maximum electric power, minimum reservoir impedance and maximum pump power; with the increasing of the vertical permeability heterogeneity, the water production rate gradually decreases, the electric power gradually declines, the reservoir impedance gradually increases and the pump power gradually declines. When the average permeability of stratified formations is constant, with the increasing of the vertical permeability heterogeneity, the injection pressure and energy efficiency only changes very slightly; this indicates that the vertical permeability heterogeneity is not the main factor affecting the system injection pressure and energy efficiency.
A Novel Kinetic Modeling Framework for the Polycondensation of Sugars Using Monte Carlo and the Method of Moments
Authors: Meimaroglou Dimitrios , Hoppe Sandrine , Boit Baptiste
Published: April 2021

The kinetics of the hydrolysis and polycondensation reactions of saccharides have made the subject of numerous studies, due to their importance in several industrial sectors. The present work, presents a novel kinetic modeling framework that is specifically well-suited to reacting systems under strict moisture control that favor the polycondensation reactions towards the formation of high-degree polysaccharides. The proposed model is based on an extended and generalized kinetic scheme, including also the presence of polyols, and is formulated using two different numerical approaches, namely a deterministic one in terms of the method of moments and a stochastic kinetic Monte Carlo approach. Accordingly, the most significant advantages and drawbacks of each technique are clearly demonstrated and the most fitted one (i.e., the Monte Carlo method) is implemented for the modeling of the system under different conditions, for which experimental data were available. Through these comparisons it is shown that the model can successfully follow the evolution of the reactions up to the formation of polysaccharides of very high degrees of polymerization.
Histone Sample Preparation for Bottom-Up Mass Spectrometry: A Roadmap to Informed Decisions
Authors: Daled Simon , Willems Sander , Van Puyvelde Bart , Corveleyn Laura , Verhelst Sigrid , De Clerck Laura , Deforce Dieter , Dhaenens Maarten
Published: April 2021

Histone-based chromatin organization enabled eukaryotic genome complexity. This epigenetic control mechanism allowed for the differentiation of stable gene-expression and thus the very existence of multicellular organisms. This existential role in biology makes histones one of the most complexly modified molecules in the biotic world, which makes these key regulators notoriously hard to analyze. We here provide a roadmap to enable fast and informed selection of a bottom-up mass spectrometry sample preparation protocol that matches a specific research question. We therefore propose a two-step assessment procedure: (i) visualization of the coverage that is attained for a given workflow and (ii) direct alignment between runs to assess potential pitfalls at the ion level. To illustrate the applicability, we compare four different sample preparation protocols while adding a new enzyme to the toolbox, i.e., RgpB (GingisREX®, Genovis, Lund, Sweden), an endoproteinase that selectively and efficiently cleaves at the c-terminal end of arginine residues. Raw data are available via ProteomeXchange with identifier PXD024423.
Sexuality and Affection in the Time of Technological Innovation: Artificial Partners in the Japanese Context
Authors: Aoki Beatriz Yumi , Kimura Takeshi
Published: April 2021

Recent years have witnessed an increase in the number of academic studies on the impact of technological advancements on human life, including possible transformations and changes in human sexuality following the development of sex-related devices, such as sex robots. In this context, terms such as posthuman sexuality, digisexuality, and techno-sexuality have emerged, signaling possible new understandings of sexual, intimacy, and emotional practices. It is important to note that ancient history shows that humankind has for a long time been fascinated with their relationship to non-living things, mostly human-like figures, such as dolls. The Ningyo (人形, the Japanese term for doll) has a long history of usage, and has deep religious and animistic significance in the Japanese context—there are records of sexual use as early as the 18th century. With this context in mind, this paper focuses on three Japanese examples, aiming to shine a light on beyond-human relationships, which include a Japanese man’s marriage to a digital character, sex dolls, and communicative robots, from both a sexual and emotional perspective. In a new horizon of sexual and romantic possibilities, how will humans respond, and what can emerge from these interactions?
A Laser-Based Direct Cable Length Measurement Sensor for CDPRs
Authors: Martin Christoph , Fabritius Marc , Stoll Johannes T. , Pott Andreas
Published: April 2021

Accuracy improvement is an important research topic in the field of cable-driven parallel robots (CDPRs). One reason for inaccuracies of CDPRs are deviations in the cable lengths. Such deviations can be caused by the elongation of the cable due to its elasticity or creep behavior. For most common CDPRs, the cable lengths are controlled using motor encoders of the winches, without feedback about the actual elongation of the cables. To address this problem, this paper proposes a direct cable length measurement sensor based on a laser distance sensor. We present the mechanical design, the first prototype and an experimental evaluation. As a result, the measurement principle works well and the accuracy of the measured cable lengths is within −2.32 mm to +1.86 mm compared to a range from −5.19 mm to +6.02 mm of the cable length set with the motor encoders. The standard deviation of the cable length error of the direct cable length measurement sensor is 58% lower compared to the one set with the motor encoders. Equipping all cables of the cable robot with direct cable length measurement sensors results in the possibility to correct cable length deviations and thus increase the accuracy of CDPRs. Furthermore, it enables new possibilities like the automatic recalibration of the home pose.
Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data
Authors: Li Guannan , Li Ying , Hou Yongchao , Wang Xiang , Wang Lin
Published: April 2021

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.
A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS
Authors: Pinto Miguel M. , Trigo Ricardo M. , Trigo Isabel F. , DaCamara Carlos C.
Published: April 2021

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.
Representative Learning via Span-Based Mutual Information for PolSAR Image Classification
Authors: Wang Jianlong , Hou Biao , Jiao Licheng , Wang Shuang
Published: April 2021

The optimal parameters of polarimetric scattering decomposition are critical to classify the pixels in polarimetric synthetic aperture radar (PolSAR) images by utilizing the method of machine learning. Therefore, span-based mutual information (Sp-MI) is proposed to lighten the dependence on labeling information, and then a heuristic representative learning scheme is also given by artificial neural network (ANN) to classify parameters separately with the increasing sequence according to the values of Sp-MI. Furthermore, an innovative method of using the sine function is presented to map the parameters of angular, and a min-max scaling method is applied to complete the procedure of normalization. Except for the support vector machine, three ANN-based classifiers are implemented to verify the rationality and effectiveness of the proposed representative learning and normalization scheme. Meanwhile, the classification method is compared with four similar comparison methods on three real PolSAR images. Finally, the classification results show the effectiveness of the proposed Sp-MI and the validation of the representative learning scheme in the aspect of classification overall accuracy and visual effect.
Monocular Visual-Inertial Navigation for Dynamic Environment
Authors: Fu Dong , Xia Hao , Qiao Yanyou
Published: April 2021

Simultaneous localization and mapping (SLAM) systems have been generally limited to static environments. Moving objects considerably reduce the location accuracy of SLAM systems, rendering them unsuitable for several applications. Using a combined vision camera and inertial measurement unit (IMU) to separate moving and static objects in dynamic scenes, we improve the location accuracy and adaptability of SLAM systems in these scenes. We develop a moving object-matched feature points elimination algorithm that uses IMU data to eliminate matches on moving objects but retains them on stationary objects. Moreover, we develop a second algorithm to validate the IMU data to avoid erroneous data from influencing image feature points matching. We test the new algorithms with public datasets and in a real-world experiment. In terms of the root mean square error of the location absolute pose error, the proposed method exhibited higher positioning accuracy for the public datasets than the traditional algorithms. Compared with the closed-loop errors obtained by OKVIS-mono and VINS-mono, those obtained in the practical experiment were lower by 50.17% and 56.91%, respectively. Thus, the proposed method eliminates the matching points on moving objects effectively and achieves feature point matching results that are realistic.
Changes in Ecosystems and Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area since the Reform and Opening Up in China
Authors: Wang Xuege , Yan Fengqin , Zeng Yinwei , Chen Ming , Su Fenzhen , Cui Yikun
Published: April 2021

Ecosystem services provide important support for the sustainable development of humans; these services are provided by various ecosystems, but they have been severely influenced by anthropogenic activities globally in the past several decades. To respond to the Sustainable Development Goals of the United Nations, this study investigated the changes in ecosystem structure and estimated the associated ecosystem services value (ESV) since China’s reform and opening-up policy in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and populous areas of China. Our results showed that dramatic changes in ecosystem structure occurred in the GBA, characterized by unpresented construction land sprawl (an average of 148 km2/yr) and extensive farmland loss (an average of 111 km2/yr). The change size and rate of ecosystems from 2000 to 2010 was the biggest and fastest, followed by that from 1990 to 2000. The ESV of the study area showed an overall decreasing trend, declining from 464 billion yuan to 346 billion yuan. The ESV supported by forest ecosystems and water body ecosystems made dominant contributions to the total ESV, ranging from 92% to 95%. Strong spatial heterogeneity of the ESV of the GBA might be noted throughout the study period, with lower values in the central region and higher values in the surrounding region. To realize sustainable development in the GBA; this study strongly suggests that local governments, and the public, scientifically use various ecosystems and their services, focusing on vigorously protecting ecosystems with high and important ESVs, such as water body, wetland, forest, and farmland ecosystems.
Potential of Sentinel-2 Satellite Images for Monitoring Green Waste Compost and Manure Amendments in Temperate Cropland
Authors: Dodin Maxence , Smith Hunter D. , Levavasseur Florent , Hadjar Dalila , Houot Sabine , Vaudour Emmanuelle
Published: April 2021

Increasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha−1) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha−1.
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5
Authors: Yan Bin , Fan Pan , Lei Xiaoyan , Liu Zhijie , Yang Fuzeng
Published: April 2021

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.
Improving DGNSS Performance through the Use of Network RTK Corrections
Authors: Weng Duojie , Ji Shengyue , Lu Yangwei , Chen Wu , Li Zhihua
Published: April 2021

The differential global navigation satellite system (DGNSS) is an enhancement system that is widely used to improve the accuracy of single-frequency receivers. However, distance-dependent errors are not considered in conventional DGNSS, and DGNSS accuracy decreases when baseline length increases. In network real-time kinematic (RTK) positioning, distance-dependent errors are accurately modelled to enable ambiguity resolution on the user side, and standard Radio Technical Commission for Maritime Services (RTCM) formats have also been developed to describe the spatial characteristics of distance-dependent errors. However, the network RTK service was mainly developed for carrier-phase measurements on professional user receivers. The purpose of this study was to modify the local-area DGNSS through the use of network RTK corrections. Distance-dependent errors can be reduced, and accuracy for a longer baseline length can be improved. The results in the low-latitude areas showed that the accuracy of the modified DGNSS could be improved by more than 50% for a 17.9 km baseline during solar active years. The method in this paper extends the use of available network RTK corrections with high accuracy to normal local-area DGNSS applications.
Improving Land Cover Classification Using Genetic Programming for Feature Construction
Authors: Batista João E. , Cabral Ana I. R. , Vasconcelos Maria J. P. , Vanneschi Leonardo , Silva Sara
Published: April 2021

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.
Determination of Venus’ Interior Structure with EnVision
Authors: Rosenblatt Pascal , Dumoulin Caroline , Marty Jean-Charles , Genova Antonio
Published: April 2021

The Venusian geological features are poorly gravity-resolved, and the state of the core is not well constrained, preventing an understanding of Venus’ cooling history. The EnVision candidate mission to the ESA’s Cosmic Vision Programme consists of a low-altitude orbiter to investigate geological and atmospheric processes. The gravity experiment aboard this mission aims to determine Venus’ geophysical parameters to fully characterize its internal structure. By analyzing the radio-tracking data that will be acquired through daily operations over six Venusian days (four Earth’s years), we will derive a highly accurate gravity field (spatial resolution better than ~170 km), allowing detection of lateral variations of the lithosphere and crust properties beneath most of the geological features. The expected 0.3% error on the Love number k2, 0.1° error on the tidal phase lag and 1.4% error on the moment of inertia are fundamental to constrain the core size and state as well as the mantle viscosity.
Mapping Arctic Lake Ice Backscatter Anomalies Using Sentinel-1 Time Series on Google Earth Engine
Authors: Pointner Georg , Bartsch Annett
Published: April 2021

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses.
Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data
Authors: Kwak Geun-Ho , Park Chan-won , Lee Kyung-do , Na Sang-il , Ahn Ho-yong , Park No-Wook
Published: April 2021

When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.
Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China
Authors: Zhu Yaohui , Yang Guijun , Yang Hao , Zhao Fa , Han Shaoyu , Chen Riqiang , Zhang Chengjian , Yang Xiaodong , Liu Miao , Cheng Jinpeng , Zhao Chunjiang
Published: April 2021

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.
Effect of Reduced Anthropogenic Activities on Water Quality in Lake Vembanad, India
Authors: Kulk Gemma , George Grinson , Abdulaziz Anas , Menon Nandini , Theenathayalan Varunan , Jayaram Chiranjivi , Brewin Robert J. W. , Sathyendranath Shubha
Published: April 2021

The United Nation’s Sustainable Development Goal Life Below Water (SDG-14) aims to “conserve and sustainably use the oceans, seas, and marine resources for sustainable development”. Within SDG-14, targets 14.1 and 14.2 deal with marine pollution and the adverse impacts of human activities on aquatic systems. Here, we present a remote-sensing-based analysis of short-term changes in the Vembanad-Kol wetland system in the southwest of India. The region has experienced high levels of anthropogenic pressures, including from agriculture, industry, and tourism, leading to adverse ecological and socioeconomic impacts with consequences not only for achieving the targets set out in SDG-14, but also those related to water quality (SDG-6) and health (SDG-3). To move towards the sustainable management of coastal and aquatic ecosystems such as Lake Vembanad, it is important to understand how both natural and anthropogenic processes affect water quality. In 2020, a unique opportunity arose to study water quality in Lake Vembanad during a period when anthropogenic pressures were reduced due to a nationwide lockdown in response to the global pandemic caused by SARS-CoV-2 (25 March–31 May 2020). Using Sentinel-2 and Landsat-8 multi-spectral remote sensing and in situ observations to analyse changes in five different water quality indicators, we show that water quality improved in large areas of Lake Vembanad during the lockdown in 2020, especially in the more central and southern regions, as evidenced by a decrease in total suspended matter, turbidity, and the absorption by coloured dissolved organic matter, all leading to clearer waters as indicated by the Forel-Ule classification of water colour. Further analysis of longer term trends (2013–2020) showed that water quality has been improving over time in the more northern regions of Lake Vembanad independent of the lockdown. The improvement in water quality during the lockdown in April–May 2020 illustrates the importance of addressing anthropogenic activities for the sustainable management of coastal ecosystems and water resources.
A Moving Target Velocity Estimation Method Based on the MC-MASA SAR Mode
Authors: Wang Yamin , Chen Jie , Liu Wei , Li Chunsheng , Yang Wei
Published: April 2021

Imaging position shift based on the multiple azimuth squint angles (MASA) mode is effective for target azimuth velocity estimation, whereas accuracy is low when target range velocity is high. In this paper, the estimation problem for both target azimuth and range velocities is considered based on the multi-channels MASA (MC-MASA) mode. Firstly, the acquisition geometry of MC-MASA mode and Doppler characteristics of a moving target are analyzed in detail, especially in squint mode. Then, for better moving target estimation, the stationary background clutter is removed using the displacement phase center antenna (DPCA) technique, and the failure in range velocity estimation with sequential SAR images is also discussed. Furthermore, a modified along-track interferometry (ATI) is proposed to preliminarily reconstruct the azimuth-and-range velocity map based on the MC-MASA mode. Since the velocity estimation accuracy is dependent on squint angle and signal-to-clutter ratio (SCR), the circumstances are divided into three cases with different iteration estimation strategies, which could expand the scene application scope of velocity estimation and achieve a high estimation accuracy along both azimuth and range directions. Finally, the performance of the proposed method is demonstrated by experimental results.
Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification
Authors: Wegmueller Sarah A. , Townsend Philip A.
Published: April 2021

Severe forest disturbance events are becoming more common due to climate change and many forest managers rely heavily upon airborne surveys to map damage. However, when the damage is extensive, airborne assets are in high demand and it can take managers several weeks to account for the damage, delaying important management actions. While some satellite-based systems exist to help with this process, their spatial resolution or latency can be too large for the needs of managers, as evidenced by the continued use of airborne imaging. Here, we present a new, operational-focused system capable of leveraging high spatial and temporal resolution Sentinel-2 and Planet Dove imagery to support the mapping process. This system, which we have named Astrape (“ah-STRAH-pee”), uses recently developed techniques in image segmentation and machine learning to produce maps of damage in different forest types and regions without requiring ground data, greatly reducing the need for potentially dangerous airborne surveys and ground sampling needed to accurately quantify severe damage. Although some limited field work is required to verify results, similar to current operational systems, Astrape-produced maps achieved 78–86% accuracy with respect to damage severity when evaluated against reference data. We present the Astrape framework and demonstrate its flexibility and potential with four case studies depicting four different disturbance types—fire, hurricane, derecho and tornado—in three disparate regions of the United States. Astrape is capable of leveraging various sources of satellite imagery and offers an efficient, flexible and economical option for mapping severe damage in forests.
Efficient Certificate-Less Aggregate Signature Scheme with Conditional Privacy-Preservation for Vehicular Ad Hoc Networks Enhanced Smart Grid System
Authors: Vallent Thokozani Felix , Hanyurwimfura Damien , Mikeka Chomora
Published: April 2021

Vehicular Ad hoc networks (VANETs) as spontaneous wireless communication technology of vehicles has a wide range of applications like road safety, navigation and other electric car technologies, however its practicability is greatly hampered by cyber-attacks. Due to message broadcasting in an open environment during communication, VANETs are inherently vulnerable to security and privacy attacks. However to address the cyber-security issues with optimal computation overhead is a matter of current security research challenge. So this paper designs a secure and efficient certificate-less aggregate scheme (ECLAS) for VANETs applicable in a smart grid scenario. The proposed scheme is based on elliptic curve cryptography to provide conditional privacy-preservation by incorporating usage of time validated pseudo-identification for communicating vehicles besides sorting out the KGC (Key Generation Center) escrow problem. The proposed scheme is comparatively more efficient to relevant related research work because it precludes expensive computation operations likes bilinear pairings as shown by the performance evaluation. Similarly, communication cost is within the ideal range to most related works while considering the security requirements of VANETs system applicable in a smart grid environment.