bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Photocatalytic Hydrogen Production of the Polyoxoniobate Modified with RGO and PPy
Authors: Heng Shiliang , Li Lei , Li Weiwei , Li Haiyan , Pang Jingyu , Zhang Mengzhen , Bai Yan , Dang Dongbin
Published: December 2020

The development of high-efficiency, recyclable, and inexpensive photocatalysts for water splitting for hydrogen production is of great significance to the application of solar energy. Herein, a series of graphene-decorated polyoxoniobate photocatalysts Nb6/PPy-RGO (Nb6 = K7HNb6O19, RGO = reduced graphene oxide, PPy = polypyrrole), with the bridging effect of polypyrrole were prepared through a simple one-step solvothermal method, which is the first example of polyoxoniobate-graphene-based nanocomposites. The as-fabricated photocatalyst showed a photocatalytic H2 evolution activity without any co-catalyst. The rate of 1038 µmol g−1 in 5 h under optimal condition is almost 43 times higher than that of pure K7HNb6O19·13H2O. The influencing factors for photocatalysts in photocatalytic hydrogen production under simulated sunlight were studied in detail and the feasible mechanism is presented in this paper. These results demonstrate that Nb6O19 acts as the main catalyst and electron donor, RGO provides active sites, and PPy acted as an electronic bridge to extend the lifetime of photo-generated carriers, which are crucial factors for photocatalytic H2 production.
Deciphering the Influence of Electrolytes on the Energy Storage Mechanism of Vertically-Oriented Graphene Nanosheet Electrodes by Using Advanced Electrogravimetric Methods
Authors: Lé Tao , Bidan Gérard , Billon Florence , Delaunay Marc , Gérard Jean-Michel , Perrot Hubert , Sel Ozlem , Aradilla David
Published: December 2020

Electrolyte composition is a crucial factor determining the capacitive properties of a supercapacitor device. However, its complex influence on the energy storage mechanisms has not yet been fully elucidated. For this purpose, in this study, the role of three different types of electrolytes based on a propylene carbonate (PC) solution containing tetrabutylammonium perchlorate (TBAClO4), lithium perchlorate (LiClO4) and butyltrimethylammonium bis(trifluoromethylsulfonyl)imide (N1114TFSI) ionic liquid on vertically-oriented graphene nanosheet electrodes has been investigated. Herein, in situ electrochemical quartz crystal microbalance (EQCM) and its coupling with electrochemical impedance spectroscopy (EIS), known as ac-electrogravimetry, have allowed the dynamic aspects of the (co)electroadsorption processes at the electrode-electrolyte interface to be examined. A major contribution of ClO4− anions (TBAClO4) was evidenced, whereas in the PC/N1114TFSI mixture (50:50 wt%) both anions (TFSI−) and cations (N1114+) were symmetrically exchanged during cycling. In the particular case of LiClO4, solvation of Li+ cations in PC was involved, affecting the kinetics of electroadsorption. These results demonstrate the suitability of dynamic electrogravimetric methods to unveil the interfacial exchange properties of mobile species for the conception of new high performance energy storage devices.
Food Bank Donations in the United States: A Landscape Review of Federal Policies
Authors: Hudak Katelin M. , Friedman Emily , Johnson Joelle , Benjamin-Neelon Sara E.
Published: December 2020

Rates of food insecurity have increased substantially in the United States (US), and more families are turning to the charitable food system to help meet their needs. Prior studies have examined the nutritional quality of foods offered through food banks, but little is known about what government policies may shape the healthy food donation landscape. The purpose of this study was to review US federal policies that impact food and beverage donations to food banks and assess whether policies encourage healthy food donations. In spring 2020, two researchers independently reviewed federal food and beverage donation policies using predefined search terms in two legal databases. We identified six categories of policies based on the existing food donation literature and themes that emerged in the policy review. We identified 42 federal policies spanning six categories that addressed food and beverage donations to food banks. The largest category was “government programs,” with 19 (45%) policies. The next largest category was “donation via schools,” with 12 (29%) policies. However, no policies specifically addressed the nutritional quality of food donations. There is an opportunity for the federal government to strengthen food bank donation policies and improve the nutritional quality of donated foods and beverages.
Association of Vegetable and Animal Flesh Intake with Inflammation in Pregnant Women from India
Authors: Yadana Su , Talegawkar Sameera A. , Mathad Jyoti S. , Alexander Mallika , Rajagopalan Kripa , Kumar Pavan , Naik Shilpa , Leu Cheng-Shiun , Kulkarni Vandana , Deshpande Prasad , Araujo-Pereira Mariana , Bhosale Ramesh , Babu Subash , Andrade Bruno B. , Caulfield Laura E. , Gupta Amita , Shivakoti Rupak
Published: December 2020

In pregnant women, studies are lacking on the relationship of vegetable and animal flesh (poultry, red meat and seafood) intake with inflammation, especially in low- and middle-income countries. We conducted a cohort study of pregnant women receiving antenatal care at BJ Medical College in Pune, India. The dietary intake of pregnant women was queried in the third trimester using a validated food frequency questionnaire. Twelve inflammatory markers were measured in plasma samples using immunoassays. Only 12% of the study population were vegetarians, although animal flesh intake levels were lower compared to Western populations. In multivariable models, higher intakes of total vegetables were associated with lower levels of the T-helper (Th) 17 cytokine interleukin (IL)-17a (p = 0.03) and the monocyte/macrophage activation marker soluble CD163 (sCD163) (p = 0.02). Additionally, higher intakes of poultry were negatively associated with intestinal fatty-acid binding protein (I-FABP) levels (p = 0.01), a marker of intestinal barrier dysfunction and Th2 cytokine IL-13 (p = 0.03), and higher seafood was associated with lower IL-13 (p = 0.005). Our data from pregnant women in India suggest that a higher quality diet emphasizing vegetables and with some animal flesh is associated with lower inflammation. Future studies should confirm these findings and test if modulating vegetables and animal flesh intake could impact specific aspects of immunity and perinatal health.
Food Timing, Circadian Rhythm and Chrononutrition: A Systematic Review of Time-Restricted Eating’s Effects on Human Health
Authors: Adafer Réda , Messaadi Wassil , Meddahi Mériem , Patey Alexia , Haderbache Abdelmalik , Bayen Sabine , Messaadi Nassir
Published: December 2020

Introduction: Recent observations have shown that lengthening the daily eating period may contribute to the onset of chronic diseases. Time-restricted eating (TRE) is a diet that especially limits this daily food window. It could represent a dietary approach that is likely to improve health markers. The aim of this study was to review how time-restricted eating affects human health. Method: Five general databases and six nutrition journals were screened to identify all studies published between January 2014 and September 2020 evaluating the effects of TRE on human populations. Results: Among 494 articles collected, 23 were finally included for analysis. The overall adherence rate to TRE was 80%, with a 20% unintentional reduction in caloric intake. TRE induced an average weight loss of 3% and a loss of fat mass. This fat loss was also observed without any caloric restriction. Interestingly, TRE produced beneficial metabolic effects independently of weight loss, suggesting an intrinsic effect based on the realignment of feeding and the circadian clock. Conclusions: TRE is a simple and well-tolerated diet that generates many beneficial health effects based on chrononutrition principles. More rigorous studies are needed, however, to confirm those effects, to understand their mechanisms and to assess their applicability to human health.
Inhibition of SARS-CoV-2 Entry into Host Cells Using Small Molecules
Authors: Al Adem Kenana , Shanti Aya , Stefanini Cesare , Lee Sungmun
Published: December 2020

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a virus belonging to the Coronavirus family, is now known to cause Coronavirus Disease (Covid-19) which was first recognized in December 2019. Covid-19 leads to respiratory illnesses ranging from mild infections to pneumonia and lung failure. Strikingly, within a few months of its first report, Covid-19 has spread worldwide at an exceptionally high speed and it has caused enormous human casualties. As yet, there is no specific treatment for Covid-19. Designing inhibitory drugs that can interfere with the viral entry process constitutes one of the main preventative therapies that could combat SARS-CoV-2 infection at an early stage. In this review, we provide a brief introduction of the main features of coronaviruses, discuss the entering mechanism of SARS-CoV-2 into human host cells and review small molecules that inhibit SARS-CoV-2 entry into host cells. Specifically, we focus on small molecules, identified by experimental validation and/or computational prediction, that target the SARS-CoV-2 spike protein, human angiotensin converting enzyme 2 (ACE2) receptor and the different host cell proteases that activate viral fusion. Given the persistent rise in Covid-19 cases to date, efforts should be directed towards validating the therapeutic effectiveness of these identified small molecule inhibitors.
How Efficient Are Indian Banks in Managing the Risk-Return Trade-Off? An Empirical Analysis
Authors: Navas Jalaludeen , Dhanavanthan Periyasamy , Lazar Daniel
Published: December 2020

Risk taking is an inherent element of the banking business. Banks make conscious decisions regarding risk taking as they expect to make more return if they take more risk. The primary objective of this study is to empirically investigate the efficiency of Indian banks in generating return relative to the risk they take. If the efficiency measurement is not adjusted for different risk preferences, then a bank earning lower return at lower risk may be misclassified as less efficient compared to peers earning the same level of return, but operating at a higher level of risk. This paper uses measures of liquidity risk, credit risk, market risk, and insolvency risk to develop a risk-return stochastic frontier in order to examine the risk efficiency of banks, a novel attempt in the Indian context. The paper further analyzes the efficiency of banks with respect to bank specific characteristics and risk management regimes. The models are estimated using data from a sample of 47 major banks for the period 2009–2018. The study reveals that Indian banks, on average, exhibited lower efficiency in trading risk against return during the sample period.
Adaptive-SFSDAF for Spatiotemporal Image Fusion that Selectively Uses Class Abundance Change Information
Authors: Hou Shuwei , Sun Wenfang , Guo Baolong , Li Cheng , Li Xiaobo , Shao Yingzhao , Zhang Jianhua
Published: December 2020

Many spatiotemporal image fusion methods in remote sensing have been developed to blend highly resolved spatial images and highly resolved temporal images to solve the problem of a trade-off between the spatial and temporal resolution from a single sensor. Yet, none of the spatiotemporal fusion methods considers how the various temporal changes between different pixels affect the performance of the fusion results; to develop an improved fusion method, these temporal changes need to be integrated into one framework. Adaptive-SFSDAF extends the existing fusion method that incorporates sub-pixel class fraction change information in Flexible Spatiotemporal DAta Fusion (SFSDAF) by modifying spectral unmixing to select spectral unmixing adaptively in order to greatly improve the efficiency of the algorithm. Accordingly, the main contributions of the proposed adaptive-SFSDAF method are twofold. One is to address the detection of outliers of temporal change in the image during the period between the origin and prediction dates, as these pixels are the most difficult to estimate and affect the performance of the spatiotemporal fusion methods. The other primary contribution is to establish an adaptive unmixing strategy according to the guided mask map, thus effectively eliminating a great number of insignificant unmixed pixels. The proposed method is compared with the state-of-the-art Flexible Spatiotemporal DAta Fusion (FSDAF), SFSDAF, FIT-FC, and Unmixing-Based Data Fusion (UBDF) methods, and the fusion accuracy is evaluated both quantitatively and visually. The experimental results show that adaptive-SFSDAF achieves outstanding performance in balancing computational efficiency and the accuracy of the fusion results.
Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment
Authors: Psomiadis Emmanouil , Diakakis Michalis , Soulis Konstantinos X.
Published: December 2020

Timely mapping, measuring and impact assessment of flood events are crucial for the coordination of flood relief efforts and the elaboration of flood management and risk mitigation plans. However, this task is often challenging and time consuming with traditional land-based techniques. In this study, Sentinel-1 radar and Landsat images were utilized in collaboration with hydraulic modelling to obtain flood characteristics and land use/cover (LULC), and to assess flood impact in agricultural areas. Furthermore, indirect estimation of the recurrence interval of a flood event in a poorly gauged catchment was attempted by combining remote sensing (RS) and hydraulic modelling. To this end, a major flood event that occurred in Sperchios river catchment, in Central Greece, which is characterized by extensive farming activity was used as a case study. The synergistic usage of multitemporal RS products and hydraulic modelling has allowed the estimation of flood characteristics, such as extent, inundation depth, peak discharge, recurrence interval and inundation duration, providing valuable information for flood impact estimation and the future examination of flood hazard in poorly gauged basins. The capabilities of the ESA Sentinel-1 mission, which provides improved spatial and temporal analysis, allowing thus the mapping of the extent and temporal dynamics of flood events more accurately and independently from the weather conditions, were also highlighted. Both radar and optical data processing methods, i.e., thresholding, image differencing and water index calculation, provided similar and satisfactory results. Conclusively, multitemporal RS data and hydraulic modelling, with the selected techniques, can provide timely and useful flood observations during and right after flood disasters, applicable in a large part of the world where instrumental hydrological data are scarce and when an apace survey of the condition and information about temporal dynamics in the influenced region is crucial. However, future missions that will reduce further revisiting times will be valuable in this endeavor.
DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars
Authors: Wilhelm Thorsten , Geis Melina , Püttschneider Jens , Sievernich Timo , Weber Tobias , Wohlfarth Kay , Wöhler Christian
Published: December 2020

Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a basis. Additionally, we introduce a novel dataset, termed DoMars16k, which contains 16,150 samples of fifteen different landforms commonly found on the Martian surface. We use a convolutional neural network to establish a relation between Mars Reconnaissance Orbiter Context Camera images and the landforms of the dataset. Afterwards, we employ a sliding-window approach in conjunction with a Markov Random field smoothing to create maps in a weakly supervised fashion. Finally, we provide encouraging results and carry out automated geomorphological analyses of Jezero crater, the Mars2020 landing site, and Oxia Planum, the prospective ExoMars landing site.
Building Extraction from High Spatial Resolution Remote Sensing Images via Multiscale-Aware and Segmentation-Prior Conditional Random Fields
Authors: Zhu Qiqi , Li Zhen , Zhang Yanan , Guan Qingfeng
Published: December 2020

Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use of the spatial neighbourhood information of both labelled and observed images. CRF is widely used in building footprint extraction. However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images. Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields (MSCRF). To solve the problem of losing building details in the downsampling process, D-LinkNet connecting the encoder and decoder is correspondingly used to generate the unary potential. By integrating multi-scale building features in the central module, D-LinkNet can integrate multiscale contextual information without loss of resolution. For the pairwise potential, the segmentation prior is fused to alleviate the influence of spectral diversity between the building and the background area. Moreover, the local class label cost term is introduced. The clear boundaries of the buildings are obtained by using the larger-scale context information. The experimental results demonstrate that the proposed MSCRF framework is superior to the state-of-the-art methods and performs well for building extraction of complex scenes.
Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing
Authors: Hsu Astrid J. , Kumagai Joy , Favoretto Fabio , Dorian John , Guerrero Martinez Benigno , Aburto-Oropeza Octavio
Published: December 2020

This study investigated how different remote sensing techniques can be combined to accurately monitor mangroves. In this paper, we present a framework to use drone imagery to calculate correction factors which can improve the accuracy of satellite-based mangrove extent. We focus on semi-arid dwarf mangroves of Baja California Sur, Mexico, where the mangroves tend to be stunted in height and found in small patches, as well as larger forests. Using a DJI Phantom 4 Pro, we imaged mangroves and labeled the extent by manual classification in QGIS. Using ArcGIS, we compared satellite-based mangrove extent maps from Global Mangrove Watch (GMW) in 2016 and Mexico’s national government agency (National Commission for the Knowledge and Use of Biodiversity, CONABIO) in 2015, with extent maps generated from in situ drone studies in 2018 and 2019. We found that satellite-based extent maps generally overestimated mangrove coverage compared to that of drone-based maps. To correct this overestimation, we developed a method to derive correction factors for GMW mangrove extent. These correction factors correspond to specific pixel patterns generated from a convolution analysis and mangrove coverage defined from drone imagery. We validated our model by using repeated k-fold cross-validation, producing an accuracy of 98.3% ± 2.1%. Overall, drones and satellites are complementary tools, and the rise of machine learning can help stakeholders further leverage the strengths of the two tools, to better monitor mangroves for local, national, and international management.
An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data
Authors: Liu Pengfei , Wang Qing , Zhang Dandan , Lu Yongzong
Published: December 2020

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.
Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan
Authors: Zhang Pengfei , Xu Chong , Ma Siyuan , Shao Xiaoyi , Tian Yingying , Wen Boyu
Published: December 2020

After a major earthquake, the rapid identification and mapping of co-seismic landslides in the whole affected area is of great significance for emergency rescue and loss assessment of seismic hazards. In recent years, researchers have achieved good results in research on a small scale and single environment characteristics of this issue. However, for the whole earthquake-affected area with large scale and complex environments, the correct rate of extracting co-seismic landslides remains low, and there is no ideal method to solve this problem. In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction of landslides triggered by the 2018 Iburi earthquake, Japan. The study area is about 671.87 km2, of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model. The results show that most of the co-seismic landslides can be identified by this method. In this experiment, the verification precision of the model is 0.7965 and the F1 score is 0.8288. This method can intelligently identify and map landslides triggered by earthquakes from Planet images. It has strong practicability and high accuracy. It can provide assistance for earthquake emergency rescue and rapid disaster assessment.
Sand Dune Dynamics Exploiting a Fully Automatic Method Using Satellite SAR Data
Authors: Delgado Blasco José Manuel , Chini Marco , Verstraeten Gert , Hanssen Ramon F.
Published: December 2020

This work presents an automatic procedure to quantify dune dynamics on isolated barchan dunes exploiting Synthetic Aperture RADAR satellite data. We use C-band datasets, allowing the multi-temporal analysis of dune dynamics in two study areas, one located between the Western Sahara and Mauritania and the second one located in the South Rayan dune field in Egypt. Our method uses an adaptive parametric thresholding algorithm and common geospatial operations. A quantitative dune dynamics analysis is also performed. We have measured dune migration rates of 2–6 m/year in the NNW-SSE direction and 11–20 m/year NNE-SSW for the South Rayan and West-Sahara dune fields, respectively. To validate our results, we have manually tracked several dunes per study area using Google Earth imagery. Results from both automatic and manual approaches are consistent. Finally, we discuss the advantages and limitations of the approach presented.
Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions
Authors: Bingham Frederick M. , Li Zhijin
Published: December 2020

Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall.
Assessment of Spatio-Temporal Landscape Changes from VHR Images in Three Different Permafrost Areas in the Western Russian Arctic
Authors: Ardelean Florina , Onaca Alexandru , Chețan Marinela-Adriana , Dornik Andrei , Georgievski Goran , Hagemann Stefan , Timofte Fabian , Berzescu Oana
Published: December 2020

Our study highlights the usefulness of very high resolution (VHR) images to detect various types of disturbances over permafrost areas using three example regions in different permafrost zones. The study focuses on detecting subtle changes in land cover classes, thermokarst water bodies, river dynamics, retrogressive thaw slumps (RTS) and infrastructure in the Yamal Peninsula, Urengoy and Pechora regions. Very high-resolution optical imagery (sub-meter) derived from WorldView, QuickBird and GeoEye in conjunction with declassified Corona images were involved in the analyses. The comparison of very high-resolution images acquired in 2003/2004 and 2016/2017 indicates a pronounced increase in the extent of tundra and a slight increase of land covered by water. The number of water bodies increased in all three regions, especially in discontinuous permafrost, where 14.86% of new lakes and ponds were initiated between 2003 and 2017. The analysis of the evolution of two river channels in Yamal and Urengoy indicates the dominance of erosion during the last two decades. An increase of both rivers’ lengths and a significant widening of the river channels were also observed. The number and total surface of RTS in the Yamal Peninsula strongly increased between 2004 and 2016. A mean annual headwall retreat rate of 1.86 m/year was calculated. Extensive networks of infrastructure occurred in the Yamal Peninsula in the last two decades, stimulating the initiation of new thermokarst features. The significant warming and seasonal variations of the hydrologic cycle, in particular, increased snow water equivalent acted in favor of deepening of the active layer; thus, an increasing number of thermokarst lake formations.
Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China
Authors: Liu Xiang , Liu Huiyu , Datta Pawanjeet , Frey Julian , Koch Barbara
Published: December 2020

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.
Comment on “Pre-Collapse Space Geodetic Observations of Critical Infrastructure: The Morandi Bridge, Genoa, Italy” by Milillo et al. (2019)
Authors: Lanari Riccardo , Reale Diego , Bonano Manuela , Verde Simona , Muhammad Yasir , Fornaro Gianfranco , Casu Francesco , Manunta Michele
Published: December 2020

We present in this comment a Multi-Temporal SAR Interferometry (MT-InSAR) analysis showing that the results published by Milillo et al. (2019) in the Remote Sensing Journal, presenting the evidence of space geodetic observations relevant to displacements occurring before the collapse of the Morandi Bridge, happened in Genova (Italy) on the 14 August 2018, are questionable. In particular, we focus on the InSAR results obtained by Milillo et al. (2019) by processing the 3 m × 3 m resolution COSMO-SkyMed (CSK) data collected from ascending and descending orbits on the area of interest. These results, thanks to the high spatial resolution and the short revisit time characterizing this multi-orbit SAR dataset, represent the cornerstone of their analysis. The main findings of their study allow Milillo et al. to conclude that the InSAR processing of this COSMO-SkyMed dataset reveals the increased deformation magnitude over time of points located near the strands of the deck next to the collapsed pier, between 12 March 2017 and August 2018. In this comment, we show the results obtained by the IREA-CNR SAR team after processing the same ascending and descending CSK dataset, but by using two alternative and independent processing techniques: the Small BAseline Subset (SBAS) and the Advanced Tomographic SAR (TomoSAR) approaches, respectively. Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results, leading us to deeply disagree with the findings of their InSAR analysis.
Lookup Table Approach for Radiometric Calibration of Miniaturized Multispectral Camera Mounted on an Unmanned Aerial Vehicle
Authors: Cao Hongtao , Gu Xingfa , Wei Xiangqin , Yu Tao , Zhang Haifeng
Published: December 2020

Over recent years, miniaturized multispectral cameras mounted on an unmanned aerial vehicle (UAV) have been widely used in remote sensing. Most of these cameras are integrated with low-cost, image-frame complementary metal-oxide semiconductor (CMOS) sensors. Compared to the typical charged coupled device (CCD) sensors or linear array sensors, consumer-grade CMOS sensors have the disadvantages of low responsivity, higher noise, and non-uniformity of pixels, which make it difficult to accurately detect optical radiation. Therefore, comprehensive radiometric calibration is crucial for quantitative remote sensing and comparison of temporal data using such sensors. In this study, we examine three procedures of radiometric calibration: relative radiometric calibration, normalization, and absolute radiometric calibration. The complex features of dark current noise, vignetting effect, and non-uniformity of detector response are analyzed. Further, appropriate procedures are used to derive the lookup table (LUT) of correction factors for these features. Subsequently, an absolute calibration coefficient based on an empirical model is used to convert the digital number (DN) of images to radiance unit. Due to the radiometric calibration, the DNs of targets observed in the image are more consistent than before calibration. Compared to the method provided by the manufacturer of the sensor, LUTs facilitate much better radiometric calibration. The root mean square error (RMSE) of measured reflectance in each band (475, 560, 668, 717, and 840 nm) are 2.30%, 2.87%, 3.66%, 3.98%, and 4.70% respectively.
A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar
Authors: Song Chong , Wang Bingnan , Xiang Maosheng , Wang Zhongbin , Xu Weidi , Sun Xiaofan
Published: December 2020

The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.
Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco
Authors: Bouras El houssaine , Jarlan Lionel , Er-Raki Salah , Albergel Clément , Richard Bastien , Balaghi Riad , Khabba Saïd
Published: December 2020

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.
Quantification and Mapping of Satellite Driven Surface Energy Balance Fluxes in Semi-Arid to Arid Inter-Mountain Region
Authors: Acharya Bibek , Sharma Vivek , Heitholt James , Tekiela Daniel , Nippgen Fabian
Published: December 2020

Crop evapotranspiration (ETc) estimates, on a regional scale, hold enormous potential in managing surface and groundwater resources. This is particularly important for the headwater state of Wyoming, which provides water to found major river basins of the US. In this study, METRIC (Mapping evapotranspiration at high resolution with internalized calibration), a satellite-based image processing model, was used to map and quantify daily, monthly, and seasonal ETc and other energy balance fluxes, i.e., net radiation (Rn), sensible heat (H), and soil heat flux (G) dynamics for different land-use classes. Monthly and seasonal ETc estimated were further used to approximate regional water consumption patterns for different land-use types for nine irrigation districts in semi-arid to arid intermountain region of Big Horn Basin (BHB), Wyoming. The validation of METRIC retrievals against Bowen ratio energy balance system (BREBS) fluxes measured over three vegetative surfaces, viz. sugar beet in 2017, dry bean in 2018, and barley in 2019, indicated high accuracy. The pooled correlation observed between estimated (pooled) and measured instantaneous fluxes had R2 values of 0.91 (RMSE = 0.08 mm h−1, NSE = 0.91), 0.81 (RMSE = 49.6 Wm−2, NSE = 0.67), 0.53 (RMSE = 27.1 Wm−2, NSE = 0.53), and 0.86 (RMSE = 59.2 Wm−2, NSE = 0.84) for ETc, Rn, G, and H, respectively. The biggest discrepancy between measured and estimated monthly ETc values was observed during times when BREBS flux tower footprint was devoid of any crops or the crops at footprint were not actively transpiring. Validation results improved when comparisons were made on monthly scales with METRIC underestimating growing season ETc in the range between 3.2% to 6.0%. Seasonal ETc by land-use type showed significant variation over the study area where crop ETc was 52% higher than natural vegetation ETc. Furthermore, it was found that, in the arid to semi-arid intermountain region of Wyoming, the contribution of irrigation to total seasonal ETc varied in the range of 73–81% in nine irrigation districts that fall within the study area. The high relative contribution of irrigation highlights the importance of identifying and quantifying ETc for improved management in irrigation system design and water allocation.
High-Resolution Thermal Imaging and Analysis of TIG Weld Pool Phase Transitions
Authors: Boone Nicholas , Davies Matthew , Willmott Jon Raffe , Marin-Reyes Hector , French Richard
Published: December 2020

Tungsten inert gas (TIG) welding is a well-established joining process and offers the user flexibility to weld a large range of materials. Ultra-thin turbine tipping is an important application for TIG welding that is exceptionally challenging due to the wide range of variables needed to accurately control the process: slope times, arc control, travel speed, etc. We offer new insight into weld pool characteristics, utilizing both on- and off-line measurements of weld tracks. High-resolution thermal imaging yields spatially and temporally resolved weld pool phase transitions coupled with post-weld photographs, which gives a novel perspective into the thermal history of a weld. Our imaging system is filtered to measure a 10 nm window at 950 nm and comprises a commercial Sigma lens to produce a near-infrared (NIR) camera. The measured near-infrared radiance is calibrated for temperature over the range of from 800 to 1350 °C.
Dynamic Properties of Microresonators with the Bionic Structure of Tympanic Membrane
Authors: Tai Yongpeng , Zhou Kai , Chen Ning
Published: December 2020

The structure of a microresonator will affect the vibration characteristics and the performance of the system. Inspired by the structural characteristics of the human tympanic membrane, this paper proposed a microresonator with the bionic structure of a tympanic membrane. The structure of a tympanic membrane was simplified to a regular shape with three structural parameters: diameter, height, and thickness. To imitate the tympanic membrane, the contour surface of the bionic structure was modeled based on the formula of transverse vibration mode of a circular thin plate. The geometric model of the bionic structure was established by using the three structural parameters and the contour surface equation. The dynamic properties of the bionic model were studied by the finite element method (FEM). We discuss the modal characteristics of the bionic structure and study the effect of structural parameters and scale on the dynamic properties. The advantages of the bionic structure were investigated by a comparison with circular plate microresonators. The results illustrate that the bionic structure can significantly improve the resonant frequency and have a much larger effective area of vibration displacement.
Wideband Multiport Antennas
Authors: Seyyedesfahlan Mehdi , Uzun Abdulkadir , K. Skrivervik Anja K. , Tekin Ibrahim
Published: December 2020

In this paper, a wideband four port 2–6 GHz antenna is proposed. One-, two-, and four-port antennas are implemented and characterized between 2 and 6 GHz. The isolation between the ports is improved by connecting and optimizing the ground plane sections. The results show that the antennas’ reflection coefficients are better than 10 dB in the frequency band. The measured isolation between the ports is greater than 15 dB (between 2.3 and 6 GHz) and 10 dB in the whole band for two- and four-port antennas, respectively, however, it is more than 20 dB around 2.4 and 5–6 GHz for both antennas. The calculated correlation coefficient between ports is below −30 dB (>2.14 GHz) and −15 dB for the two- and four-port antennas, respectively. The measured gain and efficiency scale are 3.1–6.75 dBi and 62–98%, respectively. To the best of our knowledge, an antenna both being wideband from 2 to 6 GHz and having independent four ports is only addressed in this work. The four-port antenna can be used for MIMO systems or smartphones operating on many wireless systems simultaneously such as 3G/4G/5G Sub-6 GHz and WLAN including the next generation WiFi7 with full-duplex operation.
Optical Fiber Sensors by Direct Laser Processing: A Review
Authors: Pallarés-Aldeiturriaga David , Roldán-Varona Pablo , Rodríguez-Cobo Luis , López-Higuera José Miguel
Published: December 2020

The consolidation of laser micro/nano processing technologies has led to a continuous increase in the complexity of optical fiber sensors. This new avenue offers novel possibilities for advanced sensing in a wide set of application sectors and, especially in the industrial and medical fields. In this review, the most important transducing structures carried out by laser processing in optical fiber are shown. The work covers different types of fiber Bragg gratings with an emphasis in the direct-write technique and their most interesting inscription configurations. Along with gratings, cladding waveguide structures in optical fibers have reached notable importance in the development of new optical fiber transducers. That is why a detailed study is made of the different laser inscription configurations that can be adopted, as well as their current applications. Microcavities manufactured in optical fibers can be used as both optical transducer and hybrid structure to reach advanced soft-matter optical sensing approaches based on optofluidic concepts. These in-fiber cavities manufactured by femtosecond laser irradiation followed by chemical etching are promising tools for biophotonic devices. Finally, the enhanced Rayleigh backscattering fibers by femtosecond laser dots inscription are also discussed, as a consequence of the new sensing possibilities they enable.
A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction
Authors: Li Yiming , Meng Xiangmin , Zhang Zhongchao , Song Guiqiu
Published: December 2020

The traditional predictive model for remaining useful life predictions cannot achieve adaptiveness, which is one of the main problems of said predictions. This paper proposes a LightGBM-based Remaining useful life (RUL) prediction method which considers the process and machining state. Firstly, a multi-information fusion strategy that can effectively reduce the model error and improve the generalization ability of the model is proposed. Secondly, a preprocessing method for improving the time precision and small-time granularity of feature extraction while avoiding dimensional explosion is proposed. Thirdly, an importance coefficient and a custom loss function related to the process and machining state are proposed. Finally, using the processing data of actual tool life cycle, through five evaluation indexes and 25 sets of contrast experiments, the superiority and effectiveness of the proposed method are verified.