bluuebunny/crossref_metadata_2025 huggingface dataset:
Dopant-Dependent Electrical and Biological Functionality of PEDOT in Bioelectronics
Authors: Skorupa Małgorzata , Więcławska Daria , Czerwińska-Główka Dominika , Skonieczna Magdalena , Krukiewicz Katarzyna
Published: June 2021

The aspiration to interact living cells with electronics challenges researchers to develop materials working at the interface of these two distinct environments. A successful interfacing coating should exhibit both biocompatibility and desired functionality of a bio-integrated device. Taking into account biodiversity, the tissue interface should be fine-tuned to the specific requirements of the bioelectronic systems. In this study, we pointed to electrochemical doping of conducting polymers as a strategy enabling the efficient manufacturing of interfacing platforms, in which features could be easily adjusted. Consequently, we fabricated conducting films based on a poly(3,4-ethylenedioxythiophene) (PEDOT) matrix, with properties modulated through doping with selected ions: PSS− (poly(styrene sulfonate)), ClO4− (perchlorate), and PF6− (hexafluorophosphate). Striving to extend the knowledge on the relationships governing the dopant effect on PEDOT films, the samples were characterized in terms of their chemical, morphological, and electrochemical properties. To investigate the impact of the materials on attachment and growth of cells, rat neuroblastoma B35 cells were cultured on their surface and analyzed using scanning electron microscopy and biological assays. Eventually, it was shown that through the choice of a dopant and doping conditions, PEDOT-based materials can be efficiently tuned with diversified physicochemical properties. Therefore, our results proved electrochemical doping of PEDOT as a valuable strategy facilitating the development of promising tissue interfacing materials with characteristics tailored as required.
Electron Beam Irradiation: A Method for Degradation of Composites Based on Natural Rubber and Plasticized Starch
Authors: Manaila Elena , Craciun Gabriela , Ighigeanu Daniel , Lungu Ion Bogdan , Dumitru Marius , Stelescu Maria Daniela
Published: June 2021

Polymeric composites based on natural rubber (NR) and plasticized starch (PS) obtained by peroxide cross-linking have been subjected to electron beam irradiation in order to investigate their degradation. The amount of PS ranged from 10 to 50 phr and the irradiation dose from 150 to 450 kGy. Irradiation was performed in atmospheric conditions using a linear electron accelerator of 5.5 MeV. Changes in chemical, physical, structural, and morphological properties of composites were correlated with variables, such as PS loading and irradiation dose. Thus, mechanical properties, gel fraction, cross-linking degree, water uptake, weight loss in toluene/water were compared with those obtained before irradiation. The changes in structure and morphology were studied by Fourier Transform Infrared Spectroscopy and Scanning Electron Microscopy. Both PS loading and irradiation dose were found to be responsible for the degradation installing. Moreover, it has been shown that at the dose of 450 kGy, chain scission is dominant over cross-linking.
Review: Filament Winding and Automated Fiber Placement with In Situ Consolidation for Fiber Reinforced Thermoplastic Polymer Composites
Authors: Boon Yi Di , Joshi Sunil Chandrakant , Bhudolia Somen Kumar
Published: June 2021

Fiber reinforced thermoplastic composites are gaining popularity in many industries due to their short consolidation cycles, among other advantages over thermoset-based composites. Computer aided manufacturing processes, such as filament winding and automated fiber placement, have been used conventionally for thermoset-based composites. The automated processes can be adapted to include in situ consolidation for the fabrication of thermoplastic-based composites. In this paper, a detailed literature review on the factors affecting the in situ consolidation process is presented. The models used to study the various aspects of the in situ consolidation process are discussed. The processing parameters that gave good consolidation results in past studies are compiled and highlighted. The parameters can be used as reference points for future studies to further improve the automated manufacturing processes.
Toughening Modification of Polylactic Acid by Thermoplastic Silicone Polyurethane Elastomer
Authors: Sun Mingtao , Huang Shuang , Yu Muhuo , Han Keqing
Published: June 2021

The melt blending of polylactic acid (PLA) and thermoplastic silicone polyurethane (TPSiU) elastomer was performed to toughen PLA. The molecular structure, crystallization, thermal properties, compatibility, mechanical properties and rheological properties of the PLA/TPSiU blends of different mass ratios (100/0, 95/5, 90/10, 85/15 and 80/20) were investigated. The results showed that TPSiU was effectively blended into PLA, but no chemical reaction occurred. The addition of TPSiU had no obvious effect on the glass transition temperature and melting temperature of PLA, but slightly reduced the crystallinity of PLA. The morphology and dynamic mechanical analysis results demonstrated the poor thermodynamic compatibility between PLA and TPSiU. Rheological behavior studies showed that PLA/TPSiU melt was typically pseudoplastic fluid. As the content of TPSiU increased, the apparent viscosity of PLA/TPSiU blends showed a trend of rising first and then falling. The addition of TPSiU had a significant effect on the mechanical properties of PLA/TPSiU blends. When the content of TPSiU was 15 wt%, the elongation at break of the PLA/TPSiU blend reached 22.3% (5.0 times that of pure PLA), and the impact strength reached 19.3 kJ/m2 (4.9 times that of pure PLA), suggesting the favorable toughening effect.
Structure and Properties of Polyoxymethylene/Silver/Maleic Anhydride-Grafted Polyolefin Elastomer Ternary Nanocomposites
Authors: Liu Yang , Zhang Xun , Gao Quanxin , Huang Hongliang , Liu Yongli , Min Minghua , Wang Lumin
Published: June 2021

In the present study, silver (Ag) nanoparticles and maleic anhydride-grafted polyolefin elastomer (MAH-g-POE) were used as enhancement additives to improve the performance of the polyoxymethylene (POM) homopolymer. Specifically, the POM/Ag/MAH-g-POE ternary nanocomposites with varying Ag nanoparticles and MAH-g-POE contents were prepared by a melt mixing method. The effects of the additives on the microstructure, thermal stability, crystallization behavior, mechanical properties, and dynamic mechanical thermal properties of the ternary nanocomposites were studied. It was found that the MAH-g-POE played a role in the bridging of the Ag nanoparticles and POM matrix and improved the interfacial adhesion between the Ag nanoparticles and POM matrix, owing to the good compatibility between Ag/MAH-g-POE and the POM matrix. Moreover, it was found that the combined addition of Ag nanoparticles and MAH-g-POE significantly enhanced the thermal stability, crystallization properties, and mechanical properties of the POM/Ag/MAH-g-POE ternary nanocomposites. When the Ag/MAH-g-POE content was 1 wt.%, the tensile strength reached the maximum value of 54.78 MPa. In addition, when the Ag/MAH-g-POE content increased to 15wt.%, the elongation at break reached the maximum value of 64.02%. However, when the Ag/MAH-g-POE content further increased to 20 wt.%, the elongation at break decreased again, which could be attributed to the aggregation of excessive Ag nanoparticles forming local defects in the POM/Ag/MAH-g-POE ternary nanocomposites. Furthermore, when the Ag/MAH-g-POE content was 20 wt.%, the maximum decomposition temperature of POM/Ag/MAH-g-POE ternary nanocomposites was 398.22 °C, which was 71.39 °C higher than that of pure POM. However, compared with POM, the storage modulus of POM/Ag/MAH-g-POE ternary nanocomposites decreased with the Ag/MAH-g-POE content, because the MAH-g-POE elastomer could reduce the rigidity of POM.
An Assessment of Surface Treatments for Adhesion of Polyimide Thin Films
Authors: Cen-Puc Marco , Schander Andreas , Vargas Gleason Minerva G. , Lang Walter
Published: June 2021

Polyimide films are currently of great interest for the development of flexible electronics and sensors. In order to ensure a proper integration with other materials and PI itself, some sort of surface modification is required. In this work, microwave oxygen plasma, reactive ion etching oxygen plasma, combination of KOH and HCl solutions, and polyethylenimine solution were used as surface treatments of PI films. Treatments were compared to find the best method to promote the adhesion between two polyimide films. The first selection of the treatment conditions for each method was based on changes in the contact angle with deionized water. Afterward, further qualitative (scratch test) and a quantitative adhesion assessment (peel test) were performed. Both scratch test and peel strength indicated that oxygen plasma treatment using reactive ion etching equipment is the most promising approach for promoting the adhesion between polyimide films.
Mucoadhesive and Rheological Studies on the Co-Hydrogel Systems of Poly(Ethylene Glycol) Copolymers with Fluoroalkyl and Poly(Acrylic Acid)
Authors: Sun Yang , Perez Adiel F. , Cardoza Ivy L. , Baluyot-Reyes Nina , Ba Yong
Published: June 2021

A self-assembled co-hydrogel system with sol-gel two-phase coexistence and mucoadhesive properties was developed based on the combined properties of fluoroalkyl double-ended poly(ethylene glycol) (Rf-PEG-Rf) and poly(acrylic acid) (PAA), respectively. We have synthesized an Rf-PEG-g-PAA (where g denotes grafted) copolymer and integrated it into the Rf-PEG-Rf physically cross-linked micellar network to form a co-hydrogel system. Tensile strengths between the co-hydrogel surfaces and two different sets of mucosal surfaces were acquired. One mucosal surface was made of porcine stomach mucin Type II, while the other one is a pig small intestine. The experimental results show that the largest maximum detachment stresses (MDSs) were obtained when the Rf-PEG-g-PAA’s weight percent in the dehydrated polymer mixture is ~15%. Tensile experiments also found that MDSs are greater in acidic conditions (pH = 4–5) (123.3 g/cm2 for the artificial mucus, and 43.0 g/cm2 for pig small intestine) and basic conditions (pH = 10.6) (126.9 g/cm2, and 44.6 g.cm2, respectively) than in neutral pH (45.4 g/cm2, and 30.7 g.cm2, respectively). Results of the rheological analyses using shear strain amplitude sweep and frequency sweep reveal that the Rf-PEG-g-PAA was physically integrated into the Rf-PEG-Rf micellar network, and the co-hydrogels remain physically cross-linked in three-dimensional micellar networks with long-term physical dispersion stability. Therefore, the co-hydrogel system is promising for drug delivery applications on mucosal surfaces.
Multiscale Modeling of Epoxy-Based Nanocomposites Reinforced with Functionalized and Non-Functionalized Graphene Nanoplatelets
Authors: Al Mahmud Hashim , Radue Matthew S. , Chinkanjanarot Sorayot , Odegard Gregory M.
Published: June 2021

The impact on the mechanical properties of an epoxy resin reinforced with pristine graphene nanoplatelets (GNP), highly concentrated graphene oxide (GO), and functionalized graphene oxide (FGO) has been investigated in this study. Molecular dynamics (MD) using a reactive force field (ReaxFF) has been employed in predicting the effective mechanical properties of the interphase region of the three nanocomposite materials at the nanoscale level. A systematic computational approach to simulate the reinforcing nanoplatelets and probe their influence on the mechanical properties of the epoxy matrix is established. The modeling results indicate a significant degradation of the in-plane elastic Young’s (decreased by ~89%) and shear (decreased by ~72.5%) moduli of the nanocomposite when introducing large amounts of oxygen and functional groups to the robust sp2 structure of the GNP. However, the wrinkled morphology of GO and FGO improves the nanoplatelet-matrix interlocking mechanism, which produces a significant improvement in the out-of-plane shear modulus (increased by 2 orders of magnitudes). The influence of the nanoplatelet content and aspect ratio on the mechanical response of the nanocomposites has also been determined in this study. Generally, the predicted mechanical response of the bulk nanocomposite materials demonstrates an improvement with increasing nanoplatelet content and aspect ratio. The results show good agreement with experimental data available from the literature.
Production of Sucrolytic Enzyme by Bacillus licheniformis by the Bioconversion of Pomelo Albedo as a Carbon Source
Authors: Doan Chien Thang , Tran Thi Ngoc , Nguyen Thi Thanh , Tran Thi Phuong Hanh , Nguyen Van Bon , Tran Trung Dung , Nguyen Anh Dzung , Wang San-Lang
Published: June 2021

Recently, there has been increasing use of agro-byproducts in microbial fermentation to produce a variety of value-added products. In this study, among various kinds of agro-byproducts, pomelo albedo powder (PAP) was found to be the most effective carbon source for the production of sucrose hydrolyzing enzyme by Bacillus licheniformis TKU004. The optimal medium for sucrolytic enzyme production contained 2% PAP, 0.75% NH4NO3, 0.05% MgSO4, and 0.05% NaH2PO4 and the optimal culture conditions were pH 6.7, 35 °C, 150 rpm, and 24 h. Accordingly, the highest sucrolytic activity was 1.87 U/mL, 4.79-fold higher than that from standard conditions using sucrose as the carbon source. The purified sucrolytic enzyme (sleTKU004) is a 53 kDa monomeric protein and belongs to the glycoside hydrolase family 68. The optimum temperature and pH of sleTKU004 were 50 °C, and pH = 6, respectively. SleTKU004 could hydrolyze sucrose, raffinose, and stachyose by attacking the glycoside linkage between glucose and fructose molecules of the sucrose unit. The Km and Vmax of sleTKU004 were 1.16 M and 5.99 µmol/min, respectively. Finally, sleTKU004 showed strong sucrose tolerance and presented the highest hydrolytic activity at the sucrose concentration of 1.2 M–1.5 M.
From Street to Road: An Innovative Approach to Explore Discarded Chewing Gum as a Performance-Enhancing Modifier for Road Pavement Applications
Authors: Nciri Nader , Kim Namho , Cho Namjun
Published: June 2021

To uncover the potential benefits of discarded chewing gum (DCG) as a performance-enhancing modifier for road pavement applications, its influence on the asphalt binder’s attributes was profoundly examined. The base AP-5 asphalt along with its specimens dosed with various fractions of DCG (e.g., 3, 6, and 9 wt%) were analyzed by Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thin-layer chromatography-flame ionization detection (TLC-FID), scanning electron microscopy (SEM), atomic force microscopy (AFM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Brookfield viscometer, ring and ball softening point, needle penetration, and dynamic shear rheometer (DSR) tests were adopted to inspect the physical and rheological changes of asphalt cement after DCG incorporation. FT-IR disclosed that the asphalt-gum interaction was not chemical but physical in nature, whilst XRD demonstrated the existence of talc filler in DCG, which may confer the bituminous mixes with exceptional engineering properties. Iatroscan analysis evinced that the gum treatment particularly altered the aromatic and resin fractions; meanwhile, the content of saturates and asphaltenes remained relatively unchanged. SEM divulged that the DCG has a complete dissolution within the bitumen matrix, which becomes rougher due to higher dose administration. AFM revealed that the steady gum introduction amplified the size of bee-like structures, shrunk their peri-phase domains, and wiped out the para-phase domains entirely. TGA/DTGA/DSC data highlighted that the high-temperature-stable additive slightly affected the thermal properties of blends. DSR and empirical rheological tests showed that the waste gum made the bitumen less vulnerable to heat and tender, thereby boosting its resistance against fatigue cracking at intermediate service temperatures. On top of that, DCG widened the thermal window of bitumen performance grade (PG), and preserved its viscosity at standard temperatures, leading to maintaining an appropriate workability for asphalt mix. In brief, the use of discarded chewing gum as an asphalt modifier is feasible and could mitigate plastic pollution and provide durable roadways by delivering superior performance.
Influence of the Alkali Treatment of Flax and Hemp Fibers on the Properties of PHBV Based Biocomposites
Authors: Frącz Wiesław , Janowski Grzegorz , Bąk Łukasz
Published: June 2021

This study assessed the impact of alkali treatment of hemp and flax fibers on mechanical properties (determined by means of the uniaxial tensile test, impact tensile strength test and hardness test), processing properties (the course of the extrusion and injection process) and usable properties (shrinkage of molded pieces, degree of water absorption) of biocomposites on the base of poly (3-hydroxybutyric-co-3-hydroxyvaleric acid) (PHBV) biopolymer. For this purpose, 1 mm of length flax and hemp fibers was surface-modified by means of aqueous solution of NaOH (sodium hydroxide) with concentrations of 2%, 5% and 10%. The composites were made using the extrusion technology. The test specimens were produced by injection molding technology. In total, eight types of biocomposites with modified and non-modified fibers were produced, and each biocomposite contained the same filler content (15 wt.%). Their properties were compared in some cases with pure PHBV polymer. In the case of biocomposites filled with hemp fibers, it was noted that an increase of the alkalizing solution concentration improved most of the tested properties of the obtained biocomposites. On the other hand, in the case of flax fibers, there was a significant decrease in most of the mechanical properties tested for the composite containing fibers etched by 10% NaOH solution. The obtained results were verified by examining fibers and the destroyed specimens with a scanning electron microscope (SEM) and an optical microscope, which confirmed, especially, the significant geometry changes of the flax fibers etched by 10% NaOH solution. This procedure also resulted in a significant change of processing properties—a composite of this fiber type required about 20 °C lower temperature during the extrusion and injection molding process in order to obtain the right product. These results lead to the important conclusion that for each filler of the plant-origin and polymer matrix, the fiber alkalization method should be selected individually in order to improve the specific properties of biocomposites.
Interplay of Structural Factors in Formation of Microphase-Separated or Microphase-Mixed Structures of Polyurethanes Revealed by Solid-State NMR and Dielectric Spectroscopy
Authors: Ostanin Stepan , Mokeev Maxim , Pikhurov Dmitry , Sakhatskii Aleksandr , Zuev Vjacheslav
Published: June 2021

A set of aromatic-oxyaliphatic polyurethanes (PUs) with different mass fractions of components also containing fluorinated fragments was synthesized and studied using various solid-state NMR techniques and dielectric spectroscopy. In contrast to the common model suggested by Cooper and Tobolsky in 1966, the rigid domains of microphase separated PUs are formed, not only by units containing urethane bonds, but also by oxyethylene fragments that form a common rigid phase. The urethane bonds and oxyethylene fragments are incorporated into both rigid and soft phases. Good agreement with the Cooper and Tobolsky model is observed only when solubility parameters are significantly different for the hard and soft segments, such as hydrocarbon aromatics and perfluoroaliphatic blocks.
Online Opportunities in Secularizing Societies? Clergy and the COVID-19 Pandemic in Ireland
Authors: Ganiel Gladys
Published: June 2021

This article explores how Christian clergy in Ireland have framed their adoption of online ministries during the COVID-19 pandemic as opportunities for the churches to retain some significance, even in secularizing societies. It is based on an island-wide survey of 439 faith leaders and 32 in-depth, follow-up interviews. The results of this study are analysed in light of scholarship in three areas: (1) secularization in Ireland, informed by Norris and Inglehart’s evolutionary modernization theory; (2) cross-national research that has found increasing interest in spirituality or religion during the pandemic (with the UK as the main point of comparison); and (3) wider pre-pandemic scholarship on digital religion. The article concludes by arguing that the clergy’s framing of online ministries as opportunities is important: if they regard online ministries as potential sites of religious revitalization, they are more likely to invest in them. There is some evidence that they may be assisted in this by lay volunteers. However, given the secularization already underway, it remains to be seen whether an embrace of blended online and in-person religion will have far-reaching impacts on Ireland’s religious landscape.
Popular Hymnody and Lived Catholicism in Hungary in the 1970s–1980s
Authors: Povedák Kinga
Published: June 2021

In this article, I look at how popular hymnody and the surrounding devotional and liturgical practices changed after the Second Vatican Council in Hungary. The songs amongst authoritarian, atheistic circumstances sounded astonishingly similar to the emerging “folk mass movement”. The discourse analysis of Hungarian popular hymnody contributes to a new perspective of Eastern European Catholicism and helps us understand how “lived Catholicism” reflects the post-Vatican spirit. Post-Vatican popular hymnody, a catalyst for a new style of devotional practices, is understood as “performed theology” behind the Iron Curtain expressing relationality, as it actualizes and manifests spiritual, eschatological, and ecclesial relationships.
Divine ‘Pastness’ and the Creation of Hope: The Significance of the Sepultus est…
Authors: Lindsay Mark R.
Published: June 2021

This article explores Karl Barth’s exegesis of the ‘sepultus est…’ from the Apostles’ Creed, as articulated in his 1935 Credo lectures. I argue that Barth accords the sepultus a degree of theological significance that is against the grain, not only of the majority of western interpretations of Jesus’s burial, but also of his own later interpretation of it within his Kirchliche Dogmatik. Specifically, this article argues that in his 1935 lectures, Barth exegetes the sepultus in terms of a divine self-surrender to the ‘pure pastness’ that is the ‘state and fate’ of all humanity. As a consequence, the sepultus can then be used as the pivot to a different, and more hopeful, future.
The Role of Religious Coping in Caregiving Stress
Authors: Triana Lidya , Sudjatmiko Iwan Gardono
Published: June 2021

Studies explaining how stressors and religious coping affect caregivers’ depression have been rarely conducted in the Indonesian context. Therefore, this study discusses stress process theory by examining the role of religious coping as a moderating variable between relational deprivation and loss of self on depression. In a quantitative study of 50 caregivers of persons with schizophrenia in Indonesia, this study analyzed the moderating variables using multiple regression. The results showed that higher relational deprivation will lead to increased depression, but religious coping mechanisms can reduce the effect of relational deprivation on depression (buffering effect). Religious coping can also minimize the effect of loss of self to depression. Subjective stressors and religious coping offer new theoretical insights and must be considered when studying caregiving stress. In this regard, mental health services aiming to enhance caregivers’ welfare need to be provided by the state and community.
FSRSS-Net: High-Resolution Mapping of Buildings from Middle-Resolution Satellite Images Using a Super-Resolution Semantic Segmentation Network
Authors: Zhang Tao , Tang Hong , Ding Yi , Li Penglong , Ji Chao , Xu Penglei
Published: June 2021

Satellite mapping of buildings and built-up areas used to be delineated from high spatial resolution (e.g., meters or sub-meters) and middle spatial resolution (e.g., tens of meters or hundreds of meters) satellite images, respectively. To the best of our knowledge, it is important to explore a deep-learning approach to delineate high-resolution semantic maps of buildings from middle-resolution satellite images. The approach is termed as super-resolution semantic segmentation in this paper. Specifically, we design a neural network with integrated low-level image features of super-resolution and high-level semantic features of super-resolution, which is trained with Sentinel-2A images (i.e., 10 m) and higher-resolution semantic maps (i.e., 2.5 m). The network, based on super-resolution semantic segmentation features is called FSRSS-Net. In China, the 35 cities are partitioned into three groups, i.e., 19 cities for model training, four cities for quantitative testing and the other 12 cities for qualitative generalization ability analysis of the learned networks. A large-scale sample dataset is created and utilized to train and validate the performance of the FSRSS-Net, which includes 8597 training samples and 766 quantitative accuracy evaluation samples. Quantitative evaluation results show that: (1) based on the 10 m Sentinel-2A image, the FSRSS-Net can achieve super-resolution semantic segmentation and produce 2.5 m building recognition results, and there is little difference between the accuracy of 2.5 m results by FSRSS-Net and 10 m results by U-Net. More importantly, the 2.5 m building recognition results by FSRSS-Net have higher accuracy than the 2.5 m results by U-Net 10 m building recognition results interpolation up-sampling; (2) from the spatial visualization of the results, the building recognition results of 2.5 m are more precise than those of 10 m, and the outline of the building is better depicted. Qualitative analysis shows that: (1) the learned FSRSS-Net can be also well generalized to other cities that are far from training regions; (2) the FSRSS-Net can still achieve comparable results to the U-Net 2 m building recognition results, even when the U-Net is directly trained using both 2-meter resolution GF2 satellite images and corresponding semantic labels.
Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery
Authors: Pedrayes Oscar D. , Lema Darío G. , García Daniel F. , Usamentiaga Rubén , Alonso Ángela
Published: June 2021

Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more modern alternatives do exist. In this paper, state-of-the-art methods are evaluated, consisting of semantic segmentation networks such as UNet and DeepLabV3+. In addition, two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification. These datasets, called UOPNOA and UOS2, are publicly available. In this work, the performance of these networks and the two datasets generated are evaluated. This paper demonstrates that ground sampling distance is the most important factor in obtaining good semantic segmentation results, but a suitable number of bands can be as important. This proves that both aircraft and satellite imagery can produce good results, although for different reasons. Finally, cost performance for an inference prototype is evaluated, comparing various Microsoft Azure architectures. The evaluation concludes that using a GPU is unnecessarily costly for deployment. A GPU need only be used for training.
Monitoring Lakes Surface Water Velocity with SAR: A Feasibility Study on Lake Garda, Italy
Authors: Amadori Marina , Zamparelli Virginia , De Carolis Giacomo , Fornaro Gianfranco , Toffolon Marco , Bresciani Mariano , Giardino Claudia , De Santi Francesca
Published: June 2021

The SAR Doppler frequencies are directly related to the motion of the scatterers in the illuminated area and have already been used in marine applications to monitor moving water surfaces. Here we investigate the possibility of retrieving surface water velocity from SAR Doppler analysis in medium-size lakes. ENVISAT images of the test site (Lake Garda) are processed and the Doppler Centroid Anomaly technique is adopted. The resulting surface velocity maps are compared with the outputs of a hydrodynamic model specifically validated for the case study. Thermal images from MODIS Terra are used in support of the modeling results. The surface velocity retrieved from SAR is found to overestimate the numerical results and the existence of a bias is investigated. In marine applications, such bias is traditionally removed through Geophysical Model Functions (GMFs) by ascribing it to a fully developed wind waves spectrum. We found that such an assumption is not supported in our case study, due to the small-scale variations of topography and wind. The role of wind intensity and duration on the results from SAR is evaluated, and the inclusion of lake bathymetry and the SAR backscatter gradient is recommended for the future development of GMFs suitable for lake environments.
A Case for a New Satellite Mission for Remote Sensing of Night Lights
Authors: Barentine John C. , Walczak Ken , Gyuk Geza , Tarr Cynthia , Longcore Travis
Published: June 2021

The physiology and behavior of most life at or near the Earth’s surface has evolved over billions of years to be attuned with our planet’s natural light–dark cycle of day and night. However, over a relatively short time span, humans have disrupted this natural cycle of illumination with the introduction and now widespread proliferation of artificial light at night (ALAN). Growing research in a broad range of fields, such as ecology, the environment, human health, public safety, economy, and society, increasingly shows that ALAN is taking a profound toll on our world. Much of our current understanding of light pollution comes from datasets generated by remote sensing, primarily from two missions, the Operational Linescan System (OLS) instrument of the now-declassified Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense and its follow-on platform, the Day-Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar-Orbiting Partnership satellite. Although they have both proved invaluable for ALAN research, sensing of nighttime lights was not the primary design objective for either the DMSP-OLS or VIIRS-DNB instruments; thus, they have some critical limitations. Being broadband sensors, both the DMSP-OLS and VIIRS-DNB instruments suffer from a lack of spectral information. Additionally, their spatial resolutions are too low for many ALAN research applications, though the VIIRS-DNB instrument is much improved over the DMSP-OLS in this regard, as well as in terms of dynamic range and quantization. Further, the very late local time of VIIRS-DNB observations potentially misses the true picture of ALAN. We reviewed both current literature and guiding advice from ALAN experts, aggregated from a diverse range of disciplines and Science Goals, to derive recommendations for a mission to expand knowledge of ALAN in areas that are not adequately addressed with currently existing orbital missions. We propose a stand-alone mission focused on understanding light pollution and its effects on our planet. Here we review the science cases and the subsequent mission recommendations for NITESat (Nighttime Imaging of Terrestrial Environments Satellite), a dedicated ALAN observing mission.
Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping
Authors: Contreras Acosta Isabel Cecilia , Khodadadzadeh Mahdi , Gloaguen Richard
Published: June 2021

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.
Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning
Authors: Ewing Jordan , Oommen Thomas , Jayakumar Paramsothy , Alger Russell
Published: June 2021

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.
Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests
Authors: Delcourt Clement J. F. , Combee Alisha , Izbicki Brian , Mack Michelle C. , Maximov Trofim , Petrov Roman , Rogers Brendan M. , Scholten Rebecca C. , Shestakova Tatiana A. , van Wees Dave , Veraverbeke Sander
Published: June 2021

Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R2 = 0.42, p < 0.001). The dNBR performed better to predict GeoCBI in open mature larch plots (R2 = 0.56, p < 0.001). The GeoCBI provides a holistic field assessment of fire severity yet is dominated by the effect of fire on vegetation. No significant relationships were found between GeoCBI components (overall and substrate stratum) and burn depth within our fires (p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R2 = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.
Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector
Authors: Rasmy Laila , Sebari Imane , Ettarid Mohamed
Published: June 2021

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points.
Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images
Authors: Lema Darío G. , Pedrayes Oscar D. , Usamentiaga Rubén , García Daniel F. , Alonso Ángela
Published: June 2021

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.
Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series
Authors: Weiß Thomas , Ramsauer Thomas , Jagdhuber Thomas , Löw Alexander , Marzahn Philip
Published: June 2021

This study evaluates a temporally dense VV-polarized Sentinel-1 C-band backscatter time series (revisit time of 1.5 days) for wheat fields near Munich (Germany). A dense time series consisting of images from different orbits (varying acquisition) is analyzed, and Radiative Transfer (RT)-based model combinations are adapted and evaluated with the use of radar backscatter. The model shortcomings are related to scattering mechanism changes throughout the growth period with the use of polarimetric decomposition. Furthermore, changes in the RT modeled backscatter results with spatial aggregation from the pixel to field scales are quantified and related to the sensitivity of the RT models, and their soil moisture output are quantified and related to changes in backscatter. Therefore, various (sub)sets of the dense Sentinel-1 time series are analyzed to relate and quantify the impact of the abovementioned points on the modeling results. The results indicate that the incidence angle is the main driver for backscatter differences between consecutive acquisitions with various recording scenarios. The influence of changing azimuth angles was found to be negligible. Further analyses of polarimetric entropy and scattering alpha angle using a dual polarimetric eigen-based decomposition show that scattering mechanisms change over time. The patterns analyzed in the entropy-alpha space indicate that scattering mechanism changes are mainly driven by the incidence angle and not by the azimuth angle. Besides the analysis of differences within the Sentinel-1 data, we analyze the capability of RT model approaches to capture the observed Sentinel-1 backscatter changes due to various acquisition geometries. For this, the surface models “Oh92” or “IEM_B” (Baghdadi’s version of the Integral Equation Method) are coupled with the canopy model “SSRT” (Single Scattering Radiative Transfer). To resolve the shortcomings of the RT model setup in handling varying incidence angles and therefore the backscatter changes observed between consecutive time steps of a dense winter wheat time series, an empirical calibration parameter (coef) influencing the transmissivity (T) is introduced. The results show that shortcomings of simplified RT model architectures caused by handling time series consisting of images with varied incidence angles can be at least partially compensated by including a calibration coefficient to parameterize the modeled transmissivity for the varying incidence angle scenarios individually.
HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor
Authors: Hong Yameng , Leng Chengcai , Zhang Xinyue , Pei Zhao , Cheng Irene , Basu Anup
Published: June 2021

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.
Identifying Leaf Phenology of Deciduous Broadleaf Forests from PhenoCam Images Using a Convolutional Neural Network Regression Method
Authors: Cao Mengying , Sun Ying , Jiang Xin , Li Ziming , Xin Qinchuan
Published: June 2021

Vegetation phenology plays a key role in influencing ecosystem processes and biosphere-atmosphere feedbacks. Digital cameras such as PhenoCam that monitor vegetation canopies in near real-time provide continuous images that record phenological and environmental changes. There is a need to develop methods for automated and effective detection of vegetation dynamics from PhenoCam images. Here we developed a method to predict leaf phenology of deciduous broadleaf forests from individual PhenoCam images using deep learning approaches. We tested four convolutional neural network regression (CNNR) networks on their ability to predict vegetation growing dates based on PhenoCam images at 56 sites in North America. In the one-site experiment, the predicted phenology dated to after the leaf-out events agree well with the observed data, with a coefficient of determination (R2) of nearly 0.999, a root mean square error (RMSE) of up to 3.7 days, and a mean absolute error (MAE) of up to 2.1 days. The method developed achieved lower accuracies in the all-site experiment than in the one-site experiment, and the achieved R2 was 0.843, RMSE was 25.2 days, and MAE was 9.3 days in the all-site experiment. The model accuracy increased when the deep networks used the region of interest images rather than the entire images as inputs. Compared to the existing methods that rely on time series of PhenoCam images for studying leaf phenology, we found that the deep learning method is a feasible solution to identify leaf phenology of deciduous broadleaf forests from individual PhenoCam images.
Systematic Literature Review of Realistic Simulators Applied in Educational Robotics Context
Authors: Camargo Caio , Gonçalves José , Conde Miguel Á. , Rodríguez-Sedano Francisco J. , Costa Paulo , García-Peñalvo Francisco J.
Published: June 2021

This paper presents a systematic literature review (SLR) about realistic simulators that can be applied in an educational robotics context. These simulators must include the simulation of actuators and sensors, the ability to simulate robots and their environment. During this systematic review of the literature, 559 articles were extracted from six different databases using the Population, Intervention, Comparison, Outcomes, Context (PICOC) method. After the selection process, 50 selected articles were included in this review. Several simulators were found and their features were also analyzed. As a result of this process, four realistic simulators were applied in the review’s referred context for two main reasons. The first reason is that these simulators have high fidelity in the robots’ visual modeling due to the 3D rendering engines and the second reason is because they apply physics engines, allowing the robot’s interaction with the environment.
Smartphone-Based Inertial Odometry for Blind Walkers
Authors: Ren Peng , Elyasi Fatemeh , Manduchi Roberto
Published: June 2021

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.