bluuebunny/crossref_metadata_2025 huggingface dataset:
Polyurethane/Vermiculite Foam Composite as Sustainable Material for Vertical Flame Retardant
Authors: Alves Lívia R. P. Silva Tenório , Alves Márcio Davi Tenório C. , Honorio Luzia M. Castro , Moraes Alan I. , Silva-Filho Edson C. , Peña-Garcia Ramón , Furtini Marcelo B. , da Silva Durcilene A. , Osajima Josy A.
Published: September 2022

Rigid polyurethane foams were prepared by the one-step expandable foam method using casting molding followed by forming clay-based composites. Polyurethane/vermiculite foam composites (PU/VMT) were controlled based on adding the percentage of clay in the formulation. The effects of composite modifications were evaluated by X-ray diffraction (XRD), thermogravimetric analysis (TG/DTG), and scanning electron microscopy (SEM/EDS) applied to the flame retardancy explored by the vertical burn test. The results indicated that adding clay controlled the particle size concerning polyurethane (PU) foams. However, they exhibited spherical structures with closed cells with relatively uniform distribution. XRD analysis showed the peaks defined at 2θ = 18° and 2θ = 73° relative to the crystallinity in formation and interaction of rigid segments were identified, as well as the influence of crystallinity reduction in composites. In the flame test, the flame retardant surface was successful in all composites, given the success of the dispersibility and planar orientation of the clay layers and the existence of an ideal content of vermiculite (VMT) incorporated in the foam matrix.
Trimetallic Nanoalloy of NiFeCo Embedded in Phosphidated Nitrogen Doped Carbon Catalyst for Efficient Electro-Oxidation of Kraft Lignin
Authors: Honorato Ana Maria Borges , Khalid Mohmmad , Curvelo Antonio Aprigio da Silva , Varela Hamilton , Shahgaldi Samaneh
Published: September 2022

Recently, electro-oxidation of kraft lignin has been reported as a prominent electrochemical reaction to generate hydrogen at lower overpotential in alkaline water electrolysis. However, this reaction is highly limited by the low performance of existing electrocatalysts. Herein, we report a novel yet effective catalyst that comprises nonprecious trimetallic (Ni, Fe, and Co) nanoalloy as a core in a phosphidated nitrogen-doped carbon shell (referred to as sample P-NiFeCo/NC) for efficient electro-oxidation of kraft lignin at different temperatures in alkaline medium. The as-synthesized catalyst electro-oxidizes lignin only at 0.2 V versus Hg/HgO, which is almost three times less positive potential than in the conventional oxygen evolution reaction (0.59 V versus Hg/HgO) at 6.4 mA/cm2 in 1 M KOH. The catalyst demonstrates a turnover frequency (TOF) three to five times greater in lignin containing 1 M KOH than that of pure 1 M KOH. More importantly, the catalyst P-NiFeCo/NC shows theoretical hydrogen production of about 0.37 μmoles/min in the presence of lignin, much higher than that in pure 1 M KOH (0.0078 μ moles/min). Thus, this work verifies the benefit of the NiFeCo nanoalloy incorporated in carbon matrix, providing the way to realize a highly active catalyst for the electro-oxidation of kraft lignin.
New Hybrid Nanocomposites with Catalytic Properties Obtained by In Situ Preparation of Gold Nanoparticles on Poly (Ionic Liquid)/Poly (4-Vinylpyridine) Nanofibers
Authors: Ramírez Oscar , Leal Matías , Briones Ximena , Urzúa Marcela , Bonardd Sebastián , Saldías Cesar , Leiva Angel
Published: September 2022

In this work, we report the obtaining of new hybrid nanocomposites with catalytic activity formed by nanofibers of polymer blends and gold nanoparticles. The nanofibers were obtained by electrospinning blends of a poly (ionic liquid) (PIL) and its precursor polymer, poly (4-vinyl pyridine) (P4VPy). The characteristics of the nanofibers obtained proved to be dependent on the proportion of polymer in the blends. The nanofibers obtained were used to synthesize, in situ, gold nanoparticles on their surface by two-step procedure. Firstly, the adsorption of precursor ions on the nanofibers and then their reduction with sodium borohydride to generate gold nanoparticles. The results indicated a significant improvement in the performance of PIL-containing nanofibers over pure P4VPy NFs during ion adsorption, reaching a 20% increase in the amount of adsorbed ions and a 6-fold increase in the respective adsorption constant. The catalytic performance of the obtained hybrid systems in the reduction reaction of 4-nitrophenol to 4-aminophenol was studied. Higher catalytic conversions were obtained using the hybrid nanofibers containing PIL and gold nanoparticles achieving a maximum conversion rate of 98%. Remarkably, the highest value of kinetic constant was obtained for the nanofibers with the highest PIL content.
Enhanced Mechanical Properties and Anti–Inflammation of Poly(L–Lactic Acid) by Stereocomplexes of PLLA/PDLA and Surface–Modified Magnesium Hydroxide Nanoparticles
Authors: Baek Seung-Woon , Kim Jun Hyuk , Song Duck Hyun , Kim Da-Seul , Park Chun Gwon , Han Dong Keun
Published: September 2022

Poly(L–lactic acid) (PLLA), as a biodegradable polymer, has attracted attention for use as a biomaterial. In order to apply PLLA as a cardiovascular stent, stronger mechanical properties and anti–inflammatory effects against acidic by–products are required. In this study, PLLA/PDLA stereocomplex microparticles (SC) were developed and surface–modified magnesium hydroxide (MH) nanoparticles with oligolactide were combined with these PLLA composites. The SC improved the mechanical properties of the PLLA composites through the formation of stereocomplex structures. The surface–modified MH nanoparticles showed enhanced mechanical properties due to the stereocomplex structures formed by PLLA chains and inhibited inflammatory responses by pH neutralization as a result of MH. Additionally, the MH nanoparticles containing PLLA composites had antibacterial effects and increased the viability of human vascular endothelial cells. This technology is expected to have great potential in the development of PLLA composite materials for the production of various medical devices, such as cardiovascular stents.
In Situ Coating of Polydopamine-AgNPs on Polyester Fabrics Producing Antibacterial and Antioxidant Properties
Authors: Allehyani Esam S. , Almulaiky Yaaser Q. , Al-Harbi Sami A. , El-Shishtawy Reda M.
Published: September 2022

Nanoparticles are increasingly utilized as coating materials to improve the properties of polyester textiles. In this work, polyester textiles were successfully fabricated, with hydrazide groups serving as ligands for the entrapment of sliver ions and subsequent reduction to AgNPs. Polydopamine (PDA) was used in this work to impart antibacterial and antioxidant properties to the polyester textiles through its phenolic hydroxyl groups, which can convert silver ions into AgNPs. Moreover, glucose was used as a reducing agent to create AgNPs-loaded polyester hydrazide. ATR-FTIR, SEM, EDX, thermogravimetric analysis (TGA), and tensile strength were used to characterize the pristine polyester, the polyester hydrazide, the PDA-coated AgNP-loaded polyester hydrazide and the AgNP-loaded polyester hydrazide. A broth test was also used to investigate the textile’s antimicrobial activities against Escherichia coli and Staphylococcus aureus. Overall, the composite nanocoating with PDA-AgNPs demonstrated good tensile strength and antioxidant and antibacterial characteristics, implying the practicality of PDA-AgNPs coating polyester for biomedical textile applications.
Alkyl Chain Engineering of Low Bandgap Non-Fullerene Acceptors for High-Performance Organic Solar Cells: Branched vs. Linear Alkyl Side Chains
Authors: Lee Youngwan , Raju Telugu Bhim , Yeom Hyerim , Gopikrishna Peddaboodi , Kim Kwangmin , Cho Hye Won , Moon Jung Woo , Cho Jeong Ho , Kim Jin Young , Kim BongSoo
Published: September 2022

In this work, we report the synthesis and photovoltaic properties of IEBICO-4F, IEHICO-4F, IOICO-4F, and IDICO-4F non-fullerene acceptors (NFAs) bearing different types of alkyl chains (2-ehtylhexyl (EH), 2-ethylbutyl (EB), n-octyl (O), and n-decyl (D), respectively). These NFAs are based on the central indacenodithiophene (IDT) donor core and the same terminal group of 2-(5,6-difluoro-3-oxo-2,3-dihydro-1H-inden-1-ylidene)malononitrile (IC-2F), albeit with different side chains appended to the thiophene bridge unit. Although the side chains induced negligible differences between the NFAs in terms of optical band gaps and molecular energy levels, they did lead to changes in their melting points and crystallinity. The NFAs with branched alkyl chains exhibited weaker intermolecular interactions and crystallinity than those with linear alkyl chains. Organic solar cells (OSCs) were fabricated by blending these NFAs with the p-type polymer PTB7-Th. The NFAs with appended branched alkyl chains (IEHICO-4F and IEBICO-4F) possessed superior photovoltaic properties than those with appended linear alkyl chains (IOICO-4F and IDICO-4F). This result can be ascribed mainly to the thin-film morphology. Furthermore, the NFA-based blend films with appended branched alkyl chains exhibited the optimal degree of aggregation and miscibility, whereas the NFA-based blend films with appended linear alkyl chains exhibited higher levels of self-aggregation and lower miscibility between the NFA molecule and the PTB7-Th polymer. We demonstrate that changing the alkyl chain on the π-bridging unit in fused-ring-based NFAs is an effective strategy for improving their photovoltaic performance in bulk heterojunction-type OSCs.
Polyetherimide-Reinforced Smart Inlays for Bondline Surveillance in Composites
Authors: von der Heide Chresten , Steinmetz Julian , Völkerink Oliver , Makiela Patrick , Hühne Christian , Sinapius Michael , Dietzel Andreas
Published: September 2022

An integrable sensor inlay for monitoring crack initiation and growth inside bondlines of structural carbon fiber-reinforced plastic (CFRP) components is presented. The sensing structures are sandwiched between crack-stopping poly(vinyliden fluoride) (PVDF) and a thin reinforcing polyetherimide (PEI) layer. Good adhesion at all interfaces of the sensor system and to the CFRP material is crucial, as weak bonds can counteract the desired crack-stopping functionality. At the same time, the chosen reinforcing layer must withstand high strains, safely support the metallic measuring grids, and possess outstanding fatigue strength. We show that this robust sensor system, which measures the strain at two successive fronts inside the bondline, allows to recognize cracks in the proximity of the inlay regardless of the mechanical loads. Feasibility is demonstrated by static load tests as well as cyclic long-term fatigue testing for up to 1,000,000 cycles. In addition to pure crack detection, crack distance estimation based on sensor signals is illustrated. The inlay integration process is developed with respect to industrial applicability. Thus, implementation of the proposed system will allow the potential of lightweight CFRP constructions to be better exploited by expanding the possibilities of structural adhesive bonding.
Who Can Revive Buddhist Ordinations? Explaining the Eminence of Guxin Ruxin in Late Ming China
Authors: Lepneva Mariia
Published: September 2022

Guxin Ruxin 古心如馨 (1541–1616) is credited with the revival of monastic ordinations, which ranks among the major breakthroughs of the late Ming Buddhist renewal. Despite the long-standing ban on ordination ceremonies, he managed to win the trust of fellow monastics, attract local patrons, and eventually gain imperial approval. This paper aims to unravel the reasons that this Vinaya master was recognized as legitimate by contemporaries and descendants, focusing on two key episodes in his hagiographies. In the first of them, Guxin Ruxin had a vision of Bodhisattva Mañjuśrī transmitting monastic precepts to him, whereas in the second one his identity as a reincarnation of Upāli was revealed through a miracle he performed during the restoration of Porcelain Pagoda in Nanjing. The research has shown that the second account was a later interpolation designed on the basis of the hagiography of another prominent monk, Xuelang Hong’en 雪浪洪恩 (1545–1607). By contrast, the first of the narratives, although heavily laden with the elements of local lore, could be a reflection of real experience. The inspiration Guxin Ruxin drew from it might explain his commitment to the cause of Vinaya revival and the ensuing charisma.
Preliminary Experimental Results of Context-Aware Teams of Multiple Autonomous Agents Operating under Constrained Communications
Authors: Martinez-Lorenzo Jose , Hudack Jeff , Jing Yutao , Shaham Michael , Liang Zixuan , Al Bashit Abdullah , Wu Yushu , Zhang Weite , Skopin Matthew , Heredia-Juesas Juan , Ma Yuntao , Sweeney Tristan , Ares Nicolas , Fox Ari
Published: September 2022

This work presents and experimentally tests the framework used by our context-aware, distributed team of small Unmanned Aerial Systems (SUAS) capable of operating in real time, in an autonomous fashion, and under constrained communications. Our framework relies on a three-layered approach: (1) an operational layer, where fast temporal and narrow spatial decisions are made; (2) a tactical layer, where temporal and spatial decisions are made for a team of agents; and (3) a strategical layer, where slow temporal and wide spatial decisions are made for the team of agents. These three layers are coordinated by an ad hoc, software-defined communications network, which ensures sparse but timely delivery of messages amongst groups and teams of agents at each layer, even under constrained communications. Experimental results are presented for a team of 10 small unmanned aerial systems tasked with searching for and monitoring a person in an open area. At the operational layer, our use case presents an agent autonomously performing searching, detection, localization, classification, identification, tracking, and following of the person, while avoiding malicious collisions. At the tactical layer, our experimental use case presents the cooperative interaction of a group of multiple agents that enables the monitoring of the targeted person over wider spatial and temporal regions. At the strategic layer, our use case involves the detection of complex behaviors, i.e., the person being followed enters a car and runs away, or the person being followed exits the car and runs away, which require strategic responses to successfully accomplish the mission.
An HAP and UAVs Collaboration Framework for Uplink Secure Rate Maximization in NOMA-Enabled IoT Networks
Authors: Wang Dawei , Wu Menghan , He Yixin , Pang Linna , Xu Qian , Zhang Ruonan
Published: September 2022

In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users’ power, and UAVs’ three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works.
Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning
Authors: Tang Xueyuan , Luo Kun , Dong Sheng , Zhang Zidong , Sun Bo
Published: September 2022

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure.
Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
Authors: Wang Zhongyuan , Wang Ruiguang , Wang Yangyang , Hu Chao , Liu Bingyu
Published: September 2022

The currently available triple-frequency signals give rise to new prospects for precise point positioning (PPP). However, they also bring new bias, such as time-varying parts of the phase bias in the hardware of receivers and satellites due to the fact that dual-frequency precise clock products cannot be directly applied to triple-frequency observation. These parameters generate phase-based inter-frequency clock bias (PIFCB), which impacts the PPP. However, the PIFCBs of satellites are not present in all GNSSs. In this paper, various IF1213 PPP models are constructed for these parts, namely, the triple-frequency PIFCB (TF-C) model with PIFCB estimation, the TF inter-frequency bias (IFB) (TF-F) model ignoring the PIFCB, and the TF-PIFCB-IFB (TF-CF) model with one system PIFCB estimation. Additionally, this study compares these IF1213 PPP models with the dual-frequency ionosphere-free (DF) model. We conducted single system static PPP, dual-system static and kinematic PPP experiments based on BDS/GPS observation data. The GPS static PPP experiment demonstrates the reliability of the TF-C model, as well as the non-negligibility of the GPS PIFCB. The BDS static PPP experiment demonstrates the reliability of the TF-F and TF-CF models, and that the influence of the BDS-2 PIFCB can be neglected in BDS. The BDS/GPS PPP experimental results show that the third frequency does not significantly improve the positioning accuracy but shortens the convergence time. The positioning accuracy of TF-C and TF-CF for static PPP is better than 1.0 cm, while that for kinematic PPP is better than 2.0 cm and 4.0 cm in the horizontal and vertical components, respectively. Compared with the DF model, the convergence time of the TF-C and TF-CF models for static PPP is improved by approximately 23.5%/18.1%, 13.6%/9.7%, and 19.8%/12.1%, while that for kinematic PPP is improved by approximately 46.2%/49.6%, 33.5%/32.4%, and 35.1%/36.1% in the E, N and U directions, respectively. For dual-system PPP based on BDS/GPS observations, the TF-C model is recommended.
Fifteen-Year Trends (2005–2019) in the Satellite-Derived Ozone-Sensitive Regime in East Asia: A Gradual Shift from VOC-Sensitive to NOx-Sensitive
Authors: Itahashi Syuichi , Irie Hitoshi , Shimadera Hikari , Chatani Satoru
Published: September 2022

To mitigate tropospheric ozone (O3) pollution with proper and effective emission regulations, diagnostics for the O3-sensitive regime are critical. In this study, we analyzed the satellite-measured formaldehyde (HCHO) and nitrogen dioxide (NO2) column densities and derived the HCHO to NO2 ratio (FNR) from 2005 to 2019. Over China, there was a clear increase in the NO2 column during the first 5-year period and a subsequent decrease after 2010. Over the Republic of Korea and Japan, there was a continuous decline in the NO2 column over 15 years. Over the entire East Asia, a substantial increase in the HCHO column was identified during 2015–2019. Therefore, FNR increased over almost all of East Asia, especially during 2015–2019. This increasing trend in FNR indicated the gradual shift from a volatile organic compound (VOC)-sensitive to a nitrogen oxide (NOx)-sensitive regime. The long-term changes in HCHO and NO2 columns generally corresponded to anthropogenic non-methane VOC (NMVOC) and NOx emissions trends; however, anthropogenic sources did not explain the increasing HCHO column during 2015–2019. Because of the reduction in anthropogenic sources, the relative importance of biogenic NMVOC sources has been increasing and could have a larger impact on changing the O3-sensitive regime over East Asia.
Soil Reflectance Composites—Improved Thresholding and Performance Evaluation
Authors: Heiden Uta , d’Angelo Pablo , Schwind Peter , Karlshöfer Paul , Müller Rupert , Zepp Simone , Wiesmeier Martin , Reinartz Peter
Published: September 2022

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral index-independent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid.
Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China
Authors: Zhang Jie , Liu Shanwei , Liang Hong , Wan Wei , Guo Zhizhou , Liu Baojian
Published: September 2022

Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring.
The Influence of Satellite-Derived Environmental and Oceanographic Parameters on Marine Turtle Time at Surface in the Gulf of Mexico
Authors: Roberts Kelsey E. , Garrison Lance P. , Ortega-Ortiz Joel , Hu Chuanmin , Zhang Yingjun , Sasso Christopher R. , Lamont Margaret , Hart Kristen M.
Published: September 2022

The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys.
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter
Authors: Peng Zeli , Ding Yinghui , Qu Ying , Wang Mengsi , Li Xijia
Published: September 2022

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces.
The Lunar Regolith Structure and Electromagnetic Properties of Chang’E-5 Landing Site
Authors: Li Yuxi , Zhou Bin , Shen Shaoxiang , Lu Wei , Tang Chuanjun , Li Shidong , Su Yan , Dai Shun , Fang Guangyou
Published: September 2022

On 1 December 2020, China’s Chang’E-5 (CE-5) probe successfully landed in the northeastern Oceanus Procellarum. This work mainly presents the results of Lunar Regolith Penetrating Radar (LRPR) equipped on the CE-5 Lander. The lunar regolith structure of the landing site from the surface to 3-m depth is unveiled by LRPR, which found that abundant rock fragments are distributed in uniform lunar regolith. The imaging result proved that the drilling and sampling process was prevented by big rocks at about 100 cm depth. On the basis of the response of lunar soil to electromagnetic (EM) wave, the EM properties of the landing site estimate that the relative dielectric constant and the loss tangent are 2.520 ± 0.186 and 0.0133 ± 0.0020, respectively.
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Authors: Wang Chenwei , Liu Xiaoyu , Huang Yulin , Luo Siyi , Pei Jifang , Yang Jianyu , Mao Deqing
Published: September 2022

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class.
On-Orbit Autonomous Geometric Calibration of Directional Polarimetric Camera
Authors: Xiang Guangfeng , Meng Binghuan , Tu Bihai , Lei Xuefeng , Sheng Tingrui , Han Lin , Luo Donggen , Hong Jin
Published: September 2022

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km).
Watching the Saltmarsh Grow: A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration
Authors: Rummell Ashley J. , Leon Javier X. , Borland Hayden P. , Elliott Brittany B. , Gilby Ben L. , Henderson Christopher J. , Olds Andrew D.
Published: September 2022

Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote sensing methods that can be erroneous. Recent advances in remote sensing techniques such as high-resolution sensors (<2 m resolution), object-based image analysis and shallow learning classifiers provide promising alternatives but have rarely been applied in a restoration context. We measured the changes to wetland communities at a 200 ha restoring coastal wetland in eastern Australia, using remotely sensed Worldview-2 imagery, object-based image analysis and random forest classification. Our approach used structural rasters (digital elevation and canopy height models) and a multi-temporal technique to distinguish between spectrally similar land cover. The accuracy of our land cover maps was high, with overall accuracies ranging between 91 and 95%, and this supported early detection of increases in the area of key ecosystems, including mixed she-oak and paperbark (10 ha), mangroves (0.91 ha) and saltmarsh (4.31 ha), over a 5-year monitoring period. Our approach provides coastal managers with an accurate and frequent method for quantifying early responses of coastal wetlands to restoration, which is essential for informing adaptive management in the regeneration of ecosystem services.
Scale-Space Feature Recalibration Network for Single Image Deraining
Authors: Li Pengpeng , Jin Jiyu , Jin Guiyue , Fan Lei
Published: September 2022

Computer vision technology is increasingly being used in areas such as intelligent security and autonomous driving. Users need accurate and reliable visual information, but the images obtained under severe weather conditions are often disturbed by rainy weather, causing image scenes to look blurry. Many current single image deraining algorithms achieve good performance but have limitations in retaining detailed image information. In this paper, we design a Scale-space Feature Recalibration Network (SFR-Net) for single image deraining. The proposed network improves the image feature extraction and characterization capability of a Multi-scale Extraction Recalibration Block (MERB) using dilated convolution with different convolution kernel sizes, which results in rich multi-scale rain streaks features. In addition, we develop a Subspace Coordinated Attention Mechanism (SCAM) and embed it into MERB, which combines coordinated attention recalibration and a subspace attention mechanism to recalibrate the rain streaks feature information learned from the feature extraction phase and eliminate redundant feature information to enhance the transfer of important feature information. Meanwhile, the overall SFR-Net structure uses dense connection and cross-layer feature fusion to repeatedly utilize the feature maps, thus enhancing the understanding of the network and avoiding gradient disappearance. Through extensive experiments on synthetic and real datasets, the proposed method outperforms the recent state-of-the-art deraining algorithms in terms of both the rain removal effect and the preservation of image detail information.
Investigating Methods for Cognitive Workload Estimation for Assistive Robots
Authors: Aygun Ayca , Nguyen Thuan , Haga Zachary , Aeron Shuchin , Scheutz Matthias
Published: September 2022

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.
A Novel Air-Door Opening and Closing Identification Algorithm Using a Single Wind-Velocity Sensor
Authors: Shang Wentian , Deng Lijun , Liu Jian
Published: September 2022

The air-door is an important device for adjusting the air flow in a mine. It opens and closes within a short time owing to transportation and other factors. Although the switching sensor alone can identify the air-door opening and closing, it cannot relate it to abnormal fluctuations in the wind speed. Large fluctuations in the wind-velocity sensor data during this time can lead to false alarms. To overcome this problem, we propose a method for identifying air-door opening and closing using a single wind-velocity sensor. A multi-scale sliding window (MSSW) is employed to divide the samples. Then, the data global features and fluctuation features are extracted using statistics and the discrete wavelet transform (DWT). In addition, a machine learning model is adopted to classify each sample. Further, the identification results are selected by merging the classification results using the non-maximum suppression method. Finally, considering the safety accidents caused by the air-door opening and closing in an actual production mine, a large number of experiments were carried out to verify the effect of the algorithm using a simulated tunnel model. The results show that the proposed algorithm exhibits superior performance when the gradient boosting decision tree (GBDT) is selected for classification. In the data set composed of air-door opening and closing experimental data, the accuracy, precision, and recall rates of the air-door opening and closing identification are 91.89%, 93.07%, and 91.07%, respectively. In the data set composed of air-door opening and closing and other mine production activity experimental data, the accuracy, precision, and recall rates of the air-door opening and closing identification are 89.61%, 90.31%, and 88.39%, respectively.
A Privacy-Preserved ID-Based Secure Communication Scheme in 5G-IoT Telemedicine Systems
Authors: Lin Tzu-Wei
Published: September 2022

5G networks have an efficient effect in providing quality of experience and massive Internet of things (IoT) communication. Applications of 5G-IoT networks have been expanded rapidly, including in smart medical healthcare. Emergency medical services (EMS) hold an assignable proportion in our lives, which has become a complex network of all types of professionals, including care in an ambulance. A 5G network with EMS can simplify the medical treatment process and improve the efficiency of patient treatment. The importance of healthcare-related privacy preservation is rising. If the work of privacy preservation fails, not only will medical institutes have economic and credibility losses but also property losses and even the lives of patients will be harmed. This paper proposes a privacy-preserved ID-based secure communication scheme in 5G-IoT telemedicine systems that can achieve the features below. (i) The proposed scheme is the first scheme that integrates the process of telemedicine systems and EMS; (ii) the proposed scheme allows emergency signals to be transmitted immediately with decreasing risk of secret key leakage; (iii) the information of the patient and their prehospital treatments can be transmitted securely while transferring the patient to the destination medical institute; (iv) the quality of healthcare services can be assured while preserving the privacy of the patient; (v) the proposed scheme supports not only normal situations but also emergencies. (vi) the proposed scheme can resist potential attacks.
A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields
Authors: López Emiliano , Vionnet Carlos , Ferrer-Cid Pau , Barcelo-Ordinas Jose M. , Garcia-Vidal Jorge , Contini Guillermo , Prodolliet Jorge , Maiztegui José
Published: September 2022

The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193).
ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems
Authors: Alkhabbas Fahed , Alsadi Mohammed , Alawadi Sadi , Awaysheh Feras M. , Kebande Victor R. , Moghaddam Mahyar T.
Published: September 2022

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively.
Evaluating the Reliability of a Shape Capturing Process for Transradial Residual Limb Using a Non-Contact Scanner
Authors: Ngan Calvin C. , Sivasambu Harry , Ramdial Sandra , Andrysek Jan
Published: September 2022

Advancements in digital imaging technologies hold the potential to transform prosthetic and orthotic practices. Non-contact optical scanners can capture the shape of the residual limb quickly, accurately, and reliably. However, their suitability in clinical practice, particularly for the transradial (below-elbow) residual limb, is unknown. This project aimed to evaluate the reliability of an optical scanner-based shape capture process for transradial residual limbs related to volumetric measurements and shape assessment in a clinical setting. A dedicated setup for digitally shape capturing transradial residual limbs was developed, addressing challenges with scanning of small residual limb size and aspects such as positioning and patient movement. Two observers performed three measurements each on 15 participants with transradial-level limb absence. Overall, the developed shape capture process was found to be highly repeatable, with excellent intra- and inter-rater reliability that was comparable to the scanning of residual limb cast models. Future work in this area should compare the differences between residual limb shapes captured through digital and manual methods.