bluuebunny/crossref_metadata_2025 huggingface dataset:
Polyamide 6-Aluminum Assembly Enhanced by Laser Microstructuring
Authors: Bula Karol , Korzeniewski Bartosz
Published: January 2022

The presented work’s aim is the application of low-power laser treatment for the enhancement of interfacial micromechanical adhesion between polyamide 6 (filled with glass fiber) and aluminum. A fiber laser beam was used to prepare micro-patterns on aluminum sheets. The micro-structuring was conducted in the regime of 50, 100, 200 and 300 mm/s laser beam speeds, for both sides. The joining process was realized in an injection molding process. Metallic inserts were surface engraved and overmolded in one-side and two-side configurations. A lap shear test was used to examine the strength of the joints. Engraved metallic surfaces and adequate imprints on polyamide side were checked by optical microscope with motorized stages, and roughness parameters were also determined. Microscopic observations made it possible to describe the grooves’ shape and to conclude that a huge recast melt was formed when the lowest laser beam speed was applied; thus, the roughness parameter Ra reached the highest value of 16.8 μm (compared to 3.5 μm obtained for the fastest laser speed). The maximum shear force was detected for a sample prepared with the lowest scanning speed (one-sides joints), and it was 883 N, while for two-sided joints, the ultimate force was 1410 N (for a scanning speed of 200 mm/s).
Experimental Research on Polymer-Based Coaxial Sealing Systems of Hydraulic Cylinders for Small Displacement Velocities
Authors: Deaconescu Tudor , Deaconescu Andrea
Published: January 2022

Reducing friction in the coaxial sealing systems of hydraulic cylinders is one of the solutions for increasing the energy efficiency of industrial actuations. This is a requirement, particularly in the case of small velocities that carry the risk of eigen-vibrations and/or stick-slip. The authors discuss the experimental research conducted on three coaxial sealing systems made from thermoplastic polymer and polyurethane type materials. The paper presents the equipment and method used for the experimental determination of static and kinematic friction coefficients and discusses the subsequent results obtained to test different working parameters. The experimentally determined friction coefficients yielded a range of materials recommended for coaxial seals such as to minimize the occurrence of jerky operation.
Electrospun Nanofibrous Membranes Based on Citric Acid-Functionalized Chitosan Containing rGO-TEPA with Potential Application in Wound Dressings
Authors: Cojocaru Elena , Ghitman Jana , Pircalabioru Gratiela Gradisteanu , Stavarache Cristina , Serafim Andrada , Vasile Eugeniu , Iovu Horia
Published: January 2022

The present research work is focused on the design and investigation of electrospun composite membranes based on citric acid-functionalized chitosan (CsA) containing reduced graphene oxide-tetraethylene pentamine (CsA/rGO-TEPA) as materials with opportune bio-properties for applications in wound dressings. The covalent functionalization of chitosan (CS) with citric acid (CA) was achieved through the EDC/NHS coupling system and was checked by 1H-NMR spectroscopy and FTIR spectrometry. The mixtures to be electrospun were formulated by adding three concentrations of rGO-TEPA into the 1/1 (w/w) CsA/poly (ethylene oxide) (PEO) solution. The effect of rGO-TEPA concentration on the morphology, wettability, thermal stability, cytocompatibility, cytotoxicity, and anti-biofilm activity of the nanofibrous membranes was extensively investigated. FTIR and Raman results confirmed the covalent and non-covalent interactions that appeared between the system’s compounds, and the exfoliation of rGO-TEPA sheets within the CsA in the presence of PEO (CsA/P) polymer matrix, respectively. SEM analysis emphasized the nanofibrous architecture of membranes and the presence of rGO-TEPA sheets entrapped into the CsA nanofiber structure. The MTT cellular viability assay showed a good cytocompatibility with the highest level of cell development and proliferation registered for the CsA/P composite nanofibrous membrane with 0.250 wt.% rGO-TEPA. The designed nanofibrous membranes could have potential applications in wound dressings, given that they showed a good anti-biofilm activity against Gram-negative Pseudomonas aeruginosa and Gram-positive Staphylococcus aureus bacterial strains.
Polarization-Dependent Gratings Based on Polymer-Dispersed Liquid Crystal Cells with In-Plane Switching Electrodes
Authors: Huang Chia-Yi , Lin Shih-Hung
Published: January 2022

A diffraction grating of polymer-dispersed liquid crystal (PDLC) with polarization-selective characteristics is investigated. Electrically controllable gratings are produced using In-Plane Switching (IPS) electrodes. Indium tin oxide (ITO) electrodes with a stripe pattern are used to generate a horizontal electric field parallel to the substrate on a single glass substrate. It is known from the experimental results that the number of diffraction orders can be controlled by applied voltage. Except for the zeroth order, the consistently highest intensity can be obtained for every other order of diffraction, and the polarization direction of the diffraction is perpendicular to the direction of the electrode stripes. The polarization direction of the zeroth order diffraction is parallel to the direction of the electrode stripes. Therefore, it can be used as a filter for light polarization.
Continuous Fiber-Reinforced Aramid/PETG 3D-Printed Composites with High Fiber Loading through Fused Filament Fabrication
Authors: Rijckaert Sander , Daelemans Lode , Cardon Ludwig , Boone Matthieu , Van Paepegem Wim , De Clerck Karen
Published: January 2022

Recent development in the field of additive manufacturing, also known as three-dimensional (3D) printing, has allowed for the incorporation of continuous fiber reinforcement into 3D-printed polymer parts. These fiber reinforcements allow for the improvement of the mechanical properties, but compared to traditionally produced composite materials, the fiber volume fraction often remains low. This study aims to evaluate the in-nozzle impregnation of continuous aramid fiber reinforcement with glycol-modified polyethylene terephthalate (PETG) using a modified, low-cost, tabletop 3D printer. We analyze how dimensional printing parameters such as layer height and line width affect the fiber volume fraction and fiber dispersion in printed composites. By varying these parameters, unidirectional specimens are printed that have an inner structure going from an array-like to a continuous layered-like structure with fiber loading between 20 and 45 vol%. The inner structure was analyzed by optical microscopy and Computed Tomography (µCT), achieving new insights into the structural composition of printed composites. The printed composites show good fiber alignment and the tensile modulus in the fiber direction increased from 2.2 GPa (non-reinforced) to 33 GPa (45 vol%), while the flexural modulus in the fiber direction increased from 1.6 GPa (non-reinforced) to 27 GPa (45 vol%). The continuous 3D reinforced specimens have quality and properties in the range of traditional composite materials produced by hand lay-up techniques, far exceeding the performance of typical bulk 3D-printed polymers. Hence, this technique has potential for the low-cost additive manufacturing of small, intricate parts with substantial mechanical performance, or parts of which only a small number is needed.
Lipocalin, Resistin and Gut Microbiota-Derived Propionate Could Be Used to Predict Metabolic Bariatric Surgery Selected Outcomes
Authors: Auguet Teresa , Lopez-Dupla Miguel , Ramos Jessica , Bertran Laia , Riesco David , Aguilar Carmen , Ardévol Anna , Pinent Montserrat , Sabench Fàtima , Del Castillo Daniel , Richart Cristóbal
Published: January 2022

Many patients with clinically severe obesity (CSO) need to undergo bariatric surgery, with possible side effects, so individualized predictive methods are required. Adipocytokines and gut/intestinal microbiota-derived metabolites could be predictive biomarkers of metabolic success post- surgery, but the knowledge in this field is undefined. The objective of this work was to determine whether adipocytokines and microbiota-derived metabolites can be used to predict the metabolic improvement post- surgery in women with CSO. We analyzed circulating levels of some cytokines and some microbiota-derived metabolites at baseline and 12 months post-surgery from 44 women with CSO and 21 women with normal weight. Results showed that glucose, insulin, glycosylated hemoglobin A1c (HbA1c), low-density lipoprotein (LDL-C), and triglycerides levels were decreased post-surgery, while high density lipoprotein increased. Twelve months later, leptin, resistin, lipocalin, PAI-1, TNF-α, and IL-1β levels were lower than baseline, meanwhile adiponectin, IL-8, and IL-10 levels were increased. Moreover, baseline lipocalin levels were associated with HbA1c reduction post-surgery; meanwhile baseline resistin was related to postoperative HOMA2 (insulin resistance) and baseline propionate was associated with LDL-C decrease. To conclude, the detection of lipocalin, resistin, and propionate levels may be used to predict the metabolic success following bariatric surgery, although new knowledge is needed.
Highly Active CuO/KCC−1 Catalysts for Low-Temperature CO Oxidation
Authors: Luo Yiwei , Li Yonglong , Wang Conghui , Wang Jing , Liu Wenming , Peng Honggen , Wu Daishe
Published: January 2022

Copper catalysts have been extensively studied for CO oxidation at low temperatures. Previous findings on the stability of such catalysts, on the other hand, revealed that they deactivated badly under extreme circumstances. Therefore, in this work, a series of KCC−1-supported copper oxide catalysts were successfully prepared by impregnation method, of which 5% CuO/KCC−1 exhibited the best activity: CO could be completely converted at 120 °C. The 5% CuO/KCC−1 catalyst exhibited better thermal stability, which is mainly attributed to the large specific surface area of KCC−1 that facilitates the high dispersion of CuO species, and because the dendritic layered walls can lengthen the movement distances from particle-to-particle, thus helping to slow down the tendency of active components to sinter. In addition, the 5% CuO/KCC−1 has abundant mesoporous and surface active oxygen species, which are beneficial to the mass transfer and promote the adsorption of CO and the decomposition of Cu+–CO species, thus improving the CO oxidation performance of the catalyst.
Incident Angle Dependent Formation of Ion Tracks in Quartz Crystal with C60+ Ions: Big Ions in Small Channels
Authors: Amekura Hiroshi , Narumi Kazumasa , Chiba Atsuya , Hirano Yoshimi , Yamada Keisuke , Yamamoto Shunya , Saitoh Yuichi
Published: January 2022

Quartz (SiO2) crystals possess intrinsic columnar pores perpendicular to (0001) surfaces, consisting of three- and six-membered ring (3MR and 6MR) structures of Si and O atoms. The diameters of the larger pores, i.e., 6 MRs, are ~0.49 nm, while the diameters of fullerene (C60) ions are 0.7 nm, i.e., larger than either type of the pores. Transmission electron microscopy observation evidenced approximately two times longer ion tracks in the channeling condition, i.e., 0° incidence to (0001) surface, than an off-channeling condition, i.e., 7° incidence in this case, under 6 MeV C60 ion injection. The track length at the 0° incidence decreases more steeply than that at the 7° incidence with decreasing the energy from 6 MeV to 1 MeV. Finally, the track lengths at the 0° and 7° incidences become comparable, i.e., the channeling-like effect disappears at 1 MeV irradiation. This study experimentally indicates that the channeling-like effect of C60 ions is induced in quartz crystals, while the sizes of the channels are smaller than the C60 ions.
Baseline Data of Low-Density Polyethylene Continuous Pyrolysis for Liquid Fuel Manufacture
Authors: Ketov Aleksandr , Korotaev Vladimir , Sliusar Natalia , Bosnic Vladivir , Krasnovskikh Marina , Gorbunov Aleksei
Published: January 2022

The recycling of end-of-life plastics is a problem, since small parts can be returned into circulation. The rest is burned, landfilled or recycled into low-quality heating oil by pyrolysis methods. The disadvantages of this method are the need to dispose the formed by-product, pyrolytic carbon, the poor quality of produced liquid fuel and the low productivity of the method associated with the periodicity of the process. In this work, methods of thermogravimetry and chromatography–mass spectrometry (GC-MS) have been used to study the co-pyrolysis products of low-density polyethylene (LDPE) and oxygen-containing substances at the pressures of 4–8 MPa and temperatures of 520–620 °C. Experiments have highlighted the conditions needed for producing of high-quality liquid fuel. Initial data have been prepared for the design of a continuous pyrolysis reactor to dispose polymer waste for the production of bio-oil which would be available to enter the petrochemical products market.
Religion, Gender, and Bodies: Women’s Polyvalent Roles and Experiences in the Biopolitics of Taiwan’s Presbyterian Missions
Authors: Zavala-Pelayo Edgar , Chang Hung-Chieh
Published: January 2022

The Presbyterian missions and medical missions in 19th-century Taiwan were successful enterprises that over time developed into the Presbyterian Church in Taiwan, which stands today as the largest Christian minority church in this country. Through a Foucauldian biopolitical perspective, this paper analyzes the roles of female missionaries in the management of bodies and the subjective experiences of both foreign and Native women in the missions. Going beyond descriptive narratives and control-versus-agency reductionist frames, the paper points the polyvalent semantics of such roles and experiences. It also explores the complex relations between the women’s biopolitical functions, the PCT’s industrial type of biopolitical apparatus, and the biopolitical regimes of the late Qing dynasty and the Japanese colonial government in the early 20th century. The conclusions remark on the analytical relevance of biopolitical perspectives in the study of gender and body-related phenomena in Christian missions and Christian religions beyond Western societies.
Embracing the Paradox: A Bodhisattva Path
Authors: Bhattacharjee Monica
Published: January 2022

This article addresses the significance of paradox as a steady presence in our lives. Contradictions and ambiguities often lead to aversive states of anxious uncertainty where straightforward answers are often unavailable yet sought after to alleviate existential insecurities. In conditions where narratives of ambivalence intensify, such as during the worldwide COVID-19 crises, our traditional socio-evolutionary inclinations to avoid them either through denial or active resistance become more noticeable. It also leads to distress in intersubjective spaces especially when uncertainty and perceptions of threat stand as correlates, and we start to fear what we do not understand. In this paper, I consider wisdom responses from a Buddhist perspective to help us acknowledge the value of paradox, highlighting how changes in the formulation of our self-concept can help with that. I draw upon select principles and insights from the Diamond Sutra and the Heart Sutra, two texts within the Mahaprajnaparamita sutras of Mahayana Buddhism. Through these, I examine some inherent paradoxes as vital components of a larger ontological unity, the recognition of which can act as an enabler to the Bodhisattva path. This path is worthy of exploration, allowing us to move past the need for closure and instead focus on reconciliation, disclosure, and epistemic humility.
Radar Imaging Statistics of Non-Gaussian Rough Surface: A Physics-Based Simulation Study
Authors: Chiang Cheng-Yen , Chen Kun-Shan , Yang Ying , Zhang Yang , Wu Lingbing
Published: January 2022

This paper investigates the radar image statistics of rough surfaces by simulating the scattered signal’s dependence on the surface roughness. Statistically, the roughness characteristics include the height probability density (HPD) and, to the second-order, the power spectral density (PSD). We simulated the radar backscattered signal by computing the far-field scattered field from the rough surface within the antenna beam volume in the context of synthetic aperture radar (SAR) imaging. To account for the non-Gaussian height distribution, we consider microscopic details of the roughness on comparable radar wavelength scales to include specularly, singly, and multiply scatterers. We introduce surface roughness index (RSI) to distinguish the statistical characteristics of rough surfaces with different height distributions. Results suggest that increasing the RMS height does not impact the Gaussian HPD surface but significantly affects the Weibull surface. The results confirm that as the radar frequency increases, or reaches a relatively larger roughness, the surface’s HPD causes significant changes in incoherent scattering due to more frequent multiple scattering contributions. As a result, the speckle move further away from the Rayleigh model. By examining individual RSI, we see that the Gaussian HPD surface is much less sensitive to RMS height than the Weibull HPD surface. We demonstrate that to retrieve the surface parameters (both dielectric and roughness) from the estimated RCS, less accuracy is expected for the non-Gaussian surface than the Gaussian surface under the same conditions. Therefore, results drawn from this study are helpful for system performance evaluations, parameters estimation, and target detection for SAR imaging of a rough surface.
The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean
Authors: Wrobel-Niedzwiecka Iwona , Kitowska Małgorzata , Makuch Przemyslaw , Markuszewski Piotr
Published: January 2022

A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO2 (pCO2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO2W data points from the Surface Ocean CO2 Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO2W. Using all physical–biological components improved estimates of the pCO2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO2W distribution were reproduced with very good accuracy, but the network underestimated pCO2W in the winter and overestimated pCO2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO2W distribution and Air-Sea CO2 flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO2 flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO2 sinks. Strong monthly CO2 influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m−2 day−1) occurred in the fall and winter, when the pCO2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms−1) was present.
On the Radiative Impact of Biomass-Burning Aerosols in the Arctic: The August 2017 Case Study
Authors: Calì Quaglia Filippo , Meloni Daniela , Muscari Giovanni , Di Iorio Tatiana , Ciardini Virginia , Pace Giandomenico , Becagli Silvia , Di Bernardino Annalisa , Cacciani Marco , Hannigan James W. , Ortega Ivan , di Sarra Alcide Giorgio
Published: January 2022

Boreal fires have increased during the last years and are projected to become more intense and frequent as a consequence of climate change. Wildfires produce a wide range of effects on the Arctic climate and ecosystem, and understanding these effects is crucial for predicting the future evolution of the Arctic region. This study focuses on the impact of the long-range transport of biomass-burning aerosol into the atmosphere and the corresponding radiative perturbation in the shortwave frequency range. As a case study, we investigate an intense biomass-burning (BB) event which took place in summer 2017 in Canada and subsequent northeastward transport of gases and particles in the plume leading to exceptionally high values (0.86) of Aerosol Optical Depth (AOD) at 500 nm measured in northwestern Greenland on 21 August 2017. This work characterizes the BB plume measured at the Thule High Arctic Atmospheric Observatory (THAAO; 76.53∘N, 68.74∘W) in August 2017 by assessing the associated shortwave aerosol direct radiative impact over the THAAO and extending this evaluation over the broader region (60∘N–80∘N, 110∘W–0∘E). The radiative transfer simulations with MODTRAN6.0 estimated an aerosol heating rate of up to 0.5 K/day in the upper aerosol layer (8–12 km). The direct aerosol radiative effect (ARE) vertical profile shows a maximum negative value of −45.4 Wm−2 for a 78∘ solar zenith angle above THAAO at 3 km altitude. A cumulative surface ARE of −127.5 TW is estimated to have occurred on 21 August 2017 over a portion (∼3.1×106 km2) of the considered domain (60∘N–80∘N, 110∘W–0∘E). ARE regional mean daily values over the same portion of the domain vary between −65 and −25 Wm−2. Although this is a limited temporal event, this effect can have significant influence on the Arctic radiative budget, especially in the anticipated scenario of increasing wildfires.
Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model
Authors: Koch Julian , Demirel Mehmet Cüneyd , Stisen Simon
Published: January 2022

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.
Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping
Authors: Liu Rui , Yang Xin , Xu Chong , Wei Liangshuai , Zeng Xiangqiang
Published: January 2022

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.
Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus
Authors: Ershov Dmitry V. , Gavrilyuk Egor A. , Koroleva Natalia V. , Belova Elena I. , Tikhonova Elena V. , Shopina Olga V. , Titovets Anastasia V. , Tikhonov Gleb N.
Published: January 2022

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.
Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels
Authors: Wei Pengliang , Huang Ran , Lin Tao , Huang Jingfeng
Published: January 2022

A deep semantic segmentation model-based method can achieve state-of-the-art accuracy and high computational efficiency in large-scale crop mapping. However, the model cannot be widely used in actual large-scale crop mapping applications, mainly because the annotation of ground truth data for deep semantic segmentation model training is time-consuming. At the operational level, it is extremely difficult to obtain a large amount of ground reference data by photointerpretation for the model training. Consequently, in order to solve this problem, this study introduces a workflow that aims to extract rice distribution information in training sample shortage regions, using a deep semantic segmentation model (i.e., U-Net) trained on pseudo-labels. Based on the time series Sentinel-1 images, Cropland Data Layer (CDL) and U-Net model, the optimal multi-temporal datasets for rice mapping were summarized, using the global search method. Then, based on the optimal multi-temporal datasets, the proposed workflow (a combination of K-Means and random forest) was directly used to extract the rice-distribution information of Jiangsu (i.e., the K–RF pseudo-labels). For comparison, the optimal well-trained U-Net model acquired from Arkansas (i.e., the transfer model) was also transferred to Jiangsu to extract local rice-distribution information (i.e., the TF pseudo-labels). Finally, the pseudo-labels with high confidences generated from the two methods were further used to retrain the U-Net models, which were suitable for rice mapping in Jiangsu. For different rice planting pattern regions of Jiangsu, the final results showed that, compared with the U-Net model trained on the TF pseudo-labels, the rice area extraction errors of pseudo-labels could be further reduced by using the U-Net model trained on the K–RF pseudo-labels. In addition, compared with the existing rule-based rice mapping methods, he U-Net model trained on the K–RF pseudo-labels could robustly extract the spatial distribution information of rice. Generally, this study could provide new options for applying a deep semantic segmentation model to training sample shortage regions.
An Assessment of Sea Ice Motion Products in the Robeson Channel Using Daily Sentinel-1 Images
Authors: Liu Tingting , Wang Zihan , Shokr Mohammed , Lei Ruibo , Zhang Zhaoru
Published: January 2022

Sea ice motion is an essential parameter when determining sea ice deformation, regional advection, and the outflow of ice from the Arctic Ocean. The Robeson Channel, which is located between Ellesmere Island and northwest Greenland, is a narrow but crucial channel for ice outflow. Only three Eulerian sea ice motion products derived from ocean/sea ice reanalysis are available: GLORYS12V1, PSY4V3, and TOPAZ4. In this study, we used Lagrangian ice motion in the Robeson Channel derived from Sentinel-1 images to assess GLORYS12V1, PSY4V3, and TOPAZ4. The influence of the presence of ice arches, and wind and tidal forcing on the accuracies of the reanalysis products was also investigated. The results show that the PSY4V3 product performs the best as it underestimates the motion the least, whereas TOPAZ4 grossly underestimates the motion. This is particularly true in regimes of free drift after the formation of the northern arch. In areas with slow ice motion or grounded ice floes, the GLORYS12V1 and TOPAZ4 products offer a better estimation. The spatial distribution of the deviation between the products and ice floe drift is also presented and shows a better agreement in the Robeson Channel compared to the packed ice regime north of the Robeson Channel.
An Improved Fast Estimation of Satellite Phase Fractional Cycle Biases
Authors: Qi Ke , Dang Yamin , Xu Changhui , Gu Shouzhou
Published: January 2022

Satellite phase fractional cycle biases (FCBs) are crucial to precise point positioning with ambiguity resolution (PPP–AR), and they can improve the accuracy and reliability of a solution. Traditional methods need multiple iterations and need to keep the same reference when estimating satellite phase fractional cycle biases. In this paper, we propose an improved fast estimation of FCB, which does not need any iterations and can select any reference when estimating FCB. We compare the suitability and precision of a traditional and a proposed method by BDS-3 experiments. The results of the FCB experiments show that the calculated time of the proposed method is less than the traditional method and that computation efficiency is increased by 34.71%. These two methods have a similar rate of fixed epochs and ambiguities in the static and dynamic models. However, the time to first fix (TTFF) of the proposed method decreased by 19.69% and 28.83% for the static and dynamic models, respectively. The results show that the proposed method has a better convergence time in PPP–AR.
Integrated Satellite System for Fire Detection and Prioritization
Authors: Mazzeo Giuseppe , De Santis Fortunato , Falconieri Alfredo , Filizzola Carolina , Lacava Teodosio , Lanorte Antonio , Marchese Francesco , Nolè Gabriele , Pergola Nicola , Pietrapertosa Carla , Satriano Valeria
Published: January 2022

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.
Multiscale Variability and the Comparison of Ground and Satellite Radar Based Measures of Peatland Surface Motion for Peatland Monitoring
Authors: Marshall Chris , Sterk Henk Pieter , Gilbert Peter J. , Andersen Roxane , Bradley Andrew V. , Sowter Andrew , Marsh Stuart , Large David J.
Published: January 2022

Peatland surface motion is highly diagnostic of peatland condition. Interferometric Synthetic Aperture Radar (InSAR) can measure this at the landscape scale but requires ground validation. This necessitates upscaling from point to areal measures (80 × 90 m) but is hampered by a lack of data regarding the spatial variability of peat surface motion characteristics. Using a nested precise leveling approach within two areas of upland and low-lying blanket peatland within the Flow Country, Scotland, we examine the multiscale variability of peat surface motion. We then compare this with InSAR timeseries data. We find that peat surface motion varies at multiple scales within blanket peatland with decreasing dynamism with height above the water table e.g., hummocks < lawn < hollows. This trend is dependent upon a number of factors including ecohydrology, pool size/density, peat density, and slope. At the site scale motion can be grouped into central, marginal, and upland peatlands with each showing characteristic amplitude, peak timing, and response to climate events. Ground measurements which incorporate local variability show good comparability with satellite radar derived timeseries. However, current limitations of phase unwrapping in interferometry means that during an extreme drought/event InSAR readings can only qualitatively replicate peat movement in the most dynamic parts of the peatland e.g., pool systems, quaking bog.
Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images
Authors: Berg Paul , Santana Maia Deise , Pham Minh-Tan , Lefèvre Sébastien
Published: January 2022

Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.
Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds
Authors: Letard Mathilde , Collin Antoine , Corpetti Thomas , Lague Dimitri , Pastol Yves , Ekelund Anders
Published: January 2022

Coastal areas host highly valuable ecosystems that are increasingly exposed to the threats of global and local changes. Monitoring their evolution at a high temporal and spatial scale is therefore crucial and mostly possible through remote sensing. This article demonstrates the relevance of topobathymetric lidar data for coastal and estuarine habitat mapping by classifying bispectral data to produce 3D maps of 21 land and sea covers at very high resolution. Green lidar full waveforms are processed to retrieve tailored features corresponding to the signature of those habitats. These features, along with infrared intensities and elevations, are used as predictors for random forest classifications, and their respective contribution to the accuracy of the results is assessed. We find that green waveform features, infrared intensities, and elevations are complimentary and yield the best classification results when used in combination. With this configuration, a classification accuracy of 90.5% is achieved for the segmentation of our dual-wavelength lidar dataset. Eventually, we produce an original mapping of a coastal site under the form of a point cloud, paving the way for 3D classification and management of land and sea covers.
Jitter Detection Method Based on Sequence CMOS Images Captured by Rolling Shutter Mode for High-Resolution Remote Sensing Satellite
Authors: Zhu Ying , Yang Tingting , Wang Mi , Hong Hanyu , Zhang Yaozong , Wang Lei , Rao Qilong
Published: January 2022

Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation.
As-Built Inventory and Deformation Analysis of a High Rockfill Dam under Construction with Terrestrial Laser Scanning
Authors: Xiao Peiwei , Zhao Ran , Li Duohui , Zeng Zhaogao , Qi Shunchao , Yang Xingguo
Published: January 2022

The construction of large earth/rock fill dams, albeit its remarkable progress, still relies largely on past experiences. Therefore, a comprehensive yet dependable monitoring program is particularly beneficial for guiding the practice. However, conventional measurements can only produce limited discrete data. This paper exploits the potential of the terrestrial laser scanning (TLS) for an accurate inventory of as-built states of a concrete-faced rockfill dam under construction and for a full-field analysis of the 3D deformation pattern over its upstream face. For the former, a well-designed 3D geodetic system, with a particular consideration of the topography, promises a regulated acquisition of high-quality and blind-zone-free point cloud at field and also eases the cumbersome data registration process while maintaining its precision in house. For the latter, a problem-tailored processing pipeline is proposed for deformation extraction. Its core idea is to achieve a highly precise alignment of the point clouds with Iterative Closed Point algorithms from different epochs in datum areas that displays a featured, undeformed geometry at stable positions across epochs. Then, the alignment transformation matrix is applied to the point clouds of respective upstream face for each epoch, followed by pairwise comparisons of multiple adjusted point clouds for deformation evaluation. A processing pipeline is used to exploit the peal scene data redundancy of the GLQ dam acquired at six different epochs. Statistical analysis shows that satisfactory accuracy for deformation detection can be repeatably achieved, regardless of the scanner’s positioning uncertainties. The obtained 3D deformation patterns are characterised by three different zones: practically undeformed, outward and inward deformed zones. Their evolutions comply well with real construction stages and unique 3D valley topography. Abundant deformation results highlight the potential of TLS combined with the proposed data processing pipeline for cost-efficient monitoring of huge infrastructures compared to conventional labor-intense measurements.
A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images
Authors: Islam Kh Tohidul , Wijewickrema Sudanthi , O’Leary Stephen
Published: January 2022

Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.
Research and Development of a COVID-19 Tracking System in Order to Implement Analytical Tools to Reduce the Infection Risk
Authors: Vavrinsky Erik , Zavodnik Tomas , Debnar Tomas , Cernaj Lubos , Kozarik Jozef , Micjan Michal , Nevrela Juraj , Donoval Martin , Kopani Martin , Kosnacova Helena
Published: January 2022

The whole world is currently focused on COVID-19, which causes considerable economic and social damage. The disease is spreading rapidly through the population, and the effort to stop the spread is entirely still failing. In our article, we want to contribute to the improvement of the situation. We propose a tracking system that would identify affected people with greater accuracy than medical staff can. The main goal was to design hardware and construct a device that would track anonymous risky contacts in areas with a highly concentrated population, such as schools, hospitals, large social events, and companies. We have chosen a 2.4 GHz proprietary protocol for contact monitoring and mutual communication of individual devices. The 2.4 GHz proprietary protocol has many advantages such as a low price and higher resistance to interference and thus offers benefits. We conducted a pilot experiment to catch bugs in the system. The device is in the form of a bracelet and captures signals from other bracelets worn at a particular location. In case of contact with an infected person, the alarm is activated. This article describes the concept of the tracking system, the design of the devices, initial tests, and plans for future use.