bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Evaluation of the Stiffness of Abaca-Fiber-Reinforced Bio-Polyethylene and High Density Polyethylene Composites
Authors: Seculi Faust , Espinach Francesc X. , Julián Fernando , Delgado-Aguilar Marc , Mutjé Pere , Tarrés Quim
Published: February 2023

The use of bio-based matrices together with natural fibers as reinforcement is a strategy for obtaining materials with competitive mechanical properties, costs, and environmental impacts. However, bio-based matrices, unknown by the industry, can be a market entry barrier. The use of bio-polyethylene, which has properties similar to polyethylene, can overcome that barrier. In this study, composites reinforced with abaca fibers used as reinforcement for bio-polyethylene and high density polyethylene are prepared and tensile tested. A micromechanics analysis is deployed to measure the contributions of the matrices and reinforcements and to measure the evolution of these contributions regarding AF content and matrix nature. The results show that the mechanical properties of the composites with bio-polyethylene as a matrix were slightly higher than those of the composites with polyethylene as a matrix. It was also found that the contribution of the fibers to the Young’s moduli of the composites was susceptible to the percentage of reinforcement and the nature of the matrices. The results show that it is possible to obtain fully bio-based composites with mechanical properties similar to those of partially bio-based polyolefin or even some forms of glass fiber-reinforced polyolefin.
Theoretical–Experimental Study of the Action of Trace Amounts of Formaldehyde, Propionaldehyde, and Butyraldehyde as Inhibitors of the Ziegler–Natta Catalyst and the Synthesis of an Ethylene–Propylene Copolymer
Authors: Hernández-Fernández Joaquín , Ortega-Toro Rodrigo , Castro-Suarez John R.
Published: February 2023

The copolymer synthesis process can be affected by failures in the production process or by contaminating compounds such as ketones, thiols, and gases, among others. These impurities act as an inhibiting agent of the Ziegler–Natta (ZN) catalyst affecting its productivity and disturbing the polymerization reaction. In this work, the effect of formaldehyde, propionaldehyde, and butyraldehyde on the ZN catalyst and the way in which it affects the final properties of the ethylene-propylene copolymer is presented by analyzing 30 samples with different concentrations of the mentioned aldehydes along with three control samples. It was determined that the presence of formaldehyde 26 ppm, propionaldehyde 65.2 ppm, and butyraldehyde 181.2 ppm considerably affect the productivity levels of the ZN catalyst; this effect increases as the concentration of aldehydes is higher in the process; likewise, these impurities affect the properties of the final product, such as the fluidity index (MFI), thermogravimetric analysis (TGA), bending, tension, and impact, which leads to a polymer with low-quality standards and less resistance to breakage. The computational analysis showed that the complexes formed by formaldehyde, propionaldehyde, and butyraldehyde with the active center of the catalyst are more stable than those obtained by the ethylene-Ti and propylene-Ti complexes, presenting values of −40.5, −47.22, −47.5, −5.2 and −1.3 kcal mol−1 respectively.
Deposition of Chitosan on Plasma-Treated Polymers—A Review
Authors: Vesel Alenka
Published: February 2023

Materials for biomedical applications often need to be coated to enhance their performance, such as their biocompatibility, antibacterial, antioxidant, and anti-inflammatory properties, or to assist the regeneration process and influence cell adhesion. Among naturally available substances, chitosan meets the above criteria. Most synthetic polymer materials do not enable the immobilization of the chitosan film. Therefore, their surface should be altered to ensure the interaction between the surface functional groups and the amino or hydroxyl groups in the chitosan chain. Plasma treatment can provide an effective solution to this problem. This work aims to review plasma methods for surface modification of polymers for improved chitosan immobilization. The obtained surface finish is explained in view of the different mechanisms involved in treating polymers with reactive plasma species. The reviewed literature showed that researchers usually use two different approaches: direct immobilization of chitosan on the plasma-treated surface or indirect immobilization by additional chemistry and coupling agents, which are also reviewed. Although plasma treatment leads to remarkably improved surface wettability, this was not the case for chitosan-coated samples, where a wide range of wettability was reported ranging from almost superhydrophilic to hydrophobic, which may have a negative effect on the formation of chitosan-based hydrogels.
Free Vibration Characteristics of CFRP Laminate with One-Dimensional Periodic Structures
Authors: Dou Yukuan , Zhang Jinguang , Wen Xianglong , Cheng Hui , Liu Haixin
Published: February 2023

This paper proposes an approach of stacking prepreg periodically for carbon fiber-reinforced polymer composites (CFRP) laminate. This paper will discuss the natural frequency, modal damping, and vibration characteristics of CFRP laminate with one-dimensional periodic structures. The damping ratio of CFRP laminate is calculated using the semi-analytical method which combines modal strain energy with the finite element method. The finite element method is used to calculate the natural frequency and bending stiffness which are verified with experiments. The numerical results of the damping ratio, natural frequency, and bending stiffness are in good agreement with the experiment results. Finally, the bending vibration characteristics of CFRP laminate with one-dimensional periodic structures and traditional CFRP laminate are investigated with experiments. The finding confirmed that the CFRP laminate with one-dimensional periodic structures exists band gaps. This study provides theoretical support for the promotion and application of CFRP laminate in the field of vibration and noise.
A Comparative Study on the Microscale and Macroscale Mechanical Properties of Dental Resin Composites
Authors: Yan Shuogeng , Wang Kun , Wang Zhengzhi
Published: February 2023

Dental resin composites are universal restorative materials, and various kinds of fillers are used to reinforce their mechanical properties. However, a combined study on the microscale and macroscale mechanical properties of dental resin composites is missing, and the reinforcing mechanism of the composites is still not fully clarified. In this work, the effects of the nano-silica particle on the mechanical properties of dental resin composites were studied by combined dynamic nanoindentation tests and macroscale tensile tests. The reinforcing mechanism of the composites was explored by combining near-infrared spectroscopy, scanning electron microscope, and atomic force microscope characterizations. It was found that the tensile modulus increased from 2.47 GPa to 3.17 GPa, and the ultimate tensile strength increased from 36.22 MPa to 51.75 MPa, with the particle contents increasing from 0% to 10%. From the nanoindentation tests, the storage modulus and hardness of the composites increased by 36.27% and 40.90%, respectively. The storage modulus and hardness were also found to increase by 44.11% and 46.46% when the testing frequency increased from 1 Hz to 210 Hz. Moreover, based on a modulus mapping technique, we found a boundary layer in which the modulus gradually decreased from the edge of the nanoparticle to the resin matrix. Finite element modeling was adopted to illustrate the role of this gradient boundary layer in alleviating the shear stress concentration on the filler–matrix interface. The present study validates mechanical reinforcement and provides a potential new insight for understanding the reinforcing mechanism of dental resin composites.
The Environmental Impacts of Disposable Nonwoven Fabrics during the COVID-19 Pandemic: Case Study on the Francesc de Borja Hospital
Authors: Quintana-Gallardo Alberto , del Rey Romina , González-Conca Salvador , Guillén-Guillamón Ignacio
Published: February 2023

Hospitals generate huge amounts of nonwoven residues daily. This paper focused on studying the evolution of nonwoven waste generated in the Francesc de Borja Hospital, Spain, over the last few years and its relation to the COVID-19 pandemic. The main objective was to identify the most impacting pieces of nonwoven equipment in the hospital and to analyze possible solutions. The carbon footprint of the nonwoven equipment was studied through a life-cycle assessment. The results showed an apparent increase in the carbon footprint in the hospital from 2020. Additionally, due to the higher annual volume, the simple nonwoven gown used primarily for patients had a higher carbon footprint over a year than the more sophisticated surgical gowns. It can be concluded that developing a local circular economy strategy for medical equipment could be the solution to avoid the enormous waste generation and the carbon footprint of nonwoven production.
Classification of Tumor in Brain MR Images Using Deep Convolutional Neural Network and Global Average Pooling
Authors: Malla Prince Priya , Sahu Sudhakar , Alutaibi Ahmed I.
Published: February 2023

Brain tumors can cause serious health complications and lead to death if not detected accurately. Therefore, early-stage detection of brain tumors and accurate classification of types of brain tumors play a major role in diagnosis. Recently, deep convolutional neural network (DCNN) based approaches using brain magnetic resonance imaging (MRI) images have shown excellent performance in detection and classification tasks. However, the accuracy of DCNN architectures depends on the training of data samples since it requires more precise data for better output. Thus, we propose a transfer learning-based DCNN framework to classify brain tumors for example meningioma tumors, glioma tumors, and pituitary tumors. We use a pre-trained DCNN architecture VGGNet which is previously trained on huge datasets and used to transfer its learning parameters to the target dataset. Also, we employ transfer learning aspects such as fine-tune the convolutional network and freeze the layers of the convolutional network for better performance. Further, this proposed approach uses a Global Average Pooling (GAP) layer at the output to avoid overfitting issues and vanishing gradient problems. The proposed architecture is assessed and compared with competing deep learning based brain tumor classification approaches on the Figshare dataset. Our proposed approach produces 98.93% testing accuracy and outperforms the contemporary learning-based approaches.
Analysis of Bottomhole Rock Stress in Deep-Well Drilling Considering Thermal-Hydro-Mechanical Coupling
Authors: Yang Bin , Xu Honglin
Published: February 2023

Drilling is a key step in the exploitation of deep oil and gas resources. In order to clarify the stress state of the rocks and the mechanism of rock breakage in deep-well drilling, a thermal-hydro-mechanical coupling model for deep-well drilling was established, and the effects of drilling on the temperature, pressure, and stress in the formation were studied. Furthermore, the effects of the formation parameters and wellbore parameters on the bottomhole stress were analyzed. The results revealed that after the formation was drilled, the temperatures in different horizontal in situ stress directions were not significantly different, but the difference in the pore pressure between the maximum and minimum horizontal stress directions was large. The average effective stress at the bottom of the hole was the smallest, and in some areas, it was tensile stress. For deep-well drilling, as the formation pressure increased, the in situ stress increased, and the permeability decreased, leading to greater average effective stress of the bottomhole rock. As a result, it was harder to break the rock, and the drilling efficiency decreased. Reducing the wellbore pressure and wellbore temperature is conducive to forming tensile stress near the borehole axis in the bottomhole, causing tensile damage. The average effective stress of the formation near the shoulder of the drill bit was compressive stress, and it is advisable to take advantage of the rock shear failure characteristics to improve the drilling efficiency in this area. The results of this study can help us to understand the stress state of the bottomhole rocks and the mechanism of rock breakage and can provide a reference for the optimization of drilling tools and drilling parameters in deep-well drilling.
Fresh Approaches for Structured Text Programmable Logic Controllers Programs Verification
Authors: Siboulet Émile , Pottier Louen , Ranger Tom , Riera Bernard
Published: February 2023

Programmable logic controllers (PLCs) are everywhere today and perform critical tasks in industries. They are considered as a key component for the Industry 4.0. Before they are put into operation, it is necessary to check the accuracy of the PLC programs. This verification operation can be performed using model checkers. This stage is often long and costly and requires a domain expert who can understand the system, as well as the different model checker tools able to verify the code implemented in the controller. Furthermore, this verification often requires a conversion of the PLC code into a language understood by a model checker which can influence the behavior of the observed PLC. Hence, there is a need to propose methods and tools which could be used by technicians and engineers. The aim of this paper is to propose methods that require little work to set up and are robust to program sizes used in Industry 4.0. This paper explores some fresh ideas for human-adapted PLC code verification. We present different methods to test codes in structured text (ST) compliant with the IEC 61131-3 standard. Hence, the first idea is to test the ST code that will be directly implemented on a controller. For that, we propose a method using the model checker UPPAAL which allows us to obtain exact results on short codes. Second, we propose verifying the generic properties that a PLC program must avoid: deadlocks, non-accessible states and fugitive states or actions. To solve combinatory explosion problems encountered with the UPPAAL software, the third proposition consists of using relational databases. The same verification as previously followed can be obtained, but the search time is longer. The fourth and last proposal is to process the ST code with a neural network composed of long short-term memory layers (LSTM) to quickly determine the validity of the code. This method could give an approximation of code errors in a few seconds. The different proposed methods are supported with several examples.
Lactic Acid: A Comprehensive Review of Production to Purification
Authors: Ojo Abidemi Oluranti , de Smidt Olga
Published: February 2023

Lactic acid (LA) has broad applications in the food, chemical, pharmaceutical, and cosmetics industries. LA production demand rises due to the increasing demand for polylactic acid since LA is a precursor for polylactic acid production. Fermentative LA production using renewable resources, such as lignocellulosic materials, reduces greenhouse gas emissions and offers a cheaper alternative feedstock than refined sugars. Suitable pretreatment methods must be selected to minimize LA cost production, as the successful hydrolysis of lignocellulose results in sugar-rich feedstocks for fermentation. This review broadly focused on fermentative LA production from lignocellulose. Aspects discussed include (i). low-cost materials for fermentative LA production, (ii). pretreatment methods, (iii). enzymatic hydrolysis of cellulose and hemicellulose, (iv). lactic acid-producing microorganisms, including fungi, bacteria, genetically modified microorganisms, and their fermentative pathways, and (v). fermentation modes and methods. Industrial fermentative lactic acid production and purification, difficulties in using lignocellulose in fermentative LA production, and possible strategies to circumvent the challenges were discussed. A promising option for the industrial production and purification of LA that contains enzyme and cell recycling continuous simultaneous saccharification and fermentation coupled with membrane-based separation was proposed. This proposed system can eliminate substrate-, feedback-, and end-product inhibition, thereby increasing LA concentration, productivity, and yield.
Struggling with and against the Governance of Islam in Spain
Authors: Lems Johanna M. , Planet Contreras Ana I.
Published: February 2023

The process of accommodation of Islam in Spain is based on the Constitution of 1978, which guarantees the freedom of religion. Regarding Islam, the Cooperation Agreement signed in 1992 between the State and the Islamic Commission of Spain brought with it a formal recognition of the practice of the Islamic faith. Thirty years later, the sole interlocutor appointed by the State seems to be ineffective in the pursuance of compliance with Islamic religious rights. In various regions other actors have engaged in claims-making for rights that include, among others, the access to cemetery space for Islamic burials and Islamic religious education and halal food in publicly funded schools. This paper explores the governance of Islam and Muslims in Spain by presenting a case in which a number of grassroots organizations in the northern region of La Rioja have combined their efforts to achieve compliance with the religious rights they were granted decades ago. Through claims-making outside the institutionalized structure of interlocution with the State, they are contesting the external and internal top-down governance of Islam in Spain. Based on empirical data, we analyzed the nature of their claims, the varied ways of responding to specific practices of governance, as well as the spaces in which this claims-making takes place.
Measuring Systemic Governmental Reinsurance Risks of Extreme Risk Events
Authors: Hadad Elroi , Shushi Tomer , Yosef Rami
Published: February 2023

This study presents an easy-to-handle approach to measuring the severity of reinsurance that faces a system of dependent claims, where the reinsurance contracts are of excess loss or proportional loss. The proposed approach is a natural generalization of common reinsurance methodologies providing a conservative framework that deals with the fundamental question of how much money should a government hold to prepare for natural or human-made extreme risk events that the government will cover? Although the ruin theory is commonly used for extreme risk events, we suggest a new risk measure to deal with such events in a new framework based on multivariate risk measures. We analyze the results for the log-elliptical model of dependent claims, which are commonly used in risk analysis, and illustrate our novel risk measure using a Monte Carlo simulation.
SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification
Authors: Huang Xiaohui , Zhou Yunfei , Yang Xiaofei , Zhu Xianhong , Wang Ke
Published: February 2023

Hyperspectral image (HSI) classification is a significant foundation for remote sensing image analysis, widely used in biology, aerospace, and other applications. Convolution neural networks (CNNs) and attention mechanisms have shown outstanding ability in HSI classification and have been widely studied in recent years. However, the existing CNN-based and attention mechanism-based methods cannot fully use spatial–spectral information, which is not conducive to further improving HSI classification accuracy. This paper proposes a new spatial–spectral Transformer network with multi-scale convolution (SS-TMNet), which can effectively extract local and global spatial–spectral information. SS-TMNet includes two key modules, i.e., multi-scale 3D convolution projection module (MSCP) and spatial–spectral attention module (SSAM). The MSCP uses multi-scale 3D convolutions with different depths to extract the fused spatial–spectral features. The spatial–spectral attention module includes three branches: height spatial attention, width spatial attention, and spectral attention, which can extract the fusion information of spatial and spectral features. The proposed SS-TMNet was tested on three widely used HSI datasets: Pavia University, IndianPines, and Houston2013. The experimental results show that the proposed SS-TMNet is superior to the existing methods.
Dynamic Data Augmentation Based on Imitating Real Scene for Lane Line Detection
Authors: Wang Qingwang , Wang Lu , Chi Yongke , Shen Tao , Song Jian , Gao Ju , Shen Shiquan
Published: February 2023

With the rapid development of urban ground transportation, lane line detection is gradually becoming a major technological direction to help to realize safe vehicle navigation. However, lane line detection results may have incompleteness issues, such as blurry lane lines and disappearance of the lane lines in the distance, since the lane lines may be heavily obscured by vehicles and pedestrians on the road. In addition, low-visibility environments also pose a challenge for lane line detection. To solve the above problems, we propose a dynamic data augmentation framework based on imitating real scenes (DDA-IRS). DDA-IRS contains three data augmentation strategies that simulate different realistic scenes (i.e., shadows, dazzle, and crowded). In this way, we expand from a limited scene dataset to realistically fit multiple complex scenes. Importantly, DDA-IRS is a lightweight framework that can be integrated with a variety of training-based models without modifying the original model. We evaluate the proposed DDA-IRS on the CULane dataset, and the results show that the data-enhanced model outperforms the baseline model by 0.5% in terms of F-measure. In particular, the F-measure of the “Normal”, “Crowded”, “Shadow”, “Arrow”, and “Curve” achieve a 0.4%, 0.1%, 1.6%, 0.4%, and 1.4% improvement, respectively.
Parallel Processing Method for Microseismic Signal Based on Deep Neural Network
Authors: Ma Chunchi , Yan Wenjin , Xu Weihao , Li Tianbin , Ran Xuefeng , Wan Jiangjun , Tong Ke , Lin Yu
Published: February 2023

The microseismic signals released by rock mass fracture can be captured via microseismic monitoring to evaluate the development of geological disasters. This is crucial for underground engineering construction, underground mining, and earthquake and geological disaster evaluation. However, extracting information effectively is difficult due to the low signal-to-noise ratio of microseismic signals caused by complex environmental factors. Therefore, denoising and detection (onset time picking) are essential to processing microseismic signals and extracting source information. To improve the efficiency and accuracy of microseismic signal processing, we propose a parallel dual-tasking network, which is an advanced deep learning model that can simultaneously perform microseismic denoising and detection tasks. The network, comprising one encoder and two parallel decoders, is customised to extract input data features, and two outputs can be simultaneously generated to denoise and detect microseismic signals. The model exhibits excellent denoising and detection performance for microseismic signals containing various types of noise. Compared with traditional methods, the signal-to-noise ratio of the denoised signal is greatly improved, and the waveform distortion of the denoised signal is small. Even when the signal-to-noise ratio is low, the proposed model can maintain good onset time pickup performance. This method obviates the need for different denoising methods for different types of noise and precludes setting thresholds artificially to improve the denoising effect and detection accuracy. Moreover, the dual processing characteristics of the model facilitate simultaneous denoising and detection, which improves the processing efficiency of microseismic data and meets the demand for automatically processing massive microseismic data. Therefore, this method has excellent data processing potential in exploration seismology, and earthquake and disaster assessment.
Tropical Tropopause Layer Cloud Properties from Spaceborne Active Observations
Authors: Lei Siliang , Zhu Xijuan , Ling Yuxiang , Teng Shiwen , Yao Bin
Published: February 2023

A significant part of clouds in the tropics appears over the tropopause due to intense convections and in situ condensation activity. These tropical tropopause layer (TTL) clouds not only play an important role in the radiation budget over the tropics, but also in water vapor and other chemical material transport from the troposphere to the stratosphere. This study quantifies and analyzes the properties of TTL clouds based on spaceborne active observations, which provide one of the most reliable sources of information on cloud vertical distributions. We use four years (2007–2010) of observations from the joint Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat and consider all cloudy pixels with top height above the tropopause as TTL clouds. The occurrence frequency of TTL clouds during the nighttime is found to be almost 13% and can reach ~50–60% in areas with frequent convections. The annual averages of tropical tropopause height, tropopause temperature, and cloud top height are 16.2 km, −80.7 °C, and 16.6 km, respectively, and the average cloud top exceeds tropopause by approximately 500 m. More importantly, the presence of TTL clouds causes tropopause temperature to be ~3–4 °C colder than in the all-sky condition. It also lifts the tropopause heights ~160 m during the nighttime and lowers the heights ~84 m during the daytime. From a cloud type aspect, ~91% and ~4% of the TTL clouds are high clouds and altostratus, and only ~5% of them are associated with convections (i.e., nimbostratus and deep convective clouds). Approximately 30% of the TTL clouds are single-layer clouds, and multi-layer clouds are dominated by those with 2–3 separated layers.
Characterization of Bias in Fengyun-4B/AGRI Infrared Observations Using RTTOV
Authors: Zhu Zhi , Shi Chunxiang , Gu Junxia
Published: February 2023

As China’s first operational second-generation geostationary satellite, Fengyun-4B carries the newly developed Advanced Geostationary Radiation Imager (AGRI), which adds a low-level water vapor detection channel and an adjusted spectrum range of four channels to improve the quality of observation. To characterize biases of the infrared (IR) channels of Fengyun-4B/AGRI, RTTOV was applied to simulate the brightness temperature of the IR channels during the period of Fengyun-4B trial operation (from June to November 2022) under clear-sky conditions based on ERA5 reanalysis, which may provide beneficial information for the operational applications of Fengyun-4B/AGRI, such as data assimilation and severe weather monitoring. The results are as follows: (1) due to the sun’s influence on the satellite instrument, the brightness temperature observations of the Fengyun-4B/AGRI 3.75 μm channel were abnormally high around 1500 UTC in October, although the data producer made efforts to eliminate abnormal data; (2) the RTTOV simulations were in good agreement with the observations, and the absolute mean biases of the RTTOV simulations were less than 1.39 K over the ocean, and less than 1.77 K over land, for all IR channels under clear-sky conditions, respectively; (3) for the variation of spatial distribution bias over land, channels 12–15 were more obvious than channels 9–11, which indicates that the skin temperature of ERA-5 reanalysis and surface emissivity may have greater spatial uncertainty than the water vapor profile; (4) the biases and standard deviations of Fengyun-4B/AGRI channels 9–15 had negligible dependence on the satellite zenith angles over the ocean, while the standard deviation of channels 8 and 12 had a positive correlation with satellite zenith angles when the satellite zenith angles were larger than 30°; and (5) the biases and standard deviations of Fengyun-4B/AGRI IR channels showed scene brightness temperature dependence over the ocean.
Thick Cloud Removal in Multi-Temporal Remote Sensing Images via Frequency Spectrum-Modulated Tensor Completion
Authors: Chen Zhihong , Zhang Peng , Zhang Yu , Xu Xunpeng , Ji Luyan , Tang Hairong
Published: February 2023

Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images. However, existing methods ignore different low-rank properties in the spatial and temporal dimensions, such that they cannot utilize spatial and temporal information adequately. In this paper, we propose a new frequency spectrum-modulated tensor completion method (FMTC). First, remote sensing images are rearranged as third-order spatial–temporal tensors for each band. Then, Fourier transform (FT) is introduced in the temporal dimension of the rearranged tensor to generate a spatial–frequential tensor. In view of the fact that land features represent low-frequency components and fickle clouds represent high-frequency components in the time domain, we chose adaptive weights for the completion of different low-rank spatial matrixes, according to the frequency spectrum. Then, Invert Fourier Transform (IFT) was implemented. Through this method, the joint low-rank spatial–temporal constraint was achieved. The simulated data experiments demonstrate that FMTC is applicable on different land-cover types and different missing sizes. With real data experiments, we have validated the effectiveness and stability of FMTC for time-series remote sensing image reconstruction. Compared with other algorithms, the performance of FMTC is better in quantitative and qualitative terms, especially when considering the spectral accuracy and temporal continuity.
An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales
Authors: Keuris Lars , Hetzenecker Markus , Nagler Thomas , Mölg Nico , Schwaizer Gabriele
Published: February 2023

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.
STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images
Authors: Gomroki Masoomeh , Hasanlou Mahdi , Reinartz Peter
Published: February 2023

Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method’s effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%.
Climate Change and CO2 Fertilization Have Played Important Roles in the Recent Decadal Vegetation Greening Trend on the Chinese Loess Plateau
Authors: Niu Zhongen , He Honglin , Yu Pengtao , Sitch Stephen , Zhao Ying , Wang Yanhui , Jain Atul K. , Vuichard Nicolas , Si Bingcheng
Published: February 2023

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint.
Evaluation of the High-Resolution MuSyQ LAI Product over Heterogeneous Land Surfaces
Authors: Li Dandan , Huang Yajun , Xiao Yao , He Min , Wen Jianguang , Li Yuanqing , Ma Mingguo
Published: February 2023

In recent years, the retrieval and validation of remotely-sensed leaf area index (LAI) products over complex land surfaces have received much attention due to the high-precision land surface model simulations and applications in global climate change. However, most of these related researches mainly focus on coarse resolution products. This is because few products have been specifically designed for solving the problems derived from complex land surfaces in mountain areas until now. MuSyQ LAI is a new product derived from Gaofen-1 (GF-1) satellite data. This product is characterized with a temporal resolution of 10 days and a spatial resolution of 16 m. As is well known, high-resolution products have less uncertainties because of the homogeneities of sub-pixel. Therefore, to evaluate the precision and uncertainty of MuSyQ LAI, an up-scaling strategy was employed here to validate MuSyQ LAI for three mountain regions in Southwest China. The validation strategy can be divided into three parts. First, a regression model was built by in situ LAI measured by LAI-2200 and the normalized difference vegetation index (NDVI) from unmanned aerial vehicle (UAV) images to obtain a 0.5 m resolution LAI map. Second, an up-scaled LAI map with a spatial resolution consistent with MuSyQ LAI was calculated by the pixel-averaging method from the UAV-based LAI map. Third, the MuSyQ LAI was validated by the up-scaled UAV-based LAI in pixel scale. Simultaneously, the sources of uncertainty were analyzed and compared from the view of data source, retrieval model, and scale effects. The results suggested that MuSyQ LAI in the study areas are significantly underestimated by 53.69% due to the complex terrain and heterogeneous land cover. There are three main reasons for the underestimation. The differences between GF-1 reflectance and UAV-based reflectance employed to estimate LAI are the largest factors for the validation results, even accounting for 61.47% of the total bias. Subsequently, the scale effects led to about 28.44% bias. Last but not least, the models employed to retrieve LAI contributed merely 10.09% uncertainties to the total bias. In conclusion, the accuracy of MuSyQ LAI still has a large space to be improved from the view of reflectance over complex terrain. This study is quite important for applications of MuSyQ LAI products and also provides a reference for the improvement and application of other high-resolution remotely sensed LAI products.
A Robust Stepwise Clustering Approach to Detect Individual Trees in Temperate Hardwood Plantations using Airborne LiDAR Data
Authors: Shao Gang , Fei Songlin , Shao Guofan
Published: February 2023

Precise tree inventory plays a critical role in sustainable forest planting, restoration, and management. LiDAR-based individual tree detection algorithms often focus on finding individual treetops to discern tree positions. However, deliquescent tree forms (broad, flattened crowns) in deciduous forests can make these algorithms ineffective. In this study, we propose a stepwise tree detection approach, by first identifying individual trees using horizontal point density and then analyzing their vertical structure profiles. We first project LiDAR data onto a 2D horizontal plane and apply mean shift clustering to generate candidate tree clusters. Next, we apply a series of structure analyses on the vertical phase, to overcome local variations in crown size and tree density. This study demonstrates that the horizontal point density of LiDAR data provides critical information to locate and isolate individual trees in temperate hardwood plantations with varied densities, while vertical structure profiles can identify spreading branches and reconstruct deliquescent crowns. One challenge of applying mean shift clustering is training a dynamic search kernel to identify trees of different sizes, which usually requires a large number of field measurements. The stepwise approach proposed in this study demonstrated robustness when using a constant kernel in clustering, making it an efficient tool for large-scale analysis. This stepwise approach was designed for quantifying temperate hardwood plantation inventories using relatively low-density airborne LiDAR, and it has potential applications for monitoring large-scale plantation forests. Further research is needed to adapt this method to natural stands with diverse tree ages and structures.
Matchup Strategies for Satellite Sea Surface Salinity Validation
Authors: Westbrook Elizabeth E. , Bingham Frederick M. , Fournier Severine , Hayashi Akiko
Published: February 2023

Satellite validation is the process of comparing satellite measurements with in-situ measurements to ensure their accuracy. Satellite and in-situ sea surface salinity (SSS) measurements are different due to instrumental errors (IE), retrieval errors (RE), and representation differences (RD). In real-world data, IE, RE, and RD are inseparable, but validations seek to quantify only instrumental and retrieval error. Our goal is to determine which of four methods comparing in-situ and satellite measurements minimizes RD most effectively, which includes differences due to mismatches in the location and timing of the measurement, as well as representation error caused by the averaging of satellite measurements over a footprint. IE and RE were obviated by using simulated Argo float, and L2 NASA/SAC-D Aquarius, NASA·SMAP, and ESA·SMOS data generated from the high-resolution ECCO (Estimating the Climate and Circulation of the Oceans) model SSS data. The methods tested include the all-salinity difference averaging method (ASD), the N closest method (NCLO), which is an averaging method that is optimized for different satellites and regions of the ocean, and two single salinity difference methods—closest in space (SSDS) and closest in time (SSDT). The root mean square differences (RMSD) between the simulated in-situ and satellite measurements in seven regions of the ocean are used as a measure of the effectiveness of each method. The optimization of NCLO is examined to determine how the optimum matchup strategy changes depending on satellite track and region. We find that the NCLO method marginally produces the lowest RMSD in all regions but invoking a regionally optimized method is far more computationally expensive than the other methods. We find that averaging methods smooth IE, thus perhaps misleadingly lowering the detected instrumental error in the L2 product by as much as 0.15 PSU. It is apparent from our results that the dynamics of a particular region have more of an effect on matchup success than the method used. We recommend the SSDT validation strategy because it is more computationally efficient than NCLO, considers the proximity of in-situ and satellite measurements in both time and space, does not smooth instrumental errors with averaging, and generally produces RMSD values only slightly higher than the optimized NCLO method.
HRRNet: Hierarchical Refinement Residual Network for Semantic Segmentation of Remote Sensing Images
Authors: Cheng Shiwei , Li Baozhu , Sun Le , Chen Yuwen
Published: February 2023

Semantic segmentation of high-resolution remote sensing images plays an important role in many practical applications, including precision agriculture and natural disaster assessment. With the emergence of a large number of studies on convolutional neural networks, the performance of the semantic segmentation model of remote sensing images has been dramatically promoted. However, many deep convolutional network models do not fully refine the segmentation result maps, and, in addition, the contextual dependencies of the semantic feature map have not been adequately exploited. This article proposes a hierarchical refinement residual network (HRRNet) to address these issues. The HRRNet mainly consists of ResNet50 as the backbone, attention blocks, and decoders. The attention block consists of a channel attention module (CAM) and a pooling residual attention module (PRAM) and residual structures. Specifically, the feature map output by the four blocks of Resnet50 is passed through the attention block to fully explore the contextual dependencies of the position and channel of the semantic feature map, and, then, the feature maps of each branch are fused step by step to realize the refinement of the feature maps, thereby improving the segmentation performance of the proposed HRRNet. Experiments show that the proposed HRRNet improves segmentation result maps compared with various state-of-the-art networks on Vaihingen and Potsdam datasets.
Towards a Smart Environment: Optimization of WLAN Technologies to Enable Concurrent Smart Services
Authors: Ali Ali Mohd , Hassan Mohammad R. , al-Qerem Ahmad , Hamarsheh Ala , Al-Qawasmi Khalid , Aljaidi Mohammad , Abu-Khadrah Ahmed , Kaiwartya Omprakash , Lloret Jaime
Published: February 2023

In this research paper, the spatial distributions of five different services—Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail—are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.
Adaptive Spatial Transformation Networks for Periocular Recognition
Authors: Borza Diana Laura , Yaghoubi Ehsan , Frintrop Simone , Proença Hugo
Published: February 2023

Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This work presents a periocular recognition framework based on deep learning, which automatically localises and analyses the most important areas in the periocular region. The main idea is to derive several parallel local branches from a neural network architecture, which in a semi-supervised manner learn the most discriminative areas in the feature map and solve the identification problem solely upon the corresponding cues. Here, each local branch learns a transformation matrix that allows for basic geometrical transformations (cropping and scaling), which is used to select a region of interest in the feature map, further analysed by a set of shared convolutional layers. Finally, the information extracted by the local branches and the main global branch are fused together for recognition. The experiments carried out on the challenging UBIRIS-v2 benchmark show that by integrating the proposed framework with various ResNet architectures, we consistently obtain an improvement in mAP of more than 4% over the “vanilla” architecture. In addition, extensive ablation studies were performed to better understand the behavior of the network and how the spatial transformation and the local branches influence the overall performance of the model. The proposed method can be easily adapted to other computer vision problems, which is also regarded as one of its strengths.