bluuebunny/crossref_metadata_2025 huggingface dataset:
A Brief Review of Gel Polymer Electrolytes Using In Situ Polymerization for Lithium-ion Polymer Batteries
Authors: Chae Wookil , Kim Bumsang , Ryoo Won Sun , Earmme Taeshik
Published: February 2023

Polymer electrolytes (PEs) have been thoroughly investigated due to their advantages that can prevent severe problems of Li-ion batteries, such as electrolyte leakage, flammability, and lithium dendrite growth to enhance thermal and electrochemical stabilities. Gel polymer electrolytes (GPEs) using in situ polymerization are typically prepared by thermal or UV curing methods by initially impregnating liquid precursors inside the electrode. The in situ method can resolve insufficient interfacial problems between electrode and electrolyte compared with the ex situ method, which could led to a poor cycle performance due to high interfacial resistance. In addition to the abovementioned advantage, it can enhance the form factor of bare cells since the precursor can be injected before polymerization prior to the solidification of the desired shapes. These suggest that gel polymer electrolytes prepared by in situ polymerization are a promising material for lithium-ion batteries.
Performance Simulation and Fused Filament Fabrication Modeling of the Wave-Absorbing Structure of Conductive Multi-Walled Carbon Nanotube/Polyamide 12 Composite
Authors: Han Baigang , Wang Yan
Published: February 2023

Fused filament fabrication (FFF) is a reliable method for fabricating structured electromagnetic wave (EMW) absorbers from absorbing materials. In this study, polymer-matrix composites were prepared using polyamide 12 (PA12) which was recovered from selective laser sintering (SLS) as the substrate and multi-walled carbon nanotubes (MWCNT) as the filler. The CST software is used for simulation calculation and study of electromagnetic wave absorption characteristics of composite materials. After that, based on the obtained parameters and results, modeling was carried out, and finally, EMW absorbers with various microstructures were fabricated by FFF. For the honeycomb structure sample, when the side length is 5 mm and the height is 2 mm, the minimum return loss (RL) of the composite at 15.81 GHz is −14.69 dB, and the maximum effective absorption bandwidth is 1.93 GHz. These values are consistent with the simulation results. The pyramid structure has better absorbing performance than plate structure and honeycomb structure. According to simulation calculations, the pyramid structure shows the best performance at an angle of 28°. The absorption performance of the printed pyramid structure sections exceeded the simulated values, with effective absorption bandwidth (EAB) reaching all frequencies from 2 to 18 GHz, with a minimum return loss of −47.22 dB at 8.24 GHz.
Development of Mineral-Bonded Plywood with Magnesium Oxychloride as a Binder Using the Hot-Pressing Process
Authors: Shalbafan Ali , Thoemen Heiko
Published: February 2023

Environmentally friendly plywood panels were produced by a hot-pressing process using magnesium oxychloride cement (MOC) as a no-added formaldehyde adhesive. Magnesium oxychloride cement binders were prepared with different molar ratios of MgO:MgCl2 (M/C) and H2O:MgCl2 (W/C) ranging from 6 to 12 and 15 to 21, respectively, for plywood production. The binder properties measured were gel time, differential scanning calorimetry (DSC) and Fourier transom infrared spectroscopy (FTIR). The quality of the plywood panels was analyzed based on their mechanical (shear and bending) and physical (thickness swelling and water absorption) properties. A positive effect on the properties of the MOC binder as well as on the properties of the plywood was observed by increasing the molar ratio M/C up to a value of 9. The shear and flexural properties of the plywood specimens were negatively affected by further increasing the molar ratio M/C to 12 and the molar ratio W/C from 15 to 21. Differential scanning calorimetry analysis showed a peak temperature of less than 100 °C for MOC curing, which meets the requirements of hot press technology. No delamination of the plywood specimens was observed after 24 h immersion in tap water or 6 h immersion in boiling water and after a cyclic delamination test. In general, mineral-bonded plywood with magnesium oxychloride shows promising properties for indoor and outdoor use, although the binder quality should still be improved.
Bulk Polymerization of Thermoplastic Shape Memory Epoxy Polymer for Recycling Applications
Authors: Zhuo Haitao , He Zhen , Liu Jun , Ma Guocheng , Ren Zhenghe , Zeng Youhan , Chen Shaojun
Published: February 2023

Conventional epoxy polymers are thermo-set and difficult to recycle and reuse. In this study, a series of linear thermoplastic epoxy polymers (EPx) with shape memory properties were prepared by using a bifunctional monoamine diglycolamine (DGA) as a curing agent and an equivalent amount of bifunctional rigid epoxy resin (E-51) and bifunctional flexible epoxy resin (polypropylenglycol diglycidyl ether, PPGDGE) in a bulk polymerization reaction. The results showed that these samples can fully react under the curing process of, 60 °C/2 h, followed by 80 °C/2 h, followed by 120 °C/2 h. The introduction of different contents of PPGDGE can adjust the Tg of the material to adapt to different environmental requirements, and can significantly increase the fracture strain of the material and improve its micro-phase separation structure. Thus, Rf of the material is close to 100%, and Rr is increased from 87.98% to 97.76%. Importantly, this linear chain structure allows the material to be easily recycled and reprocessed by dissolving or melting, and also means the material shows potential for 3D printing or other thermoplastic remolding.
Effect of Different Mouthwashes on the Surface Microhardness and Color Stability of Dental Nanohybrid Resin Composite
Authors: Hamdy Tamer M. , Abdelnabi Ali , Othman Maha S. , Bayoumi Rania E. , Abdelraouf Rasha M.
Published: February 2023

Background: Surface microhardness and color stability of dental restorative material should be sustained throughout its functional lifetime to maintain the esthetic quality of the restoration. However, the frequent application of mouthwash may affect their surface microhardness and color stability. The aim of this study was to evaluate the effects of different types of mouthwashes with different contents on surface microhardness and color stability of dental nanohybrid resin-based composite. Methods: Disc specimens of nanohybrid resin composite (Luna Nano-Hybrid Composite) were prepared according to manufacturing instructions; specimens were incubated for 24 h in three types of mouthwash (Chlorohexidine, Listerine Green Tea, and Colgate Optic White Whitening Mouthwash). Artificial saliva was used as a control group. Surface microhardness was evaluated using Vickers microhardness device. Color stability after and before immersion in the different mouthwashes was evaluated using extra-oral spectrophotometer; the values of color change (ΔE00) were subsequently calculated. Data were analyzed using one-way ANOVA and post hoc test (p ≤ 0.05). Results: There was no significant difference between microhardness of resin composite immersed in artificial saliva, CHX, and Green Tea mouthwashes (78.5, 78.4, and 73.5, respectively) (p ≥ 0.1), while the bleaching mouthwash led to the lowest microhardness of resin composite, with significant difference compared to the three previous immersion media (p = 0.002). Moreover, there were significant differences in the color changes (ΔE00) of resin composite exposed to the various immersion media (p = 0.0001). Conclusions: The bleaching mouthwash led to a significant reduction in nanohybrid resin composite’s microhardness compared to the chlorohexidine and Green Tea containing mouthwashes. The resin composite’s color change was accepted in bleaching mouthwash but unaccepted in chlorohexidine and Green Tea containing mouthwashes.
Selective Sorption of Cerium Ions from Uranium-Containing Solutions by Remotely Activated Ion Exchangers
Authors: Jumadilov Talkybek , Utesheva Ainamgul , Grazulevicius Juozas , Imangazy Aldan
Published: February 2023

This study investigated the effect of the remote activation of the ion exchangers Amberlite IR120 (H+ form) and AV-17-8 (OH− form) in aqueous media to increase the sorption activity of the interpolymer system “Amberlite IR120H:AV-17-8” (X:Y, molar ratio of ionic groups) towards cerium ions from uranium-containing solutions. The sorption properties of the above-mentioned interpolymer system with molar ratios X:Y of 6:0, 5:1, 4:2, 3:3, 2:4, 1:5, and 0:6 were studied using the methods of conductometry, gravimetry, and inductively coupled plasma–optical emission spectrometry. The presented research revealed the dependence of the sorption activity of the interpolymer system “Amberlite IR120H:AV-17-8” (X:Y) on the acidity of the solution. At pH 2.0, the highest cerium ion sorption degree from the model solution (containing both cerium and uranium ions) by the interpolymer system “Amberlite IR120H:AV-17-8” (4:2) was 56% after 48 h of interaction, whereas the cerium ion sorption degrees by raw Amberlite IR120H (6:0) and raw AV-17-8 (0:6) were 30% and 0%, respectively. The increased sorption ability of the interpolymer system “Amberlite IR120H:AV-17-8” (4:2) might be associated with the achievement of the highest ionization degree by this system remotely activated in an aqueous medium. Moreover, the cerium ion desorption study demonstrated a 60% degree of desorption using 2M nitric acid as a desorbing agent (eluent). The obtained results demonstrate the potential of using the remote interaction effect for the activation of the ion exchangers in aqueous media as an interpolymer system for increased cerium ion sorption from uranium-containing solutions.
Measurement Invariance of Prayer Importance Scale: Religiosity, Gender, Age
Authors: Tatala Małgorzata , Wojtasiński Marcin
Published: February 2023

The main goal of the research was to increase knowledge on the psychometric properties of the Prayer Importance Scale (PIS). The study analyzed the structure of test items using item response theory (IRT), Mokken scale analysis (MSA), and confirmatory factor analysis (CFA) to establish factorial structure of the method. Measurement invariance (MI) was calculated in groups differentiated by three criteria: religiosity, gender, and age. MI verifies whether test items measure the construct in the same way across different groups. The Structure and Level of Religiosity Test (SLRT) was used to examine the level of religiosity in participants. In a study conducted with a sample of n = 566 adults (Mage = 49.16 years; SDage = 15.72), two religious groups were identified based on a median distribution: with low (n = 275) and high (n = 291) levels of religiosity. Two groups differentiated by gender, men (n = 284) and women (n = 282), were equivalent per age period: early (age 25–39; n = 192), middle (age 40–59; n = 187), and late adulthood (age 60+; n = 187). Results of the analyses show high homogeneity of items comprising PIS, as well as strict invariance for the three distinguished criteria. Results of the study provide extended knowledge about psychometric properties of PIS and the ability to compare results due to religiosity, gender, and age.
Distribution of Enhanced Potentially Toxic Element Contaminations Due to Natural and Coexisting Gold Mining Activities Using Planet Smallsat Constellations
Authors: Kimijima Satomi , Nagai Masahiko , Sakakibara Masayuki
Published: February 2023

Potentially toxic elements (PTEs) from natural and anthropogenic activities threaten the environment and human health. The associations of PTEs with natural hazards can be powerful and prominent mechanisms to release PTEs, considerably hastening their multiple contaminations and widespread distribution. This study primarily aimed to investigate the enhanced potential distribution of PTE contaminations (arsenic, lead, and mercury) from coexisting gold mining operations combined with massive riverbank erosion in Indonesia from 2002 to 2022, where soil and water are highly contaminated naturally, using PlanetScope smallsat constellations, Google Earth imagery, and hydrographic datasets. According to the findings, increased barren extents were found because of mining deposits and road network developments. Enhanced natural and anthropogenic PTE runoffs would be transported across two different sub-basins, affecting broader parts of the Bone River. Between 2002 and 2022, 139.3% of river expansion was identified, eroding a maximum of 3,436,139.4 m3 of contaminated soil. Particularly land surfaces were repeatedly transformed from rivers to agricultural lands in the low Bone River, possibly contaminated by fertilizer spills. The combination of PTE potentials from different sources would further exacerbate the contamination level at an estuary. These findings are expected to aid in the timely monitoring of and assuming volumes, rates, and distribution of PTEs from various natural and anthropogenic activities and alert PTE contamination risks to ecosystems and human health. Future work in this area should aim to investigate contamination levels at the estuary, where contaminated materials from both natural and anthropogenic activities are accumulated.
X-Band Radar Attenuation Correction Method Based on LightGBM Algorithm
Authors: Yang Qiang , Feng Yan , Guan Li , Wu Wenyu , Wang Sichen , Li Qiangyu
Published: February 2023

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme.
Improved Analytical Formula for the SAR Doppler Centroid Estimation Standard Deviation for a Dynamic Sea Surface
Authors: Qiao Siqi , Liu Baochang , He Yijun
Published: February 2023

The existing formulas for the synthetic aperture radar (SAR) Doppler centroid estimation standard deviation (STD) suffer from various limitations, especially for a dynamic sea surface. In this study, we derive an improved version of these formulas through three steps. First, by considering the ocean wavenumber spectrum information, a new strategy for determining the number of independent samples of the sea wave velocity field is adopted in the new formula. This is contrary to the method used in the existing formulas, where the number of SAR geometric resolution cells is taken as the number of samples assuming that adjacent SAR resolution cells are statistically uncorrelated. Second, the pulse repetition frequency and Doppler bandwidth are decoupled in the new formula, unlike in the existing formulas where they are unchangeably related to each other. Third, the effects of thermal noise and Doppler aliasing are jointly quantified in a mathematically exact manner instead of being treated separately, as in the existing formulas. Comprehensive SAR raw data simulations for the ocean surface show that the new formula has a better performance in predicting the Doppler centroid estimation STD than the existing formulas.
Seasonal Variations of the Relationship between Spectral Indexes and Land Surface Temperature Based on Local Climate Zones: A Study in Three Yangtze River Megacities
Authors: Xiang Yang , Tang Yongqi , Wang Zhihua , Peng Chucai , Huang Chunbo , Dian Yuanyong , Teng Mingjun , Zhou Zhixiang
Published: February 2023

Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas.
Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China
Authors: Li Guang , Han Wenting , Dong Yuxin , Zhai Xuedong , Huang Shenjin , Ma Weitong , Cui Xin , Wang Yi
Published: February 2023

Accurately obtaining the multi-year spatial distribution information of crops combined with the corresponding agricultural production data is of great significance to the optimal management of agricultural production in the future. However, there are still some problems, such as low generality of crop type mapping models and susceptibility to cloud pollution in large-area crop mapping. Here, the models were constructed by using multi-phase images at the key periods to improve model generality. Multi-phase images in key periods masked each other to obtain large-area cloud-free images, which were combined with the general models to map large areas. The key periods were determined by calculating the global separation index (GSI) of the main crops (wheat, maize, sunflower, and squash) in different growth stages in the Hetao Irrigation District (HID) in China. The multi-phase images in the key period were used to make the data set and were then combined with a variety of deep learning algorithms (U-Net, U-Net++, Deeplabv3+, and SegFormer) to construct general models. The selection of the key periods, the acquisition of regional cloud-free images, and the construction of the general crop mapping models were all based on 2021 data. Relevant models and methods were respectively applied to crop mapping of the HID from 2017 to 2020 to study the generality of mapping methods. The results show that the images obtained by combining multi-phase images in the key period effectively avoided the influence of clouds and aerosols in large areas. Compared with the other three algorithms, U-Net had better mapping results. The F1-score, mean intersection-over-union, and overall accuracy were 78.13%, 75.39% and 96.28%, respectively. The crop mapping model was applied to images in 2020, and its average overall accuracy was more than 88.28%. When we applied the model to map crops (county food crops, cash crops, and cultivated land area) from 2017 to 2019, the regression analysis between the mapping areas obtained by the model and the ground measurements was made. The R2 was 0.856, and the RMSE was 17,221 ha, which reached the application accuracy, indicating that the mapping method has certain universality for mapping in different years.
Statistical Characteristics of the Multiscale SST Fractal Structure over the Kuroshio Extension Region Using VIIRS Data
Authors: Yu Kai , Dong Changming , Wang Jin , Cheng Xuhua , Yu Yi
Published: February 2023

The ocean behaves as a typical multiscale fractal structure, whose dynamic and thermal variabilities extend over a wide range of spatial scales, r, spanning from 10−3 to 107 m. Studying the statistical characteristics of multiscale fractal structures is crucial to understanding the interactions and energy cascade processes between different spatial scales. Remote sensing data are one of the best choices for revealing these statistical characteristics. This work analyzes the multiscale (1–1000 km) fractal structures of sea surface temperature (SST) from the Level-2+ (L2P) satellite orbit Visible Infrared Imaging Radiometer Suite (VIIRS) products over the Kuroshio Extension (KE) region (145°E–160°W, 20°N–50°N), using a conventional method (second-order structure function, D(r)) and a newly developed statistical method (spatial variance, V(r)). The results show that both the power-law distribution slopes of D(r) and V(r) are close to 2/3, which is equivalent to the −5/3 wavenumber spectrum. V(r) is found to be more robust when depicting the fractal structure and variance density, V’(r), compared with D(r). V’(r) is slightly larger at the mesoscale (50–150 km) than at the large scale (higher than 150 km) and is much smaller than that at the submesoscale (smaller than 50 km). Additionally, V’(r) has an indiscernible diurnal variation but remarkable seasonal and latitudinal variations. For the seasonal variability, the maximum V’(r) appears in winter at the large scale and mesoscale, and gradually shifts towards spring at the submesoscale, which implies that a forward energy cascade process may occur during this period. The maximum of the latitude-dependent V’(r) appears around 40°N for all the scales. It is consistent with the latitude of the strongest background SST gradient, indicating that the background SST front is the main source of the strong SST multiscale spatial variabilities over the KE region. This work benefits the application of other high-resolution remote sensing data in this research field, including the forthcoming Surface Water Ocean Topography (SWOT) satellite product.
UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms
Authors: Lawrence Brett , de Lemmus Emerson , Cho Hyuk
Published: February 2023

In recent years, deep learning-based approaches have proliferated across a variety of ecological studies. Inspired by deep learning’s emerging prominence as the preferred tool for analyzing wildlife image datasets, this study employed You Only Look Once (YOLO), a single-shot, real-time object detection algorithm, to effectively detect cavity trees of Red-cockaded Woodpeckers or RCW (Dryobates borealis). In spring 2022, using an unmanned aircraft system (UAS), we conducted presence surveys for RCW cavity trees within a 1264-hectare area in the Sam Houston National Forest (SHNF). Additionally, known occurrences of RCW cavity trees outside the surveyed area were aerially photographed, manually annotated, and used as a training dataset. Both YOLOv4-tiny and YOLOv5n architectures were selected as target models for training and later used for inferencing separate aerial photos from the study area. A traditional survey using the pedestrian methods was also conducted concurrently and used as a baseline survey to compare our new methods. Our best-performing model generated an mAP (mean Average Precision) of 95% and an F1 score of 85% while maintaining an inference speed of 2.5 frames per second (fps). Additionally, five unique cavity trees were detected using our model and UAS approach, compared with one unique detection using traditional survey methods. Model development techniques, such as preprocessing images with tiling and Sliced Aided Hyper Inferencing (SAHI), proved to be critical components of improved detection performance. Our results demonstrated the two YOLO architectures with tiling and SAHI strategies were able to successfully detect RCW cavities in heavily forested, heterogenous environments using semi-automated review. Furthermore, this case study represents progress towards eventual real-time detection where wildlife managers are targeting small objects. These results have implications for more achievable conservation goals, less costly operations, a safer work environment for personnel, and potentially more accurate survey results in environments that are difficult using traditional methods.
Local Climate Zones to Identify Surface Urban Heat Islands: A Systematic Review
Authors: Fernandes Rodrigo , Nascimento Victor , Freitas Marcos , Ometto Jean
Published: February 2023

The Land Surface Temperature (LST) obtained by remote sensing data is an essential indicator for analyzing the Surface Urban Heat Island (SUHI). A factor contributing to its occurrence is the reflections of the different materials in urban and rural areas, which significantly affect the energy balance near the surface. Therefore, recent studies have increasingly used the Local Climate Zones (LCZs) classification system to discriminate those urban areas. Therefore, our study aims to perform a systematic review using the PRISMA method of LCZ classification applied to understand the LST and the SUHI phenomenon. At first, 10,403 related articles were found in the scientific literature which, after passing through filtering stages, resulted in 51 that were further analyzed. Our results showed that these articles were very recent, beginning in 2016, and present an increasing trend. Even though all articles reviewed somehow try to understand the Urban Heat Island phenomenon, in more than half of them, the SUHI calculation method was not mentioned. China was the country with the most studies. Images from Landsat and TERRA/AQUA sensors appeared in 82% of the studies. The method that appears the most to LCZ definitions is from the World Urban Database. Finally, we conclude that LCZ mapping methodologies were accurate enough for SUHI studies considering the current climatic changes. Therefore, this systematic review can help new studies on SUHI identification through LCZ in different world areas using remote sensing data to estimate the LST.
Rust-Style Patch: A Physical and Naturalistic Camouflage Attacks on Object Detector for Remote Sensing Images
Authors: Deng Binyue , Zhang Denghui , Dong Fashan , Zhang Junjian , Shafiq Muhammad , Gu Zhaoquan
Published: February 2023

Deep neural networks (DNNs) can improve the image analysis and interpretation of remote sensing technology by extracting valuable information from images, and has extensive applications such as military affairs, agriculture, environment, transportation, and urban division. The DNNs for object detection can identify and analyze objects in remote sensing images through fruitful features of images, which improves the efficiency of image processing and enables the recognition of large-scale remote sensing images. However, many studies have shown that deep neural networks are vulnerable to adversarial attack. After adding small perturbations, the generated adversarial examples will cause deep neural network to output undesired results, which will threaten the normal recognition and detection of remote sensing systems. According to the application scenarios, attacks can be divided into the digital domain and the physical domain, the digital domain attack is directly modified on the original image, which is mainly used to simulate the attack effect, while the physical domain attack adds perturbation to the actual objects and captures them with device, which is closer to the real situation. Attacks in the physical domain are more threatening, however, existing attack methods generally generate the patch with bright style and a large attack range, which is easy to be observed by human vision. Our goal is to generate a natural patch with a small perturbation area, which can help some remote sensing images used in the military to avoid detection by object detectors and im-perceptible to human eyes. To address the above issues, we generate a rust-style adversarial patch generation framework based on style transfer. The framework takes a heat map-based interpretability method to obtain key areas of target recognition and generate irregular-shaped natural-looking patches to reduce the disturbance area and alleviates suspicion from humans. To make the generated adversarial examples have a higher attack success rate in the physical domain, we further improve the robustness of the adversarial patch through data augmentation methods such as rotation, scaling, and brightness, and finally, make it impossible for the object detector to detect the camouflage patch. We have attacked the YOLOV3 detection network on multiple datasets. The experimental results show that our model has achieved a success rate of 95.7% in the digital domain. We also conduct physical attacks in indoor and outdoor environments and achieve an attack success rate of 70.6% and 65.3%, respectively. The structural similarity index metric shows that the adversarial patches generated are more natural than existing methods.
The Potential of Monitoring Carbon Dioxide Emission in a Geostationary View with the GIIRS Meteorological Hyperspectral Infrared Sounder
Authors: Zhang Qi , Smith William , Shao Min
Published: February 2023

With the help of various polar-orbiting environment observing platforms, the atmospheric concentration of carbon dioxide (CO2) has been well established on a global scale. However, the spatial and temporal pattern of the CO2 emission and its flux dependence on daily human activity processes are not yet well understood. One of the limiting factors could be attributed to the low revisit time frequency of the polar orbiting satellites. With high revisiting frequency and CO2-sensitive spectrum, the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY-4A and FY-4B satellites have the potential to measure the CO2 concentration at a higher temporal frequency than polar-orbiting satellites. To provide a prototypical demonstration on the CO2 monitoring capability using GIIRS observations, a hybrid-3D variational data assimilation system is established in this research and a one-month-long experiment is conducted. The evaluations against the Goddard Earth Observing System version 5 (GEOS-5) analysis field and Orbiting Carbon Observatory -2/-3 (OCO-2/-3) CO2 retrieval products reveal that assimilating GIIRS observations can reduce the first guess’s CO2 concentration mean bias and standard deviation, especially over the lower troposphere (975–750 hPa) and improve the diurnal variation of near surface CO2 concentration.
PSSA: PCA-Domain Superpixelwise Singular Spectral Analysis for Unsupervised Hyperspectral Image Classification
Authors: Liu Qiaoyuan , Xue Donglin , Tang Yanhui , Zhao Yongxian , Ren Jinchang , Sun Haijiang
Published: February 2023

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online.
Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net
Authors: Tao Liurong , Ren Haoran , Gu Zhiwei
Published: February 2023

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data.
Integrating Bio-Sensing Array with Blood Plasma Separation on a Centrifugal Platform
Authors: Peshin Snehan , Madou Marc , Kulinsky Lawrence
Published: February 2023

Numerous immunoassays have been successfully integrated on disc-based centrifugal platforms (CDs) over the last 20 years. These CD devices can be used as portable point-of-care (POC) platforms with sample-to-answer capabilities where bodily fluids such as whole blood can be used as samples directly without pre-processing. In order to use whole blood as a sample on CDs, centrifugation is used to separate red blood cells from plasma on CDs. There are several techniques for using specific fluidic patterns in the centrifugal fluidic network, such as reciprocation, that enhances the sensitivity of the immunoassays, including those using microarray antigen membranes. Present work demonstrates, for the first time, simultaneous integration of blood plasma separation (BPS) and reciprocation on the CD platform. The integrated design allows plasma that is separated from the red blood cells in a sedimentation chamber to flow into the reciprocation chamber via a narrow connecting channel of 0.5 mm × 0.5 mm cross-section. Due to the small cross-section of the connecting channel, there is no inflow of the red blood cell into the reciprocation chamber during subsequent fluidic operations of the CD. While no inflow of the red blood cells into the reciprocation chamber was observed, the conditions of 20 g jerk acceleration were also simulated in ANSYS finite element analysis software, and it was found that the CD design that was used is capable of retaining red blood cells in the sedimentation chamber. Experimentally, the isolation of red blood cells in the sedimentation chamber was confirmed using the ImageJ image processor to detect the visible color-based separation of the plasma from the blood. A fluorescent analyte testing on the bio-sensing array of the presented novel integrated design and on the standard reciprocation design CD was conducted for 7 min of reciprocation in each case. The test analyte was Europium Streptavidin Polystyrene analyte (10−3 mg/mL) and the microarray consisted of Biotin bovine serum albumin (BSA) dots. The fluorescent signals for the standard and integrated designs were nearly identical (within the margin of error) for the first several minutes of reciprocation, but the fluorescent signal for the integrated design was significantly higher when the reciprocation time was increased to 7 min.
Neural Architecture Search Survey: A Computer Vision Perspective
Authors: Kang Jeon-Seong , Kang JinKyu , Kim Jung-Jun , Jeon Kwang-Woo , Chung Hyun-Joon , Park Byung-Hoon
Published: February 2023

In recent years, deep learning (DL) has been widely studied using various methods across the globe, especially with respect to training methods and network structures, proving highly effective in a wide range of tasks and applications, including image, speech, and text recognition. One important aspect of this advancement is involved in the effort of designing and upgrading neural architectures, which has been consistently attempted thus far. However, designing such architectures requires the combined knowledge and know-how of experts from each relevant discipline and a series of trial-and-error steps. In this light, automated neural architecture search (NAS) methods are increasingly at the center of attention; this paper aimed at summarizing the basic concepts of NAS while providing an overview of recent studies on the applications of NAS. It is worth noting that most previous survey studies on NAS have been focused on perspectives of hardware or search strategies. To the best knowledge of the present authors, this study is the first to look at NAS from a computer vision perspective. In the present study, computer vision areas were categorized by task, and recent trends found in each study on NAS were analyzed in detail.
Brillouin Interaction between Two Optical Modes Selectively Excited in Weakly Guiding Multimode Optical Fibers
Authors: Fotiadi Andrei , Rafailov Edik , Korobko Dmitry , Mégret Patrice , Bykov Alexander , Meglinski Igor
Published: February 2023

A multimode optical fiber supports excitation and propagation of a pure single optical mode, i.e., the field pattern that satisfies the boundary conditions and does not change along the fiber. When two counterpropagating pure optical modes are excited, they could interact through the stimulated Brillouin scattering (SBS) process. Here, we present a simple theoretical formalism describing SBS interaction between two individual optical modes selectively excited in an acoustically isotropic multimode optical fiber. Employing a weakly guiding step-index fiber approach, we have built an analytical expression for the spatial distribution of the sound field amplitude in the fiber core and explored the features of SBS gain spectra, describing the interaction between modes of different orders. In this way, we give a clear insight into the sound propagation effects accompanying SBS in multimode optical fibers, and demonstrate their specific contributions to the SBS gain spectrum.
Deflection Estimation of Truss Structures Using Inverse Finite Element Method
Authors: Zhang Zhaobo , Zheng Shuai , Li Hongnan , Ren Liang
Published: February 2023

It is well recognized that strain and deflection data are important indexes to judge the safety of truss structures. Specifically, the shape sensing technology can estimate the deformation of a structure by exploiting the discrete strain data without considering the material property conditions. To fill the gap in which most of the methods in SHM (structural health monitoring) cannot be directly used to predict the displacement field, this paper proposed a novel inverse finite element method (iFEM) algorithm based on the equivalent stiffness theory. A deflection sensor is fabricated to focus on predicting the distributed deflection variation of the truss structure. The performance of the deflection sensor was evaluated by a calibration test and a stability test. Finally, it was applied to distributed deflection monitoring in the testing of truss structures. Results of all tests verify that the deflection sensor based on the i-FEM algorithm can predict the distributed deflection variation of the truss structure accurately, in real time, and dynamically.
Comparison of Heuristic Algorithms in Identification of Parameters of Anomalous Diffusion Model Based on Measurements from Sensors
Authors: Brociek  Rafał , Wajda Agata , Słota Damian
Published: February 2023

In recent times, fractional calculus has gained popularity in various types of engineering applications. Very often, the mathematical model describing a given phenomenon consists of a differential equation with a fractional derivative. As numerous studies present, the use of the fractional derivative instead of the classical derivative allows for more accurate modeling of some processes. A numerical solution of anomalous heat conduction equation with Riemann-Liouville fractional derivative over space is presented in this paper. First, a differential scheme is provided to solve the direct problem. Then, the inverse problem is considered, which consists in identifying model parameters such as: thermal conductivity, order of derivative and heat transfer. Data on the basis of which the inverse problem is solved are the temperature values on the right boundary of the considered space. To solve the problem a functional describing the error of the solution is created. By determining the minimum of this functional, unknown parameters of the model are identified. In order to find a solution, selected heuristic algorithms are presented and compared. The following meta-heuristic algorithms are described and used in the paper: Ant Colony Optimization (ACO) for continous function, Butterfly Optimization Algorithm (BOA), Dynamic Butterfly Optimization Algorithm (DBOA) and Aquila Optimize (AO). The accuracy of the presented algorithms is illustrated by examples.
Map Space Modeling Method Reflecting Safety Margin in Coastal Water Based on Electronic Chart for Path Planning
Authors: Jang Da-un , Kim Joo-sung
Published: February 2023

Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according to ship characteristics, and the psychological buffer space of a ship navigator. First, the obstacle area was segmented using the binary method—a segmentation method—based on the international standard electronic chart image. Next, the margin space was incorporated through the morphological algorithm for the obstacle area. Finally, to minimize the space lost during the route search, the boundary simplification of the obstacle area was performed through the concave hull method. The experimental results of the proposed method resulted in a map that minimized the area lost due to obstacles. In addition, it was found that the distance and path-finding time were reduced compared to the conventional convex hull method. The study shows that the map modeling method is feasible, and that it can be applied to path planning.
An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil
Authors: Melendreras Candela , Soldado Ana , Costa-Fernández José M. , López Alberto , Valledor Marta , Campo Juan Carlos , Ferrero Francisco
Published: February 2023

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.
An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
Authors: Alsokhiry Fahad , Annuk Andres , Mohamed Mohamed A. , Marinho Manoel
Published: February 2023

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.