bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis of Water-Dispersible Poly(dimethylsiloxane) and Its Potential Application in the Paper Coating Industry as an Alternative for PFAS-Coated Paper and Single-Use Plastics
Authors: Hamdani Syeda Shamila , Elkholy Hazem M. , Alford Alexandra , Jackson Kang , Naveed Muhammad , Wyman Ian , Wang Yun , Li Kecheng , Haider Syed W. , Rabnawaz Muhammad
Published: April 2024

Polyethylene-, polyvinylidene chloride-, and per- and polyfluoroalkyl substance-coated paper generate microplastics or fluorochemicals in the environment. Here, we report an approach for the development of oil-resistant papers using an environmentally friendly, fluorine-free, water-dispersible poly(dimethylsiloxane) (PDMS) coating on kraft paper. Carboxylic-functionalized PDMS (PDMS-COOH) was synthesized and subsequently neutralized with ammonium bicarbonate to obtain a waterborne emulsion, which was then coated onto kraft paper. The water resistance of the coated paper was determined via Cobb60 measurements. The Cobb60 value was reduced to 2.70 ± 0.14 g/m2 as compared to 87.6 ± 5.1 g/m2 for uncoated paper, suggesting a remarkable improvement in water resistance. Similarly, oil resistance was found to be 12/12 on the kit test scale versus 0/12 for uncoated paper. In addition, the coated paper retained 70–90% of its inherent mechanical properties, and more importantly, the coated paper was recycled via pulp recovery using a standard protocol with a 91.1% yield.
Coarse-Grained Simulations on Polyethylene Crystal Network Formation and Microstructure Analysis
Authors: Hussain Mohammed Althaf , Yamamoto Takashi , Adil Syed Farooq , Yao Shigeru
Published: April 2024

Understanding and characterizing semi-crystalline models with crystalline and amorphous segments is crucial for industrial applications. A coarse-grained molecular dynamics (CGMD) simulations study probed the crystal network formation in high-density polyethylene (HDPE) from melt, and shed light on tensile properties for microstructure analysis. Modified Paul–Yoon–Smith (PYS/R) forcefield parameters are used to compute the interatomic forces among the PE chains. The isothermal crystallization at 300 K and 1 atm predicts the multi-nucleus crystal growth; moreover, the lamellar crystal stems and amorphous region are alternatively oriented. A one-dimensional density distribution along the alternative lamellar stems further confirms the ordering of the lamellar-stack orientation. Using this plastic model preparation approach, the semi-crystalline model density (ρcr) of ca. 0.913 g·cm−3 and amorphous model density (ρam) of ca. 0.856 g·cm−3 are obtained. Furthermore, the ratio of ρcr/ρam ≈ 1.06 is in good agreement with computational (≈1.096) and experimental (≈1.14) data, ensuring the reliability of the simulations. The degree of crystallinity (χc) of the model is ca. 52% at 300 K. Nevertheless, there is a gradual increase in crystallinity over the specified time, indicating the alignment of the lamellar stems during crystallization. The characteristic stress–strain curve mimicking tensile tests along the z-axis orientation exhibits a reversible sharp elastic regime, tensile strength at yield ca. 100 MPa, and a non-reversible tensile strength at break of 350%. The cavitation mechanism embraces the alignment of lamellar stems along the deformation axis. The study highlights an explanatory model of crystal network formation for the PE model using a PYS/R forcefield, and it produces a microstructure with ordered lamellar and amorphous segments with robust mechanical properties, which aids in predicting the microstructure–mechanical property relationships in plastics under applied forces.
A Novel Nanofiber Hydrogel Adhesive Based on Carboxymethyl Cellulose Modified by Adenine and Thymine
Authors: Xie Chong , Yang Runde , Wan Xing , Li Haorong , Ge Liangyao , Li Xiaofeng , Zhao Guanglei
Published: April 2024

Natural polymer-based adhesive hydrogels have garnered significant interest for their outstanding strength and versatile applications, in addition to being eco-friendly. However, the adhesive capabilities of purely natural products are suboptimal, which hampers their practical use. To address this, we engineered carboxymethyl cellulose (CMC) surfaces with complementary bases, adenine (A) and thymine (T), to facilitate the self-assembly of adhesive hydrogels (CMC-AT) with a nanofiber configuration. Impressively, the shear adhesive strength reached up to 6.49 MPa with a mere 2% adhesive concentration. Building upon this innovation, we conducted a comparative analysis of the shear adhesion properties between CMC and CMC-AT hydrogel adhesives when applied to delignified and non-delignified wood chips. We examined the interplay between the adhesives and the substrate, as well as the role of mechanical interlocking in overall adhesion performance. Our findings offer a fresh perspective on the development of new biodegradable polymer hydrogel adhesives.
Development and Characteristics of Protein Edible Film Derived from Pork Gelatin and Beef Broth
Authors: Ciurzyńska Agnieszka , Janowicz Monika , Karwacka Magdalena , Nowacka Małgorzata , Galus Sabina
Published: April 2024

The aim of this work was to develop edible films derived from gelatin and beef broth and to analyze the physical properties of the output products. The presented research is important from the point of view of searching for food packaging solutions that may replace traditionally used plastic packaging. This study’s conceptual framework is in line with the trend of sustainable development and zero waste. This study was conducted to develop a recipe for edible films derived from beef gelatin with gelatin concentrations at 4%, 8%, and 12% enriched with additions of beef broth in amounts of 25, 50, 75, and 100%. Selected physical properties of the output edible films were examined in terms of thickness, swelling in water, opacity, water content, water solubility, structure, and mechanical properties. The conducted research made it plausible to conclude that the addition of broth has a positive effect on the extensibility of the edible films and the other physical properties under consideration, especially on decreasing the film thickness, which was found to vary between 50.2 and 191.6 µm. When gelatin and broth were added at low concentrations, the tensile strength of the films increased, and subsequently decreased; however, an opposite effect was observed for elongation at break. The increased broth concentration caused the film opacity to increase from 0.39 to 4.54 A/mm and from 0.18 to 1.04 A/mm with gelatin concentrations of 4% and 12%, respectively. The water solubility of the gelatin films decreased as a result of the broth addition. However, it was noticed that increasing the content of broth caused the water solubility to increase in the tested films. The mere presence of broth in the gelatin films changed the microstructure of the films and also made them thinner.
Recent Progress on Conversion of Lignocellulosic Biomass by MOF-Immobilized Enzyme
Authors: Tao Juan , Song Shengjie , Qu Chen
Published: April 2024

The enzyme catalysis conversion of lignocellulosic biomass into valuable chemicals and fuels showed a bright outlook for replacing fossil resources. However, the high cost and easy deactivation of free enzymes restrict the conversion process. Immobilization of enzymes in metal–organic frameworks (MOFs) is one of the most promising strategies due to MOF materials’ tunable building units, multiple pore structures, and excellent biocompatibility. Also, MOFs are ideal support materials and could enhance the stability and reusability of enzymes. In this paper, recent progress on the conversion of cellulose, hemicellulose, and lignin by MOF-immobilized enzymes is extensively reviewed. This paper focuses on the immobilized enzyme performances and enzymatic mechanism. Finally, the challenges of the conversion of lignocellulosic biomass by MOF-immobilized enzyme are discussed.
Maximizing Interlaminar Fracture Toughness in Bidirectional GFRP through Controlled CNT Heterogeneous Toughening
Authors: Zhao Hongchen , Zhang Yunxiao , Ou Yunfu , Wu Longqiang , Li Juan , Yao Xudan , Yang Xiongwu , Mao Dongsheng
Published: April 2024

“Interleaving” is widely used for interlaminar toughening of fiber-reinforced composites, and the structure of interleaving is one of the important factors affecting the toughening efficiency of laminates. Several experiments have demonstrated that compared to continuous and dense structures, toughening layers with structural heterogeneity can trigger multiple toughening mechanisms and have better toughening effects. On this basis, this work further investigates the application of heterogeneous toughening phases in interlaminar toughening of bidirectional GFRP. CNT was selected to construct toughening phases, which was introduced into the interlaminar of composites through efficient spraying methods. By controlling the amount of CNT, various structures of CNT toughening layers were obtained. The fracture toughness of modified laminates was tested, and their toughening mechanism was analyzed based on fracture surface observation. The results indicate that the optimal CNT usage (0.5 gsm) can increase the initial and extended values of interlayer fracture toughness by 136.0% and 82.0%, respectively. The solvent acetone sprayed with CNT can dissolve and re-precipitate a portion of the sizing agent on the surface of the fibers, which improves the bonding of the fibers to the resin. More importantly, larger discrete particles are formed between the layers, guiding the cracks to deflect in the orientation of the toughened layer. This generates additional energy dissipation and ultimately presents an optimal toughening effect.
Fluorine-Containing Ionogels with Stretchable, Solvent-Resistant, Wide Temperature Tolerance, and Transparent Properties for Ionic Conductors
Authors: Fan Xiaoxi , Feng Wenlong , Wang Shuang , Chen Yinpeng , Zheng Wen Jiang , Yan Jie
Published: April 2024

Stretchable ionogels, as soft ion-conducting materials, have generated significant interest. However, the integration of multiple functions into a single ionogel, including temperature tolerance, self-adhesiveness, and stability in diverse environments, remains a challenge. In this study, a new class of fluorine-containing ionogels was synthesized through photo-initiated copolymerization of fluorinated hexafluorobutyl methacrylate and butyl acrylate in a fluorinated ionic liquid 1-butyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide. The resulting ionogels demonstrate good stretchability with a fracture strain of ~1300%. Owing to the advantages of the fluorinated network and the ionic liquid, the ionogels show excellent stability in air and vacuum, as well as in various solvent media such as water, sodium chloride solution, and hexane. Additionally, the ionogels display impressive wide temperature tolerance, functioning effectively within a wide temperature range from −60 to 350 °C. Moreover, due to their adhesive properties, the ionogels can be easily attached to various substrates, including plastic, rubber, steel, and glass. Sensors made of these ionogels reliably respond to repetitive tensile-release motion and finger bending in both air and underwater. These findings suggest that the developed ionogels hold great promise for application in wearable devices.
Computational Requirements for Modeling Thermal Conduction in Polymeric Phase-Change Materials: Periodic Hard Spheres Case
Authors: Redosado Leon Kevin A. , Lyulin Alexey , Geurts Bernard J.
Published: April 2024

This research focuses on modeling heat transfer in heterogeneous media composed of stacked spheres of paraffin as a perspective polymeric phase-change material. The main goal is to study the requirements of the numerical scheme to correctly predict the thermal conductivity in a periodic system composed of an indefinitely repeated configuration of spherical particles subjected to a temperature gradient. Based on OpenFOAM, a simulation platform is created with which the resolution requirements for accurate heat transfer predictions were inferred systematically. The approach is illustrated for unit cells containing either a single sphere or a configuration of two spheres. Asymptotic convergence rates confirming the second-order accuracy of the method are established in case the grid is fine enough to have eight or more grid cells covering the distance of the diameter of a sphere. Configurations with two spheres can be created in which small gaps remain between these spheres. It was found that even the under-resolution of these small gaps does not yield inaccurate numerical solutions for the temperature field in the domain, as long as one adheres to using eight or more grid cells per sphere diameter. Overlapping and (barely) touching spheres in a configuration can be simulated with high fidelity and realistic computing costs. This study further extends to examine the effective thermal conductivity of the unit cell, particularly focusing on the volume fraction of paraffin in cases with unit cells containing a single sphere. Finally, we explore the dependence of the effective thermal conductivity for unit cells containing two spheres at different distances between them.
TEMPO-Oxidized Nanocellulose Films Modified by Tea Saponin Derived from Camellia oleifera: Physicochemical, Mechanical, and Antibacterial Properties
Authors: Jiang Nan , Hu Yudi , Cheng Yuhang
Published: April 2024

Nanocellulose materials have been widely used in biomedicine, food packaging, aerospace, composite material, and other fields. In this work, cellulose obtained from Camellia shells through alkali boiling and subbleaching was micro-dissolved and regenerated using the DMAc (N,N-Dimethylacetamide)/LiCl system, and TOCNs (TEMPO-oxidized cellulose nanofibers) with different degrees of oxidation. The membrane was prepared by filtration of polytetrafluoroethylene (pore size 0.1 μm), and the oxidized nanocellulose film was obtained after drying, Then, the crystallinity, mechanical properties and oxygen barrier properties of the TOCN film were investigated. Furthermore, based on TS (tea saponin) from Camellia oleifera seed cake and TOCNs, TS-TOCN film was prepared by the heterogeneous reaction. The TS-TOCN film not only shows excellent oxygen barrier properties (the oxygen permeability is 2.88 cc·m−2·d−1) but also has good antibacterial effects on both Gram-negative and Gram-positive bacteria. The antibacterial property is comparable to ZnO-TOCN with the same antibacterial content prepared by the in-situ deposition method. Antioxidant activity tests in vitro showed that TS-TOCN had a significant scavenging effect on DPPH (2,2-Diphenyl-1-picrylhydrazyl) radicals. This design strategy makes it possible for inexpensive and abundant Camellia oleifera remainders to be widely used in the field of biobased materials.
Experimental and Numerical Investigation of Prepreg-RTM Co-Curing Molding Composite Bolted T-Joint under Bending Load
Authors: Zhang Tao , Luo Zhitao , Li Kenan , Cheng Xiaoquan
Published: April 2024

A set of polymer composite bolted T-joints with a novel configuration consisting of an internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under a bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint, respectively, while at only 55.4% of its weight. The damage of the joint is initiated within the resin area and leads to the degradation of the joint’s bending performance. The preferred stacking sequence of the skeleton is [0/+45/90/−45]ns when primarily subjected to bending loads. The decrease in the bending performance is within 5% of the inclining angle of the skeleton, less than 12 degrees. The more 90° layers in the skin, the better the bending performance of the joints, while the more 0° layers, the poorer the bending performance.
A Comparative Study of Removal of Acid Red 27 by Adsorption on Four Different Chitosan Morphologies
Authors: Wu Hongli , Zhou Jiaying , Zhang Sai , Niu Ping , Li Haoming , Liu Zhongmin , Zhang Ning , Li Chunhui , Wang Liping , Wang Yudong
Published: April 2024

To investigate the relationship between structures and adsorption properties, four different morphologies of chitosan, with hydrogel (CSH), aerogel (CSA), powder (CSP), and electrospinning nanofiber (CSEN) characteristics, were employed as adsorbents for the removal of Acid Red 27. The structures and morphologies of the four chitosan adsorbents were characterized with SEM, XRD, ATR-FTIR, and BET methods. The adsorption behaviors and mechanisms of the four chitosan adsorbents were comparatively studied. All adsorption behaviors exhibited a good fit with the pseudo-second-order kinetic model (R2 > 0.99) and Langmuir isotherm model (R2 > 0.99). Comparing the adsorption rates and the maximum adsorption capacities, the order was CSH > CSA > CSP > CSEN. The maximum adsorption capacities of CSH, CSA, CSP, and CSEN were 2732.2 (4.523), 676.7 (1.119), 534.8 (0.885), and 215.5 (0.357) mg/g (mmol/g) at 20 °C, respectively. The crystallinities of CSH, CSA, CSP, and CSEN were calculated as 0.41%, 6.97%, 8.76%, and 39.77%, respectively. The crystallinity of the four chitosan adsorbents was the main factor impacting the adsorption rates and adsorption capacities, compared with the specific surface area. With the decrease in crystallinity, the adsorption rates and capacities of the four chitosan adsorbents increased gradually under the same experimental conditions. CSH with a low crystallinity and large specific surface area resulted in the highest adsorption rate and capacity.
Support for Engaged Couples in Preparation for a Catholic Marriage
Authors: Dudziak Urszula
Published: April 2024

Marriage can be solemnised in a non-religious setting, through a declaration given before a civil servant. Alternatively, it may incorporate a religious dimension and take place in a church, with an accompanying prayer, the presence of a priest, and a blessing. Quoting from the Old and New Testaments, the author highlights the fundamental aspects of marriage within the Catholic Church. The author also explores the tasks and characteristics of marriage, comparing the declaration made at the Registry Office for secular marriages with the vows exchanged during a church wedding. The article also draws attention to the premarital counselling offered by the Catholic Church, provided by priests and family life counsellors. This counselling is a valuable and necessary resource for engaged couples as they prepare for marriage and parenthood. It can help couples with two important tasks: expressing love and transmitting life.
Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective
Authors: Zeng Bohan , Gao Shan , Xu Yuelei , Zhang Zhaoxiang , Li Fan , Wang Chenghang
Published: April 2024

Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.
Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network
Authors: Zhao Yao , Ou Chengwen , Tian He , Ling Bingo Wing-Kuen , Tian Ye , Zhang Zhe
Published: April 2024

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes.
Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning
Authors: Couture Heather D. , Alvara Madison , Freeman Jeremy , Davitt Aaron , Koenig Hannes , Rouzbeh Kargar Ali , O’Connor Joseph , Söldner-Rembold Isabella , Ferreira André , Jeyaratnam Jeyavinoth , Lewis Jordan , McCormick Colin , Nakano Tiffany , Dalisay Charmaine , Lewis Christy , Volpato Gabriela , Gray Matthew , McCormick Gavin
Published: April 2024

Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant water vapor plumes as proxy signals to estimate electric power generation and CO2 emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, then an aggregation model predicted plant utilization over a 30-day period. Lastly, emission factors specific to region, fuel, and plant technology were used to convert the estimated electricity generation into CO2 emissions. Models were trained with reported hourly electricity generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed the baseline approaches. In validating our model results against available generation and emissions reported data, we calculated the root mean square error as 1.75 TWh (236 plants across 17 countries over 4 years) and 2.18 Mt CO2 (207 plants across 17 countries over 4 years), respectively. Ultimately, we applied our ML method to plants that constitute 32% of global power plant CO2 emissions, as estimated by Climate TRACE, averaged over the period 2015–2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is made freely available to the public to support global emissions reduction.
Sentinel-1-Based Soil Freeze–Thaw Detection in Agro-Forested Areas: A Case Study in Southern Québec, Canada
Authors: Taghipourjavi Shahabeddin , Kinnard Christophe , Roy Alexandre
Published: April 2024

Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) data time series at two agro-forested study sites, St-Marthe and St-Maurice, in southern Québec, Canada. In total, 18 plots were instrumented to monitor soil temperature and derive soil freezing probabilities at 2 and 10 cm depths during 2020–21 and 2021–22. Three change detection algorithms were tested: backscatter differences (∆σ) derived from thawed reference (Delta), the freeze–thaw index (FTI), and a newly developed exponential freeze–thaw algorithm (EFTA). Various probabilistic mixed models were compared to identify the model and predictor variables that best predicted soil freezing probability. VH polarization backscatter signals processed with the EFTA and used as predictors in a logistic model led to improved predictions of soil freezing probability at 2 cm (Pseudo-R2 = 0.54) compared to other approaches. The EFTA could effectively address the limitations of the Delta algorithm caused by backscatter fluctuations in the shoulder seasons, resulting in more precise estimates of FT events. Furthermore, the inclusion of crop types as plot-level effects within the probabilistic model also slightly improved the soil freezing probability prediction at each monitored plot, with marginal and conditional R2 values of 0.59 and 0.61, respectively. The model accurately classified observed binary ‘frozen’ or ‘thawed’ states with 85.2% accuracy. Strong cross-level interactions were also observed between crop types and the EFTA derived from VH backscatter, indicating that crop type modulated the backscatter response to soil freezing. This study represents the first application of the EFTA and a probabilistic approach to detect frozen soil conditions in agro-forested areas in southern Quebec, Canada.
Three-Dimensional Human Pose Estimation from Micro-Doppler Signature Based on SISO UWB Radar
Authors: Zhou Xiaolong , Jin Tian , Dai Yongpeng , Song Yongping , Li Kemeng
Published: April 2024

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields.
SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm
Authors: Hiestand Mikael P. , Tollerud Heather J. , Funk Chris , Senay Gabriel B. , Fickas Kate C. , Friedrichs MacKenzie O.
Published: April 2024

The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.
Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China
Authors: Qin Kun , Wang Zhanpeng , Dai Shaoqing , Li Yuchen , Li Manyao , Li Chen , Qiu Ge , Shi Yuanyuan , Yin Chun , Yang Shujuan , Jia Peng
Published: April 2024

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.
An Improved SAR Ship Classification Method Using Text-to-Image Generation-Based Data Augmentation and Squeeze and Excitation
Authors: Wang Lu , Qi Yuhang , Mathiopoulos P. Takis , Zhao Chunhui , Mazhar Suleman
Published: April 2024

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods.
Separation of Rapidly-Varying and Slowly-Varying Processes and Development of Diffraction Decomposition Order Method in Radiative Transfer
Authors: Zhang Meng , Gao Chenxu , Sun Bingqiang , Zhang Yijun
Published: April 2024

Single scattering in radiative transfer is separated into rapidly-varying and slowly-varying processes, where the rapidly-varying process (RVP) is mainly contributed by the diffraction effect. Accordingly, the diffraction decomposition order (DDO) method is developed to solve the vector radiative transfer equation (VRTE). Instead of directly solving the original VRTE, we decompose it into a series of order equations, where the zeroth-order equation replaces the RVP with a δ-function while the high-order equations are the same as the zeroth-order one, except that the high decomposition orders of the RVP are used as driven sources. In this study, the DDO method is numerically realized using the successive order of the scattering method. The DDO is computationally efficient and accurate. More importantly, all physical processes in the VRTE are fully decomposed due to the order decomposition of the RVP and can be straightforwardly discussed.
GPU Acceleration for SAR Satellite Image Ortho-Rectification
Authors: Dong Lei , Zhang Tingtao , Liu Fangjian , Liu Rui , You Hongjian
Published: April 2024

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.
Landslide-Hazard-Avoiding Highway Alignment Selection in Mountainous Regions Based on SAR Images and High-Spatial-Resolution Precipitation Datasets: A Case Study in Southwestern China
Authors: Wang Zhiheng , Jia Yang , Li Shengfu , Zhang Rui , Xu Binzhi , Sun Xiaopeng
Published: April 2024

Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway alignment selections before construction. In this study, we proposed a new system highway alignment selection method based on coherent scatter InSAR (CSI) and ~1 km high-spatial-resolution precipitation (HSRP) analysis. Prior to the CSI, we calculated and analyzed the feasibility of Sentinel-1A ascending and descending data. To illustrate the performance of the CSI, CSI and SBAS–InSAR were both utilized to monitor 80 slow-moving landslides, which were identified by optical remote-sensing interpretation and field investigation, along the Barkam–Kangting Highway Corridor (BKHC) in southwestern China, relying on 56 Sentinel-1A descending images from September 2019 to September 2021. The results reveal that CSI has clearer deformation signals and more measurement points (MPs) than SBAS-InSAR. And the maximum cumulative displacements and rates of the landslides reach −75 mm and −64 mm/year within the monitoring period (CSI results), respectively. Furthermore, the rates of the landslides near the Jinchuan River are higher than those of the landslides far from the river. Subsequently, to optimize the highway alignment selection, we analyzed the spatiotemporal evolution characteristics of feature points on a typical landslide by combining the −1 km HSRP, which was calculated from the 30′ Climatic Research Unit (CRU) time-series datasets, with the climatology datasets of WorldClim using delta spatial downscaling. The analysis shows that the sliding rates of landslides augment from the back edge to the tongue because of fluvial erosion and that accelerated sliding is highly related to the intense precipitation between April and September each year (ASP). Consequently, three solution types were established in our method by setting thresholds for the deformation rates and ASPs of every landslide. Afterward, the risk-optimal alignment selection of the BKHC was finalized according to the solution types and consideration of the construction’s possible impacts. Ultimately, the major problems and challenges for our method were discussed, and conclusions were given.
Comparison of Cloud-Mask Algorithms and Machine-Learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain
Authors: Gao Xinyi , Chi Hong , Huang Jinliang , Han Yifei , Li Yifan , Ling Feng
Published: April 2024

Southern China, one of the traditional rice production bases, has experienced significant declines in the area of rice paddy since the beginning of this century. Monitoring the rice cropping area is becoming an urgent need for food security policy decisions. One of the main challenges for mapping rice in this area is the quantity of cloud-free observations that are vulnerable to frequent cloud cover. Another relevant issue that needs to be addressed is determining how to select the appropriate classifier for mapping paddy rice based on the cloud-masked observations. Therefore, this study was organized to quickly find a strategy for rice mapping by evaluating cloud-mask algorithms and machine-learning methods for Sentinel-2 imagery. Specifically, we compared four GEE-embedded cloud-mask algorithms (QA60, S2cloudless, CloudScore, and CDI (Cloud Displacement Index)) and analyzed the appropriateness of widely accepted machine-learning classifiers (random forest, support vector machine, classification and regression tree, gradient tree boost) for cloud-masked imagery. The S2cloudless algorithm had a clear edge over the other three algorithms based on its overall accuracy in evaluation and visual inspection. The findings showed that the algorithm with a combination of S2cloudless and random forest showed the best performance when comparing mapping results with field survey data, referenced rice maps, and statistical yearbooks. In general, the research highlighted the potential of using Sentinel-2 imagery to map paddy rice with multiple combinations of cloud-mask algorithms and machine-learning methods in a cloud-prone area, which has the potential to broaden our rice mapping strategies.
Lunar Surface Resource Exploration: Tracing Lithium, 7 Li and Black Ice Using Spectral Libraries and Apollo Mission Samples
Authors: Fernández Susana del Carmen , Alberquilla Fernando , Fernández Julia María , Díez Enrique , Rodríguez Javier , Muñiz Rubén , Calleja Javier F. , de Cos Francisco Javier , Martínez-Frías Jesús
Published: April 2024

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles.
ACTOR: Adaptive Control of Transmission Power in RPL
Authors: Rabet Iliar , Fotouhi Hossein , Alves Mário , Vahabi Maryam , Björkman Mats
Published: April 2024

RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced “ripple”)—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices’ full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.
Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach
Authors: Ibragimov Eldor , Kim Yongsoo , Lee Jung Hee , Cho Junsang , Lee Jong-Jae
Published: April 2024

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.