bluuebunny/crossref_metadata_2025 huggingface dataset:
The Processing and Electrical Properties of Isotactic Polypropylene/Copper Nanowire Composites
Authors: Lu Po-Wen , Jaihao Chonlachat , Pan Li-Chern , Tsai Po-Wei , Huang Ching-Shuan , Brangule Agnese , Zarkov Aleksej , Kareiva Aivaras , Wang Hsin-Ta , Yang Jen-Chang
Published: August 2022

Polypropylene (PP), a promising engineering thermoplastic, possesses the advantages of light weight, chemical resistance, and flexible processability, yet preserving insulative properties. For the rising demand for cost-effective electronic devices and system hardware protections, these applications require the proper conductive properties of PP, which can be easily modified. This study investigates the thermal and electrical properties of isotactic polypropylene/copper nanowires (i-PP/CuNWs). The CuNWs were harvested by chemical reduction of CuCl2 using a reducing agent of glucose, capping agent of hexadecylamine (HDA), and surfactant of PEG-7 glyceryl cocoate. Their morphology, light absorbance, and solution homogeneity were investigated by SEM, UV-visible spectrophotometry, and optical microscopy. The averaged diameters and the length of the CuNWs were 66.4 ± 16.1 nm and 32.4 ± 11.8 µm, respectively. The estimated aspect ratio (L/D, length-to-diameter) was 488 ± 215 which can be recognized as 1-D nanomaterials. Conductive i-PP/CuNWs composites were prepared by solution blending using p-xylene, then melt blending. The thermal analysis and morphology of CuNWs were characterized by DSC, polarized optical microscopy (POM), and SEM, respectively. The melting temperature decreased, but the crystallization temperature increasing of i-PP/CuNWs composites were observed when increasing the content of CuNWs by the melt blending process. The WAXD data reveal the coexistence of Cu2O and Cu in melt-blended i-PP/CuNWs composites. The fit of the electrical volume resistivity (ρ) with the modified power law equation: ρ = ρo (V − Vc)−t based on the percolation theory was used to find the percolation concentration. A low percolation threshold value of 0.237 vol% and high critical exponent t of 2.96 for i-PP/CuNWs composites were obtained. The volume resistivity for i-PP/CuNWs composite was 1.57 × 107 Ω-cm at 1 vol% of CuNWs as a potential candidate for future conductive materials.
Impact of Whey Protein Edible Coating Containing Fish Gelatin Hydrolysates on Physicochemical, Microbial, and Sensory Properties of Chicken Breast Fillets
Authors: Sabzipour-Hafshejani Forouzan , Mirzapour-Kouhdasht Armin , Khodaei Diako , Taghizadeh Mohammad Sadegh , Garcia-Vaquero Marco
Published: August 2022

This study aims to research the impact of coatings containing whey protein (WP), fish gelatin hydrolysates (FGH), and both compounds together (WP + FGH) on the shelf-life of chicken breast fillets over the course of 16 days of cold storage (4 °C, 4-day intervals), as assessed by their physicochemical, microbiological, and sensory properties. Overall, cooking loss, pH value, total volatile base nitrogen, free fatty acids, peroxide value, and thiobarbituric acid reactive substances increased with storage time in all samples. WP + FGH coated samples had significantly lower variation in all these parameters over the time of storage compared to other coated samples (WP and FGH), while these parameters increased greatly in control (uncoated) samples. WP + FGH coating also resulted in reduced bacterial counts of total mesophilic, aerobic psychrotrophic, and lactic acid bacteria compared to other coated and uncoated samples. The sensory evaluation revealed no differences in the panelists’ overall acceptance at day 0 of storage between samples. The samples were considered “non-acceptable” by day 8 of storage; however, WP + FGH coated samples maintained an overall higher acceptability score for the sensory attributes evaluated by the panelists. Overall, this study shows the potential of WP + FGH coatings for prolonging the shelf-life of chicken breast fillets.
Theoretical Investigation of Gamma- and Neutron-Shielding Properties of Polysulfone (PSU) Polymer Material Using Geant4
Authors: Akhdar Hanan
Published: August 2022

Polymers are widely used materials that have many medical and industrial applications. Some polymers have even been introduced as radiation-shielding materials; therefore, many studies are focusing on new polymers and their interactions with photons and neutrons. Research has focused on theoretical estimation of the shielding effectiveness of different materials. It is well known that theoretical studies on the shielding properties of different materials through modeling and simulation have many benefits, as they help scientists to choose the right shielding material for a specific application, and they are also much more cost-effective and take much less time compared to experimental studies. In this study, polysulfone (PSU) was investigated. PSU is a high-temperature, amber-colored, semi-transparent plastic material with good mechanical properties. It is resistant to degradation from hot water and steam and is often used in medical and food preparation applications, where repeated sterilization is required. The interactions of photons and neutrons with PSU were investigated using a Monte Carlo-based simulation toolkit, Geant4, within a wide range of energies of both photons and neutrons. The mass attenuation coefficients (µm), the half-value layers (HVL), the effective atomic numbers (Zeff), and the effective electron densities (Neff) of gammas were investigated. In addition, the effective removal cross-sections (ΣR) and the mean free paths (λ) of neutrons were also studied. The results were then compared to other commonly used polymer materials.
Modifying Anti-Compression Property and Water-Soluble Ability of Polyglycolic Acid via Melt Blending with Polyvinyl Alcohol
Authors: Wei Liao , Ma Shuyue , Hao Mengyuan , Ma Lanrong , Lin Xiang
Published: August 2022

Biodegradable polymeric materials have become the most attractive research interest in recent years and are gradually widely used in various fields in the case of environmental pollution. In this paper, binary blends, mainly including varying contents of polyglycolic acid (PGA) and poly(vinyl alcohol) (PVA), were prepared via a melt compounding strategy. The ethylene-methyl acrylate-glycidyl methacrylate (EMA-GMA) was employed as the compatibilizer to improve the compatibility between the PGA and PVA and the polyolefin elastomer (POE) was used as toughening agent. The anti-compression property and water-soluble ability of the blends were particularly studied to explore their potential application in an oil/gas exploitation field. Special attentions were paid to the evolution of the water-soluble ability of PGAX with the PVA concentration. Furthermore, isothermal shear measurement and thermogravimetric analysis were performed to evaluate the thermal stability of PGA and PGA blends (PGAX) during melt processing. The results showed that the incompatibility between PGA and PVA largely deteriorated the mechanical property, i.e., anti-compression strength, leading to fragile characteristics under a lower compressive load for the PGAX samples with varied contents of PVA. The presence of PVA and EMA-GMA greatly enhanced the viscoelasticity of the PGA melt, showing an increased storage modulus and viscosity at a low shear frequency; however, the thermal instability of PGAX was intensified owing to the greater ease of thermal degradation of PVA than that of PGA. Meanwhile, the water-soluble ability of PGAX was improved due to the high water dissolution of PVA, which played the role as a sacrificial material. The purpose of this work is to pursue an effective modification for PGA processing and application via melt blending.
Thermo-Responsive Hydrophilic Support for Polyamide Thin-Film Composite Membranes with Competitive Nanofiltration Performance
Authors: Drikvand Haniyeh Najafvand , Golgoli Mitra , Zargar Masoumeh , Ulbricht Mathias , Nejati Siamak , Mansourpanah Yaghoub
Published: August 2022

Poly(N-isopropylacrylamide) (PNIPAAm) was introduced into a polyethylene terephthalate (PET) nonwoven fabric to develop novel support for polyamide (PA) thin-film composite (TFC) membranes without using a microporous support layer. First, temperature-responsive PNIPAAm hydrogel was prepared by reactive pore-filling to adjust the pore size of non-woven fabric, creating hydrophilic support. The developed PET-based support was then used to fabricate PA TFC membranes via interfacial polymerization. SEM–EDX and AFM results confirmed the successful fabrication of hydrogel-integrated non-woven fabric and PA TFC membranes. The newly developed PA TFC membrane demonstrated an average water permeability of 1 L/m2 h bar, and an NaCl rejection of 47.0% at a low operating pressure of 1 bar. The thermo-responsive property of the prepared membrane was studied by measuring the water contact angle (WCA) below and above the lower critical solution temperature (LCST) of the PNIPAAm hydrogel. Results proved the thermo-responsive behavior of the prepared hydrogel-filled PET-supported PA TFC membrane and the ability to tune the membrane flux by changing the operating temperature was confirmed. Overall, this study provides a novel method to fabricate TFC membranes and helps to better understand the influence of the support layer on the separation performance of TFC membranes.
Characterisation of White Metakaolin-Based Geopolymers Doped with Synthetic Organic Dyes
Authors: D’Angelo Antonio , Dal Poggetto Giovanni , Piccolella Simona , Leonelli Cristina , Catauro Michelina
Published: August 2022

Over the years, many materials have been used to restore buildings, paintings, ceramics, and mosaic pieces exhibiting different types of dyes and colour hues. Recently, geopolymers have been used for restoration purposes owing to their high chemical and mechanical resistance. In this work, white metakaolin was used to obtain white geopolymers, cured at 25 and 40 °C, as bulk materials to be coloured with synthetic organic dyes, i.e., bromothymol blue, cresol red, phenolphthalein, and methyl orange. These dyes were added during the fresh paste preparation to obtain dyed geopolymeric solids. Ionic conductivity and pH measurement confirmed the chemical stability of the consolidated materials, while FT-IR analyses were used to follow the geopolymerisation occurrences at different ageing times (from 7 to 56 days). Finally, the colour hues and properties were assessed in the CIELAB colour space before and after immersion in water.
Vacuum Low-Temperature Microwave-Assisted Pyrolysis of Technical Lignins
Authors: Karthäuser Johannes , Biziks Vladimirs , Frauendorf Holm , Mai Carsten , Militz Holger
Published: August 2022

Cleavage by microwave-assisted pyrolysis is a way to obtain higher-value organic chemicals from technical lignins. In this report, pine kraft lignin (PKL), spruce and beech organosolv lignin (SOSL and BOSL), and calcium lignosulfonates from spruce wood (LS) were pyrolyzed at temperatures between 30 and 280 °C using vacuum low-temperature, microwave-assisted pyrolysis. The mass balance, energy consumption, condensation rate, and pressure changes of the products during the pyrolysis process were recorded. Phenolic condensates obtained at different temperatures during pyrolysis were collected, and their chemical composition was determined by GC-MS and GC-FID. The origin of the technical lignin had a significant influence on the pyrolysis products. Phenolic condensates were obtained in yields of approximately 15% (PKL and SOSL) as well as in lower yields of 4.5% (BOSL) or even 1.7% (LS). The main production of the phenolic condensates for the PKL and SOSL occurred at temperatures of approximately 140 and 180 °C, respectively. The main components of the phenolic fraction of the three softwood lignins were guaiacol, 4-methylguaiacol, 4-ethylguaiacol, and other guaiacol derivatives; however, the quantity varied significantly depending on the lignin source. Due to the low cleavage temperature vacuum, low-temperature, microwave-assisted pyrolysis could be an interesting approach to lignin conversion.
The Effect of the Physical and Chemical Properties of Synthetic Fabrics on the Release of Microplastics during Washing and Drying
Authors: Choi Sola , Kim Juhea , Kwon Miyeon
Published: August 2022

Synthetic fibers released during washing are the primary source of microplastic pollution. Hence, research on reducing the release of microplastic fibers during washing has recently attracted considerable attention. As a result of previous studies, there is a difference in the amount of microplastic emission according to various types of fabrics. To mitigate the release of microplastics, the study of the reason for the difference in the amount of microplastics is needed. Therefore, this study investigated different synthetic fabrics that release microplastics and the physical properties of the fabrics that affect the release of fibers. Three types of fabrics with different chemical compositions were analyzed. The washing and drying processes were improved by focusing on the mechanical factors that affected microplastic release. Furthermore, based on the mass of the collected microplastic fibers, it was found that the chemical compositions of the fabric can affect the microplastics released during washing and drying. This evaluation of physical properties helped to identify the physical factors that affect microplastic release. These results may provide a basis for reducing microplastic fiber types, thereby minimizing unintended environmental pollution.
The Length Change Ratio of Ground Granulated Blast Furnace Slag-Based Geopolymer Blended with Magnesium Oxide Cured in Various Environments
Authors: Chen Yen-Chun , Lee Wei-Hao , Cheng Ta-Wui , Chen Walter , Li Yeou-Fong
Published: August 2022

Geopolymer (GP) has been considered a potential material to replace ordinary Portland cement (OPC) because of its excellent mechanical properties and environmentally friendly process. However, the promotion of GP is limited due to the large shrinkage and the different operating procedures compared to cement. This study aims to reduce the shrinkage of ground granulated blast furnace slag (GGBFS) based GP by the hydration expansion properties of activated magnesium oxide (MgO). The slurry of GP was blended from GGBFS, MgO, and activator; and the compositions of the activator are sodium hydroxide (NaOH), sodium silicate (Na2SiO3), and alumina silicate(NaAlO2). Herein, the GGFBS and MgO were a binder and a shrinkage compensation agent of GP, respectively. After unmolding, the GP specimens were cured under four types of environments and the lengths of the specimens were measured at different time intervals to understand the length change ratio of GP. In this study, two groups of GP specimens were made by fixing the activator to binder (A/B) ratio and the fluidity. The test results show that adding MgO will reduce the shrinkage of GP as A/B ratio was fixed. However, fixing the fluidity exhibited the opposite results. The X-ray diffraction (XRD) was used to check the Mg(OH)2 that occurred due to the MgO hydration under four curing conditions. Three statistical and machine learning methods were used to analyze the length change of GP based on the test data. The testing and analysis results show that the influence of curing environments is more significant for improving the shrinkage of GP than additive MgO.
A Study Using Optimized LSSVR for Real-Time Fault Detection of Liquid Rocket Engine
Authors: Huang Peihao , Yu Huahuang , Wang Tao
Published: August 2022

Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out. Through the analysis of the results, the effectiveness and the detection accuracy of this presented real-time fault detection method using LSSVR GA-optimized is verified. The experiment results show that this method can effectively diagnose this hydrogen-oxygen rocket engine in real-time, and the method has engineering application value.
Interventional Studies on Nurses’ Mental Health in the COVID-19 Pandemic: A Systematic Review
Authors: Köse Selmin , Murat Merve
Published: August 2022

This systematic review’s primary aim is to examine the interventional studies on the mental health of nurses during the COVID-19 pandemic, and the secondary aim is to determine the interventions that can be planned for future pandemics. For this, databases were searched, and a total of 12 studies meeting the inclusion criteria were included. It was determined that most of the studies were conducted online due to pandemic restrictions. The effects of the interventions (diaphragmatic breathing relaxation, emotional freedom techniques, sending motivational messages, a mobile wellness program, acceptance and commitment therapy, a neuro-meditation program, laughter yoga, gong meditation, distant Reiki, e-aid cognitive behavioral therapy, mindfulness-based breathing, and music therapy) on the parameters affecting the mental health of the nurses working actively in the COVID-19 pandemic were examined. In these studies, the interventions applied to nurses were seen to have positive results on mental health and well-being. The COVID-19 pandemic, which is a traumatic experience for healthcare professionals, shows that more interventional studies are needed to protect and improve the mental health of nurses who take care of critically ill patients during difficult times.
Understanding of Macro Factors That Affect Yield of Government Bonds
Authors: Koroleva Ekaterina , Kopeykin Maxim
Published: August 2022

Government bonds are one of the safest and most attractive instruments in the investment portfolio for private investors and investment funds. Although bonds are perceived as an alternative to bank deposits, a number of macroeconomic factors influence their yield. The goal of the research is to investigate the relationship between macroeconomic factors and the yield of government bonds. We use regression models on a dataset of 22 countries with post-industrial economics for ten years. The main criteria for selecting countries are membership in the Organization for Economic Cooperation and Development and inclusion in the Top-25 countries on the competitiveness index. The results revealed a negative association between the yield of government bonds and gold. Moreover, we indicate a positive association between the yield of government bonds and the following indicators—inflation, oil prices, and GDP per capita. In the case of the influence of population savings and the uncertainty index, we obtain inconclusive results. The study contributes to ongoing research in the field of financial management with respect to investigating determinants of the yield of government bonds.
Towards Improved Unmanned Aerial Vehicle Edge Intelligence: A Road Infrastructure Monitoring Case Study
Authors: Tilon Sofia , Nex Francesco , Vosselman George , Sevilla de la Llave Irene , Kerle Norman
Published: August 2022

Consumer-grade Unmanned Aerial Vehicles (UAVs) are poorly suited to monitor complex scenes where multiple analysis tasks need to be carried out in real-time and in parallel to fulfil time-critical requirements. Therefore, we developed an innovative UAV agnostic system that is able to carry out multiple road infrastructure monitoring tasks simultaneously and in real-time. The aim of the paper is to discuss the system design considerations and the performance of the processing pipeline in terms of computational strain and latency. The system was deployed on a unique typology of UAV and instantiated with realistic placeholder modules that are of importance for infrastructure inspection tasks, such as vehicle detection for traffic monitoring, scene segmentation for qualitative semantic reasoning, and 3D scene reconstruction for large-scale damage detection. The system was validated by carrying out a trial on a highway in Guadalajara, Spain. By utilizing edge computation and remote processing, the end-to-end pipeline, from image capture to information dissemination to drone operators on the ground, takes on average 2.9 s, which is sufficiently quick for road monitoring purposes. The system is dynamic and, therefore, can be extended with additional modules, while continuously accommodating developments in technologies, such as IoT or 5G.
3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China
Authors: Zang Zengliang , You Wei , Ye Hancheng , Liang Yanfei , Li Yi , Wang Daichun , Hu Yiwen , Yan Peng
Published: August 2022

Based on the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme of the Weather Research and Forecasting model coupled with online Chemistry (WRF-Chem) and the three-dimensional variational (3DVAR) assimilation method, a 3DVAR data assimilation (DA) system for aerosol optical depth (AOD) and aerosol concentration observations was developed. A case study on assimilating the Himawari-8 satellite AOD and/or fine particulate matter (PM2.5) was conducted to investigate the improvement of DA on the analysis accuracy and forecast skills of the spatial distribution characteristics of aerosols, especially in the vertical dimension. The aerosol extinction coefficient (AEC) profile data from The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), surface PM2.5 and Himawari-8 AOD measurements were used for verification. One control experiment (without DA) and two DA experiments including a PM2.5 DA experiment denoted by Da_PM and a combined PM2.5 and AOD DA experiment denoted by Da_AOD_PM were conducted. Both DA experiments had positive effects on the surface PM2.5 mass concentration forecast skills for more than 60 h. However, the Da_PM showed a slight improvement in the analysis accuracy of the AOD distribution compared with the control experiment, while the Da_AOD_PM showed a considerable improvement. The Da_AOD_PM had the best positive effect on the AOD forecast skills. The correlation coefficient (CORR), root mean square error (RMSE), and mean fraction error (MFE) of the 24 h AOD forecasts for the Da_AOD_PM were 0.73, 0.38, and 0.54, which are 0.09 (14.06%), 0.08 (17.39%), and 0.22 (28.95%) better than that of the control experiment, and 0.05 (7.35%), 0.06 (13.64%), and 0.19 (26.03%) better than that of the Da_PM, respectively. Moreover, improved performance for the Da_AOD_PM occurred when the AEC profile was used for verification, as when the AOD was used for verification. The Da_AOD_PM successfully simulated the first increasing and then decreasing trend of the aerosol extinction coefficients below 1 km, while neither the control nor the Da_PM did. This indicates that assimilating AOD can effectively improve the analyses and forecast accuracy of the aerosol structure in both the horizontal and vertical dimensions, thereby compensating for the limitations associated with assimilating traditional surface aerosol observations alone.
An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat
Authors: Lu Zhuo , Deng Lei , Lu Han
Published: August 2022

Leaf area index (LAI), which is an important vegetation structure parameter, plays a crucial role in evaluating crop growth and yield. Generally, it is difficult to accurately estimate LAI only using vegetation index in remote sensing (RS), especially for unmanned aerial vehicle (UAV) based RS, as its high-resolution advantage has not been fully utilized. This study aims to propose an improved LAI estimation method that comprehensively considers spectral information and structural information provided by the UAV-based RS to improve the LAI estimation accuracy of field-grown wheat. Specifically, this method introduces the canopy height model (CHM) to compensate for the lack of structural information in LAI estimation, and then takes canopy coverage (CC) as a correction parameter to alleviate the LAI overestimation. Finally, the performance of this method is verified on RGB and multispectral images, respectively. The results show that canopy structure, namely CHM and CC, can significantly improve the accuracy of LAI estimation. Compared with the traditional method, the proposed method improves the accuracy by 22.6% on multispectral images (R2 = 0.72, RMSE = 0.556) and by 43.6% on RGB images (R2 = 0.742, RMSE = 0.534). This study provides a simple and practical method for UAV-based LAI estimation, especially for the application of low-cost RGB sensors in precision agriculture and other fields.
Deep Reinforcement Learning Based Freshness-Aware Path Planning for UAV-Assisted Edge Computing Networks with Device Mobility
Authors: Peng Yingsheng , Liu Yong , Li Dong , Zhang Han
Published: August 2022

As unmanned aerial vehicles (UAVs) can provide flexible and efficient services concerning the sparse network distribution, we study a UAV-assisted mobile edge computing (MEC) network. To satisfy the freshness requirement of IoT applications, the age of information (AoI) is incorporated as an important performance metric. Then, the path planning problem is formulated to simultaneously minimize the AoIs of mobile devices and the energy consumption of the UAV, where the movement randomness of IoT devices are taken into account. Concerning the dimension explosion, the deep reinforcement learning (DRL) framework is exploited, and a double deep Q-learning network (DDQN) algorithm is proposed to realize the intelligent and freshness-aware path planning of the UAV. Extensive simulation results validate the effectiveness of the proposed freshness-aware path planning scheme and unveil the effects of the moving speed of devices and the UAV on the achieved AoI.
Attempt to Combine Physicochemical Data with Thermal Remote Sensing to Determine the Extent of Water Mixing between River and Lake
Authors: Tritt Remigiusz , Młynarczyk Adam , Proch Jędrzej
Published: August 2022

The mixing of river and lake waters is important for the functioning of a reservoir, especially in the case of shallow polymictic reservoirs such as Lake Swarzędzkie. The extent of this mixing depends largely on the river flow rate. In lakes, which rivers with low flow values flow through, it should be expected that the flow currents only reach the narrow zone adjacent to the mouth of the river to the lake. The water of rivers generally has different chemical compositions and physicochemical parameters in relation to lake water. Therefore, to determine the range of the river in the lake and characterize the water mixing, measurements of temperature, electrolytic conductivity, and the concentrations of selected chemical elements were made in the estuary zone and at other points located on the lake and on the river near the tributary. In addition, the values and directions of horizontal currents were determined, and thermal photos were taken from a low-altitude ceiling.
Multi-Band and Polarization SAR Images Colorization Fusion
Authors: Li Xinchen , Jing Dan , Li Yachao , Guo Liang , Han Liang , Xu Qing , Xing Mengdao , Hu Yihua
Published: August 2022

The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first use non-subsampled shearlet transform (NSST) to fuse multi-band and polarization SAR images. The sub-band images decomposed from the NSST are fused by the coefficient of variation (CV) and phase consistency (PC) weighted fusion rules. Subsequently, we extract the band and polarization difference information from the multi-band and polarization SAR images. The fusion image is finally colorized according to the band and polarization differences. In the experiments, we used Ka and S-band multi-polarization SAR images to test the fusion performance. The experiment results prove that the proposed fused images not only preserve much valuable information but also can be interpreted easily.
End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations
Authors: Vient Jean-Marie , Fablet Ronan , Jourdin Frédéric , Delacourt Christophe
Published: August 2022

The characterization of suspended sediment dynamics in the coastal ocean provides key information for both scientific studies and operational challenges regarding, among others, turbidity, water transparency and the development of micro-organisms using photosynthesis, which is critical to primary production. Due to the complex interplay between natural and anthropogenic forcings, the understanding and monitoring of the dynamics of suspended sediments remain highly challenging. Numerical models still lack the capabilities to account for the variability depicted by in situ and satellite-derived datasets. Through the ever increasing availability of both in situ and satellite-derived observation data, data-driven schemes have naturally become relevant approaches to complement model-driven ones. Our previous work has stressed this potential within an observing system simulation experiment. Here, we further explore their application to the interpolation of sea surface sediment concentration fields from real gappy satellite-derived observation datasets. We demonstrate that end-to-end deep learning schemes—namely 4DVarNet, which relies on variational data assimilation formulation—apply to the considered real dataset where the training phase cannot rely on gap-free references but only on the available gappy data. 4DVarNet significantly outperforms other data-driven schemes such as optimal interpolation and DINEOF with a relative gain greater than 20% in terms of RMSLE and improves the high spatial resolution of patterns in the reconstruction process. Interestingly, 4DVarNet also shows a better agreement between the interpolation performance assessed for an OSSE and for real data. This result emphasizes the relevance of OSSE settings for future development calibration phases before the applications to real datasets.
Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data
Authors: Li Mengyu , Yang Wei , Kondoh Akihiko
Published: August 2022

Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R2 of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R2 increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future.
Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine
Authors: Zhi Feng , Dong Zhenhua , Guga Suri , Bao Yongbin , Han Aru , Zhang Jiquan , Bao Yulong
Published: August 2022

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas.
Estimation of the Center of Mass of GRACE-Type Gravity Satellites
Authors: Huang Zhiyong , Li Shanshan , Cai Lin , Fan Diao , Huang Lingyong
Published: August 2022

One of the key constraints for the accelerometer of GRACE-type gravity satellites to accurately measure the non-gravitational accelerations acting on the satellite is that the center of mass of the satellite and the proof mass of the accelerometer should maintain a coincidence. In addition, the accuracy requirement is that the center of mass offset (CM-offset) in the three directions is less than 100 microns. Since the center of mass (CoM) of the satellite will change with the consumption of cold-gas fuel in the tanks, it is necessary to regularly carry out the CoM calibration maneuver. Firstly, the observation equations consisting of the accelerometer linear acceleration, angular acceleration, and the CM-offset vector are established in order to estimate the amount of CM-offset. Then, according to the estimated CM-offset, the satellite mass trim mechanisms are used to change the satellite’s CoM, so that the satellite’s CoM always approaches the proof mass of the accelerometer, with an accuracy of 100 μm per axis. The CM-offset of the satellite of GRACE-FO is estimated by using the accelerometer, star camera, magnetic torquer, magnetometer, and the precision orbit data during the GRACE-C CM-offset calibration period on 1 February 2020. Four kinds of CM-offset results are obtained by four different angular accelerations as follows: the angular acceleration based on the attitude dynamics (“MTQ angular acceleration”), the accelerometer angular acceleration calibrated by MTQ, the accelerometer angular acceleration, and the angular acceleration calculated by the star camera. By comparing the four kinds of CM-offset results that are estimated by the four different methods, all four of the results are shown to have the same level of accuracy. Based on the accelerometer (calibrated) angular acceleration, the difference with the JPL result is 0.5 μm, while the difference between the conventional method and the JPL result is 6.0 μm. All four of the methods can achieve the requirement of 50 μm accuracy and using four CM-offset estimation methods simultaneously can improve the integrity of the calibration results. Subsequently, the CM-offset results of GRACE-C since its launch are estimated here. The calibration algorithm that is proposed in this paper can be used as a reference in the calibration of gravity satellites carrying an accelerometer payload.
LIDAR-Inertial Real-Time State Estimator with Rod-Shaped and Planar Feature
Authors: Liu Hong , Pan Shuguo , Gao Wang , Ma Chun , Jia Fengshuo , Lu Xinyu
Published: August 2022

State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud feature selection for LIDAR-inertial tightly coupled systems is proposed. In the front-end, a point cloud registration is carried out after marking rod-shaped and planar feature information which is different from the existing LIDAR and inertial measurement unit (IMU) integration scheme. This preprocessing method subsequently reduces the outliers. IMU pre-integration outputs high-frequency result and is used to provide the initial value for LIDAR solution. In the scan-to-map module, a computationally efficient graph optimization framework is applied. Moreover, the LIDAR odometry further constrains the IMU states. In the back-end, the optimization based on sliding-window incorporates the LIDAR-inertial measurement and loop closure global constraints to reduce the cumulative error. Combining the front-end and back-end, we propose the low drift and high real-time LIDAR-inertial positioning system. Furthermore, we conducted an exhaustive comparison in open data sequences and real-word experiments. The proposed system outperforms much higher positioning accuracy than the state-of-the-art methods in various scenarios. Compared with the LIO-SAM, the absolute trajectory error (ATE) average RMSE (Root Mean Square Error) in this study increases by 64.45% in M2DGR street dataset (street_01, 04, 07, 10) and 24.85% in our actual scene datasets. In the most time-consuming mapping module of each system, our system runtime can also be significantly reduced due to the front-end preprocessing and back-end graph model.
Projection of Future Extreme Precipitation in China Based on the CMIP6 from a Machine Learning Perspective
Authors: Yan Yilin , Wang Hao , Li Guoping , Xia Jin , Ge Fei , Zeng Qiangyu , Ren Xinyue , Tan Linyin
Published: August 2022

In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends.
Deep 1D Landmark Representation Learning for Space Target Pose Estimation
Authors: Liu Shengli , Zhu Xiaowen , Cao Zewei , Wang Gang
Published: August 2022

Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.
Identification of Old-Growth Mediterranean Forests Using Airborne Laser Scanning and Geostatistical Analysis
Authors: Hevia Andrea , Calzado Anabel , Alejano Reyes , Vázquez-Piqué Javier
Published: August 2022

The protection and conservation of old-growth forests (OGFs) are becoming a global concern due to their irreplaceability and high biodiversity. Nonetheless, there has been little research into the identification and characterization of OGFs of the oldest tree species in Mediterranean areas. We used forest inventory data, low-density airborne laser scanning (ALS) metrics, and geostatistical analysis to estimate old-growth indices (OGIs) as indicators of old-growth forest conditions. We selected a pilot area in European black pine (Pinus nigra subsp. salzmannii) ecosystems where the oldest known living trees in the Iberian Peninsula are found. A total of 756 inventory plots were established to characterize standard live tree and stand attributes. We estimated several structural attributes that discriminate old growth from younger age classes and calculated different types of OGI for each plot. The best OGI was based on mean tree diameter, standard deviation of tree diameter, and stand density of large trees (diameter > 50 cm). This index is useful for assessing old-growthness at different successional stages (young and OGFs) in Mediterranean black pine forests. Our results confirm that the estimation of OGIs based on a combination of forest inventory data, geostatistical analysis, and ALS is useful for identifying OGFs.
Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China
Authors: Xiong Nina , Yu Rongxia , Yan Feng , Wang Jia , Feng Zhongke
Published: August 2022

Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning.