bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Sustainability through Covalent Adaptable Networks in the Recycling of Polyurethane Plastics
Authors: Miravalle Edoardo , Bracco Pierangiola , Brunella Valentina , Barolo Claudia , Zanetti Marco
Published: September 2023

The global plastic waste problem has created an urgent need for the development of more sustainable materials and recycling processes. Polyurethane (PU) plastics, which represent 5.5% of globally produced plastics, are particularly challenging to recycle owing to their crosslinked structure. Covalent adaptable networks (CANs) based on dynamic covalent bonds have emerged as a promising solution for recycling PU waste. CANs enable the production of thermoset polymers that can be recycled using methods that are traditionally reserved for thermoplastic polymers. Reprocessing using hot-pressing techniques, in particular, proved to be more suited for the class of polyurethanes, allowing for the efficient recycling of PU materials. This Review paper explores the potential of CANs for improving the sustainability of PU recycling processes by examining different types of PU-CANs, bond types, and fillers that can be used to optimise the recycling efficiency. The paper concludes that further research is needed to develop more cost-effective and industrial-friendly techniques for recycling PU-CANs, as they can significantly contribute to sustainable development by creating recyclable thermoset polymers.
Photoluminous Response of Biocomposites Produced with Charcoal
Authors: Delatorre Fabíola Martins , Cupertino Gabriela Fontes Mayrinck , Pereira Allana Katiussya Silva , de Souza Elias Costa , da Silva Álison Moreira , Ucella Filho João Gilberto Meza , Saloni Daniel , Profeti Luciene Paula Roberto , Profeti Demetrius , Dias Júnior Ananias Francisco
Published: September 2023

Due to the possible effects of global warming, new materials that do not have a negative impact on the environment are being studied. To serve a variety of industries and outdoor applications, it is necessary to consider the impact of photoluminosity on the performance of biocomposites in order to accurately assess their durability characteristics and prevent substantial damage. Exposure to photoluminosity can result in adverse effects such as discoloration, uneven surface, loss of mass, and manipulation of the intrinsic mechanical properties of biocomposites. This study aims to evaluate general charcoal from three pyrolysis temperatures to understand which charcoal is most suitable for photoluminosity and whether higher pyrolysis temperatures have any significant effect on photoluminosity. Porosity, morphology, Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy of charcoal were analyzed. Charcoal obtained at a temperature of 800 °C demonstrates remarkable potential as a bioreinforcement in polymeric matrices, attributable to its significantly higher porosity (81.08%) and hydrophobic properties. The biocomposites were characterized for flexural strength, tensile strength, scanning electron microscopy (SEM), FTIR, and x-ray diffraction (XRD). The results showed an improvement in tensile strength after exposure to photoluminosity, with an increase of 69.24%, 68.98%, and 54.38% at temperatures of 400, 600, and 800 °C, respectively, in relation to the treatment control. It is notorious that the tensile strength and modulus of elasticity after photoluminosity initially had a negative impact on mechanical strength, the incorporation of charcoal from higher pyrolysis temperatures showed a substantial increase in mechanical strength after exposure to photoluminosity, especially at 800 °C with breaking strength of 53.40 MPa, and modulus of elasticity of 4364.30 MPA. Scanning electron microscopy revealed an improvement in morphology, with a decrease in roughness at 800 °C, which led to greater adhesion to the polyester matrix. These findings indicate promising prospects for a new type of biocomposite, particularly in comparison with other polymeric compounds, especially in engineering applications that are subject to direct interactions with the weather.
Fabrication of Nanogroove Arrays on Acrylic Film Using Micro-Embossing Technique
Authors: Raksiri Chana , Potejanasak Potejana , Dokyor Thitipoom
Published: September 2023

The fabrication of nanostructures is of great importance in producing biomedical devices. Significantly, the nanostructure of the polymeric film has a significant impact on the physical and biophysical behavior of the biomolecules. This study presents an efficient nanofabrication method of nanogroove structures on an acrylic film by the micro-embossing process. In this method, a master mold was made from a thermos oxide silicon substrate using photolithography and etching techniques. An isotropic optical polymethyl methacrylate (PMMA) film is used in the experiment. The acrylic film is known for its excellent optical properties in products such as optical lenses, medical devices, and various general purpose engineering plastics. Then, the micro-embossing process was realized to fabricate nanogroove patterns on an acrylic film by using a micro-embossing machine. However, the morphology of the nanopatterns on an acrylic film was characterized by using an atomic force microscope to measure the dimensions of the nanogroove patterns. The impact of embossing temperature on the morphology of nanogroove patterns on acrylic film is experimentally investigated. The results show that when the embossing temperature is too small, the pattern is not fully formed, and slipping occurs in nanopatterns on the acrylic film. On the other hand, the effect of increasing the embossing temperature on the morphology of nanogrooves agrees with the master mold, and the crests between the nanogrooves form straight edges. It should be noted that the micro-embossing temperature also strongly influences the transferability of nanopatterns on an acrylic film. The technique has great potential for rapidly fabricating nanostructure patterns on acrylic film.
Damping and Stiffness Analysis of Sandwich Beam with 3D-Printed Honeycomb Core Filled with Magnetorheological Elastomer (MRE): An Experimental Approach
Authors: Sharif Umer , Xiang Xinmei , Zhu Miaochang , Deng Jun , Sun Jing , Ibrahim Dauda Sh. , Adewale Orelaja Oluseyi
Published: September 2023

The current study focuses on the production and experimental examination of sandwich beams consisting of an aluminum face sheet and 3D-printed honeycomb cores that are filled with magnetorheological elastomer (MRE). These cores are loaded with different ratios of (75/25)% and (50/50)% elastomer and magnetic particles, measured by weight. In order to ascertain the dynamic characteristics of sandwich beams, the constructed specimens were subjected to classic shock (free vibration) experiments, and these experiments were conducted under two conditions: with and without the application of a changing magnetic field at the free end and center of the beam. The results of the experiments suggest that the attenuation of the damping ratio exhibited satisfactory performance, particularly with respect to the structures that were being examined. The sandwich beam constructions proposed exhibited the ability to alter the damping ratio, damping coefficient, and stiffness through the application of a magnetic field. Nevertheless, an escalation in the applied magnetic field resulted in a reduction in stiffness values, while the values of the damping ratio and damping coefficient increased. Furthermore, significant variations in damping were observed when the magnets were located in the central regions of the structures.
Study on SiO2 Nanofluid Alternating CO2 Enhanced Oil Recovery in Low-Permeability Sandstone Reservoirs
Authors: Hu Jiani , Fu Meilong , Li Minxuan , He Honglin , Hou Baofeng , Chen Lifeng , Liu Wenbo
Published: September 2023

Water alternating gas (WAG) flooding is a widely employed enhanced oil recovery method in various reservoirs worldwide. In this research, we will employ SiO2 nanofluid alternating with the CO2 injection method as a replacement for the conventional WAG process in oil flooding experiments. The conventional WAG method suffers from limitations in certain industrial applications, such as extended cycle times, susceptibility to water condensation and agglomeration, and ineffectiveness in low-permeability oil reservoirs, thus impeding the oil recovery factor. In order to solve these problems, this study introduces SiO2 nanofluid as a substitute medium and proposes a SiO2 nanofluid alternate CO2 flooding method to enhance oil recovery. Through the microcharacterization of SiO2 nanofluids, comprehensive evaluations of particle size, dispersibility, and emulsification performance were conducted. The experimental results revealed that both SiO2-I and SiO2-II nanoparticles exhibited uniform spherical morphology, with particle sizes measuring 10–20 nm and 50–60 nm, respectively. The SiO2 nanofluid formulations demonstrated excellent stability and emulsification properties, highlighting their potential utility in petroleum-related applications. Compared with other conventional oil flooding methods, the nanofluid alternating CO2 flooding effect is better, and the oil flooding effect of smaller nanoparticles is the best. Nanofluids exhibit wetting modification effects on sandstone surfaces, transforming their surface wettability from oil-wet to water-wet. This alteration reduces adhesion forces and enhances oil mobility, thereby facilitating improved fluid flow in the rock matrix. In the oil flooding experiments with different slug sizes, smaller gas and water slug sizes can delay the breakthrough time of nanofluids and CO2, thereby enhancing the effectiveness of nanofluid alternate CO2 flooding for EOR. Among them, a slug size of 0.1 PV approaches optimal performance, and further reducing the slug size has limited impact on improving the development efficiency. In oil flooding experiments with different slug ratios, the optimal slug ratio is found to be 1:1. Additionally, in oil flooding experiments using rock cores with varying permeability, lower permeability rock cores demonstrate higher oil recovery rates.
Study on Mechanism and Regularity of Rock Breaking by Pulsed Water Injection with Particles
Authors: Zhao Tian , Wu Qingxiang , Lv Haifeng , Song Heng , Yang Xinke , Fang Tiancheng
Published: September 2023

In recent years, the drilling technology applied to deep and ultra-deep formations has developed rapidly, but the drilling speed for hard formations is low. Therefore, it is very important to study the drilling methods for deep and hard strata. Particle pulsed jet drilling is a new drilling method based on particle jet impact drilling technology and high-pressure pulsed water jet impact drilling technology. In this paper, the mathematical models of the shear layer amplification coefficient and wave velocity are established based on a similar network theory, and the motion equations of a single particle and particle swarm are established according to the motion of particles in a pulsed jet environment. Then, based on the self-designed particle jet impact rock-breaking experimental platform, the numerical simulation results are compared, analyzed, and verified. The results show that the rock-breaking efficiency increases with the increase in the average velocity of the particle pulsed jet. When the average speed exceeds 160 m/s, the rock-breaking efficiency increases significantly. With the increase in the particle concentration, the rock-breaking efficiency of the particle pulsed jet also increases, but there is an optimal value. When the concentration is too high, the impact of particles on the rock is affected by the collision between particles, and the wear of the drill intensifies. This research is helpful for understanding deep-well drilling-speed-increasing technology and promotes the development and engineering applications of particle jet impact drilling theory.
Polyphenols as Antioxidant/Pro-Oxidant Compounds and Donors of Reducing Species: Relationship with Human Antioxidant Metabolism
Authors: Andrés Celia María Curieses , Pérez de la Lastra José Manuel , Juan Celia Andrés , Plou Francisco J. , Pérez-Lebeña Eduardo
Published: September 2023

In this review, we have focused mainly on the study of their antioxidant and pro-oxidant capacity, and the analysis of the oxidation of the catechol group to o-quinone. The redox balance established between the different oxidase and reductase enzymes generates reducing species, H+ and e−, and allows the oxidation of polyphenolic groups to quinones to be reversible. This continuous balance between these nucleophilic and electrophilic substances allows the activation of the NRF2/ARE axis, which regulates cellular antioxidant responses against oxidative stress, as well as cell proliferation. Understanding the ambivalent character of polyphenols, which can act simultaneously as antioxidants and pro-oxidants, will allow the design of specific therapies that can serve science and medicine in their tasks.
Effect of Hot Filament Chemical Vapor Deposition Filament Distribution on Coated Tools Performance in Milling of Zirconia Ceramics
Authors: Fan Louis Luo , Yip Wai Sze , Sun Zhanwan , Zhang Baolong , To Suet
Published: September 2023

Zirconia ceramics (ZrO2) have been used for a variety of applications due to their superior physical properties, including in machining tools and dentures. Nonetheless, due to its extreme hardness and brittleness in both sintered and half-sintered forms, zirconia is difficult to machine. In this study, half-sintered zirconia blocks are milled with tungsten carbide milling tools which arecoated with diamond film using hot filament chemical vapor deposition (HFCVD) at various substrate-to-filament distances. The objective was to determine the effect of substrate-to-filament distances on the coating thickness, diamond purity, coating grain size, and ZrO2 machining performance during HFCVD. The experimental results show that, in HFCVD, the grain size and coating thickness of the diamond film on milling tools tend to decrease when the substrate-to-filament distances decrease. Tool failure happened at a cutting time of 200 min for all coated tools. However, the machining quality in terms of surface topology, surface roughness, and tool condition is superior for diamond-coated milling tools with smaller grain sizes and thinner thicknesses. It can be concluded that diamond milling tools with a smaller grain size and lesser thickness produced under shorter substrate-to-filament distances have a superior machining performance and a longer tool life. This study could potentially be used for parameter optimization in the production of coated tools.
Mathematical Modeling of Prediction of Horizontal Wells with Gravel Pack Combined with ICD in Bottom-Water Reservoirs
Authors: Qin Shili , Zhang Ning , Cao Bobo , An Yongsheng , Huo Runshi
Published: September 2023

During the development of horizontal wells in bottom-water reservoirs, the strong heterogeneity of reservoir permeability leads to premature bottom-water breakthroughs at locations with high permeability in the horizontal wellbore, and the water content rises rapidly, which seriously affects production. To cope with this problem, a new technology has emerged in recent years that utilizes gravel filling to block the flow in the annulus between the horizontal well and the borehole and utilizes the Inflow Control Device (ICD) completion tool to carry out segmental water control in horizontal wells. Unlike conventional horizontal well ICD completions that use packers for segmentation, gravel packs combined with ICD completions break the original segmentation routine and increase the complexity of the production dynamic simulation. In this paper, the flow in different spatial dimensions, such as reservoirs, gravel-packed layers, ICD completion sections, and horizontal wellbores, is modeled separately. Furthermore, the annular pressures at different locations are used as the solution variable for the coupled solution, which realizes the prediction of oil production, water production, and the water content of gravel packs combined with ICD completion of horizontal wells. The model is used to calculate the effects of different crude oil viscosities, different reservoir permeabilities, different permeabilities of gravel-packed layers, and different development stages on the water control effects of gravel packs combined with ICD completions and conventional ICD completions under field conditions.
Anomaly Recognition, Diagnosis and Prediction of Massive Data Flow Based on Time-GAN and DBSCAN for Power Dispatching Automation System
Authors: Liu Wenjie , Lei Pengfei , Xu Dong , Zhu Xiaorong
Published: September 2023

Existing power anomaly detection is mainly based on analyzing static offline data. But this method takes a long time and has low identification accuracy when detecting timing and frequency anomalies, since this method requires offline screening, classification and preprocessing of the collected data, which is very laborious. Anomaly detection with supervised learning requires a large amount of abnormal data and cannot detect unknown anomalies. So, this paper innovatively proposes the idea of applying Time-series Generative Adversarial Networks (Time-GAN) in a dispatching automation system for the identification, diagnosis and prediction of massive data flow anomalies. First of all, regarding the problem of insufficient abnormal data, we use Time-GAN to generate a large number of reliable datasets for training fault diagnosis models. In addition, Time-GAN can ameliorate the imbalance between various types of data. Secondly, unsupervised learning methods such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-means are used to detect unknown anomalies that may exist in the power grid. Finally, some supervised learning methods are selected to compare with unsupervised learning methods. Experimental results show that the proposed algorithm has a higher recognition rate of known anomalies than other benchmark algorithms and it can find new unknown anomalies. It lays a good foundation for the safe, stable, high-quality and economical operation of the power grid.
Interpretation of Funerary Spaces in Roman Times: Insights from a Nucleus of Braga, NW Iberian Peninsula
Authors: Braga Cristina , Ribeiro Jorge , Fontes Luis , Fragata Ana
Published: September 2023

The funerary/cult archeological nucleus of Rua do Raio (Braga, in the northwest of the Iberian Peninsula) was discovered between 2007 and 2009, under the excavation works of a necropolis of Bracara Augusta. This building exhibits a set of particularities that confirms its archeological importance. It is a construction dating from the middle of the 1st century AD, subject to two reforms, one in the second half of the 1st century AD and another in the 2nd century AD, with a trapezoidal shape and comprising ten rectangular tanks rendered with opus signinum mortars. It shows a unique architectural configuration in the city, as well as in the Portuguese territory, and the space is under musealization, together with a set of graves identified in the same archeological intervention. The present investigation contextualizes the funerary and religious architecture of the city. Its description is presented, highlighting its relevance, and an interpretative possibility is formulated.
Introduction: Cultural and Religious Pluralism in the Age of Imaginaries
Authors: ten Kate Laurens , van Boxtel Bram
Published: September 2023

In recent decades, the paradigm of secularization has been under pressure [...]
The Identification and Analysis of Long-Range Aerosol Transport Pathways with Layered Cloud-Aerosol Lidar with Orthogonal Polarization Datasets from 2006 to 2016
Authors: Wang Lingyu , Wang Wensheng , Lyu Baolei , Zhang Jinghua , Han Yilun , Bai Yuqi , Guo Zhi
Published: September 2023

In this study, we used Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products acquired from 2006 to 2016 to identify global long-range aerosol transport pathways, including the trans-Atlantic, the trans-Pacific, and the trans-Arabian Sea pathways. Deep analyses were subsequently conducted focusing on two significant paths within the range of the trans-Pacific transport pathway, from which we generated a three-stage conceptual model mainly identifying aerosols from the Taklimakan Desert and aerosols from the North China Plain. The results show that in the first stage of the model, the dust or polluted-dust aerosols were emitted, raised, and mixed within the planetary boundary layer (PBL), characterized by high percentages (>70%) of aerosols in the PBL (AODPBL), while in the second stage, some aerosols were further raised into the free troposphere where the AODPBL percentages decreased to less than 40%, driven by vertical movements and turbulences; in the last stage, the aerosols gradually settled back to the surface layer due to gravity and wet deposition, inferred by increasing AODPBL percentages. We demonstrated that the proposed model is capable of characterizing different aerosol types and climate conditions on spatiotemporal scales, providing a straightforward and evident approach to exploring long-range aerosol transport pathways.
Impacts of Extreme-High-Temperature Events on Vegetation in North China
Authors: Yang Qingran , Jiang Chao , Ding Ting
Published: September 2023

Understanding the response of vegetation to temperature extremes is crucial for investigating vegetation growth and guiding ecosystem conservation. North China is a vital hub for China’s economy and food supplies, and its vegetation is highly vulnerable to complex heatwaves. In this study, based on remote sensing data, i.e., the normalized difference vegetation index (NDVI), spatio-temporal variations in vegetation and extreme high temperatures are investigated by using the methods of trend analysis, linear detrending, Pearson correlation and ridge regression. The impacts of extreme-high-temperature events on different vegetation types in North China from 1982 to 2015 are explored on multiple time scales. The results indicate that the NDVI in North China exhibits an overall increasing trend on both annual and monthly scales, with the highest values for forest vegetation and the fastest growth trend for cropland. Meanwhile, extreme-high-temperature events in North China also display an increasing trend. Before detrending, the correlations between the NDVI and certain extreme-high-temperature indices are not significant, while significant negative correlations are observed after detrending. On an annual scale, the NDVI is negatively correlated with extreme temperature indices, except for the number of warm nights, whereas, on a monthly scale, these negative correlations are only found from June to September. Grassland vegetation shows relatively strong correlations with all extreme temperature indices, while forests show nonsignificant correlations with the indices. This study offers new insight into vegetation dynamic variations and their responses to climate in North China.
In Search of Ancient Pre-Roman Imperial Roads: A Case Study of the Application of Remote Sensing in Road Archaeology in the Southern Levant
Authors: Marciak Michał , Sobczyński Daniel , Abadi Omri , Szypuła Bartłomiej , Schwimmer Lior , Čilová Miroslava
Published: September 2023

This paper presents a unique case of the application of remote sensing methods in archaeological survey devoted to ancient pre-Roman Imperial roads in the Southern Levant. The results of our preparatory remote sensing research and subsequent fieldwork in Jordan and Israel between 22 February and 23 March 2023, within the framework of the research project entitled “Travel and Mobility in Hellenistic and Early Roman Palestine”, are reported and discussed. Part of this project is a large-scale, systematic research attempt to discover additional ancient pre-Roman roads and to suggest a working methodology for future research. The methodology is supposed to combine remote sensing research and archaeological survey. The project’s first fieldwork achieved several goals. First, the modern methods enabled us to provide a high-resolution capture of the detected features and artifacts, including the courses of ancient roads and the locations of road-related archaeological sites. Altogether, 105 road remains, 62 archaeological sites, and 14 pottery findings were identified; what is more, 11 GPS (Global Positioning System) tracks of ancient roads were registered. Second, we suggested necessary revisions to the previous state of research and reported new findings. For instance, newly discovered rock art evidence found along Glueck’s Road confirms the continuity of the use of this road long into late antiquity and early Islam. Third, some methodological conclusions were reached. For example, a multi-source approach to identifying ancient roads including the use of archival cartographic sources, archival and modern satellite and aerial imagery, and the databases of archaeological sites is still necessary. However, there can be no doubt that spatial analyses and remote sensing studies must be accompanied by archaeological fieldwork, which is absolutely necessary for determining the dating of the roads (by dating the settlement and pottery) and a detailed identification of the road courses (particularly through the discovery of road-related infrastructure).
LPHOG: A Line Feature and Point Feature Combined Rotation Invariant Method for Heterologous Image Registration
Authors: He Jianmeng , Jiang Xin , Hao Zhicheng , Zhu Ming , Gao Wen , Liu Shi
Published: September 2023

Remote sensing image registration has been a very important research topic, especially the registration of heterologous images. In the research of the past few years, numerous registration algorithms for heterogenic images have been developed, especially feature-based matching algorithms, such as point feature-based or line feature-based matching methods. However, there are few matching algorithms that combine line and point features. Therefore, this study proposes a matching algorithm that combines line features and point features while achieving good rotation invariance. It comprises LSD detection of line features, keypoint extraction, and HOG-like feature descriptor construction. The matching performance is compared with state-of-the-art matching algorithms on three heterogeneous image datasets (optical–SAR dataset, optical–infrared dataset, and optical–optical dataset), verifying our method’s rotational invariance by rotating images in each dataset. Finally, the experimental results show that our algorithm outperforms the state-of-the-art algorithms in terms of matching performance while possessing very good rotation invariance.
Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
Authors: Lovati Giulia , De Michelis Paola , Alberti Tommaso , Consolini Giuseppe
Published: September 2023

The ionosphere has distinctive characteristics under different solar and geomagnetic conditions, as well as throughout the seasons, and has a direct impact on our technological life in terms of radio communication and satellite navigation systems. In the pursuit of developing highly accurate ionospheric models and/or improving existing ones, understanding the various physical mechanisms that influence electron density dynamics is critical. In this study, we apply the Multivariate Empirical Mode Decomposition (MEMD) method to the electron density distribution in the mid-to-high latitude (above 50° magnetic latitude) regions in order to identify the dominant scales at which these mechanisms operate. The data were collected by the Swarm mission in the Northern Hemisphere. MEMD allows us to separate the main intrinsic modes and assess their relative contributions to the original one, thereby identifying the most important modes and the spatial scales at which they exert influence. Our study spanned the period from 1 January 2016 to 31 December 2021, which was characterized by low solar activity levels. This choice allowed for a more focused investigation of other variables influencing electron density distribution under similar solar activity conditions. We specifically examined the variations of the resulting modes in relation to different seasons and geomagnetic activity conditions, providing valuable insights into the complex behavior of the ionosphere in response to various external factors.
Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour
Authors: Adavi Zohreh , Ghassemi Babak , Weber Robert , Hanna Natalia
Published: September 2023

Water vapour plays a key role in long-term climate studies and short-term weather forecasting. Therefore, to understand atmospheric variations, it is crucial to observe water vapour and its spatial distribution. In the current era, Global Navigation Satellite Systems (GNSS) are widely used to monitor this critical atmospheric component because GNSS signals pass through the atmosphere, allowing us to estimate water vapour at various locations and times. The amount of precipitable water vapour (PWV) is one of the most fascinating quantities, which provides meteorologists and climate scientists with valuable information. However, calculating PWV accurately from processing GNSS observations usually requires the input of further observed meteorological parameters with adequate quality and latency. To bypass this problem, hourly PWVs without meteorological parameters are computed using the Random Forest and Artificial Neural Network algorithms in this research. The first step towards this objective is establishing a regional weighted mean temperature model for Austria. To achieve this, measurements of radiosondes launched from different locations in Austria are employed. The results indicate that Random Forest is the most accurate method compared to regression (linear and polynomial), Artificial Neural Network, and empirical methods. PWV models are then developed using data from 39 GNSS stations that cover Austria’s entire territory. The models are afterwards tested under different atmospheric conditions with four radiosonde stations. Based on the obtained results, the Artificial Neural Network model with a single hidden layer slightly outperforms other investigated models, with only a 5% difference in mean absolute error. As a result, the hourly PWV can be estimated without relying on measured meteorological parameters with an average mean absolute error of less than 2.5 mm in Austria.
Actual Evapotranspiration Dominates Drought in Central Asia
Authors: Zhao Zhuoyi , Hao Xingming , Fan Xue , Zhang Jingjing , Zhang Sen , Li Xuewei
Published: September 2023

Central Asia is a drought-prone region that is sensitive to global climate change. The increased actual evapotranspiration intensifies the drought impacts in this area. However, little is known about the similarities and differences between various types of drought in Central Asia, as well as the relative importance of water income and consumption processes during drought events. Therefore, this study evaluates the trends and characteristics of meteorological, agricultural, and hydrological droughts in Central Asia using precipitation, soil moisture, and terrestrial water storage as indicators; explores the temporal correlation of and spatial similarity between various types of drought; and quantitatively assesses the contribution of water balance variables to drought intensity. The results indicate that drought has intensified in Central Asia, and the trends of precipitation, soil moisture, and terrestrial water storage in this region were −0.75 mm·yr−1 (p = 0.36), −0.0003 m3·m−3 yr−1 (p < 0.01), and −0.3742 cm·yr−1 (p < 0.001), respectively. Severe droughts are typically short in duration and high in intensity. Three various types of drought have low temporal correlation and spatial similarity. Furthermore, agricultural and hydrological droughts were primarily driven by actual evapotranspiration, accounting for relative contributions of 64.38% and 51.04% to these drought types, respectively. Moreover, the extent of increased actual evapotranspiration expanded to cover 49.88% of the region, exacerbating agricultural and hydrological droughts in 23.88% and 35.14% of the total study area, respectively. The study findings demonstrate that actual evapotranspiration plays a critical role in causing droughts. This study establishes a theoretical foundation to carry out drought assessment, the construction of multivariate drought indices, and water resource management in Central Asia.
A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression
Authors: Zhao Haixia , Zhou You , Bai Tingting , Chen Yuanzhong
Published: September 2023

Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-Unet. The MSDC-Unet mainly contains modules of deformable convolution and dilated convolution. The deformable convolution can change the shape of the convolution kernel to adjust the shape of seismic signals to fit different features, while the dilated convolution with different dilation rates is used to extract feature information at different scales. Furthermore, we combine Charbonnier loss and structure similarity index measure (SSIM) to better characterize geological structures of seismic data. Several examples of synthetic and field seismic data demonstrate that the proposed method is effective in the comprehensive results in terms of quantitative metrics and visual effect of denoising, compared with two traditional denoising methods and two deep convolutional neural network denoising models.
Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework
Authors: Zhang Kai , Zhang Rundong , Yang Ziqian , Deng Jie , Abdullah Ahsan , Zhou Congying , Lv Xuan , Wang Rui , Ma Zhanhong
Published: September 2023

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km2 was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage.
Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
Authors: Bhattacharya Ananyo , Li Cheng , Renno Nilton O. , Atreya Sushil K. , Sweeney David
Published: September 2023

Airborne dust plays an active role in determining the thermal structure and chemical composition of the present-day atmosphere of Mars and possibly the planet’s climate evolution over time through radiative–convective and cloud microphysics processes. Thus, accurate measurements of the distribution and variability of dust are required. Observations from the Mars Global Surveyor/Thermal Emission Spectrometer Mars Mars Reconnaissance Orbiter/Mars Climate Sounder and Mars Express/Fourier Transform Spectrometer and the Curiosity Rover have limited capability to measure dust. We show that spacecraft occultation of the Martian atmosphere at far-infrared frequencies between 1 and 10 THz can provide the needed global and temporal data on atmospheric dust by providing co-located measurements of temperature and dust opacity from the top of the atmosphere all the way down to the surface. In addition, spacecraft occultation by a small-satellite constellation could provide global measurements of the development of dust storms.
Improvement of Ice Surface Temperature Retrieval by Integrating Landsat 8/TIRS and Operation IceBridge Observations
Authors: Song Lijuan , Wu Yifan , Gong Jiaxing , Fan Pei , Zheng Xiaopo , Zhao Xi
Published: September 2023

Accurate retrieval of ice surface temperature (IST) over the Arctic ice-water mixture zone (IWMZ) is significantly essential for monitoring the change of the polar sea ice environment. Previous researchers have focused on evaluating the accuracy of IST retrieval in pack ice regions, possibly on account of the availability of in situ measurement data. Few of them have assessed the accuracy of IST retrieval on IWMZ. This study utilized Landsat 8/TIRS and Operation IceBridge observations (OIB) to evaluate the accuracy of the current IST retrieval method in IWMZ and proposed an adjustment method for improving the overall accuracy. An initial comparison shows that Landsat 8 IST and OIB IST have minor differences in the pack ice region with RMSE of 0.475 K, MAE of 0.370 K and cold bias of −0.256 K. In the thin ice region, however, the differences are more significant, with RMSE of 0.952 K, MAE of 0.776 K and warm bias of 0.703 K. We suggest that this phenomenon is because the current ice-water classification method misclassified thin ice as water. To address this issue, an adjusted method is proposed to refine the classification of features within the IWMZ and thus improve the accuracy of IST retrieval using Landsat 8 imagery. The results demonstrate that the accuracy of the retrieved IST in the two cases was improved in the thin ice region, with RMSE decreasing by about 0.146 K, Bias decreasing by about 0.311 K, and MAE decreasing by about 0.129 K. After the adjustment, high accuracy was achieved for both pack ice and thin ice in IWMZ.
An Improved In-Flight Calibration Scheme for CSES Magnetic Field Data
Authors: Yang Yanyan , Zhima Zeren , Shen Xuhui , Zhou Bin , Wang Jie , Magnes Werner , Pollinger Andreas , Lu Hengxin , Guo Feng , Lammegger Roland , Zhou Na , Miao Yuanqing , Tan Qiao , Li Wenjing
Published: September 2023

The CSES high precision magnetometer (HPM), consisting of two fluxgate magnetometers (FGM) and one coupled dark state magnetometer (CDSM), has worked successfully for more than 5 years providing continuous magnetic field measurements since the launch of the CSES in February 2018. After rechecking almost every year’s data, it has become possible to make an improvement to the in-flight intrinsic calibration (to estimate offsets, scale values and non-orthogonality) and alignment (to estimate three Euler angles for the rotation between the orthogonalized sensor coordinates and the coordinate system of the star tracker) of the FGM. The following efforts have been made to achieve this goal: For the sensor calibration, FGM sensor temperature corrections on offsets and scale values have been taken into account to remove seasonal effects. Based on these results, Euler angles have been estimated along with global geomagnetic field modeling to improve the alignment of the FGM sensor. With this, a latitudinal effect in the east component of the originally calibrated data could be reduced. Furthermore, it has become possible to prolong the updating period of all calibration parameters from daily to 10 days, without the separation of dayside and nightside data. The new algorithms optimize routine HPM data processing efficiency and data quality.
Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI
Authors: Mukiibi Alex , Franke Angelinus Cornelius , Steyn Joachim Martin
Published: September 2023

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.
Deciphering China’s Socio-Economic Disparities: A Comprehensive Study Using Nighttime Light Data
Authors: Chen Tianyu , Zhou Yuke , Zou Dan , Wu Jingtao , Chen Yang , Wu Jiapei , Wang Jia
Published: September 2023

Achieving equitable and harmonized socio-economic development is a vital gauge of national progress, particularly in geographically extensive nations such as China. This study, employing nighttime lights as a socio-economic development indicator and remote sensing vegetation indices, investigates spatial variations in wealth distribution across China’s eastern and western regions, delineated by the Hu Huanyong Line. It uncovers the balance between economic growth and green space preservation and discrepancies in development and green space allocation. A thorough county-level analysis using this nighttime light (NTL) and vegetation index exposes the dynamic shifts in socio-economic focal points. The Gini coefficient, assessing inequality and spatial autocorrelation within the index ratio, enriches our regional development understanding. The findings depict a heterogeneous yet rapid economic expansion, primarily within a 30 km coastal buffer zone. Despite a decrease in Gini coefficients in both eastern and western regions, the potential for inland development escalates as coastal illumination approaches saturation. This study unveils enduring, yet lessening, economic disparities between eastern and western China, underscoring the necessity for green preservation in eastern development plans. Moreover, inland regions emerge as potential areas for accelerated development. This study offers crucial insights for formulating balanced, sustainable regional development strategies in China.
Enhancing Land Cover Mapping and Monitoring: An Interactive and Explainable Machine Learning Approach Using Google Earth Engine
Authors: Chen Haifei , Yang Liping , Wu Qiusheng
Published: September 2023

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool’s capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE.
Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain–Computer Interfaces by Using Multi-Branch CNN
Authors: Chowdhury Radia Rayan , Muhammad Yar , Adeel Usman
Published: September 2023

A brain–computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.