bluuebunny/crossref_metadata_2025 huggingface dataset:
Attitude Is Not Enough to Separate Solid Waste at Home in Lima
Authors: Méndez-Lazarte Christiam , Bohorquez-Lopez Victor W. , Caycho-Chumpitaz Carlos , Estrada-Merino Alfredo
Published: March 2023

Facilitating solid waste separating behavior at home continues to be a challenge for municipal programs in emerging economies. Large cities concentrate the generation of solid waste and, in Latin America, a great percentage of this waste is not re-used. Therefore, in this research, we explore the drivers motivating solid waste separation at home in Lima. We applied 450 surveys in two municipalities of Lima and analyzed the results through Structural Equation Modeling (SEM). The results demonstrate that attitude, perception of technical knowledge, and availability of physical space influence solid waste separation behavior. Additionally, the mediating role of intention between solid waste separation attitude and behavior is demonstrated. Municipal solid waste recycling programs in emerging economies tend to focus on educational and motivational actions, without giving due importance to space at home in order to manage solid waste. The lack of urban equipment and the limited availability of space at home introduce barriers that limit solid waste separating behaviors in emerging economies.
Tension and Transaction: Dynamics of Religious Recomposition from a Multiscopic Perspective
Authors: Teixeira Alfredo
Published: March 2023

This review article is based on re-reading the Joas vs. Weber discussion about the macro-concept of “disenchantment”. For Joas, the Weberian thesis brings together, in a single explanatory model, different social processes that must be differentiated. Joas’s proposal highlights the need to build research models sensitive to the interaction and the play of different logics of action between tension and transaction. The collection of some of the most recent tendencies shows how research on religion and modernity has renewed its interest in “visible religion”, granting a fundamental place to study the different modalities of religious agency in the recomposition of the public domain. Reading these results allows the reconstruction of an epistemological model centered on the logic of action, considering that religion can no longer be studied only on the terrain of its institutional reproduction. Instead, the plurality of religious agency requires observation at different scales: a multiscopic and multisite perspective.
Pilgrim Reverence as a Pathway to Ecological Conversion: An Analysis of Phenomenological Journaling along the Way
Authors: Muthupandiyan Megan M.
Published: March 2023

By fostering reverence, walking The Way can facilitate a critical tool in the promotion of what Pope Francis calls ecological conversion in his 2015 encyclical Laudato Si’. Re-reading a series of personal phenomenological reflections she wrote during the course of her 2012 and 2017 pilgrimages along the Camino Frances through the lens of philosopher Paul Woodruff’s theory of reverence, the author explores the emotional sources of reverence pilgrims experience through the course of pilgrimage. Cultivated through a body of non-religious but characteristically pilgrim-oriented ceremonies, ritual activities, and acts of perception, the pilgrim reverence the author experiences and observes does not seem to draw from any single theological lexicon; it seems rather to extend, in all cases, into and beyond a feeling for the human community, to the earth itself. It is a spiritual exercise open to humanity itself, where pilgrims have the opportunity to foster recognition that everything is connected in our social, environmental, and economic ecologies.
The Lived Religion of Two Daimista Women in the Context of the Transnationalization of Santo Daime: Notes about a Feminized Religion
Authors: Valamiel Paulina
Published: March 2023

In this article, I discuss the experience of two daimista female leaders located in The Netherlands and in Japan. The reason why their stories are here is because of the prominence of Santo Daime in these countries per world region (Europe and Asia), where the leadership of these women have great projection in the daimista transnational field. The data discussed here were collected through in-depth interviews by using the life history technique. The objective was to analyze how these women experience this religion outside its context of origin. Their religious background appeared as something very important to their lived religion, showing cultural elements with which Santo Daime ends up having to negotiate in the religious global flows. Also, it seems that the feminization of Santo Daime is not only about women’s entrance in this religion, more than that, it is about a feminized way to live and experience religion. Over all, there is a reflexive relationship between Santo Daime and these women because their lives are changed by this religion just as their agency contributes to changes in the core of it and its transnationalization process.
The Effect of In Vitro Maturation (IVM) Protocol Changes on Measures of Oocyte/Embryo Competence
Authors: Rose Bruce , Nguyen Kevin , Brown Samuel
Published: March 2023

Purpose: In vitro maturation (IVM) continues its evolution as new ideas are introduced with the objective of making the IVM procedure easier and more effective. This study combines ideas believed likely to improve the IVM outcome or make the IVM oocyte identification process easier. Methods: A cohort of 45 women underwent an IVM cycle in which letrozole was used with the theoretical objective of improving the competence of small antral follicles, the oocyte aspiration technique was modified to minimize the time between oocyte aspiration and oocyte identification, and blastocysts were transferred during a subsequent cycle with controlled endometrial development. Results: Measures of oocyte competence used for these prospectively followed cycles were as follows: the maturation rate was 90.5%, the fertilization rate was 92.4%, the cleavage rate was 94.6%, the usable blastulation rate per zygote was 50.2%, and the implantation rate was 34.2%. Per transfer, the biochemical pregnancy rate was 63.2%, the clinical pregnancy rate was 55.3% and the ongoing/delivered pregnancy rate at the end of the first trimester was 47.4%. The miscarriage rate for clinical pregnancies in the first trimester was 14.3% and the ongoing twinning rate was 11.1%.
Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach
Authors: Hammoud Mohamad Abed El Rahman , Zhan Peng , Hakla Omar , Knio Omar , Hoteit Ibrahim
Published: March 2023

Detecting mesoscale ocean eddies provides a better understanding of the oceanic processes that govern the transport of salt, heat, and carbon. Established eddy detection techniques rely on physical or geometric criteria, and they notoriously fail to predict eddies that are neither circular nor elliptical in shape. Recently, deep learning techniques have been applied for semantic segmentation of mesoscale eddies, relying on the outputs of traditional eddy detection algorithms to supervise the training of the neural network. However, this approach limits the network’s predictions because the available annotations are either circular or elliptical. Moreover, current approaches depend on the sea-surface height, temperature, or currents as inputs to the network, and these data may not provide all the information necessary to accurately segment eddies. In the present work, we have trained a neural network for the semantic segmentation of eddies using human-based—and expert-validated—annotations of eddies in the Arabian Sea. Training with human-annotated datasets enables the network predictions to include more complex geometries, which occur commonly in the real ocean. We then examine the impact of different combinations of input surface variables on the segmentation performance of the network. The results indicate that providing additional surface variables as inputs to the network improves the accuracy of the predictions by approximately 5%. We have further fine-tuned another pre-trained neural network to segment eddies and achieved a reduced overall training time and higher accuracy compared to the results from a network trained from scratch.
3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting
Authors: Guo Shiqing , Sun Nengli , Pei Yanle , Li Qian
Published: March 2023

Radar echo extrapolation is a commonly used approach for convective nowcasting. The evolution of convective systems over a very short term can be foreseen according to the extrapolated reflectivity images. Recently, deep neural networks have been widely applied to radar echo extrapolation and have achieved better forecasting performance than traditional approaches. However, it is difficult for existing methods to combine predictive flexibility with the ability to capture temporal dependencies at the same time. To leverage the advantages of the previous networks while avoiding the mentioned limitations, a 3D-UNet-LSTM model, which has an extractor-forecaster architecture, is proposed in this paper. The extractor adopts 3D-UNet to extract comprehensive spatiotemporal features from the input radar images. In the forecaster, a newly designed Seq2Seq network exploits the extracted features and uses different convolutional long short-term memory (ConvLSTM) layers to iteratively generate hidden states for different future timestamps. Finally, the hidden states are transformed into predicted radar images through a convolutional layer. We conduct 0–1 h convective nowcasting experiments on the public MeteoNet dataset. Quantitative evaluations demonstrate the effectiveness of the 3D-UNet extractor, the newly designed forecaster, and their combination. In addition, case studies qualitatively demonstrate that the proposed model has a better spatiotemporal modeling ability for the complex nonlinear processes of convective echoes.
An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds
Authors: Deng Lu , Zhang An , Guo Jingjing , Liu Yingkai
Published: March 2023

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.
Study of the Profile Distribution of the Diffuse Attenuation Coefficient and Secchi Disk Depth in the Northwestern South China Sea
Authors: Zhang Xianqing , Li Cai , Zhou Wen , Zheng Yuanning , Cao Wenxi , Liu Cong , Xu Zhantang , Yang Yuezhong , Yang Zeming , Chen Fei
Published: March 2023

The diffuse attenuation coefficient for the downwelling irradiance Kdz,λ is a critical parameter in terms of the optical properties of the ocean. In the northwestern South China Sea, there are complex physical processes, and the accurate estimation of Kdz,λ in the northwestern South China Sea is critical for the study and application of the underwater light field and water constituents. In this study, using Hydrolight 6.0 (HL60) software, Kdz,λ was simulated based on the inherent optical properties (IOPs) and chlorophyll a concentration dataset in the northwestern South China Sea. The simulations were in good agreement with the results calculated by the model of Lee (2005), and the spectral characteristics of Kdz,λ were consistent with several oceanic types according to Jerlov’s classification. The horizontal and vertical distribution characteristics of Kd(z,490) were studied in the two typical upwelling areas of eastern Hainan Island and eastern Vietnam. Kd(z,490) in eastern Hainan Island exhibited an overall decreasing trend from west to east at the same depth, while the vertical depth of the maximum value of Kd(z,490) in eastern Hainan Island was found to increase from west to east, which was significantly associated with the distribution trend of the temperature and salinity. Kd(z,490) in eastern Vietnam exhibited unique horizontal and vertical distribution characteristics due to upwelling, with a low temperature and high salinity. A satisfactory linear relationship between Kd(z,490) and Kdz,λ was found from 420 to 580 nm with R2 > 0.76, root mean square (RMSE) ≤ 0.010 m−1, and mean absolute percentage error (MAPE) < 9%, and this result indicated that Kdz,λ from 420~580 nm could be estimated with Kd(z,490). The regression accuracy sharply decreased after 580 nm, indicating that Kdz,λ estimation based on Kd(z,490) can be more suitably achieved from 420~580 nm and becomes inaccurate after 580 nm. Based on the simulations, an empirical relationship for ZSDD estimation involving Kd,10%490 was developed, and ZSDD in the northwestern South China Sea was calculated, with a range of 5–23 m and a suitable agreement with ZSDD obtained via the method of Lee (2018).
Diffusion Height and Order of Sulfur Dioxide and Bromine Monoxide Plumes from the Hunga Tonga–Hunga Ha’apai Volcanic Eruption
Authors: Li Qidi , Qian Yuanyuan , Luo Yuhan , Cao Le , Zhou Haijin , Yang Taiping , Si Fuqi , Liu Wenqing
Published: March 2023

A violent volcanic eruption attracting considerable attention occurred on 15 January 2022 near the South Pacific island nation of Tonga. To investigate its environmental impact, we retrieved the sulfur dioxide (SO2) and bromine monoxide (BrO) vertical column densities of environmental trace gas monitoring instrument 2 (EMI-2) based on the differential optical absorption spectroscopy algorithm. The results showed westward and southeastward transport of principal parts of SO2 and BrO plumes, respectively, from the Hunga Tonga–Hunga Ha’apai (HTHH) eruption. On 15 January, most of the released SO2 entered the stratosphere (above 20 km) directly and spread rapidly westward (approximately 30 m/s). In contrast, the principal portion of the BrO spread southeastward slowly (approximately 10 m/s) within the 8–15 km altitude layer on 16 January. Our research results also suggest that during the HTHH eruption, BrO was released from the magmatic melt later than SO2. The total SO2 emissions from this eruption were approximately 0.24 Tg. The majority of SO2 and BrO plumes were transported within the Southern Hemisphere. This study is an important extension to the empirical database of volcanological and magmatic degassing research.
Double Branch Parallel Network for Segmentation of Buildings and Waters in Remote Sensing Images
Authors: Chen Jing , Xia Min , Wang Dehao , Lin Haifeng
Published: March 2023

The segmentation algorithm for buildings and waters is extremely important for the efficient planning and utilization of land resources. The temporal and space range of remote sensing pictures is growing. Due to the generic convolutional neural network’s (CNN) insensitivity to the spatial position information in remote sensing images, certain location and edge details can be lost, leading to a low level of segmentation accuracy. This research suggests a double-branch parallel interactive network to address these issues, fully using the interactivity of global information in a Swin Transformer network, and integrating CNN to capture deeper information. Then, by building a cross-scale multi-level fusion module, the model can combine features gathered using convolutional neural networks with features derived using Swin Transformer, successfully extracting the semantic information of spatial information and context. Then, an up-sampling module for multi-scale fusion is suggested. It employs the output high-level feature information to direct the low-level feature information and recover the high-resolution pixel-level features. According to experimental results, the proposed networks maximizes the benefits of the two models and increases the precision of semantic segmentation of buildings and waters.
Region-Specific and Weather-Dependent Characteristics of the Relation between GNSS-Weighted Mean Temperature and Surface Temperature over China
Authors: Wang Minghua , Chen Junping , Han Jie , Zhang Yize , Fan Mengtian , Yu Miao , Sun Chengzhi , Xie Tao
Published: March 2023

Weighted mean temperature of the atmosphere, Tm, is a key parameter for retrieving the precipitable water vapor from Global Navigation Satellite System observations. It is commonly estimated by a linear model that relates to surface temperature Ts. However, the linear relationship between Tm and Ts is associated with geographic regions and affected by the weather. To better estimate the Tm over China, we analyzed the region-specific and weather-dependent characteristics of this linear relationship using 860,054 radiosonde profiles from 88 Chinese stations between 2005 and 2018. The slope coefficients of site-specific linear models are 0.35~0.95, which generally reduce from northeast to southwest. Over southwest China, the slope coefficient changes drastically, while over the northwest, it shows little variation. We developed a Ts∼Tm linear model using the data from rainless days as well as a model using the data from rainy days for each station. At half the stations, mostly located in west and north China, the differences between the rainy-day and rainless-day Tm models are significant and larger than 0.5% (1%) in mean (maximal) relative bias. The regression precisions of the rainy-day models are higher than that of the rainless-day models averagely by 28% for the stations. Radiosonde data satisfying Tm−Ts>10 K and Ts−Tm>30 K most deviate from linear regression models. Results suggest that the former situation is related to low surface temperature (<270 K), as well as striking temperature and humidity inversions below 800 hPa, while the latter situation is related to high surface temperature (>280 K) and a distinct humidity inversion above 600 hPa.
Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data
Authors: Zhang Da , Huang Chunlin , Gu Juan , Hou Jinliang , Zhang Ying , Han Weixiao , Dou Peng , Feng Yaya
Published: March 2023

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-View Images
Authors: Zhao Kun , Gao Qian , Hao Siyuan , Sun Jie , Zhou Lijian
Published: March 2023

Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN)-based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexibly in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness.
Spatiotemporal Evolution of Residential Exposure to Green Space in Beijing
Authors: Cao Yue , Li Guangdong , Huang Yaohui
Published: March 2023

Urban green space has a critical impact on the urban ecological environment, residents’ health, and urban sustainability. Quantifying residential exposure to green space and proposing targeted enhancement strategies in urban areas is helpful to rationally plan urban green space construction, reduce the inequality in residential exposure to green space, and promote environmental equity. However, the long-time evolution analysis of residential exposure to green space at different scales and the influence of green space quality on residential exposure to green space are rarely reported. Here we produced a long-time series dataset of urban green space from 1990 to 2020 based on the 30 m Landsat data and used the Normalized Difference Vegetation Index (NDVI) as a representation of the green space quality to comprehensively analyze residential exposure to green space at the city and block scales within the 5th ring of Beijing, China. We found that the urban green space in Beijing is mainly distributed in urban areas between the 4th and 5th rings (i.e., 153.4 km2 in 2020), and there is little green space within the 2nd ring area (i.e., 12.6 km2 in 2020). There is clear spatial inequality in residential exposure to green space, and about 2.88 million (i.e., ~27%) residents have experienced different degrees of decline in residential exposure to green space from 2015 to 2020. However, the degree of inequality in residential exposure to green space has gradually weakened from a high level (Palma ratio = 2.84) in 1990 to a relatively low level (Palma ratio = 0.81) in 2020. In addition, the spatial-temporal analysis method of residential exposure to green space based on green space quality has certain advantages that can help explore the degraded and lost areas of green space.
A Clutter Parameter Estimation Method Based on Origin Moment Derivation
Authors: Yang Liru , Liu Yongxiang , Yang Wei , Su Xiaolong , Shen Qinmu
Published: March 2023

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters.
An Effective Task Sampling Strategy Based on Category Generation for Fine-Grained Few-Shot Object Recognition
Authors: Liu Shifan , Ma Ailong , Pan Shaoming , Zhong Yanfei
Published: March 2023

The recognition of fine-grained objects is crucial for future remote sensing applications, but this task is faced with the few-shot problem due to limited labeled data. In addition, the existing few-shot learning methods do not consider the unique characteristics of remote sensing objects, i.e., the complex backgrounds and the difficulty of extracting fine-grained features, leading to suboptimal performance. In this study, we developed an improved task sampling strategy for few-shot learning that optimizes the target distribution. The proposed approach incorporates broad category information, where each sample is assigned both a broad and fine category label and converts the target task distribution into a fine-grained distribution. This ensures that the model focuses on extracting fine-grained features for the corresponding broad category. We also introduce a category generation method that ensures the same number of fine-grained categories in each task to improve the model accuracy. The experimental results demonstrate that the proposed strategy outperforms the existing object recognition methods. We believe that this strategy has the potential to be applied to fine-grained few-shot object recognition, thus contributing to the development of high-precision remote sensing applications.
An Efficient Channel Imbalance Estimation Method Based on Subadditivity of Linear Normed Space of Sub-Band Spectrum for Azimuth Multichannel SAR
Authors: Xu Zongxiang , Lu Pingping , Cai Yonghua , Li Junfeng , Yang Tianyuan , Wu Yirong , Wang Robert
Published: March 2023

Azimuth multichannel (AMC) technology is one of the mainstream technical approaches to realize high-resolution wide-swath (HRWS) imaging. It has been successfully applied to several synthetic aperture radar (SAR) satellites in orbit. However, the inevitable imbalance between channels can seriously affect the azimuth reconstruction spectrum, introducing ghost targets into the final imaging results and degrading the SAR image quality. In order to address this issue, this paper proposes a channel imbalance estimation method based on minimizing the sum of the sub-band norm (MSSBN) for the reconstructed azimuth spectrum. First, the amplitude imbalance is calibrated in the range-Doppler domain. Then, the echo in each channel with phase imbalances is reconstructed by filters separately and converted to the range-Doppler domain. Finally, the global optimization algorithm is used to find the phase error of each channel so that the reconstructed postcompensation spectrum has the smallest sub-band spectrum norm sum. By two-dimensional blocking, this method can also estimate the space-varying phase imbalance in the range dimension and the time-varying phase imbalance in the azimuth dimension. Experimental results using simulated and actual AMC SAR data from the GF-3 system validate the proposed algorithm’s high estimation accuracy and excellent computational efficiency.
A High-Resolution Global Moho Model from Combining Gravimetric and Seismic Data by Using Spectral Combination Methods
Authors: Dashtbazi Arash , Voosoghi Behzad , Bagherbandi Mohammad , Tenzer Robert
Published: March 2023

The high-resolution Moho depth model is required in various geophysical studies. However, the available models’ resolutions could be improved for this purpose. Large parts of the world still need to be sufficiently covered by seismic data, but existing global Moho models do not fit the present-day requirements for accuracy and resolution. The isostatic models can relatively reproduce a Moho geometry in regions where the crustal structure is in an isostatic equilibrium, but large segments of the tectonic plates are not isostatically compensated, especially along active convergent and divergent tectonic margins. Isostatic models require a relatively good knowledge of the crustal density to correct observed gravity data. To overcome the lack of seismic data and non-uniqueness of gravity inversion, seismic and gravity data should be combined to estimate Moho geometry more accurately. In this study, we investigate the performance of two techniques for combining long- and short-wavelength Moho geometry from seismic and gravity data. Our results demonstrate that both Butterworth and spectral combination techniques can be used to model the Moho geometry. The results show the RMS of Moho depth differences between our model and the reference models are between 1.7 and 4.7 km for the Butterworth filter and between 0.4 and 4.1 km for the spectral combination.
Improved the Characterization of Flood Monitoring Based on Reconstructed Daily GRACE Solutions over the Haihe River Basin
Authors: Nie Shengkun , Zheng Wei , Yin Wenjie , Zhong Yulong , Shen Yifan , Li Kezhao
Published: March 2023

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of “Temperature-Precipitation” combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change.
Air Pollution Patterns Mapping of SO2, NO2, and CO Derived from TROPOMI over Central-East Europe
Authors: Wieczorek Beata
Published: March 2023

The analysis of changes in the level of air pollution concentration allows for the control of air quality and its compliance with the normative requirements. Currently, every country in Europe implements air quality monitoring. However, during emergencies in areas that are often difficult to monitor, the only source of information is geospatial data obtained by means of Earth observation techniques. The aims of this study were to estimate the amounts of pollutant concentrations and develop a pattern of spatiotemporal changes in Central and Eastern Europe in Poland and Ukraine. Due to the ongoing military operations in Ukraine, it is an area that is difficult to access. Pollution from industrial facilities, fires, collapsed buildings, and the use of explosive weapons poses a threat to air quality. Additionally, the impact of war on air pollution concentration levels remains unclear. This work characterized the changes in the distribution of sulfur dioxide, nitrogen dioxide and carbon monoxide concentrations in 2018–2022 in local zones in both countries. Publicly available TROPOMI-S5 satellite data were used for this study, which were compared with measurements from ground stations in Poland. It has been estimated that the concentration of NO2 (+0.67 ± 0.47 µmol/m2) in Poland has increased and the level of SO2 and CO have decreased in both studied areas: in Poland (−161.67 ± 5.48 µmol/m2, −470.85 ± 82.81 µmol/m2) and in Ukraine (−32.56 ± 23.51 µmol/m2, −438.04 ± 80.76 µmol/m2). The concentration of NO2 in Ukraine has decreased by −0.28 ± 0.21 µmol/m2.
Dynamic Vascular Imaging Using Active Breast Thermography
Authors: Gershenson Meir , Gershenson Jonathan
Published: March 2023

Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared ‘thermogram’ breast imaging can map the skin temperature, and signal inversion with components analysis can be used to identify the mechanisms of thermal image generation of the vasculature using dynamic thermal data. This work focuses on using dynamic infrared breast imaging to identify the thermal response of the stationary vascular system and the physiologic vascular response to a temperature stimulus affected by vasomodulation. The recorded data are analyzed by converting the diffusive heat propagation into a virtual wave and identifying the reflection using component analysis. Clear images of passive thermal reflection and thermal response to vasomodulation were obtained. In our limited data, the magnitude of vasoconstriction appears to depend on the presence of cancer. The authors propose future studies with supporting diagnostic and clinical data that may provide validation of the proposed paradigm.
Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
Authors: Tsipi Lefteris , Karavolos Michail , Bithas Petros , Vouyioukas Demosthenes
Published: March 2023

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.
An Improved Optimization Function to Integrate the User’s Comfort Perception into a Smart Home Controller Based on Particle Swarm Optimization and Fuzzy Logic
Authors: Costa Jonatha Rodrigues da , Barroso Giovanni Cordeiro , Souza Darielson Araújo de , Batista Josias Guimarães , Souza Junior Antonio Barbosa de , Rios Clauson Sales do Nascimento , Vasconcelos Felipe José de Sousa , Júnior José Nogueira do Nascimento , Bezerra Ismael de Souza , Lima Alanio Ferreira de , Santana Killdary Aguiar de , Oliveira Júnior José Raimundo de
Published: March 2023

Scheduling residential loads for financial savings and user comfort may be performed by smart home controllers (SHCs). For this purpose, the electricity utility’s tariff variation costs, the lowest tariff cost schedules, the user’s preferences, and the level of comfort that each load may add to the household user are examined. However, the user’s comfort modeling, found in the literature, does not take into account the user’s comfort perceptions, and only uses the user-defined preferences for load on-time when it is registered in the SHC. The user’s comfort perceptions are dynamic and fluctuating, while the comfort preferences are fixed. Therefore, this paper proposes the modeling of a comfort function that takes into account the user’s perceptions using fuzzy logic. The proposed function is integrated into an SHC that uses PSO for scheduling residential loads, and aims at economy and user comfort as multiple objectives. The analysis and validation of the proposed function includes different scenarios related to economy–comfort, load shifting, consideration of energy tariffs, user preferences, and user perceptions. The results show that it is more beneficial to use the proposed comfort function method only when the user requires SHC to prioritize comfort at the expense of financial savings. Otherwise, it is more beneficial to use a comfort function that only considers the user’s comfort preferences and not their perceptions.
Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services
Authors: Lee Kyungroul , Lee Jaehyuk , Lee Sun-Young , Yim Kangbin
Published: March 2023

A variety of data-based services such as cloud services and big data-based services have emerged in recent times. These services store data and derive the value of the data. The reliability and integrity of the data must be ensured. Unfortunately, attackers have taken valuable data as hostage for money in attacks called ransomware. It is difficult to recover original data from files in systems infected by ransomware because they are encrypted and cannot be accessed without keys. There are cloud services to backup data; however, encrypted files are synchronized with the cloud service. Therefore, the original file cannot be restored even from the cloud when the victim systems are infected. Therefore, in this paper, we propose a method to effectively detect ransomware for cloud services. The proposed method detects infected files by estimating the entropy to synchronize files based on uniformity, one of the characteristics of encrypted files. For the experiment, files containing sensitive user information and system files for system operation were selected. In this study, we detected 100% of the infected files in all file formats, with no false positives or false negatives. We demonstrate that our proposed ransomware detection method was very effective compared to other existing methods. Based on the results of this paper, we expect that this detection method will not synchronize with a cloud server by detecting infected files even if the victim systems are infected with ransomware. In addition, we expect to restore the original files by backing up the files stored on the cloud server.
Distributed Optical Measurement System for Plate Fin Heat Exchanger
Authors: Li Huajun , Yang Xiao , Wang Baoliang , Ji Haifeng
Published: March 2023

The acquirement of the flow information in plate-fin heat exchanger (PFHE) is limited by its metal structure and complex flow condition. This work develops a new distributed optical measurement system to obtain flow information and boiling intensity. The system utilizes numerous optical fibers installed at the surface of the PFHE to detect optical signals. The attenuation and fluctuation of the signals reflect the variation of the gas-liquid interfaces and can be further used to estimate the boiling intensity. Practical experiments of flow boiling in PFHE with different heating fluxes have been carried out. The results verify that the measurement system can obtain the flow condition. Meanwhile, according to the results, the boiling development in PFHE can be divided into four stages with the increase in the heating flux, including the unboiling stage, initiation stage, boiling developing stage, and fully developed stage.
Pose ResNet: 3D Human Pose Estimation Based on Self-Supervision
Authors: Bao Wenxia , Ma Zhongyu , Liang Dong , Yang Xianjun , Niu Tao
Published: March 2023

The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose ResNet. ResNet50 is used as the basic network for extract features. First, a convolutional block attention module (CBAM) was introduced to refine selection of significant pixels. Then, a waterfall atrous spatial pooling (WASP) module is used to capture multi-scale contextual information from the extracted features to increase the receptive field. Finally, the features are input into a deconvolution network to acquire the volume heat map, which is later processed by a soft argmax function to obtain the coordinates of the joints. In addition to the two learning strategies of transfer learning and synthetic occlusion, a self-supervised training method is also used in this model, in which the 3D labels are constructed by the epipolar geometry transformation to supervise the training of the network. Without the need for 3D ground truths for the dataset, accurate estimation of the 3D human pose can be realized from a single 2D image. The results show that the mean per joint position error (MPJPE) is 74.6 mm without the need for 3D ground truth labels. Compared with other approaches, the proposed method achieves better results.
Detection of Semi-Solid Materials Utilizing Triple-Rings CSRR Microwave Sensor
Authors: Al-Gburi Ahmed Jamal Abdullah , Rahman Norhanani Abd , Zakaria Zahriladha , Palandoken Merih
Published: March 2023

This article proposes the design, fabrication and measurement of a triple-rings complementary split-ring resonator (CSRR) microwave sensor for semi-solid material detection. The triple-rings CSRR sensor was developed based on the CSRR configuration with curve-feed designed together, utilizing a high-frequency structure simulator (HFSS) microwave studio. The designed triple rings CSRR sensor resonates at 2.5 GHz, performs in transmission mode, and senses shift in frequency. Six cases of the sample under tests (SUTs) were simulated and measured. These SUTs are Air (without SUT), Java turmeric, Mango ginger, Black Turmeric, Turmeric, and Di-water, and detailed sensitivity analysis is conducted for the frequency resonant at 2.5 GHz. The semi-solid tested mechanism is undertaken using a polypropylene (PP) tube. The samples of dielectric material are filled into PP tube channels and loaded in the CSRR centre hole. The e-fields near the resonator will affect the interaction with the SUTs. The finalized CSRR triple-rings sensor was incorporated with defective ground structure (DGS) to deliver high-performance characteristics in microstrip circuits, leading to a high Q-factor magnitude. The suggested sensor has a Q-factor of 520 at 2.5 GHz with high sensitivity of about 4.806 and 4.773 for Di-water and Turmeric samples, respectively. The relationship between loss tangent, permittivity, and Q-factor at the resonant frequency has been compared and discussed. These given outcomes make the presented sensor ideal for detecting semi-solid materials.
Domain Word Extension Using Curriculum Learning
Authors: Seong Sujin , Cha Jeongwon
Published: March 2023

Self-supervised learning models, such as BERT, have improved the performance of various tasks in natural language processing. Although the effect is reduced in the out-of-domain field and not the the trained domain thus representing a limitation, it is difficult to train a new language model for a specific domain since it is both time-consuming and requires large amounts of data. We propose a method to quickly and effectively apply the pre-trained language models trained in the general domain to a specific domain’s vocabulary without re-training. An extended vocabulary list is obtained by extracting a meaningful wordpiece from the training data of the downstream task. We introduce curriculum learning, training the models with two successive updates, to adapt the embedding value of the new vocabulary. It is convenient to apply because all training of the models for downstream tasks are performed in one run. To confirm the effectiveness of the proposed method, we conducted experiments on AIDA-SC, AIDA-FC, and KLUE-TC, which are Korean classification tasks, and subsequently achieved stable performance improvement.