bluuebunny/crossref_metadata_2025 huggingface dataset:
The Spirit Is within Us! Ritual Practices of Latin American Pentecostals in Barcelona
Authors: Muñoz-Henríquez Wilson , Fernández-Mostaza M. Esther , Soto Lara José Julián
Published: May 2022

In recent years, Christian Pentecostalism has been one of the most successful religious movements in the province of Barcelona, while the high level of immigration from Latin America has clearly been an influential factor in its development. Yet, despite the fact that Pentecostalism has played a prominent role in religious diversification in Catalonia, it has been the subject of very few studies. This paper seeks to address this gap in research and shed light on an area of fundamental importance to the movement: its ritual dimension. Drawing on ethnographic information from the Iglesia Evangélica el Vallès (Canovelles), we describe and analyze the principal channels of communication with the sacred established by the Latin American Pentecostals. In our conclusions, we show that the communicative practices developed during worship are oriented toward manifesting the presence of the Holy Spirit, leading to the emergence of ritual condensation around this symbolic force. For this purpose, practices such as “praise”, the “laying on of hands” and “speaking in tongues” establish sequential and progressive communication with the Holy Spirit throughout the process of worship, culminating in mimetic communication.
Diamagnetic Therapy in a Patient with Complex Regional Pain Syndrome Type I and Multiple Drug Intolerance: A Case Report
Authors: Roberti Roberta , Marcianò Gianmarco , Casarella Alessandro , Rania Vincenzo , Palleria Caterina , Vocca Cristina , Catarisano Luca , Muraca Lucia , Citraro Rita , Romeo Pietro , De Sarro Giovambattista , Gallelli Luca
Published: May 2022

Complex regional pain syndrome (CRPS) is a neurologic chronic pain condition hard to diagnose and treat, and able to significantly impact the quality of life. Currently, the available multimodal, individualized treatments (i.e., pharmacological and non-pharmacological therapies including invasive procedures) are aimed only at symptom control. Herein, we report a 69-year-old Caucasian female who came to our attention due to a 3-year history of severe (10/10) burning pain in her right ankle, along with oedema and local changes in skin color and temperature, which occurred after the ankle sprain. Previous pharmacological attempts failed due to multiple drug intolerance. Clinical examination confirmed the CRPS type I diagnosis, and a weekly diamagnetic therapy protocol was started since the patient refused further medications and interventional procedures. After 10 weeks of treatment, a significant (p < 0.01) reduction in pain severity and absence of oedema (difference in ankles’ circumference: from 3 cm to 0) were observed, with consequent improvements in quality of life and no adverse events. Although high-quality clinical evidence is still lacking, our case report suggests further investigating the potential use of diamagnetic therapy as a non-invasive and safe adjunctive treatment for CRPS, and as an alternative when patients did not benefit from drugs and/or refuse invasive procedures.
Deep Learning Methods to Reveal Important X-ray Features in COVID-19 Detection: Investigation of Explainability and Feature Reproducibility
Authors: Apostolopoulos Ioannis D. , Apostolopoulos Dimitris J. , Papathanasiou Nikolaos D.
Published: May 2022

X-ray technology has been recently employed for the detection of the lethal human coronavirus disease 2019 (COVID-19) as a timely, cheap, and helpful ancillary method for diagnosis. The scientific community evaluated deep learning methods to aid in the automatic detection of the disease, utilizing publicly available small samples of X-ray images. In the majority of cases, the results demonstrate the effectiveness of deep learning and suggest valid detection of the disease from X-ray scans. However, little has been investigated regarding the actual findings of deep learning through the image process. In the present study, a large-scale dataset of pulmonary diseases, including COVID-19, was utilized for experiments, aiming to shed light on this issue. For the detection task, MobileNet (v2) was employed, which has been proven very effective in our previous works. Through analytical experiments utilizing feature visualization techniques and altering the input dataset classes, it was suggested that MobileNet (v2) discovers important image findings and not only features. It was demonstrated that MobileNet (v2) is an effective, accurate, and low-computational-cost solution for distinguishing COVID-19 from 12 various other pulmonary abnormalities and normal subjects. This study offers an analysis of image features extracted from MobileNet (v2), aiming to investigate the validity of those features and their medical importance. The pipeline can detect abnormal X-rays with an accuracy of 95.45 ± 1.54% and can distinguish COVID-19 with an accuracy of 89.88 ± 3.66%. The visualized results of the Grad-CAM algorithm provide evidence that the methodology identifies meaningful areas on the images. Finally, the detected image features were reproducible in 98% of the times after repeating the experiment for three times.
Subcutaneous Emphysema of the Orbit after Nose-Blowing
Authors: Mihara Riku , Murai Yasuo , Sato Shun , Matano Fumihiro , Morita Akio
Published: May 2022

Orbital emphysema after nose-blowing is an uncommon condition and can appear without a trigger. Herein, we reported a case of orbital emphysema after nose-blowing and performed a literature review. A 68-year-old man fell and sustained an injury near his left orbit. No symptoms were noted. He noticed a left periorbital swelling after blowing his nose. Through computed tomography examination, he was diagnosed with subcutaneous emphysema. There are no previous reports that have reviewed the clinical features, need for surgery, and severity of symptoms of subcutaneous emphysema after nasal swallowing due to different factors. We retrospectively analyzed a cohort of 48 cases by searching PubMed to clarify these issues. Regarding the emphysema trigger, 21 cases had an injury or had previously undergone surgery. In 34 cases, conservative treatment was required, while surgery was selected in the acute phase in 6 cases and after the acute phase as a radical cure in 8 cases. Reduced visual acuity, diplopia, exophthalmos, facial hypoesthesia, and color disorders were noted and were more common among surgical cases. The literature review revealed no association between fracture location and the need for surgery; furthermore, surgery was less required in non-trauma cases, excluding osteoma, than in trauma cases (p = 0.0169). Our study reveals that a strict follow-up examination of visual symptoms is necessary for the first 2 days in cases of subcutaneous emphysema caused by nose blowing after facial trauma.
Automatic Calibration of a LiDAR–Camera System Based on Instance Segmentation
Authors: Rotter Pawel , Klemiato Maciej , Skruch Pawel
Published: May 2022

In this article, we propose a method for automatic calibration of a LiDAR–camera system, which can be used in autonomous cars. This approach does not require any calibration pattern, as calibration is only based on real traffic scenes observed by sensors; the results of camera image segmentation are compared with scanning LiDAR depth data. The proposed algorithm superimposes the edges of objects segmented by the Mask-RCNN network with depth discontinuities. The method can run in the background during driving, and it can automatically detect decalibration and correct corresponding rotation matrices in an online and near real-time mode. Experiments on the KITTI dataset demonstrated that, for input data of moderate quality, the algorithm could calculate and correct rotation matrices with an average accuracy of 0.23°.
Galileo Time Transfer with Five-Frequency Uncombined PPP: A Posteriori Weighting, Inter-Frequency Bias, Precise Products and Multi-Frequency Contribution
Authors: Zhang Zhehao , Pan Lin
Published: May 2022

Galileo satellites can broadcast signals on five frequencies, namely E1, E5A, E5B, E5 (A+B), and E6. The multi-frequency integration has become an emerging trend in Global Navigation Satellite System (GNSS) data processing. This study focused on the precise time transfer based on Galileo five-frequency uncombined precise point positioning (PPP), including the performance comparison of PPP time transfer with a priori and a posteriori weighting strategies, with different inter-frequency bias (IFB) dynamic models, and with the precise satellite products from different analysis centers, as well as the contribution of multi-frequency observations for time transfer. Compared with the a priori weighting strategy, the short-term frequency stability of time transfer adopting the Helmert variance component estimation method can be improved by 28.9–37.6% when the average time is shorter than 100 s. The effect of IFB dynamic models on Galileo five-frequency PPP time transfer is not obvious. When employing the post-processed precise satellite products from seven analysis centers, the accuracy of time transfer can be better than 0.1 ns, while an accuracy of 0.253 ns can be obtained in the real-time mode. At an average time of approximately 10,000 s, the post-processed time transfer with Galileo five-frequency PPP can provide a frequency stability of 3.283 × 10−14 to 3.459 × 10−14, while that in real-time mode can be 3.541 × 10−14. Compared with dual-frequency PPP results, the contribution of multi-frequency combination to the accuracy and frequency stability of time transfer is not significant, but multi-frequency PPP can achieve more reliable time transfer results when the signal quality is poor.
Fire Radiative Power (FRP) Values for Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) Hotspots Derived from the Advanced Himawari Imager (AHI)
Authors: Engel Chermelle B. , Jones Simon D. , Reinke Karin J.
Published: May 2022

The purpose of this research was to derive and evaluate fire radiative power (FRP) values for real-time Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT)/Advanced Himawari Imager (AHI) hotspots. While BRIGHT/AHI hotspots with 2 km nominal resolution are available every 10 min, they are without FRP values. Here, we present a method to calculate FRP values for BRIGHT/AHI hotspots and compute them over a 12-month period, day and night. FRP distributions from BRIGHT/AHI hotspots and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) hotspots are compared to assess relative agreement, with the distributions found to be broadly similar. Nuanced differences between the sensor FRP values were explored highlighting the need for a deeper understanding of the fire detection and FRP algorithms when doing intercomparisons. Notwithstanding the complexities of FRP intercomparisons, the computationally simple BRIGHT/AHI FRP definition allows for fast and real-time reporting of BRIGHT/AHI hotspots FRP.
Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale
Authors: Li Weitao , Su Xin , Feng Lan , Wu Jinyang , Zhang Yujie , Cao Mengdan
Published: May 2022

Three parallel Visible/Infrared Imager Radiometer Suite (VIIRS) aerosol products (SOAR, NOAA, and AERDT) provided data since 2012. It is necessary to study the performances and advantages of different products. This study aims to analyze the accuracy and error of these products over the ocean and compare them with each other. The results show that the three VIIRS ocean aerosol retrievals (including total aerosol optical depth (AOD), fine mode fraction, Ångström exponent (AE), and fine AOD (AODF)) correlate well with AErosol RObotic NETwork (AERONET) retrievals (e.g., correlation >0.895 for AOD and >0.825 for AE), which are comparable to the newest moderate-resolution imaging spectro-radiometer (MODIS) retrievals. Overall, the SOAR retrievals with quality filtering have the best validation accuracy of all parameters. Therefore, it is more recommended to use. The differences in the annual AOD spatial patterns of different products are small (bias < 0.016), but their AE spatial patterns are evidently different (bias > 0.315), indicating the large uncertainty of VIIRS AE. Error analysis shows that the scattering angle and wind speed affect aerosol retrieval. Application of the non-spherical dust model may reduce the dependence of retrieval bias on the scattering angle. Overall, this study provides validation support for VIIRS products usage and possible algorithm improvements.
L-Unet: A Landslide Extraction Model Using Multi-Scale Feature Fusion and Attention Mechanism
Authors: Dong Zhangyu , An Sen , Zhang Jin , Yu Jinqiu , Li Jinhui , Xu Daoli
Published: May 2022

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively.
Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
Authors: Gao Tong , Chen Hao , Lu Junhong
Published: May 2022

To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods.
SARCASTIC v2.0—High-Performance SAR Simulation for Next-Generation ATR Systems
Authors: Woollard Michael , Blacknell David , Griffiths Hugh , Ritchie Matthew A.
Published: May 2022

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes.
Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling
Authors: Bo Yong , Li Xueke , Liu Kai , Wang Shudong , Zhang Hongyan , Gao Xiaojie , Zhang Xiaoyuan
Published: May 2022

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.
Distinct Variability between Semidiurnal and Diurnal Internal Tides at the East China Sea Shelf
Authors: Wang Weidong , Robertson Robin , Wang Yang , Zhao Chen , Hao Zhanjiu , Yin Baoshu , Xu Zhenhua
Published: May 2022

Breaking internal tides and induced mixing are critical to shelf dynamics, including heat and mass exchanges. Spatiotemporal variability of internal tides and modulation factors for the southern East China Sea shelf were examined based on a combination of a three-month mooring velocity and satellite altimeter data. Semidiurnal and diurnal internal tides exhibited distinct temporal trends, with the semidiurnal internal tides enhanced by an order of magnitude during the latter half of the record, while the diurnal internal tides followed quasi spring-neap cycles with a generally stable intensity except for two specific periods of strengthening. These internal tides probably originated remotely over the shelf-slope area northeast of Taiwan. Time-varying stratification was the most important factor for the internal tidal magnitude. In addition, varying background currents influenced the diurnal critical latitude band, which explains the slightly enhanced diurnal internal tides during the two periods. Although both semidiurnal and diurnal internal tides were mode-1 dominated, the semidiurnal internal tides were surface intensified while the diurnal tides were bottom intensified. The proportion of higher mode internal tides increased during robust eddy activities. Stronger background vertical shear corresponded to high-frequency events and energy transfers from tidal frequencies to high frequencies associated with turbulent mixing.
Object Tracking and Geo-Localization from Street Images
Authors: Wilson Daniel , Alshaabi Thayer , Van Oort Colin , Zhang Xiaohan , Nelson Jonathan , Wshah Safwan
Published: May 2022

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field.
Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops
Authors: Yin Caixia , Lv Xin , Zhang Lifu , Ma Lulu , Wang Huihan , Zhang Linshan , Zhang Ze
Published: May 2022

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.
Research on Lightweight Disaster Classification Based on High-Resolution Remote Sensing Images
Authors: Yuan Jianye , Ma Xin , Han Ge , Li Song , Gong Wei
Published: May 2022

With the increasing frequency of natural disasters becoming, it is very important to classify and identify disasters. We propose a lightweight disaster classification model, which has lower computation and parameter quantities and a higher accuracy than other classification models. For this purpose, this paper specially proposes the SDS-Network algorithm, which is optimized on ResNet, to deal with the above problems of remote sensing images. First, it implements the spatial attention mechanism to improve the accuracy of the algorithm; then, the depth separable convolution is introduced to reduce the number of model calculations and parameters while ensuring the accuracy of the algorithm; finally, the effect of the model is increased by adjusting some hyperparameters. The experimental results show that, compared with the classic AlexNet, ResNet18, VGG16, VGG19, and Densenet121 classification models, the SDS-Network algorithm in this paper has a higher accuracy, and when compared with the lightweight models mobilenet series, shufflenet series, squeezenet series, and mnasnet series, it has lower model complexity and a higher accuracy rate. According to a comprehensive performance comparison of the charts made in this article, it is found that the SDS-Network algorithm is still better than the regnet series algorithm. Furthermore, after verification with a public data set, the SDS-Network algorithm in this paper is found to have a good generalization ability. Thus, we can conclude that the SDS-Network classification model of the algorithm in this paper has a good classification effect, and it is suitable for disaster classification tasks. Finally, it is verified on public data sets that the proposed SDS-Network has good generalization ability and portability.
Lidar-Derived Aerosol Properties from Ny-Ålesund, Svalbard during the MOSAiC Spring 2020
Authors: Dube Jonas , Böckmann Christine , Ritter Christoph
Published: May 2022

In this work, we present Raman lidar data (from a Nd:YAG operating at 355 nm, 532 nm and 1064 nm) from the international research village Ny-Ålesund for the time period of January to April 2020 during the Arctic haze season of the MOSAiC winter. We present values of the aerosol backscatter, the lidar ratio and the backscatter Ångström exponent, though the latter depends on wavelength. The aerosol polarization was generally below 2%, indicating mostly spherical particles. We observed that events with high backscatter and high lidar ratio did not coincide. In fact, the highest lidar ratios (LR > 75 sr at 532 nm) were already found by January and may have been caused by hygroscopic growth, rather than by advection of more continental aerosol. Further, we performed an inversion of the lidar data to retrieve a refractive index and a size distribution of the aerosol. Our results suggest that in the free troposphere (above ≈2500 m) the aerosol size distribution is quite constant in time, with dominance of small particles with a modal radius well below 100 nm. On the contrary, below ≈2000 m in altitude, we frequently found gradients in aerosol backscatter and even size distribution, sometimes in accordance with gradients of wind speed, humidity or elevated temperature inversions, as if the aerosol was strongly modified by vertical displacement in what we call the “mechanical boundary layer”. Finally, we present an indication that additional meteorological soundings during MOSAiC campaign did not necessarily improve the fidelity of air backtrajectories.
The Urban Seismic Observatory of Catania (Italy): A Real-Time Seismic Monitoring at Urban Scale
Authors: Patanè Domenico , Tusa Giuseppina , Yang William , Astuti Antonio , Colino Antonio , Costanza Antonio , D’Anna Giuseppe , Di Prima Sergio , Fertitta Gioacchino , Mangiagli Salvatore , Martino Claudio , Torrisi Orazio
Published: May 2022

We describe the first dense real-time urban seismic–accelerometric network in Italy, named OSU-CT, located in the historic center of Catania. The city lies in the region with the greatest danger, vulnerability, and earthquake exposure in the entire Italian territory. OSU-CT was planned and realized within the project called EWAS “an Early WArning System for cultural heritage”, aimed at the rapid assessment of earthquake-induced damage and the testing of an on-site earthquake early warning system. OSU-CT is mainly based on low-cost instrumentation realized ad hoc by using cutting-edge technologies and digital MEMS (micro-electro-mechanical systems) triaxial accelerometers with excellent resolution and low noise. Twenty of the forty scheduled stations have already been set up on the ground floor of significant historic public buildings. In order to assess the performance of an earthquake early warning (EEW) on-site system, we also installed wide-band velocimeters (ETL3D/5s) in three edifices chosen as test sites, which will be instrumented for a structural health monitoring (SHM). In addition to several laboratory and field validation tests on the developed instruments, an effective operational test of OSU-CT was the Mw 4.3 earthquake occurring on 23 December 2021, 16 km west, south-west of Catania. Peak ground accelerations (4.956 gal to 39.360 gal) recorded by the network allowed obtaining a first urban shakemap and determining a reliable distribution of ground motion in the historical center of the city, useful for the vulnerability studies of the historical edifices.
Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery
Authors: AbdelRahman Mohamed A. E. , Afifi Ahmed A. , D’Antonio Paola , Gabr Safwat S. , Scopa Antonio
Published: May 2022

Salinity systems are well known as extreme environmental systems that occur either naturally or by certain human activities, in arid and semiarid regions, which may harm crop production. Soil salinity identification is essential for soil management and reclamation projects. Information derived from space data acquisition systems (e.g., Landsat, ASTER) is considered as one of the most rapid techniques in mapping Salt-Affected Soil (SAfSoil). The current study tested the previously proposed salinity indices on the northern Nile Delta region, Egypt. The results indicated that most of the indices were not suitable to detect the SAfSoil in the area, due to the interaction between the bare soils, salts, and urbanization. To resolve this issue, the current work suggested a new index for detecting and monitoring the SAfSoil in the Nile Delta region. The newly proposed index takes into consideration plant health, the salt crust at the surface of the soils, as well as urbanization. It facilitates the mapping processes of SAfSoil in the area compared to any other previously proposed index. In this respect, multi-temporal Landsat-7 and 8 satellite data, acquired in 2002, 2016, and 2021, were used. The new index was prepared using the 2002 data and verified using the 2016 and 2021 data. Field measurements and data collected during 2002, 2016, and 2021 were utilized as ground truth data to assess the accuracy of the results obtained from the proposed index. The evaluation of the results indicated that the accuracy assessment for 2002, 2016, and 2021 images was 94.58, 96.08, and 95.68%, respectively. Finally, the effectiveness of using remote sensing in detecting and mapping SAfSoil is outlined.
Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency
Authors: Gong Xi , Yao Feng , Ma Jiayi , Jiang Junjun , Lu Tao , Zhang Yanduo , Zhou Huabing
Published: May 2022

Feature matching is a key method of feature-based image registration, which refers to establishing reliable correspondence between feature points extracted from two images. In order to eliminate false matchings from the initial matchings, we propose a simple and efficient method. The key principle of our method is to maintain the topological and affine transformation consistency among the neighborhood matches. We formulate this problem as a mathematical model and derive a closed solution with linear time and space complexity. More specifically, our method can remove mismatches from thousands of hypothetical correspondences within a few milliseconds. We conduct qualitative and quantitative experiments on our method on different types of remote-sensing datasets. The experimental results show that our method is general, and it can deal with all kinds of remote-sensing image pairs, whether rigid or non-rigid image deformation or image pairs with various shadow, projection distortion, noise, and geometric distortion. Furthermore, it is two orders of magnitude faster and more accurate than state-of-the-art methods and can be used for real-time applications.
Spatio-Temporal Characteristics of the Evapotranspiration in the Lower Mekong River Basin during 2008–2017
Authors: Pan Xin , Liu Suyi , Yang Yingbao , You Chaoshuai , Yang Zi , Xie Wenying , Li Tengteng
Published: May 2022

Droughts and floods have occurred frequently in the Lower Mekong River Basin in recent years. Obtaining the evapotranspiration (ET) in the basin helps people to better understand water cycle and water resources. In this study, we retrieved and validated ET in the Lower Mekong Basin over multiple years (from 2008 to 2017) using remote sensing products. Based on the retrieval ET, we analyzed the spatial-temporal variation of ET and influencing factors at the monthly, seasonal, and inter-annual scale respectively. The results revealed that the overall variation trend of ET at annual scale slightly increased during 2008 to 2017, with the highest annual ET being 1198 mm/year in 2015 and the lowest annual ET being 949 mm/year in 2008. At the seasonal scale, ET in the rainy season was lower than the dry season; at the monthly scale, March had the highest monthly ET (101 mm/month) while July had the lowest monthly ET (73 mm/month). Spatial analyzing showed that ET in the margin of this region was higher (with on average about 1250 mm/year) and lower in the middle (with on average about 840 mm/year), and monthly ET changed mostly in forest areas with the difference of 60 mm/month. Influencing analyzing results showed that ET was mainly driven by solar radiation and near-surface temperature, and precipitation had an inhibitory effect on ET in the rainy season months. The study also showed that forests in the basin are very sensitive to solar radiation, with a correlation coefficient of 0.89 in March (the month with the highest ET) and 0.45 in July (the month with the lowest ET).
A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction
Authors: Xiao Xiao , Guo Wenliang , Chen Rui , Hui Yilong , Wang Jianing , Zhao Hongyu
Published: May 2022

Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.
Accuracy of Code GNSS Receivers under Various Conditions
Authors: Magiera Weronika , Vārna Inese , Mitrofanovs Ingus , Silabrieds Gunārs , Krawczyk Artur , Skorupa Bogdan , Apollo Michal , Maciuk Kamil
Published: May 2022

The main objective of this research work was to study the accuracy of GNSS code receivers under poor sky visibility conditions based on measurements on three different objects (point, line, and surface) and additionally to test results on point positioning with good sky visibility conditions. The measurement was based on 3 smartphones (in the same mode to check repeatability) and 2 handheld receivers (working in GPS+GLONASS modes). The methodology was based on the RTK technique, whose coordinates were assumed as a reference. Based on the results, the significant influence of measuring in the vicinity of high trees on the obtained accuracy was observed for both the precise geodetic equipment and the tested code receivers. More favorable results of point positioning were observed when using mobile phones. On the other hand, in the case of measurement in motion, the handheld receivers guaranteed higher accuracy. Moreover, the study showed that handheld receivers might achieve a better accuracy than smartphones, and that position might be determined with a greater accuracy and reliability. Furthermore, handheld receivers were characterized by a smaller number of outliers.
Using Multi-Platform Satellite Observations to Study the Atmospheric Evolution of Brown Carbon in Siberian Biomass Burning Plumes
Authors: Konovalov Igor B. , Golovushkin Nikolai A. , Beekmann Matthias , Turquety Solène
Published: May 2022

A bulk of evidence from in situ observations and lab experiments suggests that brown carbon (light-absorbing organic compounds in particles) can provide a significant yet highly variable contribution to the overall light absorption by aerosol particles from biomass burning (BB). Partly stemming from the complexity of the atmospheric evolution of organic aerosol (OA), the variability in brown carbon (BrC) absorption makes it difficult to partition the radiative effects of BrC and black carbon (BC) in atmospheric and climate models; as such, there are calls for satellite-based methods that could provide a statistical characterization of BrC absorption and its evolution in different regions of the world, especially in remote BB regions, such as Siberia. This study examined the feasibility of the statistical characterization of the evolution of BrC absorption and related parameters of BB aerosol in smoke plumes from intense wildfires in Siberia through the analysis of a combination of data from three satellite instruments: OMI (Ozone Monitoring Instrument), MISR (Multi-Angle Imaging SpectroRadiometer), and MODIS (Moderate Resolution Imaging Spectroradiometer). Using a Monte Carlo method, which related the satellite retrievals of the absorption and extinction aerosol optical depths to Mie theory calculations of the optical properties of BB aerosol, we found that the BrC absorption, as well as the imaginary refractive index for the OA, decreased significantly in Siberian BB smoke plumes during about 30 h of the daylight evolution, nevertheless remaining considerable until at least 70 h of the daylight evolution. Overall, the study indicated that the analysis of multi-platform satellite observations of BB plumes can provide useful insights into the atmospheric evolution of BrC absorption and the partitioning of BrC and BC contributions to the total light absorption by BB aerosol.
Orbit Determination for All-Electric GEO Satellites Based on Space-Borne GNSS Measurements
Authors: Lu Wenqiang , Wang Haoguang , Wu Guoqiang , Huang Yong
Published: May 2022

Orbit accuracy of the transfer orbit and the mission orbit is the basis for the orbit control of all-electric-propulsion Geostationary Orbit (GEO) satellites. Global Navigation Satellite System (GNSS) simulation data are used to analyze the main factors affecting GEO satellite orbit prediction accuracy under the no-thrust condition, and an electric propulsion calibration algorithm is designed to analyze the orbit determination and prediction accuracy under the thrust condition. The calculation results show that the orbit determination accuracy of mission orbit and transfer orbit without thrust is better than 10 m using onboard GNSS technology. The calibration accuracy of electric thrust is about 10−9 m/s2 and 10−7 m/s2 with 40 h and 16 h arc length, respectively, using the satellite self-positioning data of 100 m accuracy to calibrate the electric thrust. If satellite self-positioning data accuracy is at the 10 m level, the electric thrust calibration accuracy can be improved by about one order of magnitude, and the 14-day prediction accuracy of the transfer orbit with thrust is better than 1 km.
Governing the Life Course through Lifelong Learning: A Multilevel and Multidimensional View
Authors: Parreira do Amaral Marcelo , Tikkanen Jenni
Published: May 2022

The life course in general, and the educational trajectories of young people in particular, comprise a high degree of complexity as they take place in iterative, recursive and interactive negotiation processes in which numerous actors, institutions and discourses are involved. In this paper, an attempt is made to combine two conceptual discussions—Life Course and Governance—bringing them to bear on the examination of how Lifelong Learning (LLL) policies have been used to govern young people’s life courses. The paper synthesizes different discussions of the complex relations among governance, discourses and structures of opportunity that impact the governing of the life course and particularly educational trajectories. It suggests that the combination of life course research and a governance perspective enables analyzing the governance of educational trajectories along discursive, institutional and relational dimensions of opportunity structures. Considering these various dimensions, the paper argues, allows us to attend to the social interactions, decision-making processes and processing mechanisms that precede and/or underlie educational processes and thus favor or complicate them. The contribution also critically discusses the implications of a governance perspective on life courses and closes with a discussion of the multidimensional and multilevel challenge of governing life course by means of LLL policies.
Emancipation Life Paths of Portuguese Cigano/Roma Women
Authors: Magano Olga
Published: May 2022

In Portuguese society, some Cigano/Roma women, during their life paths, distance themselves from the Cigano cultural tradition, particularly in regard to marriage, schooling, employment and social life. On the one hand, there is a feeling of attachment to traditional values as family pressure to marry or in relation to gender differentiation; on the other hand, these women express a desire for empowerment autonomy and emancipation in order to draw up their own trajectories and life projects. The results obtained with 21 in-depth interviews, 10 women and 11 men, indicate that Cigano women face greater difficulties with regard to early and inbred marriage and are more prone to be victims of family and domestic violence, but they are also the main drivers of change and social transformation of Cigano/Roma families.
The Effects of COVID-19 on Family Climate: A Fuzzy Clustering Approach to Examine Spanish Households
Authors: Martín-Quintana Juan Carlos , Martín Juan Carlos , Alemán Pedro F.
Published: May 2022

Spain was one of the countries in which more severe lockdown policies were imposed during the second term of 2020 to mitigate the unprecedented health crisis. The measures restricted citizens’ mobility, obliging families to stay confined at homes for 99 days since 15 March 2020. The measures created a number of challenges that affect the family climate. This paper aims to empirically analyse how the family climate in Spain has been affected by COVID-19. The family climate assessment was based on an online questionnaire answered by 2034 citizens. A multi-criteria decision-making method rooted in fuzzy logic and TOPSIS, and a fuzzy clustering method, are applied to analyse the effects of the COVID-19 on the family climate. The fuzzy clustering method reveals that there are three different family climate profiles, namely (1) extreme positive, (2) extreme negative, and (3) intermediate. Our results show that some traits affect having a more or less positive family climate. The authors discuss the main contributions and the policy implications that could provide insights into future measures.
The Key Role of Nutritional Elements on Sport Rehabilitation and the Effects of Nutrients Intake
Authors: Papadopoulou Sousana K. , Mantzorou Maria , Kondyli-Sarika Foivi , Alexandropoulou Ioanna , Papathanasiou Jannis , Voulgaridou Gavriela , Nikolaidis Pantelis T.
Published: May 2022

Adequate nutrition is of utmost importance for athletes, especially during rehabilitation after injury in order to achieve fast healing and return to sports. The aim of this narrative review is to define the proper nutritional elements for athletes to meet their needs and facilitate their fast return to sports after surgery or injury, as well as determine the effects of specific nutrients intake. Studies on antioxidants, which are substances that protect against free radicals, for the injured athlete are few and unclear, yet poly-phenols and especially flavonoids might improve healing and inflammation following an injury. Benefits of vitamin C or E on muscle damage are disputable in relevant studies, while optimal levels of vitamin D and calcium contribute to bone healing. Minerals are also essential for athletes. Other supplements suggested for muscle damage treatment and protein synthesis include leucine, creatine, and hydroxymethylbutyrate. Diets that include high-quality products, rich in micronutrients (like vitamins, minerals, etc.) bio-active compounds and other nutritional elements (like creatine) are suggested, while an individualized nutrition program prescribed by a trained dietitian is important. Further studies are needed to clarify the underlying mechanisms of these nutritional elements, especially regarding injury treatment.