bluuebunny/crossref_metadata_2025 huggingface dataset:
Frequency of Detection and Prevalence Factors Associated with Common Respiratory Pathogens in Equids with Acute Onset of Fever and/or Respiratory Signs (2008–2021)
Authors: Pusterla Nicola , James Kaitlyn , Barnum Samantha , Bain Fairfield , Barnett D. Craig , Chappell Duane , Gaughan Earl , Craig Bryant , Schneider Chrissie , Vaala Wendy
Published: July 2022

A voluntary biosurveillance program was established in 2008 in order to determine the shedding frequency and prevalence factors for common respiratory pathogens associated with acute onset of fever and/or respiratory signs in equids from the USA. Over a period of 13 years, a total of 10,296 equids were enrolled in the program and nasal secretions were analyzed for the qPCR detection of equine influenza virus (EIV), equine herpesvirus-1 (EHV-1), EHV-4, equine rhinitis A and B virus (ERVs), and Streptococcus equi subspecies equi (S. equi). Single infections with respiratory pathogens were detected in 21.1% of the submissions with EIV (6.8%) and EHV-4 (6.6%) as the two most prevalent viruses, followed by S. equi (4.7%), ERVs (2.3%), and EHV-1 (0.7%). Multiple pathogens were detected in 274 horses (2.7%) and no respiratory pathogens in 7836 horses (76.2%). Specific prevalence factors were determined for each of the six respiratory pathogen groups; most differences were associated with age, breed, and use of the horses, while the clinical signs were fairly consistent between viral and bacterial respiratory infections. Monitoring the frequency of detection of common respiratory pathogens is important in order to gain a better understanding of their epidemiology and to implement management practices aimed at controlling disease spread.
Dehydroisohispanolone as a Promising NLRP3 Inhibitor Agent: Bioevaluation and Molecular Docking
Authors: González-Cofrade Laura , Cuadrado Irene , Amesty Ángel , Estévez-Braun Ana , de las Heras Beatriz , Hortelano Sonsoles
Published: July 2022

Dehydroisohispanolone (DIH), is a labdane diterpene that has exhibited anti-inflammatory activity via inhibition of NF-κB activation, although its potential effects on inflammasome activation remain unexplored. This study aims to elucidate whether DIH modulates NLR family pyrin domain-containing protein 3 (NLRP3) inflammasome in macrophages. Our findings show that DIH inhibited NLRP3 activation triggered by Nigericin (Nig), adenosine triphosphate (ATP) and monosodium urate (MSU) crystals, indicating broad inhibitory effects. DIH significantly attenuated caspase-1 activation and secretion of the interleukin-1β (IL-1β) in J774A.1 cells. Interestingly, the protein expressions of NLRP3, apoptosis-associated speck-like protein containing a CARD (ASC), pro-caspase-1 and pro-IL-1β were not affected by DIH treatment. Furthermore, we found that DIH pretreatment also inhibited the lipopolysaccharide (LPS)-induced NLRP3 inflammasome priming stage. In addition, DIH alleviated pyroptosis mediated by NLRP3 inflammasome activation. Similar results on IL-1β release were observed in Nig-activated bone marrow-derived macrophages (BMDMs). Covalent molecular docking analysis revealed that DIH fits well into the ATP-binding site of NLRP3 protein, forming a covalent bond with Cys415. In conclusion, our experiments show that DIH is an effective NLRP3 inflammasome inhibitor and provide new evidence for its application in the therapy of inflammation-related diseases.
Optical and Structural Characteristics of Rare Earth-Doped ZnO Nanocrystals Prepared in Colloidal Solution
Authors: Toma Maria , Selyshchev Oleksandr , Havryliuk Yevhenii , Pop Aurel , Zahn Dietrich R. T.
Published: July 2022

ZnO nanocrystals doped with Nd, Gd, and Er were synthesized using a soft chemical process in ambient atmosphere. Pseudospherical and hexagonal nanocrystals (NC) of the wurtzite phase with a mean size of (7.4 ± 1.7) nm were obtained. The presence of rare earth (RE) dopants was confirmed by X-ray fluorescence (XRF) spectroscopy. The ZnO nanocrystals exhibited simultaneously narrow excitonic- and broad trap/surface-related photoluminescence (PL), both of which were affected by doping with RE atoms. Doping reduced the total PL intensity, suppressing the excitonic emission by a greater extent than the broad band PL. Also, doping resulted in a blue shift of the trap/surface-related emission, while the energy of the excitonic peak remained unchanged. Resonant Raman spectra additionally confirmed the wurtzite phase of ZnO NCs and revealed a shift of the A1-LO mode towards lower frequency upon doping that could be caused by the mass effect of RE atoms, point defects, and increases in charge carrier concentration. Fitting of the spectra with Voigt profiles showed better results with two surface optical (SO) phonon modes that were previously theoretically predicted for the wurtzite ZnO phase. The influence of RE doping on PL and Raman spectra can be explained by the incorporation of RE ions into the ZnO nanostructures, where the dopants act as non-radiative defects.
Green Synthesis of a Novel Silver Nanoparticle Conjugated with Thelypteris glandulosolanosa (Raqui-Raqui): Preliminary Characterization and Anticancer Activity
Authors: Vera-Nuñez Lucero Del Carmen , Cornejo-Ruiz Junior Oliver , Arenas-Chávez Carlos Alberto , de Hollanda Luciana Maria , Alvarez-Risco Aldo , Del-Aguila-Arcentales Shyla , Davies Neal M. , Yáñez Jaime A. , Vera-Gonzales Corina
Published: July 2022

In the last decade, the green synthesis of nanoparticles has had a prominent role in scientific research for industrial and biomedical applications. In this current study, silver nitrate (AgNO3) was reduced and stabilized with an aqueous extract of Thelypteris glandulosolanosa (Raqui-raqui), forming silver nanoparticles (AgNPs-RR). UV-vis spectrophotometry, dynamic light scattering (DLS), and scanning transmission electron microscopy (STEM) were utilized to analyze the structures of AgNPs-RR. The results from this analysis showed a characteristic peak at 420 nm and a mean hydrodynamic size equal to 39.16 nm, while the STEM revealed a size distribution of 6.64–51.00 nm with an average diameter of 31.45 nm. Cellular cytotoxicity assays using MCF-7 (ATCC® HTB-22™, mammary gland breast), A549 (ATCC® CCL-185, lung epithelial carcinoma), and L929 (ATCC® CCL-1, subcutaneous connective tissue of Mus musculus) demonstrated over 42.70% of MCF-7, 59.24% of A549, and 8.80% of L929 cells had cell death after 48 h showing that this nanoparticle is more selective to disrupt neoplastic than non-cancerous cells and may be further developed into an effective strategy for breast and lung cancer treatment. These results demonstrate that the nanoparticle surfaces developed are complex, have lower contact angles, and have excellent scratch and wear resistance.
Development of a Hydrokinetic Turbine Backwater Prediction Model for Inland Flow through Validated CFD Models
Authors: Niebuhr Chantel Monica , Hill Craig , Van Dijk Marco , Smith Lelanie
Published: July 2022

Hydrokinetic turbine deployment in inland water reticulation systems such as irrigation canals has potential for future renewable energy development. Although research and development analysing the hydrodynamic effects of these turbines in tidal applications has been carried out, inland canal system applications with spatial constraints leading to possible blockage and backwater effects resulting from turbine deployment have not been considered. Some attempts have been made to develop backwater models, but these were site-specific and performed under constant operational conditions. Therefore, the aim of this work was to develop a generic and simplified method for calculating the backwater effect of HK turbines in inland systems. An analytical backwater approximation based on assumptions of performance metrics and inflow conditions was tested using validated computational fluid dynamics (CFD) models. For detailed prediction of the turbine effect on the flow field, CFD models based on Reynolds-averaged Navier–Stokes equations with Reynolds stress closure models were employed. Additionally, a multiphase model was validated through experimental results to capture the water surface profile and backwater effect with reasonable accuracy. The developed analytical backwater model showed good correlation with the experimental results. The model’s energy-based approach provides a simplified tool that is easily incorporated into simple backwater approximations, while also allowing the inclusion of retaining structures as additional blockages. The model utilizes only the flow velocity and the thrust coefficient, providing a useful tool for first-order analysis of the backwater from the deployment of inland turbine systems.
Late Pleistocene and Early Holocene Birds of Northern Vietnam (Caves Dieu and Maxa I, Thanh Hoa Province)—Paleornithological Results of the Joint Bulgarian-Vietnamese Archaeological Expeditions, 1985–1991 (Paleoavifaunal Research)
Authors: Boev Zlatozar
Published: July 2022

The examined material (207 bones and bone fragments) of 53 avian taxa from two human cave dwellings is dated between 24,000 ± 1000 BP and 9400 ± 100 BP. It reveals that 49.0% of the bird species/taxa disappeared from the recent bird fauna of the Thanh Hoa Province; 39.6% disappeared from the recent bird fauna of North Vietnam (except Thanh Hoa Province); 33.9% disappeared from the recent bird fauna of Vietnam (except North Vietnam); 28.3% are not extant in the recent bird fauna of Indochina (except Vietnam); and 52.8% disappeared from the recent bird fauna of Southeast Asia (except Indochina). This suggests more considerable influence of the Late Pleistocene climatic events on the environment and bird fauna than previously accepted in the Eastern part of the Indochinese peninsula in the last 24–millenia. The gallinaceous birds are best represented. Of the 39 Southeast-Asian species, 18 species/taxa (46.2 percent) are Galliforms. They consist of 34 percent of all bird taxa recorded in both caves. Four categories of the IUCN Red List have been represented among the established birds in the sites: LC—28, NT—7, VU—2 (Buceros bicornis and Rhyticeors undulates), and CR—2 (Lophura edwardsi and Rhinoplax vigil).
Reactions of Bitcoin and Gold to Categorical Financial Stress: New Evidence from Quantile Estimation
Authors: Hoque Mohammad Enamul , Low Soo-Wah
Published: July 2022

This study examines the responses of Bitcoin and gold to categorical financial stress and compares the responses before and during the COVID-19 pandemic. The OLS and Quantile regression estimations revealed that gold and Bitcoin exhibit similar reactions in full and pre COVID-19 samples. Gold and Bitcoin respond positively to equity valuation and safe assets categories of financial stress. Gold also reacts positively to the credit category of financial stress suggesting that widening credit spreads are bullish for gold. Bitcoin and gold respond differently in the funding category, and there is no significant reaction to volatility-related financial stress. Overall, the effects of categorical financial stress on gold and Bitcoin are similar in the full sample and sub-sample before COVID-19, but the effects are heterogeneous. Interestingly, during the pandemic, the reactions of gold and Bitcoin to categorical financial stress have changed. Gold only reacts positively to the credit category of financial stress across quantiles. Bitcoin reacts positively to credit and safe asset categories but not across all quantiles. The findings offer insights into the effects of several systemic financial stress on the value of safe haven assets.
Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau
Authors: Xu Jingyi , Tang Yao , Xu Jiahui , Chen Jin , Bai Kaixu , Shu Song , Yu Bailang , Wu Jianping , Huang Yan
Published: July 2022

The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (βmax, CCRmax, G20, and RCmax) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCRmax method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).
Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China
Authors: Song Jun , Li Chunlin , Liu Miao , Hu Yuanman , Wu Wen
Published: July 2022

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.
A Novel Topography Retrieval Algorithm Based on Single-Pass Polarimetric SAR Data and Terrain Dependent Error Analysis
Authors: Yang Congrui , Zhao Fengjun , Wang Chunle , Wang Mengmeng , Liu Xiuqing , Wang Robert
Published: July 2022

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM.
CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification
Authors: Pitonak Radoslav , Mucha Jan , Dobis Lukas , Javorka Martin , Marusin Marek
Published: July 2022

CubeSats, the nanosatellites and microsatellites with a wet mass up to 60 kg, accompanied by the cost decrease of accessing the space, amplified the rapid development of the Earth Observation industry. Acquired image data serve as an essential source of information in various disciplines like environmental protection, geosciences, or the military. As the quantity of remote sensing data grows, the bandwidth resources for the data transmission (downlink) are exhausted. Therefore, new techniques that reduce the downlink utilization of the satellites must be investigated and developed. For that reason, we are presenting CloudSatNet-1: an FPGA-based hardware-accelerated quantized convolutional neural network (CNN) for satellite on-board cloud coverage classification. We aim to explore the effects of the quantization process on the proposed CNN architecture. Additionally, the performance of cloud coverage classification by biomes diversity is investigated, and the hardware architecture design space is explored to identify the optimal FPGA resource utilization. Results of this study showed that the weights and activations quantization adds a minor effect on the model performance. Nevertheless, the memory footprint reduction allows the model deployment on low-cost FPGA Xilinx Zynq-7020. Using the RGB bands only, up to 90% of accuracy was achieved, and when omitting the tiles with snow and ice, the performance increased up to 94.4% of accuracy with a low false-positive rate of 2.23% for the 4-bit width model. With the maximum parallelization settings, the hardware accelerator achieved 15 FPS with 2.5 W of average power consumption (0.2 W increase over the idle state).
Improving Soil Water Content and Surface Flux Estimation Based on Data Assimilation Technique
Authors: Chen He , Lin Rencai , Zhang Baozhong , Wei Zheng
Published: July 2022

Land surface model is a powerful tool for estimating continuous soil water content (SWC) and surface fluxes. However, simulation error tends to accumulate in the process of model simulation due to the inevitable uncertainties of forcing data and the intrinsic model errors. Data assimilation techniques consider the uncertainty of the model, update model states during the simulation period, and therefore improve the accuracy of SWC and surface fluxes estimation. In this study, an Ensemble Kalman Filter (EnKF) technique was coupled to a Hydrologically Enhanced Land Process (HELP) model to update model states, including SWC and surface temperature (Ts). The remotely sensed latent heat flux (LE) estimated by Surface Energy Balance System (SEBS) was used as the observation value in the data assimilation system to update the model states such as SWC and Ts, etc. The model was validated by the observation data in 2006 at the Weishan flux station, where the open-loop estimation without state updating was treated as the benchmark run. Results showed that the root mean square error (RMSE) of SWC was reduced by 30%~50% compared to the benchmark run. Meanwhile, the surface fluxes also had significant improvement to different extents, among which the RMSE of LE estimation from the wheat season and maize season reduced by 33% and 44%, respectively. The application of the data assimilation technique can substantially improve the estimation of surface fluxes and SWC states. It is suggested that the data assimilation system has great potential to be used in the application of land surface models in agriculture and water management.
Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion
Authors: Li Kuoyang , Zhang Min , Xu Maiping , Tang Rui , Wang Liang , Wang Hai
Published: July 2022

Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images.
Evaluating Satellite Sounders for Monitoring the Tropical Cyclone Environment in Operational Forecasting
Authors: Esmaili Rebekah , Barnet Christopher , Dunion Jason , Folmer Michael , Zawislak Jonathan
Published: July 2022

Tropical cyclones can form over open ocean where in situ observations are limited, so forecasters rely on satellite observations to monitor their development and track. We explore the utility of an operational satellite sounding product for tropical forecasting by characterizing the products retrieval skill during research flights. Scientists from both the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) research team and tropical cyclone communities collaborated to target relevant tropical cyclones during the campaign. This effort produced 130 dropsondes that are well-timed with satellite sounder overpasses over three different tropical cyclones and one Saharan Air Layer outbreak. For the combined infrared and microwave retrieval, the NUCAPS temperature has a root mean square error (RMSE) of 1.2 K near the surface (1000–600 mb) and 0.8 K in the mid-troposphere (600–300 mb), which is in line with global product requirements. The water vapor mixing ratio RMSE was 26% near the surface and 46% in the mid-troposphere. NUCAPS microwave-only retrievals can also be useful for cloudy scenes, with surface RMSE values of 4 K (temperature) and 23% (water vapor). Using information content analysis, we estimated that the vertical resolution near the surface was 1.7 km for the temperature retrievals and 2.2 km for the water vapor retrievals in this study. We discuss the feasibility of implementing NUCAPS in an operational forecasting setting, which requires rapid data delivery to forecaster software tools.
Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors
Authors: Nie Shihai , Wang Yanxia , Tu Jinsheng , Li Peng , Xu Jianhui , Li Nan , Wang Mengke , Huang Danni , Song Jia
Published: July 2022

Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results’ reliability and the proposed method’s effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.
Reliable Label-Supervised Pixel Attention Mechanism for Weakly Supervised Building Segmentation in UAV Imagery
Authors: Chen Jun , Xu Weifeng , Yu Yang , Peng Chengli , Gong Wenping
Published: July 2022

Building segmentation for Unmanned Aerial Vehicle (UAV) imagery usually requires pixel-level labels, which are time-consuming and expensive to collect. Weakly supervised semantic segmentation methods for image-level labeling have recently achieved promising performance in natural scenes, but there have been few studies on UAV remote sensing imagery. In this paper, we propose a reliable label-supervised pixel attention mechanism for building segmentation in UAV imagery. Our method is based on the class activation map. However, classification networks tend to capture discriminative parts of the object and are insensitive to over-activation; therefore, class activation maps cannot directly guide segmentation network training. To overcome these challenges, we first design a Pixel Attention Module that captures rich contextual relationships, which can further mine more discriminative regions, in order to obtain a modified class activation map. Then, we use the initial seeds generated by the classification network to synthesize reliable labels. Finally, we design a reliable label loss, which is defined as the sum of the pixel-level differences between the reliable labels and the modified class activation map. Notably, the reliable label loss can handle over-activation. The preceding steps can significantly improve the quality of the pseudo-labels. Experiments on our home-made UAV data set indicate that our method can achieve 88.8% mIoU on the test set, outperforming previous state-of-the-art weakly supervised methods.
Flight Planning for Survey-Grade 3D Reconstruction of Truss Bridges
Authors: Shang Zhexiong , Shen Zhigang
Published: July 2022

Autonomous UAV 3D reconstruction has been widely used for infrastructure inspections and asset management. However, its applications on truss structures remain a challenging task due to geometric complexity and the severe self-occlusion problem of the truss structures when constrained by camera FOV, safety clearance, and flight duration. This paper proposes a new flight planning method to effectively address the self-occlusion problem to enable autonomous and efficient data acquisition for survey-grade 3D truss reconstruction. The proposed method contains two steps: First, identifying a minimal set of viewpoints achieves the maximal reconstruction quality at every observed surface of the truss geometry through an iterative optimization schema. Second, converting the optimal viewpoints into the shortest, collision-free flight trajectories while considering the UAV constraints. The computed flight path can also be implemented in a multi-UAV fashion. Evaluations of the proposed method include a synthetic truss bridge and a real-world truss bridge. The evaluation results suggested that the proposed approach outperforms the existing works in terms of 3D reconstruction quality while taking less time in both the inflight image acquisition and the post-flight 3D reconstruction.
Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016
Authors: Recondo Carmen , Corbea-Pérez Alejandro , Peón Juanjo , Pendás Enrique , Ramos Miguel , Calleja Javier F. , de Pablo Miguel Ángel , Fernández Susana , Corrales José Antonio
Published: July 2022

In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island.
Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model
Authors: Sun Chunling , Zhang Hong , Ge Ji , Wang Chao , Li Liutong , Xu Lu
Published: July 2022

Timely and accurate information on rice cultivation makes important contributions to the profound reform of the global food and agricultural system, and promotes the development of global sustainable agriculture. With all-day and all-weather observing ability, synthetic aperture radar (SAR) can monitor the distribution of rice in tropical and subtropical areas. To solve the problem of misclassification of rice with no marked signal during the flooding period in subtropical hilly areas, this paper proposes a new feature combination and dual branch bi-directional long short-term memory (DB-BiLSTM) model to achieve high-precision rice mapping using Sentinel-1 time series data. Based on field investigation data, the backscatter time series curves of the rice area were analyzed, and a characteristic index (VV − VH)/(VV + VH) (VV: vertical emission and vertical receipt of polarization, VH: vertical emission and horizontal receipt of polarization) for small areas of hilly land was proposed to effectively distinguish rice and non-rice crops with no marked flooding period. The DB-BiLSTM model was designed, ensuring the independent learning of multiple features and effectively combining the time series information of both (VV − VH)/(VV + VH) and VH features. The city of Shanwei, Guangdong Province, China, was selected as the study area. Experimental results showed that the overall accuracy of the rice mapping results was 97.29%, and the kappa coefficient reached 0.9424. Compared to other methods, the rice mapping results obtained by the proposed method maintained good integrity and had less misclassification, which demonstrated the proposed method’s practical value in accurate and effective rice mapping tasks.
Classification of Heterogeneous Mining Areas Based on ResCapsNet and Gaofen-5 Imagery
Authors: Guan Renxiang , Li Zihao , Li Teng , Li Xianju , Yang Jinzhong , Chen Weitao
Published: July 2022

Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.
Recent Changes in Drought Events over South Asia and Their Possible Linkages with Climatic and Dynamic Factors
Authors: Ullah Irfan , Ma Xieyao , Ren Guoyu , Yin Jun , Iyakaremye Vedaste , Syed Sidra , Lu Kaidong , Xing Yun , Singh Vijay P.
Published: July 2022

South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective.
Satellite Observation of the Long-Term Dynamics of Particulate Organic Carbon in the East China Sea Based on a Hybrid Algorithm
Authors: Cai Sunbin , Wu Ming , Le Chengfeng
Published: July 2022

The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas.
AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM
Authors: Sun Jiaqi , Wang Jiarong , Hao Zhicheng , Zhu Ming , Sun Haijiang , Wei Ming , Dong Kun
Published: July 2022

Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.
Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy
Authors: Hoffman Louwrens Christiaan , Ni Dongdong , Dayananda Buddhi , Abdul Ghafar N , Cozzolino Daniel
Published: July 2022

Issues related to food authenticity, traceability, and fraud have increased in recent decades as a consequence of the deliberate and intentional substitution, addition, tampering, or misrepresentation of food ingredients, where false or misleading statements are made about a product for economic gains. This study aimed to evaluate the ability of a portable NIR instrument to classify egg samples sourced from different provenances or production systems (e.g., cage and free-range) in Australia. Whole egg samples (n: 100) were purchased from local supermarkets where the label in each of the packages was used as identification of the layers’ feeding system as per the Australian legislation and standards. The spectra of the albumin and yolk were collected using a portable NIR spectrophotometer (950–1600 nm). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data. The results obtained in this study showed how the combination of chemometrics and NIR spectroscopy allowed for the classification of egg albumin and yolk samples according to the system of production (cage and free range). The proposed method is simple, fast, environmentally friendly and avoids laborious sample pre-treatment, and is expected to become an alternative to commonly used techniques for egg quality assessment.
Technical Perspectives on Applications of Biologically Coupled Gate Field-Effect Transistors
Authors: Sakata Toshiya
Published: July 2022

Biosensing technologies are required for point-of-care testing (POCT). We determine some physical parameters such as molecular charge and mass, redox potential, and reflective index for measuring biological phenomena. Among such technologies, biologically coupled gate field-effect transistor (Bio-FET) sensors are a promising candidate as a type of potentiometric biosensor for the POCT because they enable the direct detection of ionic and biomolecular charges in a miniaturized device. However, we need to reconsider some technical issues of Bio-FET sensors to expand their possible use for biosensing in the future. In this perspective, the technical issues of Bio-FET sensors are pointed out, focusing on the shielding effect, pH signals, and unique parameters of FETs for biosensing. Moreover, other attractive features of Bio-FET sensors are described in this perspective, such as the integration and the semiconductive materials used for the Bio-FET sensors.
Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm
Authors: Quiles Eduardo , Dadone Javier , Chio Nayibe , García Emilio
Published: July 2022

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.