Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of Sentinel-3A Based Lake Level over US and Canada

Abstract: Satellite altimetry is an important contributor for measuring the water level of continental water bodies. The technique has been applied for almost three decades. In this period the data quality has increased and the applications have evolved from the study of a few large lakes and rivers, to near global applications at various scales. Products from current satellite altimetry missions should be validated to continuously improve the measurements. Sentinel-3A has been operating since 2016 and is the first mission operating in synthetic aperture radar (SAR) mode globally. Here we evaluate its performance in capturing lake level variations based on a physical and an empirical retracker provided in the official level 2 product. The validation is performed for more than 100 lakes in the United States and Canada where the altimetry based water levels are compared with in situ data. As validation measures we consider the root mean squared error, the Pearson correlation, and the percentage of outliers. For the US sites the median of the RMSE value is 25 cm and 19 cm and the median of the Pearson correlations are 0.86 and 0.93 for the physical and empirical retracker, respectively. The percentage of outliers (median) is 11% for both retrackers. The validations measures are slightly poorer for the Canadian sites; the median RMSE is approximately 5 cm larger, the Pearson correlation 0.1 lower, and the percentage of outliers 5% larger. The poorer performance for the Canadian sites is mainly related to the presence of lake ice in the winter period where the surface elevations are not able to map the surface correctly. The validation measures improve considerably when evaluated for summer data only. For both areas we show that the reconstruction of the water level variations based on the empirical retracker is significantly better compared to that of the physical retracker in terms of the RMSE and the Pearson correlation.
Antioxidant effect of genistein on ovarian tissue morphology, oxidant and antioxidant activity in rats with induced polycystic ovary syndrome

Abstract Background Oxidative stress is the most frequent cause of female infertility disorders including polycystic ovary syndrome (PCOS). Genistein as a major component of soybean isoflavone scavenges free radicals by antioxidant activities. Objective The present study examines the antioxidant effects of genistein on ovarian tissue following experimental PCOS in rats. Materials and Methods Twenty female Wistar rat were randomly divided into the following groups (n=5 each group): (I) control group (no treatment); (II) induced PCOS (injection of estradiol valerate); (III) genistein-treated non-PCOS (received genistein); and (IV) genistein-treated PCOS groups. The weight of rats were measured and the blood samples collected and centrifuged. The oxidant and antioxidant activity of plasma and ovaries were measured. All rats were sacrificed under anesthesia, and ovaries were collected and weighted. Histological examination and follicular quality were assessed by staining. Results In histological observation, the induced PCOS rats displayed more number of atretic follicles and the follicular quality in genistein-treated rats was similar to the control groups. The plasma and ovaries malondialdehyde levels significantly increased in PCOS rats (p < 0.001), while the total antioxidant capacity levels, glutathione peroxidase, and superoxide dismutase activities significantly decreased (p < 0.001). The plasma and ovary malondialdehyde levels significantly decreased in PCOS rats that were treated with genistein (p < 0.001) and the total antioxidant capacity (p < 0.05), glutathione peroxidase, and superoxide dismutase activities significantly increased (p < 0.001). Conclusion Treatment with genistein preserved follicular quality by increasing antioxidant activities and scavenging oxidant levels in PCOS rats.
The Global Wind Resource Observed by Scatterometer

A 27-year-long calibrated multi-mission scatterometer data set is used to determine the global basin-scale and near-coastal wind resource. In addition to mean and percentile values, the analysis also determines the global values of both 50- and 100-year return period wind speeds. The analysis clearly shows the seasonal variability of wind speeds and the differing response of the two hemispheres. The maximum wind speeds in each hemisphere are comparable but there is a much larger seasonal cycle in the northern hemisphere. As a result, the southern hemisphere has a more consistent year-round wind climate. Hence, coastal regions of southern Africa, southern Australia, New Zealand and southern South America appear particularly suited to coastal and offshore wind energy projects. The extreme value analysis shows that the highest extreme wind speeds occur in the North Atlantic Ocean with extreme wind regions concentrated along the western boundaries of the North Atlantic and North Pacific Oceans and the Indian Ocean sector of the Southern Ocean. The signature of tropical cyclones is clearly observed in each of the well-known tropical cyclone basins.
Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.
Toddlers track hierarchical structure dependence

ABSTRACT Previous research suggests that toddlers can rely on distributional cues in the input to track adjacent and nonadjacent grammatical dependencies. It remains unclear whether toddlers understand the hierarchical phrase structures that determine the corresponding grammatical dependencies. We addressed this question by testing toddlers on two different phrase structures in French that govern distinct patterns of grammatical gender feature agreement. We first show that the two structures are in fact extremely infrequent in children’s input. Then we report on a preferential looking experiment in which French-learning 30-month-olds were presented with French sentences in the two structures, and the grammaticality of the feature agreement was manipulated. Crucially, the contrasting structures contained the same sequentially ordered feature-bearing words in our design (e.g., correct agreement: La banane FEMININE dans le chapeau MASCLINE, elle FEMININE … ‘The banana in the hat, it …’; incorrect agreement: *La banane FEMININE et le chapeau MASCLINE, elles FEMININE … ‘The banana and the hat, they …’). Thus, children must go beyond those words and distinguish the structures hierarchically in order to determine the grammaticality of the different agreement patterns. We predicted that if toddlers rely solely on input support to acquire the structures and the agreement, they should show no discrimination of feature grammaticality. If, however, they can rely on mechanisms beyond linear input, such as the principle of structure dependence in the universal grammar (UG), they should show a grammaticality effect. The results confirmed the latter prediction, demonstrating the possible influence of the learner’s internal system, suggesting that UG knowledge might guide early language development.
Performance Evaluation of Spaceborne Integrated Path Differential Absorption Lidar for Carbon Dioxide Detection at 1572 nm

As one of the most influential greenhouse gases, carbon dioxide (CO2) has a profound impact on the global climate. The spaceborne integrated path differential absorption (IPDA) lidar will be a great sensor to obtain the columnar concentration of CO2 with high precision. This paper analyzes the performance of a spaceborne IPDA lidar, which is part of the Aerosol and Carbon Detection Lidar (ACDL) developed in China. The line-by-bine radiative transfer model was used to calculate the absorption spectra of CO2 and H2O. The laser transmission process was simulated and analyzed. The sources of random and systematic errors of IPDA lidar were quantitatively analyzed. The total systematic errors are 0.589 ppm. Monthly mean global distribution of relative random errors (RREs) was mapped based on the dataset in September 2016. Afterwards, the seasonal variations of the global distribution of RREs were studied. The global distribution of pseudo satellite measurements for a 16-day orbit repeat cycle showed relatively uniform distribution over the land of the northern hemisphere. The results demonstrated that 61.24% of the global RREs were smaller than 0.25%, or about 1 ppm, while 2.76% of the results were larger than 0.75%. The statistics reveal the future performance of the spaceborne IPDA lidar.
E-government success assessment from a public value perspective

ABSTRACT Public organizations are massively using ICTs. Leading to e-government development, they expect creating public value. However, little empirical evidence about achieving the expected benefits exists. Focusing on the private sector, the extant theoretical models for IT value are not suitable for e-government. This research builds on the DeLone and McLean (D&M) IS success model and the e-government public value perspective to propose and test a model of e-government success from the perspective of government employees. Organizational performance and environmental sustainability are investigated as the two main dimensions of e-government public value in relation with the three quality variables, intention to use and user satisfaction. Based on PLS-SEM techniques, the results show that an intention to use the taxation system and user satisfaction lead to public value measured by organizational performance and environmental sustainability. They demonstrate the adequacy of the research model to assess e-government value from the standpoint of the government employees.
Implementation of a Nowcasting Hydrometeorological System for Studying Flash Flood Events: The Case of Mandra, Greece

Severe hydrometeorological hazards such as floods, droughts, and thunderstorms are expected to increase in the future due to climate change. Due to the significant impacts of these phenomena, it is essential to develop new and advanced early warning systems for advance preparation of the population and local authorities (civil protection, government agencies, etc.). Therefore, reliable forecasts of extreme events, with high spatial and temporal resolution and a very short time horizon are needed, due to the very fast development and localized nature of these events. In very short time-periods (up to 6 h), small-scale phenomena can be described accurately by adopting a “nowcasting” approach, providing reliable short-term forecasts and warnings. To this end, a novel nowcasting system was developed and presented in this study, combining a data assimilation system (LAPS), a large amount of observed data, including XPOL radar precipitation measurements, the Chemical Hydrological Atmospheric Ocean wave System (CHAOS), and the WRF-Hydro model. The system was evaluated on the catastrophic flash flood event that occurred in the sub-urban area of Mandra in Western Attica, Greece, on 15 November 2017. The event was one of the most catastrophic flash floods with human fatalities (24 people died) and extensive infrastructure damage. The update of the simulations with assimilated radar data improved the initial precipitation description and led to an improved simulation of the evolution of the phenomenon. Statistical evaluation and comparison with flood data from the FloodHub showed that the nowcasting system could have provided reliable early warning of the flood event 1, 2, and even to 3 h in advance, giving vital time to the local authorities to mobilize and even prevent fatalities and injuries to the local population.
Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures.
From Psychoacoustics to Brain Waves: A Longitudinal Approach to Novel Word Learning

Musical expertise has been shown to positively influence high-level speech abilities such as novel word learning. This study addresses the question whether low-level enhanced perceptual skills causally drives successful novel word learning. We used a longitudinal approach with psychoacoustic procedures to train 2 groups of nonmusicians either on pitch discrimination or on intensity discrimination, using harmonic complex sounds. After short (approximately 3 hr) psychoacoustic training, discrimination thresholds were lower on the specific feature (pitch or intensity) that was trained. Moreover, compared to the intensity group, participants trained on pitch were faster to categorize words varying in pitch. Finally, although the N400 components in both the word learning phase and in the semantic task were larger in the pitch group than in the intensity group, no between-group differences were found at the behavioral level in the semantic task. Thus, these results provide mixed evidence that enhanced perception of relevant features through a few hours of acoustic training with harmonic sounds causally impacts the categorization of speech sounds as well as novel word learning. These results are discussed within the framework of near and far transfer effects from music training to speech processing.
Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation

The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks.
Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data

This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators—adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)—are evaluated in five irrigation schemes for three spatial resolutions—250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed–derived data (the Water Productivity Open Access Portal—WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (<2 ha). Our findings highlight the importance of selecting the spatial resolution appropriate to scheme characteristics when undertaking irrigation performance assessment using remote sensing.
Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation

It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensemble multi-scale residual deep learning method with the regularizer of shape representation for semantic segmentation of buildings. Based on the U-Net architecture using residual connections and multi-scale ASPP (atrous spatial pyramid pooling) modules, our method introduced the regularizer of shape representation and ensemble learning of multi-scale models to enhance model training and reduce over-fitting. In our method, the shape representation was coded in an antoencoder that was used to encode and reconstruct the shape characteristics of the buildings. In prediction, we consider multi-scale trained models for different resolution inputs and side effects to obtain an optimal semantic segmentation. With the high-resolution image of the Changshan, an island county in China, we used two-thirds of the study region image to train the model and the remaining one-third for the independent test. We obtained the accuracy of 0.98–0.99, mean intersection over union (MIoU) of 0.91–0.93 and Jaccard coefficient of 0.89–0.92 in validation. In the independent test, our method achieved state-of-the-art performance (MIoU: 0.83; Jaccard index: 0.81). By comparing with the existing representative methods on four different data sets, the proposed method consistently improved the learning process and generalization. The study shows important contributions of ensemble learning of multi-scale residual models and regularizer of shape representation to semantic segmentation of buildings.
Evaluation of Genetic Diversity of Mongolian Horse and Thoroughbred Horse Using Microsatellite Markers

13 microsatellite loci were used to detect 60 horses of two horse breeds,namely mongolian and thoroughbred in this experiment. The PCR amplified products of microsatellite were detected by 8 percent non-denatured polyacry lamide gel electrophoresis and showing in silver staining protocol. Allele frequency,numbers of effective alleles(Ne),polymorphism information contents(PIC)and heterozygosity(H)for every microsatellite locus were calculated by softwares.The results were the greatest variation in UCDEQ440 and the lowest in TKY16 in 13 microsatellite loci;Mean numbers of Ne,PIC and H in thoroughbred horses were all higher a bit than in mongolian;Mean coefficient of gene differentiation(G_(ST))of the whole breeds was 0.042 9. These results could provide references in the future that breeding new excellent breeds in our country.