Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China

Although the Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was primarily designed for glacier and sea-ice measurement, it can also be applied to monitor lake surface height (LSH). However, its performance in monitoring lakes/reservoirs has rarely been assessed. Here, we report an accuracy evaluation of the ICESat-2 laser altimetry data over 30 reservoirs in China using gauge data. To show its characteristics in large-scale lake monitoring, we also applied an advanced radar altimeter SARAL (Satellite for ARgos and ALtika) and the first laser altimeter ICESat (Ice, Cloud and land Elevation Satellite) to investigate all lakes and reservoirs (>10 km2) in China. We found that the ICESat-2 has a greatly improved altimetric capability, and the relative altimetric error was 0.06 m, while the relative altimetric error was 0.25 m for SARAL. Compared with SARAL and ICESat data, ICESat-2 data had the lowest measurement uncertainty (the standard deviation of along-track heights; 0.02 m vs. 0.17 m and 0.07 m), the greatest temporal frequency (3.43 vs. 1.35 and 1.48 times per year), and the second greatest lake coverage (636 vs. 814 and 311 lakes). The precise LSH profiles derived from the ICESat-2 data showed that most lakes (90% of 636 lakes) had a quasi-horizontal LSH profile (measurement uncertainty <0.05 m), and special methods are needed for mountainous lakes or shallow lakes to extract precise LSHs.
A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

: This paper addresses the ECG-blood pressure relationship - a fact that physicians have discussed for years. The hypothesis set in the paper is that blood pressure is related to electrocardiogram (ECG) and that the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values can be predicted by using information only from a given ECG signal. Therefore, we established a protocol for creating a database considering measurements from real patients in ambulance environment, and consequently developed methodology for analysing the collected measurements. The proposed methodology follows two steps: i) ﬁrst the signals are considered as time series data, and ii) a time series feature extraction method is applied to extract the important features from the ECG signals. Hereafter, a novel Machine learning method is applied (CLUS) that produced best results among the traditionally-used Machine learning methods. The best results obtained are 12.81 ± 2.66 mmHg for SBP and 8.12 ± 1.80 mmHg for DBP. After introducing calibration method the obtained mean absolute errors (MAEs) reduced to 6.93 ± 4.70 mmHg for SBP, and 7.13 ± 4.48 mmHg for DBP. Given the latest literature, the results are appropriately compared and conﬁrm the relation between the ECG signal and the blood pressure.
A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation

In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ETEC) at AmeriFlux sites and ET values from the water balance method (ETWB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE).
Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes

Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China.
Are Sensors and Data Processing Paving the Way to Completely Non-invasive and Not-painful Medical Tests for Widespread Screening and Diagnosis Purposes?

Effective medical tests are essential in supporting correct clinical decisions by medical doctors. But, have medical tests to be necessarily invasive and painful to be effective? During last decades, new developments of sensors and improvements of data analysis algorithms seem to paying the way to a (more or less near) future with completely non-invasive and not painful medical tests. This work aims to furnish a survey on what is going on within this frame, with an eye to new possibilities.
Influence of Data Similarity on the Scoring Power of Machine-learning Scoring Functions for Docking

: Inconsistent conclusions have been drawn from recent studies exploring the influence of data similarity on the scoring power of machine-learning scoring functions, but they were all based on the PDBbind v2007 refined set whose data size is limited to just 1300 protein-ligand complexes. Whether these conclusions can be generalized to substantially larger and more diverse datasets warrants further examinations. Besides, the previous definition of protein structure similarity, which relied on aligning monomers, might not truly reflect what it was supposed to be. Moreover, the impact of binding pocket similarity has not been investigated either. Here we have employed the updated refined set v2013 providing 2959 complexes and utilized not only protein structure and ligand fingerprint similarity but also a novel measure based on binding pocket topology dissimilarity to systematically control how similar or dissimilar complexes are incorporated for training predictive models. Three empirical scoring functions X-Score, AutoDock Vina, Cyscore and their random forest counterparts were evaluated. Results have confirmed that dissimilar training complexes may be valuable if allied with appropriate machine learning algorithms and informative descriptor sets. Machine-learning scoring functions acquire their remarkable scoring power through mining more data to advance performance persistently, whereas classical scoring functions lack such learning ability. The software code and data used in this study and supplementary results are available at https://GitHub.com/HongjianLi/MLSF.
Applied Mathematics and Computation

Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the ﬁeld, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system. © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license. ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
An Improved Descalloping Method Combined With Imaging Parameters for GaoFen-3 ScanSAR

Due to the specific working mechanism of alternately transmitting and receiving signals between multiple adjacent swaths, scanning synthetic aperture radar (ScanSAR) will cause periodical wavelike modulation of the intensity image along azimuth direction, which is known as scalloping. Conventional descalloping methods are achieved by proper azimuth antenna pattern (AAP) correction and multi-looking techniques but are limited by the accuracy of Doppler centroid estimating and the thermal noise. Another type of method extracts and suppresses the scalloping texture on the image, but the scanning parameters of ScanSAR are insufficiently considered. The period of scalloping on the image is related to the period of switching between subswathes. While the harmonics can be calculated by the period of a periodic signal, an improved frequency filtering method combined with imaging parameters is proposed. The scalloping modulation model of ScanSAR combined with imaging parameters is constructed, and the harmonics of scalloping texture are accurately calculated and filtered. For the low signal to noise ratio (SNR) image, the antenna pattern is modified according to SNR to avoid scalloping caused by noise. For non-uniform scenes, scalloping suppression is achieved by using the scalloping features acquired by uniform scenes. To separate the non-uniform scenes from uniform scenes, we still use the characteristics of harmonics caused by scalloping. Our descalloping method achieves accurate suppression of scalloping without sea-land segmentation and ship mask and the residual scalloping is reduced from 1.0 to 0.3–0.5 dB. The residual scalloping and statistical characteristics of the image are analyzed to demonstrate the effectiveness of the proposed method.
Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification

Recently, Hyperspectral Image (HSI) classification methods based on deep learning models have shown encouraging performance. However, the limited numbers of training samples, as well as the mixed pixels due to low spatial resolution, have become major obstacles for HSI classification. To tackle these problems, we propose a resource-efficient HSI classification framework which introduces adaptive spectral unmixing into a 3D/2D dense network with early-exiting strategy. More specifically, on one hand, our framework uses a cascade of intermediate classifiers throughout the 3D/2D dense network that is trained end-to-end. The proposed 3D/2D dense network that integrates 3D convolutions with 2D convolutions is more capable of handling spectral-spatial features, while containing fewer parameters compared with the conventional 3D convolutions, and further boosts the network performance with limited training samples. On another hand, considering the existence of mixed pixels in HSI data, the pixels in HSI classification are divided into hard samples and easy samples. With the early-exiting strategy in these intermediate classifiers, the average accuracy can be improved by reducing the amount of computation cost for easy samples, thus focusing on classifying hard samples. Furthermore, for hard samples, an adaptive spectral unmixing method is proposed as a complementary source of information for classification, which brings considerable benefits to the final performance. Experimental results on four HSI benchmark datasets demonstrate that the proposed method can achieve better performance than state-of-the-art deep learning-based methods and other traditional HSI classification methods.
Neural Citation Recommendation: A Reproducibility Study

Context-aware citation recommendation is used to overcome the process of manually searching for relevant citations by automatically recommending suitable papers as citations for a speciﬁed input text. In this paper, we examine the reproducibility of a state-of-the-art approach to context-aware citation recommendation, namely the neural citation network (NCN) by Ebesu and Fang [1]. We re-implement the network and run evaluations on both RefSeer, the originally used data set, and arXiv CS, as an additional data set. We provide insights on how the diﬀerent hyperparameters of the neural network aﬀect the model performance of the NCN and thus can be used to improve the model’s performance. In this way, we contribute to making citation recommendation approaches and their evaluations more transparent and creating more eﬀective neural network-based models in the future.
Geodetic Deformation versus Seismic Crustal Moment-Rates: Insights from the Ibero-Maghrebian Region

Seismic and geodetic moment-rate comparisons can reveal regions with unexpected potential seismic hazards. We performed such a comparison for the Southeastern Iberia—Maghreb region. Located at the western Mediterranean border along the Eurasia–Nubia plate convergence, the region has been subject to a number of large earthquakes (M ≥ 6.5) in the last millennium. To this end, on the basis of available geological, tectonic, and seismological data, we divided the study area into twenty-five seismogenic source zones. Many of these seismogenic source zones, comprising the Western Betics, the Western Rif mountains, and the High, Middle, and Saharan Atlas, are characterized by seismic/geodetic ratio values lower than 23%, evidencing their prevailing aseismic behavior. Intermediate seismic/geodetic ratio values (between 35% and 60%) have been observed for some zones belonging to the Eastern Betics, the central Rif, and the Middle Atlas, indicating how crustal seismicity accounts only for a moderate fraction of the total deformation-rate budget. High seismic/geodetic ratio values (> 95%) have been observed along the Tell Atlas, highlighting a fully seismic deformation.
Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.
Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam

This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.