Miracle-dz/UniFAR-FTUs huggingface dataset:
On Deriving Tidal Volume From Electrocardiogram During Maximal Effort Test

Electrocardiogram-derived respiration (EDR) is a well known method for obtaining respiratory signal estimations only from electrocardiogram (ECG). It has been extensively employed for deriving respiratory rate, whereas much less results concerning tidal volume (TV) estimation have been reported in the literature. In this work, a method for estimating TV from ECG during a maximal effort treadmill test is presented. It is based on the R-S amplitude series and a calibration process of a linear model. The test was divided in stages, and lowest estimation error was obtained for lead V4, with a median relative error as low as 7% in the best-performing stage and lower than 14% in most of them, suggesting that TV can be estimated from the ECG.
Hydrologic Mass Changes and Their Implications in Mediterranean-Climate Turkey from GRACE Measurements

Water is arguably our most precious resource, which is related to the hydrological cycle, climate change, regional drought events, and water resource management. In Turkey, besides traditional hydrological studies, Terrestrial Water Storage (TWS) is poorly investigated at a continental scale, with limited and sparse observations. Moreover, TWS is a key parameter for studying drought events through the analysis of its variation. In this paper, TWS variation, and thus drought analysis, spatial mass distribution, long-term mass change, and impact on TWS variation from the parameter scale (e.g., precipitation, rainfall rate, evapotranspiration, soil moisture) to the climatic change perspective are investigated. GRACE (Gravity Recovery and Climate Experiment) Level 3 (Release05-RL05) monthly land mass data of the Centre for Space Research (CSR) processing center covering the period from April 2002 to January 2016, Global Land Data Assimilation System (GLDAS: Mosaic (MOS), NOAH, Variable Infiltration Capacity (VIC)), and Tropical Rainfall Measuring Mission (TRMM-3B43) models and drought indices such as self-calibrating Palmer Drought Severity (SCPDSI), El Niño–Southern Oscillation (ENSO), and North Atlantic Oscillation (NAO) are used for this purpose. Turkey experienced serious drought events interpreted with a significant decrease in the TWS signal during the studied time period. GRACE can help to better predict the possible drought nine months before in terms of a decreasing trend compared to previous studies, which do not take satellite gravity data into account. Moreover, the GRACE signal is more sensitive to agricultural and hydrological drought compared to meteorological drought. Precipitation is an important parameter affecting the spatial pattern of the mass distribution and also the spatial change by inducing an acceleration signal from the eastern side to the western side. In Turkey, the La Nina effect probably has an important role in the meteorological drought turning into agricultural and hydrological drought.
Variance estimates for random disc-polygons in smooth convex discs

Abstract In this paper we prove asymptotic upper bounds on the variance of the number of vertices and the missed area of inscribed random disc-polygons in smooth convex discs whose boundary is C+2. We also consider a circumscribed variant of this probability model in which the convex disc is approximated by the intersection of random circles.
Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data

Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.
Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

The European Parliament and The Council of the European Union have established the Water Framework Directive (2000/60/EC) for all European Union member states to achieve, at least, “good” ecological status of all water bodies larger than 50 hectares in Europe. The MultiSpectral Instrument onboard European Space Agency satellite Sentinel-2 has suitable 10, 20, 60 m spatial resolution to monitor most of the Estonian lakes as required by the Water Framework Directive. The study aims to analyze the suitability of Sentinel-2 MultiSpectral Instrument data to monitor water quality in inland waters. This consists of testing various atmospheric correction processors to remove the influence of atmosphere and comparing and developing chlorophyll a algorithms to estimate the ecological status of water in Estonian lakes. This study shows that the Sentinel-2 MultiSpectral Instrument is suitable for estimating chlorophyll a in water bodies and tracking the spatial and temporal dynamics in the lakes. However, atmospheric corrections are sensitive to surrounding land and often fail in narrow and small lakes. Due to that, deriving satellite-based chlorophyll a is not possible in every case, but initial results show the Sentinel-2 MultiSpectral Instrument could still provide complementary information to in situ data to support Water Framework Directive monitoring requirements.
Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.
Efficient Numerical Full-Polarized Facet-Based Model for EM Scattering from Rough Sea Surface within a Wide Frequency Range

A full-polarized facet based scattering model (FPFSM) for investigating the electromagnetic (EM) scattering by two-dimensional electrically large sea surfaces with high efficiency at high microwave bands is proposed. For this method, the scattering field over a large sea facet in a diffuse scattering region is numerically deduced according to the Bragg scattering mechanism. In regard to near specular directions, a novel approach is proposed to calculate the scattered field from a sea surface based on the second order small slope approximation (SSA-II), which saves computer memory considerably and is able to analyze the EM scattering by electrically large sea surfaces. The feasibility of this method in evaluating the radar returns from the sea surface is proved by comparing the normalized radar cross sections (NRCS) and the Doppler spectrum with the SSA-II. Then NRCS results in monostatic and bistatic configurations under different polarization states, scattering angles and wind speeds are analyzed as well as the Doppler spectrum at Ka-band. Numerical results show that the FPFSM is a reliable and efficient method to analyze the full-polarized scattering characteristics from electrically large sea surface within a wide frequency range.
A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples

Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency’s (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod.
Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data

Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740–865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha−1 to as high as 90 Mgha−1, implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha−1, similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources—remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)—into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights.
SAR and Lidar Temporal Data Fusion Approaches to Boreal Wetland Ecosystem Monitoring

The objective of this study was to develop a decision-based methodology, focused on data fusion for wetland classification based on surface water hydroperiod and associated riparian (transitional area between aquatic and upland zones) vegetation community attributes. Multi-temporal, multi-mode data were examined from airborne Lidar (Teledyne Optech, Inc., Toronto, ON, Canada, Titan), synthetic aperture radar (Radarsat-2, single and quad polarization), and optical (SPOT) sensors with near-coincident acquisition dates. Results were compared with 31 field measurement points for six wetlands at riparian transition zones and surface water extents in the Utikuma Regional Study Area (URSA). The methodology was repeated in the Peace-Athabasca Delta (PAD) to determine the transferability of the methods to other boreal environments. Water mask frequency analysis showed accuracies of 93% to 97%, and kappa values of 0.8–0.9 when compared to optical data. Concordance results comparing the semi-permanent/permanent hydroperiod between 2015 and 2016 were found to be 98% similar, suggesting little change in wetland surface water extent between these two years. The results illustrate that the decision-based methodology and data fusion could be applied to a wide range of boreal wetland types and, so far, is not geographically limited. This provides a platform for land use permitting, reclamation monitoring, and wetland regulation in a region of rapid development and uncertainty due to climate change. The methodology offers an innovative time series-based boreal wetland classification approach using data fusion of multiple remote sensing data sources.
Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation

Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap.
Statistical Characteristics of Cyclonic Warm-Core Eddies and Anticyclonic Cold-Core Eddies in the North Pacific Based on Remote Sensing Data

A (an) cyclonic (anticyclonic) eddy is usually associated with a cold (warm) core caused by the eddy-induced divergence (convergence) motion. However, there are also some cyclonic (anticyclonic) eddies with warm (cold) cores in the North Pacific, named cyclonic warm-core eddies (CWEs) and anticyclonic cold-core eddies (ACEs) in this study, respectively. Their spatio-temporal characteristics and regional dependence are analyzed using the multi-satellite merged remote sensing datasets. The CWEs are mainly concentrated in the northwestern and southeastern North Pacific. However, besides these two areas, the ACEs are also concentrated in the northeastern Pacific. The annual mean number decreases year by year for both CWEs and ACEs, and the decreasing rate of the CWEs is about two times as large as that of the ACEs. Moreover, the CWEs and ACEs also exhibit a significant seasonal variation, which are intense in summer and weak in winter. Based on the statistics of dynamic characteristics in seven subregions, the Kuroshio Extension region could be considered as the most active area for the CWEs and ACEs. Two possible mechanisms for CW-ACEs generation are discussed by analyzing two cases.
Enhanced Warming and Intensification of the Kuroshio Extension, 1999-2013

The Pacific climate regime has anomalous warm and cool periods every decade associated with atmospheric circulation changes, which are known to have modulated the tropical and subtropical Pacific during the recent Pacific hiatus regime (1999–2013). However, the influence of the hiatus regime on the Kuroshio Extension (KE) remains unclear. Here, we show that the KE jet underwent enhanced warming (increased 1–1.5 °C), intensification (8–19%) and northward migration (0.5–1°). The KE jet became more perturbed in the upstream region (increased by 70%, west of 146°E) but became stable downstream (perturbation decreased 5–11%, east of 146°E). A poleward shift of the mid-latitude jet stream and weakened Aleutian Low (AL) contributed to the northward migration and intensification of the KE jet, respectively. The weakened AL was associated with negative wind stress curl (WSC) in the eastern Pacific, and this WSC generated an underlying positive sea surface height anomaly that propagated westward, intensifying the KE jet when it reached the KE region. Since the recent Pacific hiatus regime ended after 2013, these changes of the KE jet may reverse during the ongoing warming regime.
An Explorative Model to Assess Individuals' Phubbing Risk

Phubbing could be defined as a new form of addiction; however, checking the phone and ignoring the speaker could also be linked to the increased availability of virtual social environments. We developed a multidimensional model for phubbing considering psychological dimensions and information and communication technology related habits. We collected data through online questionnaires and surveys. The best model obtained from our data was constituted by Information and Communication Technologies’ (ICTs) usage behaviours, Trait Anxiety, Virtual Sense of Community and Neuroticism. Finally, our study confirmed a strong connection between phubbing and online addiction behaviours.
Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction

Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l 1 -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.