Miracle-dz/UniFAR-FTUs huggingface dataset:
National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach

Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H2O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (R2=0.65, CCC=0.79, RMSE=0.54), that significantly out-performed existing soil pH information for the country.
Linking Learners’ Perspectives on Language Assessment Practices to Teachers’ Assessment Literacy Enhancement (TALE): Insights from Four European Countries

ABSTRACT This article presents results from a needs analysis survey conducted in the first year of a European-funded project entitled ‘Teachers’ Assessment Literacy Enhancement (TALE)’. The survey questionnaire used asked 1788 learners of English in Cyprus, Germany, Greece and Hungary about their experiences of assessment; which of these they considered conducive to learning and the role feedback played as an instrument of formative assessment. Further questionnaire data from their 658 teachers were included in the data analysis. The results showed that practices differed across contexts. Overall, both learners and teachers reported a wide range of skills and areas to be assessed in the EFL classroom with writing, followed by speaking, being assessed the most. Based on the perceptions reported by the learners, the assessment types used revealed rather traditional approaches with frequent use of e.g. discrete-point tests with closed answers, extended writing and translation. The learners appeared to regard these types of assessment to be supportive of their learning. Feedback given was mostly restricted to marks and brief comments. The perceptions on feedback practices varied among teachers and their learners. Results of the needs analysis were taken as the basis of the online course design for enhancing teachers’ language assessment literacy.
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and Remote Sens. 2020, 12, 2833; doi:10.3390/rs12172833 www.mdpi. com/journal/remotesensing Remote Sens. 2020, 12, 2833 2 of 32 validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.
Development and Application of HECORA Cloud Retrieval Algorithm Based On the O2-O2 477 nm Absorption Band

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI.
Combining Interior Orientation Variables to Predict the Accuracy of Rpas-Sfm 3D Models

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.
Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.
Fundamental Limits and Algorithms in Decentralized and Cooperative Wireless Networks. (Limites fondamentales et algorithmes dans des réseaux sans-fils décentralisés et coopératifs)

Network cooperation is known to bring multiplicative gains under certain ideal assumptions. However, current wireless settings cope with many challenging constraints, as tight delay constraints, fast-changing channels, or rate-limited backhaul links. The topic of analyzing how the non-fulfillment of the ideal hypotheses impacts the performance has generated great interest in the research community. Nevertheless, the main focus has been on settings in which the imperfect information is shared by all the nodes, which is not feasible in many scenarios. This thesis aims for shedding light on the performance of cooperative settings in which the information available at each node may be different. We focus on the distributed Network MIMO. This setting is characterized by two main aspects: The perfect sharing of the user's information data and the imperfect sharing of the channel information. We start by characterizing the Degrees-of-Freedom metric of the setting, which is an approximation of the capacity at high SNR. The contribution is twofold, as we provide both achievable schemes that considerably outperform the solutions in the literature and upper-bounds that illustrate up to which scale the distributed setting is harmed with respect to the perfect-sharing setting. The second perspective consists in restricting the transmission to the conventional paradigm of Zero-Forcing and analyzing the achievable rate at high SNR to understand whether the performance losses from decentralized information can be accurately calculated. We propose a novel zero-forcing scheme tailored to the decentralized configuration that asymptotically attains the centralized rate.
Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.
Antioxidant Activity of Selected Apple Cultivars Studied by Electron Paramagnetic Resonance

The total antioxidant activity of three apple cultivars conventionally farmed (Jonagold, Golden Delicious and Idared) and two cultivars organically farmed (Jonagold and Golden Delicious) were investigated by electron paramagnetic resonance (EPR) spectroscopy. The spin label used for experiments was TEMPO radical (2,2,6,6tetramethylpiperidine-1-oxyl) with a well-defined EPR spectrum consisting of three equidistant peaks. The results obtained indicated difference in the total antioxidant activity of apples grown in two different ways, conventional and organic, as well as between different cultivars grown in the same way. The Golden Delicious cultivar is richer in antioxidants than the Jonagold and Idared, regardless of the method of farming. The difference between them is higher in conventionally grown apples than in organically farmed apples. Experimental data was fitted with non-linear curve fit for exponential decay of the first order based on the Levenberg-Marquardt method. The high values of the R2 parameters for all data indicated correctness in the proposition of exponential decay of the first order as a model describing the dynamic properties of spin labels which diminish in the presence of fresh apple juice.
Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation

Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. A quadrotor is regarded as an owned UAV, and it is able to estimate the distance of an incoming fixed-wing intruder. The adopted object detection method is based on the you only look once (YOLO) object detector. Deep neural network (DNN) and convolutional neural network (CNN) methods are applied to exam their performance in the distance estimation of moving objects. The feature extraction of fixed-wing UAVs is based on the VGG-16 model, and then its result is applied to the distance network to estimate the object distance. The proposed model is trained by using synthetic images from animation software and validated by using both synthetic and real flight videos. The results show that the proposed active vision-based scheme is able to detect and track a moving UAV with high detection accuracy and low distance errors.
Estimates of Conservation Tillage Practices Using Landsat Archive

The USDA Environmental Quality Incentives Program (EQIP) provides financial assistance to encourage producers to adopt conservation practices. Historically, one of the most common practices is conservation tillage, primarily the use of no-till planting. The objectives of this research were to determine crop residue using remote sensing, an indicator of tillage intensity, without using training data and examine its performance at the field level. The Landsat Thematic Mapper Series platforms can provide global temporal and spatial coverage beginning in the mid-1980s. In this study, we used the Normalized Difference Tillage Index (NDTI), which has proved to be robust and accurate in studies built upon training datasets. We completed 10 years of residue maps for the 150,000 km2 study area in South Dakota, North Dakota, and Minnesota and validated the results against field-level survey data. The overall accuracy was between 64% and 78% with additional improvement when survey points with suspect geolocation and satellite tillage estimates with fewer than four dates of Landsat images were excluded. This study demonstrates that, with Landsat Archive available at no cost, researchers can implement retrospective, untrained estimates of conservation tillage with sufficient accuracy for some applications.
Understanding fintech continuance: perspectives from self-efficacy and ECT-IS theories

The purpose of this study is to clarify theory and identify factors that could explain the level of fintech continuance intentions with an expectation confirmation model that integrates self-efficacy theory. ,With data collected from 753 fintech users, this study applies partial least square structural equation modeling to compare and select the research model with the most predictive power. ,The results show that financial self-efficacy, technological self-efficacy and confirmation positively affect perceived usefulness. Among these factors, financial self-efficacy and technological self-efficacy have both direct and indirect effects through confirmation on perceived usefulness. Perceived usefulness and confirmation are positively related to satisfaction. Finally, perceived usefulness and satisfaction positively influence fintech continuance intentions. ,To the best of our knowledge, this is one of the earliest studies that investigates the effect of domain-specific self-efficacy on fintech continuance intentions, which enriches the existing research on fintech and deepens our understanding of users' fintech continuance intentions. We distinguish between financial self-efficacy and technological self-efficacy and specify the relationship between self-efficacy and continuance intentions. Moreover, this study highlights the importance of assessing a model's predictive power using the PLSpredict technique and provides a reference for model selection.