Miracle-dz/UniFAR-FTUs huggingface dataset:
Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins.
Sequential Network Transfer: Adapting Sentence Embeddings to Human Activities and Beyond

We study the problem of adapting neural sentence embedding models to the domain of human activities to capture their relations in different dimensions. We introduce a novel approach, Sequential Network Transfer, and show that it largely improves the performance on all dimensions. We also extend this approach to other semantic similarity datasets, and show that the resulting embeddings outperform traditional transfer learning approaches in many cases, achieving state-of-the-art results on the Semantic Textual Similarity (STS) Benchmark. To account for the improvements, we provide some interpretation of what the networks have learned. Our results suggest that Sequential Network Transfer is highly effective for various sentence embedding models and tasks.
Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields

Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available.
Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB vegetation indexes in assessing the effects of this disease during the biotrophic phase of host-pathogen interaction, in which symptoms of wilt are not yet evident. While no differences were detected by measuring stomatal conductance and chlorophyll fluorescence, results obtained from RGB indexes showed significant differences between control and inoculated plants for indexes Saturation, a*, b*, green Area (GA), normalized green-red difference index (NGRDI) and triangular greenness index (TGI), presenting a reduction in plant growth as well as in green and yellow color components as an effect of inoculation. These results were contrasted across two scenarios of mineral fertilization in soil and soil amended with two different olive mill waste composts, presenting a clear interaction between the host-pathogen relationship and plant nutrition and suggesting the effect of V. dahliae infection during the biotrophic phase was not related to plant water status.
A Workflow to Estimate Topographic and Volumetric Changes and Errors in Channel Sedimentation after Disturbance

Light Detection and Ranging (LiDAR) methods, such as ground-based Terrestrial Laser Scanning (TLS), have enabled collection of high-resolution point clouds of elevation data to calculate changes in fluvial systems after disturbance, but are often accompanied by uncertainty and errors. This paper reviews and compares TLS analysis methods and develops a workflow to estimate topographic and volumetric changes in channel sedimentation after disturbance. Four analytic methods to estimate topographic and volumetric changes were compared by quantifying the uncertainty in TLS-derived products: Digital Elevation Model (DEM) of difference (DOD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiple Model to Model Cloud Comparison (M3C2). Mean errors across surfaces within each dataset contributed to a propagation error of 0.015–0.016 m and 0.017–0.018 m for the point clouds and derived DEMs, respectively. The estimated error of the total volumetric change implied increased errors in the conversion of point clouds into a surface by C2M and DOD; whereas C2C and M3C2 were generally simpler, efficient, and accurate techniques for evaluating topographic changes. The comparison of methods to analyze TLS data will contribute to applications of remote sensing of hydro-geomorphic processes in stream channels after disturbance. The workflow presented also aids in estimating uncertainties inherent in data collection and analytic methods for topographic and volumetric change analysis.
Sensitivity analysis and choosing between alternative polytomous IRT models using Bayesian model comparison criteria

ABSTRACT Polytomous Item Response Theory (IRT) models are used by specialists to score assessments and questionnaires that have items with multiple response categories. In this article, we study the performance of five model comparison criteria for comparing fit of the graded response and generalized partial credit models using the same dataset when the choice between the two is unclear. Simulation study is conducted to analyze the sensitivity of priors and compare the performance of the criteria using the No-U-Turn Sampler algorithm, under a Bayesian approach. The results were used to select a model for an application in mental health data.
Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.
Bringing the Illusion of Reality Inside Museums - A Methodological Proposal for an Advanced Museology Using Holographic Showcases

The basic idea of a hologram is an apparition of something that does not exist but appears as if it was just in front of our eyes. These illusion techniques were invented a long time ago. The philosopher and alchemist Giovanni Battista della Porta invented an effect that was later developed and brought to fame by Prof. J. H. Pepper (1821–1900) and applied in theatrical performances. The innovation nowadays consists in the adopted technology to produce them. Taking advantage of the available digital technologies, the challenge we are going to discuss is using holograms in the museum context, inside showcases, to realize a new form of scenography and dramaturgy around the exhibited objects. Case studies will be presented, with a detailed analysis of the EU project CEMEC (Connecting Early Medieval European Collections), where holographic showcases have been designed, built and experimented in EU museums. In this case, the coexistence in the same space of the real artifact and the virtual contents, and interior setup of the showcase, its dynamic lighting system, the script and the sound, converge to create an expressive unity. The reconstruction of sensory and symbolic dimensions that are ‘beyond’ any museum object can take the visitor in the middle of a lively and powerful experience with such technology, and represents an advancement in the museological sector. User experience results and a list of best practices will be presented in the second part of the paper, out of the tests and research activities conducted in these three years of the project.
Is Accurate Synoptic Altimetry Achievable by Means of Interferometric GNSS-R?

This paper evaluates the capability of interferometric global navigation satellite system reflectometry (GNSS-R) to perform sea surface altimetry in a synoptic scenario. Such purpose, which requires the combination of the results from different GNSS signals, constitutes a unique characteristic of this approach. Interferometric GNSS-R group delay altimetry has been proven to be more precise than conventional GNSS-R. However, the self-consistency and accuracy of their synoptic solutions (simultaneous multi-static results) have never been proved before. In our work, we analyze a dataset of GNSS signals reflected off the Baltic Sea acquired during an airborne campaign using a receiver that was developed for such a purpose. Among other features, it enables beamformer capability in post-processing to get multiple and simultaneous GNSS signals under the interferometric approach’s restrictions. In particular, the signals from two GPS and two Galileo satellites, at two frequency bands (L1 and L5), covering an elevation range between 28° and 83°, are processed to retrieve sea surface height estimations. The results obtained are self-consistent among the different GNSS signals and data tracks, with discrepancies between 0.01 and 0.26 m. Overall, they agree with ancillary information at 0.40 m level, following a characteristic height gradient present at the experimental site.
Code similarity and clone search in large-scale source code data

Software development is tremendously benefited from the Internet by having online code corpora that enable instant sharing of source code and online developer’s guides and documentation. Nowadays, duplicated code (i.e., code clones) not only exists within or across software projects but also between online code repositories and websites. We call them “online code clones. ” They can lead to license violations, bug propagation, and re-use of outdated code similar to classic code clones between software systems. Unfortunately, they are difficult to locate and fix since the search space in online code corpora is large and no longer confined to a local repository. This thesis presents a combined study of code similarity and online code clones. We empirically show that many code snippets on Stack Overflow are cloned from open source projects. Several of them become outdated or violate their original license and are possibly harmful to reuse. To develop a solution for finding online code clones, we study various code similarity techniques to gain insights into their strengths and weaknesses. A framework, called OCD, for evaluating code similarity and clone search tools is introduced and used to compare 34 state-of-theart techniques on pervasively modified code and boiler-plate code. We also found that clone detection techniques can be enhanced by compilation and decompilation. Using the knowledge from the comparison of code similarity analysers, we create and evaluate Siamese, a scalable token-based clone search technique via multiple code representations. Our evaluation shows that Siamese scales to largescale source code data of 365 million lines of code and offers high search precision and recall. Its clone search precision is comparable to seven state-of-the-art clone
Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.
Detection and Monitoring of Tunneling Induced Ground Movements Using Sentinel-1 SAR Interferometry

SAR interferometry is a powerful tool to obtain millimeter accuracy measurements of surface displacements. The Sentinel-1 satellite mission nowadays provides extensive spatial coverage, regular acquisitions and open availability. In this paper, we present an MT-InSAR analysis showing the spatial and temporal evolution of ground displacements arising from the construction of a 3.71 km overflow tunnel in Genoa, Italy. Underground tunneling can often modify the hydrological regime around an excavated area and might induce generalized surface subsidence phenomena due to pore pressure variations, especially under buildings. The tunnel was excavated beneath a densely urbanized area lying on upper Cretaceous marly limestone and Pliocene clays. Significant cumulative displacements up to 30 mm in the Line of Sight (LOS) direction were detected during the tunnel excavation. No displacements were recorded before until the middle of 2016. The Persistent Scatterers Interferometry (PSI) analysis reveals in high detail the areal subsidence, especially where the subsurface is characterized by clay and alluvial deposits as well as there is the presence of large building complexes. The time-series and the displacement rate cross-sections highlight a clear relation with the tunnel face advancement, responsible for the subsidence phenomena, which proceeded northward starting from the middle of 2016 to the end of 2017. The stabilization occurred in a range of five-six months from the beginning of each displacement phase. Due to the low subsidence ratio the ground settlements did not cause severe damages to the buildings.
Facebook and Face-to-Face: Examining the Short- and Long-Term Reciprocal Effects of Interactions, Perceived Social Support, and Depression among International Students

We investigated the proposition that among international students, face-to-face (FtF) interaction with the host-country network, and Facebook interaction with the host-and the home-country networks predict perceived social support, which, in turn, predicts psychological adjustment. We tested the model using cross-lagged and non-lagged reciprocal effects path analyses on three-wave panel data gathered via online surveys. The results indicated that whereas FtF interaction with the host-country increased perceived social support in the short-term, Facebook interaction with the host-country lowered perceived social support in the long-term. Perceived social support increased Facebook interaction with the host-country both in the short-and the long-term. At the same time, perceived social support, in the long-term, decreased depressive symptoms. In the short-term, perceived social support and depressive symptoms negatively reinforced each other. Our longitudinal study contributes to existing literature by elucidating the complex interplay of communication channels and their implications on international students’ experiences.
Stability Analysis of Darcy-Forchheimer Flow of Casson Type Nanofluid Over an Exponential Sheet: Investigation of Critical Points

In this paper, steady two-dimensional laminar incompressible magnetohydrodynamic flow over an exponentially shrinking sheet with the effects of slip conditions and viscous dissipation is examined. An extended Darcy Forchheimer model was considered to observe the porous medium embedded in a non-Newtonian-Casson-type nanofluid. The governing equations were converted into nonlinear ordinary differential equations using an exponential similarity transformation. The resultant equations for the boundary values problem (BVPs) were reduced to initial values problems (IVPs) and then shooting and Fourth Order Runge-Kutta method (RK-4th method) were applied to obtain numerical solutions. The results reveal that multiple solutions occur only for the high suction case. The results of the stability analysis showed that the first (second) solution is physically reliable (unreliable) and stable (unstable).
Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.