Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Resolution Monitoring of Tidal Systems Using UAV: A Case Study on Poplar Island, MD (USA)

Tidal processes regulating sediment accretion rates and vegetated platform erosion in tidal systems strongly affect salt marsh evolution. A balance between erosion and deposition in a restored salt marsh is crucial for analyzing restoration strategies to be adopted within a natural context. Marsh morphology is also coupled with tidal mudflats and channel networks and this makes micro-tidal systems crucial for a detailed assessment of restoration interventions. Here, we present a methodological approach for monitoring channel morphodynamics and vegetation variations over a time frame of six years in a low tidal energy salt marsh of the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island (Maryland, USA). The project is a restoration site where sediment dredged from the shipping channels in the upper Chesapeake Bay is used to restore a tidal marsh habitat in mid-Chesapeake Bay. Aerial surveys with an Unmanned Aerial Vehicle (UAV) have been performed for the high-resolution mapping of a small tidal system. Flight missions were planned to obtain a Ground Sample Distance (GSD) of 2 cm. Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) algorithms have been used to reconstruct the 3D geometry of the site. The mapping of channel morphology and an elevation assessment on the mudflat were performed using orthomosaics, Digital Terrain Models (DTMs) and GNSS survey. The results highlight that the workflow adopted in this pilot work is suitable to assess the geomorphological evolution over time in a micro-tidal system. However, issues were encountered for salt marsh due to the presence of dense vegetation. The UAV-based photogrammetry approach with GNSS RTK ground surveys can hence be replicated in similar sites all over the world to evaluate restoration interventions and to develop new strategies for a better management of existing shorelines.
Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning

Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method.
Using UAV Imagery to Detect and Map Woody Species Encroachment in a Subalpine Grassland: Advantages and Limits

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice.
A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.
Enhancement of Cloud-to-Ground Lightning Activity Caused by the Urban Effect: A Case Study in the Beijing Metropolitan Area

To investigate the possible impact of urban development on lightning activity, an eight-year (2010–2017) cloud-to-ground (CG) lightning dataset provided by the National-Wide Lightning Detection Network in China was analyzed to characterize the CG lightning activity in the metropolitan area of Beijing. There is a high CG flash density area over the downtown of Beijing, but different from previous studies, the downwind area of Beijing is not significantly enhanced. Compared with the upwind area, the CG flash density in the downtown area was enhanced by about 50%. Negative CG flashes mainly occurred in the downtown and industrial area, while positive CG flashes were distributed evenly. The percentage of positive CG flashes with Ipeak ≥ 75 kA is more than six times that of the corresponding negative CG flashes in the Beijing area. The enhancement of lightning activity varies with season and time. About 98% of CG flashes occurred from May to September, and the peak of CG diurnal variation is from 1900 to 2100 local time. Based on the analysis of thunderstorm types in Beijing, it is considered that the abnormal lightning activity is mainly responsible for an enhancement of the discharge number in frontal systems rather than the increase of the number of local thunderstorms. In addition, there is a non-linear relationship between pollutant concentrations and CG flash number, which indicates that there are other critical factors affecting the production of lightning.
Formal security analysis of MPC-in-the-head zero-knowledge protocols

Zero-knowledge proofs allow a prover to convince a verifier of the veracity of a statement without revealing any other information. An interesting class of zero-knowledge protocols are those following the MPC-in-the-head paradigm (Ishai et al., STOC ’07) which use secure multiparty computation (MPC) protocols as the basis. Efficient instances of this paradigm have emerged as an active research topic in the last years, starting with ZKBoo (Giacomelli et al., USENIX ’16). Zero-knowledge protocols are a vital building block in the design of privacy-preserving technologies as well as cryptographic primitives like digital signature schemes that provide post-quantum security. This work investigates the security of zero-knowledge protocols following the MPC-in-the-head paradigm. We provide the first machine-checked security proof of such a protocol on the example of ZKBoo. Our proofs are checked in the EasyCrypt proof assistant. To enable a modular security proof, we develop a new security notion for the MPC protocols used in MPC-in-the-head zero-knowledge protocols. This allows us to recast existing security proofs in a black-box fashion which we believe to be of independent interest.
A Framework for Set Similarity Join on Multi-Attribute Data

Set similarity join, which finds all pairs of similar sets in a collection, plays an important role in data cleaning and integration. Many algorithms have been proposed to efficiently answer set similarity join on single-attribute data. However, real-world data often contain multiple attributes. In this paper, we propose a framework to enhance existing algorithms with additional filters for dealing with multi-attribute data. We then present a simple, yet effective filter based on lightweight indexes, for which exact and probabilistic implementation alternatives are evaluated. Finally, we devise a cost model to identify the best attribute ordering to reduce processing time. Our experimental results show that our approach is effective and significantly outperforms previous work.
Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.
Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems.
Particle Size-Frequency Distributions of the OSIRIS-REx Candidate Sample Sites on Asteroid (101955) Bennu

We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and shed light on the processes that have shaped the surface of this rubble-pile asteroid. Building on a global survey of particles, we used higher-resolution data from regional observations to calculate particle size-frequency distributions (PSFDs) and assess the viability of four candidate sites for sample collection (presence of unobstructed particles ≤ 2 cm). The four candidate sites have common characteristics: each is situated within a crater with a relative abundance of sampleable material. Their PSFDs, however, indicate that each site has experienced different geologic processing. The PSFD power-law slopes range from −3.0 ± 0.2 to −2.3 ± 0.1 across the four sites, based on images with a 0.01-m pixel scale. These values are consistent with, or shallower than, the global survey measurements. At one site, Osprey, the particle packing density appears to reach geometric saturation. We evaluate the uncertainty in these measurements and discuss their implications for other remotely sensed and mapped particles, and their importance to OSIRIS-REx sampling operations.
Semantic Search on Scientific Repositories: A Systematic Literature Review

Open Science has been recognized as one of the most important movements for leveraging scientific collaboration, helping scientists produce high quality research through sharing and reuse. It is usually defined as a combination of three factors: open access, open data and open processes, and relies on the corresponding publication of papers, data and software in repositories that can be publicly accessed on the Web. However, finding relevant papers, data and software has become one of the associated problems. Many search mechanisms – in particular semantic search – have risen as a means to solve this issue. Nevertheless, implementing these mechanisms and integrating them into scientific repositories presents many challenges. This paper presents a systematic literature review of research efforts on mechanisms for supporting search for scientific papers, data and processes, based on extracting and analyzing the entire contents of Scopus and IEEE Xplore.
Satellite-Based Observations Reveal the Altitude-Dependent Patterns of SIFyield and Its Sensitivity to Ambient Temperature in Tibetan Meadows

Photosynthesis and its sensitivity to the changing environment in alpine regions are of great significance to the understanding of vegetation–environment interactions and other global ecological processes in the context of global change, while their variations along the elevation gradient remain unclear. Using solar-induced chlorophyll fluorescence (SIF) derived from satellite observations, we discovered an increase in solar-induced fluorescence yield (SIFyield) with rising elevation in Tibetan meadows in the summer, related to the altitudinal variation in temperature sensitivity at both seasonal and interannual scales. Results of the altitudinal patterns of SIFyield demonstrated higher temperature sensitivity at high altitudes, and the sensitivity at the interannual scale even exceeds that at seasonal scale when the elevation reaches above 4700 m. This high-temperature sensitivity of SIFyield at high altitudes implies potential adaptation of alpine plants and also indicates that changes in photosynthesis-related physiological functions at high altitudes should receive more attention in climate change research. The altitudinal SIFyield patterns revealed in this study also highlight that variations in temperature sensitivity should be considered in models, otherwise the increasing trend of SIFyield observations can never be discovered in empirical simulations.
Classic McEliece on the ARM Cortex-M4

This paper presents a constant-time implementation of Classic McEliece for ARM Cortex-M4. Specifically, our target platform is stm32f4-Discovery, a development board on which the amount of SRAM is not even large enough to hold the public key of the smallest parameter sets of Classic McEliece. Fortunately, the flash memory is large enough, so we use it to store the public key. For the level-1 parameter sets mceliece348864 and mceliece348864f, our implementation takes 582 199 cycles for encapsulation and 2 706 681 cycles for decapsulation. Compared to the level-1 parameter set of FrodoKEM, our encapsulation time is more than 80 times faster, and our decapsulation time is more than 17 times faster. For the level-3 parameter sets mceliece460896 and mceliece460896f, our implementation takes 1 081 335 cycles for encapsulation and 6 535 186 cycles for decapsulation. In addition, our implementation is also able to carry out key generation for the level-1 parameter sets and decapsulation for level-5 parameter sets on the board.
Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.
Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.
One-Pot Synthesis of (Z)-β-Halovinyl Ketones via the Cascade of Sonogashira Coupling and Hydrohalogenation

Herein, we report an efficient method for the synthesis of (Z)-β-halovinyl ketones through a one-pot Sonogashira coupling and hydrohalogenation reaction promoted by palladium-copper catalyst and Brønsted acid. The ynone intermediates are generated in situ from readily available acid chlorides and terminal alkynes at room temperature, which are directly converted to (Z)-β-halovinyl ketones by treating with triflic acid. This method avoids the use of an external halogen source and features broad substrate scope, high yield, and good to excellent stereoselectivity.