Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Characterisation & Validation within ESA Fire-CCI

Uncertainty characterisation and validation are critical phases to generate any Essential Climate Variable (ECV), and therefore both have been included as key deliverables of the ESA CCI programme [1]. All products generated by the CCI are required to have an associated per pixel uncertainty characterisation. This paper describes both the uncertainty characterisation framework and the related uncertainty validation exercise of the Fire-CCI project.
Experiments of Cryogenic Dew and Frost Point Hygrometer for Upper Air Sounding

An experiment device of cryogenic dew and frost point hygrometer is designed based on physical mechanism. Dew and frost on the chilled mirror are detected by transmitting an IR light with wavelength of 850 nm to mirror and receiving the reflected light after absorption of dew or frost by a photodiode. Special novel signal processing technologies for photoelectric integral and ambient light power suppression make the measurements can be done without any light shield. Peltier thermoelectric cooler (TEC) module is employed to heating or chilling mirror for clear or dew or frost, and served by Fuzzy-PID control logic algorithms and high-efficiency, switch-mode driver. Experiment results show that the instrument measuring dew or frost temperature is very accuracy and error less than 0.2 Celsius degree.
Identification and Characterization of NTB451 as a Potential Inhibitor of Necroptosis

Necroptosis, or caspase-independent programmed cell death, is known to be involved in various pathological conditions, such as ischemia/reperfusion injury, myocardial infarction, atherosclerosis, and inflammatory bowel diseases. Although several inhibitors of necroptosis have been identified, none of them are currently in clinical use. In the present study, we identified a new compound, 4-({[5-(4-aminophenyl)-4-ethyl-4H-1,2,4-triazol-3-yl]sulfanyl}methyl)-N-(1,3-thiazol-2-yl) benzamide (NTB451), with significant inhibitory activity on the necroptosis induced by various triggers, such as tumor necrosis factor-α (TNF-α) and toll-like receptor (TLR) agonists. Mechanistic studies revealed that NTB451 inhibited phosphorylation and oligomerization of mixed lineage kinase domain like (MLKL), and this activity was linked to its inhibitory effect on the formation of the receptor interacting serine/threonine-protein kinase 1 (RIPK1)-RIPK3 complex. Small interfering RNA (siRNA)-mediated RIPK1 knockdown, drug affinity responsive target stability assay, and molecular dynamics (MD) simulation study illustrated that RIPK1 is a specific target of NTB451. Moreover, MD simulation showed a direct interaction of NTB451 and RIPK1. Further experiments to ensure that the inhibitory effect of NTB451 was restricted to necroptosis and NTB451 had no effect on nuclear factor-κB (NF-κB) activation or apoptotic cell death upon triggering with TNF-α were also performed. Considering the data obtained, our study confirmed the potential of NTB451 as a new necroptosis inhibitor, suggesting its therapeutic implications for pathological conditions induced by necroptotic cell death.
Beginning and intermediate L2 writer’s use of N-grams: an association measures study

Abstract Acommon approach to analyzing phraseological knowledge in first language (L1) and second language (L2) learners is to employ raw frequency data. Several studies have also analyzed n-gram use on the basis of statistical association scores. Results from n-gram studies have found significant differences between L1 and L2 writers and between intermediate and advanced L2 writers in terms of their bigram use. The current study expands on this research by investigating the connection between bigram and trigram association measures and human judgments of L2 writing quality. Using multiple statistical association indices, it examines bigram and trigram use by beginner and intermediate L1 Korean learners of English in English placement test essays. Results of a logistic regression indicated that intermediate writers employed a greater number of strongly associated academic bigrams and spoken trigrams. These findings have important implications for understanding lexical development in L2 writers and notions of writing proficiency.
Sparse SAR Image Formation of Moving Targets-A Reweighted Sparse Approach

For multi-channel synthetic aperture radar of ground moving target imaging (SAR GMTIm), the moving targets in SAR image domain are sparse after clutter suppression, which provides the possibility of using sparse approach. In this paper, a reweighted sparse algorithm of SAR GMTIm is proposed to improve the imaging performance. Intuitively, both the magnitude and interferometric phase can exhibit the moving target signatures by applying displaced phase center antenna (DPCA) technique and along-track interferometry (ATI), respectively. So a hybrid metric of magnitude and interferometric phase is constructed to be as the weights of the reweighted sparse approach. Compared with the unweighted approach, the proposed reweighed approach can effectively improve the sparse imaging performance. Finally, experiments using measured data are performed to confirm the effectiveness of the proposed algorithm.
CFOSAT Mission: Using of Swim Measurements for Improving Scat Wind Vector Retrieval

CFOSAT (the China France Oceanography Satellite) is the joint mission from the Chinese and French Space Agencies to jointly perform global ocean surface wind and sea state observation and monitoring. The satellite will carry two Ku-band radars: the wave scatterometer (SWIM) and the wind scatterometer (SCAT). Such a unique configuration provides multi-look and multi-angle observations to reconstruct key parameters of local sea surface conditions: the directional wave spectrum and the determination of the sea surface wind speed and direction. From unique collocated wave and wind measurements, each single instrument observation by its own can benefit from the other one. As often discussed, radar returns, at the same location but for different angles of observations, will possibly exhibit different sensitivity with regard to various sea surface parameters, i.e. near-surface wind speed and direction, significant wave height, sea state degree of development and/or local swell wave spectrum characteristics. This often results in radar signal dispersion which limits the precision of the retrieval algorithms from radar remote sensing measurements. Accordingly, the potential to benefit from additional information from a co-located second instrument could be strongly useful in the correction of such factors. The present work considers the possibility of using the near-nadir SWIM measurements to improve the wind estimates from the scatterometer inversion algorithm. Preliminary estimates and simulations confirm that some types of errors, e.g. due to SCAT antenna geometry and configuration, can largely be reduced. As well, additional improvement can be expected for the measurements under low-wind and rapidly changing sea conditions.
The Effect of Snow on Boreal Forest Albedo

A set of high resolution photos of Sub-Arctic forests with snow covered forest floor was analyzed by unsupervised classification to demonstrate the effect of the canopy on the reflectance of the snow pixels. The mean reflectance of snow pixels decreased linearly with increasing number of canopy pixels, similarly for cloudy and clear sky images. An albedo model was used to estimate the contribution of multiple scattering from the forest floor to its total scattering. The multiple scattering dominates the NIR and broadband albedo and is not negligible in the red wavelength range for leaf area index (LAI) values exceeding 1…2, depending on the sun zenith angle.
Deep Semantic Hashing Retrieval of Remotec Sensing Images

Due to the rapid evolution of satellite systems, traditional nearest neighbor image retrieval methods used in large-scale image retrieval usually cause “curse of dimensionality” that leads to boosting feature storage and slow retrieval speed. The hashing method, which aims at mapping the high-dimensional data to compact binary hash codes in Hamming space and quickly calculates the Hamming distance by bit operation and XOR operation, can effectively achieve search and retrieval with remaining similarity for big data. In this paper, we propose a novel image retrieval method based on deep hashing learning, called deep semantic hashing(DSH), attempting to mining the semantic information of remote sensing(RS) images. Experiments carried out on an archive of RS images point out that DSH outperforms other methods to achieve the state-of-the-art performance in image retrieval applications.
Analysis of the Radar Vegetation Index and Assessment of Potential for Improvement

The Radar Vegetation Index (RVI) is widely applied to indicate vegetation cover. The index includes the backscattering intensities of co- and cross-polarization that do not only contain information coming from vegetation scattering at longer wavelength (L-band), but also from the soil underneath. A forward modelling approach using active and passive microwave-derived parameters to obtain the scattering contribution of the soil is pursued. The idea of this research study is a subtraction of the attenuated soil scattering contribution from the measured backscattering intensities, to provide a clean vegetation-based solution, called improved RVI (RVII). For latter analysis, the vegetation volume is forward modeled to calculate vegetation-only RVI-values without any soil scattering contribution. It reveals that, the pre-factor of the standard RVI leads to values up to 1.2, unfavorable for a normalized index running between zero and one. Hence, improvements for the standard RVI equation are proposed here to obtain a better suited value range and for incorporating soil scattering influences and filtering of regions with dominant soil scattering. Moreover, the improved RVI (RVII) is compared with datasets of vegetation and soil parameters (e.g. vegetation water content) for correlation analysis to find the physical parameters contributing to the index.
A Comparative Study Of Impervious Surface Estimation From Optical And Sar Data Using Deep Convolutional Networks

Incorporating optical and SAR data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism. The recent development of deep convolutional networks (DCN) opens a promising opportunity. In this study, the typical DCN, AlexNet, was modified to estimate the impervious surface from optical and SAR data. GoogLeNet and the Support Vector Machine (SVM) were employed for comparison. Experimental results indicated the effectiveness of AlexNet with an accuracy of over 99%, outperforming both GoogLeNet and SVM. Furthermore, 60~80% of training samples outperformed the results from the whole training set under certain number of epochs, indicating that large number of training samples may not necessarily produce better results, depending on other factors (e.g. number of epochs). Generally, AlexNet was able to fuse the optical and SAR data and improved the accuracy of estimating impervious surface by about 2% compared with that using optical data alone.
A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization

A Big Earth Data platform has been constructed based on a parallel distributed database management system, SciDB, to demonstrate visual analytics with interactive animation on diverse datasets. This high-performing capability is achieved by exploiting transparent multimodal parallelization, largely enabled by a unifying indexing scheme, STARE, that provides unparalleled variety scaling. Such a platform not only supports effortless interactive data exploration and analysis but also has the potential to systemize machine learning undertakings with diverse and voluminous Earth Science data.
MDEntropy: Information-Theoretic Analyses for Molecular Dynamics

MDEntropy is a Python package for information-theoretic (IT) analysis of data generated from molecular dynamics simulations. While correlation studies have long been of interest to the molecular dynamics (MD) community (McCammon, Gelin, and Karplus 1977, McClendon et al. (2009)), IT tools to analyze MD trajectories have been much less developed. MDEntropy seeks to fill this niche by providing an easy-to-use Python API that works seamlessly with other Python packages, such as mdtraj, msmbuilder, and numpy (R. T. McGibbon et al. 2015, Walt, Colbert, and Varoquaux (2011), M. P. Harrigan et al. (2017)).
Assessment of the Mapping of Aboveground Biomass and its Uncertainties Using Field Measurements, Airborne Lidar and Satellite Data in Mexico

In this work we estimated Mexican forest aboveground biomass (AGB) using Synthetic Aperture Radar (SAR) and optical remote sensing data and two different reference data: (1) extensive national forest inventory (NFl) and (2) airborne Light Detection and Ranging (LiDAR) data. For the second modelling scenario, we applied a two-stage upscaling approach: firstly AGB for the LiDAR transects were estimated and used then to calibrate satellite imagery. Furthermore, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB maps. The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics compared to the independent validation dataset. However, the AGB map based on two-stage up-scaling method (i.e., from field AGB to LiDAR and from LiDAR-AGB to satellite imagery) showed much higher uncertainties compared to the traditional field to satellite up-scaling.
Private Information Retrieval Schemes with Regenerating Codes

A private information retrieval (PIR) scheme allows a user to retrieve a file from a database without revealing any information on the file being requested. As of now, PIR schemes have been proposed for several kinds of storage systems, including replicated and MDS-coded data. In this paper, the problem of constructing a PIR scheme on regenerating codes is considered. A regenerating code is a storage code whose codewords are distributed among n nodes, enabling efficient storage of files, as well as low-bandwidth retrieval of files and repair of nodes. In this work, a PIR scheme on regenerating codes is constructed, using the product-matrix (PM) framework of Rashmi, Shah and Kumar. Both the minimum-bandwidth (MBR) and minimum-storage (MSR) settings are considered, and the structure given by the PM framework is used in order to reduce the download communication complexity of our schemes.
Spin rate estimation of projectiles from microwave signals

This paper presents a novel spin rate estimation system for artillery projectile which relies on two linearly polarized antennas and a radio frequency power detector device (PDD). Emphasis is put on the spin rate measurement results and their comparison to another system based on embedded magnetometers. The precision of the method is evaluated by real field test experiments and real firings. The system was designed to support the solution with on-board magnetometers, but can also offer a large number of possible applications.
Human-centered machine learning through interactive visualization: review and open challenges

The goal of visual analytics (VA) systems is to solve complex problems by integrating automated data analysis methods, such as machine learning (ML) algorithms, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and makes the crucial interplay between automated algorithms and interactive visualizations more concrete. The framework is illustrated through several examples. We derive three open research challenges at the intersection of ML and visualization research that will lead to more effective data analysis.
Initial Studies of Validity of the Children’s Occupational Performance Questionnaire

Studies of validity of a new caregiver report measure, the Children’s Occupational Performance Questionnaire (COPQ) designed to address children’s performance in the domains of personal and instrumental activities of daily living (IADLs), social participation, play/leisure, and education/work are presented. I examined criterion-related and discriminant validity of the COPQ. Criterion-related validity was addressed by correlating children’s COPQ scores with those from the Vineland Adaptive Behavior Scale (VABS-II). Quasi-experimental methods were used to compare COPQ scores from a heterogeneous group of children with disabilities with those from neurotypical children matched by age. COPQ scores correlated highly with scores from the VABS-II including social interaction, communication, daily living skills, and motor skills. Capacity of the COPQ to discriminate between children with and without disabilities varied dependent on age, and the occupational domain being considered. Preliminary support for the validity of COPQ as a measure of occupational performance for children was provided. Further study of the tool’s psychometrics and with larger samples is needed.
microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF

For named entity recognition (NER), bidirectional recurrent neural networks became the state-of-the-art technology in recent years. Competing approaches vary with respect to pre-trained word embeddings as well as models for character embeddings to represent sequence information most effectively. For NER in German language texts, these model variations have not been studied extensively. We evaluate the performance of different word and character embeddings on two standard German datasets and with a special focus on out-of-vocabulary words. With F-Scores above 82% for the GermEval'14 dataset and above 85% for the CoNLL'03 dataset, we achieve (near) state-of-the-art performance for this task. We publish several pre-trained models wrapped into a micro-service based on Docker to allow for easy integration of German NER into other applications via a JSON API.
Spatial Correlation Based Psinsar Technique to Estimate Ground Deformation in las Vegas Region, Us

Differential Synthetic Aperture Radar interferometry (DIn-SAR) is an efficient technique for observing and monitoring ground deformation over a large area at a millimetric level using multi-temporal SAR images. However, the phase decorrelation phenomena along with other bottlenecks, such as atmospheric nuisance, orbital, and DEM inaccuracy limit the accuracy of the traditional DInSAR technique. Persistent Scatterer InSAR (PSInSAR), based on DInSAR, circumvents these limitations using temporally stable reflectors or permanent scatterers (PS) of earth surface using a long temporal stack of SAR images. In this paper, a spatial correlation based PSInSAR technique is applied to detect the ground deformation of Las Vegas, Nevada, US between 2002 to 2010. This aim of this study is focused on the precise estimation and validation of ground deformation using the PSInSAR analysis of descending pass Envisat ASAR data, and further cross-validation of displacement time-series is carried out with GPS time-series observations. Archived C-band Envisat ASAR SAR data stack was used to estimate ground deformation map of the concerned area. A good correlation is observed between PSInSAR and GPS time-series. We have observed a mean annual deformation rates of −5 to 5.1 mm/ year during 2002–2010 along satellite line of sight (LOS).
ESA's Campaign Activities in Support of the FLEX Mission

The European Space Agency (ESA) carries out instrument developments and campaigns to provide support for the verification and validation of future Earth Observation missions and their users. A larger number of these activities took place during the last years in the context of the future FLuorescence EXplorer (FLEX) mission [1]. These activities have been related (1) to the instrument development (e.g. through the development of airborne demonstrators), (2) to the understanding and modeling of bio-geophysical processes, (3) the development of retrieval algorithms and test data sets, and (4) the validation of the remotely sensed products. The corresponding campaigns were conducted over different locations inside and outside Europe in collaboration with national research organisations in the ESA member states as well as with international organisations harmonising International campaign activities and maximizing synergies. At present, related activities produce high value datasets and analyses to support the FLEX mission, especially with respect to algorithm development and uncertainty quantification
Newly Identified Essential Amino Acids Affecting Chlorella ellipsoidea DGAT1 Function Revealed by Site-Directed Mutagenesis

Diacylglycerol acyltransferase (DGAT) is a rate-limiting enzyme in the synthesis of triacylglycerol (TAG), the most important form of energy storage in plants. Some residues have previously been proven to be crucial for DGAT1 activity. In this study, we used site-directed mutagenesis of the CeDGAT1 gene from Chlorella ellipsoidea to alter 16 amino acids to investigate effects on DGAT1 function. Of the 16 residues (L482R, E542R, Y553A, G577R, R579D, Y582R, R596D, H603D, H609D, A624R, F629R, S632A, W650R, A651R, Q658H, and P660R), we newly identified 5 (L482, R579, H603, A651, and P660) as being essential for DGAT1 function and 7 (E542, G577, R596, H609, A624, S632, and Q658) that significantly affect DGAT1 function to different degrees, as revealed by heterologous expression of the mutants in yeast strain INVSc1. Importantly, compared with CeDGAT1, expression of the mutant CeDGAT1Y553A significantly increased the total fatty acid and TAG contents of INVSc1. Comparison among CeDGAT1Y553A, GmDGAT1Y341A, AtDGAT1Y364A, BnDGAT1Y347A, and BoDGAT1Y352A, in which tyrosine at the position corresponding to the 553rd residue in CeDGAT1 is changed into alanine, indicated that the impact of changing Y to A at position 553 is specific for CeDGAT1. Overall, the results provide novel insight into the structure and function of DGAT1, as well as a mutant gene with high potential for lipid improvement in microalgae and plants.
Improving Trajectory Optimization Using a Roadmap Framework

We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners' combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process.