Miracle-dz/UniFAR-FTUs huggingface dataset:
UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables

The UltraViolet and infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) mission aims to demonstrate pioneering technologies for broadband measurement of the Earth’s radiation budget (ERB) and solar spectral irradiance (SSI) in the Herzberg continuum (200–242 nm) using high quantum efficiency ultraviolet and infrared sensors. This research and innovation mission has been initiated by the University of Versailles Saint-Quentin-en-Yvelines (UVSQ) with the support of the International Satellite Program in Research and Education (INSPIRE). The motivation of the UVSQ-SAT mission is to experiment miniaturized remote sensing sensors that could be used in the multi-point observation of Essential Climate Variables (ECV) by a small satellite constellation. UVSQ-SAT represents the first step in this ambitious satellite constellation project which is currently under development under the responsibility of the Laboratory Atmospheres, Environments, Space Observations (LATMOS), with the UVSQ-SAT CubeSat launch planned for 2020/2021. The UVSQ-SAT scientific payload consists of twelve miniaturized thermopile-based radiation sensors for monitoring incoming solar radiation and outgoing terrestrial radiation, four photodiodes that benefit from the intrinsic advantages of Ga 2 O 3 alloy-based sensors made by pulsed laser deposition for measuring solar UV spectral irradiance, and a new three-axis accelerometer/gyroscope/compass for satellite attitude estimation. We present here the scientific objectives of the UVSQ-SAT mission along the concepts and properties of the CubeSat platform and its payload. We also present the results of a numerical simulation study on the spatial reconstruction of the Earth’s radiation budget, on a geographical grid of 1 ° × 1 ° degree latitude-longitude, that could be achieved with UVSQ-SAT for different observation periods.
Predicting Carbon Accumulation in Temperate Forests of Ontario, Canada Using a LiDAR-Initialized Growth-and-Yield Model

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation.
Along-Track Multistatic Synthetic Aperture Radar Formations of Minisatellites

The paper analyses an along-track multistatic Synthetic Aperture Radar (SAR) formation. The formation aims at achieving a high azimuth resolution maintaining at the same time a large swath width. The case with one transmitting sensor and all receiving is analyzed (Single Input Multiple Output, SIMO). An effective and novel reconstruction, in the two-dimensional frequency domain is introduced that is able to keep low the azimuth ambiguity and achieve a recombination gain close to the theoretical one. Degradation of the system performance due to the loss of the control of formation position is analyzed using probabilistic considerations. Moreover, some innovative methods to mitigate the loss of optimality are introduced and evaluated using simulations. Finally, considerations on the impact of the across-track non zero baseline are discussed.
Effective and efficient Java‐type obfuscation

To protect valuable assets embedded in software against reverse‐engineering attacks, software obfuscations aim at raising the apparent complexity of programs and at removing information that is useful for attackers. In this work, we propose to combine five transformations that obfuscate the type hierarchy of Java applications and eliminate much of the type information that can be inferred from the Java bytecode. We rely on some existing algorithms, present adaptations, and introduce new algorithms for some of the transformations, which are all made available in an open‐source prototype implementation ready for take‐up. We present an extensive experimental evaluation on benchmarks of real‐world complexity, using complementary metrics that cover the protection strength against both human and tool‐based reverse‐engineering attack methods. The results indicate that the obfuscation is effective as well as much more efficient than the previous state of the art. For the first time, this makes these obfuscations practically viable in real‐world deployment scenarios.
Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics

This research presents the implementation of artificial intelligence (AI) for classification of frogs in symmetry of the bioacoustics spectral by using the feedforward neural network approach (FNNA) and support vector machine (SVM). Recently, the symmetry concept has been applied in physics, and in mathematics to help make mathematical models tractable to achieve the best learning performance. Owing to the symmetry of the bioacoustics spectral, feature extraction can be achieved by integrating the techniques of Mel-scale frequency cepstral coefficient (MFCC) and mentioned machine learning algorithms, such as SVM, neural network, and so on. At the beginning, the raw data information for our experiment is taken from a website which collects many kinds of frog sounds. This in fact saves us collecting the raw data by using a digital signal processing technique. The generally proposed system detects bioacoustic features by using the microphone sensor to record the sounds of different frogs. The data acquisition system uses an embedded controller and a dynamic signal module for making high-accuracy measurements. With regard to bioacoustic features, they are filtered through the MFCC algorithm. As the filtering process is finished, all values from ceptrum signals are collected to form the datasets. For classification and identification of frogs, we adopt the multi-layer FNNA algorithm in machine learning and the results are compared with those obtained by the SVM method at the same time. Additionally, two optimizer functions in neural network include: scaled conjugate gradient (SCG) and gradient descent adaptive learning rate (GDA). Both optimization methods are used to evaluate the classification results from the feature datasets in model training. Also, calculation results from the general central processing unit (CPU) and Nvidia graphics processing unit (GPU) processors are evaluated and discussed. The effectiveness of the experimental system on the filtered feature datasets is classified by using the FNNA and the SVM scheme. The expected experimental results of the identification with respect to different symmetry bioacoustic features of fifteen frogs are obtained and finally distinguished.
Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models for multiple grassland types based on an unmanned aerial vehicle (UAV)-borne imaging spectroscopy and (ii) to generate forage quality distribution maps using the best models obtained. The study examined data from eight grasslands in northern Hesse, Germany, which largely differed in terms of vegetation type and cutting regime. The UAV with a hyperspectral camera on board was utilised to acquire spectral images from the grasslands, and crude protein (CP) and acid detergent fibre (ADF) concentration of the forage was assessed at each cut. Five predictive modelling regression algorithms were applied to develop quality estimation models. Further, grassland forage quality distribution maps were created using the best models developed. The normalised spectral reflectance data showed the strongest relationship with both CP and ADF concentration. From all predictive algorithms, support vector regression provided the highest precision and accuracy for CP estimation (median normalised root mean square error prediction (nRMSEp) = 10.6%), while cubist regression model proved best for ADF estimation (median nRMSEp = 13.4%). The maps generated for both CP and ADF showed a distinct spatial variation in forage quality values for the different grasslands and cutting regimes. Overall, the results disclose that UAV-borne imaging spectroscopy, in combination with predictive modelling, provides a promising tool for accurate forage quality estimation of multiple grasslands.
Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review

The free open access data policy instituted for the Landsat archive since 2008 has revolutionised the use of Landsat data for forest monitoring, especially for estimating forest aboveground biomass (AGB). This paper provides a comprehensive review of recent approaches utilising Landsat time-series (LTS) for estimating AGB and its dynamics across space and time. In particular, we focus on reviewing: (1) how LTS has been utilised to improve the estimation of AGB (for both single-date and over time) and (2) recent LTS-based approaches used for estimating AGB and its dynamics across space and time. In contrast to using single-date images, the use of LTS can benefit forest AGB estimation in two broad areas. First, using LTS allows for the filling of spatial and temporal data gaps in AGB predictions, improving the quality of AGB products and enabling the estimation of AGB across large areas and long time-periods. Second, studies have demonstrated that spectral information extracted from LTS analysis, including forest disturbance and recovery metrics, can significantly improve the accuracy of AGB models. Throughout the last decade, many innovative LTS-based approaches for estimating forest AGB dynamics across space and time have been demonstrated. A general trend is that methods have evolved as demonstrated through recent studies, becoming more advanced and robust. However, most of these methods have been developed and tested in areas that are either supported by established forest inventory programs and/or can rely on Lidar data across large forest areas. Further investigations should focus on tropical forest areas where inventory data are often not systematically available and/or out-of-date.
New Insights into Non-Listed Family SMEs in Spain: Board Social Capital, Board Effectiveness, and Sustainable Performance

This study proposes an original structural model that analyzes the relationship between sustainable firm performance, and a board of directors’ external and internal social capital. Data collected in 232 non-listed and family-run small and medium-sized enterprises in Spain suggest that the effects of boards’ internal and external social capital on sustainable firm performance were partially transmitted through board effectiveness. However, external social capital influences board effectiveness and sustainable firm performance more than internal social capital. Moreover, interlocks only reinforce the relationship between a board’s external social capital and its effectiveness. Our research offers the following main contributions: (1) A proposed structural theoretical model, (2) a focus on both internal and external social capital, unlike previous literature that emphasized only one perspective, and (3) empirical evidence that supports literature on the interlocking interaction between a boards’ internal and external social capital.
English Focus Perception by Mandarin Listeners

This study compared how well native Mandarin and native English speakers can perceive prosodically marked focus in English echo questions. Twenty-five yes–no echo questions were produced with a sentence focus, a verb focus, and an object focus. After hearing each sentence, they were asked to choose a correct response. Native English listeners were more accurate than native Mandarin on verb and object focus, but not on sentence focus. More importantly, both groups confused object focus with sentence focus and vice versa. However, confusion between object and verb focus, and between object and sentence focus was infrequent. These results suggest that, in some cases, (1) acoustic prominence on the head of a phrase or its internal argument can project to the entire phrase and make the entire phrase focused, and (2) parallel transmission of the two functions of intonation, and cross-linguistic variation in focus marking (prosodically versus syntactically) may contribute to their perceptual ambiguity.
A Method for Wavelet-Based Time Series Analysis of Historical Newspapers

This thesis presents a wavelet-based method for detecting moments of fast change in the textual contents of historical newspapers. The method works by generating time series of the relative frequencies of diﬀerent words in the newspaper contents over time, and calculating their wavelet transforms. Wavelet transform is essentially a group of transformations describing the changes happening in the original time series at diﬀerent time scales, and can therefore be used to pinpoint moments of fast change in the data. The produced wavelet transforms are then used to detect fast changes in word frequencies by examining products of multiple scales of the transform. The properties of the wavelet transform and the related multi-scale product are evaluated in relation to detecting various kinds of steps and spikes in diﬀerent noise environments. The suitability of the method for analysing historical newspaper archives is examined using an example corpus consisting of 487 issues of Uusi Suometar from and 250 issues of Wiipuri from 1893–1918. Two problematic features in the newspaper data, noise caused by OCR (optical character recognition) errors and uneven temporal distribution of the data, are identiﬁed and their eﬀects on the results of the presented method are evaluated using synthetic data. Finally, the method is tested using the example corpus, and the results are examined brieﬂy. The method is found to be adversely aﬀected especially by the uneven temporal distribution of the newspaper data. Without additional processing, or improving the quality of the examined data, a signiﬁcant amount of the detected steps are due to the noise in the data. Various ways of alleviating the eﬀect are proposed, among other suggested improvements on the system.
EFFECTS OF BORON AND SULPHUR ON THE GROWTH AND YIELD OF ONION

An experiment was conducted at the Horticulture Farm, Hajee Mohammed Danesh Science and Technology University, Dinajpur from November 2006 to April 2007 to know the effect of boron and sulphur on the growth and yield of onion cv. Taherpuri. The treatment consisted of four levels of each boron (0, 0.5,1 and 2 kg/ha) and sulphur (0, 15, 30 and 60 kg/ha). There was a significant influence of boron and sulphur on yield and yield attributes of onion. Boron @ 1 kg/ha gave the highest yield of bulb (13.19 t/ha) and sulphur @ 30 kg/ha produced the highest yield (12.24 t/ha). However, a combined application of boron and sulphur produced higher yield than boron and sulphur alone. Boron @ 1 kg/ha together with sulphur @ 30 kg/ha produced maximum yield of bulb (15.38 t/ha).
Assessment of bactericidal activity of some essential oils from medicinal plants and selected food additives on Salmonella enteritidis , Staphylococcus aureus and Escherichia coli

The aim of the present study was to investigate and compare between the antibacterial properties of some essential oils derived from some medicinal plants and food additives against food borne pathogens Salmonella enteritidis, Staphylococcus aureus and E. coli. The antimicrobial potential was determined performing the disc diffusion assay and also minimal inhibitory (MIC) and bactericidal (MBC) concentrations. The inhibitory ability manifested by the essential oils and food additives against the bacterial strains varied significantly depending fundamentally on concentration, but also on bacterial species. Essential oils were the most bactericidal agents against the tested bacteria. The MIC and MBC values between 0.125 and 1.0 μg/ml for the most active essential oil of Thymus vulgaris followed by Salvia officinalis and Achillea santolina which had MIC and MBC against the tested bacteria ranged from 0.54. 0μg/ml and 1.5-6. 0μg/ml for MIC and MBC respectively. The most active food additives against the tested bacteria were acetic acid and tartrazine while nisin and phloxine B had no effect on gram negative bacteria.
Improvement of ethanol production by electrochemical redox combination of yeast cells

Electrolytic cell are used for enhancement in produ ction of microbial products. Ethanol is produced as one of the renewable source of energy for which one can use even waste products like lignocellulosic biomass. S election of best ethanol producing yeast strain and best ethanol tolerant yeast cell i s first priority for optimized production. Out of eight yeast strains isolated in Central Department of Biotechnology (CDBT). strain CDBT 2 showed good eth anol tolerance with maximum growth even at 8% ethanol. Thebest ethanol tolerant strain CDBT 2 found to grow till 18% ethanol. Total ethanol production by CDBT 2 was found to enhance by about 15±0.12% than control without supply of 4V external energy when glucose was used as carbohydrate source. Drastic increase i n thanol production by 129±0.877% was seen when external voltage was suppl ied in electrochemical cell with alginate immobilized yeast cells on porous gra phite cathode with or withput external voltage supply. However total enhancement is 7.0±0.056% than control w/o immobilization of yeast strain in cathode. 9.0±0.22 5% increament was observed when cathode was immobilized with neutral red with and without supply of external voltage. 30.64±0.30% and 28.67±0.344% increament in e hanol were observed when cathode were immobilized with neutral red only and neutral red followed by yeast immobilization than with normal graphite felt catho de without external voltage supply. The best ethanol tolerant yeast strain, CDB T 2 was grown in electrolytic cell at best electrode combination with lignocellulosic b omass, Saccharum spontanum , pretreated with ammonium hydroxide followed by hydr olysis with hydrochloric acid gave the ethanol production of 16.67% greater than without external voltage supply.
Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs

This paper examines contemporaneous causality among daily price series of the Chinese Stock Index 300 (CSI300), nearby futures, and first distant futures for April 2010 ~ November 2014 through vector error correction modeling and directed acyclic graphs. As non-Gaussian data are prominent in financial time series, the recently developed Linear Non-Gaussian Acyclic Model (LiNGAM) algorithm is utilized to facilitate analysis. It refines results derived from the PC algorithm, which does not lead to the unique identification of a directed acyclic graph. The price series studied are tied together through cointegration and the nearby futures adjusts towards long-run relationships. Contemporaneous price information is determined to be discovered in the nearby futures. The results suggest that a shock to the nearby futures could have long-lasting effects on prices across the three series under consideration. Policy makers should pay close attention to the nearby futures for financial stability. Citation: Xiaojie Xu, (2019) ''Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs'', Economics Bulletin, Volume 39, Issue 3, pages 2052-2077 Contact: Xiaojie Xu xxu6@ncsu.edu. Submitted: March 06, 2019. Published: September 07, 2019.
Adoption of mobile phone for marketing of cereals by smallholder farmers in Quang Dien District of Vietnam

The study investigated factors that shape the Vietnamese smallholders’ adoption of mobile phones for marketing. A random sample of 161 smallholders was drawn from a total of 275 smallholders who produced and marketed cereals in Quang Dien district of Vietnam. Percentages, means, standard deviations, chi squares test, t-test and a binary logistic regression model were applied to analyse the data. This study concluded that the young male smallholders with a higher education level, who are members of community-based organisations (CBOs) and live far from an electricity base, have high income and participate in credit programs, have a greater tendency to adopt mobile phones for marketing. Lack of knowledge/skills on use of applications on mobile phones, together with language barriers and mobile phone network problems hindered smallholder adoption of mobile phones for marketing. Keywords: mobile phones, adoption, marketing, smallholders, Quang Dien district