dataesr/scientific-paragraphs-categorization huggingface dataset:
Data mining approach A data mining consists of the discovery of interesting, unexpected or valuable structure in large data sets that can be called with the slogan Big Data  [28]. In other words, data mining consists of extracting the most important information from a very large data set. Indeed, the classical statistical inference has been developed for processing small samples. In the presence of very large databases, all the standard statistical indexes become significant and thus interesting (e. g. for 1 million of data, the significance threshold of correlation coefficient is very low reaching 0. 002, …). Additionally, in data mining, data collected are analyzed for highlighting the main information before to use them in the forecasting models. Rather than opposing data mining and statistics, it is best to assume that data mining is the branch of statistics devoted to the exploitation of large databases.
Nonlinear Regression Artificial Neural Networks (ANN) are being increasingly used for nonlinear regression and classification problems in meteorology due to their usefulness in data analysis and prediction  [35]. The use of ANN is particularly predominant in the realm of time series forecasting with nonlinear methods. Actually the availability of historical data on the meteorological utility databases and the fact that ANNs are data driven methods capable of performing a non-linear mapping between sets of input and output variables makes this modelling software tool very attractive. Artificial neural network with d inputs, m hidden neurons and a single linear output unit defines a nonlinear parameterized mapping from an input vector x to an output y is given by (unbiased form): (2) Each of the m hidden units are usually related to the tangent hyperbolic function For solar radiation the relationship between the output and the inputs has the form given by: (3) As shown by the preceding equation, the ANN model is equivalent to a nonlinear autoregressive (AR) model for time series forecasting problems.
Gaussian Mixture Models Gaussian Processes (GPs) are a relatively recent development in non-linear modelling  [49]. A GP is a generalization of a multivariate Gaussian distribution to infinitely many variables. A multivariate Gaussian distribution d is fully specified by a mean vector μ and covariance matrix Σ, e. g. The key assumption in GP modelling is that the data can be represented as a sample from a multivariate Gaussian distribution e. g. the observations. In order to better introduce GPs, the case is often restricted to one scalar input variable. As the data are often noisy usually from measurement errors, each observation y can be thought of as an underlying function the training database , the vector of forecasted irradiation for horizon h for new test inputs is given by the mean of the predictive Gaussian distribution predictions.
Predictors ensemble Current practice suggests that forecasts should be composed either by a number of simple-say "conventional" forecasts-or produce a simple forecast from other simple forecasts (not only point forecasts, but also probabilistic). This leads to gains in performance, relative to the contributing forecasts. In the case of statistical models, realizations coming from the same technology (for example the same neural network architecture) trained multiple times, or using different samples of the dataset; or different technologies. Once "first stage forecasts" are available, different combination approaches are possible. The simplest approach is averaging of results given by different methods. A more general approach assigns a weight to each of the contributing methods, for each time horizon, depending on different criteria and with different weighting policies.
The parameter vector governs the non-linear mapping and is estimated during a phase called the training or learning phase. During this phase, the ANN is trained using the dataset that contains a set of n input and output examples. The second phase, called the generalization phase, consists of evaluating, on the test dataset , the ability of the ANN to generalize, i. e. , to give correct outputs when it is confronted with examples that were not seen during the training phase.
multipliers, which are the solutions of a quadratic programming (QP) problem. Unlike ANNs, which are confronted with the problem of local minimum, here the problem is strictly convex and the QP problem has a unique solution. In addition, it must be stressed (that unlike GPs), not all the training patterns participate to the preceding relationship. Indeed, a convenient choice of a cost function (Vapnik's insentive function) in the QP problem allows obtaining a sparse solution. The latter means that only some of the coefficients will be nonzero. The examples that come with nonvanishing coefficients are called Support Vectors. One way to use the SVR in prediction problem is related to the fact that given the training dataset and a test input vector , the forecasted clear sky index can be computed for a specific horizon, h, like:.
[14]  using the equation (5): Starch content g:L -1 À Á ¼ A 600 2:294 ð5Þ Experimental design and data analysis The effects of F/2 medium components on cells growth, pigments and starch productions by Tetraselmis sp. were evaluated and analyzed by Box-Behnken methodology  [15]. The experiment design contained 27 trials (Table  3 ). Table  1  shows the four independent variables which are extracellular NaNO 3 concentration (x 1 ), extracellular NaH 2 PO 4 concentration (x 2 ), the metal solution initial volume (x 3 ) and the vitamin solution initial volume (x 4 ). These factors were studied at three levels, low (-1), medium (0) and high (+1). The biomass concentration (Y 1 ), total chlorophylls (Y 2 ), carotenoids (Y 3 ) and starch (Y 4 ) contents were analyzed as asked responses of the experiments design.
Experimental data were fitted with a second order polynomial model (Eq. 6): Ŷ ¼ β 0 þ X β i x i þ X β ii x i 2 þ X β ij x i x j ð6Þ Where Ŷ: response variable; β 0 : constant coefficient; x i and x j : uncoded variables ranging between the minimum and the maximum concentrations of the different factors; β i , β ii and β ij : coefficients for the linear, quadratic, and interaction effects, respectively. After Tetraselmis sp. cultivation under different nutritional conditions, the statistical analysis of Box-Behnken Design of experimental results was carried out using STATISTICA software 8. 0 (Stat Soft. Inc 2008). The RSM is an efficient statistical tool to investigate the factors effect and interactions with a minimum number of experiments  [16, 17]. Thus, to optimize the biomass, total chlorophylls, carotenoids and starch production (Y 1 , Y 2 , Y 3 , Y 4 ) by Tetraselmis sp.
, a total of 27 experiments were performed in this study (Table  3 ). Accordingly, P-values were carried out for regression analysis and to evaluate the significance of factors' effects of the first (x 1 , x 2 , x 3 , x 4 ) and the second (x 1 2 , x 2 2 , x 3 2 , x 4 2 ) order polynomials, and their interactions (x 1 x 2 , x 1 x 3 , x 1 x 4 , x 2 x 3 , x 2 x 4 , x 3 x 4 ). The optimum responses were obtained with the "Response Desirability Profiling" tool of STATISTICA Software.
In fact, the low sulfur concentration caused a decrease of the photosystem II activity. Therefore, the microalgal cells were exposed to culture stress conditions, hence increasing starch synthesis. In this study, Run 16 (Table  3 ) showed a high biomass concentration of 4. 8 g. L -1 with a high biomass productivity of 343 mg. L -1. day -  (data not shown) corresponding to a high starch content of 36% DW. Thus, this result is crucial to achieve a compromise between increasing cell growth and starch accumulation in Tetraselmis sp. strain V 2 in order to set up a promising source for bioethanol production. Based on the FT-IR spectra analysis, the relative carbohydrate content was calculated by measuring the ratio of the transmittance areas of the carbohydrate bands (1200-950 cm -1 ) to the amide I band (1639 cm -1 ).
The various load and temperature dynamics inside the subnets are aggregated in the substation patterns as hourly steady-state conditions. It is assumed that all substations present similar patterns. In order to compare the impact of the LTDH subnets on the primary network, one LTDH substation arrangement is simulated and compared to a conventional substation selected as a reference. The reference substation is based on commercially available equipment  [12] , suitable for this type of application (a multi-dwelling building with c. a. 50 multi-family households). Each substation is designed for a nominal load of 250 kW th that is assumed to occur when the outdoor ambient temperature reaches -20°C. For this study, the outdoor ambient temperature distribution data used correspond to a Typical Meteorological Year (TMY) which is a statistically representative year at the selected location, in this case Stockholm, Sweden.
Acknowledgements The research presented is performed within the framework of the  Erasmus Mundus Joint Doctorate SELECT+ 'Environomical Pathways for Sustainable Energy Services'  and funded with support from the  Education, Audiovisual, and Culture Executive Agency (EACEA)  of the  European Commission  under Erasmus Mundus Action 1 programmes. This publication reflects the views only of the author(s), and the Commission cannot be held responsible for any use which may be made of the information contained therein. The lead author would also like to express his gratitude to the  National Council on Science and Technology (CONACYT) of Mexico  for providing supplementary funding.
Characterization techniques PXRD measurements were made in a Bruker-AXS D8-Discover diffractometer with a vertical theta-theta goniometer and a Vantec linear detector. Monochromatic Cu Kα radiation was obtained from a copper X-ray tube operated at 40 kV and 40 mA and a Ni monochromator. Data were recorded over a 2θ range of 5-70° with an angular step of 0. 02° at 10. 6 s/step. Specific surface areas were determined by N2 adsorption at -196 ºC using a Micromeritics ASAP 2020 automatic analyzer. Samples were previously degassed in situ at 150 ºC under vacuum for 15 h. Surface areas were calculated using the Brunauer-Emmet-Teller (BET) method over a p/p0 range where a linear relationship was maintained. Morphology of the samples was analyzed by electron microscopy. Transmission electron microscopy (TEM) was performed in the bright field using a JEOL JEM-2100 instrument at an accelerating voltage of 100 kV.
These effects were apparently due to the deposition-resuspension mechanisms of dust accumulation on the PV panel surfaces. The ANN model performed significantly better in predicting daily ΔCI as well as cumulative CI than the linear model in term of R 2 values and statistical error indexes. The previous day environmental conditions had a significant effect on the modeling outcome. The inclusion of the wind gustiness and cumulative exposure time also considerably improved the model prediction capability. The advantage of the ANN-based model is its simplicity, ease of data fitting and no requirement of an accurate mathematical model.
Methodology Data collection of this study was carried out in the Solar Test Facility (STF) located in Doha, Qatar. The PV soiling effect, in terms of Cleanness Index (CI; a measure of PV performance loss due to soiling) and daily change in Cleanness Index (ΔCI), was calculated based on field measurement at the STF site. Concurrently, the environmental variables were also measured at the test site. In this study, we collected data for more than two and half years spanning May 29, 2014 through December 31, 2016. The CI measurements are available for 886 days, out of which 826 days were considered for modeling in which all selected variables' measurements were available for the analysis. The daily ΔCI values after a cleaning or rain event were not included in this study. The methods for data collection are described in the following sections.
Measurement of PV module performance The detailed methods of PV performance data collection and CI calculation have been described in our previous study  (Guo et al. , 2015). Briefly, two identical PV arrays at the STF were used in this study, each comprising eight 220 Wp polysilicon PV modules, tilted at 22°and facing due south, each in a single string connected to identical grid-tied inverters. The arrays' DC electrical parameters and module back surface temperatures were measured at maximum power point condition once per minute. DC power, voltage and current were measured via transducers with ± 0. 5% accuracy. Module temperatures were measured via permanently attached thermocouples, with unspecified accuracy. One array was cleaned every week, referred to as the Clean PV Array, one every second month, referred to as the Test PV Array.
From the solar irradiance and module temperature-corrected PV performance data (normalized to the standard test conditions; solar irradiance (1 kW m -2 ) and temperature (25 °C), the CI value was determined to quantify the PV soiling level. It is defined as the ratio of the performance ratio of Test PV Array to that of a clean PV Array  (Guo et al. , 2015). The value of the Cleanness Index ranges from 0 to 1, with 1 for a weekly-cleaned PV module. It must be noted that Test PV array was cleaned once every two months. The Cumulative exposure time or exposure period (i. e. a number of days since the Test PV array was last cleaned either by manually or rain event) was also calculated. Each exposure period is the total number of days between two cleanings of the PV modules.
The daily ΔCI was the target output of the ANN model. MATLAB's built-in random division technique was used, which randomly divided the data set -70% for training, and 15% each for testing and validating. Configuration optimization of the ANN model is difficult due to the stochastic nature of ANN models. Nevertheless, the number of neurons in the hidden layer was varied from two to twenty-two with randomly initialized weights. The ANN model with these varying number of hidden-layer neurons was trained and evaluated ten times each to get the optimum results  (Torres-Ramírez et al. , 2015). The LM algorithm with 20 neurons in the hidden layer for the network produced the best results, and it was used for generating the model outputs. The optimal ANN model for a particular number of input variables was obtained by training the network ten times for each case.
The ANN model was executed using the MATLAB® Neural Network toolbox release R2016a. The set of input variables to the ANN model was also varied to evaluate their ability to predict the daily ΔCI. Firstly, a set of five input variables, namely the present-day average values of PM 10 , WS, WD, Ta and RH, were used as the ANN model input. The corresponding ANN model is herein referred to as ANN-5. Secondly, three additional variables, namely the previous-day average values of WS, PM 10 , and RH, were introduced as inputs to the ANN model. The corresponding ANN model is referred to as ANN-8. The performance improvement of the ANN-8 model as compared to ANN-5 in term of R 2 was evaluated. The previous-day WD and Ta variables had insignificant (p > 0. 05) effect on the present-day PV soiling and were therefore not included in the ANN-8 model.
Multiple linear regression (MLR) model As a benchmark for evaluating the accuracy of the ANN model, a multi-variable linear regression (MLR) model was implemented in this study. MLR models had been employed to smaller sets of data in our previous studies  (Guo et al. , 2015 (Guo et al. , , 2016)). In the MLR model, the daily ΔCI was simply expressed as a linear function of the environmental variables: = + + ⋯ + CI α α α α Δ var var var i i 0 1 1 2 2 (1) where … α α α α , , i 0 1 2 are the model parameters. Microsoft Excel add-in XLSTAT™ was used to fit MLR model with the obtained dataset. The MLR model used the same ten input variables as that for the ANN-10 model. All 826 observations were used to determine the parameters of the MLR model.
Model predictions of daily ΔCI Fig. 6  shows the linear relationship between measured and predicted daily ΔCI values by using MLR and ANN-based models. It is noted that MLR model used the same ten input variables as the final ANN-10 model for evaluating the accuracy of the ANN model through comparison. In comparison to MLR model, it is evident that proposed ANN-based model has the significantly better performance for predicting the daily ΔCI of PV modules. The ANN-10 model can explain approximately 54% (R 2 = 0. 54, p < 0. 05) of the variability in the daily ΔCI as compared to MLR model (R 2 = 0. 17). The predicted data points of daily ΔCI in the case of MLR show a more scattered behavior from the fitted line as compared to that of ANN model. It was observed that out of the 826 data points used in the models, 201 and 110 predicted values in MLR and ANN model, respectively, deviate from the measurement data by more than one standard deviation of measurement data.
The ANN-based modeling approach seems to be a suitable tool to predict the PV soiling with the significantly reduced computational burden and high performance. The advantage of the ANN-based model is its simplicity, ease of data fitting and no requirement of an accurate mathematical model. However, the correlation coefficient between measured and ANN model predicted data points of daily ΔCI as a function of 24-h averages of environmental variables was not highly significant, suggesting that there are additional factors that influence the soiling-related PV performance loss. For example, the strong diurnal trends of environmental variables and their short-term relative increase or decrease may affect the diurnal variation of PV soiling. In the diurnal pattern, the environmental variables are strongly interactive and dependent on each other, so despite 24-h averages considering the short-   -0.
Acknowledgements This work was funded by the  European Community  ( FP7-NMP. 2012. 1. 1-1project  chipCAT, Reference No. 310191 ), by the  Deutsche Forschungsgemeinschaft (DFG)  within the  Excellence Cluster "Engineering of Advanced Materials " in the framework of the excellence initiative, by the  Spanish MINECO  (grants  CTQ2012-34969  and  CTQ2015-64618-R/ FEDER ), by the  Generalitat de Catalunya  (grants  2014SGR97  and XRQTC), and by the  Czech Ministry of Education  (grant  LM2015057 ). Computer resources, technical expertise and assistance were provided by the  Red Española de Supercomputación. Y. L. and coworkers thank  Elettra  for excellent working conditions and support. The authors also acknowledge the  CERIC-ERIC Consortium  for the access to experimental facilities and financial support and the  COST Action   CM1104  for travel support.
INTRODUCTION The use of solar energy in sunny countries is an effective tool to compensate the energy lack and has also environmental interest because pollution has become a major problem  [1]. Knowledge of the solar energy potential at a given site is an important parameter for designers of solar energy systems. The long-term evaluation of the performance of solar energy conversion systems is established using numerical simulation programs. In Algeria, the software for dimensioning photovoltaic and thermal installations claim, in most cases, integrated databases to calculate solar flux, based essentially on Capderou's model of the Solar Atlas of Algeria  [2]. The optimal dimensioning and the best management of energy systems requires knowledge of meteorological conditions for which extensive studies are carried out in several regions of the world to assess and model the solar potential.
In this work, some fundamental notions of astronomy are presented which give information on the radiation received by the ground (diffuse, direct and global). The data required for the establishment of the solar energy balance (direct and diffuse flux calculations) are obtained by considering a sinusoidal variation of the ambient temperature and of the overall flux received on a horizontal wall and by considering the actual data of the maximum solar flux and the minimum and maximum temperatures recorded for the considered months on the Adrar site.
II. EXPERIMENTAL DATA Currently, in Algeria, the measurement of solar radiation data is carried out in an instantaneous way on certain sites in Algeria. We chose the site of Adrar φ = 27 0 88' N and λ = 0 0 27' W for this study. The choice of this site is based on the fact that Adrar is a city very rich in solar energy, given its average sunshine rate which reaches about 9963Wh / m2•j  [4]. For this reason, we have chosen this region in order to develop a building with a solar installation with maximum efficiency by playing on the climatic conditions. The data of the solar radiation (the mean ambient solar flux, the average ambient temperature) used were provided by the Adrar Renewable Energy Research Unit in the Saharian Medium (RERUSM).
Computational details The fitting of the desorption curves and the parameter determination was performed using a least squares method implemented in Matlab. In order to determine the parameters, a genetic algorithm was used to select a set of parameters for each iteration step  [25]. This algorithm was successfully applied previously for the modeling of the Ni-catalyzed MgH 2 hydrogen absorption rate  [26, 27]. The total population for the algorithm was 200 sets of parameters and it was evolved until the convergence criterion was met. With these parameters, it was possible to simulate the desorption curve using the ode45 method provided by Matlab. The curve obtained was then compared to the experimental one and the goodness of the fitting was determined calculating the sum of the squared differences between the experimental and the calculated data.
Results and discussion Hydrogen reaction kinetics of the samples were studied by continuous cycling in the Sieverts device at 200 C with different back pressures (50e2000 kPa). The samples showed an absorption capacity between 4 and 4. 5 wt% after several cycles of hydrogen absorption/desorption. Details about the hydrogen storage capacity and stability are presented elsewhere  [23]. In our previous work, XRPD and FTIR analyses of the milled and cycled samples have been presented showing the phases present in the samples before and after the hydrogen cycling. The milled sample obtained was proved to be a mixture of Mg(NH 2 ) 2 , Li   Several equations proposed in literature were considered to fit the data  [28]. A complete summary of the different kinetic models, grouped in four subtypes according to the controlling mechanism: nucleation, geometrical contraction, diffusion and reaction order, has been presented  [24].
Tracking was performed using a ×40 microscope objective and recording videos with a monochrome CCD camera (AxioCam ICm 1, Carl Zeiss) at 28 fps. The videos were processed using specially written software based on feature-matching routines (LabVIEW NI IMAQ Vision, National Instruments) and the earlier work of Neal et al. [23]  and allowed the frame-by-frame in-plane co-ordinates of an individual particle to be obtained from the image of the caustic. When the particles aggregate the caustic changes shape from a simple circular shape (electronic supplementary material, figure  S2 ) to a set of overlapping circular shapes (electronic supplementary material, figure  S3 ) so that it was straightforward to ensure single particles were tracked. The motion of particles can be characterized by their mean square displacement, MSD  [24]  such that where (x i ,y i ) are the coordinates of a particle at the ith step; and the diffusion coefficient, D is defined as MSD = 1 N -n N-n i=0 ((x i+n -x i ) 2 + (y i+n -y i ) 2 ), ( 2 MSD(t) = 4D T t, (2.