dataesr/scientific-paragraphs-categorization huggingface dataset:
Image analysis and measurement The primary steps of image processing were devoted to 3D reconstruction. The out-of-focus information was first removed using a function of the ZEN 2. 3 pro software and the native czi format (raw format of images produced by microscope) was converted to TIFF format, using either ZEN 2. 3 pro or ImageJ. For multi-tile images, the conversion was performed after stitching by ImageJ  [26, 27]. The images were further processed using an in-house code developed in Matlab ® 2018b. The image processing operation included median filtering to reduce the noise, an automatic threshold based on the maximum entropy method and morphological operations on the binary image (filtering, endpoints connection, hole-filling, and closing). Then, the colony volume was built by merging all images of the stack.
2-0. 4, and average cell diameter and total cell concentration were determined by the Coulter counter equipped with a 30 µm diameter aperture. For the series of tests using destructive measurement at selected times, the cell number and the average cell size of the colony was determined by the Coulter counter. These data allowed for the calculation of the total volume occupied by cells. The packing factor 2 of cells in a colony is defined as the total cell volume (assuming the cell to be spherical) divided by the colony volume: 2 4π/3 × 2 × / 3 13 Where is the average cell diameter, the total number of cells, and the apparent volume of the colony determined by image processing.
Acknowledgments This study was carried out in the  Centre Européen de Biotechnologie et de Bioéconomie (CEBB) , supported by  Région Grand Est, Département de la Marne ,  Grand Reims  and the  European Union. In particular, the authors would like to thank  Département de la Marne  for its financial support. The authors thank  Cédric Guerin  of  LGPM  for the help in HPLC glucose analysis. Appendix. Supplementary data.
Analysis. In addition, the development of the air pollution scenarios has been supported by the European Union project 'Action on Black Carbon in the Arctic' (Commission Implementing Decision on the 2016 Annual Action programme for the Partnership Instrument). The contributions of the International Energy Agency (IEA) were funded through the IEA resources (this sentence needs to be refined before final publications). The contributions of the Norwegian Meteorological Institute (met. no) have been funded by the met. no.
2. 5. Principal component analysis Principal component analysis (PCA) and linear regression of experimental data were carried out using SIMCA from UMETRICS (13. 0). The methane potential, composition of liquid (sCOD) and solid (lignin, cellulose and hemicellulose and FA) fractions were included in the PCA loadings plot as dependent variables. In addition, pretreatment parameters: NaOH dose, microwaves and ultrasonic energies, temperature and alkali pretreatment time were considered in the PCA as independent variables. Correlation coefficient R² was calculated to assess the statistical relationship between two variables, while p-value was estimated to describe the probability that the null hypothesis was true.
2. 6. Relative efficiency calculation The specific energy consumed during knife milling (EM) was measured with a wattmeter and calculated as follows: Where PM (watts) is power consumed during knife milling at a time t (s), P0 (watts) is the power consumption during knife milling under idle conditions (without OP), and MOP (gTS) is the mass of olive pomace. +. (//12*) = 3 (4. - 4 Energy consumed (Ec) for NaOH production (Ec(NaOH)) was calculated using the Ecoinvent (2010) database where the global energy requirement (GER) is estimated to be around 5980 kJeq/kgNaOH  (Ruggeri et al. , 2015). When OP was pretreated with NaOH at 50 °C, the energy consumption of the heating system was added to the NaOH production energy. This energy was theoretically calculated using (Eq 6). The calorific capacity of the liquid phase was considered equivalent to that of water, as the solid to the liquid ratio was 1:50.
Where Q is the energy consumed during the alkaline pretreatment at 323°K, Mwater is the water quantity (g), Cpwater is the calorific capacity of water (4. 18 J/g. °K) and T is equal to 323°K and MOP (gTS) is the quantity of olive pomace to be treated. Then the total energies consumed by the alkaline pretreatments at 25°C and 50°C were calculated taking into account the knife milling energy. The total energies consumed (tEC) are given in Eq 7 and Eq 8. 7+N (25°P)(//12*) = EM + Ec (NaOH) = EM + GER x NaOH(%) (+< 7) 7+N (50°P)(//12*) = EM + Q + Ec (NaOH) = EM + Q + GER x NaOH(%) (+< 8) Energy consumed by microwave oven (Ec(MW)) was determined by subtracting the specific energy reflected (data not shown) from the energy supplied (Table1). The energy supplied was calculated as follow (Eq 5): +YZ [[\]^6(//12*) = PMW.
3. 3. Principal component analysis The PCA analysis was conducted in order to link the methane production to the characterization results (for both solid and liquid fractions) and operating conditions of the pretreatments. Fiber and FA contents were used as properties of solid samples. It was assumed that the sCOD refers to the overall measure of solubilization. The two principal components accounted for 44. 3% and 24. 8% of the total dataset variability. The PCA shows that alkali dose was negatively correlated to hemicelluloses and lignin content in the remaining solid and much less to cellulose. In fact, as previously mentioned, alkaline pretreatment at low temperatures partially degraded hemicelluloses and lignin while cellulose remained intact. In fact, NaOH dose was highly correlated with methane production (R²= 0.
These were complemented with bulk P K-edge spectra of other P compounds such as natural apatite (from Taiba, Sudan), amorphous calcium phosphate, iron(III) phosphate, PO 4 adsorbed to goethite, aluminum phosphate and PO 4 adsorbed to gibbsite. These standards were collected at BL8 of SLRI and are part of our database of P standards used for routine P speciation analysis in soil. The sources and preparation of the standards are described in  Gustafsson et al. (2020). Together, these compounds represent the major P associations (i. e. , Ca-P, Fe-P, Al-P, P adsorbed to Fe and Al, and organic P) that are often encountered in soils.
Analysis of X-ray fluorescence and X-ray absorption data The μ-XRF spectra were dead-time corrected and normalized to incoming beam intensities before individual elemental spectra deconvolution. Microscale-resolution elemental maps were obtained after fitting each pixel μ-XRF spectrum using the PyMca X-ray Fluorescence Toolkit  (Solé et al. , 2007). The ATHENA software package was used to subtract the background and normalize all μ-XANES and bulk soil P XANES spectra  (Ravel and Newville, 2005). Solid-phase P speciation was estimated by linear combination fitting (LCF) analysis  (Eriksson et al. , 2016; Tannazi and Bunker, 2005)  using weighted combinations of the reference spectra (Fig. S1 ). No energy shifts were permitted in the fitting procedure and the fit combinatorics were not forced to sum to 100%.
Acknowledgments This research was funded by the  Swedish Research Council Formas , grant number  2017-01139 , by the  Geological Survey of Sweden , grant number  36-2044-2016 , and by the  Swedish Farmers' Foundation for Agricultural Research , grant number  O-15-23-311. We thank the  Synchrotron Light Research Institute, Thailand  and the  SOLEIL synchrotron, France  for providing beamtime through grant number  2012-3  and grant number  20180029 , respectively. We thank  Camille Rivard  for the help with µXRF data processing. The support of the staff on beamline  LUCIA ,  SLRI 's  BL8 , XAS and laboratories at the.
Besides, good consistency of the experimental heat release behavior was found regarding the theoretical calculations. A general overview of the results performed in this study is given in Figure  9 , with the duration of the solidification plateau over nucleation temperatures for all the experiments. The solidification durations were measured by the use of a sliding average, with an interval of 9 s. Because of the uncertainty on the choice of this interval, the error bars illustrate the values for an interval of ± 1 s (respectively 8 s for the upper one, and 10 s for the lower one). The theoretical data are represented by the black and red dashed curve, respectively for the liquid and solid phase. Figure  9  Plot of the solidification durations over triggering temperatures From Figure  9 , the experimental durations are shown to follow a linear trend for a crystallization temperature from 295 K to 320 K, close to theoretical calculation.
Because there was no possibility to dissociate recalescence duration with residual solidification for the laser study, it will also be included in the total solidification duration measured by the thermocouple. In order to minimize the difference between the solidification durations of the thermocouple and laser methods, a criterion of 25 % has been found. It has been decided to choose a confidence level of ± 20 % for the data calculated using this criterion to evaluate the difference between these two methods. The results comparing the two for a criterion of 25 % methods are presented in Figure  11    From those figures, the evolution of the laser durations can be divided into two parts. The first part corresponds to a supercooling degree exceeding 20 K, whose laser's solidification duration is below 125 s, represented in red symbols in Figure  11.
Acknowledgments The authors would thank CPER (2015-2020) and  ERDF  ( 2014-2020 ) grants for supporting this work through the funding of experimental equipment. The authors would also like to thank  H. Arlaud  who built the microthermocouples and the experimental set-up,  C. Fuentes  for her help with laser techniques and  C. Calbrix  for his help for the measurements.
Model assumptions The design of the AA-CAES system is based on an analytical analysis for each component of the system. In order to simulate the global system on hourly basis for a period of one year (PV + AA-CAES + Building), a numerical code was developed in Excel VBA that is based on iterative approach. The assumptions taken into consideration in this work are: -Air is considered as ideal gas. -Maximum and minimum reservoir pressure are fixed ( , ). -High pressure reservoir is considered as constant volume and adiabatic. -Cylinder piston volumetric compressor/turbine are considered in this system. -Displacement volume for the first stage of compressor and turbine are fixed. -Compressors and turbines are mounted on the same crankshaft separately. -The efficiencies of heat exchangers (for charge and discharge phases) are fixed to 90 %.
Monthly energy source analysis Three different locations in France (Strasbourg, Nantes and Nice) are chosen in order to study different geographical scenarios for the same building model. As for the CAES system, three compressors and two turbines are chosen to be modeled. Figure  8  shows the monthly electrical energy building model demand and the monthly solar production for Nice. 1000 m 2 is the main surface estimated for the solar panels whose efficiency is 20 %. Other input data and constraints required for the sizing procedure are listed in Table  1. "Nice" location is considered for figures 8 to 15. 9. It can be noticed that the energies derived directly from the solar production (PV self-consumption) and from the CAES system (Energy from storage) have the same behavior all over the year.
The 2D modelling procedure consists of solving transient heat transport and flow equations for the entire studied area. Groundwater flow and heat transport are simulated using the finite element based software FEFLOW  [51]. The first step of the modelling process consists in simulating steady groundwater flows over the area of interest. The modelling configuration used is illustrated in Fig. 5a  and the parametrization is detailed in the appendix (Table  A. 1). Both abstraction (red dot) and injection (black dot) wells are represented by the numerical model. The doublet has an average annual pumping rate of QP = 4. 5 l s -1 which is assigned as a well boundary condition at the abstraction and injection locations. Impervious underground constructions are delineated by no-flux boundaries. The size of the elements ranges from several centimetres near underground constructions and wells to about a meter near to the domain boundaries.
With regard real experiences with self-consumption in the Netherlands, only a few studies have been conducted so far. For instance, on individual self-consumption of households with PV systems, among which an extensive study in the framework of ERA-Net smart grid plus project CESEPS  [33, 34] , which was executed at and coordinated by University of Twente  [35]. In this study, measured time series of energy data of 217 households from three smart grid pilot projects in the Netherlands (Jouw energy moment, JEM, in Breda, Powermatching City in Groningen, and Profit for All in Amersfoort) and a public reference dataset with smart meter data from 70 households (provided by Liander), were evaluated for one year to get insights in the performance of smart grid technologies, among which PV systems, but also heat pumps and micro-CHP.
The ANN could be trained with a reduced amount of data generated by a validated cell model  [13]. Once this network is trained, it can predict different operational parameters of the fuel cell reducing the computation time  [14]. This strategy has many possibilities  [15] : spectroscopic analysis, prediction of reactions, chemical process control, and the analysis of electrostatic potentials. The ANN is trained to learn the internal relationships from data. These data may be taken from the real process even if there are noisy. The database should have a significant size and contain the maximum combination of inputs-outputs covering the studied domain. Therefore it is possible to generate database from model  [14]. As shown below, electrochemical modeling can be coupled successfully with artificial intelligent approach; thus in the following subsections, we provide basic mathematical models of fuel cells.
Water production and removal are analogous to heat production and removal. Therefore this analogy (energy balance) can be added to the present electrochemical pseudo 2D approach of mass balance (CSTR-PEM). Operating conditions that can threaten the life of the PEM cell are not easy to detect; using simple models for fast computation with A. I. shall avoid the problematic lack of large experimental databases. Predictive models and A. I. can take up the challenge of "Smart Grid". A. Appendix.
g. , heating, ventilating and air conditioning -HVAC) or the effect of price signals. Physically-based models for appliances (including HVAC) are also proposed in  Muratori et al. [2013] , combined with heterogeneous Markov chain for activity patterns, to simulate households energy consumption. A similar approach was followed in  Richardson et al. [2010] , but using individual appliance consumption data. A set of physical models for appliances are proposed in  López et al. [2019] , implemented in MATLAB Simulink, and can simulate optimal on/off decisions of household appliances. Gottwalt et al. described a simulation engine for households with two modules: (a) bottom-up approach that generates consumption data for each appliance by combining statistical data about appliance use and resident presence at home; (b) optimization of appliances schedule in order to find the optimal load shift according to time-based tariffs  Gottwalt et al.
The data set contains tariffs and energy consumption records, for each client, at half-hourly intervals. Only ToU customers with more than 95% of data available (1 007 clients) are kept and the same number of Std clients are sampled to build a control group. We denote by I T oU the set of ToU households and by I Std the set of Std ones. The missing values in the time series were filled by linear interpolation, using the previous and next interval records for small gaps and the days preceding and following for longer periods of missing data. Finally, for each household, we also compute the average energy consumption, its minimum, and its maximum as well as the half-hour of the daily peak and of the daily trough, for the hot months (from April to September) and for the cold months (the others).
As the household clustering is a prior knowledge for the creation of the data generators (we create a generator per cluster), the entire data is used to cluster the clients. The (non-parametric and semi-parametric) data generators are optimized on the training set. The testing set is used to calibrate CVAE-based data generators and to choose the best combination of exogenous variables to give in input. Moreover, the best CVAE among several executions of the training process (CVAEs may converges to local minima) is selected thanks to this testing set. Finally, it also permits to compare the two approaches, nonparametric and semi-parametric, in the experiments of Section 7. To simplify notation, we re-indent the observations of the original data set: observations from 1 to T 0 = 273 form the training set, and the ones from T 0 + 1 to T = 365 form the testing set.
Then, these profiles are concatenated in a matrix M ∈ M |I|×H|P| that gathers all the households. The dimension of M is reduced with a non-negative matrix factorization (NMF): with r a small integer, M is approximated by W H, where W and H are |I| × r and r × H|P|-non-negative matrices, respectively. As soon as this approximation is good enough, line i of the matrix W is sufficient to reconstruct household i profiles (with the knowledge of matrix H -which is not used for the clustering). Thus, for each household i, from the H|P|-vector (µ 1 i (p),. , µ H i (p)) p∈P , r features are extracted: line i of W. With this low dimension representation of households in R r , k-medoids clustering algorithm provides the k clusters C 1 ,. , C k , using KMedoid function implemented in the Python-library sklearn_extra.
e. , it was fixed to 4). In the following sections we present the two data-driven methods that simulate energy consumption profiles associated with the clusters of I T oU obtained with the method described above. For both approaches, we will train a data generator per cluster. So from now on and for simplicity of notation, a record Y h t will refer to Y h t (C ), where C designs any clusters of set I T oU. 5 Energy consumption profile generation with conditional variational autoencoder The training set made of the T 0 observations (Y 1 , X 1 ), (Y 2 , X 2 ),. , (Y T 0 , X T 0 ) is considered. For a day t, Y t = (Y 1 t ,. , Y H t ) is the H-dimension vector which corresponds to the daily profile of the half-hour energy consumption of a household cluster. The vector X t gathers calendar, weather, and tariff information of day t, which will be detailed further.
Implementation details The CVAE were implemented by using the software libraries Tensorflow and Keras in Python programming language. The architecture of a CVAE is defined by the latent dimension d as well as the number of layers and units in encoder and decoder neural networks. We use dense layers which are deeply connected neural network layers. Once the architecture of the CVAEs is set, hyperparameters are chosen: the neural activation functions, the initialization method for neural weights and the parameter η, defined in Equation (4), that balances the two terms of the loss. The choice of the architecture and hyper-parameters calibration is detailed in Section 5. 2. In order to optimize a CVAE, so namely to compute weights and bias for each neural of both the encoder and the decoder, the loss is minimized by using the Adam optimizer (see  Kingma and Ba [2015] ), an extension of stochastic gradient descent method, which is commonly used in deep learning and already implemented in Keras.
Note that the learning rate of this optimizer is also an hyper-parameter to set before training CVAEs. Finally, the energy consumption records are rescaled to get values between 0 and 1 by computing the maximum Y max and minimum Y min of the energy consumption observed on the train period. The generated value are re-scaled to get coherent profile, mostly between Y min and Y max. We recall that the data described in Section 3 was divided into two data sets: the training set contains 75% for the observations (sampled randomly from the complete data set) and is used to train the CVAE (see Table  1 ); the testing set, made with the remaining daily observations, is used to calibrate hyper-parameters (see Section 5. 2). Finally, as CVAE may converge into local minima, many CVAE are trained and the testing set is also used to select the best one (see Section 5.
Simulator creation Finally, we emphasize that CVAEs may converge into local minima. To avoid it, each CVAE is trained 50 times and the one with the lowest MSE on testing set is selected. For each of the cluster presented in Section 4, we thus get a CVAE that takes as inputs the daily energy consumption profile Y t = (Y 1 t. , Y H t ) of the considered cluster (which is rescaled during the training process) and the conditional vector X t described above. Then, the decoder is isolated and enables the generation of new data. Indeed, for a new vector X t at a day t , which can either be created or extracted from the data test, we sample a vector Z t ∼ N (0, I d ) and give these two vectors as inputs of the decoder, which outputs a daily energy consumption profile. The quality of the generated data is evaluated in two situations.
First, samples for the conditional vectors X T 0 +1 ,. , X T associated with the training set are generated. Thus, we will measure the ability of the data generators to forecast energy consumption (we will see that we can deduce a foretasted density from the generated samples). Secondly, we will create new vectors X t for which we modify the variables 1 p h t =Low , and 1 p h t =High in order to measure the impact of tariff changes. These results are presented in Section 7 and compare them with data generated according to a semi-parametric data generator presented below.
The s h τ , s h τ , and s h κ functions are catching the effect of the temperatures and of the yearly seasonality. They are approximated by cubic splines, i. e. C 2 -smooth functions made up of sections of cubic polynomials joined together at points of a grid (the knots). Fixing the number of knots k and their position is sufficient to determine a linear basis of dimension k in which these functions can be projected. The mgcv R-package allows to estimate the coordinates of the splines in their basis and the coefficients α h , ξ h Low , and ξ h High that catch day of the week and tariff effects. Appendix A. 5 provides details on the estimation of the correlation matrix Σ, which makes it possible to model the correlations between the consumption profiles of two half-hours of the same day, whereas keeping a variance of the residuals that varies according to the half-hour and the price.
However, the energy score is slightly lower for the nonparametric approach (namely for CVAE-based simulator) than for the semi-parametric one (GAM-based simulator). Thus, the method that consists in adding a noise term to a forecast in expectation may have some limits whereas CVAEs seem to catch correctly the distributions of daily energy consumption. Experiments of  Scheuerer and Hamill [2015]  highlight that, when the estimation of the average value of the original data is incorrect (namely when the expectation of F differs from the expectation of y in Equation (  12 )), variogram scores increase. Moreover, a too low or a too high variance -when the variance of F differs from the one of y -also increases variogram. Given the variogram scores and the plots, we conclude that CVAE-based generators face an estimation of expected energy consumption worst than the semi-parametric generator but provide also samples with a too low variance.