dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Figure  4 , first physics-based performance calculations are carried out for multiple effective property combinations. Once such a dataset is available, the data-driven modeling is used to generate such mappings. Subsequently, it is used to estimate mesostructure properties from performance measurements. 23 The data-driven modeling avoids explicitly solving the governing equations for all possible combinations of property values, which is prohibitively expensive. For example, consider identifying mesostructure properties, e. g. , tortuosity factor, from the electrochemical performance of porous electrodes, as shown in Figure  4. Not every mesostructure property ↔ electrochemical performance mapping can be inverted, and accordingly one must ensure that the mapping is sensitive to every property one wishes to estimate.
43 unction for further analysis, such as obtaining activation energies from the slope or using the D = D(T) property relation in a temperature-dependent analysis. In essence, machine learning builds reduced order (or surrogate) models from data. Figure  5  shows three different datasets in each of the columns, and two different Neural Network (NN) representations are used to learn the underlying trends (each row respectively). The datapoints contain inaccuracies (noise in the measurements). The learning ensures that the model predicts the training data accurately, while a similar accuracy is not necessarily guaranteed for predicting datapoints not part of the training set. For example, Figure  5e ,f shows that predicted trends exhibit drastic changes away from the training datapoints. Note that not just extrapolation but also interpolation in between the two data clusters are questionable.
In essence, machine learning builds reduced order (or surrogate) models from data. Approaching this as a data-driven modeling question, testing the model accuracy on a dataset not used for training can help expose and manage artifacts. The model complexity is intrinsically tied to the accuracy of the dataset. Compare Figure  5 , panels b and e, having identical datapoints: the simpler representation in (b) is reliable if the data contains inaccuracies, while the more complex representation in (e) is meaningful if the datapoints are reliable. ("Model Parameters and Data Accuracy" in the Supporting Information further discusses the connection between model complexity and data reliability; model complexity often scales with the number of model parameters. ) Alternatively, the physics can guide through this impasse.
The slope of log(D) vs 1/T in Figure  5  represents activation energy and is typically a positive and a slowly varying property (if at all). Accordingly, the trends in Figure  5e ,f are likely unphysical. These qualifications are easier to make from Figure  5  where a one-dimensional dataset is explored, but become quite difficult to identify when higher dimensional datasets are studied. Appropriately pre-processing datasets using physical symmetries or geometrical invariances (known as feature engineering), for example, training log(D) vs 1/T, instead of D vs T, helps considerably with building data-driven models. Since any ML implementation relies on data, data generation and curation are crucial steps. If data is generated through experiments, one must ensure repeatability and reproducibility of measurements.
Such precautions minimize systematic errors so that the remaining variability is a true random error and analyzed statistically. Instead, if data is generated using physics-based calculations, the accuracy of computed trends in deterministic simulations and reliability of statistics in stochastic simulations must be ensured. Essentially, one should be mindful of the confidence in the raw data and how the uncertainty propagates to predictions. One must also be wary of over-fit models (often nicer-looking fits of the data) that may not be useful or predictive outside of the scope in which they are fit. Typically, the datasets are not as simple as D = D (T) so that one can visually assess the reliability of the data-driven model. In addition to rigorous verification of model accuracy, we should also focus on interpreting these approximations.
Such a solution essentially replaces the hardware (e. g. , exascale computing) requirements with specialized software development. As these physics-based simulations produce larger and larger datasets, their interpretation becomes challenging. ML can parse through these datasets to identify relevant information that should be visualized by the researchers. Consider a 3D simulation of an intercalating porous electrode  13, 36  where multiple small-scale entities jointly reproduce a macroscopic response. Given the sheer number of such entities, it is infeasible (and unnecessary) to visually track each of them. Rather the interest is in visualizing norms and outliers. For this electrode, the representative particles are the ones whose lithiation follows the macroscopic response (the norms) and those severely lagging or leading (i.
The sections that follow elaborate on the most relevant data and hypotheses. Table  2. Unit-by-unit performance, economic and environmental parameters, at each scale size. Whenever a range of values is shown, the nominal case refers to the arithmetic average. Lower and upper bounds were used for sensitivity analyses. As explained for Table  1 , production units must deliver heat at the temperature required for application. For a building-scale unit, that temperature was assumed to be 65 • C, i. e. , the typical temperature for DHW production, also acceptable for heating systems. On the other hand, sector-scale or district-scale units must deliver heat at the network's inlet temperature, assumed as 80 • C in this study. η T out θ out θ in η ex z CI c F ϕ OM n x CO 2 Unit Scale [%] [ • C] [-] [-] [%] [EUR/ Yearly temperature and solar irradiation profiles at an hourly time step were obtained for the city of Chambéry (France, H1 zone) from the photovoltaic geographical information system (PV-GIS) database  [37].
Typically, specific investment costs are expressed as a function of peak power and within tranches. The ADEME usually estimates these tranches statistically from a database of existing projects. The lower and upper bounds of each tranche correspond, typically, to the 1st and 3rd quartile of the overall data examined. In general, large differences between building scale and bigger scales can be discerned in terms of investment costs, fuel costs, and equipment lifetime. For solar panels and gas boilers, specific investment seems to decrease when scaling up the units. This tendency is reversed for heat pumps and the biomass boiler, according to data from the ADEME reports. On the other hand, the lifespan of equipment is longer at bigger scales. The most notable case is heat pumps, which go from 17 years at building scale up to 30 years at district scale.
Funding: This research was funded by the  ADEME  (the  French Agency on Ecological Transition)  through the  RETHINE  project (interconnected electrical and thermal networks) and has been supported by the  CNRS-MITI (Mission for Transversal and Interdisciplinary Initiatives of the French Nation Center of scientific research)  and  CSTB (French Scientific and Technical Center for Building)  through the  CITYBIOM  project, and by the  French National Research Agency  through the Invest-.
It is worth mentioning that the stable heterostructure was such that the total energy of the MoS 2 /CNT heterostructure is lower than the energy of individual CNT and MoS 2 (see Eq. (  1 )) Moreover, the DFT method was used based on the Dmol 3 code with the Generalized Gradient Approximation (GGA) approach in the Materials Studio version 7. 0 package  29, 30. Furthermore, the exchange-correlation functional used in this study was based on the work of Perdew, Burke and Ernzerhof (PBE)  31  with the Gaussian double zeta plus polarization numerical base (DNP) set. To treat the core electrons, DFT semi-core pseudopotentials (DSPPs) were selected. The geometry relaxation and energy computations were chosen with 1 × 10 -5 Ha, 0. 002 Ha/Å, and 0. 005 Å for energy, force, and displacement tolerances, respectively.
Thanks to Grimme's semi-empirical dispersion-corrected density functional theory (DFT-D2)  32, 33  for considering weak interactions with high accuracy, we used DFT-D2 instead of the standard PBE functional. Besides, to analyze the characteristics of the electron density difference, the CASTEP code  34  of plane wave and ultra-soft pseudopotentials  35  was applied with a plane-wave cutoff energy of 400 eV.
See Table  7 ). It should be mentioned that the uncertainty of the coefficients is rather high, especially in the case of CA. It can be observed that the experimental dataset for CA appears to be quasi-linear. The form of the polynomial is therefore overcorrecting and induces a large tolerance for the determination of the coefficients. If we were to model the experimental data with a linear fit (C P,S (T) = aT + b), the r 2 and σ e remain similar (0. 9811 and 0. 0156 respectively) and the coefficients and uncertainties become a = 0. 0202 ± 0. 0016 (J•g -1 •K -2 ) and b = -3. 7128 ± 0. 4598 (J•g -1 •K -1 ) which shows a more reliable approach in this temperature range. For the sake of consistency and reproducibility it was chosen to use the same third order polynomial for all materials despite their showing of higher uncertainties for the determination of coefficients due to the overcorrection of the fit.
Catalysts characterization methods The maghemite nanoparticles and the palladium decorated maghemite were examined by high-resolution transmission electron microscopy (HRTEM, FEI Technai G2 electron microscope, 200 kV). During the preparation step, drops of the aqueous suspension of samples were placed onto copper grids (Ted Pella Inc. , only carbon, 300 mesh). The size of the nanoparticles was estimated based on the HRTEM images and the original scale bar by using the ImageJ software. Powder X-ray diffraction (XRD) measurements were conducted by using a Rigaku Miniflex II diffractometer with CuKα radiation source (30 kV, 15 mA). To identify the functional groups on the surface of the maghemite nanopowder, a Bruker Vertex 70 Fourier transform infrared spectrometer (FTIR) was used. The prepared maghemite (2 mg) was added to 250 mg spectroscopic potassium bromide, and after homogenization a pellet was formed which was used in the measurements.
Catalytic performance tests The catalytic hydrogenation of 2,4-dinitrotoluene (DNT) was carried out in a Büchi Uster Picoclave reactor (200 mL stainless steel vessel with heating jacket). The pressure of H 2 was kept at 20 bar, and the reaction mixture was kept at 303, 313, 323 and 333 K. Sampling was carried out after 5, 10, 15, 20, 30, 40, 60, 120, 180, and 240 min on reaction stream. The initial concentration of DNT was 0. 05 mol L -1 in methanol, and 150 mL DNT solution and 0. 1 g catalyst were applied during the tests. The formed by-products and reaction intermediates were identified by using a JMS-T200GC AccuTOF GCx-plus chromatograph and a JEOL JMS-T200GC mass spectrometer. For the GC measurements, ZB-1MS column (30 m × 0. 25 mm, 0. 25 μm) was used. The collected data were analysed and detected molecular species were assigned by using "NIST library search", "Molecular ion search", "Exact Mass Analysis of Molecular Ion", "Isotopic Pattern Analysis" and "EI Fragment Ion Analysis".
Monosaccharide Analysis with HPAEC High-performance anion-exchange chromatography with pulsed amperometric detector (HPAEC-PAD) was used to identify and quantify the monosaccharides obtained after polysaccharide hydrolysis. The system used was ICS 3000 (Dionex Corporation, Sunnyvale, CA, USA). It consisted of an AS40 automatic injector, a pump module that can be operated in gradient mode, and a pulsed amperometric detector (PAD). Data acquisition and processing were done using Chromeleon software (version 6. 8, ThermoFisher scientific, Waltham, Massachusetts, États-Unis). The samples were filtered at 0. 22 µm, and 25 µL of the samples was injected and then separated through a CarboPac PA1 pre-column and column (Dionex, 4 × 50 mm and 4 × 250 mm, respectively) and thermostated at 25 • C. The monosaccharides were transformed into alcoholates in the mobile phase at a pH above the pKa of their hydroxyl functions.
Statistical Analysis The variability of the method was tested by measuring each test in quintuplicate. For comparison with the HPAEC method, the EPS from the three microorganisms studied were divided into 5 different samples (3 samples for the polysaccharide produced by Synechoccocus sp. ) and then hydrolyzed separately in order to match the variability related to the hydrolysis protocol with the variability of the method. The data obtained were then subjected to a Kruskal-Wallis test to detect statistical differences between the results from the two methods. The percentage of each monomer in a given polysaccharide (for a given hydrolysis) was calculated as follow: % Galactose = ∑[Galactose] TC a × n a (3) where n a and TC a represent the number of replicates used per measurement and the sum of the monomer concentrations calculated on polymer a, respectively.
Prior to the start of the GD study, the mouse was allowed to acclimatize to its new environment. For this acclimatization period, the mouse was housed in the cage for 8 h per day for a total period of 5. 0 days. During this acclimatization period, we monitored the mouse for behavioral changes and response to the pump noise, temperature, new feeding and drinking mode, etc. No data were collected during this period. The gender of the mouse was not foreseen to affect the study outcome. Only one mouse was used for this study, mainly as per the EU Directive 2010/63/EU and clauses of the ethical committee agreement used to conduct this animal study.
In this regard, we are conducting further studies to evaluate the effect of the simultaneous presence of different N-sources, organic compounds, and high urine salinity mixtures of different N-sources on the biomass and oxygen productivities of Limnospira indica. The biochemical data obtained from these ongoing studies will be used to optimize the prediction parameters of the Photosim model, which will then be used to perform future GD studies. In conclusion, this GD study provided preliminary indications toward the possibility of using NH 4 + -and urea-rich medium and CO 2 from the consumer compartment for Limnospira indica cultivation and air revitalization. The cumulative conclusions from our previous, present, and upcoming studies would not only aid in designing more efficient and robust regenerative lifesupport systems for future space missions but would also help in gaining a better understanding of the design of economically viable microalgae-based wastewater remediation systems and photosynthetic biorefineries for terrestrial purposes.
Statistical Analysis All the analyses were carried out in triplicate, and average values with standard deviation were reported. One-way ANOVA was applied using raw data to test for significant differences among the samples (the significance level was always set at p < 0. 05). The Tukey's test was used as post hoc analysis when there were significant differences among the samples. The data were analyzed using SPSS Statistics software Ver. 23 (SPSS, Inc. , Chicago, IL, 2020). The optimization process was evaluated with an RSM analysis, performed in 'R' (RStudio with 'R' version 3. 0. 2, RCore Team, Vienna, Austria, 2020). 2009 ), which used strain mh0186 of A. limacinum  [20, 21].
The electrochemical impedance CPE-R circuit, fitted data shows a goodness-of-fit-value ( χ 2 ) below -10 -4 (see Supplementary section for details). The chronoamperometry measurements were performed under chopped illumination with a 5 s period under polarization conditions + 0. 5 and + 1. 0 V vs. Ag/AgCl/0. 1 M KCl.
Acknowledgments E. C: Acknowledges the partial financial support from the  National Science Centre (NCN)  of Poland by the  OPUS grant   2019/35/B/ST5/00248. E. C, M. B and I. I: Acknowledge the partial financial support of  RISE - H2020-EU. 1. 3. 3. Grant (ID:778157 )  Can-BioSe. I. I: Acknowledges the partial financial support from the  National Science Centre (NCN)  of Poland by the  OPUS grant   2020/37/B/ST5/00576. K. S : Acknowledges the partial financial support from the  National Science Centre (NCN) of Poland by Sonata-BIS  no  2017/26/E/ST5/00416. I. G-K and M. Z : Acknowledge the partial financial support from the  National Science Centre (NCN)  of Poland by the  Sonata-BIS  no  2015/18/E/ST4/00196.
The determination of the intrinsic stability constants of the B surface species was accomplished by iterative modelling of the adsorption data with Visual Minteq v. 3. 1  (Gustafsson, 2012)  and its integrated PEST optimization procedure  (Doherty, 2010). The code provided at the same time the distribution of all adsorbed and dissolved aqueous species that describe our system. Before starting the modelling calculations the constant of formation of the aqueous complex NaHSiO3 0 (log K = -8. 30; Johnson et al. , 1992)  was added to the thermodynamic database, as a significant fraction aqueous silica is predicted to be present as this complex at basic pH. The inner and outer capacitances, C1 and C2, were used as fitting parameters and fixed respectively to values of 2. 2 and 0. 05 F/m 2 , which provide the best fit of all data and are within the interval of values typical of the silica-water interface (cf.
Summary and Conclusions Boron adsorption on the amorphous silica surface was investigated in 0. 01 M aqueous solutions of NaCl and CaCl2 at room temperature. The obtained adsorption envelopes and the corresponding B isotope fractionation data were interpreted within the framework of the triple-layer model and were found to be consistent with the formation of trigonal inner-sphere [>SiOB(OH)2 0 ] and tetrahedral inner-sphere [>SiOB(OH)3 -] complexes in NaCl solutions, whereas the formation of the only inner-sphere tetrahedral complex describes well most the data distribution of CaCl2 solutions. In the latter solutions a limited fraction of B (< 25 %) might also be adsorbed as a CaB(OH)4 + complex at pH > 8 and partially explain the relative increase in the measured B adsorbed fractions. The calculated isotope fractionation factors between adsorbed and dissolved boron species indicate that the trigonal B complex is enriched by ~16 ‰ in the light isotope ( 10 B) compared to aqueous B(OH)3 0 , whereas the composition of the tetrahedral complex is 5 and 6.
Acknowledgements We thank  Philippe Besson  for the technical assistance during the analyses of aqueous solution at the  ICP-OES , and  Alain Castillo  for his help with measuring BET surfaces of amorphous silica samples. Lucien Datas and  Teresa Hungria  are warmly thanked for the TEM characterization of the SiO2 powders at the  Raimond Castaing Microcharacterization Center in Toulouse. Laeticia Faure (IPGP)  is thanked for the boron extractions from the solution samples prior to boron isotope analyses by MC-.
Model identification methods First of all, we explored a range of nonlinear models that might be useful for characterizing the growth rate µ of the studied species according to some classical kinetic functions (µ(. )) from the literature depending on the following variables: the incident light I in or the biomass x. Then we proposed a new kinetic function depending on both these two variables. The optimal parameters of the growth functions used to explain the characteristics of the growth rates of the algal species (determined in microwell plates) were calibrated using the "fitnlm" function of Matlab, which estimates model parameters and delivers statistics. To readjust the parameters of the proposed growth function using the data of the biomass of both species in mono-culture (in chemostat), we used the function "fmincon" of Matlab to minimize the least squares criterion: k i=1 n j=1 (Xexp ij -Xsim ij ) 2 n where k =2 and n is the number of observations of X exp , and X sim results from the numerical integration of the model (describing the time evolution of the biomass in continuous mode photo-bioreactors) by the "ode45" function of Matlab.
Statistical tests Statistical results on the identified model parameters were delivered by the "fitnlm" function of Matlab (used to fit nonlinear regression models) and are given as param. ±SE with an associated p-value p for the significance of each regression parameter for n observations. The comparison between the parameters among species for the same growth model was ensured by the same function using the vector of all observations on µ (for both species) as a response variable, and the matrix of the model variable along with a dummy variable (which takes only the value 0 or 1 according to the species, thus indicating the absence or presence of some categorical effect that may be expected to shift the outcome of the parameter identification) as predictor variables  [39]. This involved the need to add to each required parameter a coefficient multiplied by the dummy variable, thus constituting the new model formulation (used in the "fitnlm" function).
These two parameters are likely to vary considerably depending on the operating conditions. Then, they were both re-identified for model calibration, depending on the culture device being used. α which depends on the culture device, was found to be equal to 0. 44, while K hsx , apparently sensitive with regard to mixing, was equal to 14 and 52 mgC L -1 for C. sorokiniana and S. pectinatus, respectively. The parameters μ, K sIatt and I 0 , considered as characteristic parameters of the species, were held the same (as given in Table  3 ). In the second step, we sought to validate our growth function (  6 ) on another data set. So, we used the experimental data of biomass (from cell count measurements being converted to mgC L -1 ) tracked over time in the same Erlenmeyer photo-bioreactor but inoculated with a culture of a mixture of the species.
Aknowledgements We thank  Patrick Raimbault  for carbon content analyses,  Tatiana Linhart  for cell count, and  Axelle Menard  for giving a helping hand to experiments monitoring. We would also like to thank  Bénédicte Fontez  for assistance with statistical analyses. This work was supported by the  ADEME French Agency  and the  LabEx NUMEV  incorporated into the  I-Site MUSE  funded by the  French Research Agency (ANR)  that have both funded the Ph. D. grant of the first author. This study was also supported by the  PHYCOVER  project, which was funded by the  French National Agency for Research  ( ANR-14-CE04-0011 ).
They can be represented by two root-mean-square (RMS) components (v rms et w rms ) transversal to the main flow. A collection of some experimental and numerical data from the literature was used to observe and analyze these velocity fluctuations in turbulent flows in a horizontal pipe. Fig. 3  presents the profiles of the ratios of the radial and azimuthal RMS variances by the friction velocity μ T corresponding to different case studies  [71, 72]. This figure indicates that these two non-dimensional fluctuating velocities are almost similar regardless of the Reynolds number, namely 17,800, 24,600, 38,000 and 44,000. In addition, their values remain almost maintained in the inner region of the tube until the beginning of the viscous sub-layer where they start to progressively decrease. Indeed, the turbulence tends towards the isotropy in the pipe center and presents an increasing anisotropy towards the boundary layer whose thickness varies with Reynolds number.