dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell spatial distribution 3. 2. 1. Cell trajectories and their combination with the irradiance field The goal here is to obtain data on the history of each cell exposure to light in order to further use it in the estimation of overall growth rate. A collection of various trajectories was acquired using the method of sampling cell movement in the reactor described above (Subsection 2. 3). The interaction between hydrodynamics and light transfer is highlighted here. To ensure the statistical convergence of numerical resolution and to guarantee a meaningful representation of all possible events that microalgae undergo in PBR, a choice of 1. 5 × 10 5 particles to be simulated, for a duration of 5 s and with a time step of 0. 05 (s), were deemed necessary to generate sufficient amount of information (i.
e. about 10 7 events). An example of trajectory recorded for an individual cell is illustrated algae is recorded over 5 (s) with frequency of 20 (Hz) as represented in Fig. 5(c ). This procedure is performed for the whole set of simulated particles in order to represent the light regime actually seen by each particle along its travel in the reaction volume. Therefore, the interaction between turbulence and heterogeneity of illumination within a volume was highlighted. Some microalgal trajectories relatively similar to the one presented in Fig. 5(c ) have been reported in previous studies  [26, 29, 35, 37]. However, the verification that the database of particle trajectory is correctly sampling the light volume distribution is almost never carried out. Here, we aim to validate the exploitation of light exposure database for the evaluation of growth rates for the different microalgae behaviors mentioned in Section 2.
Validation of the homogeneous spatial distribution of the cells in the irradiance field The previously obtained results cannot be validated experimentally nor compared to other simulation studies. Hence, to guarantee the strength of the Lagrangian tracking method (DRW model), it is important to verify that: (i) this procedure ensure a homogeneous spatial distribution of the particles in the volume and (ii) the database generated on light signals recorded by the cells allows a good sampling of the local light intensity values. First of all, Fig. 6 (a) shows a uniform spatial dispersion of cells along the tube radius, which is an important result in its own. Indeed, simulations reported in the literature  [35]  performed with CFD tools by using the standard turbulence model kε coupled to the Lagrangian tracking, overestimate the turbulent kinetic energy in this area and thus generate a numerical bias on the subsequent trajectories' calculation.
These methods overestimate the particle concentration in the near walls. In our results, although this artificial buildup exists, due to the lack of the hydrodynamic boundary layer resolution and the insufficient geometric adaptation of the rebound laws, it is however reduced to less than 6% thanks to the particle wall ejection proposed here, which imposes a constraint that forces the cells back into the flow, as described in Section 2. 3. Moreover, an "at the point" validation was conducted according to the method described by Gernigon et al. [35]  to confirm the uniformity of the distribution of cells in the volume and to analyze the acuity of the near-wall effect. This method consists in comparing the distribution of local light intensities obtained by the spatial irradiance field calculation and the database of light recorded by cells using the stochastic tracking model.
These high irradiance intensities are rarely encountered in reality except in subtropical regions. The onset of inhibition under conditions of permanent lighting was also signaled for intensities >1300 (μmol ⋅ m -2 ⋅ s -1 ). With this in mind, we analyzed our light exposure database for whole cells tracked to check the time of cell passage through the very high illuminated zones. To do this we have chosen an extreme operating condition where microalgae could be subject to inhibition (i. e. the highest incident irradiance recorded in Toulouse, France I 0 = 1402 (μmol ⋅ m -2 ⋅ s -1 ) α = 0. 33, the lower biomass concentration considered here X = 0. 3 (g/L), and at the lowest flow velocity tested U b = 0. 3 (m/s)). Then, we estimated for each cell the exposure time to irradiances above 1300 μmol ⋅ m -2 ⋅ s -1.
Biological dynamic response The database about the light history of each particle has fed the dynamic model expressed in Eq. (  7 ) to obtain the internal variable i evolution that represents the dynamic adaptation of individual cells to the detected light I p. Fig. 9  presents the internal variable signal resulting from the biological response to the recorded light fluctuations, for the same cell as in Fig. 5(c ). It was found that the magnitude of strong fluctuations in light exposure is smoothed by the internal variable i due to turbulence induced by hydrodynamic microstructures (i. e. small swirls). This could be explained by the difference in time scales of the hydrodynamic and biological processes: the relaxation time or cell response, τ = 0. 3 (s), used in the dynamic model, is much greater than the sampling frequency of light exposure and eddy lifetime.
Therefore, this allows to filter out the high variations in light exposure, to finally represent only the pattern of fluctuations inferred by the macroinstabilities (i. e. large eddies) of the studied flow. The period and amplitude of the light fluctuations seen by the cells depend on the intensity of the turbulence induced by the fluid hydrodynamics and on the spatial light gradient governed by the biomass concentration and the sunlight energy supplied. The results presented here pertinently reflect the consequence of the combination of the cells light history with a dynamic response kinetic governed by the biological characteristic time. The simulation outcomes for the temporal evolution of the internal variable i constitute a database further used to calculate  the global growth rate for a real dynamic biological behavior.
Towards a general correlation for the normalized specific growth rate Γ This whole procedure of specific growth rates calculation for both asymptotic and dynamic (real) cases, was performed for a wide range of operating conditions regarding microalgae concentration, flow rate expressed in Reynolds, and received sunlight characteristics (global incident irradiance, direct/ diffuse radiation contribution) in order to cover a wide spectrum of operating possibilities for an outdoor tubular PBR. The normalized growth rate Γ was calculated by Eq. (  1 ) for each case simulated. In other words, Γ translates the capacity of a PBR to reach its optimal performances according to the way the microalgae are exposed to light, which is driven by its operating conditions. Since numerical simulations require a coupling between large databases which is time-consuming, a reduction of the model seems attractive for application cases where PAR performance could be calculated for real operating conditions (i.
Conclusion Through a rigorous modeling of the radiative field considering the solar regime dynamics (i. e. direct light angles and diffuse light contribution), the heterogeneous light structure characterizing a tubular PBR were simulated by a Monte Carlo technique including reflection and refraction on the walls, absorption and scattering by the cells, for 10 biomass concentrations. Cells trajectories were simulated for five Reynolds number via a Discrete Random Walk model based on turbulent quantities calculated by a kε turbulence model. For the 5 cases, trajectories coupled with the light fields provided the light signals experienced by each of the 1. 5 × 10 5 cells. This database was verified to be representative of the light field inside the PBR. The time resolution (0. 05 s) is lower than the hydrodynamic and biological time scales involved.
Model Validation: Comparison with Experimental Results Experimental data from the experimental set-up were used to verify the model. The first case study was done a simple aqueous salt (NaCl) solution of 3 g/L whereas the second case was performed with 6 g/L. The initial feed tank was set at 0. 4 L, and a staircase pressure function of 10 bar + 2 bar/15 min was selected. Figure  6a ,d compare cumulative permeate variation results over time between the experiment and model simulation for the first and second case (the different initial feed concentrations are, respectively, 3 g/L and 6 g/L). The results allowed the determination of the mass transfer coefficient (k). A similar evolution of permeate production at the start of the process was observed, and both plots showed a good agreement between the model and experiment.
Owner-occupiers in this sector show a variety of energy-use behaviours that are inaccessible to suppliers due to privacy issues and costly sub-metering devices. More importantly, electricity consumption in residential buildings depends on the occupants' activities  (Bin & Dowlatabadi, 2005). Total energy demand is the sum of demand of each dwelling with one/several occupant(s) per area (e. g. neighbourhood, city, country), varying in time and space. Thus, the proliferation of datasets are both spatially and temporally indexed. So, to examine the variation of energy demand over time and in different spaces, the present study is based on a Spatio-Temporal Analysis in order to determine the contribution of each end-use in energy demand reduction  (Chen et al. , 2019). In this way, it contributes to the existing literature by focusing on domestic electric appliance use and identifying patterns of ownership and usage factors, and finally analysing the data at different urban scales.
A typical electricity-consumption day is calculated and generalized across the year. The results are presented in graph form, showing an electricity demand curve. This makes it possible to compare the analysis with future scenarios and reveals potential improvement points. To gain a clearer understanding of demand, the analyses show daily, weekly, monthly or annual variations, from neighbourhood to city scale. Apart from their similarities, the most significant difference between the above-mentioned studies is their method to predict patterns of appliance use, including time of use and average energy consumption. Overall electricity consumption data can be collected from electricity bills provided by authorities, and disaggregated and more precise information can be gained from sub-meters.
Understanding these determining factors is key to evaluating the categories of demand on the urban energy system  (Farzaneh et al. , 2014). These variables affect the decision-making process of the occupants and can lead to changes in their behaviour. Figure  2  is a flowchart showing the relationship between energy demand and these factors and how they influence decision making. In this study, we first carried out large-scale cross-national analysis using data from the French National Institute of Statistics and Economic Studies (INSEE). Assuming there are the same values across regions, we then used the result of the analysis for a smaller urban area. We also carried out housing surveys to complete the dataset. Some additional information was collected from electric appliance manufacturers.
Classification and examination: defining different profile typologies, inputting the statistical data at the urban scale and removing typologies with insignificant values The quantifiable and categorical variables that affect the electricity consumption of electric appliances and their possible categories are presented in Table  7. For a more precise analysis, we examined household type, which intrinsically includes household size, as households consisting of two people may differ in structure (e. g. a singleparent family consisting of a father and child, a couple consisting of two adults) and thus show different patterns of electricity demand. In this way, five factors were identified as having the greatest effect on residential electricity consumption related to electric appliances. Dividing each factor into its possible categories and multiplying them by each other yields various scenarios and different possibilities for each.
4 ). In this database, small dwellings are those with a floor area of less than 40m 2 , medium 385 dwellings between 40 and 100 m 2 , and large dwellings 100m 2 and over. A young reference 386 person is aged between 15 and 24 years, a middle-aged person is aged 25 to 65 years, and 387 older is 65 and over. For example, in the Centre district, 67% of households are single, 16% 388 are composed of couples without children, 7% couples with children, 5% are complex 389 households, and 5% are single-parent households. Regarding the household reference person, 390 the largest category (46%) is middle-aged; younger and older head of households represent 391 34% and 20% respectively. Regarding dwelling size, 49% of the total number have a surface 392 area between 40 and 100 m 2 , 39% less than 40 m 2 , and only 12% have a surface area of 100 393 m 2 or above (see Table  9 ).
Collecting data for each appliance; ii. Calculating values from the database (presented in appendix A); iii. Calculation of unit energy consumption of every appliance (i) for the whole year in different operational modes (active, sleep, standby, off) using Eq. 2, where EC is electricity consumption, P is power, T is time, and m is unit of time (minute): After that, the associated load profiles can be created, showing hourly, daily, weekly and seasonal variations. To this end, it is necessary to: 1) Find the proportion of time spent by each type of individual on each activity (a household that spends more time on an activity such as cooking is more likely to use more electric cooking appliances); 2) Multiply this number by the total minutes per year the appliance is running to gain the annual utilization of each appliance for each household type; 3) Apply the active occupancy rate (calculated in the previous step) per type of day (weekday, weekend/public holidays) to the annual running time of each appliance to obtain the time of use of the appliance on that type of day (e.
Conclusions An online survey conducted in Canada, Colombia, Chile, Belgium, and France is used as a tool to explore and characterize geothermal energy awareness and acceptance in a social segment primarily constituted by people with post-secondary education. Data were collected without any restriction to people who may answer. As a result, only respondents with high-level education were selected and considered for the analysis. The available participants for this research project were somewhat limited as the Ques-tionPro platform could be left open only for 3 months; reminders to invite the public to fill out the survey were regularly sent, but there were no tools to directly contact the respondents. Being an online survey, only the connected portion of the population could be reached. The acceptance and awareness of a technology can be based on the understanding of technical aspects or it can just be intuitive.
Acknowledgements This work is part of the project " IGCP 636-Unifying international research forces to unlock and strengthen geothermal exploitation of Americas and Europe " developed between 2016 and 2020. The authors acknowledge the  UNESCO (United Nations Educational, Scientific and Cultural Organization) ,  IUGS (International Union of Geothermal Sciences ) within the  International Geoscience Programme  (IGCP). We also acknowledge all the anonymous respondents who completed the survey in the five participating countries. Balzan-Alzate D. and Daniele L. acknowledge  CEGA  (fondap 15200001) support.
NLDFT analysis with SAIEUS software was employed to determine the pore size distribution. 57 Fe M€ ossbauer spectroscopy was acquired on the Fe acetates, FeeNeC catalysts and FeeNeC cathode, at room temperature or at 5 K using a 57 Co:Rh source, and applying a triangular velocity waveform using NaI scintillation detector for g-rays. The velocity calibration was performed with an a-Fe foil. A Hitachi HT7800 RuliTEM equipped with a standard lens for high contrast and resolution of 0. 204 nm (Off-axis, 100 kV) was used to capture bright-field images of the FeeNeC catalysts. For that purpose, the catalysts were deposited onto a holly carboncoated transmission electron microscopy (TEM) copper grid and images were captured at an electron energy of 100 kV. For highresolution scanning transmission electron microscopy (STEM) imaging, a JEOL 2100F 200 kV, equipped with a CEOS Cs corrector on the illumination system, was used for Z-contrast imaging.
H. A. and A. S. together performed the electron microscopy imaging and further characterization. N. U. H. and X. P. assisted with the AEMFC testing and data analysis. F. J. , P. G. S. , M. T. S. , contributed to synthesizing and providing the material and characterizing the FeeNeC catalyst. K. Y. , I. G. R. , S. B. , and D. R. D. performed the degradation modeling experiments and analyzed those results. A. Z. performed the M€ ossbauer spectroscopy and data analysis. J. R. V. oversaw the synthesis of the membrane and ionomer and assisted with the writing of the manuscript. J. R. R. oversaw the electron microscopy experiments and provided ICDD licenses for XRD. W. E. M. supervised the execution of the overall project.
Acknowledgements W. E. M and H. A. gratefully acknowledge the financial support of the  U. S. Department of Energy Office of Energy Efficiency & Renewable Energy  under the  Hydrogen and Fuel Cells Technologies Office (HFTO)  (award number:  DE-EE0008433 ) to perform the AEMFC experiments, SEM, TEM, STEM, analyze data and prepare the manuscript. J. R. V. gratefully acknowledges the support of  UK EPSRC  grant  EP/M014371/1  for the polymer synthesis. J. R. R. acknowledges the support of  Center of Catalysis for Renewable Fuels(CReF). The work performed by P. G. S and F. J. was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement CREATE [721065]. The portion of the work executed at The Technion was supported by the  French-Israeli Joint Research Project  ( PRC 2019e2021 ;  MOST and CNRS) [PRC2347] (MOST grant  No.
Results The results are presented for a one-year simulation. The simulation was run in MATLAB with a time step of 10 min. The working time and the number of starts of the fuel cell and the electrolyzer were recorded. Indeed, for these two components, multiple start-stop cycles were one of the major issues regarding their durability. The number of cycles of the battery was counted. The ratio of electrical energy consumed by the heat pump compared to the total electrical consumption (electronics load plus air conditioning) and the ratio of cooling produced by the ACS compared to the total cooling produced.
Acknowledgments: This work has been supported by the  French Research Agency  under the  RECIF project  (contract  ANR-18-CE05-0043 ), by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ), and by the  Region Bourgogne Franche-Comté. The authors thank  Antoine PERRIGOT  of the  PROMES laboratory and Franco Nicolas FERRUCCI  of the  GEPASUD laboratory  for the thermochemical process description as well as  Franck LUCAS  for the data simulation.
ACKNOWLEDGMENTS The authors are grateful to Corning ® for the loan of an  Advanced- Flow Lab Photoreactor  (AFR) and to  Marc Winter  and  Guillaume Gauron  for fruitful discussions. Pr Audrey Moores  and  Julio C. S. Terra  from  University McGill  are greatly acknowledged for the valuable discussions. The  French Ministry of Higher Education, Research and Innovation and Doctoral School "Sciences des Métiers de l'Ingénieur" (ED 432 ) are gratefully acknowledged for a PhD scholarship attributed to ML. Maria Russo  is gratefully acknowledged for fluorescence microscopy and transmission electron microscopy measurements.
Acknowledgements F. G. and M. B. contributed equally to this work. The authors acknowledge financial support from " IMPRESSIVE " project which received funding from the  European Union 's  Horizon2020 research and innovation program  under grant agreement number  n°826013. W. N. and F. S. thank the financial support from the  Agence Nationale de la Recherche (ANR)  " VISION-NIR" program  under grant agreement number  ANR-17-CE05-0037. T. T. acknowledges the financial support from  Spanish MINECO  ( CTQ2017-85393-P (Phthalophoto ),  PID2020-116490GB-I00  (Porphyrinoids)) and  ERA-NET/European Commission/MINECO , ( UNIQUE ,  SOLAR-ERA. NET Cofound 2 N° 008/ PCI2019-111889-2. IMDEA Nanociencia acknowledges support from the " Severo Ochoa" Programme for Centres of Excellence in R&D (MINECO , Grant  SEV2016-0686 ).
Morphological, Structural, and Optical Characterizations The structure of materials was determined by a powder X-ray diffractometer (XRD) from 20 • to 60 • (Kα1 1. 5405980 nm, Kα2 1. 5444260 nm, ratio K-α1/Kα2 1. 05, step size 0. 0083556 • , time per step 80. 10 s, scan speed 0. 013263 • × s -1 ) using an X'pert Pro (Pan Analytical, Malvern, UK) apparatus and quantified by Rietveld refinement on FullProf with an ISCD file of anatase phase (CollCode 92363-Weirich T. E-2000) and rutile phase (CollCode 93097-Ballirano P-2001). BET (Brunauer, Emmett, and Teller) specific surface areas and the average pore diameters of the powders were measured using an adsorption analyzer (ASAP-2010, Micromeritics, Norcross, GA, USA). To depict the chemical composition of materials in the form of thin layers, the X-ray photoelectron spectroscopy (XPS) measurements were carried out with a monochromatic source, Al Kα, as the excitation source, and photoelectron spectra were calibrated as binding energy relative to the energy of C-C of C1s at 284.
Characterizations Textural properties of the C 3 N 4 template and Mo-containing samples were studied by N 2 adsorption-desorption volumetry at À196 C on a Micrometrics ASAP 2010 device. Pore distributions have been calculated using BJH equation. The samples were outgassed before the measurements at 400 C for 2 h. Phase composition was studied by X-ray diffraction (XRD) on a Bruker D8 Advance A25 diffractometer with CuKa emission. The phases were identied by comparison with JCPDS standards database. Phase composition was quantied using Rietveld renement as implemented in the Philips XPert soware. CHONS analysis was performed on a Thermo Fisher Flash 2000 device. Transmission Electron Microscopy (TEM) images were obtained on a JEOL 2010 instrument at 200 KV. TEM images were analyzed using Digital Micrograph Gatan program package.
Acknowledgements The authors would like to acknowledge the support from the  UKRI Interdisciplinary Centre for Circular Chemical Economy  ( EP/V011863/1 ). E. Yu ,  D. Li ,  S. Rasul ,  J. -M. Fontmorin ,  P. Izadi  and  H. Xiang  thank  EPSRC   LifesCO2R  project ( EP/N009746/1 ),  EPSRC NECEM  ( EP/R021503/1 ),  NERC  MeteoRR ( NE/L014246/ 1 ),  NBIC 002POC19034  and  P. Izadi  and  H. Xiang  thank the  Doctoral Training Awards (SAgE DTA , 2015 cohort) from  Faculty of Science, Agriculture and Engineering, Newcastle University  for supporting their PhD studies. The project was also supported by the  Open Fund Project for State Key Laboratory of Clean Energy Utilization with Zhejiang University , project number  ZJUCEU2019004. A. Cowan  thanks  EPSRC  for support ( EP/N010531/1 ).
FUNDING EM was supported by the Agence Nationale de la Recherche (Alpalga ANR-20-CE02-0020, Oceanomics ANR-11-BTBR-0008, GlycoAlps ANR-15-IDEX-02, GRAL Labex ANR-10-LABEX-04, and EUR CBS ANR-17-EURE-0003) and Institut Carnot 3BCAR.