dataesr/scientific-paragraphs-categorization huggingface dataset:
The optimized variables are network demand P demand,car , amount of sunshine G car and outside temperature T ext,car. The purpose of the optimization is therefore to create typical days that minimize the objective function (Equation (  1 )) written as the sum of the squared deviations between the day that has been created and all the other days in the season provided by a database (network demand, amount of sunshine and outside temperature being furnished in this database for one year with an hourly time step). The letter "l" represents the index for the period that Energies 2021, 14, 8003 6 of 27 here represents one hour of a 24 h day, "k" is the season index denoted by 1 to 3 and "j" represents the index for the day of the season considered Seas. obj P car,k = min P Seas,k ∑ j 24 ∑ l P demand,car,k,l -P demand,j,k,l 2 , ∀ k ∈ [1, 3] (1) obj G car,k = min G Seas,k ∑ j 24 ∑ l G car,k,l -G j,k,l 2 , ∀ k ∈ [1, 3] (2) obj T car,k = min T Seas,k ∑ j 24 ∑ l T ext,car,k,l -T ext j,k,l 2 , ∀ k ∈ [1, 3] (3) This minimization is therefore carried out 3 times in order to create a typical profile for winter (k = 1, Seas = 90), summer (k = 2, Seas = 92) and mid-season (k = 3, Seas = 183).
Julia (JuMP) The open-source Julia programming language  [12]  was selected for its fast computation and its optimization package JuMP. With this package, different optimization solvers are used while keeping the same formulation. Therefore, a different complexity for the system to be optimized can be resolved with open-source Ipopt or commercial Knitro NLP solvers. In this study, optimization was carried out with the open-source Ipopt solver as the final optimization tool must be open-access.
Conclusions In the context of energy transition, heat production with low carbon emissions is needed. The use of solar thermal energy in heating networks in France but also in many other countries must be amplified. The possibility of optimizing the dimensioning of this type of system with the help of open-access tools will certainly help to increase the development of solar thermal energy. This study presents the methodology used in a new optimization tool written in Julia to size a thermal production site to supply a district heating network. The dynamic model of the system is discretized into a set of algebraic equations allowing the formulation of a multi-period nonlinear optimization problem. This model was applied to optimize the thermal production of a district heating network using inter-seasonal thermal storage.
As nonlinear optimization guarantees neither systematic convergence nor a global optimum, a solution strategy was developed to guarantee a confidence optimum. Three thermal storage connections were studied, and the results showed the relevance of a storage connection at the solar field outlet. Results show that the model works properly and that the solar plant/storage couple is an interesting solution from an economic and environmental point of view. In fact, the economic optimum, taking state aid into account, assesses the value of solar energy at 49% of total energy supplied for a total renewable rate of 69%. These first results also validate the choice of the Julia environment and the JuMP package for optimization. However, special attention must be paid to the problem-solving methodology to avoid getting a local minimum.
Acknowledgements A. O. acknowledges the financial support from the  Ministry of Science and Higher Education of the Russian Federation  (project  АААА-А17-117041710087-3 ). Dong Y,  Dang J, Wang W ,  Yin S, Wang  Y: First-principles determination of active sites of Ni metal-based electrocatalysts for hydrogen evolution reaction. ACS Appl Mater Interfaces 2018, 10: 39624-39630 , https://doi. org/10. 1021/acsami. 8b12573. DFT was employed to identify the active sites of Ni metal-based electrocatalysts for the HER. While the Ni(111) surface binds H atoms too strongly, which limits the formation and desorption of H 2 , its oxidation to a relatively low oxygen coverage decreases the H ad binding energy and results in superior HER performance.
g. , Soultz, Landau and Rittershoffen), all of the abandoned, suspended and ongoing projects are still important research facilities, providing a significant scientific database. For example, the Fenton Hill venture is described in great detail by Brown et al. [34]  and a summary of the lessons learned can be read in Kelkar et al. [41]. A compilation of the development phases of the Rosemanowes geothermal project, problems faced and unresolved issues are provided by, for example, Parker  [42]. Richards et al. [43]  discuss the performance and characteristics of the Rosemanowes hydraulically stimulated geothermal reservoir. The authors also discuss the fundamental parameters controlling the impedance, thermal performance and water losses. The contribution of the Soultz project for the scientific community and its development phases are described in detail by, for instance, Genter et al.
[51]. Four main fracture sets were identified but only one, the N-S set, is optimally oriented for slip. The sets E-W and NNW-SSE require higher additional fluid overpressure to be reactivated. Additionally, Miranda et al. [51]  present an a priori stress estimation for Kuujjuaq calibrated with published stress data for the Canadian Shield and built upon geological indicators, empirical correlations, analytical models and Monte Carlo simulations to deal with the ensemble of uncertainties (Figure  4b ). The orientation of the maximum horizontal stress was inferred N210-220 • E. All of these previous results constitute the basis for the numerical simulations carried out in the present study.
Therefore, a first analysis was carried out using Sanyal et al. [72]  costs and these were posteriorly increased by a factor ranging between 2 and 5 to assess more realistic energy costs for Kuujjuaq (Table  2 ). The costs are in USD. The imputed interest rate was assumed 9%  [72]. The annually provided energy was estimated based on the annual average heating (and electricity) consumption inferred for the community. The ensemble of uncertainties was simulated with the Monte Carlo method (Table  3 ) using the software @RISK  [74]  and the simulations were carried out for each possible engineered geothermal energy system design computed with FRACSIM3D.
Funding: This study was funded by the  Institut nordique du Québec (INQ)  through the  Chaire de recherche sur le potentiel géothermique du Nord  awarded to  Jasmin Raymond. The Centre d'études nordiques (CEN), supported by the  Fonds de recherche du Québec-nature et technologies (FRQNT) , and the  Observatoire Homme Milieu Nunavik (OHMI)  are further acknowledged for helping with field campaigns cost and logistics. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Acknowledgments: The authors would like to acknowledge Félix-Antoine Comeau, Inès Kanzari, Jean-François Dutil, Sérgio Seco and Stefan Premont for the support during the analyses of the thermophysical properties. Acknowledgments are extended to Cynthia Brind'Amour-Cote for the support during the field campaign. A special thanks is given to Fiona Chapman for the English language review. The authors are also grateful to the three anonymous reviewers whose comments and suggestions helped to make the manuscript clearer.
The samples were packed in a 4-mm-diameter rotor after rehydration to 11. 5 ( AE2. 8) % w/w. The samples were spun at 9000 Hz at room temperature. CP-MAS spectra were acquired with a contact time of 2. 5 ms and an overaccumulation of 5120 scans at intervals of 10 s as a recycling delay. The carbonyl carbon was set to 176. 03 ppm through external glycine calibration. NMR spectra deconvolution was developed using the PeakFit (v. 4. 11) software program (Systat Software, Inc. , USA). The crystallinity index (CI) was calculated as the ratio of the sum of the peak area from 86 to 91 ppm (corresponding to the crystalline part of C 4 ) to the sum of the peak area from 80 to 91 ppm (corresponding to all of the C 4 ). The 1 H spectra were acquired over eight accumulations separated by a recycling delay of 5 s.
Acknowledgments This work is a contribution to the  Labex   SERENADE  (n  ANR-11-LABX-0064 ) funded by the " Investissement d'Avenir" French Government program  of the  French National Research Agency (ANR)  through the  A*MIDEX project  (n  ANR-11-IDEX-0001-02 ). Solid state NMR experiments and STEM images were performed at the  BIBS facility  ( UR1268   BIA ,  IBiSA ,  Phenome-Emphasis-FR  (grant number  ANR-11-INBS-0012 )). We acknowledge SOLEIL for provision of synchrotron radiation facilities and we would like to thank  Gautier Landrot  for his help during data acquisition. We are also very grateful to  Benoit Duchemin  for the XRD experiments ( CASMOC Universit e du Havre -Fr ) and  Bruno Novales  ( BIBS plateform BIA-INRAE ) for the STEM pictures.
The RE production and capacity data used in this study were provided by recent literature research on a yearly basis. However, while knowing a single averaged value over the region is useful, this may not be sufficient to correct low-frequency climate variability of electricity production. On the other hand, from a climate perspective, the analyses of long-term climate data can have a substantial impact on the design of electricity mixes  [69]. Accordingly, we estimate the CFs and demand over the whole period (1981-present day) available from the MERRA-2 reanalysis climate data  [70]. Since the inter-annual variability of the mean CFs and mean demand (inputs of the optimization) from 1981 to 2019 were found to be weak (not shown here) and since the length of the regional observed demand time series is limited to one year (2018) on an hourly basis and only means of observed CFs over the same year are available, we consider that the climate over the period 1981-2019 is represented by the year 2018 to correct bias in the production models and fit the demand model.
Acknowledgments: This work was conducted in the frame of the  Energy4Climate Interdisciplinary Center (E4C) of Institut Polytechnique de Paris and Ecole des Ponts ParisTech , supported by the MED-OCEAN research contract. The E4CLIM open-source software is supported by the  3rd Programme d'Investissements d'Avenir 345  [ ANR-18-EUR-0006-02 ] and by the  Foundation of Ecole polytechnique (Chaire "Défis Technologiques pour une Énergie Responsable " ). The authors would like to acknowledge the  National Electricity Office (ONE)  in Morocco for providing the regional hourly load data. We are also grateful to the reviewers for their careful review and positive comments.
We thank  G. Caserta  for performing some of the preliminary experiments and  G. Kopiec  for preparing the viologen modified polymer. We acknowledge support from  CNRS ,  Aix Marseille Université ,  Agence Nationale de la Recherche  ( ANR-15-CE05-0020 ,  Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003 ), and the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme  ( ANR-11-IDEX-0001-02 , RESOLV, funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2033 -Projektnummer 390677874, the  ERC  starting grant  715900 , and the  ANR-DFG  project  SHIELDS  ( PL 746/2-1 ). The French authors are part of  FrenchBIC  (www. frenchbic. cnrs. fr).
Metrics to bridge the demonstration at the level of material science and proper engineering designs need further clarification. To tackle this challenge, we developed a numerical model to evaluate and optimize the performance of the PV thermal battery with coupling heat and mass transfer models in MATLAB and with PV performance simulations using PV-lib (Supplementary Note 2). In Fig. 4a , we mapped the working principle of the MOF Al-Fum thermal battery to the test conditions. The open-loop sorptive thermal battery undergoes the locus through the isosteres curves of Al-Fum, constructed from isotherms at different temperature. Usable energy density was directly determined by the cyclic water uptake between discharged (blue point) and charged (red point) states. Discharging or charging of the thermal battery can be simply triggered or deactivated by the contact to/isolation from environment with mechanical doors/valves to control the working time (green point).
Acknowledgements F. C. acknowledges funding from the  European Union 's  Horizon 2020 Programme  under  MSCA  grant agreement No. 754462  and  Bjarne Saxhofs Foundation. E. G. , F. N. and C. S. acknowledge the  CNRS, ENS ,  ESPCI  and  PSL University  for financial support. B. M. , F. C. , R. L. and J. H. acknowledge  TU/e  for support.
ACKNOWLEDGMENT V. D. -C. is grateful to the financial support given by the  COMETE  project  (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie ), which is co-funded by the  European Union  under the program 'FEDER-FSE Lorraine et Massif des Vosges 2014-2020. M. P. acknowledges  ANR   JCJC HELIOSH2  ( ANR-17-CE05-0007-01 ) project for the financial support.
Energy poverty in Japan Unlike in European countries such as France, energy poverty is not yet on the policy agenda in Japan; there are no official statistics or policy measures, particularly for tackling the energy poverty phenomenon  (Chapman and Okushima, 2019; Castaño-Rosa and Okushima, 2021). Energy poverty figures have been estimated only in academic studies, including  Okushima (2016 Okushima ( , 2017)). Hence, this study outlines the current status of energy poverty in Japan using its original estimates. Figure  4  shows the trend in energy poverty in Japan from 2013 to 2017, with the rates estimated based on the 10% indicator using microdata from the Family Income and Expenditure Survey. 7 The Japanese energy poverty rates were evaluated on a monthly basis, which allowed for a clearer presentation of seasonal variations.
As shown in this figure, the energy poverty rate peaked in February every year, and the rates are cyclical-higher in winter and lower in summer. This implies that heating needs in winter are much more significant than cooling needs in summer in the overall context of Japanese energy poverty. Furthermore, the figure shows that the energy poverty rate was the highest in the winter of 2015, but has since been on a downward trend, reflecting the decline in international and domestic energy prices. These results are in line with existing studies on energy poverty in Japan, although there are differences in the indicators and datasets used. For example, Okushima (2016) estimated the energy poverty rate in Japan using the 10% indicator, showing 8. 4% in 2013 and 15. 2% in the winter of 2013. Chapman and Okushima (2019)  reported a rate of 4.
Other studies also confirm the higher energy needs of households with elderly people especially for space heating  (Healy and Clinch, 2002; Oreszczyn et al. , 2006). As for the dwelling factor, the study separates households into those living in detached-type houses and those living in apartment-type houses. Many studies have shown that the type of housing, detached or apartmenttype, is the most important factor in determining household energy use in terms of built characteristics  (Santamouris et al. , 2007; Okushima, 2016 Okushima, , 2017;; Fizaine and Kahouli, 2019). Tables  5  and 6  show sizable differences in energy use between detached houses and apartments. These results illustrate that the typology is reasonably acceptable when it is applied to the French and Japanese datasets. 10 Note: The numbers are the means and medians (within parentheses) of energy use for domestic energy services by "type" (in GJ).
The reason behind setting 60% median thresholds is that energy poverty in developed countries is characterized by the concept of relative poverty rather than absolute poverty  (Townsend, 1979; Bradshaw et al. , 2008; Liddell et al. , 2012). The median value for each type is provided in Tables  5  and 6 , and 60% of this value is given as the threshold for each type in France and Japan. In terms of the second (income) threshold , the study defines households in the lowest 30% income group as income-dimensionally poor in accordance with previous studies such as  Boardman (1991). As for France, the study decides using this procedure. Unlike the French dataset, the Japanese dataset has only range-type information for annual income (see Table  F. 1). Therefore, the study defines households belonging to income ranges 1 and 2 (in Table  F.
J o u r n a l P r e -p r o o f Journal Pre-proof   table  shows the inequality of energy use for domestic energy services measured by the Gini coefficient. Please note that "% Contribution" means the (percentage) contribution of the inequality of energy use for each purpose to the total inequality of domestic energy use. The figures on domestic energy use by purpose are derived from the data estimated by the Ministry of the Environment (http://www. env. go. jp/earth/ondanka/ghg/kateiCO2tokei. html); the values do not exactly correspond with the original dataset in the study and there are some differences in the Gini coefficients between Tables  10  and 12. Tables  11  and 12  show the decomposition results of energy inequality by purpose, presenting the contribution of energy use for each purpose to the total energy inequality in France and Japan, respectively.
Acknowledgments The authors are grateful to the editors of this journal for their kind assistance and the three anonymous reviewers for their useful comments and suggestions. In addition, the authors would like to thank the staff of the  Statistics Bureau ,  Ministry of Internal Affairs and Communications Japan , those of the  Ministry of the Environment Japan , and  Masaya Tokutake  for their kind assistance with this study. J o u r n a l P r e -p r o o f.
Computational Details Density functional theory (DFT) through wB97XD/def2-TZVP method was used to study the molecular forces for the interaction between BU and ChA. The structures of the individual molecules and the complexes were optimized by the Gaussian 09 program  [46]. Frequency calculations were performed on the optimized structures to ensure that the structures had minimum energies on the potential energy surface, which was confirmed by the positive vibration modes. The solvent effect on the CT complexes was performed using the self-consistent reaction field (SCRF) method and polarized continuum model (PCM). Charge population and charge transfer analysis were calculated using the Multiwfn program  [47]. The g-C 3 N 4 nanosheet was constructed based on the (011) lattice plane supercell (2 × 2 × 1), according to the literature  [48].
Implementation and summary All the above mentioned expressions are implemented in Matlab® environment. Constrained non-linear programming is solved with the fmincon function. To reduce computational times, G-functions are not computed on an hourly-time scale, but based on an aggregated logarithmic scale covering the whole simulation time and then interpolated, the relative error being below 0. 1%. Convoluted powers between neighboring systems are weekly-updated (Eq. (  26 )), resulting in the rootmean-square error below 0. 1 • C. Summary of main steps is given in Fig. 2.
Reference case study The reference case study is a neighborhood of 4 × 3 = 12 identical buildings on identical 20 m wide square plots (cf. Fig. 3 ). Each plot is connected to 8 BHE; the sizing has been done with the ASHRAE method. The energy demand of each consumer is 53. 9 MWh/y (30 kW) and 14. 6 MWh/y (18. 2 kW) for heating and cooling respectively (cf. Fig. 4 ). It has been modelled in the framework of the GRETA project (project, 2017) and concerns a well-insulated hotel in the city of Turin (Italy). The thermal loads have been reduced by 20% compared to original dataset. Main parameters are given in Table  2. The results will be focused on consumers 5 and 12 since they have four and two immediate neighbors respectively and represent the worst and the best location in the neighborhood. Fig.
, are unambiguously known, multiple processes simultaneously contribute to each of these; for example, reactions and transport both contribute to species balance. Furthermore, one has to sufficiently characterize these processes (in terms of relevant constitutive relations and corresponding material properties). Machine learning (ML), on the other hand, is a type of datadriven modeling that makes predictions without knowing the underlying physics. The data-driven nature of ML substitutes knowledge of the underlying physical mechanisms with many observations of system behavior. This has revolutionized many domains in the past decade,  17, 18  especially where large datasets are available. Successful ML applications typically rely on abundant data, be it speech patterns to train personal assistants (e.
Once such a dataset of controlled factors (compositions, porosities, and thicknesses) and corresponding outcomes (e. g. , energy and power) is available, data analysis identifies an optimally performing electrode. Such an approach identifies the optimal electrode within the design space studied, but it does not offer any insight into why this electrode configuration is the optimal one. Therefore, if one were to change the active material to a different chemistry or even just change the particle morphology, the previously generated dataset would lose nearly all usefulness. The physics-based understanding of the porous electrode performance answers the why question by relying on intrinsic material properties (e. g. , diffusivities, reaction rate constants, etc. ) and predicting the performance differences across a variety of electrodes having different geometrical arrangements.
A judicious combination of data-driven and physics-based approaches can speed up scientific discoveries by translating mechanistic information across systems using physics (i. e. , causation) and substituting unknown or complex physics via data (i. e. , correlation). With the help of physics, one can partially relax the data overhead since the physics-constrained behavior can be approximated using a limited dataset. This is particularly suited for ML applications in sciences  19  where the observed response satisfies fundamental laws such as energy conservation, entropy generation, charge neutrality, etc. The goal is to improve predictive accuracy while minimizing efforts. This approach also aids in the development of transferable f unctions. In a conventional physics-based analysis, the accuracy is improved by progressively introducing advanced constitutive relations, while the fundamental laws remain unchanged (for example, replacing dilute solution theory with concentrated electrolyte transport).
Accurate 3D Mesostructures. Instead of sequentially building mesostructure ↔ effective properties and effective properties ↔ electrochemical performance relationships, if detailed mesostructure information is available, one may directly simulate electrochemical interactions at the pore scale. X-ray computed tomography (XCT) and other advances in 3D imaging allow us to study the composition and structure of critical materials as manufactured, rather than using idealized representations. The use of such realistic geometries is directly related to higher fidelity predictions of the electrochemical responses of these materials. However, many challenges are prevalent in obtaining accurate 3D mesostructures, including image segmentation (i. e. , assigning correct material phase to each voxel) and the effort required for 3D imaging, resulting in limited datasets.