dataesr/scientific-paragraphs-categorization huggingface dataset:
It has the advantage of providing an operational forecast even if the PV output measurements cannot be provided in real-time. GHI forecasts also permit the assessment of the predictability of a future PV farm not yet built. During the last decade, numerous studies proposed PV production forecast methods. A large proportion of them have been surveyed by different authors  [9] [10] [11] [12] [13]. These publications agree that each forecasting technique is suitable for a particular time horizon range. At the intraday scale, time series modeling approaches are often used. A wide range of methods modeling time-series using machine learning techniques have been developed  [14]. Such methods are trained on historical data and offer direct PV power forecasts if long-term historical datasets of this quantity are available.
More advanced methods use hybrid approaches  [15]  or include a variety of environmental data to assimilate more information driving PV power variability  [16]. Nevertheless, these approaches depend on the behavior of the training datasets. Applications to other regions or periods requires retraining based on available historical data  [17]. Other appropriate methods for this time scale are based on cloud cover forecasting using images from the geostationary meteorological satellite. These techniques can be applied at any geographical site covered by the satellite without using training datasets. Moreover, it enables us to overcome the underestimation of cloud cover by classical numerical weather prediction models (NWP)  [18]. Some studies demonstrated the superior performance of such techniques compared to NWP, over the USA  [19] , Germany  [20]  or Netherlands  [21].
Satellite Data SEVIRI is a 12-channel imager on the MSG geostationary satellites operated by EUMETSAT. In this work, we used the observations acquired from 01 July 2017 to 30 June 2020 mainly from MSG-3 and MSG-4 and punctually from MSG-1 when MSG-4 has been in maintenance. These data are delivered in real-time by the SATMOS service from Météo-France under the form of NetCDF files containing calibrated radiance. Images are continuously archived at Institut Pierre-Simon Laplace (IPSL) in Palaiseau. The computation of CMV and the forecast algorithm uses the high-resolution visible channel (HRV) observing the Earth through a broadband from 0. 4 to 1. 1 µm. The spatial resolution of HRV data is 1 km at nadir, corresponding approximately to a pixel of 2 × 1. 2 km 2 over Palaiseau (respectively, 3 km at nadir and 4 × 6 km 2 for infrared channels).
ARPEGE Outputs The NWP model ARPEGE is one of the operational forecast model used at Météo-France  [48]. We use the output "surface solar radiation downwards" of ARPEGE, as a reference model to assess the satellite-based forecast. This model is operational as a global forecast system with a horizontal resolution of about 7. 5 km over mainland France. Its initial conditions are determined by a 4D-Var data assimilation of observations. ARPEGE is a spectral hydrostatic model with a presentation in finite elements on the vertical direction. It presents 105 vertical levels from 10 m. to 75 km above sea level. For each day, we selected the run initialized at 12:00 UTC for 0-24 h ahead with an hourly time resolution. Outputs have been linearly interpolated to coincide with MSG timestamps. Spatial resolution of this global model is 5 km over Europe.
Evaluation Protocol The GHI forecasts for the single pixel colocated at Palaiseau are compared with the GHI measured by the BSRN station. In order to compare instant pixelwise value from satellites with local ground measurements, we converted both satellite and ground data irradiation sums to 15 min time steps irradiation sums. Then, for a given satellite acquisition time (e. g. , 12. 11 UTC), we compared both irradiations summed over the last 15 min from 11. 57 UTC to 12. 11 UTC. Minute resolution ground data have been simply summed over these 15 min. For satellite data, we first computed a GHI forecast value at each minute between the two acquisitions using a temporal linear interpolation between 11. 57 and 12. 11 UTC of predicted clear-sky index. Then we computed the GHI every minute, given that the clear sky model is able to provide GHI cs value at 1 min steps.
Then forecast data have been summed over these 15 min. This protocol follows the recommendations of  [60]  who advise to take into account the Sun position at each minute to synthesize the 15 min information gap due to satellite sensor resolution. We performed the forecast using all HRV images available at IPSL from 1 July 2017 to 30 June 2020. We only used images sufficiently illuminated by the Sun to compute CMV and cloud index maps. We rejected all data computed with an SZA above 78 •. In practice, the first and last daily observations used in July were time stamped at 5. 26 and 18. 26 UTC. In January, forecasts started at 9. 41 and ended at 14. 11 UTC.
Relative value corresponds to the absolute value divided by mean of GHI ground measurements. Results at time horizon of 0 min evaluate our algorithm of GHI estimation from satellite. Results are similar to the state-of-the-art for Palaiseau station. In particular,  [60]  published very close results in terms of relative RMSE (25. 5% and 24. 8% for the GHI database HelioClim-3 version 4 and version 5, respectively). The high value of the correlation coefficient ensures consistent results. Error metrics increase with time horizon except for bias error. However, results are satisfactory in the magnitude of order from similar works using ground truth data in the Netherlands  [21]  and Germany  [43]  at different periods. Figure  5a  shows the RMSE results compared with those of ARPEGE model and persistence algorithm as reference algorithms.
Funding: This work is part of the project  Grid Power Sustainability , cofinanced by the  European Union and Regional Council Ile-de-France. Europe is involved in Ile-de-France through the  European Regional Development Fund (ERDF). This action benefited from the support of the Chair of " Challenging Technology for Responsible Energy" led by l'X -Ecole polytechnique  and the  Fondation de l'Ecole polytechnique , sponsored by TOTAL. This research was supported by  3rd Programme d'Investissements d'Avenir  [ ANR-18-EUR-0006-02 ]. Acknowledgments: This work benefited from the support of the  Energy4Climate Interdisciplinary Center (E4C) of IP Paris and Ecole des Ponts ParisTech. Meteosat images have been provided by  SATMOS (Météo-France ). Meteosat images and weather regime database ( A2C  project) are maintained and up-to-date thanks to IPSL.
The main assumptions taken into consideration were (i) the evaporation only occurring at the pore entrances where the liquid-vapor interface was located; (ii) the absence of wetting, fouling or crystallization throughout the process, thus no salt penetrating the membrane; (iii) Knudsen diffusion only (collisions of vapor molecules with the pore wall)  [35]  determining the mass transfer through the pores of the membrane; (iv) neglecting the transmembrane conductive heat loss and the boundary layer on the permeate side, due to the vacuum and the total vapor phase in the permeate compartment  [33] ; (v) the properties of seawater calculated from its activity coefficient ! ) and its water molar fraction obtained using the PHREEQC software (version 2. 13. 1, US Geological Survey)  [36]. The feed bulk longitudinal model further aggregates the above models by a series of ordinary differential equations to predict the profiles of hydraulic pressure, temperature, flow velocity and salt concentration along the feed flow direction.
/ ) involving the estimation of solar radiation on the ground based on isotropic sky model where transmission, reflection, and absorption of radiation are included and referred to as functions of incoming radiations and main properties of the solar collector  [29]. Same solution procedure as the previous work was adopted  [29]. First, a system of differentialalgebraic equations (DAEs) was resolved by the feed bulk longitudinal profile discretized along the module length and the set of algebraic equations describing the process in membrane pores. Then, Scipy ODE package with the Real-valued Variable-coefficient Ordinary Differential Equation solver (Isoda)  [42]  was applied to automatically process the system dynamics Eq. 1-3. It should be noted that all models in the present work were programmed in Python (version 2.
Acknowledgment This work was supported by the  ANR (Agence Nationale de la Recherche ) project:  LabCom -Membranes pour le traitement d'eaux des Océans avec énergie Solaire en régions Tropicales (MOST ), 2018; and  China Scholarship Council (CSC)  [No. 201504490023], 2015. Declarations of interest: None.
Preparation of H 2 S Sorbent Material Montmorillonite STx-1b from Gonzales County (Monthalia, TX, USA) (Mt, for all other abbreviations see Appendix A) was obtained from the Source Clay Repository of the Clay Minerals Society. Mt was already fully characterized elsewhere  [15]. Its main characteristics are briefly summarized: mineralogical composition, obtained from quantitative X-ray powder diffraction results (QXRDP), montmorillonite 73%, cristobalite 12. 8%, tridymite 11. 6%, quartz 0. 12%, amorphous 2%; chemical formula, obtained by combining chemical data and QXRDP results, (Si The same chemicals and procedures described by the authors in a previous study were used  [11]. Specifically, CuSO 4 •5H 2 O was dissolved at room temperature in a well-stirred phenanthroline (Phen) solution in order to obtain a 6 mM solution of Cu(II)Phen; Mt (50 mg) was dispersed in 10 mL of this solution, shaken for 1 h at 250 rpm at room temperature, and then separated from the liquid phase via centrifugation at 14,000× g for 1 min.
Gas analyses were carried out in multiple ion detection mode (MID) and different m/z ratios were considered (17 and 18 for H 2 O; 28 and 44 for CO 2 ; 30 for NO and NO 2 ; 33 and 34 for H 2 S; 46 for NO 2 ; 48, 64, and 66 for SO 2 ). Experimental conditions were: heating rate = 10 • /min; heating range = 25-1050 • C; data measurement = every 0. 5 s; purging gas = ultrapure helium, flow rate = 100 µL/min. Before starting measurements, samples were equilibrated for 15 min using a 100 µL/min flow of ultrapure helium  [14]. X-ray absorption spectroscopy (XAS) spectra were measured at the Spanish CRG beamline BM25-Spline at the European Synchrotron Radiation Facility (ESRF), Grenoble (France). XAS spectra were collected at the Cu K-edge (8979 eV) in both transmission and fluorescence mode at room temperature on pellets prepared by mixing uniformly the powered samples and cellulose.
The d001 peak position is always at 1. 71 nm. However, some additional peaks (e. g. , at 0. 6040, 0. 3816, 0. 3429, 0. 3337 nm) were recognized, which can all be related to rosickyite (i. e. , monoclinic sulfur), as evaluated by means of the PANalytical X'Pert HighScore software and the ICDD PDF-2 database. These results agree with the outcome of the SEM/EDX analysis, namely that the final reaction product of the interaction between Mt-CuPhen and H2S gas is crystalline sulfur.
The d 001 peak position is always at 1. 71 nm. However, some additional peaks (e. g. , at 0. 6040, 0. 3816, 0. 3429, 0. 3337 nm) were recognized, which can all be related to rosickyite (i. e. , monoclinic sulfur), as evaluated by means of the PANalytical X'Pert HighScore software and the ICDD PDF-2 database. These results agree with the outcome of the SEM/EDX analysis, namely that the final reaction product of the interaction between Mt-CuPhen and H 2 S gas is crystalline sulfur.
0 kV and a magnication of 10 000Â. Transmission electron microscopy (TEM) images of Pd nanoparticles were obtained with a JEM 1400 (JEOL Co. , Japan, 2008) transmission electron microscope equipped with a high resolution digital camera (CCD MORADA, SiS-Olympus, Germany). Atomic force microscopy (AFM) was performed using an Easyscan Nanosurface 2. 0. Cantilevers with a spring constant of 42 N m À1 and a resonance frequency range of 146-236 kHz, which were tuned prior to further measurement, were employed. The Mott-Schottky plots were obtained from the impedance data generated upon the application of sinusoidal perturbation of frequency ranging from 100 kHz to 1 Hz with 10 mV amplitude. The potential was scanned from 0. 895 down to 0. 295 V vs. Reversible Hydrogen Electrode (RHE).
Acknowledgements  Dr Sophie Nowak  ( Université de Paris)  is gratefully acknowledged for the X-ray diffraction analysis of FTO/NiO electrodes. We acknowledge the ITODYS SEM and XRD facilities ( Université de Paris ,  CNRS UMR 7086 ,  Paris, France). This work has been realized under the auspices of Polish- French Collaboration Program "Polonium" (PHC Polonium   2018-2019 ) and  Campus France  and the  French Embassy in Warsaw  are gratefully acknowledged for mobility support. Université Paris Diderot-Université de Paris (Laboratoire ITODYS)  and  CNRS  are gratefully acknowledged for nancial support. This work has also been supported by the  National Science Center (NCN, Poland)  under Opus Project  2018/29/B/ST5/02627.
Moulichon, V. Debusschere, L. Garbuio, M. A. Rahmani, M. Alamir and N. Hadjsaid  y(k) = y(k -1) + T s f (y(k -1)) (18) The discrete model is implemented with MATLAB Simulink™ using the "Embedded Coder" toolbox of MATH-WORKS™, and the "Code Composer Studio" toolkit. The DSP's central processing unit (CPU) load is reported in Table  3  for the three SM models. All the CPU load cannot be used for the VSG model, in order to ensure a proper operation of the controller (maximum 75 %, to avoid overloading and numerical errors that could destabilize the controller). Note that only the SM model changes in the comparison, not the rest of the controller including the direct and inverse Park transformations, the mechanical equations and the regulation. This explains why the CPU load is still high even with the static model as there is a mandatory minimum CPU load needed by the local controller, independently from the SM model itself.
Funding This work was partially supported by  Normandie Université (NU) , the  Région Normandie , the  Centre National de la Recherche Scientifique (CNRS) ,  Université de Rouen Normandie (URN) ,  INSA Rouen Normandie ,  Labex SynOrg  ( ANR-11-LABX-0029 ), and  Innovation Chimie Carnot  (I2C). M. Z. thanks the  China Scholarship Council (CSC)  for a doctoral fellowship. T. P. thanks the  Institut Universitaire de France , the  CNRS Emergence program , and the  Agence Nationale pour la Recherche  ( ANR-18-CE07-0004-01 ) for funding.
For instance in  [4] , an appliance-scheduling is proposed taking into account a photovoltaic panel and a hybrid electric vehicle (PHEV). A dynamic multi-swarm with learning strategy is applied to solve the proposed model with the objective of reducing the weighted sum of the electricity payments, consumer's dissatisfaction and carbon dioxide emissions. In  [5]  the B-DSM with household appliances and a battery is proposed. The authors minimize the electricity cost and the discomfort level. The discomfort is modeled through the disparity between the baseline and the optimal schedule. The mixed integer non-linear optimization model is built and solved with AIMMS software. Authors in  [6]  proposed a framework for the HEMS with a battery, PV output and electric vehicle. They developed an improved particle swarm optimization with a heuristic approach to schedule appliances and calculate power distribution under electricity time varying prices, user preference constraint.
GP method is coded with the mathematical programming language (AMPL) and solved with CPLEX solver, and H-NSGAII algorithm is coded with ParadisEO version 2. 0. 1  [31] , a C++ white-box object-oriented framework and Concert Technology with IBM ILOG CPLEX Optimization Studio Version 12. 7. 0. Both algorithms are run on a Linux 16. 04 (64 bit) Intel® Core i7-6500U CPU @ 2. 50GHz × 4 computer with an 8. 00 GiB memory. Table  2  compares the average values of these indicators performed for each scenario. Obviously, H-NSGAII solves instances in remarkably less time than CPLEX. CPLEX cannot find the optimal Pareto solutions within a reasonable time and for some scenarios it stopped at CPLEX timelimit which is set to 1500 seconds. According to NPS indicator, H-NSGAII obtains more solutions than CPLEX, which offers more options to the decision-maker.
In this approach, discrete variables are encoded as partial solutions by the NSGAII and considered as input data for the CPLEX solver which optimally determines the continuous variables. This matheuristic is effective for our constrained MOOP when neither a black box solver nor a MOEA can find good feasible solutions in a reasonable time. Several simulations are carried out for both cold and hot weather conditions and compared with the GP solved with CPLEX under six scenarios. The results demonstrate the effectiveness of our proposed framework and show that our matheuristic is able to obtain more Pareto solutions in a much shorter time that are far better than those obtained by CPLEX according to the hypervolume difference and epsilon indicator metrics. Future extensions to this work will be mainly aimed at investigating data uncertainties due to the penetration of volatile renewable energy sources, electricity and gas prices forecasting errors and unpredictable energy consumption.
In  [3]  , a CFD approach using the RNG k-E turbulence model has been applied to some Trombe wall configurations obtained by varying various physical parameters. Their influence on the global energy performance has been determined and lead to recommendations for optimizing the operation of the Trombe system. The work  [4]  concerns the renovation of buildings with this system in Istanbul (Turkey). Results show that renovation based on the Trombe wall system is much more interesting from the point of view of saving energy than changing the materials of the existing envelope. Some configurations are presented in the review  [5]  where other useful information and data concerning this assembly are available. The study  [6]  shows that indoor temperature can be increased by the heating configuration of the Trombe wall and that the configuration efficiency is of about 10%.
Thermal measurements Thermal field is measured by means of K type thermocouples of 0. 1 mm in diameter. After conventional manufacturing through welding, they are systematically calibrated in a thermoregulated bath between 2 and 87 °C, by steps of 5 °C. A second calibration is done later in situ when all the thermocouples of the assembly are installed in the assembly. This calibration is carried out by arranging the assembly in the climatic chamber and using a calibrating thermometer with an accuracy of ±0. 1 °C. Thermocouples of the assembly are connected to a fast data acquisition system ((2) in Fig. 2 a ) controlled by a computer ((3) in Fig. 2 a ). Temperature of the glass cover is measured at 5 points distributed on its diagonals. During the measurement, deviations between the 5 temperatures are of about 0.
Thermocouple welds are installed on the surface with great care, because the surface temperatures T h are decisive for the data processing leading to quantify convective heat transfer. Surface temperature of the insulating side wall is measured with the same number of thermocouples as on the convective surface. They are used to control the thermal state of the assembly through a thermal balance and also to evaluate the conductive losses through the insulating material. • in three points on each partition: at its base, its end, and its middle. For each processed configuration, the considered temperatures are those obtained in steady state when the maximum fluctuation of all temperatures is less than 0. 5%. This condition is reached after approximately 2 to 4 h of testing, depending on the processed configuration.
The lateral conductive losses are neglected taking into account only the thermal data of the central column. Losses at the back of the wall were evaluated by using the temperatures measured on the inner and outer faces of the insulating board, corresponding as previously for the largest value of the Rayleigh number. The calculation based on the linearized Fourier law associated with the measured thermal conductivity of the insulator shows that the losses by pure conduction through the insulator are very low, varying between 1 and 2% of the global heat flux. Given these results, the heat flux generated by the heating plate is almost completely exchanged by natural convection in the active cavity. The power P generated in the central column of the wall allows determination of the corresponding heat flux ϕ = p/s whose maximal uncertainty ϕ/ϕ = P/P + 2 H/H ranges from 0.
Specific Heat Capacity Model of the Flare Gas The specific heat capacity of combustion products, flare gas, depends on the temperature variation  [34] ; this is why the gas is qualified as semi-perfect, and considering it as constant may disturb a good quantification of the available heat of the source. These risks also have a direct impact on the enthalpy and entropy calculations since both terms are defined based on the specific heat capacity. The thermodynamic database used to perform the required calculation of the ORC, Cool Prop, is not able to be used directly to prevent accurate enthalpy and entropy calculation for the present flare gas composition since such a specific mixture is not available within the mentioned data base. Thus, the calculation is made possible through two mains steps: Defining the typical flaring gas composition within the Cool Prop tool, and then deducing its specific heat.
After this, a sensitive analysis on the computed specific heat is performed and a fitting curve for the variation of the specific heat versus temperature is obtained. It should be noted that the elementary chemical compounds of the typical flare gas composition are all available within the Cool Prop database. A typical flare gas composition was considered for the present work as reported previously by Emam  [25]. Such composition was assumed for the present preliminary study. However, experimental measurements are highly recommended when a physical ORC deployment is expected in order to assure an accurate-sizing step of the whole ORC solution during the tendency modeling step. As mentioned previously, the composition of the flare gas was introduced within CoolProp  [35]  for the calculation of specific heat capacity for a range of temperature sources.
Instrumentation SECM data was taken using a combination Heka ring/disk potentiostat PG 340 and Heka Elproscan controller ESC 3. SEM images were taken on a ZEISS FE SEM ULTRA 55 using an acceleration voltage of 10kV. XPS measurements were performed with a Kratos Analytical Axis Ultra DLD. pH measurements were performed using an Oakton pH 110 series pH meter.
Modeling of the heliostat field and the particle-in-tube solar receiver The models of the solar field and the receiver are presented together because they are strongly linked. The solar field is designed using SolarPILOT, a software from NREL  [30] , with a design power, depending on the desired particle outlet temperature (750°C or 880°C). The cavity receiver consists of absorber tubes at the back positioned on a curved line when looking at the top view. The tube number and geometry are detailed in section 3. 3. The cavity is made of five refractory panels and the aperture has a 5. 5 m height and a 6 m width, with an angle of 20° relative to the vertical plane. To avoid any deterioration of the receiver due to uneven flux distribution leading to thermal hot spots and unacceptable stresses  [31] , an aiming point strategy is applied using the heliostats location optimized in SolarPILOT.