dataesr/scientific-paragraphs-categorization huggingface dataset:
Sizing of the hydraulic circuit So as to size the hydraulic system, namely: piping, flow diverters, tempering valves, valves, pumps. , we need the total flow rate of the working fluid (water in our case). The flow rate is given by the following expression: A q Q collector total × = where collector q is the flow rate of the working fluid per square meter of collector. In our case, we set. Once flow rate is calculated, PREVIS code proposes the possible configurations of our installation. So that all the solar collectors work in the same conditions, it is necessary that the route of the water is the same for each of them. The recommended assembly is said Tickelman's buckle assembly. Its principle is illustrated on the figure  3.
CONCLUSION AND PERSPECTIVE In this paper we were interested in the sizing of a solar installation of hot water production in favour of the university campus of Tangier. Such installation is successful only if its determination, its conception and its realization are made with care. We have used the dynamic simulation code (TRNSYS) to investigate our solar plant for hotwater production and we have developed software which allows us to determine the optimal sizing of the solar installation of hot water production. This software that we baptized PREVIS 1. 0 supplies, for fixed operating conditions, the dimensions of each component of the installation without having to use tables of data or diagrams. PREVIS 1. 0 will allow the fitters of solar installations of hot water production to make a good sizing in a simple and precise way.
This paper proposes a modular modeling towards a health system integration of fuel cells by considering not only the dynamics of the gases but also fault models that affect the PEMFC performances. The main goal is to simulate the faulty state in order to overcome data scarcity, since running a fuel cell to generate a database under faulty conditions is a costly process in time and resources. The degradation processes detailed in this paper allow to introduce a classification of faults that can occur, giving a better understanding of the performance losses necessary to simulate them. The faults that are detailed and modeled are the flooding, drying and aging processes. This modeling is based on a system approach, so it runs faster than real-time degradation tests, allowing the training and validation of online supervisors, such as the energy management strategy (EMS) method or diagnosis.
This is due to the inertia of the compressor and results in more or less abundance of reactant in the FC. The hysteresis effect modeling is detailed in  [46]. The equations governing the pressure and gas flow variations are also detailed in their paper. The voltage variation comes from the OCV part, which is a function of the gas pressure, as described in Equation (3). To fit more with an actual state-of-the-art FC, the parameters used in this article are fitted with open databases. The dataset used comes from  [48].
Our study proposes to model the irreversible degradation of the FC with the data and coefficient used by Pei et al. [15]  and integrates them into the ohmic losses, which results in V ohm,ageing = r ohm • i • (1 + k deg • α degradation ) (8) where k deg is a degradation coefficient which must be fitted for each FC. The resistive term r ohm is the resistance of the membrane and electrodes that is detailed in the next section. α degradation = k high. load δt hl + k low. load δt ll + k cycling n c +k start/stop n s/s + k operation t op where δt hl and δt ll , respectively, are the times spent at high load and low load, n c is the number of high/low load cycling, n s/s is the number of fuel cell start and stop and t op is the FC global operating time. Here, all the coefficients are taken from  [15] , with an additional coefficient, k operation , which represents the natural degradation of the cell which, in this paper, is fitted on the database from the IEEE challenge  [50] , where the operating load of the fuel cell is rather constant.
Plasmid construction and generation of transplastomic lines The P. aeruginosa (PAO1) rhlA gene sequence (Pseudomonas Genome DataBase code PA3479) fused to a hemagglutinin (HA) epitope YPYDVPDYA was codon optimized according to the codon usage of C. reinhardtii chloroplast genome, and a synthetic gene was obtained from GeneArt (Thermo Fisher, Waltham, USA). The psaA promoter fragment was amplified from pSRSapI vector  [45] , obtained from the Chlamydomonas Stock Center. The rbcL terminator fragment was amplified from the pLM20 vector  [46] , kindly provided by G. Allorent (CNRS-CEA Grenoble). Primers listed in Suppl. Table  S1  were used to amplify psaA promoter, rhlA-HA gene fusion, and rbcL terminator fragments by PCR. pLM20 vector was digested with EcoRV and SmaI restriction enzymes, and the four pieces were assembled by Gibson Assembly (New England BioLabs, Ipswich, MA, USA) and transformed into E.
Acknowledgements This work was supported by the  Spanish Ministry of Science and Innovation (MICINN)  [grant  PID2019-104350RB-I00 ] (to A. P. ); the  AGAUR agency of Generalitat de Catalunya  [grant  2017SGR-727 ] (to A. P. ); and the  Ramon Llull University / Obra Social la Caixa Grants  (to P. L. ). Work at UMR7141 in Paris was funded through the  "Initiative d ′ Excellence" program  (Grant " DYNAMO ,"  ANR-11-LABX-0011-01 ). Work at CRAG was funded by the  FEDER / Ministerio de Ciencia ,  Innovacioń y Universidades -Agencia Estatal de Investigacioń  [Grants  PGC2018-099987-B-I00  and  PID2021-122288NB-I00 ], the  CERCA Programme / Generalitat de Catalunya  [Grant  2017SGR-718 ], and the  Spanish Ministry of Economy and Competitiveness / "Severo Ochoa Programme for Centres of Excellence in R&D"  [Grant  CEX2019-000902-S]  (to E.
In a solar micro-grid, a hybrid renewable energy system generates electricity for a building's onsite use. The battery storage and the main power grid connection are used to facilitate the matching between the demand and production. To control energy flows optimally, an accurate day-ahead prediction of the photovoltaic (PV) panels output is required. However, this is a challenging task due to the fluctuating nature of solar radiation availability. The accuracy of the prediction is influenced by the modelling method and input parameters. In this study, the measured power and weather data is gathered from an experimental installation of PV panels to predict PV output for a 24-hours horizon in 15 min intervals. The multiple linear regression (MLR) and artificial neural network (ANN) methods are considered in the prediction modelling and compared using performance indicators.
Kalogirou  [7]  conducted a comprehensive review on the ANN method in renewable energy systems considering PV systems. Zazoum  [8]  compared different ML techniques for PV power prediction. He concluded that the ML methods are suitable and fast to predict the PV power of any system. The scientific literature has aimed to improve the accuracy of the PV power forecasting models in terms of different prediction algorithms and input data. Different sets of weather and PV operation parameters have been investigated to increase the accuracy  [9]. However, to the best of the author's knowledge, there is no work focusing on the different ML methods using as an input parameter the PV power data measured by the individual microinverters to increase the accuracy of the day-ahead power prediction. In this study, the MLR and ANN prediction models are built for each PV panel and the overall system using the input data measured from the micro-inverter and aggregated system level.
Methodology A comparative study of different PV power prediction models is conducted. First, the experimental set-up of PV panels is presented with data gathering methods. Next, the monitored data is pre-processed and used to create different data-driven models for the PV power prediction. The first method is a multiple linear regression (MLR) with interaction terms which is compared to a method called Artificial Neural Network (ANN). Both models are regression models, and the monitored data from the PV installation and weather station are used to train and validate the models in the MATLAB environment. The training data was collected from 27. 9. 2021 to 31. 3. 2022 and includes large variation of different weather conditions. The different prediction models are built by using as input the weather data and power output data from the micro-inverter and aggregated system level.
However, the micro-inverter technology enables the independent operation of each panel and partial shading has an impact only on the shaded panels. Using the micro-inverter technology has also several other advantages over traditional inverters discussed in  [11]. The four micro-inverters are connected to a gateway which is used to communicate measured system data to the web-based monitoring and analysis software provided by Enphase company  [12]. The power output of each panel (P inv,1-4 ) and the whole system (P agg ) is measured every 15 min and uploaded into Excel file from the online software. The installation includes also Lithium-Ion AC battery storage to store the PV production if not used directly in the building. The optimal battery use is managed by the gateway. In addition to the PV panels, the installation includes a weather station to measure the local weather parameters global horizontal irradiance (GHI), ambient temperature (T a ), dew point temperature (T d ), pressure ( p at ), wind speed (WS) and relative humidity (RH) in 15 min intervals.
Artificial neural network model The ANN algorithms are commonly used to solve time series prediction tasks because they have the ability to learn the behaviour of variables from historical measurements, such as PV power output and weather data in terms of the PV prediction modelling  [13]. The ANN is based on supervised learning, and the prediction performance increases with the amount of historical data used in the training of the model. In addition to the training dataset, a part of the historical dataset is used only for the test and validation of the model to show the generalization capability of the prediction model. The learning or training process is based on continuously improving the weights and biases within the neural network to reach the desired output, which minimizes MSE in Eq.
(  2 ) (or RMSE in Eq. (  3 )) between the actual and predicted output. This is performed by using a learning algorithm, such as gradient descent, error back-propagation or Levenberg-Marquardt  [13]. The back-propagation algorithms are the most used and powerful in terms of learning  [14]. In this study, the MATLAB software is used to train the ANN model and the limited-memory Broyden-Fletcher-Goldfarb-Shanno quasi-Newton (LBFGS) optimization algorithm is used to minimize the MSE  [15]. The number of inputs to the ANN model indicates the "neurons" in the input layer presented in Fig. 2. The layer structure includes the input layer, a certain number of hidden layers with hidden neurons and the output layer. In the case of PV power prediction, the input layer includes the selected weather variables (x 1 , x 2, x 3.
Multiple linear regression model performance To predict the PV power, an MLR model was trained in the MATLAB software environment. The performance indicators presented in Section 2. 5 were used to evaluate the accuracy of the model. First, the regression model was trained with the weather data and the aggregated PV power output of the system. Next, the PV power output data of each micro-inverter was used to predict the output of each panel. The predictions of each panel were summed up to have a prediction of the whole system. The performance of the prediction models generated by the aggregated PV output data and by the individual micro-inverter data was compared. The overall performance results in Table  2  show that the MLR, which was based on the micro-inverter data, showed better performance with 13.
86 MAE, 1438 MSE, 37. 92 RMSE and 0. 938 COD than the model based on the aggregated PV power data with 16. 32 MAE, 2160 MSE, 46. 48 RMSE and 0. 907 COD. The results revealed that a more accurate prediction model was achieved if the micro-inverter data was used first to model each panel output and then summed up to have PV power prediction for the whole system. This method can help to predict more accurately the PV power of the system if all the panels are not positioned equally to the sun during the day, for example, if some shading occurs. The MLR model based on the micro-inverter data was used to predict the PV power for the representative day in April, shown in Fig. 3a. This day was not used in the training dataset. Fig. 3b  presents the correlation between the actual and predicted values.
Artificial neural network model performance The same dataset, as for the MLR model, was used to train an ANN model. Again both aggregated and microinverter based PV power data were used to train the model to see if the micro-inverter data can be used to have a more accurate prediction model for the installation. The overall performance results in Table  2  show that the ANN model, which was based on the micro-inverter data, showed again better performance with 9. 87 MAE, 930 MSE, 30. 497 RMSE and 0. 9596 COD than the model based on the aggregated PV power data with 13. 34 MAE, 1517 MSE, 38. 96 RMSE and 0. 935 COD. However, in both cases, the ANN model showed the better overall performance than the MLR models. The ANN model based on the micro-inverter data was used to predict the PV power for the representative day in April, shown in Fig.
Conclusion In this paper, the PV panel installation using micro-inverter technology was realized in INSA ICUBE Laboratory in Strasbourg to build a PV power prediction model using two different methods: multiple linear regression and artificial neural network. The weather station was used to collect the weather data. The correlation analysis was used to select the most significant weather parameters correlating with the PV power output. The parameters were the ambient temperature, relative humidity, pressure, wind speed and solar irradiation. In addition to the two different modelling methods, the prediction models were built using two different approaches to collect the PV power output data from the installation. First, the aggregated PV power was used in the modelling process. Next, the PV power data, detected by each individual micro-inverter, was used to model each PV panel output to get a more accurate model of the whole system.
The results showed that the overall performance of the ANN models was better than the MLR models resulting in lower MAE, RMSE and MSE and higher COD. Additionally, the results revealed that building a model of each panel by using the PV power data from the micro-inverters resulted in more accurate prediction models. Due to this, it is recommended to use the micro-inverter technology in the PV installations to facilitate the prediction of the system output power. For future work, the PV prediction model based on the ANN and micro-inverter data will be enhanced by collecting more data from the installation. The comparison between different models will be extended with other methods, such as Support Vector Machine, Fuzzy prediction and Regression Tree. Additionally, the prediction model will be used in the model predictive control of a hybrid renewable energy system.
Characterization of the Membrane Properties 2. 3. 1. Structural Properties SEM JSM-6400 (JEOL Europe, Croissy sur Seine, France) coupled with X-ray energydispersive spectrometry XFlash 6130 EDX (Bruker, Billerica, MA, USA) and the ES-PRIT 1. 9 software was used to characterize the membrane morphology and to analyze the presence of NPs and Na and Cl elements on/in the membranes. For the cross-section analyses, samples were broken using liquid nitrogen and were observed via SEM/EDX after carbonization. The membrane thicknesses were measured using an electronic micrometer (Schuts Model-134001), and the porosity was measured using a solvent method (ethanol) with a balance (Sartorius, Göttingen, Germany, 0. 01 mg).
Wettability Indicators The contact angle and the liquid entry pressure are the main parameters used to characterize the membrane wettability, which characterizes the risk of wetting. The contact angle characterizes the membrane surface hydrophobicity. The contact angle of the prepared membranes was measured using a drop shape analyzer (DSA25, Kruss, De (https://www. kruss-scientific. com/en, accessed on 12 July 2023)) and then analyzed using the ADVANCE software (https://www. kruss-scientific. com/en/productsservices/advance-software/advance-drop-shape, accessed on 12 July 2023). The liquid entry pressure of pure water (LEP w ) is defined as the minimum hydrostatic pressure that must be applied to a membrane before water penetrates the largest pore of the membrane by overcoming the hydrophobic forces; this pressure can be detected on the permeate side  [24, 25].
To do so, in addition to the model described above, calculations using a second model, named "conduction-only" are performed. In this model, only the heat transferred by conduction in the liquid PCM is considered. Therefore, the sole equation to solve is the energy equation (Eq. (  3 )) where u = 0. Initially, both PCM and finned heat transfer tube are at the same temperature of 290 °C. A fixed and uniform temperature of 315 °C is imposed on the inner tube wall. Hexahedron elements are adopted to discretize the finned tube and PCM domains (Fig. 1 (c) ). The Computational Fluid Dynamics (CFD) simulations are performed with ANSYS Fluent software. PISO scheme is adopted for the pressure-velocity coupling; a second-order upwind scheme is used for the convective terms in the momentum and energy equations and a least-square cell-based scheme for the diffusive terms.
⋆ This research work has received support from the  ANR  Projects  Ctrl-AB  ( ANR-20-CE45-0014 ),  PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) and  ANR Project Maximic  ( ANR-17-CE40-0024 ). ⋆ This research work has received support from the  ANR Projects Ctrl-AB  ( ANR-20-CE45-0014 ),  PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) and  ANR Project Maximic  ( ANR-17-CE40-0024 ). ⋆ This research work has received support from the  ANR Projects Ctrl-AB  ( ANR-20-CE45-0014 ),  PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) and  ANR Project Maximic  ( ANR-17-CE40-0024 ). ⋆ This research work has received support from the  ANR Projects Ctrl-AB  ( ANR-20-CE45-0014 ),  PhotoBiofilm Explorer  ( ANR-20-CE43-0008 ) and  ANR Project Maximic  ( ANR-17-CE40-0024 ).
Acknowledgments: A. Higareda acknowledges CONAHCYT for the postdoctoral research fellowship 2022  (1) , application number 2415291. Diana L. Hernández-Arellano acknowledges CONAHCYT for the doctoral research fellowship, number 814166. N. Alonso-Vante acknowledges financial support from the European Union (ERDF) 'Région Nouvelle Aquitaine'.
Consequently, renewable energy resources (for example wind and solar) based off-grid become the most convenient solution to electrifying these areas  [3] [4]  [5]. In this paper, a hybrid solar-wind system is proposed to ensure a more stable energy-generating system. The proposed system ensures clean, reliable, and cheap energy on an annual basis. The system can adapt to the unpredictability of weather conditions and temperatures with two energy sources that can complement each other  [3][6]. In other terms, the complementary nature between solar energy and wind energy. For example, there is high solar irradiation and relatively low wind energy in the daytime, while there is high wind energy but little irradiation at night  [7]   [8]. The research includes using variable weather conditions and temperatures which are simulated in 144 MODELLING AND SIMULATION OF THE HYBRID SYSTEM PV-WIND the MATLAB/Simulink environment to evaluate the efficiency of the system.
Acknowledgements J. L. acknowledges  CEA  for funding through a  CFR  PhD grant. R. D. , V. M. M. , and A. J. R. E. acknowledge the  European Research Council (ERC)  for funding. This work was funded under the European Union's  Horizon 2020 research and innovation programme  (grant agreement number  832606 ; project  PISCO ). J. A. A acknowledges the  Ministerio de Ciencia e Innovación of Spain, Agencia Estatal de Investigación (AEI)  and  EU (FEDER)  under grants  PID2019-110430GB-C22  and  PCI2019-111839-2  (SCALEUP). A. J. R. E. thanks the  Spanish Ministry of Education, Culture and Sports  for its supports via a PhD grant ( FPU2017-03684 ) and  Estancias Breves FPU 2021. R. D. thanks  Dr. P. Maldivi  and  Dr. C. Aumaître  for helpful discussions and  O El-Dahshan  for his help in the editing the final text.
Yield of pyrolysis Purity & homogeneity of the carbon phase Physisorption of nitrogen at 77 K was performed with a Micromeritics ASAP2020 adsorption apparatus. The samples were outgassed for 12 h at 300 C before analysis. Specific surface areas were calculated using the BET (completed with the Rouquerol correction) and the 2D-NLDFT models. The micropore volumes and pore size distributions were obtained using the 2D-NLDFT model: this model can actually avoid overestimation of the specific surface area by the BET model for microporous solids. 2D-NLDFT calculations were carried out using the software SAIEUS, with a corrugation parameter l ¼ 4. 25 and an integration range from 0. 4 nm to 29. 3 nm. Thermogravimetric analysis (TGA) was carried out under dry air in a Setsys Evolution Setaram thermobalance.
Characterization Structural characterization of the obtained samples was performed by X-ray diffraction (XRD) using a Rigaku Ultima+ powder diffractometer with Cu-Kα radiation. During measurements the instrument used a step size of 0. 05 o , a step time of 2 s, a voltage of 40 kV and a current of 30 mA. Existing phases were identified after comparison of the obtained diagrams with the powder diffraction files (ICDD-PDF) database  [32]. Quantification of determined phases in XRD diagrams was performed following the Rietveld refinement method using the Fullprof software  [33]. The morphology of nanopowders was visualized by field-emission scanning electron microscopy (SEM) taken in a Quanta 200 ESEM FEG FEI microscope. The field-emission gun operated at 15-30 kV and the elemental analysis of the observed areas was supported by an energy-dispersive X-ray spectroscopy (EDS) analyzer of EDAX company equipped in the microscope.
For higher magnifications of the nanoparticles, transmission electron microscopy (TEM) images were taken using a JEM-1210 (JEOL) microscope, operating at 120 kV. Samples for TEM analysis were prepared by the deposition of a drop of the particles dispersion in isopropanol onto a carbon coated copper grid. For the determination of the hydrodynamic size in the range 1-10000 nm, a Horiba nanoPartica SZ-100V2 nanoparticle analyzer was used and data were fitted by a log-normal distribution function. The magnetic properties of Fe-loaded adsorbents were evaluated by using a vibrating sample magnetometer (VSM) Oxford Instruments 1. 2H/CF/HT, operating at room temperature. Hysteresis loops were measured with a varying magnetic field in the range ±1 T. Saturated adsorbents were analyzed by X-ray photoelectron spectroscopy (XPS) to identify the occurring adsorption mechanism on the solid's surface.
Funding We thank the  CNRS-PROMES laboratory , UPR 8521, belonging to the  French National Centre for Scientific Research (CNRS)  for providing access to its installations, the support of its scientific and technical staff, and the financial support of the  SFERA-III  project (Grant Agreement No  823802 ). This work was supported by the  French "Investments  for the future" program managed by the  National Agency for Research , under contact  ANR-10-EQPX-49-SOCRATE  ( Equipex SOCRATE ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101007417 , having benefited from the access provided by  CSIC-ICMAB in Barcelona  within the framework of the  NFFA-Europe Pilot Transnational Access Activity , proposal  ID262.