dataesr/scientific-paragraphs-categorization huggingface dataset:
The water flow rate supplied to the pads is determined based on the technical data provided by the manufacturer. It is shown that pads' outlet temperature increases with the increase of fans rotation speed. The total growth is of 0. 6 K whereas the relative humidity tends to decrease about 9%. Moreover, the efficiency is higher at low speeds due to the better contact of air with pads' water, providing the evaporation process sufficient time to saturate air. At fan speed 50% RPM, cooling pad's efficiency is 97% while by increasing the fans speed to 100% it will be decrease to 88%. Several external factors may impede the operation of EC using cellulose pads. That is, dirt or leaves could be sucked by the dry cooler's fans reducing the suction surface area. To simulate the impact of such conditions, especially leaves, a test simulating the suction of 20 leaves is conducted.
The EXCEM analysis was developed by Rosen and Dincer  [51]  to enhance the process simulator software Aspen Plus by the exergy approach. The analysis is used to assess the process and system and is based on the four quantities, as indicated in the name, which are exergy, cost, energy and mass  [51]. The main principle of the EXCEM analysis is that mass and energy flows are conserved, and exergy and cost flows decrease and increase, respectively, or remain constant  [51]. The widely used and general methodology of exergy cost accounting is the SPECO analysis proposed by Lazzaretto and Tsatsaronis  [52]. The name of the analysis indicates the Specific Exergy Costing, and it is used systematically to determine exergy efficiencies and costs per exergy unit in energy systems  [52]. The main principle of the SPECO is to define exergy fuel(s) and product(s) of each system component.
The measured weather and operational data were used as input for the PVT and PV models in TRNSYS simulation software to perform annual dynamic analysis. Finally, the PVT resulted in a lower levelized cost of exergy than the PV system, especially if the battery storage was connected to the system. Mourshed et al. [60]  applied the energy and exergy analysis to the small flat-plate PVT collector presented in Figure  3 , which was simultaneously cooled by water and air. They used an experimental set-up of the PVT collector to conduct the analysis. The authors conducted a semi-steady-state analysis for daily operation and used the ambient temperature of the collector as a reference temperature. They resulted in a maximum thermal exergy efficiency of 2. 89%, of which 0. 64% was covered by air cooling and 2.
The dynamic hourly simulations of the system were performed by a model developed into TRNSYS simulation software. In addition to the exergy analysis, the exergy cost analysis was conducted. The authors used the soil temperature around the system as a reference temperature for the exergy analysis, and the analysis was performed on a monthly and yearly basis. The results of  [61]  presented the monthly exergy fuel, product and irreversibility associated with different system components. The monthly exergy efficiency of the PVT Martínez-Gracia et al. [61]  presented an exergy assessment of a case study including PVT collectors, a solar-assisted heat pump and seasonal thermal storage. The real installation of the system was realized under the weather conditions of Zaragosa, Spain, to provide electricity, heat and DHW for a social housing building.
The dynamic hourly simulations of the system were performed by a model developed into TRNSYS simulation software. In addition to the exergy analysis, the exergy cost analysis was conducted. The authors used the soil temperature around the system as a reference temperature for the exergy analysis, and the analysis was performed on a monthly and yearly basis. The results of  [61]  presented the monthly exergy fuel, product and irreversibility associated with different system components. The monthly exergy efficiency of the PVT field varied between 13% in July and 17% in April. The highest thermal exergy production was in May due to cooler ambient temperature than during the highest solar fuel months from June to August. The PVT field had the lowest unit exergy cost of approximately 6 due to irreversibility because of its position at the beginning of the system.
The experimental data were used to validate a dynamic TRNSYS model of the micro-CHP system to conduct the analysis. They compared the performance of the cogeneration system to a reference system including the electric grid connection for electricity and a gas boiler for heat production. Additionally, 20% renewable energy production was added to the reference system. As comparison indicators, the energy-based PES and exergy-based RAI, introduced in Section 2. 4, were used. The authors selected the reference temperature to be constant at 10 • C to calculate the thermal exergy production of the micro-cogeneration cooling flow. The results of  [62]  showed a maximum overall exergy efficiency of 33% at full load operation and with an inlet temperature of 60 • C. At the same operation point, the thermal exergy efficiency was 9% and the electrical 24%.
The system model was built into a Matlab/Simulink environment. The analysis was conducted under three different European climates and economic conditions in Tampere, Finland; Strasbourg, France; Barcelona, Spain. The reference building was simulated by IDA ICE software, and the location-specific building construction was considered in defining the U-values of the building envelope. The reference temperature was selected to be the minimum monthly temperature in each location. The results were presented on an annual and monthly basis, and the SPECO method was used in the exergo-economic analysis by taking into account the energy levels of the products. In this study  [9] , the annual overall exergy efficiency varied from 13% to 16% depending on the location. The PVT collectors caused the highest exergy destruction during the summer, and the micro-cogeneration unit the winter.
A double-input single-output DC-DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC-DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting.
As part of intermittent renewable energy contribution in the energy mix, the electrical grid operator requires production predictions to achieve high performances, efficiency, and a secure electricity supply system. In summary, power production approaches can be classified into various categories based on mathematical, statistical, physical, artificial intelligence (AI), and hybrid. Advanced approaches, such as artificial neural networks (ANNs), are used in AI methodologies to anticipate energy output and demand variations for providing flexibility to the electrical systems  [36]. This method is used to identify data models from learning datasets. However, the necessity for a large amount of historical data to develop the ANN and anticipate electricity generation is a significant drawback  [37].
Data Preprocessing The dataset used in our work was collected from two sources. For the PV generator, the data were obtained from the Photovoltaic Geographical Information System (PVGIS) website, and the battery's SOC was obtained from the registered data on the evolution of the SOC of a battery in our laboratory throughout a year. After the collection of data, we were interested in the preprocessing step. Indeed, because the gathered data tend to be partial and incoherent, preprocessing seeks to eliminate noise, fill in missing values, and rectify inconsistencies in the data. Therefore, we utilized the simple moving average (SMA) technique to fill in the missing information. Then, we used the Z score to normalize the data, which seeks to place values within specific ranges and accelerate the model's execution.
2. Train and Test of the LSTM Model The dataset was split into two parts: training and testing. In this work, as an example, to forecast 24 h of PV power and battery 'SOC', the training group consisted of 75% of the whole dataset. This percentage was chosen to produce accurate predicted values. Indeed, the prediction model was built on the training group. The remaining 25% of the dataset was designated as the test group for model evaluation. For the prediction of PV production and SOC, we trained the LSTM model using a single hidden layer that includes 80 and 100 hidden units, respectively. We employed the ADAM optimizer, which is one of the optimization strategies used in deep learning. The learning rate was 0. 01 and decayed every 50 epochs.
General All chemicals were purchased from Sigma Aldrich, TCI Chemicals, VWR International or Chemie Brunschwig AG and used without further purification. To avoid variations in degree of degradation, solutions were always freshly prepared before experiments and/or analysis unless noted otherwise. UV-Vis absorption spectra were collected using an Agilent Cary 5000 or Perkin Elmer Lambda 35. The spectra were measured in 70 µl UV-microcuvettes (BRAND®), unless specified otherwise. The pH was measured using Mettler Toledo pH electrodes LE438 (polyester junction, 12 mm diameter) or LE422 (ceramic junction, 4. 3 mm diameter). pH data was recorded using an Oakton pH 700 Benchtop Meter and logged every 3 seconds by the CyberComm 2700 computer software. Nuclear Magnetic Resonance (NMR) spectra were measured on a Bruker 400 MHz spectrometer.
Chemical stability To study the chemical stability of MCH in different solvents, we prepared the following solutions: 0. 2 mM in D2O, 5. 3 mM in xd6-DMSO = 0. 15 and 17. 5 mM in pure d6-DMSO. The samples were measured using amber NMR tubes (5 mm, Norell™) that prior to each use were cleaned with water, acetone, and deuterated solvent and dried in a vacuum oven. Assignment of the characteristic peaks (a, b', c, c') was done in accordance with a prior study by Berton et al. 13  We tracked the degradation of the photoacid samples using positions of the (CH3)2 singlet NMR peaks, denoted as c, c*, or c' (Figure  2 , Supplementary Figure  S4 ). We also measured a reference spectrum of salicylaldehyde (D1) in D2O to reference the position of the d*. All data was processed and analyzed using the MestReNova software.
Simulation methodology Molecular dynamics simulations of periodic cubic boxes were performed in OpenMM software  67. Each system contained one solute molecule (merocyanine or spiropyran) placed at the center of a box and surrounded by a sufficient quantity of solvent to approximate infinite dilution. The total number of solvent molecules was set to 2000, and the molar fractions of DMSO, xDMSO = 0, 0. 05, 0. 15. 0. 3, and 1 were considered. Initial configurations were generated using PACKMOL  68  , and the input files were prepared with fftool  69  and polarizer 70 utilities. A cut-off of 12 Å was set for non-bonded interactions, with the tail correction applied for energy and pressure. Particle Mesh Ewald (PME) method was used to evaluate electrostatic energies, with a tolerance of 10 -5. Bonds involving hydrogen atoms and the H-O-H angle of water were constrained using SHAKE algorithm.
ACKNOWLEDGMENTS A. D. V. thanks  Dr. Bruno Marco-Dufort  for his advice on optimizing synthetic procedures and scientific discussions. M. R. L. acknowledges support from  ReMaP  project funded by the  Swiss Federal Office of Energy  under contract  SI/501810-01  and the  ETH Foundation. K. G. and M. S. acknowledge the funding from  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  771294 ). MD simulations were performed using resources from  GENCI-IDRIS  (grants  2021-A0100910463  and  2022-A0120910463 ).
This investigation was based on the RETScreen dimensioning tool after defining the adequate load profile and taking into account the real geographical and climatic parameters. (Imam et al. 2019 ) used the System Advisor Model software to carry out a technical-economic feasibility analysis of a grid-connected residential photovoltaic system for a typical apartment in Saudi Arabia, based on various key performance indicators, at namely: efficiency, capacity factor, performance ratio, levelized energy cost, net present value, internal rate of return and payback period. Bourahla et al. (2019)  analysed the economic impact of photovoltaic energy production by a future campus microgrid for the University of Science and Technology (USTO) in Algeria. The feasibility analysis made it possible to determine the number of years of recovery, the internal rate of return of 5% and the profitability index.
Solar energy production and economic evaluation are analysed in the work of  (Yaniktepe et al. 2017) , using daily solar radiation and average temperature data which are measured for three years in Osmaniye province in Turkey. The economic values of the study such as Net Present Value (NPV), Internal Rate of Return (IRR), payback year, and simple payback period were evaluated. Osmaniye has proven to be a considerable region for photovoltaic (PV) investment in power generation where investment of a PV is applicable. Hussein and Jasim (2019)  assessed the feasibility of building a 10 MW solar PV power plant in Iraq from an economic and environmental perspective. Apart from the economic parameters (discounted energy cost analysis), the cash flows (preventive maintenance, servicing. ) over the life cycle of the power plant and the net present value of the plant were assessed.
2023). The Electrical Transient Analyzer Program (ETAP) software is used to evaluate the operational performance of the hybrid system, such as bus voltage profiles and active and reactive power losses. This study revealed that the PV-wind-diesel system is the optimal energymix hybrid microgrid for the rural area of Al-Dhafrat in Oman. Further technical, environmental, and economic feasibility studies were conducted for the construction of a 5 kW PV power plant in a city in northern Iran (Sari) using the RETScreen software developed by Natural Resources Canada by  (Semeskandeh et al. 2022 ). The proposed system includes a two-stage multi-chain inverter with ZETA DC-DC converter and a modified P&O algorithm. A techno-economic analysis of a solar photovoltaic mini-grid system for five typical rural communities in Chad while promoting renewable energy system adaptation and rural electrification was presented by  (Hassanea et al.
2022). The system development costs, electricity tariff, and power generation sizing are then performed by the authors via the discounted cost of electricity (LCOE) technique. These various works show the interest given by the authors to the technical-economic and environmental feasibility study of a source of solar hybrid or renewable energy production with a view to its use to ensure quality energy autonomy for populations at various levels. The various technical, economic and environmental parameters necessary to properly carry out this studied have been highlighted. Among the software used, RETSreen is the most appropriate and easy-to-use tool for the feasibility analysis of clean energy systems  (Sanwogou et al. 2019). However no investigation has been conducted to reduce energy shortage in official buildings in Benin, nor to apply hybrid solar and quantify the techno-economical and environmental benefit of such approach.
Data collection To record the data, we used:  The GPS Test application is a reliable android application that consists of five (5) tabs: the first tab is for basic information about a GPS connection, the second tab has the same functions, except that the satellite is represented by a compass, the third tab displays the coordinates on a world map, it tells us what part of the globe we are on, the fourth tab allows us to determine our altitude, the last tab concerns information relating to GMT dates and times, local times and dates as well as sunrise and sunset times.  Google Earth: It is a software developed by Google + allowing users to browse the earth by satellite images, in 2D or in 3D. It is also possible to carry out research on the various places of a city (restaurants, hotels, etc.
Methods In this section, the methods and assumptions used to carry out the technical-economic and environmental study are described. First, we will describe what a solar-conventional hybrid system is in backup mode. Then the different consumption scenarii are presented. And finally the approach used to size the PV generator is then explained. As the system operates under several uncertain economic parameters, this study includes an economic feasibility analysis using RETScreen software to study the financial viability of the different scenarios. The applied same software generated the environmental benefits of the project.
Analysis of the economic feasibility and environmental impact of the solar generator During the development of a photovoltaic solar project, the economic analysis is very important to decide on its economic and ecological viability  (RETScreen International 2006; Owolabi et al. 2019). The hardware used for the economic analysis in this study is the RETScreen Expert-Professional-6. 0. 7. 55 software. It is a standardized clean energy management software developed by the Government of Canada for the feasibility analysis of renewable energy projects and the analysis of energy performance.
Overview of RETScreen Expert RETScreen International is an innovative and unique tool for raising awareness, decision support and capacity building in the field of renewable energies. It is developed and maintained by the Government of Canada, through the CANMET Energy Diversification Research Laboratory (CDRL) of Natural Resources Canada, whose center is located in Varennes, Quebec  (Mehmood et al. 2014; Thevenard et al. 2000). The software is the result of the contribution of more than 307 networks of experts from industry, government and academia, with the collaboration of organizations such as the National Aeronautics & Space Administration (NASA), the Renewable Energy and Energy Efficiency Partnership (REEEP), United Nations Environment Program (UNEP), Global Environment Facility (GEF), World Bank Prototype Carbon Fund (PCF), Energy + Environment Foundation and Leonardo Energy Initiative (RETScreen 2022).
Apart from the latest version of the software, known as RETScreen Expert, all other versions are readily available for free public use without any cost implications for the feasibility analysis of clean energy projects, including energy efficiency technologies and renewable energy projects such as solar photovoltaic, wind and hydroelectric projects (RETScreen 2022). The software can be used for feasibility studies of energy projects, performance evaluation of new projects and modernization projects, as well as monitoring and evaluation of existing projects. Users can select each technology project based on the objective of their feasibility study or performance evaluation. The software helps decision makers assess the potential of certain energy projects in order to implement them quickly and inexpensively.
Performance analysis The performance analysis spreadsheet allows the user to monitor, analyze and communicate key energy performance data to operators, managers and high-level decision makers, including actual energy performance of the installation compared to the expected performance. The spreadsheet uses advanced regression and prediction models that take into account normalized energy performance based on variable parameters, such as meteorological data obtained from NASA, to track a facility's actual energy performance against its performance. predicted (RETScreen 2022). As part of this study, the "Feasibility Analysis" worksheet was considered. The financial analysis spreadsheet of the software contains financial parameters such as inflation rate, discount rate, reinvestment rate, debt ratio, debt interest rate as variables input and automatically calculates key indicators of financial viability, such as internal rate of return, simple return, net present value, etc.
Conclusion In this study, an in-depth feasibility analysis was carried out to assess the viability of installing a back-up system in Beninese jurisdictions by analyzing the technical, financial and environmental impact of the project. Solar installation will serve to reduce Kandi Court's electricity bill costs and greenhouse gas emissions. NASA climate data for Kandi and financial parameters such as reinvestment rate, inflation rate and discount rate were used for financial analysis as input variables in RETScreen Expert software. The sizes of the components of the photovoltaic system were calculated from the analytical formulas. The software accurately calculated financial viability indicators such as net present value, payback time, internal rate of return and annual savings not to mention the amount of greenhouse gases reduced by the project.
Besides, after the PR reaction, the remaining weight of the precipitants was measured to be ca. 50 mg, indicating a conversion rate of ca. 95% according to the following calculation: Conversion rate M1-M2 M1  (Hu et al. , 2022; Wang, et al. , 2022) , where M 1 is the weight of reactants before PR, and M 2 is the weight loss of reactants after PR. Furthermore, all the diffraction patterns from the PET precipitants were almost eliminated in this stage (Figure  2E ), suggesting a successful decomposition of PET compounds. Further illumination conducted no product change and we found that the hydroxyl radical generation from PdO x was terminated after 4 h PR reaction (Figures  5A, B ) by electron paramagnetic resonance (EPR), meanwhile, the valence state of Pd exceeded the highest value recorded in the database.
Funding The authors declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the  Key-Area Research and Development Program of Guangdong Province  (Grant No. 2021B0101310003 ),  Guangzhou Basic and Applied Basic Research Foundation  (Grant Nos. 202201010440  and  2023A03J0024 ),  Guangdong Junior Innovative Talents Project for Ordinary Universities  (Grant No. 2021KQNCX018 ), the  National Natural Science Foundation of China  (Grant No. 62074060 ),  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2020B1515020032 ),  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2022A1515140064 ).
Review Protocol The hybridisation of renewable sources (especially PV-wind) is a relatively new research field but has reached a very advanced technological level. In this paper, a systematic search of the scientific literature was performed to cover all aspects and trends of research on PV-wind hybrid systems. The main sources of information for providing an overview of these systems were well-known journal databases. The databases used were Web of Science, Science Direct, Springer, Wiley Online Library Journals and eBooks, IEEE-Xplore, Taylor and Francis, INSPEC, ACM Digital Library, and some specialised MDPI journals, among others. As the research field of PV-wind hybrid systems is vast, thousands of scientific papers have been published in the last twenty years. Some criteria were adopted for the initial search using specific keywords (such as PV-wind hybrid systems and PV-wind energy management) in order to filter the preliminary results.