dataesr/scientific-paragraphs-categorization huggingface dataset:
In other words, eigenvectors with similar directions are positively correlated; eigenvectors with opposite directions are inversely correlated, while eigenvectors with an angle around 90° are independent (cosine → 0). The length of a variable eigenvector is called the load and demonstrates the relative importance of each principal component (PC). Three principal components (PCs) were selected to improve the representation of the data variability for H2/CO molar ratio and ηsolar to fuel by 97. 0% and 92. 6%, respectively. The raw experimental data were further processed and the final values of the response variables were statistically examined via Statistica Software (StatSoft v. 8. 0) and R Studio Software (version 1. 1. 456). Based on data input, the significant interactions were identified using PCA and Pareto charts to quantify the effect of each operating variable on selected response variables.
These unfavourable conditions may generate pyrolytic smoke, which causes fouling on the reactor window  [17, 18]. Chuayboon et al. [17]  compared ηsolar to fuel for various wood biomasses and found that it ranged between 13 -21 %, depending on biomass physical and chemical properties. Even though a temperature increase caused higher heat losses, significant benefits in promoting syngas yield, quality and reaction kinetics while reducing the formation of tars were observed. Therefore, working at an optimum temperature needs to be considered to obtain high syngas yield, while minimizing heat losses. Additional comparison of ηsolar to fuel with various gasification studies are summarized in the supplementary materials (Table  A. 4). 4 ) by R Studio Software (Version 1. 1. 456). The lack of fit or error of the model was also described.
Polynomial model validation The adequacy of the polynomial model in this study was investigated by two methods. First, the model was validated by considering the predicted and experimental values of H2/CO molar ratio (Y1) using the model building dataset, as reported by Raheem et al. [12]. Second, the model was further authenticated by the cross-validation method, which is a technique used to assess how the results of a model will generalize to an independent experimental dataset that was not used in building it  [58]. Independent dataset is reported in the supplementary materials (Table  A. 5). This method was previously adopted to validate predictive models  [59] [60] [61] [62]. Figure  5 (A, B) plots a straight line denoting the fitting between predicted and experimental data of H2/CO molar ratio using the model building dataset and the independent dataset, respectively.
3, and OPEFB flowrate of 1. 3 g/min. From Figures  6 (B  and E ), it is possible to identify the operating condition where H2/CO molar ratio and ηsolar to fuel are visually optimized. To illustrate, H2/CO molar ratio and ηsolar to fuel of 1. 5-1. 6, and 13-14 %, respectively, can be achieved at 1200-1350°C, H2O/OPEFB molar ratio of 2. 3, and OPEFB flowrate of 2. 0-2. 4 g/min. However, the operating variables of the optimal condition are beyond the operational limits of the solar reactor used for this study. Furthermore, a more detailed analysis and optimization of response variables can be done using optimization methods via softwares such as General Algebraic Modelling System (GAMS). This detailed optimization step can be a valuable future study for a potential scale up. Having analysed the technical potential of solar gasification to valorise OPEFB, it can be concluded that this promising method brings sustainable opportunities for the solid waste treatment of palm oil industry.
were plotted in Pareto charts(Figure A. 6 (D, H), supplementary materials). Both H2O/OPEFB molar ratio and OPEFB flowrate showed significant effects on H2/CO molar ratio and ηsolar to fuel. For temperature, the p-value was slightly higher than 0. 05 (Figure A. 6 (D, H), supplementary materials). Due to the importance of temperature as an operating variable in literature, the pre-selected operational range close to the optimal conditions of biomass gasification [7, 17, 18]  may have influenced the statistical outcome. The entire dataset was, therefore, subjected to a more detailed PCA. PCA (Figure3(A, B)) was conducted by considering the scaled values of the operating variables with respect to the scaled values of response variables. Plots combining PC2 and.
2 , 2 were plotted in Pareto charts(Figure A. 6 (D, H), supplementary materials). Both H2O/OPEFB molar ratio and OPEFB flowrate showed significant effects on H2/CO molar ratio and ηsolar to fuel. For temperature, the p-value was slightly higher than 0. 05 (Figure A. 6 (D, H), supplementary materials). Due to the importance of temperature as an operating variable in literature, the pre-selected operational range close to the optimal conditions of biomass gasification [7, 17, 18]  may have influenced the statistical outcome. The entire dataset was, therefore, subjected to a more detailed PCA. PCA (Figure3(A, B)) was conducted by considering the scaled values of the operating variables with respect to the scaled values of response variables. Plots combining PC2 and.
Our model can equally well be applied to such more detailed data sources, better representing economic interactions to identify and describe how each economic sector is impacted by an economic shock. For example, when including government accounts, the tax redistribution can be accounted for in the dynamics of the economy, which is particularly relevant for assessing the social impacts. In the example presented in this article, only carbon dioxide emissions were calculated. Multi-regional input-output tables, such as Exiobase, also present air pollution and other greenhouse gas emissions, energy and water use, land occupation, and other relevant environmental indicators. The integration of data from an MRIO will allow performing a complete environmental assessment. In addition, in this article, households are disaggregated into three levels of income.
More realistic data on stocks and investment are available in the databases of different organisations, such as the Organisation for Economic Co-operation and Development (OECD), the International Monetary Fund (IMF), central banks, and others. The integration of these databases into the model will help to identify the sectors that are impacted, and who is at risk, from a perturbation. These databases also provide statistical data for interest rates, which will allow obtaining more realistic modelling of economic growth.
). In addition, it provides capacitive models of peripheral storage units (electricity, gas, hydrogen, heat, cold). EHDO uses mixed-integer linear programming (MILP) to define a set of mathematical constraints for each component model and asserts energy balances, conversion efficiency ratios, maximal load limitations, and storage constraints at hourly time steps. It relies on an external solver (Gurobi) for solving optimization problems. The implementation in Python makes it possible to extend EHDO by including additional Python modules. An example for using EHDO for HENs is given in  [28] , where the developers present the full range of supported systems and carriers, and provide two illustrative case studies.
ESSIM The Energy System Simulator (ESSIM)  [45]  is an open-source tool for calculating energy flows in interconnected HENs developed by TNO (The Netherlands). ESSIM calculates energy flows between network components -referred to as assets -and between different types of networks over a period of time with hourly intervals. Assets representing peripheral storage devices (electricity, gas, hydrogen, heat) are included as capacitive models. For each time interval, the scheduling of controllable, ''flexible'' assets and the energy balance for all involved networks is determined based on marginal costs. ESSIM is implemented using the modeling language Energy System Description Language (ESDL). A GUI is provided for defining assets and their geographical locations. ESSIM computes and visualizes key performance indicators and gives insights into how well the assets in a network are dimensioned, e.
GasPowerModels. jl GasPowerModels. jl is a package for the joint optimization of power transmission networks and natural gas networks. Its development is a joint effort of the Los Alamos National Laboratory, the University of Michigan and the Pennsylvania State University. The package combines elements and steady state models from two preceding packages, i. e. , GasModels. jl for gas flows and PowerModels. jl for power flows  [48]. The model used by GasPow-erModels. jl decouples network formulations (e. g. , mixed-integer convex) from problem specifications (e. g. , network expansion planning). Thanks to this decoupling, it is possible to define a variety of optimization formulations and compare them on common problem specifications. The resulting models are in general mixed-integer nonlinear programs.
GasPowerModels. jl is implemented in JuMP which makes it possible to extend it by including additional Julia or Python modules. JuMP depends on external solvers for solving optimization problems and supports a number of open-source (SCIP, Ipopt, etc. ) and commercial solvers (Gurobi, CPLEX, etc. ) for a variety of problem classes. The application of GasPowerModels. jl to HENs is documented and illustrated in  [49] , analyzing the natural gas and electric power systems in the Northeastern United States, and  [50] , which combines the IEEE 14 bus test system with the Belgian natural gas network.
Modelica Modelica [57] is an object-oriented language for modeling cyber-physical systems, developed by the Modelica Association. A system modeled in Modelica may combine electrical, mechanical, thermal or hydraulic components. It is also possible to add controllers in order to simulate different control strategies. A comprehensive standard library is available open-source, additional relevant libraries are available either free of charge (e. g. ,  TransiEnt [58]  for HENs, OpenIPSL  [59]  for power systems, or the DisHeatLib [36] for heat networks) or commercially (e. g. , the multi-domain Modelon Library Suite [60]). Modelica relies on differential, algebraic and discrete equations for modeling, enabling the analysis of both static and dynamic system behavior. Modelica itself is a text-based modeling language but there are several Modelica-based modeling and simulation environments available (both open-source and proprietary).
Pandaplan Pandaplan  [63]  consists of the two open-source tools pandapower  [37]  and pandapipes  [26]. It is developed at the Fraunhofer Institute for Energy Economics and Energy System Technology (Kassel, Germany). Both pandapipes and pandapower are based on the programming language Python. While pandapower can be used to model and simulate power grids, pandapipes can be utilized to evaluate the pressure, flow velocity and temperature distribution in gas and district heating grids. pandapower and pandapipes use the same data structure to describe networks and can thus be combined easily in order to simulate coupled infrastructures. Coupling points, like heat pumps or power-togas plants may be modeled using controller components, which can also be used to implement a specific operational strategy.
In addition, pandapower provides capacitive models for electrical storages. Components are described by algebraic and/or differential equations, and the resulting system of equations is solved with the Newton-Raphson method. While it is possible to simulate time-dependent systems, it is not possible to study the dynamic system behavior, as inertial effects are neglected. The implementation in Python makes it possible to extend the simulation by including additional Python modules. The application of Pandaplan to HENs is illustrated in  [26] , which studies the flexibility potential of power-to-gas in a coupled power and gas grid.
Modeling is generally carried out using deterministic linear programming techniques that aim to minimize a single objective function subject to the expected dispatch costs, taking into account a number of constraints including availability and operational characteristics of generating plants, fuel costs, operator and transmission constraints, and others. PLEXOS is primarily used through a graphical user interface but also has an API that can be used from any. NET environment. PLEXOS allows to use different commercial optimization engines (e. g. , CPLEX, Gurobi). The application of PLEXOS to HENs is illustrated in  [65] , which shows a multi-vector energy analysis for interconnected power and gas systems in Britain and Ireland.
PyPSA-Eur-Sec PyPSA-Eur-Sec  [66]  is an open model of the sector-coupled European energy system. At the time of writing, the tool is maintained by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin (Germany), developed in collaboration with the KIT Karlsruhe Institute of Technology (Germany) and Aarhus University (Denmark). PyPSA-Eur-Sec is designed as a module of the PyPSA toolbox  [67] , enabling the modeling of the transmission networks of the ENTSO-E area. It can model electricity, hydrogen and gas grids, with numerous sources (e. g. , renewables, biomass or fossil fuels) and generation technologies (e. g. , heat pumps, fuel cells or CHP). In addition, it provides capacity models for energy storage at different time scales, including batteries, pumped hydro storage, hydrogen storage and hot water storage.
Moreover, PyPSA-Eur-Sec considers transformations in industry, aviation and shipping, and the use of CCS. PyPSA-Eur-Sec uses a myopic approach with 5-year time steps from 2020 to 2050, minimizing for every time step the total system cost (incl. CO 2 constraints). For every time step, the overall model is optimized with the help of an external solver (Gurobi, CPLEX, etc. ), assuming a long-term market equilibrium with perfect competition and perfect foresight. The implementation in Python makes it possible to extend the simulation by including additional Python modules. An example for using PyPSA-Eur-Sec for HENs is given in  [68] , which identifies economy-optimal energy transition paths at the European level under different carbon budgets.
The resulting system of equations is solved iteratively by a sequential linearization method which updates the boundary conditions expressed by the coupling equations at each iteration step. SAInt is able to assess the gas storage capacity in pipeline systems and also provides models for gas storage in peripheral facilities (LNG terminals, underground caverns, etc. ). SAInt is mainly programmed with Visual Basic and uses IronPython as a scripting language for interacting with the user. It provides a graphical user interface and an API that can be used from any. NET environment for accessing solvers and classes representing the different assets in gas and power systems. The application of SAInt to HENs is illustrated in  [72] , which shows how disruptions in a coupled gas and power system can affect the operation of both systems and how counter measures to mitigate their impact can be assessed.
Acknowledgments This research has been conducted as part of the IEA DHC Annex TS3 [73], an international collaboration with the aim of promoting opportunities and overcoming challenges for district heating and cooling networks in an integrated energy system context. Part of the work of the IEA DHC Annex TS3 is the collection, categorization and demonstration of recommended methods for the assessment of HENs with a focus on DHC. Part of this work has been funded by the  Austrian Research Funding Association (FFG)  under grant agreement # 876727. Part of this work has been funded by the  German Federal Ministry for Economic Affairs and Climate Action (BMWK)  under grant agreement  020E100331618. Part of this work was funded by the  Danish Energy Technology Development and Demonstration Programme (EUDP)  under grant agreement J.
Data for this study were collected every 10 minutes for each variable, and the analysis was conducted over four seasons. Zaghba et al. [12]  the others examined the impact of seasonal variation and different climatic conditions (sunny, partly cloudy, and cloudy) on the performance of a 2. 25 kWp gridconnected PV installation installed on the roof of a parking lot, the installation is based on thin-film silicon technology in the desert environment of the Ghardaïa region, Algeria. The data was recorded with a measurement step of 5 min. The performance evaluation of a grid-connected photovoltaic system of 1. 75 kWp based on monocrystalline silicon modules installed in the Saharan zone of southern Algeria (Adrar), in three typical days of the year 2014 under variable climatic conditions (clear, cloudy, and sandstorm) is presented in  [12].
Dabou et al. [13]  the others examined the impact of seasonal variation and different climatic conditions (sunny, partly cloudy, and cloudy) on the performance of a 2. 25 kWp grid-connected PV installation installed on the roof of a parking lot. The measurement data in this study were recorded at an interval of 1 min. Karami et al. [14]  presented an experimental study of different silicon-based PV modules (monocrystalline (c-si), Polycrystalline (p-si), and Amorphous (a-si)) during 3 days under varying climatic conditions (clear, cloudy, and rainy). Real-time measurements were taken every five minutes for different climate parameters. The experimental study was performed to assess the real performance of the selected technologies under real conditions in Casablanca, Morocco. In Ghana the others  [15]  examined the performance of five solar photovoltaic systems with a total capacity of 20KWp with five different solar cell technologies such as polycrystalline, monocrystalline, copper indium sulfide thin-film, amorphous silicon, and heterojunction incorporating a thin film under a humid tropical climate in Ghana.
Data acquisition system and weather station The solar power plant of Toujounine has four weather stations, two on loop A (POT A4 and POT A7), one on loop B (POT B4), and one on loop C (POT C3). Each weather station has an experimental measurement device as shown in Figure  4. Each device is composed of; i) a pyranometer (PYH) on the horizontal plane and a pyranometer (PYP) on the module plane to measure the different values of solar radiation, ii) a thermometer probe (4-wire PT-100) to measure the ambient temperature and the module temperature; and iii) a nanometer (type AIRFLOW TA2), installed at the top of a 2-meter mast, where standard measurements are made to measure wind speed.
Monitoring system The automatic data acquisition system is required to monitor the performance of a solar installation. The monitoring system is used to control the inverters and communicate the system status and weather data to the user locally or remotely. The solar power plant of Toujounine is controlled by a SCADA system with a storage capacity of 18 TB that works with the MySQL database and has Siemens Wincc type servers that archive the data of 5,000 variables over 1 year. The SCADA system of the plant provides an interface to all the components of the installation and allows it to process in real-time a large number of parameters and to control the installation remotely. Each POT contains an XM408-8C switch which is connected to the previous POT, the next POT, a thermal camera, an IP phone, and an ET200SP PLC.
DATA ACQUISITION AND ANALYSIS METHOD 3. 1. Measurement of metrological parameters The performance analysis of this plant is made based on the data recovered from the SCADA for the period from August 1, 2019, to July 31, 2020. The different measurements are collected with a measurement step of 10 seconds. The metrological parameters collected to be used to analyze the performance are sunshine, ambient air temperature, wind speed, and module temperature. The solar power plant of Toujounine has four measuring stations, each POT contains one measuring station in addition to the metrological station which serves as a test bench. The radiation data are measured in the same plane as the photovoltaic field using the pyranometer (PHY and PYP). The temperature of the ambient air is measured with temperature sensors (PT-100) located in solar shields.
Seasonal analysis of performance parameters In order to evaluate the behavior of the 50 MWp power plant in the different metrological conditions of Nouakchott, we will in the following analyze the performance of this power plant in two seasons characterizing the climate of Nouakchott the dry season from March to August and the wet season from September to February. The dry season is characterized by a clear sky with high heat and harmattan, we have selected the month of May 2020 to analyze the plant during this season. The wet season is characterized by cloudy skies and strong winds, it is subdivided into a period of rainy and cool, the month of November 2019 is our choice to analyze the plant in the wet season. The performance analysis of the power plant is done by evaluating the instantaneous parameters that are recorded by the data acquisition system incorporated in the SCADA of the power plant.
CONCLUSION This work presented a performance analysis study of the largest grid-connected solar photovoltaic power plant installed in Mauritania with a capacity of 50 MWp. The study analyzed the behavior of the plant under the different climatic conditions of Nouakchott. The seasonal analysis in two months (May and November) which reflect the dry season and the wet season characterizing the climate of Nouakchott and the daily analysis in three typical days (clear, cloudy, and sandstorms) allowed us to better understand the effect of the metrological parameters on the operation of the plant. The performance parameters of this plant were analyzed under actual plant operating conditions using one year of data from July 2019 to August 2020 based on measured data with a ten-second measurement step.
[42]  According to crystallographic data from RrCODH structure (PDB: 1JQK), minimal distances between D, B and C clusters and the protein surface are 6. 5, 12. 1 and 13. 1 Å respectively. The N-terminal amino acid bearing the histidine tag is located at 18. 4, 12. 4 and 20. 2 Å from the D, B and C clusters, respectively. These distances show that all clusters are at distances which are relevant in respect to biological tunneling distances  [43, 44]  either from the surface of the protein or from the N-terminal and considering the fact that the poly-histidine peptidic chain is highly flexible. Since we previously observed that RrCODH is efficiently adsorbed at hydrophobic surfaces. [16] and considering the fact that hydrophobic patches, as well as the histidine tags are both located near the D-and B-cluster, we can hypothesized that there is a contribution of both to the favorable immobilization of the enzyme during the incubation process.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  through the  LabEx ARCANE program  ( ANR-11-LABX-0003-01 ) and the  Graduate School on Chemistry, Biology and Health of Univ Grenoble Alpes CBH-EUR - GS  ( ANR-17-EURE-0003 ). This work was partially funded by  CNRS via « Mission pour les Initiatives Transverses et Interdisciplinaires (MITI) » and « Cellule Energie » of CNRS  through the project  «EGreen-Syn». The authors acknowledge support from the  plateforme de Chimie Nano-Bio ICMG FR 2607  ( PCN-ICMG ) and  Plateau Synthese Organique (PSO-DCM).