dataesr/scientific-paragraphs-categorization huggingface dataset:
subtilis possesses at least one FDH is the existence of a putative fdhD gene encoding a sulfurtransferase crucial for FDH activity  (12). In the B. subtilis genome, the fdhD gene is distant from the yjgC and yrhE loci (53 0 versus 18 0 and 39 0 , respectively). Interestingly, the yjgC gene is predicted to be organized in operon with yjgD as yjgC and yjgD are coexpressed with a positive correlation of 0. 98  (28, 29). The same holds true for the gene yrhE predicted to be in operon with yhrD as both are adjacent and coexpressed with a positive correlation of 0. 99 (Fig. 1B ). The yjgD and yrhD genes encode 21-kDa and 17-kDa polypeptides, with weak sequence overall identity (20%), and contain a DUF1641 domain of 38 amino acids of unknown function (PFAM PF07849). While YjgC/ YrhE are related to FdsA, as illustrated by the YjgC structural model based on the 3D structure of R.
The YjgCD and YrhED are protein complexes with formateoxidizing activity To test the activity of YjgC and YrhE as FDHs and the importance of YjgD and YrhD, the corresponding proteins were purified. First, the yjgCD and yrhED operons were expressed in B. subtilis under the control of an inducible promoter and a 6-histidine tag was added at the N terminus of the YjgC and YrhE proteins. Affinity purification of these proteins showed that YjgC and YrhE copurified with YjgD and YrhD, respectively, as identified by SDS-PAGE and MS analysis (Fig. 2A , Tables  S2  and S3 ). MS data are available via ProteomeXchange with identifier PXD028742. These results suggest that proteins YjgD and YrhD interact physically with YjgC and YrhE, respectively. A full steady-state enzyme kinetic analysis of formate oxidation by YjgCD and YrhED using benzyl viologen (BV) as an artificial electron acceptor is shown in Figure  2B.
Spectral simulations Numerical simulations of EPR spectra were performed with the EasySpin package (release 5. 0. 12) using MATLAB (The MathWorks, Inc)  (70). A field-independent (unresolved hyperfine couplings, H-strain) linewidth model was used to simulate the EPR signal of the MSK and the Mo(V) species, whereas those of the FeS centers were simulated using a fielddependent (g-strain) linewidth model. For H-strain, the full width at half maximum of Gaussian lines along the g-tensor principal axes was adjusted, whereas the corresponding gstrain distributions were considered uncorrelated. Parameters used for the simulations shown in this work are provided in Table  S8.
MS analysis Purified proteins were subjected to an SDS-PAGE, and the stacking gel band corresponding to total proteins was excised and submitted to in-gel trypsin digestion for proteomic analysis as described previously  (71)  and with the following adapted modifications. Spectra were processed by Proteome Discoverer software (ThermoFisher, version 2. 4. 1. 15) using the Sequest HT algorithm with the Bacillus subtilis database (Taxonomy ID: 224308, version 2016-08-20, downloaded from the NCBI by Protein Center including 5573 entries). In this study, proteins were also filtered by a minimum number of Peptide Spectral Match of 10. The list of identified proteins is available in Tables S2-S4.
Acknowledgments-We thank  Romain Mercier  for providing plasmids pSPH1/pSPH2, originally constructed by  H. Strahl von Schulten. We also thank  Matt Cabeen ,  Elodie Foulquier-Khadaroo , and  Anne Galinier  for providing protocols and advice for B. subtilis genetics and culture. We thank  Anne Galinier  and  Frédéric Biaso  for scientific discussions. We also thank  Genevieve Shirley Dobihal  and  David Rudner  for providing genomic DNA Bacillus subtilis 168 trpC2 ΔyjgD::ery, strain was originally constructed by Koo et al. (88). Marseille Protéomique  (MaP) has been supported by  IBiSA  ( Infrastructures en Biologie Santé et Agronomie ) and  Aix Marseille Université. We are grateful to the  EPR  facilities available at the  French EPR network (RENARD ,  IR CNRS 3443 ) and the  Aix-Marseille EPR center.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http:// proteomecentral. proteomexchange. org) via the PRIDE partner repository  (72)  with the dataset identifier PXD028742 and 10. 6019/PXD028742. Supporting information-This article contains supporting information  (73) (74) (75) (76) (77) (78) (79) (80) (81) (82) (83) (84) (85) (86) (87).
52 Typically, ca. 3. 5 mg of catalyst (25-75 μm sieve fraction) was loaded in a quartz capillary (ID 1. 5 mm, 50 μm thick), which was tightly glued into a frame connected to gas feed lines. Heating of the capillary was ensured by a hot gas blower (FMD Oxford). After the capillary was leak-checked at 20 bar, XAS data was obtained in He at room temperature. The catalyst was exposed to a 15 mL min -1 flow of 20 vol % H 2 /He and heated to 543 K (ramp 5 K min -1 ) with a hold time of 5-15 min at ambient pressure. After the H 2 treatment, the capillary was cooled to 453 K prior to introducing a syngas feed (H 2 /CO/He = 60/30/10 vol %) at 15 mL min -1. Within ca. 100 min, the capillary was pressurized to 20 bar, and subsequently, the temperature was increased to 533 K (ramp 5 K min -1 ) and held for 160 min.
Only for the CuZn-15/C catalyst was the feed subsequently switched to H 2 /CO/CO 2 /He = 60/ 27/3/10 vol %, recording XAS spectra for 160 min, and after that to H 2 /CO 2 /He = 67. 5/22. 5/10 vol %. Finally, XAS data were recorded after cooling to room temperature at 20 bar and in the last experienced gas atmosphere (H 2 /CO 2 /He and H 2 /CO/He for the CuZn-15/SiO 2 and CuZn-15/C catalysts, respectively). During all treatments, XAS spectra were recorded while scanning the X-ray energy from 8. 70 to 10. 65 keV (20 averaged scans per 10 s) in transmission mode using a Si(111) quick-XAS monochromator. Methanol production and gas compositions were recorded with a mass spectrometer (Cirrus, MKS) at ambient pressure. If the measurement involved CO, a conditioned carbonyl trap was used upstream the capillary to capture metal carbonyl compounds.
ZnO (abcr, 99. 999%), CuO (Sigma-Aldrich, 99. 999%), Cu 2 O (Sigma-Aldrich, ≥99. 99%), in-house synthesized Zn 2 SiO 4 (Figure  S27 ), all mixed with boron nitride (Sigma-Aldrich, 98%), and Cu (6 μm) and Zn (5 μm) foils were used as references, with their spectra being recorded at room temperature under air. A Zn 30 Cu 70 brass measured at the ESRF (LISA beamline (BM 08)) was also used as a reference. The optics and performance at the two beamlines are different, and hence, this might give rise to slight changes in the observed XAS spectra. Data analysis was performed using Athena and Artemis software, as further detailed in supplementary section S4. 2. 3. Catalyst Testing. A 16-reactor setup (Flowrence, Avantium) was used to evaluate the catalyst performance for methanol synthesis at 40 bar(g) and 533 K for at least 100 h.
The adsorption kinetic characteristics as well as the SCP were deduced from the temporal evolution of MOF HEx mass in a pressure-temperature-swing operation. We show a mass change profile example of a MOF HEx based on MIL-160 and resulting instant thermal flow rate in Figure  S9. Figure  2A  shows the instant cooling energy flow in the evaporator connected to the MOF HEx during adsorption (blue line). It is converted to average SCP (black line) at a given adsorption time. Note that the results manifest faster adsorption kinetics by 4-6 times more than a similar sample measured in the ambiance in a previous study,  43  which justifies the necessity of maintaining low-pressure operation conditions for adsorption chiller. A dataset is then collected on three other MOFs in Figure  2B. SCP is normalized in the function of average time to achieve equilibrium (t) as a function of temperature (T) and partial pressure (p/p 0 ) in the two scenarios of air conditioning (T ev = 10 C) and district cooling (T ev = 20 C).
ACKNOWLEDGMENTS F. S. C. and X. Z. acknowledge funding from the  European Union 's  Horizon 2020 Programme under MSCA  grant agreement no. 754462. E. G. , F. N. , and C. S. acknowledge the  CNRS ,  ENS ,  ESPCI , and  PSL University  for financial support. The authors thank  Dr. Vladimir Martis  from  Surface Measurement Systems  for assistance in the kinetics tests and  Yukiko Kamei  for drawing the figures.
For that, a spatio-temporal autoregressive model (STVAR) is applied. The measurements are collected at three sites located on Guadeloupe island. The high variability of the tropical irradiance profile allows us to test the robustness and stability of the used MPPT algorithms. Solar forecasting associated with the fuzzy MPPT technique allows us to estimate in advance the produced PV power, which is essential for optimal energy management in the case of smart energy production systems. Simulation of the proposed solution is validated under Matlab/Simulink software. The results clearly demonstrate that the proposed solution provides good PV power prediction and better optimization performance: a fast, dynamic response and stable static power output, even when irradiation is rapidly changing.
They are based on basic operations of training and testing. Their advantage is that they deal with complex nonlinear features effectively, and their main drawback is the requirement for a large quantity of data during the training stage  [39]. Deep learning structures are more popular nowadays  [40]. Hybrid approaches, which are a combination of two or more approaches that give more accuracy in PV power forecasting in different cases  [41]. Indirect methods consist of fragmenting the power prediction problem into two stages: forecasting weather parameters and introducing them in commercial PV simulation software to forecast the PV power generation. Solar forecasting was conducted for different time scale horizons and using different forecasting methods cited above. Concerning PV power short-term horizon forecasting applications, other approaches are proposed in the literature to estimate the expected power of a PV system from a ramp rate characterization.
An analysis of variability is therefore investigated, such as in  [42] , exhibiting the frequency of a given fluctuation from output PV power for a certain day by an analytic model, and  [43]  who demonstrated the smoothing of rapid ramps observed in a location point by large PV plants, which was strengthened in  [16]  by the result of the aggregation of multiple PV plants. In  [14] , a quantitative metric called the daily aggregate ramp rate (DARR) was proposed to quantify, categorize, and compare daily variability from power output, across multiple sites. In  [16] , models were developed exhibiting a capacity to extrapolate the resulting variability on an ensemble of power plants, and, in  [34] , a method was proposed to estimate the largest expected PV power ramp rates (RR). The works in the literature clearly show the purposes of power forecasting, namely, the development of strategies to mitigate the impact of solar variability on electric power grids, the optimization of the design and size of PV power plants, and, consequently, a maximization of their effectiveness and minimization of costs by using the curve fitted to the largest RR ratios, irradiance, and cloud shadow velocities dataset.
Global Structure Design A block diagram of the proposed control system is shown in Figure  1. The global structure of the studied PV plant is mainly composed of a PV generator (GPV), DC-DC boost converter, and load. From solar measurements collected at three sites on Guadeloupe island for a year, and using the STVAR prediction model, we obtained prediction results for a short time scale. Solar measurements and forecasting data are used to feed our PV production system. For the configuration of our PV system, we propose the use of a DC-DC boost converter, controlled by an MPPT fuzzy LOGIC algorithm to extract the maximum power from the photovoltaic panels. The objective of this combination is to have an indirect estimate of the power produced by our PV system. The following sections describe each part of the proposed structure illustrated in Figure  1.
Solar PV Generator For more simplicity, photovoltaic cells can be represented using the single-diode model as shown in Figure  2. The description of the proposed structure is as follows: From solar measurements collected at three sites on Guadeloupe island for a year, and using the STVAR prediction model, we obtained prediction results for a short time scale. Solar measurements and forecasting data are used to feed our PV production system. For the configuration of our PV system, we propose the use of a DC-DC boost converter, controlled by an MPPT fuzzy LOGIC algorithm to extract the maximum power from the photovoltaic panels. The objective of this combination is to have an indirect estimate of the power produced by our PV system. The following sections describe each part of the proposed structure illustrated in Figure  1.
Solar Forecasting and The global horizontal irradiance (GHI) was measured at 1 Hz with a pyranometer CM22 from Kipp and Zonen pyranometer (type SP Lite) whose response time is less than a second. The sensor accuracy given by the manufacturer is 3%. For this study we used an averaged database at 1 min of the global horizontal irradiance (GHI) measured for a period starting in January 2012 and ending in December 2012. The measurements were collected at site Petit-canal Gros-cap, along the cliffs (16 • 38 N, 61 • 49 W). The forecasting time series at Petit-canal was obtained by the STVAR model using past irradiance observations at the three measurement sites (see Figure  4 ). Figure  5  displays a five-day irradiance sequence showing high temporal variability. To perform the statistical forecasting model, the temporal trend of the irradiance time series must be removed.
Solar Forecasting and The global horizontal irradiance (GHI) was measured at 1 Hz with a pyranometer CM22 from Kipp and Zonen pyranometer (type SP Lite) whose response time is less than a second. The sensor accuracy given by the manufacturer is 3%. For this study we used an averaged database at 1 min of the global horizontal irradiance (GHI) measured for a period starting in January 2012 and ending in December 2012. The measurements were collected at site Petit-canal Gros-cap, along the cliffs (16°38 N, 61°49 W). The forecasting time series at Petit-canal was obtained by the STVAR model using past irradiance observations at the three measurement sites (see Figure  4 ). Figure  5  displays a five-day irradiance sequence showing high temporal variability. To perform the statistical forecasting model, the temporal trend of the irradiance time series must be removed.
For more efficiency, a process of the selection of optimal temporal input data (p lags optimal of past observations) and spatial input data (more relevant neighboring sites variable) was developed. This procedure is based on the calculation of the information criterion BIC (Bayesian information criterion) for sequential integration of each spatio-temporal explanatory variable. The relevant spatio-temporal input data are selected by minimization of BIC  [6, 22]. This procedure is built according to the method proposed in  [6, 22]. For more details on the modeling process see  [6, 9]. This process of spatio-temporal input selection was performed on a training dataset. The first month of the dataset was used as a model training dataset and the rest of the year as a test dataset. STVAR model showed a good predictive performance for a time scale from 5 min to 1 h  [6, 9].
14  [64] ). If all lagged time series values associated with all measurement sites had to be integrated into the modeling process as input data, the computational process would not be optimized. For more efficiency, a process of the selection of optimal temporal input data (p lags optimal of past observations) and spatial input data (more relevant neighboring sites variable) was developed. This procedure is based on the calculation of the information criterion BIC (Bayesian information criterion) for sequential integration of each spatio-temporal explanatory variable. The relevant spatio-temporal input data are selected by minimization of BIC  [6, 22]. This procedure is built according to the method proposed in  [6, 22]. For more details on the modeling process see  [6, 9]. This process of spatio-temporal input selection was performed on a training dataset.
Solar Forecasting Results: Datasets Used for the Simulation Irradiance in a tropical insular context presents particularly high intraday variability with days that can be clustered as a function of irradiance conditions  [10, 63, 65]. The proposed structure in Figure  1  contains two dynamic systems, operating with two different time scales. The MPPT PV system is very fast (sampling time equal 10 -4 s) compared with the solar forecasting system (sampling time equal 1 min). For that, the data selection operation is necessary. A sequence of 11 values was selected with a high magnitude of fluctuations to assess the robustness of the system process. Hence, values of the sequence were chosen according to the high amplitude of fluctuations between two points; that is to say between time t and t + 1 (1 min).
Output PV Power Prediction The PV power generation plant of 3 kW controlled by the MPPT fuzzy logic technique was with the measured and predicted solar data results given in Section 4. 1. The solar measurements were collected from the Petit-canal site in Guadeloupe. Predicted solar data were obtained using the STVAR model for a very short time scale of 1 min ahead. Four seasonal days, representing the fast variability of a tropical climate over 10 h of sunshine every day, were selected. The produced output power of the PV system was calculated using measured and predicted data in the cases of the four seasonal days. In order to evaluate the accuracy and efficiency of the PV power prediction, rRMSE, rMAE, and rMBE were calculated for the four seasonal days. The results are given in Table  3.
Conclusions The existence of a sudden and rapid change in the meteorological conditions, especially in a tropical context, led us to propose through the present work a robust and reliable solution based on real measured and predicted irradiance data. The proposed work deals with an indirect PV power production prediction of a PV power generation plant for a short time scale. The PV power generation plant is composed of 10 solar panels (3 kW) and a DC-DC boost converter. The PV power prediction approach is based on a combination of solar forecasting results and a fuzzy MPPT technique. Solar measurements data were collected on Guadeloupe Island. Solar forecasting using the STVAR model, applied for the first time at 1 min time scales, allowed us to obtain a representative irradiance profile to test the efficiency of the proposed MPPT PV solution.
Acknowledgements This work was supported financially by funding from  Total Research & Technology Feluy. This work was granted access to the  HPC/AI resources of TGCC  under the allocation 2020-A0090811911 made by  GENCI. We thank  Arnaud Etcheberry ,  Mathieu Fregnaux , and  Muriel Bouttemy  for XPS measurements, and  Françoise Pillier  for SEM analysis. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
out by X-ray diffraction. The diffractogram obtained is composed of the lines diffracted by the different families of crystalline planes (hkl) of the crystal lattice of the analyzed material. The crystal structure of our materials was studied using a Bruker X-ray diffractometer, type D8 ADVANCE equipped with graphite monochromatic Cu Kα radiation (λ= 0. 15418 nm) with source (40 mA,40 KV) and analyzed using software (X' Pert high score plus) type Expert Pro (PAN analytical) based on the PDF 2 Database of Joint Committee on Powder Diffraction Standards (JCPDS). The XRD patterns of the samples were recorder in the range 2θ from 5° to 80° using a step size of 0, 0150° and a scan step time of 49 s. The crystallite size was calculated using Debye-Scherrer' equation in the X' pert High Score Plus software by the following formula: D= (K * λCu-kα) / (cos θ * FWHM); where D is the crystallite size, K is a dimensionless shape factor, λ is the radiation wavelength for the X-ray tube, θ is the diffraction angle and FWHM is the width of half of the maximum diffraction (in radians).
Acknowledgements Financial support was provided by the  German Research Foundation (DFG)  (through the priority programme 1928 COORNETs, the  International Graduate School of Science and Engineering (IGSSE) ,  GSC81 , and a  Heisenberg professorship ) and the  Konrad-Adenauer-Stiftung (KAS). This work was carried out with the support of  Diamond Light Source , instrument I09 (proposal SI17634-1). The research leading to this result has been supported by the  project CALIPSOplus  under Grant Agreement  730872  from.
K E Y W O R D S database, enthalpy of vaporization, I-PC-SAFT, liquid density, liquid heat capacity, PC-SAFT, pure component, role of association, SAFT-type equation of state, vapor pressure 1 | INTRODUCTION Equations of state (EoS) are one of the most commonly used tools in chemical and process engineering due to their capacity of predicting thermodynamic properties of pure compounds and mixtures at any given state. It is therefore clear that their performance at predicting such properties must be studied carefully and systematically to quantify their strengths and weaknesses. Recently, Jaubert et al. 1 proposed a reference database to evaluate the accuracy of an EoS or to cross-compare two EoS when they are used to correlate the properties of binary mixtures. Their proposed database contains phase equilibrium data (VLE, LLE, VLLE), property change on mixing data (enthalpy or heat capacity), 2 azeotropic data and critical point data for 200 binary systems made up of different pair combinations of 107 non-associating and self-associating pure compounds.
mixing rules employed, if the vapor pressures of each pure compound are inaccurate, the predictions of the binary system are compromised. As a convincing illustration, the reader is referred to Figure  3  in Agarwal et al. 5  In this context, Piña-Martinez et al. 6  recently defined a database containing over 300,000 pseudo-experimental data points for pure components. These data come from a carefully selected portion of the DIPPR database  7  for 1800 different pure components representing all chemical families, and including 1252 non-self-associating (NSA) and 548 selfassociating (SA) compounds. For each of the 1800 pure components, 50 vapor pressure and 50 liquid density pseudo-experimental data points were generated from the correlations available in the DIPPR database. Among the 1800 fluids, 1536 also possess an accurate correlation for Δ vap H and 890 for c sat P,liq making possible to generate 50 data points for each of these properties.
The critical properties (T c , P c , and v c ) are available for the 1800 components so that in the end, the resulting database contains 306,700 high-quality pseudo-experimental data points. Although only the most accurate correlations available in the DIPPR database were kept (i. e. , correlations with the lowest errors reported by the DIPPR), we cannot exclude the case where data with high experimental uncertainty would have been used by the DIPPR to determine the parameters of a correlation (as the DIPPR error only quantifies the mean deviation between data points and correlated data). Although the real experimental uncertainty is unknown, it is believed that DIPPR correlations associated with low errors are statistically accurate and can be used for determining EoS parameters. Note: It is worth noting that EoS only predict residual properties.