dataesr/scientific-paragraphs-categorization huggingface dataset:
2-methyl tryptophan biosynthesis The mature protein coding sequence of the tryptophan 2-C-methyltransferase (TsrM) from S. laurentii (GenBank: FJ652572. 1) was codon-optimized for E. coli expression and synthetized by Twist Bioscience. The optimized TsrM sequence was inserted into a pBbA5k BglBrick backbone between the BglII and BamHI sites to obtain the vector pTsrM. Codon-optimized genes encoding electron transfer proteins were cloned between the BamHI and XhoI sites of the pTsrM vector (Table  S8 ). The vector for cobalamin importer overexpression was constructed similarly to a previously-published report  23. The genes for proteins BtuB (AYG21241. 1, GenBank), BtuC (AYG19236. 1, GenBank), BtuD (AYG19238. 1, GenBank), BtuE (AYG19237. 1, GenBank) and BtuF (AYG20683. 1, GenBank) were amplified from E.
Acknowledgements We thank members of the  Bokinsky Lab  and the  Barras Unit  for discussions throughout the work, and  Mohamed Atta  and  Julien Pérard  for their assistance with biochemistry experiments. This project (IRONPLUGNPLAY) has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant 722361 and was supported by grants from  Agence Nationale Recherche (ANR) ERA-Institut Pasteur  and the  ANR-10-LABX-62-IBEID. The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported under Contract No. DE-AC02-05CH11231. SL was supported by the  China Scholarship Council.
Acknowledgments We thank our team members and  Lionel Fôret  for critical discussions. This study was supported by the  Academy of Finland  (grants  282192 ,  312491 , and  307415  to E. I. ),  Sigrid Juselius Foundation (E. I. ) , and  ANR-17-CE11-0003-NANODROP  and  ANR-18-CE11-0012-01-MOBIL  to A. R. T. We apologize to our colleagues whose works could not be cited due to space restrictions.
All the training examples come from simulated data. In this paper, a neural structure is proposed: feedforward ANN trained with supervised learning algorithms are used for classification and properties estimation. One model is developed for each of the classes, allowing both properties to be estimated simultaneously from experimental photothermal data. The neural structure is first developed with the purpose of being operable with a wide range of materials and behaviors. In a second phase, this tool will be adjusted to CSP absorber materials. The paper is organized as follows: in Section 2, the neural structure based on feedforward artificial neural networks allowing thermal diffusivity and thermal conductivity to be simultaneously estimated for two classes of materials is presented. The database is composed of polymers and metallic alloys.
Test bench settings In order to develop a consistent neural model, we have decided for experimental photothermal data (for both the networks' training and validation phases). A photothermal test bench is set up (Fig. 3 ): a laser excites a sample thanks to a laser command connected to the computer. Then the radiance variation of the sample is detected by an infrared  (IR) camera which measure the evolution of temperature on the materials' front face. The signal is recorded directly on the computer. The laser is an Er-Yb diode laser of 3 W in collimated beam with a square cross section of 1. 1 Â 1. 1 mm 2 , and a low divergence (4 mrad). The peak wavelength is between 970 and 980 nm. The asymmetric radiation from the laser diode is transformed into a symmetrical beam using a patented micro optics.
The samples and their photothermal responses The examples dataset is made up of 30 photothermal responses associated with thermal diffusivity/thermal conductivity couples (Tab. 1). The set includes polymers such as PMMA (polymethyl methacrylate, sample n°6), metallic alloys such as Inconel 625 ® (sample n°23) and a ceramic sample, the boron nitride (sample n°30). On one hand, we recovered data about all these samples. The objective is to know as accurately as possible the real properties of each sample in order to train the artificial neural networks properly, and to evaluate the results of the validation phase with accurate data. For this purpose, we took thermal diffusivity/thermal conductivity data from the manufacturer who provided us the samples, but these values were merely estimated.
So, we compared these values with values coming from other manufacturers and from the literature (see Appendices). For each sample, we chose to work with mean value of both thermal diffusivity and thermal conductivity. The dataset (Tab. 1) is divided in several subsets in order to test different data combinations for the training phase. Set 1 is composed of the samples in green (8 samples). In Set 2, the samples in blue are added (15 samples). In Set 3, the samples in purple are added (18 samples). In Set 4, the samples in orange are added (22 samples). The samples in pink are used for validation (7 samples). The sample 20 is quartz. Because it is transparent, the laser excitation goes mainly through the material, and the signal recorded by the infrared camera in front of the sample is not significant.
Structure of the classification network We used a feedforward artificial neural network trained with a supervised learning algorithm to develop the classification algorithm, with two apparent classes. Unlike the simulated database used in previous work  [21] , we observe in Table  1  that the real materials available do not cover all the possible combinations for the thermal diffusivity/thermal conductivity couple. The higher the thermal conductivity, the higher the thermal diffusivity, and vice versa. A material with high-low or low-high values of both properties does not have a physical existence. So, we highlight a class of materials with low thermal conductivity and low thermal diffusivity (these materials are polymers, i. e. the samples 1-19 in Tab. 1) and a class of materials with higher values of both properties (these materials are metallic alloys and ceramics, i.
1% whereas the thermal conductivity of sample 28 is estimated with an error of 25%. Moreover, we observe that the values taken as references for the development of the model vary within a certain range, as shown by the properties' minimum and maximum values in the figure. For example, sample 8 (Fig. 9a ) has a thermal conductivity between 0. 22 and 0. 37 W m À1 K À1. For this sample, the model gives an estimation of 0. 20 W m À1 K À1 , which represents an error of 31% when considering as reference the average value from the literature, and an error of only 9% when considering as reference the lowest value available. This means that the values considered as references may not be accurate enough and, as a result, penalize the neural network training. To deal with this issue, it would be suitable to correct the database with measured values of thermal conductivity and thermal diffusivity.
We manage to obtain mean relative errors between 8% (for the thermal diffusivity of polymers) and 20% (for the thermal conductivity of polymers and for both thermophysical properties of metallic alloys). In this paper, we highlight that, when developing datadriven models, the training examples have to be carefully chosen. A reduced number of training examples or the addition to the training set of examples which do not provide useful information penalize generalization ability. Also, the length of the materials' thermophysical responses used as model inputs is critical. Its impact on performance has been evaluated and we highlight that 15-s long thermophysical responses are necessary to obtain good results. It should be noted that the available database is rather small, which, of course, penalizes the development of estimation models based on machine learning.
In addition, there is uncertainty over the properties' values given by manufacturers or taken from the literature. Because these values guide the weights and biases adjustment during training, the results summarized above are hopeful. In order to continue this study, it would be crucial to consolidate the database, which will allow to increase the number of training examples and improve generalization ability. So, it would be possible to create a third class for materials with higher thermal conductivity, or even to distribute the available examples into more precise classes. Furthermore, it is necessary to precise the properties' values used as target values during training thanks to measurements. In addition, it is possible to conduct a similar study with an alternative input, as the temperature evolution on the materials' rear face, instead of such an evolution on the materials' front face.
This work has received funding from the  European Union 's  Horizon 2020 research and innovation program RAISELIFE  under grant agreement No. 686008. The authors would like to thank  Goodfellow SARL  for providing us samples,  Hervé Duval  ( PROMES-CNRS ) for his help in the instrumentation of the photothermal test bench, especially for the control of the laser, as well as  Emmanuel Hernandez  ( PROMES-CNRS ) for the shaping of the samples.
The specifications of the studied ferry and its mission are presented in the next section. It is followed by the analysis and the treatment of the measurement data in order to determine the speed profile and the resistance to advancement. A simulation of the classical propulsion will then be performed in order to determine the power repartition in the different parts of the conversion chain, and its evolution in time, which is necessary for the sizing of the different elements of the hybrid structures and the determination of the relevant power management strategies. The last section concerns the hybridization of the ferry propulsion, aiming to determine the best hybrid structure.
Energy Dispersive X-ray Spectroscopy mapping was performed using a JEOL Silicon Drift Detector (DrySD60GV, sensor size 60 mm 2 ) with a solid angle of ~0. 6 srad. X-ray diffraction (XRD). XRD measurements were carried out using a Panalytical X'pert pro diffractometer equipped with a Co anode (λΚ α = 1. 79031 Å) and a multichannel X'celerator detector. All the diffractograms were fitted via MAUD program (Material Analysis Using Diffraction), a general X-ray diffraction program based mainly on the Rietveld refinement method,  37  and allowing determination of a and c parameters as well as the calculated crystallites sizes for each diffraction peak. Energy dispersive X-ray fluorescence (EDXRF). Elemental analyses of the powders were conducted by EDXRF using an epsilon 3XL spectrometer from Panalytical equipped with a silver X-ray tube.
Data reduction and analysis was performed using the Athena software. 38 The XAS data were binned, summed, and normalized using Athena, a program in the IFEFFIT package. 38 The absorption edge E 0 was measured as the first inflection point in the derivative spectrum and calibrated vs. an Ir foil. The XANES and EXAFS spectra were normalized from 42 to 115 eV below the edge and 150 to 1470 eV above the edge, respectively. Operando XAS. Ir L 3 -edge X-ray absorption spectra were collected in fluorescence mode at the SAMBA beamline (Synchrotron SOLEIL, see characteristics below). Cyclic voltamograms and constant potential electrolysis procedures were performed using a Bio-logic SP-300 potentiostat, connected to a custom-made electrochemical cell suitable for in situ/operando XAS measurements. The cell is made of a single, square-shaped glass compartment (3 × 3 cm), fitted at the top with a cylindrical screw cap (4.
5 cm inner diameter). A hole is made in one of the walls of the square part containing the electrolyte. A glassy carbon plate (500 µm thickness) working electrode was fixed with glue from the outside in front of the hole in the electrochemical cell wall. The actual working electrode surface in contact with the electrolyte was ca. 0. 5 cm 2 (R = 0. 4 cm). A plastic disk was fitted in between the screw cap and the cell top entrance, with holes to allow other electrodes. The counter electrode was a Pt mesh inserted in a glass compartment in contact with the electrochemical solution through a fritted glass. The reference electrode was an Ag/AgCl (E = + 0. 2 V vs. NHE) electrode. The incoming photon intensity was monitored using a N 2 /He-filled ionization chamber. In situ/operando XAS data were collected as fluorescence excitation spectra using a 36-pixels Ge detector (Canberra) with a 45°outgoing angle.
Acknowledgements The work of M. O. was financially supported by  College de France. This work was partially financed by  SATT Lutech. M. F. acknowledges funding from the  European Research Council (ERC)  under European Union's  Horizon 2020 Programme  (Grant Agreement no. 803220 , TEMPORE). The XAS experiments were performed on SAMBA beamline at  Synchrotron Soleil  (Proposal 20181713). We would like to acknowledge  Guillaume Alizon  for his assistance with the in situ XAS data measurements. The authors thank  Maria de Marco  for her help in spray-drying. Sophie Nowak  is gratefully acknowledged for EDXRF measurements. Philippe Decorse  is gratefully acknowledged for XPS experiments. XPS equipment was funded by the  Région Île-de-France , convention SESAME n°16016303 and the  Labex SEAM (Science and Engineering for Advanced Materials and devices).
Policy 16 993-1010 Qi T and Weng Y 2016 Economic impacts of an international carbon market in achieving the INDC targets Energy 109 886-93 Schwanitz V J, Piontek F, Bertram C and Luderer G 2014 Long-term climate policy implications of phasing out fossil fuel subsidies Energy Policy 67 882-94 Söder M, Thube S D and Winkler M 2019 The European emission trading system and renewable electricity: using the G/TAP power database to analyze the role of carbon prices on the development of renewables in the EU 22nd Annual Conference on Global Economic Analysis (Warsaw, Poland) Springer U 2003 The market for tradable GHG permits under the Kyoto protocol. A survey of model studies Energy Econ. 25 527-51 Stiglitz J E 2019 Addressing climate change through price and non-price interventions Eur. Econ. Rev.
Acknowledgments K T and M R A acknowledge support from the  Integrated Research Program for Advancing Climate Models (TOUGOU Program) , the  Ministry of Education, Culture, Sports, Science, and Technology, Japan ;  M R A  and M C were additionally supported by the  FORCeS  and  4C H2020  projects under Grant Agreement Numbers  821205  and  821003  and A M by the  Institut d'Etudes Avancées de Nantes , 5, allée  Jacques Berque ,  44000  Nantes, France. J L acknowledges funding from the  Wellcome Trust ,  Our Planet Our Health (Livestock, Environment and People-LEAP) , award number  205212/Z/16/Z. M A S, M C and M R A acknowledge support from the  Natural Environment Research Council  award number  NE/T004053/1  and the  Oxford Martin Programme on Climate Pollutants. K T also benefited from State assistance managed by the  National Research Agency in France  under the ' Programme d'Investissements d'Avenir'  under the reference ' ANR-19-MPGA-0008 '.
FEG/SEM and EDS: SEM images were acquired thanks to a FE-SEM Schottky gun SU-70 purchased from Hitachi (Japan). In order to prevent charging effect from the samples, low voltage (either 1 or 5 kV) conditions were used. Elemental atomic distribution in the near surface of samples was measured by EDS. The system used was on OXFORD X-Max 50mm 2 (SDD technology) spectrometer controlled through INCA software. The system was mounted on the FESEM Hitachi SU-70. To achieve a small volume analysis suited to the small particle size, 5 kV analyses were processed. Elemental standards samples fitted to these conditions were set up in a system (commercialized by Geller microanalytical laboratory, Boston, MA, USA). Quantifying correction model was Phi(phoz) model modified by Pouchou&Pichoir.
X-rays fluorescence (XRF): XRF spectra were measured with XEPOS (SpectroAmetek) spectrometer. With this apparatus, quantification of all elements with Z values 12<Z<92 is possible, so the oxygen is not quantified with the used equipment. The elements are quantified as milligrams per kilogram (ppm) or as atomic percentage. N2 sorption: the textural properties of the solids were determined by N2 sorption data obtained at -196°C with an ASAP 2010 Micromeritics instrument or a Bell Instrument. Prior to the analysis, the samples were out gassed at 300°C for 3 h until a stable static vacuum of 0. 263 Pa was reached. The weight of the sample used (initially around 80 mg) was measured exactly after pretreatment. The BET (Brunauer Emmett Teller) specific surface area was calculated from the adsorption data over a relative pressure range from 0.
05 to 0. 30, as recommended for mesoporous solids  [52]. The pore diameter and pore volume were determined according to the Barrett Joyner and Halenda (BJH) model applied to the desorption branch of the isotherm. Diffuse reflectance UV-visible spectroscopy: spectra of solids were recorded at room temperature for 3-4 mm thick powder samples between 200 and 900 nm with a scanning rate of 200 nm. min -  on a Varian Cary 5000 spectrometer equipped with an integrating sphere of 70 mm diameter (Apparatus name: V-630-Instrument ; serial N o C390361148 ; Abs. Start: 900 nm ; Abs. End: 200 nm ; data interval= 1. 0nm ; UV/Vis bandwidth=1. 5nm ; light-source= D2/WI ; changing source at 340 nm and standard scan speed 400 nm. min -1 ). The spectra were plotted in absorbance then reflectance modes, after subtraction of the Teflon reference PTFE signal.
1. It is known that quality of LeBail refinement is verified by two numerical statistical indicators Rwp and Rexp (eq. 4 and eq. 5 of §. 2. 5, using FullProf-2017 software). Here, with applying LeBail analysis, the true intensity of diffraction could be predicted and be the model obtained by minimizing the weighted sum of squared differences between observed and calculated intensities value. The Rwp/Rexp ratio (GoF=Rwp/Rexp: Goodness-of-Fit) varies in the following order: 3Zn/a-TiO2 (2. 75)> a-TiO2 (2. 2)> 3Cu/a-TiO2 (1. 74); it can be noticed that the GoF of 3Cu/a-TiO2 is larger than the one measured on blank a-TiO2, as can be expected because of the used calcination treatment and of the presence of nitrates salts in preparation steps. It is known that in a perfect refinement, the squared ratio Rwp 2 /Rexp 2 should be less than 4 at the end of refinement.
By comparing the results of calculation as shown in Table. 1, for the measurements made on 3Cu/a-TiO2 with "V12" with a corrected slot defined on the apparatus and with fixed slots, "FS" measurements have to be done under the two conditions but the non-coherent domain between the two files can be obtained with a SmartLab (high-resolution software). No noticeable difference was detected. The sizes of titania particles were determined by XRD using Debye-Scherrer equation (analysis of full width at half maximum, FWHM) as shown in Table. 1. Crystallites size (noncoherent domain sizes) were measured perpendicularly to the (101) indexed reticular plans that give the most intense diffraction peak and increase in the following order: Millennium-P500 (47. 4 nm)<a-TiO2 (118. 6 nm)< 3Cu/a-TiO2 (149. 0 nm)< 3Zn/a-TiO2 (223.
Acknowledgments M. R. and K. K. acknowledge the financial support by the  Swiss Government Excellence Scholarship (ESKAS). S. V. acknowledges support from the  National Research, Development and Innovation Office of Hungary (NKFIH  grant  FK135375 ). P. B. acknowledges the financial support by the  NCCR Catalysis  and by the  Swiss National Science Foundation  ( SNSF, Grant  code  200020_172507 ).
Besarati et al. developed a similar method but with a genetic algorithm as an optimization method  [9]. The ant colony optimization  [10]  associated with the ray-tracing software STRAL  [11]  was chosen for the Jülich solar tower tests. The objective is to maximize the receiver output instead of the efficiency. The heliostat field management of the Ivanpah solar thermal power plant relies on the Solar Field INtegrated Control System (SFINCS) developed by BrightSource. The optimization of the heliostats aiming points is achieved by a closed-loop control system that aims to maximize the solar energy input in the receiver while respecting the flux limitations, which are 150 kW/m 2 , 600 kW/m 2 and 300 kW/m 2 for the reheater, evaporator and superheater, respectively. Finally, several methods have been described for circular receiver and are summarized by Collado  [12].
The ant colony optimization  [10]  associated with the ray-tracing software STRAL  [11]  was chosen for the Jülich solar tower tests. The objective is to maximize the receiver output instead of the efficiency. The heliostat field management of the Ivanpah solar thermal power plant relies on the Solar Field INtegrated Control System (SFINCS) developed by BrightSource. The optimization of the heliostats aiming points is achieved by a closed-loop control system that aims to maximize the solar energy input in the receiver while respecting the flux limitations, which are 150 kW/m 2 , 600 kW/m 2 and 300 kW/m 2 for the reheater, evaporator and superheater, respectively. Finally, several methods have been described for circular receiver and are summarized by Collado  [12]. Most of the aiming strategies were developed for commercial plants and for circular or flat receivers, and the amount of published literature is limited.
5 m), and the standard deviation of the Gaussian distribution σ 2 and the parameter b are set to 12 and 0. 5, respectively. Daily APS (a single aiming point for each heliostat throughout the day) is examined and compared to hourly APS (aiming points changing every hour) to easily control the heliostat field during a full day of operation. Results of APS are introduced into SOLTICE (SOLar Simulation Tool in ConcEntrating optics), a new open-source ray-tracing software developed by the CNRS-PROMES (Processes, Materials and Solar Energy) laboratory and Meso-Star SAS (simplified jointstock company)  [17]. SOLTICE is an upgrade of the previous version Solfast with reduced computation time. It is based on the same algorithm as Solfast which was validated in previous studies  [18]  and provides more options in terms of geometry and reflection behavior of surfaces.
SOLTICE uses the Yet Another Markup Language (YAML) language to create geometries. It is designed to efficiently handle complex solar facilities. Indeed, the CAD model can be imported and therefore a complex ray's path can be simulated. The influence of the variation of surface optical properties with wavelength can also be taken into account. The flux distribution on each tube computed by SOLTICE is post-processed and considered in a simplified thermal model developed with the Matlab ® software  [19]. The thermal model is based on the Net Radiation Method, which establishes the balance on heat flux and radiosity. The discretization of the tubes for the thermal model is performed by dividing each of them into 15 parts in height, and considering the front and the back of the tube. This discretization results in 1200 elements for the forty tubes (30 elements per tube).
The cavity is discretized in 104 elements, as shown in Figure  6  when the cavity is unfolded. Daily APS (a single aiming point for each heliostat throughout the day) is examined and compared to hourly APS (aiming points changing every hour) to easily control the heliostat field during a full day of operation. Results of APS are introduced into SOLTICE (SOLar Simulation Tool in ConcEntrating optics), a new open-source ray-tracing software developed by the CNRS-PROMES (Processes, Materials and Solar Energy) laboratory and Meso-Star SAS (simplified jointstock company)  [17]. SOLTICE is an upgrade of the previous version Solfast with reduced computation time. It is based on the same algorithm as Solfast which was validated in previous studies  [18]  and provides more options in terms of geometry and reflection behavior of surfaces.