dataesr/scientific-paragraphs-categorization huggingface dataset:
CANTERA results obtained for the equivalent 1D flame are also added as reference values. In terms of computational cost in that case, JAGUAR has an efficiency (cost per iteration and per DOF) around 7. 5 µs/ite/DOF whereas AVBP using TTGC scheme is around 6. 0 µs/ite/DOF. This is quite encouraging since JAGUAR is a very recent code where no real optimization was done compared to AVBP which has more than twenty years of experience in computational fluid dynamics. Fig. C4  Comparison between JAGUAR and AVBP of temperature, heat release rate, axial and vertical velocity profiles at y = 0 mm along x-axis for the 2D burner case. Appendix D Comparison between the centred diffusion scheme and the SDLIFT diffusion scheme on a 1D flame with a coarse discretization The same 1D flame introduced in paragraph 5.
Diffuse reflectance infrared transmittance (DRIFT) DRIFT analyses were performed using a Nicolet 6700 FTIR spectrometer where data are collected using OMNIC software. The treated samples were placed in airtight DRIFT cells with a CaF 2 window inside the glove box. For each spectrum, 64 scans were collected with a resolution of 4 cm -1. All the treatments of the catalyst for DRIFT analysis were performed at room temperature.
MicroITIES support was fabricated following procedure reported elsewhere  [22]. All experiments were conducted using Autolab 108 (Met-rohm®) with Nova 1. 11 software. All experiments involving the microITIES modification with In the second picture (B), the material is pulled from the interface using a counter electrode from the organic phase. polyamide materials were performed with a single use, disposable fused silica capillary based tips. This means, that data shown in Figs. 5  and 6  (also, Figs. S5-S8), were performed in the following manner: (i) first the microITIES tip was fabricated; (ii) unmodified microITIES was tested using cyclic voltammetry in the presence of fixed concentration of TMACl dissolved in the aqueous phase; (iii) microITIES was modified with a chosen polyamide using a fixed number of voltametric scans (each condition requires the utilization of a separate tip); (iv) tip was removed from the aqueous phase containing diamines and was placed in the cell filled with the aqueous phase ([TMA + ] = 60 µM); (v) tip was removed and placed in the cell containing [TMA + ] = 180 µM.
Acknowledgements The presented research was financed by the  National Science Centre Poland  as part of the  PRELUDIUM 19  project ( UMO-2020/37/N/ST4/ 00270 ). The authors are also grateful to  Polish National Agency For Academic Exchange and Campus France  for financing  PHC Polonium  Project ( NAWA -BPN/BFR/2021/1/00006 ;  Campus France   -48163ZD ).
Acknowledgements We are greatful to  Leandro Jacomine ,  Jonathan Dijoux ,  Aurélie Hourlier-Fargette ,  Marie-Pierre Krafft ,  Dominique Langevin  and  Katja Steck  for precious scienti c discussions and technical help. The authors gratefully acknowledge the  PLAMICS facility  of the  Institute Charles Sadron  for providing access to the SEM.
Funding This work has been nanced by  BASF  and an  ERC Consolidator Grant  (agreement  819511 -METAFOAM ). It also pro ted from an  IdEx Unistra "Attractivity grant " ( Chaire W. Drenckhan ). Overall, it was conducted in the framework of the  Interdisciplinary Institute HiFunMat , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and  SFRI  ( STRATUS  project,  ANR-20-SFRI-0012 ) under the framework of the  French Investments for the Future Program.
Having built the turbulent profile, turbulent characteristics are known, especially the typical turbulent length scales that made it possible to build the required data for LES profiles. Obviously, for experimental comparison, when all these parameters are available, the data preprocessing needs less complexity. Even though wind and turbulence profiles are used as input profiles for CFD codes, they may be modified along a flat, unobstructed domain when the equilibrium state is reached by the code. Indeed, a previous study has demonstrated  [31]  the difficulties for RANS CFD models to maintain the ABL profiles along a domain longer than ~1000 m  [31]. In addition to the difficulties associated with the wall functions model  [32] , the difficulty of performing an inlet profile consistent with the turbulence model is still an open issue when users have to demonstrate the capability of the RANS CFD code to maintain a steady wind and turbulence profile along a flat, unobstructed domain.
Description of the Experimental Dataset Ammonia dispersion field tests performed by INERIS were presented in a previous paper  [38]. INERIS conducted real-scale releases of ammonia on the 950-ha flat testing site of CEA-CESTA. Figure  3  shows the whole measurement area. The use of a simple approach such as a 1-D or 2-D model to ensure the conservation of parameters from orifice to inlet boundary area is strongly recommended  [37].
Description of the Experimental Dataset Ammonia dispersion field tests performed by INERIS were presented in a previous paper  [38]. INERIS conducted real-scale releases of ammonia on the 950-ha flat testing site of CEA-CESTA. Figure  3  shows the whole measurement area. During the experiments, atmospheric conditions were assessed using a meteorological mast standing at a height of 10 m. The mast was equipped with three cup anemometers positioned at 1. 5, 4, and 7 m above the ground, a wind vane at 7 m, and an ultrasonic anemometer at 10 m. Additionally, a weather station was set up near the testing site to measure ambient temperature, relative humidity, and solar flux at 1. 5 m above the ground. Sensors were strategically placed in seven arc shapes centred on the release point. Multiple test cases were conducted involving releases with mass flow rates of up to 4.
These eddies are then transported through an 800 m long rectangular cross-section domain. The resultant, time-dependent flow field taken from a cross-section of this domain can be extracted and imposed as an inlet condition for LES. This method allows the desired Reynolds stress field to be prescribed. Inflow boundaries for the synthetic method available in FDS consist of data given by following experimental results: mean velocity (U mean ), root mean square velocity in x-direction matching with mean wind direction (RMS x ), integral time scale (T x ), integral scale in x-direction (L x ). The inflow mean velocity profile follows an exponential law. The integral length scale may be defined by assuming the advection of turbulent structures by the averaged wind, such as L x = U mean × T x with T x the observed integral time scale in the x-direction.
In a regulatory context with a minimum set of data that allows the building of a turbulent profile for the RANS model, it would be possible to assess the typical turbulent length scales that made it possible to build the required data for LES profiles, as demonstrated in Section 3. 3. 1. The choice of sub-model comes next to the crucial choice about the mesh requirements. The harmonisation in CFD codes of an equivalent source term is feasible by means of the implementation of an identical term source that is fully gaseous.
This experimental ammonia case was targeted because it represents an unobstructed environment, which is within the application domain of all AT&D model types. Based on experimental observation analysis, flow input data and source terms of different levels of complexity have been set up for each approach. The same equivalent gas term located has been set for LES and RANS approaches to harmonising practices. Expected difficulties to maintain the turbulence level along the flat domain have been encountered for CFD approaches. Considering the inherent complexity of modelling and simulating the atmospheric turbulence, the predicted concentration is in good agreement with experimental data for both the RANS and LES approaches. Harmonisation of input data for flow and boundary layer simulation between the Swift model and more sophisticated models remains a major issue within the context of regulatory studies.
Acknowledgements We thank  Shell Global Solution International B. V. for financial support. Z. J. B. acknowledges support from an  ETH Career Seed Award  as well as  ETH  + Project  SynMatLab. P. -A. Payard  and Q. Pessemesse are grateful to  Université Lyon  1 and the  Region Auvergne Rhone Alpes  for financial support. P. -A. P. and Q. P. are grateful to the  CCIR of ICBMS, PSMN ,  GENCI-TGCC  (Grant  A0120813435 ) for providing computational resources and technical support. Open Access funding provided by  Eidgenössische Technische Hochschule Zürich.
Acknowledgements This work was supported by the  French "Agence Nationale pour la Recherche (ANR) " in part through Micro- TIP  project  (Microsystem including transducers based interpenetrating networks ,  ANR-15-CE08-0032 ), in part by the  Luxembourg National Research Fund (FNR)  and  Fonds de la Recherche Scientifique (F. R. S. -FNRS)  through  FNRS-FNR  project  INFINITE  (Agreement number  INTER/ FNRS/21/16555380/INFINITE ), and in part by  European Union 's  Horizon 2020 research and innovation program  through  WEAFING  project ( Wearable Electroactive Fabrics Integrated in Garments , Grant agreement ID: 825232). Zeon Europe GmbH (Halle, Germany)  is acknowledged for supplying of Hydrin C2000XL epichlorohydrin/ ethylene oxide copolymer free of charge.
Demonstrators Depicting Applications of Developed Sensors When the validity of the senor signals had been verified, two different sensor readout systems were constructed to monitor the moisture in wood or the surrounding air. The first system, shown in Figure  6 , used a microcontroller equipped with Bluetooth that was connected to the sensors. The system was placed in a jar together with a commercial humidity sensor, and the moisture content was controlled by adding sodium chloride saturated salt solution holding the moisture level in chamber around 64%. The microcontroller sent data to a computer by wireless communication, which was displayed in an in-house developed web app. In this way, the moisture and humidity could be tracked and logged in real time while using the commercial sensor for calibration and verification.
Preliminary isothermal step was excluded purposely to obtain weight change information at lower temperatures. Raman Spectroscopy: A WITec Alpha 300 RAS system (Ulm, Germany) equipped with a 532 nm excitation laser was used to carry out the Raman spectroscopy measurements. The 50× ZEISS LD EC Epiplan-Neofluar Dic 50×/NA 0. 55 objective and an optimized power on the sample of 1 mW to minimize heat induced effects were used in the measurements. All spectra were analyzed using WITec Project Plus 5. 1 software. A Lorentzian peak was fitted to obtain D and G band intensities and FWHM. The spectrometer diffraction grating of 600 L mm -1 was implemented in the study. The spectra were also corrected for cosmic rays and the background baseline by subtracted a shape-based correction. Sensor Fabrication: Commercially used CO 2 laser engraver Speedy 300 Laser machine from Trotec served as source of laser for graphitization.
Acknowledgements The authors would like to acknowledge funding from  Vinnova for the Digital Cellulose Competence Center (DCC) , Diary number  2016-05193 , as well as financial support from  Stora Enso AB. The work was also supported by  Treesearch. se. Dr. Robert Brooke  is thankfully acknowledged for taking the picture and video for Figure 7 and Video S1, Supporting Information respectively.
Acknowledgments: We are grateful to  D. Stapfer ,  M. Steger  and  J. -F. Gil  for technical support, and to the general staff of SOLEIL for providing beamtime under project  20200627  and  99210131. EM acknowledges  ETH Zurich  and the  United States National Science Foundation  for a guest professorship, and SH acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG ,  RTG 2717 ). Funding: This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  from the  European Research Council  under grant agreement  786636 , the  Swiss National Science Foundation  (project  200020_200306 ), and the  United States National Science Foundation  (Grant No. CHE-1940456 ).
Acknowledgements This research was financially supported in part by the  University of Rennes  (Ph. D. grants to R. S. , A. D. and R. L. ), the  Lebanese University  (Ph. D. grants to R. S. and A. D. ) and the  Région Bretagne  ( Ph. D. ARED  grant to R. L. ). We are grateful to  CRMPO and UAR ScanMAT , especially to  Philippe Jéhan ,  Elsa Caytan , and  Marielle Blot  for MS, NMR and chiral HPLC chromatography analyses, respectively.
Acknowledgements This work is supported by the  Engineering and Physical Sciences Research Council ,  United Kingdom , through the  EPSRC MEMPHIS ,  United Kingdom  ( EP/K003976/1 ) and  PREMIERE  ( EP/T000414/1 ) Programme Grants. O. K. M. acknowledges funding from  PETRONAS  and the  Royal Academy of Engineering  for a Research Chair in Multiphase Fluid Dynamics. We acknowledge  HPC  facilities provided by the  Research Computing Service (RCS) of Imperial College London  for the computing time. D. J. and J. C. acknowledge support through  HPC/AI  computing time at the  Institut du Developpement et des Ressources en Informatique Scientifique (IDRIS)  of the  Centre National de la Recherche Scientifique (CNRS) , coordinated by  GENCI  ( Grand Equipement National de Calcul Intensif ), France Grant  2023 A0142B06721.
Acknowledgments The authors acknowledge the  French National Research Agency  for its support through the  Labex STORE- EX  project ( ANR-10LABX-76-01 ) and through the projects  BALWISE  ( ANR-19-CE05-0014 ). R. P thanks  Clare College, University of Cambridge  for funding via a  Junior Research Fellowship  and  Alpha A. Lee  (Cambridge),  Marcus Fletcher  ( Imperial College London ) and  Damien Laage  ( ENS ) for useful discussions on the data.
The velocity predictions of the FV model have been observed to be closer to the experiments. The energy dissipated in the body as a consequence of viscoelastic dissipation has been computed. It was observed that a significant portion of the energy consumed is dissipated through viscoelastic effects. It shall be noted that the presented methodology allows for determining the energy dissipated in the bulk and the energy consumed by the fracture processes for cases where the data is available up to the crack faces. It shall be noted that instead of extracting the displacement fields near the crack faces and imposing them on the FE model, the displacement fields in the entire body (obtained by using the DIC technique) can be used directly to evaluate energy evolution. The strain fields and their evolution in time can be evaluated from the displacement fields and the strain energy and the dissipation can be evaluated from them.
A methanizer was used to convert the CO 2 into CH 4 for the flame ionization detector. Each data point consisted of the average of 5 consecutive analyses. The GC was calibrated for C3 and acetylene, but none of these products were detected. The carbon mass balances were calculated for each experiment and were fluctuating between 98 % and 102 %. From repeated experiments a relative standard deviation on the methane conversion of 4 % was estimated. Catalytic performance indicators to quantify the OCM output are: Methane/oxygen conversion: X CH4 = F in CH4 -F out CH4 F in CH4 (1) X O2 = F in O2 -F out O2 F in O2 (2) C 2 selectivity is described as a function of the carbon yield: S C2 = 2 * (F out C2H4 + F out C2H6 ) 2 * ( F out C2H4 + F out C2H6 ) + F out CO2 + F out CO -F in CO2 (3) CO selectivity is also defined as a function of the carbon yield: S CO = F out CO 2 * ( F out C2H4 + F out C2H6 ) + F out CO2 + F out CO -F in CO2 (4) CO 2 selectivity is calculated by taking into account the inlet feed: S CO2 = F out CO2 -F in CO2 2 * ( F out C2H4 + F out C2H6 ) + F out CO2 + F out CO -F in CO2 ( 5 ) Contact time is given by the following equations: Contact time(g s -1mol -1) = m catalyst F inlet CH4 (7) Productivity of a product i (µmol g -1 s -1 ) is the amount of product per gram of catalyst: Pi = F outlet i m catalyst (8) with i representing a product formed during the OCM reaction, such as CO, CO 2 , C 2 H 4 or C 2 H 6.
IR thermography recorded the hot spots, so identifying the catalyst-bed zone responsible for the oscillatory behaviour, related to the dynamic structural change of the metal particles. Interestingly, it was demonstrated by this way that the oscillations of the Pt oxidation state and the hot spot regions evolved simultaneously and moved from the end toward the beginning of the catalyst bed with increasing reaction temperature  [176]. The polarization response of a coplanar electrochemical capacitor covered with an ionic liquid as the electrolyte has been examined using a combination of XPS and SEM. The spatiotemporal distribution of the ionic liquid surface potential was evidenced through SEM high spatial resolution and speedy imaging, together with the application of a data mining algorithm.
A Pawley refinement was successfully completed, further supporting the feasibility of this cell. The short value of the c parameter strongly suggests the formation of a MOF with a rodshaped secondary building unit (SBU), similar to previously reported structures including related pyrazole  [43, 44]  or tetrazole  [45, 46]  based linkers. Electron density maps were generated by applying the charge flipping method to the integrated intensities from the PXRD pattern, and used to build a crystal model in the P2 1 /c space group, consisting of inorganic SBUs running along the crystallographic c axis, formed by cobalt atoms coordinated to bridging tetrazole rings (Fig. 2a ). The model was geometrically optimized with force-field based energy minimization procedures coupled with Rietveld refinement cycles, using Biovia Materials Studio Software package  [47].
We noticed that the relative intensities of some peaks in the experimental pattern are lower than the calculated one, which is possibly due to preferred orientation effects, expected for the plate like morphology of the crystals (Fig. 1e ), and the diffraction data acquisition in reflexion geometry (Figs. S5  and S6 ). Nitrogen gas adsorption isotherms were measured after the evacuation at 373 K overnight to remove solvent molecules (under these conditions, PXRD pattern is maintained). The surface area calculated using Brunauer-Emmett-Teller (BET) method from the nitrogen gas adsorption data result in 43. 0 m 2 g -1 for Co-NDTz (Fig. S7 ) and 4. 47 m 2 g -1 for Co-NDPhTz. However, the CO 2 sorption measured at 273 K (Fig. 2d ) reveals for case of Co-NDTz a Dubinin-Astakhov  [48]  CO 2 specific surface area of 636 m 2 g -1 and a CO 2 uptake of 2.
Acknowledgements Authors acknowledge to Grants  PID2020-112590GB-C22  and  PID2020-112590GB-C21  funded by  MCIN/AEI/10. 13039/ 501100011033. A. V. G. thanks for  FPU17/03463. We acknowledge  Dr. Fátima Esteban  from  ICMM X-Ray Diffraction Facility  for assistance in PXRD data acquisition.
In practice, globally, sensor manufacturers calculate the term [ φ F 750 400 E(λ) × a * chla (λ)] by immersing their instruments in a monospecies culture or a dye, constructing the curve [chla] ~[Fchl] and assuming that this term is constant regardless of the conditions. The dataset used by Roesler includes CTD, moorings, and profilers, but all the data during which the PAR had values greater than or equal to 200 µ Einstein m -2. s -  (equivalent µmol m -2. s -  ) are discarded to avoid quenching (this is the value from which the yield drops by more than 10%). Roesler calculated the calibration coefficient of a sensor successively immersed in different cultures of mono-species algae. As shown in Figure  2 , the calibration slope of optical sensors varies according to the species of algae encountered.
Materials and Methods The data presented in this work were collected during campaigns carried out between 2012 and 2020. The equipment consisted of an SBE911 + CTD (Figure  4 ), a reference SBE35 probe to check the validity of temperature measurements, and a carousel of 12 sampling bottles. Before deployment, the CTDs were calibrated in pressure, temperature, and conductivity in a metrology laboratory. Therefore, we agree with the choice of HPLC as the reference method for the analysis of chlorophyll a in seawater samples. All these studies only partially address our problem. In particular, we have not observed, in our sea campaigns, either the influence of CDOM or the perturbation due to non-photochemical quenching, so we do not make any particular corrections on the raw values of our sensors.
Materials and Methods The data presented in this work were collected during campaigns carried out between 2012 and 2020. The equipment consisted of an SBE911+ CTD (Figure  4 ), a reference SBE35 probe to check the validity of temperature measurements, and a carousel of 12 sampling bottles. Before deployment, the CTDs were calibrated in pressure, temperature, and conductivity in a metrology laboratory. The values obtained in situ were compiled into. btl files generated by SBE data processing 7. 23. 2 (Seabird Company software), which is the data conversion and processing software for the Seabird SBE 9 CTD profilers. The software can be downloaded for free from the manufacturer's website (https://www. seabird. com/software (accessed on 22 November 2022). The. btl files provide very accurate values, averaged around the trigger of the sample bottle closure.
Several types of instruments allowing the measurement of chlorophyll in vivo were used during these campaigns and some of them were lent to us by external laboratories. This is the case of the Fluoroprobe BBE, the Phyto-PAM, and the flow cytometer. The Aquatracka III (Chelsea) is a sensor whose range is from 0. 01 to 100 μg/L. There is a version with a maximum immersion of 600 m, and another at 6000 m. The excitation wavelength was set at 430 nm, and the emission wavelength at 685 nm. The optics were fitted with a xenon lamp emitting flashes at 5. 5 Hz (Figure  5 ). The signal was logarithmically amplified. The values obtained in situ were compiled into. btl files generated by SBE data processing 7. 23. 2 (Seabird Company software), which is the data conversion and processing software for the Seabird SBE 9 CTD profilers.
These signals separated by optical filters were collected by photomultipliers (PMT), amplified, digitized, processed, and stored by a computer through a dichroic mirror and an optical filter. This individual analysis process (cell-by-cell) is multiparametric and can be performed at the speed of several thousand events per second. The computer calculated the statistical data associated with the distributions of the measured parameters and represented them in the form of histograms (one parameter) or cytograms (two parameters) on one or more populations, whose cellular properties were thus evaluated. The sorting function of the most advanced flow cytometers makes it possible to physically sort one or two cell populations defined by their optical properties. Flow cytometry makes it possible to count the different populations of photosynthetic phytoplankton on the basis of the fluorescence of phytoplankton pigments.
This is why our 2012 and 2013 calibrations only have three levels. Even so, the impact on our calibration was limited because a small number of trials affects the uncertainty more than the slope of the regression. In Figure  9 , it is observed that for the same instrument, the slopes differed from one culture to another. It was not possible to establish a robust calibration coefficient. In situ calibration also revealed large variations, thus confirming the literature data (Ricour et al. [39] ). When the pre-campaign calibration was completed, the sensors were deployed at sea with their new slope factor. One liter of seawater was taken from the Niskin bottles and the samples were filtered through GF/F filters, and the filters were kept in liquid nitrogen. Upon return to shore, the samples were analyzed by HPLC.