dataesr/scientific-paragraphs-categorization huggingface dataset:
Data pre-treatment To secure an appropriate analysis of the multivariate nature of the problem, the thermochemical variables obtained from the solving of Eqs. (1) and 2 are going through pre-treatments, such as centering and rescaling and unsupervised machine learning, like clustering with Kmeans  [15]  , and dimension reduction with LPCA  [10]  , before the ANN training is to be applied. This strategy is expected to close the gap between the previous works  [22]  , in which artificial data augmentation was required to secure accuracy, and the introduction of complex chemistry in the numerical simulation of large scale furnaces. Following the recent study by D'Alessio et al. [7]  in the context of adaptive chemistry for reacting flow simulation, the original dataset is first pre-processed with centering and auto-scaling (standardizing) to insure reliable and robust results.
Artificial neural network The ANN regression structure ( Table  1  ) used in this study is adopted from our previous works  [22]. The ANN consists of 1 input layer, 4 dense hidden layers with the rectified linear activation function (ReLU) and 1 output layer. The total number of parameters (weights and biases) is 179,851. The ANN input is fed by the data in PCA space, composed of 11 PCs which are obtained by the projection of the 11 original variables from composition space into PCA space. The target is the increment of species composition and temperature (i. e. , the ANN learns how to integrate the time evolution of chemistry). The database is split into training set (81%), validation set (9%) and test set (10%) for model performance evaluation. The training process is performed using Tensorflow 2 with GPU support (NVIDIA GeForce GTX 1080 Ti) and the Adam optimizer in default setting.
Conclusion A novel methodology based on the training of neural networks was proposed to reduce combustion chemistry and applied to a nonpremixed syngas oxy-flame canonical configuration. The unsupervised learning technique K-means was used to cluster the entire dataset, obtained from the solution of stochastic particles in a turbulent micromixing system, into multiple subsets. In the case of a two-inlet problem, with one of the streams bringing the energy necessary for ignition, the proposed decomposition in clusters is likely to be robust and generic. Indeed, after some turbulent mixing, the mixture will always ignite to then evolve towards equilibrium. Some level of finite rate chemistry may appear, but full quenching is very unlikely, especially in the case of oxy-combustion studied here.
For every transfer, Fig. 3  first and second parts of the operator evaluation form syringe or vial activity measurement was simulated by placing it in the well of the dose calibrator. At the end of the transfer sequence, 3 simulated patient doses were prepared. A second identical preparation sequence was repeated with the same "Eluate" vial and 2 new preparation vials. Detailed MFT-F protocol is available in Supplementary data S3.
Acknowledgements The authors thank the electro-radiology technician team of the  Nuclear Medicine unit of Montpellier Cancer Institute  for their collaboration, and  Dr. Isabelle Couret  from the  Nuclear Medicine department of CHU de Montpellier ,  Dr. Danièle Alizé  and  Dr. Laurie Barthélémi  from the  Nuclear Medicine department of CHU de Nîmes  for their technical advises.
2). In typical applications of AGNES one checks: A) the stabilization of trajectory at (at least) one gain; B) the Nernstian relationship between response functions at (at least) two gains. Some systems, in the published literature, where [In 3+ ] has been measured, are: -Speciation with NTA  [10]  confirmed with results from ref. [36]  -Speciation with oxalate  [10, 11] , confirmed with results from ref. [37] -pH impact on [In 3+ ] [9], confirmed with stability constants reported in ref. [38]  -In(OH) 3 solubility product (from pH 4 to 6)  [11]  agreeing with the database NIST 46. 7, reaching concentrations in the pmol L -1 range. The impact of the irreversibility of the redox couple In 3+ /In°on AGNES in these works was: i) the unsuitability of Differential Pulse Polarography to compute the gain associated to a given deposition potential (so the gain is obtained from a calibration), and ii) a slightly larger minimum time to reach equilibration in solutions with no added ligand ("rule of thumb" of ten times the gain, instead of seven times as in Zn, Cd and Pb when using HMDE, smallest drop 1)  [10].
Methods Quantitative 1 H NMR spectroscopy was performed on a BRUKER Avance III 400Hz spectrometer (Bruker Gmbh, Mannheim, Germany), equipped with a BBFO+ 5 mm probe for 1 H analysis. Reactive moieties were normalized to the Si-CH 3 peak, taken as the reference. Samples were dissolved in deuterated chloroform, at a concentration of 30 mg in around 0. 6 mL of solvent. A total of 256 scans were recorded with a relaxation time of 2 s between each scan. Proton NMR spectra simulations were performed using the software Mestrenova 10. 0. 2 and associated Mnova NME plugin (MestreLab Research, Santiago de Compostela, Spain). Molar mass measurements were carried out via size exclusion chromatography with toluene as an eluent. Samples were dissolved in HPLC grade toluene from Sigma Aldrich (Saint Quentin Fallavier, France) at a concentration of 3 mg/mL and then filtered on a 0.
ACKNOWLEDGMENTS This work was supported through funding from the  Agence Nationale de la Recherche  (grant  ANR-18-CE09-0018 ). A CC BY or equivalent licence is applied to Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. Computational resources were provided by  IDRIS  (national computing center of the CNRS) and the PSMN (computing center at  ENS de Lyon ).
Characterization 2. 2. 1. Lab Characterization The surface area (Brunauer-Emmett-Teller, BET) and porosity analyses of the catalysts (powder, pelletized and printed) were performed using nitrogen adsorption on a BEL-SORPmini gas sorption analyzer. The macro and microstructure and elemental distribution images were obtained using a FEI Nova NanoSEM 450 operated at 20 kV and coupled with an EDX (energy dispersive X-ray spectroscopy) QUANTAX 200 system. X-ray diffraction (XRD) analysis was carried out on a Philips/Panalytical X'Pert Pro powder diffractometer with a CuKα radiation source operated at 40 keV and 40 mA. The diffraction data was analyzed using the X'Pert High Score Plus software.
The final XRD-CT images (i. e. , reconstructed data volume) were reconstructed using the filtered back projection algorithm. The details of the Rietveld refinement of the XRD-CT data can be found in the authors' previous work  [34]. The OCM reaction was performed at atmospheric pressure with a CH 4 :O 2 :N 2 ratio of 4:1:4. The outlet gases were analyzed by mass spectrometry at ID15 using an Ecosys portable mass spectrometer (the details of the operating conditions and results are shown in the Supplementary Materials). For the tests 88 mg of structured catalyst were cut to size for a 6 mm OD quartz tube and were heated up to 840 • C in the atmosphere of 5% O 2 in He. The OCM reaction was performed with a gas hourly space velocity (GHSV) 36,000 mL g -1 h -1 and a total flow of 53 s cm 3. The sample was heated using an ID15A in-house box furnace and the gas mixture was fed from the bottom of the reactor.
and A. V. ; resources and supervision, J. -U. R. , A. M. B. ; project administration and funding acquisition, F. G. All authors have read and agreed to the published version of the manuscript. Funding: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 679933 (MEMERE project). We acknowledge the European Synchrotron Radiation Facility for provision of synchrotron radiation facilities. A. M. B. acknowledges EPSRC (award EP/K007467/1) for funding.
1 wt% LiBr) was used as eluent with a flow rate of 0. 8 mL min -1. The column temperature was set to 60 • C. Data acquisition and calculations were performed using Cirrus Multi GPC/SEC software. MALDI-TOF mass spectrometry. Analyses were performed on a MALDI-TOF/TOF Bruker Ultraflex III mass spectrometer using a nitrogen laser for MALDI (λ 337 nm). Mass spectra of 2500 shots were accumulated for the spectra at 25 kV acceleration voltage and reflectron lens potentials at 26. 3 kV. A mixture of peptides was used for external calibration. Polymer samples were dissolved in dichloromethane at a concentration of 5 mg mL -1. The cationization agent used was NaI dissolved in MeOH at a concentration of 10 mg mL -1. The matrix used was dithranol and was dissolved in dichloromethane at a concentration of 10 mg mL -1.
Survey spectra in the energy range from 0 to 1350 eV with 0. 4 eV step were recorded for all the samples. High resolution spectra were recorded with 0. 1 eV step, 100 ms dwell time and 25 eV pass energy. A ninety degree take-off angle was employed in all measurements. Curve fitting was carried out using the CasaXPS software (Casa Software Ltd. , Cheshire, England, UK), which each component of the complex envelope is described as a Gaussian-Lorentzian sum function; a constant 0. 3 (˘0. 05) G/L ratio was used. The background was fitted using a nonlinear Shirley model. Measured transmission function and Scofield sensitivity factors have been employed for quantification purposes. An aromatic carbon C 1s peak at 284. 5 eV was used as the reference of binding energy. Scanning electron microscopy images were recorded with a JEOL JSM-6300 scanning microscope (JEOL Ltd.
Characterizations The rough morphological surface properties of materials were determined with the help of scanning electron microscopy (SEM) using FE-SEM (Jeol Model JSM-6340F (Tokyo, Japan). For elaborating the fine structure of the catalysts, high-resolution transmission electron microscopy (HRTEM) was used and HRTEM images were collected on FEI-TecnaiG 2 Twin TEM (Hillsboro, OR, USA) operating at an acceleration voltage of 200 kV. For sample preparation, a very dilute suspension of material was deposited on a carbon coated TEM grid. TEM images were processed on GATAN micrograph software (Munchen, Germany). The vibration spectra (FT-IR) of samples were recorded on a Perkin-Elmer spectrum RX-1 IR spectrophotometer (Waltham, MA, USA) using a potassium bromide window. X-ray diffraction patterns for determining the phase structure and crystalline properties of the materials were obtained on a Bruker D8 Advance diffractometer (Billerica, MA, USA) working at 40 kV and 40 mA with Cu K α radiation (λ = 0.
Instrumentation A Princeton Applied Research 263A potentiostat/galvanostat was employed to perform cycle tests, and CorrWare 3. 1c software (Oak Bridge, TN, USA) was used for data acquisition. Room-temperature X-ray powder diffraction (XRPD) patterns were collected between 20 and 90 ˝(Δ2θ = 0. 08 ˝, time per step = 17 s, scan speed = 0. 02 ˝/s) with a Siemens D500 diffractometer (Berlin, Germany), using Cu K α radiation (Cu K α1 : λ = 1. 54056 Å, K α2 : λ = 1. 54433 Å). Specific surface areas were determined by the classical BET procedure using a Coulter SA 3100 apparatus (BeckmanCoulter, Fullerton, CA, USA). Desorption isotherms were used to determine the pore size distribution using the Barret Joyner Halenda (BJH) method. Particle morphology was obtained by scanning electron microscopy (SEM), using a Hitachi TM-1000 (Tokyo, Japan) equipped with an energy dispersive X-ray (EDS, SwiftED-TM) detector.
Acknowledgments: This work was supported by the  Spanish National Research Council (CSIC). The author thanks the  Ramon Areces Foundation  for financial support. M. F also thanks the  MINECO  for the research grant  SAF2014-59118-JIN  ( Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad  2014: " Proyectos de I+D+i para   jóvenes investigadores "), and co-funding by  Fondo Europeo de Desarrollo Regional (FEDER).
Acknowledgments : Supports from the  Indo-French Centre for the Promotion of Advanced Research (IFCPAR)/Centre Franco-Indien pour la Promotion de la Recherche Avancée (CEFIPRA)  (Project No. 4705-1 ) and from the  CEA-Enhanced Eurotalents program  are gratefully acknowledged. The TEM-team platform (CEA, IBITECS) is acknowledged for help with TEM images. The " Service de Chimie Bioorganique et de Marquage " belongs to the  Laboratory of   Excellence in Research on Medication and Innovative Therapeutics  ( ANR-10-LABX-0033-LERMIT ). Acknowledgments: We are grateful for financial support from the  NSFC  project ( U1401245 ,  21501052 ), the Acknowledgments:  Rafael Luque  gratefully acknowledges  Consejeria de Ciencia e Innovacion, Junta de Andalucia  for funding project  P10-FQM-6711. Funding from  Marie Curie Actions  under  Innovative Training Networks Project Photo4Future  ( H2020-MSCA-ITN-2014-641861 ), especially for funding  Weiyi Ouyang Ph.
Analytical department is kindly acknowledged for the analysis of samples. Rabah Boukherroub  acknowledges financial support from the  Centre National de la Recherche Scientifique (CNRS) ,  Lille1 University and Nord Pas de Calais region. Acknowledgments: Financial supports by the  European Union  and the  Greek Ministry of Education  ( ERC-09  and  ARISTEIA-2691 ) are kindly acknowledged. I. N. L. gratefully acknowledges the sponsorship from  COST  action  CM1201. We thank  E. Evgenidou  for obtaining the MS spectra. Acknowledgments: This work was supported by  Jiangsu Province  for specially appointed professorship to  Peizhi Zhu , research funds from  Yangzhou University , research funds from  Liuda Rencai Gaofeng , the  Technology Support Program of Science and Technology Department of Jiangsu Province  ( BE2015703 ), the  Jiangsu agricultural science and Technology Innovation Fund Project  (CX( 14 )2127) , the support from the  Testing Center of Yangzhou University , and A Project Funded by the  Priority Academic Program Development of Jiangsu Higher Education Institutions.
Acknowledgments: This work was supported by the  973 Program  (Grant  2014CB932104 ), the  National Natural Science Foundation of China (NSFC) , the  Beijing Natural Science Foundation  (Grant  2152022 ), the  Fundamental Research Funds for the Central Universities  (Grant  YS1406 ) and  Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology  (Grant  BM2012110 ). Acknowledgments: This work was supported by  National Nature Science Foundation of China  (No. 21373273 ),  Hunan Provincial Natural Science Foundation of China  (No. 13JJ1002 ),  Fundamental Research Funds for the Central Universities of Central South University  (No. 2014zzts168 ) and  Shenghua Scholar Program   of Central South University. Acknowledgments: This work was supported by the  National Natural Science Foundation of China  (No.
6 mL/min was applied for the 11 min run. Refractive index (RI) detector and photodiode array (PDA) detector at 254 nm were used. A calibration curve with polystyrene (PS) standards was carried out for molar mass determination. The molar mass calculation was performed with data collected from the UV detector. Differential scanning calorimetry (DSC) analyses were performed using a Q2000 DSC apparatus from TA Instrument (TA Instrument, New Castle, DE, USA) to estimate the polymers' glass transition temperature (T g ) as well as their melting temperature (T m ) and crystallization temperature (T c ), if applicable. Typically, a 1 to 3 mg sample in a sealed aluminum pan was treated following a three-step method: (1) fast heating to 180 • C (the sample was kept for 3 min at this high temperature to erase all thermal history), (2) cooling at 10 • C/min to -80 • C followed by an isotherm of 5 min, and (3) second heating at 10 • C/min to 180 • C.
The delay extraction was fixed at 560 ns, and the frequency of the laser (nitrogen 337 nm) was set at 5 Hz. The acquisition mass range was set to 1500-10,000 m/z with a matrix suppression deflection (cut off) set to 1500 m/z. 16,952. 31). Spectra were processed with flexAnalysis software (Version, Bruker Daltonics, Billerica, MA, USA). Sample preparation was performed with the dried droplet method using a mixture of 0. 5 µL of sample with 0. 5 µL of matrix solution dried at room temperature. The 2-[(2E)-3-(4-tert-butylphenyl)-2-methylprop-2-enylidene]malononitrile (DCTB) matrix was prepared at 20 mg/mL in dichloromethane.
Acknowledgements The authors thank the  French National Agency for Research  for financial support through a  Ph. D. grant  to  K. Onida  and a postdoctoral fellowship to  A. J. Haddleton  ( ANR-19-CE07-0006-ThermoPESO ). L. G. Borrego Sanchez de la Cuesta  and  L. Ibrahimli  are thanked for their help to prepare some of the starting materials. The authors would like to thank the  Centre Commun de RMN de Lyon  ( A. Baudoin ,  E. Chefdeville ,  C. Gilbert ) for NMR analyses. E. Jeanneau  ( Centre De Diffractométrie Henri Longchambon, Université Claude Bernard Lyon1 ) is also acknowledged for X-Ray analyses.
The vibration of the Si-O-modifier bond is detected at the frequency of ca. 1510 cm -1. During the functionalization with non-fluorinated compounds (C6), the bands at the frequency of 2850 cm -1 and cm -1 were observed and assigned to asymmetric and symmetric CH stretching vibrations of the alkyl chain. Asymmetric vibration bands of CH 3 (1462 cm -1 ) and CH 2 with in-plane deformation (1446 cm -1 ) were found for functionalized materials with C6. Moreover, bands found at 1120 cm -1 and 1245 cm -1 , with different intensity, were related to the C-C-C stretching symmetric vibrations. On the basis of the data collected, it can be stated that the functionalization process was performed successfully. Additionally, the confirmation of functionalization was assessed by XPS and HR-TEM analyses. The wide range of XPS spectrum of the selected pristine and functionalized REO -Nd 2 O 3 has been presented in Fig.
Acknowledgment The research was supported by  2017/26/D/ST4/00752 (Sonata 13 ) grant from the  National Science Centre, Poland. The research was also partly supported by the  Franco-Polish Hubert Curien's Partnership Program "Polonium "  PPN/BIL/2018/1/00222 -CAMPUS France PHC Polonium 2019  (n •  42903VH ).
Acknowledgements D. V. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  804320 ). The authors acknowledge the use of TEM instrumentation provided by the  National Facility ELECMI ICTS  ( "Division de Microscopia Electronica", Universidad de Cadiz, DME-UCA ). LL acknowledges funding from the  Andalusian regional government  ( FEDER-UCA-18-106613 ), the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  823717 -ESTEEM3 ) and the  Spanish Ministerio de Economia y Competitividad  ( PID2019-107578GA-I00 ). J. L. acknowledges financial support from the  National Science Foundation of China  (Grant No. 21808134 ) and start-up funding from  Shaanxi University of Science & Technology.
(  14 ). When g E was rigorously calculated with the PC-SAFT EoS, the infinite pressure was approximated by imposing 0. 99 η = and the pure-component parameters were extracted from the paper by Gross and Sadowski  [41]. The 2 curves, visible in Fig. 2 , are indistinguishable what makes it possible to conclude that Eq. (  14 ) is an excellent approximation of At this point, we thought it would be interesting to study the influence of the pressure on the sign of g E predicted by the PC-SAFT EoS. For this reason, two binary systems that experimentally exhibit positive deviations from ideality were selected. These 2 systems are ethane(1) + CO2 and water(1) + ethanol  (2). The pure-component parameters were extracted from the paper by Gross and Sadowski  [41]  for ethane and CO2 and from the database available in Aspen Plus for water and ethanol.
e. , below and near the LII fluence curve plateau region), n,a , B 1,a and B n,a have been set equal to n,s , B 1,s and B n,s , respectively. Following the methodology introduced and described in  [30] , a full central composite design of experiments (DoE) has been coupled to a genetic algorithm-based optimizer (the ga function of MATLAB®) to identify a set of parameters allowing obtaining simulated signals identical to measured ones. Examples of DoE response surfaces obtained by defining an objective function corresponding to a least square sum between experimental and numerical LII time decays obtained at each investigated fluence are depicted in Fig. 1. As one can see by looking at Fig. 1 (a), optimized E(m) and n,s around ~ 0. 25 and ~ 4. 5 × 10 -10 cm 2n-1 •J 1-n , respectively, can be determined.
4. Similarly, the derived B 1,s is well in line with the experimental obser- vations from  [51] , while the orders of magnitude of other parameters are coherent with expected ones  [21]. [23]. These signals have been obtained using a 1064-nm nearly top-hat laser beam and a detection wavelength of 610 nm. This comprehensive work, moreover, provides the spatial-and temporal profiles of the laser energy, NO-LIF thermometry-derived flame temperatures likewise information regarding primary particle and aggregate sizes determined by transmission electron microscopy. All these input data have, therefore, been considered to perform the calculations leading to the agreement between measured and simulated signals depicted in Figs. 3  and 4.
34, σ λn,s =4. 2•10 -10 cm 2n-1 •J 1-n ) CH 4 /O 2 / J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 J•cm -2 Fig. 4  Comparison of model predictions with LII time decays obtained at 12 mm HAB in  [23]  homogeneous beams. Additional details regarding this database can be found in  [46]  as well as in  [25]  where an attempt to model measured signals had already been undertaken. Here again, using the parameterization proposed in Sect. 2. 2. 3, while simply adjusting the E(m) value to 0. 31 allows perfectly reproducing measured LII peak intensities as a function of the fluence, as depicted in Fig. 3. The parameterized model proposed in the present work (with n,s = 4. 2 × 10 -10 cm 2n-1 •J 1-n , B 1,s = 1. 15 J•cm -2 , B n,s = 0. 41 J•cm -2 and ΔH n,s = 1.
-E(m) values comprised between 0. 18 and 0. 31 for Diesel soot have been assessed depending on the particle maturation stage. Such a result represents an important finding as data regarding the evolution of the optical properties of Diesel soot is quite rare in the literature as recalled in  [14]. Regarding methane soot, the E(m) determined by modeling the data from  [23]  led to values comprised between 0. 2 and 0. 34 which are perfectly in line with those reported in  [29]. -Integrating the effect of MS within the absorption process led to estimate E(m) values for Diesel soot 24% lower on average than when considering the classical RDG-FA theory. This, therefore, supports the importance of thoroughly considering aggregate properties to be able to derive consistent information on soot optical properties by LII modeling.
Acknowledgements This research has received the support of the  John R. Evans Leaders Fund  from the  Canada Foundation for Innovation. The authors, moreover, warmly thank  Jérôme Yon  from  CORIA  for fruitful discussions regarding the interpretation of SMPS analyses as well as  S. Bejaoui  from the  NASA -Ames Research Center  for providing the raw LII data from [23] which have been used for the purposes of the model validation described in Sect. 3.