dataesr/scientific-paragraphs-categorization huggingface dataset:
fr). F. Bezerra and R. Ecoffet are with CNES, 31401 Toulouse, France (e-mail: francoise. bezerra@cnes. fr; robert. ecoffet@cnes. fr). (e. g. , RPP-sensitive volume and critical energy), and thus the overall accuracy of this rate prediction approach. By analyzing each of the contributions (physical processes) leading to an upset and using experimental measurements of SEU sensitivity of a device, a method to calibrate all the sensitive parameters is proposed. A dedicated GEANT4  [18] ,  [19]  application has been developed to implement this method. The MicroElec  [20] ,  [21]  module has been used to simulate the full ionization cascade. This article is divided into two parts. First, the procedure used to determine the parameters of the target system is explained. Then, in a second step, the proposed method was applied to a device having a large data set involving electron, proton, and heavy ion data.
The MicroElec module  [20] ,  [21]  was used to simulate the transport of these particles down to low energies (down to 16. 7 eV for electrons, which allows one to correctly track the secondary electrons, and thus check if they leave the sensitive volume). The case of Renesas DDR SRAM 45 nm has been treated. The best set of RPP parameters has been estimated. The initial RPP parameters are to be defined. Random RPP parameters could be chosen, but to optimize the approach, RPP parameters can be found in the literature for equivalent devices (technological node, technological architecture…). A first set of RPP parameters has been defined as follows. -Overlayer thickness: 10 µm. -Sensitive volume : a 500-nm sided parallelepiped. -Critical energy: 30 keV. Based on these initial RPP parameters, the overlayer characteristics are first extracted from the position of the ionization peak.
But good prediction requires the use of a charge recombination model and the consideration of the triggering of multiple events. To do so, simple models were used. To define the intrinsic parameters of these models, a study conducted on equivalent devices (bulk technology) to the Renesas was carried out. were found in the values of these parameters. It should, however, be noted that this preliminary study was necessary to investigate the case of the Renesas device. Nevertheless, a database of these parameters could be created from all the SEU measurements available in the literature. Our conclusions on the low dependence of these parameters according to the integration levels could be then validated. In addition, a strong dependence of these parameters with the technological architecture (bulk, SOI, …) is expected.
In the later simulations we will use the data depicted on the red curve. To our knowledge, the data was obtained by the Red Bull Stratos team with a numerical weather forecast model. For the drag coefficient and the projected area, we use the relations proposed by Guerster and Walter  [7] : 𝐶 𝐷 = 𝐴 for 𝑀𝑎 = 𝑣 𝑧 𝑀𝑎𝑐ℎ 1 ≤ 0. 6 (8)   =  + ( -0. 6) 2 for 0. 6 ≤  ≤ 1. 1   =  + 0. 25 •  -( -1. 1) for 1. 1 ≤   (10)  and 𝐴 ⊥ (𝛽) = 𝐴 𝑥 sin(𝛽) + 𝐴 𝑧 cos(𝛽) (11) with   = 1. 19  2 the effective front area and   = 0. 525  2 the effective top area  [7] , A, B and C three parameters that should be estimated in the inverse problem. The last needed information, and probably the most difficult to obtain is the angle of attack  during the leap. Guerster and Walter  [7] , estimate an angle of attack  =  2 - with the full video from Baumgartner's point of view during the leap.
The numerical implementation The programming language we use is Python, a language which gives readable code that closely resembles the equations. The equation (3) is a system of two first-order equations that can be solved by numerical integration. There are a lot of numerical tools called "ODE solver" (ordinary differential equation) to solve this kind of equation  [16]. Here, we have chosen to work with the SciPy ecosystem  [17] , and in particular with the module scipy. integrate. solve_ivp. This module allows us to choose between all the classical numerical integration methods, and trigger events to stop the integration time if needed. By default, we select the Runge-Kutta method. We can also set the maximum allowed time step size during integration, which allows us to properly control any variations in the parametric functions, like the angle of attack.
Here, we have chosen to use LMFIT: Non-Linear Least-Squares Minimization and Curve-Fitting for Python  [18]. This library allows us to use more than twenty methods of minimization, from the local Levenberg-Marquardt least squares method to the Markov Chain Monte Carlo (MCMC) algorithm, amongst others. A complete discussion of inverse methods is beyond the scope of this document; the aim here is simply to give students access to up-to-date methods and algorithms as "gray-box", and not completely "black-box". The use of lmfit. minimize() module is straightforward, as long as the user starts with one of the numerous examples available on the GitHub repository ( https://lmfit. github. io/lmfitpy/examples/index. html) and adapts it to their problem. Principally, the user has to build a callable function that corresponds to the direct problem.
Simulation code Numerical simulations are performed in the cylindrical coordinate system (r, φ, z) using PHOENICS 2018  [39, 40] , a commercial computational fluid dynamics (CFD) software. PHOENICS is a general-purpose CFD code simulating steady or unsteady, two-or three-dimensional turbulent or laminar, single-phase or multi-phase, compressible or incompressible flows. The numerical procedure is of the finite-volume type in which the original partial differential equations are converted into algebraic finite-volume equations with the aid of discretization assumptions for the transient, convection, diffusion and source terms. For that purpose, the solution domain is subdivided into several control volumes on a mono-block mesh using a conventional staggered-grid approach. All field variables except velocities are stored at the grid nodes, while the velocities themselves are stored at staggered cellface locations which lie between the nodes.
Acknowledgements This work was financially supported by the  National Natural Science Foundation of China  (Grants No. 11532015 ,  U1738119 ),by China's  Manned Space Program (TZ-1 ) and by the Joint Project of  CMSA-ESA Cooperation on Utilization in Space , and was conducted within the framework of a  joint Doctoral Training Program between Aix-Marseille University  and  Institute of Mechanics (CAS)  with a fellowship provided by the  China Scholarship Council (CSC) , which is gratefully acknowledged.
Mechanical testing of 3D printed samples 3D printed samples have been tested under displacement control in quasi-static uniaxial compression (Instron 5569A, USA) with a load cell of 50 KN. All samples were compressed to a maximum strain of 70%. The Bluehill 2. 0 software was connected to the Instron system to control the machine and acquire the data. Two circular smooth platens were used to transfer the compressive force from the load cell. The platens were cleaned with alcohol before each test. The cross-bar head moved at a speed of 2. 5mm/min, corresponding to a strain rate of. Cyclic loading-unloading tests were again conducted through the Bluehill software by using a triangular waveform. Five cycles have been performed in total, with the most important parameters for these cyclic tests being the maximum and minimum displacements.
The mechanical behavior of the 3D printed samples was simulated using the finite element method (Abaqus 6. 14, Dassault Systems, France). The geometric models of the VW and MCC structures were meshed using pre-processing software (Hypermesh, Altair Engineering Inc, USA) with the C3D8 element. Both the VW and the MCC structures were meshed using solid mapping in Hypermesh with 283392 elements. We have performed convergence tests with 283392 and 198720 elements. The results indicate that the number of elements used here is sufficient to capture the mechanical response of the proposed architectures. The bottom boundary was constrained along the vertical direction, and a vertical displacement was applied to the top of the model. General self-contact interaction with a friction coefficient of 0.
2 was defined for tangential motion and hard contact for the normal one. For the weak form of the dynamic equilibrium equation, one is encouraged to refer to references  [60, 61]. A dynamic explicit solver with an adaptive time-stepping was used to perform the simulations. A user-defined viscoplastic constitutive model was adopted for the VeroWhitePlus and embedded to ABAQUS through the VUMAT interface. The VUMAT code is available for readers to repeat the analysis in the appendix of reference  [62]. Tensile experiments were performed at different strain rates in ref. [62] , i. e. , , ; even at those relatively low strain rates, significant differences were reported. Therefore, it is necessary to choose a viscoplastic model to perform the simulations. This constitutive relation was initially developed for polymers  [63, 64]  and then widely used for 3D printed photopolymers  [62, 65]  such as VeroWhitePlus used in this work.
Acknowledgements ∆A ‡ = ∆A ξ ref,R ′ →ξ * -k B T ln ( h k B T ⟨| ξ * |⟩ 2 P (ξ ref,R ′ ) ) + ∆A ξ ref,R →ξ ref,R ′ + k B T ln ( P (ξ ref,R ′ ) P (ξ ref,R ) ). The last two terms on the right hand side cancel each other because, by definition, ∆A ξ ref,R →ξ ref,R ′ = -k B T ln ( P (ξ ref,R ′ ) P (ξ ref,R ) ) leading to a desired result ∆A ‡ = ∆A ξ ref,R →ξ * -k B T ln ( h k B T ⟨| ξ * |⟩ 2 P (ξ ref,R ) ) = ∆A ξ ref,R ′ →ξ * -k B T ln ( h k B T ⟨| ξ * |⟩ 2 P (ξ ref,R ′ ) ) proving the independence of ∆A ‡ on the choice of the reference reactant state. Table  1 : Free energy of activation (eV) computed for the proton exchange reaction in chabazite at T=600 K. The reference free energies for the target methods optPBE-vdW and HSE06 were obtained by a direct MD calculation (Reference A) or through the application of FEPT to 5000 (TS) or 10000 (R) configurations selected from data generated by the production (PBE) method at T=600 K (Reference B).
T. B. acknowledges support from Slovak Research and Development Agency under the Contracts No. APVV-15-0105 and No. VEGA-1/0777/19. D. R. acknowledges financial support through the COMETE project (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie) co-funded by the European Union under the program "FEDER-FSE Lorraine et Massif des Vosges 2014-2020". Part of the results of this research have been achieved using the Computing Center of the Slovak Academy of Sciences acquired in projects ITMS 26230120002 and 26210120002 supported by the Research and Development Operational Program funded by the ERDF. Let us consider two different choices of reference states labeled as ξ ref,R and ξ ref,R ′ and let us rewrite Eq. 3 as follows:.
the choice of the refer- ence state T. B. acknowledges support from Slovak Research and Development Agency under the Contracts No. APVV-15-0105 and No. VEGA-1/0777/19. D. R. acknowledges financial support through the COMETE project (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie) co-funded by the European Union under the program "FEDER-FSE Lorraine et Massif des Vosges 2014-2020". Part of the results of this research have been achieved using the Computing Center of the Slovak Academy of Sciences acquired in projects ITMS 26230120002 and 26210120002 supported by the Research and Development Operational Program funded by the ERDF. Let us consider two different choices of reference states labeled as ξ ref,R and ξ ref,R ′ and let us rewrite Eq. 3 as follows: A Independence of ∆A ‡R→P on.
Overall, it can be concluded that the experimental database for the combustion chemistry of the aliphatic amines needs to be augmented and kinetic models need to be developed with a higher level of detail. Diethylamine has been chosen in this work as a representative model compound of secondary amine species present in bio-oils. The pyrolysis and oxidation chemistry of diethylamine (DEA) is studied -for the first time -both experimentally and theoretically through simulations with a newly developed kinetic model. Pyrolysis experiments are performed using two different experimental units: a jet-stirred reactor and a tubular reactor. Oxidation experiments are carried out using a jet-stirred reactor. The temperatures range from 500 to 1100 K, at a pressure of 1. 07 bar and both pyrolysis as well as oxidation at three different equivalence ratios are studied (φ = 1.
Kinetic model development A kinetic model is constructed for the pyrolysis and oxidation of DEA. This model consists of two parts, i. e. (1) a DEA kinetic model and (2) a kinetic model to account for the smallest species and the reactions between these species developed by Glarborg et al. [29]  as a base mechanism. The DEA kinetic model is generated automatically using the automatic kinetic model generation tool Genesys  [28]. Starting from a number of initial species, a set of user-defined reaction families and constraints, this program automatically generates an elementary step kinetic model. Each reaction family specifies the rearrangement of bonds and atoms to go from the reactant(s) to the product(s) through the course of an elementary reaction. The thermodynamic consistency of the model is accounted for by implementing the reactions as reversible.
To prevent the generation of kinetically insignificant species and reactions and hence limit the network size, the rule-based termination criterion is applied. Rules or constraints are specified to the reaction families and the product species. After the generation of the network, thermodynamic and kinetic parameters are assigned to all species and reactions. For this, Genesys is connected with user-defined databases containing species and reactions data from quantum chemical calculations. If available, entries from these databases are used. For DEA, quantum chemical calculations are performed for all the main species and reactions important to accurately describe the DEA chemistry, including the low-temperature oxidation chemistry. In case no ab initio thermodynamic data is available, Benson's group additivity method  [39]  is applied, for hydrocarbons  [40, 41] , oxygenates  [42]  and nitrogen-containing compounds.
In case no ab initio kinetic data is available, the group additivity method developed by Saeys et al. [43]  and extended by Sabbe et al. [44] [45] [46]  and Paraskevas et al. [47]  is used. For reactions with a nitrogen atom in the reactive center, either in-house developed group additivity schemes or rate rules are used  [48]. The automatically generated model for DEA is merged with the mechanism of Glarborg et al. [29]. This Glarborg et al. mechanism contains 150 species and 1397 reactions. It describes the chemistry of small hydrocarbons (C1-C2) and nitrogen-containing species, including the formation and destruction of NOx in a combustion process. In the case that the same species or reactions appear in both the automatically generated Genesys mechanism and the Glarborg et al. mechanism, it is opted to retain the kinetic parameters from the Genesys mechanism.
Acknowledgements The computational resources and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by the  Research Foundation -Flanders (FWO)  and the  Flemish Government -department EWI. Cato A. R. Pappijn  acknowledges financial support from a doctoral fellowship from the  Research Foundation -Flanders (FWO). The authors also would like to acknowledge the financial support from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  ( ERC  grant agreement no. 818607 ),  COST Action   CM1404  " Chemistry of Smart Energy Carriers and Technologies" (SMARTCATS)  and the  IMPROOF  project ( H2020-IND-CE-2016-17/H2020-SPIRE-2016 ) under European Union's  Horizon 2020 research and innovation program  (grant agreement no.
Acknowledgments The  Ministère de l'Enseignement Supérieur et de la Recherche , the  Centre National de la Recherche Scientifique (CNRS) , the  Conseil Régional de Bourgogne  (PhD JCE grant #  2015-9205AAO033S04139 / BG0003203 ), and the  French Research National Agency (ANR)  via project  JCJC ''SPID "  ANR-16-CE07-0020  and project  JCJC "WazaBY "  ANR-18-CE18-0012  are gratefully acknowledged. This work is part of the projects ''Pharmacoimagerie et agents théranostiques" et ''Chimie durable, environnement et agroalimentaire" supported by the  Université de Bourgogne  and the  Conseil Régional de Bourgogne  through the  Plan d ' Actions Régional pour l'Innovation (PARI)  and the  European Union  through the  PO FEDER-FSE  Bourgogne  2014/2020  programs. It was performed within the Dijon's pharmaco-imaging consortium, a regional center of excellence in pharmacoimaging.
This work was also supported by a  French Government Grant  managed by the  French National Research Agency (ANR)  under the program " Investissements d'Avenir " (reference  ANR-10-EQPX-05-01/IMAPPI Equipex ). Chematech® society is warmly thanked for providing DO3A-(tBu) 3 -NH 2 macrocycle. FrenchBIC, GDR AIM, and OncoDesign® are acknowledged for fruitful discussion. LAG acknowledged the  French Research National Agency (ANR)  for her grant ( SADAM ANR-16-CE07-0015-02 ). Mr Soustelle , and  Ms M. -J. Penouilh  are gratefully acknowledged for HR-MS, NMR analyses, and  Dr Myriam Laly  for ionic chromatography.
Multiple examples of each morphology of carbonaceous matter have been measured to produce a statistically significant dataset, depending upon their abundance in the studied samples. The matrix invariably contains a small amount of disaggregated carbonaceous matter of indeterminate origin, thus serves as the ideal benchmark against which elemental concentrations of specific larger microstructures are normalised (i. e. ambient conditions). For each sample, multiple analyses of the matrix adjacent to microstructures of interest were conducted, providing the average matrix value against which analyses of carbonaceous material within that microstructure were normalised. X-ray counts can be converted into concentrations following the rationale and methods described in Halden et al. 56  and Campbell et al.
The limit of detection (LOD) is defined as the concentration of an element that would give rise to an X-ray peak having an intensity equal to the three-sigma fluctuation of the background underlying the peak. In GUPIX software, LOD is defined as 3 times the square root of the background over 1 full-width half-maximum centred on the principal peak centroid. This corresponds to the LOD definition used for fluorescence spectrometry. The value of LOD can vary with the concentration of the detected element, the preparation of the sample and more generally with the conditions of analysis. In order to achieve both the best conditions of analysis and the lowest background, we used the soft borosilicate glass NIST1411 standard as a reference material. We also prepared the samples accordingly to avoid any parasitic radiation due to the interaction of the beam with the chert matrix.
PIXE standardisation is based on the accurate determination of an instrumental constant independent of the X-ray energy. In practice, several uncertainties arose mainly from the inaccurate characterisation by the detector and uncertainties in X-ray transmission. GUPIX software takes into consideration all of these deviations from the ideal measurement through the determination of a sole parameter termed the "H-factor". For that purpose, two standards were used: soft borosilicate glass NIST1411 and stainless steel AISI 15-7PH. The combination of these two standards covers the energy range from 1 to 20 keV in which all the potential trace elements could be detected. NIST1411 was also used to evaluate the matrix effect and finely tune the beam conditions and detection set-up for the analysis of these cherts.
Carbon isotope ratio mass spectrometry. In situ carbon isotope ratio determinations were performed using a CAMECA IMS1280 large-geometry SIMS instrument at the Swedish Museum of Natural History operating in scanning ion imaging mode. Following a 60 sec. pre-sputter with a 20 kV incident energy, ca. 1 nA Cs beam rastered over a 25 ×25 µm area to remove the gold coating, the beam was reduced to a critically focussed <1 µm analytical spot, with a beam current of ca. 100 pA, which was rastered over an area of 20 × 20 µm during acquisition of data. A low energy electron flooding gun was utilised to prevent charge build up on largely insulating target areas. Sputter secondary ions were steered back onto the ion optic axis using the dynamic transfer optical system, a secondary raster that is synchronised with the primary raster to permit acquisition of data from a scanned area at high mass resolution.
The secondary ion species 12 C -and 13 C -were separated at a mass resolution of 4000 (M/ΔM), in order to resolve  13  C from  12  C 1 H and detected sequentially in the axial ion-counting electron multiplier (E Max ) with counting times of 1 sec. and 5 sec. respectively over 100 cycles. The resulting ion images were processed using CAMECA Winimage2 software to identify regions of interest (ROIs) of high C concentration, from which the 13 C/12 C ratios were calculated, with the 44 ns E Max deadtime correction applied at the pixel level. Analyses of an in-house reference material, Cpyr2, a pyrolised graphite disk with a δ 13 C PDB value of -35. 4‰ (G. D. Layne, Memorial University of Newfoundland, pers. comm. ) were performed using exactly the same primary beam and raster conditions in order to retain identical sputtering conditions and, hence, identical mass bias relative to the target graphitised carbonaceous material.
Acknowledgements We are grateful to  Giorgio Gasparotto  ( BiGeA-Bologna ) for help with SEM work, and to  Sylvain Janiec  ( ISTO-Orléans ) for the production of thin sections. We acknowledge financial support from the  French National Centre for Scientific Research (CNRS) , the  French Space Agency (CNES) , the  MASE (Mars Analogues for Space Exploration) project  ( EU-FP7  Grant no. 607297 ), and the  INACMa  ( Inorganic Nanoparticles in Archaean Carbonaceous Matter -a key to early life and palaeoenvironmental reconstructions) project  ( EU-FP7 Grant no. 618657 ). The NordSIMS facility in Stockholm is supported by  Swedish Research Council  infrastructure grant no. 2017-00671  and the  Swedish Museum of Natural History ; this is NordSIMS publication no. 635. KHL received generous support from the  COST ORIGINS Action  in the form of Grant no.
1f , short pulses at -/+8 V allow recovering the initial state in Co 3 O 4 after each cycle. Both materials show no progressive irreversible gain/loss of magnetic signal upon successive cycling. However, while the M amplitude in each cycle remains stable for CoN, a loss of about 25% is observed in Co 3 O 4 after the first cycle. Overall, Fig. 2  corroborates that magneto-ionic rates, onset voltages and endurance are better when using nitrogen than oxygen migration. The data are shifted in M-axis to make them distinguishable. Cyclability was carried out under the application of 5 kOe to ensure being above the anisotropy field and, thus, in saturation.
All spectra contained at least 1×10 7 counts. The spectra were deconvoluted using the non-linearly least-squared based package PALSfit fitting software 37 into discrete lifetime components, which directly confirm different defect types -i. e. , sizes-(Fig. 4 ). The corresponding relative intensities (I i ) reflect to a large extent the concentration of each defect type. In general, positron lifetime (τ i ) is directly proportional to defect size, i. e. , the larger the open volume, the lower the probability and the longer it takes for positrons to be annihilated with electrons  [36] [37] [38] [39] [40]. The positron lifetime and its intensity are probed as a function of positron implantation energy E p or, in other words, implantation depth (thickness). A mean positron implantation depth z mean can be roughly approximated by a material density dependent formula  41  : z mean (nm) = (36/ρ(g cm -3 ))•(E p (keV))  1.