dataesr/scientific-paragraphs-categorization huggingface dataset:
The recording principle is based on 4 HD SDI cameras per box as well as an audio recording microphone connected to a QUAD (Blackmagic Multiview 4), itself connected to a recorder. The streamer (EXTRON) with 32 GB internal memory allows synchronising and merge these audio and video streams. The data comes from four different educational meetings, each one is case based and is introducing specific tools: • Banknotes transport case: students should identify and analyse the main function and associates technical solutions of a case used for transporting banknotes based on a system that tainted the banknotes in case of robbery. This involves practising functional analysis and formatting a FAST (Function Analysis System Technic) diagram. The video recording time of the meeting is 1h06. • Causal analysis of the main function of a car windshield ice scraper: students should analyse a situation where an ice scraper is used, understand the causes that require such a function and the expected consequences.
Each column contains a numerical sequence where the value 1 corresponds to the observation of the interaction and 0 to the absence of observation. The spreadsheets of the four meetings were then exported in CSV format files to be used by Python programs. The curves represented in figures 3, 4 and 5 correspond to the cumulative values of the numerical series observed. Data have been analysed according to the Granger's Null-hypothesis (H0). This null hypothesis for a test between two series x and y is that lagged x-values do not explain the variation in y. It assumes that x(t) Granger-cause y(t). The negation of H0 is the Alternative Hypothesis (HA) where a time serie x Granger-causes a time serie y. We implemented Granger Causality test using the "grangercausalitytests" function present in the statsmodels python library and following the indications of the "machinelearningplus" article 2.
This function takes two time series x and y as inputs and gives results in order to reject or not the H0 between x and y. Among the results of this function we got coefficients corresponding to past values of the first time series. The Null hypothesis for the function "grangercausalitytests" is that the time series x, does not Granger cause the time series y. Grange causality means that past values of x have a statistically significant effect on the current value of y. The null hypothesis that x does not Granger cause y, is rejected if the past values are below a desired size of the test. As in the article, we created a function designed to perform the tests on all combinations of variables of the input dataset in one shot. The result of the function is a matrix where the rows are the response variables and columns are the predictors.
RESULTS We analysed all the data available coming from 4 meetings of the same team carried out in room 4. From meeting 1 to meeting 4. Tables 1 (S2B4, meeting 1), 2 (S3B4, meeting 2), 3 (S4B4, meeting 3) and 4 (S5B4, meeting 4) show the results. Table  1  gives the results of the Granger test for the series S2B4. Based on Granger's assumptions, we analysed all the data available to us from meeting 1 to meeting 4. Tables 1 (S2B4, meeting 1), 2 (S3B4, meeting 2), 3 (S4B4, meeting 3) and 4 (S5B4, meeting 4) show the interactions causing other interactions (in the sense of Granger). We can observe a relationship between individual work and communication interactions in the first three meetings (cf. Table  1, 2  and 3 ). The values (0. 0000) in the cell Individual Work_x, Communication_y make it possible to conclude that the first set of data relating to individual work Granger causes the second -that of communication.
2022) ALMA simulator to simulate interferometric and total power observations of low S/N lines based on a known input model. The resulting simulated observations were imaged using the Physics at High Angular resolution in Nearby GalaxieS (PHANGS)-ALMA pipeline  (Leroy et al. 2021a ). Then we applied the spectral stacking method and assessed how well the stacks recover the known input. In particular, we stacked via the galactocentric radius using simulated CO(2-1) data cubes built on real observations of galaxies from the PHANGS-ALMA survey  (Leroy et al. 2021b). We also present a new public Python package, PYSTACKER that can be used to easily apply these techniques. This utility complements the tool LINESTACKER presented by  Jolly et al. (2020) , which is also validated against simulation.
Description of the spectral stacking method The main goal of the spectral stacking technique is the recovery of low S/N lines by shifting the spectra to a known velocity field defined by a high S/N prior (e. g. CO(1-0) or CO(2-1) or the 1 https://github. com/low-sky/spectral_stack HI 21 cm line) and then averaging the spectra based on another parameter such as environment, star formation rate, or line intensity. Our stacking method is based on den  Brok et al. (2022)  and  Neumann et al. (2023)  and our implementation is available as a Python package, called PYSTACKER 2. We describe the basic steps of the code in the following. We begin with a set of data cubes of the same target with different S/N levels of the input line emission. First, we homogenise the data bringing all data cubes to the same coordinate grid and convolving to the same spatial resolution.
Then we define a prior, typically the most significantly detected line, which is used to obtain the velocity field as the velocity at the peak intensity of each spectrum 3   (Koch et al. 2018 ). We use this velocity field to redefine the spectral axis for each individual spectrum in the cube 4  so that the emission of all lines should be centred at a velocity of 0 km s -1. The result is sometimes referred to as a 'shuffled' cube, in reference to the shuffle task of the Groningen Image Processing System (GIPSY;  van der Hulst et al. 1992). Figure  2  shows the basic functioning scheme of the PYS-TACKER package, which allows for two input options. The first is the PyStructure database, a numpy dictionary containing all the molecular line emission data. The PyStructure database is produced by a separate pipeline and already contains the velocity alignment analogous to the velocity shuffling performed by PyStacker.
3). If signal is present in the stacked line within a given bin, the averaged spectrum should then appear as a clear emission line (e. g. Fig. 1  right panel). For comparison, averaging across different parts of strongly rotating discs without first adjusting to the local velocity yields a broad lower signal-to-noise profile (e. g. see Fig. 2  in  Schruba et al. 2011). The PyStructure files does already include the re-sampled and velocity aligned data. If FITS files are provided, the PyStacker package will perform the velocity alignment given an input prior or take the input velocity field. In both cases, the spectra will be stacked according to the input stacking quantity and processed to retrieve average integrated intensities. The results are then returned as a Python dictionary, which can be read e.
g. following the example script coming with the PyStacker package. We can only reliably shuffle the spectra if the prior is actually detected in the respective spectrum. Thus, if the prior is only detected in a fraction of the spectra inside a bin, we rely on these spectra to infer the average of the bin. In this case we consider the emission in the spectra that could not be shuffled to be equal to zero, such that the average is always measured relative to all spectra inside a given bin 5. We expect this to be a reasonable assumption when stacking a rare faint molecular line such as HCN(1-0) using CO as a prior 6. Within any given bin n, we measure the average spectrum T n,stack (v) = 1 N tot (n) N det (n) i=0 T n,i (v), (1) where N tot (n) and N det (n) are the total number and the priordetected number of spectra in bin n.
Recovery of integrated intensities We apply our method to simulated data cubes with known input to test how well spectral stacking can recover the integrated intensities of molecular line emission as a function of the noise level of the observations. Specifically, we use a set of simulations of molecular line emission produced to validate the PHANGS-ALMA data reduction pipeline  (Leroy et al. 2021a ). As described in  Leroy et al. (2021a) , the simulated CO(2-1) data cubes were produced using the CASA tasks simdata and simobserve using inputs based on real PHANGS-ALMA CO(2-1) images. The simulated observations mimic interferometric observations of the galaxy NGC 3059 similar to the PHANGS-ALMA survey 7. The simulations included the creation of a simulated total power map constructed by convolving the input model to the resolution of the ALMA TP antennas and adding Gaussian noise of the expected magnitude for a real PHANGS-ALMA TP observation.
The input intensity cube, hereafter referred to as the template, is the actual masked NGC 3059 cube from PHANGS-ALMA. We show the integrated intensity map of this template data cube in the upper left panel of Fig. 2. These 'true' data are used to construct simulated 12 m, 7 m, and total power observations and imaged via the PHANGS-ALMA pipeline (for more details see  Leroy et al. 2021a ). Then we run these through the stacking pipeline in this study. The use of real data as a model means that there will be some observational noise in the true data, but we consider that as signal, and explore how well it gets recovered, and it should have only a modest impact on the analysis. The simulations produce images for different combinations of the ALMA main array, the ACA 7 m antennas, and the A104, page 3 of 9 total power data: 12m+7m+tp, 12m+7m, 7m+tp, 12 m and 7 m (Sect.
For the outer radii, an alternative approach could be to average over a larger region, for example binning everything beyond the radius of 6 kpc, in order to potentially recover more of the fainter emission at the cost of spatial information. However, in this case we do not recover more emission due to the steep drop in emission beyond 6 kpc. We use the S/N/1 data (i. e. the simulated data mimicking PHANGS-ALMA CO(2-1) observations) as a prior to account for the varying velocity field across the galaxy and to determine the channels of significant emission to compute the integrated intensities. For each configuration, the respective S/N/1 cube is used as a prior. This means that to stack the 12m+7m+tp cubes, we use the 12m+7m+tp S/N/1 cube as the prior; to stack the 7m+tp data, we use the 7m+tp S/N/1 cube, and so on.
g. by averaging spectra over larger regions). Although this could lose spatial information, and so was not performed here, this approach could potentially recover otherwise undetected emission. Therefore, we recommend adapting the binning parameters to the strength and distribution of the studied line emission. We note that NGC 3059 is a relatively low-luminosity galaxy, and the situation may be more optimistic in somewhat brighter targets. We highlight the differences between the recovered stacks and the true values in Fig. 3. Based on the computed uncertainties of the stacked integrated intensities, σ w , we clip at S/N (W/σ W ) levels of 3, 5, and 10. As expected, we find that with stronger σ W -clipping the stacked line intensities show better agreement with the true values such that for data above 10σ the maximum discrepancy is <35%, and <15% (<8%) in the inner 6 kpc (4 kpc).
Conclusions We performed spectral stacking of simulated interferometric data of the galaxy NGC 3059 as a function of galactocentric radius at different noise levels and combining different telescope arrays. Our main results are the following: A104, page 7 of 9 A&A 675,  A104 (2023)  1. Spectral stacking is able to recover the integrated intensities across most of the molecular disc where the prior is predominantly detected. In the most extreme case we detect a stacked spectrum, even in bins where the integrated intensities of the moment-0 map have a median S/N of 0. 54. For this specific galaxy, all data above 3σ and 10σ agrees within 23% and 15%, respectively, with the expected values if the prior is detected in at least 36% of the spectra contributing to the stack (i. e. within the inner 6 kpc).
2. Using interferometric data only (i. e. without total power information) can filter out up to 30% of the emission even at the typical PHANGS-ALMA sensitivity and even if the prior is predominantly significant. Even more extreme, for lines that are 10 times (e. g. HCN(1-0)), the 12monly or 7m-only configurations miss ∼50% of the emission in the stacked spectra throughout the full molecular gas disc. 3. The critical limitation of the spectral stacking method is connected to the quality of the prior used to align the velocity field and potentially the imaging procedure. If the prior is not detected across most of the bin, we expect to systematically find stacked line intensities that are too low. This might be improved by using low-resolution priors (e. g. HI 21 cm line) or model priors, which provide a completely defined velocity field.
Acknowledgements. We would like to thank the anonymous referee for their insightful comments that helped improve the quality of the paper. L. N. acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - 516405419. AKL gratefully acknowledges support by grants  1653300  and  2205628  from the  National Science Foundation , by award  JWST-GO-02107. 009-A , and by a  Humboldt Research Award  from the  Alexander von Humboldt Foundation. CE acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG) Sachbeihilfe , grant number  BI1546/3-1. J. P. acknowledges support from the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES. E. R. acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC) , funding reference number  RGPIN-2022-03499.
μ-XCT measurements and data processing The μ-XCT measurements were carried out on the 96:2:2 electrodes both before and after the calendering process at the P05 synchrotron imaging beamline (Desy, Hamburg, Germany)  [34] , as detailed in a former paper from our group  [26]. The electrodes of this formulation were used to extract particle morphology and validate the microstructure of the model outcome. The reconstructed data have been segmented into biphasic images (AM and pores + CBD) by using the machine learning segmentation plugin Trainable Weka  [35]  in ImageJ. The segmentation process employs the Fast Random Forest class, the features and parameters which are detailed in Table  S1. The resulting 3D microstructure has an effective voxel size of 0. 642 μm. The biphasic image stacks are imported into the commercial software package Avizo V9.
4 (Avizo, Thermo Fisher Scientific, Waltham, Massachusetts, USA)  [36]  for post-treatment. The secondary particles in the uncalendered electrode are used to construct the particles dataset. One voxel on the surface of the AM phase is eroded to separate the connected large particles. The Watershed-based Object-Separate algorithm is used to label the individual particles. Following separation, one voxel is dilated back to maintain the particles' size.
Initial structure generation The bounding box for each identified particle is then excised from the microstructure, and saved into a 'particle database' as Python serialized objects, along with some metadata, such as their volume equivalent sphere diameters. When building the physical model and establishing the initial structure, the particles are chosen randomly from the newly generated particle database while accounting for the PSD of the real electrode, until the target AM volume is obtained. In order to control the total number of particles in the physical model, preventing our CGMD simulations from getting computationally too costly, the original labeled particles are downsampled by taking every 2 × 2 × 2 voxel 3. Then, the CGMD model structure is prepared by substituting the downsampled voxels with spheres (primary AM particles) of equivalent volume, with a diameter of 1.
Here, we choose 135 GPa as Young's modulus value for the NMC111 particles, which is a reasonable value from literature  [39] [40] [41]. The parameters related to the material properties used in the simulation are shown in Table  1 , and the effective parameters between the different materials (E eff , R eff , m eff ) are calculated automatically by the LAMMPS software according to the formulas mentioned above. To simulate the cohesion between the electrode and the current collector (CC, aluminum foil for the NMC111 cathode), we endow the lower wall of the simulated box with a JKR FF. As a result, an attractive force is added between the lower wall and the particles in contact with it. The mass of the particle is used as m eff , and R eff is set to the radius of the particle. When considering the LJ force between different materials, we use the values of 0.
The FFs used in this model for the whole manufacturing process simulation include the Lennard-Jones FF (LJ) and Johnson-Kendall-Roberts model-based Granular FF (JKR), which are available in the LAMMPS software. The actual values of Young's modulus and Poisson's ratio of the material are obtained from the literature  [39] [40] [41]  and utilized as input parameters for the JKR FF. The reader is directed to the Material and Methods section for a more detailed description of the CGMD procedures. The computational workflow is schematized in Fig. 1. The slurry, dried electrode and calendering simulations (9494 AM particles and 5458 CBD particles for 94%-6% and 10330 AM particles and 3877 CBD particles for 96%-4%) take respectively ~24, ~11 and 9-37 h by using one node (128 GB of RAM) composed of 2 processors (Intel® Xeon® CPU E5-2680 v4 @ 2.
Continuing the analysis of the resulting microstructural models, we turn our attention to the porosity distribution. Here, a watershed-based algorithm (Avizo) is used to segment the pore space into individually labeled pore-fragments. A Pore Network Model (PNM) is then used to analyze these individual pores. Although the pores present diverse shapes, they are approximated as spheres. An equivalent radius is calculated to represent the size and to examine the size distribution. To account for the voxel size of the μ-XCT data (which represents the lowest limit of our analysis), only pores with a radius greater than 1 μm are included in the statistics. The connectivity of the pore network is assessed by connecting neighboring pairs of individual pores with throats. The cross-sectional area of such throats is proportional to the interfacial area shared by the corresponding pair of pores.
The commercial software GeoDict (Math 2 Market)  [47]  is used to fit the individual particles into ellipses, which are then evaluated for Krumbein sphericity ( ̅̅̅̅̅̅̅̅̅̅̅̅ bc/a 2 3 √ , where a, b, and c are the sizes of the axes of the ellipsoid). Fig. 5  gives the distribution of the Krumbein sphericities in the microstructure before and after the calendering process. It can be seen that the sphericities alter owing to calendering, but the peak is always around 0. 7-0. 8. This deformation of particles has already been reported in the experimental literature  [48]. In addition, our model offers the possibility to study the variation of particle orientation during calendering, as described in detail in the Supplemental Information. Furthermore, a comparison of the real particle shapes model and our previous model with spherical AM particles  [25]  has also been achieved.
FUNDING J. X. , A. C. N. , C. L. and A. A. F. acknowledge the  European Union 's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges  Institut Universitaire de France  for the support. F. M. Z. , O. A. and A. A. F. acknowledge the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  957189  ( BIG-MAP ).
Acknowledgments Prof. Alejandro A. Franco 's research team acknowledges the  MatriCS HPC  platform from  Université de Picardie-Jules Verne  for the support and for hosting and managing the ARTISTIC dedicated nodes used for the calculations reported in this manuscript. The team acknowledges  Markus Osenberg  and  Prof. Ingo Manke  from  Institute of Applied Materials, Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (Germany ) for the tomography data collection.
They are computed as follows: v center ðxÞ ¼ À C 4p X N i¼i ðx À v c i ðtÞÞ Â dv i ðtÞ jx À v c i ðtÞj 3 j d ðx À v c i ðtÞÞ; (21) v image ðxÞ ¼ À C 4p X 6P k¼61 X N i¼i ðx À v c i ðtÞÞ Â dv i ðtÞ jx À v c i ðtÞj 3 : (22) For the image contribution, the effect of the velocity smoothing function is neglected due to the assumption of L ) d and 6P is a cutoff number chosen to represent the number of images on either side of the domain. The choice of P is explained in Appendix C. Optimization: To compensate for the high resolution requirements for thin vortices, Knio and Klein  39  suggested three optimization techniques. The first method, henceforth referred to as M1 technique, is attractive and used in this work since it requires only minor modifications to the existing code and does not require computation of the curvature, j.
4202. The numerical core radius d ttm is computed from (15). With r 0 ðtÞ ¼ max i¼1::N jdv i j, (24) can be used to compute the two coarse radii. (3) For both core radii, (  14 ) is evaluated at each node location by applying the periodic boundary condition as illustrated in Fig. 3 , i. e. , the velocity at a node x inside the computational domain is evaluated by applying a translation along the periodicity direction such that x is at the center of the domain. (4) The corrected velocity is obtained according to Eq. (  23 ). (5) Finally, the node positions are updated with the equation of motion (25) with a fifth-order Adams-Bashforth scheme and Runge-Kutta-Fehlberg initialization. 51, 52  Both LIA and the M1 KK methods are written in python, and the filament code is validated against the static test of Klein and Knio,  32  which is shown in Appendix A.
III. TEMPORAL EVOLUTION OF A HAIRPIN FILAMENT IN STAGNANT BACKGROUND FLOW (Q 2 ¼ 0) In this section, we consider a case, in which a hairpin filament is allowed to evolve in a stagnant background flow, i. e. , Q 2 ¼ 0 as shown in the work of Hon and Walker. 40  Both the LIA and M1 KK methods are used to compute the motion of the filament. The goal of this section is twofold: (1) This case serves as a further validation of the filament code written during the course of this work, and (2) it also highlights the differences between LIA and the M1 KK approximations. The hairpin is initially inclined at c ¼ 45 with respect to the streamwise direction, having an amplitude A ¼ 0. 5, spread parameter b ¼ 20, and a dimensionless core size d ¼ 0:02. Additionally, circulation is set as C ¼ 1, and a spatial discretization of 700 nodes over a length of L ¼ 4 is used.
IV. TEMPORAL EVOLUTION OF A HAIRPIN FILAMENT IN ABL BACKGROUND FLOW The evolution of a hairpin filament immersed in an atmospheric boundary layer flow is studied here. In this section, as mentioned in the introduction, we attempt to answer our questions regarding the abundance and orientation of hairpin structures in the stably stratified ABL by studying the dynamic hairpin characteristics with respect to changes in stratification. First, the mean background flow profiles are computed from the DNS database and discussed in Sec. IV A. Then, with four initial conditions of the hairpin identified in Sec. IV B, the temporal evolution of the hairpin filament is discussed in Secs. IV C and IV D.