dataesr/scientific-paragraphs-categorization huggingface dataset:
Results ). The input of the network was an array of 64 dimensions representing the state of each channel at a certain timepoint. We used batch normalization and rectified linear unit (ReLU) activation functions in all layers, except the middle and last one which acted as linear units. The minimization of the mean square error (MSE) between the input data and the output layer was used to train the network, through the Adam optimizer  (Perl et al. , 2020). The data was previously z-scored and split into a training and test set (70-30% of the dataset). The batch size was 256 timepoints and the networks were trained for 400 epochs, or until the validation error was no longer improving for the last 50 epochs.
As the signal got binarized, we intended to prove that having only two "Micro-states" (0 and 1) it is already enough to achieve our goal of entropy calculation. We implemented a random forest classifier using the Scikit-learn library in Python. As the dataset was imbalanced (the number of windows corresponding to the three stages was not the same), we applied under-sampling without replacement to achieve the same number of elements of each class. The under-sampling process was repeated 50 times. To achieve statistical significance, we used leave-one-out cross validation, splitting the set into train (80%) and test (20%) 50 times for each sampling.
Results To investigate the functional organization of large-scale dynamics, we analyzed electrophysiological information obtained from recordings performed in ferrets. We obtained data from 4 animals in resting activity during the light cycle between 12 a. m. and 6 p. m. We recorded LFPs by using 64 custom micro-electrocorticographic (μECoG) electrodes displayed in arrays chronically implanted in the animals (Figure  1A ). To disentangle the level of temporal irreversibility, we used the electrocorticographic (µECoG) time series obtained during spontaneous behavior, including movement, quiet-awake, and sleep periods. In this study, we replicated results reported in previous articles  (Stitt et al. , 2017)  using the same dataset in order to validate the presented results, showing that the highest recording time was in the slow wave sleep state and that the most probable state transition was from active-awake/REM to quiet-awake (Supp Figure  1 ).
In contrast to similar methods, the irreversibility framework has the flexibility that allows it to be applied to many different modalities including fMRI and MEG  (Cruzat et al. , 2023; Deco, Perl, et al. , 2021; Kringelbach et al. , 2023). As it can be easily adapted to other techniques, it allows for an easy comparison of different datasets with different modalities of similar samples of subjects, in humans or other animals. In addition, we can assess whether the levels of nonequilibrium change with disease and therefore could be used as a potentially sensitive and specific biomarker. The irreversibility approach provides an indirect proxy to the nonequilibrium phenomenon of the system. Other more direct ways to unveil a system's thermodynamics, coming from methods which find other relations between dynamical and thermodynamical system properties  (Fang et al.
Funding S. I is supported by the  EU  project  euSNN  ( MSCA-ITN-ETN H2020-860563 ). G. D. received funding from the  Horizon EU ERC   Synergy Grant  Project ID:  101071900 ;  Spanish national research  project (ref. PID2019-105772GB-I00/AEI/10. 13039/501100011033 ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ; A. K. E. received funding from the  DFG  ( SFB936-178316478-A2 ) and from the  EU  project  cICMs  ( ERC-2022-AdG-101097402 ). Views and opinions expressed in this paper are those of the authors only and do not necessarily reflect those of the EU or the ERC. Neither the EU nor the ERC can be held responsible for them. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Track selection and particle identification (Anti) 3 H candidates are selected from a sample of charged-particle tracks reconstructed in the ITS and TPC in the pseudorapidity range |η| < 0. 8 (0. 75) for the Pb-Pb (pp) data sample, and at midrapidity with |y| < 0. 5 for both samples. Several track quality criteria are applied, such as a minimum number of clusters in the TPC of at least 100 out of a maximum of 159, and at least 2 in the ITS, with at least one cluster located in any of the two innermost ITS layers. Furthermore, the number of TPC crossed rows is constrained to be more than 70. A good quality of the track fit is achieved by requiring the χ 2 per TPC reconstructed point to be less than 2. 5 (4) for the Pb-Pb (pp) data sample. These cuts are stricter in the Pb-Pb data sample due to the higher track density environment.
The number of TPC clusters used in the calculation of the specific energy loss (dE/dx) is required to be larger than 100  (55)  to ensure a good dE/dx resolution in the Pb-Pb (pp) dataset. The contribution from secondary tracks in the pp data sample is reduced by selecting a maximum distance of closest approach (DCA) to the primary vertex in the transverse plane (DCA xy ) and in the longitudinal direction (DCA z ) lower than 0. 1 cm. The (anti)triton candidate tracks are identified using the information on the specific energy loss dE/dx in the TPC gas volume, which is complemented by the information on the time-of-flight measurement in the TOF detector. The nσ TPC variable represents the PID response in the TPC expressed in terms of the deviation between the measured and expected dE/dx, normalised by the detector resolution σ.
The root mean square (RMS) of these distributions is used to determine the uncertainty of the momentum p, which is propagated to the uncertainty of the measured cross section. For the analysis of the Pb-Pb data sample using the TOF-to-TPC method, the MC information on the momenta of daughter tracks originating from the 3 H annihilation is used to estimate the momentum of the particle at annihilation. This is compared to p primary to estimate the magnitude of this effect and the resulting uncertainty. The uncertainty on the inelastic cross section from the continous energy loss is evaluated to be less than 2% for the antibaryon-to-baryon method and less than 2. 5% for the TOF-to-TPC method.
Acknowledgement The authors would like to warmly thank  Mauricio Chagas da Silva  for valuable discussions and technical help. This work was supported 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 ". B. H. , S. L. , and D. R. acknowledge the financial support of the  Agence Nationale de la Recherche  under the  Lorraine Artificial Intelligence  ( LOR-AI )  project (grant number  ANR-20-THIA-0010-01 ). This work was granted access to the HPC resources of TGCC under the allocation 2022- A0120810433  by GENCI. T. B. acknowledges support from  Slovak Research and Development Agency  under the Contract No. APVV-20-0127. A. G. and A. G. thankfully acknowledge support and funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (Grant Agreement No.
A terminal ballistic analysis of the effects of 7. 62 mm Â 51 AP P80 rounds on inclined high-strength armor steel plates is the focus of the presented study. The findings of an instrumented ballistic testing combined with 3D advanced numerical simulations performed using the IMPETUS Afea® software yielded the conclusions. The experimental verification proved that slight differences in the pitch-andyaw angles of a projectile upon an impact caused different damage types to the projectile's core. The residual velocities predicted numerically were close to the experimental values and the calculated core deviations were in satisfactory agreement with the experimental results. An extended matrix of the core deviation angles with combinations of pitch-and-yaw upon impact angles was subsequently built on the basis of the numerical study.
T. Fras Defence Technology 31 (2024) 73e94 plates made up the impact arrangement. The undertaken measurements in the performed terminal ballistics experiments included flash X-rays imaging, digital high-speed cameras recording, due to which the initial and residual conditions of the impacts were registered. The impact configuration's components maybe considered as illustrative, but they are also included in the IMPETUS Afea® numerical software's commercial library. The pre-defined components in the calculation software were used to prepare the numerical study in terms of their geometry and material modeling. Therefore, the ballistic experimental investigation was compared with its numerical representation not affected by a user way of preparation of the geometrical model, meshing and last but not least, constitutive functions of the materials composing the threat/ target configuration.
Thus, as the yaw angle the Standard defines the maximum resultant angle between the main axis of the projectile and its trajectory (velocity vector) irrespective of plane  [9]. As the angle of impact, the Standard describes the angle between the projectile trajectory and the direction perpendicular to the plane tangent to the point of impact on the target  [9]. These definitions are respected (though, the yaw angles have other designations) by both the calculation code IMPETUS  [16]  and the measurement device PROTOTYPA  [17] , Fig. 4. In Fig. 5 , there are presented the yaw angles following their designation in the NATO Standard AEP-55. The total yaw angle is calculated following the definition given in the report  [18]. The total yaw angle yt of a projectile is the angle between the vector of velocity of the projectile and the vector of its axis of symmetry in the plane defined by those two vectors.
Numerical modeling The IMPETUS Afea Solver® is the non-linear transient dynamic explicit Finite Element code that takes advantage of a GPU technology. The parallelization of the solver is done with the GPU, meaning that large clusters of processors are not necessary to run a single large model. Using the IMPETUS® Lagrangian solver, the above described experimental configurations were represented numerically. Like in the experiment, the used projectile was 7. 62 mm Â 51 FMJ/PB/HC AP P80 and as the target, the 4. 5 mm thick Armox 500T steel plates were taken.
Models for the materials of the impact configuration The IMPETUS' library contains a number of predefined target materials, as well as specific threats. The user may decide to keep the material model proposed by the software or to assign the model option and values of its parameters on its own. In the current approach, the choice of the material implemented into the threat and target was limited to the option proposed by the software in its library. Both the projectile of caliber 7. 62 mm Â 51 AP P80 (so, its jacket, cup and core) and the steel Armox 500T were available in the IMPETUS' library (in the IMPETUS ver. 7. 0. 1, the models for Armox 500T steel is not available anymore). This choice has at least two advantagesethe modeled configuration remained free of the influences of the user with respect to the material models and a choice of its parameters and further, it is possible to verify to which extent the solutions proposed by the default software library may represent the tested impact configuration  [21].
Plasticity models The material models proposed by the software were not changed in the carried out simulations. Thus. (1) The plasticity model used to describe the flow behavior of the brass, lead and the Armox steel is the Johnson-Cook flow model *MAT_JC, Eq. (  5 ). (2) The plasticity model of the projectile core is the generic highstrength steel *MAT_HSS, Eqs. (  6 )e(  8 ) the material model is specified for high-strength steels and hard metals in ballistic applications. The in-house approach used in IMPETUS defined simply an elastic material model with a brittle The Johnson-Cook model formulation implemented in IMPETUS is as follow: s y ε p eff ; ε • p eff ; T ¼ A þ B ε p eff n 2 4 1 þ C ln 0 @ ε • p eff ε 0 • 1 A 3 5 1 À T À T 0 T m À T 0 m ! (5) Fig. 15. Mesh of the projectile 7. 62 Â 51 AP P80: cross-section through the jacket, core and lead cup  [16].
Calculating position of the projectile after plate perforation In IMPETUS, the global coordinate system is linked to the target, Fig. 17. Contrary to the experiment in which the target is adapted to the demanded inclination angle, the angle of impact in the code is defined by a position of the projectile, Fig. 17(b ). The code enables defining the angle of target obliquity, as well as the pitch and yaw projectile's angles. As it was stated in Section 2 and presented in Fig. 4 , IMPETUS respects the definition of the pitch and yaw angles presented in Ref. [9]. To avoid an ambiguity of the deviation angles calculation, the values of the angles were calculated based on the exact positions of the projectile's core e i. e. the X, Y, Z coordinates of the points determining the axis of symmetry of the projectile's core before u 0 and after u f plate's perforation.
Modeling of the experimental cases In the experiment, the target was inclined at 10 for the shot. The experiments showed that the subsequent six shots, during which the total initial yaw angles varied between 0. 48 and 1. 53 , resulted in different types of the residual core performance (intact, broken in the middle, fragmented). The numerical simulation was aimed to mirror the experimental set-up and repeat its initial conditions, i. e. the initial velocities of the projectile and the measured initial yaw and pitch angles. They were built on the default threat/target configuration and material modeling available in the card "terminal ballistic setting" of the IMPETUS software. The objective of the study was to verify if the numerical code may represent efficiently the experimental observations.
Last but not least, a tendency to fracture observed for highstrength, high-hardness projectiles' cores under even slight bending loads occurred during asymmetrical or oblique impacts, may also be considered as an effective defeat mechanism, which might be further optimized (e. g. Refs. [31e33]). The experimental ballistic testing proved how complex the impact tests are. The initial impact conditions of the performed shots did not vary much but they resulted in different types of the residual core damages. An accurate representation of such an experiment with a slight variation of initial conditions and the different resulting projectile failure types is a challenge for numerical modeling, independently of a chosen calculation software. The numerical simulations were performed using the default setting for the terminal ballistic scenarios available in IMPETUS Afea solver®.
Acknowledgements Sincere thanks are expressed to  Mr. Paul Beillard  ( ISL ),  Mr. Laurent Sinniger  ( ISL ) for performing the ballistic investigation and to  Mr. Fabien Rondot  ( ISL ) for valuable discussions. A feedback of  Dr. Jerome Limido  ( IMPETUS ) and  Mr. Jiri Dohnal  ( PROTYPA ) is hereby also acknowledged.
Acknowledgments The authors acknowledge  CNRS  and the  Agence Nationale de la Recherche  (Grant  ANR-15-CE09-0020 BLINK ) for financial support. B. A. , C. R. , J-C. M. and C. C. acknowledge the  Université Paul Sabatier  for financial support, the  CMEAB  platform for TEM support, and  N. Ratel-Ramond  ( CEMES ) for XRD support. B. A. acknowledges the  German Academic Exchange Service (DAAD)  for financial support ( Short-Term Grant  No. 91709165 ). A. E. , A. B. , and M. S. acknowledge the  Université de Lille and Région Hauts de France  for financial support. M. P. and F. G. acknowledge the  Université de Rennes  for financial support. F. G. acknowledges  B. Lefeuvre  for ICP measurements and  Dr. G. Loas  and  A. Carré  for technical support. We would like to thank  Prof. M. Haase  ( Univ.
In various studies  (Solomonidou et al. , 2014; 2016; 2018; 2019; 2020a , 2020b; Lopes et al. , 2016; Malaska et al. , 2016; 2020) , we used radiative transfer modeling in order to evaluate the atmospheric scattering and absorption and securely extract the surface albedo of multiple Titan areas including the major geomorphological units. We also investigated the morphological and microwave characteristics of these features using Cassini RADAR data in their SAR and radiometry mode. Here, we present a global map for Titan's surface showing the chemical composition constraints for the various units. The results show that Titan's surface composition, at the depths detected by VIMS, has significant latitudinal dependence, with its equator being dominated by organic materials from the atmosphere and a very dark unknown material, while higher latitudes contain more water ice.
In the classification of auroral image data with two different time resolutions we found that the occurrence of eight morphological classes strongly increased when the image cadence was high (24 seconds), while the occurrence of 13 morphological classes experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of the group of eight "active auroral classes". Time periods for which "active auroral classes" persisted for longer than two consecutive images with maximum cadence of six minutes coincided with ground-magnetic deflections and their occurrence was found clustered around the magnetic midnight. The active auroral onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image classification method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.
1 Pre-processing of the auroral images Manual labelling was performed for all quicklook images from the winter season 2019-2020 as well as for January and February 2019. Quicklook images have a reduced resolution of 480×480 pixels and about 6-minute cadence. The manual labelling aimed at providing a ground truth for developing an automatic classification routine to detect images which contain auroral emission. This dataset contained approximately 37000 labelled images. From this dataset, only those images that contained auroral emission and were not dominated by clouds were further used in unsupervised learning. That became approximately 12000 images. To prepare the images for unsupervised learning, each image was first processed to remove features that could lead to classification biases that are unrelated to the auroral morphology, such as a caption indicating the camera type, observatory location, date and time.
The cadence of 6 minutes of the quicklook data favours quiet aurora over active aurora. This happens because active aurora occurs in short highly dynamic bursts, which are poorly sampled by a cadence of 6 minutes, while quiet aurora experiences longer lifetimes. To study the influence of this bias and to better represent the active auroral forms, a second dataset was made. In this dataset, the cadence was reduced to 24 seconds during selected periods of active aurora. This additional dataset is also referred to as high resolution dataset in this work. These selected periods were visually identified as the 19 brightest auroral displays with large north-south extent as seen in the keograms (sample keogram in the top panel of Figure  2 ).
Classification of images In contrast to supervised learning, where we know the correct answer to a classification question and can train a classifier accordingly, in unsupervised learning we do not know the answer. The manual labelling of auroral images provided us with a dataset of unknown auroral forms. We try to learn the types of the aurora from the data by using clustering algorithms. Intuitively, similar auroral shapes in the images should belong to the same cluster. The first step is to define a numeric measure of the image content in form of a feature vector. In the second step, the images are clustered based on their feature vectors: here, the true number of clusters is, of course, unverifiable. As clustering is often based on similarity metrics, the results also depend on how well the feature vectors represent the image content.
In his Master's thesis, Teissier (2022) evaluated a number of different feature vector extractors as well as clustering approaches. The following provides a brief description of the central concepts and the choices of methodology. Classifiers based on Convolutional Neural Networks (CNN)  (LeCun et al. , 2015)  are becoming increasingly popular in image analysis. Training an artificial neural network refers to the process of optimising all parameters within the network to minimise the classification error. The analysis of auroral images in our dataset uses a processing chain which includes a CNN architecture followed by a smaller neural network. We start with the inclusion of high-time resolution data as it provides a better balance between images https://doi. org/10. 5194/egusphere-2023-2857 Preprint.
Differences between active and quiet type aurora The CNN analysis was run on the quicklook images with 6-minute cadence alone as well as on the full dataset with the inclusion of higher resolution images with 24-second cadence. As a result we found that the occurrence of some morphological classes increased much with the increasing temporal resolution, while the occurrence rate of other classes was not affected. The high sensitivity to the increased temporal resolution of the image data (at least 80% change, red line in Figure  1 ) therefore suggests that these classes primarily describe morphology of active auroral displays. These are classes number 0, 13, 26, 8, 21, 7, 14 and 35. The class occurrence changes are illustrated by Figure  1 , which shows the occurrence rate difference of all 37 classes between quicklook and high-resolution data in percentage with respect to the total occurrence rate of each class.
The classes, for which the occurrence showed little or no change (maximum 20% change, blue line in Figure  1 ) with the increasing temporal resolution (classes number  20, 4, 9, 17, 34, 22, 32, 10, 3, 18, 25, 1 and 29) , are likely to contain auroral structures mainly related to quiet auroral displays. By quiet auroral displays we refer to images of structures, which are relatively simple and evolve slowly in time. A typical example of this is an auroral arc, or a multiple arc, which changes little between consecutive images with higher temporal resolution of 24 seconds. Example images of slowly evolving features can be seen in the bottom row in Figure  3. Active auroral displays, in turn, refer to images with one or multiple more complex auroral structures, which evolve significantly between consecutive images in our higher temporal resolution dataset, and have little resemblance between consecutive images in quicklook time resolution (6 minutes) as seen in the top row of    (Tanskanen, 2009).
Three independently studied substorms were identified with good accuracy. Furthermore, 50% of independently listed 240 substorm onsets were detected within the uncertainty of their method. Currently, the only operational image classification method that runs on real-time image data is described by  Nanjo et al. (2022). They use un-pruned auroral colour images from Tromsø and Skibotn in Northern Norway and Kiruna in Northern Sweden 7. The closest to the auroral breakup detection of that approach is the beginning of an extended period of the class called discrete aurora. The class itself contains much more than just the auroral breakups, but with some further analysis those results may also become a helpful cross-validation dataset for our method in the future. While the results presented here are very promising, further research is needed to assess how efficiently this method works on unseen image data from different winter seasons.