dataesr/scientific-paragraphs-categorization huggingface dataset:
Technical Installation and the Software Tool The installation was controlled by a software tool developed at LAM (Lutheries-Acoustique-Musique [Lutheries-Acoustics-Music]) and used the Max programming language. This software enabled the transmission of a selection of sound sequences and musical excerpts to the four audiovibrotactile objects. To do this, we provided the patients with a Korg NanoKontrol2 MIDI 7 interface that allowed them to control the sensory aspects of the vibrations and sounds, such as intensity level and playback speed (Figure  3 ). This MIDI interface sent commands from the patient to the computer via a USB interface. A small mixing console (Behringer Xenys 1204USB) functioned as a digital-analog converter and fed the various mediating devices via a multichannel amplifier.
It also powered the microphones during the sessions. For the purposes of analysis, the software tool recorded the patients' choices of sounds and the subsequent audio signal transmitted to the mediating objects (Figure  4 ). Stimuli were activated almost exclusively by the patients, who had constant control over the MIDI interface. If the patients asked the clinician to select or modify stimuli for them, they could give access to the interface to the by placing it outside the carpet, which was a clinically significant action. The length of the cables connecting the devices to the computer and amplifier was sufficient to allow for these movements. All mediating objects were permanently activated and it was not possible to disconnect them during a session. Patients could choose the objects through which they wanted to explore stimuli.
Data Systematization and Analysis The observing psychologist took verbatim notes of patients' speech during the workshop sessions, both their free comments during exploration time and their responses to the final questions. The observing psychologist took notes also of patients' behavior related to selected sounds, as well as the use and duration of the contact with the objects (description and timing with chronometer). She noted body postures when the patients entered in contact with the objects. These notes of patients' speech and behavior were systematized, that is, coded and quantified. The patients' body contacts with each mediating object were counted and their body postures with these objects were classified and counted. The patients' verbal utterances were numbered. The total number of sentences uttered directly referring to the sounds (all sessions and all patients together) were classified by sound stimuli.
For each sentence, the significant semantic units (differentiated as nouns, verbs, adjectives, syntagma, etc. ) used to name, describe, and qualify each sound were indexed and counted. Additionally, the events of the workshop and the patients' speech were transcribed after each session by both psychologists in order to compare their perceptions of the observed situations and the verbal expressions. We could then analyze patients' verbal and nonverbal expressions, contextualizing their behavior and speech. Furthermore, in developing the software tool, we employed a "chirographic" recording method to record the patients' actions on the gesture control MIDI interface. Such recorded data involved the choice of specific sound and music sequences, how long they were played (start/stop), and the changes in playback levels and speed.
This automatized digital recording could measure the gestural data generated by the patient's handling of the interface during the workshop sessions, allowing quantitative gestural data analysis. In this way, the patients' actions and certain types of data on their bodily activity were recorded in a noninvasive manner, that is, without a need for body sensors. Data systematization grids were designed specifically for this experiment to capture verbal and nonverbal (i. e. , movements, body positions) data. We defined systematic analytic categories that were extracted from the data collected by the experiment in order to address the research questions formulated at the beginning of the study. Thus, we could quantify-mainly in terms of percentage, but also with mean, median, mode, variance, and standard deviationthe patients' verbal and nonverbal data related to the stimuli and mediating objects.
Use of the Mediating Objects In the presence of the clinical psychologists, each of the patients approached the setting in a highly individual manner, exploring his/her sensations through gestures and movements, and expressing them verbally. Each object lent itself to different types of use, following the patients' personal preferences and the suggestions made by the psychologists when patients requested it, depending on the conditions of each session. In regard to the data collected, we made two kinds of measurements: the number of occurrences of each object's use by each patient and total use by all patients together. The overall data of the objects selected and used by the patients during the five workshop sessions show that the ball was the most frequently used object (28% during all workshop sessions, all patients together), followed by the table (21%) and the headrest pillow (20%).
One important open question in side-channel analysis is to find out whether all the leakage samples in an implementation can be exploited by an adversary, as suggested by masking security proofs. For attacks exploiting a divide-and-conquer strategy, the answer is negative: only the leakages corresponding to the first/last rounds of a block cipher can be exploited. Soft Analytical Side-Channel Attacks (SASCA) have been introduced as a powerful solution to mitigate this limitation. They represent the target implementation and its leakages as a code (similar to a Low Density Parity Check code) that is decoded thanks to belief propagation. Previous works have shown the low data complexities that SASCA can reach in practice. In this paper, we revisit these attacks by modeling them with a variation of the Random Probing Model used in masking security proofs, that we denote as the Local Random Probing Model (LRPM).
For example, we show that we can bound the results of a SASCA against an AES implementation from  [VGS14, GS15]  in seconds (rather than hours) of computations. These results confirm that the leakage of any operation within a block cipher can be exploited. More precisely, and despite the first and last rounds' leakages are more useful than the inner rounds in known-plaintext/ciphertext attacks, inner rounds' leakages cannot be neglected in other cases (e. g. , in unknown-plaintext/ciphertext attacks). We insist that these computational gains become even more relevant when considering high-end devices. For example, propagating information through a XOR operation on a b-bit bus requires O(b • 2 b ) operations in SASCA, but this propagation has constant cost in the LRPM. So while our model already leads to considerable speed ups in the simple case of 8-bit devices investigated in previous works (due to the code size of the target implementations), these gains significantly increase if targeting a 32-bit or larger architecture.
Additionally denoting the information bound (in bytes) on the key bytes k 1 or k 2 with B (t) , we can r (0) cm→kn = 0, ( 7 ) p (t) kn→cm = N 4 + (N -1) • r (t-1) cm→kn , (8) r (t) cm→kn = p (t) kn→cm • 8 , ( 9 ) B (t) = N 4 + N • r (t) cm→kn. ( 10 ) Note that the goal of SASCA is to recover key bytes, so the information bounds on other intermediate variable nodes are excluded in these equations. A pseudo-code for this example is given in Appendix A. Taking a value of = 0. 1 bits as in the previous subsection, and using a similar approximation for the data complexity of an analytical attack N a ≥ 1 B (∞) , we can see in the left part of Figure  4  that the bound becomes larger than one for N a ≥ 27. Note that the gain of the analytical strategy over the divide-andconquer one only appears when the number of measurements becomes sufficient for the decoding to become effective.
Analysis and results Following the usual approach in simulated side-channel attacks, we analyzed the case where all the target intermediate variables leak some deterministic function of their value, with some additive Gaussian noise. From this simple setting, we can compute a MI value according to Definition 1 and normalize it. We investigated the following parameters: 1. Different number of rounds of leakage exploited. We evaluated how the data complexity of SASCA is reduced by using the leakage of more block cipher rounds.
Different deterministic parts of the leakage function. We considered the standard Hamming weight leakage function and a random one (the same as used in  [DFS19] ), with the goal to identify possible dependencies of the model on this function. 3. Different noise levels. We considered lower and higher noise levels, in order to evaluate how the improvements of SASCA over TA depend on this parameter. 4. Known or unknown plaintext attacks. We considered both the case of an attack with known plaintexts as in the standard DPA setting and the unknown plaintext context which may be considered in certain applications of side-channel attacks  [HTM09]. A pseudo-code corresponding to one AES round is given in Appenxix B. The result of our first set of experiments is given in Figure  8. It corresponds to the simplest case of an attack in the known plaintext context, for a value of = 0.
This last set of experiment is also a good one to confirm the significant gains in evaluation time that the model allows. For example, running a single 10-round SASCA on the 32 cores of an Intel Haswell architecture takes 7. 5 minutes on average with our prototype code (and the success rate curve which is computed over 40 independent experiments for this number of rounds therefore took 5 hours to be generated). The bounds provided by the model are produced in seconds of computation on the same platform. We finally checked the impact of the α parameter (from Section 3. 4) in these various experiments and concluded that small variations of it have limited impact on our conclusions. For simplicity, we therefore use a value of α = 1 in the remaining of the paper.
Connections to coding theory We conclude this section by highlighting interesting connections between SASCA and coding theory. In particular, the Tanner graph derived from one column of the first AES round (in Figure  7 ) defines an LDPC code with a parity-check matrix H 14×18 shown in Figure  11 , whose row weight is a constant 3, and the column weight vector (i. e. , the degree vector of variable nodes) equals: ( 3 3 4 4 3 2 2 2 2 2 2 2 1 1 1 1 2 5 ). The sparsity of this parity-check matrix H 14×18 explains why BP performs well. Note that this parity-check matrix becomes larger if we have more traces, but its sparsity remains. Supposing N traces are observed, from one column of the first AES round we could generate a parity-check matrix of row size 14N and column size (4 + 14N ), leading to a [4 + 14N, 4] q LDPC code.
Note also that this transformation does not mean that the description of AES is no longer non-linear. It only means that this transform could hide the non-linearity of AES by the merge trick that locally combines the leakages connected by a non-linear operation (e. g. , an S-box). Therefore, the (in)vulnerability of AES against SASCA can be characterized by the decoding performances of one particular LDPC code derived from the inherent structure of the cipher. When N increases, the dimension of the derived code is invariant, i. e. , equivalent to the key size. Its length, however, increases and the additive channels associated to some positions (i. e. , connected to so-called continuous channels) become less noisy. The latter explains why more traces lead to better attacks (and in particular SASCA) from a coding theory viewpoint, since both low-rate codes and low-noise channels generally imply better decoding performances.
[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]  These properties are acquired during fetal life and evolved during all the life of animal. [15] [16] [17] [18]  In this context, this review makes an attempt to cover the aspects related to muscle fibers and their plasticity according to several factors of variability such as the sex of the animals, their age, breed (e. g. , Continental versus British), the physiological status and the rearing practices or factors. Furthermore, the relationships between muscle fibers and several beef quality traits such pH decline, color and tenderness were reviewed. The preparation of the database that served to write this comprehensive review was based on a computerized search using different databases including Google Scholar, Web of Science and Scopus.
The image analysis can be performed using different software tools such as Visilog  36  or MyoVision  47  allowing a fast and automatic classification of the fibers after the detection of fiber types based on different antibodies raised against various MyHCs. For example, Visilog a tool efficiently applied by our group allows an objective and fast fiber typing as well as morphometric characterization of cattle muscle fibers. This tool allows saving time and gain reliability for research laboratories due to its combination with the recent advances in fluorescence microscopy, immunochemistry and image processing algorithms. Furthermore, Visilog permits also to automatically determine the mean cross sectional area per type and fiber density (number per mm 2 ) for an approximate of 300 fibers per serial image.
SEA has been prospectively applied to de-orphanize drugs without known protein targets  [7]. More concretely, these authors analysed a set of 1431 world-wide approved drugs. They found that 1079 of these drugs have known targets in bioactivity databases such as ChEMBL. The remaining 352 drugs were analysed with SEA, which managed to identify targets for 308 of these drugs (mostly the SEA-suggested target could be verified in the literature, but also experimentally confirmed in some cases). Despite these efforts, targets could not be found for a set of 41 drugs listed in their Table S4  [7]. Methods based on other principles may be able to provide useful predictions for these hard-to-predict target-orphan drugs. In particular, target prediction methods based on chemical similarity have been found to be particularly powerful  [6].
This ligand-centric approach provides by construction the maximum coverage of target space for a given dataset  [4]. The first version (v1) of this tool exploited a knowledgebase with 184,912 molecules retrieved from release 20 of the ChEMBL database  [11]  and thus were annotated with 3046 single-protein targets (a molecule is annotated with a target if the K i , K d , IC 50 or EC 50 of the pair is lower than 10 µM). The second version (v2) of MolTarPred is the current version online (http://moltarpred. marseille. inserm. fr/) and that was thoroughly described in a recent publication  [10]. MolTarPred v2 exploits a much larger knowledgebase than v1 comprising 607,659 small-molecule ligands annotated with at least one of 4553 protein targets  [10]. A comprehensive description of how to use MolTarPred and interpret its results has already been published  [10] , with two previous papers developing and validation its working principles  [4, 5].
Discussion and Conclusions Despite substantial research on actarit, e. g. , exploring its clinical phenotype  [15, 17] , improving its administration  [18, 19]  or enhancing its stability within the organism  [20, 21] , no molecular target of this drug has previously been found. MolTarPred v1 was hence able to discover the first target for actarit where SEA could not  [7]. Running the current version of SEA (http://sea. bkslab. org/) on actarit returns CAII as the 74 th predicted target and not being ranked sufficiently high seems the reason why this target-drug association was missed. ChEMBL recently supplements their compiled measurements with in silico target predictions  [22] , which also missed this target of actarit (https://www. ebi. ac. uk/chembl/compound_report_card/CHEMBL1885632).
Some examples of FDA-approved CAII inhibitors are brinzolamide to treat elevated intraocular pressure, chlorothiazide indicated for hypertension and topiramate to treat epilepsy and migraines. Therefore, our study could also contribute to expand the indications of actarit. The of actarit, as a human-safe CAII inhibitor can now be investigated in the same indications associated with CAII. MolTarPred also has the advantage of offering interpretability for its predictions. For example, the most similar molecule to actarit was CHEMBL112, also known as paracetamol (Figure  1B ), with both chemical structures only differing in a terminal functional group (Figure  1C ). Paracetamol has moderate CAII activity with a Ki of 6. 2 μM  [32]. This means that MolTarPred v1 was able to identify a new CAII inhibitor with about 15-fold higher potency (IC50 of 422 nM) than its closest top hit and that the functional group substitution (carboxymethyl in actarit, hydroxyl in paracetamol) is the cause of such potency enhancement.
Discussion and Conclusions Despite substantial research on actarit, e. g. , exploring its clinical phenotype  [15, 17] , improving its administration  [18, 19]  or enhancing its stability within the organism  [20, 21] , no molecular target of this drug has previously been found. MolTarPred v1 was hence able to discover the first target for actarit where SEA could not  [7]. Running the current version of SEA (http://sea. bkslab. org/) on actarit returns CAII as the 74th predicted target and not being ranked sufficiently high seems the reason why this target-drug association was missed. ChEMBL recently supplements their compiled measurements with in silico target predictions  [22] , which also missed this target of actarit (https://www. ebi. ac. uk/chembl/compound_report_card/CHEMBL1885632).
Some examples of FDA-approved CAII inhibitors are brinzolamide to treat elevated intraocular pressure, chlorothiazide indicated for hypertension and topiramate to treat epilepsy and migraines. Therefore, our study could also contribute to expand the indications of actarit. The of actarit, as a human-safe CAII inhibitor can now be investigated in the same indications associated with CAII. MolTarPred also has the advantage of offering interpretability for its predictions. For example, the most similar molecule to actarit was CHEMBL112, also known as paracetamol (Figure  1B ), with both chemical structures only differing in a terminal functional group (Figure  1C ). Paracetamol has moderate CAII activity with a K i of 6. 2 µM  [32]. This means that MolTarPred v1 was able to identify a new CAII inhibitor with about 15-fold higher potency (IC 50 of 422 nM) than its closest top hit and that the functional group substitution (carboxymethyl in actarit, hydroxyl in paracetamol) is the cause of such potency enhancement.
Furthermore, given the high similarity between actarit and paracetamol, it is likely that actarit also has similar activity to at least some of the other targets of paracetamol. This merits further investigation and might lead to further repositioning opportunities. Like actarit, malotilate was classified as a hard-to-predict target-orphan drug  [7]  and the ChEMBL database does not report any known target either (https://www. ebi. ac. uk/chembl/compound_report_ card/CHEMBL1697754). However, there is a 30-year-old study reporting an IC 50 of 4. 7 µM with 5-lipoxygenase  [33]. This target was not only missed by SEA, but also MolTarPred and the ChEMBL method. A possible reason is that the activity of this association is close to the threshold of activity of these methods (10 µM). By comparing Figures  1  and 3 , one can see that CAII was predicted for malotilate with higher reliability than for actarit, yet no CAII inhibition was induced by malotilate.
None of the molecules that were supporting the CAII prediction in MolTarPred v1 are among the top 10 hits in MolTarPred v2 (they are now lower in the ranking) and none of these top 10 hits in MolTarPred v2 are known to bind CAII. As a result, CAII is not predicted as a target of actarit by MolTarPred v2. This seemingly shocking outcome is not unexpected. A high rate of false negatives, i. e. , low recovery rates, is a well-known limitation of in silico target prediction, which instead excels at quickly providing target predictions with generally few false positives  [5, 6] , as it has been the case here. This high-false-negative limitation is largely due to sparse nature of ligand-target bioactivity data (even in the most comprehensive databases, only 0. 04% of all the possible ligand-target pairs in the knowledgebase have at least one bioactivity value associated  [4] ).
Considering this latter, its application can be enforced in order to evaluate the risks caused by the particle resuspension in case of LOVA. To do this, the Rock'n'Roll model was implemented in a CFD code and precisely compared to experimental data. This work develops the implementation of the Rock'n'Roll model in a CFD code (ANSYS CFX) and presents the different steps of its validation with experimental data acquired on IRSN facilities, before detailing its application to the case of a LOVA in a toroidal geometry. Although the application proposed is related to the specific issue of a LOVA, the hazards related to the particle resuspension are relevant to numerous industrial situations in which powders are manipulated, can fall on the floor and be resuspended by operator displacements. Hence, the present calculations could be extended to wider industrial applications.
CFD simulation description To evaluate the resuspension of different particle sizes from the airflows generated during a LOVA, numerical simulations were performed using ANSYS CFX software, into which the resuspension model with a variable shear velocity is implemented. The first step consists in representing the geometry of the torus with the duct that simulates the breach, which is the source of the loss of vacuum. For the computational domain, a toroidal geometry is retained with a D-shaped section, the dimensions of which are given in Table  2 ; they were chosen based on those used in a previous study of Gelain et al. [12]. To represent the break, a circular duct with a diameter of 0. 16 m and a length of 2 m is used. The duct is located halfway up the torus and is initially closed at the torus wall.
Erreur ! Source du renvoi introuvable. presents a sectional view of the shape of the torus with its dimensions. The geometry is then discretized in tetrahedral elements as presented on Figure  14 , which presents different views of the mesh; its characteristics are summarized in Table  3. The dataset parameters for the calculations are given in Table  4. The k-ra SST turbulence model was chosen, as for the airflow study presented by Gelain et al. [12]. The airflow parameters used for the calculations were set for a LOVA-type accident scenario as described by Xiao et al. [29]  and already used by Gelain et al. [12]. They are given in Table  4. The surface energy used for these calculations is equal to 4. 4 J. m-, as preconized by Rondeau  [27] , from a fit of the Biasi correlation on its experimental data of resuspended fraction acquired in the AUG tokamak.
Conclusion This article présents the implémentation of the Rock'n'Roll model in a CFD code (ANSYS CFX), its comparison with experimental tests and an application to the case of a LOVA in a toroidal geometry. First of all, the Rock'n'Roll model integrating the adhesive force correlation of Biasi et al. [15]  is described, before detailing the model implementation in ANSYS CFX, performed by the way of a source term of resuspension in the particle transport equation. The model can be used with a constant or a variable friction velocity according to the model formulation. It has to be noticed here that the Biasi correlation, which has been obtained with various experiments, could be replaced in a future work by real adhesion force distributions obtained by means of atomic force microscopy technique in the specific case of tungsten particles deposited on tungsten substrates  [17].