dataesr/scientific-paragraphs-categorization huggingface dataset:
Live experiments managed to capture a wide range of malicious behaviour, related to exploiting vulnerabilities, in an effective and efficient manner without any administrator interference. Penetration Testing: Penetration testing is an essential method to reveal how a system or network is vulnerable or how the adopted security measures suffer from an exploitable security gap. As a result, various solutions were presented. Rushing et al. described a software project named Collaborative Penetration-testing and Analysis Toolkit (CPAT) that helps network security analysts with performing penetration tests by using network analysis technologies and tools in a collaborative environment with reactive data management  [327]. However, results are still to be reported. Visoottiviseth et al. developed a PENTOS as a penetration testing system for IoT devices designed for ethical hacking and used to determine both IoT risks and vulnerabilities  [328].
e. attack or accident). Sometimes, the detection process cannot be achieved especially in the case of advanced passive attack types (i. e Footprinting, War driving, or Spying)  [352, 353]. Hence, an urgent and persistent need for more sophisticated tools to detect attacks in their early phase to prevent them from further spreading and damaging the IoT system, network, application, or device. Enable smart automated PT Solutions: which should be enabled in a real-time manner to achieve the necessary functionalities of IoT entities and application requirements. This can be achieved by relying on AI-based solutions which play a key role in detecting security vulnerabilities. Enhanced IoT Corrective Solutions: must be maintained to "correct" and overcome detected IoT security vulnerabilities. This includes enhanced Intrusion Prevention Systems, and next-generation firewalls while ensuring a secure and verified data backup, with alternative devices being available for necessary computational requirements.
Specifically, we searched academic databases for studies into connections between these two forms of extremism. As our query produced few search results, we extended the search to Google Scholar. Most of the literature we found on cumulative radicalisation was very recent. We used various permutations of the following search terms: 'cumulative extremism', 'radicalisation and anti-Islam networks', 'stop Sharia', 'Sharia for Europe', 'Salafism in Europe', 'Islamophobia', 'far-right in Europe', 'violent extremism in Europe', 'polarisation'. The searches returned a combination of academic journal articles, book reviews, newspaper articles and policy papers. A number of results were excluded due to irrelevancy with regard to interaction and reciprocity. Given the small number of search results from peer-reviewed journals, we expanded the corpus on radicalisation by adding relevant articles on extremism and radicalisation published by known scholars in this field to explain what we found in these studies.
Concordance analysis can be used to reveal not only the frequency of particular keywords within a corpus, but can also facilitate detailed analysis of the context surrounding each instance of the terms, which can expose certain discursive constructions. Collocation analysis, which was used extensively in this research, can help to reveal the strength of the relationship between two or more words. We used a rigorously developed and widely used software, Sketch Engine, to conduct the frequency, concordance, and collocation analyses for our project. Owing to the large size of the corpus, we split up the main corpus into four component corpora of manageable size (approximately 700,000 words each). For each of these component corpora, our collocation analyses were conducted as follows. Based on frequency counts, we entered a series of keywords (or lemmas as they are termed in corpus linguistics) one at a time into Sketch Engine's Word Sketch function.
e. , a product and producer of social realities) rooted in argumentative, declarative and pragmatic strategies worthy of detailed analysis. By employing a critical semiotic approach to discourse analysis, we seek to highlight the principal themes, representations and arguments utilised by journalists and columnists in online traditional Portuguese newspapers (OTPN). We use a news media corpus that includes all Portuguese daily press publications: Público, Diário de Notícias, Jornal de Notícias, Correio da Manhã, and Observador. In total, we analysed 1,553 articles taken from newspaper websites based on keywords. 1 In keeping with the French school of discourse analysis  (Charaudeau, 1992; Kerbrat-Orecchioni, 2009) , we combined lexical and semantic linguistic analysis, as well as corpus-assisted discourse analysis (CADA), using the Sketch Engine software 2 (see also chapter 5 in this book).
Via linguistic and discursive analysis, we analyse the way in which the term is invoked by Portuguese press columnists. How is the concept used by these journalists? With which enunciative, rhetorical, argumentative and pragmatic objectives is the Islamophobia term employed within the corpus? To answer these questions, the following analysis takes the form of a metadiscursive study focused on the processes of signification (reappropriation, misappropriation) guiding the usage of the Islamophobia keyword in the co-text of the sentences and the context of the article. During a first reading of the articles containing the term in the Sketch Engine software 4 we were challenged by how frequently three variations of Islamophobia appeared: 'Islamophobe(s)', 'Islamophobic(s)' and 'Islamophobia 5 ' (in quotation marks).
Furthermore, the physics and boundary conditions must be clearly identified. Secondly, the model is solved through either an analytical approach or numerical methods using either a commercial software (e. g. Matlab, COMSOL Multiphysics  [21] [22] [23] , ANSYS  [24] ) or within the framework of an in-house or open-source code (e. g. Parcell3D  [25, 26] ). The geometry and the physical equations are defined and implemented according to the hypotheses previously made. After solving the governing equations, a validation step is crucial to ensure good confidence in the results. Figure  1  illustrates the different relevant modelling scales for the optimisation of a complete SOC system. First, the electrochemistry and transport mechanisms can be modelled at the scale of the electrode microstructure.
Besides, the extended model takes into account surface diffusion, as well as 3D diffusion within particles near the interface when the characteristic utilisation length l δ is lower than the size of individual morphological features d, l δ < d [59]. A more complex 3D model was developed by Rüger et al by using a cubic-based geometry  [23]. The 3D model is based on the description of the oxygen concentration in the MIEC. The processes of gas diffusion, surface exchange, bulk diffusion and charge transfer are implemented in a 3D FEM commercial software (COMSOL Multiphysics), as shown in figure  5. Pore and MIEC cubes are randomly distributed to obtain a homogeneous structure as depicted in figure  6. The goal of the model is to calculate the area specific resistance ASR cat of a MIEC cathode as a function of the material's parameters and its microstructure.
This paper presents an automated process that detects, tracks, and classifies thermal events using infrared movies of the inside of the vessel for machine protection. This process relies on a Region-Based Convolutional Neural Network, a deep learning model, for the detection of thermal events in infrared images. This model is trained using a dataset of thermal events, obtained by manually annotating thermal events in movies from the WEST tokamak, which is equipped with 12 infrared cameras that provide information about the surface temperature of the in-vessel components. The labels characterizing the thermal events are chosen in a custom-designed ontology, which is being developed. This automated process can correctly detect, track and classify most of the regular thermal events appearing in the infrared movies of WEST, in a manner compatible with the real-time plasma operation.
Introduction The ability to analyze thermal events occurring on the internal components of fusion reactors during their operation is important both for machine protection and for science. The large quantity of data generated by diagnostics during the operation of long-pulse machines makes it cumbersome for humans to cope with the real-time analysis of thermal events. There is a strong need for a process able to detect and analyze thermal events automatically and in real-time, for feedback control and investment protection. This paper presents an automated process that detects, tracks, and classifies thermal events using infrared movies of the inside of the vessel. This process relies on deep learning models  [1] , trained on a specially created dataset of thermal events. A web-based dashboard displays the most critical thermal events detected automatically by the process, to help the machine monitoring team to take action between pulses, if necessary.
The dataset of thermal events is obtained by manually annotating thermal events in movies from the WEST tokamak, which is equipped with 12 infrared cameras that provide information about the surface temperature of the in-vessel components  [2]. This paper is structured as follows: Section 2 presents the thermal events dataset, which contains all the knowledge used to train the automated process, briefly described in Section 3. Section 4 shows results obtained with this process using dedicated key performance indicators. Section 5 details the dashboard that displays the most critical thermal events detected. Finally, Section 6 presents the conclusions and outlook of this work.
Formalisation of knowledge in a thermal events dataset Supervised deep-learning models learn from example: given couples of inputs and expected outputs, the parameters of the model are learnt for it to perform a specific task, without having to properly formalizing it. Here, the automated process needs to detect thermal events in the frames of infrared movies. This means that, to train the model, many infrared images (the inputs) must be gathered, with the corresponding thermal events manually annotated on the images using bounding boxes (the expected outputs). All these data constitute what is called a dataset, which is then partitioned in three parts: the train split, used to learn the parameters of the model; the validation split, used to select the values of the hyper-parameters associated with the training; and the test split, used to evaluate the performance of the trained model.
The dataset is constituted of 88 manually annotated infrared movies of the interior vessel of WEST, provided by its infrared thermography diagnostic  [2]. It contains movies from three types of lines of sight (divertor: 31 movies, lower-hybrid antenna: 31 movies, and wide-angle: 26 movies), and from pulses from the C4 and C5 campaigns of WEST. The training, validation and test splits contain 53, 17 and 18 movies respectively. The pulses have been chosen to maximize the variety in the types of experiments, to have movies as different from each other as possible. Table  1  presents the repartition of the different classes of thermal events in the dataset. The classes are selected in a simple custom-designed ontology, currently in development. A short description of each class can be found in  [3].
The label "hot spot" is the catchall class, which gathers all the thermal events that do not belong to any other class in the ontology. This explains why it represents more than half the events in the dataset. The manual annotation of infrared movies is a cumbersome task, which presents risks of inconsistencies between annotators. To mitigate these risks, annotation tools are developed and regularly improved; they decrease the annotation time and the inconsistencies. To create the dataset, the annotation tool initially described in  [3]  was improved to reduce the jittering and flickering of the proposed annotations. Fig. 1  shows an example of manually annotated frame of an infrared movie of the divertor of WEST. All the thermal events, either manually annotated of automatically detected, are centralized in a SQL database for easy access and analysis by the machine protection and scientific teams.
Dashboard of the most critical thermal events A web-based dashboard, accessible anywhere at WEST, displays automatically the most critical thermal events detected. It is available to the protection officers in the control room, to help them analyze the thermal events and take action on the next pulse in coordination with the operational team, if necessary for machine protection. The dashboard, called ēveniō (to "happen" or "occur" in Latin), is shown in Fig. 4 , where the thermal events detected during the last pulse of WEST's C5 campaign are displayed. The graphical interface is generated using a Streamlit 1  instance that runs on a local server, and is  linked to the centralized SQL thermal events database. The dashboard is interfaced with the visualization software ThermaVIP  [7] , so that the detected thermal events can be easily visualized, analyzed and modified, relabeled, or rejected if necessary, by displaying them in the software in one click.
Conclusions and outlook A process that detects, tracks and classifies thermal events automatically from infrared videos and signals from other diagnostics is presented, in a manner compatible with the feedback control system of fusion machines. This process relies on artificial intelligence and deep learning techniques to learn from example: it then requires the creation of a dedicated dataset, constituted of many manually annotated infrared movies. Currently, only the first stage (detection, tracking and coarse classification of thermal events from infrared movies) of the process has been implemented and tested on data from the WEST tokamak, with  encouraging results, both qualitatively and quantitatively. The implementation of stages 2 and 3 is an outlook of this paper. A dedicated web-based dashboard is developed, to display the detected thermal events automatically after an experiment.
Its aim is to provide an easy to use tool for the officers in charge of machine protection to analyze the thermal events that occurred on the first wall of a fusion machine. It is interfaced with ThermaVIP, enabling the quick visualization of the detected thermal events in the infrared movies. The dashboard is a powerful tool that is highly customizable to suit the needs of the protection and operational teams. The automated process, the thermal events dataset and the dashboard have been developed and tested on data from previous campaigns of WEST, and are to be continuously improved during its future experimental campaigns. They will also be used on other fusion reactors (W7-X, DTT, ITER, with simulation and/or experimental data when available) using transfer learning.
Acknowledgements This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the  European Union  nor the  European Commission  can be held responsible for them.
2B, C ). More importantly, the difference in the counts of synonymous variants between the two species is also stable and always significant (Kolmogorov-Smirnov test p-value << 0. 005), whereas the difference in the counts of nonsynonymous variants decrease with increasing gene expression, with this difference disappearing in the upper 50-60% of gene expression rate (Fig. 2C ). This result supports the hypothesis that gene expression overrides effective population size in driving purifying selection. Even if gene expression has been suggested to be one of the causes of non-neutrality in synonymous variants in yeast  (Shen et al 2022) , or that codon usage bias is more intense in highly expressed genes  (Frumkin et al 2018) , gene expression rate does not appear to perturb synonymous variation in our datasets from two vertebrate species.
DNA extraction, pooled library preparation and sequencing DNA extraction was performed using DNeasy Blood and Tissue kit (Qiagen) following manufacturer's instructions. Whole genome sequencing libraries were prepared and sequenced at the Norwegian Sequencing Centre, Oslo, using Illumina pcr-free single or dual-indexing kits. To minimise batch effects, genomic samples from different species and different localities were randomised in six libraries and sequenced on 1-3 lanes of Illumina HiSeq2500 and HiSeq4000 aiming at 20X coverage depth. Raw reads are publicly available at ENA database (XXX, to be made available). Supplementary Figure  1. Schematic representations of the potential distribution of ancestral and derived alleles at a biallelic site across two ingroup and one outgroup samples.
Distribution of derived alleles frequencies After removing monomorphic sites across Emperor and King penguin samples (e. g. , INFIXANC and ALLFIX configurations in Supp. Fig. 1 ) from the dataset, we checked whether the derived alleles distribution was in line with basic expectations from population genetics. To avoid downstream normalisation, we selected only sites without missing data in the target species. In addition, we choose sites which were properly polarised (flagPol=UNFOLDED) based on at least four alleles present in the outgroup and with coverage depth range between 6X and 8X, i. e. , one standard deviation (ca. 1X) from the mean (ca. 7X). Such a narrow coverage range was applied to mitigate as much as possible the inclusion of sex-chromosome related regions, which could have been incorrectly assembled within autosomal scaffolds, and multiple-copy regions, which were not properly assembled in the reference genome sequence (see Supp.
Gene expression data 2. 1 Sample collection and storage During June-September 2016 field campaigns at Dumont d'Urville Station in Antarctica and at the Alfred Faure station in Crozet archipelago, samples of five different tissues (brain, liver, kidney, skin and muscle) were collected from three freshly-predated 3-7 months-old chicks from natural populations of Emperor and King penguins, respectively (Pirri et al in preparation). All tissue samples were collected immediately after death, directly fixed in RNAlater (Applied Biosystems, Warrington, UK) and frozen at -80°C until RNA extraction.
Poly-A mRNA was fragmented for 3 minutes at 94°C, and each purification step was carried out with 1 × Agencourt AMPure XP beads. Paired-end sequencing (100 bp from each end) was then performed on the Novaseq 6000 (Illumina, San Diego, CA) at a sequencing depth of 100 million reads per library. Raw reads are publicly available at ENA database as one pool of reads per species (Project ID: PRJEB64484, sample accession ID King penguin: ERS16093259; sample accession ID Emperor penguin, ERS16093260).
Gene-based analyses Counts of synonymous and missense polymorphic sites were summarised per gene using a custom python script (FinalPipeline. ipynb; https://github. com/emitruc/ExpressionLoad). After applying the stringent filters for coverage (from 6X to 8X) and missing data (no missing genotype), we used the genomic coordinates of all genes to subset the list of SNPs in the daf. joint dataset and count the number of synonymous and missense polymorphic sites per gene. RNAseq counts per gene from HTseq were then normalised by CDS length and total number of reads as transcript per million (TPM), separately per Emperor and King penguin samples (Supp. Fig. 5 ). Supplementary Figure  5. Expression rate as transcripts per million (TPM) per gene in King (yellow) and Emperor (blue) penguins. Whole range of expression (left panel) and up to 6000 TPM (right panel).
Polymorphic sites-based analyses For each polymorphic SNP in our genomic dataset (daf. joint. no00), we separately estimated the RNA reads coverage in King and Emperor penguin samples using SAMtools module depth and a bed file listing all polymorphic sites in the genomic dataset. Per site RNA read coverage was then added to the genomic dataset, as one value (total number of reads) per species (daf. joint. no00. rnaCov; https://doi. org/10. 6084/m9. figshare. 23863503. v1). Raw mRNA coverage per site was normalised as counts per million reads (CPM) dividing this value by the sum of mapped RNA reads x 1 million, separately per species: After applying the same stringent filters for coverage (from 6X to 8X), outgroup missing data (at least 4 alleles present), and King and Emperor penguin missing data (no missing genotype), we generated different datasets of synonymous and nonsynonymous variants separately per species.
Next, after capping the maximum CPM value to 5, we grouped the variants in each dataset applying the same 100 bins of CPM. We then estimated the ratio of nonsynonymous over synonymous variants in each bin separately per species (Supp. Fig. 6 ). To investigate the effect of gene expression on the allele frequency of synonymous and nonsynonymous variants, we estimated the derived allele frequency spectra grouping the variants by discrete values of CPM (Supp. Fig. 7 ): i) CPM < 0. 3, CPM > 0. 3; ii) CPM < 0. 5, CPM > 0. 5; iii) CPM < 0. 3, 0. 3 < CPM < 2, CPM > 2. In order to exclude the possibility that a few genes were driving the observed pattern (i. e. , pseudoreplication) in the comparison of the site frequency spectra at different expression rates, we replicated ten times the analysis by grouping variants with CPM < 0.
In total, we designed four models, each of them testing three carrying capacities. For each of these 12 scenarios, we ran three replicas of 10*N generations each (except for the Ne = 100,000 model where we ran for Ne generations due to computational time), as suggested by SLiM authors in order to make the population reach an equilibrium state (i. e. , burn-in). At the end of the simulations, we estimated the pN/pS based on 24 individuals, as in our genomic and transcriptomic real data, repeating the estimate 100 times by randomly resampling 24 individuals. Slim scripts are available at XXX github repository (to be published). Supplementary Figure  8. Selection coefficients in highly expressed genes. Distributions of pN/pS estimated in 100 genes where each deleterious mutation is assigned a fixed selection coefficient of -0.
Acknowledgements We thank  Fabrizio Mafessoni ,  Paolo Gratton  and  Robin Cristofari  for helpful comments and suggestions on the analyses, and  Federica Pirri  for RNA data production. ET was supported by  PNRA_16 00164  (" Programma Nazionale di Ricerca in Antartide ". Bando PNRA 5 aprile 2016 , n. 651. - Linea B "Genomica degli adattamenti estremi alla vita in Antartide "). The study was supported by the  Institut Polaire Français Paul-Emile Victor (IPEV)  within the framework of the  Program 137-ANTAVIA  (PI: CLB), by the  Centre Scientifique de Monaco  with additional support from the  LIA-647  and  RTPI-NUTRESS (CSM/CNRS-UNISTRA ), by the  Centre National de la Recherche Scientifique (CNRS)  through the  Programme Zone Atelier de Recherches sur l' Environnement Antarctique et Subantarctique (ZATA ).