dataesr/scientific-paragraphs-categorization huggingface dataset:
Typical scenarios for SPIDER include the replication of a complete 5G network, including User Equipment (UE), RAN and the 5G Core. Mouseworld integrates a commercial traffic generator, such as Keysight IXIA Breaking Point, for experimenting with network attacks under high traffic load. Moreover, an isolated network with replicated internet services is made available to carry out additional security experiments. The Data Repository consists of a collector and storage module gathering the generated traffic in different formats from packet-level to aggregated telemetry (e. g. Netflow v9). An application layer supports the transformation of collected data into valuable datasets features, for example, by extracting statistical features from typical network flows. A Tagger entity adds labels to each flow using external and logs information output generated during the execution of each experiment.
A. Emulation Scenario Definition An instantiated emulation scenario in SPIDER consists of a configured 5G network environment including multiple assets -such as UE or UE emulators, vertical application components, VNFs and PNFs, VIM, tenant spaces, and Software Defined Network (SDN) controllers. Each of the assets involved can contain some vulnerabilities, either inherent system properties tagged according to the Common Vulnerability Enumeration (CVE) system, or "deliberate misconfigurations", that is possible to exploit using specific tactics and techniques (e. g. , by following the MITRE ATT&CK framework  [16] ) Moreover, each emulation scenario can be associated with specific learning objectives (i. e. , perform attack/defence actions). Scenarios are modeled using an Emulation Scenario Descriptor, which encodes all their aspects in a JavaScript Object Notation (JSON) format.
The Raw Log Aggregator collects logs from the log extraction agents installed on the components for normalization, enrichment, and in order to store them as meta-data for further usage in investigations and reports. Event sources include servers, switches, routers, storage arrays, operating systems, and firewalls. The VAO is responsible to initiate and configure the Log Extraction Agent as part of day 2 operations. In order to process incoming normalized events, a fast and scalable processing mechanism is required (Streaming & Rule engine). A stack of different technologies is employed to facilitate those processing requirements. The core off-theshelf components that play a significant role in the processing pipeline are Kafka, Logstash, and the Complex Event Processing (CEP) engine. Kafka has been selected among other queue systems for its exceptional scaling capabilities and its ability to perform CEP queries.
CEP capabilities are provided by dedicated components such as Kafka SQL, Esper, Siddhi. Finally, the Indexing Engine includes a high available transactional relational database, a linear scalable documentoriented storage, and an object store (artifactory). For the sake of reference implementation Elasticsearch is used, being it open source, highly scalable and extremely documented. The Indexing engine allows the definition of multiple indextemplates, indicatively including raw-logs and performanceindicators. The Indexing Engine is crucial since it is the onestop-shop for operational dashboards. All analytic-oriented queries are performed on-top of the Indexing Engine.
E. Machine Learning Orchestration The Machine Learning Orchestrator is part of the Digital Twin Entity Management (Figure  3 ) in the Mouseworld testbed. The Topology Generator employs predefined templates, interacting with OSM (e. g. , ETSI NFV SOL-005 interface) to provision an executable service graph. An Experiment Launcher is in charge of day-2 configurations and to trigger the emulation functions for dataset generation. This is based either on ProxyCharms or dedicated Ansible scripts. The Experiment Launcher uses a configuration file, to define the statistical distribution of traffic, the number of intervals in which the experiment is divided, its duration, and the type of emulated services. Currently, two SPIDER-specific scenarios have been defined: crypto mining malware detection and DNS attack.
To validate the use cases, SPIDER has defined a methodology which includes specific steps: i) monitor of the technical Key Performance Indicator (KPI) of the system's performance and efficiency of developed modules, ii) monitor of the effectiveness of the educational and learning module and ii) monitor of the perceived Quality of Experience (QoE) metrics. The technical KPIs are further categorised in: i) general KPIs, ii) 5G KPIs, iii) cybersecurity KPIs, and iv) ML KPIs. The educational KPIs, are closely monitored by the scoring system proposed by SPIDER, as well as the progress of the Red or Blue team member. Lastly for the QoE metrics there are four sub-groups, classified as follows. The first is about technology acceptability metrics and is largely based on the work in  [8] ,  [23]. The second one refers perceived trust, which is critical when assessing cyber ranges.
We comment in particular on the cases where no unitary magnetic quiver was found. Appendix A gives details on the 5d brane webs which are the main tool used to derive the quivers, and Appendix B contains the Hasse diagrams derived from said quivers by quiver subtraction. Appendices C and D focus on the interesting cases of the sp(4) 7 × sp(8) 8 theory and N = 3 theories, respectively. Before delving into the details, let us finally mention that it is remarkable that many quivers can be guessed, before any computation is made, based purely on data from the SCFT. This can be seen as one more indication of the many restrictions that constrain 4d N = 2 SCFTs, and of their geometric 'simplicity', which is apparent in the corresponding 'simplicity' of most of the magnetic quivers. It is however tempting to expect that this simplicity is a consequence of the low rank of the SCFTs, and that at higher ranks a larger and larger proportion of Higgs branch geometries won't admit such an elementary description.
Acknowledgments We would like to thank  Rudolph Kalveks  for discussions and help with computations of bad quivers, and  Behzat Ergun ,  Simone Giacomelli ,  Amihay Hanany  and  Zhenghao Zhong  for discussions. AB is supported by the  ERC  Consolidator Grant 772408-Stringlandscape, and by the  LabEx ENS-ICFP :  ANR-10-LABX-0010/ANR-10-IDEX-0001-02 PSL*. JFG is supported by  STFC  grants  ST/P000762/1  and  ST/T000791/1. MM is supported by  NSF  grants  PHY-1915093. GZ is supported in part by the  ERC-STG  grant  637844-HBQFTNCER , by the  INFN  and by the  Simons Foundation  grant  815892.
Linguistic analysis in an environmental social media corpus The main objective of this study was to examine the specialized environmental vocabulary which is incorporated in ordinary discourse found on the Internet. Specifically, we collected English-language data from two social networks, Twitter and Reddit, with a set of environmental keywords which was compiled using the term candidates extraction technique described in Shvets and Wanner (2020). Furthermore, using data from the initial corpus, we built a smaller sample of texts to serve as a ground for a preliminary linguistic analysis of the environmental term carbon. In environmental discourse, carbon is an umbrella term which has acquired multiple senses when it comes to greenhouse gas emissions. Additionally, this term has settled in ordinary communication  (Fletcher & Downing, 2011).
This means that we provide their complete lexicographic description, including a very detailed definition, usage examples, as well as semantic and combinatorial (cooccurrence) relations. Our work draws heavily on the theoretical and descriptive framework of  Explanatory and Combinatorial Lexicology (Mel'čuk et al. , 1995; Mel'čuk, 1997). Explanatory and Combinatorial Lexicology is a linguistic theory which offers a rigorous methodology to formalize and describe the lexicon of a natural language in lexicographic resources. Another essential part of our research is practical lexicographic work done in the English Lexical Network, a generalpurpose lexical database  (Gader et al. , 2014). Its design is based on a new type of a lexical network model called Lexical System  (Polguère, 2014). In such a lexical system, special emphasis is put on the relations between lexical units that are modeled with formal tools provided by Explanatory and Combinatorial Lexicology.
In this study, we assumed an interdisciplinary approach which consisted of using various techniques pertinent to lexicology, lexicography, terminology, corpus building, corpus data management, and so forth. In the following sections, we give a detailed description of these techniques. Nevertheless, we consider it important to provide a general overview of the adopted approach as well. Corpus building is one of the most significant parts of our research. We collect textual data from Twitter and Reddit using customized scripts written in the programming language Python. The collected raw data was stored in a database service called MongoDB 3. The raw textual data usually contains a lot of irrelevant/noisy texts. Thus, it needs to be further processed and filtered. In order to do this, we exploited popular Python libraries (NLTK, TextBlob, Spacy, etc.
Twitter Corpus Twitter is an American social network available worldwide with around 463 million daily active users  (Statista, 2021). Twitter users generate tweets, or short messages with a maximum length of 280 characters. There are no thematic boards with "long post -short comments" structure, but users can publish threads of connected tweets when 280 characters are not enough. It is also possible to follow and mention other users, use hashtags, and repost and like tweets. Data extraction from Twitter is straightforward and there is a clear official API documentation provided by the Twitter Developer Platform (  2021 ). An extraction technique using Twitter's Streaming API was set up to gather tweets related to the environmental topic  (Twitter, 2021). The Streaming API gives access to a random set of a global continuous stream of tweets.
In order to extract only relevant tweets from this random set, we established several criteria for a tweet to be considered relevant: (a) it contained at least one item from our keyword list, (b) it was written in English, and (c) it was not a retweet, that is, a repost of another tweet. Each tweet was extracted with the following metadata: tweet ID, creation date and time, text of the tweet, and author's ID. The raw Twitter dataset extracted in the time span of 20 days comprised 4,709,015 tweets.
Reddit Corpus Reddit is an American social network available worldwide, with around 430 million daily active users  (Statista, 2021). Communication takes place in thematic communities, in Reddit terms, subreddits. A subreddit is a collection of submissions, or posts generated by Reddit users. Each submission can be voted up or down and might trigger further discussion in the form of comments, or texts generated by users to give an opinion on a submission or to make a statement related to a submission. Reddit data was extracted using Reddit's official API wrapper PRAW  (Boe, 2016). Our extraction technique for Reddit was distinct from that of Twitter due to the difference in the structure of these two social networks. In contrast to Twitter, Reddit is a community-based platform, which means that the extraction mechanism should be adjusted accordingly.
In order to retrieve relevant content, we established the following criteria in selecting Reddit communities for data extraction: (a) they were English-language communities related to the environmental topic and (b) they had no fewer than 5,000 community members. The names of the 14 subreddits chosen for data extraction are as follows: Environment, Ecology, Environmental science, Sustainable, ZeroWaste, Climate, StormComing, Green, Enviroaction, RenewableEnergy, ClimateActionplan, Recycling, ClimateOffensive, and GlobalWarming. In order to ensure the relevance of obtained data, we extracted "top of all time" submissions which are considered the most representative of a given subreddit  (Roussel, 2019). Such submissions display high community activity, which is of the upmost importance for obtaining rich data.
Since the Reddit API automatically extracts "top of all time" submissions without a possibility to choose a more precise time span, our Reddit corpus contains comments and submissions which date back to 2004. However, such texts constitute a small minority in our corpus. The Reddit API has a maximum pull limit of 1,000 submissions per each subreddit. Comments are not subject to this constraint, which was beneficial for our study. Each submission was extracted with the following metadata: name of the subreddit it belongs to, title of the submission, submission ID, number of comments, date and time of creation, and text of the submission. As for the comments, the following metadata were extracted: comment ID, name of submission the comment belongs to, text of a comment, date and time of creation.
Data Preparation Data preparation is important for further automatic data processing such as topic modeling, concordances analysis, and other natural language processing tasks. We cleaned the obtained raw Twitter data by eliminating full duplicates, non-English tweets, and tweets with no keywords. Then, we removed mentions, emails, newline characters, hashtag symbols, URLs, numerals, special characters, emojis, and stop words  (Bird et al. , 2009). Finally, we corrected the spelling, and lowercased and lemmatized the words. We performed the same steps for the Reddit data. Additionally, we eliminated submissions with no commenting activity, that is, with zero comments. Furthermore, we eliminated submissions and comments that did not include any of the keywords from our environmental keywords list.
Furthermore, our corpora contained a number of texts that were out of our research interest. It can be explained by the fact that some keywords used for data extraction are also used in contexts that are not relevant for us, such as carbon monoxide poisoning or increased carbon dioxide inhalation as a consequence of wearing masks. In order to detect such texts, we applied a technique of topic modeling with the Gensim library  (Řehůřek & Sojka, 2010)  and the MALLET package  (McCallum, 2002)  to our "Carbon" Twitter and Reddit subcorpora. Topic modeling is a text-mining technique which allows for grouping texts in a dataset according to their content. We built latent Dirichlet allocation (LDA) models with different number of topics, from 2 to 100, in search for the highest coherence score, which is the measure that helps understand how many topics are present in a dataset.
Texts that featured the terms carbon and carbon dioxide (CO 2 ) dominated. This may be attributed to the fact that in the environmental context, carbon is, above all, associated with carbon dioxide, whereas methane is not frequently present in the picture. As a further step, we built a sample of 500 randomly chosen tweets and 500 randomly chosen Reddit comments to manually analyze the texts. Such a subcorpus was used to perform a fine-grained manual examination on the sentence level to address the following questions: (a) What are the semantic links between the terms carbon, carbon dioxide, and methane when they are found in one piece of text? (b) Are there any repeated semantic patterns in our data that would reveal how the general public conceptualizes carbon? We are aware of the important limitations that such an approach implies.
Results Our manual linguistic analysis revealed several repeated semantic patterns of how the general public conceptualizes the term carbon in the environmental context. The analysis consisted of a manual examination of 1,000 randomly chosen texts. Close readings of each text allowed us to build a list of repeated semantic patterns as we were going through the dataset. A text was either marked as an illustration of a previously detected semantic pattern or it provided a basis for creating a new semantic pattern. 4 The main part of such a semantic analysis was performed by the first author. Disputable examples and final results were examined in collaboration with a specialist in theoretical and green chemistry and a specialist in lexicology. As a result, we identified five semantic patterns which demonstrate how the general public conceptualizes the term carbon and how these conceptualizations differ throughout our sample.
Conceptualization 1: Carbon ≡ Carbon Dioxide As can be seen in Figure  1 , there was a tendency in our data to use carbon together with carbon dioxide in one piece of text. It can be attributed to the fact that in the environmental context, carbon is a very common shortcut for carbon dioxide, as it was mentioned previously. In such texts, carbon and carbon dioxide coexist as contextual synonyms, which is observable throughout most of the sample, as in the following example: (2) The average American emits 44 tons of CO 2 per year. This facility will sequester the carbon of 2. 2k Americans, aka 0. 0007% of the US population. (Reddit) It is clear that carbon in the second sentence is a full synonym of CO 2 , as both terms refer to the greenhouse gas carbon dioxide that Americans "emit" and that is to be sequestered by a certain facility.
However, it would definitely be a step towards a more effective and scientifically accurate communication within the environmental context. In our further research, we aim to propose full-fledged and clear-cut lexicographic articles for the most basic environmental terms, including carbon. Furthermore, we collected a large amount of data from Twitter and Reddit, which are a rich source of a unique linguistic content that we will continue to explore. Similarly, we aim at conducting further research to improve the cleaning of our corpus to obtain better results for automated tasks such as concordance analysis, topic modeling, and so forth. Such tasks will complement our manual linguistic analysis of the social media corpus.
Dymola computer code The computer code we use is a dynamic building energy model, which predicts the electric power consumption needed to heat a room to a prescribed set point temperature. The room is an experimental cell from the BESTLab laboratory, a description of which is given below. The model was built using BuildSysPro, a library of components for modeling buildings and energy systems (  [26] ), written in the Modelica language, and used for simulation in the Dymola software. Originally developed in  [27] , Modelica is well adapted to model large-scale structures, the behavior of which is defined through the interaction of its components, each of them described by a limited set of equations, resulting in an overall system of (usually non-linear) equations, solved by general purpose algorithms.
An important aspect of this system-based approach is its modularity, enabling to create quickly and simply large-scale models from a library of elementary building blocks. The final computer code itself is an executable built using the Dymola software. Notationwise, let P t be the actual electric power consumption in the cell at time step t, for t ∈ {1,. , T }, and let y t be the corresponding code prediction. The latter is computed given the power consumption y t-1 at the previous time step, and a vector x t ∈ R n of forcing variables (physical inputs) at time step t, including cell characteristics, average temperature inside the cell, and weather conditions such as wind speed, outside temperature and solar radiations, among others. In addition, this code depends on a vector of fixed parameters θ ∈ R p.
Experimental data The experimental cell is one of the twelve cells making up the BESTLab laboratory (see Figure  1 ), in the EDF research and development site of Les Renardières, at about 75 km southeast of Paris. BESTLab was built in 2010, in the context of increasing demand for low-energy buildings. Its primary purpose is to test innovative building envelope components and integrated solar technologies. See  [28, 17]  for a detailed description of the installation. Our study focuses on one of the ground cells (see Figure  2 ), comprising a single, windowed, wall in contact with the outside environment. A heating, ventilation and air conditioning (HVAC) system allows to heat or cool the cell at will, thus allowing to test different types of scenarios. Figure  3  shows a typical experimental sequence, wherein the cell is alternatively maintained at certain target temperatures, heated with a fixed power supply, or left unconditioned, so that the temperature evolves freely.
Posterior distribution. From Bayes' formula (3), π(θ, λ 2 |Z) ∝ L(Z|θ, λ 2 )π(θ, λ 2 ). (6) As Y : θ → Y (θ) is non-linear, the posterior distribution (6) has no closed form. It is sampled using a Metropolis-Hastings algorithm (  [24] ). In practice, calculations were carried out using the algorithm implemented in the Open-TURNS software platform for uncertainty treatment in numerical simulation (  [9] ), consisting of componentwise random-walk steps with adaptive lengths, tuned to maintain the acceptance rate within reasonable bounds. Figure  5  shows the densities of the marginal posterior distributions π(θ i |Z) and π(λ 2 |Z). These show that the 30 available data allowed to significantly reduce the parametric uncertainty, yielding posterior densities with masses concentrated in realistic regions of the parameter space.
Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field.
Introduction Structuring a health data warehouse requires the definition of a data model that allows both a precise and flexible description of the underlying concepts and the semantic links between them, due to the diversity of data sources and their high level of connectivity. The conclusive results of our approach in the PREDIMED proof of concept  [2] , allowed us to validate the use of an improved version of PREDIMED now in production. Current modeling methodologies, regardless their application domain, are mainly based on the Entity-Association model, offering a good grasp on the objects to be modeled and the underlying constraints. To explore data through a data model, an abstract model should be implemented and the most common way to do so are relational database management systems.