dataesr/scientific-paragraphs-categorization huggingface dataset:
These metrics are based on the difference e i = y i -y * i between the actual expected value y i of the creep rupture stress and the corresponding prediction y * i. MAE = 1 N N i=1 |e i | (1) MAPE = 100 N N i=1 e i y i (2) RMSE = 1 N N i=1 e 2 i (3) with N the length of the database. While mathematically different, these metrics all provide insights into the generalisation capability of the model, i. e. its ability to correctly interpolate to unknown inputs which were not in the database used for training. Without loss of generality, the smaller their values, the better the predictive capability. Five-fold cross-validation results in a MAE of 0. 0280 log MPa (equivalent to an error of ±19 MPa on a estimate of 300 MPa), a MAPE of 1. 26 % (±22 MPa) and a RMSE of 0. 0548 log MPa (±38 MPa), which highlights a reasonable generalisation capability of the model.
Microstructure suitability The CALPHAD method is used to evaluate the performance of the screened alloys on the basis of their constitution. The Thermo-Calc software [36] coupled with the thermodynamic databases TTNI8  [37]  and TCNI9  [38]  dedicated to nickel-based alloys is employed to estimate the equilibrium fractions of γ and TCP phases and the composition of the (γ) matrix and γ phase, which in turn serves in the computation of the γ/γ lattice misfit. The maximum allowed content of TCP phases is determined after computing the TCP content of actual alloys (listed in Figure  6 ) and comparing it with values reported.
Corrosion resistance The resistance to corrosion of the single crystal nickelbased superalloys is typically imparted by additions of aluminium and chromium in quantities such that a protective layer of mixed oxides (including aluminium and chromium sesquioxides) can form. Its growth is impeded by possible depletion of both elements: aluminium in γ precipitates and chromium in TCP phases. Environmental resistance can then be naively considered a direct function of the amount of these elements that remains free to diffuse outward, i. e. whose mobility is not mostly disabled by the formation of Al-and Cr-rich phases in the alloy. We thus relate corrosion resistance to the content of aluminium and chromium in the (γ) matrix. Alloys from the creep rupture stress database are used to tune the minimal thresholds of aluminium and chromium by computing their equilibrium constitution at 1200 • C (Figure  1 ), at which the harshest rate of degradation by corrosion is expected.
Following the composition of the (γ) matrix of CMSX-4, an alloy with satisfactory corrosion resistance currently used in commercial gas turbines, minima of 9. 5 at. % Al and 7. 5 at. % Cr are set. These minima are quite restrictive: if the alloys from the database were to be screened, 53 % (TTNI8) or 60 % (TCNI9) of them would be rejected (Figure  1 ).
Volumetric mass density The reduction of volumetric mass density (ρ) increases the life of turbine blades by lowering the stresses induced by centrifugal forces. The density is modelled following the approach of Hull  [42]  based on data previously gathered from the same literature search as the creep rupture stress (Equation  7 ). The database contains 210 measurements on 189 alloys, from single crystal, directionally solidified and polycrystalline cast or wrought families. ρ = 100 i w i ρ i + i a i w i (7) with ρ the alloy density in g. cm -3 provided w i is the nominal content of element i in wt. % and ρ i its density in g. cm -3. The regression coefficients a i are given in Table  3. The density of alloys to be designed is limited in order to match that of common single crystal nickel-based superalloys such as CMSX-4+ and CMSX-10K.
Search specifications A systematic grid-search in the space defined in Table 4 is conducted. Each element is varied by steps of 1 wt. % with the exception of hafnium (0. 5 wt. %). Nickel remains the balance element of every alloy. A 200 ppm carbon content is imposed to take into account its effect as a potential trace. The resulting search space contains about 293 million alloys (combinations). The explored area fits within the space covered in the CRS database, and the boundaries of alloying elements place the search space in the most densely populated area of the database. The systematic screening allows for a more complete mapping of the compositional space than advanced methods such as genetic algorithms  [2, 11, 32]  or sequential quadratic programming  [6]  which by nature only partially sample it and hence do not provide an overall view of it.
Comparison with existing alloys It is important to note that, among the alloys from the creep rupture stress database (the "competitors"), none follows the microstructural specifications forced upon selected candidates, so that any direct comparison would be biased in favour of the competitors. Nevertheless, such a comparison is here attempted to illustrate the interest of the present alloy design strategy since, as will be shown, many candidates actually possess higher specific properties than competitors. In this aim, both populations are plotted  on Figure  5  as a function of their predicted density and predicted lowered creep rupture stresses. All plots share the same abscissa. Since the existing alloys were used to train the model, their predicted properties are close to their actual values.
This is to avoid the pitfall of selecting alloys with unrealistic microstructures when relying solely on a statistical model  [22]. It was observed that CALPHAD predictions of TCP content using TTNI8 and TCNI9 were not equivalent, with difference ranging from 1mol. % to 40mol. %. These two computational thermodynamics databases mainly rely on the fitting of experimental data (as most CALPHAD databases do). It is thus not surprising to observe differences between their predictions for identical compositions. The selection process required an agreement between both (for all quantities computed using CALPHAD); having two databases with corroborating results minimises the risk of inadequate microstructure in the cast candidates. The location of these fifteen candidates in comparison to competitors is shown on Figure  5  as cyan squares.
Moreover, this matrix cannot be changed by the agents, their autonomy is therefore limited. The Repast Simphony simulation platform natively uses the first scheduling options (i. e. with a default agents' method to call and no information given to the agent), but it also allows a sort of contextual activation based on "watchers". Watchers allow an agent to be notified of a state change in another agent and schedule the resulting action. The designer specifies which agent to watch and a query condition that must be verified to trigger the resulting action. This activation process is coherent with our definition of a contextual activation. However, it is limited by the expressiveness of the watcher queries language to express the activation context. Indeed, the queries are boolean expressions that evaluate the watcher and the watchee using primitives such as colocated and linked to and the logic operators AND and OR.
Partially supported by  Lebesgue center of mathematics "Investissements d'avenir" program  - ANR-11-LABX-0020-01 , by  ANR   CAESARS -ANR-15-CE05-0024  and by  ANR   MFG -ANR-16-CE40-0015-01. Partially supported by  National Science Foundation of China  (Grant No. 11631004 ) and  Science and Technology Commission of Shanghai Municipality  (Grant No. 14XD1400400 ).
This paper summarises the results from a blind-prediction benchmark study for models used for estimating the consequences of vented hydrogen deflagrations, as well as for users of such models. The work was part of the HySEA project that received funding from the Fuel Cells and Hydrogen Joint Undertaking (FCH JU) under grant agreement no. 671461. The first blind-prediction benchmark exercise in the HySEA project focused on vented explosions with homogeneous hydrogen-air mixtures in 20-foot ISO containers. The scenarios selected for the second blind-prediction study focused on vented deflagrations in inhomogeneous hydrogen-air mixtures resulting from continuous stratification of hydrogen during vertical jet releases inside 20-foot ISO containers. The deflagrations were vented through commercial vent panels located on the roof of the containers.
The test program included two configurations and four experiments, i. e. two repeated tests for each scenario. The paper compares experimental results and model predictions and discusses the implications of the findings for safety related to hydrogen applications. Several modellers predicted the stratification of hydrogen inside the container during the release phase with reasonable accuracy. However, there is significant spread in the model predictions, especially for the maximum reduced explosion pressure, and including predictions from different modellers using the same model system. The results from the blind-prediction benchmark studies performed as part of the HySEA project constitute a strong incentive for developers of consequence models to improve their models, implement automated procedures for scenario definition and grid generation, and update training and guidelines for users of the models.
The second HySEA blind-prediction benchmark study The experimental program in the HySEA project included 66 vented hydrogen deflagration experiments performed in 20-foot shipping containers: 42 tests with initially homogeneous and quiescent mixtures, and 24 tests with inhomogeneous mixtures  (Skjold, 2018a,b; Skjold et al. , 2019b). The total number of tests was 72, which included five unignited dispersion tests and one failed test. The scenarios selected for the second HySEA blind-prediction benchmark exercise involved continuous stratification of hydrogen and subsequent ignition to deflagration and explosion venting through commercial vent panels. The members of the HySEA consortium invited researchers and engineers to submit model predictions before the actual experiments were performed.
Fig. 1  (right) shows the pipe rack mounted in the centre position, denoted configuration P2. Three one-inch pipes (outer diameter about 34 mm) prevented the upper part of the container from bulging out. The centre of these pipes was about 80 mm below the frame that supported the vent panels. type 9215 (BNC) data acquisition modules from National Instruments, mounted in a cDAQ-9188XT chassis located in the instrumentation bunker, recorded the pressure and deflection measurements at a sampling frequency of 100 kHz. A separate National Instruments NI-9201 data logger was used for the concentration measurements. Two Edgertronic SC1 high-speed video cameras and one regular JVC GZ-RX515BE video camera recorded the explosion events. ) and sampling tubes (A4′, B4′, etc. ) near the back wall implies that the continuous and intermittent concentration measurements should measure similar concentration profiles for the stratified mixture inside the container.
002 m. The opening times for the vent panels were estimated from high-speed video recordings. Based on independent observation of recordings from two cameras in different locations, the accuracy of the time estimates was estimated to be within ± 0. 002 s. The values indicated for P stat are filtered pressure data (50 Hz) measured with the two sensors P05 and P06 at time t stat. The measurement uncertainties of the concentration measurements were approximately ± 0. 2 and ± 0. 6 vol% for the point (Servomex) and continuous (Teledyne) measurements, respectively. The corresponding response times for the sensors (0-90 vol% O 2 ) were ten and six seconds. lines) and the intermittent measurements with the Servomex analyser (points). The results demonstrate clear stratification inside the container, with maximum hydrogen concentrations around 27 vol% near the ceiling.
Acknowledgements The  HySEA  project received funding from the  Fuel Cells and Hydrogen 2 Joint Undertaking  ( FCH 2 JU)  under grant agreement No  671461. This Joint Undertaking received support from the  European Union 's  Horizon 2020 research and innovation programme  and the  United Kingdom, Italy, Belgium and Norway. The members of the  HySEA consortium  gratefully acknowledge the valuable contributions from the members of the  HySEA Advisory Board :  Simon Jallais  and  Elena Vyazmina  from  Air Liquide ,  Derek Miller  from  Air Products ,  Carl Regis Bauwens  from  FM Global  and  Y. F. (John) Khalil  from  United Technologies Research Center (UTRC).
Managing privacy and understanding handling of personal data has turned into a fundamental right, at least within the European Union, with the General Data Protection Regulation (GDPR) being enforced since May 25 th 2018. This has led to tools and services that promise compliance to GDPR in terms of consent management and keeping track of personal data being processed. The information recorded within such tools, as well as that for compliance itself, needs to be interoperable to provide sufficient transparency in its usage. Additionally, interoperability is also necessary towards addressing the right to data portability under GDPR as well as creation of user-configurable and manageable privacy policies. We argue that such interoperability can be enabled through agreement over vocabularies using linked data principles.
To this end, the work presented in this paper aims to set a basis for the establishment of interoperable standards in this domain. In particular, it addresses the following gaps by complementing existing (W3C) standards: -There are no standard vocabularies to describe and interchange personal data. Such vocabularies are relevant, for instance, to support data subjects' right to data portability under Article 20 of the GDPR  [9]. -There are no agreed upon vocabularies or taxonomies for describing purposes of personal data handling and categories of processing: the GDPR requires legal bases for data processing, including consent, be tied to the specific purposes and processing of personal data to justify their lawful use. Consequently personal data processing should be logged with a standard reference to a purpose which complies with the norms set by the legal bases -such as the individual's consent.
The concrete taxonomies for representing this information in the context of personal data handling are not yet standardised. -There are no agreed upon vocabularies or ontologies that align the terminology of privacy legislations -such as the GDPR, to allow organisations to claim compliance with such regulations using machine-readable information. The herein presented Data Privacy Vocabulary (DPV) aims at addressing these challenges by providing a comprehensive, standardized way set of terms for annotating provacy policies, consent receipts, and -in general -records of personal data handling. To this end, the rest of the paper is structured as follows: Section 2 explains the setup and governance of the DPV Community group within the World Wide Web Consortium (W3C) whereafter Section 3 summarizes pre-existing relevant vocabularies and standards that served as inputs.
DPVCG: Data Privacy Vocabularies and Controls CG To address the gaps mentioned in Section 1, a W3C workshop was announced 9 , which received 32 position statements and expressions of interest. These were used to create an agenda based on standards and solutions for interoperable privacy. The workshop took place on 17 th and 18 th April 2018 in Vienna and consisted of about 40 participants. Discussions and interactions were structured into sessions around the four themes of: (1) 'relevant vocabularies and initiatives', (2) 'industry perspective', (3) 'research topics', and (4) 'governmental side and initiatives'. The workshop concluded with a discussion of the next steps and priorities in terms of standardisation and interoperability. The identified goals were (from highest to lowest priority): taxonomies for regulatory privacy terms (including GDPR), personal data, purposes, disclosure and consent (as well as other legal bases), details of anonymisation (and measures taken to protect personal data), and for recording logs of personal data processing.
The resulting vocabulary is presented in Section 5. A second face-to-face meeting was conducted on 3 rd and 4 th December 2019 at Vienna, Austria. The goal of this meeting was to analyse the collected use-cases and vocabularies, to establish agreement on the requirements for vocabularies to be delivered, and to plan ahead towards their conception and completion. A third face-to-face was organised on 4 th and 5 th April 2019 in Vienna and Dublin to finalise the vocabulary and reach an agreement towards the first public draft. The outcome of the meeting was agreement of terms used and its expression using RDF and OWL. The meeting also provided agreement over the namespace of the vocabulary, its hosting, and documentation. After over a year of collaborative effort, the CG published the 'Data Privacy Vocabulary' (DPV) on 25 th July 2019.
To this end, an analysis of existing vocabularies was carried out to determine their suitability, which revealed a lack of top-level concepts which could be readily incorporated. Therefore, the CG created the necessary concepts by inviting contributions and reviewing them through discussions. The agreement over how terms were proposed, discussed, and added was documented through a collaborative spreadsheet hosted on the Google Sheets platform  15. The spreadsheet contained separate tabs for each 'modular' ontology and a base ontology representing combined their combined usage to represent personal data handling. The columns in the spreadsheet were mapped to semantic web representations, as depicted in Table  1. The vocabulary was created by using the Google Drive API in a script  16  that extracted terms and generated RDF serialisations using rdflib  17  and documentation using ReSpec  18.
Base Ontology The 'Base Ontology' describes the top-level classes defining a policy for legal personal data handling. Classes and properties for each top-level class are further elaborated using sub-vocabularies, which are available as separate modules and are outlined in subsequent sections. While all concepts in DPV share a single dpv: namespace, the modular approach of providing the base ontology as a separate module makes it possible to use sub-vocabularies without the dpv:PersonalDataHandling class, for example to refer only to purposes. Exceptions to this are the NACE purpose taxonomy (cf. details Section 5. 3) extending the dpv:Sector concept in the Purposes vocabulary, and the GDPR legal bases taxonomy (cf. details in Section 5. 6) extending the top-level dpv:LegalBasis class -which are provided under a separate namespaces to indicate their specialisation.
For continued development of this work, the DPVCG is currently inviting participation in the form of comments, feedback, and suggestions. Specifically, the DPVCG kindly requests proposals to extend its initial taxonomies by additional terms, where these are missing or need refinements in order to describe specific use cases of personal data handling. Future plans also include producing documented examples of how the DPV could be adopted for specific use-cases. Examples include annotating privacy policies, documenting information for specific laws such as GDPR, and producing transparent processing logs by mapping the DPV to existing database schemas.
a preliminary outline of the goals of CG has been presented in ISWC2018's SWSG workshop [5] where we also gathered valuable feedback by the participants; this work is the first complete presentation of the resulting, proposed vocabulary elaborated by the DPVCG since. This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant  73160. Harshvardhan J. Pandit  is funded by the  ADAPT Centre for Digital Excellence  funded by  SFI Research Centres Programme  (Grant  13/RC/2106 ) and co-funded by  European Regional Development Fund. Pandit, Polleres, et al. 9 https://www. w3. org/2018/vocabws/ 10 https://lists. w3. org/Archives/Public/public-dpvcg/ 11 https://www. w3. org/community/dpvcg/wiki/Main_Page 12 https://mydata2018. org/.
The repository software provides a secure home for the collection of documents stored in the system. Repository content can be annotated through defined metadata fields, enhancing structured archiving and retrieval. Further, submitters are encouraged to use document templates to improve consistency of the document formats and enable improved tools to be developed 8. The repository's content is indexed by all the major search engines and harvested by such services as Google Scholar, Scopus, OpenAIRE, ASFA, etc. To support such indexing, the repository assigns DOI to submitted best practices 9. If documents already have DOIs assigned to them by an external system, their existing identifiers are added to the system and are used as the preferred DOI within the OBPS. Peer review of documents stored in the OBPS repository is supported through a partnership with the Frontiers of Marine Science.
There are many forms of peer review  (Walker and Rocha da Silva, 2015)  and the Frontiers in Marine Science provides an open access platform and identifies reviewers for accepted articles. This was judged to be an important part of the community forum process desired for OBPS in that it would help build consensus around what is indeed "best" in different settings. However, peer review will not always resolve issues among competing practices, or determine at what point a new practice should replace an existing one. In addition to the peer review offered by academic journals, the review of submissions by expert panels from programs like GOOS 10  , the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) 11  , the International Oceanographic Data and Information Exchange (IODE) 12  , and others, will help identify recommended practices for respective stakeholders.
L. et al. , 2016)  help connect the content of best practice/method description documents with widely-used, machine-readable descriptors commonly applied to data. On the other hand, the text-mined content of the OBPS collection can serve to identify and fill gaps in the terminologies. Through the OBPS, there is a two-way benefit in both (1) vocabularies such as the widely adopted British Oceanographic Data Centre vocabularies 15  and (2) ontology resources covering a broad range of disciplines such as environments  (Buttigieg P. et al. , 2016) , populations and communities  (Walls et al. , 2014) , devices and protocols  (Brinkman et al. , 2010) , chemicals  (Degtyarenko et al. , 2008) , qualities  (Mabee et al. , 2007) , and the SDGs  (Buttigieg P. L. et al. , 2016) , all developed using the best practices of the Open Biological and Biomedical Ontologies (OBO) Foundry and Library  (Smith et al.
html 21 http://marine. copernicus. eu/usecases/harmful-algae-bloom-monitoringaquaculture-farms-spain/ 22 Visited 1. April 2019 23 Ireland (2), US (2), EU (3) optimized observing approaches, such as the European EMSO-ERIC (formerly FixO3), Tropical Arrays (e. g. , PIRATA) and Regional Alliances such as IMOS. Driven by the wide variability, almost all consortia have created their "own" best practices for sensors, infrastructure, and data that are tailored to their needs. Many of them are based on field experience and personal and institutional expertise. Given the national or consortia driven operation of the infrastructure, Eulerian observatories documentation is highly fragmented. Under the umbrella of the global Eulerian observatory network OceanSITES, community agreement has been achieved for selected best practice segments such as data dissemination.