dataesr/scientific-paragraphs-categorization huggingface dataset:
GVP and GEOROC datasets The datasets of GVP and GEOROC were downloaded in 2021 (more precisely in June 2021 for GEOROC data). From GVP we accessed ~1,400 Holocene volcanoes and ~9,800 eruption records, with ~860 volcanoes having known eruption date(s), and included some eruptions older than Holocene for Yellowstone caldera. GEOROC precompiled files provided ~384,000 volcanic rock analyses, ~138,000 of them matched and found around GVP Holocene volcanoes. Following the matching procedure described above, we obtained around 1000 GVP volcanoes that matched rock samples, around 700 among those have eruption date(s). We expect that most of the GVP Holocene are (at least partially) matched.
Matching GEOROC dates with GVP eruptions Once the GEOROC samples are matched with a GVP volcano, the correspondence of the samples to eruptions was done based on temporal data, more precisely on the ERUPTION YEAR field, and on the Start Year and End Year fields, respectively (if the GVP End Year is missing, the GVP Start Year is used). The algorithm worked as follows: 1) if the ERUPTION YEAR is in between the Start Year and End Year, a match is found; 2) otherwise, look for a Start Year before the ERUPTION YEAR, and an End Year which has not been identified in 1). If the Start Year and End Year are the same, and matching an ERUPTION YEAR, then we searched for a match at the month level. Many eruption dates in the GVP dataset are incomplete and only part of the start/end date is known. Moreover, for eruptions after 1,679 (the year 1,679 is used because of a programming viewpoint, not a volcanology one), many records also contain the start and end day, whereas for most older eruptions, only the year is known.
The DashVolcano tool To make the integration of GVP and GEOROC databases easily accessible and explorable, we designed a dashboard which, given a volcano of interest, comprises five types of visualization of the joint data which are described in sequence below: 1. A map of the world (using ©OpenStreetMap), showing GVP volcanoes, and GEOROC samples around volcanoes, 2. TAS (total-alkalis versus silica) diagrams (Le  Bas et al. , 1986; Le Bas et al. , 1992) , 3. Harker diagrams (e. g. ,  Winter, 2001)  from the GEOROC oxide compositions, together with, 4. GVP bar plots, summarizing known VEI, and 5. A chronogram with GVP eruptive history and GEOROC silica and alkalis evolution.
GVP bar plots of eruption history with known VEI We display the TAS diagram of a selected volcano using GEOROC data (Figure  6A ), together with a bar plot of the values of VEI from eruptions in GVP (Figure  6C ). The bar plot illustrates the volcanic explosivity history of a given volcano using the number of eruptions with a given VEI. Each VEI point is displayed with a date if provided by GVP. Unknown values of VEI are also indicated. The list of major and minor rocks (see description in Table  1 ) from the GVP dataset is given and reflected in the color of the histogram (from warmer red colors for felsic, towards colder blue colors for mafic). When a date is chosen from the GEOROC date menu, the corresponding eruption is reflected in the GVP histogram if an automatic match is found.
GVP eruption chronogram A chronogram is the eruption timeline of a volcano extracted from the GVP dataset. We distinguish three time periods: before common era (BC), before 1,679 (CE), and after 1,679 (we note that this year has no geological significance, it is related to the precision of the time recordings of Python). This was necessary because they have different levels of granularity: recent eruptions are often precise to the day, whereas BC data are generally less precise but may be known at the year level. All known eruptive history is displayed in chronological order; each colored bar represents the duration of an eruption based on the known start and end date, so short and long eruption periods are distinguishable. The actual dates in the GVP dataset are available upon hovering on the chronogram, so the user knows which dates are present in the dataset versus the dates used for plotting purposes.
GEOROC-GVP index files The DashVolcano tool automates the integration of GEOROC and GVP datasets, it is not a new database that stores these data. It instead generates index files to cross-reference the volcanoes and rock samples. To link a GVP volcano to GEOROC rock samples, an noting that all data have been recalculated to 100% volatile-free and with total iron oxide as FeO* (see text for details). Different symbol corresponds to different material types, which can be deactivated when not needed. The datasets for Campi Flegrei, which consist of around 2,300 analyses, can be downloaded in CSV format file by clicking "DOWNLOAD" button located between the map and TAS diagram plot. The downloaded file, which stored all the data being plotted, will be stored under the DashVolcano. 1. 0 folder.
This file contains the samples from MOUNT MAZAMA, under two names: MOUNT MAZAMA, PRE-MAZAMA GROUP. In the GVP database, MOUNT MAZAMA is known as a synonym of Crater Lake. The index file for PVFZCE_ CASCADES. csv thus contains one line of the form Crater Lake; MOUNT MAZAMA, PRE-MAZAMA GROUP. We note that if a user only wishes to study GEOROC data which do not correspond to FIGURE 5 (A) The wider Neapolitan Volcanic area can also be selected to include more data around the volcano of interest, or when needed to cover many volcanoes at once using the box or lasso select tools provided. (B) The TAS diagram is automatically updated to plot the chemical compositions of the rock samples within the selected area. any GVP volcanoes, it is also possible to use the above mechanism to link several GEOROC samples together, by tying them with a "GVP name" that is not present in the GVP volcano list.
Manual data curation Some users may want to include their own data collection, and combine it with the matched GEOROC datasets, to improve data coverage of a volcano of interest and enable data visualization using the DashVolcano tools. It is possible to do so, but it may be required to edit mapping files accordingly. The format of the manual data must follow the GEOROC Precompile File spreadsheet template with the compulsory fields SAMPLE NAME, LOCATION (name), geographic location (LATITUDE, LONGITUDE), MATERIAL, TECTONIC SETTING and the major element composition. Manual curation is also a way to check the performance and consistency of the code to link information between geochemical data (GEOROC or manual curation) and those from GVP. As examples of manual curation, we compiled geochemical analyses of volcanic rock samples  (Widiwijayanti, 2022a; Widiwijayanti, 2022b; Widiwijayanti, 2022c)  from Mt.
Fuji  (Takahashi et al. , 2003; Yamamoto et al. , 2007; Yamamoto et al. , 2020; Miyaji et al. , 2011; Yamamoto, 2011; Ishizuka et al. , 2021) , Rinjani  (Vidal et al. , 2015; Métrich et al. , 2017)  and Merapi  (Costa et al. , 2013)  volcanoes. These manually curated datasets contain data that are not currently in the GEOROC database. We also provide detail explanation about the manual data curation including stepby-step how to add data manually and example of uses in the Supplementary Appendix II of the paper.
Any unexpected result may reflect an error or omission in the automatic matching, or an error in the GEOROC database. The map (Figure  5A ) also shows two others active GVP volcanoes close by, which are Vesuvius and Ischia (red dots), and the distribution of the GEOROC rock samples for the three volcanoes in the Neapolitan Volcanic Area. Selecting Campi Flegrei from the drop-down menu in the Map visualization tool will generates its TAS diagram, in which all the corresponding plotted samples belong to Campi Flegrei, shown in blue on the map (Figure  4A ). Using the Mapview tool, a user may select the area of interest interactively from the map using box or lasso select tools depending on the desired shape. For example, a user wishes to include more GEOROC samples around Campi Flegrei or to select wider Neapolitan Volcanic area.
A new TAS diagram will be automatically generated to include all the GEOROC samples in the area selected. The TAS diagram shown in Figure  5B  can be compared to the Neapolitan Volcanic rock data plotted in Figure  2  of  Pignatelli and Piochi (2021)  for validation purposes. It is also interesting to compare the rock compositions of a given volcano (here Campi Flegrei volcano, in Figure  4B ) with the rock compositions of a larger area to which it belongs (here the Neapolitan Volcanic area, in Figure  5B ).
Comparative studies and side-by-side plots In order to facilitate comparative studies between two volcanoes, or two eruptions, side-by-side plots are provided, comprising TAS and Harker diagrams for analyzing geochemical variability, and bar plots to summarize the VEI's of the historical eruptions. The bar plots show how many eruptions for each VEI and the eruption date will be shown when user mouseover the dots inside the bar. Below the bar plots, the GVP major and minor rock types are reported  (Global Volcanism Program, 2013). Side-by-side plots allow to quickly visualize, for example, which volcano has the most continuous range in composition, what are the predominant rock types in each volcano, and more generally provides a comparison between the general trends of the GEOROC datasets.
As an example (Figure  6 ), we compare two large caldera-forming volcanoes in the North American continent, Yellowstone and Crater Lake (Mount Mazama). Both feature a variety of well-studied volcanic rocks that originated from some of the most explosive eruption to have occurred within the last 2 million years  (Bacon and Lowenstern, 2005; Bindeman et al. , 2007)  but the causes of their eruptive behavior are quite different and are closely linked to plate tectonic setting, Yellowstone in continental hotspot  (Smith and Braile, 1994; Christiansen, 2001; Smith et al. , 2009)  and Crater Lake in convergent margin of Cascadia subduction zone  (Bacon, 1983). The volcanism at Mt. Mazama and Yellowstone highlights these differences and forms the basis of comparative studies of diverse origins of two caldera forming volcanoes that are being used as exemplary teaching activities to explore compositional variations using GEOROC datasets  (Ratajeski, 2004).
Data visualization with an eruption chronogram The linked information between GEOROC and GVP data allows extraction of data to relate the volcanic rock samples composition to their eruption time and VEI, hereafter expressed as a pair of TAS diagrams and a chronogram (Figure  8 ). The GEOROC rock samples that matched a chosen volcano, or a specific eruption data for this volcano if available, are plotted in two TAS diagrams. The left side diagram contains only the GEOROC data and the right side are combined with GVP data but only shown eruptions that are matched in both databases (with VEI data if available). The eruption history of this volcano is illustrated in a chronogram. The chronogram summarizes the timeline of the historical eruptions, the events occurred during the eruption and VEI information as reported by GVP.
The total alkali oxides (Na 2 O + K 2 O) and silica (SiO 2 ) content of samples with known eruption time (blue dots) are plotted together with the GVP historical eruption records (red bars, the more event reported in an eruption, the redder the bar) with the VEI data (purple dots, a line connects them to highlight the variations in VEI). All known eruptive history will be displayed in chronological order; each coloured bar represents duration of an eruption based on the known start and end date. A list of corresponding events  (Global Volcanism Program, 2013)  is shown when hovering the mouse above the data point. We selected Mount Etna as a comprehensive example (Figure  8 ) as it erupts frequently  (Tanguy et al. , 2007) , has well documented historical eruptions (with known start and end date, VEI, events), and many rock sample analyses that allow to illustrate the chemical composition variation along the eruption timeline.
Conclusion The open-source DashVolcano app automatically integrates two global databases, the Geochemistry of Rocks of the Oceans and Continents (GEOROC) and the Holocene volcanoes of the world of the Global Volcanism Program (VOTW-GVP), with a graphical user interface (GUI). The app provides the user a dashboard to explore and compare the joint data for volcano(es) and/or eruption(s) in terms of chemical rock composition, analyzed rock's material, eruption timeline, eruption events, and VEI information. DashVolcano can be used to find evidence to support known hypotheses or to articulate new ones based on the combined datasets, which are now widely accessible and available for the first time. This work is a significant contribution to the community, from the point of view of methodology on establishing interoperability between two global Earth science databases, helping the reuse of existing datasets, and providing new tools to explore the integrated datasets.
( 98 ) Using the Voigt notation for the stress σ = σ 11 σ 22 σ 33 σ 23 σ 13 σ 12 ⊤ , the J 2 invariant can be expressed as a quadratic function s ⊤ s = σ ⊤ Jσ, (99) with J = 1 3         2 -1 -1 0 0 0 -1 2 -1 0 0 0 -1 -1 2 0 0 0 0 0 0 6 0 0 0 0 0 0 6 0 0 0 0 0 0 6        . ( 100 ) The matrix J is semi-definite positive which shows that C is convex. In that case, we have to solve a quadratic program with quadratic constraints that can be solved efficiently with SQP solvers min σ,a,pN 1 2   σ p N a   ⊤ Q   σ p N a   + p ⊤   σ p N a   s. t. 3σ ⊤ g Jσ g ⩾ κ 2 for g ∈ 1, n e,g p N ⩾ 0. (101) The case of Drucker-Prager plasticity. The Drucker-Prager plasticity involves the following definition of the convex set C as C := K σ = (σ h , s) | 1 k d ∥s∥ + σ h tan(φ) ⩽ c (102) where ∥s∥ = J 2 , k d is a constant that depends on the dimension d, c is the cohesion and φ the friction angle.
Safety ontologies In relation to safety, ontologies are commonly used for obtaining safety risk knowledge and handling safety management. For safety risk knowledge, it is possible to develop an ontological method which organizes this knowledge into seven unified classes (i. e. project, construction activity, risk factor, risk, risk grade, risk consequence, and risk prevention measure)  [4]. For handling the risk management, an ontology with a case-based reasoning is used as a decision-making approach for safety risk management  [5]. Moreover, safety ontologies are able to represent specifically extracted information from databases. In this way, ontologies can assist for identifying additional capabilities of these information  [6]. However, ontologies can be integrated with other technologies, algorithms, or methodologies in order to enhance their capabilities.
Reliability ontologies Reliability with respect to ontologies is expressed as a way to make ontologies reliable, or to use ontologies for increasing reliability in various systems. Agile methodology uses agile principles and practices for ontology development. In this way, it is possible to utilize software engineering to build reliable ontologies  [9]. Moreover, ontology alignment is a way to create reliable ontologies. In  [10] , machine learning techniques are used to automatically align ontologies to make them more reliable. However, ontologies are able to be used in various methodologies in order to provide a variety of different types of reliability. In general, an ontology-based text mining methodology is able to maximize system reliability, since it is able to extract knowledge from databases  [11].
Evaluation and results For the implementation and evaluation phase, a testbed is constructed with the intention to test our proposed safety orchestrator. Our testbed is composed of six Virtual Machines (VMs). These VMs are executed in a computer with an Intel core i7 processor which is running at 4. 6 GHz. More specifically, the number of threads that are able to execute instructions at once is 16. Our machine uses 15. 744 GB of RAM, with an additional Swap memory of 15. 6 GB. The OS running is Linux, and more especially Pop OS 20. 04 focal which is based on Ubuntu 20. 04. Each VM uses 6 GB of RAM and 3 vCPU. Open Source MANO (OSM) handles all the VNF by using the VNF and NS descriptors for instantiation. And Openstack handles the whole architecture of the servers. Furthermore, each orchestrator of our proposed safety architecture corresponds to one VNF and one instantiated VM.
For example, the VNF which corresponds to UPF function is not able to migrate. In this way, it is possible to simulate the issues of a NFV with respect to VNF. This is structured in a docker environment with each VNF occupying a container which uses Ubuntu 20. 04. Furthermore, the safety orchestrator needs to be able to receive the anomaly messages from the rest of the orchestrators. For this to happen, a client-server architecture is implemented. This architecture uses the WSGIserver technology. According to this architecture, (i) safety orchestrator is the server which receives the anomaly message, treats it, and sends back the best response, (ii) and each one of the rest of the orchestrators is the client which sends the anomaly message and then receives the reply. Furthermore, the server sends two types of messages to the client: (i) one message is for sending the course of action to the act module of the client in order to implement them in case that the anomaly type is safety-related, (ii) and the second message is for sending back the anomaly information in case that the anomaly type is not safety-related.
Generation step: Making of the 3D model prototype During the generation phase, our goal was to address RQ#2: Can we create a low-cost high-fidelity prototype that meets the OMIs' needs? 3. 4. 1 Materials and prototyping. Our goal was to address OMIs' needs by making a low-cost high-fidelity prototype. We used the free version of the Autodesk Fusion 360 software for modelling the elements of the model. In terms of hardware, we used a Creality CR10-v2 3D printer with PLA filament. For creating the 3D models, we used the maps of the station that are available online, but we also went onsite to take photos when details were missing. Thanks to 3D printing, we were able to make many trials in a short time to answer specific technical and functional questions regarding the model. For instance, we observed with the expert participant (blind adult) that four steps were not enough to easily identify half of a printed staircase.
Collected data and Analysis We obtained informed consent from all the participants (guardians of children under 18) to record the exploration sessions (audio and hands images) and interviews (audio only). After the two classroom sessions, participants were asked about their preferences between the two devices. The answers have been recorded, transcribed, and gathered in a table in order to highlight each participant's preference and reasons for their choice. Then, a debrief session with the whole research team aimed at summarizing the relevant comments regarding both devices. During the on-site session, one researcher was following the pairs at the railway station and wrote reports with observations made during the on-site session. The collected data included behavioral observations and comments related to the ongoing session as well as comments related to the previous classroom sessions and the devices.
The reports were then summarized and discussed with the whole research team. The semi-structured interview with OMIs were recorded and transcribed for thematic analysis  [5]. We reduced the data in three steps: i) one researcher gathered the data in a table (rows corresponding to participants and columns to the questions' topics); ii) two researchers worked separately on the table, gathering the comments that were relevant (everything concerning the impact of the devices on their professional practice); iii) themes were identified by the two researchers and discussed with the whole team. In addition to the observations, we collected data regarding the OMIs' skills. They had to answer a set of questions regarding the frequency of use of different tools: tactile maps, wooden mockups, 3D models (representing an environment), 3D objects (ex: 3D-printed sidewalk), magnetic cardboards and software (InkScape, Adobe Illustrator, Fusion 360, TinckerCAD, StechUp, GIMP) on a Likert scale with 5 levels (from "never" to "everyday use").
OMIs IT skills and Frequency of use The survey with OMIs shows that the magnet board is the most used teaching tool (Figure  8 ). Other tools are less used because of the lack of time and skills. For example, OM5 said that: "Despite the benefits of these tools, they require time (all the tools expect magnet board) and we lack it most of the time". Concerning 3D objects and models, OM3 and OM5 use Lego bricks, OM5 also uses cardboard boxes. OM1 and OM4 collaborate with makers for the making of 3D-printed objects (see Figure  9 ). 3 out of 5 OMIs would like to learn (OM4,5) and improve (OM1) 3D modelling and printing skills. Regarding frequency of use of different software, OM1 uses TinkerCAD and GIMP several times a year and OM4&5 use SketchUp and InkScape respectively but they are not autonomous.
Thus, we fully agree with  [19]  suggesting that some of the obstacles can be overcome, especially using open-source databases of 3D models of numerous objects (see e. g. , the Thingiverse or Btactile databases ). In addition, all the OMIs from our two studies have access to 3D printers or local FabLabs, and two of them have already experienced 3D printing (OM1 already made 3D models and OM4 ordered 3D objects to a FabLab). (ii) Having concerns regarding the making of 3D models and objects is legitimate, but the making of 2D tactile maps requires specific skills too  [62]. The survey showed that some of the OMIs involved in our study do not know how to make their own tactile maps and rely on an expert making tactile documents in the center (the transcriber). Informal discussions that we had with transcribers following this study showed that they would be interested in making 3D modeling and printing, and that they would consider this task as part of their duty.
We are confident that the numerous online databases, tools and methods  [8]  but also the existence of numerous local FabLab can support the spreading of such tools in the special education community. However, we made two observations in our study that would be worth future work. The first suggestion would be the creation of a dedicated library of objects (staircase, escalator, etc. ) that could be used by themselves to learn specific knowledge (e. g. , what is a staircase) but also to build complex 3D models. Another interesting future research work could focus on a portable device at the intersection between the magnetic board and the 3D model, allowing OMIs to build quickly and easily a spatial model. A final extension of our work would be to use a touch sensitive technology  [51]  that allows to do without the PenFriend.
ACKNOWLEDGMENTS We thank all the Orientation and Mobility Instrocturs who volunteered to participate in this study;  Carine Briant ,  Isabelle Campaignolle  and  Lauriane Stocker  ( IRSA Bordeaux ),  Carol Gay Brown  ( UNADEV ),  Charlotte Picaud  ( GIHP ) and  Maud Dupeux  ( CESDVIJA Toulouse ), as well as the 6 participants with visual impairments. We thank  Anthony Bordeau  for technical assistance and " Cherchons Pour Voir ". "This research was funded, in part, by  l'Agence Nationale de la Recherche (ANR) , project  ANR-19-CE19-0005. For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ".