dataesr/scientific-paragraphs-categorization huggingface dataset:
HARPS We acquired 90 high-resolution (R ≈ 115 000) spectra with the High Accuracy Radial Velocity Planet Searcher (HARPS;  Mayor et al. 2003  ) spectrograph mounted at the 3. 6 m ESO telescope at La Silla Observatory. HARPS observes in the visible spectrum within the wavelength range of 380 to 690 nm. The typical exposure time per observation was 1500 s, which produced spectra with a typical S/N of 70-80 at 550 nm. The observations were carried out between 2019 April and 2020 March, as part of the two large observing programs 1102. C-0923 (PI: Gandolfi) and 1102. C-0249 (PI: Armstrong), and the ESO programs 60. A-9700 and 60. A-9709. We reduced the data using the dedicated HARPS data reduction software (DRS) and extracted the RV measurements by cross-correlating the Echelle spectra with a K5 numerical mask  (Baranne et al.
Spectroscopic parameters We used the HARPS observations to produce a high signal-to-noise (S/N = 700 at 550 nm) spectrum of HD 73583. Briefly, we arbitrarily chose one of the RV observations as the zero velocity and through a cross-correlation procedure shift and add all the other observations to this one. Because of the inherent lack of accuracy in all available methods, we use three independent methods in order to derive the basic three spectroscopic parameters: ef fecti ve temperature ( T eff ), surface gravity (log g ), and metallicity ([Fe/H]). The first method, using SpecMatch-Emp (Yee, Petigura & von Braun 2017 ), compares a standardized version of our spectrum to a library of more than 400 spectra of stars with well-determined parameters. Through interpolation and a minimizing process the code provides a set of stellar fundamental parameters such as T eff , metallicity ([Fe/H]), log g.
3. We note that these parameters are in full agreement within 1 σ with the mass and radius estimated using SpecMatch-Emp. As an extra check to our stellar mass and radius determination, we also perform a spectral energy distribution (SED) modelling using the software ARIADNE  (Acton et al. 2020 ). Grids of four stellar atmospheric models, Phoenix v2  (Husser et al. 2013 ) , BtSettl (Allard, Homeier & Freytag 2012 ),  Castelli & Kurucz ( 2004 ) , and  Kurucz ( 1993 )  were interpolated with priors for T eff , log g , [Fe/H] from SpecMatch-Emp (Table  3  ). Stellar radius, distance, and extinction ( A V ) were treated as free parameters. We used broadband photometry from 2MASS J, H, and K, WISE W1 and W2, the Johnson B and V magnitudes from APASS, and Gaia G, G BP , and G RP magnitudes and parallax from eDR-3.
Transit analysis We first perform a transit analysis in order to obtain planet ephemerides to use for a preliminary RV data analysis in Section 5. 2, as well as to check for uniform transit depths in all bands for both planets. We use the code pyaneti (Barrag án,  Gandolfi & Antoniciello 2019 ; Barrag án et al. 2022 )  to model the flattened TESS (see Section 2), CHEOPS (see Section 3. 2), and ground-base transits (see Section 3. 3). To speed up the analysis, we just model data chunks of maximum 3. 5 h either side of each transit mid-time. We sample for the stellar density, ρ , and we reco v er the scaled semimajor axis ( a / R ) for both planets using Kepler's third law (see e. g. Winn 2010  ). For the limb-darkening model we use the quadratic limb-darkening approach described in  Mandel & Agol ( 2002 )  with the q 1 and q 2 parametrization given by  Kipping ( 2013 ).
Dynamical analysis We performed an orbital stability analysis of the HD 73583 system using the software mercury6  (Chambers 1999 ). We assume that both planets have coplanar orbits and we use our derived parameters in Table  5  to create our mercury6 set-up. We evolved the system for 1 Gyr with steps of 0. 5 d per inte gration. F or HD 73583 b we found negligible changes on the orbital parameters of the planet, except for the eccentricity that fluctuated with a maximum change of 0. 08. For HD 73583 c we found changes of its eccentricity < 0. 08 and a maximum variation of 5 × 10 -5 AU in its semimajor axis. Therefore, we conclude that the orbital and planetary parameters derived for HD 73583 are consistent with a dynamically stable system.
MNRAS  514 ,  1606-1627 (2022 ) di Torino foundation under Grant No. 2018No. 2323'Gaseous or rock y? Unv eiling the nature of small worlds'. DJA acknowledges support from the  STFC  via an  Ernest Rutherford Fellowship  ( ST/R00384X/1 ). APH and ME acknowledges grant  HA 3279/12-1  within the  DFG Schwerpunkt SPP 1992, 'Exploring the Diversity of Extrasolar Planets '. JS and PK would like to acknowledge support from  MSMT  grant  L TT -20015. We acknowledges the support by FCT -Funda c˜ ao para a Ci encia e a Tecnologia through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional Competitividade e Internacionaliza c˜  ao by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113 ;  PTDC/FISAST /28953/2017 & POCI-01-0145-FEDER-028953.
In Europe, the more relevant "host" for this is the EPOS platform (https://www. epos-eu. org). EPOS is the natural environment for integrating the European tsunami research network. Building research and development activities in the framework of the EPOS platform will foster community progress in such important aspects as: -preservation, standardisation, and dissemination of scientific data and products; -converting research and engineering assets (data, software) into offline and online services with standardised access policy; -ensuring sustainability of service provision according to FAIR principles; -need to better integrate tsunami products with products and services of other research communities (other EPOS Thematic Core Services, or TCSs). In July 2021, the tsunami community got the official status as a candidate Thematic Core Service (cTCS).
General characteristics of the community: from science to risk reduction The tsunami community involved in the effort of the TCS construction currently includes 30 partner institutions across Europe from 14 countries (Figure  1 ). The European tsunami research community is composed of universities and public and private research centres. There is a well-established tradition of collaborative research, as testified by a long record of EU-funded projects. In addition to the seminal ones, already mentioned in the introduction, among collaborative projects with a specific focus on tsunamis we note the NEAREST, SEAHELLARC, and TRANSFER FP6 projects, the TRIDEC and ASTARTE FP7 projects, and the NEARTOWARN and TSUMAPS-NEAM prevention and preparedness projects funded by the DG-ECHO [see e. g. Hammitzsch et al.
Envisaged community services The tsunami community has from its outset transferred science into hazard and risk assessment and tsunami warning operations. This community is also multi-disciplinary and multi-faceted, as it includes a broad range of disciplines such as geophysics, oceanography, engineering, numerical modelling, hazard and risk analysis. The rich spectrum of community activities will be naturally reflected in corresponding EPOS-related products and services that can be roughly systematised by four categories presented in Figure  2. Correspondingly, the future EPOS Tsunami TCS plans to organise its work within four thematic Pillars each representing a specific family of DDSS (Data, Data-products, Services and Softwares, https://www. epos-eu. org/eposservices-list) elements. Figure  3  illustrates the four Pillars, and the four sub-sections below describe the envisaged services in more detail.
Pillar 1: Support to Tsunami Service Providers This pillar will incorporate tools, workflows, and workspaces to facilitate interoperability of the official NEA-MTWS Tsunami Service Providers (TSP) in their operational (= event processing in real time) but also preparatory  work. Initially oriented towards the 5 NEAMTWS presently accredited TSPs (in Portugal, France, Italy, Greece, and Turkey), and to the national tsunami warning centres, the services included in this Pillar will be open for further extension. In particular, it is envisaged that the real-time and subsequent rapid characterization of the tsunami events will constitute the basis for further research and understanding of the tsunami and its source. The first DDSS element in this Pillar will be the so-called "TSP-IOT Interoperability tool": a virtual access (VA) to a web service including a common database (forecast points, sensor locations, bathymetry and topography models, etc.
). Tools to enable data exchange (e. g. , earthquake parameters and mechanism), warning information and enhanced products (e. g. alert level and tsunami travel time maps) in real-time are also under discussion. TSP-IOT tool will help warning centres to make their early warning operations interoperable and consistent, and to increase redundancy and fall-back solutions during acute event processing. An end-user oriented interface of the tool would allow accredited subscribers to access products and messages from all TSPs through a single point-ofentry. In a second stage of the implementation, additional tools, such as pre-computed scenario and/or earthquake finite fault model databases, will be used by the TSPs for continuous training and to retrospectively analyse past events. The TSP-IOT will be also enhanced with the functionality to access other databases and tools that will be facilitated under the TCS Tsunami such as, in a not too distant future, cloud or HPC simulation capabilities.
Pillar 2: Tsunami Data Pillar 2 will provide services both via Virtual Accesses (VA) and Trans National Accesses (TNA). It will include VA to real-time sea-level monitoring facilities as well as to catalogues of historical and paleotsunamis. VA to datasets on submarine landslides, bathymetry-topography models, in accordance with FAIR principles, will also assist tsunami researchers and modellers. A link to potential earthquake sources, such as crustal faults and subduction systems, already offered in the TCS Seismology  [Haslinger et al. , 2022]  will be provided, and collaborations will be sought to enhance the existing services for seamless use within the tsunami community. This Pillar also incorporates access to large experimental hydrodynamic experimental facilities as TNAs. The sea level monitoring facility (SLMF) (Figure  4 , http://www.
ioc-sealevelmonitoring. org/) collects and processes in real time data from 1028 sea level measuring stations operated by 164 institutes (station operators) world wide. The system was built and is operated by Flanders Marine Institute (VLIZ) for UNESCO as part of the GLOSS (Global Sea Level Observing System) program of the Intergovernmental Oceanographic Commission. The station operators push the raw data to the SLMF using different data protocols: WMO GTS (Global Telecommunication System), FTP push, HTTP and even Email. Data is parsed in real-time and stored in the database. SLMF redistributes the parsed real time data through a series of interfaces: web-pages, rest-API, or GTS to the different users, including tsunami warning centers, station operators and scientific community. SLMF database contains sealevel data coupled with metadata, contact information and configuration data for sea-level stations and their sensors.
Access to the SLMF data is one of the two cTCS-Tsunami pilot services to be integrated into the EPOS ICS Data Portal. The distribution of historical tsunami data, and other services related to the Euro Mediterranean Tsunami Catalogue [EMTCv2,  Maramai et al. , 2021]  will be available on the TCS portal. Information on historical tsunamis generated in the NEAMTWS area is classified by the generating cause, tsunami intensity, and reliability. A general description of the tsunami, related parameters, and bibliographic references are linked to each event. In addition, a second database, the Italian Tsunami Effects Database (ITED), documenting the tsunami effects observed along the Italian coasts will be provided. The description of the effects and quantitative data (local intensity, run-up, inundation, sea retreat, etc.
), as well as the respective bibliographic references are indicated for each site in the database. The distribution of tsunami intensities over time (tsunami history) is available for each locality. Both databases are already available as OGC (Open Geospatial Consortium) services. An additional database about the Greek Tsunami Effects Database (GTED), which is under construction will be also provided by the Dept. of Geology, National and Kapodistrian University of Athens. The ASTARTE Paleotsunami Deposits database is a relational geographic database on paleotsunami deposits. It comprises a total of 151 sites and 220 tsunami evidence (events) recorded for the whole North-East Atlantic and Mediterranean Sea region. Extending in the past the tsunami catalogues may help constrain or validate at least large event recurrence  [Behrens et al.
Andrey Babeyko et al. The Euro-Mediterranean Submarine landSlide database [EMSS21;  Urgeles et al. , 2021; see Figure 5]  will provide services allowing scientists to derive information relative to the source and subsequent dynamics, which can be useful for Probabilistic Tsunami Hazard Assessment (PTHA) from submarine landslides. The relevant information to construct individual landslide tsunami scenarios will also be available. The dataset includes polygons and polylines for the landslide deposits, source areas and scars as well as information relative to age, volume, area, runout, thickness, typology, scar elevation, depth, and slope as well as the bibliographic information relative to each event amongst others. This dataset will be augmented with information from the recently compiled submarine landslide open-access database in the south and southwest Iberia Margin  [Gamboa et al.
, 2021a]. The latter incorporates more than 1500 events, including their morphic features and the spatial and morphometric relationships between them  [Gamboa et al. , 2021b]. Currently, two experimental facilities have expressed their interest in the TNA program within the EPOS Delivery framework. The HR Wallingford's Froude Modelling Hall (Figure  6 ) has a total extent of approximately 14,400m 2. These physical laboratory test facilities provide a global service to support water related engineering projects, including the installation offered for TNA: the Fast Flow Facility (FFF) with Tsunami Simulator. HR Wallingford's state-of-theart Tsunami Simulator is the outcome of 10 years of collaboration between HRW and UCL [see  Chandler et al. , 2021]. It is situated within the FFF and data acquisition systems are backed up by a range of instrumentation that enables detailed measurements to be made.
Towards the new EPOS TCS Tsunami For meteorological tsunamis (also known as meteotsunamis), a global 1-min sea-level dataset named MISELA has been recently published  [Zemunik et al. , 2021]  and will be accessible in EPOS. It encompasses quality-checked records of non-seismic sea-level oscillations at tsunami timescales, that is with periods below 2 h, obtained from 331 worldwide tide-gauge sites. To account for historical events, EPOS will make accessible information from the global scale meteotsunami catalogues that have been recently compiled [e. g. ,  Gusiakov et al. , 2021] , suggesting 235 events as confirmed or suspected meteotsunamis.
Pillar 3: Numerical Models Pillar 3 will provide access to all kind of software and simulation data resources including: -Software repositories with source codes (e. g. GitHub); -Simulation-data repositories (e. g. , amplification factors or pre-calculated Green's functions); -Virtual accesses (VA) to online tsunami simulation tools; -Virtual and Transnational Accesses (VA/TNA) to HPC Workflows as a Service (WaaS). Since tsunami science is mostly simulation-oriented, this Pillar will be broadly relevant. Some examples are described in the following. The representation of earthquakes as tsunamigenic sources is a major component in tsunami simulations. Spatial variation of slip, particularly at shallow depths, can have a large impact on tsunami wave height, particularly in the near field  [Geist et al.
, 2002; Ulrich et al. , 2019]  which can influence tsunami hazard assessment  [Scala et al. , 2020; Basili et al. , 2021]  and the probabilistic tsunami forecasting  [Selva et al. , 2021b]. cTCS Tsunami will provide services for generation of earthquake source scenarios, based on stochastic and physics-based, depth-dependent slip distributions, representative of the earthquake rupture history on planar and non-planar geometries of the subduction zones. These distributions obey the k-square fractal law in the definition of the slip asperities and are implemented in the K223d software  [Herrero and Murphy, 2018; Murphy and Herrero, 2020]. Tsunami propagation modelling suite will include the flagship Tsunami-HySEA code designed for quake generated tsunami simulations  [Macías et al. , 2017 [Macías et al.
Different versions of advection and run-up schemes are implemented. The model was developed in the framework of the GITEWS project and is currently used for the determination of warning products for the tsunami database of the Indonesian Tsunami Early Warning System (InaTEWS) as described in  Harig et al. [2020]. An alternative to high-resolution local inundation modelling is the application of amplification factors. In EPOS, a set of amplification factors will be provided for two datasets, either global  [Løvholt et al. , 2012]  or related to the NEAM region  [Glimsdal et al. , 2019]. Amplification factors relate the maximum offshore wave amplitude at a given reference depth to the maximum inundation height plus its uncertainty. Being computed along transects assuming linear non-breaking waves implies that the amplification factors involve a much higher uncertainty than local inundation models do.
In EPOS, we will provide a service related to modelling cohesive landslide dynamics using the BingClaw model  [Løvholt et al. , 2017; Kim et al. , 2019]. This model has been used extensively over the last few years for modelling landslide dynamics, and the coupling to tsunami generation. BingClaw is a two-layer depth averaged landslide model based on the Hershel-Bulkley rheology. It offers the opportunity to link tsunamigenesis to the material properties of the sediments. Landslide-HySEA is a numerical model of the HySEA family designed for landslide generated tsunami simulations  [Macías et al. , 2015; González-Vida et al. , 2019]. This code implements a 2D coupled system composed of two models, one for the slide material represented by a Savage-Hutter type of model and the water dynamics model is represented by the fully non-linear shallow-water equations.
The developed surrogate model has been successfully applied for several meteotsunami events in the Adriatic Sea. It provides conservative extreme sea-level forecasts even for events missed with the deterministic AdriSC model  [Denamiel et al. , 2019b; Tojčić et al. , 2021]. In EPOS, the polynomial coefficients, the Matlab scripts and the user interface of the AdriSC MTsurrogate model will be provided. An innovative aspect of the TCS Tsunami delivery framework will be the provision of both VAs and TNAs to Workflows as a Service (WaaS), based on HPC resources, in cooperation with the ChEESE Centre of Excellence (ChEESE CoE) for Exascale supercomputing in the area of solid Earth. Numerical services and workflows were developed during several European projects (e. g. ChEESE, eFlows4HPC) and initiatives, and tested with PRACE awarded simulation campaigns.
For both VAs and TNAs, it is foreseeable that the TCS will be offering a training, a project design and a project execution phase. The users will be trained for the usage of the softwares and of the computational infrastructure, and accompanied in the development of new experiments exploiting the TCS workflows. One example is the probabilistic tsunami forecasting based on large ensembles of high-resolution tsunami Towards the new EPOS TCS Tsunami simulations  [Gibbons et al. , 2020; Selva et al. , 2021b]. Another examples are innovative and efficient numerical techniques to model physics-based tsunami-earthquake interaction  [Wirp et al. , 2021; Krenz et al. , 2021]. While this manuscript was in the revision phase, two new projects were funded by the Horizon Europe program 2021 call funding Research Infrastructures: Geo-INQUIRE and DT-GEO, both launched by the broader geo-hazard community.
Pillar 4: Hazard and Risk Products The last Pillar will incorporate access to data repositories, codes, and online tools aimed at generating and presenting hazard and risk results. Datasets on existing tsunami exposure and vulnerability models will be made accessible, together with methodological guidelines, engineering assessment tools and statistical codes for future exposure capture and vulnerability model development. Whenever possible, data will be published through open standard web services like those described by the Open Geospatial Consortium. Existing and future hazard, exposure, and vulnerability models will be harmonised with those of the seismic community (to foster multi-hazard risk assessment) and made available to different end-users: researchers, civil protection authorities, decision and policy makers, and the general public.
It hosts survey forms for exposure capture pre-tsunami that specifically account for attributes that reduce or increase an asset's vulnerability to this hazard. It also provides access to post-tsunami damage data collection tools for use in reconnaissance missions. The service will provide training materials on the use of the different tools, developed by the TSEA-Hub community of international contributors. Under the framework of the new cTCS, TSEA-Hub will link with the European Tsunami Risk Service, developing shared taxonomies and ensuring data is interoperable. The European Tsunami Risk Service (ETRiS) provides DDSS useful for performing tsunami risk analysis. This service, for the most part, collects the available vulnerability and risk-related datasets and models and ensures that they adhere to FAIR principles.
More specifically, it includes a tsunami consequence database collecting available datasets reporting impacts, incurred damages and societal consequences of past tsunami events. It also features selected available tsunami fragility models (empirical and analytic), vulnerability, and consequence models in the literature. The service provides software tools useful for statistical post-processing of the consequence datasets to derive fragility and vulnerability models, estimate the uncertainties in a given model, and select the most suitable one considering both the goodness of fit and model parsimony. ETRiS will link with the PTHA-related datasets and services (e. g. , European NEAMTHM18 Model and local PTHA workflows) and the Tsunami Structural Engineering Analysis Hub to ensure interoperability, in hazard, exposure and vulnerability models.
Outlook The next steps for the development of the new EPOS cTCS Tsunami will be implemented by several dedicated working groups (WGs). Up to now, four working groups have been established, each covering specific responsibilities towards construction of a full-fledged TCS within the next three years of candidacy. 1) WG-Core is a core team coordinating all TCS capacity building activities. 2) WG-Governance: this group will develop the TCS Tsunami Governance model: structure, composition of boards, roles of service and data providers, liaisons with other TCSs and external organisations. 3) WG-Consortium Agreement has an ultimate goal to organise compilation, discussion, dissemination, and finally signature of the TCS Consortium Agreement by all TCS partners. 4) WG-Services is the largest working group.