dataesr/scientific-paragraphs-categorization huggingface dataset:
The most widely applied design in this application is the row-column paradigm (RCP). The RCP, initially proposed by Farwell and Donchin  [4] , remains a significant reference in the field and continues to be extensively studied. In this particular BCI system, a 6x6 grid containing alphanumeric characters is presented to the user. The characters are arranged in rows and columns. As each row and column is randomly flashed or intensified one after the other, the user directs their attention to the specific characters within the grid that they wish to select. Following several rounds of flashing, the system identifies and presents on the screen the character that the user was supposedly focusing on during the process. Nowadays, there are several BCI software platforms available for the configuration and control of a P300-based BCI, such as BCI2000  [5] , BCILAB  [6] , Open-ViBE  [7]  and PMW  [8].
However, according to Stegman et al. [9] , the two most popular platforms for developing BCI systems are BCI2000 and Open-ViBE. These platforms are widely utilized and offer up-to-date software releases, comprehensive documentation, and support. Although these platforms are designed for building end-user BCI applications, implementing a P300 speller still requires technical expertise. Due to their general-purpose nature and high configurability, parameterizing them to achieve the desired speller functionality can be complex. In response to the complexity of previous platforms and the difficulty in providing access to a target population with limited technical knowledge, the BCI research group at the University of Malaga (UMA-BCI) has recently developed a user-friendly and highly flexible BCI platform specifically tailored for P300 spellers.
This platform is known as the UMA-BCI Speller  [10]. The UMA-BCI Speller offers ease of use and greater adaptability, allowing users to configure any type of speller according to their specific requirements. The following is a brief summary, taken from  [9]  and  [10] , of the main features of each of these tools: • BCI2000. The major objective of this platform was to give researchers a versatile and standard BCI experimental tool. BCI2000 has a generic framework and uses a modular program design approach. Consequently, BCI2000 can work with a variety of BCI protocol designs without requiring modifications to the main software modules. BCI2000 was written in C++, making it suitable for use on a wide range of platforms. MATLAB and Python are also supported. Online signal processing programs built in these languages enable this compatibility.
It is available for Linux, Windows, and Mac OS. • Open-ViBE. Open-ViBE allows users to construct whole scenarios using a graphical language. Therefore, no prior programming experience is necessary to construct basic BCI applications. This was a relatively new notion for BCI development at the time. This platform also comes with a full software development kit (SDK) for programmers who want to create new bespoke features. Open-ViBE was designed as a modular system that makes use of freely available portable C++ libraries. • UMA-BCI Speller. This is an easily configurable platform oriented for patients and caregivers. Specifically, the UMA-BCI Speller acts as a wrapper of BCI2000 to build a P300-based speller application and simplifies its use. Most of BCI2000′s configurability is intentionally limited because the UMA-BCI Speller aims to control the visual layout of the speller through new multiple options outside BCI2000.
695; p < 0. 001), as it was selected by 14 out of 15 participants, while only one participant chose BCI2000 and none chose Open-ViBE. Secondly, in relation to the variable most difficult to use, the 3. Average score (±standard error) selected by participants for each software platforms in the subdimensions of the individual questionnaire. The score for each dimension of the individual questionnaire (simplicity, speed and information) was obtained by averaging the responses from four Likert-type scale items, ranging from 1 (completely disagree) to 5 (completely agree). UMA-BCI Speller platform was significantly selected below the chance level (0 out of 15, Z = -2. 444; p = 0. 002), as it was selected by none of them, while 7 chose BCI2000 and 8 chose Open-ViBE. Thirdly, in relation to the most intuitive to use variable, the UMA-BCI Speller platform was again significantly chosen over chance (15 out of 15, Z = 5.
Acknowledgments This work was partially supported by the project  PID2021-127261OB-I00  (SICODIS), funded by  MCIN (Ministerio de Ciencia e Innovación) /AEI  ( Agencia Estatal de Investigación )  /10. 13039/ 501100011033/ FEDER ,  UE  ( Fondo Europeo de Desarrollo Regional). The work was also partially supported by the  University of Málaga (Universidad de Málaga ). The authors would like to specially thank the college students  Diane-Devi Lacourte ,  Ali Doukkali ,  Akram Sanad  and  Yannis Petitpas  for their cooperation during the experiments. The authors would also like to thank all participants for their cooperation.
Implementation In this section we provide details on our implementation to ensure the reproducibility of our results. Mmg platform. (Mmg, 2022 ) is an open source software for triangle and tetrahedral remeshing. It provides tools (1) to generate, adapt and optimize 2D triangulations, (2) to adapt and optimize 3D surface triangulation and isovalue discretization, and (3) to adapt and optimize tetrahedral meshes and implicit domain meshes. Mmg has been mostly developed for computational fluid dynamics applications and for shape optimization applications  (Dobrzynski, 2005; Dapogny et al. , 2014). Available as a C library or as an executable, Mmg can be easily integrated in a workflow and its functionalities were demonstrated on simulation of the subsonic turbulent nozzle jet flow, to optimize an elastic mechanical system, to accelerate the full waveform inversion, see e.
g. Balarac et al. (2021 ), Jacquet (2021). Mmg is an open source software jointly developed by Inria, 6 the University of 6 https://www. inria. fr/en Bordeaux, Bordeaux INP, 7  the UPMC 8  and the CNRS. 9 Mmg is multiplatform (Windows, Linux and MacOS), distributed under LGPL license and available on Github. 10 Lumos3D. To generate automatically the meshes for a potentially large number of alternative geological models, we implemented an extension of the library Lumos2D  (Legentil et al. , 2022) : the Lumos3D library, 11  which is written in C++ and depends on the C mmg application programming interface (API). From an input meshed model, it allows to restrict the discretization and the optimization to the modified region. To prevent remeshing of the entire mesh, the options -nomove,noinsert, -noswap are used by calling the Mmg API for level-set discretization.
The -nomove option blocks the movement of vertices and the -noinsert one prevents the creation of new vertices. The -noswap option prevents the edge swapping between two adjacent tetrahedra. For the optimization step, the options hmin and hmax are also used to set the mesh size to adapt the mesh resolution. To control the maximal distance between the piecewise linear representation boundary representation and the reconstructed boundary, the -hausd option is set. A decreasing Hausdorff distance, decreases the distance between the level-set and the triangle surface, high curvature areas are then refined. Mmg uses the Medit file format (. mesh). We encode geological model entities with integer tags defined on the tetrahedra. The scalar field and/or metric for remeshing values are defined on the vertices of the mesh and are stored in another file format (.
GEOSX GEOSX is an open-source, multiphysics, and multilevel physics simulation tool jointly created by Lawrence Livermore National Laboratory, Stanford University, and TotalEnergies  (Gross and Mazuyer, 2021). GEOSX 12  is a code actively developed, it is distributed under LGPL 2. 1 license terms. Designed for scalability on multiple CPUs and multiple GPUs, GEOSX offers a suite of physical solvers that rely on various numerical schemes such as Finite Volume methods and Finite Element methods. These different schemes are implemented for unstructured grids to accommodate the complex geometries of the subsurface. For flow simulations, GEOSX offers the choice to use the classical two-point flux approximation (TPFA) or a more advanced hybrid mimetic discretization scheme (MFD), see e. g. Lipnikov et al.
Discussion In this paper, we set the ground for and incremental geometric model updating workflow and showed how it can be used for a relatively simple CO 2 sequestration case study. Nonetheless, several aspects can be discussed and extended in future studies. For demonstration purposes, we chose constant permeability and porosity per layer to demonstrate the workflow up to simulation. When working with real data models, property geostatistical filling is a key step to represent geological features. On unstructured grids, classical algorithms operating on structured grids cannot be used, but recent geostatistical algorithms, e. g. Biver et al. (2019) ,  Mourlanette et al. (2020)  have demonstrated their capabilities on tetrahedral meshes to fill heterogeneous porosity, permeability, and facies values consistent with available data.
To account for faults cutting the model in two blocks, inserting iteratively the horizons in the fault blocks is a possibility with the current workflow. The fault blocks would then be considered independently while maintaining conformity. The additional challenge is to account for faults that are ending in the model. Remeshing also raises practical challenges for automation. For instance, we chose to inject the CO 2 right below the reservoir in order to keep the same parameter set. Some adaptation would be needed to specify boundary conspirions in the simulator, even if the mesh changes. More significantly, exploring effectively a larger range of scenarios would call for a tighter integration of the model construction, perturbation and mesh updating. As mentioned before, the combination of LUMOS and Mmg with the open-source implicit modeling code LoopStructural could be a valuable option to explore this in the future.
Acknowledgments This work was performed in the frame of the  RING  project (http:// ring. georessources. univ-lorraine. fr)supported by the  RING  Consortium managed by ASGA. This work is additionally supported by  TotalEnergies. We acknowledge Inria and the  Mmg Consortium for Mmg libraries. The authors would also like to thank  Gilles Darche  and  Francois Hamon  for their help, and  Florian Wellmann , an anonymous reviewer and the editor  Boyan Brodaric  for their constructive comments.
Since its start in 2014, the lightweight open source Python library Gammapy has come a long way to become a popular data analysis package for high-energy astrophysics. Selected as the official CTAO Science Analysis tool, it is also an approved analysis software within the H. E. S. S. and MAGIC collaborations. The first long-term version, Gammapy v1. 0 was released on late 2022. It is compliant with several well-established data conventions in high-energy astrophysics, and provides serialised data products that are interoperable with other software. Event lists and instrument response functions curated within the same format from various instruments can be reduced to data binned in energy, time or spatial coordinates. Thereafter, the flux and morphology of one or more gamma-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal and spatial models.
Flux points, likelihood profiles and light curves extractions are supported. Complex user defined likelihoods and models can also be implemented. In this contribution, we will highlight the main features of Gammapy v1. 0, including data reduction and analysis examples from different space and ground-based instruments, applications of various background rejection techniques, and a simultaneous fitting across multiple instruments with astrophysical models. We will also present our plans for the future, showcasing new features such as the support of different event types, unbinned likelihood analysis, spectral unfolding and transient source detections. In addition to an improved API with distributed computing for scalable analysis, enhanced support for all-sky instruments like Fermi-LAT and HAWC is foreseen.
Gammapy, a Python package for 𝛾-ray astronomy Gammapy1 is an open Python library  [1]  aiming to derive astrophysical products and catalogs from very-high energy (VHE) gamma-ray high-level data, such as those from H. E. S. S. , MAGIC, VERITAS, HAWC and also Fermi-LAT. These high-level data are produced by Imaging Atmospheric Cherenkov Telescopes (IACTs) or Water Cherenkov Detectors (WCD) as outputs of their low-level analysis pipelines, consisting of the steps of calibration, reconstruction and background reduction. These high-level data contain event list, Instrument Response Functions (IRFs) and their associated metadata, or also data cubes like an exposure map. Gammapy reads data curated with the gamma-astro-data-format (GADF) format  [2]  and serialised into FITS  [3]. This package is built within the Python ecosystem and has as main dependencies numpy  [4]  for the n-dimensional data structures, astropy  [5]  (to which Gammapy is affiliated) for the astronomical functionalities, scipy  [6]  for numerical algorithms, iminuit  [7]  for numerical minimisation and matplotlib  [8]  for visualisation.
The Long-Term Stable version and the current v1. 1 version After the establishment of a roadmap in 20185, the Gammapy team has released in November 2022 its first Long-Term Stable (LTS) version, the v1. 0 (and its minor bug release v1. 0. 1 in March 2023). An accompagning paper has been very recently accepted by A&A and is about to be published6. This LTS version will be supported with as many minor bug releases as necessary (following the convention v1. 0. x), until the release of the next LTS version (see the section 3). This release is light-weight with about 50,000 lines of code, of which 34% being the Python code, 22% being the code test functions, 26% on the code documentation and 9% on the package documentation. The design of the data flow has been settled, as illustrated in Fig. 2.
Data • the Data Reduction: from user's choices of data selection (or filtering), of geometry (spatial, spectral or temporal) and of analysis type, the IRFs and the event list are selected, projected and reduced into data cubes and stored into a data container class inheriting from the class Dataset, that is the central element of Gammapy, • the Modeling and Fitting: from user's choices of analysis and its associated parameters, source model(s) is(are) associated to Dataset(s) and their parameters are estimated using Poisson maximum likelihood fitting on the reduced data.
B. Khélifi Compared to its previous release (v0. 20. 1), most of the changes7 are in the package infrastructure (e. g tutorial gallery relying on Sphinx gallery, better compliance with astropy), which permits a simplification of the maintenance. One can highlight that the support for HAWC data analysis has been highly improved, as illustrated in  [11]  showing the use of Gammapy by the HAWC collaboration. The package allows to produce different types of VHE astrophysical products using algorithms that have been developed by the TeV community, the GeV community or the X-ray community: 1D spectral analyses From any spatial region, a spectral model can be adjusted using IRFs made with energy-dependent directional cuts or without and by estimating the background level with the Reflected Background method  [12] ; point-source sensitivity can be computed using ON and OFF estimations; flux points with their likelihood profiles can be derived from any spatial region; 1D spectrum simulation can be performed.
B. Khélifi Ray  [15] ), on the handling of the future Very-high-energy Open Data Format (see Section 5) (clarifying our internal data format and by adding I/O functions), on the handling of meta data and provenance information to respect the FAIR principles for Reseach Softwares  [16] , on the DevOps tools to facilitate the maintenance and the development of the package (documentation, CI, benchmarks).
The preparation of the CTAO contribution Gammapy has been selected as the core library of the open Science Analysis Tool of CTAO. The SAT will contain the science tools features of Gammapy and also the functionalities needed for some CTAO operations, such as the real time analysis pipeline. For the CTAO construction, a group of institutions that are strongly involved in Gammapy development and organization have committed to contribute all features to Gammapy to fulfill the minimal requirements of the CTAO as In-Kind Contribution. The acceptance by CTAO of a Gammapy release will follow the CTAO management plan that contains a Preliminary Design Review, a Critical Design Review and an Acceptance Review. While maintaining Gammapy as an independent open research software aiming for interoperability between instruments, the Gammapy team is now working in close cooperation with the CTAO computing team.
Regular technical meetings permit to define in detail the future SAT features in the context of the global computing needs of CTAO and to collaborate for the improvement of the Gammapy API and documentation. And at the management level, two CTAO representatives are now members of the Gammapy Coordination Committee13. In the meantime, CTAO is preparing the community to use the future data for scientific purposes. An open Science Data Challenge (SDC) is under consideration with objectives to test some CTAO tools (documentation, data dissemination tools, software distribution), to train the CTA consortium and the wider scientific community to use the SAT to analyse data, and to explore the CTAO expected performances. In this perspective, Gammapy as a core library of the CTAO SAT will be used to produce the simulated data of this future SDC and to analyse the SDC data.
PoS(ICRC2023)959 Gammapy, a Python package for 𝛾-ray astronomy B. Khélifi under a VHE data format, the gamma-astro-data-format (GADF). The light-weight Gammapy package is officially in use by several TeV facilities and has been chosen by the CTA Observatory to be the core library of its future open Science Analysis Tool. With the common data format GADF, it permits multi-instrument analyses by supporting joint fit of their data. This mature research software can be used for data from ASTRI, CTA, FACT, Fermi-LAT, HAWC, H. E. S. S. , MAGIC, VERITAS. The LHAASO, SWGO, KM3NeT facilities are also testing and evaluating Gammapy. With more than 70 references to Gammapy and with its use for academic purposes, this software grows in popularity and is recognised as a major research software by the VHE community.
The Gammapy team released the first Long-Term Stable version, the v1. 0, that offers users a maintained and stable package for their scientific analyses. Its stable user interface associated with minor bug releases offers a comfortable framework for physicists. This LTS offers all the standard and historical VHE algorithms, as well as algorithms coming from other astrophysics domains like the data cube analysis. It also allows a proper statistical treatment of joint analyses of high-level data of different types. In parallel, the project developers team is working to improve the capabilities of the package, e. g. with the support of Bayesian analysis or unbinned analysis. Until the release of the new LTS, several feature releases are expected and will contain new functionalities and scientific algorithms.
It is also expected that the high-level data format will evolve as the GADF initiative has ended. Eleven gamma-ray and neutrino experiments have created a new initiative, called Very-high-energy Open Data Format (VODF)16 (see  [17]  for a complete description), aiming to create new standards for VHE astrophysics detectors that respect the FAIR principles  [18]  and follow as much as possible the IVOA17 standards. In this context, the internal data model of Gammapy is being separated from the read data model with the use of an I/O layer dealing with formats and their versions. To conclude, the Gammapy team is continuing to improve the open package by offering even more functionalities. As an open project, we encourage any reader to participate to the improvement of the package or to the animation of schools, while taking a special care to recognise any effort.
2021; Langlois et al. 2021; Vigan et al. 2021 ). These observations were carried out as a follow up to the ones presented in  Samland et al. (2017)  and have the highest signal-to-noise ratio (S/N) achieved so far (S/N ∼23 for K1). We use the radiative transfer code petitRADTRANS to model the atmospheric spectrum of the planet. Additionally, we attempt to reproduce the results in SAM17 using a retrieval approach. The paper is structured as follows: in Sect. 2 we describe our spectro-photometric observations. In Sect. 3, we detail our data-reduction and spectrum-extraction procedures, as well as (a)  Abt & Morrell (1995) , (b)  Lee et al. (2022) , (c)  Macintosh et al. (2015) , (d) Gaia Collaboration (  2021 ), (e)  Simon & Schaefer (2011) , (f)  Høg et al. (2000) , (g)  Arentsen et al. (2019).
Observations New data for 51 Eri were obtained with the VLT/SPHERE high-contrast instrument  (Beuzit et al. 2019 ) within the SHINE (SpHere INfrared survey for Exoplanets,  Chauvin et al. 2017)  Guaranteed Time Observations (GTO) program on the night of September 28, 2017. These observations were carried out in the IRDIS_EXT mode, which combines IRDIS in dual-band imaging mode (DBI;  Vigan et al. 2010)  in the K1K2 (K1 = 2. 110 µm and K2 = 2. 251 µm) filters with IFS  (Claudi et al. 2008)  in the Y H spectral bands (between 0. 95 and 1. 65 µm, with spectral resolution R ∼ 33). An apodised pupil Lyot coronagraph with a focal mask diameter of 185 marcsec, was used for the observations  (Carbillet et al. 2011). In order to reduce residual speckle noise, the observations were carried out close to meridian passage using the pupil-stabilised mode, which allows the use of ADI post-processing  (Marois et al.
Data reduction The data were reduced with the SPHERE Data Center pipeline  (Delorme et al. 2017 ) using the SPHERE Data Reduction Handling (DRH) software (version 15. 0;  Pavlov et al. 2008). This basic reduction consists in performing the usual sky-background subtraction, flat fielding, bad-pixel identification and interpolation, star-centring corrections and, for IFS, the calibration of the wavelengths and of the cross-talk between spectral channels. We then removed poor-quality frames where a significant drop in stellar flux is detected from the photometry of satellite spots. Because of interfering cirrus clouds near the end of the observing sequence, only the first 140 of the total of 154 IFS frames were used for the post-processing analysis. We also discarded the second PSF for the flux normalisation and only used the first PSF frames.
The SED was built with photometry from Tycho B T , V T  (Hoeg et al. 1997) , WISE W3  (Cutri et al. 2013) , Johnson U, V, B  (Mermilliod 2006) , and IRAS 12 µm  (Helou & Walker 1988). Finally, we scaled the model spectrum to the resolution of our IFS data using the Python function SpectRes 1. A similar procedure along with the respective transmission curves for the filters was used to obtain updated IRDIS photometry for K1 and K2. As the planet is not detected significantly in the K2 filter, we applied forced photometry with ANDROMEDA. This consists in performing a photometric measurement in the K2 images at the position of the planet in the K1 frames. To estimate the uncertainties in the flux of both the   2017 ), as well as the updated GPI J and H spectra and the K1 and K2 spectra from  Rajan et al.