dataesr/scientific-paragraphs-categorization huggingface dataset:
g. assemblages), and assign objects to each group on the projections. These new groups can later be used in the quick sidebar panel like any other variable, loading them in the Load data panel. The first step is to go to the record new group subpanel in the Additional settings panel. Then, the user chooses a name for the new group variable and creates it. At this step, data from another variable can be copied, a useful option if the new group consists of a slight modification of an already present variable. The user can create as many new group variables as necessary. When the new group is created, you can select points in 2D plots by using the "box" or "lasso" tools and change their attribution by using the button "Change Group Assignment". The list of points selected is detailed below the latter.
Conclusion Since the middle of the 20th century, archaeologists have been recording an increasing amount of spatial data during fieldwork, with the intent of preserving contextual information as much as possible. Such tedious work is now indispensable to propose reliable spatial (i. e. planimetrical and stratigraphical) interpretations. As a result, it often leads to the accumulation of several thousands of object coordinates, the analysis of which requires both adequate software and extensive training in GIS software. The aim of the SEAHORS application is to offer tools for the spatial exploration of archaeological objects to a broader audience, through a point-and-click interactive interface, in a free and open-source environment. We hope that this software (in its current form and through future additions) will foster the spatial exploration of archaeological sites and, in particular, the practice of post-excavation stratigraphic analysis  (Discamps et al.
R Package: https://cran. r-project. org/package=SEAHORS R shiny script: https://github. com/AurelienRoyer/SEAHORS DOI of SEAHORS v1. 7: https://doi. org/10. 5281/zenodo. 7929542 First version of R shiny script: https://github. com/AurelienRoyer/SEAHORS-raw-script DOI of SEAHORS v1. 0: https://doi. org/10. 5281/zenodo. 7664192 A web application: https://aurelienroyer. shinyapps. io/Seahors/ The web application does not allow to import orthophotographic images nor to export a rmarkdown report. The tutorial video of SEAHORS in english: https://nakala. fr/10. 34847/nkl. 3fdd6h8j The tutorial video of SEAHORS in french: https://nakala. fr/10. 34847/nkl. 65bf1h72 Cassenade spatial data: https://nakala. fr/10. 34847/nkl. e30aie2u Cassenade refit: https://nakala. fr/10. 34847/nkl. fe27j10z Cassenade orthophoto: https://nakala.
Acknowledgements The authors are grateful to  Rémi Laffont  for his help and to  Igor Djakovic  for his English language corrections. The authors thank also  Reuven Yeshurun  and the three reviewers,  Frédéric Santos ,  Jacqueline Meier  and  Ma'ayan Lev  for their comments and corrections that improved the manuscript. Preprint version 2 of this article has been peer-reviewed and recommended by  Peer Community In Archaeology  (https://doi. org/10. 24072/pci. archaeo. 100320).
A 2. 5D time-frequency model was created in Ref. [30]  to study the railway vibrations due to rail-wheel defects. Other researchers  [31] [32] [33]  combined the thin-layer method (TLM) with the 2. 5D finite element modelling to predict the ground vibrations induced by passing trains at various speeds. Ground induced vibration due to the passage of a high-speed locomotive was investigated using a coupled Railway Engineering Science (2024) 32(1):36-58 train-track dynamic finite element model using 3D ground modelling  [1, 34]. A full 3D model with a similar coupling approach was developed by  Banimahd et al. 35 ] using an in-house software called DART3D (Dynamic Analysis of Railway Track 3D) to investigate deflection changes in the vicinity of transition zones. Woodward et al. [14]  used DART3D to study ground dynamics when a train approaches the critical track velocity.
A finite element model of the track section tested in the full-scale testing rig was developed using the commercial software Abaqus. The slab and the ballasted tracks on the conventional embankmnet were simulated, with the slab track being a precast reinforced concrete Max-Bögl slab and the ballasted track composed of a ballast layer and precast reinforced concrete sleepers. After performing a mesh size sensitivity study, the calibrated models and chosen parameters were then used to develop the NBT model to investigate its performance in a high-speed rail context, in the dynamic anlaysis part of this work. It is worth mentioning that the experimental results obtained in this research have also served in similar numerical calibrations. Ramos et al. [61]  calibrated 3D FE models developed in ANSYS and consisting of slab and ballasted tracks using the experimental results of the GRAFT-II tests.
They investigated transient displacement and stress behaviour of the subgrade, and looked at the long-term settlement models based on the experimental and numerical outputs. In another work, Sains-Aja et al. [62]  created a 3D FE slab track model also using the commercial software ANSYS and calibrated it via the GRAFT-II laboratory results. They also performed experiments to identify the mechanical characteristics of EVA (ethylene vinyl acetate) and EPDM (ethylene-propylene diene monomer) railpads. Thölken et al. [63]  used the GRAFT-II data to calibrate a 3D FE model developed in Abaqus to reproduce the measured displacement and acceleration test results. A parametric analysis was then performed to establish which material characteristics of the system the model was more sensitive to. Duration (s) c y C _ l i a R _ d e t s a l l a B _ a t a D b a L c y C _ r e p e e l S _ d e t s a l l a B _ a t a D b a L c y C _ l i a R _ d e t s a l l a B _ a t a D c i r e m u N.
( 2018 ). We used the ATLAS12 stellar atmosphere grid  (Kurucz 2013 )  and atomic and molecular line data from VALD 9 (Vienna Atomic Line Database; Ryabchikova et al. 2015 ). The macro-and microturbulent velocities were held fixed to 1. 5 and 0. 5 kms -1 , respectively. The resulting T eff , log g , and abundances were in excellent agreement with the ARES + MOOG analysis. We additionally derived the projected rotational velocity, vsin i = 2. 7 ± 0. 7 kms -1.
Global photometric analysis Once we had confirmed the true period of HD 15906 c with CHEOPS , we performed a joint fit of the TESS , CHEOPS , and LCOGT photometric data using JULIET 13   (Espinoza, Kossakowski & Brahm 2019 ). This package combines transit models from BATMAN  (Kreidberg 2015 )  with the option to include linear models and GPs to model instrumental noise and stellar variability. We created a model consisting of two transiting planets, using the following parametrization: (i) Orbital period, P , and mid-transit time, T 0 , for both planets. We set broad uniform priors on P and T 0 from a visual inspection of the TESS and CHEOPS light curves. (ii) Planet-to-star radius ratio, p = R P / R , and impact parameter, b , for both planets. We set uniform priors to allow exploration of all physically plausible solutions.
CHEOPS detrending The CHEOPS light curves contain trends that are correlated with instrumental parameters such as background flux (bg) and centroid position (x, y). There are also periodic noise features that repeat once per CHEOPS orbit due to the satellite rolling around its pointing direction. Detrending the light curve against the sine or cosine of the roll angle ( φ) can remo v e these periodic instrumental effects. The CHEOPS light curves also include stellar variability. From the TESS LC we kno w that HD 15906 sho ws stellar v ariabil-  From the TESS data alone, HD 15906 c was a duotransit with 36 possible period aliases. This plot shows these aliases and their corresponding probabilities, derived using MONOTOOLS. The true period, as determined by CHEOPS follow-up observations, had the highest probability.
The M32 kernel was intended to capture the long-term variability and the SHO kernel was used to capture the short-term quasi-sinusoidal noise. We defined a normal prior on the natural frequency of the SHO kernel, ω 0 , using the peak and its width from the periodogram analysis. We performed one fit where we jointly modelled the sector 4 and sector 31 data with this kernel and we also performed a fit where we decoupled the sector 4 and sector 31 data. We used the M32 plus SHO kernel for sector 4 and the M32 kernel for sector 31, moti v ated by the fact we only detect the periodic trend in sector 4. After performing these two fits, we checked for periodicity in the TESS sector 4 residuals. In both cases, the peak of the periodogram was still at 0. 22004 d but with a FAP greater than 68 per cent.
For the outer planet, we included the TESS data and CHEOPS visits 3 and 6. In total, we had seven transits of the inner planet and four transits of the outer planet. For the fit of each planet, we used a model consisting of one transiting planet and the same detrending as described in Section 4. 2. The only difference in the transit model was that we fit for the individual transit times instead of P and T 0. We set a uniform prior of width 0. 1 d on each transit time based upon a visual inspection of the data. All other priors were unchanged from the global photometric analysis and we used DYNESTY to sample the posterior of the model with nested sampling. Our results are presented in Section 5. 2. 7  , are indicated by the blue and pink vertical dashed lines and the red horizontal line represents the 1 per cent false alarm probability level.
Radial velocity analysis From HARPS and FIES, we have 25 sparsely sampled RV data points that show a relatively large scatter (see Fig. 7  ). We ran a GLS periodogram on the RV data and found no significant peaks at the planetary periods. The strongest peak was at 12. 27 d and the bestfitting sinusoid with this period had an amplitude of ∼ 10 ms -1. It is possible that this signal is caused by stellar activity, but with such large gaps between each observation, the short-period peaks in the GLS periodogram are unreliable. We remo v ed the best-fitting sinusoid from the RV data and re-ran the GLS periodogram -no additional peaks emerged. To search for the planetary signals, we performed a series of fits to the HARPS and FIES RV data using JULIET. For our first fit, we assumed that there were no planets in the system and we fit only for an offset and a white noise term for each instrument.
1 d). The other two hyperparameters were allowed to vary uniformly o v er a broad range. With such a small number of sparsely sampled RVs, the GP was unlikely to yield a meaningful result but we chose to include it for completeness. The results of our RV modelling are presented in Section 5. 3. We note that we also tried a joint fit of the TESS , CHEOPS , and LCOGT photometric data with the HARPS and FIES RV data using JULIET. The photometric model was identical to that presented in Section 4. 2 and we used the RV model with two planets but no GP. Ho we ver, due to the small number of sparse RVs, the fitted planet parameters were adversely affected compared to those from the global photometric model. Therefore, we decided to present independent analyses of the photometry and RVs in this paper.
Global photometric results In Section 4. 2 , we described our joint fit of the TESS , CHEOPS , and LCOGT photometry and we present the resulting fitted planetary parameters in Table  7. We also include the following derived planetary parameters: transit depth ( δ = ( R P / R ) 2 ), planet radius ( R P ), semimajor axis ( a ), orbital inclination ( i ), total transit duration ( T dur ), insolation flux ( S P ), and equilibrium temperature assuming zero bond albedo and full day-night heat redistribution ( T eq ). Figs 1 , 8 , and 9 show the TESS , CHEOPS , and LCOGT data alongside the global photometric model. Fig. 10  shows the detrended TESS and CHEOPS data, phase-folded on each planet with the bestfitting transit model, and Fig. 11  shows the same for the LCOGT data. For a full list of posterior values and the corner plots presenting the posterior distributions of the fitted planetary parameters, see Appendix A.
2015 ; Furlan & Howell 2017 , 2020 ). MNRAS 523,  3090-3118 (2023)  Figure  13. HARPS (green squares and blue triangles) and FIES (red circles) RV data folded on the inner (left-hand panel) and outer (right-hand panel) planet. The transits occur at phase zero. A Keplerian model (dotted line) has been plotted on each axis to guide the eye and the arrows illustrate that this is an upper limit. The model represents a planet on a circular orbit with a semi-amplitude equi v alent to the RMS of the HARPS data (10. 70 ms -1 ), a proxy for its maximum value. As mentioned in Section 2. 1 , HD 15906 b passed all of the SPOC vetting tests. In addition, it has been shown that multiplanet systems are significantly less likely to be false positives than single planet systems, especially when the planets are smaller than 6 R ⊕  (Lissauer et al.
High-r esolution spectr oscopy Using the HARPS and FIES data, we did not detect the RV signals induced by the two transiting objects (see Section 5. 3 ). In this section, we use the HARPS data to rule out stellar masses for the transiting objects and place limits on the presence of a bound stellar companion. In Fig. 13  , we show the HARPS and FIES RVs folded on HD 15906 b and c using the ephemerides obtained in the global photometric analysis (Table  7  ). The RMS of the HARPS data (10. 70 ms -1 ) can be used as a proxy for the maximum possible semi-amplitudes of the two transiting objects. Using the stellar mass presented in Table  5  , the orbital parameters presented in Table  7  and a semi-amplitude of 10. 70 ms -1 , HD 15906 b has an upper mass limit of ∼ 32 M ⊕ and HD 15906 c has an upper mass limit of ∼ 39 M ⊕.
This confirms that the two transiting objects must be of planetary mass. Furthermore, under the assumption of a circular orbit and an orbital inclination of 90 degrees, the RMS of the HARPS data rules out a bound brown dwarf or star, with a mass greater than 13 M Jupiter , out to ∼ 1500 AU. At the distance of HD 15906, this corresponds to an angular separation of ∼ 32 arcsec. Even down to an orbital inclination of 10 degrees, we can rule out a brown dwarf or stellar companion out to ∼ 45 AU, corresponding to an angular separation of ∼ 1 arcsec. Finally, we checked for a linear drift in the RV data because this could be indicative of a long-period bound stellar companion. We chose the pre-upgrade HARPS data for this purpose because it has the longest baseline ( > 9 yr). We used JULIET to perform a fit of this data, using a model consisting of no planets, an offset, white noise, and a linear trend.
Gemini-North HD 15906 was observed on 2019 October 15 using the 'Alopeke speckle instrument on the Gemini-North 8 m telescope  (Scott et al. 2021 ; Howell & Furlan 2022 ). 'Alopeke provides simultaneous speckle imaging in two bands (562 and 832 nm) with output data products including a reconstructed image with robust contrast limits 15 https:// github. com/arjunsavel/ SImMER on companion detections. Three sets of 1000 × 0. 06 s images were obtained and processed in our standard reduction pipeline (see  Howell et al. 2011 ). Fig. 15  includes our final 5 σ contrast curves and the 832 nm reconstructed speckle image. We find that HD 15906 has no companion stars brighter than 5-8 magnitudes below that of the target star within the angular and image contrast le vels achie ved. The angular re gion co v ered ranges from the 8 m telescope diffraction limit (20 mas) out to 1.
This work has made use of data from the ESA mission Gaia ( https: //www. cosmos. esa. int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, ht tps://www. cosmos. esa. int /web /gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This paper utilizes data collected at Lick Observatory and analysed using funds provided by the  NASA   Exoplanets Research Program  (XRP) through grant  80NSSC20K0250  (PI:  Courtney D. Dressing ). Some of the observations in the paper made use of the High-Resolution Imaging instrument 'Alopeke obtained under Gemini LLP Proposal Number:  GN/S-2021A-LP-105. 'Alopek e w as funded by the  NASA   Exoplanet Exploration Program  and built at the  NASA Ames Research Center  by  Steve B.
The  Pan-STARRS1 Surveys  (PS1) and the PS1 public science archiv e hav e been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the  Pan-STARRS Project Office , the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins Uni versity, Durham Uni versity , the  Uni versity of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No. NNX08AR22G  issued through the  Planetary Science Division  of the  NASA Science Mission Directorate , the  National Science Foundation  Grant No.
AST-1238877 , the  University of Maryland ,  Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation  AT thanks the  Science and Technology Facilities Council (STFC)  for a PhD studentship. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). This work has been carried out within the framework of the NCCR PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40 182901 and51NF40 205606. ACC and TW acknowledge support from  STFC  consolidated grant numbers  ST/R000824/1  and  ST/V000861/1 , and  UKSA  grant number  ST/R003203/1. ML acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2 194576. ABr was supported by the  SNSA.
DRC  acknowledges partial support from the  National Aeronautics and Space Administration  through the  Exoplanet Research Program  grant  18-2XRP18 2-0007. KAC and DWL acknowledge support from the TESS mission via subaward  s3449  from  MIT. DG and LMS gratefully acknowledge financial support from the  CRT Foundation  under Grant No. 2018. 2323 'Gaseous or  rock y? Unv eiling the nature of small worlds '. ZG acknowledges the support of the  Hungarian National Research, Development and Innovation Office (NKFIH)  grant  K-125015 , the  PRODEX Experiment  Agreement No. 4000137122  between the  ELTE E ötv ös Lor ánd University  and the  European Space Agency  ( ESA-D/SCI-LE-2021-0025 ), the  VEGA  grant of the  Slovak Academy of Sciences  No. 2/0031/22 , the  Slovak Research and Dev elopment Agenc  y contract No.
APVV-20-0148 , and the support of the city of Szombathely. SGS acknowledges support from  FCT  through FCT contract nr. CEECIND/00826/2018  and  POPH/FSE  (EC). YAl acknowledges the support of the  Swiss National Fund  under grant  200020 172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the European Downloaded from https://academic. oup. com/mnras/article/523/2/3090/7191658 by INIST- CNRS INEE INSB  user on 07 July 2023.
LD is an F. R. S. -FNRS Postdoctoral Researcher. This w ork w as supported by FCT -Funda c˜ ao para a Ci encia e a Tecnologia through national funds and by FEDER through COMPETE2020 -Programa Operacional Competitividade e Internacionalizac ˜ 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/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953, PTDC/FIS-AST/28987/2017 & POCI-01-0145-FEDER-028987, ODSD is supported in the form of work contract (DL 57/2016/CP1364/CT0004) funded by national funds through FCT. B-OD acknowledges support from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number MB22. 00046. DD acknowledges support from the TESS Guest Investigator Program grants 80NSSC21K0108 and 80NSSC22K0185.
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (project FOUR ACES. grant agreement No 724427). It has also been carried out in the frame of the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation (SNSF). DE acknowledges financial support from the Swiss National Science Foundation for project 200021 200726. MF and CMP gratefully acknowledge the support of the Swedish National Space Agency (DNR 65/19, 174/18). MGi is an F. R. S. -FNRS Senior Research Associate. SeHo gratefully acknowledges CNES funding through the grant 837319. KGI and MNG are the ESA CHEOPS Project Scientists and are responsible for the ESA CHEOPS Guest Observers Programme. They do not participate in, or contribute to, the definition of the Guaranteed Time Programme of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme.
This work was granted access to the HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR-10-EQPX-29-01) of the programme Inv estissements d'Av enir supervised by the Agence Nationale pour la Recherche. RL acknowledges funding from University of La Laguna through the Margarita Salas Fellowship from the Spanish Ministry of Universities ref. UNI/551/2021-May 26, and under the EU Next Generation funds. PFLM acknowledges support from STFC research grant number ST/M001040/1. IR acknowledges support from the Spanish Ministry of Science and Innovation and the European Re gional Dev elopment Fund through grant PGC2018-098153-B-C33, as well as the support of the Generalitat de Catalunya/CERCA programme. NCS acknowledges funding by the European Union (ERC, FIERCE, 101052347).
Contributions. The main contributions of this paper are • The description of a new method, SMACE, for the interpretability of composite decision-making systems; • The Python implementation of SMACE, available as an open source package at https://github. com/ gianluigilopardo/smace; • The evaluation of SMACE vs some popular methods showing that the latter perform poorly in our setting. The rest of the paper is organized as follows. In Section 2, we briefly present some related work on both decision trees and post hoc methods for machine learning. Section 3 outlines the main challenges we want to address. In Section 4 the mechanisms behind SMACE are explained step by step; an overview is given in Section 4. 3. Finally, we provide an evaluation of our method compared to established post hoc solutions in Section 5, before concluding in Section 6.
Rules only Let us first evaluate the case of a decision system consisting of only three simple conditions applied to only three input features. The decision policy contains rule R 1 : if x 1 ≤ 0. 5 and x 2 ≥ 0. 6 and x 3 ≥ 0. 2 then 1 , else 0. 2 https://github. com/slundberg/shap 3 https://github. com/marcotcr/lime Table  1 : Example in generic position, three conditions on three input features. LIME and SHAP are producing flat explanations on the variables x1 and x2, even if their sensitivities for the decision are very different. SMACE is able to capture this information. condition example (ξ (1) ) SMACE SHAP LIME x 1 ≤ 0. 5 0. 6 -0. 9 -0. 08 -0. 21 x 2 ≥ 0. 6 0. 1 -0. 5 -0. 08 -0. 21 x 3 ≥ 0. 2 0. 4 0. 8 0. 02 0. 04 Note that there are no models, R 1 is based solely on the input data. The method then reduces to the application of Eq.