dataesr/scientific-paragraphs-categorization huggingface dataset:
During our iterative development of the data sphere system, we asked professionals and researchers in urbanism for their feedback and informal evaluation of most elements we implemented. Their input helped inform our choices of the parameters for the navigation methods, and how the projection parameters should be constrained and controlled, as well as aesthetic choices for colors and transparencies. As the implementation neared a more complete state, we invited seven participants from the same group to perform a series of exploration tasks with the system, to gauge their reaction to the visualization and the interaction methods. We presented them with a 3D terrain of the city they live in, textured with aerial imagery, which visualized all 3D buildings and the transportation networks in a stylized way, similar to the right image in fig.
Once a estimation of residual displacements is obtained, a criterion based on kinematic error is proposed in order to predict whether the braking system would be operational after an emergency stop or not. 2 Resolution of the elastic-plastic, multi-scale, multi-contact problem 2. 1 General procedure Figure  2  is a diagram depicting the different steps involved in the dialogue between three numerical simulation software, namely: ROUVISLAM, ABAQUS and ISAAC, in order to predict the elastic-plastic, multi-scale and multi-contact displacement in worm gears. Since the contact line location between the gears flanks is not known when an emergency stop occurs, the three simulation tools are used to perform the following computations at any kinematic position of the worm/ wheel pair. -In step 1, the torque is applied on the worm/wheel pair in ROUVISLAM (developed by the LaMCoS Lab.
and Mecalam Cie) in order to compute semi-analytically the quasi-static load sharing due to the applied torque. In other terms, it provides the nodal force field or equivalently the pressure field on the different worm and wheel teeth in contact, for a given kinematic position, assuming an elastic behavior. -In steps 2-3, the nodal force field is transferred to the standard ABAQUS software in order to compute by finite element method the residual teeth bending displacement field. -In steps 4-5, the pressure field is transferred to the ISAAC software (developed by the LaMCoS Lab. ) in order to compute semi-analytically the residual contact displacement field. The choice of a semi-analytical solver for the resolution of the contact problem is justified by its faster convergence and its robustness compared to a finite element solver.
In addition, the semi-analytical solver allows overcoming difficulties of finite element method in dealing with nodal forces. -In steps 6-8, the bending and the contact residual displacement fields resulting from the plastic deformation at the local (contact deformation) and global (tooth bending) scales are transferred to ROUVISLAM, in order to compute the kinematic errors, and to predict whether the worm gear will be able to continue to ensure the safety of the lifting system. Several codes have been implemented in python and MATLAB in order to couple the different numerical simulation software. Namely, these codes allow for the transfer of the different fields (nodal force, pressure, bending residual displacement and contact residual displacement) between ROUVISLAM, ABAQUS and ISAAC software (steps 2, 4, 6 and 7).
ABAQUS: Elastoplastic global bending problem An automatic procedure creates the ABAQUS model, including the importation of the CATIA model (generated via ROUVISLAM software), the application of the nodal forces as well as the application of boundary conditions. ABAQUS software is customized in order to carry out the calculation of the residual bending displacements. In this model, the wheel's bore and the worm's shaft are clamped. In addition, quadratic prism elements are also used to mesh the geometry. In order to account only for the residual displacements, the computations are carried in 2 steps. In the 1st step (loading step), the geometry is loaded so the elastoplastic displacements can be determined. In the 2nd step (unloading step), the geometry is unloaded so to restore the elastic displacements and get pure residual displacements.
ISAAC: Elastoplastic local contact problem The computation of the contact residual displacements is performed with ISAAC which is far more accurate in solving contact problems than the standard ABAQUS software with equivalent computation time. ISAAC is a 3D elastic-plastic contact code based on a semi-analytical solver, initially developed by Jacq et al. [1]. It integrates two principal modules: an elastic contact solver, and a plasticity loop. The elastic contact solver module takes the external load W as input, and gives the contact surface G C , the pressure distribution p and the elastic surface displacement u e as output. This module solves simultaneously a set of three equations. The 1st equation is the load balance between the total external load W and the pressure p applied at any point x = (x 1 , x 2 ) on the contact surface G C : W ¼ ∫ G C p x ð ÞdG: ð13Þ The 2nd equation expresses the surface separation h as the sum of the initial surface separation h i , the rigid body displacement d, and the elastic normal displacements u e 3 of the two bodies in contact: h x ð Þ ¼ h i x ð Þ þ d þ u e 3 x ð Þ; ð14Þ The elastic normal displacement u e 3 can be expressed using the Boussinesq relation: u e 3 x ð Þ ¼ ∫ G C U x; j ð Þp j ð ÞdG: ð15Þ In this expression, U (x, j) is the influence matrix (also called Green's function) which contains the displacement at any point x as a result of a unit load at a point j.
In order to fully solve the elastoplastic contact problem, it is necessary to account for the geometry modification as a result of the residual surface displacements u r (determined in the plasticity loop) in the elastic contact solver. Actually, an iteration between the two modules is necessary to get a converging solution of the elastoplastic problem. ISAAC software has been used together with its various modules to solve not only plastic contact problems  [1, [14] [15] [16] [17] , but also to account for the effect of the viscoelasticity  [18, 19] , the anisotropy  [20] , the presence of sub-surface heterogeneities or surface coating  [21] [22] [23]  as well as the wear of the surfaces in contact  [17, 24]. The current work considers only the plasticity loop, whereas the contact surface G C , the pressure distribution p and the elastic surface displacement u e are computed in the contact solver of ROUVISLAM.
Validation Numerical simulations are compared to measurements in order to validate the method presented above to solve the elastic-plastic, multi-scale and multi-contact problem encountered in the worm gear used in the Fastbrake© system. For the numerical simulations, the geometrical parameters were precisely extracted from 3D point cloud as obtained with a Coordinate Measuring Machine (CMM) (Zeiss MC 550, Germany). A portion of the wheel containing 1/5 of the teeth was used for the different simulations in order to reduce computation time. In addition, the constitutive elastic-plastic laws of the material of the wheel and the one of the worm were identified via a tensile test machine (ZwickRoell, France). The measured data were used directly in ABAQUS model or fitted with a swift law for ISAAC simulations.
The contact tests data were provided by FOC Transmissions. These tests were conducted with the help of a pressing device (see Fig. 10a ). In this device, the worm gears were subjected to impacts of various forces, by pressing the worm against the wheel, with the latter being clamped inside a housing. Figure  10b  shows an example of the impacted wheel. The geometrical parameters were measured again for the impacted worm gears using the CMM and compared to those obtained previously on the non-impacted worm gears, in order to determine the residual displacements. The kinematic position of the worm/wheel pair in which the impact occurred was identified by comparing the position and the shape of the impact-induced marks with those of the resulting pressure zones as obtained with ROUVISLAM software (see Fig.
Semi-analytical method is used to compute the load sharing due to the applied torque (ROUVISLAM), and to find out the contact residual displacements due to the applied pressure field (ISAAC). The method gives good agreement with measurements. A criteria based on the kinematic errors is proposed to predict whether the system will continue to run after a first emergency stop or not. A parametric study was carried out in order to find the maximum load to which the system can be subjected so it can continue to run after the emergency stop. The procedure can be further enhanced for large displacements, by including an iteration process between load sharing calculation (ROUVISLAM software) and residual displacements calculation (ABAQUS and ISAAC  softwares). Indeed, the computed load sharing is made under elastic linear assumption, which can be considered valid for small displacements.
Acknowledgements. This work is funded by  "la Région Auvergne-Rhone-Alpes ", as part of the  LICEF  project  À Launch of Industrialization, Commercialization , and  Exploitation of FAST-BRAKE©. We thank  Mr. Jérôme Machabert  ( FOC Transmissions ) for the trust and for providing the data and the specimens used in this work.
Forest canopies buffer macroclimatic temperature fluctuations. However, we do not know if and how the capacity of canopies to buffer understorey temperature will change with accelerating climate change. Here we map the difference (offset) between temperatures inside and outside forests in the recent past and project these into the future in boreal, temperate and tropical forests. Using linear mixed-effect models, we combined a global database of 714 paired time series of temperatures (mean, minimum and maximum) measured inside forests vs. in nearby open habitats with maps of macroclimate, topography and forest cover to hindcast past  and to project future (2060-2080) temperature differences between free-air temperatures and sub-canopy microclimates. For all tested future climate scenarios, we project that the difference between maximum temperatures inside and outside forests across the globe will increase (i.
Paired plot data We used a unique data set with 714 temperature offset data points involving paired plots from 74 studies spread across 5 continents (Supplementary Material Fig. S1 ; Data available in De  Frenne et al. , 2019). Focus was on air temperature below tree canopies (~72% of observations) and the temperature of the topsoil (~28%), given their importance for responses of forest organisms and ecosystem functioning to macroclimate warming. A key asset of this database is the paired nature of the data, which always combines below-canopy temperature data at a given forest site with open-air temperature data from a neighbouring reference nonforest site. Temperature measurement were performed by various logger types such as HOBO loggers (~15% of observations), iButton loggers (~10%), full weather stations (~5%) and various other logger types (e.
, 2019 for full details). Temperature offsets were calculated as the temperature inside the forest minus the temperature outside the forest, or extracted directly from the original study; negative values reflect cooler temperatures below tree canopies while positive values reflect warmer understorey temperatures. This was done for three temperature response variables, i. e. mean, maximum, and minimum temperature (further referred to as Tmean, Tmin and Tmax, respectively) that were computed during a specific time period that could differ between sites but that was exactly the same between paired sensors installed outside and inside the forest at a given site. Multiple forest sites (at least several kilometres apart), seasons (meteorological seasons, later aggregated to growing versus non-growing season) and temperature metrics (maximum, mean, minimum, air or soil temperatures) originating from the same study were entered into different rows of the database but tagged under the same study ID.
Temperature values of long time series were always aggregated per season and/or year, which means that several temperature values for Tmean, Tmin or Tmax could be generated for the same study site. Temperature measurements were classified as having taken place during the growing season, the non-growing season or throughout the whole year. This classification was performed on the basis of reported meteorological seasons and/or climate information in the original study. The dry and winter season were classified as the non-growing season in tropical and temperate biomes, respectively. Estimates of uncertainty (standard error, standard deviation, coefficient of variation or confidence intervals) of the temperature measurements were only reported for a small minority (13. 6%) of offset values in the database and were thus not included in our analyses.
1  (Fick and Hijmans, 2017). In addition, we gathered future projections (2060-2080) for the exact same set of temperature and precipitation variables described in the previous sentence but based on the contrasting "very stringent" representative concentration pathway (RCP) 2. 6 and "worst case" RCP 8. 5 from three different general circulation models (GCMs) with minimal interdependency, based on  Sanderson et al. (2015) , i. e. HadGEM2-ES, MPI-ESM-LR and MIROC5 (downscaled CMIP5 data from WorldClim; 30 arcsec resolution). Topographic variables and distance to the coast. We gathered six variables related to topography using raster layers derived from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) dataset at 30 arcsec resolution  (Amatulli et al. , 2018). Maps on northness and eastness, elevation (m a.
Data analysis All statistical analyses were performed in the open-source statistical software environment of R, version 4. 0. 2 (R Core Team, 2021). The temperature offsets for Tmean, Tmax and Tmin were modelled (274, 184 and 202 plot pairs respectively), after removing missing values for sensor height, i. e. not mentioned in the original study, and data points with canopy cover zero (based on the tree canopy cover map introduced above; Hansen et al. , 2013) using linear mixed-effect models with random intercept (LMMs) (lme4 package;  Bates et al. , 2015). In our main models, we combined the seasonal (growing vs. non-growing and annual) time series and performed additional analyses for the different three different time periods (see further and Supplementary Material Appendix S2). We included 'study ID' as a random intercept term to account for non-independence between samples within studies.
To test for non-linear relationships, we also used generalized additive mixed-effect models (GAMMs) (cf. the gamm4 package)  (Wood and Scheipl, 2014)  on the same dataset. We applied smoothers to the same set of fixed-effect terms, included the same random intercept term 'study ID' and followed the same model selection procedure as for the LMMs. For each of the three studied response variables (Tmean, Tmax, Tmin) and for each of the two modelling approaches, we performed a leave-one-out cross validation (LOOcv) and compared root mean square errors (RMSE) among models (LMMs vs. GAMMs). We found no difference (t test, p-value > 0. 05) in RMSE between LMMs and GAMMs, justifying our choice of LMMs (see also Supplementary Fig. S4 ). Furthermore, we checked spatial autocorrelation in the model residuals for the LMMs using Moran ś I-test from the ape package  (Paradis and Schliep, 2019).
For each of the temperature responses, we fitted single best models using 30 bootstrap samples. Using these 30 models, we generated per-pixel standard deviation mapped at the global extent (Supplementary Fig. S5 ). To map uncertainty for the future predictions, the same procedure was followed for each of the three GCMs, i. e. 30 bootstraps per GCM. Furthermore, we provide maps indicating where the models are extrapolating beyond the values of data used to fit the models. Predictive performance and uncertainty mapping were performed considering fixed effects of the models, excluding uncertainty of the random (study) effects. Predictions were made using the raster package  (Hijmans and van Etten, 2012). Graphical plots were created using ggplot2  (Wickham, 2016)  and Tmap packages  (Tennekes, 2018).
At the regional and even continental scale, novel high-resolution data on forest structure and composition based on remote sensing imagery (e. g. GEDI LiDAR data) are becoming available  (De Frenne et al. , 2021; Lembrechts et al. , 2019; Randin et al. , 2020; Zellweger et al. , 2018). Including these microclimate measurements and novel spatial map data (e. g. Haesen et al. , 2021; Lembrechts et al. , 2020)  in future models and mapping efforts will increase accuracy of future predictions  (Lembrechts et al. , 2021a). Our study illustrates that forest microclimates themselves are subject to climate change, which will have important consequences for forest-dwelling species and must hence not be neglected. PRocessus mIcroclimatiques sur la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat" (grant number: ANR-19-CE32-0005-01).
Acknowledgements: EDL, PV, PDF, LD, PS, CM and TV received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Starting Grant   FORMICA 757833 ). FZ received funding from the  Swiss National Science Foundation  (grant number  193645 ). SH received funding from a  FLOF fellowship of the KU Leuven  (project nr. 3E190655 ). Funding for DHK was provided by the  National Science Foundation Graduate Research Fellowship Program  ( DGE-1842473 ). JA acknowledges  Academy of Finland Flagship funding  (grant no. 337552 ). KH received funding from the  Swedish Research Council Formas  (grants  2014-530  and  2018-2829 ) and the  Bolin Centre for Climate Research, Stockholm University. SG was supported by the  Research Foundation Flanders (FWO)  (project  G0H1517N ).
Method 2. We modelled the stellar activity modulation using the Gaussian Process (GP) implemented within the public code citlalicue  (Barragán et al. 2022). Briefly, the routine masks out the planetary transits and bins the data using 3 h bins. It performs an iterative maximum likelihood optimization coupled with a 5-sigma clipping algorithm, in order to fit a GP with quasi-periodic kernel and remove possible outliers. To maximize the performance of citlalicue, we masked out the transits of the 3. 65 d planet, as they can affect the modelling of the stellar activity signal. We flattened the light curve, dividing the full PDC-SAP time series (including the data points previously masked out) by the retrieved correlation model and we searched the TESS detrended light curve for transits, by repeatedly applying the Transit Least Square algorithm (TLS,  Hippke & Heller 2019).
CHEOPS light curve detrending We detrended the nine CHEOPS visits with the code pycheops  (Maxted et al. 2022 ), which allows one to de-correlate the data using the vectors that describe the short-term instrumental photometric trends: roll angle, centroid movements, smear curve, and contamination by background stars. pycheops can also detrend the CHEOPS light curves using simple linear or quadratic trends. We first applied a 3 σ clipping algorithm as implemented in pycheops to remove outliers from the CHEOPS data and analysed each visit individually to check if it is affected by any of the known CHEOPS instrumental systematics. We excluded from the process the smear and contamination effects, because they are already accounted for by the PIPE extraction. We chose the detrending vectors via a Bayes factor pre-selection method  (Trotta 2007) , which routinely fits each visit with a least mean square method, accounting for the transit model and one of the possible decorrelation terms.
2018 ), a χ 2 minimization procedure provides an estimate of the following parameters: T eff = 5875 ± 110 K, [Fe/H] = 0. 10 ± 0. 09, and R ⋆ = 1. 10 ± 0. 18 R ⊕. These results are compatible within 1 σ with the parameters retrieved with ARES/MOOG. We finally utilized the package Spectroscopy Made Easy (SME,  Valenti & Piskunov 1996; Piskunov & Valenti 2017)  to determine the projected rotational velocity of the star v ⋆ sin i ⋆. Based on T eff , log g ⋆ and [Fe/H] as derived with Specmatch-emp, v mic as determined with ARES/MOOG, and v mac = 4. 1 ± 0. 4 km s -1 from the Doyle et al. (  2014 )'s empirical equation, we found v ⋆ sin i ⋆ = 5. 9 ± 0. 8 km s -1 , in very good agreement with the TRES results (see Sect. 5. 1). Using the Pecaut & Mamajek (2013)'s calibration for main sequence stars 10  , we found that the effective temperature implies that HD 93963 A is a G0 dwarf star.
Joint analysis of the TESS and CHEOPS transit light curves We performed a joint analysis of the detrended TESS and CHEOPS light curves (Sects. 2. 2 and 3. 2). We excluded the LCOGT data due to their poor quality when compared to the space-based TESS and CHEOPS photometry. For the joint fit, we used the software suite pyaneti  (Barragán et al. 2019 (Barragán et al. , 2022)) , which couples a Bayesian framework with an MCMC sampling to produce posterior distributions of the fitted parameters. pyaneti uses the quadratic limb-darkening transit model of  Mandel & Agol (2002)  following the q 1 and q 2 parametrization proposed by  Kipping (2013). We set Gaussian priors on q 1 and q 2 using the limb darkening coefficients derived by  Claret (2017 Claret ( , 2021) )  for the TESS and CHEOPS passbands, respectively.
Ruling out false positive scenarios We used the Tool for Rating Interesting Candidate Exoplanets and Reliability Analysis of Transits Originating from Proximate Stars (TRICERATOPS;  Giacalone et al. 2021)  to further verify that the transits are not due to contaminating eclipsing binaries. TRICERATOPS is a Bayesian tool that uses the phase-folded primary transit, any pre-existing knowledge about the stellar host and contaminants, and the understanding of planet occurrence and stellar multiplicity. For each planet, the code requires the photometric phase-folded light curve (with phase equal to zero corresponding to the transit centre), the target TESS input catalogue ID and the TESS sector in which the target was observed. The code refers to the MAST database to recover the list of stars within 10 pixels (each pixel corresponds to 21 ′′ ) from the target.
TRICERATOPS uses these inputs to calculate the contribution of nearby stars to the observed flux, and identifies those that are bright enough to produce the observed transit. The accounted for scenarios of false positive detections are the following: an unresolved bound companion with a planet transiting the secondary star, an unresolved foreground or background star hosting a planet, a foreground or background eclipsing binary, a transit on a nearby star, and a nearby eclipsing binary (NEB). The code uses a Bayesian framework to estimate the probability associated with each scenario, by fitting transit and eclipsing binary models accounting for the input orbital period and twice the period. A1, page 15 of 25  A&A 667, A1 (2022)  We ran TRICERATOPS for both candidates. The pipeline identified a total of 23 stars within 10 ′′ from the target star, although only 2 nearby stars were considered to be potential NEBs: the gravitationally bound stellar companion DR3 729899906357408640, and the star DR3 729899902062379776.