dataesr/scientific-paragraphs-categorization huggingface dataset:
7-0. 8 m s -1 for the other nights (red and green lines). To determine if these two behaviors are consistent with solar observations, we compute the RV RMS as a function of τ for each of the 75 GOLF solar 8-h subseries and for each corresponding WGN. We see that solar RV RMS measurements 12  show a large dispersion but do not match the decrease observed for a WGN. The RV RMS derived on HD 67458 dataset are statistically compatible with solar data at large τ. Comparing with the RV semi-amplitude of an Earth-mass planet in the habitable-zone (K ∼ 9 cm s -1 for HD 67458, see Appendix B), this demonstrates that this strategy is not robust to mitigate enough the shorttimescale stellar signal. This is in total agreement with  Meunier et al. (2015) , who found that we need to bin over τ > 8-h to get down to the level of an Earth-like RV signature.
Periodograms of the granulation signal The power spectral density of stellar granulation is well-known to act as a red-like noise, that is a power increase in a given frequency range. For main-sequence stars, this frequency range correspond to ν > 10 µHz. In Fig. 7 , we show the Lomb-Scargle periodograms (Scargle 1982) of each CHEOPS, ESPRESSO and TESS datasets. To fit these periodograms, classical models are Harvey-functions  (Harvey 1988). These functions are defined as the sum of Lorentzian functions parameterized by different timescales and amplitudes to distinguish the stellar activity components that dominate different frequency regions in the periodogram (generally attributed from high to low frequencies to: instrumental noise, stellar oscillation modes, granulation, supergranulation, and active regions).
5±60 µHz, with T eff ,⊙ = 5777 K and ν max,⊙ = 3090 µHz  (Huber et al. 2011 ) and stellar parameters from Table  4. In each panel of Fig. 7 , we have indicated the cut-off frequencies ν cut that delineate the white noise and granulation regimes (green vertical lines). In the CHEOPS and ESPRESSO datasets, ν cut correspond to periods in the interval  [244, 810]  s. In the TESS dataset, ν cut corresponds to periods around 100 min. We note that, in the dataset of ESPRESSO observations of HD 88595, the approximation of white noise is only partially true (the high frequency part of the periodogram is not perfectly flat). These levels of white noise impact the detection of the granulation signal, with the periodogram slope in the frequency region of stellar granulation that decreases with the increase of WGN.
As discussed in Sect. 4. 2, this is due to the large level of white noise in CHEOPS data. For comparing different stellar observations, we need to correct the flicker index values for this effect. This will be done in Sect. 5. 2. On the other hand, the flicker index deduced from the ESPRESSO periodogram of HD 67458 is consistent with zero, leading us to the conclusion that the granulation signal is this dataset is not clearly identified. On the contrary, indexes inferred from CHEOPS and ESPRESSO averaged periodograms of HD 88595 are both large (i. e. , α g ≫0), and are consistent within their 1σ errorbars. Finally, the flicker indexes inferred from TESS averaged periodograms of HD 67458 and HD 88595 are α g < 0. 1, both consistent with a nondetection. Analyzing the two TESS sectors of HD 88595 allows, however, a marginal detection of the granulation signal with α g = 0.
Comparison with Kepler bright stars To compare the flicker indexes for stars of different apparent magnitudes (i. e. , different white noise levels), we have to interpolate the behavior of the periodogram's slope at a constant white noise level. Following  Sulis et al. (2020a) , we target the level σ w = 30 ppm (hereafter σ 30 ), which was arbitrarily chosen as a good compromise for all studied stars (since all estimates of σ w are above 30 ppm). For each photometric dataset (VIRGO's three SPM channels, CHEOPS observations), we then applied the following procedure. (a) We added a synthetic white Gaussian noise of standard deviation σ (i) w to each L available light curves. (b) We compute the averaged periodogram P (i) L with Eq. (  3 ) using these L new light curves. (c) We evaluate the flicker index α g (σ (i)  w ) by fitting model (4) to P (i) L.
We found F8 HD ∝ ν -0. 530 max. Using such scaling and the parameters given in Table  4 , the two values predicted by the 3D simulations are F8 HD ≈ 48 ppm (HD 67458) and F8 HD ≈ 66 ppm (HD 88595). The predicted characteristic timescales are τ ACF ≈ 203. 2 s (HD 67458) and τ ACF ≈ 369. 3 s (HD 88595). For HD 67458, the F8 values inferred from CHEOPS dataset are larger than the predictions from HD simulations, indicating that white noises are dominating the dataset. For HD 88595, the inferred and predicted F8 are in very good agreement (see Sect. 4. 1 and Table  6 ). The length of all the synthetic time series correspond to at least 1000 convective turnover times, with these turnover times that are adapted to the stellar properties of the synthetic target stars. For all of these target stars, we compute the classical periodogram and scale the two cut-off frequencies f g and f c ≥ f H with the stellar parameters, with f c the corner frequency that marks the p-modes frequency domination regime out (see  Sulis et al.
Link between the spectroscopic and photometric signatures Only a few variations of the physical granulation properties were observed during the solar magnetic cycles  (Garcia et al. 2005) , with 2% variation in the density and mean granules' area observed during the solar cycle  (Ballot et al. 2021 ). This cannot be directly verified on other stars since their surfaces cannot be resolved on the scale of the granulation cells. However, this can also be confirmed indirectly by studying variations in their power spectra  (Seleznyov et al. 2011; Muller et al. 2018; Sulis et al. 2020a). A comparison of the current periodogram of HD 88595 with future observations taken in a few years would allow us to confirm if the granulation signal is indeed stationary with the stellar magnetic cycle. Without having any datasetto our knowledge -to verify this statement, we assume in this section that photometric observations of the granulation signal can allow us to predict its signature (amplitude and timescales) in spectroscopic observations (e.
Comparison of the periodograms In the left panel of Fig. 12 , we compare the (arbitrarily normalized 17  ) averaged periodograms of solar VIRGO (red) and GOLF (black) observations. We observe comparable periodogram' slopes on data taken in photometry and spectrophotometry with flicker indexes α g = 1. 33 ± 0. 03 and α g = 1. 34 ± 0. 14, respectively (see Sect. 5. 1). In both dataset, the level of white noise is low and does not affect the characterization of the stellar granulation signal. We note that, in the frequency region where the supergranulation starts to dominate (ν<200 µHz), the periodogram of brightness becomes flat while it still increases in the periodogram of RV. In the two other panels, we show the averaged periodograms of CHEOPS and ESPRESSO observations of HD 67 458 and HD 88595.
Prediction from empirical laws Some relations between the amplitudes of the stellar granulation signal in spectroscopy (RV RMS) and photometry (F8 metric) have been derived in the literature. We aim to test these predictions with the CHEOPS and ESPRESSO datasets of HD 67458 and HD 88595. A first relation has been derived in  Bastien et al. (2014) , based on a small sample of 12 stars observed first by Kepler and later by RV ground-based surveys at the Keck and Lick observatories. The stars in this sample have been characterized as "chromospherically quiet stars" since they showed lowamplitude photometric variability (≤3 ppt) in their Kepler light curves. However, the RV precision was limited to ≥4 m s -1 leading to a difficult characterization of the stellar granulation signal. Fitting a linear law between F8 and the corresponding RV RMS for each stars, the relation: RV RMS = (31.
Data filtering Motivated by the large timespans required to bin out the p-modes for HD 88595 (and likelihood for a large uncertainty on the optimal binning), we also attempt to filter out the p-mode impact in the RVs and shape indicators. For this, we apply a Butterworth filter provided by the Python library scipy. Here, we use the individual RVs, the FWHM and contrast provided by the ESPRESSO DRS. We fit each of the remaining shape indicators for each individual CCF. For nights where there is an overlap between CHEOPS and ESPRESSO observations, we (linearly) interpolate the CHEOPS lightcurves onto the ESPRESSO timestamps. Then, on a night by night basis, we apply the Butterworth filter to the RV and shape indicator time-series with a cutoff frequency corresponding to 5 min and increasing in 5 min intervals up to 80 and 255 min for HD 67458 and HD 88595, respectively (with the latter being approximately three times the estimated time to bin the p-modes down to 10 cm s -1 RMS for this target).
Conclusions The first goal of this paper was to detect the stellar granulation signal of two bright stars for the first time with highprecision photometric CHEOPS observations taken simultaneously or contemporaneously with high-resolution spectroscopic ESPRESSO observations. We detected the stellar granulation signal for the F star HD 88595 in both datasets, but only in the CHEOPS dataset for the solar-like star HD 67458 (Sect. 4). In particular, we observed significant variabilities between the three nights of observations of HD 67458 with ESPRESSO. Although we could not clearly identify the origin of these variabilities, we showed that they are induced by an excess of white noise in the observations masking the signatures of the stellar granulation (Sect. 4. 1). We demonstrated the sensitivity of high-precision photometric CHEOPS observations to this stellar signal, which was not detected on TESS observations (large photon noise and small amplitude of the granulation signal in the TESS passband).
The second goal of this study was to test the predictions from 3D hydrodynamic models of convection (Sect. 6). We observed photometric amplitudes with CHEOPS that are consistent with the expectation from 3D models: 48 ppm for the solar-like star and 66 ppm for the F star. The flicker index and flicker frequency derived on synthetic granulation time series generated by these 3D model predictions are also in agreement with both Kepler and CHEOPS observations. The last goal of this study was to link the spectroscopic and photometric signatures of convection for main-sequence stars with high-precision observations (Sect. 7). We observed a very good match between the periodograms of CHEOPS and ESPRESSO observations for the F star in the frequency regime dominated by stellar granulation. This was quantitatively confirmed by similar flicker indexes inferred on both datasets and A24, page 21 of 30 A&A 670, A24 (2023) Fig.
Acknowledgements. The authors thank the anonymous referee for her/his helpful comments that improved the quality of the manuscript. Overall, the paper has been significantly improved in terms of both science and writing. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. The authors acknowledge the  ESPRESSO  project team for its effort and dedication in building the ESPRESSO instrument.
26093/cds/vizier). The original description of the VizieR service was published in 2000, A&AS, 143, 23. We acknowledge  M. Oshagh  for sharing the dataset from Oshagh et al. (2017). S. S. acknowledges support from  CNES , the  Programme National de Planétologie (PNP) , and the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU. M. L. and S. S. acknowledge support of the Austrian  A24 , page 22 of 30 S. Sulis et al. : Connecting photometric and spectroscopic granulation signals with CHEOPS and  ESPRESSO Research Promotion Agency (FFG) , under project number  859724  (GRAPPA). M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. The contributions of M. L. and A. K. have been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation.
H. M. C. acknowledges support from the  UKRI  under a  Future Leader Fellowship  (grant number  MR/S035214/1 ). Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Research Foundation  (Grant agreement no. :  DNRF106 ). L. F. R. D. acknowledges support from the  Independent Research Fund Denmark  (Research grant  7027-00096B ). The numerical results presented in this work were obtained at the  Centre for Scientific Computing , Aarhus https://phys. au. dk/forskning/. Part of the calculations have also been performed using the OCA/SIGAMM mesocentre. This research was undertaken with the assistance of resources provided at the  NCI National Facility  systems at the  Australian National University  through the  National Computational Merit Allocation Scheme  supported by the  Australian Government.
S. H. gratefully acknowledges CNES funding through the grant  837319. 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. Y. A. and M. J. H. acknowledge 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 Regional Development Fund  through grants  ESP2016-80435 MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiologia (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme. The MOC activities have been supported by the  ESA  contract No. 4000124370. S. C. C. B. acknowledges support from FCT through FCT contracts nr.
IF/01312/2014/CP1215/CT0004. X. B. , S. C. , D. G. , M. F. and J. L. acknowledge their role as ESA-appointed CHEOPS science team members. ABr was supported by the  SNSA. This project was supported by the  CNES. 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). A. De. acknowledges the financial support of the  SNSF. The Belgian participation to CHEOPS has been supported by the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Program , and by the  University of Liège  through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation.
L. D. is an F. R. S. -FNRS Postdoctoral Researcher. This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalizacão 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 ,  O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B. -O. D. acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). D. G. gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseousor rocky?Unveiling the nature of small worlds".
M. G. is an F. R. S. -FNRS Senior Research Associate. K. G. I. is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does 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 Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. This work was also partially supported by a grant from the  Simons Foundation  (PI:  Queloz , grant number  327127 ). I. R. I. acknowledges support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
07 0. 12 ± 0. 05 -0. 01 ± 0. 05 3. 99 ± 0. 06 4. 148 ± 0. 017 1. 616 ± 0. 017 1. 351 +0. 031 -0. 058 2. 8 ± 0. 4 1534 ± 85 This work (spectroscopy) This work (spectroscopy) This work (spectroscopy) This work (spectroscopy) This work (spectroscopy) This work (spectroscopy) This work (from R  *  and M  *  ) This work (IRFM) This work (isochrones) This work (isochrones) Eq. (2), This work (ESPRESSO) Mean log R ′ HK Rotation period [days] v sin i [km s -1 ] Stellar inclination [degrees] -4. 99 ± 0. 14 10. 57 ± 0. 06 2. 179 ± 0. 2 ∼26 -4. 96 ± 0. 15 3. 1151 ± 0. 0003 7. 24 ± 0. 35 ∼16 This work (spectroscopy) This work (TESS photometry) HARPS (d) , This work This work Notes. (a) SIMBAD astronomical database from the Centre de Données astronomiques de Strasbourg (http://simbad. u-strasbg. fr/ simbad/).
Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important for the programming of Cyber-Physical Systems (CPS). This paper describes TeamPlay, a research project funded under the EU Horizon 2020 programme between January 2018 and June 2021. TeamPlay aimed to provide the system designer with a toolchain for developing embedded applications where ETS properties are first-class citizens, allowing the developer to reflect directly on energy, time and security properties at the source code level. In this paper we give an overview of the TeamPlay methodology, introduce the challenges and solutions of our approach and summarise the results achieved. Overall, applying our TeamPlay methodology led to an improvement of up to 18% performance and 52% energy usage over traditional approaches.
Furthermore, developing software that is optimised for multiple non-functional properties at once is an even greater challenge. The TeamPlay project addressed these challenges by providing the developer with tool support to analyse ETS properties at the source code level together with a methodology for developing embedded applications where ETS properties are treated as first-class citizens. The TeamPlay approach, therefore, allows developers to reason about the energy, time, and security properties of their applications very early in the process and as an integral part of their typical development activities. The Teamplay methodology comprises a number of key components: • A cross-layer management of ETS properties from source code to binary, through a complex set of optimisations arising from the different elements of the developer workflow, i.
In this paper,  (1)  we give an overview of the TeamPlay toolchain and methodology for both predictable and complex architectures; (2) we present a high-level overview of the key technical challenges that TeamPlay addressed, including making ETS properties first-class citizens in a transparent manner, enabling the programmer to reason about ETS constraints as part of their development process, energy-modelling targetting multiple different architecture types and generally software development with stringent ETS goals and requirements from the outset; and (3) we demonstrate the TeamPlay methodology on a number of industrial use cases from the domains of medical devices, space, aerial vehicles, and deep learning.
II. OVERVIEW OF THE TEAMPLAY TOOLCHAIN The TeamPlay approach is focussed around providing programmer-friendly abstractions that turn ETS properties into first-class citizens of the language they are embedded in. This allows the programmer to annotate source code with timing, energy, and security information, assign them to structures in the code, and write assertions that can be proven about these properties. By exposing ETS properties as first-class citizens and by making them easy to use and understand for application programmers, we enable the programmer to manipulate, and precisely reason about, ETS as normal program values, directly interacting with analytical and optimisation frameworks, including the physical properties of the underlying hardware. Depending on the criticality and performance constraints of the system, we consider different platforms ranging from simple predictable hardware platforms, e.
The multi-criteria optimising compiler (implemented in the WCET-aware C compiler WCC  [2] -  [4] ) is then provided with the collected POIs. It applies a set of multi-objective optimisations  [5]  regarding energy usage, timing control, and security risks. WCC uses three plug-in tools to accomplish this, each targeting a specific non-functional property. The aiT tool  [6]  statically computes a bound for the Worst-Case Execution Time (WCET) for each identified task in the code. The EnergyAnalyser tool  [7]  uses robust and accurate energy models  [8] ,  [9]  for the target hardware platforms to predict the energy consumed by each task. The SecurityAnalyser quantifies the security protection level against timing and power side-channel attacks with novel metrics  [10] , and the SecurityOptimiser transforms the code to increase protection against these side-channel attacks  [11] ,  [12]  if required.
The resulting sequential binary is then instrumented to integrate measuring points which, when run multiple times, allow us to create a dynamic profile of the energy consumption and execution time for each task. In this toolflow energy measurement and execution time extraction are performed using PowProfiler  [18] ,  [19]. Reconnecting with the previous workflow, the CSL and coordination layer are provided with the estimated ETS properties of the application. The coordination layer uses this information to schedule the application  [20] ,  [21] , generating the glue code for the management of the tasks accounting for the parallel capability of the underlying platform (dashed path in Figure  2 ).
A. The Transparency Challenge During the design of a CPS it is common practice to analyse the ETS effects once the application has been completed. The challenge we address with TeamPlay is to enable programmers to reason about these non-functional properties in an early stage of the development cycle instead. TeamPlay promotes ETS information from the binary to the source code level. Full transparency of ETS properties requires correlating source code features with the corresponding parts of the binary  [22]. A contract must be formally proven to guarantee the ETS constraints across the different levels of abstraction. Clear, human-understandable feedback needs to be provided in order to allow the developer to take actions should the application code fail to satisfy some of the constraints.