dataesr/scientific-paragraphs-categorization huggingface dataset:
This is especially true for hot Jupiters, to understand whether the exoplanet flux observed at visible wavelengths is due to thermal emission leaking into the shorter wavelengths or to reflected light due to high-altitude condensates or Rayleigh scattering. One example is the hot Jupiter Kepler-7b, which shows a prominent occultation and phase-curve signal in the Kepler data but no signal at all in the infrared, as measured by Spitzer at 3. 6 and 4. 5 microns  [12, 19]. This points to Kepler-7b harbouring high-altitude clouds or hazes. Such inference would not have been possible without (broadband) observations at visible wavelengths. Other examples include a recent study  [39]  which combines phase-curves of multiple hot-Jupiter phase-curves observed by TESS and shows a possible trend between equilibrium temperature, day-to-nightside temperature contrast and recirculation efficiency.
Back End Electronics (BEE) The BEE is the main computer of the instrument. On one hand, it provides power to the entire payload and data interfaces to the platform on-board computer, on the other hand, it interfaces with the SEM. Similarly to the SEM, it contains a Data Processing Unit (DPU) and a Power Supply and Distribution Unit that provides conditioned power for itself and the SEM. The DPU hardware is based on the GR712, which contains two LEON3 processors and provides the space wire interface to the SEM and MIL-1553 interfaces towards the spacecraft. The DPU mass memory from 3D-Plus provides a FLASH memory in the configuration of 4 Gbit times eight-bit. For the effective operation of the processor, four components are used to provide 32-bit access and error detection and correction. The BEE is hosting the main instrument software, the In-Flight Software (IFSW), which provides a high-level control of the SEM and FPM.
) are collected in a file and compressed using arithmetic compression. This file is called the "compression entity". The compression achieved depends upon the entropy of the image, which is a function of numerous factors such as, for example, the number of stars in the background, exposure time, size of the image, etc. In practise, the compression factors actually measured, range between 2. 5 and 3. 3. 4. This compression entity is sent to the SC mass memory via PUS service 13. The detailed treatment of all side margins, imagettes, etc. is highly configurable and can be adjusted to match available bandwidth and data reduction needs.
Science Operations Centre (SOC) The Science Operations Centre (SOC) is under the responsibility of the University of Geneva and is physically located at the Department of Astronomy of the University. Seven additional institutes or industrial partners from the CHEOPS Mission Consortium countries have made important contributions to the SOC. The ultimate objective of the CHEOPS Science Operations Centre (SOC) is to enable the best possible science with the CHEOPS satellite by delivering the following elements: -Mission planning after spacecraft commissioning; -Science operations visibility, reporting, and knowledge management; -Support to the CHEOPS science team and, in exceptional cases, indirect support to guest observers; -Science operations system study, design, and requirements; -Mission planning tool development; -Quick look analysis to monitor the performance of the instrument; -Development and maintenance of the data reduction pipeline to process raw data and deliver science ready data products (images and light-curves); -Mission data archiving; -Science data distribution.
Detailed calibration of star trackers optical heads with respect to the spacecrafts reference frame was performed based on instrument data but without enabling payload measurements to enter the AOCS control loop in order to allow for an accurate pointing even without instrument measurements. Subsequently, observations with the Payload in the Loop for tracking the target star have been performed. This has led to a full characterisation of the pointing abilities of the system. Several additional detailed characterisation were performed during this phase aiming at defining the point-spread function (see Sect. 11. 1), the extent of stray light as a function of angle of incidence, the amount of dark current, the number of hot pixels, the measure of the gain stability, as well as an update of the location of the South-Atlantic Anomaly.
A more difficult issue, which is still present, was the evidence of a much larger number of hot pixels than expected based on the COROT data (of order 70% more). Also, most of these hot pixels being telegraphic, their correction is somewhat more difficult. To reduce their number, the operating temperature of the CCD has been reduced in several tests from 233 K to 228 K and finally to 223 K. Because the latter lower temperature might create temperature stability issues in some extreme pointings and because all calibrations had been carried out at 233 K, it was decided to operate the CCD at the intermediate temperature of 228 K which resulted in a decrease of hot pixels by a factor 3.
A second 3 hour observation was made later on. A 75 ppm precision was achieved for this observation, which makes Fig. 14  Left: Photometric light-curve of the star HD 88111 obtained from images taken every 30 seconds over a period of 47 hours without any detrending. The gaps in the data are due to the Earth occulting the target star and to the passage of the spacecraft through the South Atlantic Anomaly, a period during which the data are discarded. Over a period of 6 hours, the precision achieved is 15. 5 ppm; Right: One of the image of HD 88111 as recorded by CHEOPS this case compliant with science requirement 1. 2. Note that, as in the case of HD 88111, no detrending of the data was performed. In summary, measurements taken during commissioning demonstrate that CHEOPS meets the photometric precision requirements on both the bright and faint stars.
KELT-11b: Radius determination of a bloated planet During IOC-D a few stars known to host planets were targeted by CHEOPS as part of the end-to-end validation of the operational process. The giant planet KELT-11b was among these targets. Discovered by the KELT survey in 2016  [31] , the radius of the planet measured by transit photometry is R p = 1. 37 +0. 15  -0. 12 R Jup while its mass derived from radial velocity data is M p = 0. 195 ± 0. 18 M Jup. The planet orbits the evolved subgiant star HD 93396 of magnitude V = 8 in a period of P = 4. 736529 ± 0. 00006 days. As evidenced by these data, KELT-11b is a so-called bloated giant planet having about 20% of the mass of Jupiter but a radius larger by almost 40%. To analyse the data we used pycheops 5  version 0. 7. 0. pycheops has been developed specifically for the analysis of CHEOPS data and uses the qpower2 algorithm  [29]  for fast computation of transit light curves.
Acknowledgements 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 Swiss participation to CHEOPS has been supported by the  Swiss Space Office (SSO)  in the framework of the  Prodex Programme  and the  Activité Nationales Complémentaires (ANC) , the  Universities of Bern and Geneva  as well as well as of the  NCCR PlanetS  and the  Swiss National Science Foundation.
He further extended the method to integrate the case of inhomegeneous turbulence distribution featuring a higher level of turbulence in the rotor wakes. Finally, he generalised the solution to rotating blades. Hanson proposed to apply a strip-based approach, which consists in dividing the stator into radial slices, in order to take into account the radial variation of the turbulence characteristics and of the geometry. The sound power is obtained by summing up the contribution of all strips. Hanson performed comparisons of his calculations (made for a typical radial position) to the experimental data of several fans covering a large range of parameters. Because of a lack of experimental data, turbulence intensity and integrale length scales were chosen to minimise the offset between predicted and experimental results.
Finally, it should be noted that comparisons between Posson's model and some of the other models cited above (Ventres, Hanson, Amiet) using the SDT experimental results for validation were presented, for example, by de Laborderie  [27]  and Lewis et al. [28]. Their results evidenced discrepancies between the models, especially at low frequency. Accounting for the cascade effect for skewed gusts greatly improved the prediction compared to solutions calculated either with a two-dimensional cascade model or with an isolated-airfoil model. The presence of the duct proved to be important too. As shown by the literature review, the validation of RANS-informed analytical methods has been mostly restricted to the SDT data provided by NASA. The present study uses a new, independent data set obtained in 2018 at AneCom AeroTest during a test campaign organised in the framework of the European project TurboNoiseBB  [29].
These data give the opportunity to further assess the RANS-informed analytical method. With the two questions raised at the beginning of the introduction in mind, a benchmark was organised reconsidering the impact of the two main ingredients of the method-(1) the RANS calculation (in particular the choice of the turbulence model) and (2) the aeroacoustic models. While the first part of the study is addressed in a companion paper by Kissner et al. [1] , this second part deals with the impact of the acoustic models. The benchmark character is unique as more than ten independent European institutions using different CFD and acoustic solvers were involved. This large diversity guarantees that several key aspects of the RANS-informed analytical approach for fan broadband noise will be addressed.
Acoustic Data The acoustic and aerodynamic tests were conducted separately in order to avoid a contamination of the acoustic results by the instrumentation (hot-wire probes and total pressure sensor rakes mounted along the stator leading edge). The noise results for the bypass duct are based on measurement data provided by the line array of condenser microphones AX1 indicated in Figure  2. The microphones were wall-flush mounted in a section of constant radii located far downstream of the stator. The microphone signals were filtered using an axial wavenumber decomposition technique to efficiently separate hydrodynamic and acoustic pressure fluctuations  [33]. The signals were synchronised with the rotor shaft so that the rotor-locked part of the fluctuations could be removed  [34]. The model used to deduce the sound power spectra based on the sound pressure assumes an equal energy density distribution between the propagating acoustic modes of the same frequency band  [33, 35].
RANS Data Processing For a given operating point, all the acoustic simulations were based on the same input obtained by analysing the geometry and the flow solution of the RANS calculation. The data analysis-done with the DLR in-house tool C3D_T2P  [40] -was conducted at 97 radial positions equally distributed along the whole span (see Figure  4 ). The streamline positions in the (x, r)-plane were determined after having circumferentially averaged the mean flow. Only the part of the flow going into the bypass duct was considered in the prediction. Thus, the interaction with the Engine Support Stator (ESS) located at the core entry was ignored. For each radial position of the stator, the following values were provided: the axial and tangential speeds up-and downstream of the stator (see Figure  5 ), the turbulent kinetic energy and the turbulence length scale (see Figure  6 ).
Solutions PN1 and PN2 The methods implemented in the DLR in-house code PropNoise  [42]  is among the simpler methods used in the benchmark. It is subsequently presented using the formalism of Moreau and Guérin  [43]. The objective is to enumerate the various steps necessary in order to obtain a noise prediction based on RANS data and to illustrate the ambiguity and complexity of some choices. As mentioned before, the acoustic prediction relies on the acoustic analogy. For the prediction of RSI noise, only the source term related to the vane unsteady loading mechanism is considered. The other contributions for example, due to the turbulence-potential-field interaction are not modelled. The unsteady loading is calculated by assuming that the vanes are isolated, which means that blade-to-blade interactions are not considered.
Solution LN3 The method applied for the results labelled LN3 was presented by Cheong et al. [52]. It relies on the two-dimensional cascade response model implemented by  Whitehead [53]  in the code LINSUB. That model is based on Smith's work  [54]. An integral equation, which relates the source strength to the velocity disturbance, is solved by using a collocation procedure. The applied turbulence spectrum is the two-dimensional spectrum Φ 22 (K 1 , K 2 ) obtained by integrating the 3D wavenumber spectrum Φ 22 (K 1 , K 2 , K 3 ) over its spanwise component K 3. Through this integration, the contribution of subcritical waves such as |K 3 | > |K 1 |M √ 1 -M 2 , ( 26 ) are included even though they are acoustically cut-off. Cheong and co-authors showed that there is a critical frequency below which the interaction between neighbouring blades is important to include.
Solution BB1 The method applied to obtain the results BB1 was described by Blázquez and Corral  [56]. It is similar in many respects to LN1. The main difference is the use of a linearized Navier-Stokes (LNS) solver called Mu 2 s 2 T -L  [57]  to calculate the acoustic response instead of LINSUB. The objective of the BBNANEMS code is to work for turbomachine components like turbines, where for example, the infinitely thin flat plate approximation is no longer satisfying. This way, the blade shape (camber and thickness) and its effect on the mean flow (overspeed at the leading edge and flow deviation between leading and trailing edge) can be considered. To save computation time, the calculations are done on a reduced number of 2D strips equally distributed in the radial direction. In principle, the method could be extended to 3D blade geometry but at the expense of a dramatic increase in computational cost.
Results and Discussion The results of Part II of the TurboNoiseBB benchmark using the solvers presented in the previous sections are now presented. As the same RANS inputs were used for all simulations, the different acoustic codes can be directly and fairly compared. However, it should be noted that differences between simulations and experimental values should be interpreted very carefully as the acoustic models are sensitive to the RANS input and the way the integral length scale is calculated, as shown in Part I  [1]. Furthermore, the experimental data are not limited to fan-OGV interaction broadband noise but rather contain all aerodynamic noise sources of the test rig as briefly discussed in Section 2. 1. 2.
Conclusions A benchmark concerning RANS-informed analytical methods applied to rotor-stator interaction broadband noise of turbofan was organised within the framework of the European project TurboNoiseBB. The objective was twofold: (i) to analyse the impact of the Reynolds-Averaged Navier-Stokes calculation on the intermediate turbulence statistics and the final acoustic prediction, (ii) to quantify and assess the influence of the acoustic models. The first question was addressed in the companion paper  [1]  considering various turbulence models while the same acoustic code was used to predict the noise. For this second part, an inverse strategy was applied: the acoustic models were varied while the same RANS solutions were used as input. The experimental data of the benchmark were obtained for the transonic fan ACAT1 of AneCom AeroTest.
The benchmark made use of the data recorded at the three operating conditions, Approach, Cutback and Sideline along the sea level static working line. The acoustic tools of the benchmark have differing application objectives and therefore do not need the same accuracy. This explains the very broad range of measured computation time: between one minute and more than one day per acoustic simulation. A classification of the models in two categories was proposed. Group A contains methods based on the acoustic analogy and Group B, the direct acoustic methods bypassing the calculation of a source term. Regarding methods of type A, the following issues were identified: (1) a mismatch between the source model and the Green's function (swirl was considered either with a simplified model or not at all), (2) significant variations of the power radiation with the choice of the stagger angle, (3) an uncertainty regarding the consideration of the oblique, spanwise gust component in results using a 2D cascade unsteady lift response.
• The split between the up-and downstream power levels exhibited different trends for the single-airfoil and the cascade response results. It was not possible to determine the best combination of models in terms of accuracy because of the uncertainties regarding (i) the turbulence statistics issued from the RANS calculations used to feed the acoustic models, and (ii) the acoustic validation data, as these included the contribution of all acoustic sources in the test rig-particularly strong at low frequency-and not solely rotor-stator interaction noise. Furthermore, rotor shielding is clearly visible in the experimental data measured in the forward arc but was not accounted for in the simulations. In the benchmark, the turbulence was considered as isotropic. However, the hot-wire measurements in the interstage indicate that this assumption is no more suitable near the casing wall due to the rotor tip vortex and the boundary layer.
Acknowledgments: The authors would like to gratefully acknowledge the support of following people:  Azucena Pintado  and  Thomas Nodé-Langlois  (AIRBUS),  Marc Jacob  and  Stéphane Moreau  (ECL),  Maximilian Behn  and  Sebastian Hakansson  (DLR),  Phil Joseph  (ISVR), and  Adolfo Serrano  ( ITP Aero ).
Ways ahead From the above it follows that the origin of granitic rocks is likely to remain a matter of passionate debate for years to come. It will certainly be fuelled by an increasing demand for raw materials, increasing computer power and progress in analytical techniques. The latter will secure a never-finishing flux of increasingly affordable, high-quality compositional data, including non-traditional isotopic systems such as Fe, Ca, Li, Mg, Si, Cr or stable Sr  (Johnson et al. 2004; Foden et al. 2015; Tomascak et al. 2016; Teng et al. 2017) , as well as in situ elemental and isotopic data for both individual minerals and melt inclusions. To tackle the flood of data, an increasing role will be played by statistical and computing methods, data mining and online databases, shared through the World Wide Web.
In their first contribution, Mayne et al. (2019b) present the background, overall philosophy and explore full potential of their new software (RCRUST)  (Mayne et al. 2016 ) which allows phase-equilibria modelling along pressure-temperature paths with composition as a variable. To demonstrate the utility of RCRUST for process-orientated investigations, the authors briefly present four possible fields of application. In the first, they explore the evolution of the water content in a fully-hydrated but fluidabsent rock composition along pressure-temperature trajectories that evolve towards granulite-and eclogite-facies peak metamorphic conditions. The second and third cases investigate the effect of melt loss during partial melting and the role of peritectic crystals entrained from the source.
Another interesting application of the pathdependent phase-equilibria modelling using the software RCRUST is presented in the case study of the Buddusò Pluton in NE Sardinia by  Farina et al. (2019). The authors propose that the internal chemical variability of this normally-zoned pluton, homogeneous in terms of whole-rock Sr-Nd isotopic compositions, could have been generated by crystallization differentiation of the same hydrous tonalitic parental magma. The inferred mechanism invokes compaction in a rheologically locked crystal-rich magma chamber  (Bachmann & Bergantz 2004). Petrogenesis of igneous suites, including granitoids, may be deciphered by whole-rock geochemical modelling. Janoušek & Moyen (2019)  provide an overview of the current approaches and argue that petrogenetic modelling is a powerful tool to rule out impossible scenarios and to constrain likely processes inferred from geological and petrological observations.
Acknowledgements The editors are indebted to all presenters of the granite session at the  Goldschmidt Conference in Paris  for having provided such an exciting account of many aspects of the multifaceted granite science. In particular, all the authors of this volume are thanked for their deep knowledge, hard work and tireless dedication to finish this book. We are equally grateful to all reviewers for their helpful comments and expertise, and also for sticking to the tight deadlines imposed by the editors. Last but not least, we would like to acknowledge  Jo Armstrong , the Production Editor of the current volume, with  Bethan Phillips  and  Angharad Hills , the commissioning assistants, for their tireless and competent work and good humour, as well as all the others at the Geological Society in London, without whom this volume could not have materialized.
Abstract. The new AquiFR hydrometeorological modelling platform was developed to provide short-to-long-term forecasts for groundwater resource management in France. This study aims to describe and assess this new tool over a long period of 60 years. This platform gathers in a single numerical tool several hydrogeological models covering much of the French metropolitan area. A total of 11 aquifer systems are simulated through spatially distributed models using either the MARTHE (Modélisation d'Aquifères avec un maillage Rectangulaire, Transport et HydrodynamiquE; Modelling Aquifers with Rectangular cells, Transport and Hydrodynamics) groundwater modelling software programme or the EauDyssée hydrogeological platform. A total of 23 karstic systems are simulated by a lumped reservoir ap-proach using the EROS (Ensemble de Rivières Organisés en Sous-bassins; set of rivers organized in sub-basins) software programme.
The main objectives of this paper are to describe this platform, to evaluate its performance against observations, and to prove its suitability and robustness for operational and research purposes. Prior to real-time monitoring and forecast, AquiFR needs to be assessed over a long-term period, which is presented in the present study. The evaluation is carried out over a 60-year period from 1958 to 2018 at a daily time step. This long-term simulation provides a unique insight on the long-term evolution of groundwater in France, as most of the groundwater data are available over about 30 years. This long-term simulation can then be used to characterize the daily situation compared to past events. A wide range of gauging stations and piezometers were selected in order to perform the evaluation of the simulated piezometric heads, river flows and karstic spring flows.