dataesr/scientific-paragraphs-categorization huggingface dataset:
Context. The ESA PLATO space mission is devoted to unveiling and characterizing new extrasolar planets and their host stars. This mission will encompass a very large (>2,100 deg 2 ) field of view, granting it the potential to survey up to one million stars depending on the final observation strategy. The telemetry budget of the spacecraft cannot handle transmitting individual images for such a huge stellar sample at the right cadence, so the development of an appropriate strategy to perform on-board data reduction is mandatory. Aims. We employ mask-based (aperture) photometry to produce stellar light curves in flight. Our aim is thus to find the mask model that optimizes the scientific performance of the reduced data. Methods. We considered three distinct aperture models: binary mask, weighted Gaussian mask, and weighted gradient mask giving lowest noise-to-signal ratio, computed through a novel direct method.
2010)  and Transiting Exoplanet Survey Satellite (TESS)  (Ricker et al. 2014)  targets. In view of its acknowledged high performance and straightforward implementation, mask-based (aperture) was adopted as in-flight photometry extraction method to be implemented in the PLATO data processing pipeline. In such technique, each light curve sample is generated by integrating the target flux over a limited number of pixels, which shall be appropriately selected to maximize the scientific exploitability of the resulting timeseries light curve. In this context, the present work unfolds the development carried out for defining the optimal collection of pixels for extracting photometry from non-saturated stars in the P5 sample. There is a noteworthy number of publications on the theme of photometric masks.
Among the oldest, we put some emphasis on the work of  Howell (1989) , in which the idea of a growth curve (signal-to-noise ratio as a function of aperture radius) for point-source observations is presented; on the stellar photometry package DAOPHOT 3  from  Stetson (1987) , which is still widely used today; and on the solution proposed by  Naylor (1998) , which consists of employing weighted masks for imaging photometry, providing improved noise-to-signal ratio (NSR) performance compared to binary masks. Later on, and orientated to planet transit finding and asteroseismology,  Llebaria & Guterman (2006)  and  Bryson et al. (2010)  developed strategies to compute optimized binary masks 4  for extracting light curves from CoRoT and Kepler targets, respectively. More recently,  Smith et al. (2016)  proposed a new method to assign apertures for Kepler targets, focussed on planet detection and mitigation of systematic errors, through an optimization scheme based on NSR and Combined Differential Photometric Precision (CDPP) 5  (Jenkins et al.
2010b). As described in Kepler's Data Processing Handbook 6 , this method is implemented within the photometry analysis component of Kepler's science pipeline. Alongside,  Aigrain et al. (2015)  and  Lund et al. (2015)  provided techniques for mask pixel selection for Kepler K2 targets. The former proposes circular apertures, which has satisfactory performance for sufficiently bright targets and is relatively robust to systematic errors. The latter uses clustering of pixels, which best fits the flux distribution of the targets, being therefore more suitable for dense fields. A modified version of this method is employed in  Handberg & Lund (2016)  for reducing the data of Kepler K2 targets from campaigns 0 to 4. Besides, it is also considered as one of the possible solutions for extracting light curves from TESS targets  (Lund et al.
Then we determine whether or not the best compromise between these two parameters is obtained from apertures having overall lower NSR. This paper is organized as follows (see Fig. 1 ). Section 2 describes the main payload characteristics, including instrument point spread function (PSF), spectral response, and noise. Also, put for determining a halo of pixels to be downlinked along with the aperture pixels. 5 Roughly speaking, CDPP is an estimate of how well a transit-like signal can be detected  (Smith et al. 2016 ). an expression is derived to provide an estimation on the intensities of zodiacal light entering each PLATO telescope. Section 3 gives details on the extracted data from the adopted input catalogue (Gaia DR2). That information is used to build synthetic input images, called imagettes, to characterize the performance of aperture photometry.
Spectral response and vignetting The spectral response of a photometer represents its efficiency in converting incident photons into effective counts of electrons at detector level. It is derived from the combined effect of optical transmission and CCD quantum efficiency. In parallel, another parameter impacting instrument efficiency is vignetting, which consists of an inherent optical feature that causes attenuation of image brightness. Such an effect increases non-linearly as the angular position, α, of the source augments with respect to the optical axis (α = 0) of the instrument. A preliminary spectral response curve of the PLATO cameras is presented in Fig. 6 , alongside the Gaia G passband 10  , Johnson's V filter from  Bessell (1990) , Vega A0V Kurucz template (alpha_lyr_stis_008) from the CALSPEC 11  database, E-490 12  reference solar spectrum from the American Society for Testing and Materials (ASTM), and a M2V-type star synthetic spectrum from the Pickles atlas 13   (Pickles 1998).
023 mag  (Bohlin 2007). The P band zero point is given by zp = 2. 5 log 10 (h c) -1 Θ λ 2 λ 1 f Vega (λ) S (λ) λ dλ + P Vega. (5) This constant (see Table  6 ) provides a straightforward way for switching between stellar flux and magnitudes using P = -2. 5 log 10 (h c) -1 Θ λ 2 λ 1 f (λ) S (λ) λ dλ + zp. (6) Thus, having the zero point zp and the magnitude P of a given star, its respective total flux f P (per camera and expressed in units of e -s -1 ) can be estimated with f P = 10 -0. 4(P-zp). (7) For switching between P and V magnitudes, we determine the V -P relationship using the Johnson-Cousins V filter (Fig. 6 ) and modelling f (λ) with synthetic stellar spectra extracted from the POLLUX database  (Palacios et al. 2010). As for the calibration star Vega, we adopted the template alpha_lyr_stis_008 (Fig.
2016) , our gradient and Gaussian masks are much simpler and faster to calculate. They might be suitable for not too crowded fields or in situations in which the existence of contaminants may not be too critical (e. g. for asteroseismology targets). We note however that these masks adapt their size to the presence of contaminant stars. This is possible since our expression for the NSR (Eq. 11) takes into account the fluxes coming from contaminant companions, so whenever their signals are sufficiently strong compared to those of the targets the masks are reduced in width to keep NSR as low as possible. Moreover, our weighted masks can be implemented with ease in both Kepler and TESS data processing pipelines, so their usage is not limited to PLATO targets. We expect that the ensemble of results and discussions derived from this work might be particularly useful during the next steps of the preparation phases of the PLATO mission, in particular for the definition of algorithms in the exoplanet validation pipeline, for the construction of the PIC, and later on for the selection of targets.
Therefore, the P5 sample will be composed of a photometry extraction method (binary masks) that is intrinsically insensitive to detect most of the potential background false positives, plus a non-negligible number of imagettes and COBs that can be strategically allocated to targets of interest. Overall, that should be enough to identify properly a substantial fraction of the TCEs, which will be dominated by short period transits, in the P5 sample. Aside from that, the PLATO data processing team is currently studying the feasibility and effectiveness of applying imagette-independent methods for identifying background false positives from the P5 detections.
Number N bad TCE of contaminant stars for which SPR raw k ≥ SPR critk , which supposes photometry with no background correction, as a function of the number N T of telescopes observing the host star and the aperture model. The presented values were determined from our dataset of ∼ 3. 25 million contaminant stars. The SPR crit k was computed with δ back,k ∼ 0. 1 mag, t d = 4h, η = 7. 1, and n tr = 3. The roman numerals correspond to the areas indicated in Fig. 19. The percentiles indicate the amount of deviation of the values from weighted masks with respect to those from binary mask. The weighted values in the lower row correspond to the effective N bad TCE , obtained by assuming uniform star distribution and a fractional field of view as given in Table1.
Acknowledgements. This work has benefited from financial support by the  French National Centre for Space Studies (CNES) , the  Paris Observatory -PSL  and the  Brazilian National Council for Scientific and Technological Development (CNPq). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://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 research was achieved using the POLLUX database 22 operated at  LUPM (Université Montpellier -CNRS, France  with the support of the PNPS and INSU. This research uses solar spectrum data from the  National Renewable Energy Laboratory , operated for the  U.
5 µm thick (Fig. 2b ). The calcite is always recognizable as distinctive crystal stacks with large faces (Fig. 1b , label 1), as are the needle-like Na enriched carbonates (Fig. 1b , label 2). Due to the insufficient amount of salts, only the calcite phase was identified by XRD. The microRaman spectrum of a glass powder (6-10 µm) altered for 6 months at 40°C, bears the distinctive peaks of calcite (283, 715 and 1087 cm -1 ) and an additional peak at 1071 cm -1 that could indicate the presence of pirssonite, a mixed soda-lime carbonate of composition Na2Ca(CO3)2. 2H2O (RRUFF database, reference R040146). At 40°C, the cracking of the hydrated layer starts after 3 months of ageing, when the hydrated layer is 1±0. 2 µm thick. The cracks cut across the salts (see for instance Fig. 1d , the calcite crystal on the left).
MGS 3. 1. 1. MGS-MAG One can categorize MGS vector measurements into two low-and high-altitude data sets. The first is from the premapping phases of the mission, namely, the AeroBraking and the Science Phase Orbits (AB/SPO;  Albee et al. , 2001). MGS was in a quasi-polar, elliptical orbit with a periapsis oscillating between 90 and 160 km. Low-altitude measurements (i. e. , those useful to describe and model the crustal magnetic field) were mostly acquired on the dayside. The AB/SPO phase lasted from orbit insertion in September 1997 until March 1999, whereupon the Mapping Orbits (MO) phases (nominal and extended) started. These phases lasted until November 2006. The orbit was almost circular, with a quasi-constant altitude at about 360 km close to the south pole and 415 km close to the north pole.
Data Selection and Decimation: Minimization of External Fields When dealing with the modeling of magnetic field, one typically faces a problem related to the enormous amount of available measurements. MGS returned more than 180 million vector triplets, and MAVEN will possibly return more data. These numbers make it impossible to use all measurements, and data selection/decimation procedures have to be defined. They are usually optimized so that those measurements possibly more influenced by external fields are eliminated or reduced. Several options have been pursued in the past. Data can be stacked and binned over relatively small volumes, in an attempt to average out time varying magnetic fields  (Acuña et al. , 1999; Purucker et al. , 2000). A nighttime selection can be performed before this binning procedure  (Connerney et al.
This high correlation supports the pertinence of such locally derived proxies for magnetic data selection in order to minimize the external field perturbations. The daily index series covers the entire time period when MGS was in the MO phase. The index varies from 2. 5 to 31. 1 nT, with a mean value of 7. 1 nT. We impose a maximum daily root-mean-square (rms) value of 4. 5 nT. This arbitrary choice is actually consistent with terrestrial studies, as this corresponds to keeping only the quietest 10% of days. Most of the selected measurements were acquired during the last Martian year of the MGS mission. This epoch is closer to the solar minimum, which was reached in 2008 (see  Langlais et al. , 2017, for details). This selection leads to a data set made of 5. 8 million vector triplets. This large number is further decreased using an along-track filtering procedure, which is described below and in Appendix C.
After looking at all orbits we defined the following criteria: (i) the correlation coefficient has to be larger than 0. 4 for at least two components, and (ii) it has to be larger than 0 for the last component (see Figures  C1  and C2 ). These criteria may seem to be loose, but they are preferable to include more measurements than to exclude too many. This scheme leads to the selection of about 65% of the low-altitude orbit tracks, that is, 633 out of 967 for the MGS AB and SPO orbits. The same selection was made for MAVEN tracks below 600 km, except that only nightside measurements (between 19:00 and 5:00 local time) were initially kept. Using the correlation coefficients between observations and predictions, 1,585 out of 1,972 orbits (on the nightside) were selected, or approximately 80% of the data set.
Note. For MGS MO and MAVEN, these numbers are for nightside measurements. The number of selected data corresponds to data available on selected orbits. "Final" and "second run" numbers are those after along-track decimation and after outliers removal, respectively. n/a = not applicable; MGS = Mars Global Surveyor; AB/SPO = AeroBraking and the Science Phase Orbits; MO = Mapping Orbits; ER = Electron Reflectometer instrument; MAVEN = Mars Atmosphere and Volatile EvolutioN.
18 nT. Those associated with the AB/SPO data set below 350-km altitude are given for a comparison with those of  Langlais et al. (2004). In their model, AB/SPO data were limited to a maximum altitude of 350 km, before being spatially averaged. Associated rms misfits were close to 20 nT, to be compared to about 17 nT here. For each data set and field components, histograms of the residuals are shown in Figure  3. The residuals are globally symmetrically distributed, with the exception of the ER data set: the mean misfit is positive, with very few residuals below -1 and many more above +1. The lowest mean misfits are associated with the high-altitude MGS MO data set and with the radial component. Correlation coefficients are all above 0. 92 and even excess 0. 98 for the radial components. High-altitude measurements are associated with remarkably low misfits, below 4 nT.
Morschhauser et al. (2014)  had misfit between 5. 7 and 7. 2 nT, depending on the component. Misfits associated with the MGS AB/SPO data set are comparable with those of  Morschhauser et al. (2014) , closer to 16 nT. We interpret the better high-altitude misfits of our model as being a direct consequence of the external magnetic field proxy we used for data selection. The horizontal components are associated with slightly poorer rms misfits while showing nonzero biases. These are usually interpreted as being caused by nonlithospheric contributions, that is, external magnetic field contributions  (Langlais et al. , 2004; Morschhauser et al. , 2014). The mean misfit of B  and B  have opposite signs for the MGS AB/SPO and MAVEN data sets, respectively. The MAVEN data set contains exclusively nightside data (as does the MGS MO one), while the MGS AB/SPO one is strongly dominated by dayside measurements.
XCH 4 Figure  6  shows the XCH 4 time series of the different data sets. As for XCO 2 , the general features are in agreement  for all data sets. There is a slight annual increase of about 10 ppbv. Also, there is a seasonal cycle with a variability of ≈ 30 ppbv; however, compared to XCO 2 the interannual seasonality strength and phase vary significantly between the years due to the many different variable sinks and sources of methane, e. g. ,  Dlugokencky et al. (1997). The differences between the data sets largely resemble the differences observed for XCO 2. The bias between EM27/SUN and TCCON is 0. 72 %; see Table  4. This bias is close to the bias observed by Hedelius et al. (  2016 ), 0. 75 %, where they used the GGG software package for the analysis of EM27/SUN spectra. Although a single bias is reported, as was observed for XCO 2 the offset is not constant, but rather shows a seasonality.
The calibration uncertainty between COCCON and TCCON is estimated to amount to 5 ppbv for XCH 4 ; see Table  4. The retrievals between EM27/SUN and HR125 LR agree within 1σ precision (0. 9997±0. 0008). Panel (a) of Fig. 7  shows the ratio between EM27/SUN and HR125 LR color-coded with the observation date. As for XCO 2 , no trend is apparent. An explicit linear fit to the XCH 4 ratio produces a slope coefficient of 0. 0001, 1 order of magnitude smaller than the 1σ precision of the ratio (0. 0008). An interesting feature is observed in the ratio between EM27/SUN and TCCON data sets; see panel (b) of Fig. 7. In general the pattern is similar to that of XCO 2 , with a slight dependence on the SEA. The ratio in the figure is color-coded with the date of observation rather than the SEA. It can be seen that for 1 and 14 March 2016 (shaded area in Fig.
ILS measurements and instrumental examination The measurement of the ILS is a valuable diagnostic for detecting misalignments of spectrometers. Differences in the ILS of the EM27/SUN spectrometers due to misalignment can lead to biases in the data products between the instruments. Here the spread of ILS values of all EM27/SUN spectrometers that were checked at KIT in the past 4 years is estimated. Numerical values are given in Table  5 ; the results are shown in Fig. 9. The black square denotes an ILS measurement of the HR125 spectrometer, also with 1. 8 cm MOPD. This test was done to check for an absolute offset of our method. The HR125 would be expected to show an ideal ILS for short optical path differences, but a value of 0. 9824 was obtained. From this measurement it is concluded that our method shows an absolute offset and that values between 0.
Therefore the number of coincident measurements for SN62 and 63 that were checked on this date are sparse. A typical calibration day is depicted in Fig. 10. The calibration factors and standard deviations for all instruments with respect to the reference spectrometer are also depicted in Table  6. Calibration factors and standard deviations were obtained using the methods described in Sect. 3. 4. The calibration factors are close to nominal for all species and instruments. For XCO 2 the ensemble mean is high compared to the reference EM27/SUN, with a mean calibration factor of 0. 9993 and a standard deviation of 0. 0007. In Fig. 11  histograms of the calibration factor distributions are depicted for XCO 2 , XCH 4 , and O 2 , respectively. The histograms are not conspicuous. Applying the mean calibration factor to all calculated calibration factors centers the data around the ensemble mean.
(  2017 ) study only data within ±2 h of local noon were taken into account, whereas here no constraints regarding the time of measurement were applied. As another sensitive test the O 2 total column calibration factors are given. In contrast to XCO 2 and XCH 4 , there is no canceling of errors in this quantity. The ensemble mean is slightly high compared to the reference EM27/SUN (0. 9999 ± 0. 0014). The average bias is 0. 11 % O 2 with an average standard deviation of 0. 04 % O 2. Note that for our setup this average bias is a worst case scenario. The bias only applies if no calibration factor is used in the subsequent analysis. The strength of this calibration routine is that the computed calibration factors can be used, thereby significantly lowering the bias between different EM27/SUN spectrometers.
Acknowledgements. We acknowledge support by the  ACROSS research infrastructure of the Helmholtz Association of German Research Centres (HGF). This work was supported by funding from the  Helmholtz Association  in the framework of MOSES (Modular Observation Solutions for Earth Systems). We thank the  National Center for Environmental Prediction (NCEP)  for providing atmospheric temperature profiles. We thank the  NASA  science team for providing MLS data from the Aura satellite. We thank the  Jungfraujoch NDACC team  for providing Jungfraujoch FTIR data. Isamu Morino  and  Akihiro Hori  contributed by procuring the  NIES  instrument and performing additional instrumental line shape measurements in Tsukuba. We acknowledge funding from the  Australian Space Research Program -Greenhouse  Gas Monitoring Project,  Australian Research Council  project  DE140100178 , and the  Centre for Atmospheric Chemistry (CAC) Research Cluster  supported by the  University of Wollongong Faculty of Science, Medicine and Health.
In this geographic area, the RESeau des URgences CORonaires (RESURCOR) network is a coordinated regional system of care for patients with cardiac emergencies, and forms part of REseau Nord Alpin des Urgences (RENAU). The RESURCOR network disseminates protocols via pocket cards and booklets, available at a dedicated website (www. renau. org). An ongoing regional prospective registry of STEMI patients has provided data since 2002 on the acute phase of patient care  [11] [12] [13] [14] [15]. One of the protocols of the RESURCOR networkthe SCA-Alp protocol (Fig. 2 )is used to implement early transfers from remote hospitals to and from the PCI centre for intermediate-risk patients with acute NSTEMI; and early return transfers from PCI centres to remote hospitals for intermediate-risk patients with acute STEMI.
Data collected and study endpoints Descriptive data were derived from the RESURCOR STEMI registry and the cath-lab database. Troponin concentrations were obtained from the remote hospital lab databases. In-hospital mortality data were derived from each hospital's database; retransfer from a remote The aim of this study was to determine the rate of adverse events associated with the SCA-Alp approach, to ascertain whether this approach is safe. The primary endpoints of this study were death within 48 h of return to the remote hospital (including during transfer) and the requirement for retransfer back to the PCI centre within 48 h for a coronary angiogram. Secondary endpoints were in-hospital stroke, Thrombolysis In Myocardial Infarction (TIMI) major bleeding, major haematoma at the puncture site (defined as the need for a transfused haemostatic agent or surgical intervention), and intrastent thrombosis (or reocclusion of the dilated lesion) > 48 h after PCI.
Acknowledgements The authors would like to thank  RENAU Investigators (Emergency Departments MD): Frederic Champly (Hôpital de Sallanches) ,  Christian Curvat  ( Hôpital privé Haute Savoie nord ),  Marc Haesevoets  ( Hôpital d'Albertville ),  Guillaume Peribois  ( Hôpital de Thonon ),  Dominique Savary  ( Hôpital d'Annecy), Pascal Usseglio (Hôpital de Chambery ),  Claire Vallenet  ( Hôpital d'Annemasse ). We also like to thank  Jean-Pierre Perfus  (MD) that set up this protocol,  Blandine Lafitte  for data collection,  Hende Madiot  for data management and  Cecile Ricard  for statistical analysis. Editorial assistance was provided by  MedLink Healthcare Communications Limited.