dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurement errors As shown in  Clerc et al. (2012b) , the inclusion of measurement errors changes the shape of the predicted X-ray observable diagrams. A precise estimate of the measurement errors is thus a key step of the analysis. The pyproffit package provides us with error estimates for each measurement. This allows us to subsequently model the relative measurement errors on CR, HR and θ c as a function of CR and θ c , using the following parametrisation: %err x = a x CR b x θ c x c (x = CR, HR, θ c ) (6) The choice of the CR-θ c plane is a natural second order approximation, reflecting the fact that, physically, the brighter and more peaked a cluster, the better the measurement. We perform a non-linear least square fit using the Levenberg-Marquardt algorithm  (Levenberg 1944; Marquardt 1963 ) to constrain the {a x }, {b x } and {c x } coefficients.
Acknowledgements XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼ 6 × 10 -15 erg cm -2 s -1 in the [0. 5-2] keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. The Saclay team acknowledges long term support from the  Centre National d'Etudes Spatiales. This work was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. MS acknowledges financial contribution from contract  ASI-INAF n. 2017-14-H. 0  and  INAF  ' Call per interventi aggiuntivi a sostegno della ricerca di main stream di INAF '. LM acknowledges the grants  PRIN-MIUR 2017 WSCC32  and  ASI-INAF n. 2018-23-HH. 0. KU acknowledges support from the  Ministry of Science and Technology of Taiwan  (grants MOST  106-2628-M-001-003-MY3  and  MOST 109-2112-M-001-018-MY3 ) and from the  Academia Sinica Investigator Award  (grant no.
The second analysis measured the oxygen isotopic ratio 16 O/ 18 O of all new cold dLHdC stars for which the CO band heads were detectable in the K band and compared them to values obtained for RCB stars. They confirmed the lower 16 O/ 18 O ratio observed in dLHdC stars as indicated by  Clayton et al. (2007)  and  García-Hernández et al. (2009) , who suggested that dLHdC stars have 16 O/ 18 O<1 and most RCB stars have 16 O/ 18 O>1. We searched for new dLHdC stars using the datasets of two all-sky surveys: 2MASS  (Skrutskie et al. 2006), and GAIA eDR3 (Gaia Collaboration et al. 2021). We limited ourselves to stars whose lines of sight are affected by an integrated interstellar reddening E(B-V) < 1 mag  (Schlafly & Finkbeiner 2011). The selection of candidates based on their brightness and colour is detailed in section 2, then in section 3 we describe the spectroscopic follow-up of these candidates and the subsequent discovery of new dLHdC, RCB, and EHe stars.
Finally, for the third and last selection, we queried the CDS/SIMBAD database  (Wenger et al. 2000)  for information about all stars that passed the selection cuts described above. We then rejected all targets that already possessed a well-defined spectroscopic type that is not carbon rich, and all targets associated with a globular cluster. We also removed all known dLHdC stars and RCB stars that have passed all the applied criteria; there were 18 of them, as listed in Table  1. About 38% of the stars selected in cut 2 were removed at this stage from the final target list. We selected 631 stars from groups A to D for spectroscopic follow-up, ∼44% of which were located in a sky area of low interstellar reddening, that is, E(B-V) 0. 5 mag. As we started with an initial dataset in which these stars represented ∼85% of the total, our selection process was less efficient with stars impacted by higher extinction.
For the nearly 400 thousand stars belonging to groups F and G, whose measured parallax S/N is lower than 3, we applied a stricter selection on their 2MASS near-IR colours with α=0. 2 in Eq. 1 because of a higher dispersion. We also requested a tighter constraint on their GAIA (BP-RP) 0 colour, ranging between 0. 4 and 1. 2 mag after interstellar dust correction, and on their GAIA G apparent brightness with G<13. 5 mag. Then, for group F alone, we added a final cut to reject stars that presented magnitude limits in the  [12]  or [24] WISE photometric bands. After a cross match with the SIMBAD database, we selected 89 supplementary targets after removing two known RCB stars: ASAS J050232-7218. 9 and UX Ant.
(2020). Additionally, we note that 6 of the new dLHdC stars (A182, A183, A223, B42, C20, and C27) were already catalogued as carbon-rich stars in the general catalogue of carbon stars  (Alksnis et al. 2001). A spectral classification of type R (R2 for A182) was given by the CDS/SIMBAD database, indicating they are warmer than classical carbon stars. One first marked difference we observed is the general weakness of the CN bands in the new dLHdC stars compared to known RCB stars. For a new dLHdC star and a known RCB star   (1932)  with similar C 2 band strengths, and thus presumably similar temperatures, the dLHdC stars generally show significantly weaker CN bands. Assuming the similarities in C 2 band strength of these two stars indicates a similar carbon abundance, the weakness of the CN bands implies that the new dLHdC stars have a lower nitrogen abundance.
New extreme helium stars Extreme helium (EHe) stars are blue supergiant stars with surface temperatures ranging from 9000 to 35000 K. They are thought to share an evolutionary connection with HdC stars as  the subsequent phase of the latter as they share similar spectroscopic characteristics due to an atmosphere that is poor in hydrogen, but highly enriched in carbon  (Jeffery 2008; Jeffery et al. 2011; Pandey et al. 2001). Most known EHe stars (13 out of 21) were not part of the initial datasets of GAIA giant stars used in our analysis because their uncorrected BP-RP colour indexes were bluer than 0. 5 mag. Furthermore, of the remaining 8 EHe stars, none passed the criteria we applied to the photometry recorded in the 2MASS and GAIA eDR3 catalogues. It is therefore surprising that we dis-covered 2 new EHe stars as they were not an objective of our search.
2 kpc, respectively, closer than the three closest RCB stars, R CrB, XX Cam, and RY Sgr, whose respective distances are ∼1. 3, ∼1. 3, and ∼1. 6 kpc. We obtained the absolute V magnitudes and the unreddened (V-I) 0 colours for all dLHdC stars, the known EHe stars, some of the Magellanic RCB stars, and 31 of the known Galactic RCB stars that have an eDR3 GAIA measured parallax with an S/N higher than 2 σ (only 3 of them have a parallax S/N between 2 and 3). We kept only the RCB stars for which we were able to convincingly estimate their maximum brightness in both V and I photometric bands. We used the light curves of the OGLE surveys for the Magellanic RCBs  (Udalski 2008 ) and mainly the ASAS-3 and ASAS-SN surveys for the Galactic RCB stars, in association with the AAVSO database for the bright ones.
2 and 0. 4 mag on a timescale between 20 to 60 days. This indicates clear physical differences between these two populations of HdC stars. The low-amplitude photometric variability of the dLHdC stars is supported by other surveys. We note that using the Hipparcos dataset,  Rimoldini et al. (2012)  found that HD 137613 presents variabilities with a peak-to-peak amplitude of 0. 05 mag on a timescale of 0. 1 day, but this is not confirmed by the KELT survey  (Oelkers et al. 2018) , which did not find variabilities greater than 0. 04 mag for that star on timescales ranging from 30 mins to one day. Rimoldini et al. (2012)  classified the brightest dLHdC star, HD 182040, as a variable star with a peak-topeak amplitude of 0. 04 mag and a period of ∼1. 3 days. This variability is confirmed by the Mascara monitoring survey of bright stars  (Burggraaff et al.
26 mag  (Jayasinghe et al. 2018). This is also the only object discovered in the present analysis that was listed as a variable star in the SIMBAD database due to the high-frequency light curve published by the HATNET variability survey  (Hartman et al. 2004) , which shows a smooth variation of 0. 12 mag amplitude over 30 days. Finally,  Heinze et al. (2019)  reported variations of ∼0. 35 mag detected by the ATLAS survey. We also studied the ASAS-SN light curve of HD 175893 and found variabilities of 0. 3 mag on a timescale of 20 days (Table 4). We did not detect photometric declines in any of the dLHdC stars except one. The ASAS-3 light curve of F75 presents two declines at JD∼2453450 and ∼2454600 days that last ∼300 and ∼150 days, respectively, with a maximum obscuration of ∼0. 55 mag in the first event and ∼0.
This shows that RCB stars of any temperature can stop or slow their production of dust for a period of time before restarting it again. Finally,  Montiel et al. (2018)  presented high-resolution mid-IR images of a few RCB stars showing the existence of extended cold shells. The dust production may be active throughout the lifetime of an RCB star. The four historic dLHdC stars are known to be deprived of any warm circumstellar inner dust shell, unlike RCB stars since the data release of the WISE mid-IR photometry and thus since the study of their SED up to 22 µm  (Tisserand 2012). Before this, we simply knew that these four HdC stars were never detected to undergo large photometric declines. Consequently, they may be thought to either not have started a dust production process yet or perhaps this process stopped long ago.
Summary After the release of the GAIA eDR3 dataset (Gaia Collaboration et al. 2021), which contains the full astrometric solution for nearly 1. 5 billion sources between 3<G<21 magnitudes, we selected about 720 dLHdC star candidates using the GAIA photometry and the near-IR photometry from the 2MASS survey in sky areas affected by interstellar reddening E(B-V) lower than 1. 0 mag. We defined our pragmatic selection cuts based only on the four bright dLHdC stars known at the time to optimise our rejection rate while keeping open the possibility of finding new dLHdC stars over a wider range of temperature and brightness. We spectroscopically followed up ∼70% of all our targets, the remaining ones were either unreachable with our telescope during our seven-month observation campaign, or had a very low priority as they were suspected of being blends with nearby objects.
Acknowledgements. PT is profoundly grateful for the long unconditional support and love from his father,  Claude Tisserand  (1945Tisserand ( -2020)), and this paper is dedicated to his memory. He personally thanks  Tony Martin-Jones  for his usual highly careful reading and comment. PT acknowledge also financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU, France. AJR is funded through the  Australian Research Council  under award number  FT170100243. We also thank the team located at Siding Spring Observatory that keeps the 2. 3m telescope and its instruments in good shape, as well as the engineer, computer and technician teams located at Mount Stromlo Observatory that have facilitated the observations. Palomar Gattini-IR  (PGIR) is generously funded by  Caltech , the  Australian National University , the  Mt Cuba Foundation , the  Heising Simons Foundation  and the  Binational Science Foundation.
Relativistic effects were included by means of the effective-core potential on the central metal atom in the calculation: ECP28MDF on ruthenium  [57]  and ECP60MDF on osmium  [58]. The relativistic DFT calculations were performed with the DIRAC18 computational program  [59]. As the reference for these calculations we take the CAM-B3LYP functional  [29] , which is a Coulomb-attenuated B3LYP functional  [60]  with parameters optimized for reproducing PV shifts obtained at the coupled cluster level of theory. We used Dyall's v3z basis set on the ruthenium/osmium and oxygen atoms and Dyall's v2z basis set on the carbon and hydrogen atoms  [61, 62]. In order to reduce the computational effort, these calculations were carried out in the two-component framework, where the Dirac-Coulomb Hamiltonian is transformed into the so-called exact two-component (X2C) Hamiltonian  [63].
The refractivity profiles show an excellent agreement of better than 0. 2 % with co-located radiosonde, spaceborne COSMIC-2 RO, and reanalysis products. The 200-500 m vertical resolution and the spatial and temporal continuity of sampling make it possible to extract properties of Kelvin waves and gravity waves with vertical wavelengths as short as 2-3 km. The results illustrate the difference in the Kelvin wave period (20 vs. 16 d) in the Lagrangian versus groundfixed reference and as much as a 20 % difference in amplitude compared to COSMIC-2, both of which impact estimates of momentum flux. A small dataset from the extra Galileo, GLONASS, and BeiDou constellations demonstrates the feasibility of nearly doubling the sampling density in planned follow-on campaigns when data with full equatorial coverage will contribute to a better estimate of wave forcing on the quasi-biennial oscillation (QBO) and improved QBO representation in models.
, 2020)  can now provide profiles from GLONASS as well as GPS, and Galileo profiles have been demonstrated from aircraft  (Haase et al. , 2021). The global coverage and high vertical resolution of the spaceborne RO datasets have provided the opportunity to quantify the global properties of gravity waves and wave sources of variability in the stratosphere. Reviews of the success of RO in capturing equatorial waves are provided in  Ho et al. (2019)  and  Scherllin-Pirscher et al. (2021)  and references therein. Several of these studies retrieved information on individual wave events but were limited to waves with very large horizontal scales and typically greater than 4 km vertical wavelength because the spacing between profiles is irregular and quasi-random in time and space. There is a continuing need to increase the vertical resolution to gain a complete view of the fine-scale wave-mean flow interaction that is unresolved in global models.
Section 4 introduces the data analysis procedures and estimates the vertical resolution of balloon-borne RO. Section 5 presents comparisons of the profiles with other independent datasets from radiosondes and spaceborne RO, as well as comparisons with numerical model reanalyses. Section 6 presents the results illustrating the equatorial waves that are captured with different periods and scales. Section 7 discusses some future perspectives and limitations of the balloon-borne RO observations. Conclusions summarizing the results are presented in Sect. 8.
, , 2021) )  and balloons  (Haase et al. , 2012)  provide a unique opportunity to perform RO measurements continuously in time and space over a localized area, rather than the pseudo-random distribution of the global SRO datasets. The density and continuity are advantageous for reconnaissance flights in an area of interest, such as at the locations of hurricanes  (Murphy et al. , 2015; Chen et al. , 2018)  and atmospheric rivers  (Ralph et al. , 2020) , but can also be applied to the problem of resolving fine-scale wave structures from free-floating balloons. The physics and geometry of ARO and BRO are similar to those of SRO, but there are some geometric modifications required to implement the typical RO retrieval technique. Detailed descriptions of the geometry and theory of BRO are provided in Appendix A.
The TSEN package included an U-blox singlefrequency GPS receiver on the gondola to provide timing and wind velocity through real-time positioning solutions. The TSEN thermistor and thermocouple were suspended 8 m below the Zephyr gondola to provide in situ measurements of ambient temperature at 30 s sampling intervals, and the TSEN barometer was located above the Euros gondola, measuring pressure at the same data rate.
Dataset The ROC receiver continuously tracked multiple constellations including GPS, Galileo, and GLONASS and logged the carrier phase, pseudorange, and signal-to-noise ratio (SNR) at a 5 s sampling interval throughout the whole flight. The logged data were stored in the internal storage of the ROC receiver and were transmitted by an Iridium data link to the CCMz every hour. Due to a technical problem that limited the bandwidth of the Iridium satellite link, data from only 23 d of the 57 d flight were recovered. The recovered dataset includes GPS-only data from 6-22 December 2019 (largest box on the left in Fig. 1 ), 1 January 2020 (box in the middle in Fig. 1 ), and 9 and 11-14 January 2021 (box on the right in Fig. 1 ). The data from three constellations (GPS, GLONASS, Galileo) were recovered on 15 December 2019.
Retrieval procedures The precise 3D positions of the balloon were calculated from the GPS pseudorange and carrier-phase data using precise point positioning with ambiguity resolution (PPPAR)  (Ge et al. , 2008; Geng et al. , 2019)  using the Position And Navigation Data Analyst (PANDA) software package developed at Wuhan University  (Shi et al. , 2008). The procedure was carried out in three steps. The first step was to estimate highrate (30 s) satellite clock errors based on regional data, instead of using global satellite clock products, since the estimated regional clock solutions can compensate for other unmodeled errors in the PPPAR solution  (Lou et al. , 2014). GPS satellite orbits were fixed to the Center for Orbit Determination in Europe (CODE) final orbits  (Dach et al. , 2020). Ground GPS observations were used from a list of 26 stations from the International GNSS Service (IGS) network in the equatorial region around the balloon trajectory to solve for the clock parameters (see Fig.
D1 in Appendix D ). In the next step, fractional cycle biases (FCBs) were estimated from the same dataset to improve the resolution of the carrier-phase ambiguities  (Geng et al. , 2011). The final calculation was solved for the coordinates of the balloon antenna at each time sample. The receiver clock errors were also estimated at each epoch as parameters with white noise variance. The zenith hydrostatic delay is constrained to the Saastamoinen model  (Saastamoinen, 1972) , and the zenith wet delay is assumed to be negligible at the balloon flight altitude and thus not included in the procedures, which reduces the number of estimated parameters. The estimated position accuracy is ∼ 10 cm in the horizontal, ∼ 20 cm in the vertical, and 0. 05 m s -1 in velocity. The precise position calculation was shown to be a significant improvement over the onboard real-time GPS solution.
The horizontal location at a given tangent point height is determined by a simulation using forward ray tracing implemented in the Radio Occultation Simulator for Atmospheric Profiling (ROSAP) model  (Hoeg et al. , 1996; Syndergaard, 1998) , assuming a climatological refractivity profile from the CIRA+Q model appropriate for the month and latitude  (Kirchengast et al. , 1999). The final BRO slant profile dataset includes a horizontal location (latitude and longitude) and time for each tangent point height in the profile, and the location and time of the lowest tangent point of the profile are provided as the reference for the profile. Figure  2  shows the intermediate products of excess Doppler and bending angle for a rising occultation labeled "G29r_G13". This occultation lasted about 15 min from when the receiver started tracking satellite PRN29 until it rose above the local horizontal.
7. The background has its coldest point significantly higher, at about 17. 5 km. This fine-vertical-scale (∼ 3-4 km wavelength) wave pattern seen in the BRO data must have a long intrinsic period (> 1 d), since no obvious progression in phase is visible. A statistical comparison between BRO and the corresponding ERA5 reanalysis profiles for the entire 17 d period is shown in Fig. 8. The number of points from the BRO profiles at each height is indicated in black lines. The lowest tangent point altitude in each profile varies because the PLL receiver loses or initiates tracking of different satellites at different altitudes. Most of the profiles (∼ 600) penetrate below 10 km, and ∼ 400 profiles penetrate below 8 km. The differences are characterized by the mean and standard deviation (SD) of the absolute and percentage differences between the two datasets.
The mean difference between BRO refractivity and ERA5 is within 0. 1 N-units, and the corresponding percentage difference is about 0. 2 % above 7 km. The mean difference between retrieved dry temperature and ERA5 is within 0. 5 K above 11 km, where moisture is negligible, with BRO showing higher temperatures. A residual wave-like pattern is apparent in the differences above 15 km, which is likely the signature of a large-scale and slowly varying wave captured by BRO that is coherent over the 17 d and is not captured accurately or is underrepresented in ERA5, as seen in Figs. 6c  and 7. Because the dataset extends only ∼ 100 • in longitude along the Equator, the large-scale wave perturbations were not averaged out and are still evident in the BRO dataset. Although there are many COSMIC-2 profiles over the equatorial region during the period of the Strateole-2 campaign, the number of profiles available to implement a profile-to-profile comparison between the two datasets is quite small.
To compare the two types of RO profile, the daily mean refractivity and temperature were calculated for each dataset by averaging all RO profiles that occurred over a day within a box of 10 • latitude by 10 • longitude centered on the mean balloon position for the day. There were typically about 15-25 COSMIC-2 profiles per day falling in such an area compared to ∼ 45 BRO profiles. There are in total 17 daily mean profiles calculated for both RO datasets. Figure  9  shows the absolute and percentage differences in daily mean refractivity between BRO and COSMIC-2. There is a ∼ 0. 1 N-unit mean difference between the two datasets above 15 km, corresponding to a 0. 2 % difference. The mean temperature difference is within ∼ 0. 5 K between two RO datasets above 8 km, with BRO showing higher temperatures.
Equatorial waves in the BRO dataset A primary objective of this work is to determine the properties of the equatorial waves that are evident in the BRO dataset, such as in the data presented in Fig. 7. The TSEN instrumentation on the Euros gondola measures flight level temperature and horizontal wind from the balloon position and reveals some properties of the wave spectrum. The Lagrangian frame of reference on the floating balloon enables a direct measurement of intrinsic wave frequencies. From these measurements, we compute the wave energy spectrum as a function of intrinsic frequency over the 57 d flight, which contains the period of high-density BRO measurements. Figure  10  shows the spectrum for waves with periods of 1-30 d. The spectrum shows a main peak near 20 d and secondary peaks at 3-4 d plus a background of higher-frequency waves.
This frame-of-reference difference is an important distinction, and we will show clear differences in waves observed in these two frames of reference in what follows. While mixed Rossby-gravity waves and Kelvin waves can occur near a 5 d intrinsic period, we do not observe these signals around this period of high-density BRO data. Figure  11c  shows the filtered temperature perturbation within the period band of 5-30 d, which contains Kelvin waves with a peak period of ∼ 20 d. The vertical structure of the 5-30 d signal in the stratosphere (Fig. 11c ) has a dominant vertical wavelength of ∼ 6 km until day 350. Then after day 352, a ∼ 3 km vertical wavelength signal is more prominent. We can estimate the horizontal wavelength using the  (c) absolute temperature difference. Solid and dashed blue lines are the mean and standard derivation (SD) of the differences between the two datasets.