dataesr/scientific-paragraphs-categorization huggingface dataset:
This difference would be reversed if the QBO were in the westward phase. Here we use two methods to make a rough estimate of momentum flux, one relying primarily on temperature perturbations from BRO data and one relying primarily on TSEN data. As presented in the Appendix of  Ern and Preusse (2009) , the gravity and Kelvin wave momentum fluxes can be estimated from relative temperature perturbations with known intrinsic wave frequency, vertical and zonal wavenumbers, atmospheric density, and buoyancy frequency. F m = ρ 0 2 k m g 2 N 2 T T 2 = ρ 0 2 ω N g 2 N 2 T T 2 , (7) Atmos. Chem. Phys. , 22, 15379-15402, 2022 https://doi. org/10. 5194/acp-22-15379-2022 in which ρ 0 is the atmospheric density, T is the background temperature, and N 2 is the squared buoyancy frequency. These formulas were applied to SABER  (Ern and Preusse, 2009)  and HIRDLS  (Alexander and Ortland, 2010)  observations and also SRO datasets, such as COSMIC-1  (Schmidt et al.
, 2016). With BRO data retrieved in the Lagrangian framework and better resolution of the wave amplitudes, a more accurate estimate of the wave momentum flux could be achieved near the balloon flight altitude. Assuming a background temperature of 195 K, a density of ∼ 0. 1 kg m -3 , and a buoyancy frequency of 0. 025 rad s -1 at about 20 km, the F m = ρ 0 u w = - ρ 0 g T N 2 ω 2 ω 1 Q Tu ω ω d ω, (8) where Q Tu ω is the quadrature spectra of the temperature and zonal wind fluctuations at intrinsic frequency ω. This is a direct estimation of total momentum flux that contains both positive and negative momentum flux. The quadrature spectra of temperature and zonal wind perturbations were calculated from the TSEN measurements; a peak of about -3 K m s -1 was found for the 20 d Kelvin wave around the BRO high-density observation period (not shown).
Discussion We have shown the level of consistency of the BRO observations with radiosonde observations and made statistical comparisons with the ERA5 reanalysis and COSMIC-2 RO for the larger-horizontal-scale Kelvin waves, providing confidence in the information extracted from the BRO observations. The RO technology has been deployed on many satellite missions to provide datasets with global coverage, either as a primary payload in dedicated constellations or as a secondary payload in other science missions and recently in commercial CubeSat constellations. The BRO observations extend the general capabilities of GNSS RO to dense consecutive profiles that can be exploited to investigate coherent finer-vertical-scale wave variability, as illustrated above. There is a rich dataset still to be explored in terms of three-dimensional propagation characteristics and directionality by analyzing transects with different orientations from the orthogonal pattern of tangent point profiles.
These capabilities will be further enriched by recovering data from multiple constellations, including Galileo, GLONASS, and BeiDou. A 24 h dataset was retrieved from the Galileo and GLONASS constellations that was sufficient to demonstrate quality comparable to GPS. A 12 h dataset was retrieved from BeiDou. Due to data transmission limitations, however, it was not possible to transmit the additional constellation datasets from the entire flight. The data were sufficient to verify the number of occultations from each constellation. On average, there were approximately two profiles retrieved per hour from the GPS constellation. Including data from all GNSS constellations more than doubles the number of profiles. The multi-constellation recordings that were successfully tested for 1 d during this campaign are planned to be implemented for future Strateole-2 science campaigns, which will have increased communication bandwidth.
We computed the daily regional mean from the much sparser COSMIC-2 RO profiles to compare them to the daily mean BRO profiles and found excellent agreement from balloon flight level down to 8 km altitude, with an rms difference of less than 0. 1 % refractivity. Slightly larger differences at 17-18 km are a possible indication that wave variations with a less than 3 km wavelength are not resolved in the sparser spaceborne RO dataset. The dominant signal in the individual BRO profile transects, made visible by the consecutive sampling in time and space, is a large-scale Kelvin wave with a ∼ 3-6 km vertical wavelength. The same Kelvin wave is also visible in the COSMIC-2 data, when a new approach is used to bin the COSMIC-2 data following the balloon trajectory, essentially mapping the COSMIC-2 observations into the intrinsic reference frame.
However, the BRO observations with slightly higher vertical resolution and denser sampling retrieved as much as 20 % higher amplitude temperature variation associated with the wave. The BRO observations present the advantage that the waves are naturally measured in the intrinsic reference frame. The BRO profiles show an intrinsic period of 20 d for the Kelvin wave as compared to 16 d period for the Kelvin wave in the ground-referenced COSMIC-2 dataset. The difference in wave amplitudes and wave periods determined from the BRO-vs. -COSMIC-2 datasets affects the calculation of momentum flux. After removing the large-scale signal of the Kelvin wave, the BRO observations show wave variations with a 2-3 km vertical wavelength that are interpreted to be westward-propagating inertia-gravity waves with a 3-4 d period.
These results demonstrate the ability to extract fine-vertical-scale wave properties continuously in time and space in poorly sampled regions of the globe. The results are promising and indicate that the data will be useful for distinguishing contributions that waves of different scales make to momentum forces for wave driving of the QBO, which is expected to lead to improved QBO representation in models. We retrieved a small dataset from the Galileo and GLONASS constellations, which have comparable accuracy to GPS, and demonstrated the feasibility to nearly double https://doi. org/10. 5194/acp-22-15379-2022 Atmos. Chem. Phys. , 22, 15379-15402, 2022 the sampling density. Experimental data from the BeiDou constellation were also collected. Five balloons carrying the ROC receiver will be deployed to perform RO observations in a follow-on campaign.
Improved Iridium data rates are expected to be high enough to support the continuous transmission of all constellations throughout the entire flight. Exploiting the full dataset and the information available from the directional sampling of the profiles on the north and south sides of the balloon paths will provide opportunities to capture waves over an even broader range of scales. BRO observations contribute to the larger objectives of the Strateole-2 campaigns that include multiple types of instruments to be flown in the coming years. The high-verticalresolution dataset provides new knowledge on fine-verticalscale waves that are unresolved in global models and provides direct observations of their spatial structure. When combined with other observations, such as the BeCOOL micro-lidar nadir cloud observations, the BRO observations will help determine the relationship of upper-troposphere waves to the presence of cirrus clouds that can be used for improving model representations of these clouds.
For restricted active spaces, the RAS(n,l,m;i,j,k) notation is used,  72  with n the total number of active electrons, l the number of holes allowed in RAS1, and m the number of electrons allowed in RAS3. Labels i, j and k refer to the number of orbitals in RAS1, RAS2 and RAS3, respectively. ] [79]  Pople correction to the MRCISD energy for size-consistency was applied. 80 The CASSCF wave functions were recomputed with the Molpro program package,  [81] [82] [83] [84]  with total energies not substantially differing from those obtained with OpenMolcas (deviations of about 10 - 4  hartree). For the CCSD(T) calculations with Molpro 2021, triplet systems were treated using the spinunrestricted UCCSD(T) formalism on a ROHF (restricted-open-shell) reference wave function, while closed-shell singlet systems were treated with the spin-restricted RCCSD(T) program.
Acknowledgements We wish to thank the  French Research Ministry, Aix-Marseille University  and  CNRS  for financial support and  'Centre de Calcul Intensif d'Aix-Marseille'  for granting us access to its high performance computing resources. This work was also supported by the computing facilities of the  CRCMM, 'Centre Régional de Compétences en Modélisation Moléculaire de Marseille '.
Determination of the Saharan Dust and Sea Salt Deposition Flux For each sample, the elemental deposition fluxes (F i ) for element i were calculated by dividing the mass of measured element i (Q i ), corrected from field blanks (Q 0,i ), by the aperture area of the funnel (S funnel = 0. 0113 m 2 ), and by the exposition duration (T exposure ). Blank data are reported in Tables  S3  and S4  in Supporting Information S1. 𝐹𝐹𝑖𝑖 = 𝑄𝑄𝑖𝑖 -𝑄𝑄0,𝑖𝑖 𝑆𝑆𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 × 𝑇𝑇𝑓𝑓𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑓𝑓𝑒𝑒𝑓𝑓 (1) Because two samples are missing, the mean deposition flux value for a given period was calculated as the average flux measured with the existing samples: = ∑ × (2) The measured fluxes were broken down into three sources: North Africa dust (F dust ), sea salt (F sea salt ), and excess flux (F excess ) probably from biological sourced elements such as living materials debris (insect, plants, etc.
If accumulated, the amount of lost rainfall represents 7% of the total amount of water collected during the whole sampling period and is therefore considered negligible. To identify potential dust sources, the periods for which the deposition flux of aluminum is higher than 2. 9 mg. m -2. day -  are selected because this represents the most loaded top third of the sample set. Over these periods, a backward-trajectory was calculated every hour over a period of 12 days ending 2,000 m above the sampling site using the Hysplit 4 rev. 761 model  (Stein et al. , 2015)  based on the Global Data Assimilation System (GDAS). This backward-trajectory ensemble is plotted using the "openair" R package with the "trajLevel" function  (Carslaw & Ropkins, 2012; R Core Team, 2021)  showing the flyover frequencies (Figure  S4  in Supporting Information S1).
Figure5. Ternary plot, the composition of various marine phytoplankton species are taken from Ho et al. (2003) ; the insect, crustacean, bacteria, fungi, bryophyte, brown algae, and angiosperm compositions are taken from Bowen (1966) , the litter composition is taken from Dessert et al. (2020) , the pollen composition is taken from Basso et al. (2019) , the composition of the different insects' species are taken from Köhler et al. (2019) , the composition of biomass burning smoke is taken from Yamasoe et al. (2000)  and the fresh vegetation composition is taken from Rocha et al. (2018). The Figure is made using R with "ggtern" package (Hamilton & Ferry, 2018; R Core Team, 2021).
Acknowledgments This work would not have been achieved without logistical support from two  INSU-CNRS  observatories run by the IPGP: the  Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG)  and the  Observatoire de l'Eau et de l'Erosion aux Antilles (ObsErA, OZCAR Research Infrastructure). A special thank you to  Vincent Robert  who helped us with the sampling, and to  Jessica Chane-Teng  for her work done at the beginning of this project. The authors would also like to thank  S. Mullin  for proofreading the English content. This work benefit of  EC2CO  ( CNRS-INSU ) support with grant " SAHAFRAN. ".
Data Availability Statement Data used in the creation of the manuscript are available at French Generic Research Data Center at https://doi. org/10. 57745/WEUNPS (Losno et al. , 2022)  and in the Supporting Information (Table  S5  in Supporting Information S1). Figures S4 and S5  in Supporting Information S1 are made with R version 4. 1. 1 (R Core Team, 2021) using different packages. Figure 2, Figures S4 and S5  in Supporting Information S1 are made with "openair" package version 2. 9. 1  (Carslaw & Ropkins, 2012). Figure  4  is made with "ggplot2" package version 3. 3. 5  (Wickham, 2016). Figure  5  is made with "ggtern" package version 3. 3. 5  (Hamilton & Ferry, 2018). Backward trajectories displayed in Figure  S4  in Supporting Information S1 were calculated using the Hysplit 4 rev. 761 model  (Stein et al.
In both cases, the J/ψ decay into dielectrons is simulated using the EvtGen package  [83]  in combination with the PHOTOS model  [84]  in order to provide a proper description of the radiative decay channel. In the simulation, GEANT3  [85]  is used to reproduce the propagation of particles through the ALICE experimental setup, taking into account the response of the detectors. The same reconstruction procedure used for data is then applied to the simulated events in order to evaluate the product of acceptance times efficiency (A × ε), which accounts for: the detector acceptance, the track quality requirements, the electron identification criteria, and the fraction of the signal counted within the invariant mass interval 2. 92 < m e + e -< 3. 16 GeV/c 2. The A × ε retrieved from MC exhibits a smooth variation with the J/ψ p T , ranging from ∼8.
In order to improve the resolution of the secondary decay vertices, it is required that at least one of the two J/ψ candidate decay tracks has a hit in the innermost SPD layer. A tune-on-data procedure  [51]  is applied to the MC sample in order to reproduce the observed single-track impact parameter distributions. This minimises the discrepancy between data and simulation, reducing the systematic uncertainty related to the R(x) determination. The F b (x) PrDF is obtained as the convolution of the R(x) function and a template of the x distribution for the mixture  In this simulation, the p T -distribution of the b-hadrons is obtained from pQCD calculations at fixed order with next-to leading-log re-summation (FONLL)  [92]. The decay description is based on the EvtGen package  [83] , and the relative abundance of b-hadron species as a function of p T is based on the precise measurements reported by the LHCb collaboration in pp collisions  [93] , which are consistent with those measured in p-Pb collisions  [67].
The p T -differential cross sections of inclusive and prompt J/ψ are shown, in comparison with ATLAS measurements  [14]  at high p T and for -2 < y < 1. 5, in the right panel of Fig. 4. The ALICE results, covering the low p T region at midrapidity, are complementary to the measurements from both the LHCb and ATLAS collaborations. The data are reported in comparison with model calculations for prompt J/ψ (Lansberg et al. [99] [100] [101] [102] ) based on the EPPS16  [94]  and the nCTEQ15  [103]  sets of nuclear parton distribution functions (nPDFs). In both cases, the shaded bands represent the envelope of the computations for different assumptions of the values of the pQCD factorisation (µ F ) and renormalisation (µ R ) scales (varied within 0. 5 < µ F /µ R < 2) computed at the 90% confidence level.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences, Austrian Science Fund (FWF) : [M  2467-N36 ] and  Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (Finep) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  and  Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Ministry of Education of China (MOEC)  ,  Ministry of Science & Technology of China (MSTC)  and  National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation , Croatia;  Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear  ( CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic ; The  Danish Council for Independent Research | Natural Sciences , the  VILLUM FONDEN  and  Danish National Research Foundation (DNRF) ,  Denmark ;  Helsinki Institute of Physics (HIP) ,  Finland ;  Commissariat à l'Energie Atomique (CEA)  and  Institut National de Physique Nucléaire et de Physique s NN = 5.
GAMA clusters unmatched in XXL As found in Section 2, 13 GAMA groups are not associated with significant extended X-ray emission based (by this we mean not associated with a C1 or C2 defined source) on the XXL observations. For each of these clusters, the analysis of the X-ray data to determine the luminosity was performed using an aperture photometry method (see Section 3. 1. 2). Here, we discuss each of these clusters in detail, highlighting possible reasons for the lack of X-ray emission. In each case, the cluster is identified by its six-digit GAMA Group ID. While it is difficult to attribute an astrophysical reason as to why these clusters are unmatched to an XXL C1/C2 source due to observational reasons (e. g. six of the GAMA clusters fall at an off-axis position > 10 arcmin from the aimpoint, making detection difficult due to the lower sensitivity), these clusters are discussed further in Section 6.
The scaling of L X and σ v The L XXL X σ v (upper plot) and L GAMA X σ v (lower plot) relations are presented in Fig. 6. In each case, a fit to the data using a power law of the form L X L 0 = E( z) γ Lσ A Lσ σ v σ 0 B Lσ (4) was performed. Unless stated otherwise, we used L 0 = 5 × 10 42 erg s -1 and σ 0 = 400 km s -1. As in Section 5. 1, the model was fit to the data using LIRA. In the case of the GAMA sample, four of the X-ray luminosity measurements are upper limits. LIRA treats these upper limits as /L 0 = E( z) γ Lσ A Lσ ( σ v /σ 0 ) B Lσ and σ v /σ 0 = A σ L ( L/E( z) γ σ L L o ) B σ L , where L 0 = 5 × 10 42 erg s -1 , σ 0 = 400 km s -1 , and γ L σ = 1. The fit highlighted in bold represents our main result obtained via the bias correction method (see Section 5. 3).
( 2011 , hereafter Z11 ) , based on the X-ray selected HIFLUGCS surv e y. We use this study as it is one of the few studies that matches as closely as possible the data analysis used in this work i. e. luminosities obtained from XMM and derived within r 500. We note ho we ver that the Z11 sample contains more massive clusters, with the majority of their cluster having σ 400 km s -1. In order to avoid mis-interpretations of the comparison with Z11 , we make use of the data provided in Z11 (see tables 1 and C. 1 in Z11 ) and refit using the LIRA fitting method as used in Section 5. 2. Ho we ver, only bolometric luminosities are provided in Z11 and we therefore compare to bolometric luminosities derived for our XXL C1 selected sample. The bolometric luminosities are estimated via the same method as described in Section 3.
1, and determined o v er the energy range 0. 01-100 keV. We fit to both our XXL selected sample and the Z11 sample assuming equation (4) (with L 0 = 1 × 10 43 erg s -1 and σ 0 = 500 km s -1 ). The data and relations of the Z11 data and our XXL sample are shown in Fig. 10 (a). The fit to the Z11 data yields a normalization and slope of A L σ , Z 11 = 6. 21 ± 1. 03 and B L σ , Z 11 = 3. 49 ± 0. 30. When comparing to the XXL L bol Xσ v relation where A L σ = 1. 55 ± 0. 28 and B L σ = 2. 41 ± 0. 55, we find some clear differences. While the slope of the Z11 sample is steeper than our XXL sample, the difference is only significant at the ∼1. 7 σ le vel, ho we ver the normalization of the Z11 is significantly higher than the XXL sample. One plausible cause is that this offset is due to the presence of very strong cool-core clusters in the HIFLUCGS sample that is not present in the XXL sample.
GAMA is funded by the  STFC (UK) , the  ARC (Australia) , the  AAO , and the participating institutions. The GAMA website is http://www. gama-survey. org/. PAG BJM, SM, and SP acknowledge support from the  UK Science and Technology Facilities Council  via grants  ST/P000525/1  (PG),  ST/T000473/1  (PG), and  ST/R000700/1  (BJM). 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 '. The Saclay group acknowledges long-term support from the  Centre National d'Etudes Spatiales (CNES). CA was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. Based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA.
Nevertheless, the long coverage of photometric data allows us to gather multiple eclipses of our targets and thus impro v e and compare orbital parameters, as well as to optimize our analysis of CHEOPS observations. The three EBLM binaries, analysed in our CHEOPS programme EBLM J1741 + 31, EBLM J1934 -42, and EBLM J2046 + 06 have shown that M-dwarfs with precisely measured radii and metallicities open up the possibility to disentangle the effect of metallicity from dif ferent ef fects on the radius inflation problem for lo w-mass Mdw arfs  (Sw ayne et al. 2021 , hereafter SW21 ). In this paper, we present the analysis of five EBLM binaries with fully conv ectiv e M-dwarfs companions, observ ed in our CHEOPS programme and compare them to the analysis of TESS observations.
O B S E RVAT I O N S A N D M E T H O D S Primary and secondary eclipses for all our five eclipsing binaries were observed with CHEOPS between 2020 November and 2021 January as part of CHEOPS Guaranteed Time Observation programme (ID-037). We obtained one primary eclipse and, depending on the depth of the secondary eclipse, one to three secondary eclipse observations in order to obtain sufficient signal-to-noise ratio (SNR) to measure both eclipses. Table  1  gives an overview of the CHEOPS observations and data extraction. All data were reduced by the CHEOPS data reduction pipeline v13. 1  (Hoyer et al. 2020 ) , which performs an aperture photometry of the target star, taking contamination in the field as well as instrumental effects like the rotation of the satellite into account. The pipeline offers light curves for different aperture sizes.
For our analysis, we selected the aperture size with minimal median absolute deviation of the point-to-point difference in the light curve. The resulting aperture radii are listed as R ap in Table  1. The observations were interrupted due to the low-Earth-orbit of CHEOPS by Earth occultations, as well as crossings of the South Atlantic Anomaly. We derive the time spent on target as the fraction of valid observations compared to the total observation interval. The TESS surv e y co v ered all of our targets with 2-min cadence data made available by TESS Guest Investigator (GI) programmes. EBLM J0239 -20 (TIC64108432) has been observed in sectors 4 and 31 under GI programmes G011278 and G03216. EBLM J0540 -17 (TIC46627823) has been observed in sectors 6 and 32 under GI programmes G011278, G03216, and G03251.