dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling of background plasma in a WEST discharge The WEST shot #54903 in L-mode of the C4 campaign is selected as a reference case for the numerical modelling work. We focus on the time when the plasma is relatively stable at 𝑡 f lattop = 8 s (time since the start of the pulse). The operational parameters are shown in Table  1. The magnetic configuration and wall geometry are shown in Fig. 1 (a) with the gas puff position set inside the PFR (private flux region), and the pump position set under the baffle. As this experimental shot case is performed with low heating power and inside a W-covered chamber, the influence of impurities can be very small. The simulation work in this paper was done through SOLEDGE-EIRENE transport code based on merging the transport code SOLEDGE2D  [22]  and turbulence code TOKAM3X  [23].
Comparing with experiment Results from the SOLEDGE-EIRENE simulation are confronted with four WEST discharges, as shown in Table  3. In WEST experimental campaign C5, the space between the divertor outer baffle and the vacuum vessel was sealed. The experimental data from the LSN shot case in C5 can be considered as baffle closed case when compared with the simulation, the one in C4 can be considered as the case with small leakage. For the case #56923 in C5, we can observe about 50% higher neutral pressure under baffle 𝑃 neu,baf f le , 17% higher upstream 𝑛 e,sep and 13% higher central line integrated density comparing with the case #55077 in C4. Both cases have very close operational parameters. In the prediction of simulation for the neutral pressure under the baffle as shown in Fig. 16 , the LSN baffle closed case indeed has higher.
2 eV, the average electron temperature inside the radiation front around 4. 2 eV, the vertical distance between the position of radiator and target about 1. 4 cm, on which the baffle closure has no obvious influence. The sensitivity of the detachment process to multiple parameters (e. g. upstream density, target plasma temperature) shows a different level of impact from leakage, the impact can be neglected when it is as a function of the target temperature. Controlling a stable detachment through analysing the diagnostic signal of target parameters can be a preferred choice when operating future devices. Finally, simulation results are confronted with experimental data from the configurations USN and LSN. The experimental results show good consistency with the prediction of SOLEDGE-EIRENE code in the aspect of 𝑃 neu,omp change ratio.
Acknowledgements This work has been carried out within the framework of the  EU-ROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the  European Union  nor the  European Commission  can be held responsible for them.
Previously, this was studied using isolated single hadrons from inclusive   collisions  [5, 6]. In this paper, the large dataset accumulated in Run 2 is exploited to select events with isolated charged pions from -lepton decays so that a much higher energy regime can be probed. The calorimeter response to electromagnetic particles such as electrons and photons is very well known due to their showers being easier to simulate accurately and precise measurements in situ using  →  events. The energy scale of electrons is therefore known with an uncertainty of less than 0. 1% for transverse momenta 25 <  T < 70 GeV in the central region  [7]. The large variety of physics processes in hadronic interactions and the non-compensating nature of the ATLAS calorimeters make the response to hadrons harder to model in simulation, motivating in situ measurements using highly energetic single pions from -lepton decays.
In this paper the calorimeter response across a wide range of transverse momenta is investigated. -bosons are produced copiously at the LHC and can decay via -leptons to single charged pions, providing a source of isolated pions:  ± →  ± (→  ± ). Using the Run 2 dataset recorded in 2015-2018, large samples of pions are selected in order to accurately measure the calorimeter response using the ratio of calorimeter energy to track momentum. The measured response is corrected for the contributions from the multiple   interactions per bunch crossing and the residual signals remaining in the calorimeter from adjacent bunch crossings, contributions collectively referred to as pile-up. The charged-pion response is measured as a function of track pseudorapidity,  trk , across the tracker volume and as a function of track transverse momentum,  trk T , in the barrel and endcaps.
Likewise, when tagging hadronically decaying heavy particles  [21]  the jet substructure variables that are used cannot be corrected easily for calorimeter scale discrepancies, so they depend on good modelling of the underlying calorimeter energy scale. The paper is organised as follows. Section 2 introduces the ATLAS detector, and Section 3 describes the dataset and samples of simulated events. The selection of events for the measurement and the variable of interest are defined in Section 4. The energy response is evaluated in Section 5 and uncertainties in the measurements are evaluated in Section 6. The width of the distribution of the variable of interest is measured in Section 7 and the longitudinal segmentation of the calorimeters is exploited in Section 8 to investigate discrepancies between simulation and data.
This is tested by taking half the difference between the responses for two large single-particle samples with different pile-up,  ∼ 20 and  ∼ 50, after the corrections. Additionally, the residual correction is determined from simulation where pile-up might not be well modelled. Studies of the  dependence of the median  T density of the event, , the number of clusters, and the cell energy and noise find that the modelling of the energy flow in the simulation is within 10%-20% of the data across the detector. Therefore, 25% of the residual correction is taken as an uncertainty in the modelling of pile-up, which forms a minor uncertainty in the analysis. In data it is checked that the fitted response is stable with respect to pile-up by splitting the dataset into two independent datasets via high pile-up and low pile-up selections based on , and also into three independent datasets, and no significant differences given the statistical uncertainties are seen in the measured response.
For this quantity the simulation is found to agree with the data to within 15%; however, the data are generally observed to have a wider distribution. Finally, the average energy in the layers of the hadronic calorimeter was probed as a method of investigating the cause of the discrepancies between the simulation and the data. These measurements will be used in the tuning of the ATLAS simulation to further improve the hadron shower modelling and detector geometry. This powerful new method of measuring the hadronic energy response of the ATLAS calorimeter can achieve a precision of 1% for the response of the calorimeter to charged pions. This detailed understanding of the response of the calorimeter can be used in gaining better understanding of the jet energy scale, its uncertainty for the highest- T jets, the energy scale of hadronically decaying -leptons, and in measuring the properties of jets.
Figure10: The average energy within each hadronic layer normalised to the track momentum as a function of track  T in the barrel (left) and endcaps (right). Also shown are the ratios of the sum of energies in all layers of the hadronic calorimeter to the track momentum. Data are required to have 0. 2 <  EM T / trk T < 1. 1 to primarily select single-pion events. Shown in the lower panel is the ratio of data to simulation. The error bars and uncertainty band show the uncertainties from the limited number of events in the dataset and simulated samples.
They obtained approximate values for the coefficients at 1. 5 MHz using data from the Cassini spacecraft that showed the flux from the Galactic poles to be stronger than that from the plane. We used measurements from Parker Solar Probe (PSP) to determine the l = 2 coefficients between 0. 5 and 7 MHz, which depict the evolution of the brightness distribution to this state of relatively diminished plane flux. To assess the compatibility of the l = 0 and l = 2 coefficients with free-free absorption effects, we employed a model of the distribution of free electrons in the Galaxy that was principally derived from observations of pulsar signal dispersion and broadening (e. g. ,  Manchester & Taylor 1981; Taylor & Cordes 1993; Cordes & Lazio 2002; Yao et al. 2017 ). An early set of pulsar data conformed to a distribution consisting of two axially symmetric components, one with a scale height of 70 pc and scale radius 10 kpc and the other extending to all heights with the same radial profile  (Manchester & Taylor 1981).
Conclusive interpretation of the Re(b 2,1 ) measurement will require inclusion of such features in the emission model, which could reduce the offset while preserving the correlation and thereby provide evidence for the local components of the YMW16 density model. The Im(b 2,1 ) and Im(b 2,2 ) measurements cannot be similarly decomposed into possible imprints of asymmetric absorption and contributions from asymmetric emission. Increasing prominence of local emission irregularities with decreasing frequency, dependence of the synthetic absorption signatures on a flawed emission model, and errors in the adopted free electron A127, page 10 of 11 B. Page et al. : Evolution of the skybrightness distribution between 0. 5 and 7 MHz distribution may all play a role in the lack of correlation between the synthetic results and the data here.
Acknowledgements. The FIELDS instrument and  Parker Solar Probe  are funded under task order  NNN10AA08T  of  NASA  contract  NNN06AA01C. Development of the spacecraft was led by the  Johns Hopkins Applied Physics Laboratory. Institutions that contributed to the design and assembly of the FIELDS instrument are noted on the "Team" page of https://fields. ssl. berkeley. edu. The data used in this study can be accessed from the "Data" page of the same web site. The work of NB was supported by the  NASA Solar System Exploration Research Virtual Institute  cooperative agreement number  80ARC017M0006. This study was also partially supported by the  Universities Space Research Association  via DR using internal funds for research development. DR's work was also in part funded by  NASA  grant  80NSSC23K0013.
Acknowledgements We thank  Karim Benakli  and  Dominik Stöckinger  for useful discussions. The work of W. K. is supported by the  Contrat Doctoral Spécifique Normalien (CDSN) of Ecole Normale Supérieure -PSL. The work of P. S. is supported in part by  French  state funds managed by the  Agence Nationale de la Recherche (ANR) , in the context of the Grant " HiggsAutomator " ( ANR-15-CE31-0002 ).
34 Conceptually, the 1D spectrum of each component can be found by simply drawing a line at the corresponding diffusion coefficient in the 2D DOSY display. PFG NMR and DOSY have found numerous applications to the analysis of mixtures. 35 Note that while the term DOSY originally refers to a specific data processing and display method, it can refer nowadays also to the experiment itself. One class of applications consists of studying interactions in solution, for aggregates and oligomers formed by, e. g. , organometallic species. 36 DOSY is also largely used in supramolecular chemistry, to characterise the size and shape of supramolecular objects. 37 Virtual separation by DOSY NMR has been assessed for different classes of samples, such as dietary supplements and pharmaceuticals, as a tool to detect counterfeit products.
38 he virtual separation of NMR spectra with standard DOSY methods, however, is limited by the difficulty to resolve overlapping signals. The most common method for data analysis consists of a non-linear least square fit of the data with one or several exponential functions. When mono-exponential fitting is used, the resulting peak in the DOSY display appears at an intermediate value, that cannot be immediately assigned to one component. Multi-exponential fitting can resolve overlapping peaks,  39  but only with excellent SNR and a sufficient difference in diffusion coefficients between the two compounds. As a result, standard DOSY methods are useful for assignment and identification, but they do not increase signal dispersion. A possible approach to improve the separation of overlapping spectra is to use multivariate processing methods.
46, 47 ] [50]  Since signal dispersion is improved in 2D spectra, 3D DOSY can help to address the overlap issues of 2D DOSY. In addition, with 3D DOSY structural information is obtained for all the separated components. This is illustrated in Fig. 3 , with the separation of HSQC spectra of five B vitamins. Importantly, in this case the 1 H spectra could not be separated by regular 2D DOSY because of peak overlap. 51 Conveniently, a large variety of methods to analyse PFG NMR data have been implemented in the open-source GNAT package. 52 he separation power of diffusion NMR can be increased by adding macromolecules that have different exchange or binding properties with the different analytes. Using such ''matrix-assisted DOSY'' approach, spectra that could not be separated without the matrix can become well resolved in the diffusion dimension.
Polarisation transfer experiments Polarisation transfer experiment make it possible to simultaneously address the overlap and assignment issues of mixture analyses by NMR. The most popular polarisation transfer scheme for such applications is the TOCSY (total correlation spectroscopy) block, which drives polarisation transfer among all the spins that belong to the same spin system. In 2D TOCSY spectra, the spectrum of molecular fragment can be extracted from the data, provided that a peak is well resolved for at least one chemical site in the corresponding spin system. This strategy was used by Bru ¨schweiler and co-workers, together with a database of fragment spectra, to help identify compounds in complex metabolic mixtures. 64, 65 ince the TOCSY block only correlates nuclear spins within the same spin systems, it cannot in general reveal the entire 1D spectrum of a mixture component.
The sample thus has to be moved between a polarisation region and the NMR spectrometer. The most used methods consist of dropping the sample in the magnet after shaking, or using a flow system to shuttle the sample between a polarisation region (where parahydrogen gas is bubbled) and the detection region. 112 he potential of SABRE for mixture analysis has been explored by Tessari and co-workers, who showed that the method can be applied to complex mixtures of molecules with concentrations in the micromolar range, provided that an appropriate co-substrate is also added to the mixture. 113, 114 They also showed that the signal intensity changes linearly with concentration in that regime. With a standard-addition method, they were able to quantify several analytes in an artificial mixture. 115 Also working with a co-substrate, they recorded 2D DOSY data with a flow shuttle.
159 The method was further extended by Concilio et al. , who demonstrated the spatial encoding of multiple-quantum coherences of order up to 5, and used the resulting approach for a maximumquantum analysis of a mixture of aromatic compounds. 158 iffusion NMR experiments can also be accelerated by spatial parallelisation. In this case, different slices in the sample undergo magnetic-field gradient pulses of different effective duration. 136, 160, 161  Thrippleton et al. ,  136  and later revisited by Shrot et al. ,  160  who used a more general detection scheme. More recently, Guduff et al. showed that UF (also called spatially encoded or SPEN) DOSY NMR can be extended to higher dimensional experiments, in order to acquire 3D DOSY data set (as described in Section 2. 2) in a few minutes instead of a few hours.
While each spectrum is effectively acquired over a duration of N w Â T R , and represents an average sample composition over that duration, the time resolution is improved compared to the processing of nonoverlapping data sets. TR-NUS was used by Dass et al. to monitor the fermentation of wheat flour. 180 They collected a series of points of 2D HSQC experiments, randomly sampled from a full grid of 200 points. From this data, a times series of 2D HSQC spectra was obtained, and analysed by integration of selected peaks to track the concentration of ethanol and glycerol during fermentation, as illustrated in Fig. 9. The use of TR-NUS was recently made easier by the release of a software package for data processing and analysis. 181 5] [186]  In this case a randomised list of gradient amplitudes is generated, and used to collect a time series of diffusion-attenuated spectra.
Acknowledgements I am very grateful to the many colleagues with whom I have add the chance to work on these topics. I would particularly like to thank  Patrick Giraudeau  for useful critical feedback on the manuscript. This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreements no  801774/DINAMIX ) the  Region Pays de la Loire (Connect Talent HPNMR) , the  Agence Nationale de la Recherche  ( ANR-21-CE07-0056-02 ), and the  I-SITE NExT  ( iChem4. 0 ). The NExT initiative is supported by  Nantes Universite , the  Region des Pays de la Loire and Nantes Metropole in the frame of the Programme d'Investissement d'Avenir (PIA). The author also acknowledges the  French National Infrastructure for Metabolomics and Fluxomics MetaboHUB - ANR-11-INBS-0010  (www.
Data acquisition Defects were spectroscopically studied using a home-built confocal microscope with 410 nm linear polarized laser excitation schematically displayed in Supplementary Fig. 1. All experiments were performed at room temperature. The sample was scanned through the focal point by a 3D nano-positioner. The emitted fluorescence was split into two paths and detected by two single photon detectors for integral fluorescence and fluorescence auto-correlation measurements. Alternatively, the fluorescence could be deflected into a spectrometer for identification and spectral characterization of the emitters. Microwave radiation was supplied to the sample by a golden microwave waveguide lithographically defined Fig. 4  ODMR contrast of a single TR12 center. Simulated optically detected magnetic resonance contrast as a function of the magnetic field orientation for TR12 (multicolored) and nitrogen-vacancy (red) for 30 mT.
ACKNOWLEDGEMENTS We thank  Adam Gali ,  Kangwei Xia  and  Denis Dertli  for fruitful discussions and help with the preparation of the manuscript. This work was supported by  Bundesministerium für Bildung und Forschung  (project  UNIQ ),  Deutsche Forschungsgemeinschaft  (grant  KO4999/3-1 ),  FET-Flagship  Project  SQUARE ,  ERC  grant  SMeL ,  EU  project  ASTERIQS ,  DFG  research group FOR 2724 and QTBW.
As explained by  Blomme et al. (2022) , the Gaia spectra of hot stars are dominated by strong Ca and H Paschen lines, which are not optimal for RV determinations. This results in larger RV uncertainties and introduces magnitude-dependent systematic effects. We corrected for the systematics using the formula in  Blomme et al. (2022) , but the large dispersion in RVs for these hotter stars is still very obvious in Figure  2. 5  For the mentioned reasons we do not consider these RV to settle the membership of the spectroscopic targets. In addition, as pointed out by Alonso-Santiago et al. (2021), Stock 2 is located in a region affected by differential reddening. To produce a dereddened CMD of the cluster, we used the 3D extinction map of  Lallement et al. (2022). These authors combined EDR3 with 2MASS data  (Skrutskie et al.
ESPaDOnS During the 2020B semester, we observed 64 hot Stock 2 MS stars with the ESPaDOnS spectrograph mounted on the 3. 6 m Canada-France-Hawaii telescope located at the Maunakea Observatory, USA (PI: Casamiquela, ID: 20BF003). We used the Star+Sky mode, which gives a full optical spectrum (3700-10,500 Å) with a mean resolution R = 68,000. The data were reduced using the instrument pipeline (Upena version 1. 1).
Atmospheric Parameters We derived effective temperature (T eff ), surface gravity  and v  i sin e using a combination of  principal component analysis (PCA;  Gebran et al. 2016 ) and sliced inverse regression (SIR;  Kassounian et al. 2019)  techniques. In brief, the method is based on a dimensionality reduction tool that makes the comparison of the observed spectrum with a trained synthetic database of spectra. Details about our method are provided in Sections 4. 1 and 4. 2, and our results are described in Section 4. 3. ( g log ), metallicity ([M/H]),.
Training Database We used three databases, with different resolving power, in this work. Each database consists of a set of ∼80,000 synthetic spectra with stellar parameters randomly selected in the ranges given in Table  1. The wavelength range, between 4450 and 5400 Å, was selected because it is very sensitive to all the stellar parameters, especially for A, F, and G stars  (Paletou et al. 2015a (Paletou et al. , 2015b;; Gebran et al. 2016; Kassounian et al. 2019). This is mainly due to the presence of the Balmer Hβ line and of moderately strong and weak metallic lines in this window, and is helped by the existence of high precision atomic data for the available transitions. 7 The microturbulent velocity parameter ξ t is selected between 2 and 4 km s -1 , which are typical values for A and Am stars  (Gebran et al.
2014). The studied region is only moderately affected by variations in this parameter. We calculated atmospheres using the 1D plane-parallel ATLAS9 models  (Kurucz 1992). These models account for local thermodynamic equilibrium (LTE) layers in hydrostatic and radiative equilibrium. We implemented the new opacity distribution function of  Castelli & Kurucz (2003) , and the mixing length parameter was used as prescribed by  Smalley (2004). Synthetic spectra were calculated using the SYNSPEC48 code (Hubeny & Lanz 1992). We used the line list compiled in  Gebran et al. (2022)  based on the three databases gfhyperall, 8  VALD, 9  and NIST. 10.
Sliced Inverse Regression SIR requires the preprocessing of the training database using PCA. A detailed description of the PCA technique and its calculation steps can be found in  Gebran et al. (2016). In summary, the training database was gathered into a matrix of dimension N Spectra × N λ , where N Spectra was the number of spectra in each database (∼80,000) and N λ was the number of wavelength points per synthetic spectrum. The covariance matrix C was then calculated as follows: C N x x x x 1. 1 i N i i T Spectra 1 Spectra å = - - = ( ¯) • ( ¯) () The superscript T stands for the transpose operator, and x ¯is the average spectrum calculated using the classical function x N x 1. 2 i N i Spectra 1 Spectra å = = ¯( ) The covariance matrix C has a dimension of N λ × N λ. The N λ eigenvectors of C, e k , were then derived and only the first 12 vectors were considered in our study.
The choice of 12 eigenvectors was based on the analysis of the reconstructed error: choosing k = 12 leads to a reconstructed error of less than 1%  (Gebran et al. 2016; Kassounian et al. 2019). The synthetic spectra of the database were then projected on these 12 eigenvectors and 12 coefficients (p k ) were derived for each spectrum. In the same way, observations were projected onto e k and the 12 coefficients (ρ k ) qwre derived for each observation. Finally, the values of the stellar parameters of the observation were the ones of the nearest neighbor, found by minimizing the difference: d p , 3 j k k j k 1 12 2 å r = - = ( ) ( ) where j covers the number of synthetic spectra. During the minimization procedure, the continuum of the observed spectrum was corrected iteratively by applying the  Gazzano et al.