dataesr/scientific-paragraphs-categorization huggingface dataset:
2014 Bohlin et al. , 2020) )  has met this need, providing a practical intermediary between the fundamental WDs and the stars used to flux calibrate essentially all astronomical surveys  (Bohlin et al. 2011; Betoule et al. 2013; Rubin et al. 2015; Scolnic et al. 2015; Bohlin 2016; Currie et al. 2020; Brout et al. 2022 ). Despite its success, CALSPEC is observationally expensive to expand, with the highest-quality optical observations coming only from the Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS). CALSPEC also does not include many of the standards in common use (e. g. ,  Oke 1990; Hamuy et al. 1992 Hamuy et al. , 1994)). In this work, we present an extended optical spectrophotometric standard-star network, which we are able to tightly tie to CALSPEC. The spectrophotometric data that will be discussed here were taken with the SuperNova Integral-Field Spectrograph (SNIFS;  Aldering et al.
Our Dataset Our data set of spectrophotometric standard stars has been obtained by the SNfactory using the SNIFS integral-field spectrograph in the course of obtaining the spectrophotometric time series of nearby SNe Ia in order to improve constraints on the dark energy equation of state. SNfactory typically observed stars during evening and morning twilight, at midnight, and 2-3 times in between. During bright twilight, the bright standard stars are chosen, and at midnight a CALSPEC star was given highest priority in the selection. Priority is first given to a star at low airmass, then a star at high airmass. Thereafter priority is given according to which star can best improve the calibration solution. In this calculation, the bright stars (requiring ∼1 s exposures) are given lower weight since their PSFs can exhibit more structure because few atmospheric turbulence phase distortion cells pass over the telescope for short exposures.
S. Department of Energy  under Contract No. DE-AC02-05CH11231. Support in France was provided by  CNRS/IN2P3 ,  CNRS/INSU , and  Département Physique Nucléaire et Corpusculaire and French  state funds managed by the  National Research Agency  within the  Investissements d'Avenir program  under grant reference numbers  ANR-10-LABX-0066 ,  ANR-11-IDEX-0004-02 , and  ANR-11-IDEX-0007. Additional support comes from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 759194-USNAC ). Support in Germany was provided by the  German Research Foundation  through  TRR33  " The Dark Universe " and by the  German Aerospace Center  through grants  FKZ 50OR1503  and  FKZ 50OR1602. In China the support was provided from  Tsinghua University   985 grant  and  NSFC  grant No.
Experimental Section Statistical Analysis: All the experiments were performed in triplicates and repeated at least three times (n = 3), unless otherwise stated. Statistical analyses were performed using GraphPad Prism software v. 5. 01 (GraphPad Prism software Inc. , La Jolla, CA, USA) using unpaired Student's t-test for comparison of two groups. Data if necessary is presented as mean ± SD (standard deviation) (see Table  S7 , Supporting Information). The differences were considered to be statistically significant if p < 0. 05 for either test used. Each Raman spectrum was acquired in 80 s or 200 s, i. e. , a CCD integration time of 2 s was used in both measurements and several spectra (n = 40 with RamanRxn1 and n = 100 with Apha 300S) were averaged in order to improve the signal-to-noise ratio.
Acknowledgements The authors are grateful for inspiring scientific discussions shared with  Prof. Daniel Morse ,  Dr. Elizabeth Bailey  ( UCA, USA ),  Prof. Gert Wörheide  ( LMU Munich, Germany ), and  Prof. Horst Hinssen  ( Universität Bielefeld, Germany ). Authors gratefully thank  Alexandre Altié  of  Plateforme C2VN de Microscopie Électronique TIMONE, Aix-Marseille Université, France ;  Daria Tokina ,  Sonia Żółtowska , and  Sabine Kaiser  for technical support; and  Dr. Paco Cardenas  ( Uppsala University, Sweden ) and  Dr. Heike Meissner  ( TU Dresden, Germany ) for the SEM photos of spicules. This research was partially supported by  DFG  Project  HE 394/3 , by a  Polish Honourable Alexander von Humboldt Fellowship  ( FNP, Poland ), by  OPUS  and  MAE-STRO  Projects  2020  ( NCN, Poland ).
This chapter offers a general overview of the available tools and strategies for validating Land Use Cover (LUC) dataspecifically LUC maps-and Land Use Cover Change Modelling (LUCCM) exercises. We give readers some guidelines according to the type of maps they want to validate: single LUC maps (Sect. 3), time series of LUC maps (Sect. 4) or the results of LUCCM exercises (Sect. 5). Despite the fact that some of the available methods are applicable to all these maps, each type of validation exercise has its own particularities which must be taken into account. Each section of this chapter starts with a brief introduction about the specific type of maps (single, time series or modelling exercises) and the reference data needed to validate them. We also present the validation methods/functions and the corresponding exercises developed in Part III of this book.
Validation Methods/Functions and Exercises Presented in Part III of This Book This chapter is intended as a presentation of Part III of this book. Figure  1   The exercises presented in Part III have been applied using the Quantum GIS (QGIS) software and R scripts. To homogenize the exercises across the different chapters, they have the same standard objectives: to validate a map (t 1 ) against reference data/map (t 1 ) (single LUC map); to validate a series of maps with two or more time points (t 0 , t 1, t 2 …) (LUC maps series/ LUC changes); and, for results from LUCCM exercise, to validate soft maps produced by the model against a reference map of changes (t 0t 1 ) (soft LUC maps), to validate a simulation (T 1 ) against a reference map (t 1 ) (single LUC map -hard LUC maps) and to validate simulated changes (t 0 -T 1 ) against a reference map of changes (t 0t 1 ) (LUC maps series / LUC changeshard LUC maps).
The application of Overall, user's and producer's accuracy metrics through GWR (see Sect. 1 in chapter "Geographically Weighted Methods to Validate Land Use Cover Maps") can help the user to assess the suitability of the LUC data and to observe local variations in accuracy and error on the map  (Comber 2013). In some cases, local assessments may be necessary because they can uncover possible clusters of errors in the LUC data. By adapting logistic Geographically Weighted Regression (GWR)  (Brunsdon et al. 1996) , the spatial variations in Boolean LUC (classified data) and fuzzy LUC (reference data) can be modelled, providing maps that show the distribution of the overall, user's and producer's accuracy metrics.
Validation of Land Use Cover Maps Series/Land Use Cover Changes There is no common practice or set of methods for validating or evaluating the uncertainty of a LUC map series with two or more time points (t 0 , t 1, t 2 …). Most of the exercises for the validation of LUC data only refer to single LUC maps, without focusing specifically on the LUC change studied through a series of LUC maps. One of the facets that users most demand from LUC data is the ability to study and display LUC changes over time. We therefore need methods and tools to assess the uncertainty of the changes that are measured from LUC maps. It is worth noting that the individual accuracy of two LUC maps involved in a post-classification comparison offers few clues as to the accuracy of change, because the relation between the errors in the two maps is unknown.
do that explore in deep the sociological and ethnological dimensions of Malaysian food habits. To do it MFB collects face to face data on food-related practices, but also social norms, attitudes, cultural representations, and routines as well as their supposed socio-cultural and demographic determinants. The focus includes the repartition of food practices between home and away from home, the sources of food, the socialization of the meals and food intakes, the food temporality, the perception of risks in food, etc. A series of indexes assign individuals within the phenomena of transition (demographic, nutrition, food and protein) and of "compacted modernization" experienced by certain some Asian countries  (Poulain et al. , 2014 , 2015 , 2020 , Fournier et al. 2016). The theoretical objective of the project consists of studying the social, ethnic and cultural diversification of food patterns and its changes in the context of modernization.
It studies the evolution of food consumption both at home and outside the home and identifies the consequences in terms of market factors and public health. It is based on a national representative sample (n = 2000 for MFB1 and 1600 for MFB2), of Malaysians from 18 years old to the very elderly according to the National Census. The data collection is conducted on 'normal' periods of the year (out of the religious or national feasts). The questionnaire has 6 main parts: socio demographics and ethnicity indicators, food norms, food intake in the last 24 hours (recall), cooking practices, social representations of food, health, and risk issues. The questionnaire comprises 60 items and more than 1200 variables. The method of sampling is the quota methods based on four quotas: age, ethnicity, level of urbanisation and region of residence.
To reconstruct the interviewee's 24-hour food intake, the style of data collection will be pitched in the middle ground between a qualitative and a quantitative approach. The questions are intended to be a sort of guide to help the interviewee to remember the food intake composition and structure, the time of consumption, the terms/conditions? of the acquisition and the socio-technical contexts of consumption, including the ethnicity of the persons with whom the food intake was shared. The form and sequence of questions have been developed to reflect the Malaysian context, including the high frequency of consuming food away from home, or as takeaway food, and the variety in places of consumption. The database includes two MBF waves of data collection  (2012-13, N= 2000 and 2018-2019, N= 1600).
-Snacks and all food intakes can be analyzed in terms of social norms using the following dimension. The comparison between the meal standards and the practices implemented reveals a process of individualization. Lunches, for example, are practices with in particular individual portions, while lunch social norms are based on shared dishes. Practices change faster than the norms. However, meals are very largely socialized. The localization of food at home and out of the home is done with 2 complementary methods the 24 hours recall and a reconstruction of the week before. The eating out can be studied by individual population and by meals (all meals together). These data can be crossed with different descriptive variables. The MFB database is made available in an "open science" philosophy. Let us me remember the spirit of Open Science.
It is a movement which consists in opening the research process to all types of actors concerned by the topic (scientific partners, economical actors, public institutions, citizens, NGOs. ). It uses the opportunity represented by the "digital mutation" to make research data easily accessible and reusable (CNRS, 2019). In our case, the MFB1 report has already been freely accessible since 2015 and the MFB2 will be available shortly. The provision of the database itself is a new step. Sharing research results and data, not only between scientists, but also with all stakeholders in society, is a way of disseminating, a way of developing partnership relations with all the different actors concerned by the subject of the research. But it is also a way of advancing knowledge, a way to co-construct the knowledge.
Optical properties of TPE-MRh The absorption and emission properties of TPE-MRh were determined in chloroform, toluene, and tetrahydrofuran (THF) solutions. TPE-MRh displays three absorption peaks at 303-307, 370-374, and 489-504 nm depending on the solvent (Figure  1A  and Figures  S6-S8 ), among them the middle peak is the most intense. For each solvent, different concentrations in the range of 10 -6 -10 -5 M were analyzed, the absorption rises until it exceeds the unitary value for peaks at all wavelengths. Notably, in the range of concentrations of 10 -6 -10 -3 M the emission intensity remains very low for all solvents (Figures S12-S14), although not completely suppressed. The extinction molar coefficients (ɛ, Table  1 ), extrapolated from the linear fitting of the absorption data (Figures  S9-S11 ), are maximum in THF (about 3.
A C K N O W L E D G M E N T S This work was supported by the  MIUR-PRIN 20179BJNA2. This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (grant agreement no. 648558   STRIGES CoG ). Support from grant no. H2020-MSCA-ITN-2017  training network " COSINE-COmputational Spectroscopy In Natural sciences and Engineering " is also acknowledged. Q. W. acknowledges the financial support from the  China Scholarship Council  (grant no. 202006890004 ). data that support the findings of this study are available from the corresponding author upon reasonable request.
We analyse four XMM-Newton observations obtained between January 2021 and January 2022 at key phases of the episode of enhanced disc activity. Archival XMM-Newton, Chandra, MAXI, and RXTE-ASM data are also used to study the long-term correlation between optical and X-ray emission. Results. Optical spectroscopy unveils a clear increase in the radial extent of the emission regions during the episode of enhanced disc activity, whilst no increase in the V-band flux is recorded. Our Doppler maps do not reveal any stable feature in the disc resulting from the putative action of the companion on the outer parts of the Be disc. Whilst the hard X-ray emission is found to display the usual level and type of variability, no specific increase in the hard emission is observed in relation to the enhanced disc activity.
The detector was an Andor Newton CCD with 2048 × 512 pixels of 13. 5 µm squared. Typical integration times of individual exposures ranged between 10 s and 2 min depending on the atmospheric conditions. Between 75 and 100 exposures were taken per night and were combined to provide a nightly mean spectrum. The mean S/N of these nightly means, evaluated in the continuum between 6736 and 6745 Å, was 1200. The data reduction was performed using version 17FEBpl 1. 2 of the MIDAS software developed at the European Southern Observatory. These data were complemented by dedicated amateur spectra taken by co-author Joan Guarro Fló either from Piera or remotely from Santa Maria de Montmagastrell (Tàrrega) in Catalonia. The equipment used on both sites was a 40. 6 cm Schmidt-Cassegrain with a focal reducer to f/6.
5 along with a self-made echelle spectrograph. The spectrograph provided a resolving power of 8500 over the range from 3750 to 9500 Å with an ASI2600MM CCD camera. The data were reduced using the ISIS-V6. 1. 1 software. Additional archival amateur spectra of γ Cas taken during the visibility gap of the star with TIGRE or obtained contemporaneously with older X-ray observations were extracted from the Be Star Spectra (BeSS) database 2   (Neiner et al. 2011). We used the telluric tool within IRAF along with the atlas of telluric lines of  Hinkle et al. (2000)  to remove the telluric absorptions in the spectral regions around the He I λ 5876 and Hα lines. To achieve as homogeneous a normalisation as possible, the spectra were continuum normalised using the MIDAS software adopting best-fit spline functions adjusted to the same set of carefully chosen continuum windows for all spectra.
2001). The data were processed with the Science Analysis System (SAS) software version 18. 0. 0 and using the current calibration files available in January 2021. The epatplot SAS command was used to check for the presence of pileup in the MOS1 data. No obvious indication of severe pileup was found. Yet, the MOS1 count rate during the observation significantly exceeded the pileup limit for small window mode. As a result, the MOS1 spectra were extracted over an annulus of inner and outer radii of 10 and 40 arcsec. In this way, the core of the point spread function where pileup should be more severe was excluded from the spectral extraction. Whilst our first and third observations (revolutions 3859 and 3960) were free of any significant soft proton background flares, the second and fourth observations (revolutions 3881 and 4049) were strongly impacted by such events.
We thus filtered our data against high background (i. e. discarding time intervals with count rates exceeding 0. 25 ct s -1 and 0. 5 ct s -1 at energies above 10 keV for the EPIC-MOS and EPIC-pn cameras, respectively) and kept only the clean part of these exposures. We further retrieved all archival (2004-2014) XMM-Newton observations of γ Cas (Lopes de  Oliveira et al. 2010; Smith et al. 2012; Smith 2019 ) and reprocessed them with SAS version 18. 0. 0 in the same way as our new data. In addition, Chandra High-Energy Transmission Grating (HETG) data  (Smith et al. 2004)  were extracted from the Chandra Transmission Grating Data Archive and Catalog (TGCat;  Huenemoerder et al. 2011). The properties of the X-ray observations considered in this paper are summarised in Table  1. To investigate the long-term variability of the X-ray flux and its correlation with optical photometry, we also considered data collected with the Monitor of All-sky X-ray Image (MAXI) installed since August 2009 on the Japanese Experiment Module Exposed Facility of the International Space Station (ISS;  Matsuoka et al.
X-ray data The new X-ray observations obtained during the 2020-2021 high-emission event sample an emission state of the γ Cas Be disc that was never analysed before with Chandra or XMM-Newton data. Indeed, optical spectroscopy of γ Cas at the epochs of previous X-ray observations, extracted from the BeSS database, shows that the Hα line was much stronger in 2020-2021 than for any of these previous observations (Fig. 9 ). In this section we analyse the new X-ray data along with the previous Chandra and XMM-Newton data. Spectral analysis of the data was performed under xspec version 12. 9. 0i with solar abundances set to the values of  Asplund et al. (2009).
Broadband spectra Whilst the properties of the cooler plasma components in the Xray spectrum of γ Cas are best determined by the lines in the RGS spectrum (see the previous subsection), the properties of the hot plasma component are best derived from the continuum and from the Fe XXV and Fe XXVI lines around 6. 7-6. 97 keV. As a next step, we thus attempted to fit the EPIC spectra (at energies above 0. 4 keV) accounting for the information obtained from the RGS data. The same fitting procedure was applied also to the HETG data to establish the values of the fluxes for the Chandra observation 11. For this purpose, we used a model given by phabs(ISM)* [phabs 1 *(bvapec(3T)+bvapec(kT hot )) + phabs 2 *(bvapec (kT hot )+gauss)]. This model is directly inspired by the results of Lopes de Oliveira et al.
Similar plots are provided for the other datasets in Figs. B. 1-B. 7. We caution that these model parameters should not be overinterpreted as our overall description of the emission of γ Cas is certainly too simplistic compared to reality. These parameters also depend on the ingredients of the plasma models. Indeed, we tested the models of  Smith et al. (2012) , which used mekal  (Kaastra & Mewe 2000)  plasma models with solar abundances from  Anders & Grevesse (1989)  on the data from the 2010 campaign. Whilst these models provide a good description of the data, with parameters in good agreement with those of  Smith et al. (2012) , they require a plasma component in the 3. 4-4. 3 keV 11 Because uncertainties on the cross-calibration between Chandra and XMM-Newton could bias the comparison of the spectral parameters, we do not quote the parameters of the HETG fit in Table  3.
15 ). To allow for unbiased comparison with optical photometry, we built a long-term light curve with time steps of one year, but only retaining those dates that fall into the APT optical observation season. Only daily integrations with errors smaller than 0. 03 ct s -1 were retained, and the averages were weighted by the inverse of their error. Since we are dealing with X-ray data from two different instruments here, we must pay attention to their cross-calibration. The ASM data cover the energy range from 1. 5 to 12 keV. Yet, to keep contamination of the MAXI data by ISS precession-induced background variations to a minimum, we restricted our analysis to the least affected 2-6 keV energy band for this instrument. ASM and MAXI count rates were converted into fluxes using conversion factors of respectively 3.
Nonetheless, the correlation between V-band and X-ray flux remains highly significant for the individual instruments: the Spearman rank correlation coefficient amounts to r S = 0. 75 for the ASM data taken between 1997 and 2011, and to r S = 0. 58 for the MAXI data collected between 2010 and 2019. In contrast with    4 ). Finally, the bottom panel yields the evolution of the 1. 5-12 keV X-ray flux of γ Cas, averaged over the yearly observing seasons as in Fig. 15. The fluxes in the same band derived from the Chandra and XMM-Newton observations (Table  4 ) are illustrated by the magenta triangles. a significant dispersion (that also exists among the daily ASM and MAXI fluxes, but is averaged out in the yearly means). Finally, we searched the MAXI data for a signature of the orbital period. No significant signal was found at or close to the 203 day period and its harmonics, independently confirming the lack of such a signal in the fluxes inferred from XMM-Newton and Chandra spectra (see Fig.
Acknowledgements. We thank  Ernst Pollmann  for sharing his compilation of Hα EWs with us. We deeply acknowledge the variable star observations from the AAVSO International Database contributed by observers worldwide, as well as the amateur spectroscopists who contributed data to the BeSS database. The Liège team thanks the  European Space Agency (ESA)  and the  Belgian Federal Science Policy Office (BELSPO)  for their support in the framework of the  PRODEX Programme  (contract HERMeS). R. L. O. acknowledges financial support from the  Brazilian institutions CNPq  ( PQ-312705/2020-4 ) and  FAPESP  (# 2020/00457-4 ). This work is based on observations collected with  XMM-Newton , an  ESA Science Mission  with instruments and contributions directly funded by  ESA Member States  and the  USA (NASA).
We thank the  XMM-Newton-SOC  for their assistance in the scheduling of the ToO observations. This research uses optical spectra collected at the OHP and with the TIGRE telescope ( La Luz , Mexico). TIGRE is a collaboration of  Hamburger Sternwarte , the  Universities of Hamburg, Guanajuato and Liège. This research also made use of MAXI data provided by  RIKEN ,  JAXA  and the MAXI team. The ADS, CDS and SIMBAD databases were used in this work.
Study design and participants Patients were enrolled in the open-label phase 1b/2 CARTITUDE-1 study (NCT03548207) in the USA  (16 centers)  and Japan (four centers). Full study design details and eligibility criteria have been previously described  [12]. Briefly, eligible patients had a diagnosis of MM per International Myeloma Working Group criteria  [16] , received ≥3 prior lines of therapy or were double refractory to a proteasome inhibitor and an immunomodulatory drug, and had received a proteasome inhibitor, an immunomodulatory drug, and an anti-CD38 antibody. Patients had to have documented disease progression ≤12 months after the last line of therapy and were excluded if they had previously been treated with a CAR T-cell or BCMAtargeted therapy. Patients with known active or prior history of central nervous system involvement and those who exhibited clinical signs of meningeal involvement of MM were excluded from the study.