dataesr/scientific-paragraphs-categorization huggingface dataset:
Context. Asymptotic giant branch (AGB) stars are one of the main sources of dust production in the Galaxy. However, it is not yet clear what this process looks like and where the dust happens to be condensing in the circumstellar environment. Aims. By characterizing the location of the dust and the molecules in the close environment of an AGB star, we aim to achieve a better understanding the history of the dust formation process. Methods. We observed the carbon star R Scl with the thermal-infrared VLTI/MATISSE instrument in L-and N-bands. The high angular resolution of the VLTI observations (as small as 4. 4 mas in the L-band and 15 mas in the N-band with ATs), combined with a large uv-plane coverage allowed us to use image reconstruction methods. To constrain the dust and molecules' location, we used two different methods: one using MIRA image reconstruction algorithm and the second using the 1D code RHAPSODY.
2017). Polarimetric observations confirm the clumpy structure of the dust forming region  (Yudin & Evans 2002). However, such clumps were not detected in the MIDI observations of  Sacuto et al. (2011). Such a non-detection was very likely due to the limitations in terms of uv-coverage and visibility sampling of MIDI, which is not optimized for the detection of large-scale asymmetric structures  (Paladini et al. 2012 (Paladini et al. , 2017)). This paper is organized as follows. In Section 2, we present new VLTI/MATISSE observations in L-and N-bands of the AGB star R Scl, as well as the data reduction processes. In Section 3, we present the photometric data of this star and we derived a hydrostatic model to describe the central source. The resulting best-fit model is used as input for the DUSTY modeling used to interpret both the photometry and the interferometric MATISSE data.
Observations and data processing The Multi Aperture Interferometric SpectroScopic Experiment (MATISSE) is a four-telescope interferometric beam combiner that disperses the coherent light from four telescopes of the Very Large Telescope Interferometer (VLTI) in the L (3-4 µm), M (4. 5-5 µm), and N (8-13 µm) mid-infrared bands  (Lopez et al. 2022). It can combine either the four 1. 8 m telescopes (auxiliary telescopes, AT), or the four 8 m telescopes (unit telescopes, UT). MATISSE delivers coherent fluxes, closures phases, differential phases, and photometry which can be used with the coherent flux to derive the relevant visibilities. The four ATs can be moved along rails on the VLTI site, allowing for the baselines (separation vector between two telescopes with coordinates (u,v)) to more densely populate the (u,v)-plane than when only 4 fixed telescopes are used.
This makes MATISSE an imaging instrument that offers an exquisite spatial resolution: reaching, in the L-band, up to λ L = 3 µm, λ L /B = 4. 4 mas, and in the N-band up to λ N = 10 µm, λ N /B = 15 mas for a baseline of B = 140 m. The reliability of the image reconstruction process, the signal-to-noise-ratio (S/N), and the dynamic range of the image depend on the completeness of the (u,v)-plane coverage. The observations of R Scl were conducted using the ATs of the VLTI during the commissioning run of MATISSE in December 2018. Table  A. 1 presents the logbook of the observations with the used AT configurations. Since on December 5 and 6, the observing nights were of poor quality characterized by technical glitches, only a fraction of the data from these two nights are used here. The data are processed using the MATISSE data reduction software drsmat version 1.
It is important to notice that calibrators have been chosen with no IR excess (see  Cruzalèbes et al. (2019)  for a description of the IR excess-free criterion) to avoid bias on the infrared measurements and in the calibration process. The drsmat has been developed in C with the ESO-specific library CPL, and is interfaced with the ESO recipes environment esorex. A graphical user interface can be used in the form of python tools developed by the consortium 2  to handle the data and recipes, and display the results. Table  A. 2 lists the stars used as calibrators for R Scl during the MATISSE commissioning run.
Stellar pulsation To estimate the pulsation phase of the MATISSE data, we made a periodogram analysis of the AAVSO V-band photometry. For this purpose, we downloaded data from between JD 2411972. 3 and 2458893. 5, and we selected only the best-quality data (based on the AAVSO data flag system). The periodogram not shown in this article is produced using the Lomb-Scargle method, an efficient algorithm for detecting and characterizing periodicity in unevenly-sampled time-data (VanderPlas 2018). The primary peak was detected at P = 372 ± 5 days which is consistent with the period 376 days from  Wittkowski et al. (2017). We also detected a secondary peak at 15000 days (≈ 41 years), which was previously unknown. We ensured this was not an alias of the shorter period by checking that the secondary peak remains after we removed the primary pulsation from the data.
Stellar contribution: COMARCS model fitting In this section, the stellar contribution will be described using hydrostatic models. The detailed fit of the spectral energy distribution which would require more advanced models such as dynamic ones  (Höfner et al. 2005 ) is beyond the scope of the current paper. Our aim here is to derive the stellar parameters and get a hydrostatic model that will be used in the following section as central source for the radiative transfer equation solver DUSTY 3  in order to model the total emission of R Scl. We fit the photometric data with the grid of precomputed low-resolution synthetic spectra COMARCS  (Aringer et al. 2016 (Aringer et al. , 2019)). The spectra are calculated starting from 1D hydrostatic model atmospheres that take into account atomic and molecular absorption directly computed using the opacity generation code from the Copenhagen Opacities for Model Atmospheres (COMA) program  (Aringer et al.
Dust contribution: DUSTY model fitting To describe the circumstellar envelope of the star, we use the 1D radiative transfer code DUSTY  (Ivezić & Elitzur 1997). We set: (1) the central source as our best-fitting COMARCS model; (2) the dust grain-size distribution following the classical MRN (Mathis Rumpl Nordsieck) distribution  (Mathis et al. 1977). For the latter, we assumed a minimum and maximum grain size a min = 0. 005 µm and a max = 0. 25 µm, respectively, which are largely adopted for size distribution among stellar envelopes and interstellar dust; (3) the inner shell temperature was set to 1200 K, which aptly represents the sublimation-condensation temperature of dust in particular AmC  (Gail & Sedlmayr 1999). Such a temperature is affected, in practice, by unknowns related to dust grain optical and physical properties.
2019). Since DUSTY does not handle molecules, and knowing the coexistence of molecules and dust, we expected to see strong discrepancies between the DUSTY model and the MATISSE data in the L-band, where strong molecular features are located. Although dust emission usually dominates the N-band, molecular contributions are also expected there, namely: CS and possibly SiO at 8 µm; HCN+C 2 H 2 molecular bands between 11 and 16 µm  (Chubb et al. 2020 ) which can affect the shape of the SiC band at 11. 3 µm. The resulting fit to the SED (black curve in Fig. 1 ) provides results that are in reasonable agreement with the above expectations: a satisfactory fit to the N-band visibilities from MATISSE is found (orange curves in Fig. 2 c  and d ), but the L-band visibilities are not well matched with this star+dust model alone (Fig.
We present the main steps of our work as follows: 1. Data obtained with the VLTI/MATISSE instrument during the seven-days of the long commissioning session from 2018-12-03 to 2018-12-15 was used in this work. 2. Using the COMARCS stellar atmospheric model and the dust radiative transfer code DUSTY, we confirm the previously published characteristics of a thin dust shell, mainly composed of amorphous carbon with a small amount of silicon carbide (10%), leading to the location of the inner boundary estimated by DUSTY at about 4. 6 R from the central star (i. e. , 24. 4 mas) and a mass-loss rate of 1. 2 ± 0. 4 × 10 -6 M yr -1. A&A proofs: manuscript no. RScl A. 5. Closure phase.
Acknowledgements. MATISSE has been built by a  consortium composed of French (INSU-CNRS in Paris and OCA in Nice) ,  German  ( MPIA, MPIfR and University of Kiel ),  Dutch (NOVA and University of Leiden), and Austrian (University of Vienna ) institutes. It was defined, funded and built in close collaboration with  ESO. The  Conseil Départemental des Alpes-Maritimes in France , the  Konkoly Observatory and Cologne University  have also provided resources to manufacture the instrument. The results are based on public data released from the MATISSE commissioning observations at the  VLT Interferometer under Programme IDs 60. A-9257(E). We have great remembrance of the contributions from our two deceased colleagues,  Olivier Chesneau  and  Michel Dugué  to the MATISSE instrument. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research.
This work was supported by the  Action Spécifique Haute Résolution Angulaire (ASHRA)  of  CNRS/INSU  co-funded by  CNES , as well as from the  Programme National de Physique Stellaire (PNPS)  from  CNRS. It has also been supported by the  French government  through the  UCA-JEDI Investments  in the  Future  project managed by the  National research Agency (ANR)  with the reference number  ANR-15-IDEX-01  and the  Hungarian governent through NKFIH  grant  K132406. Vincent Hocdé  is supported by the  National Science Center, Poland ,  Sonata BIS  project  2018/30/E/ST9/00598. FM acknowledges support from the  French National Research Agency (ANR)  funded project  PEPPER  ( ANR-20-CE31-0002 ). The authors made extensive use of the  Jean-Marie Mariotti Center (JMMC)  tools to prepare this.
• The additional convenience sample of health care professionals was recruited via an online campaign by sending emails to professional associations and posting advertisements on social networks (Multimedia Appendices 4). All participants provided informed electronic consent before enrollment. The Institutional Review Board CERAPHP. 5, Paris, France, gave ethical approval (IRB0001198). To recruit patients with various combinations of chronic conditions and socioeconomic status, perform quantitative analysis using logistic regression, and ensure that data saturation was reached based on previous experience with an online survey and considering the fact that 34. 5% of the French population lives with a chronic condition, we targeted a representative sample of 2000 participants  [22, 23]. Regarding the convenience sample of health care professionals, we decided to stop recruitment after reaching 95% data saturation  [22].
• The manufacturer says that this device will allow for greater consistency between the prescription and the actual taking of medications. In your opinion, why would a person take their treatment more consistently if they were equipped with this device? In addition, we collected sociodemographic data (eg, sex, age, level of education) for patients and public participants, professional status data (profession and duration of professional experience) for health care professionals, and health characteristics for all participants (eg, chronic condition, current use of a connected device to collect health data) (Multimedia Appendices 1 and 2). The survey for patients and public participants was available online in a dedicated IPSOS platform. The survey for health care professionals was available in a LimeSurvey form.
The coding was limited to the manifest content of the responses, except for the first open-ended question on the emotional reaction to the digital pill, for which the latent emotional content was interpreted when possible  [25]. The second and third steps occurred in a meeting in which the 4 coders and SS inductively condensed all the codes using semantic, psychological, and anthropological considerations. Then, they categorized the codes inductively into themes and classified them deductively into the 5 theoretical dimensions of acceptability  [19]. We then calculated the number of citations for each code identified with the qualitative content analysis in each group using the weighted data for patients and public participants. Data saturation was assessed for patients, public participants, and health care professionals with a mathematical model  [22].
Willingness to Take Digital Pills We calculated the number and proportion of participants who agreed to take digital pills. We estimated odds ratios (ORs) and 95% CIs using the weighted data of the representative sample to calculate the association of the willingness to take digital pills with all socioeconomic and health characteristics. We used the same data for a multivariate logistic regression model, including sex, age, socioprofessional status, residential area, chronic condition, current use of a connected device to collect health data, and ease to talk about treatment with the doctor. For the regression, we considered the response "I do not wish to answer" to be a refusal to take digital pills. The significance threshold for P values was calculated with Bonferroni correction to correct for multiple testing.
Acknowledgments We thank  Florence Weber  for the expert advice in social anthropology and  Marion Plaze  for expert advice in cognitive psychology. We thank  Raphaël Porcher  for statistical guidance,  Elise Diard  for expertise in the design of the figures,  Nivantha Naidoo  for help in interpreting the data, and  Laura Smales  for copyediting the manuscript. The study received a grant from the  Fondation Université de Paris  ( 19_DON22_001_U4 ). The funder had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or decision to publish.
The  Dugo-Cota et al. (2019)  phylogeny includes 57 of the 66 Hispaniola species. Because fitting the IW model assumes complete sampling of the extant species of the focal island, we inserted the missing Hispaniola species by assigning them at random locations within the Hispaniola subclades that they have been hypothesized to belong to. Information on the nine missing species and the detailed rationale for including them in a given subclade are given in supplementary Table  S1. There is no genetic data available on GenBank for these missing species, because they have been recently described, are known from a single specimen or are possibly extinct. We used a set of functions from the phytools R package to assign missing species to clades  (Revell, 2012). Four of the missing species were previously considered subspecies, and have recently been elevated to species, and we thus randomly inserted them 100 times at any height along the tip branch of the species they were previously assigned to.
The preferred model in all trees was CS. We thus used only tree parameter estimates and all loglikelihood differences were very similar across the 100 sets of trees, and would have led to the same conclusions in our model comparison. Moreover, tree set 52 did not have the largest loglikelihood difference between CS and IW. All sets of trees and corresponding DAISIE colonization/branching time R objects are provided in the supplementary material. The simulation code, functions to compute the likelihood under the two models, and a tutorial on how to run simulations and perform model-fitting are available in the R package DAISIE on CRAN and on Github (https://github. com/rsetienne/DAISIE).
Bootstraps The analyses fitting the CS model to each of 1000 CS and 1000 IW simulated data sets all completed successfully. For the analyses fitting the IW model to the same data sets, some runs runs could not be completed within the limit we set (10 days). For the CS data sets this was 0. 8% of the simulations and for IW this was 1. 6%. These were all data sets with little information (only a single clade) where the estimation procedure went to very high values of the rate of cladogenesis and colonization. The maximum IW loglikelihood is thus higher in these cases, although it may not be much higher. Even if we make the unlikely assumption that in all of these aberrant simulated data sets the IW is a better fit, they are so rare that our qualitative conclusion that the CS model is a better fit does not change.
The initial condition at colonization time t 2 connects the solutions before (equations (S3-S4)) and after (equations (S6-S9)) the colonization event, Q 0,0 m,e (t 2 ) = γ m+e+1 M -m-1 M Q 0 m,e (t 2 ) + γ m+e+2 m+1 M Q 0 m+1,e (t 2 ) dt Q 0,1 m,e (t 2 ) = 0 Q 1,0 m,e (t 2 ) = γ m+e+1 M -m-1 M Q 1 m,e (t 2 ) + γ m+e+2 m+1 M Q 1 m+1,e (t 2 ) dt Q 1,1 m,e (t 2 ) = 0 (S10) After -th colonization recorded in data Now we generalize equations (S3-S4) and (S6-S9) to an arbitary number of colonization events recorded in the data. We define the number as the number of colonizations from which originate the species at time t represented in the phylogeny (see Fig. S6  for an illustration). Recall that there are k such species, corresponding lineage (see previous sections for the cases = 1 and = 2). Therefore, we introduce a binary vector σ of length , the i-th component of which indicates whether speciation has taken place (σ i = 1) or not (σ i = 0) in the lineage that originates from the i-th colonization event.
The likelihood can then be extracted from components Q σ m,e (t p ) at the present time t p. To do so, we assume that we have complete information about the extant species. That is, we assume that there are no other species on the island at the present time than those represented in the dataset. This allows us to set the number of missing non-endemic and endemic species to zero, i. e. , we impose that m = e = 0 at time t p. The data also allow us to determine the vector σ p at time t p. We set σ i = 0 for colonizations that correspond to lineages that are still non-endemic at the present time (represented by dashed lines in Fig. S5 ). For all other colonizations we set σ i = 1. Finally, the likelihood is given by Q σp 0,0 (t p ). In practice we often do not have complete information. For example, there might be large uncertainty about colonization times, or there might be missing species at the present time, i.
Acknowledgements We thank  Ally Phillimore  for early discussions that provided the original motivation for this study. We thank  Pedro Neves ,  Joshua Lambert ,  Ally Phillimore ,  Tanja Stadler  and two anonymous reviewers for helpful comments on the manuscript, and  Hanno Hildenbrandt  for a major improvement in the performance of the DAISIE PACKAGE. We thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high performance computing cluster. RSE and LV thank the  Netherlands Organization for Scientific Research (NWO)  for financial support through  NWO-VICI  and  NWO-VIDI  grants, respectively. LV thanks the  DFG for an Eigene Stelle grant  ( VA 1102/1-1 ). BH was supported by the  Laboratoires d'Excellences (LABEX)   TULIP  ( ANR-10-LABX-41 ).
nasa. gov/earthscience/decadal-sbg) exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of spatio-ecological heterogeneity in vast areas over time. In fact, spectral heterogeneity of an optical image -associated with the reflectance values of the pixelscan be a proxy of the spatio-ecological heterogeneity  (Rocchini, 2007). Hence, the variation of spatio-ecological heterogeneity in space and time (e. g. , phenological cycles) can be effectively inferred using remote sensing  (Schneider et al. , 2017). Therefore, the measure of ecosystem heterogeneity over time from satellite through Free and Open Source Software and algorithms allows robust, reproducible and standardized estimates of ecosystem patterns and processes  (Rocchini and Neteler, 2012). Also, its use brings many advantages: availability, transparency and shareability.
In this context, the R platform is one of the most used statistical and computational environment in ecology, partially thanks to the continuous development of relevant packages. In particular, the rasterdiv package  (Marcantonio et al. , 2021; Rocchini et al. , 2021b; Thouverai et al. , 2021)  allows to calculate a plethora of different indices to measure spatio-ecological heterogeneity from space. Most of the algorithms have been related to Information Theory relying on abundance-based metrics, starting from Shannon's index  (Simpson, 1949)  (see Section 2). However, some information about the spectral distance among pixel reflectance values might be lost if not considered in the calculation  (Rocchini et al. , 2017). Currently, the candidate for solving the problem is Rao's Quadratic Entropy index (hereafter Rao's Q)  (Rao, 1982) : this index, besides the relative abundance of pixel values in a given moving window or polygonal area, incorporates also their spectral distances (Section 2).
Both Shannon and Rao's Q indices are point descriptors of heterogeneity, namely they can only show part of the whole heterogeneity spectrum. Recently  Rocchini et al. (2021a)  proposed an implementation of the Rao's Q index by parameterizing the original formula, and allowing the whole continuum of heterogeneity to be measured thanks to Rao's Q continuous profiles (see Section 2). This paper aims to show how to make proper use of the Rao's continuum heterogeneity variation profile by proposing a new R functionintegrated into the rasterdiv R package  (Marcantonio et al. , 2021)  which calculates AUC, the area under the curve formed by applying the parametric Rao's Q index (see Section 2). Two case studies on multiand hyperspectral satellite images are also provided in order to verify if the new metric proposed could be an effective tool for the study of spatio-ecological heterogeneity.
The theory Algorithms that aim to measure environmental heterogeneity through remote sensing data can rely on the moving window technique, which divides remotely sensed imagery into user-defined squares (windows) to derive measures of heterogeneity. Examples are included in the rasterdiv R package  (Rocchini et al. , 2021b). One of the most used metrics included in the package is the Shannon entropy index H  (Shannon, 1948) : 𝐻 = - 𝑁 ∑ 𝑖=1 𝑝 𝑖 ln 𝑝 𝑖 (1) where the relative abundance of every pixel reflectance value calculated as the ratio between the actual value of the pixel 𝑖 ∈ {1, … , 𝑁} and the sum of the pixel values of the moving window (𝑝 𝑖 ) in an image of 𝑁 pixels is considered. It is usually calculated of one layer images, such as a vegetation index or the first axis of a PCA. However, Shannon's H does not consider the spectral distances among pixel reflectance values, overestimating the heterogeneity of homogeneous surfaces  (Rocchini et al.
A theoretical example In this section, we will show how to use the function accRao() from the rasterdiv package to calculate the accumulation function (integral) of Rao values obtained using a range of alpha-values. We used a raster for the global average NDVI rescaled at 8-bit available from rasterdiv. This raster was first cropped on the islands of Sardinia and Corsica. In order to simulate the effects of an ecological perturbation, for example widespread drought, we created a new raster with perturbed NDVI values for these two islands. Pixels with NDVI higher than 150 were decreased using values from a normal distribution centred on 50 with a standard deviation of 5. Then, we applied accRao() both on the original and simulated raster by using alphas ranging from 1 to 10: Afterwards, the difference between the two rasters, before and after the simulated perturbation, was calculated (Fig.