dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, while this current project correlates the presence of NCCPs with machine availability, evaluating for correlations with cancer outcomes is beyond the scope of this work. Complete GLOBOCAN, DIRAC and NCCP data was only available for 143 countries worldwide. The exclusion of countries with incomplete data may generate a selection bias, and ongoing research to collect and analyses data for missing countries is warranted. At present DIRAC data does not include specific information on brachytherapy, and ongoing work is underway to increase the level of detail which could inform future analyses. The current study demonstrates a correlation between NCCPs and machine availability but does not demonstrate causation, or directionality. Countries with a high supply of radiotherapy machines may be more likely to include radiotherapy within their NCCPs, due to the need for clear strategies for maintenance, equipment procurement and training.
The upper limit for the black hole mass set by the PPISN process is used in ref. [40] to demonstrate that it is possible to constrain the Hubble constant at z = 0. 8 with a simulated dataset from one to 5 years of Advanced LIGO and advanced Virgo at design sensitivity. The implications of wrong population assumptions are explored in ref. [41], demonstrating that, once the right population model is assumed, a joint inference of the population parameters Λ and cosmological parameters  is required to avoid incurring in biases. In refs. [23] an. [16] the authors apply this method to the GWTC-3. Neutron Star Mass Function: The very same method can be applied to BNS observations. Differently from black holes, neutron stars are expected to span a narrower mass range. Several studies show the proof of principle of this method with current ground-based GW detectors  [42]  and next-generation GW detectors such as the Einstein telescope.
Prostate cancer is the most common men cancer in France. Continuous progress in oncology led to develop robot-assisted Radical Prostatectomies (rRP) and robot-assisted stereotactic body radiotherapy (rSBRT). The present study aims at comparing economic and clinical impacts of prostate cancer treatments performed either with rSBRT or rRP in France. A Markov model using TreeAge Pro software was chosen to calculate annual costs; utilities and transition probabilities of localized prostate cancer treatments. Patients were eligible for radiotherapy or surgery and the therapeutic decision was a robot-assisted intervention. Over a 10-year period, rSBRT yielded a significantly higher number of qualityadjusted life years than rRP (8. 37 vs 6. 85). In France, rSBRT seemed more expensive than rRP (€19,475 vs €18,968, respectively).
Depending on the type of intervention, we compared values from the Katz et al. study  (5) , the PACE-B study  (10)  and the PROTECT trial  (11)  and the probability of occurrence of any adverse effects to calculate the average utility per year  (5). Utility values decreased after intervention because they captured the decrement in QoL due to age but also due to the burden of prostate cancer, while the probability of death increased over the 10-year period. This decreasing utility values were consistent with the age-related decline of the general population reported in the French INSEE database  (14). Quality-adjusted life years (QALYs) were calculated by multiplying the length of time in a state by the utility for the given state. QALYs were discounted at an annual rate of 4% as recommended by the French Health Agency  (15).
Costs Inputs The analysis considered direct costs as well as costs associated with long-term disability care provided in facilities. Cost data was collated from multiple sources including the French Diagnosis Related Group (DRG) system for 2021 and published costs (Table  1 )  (12, 16). The cost data used in our model refers to French national data. Indeed, the DRGs correspond to the price of a hospitalization for prostatectomy or radiotherapy session in France (regardless of the type of hospital). The details of the calculations specified in our Supplementary File correspond to the average national costs of prostate cancer treatment in France. The "Initial state" costs were calculated from French databases for each type of intervention (Supplementary File)  (16) (17) (18) (19) (20) (21). All costs were in Euros for the year 2021.
However, few studies have dealt with the analysis of AR lifetimes. In a series of papers,  Donahue et al. (1997a,b)  used the pooled variance technique on calcium data to infer the AR lifetimes of approximately 100 lower main sequence stars. The authors show that ARs have rather irregular lifetimes and that different stars might show very different pooled variance diagrams depending on their level of activity (age) and colour (mass). More recently, several works have developed a methodology based on the decay of the autocorrelation function of light curves (in particular using data from the Kepler mission) to put constraints on starspots and AR lifetime. Giles et al. (2017)  find that big starspots live longer irrespective of the spectral type of the star and that starspots decay more slowly on cooler stars.
Photometry time series from the TESS mission are also available for some of these stars. In addition, we take advantage of Gaussian processing, a state-of-the-art statistical analysis, to model stellar activity. This paper is organised as follows. We present our stellar sample in Sect. 2. Section 3 describes the analysis of the data while in Sect. 4 we use our dataset to revisit the activity, rotation, and age relationships. The dependency of AR lifetime on spectral type, activity, and stellar age is discussed in Sect. 5 and we present our conclusions in Sect. 6.
2012)  or HARPS  (Mayor et al. 2003 ) optical échelle spectra. The instrumental setup of HARPS and HARPS-N is almost identical. The spectra cover the ranges 378-691 nm (HARPS) and 383-693 nm (HARPS-N) with a resolving power of R ∼ 115 000. The spectra are provided already reduced using HARPS-N/ESO standard calibration pipelines (Data Reduction Software, DRS version 3. 7 and 3. 8, respectively) and were retrieved from the corresponding ESO 1  and TNG 2  archives. In addition, several solar spectra taken by the HARPS-N solar telescope  (Dumusque et al. 2021)  were analysed in order to use the Sun as a benchmark.
In our first test, we took advantage of the solar spectra taken by the HARPS-N solar telescope and used the three years of S index values published in  Maldonado et al. (2019). We computed the PV diagram using the original dataset and compared it with the PV diagram obtained by sampling the data using the observation times of three selected stars. The corresponding plot is shown in Fig. 8  (top left). Specifically, 'sampling 1' contains 83 data points covering a time-span of ∼1. 5 yr. The data are divided into two main observing seasons with a gap of ∼200 d between them. 'Sampling 2' is composed of 41 observation points taken in 1. 3 yr. As in 'sampling 1' there is a gap of 200 d between the two main observing seasons, but the second season is less populated than the first one. Finally, in 'Sampling 3' we consider only 22 data points, covering ∼200 d, with a gap of ∼100 d between the two main observing seasons.
4 d, and the AR lifetime to four rotation periods. The simulations were performed while considering a sinusoidal behaviour, modulated with an exponential decay. In order to simulate the effect of spot growth and decay, as the time runs and the amplitude of the variability decays, another sinusoidal signal (with the same period but a different phase) is included. It can be seen that in the short-period cases, we are not able to recover the injected rotation period (even with the original dataset), as the PV steadily increases until the AR lifetime is reached. However, the AR lifetime seems well constrained even for samplings 1 and 2. In the case of sampling 3, without any priory knowledge of the AR lifetime, we would have concluded that the PV diagram is too complex to derive any meaningful conclusion.
Whilst the AR lifetime correlation with T eff appears to be statistically significant (with a p-value lower than 0. 02), the correlation between AR lifetime and activity level does not (p-value ∼0. 12). These results are in agreement with the findings of GI17, SA21, and BA22 who analysed a large dataset of KEPLER light curves and concluded that ARs decay more slowly in cooler stars. It is worth noting that when comparing stars with different stellar parameters, other properties like the convective turnover time might be different as well. The use of the Rossby number has been shown to substantially improve the observed activityrotation relations for main sequence, solar-type stars (e. g. Noyes et al. 1984). To compute the Rossby number, we first estimate the mass of our stars using the Gaia DR2 luminosities (Gaia Collaboration 2018) and the mass-luminosity relationship provided by  Wang & Zhong (2018).
Acknowledgements. J. M. , S. C. , A. P. , G. M. acknowledge support from the  Accordo Attuativo ASI-INAF  no. 2021-5-HH. 0, Partecipazione alla fase B2/C della missione Ariel (ref. G. Micela). S. C. acknowledge financial contribution from the agreement  ASI -INAF no. 2018-16-HH. 0  ( THE StellaR PAth  project). We sincerely appreciate the careful reading of the manuscript and the constructive comments of the referee  Gibor Basri.
In a companion paper  (Eckert et al. 2022, hereafter Paper I) , we present a general framework to recover the mass profiles of galaxy clusters from X-ray and SZ data under the assumption that the gas is in hydrostatic equilibrium within the DM potential well. Here we apply our framework to recover the parameters of the Einasto functional form by fitting the X-COP data over a wide range in radius, which we subsequently use to search for the effect of DM self-interaction. Throughout the paper, we assume a Planck 2015 ΛCDM cosmology with Ω m = 0. 308, Ω Λ = 0. 692, and H 0 = 67. 8 km s -1 Mpc -1 (Planck Collaboration XIII 2016).
The sample The X-COP sample  (Eckert et al. 2017 ) consists of a set of 12 massive galaxy clusters in the redshift range 0. 04 < z < 0. 1. The sample contains the most significant SZ detections from the Planck PSZ1 catalog (Planck Collaboration XXIX 2014) with the exception of three highly perturbed systems for which the validity of a radially averaged analysis cannot be guaranteed. For each cluster, a minimum of five XMM-Newton pointings are available to cover the gas distribution uniformly, at least out to 2 × R 500. The XMM-Newton data were reduced in a homogeneous way  (Ghirardini et al. 2019; Eckert et al. 2019; Ettori et al. 2019 ) and the reduced data and thermodynamic profiles (gas density and spectroscopic temperature) are publicly available 1. In addition, Planck SZ pressure profiles were derived for all systems following Planck Collaboration V (2013) from the SZ signal reconstructed using the MILCA algorithm  (Hurier et al.
Conclusion We have measured the internal DM structure of galaxy clusters and compared our observations with clusters in CDM and SIDM simulations. Our results can be summarized as follows: -The hydrostatic mass profiles of the 12 X-COP galaxy clusters are well represented by an Einasto model over more than two decades in radius. The data quality is sufficient to break the degeneracy between the various parameters of the Einasto model, and determine the Einasto index with high precision. A41, page 6 of 10 -Most X-COP clusters have an Einasto index of 0. 15 < α < 0. 3, with mean α = 0. 22 ± 0. 04. The substantial intrinsic scatter, σ α = 0. 14 +0. 10 -0. 05 , is dominated by a few outliers with α 0. 5, all of which appear morphologically disturbed, as indicated by a large centroid shift. The high Einasto indices are therefore likely a systematic effect in the reconstruction of the mass profiles, caused by hydrostatic bias and/or miscentering.
Acknowledgments The computations were performed on JUQUEEN, JURECA, JUWELS and QPACE at Forschungszentrum Jülich, on SuperMUC and SuperMUC-NG at Leibniz Supercomputing Centre in München, on Hazel Hen and HAWK at the High Performance Computing Center in Stuttgart, on Turing and Jean Zay at CNRS' IDRIS, on Joliot-Curie at CEA's TGCC, on Marconi in Roma and.
on GPU clusters in Wuppertal and Budapest. We thank the  Gauss Centre for Supercomputing, PRACE  and  GENCI  (grant  52275 ) for awarding us computer time on these machines. This project was partially funded by the  DFG  grant  SFB/TR55 , by the  BMBF  Grant No. 05P18PXFCA , by the  Hungarian National Research, Development and Innovation Office  grant  KKP126769  and by the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" program , through grants  AMX-18-ACE-005 ,  AMX-19-IET-008 -IPhU  and  ANR-11-LABX-0060.
Observations and data analysis We analyzed CARMENES transit data of KELT-9 b obtained on August 6, 2017, and available from the Calar Alto public archive. The observations consist of 54 exposures with integration times from 300 to 400 s. This introduces a small orbital blurring, as the planet's orbital velocity with respect to the Earth changes by ∼3. 5 km s -1 on average during exposures (the resolution element is ∼3 km s -1 ). The mean signal-to-noise ratio (S/N) per pixel is ∼60 in the spectral order containing the Paβ absorption. We discarded four spectra with low signal (S/N < 35) due to passing clouds. Two additional transits of KELT-9 b are available in the public archive, observed on September 21, 2017, and 16 June, 2018, but under significantly worse conditions in terms of the mean S/N around the Paβ line or the transit phase coverage, which did not allow us to recover planet signals.
In order to enhance its detectability, the standard approach consists in shifting all in-transit spectra to the rest frame of the planet and coadding them (F in /F out ) before subtracting one to obtain the final transmission spectrum around the Paβ line. In this step, we used a semi-amplitude of the orbital velocity of the planet (K P ) of 234. 24 ± 0. 90 km s -1 , as reported by  Hoeijmakers et al. (2019) , and the in-transit spectra were weighted by their mean S/N. In order to explore the multidimensional parameter space, we used the statistical framework MULTINEST  (Feroz et al. 2009 ) by means of the pyMultinest package  (Buchner et al. 2014; Buchner 2016 ), which allows parameter estimation and model L1, page 2 of 7.
Acknowledgements. We acknowledge funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 694513. IAA-CSIC authors acknowledge financial support from the  Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación y Universidades through projects Ref. PID2019-110689RB-I00/AEI/10. 13039/501100011033  and the  Centre of Excellence "Severo Ochoa " award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). This research has made use of the  Spanish Virtual Observatory  (http://svo. cab. inta-csic. es)supported by the  MINECO/FEDER  through grant  AyA2017-84089. 7.
Acknowledgments The authors thank two anonymous reviewers for their helpful reviews. C. Michaut  has received financial support of the  IDEXLyon Project of the University of Lyon  in the frame of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) as well as funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement No. 101 001 689 ) and from  PNP/INSU/CNES. J. A. Neufeld  is grateful for visiting Professorships at the  IPGP  and  ENS Lyon.
Acknowledgments This work is supported by the " QuantiXLie Centre of Excellence ", a project co-financed by the  Croatian Government and European Union  through the  European Regional Development Fund , the  Competitiveness and Cohesion Operational Programme  ( KK. 01. 1. 1. 01. 0004 ). Y. F. N. acknowledges the support from  National Natural Science Foundation of China  under Grant No. 12075104.
Reference  [18]  used US influenza-related mortality data to investigate the between-state progression of the influenza pandemic. The research indicated higher pairwise synchrony between populous states through a correlation analysis between locations. Reference  [16]  focused on the spatial structure of influenza transmission from June 1918 to April 1919 in England and Wales. The study used statistical methods (lag and correlation analysis) to better understand the spatial and temporal characteristics of the pandemic. Reference  [19]  investigated the spatiotemporal pattern of dengue hemorrhagic fever incidence by collecting a time series dataset containing 850,000 dengue hemorrhagic fever infections, from 1983 to 1997, in 72 provinces in Thailand. The study used cross-correlation functions to provide metrics of the spatial dependency of temporal correlation among time series.
Methods and Procedures The following steps were used to develop the diffusion graphs based on the PDND approach: Step 1. Defining the nodes of the graph. For the US graph, 54 nodes represented US states plus New York City, the District of Columbia, Puerto Rico, and Guam; for the Japan graph, 47 nodes represented prefectures. Step 2. Collecting the time series COVID-19 datasets. To build two COVID-19 diffusion graphs for the US and Japan, we used the number of daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021. For the US, we used daily records for all states plus other territories in the US from the Centers for Disease Control and Prevention  [6]. For Japan, we used data for all prefectures from the Japan Ministry of Health, Labour, and Welfare  [11]. Step 3. Defining the edges of the network.
The edges represent connections between nodes. In the COVID-19 dataset, edges indicated a connection between two locations in terms of synchrony of their COVID-19 dynamics. Network edges can be classified as binary or weighted, and can show the directionality among regions (directed or undirected)  [24]. We assumed that all geographical entities were connected with each other. Step 4. Selecting a method to discover synchronized location. A statistical method, correlation analysis, was used to identify if there was a strong relationship between the COVID-19 time series datasets of different geographical entities. Following numerous other studies  [21] , we adopted a lag of 0 between the analyzed time series. Step 5. Forming the connectivity matrix. Computed connectivity between nodes can be used to create a connectivity matrix, which is also known as an adjacency matrix.
In this matrix, nodes are represented by rows (i), and columns (j) and edges are represented by matrix entries (aij), as presented in Figures  1  and 2. Step 1. Defining the nodes of the graph. For the US graph, 54 nodes represented US states plus New York City, the District of Columbia, Puerto Rico, and Guam; for the Japan graph, 47 nodes represented prefectures. Step 2. Collecting the time series COVID-19 datasets. To build two COVID-19 diffusion graphs for the US and Japan, we used the number of daily confirmed cases of COVID-19 from 5 January, 2020 to 31 July, 2021. For the US, we used daily records for all states plus other territories in the US from the Centers for Disease Control and Prevention  [6]. For Japan, we used data for all prefectures from the Japan Ministry of Health, Labour, and Welfare  [11].
Step 3. Defining the edges of the network. The edges represent connections between nodes. In the COVID-19 dataset, edges indicated a connection between two locations in terms of synchrony of their COVID-19 dynamics. Network edges can be classified as binary or weighted, and can show the directionality among regions (directed or undirected)  [24]. We assumed that all geographical entities were connected with each other. Step 4. Selecting a method to discover synchronized location. A statistical method, correlation analysis, was used to identify if there was a strong relationship between the COVID-19 time series datasets of different geographical entities. Following numerous other studies  [21] , we adopted a lag of 0 between the analyzed time series. Step 5. Forming the connectivity matrix.
Limitations of the Study Certain limitations should be acknowledged regarding this study. First, we only focused on two countries: the US and Japan. Investigations of other countries in different parts of the world could produce different results. Second, although we used a correlation analysis to understand connectivity and develop COVID-19 networks, other methods, such as coherence analysis, should also be considered. Finally, the analyzed COVID-19 confirmed case dataset was subject to COVID-19 testing bias, in that the number of confirmed cases was the function of the number of tests conducted in different US states and prefectures in Japan.
Numerical set-up and conventions We perform GRRMHD simulations of accretion on to a 10 M Schwarzschild (non-spinning) black hole using KORAL , 2 a highly parallelized GRRMHD code  (S ądowski et al. 2013 , 2014 , 2017 ). The details of the numerical set-up have been described in S ądowski ( 2016 ). The radiative part of the simulation set-up accounts for the electron scattering that dominates the total opacity in the considered regime, as well as free-free absorption and thermal Comptonization (follo wing S ądo wski &  Narayan 2015 ). At this stage, frequencyaveraged (bolometric) opacities are used, with the aim of capturing the radiation feedback on the disc structure and dynamics. We then use the relati vistic radiati ve post-processing code HEROIC  (Zhu et al. 2015 ; Narayan et al. 2016 )  to compute puffy disc spectra and images, solving the complete radiative transfer problem on the conv erged, time-av eraged output of GRRMHD models (gas density, temperature, velocity, and magnetic field maps in the inner part of the simulation volume).
All these effects influence the spectra in a highly non-trivial and non-linear way that cannot be fully reproduced by adding and/or multiplying a series of analytical model components. Synthetic spectra from GRRMHD simulations of hard state BHXBs were recently discussed by  Dexter, Scepi & Begelman ( 2021 ). We present synthetic puffy disc spectra computed using the HEROIC code in Figs 5 -6. In the left-hand panel, we explore the influence  of the inclination, expanding on the total luminosity discussion from Section 3. The inclination θ has a strong impact on the puffy disc spectra because of the beaming at low inclinations and obscuration at high inclinations. Between θ = 10 • and 80 • , the maximum value of the spectral energy distribution (SED) changes by two orders of magnitude. Ho we ver, the location of the spectral peak is only mildly affected, and remains around 3 keV.
Hardness is defined as the ratio of photon counts in the 4-10 and 2-4 keV bands. For comparison, the location of the kerrbb and slimbh spectra for ṁ = 0. 6 is shown as well; for small and moderate inclinations, they both indicate less luminous and softer spectra than a puffy disc at the same mass accretion rate. by the standard spectral fitting tools? To address this question, we generated synthetic test spectra from a puffy disc simulation in the energy range of 0. 1-100 keV, and fitted them with the XSPEC software package  (Arnaud 1996 ). The synthetic puffy spectra correspond to a GRRMHD simulation of a Schwarzschild black hole of spin a * = 0, mass accretion rate ṁ = 0. 6 (as defined in equation 1 ), and mass M = 10 M , at a distance D = 10 kpc, seen from two different inclinations, θ = 10 • and 60 •.