dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 3 for each time step (I λ1-λ2 (t)). 5. Plot with software of choice the spectrally integrated emission (I λ1-λ2 (t)) of each spectrometer (step 5. 2. 4) as a function of the spectrometer distances from the surface (step 5. 1. 6) (Figure  2 ) (For example, plot(d(:,1:3),I(:,1:3),'x') ). 6. For better interpretation of the results, perform a polynomial fit of the data and plot the results (e. g. , using MATLAB's polyfit command: [p,ErrorEst] = polyfit(d(:,1:3),I(:,1:3,9); [fit,delta] = polyval(p,d(:,1:3),ErrorEst); plot(d,fit) ) Note: Depending on the location and resolution of the recorded spectra, the excited states temperature of molecules can be determined. Use a radiation simulation tool to fit numerical spectra to experimental spectra of the CN violet and C 2 swan systems. Several online tools include spectral fitting tools to obtain translational, rotational, vibrational, and electronic temperatures 30.
50. A material code was developed at the VKI using discontinuous Galerkin discretization to simulate the complex in-depth thermal response of ablative composite materials 51. This code makes use of the new thorough physico-chemical library Mutation ++ , providing the thermal and transport properties of gas mixtures, including the calculation of both finite-rate gas-phase chemistry and homogeneous/heterogeneous gas/gas-solid equilibrium chemistry 52. We envisage comparison of our experimental data to the material response code, which is able to represent the microscale state of the porous medium.
Acknowledgements The research of B. Helber is supported by  a fellowship of the Agency for Innovation by Science and Technology (IWT , dossier # 111529 ) in Flanders, and research of T. E. Magin by the  European Research Council   Starting Grant  # 259354. We acknowledge  Mr. P. Collin  for his valuable help as Plasmatron operator. We gratefully acknowledge  George Law  and  Stephen Ellacott  for providing the test material and for informative support.
Value of the data This data facilitates the data collection of other researchers attempting to follow the same tech- nique or to evaluate future methods for analysis of the data. There are limited public datasets available on the monthly abundance/recruitment of field popu- lations of U. pinnatifida despite their importance for understanding invasion dynamics. Environmental parameters included so that the quantitative relationship between the population dynamics and environmental factors can be explored. Allows researchers to independently verify the model predictions versus field results.
Acknowledgements This research is supported by an  Irish Research Council   ELEVATE international career development fellowship , co-funded by  Marie Curie Actions  under the European Union's  Seventh Framework Programme. The data presented here that were collected in Brest were obtained as part of the PhD thesis of  Marie Voisin  who benefitted from a  Ph. D. fellowship ("Renouvellement des Compétences" Program)  from the  Region Bretagne. MV and FV are thankful to the many people from the Department AD2M of the  Station Biologique de Roscoff  who provided help for the surveys carried out in the field.
An angular analysis of the B 0 → K * 0 (→ K + π -)µ + µ -decay is presented. The dataset corresponds to an integrated luminosity of 3. 0 fb -1 of pp collision data collected at the LHCb experiment. The complete angular information from the decay is used to determine CP -averaged observables and CP asymmetries, taking account of possible contamination from decays with the K + π -system in an Swave configuration. The angular observables and their correlations are reported in bins of q 2 , the invariant mass squared of the dimuon system. The observables are determined both from an unbinned maximum likelihood fit and by using the principal moments of the angular distribution. In addition, by fitting for q 2 -dependent decay amplitudes in the region 1. 1 < q 2 < 6. 0 GeV 2 /c 4 , the zero-crossing points of several angular observables are computed.
0 fb -1  [1]. Different subsets of these angular observables have also been measured by the BaBar, Belle, CDF and CMS collaborations  [2] [3] [4] [5] [6] [7]  and all of these measurements are in good agreement with SM predictions. The LHCb collaboration has also used the 2011 dataset to determine an alternative set of angular observables that have reduced theoretical uncertainties  [8]. In contrast to the previous analyses, these observables cannot be extracted from single angle distributions. This second LHCb analysis found a local deviation with respect to the SM prediction in one observable, P 5 , with a significance corresponding to 3. 7 standard deviations. Possible interpretations of this discrepancy and the consistency of all of the measurements of b → s transitions have been widely discussed in the literature  [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21].
The present paper describes an updated angular analysis of the B 0 → K * 0 µ + µ -decay, using the LHCb Run 1 data sample, corresponding to an integrated luminosity of 3. 0 fb -1. The data were recorded in pp collisions at centre-of-mass energies of 7 and 8 TeV during 2011 and 2012, respectively. All previous analyses of the B 0 → K * 0 µ + µ -decay have extracted only part of the information available, by fitting simplified forms of the angular distribution. This paper presents a complete set of observables for the first time, based on the full angular distribution. The simultaneous determination of these observables allows correlations between the measured quantities to be computed, enabling the use of the results in global fits to theoretical models. This is critical to understand whether SM dynamics are sufficient to explain the above discrepancy, or if extensions to the SM are necessary.
Determination of angular observables using the method of moments The angular observables are also determined using a principal moment analysis of the angular distribution, without making any angular fit to the data  [25, 48]. As a continuous function of q 2 , the moments are given by M i (q 2 ) = 1 d(Γ + Γ)/dq 2 d 4 (Γ + Γ) dq 2 d Ω f i ( Ω)d Ω. (9) The average M i (q 2 ) in a bin of q 2 is estimated by replacing the integral in Eq. 9 with a sum over the candidates in the dataset. The angular acceptance is accounted for by weighting the candidates in the sum, M i = 1 e w e e w e f i ( Ω e ). ( 10 ) The sum is evaluated for candidates within ±50 MeV/c 2 of the B 0 mass. The weight, w e , is the reciprocal of the candidate's efficiency and is computed as described in Sec. 5. The mass window contains more than 95% of the signal candidates.
6. The statistical uncertainties of the angular moments are estimated using a bootstrapping technique  [49]. Confidence intervals are defined such that they include the 16 th -84 th percentiles of the bootstrap distribution of the observables. When computing the P ( ) i observables, bootstrapped data with unphysical F L (F L < 0 or F L > 1) are added at ±∞ to ensure that the resulting intervals do not undercover. As in the likelihood method, the CP asymmetries are determined by flipping the sign of the relevant B 0 angular terms. The resulting moments are then used to determine the CP asymmetries by substituting A i for S i in Eq. (  12 ). In the moment analysis, an additional angular observable that is not present in the massless limit is determined. This observable is sensitive to large new scalar or tensor contributions to the decay and is associated with a new forward-backward asymmetry of the dimuon system, f 6c ( Ω) = cos 2 θ K cos θ l  [22, 50].
With the 3 fb -1 dataset, it is not possible to determine the parameters describing both the B 0 and B 0 decays separately. It is therefore assumed that CP symmetry holds in the decay such that the amplitudes describing the B 0 and B 0 decays are identical. An unbinned maximum likelihood fit to the distributions of m(K + π -µ + µ -), cos θ l , cos θ K , φ and q 2 is used to determine the amplitude parameters. The integral of the angular distribution is required to be consistent with the number of signal candidates in the fit. For simplicity, m(K + π -) is not included in the fit. The variation of the amplitudes with m(K + π -) is accounted for by replacing products of amplitudes A L,R i A * L,R j with A L,R i A * L,R j g i (m(K + π -))g * j (m(K + π -))dm(K + π -) , (15) where g i (m(K + π -)) describes the variation of the amplitude A L,R i with m(K + π -).
The figure therefore shows the distribution of the candidates rather than the candidates weighted by the inverse of their selection efficiency. Good agreement of the fitted function and the data is observed. The amplitude parameters are used to construct observables as continuous functions of q 2. The observables S 4 , S 5 and A FB have zero-crossing points and these are determined by solving a quartic equation. The different solutions of this equation are separable based on the sign of the slope of the observable in the vicinity of the zero-crossing point. Only zero-crossing points in the range 1. 1 < q 2 < 6. 0 GeV 2 /c 4 with a local slope consistent with the data above 6. 0 GeV 2 /c 4 are retained. The large number of parameters floating in the fit, coupled with the limited number of signal candidates present in the dataset, results in a non-parabolic likelihood surface.
Therefore, as in the determination of the angular moments, the statistical uncertainties of the q 2 -dependent observables and their corresponding zero-crossing points are determined using a bootstrapping technique  [49]. The statistical coverage of the resulting intervals is checked using simulated events and is found to be correct for the observables S 4 , S 5 and A FB. Despite the coverage being correct, approximately 10% of the bootstrapped datasets result  in no zero-crossing point with the correct slope in the q 2 range 1. 1 < q 2 < 6. 0 GeV 2 /c 4. In these cases, the zero-crossing point is added to the bootstrap distribution at ±∞ to ensure that the method does not undercover. The determination of the q 2 -dependent amplitudes in principle allows the full observable basis to be determined.
01 or less. Finally, at high q 2 , the statistical precision on the acceptance correction leads to a systematic uncertainty at the level of 0. 01 or less. For the P ( ) i observables, the situation is more complex and the systematic uncertainty is shared more evenly between the different sources (see Table  2 ). The dominant sources of systematic uncertainty can all be reduced in future analyses with larger datasets. Propagating the above sources of systematic uncertainty to the zero-crossing points yields uncertainties at the level of 0. 07 GeV 2 /c 4 for S 4 , 0. 02 GeV 2 /c 4 for S 5 and 0. 03 GeV 2 /c 4 for A FB. These uncertainties are negligible compared to the statistical uncertainties.
The results are in agreement with those obtained from the likelihood fit of the angular observables and the moments analysis. For A FB , the best-fit to the data (the line in the figure) has two zero-crossing points in the range 1. 1 < q 2 < 6. 0 GeV 2 /c 4 with different slopes. As discussed in Sec. 7. 3, only the solution consistent with the data in the range q 2 > 6. 0 GeV 2 /c 4 is cited, i. e. the solution with the positive slope (see Figs. 6  and 9 ). The zero-crossing points determined from the amplitude fit are  The observables S 4 , S 5 and A FB determined by fitting for the q 2 dependent decay amplitudes. The line indicates the best-fit to the dataset. The band indicates the 68% interval on the bootstraps at each point in q 2. Note that, the correlation between points in the bands means it is not possible to extract the uncertainty on the zero-crossing points from these figures.
Compatibility with the Standard Model The EOS software package  [57]  is used to determine the level of compatibility of the data with the SM. It provides predictions for the observables integrated over the q 2 bins used in the analysis. A χ 2 fit is performed to the CP -averaged angular observables F L , A FB and S 3 -S 9 obtained from the likelihood fit to the data. The χ 2 fit uses observables in the range q 2 < 8. 0 GeV 2 /c 4 and a wide q 2 bin covering the range 15. 0 < q 2 < 19. 0 GeV 2 /c 4. Previous analyses  [9] [10] [11] [12] [19] [20] [21]  have shown that the existing measurements of decays involving a b → s quark transition, including the previous LHCb P 5 result from Ref. [8] , can be accounted for by modifying only the real part of the vector coupling strength of the decays, conventionally denoted Re(C 9 ).
Conclusions This paper presents the first analysis of the full angular distribution of the B 0 → K * 0 µ + µ - decay. The analysis uses the complete LHCb Run 1 dataset and supersedes the results presented in Refs. [1, 8]. In addition to CP -averaged observables, a complete set of CP asymmetries of the angular distribution are measured for the first time. Correlations between the different observables are computed to allow the results to be included in global fits of b → s data. Three separate techniques are used to analyse the data. An unbinned maximum likelihood fit to the full angular distribution is made in approximately 2 GeV 2 /c 4 wide q 2 bins. Observables are also determined by computing moments of the angular distribution in q 2 bins approximately 1 GeV 2 /c 4 wide. In addition, for the first time, a q 2 -dependent fit is performed to the angular distribution in order to determine the six complex decay amplitudes that describe the decay.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FANO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF  (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
Modern dromedary populations from the Arabian Peninsula therefore were expected to display the highest level of genetic diversity and variation. To test this expectation, we combined two comprehensive datasets encompassing 759 mitochondrial (867 bp; end of cytochrome B, tRNAs threonine and proline, beginning of control region; MT-CR) and 970 multiloci (17 autosomal microsatellites) genotypes, covering five defined geographical regions  (26) : Eastern Africa (EAF, n = 170), Western and Northern Africa (WNAF, n = 233), North Arabian Peninsula (NAP, n = 349), South Arabian Peninsula (SAP, n = 181), and Southern Asia including Australia (SAS, n = 150) (Dataset S1). Shared genetic diversity and population structure in modern dromedaries. In contrast to the hypothesis that the greatest ancestral variation is retained close to the area of domestication  (20) , we observed similar amounts of heterozygosity (H E : 0.
g. , the Ethiopian Plateau and the swamps of the Sudd), physiological constraints (humidity, food plants, lack of salt, disease) and, perhaps most importantly, cultural barriers (SI Appendix, Fig. S4 ). Subtle population structure within the SAP. To investigate subtle population structure that might have been masked by the high distinctiveness of EAF, we excluded the latter from structure analysis and observed nine independent clusters (Fig. 2  and SI Appendix, Fig. S2B ). Despite substantial admixture, two dromedary populations (Awarik and Awadi; Dataset S1) from an isolated mountainous region in southwestern Saudi Arabia segregated. Dromedaries from Oman and UAE separated from the cluster containing Southern Asian individuals, whereas WNAF and NAP populations shared common ancestry and genetic diversity.
Within the latter only the Hadana breed (Dataset S1) appeared to have a contrasting genetic makeup (Fig. 2 ). Introduction of Arabian dromedaries into Africa. The absence of genetic structure between WNAF and NAP (ϕ ST = 0. 006; P < 0. 001; F ST = -0. 002; P > 0. 05) points to an extensive exchange of dromedaries introduced into northeastern Africa from the Arabian Peninsula via the Sinai (SI Appendix, Fig. S4 ), possibly starting in the early first millennium BCE and intensifying in the Ptolemaic period  (1, 17). From here, dromedaries spread across northern Africa, but their adoption into local economies may have been slow, considering that the first unequivocal evidence for their presence in northwestern Africa comes from archaeological layers dating to the fourth to the seventh century CE (Late Antiquity/Early Middle Ages) (SI Appendix).
This exchange was almost certainly camel-borne  (13). Approximate Bayesian computation inferences of domestication scenarios. Four scenarios can potentially explain the patterns of genetic diversity recorded in modern dromedaries: at the time of domestication, the initial gene pool was captured from: (i) one unique and diverse wild dromedary population; (ii) a primary small population of domesticates, with subsequent introgression of wild lineages into the early-domesticated gene pool; (iii) two independent source populations, each represented by one of the two observed ancestral lineages; or (iv) two source populations at successive time periods. Using approximate Bayesian computation (ABC) algorithms (  43 ) on a combined mitochondrial and microsatellite dataset (n = 642), we simulated these four different scenarios (SI Appendix, Fig.
This endorsement of the second scenario mirrors recent studies in pigs and other livestock in which a model incorporating continuous gene flow between a wild and a domestic species was better supported than traditional    S1  and Dataset S1. Fig. 3. BSP derived from the alignment of 759 modern with seven earlydomesticated dromedary MT-CR sequences. The thick solid line depicts the median estimate of N e , with black thin lines delimiting the 95% HPD. We used the archaeological dating of the wild and early-domesticated dromedary samples (SI Appendix, Table  S4 ) to estimate the substitution rate μ = 1. 232 × 10 -06 substitution•site -1 •y -1 (95% HPD: 4. 435 × 10 -07 , 2. 213 × 10 -06 ). LA, Late Antiquity; MA, Middle Ages. hypotheses assuming reproductive isolation  (15, 16). Because wild and early-domesticated dromedaries coexisted in the Arabian Peninsula for only a short time [probably less than 2,000 y  (8) ], the period of potential gene flow was rather short compared those for cattle  (16) , pigs  (15) , or horses  (25, 44, 45).
Conclusion The dromedary's fundamental role in the tradition of crosscontinental caravan networks gave rise to an intense sharing of genetic variation, blurring genetic signals about ancestral diversity and possible center of domestication. Nevertheless, using a large modern DNA dataset in combination with a number of ancient sequences, we were able to support a scenario with an initial domestication followed by consecutive introgression from wild populations echoing findings from other species (57), such as horses  (25, 44, 45) , cattle  (16) , and pigs  (15). Interestingly, in dromedaries, this restocking occurred from an unsourced wild "ghost" population, a pattern thus far observed in only few other domestic species (e. g. , pigs and dogs). A remarkable feature in the history of dromedary domestication is the substantial genetic diversity of the domestic population, given the temporally and geographically restricted coexistence of early-domestic animals and their wild ancestors, which already were heading to extinction when the domestic form emerged.
Materials and Methods Modern and Ancestral Genetic Diversity. Hair, blood, and saliva samples were collected commensally during routine veterinary treatments, and all owners agreed to the analysis; no further specific permissions were required from the Ethics Committee of the Vetmeduni Vienna for this study. To infer the genetic diversity, population structure, and differentiation of the modern and ancient dromedary populations, we performed genetic analyses on a total of 1,083 modern dromedaries originating from 21 countries, seven early-domesticated (400-1870 CE) specimens, and eight wild dromedary specimens (5000-1000 BCE) (Fig. 1A ). Wild dromedaries were classified based on the archaeological context (SI Appendix) and morphological differentiation  (12). Detailed information about samples is given in Dataset S1; collection, wet-laboratory, and in silico procedures are given in SI Appendix, Table  S4.
restocking from the wild, we used ABCtoolbox  (43)  on the combined (n = 642) mitochondrial and microsatellite dataset. For each of the four scenarios (SI Appendix, Fig. S6 ) we simulated a large number of datasets (1,000,000) using Fastsimcoal2  (64)  under the coalescent model drawing parameter values from prior distribution ranges (SI Appendix, Table  S10 ). We tested a maximum of 11 historical parameters and generated 15 summary statistics for each simulation in Arlequin3. 5  (65)  (SI Appendix, Table  S11 ). Summary statistics with highest pairwise correlations (R correlation test with Spearman's rho statistics; SI Appendix, Fig. S9 ) were removed, resulting in 12 summary statistics for further analysis. With the 5,000 simulations closest to the observed dataset, we evaluated model differentiation with the R package abc (66) (SI Appendix, Fig.
ACKNOWLEDGMENTS. We specifically thank the camel owners; the research group at the  Management of Scientific Centres and Presidential Camels ;  His Highness the President Sheikh Khalifia Bin Zayed Al Nahayan ;  R. Saleh ,  D. Tabbaa ,  I. Awaddi ,  A. Perret ,  J. Perret ,  P. Spencer ,  S. Shahkarami , and  M. Ismail , who collected samples; the  International Livestock Research Institute ;  S. A. Jasim ,  Director of Archaeology in the Emirate of Sharjah, UAE , who facilitated research at al-Buhais and  Tell Abraq ;  C. Vogl ,  C. Schlötterer , and  M. Müller  for scientific support; and the editor and two reviewers for their thoughtful comments on an early version of the manuscript. We acknowledge the use of the  High Performance Computing Cluster, Organisms and Environment Division, School of Biosciences, Cardiff University.
Funding was provided by Grant  KFU ,  F289  from  King Faisal University, Saudi Arabia  (to F. A. ), Grant  NBAF419  from the  Natural Environment Research Council ,  Sheffield University ,  United Kingdom  (to O. H. );  European Research Council   Consolidator Grant 310763 GeneFlow  (to M. H. ); and  Austrian Science Fund  Grants  P21084-B17  and  P24706-B25  (to P. A. B. ). P. A. B. is the recipient of  Austrian Programme for Advanced Research and Technology Fellowship   1106/12  from the  Austrian Academy of Sciences.
The rest of the observations included in this work are published here for the first time. We reduced the NuSTAR observations using the standard pipeline, part of the NuSTAR Data Analysis Software v1. 4. 1 FIG. 2. -Left panel: The X-ray spectra from the four NuSTAR observations of Cygnus X-1 in its soft state. The spectra have been unfolded through a model that is constant with energy. Right panel: Data/model ratios for these data after being fit with a simple model consisting of an accretion disk and a high-energy powerlaw tail, fit to the 3-4, 8-10 and 50-79 keV bands. In each case, the residuals imply the presence of a strong reflection component from the accretion disk (see  Tomsick et al. 2014). Notably, a relativistically broadened iron line is seen in all four NuSTAR soft state observations.