dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work the impact of nuclear models on fission observables, i. e. the outputs of the code, is investigated. By comparing the observables estimated by the code with the available experimental data, the relevance of the models can be inferred. If results related to major observables are in good agreement with available data, other calculated observables, which could not be measured, could be legitimately considered as pertinent. This confers to FIFRELIN a predictive power. This paper will focus on the impact of the spin cut-off parameter models which defined the total angular momentum distribution from which the initial spins of the fragments are sampled. This entity mainly drives the characteristics of gammas such as their energy, their multiplicity and their spectrum. In order to perform this study, only the spin cut-off parameters of the simulation are tuned, the other parameters are unchanged.
5 The results presented here consist of the analysis of previously obtained Doppler measurements (pre-2016 data), and the confirmation of a signal in a specifically designed follow-up campaign in 2016. The Doppler data comes from two precision radial velocity instruments, both at the European Southern Observatory (ESO): the High Accuracy Radial velocity Planet Searcher (HARPS) and the Ultraviolet and Visual Echelle Spectrograph (UVES). HARPS is a high-resolution stabilized echelle spectrometer installed at the ESO 3. 6m telescope (La Silla observatory, Chile), and is calibrated in wavelength using hollow cathode lamps. HARPS has demonstrated radial velocity measurements at ∼1 ms -1 precision over time-scales of years, 6 including on low-mass stars. 7 All HARPS spectra were extracted and calibrated with the standard ESO Data Reduction Software, and radial velocities were measured using a least-squares template matching technique.
7 HARPS data is separated into two datasets. The first set includes all data obtained before 2016 by several programmes (HARPS pre-2016). The second HARPS set comes from the more recent Pale Red Dot campaign (PRD hereafter), which was designed to eliminate period ambiguities using new HARPS observations and quasi-simultaneous photometry. The HARPS PRD observations consisted of obtaining one spectrum almost every night between Jan 19th and March 31st 2016. The UVES observations used the Iodine cell technique 8 and were obtained in the framework of the UVES survey for terrestrial planets around M-dwarfs between 2000 and 2008. The spectra were extracted using the standard procedures of the UVES survey, 9 and new radial velocities were re-obtained using up-to-date Iodine reduction codes. 10 Since systematic calibration errors produce correlations among observations within each night, 11 we consolidated Doppler measurements through nightly averages to present a simpler and more conservative signal search.
If the evidence ratio exceeds some threshold (e. g. B 1 /B 0 > 10 3 ), then the model in the numerator (with one planet) is favoured against the model in the denominator (no planet). A well isolated peak at ∼11. 2 days was recovered when analyzing all the night averages in the pre-2016 datasets (Figure  1 , panel a). Despite the significance of the signal, the analysis of pre-2016 subsets produced slightly different periods depending on the noise assumptions and which subsets were considered. Confirmation or refutation of this signal at 11. 2 days was the main driver for proposing the HARPS PRD campaign. The analysis of the HARPS PRD data revealed a single significant signal at the same ∼ 11. 3 ± 0. 1 day period (Figure  1  Radial velocity measurements phase folded at the 11. 2 day period of the planet candidate for 16 years of observations.
Although its nature is unclear, a second signal at P∼ 200 days was fitted and subtracted from the data to produce this plot and improve visualization. Circles correspond to HARPS PRD, squares are HARPS pre-2016 and triangles are UVES observations. The black line represents the best Keplerian fit to this phase folded representation of the data. Error bars correspond to formal 1-σ uncertainties. alone did not prove consistency with the pre-2016 data. Final confirmation was achieved when all the sets were combined (Figure  1 , panel c). In this case statistical significance of the signal at 11. 2 days increases dramatically (false-alarm probability < 10 -7 , Bayesian evidence ratio B 1,0 > 10 6 ). This implies that not only the period, but also the amplitude and phase are consistent during the 16 years of accumulated observations (see Figure  2 ).
Spectroscopic datasets 2. 1 New reduction of the UVES M-dwarf programme data. Between 2000 and 2008, Proxima was observed in the framework of a precision RV survey of M dwarfs in search for extrasolar planets with the Ultraviolet and Visual Echelle Spectrograph (UVES) installed in the Very Large Telescope (VLT) unit 2 (UT2). To attain high-precision RV measurements, UVES was self-calibrated with its iodine gas absorption cell operated at a temperature of 70 • C. The image slicer #3 was chosen which redistributes the light from a 1 ′′ × 1 ′′ aperture along the chosen 0. 3 ′′ wide slit. In this way, a resolving power of R = 100, 000 -120, 000 was attained. At the selected central wavelength of 600 nm, the useful spectral range containing iodine (I 2 ) absorption lines (≈ 500 -600 nm) falls entirely on the better quality detector of the mosaic of two 4K × 2K CCDs.
More details can be found in the several papers from the UVES survey. 9, 45, 50  T extracted UVES spectra include 241 observations taken through the Iodine cell, three template (no Iodine) shots of Proxima, and three spectra of the rapidly rotating B star HR 5987 taken through the Iodine cell as well, and almost consecutive to the three template shots. The B star has a smooth spectrum devoid of spectral features and it was used to calibrate the three template observations of the target. Ten of the Iodine observations of Proxima were eliminated due to low exposure levels. The remaining 231 iodine shots of Proxima were taken on 77 nights, typically 3 consecutive shots per night. The first steps in the process of I 2 calibrated data consists of constructing the high signal to noise template spectrum of the star without iodine: 1) a custom model of the UVES instrumental profile is generated based on the observations of the B star by forward modeling the observations using a higher-resolution (R = 700, 000 -1, 000, 000) template spectrum of the I 2 cell obtained with the McMath Fourier Transform Spectrometer (FTS) on Kitt Peak, 2) the three template observations of Proxima are then co-added and filtered for outliers, and 3) based on the instrument profile model and wavelength solution derived from the three B star observations, the template is deconvolved with our standard software.
Chromospheric H α emission. This index was measured in a similar way to the S-indices, such that we summed the fluxes in the center of the lines, calculated to be 6562. 808 Å, this time utilising square bandpasses of 0. 678 Å not triangular shapes, and those were normalized to the summed fluxes of two square continuum band regions surrounding each of the lines in the time series. The continuum square bandpasses were centered at 6550. 870 Å and 6580. 309 Å and had widths of 10. 75 Å and 8. 75 Å, respectively. Again the uncertainties were calculated from the quadrature sum of the variance of the data within the bandpasses. The number of images collected per night was increased during the second part of the campaign (until about 40 images in each filter per night). All CCD measurements were obtained by the method of synthetic aperture photometry using a 2 × 2 binning.
Module of the Window function. We first present the so-called window function of the three sets under discussion. The window function is the Fourier transform of the sampling. 61 Its module shows the frequencies (or periods) where a signal with 0 frequency (or infinite period) would have its aliases. As shown in Extended Data Figure  1 , both the UVES and HARPS PRD campaigns have a relatively clear window function between 1 and 360 days, meaning that peaks in periodograms can be interpreted in a very straightforward way (no aliasing ambiguities). For the UVES case, this happens because the measurements were uniformly spread over several years without severe clustering, producing only strong aliases at frequencies beating caused by the usual daily and yearly sampling (peaks at 360, 1, 0. 5 and 0.
Radial velocities. Here we present likelihood-ratio periodogram searches for signals in the three Doppler time-series separately (PRD, HARPS pre-2016, and UVES). They are analyzed in the same way as the activity indices to enable direct visual comparison. They differ from the ones presented in the main manuscript in the sense that they do not include MA terms and the signals are modelled as pure sinusoids to mirror the analysis of the other time-series as close as possible. The resulting periodograms are shown in Extended data Figure  2. A signal at 11. 2 days was close to detection using UVES data-only. However, let us note that the signal was not clearly detectable using the Doppler measurements as provided by the UVES survey,  45  and it only became obvious when new Doppler measurements were re-derived using up-to-date Iodine codes (Section 2.
1). The signal is weaker in the HARPS pre-2016 dataset, but it still appears as a possible second signal after modeling the longer term variability with a Keplerian at 200 days. Sub-sets of the HARPS pre-2106 data taken in consecutive nights (eg. HARPS high-cadence runs) also show strong evidence of the same signal. However splitting the data in subsets adds substantial complexity to the analysis and the results become quite sensitive to subjective choices (how to split the data and how to weight each subset). The combination UVES with all the HARPS pre-2016 (Figure  1 , panel a) already produced a FAP of ∼1%, but a dedicated campaign was deemed necessary given the caveats with the sampling and activity related variability. The HARPS PRD campaign unambiguously identifies a signal with the same ∼ 11.
Photometry. Signals and calculation of the FF ′ index. The nightly average of the four photometric series was computed after removing the measurements clearly contaminated by flares (see Figure  3  in main manuscript). This produces 43 LCOGT epochs in the B and V bands (80 nights), and 66 ASH2 epochs in both SII and H α bands (100 nights covered). The precision of each epoch was estimated using the internal dispersion within a given night. All four photometric series show evidence of a long period signal compatible with a photometric cycle at 83-d (likely rotation) reported before. 3 See periodograms in Extended data Figure  3. In the presence of spots, it has been proposed that spurious variability should be linearly correlated with the value of the normalized flux of the star F , the derivative of the flux F ′ , and the product of FF ′62 in what is sometimes called the FF ′ model.
Width of the mean spectral line as measured by m 2. The m 2 measurement contains a strong variability that closely mirrors the measurements from the photometric time-series (see Figure  3  in the main manuscript). As in the photometry, the rotation period and its first harmonic (∼ 40 days) are clearly detected in the PRD campaign (see Extended data Figure  4 ). This apparently good match needs to be verified on other stars as it might become a strong diagnostic for stellar activity in M-stars. The analysis of the HARPS pre-2016 also shows very strong evidence that m 2 is tracing the photometric rotation period of 83 days. The modelling of this HARPS pre-2016 requires a second sinusoid with P 2 ∼ 85 days, which is peculiar given how close it is to P 1. We suspect this is caused by photospheric features on the surface changing over time.
Asymmetry of the mean spectral lines as monitored by m 3 The periodogram analysis of m 3 of the PRD run suggests a signal at 24 days which is close to twice the Doppler signal of the planet candidate (see Extended Data Figure  5 ). However, line asymmetries are expected to be directly correlated with Doppler signals, not at twice nor integer multiples of the Doppler period. In addition, the peak has a FAP∼ 5% which makes it non-significantly different from white noise. When looking at the HARPS pre-2016 data, some strong beating is observed at 179 and 360 days, which is likely caused by a poorly sampled signal at that period or longer (magnetic cycle?), or some residual systematic effect (contamination by tellurics?). In summary, m 3 does not show evidence of any stable signal in the range of interest.
This indicates that there is likely to be strong interference patterns from the sampling in this region, and that the signal in the radial velocity data is also not due to the sampling of the data. A similar study in the context of the HARPS M-dwarf program was also done on Proxima. 63 ey compared several indices and finally decided to use the intensity of the chromospheric sodium doublet lines. They did not report any significant period at the time, but we suspect this was due to using fewer measurements, and not removing the frequent flaring events from the series, which also requires compilation of a number of observations to reliably identify outliers caused by flares.
Signal searches in H α emission Our likelihood-ratio periodograms for H α (Extended Data Figure  7 ) only show low significance peaks in the 30-40 days period range. It is important to note that the analyses described above have been performed on multiple versions of the dataset, in the sense that we analysed the full dataset without removing measurements affected by flaring, then proceeded to reanalyse the activities by dropping data clearly following the flaring periods that Proxima went through when we observed the star. This allowed us to better understand the impact that flares and outliers have on signal interference in the activity indices. Although the distribution of peaks in periodograms changes somewhat depending on how stringent the cuts are, no emerging peaks were seen close to an 11 day period.
where ξ runs over all the activity indices used to model each INS dataset (e. g. m 2 , m 3 , S-index, etc. whose description is provided below). To avoid any confusion with other discussions about correlations, we call these C ξ,INS activity coefficients. Note that each activity coefficient C ξ,INS is associated to one activity index (ξ i ) obtained simultaneously with the i-th radial velocity measurement (e. g. chromospheric emission from the H α line, second moment of the mean-line profile, interpolated photometric flux, etc. ). When fitting a model to the data, an activity coefficient significantly different from 0 indicates evidence of Doppler variability correlated with the corresponding activity index. Formally speaking, these C ξ,INS correspond to the coefficient of the first order Taylor expansion of a physical model for the apparent radial velocities as a function of the activity indices and other physical properties of the star.
Signal searches on the assymetry of the spectral lines. Likelihood periodogram searches on the line asymmetry as measured by m 3 from the HARPS pre-2016 (panel a) and HARPS PRD (panel b) datasets. A signal beating at ∼ 1 year and 1/2 year is detected in the HARPS pre-2016 data, possibly related to instrumental systematic effects or telluric contamination. No signals are detected above 1% threshold in the HARPS PRD campaign. Black and red lines represent the search for first and second signals respectively.
Acknowledgements. We thank  Enrico Gerlach ,  Rachel Street  and  Ulf Seemann  for their support to the science preparations. We thank  Javier Pascual-Granado  and  Rafael Garrido  for their insights on the properties of the putative signals in the Doppler time-series. We thank  Predrag Micakovic ,  Matthew M. Mutter  (QMUL),  Rob Ivison ,  Gaitee Hussain ,  Ivo Saviane ,  Oana Sandu ,  Lars Lindberg Christensen ,  Richard Hook  and the personnel at  La Silla  ( ESO ) for making the Pale Red Dot campaign possible. The authors acknowledge support from funding grants;  Leverhulme Trust/UK  RPG-2014-281 (HRAJ, GAE, MT),  MINECO/Spain   AYA-2014-54348-C3-1-R  (PJA, CRL, ZMB, ER),  MINECO/Spain   ESP2014-54362-P (MJLG ),  MINECO/Spain   AYA-2014-56637-C2-1-P ( JLO, NM ), J. A. /Spain 2012-FQM1776 (JLO, NM),  CATA-Basal/Chile   PB06  Conicyt (JSJ),  Fondecyt/Chile  project # 1161218  (JSJ),  STFC/UK ST/M001008/1  ( RPN, GALC, GAE ),  STFC/UK ST/L000776/1  (JB),  ERC/EU  Starting Grant # 279347  (AR, LS, SVJ),  DFG/Germany Research  Grants  RE 1664/9-2 (AR),  RE 1664/12-1 (MZ),  DFG/Germany Colloborative Research Center 963(CJM, SD) ,  DFG/Germany Research Training Group 1351 (LS), and  NSF/USA  grant  AST-1313075  (ME).
5 : 5 Signal searches on the assymetry of the spectral lines. Likelihood periodogram searches on the line asymmetry as measured by m 3 from the HARPS pre-2016 (panel a) and HARPS PRD (panel b) datasets. A signal beating at ∼ 1 year and 1/2 year is detected in the HARPS pre-2016 data, possibly related to instrumental systematic effects or telluric contamination. No signals are detected above 1% threshold in the HARPS PRD campaign. Black and red lines represent the search for first and second signals respectively.
4 Photometric datasets 4. 1 Astrograph Southern Hemisphere II. The ASH2 (Astrograph for the South Hemisphere II) telescope is a 40 cm robotic telescope with a CCD camera STL11000 2. 7K x 4K, and a field-of-view (FOV) of 54 x 82 arcmin. Observations were obtained in two narrow-band filters centered on H α and SII lines, respectively (H α is centred on 656 nm, SII is centered on 672 nm, and both filters have a Gaussian-like transmission with a FWHM of 12 nm). The telescope is at SPACEOBS (San Pedro de Atacama Celestial Explorations Observatory), at 2450 m above sea level, located in the northern Atacama Desert, in Chile. This telescope is managed and supported by the Instituto de Astrofísica de Andalucía (Spain). During the present work, only subframes with 40% of the total field of view were used, resulting in a useful FOV of 21.
Acknowledgments We would like to thank anonymous reviewers for their helpful comments and suggestions. We also thank  Jichang Zhao ,  Qiao Kan ,  Xu Feng  and  Shaowei Cai  for their proofreads and suggestions. Part of this work was done while the first author was a joint Ph. D. student at  Université de Picardie Jules Verne. This research was partly supported by  NSFC  (Grant No. 61421003 ), the fund of the  State Key Lab of Software Development Environment  (Grant No. SKLSDE-2015ZX-05 ), the  Chinese State Key Laboratory of Software Development Environment Open Fund  (Grant No. SKLSDE-2012KF-07 ), and the  MeCS platform of Université de Picardie Jules Verne.
Progress on the development of the multi-channel, ground penetrating radar imaging system is presented from hardware and software perspectives. A new exponentially tapered slot antenna, with an operating bandwidth from 100 MHz to 1. 5 GHz was fabricated and tested using the eight-port vector network analyzer, designed by Rhode and Schwarz Incorporated for this imaging project. An eight element antenna array mounted on two carts with automatic motor drive, was designed for optimal common midpoint (CMP) data acquisition. Data acquisition scenarios were tested using the acoustic version of the NORSAR2D seismic raytracing software. This package enables the synthesis and analysis of multi-channel, multi-offset data acquisitions comprising more than a hundred thousand traces. Preliminary processing is in good agreement with published bistatic ground-penetrating radar images obtained in the tunnels of the Low-noise Underground Laboratory (LSBB) at Rustrel, France.
i-DUST 2016 Concurrent progress in the development of a data simulation platform was demonstrated by using the NORSAR2D ray-tracing software to simulate data that will be recorded at LSBB. Preliminary results demonstrate the viability of the ray-tracing approach necessary to model the 128,000 traces that will be collected at LSBB. This modelling is a necessary first step in creating an inversion algorithm for the determination of subsurface permittivity.
To quantify HRP-BoNT/A-Hc endocytosis, and the colocalization between HRP-CTB and BoNT/A-Hc-5 nm Au, ultrathin (55 nm) presynaptic regions were visualized at 60,000x using a transmission electron microscope (model 1011; JEOL) equipped with a Morada cooled CCD camera and the iTEM AnalySIS software. Membrane-bound compartments within the presynaptic region were counted and scored for the presence or absence of the DAB reaction product or gold particles. The maximum diameter was measured using Image J software. Compartments <80 nm in diameter were classified as synaptic vesicles and compartments >80 nm as endosomes. For high-resolution tomography, both sides of a thick section (300 nm) were labeled for 8 min with 10 nm colloidal gold diluted 1:10 in H 2 O, followed by three 5 min water washes, to generate fiducial markers.
A Tecnai F30 microscope (FEI) was used to image dual-axis tilt series at 1. 5° increments from -66° to +66° at an accelerating voltage of 300 kV. Images were captured using an LC-1100 4K x 4K camera (Direct Electron), at a binning of 2, using SerialEM image acquisition software  40. Tilt series alignments and weighted back projection reconstructions were performed using IMOD software (Boulder Laboratories for 3D Electron Microscopy of the Cell, University of Colorado). Areas to be imaged were selected from five different presynaptic regions in groups of neurites from two independent experiments. Synapses were identified by dense clusters of 40-60 nm vesicles together with the post-synaptic density. Organelles were manually segmented at high fidelity and meshed in 3dmod as previously described  41.
The cultured neurons were then washed extensively with low K + buffer containing 10 μ M CNQX/ NBQX and allowed to recover for 12-15 min. Cells were subsequently imaged on an inverted microscope (Nikon Eclipse Ti, 100x, NA 1. 49) equipped with a Perfect Focus System and an EMCCD camera (EVOLVE 512 Delta, Photometrics), and controlled using Metamorph software (Molecular Devices). Images were acquired every 4 s, during which the neurons were stimulated by adding 1 M KCl or NaCl in low K + buffer to a final concentration of 30 mM. Alternatively, 50 mM NH 4 Cl was added during imaging to quench the fluorescence of the pHrodo dye. Time-lapse movies were analyzed using ImageJ. Nerve terminals were selected based on their fluorescence intensity and punctate structure, and their average fluorescent intensity was determined using the plugin, Time Series Analyzer.
GSDIM data analysis. Raw time-lapse movies acquired on a GSDIM microscope were processed using the open source software, Localizer, run from Igor Pro (Wavemetrics)  45  to obtain coordinates for the individual molecules. To partially account for multiple appearances of molecules, the localizations that appeared within 2 frames and 50 nm of one another were consolidated. Coordinates with a calculated localization precision  46  between 10 and 40 nm were retained for further analysis. A custom written program in Matlab (The Mathworks) was used to correct for spatial misalignment of the two channels  47. Datasets were reconstructed with a pixel size of 10 nm and regions of interest were selected from reconstructed 2D histograms to quantify the clustering of the proteins using two methods: Ripley's K-function was used to generate cluster maps  18, 19, 48  , whereas pair-correlation analyses were carried out to quantify the clustering of the proteins  20, 21.