dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization of Fine Cellulosic Materials The morphology of the fibers and fibrils from the samples was determined using a L&W Fiber Tester+. Frequency distribution, average fiber length and ECD (Equivalent Circular Diameter) were calculated (weight weighted) using a MATLAB routine processing the raw data obtained by the device. The fines fraction of the different cellulosic materials produced in high-pressure homogenization was measured using a Britt Dynamic Drainage Jar according to TAPPI Test Method T-261 cm-10 (fines fraction by weight of paper stock by wet screening). Highly fibrillated material is difficult to detect using flow imaging. The contrast of such highly swollen particles in the flow cell images from the L&W Fiber Tester+ is sometimes too low, which strongly affects the detection of the highly fibrillated parts.
Thus, in order to accurately identify the fibrillar content of the cellulosic fine materials, additional microscopic samples were produced, using a specific staining procedure to improve the identification of the fibrillar content. The different cellulosic materials were diluted to 0. 01% w/w and dyed using a combination of a tall oil-water emulsion (0. 2 wt. % in water) with a methylene blue solution (1 wt. % in water), reaching a uniform coloration  [21]. Microscope slides were prepared from the stained samples and characterized using a method described in Mayr  [21] , using a conventional transmission light microscope (Leica 301-371. 010) equipped with a standard CCD camera (Jai AM-200GE/AB-200GE) and an automated stage control (Märzhäuser Multicontrol 2000). Image analysis was performed using the open source software ImageJ (Bethesda, Maryland, USA) for automated capturing of 700-800 single images per microscope slide (1600 × 1200 pixels per image; image area: 1380 × 1035 µm 2 ).
Data analysis was performed using R program (R Development Core Team, 2008). The surface topography of the different papers was investigated using low-voltage scanning electron microscopy (LVSEM, Everhart-Thornley detector for the detection of secondary electrons; Zeiss Sigma 300, Oberkochen, Germany) using the method described in Fisher et al. , 2014. The samples were cut (1 cm × 1 cm), then attached to SEM stubs using a double-sided conductive carbon tape, and imaging (magnification 500×) was performed at an acceleration voltage of 0. 65 kV. Finally, the secondary fines (SFines) present the shortest fibers (185. 3 µm in average) with the narrowest distribution. Since this material results from fractionation through a pressure screen, it is natural to observe the narrowest distribution, since long fibers have been separated.
Acknowledgments: We are grateful for the support by our industry partners in the frame of the FLIPPR 2 project,  Mondi, Sappi ,  Zellstoff Pöls AG , a member of Heinzel pulp, and  Papierholz Austria. The K-Project Flippr 2 is funded as part of  COMET-Competence Centers for Excellent Technologies  promoted by  BMVIT, BMWFW, Styria and Carinthia. The COMET program is managed by  FFG.
Image Pro-Plus imaging software (Media Cybernetics, MD, USA) was used to count bubbles and estimate bubble size and shape parameters. When the column operated under steady-state conditions, the sampling procedure consisted in collecting online the outlet stream directly in a rectangular quartz cell of 2 mm in width designed to be mounted on the microscope. This procedure prevented bubble loss with time, limited distortion due to structural damage, and provided a constant sample thickness, which resulted in a good sampling reproducibility. As bubbles were spherical, only bubble diameter was measured, and a semi-automated image analysis procedure could be applied  [10, 15]. When all the bubbles present in an image were treated, bubble size distribution and average bubble size could be calculated automatically using the Sauter mean diameter d 32 equation: d 32 = ∑ n i=1 d 3 i ∑ n i=1 d 2 i , n ∈ N, 3 which takes both bubble volume and bubble interfacial area into account.
Device-Specific Issues TriOS RAMSES series instruments include both the radiance (ARC) and irradiance (ACC) sensors. The raw spectra are stored in American Standard Code for Information Interchange (ASCII) and/or Microsoft ACCESS database files. Data processing for these radiometers is fully unified based on the measured data (2-byte integer numbers) and calibration files provided by the manufacturer and TO. The detailed procedure to derive the calibrated results is described in  [4]. RAMSES instruments are equipped with black-painted pixels on the photodiode array used to compensate for the dark signal and electronical drifts. The background spectrum (with the external shutter closed) was subtracted as well. For subtraction, only the spectra with matching integration times were used. Before division by the responsivity coefficients, linearity correction was applied, see Section 4.
Users can start the acquisition of the spectra by pressing a button, the internal computer is setting the measurement sequence, determining the integration times, and storing the data. All three channels are acquired simultaneously and the data are stored into a single ASCII file. The spectrometers have painted detector array pixels like the RAMSES radiometers. The internal dark signal is subtracted automatically and resulting data are stored in the form of floating point numbers. The only operation needed was the division by the responsivity coefficients determined by TO using the same manual measurement sequence. The linearity correction described in Section 4. 1. 4 was not used. Spectral Evolution SR-3500 spectrometer is equipped with an optical fiber input and interchangeable radiance and irradiance fore-optics.
In these figures, blue dashed lines show the expanded uncertainty covering 95% of all data points on the right graphs. Solid lines represent RAMSES sensors, dashed lines-HyperOCR sensors, double line-SR-3500, and dotted lines-WISP-3 sensors. Larger variability of the results initially reported by participants was caused by applying out-of-date calibration coefficients, by diversely applying or not applying the non-linearity correction (Section 4. 1. 4) or calculating the OLCI band values differently. For unified data handling carried out by the pilot and described in 3. 1 to 3. 3, the calibration results obtained during LCE-2 were used, non-linearity correction was applied, OLCI band values were calculated by using individual weights as determined from the wavelength scale of each radiometer.
2 and 2. 3). Two sets of calibration coefficients were obtained for each sensor and the difference between the lamps was presented as the ratio of these coefficients. The curves in Figure  9  are calculated as standard deviations from the ratios of a whole set of calibration coefficients determined by using the two standard lamps. The systematic difference between lamps (due to small difference in traceability to SI) is neglected and only the other uncertainty components related to individual setting up and measurement of radiometers are accounted for by using standard deviation. Data in Figure  9  include calibration of more than 25 sensors for LCE-2 intercomparison and also for FICE-AAOT intercomparison one year later when a different pair of lamps was used. Remarkable in Figure  9  is the rapid increase of variability between sensors in the UV region.
However, rapid changes in the responsivity of some TriOS RAMSES irradiance sensors may cause even larger deviations which cannot be explained by other factors than the instability of the sensor itself. No quick changes were observed for the RAMSES radiance sensors, however, even after omitting outliers from the stability data of irradiance sensors, the stability of RAMSES radiance sensors is still better. stability of the sensors is likely better than 5% to 10% revealed from the previous calibration history, where the differences were likely caused by other factors such as different calibration standards, environmental conditions, calibration setups and methodologies, etc. However, rapid changes in the responsivity of some TriOS RAMSES irradiance sensors may cause even larger deviations which cannot be explained by other factors than the instability of the sensor itself.
No quick changes were observed for the RAMSES radiance sensors, however, even after omitting outliers from the stability data of irradiance sensors, the stability of RAMSES radiance sensors is still better. stability of the sensors is likely better than 5% to 10% revealed from the previous calibration history, where the differences were likely caused by other factors such as different calibration standards, environmental conditions, calibration setups and methodologies, etc. However, rapid changes in the responsivity of some TriOS RAMSES irradiance sensors may cause even larger deviations which cannot be explained by other factors than the instability of the sensor itself. No quick changes were observed for the RAMSES radiance sensors, however, even after omitting outliers from the stability data of irradiance sensors, the stability of RAMSES radiance sensors is still better.
Interpolation Interpolation of radiometer's data is needed due to differences between individual wavelength scales of the radiometers. Therefore, measured values were transferred to a common scale basis (Sentinel-3/OLCI bands) for comparison, see 3. 3. The uncertainty contribution associated with interpolation of spectra is estimated from calculations using different interpolation algorithms. The weights used for binning hyperspectral data to OLCI bands depend on the wavelength scale and exact pixel positions of the hyperspectral sensor. In Table  3 , the interpolation components include the contribution of wavelength scale uncertainty estimated from data presented in Figure  14  of  [3].
The calibration of the CHANG-ES data was conducted with the Common Astronomy Software Application Package (CASA;  McMullin et al. 2007 ) and a standard calibration technique was used 4. Because of the available resolution of the LoTSS data, ≈5 and ≈20 , and the aim of looking at the halo of the galaxy, the C band and L band CHANG-ES data were used for the analysis presented in this work.
Hα imaging We retrieved Hα and R-band images and the corresponding flat-field and bias images from the Isaac Newton Group (ING) archive. The data were taken on 5 Feburary 2008 with the Isaac Newton 2. 5 m telescope (INT) and the Wide Field Prime Focus CCD mosaic camera (WFC). Three exposures of 250 s through the WFCH6568 Hα filter (central wavelength 656. 8 nm, filter band Full Width Half Maximum (FWHM) = 9. 5 nm) and one 200 s exposure in the Harris R-band filter (central wavelength 638. 0 nm, filter band FWHM = 152. 0 nm) were taken. Since NGC 3556 fits fully into one CCD chip, we only reduced this part of the data using the standard CCD reductions tools in IRAF 5. Pixels that were affected by cosmic rays have been corrected using the Image Reduction and Analysis Facility (IRAF) version of the LACOSMIC routine (van Dokkum 2001).
Imaging In Sect. 3 we present total power and spectral index maps from the CHANG-ES and LOFAR data and a map of the magnetic field strength. For the CHANG-ES data, the image deconvolution with the CLEAN algorithm was performed via CASA. For deconvolution, the robust value of the Briggs weighting scheme  (Briggs 1995)  was set to 2 (close to natural weighting) to emphasise faint halo emission. Subsequent images were smoothed, if not indicated otherwise, to a circular synthesised Gaussian beam of 21 FWHM in order to have a uniform set of images while emphasising faint radio haloes. Table  1  summarises the properties of the images.
Acknowledgements. The data used in work was in part processed on the Dutch national e-infrastructure with the support of  SURF Cooperative  through grant e-infra 160022 & 160152. BNW acknowledges support from the  Polish National Centre of Sciences (NCN) , grant no. UMO-2016/23/D/ST9/00386. This paper is based (in part) on data obtained with the  International LO-FAR Telescope (ILT)  under project code  LC3_008. LOFAR (van Haarlem et al. 2013) is the LOw Frequency ARray designed and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources) and are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris , and  Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education , Poland.
Part of this work was carried out on the Dutch national e-infrastructure with the support of the SURF Cooperative through grant e-infra 160022 & 160152. The LOFAR software and dedicated reduction packages on https://github. com/apmechev/GRID_LRTwere deployed on the einfrastructure by the LOFAR e-infragroup, consisting of  J. B. R. Oonk  ( ASTRON & Leiden Observatory ),  A. P. Mechev  ( Leiden Observatory ) and  T. Shimwell  ( ASTRON ) with support from  N. Danezi  (SURFsara) and  C. Schrijvers  (SURFsara). This research has made use of data analysed using the  University of Hertfordshire  high-performance computing facility (http://uhhpc. herts. ac. uk/) and the  LOFAR-UK computing facility  located at the  University of Hertfordshire  and supported by  STFC  [ ST/P000096/1 ]. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P.
Assessment of performances Our results suggest that the dissimilarity curve obtained by comparing a given network and its different perturbed versions captures relevant features of the original connectivity, which suggests it can be directly used to compare two networks. To assess the different metrics' performances we quantify the 'discriminability' and the 'runtime'. Discriminability assesses whether a given function h is sensitive at certain perturbation p, and whether it is suitable to distinguish two different group of networks at a given p. Discriminability is defined as the percentage of times a function h distinguishes the differences of each group of networks at certain perturbation level. The more times h distinguishes two different datasets, the better the h discriminability is.
Real networks In this work, we evaluate metric's performances upon two dataset of different nature: functional brain connectomes and social networks. We use a recently published brain connectivity dataset  [22] , which includes functional connectivity matrices estimated from magnetoencephalographic (MEG) signals recorded from 23 Alzheimer patients (P) and a set of controls subjects (C) during a condition of resting-state with eyes-closed  [23]. Alzheimer disease is caracterised by anatomical brain deteriorations, which are reflected in an abnormal brain connectivity. MEG activity was reconstructed on the cortical surface by using a source imaging technique  [23]. Connectivity matrices were obtained from N=148 regions of interest by means of the spectral coherence between activities in the band of 11-13 Hz.
We repeat this process for 20 independent realizations and then average the distance profiles for each h. Results displayed in figure  3  suggest that Euclidean distance distinguishes better than the other two metrics the change of alliance patterns among workers observed during the two periods of time t 1 and t 2. We assessed the execution time for computing a distances profile for each subject (we used MATLAB R2017a ran in an OS 10. 12. 6, 4 GHz Intel dual core i7 processor and 32 GB memory). Figure  4  shows the relatives orders of magnitude in seconds that each metric takes to compute the networks differences. For the analysis of brain connectomes, the average times obtained are: t f =6. 83×10 -  , t d =2. 68×10 -  , t k =1. 90×10 -1 for the Euclidean distance, the dissimilarity metric and the kernel-based method, respectively.
This procedure allows 'normalizing' for the potential influences of changes in the number of connections. For brain connectomes, we estimate the distance between the aggregate (averaged) network of each group of subjects/patients. For the analysis of social networks, instead, we used the original social interaction matrices. For both dataset we create a set of 100 surrogate networks as described above and compare, by means of a zvalue, a given distance between the original networks with that obtained from surrogate pairs. Table  1  depicts z-values for the three metrics. Interestingly, the low z-values obtained by D d suggest that this distance mainly reflects differences in the degree distribution. In contrast, the Euclidean and kernel-based distances seem to capture structural differences beyond the degree distribution or density.
| Environmental data Remotely sensed environmental data were analysed to test the effects of temperature and upwelling intensity on the reproductive tactics and productivity of E. fimbriata. Monthly environmental data [sea surface temperature (SST in °C), Chlorophyll-a (Chl-a in mg/m 3 ) concentration and coastal upwelling index (CUI in m 3 s -1 m -1 )] were compiled for the area of the PCS over an eight-year period (Figure  1 ). SST and Chl-a data from January 2004 to December 2012 were obtained from the website of the U. S. National Aeronautics and Space Administration (NASA) (http://ocean color. gsfc. nasa. gov, last visited: 12/11/2017). Mean monthly remote-sensed data were acquired at a 5-km resolution from the coastline to the 200-m isobath, where upwelling-induced SST anomalies are generally maximal  (Thiaw et al.
, 2017). A CUI was deduced from wind speed data obtained from the website of the U. S. National Oceanic and Atmospheric Administration (NOAA) (http://www. pfeg. noaa. gov: Upwelling and Environmental Index Products, last visited: 20/11/2017). In order to calculate the CUI in the PCS, we have considered the Ekman hypothesis  (Ekman, 1905)  of the transport of surface water masses by wind in the north or north-east direction, coupled with the rotation of the earth. Thus, the CUI was estimated from the following equation  (Diankha et al. , 2018; Thiaw et al. , 2017) : Where is the along-shore component of wind stress within 270 km of the coastline (positive southward), is the seawater density (1,025 kg/m 3 ) and f is the Coriolis parameter (=2 Ω sin(θ), with Ω and θ equal to the Earth's angular velocity and latitude, respectively).
| Biological data Landing data, by size class, were used to estimate abundance and recruitment of E. fimbriata by applying a virtual population analysis (VPA)  (Jones, 1984; Lassen & Medley, 2001; Pope, 1972). Previously, this method was used to analyse population dynamics for several species from data-poor fisheries in Senegal (e. g. Diankha et al. , 2018). Two data sets were combined to accomplish the VPA. The first data set was total landing of E. fimbriata by the Senegal artisanal fishery from 2004 to 2012. The study was limited to 2012 due to lack of biological data (length size spectra) for E. fimbriata after this year. The data set was extracted from the Centre de Recherches Océanographiques de Dakar-Thiaroye (CRODT) database, collected at three main landing ports along the PCS: Hann, Mbour and Joal (Figure  1 ).
The number of trips per fishing gear was recorded on a daily basis, whereas fisheries landing data were collected randomly at about 5 days per week. After sorting the data by port, gear type and period (fortnightly intervals), total landings per port were estimated by multiplying the mean number of fish landed for the sampled trips by the total number of fishing trips  (Diankha et al. , 2018). The landings were summed by month for each landing port. A second data set, which was used only for VPA modelling, contained monthly size distributions of commercially landed E. fimbriata specimens by port over a 12-month period from July 2014 to June 2015. Weight (W) and total length (TL) were measured for 28,500 randomly sampled fish. To estimate abundance and recruitment using VPA modelling, the number of individuals per size class was first sorted into size frequency distributions (Figure  2 ).
We then assumed that all monthly landings, by port, were distributed according to this average size frequency. We used a size-age key to convert size class data into seasonal age groups by applying the growth equation of von  Bertalanffy (1938)  using growth parameters we had obtained from size frequency data of E. fimbriata (from July 2014 to June 2015). Seasonal time steps [winter (January-March), spring (April-June), summer (July-September) and autumn (October-December)] were chosen to evaluate growth characteristics of E. fimbriata, by assuming a fast growth and short life cycle  (Charles-Dominique, 1982). Catch (numbers at age for each month) was estimated from: The VPA was adjusted according to the approximation of  Pope (1972). This approximation basically assumes that the entire catch was made instantly in the middle of the month.
| Effect of fish length on population fecundity The relationship between fish length and log total fecundity per size class in E. fimbriata (for the period 2004-2012) was best described by second-order polynomial functions (parabolic curve). A maximum total fecundity per size class was observed at body lengths of 25 and 26 cm. Length depending on total fecundity varied among years, as shown in the shift of the parabolic curves fitted to the data on a yearly basis (Table  2 ; Figure  6 ). The results of the GAM (total and partial deviances) are summarized in Table  2 ; the total explanation reaches 91% of the variation in E. fimbriata PFm during the study period. Length accounts for 47% of the total variability and is the most determinant factor while the environmental parameters represent 44%.
ACK N OWLED G EM ENTS Data analysis was done inside the AWA project (Ecosystem Approach to the management of fisheries and the marine environment in West Agreement number 57314022. We also thank MAVA foundation and the Sub Regional Fisheries Commission (SRFC) for support within AGDpélagique project to better manage small pelagic fishes in West Africa. The data for this study were collected through the CRODT (Senegal) fisheries information system from 2004 to 2012. Many thanks to the editor and the two anonymous reviewers for their comments which led to significant improvements in this manuscript.
African waters) funded by IRD and the BMBF (grant 01DG12073E), the Preface project funded by the European Commission's Seventh Framework Program (2007-2013) under Grant Agreement number 603521, https ://prefa ce. b. uib. no/ and the DAAD program under Grant F I G U R E 6 Variation in population fecundity of Ethmalosa fimbriata (2004-2012): (a) monthly and (b) annually.
No approval was required from the Institutional Animal Care and Use Committee, and no field licence was required during any part of the experiment. DATA AVA I L A B I L I T Y S TAT E M E N TBiological data on reproduction of Ethmalosa fimbriata as recorded in Senegal (Atlantic coast and Sine Saloum estuary) can be found on PANGAEA at https ://doi. org/10. 1594/PANGA EA. 880051. Further biological, fisheries landing and environmental data that support the findings of this study are available on request from the correspond-.