dataesr/scientific-paragraphs-categorization huggingface dataset:
MPA measurements MPA measurements are globally of good quality. The temporal resolution is 86 s most of the time, but it can be doubled (172 s) for short periods of time. The detector aged with time; it drifted. This drift is compensated for over time, but after several years it is impossible to measure the highest energies anymore (typically after 10 years, it is impossible to obtain measurements above 10 keV). For the development of a proton specification model, data between 1 and 32 keV have been used. Fluxes below 1 keV have not been used, due to uncertainties in the spacecraft potential determination. Thus, we determined monthly averages of the proton flux for each satellite. These monthly averages were made in order to analyze possible solar cycle or seasonal effects (linked to the magnetic field or to its activity).
5 mean. This figure shows that beyond L * = 2. 5 fluxes resulting from GREEN-e are in agreement with NOAA-POES data, with less than a factor 3 between the two, particularly in the L range of the SLOT model (2. 5 < L * < 5), which is based on these data. At high energy (> 3 MeV) for L * > 6, POES data seem to  reach the background of the instrument, probably due to cosmic particle measurements, while fluxes from the GREEN-e model continue to decline while L * increases. We can note that for low energy (∼ 30 keV) there is a big difference between GREEN-e and NOAA-POES measurements and that for some L * values this flux is lower than > 100 keV, which is not usual. It is important to keep in mind that for low energy (∼ 30 keV), electron fluxes in GREEN-e come from AE8, while fluxes for higher energies come from the SLOT model and OZONE.
16  showed a very good correlation between GREEN, AE9 and NOAA measurements in the same region (L * < 3. 5). Why was there an agreement between the results of GREEN and NOAA that no longer appears with the Jason-2 measurements? Is this due to the difference of altitude between the two spacecraft (800 km for NOAA and 1336 km for Jason-2)? In order to illustrate the reason for this difference between Jason-2 measurements and GREEN and AE9 results, Fig. 18  has been plotted. It is the same figure as Fig. 16  but not during the same period of time: 2009 to 2015 for Fig. 18  as opposed to 1999 to 2010 for Fig. 16. This figure shows that the comparison between in situ measurements and GREEN results depends on the period of time. If the period of time of in situ measurements is long enough (several solar cycles) or is representative of a mean flux, data will easily be compared to GREEN results.
In Section 2 , we will re-iterate and expand upon the technique used by  Thomas and Keller (1990)  which includes the definition of a quantity we refer to as the "azimuthal average" and which is related to the commonly used definition of "Af ρ"  ( A'Hearn et al. 1984 ). This relationship will be derived in Section 4. 3. Given the apparent dominance of large slow moving particles in the inner coma of 67P  ( Fulle et al. , 2015 ) , we also provide an assessment of the influence of these particles on the azimuthal average. In Section 3 , we will describe the construction of a database of OSIRIS images in which quantities such as azimuthal average values at different impact parameters are fit and tabulated. In Section 4 , this database will be analysed in different ways to provide insight into the processes involved.
The OSIRIS dataset The Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS,  Keller et al. , 2007 )  was the scientific camera system onboard the Rosetta spacecraft. It consisted of two cameras, the narrow angle camera (NAC) and the wide angle camera (WAC). Both cameras were equipped with a series of band-pass filters to study the comet in different wavelength ranges. The 12 NAC filters cover a wavelength range of 250-10 0 0 nm and the 14 WAC filters a range of 240-720 nm. The two cameras have fields of view of 2. 20 °× 2. 22 °(NAC) and 11. 35 °× 12. 11 °(WAC) and an angular resolution of 18. 6 μrad/px and 101. 0 μrad/px, respectively. The images are acquired using 2048 × 2048 pixel backside illuminated CCD detectors with antiblooming gates, allowing overexposure of the nucleus without leading to saturation artefacts in unsaturated pixels.
This is a key property for enabling studies of the fainter cometary dust coma around the bright nucleus. The entire OSIRIS dataset of comet 67P contains just over 70,0 0 0 images taken throughout the mission. The images were acquired in the course of different imaging campaigns dedicated to achieving the scientific objectives although sequences can be used to address more than one objective. The raw image data from the camera was corrected through a calibration pipeline  ( Tubiana et al. , 2015 )  and organised into different levels according to the degree of correction. The calibration and correction pipeline includes corrections for analogue-to-digital converter (ADC) offset and gain, bias subtraction, high and low spatial frequency flat fielding, bad pixel and bad column removal, an exposure time normalisation, radiometric calibration and a correction for geometric distortion (resulting in level 3 data, in units of [W m -2 sr -1 nm -1 ]).
For the analysis presented in this paper, we used images on data correction level 3B, which are additionally transformed from radiometric units [W m -2 sr -1 nm -1 ] into dimensionless reflectance units. The reflectance factor is defined as R = π I ( i, e, α, λ) F ( λ) , ( 11 ) with the observed spectral radiance I , the solar spectral irradiance at heliocentric distance from the comet F , the incidence angle i , the emission angle e , the phase angle α, and the wavelength λ. Note that the solar irradiance was calculated at the central wavelength of each filter.
Image selection criteria Since the OSIRIS image campaigns are dedicated to different scientific goals, not all the images are suited to studying dust fea-tures in the innermost coma. To select a suitable dataset for analysis, several selection criteria were defined. They were as follows. -Data level 3B: Not all the OSIRIS images were corrected with the full calibration pipeline. We excluded all images from our analysis that were not provided in data level 3B. -Full image 2048 × 2048 pixels: Both OSIRIS cameras use a 2048 × 2048 pixel CCD detector, but not all the images were acquired with the maximum possible field of view (FOV). Since our analysis focusses on dust distribution in the coma, images with the largest FOV are desirable to study changes in the dust outflow from the nucleus as far out as possible.
-Shutter errors: During certain periods of the mission some problems with the mechanical shutters occurred leading to incorrect exposure of the detector. OSIRIS images acquired with a malfunctioning shutter were not a priori excluded from the database, but could be identified and were excluded from analysis. Some recovery of these data might be possible in some cases at a later date. -Ghosts: On some images, artificial features are seen superimposed on the dust coma. These are so called ghosts arising from internal reflections from optical components of the system. The ghosts are always located in the same place if they appear in an image, but they are in general not very prominent. Nevertheless, the ghost blends with the dust coma around the nucleus and influences our dust analysis. At the moment, we do not apply any corrections for the effects of the ghosts.
Although  Bodewits et al. (2016)  suggested that there is gas contamination in the F18 (OI continuum filter at 610 nm), the azimuthal average plots in that filter and in the F16 (Na filter at 589 nm) show similar behaviour which only deviates in the order of the image error. Hence, the F17 images were excluded in our dust coma analysis to prevent bias from gas contribution. We return to this issue below. This selection left us with a total of 3553 images in the dataset for analysis, thereof 1880 WAC images and 1673 NAC images.
Single image analysis To generate the data included in the database, every suitable image was passed through an automated image analysis sequence. In this paragraph, we explain with an example image ( Fig. 7  ) how the image analysis was carried out. -Transformation into polar coordinates: In a first step, the original OSIRIS image was transformed into polar coordinates ( Fig. 7  ). -The origin was set to the image coordinates of the nucleus centre of mass, which were determined using the SPICE library and appropriate kernels. The same origin was also used for the calculation of our impact parameter b ( b = 0 at the image coordinates of the nucleus centre of mass). In the polar transformed image, two regions were defined: the dayside and the nightside. Both regions are defined taking into account the projected direction to the sun in the image plane.
The dayside includes the regions defined as the solar azimuth angle ±90 °and the nightside includes the regions corresponding to the complementary 180 °in transformation angle. It is important to keep in mind that the definitions for day-and nightside as introduced here do not follow the terminator definition for the illuminated dayside and non-illuminated nightside except in the case when the phase angle takes a value of exactly 90 °. But since about 50% of the images in the dataset were taken with a phase angle between 80 °-100 °, the nomenclature is reasonably accurate. This division of the full angle into two regions was intended to gain a better understanding of the dust outflow. As will be seen, this does indeed provide some additional insight. -Average azimuthal dust reflectance (A/ 2 π ): In a second step, dust profiles of azimuthally averaged dust reflectance values with increasing impact parameter were generated ( Fig.
The Osimana database The main purpose of the Osiris Image Analysis (Osimana) database is to collect the data from the single image analysis of the whole dataset. The following parameters were produced. Average azimuthal dust reflectance: From the averaged azimuthal dust reflectance profile over the full angle four values at different impact parameters ( b = 3 km, 5 km, 10 km and b max ) were saved. The maximum impact parameter b max is different for each image and depends on the field of view and the position of the comet in the image and is included in the database. Fit parameters: The results from the fits to the Ā-curves were saved in the form of the four fit parameters and the corresponding parameter errors for the full angle, the dayside and the nightside analysis. Other image information : In addition to the data from image analysis, the database contains selected image information extracted from the image header or calculated with the help of the SPICE kernels such as comet-centric distance of the spacecraft or the phase angle of the observation.
Approach to "1/r" behaviour As illustrated in Section 2 , in the absence of effects from gravitationally-influenced particles and effects influencing the total particle scattering cross-section (e. g. fragmentation), we expect the azimuthal average to approach a "1/r law" as the impact parameter increases and thus the linear slope, w , should be zero for the azimuthal average, Ā. We have plotted histograms showing the distribution of the fit parameter w , which corresponds to the slope in the linear regime of the Ā-curve. The histograms in Fig. 9  are for the WAC dataset while Fig. 10  shows the analysis for the NAC. The bin size in each histogram was chosen to be the median value of the uncertainties of the corresponding slope parameters w in [%/km], σ ' w. In Fig. 9  a), the complete azimuthal average is shown.
Therefore, we are probably biased by images where the decrease of the Ā-curve in the acceleration region still dominates the general behaviour and where there are insufficient data points in the linear regime to guide the solution. This is also apparent from the error on the linear slope parameter which is systematically higher for the NAC. The phase angle distribution (for the NAC most images were at phase angles below 90 °) may also play a role. Based on our statistical analysis of the linear gradients we conclude that the dust outflow in general converges towards a 1/r behaviour in the inner coma. Although the statistical analysis provides a clear picture, the rather broad distributions leave some room for further investigation of individual images to establish why some images deviate from the mean.
Hence, there is actually consistency -at least in the trend -with the Snodgrass et al. result because at 90 °their Af ρ leads to a value that must be < 400 cm. In our data analysis we see significant diurnal variations in the observed Af ρ values. In Fig. 13  , an example of the observed variations is given. The data were acquired between 04-05. 05. 2015. The data points follow a periodic curve with two maxima, one with a large amplitude and one with a smaller amplitude. The variation is around 30% peak-to-peak. We observe a periodicity of roughly 0. 5 Earth day which corresponds to the spin period of 67P of ∼12. 4 h. The diurnal variations are thus most probably an effect of the irregular shape and an inhomogeneous surface activity of comet 67P.
Derivation of the starting point of linear behaviour The fitting routine can also provide an estimate of the distance from the nucleus at which 1/r behaviour can be assumed. We referred to this above as the "starting point". We determined starting points for every image in the analysis ( Fig. 14  ). Because we observe significant changes in the starting point due to different viewing geometries, the major contribution being a periodic change arising from comet rotation, we calculated the average starting point  for each comet day as the mean value of the starting points of all single images acquired at that same day. The error on the average starting point is merely the standard deviation of the single starting points. The mean value of the starting point over the period of time covered by our dataset was then found to be 11.
The DSMC approach In the introduction, we have already shown an example of a DSMC gas dynamics simulation for a spherically symmetric case to illustrate the effect of acceleration on the azimuthal average in a simplified model and to show that we can deduce profiles from our simulations that can be compared to results from data analysis. The code used is called UltraSPARTS ( http://www. plasmati. tw ) and is a commercialized derivative of the PDSC ++ code  ( Su, 2013 )  used in previous papers (e. g. Marschall et al. , 2016 Marschall et al. , , 2018 ) ). PDSC ++ is a C ++ based, parallel DSMC code which is capable of simulating 2D, 2D-axisymmetric, and 3D flow fields. The code has been developed over the past 15 years  ( Wu and Lian, 2003; Wu et al. , 2004; Wu and Tseng, 2005 )  and contains several important features including the implementation of 2D and 3D hybrid unstructured grids, a transient adaptive sub-cell method (TAS) for denser flows, and a variable time-step scheme (VTS).
In the parallel version, computational tasks are distributed using the Message Passing Interface (MPI) protocol. A Domain Re-Decomposition (DRD) method, to optimize the parallel performance, has also been implemented  ( Liao, 2017 ). In addition to spherical cases, the 3D shape model of the nucleus can be integrated as the input surface into the computation with the help of a grid generation program. Here, we use the GRIDGEN program from Pointwise to map the SHAP7 shape model  ( Preusker et al. , 2018 )  of the nucleus of 67P into the simulation code. To calculate the surface gas flux used for the boundary condition in our numerical simulation we impose a simple thermal model neglecting thermal conductivity  ( Gulkis et al. (2015)  ;  Schloerb et al. , 2015 )  but including sublimation of water ice.
The profile curves show the same fundamental behaviour seen in Fig. 15 (a). Because of the complex shape of the nucleus of 67P, the azimuthal average cannot be evaluated down to the surface and a large part of the "acceleration region" is lost for analysis. In both cases, the differences between the profile curves of different particle sizes are rather small. The slightly less steep decrease in the "acceleration region" for large particles could be an effect of gravity which is trapping particles in ballistic trajectories close to the comet and thus causing the dust brightness to decrease less steeply. However, the differences are so small one could probably not distinguish between the different particle sizes only from the behaviour of the azimuthal average in data analysis from OSIRIS images.
A large number of OSIRIS images were analysed and the essential information from the single image analysis was collected into a database. This enables statistical analysis of selected parameters over an extended dataset. We studied the dust outflow behaviour in the linear regime more closely by performing a statistical analysis regarding the distribution of the linear slope parameter w of a fit to the Ā-curves. We found that the distributions of the linear slope gradient closely follow Gaussian distributions with mean values that are closer to zero than 2 σ ' w indicating adherence to a 1/r law. There is good agreement between the distributions calculated separately for the NAC and the WAC images. The NAC uses broader bandwidth filters while the WAC uses narrow-band filters. The agreement indicates that the WAC filters used for the analysis are not significantly affected by gas emissions within the band.
Acknowledgements The team from the  University of Bern  is supported through the  Swiss National Science Foundation  and through the  NCCR PlanetS. The project has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 686709. This work was supported by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  16. 0 0 08-2. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Swiss Government. OSIRIS was built by a consortium led by the  Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany , in collaboration with CISAS,  University of Padova, Italy , the  Laboratoire d'Astrophysique de Marseille, France , the  Instituto de Astrofísica de Andalucia, CSIC, Granada, Spain , the Scientific Support Office of the  European Space Agency, Noordwijk, The Netherlands , the  Instituto Nacional de Técnica Aeroespacial, Madrid, Spain , the  Universidad Politéchnica de Madrid, Spain , the  Department of Physics and Astronomy of Uppsala University, Sweden , and the  Institut für Da-tentechnik und Kommunikationsnetze der Technischen Universität Braunschweig, Germany.
Results We used a Piezzo drive (PIFOC, PI) to acquire z-stacks containing 100 images, 100 nm apart, of E. coli using a 100x oil objective (UPlanFL 1. 3NA) and CoolSNAP HQ2 camera with a resolution of 1040 × 1392 pixels (Fig. 1A, B ). E. coli cells were loaded into a microfluidic device where they were cultured in narrow chambers (Fig. 1C ). A graphical user interface (GUI, Supplementary Text) was developed to manually label a training dataset by defining regions of interest (classes), such as the interior of the cell and its contours, then a machine learning classifier was trained on this dataset (see Supplementary Text). Importantly, the algorithm was not trained using the morphological features in the (x-y) plane -as it is classically done -but only a representative set of z-pixels (Fig.
Additionally, we succeeded in identifying individual epithelial HeLa cells in a confluent monolayer (Fig. 3C , Supplementary Fig. S6 ). Mammalian cell segmentation is hard, and although deep learning methods have already demonstrated their potential to segment mammalian cells with complex shapes, it came at the expense of large dataset and long computing time  14. In our case, a simple machine learning algorithm and limited training datasets were sufficient to enable rapid identification of cells with various shapes in a timescale that allow cell classification to be done on the fly. Importantly, our method showed better classification results (see Supplementary Fig. S7 ) than the Ilastik classifier  10  , a reference image analysis software, which does not use axial information, but encodes information from neighboring pixels.
Discussion The method presented in this manuscript does not require a complex imaging setup, since only a few images (~10) in a z-stack are required for robust identification of cells. It comes with a GUI to facilitate drawing of the regions of interest in the training dataset. Importantly, it can identify bacteria, yeast, mixture of yeast and bacteria, and mammalian cells (Fig. 3 ) with the same workflow. Therefore, it appears as a versatile method that can be used to facilitate complex segmentation problems. Moreover, its performance can be further improved. preliminary analysis using Random Forest and Neural Network (See Supplementary Text, Supplementary Fig. S8 ) showed comparable classification accuracy than SVM, but at much faster speed. Specifically, Random Forest classification was typically 20 times faster than SVM classification.
Z-pixel classification. A graphical user interface (GUI, see supplementary text) was used to simplify training set construction. Images were normalized by performing a standard histogram equalization procedure with 1% loss on the histograms. We used principal component analysis (PCA) to reduce dimensionality of the problem. Only a subset of the main principal component (N < 20) dimensions were used to represent the data used to train the classifiers and later generate predictions. For SVMs, we used a method known as winner-takes-all SVM (WTA-SVM), which has the double advantage of providing a classification score for each class and does not require the experimenter to establish a classification tree. The fit implementation of the SVMs was based on the Matlab fitcsvm package, which features automatic hyper-parameter optimization, with a Gaussian radial basis function as a kernel and a hinge loss function.
Acknowledgements The authors thank  Peter Swain ,  Julian Pietsch , our team members and an anonymous referee for their suggestions and critical reading of this manuscript. This work was supported by the  Agence Nationale de la Recherche  ( ANR-10-BINF-0006 ;  ANR-16-CE12-0025 ;  ANR-16-CE33-0018 ) and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  724813 ).
Some of these techniques can provide multiple alternative models that individually fit the input map to a certain extent  (24, 38) , in the same way as multiple molecular models are derived from NMR spectroscopy data  (39) , and routinely deposited in the PDB database. The sets of these models can be considered as uncertainty ensembles  (40) , because they reflect the limited information available on the systems and thus the fact that different models might be equally consistent with the input data; they do not, however, reflect the conformational heterogeneity arising from the internal dynamics of the systems themselves  (40) (41) (42) (43) (44). In many cases, as for instance in inferential structure determination  (45) , weights can be associated to the members of these uncertainty ensembles.
The metainference approach for cryo-EM density maps In the following, we define the metainference components, previously introduced in general terms, specifically for the case of cryo-EM data. The p MI À X; s SEM ; s B D Á ¼ Y N r ¼ 1 pðX r Þ Y N d i ¼ 1 1 ffiffiffiffiffi ffi 2p p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi À s B r;i Á 2 þ À s SEM r;i Á 2 q exp ( À 1 2 ½d i À f i ðXÞ 2 À s B r;i Á 2 þ À s SEM r;i Á 2 ) p s B r;i ; (1) development of these elements builds on the approach proposed in  (49). A summary of all the elements at the basis of our approach is reported in Table  S1. The method is implemented in the PLUMED-ISDB module (  51 ) of the open-source, freely available PLUMED library (http://www. plumed. org) (52). Experimental data.