dataesr/scientific-paragraphs-categorization huggingface dataset:
The two data acquisitions gave good images with similar contrast with the Selectris X and Omega energy filters when compared at the same defocus (Fig. 2A ). High resolution information is present as judged from the power spectra in which Thon rings are visible up to a resolution range of 2. 3-2. 5 Å (Fig. 2B ). Data collection speed is comparable for the 2 acquisitions with about 300-400 images per hour. This could potentially be increased by more extensive multi-hole acquisitions but may become also limited at some point by the data acquisition speed of the cameras and reduced stabilisation after stage shift. Acquisition speed can vary significantly depending on settings used, in particular whether multi-spot, AFIS etc. are used or not; e. g. reducing the beam size (diameter) is beneficial for multi-spot data collection strategies (data set 1 and 2 for the TFS and Jeol microscopes have 0.
The obtained resolution of the reconstructions was plotted against the amount of particles used to represent a linear dependence  (Rosenthal and Henderson, 2003)  (Fig. 2H ) as calculated from random subsets of each dataset and using independent full 3D refinements (see methods). The plot shows excellent B-factor values in the 30 Å 2 range, with slightly better values for data set 1 but this does not translate to noticeable differences in the two cryo-EM maps. However, more detailed analysis of refinement parameters during image processing revealed that the data sets have rather different characteristics: (i) data set 2 (Jeol microscope) exhibited significantly higher astigmatism values (e. g. ~ 71 nm for data set 2 as compared to ~ 9 nm for data set 1, TFS microscope), (ii) data set 2 required a larger amount of anisotropic magnification correction (0.
This is consistent with the fact that a cold FEG provides an increased envelope function at high spatial frequencies at higher underfocus  (Hamaguchi et al. , 2019). Finally, we noticed differing properties of the two data sets with regards to astigmatism, and as it turns out, beam tilt / coma (Fig. 3C-G ). While data set 1 (TFS microscope) has a narrow distribution of astigmatism values irrespective of the spot position (between 0 and 20 nm), data set 2 (Jeol microscope) shows a series of peaks (with values of ~ 20 nm width but covering a full range between 0 and +/-170 nm). As these are relatively narrow distributions it is possible that the high values originate from the data collection procedure in which multi-hole acquisitions are achieved by beam tilting, i. e. this is in fact off axis coma, which results in a phase error that can affect high spatial frequencies  (Glaeser et al.
3C ; further analysis of individual optics groups for a given stage position appeared therefore not necessary and was also not possible due to the irregular pattern of AFIS data collection; see methods). All images in data set 1 were treated as one optics group because there were very little aberrations after AFIS correction, while for data set 2 aberrations were estimated per-hole (see methods). The early astigmatism correction, CTF refinement and correction of higher order aberrations during image processing  (Zivanov et al. , 2018)  of data set 2 (Fig. 3D -G; see methods) was able to account for the astigmatism/coma issues and still allowed reaching high resolution. It could be useful to have coma correction done in more detail when performing multi-hole data collections, which would make it also more robust with regards to processing with different software.
Taken together, while the implemented engineering technologies and specific settings of the TFS and Jeol systems are rather distinct, the present benchmarking results show that overall similar results can be obtained. Nevertheless, specific data characteristics such as coma, anisotropic magnifications etc. required more advanced image processing procedures (not necessarily available in all software) to provide high-resolution information to a similar level. For routine usage, including for projects with different sample types and variable quality (homogeneity, particle distribution etc. ), robust and easy-to-use equipment and software interfaces are an advantage. A practical issue to also consider for microscope installations are the room specifications, e. g. the required ceiling height (3.
04 m for a Titan Krios G4 and 3. 85 m for a CRYO ARM 300). Regarding sample handling, both systems provide up to 12 specimen slots, but in the Jeol version only 4 grids can be mounted (e) Distribution of astigmatism over the 9 spots according to the optics groups (data set 2). (f) Distribution of beam tilt values in x and y direction over the 9 spots for each optics groups. (g) anti-symmetrical plots for the 9 spots of the optics groups revealing beam tilt / off-axis coma according to highorder aberration estimation with the software Relion  (Zivanov et al. , 2020). at once, hence 3 transfers are required for uploading 12 specimens. This also points to the issue of compatibility between instruments, e. g. sample transfer between Jeol JEM FSC and CRYO ARM series requires unclipping the grid, between Glacios and Titan Krios the same sample holding dewar can be used, and between Jeol and TFS instruments the grids can be transferred but this requires physically removing and relocating the grid from one holder to another (though no unclipping is needed if "autogrids" are used in the Jeol cartridge), which increases the risks of grid loss and contaminations and hampers the workflow and speed of sample handling.
Sample preparation Human 80S ribosomes were purified from HeLa cells for both data sets following our previously established procedures  (Khatter et al. , 2014; Khatter et al. , 2015; Myasnikov et al. , 2016; Natchiar et al. , 2017). After resuspension in buffer A (100 mM KOAc, 5 mM Mg(OAc) 2 , 20 mM sodium cacodylate) for data set 1 and in buffer B (100 mM KCl, 5 mM Mg (OAc) 2 , 20 mM HEPES pH 7. 6, 1 mM DTT) for data set 2, both samples were diluted to a concentration of ~1-2 mg/mL (0. 24-0. 48 µM) into their respective buffers. For data set 2 an antibiotic was added to a final concentration of 1 mM and the sample was incubated 5 minutes at 37 • C and kept at room temperature until grid preparation. Both samples were then applied to holey R 2/2 carbon 300 mesh rhodium plated copper grids (Quantifoil) coated with a thin carbon film and treated with a plasma-cleaner (Fischione; 90% Argon, 10% oxygen), then blotted and cryo cooled into liquid ethane using a Vitrobot IV (Thermo Fisher Scientific, The Netherlands) with the chamber operating at 100% humidity and at 10 • C for dataset 1 or 17 • C for dataset 2.
Cryo-EM data collection Cryo-EM grids were sent in a dry shipper and test data were collected at the TFS factory in Eindhoven for data set 1 and at the RIKEN Osaka cryo-EM facility for data set 2. For data set 1, data acquisition was done with the software EPU2 (Thermofisher Scientific) on a Titan Krios G4 electron microscope equipped with an E-CFEG electron source, a postcolumn mounted Selectris X energy filter and a Falcon 4 camera operating in electron event representation (EER) mode  (Guo et al. , 2020). With the Selectris energy filter the microscope needs to be operated in EFTEM (energy filtered transmission electron microscopy) mode, similarly to the settings for a K3 camera; the difference is that the path length is smaller for the Selectris and the change in nominal magnification is only about 10% (see Table  1  regarding the effective field of view).
85 Å, defocus range -0. 4 to -2. 5 μm, 6528 micrographs, 1 exposure per stage position, total dose 60 e-/ Å 2 , 1 s exposure time, fraction dose 3. 5 e-/Å 2. For data set 2, data acquisition was done with the SerialEM software 41 on the Jeol CRYO ARM 300 at the RIKEN centre of the Osaka University equipped with a CFEG electron source, an Omega energy filter and a Gatan K3 camera operating in correlated double sampling (CDS) mode  (Sun et al. , 2021). 4580 movies were acquired in a fringefree setup with 4 shots per hole acquiring multiple holes (3x3 per stage position) using beam-image shift  (Cheng et al. , 2018)  (aberration free image shift, 7 µm) with a waiting time of 10 seconds after each stage shift (during focusing) at an acquisition rate of 400-500 images / hour. The acquisition was done in super resolution mode with a pixel size of 0.
Cryo-EM data processing Data pre-processing of both data sets was done with Relion  (Zivanov et al. , 2018). In the case of data set 1 (TFS microscope) the EER frames were regrouped into 30 frames. To get a dose equivalent to 30e -/ Å over 30 frames, every 28 frames were grouped as one dose fraction, ignoring the last 21 higher dose frames. First, Relion's motion correction algorithm  (Zivanov et al. , 2018)  was applied on the frames to generate micrographs, which included dose weighting as part of the movie alignment. During this step data set 1 was coarsened to 0. 72 Å / pixel while data set 2 (Jeol microscope) was kept at the original pixel size of 0. 72 Å / pixel. CTF estimation was done using Gctf  (Zhang, 2016)  inside Relion  (Zivanov et al. , 2018)  using a Cs value of 2. 7 mm using equiphase averaging (EPA)  (Zhang, 2016).
Further analysis of individual optics groups was not possible for data set 1 due to the irregular pattern of AFIS / EPU data collection which selects neighbouring holes in a non-consecutive manner (also not evident from the image file names); hence, all images in data set 1 were treated as one optics group because there are very little aberrations (thanks to AFIS correction which comprises beam-image shifts combined with off-axis coma and astigmatism correction), while for data set 2 aberrations were estimated per-hole (grouping the 4 shots per hole) to account for the aberrations during image processing. Half subsets of the data were extracted randomly and refined independently. The obtained resolution of the 3D reconstructions was plotted against the amount of particles  (Rosenthal and Henderson, 2003)  using the formula ln (number of particles) against 1 / (obtained resolution)  (Klaholz, 2019).
A linear fit line for each dataset was plotted in Excel; the B-factors were calculated as 2 x the slope of each curve. Resolution estimation by Fourier shell correlation (FSC)  (Rosenthal and Henderson, 2003; van Heel and Schatz, 2005)  calculations (0. 143 criterion) were done in Relion  (Zivanov et al. , 2018)  using half maps. Map interpretation was done by docking the atomic model of the 80S human ribosome (PDB ID 6QZP)  (Wang et al. , 2021) into the final maps followed by an automated rigid body fit in Chimera  (Pettersen et al. , 2004)  and subsequent refinements including manual rebuilding in Coot  (Emsley et al. , 2010)  and real space refinement in Phenix1,  (Afonine et al. , 2018). Figures were prepared using ChimeraX 1. 2. 5  (Pettersen et al. , 2021)  and Pymol  (DeLano, 2002).
Acknowledgement We thank the  TFS and Jeol companies  for kindly making their microscope setups available for test data collection, which was performed by  Evgeniya Pechnikova  ( TFS, Eindhoven, NL ) and  Fumiaki Makino  (on behalf of Jeol, Osaka, Japan). We acknowledge  Jonathan Michalon ,  Mathieu Schaeffer  and  Nicolas Ballet  for IT support. This work was supported by  CNRS ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa_16099) , the  Fondation pour la Recherche Médicale (FRM) ,  Ligue nationale contre le cancer (Ligue)  and  Agence Nationale pour la Recherche (ANR)  and  USIAS of the University of Strasbourg  ( USIAS-2018-012 ). This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  France 2030 Investment Program.
The electron microscope facility was supported by the Alsace Region and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  under the  France 2030 Program , by the epiRNA grant ( Fonds Régional de Coopération, Région Grand Est ; Compétitivité Alsace 2014-2020, FEDER), by  Instruct-ULTRA, Instruct-ERIC and iNEXT-Discovery. We thank the referees for their useful suggestions.
We can thus confidently present statistics on the WNA of the whistlers and their frequent collocation with magnetic dips in Sect. 4. Our study is thus a complement to other statistical studies analysing the same dataset, such as  Jagarlamudi et al. (2021)  and  Cattell et al. (2022) , which have a different focus. Finally, we discuss the implications in terms of wave-particle interactions in Sect. 5 and summarize our results in Sect. 6.
2020) , at a cadence of 0. 87 s. The electric field is measured by the electric fields instrument (EF) consisting of two pairs of dipole electric field antennas oriented in the TN-plane and extending beyond the PSP heat shield, and a fifth antenna located behind the heat shield on the instru-ment boom; the location of antenna 5 in the wake of PSP means the R-component is susceptible to detrimental interference by the wake electric field and cannot be reliably interpreted  (Bale et al. 2016). The R-component of the wave electric field, not used in the present study, can be reconstructed from E • B = 0 in the whistler frequency range. The dataset we explore corresponds to the first encounter of PSP with the Sun from November 1, 2018, to November 11, 2018. During this time frame both the cross-spectra and BPF data products were available for studying magnetic field fluctuations.
Detection of whistler wave packets and polarization analysis We detected whistler waves in the trace of cross-spectral matrices of the fluctuating magnetic field. We applied the following criteria to detect them. First, we ensure that the power spectral density (PSD) is at least four times above the ambient fluctuation level. The ambient level of magnetic field fluctuation is computed for each day of the dataset by taking the median PSD to account for the increasing power level of turbulence when approaching the Sun. This criterion was tested on the data and was found to be effective in detecting the spectral bumps in the PSDs that are characteristic of whistlers  (Jagarlamudi et al. 2020). We then determine the ellipticity and planarity of the candidate waves to check whether they are coherent waves and not enhanced turbulence.
3. 1). We categorize whistlers as oblique when their WNA θ is greater than 45 •. This threshold is the same for the entire dataset, even though the Gendrin angle cos θ G = 2 f / f ce  (Gendrin 1961)  varies from 45 • to 87 • (for f / f ce = 0. 35 and f / f ce = 0. 03, respectively, which are the maximum and minimum f / f ce ratios encountered in our analysis).
Detailed analysis of events We present in this section the detailed analysis of three cases of whistler waves based on the DFB cross-spectra and for which we compare the polarization processing results with those derived from waveform measurements. This comparison allows us to validate the use of cross-spectra with a 28-s time resolution to compute the WNA of transient whistlers with the method described in Sect. 2. 3. These events are representative of the diversity of the whistlers detected in the datasets explored in terms of obliquity (quasi-parallel and oblique waves), intermittency, frequency, and collocation with magnetic dips. 3. 1. 1. Event 1: Quasi-parallel whistlers collocated with magnetic dips at the boundary of a switchback The first event we analyze occurred on November 4, 2018, around 15:07 UTC.
From the cross-spectra, the WNA goes up to 24. 7 • , with a median value of 6. 2 •. From the waveform, the median value is higher at 15. 7 • , and a small percentage of the whistlers are oblique (2. 7%) 3. 1. 2. Event 2: Quasi-parallel and oblique whistlers collocated with magnetic dips at the boundary of a switchback The second example we highlight here was recorded on November 3, 2018, around 14:26 UTC. The context of this detection is presented in Fig. 3. The trace of the cross-spectral matrix shows an excess of spectral power for one 28-s bin (on average 186 times above the ambient fluctuation level). This is a strong signature, even though the signature in the BPF data lasts for only a few seconds by the end of the cross-spectral bin. The whistler wave packets are located at the trailing edge of a switchback (similar to event 1).
The frequency of the whistlers detected in the cross-spectra ranges from 96 Hz to 237 Hz (0. 05-0. 14 f ce ) in the spacecraft frame. For this event burst waveforms are available. The burst waveform interval starts at 14:26:11. 1 UTC. The burst covers the main peaks seen in the BPF data. In particular, the wave packet in the magnetic dip as can be seen in Fig. 3  (dashed interval on the BPF measurements). The polarization analysis results are presented in Fig. 4. The spectrogram derived from the waveforms reveals two wave packets: the first is cotemporal with the magnetic dip, and the second is the start of the second wave packet seen in the BPF data during this interval. The planarity and ellipticity are quite high (above 0. 7 and 0. 8, respectively) for both the cross-spectra and the waveforms.
Wave normal angle Figure  10  gives the histogram of the WNA for all the 2701 wave packets. Most of the whistlers are quasi-parallel. This is consistent with recent studies with Solar Orbiter data  (Kretzschmar et al. 2021 ) and the analysis of the same dataset by  Cattell et al. (2022). However, a significant part of the whistlers (3%) have WNAs above 45 •. We note a depletion of whistlers around the Gendrin angle, which is about 66 • for 0. 2 f / f ce (also observed in magnetospheric studies; e. g. ,  Agapitov et al. 2018). The detailed analyses in Sect. 3. 1 showed that quasiparallel whistler wave packets, as observed in the cross-spectra, can show a small proportion of oblique whistlers. Moreover, the oblique cases usually have lower planarity values; 45% of the oblique cases have planarity below 0.
e. , in a specific frequency band) with respect to the local magnitude of the background magnetic field. We consider all the BPF bins within one cross-spectral bin and attribute to each of them the corresponding WNA. This means that the total number of BPF wave packets is higher than the number of cross-spectral wave packets. A135, page 9 of 15 However, we checked that this does not change the proportion of oblique whistlers, and distributions of the wave parameters. We discard the values of peak B u /|B| that are lower than 0. 001. Since some whistlers detected in the cross-spectral bin show a very narrow response in time in the BPF data (e. g. , event 2), this arbitrary threshold is meant to dismiss BPF bins that may not be whistlers. Most of the wave packets have a relative amplitude below 0.
Since we re-interpolate the depth computed (see Sect. 2. 4) to the cadence of the BPF, we do not detect the dips that are shorter than 0. 87 s. In addition, in the dataset we analyzed, wave clusters appear in series, which means a succession of whistler wave clusters, separated by a few minutes, that can last from minutes to hours. Some of these storms seem to appear during macro (and shallow) magnetic dips and switchback intervals that are not detected by our method.
Summary Characterizing whistler wave properties in the solar wind is at the heart of understanding the dynamics and evolution of the eVDF that carries the heat flux. Our study has two main objectives: to shed light on whistler wave properties in the young solar wind by using magnetic field data that cover the appropriate range of frequencies, and to validate the statistics we present by studying in detail a few examples that are cross-validated with complementary datasets in terms of cadence, spatial component measured, and frequency and time coverage (cross-spectra, BPF, waveforms). In Sect. 3 we demonstrate that even though the cross-spectra have a low cadence of 28 s during encounter 1, which is very long compared to typical timescale variations of the background magnetic field, we can derive meaningful statistics of the WNA.
Acknowledgements. C. F. , V. K. , T. D. , L. C. , and M. K. acknowledge funding from the  CNES. V. K. and O. V. A. were supported by  NASA  grant  80NSSC20K0697  and  80NSSC20K0697 ; A. L. was supported by  STFC  Consolidated Grant  ST/T00018X/1 ; O. V. A. and S. K. were partially supported by  NSF  grant number  1914670 ,  NASA 's Living with a  Star (LWS) program  (contract  80NSSC20K0218 ), and  NASA  grants contracts  80NNSC19K0848 ,  80NSSC22K0433 ,  80NSSC22K0522. S. D. B. acknowledges the support of the  Leverhulme Trust Visiting Professorship program. V. K. J. acknowledges support from the  Parker Solar Probe mission as part of NASA's Living with a Star (LWS) program  under contract  NNN06AA01C. Parker Solar Probe was designed, built, and is now operated by the  Johns Hopkins Applied Physics Laboratory  as part of  NASA 's Living with a  Star (LWS) program  (contract  NNN06AA01C ).
Here, an influence graph having a periodic attractor means that the associated discrete model (which is unique because of Assumption 2) has a periodic attractor. 3. Compute manually some features which could be potentially used to predict the existence of a periodic attractor. 4. Construct a decision tree model which uses the features in the previous step to predict the existence of a periodic attractor. 5. Manually drop some features which do not influence the prediction result. In step 1, there are 50625 influence graphs with thresholds in total without removing the graphs that are equivalent. In step 2, we use the function attracting components of the Python library NetworkX to verify the existence of a periodic attractor. We find that any influence graph with thresholds which has a periodic attractor has only one periodic attractor.
However, the topologies of these two influence graphs are similar: the numbers of cycles of length 2, 3 and 4 of these two graphs are identical and they both have 6 arcs. In order to find a condition for periodic attractor, we need to find a way to exhibit the topological difference between these two graphs, and these new features are effective: for the left graph, OD2 = 1, OD3 = 3; for the right one, OD2 = 2, OD3 = 2. Note that these features do not depend on the relations between thresholds. In step 4, we construct a decision tree to predict the existence of a periodic attractor based on the 5 features C2, C3, C4, OD2 and OD3 using all influence graphs with thresholds considered in this work. This decision tree is constructed automatically using the decision tree model of the Python library Scikit-learn.
7 Ga 0. 3 N layer was grown at 820 • C. Finally, the sixth and last QD plane was deposited on the surface of the top cladding layer for both samples. Atomic force microscopy (AFM) EDGE-DIMENSION (BRUKER, Billerica, MA, USA), operating in tapping mode with a silicon tip with a radius between 5 and 10 nm, was used to study the surface morphologies of h-BN and AlN. In addition, a diamond-coated tip with a typical radius between 5 and 10 nm was also used to investigate the morphology of the surface QDs plane, and all data were processed using WSxM software  [18]. Furthermore, X-ray diffraction (XRD) measurements were conducted using a PANalytical X'Pert PRO MRD four-circle diffractometer (Malvern Panalytical, Malvern, United Kingdom) to assess the crystalline quality of the Al x Ga 1-x N/AlN layers and examine the impact of h-BN on the subsequent layers.