dataesr/scientific-paragraphs-categorization huggingface dataset:
The PLO3S method has the highest recall compared to HKS and WKS for all sizes of the positives set (Figure  7b ). The recall curve of PLO3S increases rapidly at the beginning and stabilizes around 62% of the dataset size, while the recall of HKS and WKS increases steadily. The PLO3S method outperforms both HKS and WKS methods for Negative Predictive Value (NPV) (Figure  7c ). NPV ranges from 1 to 0. 9, as the size of the true negatives set is approximately 13 times larger than the size of the true positives set. The NPV for PLO3S ranges from 0. 93 to 0. 99 and its maximum is at 37% of the dataset size. On average, HKS and WKS show stable NPV, around 0. 93 for WKS and 0. 92 for HKS which is globally inferior to PLO3S We determined the average descriptor computation time and comparison time in Table  1  for the three methods.
Comparative evaluation of the performance of PLO3S in enrichment with protein structure comparison methods The 3 standard protein structure comparison methods, CE, TM-Align and DeepAlign outperformed the PLO3S method in all three measures, precision-recall, recall and negative predictive value (Figure  7 ). While the precision of PLO3S is about 0. 9 for low recall and 0. 2 for high recall, DeepAlign and TM-Align have higher overall precision, ranging from 0. 89 to 1 for different associated recall. The PLO3S recall stabilizes around 0. 9 for a dataset size of 62%. The recall of protein structure comparison methods is 1 after the first 20% of the results. The PLO3S NPV is inferior to the protein structure comparison methods, with its highest NPV of 0. 99 at 37% of the dataset size. The NPV of the protein structure comparison methods equals 1 for a 2% to 75% dataset size and reaches 0.
93 for 100% of the dataset. The protein structure comparison methods do not require the computation of a descriptor because the comparison and scoring parts are directly based on the structure (Figure  1 ). The comparison time is 0. 8 second, 0. 7 second, 0. 1 second and inferior to 0. 1 second for CE, PLO3S, DeepAlign and TM-Align respectively. The comparison time of PLO3S is thus similar to the comparison times of the protein structure comparison methods.
Evaluation of PLO3S in enrichment In order to evaluate the PLO3S performance in shape retrieval, we used a benchmarking data set with 403 protein conformations for 14 protein shape classes. The performance of the PLOS3S method was measured using Precision-Recall and negative predicted value curves. Around a threshold of 62% of the dataset size, the recall of the PLO3S method reaches a value of 92% The highest value for NPV is around 38% which means that further analysis of the last 38% of the dataset is not relevant. Thus, the dataset size can be reduced by 38% (while discarding less than 10% of the real positive objects). This allows to dramatically reduce the effort required to screen a large dataset in a hierarchical protocol. The performance of PLO3S in terms of recall and NPV indicates that our method meets its main objective: selecting most of the positive objects while discarding a large number of negative objects with high confidence to safely decrease the size of very large datasets in a context of protein surface shapes screening.
The average computational time required to compute the SWIMs, the PLO3S descriptors, accounts for most of the time of the method. This preprocessing step is performed once for a given object and the resulting SWIMs can be stored for later comparisons/screens in a SWIM database describing protein surface shapes. Here, the critical aspect for a large database screeening is the computational time required to compute the shape dissimilarity between two protein shapes which is satisfactory (0. 7 seconds in average).
To our knowledge, no method has been designed to find local surficial similarities of proteins independently of the sequence using the spectra of the Laplace-Beltrami operator. The Surface Wave Interpolated Maps (SWIM) descriptor has been developed on a protein surfaces dataset and is based on the spectra of the Laplace-Beltrami operator through the Wave Kernel Signature (WKS) descriptor. The SWIM descriptor also reduces the dimensions to a 2D space, which reduces the computation time compared to classic spectra-based methods used for 3D objects comparison such as HKS and WKS. Although SWIM is a 2D descriptor and some information is lost when the dimensions are reduced, the precision-recall curves indicate a better performance of PLO3S compared to the other computer vision methods. Protein surfaces are rough, displaying many variations over their surface.
On the contrary, the computation of dissimilarity in PLO3S is 272 times faster than in WKS and 344 times faster than in HKS. This is due to (1) the definition of SWIM in the 2D space (2) the creation of a specific data structure that can be manipulated by the GPU with GPGPU. To the best of our knowledge, there is no GPGPU implementation of HKS and WKS. The performances of PLO3S in enrichment are similar or higher than the reference computer vision methods evaluated in the present work with a faster comparison time to compute the dissimilarity. PLO3S can be used to quickly and efficiently decrease the size of a very large dataset while retaining the real positive objects. This method can be used for a screening in a big data environment to reduce a protein dataset that can be refined with a finer-grained method in a hierarchical protocol.
Conclusion In the present work, we introduce PLO3S that relies on a 2D representation of the surface topology based on a conformal projection of the protein surface, the SWIM descriptor. The SWIM descriptor allies the advantages of being spectrum-based, i. e invariant to isometry and accounting for local surficial features of the shape, and of being in 2D, allowing very fast computation of the shape dissimilarity for the screening of large protein surface datasets. In addition, SWIM is a local descriptor that allows for a partial comparison and can therefore be used to find similarities between protein regions. The performance of PLO3S in enrichment has been evaluated in a blind comparison of protein surfaces using the SHREC 2020 protein shapes database. The PLO3S method can be used as a fast, coarse grained protein surface shape screening method that efficiently eliminates proteins displaying dissimilar surfaces to downsize large datasets of protein surface shapes.
Solar Wind Ion Composition The ACE satellite has been monitoring, among other quantities, the solar wind density, velocity and composition from the Lagrange L1 point since 1998. [ X q+ X ] [ X O ] [ X q+ O ] He ++ ( He ++ p ) We use the ACE/SWICS 1. 1 Level 2 database 1 from which we extract the ion charge state distributions and elemental abundances relevant to oxygen , in order to calculate the ion charge state relative abundances , as well as the alpha ( ) particle speed which is equatable with the proton speed. An anomaly that occurred in August 2011 has impacted the ACE/ SWICS operations, and detailed charge-state distributions and most elemental abundances are no longer provided since that date. We use the ACE/SWEPAM Level 2 database 2 to obtain the proton parameters (density and velocity) and alpha to proton ratio.
In Figure  2  we present a number of parameters showcasing the changes in solar wind properties during the solar cycle, between 1997 and 2012. O 7+ O 6+ O 7+ O 6+ O 7+ O 6+ ≤ 0. 145 The ACE 1. 1 database provides a parameter that allows a rough classification of the solar wind type, in streamer, coronal hole (CH) and interplanetary coronal mass ejection (ICME), based on the ratio versus proton speed functions described by  Zhao L et al. (2009)  3. However, according to von  Steiger and Zurbuchen (2015)  the ratio alone, and in particular the threshold ( ) employed by  Zhao L et al. (2009)   O 7+ O 6+ × C 6+ C 5+ O 7+ O 6+ × C 6+ C 5+ ≤ 0. 01 O 7+ O 6+ from  Zhao L et al. , 2009). von  Steiger and Zurbuchen (2015)  have demonstrated from Ulysses data that a better parameter to separate streamer from CH wind is the product.
In this analysis we adopt the following classification for the solar wind type: (1) we identify ICMEs based on the list provided by  Richardson and Cane (2004)   4 O 7+ O 6+ × C 6+ C 5+ ≤ 0. 01 (2) we adopt the threshold to identify the CH (fast) wind, and (3) we exclude the outlier and UDW slow wind populations from the streamer type. The thresholds applied for the different populations are illustrated in Figure  3a  of the non-ICME wind population. It is also worth noting that ion ratios and elemental abundances in the ACE database show a significant change between solar maximum and solar minimum (see for example panels f, g of Figure  2 , and Figure  4 ). This seems to be in agreement with previous studies showing that the 2008-2010 minimum exhibited peculiar properties such as cooler temperatures  (Issautier et al.
, 2008)  that may explain the depleted ion abundances  (Lepri et al. , 2013). We therefore decide to add an additional separation in solar maximum and solar minimum periods to test for changes of the compound cross section results. The detailed results per solar wind type and solar period for solar wind He ++ density and velocity, elemental and charge-state abun-dances, are given in Tables  1  and 2. The uncertainties of the measures are determined as the dispersion (width) at half maximum of the histogram distributions used to calculate the mode values per solar wind type. O 7+ O 6+ × C 6+ C 5+ O 7+ O 6+ The elemental abundances (Table  1 ) calculated for the full ACE database span for streamer and CH populations are consistent with the most recent analysis of the Ulysses data ( Changes from maximum to minimum are also noticeable in some charge-state abundance ratios (Table  2 ), with the highest charge states showing a decreasing trend, while the lower charge states, such as , and the heavier metals ( , , ) seem more abundant during solar minimum, particularly for CH solar wind type.
However, these trends should be considered with caution, due to the lower statistics of the ACE data during solar minimum. In Figure  5  we plot the ACE 1. 1 to the SC00 charge state abundance ratio for the streamer (slow) and CH (fast) populations, for the complete database, as well as for the maximum and minimum periods. We also plot indicatively the error-bars for the complete database values (we omit the error-bars for the solar maximum and minimum periods so as to not overcrowd the plot). SC00 did not provide any uncertainties, thus the plotted error-bars are proportional to the uncertainties of the ACE 1. 1 charge state abundances from.
Velocity-Dependent Cross-Sections For the CX cross-sections we are using the compilation provided by the KRONOS 5  package at the University of Georgia  (Cumbee et al. , 2021). The KRONOS database includes comprehensive single electron cross-section values for many ion-neutral pairs, in particular H-and He-like charge states, and a wide range of collision energies. The calculations are based on the Multi-Channel Landau-Zener (MCLZ) approximation  (Lyons et al. , 2017)   on more accurate methods, such as atomic-orbital close-coupling (AOCC;  Fritsch and Lin, 1991) , molecular-orbital close-coupling (MOCC;  Janev et al. , 1993; Harel et al. , 1998) , quantum-mechanical molecular-orbital close-coupling (QMOCC;  Nolte et al. , 2012)  and classical trajectory Monte Carlo (CTMC;  Abrines et al. , 1966) , are also provided.
The KRONOS database package has been applied successfully on both solar system and astrophysical CX spectra  (Mullen et al. , 2017; Cumbee et al. , 2016 Cumbee et al. , , 2017)) , and is also employed in the AtomDB Charge eXchange v2. 0 (ACX2) spectral model  (Smith et al. , 2012 , and http://www. atomdb. org/CX). → → → → He ++ X q+ , M In this study, we use the velocity-dependent cross sections according to the KRONOS preferred order QMOCC MOCC AOCC CTMC MCLZ when available. The velocity-dependent cross sections from KRONOS are interpolated to the ACE velocity time series. In short, this includes all the bare and He-like ions (C, N, O, Ne, Mg) and only a few Li-like, or lower charge states, of Ne, Mg, and S from Table  2. If the cross-sections of any couple are not available through KRONOS, we use the SC00 crosssection values.
Emission Line Probabilities The emission line probabilities (or yields) are based on two types of calculations, as described in  Koutroumpa et al. (2006 Koutroumpa et al. ( , 2009) )  and references therein. For ions C, O, N, Ne, Mg the quantum yield cascades and emission line energies are calculated by  Kharchenko (2005)  for collisions with H and He respectively, and for the slow and fast SW regimes. These spectra have been successfully applied to cometary SWCX emission spectra for slow and fast SW velocities (e. g. ,  Kharchenko and Dalgarno, 2000, 2001; Rigazio et al. , 2002; Kharchenko, 2005). For heavier ions (Fe, Si, S, Mg), the quantum yield database was updated using the hydrogenic approximation (see details in  Kharchenko, 2005; Koutroumpa et al. , 2009)  without distinction between H and He targets, or slow and fast SW regimes.
Within this model, the exact positions of emission lines are not accurate compared to real emission spectra, but the total energy budget of the cascades in a given energy range is correct. Since many of those ion lines are blended with each other in the lower energy range (0. 1-0. 3 keV), this defect is less important at low to moderate spectral resolution of current instruments. However, future missions such as the Line Emission Mapper proposed concept (LEM;  Kraft et al. , 2022 ) and ESA's Athena mission  (Barret et al. , 2020) , which will provide microcalorimeter-resolution data, will require a deep reevaluation of the spectral line ratios in the lower energy range. The complete spectral line database may be consulted in  Koutroumpa (2007)   6.
Compound Cross-section Results α [ X q+ p ] [ X q+ p ] = [ X q+ O ] [ O He ] [ He p ] We calculate the compound cross-section based on Equation (3), where the ratio is calculated as , from the ACE SWICS and SWEPAM data presented previously 7. Figure  6  shows the 27-day average values for the compound crosssections with H and He in three energy ranges. We have chosen the full pass-band of SXI 0. 1-2. 0 keV, a slightly narrower band 3-2. 0 keV that may be applied to the XMM-Newton PN and MOS detectors, and a very narrow band around the oxygen lines 0. 5-0. 7 keV. In Figure  7  we compare the distributions (the histograms represent the number of occurrences) per solar wind type and solar activity period for these bands. In Table  3  we list the mode values of the distributions presented in Figure  7  for streamer, CH and ICME solar wind type for the complete ACE 1.
1 database, as well as for the solar maximum and solar minimum periods. The uncertainties are again determined as the dispersion (width) of the distribution at half maximum. During the full ACE 1. 1 database period, the broad band calcula-10 -16 α tions (0. 1-2 keV) show little variations with the solar wind type, as it would be expected, since the blend of spectral lines of several ion charge states smooths out any individual variations. The values are of the same order as the  Cravens et al. (2001)  empirical value of 6 × for photon energies > 0. 1 keV. However, if we compare to the calculated based on the SC00 charge-state abundances and cross-sections (Table  3 ), our values are more than 2 times lower. This is most probably due to the higher crosssections used in the SC00 study, since we have demonstrated that the ion abundances are similar in most cases between the SC00 and ACE 1.
1 analysis. From top to bottom, histograms (in number of occurrences) for the 0. 1-2 keV, 0. 3-2 keV, 0. 5-0. 7 keV range, and for the 0. 1-2 keV, 0. 3-2 keV, 0. 5-0. 7 keV range. The first column includes histograms for streamer (dark red), coronal hole (blue) and ICME (yellow) SW for the complete ACE 1. 1 database  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011). The second, third and fourth columns include histograms for the streamer, C. H. and ICME types of SW respectively, separated in solar maximum (orange) and solar minimum (blue). The red and blue vertical lines in panel three of the first column correspond to the  Whittaker and Sembay (2016)  values for the same energy range. α The differences between solar wind types become more noticeable as the spectral range becomes narrower.
Conclusion α We have compiled ion charge state abundances from the ACE 1. 1 database between 1998 and 2011, charge-exchange crosssections from the KRONOS database, and line emission probabilities from literature. We have calculated the compound crosssections for charge-exchange collisions with H and He atoms for broad (0. 1-2 keV), average (0. 3-2 keV) and narrow (0. 5-0. 7 keV) spectral ranges, for streamer, CH and ICME solar wind types and in different solar cycle periods. α We find that for broad band ranges (0. 1-2 keV), there is little variation in for the different solar wind types in each solar period. The distinction between solar wind types becomes significant as we narrow down the spectral band to single ion emission range, such as the oxygen band (0. 5-0. 7 keV). This is consistent with previous studies showing that the SWCX signal is more influenced by individual ion variations when analysing bands where only few ions dominate the spectrum, as opposed to broad bands where blends of many ion spectral lines are measured  (Kuntz et al.
g. with test-particle models). The SMILE/SXI instrument has both a broad energy range (0. 1-2 keV) and enough spectroscopic resolution to separate several strong lines (e. g. from O, C, N and Ne ions), and will certainly benefit from these combined approaches. In that context, and in view of future X-ray missions (e. g. , LEM, ATHENA) that will provide high-resolution spectra of the SWCX emission in the 0. 1-2 keV range, it is crucial to improve developments in two areas. First, we need to improve the databases of cross-sections and emission probabilities for complex ion structures, such as the ions Mg, Si, S, Fe, populating the low-energy band 0. 1-0. 3 keV. For this, we need accurate quantum calculations, corroborated by experimental measurements in collision energies similar to astrophysical conditions.
Acknowledgments This work was supported by  CNES. I wish to thank the  SWEPAM  and SWICS instrument teams for providing the ACE data through the ACE Science Center: http://www. srl. caltech. edu/ACE/ASC/ index. html. I am grateful to  Sue Lepri ,  Jim Raines ,  Ruedi von Steiger  and  Liang Zhao  for invaluable discussions on the SWICS composition data. I am very thankful to  Renata Cumbee  for her advice on the use of the cross sections in the KRONOS charge transfer database. I cannot omit to thank  Vasili Kharchenko  for always happily answering my endless questions on line emission probabilities. Finally, I deeply appreciate the two referees' thorough evaluations that greatly improved this paper.
Figure7. From top to bottom, histograms (in number of occurrences) for the 0. 1-2 keV, 0. 3-2 keV, 0. 5-0. 7 keV range, and for the 0. 1-2 keV, 0. 3-2 keV, 0. 5-0. 7 keV range. The first column includes histograms for streamer (dark red), coronal hole (blue) and ICME (yellow) SW for the complete ACE 1. 1 database(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). The second, third and fourth columns include histograms for the streamer, C. H. and ICME types of SW respectively, separated in solar maximum (orange) and solar minimum (blue). The red and blue vertical lines in panel three of the first column correspond to the Whittaker and Sembay (2016)  values for the same energy range.
092 +0. 024 -0. 019 0. 127 +0. 096 -0. 055 0. 136 +0. 077 -0. 049 0. 096 +0. 035 -0. 026 0. 145 +0. 107 -0. 062 0. 108 +0. 067 -0. 041 0. 092 +0. 025 -0. 019 +0. 066 0. 109 -0. 041 O 7+ O 6+ / 0. 131 +0. 210 -0. 080 0. 020 +0. 017 -0. 009 0. 317 +0. 585 -0. 204 0. 180 +0. 291 -0. 110 0. 029 +0. 010 -0. 007 0. 353 +0. 548 -0. 218 0. 073 +0. 069 -0. 036 +0. 016 0. 015 -0. 008 0. 103 +0. 068 -0. 038 C 6+ C 5+ / 0. 859 +0. 876 -0. 420 0. 187 +0. 124 -0. 076 1. 291 +1. 560 -0. 718 0. 990 +0. 956 -0. 498 0. 196 +0. 071 -0. 051 1. 354 +1. 497 -0. 711 0. 654 +0. 443 -0. 263 0. 176 +0. 160 -0. 081 +0. 433 0. 797 -0. 286 particle parameters and element abundance ratios from ACE 1. 1 database for streamer (slow), CH (fast) and ICME solar wind for different solar activity periods. The uncertainties represent the dispersion of the measures at half maximum of the distribution.
Comparison with LinearFold and RNAFold When comparing SparseRNAFolD-Standard with LinearFold and RNAFold, we look at the "always dangle" model, as LinearFold does not implement the "exclusive dangle" model. We first compared the three algorithms by their predictive accuracy (F-measure). For comparison, we selected all sequences from our dataset whose structure was available on RNAstrand. We further constrained it to sequences that contained hairpins greater than 3 and no pseudoknots. This resulted in 986 sequences. We found that SparseRNAFolD-Standard had a marginally better, but not significant, average Fmeasure of 0. 6394 compared to 0. 6391 of LinearFold. As described in Section "Proof of concept with RNAFold", RNAFold and SparseRNAFold-standard are identical in predictive accuracy. We then assessed their time and space usage.
To increase the size of our dataset for this testing, we included a dinucleotide shifted version of our dataset in our test Fig. 4  We plot the results of the three versions of SparseRNAFolD when given RNA sequence only as input against each other and an "exclusive dangle" model based on the dataset. (a) Memory Usage (maximum resident set size in KB) versus length (log-log plot) over all benchmark instances. The solid line shows an asymptotic fit (c 1 + c 2 n x ) for sequence length n, constants c 1 , c 2 , and exponent x for the fit. We ignored all values < 1000. (b) Run-time (s) versus length (log-log plot) over all benchmark instances. For each tool in both plots, we report (in parenthesis) the exponent x that we estimated from the benchmark results; it describes the observed complexity as Θ(n x ).
data. We then constrained the size of sequences to those > 400. The maximum time and memory used by LinearFold on this dataset were 3. 34 seconds and 118, 848 KB. The maximum time and memory used by RNAFold were 22. 26 seconds and 109136 KB. In contrast, the maximum time and memory spent by SparseRNAFold were 18. 32 seconds and 13, 000 KB, respectively. This is illustrated in Figures  5a  and 5b. The results show that SparseRNAFolD-Standard uses far less memory on even the largest pseudoknot-free sequences in our dataset. Note that the maximum resident set size is nine times lower than that of LinearFold and eight times lower than that of RNAFold. RNAFold's time remained consistent with SparseRNAFolD-Standard until longer sequences where it falls behind. LinearFold, whose time complexity is O(nb log(b)) , where n is the length of the sequence and b is the beam width, did perform faster than SparseRNAFolD-Standard as the length of the sequence increased.
Highlighting RNAFold To highlight the difference in space between RNAFold and SparseRNAFolD-Standard, we selected 81 sequences from our dataset with size greater than or equal to 2500. The sequence with the maximum length in the set was 4381 nucleotides long. As seen in Table  1 , while SparseRNAFolD-Standard's runtime is comparable to RNAFold's, its memory consumption is about five times lower.