dataesr/scientific-paragraphs-categorization huggingface dataset:
The Band function in this region peaks in energy (E peak ) at ∼1. 4 MeV. The additional PL of this fit has photon index of ∼-1. 9, which is consistent with the canonical PL value of Γ= -2 expected from the high-energy component of the electron synchrotron spectrum for both the slow-and fast-cooling regime, for an assumed powerlaw electron energy distribution of p = 2  (Granot & Sari 2002). This spectral component is consistent with the emergence of the early afterglow, over which the rest of the prompt emission is superimposed, similar to behaviour observed in GRB 190114C  (Ajello et al. 2020). The model spectrum for this fit can be found in Figure  5. Region IVb is the time of the first Fermi -GBM BTI. As mentioned previously, the TTE data within the Fermi -GBM BTIs is irrecoverable. Although the CSPEC data experienced PPU and deadtime effects, the data are corrected via the method described in Section 3.
1. Within this BTI the lightcurve consists of two distinct peaks. Our assumption of a Band function for the underlying spectral shape produces adequate fits throughout. However, we observe varying goodness-of-fit measures that coincide with the times of these two peaks. Examples of PPU-corrected counts spectra during these peaks are shown in Figure  5. Due to the current limitations of our PPU-correction technique described in Section 3. 1 we are unable to discern whether these variations are due to spectrotemporal evolution in the data or uncorrected PPU effects. Spectral fit parameters are not reported for fits within the Fermi -GBM BTI regions because the purpose of these fits is to determine the energetics of GRB 221009A. Further analysis is needed to determine the reliability of our PPU-correction technique for spectral modeling of this GRB with additional spectral models being considered.
ACKNOWLEDGMENTS The UAH co-authors gratefully acknowledge NASA funding from co-operative agreement  NNM11AA01A. The USRA co-authors gratefully acknowledge NASA funding through contract  NNM13AA43C. D. K. , C. A. W. H. , and C. M. H. gratefully acknowledge NASA funding through the  Fermi GBM  project. Support for the German contribution to GBM was provided by the  Bundesministerium f ur Bildung und Forschung (BMBF)  via the  Deutsches Zentrum f ur Luft und Raumfahrt (DLR)  under contract number  50 QV 0301. The  Fermi -LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the  National Aeronautics and Space Administration  and the  Department of Energy in the United States , the  Commissariat à l'Energie Atomique  and the  Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France , the  Agenzia Spaziale Italiana  and the  Istituto Nazionale di Fisica Nucleare in Italy , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  High Energy Accelerator Research Organization (KEK)  and  Japan Aerospace Exploration Agency (JAXA) in Japan , and the  K.
ACKNOWLEDGMENTS We thank  J. Papavassiliou  and  J. Rodríguez-Quintero  for discussions and collaboration in the early stages of this work. We also thank  P. Boucaud  and  F. De Soto  for an earlier collaboration and for their help in the preparation of the lattice data. We are indebted to the  RBC/UKQCD collaboration , especially to  P. Boyle ,  N. Christ ,  Z. Dong ,  N. Garron ,  C. Jung ,  B. Mawhinney , and  O. Witzel , for access to the lattices used in this work. Numerical computations have used resources of CINES,  GENCI  IDRIS (Project  ID   52271 ), and of the IN2P3 computing facility in France. This work is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy No. EXC 2181/1-390900948  (the  Heidelberg STRUCTURES Excellence Cluster ) and the  Collaborative Research Centre No.
SFB 1225 (ISOQUANT). J. T. and N. W. acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project No. 315477589-TRR 211. N. W. acknowledges the support by the  State of Hesse  within the  Research Cluster ELEMENTS (Project ID   500/10. 006 ). J. M. U. is supported in part by  Simons Foundation  Grant No. 994314  ( Simons Collaboration on Confinement and QCD Strings ) and the  U. S. Department of Energy, Office of Science, Office of Nuclear Physics , under grant Contract No. DE-SC0011090. This work is funded by the  U. S. National Science Foundation  under Cooperative Agreement No. PHY-2019786  (The  NSF AI Institute for Artificial Intelligence and Fundamental Interactions , [94]). J. H. and J. M. U. acknowledge support by the  Studienstiftung des deutschen Volkes.
Kinematics of the DIB carrier Although most of the DIB carriers are unknown, they have been proven to be a powerful tool for ISM tomography and consequently can probe the Galactic structure and interstellar environments. The most comprehensive kinematic study to date was performed by  Zasowski (2015)  using APOGEE (SDSS-III) data, and allowed the authors to reveal the average Galactic rotation curve of the λ1527 DIB carriers spanning several kiloparsecs (kpc) from the Sun. They probed the DIB λ1527 carrier distribution in 3D and showed that DIBs λ1527 can be used to trace large-scale Galactic structures, such as the Galactic long bar and the warp of the outer disk. Zhao et al. (2021b)  studied the kinematics of the DIB λ862 in the Galactic Bulge using Gaia-ESO  (Gilmore et al. 2012 ) and GIBS data  (Zoccali et al.
Correlations between the DIB λ862 carrier and gas kinematics using different tracers such as CO and HI can provide additional clues as to the origin of the DIB λ862 carrier. Figure  17  shows one example with the comparison of the 12 CO data from  Dame et al. (2001). In the present study we use the momentummasked cube restricted to the latitude range ±5 • 1. We see that,  in general, the DIB λ862 closely follows the CO gas pattern, especially in the Galactic anticentre region, while higher velocities are seen in CO for | | < 50 •. This close relation between the DIB λ862 and the gas reinforces the suggestion that the DIB λ862 carrier could be related to macro molecules. We want to stress again that Gaia data allow us to discuss such a largescale picture for the first time.
Acknowledgements. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. Full acknowledgements are given in Appendix A. T. Z. acknowledges financial support of the  Slovenian Research Agency  (research core funding No. P1-0188 ) and the  European Space Agency (Prodex Experiment  Arrangement No. C4000127986 ). Part of the calculations have been performed with the high-performance computing facility SIGAMM, hosted by the  Observatoire de la Côte d'Azur.
The GSP-spec group acknowledges financial supports from the french space agency (CNES),  Agence National de la Recherche  ( ANR 14-CE33-014-01 ) and  Programmes Nationaux de Physique Stellaire & Cosmologie et Galaxies (PNPS & PNCG)  of  CNRS/INSU. H. Z. is funded by the  China Scholarship Council  (No. 201806040200 ). Y. F. acknowledges the  BELgian federal Science Policy Office (BELSPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX) grants. Artificial Intelligence and Data Science (TAD)  through a grant; the  Agenzia Spaziale Italiana (ASI)  through contracts  I/037/08/0, I/058/10/0 ,  2014-025-R. 0 , 2014-025-R. 1. 2015 ,and  2018-24-HH. 0  to the  Italian Istituto Nazionale di Astrofisica (INAF) , contract  2014-049-R. 0/1/2  to  INAF for the Space Science Data Centre (SSDC , formerly known as the  ASI Science Data Center, ASDC) , contracts  I/008/10/0 ,  2013/030/I.
1b ) to the outside, which contained background ions from spray solvent and/or surrounding air. The obtained mass spectral data were converted to a text data file for imaging by Firefly software (Prosolia Inc. ) and the DESI images were visualized using BioMAP (maldi-msi. org). For the image analysis, the mass resolution of m/z of ± ~ 0. 001 was adapted. The chemical formulae of identified peaks were assigned using 12 C, 13 C, H, 14 N, 16 O, 23 Na, and 32 S, then filtered by ± 3 ppm mass precision, that is defined as [(measured m/z) -(calculated m/z)]/(calculated m/z) -1] × 10 6 (ppm). After the imaging, the ToF-SIMS measurement was performed on the same surface of A0080 using a TRIFT III spectrometer (ULVAC-PHI, Inc. ) at Nagoya University. Positive and negative spectra were obtained using an Au + beam.
Acknowledgements The  Hayabusa2  project has been led by  JAXA  ( Japan Aerospace Exploration Agency)  in collaboration with  DLR (German Space Center)  and  CNES (French Space Center)  and supported by  NASA  and  ASA (Australian Space Agency). We are grateful to all the members of the Hayabusa2 project for their technical and scientific contributions. We sincerely thank  Dr. Conel M. O'. D. Alexander , an anonymous reviewer, and the editors of  EPS  for their constructive comments.
Conclusions The ESSnuSB is designed to be a next-to-next generation neutrino oscillation experiment to precisely measure the CP violation phase δ CP at the second oscillation maximum by employing the uniquely powerful ESS accelerator as a proton driver for neutrino beam production. It is expected to start taking data after HyperK and DUNE have already conducted their measurement and either confirmed the existence of leptonic CPV or excluded it in their sensitivity regions. If the existence of CPV is confirmed, ESSnuSB will start the precision era of leptonic CPV measurement; if not, it will have an equally important mission of searching for CPV in the part of parameter space inaccessible to its two predecessor experiments. The conceptual design for the ESSnuSB experiment has been published  [25, 26] , and the new project ESSnuSB+ is underway to design intermediate facilities for R&D and measurement of the neutrino interaction cross-section, perform civil engineering conceptual studies, and explore the additional physics possibilities of the proposed infrastructure.
The Gini coefficient of the link strength distribution denoted Gini(w). The Gini coefficient was here computed for all the links in the networks, whereas g t from Eq. (  4b ) is for three links only. Next I computed five classic network-level summary metrics: 4. The binary edge density D 0 , corresponding to the proportion of nonmissing links among all the possible links in the network. 5. The binary network transitivity, C 0 (Eq. (  1 )). 6. The weighted network transitivity with the geometric mean of link strengths as weight, C 1,geom (Eq. (  3a )). 7. The network modularity M. I delineated modules of individuals that interact more among themselves than across modules using the short random walk community-finding algorithm: routine clus-ter_walktrap  (Pons and Latapy, 2005)  from igraph  (Csardi and Nepusz, 2006).
1) and 15 % of the links were strong (assigned a weight drawn at random between 0. 75 and 1). This procedure generated more triangles with two weak links and one strong link than if the link strength distribution was unimodal for example. 3. Scenario 3 with many strong triplets closed by a weak link. First I generated networks with a C 0 -score of 0. 8, using the rguman routine in R-package sna  (Butts, 2020). Next, processing each triangle one by one in a random order, I made one of the three links weaker than the other two (by assigning a weight drawn at random between 0 and 0. 1). This routine generated more triangles with two strong links and one weak link than if I omitted the reassignment step. Note that the routine processed each link multiple times because some links belonged to several triangles.
7. Lattice graphs. In lattice graphs, each individual is connected to a fixed number of immediate neighbors. I considered 2D, 3D, and 4D lattices. The link strengths were drawn from either a Gaussian or uniform distribution. 8. Modular graphs made of a set number of full networks (from 1 to 8 full networks) of which the link strengths varied between 0. 75 and 1. These modules were then connected by weak links varying between 0 and 0. 25, thereby ultimately obtaining a full network with no missing link, but a high modularity score as defined above. 9. Star graphs made of a central node connected to all other individuals by strong links varying between 0. 9 and 1, while the remaining links were all weak and varied between 0 and 0. 1. I generated 100 examples of these 9 types of theoretical networks using igraph for R  (Csardi and Nepusz, 2006).
Using the new metrics as synthetic variables Even without triadic mechanism, the new metrics can prove useful because they summarize information that would otherwise require many metrics. As an illustration, I reanalyzed and compared 6 datasets of grooming interactions within groups of rhesus macaques (Macaca mulatta), that I obtained through the ASNR repository  (Sah et al. , 2019). Rhesus macaques exhibit an "intolerant social style" characterized in particular by strong preference for kin in social interactions such as grooming  (Thierry, 2007). Therefore, the kinship structure of the group is expected to influence who grooms whom in a macaque group. Among the 6 datasets, there were three groups of nonkin that met each other after weaning age (all from the same study:  Balasubramaniam et al.
, 2018). Then there were two free-ranging groups with naturally occurring kinship structure  (Griffin and Nunn, 2012; Puga-Gonzalez et al. , 2018)  (these are from the same locality but different time periods). Another group was captive but featured locally born adult and immature offspring, therefore similar to the natural kinship structure  (Massen and Sterck, 2013). In summary, I obtained data from free-ranging groups of kin, captive groups of kin, and captive groups of nonkin, making it possible to decipher the influence of kinship and captivity. I used Hotelling's T2 statistic for this purpose. Link strength corresponded to the frequency of recorded allogrooming interactions for each dyad during standardized observation sessions. I did not distinguish who was the groomer and who was the recipient.
Multiple recent studies have proposed a theoretical explanation for the non-linearity of this correlation. For instance,  Kubota & Done (2018)  developed a broadband spectral model for bright luminous quasars using three different regions, an outer cold disk, a warm Comptonizing region, in the middle, producing the soft X-ray excess observed in most AGN, and a hot corona somewhere close to the black hole. The hot corona is fixed in the inner region of the accretion flow. They define thus four different radii, the ISCO, the outer radius of the hot corona and the outer radius of the warm corona, and the outer radius of the accretion flow. They tie the different regions energetically assuming a Novikov-Thorne emissivity. They fit the data from three AGN with different spectral shape akin to a hard, an intermediate, and a close to soft X-ray binary accretion states.
The Mars' ionospheric behavior during two consecutive solar minima (23/24 and 24/25) is investigated with the same dataset. In particular, we use the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) on board Mars Express to investigate the total electron content behavior of the whole atmosphere in relation to the solar irradiance (EUV and X-ray fluxes), the solar zenith angle and the heliocentric distance. The topside variability of the electron density profiles is also investigated through variations in the peak density and neutral scale height. Moreover, the equations of the NeMars empirical model of the Martian ionosphere for low solar activity are tested for both minima. We have found that the topside ionosphere of Mars behaved similarly at both solar minima. However, when considering the bottomside, a pronounced reduction in ionization in particular cases is suggested.
, 2016a (Hall et al. , , 2016b;; Hall et al. , 2019). Focusing on the effect of the solar cycle at Mars, the ionosphere behaves slightly differently at each phase of the solar cycle as demonstrated by  Sánchez-Cano et al. (2015b) ;  Sánchez-Cano et al. , 2016)  using different Mars Express datasets during the solar cycle 23/24. For instance, significant variability in the distribution of the ionization in the topside profiles was found through the analysis of neutral scale heights for a full solar cycle. In particular, the smallest and more altitude-constant neutral scale heights were found during the low solar activity period in 2007-2010, and the largest and more prominent altitude-dependent scale heights were found during moderate and high solar activity periods  (Sánchez-Cano et al. , 2015b , 2016).
, 2016). Therefore, the solar minimum of solar cycle 23/24 was unique in many aspects, and it gave us the opportunity to investigate the behavior of the Martian ionosphere under conditions not explored before at Mars. Since we are now at a new minimum of solar activity (solar cycle 24/25), we have the opportunity to investigate whether the Martian ionosphere behaves similarly to the previous solar cycle, or if not, whether the ionospheric behavior of the previous solar minimum may have been unique in that respect. In order to investigate whether this was an exceptional case or a recurrent situation during solar minima, this paper focuses on the analysis of ionospheric data during two consecutive solar minima at Mars, the 23/24 and 24/25 minima, using the same dataset. In particular, we use the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) instrument on board Mars Express.
Datasets and ionospheric model used in this study This study focuses on ionospheric data analysis of the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS)  (Picardi et al. , 2004; Orosei et al. , 2015)  onboard Mars Express  (Chicarro et al. , 2004)  during the two solar minima when Mars Express was in orbit around Mars. The radar has two different operational modes from which different ionospheric information can be retrieved. In particular, we use the total electron content (TEC) from MARSIS when operating in subsurface mode. In this mode, the radar sounds the surface and subsurface of the planet and the TEC is obtained after considering the signal delay introduced by the ionosphere on the radar signals  (Safaeinili et al. , 2007; Mouginot et al. , 2008; Cartacci et al.
(2021)  has demonstrated that an uncertainty of about days could be present when extrapolating Earth irradiance data to Mars at large phase angles. We use the 30. 5 nm and 9. 5 nm wavelengths as the most representative EUV and X-ray wavelengths to ionize the main and secondary layers of the Martian ionosphere, respectively (e. g. Martinis et al. , 2003; Fox and Yeager, 2006; Fallows et al. , 2015). Level 3 data are used, which includes daily averages after applying corrections for atmospheric absorption, degradation, flare removal, and to 1-AU (https://lasp. colorado. edu/data/timed_see/level3/README_SEE _L3_012. TXT). Moreover, a 27-day running average filter is applied to remove any solar-rotation dependence. Ionospheric observations at Mars are compared to predictions made by the NeMars model  (Sánchez-Cano et al.
, 2013; Sánchez-Cano et al. , 2016). NeMars is a powerful tool to accurately and quickly describe the undisturbed ionosphere of Mars on the same basis as the well-known and widely used NeQuick model for Earth (e. g. Radicella, 2009). It is an empirical model based on MARSIS-AIS data from the solar cycle 23/24, and to a lesser extent on Mars Global Surveyor (MGS) radio science data. It describes the electron density distribution with altitude of the two main ionospheric layers in Mars' ionosphere, as well as peak characteristics, scale heights with respect to solar activity, Sun's distance, seasons, latitude, and local time. The model is widely used, e. g. in support of Mars crosslink radio-occultation between two spacecraft at Mars  (Ao et al. , 2015; Nava et al. , 2020) , or to demonstrate the effect of the coma of comet C/2013 A1 (Siding Spring) on Mars  (Sánchez-Cano et al.
5c  and f in abscises). If the data points follow the 1:1 relationship, it indicates that the model properly reproduces the observations. As can be seen, the best linear fit to both datasets gives a robust result that is very close to the 1:1 relationship between the data and the model. Consequently, this figure confirms that the NeMars model for low solar activity conditions (particularly the neutral scale height) reproduces very well the topside of the ionosphere of Mars for both solar minima under a broad range of F10. 7 flux, SZA and Mars-Sun distance conditions, and so, the empirical equations developed in  Sánchez-Cano et al. (2016)  are valid for both periods. Furthermore, it also indicates that the topside ionosphere during the two solar minima was similar and that the differences observed in Fig.
Although the TEC model results match the data within the errorbars (standard deviation), we note that there are some TEC reductions of up to about 16% of the data that can be explained by a reduction on the ionization of the bottomside ionosphere. This model-data discrepancy could be caused by the uncertainty of both the model and datasets. The model has been extensively tested with observations from the solar cycle 23/24 and this is the first time that it has been tested with data from the solar cycle 24/25. Previous work indicated that the mean and median differences with respect to independent AIS observations (not used to build the model) are below 3. 5% for the electron density and below 1% for the altitude, while the electron density standard deviation is typically below 6% and below 8% for the altitude  (Sánchez-Cano et al.
, 2013; Sánchez-Cano et al. , 2015a). For the TEC, which is obtained by integration of the electron density profile with altitude, this is below 5%. Therefore, the uncertainty of the model is lower than the difference observed between the two minima at close distances to the Sun. Regarding the datasets, several algorithms have historically produced different estimates of the TEC retrieved when MARSIS operates in the subsurface mode  (Safaeinili et al. , 2007; Mouginot et al. , 2008; Cartacci et al. , 2013 Cartacci et al. , , 2018;; Conroy et al. , 2019). In particular, Sánchez-Cano et al. (2015a) undertook a critical assessment of the different algorithms finding that for SZA > 75 • all of them agree well. However, the main differences appeared for SZA < 75 •. After that,  Cartacci et al. (2018)  revisited their algorithm in order to solve an issue on their former algorithm described in  Cartacci et al.
(2013) , which produced an overestimate of TEC estimates on the dayside. Despite any possible remaining dayside differences on the TEC magnitude, several studies have shown that both TEC algorithms are appropriate for science, especially at large SZA and for temporal variability studies, as is the case of this work (e. g. Lillis et al. , 2010; Mendillo et al. , 2013; Sánchez-Cano et al. , 2018; Bergeot et al. , 2019; Burrell et al. , 2020). Moreover, these studies have proven that the science done with both algorithms is complementary. Therefore, since we are using the same dataset for the comparison of the ionospheric variability during both minima, and most of the available data come in any case from large solar zenith angles, we consider that the use of the  Cartacci et al. (2018)  algorithm for the TEC is appropriate.