dataesr/scientific-paragraphs-categorization huggingface dataset:
√ E/GeV ⊕ 3% for charged pions  [79]. In the backward region (153 • < θ < 177. 5 • ), energies are measured with a leadscintillating fiber calorimeter  [76]. Results are reported using data recorded by the H1 detector in the years 2006 and 2007 when protons and electrons/positrons (henceforth referred to as 'electrons') were collided at energies of 920 GeV and 27. 6 GeV, respectively. The total integrated luminosity of this data sample corresponds to 228 pb -1  [80]. Events are triggered by requiring a high energy cluster in the electromagnetic part of the LAr calorimeter. The scattered electron is identified as the highest transverse momentum LAr cluster matched to a track passing an isolation criteria  [81]. Events containing scattered electrons with energy E e > 11 GeV are kept for further analysis, resulting in a trigger efficiency higher than 99.
Jets are defined in the laboratory frame by clustering HFS objects satisfying -1. 5 < η lab < 2. 75. The FastJet 3. 3. 2 package  [89, 90]  is used with longitudinally invariant k T clustering algorithm  [91, 92]  using the default E-scheme and distance parameter R 0 = 1. All jets in the event with p T > 5 GeV are kept for further analysis. The unfolding targets ('particle-level') are calculated in the simulation using final-state particles with proper lifetime cτ > 10 mm and excluding the scattered lepton and photons radiated from one of the lepton lines. Reconstructed and generator level jets are matched by requiring the distance R = (φ jet gen -φ jet reco ) 2 + (η jet gen -η jet reco ) 2 < 0. 9. Results are presented after unfolding the data to particle level for events in the kinematic region defined by Q 2 > 150 GeV 2 , 0.
Each of these generators is combined with a detailed simulation of the H1 detector response based on the Geant3 simulation program  [102]  and reconstructed in the same way as data. Additional predictions, yet without a detailed simulation of the H1 detector, are made using a set of state-of-the-art generators developed mostly for pp collisions. Predictions from Pythia 8. 3  [103, 104]  are used for comparison using the default implementation and two additional parton shower implementations: Vincia  [105, 106]  and Dire  [107]. Vincia uses a p T -ordered model for QCD + QED showers based on the antenna formalism while Dire implements a p T ordered dipole shower similar to Ariadne. The NNPDF3. 1 PDF set  [108]  is used for both default and Vincia implementation and the MMHT14nlo68cl PDF set  [109]  is used for the Dire implementation.
Uncertainties Systematic uncertainties on the reconstruction of the data observables are estimated by varying the relevant parameters in the simulation and carrying out the full unfolding procedure for each simulation variant. Uncertainties on HFS objects include the energy scale from two different contributions: HFS objects contained in high p T jets and other HFS objects. In both cases, the energyscale uncertainty is ±1%. Both uncertainty sources are estimated separately  [83, 142]  by varying the corresponding HFS energy by ±1%. An uncertainty of ±20 mrad is assigned to the azimuthal angle determination of HFS objects. Lepton uncertainties are considered in the Q 2 determination and the uncertainty on the lepton energy scale ranges from ±0. 5% to ±1%  [83, 143]. Uncertainties on the azimuthal angle of the scattered lepton are estimated to be ±1 mrad  [82].
The success of this approach demonstrates that different sets of unbinned observables at detector level and particle level can be used to determine the unfolded distributions. The training of over two thousand neural networks is carried out on the Perlmutter supercomputer using 128 GPUs simultaneously during the unfolding procedure. While the unfolding procedure is unbinned, results are provided as histograms for both single-and multi-differential cross sections to ease the comparison with different theory predictions. Mean and standard deviation of all observables are calculated after unfolding in multiple Q 2 ranges. Theory predictions from dedicated DIS simulators provide an overall good description of all measured quantities. In particular, a good agreement between data and Rapgap simulation is observed for all distributions and Q 2 intervals.
Acknowledgements We are grateful to the HERA machine group whose outstanding efforts have made this experiment possible. We thank the engineers and technicians for their work in constructing and maintaining the H1 detector, our funding agencies for financial support, the  DESY  technical staff for continual assistance and the  DESY directorate  for support and for the hospitality which they extend to the non-DESY members of the collaboration. We express our thanks to all those involved in securing not only the H1 data but also the software and working environment for long term use, allowing the unique H1 data set to continue to be explored. The transfer from experiment specific to central resources with long term support, including both storage and batch systems, has also been crucial to this enterprise.
Study Design. Te main objective of this multicenter and cross-sectional study was to assess the overall satisfaction of cancer patients with the quality of OAMs dispensing in community pharmacies. Te secondary objectives were to assess the relationship between patient satisfaction with (1) the quality and type of information on OAMs given by the pharmacist, general practitioner (GP), and oncologist; (2) medication adherence; (3) oncological characteristics; (4) patient characteristics; and (5) symptoms and health-related quality of life (HRQoL). Te study protocol was designed to conform to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies  [9]. Te study has been registered on the ClinicalTrials. gov Identifer NCT03961789 (May 23, 2019).
2. 2. Setting. Tis multicenter study was coordinated by the University Hospital of Clermont-Ferrand (France). All the participants were recruited through the oncology departments of hematology, urology, pulmonology, gastroenterology, and dermatology in three hospital centers (University Hospital of Clermont-Ferrand, Hospital of Le Puy-en-Velay, and Hospital of Mende, France) and from community pharmacies (fve pharmacies in Clermont-Ferrand, France, known to manage cancer patients). Tis study (patient inclusion and data collection) was conducted between June 2017 and July 2018. Cancer patients answered once to the questionnaire, with no follow-up.
2. 5. Data Sources/Measurement. All the data were obtained from a paper questionnaire completed by the patients. All the study data were collected and managed using REDCap electronic data capture tools (Vanderbilt University, Tennessee, US) hosted at CHU Clermont-Ferrand  [12]. 2. 6. Study Size. Tis cross-sectional study assessed the satisfaction of patients treated with OAMs regarding the dispensing of the latter in community pharmacies. Te sample size has been estimated to describe satisfaction scores with a satisfactory accuracy. For a standard deviation of satisfaction scores expected at 25 (out of 100) and an accuracy close to 5 (out of 100), at least 90 patients were needed for a two-sided type I error at 5%  [13].
Statistical Methods. Continuous data were expressed as the median and interquartile range. Te normality was assessed using the Shapiro-Wilk test. Patients' satisfaction for the dispensing of their OAMs was compared between independent groups (information on methods of administration, management of adverse efects, and drug interactions) using the Student's t-test or the Mann-Whitney U test when the assumptions of the t-test were not met. Te homoscedasticity of the data was assessed using the Fisher-Snedecor test. Te results were expressed using Hedge's efect-size (ES) and 95% confdence interval (95% CI) and were interpreted according to the rules of thumb reported by Cohen  [14]  (small ES � 0. 2, medium ES � 0. 5, and large ES � 0. 8). Categorical data (satisfaction categorized according to statistical distribution, i.
We extend this study for different heliospheric distances in Section 4 and also explore a possible asymmetry between the shapes of the shock driver with the east/west direction of the structure. We discuss and conclude the study in Section 5. Several authors have previously studied IP shocks using in situ observations. The duration of some space missions has allowed reporting all these IP shocks in catalogs of events spanning a few years up to several solar cycles. Based on these previous studies, we made an inspection throughout different websites and research papers to obtain IP shocks and, if available, ICMEs associated with IP shocks detected at 1 AU and completed the dataset with IP shocks observed by Helios-1/2 as well as Ulysses.
IP Shocks The Helios-1 and Helios-2 space missions were launched in 1974 and 1976, respectively, and covered the heliospheric distance range between 0. 29 and 0. 99 AU from the Sun. The main purpose of the missions was to make pioneering measurements of the IP medium from the vicinity of the Earth to near the Sun. Measurements of the IP medium were made with the Helios magnetic-field and plasma experiments, described in  Neubauer et al. (1976)  and  Rosenbauer et al. (1977). For the data at 1 AU, we used a combination of data from the Wind, ACE, and STEREO A-B missions. The Wind spacecraft was launched on 1 November 1994 with the aim of studying the IP medium and its effects on the Earth's magnetosphere. After several orbits through the magnetosphere, the Wind spacecraft was placed in a halo orbit around the L1 Lagrange point, where it currently remains and constantly records measurements of the solar wind from its instruments on board  (Ogilvie and Desch, 1997).
Interplanetary Shock Database While several papers have reported different catalogs for IP shocks detected by the space missions described above, it is also important that the shock analysis is consistent from one catalog to another. On the one hand, numerous techniques are aimed at determining shock normals, shock speeds, values of upstream/downstream plasma, and field parameters, such as magnetic/velocity coplanarity or mixed mode method  (Paschmann and Daly, 1998). On the other hand, the detection of IP shocks depends on the temporal accuracy of the measurements, as well as a definition of what is considered the upstream and downstream intervals of the shock. Therefore, the databases must be consistent from one catalog to another to provide well-documented shocks for future investigations and to evaluate the accuracy of several shock normal determination techniques  (Russell et al.
, 1983). In our study, we chose the comprehensive Heliospheric Shock Database (HSD) (www. ipshocks. fi/), which provides shock properties detected by different missions  (Kilpua et al. , 2015). This database was compared with the Harvard Smithsonian Center of Astrophysics Wind shock database (CfA Interplanetary Shock Database), which can be found at lweb. cfa. harvard. edu/shocks/. The latter also provides detailed analyses of IP shocks observed at 1 AU by Wind and ACE spacecraft. Since each database offers different methods to identify and characterize the observed shocks, we compared in Appendix both databases, through the correlation of the shock normal values, derived by different methods, finding that the mixed-mode method offers the best correlations. We found a good agreement between the two databases, which gives us the confidence to use the data within HSD in the following.
In this database, the shocks are identified using two techniques: (1) through visual inspection of the solar-wind plasma and magnetic-field observations, and (2) with an automated shock detection algorithm (see the documentation available on the website, and references therein). For each spacecraft, the parameters available in the database are: the IP magneticfield vector, the solar-wind velocity and the bulk speed of protons/ions, the solar-wind proton/ion number density, the solar-wind proton/ion temperature or the most probable thermal speed, the spacecraft position, and the shock type (fast forward (FF) or fast reverse (FR)). The upstream and downstream intervals were chosen so that the mean values are taken sufficiently far from the peak of the shock and determined over a fixed analysis interval (∼ 8 minutes).
ICME Database Whenever possible, we associate IP shocks with a driver, namely an ICME, by crossreferencing the list of shocks with that of reported ICMEs. Unfortunately, the number of ICMEs detected by the Helios mission being scarce for a statistical study (30 events), we focus here on ICME-driven shocks detected close to the Earth's orbit and Ulysses. For the near-Earth analysis, we used the Wind data catalog from Nieves-Chinchilla et al. (2018) , which can also be found on the Wind NASA website. 1  In this catalog, the authors report the start and end time of the ICME and magnetic ejecta. For ACE data, we used the catalog provided by  Regnault et al. (2020)  from 1997 to 2017, which is based on the revision of the Richardson and Cane ICME list  (Cane and Richardson, 2003; Richardson and Cane, 2010).
Finally,  Jian et al. (2006 Jian et al. ( , 2013) )  provide us with an ICME list observed by STEREO-A/B between 2006 -2014. For ICMEs observed by Ulysses, we used the list provided by  Richardson (2014) , which covers three spacecraft orbits, during 1996 -2009, along with the start/end time of the events, solar-wind parameters during the ICME interval, the spacecraft heliocentric distance, heliolatitude, among others. Through an inspection of both IP shock and ICMEs databases, we associate each ICME with the closest shock from the HSD list by comparing the time difference of the shock and the disturbance of the ICME. We select events with a time difference of less than two hours between the ICME fronts and shocks, leading to the creation of a catalog of ICMEassociated IP shocks observed by all these spacecraft.
SIRs Database Stream interaction regions (SIRs) are characterized by a fast wind catching up with a slower one. This induces a region of compression, and the total pressure (P t ) reaches a maximum inside. Other characteristics may also be present in SIRs, such as the compression of proton number density and the magnetic field, as well as a significant temperature increase. The plasma flow deflection, both in the preceding and the following fast wind, is also frequently observed. These properties are useful for the better identification of SIRs. We used several catalogs of SIRs around L1: for Wind and ACE we considered the catalog developed by  Jian et al. (2006)  covering the period of  1995 -2004 (Wind) and 1998 -2004 (ACE). For SIRs observed by STEREO-A/B, we used the catalog provided by  Jian et al.
Distribution of the Location Angle λ Considering the number of shocks detected at 1 AU in our study (720 events), we are extending the shocks database from  Janvier, Démoulin, and Dasso (2014)  to obtain more reliable results. Since most of the shocks, detected from Ulysses, were between 4 and 5. 5 AU and at low latitudes ±30 • , in the present section, we will focus on this interval. To further investigate a possible asymmetry between the shape of the ICME/shock, we have separated eastward and westward values as an artificial construction with the east/west solar direction of the shock normal. In Figure  9 , the λ distributions for those shocks where the angle i is between [320 • , 40 • ], corresponding to the east solar direction (±40 • ), are in blue, and for those shocks with i between [140 • , 220 • ], corresponding to the west solar direction (±40 • ), are in red.
Conclusions The main aim of this paper is to present a statistical study of a large number of shocks to understand the evolution of shock properties with heliospheric distance, as well as to study if there a possible preferential orientation of shocks as a function of the inclination angle i and the location angle λ. Our study is based on a series of catalogs reporting parameters associated with ICMEs, IP shocks, and SIRs detected at several heliocentric distances, from 0. 29 to 0. 99 AU with Helios-1/2; near 1 AU using Wind, ACE, and STEREO-A/B; and 1. 3 to 5. 4 AU with Ulysses. We developed a multi-spacecraft analysis of interplanetary shocks at 1 AU to investigate spacecraft biases. present a comparison between the shock normal vectors, for those events detected at 1 AU by Wind, reported in two different databases: from the Heliospheric Shock Database and CfA Interplanetary Shock Database.
From this comparison, we found a strong correlation (above 0. 8) between the normal components from the HSD and the shock normal methods reported in CfA database. This result gave us confidence that the Heliospheric Shock Database offers us quite reliable results. We do not find significant differences, in the shock properties, for spacecraft positioned at 1 AU, as expected. We investigated the distribution of all shocks with detected and nondetected ICMEs at 1 AU. Shocks associated with detected ICME showed higher mean values of the downstream-to-upstream ratios of the shock for the magnetic-field strength, density, and temperature than the shocks without a detected ICME. The obtained result that faster and stronger shocks are associated with ICMEs agrees with several related studies  (Janvier et al.
Appendix: How Well is the Shock Normal Determined? In the present study, we used the IP shock catalog provided by the Heliospheric Shock Database (HSD) maintained at the University of Helsinki to calculate the location angle λ and compare it using different datasets of shock normal to check whether it is a quantity that is well-defined. Two methods to identify shocks have been used to construct this database: (1) through a visual inspection of solar-wind plasma and magnetic-field parameters, simultaneously looking for sudden jumps in the plasma and magnetic field parameters. These jumps should satisfy the shock wave signatures to be considered as forward (FF) or fast reverse shock (FR), and (2) using an automated shock detection machine-learning algorithm called IPSVM (InterPlanetary Support Vector Machine)  (Isavnin, 2017).
According to the documentation of the HSD, the shock normal vector ( nshock ) is calculated using the mixed-mode method (MD3). This method follows the conditions that the cross product of the upstream and downstream magnetic fields should be perpendicular to the shock normal, provided there are no gaps in the velocity components. In the case of a data gap in the velocity components, the normal vector is calculated using magnetic-field coplanarity, the most widely used method. It relies on the coplanarity theorem and is based on the idea that the normal vector to a planar surface can be determined if two vectors lie within this surface. For compressible shocks, the magnetic field on both sides of the shock and the shock normal all lie in the same plane. Thus, a variety of vectors lie in the shock plane.
However, the upstream and downstream intervals vary depending on the shock, and in most cases, there are no well-defined criteria for choosing these intervals. We then calculated the angle λ from the normal shock vectors applying Equation  1. This database has the advantage of applying the same method to a large set of spacecraft data (see Section 2) and therefore ensures consistency throughout the analysis. However, other authors have also worked on the detection as well as the characterization of the shock parameters, including the normal vector that we use to obtain the location angle. In the following, we, therefore, compare different available datasets of shock normal to check whether it is a quantity that is well determined.
Shocks at 1 AU For shocks detected at 1 AU, the CfA Interplanetary Shock Database 2  provides IP shocks observed by the Wind spacecraft. Therefore, in the following, we explore the different methods used in this database and compare the location angle outputs with that of the Heliospheric Shock Database. In the CfA Interplanetary Shock Database, the events are listed by year with a detailed analysis for each shock detected. Each shock was analyzed with the shock normal vectors derived by three methods: magnetic coplanarity (MC), velocity coplanarity (VC), and three mixed mode (MX1). The velocity coplanarity (VC), not used in the Heliospheric database, offers a good approximation to the normal vector, which is valid at high Mach numbers and for θ Bn near 0 • or 90 •  (Daly and Paschmann, 2000).
The three mixed mode method, referred to as MX1 in the following, requires both plasma and field data to calculate the components of the shock normal vector. For this, each component of the upstream, downstream, and magnitude of the magnetic field is used. Through a comparative analysis, we selected events with 1 hour or less difference between the detection time reported in both catalogs. Most events can be found in both catalogs, so we ended up with 358 total events, around 69% of the total amount of events reported in the Heliospheric Shock Database. Figure  11  shows the correlations between each component of the normal vector (N x , N y , N z ) from the Heliospheric Shock Database with the normal component of the CfA Interplanetary Shock Database from each method: (N xMC , N yMC , N zMC ) for the magnetic coplanarity method, (N xVC , N yVC , N zVC ) for the velocity coplanarity method and (N xMX1 , N yMX1 , N zMX1 ) for the three mixed mode method.
We use the absolute value of the x-component to compare positive values. These components are grouped in a small region (Figure  11 , right panels). We find that the MC method shows the lowest correlation with the results from the HSD, as shown in the first row, and the MX1 method shows the best correlations for all components, with Pearson and Spearman correlation around 0. 7 ∼ 0. 8 for all components. In Figure  12 , we show the correlation between the location angle derived by the shock normal vector from the mixed mode method (λ HSD reported in the Heliospheric Shock Database), with the location angle derived by the shock normal vector from the MX1 method (λ MX1 , reported in the CfA Interplanetary Shock Database). Both Pearson and Spearman coefficients (C p and C s ) indicate a strong correlation between both λ values with 0.
83 and 0. 84, respectively. In summary, we do not find significant differences between the λ values derived by different shock normal methods. The strongest correlations are obtained, as expected, for the mixed mode method used for both the HSD and the CfA Interplanetary shock database. Since the results are highly correlated, with correlation coefficients above 0. 8, this comparison gives us confidence that the Heliospheric Shock Database offers us quite reliable results while also providing broad coverage of several spacecraft at different heliodistances.
Introduction Care for head and neck cancers is complex in particular for the rare ones. Knowledge is limited and histological heterogeneity adds complexity to the rarity. There is a wide consensus that to support clinical research on rare cancer, clinical registries should be developed within networks specializing in rare cancers. In the EU, a unique opportunity is provided by the European Reference Networks (ERN). The ERN EURACAN is dedicated to rare adults solid cancers, here we present the protocol of the EURACAN registry on rare head and neck cancers (ClinicalTrials. gov Identifier: NCT05483374).