dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The DIFI models described in this paper were derived from a combination of Swarm satellite data and ground observatory data. The Swarm data were acquired between December 1, 2013, and July 17, 2015. The observatory data cover the time interval December 1, 2013, to February 22, 2015. The Swarm mission consists of three identical satellites in low Earth, near-polar orbits simultaneously launched on November 23, 2013, at an initial altitude of slightly more than 500 km (with respect to a reference radius of 6371. 2 km). Subsequent maneuvers between end of January and mid-April 2014 brought Swarm A and C to sideby-side orbits of inclination 87. 4 • , separated by roughly 150 km at the equator, and at an altitude decaying from 475 km in mid-April 2014 to slightly less than 460 km in mid-July 2015.
In the present modeling study, we used level 1b vector data, baseline 0405, retrieved from the European Space Agency Web site. We only used data from the A and B satellites. Data from the C satellite were not used for two reasons. First, the local time separation between A and C is very small compared with the longitudinal spatial scales of the Sq and EEJ fields, and therefore the two satellites are redundant when modeling these fields. Second, vector data provided by the A satellite are expected to be of better quality because ASM instruments onboard C ceased to deliver data on November 5, 2014  (Fratter et al. 2016) , and the VFM instrument onboard C had to be calibrated using ASM scalar data from the A satellite. Observatory hourly mean values were retrieved from the Swarm level 2 project server for the following 79 observatories: API, ASC, ASP, BDV, BEL, BGY, BOU, BOX, CDP, CKI, CLF, CNB, CTA, CZT, DLT, DOU, EBR, ELT, ESA, EYR, FRD, FRN, FUR, GAN, GLM, GNG, GUA, HAD, HER, HON, HRB, HUA, HYB, IPM, IRT, IZN, KAK, KDU, KEP, KHB, KNY, KNZ, KOU, KSH, LIV, LRM, LZH, MAB, MBO, MIZ, MMB, MZL, NEW, NGK, NVS, ORC, PAG, PET, PHU, PST, QGZ, QIX, QZH, SBL, SFS, SHU, SJG, SPT, SUA, TAM, TDC, THJ, THY, TRW, TUC, VIC, WHN, WNG, YAK (Fig.
Data preprocessing The general satellite data preprocessing workflow is described in  Chulliat et al. (2013). The first step, data selection, aims at extracting quiet-time data from the initial dataset. We used the following selection criteria for that purpose: K p < 2o, |D st | < 20 nT, -8 < IMF By < 8 and -2 < IMF Bz < 6 (where IMF stands for interplan- etary magnetic field). All indices were also retrieved from the Swarm level 2 server. The data were subsequently decimated to one sample every 60 s. Working with real data instead of synthetic data led us to make several modifications to the way data were corrected prior to the inversion. Data corrections were applied for the core, crustal and magnetospheric fields using the following models: (a) a Swarm-based, core field model derived by Patrick Alken (University of Colorado Boulder) using an algorithm recently developed for the IGRF project  (Alken et al.
The final set of models was used to correct all data. Once corrected, high-latitude satellite data were downweighted using a low-pass filter with respect to dipole latitude. We used a Tukey (tapered cosine) window defined as: where θ qd is the quasi-dipole (QD) colatitude  (Richmond 1995) , θ 0 = 45 • between 0600 and 1800 LT and θ 0 = 50 • before 0600 and after 1800 LT. This procedure effectively damps the residual signal at high latitudes, i. e. , outside areas where Sq and EEJ currents develop, while minimizing the Gibbs effect in the final model. It follows that the domain of validity of the model is the area between quasi-dipole latitudes ±55 •. (1) w(θ qd ) =        1 2 � 1 + cos � π � θ qd θ 0 -1 ��� for θ qd < θ 0 1 for θ 0 ≤ θ qd ≤ 180 -θ 0 1 2 � 1 + cos � π � θ qd θ 0 -180 θ 0 + 1 ��� for θ qd > 180 -θ 0 Observatory data were selected using the same criteria as for the satellite data and were not further decimated.
They were corrected using the Swarm level 2 models for the core  (Rother et al. 2013) , crustal  (Thébault et al. 2013)  and magnetospheric  (Hamilton 2013)  fields. The average night time level was then determined for each day using values between 2100 and 0300 LT and subsequently removed from the data. The preprocessing yielded 404,068 satellite data point triples and 242,485 observatory data point triples, covering the time interval December 1, 2013, to July 17, 2015.
The DIFI algorithm solves for the (q m(c,s) nsp , s m(c,s) nsp ) coefficients, from which the other coefficients can be derived. In order to further reduce the size of the model vector, the inversion was performed in the QD coordinate system, using the same QD basis functions as  Sabaka et al. (2002). The transformation from dipole to QD coefficients involves a matrix that was precomputed during the development phase (see Figure  2  in  Chulliat et al. 2013 ). In the present modeling work, we took N = 14. 85 × 10 -3 SFU -1 , as previously determined by  Sabaka et al. (2002)  (using the technique of Olsen 1993) from observatory data, and we used daily observed values of F 10. 7 for all datasets. The maxi- mum degree and order were set to N max = 60 and M max = 12, the minimum/maximum diurnal wave- numbers to p min = 0 and p max = 4, and the minimum/ maximum seasonal wavenumbers to s min = -2 and s max = 2.
Conclusion and outlook In the present paper, we reported on a new quiet-time, Sq and EEJ spherical harmonic field model, DIFI-2015b, derived from a combination of Swarm satellite and ground observatory data between December 2013 and July 2015. This model was calculated using a so-called Dedicated Inversion algorithm developed as part of the Swarm level 2 data processor and where the average quiet-time ionospheric field is modeled after removing contributions from all other sources to the measured magnetic field. The algorithm separates the primary and induced fields using a 3D conductivity model with a surface shell on top of a 1D mantle conductivity profile. The parameterization includes Fourier series for diurnal and seasonal variations and a linear dependence with respect to the solar radio flux index.
Southern hemisphere Fig. 13  Evolution with UT and season of the difference between the LT of the current system focus (defined as the point where the current function Ψ 1 takes its maximum absolute value) and noon in each hemisphere. A positive value indicates that the current system is trailing behind the noon meridian, while a negative value indicates that it is leading it. F 10. 7 = 100 SFU DIFI-2015b satisfactorily fits both the satellite and observatory data, with very little bias and RMS misfits around 7 nT on the ground and up to 10 nT at satellite altitude. The data residuals reflect the day-to-day variability of the Sq and EEJ currents, as well as the contamination by IHFAC fields on the polar and azimuthal components at satellite altitude. The data fit deteriorates somewhat at one ground location away from the observatories used in the inversion dataset.
However, the current performance assessment will have to be re-examined in the future, based on more independent data. This could be done either by waiting until more observatory data become available for the considered time interval, or by calculating new models from subsampled datasets. Satellite data could also be subsampled to determine the robustness of the model, but a larger time interval will be needed first to ensure full local time coverage for all subsamples. Another way to validate results from the DIFI algorithm will be to compare it with other relevant ionospheric data products derived from Swarm. One type of such products is the model output of the Comprehensive Inversion (CI) algorithm  (Sabaka et al. 2013) , an updated, Swarm-based version of the CM. Preliminary comparisons revealed generally good agreement between the latest version of the CI model and DIFI-2015b, but more detailed comparisons will be needed in the future.
e. , southern summer) and (b) a wave-4 structure in the longitudinal variation of the total Sq currents. Both phenomena are enhanced in the American sector, where the dip equator is bended, which suggests some coupling between the EEJ and Sq, in agreement with some earlier studies. The DIFI-2015b is available from https://geomag. colorado. edu/geomagnetic-and-electric-field-models. html. An earlier version, DIFI-2015a, was built from a satellite dataset covering a smaller time interval and using a less recent mantle conductivity model; this version is available as version 0101 from the Swarm data access Web site at https://earth. esa. int/web/guest/swarm/data-access.
Acknowledgements The DIFI algorithm development was funded by the  European Space Agency  through  ESTEC  contracts  4000102140/10/NL/JA  " Development of the Swarm Level 2 Algorithms and Associated Level 2 Processing Facility " and  4000109587/13/I-NB "SWARM ESL" , as well as by the  Centre National d'Etudes Spatiales  through the  "Travaux préparatoires et exploitation de la mission Swarm " project. We thank  Patrick Alken  for providing us with an updated version of his core field model,  Alexei Kuvshinov  for making available to us his updated Q matrix and  Nils Olsen  for helpful discussions at various stages of this work. This is IPGP contribution # 3735.
Context. Periodic comets are known to be the parent bodies of meteoroid streams. The stream of a given comet can split into several filaments. These can be observed in the Earth's atmosphere as more than just a single meteor shower. One such comet is C/1917 F1 (Mellish), which associates at least two, possibly four, meteor showers that have been recorded in the meteor databases. In a recent study, the dynamical evolution of the C/1917 F1's theoretical stream was followed by only considering the gravitational perturbations. The properties of individual filaments of this stream, corresponding to the appropriate meteor showers, were not predicted perfectly. Aims. To reach better agreement between theory and observation, we repeatedly model the theoretical stream of C/1917 F1. In the modeling, we also include the Poynting-Robertson drag acting on meteoroids and assume an action of the non-gravitational effects on the parent comet dynamics.
348), with the corresponding predicted filament F1, is not very obvious. However, there is at least a strong suspicion that a reliable identification will be possible after a larger set of data becomes available. NH14 used, in general, the photographic IAU MDC  (Neslušan et al. 2014 ), SonotaCo video (SonotaCo 2009) , and radio-meteor  (Hawkins 1963; Sekanina & Southworth 1975; Lindblad 2003, priv. comm. ) databases in 1 https://www. ta3. sk/IAUC22DB/MDC2007/Roje/roje_lista. php?corobic_roje=1&sort_roje=0 their shower identification. With the angular distance of mean radiant from the Sun in the activity maximum lower than 70 • , ρ-Cygnids were only, however, found in the video data. A similar, non-definitive conclusion about the April ρ-Cygnids, selected from several catalogs, and their relationship to C/1917 F1 was also made by  Hajduková et al.
(2015). Of another two predicted showers, one (F2) is a daytime shower, which seems to be present in the SonotaCo video and radio-meteor databases. Currently, no corresponding shower can be found in the IAU MDC list of all showers. In the SonotaCo video data, there is also an indication of a shower that resembles the last predicted filament, F4. Not only the unclear identifications of filaments F1, F2, and F4, but also an imperfect match between the predicted filament F3 and the December Monocerotids demand improvement in any modeling of the creation and subsequent dynamical evolution of the C/1917 F1's stream. In NH14, the influence of the Poynting-Robertson (P-R) drag was not considered in the dynamics of modeled stream particles, so we consider its influence in this work. As we show in the following, the P-R drag alone does not, however, yield a perfect match, either.
The TPs in the orbits that approach the Earth's orbit within 0. 05 AU at the present time are selected and used to predict the properties of relevant showers. Our theoretical predictions are evaluated by comparing them to the actually observed meteors. In this comparison, we use the photographic IAU MDC  (Neslušan et al. 2014 ), SonotaCo video (SonotaCo 2009) , and radio-meteor  (Hawkins 1963; Sekanina & Southworth 1975; Lindblad 2003, priv. comm. ) databases. In contrast to NH14, we also use the EDMOND video database  (Kornoš et al. 2014). In this research note, we describe our attempt to find the appropriate strength of the P-R drag influencing the dynamical evolution of the C/1917 F1 stream and causing four meteor showers in the Earth's atmosphere. The strength of the drag is given with the help of parameter β, which is the ratio of the acceleration due to the P-R drag and gravitational acceleration of the Sun.
Likewise, because of non-gravitational effects in general, we try to find the appropriate modification of the orbit of this parent body in the time when the meteoroids of its stream were released. A more detailed description of the problem solved is given in the next section. This work aims not only to better predict the properties of the showers associated with the parent comet, but also to point out the possibility of an eventual recovery of the past orbital history of the comet by studying its observed meteor showers. If our work or other future work of this kind is successful, then the meteor databases will become a tool for tracing the past dynamical history (caused by the non-gravitational effects) of those parent bodies that associate more than a single meteor shower.
Description of problem We first recall the differences between the predicted and observed filaments of the meteoroid stream of comet C/1917 F1 (Mellish), which have already been found in NH14. In their work, the authors exclusively considered the gravitational perturbations of planets on the meteoroid particles. 2 http://ssd. jpl. nasa. gov/sbdb. cgi These differences are discussed in terms of the positions of the radiant areas of individual filaments from F1 to F4. We note that the members of all four filaments were selected from the observational databases considered, using the "break-point method"  (Neslušan et al. 1995 (Neslušan et al. , 2013)). Filaments F1, F2, and F4 can be separated well only in the SonotaCo catalog, and filament F2 also seems to be present in the radio data. The break point is not very apparent when the EDMOND video data are used to separate F1, F2, and F4.
Concerning the F3 filament, very similar mean shower characteristics are obtained from both the SonotaCo and EDMOND catalogs, and this rich filament is present in the photographic IAU MDC and radio-meteor data, of course. The positions of the observed, as well as the predicted, radiants are shown in Fig. 1  for the model with the evolutionary time t ev = 50 kyr. From the observed meteors, the photographic and SonotaCo video meteors are shown. Since the radiant area of filament F3 separated from the EDMOND catalog is almost identical to the area separated from the SonotaCo database, the EDMOND video meteors are not plotted in Fig. 1  or in the other figures. The predicted radiant positions represent all TPs approaching the Earth's orbit within 0. 05 AU at present. We can see that the radiant area of filament F1, which can hypothetically be identified to the April ρ-Cygnids (No.
6 • , δ = -5. 1 • , and V g = 33. 5 km s -1 and q = 0. 189 AU, α = 102. 0 • , δ = 7. 8 • , and V g = 41. 9 km s -1 ; so while the first A100, page 5 of 10 radiant area is situated below, the second radiant area is situated above the ecliptic). This filament is not indicated in the databases of the observed meteors. For β = 0. 005 and 0. 01, all TPs are deflected from the vicinity of the Earth's orbit and, thus, no filament occurs.
Nevertheless, if we know a relatively large number of the orbits of meteoroids in a given stream, there is still a chance that their backward integration will reveal a convergence of at least The positions of radiants are shown in the same way as those in Fig. 1a. However, the initial orbit to model the stream was that of video meteor SNTC-118490, which was obtained via numerical integration of the current orbit for time 30 kyr before the present in this case. The P-R drag influencing the motion of modeled particles characterized with β = 0. 003 was considered, too. a fraction of them. Such a convergence could perhaps indicate the parameters of the orbit of the parent in the time of meteoroid production. Considering this possibility, we integrate the sets of known orbits from the photographic and video databases used, which correspond to the filaments F1-F4 of the C/1917 F1 stream, and search to see if an orbit from one filament became similar in 10, 20, 30, or 50 kyr in the past to an orbit (or orbits) from another filament (or filaments).
Acknowledgements. J. Vaubaillon  is grateful to the  CINES  staff for the use of the occigen machine on which heavy calculations were performed. L. Neslušan  and  M. Hajduková  acknowledge the support from the  Project  ITMS No. 26220120009 , based on the supporting operational  Research and development program  financed by the  European Regional Development Fund. Their work was also supported by the  Slovak Research and Development Agency  under contract No. APVV-0517-12  (M. H. ) and by the  VEGA -the Slovak Grant Agency for Science , grant No. 2/0031/14  (L. N. ).
Search strategy A highly sensitive search strategy has been developed, and validated study design filters will be applied to retrieve articles pertaining to the use of AIT for allergic rhinoconjunctivitis from electronic bibliographic databases. We have conceptualized the search to incorporate the four elements shown in Fig. 1. To retrieve systematic reviews, we will use the systematic review filter developed at McMaster University Health Information Research Unit (HIRU) (http://www. hiru. mcmaster. ca/hiru/HIRU_Hedges_MEDLINE_Strategies. aspx#Reviews). To retrieve randomized controlled trials (RCTs), we will apply the Cochrane highly sensitive search strategy for identifying RCTs in MEDLINE  [16]. To retrieve case series, we will use the filter developed by librarians at Clinical Evidence: http://clinicalevidence.
bmj. com/x/set/static/ebm/learn/665076. html. We will search the following databases: The search strategy has been developed on OVID MEDLINE and then adapted for the other databases (see Appendix 1). In all cases, the databases will be searched from inception to October 31, 2015. Additional references will be located through searching the references cited by the identified studies, and unpublished work, while research in progress will be identified through discussion with experts in the field. We will invite experts who are active in the field from a range of disciplines and regions to add to the list of included studies by identifying additional published and unpublished papers they are aware of and research in progress. There will be no language restrictions employed; where possible, relevant literature will be translated into English.
Data extraction, analysis and synthesis Data will be independently extracted onto a customized data extraction sheet in Distiller by two reviewers, and any discrepancies will be resolved by discussion or, if agreement cannot be reached, by arbitration by a third reviewer. A descriptive summary with summary data tables will be produced to summarize the literature. If clinically and statistically appropriate, meta-analysis using either fixedeffect or random-effects modeling will be undertaken  [16]. A narrative synthesis of the data will also be undertaken. It is expected that it will only be appropriate to include the SLIT and SCIT studies in a meta-analysis to minimize heterogeneity; studies using other approaches will be described narratively.
ISI Web of Science (Thomson Web of Knowledge) • TRIP Database (http://www. tripdatabase. com) • Clinicaltrials. gov (NIH web). • Cochrane Library including, -Cochrane Database of Systematic Reviews (CDSR) -Database of Reviews of Effectiveness (DARE) -CENTRAL (Trials) -Methods Studies -Health Technology Assessments (HTA) -Economic Evaluations Database (EED) • MEDLINE (OVID) • Embase (OVID) • CINAHL (Ebscohost) • • Clinical trials register (http://www. clinicaltrialsregister. eu) launched by the European Medicines Agency (EMA) • Current controlled trials (http://www. controlled-trials. com) • Australian and New Zealand Clinical Trials Registry (http://www. anzctr. org. au).
ABSTRACT Why the cohort was set up? CONSTANCES is a general-purpose cohort with a focus on occupational and environmental factors. Cohort participants CONSTANCES was designed as a randomly selected sample of French adults aged 18-69 years at inception; 200 000 participants will be included. Data collection phases At enrolment, the participants are invited to complete questionnaires and to attend a health screening centre (HSC) for a health examination. A biobank will be set up. The follow-up includes an yearly self-administered questionnaire, a periodic visit to an HSC and linkage to social and national health administrative databases. Main types of data collected Data collected for participants include social and demographic characteristics, socioeconomic status, life events and behaviours.
Regarding occupational and environmental factors, a wealth of data on organisational, chemical, biological, biomechanical and psychosocial lifelong exposure, as well as residential characteristics, are collected at enrolment and during follow-up. The health data cover a wide spectrum: self-reported health scales, reported prevalent and incident diseases, long-term chronic diseases and hospitalisations, sick-leaves, handicaps, limitations, disabilities and injuries, healthcare usage and services provided, and causes of death. Control of selection effects To take into account non-participation and attrition, a random cohort of nonparticipants was set up and will be followed through the same national databases as participants. Data access Inclusions begun at the end of 2012 and more than 110 000 participants were already included by September 2016.
WHO IS IN THE COHORT? The general design of CONSTANCES has been detailed elsewhere;  29 30  here we summarise the main characteristics of the cohort participants. We plan to gradually enrol 200 000 participants aged 18-69 years at inception in 22 selected health screening centres (HSCs) located in 20 'départements' in the principal regions of France. For practical reasons, the source population of CONSTANCES is restricted to persons living in one of the 20 CONSTANCES 'départements' and affiliated to the National Health Insurance Fund, that is, salaried workers, whether they are professionally active, unemployed or retired and their family (more than 85% of the French population), thus excluding agricultural and selfemployed workers. Eligible persons are selected from the national social security database, which includes all persons living in France, according to a random sampling scheme stratified on age, gender, socioeconomic status (SES) and region of France in order to be representative of the source population.
They receive at home an invitation and those who volunteer receive questionnaires to complete and attend a HSC for a health examination. After a field pilot, the recruitment started in late 2012 and the cohort will be completed in 2018. Currently (September 2016), more than 112 000 participants are enrolled in the cohort, with a participation rate of 7. 3%. The already available data showed that there was quite a diverse distribution of main socioeconomic variables, as shown in table  1.