dataesr/scientific-paragraphs-categorization huggingface dataset:
2008). We analyze the EUV multi-wavelength imaging data from SDO/AIA, the vector magnetic field, and continuum intensity data by the Helioseismic and Magnetic Imager (HMI)  (Schou et al. 2012 ) on board the SDO spacecraft. AIA observes the Sun in ten different wavebands, covering a wide range of temperatures and reveals physical processes at various layers of the solar atmosphere. The pixel size of the data is 0. 6 and the temporal cadence is 12 s. Generally, HMI provides four main types of data: dopplergrams (maps of solar surface velocity), continuum filtergrams (broad-wavelength photographs of the solar photosphere), and both line-of-sight and vector magnetograms (maps of the photospheric magnetic field). The processed HMI continuum intensities and magnetograms data are obtained with a 45 s cadence and a 0.
6 pixel size, provided by the HMI team. Here, we mainly used the data from seven EUV channels of AIA and HMI continuum intensity. For comparison with NIRIS we analyzed the HMI magnetograms in the 24 h before the event. Continuum intensity maps of HMI help us to co-align the TiO, H α images and the magnetograms taken by GST. The GST images taken at each wavelength position were internally aligned using the cross-correlation technique provided by the BBSO programmers. The co-alignment between SDO/HMI continuum and GST images was achieved by comparing commonly observed features of sunspots in Fe I 6173 Å images and TiO, H α ± 0. 8 Å images taken frame by frame. 1 https://www. solarmonitor. org/.
Acknowledgements. Y. Liu  and  G. P. Ruan  acknowledge the support by the  NNSFC  grant  12173022 ,  U1831107 ,  11790303  and  11973031. We are grateful to  Véronique Bommier  for the discussion on HMI vector magnetograms, to  Jin Chunlan ,  Chen Yajie ,  Hou Zhenyong  for the data discussion and to  Vasilis Archontis  and  Robert Cameron  for the possible mechanisms of jet initiation during the " Whole-Sun workshop in Blaise Pascal Institute ". W. Cao acknowledges support from  US NSF   AST-2108235  and  AGS-1821294  grants. BBSO operation is supported by  NJIT  and  US NSF   AGS-1821294  grant. GST operation is partly supported by the  Korea Astronomy and Space Science Institute  and the  Seoul National University. We thank  SDO/HMI ,  SDO/AIA  teams for the free access to the data.
Experimental Section Data acquisition and reduction: The series of annealing experiments was performed and followed by in situ XRD at the DiffAbs beamline of SOLEIL synchrotron. The specimens were annealed under N 2 atmosphere up to 450-500 C at a rate of 2 C/min using an Anton Paar heating stage that was mounted on the six-circle diffractometer (Kappa geometry). The beam size was 205 Â 255μm 2 (FWHM ¼ V Â H). The XRD patterns were recorded in vertical scattering geometry at a fixed incident angle omega ¼ 10 (yielding a beam footprint on the specimen surface: 1473 Â 255 μm2) using an XPAD S140 hybrid pixel array X-ray detector. [14, 15] An incident photon energy E ¼ 18 keV was chosen in order to measure a large reciprocal space region with one single detector position. With diffraction angles investigated close to 20 , the direction of the scattering vector is approximately normal to the specimen surface.
In the limiting cases of σ ¼ 0 or γ ¼ 0 Vðq; σ, γÞ simplifies to Lðq; γÞ or Gðq; σÞ. Analyses of the data were performed by a custom config file-based software solution written in the Python3 scripting language  [21]  and might be discussed in more detail in a dedicated publication or on https:// github. com/phlpphns. Particular attention was given to find and handle also very weak diffraction peaks on a strong background. The procedure coarsely consists of three parts: 1) baseline estimation (the python pybaselines module was used) applying a Baseline Estimation And Denoising with Sparsity (BEADS)  [22]  algorithm for baseline identification, 2) manual identification of peaks and experimental range of interest to configure the general boundary conditions of the high throughput fitting, and 3) fitting the peaks of interest in defined intervals by means of a differential evolutionary approach with the lmfit library.
Acknowledgements The authors would like to thank  SOLEIL Synchrotron  for allocating beamtime on DiffAbs beamline. Ph. Joly  ( Synchrotron SOLEIL, DiffAbs ) is thanked for technical support. IPCEI/Nano 2022 program  is acknowledged for partial funding of this work. The authors thank  Christophe Guichet  for help with data acquisition.
4e, f ), and even shows a slight upshift of the mean frequency, as shown in Fig. 3b , whereas the downshift in the measurement is dramatic. For the focused irregular waves, the architecture and training procedure of the FDML algorithm is identical to that used for the modulated plane wave, but the training data set consists of focused wave groups with initial JONSWAP spectra. The MNLS +FDML model is able to reproduce accurately the measured evolution in both the time and the frequency domain. The significant improvement compared to the MNLS model is indicated by the mean squared error (MSE) evolution in Fig. 2d, h. Examining the effect of breaking, Fig. 2b  shows the maximum amplitude of the envelope is attenuated to a value close to the measurement value, as corroborated by the sharp decline of the norm in Fig.
The FDML correction is responsible for an order-of-magnitude improvement compared to the MNLS prediction. To verify the generality of our MNLS + FDML model, we also predict wave breaking in an external data set of focused breaking waves recorded in the Multifunctional Ship Model Towing Tank at Shanghai Jiao Tong University, which has a much larger tank and wave dimensions than our training set and find comparable results to those reported here. Results are displayed in SI section 3. 4. Wave Category III: random irregular waves. Moving yet further to a realistic random sea state, we investigate the ability of our   MNLS + FDML model to provide forecasts of (unidirectional) irregular waves based on a broad-banded JONSWAP spectrum with peak enhancement factor γ = 3. 3, with random instead of focused phases (Wave Category II).
Methods Wave tank experiments. For Wave Categories I (modulated plane waves) and II (focused irregular waves), ground-truth training data was generated in the 40 m long, 2. 7 m wide wave facility at Aix Marseille University, France, by means of a piston wavemaker. Waves were measured by 12 wave gauges placed along the center-line of the tank with a sampling rate of 400 Hz. Experiments for Wave Category III (random irregular waves) were performed in the 30 m long, 1 m wide facility at the University of Sydney, Australia, again by means of a piston wavemaker. An array of eight wave gauges was moved along the tank for repeated measurements, to obtain a final number of 24 wave gauges positions spaced 0. 83 m apart (covering 19. 1 m). The gauges had a sampling rate of 32 Hz. See Fig. 7a  for the experimental set-up.
Due to modulation instability (MI)  39, 81  , the spectrum will broaden, and the amplitude of the wave will increase, attaining a maximum at the focus position ξ f , after which the modulation will decrease: the FPUT recurrence cycle  [50] [51] [52]. By selecting the initial steepness ã0 k 0 = ffiffi ffi 2 p , b F , α, and Ω M , we can set the steepness at ξ f. To generate a variety of waves, parameters were drawn from the following ranges: f 0 = ω 0 /(2π) ∈ [1. 30, 1. 55] Hz, ã0 k 0 2 ½0:12; 0:25, b F ∈ [0. 01, 0. 1], Ω M ∈ [0. 7, 1. 55], ψ ∈ [0, 2π] and α ∈ [-0. 16, 0. 16]. A data set of 258 wave trajectories were generated and measured. Figure  1  shows the evolution of a breaking wave with initial parameters f 0 = 1. 40 Hz, ã0 k 0 ¼ 0:19, b F = 0. 09, Ω M = 1, ψ = π, and α = 0. Wave Category II.
5, 1. 25] Hz, x f ∈ [10, 24] m, such that each initial condition was generated based on a unique combination of these parameters. A data set of 180 wave trajectories were generated and measured. Note that for both wave categories, parameters cannot be selected randomly, as not every combination leads to a breaking event within the length of the tank. Figures  2 to 4  show the evolution of a breaking wave with initial parameters f p = 0. 91 Hz, (ω p = 5. 7 rad/s), γ = 4, H s = 30 mm and x f = 14 m. Wave Category III. For the random irregular waves, the JONSWAP spectral density eq. (  5 ) is used. Instead of manipulating the phases to exhibit dispersive wave focusing, they are randomly drawn from a uniform distribution for each wave frequency in the spectrum. Parameters for the initial condition were γ = 3.
c For the finite-domain method the complex envelope is only needed at the wave gauge positions, and only the absolute value of the envelope is needed at each solver step. d When given a (complex) initial condition, the MNLS solver can predict the evolution for an arbitrary propagation distance. The two separate RNNs then independently provide corrections to the spectral evolution and the physical-space evolution, respectively. envelope-field at interval Δξ for both domains: a true ðξ wg ÞÀ!jaj true ðξÞ; jâj true ðξÞ ð7Þ • Data augmentation-Simply using the entire experiment would not yield enough data, and we, therefore, augment the data by creating smaller propagation segments. In order to account for different stages of evolution of the wave, we can choose training samples starting at different positions ξ 0.
Acknowledgements D. E. acknowledges financial support from the  Swiss National Science Foundation  ( Mobility Fellowship   P2GEP2-191480 ). T. P. S. and D. E. acknowledge the ONR Grants  N00014-21-1-2357  and  N00014-20-1-2366. J. K. and D. E. acknowledge  SNSF   200020-175697. Y. H. was supported by the  University of Sydney International Scholarship (USydIS). T. S. vd. B. was supported by a  Royal Academy of Engineering Research Fellowship. The authors acknowledge Thomas Adcock and  Ye Li  for access to the external data and thank  Maura Brunetti  and  Alexis Gomel  for fruitful discussions and  Michel Moret  for providing access to the computation infrastructure.
TSH association with 260 variants Using annotation informed fine-mapping (Online Methods), and a genome-wide significance threshold of P<5×10 -8 , we identified 260 independent signals for TSH at 156 unique genomic loci, of which 158 signals at 78 genomic loci are new. In addition to reaching P<5×10 -8 in the meta-analysis, 226 of the 260 signals (128 of the 158 novel signals) showed consistent direction of effect and P<0. 01 in at least two out of three large datasets available to us (UK Biobank  (9) , the analysis by Zhou et al  (6) , and an independent analysis of 40,948 individuals in Estonian Biobank, EstBB  (10) ), whilst 239 out of 260 signals were consistent if using a threshold of P<0. 05 (Supplementary Table  1 ). The SNP heritability of TSH was 16. 6% (95% CI: 12. 5%, 20. 7%); together the 260 signals explain  22.
Druggable targets For the 112 genes supported by ≥2 criteria, we surveyed gene-drug interactions using the Drug Gene Interaction Database (DGIDB). The protein products of these genes include targets for treatments to stimulate (thyrotropin [TSH]) or suppress (methimazole, targeting thyroid peroxidase, TPO) thyroid function, and drugs to treat thyroid cancer (e. g. the KDR inhibitor, vandetanib) as well as PDE4 inhibitors and AKT inhibitors utilised in immunoinflammatory conditions and cancers (Supplementary Table  7 ).
Polygenic score associations We constructed a polygenic score (PGS) for TSH using the summary statistics of approximately 1. 12 million SNPs from our discovery GWAS (meta-analysed from UK Biobank, EXCEED and results from Zhou et al, with total sample size of 247,107 European-ancestry individuals) as the training dataset (Online Methods, Figure  3 : PGS performance across ancestries). The TSH PGS showed distinct patterns of associations with relevant thyroid and non-thyroid phenotypes in our PheWAS (Supplementary Figure  4 ). Thyroid-relevant PGS associations included increased risk of hypothyroidism, lower risk of non-toxic (multi)nodular goitre, thyrotoxicosis, Graves' disease, and thyroid cancer, and reduced tyrosine. Other PGS associations included increased FEV 1 /FVC, lower risk of chronic obstructive pulmonary disease (COPD), pneumonia, coeliac disease, common cancers and multi-site chronic pain, lower arterial stiffness, increased creatinine and urate, increased alkaline phosphatase and aspartate aminotransferase, increased eosinophils, decreased sex hormone-binding globulin, testosterone and IGF-1, decreased glucose (Supplementary Table  10 ) as well as altered lipid levels and composition.
In addition, thyroid function within the euthyroid range is associated with adverse outcomes  (20)  and thus TSH-associated variants that have not yet been overtly associated with thyroid disease remain highly relevant. GWAS of other quantitative traits -including those on the UK Biobank biomarker panel -have highlighted a number of targets leading to active drug development for related diseases  (3) (4) (5). However, TSH has not yet been measured in UK Biobank samples. Thus, harnessing TSH levels measured in primary care in the EXCEED and UK Biobank studies more than doubled available sample sizes. As with other contemporary genome-wide association meta-analysis, maximising power for discovery does not leave available datasets suitable for independent replication. In our study, 226 of the 260 independent sentinels (128 of the 158 novel signals) showed consistent direction of effect and P<0.
01 in at least two of three large datasets (UK Biobank, EstBB, Zhou et al) in addition to reaching genome-wide significance in the meta-analysis. Not all SNPs were represented in the Zhou et al dataset due to contributing datasets using less dense imputation platforms than we were able to use in UK Biobank and EXCEED. The PGS we developed for TSH utilised the full genome-wide association statistics to maximise predictive power. To our knowledge this is the first TSH PGS to be shown to be associated with TSH levels across all ethnic groups in UK Biobank. Power was more limited for testing other traits and diseases in the much smaller sample sizes available in non-European ancestries in UK Biobank. Nevertheless, we showed a consistent direction of association on T 4 levels in all ancestries and association with hypothyroidism in South Asian participants (P=9.
Cohort details We analysed data from UK Biobank  (24)  and the Extended Cohort for E-health, Environment and DNA (EXCEED) study  (25). UK Biobank is a cohort of approximately 500,000 individuals recruited from across the United Kingdom. Individuals aged between 40 and 69 years were recruited from the general population between 2006 and 2010. EXCEED recruited approximately 10,000 individuals primarily through local general practices in Leicester City, Leicestershire and Rutland. Recruitment started in 2013. Individuals invited to contribute to EXCEED were aged between 40 and 69 years. Both UK Biobank and EXCEED collected information at baseline concerning lifestyle and health outcomes, as well as providing linkage to electronic health records and genetic data.
Genome-wide association study We applied an inverse normal transformation to the residuals from linear regression of the TSH phenotype against age (at time of measurement) and sex. This transformed phenotype was used for genome-wide association testing under an additive genetic model adjusted for age, genotyping array, sex and the first 10 principal components of ancestry using PLINK 2. 0  (26). We analysed individuals of European ancestry, as defined by the Pan-UK Biobank initiative  (27) , who were not more closely related than third-degree relatives using a KING software relatedness coefficient >0. 0884 to indicate second-degree relatives or closer  (28). In UK Biobank, we tested genetic variants with a minor allele count >20 and imputation score >0. 5. In EXCEED, due to the smaller sample size, we tested genetic variants with a minor allele frequency >0.
Loci for which PolyFun and SuSiE did not identify any credible sets, as well as HLA and chromosome X loci, were fine-mapped using the Wakefield method(34) with the prior W set as 0. 04 in the approximate Bayes factor formula. 95% credible sets were generated for all loci, and variants with the highest posterior inclusion probability (PIP) per credible set identified (Supplementary Table  14 ). We assessed whether signals were consistent (consistent direction of effect and P-value less than 0. 01 or 0. 05) in at least two of: (a) our results from UK Biobank, (b) summary statistics from the analysis by Zhou et al, and (c) an independent dataset from Estonian Biobank. Estonian Biobank is a population-based biobank with 212,955 participants in the current data freeze (2022v2). All biobank participants have signed a broad informed consent form and information on ICD codes is obtained via regular linking with the national Health Insurance Fund and other relevant databases, with a majority of the electronic health records having been collected since 2004  (10).
Expression quantitative trait loci (eQTLs) We used the SNP2GENE function implemented by FUMA  (42)  to facilitate the eQTL analysis. FUMA contains several eQTL datasets across a broad range of tissue types. We ran SNP2GENE requesting eQTL results from the GTEx v8 (thyroid, hypothalamus and pituitary tissues) and eQTLGen (blood, cis-and trans-eQTLs) datasets. We performed approximate colocalisation between our GWAS and eQTL signals by identifying whether the top variant in an eQTL signal was in one of our 95% credible sets (Supplementary Table  16 ).
Protein quantitative trait loci (pQTLs) Two pQTL datasets were included in the pQTL analyses: deCODE Genetics  (43) , with data for 4,719 proteins measured by 4,907 aptamers, and the SCALLOP Consortium  (44) , including 90 cardiovascular proteins. The significance level for pQTL associations were set as in the original publications: P-value<1. 8×10 -9 for deCODE Genetics  (43)  and P-value<5×10 -8 for the SCALLOP Consortium  (44). We performed approximate colocalisation between our GWAS and pQTL signals by identifying whether the sentinel variant in a pQTL signal was in one of our 95% credible sets.
Polygenic score (PGS) We applied PRS-CS-auto  (46)  to construct a polygenic score (PGS) using the summary statistics from our discovery GWAS (a meta-analysis of UK Biobank, EXCEED and results from Zhou et al (HUNT, Michigan Genomic Initiative [MGI], ThyroidOmics)) as the training dataset. PRS-CS-auto is a Bayesian approach, which automatically learns hyper-parameters from the training data; no validation dataset is required. We tested the association of this PGS trained from EUR ancestry group with TSH in non-EUR ancestry groups in UK Biobank, including AFR, AMR, CSA, EAS, MID. Associations were tested using a linear regression model, adjusted for genotyping array, age at TSH measurement, sex and the first 10 principal components of ancestry. We evaluated the association of the TSH PGS with susceptibility to hypothyroidism, hyperthyroidism, thyroid cancer, other thyroid diseases and thyroid eye disease in ancestry groups with more than 100 cases in UK Biobank.
Acknowledgements The research was partially supported by the  NIHR Leicester Biomedical Research Centre  and through an  NIHR Senior Investigator Award  to  M. D. Tobin ; views expressed are those of the author(s) and We want to acknowledge the participants of the  Estonian Biobank  for their contributions. The Estonian Biobank analyses were partially carried in the  High Performance Computing Center, University of Tartu. We would like to thank the  ThyroidOmics consortium, Nord-Trøndelag Health (HUNT) Study , and  Michigan Genomics Initiative  for depositing genome-wide summary statistics from previous studies in GWAS Catalog, and we are grateful to the staff who develop and maintain the  GWAS Catalog.
org/10. 35802/202849] and by Cohort Access fees from studies funded by the Medical Research Council (MRC), BBRSC, NIHR, the UK Space Agency, and GSK. It was previously supported by MRC grant G0902313. This work is supported by BREATHE -The Health Data Research Hub for Respiratory Health [UKR_PC_19004] in partnership with SAIL Databank. We also thank all participants and staff who have contributed their time to the study. Our analyses of UK Biobank and EXCEED used the ALICE and SPECTRE High Performance Computing Facilities at the University of Leicester.
Acknowledgements This work was supported by the  Délégation Générale de l'Armement (ER, RD) , by the  Agence Nationale de la Recherche  ( ANR-12-CORD-0030   Roboergosum  Project), by joint funding from  ANR  and the  Austrian Science Fund FWF  ( ANR-21-CE33-0019-01 ), by the  Centre National de la Recherche Scientifique  ( INS2I Appel Unique  programme; MK), and by the  European Union   Horizon 2020 research and innovation programme  under grant agreement No 761758.
In this paper, we describe the transients follow-up system of H. E. S. S. which became operational in 2016. The system allows H. E. S. S. to conduct a more versatile, optimised, and largely autonomous transient follow-up programme, combining all major functionalities in one systematic approach. We describe the design, central functionalities, and interfaces of the follow-up system in general and its three main components in detail: the Target of Opportunity (ToO) alert system, the data acquisition and central control system, and the real-time analysis. We highlight architectural decisions and features that enable fully automatic ToO follow-up and indicate key performance metrics of the subsystems. We discuss the system's capabilities and highlight the need for a fine-tuned interplay of the different subsystems in order to react quickly and reliably.
E. S. S. is receiving Nova candidates; (iii) Astro-COLIBRI  (Reichherzer et al. 2021) , which allows for visual monitoring of transient alert localisations and timelines which many H. E. S. S. Target-of-Opportunity (ToO) principal investigators (PIs) use to monitor recent alerts; and (iv) FLaapLUC  (Lenain 2018) , which continuously runs likelihood Fermi-LAT data analyses of potentially variable sources in order to trigger follow-up observations manually. The follow-up of transient alerts by pointed instruments requires dedicated and integrated transients follow-up systems that connect external alerts to the instrument. They are optimised for a fast and flexible handling of external transient alerts, including the following: alert reception and processing, execution of follow-up observations, real-time analysis (RTA) of incoming data, and communication of results and follow-up alerts to the operators, PIs, and the scientific community.
In this work, we present the transients' follow-up system of the H. E. S. S. experiment, an IACT system situated in Namibia, probing the very-high-energy (VHE, 30 GeV to 100 TeV) gamma-ray sky. The overall concept, design, and main building blocks of the system are presented in Sect. 2. The ToO alert system, the Data AcQuisition (DAQ) and central control ToO system, and RTA are presented in Sects. 3-5, respectively. The interplay of the different components and application to science cases are discussed in Sect. 6. A summary and outlook are given in Sect. 7.
Functionalities of a transients follow-up system The transients follow-up system connects H. E. S. S. with groundand space-based telescopes and transients data networks such as GCN. Based on the type and properties of newly detected astrophysical transients, the system is designed to dynamically schedule and execute automatic follow-up observations that are tailored to pre-defined science cases. As such, the system has to be able to digest diverse and limited external alert information, and to consider the visibility of objects as well as the ranking of follow-up observations against ongoing or scheduled observations. It is crucial that all parts of the system are smoothly connected, from the reception of alerts, to the reaction of the telescopes, and optimised RTA of the H. E. S. S. data on site.