dataesr/scientific-paragraphs-categorization huggingface dataset:
For further examination, the complete fliC nucleotide sequences of both EHEC O145:H25 and EHEC O145: H28 strains were determined. The H25 flagellin encoding genes derived from two E. coli O145:H25 strains CB12641 (GenBank Accession LN555738) and CB12671 (GenBank Accession LN555739) had each a length of 1332 bp and were identical between both strains. BLAST search revealed 100% and 99% sequence identity with partial fliC sequences of O145 strains 4392/97 (GenBank Accession AJ566340. 1) and 2839/98 (GenBank Accession AJ566341. 1) respectively  [22]  and 98% identity with the partial fliC H25 sequence (GenBank Accession AY250007) of the O15:H25 type reference strain HW26  [23]. Nucleotide sequences identical to those obtained from CB12641 and CB12671 were found in whole genome sequencing (WGS)-contigs deposited at GenBank (Accession numbers JHMZ01000105.
1, JHHD01000016. 1, JASO01000012. 1, AIAX01000332. 1 and AIAQ01000055. 1). Among the WGS-contigs deposited at GenBank two are of unknown serotype, one is O145:H25 and one is O177:H25. The complete fliC sequences of two EHEC O145:H28 strains, CB12513 (GenBank Accession LN555740) and CB12663 (GenBank Accession LN555741) were determined. These were identical to each other with a length of the flagellin encoding region of 1743bp (580 aa). The fliC sequences of CB12513 and CB12663 were identical to four fliC sequences (all 1743bp) derived from O145:H28 strains (GenBank Accession CP007136. 1, CP007133. 1, CP006027. 1 and CP006262. 1) [36] [37]. In contrast, the fliC H28 sequences of O145:H28 strains showed only 92% similarity (1617/1752 bp) to the complete fliC H28 gene of the H28 type reference strain HW30 (O132:H28) which has a length of 1752bp  [23]  (GenBank Accession AY250010) and 92.
5% identity to the fliC H28 sequence of strain SE11 (O152:H28) (GenBank Accession AP009240. 1) which has a total length of 1740 bp  [38]  (GenBank Accession AP009240. 1). The differences found in the fliC H25 and fliC H28 genes of EHEC O145 strains compared to non-O145 strains carrying the same flagellar antigens prompted us to develop real-time PCR assays to improve the identification of these clinically important EHEC O145 types. 4 ). Since fliC H25[O145] PCR showed cross-reactions with some non-O145:H25 strains we determined the fliC H25 sequence of each one representative strain per serogroup O2:H25 (CB13990), O100:H25 (CB9767), O165:H25 (CB12546), O172:H25 (CB10528) O177:H25 (CB14727). The flagellin encoding gene derived from these strains had each a length of 1332 bp (443 aa). Homology between the fliC H25 genes of E.
Development of a fliC real-time PCR for detection of EHEC O145:H28 strains The real-time PCR developed from the fliC H28[O145] sequence was tested on 70 strains identified as H28 positive by serotyping or nucleotide sequence typing of fliC genes and by a generic fliC H28 PCR targeting common regions of the fliC H28 genes. The strains belonged to 14 different O groups, as well as to ONT and Orough strains. The results are summarized in Table  5. The fliC H28[O145] PCR reacted with all 35 O145:H28 and two Orough:H28 strains which were positive for the O145wzy gene. Except for four O28:H28 strains, no other strain expressing flagellar type H28 reacted in the fliC H28[O145] PCR. In order to explore the nature of the observed reaction with O28:H28 strains we determined the nucleotide sequence of the fliC gene of a representative strain (CB9651, O28:H28, GenBank LN649615).
The sequence had the same length and was identical to that found with O145:H28 strains. A cluster analysis of fliC H28 sequences derived from fliC H28[O145] PCR reacting (E. coli O145:H28 and O28:H28) and non-reacting (E. coli O132:H28 and O152:H28) strains revealed two genetic clusters of strains that were distinguishable by the fliC H28[O145] PCR (Fig  4 ). None of the E. coli H-type reference strains H1-H56 reacted in the fliC H28[O145] PCR except for strain 5306-56 (O26:H46) which gave a positive reaction with a cycle threshold (CT) value of 21. 3. The reaction was supposed to be fliC specific as another H46 strain from our collection (CB13742, Or:H46) reacted equally well (CT value of 21. 9) in the PCR. By comparing the corresponding regions in the EHEC fliC H28[O145] genes and the fliC H46 gene of strain 5306-56 (GenBank AY250024, 1719bp), only small differences were found (one mismatch in the backward primer and one mismatch in the probe sequence).
The first camera model for orbiting pushbroom cameras was published in 1983 by Guichard and Toutin  [10, 35]. The model was used for SPOT-1 which was the first civilian Earth observation satellite to be launched after the American Landsat series. Later this model became known as the Toutin Model and was embedded in the commercial software PCI Geomatica. This model assumes that the satellite flies on a perturbed elliptical orbit and that its nominal constant attitude is perturbed by second degree polynomial functions of time. Later the model was extended and adapted to newer observation satellites  [36, 37, 39, 40]. In 1987, Konecny proposed to model the movement of orbiting pushbroom cameras with a straight path and a constant attitude, which leads to a model with six parameters  [20]. The differences between that model and reality were handled by eight perturbation parameters.
This model was extended later by Jacobsen  [14, 15, 16]. In 1987, Gugan proposed an orbiting pushbroom camera model for SPOT-1 in which the satellite flies an elliptical orbit whose longitude of the ascending node changes linearly with time, as well as the satellite true anomaly  [9]. The attitudes are assumed to drift linearly with time, and their initial value is unknown. This model was extended by Michalis and Dowman in 2008  [24]. In 1988, Kratky proposed to model the attitude variations of SPOT-1 by quadratic functions of time  [21] ,  [23]. This model was implemented in the software SPOTCHECK+ and was later extended by Fritsch and Stallmann  [8]. In 1990, Westin proposed to simplify the usual models by approximating the orbit of SPOT-1 by a plane circular orbit  [42]. This approximation has to be valid only during the timespan of one scene.
These methods solve the problem by applying Newton's method to a sequence of equality constrained problems  [3]. A quadratic program solver is available in the cvxopt package, which is a free software package for convex optimization based on the Python programming language. It is developed by Martin Andersen, Joachim Dahl and Lieven Vandenberghe. This solver implements a primal-dual path-following method based on the Nesterov-Todd scaling  [41, 1]. Note that for simplicity we detailed only the roll function estimation, but the method applies to pitch as well. The complete procedure to refine the roll and pitch functions is summarized in Algorithm 3. Algorithm 3: Refinement of roll and pitch coefficients from world to image correspondences. Input: {X i ↔ x i }: set of n ≥ 1 world to image correspondences δ t , w, f, y 0 : camera internal parameters a, i, λ 0 : orbit parameters α 0 : satellite initial position on the orbit ϕ, ψ, ω: attitude functions measured on board η: nominal attitude accuracy Output: φ, ψ: roll and pitch refined functions begin 1 for k ∈ {1.
Acknowledgment This work was partly founded by  Centre National d'Etudes Spatiales (CNES ,  MISS  Project),  European Research Council  (advanced grant Twelve Labours),  Office of Naval research (ONR  grant  N00014-14-1-0023 ),  DGA   Stéréo  project,  ANR-DGA  (project  ANR-12-ASTR-0035 ) and  Institut Universitaire de France.
The authors greatly appreciate the financial support of the Ministry of Higher Education Malaysia through High Impact Research Grant MOHE-HIR D000008-16001, University of Malaya (UM) and from the Embassy of France in Malaysia. Saintier, N. , Verron, E. , 2010. Configurational Mechanics and Critical Plane Approach: Concept and applicationto fatigue failure analysis of rubberlike materials. Int. J. Fatigue 32 (10), 1627-1638. Murthy, V. M. , Bhowmick, A. K. , De, S. K. , 1982. Scanning electron microscopy studies of failure surfaces of short glass fibre-rubber composites. Journal of Materials Science. 17 (3), 709-716. Richaud, E. , Djouani, F. , Fayolle, B. , Verdi, J. , Flaconneche, B. , 2013. New insights in polymer-biofuels interaction. Oil & Gas Science and Technology-Revue dIFP Energies nouvelles.
Searches for long-duration continuous waves (CWs) and a SGWB are sensitive to disturbances from spectral lines and other sustained noise artefacts. These effects cause elevated noise at a given frequency and so impair any search over these data. This paper describes the work done to characterize the LIGO detectors and their data during S6, and estimates the increase in sensitivity for analyses resulting from detector improvements and data quality vetoes. This work follows from previous studies of LIGO data quality during Science Run 5 (S5)  [14, 15]  and S6  [16, 17].
Beamsplitter 4km Figure  1 : Optical layout of the LIGO interferometers during S6  [21]. The layout differs from that used in S5 with the addition of the output mode cleaner. Similar studies have also been performed for the Virgo detector relating to data taking during Virgo Science Runs (VSRs) 2, 3 and 4  [18, 19]. Section 2 details the configuration of the LIGO detectors during S6, and section 3 details their performance over this period, outlining some of the problems observed and improvements seen. Section 4 describes examples of important noise sources that were identified at each site and steps taken to mitigate them. In section 5, we present the performance of data-quality vetoes when applied to each of two astrophysical data searches: the ihope CBC pipeline  [13]  and the Coherent WaveBurst (cWB) burst pipeline  [10].
Detector sensitivity during S6 The maximum sensitivity of any GW search, such as those cited in section 1, is determined by the amount of coincident multi-detector operation time and astrophysical reach of each detector. In searches for transient signals these factors determine the number of sources that could be detected during a science run, while in those for continuous signals they determine the accumulated signal power over that run. The S6 run took place between July 7th 2009 and October 20th 2010, with each detector recording over seven months of data in that period. The data-taking was split into four epochs, A-D, identifying distinct analysis periods set by changes in detector performance or the detector network itself. Epochs A and B ran alongside the second Virgo Science Run (VSR2) before that detector was taken off-line for a major upgrade  [19].
S6A ran for ∼2 months before a month-long instrumental commissioning break, and S6B ran to the end of 2009 before another commissioning break. The final 2 epochs, C and D, spanned a continuous period of detector operation, over nine months in all, with the distinction marking the start of VSR3 and the return of a three-detector network. Instrumental stability over these epochs was measured by the detector duty factor -the fraction of the total run time during which science-quality data was recorded. Each continuous period of operation is known as a science segment, defined as time when the interferometer is operating in a nominal state and the spectral sensitivity is deemed acceptable by the operator and scientists on duty. A science segment is typically ended by a critically large noise level in the instrument at which time interferometer control cannot be maintained by the electronic control system (known as lock-loss).
Data-quality problems in S6 While the previous section described the performance of the LIGO detectors over the full span of the S6 science run, there were a number of isolated problems that had detrimental effects on the performance of each of the observatories at some time. Each of these problems, some of which are detailed below, introduced excess noise at specific times or frequencies that hindered astrophysical searches over the data. Under ideal conditions, all excess noise sources can be quickly identified in the experimental set-up and corrected, either with a hardware change, or a modification of the control system. However, not all such fixes can be implemented immediately, or at all, and so noisy periods in auxiliary data (other data streams not directly associated with gravitational-wave readout) must be noted and recorded as likely to adversely affect the GW data.
Seismic noise Throughout the first-generation LIGO experiment, the impact of seismic noise was a fundamental limit to the sensitivity to GWs below 40 Hz. However, throughout S6 (and earlier science runs), seismic noise was also observed to be strongly correlated with transient noise glitches in the detector output, not only at low frequencies, but also at much higher frequencies (∼100-200 Hz). The top panels of fig. 5  show the seismic ground motion at LHO, both in specific frequency bands (left) and as seen by the Ω-pipeline transient search algorithm  [37]  (right) -this panel shows localised seismic noise events in time and frequency coloured by their SNR. The lower left and right panels show transient events in the gravitational-wave strain data as recorded by single-interferomter burst and CBC inspiral searches respectively.
To investigate this possibility, the interferometer was run in a configuration where the light did not enter the arm cavities, but went almost directly into the OMC, removing the length and angular control servos from consideration. Sharp downward dips in the light were seen during this test, although they were 0. 2 milliseconds wide, much narrower than the initial dips of the spike glitches. Despite this investigation and many others, the cause of the spike glitch was never determined. However, these glitches were clearly not of astrophysical origin, and were not coherent with similar events in H1, allowing the CBC signal search to excise them from analyses by vetoing time around glitches detected in L1 with unreasonably high SNR. For future science runs, Advanced LIGO will consist of almost entirely new hardware, so whether the spike glitch or something very similar will be seen in new data remains to be seen.
The impact of data quality on gravitational wave searches The impact of non-Gaussian, non-stationary noise in the LIGO detectors on searches for GWs is significant. Such loud glitches, such as the spike glitch, can mask or greatly disrupt transient GW signals present in the data at the same time, while high rates of lower SNR glitches can significantly increase the background in searches for these sources. Additionally, spectral lines and continued glitching in a given frequency range  Non-Gaussian noise in the detector outputs that can be correlated with auxiliary signals that have negligible sensitivity to GWs can be used to create flags for noisy data; these flags can then be used in astrophysical searches to remove artefacts and improve sensitivity. With transient noise, the flags are used to identify time segments in which the data may contain glitches.
In both cases, the multi-detector events identified are then subject to a number of consistency tests before being considered detection candidates. The background of each search is determined by relatively shifting the data from multiple detectors in time. These time shifts are much greater than the time taken for  a GW to travel between sites, ensuring that any multi-detector events in these data cannot have been produced by a single astrophysical signal. Although both searches require signal power in at least two detectors, strong glitches in a single detector coupled with Gaussian noise in others still contributed significantly to the search background during S6. DQ flags were highly effective in removing these noise artefacts from the analyses. The effect of a time-domain DQ flag can be described by its deadtime, the fraction of analysis time that has been vetoed; and its efficiency, the fractional number of GW candidate events removed by a veto in the corresponding deadtime.
Flag performances are determined by their efficiency-to-deadtime ratio (EDR); random flagging and vetoing of data gives EDR 1, whereas effective removal of glitches gives a much higher value. Additionally, the used percentage -the fraction of auxiliary channel glitches which coincide with a GW candidate event -allows a measure of the strength of the correlation between the auxiliary and GW channel data. Each search group chose to apply a unique set of DQ flags in order to minimise deadtime whilst maximising search sensitivity; for example, the CBC search teams did not use a number of flags correlated with very short, high-frequency disturbances, as these do not trigger their search algorithm, while these flags were used in searches for unmodelled GW bursts. We present the effect of three categories of veto on each of the above searches in terms of reduction in analysable time and removal of noise artefacts from the search backgrounds.
Only brief category definitions are given, for full descriptions see  [15]. 5. 1. 1. Category 1 vetoes. The most egregious interferometer performance problems are flagged as category 1. These flags denote times during data taking when the instrument was not running under the designed configuration, and so should not be included in any analysis. The Data Monitoring Tool (DMT) automatically identified certain problems in real time, including losses of cavity resonance, and errors in the h(t) calibration. Additionally, scientists monitoring detector operation in the control room at each observatory manually flagged individual time segments that contained observed instrumental issues and errors. All LIGO-Virgo search groups used category 1 vetoes to omit unusable segments of data; as a result their primary effect was in the reduction in analysable time over which searches were performed.
5. 2. 1. Searches for continuous GWs. The PowerFlux pipeline  [51, 52]  is one method used to conduct an all-sky search for GW signals from pulsars. This search, currently in progress, has chosen the final seven months of the S6 dataset in order to minimise the impact of poor detector performance from the earlier epochs. A preliminary analysis of the data has shown instrumental features at high frequency causing the search sensitivity to drop towards that observed during S5. In all, ∼20% of frequency bands, each a few hundred mHz wide, have been identified as non-Gaussian, compared to almost zero in S5. Additionally, the noise due to beam jitter (see section 4. 4), has had a detrimental effect on sensitivity around 180-200 Hz.
5. 2. 2. Searches for a SGWB. For the S6 search for a SGWB, DQ cuts were made to eliminate data in H1 and L1 that were too noisy, too non-stationary, or that had apparent correlated noise between detectors ‡. The analyses ran over times when both LIGO detectors were taking science-quality data, excluding those times flagged as category 1 or category 4 veto segments. The category 1 segments chosen for this search caused a 2% reduction in coincident data for the LIGO detector pair. In addition, up to 5. 5% of data segments deviate from the stationary noise ‡ In the absence of a signal model, correlated noise and a GW signal are indistinguishable in a stochastic search. However, a stochastic isotropic search assumes that the signal is broadband, and so narrow-band line features can be considered to be of instrumental, usually electronic, origin.
assumption, depending on frequency. These were removed from the analysis by identifying segments whose standard deviation, σ, varies from neighbouring segments by greater than 20%. After applying all of the data quality cuts, ∼117 days of coincident live time for the LIGO network remained. Spectral noise lines are also a problem for the SGWB search. It is improbable to have a spectral noise line present in the same frequency bin (0. 25 Hz) in both H1 and L1, but it is possible. In addition, a loud line in one detector can couple with a noise fluctuation in the other and produce an excess when the correlation is calculated between the two data streams. In order to examine frequency bins for contamination, the coherence between two interferometers was calculated, Γ(f ) = | P 12 (f ) | 2 P 1 (f ) P 2 (f ) , (1) where P 12 (f ) is the average cross-spectral density and P i (f ) is the power spectral density for the i th interferometer.
This was used to identify high coherence bins, searching at resolutions of 1 Hz and 100 mHz, using the method in  [9]. This identified power line harmonics, 16 Hz harmonics from data acquisition, violin modes of the interferometer mirror suspension, and injected calibration signals. These frequencies were excluded from the analysis, as were some frequency bins where a clear association with an environmentally produced noise line in either the H1 or L1 data could be made. In total, 87 frequency bins (each 0. 25 Hz wide, in the range from 40-1000 Hz) were removed from the S6 LIGO SGWB search. The study of the coherence also revealed a small amount (0. 2%) of additional non-stationary time series data, and these were excluded. In addition, the SGWB search pipeline was run over LIGO data after a nonphysical time-shift had been applied.
Conclusions and outlook for Advanced LIGO The LIGO instruments, at both Hanford and Livingston, are regularly affected by both non-Gaussian noise transients and long-duration spectral features. Throughout S6 a number of problems were identified as detrimental to stable and sensitive data-taking at the observatories, as well as to the astrophysical searches performed on the data. Instrumental fixes employed throughout the science run resulted in increasingly stable and sensitive instruments. Median segment duration and overall duty factor improved from epoch to epoch (table  1 ) and the detection range to the canonical binary neutron star inspiral increased by a significant factor (fig. 4 ). Data quality flags, used to identify known correlations between noise in auxiliary systems and the GW data, allowed for a significant reduction in the event background of both core transient searches, ihope and cWB (figs.
Acknowledgements The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  National Science and Engineering Research Council Canada , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.