dataesr/scientific-paragraphs-categorization huggingface dataset:
It was set up in 1985 to collect long-term tide gauge observations and to develop systems and standards ''for ocean monitoring and flood warning purposes'' and to ''support the activities of the Permanent Service for Mean Sea Level'' (PSMSL)  [11]. The GLOSS Core Network of tide gauges, defined in 2010 (Fig. 1 ), consists of 290 stations around the world  [12]. Most of these stations are operating currently but the distribution of long-term series is mainly concentrated in the northern hemisphere. The PSMSL is the global data bank of long-term sea level change information from tide gauges. It contains monthly and annual mean sea level data. The higher frequency GLOSS data feeds into this database. There are no records at PSMSL longer than 100 years in the Arctic, Africa, South America or Antarctica (Fig.
Digitising charts The most common form of analogue sea level records are paper charts generated by float tide gauges. As a float on the surface of the water rises it causes a weight (connected by a cable over a pulley) to descend. The angle of rotation of the pulley is directly proportional to the change in water level. A second pulley fastened to the same shaft drives a pen to trace on a chart (Fig. 5 )  [20]. Tide gauge charts are generated by a mechanical instrument driving a pen trace (e. g. Fig. 6 ). There are two main methods for obtaining digital data from these analogue charts. The first is to digitise the chart manually using a digitising table, which is labour intensive and time consuming. The second method, as employed by several GLOSS members, is to use software developed to extract data automatically from a digital image (scan or photograph) of the chart.
Various methods for converting a line on a digital image to a data value were reported in a paper on automated techniques for the digitisation of archived mareograms (charts), delivered to the 13th GLOSS Group of Experts (GLOSS GE) meeting  [21]. NUNIEAU (Logiciel de NUmérisation des enregistrements graphiques de NIveaux d'EAU, Ullman et al. [22] ) is the only publically available software for digitising tide gauge charts. Other organisations have developed their own software but have not made it available to external users. As well as software specifically developed for digitising tide gauge charts, there are several freely available applications designed to convert a pixel (e. g. line on an image) to a number value. The data reduction section of the Intergovernmental Oceanographic Commission (IOC) Manual on Sea-Level Measurement and Interpretation  [20]  details several of the errors that can occur with tide gauge charts that cause problems with the digitisation of the record.
In some cases, charts have been left on the rotating drum for over a month, which leads to an overlapping of the pen traces, making following the correct curve difficult. Also, in areas where the tidal curve is not sinusoidal (e. g. Fig. 7 ) selecting the correct curve to digitise requires some effort. GLOSS should set up a comparison study to determine which programs are best suited to the task. This could be carried out by sending the same chart images to each organisation, asking everyone to extract the data using their own procedures. Comparing the results would allow GLOSS to provide recommendations to the user community and allow an estimate of the relative accuracy of each method of automatic digitisation. GLOSS will create a repository of software for scanning analogue charts but the ultimate aim is to create a web application for the GLOSS community where users could upload, calibrate and then digitise their images in their browser.
(see Fig. 9 ) The GLOSS GE is investigating developments in Handwritten Text Recognition (HTR) technology. Current projects to improve HTR tend to work with the written word and so require knowledge of sentence structure and word occurrence probabilities to reconstruct sentences  [29]. This approach would not be directly applicable to sea level records, however tidal data by its very nature is periodic and predictable, so HTR technology could be adapted to take this into account and improve the automatic digitisation of handwritten tide gauge ledgers. The British Oceanographic Data Centre (BODC) has plans to set up a 'big data' project using existing tide gauge data to create a climatology for a given location and time. This could then be used as a training dataset to aid the transcription of the ledgers.
Metadata In order for any recovered data to be useful, it must, at the very least, be accompanied by basic metadata. GLOSS has produced a Manual on Quality Control of Sea Level Observations  [30] , which includes recommendations for the metadata supplied with sea level data. The metadata can be divided into three categories: information about the site (e. g. name, latitude and longitude) information about the instrument (e. g. type of gauge, manufacturer, calibration) information about the data processing (e. g. sampling schema, corrections applied, gaps in the record) In order for the data to be incorporated into the PSMSL database, benchmark and datum history is required, which is often difficult to find with analogue data, but can be found in related records in hydrographic and geodetic agencies.
Conclusions There are many challenges facing the sea level data archaeology community but it is hoped that improvements in technology, such as faster automated digitisation of tide gauge charts with minimal user input and automatic transcribing of handwritten ledgers, can overcome some of the obstacles. The GLOSS GE is aware of several organisations globally that are looking to start data archaeology projects and GLOSS would like to help coordinate efforts and share knowledge. The GLOSS website will act as a central location to view metadata relating to tide gauge records in archives, libraries, collections etc. and will provide a mechanism for users to report data they have uncovered. The GLOSS GE will ask its members to share software for digitising and quality controlling tide gauge data and will use the GLOSS website as a central location to access software and guidelines for quality controlling digitised data.
Acknowledgements The authors would like to acknowledge the  GLOSS Group of Experts and user community  for their input and assistance. The  National Oceanography Centre  funded a three-month  Citizen Science scoping study , which included a visit to the  UK National Archives  and the  British Library. The Sheerness 1832 tide gauge register was scanned under the Jisc Content programme  2011-13  ( Strand B: Mass Digitisation ). The authors would also like to thank  Andy Matthews  for producing the maps,  Marta Marcos  for supplying the Cadiz image,  Adam Leadbetter ,  Justin Buck  and  Svetlana Jevrejeva  for providing writing assistance and  Paul McGarrigle  for proofreading.
Usually, multiphonon scattering contributes as a smoothly varying spectrum and should be removed with adequate methods. The spectrometers most suited to obtain phonon DOS spectra are the neutron time of flight or a neutron filter ones. These techniques are particularly convenient at pulsed neutron sources. Different spectra are obtained for several different Q, which may differ due to the coherent scattering, and then averaged over Q to obtain the phonon DOS. Various corrections to the data are needed due to the background scattering, geometrical factors, efficiency of the detectors, second-order reflections from the monochromator and analyzer crystals, etc. The measured spectra also deviate from the true phonon DOS because different atoms contribute differently in proportion to their scattering cross sections divided by their masses.
Measurements of using a Quantum Design physical property measurement system on a crystal of the same composition are presented in Fig. 6a. The magnitude of , in the region of a few watts per Kelvin-meter, is similar to the values reported for crystals of comparable composition and the temperature dependence clearly indicates a contribution from phonon scattering. Phonon line-widths at high resolution have been measured in a region of the dispersion dominated by a single optical phonon at Q (1. 01;1. 01;1) and E ≈ 8 meV using the triple-axis neutron spectrometer IN8 at ILL with a 30 -20 -30 -30 collimation configuration. The energy line shape is resolution-limited below T ≈ 80 K, while above this temperature we fitted the data using a Lorentzian convolved with the instrumental resolution function.
The second one is assured by a steel cylinder with steel plates in the radiographic axes. The holes in the cylinder are used as collimators for the X-rays. In front of each source, a box containing sensitive films is placed (Fig. 5 ). For each experiment, all data were acquired. The target flight is recorded on the films and the perturbation length is measured (Fig. 6 ). Its evolution is clearly visible on radiography at the three times. One can noticed on the bottom of the image at 20. 4 µs the trace of support in copper, whereas the one in tungsten stay at the top of the image.
The support of the CHAMP mission by the  German Aerospace Center (DLR)  and the  Federal Ministry of Education and Research  is gratefully acknowledged. The Ørsted Project was made possible by extensive support from the  Danish Government ,  NASA ,  ESA ,  CNES, DARA , and the  Thomas B. Thriges Foundation. The authors also acknowledge  ESA  for providing access to the Swarm L1b data. E. Canet acknowledges the support of  ESA  through the  Support to Science Element (STSE) program. This work was partly funded by the  Centre National des Etudes Spatiales (CNES)  within the context of the project of the ' Travaux préparatoires et exploitation de la mission Swarm. '  W. Kuang  and  A. Tangborn  were funded by  NASA  and the  NSF. This work was partly supported by the  French 'Agence Nationale de la Recherche ' under the grant  ANR-11-BS56-011  and by the  Région Pays de Loire, France.
ficients: http://www. ngdc. noaa. gov/IAGA/vmod/igrf12. f C software for synthesizing the field from the coefficients (Linux): http://www. ngdc. noaa. gov/IAGA/vmod/geomag70_ linux. tar. gz C software for synthesizing the field from the coefficients (Windows): http://www. ngdc. noaa. gov/IAGA/vmod/geomag70_ windows. zip Online computation of field components from the IGRF-12 model: http://www. ngdc. noaa. gov/geomag-web/?model=igrf http://www. geomag. bgs. ac. uk/data_service/models_ compass/igrf_form. shtml http://wdc. kugi. kyoto-u. ac. jp/igrf/point/index. html Archive of legacy versions of the IGRF model: http://www. ngdc. noaa. gov/IAGA/vmod/igrf_old_ models. html.
Altimetry Data Jason-2. Jason-2 mission was launched on 20 June 2008 as a cooperative effort among CNES, EUMETSAT, NASA and NOAA. Its payload is mostly composed of the Poseidon-3 radar altimeter from CNES, the Advanced Microwave Radiometer (AMR) from JPL/NASA, and a triple system for precise orbit determination: the real-time tracking system DIODE of DORIS instrument from CNES, a GNSS receiver and a Laser Retroflector Array (LRA) from NASA. Jason-2 orbits at an altitude of 1336 km, with an inclination of 66 , on a 10-day repeat cycle, providing observations of the Earth surface (ocean and land) from 66 latitude North to 66 latitude South. This orbit was formerly used by Topex/Poseidon, Jason-1 and 2, with an equatorial ground-track spacing of about 315 km. Poseidon-3 radar altimeter is a two-frequency solid-state altimeter that measures accurately the distance between the satellite and the surface (range) and provides ionospheric corrections over the ocean.
It operates at Ku (13. 575 GHz) and C (5. 3 GHz) bands. Its accuracy is expected to be about 2 cm over ocean. Raw data are processed by  SSALTO (Segment Sol multimissions d'ALTim etrie, d'Orbitographie). SARAL. SARAL mission was launched on 25 February 2013 as a joint effort of CNES and ISRO. Its payload comprised the AltiKa radar altimeter and bi-frequency radiometer, and a triple system for precise orbit determination: the real-time tracking system DIODE of DORIS instrument, a Laser Retroflector Array (LRA), and the Advanced Research and Global Observation Satellite (ARGOS-3). SARAL orbits at an altitude of 790 km, with an inclination of 98. 54 , on a 35-day repeat cycle, providing observations of the Earth surface (ocean and land) from 82. 4 latitude North to 82. 4 latitude South. This orbit was formerly used by ERS-1 & 2 and ENVISAT, with an equatorial groundtrack spacing of about 85 km.
AltiKa radar altimeter is a solid-state mono-frequency altimeter that provides accurate range measurements. It is the first altimeter to operate at. Its accuracy is expected to be about 1 cm over ocean. More details about the AltiKa-SARAL mission can be found in  (Verron et al. 2015). The data used in this study come from the Geophysical Data Records (GDRs) for Jason-2 (GDR D) and SARAL (GDR T patch 2) and are made available by Centre de Topographie de l'Oc ean et de l'Hydrosph ere (CTOH, http://ctoh. legos. obs-mip. fr/). Ranges are available every 20 Hz for Jason-2 and 40 Hz for SARAL. It corresponds to a measurement every 350 and 165 m, respectively, along the track. Range values suitable to derive SSH are retracked using MLE4  (Amarouche et al. 2004 ) and MLE3  (Rodriguez 1988 ) algorithms, Maximum Likelihood Estimator with 4(3) degrees of freedom, applied to the Brown model  (Brown 1977).
ship, 28 m in length and 6. 5 ms beam, and suitable for this kind of mission. The GNSS buoys were carried on the patrol boat deck to the calibration area. The crew placed the buoys on the sea, at the estimated observing points around the calculated satellite track two hours before the expected time of overflight. The boat kept sailing out of the survey area about  4 hours, and then she proceeded to remove the buoys from the water. The same operation achieved for the calibration of Jason-2 was repeated the day after for SARAL. The buoys were hence deployed at least 4 hours on site to allow a precise determination of their position using GNSS Precise Point Positioning (PPP) technique. The locations of buoys deployment were estimated from Jason-2 and SARAL orbit predictions obtained from the GINS/GRGS software fitting SLR data from previous days and current and wind predictions from Mercator and M et eo-France to predict the drift of the buoys between their deployment and the overflight of the satellite.
obs-mip. fr/en/research/software; the GPS Inferred Positioning System and Orbit Analysis Simulation Software (GIPSY-OASIS) from Jet Propulsion Laboratory (JPL), available upon request from https://gipsy-oasis. jpl. nasa. gov; and the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP, http://webapp. geod. nrcan. gc. ca/geod/tools-outils/ppp. php) which is an online Table  2  Characteristics of the PPP processing for GINS and GIPSY softwares, and CSRS-PPP online service GINS The main characteristics of the PPP processing, which allows a centimeter error level GNSS zero-differenced positioning, are summarized in Table  2.
Acknowledgements We acknowledge the  Spanish Navy (Armada Española)  for its support and particularly T. N. Paredes, Commanding Officer of the Toralla patrol ship, and his crew, for the excellent job made during the survey. We thank the  Real Instituto y Observatorio de la Armada (ROA)  for its support. We also thank the  CTOH/LEGOS-OMP, France , for providing the altimetry dataset used in this study.
Material and methods A new set of superficial sediments samples (Table  1 ) has been collected in the mouth of the Seine estuary during the years 2009-2010 (COLMATAGE project, in the framework of Seine-Aval program). Sample stations were chosen according to a regular grid (1 nautical mile spaced out). The set of data consists of a main sampling made during May 2009 (300 samples). Some additional samples were realized during years 2009-2010. Some of them (27) were reacquiring sample stations to study seasonal variations of the sedimentation. A total of 561 samples were analysed. It must be noticed that mapping by marine geophysics, such as side-scan sonar, is not relevant in the study area because the pictures show generally monotonous acoustics facies  (Lesourd, unpublished data). Samples were taken with a Shipek grab and a Reineck box-corer; several samples were realized on each point to account for local facies variations and/or when the samples were not relevant (reworking of sedimentary layers during sampling, or empty grab).
Each sample was described and photographed. Subsamplings were realized according to the sediment structuration. In the case of coarse sediments (gravel), the sediment was sieved in a dry environment using 15 sieves with French standard mesh sizes (i. e. AFNOR definition); the particle size distribution of the other samples was obtained with a Laser Coulter LS 230. The sedimentological classification of samples was established using both structural (as described in the grab after the sampling) and textural (grain size used to reinforce structural description) characteristics. The classification is displayed in Fig. 1. The 2009 map (Fig. 1 ) has been obtained with manual correlation  (Diesing et al. , 2014) , using bathymetric data associated with a knowledge based on more than 1500 samples realized in the Seine estuary since the 1990s.
Trapped mud balls are also mixed within the sediment, leading to an internal mud supply. Observations of many grab contents show pebbles being eroded (friction wear), so that they appear as ''ghosts'' of mud pebbles, their boundaries being erased when integrated in the sandy matrix. The Seine River flows have been measured at the upper limit of the estuary from 1941 up to today (GIP Seine-Aval). During the end of the 1970s, flow rates were generally very low (Fig. 3 ), which could explain the remaining of sandy areas, even taking into account the fact that engineering work was already finished upstream before this period. For the years 1993-1996, periods of high flow rates occurred frequently, although 2009 data were collected after different conditions; superimposed on seasonal variations, long periods (about 9 years) of low river flows (when storage of fine-grained sediments occurred within the estuary) were disrupted by few and short periods of high river discharges (when mud supply increased in the downstream part of the estuary).
The average crustal radiogenic heat production is 0. 5 μW. m -3 for continental crust and 0. 3 μW. m -3 for oceanic crust  (Vilà et al. , 2010). The heat production in the lithospheric mantle is considered as null because of its very low contents in radiogenic elements. Fullea et al. (2007)  performed a sensitivity analysis for the calculation of CMB and LAB depths induced by the typical RMS error of elevation and geoid anomaly databases. They found that the inaccuracy for the crustal thickness in emerged regions is less than 2 km whereas maximum error in lithospheric thickness is less than 10 km. The effect of varying crustal and lithospheric mantle conductivity has been tested and the resulting lithospheric thickness is affected up to 6-8 km whereas varying the coefficient of thermal expansion (α) affects the lithospheric thickness up to ~12 km.
Topography and Bathymetry Topographic and bathymetric data are extracted from the ETOPO1 database (Amante & Eakins, 2009) (Figure  3a ). The most remarkable geomorphological feature of Central Eurasia is the Tibetan Plateau which covers an area of more than 2. 5x10 6 km 2 with a mean elevation of around 4500 m. High elevations are also found to the North of the Tibetan Plateau, along the Tien Shan or the Qilian Shan ranges. The Tarim basin presents a low relief with a mean elevation of around 1000 m. To the West, the highest elevations are located in the Zagros Mountains and they extend towards the Anatolian Plateau. The Alborz, Kopet Dagh, Caucasus and Sistan ranges show also remarkable topographies with elevations up to 5000 m. To avoid unrealistic high frequency variations of the Moho and LAB depths we have filtered the elevation using a Gaussian filter with a wavelength of 100 km.
Comparison with global crustal models The most widely used global datasets related to crustal thickness is the CRUST2. 0 (Bassin et al. , 2000) , which has recently been replaced by the CRUST1. 0 that incorporates an updated version of global sediment thickness and presents a better accuracy  (Laske et al. , 2013). Reguzzoni et al. , (2013)  combined CRUST2. 0 model with gravity observations from the GOCE satellite to propose the new global crustal GEMMA model that we compare with our results (Figure  7 ). The 1x1° grid global crustal model CRUST1. 0 is obtained from the analysis of seismic wave travel-times and seismic refraction data  (Laske et al. , 2013). The resulting crustal thickness from the CRUST1. 0 model (Figure  8a  Therefore, the crustal thickness derived from our model exceeds that from CRUST1.
a) this map highlights the accreted terranes in the region resulting from the long-lived convergence between Gondwana-derived blocks and the Southern margin of Eurasia since the end of the Paleozoic. b) Simplified map showing the different zones delimited in this study. The shaded relief was obtained from the ETOPO1 database (Amante & Eakins, 2009). The scale bars corresponds to 15° latitude.
In this paper we describe the application of such a method to a narrow-band search of CW from Crab and Vela pulsars in the data of Virgo VSR4 run. As no evidence for a signal is found, we place upper limits on signal amplitude. The outline of the paper is the following. In Sec. II the expected gravitational wave signal from spinning neutron stars is introduced. In Sec. III the main characteristics of Virgo VSR4 data are presented. In Sec. IV the analysis method is described, while in Sec. V the analysis results are discussed. Sec. VI is dedicated to the validation tests of the analysis procedure. Finally, Sec. VII contains the conclusions.
III. INSTRUMENTAL PERFORMANCE IN VIRGO VSR4 RUN Interferometric gravitational wave detectors, LIGO  [13] , Virgo  [14] , GEO  [15] , have collected a large amount of data in recent years. For the analysis described in this paper we have used calibrated data, sampled at 4096 Hz, from Virgo VSR4 science run. The run extended from June 3rd, 2011 (10:27 UTC) to September, 5th 2011 (13:26 UTC), with a duty factor of about 81%, corresponding to an effective duration of 76 days. Calibration uncertainties amounted to 7. 5% in amplitude and (40 + 50f kHz ) mrad in phase up to 500 Hz, where f kHz is the frequency in kilohertz. The uncertainty on the amplitude will contribute to the uncertainty on the upper limit on signal amplitude, together with that coming from the finite size of the Monte Carlo simulation used to compute it, see Sec.
IV. SEARCH DESCRIPTION The analysis pipeline consists of several steps, schematically depicted in Fig. 3 , which we summarize here. More details are given in  [11]. The starting point is a collection of windowed and interlaced (by half) "short" (1024 seconds) FFTs (the Short FFT Database -SFDB) built from calibrated detector data  [16]. At this stage a first cleaning procedure is applied to the data in order to remove big and short time duration disturbances, that could reduce the search sensitivity. A small frequency band is then extracted from the SFDB, but large enough to contain the Doppler and spin-down variations of the signal possibly emitted by the target pulsar. In the analyses described in this paper, for instance, it was of 0. 15 Hz. At this point we go back to the time domain (the sampling frequency is still the original one, 4096 Hz) and make barycentric and spin-down corrections.