dataesr/scientific-paragraphs-categorization huggingface dataset:
The air dose rates at 1 m above the ground at the measurement locations were derived from the inventory in accordance with the ICRU Report 53, with the obtained relaxation mass depth based on an environmental study performed in Fukushima Prefecture, in which observed depth profiles of radiocesium were analyzed by fitting with exponential functions  (Matsuda et al. , 2014). The relaxation mass depth (also called relaxation mass per unit area or b) corresponds to the depth where the radiocesium concentration is 1/e of that at the ground surface. The values of b in March, September, and December 2012 were determined as 1. 30, 1. 57 and 1. 90 g cm À2 from the arithmetic mean of 83, 82, and 82 data points, respectively. The radionuclide inventory was calculated under the assumption that a gamma-ray spectrum was measured under an ideal condition, that the detector was located in an open field with flat terrain with no attenuation of the gamma rays, and that the radionuclides were exponentially distributed in depth in the soil.
Computational Details The DFT calculations were performed with the Vienna ab initio simulation package (VASP)  [28, 29] , making use of the projector augmented waves (PAW) technique  [30, 31]. For the generalized gradient approximation (GGA) exchange correlation functional, we have used the Perdew-Burke-Ernzerhof parameterization (PBE)  [32]. A plane-wave cutoff energy of 400 eV for each element has been taken. For the Brillouin-zone integration, the Methfessel-Paxton  [33]  technique with a modest smearing of the one-electron levels (0. 2 eV) was used. The structures are fully relaxed. Care is taken that no additional symmetries are gained during the relaxation procedure. For all the hexagonal structures, a gamma centered k-point grid is used. For the other structures, a Monkhorst-Pack  [34]  grid is chosen.
Acknowledgments Financial support by program  ECOS-Nord Nº   Co8P03 COLCIENCIAS /   ICFES/ ICETEX (Colombia-France ), program  Picasso Nº 22905RD -Nº FR 2009-0105  ( Spain-France ) and " Ministerio de Ciencia e Innovación" Ref. Nº JCI-2011-10059  (Spain) are gratefully acknowledged.
The choice of this algorithm is made as it only requires the computation of the gradient of the misfit function and shows however improved convergence properties regarding standard steepest descent or non-linear conjugate gradient algorithms. The parameter l sets the number of gradients stored to build the l-BFGS approximation of the inverse Hessian, which is 20 in this study  (Nocedal & Wright 2006). The adjoint state method is used to compute the gradient of the misfit functions  (Chavent 1974; Plessix 2006). From the implementation point of view, the l-BFGS optimization is performed with the SEIS-COPE optimization toolbox, which is interfaced within the code used to achieve these experiments  (Métivier & Brossier 2015). The strategy retained for implementing the asymptotic preconditioning strategy consists in computing the Beylkin operator in discrete form off-line.
A C K N O W L E D G E M E N T S This study was partially funded by the SEISCOPE consortium (http://seiscope2. osug. fr), sponsored by BP, CGG, CHEVRON, EXXON-MOBIL, JGI, PETROBRAS, SAUDI ARAMCO, SCHLUMBERGER, SHELL, SINOPEC, STATOIL, TOTAL and WOODSIDE. This study was granted access to the HPC resources of the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA), the OSUG@2020 labex (reference ANR10 LABX56) and the Equip@Meso project (reference ANR-10-EQPX-29-01) of the programme Investissements d'Avenir supervised by the Agence Nationale pour la Recherche, and the HPC resources of CINES/IDRIS under the allocation 046091 made by GENCI. The authors thank all the actors of the SEISCOPE consortium research group for their active and constant support, especially Stéphane Operto for helpful and animated discussions.
SPOT4 Take 5 Whilst the Sentinel have not been launched yet, in order to prepare for the use of its data, on the 29 th of January 2013 the French space agency CNES lowered the orbit of SPOT4 to put it on the same repeat cycle of Sentinel-2 until 19 th June of the same year. During this period, SPOT passed over by the same 45 selected places every 5 days, one of them in the dry forest in Tanzania as requested by JRC. SPOT4 records in 5 spectral bands: three visible, one near-infrared and one SWIR at 20 m spatial resolution  (Hagolle et al. , n. d. ). This experiment, SPOT4 Take 5, does not simulate the full spectral and radiometric capabilities of Sentinel-2, but does simulate the revisit frequency and the spatial resolution of Sentinel-2. For the selected place in Tanzania we obtained 23 SPOT images from 6 th Feb to 19 th June 2013 at level 2A (ortho-rectified surface reflectance data provided with a cloud mask).
SPOT image segmentation To divide the images in land units we created two 'seasonal' mosaics, using SPOT images from the wet and dry season respectively. These were then segmented in combination to create polygons of a minimum mapping unit (MMU) of 0. 5 ha and 1 ha size. This method was employed so as to retain and discriminate land features that may be distinct in either season. NDVI, SAVI and MSAVI indices were calculated for the polygons. Analysis showed no significant difference between NDVI and SAVI trends, and the he MSAVI was not found to be effective at discriminating between woody and non-woody vegetation. For easy of processing we reduced the data to the NDVI series. The NDVI was calculated for each single data image and for the layer stack. From the layer stack we extracted the average NDVI for each of the segments created from the seasonal mosaic.
Object vegetation classification The SPOT data was classified in a two steps processing. Firstly, the WorldView-2 image was segmented so as to obtain polygons with a mean of 0. 1 ha. Areas of bare soil and grass were identified using a 5% reflectance threshold in the red channel. Woody vegetation was then divided into tree cover (woody vegetation higher than 5 m) and shrub cover (woody vegetation lower than 5 m). Field data provided information on the ratio of woody vegetation height to crown width, which was found to be around 1. This was effected by classifying crown width less than 5 m as shrub formations. Secondly, the segments from the SPOT data containing the NDVI profiles were then cross tabulated with the very high resolution (VHR) reference data. Therefore the segments from the SPOT data contain proportion of tree cover, shrub cover and non-woody land cover (grass or bare soil).
Random Forest Random Forest is a learning algorithm widely used in the statistical community to cluster data in different classes (among other analysis), constructing a multitude of decision trees at training time  (Breiman, 2001). It has been shown to be effective at land cover classification  (Rodriguez-Galiano et al. , 2012). Our data were examined in Random Forest with a training of 150 of the 832 sample sites, also simulating 5 days, 10 days and 16 days revisit frequency.
, 2013  for details). In the smog chamber, VOCs were analyzed by a PTR-ToF-MS. The instrument was operated in standard conditions, with a reaction chamber pressure fixed at 2. 1 mbar, drift tube voltage and temperature at 500 V and 333 K, respectively, corresponding to an electric field strength applied to the drift tube (E) to buffer gas density (N) ratio of 125 Td. The data reduction procedure is fully described in Hellebust et al. Briefly, 164 ions were identified in terms of their molecular formulas with a mass accuracy better than 20 ppm. These ions were then classified into families: aromatics, alcohols, acids, carbonyls or aliphatic hydrocarbons. In cases where the compound could neither be assigned to a chemical structure nor assigned to one of the families, it was classified according to its chemical formula either as an N-containing compound or an Ocontaining compound.
For known compounds (benzene, toluene, acetaldehyde, etc) the concentration was calculated using the k values detailed in  Cappellin et al. (2012). For ions with unknown rate constant, a default value of 3 Â 10 À9 cm 3 s À1 was used for oxygen-containing compounds and 2 Â 10 À9 cm 3 s À1 was used for compounds without oxygen in their formulas. Black carbon (BC) was monitored using an aethalometer (Aethalometer AE 33 beta, Aerosol d. o. o. )  (Drinovec et al. , 2014). Scanning mobility particle sizer (SMPS, custom built) data from the chamber were corrected for density, based on the particle chemical composition measured by the HR-ToF-AMS and subtracting BC, to provide a second measurement of the total non-refractory PM mass (i. e. those species quantified in the HR-ToF-AMS). This was used to correct for collection efficiency, CE, which was around 0.
Acknowledgments The  VELA  staff is acknowledged for the skilful technical assistance, in particular  M. Cadario ,  R. Colombo ,  G. Lanappe ,  P. Le Lijour ,  F. Muehlberger , and  M. Sculati  as well as  Rene Richter  from  PSI. We acknowledge the financial support by the  Swiss Federal Office for the Environment (FOEN) , the  Swiss Federal Roads Office (FEDRO)  and the  Swiss National Science Foundation  ( SAPMAV 200021_13016 ). The authors also acknowledge the  MASSALYA instrumental platform (Aix Marseille Universit  e, lce. univ-amu. fr)for the provision of the PTR-ToF-MS measurements used in this publication and the  French Environment and Energy Management Agency  ( ADEME, Grant  numbers  1162C0002  and  1262C0017 ).
MOBY and MOBY-MS Since 1997, MOBY has been deployed approximately 11 nautical miles from Lanai (Hawaii) in 1200 m water depth  (Clark et al. 1997 , Clark et al. 2002). The site was selected based on requirements for an ideal SVC location and accounting for the need to ensure economical and convenient access to shore facilities. The main components of the MOBY system are: i. a spar buoy tethered to a moored buoy; and ii. a hyper-spectral radiometer operating in the 340-955 nm spectral region with 1 nm resolution, coupled via fiber optics to a number of radiance and irradiance collectors. These collectors ensure measurements of in-water downward irradiance and upwelling radiance at 1, 5 and 9 m depth. Above-water downward irradiance is additionally measured at 2. 5 m above the sea surface. The MOBY radiometer system undergoes regular characterizations and calibrations to guarantee high accuracy and traceability of data to the US National Institute of Standards and Technology (NIST).
g. , indicating cloud contamination, glint perturbations, navigation problems or failure of the atmospheric correction); satellite viewing angle less than 56 degrees; sun zenith angle less than 70 degrees; Chla lower than 0. 2 g l -1 ;  a in the near infrared lower than 0. 15; and coefficient of variation less than 0. 15 for L WN in the blue-green spectral regions and  a in the near-infrared. It is anticipated that similar matchup selection criteria were applied to the other datasets included in this review. The qualified matchups were constructed by convolving MOBY hyperspectral L w data with the actual SeaWiFS spectral band responses. Bailey et al. (2008)  also considered the parallel case of MOBY L w averaged over 10 nm bandwidths with center-wavelengths corresponding to those of SeaWiFS.
2008b ). Since 2008, BOUSSOLE is also equipped with hyperspectral radiometers to measure the inwater upwelling radiance and downward irradiance, and the above-water downward irradiance. Data from these instruments, which are not part of this study, will be relevant for vicarious calibration activities of future missions. A significant difference characterizes the extrapolation methods applied to subsurface radiometric data from MOBY and BOUSSOLE. While MOBY values are simply determined from the linear fit of logtransformed radiometric measurements with respect to depth, BOUSSOLE sub-surface values result from the propagation of the 4 and 9 m depth values to the surface through models. This latter data reduction scheme, requiring estimates of Chla, takes into account Raman effects and the related nonlinearity of the log-transformed radiometric measurements with depth.
NOMAD The NASA bio-Optical Algorithm Data set (NOMAD, Werdell and Bailey 2005) includes multi-site and multi-source data resulting from the reprocessing and strict quality control of radiometric measurements from the SeaWiFS Bio-Optical Archive and Storage System (SeaBASS). The variety of measurement methods, instruments, calibration and also data reduction schemes, make it difficult to assign well-defined uncertainties to the NOMAD radiometric data set. The SeaWiFS g-factors determined from NOMAD  (Bailey et al. 2008)  were computed using 64 matchups fulfilling SVC selection criteria -out of a total of 1039. These field radiometry data result from overall 3475 quality controlled measurements out of 15400 from 1350 field campaigns included in SeaBASS. These numbers clearly indicate the difficulty of supporting SVC with in situ L w data from repositories constructed for applications more focused on validation and development rather than vicarious calibration.
g. ,  Zibordi et al. 2006 , Mélin et al. 2011 , Zibordi et al. 2012 ). Uncertainties of 5% in the blue-green and 8% in the red spectral regions have been quantified for the AAOT fully quality assured normalized water-leaving radiance determined from L w  (Gergely and Zibordi 2014). AERONET-OC data from the AAOT were used by  Mélin and Zibordi (2010)  for the determination of regional SeaWiFS g-factors. Specifically, 99 qualified matchups were identified from a 5-year data set by relaxing some selection criteria (e. g. , accepting Chla up to 3 g l -1 and coefficient of variation up to 0. 20 in the blue-green spectral region for satellite data). A particular effort was devoted to correct in situ L w spectra for the effects of differences in center-wavelengths with respect to SeaWiFS bands. Results from the study give insight on the relevance of coastal vicarious calibration sites for regional investigations and additionally provide elements to evaluate their suitability for global applications.
This finding and differences among g-factors determined for the SeaWiFS spectral bands using various data sources, but relying on the same atmospheric models and atmospheric correction algorithms, provide suggestions on the suitability of in situ L w data sources for SVC devoted to support the construction of CDRs. Specifically, when considering the blue and green center-wavelengths commonly applied for the determination of Chla, satellite-derived L w resulting from the application of gfactors differing by as little as 0. 3% can result in spectral biases close to 5%. These biases are several times higher than the 0. 5% target stability value per decade indicated for satellite ocean color data products expected to contribute to CDRs. Thus, in view of avoiding inconsistencies in long-term data records resulting from the combination of satellite products from multiple missions, a careful evaluation of sites and in situ measurements supporting SVC is needed.
Acknowledgments The authors would like to express appreciation to the  International Ocean Color Coordination Group (IOCCG)  for promoting a number of initiatives stimulating discussions on satellite ocean color system vicarious calibration which encouraged this study. This work is dedicated to the memory of  Dennis Clark  whose commitment to  Ocean Color  led to the design, implementation and long-term operation of MOBY. The contents of this paper reflect the views of the authors and should not be interpreted as an official statement of policy, decision, or position on behalf of any of the organizations mentioned.
Field Methods Standard methods were used to measure topographical and sedimentological information about the site. A centerline profile of the channel and channel cross-sections were measured to help understand particle displacements and their relation to channel morphology. A total of 18 cross-sections were established and additional points were added at slope breaks and within the floodplain following the recommendations by Heritage et al. [58]. A digital elevation model (DEM) was created from this data using linear interpolation between data points. Wolman pebble counts within the restored and the upstream control sections were completed to characterize the surface bed material (Figure  4 ). A minimum of 200 particles >5 mm in diameter were counted, with the number of fine particles and clay till exposures also recorded.
The screens prevented the majority of sediment from coming in, although it was found necessary to pump out the material using a custom built hand pump in the summer of 2014. The gauges were Hobo™ U20 water level loggers with a calibrated accuracy of ±3 mm and a range of 0-4 m. Gauge data were downloaded periodically and corrected for atmospheric variations with the barometric gauge. Gauges were set to measure pressure at a one minute interval during the summer and a five minute interval during the winter. Through the winter the upstream gauge was removed due to the possibility that it would be damaged as a result of freezing. The downstream gauge was left in as a test and was found to have functioned normally throughout this period. Due to poorly defined rating curves, the gauge data was used to estimate shear stress but not discharge.
The positions of tagged particles were mapped using a total station. Between the period of August 2013 and August 2014, at least four events reached the design bankfull depth of 1. 0 m (0. 63 m above baseflow) at the gauge located at the upstream limit of the restoration site (Figure  5 ). Six surveys were completed at each site to characterize particle displacements. For the current analysis, only the flooding periods in 2014 ending on 22 May, 25 July and 21 August were included (Table  1 ). Smaller floods prior to the 22 May date did not result in significant mobility and the data were aggregated with the 22 May survey. All three flooding periods had dimensionless shear stress values that are close to a threshold value (τ * c ~ 0. 047 from  [60] ) in the restoration reach but in the order of ~2τ * c in the control reach.
Acknowledgments The authors would like to thank the four reviewers for their detailed and insightful comments that improved an earlier version of this manuscript. The authors are indebted to our partners on this project including  Bill Snodgrass  at the  City of Toronto ,  Ryan Ness  and  Christine Tu  at the  Toronto Region Conservation Authority , and  John Parish  at  Parish Geomorphic Ltd. (now Matrix Solutions Ltd. ). This project would not have been possible without their support. Margot Chapuis  and  Emma Buckrell  were partially supported through an  NSERC   ENGAGE grant  with  Parish Geomorphic. Funding was also supplied through  NSERC   Discovery Grants  to the first and last authors.
Particle-In-Cell simulations were used to get some insight into the injection mechanism. They were performed with the 3D, fully electromagnetic code CALDER-CIRC which uses cylindrical coordinates and a Fourier decomposition in the poloidal direction  25. Two Fourier modes were used with 70 Electrons ionized close to the center of the plasma cavity when the laser crosses the density transition (between I and II) spend a long time in the accelerating field due to the expansion of the cavity in the shock front. Therefore, they are much more likely to be trapped than electrons ionized in the up-ramp density gradient (III) which have to catch up a shrinking (accelerating) cavity. For low enough laser intensity and plasma density the injection can thus be restricted to the shock front. The shaded area indicates the standard error on the measurement.
Acknowledgements This work was supported by the  European Research Council  through the  ERC projects X-Five  (Contract No. 339128 ), and by the  Agence Nationale pour la recherche  through the project  LUCEL-X  ( ANR-13-BS04-0011 ) and  CILEX  ( ANR-10-EQPX-CILEX ). O. L. and M. H. acknowledge support by the  Swedish Research Council , the  Swedish Foundation for Strategic Research and the Knut and Alice Wallenberg Foundation.
• Characterization The crystal structure of the materials was characterized by X-ray powder diffraction (XRD) with a SIEMENS D5000D diffractometer with a copper anticathode. The data were collected by step-scanning from 20 to 70º 2 with a step size of 0. 05o 2 and 1s counting time per step. The X-ray photoelectron spectra (XPS) of the samples were recorded with a SPECS apparatus equipped with a Phoibos 100 analyzer and a 5MCD detector. The morphology of powders and films were determined by Scanning Electron Microscopy (SEM), using a Leica Leo 440i microscope, and Transmission Electron Microscopy (TEM) applying a JEOL 2100 microscope. Both instruments were equipped with a spectrometer for Energy dispersive X-ray energy spectroscopy (EDS). Current-voltage (I-V) characteristics of the solar cells were carried out using a solar simulator in standard illumination conditions: AM 1.
Acknowledgments This work was supported by the  Spanish Ministry of Science and Competiveness under INNPACTO Program  ( IPT-2011-0913-920000 ). The authors would like to thanks to  Manuel Ocaña Jurado  ( ICMS-CISC ) for his help in the XPS measurements. L. Oliveira would like to thank the support of the  National Council for Scientific and Technological Development (CNPq) -Brazil.
The Doppler linewidth (Gaussian contributions) of the five components has been fixed to a calculated value at room temperature of 0. 4 MHz (FWHM). The centre frequencies and the collisional linewidths (Lorentzian contributions) were fitted for K ¼ 0-5 components of the R(10) transitions. In Table  1 , the fitted frequencies f fitted are compared with the frequencies f calc listed in the JPL database  19  and calculated from the latest combined fit reported by Mu ¨ller et al. 20. The r. m. s determined from the (f fitted À f calc ) values is evaluated to be to 79 kHz. The collisional linewidths were fitted to 3. 4 MHz (FWHM) for the five components (no K dependence of the R(10) self-broadening was observed). At a pressure of 50 mbar a collisional linewidth of B4. 5 MHz (FWHM) may be estimated for the R(10) transitions from CH 3 CN self-broadening measurements  21, 22.
Acknowledgements This work was partly supported by the  Universitedu Littoral Cote d'Opale in the framework of the 'Bonus Qualite  ´Recherche' and by the  PALM Labex  (project  FC THz, theme Emergence ), by the synchrotron SOLEIL under the proposal 20131346, as well as by the  Agence Nationale pour la Recherche  (project  ANR-12-NANO-0014 ). We would like to acknowledge the support of  Jean-Blaise Brubach  (SOLEIL),  Laurent Manceron  (SOLEIL) and  Marc Fourmentin  (LPCA). We are indebted to the SOLEIL accelerators department (in particular  M. A. Tordeux ,  C. Evain  and  M. E. Couprie ) and to the scientific director  P. Morin  for their continuous support.
Thermodynamic data are essential for the understanding, developing, and processing of materials. The CALPHAD (Calculation of Phase Diagrams) technique has made it possible to calculate properties of multicomponent systems using databases of thermodynamic descriptions with models that were assessed from experimental data. A large variety of data, such as phase diagram and solubility data, including consistent thermodynamic values of chemical potentials, enthalpies, entropies, thermal expansions, heats of transformations, and heat capacities, can be obtained from these databases. CALPHAD calculations can be carried out as stand-alone calculations or can be carried out coupled with simulation codes using the result from these calculations as input. A number of CALPHAD software are available for the calculation of properties of multicomponent systems, and the majority are commercial products.