dataesr/scientific-paragraphs-categorization huggingface dataset:
The SLs and AWLs thickness variability is the reason why the step in the TVT in the early 1920s (Fig. 6d ) is less perceptible in the DLT series (Fig. 6e ). The ESLs and AWLs both show high thickness values during this period. The mean P99D0 yields the strongest correlation in our dataset (Tab. 3) and is then the robust proxy used to reconstruct maximum annual discharges (Q-max). Moreover, this indicator is not sensitive to compaction, which may affect other proxies based on thickness. Reconstructed Q-max series reveals more significant interannual and decadal variability. The good relations between sediment parameters and Snow-Win, E-Qnival and even Temp-Spring (Tab. 3), demonstrate that Grand Lake varve predominantly reflects spring discharge conditions (e. g. Ojala and Alenius 2005; Lamoureux et al.
the recording periods of each hydrometric stations. Climatic series (daily air temperature and precipitation) have been extracted from the CANOPEX dataset (Arsenault et al. , 2016), built using Environment Canada weather stations and Thiessen polygons to calculate climatic series at the catchment scale. Daily air temperature series have been used for calculating daily potential evapotranspiration at the catchment scale, thanks to the Oudin et al. (2005) formula, designed for rainfall-runoff modelling. https://doi. org/10. 5194/cp-2020-87 Preprint. Discussion started: 20 July 2020 c Author(s) 2020. CC BY 4. 0 License.
This research was financially supported by  NSERC-Ouranos-Hydro-Québec-Hydro-Manitoba  through a  CRD grant  to P. F. and P. L. ( PERSISTANCE  project,  É. Boucher  et al. ). This work was also supported by the  FRQNT  through a doctoral ( B2X ) research scholarship to A. G. P. and by the  MOPGA Short Stay program  grant at  Université Côte d'Azur, Nice, France  to A. G. P and P. B. A financial support for the fieldwork campaign at Grand Lake was provided by  POLAR  through the  NSTP  to A. G. P. The authors are grateful.
The samples were injected into a 0. 5 mL sample loop made of passivated stainless steel. A two-position 10-port diaphragm valve (VICI AG, Schenkon, Switzerland) combined with a 3-way solenoid valve as the actuator, provided rapid switching between real-time measurements and chromatographic runs. The switching process was performed fully automated via the PTR-MS Control v2. 7 software (IONICON Analytik, Innsbruck, Austria). Make-up gas was injected into a custom made 5-port virtual valve (FastGC inlet, Figure  1 ), to ensure that only the sample introduced by the carrier gas flow entered the drift tube. The major improvement in the present prototype is the replacement of the inlet valve system by a single multiport valve. This change did not only reduce the internal dead volume, but also enabled the column back-flushing during real-time measurements, since the column could be decoupled from the PTR-MS.
Data Analysis Chromatograms were analyzed with custom written Matlab scripts (MATLAB R2019a, MathWorks). Since the signals recorded by the PTR-MS are rather noisy at the short dwell times used, signals were first smoothed with a 5-point Savitzky Golay filter. To account for fluctuations in the starting temperature and small delays in the automated starting of the ramp, chromatograms were aligned on the O 2 + peak. O 2 traverses the column without interactions and is thus a good reference point. Signals were further baseline-corrected and peaks were identified with Matlab's "findpeaks" function. The threshold for peaks was set to 3-times the standard deviation plus the average obtained from the first 100 datapoints of the chromatogram. Peaks were fitted using the custom peakfit. m function assuming a Gaussian peak shape.
Emission Rate Calculation In order to compare the total quantity of MTs emitted by the four branches, emission rates were calculated using Equation (1): F = (C out (MTs) -C in (MTs)) × Q DW ( 1 ) where F is the MT emission rate expressed in µg g -1 (biomass dry weight) h -1 , C out (MTs) and C in (MTs) are the outlet and inlet total MT concentrations in µg m -3 , DW is the dry biomass (g) of the leaves inside the enclosure (determined after 2 days at 60 • C) and Q is the air flow rate in m 3 h -1. The emission rates per projected leaf area ng m -2 (total leaf area) s -1 were also determined based on Equation (1), replacing DW (g) with leaves total area in m 2 (determined from the analysis of scanned leaves images with the Image J software) and expressing C out (MTs) and C in (MTs) in ng m -3 and Q in m 3 s -1.
Conclusions Using a conventional PTR-MS, an adapted prototype of a FastGC was added and optimized for the separation of MT isomers and tested under real field conditions to monitor, with a fine time resolution, MTs emissions from holm oak foliage. The results highlight the ability of the FastGC/PTR-MS system to provide an online separation and detection of individual MTs. Using helium as the carrier gas and an optimized voltage ramp (temperature program), we were able to monitor the temporal variation of five MTs: α-pinene, sabinene, β-pinene, myrcene and limonene (+ocimene) with a data point every 10 min. However, even though our system was able to separate sabinene and β-pinene, which was an issue in a previous study  [23] , the resolution could be further improved, since limonene still co-eluted with at least one ocimene isomer.
In its present state, the FastGC/PTR-MS system demonstrated a good performance regarding MTs measurements from dynamic enclosures. Therefore, it could be deployed in parallel with OH reactivity measurements conducted on a chamber scale, providing high time resolution data on individual MTs. This can lower the uncertainties related to the MTs reactivity calculation, allowing a more precise estimation of the missing OH reactivity. However, the performance of the system should be further optimized in order to gain in sensitivity and resolution, so it could also be deployed in the measurement of individual ambient MTs profiles, for example. Improvement can be made by changing the column (length, internal diameter, the type and thickness of the stationary phase) and developing new FastGC (carrier gas flow rates, temperature ramps, etc.
Long-term changes in ocean surface waves are relevant to society and climate research. Significant wave height climatologies and trends over 1992-2017 are intercompared in four recent high-quality global datasets using a consistent methodology. For two products based on satellite altimetry, including one from the European Space Agency Climate Change Initiative for Sea State, regional differences in mean climatology are linked to low and high sea states. Trends from the altimetry products, and two reanalysis and hindcast datasets, show general similarity in spatial variation and magnitude but with major differences in equatorial regions and the Indian Ocean. Discrepancies between altimetry products likely arise from differences in calibration and quality control. However, multidecadal observations at two buoy stations also highlight issues with wave buoy data, raising questions about their unqualified use, and more fundamentally about uncertainty in all products.
Camus et al. (2017)  highlighted that numerous factors contribute to the variability seen in sea state simulations, beginning with the generation of the wind fields, the downscaling approach and postprocessing, for example, post hoc bias correction. The diversity of source data arising from these factors gives rise to questions of consistency between datasets, absolute accuracy, and available means of independent validation. Unfortunately, the use of data buoys to study long-term sea state variability is fraught by problems associated with consistency of buoy output and validation methodologies. Aside from the impact of buoy selection, quality control and postprocessing, exploiting wave buoy data for climate studies can be hindered by steps in buoy output linked to poorly documented changes in sensors and hulls, processing upgrades, occasional deployment relocations and prolonged data losses due to operational malfunction and maintenance.
Nadir-pointing altimeters provide high-quality measurements of significant wave height and wind speed  (Gommenginger et al. , 2002; Queffeulou, 2004) , while valuable measurements of swell height, period, and direction can be extracted from spaceborne synthetic aperture radar images  (Collard et al. , 2005; Lehner et al. , 2000). Although the long-term consistency and accuracy of multimission satellite data depend on careful calibration and quality control-to address instrument biases and drifts and discrepancies between retrieval algorithms  (Ash et al. , 2010; ESA, 2019; Queffeulou, 2004; Ribal & Young, 2019; Zieger et al. , 2009) -these data are attractive for their global coverage, fine spatial resolution, and regular temporal revisits. For altimetry, the satellite time series now spans over 30 years, starting in 1985 with GEOSAT and presently supported by six concurrent satellites (SARAL AltiKa, CRYOSAT-2, JASON-3, SENTINEL-3A, SENTINEL-3B, and CFOSAT).
The RY2019 calibrated altimeter dataset underpins the global trends for mean and extreme Hs reported by  (Young & Ribal, 2019) , which represent the most comprehensive and up-to-date observation-based assessment of global sea state variability to date, and has become the key reference dataset used to validate wave climate projections  (Morim et al. , 2019). Here, comparisons focus primarily on global climatologies and temporal trends in mean Hs between the four products during the recent satellite era. No attempt is made to examine how different products represent extreme sea states, as the issue of extremes in satellite altimeter and model data raises additional complexities that are beyond the scope of this short paper. Instead, we take advantage of the availability of the new CCI2019 altimeter product (ESA, 2019) to investigate for the first time wave height variability as represented by different altimeter products, prior to their wider uptake as reference datasets in climate research.
Multimission Satellite Altimeter Products: RY2019 and CCI2019 The RY2019 dataset  (Ribal & Young, 2019 ) is a level 2 product providing 32 years (1985 to 2017) of global altimeter measurements in a common standard form for 13 satellites including GEOSAT, ERS-1, TOPEX, ERS-2, GFO, JASON-1, ENVISAT, JASON-2, CRYOSAT-2, HY-2A, SARAL, JASON-3, and SENTINEL-3A. RY2019 gives access to unfiltered 1 Hz along-track measurements of Hs and wind speed for each individual altimeter mission. All data are subjected to calibration and validation against a selection of National Oceanographic Data Center (NODC) buoys. Careful quality control and calibration are applied to ensure long-term stability and cross-mission consistency. Results in this paper are based on gridded products (1 • and 2 • ) generated for this study which exclude data from SARAL, JASON-3, and SENTINEL-3A.
The CCI2019 dataset corresponds to CCI Sea State version 1. 1 level 4 product developed within the European Space Agency Climate Change Initiative for Sea State  (Piollé, Dodet & Quilfen, et al. , 2020). The CCI2019 dataset is a multimission-gridded (1 • ) product spanning 1992-2018. It extends the GlobWave dataset  up to 2018 by including more recent missions (JASON-3, SARAL, and CRYOSAT-2) but does not currently include GEOSAT nor SENTINEL-3A. CCI2019 is based on filtered 1 Hz along-track measurements of significant wave height, using empirical mode decomposition filtering  (Huang et al. , 1998)  inspired from wavelet thresholding, as described in  Kopsinis and McLaughlin (2009). The empirical mode decomposition filter significantly increases the signal-to-noise ratio at scales below 100 km  (Quilfen & Chapron, 2019)  which give clear benefits for the representation of Hs variability at the submesoscale but has limited impact on the large scales considered here.
Reanalysis and Hindcast Products: ERA5 and CY46R1 ERA5  (Hersbach et al. , 2018)  is the most recent release of the well-known and widely used reanalysis products developed and distributed by ECMWF. Replacing the previous successful ERA40 and ERA-Interim reanalyses, ERA5 provides a comprehensive high-resolution record of the global atmosphere, land surface, and ocean waves from 1950 onwards. ERA5 features a number of innovations, notably with hourly assimilation of altimeter significant wave height data. At the time of writing, ERA5 assimilates altimeter wave height data from most missions, except GEOSAT, TOPEX, SENTINEL-3A, and JASON-3. In this study, monthly mean Hs data at 0. 5 • horizontal resolution are used. In addition, a more recent simulation with the ECMWF wave prediction system but run without assimilation has become available (ECMWF, 2019) to produce a long-term sea state hindcast.
Differences Between Altimeter-Based Sea State Products The release of the CCI Sea State products, following closely after RY2019, provides a valuable means of reviewing the robustness of altimetry-based wave climate assessments obtained with independent processing of the same satellite source data. The preliminary analyses in this study appear to reveal important differences in climatologies and trends that raise immediate questions about uncertainty and the attribution of observed discrepancies to possible causes. The processing of these satellite observations is likely to be important, and it remains unclear whether, for example, the omission of SARAL mission data (2014 to 2016 inclusive) in our analysis of RY2019 has any impact. The substantial climatological differences between the CCI2019 and RY2019 in low and high sea states noted in section 3.
Differences in Trends Quantifying long-term trends in sea state is an active area of research  (Aarnes et al. , 2015; Morim et al. , 2019; Young et al. , 2012; Young & Ribal, 2019). Most recently,  Young and Ribal (2019)  reported trends in Hs derived from the RY2019 data, which differ somewhat from our findings. Qualitatively, their trends (their Figure  2a ) are generally within the same range globally, but they are markedly more positive than ours in some regions, including the Atlantic and Southern Ocean (Figure  3a ). Direct quantitative comparison is not appropriate, however, given known differences in source data, postprocessing, and methodology. For example, Young and Ribal (2019) use all altimeter data from 1985 onwards (including GEOSAT), while our data start in 1992.
Conclusions The recent release of the CCI2019 products presents a valuable opportunity to compare long-term changes in sea state in observational satellite records and highlighted the sensitivity of estimated trends to satellite data and methodological choices. Applying the same methodology to four high quality Sea State Climate Data records revealed important discrepancies, particularly in magnitude, and a tendency for later products to report larger positive trends globally. It appears that, despite increasing availability and duration of the observational records, many challenges still remain, among which is the question of what source can be regarded as the gold standard. Our findings point to several areas for further research, notably about the reliability of buoy data for climate and the sensitivity of altimeter wave height to calibration.
Therefore, this contribution, combining available geological and geophysical datasets, aims to analyse the nature and architecture of the SE China-NW Palawan conjugate margins to assess the interactions between extension, faulting and magmatic additions during breakup and early stages of oceanic accretion. We use reflection seismic lines on both the distal SE China and NW Palawan margins combined with 3D free-air gravity anomaly inversion to determine their crustal architectures. In addition, we have performed joint inversion of seismic reflection and gravity anomaly data. This allows us to determine crustal thickness and crustal density variations reflecting structural and crustal domain boundaries on a regional scale. The IODP drilling results offer critical local constraints on basement nature and on the timing of rifting and breakup.
3. 1) Dataset and mapping methodology The SE China margin is covered by a dense grid of geophysical data from which we use two previously published reflection seismic profiles  (lines 1555 and 1530 Figs. 4-5, acquisition  parameters can be found in  Gao et al. , 2015). These profiles are located near to IODP drilling Sites  (Figs. 2, 3, 4 and 5). For the NW Palawan margin, we use the reflection seismic profile (BGR08-109 Fig. 6 , 7) published by  Franke et al. (2011). In addition, we include in our analysis of the area: 1) a range of published seismic dataset acquired by both academic and industrial surveys  (Cameselle et al. , 2017; Lei et al. , 2019; Yang et al. , 2018)  ; 2) a compilation of structural maps  (Cullen et al. , 2010; Pubellier et al. , 2017)  ; 3) different borehole data  (Larsen et al.
Acknowledgments This research used data provided by the  International Ocean Discovery Program (IODP). We thanks the participants to IODP Expedition  367-368  as well as the captains and crew of the Joides Resolution. We acknowledge  IODP France  for post cruise support. Funding for this research and access to industrial seismic and borehole data were provided by  Total SA R&D (J. N. Ferry). We are grateful to the  Editor Tiago Alves. Chao Lei ,  William Bosworth  and two anonymous reviewers are thanked for their helpful suggestions and comments.
Methods The study design was to deploy a 4 m (metal) beam trawl  (Lewis 2010 ) and Brenke sled  (Brenke 2005)  to collect samples on soft sediment substrata at the target seafloor depths of 2500 and 4000 m at every 1. 5 degrees of latitude along the western boundary of the Tasman Sea from 42°to 23°S, traversing seven Australian Marine Parks (AMPs) (Fig. 1 ). In addition, a few beam trawl samples were collected at 1000 m and a few box-corer samples at southern sites (until technical difficulties precluded its further use). A single deep-towed video transect (1500-2500 m) was completed within each Marine Park. Each site was mapped (bathymetry and backscatter) prior to deployment using a Kongsberg EM 122 multibeam sonar. Environmental data (temperature, salinity, pressure) were collected by a Sea-Bird SBE37SM sensor mounted on the Brenke Sled.
Discussion The IN2017_V03 expedition of Australia's premier research vessel Investigator was the first to systematically sample lower bathyal to abyssal habitats (2000-5000 m) in Australia's Exclusive Economic Zone. It traversed seven newly declared marine parks in Commonwealth waters off the eastern margin of Australia from 15th May to 16th June 2017, including Freycinet, Flinders, East Gippsland, Jervis, Hunter, Central Eastern and Coral Sea Marine Parks. The voyage mapped much of the deeper water habitats of these parks using high resolution multi-beam data and systematically sampled the benthic habitats, principally using video transects, small beam trawls and epibenthic sleds. Importantly, the survey traversed both areas of high primary productivity at temperate latitudes near Tasmania and more oligotrophic areas of the northern Tasman Sea.
Acknowledgements The authors wish to thank the  CSIRO Marine National Facility (MNF)  for its support in the form of sea time on RV Investigator, support personnel, scientific equipment and data management. All data and samples acquired on the voyage are made publicly available in accordance with MNF Policy. We also thank all the scientific staff and crew who participated in voyage IN2017_V03; participating taxonomists who chose not to join the list of authors,  Lisa Goudie  (sponges),  Ella Lejune  (sponges,  Queensland Museum ) and  Candice Untiedt  (octocorals,  University of Tasmania ); and voyage participants,  Jérôme Mallefet  ( Université Catholique de Louvain ) and  Robert Zugaro  (MV), who contributed photos.
Usually, a network of HFR systems is established in response to specific scientific questions or operational needs. One of the most important criteria in the planning phase of a HFR network is the selection of the desired spatial and temporal resolution, along with maximum range and spatial coverage, that optimize ocean surface current sampling. These parameters will determine the operating frequency, type of system to be used, number of systems and their relative location. Other practical aspects need to be considered, such as available space, infrastructure (power supply, communication, access roads), noise and interference sources (power lines, industry, nearby antennas, metal fences). All these aspects are discussed in the section "New Deployment Requirements and Planning". A number of factors affect HFR performances during setup and operation with impact on data accuracy or with an interruption of data flow.
Data Acquisition In most commercial HFRs the control of the electronics and data acquisition are performed by x86-based computers ranging from consumer PCs to entry level servers and running Mac OS X or Linux operating systems. They are typically provided in bundle with the HF radar system and already pre-configured with all the needed control and processing software. They only need to be configured for a few parameters such as network settings, site-specific information, processing options. As the lifetime of the computer is typically much shorter than the electronics of the HF radar system, care should be taken with respect to compatibility between the manufacturer's software and newer operating systems. A redundant external data storage system is recommended on-site as a backup for the data acquired and saved on the computer's disk.
, 2008). This is particularly important for compact DF antenna like SeaSonde, where the electromagnetic environment can affect phase and gain more significantly than in case of large antenna array. For BF configurations, the analytical antenna array response function or antenna manifold is used. It is computed by solving the electromagnetic equations for the waves propagating to the array using the precise positions (less than 0. 2 m accuracy) of each antenna element. Phase and gain calibration is required and is performed internally. Furthermore, dedicated software for the BF systems can compensate variations of the antenna characteristic caused by environmental or technical conditions  (Helzel and Kniephoff, 2010). For WERA systems, all antenna parameters are automatically monitored, at least once per hour and a warning is automatically generated if any parameter reaches a critical value, which might cause the automatic calibration procedure to fail.
First Order Limits Detection When setting up a new HF radar system, operators are requested to insert specific configuration parameters such as the identification of the system, the geographical information, the operating frequency and bandwidth, the processing options. One important step in HF radars signal processing chain is the detection, inside the power spectra maps, of the regions containing the first order bragg peaks, i. e. , the portion of the spectra containing information on surface currents. Such detection, better known as first order limits (FOLs) determination, is usually performed by the HF radar acquisition and processing software with automatic algorithms that look for the Doppler peaks at one channel (usually the omnidirectional vertical loop for SeaSonde systems) and must be tuned by the operator during the initial period of acquisition through the selection of some parameters and visual inspection of the results.