dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations and Challenges PMF has been widely applied to apportion the sources of PM based on the offline chemical analysis of filter samples. Such datasets generally include major chemical species, such as OC, EC, water-soluble inorganics, and metals. However, many of these species are not source specific and/or of secondary origins, making it difficult to directly link PMF factors with the different OA sources and/or (trans-)formation processes. In such cases, SOC is commonly calculated as the sum of OC loadings in sulfate-and nitrate-rich PMF factors or individually  [93, 205, 302, 303]. Few studies also reported the use of species such as WSOC and humic-like substances (HuLiS) to characterize SOC  [174]. By comparison, molecular organic markers (tracers) are mostly source-class specific and may provide a more definitive link between factors and source classes.
In addition, filter samplings rely on measurements performed over several hours to days, making it difficult to capture the fast-atmospheric chemical processes related to OA. By comparison, online aerosol chemical characterization techniques, such as AMS, are faster, less labor-intensive and allow the quantitatively determination of non-refractory PM with high time resolution  [34, [307] [308] [309] [310] [311]. The cost and complex maintenance requirements of the AMS make its deployment barely practicable for long-term monitoring. Consequently, most available datasets are often limited to a few weeks of measurements. This hinders the determination of the regional and seasonal aerosol characteristics and the identification of changes in the pollution trends representing a significant limitation to validate atmospheric chemistry model validation and for the evaluation of air quality policies.
The lack of emission profiles for uncommon and thus un-apportioned sources inducing an overestimation of SOC, is the principal limitation of this methodology. This is especially the case when the sampling site is strongly affected by local emission sources. Thus, CMB is probably one of the most difficult approaches to apply, as source profiles for SOC do not exist and this may lead to an over/underestimation depending on the conditions. Therefore, CMB is not the first recommended approach. The current development of emission profile databases, such as SPECIATE or SPECIEUROPE, is of great value to get the appropriate input to the CMB model for each case study and can clearly improve the evaluation of SOC using such approaches  [357, 358]. In combination with such methodologies, the use of radiocarbon measurements ( 14 C) may provide detailed information on the SOC origins and can be helpful to improve the understanding of the atmospheric involved processes.
Nevertheless, the main limitations are related to the analytical costs and the amount of material required. Thus, this methodology is difficult to implement routinely. Such a method should be applied only on short-term campaign datasets in combination with other approaches (EC tracer or CMB) to refine and improve the SOC estimates. Similarly, PMF-AMS/ACSM provides a good estimation of SOC, together with additional information about the SOA oxidation state and/or the volatility range. Detailed analyses can highlight additional sub-SOA fractions such as IEPOX-SOA or marine SOA  [298, [317] [318] [319] [320]. More sub-SOA factors are then expected but they are difficult to be resolved due to the non-specific nature of the obtained mass fragments. Only a detailed chemical characterization at the molecular level and the use of key species, i.
However, these instruments are still costly and only appropriate for short term studies. In parallel, like in the case of 14 C measurements, several authors have reported the combination of different datasets from several measurement systems to refine the source apportionment of SOA using PMF. Slowik et al. [372]  were the first to combine AMS and PTR-MS data measurements, highlighting the capability of PMF to resolve more SOA (OOA) factors and improving the interpretations of their sources and photochemical processes  [333, 372]. This kind of approach has been explored in other studies, with the combination of AMS or ACSM data with other measurements, such as ambient and thermally denuded OA spectra (TD-PMF-AMS)  [373]  or with single particle mass spectrometry  [374] , by merging high resolution mass spectra of organic and inorganic aerosols from AMS measurements  [375, 376]  or combining offline AMS data and organic markers or 14 C measurements  [145, 377].
This article proposes to use the Köppen classification to evidence climate change in Brazil. To do so, the average monthly temperature and precipitation data were computed for a set of 208 stations, representative of the climatic diversity of Brazil for the period from 1964 to 2015. The originality of our approach is not only to look at the shift between the averages of two references periods but also to observe how each year can be classified using Köppen's classification; thus, we defined "annual climate types" (ACT) and established the frequency of ACTs for each station and each period of  26 years: 1964-1989 and 1990-2015. The statistical and cartographic treatment of this information makes it possible to define the limits of the climates of Brazil and look at its evolution showing the diversity of regional situations in Brazil.
| Database used For this study, monthly average temperature and precipitation data were used for all years covering the period from 1964 to 2015, that is, 52 full years. All of the data used were acquired from the database of the Banco de Dados Meteorológicos para Ensino e Pesquisa (BMDP) of the Instituto Nacional de Meteorologia (INMET, 2017) of Brazil, available online. Missing data were obtained (also online) from the Hydrology Department (Hidroweb) of Agência Nacional de Aguas (ANA, 2017) in Brazil: these data mainly concern precipitation and allowed to fill most of the gaps using neighbouring stations of the same municipality. Nearly 350 series were collected but several series had to be eliminated because of too large gaps or major discontinuities detected following statistical homogeneity tests of series breaks (see details in  Delahaye et al.
, 2017). The database thus constituted represents nearly 250,000 monthly data, making it possible to establish nearly 10,500 ACT for the entire Brazilian territory. On the other hand, the large number of gaps before 1964 (especially in the north) prevents the comparison of two periods of 30 years. Also, for the 208 stations representative of Brazil's climatic diversity, we compared the frequencies of the ACT between the two sub-periods  1964-1989 and 1990-2015 , that is, 26 years each. Over these two periods, the Köppen classification has been applied to the averages as well as to each individual year (ACT).
ACKNOWLEDGEMENTS This work was supported by the  French National Agency for Research (ANR)  which funded the  DURAMAZ2  project (grant agreement  ANR-11-BSH1-0003 ), and the  European Union , which funded the  H2020-MSCA-RISE-2015   ODYS-SEA  project (Project Reference: 691053). V. Dubreuil also acknowledges support from the  program of the Franco-Brazilian Chairs in the State of São Paulo (UNESP ,  Presidente Prudente ,  2014 ).
Note that the spectra for the Site Z data were computed using the continuous ~4-year records available from 2013-2017. Spectra were determined via the Welch's averaged periodogram method using a 2-year window allowing 1 year of overlap; the thin lines in spectra indicate 67% confidence limits. Topography in the top panel (gray shading with 500 m intervals; every 2,000 m contour shown as black contour) is from  Smith and Sandwell (1997).
Those studies therefore include their "residual barotropic" term inextricably intertwined with their geostrophic thermal-wind-based density gradient term (herein called "relative"). As is the nature of residual calculations, the geostrophic term in those previous studies also accumulates any errors in the Ekman and boundary transports, and any errors resulting from data coverage limitations (e. g. , below 850 m for the XBT data). As such, it is difficult to diagnose the reason for the disparity between annual seasonality in the prior studies versus semiannual in the relative term estimated here. Hopefully, longer records and analyses using numerical models will aid in diagnosing this apparent contradiction. Because the present study does not use a residual method, and obtains the reference (barotropic) velocity variability directly from data (bottom pressure gradients), it is possible for the first time to directly estimate the seasonal cycle of this term from data.
Acknowledgments The authors would like to thank the ship captains and crews who have supported our research cruises. Thanks also to the support/technical teams in Miami,  São Paulo ,  Buenos Aires ,  Cape Town , and Brest who have helped collect and process the data presented herein. The U. S. PIES observations and the participation of C. M. , R. P. , S. D. , and S. G. were supported via the  NOAA Climate Program Office-Ocean Observing and Monitoring Division  ( FundRef 100007298 ) under the  Southwest Atlantic Meridional Overturning Circulation ("SAM") project. S. G. , M. L. H. , and M. K. were supported in part under the auspices of the  Cooperative Institute for Marine and Atmospheric Studies (CIMAS) , a cooperative institute of the  University of Miami  and  NOAA , cooperative agreement  NA10OAR4320143 , and under a grant from the  NOAA   Climate Variability Program  ( GC16-212 ).
R. P. acknowledges additional support from  NOAA  (grant  NA13OAR4310131 ) and  NASA  (grant  NNX14AH60G ). C. M. , R. P. , S. D. ,  S. G, M. L. H. , and M. K. also acknowledge additional support from the  NOAA Atlantic Oceanographic and Meteorological Laboratory. S. S. and T. T. acknowledge support from the  11-ANR-56-004   SAMOC  research project. S. S. also received support from the  European Union   Horizon 2020 research and innovation programme  under grant agreement  633211  (AtlantOS). E. C. and O. S. acknowledge  CNPq  for a research fellowship (grant  302018/2014-0 ) and  FAPESP  for support through the  SAMOC-Br  project (grants  2011/50552-4  and  2017/09659-6 ). I. A. , T. L. , and MvdB acknowledge support from the  DST-NRF-SANAP program  and the  South African DEA. Additional support for A.
To expand knowledge of MMPs in coral reef habitats we investigated the biogeography and occurrence of these microbes in the Paracel Islands (South China Sea). We used microscopy and micromanipulation combined with whole genome amplification (WGA) to study the morphological characteristics and taxonomic diversity of MMPs from coral reef ecosystems of the Paracel Islands. Additionally, we discussed about the potential relationship between the physical and chemical characteristics and occurrence of MMPs. These results provide newly data for a better understanding of prevalence and abundance of MMPs in coral reef systems, may imply the cosmopolitism distribution of MMPs in these distinctive systems, and reveal the role of MMPs in coral reef habitats to some extent.
, 2008) , which was performed using a Mastercycler (Eppendorf, German). The resulting PCR products were cloned into the pMD18-T vector (Takara, Dalian, China) and transformed into competent E. coli TOP10 cells. The clones were randomly selected for sequencing, which was carried out by Nanjing Genscript Biotechnology (Nanjing, China)  (Teng et al. , 2017). The 16S rRNA gene sequences of MMPs obtained in this study were analyzed using the BLAST search program 1. All the sequences were aligned using CLUSTAL W multiple alignment software, and sequence identities were calculated using the BIOEDIT software. A phylogenetic tree was constructed using the neighbor-joining method in MEGA 6. 0, and bootstrap values were calculated from 1000 replicates  (Teng et al. , 2017). The sequences were submitted in the GenBank database under accession numbers KY21895-KY21900.
The 5 species of s-MMPs were named as the uncultured Deltaproteobacteria MMP clones PI7B-6 (KY21895; 16 sequences), PI7B-11 (KY21896; 1 sequence), PI7B-17 (KY21897; 1 sequence), PI7B-31 (KY21898; 15 sequences), and PI7B-40 (KY21899; 3 sequences), and the e-MMP species was designated as uncultured Deltaproteobacteria MMP clone PI3B-7 (KY21900). Based on a BLAST search of the GenBank database, the uncultured Deltaproteobacteria clone PI7B-6 was most closely related (98. 9% sequence identity) to the uncultured Deltaproteobacteria MMP clone mmp2_19 (DQ630701) from the Sippewissett salt marsh in Falmouth (United States)  (Simmons and Edwards, 2007) , and to PI7B-11 (96% sequence identity). Candidatus Magnetomorum sp. 1-7 (KF498702), from Lake Yuehu in Rongcheng Bay of China  (Zhang et al. , 2014) , showed the highest level of identity with the uncultured Deltaproteobacteria MMP clones PI7B-17 (97% sequence identity), PI7B-31 (97.
The contribution of deep convection to the amount of water vapour and ice in the tropical tropopause layer (TTL) from the tropical upper troposphere (UT; around 146 hPa) to the tropopause level (TL; around 100 hPa) is investigated. Ice water content (IWC) and water vapour (WV) measured in the UT and the TL by the Microwave Limb Sounder (MLS; Version 4. 2) are compared to the precipitation (Prec) measured by the Tropical Rainfall Measurement Mission (TRMM; Version 007). The two datasets, gridded within 2 • × 2 • horizontal bins, have been analysed during the austral convective season, December, January, and February (DJF), from 2004 to 2017. MLS observations are performed at 01:30 and 13:30 local solar time, whilst the Prec dataset is constructed with a time resolution of 1 h. The new contribution of this study is to provide a much more detailed picture of the diurnal variation of ice than is provided by the very limited (two per day) MLS observations.
TRMM The TRMM satellite was launched in November 1997 (https: //trmm. gsfc. nasa. gov/publications_dir/publications. html, last access: April 2019). The TRMM carried five instruments: a 3-sensor rainfall suite, the precipitation radar, the TRMM Microwave Imager, the Visible and Infrared Scanner, and two related instruments, the Lightning Imaging Sensor and the Clouds and the Earth's Radiant Energy System. In our study, we use Algorithm 3B42 which produces high-quality infrared precipitation and root-mean-square precipitation-error estimation from the TRMM Version 007. The rainfall level 3 measurements (mm h -1 ) are vertically integrated precipitation (Prec). TRMM Prec data are provided in the tropics within a 0. 25 • × 0. 25 • horizontal resolution extending from 50 • S to 50 • N. TRMM datasets used in our study are in coincidence with the MLS period of measurements from 2004 to 2017.
To investigate the vertical distribution and the diurnal cycles of water species in the TL, we have defined, from results presented in Figs. 1 2 3 , seven tropical convective zones, shown in Fig. 4 : South America (SouthAm; 0 • N-30 • S), South Africa (SouthAfr; 0 • N- 30 • S), Pacific Ocean (PacOc; 0 • N-30 • S; 180 • W-150 • W), Indian Ocean (IndOc; 0 • N-30 • S; 60 • E-90 • E), MariCont (10 • N-15 • S; 90 • E-160 • E), MariCont land (MariCont-L), and MariCont ocean (MariCont-O). Land and ocean over the study zones have been separated using the Solar Radiation Data (SoDa; http://www. soda-pro. com/web-services/ altitude/srtm-in-a-tile, last access: April 2019), providing a TIFF image with values from the Shuttle Radar Topography Mission (SRTM) digital elevation model. Each zone is defined at a horizontal resolution of 2 • × 2 •.
Acknowledgements. Our study takes place within the Turbulence Effects on Active Species in Atmosphere (TEASAO; http://www. legos. obs-mip. fr/projets/axes-transverses-processus/teasao,last access:  30 April 2019 ) project. We thank the  National Center for Scientific Research (CNRS)  and the  Excellence Initiative (Idex) of Toulouse, France  ( TEASAO  project,  Peter Haynes , Attractivity Chair), for funding this study. We would like to thank the teams that have provided the MLS data (https://disc. gsfc. nasa. gov/datasets/ML2IWC_V004,last access: 1 January 2018), the TRMM data (https://pmm. nasa. gov/data-access/downloads/trmm, last access: 1 January 2019), and the SMILES data (https://mls. jpl. nasa. gov/data/smiles,last access: 1 December 2018). Review statement. This paper was edited by  Rolf Müller  and reviewed by three anonymous referees.
Data availability. The observational datasets are available from the following websites: https://trmm. gsfc. nasa. gov/ publications_dir/publications. html (last access: 1 January 2019, Prec from TRMM), https://disc. gsfc. nasa. gov/datasets?age= 1&keywords=ML2IWC_004 (last access: 1 January 2018, IWC from MLS), ftp://mls. jpl. nasa. gov/pub/outgoing/smiles (last access: 1 December 2018, IWC and pIWC from SMILES), and https://www. esrl. noaa. gov/psd/cgi-bin/db_search/DBSearch. pl? Variable=Tropopause+Pressure&group=0&submit=Search (last access: 1 January 2019, tropopause pressure from NCEP).
ACKNOWLEDGEMENTS G. S. acknowledges the  Postdoctoral Scholar Program  at the  Woods Hole Oceanographic Institution (WHOI)  with funding provided by the  NOSAMS facility  ( OCE-1239667 ). He thanks the entire NOSAMS crew with special thanks to  Ann McNichol  and  William Jenkins , as well as  Mary Lardie ,  Liz Klein  and  Al Gagnon  for assistance in the lab. L. G. thanks  Florin Filip  and  Stefan Constantinescu  for help in collecting Danube delta cores as well as to WHOI for continuous support to his work in the Danube delta and the  Black Sea. We thank  Gilles Lericolais from Ifremer  for providing access to core MD04-2774. This work is a contribution to the  ASSEMBLAGE  project funded by the  European Commission  ( EVK3-CT-2002-00090 ).
Acknowledgments This is a contribution to  ERC  project  ACCLIMATE ; the research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  339108. The authors thank  J. Duprat  for planktonic foraminifer census counts;  M. Roy-Barman  for expert advice on radioisotope measurements on LSCE MC-ICP-MS; and  S. Moreira  for his help with R programming. S. Pichat was supported by a research sabbatical (CRCT) from the  ENS de Lyon. S. Pichat is thankful to  S. J. G. Galer  for allowing him to use the  Max Planck Institute Q-ICP-MS  and to  K. P. Jochum  for his help with standard reference material. This is LSCE contribution 6412.
After eliminating unwanted signals by analyzing the dynamic spectrum of the signal recorded in time, we propose to use an automated radio burst detection based on the Constant False Alarm Rate (CFAR) method cited above. Our contributions can be summarized as follows (see Fig. 2 ): -Dedicated denoising and interference mitigation techniques for the enhancement of the signals of interest and consequently the improvement of the detection performance. -Development of a new detection method based on a CFAR-like technique. -Creation of a labeled database using the recorded NDA data for performance assessments and comparisons of different detection methods. -A performance analysis of the proposed method on realworld data. As a byproduct, we have developed an ergonomic software interface for event detection and performance evaluation, which can be used in particular to further enrich our database for future investigations.
Helens in 2004-2006. The set of instrumental seismic data during the latter is thus unique. Only a handful of other spine extrusion episodes, generally smaller or more inclined, are known to have occurred at Mt. Unzen (dacites, 1994 , 2007 , Japan), Santiaguito (andesites, 1958 , 1967 , Guatemala), Bezymianny (andesites, 1997 , Kamchatka), and Chaiten (rhyolites, 2009, Chile). The most recent episodes at Unzen and Chaiten have been less completely studied seismically than Mount St. Helens. Our paper is thus primarily based on the Mount St. Helens dataset during the large spine extrusions of late 2004 and 2005. Our goal here is not to account for all volcanic seismicity in general, but only for seismic events that are coeval with spine extrusion and occur at stationary depths. We show how the unique earthquake fault plane solutions dataset of  Moran et al.
Earthquake mechanisms during Mount St. Helens spine extrusions To date, the most quantitative dataset of volcanic seismicity during silicic-spine extrusion remains that of the 2004-2005 eruptions of Mount St. Helens. Based on waveform inversions results,  Waite et al. (2008)  and  Matoza et al. (2015)  prefer to explain a number of volcanic earthquake mechanisms by singleforce sources, corresponding to volumetric oscillation of subhorizontal cracks. Harrington and Brodsky (2007)  and  Moran et al. (2008) , on the other hand, favor stick-slip (shear dislocation) to explain the corner frequencies and first motion polarities for most such earthquakes. Recent field and structural observations  (Pallister et al. , 2013)  are also more consistent with stick-slip models, although they cannot rule out resonance of steam-filled fractures.
1+1. 5•M w ) of 3. 0 results in a size of ∼300 m. We use a slightly smaller M w than the cutoff coda duration magnitude (M d ), which is often grossly overestimated  (Qamar et al. , 2008). As is common in most volcanic seismicity studies, not all the 216 focal mechanisms in  Moran et al. (2008)  are simple doublecouples, likely due to source complexity, and many suffer from sparse arrivals at the center of the focal sphere (Supplementary Figs. S2  and S5 ). Nevertheless, most of the solutions are of very good quality (more than nine clear first arrivals, compared to  Roman et al. (2006)  "high-quality" criteria of six clear first motion polarities). We thus use them at face value to clarify deformation mechanisms below the crater. The faulting patterns that emerge from this exceptional dataset are shown in Fig.
He- lens (Supplementary Fig. S7 ). It is thus consistent with the regional extension and minimum horizontal stress directions (N 95-104 • E) in northern Cascadia (e. g. ,  Zoback and Zoback, 1980). The less frequent normal fault events (Fig. 4c  and Supplementary Fig. S6c ) may also be related to shear along the spine walls, but at various levels of the conduit. Magma rise on the inwardleaning sides of the neck would generate normal faulting (Fig. 5a ). So would, at very shallow depth, shear on the downside of the inclined whaleback spines (Fig. 5e ). At greater depths, lateral inflation of the magma reservoir due to increased magma pressure might trigger normal-fault collapse of the host rock in the reservoir roof (Fig. 5c ), though the available 2004-2005 Mount St. Helens dataset is too shallow to test such a mechanism.
Acknowledgments The research was entirely funded by the  Earth Observatory of Singapore (EOS, NTU, Singapore ;  M4430233. B50. 706022 ). Professor Paul Tapponnier is grateful to  Dr. Chris Newhall  for the initial scientific exchanges that inspired the development of this project. Dr. Liqing Jiao  started this project for her Ph. D. -qualifying examination under the supervision of Associate Professor Fidel Costa. We thank  Dr. Corentin Caudron  for fruitful discussion about volcanic seismicity. We thank two anonymous reviewers for constructive comments. We also thank  Yves Descatoire  for his help in drafting Fig. 3. This work is Earth Observatory of Singapore contribution No. 207. This research is supported by the  National Research Foundation Singapore  and the  Ministry of Education -Singapore  under the  Research Centres of Excellence initiative.