dataesr/scientific-paragraphs-categorization huggingface dataset:
1, respectively. Figure  6  shows the matchup comparison for the aerosol optical thickness at 865 nm retrieved by OLCIPAR applied to the MERIS data with AERONET data. A significant correlation is observed (R 2 >0. 81) with a RMSE of about 0. 1 and MAPE of 25% which is consistent with other studies  [43] [44] [45] [46]. Aerosol load is satisfactorily retrieved by OLCIPAR thus leading to confidence for the subsequent derivation of daily PAR. Note that the value of the slope of the regression line suggests a slight underestimation of the highest values of AOT. Fig. 6. Matchup between the aerosol optical thickness (AOT) retrieved at 865 nm by OLCIPAR from MERIS data and AOT AERONET level 2 products at 870 nm for the site of Capo Verde (Atlantic Ocean). The number of valid pixels used for the averaging is displayed in color.
Daily PAR reduction over the subtropical Atlantic waters The OLCIPAR algorithm was applied to the level 1 MERIS archive over the northern part of the subtropical Atlantic Ocean for April 2007. Level 3 binning was then applied to generate the timely averaged values of the aerosol optical thickness and daily PAR. The monthly means of the AOT retrieval is compared to the monthly level 3 MODIS Aqua product generated from the Collection-6 Aqua product  [47]  (Fig. 8 ). A qualitative agreement between the two data sets is observed with AOT smaller than 0. 2 in the central part of the zone of interest (between 10°N and 30°N) and AOT reaching 0. 5-0. 6 in the dust aerosol trajectory at the bottom of the image (below ~13°N). In addition to the validation obtained against AERONET data, such a qualitative agreement with the MODIS reference products demonstrates that the OLCIPAR algorithm is reliable for examination of the role of aerosols on the daily PAR modulation at the monthly scale.
9(c ). OLCIPAR provides higher PAR values than the NASA product where aerosol load is low (i. e. , between 12°N and 25°N). Conversely, a tremendous PAR reduction is observed with OLCIPAR for regions with significantly higher AOT (> 0. 4) with relative differences reaching 10%. In parallel, we tried to evaluate the performance of the PAR product derived from OLCIPAR algorithm against the surface shortwave irradiance (SSI) data that is available from the geostationary sensor SEVIRI distributed by EUMETSAT. It should be highlighted that the proportion of photosynthetically active radiation from SSI is variable with intensity of the incident irradiance from 41% to 58%  [48] , thus resulting to significant uncertainty in the PAR calculation from SSI. The analysis of the ratio of PAR over SSI data taken from the NASA SeaWiFS distribution (both parameters in Einstein m-2 day-1) that we have conducted reveals that there is no trivial conversion of SSI into PAR at global scale.
The use of look up tables within the algorithm is also efficient to ensure a routine operational processing of a large amount of remote sensing data. The performance and validation of OLCIPAR algorithm was evaluated using comparisons between a time series of MERIS satellite data for which OLCIPAR was applied and in situ measurements acquired by a permanent optical mooring (BOUSSOLE buoy) in the Mediterranean Sea. The discrepancies observed between the satellite derived daily PAR and ground-based measurements is 10%. The results obtained by OLCIPAR algorithm for clear sky conditions were compared with those obtained by NASA algorithm. The results of the comparisons revealed that the daily PAR retrieved by the NASA algorithm, which is based on aerosol climatology, might overestimate the daily PAR by up to 20%.
Acknowledgments We are grateful to the  Institut Universitaire de France (IUF)  for other financial support. We would like to thank the  European Space Agency  for providing the MERIS satellite data. We wish to thank  Robert Frouin  and  NASA  for making available the Frouin's algorithm used in this study to process the MERIS data. Didier Tanré  is acknowledged for his effort in establishing and maintaining the Capo Verde AERONET site. We would like to thank  David Antoine  ( Laboratoire d'Océanographie de Villefranche ) for making publicly available the data acquired through the  BOUSSOLE  project (funded by  CNES ,  CNRS-INSU ,  ESA ,  NASA ,  Université Pierre et Marie Curie ). The authors are grateful to  Bernard Gentili  and  Enzo Vellucci  from the  Laboratoire Océanographie Villefranche (France)  for their help in the processing of BOUSSOLE data.
Here σ = (ρc) * /(ρc) f where (ρc) * is the effective volumetric heat capacity of the porous medium, and a * the effective thermal diffusivity of the porous medium. Using polar coordinates (r, ϕ) and the associated local database (e r , e ϕ ), the velocity can be defined by: V = V r e r + V ϕ e ϕ ( 5 ) The vector e Z takes the following form: e z = cos (ϕ) e rsin(ϕ)e ϕ. With V r and V ϕ are the radial and tangential velocity component, respectively. Using stream function formalism, the velocity components can be expressed as: V r = 1 r ∂ψ ∂ϕ and V ϕ = - ∂ψ ∂r The continuity equation is identically satisfied, by taking the rotational of Darcy's equation. Studying the steady state we have to solve the following system: ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ∇ 2 ψ = Ra sin (ϕ) ∂ ∂r (T + sp C) + cos (ϕ) r ∂ ∂ϕ (T + sp C ) ∇ 2 T = 1 r ∂ψ ∂ϕ ∂T ∂r - ∂ψ ∂r ∂T ∂ϕ ∇ 2 C -∇ 2 T = Le r ∂ψ ∂ϕ ∂C ∂r - ∂ψ ∂r ∂C ∂ϕ ∇ 2.
Numerical results To highlight the developed analytical method, analytical results are compared against those obtained by solving the basic equations of the coupled model. It is undertaken using COMSOL Multiphysics code  [16]  which is a powerful environment for modeling and solving a variety of research and engineering problems based on the finite element method (FE). Moreover, this software is particularly adapted for the treatment of Multiphysical problems where several phenomena are simultaneously studied. Thus, the assessment of the incidence of heat and mass transfer on other phenomena such as the ingress of aggressive agents (chlorides, ions) and the ingress of pollutants (Volatile Organic Compounds) becomes possible. Also, different kind of meshes are easily generated which is more convenient for 3D studies.
Background. Neuropeptides are important regulators of physiological processes and behavior. Although they tend to be generally well conserved, recent results using trancriptome sequencing on decapod crustaceans give the impression of significant differences between species, raising the question whether such differences are real or artefacts. Methods. The BLAST+ program was used to find short reads coding neuropeptides and neurohormons in publicly available short read archives. Such reads were then used to find similar reads in the same archives, and the DNA assembly program Trinity was employed to construct contigs encoding the neuropeptide precursors as completely as possible. Results. The seven decapod species analyzed in this fashion, the crabs Eriocheir sinensis, Carcinus maenas and Scylla paramamosain, the shrimp Litopenaeus vannamei, the lobster Homarus americanus, the fresh water prawn Macrobrachium rosenbergii and the crayfish Procambarus clarkii had remarkably similar neuropeptidomes.
As a consequence we now have fairly long lists of neuropeptides for several decapods. These include Sagmariasus verrauxi  (Ventura et al. , 2014) , M. rosenbergii  (Suwansa-Ard et al. , 2015) , Procambarus clarkii  (Veenstra, 2015) , Scylla paramamosain  (Bao et al. , 2015)  and Homarus americanus  (Christie et al. , 2015) , while for other decapods significant amounts of data are available to analyze their neuropeptidomes. This is for example the case for Carcinus maenas, Litopenaeus vannamei and Eriocheir sinensis  (Li et al. , 2012; Ghaffari et al. , 2014; Verbruggen et al. , 2015; Xu et al. , 2015). Some of the ESTs (expressed sequence tags) present in the publicly available databases have been summarized by Christie and his collaborators  (Ma et al. , 2009; Ma et al. , 2010; Christie, 2014; Christie, 2016; Christie & Chi, 2015).
DNA sequences The following short read archives (SRAs) were downloaded from NCBI using the prefetch command from the SRA Toolkit (http://www. ncbi. nlm. nih. gov/books/NBK158900/): for C. maenas: SRR1564428, SRR1572181, SRR1586326, SRR1589617, SRR1612556, reads using the blastn command. The latter were used as input for the Trinity program  (Grabherr et al. , 2011)  and resulting transcripts were recursively used as input until either the transcript stopped increasing in length or it was judged to be complete based on the location of in-frame stop codons and/or a signal peptide at the N-terminal of the protein predicted from the transcript. Calculations were run on a desktop computer with a AMD FX TM -6100 six-core processor and 15. 4 Gb of memory under Ubuntu Linux. This method is very efficient for the extraction of transcripts from single copy genes.
Residues conserved between the different species are in blue. one usually finds in neuropeptide precursors and hence its status as a neuropeptide is unclear. Such proteins are also found in the other decapods. Although it was not possible for Trinity to produce a complete contig for S. paramamosain, a similar sequence is present in the databanks for Scylla olivacea. I was unable to find similar proteins in insects, but an orthologous protein was detected in the SRA from Euphausia crystallorophias. The latter sequence shows that the only conserved part is the same as in the decapods (Fig. 15 ). This peptide was called hyrg (pronounced hirg), for four of the conserved amino acids. Interestingly, the eyestalk seems to be the tissue where expression of hyrg is the highest (Table  S9 ), thus suggesting that it is likely a neurohormone.
With one exception, in all other instances where a transcript seems to be lacking it is either from a gene for which an alternative transcript was found (e. g. , in the case of the CNMamide and Neuropeptide F 1 genes), or the number of paralogs may differ between the various species (neuroparsin, CHH, MIH, PDH). The only exception is a typical MIH in H. americanus. In spite of using the MIH sequence of the closely related species H. gammarus  (Ollivaux et al. , 2006)  as a query in the BLAST program, no transcript was found in the H. americanus SRAs. Although MIH has been reported by mass spectrometry from the stomatogastric ganglion of H. americanus  (Ma et al. , 2008) , this peptide is not a typical MIH but has the structure of a CHH  (Chang, Prestwich & Bruce, 1990). This suggests that in H.
Neuropeptide evolution It thus appears that the neuropeptidome of decapods has been remarkably well conserved during evolution. Differences that are found between the insect and decapod neuropeptidomes are the loss or the gain of a neuropeptide. Although there possibly still remain arthropod neuropeptides to be discovered, it appears that the loss of neuropeptides in decapods is limited to a single neuropeptide, i. e. , allatotropin. Allatotropin is present in mollusks, annelids as well as chelicerates  (Veenstra, 2010a; Veenstra, 2011; Veenstra, 2016a)  and hence, it must have been present in the arthropod ancestor. Small peptides are sometimes hard to find using the BLAST program and allatotropin is no exception to this rule  (Veenstra, Rodriguez & Weaver, 2012). Nevertheless, as I was neither able to find even a single read correponding to its receptor, including in the very abundant number of transcriptome reads from H.
aureus strain HUK16 degrades HCH using metabolic pathways and enzymes not previously described in the literature, as suggested by previous studies  (7). Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession no. LSMV00000000. The version described in this paper is the first version, LSMV00000000. 1.
5 km in length, covering the summit craters which produced all recent tephra plumes. The backscattered power, related to the amount of particles crossing the beam, and particle along-beam velocities are recorded every 0. 23 s, providing a proxy for the tephra mass eruption rate. Radar raw data are transmitted in real-time to the volcano monitoring center of INGV-OE in Catania and are used to automatically release alerts at onset and end of eruptive events. Processed radar parameters are also made available from the VOLDORAD database online (http://voldorad. opgc. fr/). In addition to eruptive crater discrimination by range gating, relative variations of eruption intensity can be tracked, including through overcast weather when other optical or infrared methods may fail to provide information.
274 GHz, called VOLDORAD 2B, to monitor the tephra emissions of Etna's summit craters. Although the VOLDORAD 2B radar is owned by the OPGC, the radar data acquired on Etna are shared between INGV-OE and OPGC in the frame of a collaborative research agreement. As part of the same project, a dedicated micro-patch array antenna, well suited for longterm monitoring in harsh conditions in altitude, was also designed and built at the University of Calabria  (Boccia et al. , 2010)  for INGV-OE. In this paper we first describe the radar system, data acquisition and processing, and type of retrieved parameters and their integration within the existing monitoring system of INGV-OE. Furthermore, we document records of volcanic ash plumes detected by VOLDORAD 2B during the activity of Etna summit craters in 2010 and the first of the recent series of lava fountaining events, on 12 January 2011.
A technical description of the VOLDORAD 2 radar system is detailed in  Donnadieu (2012)  and references therein. In the following, only the main properties and modifications of the system proper to VOLDORAD 2B are described. The main characteristics and settings of VOLDORAD 2B at Etna (Fig. 2 ) are summarized in Table  1. A new acquisition system was developed by the OPGC using a Field-Programmable Gate Array chip and LabVIEW control programs in Windows XP environment to provide fast PC-radar communications, a friendly user interface for parameter settings and network communication facilities, while ensuring long-term data and material integrity. The system is controlled remotely through Ethernet connection and the acquisition window can be viewed remotely in real-time. Acquisition can be restarted remotely with modified settings, including three different levels of attenuation (10 dB increment set among five by hardware connections) that can be modified during acquisition.
Data format and transmission The data acquisition software allows the recording of the raw signal or the power spectra of each gate, or both. Only Doppler spectra are recorded at Etna, being directly computed from the raw time series (6 coherent integrations) and then averaged in the frequency domain (3 incoherent integrations). These settings (Table  1 ) turn out to be a good compromise between data storage and transfer rate (1 GB/day) at relatively high acquisition rate of 4. 4 s -1 (0. 228 s). The power spectral density, i. e. , power values of the 64 velocity classes, is recorded for each gate at each time step and stored in 1 min-long, time-labeled binary files. The clock of the acquisition PC, used to time-reference the radar data, is synchronized every hour to Coordinated Universal Time (UTC) time by the local server, allowing precise comparison with other timereferenced data recorded by the INGV-OE instrumental network.
Real-time products and radar database Upon reception at INGV-OE, the radar data are debiased and filtered with respect to the time and subsampled to get 5 spectra per minute. Following a method similar to  Valade et al. (2012) , a radar-derived detection index is computed from the radar-derived parameters in order to best identify tephra produced by the explosive activity. The temporal evolution of this index is displayed in real-time in the INGV-OE monitoring room and used to trigger automatic alert messages. The radar index is obtained as follows: (i) ground echoes in the Doppler spectra (around the zero velocity) were sampled at various moments and averaged, taking advantage of their remarkable stability through time; (ii) this average is then used to filter ground echoes out of Doppler spectra stacked over a large number of successive samples; (iii) the stacked filtered Doppler spectrum is integrated over the full velocity spectrum providing a power-based time series.
These filtered data are integrated in order to obtain the power echo of the signal for each gate. The values are also stored inside the multidisciplinary database of INGV-OE in order to allow a real-time consultation and visualization in the monitoring room. The resulting time series allows unambiguously detecting lava fountains feeding ash and lapilli plumes. A simple automatic system was implemented for early warning at the onset of a tephra emission from Etna's summit craters. A double threshold method is used to release automatic alert messages minimizing false alarms. Whenever the radar signal increases above the first threshold and keeps increasing for more than 5 min (second higher threshold), the system alerts the operators in the monitoring room with a sound and sends an e-mail to a predefined mailing list to notify of the start and the end of the explosive event.
An open database of radar products computed from the raw data is made available from the VOLDORAD service on the OPGC website: http://voldorad. opgc. fr/ (Donnadieu et al. , 2015). Upon a user's request, up to 9 parameters are automatically computed for each gate from the raw data files (e. g. ,  Dubosclard et al. (2004) ) and released into a table which can be downloaded.
Expected benefits The VOLDORAD 2B Doppler radar turns out to be a powerful allweather tool for detection and tracking of tephra emissions through high rate continuous sounding of juxtaposed atmospheric volumes, providing reflectivity and velocity data in the spatio-temporal domain. It allows us to both improve the monitoring of Etna and address quantitatively scientific issues on ash plume dynamics and eruptive behavior in the short-and mid-term. This is particularly valuable in the context of a rapidly evolving system, as occurring nowadays, during the fast reactivation of volcanic activity at Etna's summit craters, such as the formation and growth of the NSEC. Radar retrievals are also complementary with models of plume ascent and ash dispersal to improve hazard assessment and risk mitigation; it is expected, in the nearfuture, that estimates of mass fluxes in quasi real-time (e.
, 2010). In addition, sensitivity analysis and parametric studies on tephra dispersal models have also shown that if values of some input parameters are modified (e. g. , the total mass), the results of numerical models may differ greatly  (Connor and Connor, 2006; Scollo et al. , 2008a Scollo et al. , , 2008b;; Webley et al. , 2009). Consequently, their setting should be done with great care in order to make volcanic ash forecast as close as possible to the real dispersal. To reach this aim, inverse modeling and data assimilation represent the future challenge but they need automatic techniques able to combine numerical models with source terms obtained for example by remote sensing system such as VOLDORAD 2B. Another possible outcome is, importantly, the estimation of the mass eruption rate of tephra and its temporal variations, and the total mass of particles emitted.
Acknowledgments The  VOLDORAD 2B project was initially developed in the framework of the DPC-INGV  project " Sviluppo ed applicazione di tecniche di telerilevamento per il monitoraggio dei vulcani attivi italiani " funded in the [2000][2001][2002][2003][2004] agreement between the  Italian Department of Civil Protection  and  INGV  ( Progetti Sismologici e Vulcanologici di interesse per il Dipartimento della Protezione Civile ) and managed by  INGV-OE  (PI:  M. Coltelli ), and then the instrument setting on  La Montagnola  was funded through a research contract between OPGC and  INGV-OE  in the framework of the  FIRB  project " Sviluppo Nuove Tecnologie per la Protezione e Difesa del Territorio dai Rischi Naturali " funded by the  Italian Minister of University  and Research and managed by  INGV-OE  (PI:  M.
Coltelli ). VOLDORAD 2B radar measurements on Etna are carried out in the frame of a collaborative research agreement between the  Observatoire de Physique du Globe de Clermont-Ferrand (OPGC, Université Blaise Pascal, Clermont-Ferrand, France) , the  French CNRS , and the  Istituto Nazionale di Geofisica e Vulcanologia ,  Osservatorio Etneo , sezione di Catania (INGV-OE). Support was also obtained from the  TerMex-MISTRALS program  of the  French CNRS-INSU  (PI:  F. Donnadieu ), from the  MED-SUV  project funded by the  European Union Seventh Framework Programme  ( FP7)  under Grant agreement no. 308665  (PI G. Puglisi; Coord. UBP-LMV partner F. Donnadieu), and from the  European Science Foundation (ESF) , in the framework of the  Research Networking Programme MeMoVolc. Mathieu Gouhier ,  Sébastien Valade  helped in radar data processing during PhD fellowship (French M.
2. Comparisons are made with the LHCb prompt J/ψ production data  [63]  in both the forward (1. 5 < y J/ψ c. m. s. < 4. 0)  5  and backward (-5. 0 < y J/ψ c. m. s. < -2. 5) rapidity regions in Fig. 2a. Fig. 2b  shows a comparison with the double differential cross sections d 2 σ pPb /dP T dy of J/ψ production of LHCb. Similarly, comparisons with the ALICE data  [64]  and ATLAS data  [65]  are given in Fig. 2c  and Fig. 2d  respectively. We note that ALICE data do not exclude the contribution from b-hadron decays. In general, the agreement with the yields differential in P J/ψ T is satisfactory both at low P T and high P T. TeV: comparison between our results and the measurements of LHCb  [63] , ALICE  [66]  and ATLAS  [65]. parison of (a-d) the rapidity dependence obtained with our procedure with the measurements by LHCb  [67] , ALICE  [68]  and ATLAS  [69]  and (e) the transverse-momentum dependence as measured by ATLAS  [69].
In Fig. 3 , we have compared the LHCb  [63] , ALICE  [66]  and ATLAS data  [65]  with the J/ψ cross-section differential in y. It is interesting to notice that the results with the three nPDF show different uncertainties. In the forward region (low x 2 ), the result with EPS09NLO has the smallest uncertainty and tend to overshoot the LHCb data  [63]  (see Fig. 3a ). Such a discrepancy does not appear in Fig. 3b. One can also note that the EPS09LO uncertainty can be considered as the combination of both EPS09NLO and nCTEQ15 uncertainties in the forward region. In the backward region, owing to both the significant experimental and nPDF uncertainties, the three nPDFs are compatible with the data. At high P T (in Fig. 3c  for the ATLAS data  [65] ), although the central values of the experimental data are systematically higher than our theoretical bands, they remain compatible within one standard deviation.
There could indeed be an overestimation of the nPDF suppression in this region or an offset in the ATLAS data. As for the Υ(1S ), Fig. 4a  and 4b  show comparisons with the LHCb data. The agreement is better when the full LHCb range is considered as opposed to that when the LHCb acceptance is restricted to a range where equal positive and negative y can be accessed (Fig. 4b ). A good agreement is also obtained with the ALICE data (Fig. 4c ) in a similar rapidity domain. In the ATLAS acceptance, all three nPDF magnitudes correctly account for the yield differential in y and P T (Fig. 4d  and 4e ). Fig. 5  and Fig. 6  show comparisons for the D 0 case between our results for the 3 nPDFs and the LHCb and ALICE measurements. The agreement is overall good. The yields tend to lie on the upper half of the uncertainty band.