dataesr/scientific-paragraphs-categorization huggingface dataset:
Monosized water-in-oil droplets are generated at frequencies of about 1 kHz from two aqueous solutions flowing in parallel into a T-junction against an oil phase flow (Fig. 23 ). To make use of the fluorescence, two different fluorescent dyes are introduced in the solutions. Their lifetime should be different enough to separate their signals in the time domain. The fluorescence decay of the mixture takes on a biexponential form. In order to extract fluorescence lifetime, a timecorrelated single-photon counting (TCSPC) methodology  combined with confocal microscopy is carried out to record fluorescence data in the time domain. Femtosecond or picosecond pulse lasers operating at several MHz are used to provide single or two-photon excitation. The objective lens of the confocal microscope brings the laser light to a tight focus to probe volumes down to a few picoliters.
Acknowledgements This manuscript benefited from the discussions in the  IUCN International Task Force on Systemic Pesticides during its plenary meetings in Bath  (2011), Cambridge (2012), Padua (2012) and Louvain-la-Neuve (2013). Part of the work by authors  JvdS  and NSD has been funded by a gift by the  Triodos Foundation's Support Fund for Independent Research on Bee Decline and Systemic Pesticides. This Support Fund has been created from donations by  Adessium Foundation  (The Netherlands),  Act Beyond Trust (Japan) ,  Universiteit Utrecht (Netherlands) ,  Stichting Triodos Foundation  (The Netherlands),  Gesellschaft fu ¨r Schmetterlingsschutz (Germany) ,  M. A. O. C. Gravin  van  Bylandt Stichting  (The Netherlands),  Zukunft Stiftung Landwirtschaft (Germany) ,  Beekeepers Union ABTB (Netherlands), Study Association Storm (Student Association Environmental Sciences Utrecht University ) and citizens.
Acknowledgements We thank  T. Altinoluk ,  G. Beuf  and  K. Tywoniuk  for their valuable comments and useful discussions. The work of NA, HM, MM and CAS is supported by  European Research Council  grant  HotLHC ERC-2011-StG-279579 ; by  Ministerio de Ciencia e Innovación of Spain  under projects  FPA2008-01177 ,  FPA2009-06867-E  and  FPA2011-22776 ; by  Xunta de Galicia  ( Consellería de Educación  and  Consellería de Innovación e Industria -Programa Incite) ; by the  Spanish  Consolider-Ingenio 2010 Programme CPAN and by  FEDER. The work of YMT is supported by the  European Research Council  under the  Advanced Investigator Grant   ERC-AD-267258. NA, MM and YMT acknowledge financial support from  ECT * during the workshop h3QCD (high energy, high density and hot QCD) where parts of this work were completed.
Acknowledgments We acknowledge the support from a  DRDC   Applied Research Program  and a  DRDC Technology Investment Fund  and from  Délégation Générale pour l'Armement DGA  through grant  REI 2009 34 0025. We thank also the technical support from  Michèle Cardinal ,  Marcellin Jean  and  Alain Fernet  from  DRDC  and the participation of  Bruno Desruelle  from  DGA.
Analysis of L-and C-Band SAR Image Time Series Over a Sahelian Area P. -L. Frison, G. Mercier, G. Faye, E. Mougin, P. Hiernaux, C. Lardeux, and J. -P. Rudant Abstract-This letter presents an analysis of two synthetic aperture radar (SAR) images time series over a Sahelian area, which is located in the Gourma region, Mali. The first one is acquired at the C-band by the Environment Satellite-Advanced SAR sensor in wide swath (WS) mode and the second one at the L-band by the ALOS-Phased Array-type L-band SAR in fine, dual, and WS modes. A change detection method based on random projection and the hue-saturation-value transform appears to be very well appropriated to detect the temporal changes appearing on both data sets. The results illustrate the higher penetration depth of the L-band over such sandy soils.
In particular, the sand dunes, up to 10 m thick, oriented from west to east, appear with the C-band [see Fig. 1(b) ] in alternate dark and lighter rectilinear structures in the upper third of the image, whereas they are not visible with the L-band [see Fig. 1(c)]. The sensitivity of L-and C-bands to seasonal changes have been analyzed by applying the change detection algorithm presented in Section III to different time series. First, a special analysis has been made on the PALSAR data acquired at fine beam mode at HH polarization, as these 12 acquisitions form the most consistent time series of the whole PALSAR data set. Temporal color composite image acquisitions made at three different dates at HH polarization is given in Fig. 2(a). The corresponding acquisition area is the red square shown in Fig.
Lateral vibrations of a mass chain coupled by strings are considered in Ref. [11]. Every mass of the chain has nonlinear supports with impacts. The Kauderer-Rosenberg NNMs are used to obtain this system motions. The Kauderer-Rosenberg NNMs are investigated in the selfsustained two-degree-of-freedom system  [12] : € q 1 þ q 1 þ aq 3 1 þ b q 1 À q 2 ð ÞÀe 1 À q 2 1 À Á _ q 1 ¼ 0 € q 2 þ q 2 þ aq 3 2 þ b q 2 À q 1 ð ÞÀec 1 À q 2 2 À Á _ q 2 ¼ 0 where a, b, c are system parameters. Normal modes of selfsustained vibrations are analyzed in the configuration space. Periodic motions of forced viscoelastic nonlinear systems are considered in this paper, too. An attempt to create a method for construction the forced system NNMs from experimental data is undertaken in Ref. [13]. The harmonic balance method is used to calculate NNMs numerically.
We study the carbon monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua satellite. The focus of this study is to analyze CO variability and short-term trends separately for background CO and fresh CO emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm utilizes cloud clearing to treat cloud contaminations in the signals, and this increases the data coverage significantly to a yield of more than 50 % of the total measurements. We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask to identify AIRS clear sky scenes. We then carry out a science analysis using AIRS CO data individually for the clear and cloud-cleared scenes to identify any potential effects due to cloud clearing.
We also introduce a new technique to separate background and recently emitted CO observations, which aims to constrain emissions using only satellite CO data. We validate the CO variability of the recent emissions estimated from AIRS against other emission inventory databases (i. e. , Global Fire Emissions Database -GFED3 and the MACC/CityZEN UE -MACCity) and calculate that the correlation coefficients between the AIRS CO recently emitted and the emission inventory databases are 0. 726 for the Northern Hemisphere (NH) and 0. 915 for the Southern Hemisphere (SH). The high degree of agreement between emissions identified using only AIRS CO and independent inventory sources demonstrates the validity of this approach to separate recent emissions from the background CO using one satellite data set.
Identifying AIRS clear-sky coverage To select AIRS clear sky pixels, we use the MODIS cloud mask (MYD35_L2) (ftp://ladsftp. nascom. nasa. gov/allData/ 5/MYD35_L2/) taking advantage of the fact that MODIS is on the same Aqua satellite platform as AIRS. An example in Fig. 1  illustrates the method we used to collocate the AIRS and MODIS pixels. We first select granules (units of data stored as files for satellite data) that coincide in time from the two data sets, and then match one center pixel of a granule from each sensor using geo-location information. There are a total of 240 granules a day for AIRS and 288 granules a day for MODIS. A predetermined index system, marked as colored boxes in Fig. 1 , is then used to include a certain number of the surrounding MODIS pixels for each AIRS pixel.
AIRS CO variability for clear sky and cloud-cleared scenes In this section, we discuss the CO differences between AIRS clear sky coverage using the MODIS cloud mask and cloudcleared data sets to assess the performances of AIRS cloud clearing and identify possible limitations. We analyze the statistics of the AIRS CO distribution and variability using clear pixels and cloud-cleared pixels independently. Note that the CO values for clear pixels are selected from AIRS V5 L2 CO data sets where the cloud-cleared radiances (CCRs) were used. Accurate CO values under clear sky conditions should be retrieved CO from Level-1 (L1) clear radiances. Using the CO retrievals from the CCRs as an approximation for clear sky conditions of the same pixels could cause some errors; however, we do not expect large differences between the two data sets.
2), in the CO retrievals. Note also that the DOFSs over land are generally higher than over the oceans, and the daytime values are higher than nighttime values, which is due to the differences in surface thermal contrast. The 10 yr variability of tropospheric CO VMRs at 500 hPa during daytime from 2003 through 2012 is summarized in Fig. 5a  Emission inventories based on direct CO measurements have not been available except with the use of inverse modeling techniques  (Pfister et al. , 2005; Arellano et al. , 2006; Kopacz et al. , 2010). This study attempts to draw information on recent emissions from satellite CO data only to build toward the ultimate goal of monitoring fire activities in near-realtime using CO. AIRS CO-based biomass burning detection will complement the current real-time fire alarm system using MODIS thermal signals, because AIRS CO products are less constrained by smoke and heterogeneous clouds.
5. The short-term CO trends for clear and cloud-cleared retrievals are very similar (i. e. , with differences less than -0. 07 (ppbv yr -1 )) except for the SH land cases, where the difference is -0. 22 (ppbv yr -1 ); in both cases, the trends of cloud-cleared decrease faster than those of the clear. The trends for the segregated background CO and the recent emissions are larger than the un-segregated CO trends, especially over land, where the trends of the recent emissions are nearly double of the clear un-segregated values. To quantify the quality of the emission data from AIRS CO, we compare them with existing biomass burning and anthropogenic emission inventories. The version 3 of the Global Fire Emissions Database (GFED3) biomass burning inventory  (Van der Werf et al. , 2010)  used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997-2009 period on a 0.
5 × 0. 5 degree spatial resolution with a monthly time step. For November 2000 onwards, estimates were based on burned area, active fire detections, and plant productivity from the MODIS sensor. For anthropogenic emissions that exclude biomass burnings, we use the data that were produced as part of the MACC/CityZEN UE (MACCity) project and are available in the Ether/ECCAD-GEIA database. The data set MACCity is part of the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), and focuses on the anthropogenic emissions from 1960 to 2010 with a spatial resolution of 0. 5 × 0. 5 •. Figure  8a  shows the variability of AIRS CO recent emissions (red dotted curves) and that of other inventory data (green dotted curves), i. e. , the total amount of Fig. 8a. The variability of AIRS CO recent emissions (red dotted curves) and that of other inventory data (green dotted curves), i.
Summary The goal of this study is to understand the global CO variability and short-term trends for the CO background values and recent emissions separately. We use an innovative approach to separate statistically the recently emitted CO from the background CO in the satellite data sets by using PDF analyses. We have demonstrated that this technique works well by showing high correlation between the AIRS CO emissions we obtained and the established inventory database (i. e. , GFED3 and MACCity) with correlation coefficients of 0. 726 in the NH and 0. 915 in the SH. To ensure that we used the highest quality data for this study, we examined a potential error source due to the treatment of clouds in AIRS retrieval algorithm. We first identified AIRS clear sky single FOV pixels by using collocated MODIS cloud masks such that in each AIRS pixel 99 % of MODIS pixels are flagged as being clear.
Acknowledgements. This study is supported by the  NASA Earth Sciences  through ROSES by the  Climate Record Uncertainty Analysis Program  ( NNX11AL22A ), and by a sub-contract by the  NASA   JPL AIRS  team (2009)(2010). We have also been partially supported by the  RTRA/STAE foundation from Toulouse, France. The authors wish to thank AIRS and MODIS science teams and Ether for the wonderful products that made these measurements possible. Edited by:  B. N. Duncan.
This paper proposes a new approach for discriminating ships lying in complex backgrounds, based on the coherent Time-Frequency (TF) analysis of polarimetric SAR data, introduced in  [6]  and  [7]  for the study of anisotropic scattering behaviors, and in  [8]  and  [9]  for the characterization of complex urban environments using intermediate resolution PolSAR images. Time-Frequency techniques aim to estimate, from a full-resolution data set, spectral properties around specific spatial locations or equivalently spatial properties around specific spectral locations, with a loss of spatial or spectral resolution. When applied to coherent, i. e. , Single Look Complex (SLC) SAR data sets, spectral locations along azimuth and range direction have a strong physical meaning and may be interpreted in terms of azimuthal look angle and illumination frequency, respectively.
SAR Data Time-Frequency Decomposition Due to the linearity of SAR focusing, the TF decomposition technique introduced above may either be applied on raw or focused data. Nevertheless, due to the generally limited access to raw data for common end-users and in order to benefit from error corrections and specific signal compensation operated during the focusing of SAR data, TF analysis is usually performed over focused Single-Look Complex (SLC) SAR images. When dealing with coherent SAR images, a 2D spatial location l = [x, y] represents the position of a scatterer in range and azimuth whereas a 2D spectral coordinate ω = [ω x , ω y ] corresponds to a specific illumination frequency and azimuthal angle of observation. It is important to note that this interpretation of a spectral location in terms of physical parameters of the SAR acquisition is valid when dealing with coherent SAR data sets and does not hold for incoherent data, like intensity or cross-correlation images.
Experimental Analysis and Discussions In this section, the proposed polarimetric TF analysis technique is applied to the discrimination of ships in complex scenes, involving severe ambiguity or processing artifact related ghost echoes as well as small emerged lands, using experimental fully polarimetric data set composed of three Fine-Quad (FQ) mode RADARSAT-2 images over the scenes of Vancouver, Canada, (FQ15, two images) and San Francisco, USA, (FQ9). The choice of a TF decomposition mode is assessed from the performance of the polarimetric TF coherence indicator for ship discrimination in open sea area in the presence of strong ghost echoes and some natural islands.
Drosophila strains The transgenic strains that carry insertions of the I-element fragment in sense (1. 9, 2. 1, 2. 3, 2. 6 and 2. 10) or antisense (3. 1, 3. 6, 3. 9 and 3. 10) orientation are described in Jensen et al. (21). Briefly, the 167-2484-nt region of the GenBank sequence M14954, corresponding to the I-element, was inserted into the pW8-hsp-pA vector in sense (hsp[i1-2Á/S]pA) or antisense (hsp[i1-2Á/AS]pA) orientation and introduced into the genome of the Drosophila reactive w K strain. The control strain 62. 5. 2 (T5) contains insertion of pW8-hsp-pA vector; strain 67. 2. 1 (7. 1) carries a promoterless construct pA'[i1-2Á]pA in which the hsp70 polyadenylation sequence was inserted instead of the hsp70 promoter upstream of the I-element fragment. These constructs were introduced into a Drosophila strain devoid of functional I-elements (the reactive w K strain)  (21).
Small RNA library preparation and analysis Small RNAs 19-29 nt in size from total ovarian RNA extracts were cloned as previously described in Muerdter et al. (11). Libraries were barcoded according to Illumina TrueSeq Small RNA sample prep kit instructions and submitted for sequencing using the Illumina HiSeq-2000 sequencing system. Bioinformatic analysis of small RNA libraries is described in Supplementary Materials and Methods. Published small RNA deep sequencing data from previously published data  (23, 24)  were also analysed. Small RNA sequencing data are deposited at Gene Expression Omnibus (GEO), accession number GSE41780.
Northern analysis of short RNAs Northern analysis of short RNAs was done as previously described  (25). A chemical cross-linking step that enhances detection of small RNAs was used  (26). For this, the damp membrane with RNA side facing up was placed onto 3 MM saturated in freshly prepared crosslinking EDC reagent [0. 16 M 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and 0. 13 M 1-methylimidazole, pH 8) and incubated at 60 C for 2 h. After cross-linking, membrane was rinsed in excess RNase-free distilled water to remove any residual crosslinking solution. Enrichment for short RNA species was done using the miRNeasy Mini Kit (Qiagen). P 32 -labelled riboprobes corresponding to the sense strand of the I-element were synthesized. The I-element probe contained a fragment of open reading frame (ORF) 2 corresponding to nucleotides 2109-2481 of the GenBank sequence M14954.
The diffuse radiation fraction also influences the albedo, because the purely diffuse radiation results in an albedo similar to that of direct radiation with a SZA of ∼ 50 • over a horizontal snow surface (Wiscombe and  Warren, 1980). Finally, the surface roughness can affect surface albedo by trapping radiation (which leads to lower albedo values) and by modifying the effective angle of incidence relative to a flat surface. At high zenith angles, this can have a significant impact on the broadband albedo  (Zhuravleva and Kokhanovsky, 2011). Direct ground-based measurements of snow and ice optical properties are sparse and discontinuous  (Hansen and Nazarenko, 2004; Wang and Zender, 2011). The available field data cover only few areas and a small range of snow and ice types. The database is limited especially for remote regions such as the Arctic and Antarctic  (Kondratyev and Cracknell, 1998).
These variations in meteorological conditions strongly affect the microstructure of surface snow and account for the great variability of the snowpack  (Albert and Shultz, 2002). Low amounts of impurities are present in the snow, BC and dust being the types with the strongest effect on the albedo  (Warren and Wiscombe, 1980; Hagler et al. , 2007a). Figure  1  shows meteorological data collected by instruments placed on the Summit AWS. All reported measurements were acquired in May and June 2011. During that period, the air temperature, measured at 3. 7 m above snow with a type-E thermocouple (estimated accuracy of 0. 1 • C), was always negative. No liquid water was ever found in the snowpack. The wind speed data recorded by a RM Young propeller-type vane (estimated accuracy of 0. 1 m s -1 ) indicate that there were no strong wind events, except on 4 June (with a wind speed higher than 11 m s -1 ).
Raw data Spectral albedo measurements were obtained using an ASD FieldSpec Pro spectroradiometer (see Fig. 2 ). This instrument has a spectral range of 350 to 2200 nm, with a spectral resolution ranging from 3 nm in the UV to 12 nm in the IR. It was equipped with a remote cosine receptor (RCR), a lightdiffusing fore optic used for upwelling and downwelling flux measurements. The albedo was then simply computed by making the ratio of these fluxes. The downwelling diffuse radiation was also measured by manually shielding the spectroradiometer receptor from direct sunlight. Four repeated measurements were made at each location. The raw data were carefully examined, removing those affected by errors. Specifically, we have removed individual albedo measurements which were greater than 1 in the UV/visible range, displayed instrumental error on the first detector (wavelengths < 976 nm), or were obvious outliers compared to the three other measurements, which otherwise showed high repeatability.
Conclusions We present in this work the results of a two-month field campaign at Summit, Greenland. In May and June 2011, we performed simultaneous, co-located measurements of the snow spectral albedo and the snow physical properties, in particular the density and the specific surface area (SSA). To evaluate the impact of particulate absorbers in snow, we also collected samples for chemical analyses including dust and black carbon (BC). The measured snow spectral albedo data were then compared to the albedo computed from the snow physical and chemical properties using the DISORT radiative transfer model. The ability of this model to simulate the albedo was tested, investigating the effects of BC and dust in the visible wavelengths and describing some possible sources of discrepancies between simulated and measured albedo in the near-infrared region.
Acknowledgements. The Summit field work was funded by grant No. 1088  from  French Polar Institute (IPEV)  to  F. Domine , by grant No. 1023227  from  National Science Foundation (NSF)  to  M. Bergin  and by grant  ARC1022996  from  NSF  to  J. Dibb. We thank 109th  Air National Guard  and all crew at  Summit  for logistic support during the field campaign,  K. Steffen ,  N. Bayou  and  Greenland Climate Network  (project sponsored by  NASA  and  NSF ) for providing meteorological data and  Fondation Eau ,  Neige et Glace and CNRS  for funding the purchase of the SVC spectroradiometer. Additional funding was provided by  Agence Nationale de la Recherche  ( MONISNOW ANR-11-JS56-005-01 ). The angular response of the RCR was characterized using the RCR provided by  D. Perovich  and the  ASD  provided by  Norwegian Polar Institute  ( J.
2005b; Murase et al. 2006; Wang et al. 2007 ), relativistic shock breakout in compact CCSN progenitor stars  (Waxman & Loeb 2001) , and cosmic string cusps  (Damour & Vilenkin 2000; Siemens et al. 2006; Berezinsky et al. 2011). Searches for HENs and GWs from such events have thus far relied on blind (i. e. , untriggered) all-sky searches. Abbasi et al. (2012c)  performed an all-sky search for point sources of HENs in IceCube data and  Adrián-Martínez et al. (2012b)  carried out a similar study with ANTARES data. LIGO and Virgo have carried out a number of all-sky searches for GWs. The most recent and most sensitive such search for model-independent GW bursts was published in  Abadie et al. (2012a) , whereas the most recent all-sky search for binary inspiral-merger is  Abadie et al. (2012c).
(2008)  and  Pradier (2009) , and  Bartos et al. (2011)  have derived constraints on joint GW-HEN signals based on the interpretation of independent GW and HEN observational results. Here we present the first direct search for coincident GW-HEN events, using data taken by the ANTARES HEN telescope and by the LIGO and Virgo GW observatories from January to September 2007. At this time, ANTARES was still under construction and operating with only 5 active lines. At the same time, the fifth LIGO science run (S5) and the first Virgo science run (VSR1) were carried out. This was the first joint run of the LIGO-Virgo network with the detectors operating near their design sensitivities. The basic principle of the analysis presented here is that of a "triggered" search: HEN candidates are identified in the ANTARES data, then the GW data around the time of the HEN event are analyzed for a GW incident from the HEN estimated arrival direction.