dataesr/scientific-paragraphs-categorization huggingface dataset:
All developments were done with Qt and Marble / QML libraries with Windows and Linux compatibility. The software development presented in this paper has been used during an experimental campaign of acquisition in the Mediterranean Sea. A target of interest has been imaged by the X-band sensor  [7] , the flying trajectory of the remote sensing system has been estimated in real time as illustrated in Fig. 8. In this figure, the aircraft is displayed in green and its actual trajectory is in red. The boat is near the center of the solenoid, the antenna pattern is also shown as well as the radar line of sight. The solenoid has an approximate duration of 10 mn. The target velocity is around 7 m/s, resulting in a total displacement during the acquisition of more than 4 200 m.
VII. CONCLUSION Airborne SAR acquisition campaign dedicated to tracking cooperative maritime target has been performed in the Mediterranean Sea and validated the newly developped airborne guidance algorithms presented in this paper. The SETHI platform is now equipped with a new system allowing real time tracking of maritime target by the Xband SAR sensor. It relies on the use of AIS signal emitted by boat and received onboard. Software programs allow the processing of AIS and GPS data and perform a prediction of the vessel's trajectory. A spotlight mode predictive trajectory for the aircraft is then estimated and displayed for the pilot. Results in operational context have shown that the application method is consistent with ultrahigh spatial resolution imagery and allows collecting radar dataset over the full 360° of the azimuth range.
Computational Details The ground-state geometries were optimized in gas phase by a density functional theory DFT/B3LYP called the hybrid method based on three Becke parameters using the base 6-31G(d) level of theories implemented in the GAUSSIAN 09 program package  [43, 44]. With this method the dynamic second order hyperpolarizability γ (-2ω; ω, ω, 0) and the HOMO and LUMO energy levels of the investigated molecules were calculated. The addition of d polarization functions on the carbon and nitrogen atoms are critical in order to have a precise estimation of the hyperpolarizabilities.
Topography and Structural Properties The surface morphology of the investigated metalorganic thin films deposited by the PVD technique was analyzed by Atomic Force Microscopy (AFM). Figure  2a-f  shows AFM images of the Znq 2 , Cuq 2 , and Alq 3 thin films for samples deposited on the substrate at room temperature, respectively. The scanning area of the films was equal to 1 × 1 µm 2. Presented figures were drawn using Gwyddion software and they visualize 2D and 3D images for all of the investigated metalorganic complexes. The Znq2 and Cuq2 thin films deposited at room temperature showed nanocrystalline characters. The structure of the films contained clearly visible hills of nanocrystallites and valleys between them. The average differences between them were equal to ca. 40 nm for Cuq2 and 30 nm for Znq2.
Acknowledgments This work was supported by grants from the  French national research agency  ( ANR Natlurim  and  ANR PEPSI 14-CE08-0016-01 ), by  Idex Sorbonne Paris-Cité , by  CNRS , by  Biospace Lab , by the  PRC Japon JSPS/French Bilateral  grant ( PRC1248 ) and by  PHC Procore  ( 33122PA ). The authors also acknowledge all their collaborators: Q. le  Masne de Chermont ,  T. Maldiney ,  E. Teston ,  G. Ramirez-Garcia ,  D. Gourier ,  A. Bessière ,  C. Chanéac ,  M. Pellerin ,  F. Gazeau  and  L. Laumonier.
It is the most important base in the atmosphere  (Seinfeld and Pandis, 2012)  and can react very rapidly with sulfuric acid (H 2 SO 4 ), which is formed from the oxidation of SO 2 with OH (in the gas phase), O 3 and hydrogen peroxide (H 2 O 2 ) in the aqueous phase, to form ammonium sulfate or ammonium bisulfate  (Seinfeld and Pandis, 2012). If enough NH 3 is available after the neutralization of H 2 SO 4 , it can react with nitric acid (HNO 3 ), which is mainly formed from the oxidation of NO and NO 2 , to produce the semivolatile ammonium nitrate. Formation of ammonium nitrate usually occurs when the molar concentration of NH 3 + NH + is more than twice the sulfate concentration (i. e. , "free ammonia regime")  (Tsimpidi et al. , 2007). Past and future trends in the total PM concentration have recently received great attention thanks to the availability of long-term observational datasets and increased computational power available for long-term chemical transport model (CTM) simulations.
On the other hand, other organic compounds will obtain lower molecular weight and will fall into higher saturation concentration ranges through fragmentation, and they will likely reside in the gas phase. A recent model intercomparison exercise, AeroCom  (Tsigaridis et al. , 2014) , investigated the performance of 31 global models with respect to OA for the year 2006, revealing large differences between models in terms of SOA formation, mainly because of the assumptions made in the SOA scheme used (e. g. , chemical aging, multiphase chemistry and semi-volatile SOA assumptions). In addition, comparison with several observational datasets revealed that even though the models were able to simulate the secondary nature of OA, they tended to largely underestimate the observed OA, especially in urban areas  (Tsigaridis et al.
, 2018; Chrit et al. , 2017; Ciarelli et al. , 2016). In this study, we investigate the trends in secondary inorganic aerosol (SIA) and SOA in Europe during the 1990-2010 period calculated by five CTMs that participated in the EURODELTA-Trends exercise  (Colette et al. , 2017). The novel multi-model EURODELTA-Trends (EDT) exercise (launched within the Task Force on Measurement and Modelling of the EMEP program supporting the Convention on Long Range Transboundary Air pollution (CLRTAP)) provided 21 years of continuous particulate matter components and their gas-phase precursor concentrations over Eu- rope from the year 1990 and with "real" year-to-year meteorological input data. It provides a base for validating the performance of multiple models over an extended  period (i. e. , 1990-2010)  and for assessing the variation of various chemical species not routinely measured in Europe.
The paper is organized as follows. Section 2 provides a general overview of the EURODELTA-Trends experiment, with a description of the models participating in the exercise and the input data used to perform the experiment. The observational data are described in Sect. 2, along with information regarding the quality-control criteria. Results and discussions are presented in Sect. 3. The trends in anthropogenic emissions and inorganic species are discussed in Sect. 3. 1 and 3. 2, respectively. An evaluation of the secondary organic aerosol fraction is presented in Sect. 3. 3 (for the 2000-2010 period) together with the trends in biogenic emission and anthropogenic and biogenic SOA concentrations. Finally, conclusions are presented in Sect. 4.
Description of the participating models A total of eight state-of-the-art air quality CTMs delivered their results for the EDT experiments: CHIMERE  (Mailler et al. , 2017; Menut et al. , 2013) , CMAQ  (Byun and Schere, 2006) , EMEP MSC-W  (Simpson et al. , 2012) , LOTOS-EUROS  (Manders et al. , 2017; Schaap et al. , 2008) , MATCH  (Andersson et al. , 2015 (Andersson et al. , , 2007;; Robertson et al. , 1999) , MINNI  (Mircea, 2016) , Polyphemus  (Mallet et al. , 2007; Sartelet et al. , 2012)  and WRF-Chem  (Grell et al. , 2005; Mar et al. , 2016). Given the large computational demand of the simulations, only five modeling teams were able to deliver 21 years of continuous air-modeled data: CHIMERE, EMEP MSC-W, LOTOS-EUROS, MATCH and MINNI, the results of which are used in this study. Most of the other models provided air quality data for 3 intermediate  years: 1990, 2000 and 2010.
, 2012). In the EMEP (VBS-NPAS) model, the OH reaction rate for SOA aging is set to 4. 0 × 10 -12 cm 3 molecule -1 s -1 ; each reaction of the organic compounds in the gas-phase decreases the volatility by 1 order of magnitude and increases the mass by +7. 5 % to account for oxygen addition (fragmentation processes are not included). SOA aging is not included in the VBS-NPNA scheme. -The H 2 O organic aerosol module  (Couvidat et al. , 2012) , used by the CHIMERE model, uses different types of surrogate organic species: hydrophilic species (which condense preferentially into an aqueous phase) and hydrophobic species (which condense only into an organic phase). These surrogate species are produced from the oxidation of volatile organic compounds. In H 2 O, SOAs are formed from four classes of precursors: aromatic compounds, isoprene, monoterpenes and sesquiterpenes.
Biogenic and natural emissions Emissions of biogenic volatile organic compounds (BVOCs) were not prescribed by the EDT experiments. Each participating team used their own emission model to calculate biogenic emissions. One group of models used the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2. 04  (Guenther et al. , 2006)  and MEGANv2. 1  (Guenther et al. , 2012)  emission models: CHIMERE and MINNI, respectively. CHIMERE uses highly resolved spatiotemporal data (30 arcsec every 8 d) generated from MODIS for leaf area index (LAI) inputs. The 30 arcsec USGS (US Geophysical Survey) land-use database is used to provide information on the plant functional type (PFT). The PFT is then combined with the emission factors for each functional type of  Guenther et al. (2012)  to compute the landscape average emission factors.
MINNI derived them from the CORINE Land Cover (CLC2006) inventory. The MEGAN model is driven with meteorological variables, such as temperature, wind speed, humidity, solar radiation and soil moisture. The leaf area index retrieved from the Terra MODIS satellite is used to simulate the vegetation growth (8 d and 1-month average LAI data at 0. 25 • × 0. 25 • resolution for CHIMERE and MINNI, respectively). Common BVOCs species such as isoprene, αpinene as well as other classes of monoterpenes are generated for each hour and grid cell of the domain. In the CHIMERE model, emissions of sesquiterpenes are also included and used as an input for SOA chemistry. More information on the MEGAN emission algorithms can be found in  Guenther et al. (2006 Guenther et al. ( , 2012)). The second group of models (LOTOS-EUROS, MATCH and EMEP MSC-W) used a detailed tree inventory of 115 species for 30 European countries based on the work of Koeble and Seufert (  2001 ) and aggregated tree species based on land-cover types.
Anthropogenic emissions Anthropogenic gridded emissions by country and activity, i. e. , SNAP (Selected Nomenclature for reporting of Air Pollutants) codes, were estimated using the Greenhouse gas -Air Pollution Interactions and Synergies (GAINS) model  (Amann et al. , 2011). Emission of SO x , NO x , NH 3 , nonmethane volatile organic compounds (NMVOCs) as well as primary PM 2. 5 , PM 10 , black carbon and primary organic aerosol were prepared at a 0. 25 • × 0. 40 • resolution (latitude × longitude). Anthropogenic emissions were calculated for the years  1990, 1995, 2000, 2005 and 2010 , and linearly interpolated by country and activity sector for the 5-year periods to obtain the continuous 21-year emission dataset. Data for the different emission sectors were obtained from Eurostat (http://ec.
europa. eu/eurostat, last ac-cess: 14 November 2019), the International Energy Agency (IEA, 2012) and the UN Food and Agriculture Organization (FAO) (http://www. fao. org/statistics/en/, last access: 14 November 2019). Additionally, data from the International Fertilizer Association (IFA) and the COPERT model  (Athanasiadis et al. , 2009)  were used for the agriculture and transportation sectors, respectively. An error in primary particulate emission matter for Russia, north Africa and maritime areas for the period of 1991-1999 was identified at the end of the exercise. However, the effect of the error was estimated to be very limited  (Theobald et al. , 2019). The complete anthropogenic emission dataset accounts for source-specific emission limits as well as for various European air quality directives (e.
g. , the UNECE Gothenburg Protocol;  UNECE, 1999). This emission dataset, referred to as ECLIPSE_V5, was delivered by IIASA as country national totals by activity sector. It was subsequently spatialized by INERIS on the EURODELTA-Trends grid for use in the CTMs using the gridding process described in  Terrenoire et al. (2015)  and  Bessagnet et al. (2016). For the residential heating sector (SNAP2), a proxy based on population density was applied using a bottom-up inventory available for France. More information about the regridding can be found in  Colette et al. (2017).
Meteorological data To provide meteorological inputs to the modeling teams, dynamically downscaled regional climate model simulations were used in combination with ERA-Interim global reanalysis data  (Dee et al. , 2011). The Weather Research and Forecasting model (WRF version 3. 3. 1; Skamarock et al. , 2008)  was used at a resolution of 0. 44 • to generate the meteorological parameters. To reduce the uncertainty of the meteorological data, WRF was re-run with ERA-Interim reanalysis data in grid-nudging mode as described in  Stegehuis et al. (2015)  and subsequently interpolated at a 25 km resolution to match the EDT grid, although there were a few differences between the procedures of the modeling team. LOTOS-EUROS used RACMO2-downscaled data and MATCH used HIRLAMdownscaled data. More information on the meteorological inputs can be found in  Colette et al.
Observational data and trend assessment The observations are reported to EMEP, and the original time series are available in EBAS (http://ebas. nilu. no, last access: 14 November 2019). The annual observational datasets chosen for the trend assessment have passed the completeness criteria of 75 % of data available over the full 1990-2010 period and have undergone visual screening tests. The secondary dataset with annual and seasonal average concentrations is available from the webpage set up by the Task Force on Measurements and Modeling (TFMM) for this study (https://wiki. met. no/emep/emep-experts/tfmmtrendstations, last access: 14 November 2019). The datasets include yearly measurements of long-term air concentrations of sulfur dioxide (SO 2 ), particle sulfate (SO 2- 4 ), nitrogen dioxide (NO 2 ) total nitrate (TNO 3 = HNO 3 (g) + NO - 3 (p)) and total ammonium (TNH x = NH 3 (g) + NH + 4 (p)) performed in Europe between 1990 and 2010.
, 2016) , but it is not assumed that the trends will be largely affected when same method is used during the whole period. The other components are mostly measured using a filterpack sampler with no size cutoff in the inlet. The threestage filterpack separates gas and aerosol species, but for nitrogen compounds this separation might be biased due to the volatile nature of NH 4 NO 3. Therefore, TNO 3 and TNH x are usually used for robust estimate of the atmospheric nitrogen loading  (Tørseth et al. , 2012). However, it is recommended to report the measurements of all the species since it may give valuable insight into the gas-particle ratio despite possible biases. Details of the method used are found in the annual data report (i. e. , EMEP, 2012, for the 2010 data). Table  2. Relative and absolute trends in emissions of SO x , NO x , NH 3 and NMVOCs in the EURODELTA-Trends exercise (whole domain).
The linear trends are presented as relative changes with respect to the years 1990 and 2000 for the two 11-year periods and as relative changes with respect to the year 1990 for the full 21-year period. In addition, to provide a more comprehensive picture of the trends in the air pollutant concentrations, the trend analysis was also performed for several subregions adapted from the commonly used the Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects (PRUDENCE) climatic zone classification (http://ensemblesrt3. dmi. dk/quicklook/regions. html, last access: 14 November 2019). The extension of the subregions used in the study is reported in Fig. 1. The evaluation of modeled SOA was performed using an extensive dataset of secondary organic aerosol concentrations retrieved with positive matrix factorization (PMF) analysis  (Paatero, 1999)  and recently compiled by  Tsimpidi et al.
(2016). This dataset includes SOA average concentrations at various sites in Europe during the P2 period. In order to remove local pollution events, likely not included in emission inventories, stations with average SOA concentrations higher than 7 µg m -3 during the measurement period were excluded from the analysis (three sites excluded -in total, 28 sites were kept: six urban sites, eight urban downwind sites and 14 rural/remote sites). Most of the measurements were performed during short campaigns using aerosol mass spectrometers (AMSs) in different periods of the years, lasting from about 2 weeks to 1 month. The spatial distribution of the stations is presented in Fig. S1. The complete list of stations used is reported in Table  S2  along with information regarding the year and the seasons during which measurements were made.
NH 3 emissions declined by 19 % over the P1 period but only by 6 % over the P2 period. P1 (1990-2000) ,  P2 (2000-2010)  and  PT (1990 PT ( -2010) )  periods and percentage of points in Fig. 3   Emissions of NMVOCs showed a decline of 59 % over the full 1990-2010 period with similar relative reductions achieved during the P1 and P2 periods: 33 % per period. NMVOC emission reductions were mainly driven by the road transport sector, and by the year 2010, most of the NMVOC emissions arose from the use of solvents (EEA, 2012). Huang et al. (2017)  compiled a global gridded dataset of speciated NMVOC emissions for the 1970-2010 period and analyzed the trends. Among the different world regions, North America and Europe were reported to have reduced their NMVOC emissions since 1970 due to the introduction of EURO emission standards for vehicles.
SOA evaluation During the P2 period, various field campaigns were performed in Europe using AMS instruments to measure ambient OA concentrations at various sites. Using factor-analysis Geosci. Model Dev. ,  12, 2019  www. geosci-model-dev. net/12/4923/2019/ techniques, i. e. , PMF, it was possible to apportion the measured OA to a direct emitted organic factor, i. e. , POA, and a more oxidized secondary factor, referred to as SOA. Even though this methodology is affected by various sources of uncertainties, we used this dataset to provide a general benchmark for modeled SOA performance during the P2 period. Figure  14  shows the average modeled and observed SOA concentrations (retrieved from PMF analysis) for all the sites included in the analysis (Table  S2  and Fig. S1 ), as well as for campaigns carried out during winter and summer periods (astronomical seasons).
Because HNO 3 deposits faster than NO - 3 , the shift of TNO 3 partitioning towards particles increases the lifetime of atmospheric nitrogen (as reported by  Simpson et al. , 2014) , which contributes to the explaination that TNO 3 decreases less than NO x emission. A comprehensive dataset of SOA concentrations retrieved from PMF analyses was used to investigate the models' capabilities of reproducing the SOA concentrations during the 2000-2010 period. The analysis of modeled SOA concentrations indicated that the models underestimated the SOA fraction by varying extents, by a factor of 3 to 11, depending on the model, suggesting that large uncertainties in the SOA formation mechanisms as well as in the emissions of SOA precursors remain, and more studies are needed to better elucidate the evolution of the SOA fraction.
Code and data availability. Technical details of the EURODELTA project simulations that permit the replication of the experiment are available on the wiki of the EMEP Task Force on Measurement and Modelling (https://wiki. met. no/emep/emepexperts/tfmmtrendeurodelta, last access: 21 December 2018), which also includes ESGF links to corresponding input forcing data. The EURODELTA-Trends model results are made available for public use on the AeroCom server (information to gain access to the AeroCom server are available at https://wiki. met. no/aerocom/ user-server, last access: 21 December 2018). Model input and output data are permanently stored under the /metno/aerocom-usersdatabase/EURODELTA folder on the AeroCom Server. See  Colette et al. (2017)  for full terms and conditions for the use of these data.
Acknowledgements. Giancarlo Ciarelli  was supported by  ADEME  in the framework of the  MISTRALS/ChArMEx  project and the  Swiss National Science Foundation  (grant no. P2EZP2_175166 ). The Ineris coordination of the EURODELTA-Trends exercise was supported by the  French Ministry in charge of Ecology  in the context of the  Task Force on Measurement and Modelling of the EMEP program  of the LRTAP Convention. Meteorological forcing with the WRF model was provided by  Robert Vautard  and  Annemiek Stegehuis  from LSCE/IPSL. The CHIMERE simulations where performed using the TGCC supercomputers under GENCI computing allocation. The participation of CIEMAT was financed by the  Spanish Ministry of Agriculture and Fishing, Food and Environment. MATCH participation was partly funded by the  Swedish Environmental Protection Agency  through the research program  Swedish Clean Air and Climate (SCAC)  and partly by  NordForsk  through the research program  Nordic WelfAir  (grant no.
75007 ). The computing resources and the related technical support used for MINNI simulations have been provided by  CRESCO/ENEAGRID highperformance computing infrastructure  and its staff. The infrastructure is funded by  ENEA , the  Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian  and European research programs (http://www. cresco. enea. it/english, last access: 14 November 2019). MINNI participation to this project was supported by the "Cooperation Agreement for support to international Conventions, Protocols and related negotiations on air pollution issues", funded by the  Italian Ministry for Environment and Territory and Sea. The simulations with the EMEP MSC-W model were supported by the  Research Council of Norway  in the framework of the Programme for Supercomputing: through the  EMEP  project (grant  NN2890K ) for  CPU  and the  Norstore  project " European Monitoring and Evaluation Programme " (grant  NS9005K ) for data storage.
The GAINS emission trends were produced as part of the  FP7 European Research Project ECLIPSE  ( Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants ) grant no. 282688. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. P2EZP2_175166 ) and ADEME in the framework of a convention with laboratories within the  MISTRALS/ChArMEx  project (convention no. 1562C0001 ). Review statement. This paper was edited by  Samuel Remy  and reviewed by two anonymous referees.
Statistical analysis For the case in which a comparative analysis was made of the PDPA, WT1 and WT2 methodologies, the results shown in the tables and figures correspond to the mean value of the 3 replicate tests made in each case. To study the effect of nozzle type, size and working pressure from the results obtained, a one-way analysis of variance (ANOVA) with Fisher's least significant difference (LSD) test was performed for each effect. Previously, the normality and homogeneity of variance of the studied variables were verified with the Shapiro-Wilk test and Levene's test, respectively. A confidence level of 95% was considered in all tests. Statistical analyses were done using JMP® Pro 13 (SAS Institute Inc. , Cary, NC, 1989-2007 for Windows).