dataesr/scientific-paragraphs-categorization huggingface dataset:
The measured time was of 2. 4 seconds, from which 0. 986 ms are needed by Apache Felix to start the OSGi container and the rest is spent on loading the plug-ins from a specific folder of Apache Felix. In summary, the code required to encapsulate the extension component functionality into a plug-in is small, a few KB, and most of this extra code comes from iPOJO. In contrast, the extra code required to create an OSGi bundle is minimum. Moreover, this plug-in platform implementation is very light and adds very little latency when communicating the plug-ins with the middleware, however the first time these two modules communicate generates a significant latency. Furthermore, the 2. 4s initiation time of this plug-in platform implementation is lengthy, which in addition to the middleware's initiation time of the ( pressented in Section VI-A2) sums up a total of approximately 3 seconds of initiation time before the NL protocols and applications start working.
B. Evaluation of case study 1 In this section we present an evaluation of the adaptive protocol proposed for the case study 1 example throughout simulation. The idea of this evaluation is to show a brief quantitative evaluation on the impact of specific proposed adaptation, which is built with our middleware. We want to assess if even this simplistic adaptation proposal indeed generates a performance increase as reported by Huag et al. [19]. Moving on to the details of this evaluation, essentially we compared two protocols, the default OLSR protocol and the adaptive OLSR protocol which includes the adaptation proposed for case study 1. Both are based on UM-OLSRv0. 8. 8  19 an OLSR implementation for NS-2. We measured the following metrics in the simulation to compare the performance of the two protocols: • Average goodput of all data connections in the simulation, the goodput is defined as the amount of useful data delivered (in bytes) divided by the data connection transfer time.
• Normalized routing overhead (NRO), which is defined as the ratio between the number of control packets propagated by every node in the network, and the number of data packets received by the destination nodes. This metric is strongly related to the control packet overhead of the NL protocol. We also measured the average end-to-end delay, however the resulted delay values were too scattered thus not useful when comparing the two protocols, this can be explained because the OLSR protocol itself is not sensitive to delay. For the NS-2 simulation, the speed context element for each node is obtained from the speed value in the MobileNode class in the NS-2 code; this speed value has 100% precision contrary to the speed values obtained from a real world GPS. Moreover, instead of porting the entire middleware to NS-2, we only added the CUPIB mode functionality to the adaptive OLSR protocol, more specifically we added a delay of 0.
1916 ms obtained from the cost evaluation of the prototype, in order to emulate the processing overhead of the adaptation middleware when executing the CUPIB mode. In addition, to simulate a city scenario we used real data (roads specification) concerning a urban area of the city of Ensenada in Mexico, this data was exported from openstreetmap 20 (see Fig. 20 ). For the simulation of vehicles and their movements (considering traffic lights) we used the SUMO tool 21. We generated the flow of vehicles from 41 routes in the map by defining the begin and end points of the routes. The internal vertices of routes were generated automatically by the SUMO duarouter program that uses Dijkstra's shortest route algorithm, where the edge weights are a combination of the max speed of a street, the distance to the destination and also from traffic situation.
g. Eclipse) supporting a high number of modules. Furthermore, to get a more comprehensive view of the performance and efficiency of PLUGAM we emphasize on the extra overhead generated by our generic solution in contrast with a particular adaptive protocol. We will use as example the proposal of Chen et al. [20]  which performs an adaptation based on switching between two different NL protocols (more information can be seen in Table  IV  of Section VII). The extra overhead generated by our generic solution is the following: • An additional processing cost exists due to the indirect access between the extension components involved in the adaptation solution, contrary to particular adaptive protocols like Chen et al. where these elements are tightly integrated and directly communicating. This processing cost mostly depends on the communication delay between plug-ins and the middleware passing through the plug-in platform, in our prototype implementation this delay is very small, around 0.
Objectives and Organisation The objectives of the OCTAVIUS project are the following: -to demonstrate operability and flexibility of first generation post-combustion processes on pilot plants in preparation of full scale demonstration projects such as the ROAD and Porto Tolle projects. Experimentation will be carried out on 3 different industrial pilot plants (TNO pilot at Maasvlakte, ENEL pilot at Brindisi, EnBW pilot at Heilbronn). Based on the results of the pilot campaigns, OCTAVIUS will establish detailed guidelines with relevant data on emissions, operability, flexibility and cost aspects as well as HSE issues, for first generation CO 2 capture processes. -to demonstrate the DMX TM process on the ENEL pilot plant at Brindisi which will be retrofitted. This second generation capture process issued from IFPEN researches uses phase change solvents and aims at an energy consumption of around 2.
SP2 will use dedicated pilot plant testing campaigns on the three pilot plants to provide the following: -demonstration of emission control options including novel countermeasures; -demonstration of solvent life time aspects and key dependencies; -demonstration of operability and flexibility options to determine operability limits; -demonstration of process analytical techniques and control options (including dynamic response); -demonstration of alternative materials. A significant amount of operational data will be collected from the demonstration campaigns and then used to validate and verify the models developed within OCTAVIUS. These models shall provide insight into methods required to address the issues facing demo projects in the near future. The work carried out within SP2 will conclude by preparing detailed guidelines for the demonstration scale units, containing relevant data on emissions and emission minimisation, HSE, operability and flexibility aspects.
I. INTRODUCTION Orderliness, reflected in either scaling properties  1  or power laws  [2] [3] [4]  , is encountered in different frameworks involving social groups. Salient example is Zipf's law  5  , a power law with exponent -2 for the density distribution function that is observed in describing urban agglomerations  6  and firm sizes all over the world  7. This kind of "order" has got immense attention in the literature. The pertinent regularities have been found in other scenarios as well, ranging from percolation theory and nuclear multi-fragmentation 8 to the abundances of genes in various organisms and tissues  9  , the frequency of words in natural languages  5, 10  , the scientific collaboration networks  11  , the total number of cites of physics journals  12  , the Internet traffic  13  or the Linux packages links  14.
III. EMPIRICAL OBSERVATIONS A. Description of the data used in this work The raw data are obtained from the Spanish state institute INE and cover annually the period 1996-2010 (with the exception of 1997). It encompasses up to 8000 municipalities (the smallest Spanish administrative unit) distributed within 50 provinces (the building blocks of the autonomous communities, equivalent of the USA-states). The autonomous cities of Ceuta and Melilla are not included. We use provinces and municipalities as the closest representatives of the ideal of a closed system's fundamental elements. But some arbitrariness remains in the data, since i) there are many municipalities that actually contain more than one population-center, ii) provinces that contains more than a single socioeconomic cluster, and iii) economic regions that extend beyond the frontiers of a province.
ACKNOWLEDGMENT: This work was partially supported by ANR DYNHELIUM (ANR-08-BLAN-0146-01) Toulouse, and the Projects FQM-2445 and FQM-207 of the Junta de Andalucía. AP acknowledges support from the Senior Grant CEI Bio-Tic GENIL-SPR. excluding some points from the tails (largest or smallest) until reaching a satisfactory convergency of the three results. The most frequently outsiders-case obtains for the capital city. The second case refers to a few small villages with undersized population. All outsiders are indicated in the Additional Material 24 , together with the results of the three estimations. On the other hand, as commented in the text, we have found three notorious cases -Salamanca, Orense, and Zamora-where no satisfactory outcome has been achieved with this procedure.
Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM 1 ) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of EUCAARI and the intensive campaigns of EMEP during 2008 (May-June and September-October) and 2009 (February-March). In this paper we focus on the identification of the main organic aerosol sources and we propose a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 datasets accounting for urban, rural, remote and high altitude sites and therefore it is likely suitable for the treatment of AMS-related ambient datasets.
An introduction about project objectives and an overview on the goals reached within the EUCAARI project are provided by several works  (Kulmala et al. , 2009 (Kulmala et al. , , 2011;; Knote et al. , 2011; Fountoukis et al. , 2011; Murphy et al. , 2012). To analyze the aerosol chemical composition and its sources, the coordinated EUCAARI and EMEP campaigns of 2008 and 2009 provide significantly more aerosol mass spectrometer (AMS) datasets in comparison to previous efforts  (Aas et al. , 2012). For the investigation of the aerosol chemical composition and organic aerosol (OA) sources dedicated studies were performed. Nemitz et al. (2013)  discuss the organic and inorganic components of atmospheric aerosols, using AMS measurements performed during these three intensive field campaigns in 2008 and 2009.
Our work includes 17 measurement sites all over Europe, comprising 25 unit mass resolution AMS datasets, and represents therefore an unprecedented overview of OA sources in Europe. Moreover, the application of advanced source apportionment methods allows us to overcome limitations of commonly used source apportionment techniques for AMS data, such as the purely unconstrained positive matrix factorization  (Canonaco et al. , 2013a). The positive matrix factorization (PMF) model does not always succeed since the co-variance of the sources might be large due to the meteorology and the relative source contributions which vary too little. This can often be the case at rural and remote sites but it was also shown for an urban background site in Zurich, for which  Lanz et al. (2008)  pioneered the use of ME-2 for AMS data.
Briefly, air is sampled through a critical orifice into an aerodynamic lens, where the aerosol particles are focused into a narrow beam and accelerated to a velocity inversely related to their aerodynamic size. Particles are transmitted into a high vacuum detection chamber (10 -5 Torr) and impact on a heated surface (600 • C) where the non-refractory species vaporize. The resulting gas molecules are ionized by electron impact (EI, 70 eV) and the ions are extracted into the detection region to be classified by the mass spectrometer. Details about our AMS measurements quantification and data treatment (e. g. ionization efficiency calibrations, collection efficiency estimation, air corrections, etc. ) are described elsewhere  (Nemitz et al. , 2013). As in previous AMS studies the collection efficiency was found to vary with the nitrate fraction  (Middlebrook et al.
, 2013a). f j,solution = f j ± α • f j (3a) g i,solution = g i ± α • g i (3b) A softer constraining technique is provided by the pulling approach  (Paatero and Hopke, 2009; Brown et al. , 2012) , which is available within the SoFi package  (Canonaco et al. , 2013a)  and which is explained in more detail in  Canonaco et al. (2013b). The ME-2 solver is here successfully applied to the time series of the unit mass resolution organic mass spectra measured by the AMS, including for most of the sites m/z up to 200 (for a few sites the analysis was performed only up to m/z 150 due to the low signal to noise ratio (SNR) observed). Data are first averaged to 15-30 min time resolution and after performing the source apportionment analysis they are averaged to 1 h for modeling purposes. The error matrix preparation before running the source apportionment algorithm is performed following the procedure introduced by  Ulbrich et al.
A standardized source apportionment strategy In this section we provide technical guidelines to perform a standard source apportionment analysis on AMS data in order to identify well known organic aerosol sources, like hydrocarbon-like (HOA), biomass burning (BBOA), cooking (COA) and oxygenated components (OOA), but also site-specific sources. Our approach is particularly relevant for rural locations where the temporal variability of emission sources is less distinct and where the aging processes are dominant compared to fresh emissions. A detailed description of the main features of these OA sources (source profile, diurnal patterns, etc. ) is presented in Sect. 4. Here, we define first our source apportionment strategy and then we provide details about the application of this methodology on the EUCAARI-EMEP data.
3. 2. 3). For each step the residual plots should always be consulted in order to evaluate whether the constrained profile(s) has (have) caused structures in the residuals. If so, the constrained profile should be tested with a higher scalar a value. 6. In general the OOA components are not fixed, but are left as 1 to 3 additional unconstrained factors. Our approach starts with an unconstrained run, where no a priori information concerning the source mass spectra is added. This first step is important because it reveals the possibility to separate or not several OA components with PMF. It also gives the user an idea of the number of possible sources for that site, even though they might not be clearly separated yet. If the HOA mass spectrum is not identified in the first step (e. g. considering from 1 to 5 unconstrained factors), the user should fix the HOA mass spectrum with the a value approach and, to evaluate the presence of this source into the dataset, investigate its diurnal pattern and correlation with available external tracers (e.
Application to the EUCAARI-EMEP data The previously mentioned procedure is successfully applied on the 25 available organic AMS datasets and therefore it provides a consistent methodology. Deploying the ME-2 solver allows the discrimination of traffic and biomass burning within the primary sources and of two OOA components for the secondary fraction for most of the sites. In order to perform a value runs within the ME-2 solver, it is necessary to select reference mass spectra to be constrained in the model. Due to the similar features of the HOA and COA mass spectra, in our work we choose as reference the HOA and COA mass spectra identified in Paris by  Crippa et al. (2013a)  because of the significant contribution of the cooking source to the total OA mass and its strong diurnal pattern at that site.
Primary and secondary OA source contributions The standardized source apportionment strategy introduced in Sect. 3. 2 was systematically applied to the 25 available AMS datasets, consisting of the AMS matrices with the organic mass spectra over time and the corresponding errors. Table  SI -2 reports the comparison of the number of OA components identified with the a value approach in comparison with the unconstrained run (PMF)  (Ulbrich et al. , 2009) , highlighting in red the constrained source mass spectra. The unconstrained PMF often cannot provide a clear separation of OA sources in rural and remote sites  (Zhang et al. , 2007; Jimenez et al. , 2009). This may also happen at urban background sites where the effect of meteorology can still dominate over the source temporal variability  (Lanz et al.
, 2008; Canonaco et al. , 2013a). In our 25 datasets, unconstrained PMF allows mainly the identification of POA (often including HOA and BBOA in one factor typically characterized by high signal at m/z 44 and 60) and SOA sources (refer to Table SI-2). Even when HOA is identified, it is often not clean due to the high contribution of m/z 44 (which should be rather small for primary traffic emissions). In some cases it is only possible to separate two secondary oxygenated components, but no primary source is retrieved, although expected. On the contrary, with our approach, primary and secondary OA sources are retrieved for all the analyzed datasets, including traffic (HOA), cooking (COA) and biomass burning (BBOA) as primary sources, and methane sulfonic acid (MSA), semi-volatile and low-volatility oxygenated OA (SV-OOA and LV-OOA) as secondary components.
For the Cabauw 2008 and Finokalia 2009 datasets the contribution of local (site specific) sources is also observed. The ME-2 solution for the Cabauw 2008 campaign includes a site specific factor (named here LOA) which is interpreted to be HULISrelated (humic-like substances), as explained in detail by  Paglione et al. (2013). These results represent a great improvement in the source apportionment field, since we demonstrate the possibility to identify several primary and secondary OA components 23340 Figures.
, although requiring often very high computational time. Figure  4  summarizes the average organic aerosol concentration and the average relative contribution of each OA source for each site (see also Table  2 ). For all sites and campaigns it is possible to separate a hydrocarbon-like OA factor (average equal to 11 ± 6 %), whose contribution on average ranges from 3 % to 24 % to the total OA mass. The HOA average concentration ranges between 0. 1 and 2. 1 µg m -3 depending on the site location and campaign. Biomass burning is identified in 22 datasets and is presumably associated with a mix of domestic heating during cold periods and open fires (e. g. agricultural, forest and gardening waste) and barbecuing activities etc. The BBOA contribution to the total OA mass varies on average between 5 and 27 % (corresponding to 0.
the OA concentration for all the rural, marine and urban sites, highlighting the role of specific sources within different concentration ranges (average over 21, 1 and 3 datasets for panel a, b and c, respectively). The number of measurements happening for each concentration bin is reported (black line with markers) and shows a decreasing trend for higher concentration. The last concentration bin includes all data points above that concentration. For rural sites (see Fig. 5a ), the HOA contribution decreases with increasing OA concentrations, while the BBOA contribution is very small for very low concentrations (< 1 µg m -3 ), but stable over the rest of the concentration range. No general pattern can be observed for the two secondary components. However the LV-OOA fraction seems to increase at higher concentration.
POA and SOA mass spectral variability Figure  7  shows the median mass spectra of HOA (n = 25), BBOA (n = 22), SV-OOA (n = 21) and LV-OOA (n = 25) together with the 25th and 75th percentiles. The presented mass spectra (MS) can be considered as reference profiles for European sites in addition to the work presented by  Ng et al. (2011a) , where only for a reduced number of datasets it was possible to retrieve a good separation of primary sources (e. g. HOA and BBOA). The comparison between the Ng et al. MS (2011a) and our median profiles provides an R 2 equal to 0. 99, 0. 93, 0. 86 and 0. 95 for HOA, BBOA, SV-OOA and LV-OOA, respectively. However, as shown in Fig. 6 , specific m/z express a stronger variability within a source profile compared to others, as discussed later. The hydrocarbon-like OA (HOA) profile is characterized by peaks corresponding to aliphatic hydrocarbons (including m/z  27, 41, 43, 55, 57, 69, 71, etc.
7 , where the SV-and LV-OOA components retrieved for all our datasets are represented within the triangular space defined by  Ng et al. (2010). The aging of secondary OA components can be studied considering the evolution in the f 44 (corresponding to the CO + C ratio is 0. 13 for HOA, 0. 22 for BBOA, 0. 41 for SV-OOA and 0. 81 for LV-OOA, consistently with AMS high resolution analyses available in the literature  (Jimenez et al. , 2009; Ng et al. , 2010; DeCarlo et al. , 2010).
Conclusions We develop a new standardized approach for source apportionment analysis applicable for aerosol mass spectrometer measurements. Our source apportionment procedure is tested and systematically applied to 25 organic aerosol datasets (consisting of 13 rural/remote, 2 marine and 2 urban sites across Europe), demonstrating the possibility to separate the main primary and secondary organic aerosol components also for rural sites. This represents a significant advancement compared to previous literature studies which showed limitations of the unconstrained positive matrix factorization when applied on rural/background site datasets. Our source apportionment strategy is significantly improved through the use of the multilinear engine  (Paatero, 1999)  and the SoFi toolkit developed by  Canonaco et al.
online at http://www. atmos-chem-phys-discuss. net/13/23325Discussion Paper | Discussion Paper | Discussion Paper | and Communications Finland, the Academy of Finland Center of Excellence Program (project no. 1118615), the German Umweltbundesamt (contracts 351 01 031 & 038), the UK Natural Environment Research Council (NERC) through NCAS scientist contributions and the APPRAISE program, the Swedish Research Council and Swedish Environmental Protection Agency, the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FOR-MAS), as well as the Nordic Council of Ministers. Several groups were supported by the EU ACCENT NoE and several sites by the EUSAAR Infrastructure Network. The Chilbolton dataset was supported by the UK Natural Environment Research Council (NERC) through the Aerosol Interactions in Mixed Phase Clouds project (Grant ref: NE/E01125X/1), part of the APPRAISE directed programme.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. This work was funded through the  European EUCAARI IP , the  EU   FP7 Infrastructure Network ACTRIS , other collaborating initiatives and through national funding through individual members states to the CLRTAP as a contribution to the  EMEP monitoring program. The CEH and UK contribution, including much of the synthesizing analysis, was funded by the  UK Department for Environment, Food and Rural Affairs (Defra). Measurements were further funded by the following national sources: the  Spanish Ministry of Science and Innovation , the  Swiss Federal Office for Environment, Helsinki Energy , the  Ministry of Transport ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.