dataesr/scientific-paragraphs-categorization huggingface dataset:
Input Human health is a response to the exposure to a given air quality (STATE), and can be calculated using data that describe the air quality (such as level of concentration measured at a monitoring site, levels of concentration averaged for several monitoring stations or determined using an AQ model) and dose-response functions or concentration-response functions when available. In some case, the health impact can be calculated using data such as intake fractions computed after modelling the emissions to take into consideration (PRESSURES). The choice of a pollutant to perform HIA (Health Impact Assessment) is often more restricted by the available knowledge on health effects and on the way to measure those effects, than by the input provided by the STATE block. The selection of input data depends in fact on the availability of a causal function to derive health output.
In some cases, the implementation would simply mean an expert advice, in other cases, the use of some piece of software running a suitable optimization procedure. The RESPONSES block can again be described by three levels of complexity: -LEVEL 1: Expert judgment and Scenario analysis. In this case the selection of measures to be adopted is based on expert opinion, with/without modelling support to test the consequences of a predefined emission reduction scenario. In this context, the costs of the emission reduction actions can be evaluated as an output of the procedure (even if in many cases they are not considered). -LEVEL 2: Source Apportionment and Scenario analysis. In this case, the most significant sources of emissions are derived through a formal approach; this then allows to select the measures that should be applied.
The VNOs are perfectly isolated thanks to the allocations made by the Dynamic Resource Allocation Controller and the V NCC that use different slots. VNO can manage the return link resources (and moreover the forward link resources) as a pool of resources shared between the different STs. However, the isolation that must be ensured by OF meters implemented on the VNOs virtual switches suffers from the ofsoftswitch software implementation. The shaping is not stable as it must be. For an operational setup, we recommend to rely on a hardware implementation of OF meters that can be found in Openflow switches. The testbed is not fully compliant with the above specifications, as the (V NMC ) has been simplified and does not offer a full SNMP interface. The dynamic resource allocator is also simplified and is implemented as a hack of the existing OpenSAND allocator.
The IUCN Red List of Threatened Species (RLTS) is recognized as the most comprehensive tool for assessing risk of species extinction, based on a standardized methodology that is applicable for most taxa and geographic regions. Now established at regional, national and subnational scales, Red Lists have grown rapidly and autonomously thanks to active local initiatives. We investigated national and subnational Red Lists in 53 European and Mediterranean countries to get an overview of their ongoing development and uses in this region in 2014. Through an online questionnaire, we identified national Red Lists in 41 countries and subnational Red Lists in 16 countries. The public sector is the main coordinator and funder of RLTSs, while the IUCN has a limited involvement in local initiatives. Availability of data and expertise are the main factors determining the choice of taxonomic groups to be assessed.
Online survey We developed a questionnaire to obtain information on RLTS production, national conservation programs/ policies and the use of RLTSs in those programs (see Supplement 1 at www. int-res. com/ articles/ suppl/ n030 p255_supp. pdf). We adopted several typologies to describe stakeholders, RLTS methodologies, taxonomic groups (19 groups se lected), the determining factors for the development of RLTSs, conservation programs/ policies, and uses of RLTSs. The questionnaire included 398 conditional questions (questions to be answered de pending on the previous answer). It included closed and open questions, mandatory and non-mandatory questions as well as simple and multiple choice questions. In this last case, the sum of percentage in the results can be more than 100%. We built the online questionnaire (English/French) using the open source software LimeSurvey ® (Lime Survey 2011) (Supplement 1).
Role of RLTSs in biodiversity conservation programs and policies Our review of biodiversity conservation programs and policies in the Euro-Mediterranean countries did not aim to be exhaustive but instead to give an overview of the conservation framework in the region. The review documents a 'north-south' divide in conservation programs, and a stronger development of programs linked to international commitments compared to locally based initiatives. The survey shows that the European Union, and especially its Birds and Habitats Directives, has been a driving force to strengthen conservation strategies in the member countries. Species diversity knowledge and data collection are stimulated by the Habitat Directive progress reports and the development of protected areas (and green corridors) by the Natura 2000 network.
Acknowledgements. We thank all individuals across Euro-Mediterranean countries who helped us gather this information and especially those who answered the questionnaire. For their contributions in this study, we also thank  L. Billon ,  P. Dupont ,  P. Haffner ,  A. Horellou ,  A. Savouret Soubelet ,  R. Sordello  and  J. Touroult  ( Service du Patrimoine N aturel-Muséum N ational d'Histoire N aturelle, SPN -MN HN  ),  A. Carvois  and  F. Kirshner  ( UICN France ),  S. Bachman  ( Royal.
We reduced the data using the standard XMMSAS software package v13. 5 and the ESAS data-reduction scheme  (Snowden et al. 2008). Lightcurves were extracted for each of the three EPIC instruments and filtered to remove time periods of enhanced background caused by contamination from soft-proton flares. We used a collection of closed-filter observations to create a model image of the particle background. Long-term variations in the background rate were accounted for by rescaling the resulting image by the ratio between the count rates measured in the unexposed corners of the three instruments and the closed-filter observations. We then extracted source and background images in five energy bands (0. 5-0. 7, 0. 7-1. 2, 1. 2-2. 0, 2. 0-4. 0, and 4. 0-7. 0 keV). Vignetting effects were corrected by creating effective exposure maps in each band using the XMMSAS task eexpmap.
Chandra X-ray Observatory Chandra observed A2744 on several occasions (2001-09-13 with ACIS-S for 25 ks,  PI: David; 2006 -11-08, 2007 -06-10, and 2007-06-14 , for a total of 100 ks with ACIS-I, PI: Kempner; see  Owers et al. 2011). We analysed all archival data using CIAO v4. 6 and the corresponding calibration database by reprocessing all individual observations using the CIAO task chandra repro, examining the light curves of each individual observation for flares, and filtering out periods of increased background. The resulting event files were then merged and a mosaic image extracted in the 0. 7-7 keV band using the CIAO tool fluximage.
The ACS Source Catalogue For the HST weak-lensing catalogue, the shape measurements are made in the ACS/F814W band. The methodology used to build the catalogue was presented in previous analyses  (Jauzac et al. 2012, 2015, J12 and J15 hereafter). We thus here only give a suummary of the procedure, and refer the reader to these papers for more details. Our method is based on  Leauthaud et al. (2007)  (hereafter L07) who presented a weak-lensing analysis for the COSMOS survey. We use the SEXTRACTOR photometry package  (Bertin & Arnouts 1996)  for the detection of the sources with the 'Hot-Cold' method  (Rix et al. 2004, L07). The source catalogue is then cleaned by removing spurious or duplicate detections using a semiautomatic algorithm that identifies stars and saturated pixels, and designs polygonal masks around them.
6 < d < 30 pixels. The RRG method allows ellipticities to be greater than 1 due to noise. We thus by definition, restrict the ellipticity to be e 1. The limits applied to the size of the galaxies aim at eliminating: i) small galaxies, theoretically smaller than the PSF itself, which thus would have non-accurate shape measurements, and ii) large galaxies with a size similar to elliptical cluster members. Finally, in order to ensure we are only considering galaxies that are weakly-lensed, we remove all galaxies within the multiple-image regions (where the non-linear regime dominates), which can be approximated by an ellipse aligned with the elongation of the cluster predicted by the strong-lensing model (a = 60 , b = 42 , θ = 60 deg, α = 3. 5890837 deg, δ = -30. 399917 deg). The HFF-based catalogue extends to ACS-F814W magnitudes of 29, two magnitudes fainter than the pre-HFF dataset, which covers an extended region of the cluster (as in  Merten et al.
Another point that will be discussed in more detail in Sect. 8 is the position of S4. Indeed, S4 coincides with a remnant-core detected in the Chandra data (X2 in Fig. 4 ). In M11, no dark matter counterpart was found for this Xray peak, and this structure was then 'nick-named' the interloper. Our analysis contradicts M11's interpretation. While the Core, N, and NW substructures from M11 are recovered by our mass reconstruction, the W substructure, nick-named the Ghost clump by M11 because of a lack of X-ray counterpart, is not detected in our analysis. Fig. 4  shows the position of M11's W substructure as a red diamond, and as one can see while looking at the mass contours, no clear mass peak is detected at this location. The enclosed mass we measure at the M11's position is ID M this work M M erten+11 M M edezinski+16 Core 27.
X-RAY ANALYSIS To study the state of the hot ICM of A2744, we used our XMM-Newton data to extract thermodynamic maps of the central regions of the cluster. To this aim, we use the XMM-Newton images in 5 energy bands spanning the [0. 5-7] keV range (see Sect. 2. 2) and the corresponding exposure maps and background maps. The intensity of the sky background is estimated in each band by computing the mean surface brightness in a source-free region. Assuming that the X-ray emission can be locally described by a single-temperature absorbed APEC model  (Smith et al. 2001) , we use XSPEC to fold the model with the XMM-Newton response. We fix the metallicity to the canonical value of 0. 3 Z  (Leccardi & Molendi 2008)  and the Galactic column density to the value of 1. 5 × 10 20 cm 2 estimated from 21cm maps in the direction of A2744  (Kalberla et al.
2005). We then vary the plasma temperature to predict the expected count rate in each of our five bands as a function of temperature and create templates relating count rate to temperature. In Fig. 5  we show the expected count rate at fixed emission measured in the five energy bands. Around each pixel, we define a circular region containing at least 500 counts in the full [0. 5-7] keV band, and we measure the vignetting-corrected and background-subtracted count rate in each of our five bands. We then use a maximum-likelihood algorithm to fit the model APEC templates shown in Fig. 5  to the five data points and provide an estimate of the local temperature and emission measure with their uncertainty. We then construct a temperature map by gathering the best-fit values for each pixel. We also compute a (projected) entropy map by combining the local best-fit temperature and emission-measure values and computing the (pseudo-)entropy K = kT × (EM ) -1/3.
We use the form of the halo mass function obtained by a fit to the subhaloes in all three Millennium runs (Eq. 3,  Angulo et al. 2012). A similar adjustment is made for the mass of subhaloes identified by SUBFIND. Either halo mass function is calculated at redshift z = 0. 28 and z = 0. 32 with Planck 2015 parameters using the python module hmf  (Murray et al. 2013) , which contains implementations of both  Angulo et al. (2012)  mass functions. Fig. 7  shows that the changes in Ωm and σ8 move the halo mass function in different directions. The smaller value of σ8 from Planck results in a lower abundance of massive haloes, while the increased value of Ωm causes an overall shift of the mass function to higher values, which visibly affects masses below 10 14 h -1 M. By design, the adjusted halo masses follow the updated version of the halo mass function (green data points in Fig.
ACKNOWLEDGMENTS This work was supported by the  Science and Technology Facilities Council  [grant number  ST/L00075X/1 ,  ST/F001166/1 ,  ST/K501979/1 & ST/K501979/1 ] and used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk [www. dirac. ac. uk]). This equip-The Extraordinary Abell 2744 17 ment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  STFC  capital grant  ST/H008519/1 , and  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University. is part of the  National E-Infrastructure. MJ, DE, and HI thank  T. Erben  and  M. Klein  for their help with the WFI data. MJ, ML, and EJ acknowledge the  Mésocentre d'Aix-Marseille Université  (project number: 14b030).
HYS acknowledges the support from  Marie-Curie International Incoming Fellowship  ( FP7-PEOPLE-2012-IIF/327561 ) and  NSFC of China  under grants  11103011. JPK acknowledges support from the  ERC  advanced grant LIDA. JR acknowledges support from the  ERC  starting grant CALENDS and the  CIG  grant  294074. ML acknowledges the  Centre National de la Recherche Scientifique (CNRS)  for its support. PN acknowledges support from the  National Science Foundation  via the grant  AST-1044455 ,  AST-1044455 , and a theory grant from the  Space Telescope Science Institute   HST-AR-12144. 01-A. CT acknowledges the financial support from the  Swiss National Science Foundation.
The three aspects the recommendation covers are NS combination/orchestration, service configuration on the BNG, and BNG resources, status and event notification to the service platform. (3) BBF There are several ongoing activities within broadband forum (BBF) that is related to the MANO plane of telecommunication networks. The architecture and migration work area at BBF identifies the relationships between entities to facilitate the transition of networks towards virtualisation while documenting the key functionalities that need to be brought forward to enable a seamless evolution path. The work underpins all the new valueadded services and application delivery for fixed and mobile access networks, home and business that can now be deployed at the pace of each market. The models reflect the control, management and data plane aspects of BBF defined architectures and are augmented to subsume new industry directions such as SDN and NFV while carrying forward key aspects of broadband as currently deployed.
For the service delivery, NGSON extracts the requirements for service-and transport-related QoS to enforce corresponding QoS control mechanisms across the underlying networks. Software-defined networking  [35]  and NFV  [36]  initiatives can significantly contribute to achieve greater elasticity in NS deployments and accelerate a prominent innovation in NGSON service provisioning and delivery functions. In particular, SDN and NFV allow SPs to cre-ate a more powerful service logics based on NGSON that include VNFs handled as service components and participating to the service composition and orchestration process  [37]. In this regard, initiatives in IEEE and in NGSON WG have been recently undertaken to identify potential new standards in SDN/NFV and related areas to be developed in IEEE.
(4) Redzinc VELOX equipment manager  [47]   The information in the table is organised as follows: resource domain, resource type, resource domain orchestrator(s) options; a brief description of the domain functionalities; and the options currently available for the implementation of interface I5. In the case of networking resources, a first I5 implementation option relies on both (i) BGP-LS to expose network resource abstraction to domain orchestrator and (ii) PCEP to enable the orchestrator to issue network provisioning requests to the domain controllers. In particular, BGP-LS provides a distribution mechanism for exchanging link-state data either representing the real-physical topology retrieved from routing protocol databases or an abstracted topology including specifically created virtual paths.
The T-NOVA orchestration layer main capabilities are as follows: NS/VNF management and NS/VNF catalogues management, exposing the catalogues to the marketplace; Management of networking and cloud resources for NS/VNF hosting; Service mapping: resources allocation to NSs; NS/VNF instantiation requests management: the T-NOVA orchestrator receives NS/VNF instantiation requests from the marketplace, executes the instantiation and manages the NS/VNF instances repository; NS/VNF monitoring: VM-based monitoring data from the lower VIM layer and map it to the corresponding NS/VNF instances. (2) ESCAPE  [39] , as we have seen in Section 4. 1, is a proof of concept prototype of the UNIFY FP7 project  [40]. On the one hand, it can operate as a single-domain orchestrator for different technological domains.
Many palaeoclimate data and modelling studies have focused on the contrasts between the LGM, the mid-Holocene (6 ka BP), and the pre-industrial period. The BIOME 6000 project (http://www. bridge. bris. ac. uk/resources/Databases/ BIOMES_data) synthesized palaeovegetation records from many sites to provide global data sets for the LGM and mid-Holocene. Data syntheses are valuable in allowing researchers to see the global picture from scattered, individual records, and to enable model-data comparisons. The data can be interpreted in the context of a global, physically based model that allows the point-wise data to be seen in a coherent way. There are continuous, multi-millennial pollen records that stretch much further back in time than the LGM but they have not previously been brought together in a global synthesis to study changes of the last glacialinterglacial cycle.
bridge. bris. ac. uk/resources/ Databases/BIOMES_data), and our new biomized synthesis of terrestrial pollen-data records, focusing on the preindustrial period and 6 (mid-Holocene), 21 (LGM), 54 (a relatively warm interval in the last glacial period), 64 (a relatively cool interval in the glacial period), 84 (the early part of the glacial cycle), and 120 ka BP (the Eemian interglacial). The effects of rapid millennial-scale climate fluctuations were not simulated. Finally, in Sect. 4 we use our biome simulations to estimate net primary productivity.
Biomization Biomization assigns pollen taxa to one or more plant functional types (PFTs). The PFTs are assigned to their respective biomes and affinity scores are calculated for each biome (sum of the square roots of pollen percentages contributed by the PFTs in each biome). This method was first developed for Europe  (Prentice et al. , 1996)  and versions of it have been applied to most regions of the world  (Jolly et al. , 1998; Takahara et al. , 1999; Tarasov et al. , 2000; Thompson and Anderson, 2000; Williams et al. , 2000; Elenga et al. , 2004; Pickett et al. , 2004; Marchant et al. , 2009). We apply these regional PFT schemes (Table  1 ) to pollen records that generally extend > 40 kyr, assigning the pollen data to megabiomes (tropical forest, warm-temperate forest, temperate forest, boreal forest, savanna/dry woodland, grassland/dry shrubland, desert, and tundra) as defined by Harrison and  Prentice (2003)  in order to harmonize regional variations in PFT to biome assignments and to allow globally consistent model-data comparisons.
, 2008 and Spahni et al. , 2005 , mapped onto the EDC3 Parrenin et al. , 2007, age scale)  and orbital forcing from  Berger (1978). The composite CO 2 record contained in  Lüthi et al. (2008)  uses data from the Vostok core  (Petit et al. , 1999)  between 22 and 393 kyr. The Vostok record is now believed  (Bereiter et al. , 2012)  to be erroneously low during the early part of Marine Isotope Stage 3. For this reason, the FAMOUS results during this period are likely biased too cold. Although of a lower spatial resolution than HadCM3, these FAMOUS simulations have the benefit of being transient and representing low-frequency variability within the climate system, as well as using more physically plausible ice-sheet extents before the LGM than were used in the HadCM3 simulations. To allow the transient experiments to be conducted in a tractable amount of time, these forcings were all "accelerated" by a factor of 10, so that the 120 kyr of climate are simulated in 12model kyr -this method has been shown to have little effect on the surface climate  (Timm and Timmerman, 2007; Ganopolski et al.
We chose to use the actual climate model grids for the BIOME4 simulations, rather than interpolating onto the higher-resolution observational climatology grid, to avoid concealing the significant impact that the climate model resolution has on the vegetation simulation, and to highlight the differences between the physical representation of the climate between the two different models. Because of its lower resolution, FAMOUS cannot represent geographic variation at the same scale as HadCM3, which affects not only the areal extent of individual biomes but also how altitude is represented in the model, which can have a significant effect on the local climate and resulting biome affinity. The frequency of data available from the FAMOUS run also limits the accuracy of the minimum surface air temperature it can force BIOME4 with, as only monthly average temperatures were available.
, 2002). These mostly unchanged biome assignments derived from our pollen-data records are supported by our BIOME4 simulations in that, although both FAMOUS and HadCM3 do produce relatively warm anomalies compared to the LGM, both B4H and B4F simulations at 54 ka BP are similar to the LGM close to the pollen sites in the Americas, most of southern Europe (apart from Ioannina, where the data show highest affinity scores for temperate forest) and east Africa. In other parts of the world, the biomes simulated at 54 ka BP in B4H and B4F do differ significantly from those of the LGM. Both BIOME4 simulations show increased vege-tation in Europe and central Eurasia due to the climate influenced by the smaller Fennoscandian ice sheet, as well as reduced desert areas in North Africa and Australia, generally reflecting a warmer and wetter climate under higher CO 2 availability than at the LGM.
It is, however, noted that in most cases the biomized pre-industrial pollen agree with pre-industrial biomes. The climate models produce some differences in climate forcing of the vegetation due to (1) difference in resolution, affecting the biome areal extent and altitude and (2) ice-sheet extent, affecting temperature (Sect. 3. 2). We can use the pre-industrial period as a test bed to compare model outputs and pollen reconstructions (using the BIOME 6000 database), showing that there are some biases that can be attributed to biases in BIOME4, some to the biomization method, and some to the models' limiting geographical resolution.