dataesr/scientific-paragraphs-categorization huggingface dataset:
A phase delay between δO 2 /N 2 and δ 18 O atm is then deduced through the conversion of the phase calculated between the δO 2 /N 2 and δ 18 O atm filtered records after cross-correlation (hereafter Matlab delay). An independent estimate of the offset has been manually calculated from the identification of the timing of extrema in both records following the same methodology as in Appendix C (hereafter manual delay). During periods of weak eccentricity (e. g. around 400 ka and before 720 ka), there is no clear correspondence between the variations of δO 2 /N 2 and δ 18 O atm compared to the variations of their orbital target curves, as previously noted  (Dreyfus et al. , 2007; Landais et al. , 2012). During these periods, the variations of insolation in the precession band are probably too small to be imprinted in either δO 2 /N 2 or δ 18 O atm records.
During the remaining intervals of intermediate to strong eccentricity, the offset between δO 2 /N 2 and δ 18 O atm varies between -6 and -1 ka in the Matlab delay and between -8 and +1 ka for the manually calculated one (Fig. 4 ). The Matlab delay tends to present smoother and less marked varia-Clim. Past, 12, 729-748, 2016  www. clim-past. net/12/729/2016/ tions than the manual estimate. For Termination II, we obtain a δ 18 O atm vs δO 2 /N 2 phase delay of 4. 5 ka, which is in good agreement with the δ 18 O atm vs precession lag observed on raw data (5. 2 ka) and a zero phase between δO 2 /N 2 and summer solstice insolation as displayed on Fig. 3. On Fig. 4 , we observe minimal offsets during MIS 6-7, the end of MIS 9, the end of MIS 14-start of MIS 15 and the end of MIS 17. These periods are marked by high eccentricity levels together with intermediate ice-sheet extents (i.
e. neither full glacial conditions nor extremely warm interglacial conditions). On the contrary, local maxima of the δO 2 /N 2 -δ 18 O atm phase delay are observed for Termination II (-4. 5 ka), MIS 8 (-5 ka) and MIS 16 (-2 ka). Over MIS 15, the offset calculated with Matlab tends to increase while the manually calculated one presents a larger variability. Part of the variations in the offset value between δO 2 /N 2 and δ 18 O atm may be due to the uncertainty in the age difference between ice and gas ages since δ 18 O atm is expressed on a gas timescale while δO 2 /N 2 is on an ice timescale. Such uncertainty is largest during glacial periods, when the age is larger and the uncertainties on accumulation rate and temperature (i. e. main parameters controlling the firn depth) are larger. However, this uncertainty always stays below 1 ka, and therefore cannot explain the observed variations in delay values between δO 2 /N 2 and δ 18 O atm.
The marine cores data on Fig. 4  are presented on their original chronologies, constructed by tuning of their δ 18 O to the LR04 benthic stack  (Lisiecki and Raymo, 2005). The uncertainty associated with this dating method is estimated to be 4 ka for the last 1 million years. Such a large uncertainty prevents us from any comparison of the absolute timing of ice sheets discharge events with our ice core records. However, the occurrence of IRD events against the δ 18 O record of foraminifer gives us information about their relative timing within the stratigraphy. We thus only discuss the occurrence of Heinrich-like Clim. Past, 12, 729-748, 2016  www. clim-past. net/12/729/2016/ events and Greenland and/or European ice sheets discharges in regards to the variation of the δO 2 /N 2 -δ 18 O atm offset.
Acknowledgements. We address our first acknowledgements to the two anonymous reviewers and the editor for their implication in the review process. We thank  Jean-Robert Petit  and  Laurent Arnaud  for the help with the surface temperature data of Vostok and EDC. We thank  James Channell  for the marine core data. The present research project no. 902 has been performed at Concordia Station and was supported by the  French Polar Institute (IPEV). This project was funded by the  "Fondation de France Ars Cuttoli" and the "ANR Citronnier ". The research leading to these results has received funding from the  European Union 's  Seventh Framework programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no. 243908 , " Past4Future. Climate change -Learning  from the past climate". This is LSCE contribution no.
Vehicular ad hoc networking is an emerging paradigm which is gaining much interest with the development of new topics such as the connected vehicle, the autonomous vehicle, and also new high-speed mobile communication technologies such as 802. 11p and LTE-D. This paper presents a brief review of different mobility models used for evaluating performance of routing protocols and applications designed for vehicular ad hoc networks. Particularly, it describes how accurate mobility traces can be built from a real-world car traffic dataset that embeds the main characteristics affecting vehicle-to-vehicle communications. An effective use of the proposed mobility models is illustrated in various road traffic conditions involving communicating vehicles equipped with 802. 11p. This study shows that such dataset actually contains additional information that cannot completely be obtained with other analytical or simulated mobility models, while impacting the results of performance evaluation in vehicular ad hoc networks.
The second solution, namely modelling, is easier to realize by means of computers and may include many complex models. However, the accuracy depends notably on the availability of realistic mobility models. The authors of  [1]  and  [2]  show that, depending on the mobility model, the results of performance evaluation are different for the same routing protocol. An example is developed in  [1]  based on the DSR protocol. Generally in the literature, the modelling of vehicle mobility are made thanks to random patterns, microscopic traffic simulators, or real traces of vehicles. In this paper, the authors discuss the benefit of using real traces of vehicles extracted from car traffic dataset in simulation-based evaluation of vehicular ad hoc network performance. This study uses the real traces processed by  [3]  based on road videos filmed by helicopter within the context of the French research project named MOCoPo (Measuring and Modelling Congestion and Pollution).
D. Real traces of vehicles Mobility traces of vehicles through GPS data can be easily found nowadays. As an example, the Community Resource for Archiving Wireless Data At Dartmouth (CRAWDAD,  [23] ) provides trajectory datasets for VANET studies. GPS traces are obtained with map-matching algorithms. They come from specific vehicle fleets, such as bus or taxi fleets, which sometimes travel with dedicated work constraints. They do not come from common car users which are the first user class of road. Real traces based on GPS can be used to study the connection time between vehicles. In this way,  [24]  uses GPS traces from taxis and shows that the high mobility of VANETs induces time-limited connections between vehicles. Indeed, two vehicles, travelling from opposite directions on the highway at 130 km/h and having a maximum communication range of 500-meter long, will only communicate for up to 15 s  [25].
In fact, a difficulty of using GPS real traces is that, currently, either vehicles are not close together (dispersed within a wide area) or they include very few vehicles. Consequently, such traces do not allow an accurate study of V2V ad hoc communications with various vehicle density. However, the traces provided by CRAWDAD could be relevant to study the procotocols and applications relying on vehicleto-infrastructure communications. It is possible to obtain real traces of vehicle trajectories with aerial road traffic recordings. Studies had been achieved in the past by mounting a camera on a fixed position or onboard aerial platform  [26]. [3]  notes that two main groups of datasets were constructed since the beginning of this century: series of datasets resulting from the experiments conducted in the Netherlands by the TU Delft  [27]  and the datasets of the NGSim project, conducted with the funding and supervision of FHWA  [28].
A. Presentation of the MOCoPo data The MOCoPo research project (Measuring and Modelling Congestion and Pollution) was a French project aiming to improve the modelling of congestion and pollutant in urban highways (January 2011 -December 2013). The data collection was performed at the RN 87 (in the southern suburbs of Grenoble) in September 2011. A helicopter equipped with three high-definition cameras filmed the highway for several hours. In addition, pollutant measurement and loop detector data were collected. The site of the project  [30]  provides maps and access to the data. Collected data are from a 500-meter-long express road with a speed limit of 70 km/h. For this area, data collection periods were chosen to cover the congestion occurring in morning rush hours. In order to be transformed into trajectories, the MOCoPo videos were processed.
B. MOCoPo trajectory interest for VANETs Getting real-world traces usable for VANETs studies is not obvious. GPS-trace databases concern vehicles geographically dispersed or few vehicles; NGSim datasets need to be processed in order to obtain the final usable trajectories. The MOCoPo database consists of vehicles close to each other recorded during rush hours. Its data contain the information on the driver behaviours (speed, acceleration, lane changing. ). Moreover, the data show periods of congestion where vehicles stop and restart after a few moments (socalled stop and go wave) and where vehicle speeds are highly variable. It is therefore interesting to find time periods in the database where vehicles have high speed or respectively low speed.
C. Mobility profiles in MOCoPo data The authors split the trajectory database of MOCoPo into groups corresponding to different congestion scenarios characterised by the speed of the vehicles. The result is shown in figure  2. A significant variation in travel time is observed over time. Some vehicles pass through the area in less than 30 s, i. e with an average speed of 60 km/h, others in more than 120 s, i. e. with an average speed below 15 km/h. In light of these results, it is relevant to define three groups of vehicles with different mobility features presented in table  I. Figure  3  shows the travel distance versus the time for each three mobility groups. The slope of the curve representing each vehicle corresponds to their instantaneous speed. As it can be seen in the figure, the vehicles of group 1 maintain a high speed while the vehicles of group 2 suffer a period of stop and go wave (the slopes of the travel distance curve is horizontal, which means a zero speed).
C. Detector-based model -SUMO simulation The detector-based model only keeps the knowledge of the departure and arrival times of the vehicles extracted from the MOCoPo data. The data used are then similar to the outputs of a system composed of two electromagnetic loop detectors and a vehicle identification system (such as an automatic number plate recognition, a camera, or a bluetooth system). The trajectories of vehicles are still to be built from these departure and arrival times. The SUMO microscopic traffic simulator software has been chosen to simulate the behaviour of the vehicles and create the trajectories. A 500-meter-long two-lane road is represented under SUMO with a RN87-like curvature. The parameters of vehicle length is set to 5 meters and the speed limit is set to 70 km/h which corresponds to the legal speed limit on this road.
E. Comparison of the resulting trajectories From the figures 7, 8, 9, it can be seen that despite the convergence of the positions, the speed profiles vary considerably according to the mobility model used. Especially, When considering the different approximations of this latter, namely the Fixed speed 0. 5, 1, 3, 5 and 10, the curves show that they tend to get closer to SUMO curves as they are less precise in comparison with the original MOCoPo. This means that the simulated trajectory obtained through SUMO using the parameters that reproduce a specific traffic condition may result in a faithful trajectory in terms of positions while not offering any guarantee on the speed profile of the vehicles. Consequently, though simulated trajectories from SUMO could be useful for global simulation requiring realistic vehicle trajectories on the roads, the specific studies that investigate the performance in realistic traffic conditions, especially when the speed profile is important, should better rely on real-world traces such as car traffic dataset.
V. SIMULATION DESCRIPTION AND RESULTS The purpose is to evaluate the impact of the differences in the speed profiles of the mobility models (the position traces are almost the same for all) on network performance through simulation results. The results obtained with original MOCoPo dataset traces are compared with those obtained using two other mobility models (fixed speed and SUMO) regarding the three groups of mobility (group 1, group 2, and group 3). These simulations rely on the ability of Riverbed OPNET Modeler software to assign a trajectory to each node of a communication network by means of external TRJ files in the OPNET format. The following paragraphs present the simulation system configuration and the application. Then, the results are discussed.
Inspired from the open shortest path first (OSPF) protocol, OLSR maintains topology information and periodically computes the shortest path route (hop count) to  each known destination. Moreover, OLSR tries to reduce the impact of flooding due to broadcast traffic by selecting a subset of special nodes called the multipoint relays (MPR) which are the only allowed to retransmit broadcast traffic in the entire ad hoc network. This latter reason makes it a good choice as the routing protocol for the evaluations performed in this paper since the dataset contains dense VANETs  [33]  in addition to the fact that a built-in implementation of OLSR is proposed in the OPNET modeler suite.
VI. CONCLUSION The development of big data announces the availability of many datasets including vehicle trajectories in smart city. Targeting a future generalization of such data in the evaluation of vehicular networks, this paper demonstrates the use of the MoCoPo dataset which includes the real-world traces of the cars in a road section. Though they were early acquired for studies in the domain of road traffic theory, one of the goals of this paper was to show that the MOCoPo data particularly fit to the evaluation of vehicular ad hoc networks in comparison with other mobility models such as those coming traffic simulators. After they have been split into three groups corresponding to different mobility profiles namely fluid (group 1) hybrid (group 2) and congested (group 3) traffics, the MOCoPo dataset traces have been used to simulate the scenarios of a group of communicating vehicles.
Once a year, during the cruise for servicing the mooring, seawater samples are taken for TCO 2 and TA measurements. The samples are analysed using potentiometric titration derived from the method developed by  Edmond (1970)  with a closed cell. The calculations of the equivalent points are estimated using a non-linear regression method  (DOE, 1994). For calibration, Certified Reference Materials (CRMs) provided by Prof. A. Dickson (Scripps Institution of Oceanography, San Diego, USA) are used. From 2005 to 2007, during the AMMA program  (Redelsperger et al. , 2006) , two cruises per year were realised. The accuracy is estimated at93 mmol/kg for both TCO 2 and TA. Measurements collected from 2005 to 2007 during the EGEE cruises in the eastern tropical Atlantic have been used to determine an alkalinityÁsalinity relationship for the region 108SÁ68N 108WÁ108E  (Koffi et al.
, 2010)  with a standard error on predicted alkalinity of97. 2 mmol/kg: TA ¼ 65:52 Ã SSS þ 2:50 (1) Recent TA measurements made from 2009 to 2015 during the PIRATA France (PIRATA FR) cruises confirm that this relationship is still valid (Fig. 1 ). We have calculated the 10-quantiles that divide the dataset into 10 subsets of equal size. The 10-quantiles are indicated on Fig. 1a  (red squares) and follow the relationship of  Koffi et al. (2010)  even for low and high SSS values. In addition, the comparison of the relationship with the relationships of  Takahashi et al. (2014)  (Fig. 1a ) and of  Lee et al. (2006)  (Fig. 1b ) shows that the  precipitation/evaporation, TCO 2 is normalised to a mean SSS of 36 (nTCO 2 036*TCO 2 /SSS). According to  Lauvset and Gruber (2014) , when pH is not measured, the best pair of the carbon system to calculate pH is fCO 2 and TA, even with TA estimated from SSS.
86 8C in April and a minimum of 23. 98 8C in September (Table  1 ). SSS and fCO 2 show a large range (resp. 0. 5 and 43 matm) with their minimum values occurring during the cold season, in June and higher outside the ACT period (Fig. 5b  and c  and Table  1 ). The interquartile range (IQR) is also presented to show the variability of the dataset. It is defined as the difference between the third quartile and the first quartile and measures how spread the 50 % of the dataset is. The largest IQR are observed from May to August for both variables (Fig. 5b  and c ), that is, during the ACT period. A high correlation (0. 92) exists between SSS and fCO 2 , while the correlation between fCO 2 and SST is weak and statistically non-significant (Table  2 ). At a seasonal timescale, the lack of correlation between fCO 2 and SST can be explained by two opposed effects: the seasonal cooling in the ACT favours fCO 2 decrease, but as the cooling in the ACT is mainly a consequence of mixing between mixed layer and upper thermocline waters, high fCO 2 are brought up from the subsurface thus leading to an fCO 2 increase.
58WÁ10. 58W. Monthly salinity advection anomalies are calculated by removing monthly data climatology. The meridional salinity advection anomalies confirm the intrusion of saltier waters south of the mooring, which is particularly pronounced in 2011 (Fig. 7 ), from the surface down to 40 m depth, and especially from May to July. In order to highlight the variations of both fCO 2 and SSS, and to plot the data on the same scale, we normalise them by subtracting the mean of the dataset and dividing the difference by the standard deviation. The distributions of normalised fCO 2 and normalised SSS anomalies show that fCO 2 decreases (increases) are associated with SSS increases (decreases) after mid-August (Fig. 8 ), a pattern still observed until the end of the year 2011 although it becomes weaker from November.
Over the 7 years of the time series at 68S 108W, no significant trend can be detected in fCO 2 , pH and TCO 2 in the surface layer. The data period is short and the gaps in the record due to technical failures make it difficult to determine a trend. However, even the SST and SSS do not present any trend over a longer period (2000Á2013) and on a record without data gaps. This is in contrast with the western tropical Atlantic, where an increase of fCO 2 over time is detected at the CARIACO site and is mostly explained by the trend on SST  (Astor et al. , 2013). Detecting a trend in fCO 2 and pH at 68S 108W is impeded by the complex ocean circulation that causes high variability in the carbon parameters. Over the period 2006Á2014 no ocean acidification is detected. Long-term sustained observations are necessary to better document the processes affecting this region given the strong variability at this site.
Acknowledgements We acknowledge support from the European Integrated Projects CARBOOCEAN (contract 511176-2) and CARBOCHANGE (grant agreement 264879), the Institut de Recherche pour le De´veloppement (IRD) and the national program LEFE CYBER. We are very grateful to US IMAGO of IRD, and especially to Jacques Grelet and Fabrice Roubaud for their technical support at sea and the deployment of the sensor. We thank the DT INSU for preparation and calibration of the CARIOCA sensor. Seawater samples were analysed for TCO 2 and TA by the SNAPO-CO 2 at LOCEAN in Paris. Data management for PIRATA moorings is conducted by the TAO project office at NOAA/PMEL in collaboration with many research institutes listed on the PIRATA website (www. pmelnoaa. gov/pirata). Precipitation data from the GPCP were downloaded from the Giovanni online data system, developed and maintained by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).
We also acknowledge the TRMM mission scientists and associated NASA personnel for production of the data used in this research effort. TMI data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science and REASoN DISCOVER project. They are available at www. remss. com. We thank Fabrice Hernandez from IRD for making available to us the products of the Mercator Ocean (Toulouse, France) GLORYS2V3 global ocean reanalysis. We acknowledge the OSCAR Project Office for providing the ocean surface current analyses. The fCO 2 data at 68S 108W presented here are publicly available at the Carbon Dioxide Information and Analysis Center (CDIAC), www. cdiac. ornl. gov/oceans/ and in the SOCAT database (www. socat. info). We are grateful to Rik Wanninkhof and an anonymous reviewer for their comments that improve the manuscript.
The scattered field was then detected through its beat note with the probe field transmitted through the sample, which plays the role of a local oscillator and is extracted by lock-in detection. Samples were mounted on a piezo-scanner stage that allowed scanning the sample to acquire 2D images of nanorods. All data where acquired with integration times of 5 ms/pixel using a resolution of 100 nm/pixel. The setup also included white light trans-illumination and a CCD camera for bright-field imaging of the biological samples. Samples were first prepared by spin coating a mixture of (1:1 ratio) CTAB stabilized nanorods and polyvinyl alcohol (PVA, 1. 5%) on the surface of plasma cleaned glass slides. A drop of silicon oil was then added on the sample to ensure homogenous heat diffusion. PhI images of nanorod fraction with a SPR peak at 642 nm were acquired using laser excitation at 532 nm (Figure  3B ) and 640 nm (Figure  3C ) with the same intensities at the imaging plane.
Acknowledgements This work was supported by  CNRS ,  Agence Nationale de la Recherche  ( ANR-14-CE09-0018-01 ),  Conseil Régional d'Aquitaine , the  France-BioImaging national infrastructure  ( ANR-10-INBS-04-01 ) and  IdEx Bordeaux  ( ANR-10-IDEX-03-02 , LAPHIA) and  SIRIC BRIO. E. S. S. acknowledges Marie Curie Individual Fellowship  (655890 funding).
This is the motivation of the present note. Examples of quasimodes are provided by high-energy eigenfunctions of (-∆ + V) or clusters of eigenfunctions: denote by (ψ j ) a Hilbert basis of L 2 (D) consisting of orthonormal eigenfunctions of the operator -∆ + V with Dirichlet conditions, associated to the eigenvalues λ j → +∞. Then the family v λ = λ j ∈[λ-R(λ),λ+R(λ)] v j ψ j , with λ j ∈[λ-R(λ),λ+R(λ)] |v j | 2 = 1, λ → +∞, * Acknowledgement. NA and ML are partially supported by the Agence Nationale de la Recherche under grant GERASIC ANR-13-BS01-0007-01. FM is partially supported by grants MTM2013-41780-P (MEC) and ERC Starting Grant 277778. † Université de Strasbourg, IRMA, 7 rue René-Descartes, 67084 Strasbourg Cedex, France. e-mail: anantharaman@unistra. fr ‡ Université Paris Diderot, Institut de Mathématiques de Jussieu-Paris Rive Gauche, UMR 7586, Bâtiment Sophie Germain, 75205 Paris Cedex 13 France.