dataesr/scientific-paragraphs-categorization huggingface dataset:
Megasatellites are also found in adhesins in C. albicans (ALS1 to ALS7 and ALS9 genes)  (9)  and in C. glabrata (EPA1, EPA2, and EPA13 genes)  (53, 54). In the well-studied model organism Schizosaccharomyces pombe, megasatellites are detected in the MAP2 P-factor pheromone gene (SPCC1795. 06), the MAP4 gene (SPBC21D10. 06c) (encoding an adhesin required for mating), and the MAM3 gene (SPAP11E10. 02c) (involved in cell-tocell adhesion). Comparison of peptide motifs to databases of known motifs shows the presence of possible phosphorylation and glycosylation sites found in flocculins, as well as putative sites of myristoylation. Addition of myristate (a 14-carbon fatty acid) to proteins is a common posttranslational modification of proteins generally associated with the plasma membrane and/or involved in signal cascades.
Maps. Because the recent S57 vector maritime maps are free in some countries and include many geographic objects, we developed "Handinav" software. This transforms the new S57 data into XML structured files. Thus, an important number of objects can be chosen to be displayed or not: sea areas, coastlines, land areas, beacons, buoys, landmarks, and a great deal of other data are contained in these maritime XML maps. SeaTouch software builds JAVA3D maps from XML data.
Data Collection. In this study, we assessed two types of data. Firstly, we measured the seven Euclidean distances between the waypoints and the nearest positions of the ship during the itineraries. This provided us with an indicator of the distance accuracy of the navigation control. We, secondly, compared the layouts of waypoints drawn on the tactile paper and their real positions. To be able to compare these two configurations, we considered that the first waypoint was drawn at its right position. Then, the distances were managed by applying the scale used in the phantom workspace during navigation (1 : 1000).
8 × 2. 8 deg horizontal resolution. The contribution of stratospheric CO from pressures below 100 hPa to the total column is typically less than 5 % for mid-latitudes and less than 9 % for the tropics. The MOZART climatology has monthly, but not interannual variability, hence it is not a source of any decadal trends observed in the MOPITT data. In this study we use MOPITT V5 data  (Worden et al. , 2010; Deeter et al. , 2011; Deeter et al. , 2012). In order to compare with the other TIR instruments (AIRS, TES and IASI), we use the retrievals of CO from the TIR channels only (V5T data). The retrievals from the NIR channel are only possible over land for MOPITT. CO total columns from the MOPITT multispectral retrievals combining TIR and NIR channels are very similar to the TIR-only results, but are not directly comparable to any of the other instruments.
AIRS AIRS is a TIR grating spectrometer on board EOS-Aqua launched in 2002 into a sun-synchronous polar orbit with ∼13:15 local time for the ascending node equator crossing  (Aumman et al. , 2003). AIRS has 13. 5 km × 13. 5 km horizontal spatial resolution, however, the AIRS algorithm utilizes cloud-clearing processes  (Susskind et al. , 2003)  that increase the global coverage significantly, but reduce the spatial resolution to 45 km × 45 km. AIRS has a 1650 km swath providing near global coverage twice daily. AIRS resolving power is λ/ λ = 1200, resulting in a spectral resolution ∼1. 8 cm -1 for the 4. 6 µm CO absorption. AIRS radiance spectra are used to determine cloud and surface properties along with vertical profiles of atmospheric trace gases and temperature. AIRS CO retrievals have been compared to MOPITT and in situ data  (Warner et al.
, 2007b)  and to MOPITT data processed with the TES a priori profiles and covariances  (Ho et al. , 2009). When a priori profiles and covariances are the same for both TES and MOPITT retrievals, column differences are less than 6. 5 % globally, with MOPITT higher than TES. Unlike MO-PITT, AIRS and IASI, TES has relatively sparse sampling, only along the orbit track. However, sampling was sufficient for comparisons of monthly averages in the regions chosen for trend analysis.
MOPITT data MOPITT retrievals are performed for cloud free scenes using a cloud mask determined by data from Terra-MODIS  (Ackerman et al. , 1998)  and MOPITT  (Warner et al. , 2001). For the Northern and Southern Hemisphere (NH and SH) monthly averages, we use MOPITT level 3 (L3) monthly averages with latitudes 0 to 60 • , all longitudes, daytime only. MOPITT L3 data are weighted by an observation quality index (OQI) before averaging. The OQI is defined in the MOPITT V5 Users Guide, March 2011, available at http://www. acd. ucar. edu/mopitt/publications. shtml. Weighting by the OQI gives more weight to observations with less geophysical noise  (Deeter et al. , 2011). In order to reject retrievals that have a significant influence from the apriori profile while maintaining relatively uniform sampling across latitudes, we selected data with degrees of freedom for signal (DFS) > 0.
75. For the regional averages, we use MO-PITT L2 data, also weighted by the OQI and we selected daytime, land-only scenes with DFS > 1. 0. MOPITT L2 data were used so that we could apply a correction for geolocation errors in MOPITT data processing (see the Geolocation Bias Report, 2012, also at http://www. acd. ucar. edu/mopitt/publications. shtml. ) To account for this error, we added 0. 35 • to the longitude values before selecting and averaging over the latitude and longitude ranges used for this study.
AIRS data For this study we use AIRS V5 L3 monthly mean products from the Giovanni application provided by GES-DISC (Goddard Earth Sciences Data and Information Center). Giovanni allows selection of AIRS total column CO for user-selected geographic areas and time ranges. Monthly average L3 data are gridded by 1 • × 1 • (latitude × longitude) and filtered by Quality Assurance (QA) as good (QA = 0 or 1), where values of 0, 1 and 2 indicate "best", "good" and "do not use", respectively. AIRS V5 L3 monthly data were selected for daytime (ascending orbits) and averaged for the latitude and longitude ranges used in this study without further weighting or filtering.
TES data The TES L2 algorithm performs retrievals on both clear and cloudy scenes and cloudy scenes were removed before computing the monthly averages used in this study. TES V4 L2 data were selected for daytime retrievals with quality flag = 1 (good) and DFS > 0. 9. Criteria for cloud-free data are an effective cloud optical depth (OD) <0. 4 and land-only data were used in the regional averages. Note that starting in 2010, routine sampling of the SH was limited to northward of 30 • S in order to conserve instrument lifetime. Therefore, TES data are not included for the SH after 2009.
This is apparent in the IASI data for October, 2010 when IASI sampled intense burning in southern Africa about twice as frequently than MOPITT. Total column results are less sensitive than profile retrievals to differences in vertical sensitivity that are due to instrument parameters, such as spectral resolution, and the choice of constraints in the retrieval algorithms. However there are differences in the instrument column averaging kernels, as shown in  George et al. (2009) , that would give slightly different results if applied to the same atmospheric distribution.
Linear trend analysis To compute linear trends we apply a linear least squares fit to the 12-month running averages, weighted by the standard deviation of the measurements used in the monthly average. For the datasets with months where there were gaps in the data, we use the data record mean standard deviation for each region scaled by a factor of 100 so that these months are deweighted in the fit. We report 1σ errors from the linear fits. Figure  5  shows the 12-month running averages and linear fits for 11-yr trends with MOPITT CO total column data for all the spatial regions considered. Table  4  lists slope values, in % yr -1 , for the time periods covered by each of the instruments (where we have cut out at least 6 months at the beginning and end, to account for edge truncation in the 12month running average and to avoid fractional years, if possible).
For the regional trends, we find > 2σ significance in China (-1. 6 to -1 % yr -1 ), E. USA (-1. 4 to -1 % yr -1 ) and Europe (-1. 4 to -1 % yr -1 ), for the MOPITT 11-yr and AIRS 8-yr records respectively. The TES and IASI data records do not show significant trends over any of the regions, but show overall consistency with the MOPITT and AIRS time dependence and give confidence to the use of the longer MOPITT and AIRS data records for trend determination.
Interannual variability Figures 6-8 demonstrate good agreement in interannual variability for all instruments except in the cases of known sampling issues discussed in Sec. 3. As further verification of the consistency in the MOPITT and AIRS datasets for detecting patterns in interannual variability, Figure  9  shows a zonal average time record of normalized CO total column. 12-month running averages of MOPITT and AIRS CO total columns in 10 • latitude bands are normalized to the time average for each band. The variations in CO for both instruments are very similar in time and latitude, and both observe a deep minimum in 2009-2010. In Fig. 10 , we show the MOPITT and AIRS 12-month running averages, normalized to the 2008 average column, for a 10 • ×10 • spatial average around the NOAA Mauna Loa (MLO) site at 19.
5 • N and 155. 6 • W  (Novelli and Masarie, 2010). Due to the high altitude of the site, Mauna Loa measurements are more representative of the free troposphere than other surface measurements and often show the influence of air transported from Asia. Although the in-situ measurements of CO vmr are not directly comparable to total column CO, and have significantly more interannual variability, MOPITT and AIRS observe many of the same features seen in the MLO in-situ data time series. All of the 12 month running average time series of total column CO show clear features due to changes in fire emissions such as the broad peak around 2002-2003 for NH observations due to a significant number of boreal fires (e. g. ,  Yurganov et al. , 2005)  and an overall decline in global fire emissions from 2007 to 2009 as shown in the GFED3 inventory  (van der Werf et al.
Acknowledgements. The MOPITT, AIRS and TES projects are supported by the  National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Program. The MOPITT team also acknowledges support from the  Canadian Space Agency (CSA) , the  Natural Sciences and Engineering Research Council (NSERC)  and  Environment Canada , along with the contributions of COMDEV (the prime contractor) and ABB BOMEM. IASI was developed and built under the responsibility of  CNES  and flies onboard the MetOp satellite as part of the Eumetsat Polar system. The authors acknowledge the Ether French atmospheric database (http://ether. ipsl. jussieu. fr)for distributing the IASI L1C and L2-CO data. AIRS data records and analyses used in this paper were produced with the Giovanni online data system, developed and maintained by the  NASA GES DISC.
Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. C. Clerbaux  and  M. George  are grateful for the CNES financial support. P. F. Coheur is a Research Associate with F. R. S. -FNRS. The research in Belgium was also funded by  BELSPO  and  ESA  (Prodex arrangements). The authors thank.
A. Why RRM Diagrams The SL/SF modeling notation is widely used in industry for developing control applications. The benefits are: (a) graphical models are better understood, and (b) model-inloop and processor-in-loop simulation platforms are readily available around SL/SF. The negative points are: (a) models are usually not amenable to formal reasoning, and (b) simulation alone may not remove bugs. Control systems can also be developed using formal methods like Event-B  [1]. The plus points are: (a) design steps can be verified, and (b) incremental development can deal with scalability. The negative points here are: (a) the languages are text based, and (b) control/data flow information are not explicit in a model. RRM Diagrams are formal models which incorporate the benefits of both the above formalisms.
VIII. CONCLUSIONS We have discussed a novel method in which the requirements of a typical controller is partitioned into requirements meant for the modulating component and those for the discrete component. The first component is developed by a control engineer and the second component is developed by a software engineer using a formal approach; both the components are integrated compositionally to obtain the final design. Our mixed approach produces high quality designs for modulating controllers. We have discussed a simplified ACC to illustrate our approach. Even in this simple controller design, there are a few non-trivial features -like hierarchical Stateflow with complex transition guards and actions -which are difficult to get right if a manual approach is used. In future we wish to perform industrial strength case studies and to make our Simulink auto-generation process robust.
ACKNOWLEDGMENT Initial part of this research was carried out under the  Discovery  Project titled "Correct-by-construction Methodology for Distributed Automotive Control Software," awarded by Global General Motors R&D. This work was also supported by the project  FP7 ICT 287563 ADVANCE (Advanced Design and Verification Environment  for Cyber-physical System Engineering; http://www. advance-ict. eu). We would like to thank  T. K. Ghoshal  and  S. Mohalik  for useful discussions.
III. OVERVIEW OF OUR SOLUTION: ROTHAR Our solution is called ROThAr, for Real-time On-line Traffic Assignment with load estimation. It is composed of three modules that work together to come up with an optimised TA (see Figure  1 ). Users give their original position, destination and time of departure to the on-line routing module. This module uses the global knowledge about the road traffic conditions given by the traffic condition estimation module. The module queries and updates a structure called LM: see Section IV. The route optimisation module is triggered by some events happening to the traffic conditions (e. g. , accidents) to modify the routes. It can also run on a regular basis (e. g. , every 5 minutes) to adapt the system to small divergence on the traffic situation and so on.
B. Estimation of Road Segments Travel Time and Load This module is the core of our system as it is the one which is supposed to offer ROThAr with an accurate view on the traffic condition and its evolution during the assignments. We show in Section IV that computing the position of vehicles accurately is unrealistic: traffic is too stochastic and any minor variation can lead to large differences in what would be the real position of vehicles. For instance, reaction time at traffic lights varies greatly between drivers, and that has an obvious impact on the flows of vehicles. Besides, computing the position of every vehicle and their impact on load and travel time on road segments seems heavy. In this paper we propose to split the time in similar time slots, and to give an estimated position of vehicles at every time slot.
C. Rerouting and Traffic Assignment Optimisation Once routes have been assigned to all vehicles, ROThAr tries to optimise the traffic flows by rerouting some of the vehicles. We will see later that it is an action which is undertaken only if time permits -i. e. , this does not have an impact on routing features of the system, especially as we want a real-time routing/rerouting. The rerouting component iterates on the routes as long as it does not take too much time: ROThAr takes some critical trips (e. g. , those which create too much load on some segments) and try to assign them better routes (e. g. , avoiding overloaded segments); Section V describes the details of this module.
The first one is computationally expensive, while second one is less realistic. ROThAr implements a third option: when U is too small for a segment s i , v is penalised by a certain number of time units where it has to stay in s i before going to the next one in r(v): penalty(v, s i ) = tt(si,l(si,u)) U if U < tt(s i , l(s i , u)) 0 otherwise (5) 4) Evolution by time slot: The process of computing LM is rather simple: • As long as there are vehicles such that s pos(v,u) = d(v), a new entry u + 1 is added to LM. • We update the LM for every vehicle. We propagate congestion and we reposition vehicles according to corresponding travel time. Algorithm 1 shows the pseudo code for the computation of loads matrix: The variation of the time-unit induces a variation in the precision and the execution time.
With this scheme, 1024 values can be encoded. A Public software developed by Muñoz-Salinas (  2012 ) was adapted to automatically perform the location, identification and accurate measurement of the four corners of the external crown of targets of the calibration field  (Silva et al. , 2012). With this software, most of the existing coded targets are automatically extracted. Some missing corners were interactively measured to provide enough points with suitable geometry for camera calibration. After automatic measurement (complemented by interactive measurement) of corners targets in 12 images, suitably acquired for camera calibration, 3216 observations were generated, with 411 ground points. The object coordinates of the corners were measured with topographic methods with an accuracy of 3 mm.
The CMC software (Calibration with Multiple Cameras -  Bazan et al. , 2009) , developed in-house, which uses the well know collinearity model with Conrady Brown distortion model, was used for the first calibration trials. This software was modified to use also the equidistant models including the Conrady-Brown distortion equations. The results of camera calibration with the collinearity equations are presented in Table  3. Results of the camera calibration process with the equidistant model. Comparing the parameters values and their estimated standard deviations achieved with both models (Tables  2  and 3 ) it can be seen that the σ Naught and the standard deviations of the parameters are lower with the equidistant model, and, for this reason, these parameters were used to generate the resampled images.
A set of 7 ground control points and 2 check points were also surveyed in the cemetery using a combined process with some stations determined by GPS tracking in static mode and other by combination of intersection and radiation. The estimated standard deviations for the ground control points are within 1cm. Figure  6. Configuration of the photogrammetric project with the images, GCP and check points. The resampled images were imported in the Leica Photogrammetric Suite (LPS) along with the coordinates of the exposure stations and the GCP coordinates. Firstly, the image coordinates of the GCP were interactively measured with the classic point measurement tool. Then, tie points were also interactively measured and, finally some tie points were automatically extracted. Owing to the perspective displacements caused by the height of objects and image blurring, the number of tie points correctly extracted by the software was lower than in conventional aerial images.
This happens because the tie points transfer process uses area based matching which is sensitive to geometric changes such as existing in the images acquired by fisheye lenses. Nevertheless, 389 object points were finally estimated in the bundle adjustment. Most of the tie points interactively measured are grave corners that will be used to produce the final plot. Figure  6  shows the configuration of the project with the images footprints, GCP and tie points. The bundle adjustment was computed with the LPS software. Some results of the bundle adjustment are summarized in Table  4. It can be seen that the σ Naught is 2. 3 pixels, which is likely owed to the errors in the image measurements mainly in the areas out of the image centre, which are more affected by the distortion and image aberrations.
g. , by 30% for K 0 S production and 50% for Ã production at ffiffi ffi s p ¼ 7 TeV for PYTHIA 6 tune D6T  [2, 14] , with little improvement for more recent tunes. The simulations are performed with versions of PYTHIA that include MPI. The most recent versions have been tuned to reproduce the UE activity observed with primary charged particles at the LHC at 0. 9 TeV and 7 TeV centerof-mass energies. The parameters describing strangeness production, however, have not been tuned to LHC data yet. All Monte Carlo samples used in this paper have been generated with the default values of these parameters. Recent literature  [15] [16] [17]  discussing the tuning of the strangeness suppression parameters in commonly available generators is limited. A tuning of the PYTHIA 6 parameters to LEP, SLAC Linear Collider, and Tevatron data performed with the PROFESSOR program  [15]  produced bestfit parameters in disagreement with the current PYTHIA default parameters.
II. EXPERIMENTAL SETUP, DATA SETS, AND DATA ANALYSIS The central feature of CMS is a superconducting solenoid of 6 m internal diameter. Within the superconducting solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass and scintillator hadron calorimeter. Muons are measured in gas-ionization detectors embedded in the flux-return yoke. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors. CMS uses a right-handed coordinate system, with the origin at the nominal interaction point, the x axis pointing to the center of the LHC, the y axis pointing up (perpendicular to the LHC plane), and the z axis along the anticlockwise-beam direction. The polar angle is measured from the positive z axis, and the azimuthal angle is measured in the x À y plane.
A. Event selection, data sets, and Monte Carlo simulation The event selection is identical to the one described in  [1] , unless explicitly stated otherwise. Minimum-bias events were triggered by requiring coincident signals in beam scintillator counters located on both sides of the experiment and covering the pseudorapidity range 3:23 < jj < 4:65, and in the beam pickup devices  [18]. Events were then recorded with a prescaled trigger requiring the presence of at least one track segment in the pixel detector with p T > 200 MeV=c. The trigger conditions are applied to both data and simulated samples. The trigger efficiency for the events selected in the analysis is close to 100%, and no bias from the trigger selection is found. The data used in this analysis were collected in early 2010 when pileup (multiple pp collisions per proton bunch crossing) was very low.
The two latter PYTHIA 6 tunes, as well as PYTHIA 8, include p T ordering of the parton showers, and a new model  [27]  where MPI are interleaved with parton showering. PYTHIA 8 includes hard diffraction in addition to the new MPI model. The parton distribution functions used for PYTHIA 6 D6T and PYTHIA 8 tune 1 are the CTEQ6L1 and CTEQ5L sets, respectively. The Z1 tune uses the CTEQ5L parton distribution set, whereas Z2 Ã is updated to CTEQ6L1  [28]  and retuned to the underlying event activity at 7 TeV from Ref. [1]  with the PROFESSOR tool  [15]. The simulated data are generated with PYTHIA 6 version 6. 422 for tunes D6T and Z1, version 6. 424 for tune Z2 Ã , and version 8. 135 for PYTHIA 8 tune 1. Simulated primary stable charged particles with a proper lifetime c > 1 cm are clustered into jets with the anti-k T algorithm (ÁR ¼ 0:5).