dataesr/scientific-paragraphs-categorization huggingface dataset:
encryption parameters list EncParams, upload/download usable bandwidth (U, D), target optimization function ftarget, index of the element to retrieve i (pot. undefined), boolean serverDriven Output: Chosen database element 1. If serverDriven is true -Server: Send mandatory parameters to the client -Client: Check if the parameters give enough security -Jump to step 4 2. If performance results do not exist for all parameters in EncParams -Client and Server: Run the Performance cache algorithm → Performance results 3. If multiple PIR and encryption parameters are possible given the input constraints -Client and Server: Run the Optimization algorithm Else use the only possible choice → Optimal parameters for this setting given the constraints 4. If i is undefined -Client and Server: Run the Choice algorithm → Chosen index i 5.
Trivial PIR (from top to bottom the second filled line) is faster than cPIR for databases under ten elements, which is natural as cPIR has a reply expansion factor around five. For databases with more elements, cPIR can be up to fifty times faster. When the client is local, RTT (red thick dashed line) matches RP (green thick filled line), specially for large databases. Each thin blue line gives RTT for a fixed n and varying database sizes. For large databases reply size is the limiting factor, which explains why performance is closer to ideal RTT as n grows (when n grows for a fixed database size shrinks). For small databases, query size is the limiting factor. RTT does not grow as fast as n because the optimizer uses aggregation. 100000 10000 1000 100 Time (s) 1 10 RP RTT no network 0. 1 0.
01 RTT FTTH n=1 RTT FTTH n=10 RTT FTTH n=100 0. 001 RTT FTTH n=1000 Trivial FTTH PIR 0. 0001 1M 10M 100M 1G 10G 100G 1T n. l size of the db Fig. 10. Round-trip time (RTT) and request processing (RP) times of XPIR serving static data on a MSI GT60 laptop with a Core i7-3630QM 2. 67GHz on a FTTH network (database sizes are in bits).
The users can then do a cPIR-based keyword search (see Section 2. 3) to get the profiles matching a set of private keywords. If we suppose the database has one million profiles, each of one megabit, the complete database will be of one Terabit. We must also take into account that each profile may match a set of keywords and that reply generation costs are multiplied by the average number of matching keywords in a private keyword search. If we sup- 100000 10000 1000 100 Time (s) 1 10 RP 0. 1 0. 01 RTT no network RTT FTTH n=1 RTT FTTH n=10 0. 001 RTT FTTH n=100 RTT FTTH n=1000 0. 0001 1M 10M 100M 1G 10G 100G 1T n. l size of the db.
For each sample, the spectral data was obtained from two areas 80 x 80 µm 2 that had been previously analyzed by COSIMA. Two kinds of Raman analyses were made: first a slice cutting through a selected area was scanned in depth mode along a line parallel to the x--axis in the x--z--plane, and then, an image mode scan was made parallel to the focal plane in an x-y plane encompassing the sample surface. In the latter case, fractions of the rectangular scan matrix were in or out of focus due to the surface roughness of the mineral grain samples. The recorded fluorescence and Raman emission spectra were corrected for cosmic ray particle events. Spectra were summed up and averaged for selected adjoining measurement points within each scan to improve the signal--to--noise ratio. False color images for both scan modes were plotted for selected spectral bandwidth, resulting in depth and image scans each representing other spectral features and thereby allowing the spatial identification and feasible separation of the emission sources.
Oxygen, on the other hand, has a very low ionization probability for positive secondary ions, which makes it not suitable as a reference element. RSFs are obtained by analyzing standard materials with known elemental composition under the same conditions as the samples to be analyzed (i. e. , cometary grains in this case). Following data evaluation steps as described by  Stephan (2001) , secondary ion ratios for numerous elements relative to Si were obtained. For some mineral samples, either no statistically significant Si element data were available or the Si peaks were compromised by silicone oil contamination. In such cases, we used Mg as a reference element and renormalized the result to Si using an RSF(Mg/Si) of 3. 71 (geometric mean of RSF(Mg/Si) values calculated for minerals with reliable Si data).
Acknowledgements COSIMA was built by a consortium led by the  Max--Planck--Institut für Extraterrestrische Physik, Garching, Germany in collaboration with Laboratoire de Physique et Chimie de l'Environnement et de l'Espace, Orléans, France, Institut d'Astrophysique Spatiale ,  CNRS/Université Paris Sud, Orsay, France, Finnish Meteorological Institute, Helsinki, Finland, Universität Wuppertal, Wuppertal, Germany, von Hoerner und Sulger GmbH, Schwetzingen, Germany, Universität der Bundeswehr, Neubiberg, Germany, Institut für Physik, Forschungszentrum Seibersdorf, Seibersdorf, Austria, Institut für Weltraumforschung, Österreichische Akademie der Wissenschaften, Graz, Austria  and is lead by the  Max--Planck--Institut für Sonnensystemforschung, Göttingen, Germany. The support of the national funding agencies of Germany (DLR, grant  50 QP 1302 ),  France (CNES) ,  Austria, Finland, Sweden (Swedish National Space Board , contract No.
91 The DNA constituents of interest were tetrahydrofuran, trimethylphosphate and pyrimidine, serving as models for the deoxyribose, phosphate group and PY nucleobases, respectively. In these experiments, cross sections for total scattering were measured for energies between 6 and 1 keV, while differential elastic and double-differential inelastic scattering cross sections were measured for energies between 20 and 1 keV and scattering angles between 15°and 135°. To enable a convenient implementation in the simulation code, the cross-section data were interpolated by suitable model functions, offering an extrapolation of the measured differential cross sections to forward and backward scattering angles. Photon g (1 eV-100 GeV) Electron e 2 /positron e 1 (10 eV-10 MeV) Proton p 1 (1 keV-1 GeV) Heavy ions (1 MeVu 21 -1 GeVu 2 1 ) Alpha particles a (1 keV-1 GeV) Extended to electron, photon and ion interactions, DNA targets, double-helix and chromosome models, chromatin fibre in atomic resolution, liquid water cross sections, stochastic chemistry, track structures within heterogeneous targets, cross sections for ion interactions and simulations of radiation damage to DNA by ions and stochastic model of DNA DSB repair via non-homologous end-joining pathway Photons (taken from EPDL97 data library): coherent scattering, photoelectric effect, Compton scattering, pair production, Auger electron and fluorescence photon emission, relaxation processes.
64, 93, 94 In the BioQuaRT project, the geometrical model developed in the frame of the Geant4-DNA project  95  is used in the multiscale simulation tool. The different compaction levels of the DNA molecule implemented in this model are shown in Figure  6. This geometrical representation of the DNA molecule can be sufficient for calculating the direct DNA damages by considering the position of the energy transferred during the physical stage of interaction. Nevertheless, in order to take into account the "indirect effects" owing to the interaction with the radicals created by the irradiation in water surrounding the DNA molecule (chemical stage), a more realistic chemical description of the DNA is needed (for example see the RADAMOL code  96  ). The geometry shown in Figure  6  is currently being adapted to be used in the BioQuaRT multiscale simulation tool, including the chemical stage developed in the frame of the Geant4-DNA project.
The multiscale simulation tool under development in BioQuaRT will be primarily based on the Monte Carlo calculation of ionization and energy deposition at the nanometre scale. At this first simulation stage, it is important to strive for the highest achievable accuracy since these nanometric distributions are at the origin of the biological consequences and differences at this scale are responsible for the variation in the biological effectiveness between different radiation qualities. Specifically, this first simulation stage requires the development of the physical models for radiation interaction cross sections with the biomolecules in the target and experimental data for evaluating these physical models, as these cross-section models define the outcome of the Monte Carlo simulation. This stage also requires the geometrical target description that determines the energy transfer points from which chemical bond breaks may originate.
The present status of efforts to achieve direct lineal energy measurements, rather than deriving lineal energy from ionization measurements, is discussed as well as efforts to improve the measurement and simulation of nanodosimetric quantities, quantification of reactive chemical species and progress to improved biological data by using a rigorous metrological approach. A conceptual description of a multiscale model that can form the basis of the new quantities is described, and it is anticipated that the European metrology project BioQuaRT will be able to contribute substantially to the progress towards realizing such a multiscale modelling tool. A generic formal expression of how the weighting of physical quantities at different length scales and the separation from biological effects could possibly be carried out is presented for further discussion, debate and refinement.
ACKNOWLEDGMENTS The authors are indebted to  Ms Heike Nittmann  for uniformly formatting the graphs, to  Prof. Dr Reinhard Schulte ,  Dr Werner Friedland ,  Dr Marie Davídková ,  Prof. Dr Karen Kirkby  and  Prof. Dr Andy Nisbet  for advice on activities within the  BioQuaRT  project, which has contributed to insights on the matter of this review article and to  Dr Antonella Testa ,  Dr Octavia Monteiro Gil  and  Dr Clarice Patrono  for their contributions to the biological dosimetry work within the  BioQuaRT  project.
During autumn 2012 and winter 2013, two special observation periods (SOPs) of the HYdrological cycle in the Mediterranean EXperiment (HyMeX) took place. For the preparatory studies and to support the instrument deployment during the field campaign, a dedicated version of the operational convective-scale Application of Research to Operations at Mesoscale (AROME)-France model was developed: the AROME-WMED (West Mediterranean Sea) model. It covers the western Mediterranean basin with a 48 h forecast range. It provided real-time analyses and forecasts which were sent daily to the HyMeX operational centre to forecast high-precipitation events and to help decision makers on the deployment of meteorological instruments. This paper presents the main features of this numerical weather prediction system in terms of data assimilation and forecast.
Some specific data of the HyMeX SOP were assimilated in real time. The forecast skill of AROME-WMED is then assessed with objective scores and compared to the operational AROME-France model, for both autumn 2012 (05 September to 06 November 2012) and winter 2013 (01 February to 15 March 2013) SOPs. The overall performance of AROME-WMED is good for the first HyMeX special observation period (SOP1) (i. e. mean 2 m temperature root mean square error (RMSE) of 1. 7 • C and mean 2 m relative humidity RMSE of 10 % for the 0-30 h forecast ranges) and similar to those of AROME-France for the 0-30 h common forecast range (maximal absolute difference of 2 m temperature RMSE of 0. 2 • C and 0. 21 % for the 2 m relative humidity); conversely, for the 24-48 h forecast range it is less accurate (relative loss between 10 and 12 % in 2 m temperature and relative humidity RMSE, and equitable threat score (ETS) for 24 h accumulated rainfall), but it remains useful for scheduling observation deployment.
, 2009) , the Convective and Orographically induced Precipitation Study (COPS;  Wulfmeyer et al. , 2008)  or the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST;  Lothon et al. , 2014). In the latter experiment, only specific AROME forecast output was made available every hour over the small BLLAST domain. To be able to make forecast during MAP-D PHASE and COPS experiments, an AROME domain was created over the Alps. This model was initialized using Aire Limitée Adaptation Dynamique Développement International (ALADIN)-France, which was at the time the operational regional Météo-France model, taking its lateral boundary conditions from ARPEGE (Action de Recherche Petite Echelle Grande Echelle) and its initial state from a three-dimensional variational data assimilation (3D-Var) scheme  (Fischer et al.
Radiosondes were also launched from mobile platforms along the French and Italian Mediterranean coasts and in Corsica according to meteorological situations. Moreover, additional operational radiosondes were activated on request at 06:00 and 18:00 UTC through the data targeting system (DTS) implemented by ECMWF (European Centre for Medium-range Weather Forecasts;  Prates et al. , 2009)  in the EUCOS (EUMETNET Composite Observing System) framework (former EUMETNET Economic Interest Grouping (EIG) Observation Programme). The aim of this paper is to review the main characteristics of the AROME-WMED real-time version in terms of data assimilation and forecast. The AROME-WMED model configuration is described in Sect. 2. In Sect. 3, the performances of AROME-WMED and AROME-France models are evaluated during the SOP1 over a common area.
For AROME-France, the grid is defined by 750 × 720 points from the centre at 46. 4 • N, 2. 2 • E, whereas the AROME-WMED one is defined with 960 × 640 points from 41. 5 • N, 4. 1 • E; 47 % of grid points are covered by sea surface versus 41 % for the AROME-France model. As both domain and grid points of AROME-WMED and the AROME-France differ, it was considered necessary to use new climatologies for the SURFEX scheme  (Masson et al. , 2013). The same method as the one used in AROME-France was used to set up these climatologies  (Seity et al. , 2011). Physiographic data are initialized over the AROME-WMED domain using the so-called ECOCLIMAP database  (Masson et al. , 2003)  at 1 km resolution. As in AROME-France, the SURFEX orography comes from the atmospheric model one, that avoids issues with altitude inconsistencies.
It is extracted from the Global 30 Arc-Second Elevation Data Set (GTOPO30) database. For forecast purposes, lateral boundary conditions are hourly provided by the French operational global model ARPEGE  (Courtier et al. , 1991)  forecasts. Once per day, a 48 h forecast was started at 00:00 UTC with AROME-WMED. One 30 h forecast was made by the operational AROME-France model at 00:00 UTC as well as at 06:00, 12:00 and 18:00 UTC. The decision to use AROME-WMED was motivated by computational costs and by the forecasters' requirements during HyMeX SOPs. Indeed, from this 48 h forecast, it was possible to study the weather conditions for the next day during the 07:30 UTC briefing, while at Météo-France it is made routinely from the 12:00 UTC forecast.
Observations The observations assimilated are of the same type as the ones used in the operational AROME-France system: radiosondes, automated land surface stations, wind profilers, ships, buoys and aircraft reports. Satellite data are also used: infrared radiances from SEVIRI (Spinning Enhanced Visible and InfraRed Imager) on board the geostationary satellite Meteosat Second Generation, infrared and microwave radiances from polar orbiting satellites, satellite winds from atmospheric motion vectors (AMVs) and scatterometer surface winds. The GPS zenith total delay (GPS-ZTD) observations from the EUMETNET EIG GNSS (Global Navigation Satellite System) water vapour programme (E-GVAP) network are assimilated as well. Doppler winds  (Montmerle and Faccani, 2009)  and reflectivities which provide information on relative humidity profiles  (Caumont et al.
, 2010; Wattrelot et al. , 2014)   deployed for the DTS and the additional SOP1 ones were assimilated by AROME-WMED and AROME-France in real time as they were transmitted through the GTS. Thus, 373 out of the 403 additional EUCOS radiosondes launched during the SOP1 were assimilated in AROME-WMED. Data from 18 BLPBs were also assimilated during SOP1 and 16 during SOP2 (Fig. 4 ). These BLPBs were launched on request from the HyMeX Operational Centre. These data were available in real time only in the Météo-France database, temperature and wind data were thus assimilated in AROME-France and AROME-WMED. This represents a total of nearly 850 reports (351 in SOP1 and 497 in SOP2). From these BLPBs, it was possible to collect data close to the sea surface, which is generally a data void area. The high rate of observations (two per minute) was filtered and averaged (10 ) to generate representative observational reports, from a meteorological point of view, which were then coded in the form of commercial aircraft data with distinctive identifiers.
These reports included humidity (mixing ratio) in addition to temperature, pressure, wind and geographical elevation. The winds were derived from the drift of balloons, thanks to a 3-D GPS positioning. The average range of the altitude was 400 to 900 m. Only data from stable parts of the flights were used to generate this special data set, which has been derived from raw BLPB data by time filtering. Some balloons ascended rapidly when encountering strong updrafts which were generated by deep convection. The data corresponding to these rapid vertical variations were discarded. The sampling ratio of the BLPBs is not meant to capture very rapid changes in the measured parameters. In AROME-WMED, only temperature and wind data (at pressure levels) were assimilated in real time, whereas humidity was not, because the format used for the observation dissemination did not allow it.
Ancillary surface data from AEMET (Agencia Estatal de METeorologìa) have also been assimilated in real time only in AROME-WMED because these data were available on a ftp site. Figure  5  shows the amount of assimilated observations by both models over their specific domain and over their common area, ranging from 39 to 47. 5 • N and from 8 • W to 11 • E for SOP1 and SOP2. Aircraft data are fewer in the AROME-WMED model as most major European airports lie outside the domain. The same holds true for GPS data which are denser over northern Europe. Despite real-time constraints, many more satellite microwave observations were assimilated through an increase of the horizontal sampling (one observation per 80 km 2 square box, instead of one per 125 km 2 box in AROME-France) which gave a better observation coverage over the sea similar to the operational 80 km sampling of IASI (Infrared Atmospheric Sounding Interferometer)  (Guidard et al.
, 2011)  in AROME-France. Additional Spanish surface data were used in the AROME-WMED analyses to improve coverage over Spain as shown in Fig. 5. In total, over the common area, 3. 3 % of additional observations are taken into account in AROME-WMED for SOP1 and 5. 2 % for SOP2. The discrepancy is due to the increased horizontal resolution for the microwave radiance assimilation (+35. 7 % for SOP1 and +42. 4 % for SOP2) and to a later AROME-WMED analysis (half an hour later than the AROME-France). It follows that more observations were available in the meteorological database for the assimilation. Buoys provided around 5 % additional data for both SOPs as the assimilation was made after that of AROME-France. However, fewer data from AMVs, scatterometers, profilers and ground GPS stations were used during SOP1.
The lack of assimilated ground GPS data in AROME-WMED  (-22. 4 and -21. 4  % for SOP1 and SOP2, respectively) was caused by an inadequate bias correction in AROME-WMED, which discarded many GPS observations from assimilation. The main difference between the results of SOP2 and those of SOP1 lies in the activation of four additional wind profilers (+27 %) in southern France. A quality control was made against the AROME-WMED model during SOP1. Once assessed, these data were assimilated in AROME-WMED in real time, as of December 2012. The main changes in the AROME model reside in the revision of the cloud scheme with a realistic increase of clouds with partial cloud cover, in addition to changes concerning observation use. As it was not possible to simultaneously run both cycles of the AROME-WMED, we could not quantify the impact of the code change on the AROME-WMED forecast during SOP1.
This update procedure was done every 20 min. For safety reason, the dropsondes were not allowed to reach inhabited land. The drift of the dropsondes launched by the research Falcon 20 aircraft was thus extrapolated from AROME-WMED forecasts. A data subset composed of precipitation, temperature, wind, wet bulb temperature, relative humidity, brightness temperature, convective available potential energy, integrated water vapour, mean sea level pressure, sea surface temperature and radar reflectivity was also made available in real time to HyMeX Operational Centre website for secondary operational centres (L'Aquila, Italy; Palma de Mallorca; Mahon, Menorca; San Giuliano, Corsica; Toulouse, France). The real-time fields of AROME-WMED were also stored in the HyMeX database (doi:10. 6096/HYMEX. AROME_WMED.
Temperature, relative humidity at 2 m and wind at 10 m The observations used for the evaluation came from the HyMeX database, where the SYNOP (surface synoptic observations) network over the HyMeX area was stored. To complete this network, more 2 m temperature and 2 m relative humidity were also provided each hour by France, AEMET and Catalonia. The wind at 10 m was also provided from some surface stations. These data are spatially unevenly distributed (Fig. 6 ). The network is denser over Spain, France and Switzerland than elsewhere. Over the ocean, it was supplemented by data from ships, buoys and marisondes. The forecast error bias and the forecast root mean square error (RMSE) were computed for these surface parameters for each forecast lead time with respect to the surface stations (Fig.
24 h accumulated precipitation The evaluation was carried out using the 24 h accumulated precipitation (05 September to 05 November 2012), from the HyMeX database available on 01 January 2014. These data had been subject to a quality control prior to computing scores. Duplicates and outliers were removed from the validation data set. For France and Spain, the 24 h accumulated precipitation data were directly stored in the HyMeX database, whereas for Italy they were computed by adding each 1 h precipitation data for 24 h periods. Hence, if one 1 h interval was missing, the total 24 h precipitation could not be calculated. A good coverage is obtained over France, Spain and the north-west of Italy, where stations had provided data from 06:00 to 06:00 UTC the next day, for the whole 62 days of the SOP1 period (Fig.
Concluding remarks The AROME-WMED model was initially developed to forecast heavy precipitation events around the north-western Mediterranean basin during the HyMeX special observation periods (SOPs). For the first time, a research version of AROME with its own data assimilation system and with a wider domain than that of the operational AROME-France was run in real time over a long period (9 months). Moreover, some experimental data from the HyMeX campaign were assimilated in real time. This project reached its main goal with an availability in real time of 95 % during SOP1 and 93 % during SOP2, which helped the decision process on the observation deployment, such as dropsondes launched from aircraft or boundary layer pressurized balloons (BLPBs). Once the field campaign was over, the quality of forecasts was compared with those of AROME-France, thanks to specific scores that used additional surface observations made available from the HyMeX database and to standard probabilistic scores used operationally at Météo-France.