dataesr/scientific-paragraphs-categorization huggingface dataset:
records from the encrypted database based on a search criterion denoted ω, the user translates a plain SQL query such as SELECT * FROM PATIENT WHERE Name='David' (here ω corresponds to Name='David') into an encrypted SQL query, denoted SE query. This SE query requests the server to execute algorithm Search over the search index I using the search token generated with algorithm Trapdoor. Therefore, the search procedure operates in a challenge-response interaction between user U and server S with the following steps: 1. U executes algorithm Trapdoor to generate the search token τ ω for keyword ω. Then, U forms the following SE query: SELECT * FROM encrypted table WHERE RecordsID IN Search(I , τ ω ) (1) The most important and challenging part of this SE query is RecordsID IN Search(I , τ ω ).
This PostgreSQL server is in charge of executing the function Search defined in Algorithm 6. As shown in Figure1, our implemented scheme is divided into three phases: Upload: The user outsources her database by executing the CREATE and INSERT INTO queries. The proxy receives them, generates keys, runs CreateDictionary, BuildIndex and EncryptTables and converts the queries into an SE query. This SE query includes the encrypted version of CREATE and INSERT INTO queries for the data and the CREATE and INSERT INTO queries for storing the index into an additional table denoted INDEX. Query: The user executes a SELECT-FROM-WHERE query to retrieve some records based on some criteria (the WHERE conditions). The proxy receives this query, extracts the criteria, retrieves the keys and executes Trapdoor for each of these criteria.
T4 [pos i ] stores the address in array A of N i,1 and the key k i,0. Therefore, τ ω consists of two values: pos = π(K π , ω) and K ω = ϕ(K ϕ , ω). Search(I , τ ω ) → D(ω): As depicted in Algorithm 2, this algorithm parses τ ω as pos and K ω. Then it locates entry T [pos] and XOR-decrypts it using K ω. It obtains the address in array A of N i,1 and the key k i,0. Starting from N i,1 , algorithm Search can decrypt all the nodes N i, j , 1 ≤ j ≤ |L i | of L i. Finally, the algorithm outputs the list D(ω i ). If one of these steps fails, then algorithm Search aborts and indicates that the keyword was not found. Algorithm 1: I ← BuildIndex(D, K ). Inputs : Database D, Secret Key K Output: Index I 1 Create dictionary W 2 for ω i ∈ W do determine D(ω i ) 3 Set global counter ctr = 1 // Array A creation 5 Initialize an empty array A of size m 6 for ω i ∈ W do // Build a linked list L i with nodes N i, j 7 Generate random key k i,0 8 for 1 ≤ j ≤ |D(ω i )| do 9 Generate random key k i, j 10.
Fifty experimental situations (site × year × management) were chosen for calibration. AqYield was evaluated (i) for winter wheat in nine experimental situations, using daily drainage and ET data, and (ii) for two crop rotations on two fields with 7-years of continuous measurements of daily ET flux. During calibration, AqYield predicted soil AW in the contrasting situations with a model efficiency of 0. 83, in the same range of accuracy as those of other widely published models. AqYield also predicted ET accurately from calibration and validation datasets, with a model efficiency of 0. 84 and 0. 69, respectively, for monthly ET. AqYield predicted daily and monthly drainage less accurately, although the range of drainage during the cropping period was predicted well. At the crop-rotation scale, AqYield yielded acceptable predictions of ET for contrasting climate conditions and crops.
, 2015). Consequently, it has shorter calibration and calculation times and requires only basic input data, which are more easily collected than detailed data at a watershed level. Moreover, simple models, like AqYield, have the advantage to avoid "black box" effect (transparency of internal structure and behavior of the model) and to ease communication with various stakeholders  (Voinov and Bousquet, 2010). AqYield was recently found to predict AW  (Constantin et al. , 2015)  of spring crops as accurately as a more complex crop model such as STICS  (Brisson et al. , 2003). In this recent study, AqYield predictions were evaluated for three rainfed and irrigated spring crops (sunflower, maize, and sorghum) at four sites in a region with dry summers in southwestern France. An evaluation of AqYield's performance for new crop species and at the crop-rotation scale is thus required.
Compilation of databases for wheat calibration and validation We calibrated and validated the model for winter wheat (durum wheat and soft wheat). We selected AW, ET and/or drainage data from 14 experimental sites monitored in France to consider a wide range of contrasting soil, climate and crop management (mainly irrigation) situations which correspond to various European climates. Situations were distributed between calibration and validation databases to have contrasting situations (years) in both databases. We chose sites not limited by N (at least 110 kg N ha -1 added from fertilizers or manure), since AqYield does not represent N stress. For calibration, a total of 50 situations (site × year × management) were simulated; these situations had contrasting soil features, ranging from 10 to 50% clay content, 0.
8-2. 0 m soil depth and 81-313 mm of AW capacity (Table  1 ). Mean monthly temperature during the wheat cropping period (November to June) ranged from 6. 7 to 11. 9 °C. For each situation, water deficit (WD) was calculated from wheat sowing to harvest dates as cumulative daily rainfall (R) minus ET 0, excluding irrigation. Mean WD during the cropping period ranged from -187 to 132 mm among sites. WD varied among years for a given site; for example, at Fagnières it ranged from -110 to 81 mm (in 2011 and 1998, respectively   1987; 1998; 2004; 2009; 2010; 2012 a  One situation represents a combination of site × year × management. WD is water deficit, ET is evapotranspiration (in mm), AW is soil available water for plant in the soil (in mm). b Only one kind of data was available on each site (ET, drainage or AW).
, 2015) , and K phot is the photoperiod coefficient (active for crops growing during winter), which is applied only during a given period (generally from sowing to February, depending on the latitude). The end date of K phot application (End Kphot ) is estimated as follows: =× End DLindex 45 ( 0. 28 ) K 1. 5 phot where DL index is the day length index, calculated as: =+ DL latitude ( 80 )0. 1 2 index 3 By trial and error, we optimized K phot for winter wheat to predict the flowering dates observed in the calibration database more accurately. The K phot was optimized for all simulations in order to have a generic value (K phot of winter wheat = 0. 43). In a second step, we optimized the coefficient α, which influences soil evaporation (see Appendix for details). By trial-and-error, we optimized α based on measured AW, daily ET and drainage in the calibration database.
This coefficient is generic for all crops; we thus took care to check that predictions for previously evaluated spring crops  (Constantin et al. , 2015)  were not negatively impacted by this calibration. Finally, by trial-and-error, we optimized the root-growth coefficient (RootG, in °C mm -1 ), which influences root exploration in the soil, to 8. 0 for durum wheat and 9. 0 for soft wheat, based on measured AW, daily ET and drainage in the calibration database.
Crop rotations and site descriptions used to evaluate ET We evaluated predicted daily and cumulative ET at the crop-rotation scale by comparing them to observed data from two of the experimental sites: Auradé and Lamasquère. These sites are part of the Integrated Carbon Observation System (ICOS) network  (Paris et al. , 2012)  as FR-Aur and FR-Lam, respectively. For the evaluation, we ran AqYield continuously from sowing of the first crop to harvest of the last crop in each rotation. Since data from these two sites had been used to calibrate the model from wheat sowing to harvest (dataset A, Table  1 ), we excluded these calibration data when calculating statistical criteria used to evaluate predictions of the crop rotation (dataset B, Table  2 ). Auradé followed mainly a winter wheat/rapeseed rotation, sown in September or October and harvested in June or July, with mineral fertilization (annual average of 140 kg N ha -1 ), straw incorporated into the soil and no irrigation.
Sunflower was grown at this site in 2007. Lamasquère followed a winter wheat/maize rotation, with mineral and organic (manure or slurry) fertilizer (annual average of 277 kg N ha -1 ). Irrigation was applied to maize (50-148 mm year -1 ). The required meteorological data at a daily time-step (temperature, ET 0 , rainfall) were obtained from data initially recorded at a 30-minute time step at each experimental site. Daily ET was calculated from 30-minute ET fluxes recorded from 2006 to 2012, corresponding to seven cropping years (Table  2 ). Eddy covariance was used to continuously monitor ET, and included a system with a fast (20 Hz) infra-red gas analyzer (Li7500, Licor) and a 3D sonic anemometer (CSAT, Campbell Scientific Ltd). Filtering, quality controls and gap filling were performed following CarboEurope-IP recommendations  (Aubinet et al.
, 2012). Ultimately, the dataset contained 4498 observations (2455 for Auradé and 2043 for Lamasquère). No data were recorded at Lamasquère from 1 January to 15 March 2011 due to instrument failure. We also calculated cumulative ET from sowing of a given crop until the sowing of the subsequent crop (including the fallow period between them). WD showed contrasting years of AW at both sites, since 2011 and 2012 were extremely dry and 2008 was wet (Table  2 ).
Water balance component predictions for wheat 3. 1. 1. Soil available water for wheat During calibration (dataset A), AqYield predicted AW during the wheat cropping period well, with Ef = 0. 83, rRMSE = 0. 23 (corresponding to 31 mm) and MD = 13 mm (Fig. 1 ). Model predictions of AW ranged from 9 to 311 mm, while observations ranged from 0 to 314 mm. The model predicted most situations well (median difference of 16 mm in absolute value; 13% in relative value), although it tended to overpredict the low and medium observed AW slightly.
Daily, monthly and cumulative evapotranspiration for wheat During calibration (dataset A), AqYield predicted daily ET for wheat relatively well overall, with Ef = 0. 54, rRMSE = 0. 55 (corresponding to 0. 7 mm) and MD = -0. 1 mm for the four situations combined. The median difference between daily predicted and observed values was 0. 34 mm in absolute value (25% in relative value). Prediction quality varied according to the situation, however, with Ef from 0. 23-0. 73, rRMSE from 0. 45-0. 65 and MD from -0. 3 to 0. 3 (Fig. 2 ). Generally, daily ET was predicted well at the beginning of wheat.
Drainage for wheat Observed cumulative drainage at Fagnières during the cropping period ranged from 0 to 287 mm, depending on the situation. During calibration, AqYield predicted cumulative drainage well, since it predicted no drainage for the two situations (out of six in dataset A) in which no drainage occurred during the cropping period (data not shown). For the four situations in which drainage occurred, the model accurately predicted the range of cumulative drainage, despite an overprediction in 1998 (Fig. 4 ). The median difference between observed and predicted cumulative drainage during the cropping period was 6 mm in absolute value (8% in relative value) for all situations and 18 mm in absolute value (16% in relative value) for the four situations in which drainage occurred. The differences ranged from -11 mm to 61 mm.
8 mm in absolute value (9% in relative value). 1 ). "MD" is the mean deviation, "rRMSE" is the relative root mean square error and "Ef" is the model efficiency. Fig. 3. Observed vs. predicted monthly evapotranspiration (ET) for wheat at Lamasquère and Auradé for model calibration and validation. For calibration, the data are black points, the regression line is the black solid line and statistical criteria are in black. For validation, the data are crosses, the adjustment line is gray and statistical criteria are gray. The 1:1 line is black and dashed. The data came from dataset A (Table  1 ). "MD" is the mean deviation, "rRMSE" is the relative root mean square error and "Ef" is the model efficiency. In the validation dataset for Fagnières, cumulative drainage during the cropping period ranged from 0 to 121 mm for predictions and 0-88 mm for observations.
AqYield's ability to predict dynamics of winter wheat water flux components With only few input data and simple equations, AqYield accurately predicted the amount of AW in the soil during wheat cropping periods in a variety of contrasting situations (soil, climate and management practices). It slightly overpredicted AW in certain situations, especially those with low AW, which might have been due in part to uncertainty in observed AW. Observed AW was calculated as the amount of water available for the crop, subtracting water content at wilting point from soil water content measurements. Determining the wilting point and its uncertainty for each experimental field can thus influence the observed   1 ). Fig. 5. Observed vs. predicted monthly drainage for wheat for model calibration and validation.
We focused model evaluation on water fluxes (ET and drainage) because they are processes that directly impact soil water content. For wheat, AqYield predicted ET accurately from the calibration and validation datasets. However, the difference between observations and predictions increased when observed ET increased, which usually corresponded to the end of a cropping period. This could be due to AqYield'sd i fficulty in representing processes that occur during plant senescence and their influence on soil evaporation  (Dunin et al. , 1989). AqYield predicted daily drainage less accurately, overpredicting it immediately after rainfall events, because the model drains all excess water within one day, while observed rainfall events drained over several days. Notably, AqYield does not consider the saturated hydraulic conductivity of soil, a property that can decreases the rate of vertical water flux.
For cumulative drainage during the cropping period, however, AqYield accurately distinguished situations with and without drainage during wheat development. It accurately predicted   2 ). Gray areas represent wheat crops used for model calibration (dataset A). the total amount of drainage during the cropping period, which is a concern for water management and cropping system issues at this temporal scale. When simulating at the annual or cropping period scale, accurate AW is required to initialize the simulation in order to predict drainage accurately, especially the beginning of the drainage period. Initial AW thus requires careful measurement or estimation from multiyear simulations, even though AqYield can predict other variables accurately without knowing the initial AW, as shown by  Constantin et al.
Daily ET predictions at Auradé were as accurate or more accurate for certain years than those of the spatially distributed TNT2 model, which was evaluated using the same dataset from 2006 to 2010  (Ferrant et al. , 2014). For example, AqYield overpredicted ET within the same range as TNT2 for the wheat in 2010 and both models underpredicted daily ET in the late sunflower cropping period in July 2007. The agreement between the two models indicates that a limiting factor exists that was not represented by either model. Another source of discrepancy between observations and predictions is the measurement of ET itself. Béziat (2009)  showed that the uncertainty in ET measurements increases with ET intensity when using eddy covariance. It is difficult to obtain accurate daily ET, due to the use of heavy instruments that are subject to failure or calibration drift, the complexity of calculating flux from data measured in the field that rely on turbulence assumptions, and data filtering and processing, including filling data gaps using statistical models that are also subject to uncertainties.
Validity range of AqYield Model calibration of wheat predictions was based on datasets from several experimental sites across France. For most of these sites, several years were used, which represent contrasting soil and climate conditions. The validity range for wheat predictions is thus temperate climates with a large gradient of dryness, especially in summer, and without strong nutrient limitations or pest impacts. Predictions of daily ET in cropping systems were validated only for southwestern France, which has a temperate climate with dry summers. Global radiation, which varies by location, is not included in AqYield because the model does not represent biomass. Low global radiation decreases crop growth, which consequently influences water balance and fluxes. Adding global radiation as input data for AqYield, however, would increase the complexity of this simple model.
Applications for cropping system evaluation Our results indicate that AqYield adequately predicts water balance during crop rotations with the same accuracy as other published crop models that are more complex and require larger amounts of input data. A simple and accurate crop model such as AqYield, which requires only a few input data, seems useful as a module in more complex modeling approaches at the watershed level. For example, it can be implemented in the modeling platform MAELIA  (Gaudou et al. , 2014; Martin and Isaac, 2015; Mazzega et al. , 2014; Therond et al. , 2014). AqYield has been calibrated and evaluated as sufficiently accurate for wheat (main winter crop) and spring crops  (Constantin et al. , 2015). Its ability to predict ET in crop rotations including rapeseed was also satisfactory, although it requires more complete evaluation for this crop.
Acknowledgments This research is part of the  BAG ' AGES  project, supported by the  Adour-Garonne Water Agency. The authors gratefully acknowledge  Jean-Marie Nolot , who first developed the AqYield model. For access to the wheat database, we thank  INRA  and  Arvalis-Institut du Végétal , the  STICS community , the  MicMac Design project  and  LGBI  Project. We thank  Christian Herre  for data acquisition at Fagnières. ET data acquisition at Lamasquère and Auradé was funded mainly by  ICOS-France. These experimental sites benefited from the support and facilities of the  Regional Spatial Observatory (OSR) ,  CNRS  ( Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etudes Spatiales), University of Toulouse , the  GHG-Europe  ( FP7 European  grant no. 244122 ) and  FLUXPYR (FEDER Interreg IVa program , ref  POCTEFA 08/34 ) research projects,  ADEME  ( REACCTIF program  through the  CICC  project), the  Adour-Garonne Water Agency and the Regional Council of Languedoc Roussillon-Midi-Pyrénées.
). Wheat was not Descriptions of the sites in the wheat calibration and validation databases (dataset A). Abbreviations, definitions and units of variables in water balance equations. Units are mm unless otherwise specified Irr Daily irrigation PET Daily potential evapotranspiration R Daily rainfall Harvest years 2006; 2010 2007; 2011 1985; 1989;1998; 2005; 2006; 2011 2002-2004 1991 2002-2004 2004; 2006; 2007; 2010; 2012; 2015 a given day RootAWC max Maximum Available Water Content in the layer ex-plored by roots on a given day SurfAWC Available Water Content in the shallow layer on a given day SurfAWC max Maximum Available Water Content in the shallow 2006-2008 1996-1997 1989 1994-1995 1995 1996-1998 2002 2008;2012 2009 Drain Daily amount of water loss by drainage layer EVA Kc RootAWC Available Water Content in the layer explored by roots on Daily amount of evaporation from the soil Crop coefficient, index of foliar expansion (unitless) Observed data b ET ET drainage AW AW AW AW TotAWC Available Water Content in the soil on a given day TR Daily actual crop transpiration TR max Daily maximum crop transpiration AW AW AW AW AW AW AW ET ET drainage No.
8 10. 3 7. 8 11. 5 6. 7 10. 5 11. 0 11. 9 8. 5 8. 2 8. 3 9. 1 7. 4 8. 9 10. 5 10. 6 8. 5 AW capacity (mm) 128 130 281 81 142 156 180 200 205 218 238 267 269 313 128 130 281 Clay content (%) 32 50 10 33 20 33 25 20 27 13 17 23 20 14 32 50 10 Table 1 Database Site Latitude/ Longitude Calibration Auradé 43. 6/ 1. 1 Lamasquère 43. 5/ 1. 2 Fagnières 49. 0/ 4. 3 Bois-Joly 46. 2/ -0. 46 Pouilly 49. 7/ 3. 6 Magneraud 46. 2/ -0. 46 Auzeville 43. 5/ 1. 5 StGilles 43. 7/ 4. 4 Villamblain 48. 0/ 1. 8 Aulnois 49. 6/ 3. 6 Estrées-Mons 49. 9/ 3. 0 Grignon 48. 8/ 1. 9 Bruyères 49. 5/ 3. 7 Minière 48. 8/ 2. 1 Validation Auradé 43. 6/ 1. 1 Lamasquère 43. 5/ 1. 2 Fagnières 49. 0/ 4. 3.
The Martian mesosphere and thermosphere, the region above about 60 km, is not the primary target of the Exomars 2016 mission but its Trace Gas Orbiter (TGO) can explore it and address many interesting issues, either in-situ during the aerobraking period or remotely during the regular mission. In the aerobraking phase TGO peeks into thermospheric densities and temperatures, in a broad range of latitudes and during a long continuous period. TGO carries two instruments designed for the detection of trace species, NOMAD and ACS, which will use the solar occultation technique. Their regular sounding at the terminator up to very high altitudes in many different molecular bands will represents the first time that an extensive and precise dataset of densities and hopefully temperatures are obtained at those altitudes and local times on Mars.
Introduction Exomars 2016 was launched on March 14th, 2016, carrying two major modules: (i) the Trace Gas Orbiter, or TGO in short, a spacecraft in orbit around Mars with four remote sounding instruments on board, and (ii) the Schiaparelli surface platform. The release of the Schiaparelli module and the successful insertion of TGO into Mars orbit took place on October 19th, 2016 (  [31] ). A special period of high elliptic 4-sols (sol = Martian day) orbits around Mars immediately started, the so called Mars Capture Orbit phase (MCO hereinafter), which lasted for about two months. Around mid January 2017 maneuvers to change the orbit inclination and to lower the apocentre took place, shifting the orbital plane of TGO from the original nearly equatorial to a nearly polar orientation and reducing the period to about 1 day.
In March 2017, a phase of aerobraking started, which will last for months, possibly until February 2018, when the regular operations should start. The final orbit will have a 74 o inclination and a period of 2 h. The driving scientific goals of TGO and the individual instruments on-board can be found in several accompanying papers  (Vandaele et al, 2017 [131] ;  Korablev et al. , 2017 [72] ). The Martian mesosphere (altitudes between about 60 km and 120 km) and thermosphere (above about 120 km) are not the primary targets of the Exomars 2016 mission. However, this mission's instrumentation has capabilities to sound high in the Mars atmosphere, and the orbit configuration offers possibilities to address a number of open problems at high altitudes. Here we review these possibilities and their scientific interest, and try to motivate the Mars research community to pay attention to the upcoming data from the mission for exploiting them and to think about further upper atmosphere studies.
This mission is supplying a significant amount of new in-situ and remotely sensed measurements with its suite of instruments. This includes a mass spectrometer (NGIMS) for neutral and ion densities, a Langmuir probe (LPW) for electron densities and temperatures, and an UV spectrograph (IUVS) yielding selected neutral and ion densities from dayglow emissions and stellar occultations. Here we paint a succinct panorama of half a dozen open and interesting issues in our understanding of the Mars upper atmosphere, which clearly demand further attention and which the TGO data may permit insightful analysis, both as a standalone experiment and as a new tool in combination with previous and future data. I EUV and non-EUV heating sources The atmospheric heating by the solar flux in the extreme UV is the dominant heating source of the Martian thermosphere and the major driver of the daily variations.