dataesr/scientific-paragraphs-categorization huggingface dataset:
A fortuitous aspect of the Yukon Flats model is the fact that the silt layer (unit 3) is relatively conductive compared with the overlying gravels (unit 2), making it a good target for electromagnetic methods. If the order of these layers were reversed, if the base of permafrost were hosted in a relatively resistive lithology, or if the base of permafrost was significantly deeper, AEM data would not likely resolve the overall structure with such good fidelity. In addition, knowledge of the stratigraphy helps to remove the ambiguity between unfrozen gravels and frozen silts, which have similar intermediate resistivity values (Figs. 4  and 5 ). The methods developed here that use a physical property model to link hydrologic and geophysical properties provide the necessary framework to test other more challenging hydrogeological scenarios.
The second challenge, to resolve near-surface details associated with supra-permafrost thaw, is addressed in Fig. 10. For the scenarios considered here, AEM data can identify shallow thaw features but have difficulty in discriminating their specific details. There are many combinations of resistivity and thickness that produce the same electromagnetic response; therefore, without additional information it is not possible to uniquely characterize both thaw depth and resistivity. Ground-based electromagnetic data show improved sensitivity to the shallow channels and also limited sensitivity to the interface between resistive frozen gravels and frozen silty sands (Fig. 5 ). By restricting the possible values of resistivity and/or thickness for one or more layers based on prior assumptions,  Dafflon et al.
Summary Analysis of AEM surveys provide a means for remotely detecting subsurface electrical resistivity associated with the co-evolution of permafrost and hydrologic systems over areas relevant to catchment-scale and larger processes. Coupled hydrogeophysical simulations using a novel physical property relationship that accounts for the effects of lithology, ice saturation, and temperature on electrical resistivity provide a systematic framework for exploring the geophysical response to various scenarios of permafrost evolution under different hydrological forcing. This modeling approach provides a means of robustly testing the interpretation of AEM data given the paucity of deep boreholes and other ground truth data that are needed to characterize subsurface permafrost. A robust uncertainty analysis of the geophysical simulations provides important new quantitative information about the types of features that can be resolved using AEM data given the inherent resolution limitations of geophysical measurements and ambiguities in the physical property relationships.
Experimental Results This section is devoted to showing the effectiveness of the proposed control approaches. Experiments on the quadrotor prototype (Figure  4 ) by the "Dynamical Systems and Control" group of the Autonomous University of Puebla (BUAP) are carried out in real time. The control laws are executed on a Spartan-6 FPGA LX9 MicroBoard. The Spartan-6 has the ability to implement a "MicroBlaze" soft processor running at 66. 7 MHz. Furthermore, the Spartan-6 has the advantage of being able to develop custom modules, such as PWM generators and Universal Synchronous/Asynchronous Receiver/Transmitter (USART) ports. A sensor suite composed of a triaxial magnetometer, a triaxial accelerometer and three rate gyros mounted orthogonally is used to obtain the vector observations and angular velocity by means of a serial communication (RS-232 ).
Acknowledgments This work was partially supported by the  Research and Postgraduate Office of the Autonomous University of Puebla (VIEP-BUAP)  under grant  GUCJ-ING15-I , the  Mexico's National Council for Science and Technology (CONACYT) , the  Grenoble Institute of Technology (Grenoble-INP)  and the " Entomoptère Volant Autonome" (EVA)  project, supported by the  French National Research Agency (ANR).
Sources of Data A cross-sectional study was carried out using a sample extracted from the French National Insurance Healthcare System database called "Echantillon généraliste de bénéficiaires" (EGB). The EGB is based on a survey at the 97th percentile on the social security (national health insurance) number of French health insurance beneficiaries, whether they have received healthcare reimbursements or not  [8]. Over 500,000 people are currently registered, representative of the population (excluding public service employees and students). It contains anonymous information on the socio-demographic and medical characteristics of beneficiaries, the specialty of the prescriber and date and the drug motivating the reimbursement.
To minimize misuse patients with psychiatric follow-up but without ALD were considered as having psychiatric disease allowing long term treatment according to the authorized information of the antipsychotic drugs but not yet registered in the health insurance system. To quantify the observance in the psychiatric group the Cumulative Mean Gap has been assessed. The ratio is the total days of treatment gaps divided by the total days to next fill or end of observation period  [10]. The extraction and use of data were carried out with the SAS software, version 9. 1.
Discussion In this study, we examined the patterns of use of antipsychotics in France, we found that 2. 23% of French health insurance beneficiaries had at least one reimbursement for an antipsychotic medication in 2010. An high number of patients received it without an ALD for long-term psychiatric disorder and without a follow-up by a psychiatrist. Near half of them took antipsychotics chronically. These results suggest overuse and misuse in patients without psychiatric disease. The data obtained from the present observational study based on the EGB data are representative of the French population  [8]. However, this database did not include public hospitalization in psychiatry and diagnoses were not confirmed. We used ALD, medical interventions and prescribed medicines as proxies for the assessment of diagnoses.
Another limitation of this study is that it is based on information given by the French National Insurance Healthcare System (all prescription for which the patient went to the pharmacy are in the database), so, we have no idea if the subject really took his treatment nor are we sure whether long the treatment actually lasted. Data concerning the patterns of use of antipsychotic are rare in France  [11] [12]. Antipsychotics were prescribed to 0. 7% of the population covered by the general health fund between 1990 and 1994, while 2. 7% used them during the year 2000  [11]. This was close to the 2. 23% observed in 2010 and similar to the 2. 4% reported in 2005 in Norway  [13]. The overall level of antipsychotics reimbursement was stable in France while it increased in the United States, for example  [14].
However, the TES method exhibits significant errors under hot and wet atmospheric conditions  [20]. The multi-temporal physical method  [14]  and the Kalman filter physical method  [15]  utilize the invariance feature of LSE measured within a short time period (six hours) to separate LST and LSE from geostationary thermal infrared radiances provided that good atmospheric correction has been perform. The TRSM method simultaneously retrieves the atmospheric profiles, LST and LSE from the MODIS channel data, which does not require atmospheric correction  [17]. However, the requirement of adequate channels and the TRSM method's complex nature make it difficult to apply. The methods mentioned above use satellite data measured at several broad channels, therefore they cannot be applied to hyperspectral thermal infrared (HypTIR) data.
The HypTIR data with thousands of channels provide plenty of information on the atmosphere and the observed surface. Therefore, new methods are required for retrieving ST for high-emissivity surfaces from HypTIR satellite data. Currently, there is large amount of HypTIR data acquired by space-borne sensors. The earliest sensor, Interferometric Monitor for Greenhouse Gases (IMG), was launched in 1996, but it failed in 1997. The first successful sensor, the Atmospheric InfraRed Sounder (AIRS)  [21] , was launched in 2002 and has been providing HypTIR data ever since. There are HyperTIR data observed by other space-borne sensors, such as the Infrared Atmospheric Sounding Interferometer (IASI)  [22]  and the Cross-track Infrared Sounder (CrIS)  [23]. In the future, the infrared sounder (IRS)  [24]  will also provide this type of HypeTIR data.
There is a pressing need for methodological development in order to retrieve ST from these space-borne HypTIR data. There are mainly five types of methods for retrieving ST for high-emissivity surfaces from space-borne HypTIR data: the principal component regression method  [25] [26] [27] [28] [29] , the artificial neural network (ANN) method  [30, 31] , the stepwise LST and LSE retrieval method  [32] , the simultaneous LST and LSE retrieval method  [33] , the physical simultaneous atmospheric profiles, LST and LSE retrieval method  [34] [35] [36] [37]. The ANN method and principal component regression method are based on a linear/nonlinear empirical relationship between principal component amplitudes of brightness temperature spectrum at TOA and ST. The principal component regression method and the ANN method do not require extra atmospheric data and are fast enough for near real-time application  [28].
The physical simultaneous atmospheric profile, LST and LSE retrieval method utilizes physical constraint based on spectral smoothness characteristic of LSE to iterative solve LST, LSE and atmospheric profile simultaneously with the support of atmospheric radiative transfer model. The physical simultaneous retrieval method does not require atmospheric profile, but it has low computation efficiency because of its complex nature  [31]. The objective of this paper is to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR images. The proposed method is suitable for retrieving ST for high-emissivity surfaces from HypTIR measurements containing damaged data and provides flexibility for retrieving ST for high-emissivity surfaces with taking the complex physical situations mentioned above into account in future.
The proposed method can be used for near real time production of ST for high-emissivity surfaces from space-borne HypTIR data and for providing first-guess value of ST for the physical method. This paper is organized as follows: Section 2 presents the proposed method. The evaluation of the proposed method is given in Section 3. The sensitivity of the proposed method to spectral sampling frequency and instrumental noise is shown in Section 4. The application of the proposed method to satellite data is shown in Section 5, and the last section provides the conclusion.
Data for Simulation Although there are large amounts of HypTIR data measured at TOA, it is still difficult to find spatially and temporally collocated atmospheric moisture and temperature profile data. Additionally, there are few field-measured ST data at the spatial scale of a satellite FOV (12 km for IASI). Therefore, we have resorted to simulation data for determining the parameters in Equation  (3). Specifically, we have used typical atmospheric profile data and typical ST data to create simulation data for determining the parameters in Equation (3). We have selected typical profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database for simulation  [38, 39]  in two steps. First, we have classified the 2311 clear-sky TIGR profiles into six groups according the concentration of water vapour.
The method for determining clear-sky atmospheric profiles from the TIGR database has been detailed by  [40]. To describe the rapid variation of LST for each profile, the six STs for simulation are sums of bottom atmospheric temperature (Ta0) and one out of six perturbations. The six perturbations are [-15 K, -5 K, 0 K, 5 K, 10 K, and 15 K] when Ta0 < 280 K, and these perturbations are [-10 K, -5 K, 0 K, 5 K, 10 K, and 20 K] when Ta0 ≥ 280 K  [30].
Determination of Centre Wavenumbers of Channel i (i = [1,p]) and Coefficients wi (i = [0,p]) We have used the stepwise regression method with the simulation data to determine the centre wavenumbers at channel i and coefficients wi in Equation (3) for IASI. The procedure is shown in Figure  2. For this study, we have simulated large IASI data using Operational release for Automatized Atmospheric Absorption Atlas (4A/OP)  [41, 42]  using the data mentioned in Section 2. 2. The 4A/OP is used to simulate atmospheric transmittance and upward radiance using atmospheric profile data. Brightness temperature database is calculated using Equation (1) with output atmospheric radiative terms of 4A/OP. The spectral interval and spectral sampling frequency for simulation are 800-1200 cm -1 and 0. 25 cm -1 , respectively.
The viewing angle for simulation is restricted to nadir observation. For each simulation case, a random noise with dimension of 1601 generated by a Matlab random number generator with NE∆T of 0. 1 K is added. The noise equivalent temperature difference (NE∆T) has been set according to that of the IASI  [43]. Because O3 has a strong absorption feature in HypTIR radiance spectrum, only HypTIR data at channels in the spectral interval of 800-985 cm -1 and in the spectral interval of 1150-1200 cm -1 have been used for stepwise regression  [44]. The stepwise regression is used to determine the centre wavenumbers of channel i and the coefficients wi with the simulation data above. First, the channel centred at a wavenumber of 1158. 5 cm -1 , where transmittance is the largest, is selected as the initial channel.
The sum of squares for regression is defined as: 2 0 0 1 1 n l ij i j SS w w Tb y              ( 7 ) where l is the number of channels and n is the number of simulation cases, and ȳ is the mean of n samples of LSTs. The (m + 1)-th channel HypTIR brightness temperature added by the stepwise regression is the one with largest partial regression square sums among the remaining channel HypTIR brightness temperatures. We also check to see if the spectral interval of two nearby central wavenumbers is larger than 4. 5 cm -1. If not, HypTIR brightness temperature of this channel is replaced by the one with second-largest sum of squares for partial regression. The criterion for determining the number of channels for Equation (  3 ) is that the root mean square error (RMSE) of the ST retrieved using Equation (3) from the simulation data mentioned above is less than 0.
2 K. The output coefficients wi and centre wavenumbers of channel i are the solutions. The variation of RMSE of the retrieved ST with the number of channels in the process of determining the centre wavenumbers of channel i and the coefficients wi is shown in Figure  3 : the error of the ST that is retrieved using the corresponding regression equation with the simulation data above decreases with the growing number of channels, and the RMSE of the retrieved ST is less than 0. 2 K when the number of channels is larger than 10. The centre wavenumbers of channel i (i =  [1,p] ) and the coefficients wi are shown in Figure  4. From this figure, we can see that the centre wavenumbers correspond to the wavenumbers where water vapour absorption is weak. The reason may be that the assumption that ST can be expressed as a linear function of p HypTIR brightness temperatures is more reliable at these wavenumbers.
Evaluation of the Method First, we have evaluated the developed method by comparing the ST retrieved by that developed method from simulation data in Section 2 with the true input ST. The error of the retrieved ST is shown in Figure  5. From this figure, we can see that the RMSE of the retrieved ST is approximately 0. 20 K, and the error of the retrieved ST ranges from -0. 6 to 0. 9 K. Consequently, ST can be accurately retrieved using Equation (  3 ), with only 10 measurements in the spectral interval of 800-1200 cm -1. To evaluate the method independently, we have selected 39 other profiles from the clear-sky TIGR database as mentioned in Section 2. 2. We have classified all the clear-sky TIGR profiles into five groups, except for the 139 profiles mentioned in Section 2. 2, and selected nearly eight profiles from each of these five groups for evaluation.
Sensitivity to Spectral Sampling Frequency We have analysed the sensitivity of the method to spectral sampling frequency by refitting the coefficients wi in Equation (3) for each spectral sampling frequency, using five simulation databases and studying the error of ST retrieved using these refitted coefficients wi from five other independent simulation databases. To refit the coefficients wi, we have created five simulation databases for five HypTIR sensors with spectral sampling frequencies = 0. 5, 1, 2, 4, 8 cm -1. We assumed that the five sensors have 10 channels with the centre wavenumbers shown in Figure  4. The instrumental spectral response functions (ISRFs) for the five sensors are rectangular impulse functions. The ISRFs for the five sensors at one channel are shown in Figure  7. The simulation database for each of the five sensors is resampled from the simulation data mentioned in Section 2 using each ISRF.
The coefficients wi refitted for each spectral sampling frequency are shown in Table  1. To evaluate the accuracy of the ST retrieved using refitted coefficients wi, an independent simulation database for each spectral sampling frequency is resampled from the simulation data mentioned in Section 3 using each ISRF. The refitted coefficients wi and the 10 centre wavenumbers determined in Section 2 are then used for retrieving ST from these five independent simulation databases. The ST errors that were retrieved using Equation (3), with the refitted coefficients from each of the five independent simulation databases, are analysed with the spectral sampling frequencies and shown in Figure  8. The coefficients wi refitted for each spectral sampling frequency using simulation data vary significantly with spectral sampling frequencies.
Therefore, the coefficients in Equation (  3 ) are dependent on spectral sampling frequency. The biases of the ST retrieved by Equation (3) with refitted coefficients wi from each of the five independent simulation databases vary between -0. 01 K and -0. 05 K, and the corresponding standard errors of the retrieved ST for each independent simulation database are less than 0. 30 K. The ST can be retrieved accurately using Equation (3) with the refitted coefficients wi (i = [0,p]) for each spectral sampling frequency.
Sensitivity to Instrumental Noise To do this sensitivity analysis, three simulation databases are created by adding to noiseless IASI data noises with NE∆T = 0. 1 K, 0. 2 K, and 0. 3 K, respectively. The noiseless IASI data is created using simulation data with 39 atmospheric profiles, as mentioned in Section 3. For each noiseless IASI spectrum, 20 noise-added IASI spectrums are created for each level of noise, including 20 random noises with the dimension of 1601. Each random noise is generated by the Matlab random number generator with corresponding NE∆T. The centre wavenumbers at channel i and coefficients wi, used for retrieving ST from the three simulation databases above, are those determined in Section 2. With NE∆T instrumental noise growing from 0. 1 K to 0. 3 K, the standard error of the retrieved ST goes from 0.
Data for This Application The L1c image of Metop-A IASI has 8461 thermal infrared channels in the spectral interval of 645-2760 cm -1 (3. 6-15. 5 μm) with a spectral sampling frequency of 0. 25 cm -1. The spatial resolution of IASI image at nadir point is 12 km. The scan angle at the end of each scan line is 48. 98°. IASI on Metop-A scans the Mediterranean area in mid-morning orbit every day. The five-minute Metop-A IASI images sensed in the morning on three days in the year 2014 (February 2, August 1 and November 4) are collected for this study. The Advanced Very High Resolution Radiometer (AVHRR) on the same satellite has two thermal infrared channels centred at 10. 8 μm and 12 μm. The level 2 sea surface temperature (SST) product form AVHRR with a spatial resolution of 1 km is used to validate the SST retrieved by our developed method.
Application to the Mediterranean Sea Our developed method is applied to part of the collected IASI data, which has a viewing zenith angle less than 15° (the surface area covered by this selected data is shown in Figure  10  with rectangles). The cloud information in the AVHRR SST product is used to select the clear-sky IASI data. Only IASI data with more than 90% clear AVHRR pixels is used for this evaluation. In total, there are 386 matched IASI samples used for this application. The comparison of the ST retrieved by our developed method from IASI with the SST product from AVHRR is shown in Figure  11. From this figure, we can see that the RMSE of the ST from IASI is 0. 43 K. The ST of high-emissivity surfaces can be retrieved accurately from satellite data by our method.
Conclusions With the assumption that LSE is equal to unity, we have developed a multi-channel ST retrieval method for high-emissivity surfaces based on 10 HypTIR measurements of a radiometer with a spectral interval of 800-1200 cm -1 and a spectral sampling frequency of 0. 25 cm -1. We have evaluated the method using independent simulation data. Moreover, we have analysed the sensitivity of the method to spectral sampling frequency and instrumental noise. This work draws the following conclusions: (1) ST can be retrieved by our method from independent simulation data with RMSE of 0. 21 K, using only 10 HypTIR measurements. This method is very accurate and promising. (2) The coefficients wi of the method are dependent on a spectral sampling frequency. Nevertheless, ST of high-emissivity surfaces can still be retrieved accurately when the coefficients are refitted for each spectral sampling frequency.
Acknowledgments The authors would like to thank the  LMD ARA  for providing TIGR dataset, to thank  LMD ,  NOVELTIS  and  CNES  for providing the 4A/OP software and. This work has been supported by the  National High Technology Research and Development Program of China  under Grant  2012AA121103  and by the  National Natural Science Foundation of China  under Grant  41231170  and  41431179  and  41162011. IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales  ( CNES, Toulouse, France ). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service.
Instruments for Conducting the Study We have developed standard operating procedures to maintain uniformity across sites for the operational and administrative steps to be followed during the study. These are distributed through the Consortium's website www. genomalc. org. The website provides an overview of the study, which is open to all, and will provide a means for public dissemination of research outcomes as these emerge. The protocols and resources are restricted to researchers involved in the study. A data entry spreadsheet designed for the study records data collected through the questionnaires. The data entry design has built in formulae and conditional formatting that take into account the units and normal ranges of biochemical data that vary across the 6 countries and converts these into standard units.