dataesr/scientific-paragraphs-categorization huggingface dataset:
Background The goal of our study was to explore, analyse and understand the users' experience of SNS in a learning context. We therefore chose a qualitative approach, aiming to explore the behaviours, feelings and experiences  [24]  of SNS users in these kinds of situations. We carried out semi-structured interviews with general practice residents at the Faculty of Medicine of Strasbourg. We recruited participants on a voluntary basis and asked them to sign a consent form containing information on the study objectives and procedures, the voluntary nature of participation, foreseeable risks, expected benefits and data confidentiality methods. The Strasbourg University ethical review committee approved the study protocol.
Among the 13 teachers of the general practice department, 2 teachers (HM and FR), both medical doctors holding master degrees in medical education created a virtual community of practice using (Facebook®) for asynchronous distance learning. A closed (Facebook®) group was created to this end. Residents were asked to post the building blocks they had used to create their projects, their references, to read others residents' posts and provide feedback when possible. Residents were free to use e-mail instead of SNS to communicate with the supervisor and receive feedback. Participation in the virtual community of practice was not compulsory and the participation in the virtual community didn't influence residents' evaluations. We asked students to make all patient-related data they published anonymous.
Interviews HM carried out all the semi-structured interviews based on an interview guide. The interview guide (Additional file 1) was written by HM, based on the concept of social appropriation. It aimed to document, in particular, the experience of using SNS, mastering it, ways of using it, how each interviewee contributed to carrying out his or her personal project, interactions with peers, and feelings when faced with the tool and its integration into the learning environment. The user's age was also recorded, as well as the number of learning sessions attended by residents using SNS. At the end of the interview, the interviewees were invited to comment freely on using SNS in a learning situation. The interview guide was tested during a preliminary interview with residents. We modified it gradually as data were analysed, as recommended in the practice of qualitative research  [29].
Strengths and weaknesses This study addresses the need to understand the particularities of SNS usage in family practice training. The theoretical framework, methodology and interview guide enabled us to collect relevant data in relation to our research objectives. Data saturation and investigator triangulation are important criteria in qualitative research. Our study has several limitations. Although we believe that our findings can also be applicable to other fields of study, the scope and the methodology we used prevent any generalization and transferability of our results to other teaching and learning environments. Also, there are clear limitations to data saturation when purposive sampling is used, as saturation may occur sooner if all the interviewees belong to a similar subset. This could explain why we rapidly reached saturation.
2015, 7 3251 below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1. 68 K for grass, and the maximum RMSE was 3. 59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1. 82 K.
However, because of the large spatial and temporal variations in the LST, this method is very sensitive to the scale differences over space, time and viewing angle between the two sensors. The present work aims to estimate accurately the LST from Chinese FengYun-3A satellite data by using a split-window technique and then to validate the proposed algorithm using different methods. Section 2 describes the data, including satellite data and in situ measurements. Section 3 presents the theory associated with the LST retrieval and the simulations using the atmospheric radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN). The results and discussion are presented in Section 4, where the sensitivity analyses in terms of the uncertainty in the LSEs and atmospheric water vapor content (WVC) and the instrument noise are given.
FY-3A Satellite Data FengYun-3A (FY-3A) was launched on 27 May 2008, by the Taiyuan Launch Center, China. The second-generation polar-orbiting meteorological satellite carries ten payloads of optical and microwave sensors to collect a range of remote sensing data. The missions are meteorological forecasting, environmental change evaluation, natural disaster monitoring, etc. One of the major sensors is the Visible and Infrared Radiometer (VIRR), which includes seven visible and near-infrared channels and three infrared channels. The channel wavelengths of the instrument are depicted in Table  1. The thermal-infrared Channels 4 (centered at 10. 8 µm) and 5 (centered at 12. 0 µm) data observed by the VIRR at the TOA were used to estimate the LST, and the seven visible and near-infrared channels were used to determine the LSE in this work.
MODIS Satellite Data MODIS is a sensor aboard the NASA Earth Observing System (EOS) Terra satellite launched in 1999 and Aqua satellite launched in 2002. The passive imaging spectroradiometer consists of 36 spectral channels covering the visible and infrared wavelengths, from approximately 0. 4 to 14. 0 µm  [30]. MODIS is intended to satisfy a diverse set of atmospheric, oceanographic and terrestrial science observational requirements. The data used in this work are the MOD11_L2, MOD03 and MCD12Q1 products provided by NASA Goddard Space Flight Center (GSFC) Level 1, the Atmosphere Archive and Distribution System (LAADS)  [31]  and the NASA-developed EOS Clearinghouse (ECHO)  [32]. MOD11_L2 is an LST/LSE product that retrieves the LST from a generalized split-window algorithm  [10] , and LSEs are determined for Channels 31 and 32 via the classification-based emissivity method  [33]  at a 1-km spatial resolution.
The LST product is used to cross-validate the LST estimated from the FY-3A VIRR data. MOD03 is the geolocation field data that are calculated for each 1-km MODIS instantaneous field of view (IFOV) for all daytime orbits. The geodetic latitude, longitude and satellite zenith angle of the geolocation fields are used in the inter-comparison. MCD12Q1, the MODIS land cover type yearly level 3 (L3) global 500-m sine (SIN) grid product, is classified by the International Geosphere-Biosphere Programme (IGBP) 17-class scheme  [34]. This product is combined with the MOD11_L2 LST product to cross-validate the estimated VIRR LSTs for different land cover types.
Algorithm Development for FY-3A VIRR Data To develop an LST retrieval algorithm for FY-3A VIRR data, the atmospheric radiative transfer model,  MODTRAN 4 [40] , was used to simulate at-sensor radiance with the appropriate thermal-infrared channel response function of the VIRR. Figure  2  shows the corresponding spectral response functions of the FY-3A VIRR channels. For comparison, the spectral response functions of MODIS TIR Channels 31 (centered at 11. 0 µm) and 32 (centered at 12. 0 µm) are also shown in this figure. To broaden atmospheric variations to cover all possible real situations, two radiosonde observation databases are considered. One database is the latest version of the Thermodynamic Initial Guess Retrieval (TIGR), i. e. , TIGR2002, which was constructed by the Laboratoire de Meteorologie Dynamique (LMD) to represent a worldwide set of atmospheric situations (2311 radio soundings) from polar to tropical atmospheres with varying water vapor amounts (0.
1 to 8. 0 g/cm 2 ) and varying atmospheric surface temperatures (231 K to 315 K)  [41]. The other database is the six standard atmospheric profiles for the tropics, mid-latitude summer, mid-latitude winter, sub-arctic summer, sub-arctic winter and 1976 United States standard atmosphere (US76) in MODTRAN 4. Because we only accounted for atmospheric variation in clear-sky conditions for LST retrievals, the TIGR2002 profiles with a relative humidity (RH) greater than 90% are discarded, considering that this high RH seldom occurs under clear-sky conditions. In addition, the profiles with a low-level warm layer that is more than 10 K warmer than the surface are also discarded, because the inversion could cause a negative atmospheric correction when used in the split-window technique  [42]. Therefore, 1396 representative atmospheric situations in which the lowest atmospheric temperature ranges from 231 K to 315 K and WVC ranges from 0.
Specifically, the LST varies from 5 K to 15 K in steps of 5 K for 290 K and from 5 K to 5 K in steps of 5 K for 290 K. Moreover, considering most land cover types, an average emissivity ̅ between 0. 90 and 1. 0 (a 0. 02 interval) and an emissivity difference Δε between -0. 02 and 0. 02 (a 0. 005 interval) exist for VIRR Channels 4 and 5  [10]. To demonstrate the assumption of emissivity, Figure  3  shows the emissivities of VIRR Channels 4 and 5 and the range of the emissivity difference Δε between the two TIR channels, as calculated from 146 spectrum samples of soil, vegetation, water and snow in the University of California Santa Barbara (UCSB) spectral database  [43]  and the Johns Hopkins University (JHU)  [44]  spectral database. ), and , the channel brightness temperature (i = 4, 5) at the TOA can be determined according to Equation (1) with the inverse of Planck's law.
At this stage, is directly related to the TOA-measured brightness temperature (i = 4, 5). In total, for the TIGR2002 database, 257,256 situations represent the simulated database for the LST estimation and for calculating the regression coefficients; 1188 situations are obtained for the six standard MODTRAN 4 atmospheres to validate the present algorithm, for each VZA. Figure  4  shows the relationship between and calculated for all of the above-mentioned conditions with six sub-ranges of WVC between 0 and 6. 5 g/cm 2 ; for a mean emissivity ̅ = 1, the emissivity difference Δε = 0, and VZA = 0°. From this figure, one can see that the relationship between and varies with the WVC sub-ranges. Therefore, the WVC should be divided into sub-ranges to retrieve the LST accurately. Based on Figure  4  and the non-linearity of the Planck function for temperature, a quadratic form is proposed  [45] [46] [47] : * * * (2) where is the land surface temperature with surface emissivity ε = 1; and are the TOA brightness temperatures of FY-3A VIRR Channels 4 and 5, respectively; and are the coefficients as functions of the WVC and VZA.
Bare Soil Pixels Sobrino and Raissouni  [46]  and Sobrino et al. [6]  noted that a NDVI value of 0. 2 for bare soil and 0. 5 for full vegetation are appropriate for global applications of a 1-km remotely-sensed land dataset. Thus, pixels with NDVI values below 0. 2 are considered bare soil in this work. In this case, the typical soil spectra from the Johns Hopkins University (JHU)  [44]  spectral library database are used to simulate the VIRR channel reflectances (1-2 and 6-10) and emissivities (4-5) by combining the spectral response functions of those channels. Seventeen samples with unique major soil types, including clay loam, silty loam, sandy loam and loamy sand, are used in this investigation. Two statistical relationships are obtained by correlating the emissivities in Channels 4 and 5 to the channel reflectances ( ) for the seven visible and near-infrared channels.
The multiple linear regression relationships can be written as: _ 4,5 ~ , ~ (4) where _ (i = 4, 5) are the bare soil emissivities for Channels 4 and 5, respectively. c is the regression coefficient. Figure  6  compares the estimated LSEs to the actual LSEs calculated from the JHU spectra database for various bare soil types. This figure shows that the emissivities for Channels 4 and 5 can be relatively well described using the visible and near-infrared channel reflectances. These fitting results exhibited RMSE (R-square) values between the actual and estimated emissivities: 0. 003 (0. 74) for Channel 4, and 0. 003 (0. 72) for Channel 5.
Fully Vegetated Pixels Pixels with NDVI values above 0. 5 are considered fully vegetated ( = 1)  [6, 46]. Because the United States Geological Survey (USGS) spectral library only provides reflectivity spectra and because the UCSB spectral library (University of California, Santa Barbara) only provides emissivity spectra, the JHU spectral library with both reflectivity and emissivity spectra for different surface types was used. Unfortunately, JHU only provides four vegetation spectra (conifer, broadleaf, green grass and dry grass), so all four vegetation spectra are used to simulate the VIRR channel reflectances (1-2) and channel emissivities  (4) (5). Two statistical relationships are obtained by correlating the Channel 4 and 5 emissivities ( _ and _ ) to the NDVI: _ 4 0. 889 0. 119 (5) Comparing the estimated emissivities and the actual emissivities calculated from the JHU spectra database shows that the RMSEs (R-squares) are 0.
Validation Using the Simulated Data To test the performance of the proposed algorithm, data simulated with the same viewing geometry and land surface type conditions, but with the six standard atmospheres provided by MODTRAN 4, as mentioned in Section 3. 2, are used. The TOA brightness temperatures for VIRR Channels 4 and 5 are obtained in combination with the MODTRAN outputs of the atmospheric parameters ( , _ ↑ , _ ↓ ) and reasonable variations in the and. We then calculate the surface temperature using the proposed split-window algorithm. Figure  9  shows a comparison and difference histogram of the actual and the estimated using the proposed algorithm for different types of surfaces and atmospheric profiles. The RMSEs listed in this figure stem from estimating the LST at various VZAs. The results are quite consistent with the maximum RMSE below 1 K.
Validation Using the MODIS LST Product MOD11_L2 An inter-comparison method using the MODIS-derived validated LST V5 product MOD11_L2 at a 1-km resolution is used to validate the proposed LST retrieval algorithm. Considering that the LST at the satellite pixel scale rapidly varies with high spatiotemporal variations, the accuracy of the inter-comparison method depends on the geographic coordinate matching, temporal matching and VZA matching of the two satellite-derived LST products  [1, 24, 25]. Considering that the spatial resolutions for VIRR and MODIS are 1. 1 km and 1 km, respectively, we do not take into account the spatial difference between these two estimated LSTs. The LSTs estimated using the proposed method from FY-3A VIRR data are resampled to a 1-km resolution according to the nearest-neighbor interpolation method.
To match the MODIS and VIRR LSTs, the 500-m SIN grid data are projected onto a 1-km Albers equal area projection using the MODIS Reprojection Tool (MRT)  [52]. Compared with MOD11_L2 LST, the proposed method underestimates the VIRR LST, with an RMSE of 2. 16 K for a cropland/natural vegetation mosaic, 2. 37 K for croplands, 1. 68 K for grassland, 2. 32 K for open shrub land, 2. 60 K for permanent wetlands, 1. 75 K for savannas, 2. 79 K for urban and built-up land, 1. 75 K for woody savannas and 3. 59 K for barren or sparsely vegetated surfaces. Therefore, the difference between two estimated LSTs is relative small for homogeneous land surfaces, such as grasslands, while the difference is large for heterogeneous land surfaces, particularly barren or sparsely vegetated land cover. Note that spatial and angular differences exist between MODIS and VIRR and somewhat affect the resultant LSTs for comparison.
Validation Using In Situ Measurements To validate the proposed LST retrieval algorithm, the Hailar field measurements are used. Note that the data measured by the infrared radiometer are ground-based radiances that couple LST, LSE and downwelling atmospheric radiance. The ground-based LST can be retrieved by: 1 , 1 , ↓ (17) where is the inversion of the Planck function, , is the radiance emitted from the surface, , ↓ is the downwelling radiance from the sky and is the land surface emissivity. Because the experimental campaign was conducted over one year from October, 2013, to September, 2014, the land cover of the field site was dry grass from 22 October to 5 November 2013. From 6 November 2013, to 25 March 2014, the site was covered by snow. From April to May, the snow was melting, and the site was again covered by dry grass.
From June to September, the land cover at the site was green grass. Consequently, the value of is assigned a constant 0. 940 for dry grass, 0. 983 for green grass and 0. 988 for snow cover, as calculated by combining the spectral response function of SI-111 with the dry grass spectra, green grass spectra and snow spectra, respectively, provided by the UCSB spectral library database. Figure  12  illustrates the number of days with cloud-free conditions at the time of the FY-3A VIRR overpasses during the experiment period at the Hailar field site. Figure  13  provides a comparison between the LSTs retrieved from FY-3A VIRR data using the proposed method and those calculated using Equation  (17)  from the in situ measurements for all days with cloud-free conditions at the time of the VIRR overpasses.
940 for dry grass; this value may result in a slight overestimation of the LST calculated from the ground-based measurements. In addition, the ground data measured at the site are point-based, while the retrievals with FY-3A VIRR data using our proposed method have a spatial resolution of 1. 1 km. The viewing direction of the ground-based radiometers is vertically downward, while the VZAs of the VIRR are variable for each pixel. Consequently, both the spatial scale difference and the viewing angle difference may somewhat affect the comparison of the two resultant LSTs. Comparison between the LSTs estimated using the proposed method and those calculated using in situ measurements for cloud-free conditions at the time of the FY-3A VIRR overpass during the experiment period at the Hailar site (R 2 is the square of the correlation coefficient).
Conclusions In this paper, a split-window algorithm was proposed to retrieve the land surface temperature (LST) from thermal-infrared Channels 4 (centered at 10. 8 µm) and 5 (centered at 12. 0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the Chinese second-generation polar-orbiting FengYun-3A (FY-3A) meteorological satellite. On the basis of radiative transfer theory, the proposed split-window algorithm was developed using an accurate atmospheric radiative transfer model,  MODTRAN 4 , with the latest version of the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric profile database. To improve the accuracy of the split-window algorithm, the LST, the mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges. The simulation analysis showed that the fitting root mean square errors (RMSEs) were proportional to viewing zenith angle (VZA), WVC and LST; the LST could be estimated with RMSEs below 1 K for the VZA sub-ranges less than 30° or for the VZA sub-range less than 60° and the atmospheric WVC sub-range less than 3.
5 g/cm 2 , provided that the LSEs are known. Sensitivity analyses in terms of the instrument noise and the uncertainty in the LSE and atmospheric WVC were performed. The results showed that, given an instrument noise NEΔT = 0. 2 K and 1% uncertainties in 1 and Δε, the total error associated with the LST retrieval was approximately 1. 1 K. Some data that were simulated independently with the same viewing geometry and land surface type conditions, but with different atmospheric conditions, were used to test the performance of the proposed algorithm. Compared with the actual input LSTs, the retrieved results are quite consistent for VZAs with a maximum RMSE of less than 1 K. The MODIS land surface temperature/emissivity (LST/E) products MOD11_L2 with a 1-km spatial resolution LST were used to inter-validate the proposed algorithm.
A comparison of the two satellite-derived LSTs in terms of the geographic coordinate matching, temporal matching and VZA matching showed that the two datasets agree well for homogeneous land surfaces, such as grasses, with an RMSE of 1. 68 K. The agreement is poor for heterogeneous land surfaces, particularly barren or sparsely vegetated surfaces, with a RMSE of 3. 59 K. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed algorithm. The results showed that the bias and RMSE of the LSTs between the two types of measurements (ground-based and satellite-based methods) were 0. 17 K and 1. 82 K, respectively.
Acknowledgments This work was jointly supported by the  Excellent Young Talent Funds for Kezhen Distinguished Young Scholar  in the  Institute of Geographic Sciences and Natural Resources Research (IGSNRR) ,  Chinese Academy of Sciences (CAS)  under Grant  2012RC101 , the  National Natural Science Foundation of China  under Grants  41171287  and  41231170  and the " Strategic Priority Research Program " of the  Chinese Academy of Sciences, Climate Change :  Carbon Budget  and  Relevant Issues , under Grant  XDA05040204.
Cryptic type error messages are a major obstacle to learning OCaml or other ML-based languages. In many cases, error messages cannot be interpreted without a sufficiently-precise model of the type inference algorithm. The problem of improving type error messages in ML has received quite a bit of attention over the past two decades, and many different strategies have been considered. The challenge is not only to produce error messages that are both sufficiently concise and systematically useful to the programmer, but also to handle a full-blown programming language and to cope with large-sized programs efficiently. In this work, we present a modification to the traditional ML type inference algorithm implemented in OCaml that, by significantly reducing the left-to-right bias, allows us to report error messages that are more helpful to the programmer.
Introduction Typing errors in OCaml are a major obstacle to the adoption of the language, leading many potential adopters of OCaml to run away before they really get a chance to appreciate the language. Yet, producing "good" error messages for realistic ML programs turns out to be an unexpectedly hard problem. On the one hand, it is tempting to report everything that contributes to a type error. This strategy, however, can quickly lead to very verbose messages, so long that the programmer will not read them. Interactive type-debugging sessions have been proposed as a solution to verbosity (e. g. [5] ), however they can be very time consuming for the programmer. Another proposed approach is the extraction of minimal error slices  [7]. Although quite appealing, error slices do not fully address the problem: it remains to select which of those minimal slices to first present to the user, and to select on which locations to attract the user's attention when the slice involves locations spanning over remote lines of code.