dataesr/scientific-paragraphs-categorization huggingface dataset:
The implementation of Eliom as an extension of an existing language makes it possible to reuse a large set of existing libraries and benefit from an already large community of users. Web programming is never about the Web per se, but almost always related to other fields for which dedicated libraries are necessary. Explicit annotations indicate at which location the program execution takes place. Adding them is really easy for programmers and is a good way to help them see exactly where computation is going to happen, which is crucial when developing real-size applications. Eliom makes it impossible to introduce by mistake unwanted communication. Eliom makes strong use of static typing to guarantee many properties of the program at compile time. Developing both the client and server parts as a single program allows to guarantee the consistency between the two parts, to check all communications: injections, server push, remote procedure calls,.
These design choices have always been guided by concrete uses. From the beginning, Ocsigen has been used for developing real-scale applications. The experience of users has shown that the use of a tierless language is more than a viable alternative to the traditional Web development techniques, and is well suited to the current evolution of the Web into an application platform. The fluidity gained by using a tierless programming style with static typing matches the need of a new style of applications, combining both the advantages of sophisticated user interfaces and the specificities of Web sites (connectivity, traditional Web interaction, with URLs, back button,. ). This is made even more convenient through the use of features such as an advanced service identification model and the integration of reactive functional programming that are provided by Eliom but have not been covered here.
The order corresponds to the order of execution for our common subset of ML. As in Section 4. 3, we assume that fragments are first rewritten in the following manner: f %e s -→ fragment%(decode f ) serial%(encode f e s ) We also assume that expressions inside injections are hoisted out of the local client scope. For example {{ f %(3+x) }} is transformed to λy. {{ f %y }} (3+x). More formally, we apply the following transformations: {{ C[f %e s ] }} -→ (λx. C[f %x]) e s let c y = C[f %e s ] in p -→ let s x = e s in let c y = C[f %x] in p Where x is fresh. This transformation preserves the semantics, thanks to the validity constraint on programs presented in Section 3. 1. We can now define ρ s and ρ c by induction over Eliom ε programs. We refer to Section 4. 3 for a textual explanation. Since we already translated custom converters, all the converters considered are either fragment or serial.
B. 1 Properties on Eliom ε executions The execution of a program can be separated into two phases: execution of server-side code, then execution of client-side code. Proof. We consider the first step t c/s where the program is a return expression e c , and proceed then by contradiction. Corollary 1. ξ is increasing before t c/s is reached and decreasing afterwards. This corollary provides us some indications about how a typical Eliom ε execution looks like. First, ξ is filled until the final variable is reached, then all the code contained in ξ is executed until it's empty, then the final value is computed. We can illustrate this on a drawing: p | ∅ e | ξ v | ∅ ξ Let s , Context s , Converter s or Let c ξ Exec, Context c , BindContext c or Bind Of course, since Y is in the language, computation may also diverge.
B. 2 Simulation We now define a simulation between Eliom ε and ML ε. That is, given a program Eliom ε and its translation into ML ε , we can replay in ML ε any reduction done in Eliom ε. The difficulty here is that Eliom ε is a single language while ML ε is composed of two languages. Also, some information gets spread in different places during the translation and we need it all to be able to do a simulation. This relation, while complicated, gives us all the necessary information to prove that the behavior of Eliom ε and ML s is the same.
Lemma 2 (Relation over translation). The translation from Eliom ε to ML ε respects the server relation. Proof. We note p s = ρ s (p) and p c = ρ c (p). According to Lemma 1 there exists a step t c/s , a program p and a queue ξ such that p | ∅ - → * v | ξ - → * v | ∅ Additionally, according to Lemma 1, we can apply Lemma 3 on each step until t c/s and Lemma 4 on each step after t c/s. According to Lemma 2, (∅, p)R s (∅, ∅, ∅, p s , p c ) By applying Lemma 3 over each step of the execution until t c/s , there exists ξ c and γ inj such that (ξ, v )R s (γ inj , ξ c , ∅, p c , ()) which implies (ξ, v )R c (γ inj , ξ c , p c ). By applying Lemma 4 over each step of the execution after t c/s , there exists v such that (∅, v )R c (γ inj , ∅, v ). We thus exhibit a chained execution of a server and a client program that is synchronized with the original Eliom ε program.
If a declaration is in the client program, there must be an equivalent injection in the Eliom ε program. p c = let x = e c in p c ξ = [x → e] + + ξ e c = e[γ inj ] (ξ, p )R c (γ inj , ξ c , p c ) If a client closure is declared in the client program, it must be substituted in the queue. p c = bind f = λa. e in p c (ξ, p )R c (γ inj , ξ c [λa. e/f ], p c ) Lemma 4 (Relation over reduction). The execution respects the client relation. If (ξ, p)R c (γ inj , ξ c , ∅, p c ) and if p | ξ -→ p | ξ with rules Exec, Context ς , ExecVal or Final c , then we can execute k steps of reduction p c | ξ c -→ c k p c | ξ c such that (ξ , p )R c (γ inj , ξ c , ∅, p c ) Proof. By induction over the Eliom ε reduction relation -→. Simulation). Let p be an Eliom ε program with an execution p | ∅ -→ * v | ∅ For an execution of p that terminates, we can exhibit a chained execution of ρ s (p) and ρ c (p) such that evaluation is synchronized with p.
(ξ, p)R s (γ inj , ξ c , ∅, p c , ()) with () the empty program. If a declaration is in the client program, there must be an equivalent injection in the Eliom ε program. p c = let x = e c in p c ξ = [x → e] + + ξ e c = e[γ inj ] (ξ, p )R c (γ inj , ξ c , p c ) If a client closure is declared in the client program, it must be substituted in the queue. p c = bind f = λa. e in p c (ξ, p )R c (γ inj , ξ c [λa. e/f ], p c ) Lemma 4 (Relation over reduction). The execution respects the client relation. If (ξ, p)R c (γ inj , ξ c , ∅, p c ) and if p | ξ -→ p | ξ with rules Exec, Context ς , ExecVal or Final c , then we can execute k steps of reduction p c | ξ c -→ c k p c | ξ c such that (ξ , p )R c (γ inj , ξ c , ∅, p c ) Proof. By induction over the Eliom ε reduction relation -→. Simulation). Let p be an Eliom ε program with an execution p | ∅ -→ * v | ∅ For an execution of p that terminates, we can exhibit a chained execution of ρ s (p) and ρ c (p) such that evaluation is synchronized with p.
Land surface reflectance is not isotropic. It varies with the observation geometry that is defined by the sun, view zenith angles, and the relative azimuth. In addition, the reflectance is linearly polarized. The reflectance anisotropy is quantified by the bidirectional reflectance distribution function (BRDF), while its polarization properties are defined by the bidirectional polarization distribution function (BPDF). The POLDER radiometer that flew onboard the PARASOL microsatellite remains the only space instrument that measured numerous samples of the BRDF and BPDF of Earth targets. Here, we describe a database of representative BRDFs and BPDFs derived from the POLDER measurements. From the huge number of data acquired by the spaceborne instrument over a period of 7 years, we selected a set of targets with high-quality observations.
The selection aimed for a large number of observations, free of significant cloud or aerosol contamination, acquired in diverse observation geometries with a focus on the backscatter direction that shows the specific hot spot signature. The targets are sorted according to the 16-class International Geosphere-Biosphere Programme (IGBP) land cover classification system, and the target selection aims at a spatial representativeness within the class. The database thus provides a set of high-quality BRDF and BPDF samples that can be used to assess the typical variability of natural surface reflectances or to evaluate models. It is available freely from the PANGAEA website (doi:10. 1594/PANGAEA. 864090). In addition to the database, we provide a visualization and analysis tool based on the Interactive Data Language (IDL).
This quantity is difficult to measure because a typical radiometer measures the reflected light in a single direction. This is particularly true for spaceborne observations where a target is observed from a given direction. As a direct consequence, the radiometer is not sensitive to the albedo but rather to the reflectance  (Schaepman-Strub et al. , 2006). Global observation from space and multi-temporal monitoring of a given target impose the combination of mea-Published by Copernicus Publications. F. -M. Breon and F. Maignan: A BRDF-BPDF database for the analysis of Earth target reflectances surements acquired with different observation geometries  (Lunetta et al. , 2006). It is well known that the reflectance of natural surfaces is far from isotropic  (Bicheron and Leroy, 2000). For most land surfaces, the reflectance is larger in the backscatter direction than it is in the forward direction.
In practice, and except for targets that show a preferential direction such as crops planted along rows, the two azimuths (solar and view) are only significant for the BRDF through their difference. Thus, the BRDF is most often described as a function of (θ s , θ v , φ), where θ s and θ v are the solar and view zenith angles respectively and ϕ is the relative azimuth. The goal of this paper is to describe a database of BRDF samples that has been developed based on spaceborne measurements of Earth reflectances. This database may be used to assess the variability of land surface BRDF for the development and validation of BRDF models and as a boundary condition for atmospheric radiative transfer studies. Other characteristics of the land surface reflectance are its polarization properties. The incoming direct solar light is unpolarized.
, 2015). A primary objective of this space mission is the monitoring of atmospheric aerosols and clouds using the polarization characteristics of the reflected light. Information about the land surface polarization characteristics is therefore needed. The database that is presented in this paper includes, in addition to the spectral reflectances, the polarization characteristics in one channel. In the following, we describe the input data, the data processing and selection, and the database format. In addition, we have developed an interactive tool to allow a simple graphical analysis of the database and a comparison to analytical models. The tool is therefore described in the second part of the paper with a few examples of its outputs.
This is possible thanks to its optical design that consists of a wide field-of-view lens associated with a bi-dimensional CCD (charge-coupled device) matrix. This combination generates a bi-dimensional field of view with forward-backward angles of ±51 • and cross-track angles of ±43 •. The maximum view angle at the surface is close to 70 • and corresponds to measurements acquired around the corners of the CCD matrix. As the satellite flies over, up to 16 (average is 14) observations of the target are available. These observations provide a sampling of the target BRDF. During the following days, the PARASOL satellite flies over that target again, albeit in a different orbit, which provides another set of BRDF sam- N Y N Y N N Y N N In database N Y Y Y N Y Y N Y ples. Depending on cloud cover, these successive measurements allow a very broad sampling of the BRDF for view angles up to ≈ 60 • , assuming a stability of the target during the composition period.
Note however that PARASOL is on a helio-synchronous orbit so that the various acquisitions are made at a near constant solar time. As a consequence, there is little variation in the sun angle in the measurements of the target reflectance during a short period. The PARASOL satellite was launched in December 2004. Data acquisition started in early 2005 and was nearly continuous until October 2013. However, due to lack of fuel, the satellite left the A-Train and was on a slowly drifting orbit after December 2009. There have been some data acquisition interruptions during the lifetime of the satellite, mostly resulting from malfunctions of the stellar sensor. The best year in terms of data acquisition continuity was 2008. As a consequence, we selected that year to build the BRDF-BPDF (bidirectional polarization distribution function) database.
After the end of the in-orbit operations, POLDER/PARASOL data benefited from further development in calibration and data processing. Using several vicarious calibration techniques, all based on natural targets, it was possible to derive an accurate set of calibration parameters that account for the temporal evolution of the instrument sensitivity, characterized by a mean decrease modulated by a variation within the field of view  (Fougnie, 2016). These developments led to a full reprocessing of the POLDER/PARASOL dataset at the end of 2015.
Data selection The objective is to sample the variability of land surface BRDF and BPDF while selecting only the observations that are free from significant aerosol and cloud contamination and for which a large number of observations are available. Early studies have shown that there are systematic changes in the BRDF with the land surface type  (Bacour and Breon, 2005). As mentioned above, snow has a very specific directional signature; deserts show a more isotropic directional F. -M. Breon and F. Maignan: A BRDF-BPDF database for the analysis of Earth target reflectances signature than vegetated surfaces, and wetlands sometimes show a glint signature in addition to the classical maximum in the backscatter direction. It is then natural to sample BRDFs as a function of the land surface cover.
For this objective, we make use of the IGBP (International Geosphere-Biosphere Programme) classification  (Loveland et al. , 1999). We used the official MODIS land cover product (MCD12Q1) for the year 2008 at 5 min resolution  (Liang et al. , 2015). For each POLDER pixel (≈ 6. 2 × 6. 2 km 2 ) we analyse the land cover type for the 5 × 5 MODIS cells centred on the POLDER pixel. Only the POLDER pixels for which there is a clear dominance of one land cover type (> 75 %) are kept for further processing. The threshold was empirically chosen to ensure maximal homogeneity while keeping a representative spatial distribution of the selected targets. The POLDER pixels are assigned the IGBP land cover type as identified from the MODIS product, and the relative fraction of the dominant type is kept for inclusion as ancillary information in the database.
As a result of this procedure, we independently select 50 targets for each of the 12 months and each of the 16 IGBP surface types. This procedure leads to the monthly database. In addition, we generate a yearly database where the selection is based on the yearly score (scoreY) rather than the monthly scores. The procedure is very similar. In the yearly database, the same targets are selected for the 12 months. Conversely, the monthly database selects pixels independently for each month, which results in different target sets in most cases. The monthly database is best for analysing targets of high quality for each month independently. The yearly database shall be used to assess the variability of the BRDF and BPDF along the year, as shown in Sect. 3. 7 below, although some months may be poorly sampled.
Database structure The two databases (monthly and yearly) are built around a large number of text files (≈ 16 × 12 × 50). Each file includes the surface reflectance and polarized reflectance acquired during the month. The files are sorted by IGBP surface types (nn from 01 to 16) and then by month (mm from 01 to 12): The directory IGBP_nn contains the subdirectories 2008 mm, which contain the files. The file format is described in Appendix A. In addition, the database includes a binary file map_IGBP. bin. It reproduces the IGBP classification used for the data selection on a 540 × 270 (long × lat) grid. This file is used by the graphic analysis tool. 3 Analyses of the database features using the visu_brdf analysis tool.
Visu_brdf tool set-up A graphical interface tool has been developed to analyse the BRDF-BPDF data file described above. The code visu_brdf. pro is based on the IDL and its use requires an IDL licence. Another option, which does not require an IDL licence, is to download the IDL Virtual Machine from the Harris Geospatial website (https://www. harris. com/ what-we-do/geospatial-solutions). The virtual machine lets you run the compiled version of the analysis tool, provided in the visu_brdf. sav file. The first step is to locate the database. Inside the code, you change the variable "HomePath" to the directory that contains the monthly and yearly databases. When using IDL with a proper licence, type IDL >. compile visu_brdf. pro IDL > visu_brdf. If the HomePath was not set properly, a warning message indicates that one must select the path for the "POLDER BRDF" database and a window opens up for that purpose.
The Main Command window Figure  1  shows the Main Command window of the BRDF analysis tool. In the following, it is referred to as MCW. One can select one of the IGBP surface types with the "BIOME" drop-down list or successive clicks on the "NEXT" button that is next to it. Similarly, one can select the time period with the "Month" drop-down list and the NDVI range with the "NDVI" drop-down list. The available targets are shown on the map. Note the lighter grey areas that indicate the Earth surface that corresponds to the selected IGBP surface type. The squares indicate the locations of the targets in the BRDF database that correspond to the criteria (IGBP type, month, and NDVI range). According to the NDVI, the colour of the squares ranges from black to red (we use a rainbow palette) if "ALL NDVI" is selected or red if a specific range of NDVI is selected.
The Meas/Surf/Isoc checkboxes affect the Target BRDF window and are described below. The "Polariz" checkbox controls the display of a specific window for the BPDF. The "PPlog" checkbox toggles between a linear and log scale for the Principal Plane and Perpendicular Plane windows that are described below. Further below, the checkboxes with the band central wavelengths make it possible to select three bands, out of the six provided in the database, for display in the Target BRDF, Principal Plane, and Perpendicular Plane windows. Finally, the different buttons of the Main Command window located below the information about the target allow one to save a measurement-model comparison in various formats: "PNG" saves the window in PNG format. "Poscript" generates a poscript file. "Encaps. " generates encapsulated Poscript files (file.
Analysis of directional signatures in the principal and perpendicular planes The Y axis can be on either a linear or log scale depending on the "PPlog" option in the MCW. Similarly, the three channels that are displayed can be modified among the six that are available in the database. These figures confirm the general observation made from Fig. 2 : the reflectance is significantly larger in the near-infrared than it is in the visible; it increases markedly from forward scatter towards backscatter, while there are insignificant variations in the perpendicular plane. The model accurately reproduces the observed variations.
Analysis of a BPDF target Figure  4  shows an example of the content of the Target BPDF window, which is very similar to the Target BRDF except that it shows the surface polarized reflectance at a single wavelength (865 nm). Although the POLDER instrument made polarized measurements in three channels, only the longer wavelength channel is provided in the database and is therefore accessible through the visu_brdf tool. Our experience is that the surface polarized reflectance is spectrally neutral, or the spectral variations are smaller than the measurement noise. We thus provide the longer wavelength channel estimates that are the least contaminated by atmospheric scattering. The left image shows the measurements, the middle image is the model-measurement difference, and the right figure is a scatter plot of the measurement and model.
Analysis of the temporal variations of the BRDF parameters using the yearly database As explained in the database description above, there are in fact two databases. The monthly database processes all months independently and selects the best targets for each month. Conversely, the yearly database selects a set of targets for the full year. With the latter, it is then possible to analyse how the BRDF and BPDF parameters vary during the year. To use the yearly database with the visu_brdf tool, either use the keyword /YEAR if the HomePath has been properly defined, IDL > visu_brdf,/YEAR, or provide the full path to the yearly database, IDL > visu_brdf, pathin = '/home/users/breon/BRDF_database_ Year'. In such a case, the MCW is slightly different than the monthly database. There are three additional checkboxes below the drop-down buttons used for model selection.
Conclusions The main focus of the POLDER spaceborne instrument was for atmospheric studies, i. e. the monitoring of aerosols and clouds. It may be argued that the spatial resolution of the instrument, 6 × 6 km 2 , is not suitable for the analysis of land surface processes. Indeed, most landscapes show significant variability within a target of such a surface. The averaging may then smooth out some specific BRDF features. We have attempted here to select homogeneous targets, in which case the spatial resolution of the measurement is not an issue. The analysis of BRDF derived from airborne measurements on a finer scale  (Gatebe and King, 2016)  does not reveal specific features that are not present in the database described here. We thus argue that our database does describe the variability of land surface BRDF since there is presently no evidence of a significant scale effect.
A full comparison of the BRDF database described in  Gatebe and King (2016)  and that described here may be necessary to quantify the potential scale effect on the BRDF. Although the first version of the instrument was launched 20 years ago (1996 on the ADEOS-1 platform), it remains the only instrument that measures the full linear polarization of the Earth reflectance in the solar domain. Addition-ally, the directionalcoverage of POLDER is better than that of multiangle imaging spectroradiometer (MISR)  (Diner et al. , 1998) , the only other instrument that provides multidirectional sampling of the Earth reflectances  (Lallart et al. , 2008). Therefore, POLDER remains an up-to-date tool for the analysis of the directionality and polarization of land surface reflectances. We developed a database for the remote sensing community that provides a description of representative Earth targets.
A similar undertaking was achieved based on airborne measurements at a higher spatial resolution  (Gatebe and King, 2016). Earlier version of the database were developed based on the measurements from the POLDER instrument onboard the ADEOS and PARASOL satellites. Although these versions have not been properly described in the peer-reviewed literature, they have been used for several analyses of the surface directional (e. g. Kokhanovsky and Breon, 2012; Cui et al. , 2009; Jiao et al. , 2014; Bacour and Breon, 2005; Maignan et al. , 2004)  and polarization (e. g. Litvinov et al. , 2012; Maignan et al. , 2009)  signatures. The new version, which is described in this paper, is of better quality. It benefits from improved calibration and data selection scheme, provides the reflectance measurement over an extended spectral range (up to 1020 nm), and is associated with an interactive analysis tool.