dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides radiative energy flux calculation, the other important application area for RT software is remote sensing. This started almost at the same time as the energy flux simulations, an early example is  Kaplan (1959). From the early days onwards, high-level computer codes for energy flux computation and remote-sensing simulations developed somewhat independently, and not many complex codes can be used for both applications. Notable exceptions are libRadtran  (Emde et al. , 2016) , which can be used for sensor simulation and flux calculation in the shortwave, and the family of models by AER  (Atmospheric and Environmental Research; Clough et al. , 2005). The tendency for models to specialize is often not driven by physics (for example, low-level solvers like DISORT  (Stamnes et al. , 1988)  are suitable for both applications); it rather seems to be driven by practical constraints, resulting from the requirements of the two communities.
Out of an appreciation of this, a few RT codes have been developed that are fairly broad in scope, agnostic of a particular sensor, and used for a wide range of applications. Besides the already mentioned AER model family  (Clough et al. , 2005)  and li-bRadtran  (Emde et al. , 2016) ,  Dudhia (2017) ,  and Schreier et al. (2014)  could be named here as general-purpose models for the infrared spectral range, as well as, of course, the Atmospheric Radiative Transfer Simulator (ARTS), the subject of this article. The ARTS project started in the year 2000 as a joint initiative of Patrick  Eriksson (Chalmers)  and Stefan Buehler (then at the University of Bremen). Table  1  presents a very brief summary of general ARTS features. Right from the start the code was open source (GNU's Not Unix (GNU) public license); the current version is freely available at www.
, 2004a, b) , employed for example in  Rydberg et al. (2007)  and  Sreerekha et al. (2008) , and a Monte Carlo solver (MC;  Davis et al. , 2005 Davis et al. , , 2007)) , for example used in  Rydberg et al. (2009)  and  Eriksson et al. (2011d). ARTS comes with quite a complete set of documentation, consisting of four main elements. First, the top-level directory of the distribution contains several readme files that describe the program configuration, compilation, and execution. Command line options are also explained by the program itself when run with the "-h" or "-help" command line option. Configuration and compilation follow standard open-source unix programming conventions. Second, there are the guidebooks (User Guide, Theory Guide, and Developer Guide), which give a comprehensive overview of the program from a user perspective, from a theoretical perspective, and from a programming perspective, respectively.
Third, ARTS works like a scripting language with functions (in ARTS called methods) that work on variables (in ARTS called workspace variables), and each of these functions and variables has built-in documentation, perhaps comparable to a Unix man page, that can be browsed online at the ARTS website. Fourth, the distribution includes a large set of sample control files for ARTS that contain predefined setups for various remote-sensing instruments and demonstration cases for various ARTS features. There is also a build target "make check" that runs a selection of the included control files and compares their computation results to reference data. This allows the user to verify that the model works correctly. For the developer, perhaps even more importantly, it helps to ensure continuity and prevents unintentional changes in model output due to source code changes.
, 2006). Important tools around ARTS are the Qpack Matlab package  (Eriksson et al. , 2005)  that, among many other things, allows optimal estimation inversions (going from measured or simulated radiation back to an estimate of the atmospheric state) and a Matlab package for frequency grid optimization by simulated annealing  (Buehler et al. , 2010) , which are both part of the bigger Matlab package ATMLAB (ATMospheric matLAB), freely available from the ARTS website. The web-site also holds "arts-xml-data", a data package with model atmospheres, spectroscopic data, and other data that are required or useful for running radiative transfer simulations. And, last but not least, there is a growing set of Python interface and helper functions, collected in a package called Typhon. This article describes ARTS version 2.
1. First of all, nadir brightness temperatures are shown, which should be kept in mind when comparing to disk-integrated measured brightness temperatures. Second, these are clear-sky simulations, neglecting the influence of cloud or precipitation particles, which may affect observed spectra. Third, the NH 3 absorption for Jupiter, especially, has been shown to be highly sensitive to the choice of spectral line shape  (Encrenaz and Moreno, 2002) ; we have used a Voigt shape. Last, of course, spectra may also differ strongly for other atmospheric scenarios. Besides the planetary toolbox, there were numerous other additions and improvements: to start with, ARTS now includes collision-induced absorption continua from the HIghresolution TRANsmission molecular absorption database (HITRAN;  Richard et al.
Line catalogue ARTS has its own internal representation of spectral line data that maps naturally to a native catalogue format. Two variants of this internal catalogue data exist, corresponding to the two line broadening and shift algorithms introduced above. Beside other spectroscopic parameters, the catalogue format related to the classic algorithm, called ARTSCAT-3, contains the air broadening and shift parameters γ a , n a , and δν representative of Earth conditions. Besides its internal formats, ARTS can digest other catalogues with different formats, e. g. , the HITRAN format. These other databases typically report "classic" Earth-representative spectroscopic parameters; hence, their data are internally converted to the ARTSCAT-3 format. A detailed description of the ARTSCAT-3 format is given in  Eriksson et al.
, 2011) , and JPL (Jet Propulsion Laboratory;  Pickett et al. , 1998)  spectroscopic databases. The sources of the data are given explicitly and in detail for each molecule in  Mendrok and Eriksson (2014). Selected examples of the compilation procedure are detailed below. Species of obvious planetological interest but without line absorption signatures in the THz region, like ethane, germane, ethylene, or benzine, have been neglected. In order to be able to also use the database for Earth applications, species only relevant in the Earth atmosphere, but none of the other planets (see Table  4 ) have been included as well. Line parameters of these species have been taken from the HITRAN edition current at the time of compilation  (Rothman et al. , 2013; update 13 June 2013)  and converted to ARTSCAT-3 format without any further changes.
Note that by using ARTS functionality, users themselves can create ARTSCAT spectroscopic files from HITRAN data, e. g. , from more recent editions or updates. Foreign species-specific line parameters have been derived by a careful literature investigation searching for experimental or theoretical studies specifically devoted to line broadening and shift by He, H 2 , CO 2 , or H 2. Furthermore, air broadening and shift parameters reported in the HITRAN database are often deduced from individually determined and reported N 2 and O 2 broadening and shift data. In such cases, we applied the original N 2 and O 2 literature data in our catalogue compilation. For some combinations of gas species, absorption line and perturbing gas, the broadening and line shift parameters are absent in the literature, simply because spectroscopic studies dealing with these line parameters were never performed.
75 was set for the pressure broadening temperature exponent. One exception here is helium, for which the default value was estimated as γ He = 0. 04 cm -1 atm -1 (12 000 Hz Pa -1 ). The pressure shift parameter δν, which is often unknown in the THz region for most of the perturbing gases considered here, has been set to a default value of 0 in the absence of any data in the literature. As indicated above, the primary source for perturberindependent parameters like line positions and intensities has been the HITRAN and GEISA databases. However, several molecules considered of interest in planetary atmospheres are so far not covered by HITRAN or GEISA. This concerns, for example, sulfur monoxide (SO), sulfuric acid (H 2 SO 4 ), propane (C 3 H 8 ), and phosphine (PH 3 ). To generate the line lists for our catalogue, we used the line positions and intensities quoted in the JPL catalog.
Some further species not (yet) in HITRAN but required for the planetary toolbox have been added with species data (molecular mass, isotopologue ratio, partition function information) taken from the JPL spectroscopic database  (Pickett et al. , 1998 , retrieved from http://spec. jpl. nasa. gov/ (last access: September 2013)) or from educated guesses. The latter regards species that were rated as being of interest in the atmospheres of the toolbox planets and for which spectroscopic line data have been collected (e. g. , C 3 H 8 ) but also inert species that are required for the planet-suitable line broadening and shift algorithm introduced (e. g. , He). Newly added species are identified in Table 4. Table  4. Overview of the absorption species covered by the ARTS spectroscopic database. For "planet interest" species, ARTSCAT-4 type data with foreign species-specific line parameters have been compiled, while data for "Earth-only" species have been taken from HITRAN without modifications and are provided in ARTSCAT-3 format.
Empty data files are provided for "no transition" species, which exhibit no absorption lines within the spectral region of interest of the planetary toolbox but have to be considered as perturbing species. Species with "ARTS 2. 0" history are known species in ARTS' pre-toolbox version. "New" species have been added in ARTS 2. 2 with species data taken from HITRAN (default) or other sources like the JPL database (denoted by "*"). The spectroscopic catalogue data are available from the arts-xml-data package, where data are organized into one file per absorption species. It should be noted that our spectroscopic catalogue is a snapshot in time of the available spectroscopic data of interest for planetary atmospheric remote sensing, at the time of development. The snapshot is from early 2012, when the catalogue was compiled.
Species HITRAN, the most commonly used general spectroscopic line database has been undergoing very significant development in recent years  (Hill et al. , 2013). The new 2016 edition for the first time includes explicit broadening parameters for H 2 , He, and CO 2  (Gordon et al. , 2017) , as well as many other new crucial parameters, for example, for handling line mixing. We enthusiastically welcome the new HI-TRAN paradigm, since it means that it will be possible to drive the new broadening calculation in ARTS with parameters directly from HITRAN in the future. The ARTS interface to the new HITRAN is not yet available but will be worked on with high priority.
g. , for planetary use. As part of the planetary toolbox, tables of relative isotopologue abundances for Venus, Mars, and Jupiter are provided with the arts-xml-data package. We generated these, based on the available planetary literature. What can readily be found there are not isotopologue abundances for all different molecules but rather isotope ratios for important atoms, for example, the ratio of deuterium to normal hydrogen. From these, we generated the molecular isotopologue tables by rescaling Earth isotopologue abundances with the planetary isotope ratios reported in Table  5. Isotope ratios of D (in all planets) and 15 N (in Mars and Jupiter) were found to significantly differ from Earth values, while other species are within 5 % of their Earth values. Adaptation of isotopologue abundances was, hence, restricted to species containing hydrogen and nitrogen.
Handling of non-particle polarization Older ARTS versions have assumed that only particulate matter (including the planet's surface) causes effects that go beyond a scalar description. That is, gaseous absorption has been seen as scalar attenuation coefficients. This limitation is now removed, for two reasons. First of all, the Zeeman effect (Sect. 3. 2. 3) causes the absorption to depend on polarization, which cannot be described in a scalar manner. Secondly, effects of magneto-optical  (del Toro Iniesta, 2003, Sect. 1)  character also do not fit into a scalar formalism, and both Faraday rotation and parts of the Zeeman effects fall into this category. Accordingly, the code has been revised to allow for a matrix description of propagation effects throughout. As a consequence, the terminology used in ARTS has also changed: what was before denoted "absorption coefficient" is now called "propagation matrix"  (following, e.
Continuum models A number of gas absorption continuum models are available with ARTS. This particularly covers models for the micro-and millimeter-wave region (e. g. ,  Liebe et al. , 1993; Rosenkranz, 1993 Rosenkranz, , 1998) )  but also several editions of the Clough-Kneizys-Davies continuum model (CKD;  Clough et al. , 1989 Clough et al. , , 2005)) , later enhanced by  Mlawer and Tobin (MT_CKD; Mlawer et al. , 2012) , models that cover the entire millimeter to infrared spectral range. However, all of these continua have been developed with a focus on Earth observations. In atmospheres with different major atmospheric constituents as well as pressure and temperature conditions, different continua play important roles. Recent editions of the HITRAN database offer collisioninduced absorption (CIA) data  (Richard et al.
A mechanism to consider bispecies-dependent absorption has been implemented, where tabulated binary cross sections have to be provided to ARTS. A method to derive κ i,j from HITRAN data is available. To make the HITRAN data seamlessly work with ARTS, we created a slightly modified version of the data in a native ARTS format: data sets covering the same frequency range have been merged into one frequency-temperature table, data sets only covering visible and shorter wavelengths have been removed, and all binary cross sections have been converted from HITRAN (cm 5 molec -2 ) to ARTS (m 5 molec -2 ) units. These data are available as part of the arts-xml-data package.
Acknowledgements. We would like to acknowledge the tremendous support from the  ARTS developer and user community. For example, they contribute by answering questions by new users on the ARTS email lists. We also acknowledge financial support, most importantly from the  European Space Agency (ESA)  in the context of contract No. 4000104175/11/NL/AF  "Microwave propagation toolbox for planetary atmospheres". As part of this contract,  Bengt Rydberg  performed work that helped to implement the handling of radio links. Stefan Buehler 's contribution was also supported through the  Cluster of Excellence CliSAP 328  ( EXC177 ),  Universität Hamburg , funded through the  German Science Foundation (DFG). Edited by:  Klaus Gierens Reviewed  by: two anonymous referees.
Overview MuSCA is a generic, functional structural plant model (FSPM) (White and Hanan 2012) representing the carbon allocation and growth of a plant at different, user-defined, spatial scales, while taking into account competition and distances between carbon sinks and supplies present on the plant (Fig. 1 ). The model is designed as a set of modules. In this modular architecture, each module is generalized so that only a few of them are specific to a given cultivar or species (Table  1 , Table  2 ). The model is integrated in the open-source OpenAlea environment  (Pradal et al. 2008 (Pradal et al. , 2015) )  and implemented in the Python language. It makes large use of other components available in OpenAlea, such as the MTG dynamic data-structure (https://github. com/openalea/mtg), used to represents the topological connections in plant structures, the PlantGL library  (Pradal et al.
ACKNOWLEDGEMENTS We acknowledge  Jerome Ngao  and  Marc Saudreau  from  UMR PIAF, INRA at Clermont-Ferrand  respectively for their help in linking RATP to MAppleT and in linking MTG and RATP, in the OpenAlea platform. F. Boudon  from  UMR AGAP, Cirad at Montpellier , for some tips on Python coding. The authors thank also  Roberto Zampedri  and  Mr Mauro Cavagna  for field assistance and  Maddalena Campi  for useful discussions.
g. whether a planet was born in the orbit it is observed in or whether it has migrated over a large distance), but also those of the planetary systems they belong to. Knowledge of the chemical makeup of a large sample of planets will also allow us to determine the key mechanisms that govern planetary evolution at different time scales (see Fig. 2 ). A statistically significant number of planets need to be observed in order to fully test models and understand which physical parameters are most relevant. This aim requires observations of a large sample of objects (hundreds), generally repeatedly or on long timescales, which can only be done with a dedicated instrument from space, rather than with multi-purpose telescopes, such as JWST and E-ELT (see Section 1. 5 for details). The way forward: ARIEL In order to fulfil the ambitious scientific program outlined in the previous section, ARIEL has been conceived as a dedicated survey mission for transit, eclipse & phase-curve spectroscopy capable of observing a large, diverse and well-defined planet sample.
The broader wavelength range provided by ARIEL enables allows to constrain the molecular abundances and temperature with great precision, and with little or no correlation among the parameters. The opposite is true for the WFC3, whose narrow spectral range does not permit one to separate the various atmospheric parameters with confidence Despite some early successes, currently available data remain sparsein particular, there is insufficient wavelength coverage and most observations were not made simultaneously. Because an absolute calibration at the level of 10-100 ppm is not guaranteed by current instruments, great caution is needed when one combines multiple datasets at different wavelengths which were not recorded simultaneously. The degeneracy of solutions embedded in the current transit observations (e.
This approach could of course be more difficult for some elements forming relatively transparent molecular species, e. g. nitrogen in the form of N 2. For these species some chemical modeling will be needed. However, rather counter intuitively, thermochemistry at these warm to high temperatures is much better known and dataconstrained than at the low temperatures encountered in Solar System giant planets. This is due to the huge databases used in industry to model combustion in engines which deal with the same type of pressure, temperature, and compositions  [32, 243, 244] ; these data are available to the scientific community through the KIDA database (Wakelam et al. [250, 249] ). In addition, measurements of the radiatively active species and of the thermal structure of the atmosphere by ARIEL will ensure that the results of such chemical models will be better constrained, and thus more reliable than at present.
Increases in the noise levels lead only to a gradual degradation of the number of targets that can be successfully observed in each of the 3 tiers during the mission life. The ARIEL end-to-end instrument simulator: ARIEL-Sim The ExoSim tool  [199]  has been used to simulate the performance of the ARIEL system (Fig. 40 ). ExoSim is a generic, numerical end-to-end simulator of transit spectroscopy intended as open-access software. It permits the simulation of a time-resolved spectroscopic observation in either primary transit or secondary eclipse. The observational parameters can be adjusted, and the telescope and instrument parameters changed in a simple manner to simulate a variety of existing or proposed instruments. ExoSim is a tool to explore a variety of signal and noise issues that occur in transit spectroscopy observations, including the effects of the instrument systematics, correlated noise sources, and stellar variability.
The ARIEL data policy The ARIEL data policy has been designed to embrace the astronomy community in general and the exoplanet community in particular. It is recognised that ARIEL data and data products will be of huge interest to the entire exoplanet community, not only to those directly involved in the mission. The intention is to provide high quality data in a timely manner and to have a continuous dialogue with the wider community, maximising the science that can be achieved by the mission. Inputs to the target listto be observed will be solicited from the wider community (e. g. through whitepapers, meetings, and other mechanisms), the community will be kept informed about the status of the target list, as will the ESA Advisory Bodies whose feedback will be solicited. A Science Demonstration Phase (SDP) will be conducted as the final step before routine science phase operations commence.
The SDP is foreseen to provide approximately one month worth of data observed in the same manner that the core survey will be conducted. These data will be made public on a timescale of about a month, in conjunction with organisation of a major public workshop. Regular timely public releases of high quality data products at various processing levels will be provided throughout the mission. The data will be pipeline processed to different levels of data products labelled 'raw telemetry' (level 0), 'raw spectral frame cubes' (level 1), 'target (star + planet) spectra' (level 2), and 'individual planet spectra' (level 3), respectively. The lower levels can generally be released quicker than higher levels, but the objective is to release all levels timely in order to maximise the science return and impact of ARIEL.
Acknowledgments The payload consortium would like to thank the national agencies for their support during the assessment study. Many co-authors of this paper were supported by  European Research Council  grants (programs AEROSOL,  E3ARTHS ,  ExoAI, ExoLights, ExoMol, MoltenEarths, SPECULOOS, WHIPLASH ),  H2020-COMPET-2017  ( ExoplANETS A ) and  ANR (e-PYTHEAS) UKSA  grant  ST/S002634/1. We would like to thank the  ESA CDF team , as well as TEC and SRE directorate colleagues and industrial teams led by  Airbus Defense and Space -France  and  TAS -France  who provided support during the course of the study.
supervised methods in extracting potential physical regulatory interactions between genes has been demonstrated using simulated and real E. coli and S. cerevisiae subnetworks  8. This study has revealed that prediction accuracy is higher with smaller networks and concluded that inferring genome-scale networks remains elusive unless performing a feature selection step to reduce inference problem size (because of the under determined nature of current expression datasets). Contrastingly, unsupervised approaches are used to capture transcriptional associations in co-expression networks. Although these associations may not be causative, a higher propensity of strongly correlated genes is a valuable information which may have a biological meaning in a cellular or physiological context. Among the unsupervised methods, four are commonly used and have been thoroughly tested.