dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , Ca, Sr) and some of the lanthanides (e. g. , Yb, Ho  [20] ). While several groups have performed calculations of Stark maps  [21, 22, 23]  and interaction potentials  [24, 25, 26] , open-source codes like those provided by ARC 1. 0  [18]  and Pairinteraction  [27]  for the alkalis are not available for these species. This motivated the major upgrade of the ARC library we are presenting here. Specifically, we have extended ARC to divalent atoms, modifying the algorithms as necessary and including supporting data for 88 Sr, 40 Ca and 174 Yb. Methods for calculating long-range van der Waals interactions have been extended to degenerate perturbative calculations and pair-state calculations to arbitrary interspecies calculations. This new version of ARC also adds functionalities for atom-surface interactions, optical trapping and visualisation of results.
The miniaturisation of atom-based sensors  [28] , and the strong long-wavelength transitions between Rydberg states  [3] , make atom-surface interaction effects prominent  [29]. We have therefore included non-retarded van der Waals atom-surface potential calculations in ARC 3. 0. Data are currently included only for sapphire and perfectly reflective surfaces, but other surfaces can easily be incorporated. The importance of optical trapping stimulated the addition of two modules. OpticalLattice1D is dedicated to optical lattices and allows easy calculation of Bloch bands, Bloch states and Wannier states. DynamicPolarizability calculates the wavelength-dependent atomic polarizability relevant for optical trapping, and enables searches for "magic-wavelengths" where two different atomic states have the same polarizability.
Installation and getting started ARC 3. 0 is available from the online repository Python Package Index (PyPI) and can be installed from the command line simply by invoking pip install ARC-Alkali-Rydberg-Calculator This installs the package correct for the user's environment, which can be based on Python 3 for Windows, Mac OS or Linux. All the methods discussed in the following can be used after importing them from the arc library: from arc import * # write your code The examples of code given in the paper assume that this line was included at the beginning of the program to import the library. The detailed documentation of the ARC library is available online  [32]  and is updated at each upgrade of the code. New users are recommended to consult the accompanying Jupyter iPython interactive notebooks listed on the "Getting started with ARC" page of the online documentation  [32] , which provide examples of calculations and describe the relevant physics.
The range of divalent atom data currently included in ARC 3. 0 is discussed in Appendix Appendix B. New species/series may easily be added by generating the appropriate data files. Inter-species calculations for both PairStateInteractions and StarkMapResonances are possible. In addition, PairStateInteractions. getC6perturbatively now supports degenerate perturbation theory for the calculation of C 6 coefficients, The possibility of taking into account a weak magnetic field directed along the quantization axis has also been introduced (only paramagnetic terms linear in field strength which shift energy levels but do not mix states are included in the calculation). Moreover, the new single-atom calculations Wavefunction, AtomSurfaceVdW, OpticalLattice1D and DynamicPolarizability have been added in calculations_atom_single.
(The calculations of atom-surface interactions implemented in AtomSurfaceVdW require values  of the refractive index for the surface of interest for a range of frequencies. Methods for general optical surface properties are implemented in materials. ) Finally, we have also created a group of advanced calculations which are likely to be too specialized for a core toolbox. The corresponding modules are intended for expert use and can be imported with # from arc. advanced. <mn> import * # where <mn> is module name, for example: from arc. advanced. population_lifetime import getPopulationLifetime The modules in this advanced library will be built on top of the ARC core library and will provide solutions for specialised research questions. The first of these modules is population_lifetime, which has been contributed by the authors of Ref.
Degenerate perturbation theory can be used within the getC6perturbatively function by setting the flag degener-atePerturbation=True. The function will then return the C (i) 6 eigenvalues and the corresponding eigenvectors of the relevant C matrix. For example, the following fragment of code produces the results displayed in the third column of Fig. 2(c  Here theta and phi are, respectively, the polar angle (Θ) and azimuthal angle (Φ) defining the orientation of the inter-atomic axis in a referential whose z-axis is parallel to the axis of quantisation of the angular momenta. The dependence on Θ and Φ of the elements of C is taken into account by rotating the atomic basis states using Wigner D-matrices, as in ARC 1. 0  [18]. I. e. , V dd (R) defined above is replaced by the angle-dependent V dd (R, Θ, Φ), with, in a simplified notation, V dd (R, Θ, Φ) =[D(J 1 , Θ, Φ) ⊗ D(J 2 , Θ, Φ)] V dd (R) [D(J 1 , Θ, Φ) ⊗ D(J 2 , Θ, Φ)] † , (11) where the D(J m J , Θ, Φ) represent the relevant rotation matrices.
0 to divalent atoms. For example, Stark maps can be obtained for divalent atoms by setting the additional key argument s to define the spin state. Note that in the single electron approximation, states of different total spin are not coupled. Example results from such a calculation are shown in Fig. 5. Static electric fields are often used to adjust pair-state energies. StarkMapResonances allows the user to search for electric field strengths where two pair-states have same energies (Förster resonances). Lastly LevelPlot allows the plotting and interactive exploration of energy level diagrams. These diagrams may be opened as interactive stand-alone plots (from a command line Python call or in a Jupyter notebook with %matplotlib qt); then will then display the transition wavelength and transition frequency for pairs of states selected interactively by clicking on energy levels.
Quick 2D and 3D visualisations are possible, with a choice of units (atomic or SI). For example, the following code can be used to obtain and plot the probability density function for the 10f 2  Wavefunction can be integrated with other ARC functions. For example, one can find the electronic wave function for an atom perturbed by an electric field by getting the state from the corresponding StarkMap and using Wavefunction as per the following code to plot the result. An example is shown in Figs. 6(c-d ). wf. plot2D(plane="x-z", units="nm", pointsPerAxis =400, axisLength=2800) plt. show() The density and scale of the mesh on which the wave functions are calculated can be adjusted with optional keyword parameters. The probability density functions can be provided in Cartesian as well as in spherical coordinates (respectively through getRtimesPsi and getRtimesPsiSpherical), and it is also possible to obtain arrays of wave functions for all different possible spin states (using the getPsi method).
The z-axis is taken to be perpendicular to the surface. Note that different states will have different C 3 values, which leads to a modification of the atomic transition frequencies near the surface. To specify the surface material, ARC provides an abstract class materials. OpticalMaterial, with a method getN returning the refractive index n for a specified wavelength. A subclass Sapphire is provided as an example. The AtomSurfaceVdW uses information on the optical properties of the surface and the atomic transition frequencies to calculate C 3. For example, the following code will return the energy shift with error for the 6s 2 S 1/2 state of caesium in the proximity of a sapphire surface (the result is 1. 259(2) kHz µm Such calculations are possible for all the atomic species supported by ARC, to the extent that the required dipole matrix elements are available.
Using the experimental energies is thus often preferable for small values of n. For clarity, we have tabulated the range of values of n over which the quantum defects have been fitted using a dictionary variable defectFittingRange indexed by terms. For each term the dictionary returns the smallest and largest principal quantum numbers defining the range of principal quantum numbers used in the calculation of the Rydberg-Ritz coefficients provided in ARC. For example, the following code will do this for the 1  print("Min n = %d" % fitRange[0]) print("Max n = %d" % fitRange  [1] ) For principal quantum numbers above the fitted range, the energies are calculated by extrapolation of the Rydberg-Ritz formula to outside this range. No extrapolation is done for principal quantum numbers below the fitted range.
Instead, ARC tries to use tabulated energies if they exist -either energies provided by the user or the energies provided by ARC, which are literature values as described in the online documentation  [32]. In the case of S, P or D series, the calculation is aborted and a value error raised, explaining the problem, if the program requires a missing tabulated energy. For L > 2, the calculation is not aborted but a value of zero is assumed for the quantum defect. These tabulated energies must be stored in the local data directory (Sec. 4. 3) under a file name defined for each atom in the levelDataFromNIST variable. For example for strontium they are stored in sr_level_data. csv. The energyLevelsExtrapolated variable of the used atom will be set to True if the calculation uses energies obtained by extrapolation of the Rydberg-Ritz formula to outside the range of values of n used in the fitting.
csv as per the table header. This can be conveniently done by loading the appropriate. csv file in any spreadsheet program, as long as the file format is preserved in terms of separators and header comments. To change other atomic properties, like quantum defects, users should make their own subclasses which inherit classes defined in the ARC. An example is given in the following code, which could be used to update the quantum defects for calcium: class MyCalcium40(Calcium40): quantumDefect =. # write updated quantum defects # list in order specified in documentation dipoleMatrixElementFile = "my_ca_dme. npy" Once defined, MyCalcium40 can be used instead of Calcium40 in all the ARC calculations. To ensure the program does not use old cached values, new names of caching files should be provided when redefining atomic species in this way.
Outlook In summary, we have presented ARC 3. 0, a major new release of the ARC Python library that extends the library to divalent atoms and adds a number of new methods of general interest in Rydberg physics and beyond. We believe this common code base and consistent interface for many different atomic properties can speed up the development of new applications and lower knowledge barriers, e. g, in quantum technologies based on neutral atoms. The library also offers rich possibilities of advanced educational projects for students in atomic and quantum physics. Future improvements of ARC could include the addition of the calculation of wave functions for divalent atoms and additional methods for the accurate calculation of low-lying wave functions  [58] , support for multi-channel quantum defect theory calculations  [51] , and the calculation of scattering properties.
Acknowledgements We thank  Tsz-Chun Tsui  and  Trey Porto  for tabulating the ytterbium data. We also thank  Hei Yin Andrea Kam  for the use of her code to check the calculations of strontium dipole matrix elements,  Paul Huillery  for help with Stark map calculations, and  Ifan Hughes  for suggesting the bootstrap method. N. Š. is supported by the  H2020 Marie Skłodowska-Curie Actions (CO-QUDDE ,  H2020-MSCA-IF-2017  grant agreement No. 786702 ). The project was supported by the  EPSRC Platform  grant  EP/R002061/1  and  Standard grant   EP/R035482/1. We also acknowledge funding from the project  EMPIR-USOQS  (EMPIR projects are cofunded by the  European Union 's  Horizon2020 research and innovation programme  and the  EMPIR Participating States).
Nevertheless, in the case of N 2 O top-down methods are based on continuous surface monitoring sometimes combined to air-or ship-borne campaigns  [10, 15]  because no global validated spaceborne data are available to the community. The first retrieval of N 2 O atmospheric content from satellite data has been performed by  [16]  using NOAA/TOVS observations. They reported an increase of 1. 4 ppbv•yr -1 for the 20-60 N latitude band between 1987 and 1991 which is clearly overestimating surface observations. Preliminary N 2 O total columns retrieved from IASI with a beta version of the EUMETSAT software were reported for a narrow tropical band (20 N-20 S) in  Ricaud et al. [17]. Garcia et al. [18]  is the first study to report a thorough validation of N 2 O retrievals from the Metop/IASI sensors.
In the present paper, we report IASI N 2 O profiles retrieved with the SOftware for a Fast Retrival of IASI Data (SOFRID) and their global validation with NDACC-FTIR data at 12 stations for the 2008-2018 period. We first describe the adaptation and settings of SOFRID for the retrieval of N 2 O profiles. The characterization of SOFRID and FTIR retrievals are then reported. Based on information content analysis, the results of the comparisons are analyzed for three tropospheric layers in terms of general statistics, biases, trends and seasonal variability. Global distributions of mid-tropospheric SOFRID-N 2 O are presented and analysed. We finally present our conclusions and prospective activities. (2006-2012-2018). IASI measures the radiance emitted by the Earthatmosphere system in the thermal infrared (645 to 2760 cm -1 ) with a moderate spectral resolution of 0.
IASI-SOFRID N 2 O Retrievals The SOFRID has been developed for near-real time retrieval of global O 3 and CO profiles from IASI radiances. SOFRID is based on the RTTOV [25] radiative transfer model coupled to the 1D-Var algorithm developed at UK Met Office (UKMO)  [26]. SOFRID O 3 retrievals are described in details in Barret et al. [23]  and SOFRID CO retrievals in  [22]. The radiative transfer calculations are performed with the RTTOV-9. 3 model [25,  27, 28]. RTTOV is a regression model where optical depths are parameterised by a set of predefined profile dependent-predictors, which are functions of temperature, pressure, absorber amount and viewing angle  [27]. The RTTOV regression coefficients are derived from line-by-line calculations performed with the LBLRTM radiative transfer model  [29]  with line parameters from the HITRAN 2004 spectroscopic database  [30].
The land emissivity is computed with the UWiremis IR land surface emissivity module implemented in RTTOV  [31]. This module is based on a monthly climatology combining laboratory measurements and land surface emissivity product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the UW Baseline Fit Emissivity Database  [32]. Surface and atmospheric parameters (Surface pressure and temperature, wind speed, temperature and humidity atmospheric profiles) are interpolated in space and time from ECMWF (European Centre for Medium-range Weather Forecast) 6-hourly analyses. The UKMO 1D-Var algorithm  [26]  has been developed for the assimilation of radiances from nadir-viewing sensors within the EUMETSAT Satellite Application Facility for Numerical Weather Prediction (NWP SAF). It is based on the Optimal Estimation Method (OEM) described by Rodgers  [33].
A single a priori profile x a is used globally. The N 2 O a priori profile has a Volume Mixing Ratio (VMR) that decreases from 324 to 313 ppbv from the surface to 300 hPa with a value of 321 ppbv at 550 hPa. The global N 2 O variability is poorly known. Aircraft observations from the Hiaper Pole to Pole Obsevations (HIPPO) programm show that over the whole troposphere N 2 O variations over the Pacific ocean are of 3-5 ppbv around 322 ppbv which represents less than 2%  [36]. Nevertheless, Pacific ocean is not characterized by important emissions of   [12]  report 0. 2-0. 4%•yr -  positive trends from total N 2 O columns retrieved from NDACC FTIR data. Data from the AGAGE and NOAA/ESRL surface in-situ networks indicate a seasonal variability between 0. 3 and 0. 9 ppbv depending on the station  [37].
For both spectra, the Root Mean Square Deviations (RMSD) corresponding to the fit residuals (0. 016 and 0. 011 mW/(m 2 •sr•cm -1 )) are below 0. 020 mW/(m 2 •sr•cm -1 ). Only pixels with RMSDs of the fit residuals below this value corresponding to the estimated noise in Clerbaux et al. [34]  are selected in our analysis. This conservative approach is justified by the very low N 2 O variability (see Section 3) which requires the best data to be detected. With this selection criterium, about 15% of the data are further elliminated. Retrievals are performed for cloud-clear scenes only (cloud fraction derived from AVHRR below 15%). In addition, for missing AVHRR cloud data, a cloud filtering based on IASI brightness temperatures at 11 and 12 µm is applied  [23]. Preliminary comparisons based on SOFRID retrievals selected according to the previous criteria have shown that nighttime retrievals provide better agreement with FTIR data than daytime retrievals.
FTIR N 2 O from NDACC Stations N 2 O FTIR data are available for the IASI period at 17 stations of the NDACC network (http://www. ndaccdemo. org/ (accessed on 13 March 2020)). For high latitude stations (Eureka, Ny-Alesund, Thule in the NH and Arrival-Heights in Antarctica), surface temperatures and therefore the IASI radiances are very low. The N 2 O retrievals therefore contain little information and we have decided not to use FTIR data from these stations. At the Paramaribo equatorial station, there are not enough months with FTIR data over the 11 years (less than 25 months with more than 3 days with data) and we also disregard this station. We have therefore performed our comparisons with data from the 12 remaining NDACC stations (see Table  1 ). At 11 among these 12 stations the a priori mixing ratios in the troposphere (below 300 hPa) range from 313 to 318 ppbv and from 316 to 317 at ∼550 hPa.
Finally, for each station, we kept monthly averages for months with more than 3 days with valid observations. The Table  1  shows that the datasets from these 12 stations are not equivalent in terms of time sampling and period covered. Some stations are providing data continuously over the 11 years with more than 89 months with more than 3 days with valid data (Kiruna, Jungfraujoch, Zugspitze, Toronto, Izana, Mauna-Loa, Lauder, Wollongong). Some stations do not provide continuous data on the whole 2008-2018 period. The japanese station Rikubetsu does not provide data for the 2011-2013 period and the time sampling is not very high for the periods with data. The Bremen station provides data over the whole IASI period but with a rather low sampling frequency and no data for fall and winter months (51 months with more than 3 days with valid data).
General Statistics The statsistics of the comparisons at the 12 stations and for the three tropospheric layers (LT, MT and UT) are provided synthetically in a Taylor diagram  [44]  in Figure  5. In this diagram, each observation (corresponding to one of the twelve NDACC stations and one of the three tropospheric layers in our case) to be validated is represented by a point placed within a quarter circle. The reference is located in the middle of the X-axis (see Figure  5 ). The correlation coefficient between the reference and test dataset is given by the azimuthal position of the point. The RMSD is proportional to the distance between the test and the reference point. Finally, the radial distance from the origin is proportional to the variance of the experiment. The normalisation of the RMSDs and standard deviations by the standard deviation of the reference allows us to display the results from all the stations and layers on a single diagram (see Taylor  [44]  for details).
For the 6 NH stations the amplitude of the MT cycles (4 to 8 ppbv) are in very good agreement for both observing systems. In the LT, the cycles are very similar to the MT cycles with summer minima and winter maxima. The correlation coefficients (0. 38-0. 96) are comparable to those in the MT. The amplitude of the cycles is generally larger for SOFRID (4 to 9 ppbv) than for FTIR (2 to 6 ppbv). The NH UT seasonal cycles are characterized by a July-August and August-October maxima for SOFRID and FTIR respectively. This relative coincidence lead to a correct agreement with correlation coefficients from 0. 37 to 0. 67 between both datasets. The summer shift of the N 2 O maxima is caused by the position of the tropopause. In summer the tropopause is high and the retrieved UT columns are less impacted by stratospheric N 2 O poor air masses than in winter.
Acknowledgments: The FTIR data used in this publication were obtained from  J. W. Hannigan  ( Mauna-Loa ),  E. Mahieu  (Jungfraujoch),  D. Smale  (Lauder),  K. Strong  (Toronto),  R. Sussmann  (Zugspitze),  J. Notholt  ( Bremen ),  T. Blumenstock  (Kiruna),  T. Nagahama  (Rikubetsu),  M. Schneider  (Izana),  N. Jones  (Wollongong),  M. Grutter  ( Altzomoni ) and  M. De Mazieres  (Maido) as part of the  Network for the Detection of Atmospheric Composition Change (NDACC)  and are available through the NDACC website (www. ndacc. org(accessed on 5 March 2020)). This work has benefited from the support of the research infrastructure ACTRIS-FR, registered on the Roadmap of the  French Ministry of Research. NOAA-ESRL N 2 O surface measurements were obtained from their website (https://www. esrl. noaa.
In this TPS, the eye model is interactively adapted to computed tomography (CT) or magnetic resonance imaging (MRI) datasets  21  and afterwards it is registered to the image data. Since diagnosis of uveal melanomas increasingly incorporates MRI information,  [22] [23] [24]  there is a need to develop MRI-based treatment planning. The MR-compatibility of tantalum clips, which are currently being used as landmarks, has been proved. 25 In addition, 3 and 7 Tesla high-resolution MRI sequences have been developed to enable improved UM and OARs delineation compared to conventional MRI procedures which do not clearly discriminate ocular structures. 26 The Paul Scherrer Institute is currently also investigating how MRI sequences can be used for improvement in the eye model definition. 27 his emerging imaging modality strengthens the justification to shift toward MRI for ocular proton therapy planning with the ultimate goal of providing a non-invasive, that is, clipless, ocular proton therapy treatment.
2. C. 1. 7 Tesla high-resolution MRI patient imaging protocol For the development and testing of the high-resolution MRI ocular proton therapy treatment planning approach, MRI data from a single institution were collected. The study was approved by the Institutional ethics board and followed the recommendation of the Declaration of Helsinki. Written informed consent was obtained from all the patients of the study prior to their MRI scan. The orbits were imaged with a Philips 7 Tesla Achieva whole-body MRI (Philips Healthcare, Best, The Netherlands). A dedicated mask supporting a custom-made three-channel micro-eye-coil array has been designed in-house and wore by every patient. The patient head was immobilized in a volume transmit head coil (Nova Medical Inc. , Wilmington, MA, USA). None of retro-or peri-bulbar anesthesia was provided during the MRI acquisition; the patients were instructed before the scan to adhere to the cued blinking acquisition scheme.