dataesr/scientific-paragraphs-categorization huggingface dataset:
An example to this use of DBAT as a quality control tool will be further elaborated in Section 4. 2. In the event that an unknown error comes with the project, this may be useful for the user to find these errors, which otherwise may not be detectable in PhotoScan alone. The goal of Experiment 2 was to reprocess the original images independently. Thus, in this experiment, each software detected its own set of tie points from the same input images. Furthermore, the control points were measured manually in each software. As in Experiment 1, the observation weights for both the automatic and manual tie point measurements were set to be identical in both software in order to assess the bundle adjustment process in similar conditions. In Apero, this was regulated by using the Campari command to perform a block bundle adjustment with pre-assigned weights.
However, it is still within the tolerance when considering the 5-mm GCP precision. The Apero CP residual RMS was similar to the the ones generated by the PhotoScan/DBAT results. The observed difference in residual size may be due to the different processes used to detect and filter tie points. Especially, a less aggressive tie point filtering strategy in Apero might explain the elevated residuals. An alternate hypothesis is that PhotoScan employs a better blunder detection algorithm for the automatic tie points. Nevertheless, the Apero results show that millimetric result can be obtained from this dataset.
Conclusions This paper aims to demonstrate how open source solutions can be used to provide valuable survey diagnostics for bundle adjustment results generated by a commercial software, in this case PhotoScan. The experiments were conducted using a close-range UAV dataset. The relatively complex network configuration of a close-range UAV dataset compared to classical aerial photogrammetry, the inconsistent nature of its overlaps and intersecting angles, as well as the varying average GSD make it more important and difficult to detect weak parts of the network. Two open source packages were used to provide a comparison of the bundle adjustment results. The DBAT toolbox processed the same tie point measurements as PhotoScan, whereas a comparison with the Apero package provided a case where different tie points from the same image set were used.
The results show that for this particular project, PhotoScan generated bundle adjustment results that are of the same millimetric order as the theoretical GSD. This was validated by both DBAT and Apero, which produced similar precision as estimated by the GCP and CP residuals. The results were achieved under the same weighting conditions. A downside of PhotoScan is the relative lack of detailed metrics for its orientation computation. This lack may mask potential problems in the dataset. In this paper, the DBAT results suggest that some improvements on the project can be made, particularly by adding more overlapping photos for some zones with lower exterior orientation precision. The Apero results provide some indication of the image quality that might be used for similar reasons. Overall, this paper shows that an objective assessment of photogrammetric results is important.
Errors may be hidden within the project, which limits the potential of the photogrammetric data. If the generated precision is within the project requirement, this may not be an issue. However, when problems occur within a dataset, bundle adjustment metrics, such as those provided by DBAT and, to a lesser degree, Apero, can be very helpful to detect the error. Unfortunately, this has yet to be integrated fully into commercial software such as PhotoScan. In this case, open source methods may be used to check the validity of the projects and to analyze the results in more detail. However, with the ever-increasing use of commercial photogrammetry/SfM software for mapping purposes, we have seen many improvements in this regard. If the recent addition of internal orientation errors and correlations in PhotoScan is anything to go by, open source solutions might lead the commercial software authors in the right direction.
An eigenvalue analysis made it possible to compute the buckling temperature as well as the deformed shape of the membrane, after buckling. The computations yielded a buckling temperature of 44. 5 • C, which constitutes a bifurcation point. Above this point, the membrane can deform (buckle) in one of the two possible directions, up and down. As both directions are equivalent, a solution must be found to impose the deformation in one chosen direction, numerically. This was achieved by fixing a level of deformation for the membrane (i. e. , by applying a pressure or a force on it), before letting the FEM software look for the temperature that would generate this pre-defined deformation, in the buckled state (see Figure  9a, b ). The results show a maximal displacement of 35 µm at 60 • C. This is 40% larger than the experimentally observed displacement (see Figure  8 ).
The authors obtained a formal verification of various masked implementations, up to second order masked implementation of AES, and up to 5-th order for the masked Rivain-Prouff multiplication  [RP10]. In this paper we have used the CheckMasks tool described in  [Cor17b] ; the source code of the CheckMasks library is publicly available at  [Cor17a] , under the GPL v2. 0 license. The circuit to be verified is represented as nested lists, which leads to a simple and concise implementation in Common Lisp, a programming language well suited to formal manipulations. In  [Cor17b]  a formal verification of the security of the Rivain-Prouff multiplication is described, with running times similar to those in [BBD + 15]; additionally a formal verification of lemmas 4 and 5 for the RefreshMasks algorithm is described.
There are over 1000 users worldwide of 30 RTTOV and it is now used in many NWP centres around the world as part of their data assimilation system both for weather forecasting and producing atmospheric reanalyses. Although initially developed for the old TIROS Operational Vertical Sounder (TOVS) radiometers, RTTOV can now simulate around 90 different satellite sensors measuring in the microwave (MW), infrared (IR) and visible (VIS) regions of the spectrum. Some of these instruments flew in the 1970s and now RTTOV enables their radiances to be assimilated into historical  35  atmospheric reanalyses exploiting these data, for the first time, with modern data assimilation methods  (Poli et al. , 2017). RTTOV is also part of the CFMIP Observation Simulator Package (COSP)  (Bodas-Salcedo et al. , 2011)  used in climate model simulations to provide top of atmosphere radiances for climate model runs.
There have been several other fast models developed over the years notably the Community Radiative Transfer Model (CRTM)  (Chen et al. , 2008; Ding et al. , 2011)  which is also used in several NWP models, the Optimal Spectral Sampling (OSS) model  (Moncet et al. , 2015) , and many others which have taken part in several inter-5 comparisons with RTTOV (e. g. Garand et al. , 2001; Saunders et al. , 2007; Aumann et al. , 2018). These fast models are not only forward models (i. e. compute the top of atmosphere radiance from a given atmospheric state) but also compute the Jacobian matrix which gives the change in radiance for a change in any element of the atmospheric state assuming a linear relationship about a given atmospheric state. Not all applications require the full Jacobian matrix to be stored and so tangent-linear and adjoint versions of the code 10 are also provided as options.
During the mid 1990s EUMETSAT were setting up their SAFs one of which was the NWP SAF led by the Met Office (UK) which aimed to provide software packages to enable NWP centres to better exploit satellite data in their NWP systems. RTTOV was adopted by the NWP SAF as one of its main packages and has been developed within the SAF ever since and distributed to 35 users worldwide with currently over 1000 users of RTTOV in 2017. 1 http://nwpsaf. eu The ECMWF version of RTTOV in 1999 was described in  Saunders et al. (1999). Since then there have been many enhancements developed under the NWP SAF activities and the interested reader is referred to the RTTOV web site and the various science and validation reports for full details. For example one new innovation in recent years has been to compute the infrared spectrum as principal component scores, PCs (e.
Profile training datasets In order to compute the regression coefficients for RTTOV layer-to-space transmittances computed from lineby-line radiative transfer models using a diverse set of atmospheric profiles are used. For the visible and infrared transmittances they are stored in a database, whereas for the microwave they are produced at run time. The version of RTTOV in 1999  (Saunders et al. , 1999)  was trained on a dataset of 32 radiosonde profiles with 40 levels  (Chevallier et al. , 2000). Now diverse temperature, water vapour and ozone profiles which are thermodynamically consistent are sampled from the ECMWF reanalysis fields  (Chevalier et al. , 2006 ) and for the variable trace gas profiles the Copernicus Atmosphere Model reanalysis fields were also used (http://www. copernicus-atmosphere.
Transmittance model The physical basis of the fast model to compute atmospheric transmittance has not changed much since the original ideas of  McMillin and Fleming (1976)  and  Eyre and Woolf (1988)  where the layer optical depth for a specific gas and channel is parameterised in terms of layer mean temperature, absorber amount, pressure and viewing angle which are predictors for the optical depth for layer j and σj is the level j to space optical depth for that gas using the following formulation: 𝜎 " = 𝜎 "$% + ∑ 𝑎 "$%,* 𝑋 "$%,* *,- *,% 𝑗 = 2 𝑡𝑜 𝑛 (1) where j is the level number where there are n levels, m is the number of predictors indexed by k and Xj,k are the predictors and a are the coefficients for n levels and k predictors. The diverse profile datasets are used to compute layer optical depths for each gas and combinations of gases from a line by line model.
The LBLRTMv12. 2 model  (Clough et al. , 2005)  with AER v3. 2 molecular database and MT-CKD2. 5. 2 for continuum absorption is used to calculate the VIS/near IR layer optical depths in the range 2000-25500 cm -1 at a spectral resolution of 0. 01cm -1 and in the IR (175-3300cm -1 ) at 0. 001cm -1 on 54 and 101 levels. The   2006 ) suggests a simpler approach may be feasible but this has not been implemented in RTTOV to date. The layer optical depths for mixed gases and each variable gas are combined to give the total layer transmittance as in equation 2. 5 Over the years there has been research on improving the predictors used and there are now three possible sets of predictors, Xj,k , which can be invoked when running RTTOV: • The original predictors: H2O and O3 variable, all other gases fixed (referred to as v7 in RTTOV guide) • Updated predictors that include CO2 as a variable gas (referred to as v8 in RTTOV guide) 10 • Updated predictor set (referred to as v9 in RTTOV guide) optimised for water vapour channels, allow for additional optional variable trace gases (N2O, CO, CO2 and CH4) and are designed to enable inclusion of solar radiation and zenith angles beyond 60 deg.
The most recent NLTE correction is computed using a predictor-based regression scheme  (Matricardi et al. , 2017). The predictors consist of various combinations of the solar zenith angle, the sensor zenith angle, and the average kinetic temperature in two broad 15 layers above ~51 hPa. The regression has been trained using a database of accurate vibrational temperatures computed using the Granada NLTE population algorithm  (Funke et al. , 2012).
30 To solve the radiative transfer for an atmosphere partially covered by clouds, a stream method is used that divides the field of view into a number of horizontally homogeneous columns, each column containing either cloud-free layers or totally cloudy layers. Each column is assigned a fractional coverage and the number of columns is determined by the cloud overlapping assumption (maximum-random overlap) and the number of layers the atmosphere is divided up into. The total radiance is then obtained as the sum of the radiances for the 35 single columns weighted by the column fractional coverage. For aerosols, the range and shape vary from quasi-spherical to highly irregular with a size typically less than 1 µm although particles as large as 10 to 20 µm have been observed. A database of optical properties assuming spherical particles has been generated using the microphysical properties assembled in the Optical Properties of Aerosols and Clouds (OPAC) software package  (Hess et al.
, 1998)  with some additional components added (old and new volcanic ash and Asian dust). This database provides the microphysical properties (size distribution and 5 refractive indices) for thirteen aerosol components (insoluble, water soluble, soot, two sea salt (accumulation mode and coarse mode), four mineral (nucleation, accumulation, coarse modes and transported), sulphated droplets and volcanic ash). Additionally a new volcanic ash and Asian dust components were provided in RTTOVv11. Some components are hydrophilic and so the properties are interpolated according to the local relative humidity. For water clouds a cloud liquid water content must be provided in one or more atmospheric 10 layers. It is then converted into a particle number concentration and the absorption, scattering and extinction optical depths can be derived from the normalized values of the optical parameters.
RTTOV has parameters from OPAC for two stratus cloud types (stratus continental and stratus maritime) and 3 cumulus clouds (cumulus continental clean, cumulus continental polluted, and cumulus maritime) with the size distribution described by the modified gamma distribution. 15 There are two ice cloud optical property parameterisations included in RTTOV for the VIS and IR wavelengths. The first uses the ice crystal properties dataset developed by  (Baum et al. , 2011)  which is interpolated to obtain the scattering properties used in RTTOV (i. e. extinction coefficient (βext), single scattering albedo (ω0)) from the input profile of ice crystal effective diameter. Users can explicitly provide ice effective diameter or can choose 20 among four parameterisations in terms of ice water content and temperature  (Ou and Liou, 1995; Wyser, 1998; Boudala et al.
, 2002; McFarquhar et al. , 2003). The second scheme uses the methodology developed initially for the IR  (Vidot et al. , 2015)  by using a large database of optical properties of ice clouds provided by  Baran et al. (2014). It consists of 20662 particle size distributions using different in situ measured temperature (T) and estimated ice water content (IWC) observations which simulates an ensemble of different ice cloud particle 25 shapes which is expected to be more realistic than just assuming specific shapes as was done previously. It allows a direct parameterisation of the optical properties from the cloud temperature and the ice water content. For each pair of ice water content and temperature observations, the database contains the absorption and scattering coefficients, the asymmetry parameter (to compute the phase function), and also b (equation 10) used for the Chou parameterisation at wavelengths between 0.
2 and 19 microns. The formulation that has been 30 implemented in RTTOV is given by the following equations: log %z [𝛽 •86 (𝜆, 𝑇, 𝐼𝑊𝐶)] = 𝐴 Š (𝜆) + 𝐵 Š (𝜆)𝑇 + 𝐶 Š (𝜆) log %z (𝐼𝑊𝐶) + 𝐷 Š (𝜆)𝑇 \ + 𝐸 Š (𝜆)(log %z (𝐼𝑊𝐶)) \ + 𝐹 Š (𝜆)𝑇 log %z (𝐼𝑊𝐶) (11) 𝜔 z ŽŽŽŽ(𝜆, 𝑇, 𝐼𝑊𝐶) = 𝐴 • • ' (𝜆) + 𝐵 • • ' (𝜆)𝑇 + 𝐶 • • ' (𝜆) log %z (𝐼𝑊𝐶) (12) (, , ) =  " () +  " () +  " () log %z () (13) 35 The parameterisation coefficients A to F of βext, ω0 and g the asymmetry parameter were calculated by using a non-linear least squares fitting procedure over the database, and are also functions of wavelength. Note the size distributions of the ice crystals assumed by the user (or their cloud model) should be consistent with the cloud parameterisation scheme used in RTTOV. Users can input aerosol and/or cloud optical properties explicitly, so 40.
For solar calculations the surface albedo is calculated as  *  and this value is 20 capped at one to prevent unphysical albedo values being used. For each layer the absorption and scattering coefficients, the Legendre coefficients corresponding to the phase function and, for solar channels, the phase function itself have to be specified. The cloud and aerosol coefficient files include these properties for the aerosol and water cloud particle types defined in section 3. 4. As for the IR the  Baran et al. (2014)  or  Baum et al. (2011)  scattering properties datasets can be selected for ice cloud. The phase function is calculated from the 25 asymmetry parameter following  Baran et al. (2001)  and the Legendre expansion of the phase function is calculated internally in RTTOV. DOM is a solver for monochromatic radiances.
The existing Rayleigh single-scattering calculation is included as an "additive" effect alongside DOM so there is no interaction between the Rayleigh scattered radiation and the clouds/aerosols except for increased extinction by Rayleigh scattering (included in the gaseous optical depths used in DOM) and by clouds/aerosols (included in the Rayleigh single-scattering calculation). This leads to an underestimation of the top-of-atmosphere reflectances as the optical thickness of the scattering layers increases and as the wavelength decreases  (Scheck, 2016a). Improvements to the treatment of Rayleigh scattering will be investigated for a future version of RTTOV. A faster scattering model at VIS wavelengths, MFASIS  (Scheck et al. , 2016b) , is being developed for future versions of RTTOV which will allow VIS channel radiances to be used for real time data assimilation applications.
Scattering at microwave frequencies Although scattering by hydrometeors (e. g. rain and snow) at MW frequencies is not included in the core RTTOV package there is a wrapper programme (RTTOV-SCATT,  Bauer et al. , 2006 ) that provides this capability outside of RTTOV. Full details of the initial formulation of the model are given in the RTTOV-8 Science and Validation Report  (Saunders et al. 2006). RTTOV-SCATT is a multiple-scattering radiative transfer model which enables all-sky MW radiance assimilation in numerical weather prediction models and as for the core RTTOV code includes forward, tangent-linear, adjoint and Jacobian models. The all-sky brightness temperature is calculated, as in Eq. 3, as the combination of independent clear and cloudy columns weighted by an effective cloud fraction.
The gaseous absorption component of both columns is computed by RTTOV. The scattering calculation in the cloudy column is based on the Delta-Eddington approximation  (Joseph et al. , 1976 ) so that only one angle (i. e. the observation angle) is needed and the anisotropic radiance field is decomposed into an isotropic and anisotropic component. Compared to reference doubling-adding simulations, this produces mean errors of less than 0. 5 K at the targeted MW frequencies between 10 and 200 GHz, based on a dataset of 8290 model profiles located in tropical areas to ensure the presence of deep clouds and intense precipitation so that multiple scattering is maximized  (Bauer et al. , 2006). The hydrometeor types assumed in RTTOV-SCATT are rain, snow, cloud liquid water and cloud ice. Tables of hydrometeor optical properties are pre-calculated for the required frequencies, temperatures and hydrometeor classes.
Simulating principal components of the infrared spectrum A major update to RTTOV at version 10 was the introduction of the capability of performing radiances from the IR spectrum in the form of principal components (PCs) which is a useful way to represent all the measurements from the advanced IR sounders such as IASI. The method used for the simulation of the PC scores for clear skies is described in  Matricardi (2010)  and referred to as PC-RTTOV. The dataset of atmospheric profiles used to train the PC model consists of profiles generated using the operational suite of the ECMWF forecast model. It comprises 12500 vertical profiles of temperature, water vapour, ozone and ancillary information on surface properties over all surface types. Aerosols, trace gases and NLTE effects are all included in the latest version.
The different combinations of eigenvectors/predictors can provide a trade-off between more accurate but less computationally efficient, simulations. PC score regression coefficients are available based on 300, 400, 500, and 600 predictors for IASI whereas the number of eigenvectors can be up to 400. A detailed description of the PC-RTTOV model and an assessment of its accuracy can be found in  Matricardi (2010). HT-FRTC is another model that calculates transmittance and radiance spectra using PCs  (Havemann et al. , 2009)  and has been included as an optional module called from the RTTOV interface. The PCs cover the spectrum at very high spectral resolution, similar to that of conventional line-by-line models, so that individual spectral lines are resolved. This approach allows very fast calculations of spectra with line-by-line-like accuracy for clear scenes.
04 K) but this is an order of magnitude below the instrument noise. The largest bias for channel 20 is only 0. 002 K. The equivalent results shown in  Saunders et al. (1999)  show much higher standard RTTOVv7 onwards (referred to as version 7 predictors), but also better more diverse profile datasets with more profiles (32 TIGR vs 83 ECMWF) and more levels (40 vs 54) have also helped. For HIRS the differences from the line-by-line model are shown in Figure  2 , this time for the 83 profile dependent set though the results are similar for the independent profiles. The results are harder to compare with 5 those shown in Figure  1  of  Saunders et al. (1999)  as the profile dataset they used then was much bigger. An unpublished report at the time showing standard deviations of the differences with a 32 profile dataset for RTTOVv5 shows the errors for the HIRS temperature sounding channels are only slightly improved in RTTOVv12 but the errors for the water vapour channels have been reduced by a factor of 2 using the version 7 predictors.
Code and Data Availability The latest version of the RTTOV model can be downloaded free of charge from the NWP SAF web site (nwpsaf. eu) once users have registered on the site to agree to the licence conditions. Updates to the code and 15 coefficients for new instruments are also posted on the site. There is also a discussion forum for RTTOV (under the support tab) for users to share experiences with using the RTTOV model. Table 1.
AMSUTRAN model, the 1989 version of the Liebe Millimeter-wave Propagation Model (MPM-89) (Liebe et al. ,1989)  is used for microwave instruments at all frequencies below 1000 GHz with a parameterisation to include the Zeeman effect for high peaking channels around the 50-60 GHz oxygen lines. Spectroscopic parameters have been updated for the 22 GHz and 183 GHz water vapour lines based on half-width data from Liljegran et al. (2005) and Payne et al. (2008), respectively. All oxygen line parameters are updated to those from Tretyakov et al. (2005), and 35 ozone lines below 300 GHz from the HITRAN molecular database have been added. The strategy has been to update the reference line-by-line model calculations at least once every 5 years to benefit from improved spectroscopic databases and diverse profiles.