dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of posterior ammonia against independent measurements The optimized emissions of ammonia were validated against independent observations. The inversion algorithm has been designed to reduce the modelobservation mismatches. This means that the reduction of the posterior concentration differences with the observations is determined by the weighting that is given to the observations and, hence, such a comparison depends on this weighting (dependent observations). Therefore, the ideal comparison of any posterior emission resulting from top-down methods would be against measurements that were not included in the inversion algorithm (independent observations). Here, we have used ammonia observations from all EMEP stations (https://emep. int/mscw/) for the period of our study as an independent dataset for validation.
58  for the construction of prior emission error covariance matrix. For the second source of uncertainty, we performed the same inversion using not only EC6G4 and VD priors, but also two more datasets for ammonia, which have 10 times higher emissions (EGG  41, 59, 60  and NE  44  ). The calculated uncertainties are depicted in Fig. 5  together with the combined and the relative uncertainty with respect to the posterior emissions of ammonia presented in Fig. 1. The rst source of uncertainty (different surroundings) slightly affects the resulting posterior emissions of ammonia (Fig. 5a ) causing an average relative uncertainty of 4% in the European emissions. The second source of uncertainty (use of different priors) causes much larger bias as shown in Fig. 5b  (average relative uncertainty 35%).
The reason for this is obviously the large variation of the EGG  41, 59, 60  and NE  44  prior datasets that have total emissions in the rst have of 2020 of 63. 5 and 53. 3 Tg, respectively, in contrast to only 6. 2 and 5. 7 Tg for EC6G4 and VD. The latter two priors cause a very small variation in the posterior emissions in Europe as shown in Supplementary Figure S 4 and S 5. Hence, the results presented here are sensitive to the use of the prior emission dataset. The modelled column concentration (that replaces the hypothetical true column concentration in Eq. 5) is calculated by the SRMs and the prior emission and, therefore, plays a key role in the comparison of the CrIS value and retrieved value (see Eq. 6). Also, the modelled column concentration stands as the argument of the natural logarithm weighted by the averaging kernel in logarithmic space.
LMDz-OR-INCA includes a full chemical scheme for the ammonia cycle and nitrate particle formation, as well as a state-of-the-art CH 4 /NO x /CO/NMHC/O 3 tropospheric photochemistry  80. The global transport of ammonia was simulated for 2020 with a month of spin-up by nudging the winds of the 6-hourly ERA Interim Reanalysis data 81 with a relaxation time of 10 days  74. For the calculation of ammonia's lifetime, LMDz-OR-INCA ran with traditional emissions for anthropogenic, biomass burning and oceanic emission sources from ECLIPSEv5 (Evaluating the CLimate and Air Quality ImPacts of Short-livEd Pollutants), GFED4 (Global Fire Emission Dataset) and GEIA (Global Emissions InitiAtive) (hereafter called EGG)  41, 59, 60. Then we calculated the loss of ammonia due to chemical reactions and deposition as: Eq.
It is shown that the method is less sensible to manual tunning of regularization parameters (see sensitivity tests in Tichy et al. 88  ) than classical optimization procedures, which is crucial for such a large dataset where each spatial element represents a separate inverse problem. A detailed description of the algorithm is given in Tichy et al. 87. Here, we do not describe the algorithm again but explain a few modi cations that were necessary for this study. By estimating the correction coe cients for each grid-cell of the spatial domain (10°W-50°E, 25°N-75°N), we can propagate the coe cients through Eq. 6 to update a priori emissions in the model concentration term ( denotes the FLEXPART SRMs). We follow Li et al. 66  and Cao et al. 58  to bound the ratio between the prior and the posterior emissions.
In this paper, we analysed the effect of trench-hill soil surface on ground-penetrating radar (GPR) full-wave inversion for soil moisture measurement. We conducted numerical experiments by modelling the trench-hill surface using finite-difference time-domain (FDTD) simulations. The FDTD simulations were carried out with the open-source software gprMax, using different centre frequencies, namely, 150 MHz, 250 MHz and 450 MHz. The gprMax source/receiver for each centre frequency was calibrated, respectively, to transform the FDTD radar signal to normalized Green's functions for wave propagation in multilayered media. The radar signals and inversion results of the three different frequency ranges are compared. The FDTD Green's functions of the trench-hill surface with a flat surface are also compared.
Satellite sensing is used for large-and global-scale observations and provides only shallow soil information (<5 cm)  [6]. GPR has been considered for soil moisture measurement for more than three decades  [7] [8] [9] [10]. Recent reviews are given  [11] [12] [13]. Most GPR data processing techniques used for soil moisture retrieval have been based on the straight-ray or one-dimensional wave propagation approximation. The soil dielectric permittivity is retrieved from either the soil surface reflexion coefficient or the propagation velocity derived from the propagation time along the considered propagation path. For instance, the common midpoint (CMP) and wide angle reflexion and refraction (WARR) provide electromagnetic (EM) wave velocity information  [8, 9, [14] [15] [16]. The average envelope amplitude (AEA) method or early-time signal analysis focuses on changes of the first arrival signals in terms of amplitude, shape and duration, resulting from variations of soil properties such as dielectric permittivity and electrical conductivity  [17] [18] [19] [20] [21].
2 m. There is still a need to investigate its feasibility, and also to find out the proper operating frequencies. As a first approximation, neglecting vertical variability in the medium, the hilling system for the potato crop can be seen as a semi-infinite homogeneous medium with a very rough, structured surface. On this consideration, the objective then goes back to the investigation of the effect of soil roughness on moisture measurement. In this paper, we conducted numerical experiments and field measurements to investigate the effect of the potato hilling system on the full-wave inversion method for soil moisture measurements. The numerical experiments were conducted using the FDTD simulations carried out with the open-source software gprMax of Giannopoulos  [48]  and Warren et al. [49] , which modelled the 3D hill-shape soil.
In addition, 2D simulations were conducted as well to observe the EM wave propagation. The centre frequencies were set to 150, 250 and 450 MHz, respectively. For the numerical data pre-processing, a radar equation originating from the one proposed by Lambot et al. [25, 26]  was applied to calculate the transfer functions of the gprMax Hertzian dipole antenna, or the source/receiver, which was then used to transform the FDTD radar signal to the multilayered Green's functions. We compared the radar signals of the three centre frequencies, and compared the Green's functions of the trench-hill and flat surface. We also investigated the effects of the trench-hill structure on the full-wave inversion results for the different frequency ranges. Finally, a radar system operating in the less affected range was used in real field conditions to measure soil moisture, within the frame of the EU agROBOfood MIRAGE project.
FDTD Numerical Simulation and Calibration We used gprMax3D to model the trench-hill soil surface. GprMax is an open-source software that simulates EM wave propagation, solving Maxwell's equations with the FDTD method and Yee's algorithm  [49, 53]. For the inversion, the FDTD simulated data needed to be transformed into multilayered media Green's functions as in Equation (  1 ). We define a(t) as the gprMax electric source signal in the time domain and b(t) as the simulated backscattered electric field, respectively. A(ω) and B(ω) are their frequency-domain counterparts. Therefore, Equation (  1 ) becomes  [47] : B(ω) = H i (ω) + H(ω)G xx (ω) (4) where H i (ω) = A(ω)R i (ω) and H(ω) = A(ω)T(ω). In gprMax, the antenna reduces to point sources and receivers, so R s (ω) = 0. Indeed, in that case, there is no physical antenna described and, hence, no variation of impedance within the antenna (the antenna does not act as a reflector).
Field Experiments 4. 1. Ground-Penetrating Radar The radar system consists of a lightweight vector network analyser (Planar R60, Copper Mountain Technologies, Indianapolis, IN, USA), an Intel computer stick, a power bank and a smartphone. An ultrawide band log-periodic antenna (model VULP 9118 C, Schwarzbeck Mess-Elektronik, Schönau, Germany) was used as a transmitter and receiver. The radar prototype was set up in the frame of the EU agROBOfood MIRAGE project. The measurements and full-wave inversions were performed in real-time using a prototype of the gprSense software (Sensar Consulting, Chaumont-Gistoux, Belgium). The radar measurements were performed in the 130-190 MHz frequency range. The higher frequency was chosen to minimize trench-hill effects and the lower cut off frequency was chosen to maximize SNR (see antenna calibration results below), minimize soil electrical conductivity effects and ensure a reasonable size for the field set up.
Results and Discussion The GPR results are shown in Figure  9a , with the corresponding orthophoto as the background. The observed soil moisture values highlight the inherent soil variability and also show some spatial correlation. From the southwest to the northeast, the water content is getting higher, since the elevation is getting lower from the southwest to the northeast (see Figure  9b ). The relative elevation map was obtained from aerial drone photographies using photogrammetry, whose processing was performed using the Agisoft Metashape software. The DJI Phantom 4Pro was used for the image acquisitions. For comparison, we plot the GPR and TDR results in Figure  10. TDR measurement at 5 cm depth is generally lower than the GPR. GPR result is closer to TDR measurements at 15 cm depth with an RMSE of 0.
Trans Cis This work Kirby & Kroto (1978) Calculated (a)  This work  Kirby & Kroto (1978)    (a) Calculated at CCSD/cc-pVTZ level of theory. ( b) The numbers in parentheses are the parameter uncertainties in units of the last decimal digits. Their values are close to 1σ standard uncertainties (67% confidence level) because the unitless (weighted) deviation of the fit is close to 1. 0. SPFIT/SPCAT program package  (Pickett 1991)  was used for the analysis. ( c) Fixed to the calculated value, which is usually a preferred constraint over the zero or poorly determined value  (Urban & Sarka 1990; Koucký et al. 2013 ). ( d) Relative energy with respect to the global minimum, taking into account the zero-point energy. ( e) Number of distinct frequency lines in the fit. ( f ) Root-mean-square deviation of the fit.
Quantum-chemical calculations Although vinyl isocyanate has been the subject of some computational studies  (Badawi et al. 2001; Olsen 1979) , we carried out our own calculations in order to obtain a reasonable estimation for the spectroscopic parameters relevant to this work. We used coupled-cluster approximation on the level of the coupled-cluster-single-double (CCSD) model  (Purvis & Bartlett 1982)  as it is implemented in the CFOUR program package  (Matthews et al. 2020) , in conjunction with Dunning's correlation consistent triple-ζ (cc-pVTZ) basis set  (Dunning 1989 ). The convergence criteria for the HF-SCF equations, the CC amplitude equations, and the linear equations were set to 10 -8 atomic units. This means the convergence structure calculations and the analytical second derivatives were followed by finite difference techniques to obtain the full cubic force field.
2020 ). The program is written in the Matlab software (MATLAB 2020) and allows line assignments to quantum numbers, precise determination of the line frequencies employing the Voight profile function, and the data to be saved in a line list among other features. It generates input files for the SPFIT/SPCAT program package  (Pickett 1991)  which is executed directly from the user interface, allowing straightforward analysis of the assigned lines. On this basis, we easily expanded the assignments for K a = 0-6 transitions up to J = 69 and identified weak b-type R-branch and Q-branch transitions. Difficulties were encountered in assignments and fitting K a ≥ 7 transitions. In the absence of perturbations, these transitions are expected to progressively run to higher frequencies as shown in the lower panel of Fig.
Partition functions The spectroscopic constants from Table  1  were used to evaluate the rotational partition function (Q rot ) for both isomers. We used the SPCAT program  (Pickett 1991)  to undertake the numerical summation over the ground-state energy levels up to J = 240 and K a = 46 for the trans isomer and J = 210 and K a = 72 for the cis isomer. In addition, for the cis form, this summation was corrected for the energy difference between the cis and trans form. We used ∆E ZPE = 301 cm -1 from our quantum-chemical calculations. This value reproduces quite well the experimental intensities of the cis isomer lines with respect to neighboring lines of the trans isomer in our spectrum. On the other hand, the experimental value 417(20) cm -1 from  Kirby & Kroto (1978)  underestimates these cis isomer lines relative intensities.
However, we emphasize that our continuum-subtraction procedure is applied to each spectral window of 1. 8 GHz width as a whole, and therefore is not affected by spectral confusion that arises at scales of tens or hundreds of MHz. We analyzed the spectrum obtained toward the position Sgr B2(N1S) at (α, δ) J2000 = (17 h 47 m 19. s 870, -28 • 22 19. 48) following the strategy employed by  Belloche et al. (2019). This position is offset by about 1 to the south of the main hot core Sgr B2(N1) and has a lower continuum opacity compared to the peak of the hot core. We assumed local thermodynamic equilibrium (LTE) and produced synthetic spectra with the astronomical software Weeds  (Maret et al. 2011 ) in order to analyze the observed spectrum. The LTE assumption is justified by the high densities of the regions where hot-core emission is detected in Sgr B2(N) (>1 × 10 7 cm -3 , see  Bonfand et al.
The upper limit on the total column density of vinyl isocyanate derived from each conformer is reported in Table  3 , after accounting for the vibrational partition function Q trans+cis vib provided in Sect. 4. We also report in Table  3  the column density upper limit that we obtained with the ReMoCA survey for ethyl isocyanate, C 2 H 5 NCO. The nondetection of this molecule, both in its torsional ground state and its first torsionally excited state, is illustrated in Figs. 5  and 6 , respectively. We employed the spectroscopic entry 71508 (version 1) of the Cologne Database for Molecular Spectroscopy 1 (CDMS,  Müller et al. 2005)  to compute the LTE synthetic spectra of the torsional ground state 1 https://cdms. astro. uni-koeln. de/ A50, page 6 of 16 K. Vávra et al. :  The rotational spectrum and ISM search of vinyl isocyanate Fig.
Conclusions We carried out laboratory rotational spectroscopy of vinyl isocyanate in the frequency regions 127. 5-218 and 285-330 GHz. Over 1000 transition lines were assigned and measured for the ground vibrational states of its trans and cis isomers. The present work provides significantly more precise values of the spectroscopic parameters, which agree with those from accompanying high-level quantum-chemical computations. We report a nondetection of both vinyl and ethyl isocyanate toward the main hot core of Sgr B2(N) with ALMA. We find that these molecules are at least 11 and 3 times less abundant than methyl isocyanate, respectively. Despite the nondetection of vinyl isocyanate in Sgr B2(N), the present work represents a substantial improvement on previous microwave studies below 40 GHz and provides accurate reference data for further searches for this species in interstellar space.
Acknowledgements. The spectroscopic part of this work has been funded by the  Czech Science Foundation  (GACR, grant No. 19-25116Y ). L. K. , K. V. , J. K. , and K. L. gratefully acknowledge this financial support. L. K. , J. K. , K. L. , and P. K. thank the financial support from the  Ministry of Education, Youth and Sports of the Czech Republic (MSMT)  within the Mobility grant No. 8J21FR006. Computational resources were supplied by the project " e-Infrastruktura CZ" (e-INFRA CZ LM2018140 ) supported by the  Ministry of Education, Youth and Sports of the Czech Republic. Computational resources were provided by the  ELIXIR-CZ  project ( LM2018131 ), part of the international ELIXIR infrastructure. R. T. G. thanks  E. Herbst  for helpful discussions. This paper makes use of the following ALMA data: ADS/JAO.
7 and ∼1. The shape of particles is independent on their size, which is varied by scaling up/down the fragment while preserving its shape. As for fragment size distribution, we consider both monodisperse and polydisperse with power law distribution, with an exponent of -2. 70 (e. g. Elek and Jaramaz, 2008; Jutzi et al. , 2019). More details on the effects of non-spherical particle shapes and their combined effects with friction coefficient are provided in  Ferrari and Tanga (2020). We address the problem of global shape and surface stability by investigating the internal structure and evolution of rubble-pile objects. The analysis builds upon a large dataset obtained from an extensive numerical simulation campaign. We performed several (82 overall) N-body simulations to reproduce the dynamics of representative rubblepile aggregates with a range of different internal structures, and subject to different evolution histories.
Acknowledgments We thank  M. Jutzi  for insights and feedback. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant No. 193346 , and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 800060. This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation.
Deep learning-based land cover classifiers learnt from Satellite Image Time Series (SITS) are known to reach high performances. In order to explain, at least partly, the rationale leading to each one of their decisions, attention-based architectures have been proposed to automatically weight the importance of predefined data components in the classification process. Though generated for each decision separately, the informational content conveyed by such explanations can remain insufficient to end-users because of the complex nature of SITS. Moreover, getting a general perspective about the way a classifier works requires merging all explanations for each class and relating them to its mode of operation, which is not always straightforward. A preliminary and complementary approach for automatically identifying the data features detected by a pixel-wise deep spatiotemporal land cover classifier and explaining its behavior at the class level is therefore proposed in this paper.
A preliminary and complementary approach for identifying automatically the dataset features detected by a pixel-wise deep spatiotemporal land cover classifier and explaining its functioning at the class level is therefore proposed in this paper. Each classified pixel is first described using interpretable features coming under the form of data mining patterns. These features, built using all SITS dimensions, are detailed in Section 2. Each pixel is then also characterized by the activation levels of the layer that is assumed to summarize a large part of the processing performed by the network and capture high level concepts similar to the aforementioned intepretable features. Finally, pixel descriptions are analyzed for each class separately using redescription mining  (Galbrun and Miettinen, 2017)  to automatically extract correspondence rules between the interpretable features and the different activation level configurations.
e. , the patterns that are the less or the more likely to occur in randomized versions of the symbolic datasets. More details about this ranking method can be found in  (Méger et al. , 2019). Back to this paper, it is proposed to describe each pixel for which a ground truth is available by indicating, for each channel, which are the most promising maximal GFS-patterns occurring in its symbolic series, if any. In the following, the most promising maximal GFS-patterns are simply referred to as patterns when clear from the context.
Training of the DSLCC The model optimisation is performed using TensorFlow2. Inputs are normalised channel-wise between 0 and 1 using a min-max feature scaling to facilitate the convergence of the training process. A stratified sampling preserving class ratios is employed to split annotated pixels into a training dataset (60%), a validation and test dataset (20% each). Pixels belonging to a same object all belong to the same dataset. The network is learnt by considering the standard unweighted categorical cross-entropy CE and defining the loss as L global = CE(Y, Y main) +0. 5×CE(Y, Yaux) where Ymain and Yaux are the model main and auxiliary outputs. Gradients are back-propagated using an Adam optimizer and an L 2 -regularization with a weight decay of 1. 10 -6 to avoid overfitting on all layers.
CONCLUSION An original approach for explaining the behavior of a deep spatiotemporal land cover classifier is presented in this paper. It unveils, for each channel and each class, the intepretable spatiotemporal features captured at the pixel level by the last convolutional layer. These features are extracted using dedicated data mining patterns and matched with the different neuronal activation level configurations using redescription mining. In order to demonstrate the feasibility of the proposed approach, a simple architecture applying spatiotemporal and and temporal convolutions for each channel separately is considered for the deep classifier. As shown by experiments on a Sentinel-2 time series, such explanations are meaningful and can enrich channel attention-based explanations. Nevertheless, though indeed captured by the network, it is yet unclear to which extent they do account for the final decisions.
Parse Figure  1 : Division of labor between the preprocessor and the grammar engine logic and categorial logic. However, there is no need to resort to unification, and it can deal with structure-sharing. Although it does not provide a different grammatical system, this solution has the advantage to be easier to implement. We focus here on Abstract Categorial Grammars (ACGs). We present a first implementation of the preprocessor, called meta-ACG preprocessor. As feature structure are not yet implemented in ACGtk  (Pogodalla, 2016) , this program brings the possibility to work with ACGs and FSs. We also mention how it reduces labor when defining a grammar. The motivation of this work is thus twofold: 1. Formalize a way to work with features structures and categorial logic, in particular ACGs 2.