dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were pipeline calibrated and imaged with the Common Astronomy Software Applications (CASA) package version 5. 6. 1  (McMullin et al. 2007 ). The observational parameters for the line rich sources used in this paper are presented in Table  E. 2, whereas Table  E. 3 lists source properties. The spectrum for each source is extracted from the peak pixel of the CH 3 CN 12 4 -11 4 integrated intensity map. The reason for this choice is that here we focus on the N-bearing complex organics emission and CH 3 CN is used as a proxy for overall COM emission. An exception to this is source 787212, where the spectrum is extracted ∼1 beam off-source to decrease line blending and infall-related signatures (see Table  E. 2 for the coordinates of the pixel at which spectra are extracted). It is important to note that the spectrum extracted from a pixel contains the information from the beam and thus, the column densities reported in this work are the column densities within one beam.
The procedure here is similar to the grid fitting and fit by eye method of  (Nazari et al. 2021 ; also see  van Gelder et al. 2020). The spectroscopic data for each molecule are obtained either from the Jet Propulsion Laboratory database (JPL;  Pickett et al. 1998)  or the Cologne Database for Molecular Spectroscopy (CDMS;  Müller et al. 2001; Müller et al. 2005). For more information on the spectroscopy see Appendix A and Table  E. 7. The grid fitting method works if there are enough relatively unblended lines of a species present in the data. Therefore, this method is used for C 2 H 3 CN, C 2 H 5 CN and CH 18  3 OH in most sources. The fits are then carefully inspected by eye and if the grid fitting fails to provide a good fit they are fitted using the byeye method (CH 18  3 OH for most sources was done by eye).
Acknowledgements. We would like to thank the referee for very constructive comments. We also thank  R. T. Garrod  for providing us with some of their model outputs. Astrochemistry in Leiden is supported by the  Netherlands Research School for Astronomy (NOVA) , by funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 101019751 MOLDISK ), and by the  Dutch Research Council (NWO)  grants  618. 000. 001 ,  TOP-1 614. 001. 751 , and  648. 000. 022. Support by the  Danish National Research Foundation  through the  Center of Excellence "InterCat"  (Grant agreement no. :  DNRF150 ) is also acknowledged. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2019. 1. 00195. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI ( Republic of Korea) , in cooperation with the  Republic of Chile.
Acknowledgements. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 870377. P. M. also thanks  ESA, CNES  and the  CNRS  through the  MITI interdisciplinary programs  for funding support. JO was supported by the  Spanish State Research Agency (AEI)  Project No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu" -Centro de Astrobiología (CSIC-INTA ).
To relate the oberserved changes in magnetic properties with variations in crystallographic structure, X-ray diffraction analysis was carried out. An XRD waterfall pattern measured along a vertical section of the film annealed at 600°C for 30 min. , identified by a blue rectangle in Figure  7c , is shown in Figure  8. The distance between each measurement point is 2. 5 mm while the X-ray beam spot is roughly 2-3 mm, allowing us to probe changes in crystallographic phases in an almost continuous fashion. The experimental data set is overlaid with the diffraction patterns of chemically ordered L12-FePt3, L10-FePt and L12-Fe3Pt, taken from ICSD reference cards 04-017-4976, 03-065-9121 and 04-001-3171, respectively. Peak indexation is the same for all three phases, with the L10 phase having additional peaks due to its tetragonal nature.
5. Acknowledgements Y. H. received a grant from the  China Scholarship Council  to carry out part of his PhD training at  Institut Néel. This study is partially based on results obtained from the future pioneering program "Development of magnetic material technology for high-efficiency motors (MagHEM), grant number  JPNP14015 , commissioned by the  New Energy and Industrial Technology Development Organization (NEDO), Japan. Funding from the  Toyota Motor Corporation  is gratefully acknowledged. The LANEF framework (No  ANR-10-LABX-51-01 ) is acknowledged for its support with mutualized infrastructure. 6. References.
X-ray diffractions analysis The solid-state LCG is studied by powder X-ray diffraction for the analysis of the crystal structure. The Fig. 1  shows the experimental and calculated XRD prole of Li 2 CoGeO 4 compounds. The Rietveld renement of LCG samples was done to collect the detailed crystal structure information with the unique crystallographic data of LCG as the rst model. It is clear that no secondary phase and no impurity peak was detected which conrms the pure and good quality of the sample. The crystal structure of Li 2 ZnGeO 4 is taken as an initial model and the starting positions of all atoms were rst considered by analogy to the structure of this compound. A renement is then done which converged swily to reasonable and reliable factors. The best renement of the experimental prole is carried out by the monoclinic system with the Pn space group.
We present a user-friendly open-source MATLAB ® package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg. This package enables to perform a standard analysis of given turbulent data and extracts the stochastic equations describing the scale-dependent cascade process in turbulent flows through Fokker-Planck equations. As a precondition, Markovian properties of the process in scale are tested. Such a stochastic scale-dependent cascade process allows a comprehensive statistical description in terms of the complexity of the data. Cascade trajectories can be defined as single events, for each of which a total entropy production can be determined. For such entropy fluctuations a rigorous law of non-equilibrium stochastic thermodynamics, namely the integral fluctuation theorem, will be verified.
• Statistics and Machine Learning Toolbox As these MATLAB toolboxes are essential for the application of the package, the compatibility to Octave can not be provided. But to enhance the accessibility, standalone applications (64bit) for Windows, macOS and Linux are also created to run the MATLAB code on target machines that do not have a MATLAB license. To apply our method, two important features must be met. First, the set of data from which one extracts the sequence of velocity increments in scale must be a stationary process (performing multipoint analysis this condition is not always required). Second, the process in scale must be Markovian. These preconditions are tested during the execution of the program. Overall, the analysis consists of three main steps: pre-processing, estimation of Kramers-Moyal coefficients and estimation of the total entropy variation.
Exemplary dataset and system requirements To demonstrate the application of this program an exemplary dataset obtained in a turbulent air jet experiment  Renner et al. (2001)  is used within this document. The local velocity was sampled by a time-resolved hot-wire measurement. The data acquisition comprises 1. 25 × 10 7 samples at a sampling frequency of 8 kHz. The package additionally includes this exemplary dataset. We also provide the generated plots/results allowing the user to verify if the software is operating correctly. Table  1  lists all parameters that the user must enter during the analysis to reconstruct the results shown in this paper. The system requirements (memory, disk space, processor, total run time) demanded by the script depend very much on the size of the data set to be examined, the resolution in relation to the number of bins as well as the available number of CPUs (required memory will be allocated to each worker during parallel computations).
Loading data and variables uiimport is the first command in the program which asks the user to select interactively the data file which will be used for the analysis. The name of the variable/time series to be analyzed must be data. At this point, it is possible to specify the percentage of the total data that will be used to perform the analysis (for example, the first 20 % of the data). Note that this parameter has a significant effect on the overall performance of the script. save_path opens a dialog box to navigate through the local directories in order to select a folder for saving figures and files. Fs generates a pop-up dialog box to enter the sampling frequency of the data in Hz. kin_vis generates a pop-up dialog box to enter the value of kinematic viscosity ν in m 2 /s of the fluid for which the experimental data has been acquired.
spectrum This function calculates the energy spectral density (ESD) of the time series using fft function of MATLAB. Also, the ESD with and without averaging (moving average with equally spaced frequency interval in log-space) as a function of frequency will be plotted in Figure  3. low_freq generates a pop-up dialog box to select whether the data should be filtered (lowpass filter) or not. If a filter is to be applied then in the next step the frequency in Hz at which the data will be filtered by a low-pass filter have to be specified (for example 1800 Hz). If the pop-up dialog box is denied, it is set to the value low_freq=Fs/2. frequency_filter This function returns the filtered data and the filtered energy spectral density in the frequency domain (see Figure  4 ) using the low-pass filter at the previously set frequency using butter and filtfilt function of MATLAB.
This optimization use the function fmincon implemented in MATLAB. The constraints were set in a physically and mathematically meaningful way: d 11 ≤ 0, d 20 ≥ 0 and d 22 ≥ 0 (see for more details FIT_KM). In addition, this function generates a pop-up dialog box whether an example optimization is required. If the pop-up dialog box is denied, then this function straightaway performs the optimization for all scales and all velocity increments without plotting anything. If this is confirmed, then the conditional PDFs will be plotted using different representations (shown here are only two see Figure  23 ) to see the differences between optimized and non-optimized and experimental conditional PDFs. Note, if the variable scale_steps is equal to 9 then it is possible to enter any scale number from 1 up to scale number 9 (smallest respectively the largest scale).
OPTI_IFT_dij This function performs the optimization of D (1,2) (u r , r) at each scale and at each value of velocity increment in order to satisfy the integral fluctuation theorem with minimum possible error and plots the optimized d ij as a function of scale (see Figure  27 ). The optimization procedure systematically changes D (1,2) (u r , r) until the error function ξ = |1 -e -∆Stot max(N ) | (50) is minimized. Within the optimization process, the user is asked which d ij (r) should be optimized. This optimization use the function fmincon implemented in MATLAB. Using the function checkFT and plot_entropy with dr_ind=1 and overlapping cascade trajectories (z=1) and the optimized Kramers-Moyal coefficients the results presented in Figure  28  are obtained for the calculation of ∆S tot.
Acknowledgments The software resulted from funded research. We acknowledge financial support by Volk-swagenStiftung. We acknowledge the following people for helpful discussions and testing pre-version of the package  A. Abdulrazek ,  J. Ehrich ,  A. Engel ,  J. Friedrich ,  A. Girard ,  G. Gülker ,  P. G. Lind ,  D. Nickelsen ,  N. Reinke ,  T. Wester ,  M. Obligado.
Here, we derive the simplest possible global event-driven model encompassed within a ML model and built on an existing large database of event-driven diffusion coefficients. This means that we have to do prediction-error experiments, trying to model pitch angle diffusion for storms for all their given variables, evaluate model errors with the reference data, and modify the type or the number of the used variables to improve the model at best. A similar problem was addressed in Zhelavskaya et al. [33]  for a different quantity: the prediction of the cold electron density, training multiple neural networks with different variables and producing different time-averages. Time averaging is also at stake when constructing a global model: the longer the averaging period, the more regularized the model.
With a regularized model, the machine learning model is easier to obtain, but its predictive ability is degraded considering a sample event. Yet, regularization should also help in generalizing the model to out-of-sample events. As a first step, we construct a specific-event model using data from one storm (i. e. ,  March 1, 2013). In other words, we build a regression model for D αα (L, E, α) in 3 dimensions. We compare the results of 8 machine learning methods, such as deep neural networks, functional approximation, and tree-based models, and we use different sizes of training dataset to test each model. As a second step, we construct a global event-driven model D αα (t, K p , L, E, α) with a deep neural network using data from the 32 high-speed streams (HSS) storms. For each storm, we extract the geomagnetic index K p evolving in time during the 3 days of the main and recovery phases of the HSS storms  [43].
Time will be kept as a main parameter and serve to produce a superposed epoch analysis of diffusion during the 3 first days of the HSS storms. This is based on the recognition that each storm has a time history, considering, for instance, that two storms having the same geomagnetic activity index at the beginning of the storm, or at the end, can still give different pitch-angle diffusion coefficients (as the data show). The deep neural network is thus used to learn from a giant diffusion coefficient database and construct the first statistical event-driven model diffusion coefficient by whistler-mode hiss waves during HSS events, parameterized by both epoch time and Kp index. The machine learning model is thus used to replace averages and interpolations of the database elements, which one would perform usually by hand, by a numerical expression, which is afterward extremely easy to call for any epoch time, K p index, location, energy, and pitch angle, without notably altering the accuracy of the initial database.
The article is organized as follows. After the introduction in Section 1, we present in Section 2 the dataset and the machine learning methods that are used and tested in this study. In Section 3, we present our results first for all methods for the March 1, 2013, storm with regularized data and, then, for the global, i. e. , statistical, even-driven model diffusion coefficient of HSS events made from a database of 32 HSS storms. In Section 4, we discuss the global DNN pitch angle diffusion model and its use for exploration of the database. Conclusions are given in Section 5.
However, all averages will be made directly on the pitch-angle diffusion coefficient, since averaging instead its logarithm would have weighted excessively the lowest diffusion coefficients and biased them. During the storm evolution, some of the highest L-shells are located outside the plasmasphere where hiss waves are absent, which produces at best a null (when there are traces of the wave in some denser detached regions) or undefined diffusion coefficients (when the absence of the wave makes the main parameters missing). In this case, the coefficients need to be kept as a null pitch-angle diffusion coefficient in the database and in the statistics. If they were removed from the data, it would result in the rare events in which the wave are presents wrongly dominating the statistics.
Original Full Dataset In this study, we consider either 1 or 32 storms, 1 or 9 time intervals, 43 positions, 60 energies, and 256 pitch angles. This corresponds to 190 million data points, which we call the full dataset, DS1, in Table  1. This original set is too large for the herein regression in dimension 3 (i. e. , L, E, α) or 5 (i. e. , t, geomagnetic index, L, E, α) and the first task is a strategy to reduce the amount of data. In this article, we first restrain the dataset by choosing values of L at a few discrete points L = 2, 3, 4 and 5, which gives around 18 million data. Five L-shells are enough to be representative of the general behavior of the diffusion coefficients, i. e. , the spread of the cyclotron component over pitch angle, in order to first focus on the reduction in (E, α) at fixed L.
Dataset for the Storm of March 2013 The dynamics of the electron radiation belts during the month of March 2013 have been subject to much attention (e. g. ,  [3, 11, 12, 16, 62, 63] ). The storm of March 1, 2013, is associated with a high-speed solar wind stream that created strong erosion of the plasmasphere and resulted in outer belt flux dropout events. The storm was followed by enhancements of relativistic electrons in the slot region and outer belt during the 3 days. An extended period of quiet solar wind conditions persisted then for the 11 next days, with the plasmasphere expanding outward to L ~5. 5. For this event, Ripoll et al. [11]  showed the electron depletion in both the slot region and the outer belt was caused by pitch angle scattering from whistler mode hiss waves. Ripoll et al.
[16]  extended the demonstration to a global analysis of the 3D (L, E, α) structure of the radiation belts during the quiet times from March 4-15 and compared the output of event-driven Fokker-Planck simulations to pitch angle-resolved Van Allen Probes flux observations with good agreement. In this section, we focus on the specific storm of March 1, 2013, and we use the event-driven diffusion coefficients database that was generated for the studies of Ripoll et al. [16]. Specific parameters of the diffusion coefficients are given there and not recalled here. These coefficients use the local wave and data parameters and as such can contain the noise and the variability of the measurements. But since the expression of the diffusion coefficients is made of the combination of tractable mathematical expressions, with some oscillating Bessel functions, and a series of summation (over the harmonics) and integration (over both frequency and wave normal angle) (e.
g. , Albert  [48] ), the database ends up being quite smooth and not too noisy. This will be a key property of the data for choosing or developing an adapted machine learning method. In addition, the diffusion coefficients are also time-averaged from March 1 to March 5 in order to provide a single diffusion coefficient defined for L-shell L, energy E, and pitch angle α. This time-averaging made over 5 days (representing 15 temporal bins of 8-h averaged together) produces smoothed data, i. e. , a regularized dataset, which may otherwise be more variable over time and less smooth (e. g. , Figure  5  in Ripoll et al. [12] ). As we average, we mix different geomagnetic conditions and create a mean diffusion coefficient for that 5-days event. The time-averaging is only done in this section and will not be done in the HSS section in which we will keep time as another variable.
Absence of noise and regularized data make our problem specific. On the contrary, in general, data have uncertainties coming either from our partial knowledge of the variables, or from data variability. In our case, we can have experimental and simulation uncertainties. In such cases, machine learning models have to avoid over-fitting, by not being too close to the data during training. In this article, regularization of data was such that over-fitting was not an issue. For this storm, we use 4 positions, 60 energies, 256 pitchangles, i. e. , 61440 data points for (L, E, α, D αα ) listed as DS3_M13 in Table  1. We extract a subset of DS3_M13 that is composed of 84 pitch angles and 60 energies bins, thus 20,160 data points, listed as TRAIN_M13 in Table  2. This dataset is used for training and calibrating the internal parameters of the various machine learning models using cross-validation.
To evaluate the ability of the machine learning models that we trained on the TRAIN_M13 dataset, to generalize on new data, we consider 2 test datasets; see Table  2. The first dataset TEST_M13_L contains more values in the L input. The model was trained with 4 L-values (L = 2, 3, 4, 5), and here we have 37 values from L = 2 to L = 5: thus we test the interpolation between the discretization used during the training in the case of a very low resolution. The other test dataset (TEST_M13) has full resolution in angles and energies, but the same resolution in L. The test datasets have no intersection with the training dataset. We have also excluded all extrapolation points (with an exception for K p in Section 3. 2. 3), signifying that we bound the test datasets with the bounds of the corresponding training datasets, when evaluating errors.
Dataset for the 32 HSS Storms In this section, we extend massively the previous problem from 1 storm to 32 storms. We choose storms all among the same family of storms called high-speed streams (HSS) so that we can compare them together, characterize the differences, and compute relevant statistics. By doing so, we try to optimize our chances to address similar physical processes and their spatio-temporal timescales. These 32 HSS were each identified in Turner et al. [43]  between September 2012 and December 2016 (listed in Table  3 ). Each storm is observed at various MLT positions, changing with the Probes orbit. When Van Allen Probe B is at its apogee, the corresponding MLT is reported in the right column of Table  3. This MLT corresponds roughly to the most observed MLTs from L above ~4 up to L ~6.
The 32 storms are such that we have 10 events observed from the night side (MLT = 21-3), 11 from the dusk side (MLT = 15-21), 4 from the day side (MLT = 9-15), and 7 from the dawn side (MLT = 3-9). Some of the differences we found may be attributed to MLT variations, though keep in mind that the statistical MLT-rescaling of the wave power makes the coefficients valid and comparable over all MLTs. For each observed storm, we extract wave and plasma data from the Van Allen Probes during 3 days, every 8 h, which gives 9 intervals of 8 h. The timescale of 3 days is representative of the HSS main and recovery phases seen in Turner et al. [43]. The measurements are used as inputs in the simulations of the quasilinear pitch-angle diffusion coefficients Ripoll et al. [16]  outputted at this rate, producing the full database DS1.
This approach is different from making a superposed epoch model of the wave properties of HSS and computing afterwards a single diffusion coefficient from them. The latter approach has low numerical cost but neglects correlations between all the properties of the ambient domain and, therefore, introduces some error (e. g. , Ripoll et al. [17] ). From a machine learning perspective, the Kp averaging helps produce smoothed data, acting as a regularization of the solution that makes the solution less fluctuating, i. e. , less noisy from a MLperspective, similarly to the temporally-averaged data of the March 2013 storm (as discussed in section 2. 1. 3). From DS3_AVG, we train on 10% of the data, listed as TRAIN_AVG in Table  2. All datasets are described in Table  1 , training and validation datasets in Table  2  (2 first rows), and test datasets in Table  2  (3 last rows).