dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Dr. Armenise ,  Dr. Wong Syie Luing , and  Dr. Daniel Wuebben  have received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 754382 , GOT ENERGY TALENT. The content of this publication does not reflect the official opinion of the  European Union. Responsibility for the information and views expressed in this paper lies entirely with the authors. Dr. Armenise also wishes to thank  Glenia Pena Lugo  for the important feedback on the manuscript and  Pierina Lemus  to help to design several images of this manuscript.
SIMULATION OF THE INTRINSIC AGN COUNTS FROM XRB SYNTHESIS MODEL We have estimated the 'intrinsic' AGN number as a function of the redshift z, 2-10 keV intrinsic X-ray lu-   20, 26]. We, then, make use of the software POMPA 5  to get the log N (> S) source density in each of those bins, with S being the 2-10 keV source flux. From the N (> S) we also obtained the total intensity of each bin (energy received per unit area, unit time and unit sky area). Instead of the composite power law plus reflection spectral model in  Gilli et al. (2007) , we have adopted here a torus model from  Brightman & Nandra (2011)  with photon index Γ = 1. 9, aperture angle 30 deg and inclination 80 deg. We have also included an additional scattering component modelled with a powerlaw with the same photon index and normalization equal to one percent of that of the primary emission.
We used a spectral resolution of R = 50 at 17 µm and R= 120 at 36 µm and assumed a linear regime between these two values. We used the LR continuum sensitivity (see section 2. 1. 3), rescaled to the Ultradeep survey, as standard deviation of the Gaussian noise that we included in the spectra. Figure  7  shows two of the simulated spectra. Finally, we exploit a modified version of the PAHFIT code  (Smith et al. 2007 ) that uses a power-law continuum, several PAH emission lines and silicate absorption, to fit the spectra and provide the source redshift (Negrello et al. 2017 ) SEDs (inset in the upper left of the plots, where the red line is the AGN component, the blue one is related to the host-galaxy and the black one is the total AGN+galaxy emission) as spectral templates and added a white noise with amplitude based on the LR continuum sensitivity expected for a DEEP survey.
5. Moreover, having a sufficient number of photometric points, would allow us to break the SF-AGN emission degeneracy and to estimate the AGN properties using SED-fitting decomposition techniques (jointly with additional instruments at different wavelengths) when spectra are not available. Although X-ray photometry and/or low-resolution spectroscopic data may not be used in SED fitting (but see X-CIGALE,  Yang et al. 2020 ), X-ray information could be crucial to select the templates to fit in the IR. As a quick test for this statement, we simulated     2016 ) photometric data (from optical to sub-mm including Spitzer and Herschel, although not all the photometric points were available for all the sources) to obtain the AGN, hostgalaxy and total SED. We used the total SED to estimate the SPICA SMI and B-BOP fluxes, added these four photometric points to the previous 33 and used all of them in new runs of sed3fit.
ACKNOWLEDGEMENTS This paper is dedicated to the memory of  Bruce Swinyard , who initiated the  SPICA  project in Europe, but unfortunately died on 22 May 2015 at the age of 52. He was ISO-LWS calibration scientist, Herschel-SPIRE instrument scientist, first European PI of SPICA and first design lead of SAFARI. We acknowledge the whole SPICA Collaboration Team. FP, CV, CG, LB, and LS acknowledge financial support by the  Agenzia Spaziale Italiana (ASI)  under the research contract  2018-31-HH. 0. FJC acknowledges financial support from the  Spanish Ministry MCIU  under project  RTI2018-096686-B-C21  ( MCIU/AEI/FEDER/UE ), cofunded by  FEDER  funds and from the  Agencia Estatal de Investigación ,  Unidad de Excelencia María de Maeztu , ref. MDM-2017-0765. MPS acknowledges support from the  Comunidad de Madrid, Spain , through  Atracción de Talento Investigador Grant   2018-T1/TIC-11035  and  PID2019-105423GA-I00  ( MCIU/AEI/FEDER ,UE).
Emitted photons are recorded in a wavelength range covering the complete VUV/FUV spectral range emitted by transitions between rovibronic levels of the singly excited states to discrete levels and the continuum of the ground state, including emission from excited atomic dissociation fragments. The data is displayed as a twodimensional photon-excitation photon-emission map (henceforth called PhexPhem map), where intensities are plotted as a function of both, the emitted-photon wavelength (or energy) and the exciting-photon energy. In a PhexPhem map molecular processes like dipole-allowed rovibronic transitions between bound states, transitions between a bound state and a dissociation continuum, predissociation, and ionization will be visible at the same time. The current paper describes proof of concept experiments where it is shown that it is possible to record dispersed photon emission in the full excitation-emission range relevant to excitation and emission of all singly excited H 2 states and to display such spectra in one image.
A quantitative intensity determination will be the task of future experiments. In the first section the experiments and the representation of the data as a PhexPhem map is described in greater detail. It follows a discussion of its main features by a comparison to simulations. The presented simulations do not have the ambition, to advance the description of a specific molecular physics problem, rather than to correlate specific molecular processes to signatures in the PhexPhem map. Although the main features are all known to some detail from past work the PhexPhem map representation of the spectra has its value on its own, as it simplifies considerably the orientation in the complex H 2 emission spectrum.
The intensities integrated over the overlap spectral range for each exciting-photon energy have been used to append the spectra of different ga. An example how this has been done is shown in figure  4  for three different ga. To cover the full range of E exc in 2. 5 meV steps 2801 exciting-photon energy steps were necessary with 8 spectra taken at each E exc to cover the full fluorescence wavelength range resulting in more than 20000 spectra. The full data set is shown as a two-dimensional color-scale PhexPhem intensity map as a function of both the excitingphoton energy (horizontal axis) and the emission wavelength (vertical axis). The intensity scale in such a representation has to be chosen carefully in order to visualize both, features due to small intensity variations and very strong spectral features at the same time.
For this purpose we used the colormap viridis introduced by van der Walt and Smith into the Mat-PlotLib library of Python. Still, the dynamic range between very intense emissions (e. g. the Lyman-α line of atomic hydrogen fragments) and the majority of features is too large for a joint presentation. The intensities I (E exc , λ) have, therefore, been scaled by a non-linear transformation. We used a bound exponential scaling function with a single parameter a. The relative intensity F = I I max determined from the measured intensities I and I max is scaled to the displayed relative intensity values F d = I d I max,d by the transformation: F d = 1 -e -aF. (2) The effect of this transformation is shown in figure  5 (a) for different a. The two-dimensional PhexPhem map of H 2 has been displayed in this work with a = 15 after reducing the intensity of the strongest spectral feature (the Ly α -line) by a factor of 10.
The complete PhexPhem map is finally obtained by joining the spectra of the two different spectral ranges. Here the transition between the different emission-wavelength calibrations in the FUV and VUV must be considered. Due to the available calibration points, the two intercepts of the calibration curves coincide well in the overlap region of the two spectral ranges as shown in figure 5(b). A completed result for the two-dimensional VUV/FUV PhexPhem map of H 2 is shown in figure  6. The underlying data represents a set of photon excitation emission intensities of H 2 covering the complete exciting-photon energy range from room-temperature populated rovibronic levels of the vibronic ground state to the singly excited molecular states and the subsequent complete dispersed emission in the VUV/FUV spectral range on a joint  The sheer number of individual measurements assembled for this single representation demands a particular attention to proper normalization if all features are to be put on the same relative intensity scale.
Discussion As the collected data and representation contains a wealth of information it is necessary to familiarize with its major features, most of which are well known from past work dedicated to a particular aspect of the whole PhexPhem map. To identify these features the present PhexPhem map is compared to a comparatively simple simulation. The calculation is based on solving the non-relativistic nuclear part of the Schrödinger equation in Born-Oppenheimer approximation (Born and Oppenheimer 1927) with p the momentum operator, μ the reduced mass, J the rotational quantum number, χ the nuclear wavefunction and E the nuclear term energy. p2 2μ + U (R) - 2 2μR 2 (J (J + 1)) χ (R) = Eχ (R) (3) The potential energy curves U (R) for the electronic states have been taken theoretical term values where a variety of corrections have already been considered to increase the accuracy of the Born-Oppenheimer approximation (Dressler and  Wolniewicz 1986 , Wolniewicz and Dressler 1988 , Wolniewicz 1993 , 1995a , Staszewska and Wolniewicz 2002).
Conclusion We have shown experiments as a proof of concept where dispersed photon emission has been recorded after photon excitation of all singly excited states of H 2 as a function of the exciting-photon energy, covering the complete VUV/FUV emission spectral range. About 20 000 individual spectra have been appended to represent the emission in a two-dimensional PhexPhem map. In the PhexPhem map molecular processes like dipole-allowed rovibronic transitions between bound states, transitions between a bound state and a dissociation continuum, predissociation, and ionization are visible at the same time, facilitating the identification of particular features. This representation of the data has the potential to be included in standard molecular physics textbooks to explain theoretical concepts of molecular physics like the Franck-Condon principle, potential energy curves, and molecular state interactions.
Reverse Transcription Polymerase Chain Reaction (RT-PCR) Total RNA from HDFPCs was extracted using TRIzol reagent, and cDNA was synthesized from 2 µg of the total RNA  [31]. We used PCR premix (Bioneer, Daejeon, Korea); the primers' sequence in Table  1. The PCR products were analyzed using 2% agarose gel stained with eco dye, and the product band intensities were quantified using ImageJ 1. 47 software (NIH, Bethesda, MD, USA).
In daily practice, there is a paucity of published data on which to provide comprehensive evidence-based recommendations on when and how to intervene when patients with a brain tumour wish to resume driving. To address this situation, 22 members of the interdisciplinary Swiss Neuro-Oncology Society (SwissNOS), one traffic physician and representative of the Swiss Society of Legal Medicine with Level 4 qualification (Institute of Legal Medicine, Zurich) and an electrocencephalogram (EEG) specialist were invited to participate in a modified Delphi process in April and in August 2020. The aim was to shed light on procedures used in daily practice and to agree on minimum requirements to determine "fitness-todrive" for brain tumour patients in Switzerland. Glioblastoma, the most common and aggressive brain tumour, was selected in a first step as an appropriate candidate tumour that allows to study a relatively homogeneous brain tumour subtype with standardized intial care, a relatively uniform prognosis and a lower incidence of seizures than lower grade gliomas  [4, 5].
Open access US and UK samples were recruited via Prolific ( prolific. ac). Although some later samples from these countries were recruited via Respondi, we continued to also recruit Prolific samples to allow comparisons with our earliest data points in the pandemic. As we did not have matching Prolific and Respondi samples at each time point and results differed slightly between these providers, we report these samples separately for transparency. Recruitment was managed by these external providers, and exact response rates were not provided. However, Respondi provided a broad estimate of a 30% response rate across surveys (personal communication; Respondi, 2021). Nonresponse bias was mitigated by quota-based sampling, which ensured all samples were representative of each country's population in terms of age and gender (using interlocking quotas based on national census data) and, in Prolific samples, ethnicity.
The β Nestedness dissimilarity component accounts for the proportion of dissimilarity of an assemblage that is due to the loss of species initially present in another richer assemblage. These two components are of crucial importance as the interpretation of the dissimilarity between two or more sites can strongly differ, depending on the value of each of them. The dataset used to compute these analyses is presented in Fig. 6. Only the taxa identified at a sufficient taxonomic level (variable depending on the phylum) to be confidently distinct from other taxa were included (Table  1 ). Coprolites that cannot yet be safely attributed to any specific taxa were removed from the dataset. In addition, multiple bivalves, brachiopods and crustaceans that could not be identified at a sufficient taxonomical level due to preservation were also removed.
Acknowledgements We wish to thank  Evelyn Kustatscher  and the two anonymous reviewers who commented on the manuscript. This work was supported by the  ANR  project  AFTER  ( ANR-13-JS06-0001-01 ) and the  French "Investissements d' Avenir" program , project  ISITE-BFC  ( ANR-15-IDEX-03 ). We thank the  BLM (Permit N-094706 ) and  US Forest Service  ( Caribou National Forest , Special Use Permit  CT5. 01 ) for allowing access and collection at NN and  STW. The Winecup  Gamble Ranch  is thanked for allowing access to IC. Sample management and valorisation benefited from  ANR-11-INBS-0004-RECOLNAT.
h) that is simultaneously estimated with the relative antenna height h using a Least Square Estimation (LSE)  [29]. This allowed it to retrieve the temporal variations of the SL in macro-tidal environments (i. e. , with a tidal range higher than 4 m)  [19, 29, 30]. Furthermore, this method introduces a moving window to estimate dominant frequencies along the satellite tracks, while previous approaches estimate only one frequency per track at low elevations  [22]. The use of the dynamic SNR method can provide more information when few GNSS satellite tracks are available, and integrates reflections from satellites with high elevation. In this study, we analyzed SNR data acquired using a GNSS geodetic receiver on a platform over the Garonne River (France), 130 km upstream of its mouth, during one semidiurnal tide.
Study Area The study area is located along the Garonne River in Podensac, southwest France (see Figure  1a, b ). The confluence between the Garonne and Dordogne rivers forms the Gironde estuary in the Bay of Biscay. Tidal waves propagate in both rivers up to 160 km from the mouth of the estuary, with a tidal range exceeding 6m in Podensac  [26, 27]. Podensac is about 120 km upstream the mouth of the estuary and is the place where both the tidal range and the asymmetry of the tides are maximal due to a narrow river configuration. Tides in alluvial estuaries, such as the Gironde/Garonne/Dordogne estuary, are highly asymmetric  [27, 31] , unlike coastal areas where GNSS-R classical retrieval methods compare well to tide gauges  [22]. While symmetrical patterns are found in most studies, asymmetric tides in Podensac compose a challenging dataset to explore the ability to retrieve high amplitude water level variations using GNSS-R.
GNSS-IR Data A GNSS station composed of a conventional Leica AR10 antenna and GR25 receiver was installed on a platform above the river, ~4 m to 10 m above, depending on the tides (Figure  1d ). It was used as a reference station to process the data acquired by a mobile GNSS receiver onboard a buoy using Differential GNSS (D-GNSS) technique, to retrieve water heights during tidal bore occurrence  [28]. This reference station was also employed to collect SNR information at L1 (1575. 42 MHz for GPS and 1602 MHz for GLONASS with a 562. 5 kHz frequency shift between each carrier), L2 (1227. 60 MHz for GPS and 1246 MHz for GLONASS with a 437. 5 kHz frequency shift between each carrier), and L5 (1176. 45 MHz for GPS) GNSS bands from GPS and GLONASS satellites reflected over the river. The SNR acquisition was performed during a time span of 15 h, starting on October 17, 2016, at 2:35 p.
m. UTC and ending on October 18, 2016, at 5:50 a. m. UTC. The nominal sampling rate was set to 20 Hz to further evaluate the ability of SNR-based retrieval methods to detect tidal bore propagation. A first tidal bore occurred on October 17th at 05:03 p. m. , and a second on October 18th at 05:30 a. m. UTC (see Figure  2 ). In this study, we only focused on tide retrieval. Thus, 1 Hz resampled SNR data were used as inputs over the entire time span to retrieve the evolution of water heights during asymmetric tides. The output of our method produced a regularly spaced antenna height time series. Sampling was set to one minute for this case study.
Validation: Pressure Data Several instruments were installed on the riverbed from 12 October to 20 October 2016, to monitor tidal bore occurrences during spring tides, including an Acoustic Doppler Current Profiler (ADCP, Nortek Signature 1000) and a pressure sensor (Ocean Sensor Systems). Water heights estimated with pressure measurements using the hydrostatic assumption were used for comparison with the GNSS-R SNR height estimation (Figure  2 ). The specifications from the constructor indicated an accuracy of 0. 05% on the pressure measurements, corresponding to an accuracy of about 0. 005 m on the water height determination at high tides in Podensac. Nonetheless, the maximum error could reach up to 0. 10 m in extreme conditions. The nominal sampling rate of pressure measurements was 10 Hz.
Validation Pressure-derived water heights were used to validate the GNSS-R antenna heights. The absolute ellipsoidal height of the pressure sensor was not available which was the major issue in the validation procedure. Only a relative comparison between the dynamic SNR output and the pressure dataset was possible. We further assumed that bias was zero, so the means of both datasets were adjusted for validation. Two metrics were then estimated: unbiased Root Mean Square Deviation (ubRMSD), adapted because of the no-bias assumption, and Pearson's correlation coefficient (R). We compute ubRMSD and R for the entire water height time series, and we also calculated metrics on subsampled times series to study the influence of both the vertical velocity and the number of satellites visible on the SNR heights.
In our case, the combination of these factors required strong filtering. Figure  4  presents the frequencies estimated before and after these two filtering operations for a L1-only retrieval. As the occurrence of tidal bores (here at ~5:03 p. m. ) affects the frequency estimation in the moving window, we decided to remove estimations using a one hour buffer around the phenomena. The second gap from 9:00 to 10:00 p. m. was due to a stop in the acquisition recording. Table  1  presents the statistical results obtained between each configuration of the adapted dynamic SNR algorithm (user-determined value of parameter k, GNSS bands used and minimal number of satellites for the inversion process) and the validation dataset. The values for R, ubRMSD, the maximum error and the number of points calculated with LSE using the configuration (N h ) are shown.
Although presenting excellent statistical results, N h = 484 for the L2-only case, confirmed that the absence of the L2 civilian frequency for some GPS satellites led to data gaps, especially when few satellites were visible. Finally, when combining all the L1, L2 and L5 bands in a unique system of equations, N h = 664 was obtained. Compared to this result, the output time series of antenna height contained 19. 4% fewer points for the L1-only case and 27. 1% less for the L2, for quasi-identical statistical results. This loss of information was particularly accentuated when the configuration of the study area provided a restricted number of available satellite tracks for SNR based inversion, as in the Podensac case.
Furthermore, frequencies are extracted for each entire low-elevation subsampled satellite track, compromising the detection of rapid and unpredictable changes in the surface state with SNR time series. On the contrary, the dynamic SNR method has arguments to deal with the problems enunciated above. First, the integration of the rate of change into a system of equations where h and. h are jointly estimated with LSE being important: true values are directly extracted and no modelling or a priori knowledge of the phenomena is necessary. Second, the use of all-elevation SNR data (when not masked) as input, and the windowing of time series in the frequency estimations, produce finer detection of rapid changes in surface state. This is limited, as the influence of tidal bores, as an example, contaminated the results around 5:00 p.
h. The LSE estimation then provides all h,. h was calculated from true values or models. In our case, estimation was made from noisy frequencies based on the SNR time series (see Figure  4a ). The derivative of h already adds noise in Equation (  4 ). When deriving a second time for. h the noise level becomes prominent for a low gain, so including the acceleration has no advantage. The noisy SNR time series in Podensac composed a challenging dataset. We were able to clean up the extracted dominant frequencies to retrieve h and. h using two levels of filtering noise: first removal of the multipeaks in the LSP output that materialize other reflection surfaces or high frequency perturbations, then filtering of frequencies up to 3σ (99%) with an iterative approach of LSE. These additions to the initial method  [29]  were mandatory to work with GNSS-IR on very constraining river environments, of which Podensac is a perfect example.
56 m for L1. When combining all GNSS bands, a more robust estimation was found when enough satellites were visible; therefore it increased the number of samples in the output time series. Other factors are discussed including the influence of the vertical velocity of the reflecting surface and the number of distinct satellite tracks used in the LSE inversion. The influence of. h on both R and ubRMSD values was low in the analyzed 0 -5 × 10 -4 m • s -1 span. The dynamic SNR method fitted the reference data well, whatever the elevation rate of the reflecting surface. On the contrary, the number of tracks available had a major impact on the results. R and ubRMSD were consistent when n sat ≥ 4 with values greater than 0. 96 and lower than 0. 33 m, respectively. A clear degradation was observed for n sat = 2 (R = 0.