dataesr/scientific-paragraphs-categorization huggingface dataset:
Facility Details and Experimental Setup Five tests at four facilities were carried out as part of the round robin test program: Ifremer, Centrale Nantes (ECN), University of Strathclyde (UoS) and two tests at University College Cork (UCC). The primary purpose of the second test at UCC was to test a second method of wind emulation a number of months after the original test. However, the original test plan with (and without) the thruster was also partly repeated. These latter results comprise the fifth dataset used in the present research paper. The main features of the wave basins in each facility are given in Table  1  and illustrated in Figure  2. ECN, UoS and UCC are all fresh water basins, whereas Ifremer is filled with salt water. Additional masses were added to the platform at Ifremer to achieve the same inertias calculated for fresh water.
Test Plan The test plan was developed by Ifremer, who performed preliminary computations of the RAOs using Bureau Veritas' Hydrostar software. The results of these computations were used to help select the wave parameters for the physical tests. The test plan for the wind round robin included the following elements:   3 ) This test plan was generally followed by all facilities. Some facilities tested extra waves (mainly repeats) in addition to what is listed in Tables  2  and 3.
Responses in Waves under Constant Thrust Tests in irregular waves with the thruster in operation were completed as outlined in Table  3. Most facilities completed tests involving JONSWAP spectra for two peak periods and some variations of significant wave height with constant thrust of 5N and 7 N. Additionally, Pink-Noise waves were tested in combination with constant thrust in two basins. One basin (D) tested the system in the longest wave (JONSWAP T p = 2. 58 s) in combination with constant thrust. The motion RAOs are examined and then their evolution under the effect of the thrust are compared. This is done for at least three levels of thrust (0 N, 5 N and 7 N) plus additional levels when the data are available. The average RAOs over all facilities and the associated envelope defined by the minimum and maximum values are plotted in Figures  22 23 24.
A first release of this data set is now available on the EUREC 4 A database hosted by the French data and service centre for atmosphere (AERIS)  (Bock, 2020b). This paper aims at describing the ground-based GNSS data processing details and assessing the quality of the IWV retrievals from both the operational and research processing streams. Section 2 describes the GNSS data processing and IWV conversion details. Section 3 compares the zenith tropospheric delay (ZTD) estimates and the IWV retrievals from the various data processing streams for the two BCO stations. The intercomparison is completed with IWV measurements from the Vaisala RS41 radiosondes and the humidity and temperature profiler (HATPRO) microwave radiometer operated during the EUREC 4 A campaign, as well the operational radiosonde measurements operated by the Barbados weather service at GAIA.
Both streams provide ZTD estimates with a sampling of 5 min. Apart from the usage of different clock and orbit products, the processing streams run by both analysis centres also differ by their software packages and related processing options as summarized in Table  1. Most importantly, IGN uses the Bernese GNSS version 5. 2 software in double-difference processing mode  (Dach et al. , 2015) , while ENSTA_B/IPGP use the GIPSY OASIS II version 6. 2 software in precise point positioning (PPP) mode  (Zumberge et al. , 1997). Various studies have compared the results and discussed the benefits and drawbacks of both approaches. When consistent clock and orbit products are used, there is generally a good level of agreement (i. e. a few millimetres of root mean square differences (RMSDs) in ZTD estimates).
Discussion and conclusions This paper describes the data processing streams and discussed the quality of GNSS ZTD and IWV retrievals from four operational streams run by IGN and ENSTA-B/IPGP and one research stream (GIPSY repro1); see Table  1. The two operational streams run by IGN (RGP NRT and RGP daily) provide data for the BCON and BCOS stations; the two GNSS stations installed at the BCO provide data for EUREC 4 A in October 2019, as a well as a few other sites in the French overseas territories of the Antilles. The RGP NRT stream provides ZTD estimate within 45 min after the end of measurements which are generally available for assimilation into NWP models. We showed that this stream had some instabilities, especially in the most recent 1 h time slot, which are corrected in the t -2 h time slot.
It is planned in the near future to improve and homogenize the operational IWV conversion approach for all four operational streams and make these data available to the users. The GIPSY repro1 research stream includes 49 GNSS stations covering the Caribbean arc (see Fig. 1 ). The ZTD and IWV estimates from this stream, which is the main focus of this paper, have been analysed for the period 1 January-29 February 2020 and made available with 5 min time sampling for scientific applications. This stream used a slightly improved processing approach and IWV conversion method and auxiliary data with reduced uncertainties. The uncertainty due to the IWV conversion is believed to be at the level of ±0. 2 kg m -2 (see the Appendix). The data have been thoroughly quality checked and screened for outliers (see the Supplement).
However, GNSS is not an absolute remote sensing technique, and unless the IWV estimates are compared to an adequate reference it is difficult to figure out which part of the observed bias is due to GNSS or to the RS41 sondes. IWV estimates from the ERA5 reanalysis were also compared to GNSS data and to BCO and GAIA sonde data. It was found that the IWV content from reanalysis over Barbados is overall close to the GAIA observations. Indeed, ERA5 assimilated the GAIA sondes but not the sondes from the BCO during EUREC 4 A (Irina Sandu, ECMWF, personal communication). On several occasions, ERA5 was also shown to significantly underestimate IWV peaks observed by all systems (sondes, GNSS, and MWR) by 5 to 8 kg m -2 (see Fig. 5 ). Two such events are documented (22 January and 23/24 January) during which a deep moist layer extended from the surface up to altitudes of 3.
5 and 5 km (see Fig. 7 ). It was shown that ERA5 significantly underestimated the moisture content in the upper part of these layers, possibly due to the assimilation of other data over the domain that were biased low. Overall, the reanalysis showed a small dry bias (0. 34 ± 0. 50 kg m -2 ) over the study area in comparison to the 49 GNSS stations. Although the assimilation of biased radiosonde data might be thought as a potential reason, recent experiments with the ECMWF Integrated Forecasting System show that removing all the radiosondes and dropsondes in the EUREC 4 A domain does not significantly impact the simulated humidity field (Alessandro Savazzi and Irina Sandu, ECMWF, personal communication, 2021). Understanding the origin of the mean and occasional dry biases in ERA5 needs further investigation.
The CRT monitor was gamma corrected by a psychophysical procedure provided by the software PsychoPy  (Peirce, 2007). Responses were given by pressing either a left or a right button with the corresponding thumb. The buttons were fixed on top of two cylinders (3 cm in diameter, 7. 5 cm in height). The cylinders were fixed on a tablet and separated by a distance of 20 cm. The buttons were mounted on force sensors that recorded a continuous measure of the force produced at a sampling rate of 2048 Hz. The behavioral response was recorded when a force threshold was exceeded. The device allowed adjusting the force threshold needed for a response to be received. The threshold was manipulated across conditions, as described below. Response signals were transmitted to the parallel port of the recording computer.
At button press, participants received a 3ms sound feedback (1000 Hz pure tone). The participants' forearms and hypothenar muscles rested comfortably on the table, to minimize muscle recruitment during response execution. We measured the EMG activity of the flexor pollicis brevis of both hands with two electrodes placed 2 cm apart on the thenar eminences. This activity was recorded using a BioSemi Active II system (BioSemi Instrumentation, Amsterdam, the Netherlands). The sampling rate was 2048 Hz. Stimuli. Stimulus presentation was controlled by the software PsychoPy  (Peirce, 2007). Each stimulus was com-posed of two vertical oriented Gabor patches, on the left and right of a fixation cross separated by 1. 4 visual angle degrees. The Gabor patches had a spatial frequency of 1. 2 cycles per visual angle degree and a size of 2.
During the experimental session, SAT instructions alternated every three consecutive blocks. The force settings varied every six blocks, with an on-screen message to inform the participant beforehand. The order of the SAT instructions and the force requirement was counterbalanced across participants so that every possible order combination was presented to 4 participants. Within each block, the 6 levels of mean contrast value were fully randomized across trials. No response deadline was applied, and the inter-trial interval was fixed to 1000 ms from button press to next stimulus onset. EMG processing. The EMG recordings were read in Python using the MNE module  (Gramfort et al. , 2013). The signal was filtered using a Butterworth 3rd order high pass filter at 10Hz from the scipy Python module  (Oliphant, 2007) , then segmented by-trial in windows between 150 ms before and 1500 ms after stimulus onset.
). The summary statistics and plots of the parameters were created using arviz python package (version 0. 4. 1, Kumar, Carroll, Hartikainen, & Martin, 2019). Priors for the g/LMMs. The priors for the LMM and gLMM are intended to ease the fitting procedure, we chose to use the relatively broad informative priors described as described in  Weindel et al. (2021). Estimated difference between condition levels d. In order to estimate the magnitude of the difference ( d) between the levels of the experimental factors Contrast, Force and SAT, we chose to use the predictions of the fitted linear models. For each dependent variable, we first computed the predicted difference between both SAT level with all other predictors set at 0 (see Appendix A). We then computed the predicted differences between the lowest and highest contrast level as well as the weak and high force condition for each SAT instruction separately.
Drift Diffusion Modelling. Model fitting procedure. We used the implementation of a hierarchical Bayesian DDM provided in the HDDM python package (T. V. Wiecki, Sofer, & Frank, 2013). Note that HDDM uses the diffusion coefficient (See  Ratcliff & McKoon, 2008 , for a review of the DDM parameters) as a scaling parameter by fixing it to a value of 1 (contrary to a value of 0. 1 in some applications of the DDM). For each model on RT and on PMT , both in the "Model selection" section below and for the model including co-variates, we ran 32500 burn-in samples and 2500 actual recorded samples across four Markov chains Monte-Carlo (MCMC). We inspected each parameter of each chain visually to assess whether they reached their stationary distribution, and whether the R  (Gelman et al. , 1992)  was under the conventional threshold of 1.
We compared the results of joint DDM-regression fits on the PMT and RT. The hierarchical nature of the data is preserved in these models because each intercept and slope parameters is estimated as being drawn from a population distribution. The parameters that do not vary with experimental factors (i. e. , inter-trial variability of the drift rate and the non-decision time) are estimated as described in the model selection section. The inter-trial variability of the bias was free to vary between SAT instructions but the corresponding effect size was not estimated with a regression. This is because, first, we only have one estimate for the population due to the difficulty to estimate it and, second, we do not have specific hypothesis about this parameter. As in the model selection procedure, the models were fitted using the HDDM python package (T.
PMT s rather that RT s were used for the EWMA, first because  Weindel et al. (2021)  showed a high reliability when the method was applied separately to PMT and RT , and second because trials that do not appear very fast on RT can sometimes be fast on the PMT and therefore be problematic when fitting a DDM on the PMT as done here. The figures illustrating these rejection procedures can be found in the online repository. We thank Michael Nunez for kindly providing the code used for this method 3.
In the next section, we quantify factor effects on MT and we build on our assumption linking MT to T response to assess whether the more faithful capture of motor processes comes from linear regression on RT or from DDM fitting. Regarding the parameters affecting the decision time T D , the observed effects were exactly as predicted: the drift was selectively affected by the Contrast manipulation and the boundary was selectively affected by the SAT manipulation. All effects found on the DDM parameters fitted on RT are replicated in a different dataset to the exception of the interaction between SAT and Force on T 0 (see Appendix C).
In the General Discussion section we describe external data whose fit is represented by the plain line and compared to the mean T 0 of each condition represented by the dashed lines. The best fit, as assessed using an R 2 , between both lines is colored according to the color code of the condition. Panels in the bottom row show the posterior distribution of the effect of contrast in accuracy and speed along with the difference between both conditions (T 0 SAT × FC in Table  1  and in the replication Table  C1 ). present in the Speed condition only (Figure  5 ). PMT does not include MT , which we hypothesized to be strongly linked to T response. Therefore, in this analysis, T 0 provides a reasonable estimate of T encoding. In sum, encoding processes are orderly affected by Contrast in the Speed condition only.
Therefore, if our estimated T encoding reflects these stages, it should be non-linearly related to the contrast manipulation similarly across SAT levels. This prediction was tested against external data from Reynaud et al. (2012). These authors measured the latency of the onset of cortical neuron activity in visual area V1 in awake monkeys (described in Appendix D1). Their data provide onset times of V1 neurons, revealing a non-linear increase of onset-latencies across visual gratings of decreasing contrast. Reynaud et al. (2012)  then used these latencies to fit a well established neurophysiologically motivated model  (Naka & Rushton, 1966)  using the inverted equation as in  Barthélemy, Fleuriet, and Masson (2010)  (summarized in Appendix D). If T encoding really reflects the low level extraction of stimulus features, its latency should follow the same quantitative relationship.
The authors thank  Iván Ballasch  for his help in data collection,  Anna Montagnini ,  Mathieu Servant , and  Thierry Hasbroucq  for fruitful discussions,  Frédéric Chavane  and  Alexandre Reynaud  for help with the data from their article. This research was supported by grant  ANR-16-CONV-0002  (ILCB) and the  Programme "Investissements d'Avenir", Initiative d'Excellence d'Aix-Marseille Université  via  A*Midex  funding ( AMX-19-IET-004 ), and  ANR  ( ANR-17-EURE-0029 ).
When computing the R 2 on T 0 estimated from RT rather than from PMT we find the similar results: R 2 = 0. 91 and R 2 = 0. 88 in speed, low and high force respectively vs. R 2 = -1. 11 and R 2 = -0. 02 in accuracy, low and high force. The different levels of adequacy between the V1 data (Reynaud et al. , 2012)  and DDMs T 0 were further replicated with a different dataset, see Appendix C.
Abstract. Based on advanced spectroscopic databases, lineby-line and layer-by-layer radiative transfer codes numerically solve the radiative transfer equation with very high accuracy. Taking advantage of its pre-calculated optical depth lookup table, the fast and accurate radiative transfer model Automatized Atmospheric Absorption Atlas OPerational (4A/OP) calculates the transmission and radiance spectra for a user-defined layered atmospheric model. Here, we present a module called 4A-Flux, which is developed and implemented into 4A/OP in order to include the calculation of the clear-sky longwave radiative flux profiles and heating rate profiles at a very high spectral resolution. Calculations are performed under the assumption of local thermodynamic equilibrium, a plane-parallel atmosphere, and specular reflection on the surface.
4A/OP is applied to the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database to analyze the response of the OLR and vertical heating rate to several perturbations of temperature or gas concentration. This work shows that 4A/OP with 4A-Flux module can successfully be used to simulate accurate flux and heating rate profiles and provide useful sensitivity studies including sensitivities to minor trace gases such as HFC134a, HCFC22, and CFC113. We also highlight the interest for the modeling community to extend intercomparison between models to comparisons between spectroscopic databases and modeling to improve the confidence in model simulations.
Thus, radiative transfer Published by Copernicus Publications on behalf of the European Geosciences Union. Y. Tellier et al. :  Computing longwave radiative flux and vertical heating rate with 4A-Flux v1. 0 models participate in the improvement of our understanding of the atmosphere and the climate. By resolving the individual spectral lines for every absorbing species at every layer of the atmosphere, line-by-line and layer-by-layer radiative transfer codes offer a very accurate way to compute radiative transfer. Their precision relies on spectroscopic databases such as GEISA 1  (Jacquinet-Husson et al. , 2016; Jacquinet-Husson et al. , 1999)  or HITRAN 2  (Rothman et al. , 2013)  that contain the parameters of absorption lines. Being resource-intensive, these line-by-line calculations are not used in GCMs that instead use fast models based on a radiation parameterization that approximates the radiative transfer.
Ongoing efforts will soon lead to a CO 2 line mixing that will be based on the 2020 edition of the GEISA database  (Delahaye et al. , 2021). The pressure grid, the vertical distribution of temperature and gas concentrations, and surface properties such as surface temperature and spectral emittance can be specified by the user. Based on this description of the atmosphere and optical properties of gases, 4A-Flux computes the upwelling and downwelling monochromatic radiative flux profiles, also referred to as hemispherical monochromatic flux profiles [unit: W m -2 (cm -1 ) -1 ]. This computation is based on an exponential integral evaluation to perform the angular integration and a vertical integration. In the distributed version of 4A/OP, the vertical integration of the radiative transfer equation is performed under the assumption of isothermal layers (emission at the mean layer temperature), whereas in 4A-Flux, to better represent the real emission of each layer, we have implemented a sub-layer Planck function that varies linearly with the optical depth.
All six benchmark models have provided longwave integrated upwelling and downwelling flux profiles for the whole set of atmospheric profiles. Participating models have usually provided several sets of spectrally integrated longwave flux profiles for slightly different sets of greenhouse gases, called forcing variants as de-4 RFMIP website: https://rfmip. leeds. ac. uk/ (last access: 12 June 2022). scribed in  Meinshausen et al. (2017). For the first forcing variant (denoted f1), models are free to use any subset of the 43 greenhouse gases specified in  Meinshausen et al. (2017). For forcing variant f1, 4A/OP takes into account 16 gases out of the 43 gases included into the RFMIP database 5. Both the second and third forcing variant (respectively denoted f2 and f3) use CO 2 , CH 4 , and N 2 O.
Using the RFMIP database, we have evaluated the downwelling and upwelling fluxes computed with the 4A-Flux module of the 4A/OP model against calculations of other participating radiative transfer models. To perform this comparison, we have averaged downwelling and upwelling fluxes for the 1800 atmospheric situations using the provided weights to average the 100 atmospheric situations and uniformly averaging the 18 perturbations. For any given pair of model and physics variant, we have also averaged the downwelling and upwelling fluxes for every provided forcing variant. Forcing variants are averaged for clarity purposes as for every model, variability between forcing variants is smaller than the variability between models and physics variants (not shown here). For HadGEM3-GC3. 1, we have not retained 5218 Y.
This good very close agreement, while the two models differ completely in both numerical methods and the spectroscopic databases used (GEISA for 4A/OP, HITRAN for LBLRTM), is an im-portant factor in the confidence that we can have in their results. Furthermore, these deviations between models are well below the sensitivities to typical perturbations of surface and atmospheric parameters that we will describe next. 4 Sensitivity of radiative fluxes and vertical heating rate to temperature and composition In this section, we present a first application of the 4A-Flux module newly implemented in the radiative transfer code 4A/OP. Following  Clough and Iacono (1995) , we perform a sensitivity study of the OLR and the vertical heating rate to various atmospheric and surface parameters. This sensitivity is based on the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database that is developed at the Laboratoire de Météorologie Dynamique.
TIGR atmospheric database The Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database is a climatological library containing 2311 atmospheric situations that have been carefully selected by statistical methods from radiosonde reports  (Chédin et al. , 1985; Achard, 1991; Chevallier et al. , 1998). The atmospheric samples are described by their vertical profiles of temperature, water vapor, and ozone concentration on a pressure grid of 43 levels. The pressure levels range from the surface at 1013 up to 0. 0026 hPa and the density of points along the vertical increases, while pressure decreases to correctly account for the upper atmospheric layers. The 2311 atmospheres are classified into five air mass types. The Tropical class contains situations in the tropics, the Midlat 1 class contains midlatitude situations, the Midlat 2 class contains cold midlatitude and summer polar situations, the Polar 1 class contains Northern Hemisphere very cold polar situations, and the Polar 2 class contains winter polar situations for both hemispheres.
This classification has been performed by a hierarchical ascending classification depending on their virtual temperature profiles  (Achard, 1991; Chédin et al. , 1994). Figure  3  represents the mean profiles of temperature, water vapor, and ozone concentration computed for each of the five classes of the TIGR database. OLR value calculated by 4A-Flux using these five averaged atmospheres are represented in Fig. 4. Except for temperature, water vapor, and ozone, other simulated variables are identical for all five atmospheres. Table  4  presents the concentration of well-mixed greenhouse gases (WMGHGs) considered as a reference for our simulations. For all the simulations we perform here, the surface spectral diffuse emittance is set at a constant value of 0. 98 on the entire longwave spectrum.