dataesr/scientific-paragraphs-categorization huggingface dataset:
A pilot study allowed verifying that the headset did not cause any interference to the EEG recordings. The visual stimuli were displayed in gray over a black background, on a 50 cm diagonal screen (format 4/3), with a refresh rate of 66 Hz. Participants were seated 60 cm from the screen. Data preprocessing. The following processing steps were applied to the EEG data using the Matlab based FieldTrip toolbox (http://www. fieldtriptoolbox. org/) 72 : • High-pass filtering at 0. 4 Hz and stop-band at 50 Hz with deletion of the linear component, performed on the continuous signal. • Visual detection of electrodes with poor signal and reconstruction by interpolation of neighboring electrodes (separately for each experimental block). • Removal of eye movement artifacts (blinks and saccades) was performed by identifying and removing the eye movement components using an independent component analysis (ICA) (separately for each block).
In order to adjust for variations across subjects in the overall mean and variability levels, the mean and standard deviation recorded in the absence of stimuli ("noise") was subtracted respectively to the means or standard deviations observed at the other stimulation levels. These mean and variability response profiles were then averaged across participants (Fig. 1F ). Multivariate pattern analysis (MVPA). Multivariate pattern analysis was performed using the MNE-Python toolbox (https://martinos. org/mne/stable/index. html)  73, 74 , with a suit of tools for temporal decoding and generalization of decoding developed by Jean-Rémi King  75  , who also developed custom-made scripts to fit the particular purpose of the present analysis. The aim of the analysis was to assess how well the multivariate pattern of activity across electrodes on each trial predicted whether a sound stimulus had been presented or not on that trial.
Beyond this classical measure of classification performance, we also kept trial-by-trial prediction values, as required for testing our different dynamical models and predictions. This procedure was applied at each time step independently. Temporal generalization patterns. Temporal generalization was assessed by training at a specific time step and testing at other time steps. Notice that, even for temporal generalization, training was always performed on the reference contrast: maximal SNR versus stimulus absent. To reduce computation time and data size for the temporal generalization analysis we applied a decimation factor of 5 (from a sampling rate of 500 Hz rate to 100 Hz). Other than that the procedure was identical to the one described above. As with evoked potentials, classification sensitivities at different SNRs were filtered along the testing time with a low-pass filter at 30 Hz for visualization purpose (Fig.
We next looked at the distributions of trial-to-trial MVPA projected activities and compared them with the distributions predicted by the different dynamical models (Supplementary Fig. 2 ). For each individual the trial-bytrial projected activities within each time window of interest were first z-scored, with all conditions together, in order to homogenize the range of prediction values across participants. Then for each subject the distribution of activity within a particular experimental condition was assessed using kernel density estimation (ksdensity function in matlab, default bandwidth, i. e. , optimal for normal densities) over 91 points between -9 and +9. These individual distributions were averaged across subjects to obtain the group distributions shown in Supplementary Fig. 2B. "Heard" and "not heard" trials were sorted based on audibility ratings, ratings at 30% or above being considered as "heard" (30% is the median audibility observed at threshold stimulation -9 dB, see Fig.
Some trials were composed of noise alone, without superimposed stimuli. The audio stimuli were presented via a supraauricular headset (Beyerdynamic DT 770 PRO 80 ohm). Task. The protocol was the same as in the main experiment, except that in the active sessions, instead of vowel recognition, participants were required to distinguish tone 1 and tone 2 (designated as "low-pitch" and "high-pitch" respectively). Four participants performed the passive session first, and one performed the active session first. Preprocessing. Preprocessing was the same as for Control Experiment 1. Data analysis. Data analysis was the same as for the other experiments. Statistics and reproducibility. The present study includes one replication of the passive session of the main experiment (10 new participants, see above).
Acknowledgements We thank  Sebastien Marti ,  Jérôme Sackur ,  Stanislas Dehaene ,  Daphné Rimsky-Robert ,  Jacobo Sitt ,  Paolo Bartolomeo , and  Lionel Naccache  for useful comments. We thank  Brian C. J. Moore  for providing threshold equalizing noise. This work was funded by two grants from the  Agence National de la Recherche  to C. S. :  ANR-14-CE15-0013-03  ( CogniComa ) and  ANR-17-CE37-0004-01  (FlexConscious) and two grants to D. P. :  ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02.
Image sequences were calibrated, corrected, processed and analysed using a custom-made Matlab program available online (see: https://github. com/rm1720/bees-applications/wiki). This program automatically determined the honeybees' flight height in each frame as a function of the abscissa along the tunnel axis so that the Step ii (Impoverished visual Conditions IC, video-recording -1 flight): immediately after recording the bees' trajectories under "Constant floor-roof condition", one mirror (Experimental conditions A & B), two mirrors (Experimental condition C), or the half low mirror (Experimental condition D) were uncovered, and the bees' trajectories were recorded in the presence of an optical manipulation (either virtually doubling the tunnel height when there is one mirror uncovered, or giving virtually an infinite tunnel height when there are two mirrors); the entrance to the reward box was again closed during these flights so that no visual cues were available about the position of the reward.
Statistical analysis All the data recorded were included in the statistical analysis without removing any outliers. Statistical data analyses were performed with the Matlab R2018a software program. Not all dataset exhibit the same number of measurements as a function of the abscissa because the bees' crash creates an incomplete trajectory. As a consequence, median and Median Absolute Deviation (Median ± MAD) values were computed for all datasets by binning each bee's trajectory with an 8cm window in abscissa (see Table  1 ). Mann-Whitney U tests are used to compare altitude binning distribution by pairs : control conditions (CC) versus impoverished visual conditions (IC) in each experiment (A, B, C, and D) (see Table  1 ).
Occultations of the Jovian low frequency radio emissions by the Galilean moons have been observed by the PWS (Plasma Wave Science) instrument of the Galileo spacecraft. We show that the ExPRES (Exoplanetary and Planetary Radio Emission Simulator) code accurately models the temporal occurrence of the occultations in the whole spectral range observed by Galileo/PWS. This validates of the ExPRES code. In addition to supporting the analysis of the science observations, the method can be applied for preparing the JUICE moon flyby science operation planning.
The Jovian radio emission may interfere with RIME active radar mode  (Cecconi et al. , 2012) , but can be also used in a passive radar experiment mode during icy moon flybys  (Romero-Wolf et al. , 2015; Schroeder et al. , 2016; Kumamoto et al. , 2017). The ExPRES code (Exoplanetary and Planetary Radio Emissions Simulator,  Louis et al. , 2019a)  simulates for a given observer the geometrical visibility of radio emissions of a magnetised body. This visibility depends in particular of the angle between the magnetic field vector at the source and the emitted wave vector, which is computed self-consistently in the frame of the CMI theory. The anisotropic shape of the radio source and their geometrical observability conditions are well described in Fig. 1  (panels c, d and e) of  Louis et al. (2019a).
Galileo PWS Observations All Galilean moon flyby of Galileo with PWS data have been modelled and analysed. The Galileo PWS (hereafter referred to as GLL/PWS) data have been retrieved from the University of Iowa das2 server interface  (Piker et al. , 2019) , using the das2py 1  python module. We have used the GLL/PWS LRS 152-channel calibratedelectric collection 2  from that das2 server. The data are radio-electric power spectral densities provided in units of V 2 /m 2 /Hz. This dataset doesn't include the instrument's antenna gain calibration, but this has no consequence on this study. The data set has a native time resolution of 18. 67 seconds. These data are also available in the full resolution GLL/PWS dataset (GO-J-PWS-2-REDR-LPW-SA-FULL-V1. 0, Gurnett et al. , 1997) , at NASA PDS (Planetary Data System) PPI (Planetary Plasma Interaction) node.
, 2021) , which contains all the material used to conduct this study. For each Galileo flyby, we provide: (a) a figure showing the GLL/PWS data and the observable radio sources modelled by ExPRES, and (b) movies showing a subset of Jovian radio sources (a selected sub-set of frequencies), as seen from Galileo ('pov' movies), or from the top of the Jovian system ('top' movies).
Temporal Occurrence and Spectral Coverage of Jovian Auroral Radio Signals The first result of this study is the fact that Jovian auroral radio emissions are present almost continuously, although this may not be obvious in some observational data sets, due to the limited sensitivity of instruments. Ground based radio telescopes (such as the Nançay Decametre Array, in France) provide the longest observation collections  (Marques et al. , 2017) , but the distance to Jupiter limits the detection of low intensity events despite their intrinsic sensitivity. Space borne radio instruments (such as Voyager/PRA, Cassini/RPWS, GLL/PWS or Juno/Waves), provide observations from a closer range to Jupiter. Cassini and Galileo data show quasi-continuous radio signals (as shown on Figure  1 ). The ExPRES modelling of auroral radio sources, configured with radio sources every 1 • in longitude, is consistent with this observation (with the noticeable exception of the ESZ, for an observer located around Io's orbital distance to Jupiter).
ExPRES assumes a straight line propagation between the radio source and the observer. the CReMA 3. 0 trajectory scenario. Figure  14  shows the resulting ionospheric characterisation opportunities as a function of the JUICE spacecraft altitude in km (green background). There are a few gaps within the ingress and egress ionospheric characterisation opportunities. Those gaps of 1-2 minutes are ignored to compute the final iono ingress and iono egress envelops used for the observation planning. Table  4  lists the RPWI in-situ and radio measurement sequence for the 21C13 scenario based on the Closest Approach (CA), and the ingress and egress windows envelope for ionosphere characterisation. Using equation 1, we can estimate the uncertainty of the occultation timing (see Table  3 ). Finally, figure  15  is a screenshot of this measurement sequence as shown by the JUICE SOC Mapping and Planning Payload Science software (MAPPS), used to simulate the spacecraft and payload resource status (i.
e. , power, data rate, on-board solid-state mass memory (SSMM)). The RPWI operations are planned around the CA and around ingress and egress ionosphere characterisation opportunities as described in Table  4. The high-resolution in-situ measurement mode is scheduled +/-10 minutes around the closest approach (CA), while the high resolution radio measurement mode is scheduled during the ionosphere characterisation opportunities. High-resolution modes are the most demanding in term of resources (power, data generated, stored in SSMM and to be downloaded (data rate)) and they are reserved for priority scientific objectives. So it is crucial to be able to calculate the corresponding opportunities windows. The JUICE mission is in development phase, with a planned launch in September 2022. At this stage the JUICE mission science planning process is being exercised by analysing representative science scenarios similar to 21C13.
Conclusions and Perspectives The Galileo radio occultations observed in the PWS data set are well modelled by ExPRES simulation, with a sampling of the order of one minute. Discrepancies between predicted and observed ingress or egress times can be attributed to refraction effects, which are not included in the current modelling scheme. The validation on GLL/PWS data allows us to apply the same modelling to the JUICE mission planning, in order to support the scientific segmentation of the Galilean moon flybys. The ExPRES modelling will also be useful for data analysis of "passive radar" observations, since it provides the location of the Jovian radio sources used to probe the moon's sub-surface. On a technical point of view, the JUICE modelling have been done running ExPRES through the PADC operated UWS interface based on OPUS.
Acknowledgments The authors acknowledge support from  Observatoire de Paris-PSL ,  CNRS ,  CNES  and  ESA  for funding the research. CKL's work at  DIAS  is supported by the  Science Foundation Ireland  Grant  18/FRL/6199. The authors also acknowledge support from the  Europlanet 2024 Research Infrastructure  ( EPN2024RI )  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  871149 ). They want to emphasise the use of community developed tools and standards, which greatly facilitated this study (such as Autoplot, Das2, OPUS,  UWS and Cosmographia and WebGeoCalc). They thank PADC  for providing computing and storage resources. The authors thank  Ronan Modolo  for fruitful discussions during the preparation of the manuscript.
g. , zenithal delay, integrated water vapor amounts) and positioning data throughout the western part of the basin (see Section 3. 2. 2. ). The first analysis of GNSS-derived observations collected during RNR-CYC have already demonstrated the benefit of these new permanent stations to investigate the water vapor cycle at diurnal to inter-annual time scales  [34, 35] , but also to provide new and continuous observations to investigate the dynamics of the Earth's crust in this particularly active part of the world  [34, 36]. (ii) An "experimental" approach, based on the temporary collection of atmospheric and oceanic observations at various locations in the basin. For this purpose, several atmospheric and oceanographic field campaigns have been organized throughout the 3. 5-year duration of RNR-CYC, with the goal to provide novel datasets to evaluate numerical model developments and simulations performed in the frame of the project  [33].
Thanks to realtime trajectory optimization, both systems were nevertheless able to approach relatively close to the storm, as shown by black ellipses displayed in Figure  12a. For instance, glider GLNE was able to move to a distance of about 290 km from the TC center and to collect ocean data in the direct vicinity of this storm. The two gliders were programmed to sample the ocean during descending (downcast) and ascending (upcast) profiles. Observations were transmitted in real time by satellite telemetry after each upcast, when directives for modifying the sampling strategy and glider navigation (based on operational ocean model forecasts) were also received. Each glider was equipped with various physical and optical biogeochemical instruments for sampling the ocean temperature, salinity, oxygen, turbidity, and chlorophyll-a concentration at different rates according to depth, as shown in Table  1.
These results were predictable because the 1 m increase in wave height is relatively small and not expected to generate a significant difference in PMA emissions. height (and relatively constant wind field), a horizontal gradient in the aerosol concentration and size distribution at 100 m asl was not observed during this swell event. These results were predictable because the 1 m increase in wave height is relatively small and not expected to generate a significant difference in PMA emissions. Due to the low number of TCs passing in the immediate vicinity of Réunion Island during the TC season 2018-2019, the range of wind speeds and ocean wave conditions were not as large as expected to fully assess parameterizations for PMA emissions. Nevertheless, the datasets collected during this campaign have clearly demonstrated the scientific potential of the BOREAL UAS, and of its associated payload, to collect key observations in remote oceanic areas and in the vicinity to tropical cyclones.
The extension of the SHOC program to the SWIO basin (referred to as SHOC-V2) was initiated in 2017, under the frame of RNR-CYC. SAR missions cannot continuously acquire wide swaths of data in high-resolution modes; therefore, a dedicated acquisition procedure was set up to collect data without impacting the operational duty cycle of the satellites. Acquisition requests were passed along to both MDA (MacDonald Dettwiler and Associates, the private company that owns the satellite RS2) and ESA S1 mission planner portals on a 24-to-48 h notice, based on satellite orbit and five-day track forecasts provided by RSMC La Réunion. In order to reduce the workload of participating space agencies, which contributed to this experimental program on a voluntary basis, acquisition requests were also generally limited to storms presenting a threat to SWIO populations.
Between 2017 and 2021, about 150 SAR images were acquired by S1A, S1B and RS2 satellites, allowing the sampling of 20 tropical storms and cyclones over four TC seasons (raw S1 data are available at https://scihub. copernicus. eu, accessed on 20 March 2021). Among these acquisitions, nearly 40% were directly obtained within the eye or the eyewall of the systems (Table  2 , "hits"). SAR images were collected throughout the SWIO basin (Figure  23 ) at various storm evolution stages (cyclogenesis, intensification, dissipation). Collected wind data were used for nowcasting and best-track data reanalysis purposes at RSMC La Réunion, model verification  [33]  and assimilation in NWP systems  [125]  (this Special Issue). 2. Examples of SAR-derived surface wind fields obtained through the application of the retrieval algorithm proposed by  [126]  are presented hereafter.
The innovative data collected in the SWIO during RNR-CYC have also proven particularly useful to evaluate the performance of both research and operational TC forecasting models in this undersampled region of the world. SAR measurements, which allow for accurate evaluations of TC forecasts, can also be assimilated into NWP systems  [126] ; in situ and remote swell measurements can be relied upon to evaluate wave model performance; while biologging measurements are priceless to assess ocean model representation of the ocean mixed layer in cyclonic conditions. The promising results obtained during the 3. 5-year observation component of RNR-CYC have already led to the development of numerous new research projects aimed at maintaining some of these novel measurements and further reinforcing overall observation capabilities in the SWIO beyond the end of this program (July 2021).
This transdisciplinary project, constructed in close cooperation with ST specialists and oceanographers, will extend the research initiated during RNR-CYC to: (i) investigate Indian Ocean properties and spatio-temporal variability; and (ii) improve knowledge of the ecology of the five species of sea turtles living in the Indian Ocean. A particular emphasis will be put on the observation of mesoscale eddies and coastal currents that develop in the Mozambique Channel (to assess their impact on water mass distribution, transport, and mixing, as well as their role in the dynamics of the Greater Agulhas current system), and on the assimilation of collected ST-borne data in global and limited-area configurations of the ocean model NEMO  [129]. This new component of the global STORM program, which is now strongly supported by the EU, UNESCO (through the GOOS/AniBOS), CNES and CNRS, among others, will be conducted for the many years to come, and is likely to be extended to other ocean basins in the mid-term.
With the deployment of nearly 20 new sensors on-board this RV (e. g. , cytometer, titrator, thermosalinograph, cytometer, titrator, NO x , CO, CO 2 , O 3 and CH 4 analyzers, GNSS, UV radiometer, CCN, photometer, weather station), observations at the OA interface and within the atmospheric column will be collected over the long term in this region particularly sensitive to the impact of climate change (note that all observations will be transmitted in near-real-time through the RV onboard satellite communication system). The data collected in the Southern Ocean for sea and wind conditions close to those encountered in tropical cyclones will also be used to carry on with the evaluation of OA exchange parameterizations (turbulent fluxes, aerosol, and sea-spray emissions) in extreme conditions  [33].
With the deployment of nearly 20 new sensors on-board this RV (e. g. , cytometer, titrator, thermosalinograph, cytometer, titrator, NOx, CO, CO2, O3 and CH4 analyzers, GNSS, UV radiometer, CCN, photometer, weather station), observations at the OA interface and within the atmospheric column will be collected over the long term in this region particularly sensitive to the impact of climate change (note that all observations will be transmitted in near-real-time through the RV onboard satellite communication system). The data collected in the Southern Ocean for sea and wind conditions close to those encountered in tropical cyclones will also be used to carry on with the evaluation of OA exchange parameterizations (turbulent fluxes, aerosol, and sea-spray emissions) in extreme conditions [33].
With the deployment of nearly 20 new sensors on-board this RV (e. g. , cytometer, titrator, thermosalinograph, cytometer, titrator, NO x , CO, CO 2 , O 3 and CH 4 analyzers, GNSS, UV radiometer, CCN, photometer, weather station), observations at the OA interface and within the atmospheric column will be collected over the long term in this region particularly sensitive to the impact of climate change (note that all observations will be transmitted in near-real-time through the RV onboard satellite communication system). The data collected in the Southern Ocean for sea and wind conditions close to those encountered in tropical cyclones will also be used to carry on with the evaluation of OA exchange parameterizations (turbulent fluxes, aerosol, and sea-spray emissions) in extreme conditions [33].
Established methods of this form include those of Jeener  [56] , and Levitt and di Bari  [57, 58]  as modified by Levante and Ernst  [59]. The method of this form that is employed here is the simplied Levittdi Bari (sLdB) method  [57] [58] [59] , for this thermal correction superoperator ĤsLdB is ĤsLdB ¼ 1IS À q 0 j Þ 1 IS ð j; ð36Þ where 1IS is the identity superoperator. The sLdB method is already implemented in the simulation package SpinDynamica 3. 4. 2 [89] , and ensures that the density operator does indeed return to its equilibrium expression. However, it does not satisfy the conditions of detailed balance, nor is it applicable outside the hightemperature and weak-order limits. Therefore, an alternative thermalization procedure is required.