dataesr/scientific-paragraphs-categorization huggingface dataset:
5. Sensor-space results for the MEG data. Time courses depict RMS across the MEG sensors significantly different from the baseline interval (marked by bold dots in the topoplots). To identify the most sensitive sensors the data was collapsed over all three conditions and tested against the baseline interval. The left and right panels depict RI SL and RI RL data, respectively. Topoplots depict CMV (1. 5-2 s), iCMV (0. 3-0. 6 s), and preMMF ( À 0. 5 to 0 s) components. RI RL. For RI SL , the midfrontal areas are active in the all conditions. However, as the duration becomes longer it can be noticed that activations become less visible. This decrease is likely caused by the smaller contribution of the preMMF activity in the time period ranging from 1. 5 to 2 s for longer intervals. Thus, this supports the idea that the CMV-like patterns that might be visible in the RI SL data should be interpreted as a product of dispersed preMMFs.
(1996, Fig. 4 ) just before (voluntary) movement. In sum, the magnetic data does not provide a CNV-like, steady build up of activation that starts at the interval onset as can be observed in the EEG data. However, the MEG data does provide evidence for a preMMF originating from the SMA that builds up about 6 s before the response that indicates the end of the interval is given, most likely reflecting preparatory processes.
CNV amplitude enhancement for shorter duration The earlier work by  Elbert et al. (1991, cf. Gibbons and Rammsayer, 2004)  has shown that the reproductions of shorter intervals were associated with more negative amplitudes, a finding which is at odds with the assumption that the CNV reflects the accumulator. Visual inspection of Fig. 3  (right column) indicates that also in this study, for both the fronto-central and the centroparietal CNV, a more negative build-up can be observed for the 2-s condition than for the 3-and 4-s conditions in RI SL and RI RL stages. To quantify this observation, we performed a cluster-based nonparametric permutation test implementing MANOVA F-test on the RI SL data for two time periods from 0. 3 to 0. 6 s (iCNV) and from 1. 5 to 2 s (CNV) after the tone indicating the start of RI.
We found a significant cluster for the iCNV (p ¼0. 035; AFz, F7, F3, Fz, F4, FC5, FC1, FCz, FC2, C3, Cz, CP5, CP1, CPz, CP2, CP6, P7, P3, Pz, P4, P8), and for the CNV (p ¼0. 045, AFz, F7, F3, Fz, F4, FC5, FC1, FCz, FC2, C3, Cz, CP5, CP1, CPz, CP2, CP6, P7, P3, Pz, P4, P8). For the RI RL data, cluster-based permutations showed significant differences for the mean activity in last 500 ms of the reproduced interval (p ¼0. 018; C3, Cz, CP1, CP2, P3, Pz). No such effects were found for the MEG data (all p values 40. 1). Both iCNV and CNV topographies (Fig. 3 ) indicate that 2 s condition is mainly enhanced at the fronto-central sites,  suggesting that response preparation drives the difference between conditions (e. g. ,  Lütcke et al. , 2009). Given that the magnetic data does not provide a CNV-like, steady build up of activation that starts at the interval onset, we did not perform comparison for time period ranging from 1.
The CMV data was based on RMS values to prevent from canceling out of inflowing and outflowing magnetic fields. To allow for inclusion of all three reproduction intervals into one model we used linear mixed models  (Pinheiro and Bates, 2006). The CNV/CMV amplitude and condition were entered as predictors and subject was used as a random factor and subject was used as a random factor. Formal model comparisons showed that the CNV amplitude measured during the RI predicts the length of reproduced duration expressed as a ratio between reproduced duration and target duration (note that no evidence was found for the SI CNV amplitude influencing reproduced duration). This conclusion is based on a comparison of a model that includes both RI CNV amplitude and SI duration to a model that only includes an effect of SI duration (2, 3, 4 s).
8 , resulted in fairly similar slope lines, the individual data points are much more scattered. An analogical model fitting procedure as executed for the CNV data was applied to the MEG data, showing that including the MEG amplitude did not improve the fit (ΔAIC ¼ 1. 9, χ 2 (1) ¼ 0. 1, p ¼0. 752), nor was the inclusion of the interaction between SI duration and CMV warranted (ΔAIC ¼0. 9, χ 2 (1) ¼ 3. 12, p¼ 0. 209). In line with the above model comparisons, the estimated effect for MEG amplitude failed to reach significance (β ¼-0. 001, F(1, 45. 3) ¼1. 3, p ¼ 0. 268), but the condition factor did reach significance (F(2,32. 7) ¼46. 7, p o0. 001). These numerical differences steam from the fact that the mean reproductions used in the CNV and CMV models differed as trials shorter than 2 s were removed in the previous analyzes of the MEG data to exclude the influence of the postMMF on the RI SL data.
All trials were kept for the analysis of the CNV. To assess whether MEG amplitudes might improve the fit of the EEG-based models, we extended the best fitting EEG-model by adding the MEG amplitudes as additional predictor. However, addition of the MEG data to the model containing CNV data was not warranted (ΔAIC ¼0. 7, χ 2 (2) ¼ 2. 78, p ¼0. 096). As predicted, the correlation analysis showed that the shorter intervals were associated with larger CNV amplitude. This result is in line with the notion that the CNV amplitude in the reproduction stage indexes the level of preparedness.
Each point depicts the CNV/CMV amplitude per participant and per condition. amplitude ranging from 0. 3 s to 0. 6 s was calculated per condition for every participant, and we assessed to what extend the amplitude predicts the averaged time reproduction (see Fig. 9 ). In addition to iCNV amplitude, we also assessed the iCMV amplitude. The average iCMV and iCNV amplitudes are based on all MEG sensors and EEG electrodes that differed from the baseline. As for the CMV analysis, the iCMV data was based on absolute values to prevent that inflowing and outflowing magnetic fields cancel each other out. The iCNV or iCMV amplitude and condition were entered as predictors, subject was used as a random factor, and the average reproduced duration was entered as dependent variable. To assess whether the predictive value of the iCNV amplitude differs per condition, we compared a model that included iCNV amplitude and condition as main effects, and a model that included both main effects and the interaction.
While some studies have shown that the SMA is the main source contributing to CNV (e. g. ,  Gómez et al. , 2007) , the status of this structure with respect to magnetic activity has not been settled  (N'Diaye et al. , 2004). To this end, we replicated two classical studies that used the time reproduction paradigm using supra-second durations  (Elbert et al. , 1991 ; also see  Gibbons and Rammsayer (2004) ). Similarly to the previous work, we obtained a clear ramping activity in the EEG data for both perception (SI) and reproduction (RI) stages. CNV was obtained irrespective of whether trials were averaged in stimulus-locked or response-locked manner, and was maximal at fronto-central locations. These results are in line with the previous work on which this study was based  (Elbert et al. , 1991; Gibbons and Rammsayer, 2004).
With respect to the MEG data, a more diffuse picture emerged. No magnetic signal related to the encoding of temporal intervals was found. Nevertheless, the SMA and midfrontal structures were involved in the reproduction stage. Although the stimulus locked analysis showed signs of slow rising activity in the MEG data, these effects were absent in the response locked data. For the response locked data we found a magnetic field that starts rising approximately 0. 8 s before the key press terminating the timed interval, whereas the response locked CNV starts to build up earlier. As has been suggested by previous work (e. g. ,  Cheyne et al. , 2006)  this slow magnetic field (termed preMMF) is directly related to late movement preparation. The small effects present in the RI SL might therefore well reflect a smeared preMMF (a hypothesis supported by the similar source reconstructions of the RI SL and RI RL data), and should therefore be interpreted with caution.
(2004)  who did not find any activation in midfrontal areas. However, as we used a time reproduction task, the observed SMA activity could be partially driven by the response required to end the RI. This interpretation is further supported by the observation that the evidence for sustained activity in the SI stage was much weaker than in the RI stage. Unlike the MEG data, the EEG data provide a signal that is compatible for RI RL and RI SL averages. This compatibility is manifested by the similarity between RI RL and RI SL time courses and their topographies. Therefore, by means of the difference in morphology of the RI RL and RI SL plots for the EEG and MEG data, the CNV can be interpreted as the continuous signal related to timed interval, whereas the CMV/preMMF signal builds up only prior to the interval termination.
, 1999). What processes are reflected in the CNV, and to what extend they are linked to the conceptual models of interval timing, have recently become a subject of a lively debate (e. g. ,  Tamm et al. , 2014; Wittmann 2013; Wiener et al. , 2012; Van Rijn et al. , 2011). The notion of temporal accumulation predicts that longer intervals should be associated with larger amplitude of the CNV. Strikingly, the CNV amplitude in the RI SL and RI RL data was larger under the 2 s condition than under the 3 s and 4 s conditions. Although the larger amplitude in the 2 s interval in the RI SL data could be caused by the narrower distribution of responses under the 2 s condition, we also found enhanced amplitudes for the 2 s interval in the RI RL data, a finding that cannot be attributed to the wider distributions of time reproductions for longer intervals.
, 1991; cf. , Gibbons and Rammsayer, 2004). Elbert et al. (1991)  proposed that timing mechanisms for intervals longer than 3 s are different from the timing mechanisms for intervals shorter than 3 s. However, the results of the source localization of the MEG data and topographies of the EEG data do not support that view. Instead, our data suggest that reproduction of temporal intervals might differ in terms of timing of engagement and initiation of preparatory processes as reflected by the CNV. Importantly, our interpretation is in line with the foreperiod studies, demonstrating a decrease in the late component of the CNV amplitude for long intervals  (Müller-Gethmann et al. , 2003). In the time reproduction task, similarly to the foreperiod paradigm, expectancy can be affected by a decrease in the accuracy of internal timing signal as a temporal interval ages  (Gibbon, 1977; Gibbon, 1992; Gibbon et al.
, 1984). Thus this decreased accuracy of internal timing signal could influence the CNV amplitude via expectancy or changes of temporal uncertainty, both of which are expressed in the modulation of the amplitude of CNA  (Trillenberg et al. , 2000; Janssen and Shadlen, 2005). These effects, in turn, suggest that the late part of the CNV is not exclusively devoted to motor preparation, but it also exhibits a cognitive-based activity  (Baker et al. , 2012; Boehm et al. , 2014). Summarizing, these studies support the view that many different cognitive systems are involved in the timing of an interval (e. g. ,  Taatgen et al. , 2007; Gu et al. , (2015) ). Interestingly, a similar line of reasoning holds for the early component (iCNV). The electric data in the RI stage also showed larger amplitude for the iCNV, when participants reproduced the 2 s interval.
Acknowledgments This work has been supported by the European Project  COST ISCH Action TD0904  ( COST 286/09 ) " Time In Mental activity: Theoretical , behavioral, bioimaging, and clinical perspectives" (TIMELY: www. timely-cost. eu). We thank  Udo Boehm  for assistance with data acquisition,  Nele Russwinkel  for assistance with recruiting participants, and  Alexandre Gramfort  and  Robert Oostenveld  for the comments on beamforming technique.
© 2015 Rouanne et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Abstract Monitoring upper tropospheric-lower stratospheric (UTLS) secondary sulphate aerosols and their chemical and micro-physical properties from satellite nadir observations is crucial to better understand their formation and evolution processes and then to estimate their impact to the UTLS chemistry, and on regional and global radiative balance. Here we present a study aimed at the evaluation of the sensitivity of thermal infrared (TIR) satellite nadir observations to the chemical composition and the size distribution of idealized UTLS sulphate aerosol layers. The extinction properties of sulphuric acid/water droplets, for different sulphuric acid mixing ratios and temperatures, are systematically analysed. The extinction coefficients are derived by means of a Mie code, using refractive indexes taken from the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database and log-normal size distributions with different effective radii and number concentrations.
Back Close Full site for an optimal retrieval of their properties from satellite data. To do so, the extinction properties of sulphuric acid/water droplets, for different sulphuric acid mixing ratios and temperatures, are systematically analysed. The extinction coefficients are derived by means of a Mie code, using refractive indices taken from the GEISA (Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Spectroscopic Information) spectroscopic database and log-normal size distributions with different effective radii and number concentrations. IASI (Infrared Atmospheric Sounding Interferometer) pseudo-observations are generated using forward radiative transfer calculations performed with the 4A (Automatized Atmospheric Absorption Atlas) radiative transfer model, to estimate the impact of the absorption of idealized aerosol layers, at typical UTLS conditions, on the brightness temperature (BT) spectra observed by this simulated satellite instruments.
, 2002) , are excluded from this study. We have used temperature dependent refractive indices for different mixing ratios of the binary system H 2 SO 4 /H 2 O from the laboratory study of  Biermann et al. (2000). These data are available from the GEISA database  (Jacquinet-Husson et al. , 2008). Refractive indices are available for a wide array of temperatures, from 183 to 293 K, and H 2 SO 4 mixing ratios (in mass, i. e. the ratio of the H 2 SO 4 mass to the total mass of the droplets) ranging from 10 to 80 %, so covering tropospheric and stratospheric conditions. These refractive indices are measured in the region 500. 0-5000. 0 cm -1 , where major features of the absorption spectra of sulphate droplets-radiation can be found, i. e. , the OH stretching region (3200-3500 cm -1 ), bi-sulphate ion absorption  (1341, 1030, 1050, 885  and 593 cm -1 ) and sulphate ion absorption (1104 and 613 cm -1 ).
For higher concentrations, features of the undissolved H 2 SO 4 molecules (absorption at 1370, 1170, 965, 905 and 564 cm -1 ) appear as well. Please refer to  (Biermann et al. , 2000)  for more details on this dataset and to, e. g. ,  Miller et al. (2005)  and  Boer et al. (2007)  for a detailed description of the spectroscopic absorption vibration modes for the sulphate and bi-sulphate ions, and the sulphuric acid molecule. The size distributions for our simulated layers (i. e. , a function n(r) defined so that n(r)dr is the number of particles per unit volume, with radius between r and r + dr) are Figures.
1 µm, while a volcanically perturbed layer can be represented by a log-normal distribution with N 0 of a few tents particles cm -3 and a r m higher than 0. 2 µm  (SPARC, 2006). Thus, our database can represent several naturally occurring conditions. It must be noted here that multi-modal log-normal distributions have also been observed, especially in volcanically perturbed situations (see, e. g. ,  Deshler et al. , 2003) , but are not simulated in the present study. We plan in the future to extend our simulations to multi-modal log-normal size distributions. e. g. ,  Korolev et al. , 1999) : r e = r 3 n(r)dr r 2 n(r)dr (2) N e = r 2 n(r)dr 3 r 3 n(r)dr 2 (3) Because σ r is fixed in our simulations, it follows that:  4 ) r e = r m e 2. 5ln 2 σ r = 2. 619 r m ( N e = N 0 e -3. 0ln 2 σ r = 0.
g. ,  McCormick et al. , 1995; Deshler et al. , 2003). In Fig. 1 , as well as in the other figures of this section, the spectral discretization (available wavenumbers) originates from the spectral discretization of the refractive indexes in the database of  Biermann et al. (2000). While the absorption dominates the extinction in the TIR spectral region, the scattering component has increasing values for bigger particles and can reach values up to about 20 % in volcanic conditions. This result suggests that the scattering component of the extinction, even if relatively small with respect to the absorption, cannot be neglected. Correspondingly, in the following sections we study the extinction coefficient of the layer. The parameters of the aerosol layer are the H 2 SO 4 mixing ratio, the temperature and the size distribution.
Dependence of the extinction coefficient to the H 2 SO 4 mixing ratio and the temperature To study how the extinction of the layer varies with respect to the H 2 SO 4 mixing ratio and the temperature, we use the tabulated combinations of mixing ratios and temperatures in GEISA, as described in Sect. 2, and we fix the size distribution. We consider the two size distributions, background and volcanic, introduced in Sect. 3. 1. As said before, the absorption dominates over the scattering and then the extinction coefficient discussed in the following is mostly defined by the sulphate absorption spectral features, except for a small correction for layers with bigger particles (volcanic conditions). Figure  2  shows the extinction coefficient in the range 600 to 1400 cm -1 for sulphate aerosol layers at temperatures of 293 and 213 K, and different H 2 SO 4 mixing ratios, depending on the availability of mixing ratio/temperature combinations in the dataset.
The complete dataset, for all available temperatures in the interval 188-293 K can be found in Figs. 1 and 2 of the Supplement. In all cases, the extinction in volcanically-perturbed conditions is at least 50 times larger than in non-volcanic conditions, as a result of more (N 0 = 20 vs. 8 cm -3 ) and bigger (r m = 0. 3 vs. 0. 06 µm) particles. This suggests a strong sensitivity of the sulphate aerosols extinction to the size distribution, which is more thoroughly discussed in Sect. 3. 3. The H 2 SO 4 mixing ratio is a relatively sensitive parameter as well, as the variability of the extinction coefficient with respect to this parameter is over a factor 3 to 5, for each given temperature. The sulphate absorption region is located in this spectral range and is clearly visible. A minimum extinction occurs at 650-800 cm -1.
From a different perspective, this is beneficial for the retrieval of sulphate aerosol chemical and micro-physical parameters because this property limits the number of sensitive factors affecting the extinction spectra variability. The extinction, and more in particular the absorption signature, are very sensitive to the H 2 SO 4 mixing ratio of the binary solution H 2 SO 4 /H 2 O, both in background and volcanically-enhanced situations, paving the way to a retrieval. close values, 213 and 215 K, among those available in the database. This is intended to simulate typical tropical UTLS temperatures (see, e. g. ,  Lamsal et al. , 2004)  and typical mixing ratios within dehydrated atmospheric regions  (SPARC, 2006). Figure  3a  and b shows the extinction coefficient in the spectral range 600-1400 cm.
Interference with temperature and humidity profiles variability We consider here how uncertainties in the temperature and humidity profile may mask the sensitivity to sulphate aerosols. The TIGR (Thermodynamic Initial Guess Retrieval) database is a library of 2311 representative atmospheric situations (tropical, mid-latitude and polar) selected by statistical methods from an ensemble of 80 000 radiosonde measurements  (Chédin et al. , 1985; Chevallier et al. , 1998). We have calculated the average temperature and water vapour mixing ratio profile from TIGR. Only the tropical atmosphere situations (872 profiles) have been considered. and the average profiles with standard deviations. We then generate two datasets of 128 profiles each, by perturbing the average temperature (one set of 128 perturbed profiles) and water vapour profile (one set of 128 perturbed profiles) with a heightdependent Gaussian noise.
For each level, we added a random value from a Gaussian distribution of zero-mean-value and variable standard deviation, depending on the altitude level, to simulate the actual temperature and water vapour retrieval uncertainties of IASI, as estimated, e. g. , by  Pougatchev et al. (2009). In this latter work, temperature and water vapour profiles uncertainties are estimated by comparing a dataset of IASI temperature and water vapour profile retrievals with co-located radiosonde profiles. The standard deviations of the Gaussian perturbation are assumed between 0. 5 and 2. 0 K (temperature) and between 3 and 10 % (water vapour mixing ratio), as shown in Fig. 7a  and b , respectively. We then performed two sets of 128 4A/OP forward radiative transfer runs with: (1) the average temperature profile and the 128 perturbed water vapour profiles (H 2 O-perturbed runs), (2) the average water vapour profile and the 128 perturbed temperature profile (T-perturbed runs), and a further run with average temperature and water vapour profiles (baseline run for this analysis).
in Sect. 2. As done for Sects. 3. 3 and 3. 4, we restrict our attention to H 2 SO 4 mixing ratios of 60, 64, 70 and 75 % and temperature of the droplets of 213-215 K, within the set of tabulated values. As demonstrated in Sect. 3. 2, the extinction properties of the H 2 SO 4 /H 2 O droplets layers are very weakly dependent on the temperature. The chosen parameters are intended to mimic a realistic tropical UTLS situation, given the constrains of our dataset. The altitude of the sulphate aerosol layer has been fixed at about 150 hPa. A typical tropical atmosphere (temperature, humidity and gas concentration profiles) is selected from those available in 4A/OP. Then we conduct 256 simulations by varying the four H 2 SO 4 mixing ratio/temperature combinations and all the available effective number concentrations and effective radii (see Tables.
In this paper we have presented sensitivity analyses on the optical properties and BT signatures of secondary sulphate aerosols in the UTLS, based on TIR IASI pseudoobservations obtained with the 4A/OP radiative transfer model. We have modelled the sulphate aerosols as layers of H 2 O/H 2 SO 4 binary solution droplets with varying temperatures, mixing ratios and size distributions. The optical properties of these layers are obtained with a Mie code, taking as inputs the complex refractive indexes of H 2 O/H 2 SO 4 bynary solution with different temperatures and H 2 SO 4 mixing ratios from laboratory measurements and archived in the GEISA database, and size distribution with different effective radii and effective number concentrations.