dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, data from left hemisphere C3 electrodes during (ipsilateral) left-hand responses were averaged with data from right hemisphere C4 electrodes during (ipsilateral) right-hand responses (weighted average) to reflect the activity of the cortex involved in producing an incorrect response. These averaged ipsilateral responses were attributed to the C4 electrode for visualization purposes. We analyzed activity shortly preceding muscle activation in a time window from -100 msec to 50 msec relative to the EMG onset. To obtain a baseline-independent index of phasic activity, we calculated the slopes of neural activity with a customized Python script by fitting a linear regression to the data in the time window of interest. Finally, statistical analysis of the slopes was conducted using paired-samples t tests (temporal short vs.
Response Selection To examine the effects of temporal predictability on response selection, we measured the frontocentral negative activity (N-40 component), which occurs shortly before EMG onset in choice RT tasks. The N-40 has been found to be more pronounced (i. e. , more negative) for more difficult response choice demands  (Carbonnell et al. , 2013; Vidal et al. , 2003 Vidal et al. , , 2011)). We hypothesized that acting at predictable moments in time would facilitate response selection, which should be observed empirically as a smaller N-40 in temporal versus neutral cue conditions. We conducted peak analysis in the time window from -150 msec to 0 msec (time-locked to the EMG onset) on CSD-transformed data from the go trials. The N-40 activity was less negative following temporal than neutral cues, t(28) = 3.
Ammonia (NH 3 ) is the most abundant alkaline compound in the atmosphere, with consequences for the environment, human health, and radiative forcing. In urban environments, it is known to play a key role in the formation of secondary aerosols through its reactions with nitric and sulfuric acids. However, there are only a few studies about NH 3 in Mexico City. In this work, atmospheric NH 3 was measured over Mexico City between 2012 and 2020 by means of ground-based solar absorption spectroscopy using Fourier transform infrared (FTIR) spectrometers at two sites (urban and remote). Total columns of NH 3 were retrieved from the FTIR spectra and compared with data obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. The diurnal variability of NH 3 differs between the two FTIR stations and is strongly influenced by the urban sources.
In this work, the diurnal and seasonal variability of NH 3 over Mexico City is investigated using datasets from two ground-based FTIR spectrometers, including an extension of the FTIR-NH 3 total column time series of the station in Mexico City used in  Dammers et al. (2016) , and of the FTIR-NH 3 total columns measured at Altzomoni, a remote high-altitude station close to Mexico City, that are retrieved for the first time. The locations of these two sites are shown in Fig. 1. The analysis is complemented with IASI satellite observations over the region, and back-trajectories that were constructed for anomalous NH 3 columns detected at the urban site to assess the influence of local and remote sources. The FTIR spectra used for the retrieval of NH 3 were collected with the HgCdTe detector at both sites with a spectral resolution of 0.
(2015)  was used, comprising two microwindows (929. 1-931. 8 and 963. 7-970. 0 cm -1 ) to cover the NH 3 absorption lines from the υ 2 vibrational band in the mid-IR region. The trace gases H 2 O, CO 2 , O 3 , CO, and N 2 O were taken into account as interfering species for the retrieval for both stations, and the temperature and pressure profiles were obtained from the US National Centers for Environmental Prediction (NCEP). Spectroscopic parameters were obtained from the high-resolution transmission molecular absorption database  HITRAN 2008 Rothman et al. , 2009) , and the a priori profile information about the interfering gases was obtained from 40-year averages of simulations from the Whole Atmosphere Community Climate Model (WACCM)  (Eyring et al. , 2007; Marsh et al. , 2013). Since NH 3 profiles from WACCM are only representative of remote regions, scaled a priori profiles derived from 5 years of averaged NH 3 simulations from the global chemical transport model GEOS-Chem v11 were used instead.
IASI-NH 3 data product and comparison methodology IASI measures the infrared thermal radiation emitted by the Earth's surface and the atmosphere from a Sun-synchronous orbit on board the MetOp platform. Spectra are recorded in the 645-2760 cm -1 spectral range at a spectral resolution of 0. 5 cm -1  (Clerbaux et al. , 2009). The instrument crosses the Equator at mean local solar times of 09:30 LT and 21:30 LT providing global coverage of the Earth twice a day. IASI has a field-of-view composed of four circular footprints each with a diameter of 12 km at the nadir view and up to 20 km × 39 km elliptical pixels outside the nadir depending on the satellite viewing angle, complemented by scanning along a swath width of 2200 km off-nadir perpendicular to the ground track  (Clarisse et al. , 2009; Van Damme et al.
, 2010). For this study, 11 years of the IASI-A NH 3 total columns (ANNs for IASI (ANNI)-NH 3 -v3) between 2008-2018 were used; details of this version 3 can be found in Appendix A of  Van Damme et al. (2021) , and was also used by  Yamanouchi et al. (2021)  and  Viatte et al. (2022). The spatial distribution of NH 3 over Mexico City was obtained by averaging all the IASI-A morning observations between January 2008 and December 2018 over this region. The FTIR-NH 3 total columns at UNAM were compared against the IASI-NH 3 total columns over Mexico City to assess the agreement between both data sets. Due to the high spatiotemporal variability of NH 3 , the temporal and spatial coincidence criteria were tested and assessed using the correlations (both R and slope). In addition, as suggested in  Dammers et al.
(2016) , an elevation filter (FTIR station altitude minus IASI observation < 300 m) was applied. The criteria resulting in the best correlations were elevation filter < 300 m, spatial sampling difference < 20 km, maximum temporal sampling difference ±40 min, and maximum IASI-NH 3 retrieval error of 100 %. The seasonal variability comparison and annual averages were performed using only FTIR-NH 3 retrievals between 09:00 LT and 10:59 LT, corresponding to the IASI overpass time over Mexico City, and the < 20 km spatial criterion for IASI-NH 3 total columns. Altzomoni correlation plots with IASI-NH 3 data were not included due to the few coincidences between the FTIR and IASI.
The wind data used for the back-trajectories were derived from the NCEP North American Mesoscale (NAM) analysis product at 12 km and 1 h of spatial and temporal resolution, respectively (NCEPT NAM, 2015). The cluster analysis is an embedded routine in HYSPLIT and is based on the Ward's agglomerative hierarchical clustering algorithm  (Ward, 1963). Finally, the total spatial variance (TSV) method  (Draxler et al. , 2021) , included in HYSPLIT, was used to fit the number of clusters that represent the data.
Van Damme et al. (2021)  reported a trend from 2008 to 2018 of 2. 5 ± 1. 5 × 10 13 molecules cm -2 yr -1 for all of Mexico, which is closer to the Altzomoni value. The difference in magnitude can be attributed to the datasets and methodology as the present study uses ground-based FTIR measurements from two sites with higher values in 2019 and 2020, while  Van Damme et al. (2021)  used IASI satellite data over a wider region between 2008 to 2018. The average diurnal variability of the FTIR NH 3 total columns at both stations is displayed in Fig. 3. The largest average NH 3 columns at the urban station are on the order of 1. 50×10 16 molecules cm -2 and were observed during the morning and the evening. Although the diurnal pattern is not as evident as in other cities where motor vehicles have been found to be a dominant source of urban NH 3 (e.
8 ± 43. 9 % reported by  Dammers et al. (2016)  for this region using an older version of the IASI-NH 3 product. The correlation is also similar to that of  Tournadre et al. (2020) , who obtained R = 0. 79, when comparing IASI-NH 3 to FTIR-NH 3 columns using a similar instrument (Vertex 80) in Paris, and to the R = 0. 80 and MRD = -32. 4 ± 56. 3 % reported for 547 coincidences from several ground-based FTIR stations  and IASI-NH 3  (Dammers et al. , 2016). The ANNI-NH 3 -v3 product is thus in agreement with the ground-based data and even presents an improved correlation compared to the previous result. However, an underestimation in the IASI-NH 3 total columns of approximately 32 % over Mexico City persists. Dammers et al. (2017) , using an older version of the ANNI-NH 3 product, attributed these differences to a combination of more randomly distributed error sources and large systematic errors, however these reasons need to be investigated further.
9a ). The NH 3 seasonal variability over Mexico City in Fig. 9a  is similar for both the IASI and ground-based FTIR NH 3 columns and is in agreement with Fig. 4. However, IASI-NH 3 shows a consistent negative bias. The evolution with time is represented by the IASI-NH 3 and FTIR-NH 3 annual averages in Fig. 9b. The datasets suggest an increasing trend in the annual averages of the NH 3 to- tal columns, with larger columns observed in the most recent years, even in Altzomoni, except for 2013 which was affected by the 27 February event as discussed previously. The average annual increase of the IASI-NH 3 columns between 2008 and 2018 (using the < 20 km spatial criterion and the same Fourier fit as for the FTIR data) is 38 ± 7. 6 × 10 13 molecules cm -2 yr -1 for Mexico City. This is a larger positive trend than that of 2.
Conclusions This work presented the temporal and spatial distribution of NH 3 total columns over the Mexico City metropolitan area derived from two ground-based FTIR spectrometers and IASI satellite observations. The average NH 3 total column at the urban UNAM site (1. 46 ± 0. 64 × 10 16 molecules cm -2 ) is considerably higher than that at the remote station Altzomoni (1. 87±2. 40×10 15 molecules cm -2 ), with a clear difference in the diurnal cycle but similar seasonal variability. NH 3 spatial distribution from IASI shows the highest NH 3 columns in the northeast part of the city, an area surrounded by water bodies, a landfill, a compost plant for the treatment of the organic waste, and the airport. The IASI ANNI-NH 3 -v3 data product underestimates the NH 3 total columns over Mexico City, with a mean relative difference of 32 %, over the period 2008-2018 but showed a similar temporal variability and a good correlation with FTIR measurements (R = 0.
These observations, together with model data, will examine the role of reactive nitrogen in the pollution of Mexico City. A revaluation of NH 3 emission sources contribution in the Mexico City inventory is suggested. Measures to mitigate NH 3 emissions and reduce these positive trends are important, given that NH 3 is closely linked to secondary aerosol formation and the deterioration of ecosystems. Data availability. The UNAM and Altzomoni FTIR data used in this study are available at https://doi. org/10. 5281/zenodo. 7199948 (Herrera et al. , 2022)  Financial support. This research has been supported by the Consejo Nacional de Ciencia y Tecnología (CONACYT) (PhD grant no. 665043). Review statement. This paper was edited by Jayanarayanan Kuttippurath and reviewed by two anonymous referees.
The meteorological data for both FTIR stations are available at https://www. ruoa. unam. mx/csv_data/unam/minuto. php(RUOA, 2022a) and https://www. ruoa. unam. mx/csv_data/altz/minuto. php(RUOA, 2022b). The IASI ANNI-NH 3 -v3 L2 data are freely available and can be accessed through the AERIS database at https://iasi. aeris-data. fr/NH3/(AERIS,2022). MODIS active fire snapshots are available at https://wvs. earthdata. nasa. gov/(NASA,2022). The code of the HYSPLIT model can be obtained from https://www. arl. noaa. gov/hysplit/getrun-hysplit/(NOAA,2022). Last access to all URLs: 14 October 2022. Author contributions. BH is the main author of the paper, analysed the data, made most of the figures, and wrote the text. WS contributed to the data analysis. MG contributed to create some figures. AM contributed with the back-trajectory analysis.
MRI data acquisition. All acquisitions were performed on a 1. 5 T Aera MRI system (Siemens Healthcare, Erlangen, Germany) equipped with spine and body coils with 32 and 18 elements, respectively. The radial golden angle (GA) acquisitions were performed with a gradient echo sequence (GRE) without respiratory and ECG triggering. The acquisition parameters were: 220 × 220 mm 2 Field of View (FoV); 512 readout points accounting for an oversampling factor of 2; 3 mm-thick slice; 250 Hz/pixel readout bandwidth; 20 ms echo time (TE); 24. 14 ms repetition time (TR); flip angle (FA) optimized at 15° for the 3%-Agar gel and 10° for the in vivo experiment. Prior to ablation, a pre-scan of the radial GA GRE sequence was run for a few seconds to ensure motion detection via the micro coils' signals and localization of the catheter tip in the acquisition slice.
A magnitude image was reconstructed by NUFFT using gpuNUFFT  39  on an external computer in Matlab R2018b after TCP-IP transfer of the raw data using the Gadgetron Framework  40. Following this step, the GRE sequence was run for 8 min to acquire 20,000 radial projections to monitor the ablation procedure. The in vivo ablation presented in the main text was performed with the slice positioned in a sagittal orientation to minimize the out-of-plane motion due to respiration and the second ablation presented in Supporting Information was monitored in a short-axis orientation. Reconstruction of the temperature maps was performed retrospectively after the acquisition. The gradient echo-echo planar imaging (GE-EPI) phantom experiment was performed with the same 220 × 220 mm 2 FoV; 256 readout points including 2× oversampling and 128 phase encode steps with 6/8 Partial Fourier; one 3 mm-thick slice; 1502 Hz/pixel readout bandwidth; 20 ms TE; 1200 ms TR; 90° FA.
The matrix size of 128 and the acceleration factor of 2 with GRAPPA reconstruction led to an equivalent inter-echo delay of 0. 77 ms and a bandwidth of 10 Hz/pixel in the phase-encoded dimension. The use of factor-two zero filling led to a reconstructed in-plane pixel size of 0. 86 × 0. 86 mm 2. Data processing. The datasets were converted to ISMRMRD format  41  and a vendor-neutral processing was implemented in Matlab R2018a (Mathworks, Natick, MA) on a server with 40 CPU cores. The overall data processing is presented by a flowchart in Supporting Information Fig. 1  and detailed in the following section.
For the phantom experiment, a library of 10 images with 180 projections each was created based on the X motion curve. For the in vivo experiment, a library of 24 images corresponding to three cardiac and eight respiratory phases were created based on the respiratory and cardiac motion detected by the catheter micro-coils. This number of cardiac and respiratory phases was found optimal to minimize the susceptibility phase variations (data not shown) while maintaining a sufficient number of spokes per library images comprising between 80 and 172 spokes. For all the experiments a 6th order polynomial was used to create the A basis function correcting for B 0 drift. The temperature maps were obtained using the method of Gaur et al. 37  which fits a constrained model directly to undersampled k-space data without an image reconstruction step according to: where y j is one k-space data sample, i = 1 to N k indexes the acquired samples, N s is the number of image voxels, k i is the k-space location of sample i, and {b i } N b i=1 are the complex baseline library images.
The estimation of θ involves fitting the k-space data with two regularization parameters: λ controlling the sparsity of θ and β the regularization parameter of a second-order finite differentiating spatial roughness penalty  37, 44. The performance of this method was assessed for different temporal and spatial resolutions. Temperature maps were reconstructed with 20, 30, and 40 projections leading to temporal resolutions of: 0. 48, 0. 72, and 0. 97 s, respectively, at three in-plane spatial resolutions of 1 × 1 mm 2 , 2 × 2 mm 2 and 3 × 3 mm 2 obtained by truncation of the k-space readout. This set of parameters enabled model performance evaluation with acceleration factor ranging from 2. 88 to 17. 28. The uncertainty of the temperature measurements was computed by the mean and standard deviation of its temporal standard deviation in non-heated regions-of-interest (ROIs) after the training period and before heating.
The regularization parameter β was kept constant while λ was optimized for each spatial resolution by progressively decreasing its value as long as the calculated temperature remains constant allowing accurate temperature determination while minimizing temperature uncertainty. Finally, the temperature maps were filtered temporally using a low-pass Butterworth filter with a cutoff frequency of 0. 14 Hz  33. The vendor neutral Matlab® processing code is available at: https:// github. com/ maxim eYon/ Conti nuous_ Radial_ Moco_ Thermo. The phantom and in vivo datasets are available at: https:// doi. org/ 10. 5281/ zenodo. 49061 22.
2d, g , is also decreased from 1. 5 to 1 °C by the motion correction. Acceleration, temporal resolution, spatial resolution, and uncertainty of phantom temperature measurement. A comparison between three in-plane resolutions: 1 × 1, 2 × 2, and 3 × 3 mm 2 for three different temporal resolutions based on the same dataset is shown in Fig. 3. The spatial resolution of the thermometry map was decreased by truncation of the k-space readout, decreasing, in turn, the number of projections required to sample the entire k-space. The temporal resolution was tuned to 0. 48, 0. 72, and 0. 97 s by adjusting the number of projections used to compute the temperature maps to 20, 30, and 40, respectively. At a defined update rate of thermometry images the acceleration factor decreases with the decrease in spatial resolution.
We simulate a DCB with a length of 2 mm, with a pre-existing crack of 0. 4 mm length. e beam is taken to have a thickness of 1 mm, and to be loaded in plane stress. e beam is meshed using Gmsh 4. 8. 1  (Geuzaine and Remacle, 2009) , and read into Python with Meshio 5. 3. 0 (Schlömer, 2022) (as are all the nite element meshes in this work). e bulk material has Young's modulus E = 2. 7 × 10 3 MPa, a Poisson's ratio ν = 0. 39  (Doitrand et al. , 2021b) , and a mass density ρ = 1. 18 × 10 -3 g/mm 3 (Doitrand et al. , 2019). We use constant strain (T3) triangular elements in our nite element discretisation, with a consistent mass matrix. e cohesive zone parameters are the critical traction σ c = 45 MPa, the fracture energy G c = 0. 14 N/mm, and the critical distance δ c = 0. 0062 mm, while the coe cient of restitution e = 0.
Acknowledgements e authors thank  Aurelian Doitrand ,  Rafael Estevez  and  Dominique Leguillon  for kindly providing their data and  Yann Monerie  for fruitful discussions on the extrinsic cohesive model. is work was supported by Project  SMART-PROTECT , granted by the  Auvergne-Rhône-Alpes region. Code and Data availability e code required to run the simulations described in this paper is available in a GitHub repository archived on So ware Heritage, or alternatively on Zenodo. e data outputs of the codes are also available on Zenodo.
g. Section 6. 3. 1). While standard software such as Tobii PRO Lab allows visualisation of gaze trajectories over time such software lacks the kind of holistic visual workflow supporting all aspects of the exploratory sensemaking loop, from foraging through data, searching for evidence or schematising of knowledge to high-level sensemaking. Motivated by an observational study in which we collected control-room operators' gaze data while they perform their actual tasks, we present Visual Eye-Tracking Analytics (VETA), a system to visually explore and analyse eye-tracking data including, but not limited to, wearable eye tracking devices. This combines a number of distinct visual analytical features through a novel perspective of using eye-tracking data through spatial and temporal linked views, together with an immediate link between the data and the eye-tracking video, to allow for video-driven analytics (see Section 4).
Motivational use case: Network control room The design of VETA was motivated by the need for an efficient and interactive tool to analyse eye tracking data from a study 1  measuring the activities of operators in the Australian national energy network control room. Because of the complexity and duration of such activities, the data coming from such studies often contain noise and are hard to classify, and thus, difficult to handle using existing commercial analysis software (e. g. Tobii Pro Lab 2  ). In the control room there are 2-3 operators at any time, each of them has a workstation which is composed of 7 screens on two rows (see Fig. 4 ). The screens present numerous software applications simultaneously and the operators are able to organise these applications on any of their screens in a way that they believe maximises their understanding and interaction between those applications.
As part of the study we recorded the gaze of the operator using a wearable eye tracker (Tobii Pro Glasses 2 3  ). These wearable eye trackers have outward facing cameras that record video of what the operator was looking at, i. e. on their screens or any other elements of the environment. As per Tobii instructions, these were calibrated to 60 centimetres, which is similar to the distance that the operators typically sit away from the screens. They tend to work facing the screens most of the time. The study was designed to provide quantitative data to allow us to derive insights and recommendations for improvements to the control systems and the operators' workspace. More precisely, we expected to use the eye tracking data to achieve three outcomes. Outcome 1: To better comprehend the actions of operators during both routine activities and in response to exceptional events.
However, in the case of an unexpected event, operators regroup to collaboratively elaborate an appropriate response. Directly comparing between operator's gaze behaviour is not possible without understanding the wider context. AOIs for one participant are different from another, and can totally depend on the applications used and their location on the workspace. Whilst applications tend to stay in one place when they are open, they can be resized and new application windows may be opened to do a particular task. Through our analysis we found that although types of operator tasks may be determined by patterns of gaze and behaviour, operators' tasks need to be analysed individually. Identifying whether specific data patterns related to a particular task needed to be verified using the eye tracking video to determine which application was open on that operator's screen at the time of recording.