dataesr/scientific-paragraphs-categorization huggingface dataset:
The average amplitude of the high-state in the LFP is of (206 ± 26) µV for the spiking case and (209 ± 2) µV for the mean-field case. The average duration of the high-state in the LFP is (0. 59 ± 0. 14) s for the method applied to the spiking network and (0. 57 ± 0. 09) s for the meanfield case. The duration is measured as the time between the beginning and the end of the high state, where the criteria for the starting and ending points is defined as the crossing point of 3 standard deviations from the base state. The second example we present consists in a large-scale simulation of the human brain, performed with the TVB simulator platform. In this simulation each region of the brain is represented by one neuronal ensemble of AdEx neurons (as described in the first example). The connection between regions was defined by human tractography data (https:// zenodo.
org/record/4263723,Berlinsubjects/QL_20120814) from the Berlin empirical data processing pipeline  (Schirner et al. , 2015; Goldman et al. , 2021). A parcellation of 68 regions is used, with long-range excitatory connections and delays defined by 120 tract length and weight estimates in human diffusion magnetic resonance imaging (dMRI) data  (Sanz-Leon et al. , 2015; Goldman et al. , 2021). The LFP is calculated using the kernel-method, for which it has been incorporated in the TVB platform. For details on the connectome and the simulations with the TVB see Sanz  Leon et al. (2013) ,  Schirner et al. (2015) ,  Sanz-Leon et al. (2015) , and  Goldman et al. (2021). The results  are shown in Figure  3 , where we show only the LFP for the Surface depth. All the simulations presented in the paper were performed using the python language and the TVB simulation platform for whole brain simulations.
, 2016b; Hill et al. , 2017; Soberon and Nakamura, 2009). Particularly for invasive insects, human-associated factors, e. g. , urbanisation and general human ecological footprint, have ranked among the most important drivers of range expansion  (Gallardo et al. , 2015; Sanderson et al. , 2002). Thus, our objective here is to predict the current and future areas at-risk of invasion by the invasive bee M. sculpturalis. Existing studies on the distribution of M. sculpturalis were limited to climate predictors only  (Hinojosa-Diaz et al. , 2005; Polidori and Sánchez-Fernández, 2020) , which make them unsuitable to estimate the areas at-risk of invasion, because they miss important habitat predictors as well as socio-economic correlates of invasion risks. We address all these points here. In addition, we compiled a dataset of 2000 global occurrences for this species, a 3-fold increase including data of its current range expansion compared to a former study of  Polidori and Sánchez-Fernández (2020).
, 2015; Tonietto and Ascher, 2008; Wolf and Ascher, 2008)  and natural history platforms (GBIF, observations. org, naturgucker. de, Le Monde des Insectes, Spipoll, Faune-France, INPN Espèces). Data collection for modelling was terminated on 30th of October 2020. Wild bee experts verified each occurrence based on pictures or videos, museum specimens and collected specimens. We aggregated the occurrence data at the resolution of the environmental data as bioclimatic variables have a resolution of 10 km and due to the degree of uncertainty of several data points derived from public platforms (i. e. , 10 km spatial thinning;  Steen et al. , 2021; Vollering et al. , 2019) ; keeping one occurrence per pixel of the environmental variables to avoid duplicate records resulting in biased outputs towards oversampled regions.
Environmental data We included bioclimatic variables at 5 arc-minutes (approximately 10 km) resolution of the current climate data and of the 2070 projections from Chelsea version 1. 2  (Karger et al. , 2017). Variable sub-setting is detailed in a following paragraph. We used four General Circulation Models (GCMsi. e. , GFDL-ESM2M, IPSL-CM5A-LR, MIROC5, NorESM1-M) and four socio-economic pathway scenarios (SSP1/RCP2. 6; SSP2/RCP4. 5; SSP4/RCP6. 0; SSP5/RCP8. 5) to construct future scenario models. For anthropogenic variables and major vegetation forms, we used data from the Land Use Harmonization dataset version 2 at a 0. 05°resolution  (Chen et al. , 2020). We included the habitat categories, corresponding to vegetation cover, that are found in temperate climates, which were the most relevant to the biology of this species (Tables  1  and 2 ).
For future projections of landuse, we used the same GCMs, SSPs and time horizons as for climate data. To account for the risks of introduction and spread in our model predictions, we used anthropogenic predictors that we assumed to be related to the propagule pressure and spread of this species. Because the species was most likely accidentally introduced with the transport of goods, we used three categories of drivers of propagule pressure (number of individuals released to new environment) and factors of spread. First, we used distance to port and airports as a proxy for propagule pressure. Port data was downloaded from the World Port Index (https://msi. nga. mil/Publications/ WPI, accessed December 2020) and airport data was downloaded from the OpenFlights Airport database (https://openflights.
org/data. html, accessed December 2020). Second, we used distance to main roads and highways as an indicator of potential spread facilitation based on former studies identifying roads as used pathways for invasive species  (Gippet et al. , 2019). We calculated distance from roads using the Global Roads Inventory Project (GRIP4) dataset  (Meijer et al. , 2018). We selected the first two categories regarding the size of roads (highways, primary roads). For both distance to ports and airports, and distance to roads, we could not include future scenarios of change as such data are unavailablehence our future scenarios do not account for potential changes in these variables. Third, we used human population density as a proxy for both introduction and expansion risks, as observations suggest that M.
Species distribution modelling (SDM) We modelled and projected species distributions with the Biomod2 R package  (Thuiller et al. , 2009) , using 10 modelling techniques: generalised linear model (GLM), generalised additive model (GAM), classification tree analysis (CTA), artificial neural network (ANN), surface range envelop (SRE), flexible discriminant analysis (FDA) and random forest (RF), Multiple Adaptive Regression Splines (MARS), Generalised Boosting Model (GBM) and Maximum Entropy (MaxEnt). For all models, we generated five different sets of 10,000 pseudo-absences down-weighted for a 0. 5 prevalence  (Barbet-Massin et al. , 2012). The calibration area is the world, excluding Antarctica (we used a 5-fold block cross validation, with four folds used for calibration and one for evaluation; see below).
To select the predictors best explaining the distribution of M. sculpturalis, we applied a variable selection protocol  (Bellard et al. , 2016a (Bellard et al. , , 2016b;; Leroy et al. , 2014). We tested for correlations among all predictors and selected one variable per group of inter-correlated variables to avoid collinearity (Pearson's r > 0. 7) using the removeCollinearity function of the virtualspecies R package  (Leroy et al. , 2016). We used the variable importance procedure as implemented in biomod2 to select the best predictors. This procedure evaluates the importance of each variable by randomising its values (i. e. , permutation) and comparing the correlation in model predictions between non-randomised and randomised runs. We analysed the importance of all uncorrelated variables (i.
, 2006). We evaluated our models with a block-cross validation procedure to reduce autocorrelation between calibration and evaluation datasets, using the blockCV R package  (Roberts et al. , 2017; Valavi et al. , 2018). We chose a five-fold cross-validation (i. e. , spatial partitioning); for each fold we used 80% of data for calibration and 20% for evaluation (each presence point was evaluated once). We chose a block size of 500 km for the block cross-validation as a trade-off between the autocorrelation range of variables and balanced folds. We assessed the reliability of our models with a procedure adequate for presence-only models, the Boyce index  (Hirzel et al. , 2006; Leroy et al. , 2018). A value of 1 indicates projections which perfectly predicted the presence points, a value of 0 indicates that projections were not better than random, and a value of -1 indicates a counter prediction.
To estimate the area already invaded, we calculated the extent of invasive occurrence within each non-native region (Europe, North America). We computed the extent of occurrence using alpha hull polygons with the SpatialEco R package (version 1. 3-6; Evans and Murphy, 2021; Fig. S2 ). We recovered the distribution of suitability scores for both non-native regions (Europe and North America). We also provided the proportion of total suitability inside the non-native area relative to the total suitability scores. We preferred to consider the total suitability scores rather than values beyond a given threshold of suitability because the latter suggests binary transformations, which are generally misleading and inappropriate for presence-only data  (Muscatello et al. , 2021). In addition, we tested for niche overlaps computing a PCA approach between native and non-native American and native and non-native European ranges based on climate variables solely (bio13, bio15 and bio19) following the methodology proposed by  Warren et al.
Acknowledgements We sincerely thank all participants of our citizen science programs and our colleagues  Katharina Huchler ,  Philipp Meyer ,  Jovana Raičević ,  Milan Plećaš  for sharing occurrence data. This study was partly funded by the  DRF Pollinéco n°2058 (France) , which provided support in the form of salaries for the author VLF. Further it was partly funded by the  Austrian Academy of Science  as the author JL is a recipient of the  DOC fellowship program. Both funding sources did not have any additional role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. Open access funding provided by  University of Natural Resources and Life Sciences Vienna.
We can scale a correlation coefficient r i-j measured on a baseline ij in units of thermal noise to a visibility V i-j in units of Jy via ( ) = - - V r SEFD SEFD. 2 i j i j i j We have performed this flux density calibration with a common framework 164  for both the CASA and HOPS data. Compared to the SEFDs used to calibrate the data released for M87 * and 3C 279 (M87 * Paper III), we have updated several a priori calibration parameters. We are using the  Butler (2012)  planet models (updated for the year 2017) for the gain calibration of the APEX, PV, and SMT telescopes, instead of the previously used GILDAS 165  models. Additionally, we have used 10 1 instead of 8 5 as an updated estimate for the beam size of the LMT and subsequently fitted for the variable station gain on a per-day basis (Table  1 ).
To overcome this limitation, we calibrate the Sgr A * ALMA gains with the source's light curve, which is computed under the assumption that the flux density distribution of the minispiral remains stable across the extent of the observations  (Wielgus et al. 2022). We note that, even though the minispiral modeling allows us to calibrate relative ALMA gains at the ∼ 1% level, a constant ∼ 10% calibration uncertainty remains for the overall gain of the phased array, which is tied to the estimated flux density of Ganymede (the source used as primary calibrator;  Goddi et al. 2019). We perform a time-dependent variant of the network calibration (M87 * Paper III) of the EHT VLBI data to a merged light curve from ALMA and the SMA (presented alongside the MWL light curves in Section 4). The ALMA light curve is computed through the minispiral method described above, and a description of the SMA light curve is given in  Wielgus et al.
For the Butler (2012) models, we have a 10% uncertainty for Saturn and 5% for Mars, Jupiter, and Uranus. The error contribution from T sys * measurements is negligible. Finally, ALMA and SMA exhibit an additional calibration uncertainty from the phasing efficiency. The performance of ALMA has been determined in the quality assurance stage 2 (QA2) ALMA interferometric reduction of data  (Goddi et al. 2019). The final calibration method is described in Section 3. 2. 1, for which we have estimated a global 10% flux density calibration uncertainty. The small SMA dishes are well characterized. Here the phasing efficiency dominates the overall calibration uncertainty, which ranges between 5% in optimal observing conditions and 15% when the phasing efficiency is low. The a priori flux calibration parameters are summarized in Table  3.
The systematic error budgets derived for Sgr A * , NRAO 530, and J1924-2914 are shown in Table  4. These are estimated using only data on days when ALMA was observing, i. e. , 2017 April 6, 7, and 11. However, we exclude Sgr A * data from 2017 April 11 owing to flaring activity-these will be the subject of a forthcoming study (see also  Wielgus et al. 2022). The underlying distributions of the various data quantities, with and without added systematic error, are shown in Figure  5. From the combined data on all baselines, an excess in systematic errors of RR -LL closure quantities can be seen for Sgr A * compared to the observed NRAO 530 and J1924-291 calibrator sources. These offsets may be indicative of intrinsic source polarization in Sgr A *. However, the overall offsets are just at the level of thermal noise for scan-averaged data.
It is likely for source polarization to be significant on particular baselines only. Horizon-scale polarization signatures in the 2017 Sgr A * EHT data will be analyzed in future work. The systematic error budgets agree between the traditional closure quantities and the novel closure traces. It is therefore unlikely for large-scale source structure and uncorrected polarization leakage effects to be the dominant sources of systematic uncertainties. There are several EHT data issues affecting Sgr A * observations that require special care. These are described in detail in M87 * Paper III and briefly summarized below. 1. The JCMT and SMA used an identical frequency setup derived from a shared frequency standard in 2017. Radio frequency interference (RFI) is not washed out due to low fringe rate.
4. High-band SMA SEFDs on the first three observing days have been up-scaled to correct for occasionally corrupted frequency channels in the beamformer system. 5. A negligible <0. 1% amplitude loss due to a periodic ALMA correlator glitch occurring every 18. 192 s was left uncorrected. Moreover, a baseline-dependent ∼0. 67% signal loss due to finite fast Fourier transform lengths over the full 2048 MHz band on non-ALMA baselines has not been corrected. The systematic error studies presented in Figure  5  and Table  4  reflect a comparable level of data quality and internal selfconsistency for the CASA and HOPS data products. All data distributions are well behaved, and the different amounts of estimated systematic noise are balanced, being sometimes higher in one or the other data set. Small differences in the number of recorded visibilities are due to the effects of thresholding near the detection limit, as well as slight differences in flagging between the two pipelines.
Global 3 mm VLBI Array VLBI observations of Sgr A * at 86 GHz were conducted on 2017 April 3 with the Global Millimeter VLBI Array (GMVA). 168 Eight Very Long Baseline Array antennas equipped with 86 GHz receivers, the Robert C. Byrd Green Bank Telescope (GBT), the Yebes 40 m telescope, the Effelsberg 100 m telescope, PV, and 37 phased-ALMA antennas participated in the observation (project code MB007, published in  Issaoun et al. 2019b). The data were recorded with a bandwidth of 256 MHz for each polarization, and fringes were detected out to 2. 3 Gλ. The total on-source integration time on Sgr A * was 5. 76 hr over a 12 hr track, with ALMA co-observing for 8 hr. The results of the experiment rule out jet-dominated radio emission models of Sgr A * with large viewing angles (> 20°) and provide stringent constraints on the amount of refractive noise added by the interstellar scattering screen toward the source  (Issaoun et al.
Neil Gehrels Swift Observatory Observations from the Swift X-ray Telescope (XRT;  Gehrels et al. 2004; Burrows et al. 2005)  were reprocessed with the latest calibration database files and the Swift tools contained in. footnote https://heasarc. gsfc. nasa. gov/lheasoft/. Source flux in the 2-10 keV energy band is extracted from a 10″radius circular region centered on the position of Sgr A *. Count rates are reported as measured (e. g. ,  Degenaar et al. 2013) , i. e. , without any correction for the known significant absorption along the Sgr A * sight line (N H ∼ 9 × 10 22 cm -2 ). There are 48 Swift observations of the Galactic center between 2017 April 5 and 12, with a total exposure time of 26. 3 ks (Figure  2 ). These observations include a dedicated dense sampling schedule to coincide with the EHT observing window and two observations from the regular Galactic center monitoring program  (Degenaar et al.
Chandra X-Ray Observatory A series of Chandra X-ray Observatory  (Weisskopf et al. 2002)  exposures of Sgr A * were acquired on 2017 April 6, 7, 11, and 12 using the ACIS-S3 chip in FAINT mode with a 1/8 subarray  (observations IDs 19726, 19727, 20041, 20040 ; PI: Garmire), for a total of ∼133 ks coordinated with the EHT campaign (Figure  2 ). The small subarray mitigates photon pileup during bright Sgr A * flares, as well as contamination from the magnetar SGR J1745-2900, which peaked at X-ray wavelengths in 2013 and has faded over the more than 6 yr since  (Mori et al. 2013; Rea et al. 2013 Rea et al. , 2020;; Coti Zelati et al. 2015 , 2017). It also achieves a frame rate of 0. 44 s versus Chandra's standard rate of 3. 2 s. Chandra data reduction and analysis are performed with the CIAO v4.
9 ks and were coordinated with the EHT campaign. We reduced the data using the NuSTAR Data Analysis Software NuSTARDAS-v. 1. 6. 0 170  and HEASOFTv. 6. 19, filtered for periods of high instrumental background due to South Atlantic Anomaly (SAA) passages and known bad detector pixels. Photon arrival times were corrected for onboard clock drift and processed to the solar system barycenter using the JPL-DE200 ephemeris. We used a source extraction region with 50″ radius centered on the radio position of Sgr A * and extracted 3-79 keV light curves in 100 s bins with dead time, point-spread function, and vignetting effects corrected (see  Zhang et al. 2017 , for further details on NuSTAR Sgr A * data reduction). For all three observations we made use of the data obtained by both focal plane modules FPMA and FPMB.
The substructure manifests in the visibility domain as "refractive noise," which is an additive complex noise component with broad correlation structure across baselines and time  (Johnson & Narayan 2016). Using observations of Sgr A * from 1. 3 mm to 30 cm,  Johnson et al. (2018)  have shown that the combined image broadening and substructure strongly constrain the power spectrum of density fluctuations, which is consistent with later observations that occurred close to the 2017 EHT observations of Sgr A * described in this work (Sections 4. 1. 1 and 4. 1. 2) and in  Issaoun et al. (2019b Issaoun et al. ( , 2021) )  and  Cho et al. (2022). Calibrated EHT data sets indicate that the intrinsic structure is dominant in measured visibilities and that both diffractive and refractive scattering effects are limited, as anticipated from the empirically obtained scattering model and early EHT observations.
1 0 cpt SMT LMT LMT 2 SMT 2 LMT SMT    In the above equation, the equality is satisfied in the extreme situation when the source is completely unresolved at the LMT-SMT baseline, whereas in reality it partially resolves the source structure. A stronger constraint is given by the equivalent circular Gaussian with the minimum source size (Constraint 1) extrapolated from the LMT-SMT baselines, described by | | ( ) ( | | ) - p q F e. 1 1 cpt LMT SMT u cpt 2 4 ln 2   We note that the upper and lower limits on the source size obtained from Constraints 1 and 2 are along the directions of LMT-SMT and LMT-ALMA baselines. Using synthetic data from general relativistic magnetohydrodynamics (GRMHD) and semianalytic geometric models in Paper III, we have verified that these size constraints remain valid for various morphologies sharing similar profiles of visibility amplitudes with EHT Sgr A * data (see Paper III for details).
On 2017 April 11 the millimeter flux rises by over 50%, following the X-ray flare maximum with a delay of ∼2 hr. The data on this day appear significantly more variable. An analysis of the 2017 April 11 VLBI data will be presented in a separate future publication. A detailed analysis of the full data set of Sgr A * light curves contemporaneous with the EHT 2017 observations is presented in the companion paper  Wielgus et al. (2022). The -metric  (Roelofs et al. 2017)  quantifies the variability of a compact source resolved on VLBI baselines in a series of closure phases as a function of time on a specific baseline triangle. The metric compares the observed closure phase variations (ŝ 2 ) to those expected from thermal noise (˜2  ), after detrending the data to account for slow variations due to the evolution of baselines as Earth rotates (see  Roelofs et al.
5. Scripts, spectral, and response files for modeling Chandra and NuSTAR data (format: standard X-ray data formats). 6. Sampled posterior distributions of X-ray spectral model based on Chandra and NuSTAR data (format: FITS). The EHT data portal also contains the broadband spectrum presented in Table  2  (see also Section 5. 2. 2 below) with frequency, flux density, its uncertainty, and instrument index (format: CSV). 2. The quiescent and flare X-ray luminosities in the SED are νL ν in units of erg s -1 ; their equivalent νF ν values in erg s -1 cm -2 (Table  2 ) differ slightly from the integrated flux values in Table  5.
Summary and Conclusions Sgr A * and M87 * are the primary targets of the EHT, for which the 2017 April observing campaign provided the required resolution and sensitivity to obtain horizon-scale images of these SMBHs. M87 * total-intensity and polarization results from the 2017 data have been published earlier (M87 * Papers I-VIII). In this new series of papers (Papers I-VI), we present the imaging, analysis, and interpretation of the 2017 Sgr A * EHT and accompanying data at X-ray, NIR, and millimeter wavelengths from EAVN, GMVA, VLT/NACO, Swift, Chandra, and NuSTAR. Sgr A * was observed by the EHT on 2017  April 5, 6, 7, 10, and 11. In this series of papers, we focus our analysis on the days with the best (u, v)-coverage, 2017 April 6 and 7; a detailed analysis of the full set of Sgr A * millimeter light curves can be found in the companion paper by  Wielgus et al.