dataesr/scientific-paragraphs-categorization huggingface dataset:
5 cm) orthophotograph produced with photographs acquired during a dedicated helicopter flight on 4 June 2021, using structure-from-motion photogrammetry and the Agisoft Metashape (v. 1. 6. 5) software. The eruptive fissures were mapped in detail using the Planetscope 46 images and the very-high-resolution orthophotograph produced. The ground fissures were mapped from S1 and CSK co-and post-eruptive wrapped interferograms computed with MasTer  53, 54. Crater shape from SAR analysis SAR imagery from a variety of satellite-borne sensors (Extended Data Table  2 ) provide subdaily observations of the Nyiragongo summit and enable the tracking of the evolution of the crater collapse. Assuming that the crater has a nearly circular shape and constant rim elevation, the crater rim has an elliptical shape in the SAR image geometry.
Ash and SO 2 detection Space measurements of SO 2 and aerosols were processed in near real time during the crisis. The total mass of SO 2 from the TROPOspheric Monitoring Instrument (TROPOMI) hyperspectral sensor onboard the Sentinel-5 Precursor European Space Agency (ESA) satellite  60, 61  was estimated in an automated way within the SACS monitoring system  62. Daily overpass of TROPOMI over the Kivu region is at about 11:30 UTC. Here we assumed the height of the SO 2 plume was at 4 km. To retrieve the total SO 2 mass over the Virunga region, we used the same pixels selection as in ref. 19. Data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) broadband sensor, onboard the geostationary satellite MSG-4, was used to detect ash plumes with a temporal resolution of 15 min. Considering the eight thermal infrared channels of SEVIRI, the spectral significance of ash in the data was estimated for each SEVIRI pixel in the form of an ash index  63.
1%) and MnO (±4. 4%). Likewise, repeated analyses indicate the 1σ analytical precision to be within 5% for major elements, with the exception of Na 2 O (6. 6%). An additional 7 g of unheated material was used to make pressed powder pellets on which select trace elements were measured (V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Ba, Ce and Th). Trace element data determined by XRF are primarily used for comparison with inductively coupled plasma mass spectrometry, with the exception of Nb and Zr, for which XRF data are preferred. The quality of the trace element acquisition was estimated by repeated measurement of 11 international standards (GSD-10, GSD-9, JF-1, JR-1, Nim-D, Nim-G, Nim-L, Nim-N, Nim-P, STM-1 and SY-2). Accuracy is estimated to be better than ±10% for all elements except for Cr (±19%), Co (±20%), Ba (±19%) and Ce (±24%).
Acknowledgements Sentinel-1 SAR images and Sentinel-5P TROPOMI data are provided by  ESA. ALOS-2 SAR images are provided by  JAXA  under the terms and conditions of the Second Earth Observation Research Announcement (PI No. ER2A2N086 ). We acknowledge  S. Sobue  and  Y. Aoki  for assisting in the quick response of JAXA. CSK images were obtained from  ASI  through the  GEO GSNL Supersite initiative. We thank  M. Poland ,  S. Ebmeier  and  M. Bonano  for having helped unlock the delivery of CSK images to the  Virunga Supersite , and  C. Tinel  and  C. Proy  ( French Centre National d'Etude Spatiales (CNES) ) for facilitating coordination between the scientific response and space agencies participating in the  United Nation (UN) 'Space and Major Disasters' International Charter. TerraSAR-X SAR images were obtained from  DLR  through the  International Charter 'Space and Major Disasters' (© DLR  e.
Y. Morishita ,  P. Lundgren  and  F. Delgado  helped improve the processing and ionospheric corrections of the ALOS-2 interferograms. Deformation modelling benefited from the infrastructure and assistance of the Mésocentre from the  University of Clermont Auvergne , from funds from  French Government Laboratory of Excellence initiative  no. ANR-10-LABX-0006. This is Laboratory of Excellence ClerVolc contribution no. 548. This work is a contribution to the  EUROVOLC  project, under the  EU Horizon 2020 and Innovation Action , grant no. 731070. S. P. was supported by a  FRS-FNRS postdoctoral fellowship  at  Université libre de Bruxelles. C. W. acknowledges funding by the  National Science Foundation  (grant no. EAR 1923943 ).
Data availability Data used in this study are available for download from https://doi. org/10. 5281/zenodo. 6539427. The SAR imagery that supports the findings of this study is available from the space agencies and satellite operators (ESA/Copernicus, DLR, JAXA, ASI, CNES/Airbus, CSA, CONAE, ICEYE, Capella, Planet) but restrictions apply to the availability of some of these data, which were used under license for the current study, and so are not systematically available publicly. Data are, however, available from the authors upon reasonable request and with permission of the relevant data provider. Sentinel data are made available by ESA, https://scihub. copernicus. eu/. SAR images of Nyiragongo were accessed through the International Charter 'Space and Major Disasters' activation no. 713 https://disasterscharter.
, 2022d, 2022e, and 2022f, 2022e, and 2022f , hereafter Papers I, II, III, IV, V, and VI) on 2017 April 5-11. These observations consist of 5 days of SMA monitoring and 3 days of ALMA monitoring of the source for 3-10 hr each day. They constitute a uniquely long, homogeneously processed, high-cadence and high-S/N mm Sgr A * light-curve data set. We compare these observations with the historic data available at 230 GHz. During the 2017 EHT observations, Sgr A * was mostly in a low variability state, with slowly varying mm flux density of 2-3 Jy. However, on 2017 April 11, ALMA observations immediately followed a 5. 5 ks X-ray flare seen by Chandra, peaking at about 8. 8 UT (Paper II). The mm variability on that day was strongly enhanced, with the flux density growing by about 50% and reaching a maximum at about 10.
Observations and Data Reduction The EHT observed Sgr A * in April 2017 with a very long baseline interferometry (VLBI) array of eight stations at six distinct geographic locations (Event Horizon Telescope  Collaboration et al. 2019a Collaboration et al. , 2019b , hereafter M87 * Papers I and II). 149 A detailed analysis of the VLBI observations of Sgr A * on 2017 April 6 and 7 is presented in Papers I, II, III, IV, V, and VI. Two of the participating EHT stations are connected interferometers, formed by the coherent combination of their elements: SMA located on Maunakea (Hawai'i, USA), and ALMA located on the Chajnantor plateau (Atacama Desert, Chile). An advantage of using connected-element interferometers as EHT stations, besides the enhanced sensitivity of the resulting array, is that it is possible to compute the coherency matrices among their connected elements simultaneously with the summed signals that are recorded for their later use in VLBI  (Goddi et al.
2019). Therefore, as a by-product of EHT VLBI observations, we can make use of the connected-element visibilities to obtain Sgr A * light curves with long duration, high cadence, and high S/N. Apart from their standalone scientific value, the light-curve products are also employed downstream in the EHT VLBI data calibration; see Appendix A. Utilizing observations in the VLBI mode to produce Sgr A * light curves allows us to access the particularly long observing windows needed for the VLBI aperture synthesis, at the cost of using a phased array in a compact configuration with relatively low resolution and observing the source partly at an unusually low elevation. Since this is a nonstandard procedure that could be employed for similar observations in the future, in this paper we dedicate some additional effort to addressing the comparisons between data reduction pipelines and to recommending procedures for future VLBI observing campaigns.
The ALMA observations were carried out across four frequency subbands (spectral windows), each with a bandwidth of 2 GHz, centered at 213. 1 and 215. 1 GHz (B1 and B2, lower sideband) and 227. 1 and 229. 1 GHz (LO and HI, upper sideband). ALMA observed Sgr A * on 2017 April 6, 7, and 11, typically with ∼37 dishes of 12 m diameter in the phased array, with 4-10 hr tracks; see Table  1. The integration time used by the ALMA correlator was set to 4 s. Due to the array phasing requirements, ALMA observed in a compact configuration, with the longest projected baselines reaching 160 m on 2017 April 6, 278 m on 2017 April 7, and 374 m on 2017 April 11. The use of the VLBI phased array mode at ALMA has several implications for the data properties and calibration procedures, as compared to standard ALMA observations  (Matthews et al.
These limitations in the official quality assurance (QA2) calibration of the ALMA VLBI observations can be overcome with the development of independent calibration scripts, which have to handle the aforementioned peculiarities of the ALMA phasing system. In an attempt to further limit and characterize the influence of the systematic calibration errors on the analysis, we have corrected the limitations of the QA2 calibration of the Sgr A * observations using two independent procedures (A1 and A2), described in more detail in the following subsections, along with the SMA data reduction procedures. We consider the A1 pipeline to be the most selfconsistent and reliable, given the robust assumption of a lack of structural variability of a parsec-scale image on a timescale of several days, enabling the time-dependent self-calibration of the amplitude gains.
In Figure  1 , we show two model images of the field around Sgr A * ; the left panel corresponds to the original QA2 calibrated data (the initial condition for the iterative procedure) and shows the complete source structure (minispiral and Sgr A * ); the right panel is the final minispiral model obtained after the convergence of the intrafield calibration. The high noise level seen in the QA2 image (i. e. , the artifacts that are distributed across the whole field of view) is due to the effects of the time variability of Sgr A *. By modeling the source variability with Equation (1), the noise level in the final minispiral image (Figure  1 , right) is reduced. We notice that the size of the ALMA primary beam at 230 GHz is of the order of the size of the minispiral structure. This implies that different points across the image are affected by a different ALMA sensitivity.
Data corresponding to a source elevation below 25°, exhibiting significant quality loss, are flagged. The time-dependent factor, G t , can also be used to correct the amplitudes of the VLBI visibilities related to the phased ALMA. Based on  Martí-Vidal et al. (2016) , the scaling gain factor to correct ALMA amplitudes on VLBI baselines is G t. This approach has been employed for a priori amplitude calibration of the EHT VLBI Sgr A * data (see Appendix A and Paper II).
A2: SEFD-based ALMA Calibration A custom script was prepared to process the nonstandard array data acquired during the phased ALMA observations of Sgr A * using measurements of the system equivalent flux density (SEFD) of each ALMA antenna. While similar to the standard ALMA QA2 pipeline, it includes additional calibration steps necessary to produce the time-dependent light-curve data. The ALMA observations are grouped in scans, which consist of subscans of 18 s cadence, with 16 s of correlated data  (Goddi et al. 2019). A nearby phase calibrator, J1744-3116 (J1744-312), was observed for 30 s every 20 minutes. Observations of two bright quasars, NRAO 530 (J1733-1304, B1730-130) and J1924-2914 (B1921-293), were also included for the amplitude calibration; see Table  2. First, the phase delays associated with the atmospheric water vapor were estimated from measurements of the 183 GHz water line, performed with high time cadence using radiometers located at each ALMA antenna.
The radiometer measurements allowed us to estimate the column of water vapor above each ALMA antenna, which were then converted into a phase correction related to the atmospheric optical path. Conversion from the relative visibility correlation amplitude to a flux density scale was performed by applying the system temperature measurements performed routinely at each antenna. The corrected data were concatenated and reduced to produce a single CASA measurement set  (McMullin et al. 2007)  for each observing day, containing relevant data for all four subbands. The second step was the bandpass calibration of all of the frequency channels in each subband. We used NRAO 530 to generate the bandpass calibration tables, choosing a scan when the source was nearest to the zenith. The chosen reference antenna was located near the array center and not shadowed by neighboring antennas.
Lower-sensitivity channels near the edges of each subband, as well as data from shadowed antennas, were flagged. Following bandpass calibration, all channels within each subband were averaged. In the third step, we determined the amplitude scale of the observations on each day, and we applied the phase-referencing calibration. Since all three calibrators were observed as unresolved point sources, anomalously low amplitudes on some antennas were apparent. The few low-amplitude data Left: an image of the Sgr A * field obtained from the original QA2 calibrated data, using natural weighting and a Gaussian taper in Fourier space to boost the sensitivity to the extended (minispiral) structure. Right: a final image of the minispiral, after applying the intrafield calibration and removing the signal from Sgr A *.
Next, amplitude gains for individual antennas were derived using bright quasars, NRAO 530 and J1924-2914. Analysis of the gain solutions showed that the most significant trends are correlated with the elevation of the gain calibrator, consistently with known issues with antenna pointing on the SMA at very low elevations (∼15°; data corresponding to lower elevations were flagged). In light of this, gain amplitudes were interpolated using a third-order polynomial fitted based on the elevation of Sgr A * , with the median amplitude elevation-dependent correction below 25°being approximately 5%. Due to the rapid fluctuations in the instrumental phase arising from the real-time phasing loop used in VLBI beam forming, the first three integrations (≈30 s in total) were flagged whenever the telescopes moved onto Sgr A * from a calibrator source.
The remaining long-baseline data are mostly sensitive to the flux density from the unresolved Sgr A * point source; see Figure  2. Once phase self-calibration corrections were applied, an Sgr A * light curve was generated by taking the naturally weighted vector average of all baselines, for each spectral window observed by the SMA. The resultant light curves were evaluated for large fluctuations in amplitude, under the assumption that, over a 30 s interval, changes in the brightness of Sgr A * should be subdominant to the instrumental noise. Where fluctuations greater than 3σ were seen, the measurement in question was flagged, with the total volume of data flagged in this way amounting to ∼ 1%. Finally, to help improve the S/ N of the data, they were time-averaged over 62 s intervals (six integration steps).
As a comparison between the data sets, we compute the LNDCF(0) ≡ LNDCF 0 , presented in Table  3. The correlation is generally high between the two ALMA pipelines, A1 and A2, with values higher than 0. 8 on each individual day and band. It remains larger than 0. 7 if we consider full light curves formed by joining the individual days. Similarly, there is a rather high correlation between the SMA data set and the ALMA pipeline A1, reaching above 0. 75 in all cases. The correlation is less satisfactory between the A2 pipeline and the SMA, dropping below 0. 5 in some cases on 2017 April 6 and 11, but remaining high for the longest and most informative light curve from 2017 April 7. Some discrepancies between A2 and SM can be directly seen in Figure  3. Note that the ALMA-SMA correlation is calculated only in the short overlapping time of 2-3 hr, when Sgr A * is seen at low elevation by both ALMA (where it is setting) and the SMA (where it is rising), contributing additional difficulty to constraining systematic errors.
2015) , thus shifting the emission to near-IR and X-ray wavelengths and causing an inverted spectrum (i. e. , α < 0). As the electrons cool down radiatively, and subsequently the reconnection layer powering the flare depletes  (Ripperda et al. 2021) , the optically thin emission shifts back to mm and radio wavelengths (e. g. ,  Brinkerink et al. 2015) , and the source eventually settles back to the state before the flare. An elevated X-ray activity was also reported in Chandra observations on 2017 April 7 at 11-13 UT (Paper II). Here we see that this X-ray event was accompanied by a decreased spectral index period in our A1 pipeline data (as seen in Figure  6 ) and a total flux density decrease at 11-13 UT in the A1 pipeline and the SMA observations. This was then followed up by a flux density recovery seen in the SMA data around 14 UT (as seen in Figure  3 ).
In Table  7 , we give nonoverlapping values of ( ) 3 hr s m from all days and sites/pipelines (three nonoverlapping samples for ALMA 2017 April 7, a single 3 hr modulation index measurement for all of the other light curves). The measurements presented in Table  7  show a factor of 2 enhancement of the 3 hr modulation index on the X-ray flare day of 2017 April 11. On the remaining days, the modulation index varies between 0. 024 and 0. 051, while the damped random walk model fitted to all of the 2005-2019 data sets.
These uncertainties turn out to be generally larger than the statistical errors reported in the data sets, with values on the order of ∼0. 5% of the measured flux densities, or about 0. 01 Jy. A cross-correlation of the ALMA noise component extracted from the two independent calibration procedures shows that for all epochs and frequencies there is no correlation between them. This result allows us to conclude that the random noise is mainly due to the calibration-specific uncertainties.
Autocorrelations In the case of stationary signals, there is a unique relationship between the autocorrelation and the SF (see Section 5). Nevertheless, apart from the uncertainty in the stationarity assumption, studying autocorrelations separately offers a different perspective into the data. We study the signal autocorrelation using the LNDCF method  (Lehar et al. 1992) , Equation (4). A summary of these results is shown in Figure  9. 8 , and the value of twice the variance, where the SF is expected to asymptote for long lags in the case of stationary signals. The gray lines represent the SF calculated for the extracted noise component; its flat slope confirms the whitenoise characteristic of this component. Right: same as the left panel, but for the ALMA A2 pipeline HI band data (red lines).
2 L A1 HI 0. 23, 1. 2 ± 0. 4, >6 1. 6, 1. 2 L A2 B1 0. 26, 1. 5 ± 0. 3 1. 3, 1. 0 L A2 B2 0. 26, 1. 5 ± 0. 3 1. 3, 1. 0 L A2 LO 0. 3, 1. 4 ± 0. 4 1. 3, 1. 1 L A2 HI 0. 3, 1. 4 ± 0. 4 1. 3, 1. 1 L Note. The first reported slope corresponds to lags shorter than the first reported timescale, and so on. LO 3. 3 ± 0. 1 1. 1 L SM HI 3. 3 ± 0. 1 1. 1 L theoretical framework, we could expect lags of 1-2 minutes between the HI and B1 bands, easily detectable with the cadence of the ALMA data. However, no indication of a correlation lag between the bands in any of our data sets is found, with the uncertainty no larger than 20 s. The crosscorrelation function very clearly peaks at zero for all days and for both ALMA reduction pipelines, as shown in Figure  10. We interpret this lack of detectable delays as a signature that the emission region in the 213-229 GHz range is already optically thin all the way to the horizon, possibly with patches of higher optical depth material formed in the turbulent accretion flow, necessary to explain the intermediate spectral index α ≈ 0 (identified in Section 3.
The reason is the corruption related to the sampling window. To study whether the stochastic model can reproduce the data periodogram, we need to incorporate the real data sampling into the discussion. Hence, we take a Monte Carlo  approach, similar to the procedure employed by  Haggard et al. (2019). We generate 5 × 10 4 realizations of the light curves from the best-fitting models, sampled with the original sampling windows of the ALMA A1 data on 2017 April 6, 7, and 11. We use astroML  (Vanderplas et al. 2012)  for the DRW and Stheno for the Matérn process light-curve generation. We then calculate the L-S periodogram for each synthetic light curve with Astropy (Astropy Collaboration et al. 2018) and compare the results with the L-S periodograms calculated for the actual Sgr A * light-curve data sets.
Summary and Discussion We have developed algorithms to generate light-curve data from observations with phased interferometric arrays, enabling simultaneous participation in VLBI observations. We apply them here and present the high-cadence and high-S/N 1. 3 mm light curves of Sgr A * obtained during the EHT observing campaign in 2017 April with ALMA and SMA. There are several noteworthy conclusions: 1. With the very high S/N of ALMA, thermal noise is not limiting in the analysis. However, significant systematic uncertainties related to the data calibration persist. We elucidate that issue by comparing three independent data reduction pipelines (two for ALMA, one for SMA). While we show general consistency between them, some results, such as the GP correlation timescales (τ) or the presence of SF break timescales, are sensitive to the pipeline choice.
The statistical PSD properties of the 2017 April 11 observations are inconsistent with those of the GP models fitted to the Sgr A * light-curve data sets. 3. We measure the average spectral index at 220 GHz to be α = 0. 0 ± 0. 1, where the uncertainties are dominated by the calibration systematics and by the rapid time variability of the spectral index, wandering between ±0. 2 on a timescale of ∼1 hr. The spectral index immediately following the X-ray flare of 2017 April 11 is significantly lower, -0. 25 ± 0. 10. 4. No statistically significant autocorrelations are found. If detected, these persistent correlations could be attributed to the presence of the photon shell in the strongly curved spacetime around the black hole. They continue to be expected if sufficiently long observations are aggregated, e.
We thank  Yuhei Iwata ,  Lena Murchikova ,  Rebecca Phillipson , and  Chris White  for comments and discussions, as well as  Alexandra Elbakyan  for her contributions to the open science initiative. We also thank the anonymous ApJL referee for helpful and constructive comments. The Event Horizon Telescope Collaboration thanks the following organizations and programs:  National Science Foundation  (awards  OISE-1743747 ,  AST-1816420 ,  AST-1716536 ,  AST-1440254 ,  AST-1935980 ); the  Black Hole Initiative , which is funded by grants from the  John Templeton Foundation  and the  Gordon and Betty Moore Foundation  (although the opinions expressed in this work are those of the author(s) and do not necessarily reflect the views of these foundations);  NASA   Hubble Fellowship  grant  HST-HF2-51431.