dataesr/scientific-paragraphs-categorization huggingface dataset:
(2021) , all the three-component templates and the continuous data streams ground velocities of each analyzed station were first band-pass filtered in the range 1-35 Hz, and then differentiated to obtain their ground acceleration to amplify the peak frequency of the signals. For each template, a cross-correlation window of 1. 2 s was selected around the first P-wave arrival on the vertical component (CHZ) and around the S-wave arrival on the horizontal components: CH1, CH2 for SARCL and CHE, CHN for SPINS. Finally, only the signals that simultaneously fulfilled the following two main conditions were detected: (1) the obtained cross-correlation parameter (XC') between the master template and the station component data stream was greater than 0. 5; (2) the previous condition was satisfied on the vertical component and at least on one of the two horizontal components.
We set a relatively low value of the detection threshold (XC' ¼ 0. 5) as we wanted to maximize the number of detected events. However, these led to a high number of false detected events which were manually dismissed by visual inspection performed by expert seismologists (e. g. Stabile et al. 2021). The procedure enabled us to increase our manually detected dataset of more than a hundred events (see Section 4) and to obtain a total number of 202 detected earthquakes in the analyzed timespan (July-October 2020). For the P-and S-waves arrival time pickings, we applied a completely automated approach through the application of the PhaseNet algorithm  (Zhu and Beroza 2018). PhaseNet is a deep neural network algorithm, which allows to perform seismic phase picking without any hand-designed input parameter.
Indeed, thanks to their multilayer neural networks structures, deep-learning algorithms are capable of 'understanding' data in a hierarchical way, avoiding the need for human operators to formally specify the training features that machines require to 'learn' efficiently. For each earthquake, three minutes-long waveforms recorded on the three-components of all the stations of the virtual network, bandpass filtered in the range 2-40 Hz, have been given in input to PhaseNet, starting 60 s before the detection time (obtained from the single-station template matching algorithm), and ending 120 s later; for each record, a 30-seconds sliding window was chosen for the analysis. The P-wave, S-wave and noise probability distributions are calculated in output from the deep-learning algorithm, which defines the corresponding P-and S-wave arrival times (Figure  3 ) where the resulting probability distribution peak values overcome a certain threshold.
We set a maximum distance between linked pair of seismic events equal to 2 km (WDCC and WDCT parameters), and a threshold value for phase residuals equal to 0. 1 s (WRCC and WRCT parameters). Relative locations were performed by linking each event to a maximum of 20 other events of the cluster (MAXNGH parameter), thus obtaining a total number of 14382 differential arrival times of P-and S-waves, the latter being computed both using catalog data (CT) and waveforms cross-correlation (CC) computed in the frequency domain using the CCHAR program  (Rowe et al. 2002). The conjugate gradient method (LSQR, Paige and Saunders 1982) implemented in HypoDD was used for the minimization of the double-difference residuals for pairs of earthquakes at each station. Indeed, it is able to solve a large system of equations efficiently and hence it is more suitable for automatic locations  (Waldhauser and Ellsworth 2000).
A total of 17 stations of the virtual network (out of 27) were used for the inversion: seven stations from the HAVO Network (sampled at 250 Hz); MARCO, CAGG, SIRI, MGR, SLCN, PTRP (data sampled at 100 Hz) and MRN3, CGG3, PGN3, SRN3 (data sampled at 125 Hz). We performed the inversion only for stations with a minimum signal-to-noise spectral ratio (spectral_sn_min) equal to 5. 0, ignoring also the ones lacking in automatic picks. The source parameters inversion was performed through a full grid search approach to efficiently sample all the combinations in the (M 0 , f c , t Ã ) parameters space. The t Ã is the attenuation parameter, defined as the ratio between the wave travel time and the quality factor Q. We discretized the parameters domain in an 80 Â 200 x 150 nodes' grid. The initial values of corner frequency and seismic moment were estimated at the first inversion step by the code.
Indeed, earthquake relocations are characterized by both low horizontal and vertical location errors (median values of 0. 08 km and 0. 07 km, respectively), which are one order of magnitude smaller than the ones of the absolute locations (Supplementary Figure  S7 ). The a posteriori assessment of relocation errors through SVD, performed on a subset of 50 events located near the mainshock (within 1. 5 km epicentral distance), provided equivalent results (median horizontal error of 0. 05 km; median vertical error of 0. 04 km). In conclusion, the initial number of 202 manually and automatedly detected earthquakes was reduced to a final dataset of 101 relatively relocated events. It comprised earthquakes that occurred between August 7 and October 12, 2020. Most of these fall inside the same cluster around the mainshock identified from absolute locations (Figure  4a ).
Acknowledgements Most of the figures are made using the free software QGIS 3. 22. 11(https://www. qgis. org/),Matplotlib and  Cartopy Python  (version 3. 7. 8)packages, Inkscape (https://inkscape. org/), and GNUPLOT (http://www. gnuplot. info/). Some of the analyses were performed with the support of Obspy version 1. 2. 2 seismic processing tools. We are deeply grateful to  Guido Maria Adinolfi , for the substantial help in the computation of focal mechanisms by means of BISTROP method. We would like to acknowledge the three anonymous reviewers, whose very useful and constructive comments allowed us to significantly improve the quality of the manuscript.
Data availability statement The continuous data-stream of the HAVO seismic network, formerly INSIEME (FDSN code: VD, https://doi. org/10. 7914/SN/VD), and of the GEOFON network (FDSN code: GE: https:// doi. org/10. 14470/TR560404) are open-access under the license CC BY 4. 0 and they are available from IRIS DMC FDSN Web Services, http://service. iris. edu/fdsnws/dataselect/1/. The continuous data-stream of the Italian National Seismic Network (RSN) (FDSN codes: IV, https:// doi. org/10. 13127/SD/X0FXnH7QfY; MN: https://doi. org/10. 13127/SD/fBBBtDtd6q) and of the Irpinia Seismic NETwork (ISNET) (FDSN code: IX) are open-access under the license CC BY 4. 0 and they are available from http://webservices. ingv. it/fdsnws/dataselect/1/.
The results presented next were obtained numerically by the joint minimisation of the functional with respect to n(z) and P (z) in the presence of a small and constant external field E 0 = 0. 1 V/nm. We have written a simple, dedicated Python code for that purpose. For a discretisation of the fields over typically N = 1024 points, the minimisation procedure is instantaneous on a laptop (less than a second). Following the simulations of Ref. 13  , we first consider a relatively wide slit of width h = 50 A • (16. 5 σ LJ ). We plot the equilibrium density field n(z) as well as the response function f (z) in reduced units in Fig. 2. Both present strong structural oscillations up to 6 sigma from the walls. These two curves appear very similar to the ones obtained by Ballenegger and Hansen  13  via MD-although their study was mainly focused on the less polar case µ * = 1.
During this stage, the software performs in parallel a series of CHIMERE-based offline simulations (with meteorological data available from WRF) with and without accounting for emissions from fires. The series of the CHIMERE simulations starts with global (climatological) initial conditions, and every next six-hour calculation uses results from preceding model run as initial conditions. During the second stage, the software performs a series of online simulations (Fig. 1b ) taking into account the ARI for the BBA DRE and SDRE estimation. Like during the preceding stage, each of the WRF simulations starts with preset global initial conditions. The CHIMERE simulations, performed in parallel, are initialized using concentrations of gaseous and aerosol components found during the first stage. At the very beginning of each six-hour interval, the radi-ative fluxes are calculated with and without accounting for BBA at nearly the same state of the atmosphere, thereby characterizing DRE.
NUMERICAL EXPERIMENTS The above-described computational technology was applied in numerical experiments aimed at estimating the radiative effects of Siberian BBA for July  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] 2016. This was the period when in Siberia there were large wild fires that caused smoke pollution on the scales of a few thousand kilometers  [17, 29]. The simulations mainly used the typical options and parameterizations recommended by CHIMERE developers and described comprehensively in developers' review papers  [24, 27] , as well in the model documentation (https://www. lmd. polytechnique. fr/ chimere/docs/). Below, we briefly describe the configurations of the performed simulations and indicate a number of specific software features which are very significant in the context of the present research.
The estimate f BC = 1. 5 was obtained using the ratio of Siberian smoke emissions of black and organic carbon, which was optimized in  [15]  after the analysis of data from satellite measurements and three-dimensional CHIMERE modeling, with the same simplified OA representation being used as in the present work. Fulfillment of condition (  4 ) is achieved by varying f OA (and f BC , linearly related to the former) during an iteration process  [15] , which rapidly converges in this situation because the contribution of organic aerosol to AOD m is, on average, dominant (>90%). The evaluated mass concentrations of aerosol admixtures and preset values of their complex refractive indices were used to calculate the aerosol optical properties (AOD, SSA, and IF) within CHIMERE based on the Mie theory.
"Standard" (see discussion above) emissions of gas and aerosol admixtures from fires into the atmosphere were specified in each model grid cell with 1-hour resolution using the CAMS-GFAS global database  [36]. This database aggregates the estimates of emissions obtained using satellite measurements of the fire radiative power  [37]. The hourly anthropogenic emissions of aerosol and gas admixtures were calculated using the CAMS regional anthropogenic emission inventory  [38]. The emissions of biogenic, mineral, and marine aerosols were calculated directly by CHIMERE using parameterizations and databases, whose description and validity issues were dealt with in the model documentation and papers  [24, 27]. In total, our simulations covered the period from July 1 to 31, 2016, with the first 15 days considered as a transition period and omitted in the subsequent analysis.
Data calculated by the LMDz4_INCA3 global climatological model, which are a part of the CHIMERE distribution kit, were used as initial and boundary conditions for the concentrations of gaseous substances and aerosols. The initial and boundary conditions for meteorological parameters were specified using global data from NCEP final reanalysis (https://rda. ucar. edu/). The simulations were performed using computational technology described in section 1, both with and without accounting for the emissions from Siberian fires. In addition, we performed a test calculation, in which BBA was modeled as a nonabsorbing component (SSA = 1) for the same emissions of primary OA and gas admixtures. The calculated AOD values are compared with satellite measurements in Fig. 2. In particular, we can note that the regions of increased AOD in Western Siberia and in European Russia stand out in the spatial distributions of both calculated and measured AODs (see Figs.
(2007)  to suggest that X7 results from a bow shock caused by winds (either from nearby massive stars or from the supermassive black hole itself). However, the bow-shock appearance has not persisted in subsequent years. More recently,  Peißker et al. (2021)  proposed instead that X7 is the product of the interaction between the circumstellar envelope of an S-star (a member of a group of early-type stars on tight, eccentric orbits around the SMBH) and a wind emanating from the neighborhood of Sgr A *. In this study, we use the GCOI 7 database from the long-term monitoring of this region with the W. M. Keck Observatory, to characterize the morphological and dynamical evolution of X7. We find that X7 is a complex feature with a rapidly evolving spatial-velocity structure most likely due to tidal interactions with the SMBH.
Preliminary results of this analysis have previously been presented in  Campbell et al. (2021). With the GCOI database, we have access to a set of imaging data that are similar to, but completely independent of, those presented by  Peißker et al. (2021). Additionally, we present and analyze a much more extensive set of spectroscopic data than those that have previously been published, which allows us to characterize the dynamical evolution of X7 over more than 15 yr for the first time. These data, in combination with the imaging data, provide comprehensive new insights into the characteristics and behavior of this object. For the first time, we compute the orbit of the leading edge of X7 and use it to model X7ʼs response to the gravity of the SMBH. We find here that the evolution of X7ʼs shape can be well-explained by the effect of the gravity of the SMBH alone.
Spectroscopic Data Our spectroscopic data set, gathered between 2006 and 2021, consists of 33 nights of OSIRIS observations, of which the most recent seven (post-2018) are newly reported. We use 32 observations taken with a pixel scale of 35 mas pixel -1. Additionally, we use one 20 mas pixel -1 observation to illustrate the structure of X7 with slightly higher spatial resolution. The 35 mas pixel -1 observations selected from the GCOI archive are those lacking substantial residuals from telluric OH line subtraction (visible as extremely strong absorption lines). All observations were taken at a position angle of 285°with a spectral resolution of R ∼ 3800 and were obtained through the Kn3 bandpass filter (2. 121-2. 220 μm), which covers the hydrogen recombination line Br-γ (rest wavelength 2.
1661 μm), forbidden lines of iron [Fe III] (rest wavelengths 2. 1457 and 2. 2184 μm), and (near the edge of the filter) the molecular hydrogen H 2 1-0 S(1) line (rest wavelength 2. 1218 μm). The new data were calibrated in the same manner as the existing GCOI OSIRIS data used for this study, which have been reported in earlier GCOI publications  (Ghez et al. 2008; Boehle et al. 2016; Chen et al. 2018; Do et al. 2019). The selected observations consist of calibrated and mosaicked data cubes from the GCOI Archive. For the 35 mas pix -1 observations, this resulted in a final field of view of roughly to 3″ × 2 5 centered on Sgr A * and a typical angular resolution of 78 mas (evaluated on the star S0-2), while the 20 mas pix -1 observation has a resolution of 46 mas. The GCOI experimental design prioritized the observation of shortperiod stars close to Sgr A * rather than X7, resulting in X7 being only partially within this smaller field of view.
Third, we use the line map to measure the tip location as explained above for the NIRC2 measurement, with the only difference being the number of cuts used to cover the same length of the ridge, due to the different spatial sampling in the two instruments (0 01 per pixel for NIRC2 versus 0 035 for OSIRIS). For OSIRIS, a total of 11 cuts were used for the orientation measurement. The position uncertainty is determined as for NIRC2, with the addition of varying the aperture size for the spectral extraction and the velocity width to use for the slice of the OSIRIS data cube. The new tip location is used to remeasure the radial velocity. The largest source of uncertainty to the radial velocity measurement is the location at which the spectra is extracted. Therefore, we characterize the uncertainties by varying the extraction position by 1-1.
Additionally, the total surface brightness of the Br-γ line can be used to estimate the density and the mass of X7 (see Section 4. 5). We select the 2017 spectrum for this derivation because it has the smallest difference between tip and tail radial velocity. In order to maximize the signal-to-noise ratio, we combined all 2017 data sets (same procedure as in  Ciurlo et al. 2020). This permits X7 emission to be easily isolated from superimposed sources when slicing the cube (we select the wavelength range (shown as a red dot) is determined with a Gaussian. A linear fit to these peaks (green line) defines the ridge of X7, while the half-maximum along ridge profile defines the location of the tip (shown as a blue dot). Bottom left: 3D rendering of the NIRC2 cuts (the vertical axis represents the intensity) in black, together with the corresponding Gaussian fit overplotted in red and the tip measurement in blue.
Therefore, we opt for the most conservative approach and use OSIRIS measurements only: OSIRIS astrometry data is selfconsistent with the radial velocity measurements. Our analysis employs a new orbit-fitting software package developed by the GCOI, NStarOrbits (G. D. Martinez et al. 2023, in preparation). We perform a Bayesian analysis with a multi-modal nested sampling algorithm, MultiNest  (Feroz et al. 2009 )-the same orbit-fitting methodology described in previous GCOI publications (for example, see  Do et al. 2019; Ciurlo et al. 2020) , but implemented in a much more efficient and modifiable way with NStarOrbits (further details on the orbit-fitting procedure are described in Appendix D). In Table  4 , we present the weighted median and associated 68% confidence intervals (statistical uncertainties only) for X7ʼs orbital parameters.
The orbital fit results are also illustrated in Figure  7. For the reported orbit fit, we use observable-based priors  (O'Neil et al. 2019 ) and fix the central potential parameters to the average of the latest published estimates from Gravity  Collaboration et al. (2019 Collaboration et al. ( , 2020) )  and  Do et al. (2019). To confirm that this orbit-fitting strategy is robust, we also test several other orbit-fitting setups. For example, as described in Appendix D, we also fit X7 and S0-2 simultaneously. The resulting parameter estimates from the joint fit are consistent with the case we report to within 1σ. In Appendix E, we show that including the NIRC2 astrometry in addition to the OSIRIS data produces compatible results within the combined uncertainties. We find that X7ʼs tip is moving toward us, in front of the plane of the sky containing Sgr A * , and orbiting Sgr A * with a period of nearly 200 yr.
In contrast, OSIRIS data have lower signal-to-noise ratio and they are slightly undersampled at the 0 035 platescale, with an inherent PSF of ∼0 050 FWHM. We also note that, because of the extended nature of X7, given one set of measurements made over time with a particular instrument, there is no guarantee that the tip location always follows the same parcel of orbiting material in both data sets. Given these limitations, in the main text we opt for using only OSIRIS astrometry and radial velocity measurements for orbit fitting, which is selfconsistent. We investigate the influence of NIRC2 measurements on the orbit in Appendix E. algorithm is designed to deal with crowded regions, and it eliminates outliers. We estimate the uncertainties in the photometry from the variances of the measurements when changing background annular size, photometry aperture size, and the centering aperture size.
2003; Wang et al. 2013) , where the accretion flow encounters a shock that heats the inflowing gas to temperatures exceeding 10 7 K. The hot gas surrounding X7 and the X-rays emitted there are likely to raise the dust temperature, which would counteract to some extent the decline of the surface brightness of the Lp dust emission resulting from tidal stretching. In addition, the increased pressure of the external medium as X7 gets closer to the black hole, owing to both the higher temperature within and inside of the accretion shock and Figure  16. Orbital parameters for different data sets: OSIRIS-only orbit in blue, OSIRIS plus NIRC2 orbit in magenta, and NIRC2-only in red. Note that the NIRC2only orbit does not have any RV information, and it provides poor constraints on several parameters.
Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle.
Federated learning types FL allows several models to be trained and aggregated before reaching the final model. To achieve that, FL is divided into 5 main types with different aspects, which are further detailed in Ref. [75] , and are presented and discussed as follows:  Data Partitioning: includes the datasets of different clients along with the degree of these features' similarity. In fact, data partitioning can be divided into three main types  [76]. -Horizontal Data Partitioning: occurs when the clients' datasets have the same features but with the least sample space intersection. -Vertical Data Partitioning: occurs when the client datasets are exposed to the same sample space but with different feature spaces. -Hybrid Data Partitioning: combines both horizontal and vertical data partitioning but is rarely used.
ML Models: The choice of each homogeneous/heterogeneous model is not uniform. This means that it varies depending on the problem to solve and the dataset. However, Deep Learning (DL) models and treebased models (Random Forest, Xgboost) are the most commonly adopted models  [77]. Privacy Mechanisms: rely on the adoption of FL to overcome privacy and privacy-related issues, most importantly preventing the leakage of clients' data and information. In order to achieve that, FL relies on two techniques, which include differential privacy and other cryptographic methods. -Differential Privacy: protects privacy by adding random noise to data to mask the gradients. However, adding noise comes at the cost of affecting the model's accuracy. -Cryptographic Methods: adopts data encryption techniques, most commonly homomorphic encryption and/or secure multi-party computation, to protect the clients data before securely transmitting it on the server.
FL IoT data services FL has the potential to cover a wider range of IoT domains while also achieving a wider range of IoT data services, which are presented below as follows: Data production: occurs frequently and on a daily basis with ongoing tasks and processes, making it readily available for both frequent and efficient access. Data sharing: makes data sharing available and securely transmitted from an FL server to local clients' devices. In the event that there is a large volume of data, it can be compressed and transmitted to avoid network congestion and bottlenecks. Data offloading: relies on complementary IoT network technologies to ensure that the data has been delivered to the right FL server, often to reduce bandwidth usage. Data caching: allows high-speed data storage that stores data subsets to ensure that data is transmitted at a faster pace at each future operation.