dataesr/scientific-paragraphs-categorization huggingface dataset:
System Applications All basic system applications (init, shell, etc) are provided by Busybox (currently v1. 33. 1)  [20]. The most notable applications are: Telnet daemon, watchdog user-space application, NTP client, DHCP client and SPI flash. The DHCP client has been configured to obtain the client hostname published by the DHCP server (required by AMIGA software to identify the module), the station LSID (used by the monitoring software to identify the station), and the NTP server used to synchronize the internal time.
Software Development Kit (SDK) The tool-chain used for software development (compiler, libc, linker and related utilities) was custom-built. It consists of a GCC 8. 1 C compiler targeting by default the Leon3 architecture with software floating point support, using binutils 2. 30 and uCLibc-ng 1. 30 C library. The tool-chain was built for Ubuntu 18. 04 running on Intel x86_64 compatible CPUs.
Software In this section we describe in more detail the software structure and how it performs the tasks needed for the UMD to store data and transfer it to the CDAS. We organize this section into three main subsections that describe the most important routines and their functionality: Module configuration, data transfer, and calibration. The software was written to control the module and to manage event and monitoring data acquisition. It consists of five pairs of client-server applications. With the single exception of the Calibration software (MdCalib), all clients run automatically after booting in Application mode on each buried module electronics and all servers run automatically on boot on a server in CDAS, with the clients keeping a persistent connection to the server (unless specified).
Module configuration -MdCfg/MdCfgd This software configures the front-end (CITIROCs, ADCs, and HV), the firmware parameters and provides configuration/status information (ex: acquisition mode, masked channels, etc) to other programs using a Unix socket. On start-up, the client connects to the server and requests the initial module configuration (stored in the central server in INI files). Once the configuration data has been sent and applied, this daemon disconnects from the server and keeps running in background, handling configuration and status requests from all other programs (these requests will be specified in each program's section).
Data transfer -MdSend/MdRcvd This pair of programs handle the event data transfer and storage. When idle, the client periodically consults with MdCfg the current acquisition mode and the UDP broadcast port for data requests (only if in acquisition modes sending of data is requested). Upon a data request, the software instructs the firmware to search for the specified event, receiving either the event data or an error code, and sends the result to the server. The server keeps in RAM a dictionary of events indexed by the 16-bit internal CDAS Event ID. For each Event ID, the data for all participating counters is stored in a dictionary indexed by the counter ID. The server has a TTL counter for each event, which is decreased every 30 seconds and increased every time data for a new module arrives.
Once this counter reaches zero, the event is written to disk and discarded from RAM (if more data for the same event arrives, it is treated as a new event and merged as part of the standard offline post-processing of AMIGA data). The event data is stored in JSON format, using a single file which is periodically restored and compressed (in gzip format) once per day. This format allows easy parsing using libraries available in most programming languages, and has a very good compression ratio when using gzip  [21].
Calibration -MdCalib These programs run the pre-defined Calibration routines on each module as described in  [11]. On the module, MdCalib starts on boot and listens to calibration requests. Once a calibration request is received, the program acquires the file-lock for background data, sets the acquisition mode to 'Calibration' (all data requests are sent to the server indicating that the sent data is invalid due to the module being calibrated) and runs the requested calibration routine, setting the needed configuration parameters via MdCfg and sending the resulting background data to the requester. Once the calibration finishes, the module is returned to its previous state and the file-lock is released. On a remote machine, a dedicated program communicates with the calibration software running on one or more modules (identified by IP address) and receives the calibration data.
Background signal monitoring -backpulse_client/backpulse_server These programs read and store the background pulse counters from the firmware. Background pulses are all the current pulses produced by the SiPM (not necessarily belonging to a possible event). The rate of these pulses for a given SiPM is expected to be relatively constant throughout the operating lifetime of the detector; variations on this rate over time may indicate aging or damage of any of the optical parts. The client periodically orders the firmware to snapshot the current counter values, which are then sent to the server and saved in space-separated files (one file per day and module), formatted with the module time-stamp and the 64 counter values. Since the background data client shares resources with the calibration daemon, this program tries to acquire a lock-file in a predetermined location before reading the counter data, and immediately closes this file after reading.
Environmental monitoring -monitoring_client/monitoring_server/data_handler This trio of programs handle the monitoring data readout and storage. The client periodically reads the data directly from the monitoring ADCs and data from the HV power supply via MdCfg, and sends it to the monitoring server upon request. Monitoring data is taken every 5 minutes. The server spawns a new process for each connection request from a client which periodically requests its monitoring data. Every 30 minutes up to 432 Bytes of monitoring data plus a header of 35 Bytes is sent per station. This data is then immediately sent to the data_handler process via a unix socket. The data_handler process stores all the monitoring data it receives from its unix socket to a MySQL database and, as a backup, to a. csv file (rotated every 12 hours) that can be imported manually into the database.
Online-Monitoring At the server side, the monitoring data is received and stored in a database. In this section we describe the monitoring data handling from all the UMDs deployed in the field and how it is presented to the user in order to visualize it and make decisions accordingly. The monitoring system allows access to the most relevant information of the different components of the MC via a web-interface. It also enables the visualisation and analysis of the environmental and trigger monitoring parameters over time, which can be very useful to find anomalies that could indicate the presence of failures in some component of the MC. The main page of the monitoring system shows a Map option with the position of each SD station. Figure  10  shows an example, where the coloured marker depends on the alarm state of all the scintillator modules for each SD station.
In the figure, green marks represent a station working in normal state, and gray marks represent stations that have yet to be installed. Selecting a SD station shows the deploy view associated to this station and the monitoring data from the database for a particular date. An example of the monitoring system displaying a typical AMIGA station after deployment is shown in figure  11. Selecting a module shows the monitoring information and alarm state for each variable of the chosen module. This view also gives the option to mask alarms of any monitoring parameters and visualise the full monitoring history of the corresponding module.
AMIGA sample event As an example of the acquisition process, this section presents data collected by the AMIGA UMDs for an UHECR of 4 × 10 18 eV impinging with a 20 • zenith angle , that hit the border of the muon counters. This event was recorded on May 9, 2018 at 14:44:56 UTC. The temperature measured in the SiPMs for the four modules belonging to station 1764 (which participated in the event) is shown in figure  12 , taken form the monitoring system. Event data for each AMIGA station is shown in this section to illustrate the reconstruction procedure applied to the binary acquisition mode only.
UMD module binary data AMIGA event data for each buried scintillator module is stored as lists of bin number/anode sample, only for samples where at least one channel has detected light (all other samples are not stored), and a list of bin number/sampled value for each ADC. The anode bin number indicates a time-stamp in 3. 125 ns increments. These traces have a length of 2048 bins (6. 4 µs). The length of the pre-trigger / post-trigger window is a configurable parameter of the electronics. In this example, the pre-trigger and post-trigger windows are about 4. 4 µs/2 µs long, thus the trigger arrived at bin 1400. Muon counts are obtained from these raw traces (shown as illustration in top panels of figure  14 ) in an offline analysis, searching for patterns in the signals of each channel using the technique described in  [11].
The reconstructed number of muons for all AMIGA modules triggered in this sample event is presented in table 4. These counts were obtained using the Auger Offline software  [23] , which applies the already described counting strategy and the pile-up correction described in  [5]. After the muon densities as a function of the distance are reconstructed, the observations are fit to obtain the estimator   (450), the muon density at 450 m from the shower core. This observable is sensitive to the chemical composition of the primary cosmic ray entering the Earth atmosphere and is the one used for higher-level physical analysis. Combining this parameter with others from the shower reconstruction (as the energy, X max , etc), gives valuable information regarding the primary particle mass. Figure  15  shows the fit of the muon lateral distribution function made for the event studied following the procedure in  [24].
Conclusions The AMIGA front-end electronics was designed as an embedded system around the Cylone FPGA family. A soft-core LEON3 processor runs Linux as its operating system for high flexibility, short development time and other benefits. A front-end compatible with this board was designed and built for AMIGA buried muon counters using SiPMs as the sensing device and two CITIROC ASICs for signal conditioning. The system is capable of 64 SiPM channels sampled at frequencies of 320 Msps and two 14 bit ADCs (high and low gain) measured at 160 Msps. The first eight prototypes of this system were installed in October 2016. Afterwards, 53 electronics were built and are acquiring data since February 2018. After several tests, this design will be produced to complete the AMIGA muon counters, by installing three 10 m 2 modules in 61 stations.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and admin-istrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support: Comisión  Nacional de Energía Atómica, Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) ,  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Gobierno de la Provincia de Mendoza ,  Municipalidad de Malargüe ,  NDM Holdings  and  Valle Las Leñas , in gratitude for their continuing cooperation over land access, Argentina; the  Australian Research Council ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundação de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ,  São Paulo Research Foundation (FAPESP)  Grants No.
Aims. We aim to study how the orbits of galaxies in clusters depend on the prominence of the corresponding central galaxies. Methods. We divided our data set of ∼ 100 clusters and groups into four samples based on their magnitude gap between the two brightest members, ∆m 12. We then stacked all the systems in each sample, in order to create four stacked clusters, and derive the mass and velocity anisotropy profiles for the four groups of clusters using the MAMPOSSt procedure. Once the mass profile is known, we also obtain the (non parametric) velocity anisotropy profile via the inversion of the Jeans equation. Results. In systems with the largest ∆m 12 , galaxy orbits are generally radial, except near the centre, where orbits are isotropic (or tangential when also the central galaxies are considered in the analysis).
m r = 18). Of the 88 available clusters, we selected only those 76 with spectroscopically confirmed ∆m 12. The total number of spectroscopic members in the S2 data set is 4338. As the goal of this work is to study the dependence of β(r) on ∆m 12 , we divided our 97 S1+S2 galaxy systems into 4 samples in bins of ∆m 12 , chosen to ensure at least 20 systems in each bin. The four samples contain 31 systems with ∆m 12 ≤ 0. 5, 23 with 0. 5 < ∆m 12 ≤ 1. 0, 23 with 1. 0 < ∆m 12 ≤ 1. 5, and 20 with ∆m 12 > 1. 5 (see Table  1 ). The properties of the systems in the four samples are given in Tables A. 1, A. 2, A. 3, and A. 4.
( 6 ) A&A proofs: manuscript no. main -"ML": from  Mamon & Łokas (2005) , β ML (r) = 1 2 r r + r β , (7) where r β is the anisotropy radius. -"OM": from  Osipkov (1979)  and  Merritt (1985) , β OM (r) = r 2 r 2 + r 2 β. (8) All these β(r) models have one free parameter each (β C , β ∞ , or r β ). We run MAMPOSSt in the so-called Split mode  (Mamon et al. 2013) , that is we use an external maximumlikelihood analysis to determine the value of the scale radius of the galaxies number density profile, r ν. We fit the radial distributions of the galaxies in each stack with NFW models (in projection), taking into account the correction for sample incompleteness as in  Zarattini et al. (2019). The best fit values for r ν are given in Table  1. The ∆m 12 > 1. 5 sample has a slightly more concentrated distribution of galaxies than the other three samples.
For this, we follow the method of  Binney & Tremaine (1987)  in the implementations of  Solanes & Salvador-Sole (1990)  and  Dejonghe & Merritt (1992). Our procedure is the following. We fix M(r) to the MAM-POSSt solution. The two observables we need to consider are the number density and velocity dispersion profiles. We apply the LOWESS technique (see, e. g. ,  Gebhardt et al. 1994)  to smooth these profiles. The number density profile is then de-projected numerically, (using Abel's equation, see  Binney & Tremaine 1987). Since the equations to be solved contain integrals up to infinity, we extrapolate the profiles to a large-enough radiuswe find 30 Mpc to be sufficient for our results to be stable. The extrapolations are performed as in  Biviano et al. (2013). Uncertainties in the β(r) profiles are estimated by performing the Jeans inversion on 100 bootstrap resamplings of the original datasets.
0 < ∆m 12 ≤ 1. 5. Bottom-right panel: systems with ∆m 12 > 1. 5. no longer DM-dominated (e. g. Biviano & Salucci 2006) , while the outer region, R > r 200 , may not have reached dynamical equilibrium yet. We compare the MAMPOSSt solutions obtained from the 15 combinations of the three M(r) and the five β(r) models (see Sect. 3. 2) using the Bayesian Information Criterion (BIC  Schwarz 1978) , BIC = N pars ln N data -2 ln( L), (9) where N data is the sample size, and N pars is the number of free parameters used in the model and L is the MAMPOSSt-derived likelihood. The BIC values obtained using two free parameters (r -2 , and the β(r) parameter) are systematically lower than the BIC values obtained using three free parameters (i. e. adding r 200 as a free parameter) for all combinations of M(r) and β(r) models.
5 bin. The S1 data set includes many dwarf galaxies, up to three magnitudes fainter than SDSS spectroscopy. Dwarf galaxies in clusters/groups are mostly early-type (e. g. Jerjen & Tammann 1997; Lisker et al. 2013) , and therefore occupy the red tail of the gr distribution. Different β(r) have also been reported for galaxies of different stellar masses  (Annunziatella et al. 2016)  or luminosity  (Aguerri et al. 2017). To check if a different magnitude distri-Fig. 4. gr colour distribution of the galaxies in the four samples. Dotted black histogram and grey shading: ∆m 12 ≤ 0. 5. Dashed red histogram and orange shading: 0. 5 < ∆m 12 ≤ 1. 0. Dash-dotted violet histogram and pink shading: 1. 0 < ∆m 12 ≤ 1. 5. Solid green histogram and turquoise shading: ∆m 12 > 1. 5. Fig. 5. Velocity anisotropy profile σ r /σ θ and 1 σ confidence regions for the systems with ∆m 12 > 1.
5 sample the three richest clusters, FGS03, FGS27 and FGS30 that together contain almost 1/3 of all members in their sample (see Table  A. 4 ). We performed the full analysis on the remaining sample of 17 systems. The resulting σ r /σ θ profile is very similar to the original one based on all 20 systems (see Fig. 5 ). The S1 and S2 data sets cover different z ranges, their median z are 0. 16 and 0. 07, respectively. This difference reflects in an increase of the <z> of the four samples with increasing ∆m 12 , since the higher-∆m 12 systems are mostly from the S1 data set. To check for a z-dependence of β(r) we divided our ∆m 12 > 1. 5 sample in two subsamples, of ten clusters at z < 0. 12 and another ten at z > 0. 12. After performing the dynamical analysis separately on the two subsamples, we found no significant difference in their β(r), but the uncertainties are large due to the small size of the subsamples, so this test cannot be considered very significant.
Discussion and conclusions We analysed a data set of 97 galaxy clusters and groups to study the dependence of β(r), and therefore of the orbital distribution of galaxies, on ∆m 12. We split our data set in four samples of different ∆m 12. We then stacked together the systems in each of the four samples. We then run MAMPOSSt to derive the mass and the (parametric) anisotropy profiles of the four samples. Finally, with the mass profiles obtained from MAMPOSSt, we perform the inversion of the Jeans equation, allowing us to determine β(r) in a model-independent way. We find that β(r) shows a steeper dependence on r for the systems with ∆m 12 > 1. 5 than for the other three samples with smaller ∆m 12. The orbits of galaxies in the ∆m 12 > 1. 5 stack are more radial (with marginal significance, at more than 1σ level) at large radii (r 0.
Acknowledgements. We thank the anonymous referee for his/her useful comments. S. Z. acknowledges funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n. 340519. M. G. acknowledges the support from the grant  MIUR PRIN 2015  "Cosmology and Fundamental Physics: illuminating the  Dark Universe with Euclid". A. B. acknowledges the hospitality of the  Instituto de Astrofísica de Canarias  during a workshop where the foundations of this project were set.
ACKNOWLEDGEMENTS General: The authors acknowledge  K-R. Jeon  for fruitful discussions. Funding: S. K. , J. M. D-S. , G. Y. , X. M. , L. F. C. , H. K. , M. G. B. and J. W. A. R. acknowledge funding from the  EPSRC Programme Grant "Superspin"  (No. EP/N017242/1 ) and  EPSRC   International Network Grant  " Oxide Superspin " (No. EP/P026311/1 ). K. O. acknowledges the  JSPS Programme "Fostering Globally Talented Researchers " ( JPMXS05R2900005 ). S. M. and A. I. B. acknowledge funding from  Russian Science Foundation  (Grant No. 20-12-00053 , in part related to the theoretical calculations). Zh. D. and S. M. acknowledge financial support from the  Foundation  for the advancement of theoretical physics "BASIS". S. M. acknowledges financial support from the  Russian Presidential Scholarship  ( SP-3938.
Introduction To advance towards a broader understanding of the chemical evolution of the Milky Way, detailed element abundances from high-quality spectroscopic data and precise ages are needed (Soderblom 2010;  Jofré et al. 2019). Stars change their chemical composition due to several nucleosynthesis processes, and particularly during their deaths, which lead to an evolution of the overall chemical composition of galaxies. Newly synthesized elements will be inherited to form the next generation of stars, which ultimately guides the chemical evolution in the Universe (see  Freeman & Bland-Hawthorn 2002; Matteucci 2012, for reviews). Therefore, the dependency of different element abundances with time is one of the most informative constraints to shape chemical evolution models. Thanks to observations at Telescope Bernard Lyot, Mercator Telescope and Nordic Optical Telescope, and data retrieved from the archives: ESO, FIES, ELODIE, ESPaDOnS and NARVAL It is usually assumed that the measured atmospheric chemical abundances are representative of the chemical pattern of the interstellar medium at the stars' birthplaces.
Radial velocities and atmospheric parameters To perform the analysis of the spectra we used the public spectroscopic software iSpec  (Blanco-Cuaresma et al. 2014; Blanco-Cuaresma 2019). We make use of the same pipeline built in Paper I (we refer the reader to that paper for further details). In brief, the strategy goes as follows: -As a first step, the spectra coming from the different instruments are homogenised cutting them to a common wavelength range (480-680 nm), and downgraded to a common spectral resolution (45, 000). After the determination of radial velocities using a cross-correlation algorithm, telluric regions and emission lines due to cosmics are masked, and a normalization is performed using a median and maximum filtering. -Afterwards, we use the spectral synthesis fitting method to compute atmospheric parameters (T eff , log g, [M/H], [α/M], and mic ).
Membership refinement and red clump selection As in Paper I, we have done a membership refinement using the radial velocities computed with iSpec, to reject stars which have 3D kinematics not consistent with that of the cluster, which are candidates to be non-members or spectroscopic binaries. For the three clusters already analyzed in Paper I (Hyades, A&A proofs: manuscript no. paper2_v7. 1subm. Even though the precision in the Gaia radial velocities is much lower than that coming from iSpec, it is straightforward to identify the most discrepant cases if we also take into account the internal consistency among the spectroscopic targets. We rejected 8 stars. We realized that some of the remaining stars appear to have T eff and log g away from the red clump, tipically stars in the asymptotic giant branch (AGB) or in the subgiant branch (see Fig A.
Acknowledgements. We thank the anonymous referee for the useful comments which helped to improve the quality and results of the paper. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium). We acknowledge the  Gaia Project Scientist Support Team  and the  Gaia DPAC. Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. This research made extensive use of the SIMBAD database, and the VizieR catalogue access tool operated at the  CDS, Strasbourg, France , and of  NASA Astrophysics Data System Bibliographic Services.