dataesr/scientific-paragraphs-categorization huggingface dataset:
CMS Calibration and Monitoring System. KATRIN KArlsruhe TRItium Neutrino. PULCINELLA Precision Ultra-Low Current Integrating Normalization Electrometer for Low-CPS Cryogenic Pumping Section. KDB KATRIN Database. Level Analysis. cRIO compact Reconfigurable Input Output. KIT Karlsruhe Institute of Technology. REST Representational State Transfer. DAQ Data acquisition. LARA Laser Raman Spectroscopy. RMMS Radial Magnetic Monitoring System. DET "Detector magnet". LDLS Laser-Driven Light Source. ROAST Realtime Orca Analysis on STreaming. DPS Differential Pumping Section. LFCS Low-Field Correction System. RS Rear System. DVM precision digital voltmeter. LN2 liquid nitrogen. RSCM Re-condensing Superconducting Magnet. e-gun electron gun. MAC-E Magnetic Adiabatic Collimation with Electrostatic. RW Rear Wall.
Using this technique, isolates having at least 6 alleles in common out of these seven genes are grouped into the same clonal complex (CC). With advances in whole genome sequencing, this method has been extended to larger sets of loci (typically thousands) to allow strain differentiation at the level of the whole-or core genome (wgMLST or cgMLST, respectively)  [71] [72] [73] [74] [75]. A standard nomenclature system has been proposed based on the cgMLST scheme of 1,748 core loci  [75]. Isolates sharing less than 150 and 7 allelic mismatches are grouped into sublineages (SL) and cgMLST types (CT), respectively, which allows for efficient inter-laboratory communication, in particular for emerging strain types  [76]  (Figure  2A ). In addition, the compilation of genotypic data in dedicated databases, such as BIGSdb-Lm (http://bigsdb.
B) Frequency of the most prevalent L. monocytogenes MLST clonal complexes in clinical (yaxis) versus food (x-axis) isolates, based on a exhaustively collected isolates dataset obtained in the scope of listeriosis surveillance (N=6,633 isolates, France 2005-2013). (reproduced with permission from [25] ). Each circle represents a clonal complex, and is colored according to its lineage. The size of each circle is proportional to the number of isolates it represents. The clonal complexes of common reference lab strains (CC7: strains EGD and 10403S; CC9: EGDe and LO28), are also indicated.
The landscape of high-and ultra-high-energy astrophysics has changed in the last decade, in large part owing to the inflow of high-quality data collected by present cosmic-ray, gamma-ray, and neutrino observatories. At the dawn of the multimessenger era, the interpretation of these observations within a consistent framework is important to elucidate the open questions in this field. CRPropa 3. 2 is a Monte Carlo code for simulating the propagation of high-energy particles in the Universe. This new version represents a step further towards a more complete simulation framework for multimessenger studies. Some of the new developments include: cosmic-ray acceleration, support for particle interactions within astrophysical sources, full Monte Carlo treatment of electromagnetic cascades, improved ensemble-averaged Galactic propagation, and a number of technical enhancements.
2 extends the applicability of the code to a variety of applications beyond UHECR astrophysics, which range from the propagation of CRs in the Galaxy to gamma-ray and neutrino astronomy. Here we introduce the capabilities of CRPropa in section 2, followed by a description of some of the improvements made in this new version in section 3. We illustrate some applications of the code in section 4, and briefly address the current status of CRPropa 3. 2 and future developments in section 5.
The CRPropa Framework CRPropa 3  [15, 16]  is a flexible framework for high-energy astrophysical studies. It is written in C++ with Python bindings. The code structure is fully modular, handling each ingredient of the transport problem separately through the usage of individual modules that describe, e. g. , sources, observer, particles, interactions, and magnetic fields. Each module acts on properties of an object from the Candidate class, which stores all the relevant information about a particle such as its position, energy, nature (type of particle), and momentum. At each step of propagation the modules update these properties according to prescriptions which may involve, for instance, kinematic changes due to a given physical process such as interactions, magnetic deflections, etc. Particles are tracked until some specified breaking condition is met, which may include trajectory length constraints or detection, for example.
New Features In CRPropa 3. 2 we have extended the physics capabilities of the code by introducing a number of new modules to treat phenomena that so far could not be modelled with the code. Below we list some of them. Acceleration. CRPropa 3. 2 contains modules to simulate the acceleration of charged particles scattering off magnetic-field irregularities. In particular, first-and second-order Fermi acceleration are available, generalising the implementation of ref. [17]. New Photon Production Channels. Processes such as photodisintegration of nuclei and radiative decays can produce (ultra-)high-energy photons. For this reason, we added such production channels to CRPropa, together with elastic scattering of CR nuclei off background photons  [18]. The latter, in particular, does not incur substantial energy losses for cosmic rays, but can be important to obtain a consistent multimessenger picture.
Electromagnetic Cascades. So far, the propagation of photons in CRPropa was done using external packages such as the transport-equation solver DINT  [19]  or the Monte Carlo code EleCa  [20]. For consistency and to enable a full three-dimensional treatment of photon propagation, we implemented all relevant electromagnetic processes for high-energy photons and electrons natively within CRPropa. These interactions can occur many times during the cascade process, leading to a rapid growth in the number of particles in the simulation, which renders a Monte Carlo treatment over a wide range of energies impractical. To enable faster simulations, we also implemented a thinning procedure following refs. [21, 22] , leading to speedups of up to four orders of magnitude. Ensemble-Averaged Propagation.
Cosmological Propagation of Gamma Rays With the new modules for electromagnetic interactions recently added to CRPropa, it is possible to simulate a wide variety of scenarios. An interesting application is the study of gamma-ray induced electromagnetic cascades in intergalactic space. This includes effects such as absorption by the extragalactic background light (EBL) and the deflection of electron-positron pairs by intergalactic magnetic fields (IGMF)  [41]. One such example is shown in fig. 2 , in which we simulate the propagation of gamma rays through turbulent helical magnetic fields -another novel feature of CRPropa 3. 2.
Conclusions and Outlook To interpret current astrophysics observations at high-and ultra-high energies, detailed theoretical models with multiple types of messengers are necessary to obtain a self-consistent picture of the Universe across many decades in energy. CRPropa 3. 2 is a simulation framework with a broad scope that enables such studies. Our recent developments make substantial technical improvements to optimise the code performance in simulations with magnetic fields, whether in the Milky Way or outside of it. They also extend the range of possible applications of CRPropa by treating particle acceleration, by implementing powerful tools for transporting cosmic rays in the diffusion regime, by adding new channels for the production of photons, and by implementing a native treatment of electromagnetic interactions.
Acknowledgements RAB acknowledges the support from the  Radboud Excellence Initiative. LM acknowledges financial support from the  Austrian Science Fund (FWF)  under grant agreement number  I 4144-N27. KHK, LS and JT acknowledge support from the  Deutsche Forschungsgemeinschaft  for the project  Multi-messenger probe of Cosmic Ray Origins (MICRO ), grant numbers  TJ 62/8-1  and  KA 710/5-1. The work of AS is supported by the  Russian Science Foundation  under grant no. 19-11-00032. AvV acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant No. 646623 ).
ICOS was first designed to serve as a backbone network to monitor fluxes away from main anthropogenic sources. The concentration gradients between European sites are typically of only a few parts per million on seasonal timescales. These small atmospheric signals combined with atmospheric transport models are used to deduce surface fluxes. For these atmospheric inversions, a high-precision and integrated net-work is mandatory. As a precise example,  Ramonet et al. (2020)  show that a strong drought in Europe like the one seen in summer 2018 produces an atmospheric signal of only 1 to 2 ppm. During the preparatory phase and the demonstration experiment, standard operating procedures for testing the instruments and measuring air in the most precise and unbiased way were defined. Data management plans were created and required IT infrastructure such as databases, and the quality control software tools were developed.
Regular monitoring station assembly (MSA) meetings facilitate discussions of technical and scientific matters. The Atmosphere Thematic Center (ATC; https://icos-atc. lsce. ipsl. fr/, last access: 28 December 2020) is divided into three components: the metrology laboratory (MLab) responsible for instrument evaluation, protocol definition and PI support, the data unit responsible for data processing, code development and graphical tools for PIs, both located in Gifsur-Yvette, France, and finally the MobileLab in Helsinki, Finland, tasked with the audit of the stations during and after the labeling process. One very important task for the ATC is ensuring that the stations reach the quality objectives defined within ICOS, which are based on the compatibility goals of the WMO  (WMO, 2018)  and detailed in the ICOS atmosphere station specifications (ICOS RI, 2020a).
Usually, if there are some problematic points, the ATC first contacts the PI to see if improvements can be done to meet the requirements or ask for additional docu-ments. ICOS atmosphere is mainly focused on tall tower sites measuring regional signals but accepts a limited number of a high-altitude and coastal sites (ICOS RI, 2020a). Once Step 1 is approved, the station can be built, equipped and set up to fulfill the AS specifications. Once the nearreal-time dataflow to the ATC database is established  (Hazan et al. , 2016) , stations can apply for Step 2. The time lapse between Step 1 and Step 2 can vary greatly depending on the site. Indeed, in the case of already existing stations, they are entering ICOS with running instruments and historical datasets already and need only small changes such as getting the calibration cylinders from the CAL-FCL and modifying some procedures to have their data processed into the database before beginning Step 2.
The eddy covariance fluxes have been selected as well with the idea of characterizing the local surface fluxes from either biogenic and/or anthropogenic activities and of monitoring possible long-term changes around the ICOS sites. So far this parameter is not required for the labeling process due to logistical difficulties in performing such measurements at several atmosphere sites. https://doi. org/10. 5194/amt-14-89-2021 Atmos. Meas. Tech. , 14, 89-116, 2021 At the beginning of the initial test period, a station must provide at minimum continuous in situ greenhouse gas data to the database on a daily basis, and by the end, meteorological parameters (wind speed, wind direction, atmospheric temperature, relative humidity and pressure) and additional diagnostic data (room temperature, instrument and flushing pump flow rates).
Depending on the stability of one calibration to the other, ATC will recommend if the frequency can be reduced, but in any case, at least one calibration sequence per month is required. Cylinder numbers and positions in the sampling system at which they are connected, as well as the sequence of injections, have to be entered into the ATC configuration software. An automatic quality control of raw measurements  (Hazan et al. , 2016 ) is performed on the calibration data based on a check of instrumental parameters, such as temperature and pressure of the analyzer cavity, to ensure the instrument is working properly. For example, the typical accepted range for cavity ring-down spectroscopy (CRDS) cavity pressure is 139. 8 to  140. 2 Torr (186. 38 to 186. 92 hPa). Then a flushing period, whose duration is configured via the ATC configuration tool, is automatically filtered out.
Manual data quality control All the data processed by ICOS ATC go through an automatic quality control (QC) based on various criteria  (Hazan et al. , 2016). However, as a second and final quality control step, the PI of the station has to review and validate the data on a regular basis using the logbook information from the station (e. g. , contamination due to maintenance on site). No data is flagged invalid without an objective reason. To harmonize the quality control, the ATC provides dedicated software tools and organizes mandatory training that must be attended before beginning the operation at the station. The data validation is done with a software developed at the ATC directly in the server. On a daily basis, the station PI checks the ATC data products generated daily on the ATC website (https://icos-atc.
lsce. ipsl. fr/dp, last access: 28 December 2020) as an early detection of any issue related to the analyzers, sampling lines, data transmission or processing. On a weekly basis, raw greenhouse gas data have to be checked and (in)validated using ATC QC software via a flagging scheme. Raw data are reviewed day by day. For valid data, we can choose additional information such as "quality assurance operation" or "non-background conditions", but this is not mandatory. Data have to be invalidated only for an objective reason which has to be chosen from a list to be able to carry on with the QC. The reasons can be (non-exhaustive list): "calibration Issue", "flushing period", "maintenance with contamination", "inlet leakage", etc. On a monthly basis, the hourly average of greenhouse gas and meteorological data must also be verified.
Percentage of data validated by station PIs Quality control by the PIs is paramount to ensure the high quality of the ICOS dataset. When preparing the reports, we make sure that the quality control is done as detailed in the https://doi. org/10. 5194/amt-14-89-2021 Atmos. Meas. Tech. , 14, 89-116, 2021 AS specifications, i. e. , weekly for the greenhouse gas air raw data and monthly to every 2 months for the hourly means or injections of greenhouse gas (air and quality control gases) and meteorological data. Figure  3  shows the status of hourly data validation at a given time for six stations. The hourly means are computed automatically using minute means which are themselves computed using raw data. If there is at least one valid value from the raw data within a given minute, the corresponding minute mean is considered valid.
Similarly, if there is at least one valid minute mean to compute an hourly mean, the hourly mean is considered valid. There is no automatic quality control criterion applied to the hourly means; the criteria are only applied to the raw data. Valid data are shown in green and invalid data in red. Dark colors indicate automatic (by the ATC software) validation prior to the manual data inspection by the PI (light colors). The dark red color will usually indicate flushing periods or instrument failure (hence no data when the database expects some) that are automatically flagged. For each station, the analyzers are identified by their unique ICOS ID attributed by the ATC. This is the number shown in the second column in the graph. The third column shows the sampling heights. In this plot, however, we will mostly focus on the amount of manual validation to ensure that the data have been indeed controlled by the PIs.
All interventions of the PIs to flag data are done through the ATC software and are recorded for traceability. When raw data are rejected, the PI has to select a reason for the problem within a predefined list of 11 issues (such as flushing period, instrument failure, maintenance, etc. ). The PIs first validate the data on the raw level, then every 1 to 2 months on the hourly level. The second validation of the hourly dataset aims to verify the longer-term consistency of the time series. Every time a data flagging is performed either on the raw or hourly data, a reprocessing is automatically applied to the other aggregated levels (raw, minute, hour) for consistency.
Percentage of air measurement vs. calibrations or target and flushing time When switching from one sample to the other, there is some flushing period (defined by the PI in agreement with the ATC) that is automatically removed from the valid data. Figure  4  allows us to evaluate whether the time spent measuring "invalid" air is not too high compared to the time measuring "valid" air. It is mostly important for tall towers that switch between many levels and may end up spending too much time flushing. In the figure, when the percentage does not reach 100 %, it means that the station has not yet provided data for the whole year. For example at Lindenberg (LIN), the analyzer for CO 2 and CH 4 was at least running since October 2017, but the CO / N 2 O analyzer was installed only in August 2018, thus showing only 2 months of data.
Temperature dependence of the instruments A few of the instruments tested at the ATC MLab have shown a significant sensitivity of the GHG measurements to temperature. On site, the temperature variation is supposed to be small, but in case of problems with the air conditioning, we can use the target gas measurement to evaluate the impact of the temperature changes on the measurements, as seen in Fig. 8. Currently, there is no correction derived from the MLab tests applied in the ICOS data processing. For instruments showing a significant variability due to the temperature or other parameters (i. e. , leading to the WMO goals within the observed range of temperature being exceeded), it is recommended to use a short-term working standard in order to correct the short-term variability induced by these sensitivities.
Meteorological measurements Meteorological parameters are mandatory as they are used to analyze the atmospheric signals measured at the station location and associate them with regional or large-scale processes. During the initial test period, the ATC checks that the sensors are compliant with the list from the AS specifications and that the data are transmitted correctly to the ATC data unit database for all mandatory levels (see Fig. 9 ). The ATC checks the data availability and consistency. The ATC performs simple filtering on the raw data based on valid ranges (min/max values) for the five mandatory species, which are pressure, temperature, relative humidity, wind speed and wind direction. Except for relative humidity, the data are also marked as invalid if the measurement is constant for more than X min in a row.
X is set to 10 for the wind vari- ables and to 60 for the other species. This criterion is used to cope with blocked sensors. ATC is also working on a model vs. measurement comparison with the European Center for Medium-range Weather Forecasts (ECMWF) data to highlight potential drifts or outliers. In terms of instrumentation, ATC is working on instating a 2-year recalibration of the humidity sensors that are the ones drifting the fastest over time. If meteorological data are sent to the database at the same time as the greenhouse gas data and not at the end of the test period, they can be used to understand the variability in the greenhouse gas data.
Time series and associated uncertainties For most of the stations that enter labeling Step 2, data have already been collected before the initial test period which allows an analysis of the previous year (Fig. 11 ) and previous month (not shown) and the ability to plot a wind rose (not shown) allocating the mixing ratios to the wind direction and intensity for the whole year and by season. This figure is of interest to evaluate the influence of different sources that can surround the site at a more or less large scale. On the yearly figure, ATC looks for patterns in the target gases (biases, drifts), data gaps and outliers, whereas the ambient air signals are much more visible in the figures of shorter periods. To allocate mixing ratios to wind sectors or to compare two instruments measuring the same species, all instruments and sensors have to have access to a time server and update their clock regularly.
Presentation of the 2labeled stations The 23 labeled stations described here passed Step 1 between 2016 and 2019 (see Table  3 ). For most of them, this was a straightforward step. For four of them (Ispra, IPR, Observatoire de l'Atmosphère du Maïdo, RUN, Lutjewad, LUT, and Karlsruhe, KIT), additional documents and preliminary studies were requested mainly to address potential local contaminations. After Step 1 approval, they entered Step 2 up to 2. 5 years later depending on the existing infrastructure and instrumentation. At the end of the initial test period, a scientific report summing up the setup done during this period and the resulting data was sent to the PI, and the station was proposed for labeling. The stations were then approved by the ICOS general assembly. In November 2017, the first four ICOS atmosphere stations were labeled following this procedure.
13  and Table  3 ). The first ICOS compliant data date from the end of 2015 for two German stations (which began measurements following the ICOS procedure before the operational phase and so the Step 1 application), and the more recent stations have had compliant data since September 2019. A total of 15 stations are continental sites equipped with tall towers with up to six air sampling levels. Four are classified as mountain stations, two as coastal sites with one sampling level and two as remote sites. The AS specifications also provide guidelines for sampling periphery such as regulators, sampling valves and tubing. This allows a high level of standardization while allowing flexibility for the PIs to design the setup. For the gas distribution to the analyzer, the required equipment is a rotary valve from Valco (model EMT2SD).
noaa. gov/gmd/ccgg/mbl/crvfit/crvfit. html, last access: 28 December 2020,  Thoning et al. , 1989). Karlsruhe, KIT, Ochsenkopf, OXK, and Steinkimmen, STE) were also using OA-ICOS N 2 O / CO instruments and using the recommended Nafion drier. In May 2020, seven sites (CMN, HTM, PUY, SVB, RUN, Trainou, TRN, JFJ) which were not equipped with a drier during the test period installed a Nafion drier for all their samples, while Hohenpeissenberg (HPB) and Gartow (GAT) added a Nafion drier for their OA-ICOS N 2 O / CO instruments. IPR stopped using its chiller in October 2018 and then installed a Nafion drier in May 2020. In Fig. 14 , the availability and distribution of data over the past year for CO 2 , CH 4 and CO is shown for the 23 stations. Calibration is in red, target in blue and air in green/gray.
Some of the sites show outliers in the left boxes (initial test period) due to problems that have been solved during the initial test period. At HTM, the instrument is known to be poor for CO. It was replaced between May 2018 and September 2019 by an instrument performing better (not shown). Unfortunately, due to a storm at the station, this instrument was damaged, and the old one was re-Figure  14. Data distribution between ambient air and target and calibration gases for the 23 stations for CO 2 , CH 4 and CO over the past year. Calibration is in red, target in blue and air in green/gray. Gray and darker colors are invalid data. The 100 % level is reached when data are available for the full year. For some stations, two instruments are used to measure CO 2 , CH 4 and CO (see their ICOS ID on the second line).