dataesr/scientific-paragraphs-categorization huggingface dataset:
3 nT. The mean magnitude of the spikes after cleaning, which is determined as the difference between the spike peak and the median over the 20 s intervals before and after the spike, is reduced to about 0. 3 nT. The remaining magnitude of the high frequency disturbance is determined as the peak-to-peak amplitude of the disturbed data from which the peak-to-peak amplitude during a time interval when the disturbance is absent is subtracted. Not all AC disturbances can be corrected using the PiCoG method. One class of such disturbances is represented by the sensor offset oscillations (SOO) triggered by large temperature gradients. The accommodation of the SOSMAG boom combined with its short Table  6  The magnitude of the identified disturbances as initially measured by the FGOB and the final magnitudes after AC cleaning.
The GOES-14 satellite is at geostationary position with 255. 6 • eastern longitude  (Singer et al. 1996 ; GOES-14 data from www. swpc. noaa. gov/products/ goes-magnetometer). Again, the model magnetic field is calculated with T04 using the WIND data as input for the solar wind conditions. The three panels on the right of Fig. 19  show the deviation of the GOES-14 data from the model as a function of local time at the spacecraft. A dependence of the differences on the local time can be seen here as well. Similar to SOSMAG, the daily variation in the Hp component is about 8 nT peak-topeak with the minimum at local midnight and a broadened maximum around noon. The He component also shows a significant daily variation (5-8 nT peak-to-peak) but with the maximum around local noon, as for Hp. The variation in Hn is less than 5 nT peak-to-peak but with two maxima during 24 hours.
It is also obvious that the GOES-14 data do not vary around zero nanotesla which indicates that the GOES-14 offset processing is not linked to the T04 model. The daily variations of GOES-14 compared to SOSMAG are about the same in Hp but different in He and Hn. This may be explained by the tilt of the magnetic axis relative to the rotational axis of the Earth with GOES-14 being located slightly north of the magnetic equator and GK-2A slightly south of it. Taking into account that the peak-to-peak variation is nearly the same for both, measurements on GOES-14 and GK-2A, it can be concluded that the local time dependence of the deviation of the SOSMAG data from the model data is to a large extend neither caused by the SOSMAG sensors nor by spacecraft interferences but is inherent to the model data.
In other words, the daily variation of the Tsyganenko model data is not fully representing the real daily variation at the position of the spacecraft. A similar effect was reported for the GOES-16 magnetometer data  (Loto'aniu et al. 2019). A comparison of these data with the Fig. 20  Final values for DC offsets (in Hpen) determined from times with Kp-index ≤ 1. 0 for 10 months of data (2019-03 to 2019-12) using sliding windows; same scale on y-axis in all panels. Standard deviations were determined from the selected data points (Kp ≤ 1. 0) only T04 model revealed differences to the model, where the Hp component showed the strongest and diurnally varying deviations. To enable a smooth transition of the final SOSMAG offsets from month to month, it was decided to use a sliding window approach for the processing.
A rectangular window with a length of 28 days was chosen for each DC offset determination. The window is shifted by seven days for the next calculation, leading to a value every seventh day. For all data in 2019 the thus obtained DC offsets are shown in Fig. 20. The offsets vary with less than about 8 nT in Hp, 5 nT in He and 2 nT in Hn. The behaviour the three components is comparable to the daily variations in Fig. 19  for which the largest and the smallest variation are also seen in Hp and Hn, respectively. In general, the long-term variation is fairly low and as such not much worse compared to offset drifts of e. g. a magnetospheric mission like Cluster  (Alconcel et al. 2014). The subtraction of the DC offsets from the AC cleaned data is done with linear interpolation between the calculated offset points to produce the fully corrected SOSMAG data.
2019) , including the determination of the spin plane offsets to accuracies of the order or better than 0. 1 nT. The   (Torbert et al. 2016b ) allows for regular in-flight calibration of the spin axis offsets, as well. Thereby, overall uncertainties on the order of 0. 1 to maximally 0. 3 nT are regularly achieved in low fields within the regions of interest for the MMS mission  (Plaschke 2019). Hence, we can use the MMS data as a standard against which the accuracy of the Tsyganenko model as well as of the GK-2A magnetometer offset calibration can be judged. For this comparison, we used SOSMAG AC cleaned data of 1 March 2019 of which the offset vector (10. 9, 37. 6, -90. 6) nT in Hpen and the T04 model field were subtracted. The Kp index at this time was 4. The result is shown in blue in the top three panels of Fig.
21. In red we show the difference between the fluxgate magnetometer data of MMS spacecraft four and the corresponding T04 field data, also in Hpen coordinates. Within the interval shown, the two spacecraft were located at distances below one Earth radii. As it can be seen, the instantaneous difference between the measured data and the model field can easily become as large as a few nT. Even the MMS 4 data show this, which we regard as a highly accurate standard for comparison. If the GK-2A magnetometer data were equally accurately calibrated, then we would expect the differences to the model field to be equal to those determined from MMS 4 data during closest approach. For the Hp and He components this is indeed approximately the case within MMS accuracy margins. For the Hn component, however, we see a systematic difference between GK-2A and MMS data of approximately 2 nT.
g. Kavosi and Raeder 2015). SOSMAG aboard GK-2A is able to measure and clearly resolve even small-scale ULF wave activity on geostationary orbit. This can be seen in Fig. 25 , which displays the SOS-MAG IMF data for 11 May 2019 in the dayside noon and afternoon local time sectors. The ULF fluctuations can be discerned already in the Hpen time series (top panel). After subtracting a 10 minute moving average, they are more clearly visible in the second panel. The first wave packet between 2:20 and 4:20 UT occurs during southward IMF conditions (see GSM Z in third panel), and may therefore be associated to reconnection or to resulting flux transfer events (e. g. Lockwood and Wild 1993). Such an explanation would, however, raise the question of why the wave activity did not start earlier when the IMF was pointing even more in southward direction.
Data Product Flow SOSMAG data are transmitted from GK-2A to the South Korean ground station in real-time and first stored in the GK-2A ground system managed by the National Meteorological Satellite Center of the Korea Meteorological Administration (KMA). From there, the data are retrieved by the SOSMAG Ground Processor (GP), which is installed at the Space Weather Service Centre of ESA, every minute. Data within the GP is processed according to the flow presented in Fig. 26. Data which are retrieved from KMA are used to process both, on-board cleaned and raw data (dependent on availability) from binary data to magnetic field data in nT. The presented processing flow is executed three times with different delays as well as different parameter qualities. The properties of the resulting data sets are summarized in Table  7.
From October 2020, the processed data will be made available within the frame of ESA's Space Weather Portal (http://swe. ssa. esa. int/) which is open to registered public users free of charge. A Representational State Transfer (REST) Application Programming Interface (API) like the Heliophysics Application Programming Interface (HAPI) managed by NASA is currently in discussion, but not yet decided. In parallel to the provision of the data via the Space Weather Portal, which mainly supports the space weather application driven activities, it is planned to make the final data (Table  7 ) available in Common Data Format from a server at the Space Research Institute of the Austrian Academy of Sciences.
Summary and Conclusions The Service Oriented Spacecraft Magnetometer (SOSMAG) is equipped with four magnetic field sensors whereas two sensors are mounted on an approximately one meter long boom, which was developed as part of the instrument. Aboard the South Korean meteorological Real-time data are generated almost immediately, but uses cleaning and calibration parameters which were determined with data which were measured up to 98 days before. Preliminary 1-2 days Preliminary data are generated in the same way as real-time data, but they include data that might have arrived late, e. g. due to service outages.
Final 98 days Final data are processed using the newest parameter sets that were generated specifically for these data. In addition, data quality flags are added. and environmental satellite GEO-KOMPSAT-2A (GK-2A), SOSMAG was launched into a geostationary orbit at a geographic longitude of 128. 2 • East as part of the Korea Space Environment Monitor instrument suite on 4 December 2018. The nominal operation of SOSMAG started on 25 February 2019. The hosting spacecraft was designed and built without magnetic cleanliness requirements, and the location of the boom and of the two magnetic sensors within the spacecraft was chosen without magnetic cleanliness related considerations. Furthermore, the SOSMAG team had no access to operation time tables and housekeeping data of the spacecraft during the first 12 months of operation.
The combination of multiple sensors with at least two sensors on a short boom was chosen to achieve an accuracy level which is good enough for space weather related forecast and science under conditions which are not ideal for spaceborne magnetic measurements. It has been the goal to derive the correction and calibration parameters during the first months of operation with post-processing on ground so that, after an upload of the parameters to the spacecraft, SOSMAG is able to process corrected and cleaned magnetic vectors on board. In an ideal case, this should allow for a change of the data transmission mode from the raw data of the four sensors with one vector per second each to a cleaned data product from a virtually single sensor with four vectors per second. The cleaning of the SOSMAG data from dynamic disturbances generated by the GK-2A spacecraft is done with a newly developed Principal Component Gradiometer (PiCoG) method which employs principal component analysis to decouple multiple disturbance sources and to minimize the introduction of artefacts to the components free of the targeted disturbance.
The magnitudes of the identified disturbances, which can be as large as 40 nT in the sensor furthest away from the spacecraft, are reduced by a minimum factor of 2 and a maximum factor of 35, depending on the location and the signature of the disturbance. The magnitude of the largest remnant field in the cleaned data from a spacecraft disturber is 2. 0 nT. A better reduction is hindered by the quite high intrinsic noise as well as by the non-ideal mounting location of the two AMR based spacecraft sensors. A magnetic screening campaign has been conducted at KARI facilities pre-flight. The magnetic signatures of the reaction wheels and the Advance Meteorological Imager (AMI), which is the primary payload of GK-2A, have been investigated by the SOSMAG sensors and two fluxgate based gradiometers which were placed nearby the spacecraft.
It was demonstrated that the offset is below the required 5 nT. Furthermore, the drift of the offsets over 10 months is less than 7 nT. The discussion of two science phenomena observed by SOSMAG, a substorm event and ultra-low frequency waves, clearly demonstrate the suitability of the SOSMAG data for space weather forecast applications in particular and magnetospheric science in general. This is of specific importance because the magnetic field measurements aboard GK-2A complement the measurements of the geostationary GOES satellites in a completely different longitudinal sector. Future work could include a further reduction of the remaining artefacts in the final data product with a focus on lessening the temperature driven sensor oscillations with an epoch based identification and correction of the offset drift.
and in-flight calibration has been funded by the  Space Safety (formerly Space  was funded by the  General Support Technology Programme of ESA  (project no. 4000105630 ). Both optional programmes were accordingly and gratefully supported by the national space agencies of the participating countries:  German Aerospace Center, Austrian Aeronautics and Space Agency and UK Space Agency. We acknowledge the use of magnetometer data from the GOES satellites, Level 2 fluxgate magnetometer and fast plasma investigation data from the Magnetospheric Multiscale (MMS) mission, solar wind data from the OMNI webpage of NASA as well as ground based measurements from the THEMIS website. The MMS and OMNIWeb data are stored at https://lasp. colorado. edu/mms/sdc/and https://omniweb. gsfc. nasa. gov/,respectively.
Background In the two main adverse event reporting databases, Vigibase (the World Health Organization's database of individual case safety reports from 130 countries) and FAERS (US Food and Drug Administration Adverse Event Reporting System), the number of sudden cardiac deaths reported with potential COVID-19 treatments are, respectively, 81 and 105 for hydroxychloroquine, 26 and 48 for lopinovir/ritonavir, 54 and 151 for imatinib and 27 and 251 for azithromycin. Although a degree of underreporting of adverse events in these databases is likely, it is worthwhile noting that hydroxychloroquine or azithromycin, for instance, have been prescribed for decades to hundreds of millions of patients worldwide. In a recent study by Raoult et al. on the potential benefits of combined hydroxychloroquine and azithromycin  [5] , an ECG was performed before initiating treatment and at day 2.
All other QT-prolonging drugs were stopped, and the treatment was not started in patients with a QTc > 500 ms. Our cardiology colleagues in Marseille have collected electrocardiogram (ECG) data from 502 inpatients and outpatients who have received this drug combination (Baptiste Maille et al. manuscript under review). In their study, hydroxychloroquine/azithromycin was withheld for cardiac reasons (either clinical or electrocardiographic) in 2. 3%. Among the 413 patients in whom both baseline and day 2 ECGs were available, the mean QTc prolongation was modest. Nine patients had QT prolongations of > 60 ms, none of whom had symptomatic cardiac events. In another series of 84 inpatients with more severe symptoms of COVID-19, including some in critical care, hydroxychloroquine/azithromycin prolonged the QTc to > 500 ms in 11%  [6].
Aethalometer (AE-31) 203 In this study, the equivalent BC (eBC) concentration in the lower atmosphere (around 3 m a. g. l) 204 was measured by an AE-31 aethalometer  (Gundel et al. , 1983) , during the 3-days campaign. The device is 205 equipped with 7-wavelengths (370, 470, 520, 590, 660, 880, 950 nm) and determines the attenuation 206 coefficient by using the ratio of light attenuated through a sensing spot (where aerosols are deposited) and 207 a referenced clean spot, both on a quartz fiber filter substrate. The sampling and reference spots surface 208 areas are 0. 5 cm 2 , while volumetric flow rate is 4 l min -1. The flow rate was calibrated with a TetraCal 209 (BGI Instruments) volumetric airflow before and after the field campaign. A 5 minutes temporal 210 resolution was used for data acquisition.
However, due to the low background concentration in the Arctic, 211 the signal/noise ratio is high, so that data were average in an hour interval. Most of the filter-based 212 techniques used to measure the aerosol absorption coefficient and eBC suffer from different systematic 213 errors that must be corrected. In case of the aerosol absorption coefficient, the most important are the 214 corrections for multiple scattering by the filter fibers and aerosol particles, and for filter loading effects. measurements of 5 days. In order for the retrieved eBC mass concentration from the two instruments to be 233 equal during the overlapping period, the PSAP eBC was calculated dividing the absorption measurements 234 (at 530 nm) with a MAC equal to 7. 25 m 2 g -1 (keeping the AE31 data as reference).
The calibration points were fitted using 269 a linear fit. The mass equivalent diameter is calculated assuming the sphericity of the BC particles and an 270 effective density of 1. 8 g cm -3 (Moteki and Kondo, 2010). 271 The SP2 data were analyzed using the IGOR based toolkit from M. Gysel (Laboratory of 272 Atmospheric Chemistry, Paul Scherrer Institute, Switzerland). The large amount of signals derived from 273 every single particle are elaborated achieving rBC mass and number concentrations and size distributions. 274 It's important to remark that the eBC and the rBC mass concentrations are not exactly the same physical 275 quantities: the former is obtained from an absorption measurement assuming a constant MAC, whereas 276 the second is obtained via a laser-induced-incandescence method with an SP2 empirically calibrated with 277 a reference material  (Petzold et al.
, 2015). The meteorological and surface 302 radiation measurements are available in a 1 minute time resolution via the PANGAEA data repository 303  (Maturilli et al. , 2020). The daily/hourly mean values of the meteorological parameters were used in the 304 statistical analyses of the 80-days/3-days experiment and in Figure  2  and Figure  3  (the physico-chemical 305 parameters from the snow samples are instead punctual value). 306 307.
Back trajectories calculation and Potential Source Contribution Function analysis 331 Air mass back-trajectories (BT) were calculated using the NOAA ARL HYSPLIT 4 rev. 513 332 transport model  (Stein et al. , 2015). Global Data Assimilation System (GDAS) meteorological input 333 fields with 0. 5x0. 5 degree resolution and a propagation time of 240 hours was employed. The trajectories 334 https://doi. org/10. 5194/acp-2020-574 Preprint. Discussion started: 8 July 2020 c Author(s) 2020. CC BY 4. 0 License. were calculated every hour for an endpoint of 500 m above ground level in Ny-Ålesund. A potential 335 source contribution function (PSCF) analysis has been applied to the BTs exploiting a specifically 336 developed FORTRAN computer code  (Petroselli et al. , 2018). That analysis considered BC concentration 337 measured in the air by both AE31 and PSAP.
Briefly, the method calculates the probability of finding a 338 source of a particular pollutant on a certain region by superimposing grid cells to it and estimating the 339 fraction of the total time spent on each cell by trajectories associated with a high concentration measured 340 at the receptor site. The 90th percentile was used to define the high concentration limit and cells of 3 x 3 341 degrees (lat-long) were exploited in the calculation of probabilities. Details of the PSCF methodology 342 employed here are described in  Petroselli et al. , (2018). The data of the active fires, covering the last 12 343 days before the sampling day, are from the MODIS active fire products (https://firms. 344 modaps. eosdis. nasa. gov/firemap/), offered by NASA LANCE. 345 346.
CCD camera data were corrected for read-out noise, distortion, flat-field and background noise. In situ experiments were carried out using a dedicated furnace in which the samples, thinned to 70-100 μm, were placed. The diameter of the X-ray beam was 1 mm, and the signal was recorded from a location placed at the mid-distance between the center and the edge of the HPT-processed disc in order to probe a reproducible microstructure from one sample to the other and to be able to correlate this data with STEM observations. The SAXS 2D-data was radially averaged and normalized into the 1D-profile of scattered intensity I versus the scattering vector amplitude q; detailed procedures for data interpretation and used model can be found in  [9] [10] [11]. This model needs some hypothesis which can be given by other experimental methods like precipitates composition in Zn (around 53%) and their shape (assumed to be spherical).
Acknowledgements Both the  Agence Nationale de la Recherche  and the joint  CNRS-JSPS 2017 summer program  for the support to the trans-national collaboration are gratefully acknowledged for financial support ( PRASA  project -ANR-15-CE08-0029 ). This work was also supported in part by  Grant-in-Aid for Scientific Research (S)  from the  MEXT, Japan  (No. 26220909 ). HPT was carried out in the  International Research Center on Giant Straining for Advanced Materials (IRC-GSAM ) at  Kyushu University. Thomas Dorin at Deakin university is thanked for casting the alloy.
Similarly, about 50% of AGA pausing genes with increased AGA occupancy intersected between B6J. Gtpbp1 -/-and B6J-Gtpbp2   S1 ). Gene ontology (GO) analysis of pausing genes revealed enrichment in numerous biological processes and the majority of these processes were enriched in both Gtpbp1 and Gtpbp2 (Figure  2  -figure  supplement 1C ). Thus, our data suggest that both GTPBP1 and GTPBP2 rescue stochastic ribosome pausing events when the tRNA Arg UCU pool is reduced. To determine if transcriptional alterations are shared upon loss of Gtpbp1 and Gtpbp2, we performed RNA-Sequencing analysis (Table  S2 ). Comparison of data from the cerebellum of 3week-old B6J. Gtpbp1 -/-and B6J mice revealed differential expression (q-value ≤ 0. 05) of approximately 12% of detected genes, with 46% upregulated and 54% downregulated.
, 2000). However, we show here that loss of Gtpbp1 leads to widespread neurodegeneration in B6J mice. As observed in Gtpbp2 -/-mice, neuron loss in Gtpbp1 -/-mice is dependent on a mutation in B6J strain that disrupts n-Tr20 tRNA Arg UCU processing  (Ishimura et al. , 2014). Ribosome profiling of the Gtpbp1 -/-cerebellum revealed increased ribosome occupancy at AGA codons as we previously observed for Gtpbp2 -/- mice  (Ishimura et al. , 2014). These data suggest that both GTPBP1 and GTPBP2 function as ribosome rescue factors and are essential genes in many neurons when n-Tr20 levels are diminished. In addition, the lack of neurodegeneration in mice heterozygous for mutations in both Gtpbp1 and Gtpbp2 and the lack of an additive phenotype in the brain of mice lacking both genes, suggest that these trGTPases function in the same pathways to mitigate ribosome stalling, perhaps mediating different steps in this pathway.
Ribosome profiling data analysis Reads were clipped to remove adaptor sequences (CTGTAGGCACCATCAAT) using fastx_clipper and trimmed so that reads start on the second base using fastx_trimmer (http://hannonlab. cshl. edu/fastx_toolkit/). Reads containing ribosomal RNA were then filtered out by mapping to a ribosomal RNA reference using bowtie2 version 2. 2. 3 using parameters -L 13  (Langmead and Salzberg, 2012). Remaining reads were mapped to a mm10 mouse reference using a Gencode M20 annotation, or a Gencode M20 transcript reference using hisat2 version 2. 1. 0  (Kim et al. , 2019). Ribosomal A-sites were identified using RiboWaltz version 1. 0. 1  (Lauria et al. , 2018) , and read lengths 29-33 were retained for further analysis. Observed/expected reads were calculated for each transcript with alignments with the expected reads being the read density expected at a given site with a given codon, assuming that reads are uniformly distributed across the coding part of the transcript.