dataesr/scientific-paragraphs-categorization huggingface dataset:
physics, the study of TLEs became one of the main physical subjects of interest after their discovery in 1989. In the years 1992 -1994 detection was performed from satellite, aircraft and space shuttle and recently from the International Space Station. These events have short duration (milliseconds) and small scales (km to tens of km) and appear at altitudes 50 -100 km. Their nature is still not clear and each new experimental data can be useful for a better understanding of these mysterious phenomena.
The detector JEM-EUSO will be attached to the Japanese Experiment Module (JEM) of the International Space Station (ISS) at an orbit of 400 km above the surface of the Earth. The JEM-EUSO detecting system consists of a telescope with three Fresnel lenses and a focal surface made of photomultipliers and associated electronics, which converts the photons collected by the lenses into photoelectrons. The focal surface consists of some 3 • 10 5 pixels, each with an independent read out. The diameter of the telescope aperture is about 2. 5 meters. The instrument will work in two modes: the nadir mode and the tilted mode. It is planned to have the detector work in the nadir mode for 2 years and then in the tilted mode for further 3 years. The time unit used for sampling the data is 2. 5 microseconds.
The field of view (FoV) of the detector is 60 •. The wavelengths measured by the detector may range from 290 to 430 nm. In addition to this main telescope part, the detection system is completed by an Atmospheric Monitoring system consisting of an Infrared Camera and a LIDAR to provide information concerning the cloud top height. A calibration system, which measures the efficiencies of the optics, of the focal surface and the data acquisition system is provided as well.
Estimation of TLE frequency for JEM-EUSO based on Tatiana-2 experimental data The global occurrence of TLE can be obtained in space-borne experiments. Only few detectors devoted to TLE measurements were launched to the orbit: ISUAL on board FORMOSAT-2 satellite  [7] , UV detector on board Universitetsky-Tatiana satellite  [8] , UV and IR detector on board Universitetsky-Tatiana-2 satellite  [9]  and JEM-GLIMS on board ISS  [10]. ISUAL and JEM-GLIMS have spatial resolution and provide spectral measurements in wide wavelength bands, but Tatiana-1 and Tatiana-2 detectors provide measurements in 240 -400 nm, in the same wavelength range as EAS fluorescent radiation. It is important to mention that in both Tatiana experiments and in JEM-EUSO PMTs with UV filters are used to measure radiation. Oscillograms of fast luminous events are recorded.
The data of Tatiana-2 satellite of TLE events is recorded in a digital way and consists of 128 ms oscillograms of ADC code (N ADC ) and PMT high voltage code (M). ADC code means the signal amplitude and M corresponds to a PMT gain. The average rate of TLE events from this data is 0. 13 min -1 or 10 -4 hr -1 km -2. The instantaneous frequency of TLE in local thunderstorm area could be significantly higher (∼ 10 -3 hr -1 km -2 ). It is worth noticing that this number is order of magnitude higher than TLE frequency, measured by ISUAL  [12, 13]. It could be explained by the sensitivity of the detector whose instrumental threshold is ∼ 10 20 in photon number. As it was mentioned above, these faint flashes are distributed more evenly in global map. ISUAL can not measure them. To estimate the number of TLE events which could be measured by JEM-EUSO or influence its operation, we should take into account the ratio of detectors fields of view.
Conclusions and the future The frequency of occurrence of the TLE events is large. Their characteristics have been described by different groups working with detectors into orbit and with on ground detecting systems. JEM-EUSO is unique in that its time and spatial resolution is very high and will allow time evolution measurement. Two more experiments dedicated to measuring TLEs will soon be put into orbit: ASIM and Taranis. The set of data produced by all these experiments may create an exceptional chance to further investigate the spatial and time evolution of the TLEs. The amount of light produced by the two most common types of TLEs does not pose danger to the photomultipliers of the detector. However, these phenomena constitute a significant background to the detection of UHECRs. Many aspects of the detection of the signal from the TLEs remain to be considered in next developments of this work.
Historically, the scarcity of in situ data for validation has affected our ability to accurately simulate contemporary mean NPP and its variability in the AO  [Wassmann and Reigstad, 2011] ; however, a recent data compilation  [Hill et al. , 2013; Matrai et al. , 2013; Codispoti et al. , 2013]  including historical NPP, chlorophyll-a concentration (hereafter chlorophyll), and nutrient data , as well as recent in situ data collected during the  International Polar Year (2007 -2008)  are now available, together with data from more recent polar research field campaigns during 2009-2011. These data sets allow validation and calibration of OCMs and BOGCMs in the AO, as well as the testing of multiple ecological hypotheses [e. g. ,  Ardyna et al. , 2013; Arrigo et al. , 2011]. In order to better understand the strengths and limitations of currently available NPP estimates for the AO, we conducted a Primary Productivity Algorithm Round Robin (PPARR) activity to assess the skill and sensitivity of NPP models in the AO.
, 2006]. This was followed by an evaluation of model skill in $30 models (model-in situ data comparisons), and specifically focused on a large, quality-controlled database ($1000 measurements) from the tropical Pacific  [Friedrichs et al. , 2009]. By examining uncertainties associated with the input variables (e. g. , surface chlorophyll, PAR, and SST), it was determined that more than half of the model-data NPP differences could be accounted for by errors/uncertainties in these input variables. The PPARR4 study expanded these comparisons with in situ data in key regions critical for NPP models such as coastal waters, the oligotrophic subtropics, and the Southern Ocean  [Saba et al. , 2010 [Saba et al. , , 2011]]. These previous PPARR studies revealed that ocean color model skill was lowest in coastal waters where NPP was typically overestimated.
Data and Methods We assembled a quality-controlled in situ database that includes a set of productivity-related variables at 455 stations sampled mostly in the Chukchi, Beaufort, Barents, and Nordic Seas, and the Canadian Archipelago from 1988 to 2011 (Figure  1a ). This database was built upon the ARCSS-PP data set  [Matrai et al. , 2013] , available at NOAA/National Oceanographic Data Center (NODC) (http://www. nodc. noaa. gov/cgi-bin/OAS/ prd/accession/details/63065), and was supplemented with more recent international polar research activities (Table  1 ). Also, daily sea ice concentration at each sampling station was determined using NOAA/ National Snow and Ice Data Center (NSIDC) Climate Data Record of Passive Microwave Sea Ice Concentration, Version 2  [Meier et al. , 2013]. Roughly half of the stations (47%) were sampled in ice-free regions (sea ice concentration < 15%, a threshold to determine sea ice extent used by NOAA/NSIDC) (Figure  1b ).
In Situ Net Primary Productivity The 455 stations (Table  1 ) were chosen such that (1) NPP measurements, using 13 C-or 14 C-labeled compounds, were available at four or more different depths, (2) the minimum sampling depth of NPP was between 0 and 5 m depth, and (3) additional in situ data were available including surface chlorophyll, sea surface temperature (SST) and mixed layer depth (MLD). Most in situ NPP using a 14 C label (N5324) were measured after 24 h in situ or simulated in situ incubations and the rest of the 14 C data (N550) were measured after less than a 12 h incubation or an unknown time period; most data were reported as daily values. In situ carbon uptake rates using a 13 C label (N581) were measured after 3-6 h on-deck incubations  [Lee and Whitledge, 2005; Lee et al. , 2010] ; a good agreement between estimates of 13 C and 14 C productivity has been reported  [Cota et al.
Input Variables The majority of NPP models participating in this comparison exercise required a variety of input data fields, including surface chlorophyll, PAR, SST, and MLD (Table  3 ). In order to test the sensitivity of the models to the input data provided, two sources of data were provided to the modeling teams for each of the primary input variables: chlorophyll (in situ and satellite), PAR (reanalysis and satellite), SST (in situ and reanalysis), a The data from the ARCSS-PP database  [Matrai et al. , 2013]  are italicized and sub-Arctic regions are labeled as following: 1--Arctic Basin, 2--Beaufort Sea, 3--Canadian Archipelago including Baffin Bay, 4--Chukchi Sea, 5--East Siberian Sea, and 6--Nordic Seas (Greenland and Barents Sea). b See ARCSS-PP database for further details. Journal of Geophysical Research: Oceans and MLD (in situ and climatology).
Surface Chlorophyll and Subsurface Chlorophyll-a Maximum (SCM) For each station where NPP was measured, in situ fluorometric chlorophyll was collected at the surface layer between 0 and 5 m depth (N5455). The comparison of in situ and remotely sensed chlorophyll was limited not only by the scarcity of in situ measurements, but also by contamination of the remotely sensed data due to sea ice and clouds in the AO. The retrieval of chlorophyll did not significantly differ between the 1 day and 8 day averaged match-ups within the period 1998 to 2007  [Matrai et al. , 2013]. In order to maximize the number of satellite matchups, remotely sensed chlorophyll (N585) was retrieved from the level-3, 9 km, 8 day average   2c  and 2d , Figures  2e  and 2f , and Figures  2g  and 2h  show where and when those in situ NPP were measured in terms of latitude, year, and month, respectively.
1002/2015JC011018 concentration (<1 mg m 23 ) and underestimated at higher concentrations (>1 mg m 23 ), which is consistent with the results of  Cota et al. [2004]. There was little difference in in situ chlorophyll between the N5455 and N585 datasets in their magnitude distributions (Figure  3b ). In addition, SCM were defined when the maximum chlorophyll in the water column was greater than 1 mg m 23 and more than 150% of the surface value. Among the 85 stations, SCM existed at 44 stations and were absent at 39 stations: two stations were excluded because no chlorophyll profile was available. At the stations with SCM, the mean in situ logNPP was 2. 64 (437 mgC m 22 d 21 ). At the stations without SCM, the mean in situ logNPP was 2. 37 (234 mgC m 22 d 21 ).
Photosynthetically Active Radiation (PAR) Reanalysis estimates, incorporating observations and numerical simulations with data assimilation, of daily average downward solar radiation flux at $1. 98 resolution (N5455) were obtained from NOAA/National Centers for Environmental Prediction (NCEP; http://www. esrl. noaa. gov/psd/data/gridded/data. ncep. reanalysis. surfaceflux. html) and converted to PAR by multiplying by 0. 43  [Olofsson et al. , 2007]. In addition, satellite PAR matchups (N585) were extracted from the level-3, 9 km, 8 day average imagery from SeaWiFS (N513 from November 1997 to June 2002) and MODIS-Aqua (N572 from July 2002 to October 2011). Reanalysis and satellite PAR were highly correlated (N585, r50. 79, p<0. 01) and the range of values was similar between them, but the reanalysis PAR was generally higher than the satellite values (Figure  3c ).
Mixed Layer Depth (MLD) Estimates of MLD were calculated using CTD profiles from corresponding polar research cruises and from the NOAA/NODC ArcNut database. In order to calculate in situ MLD (N5455), we first used CTD data to compute potential density (q h ), disregarding surface values between 0 and 5 m due to possible instrumental errors. Then, MLD was estimated for each profile by using a threshold criterion of Dr h 5 0. 1 kg m 23 [Peralta-Ferriz and Woodgate, 2015], where Dr h 5 r h (z)r h (z min ); r h (z) and r h (z min ) are the potential density anomaly (r h 5 q h -1000 kg m 23 ) at a given depth (z) and the shallowest depth between 5 and 10 m (z min ), respectively. For climatological MLD (N585), we used the Polar Science Center Hydrographic Climatology (PHC3. 0; http://psc. apl.
Model Skill NPP model skill was assessed using root-mean-square difference (RMSD) for each participating model, where N is the number of observations: RMSD 2 5 P N i51 logNPP i model 2logNPP i in situ À Á 2 N 5bias 2 1uRMSD 2 Generally, the smaller a RSMD value becomes, the better a model performs. The RMSD consists of two components: bias representing the difference between the means of in situ and model data and unbiased RMSD (uRMSD; often referred to as centered RMSD) representing the difference in variability between model output and in situ data. Thus, bias and uRMSD provide measures of how well mean and variability are modeled, respectively: bias5log NPP model ð Þ log NPP in situ ð Þ uRMSD 5 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 N X N i51 logNPP i model logNPP model À Á 2 logNPP i in situ logNPP in situ À Á Â Ã 2 r The Target diagram  [Jolliff et al.
1<bias<0. 1) and (2) correlation coefficient was greater than the model average (r>0. 38 for Case 1 and r>0. 54 for Case 9). Four model results (Models 6, 12, 16, and 27) were selected for Case 1 and five model results (Models 3, 12, 14, 26, and 27) for Case 9. Using t-test for equal mean, it was determined that the estimated NPP from the selected models was not statistically different from the in situ NPP N585 mean (not shown). From the cumulative distribution of logNPP N585 for these model results (Figure  9 ), it is again clear that the variability of NPP was underestimated using satellite chlorophyll (Case 1) although mean NPP was reproduced. Unlike Case 1, models using in situ chlorophyll (Case 9) successfully reproduced mean and variance in terms of magnitude distribution. Among the seven selected models, four (Models 3, 12, 14, and 16) required chlorophyll as well as satellite-derived Rrs and two (Models 26 and 27) used empirically derived Z eu_derived that was based on the relationship between in situ Z eu and in situ surface chlorophyll using the AO data sets listed in  mean and/or variability of NPP as compared to when all data were used.
[Codispoti et al. , 2013]Models a reanalysis PAR had lower values (1 to 17 Ein m 22 d 21 ) in the N5455 data set compared to the N585 data subset, and thus provided a wider range of values. 3. 2. 3. Sea Surface Temperature (SST)In situ SST was obtained from surface (0 -5m) CTD data from corresponding polar research cruises and from the NOAA/NODC ArcNut database[Codispoti et al. , 2013](http://www. nodc. noaa. gov/archive/arc0034/ 0072133/). The reanalysis SST, constructed by combining observations from different platforms (satellites, ships, and buoys) on a regular global grid, was retrieved from the NOAA National Climate Data Center (NCDC) Optimum Interpolation SST (daily 1 =4-degree resolution from 1981; http://www. ncdc. noaa. gov/sst/). Although SST ranged from 22 to 128C (Figure.
The Early Medieval valley settlement of Thunau am Kamp in Lower Austria has been under archaeological excavation for 10 years. The site was occupied during the 9th and 10th centuries AD according to potsherds, which seem to indicate two phases of activity: in the older phase ovens were placed in the corners of houses while during the younger phase they are found in the middle of the wall. The present study has been conducted in order to increase the archaeomagnetic database and fill the temporal gap around 900 AD. For this purpose 14 ovens have been sampled for their paleaomagnetic signals. Laboratory treatment generally confirmed that the baked clay has preserved stable directions. Apart from one exception, all the mean characteristic remanent magnetisation directions are concentrated on the Early Medieval part of the directional archaeomagnetic reference curve of Austria at about 900 AD.
Using this curve archaeomagnetic dating provides ages between 800 and 1100 AD, which are in agreement with the archaeological dating. Together with the archaeological age estimates and stratigraphic information the new data have been included into the database of the Austrian curve and it has been recalculated using a new version of RenCurve. The new data confine the curve and its error band considerably in the time interval 800 to 1100 AD. This calibration process also provides probability density distributions for each included structure, which allows for posterior dating and refines temporal errors considerably. Because such dating includes archaeological information it is not an independent age estimate but is a combination of all available dating methods.
Nevertheless, dating can be difficult through analogy to other sites. This problem is further aggravated by the limited use of radiocarbon dating on such sites, since the calibration curve is characterised by three plateaus in the time interval between 650 and 950 AD  (Reimer et al. , 2004). Hence, radiocarbon dating is very imprecise in this time interval. In comparison, the archaeomagnetic calibration curve for Austria shows a strong movement of the geomagnetic field direction from 500 to 1000 AD  (Schnepp and Lanos, 2006). Unfortunately, due to the limited dataset the error envelope is large and accordingly the calibration curve also produces relatively large errors in calendar dates when used for archaeomagnetic dating of this time period. The archaeological site of Thunau am Kamp is situated in the middle Kamp-region in the north-western part of Lower Austria  (Waldviertel, 48.
In 2009 14 of the fired structures were sampled for an archaeomagnetic investigation. These structures could be dated in two ways: on the one hand by using the existing curve  (Schnepp and Lanos, 2006)  taking into account stratigraphic constraints and some evidences for contemporaneity; or on the other hand, by constructing a new curve, which would incorporate the data provided by the 14 new structures with some a priori dating information. Bayesian modelling as part of the new curve construction process could then provide both posterior curves and posterior dating for each structure. The purpose of this paper is to compare the dating results from using both these approaches.
Curve and reference dating estimation using Bayesian modelling The archaeomagnetic dating procedure requires two steps: firstly, a dataset of archaeomagnetic directions obtained from well-dated features for a region of 500 to 1000 km radius. From this dataset an archaeomagnetic reference curve is calculated. Secondly, as soon as such a calibration curve is constructed it can be used for dating other archaeological structures from the same region. A calibration reference curve for Austria has been published by  Schnepp and Lanos (2006). The model used for creating such archaeomagnetic reference curves is based on a formal Bayesian framework for constructing chronologies. A first description of the algorithm was published by  Lanos (2004). All prior information on the observations that includes experimental errors on direction and uncertainty on age as well as stratigraphic information is translated into prior probabilities.
TH4 obviously plots on the younger part of the calibration curve (Fig. 8 ) following the stratigraphy. The time interval of the corresponding archaeomagnetic direction agrees well with the age of the potsherds found in its fill. The investigated archaeological features mostly yield well-defined archaeomagnetic directions, which allow for refinement of the Austrian calibration curve  (Schnepp and Lanos, 2006)  for the Early Medieval period. Although archaeological fine dating of the site is not yet complete, there is good agreement with the reference curve, which underlines its reliability. With age intervals between 20 and 100 years (cf. Table  2  column 3), the dating precision is sufficient to include the new data into the database of the Austrian curve. By doing this, the number of directions in the database has increased from 16 to 29 for the interval between 800 and 1000 AD.
The reference curve has been recalculated using the new version of RenCurve as described above. The results of declination and inclination are shown together with the whole dataset in Fig. 9. In particular, it shows that the 9th century is much better represented by data now. The published marginal curves of declination and inclination  (Schnepp and Lanos, 2006)  are drawn in comparison with the recalculated curves. This shows that the old and new curves are in very good agreement and the error bands become narrower along the whole curves. Apart from this, the effect is stronger in the 9th and 10th century because of the additional data and accordingly dating of this time interval will become more precise. The high accordance between the Thunau data and the archaeomagnetic reference curve underlines that the archaeological dating is reliable and in agreement with other sites in the region, especially from Hungary, which provided most of the archaeomagnetic data for the reference curve (see  Schnepp and Lanos, 2006 and references therein).
The new data have also been used to perform archaeomagnetic dating. This was undertaken using the RenDateModel software in two ways: (1. ) every oven was dated independently and (2. ) stratigraphic constraints according to Fig. 2  and Table  1  were used. The results of the dating procedure are listed in Table  2  and the probability densities are shown in Fig. 10 (a  and b ). Except for structure THM the archaeomagnetic dating confirms the archaeological age estimates, but only for TH6 that dating was confined (cf. Table  2 ). As pointed out already, the archaeological analysis of finds is in progress and not finished yet. So the absolute chronological data, as given in Table  2  column 3, display only the state of research to date, and sampling of features in 2009 should be regarded as a coarse chronological aid for the archaeomagnetic research.
e. the last use of an oven for example. Archaeomagnetic data which were not obtained from already dated archaeological features cannot be used for reference curve building. Dating with stratigraphic constraint or fact Fig. 10. Dating of the Thunau ovens: probability distributions (arbitrary scale) versus calendar time are plotted. They were obtained from dating approaches calculated with RenDateModel software using overlap with 95% confidence of archaeomagnetic direction with secular variation curve (Fig. 8 ). (a) Each feature independently dated, (b) stratigraphic model and facts were used (Fig. 2c ), and (c) posterior probability distributions obtained from Bayesian secular variation curve modelling using the new version of RenCurve software. example on comparison of finds and results from other sites, such data can be used for curve building.
Conclusions New reliable archaeomagnetic directions have been obtained from 13 ovens found in an Early Medieval settlement situated in Lower Austria. The new results fill a gap that exists in the archaeomagnetic database of Austria around 900 AD. These directions agree well with the archaeomagnetic reference curve and contribute to its further refinement between 800 and 1000 AD. Independent of the archaeological age estimates for the site, the archaeomagnetic dating provides an age interval of about 200 years from the late 8th up to the beginning of the 11th century AD for the occupation of the lower part of the settlements at Thunau am Kamp. Generally archaeological age estimates and chronological relevance of different placement of ovens in the houses are supported by the archaeomagnetic directions.