dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, as the mode shapes obtained are quite noisy, if they are normalized to the maximum without precaution, a higher relative standard deviation (RSD) would result (Figure  10a ). To avoid this, the mode shapes are filtered with a low-pass filter to remove the noise disturbance and the normalization factors are deduced from these filtered mode shapes. The low-pass filter is applied by performing zero-phase digital filtering by processing the input data, i. e. , the mode shapes, in both the forward and reverse directions (filtfilt function in MATLAB ® ). The filter used in the filtfilt function is a 4th-order lowpass elliptic filter (ellip function in MATLAB ® ) with 0. 5 dB of passband ripple, 40 dB of stopband attenuation, and a passband edge spatial frequency of 20 m -1 , which, for mode shapes sampled at nearly 592 m -1 , corresponds to 0.
Cantilever Beam Figure  22  shows the averages and standard deviations over the ten tests for the mode shapes, while Tables  4  and 5 , respectively, report the frequencies and the modal damping ratios determined using the full-field measurement and the accelerometer data set. DIC measurements are obtained after a numerical processing performed on each image. The time signal being obtained after a numerical post-processing and not in an analogue way, as for accelerometers, the signal is therefore necessarily subjected to aliasing because no analogue filter can be applied. Thus, the eighth mode observed from DIC measurements, at the frequency of 2178 Hz (see Table  4 ), is a folding frequency of the frequency obtained with the accelerometer measurements for this mode, which is equal to 2327 Hz.
eROSITA After its launch in July 2019, eROSITA started to scan the sky in December 2019 and completed four full sky surveys (eRASS1 to eRASS4) by December 2021. eROSITA consists of seven coaligned Wolter type-I telescopes, each equipped with a camera based on a charge coupled device (CCD) of pn type with an integration time of 50 ms  (Predehl et al. 2021). eROSITA scanned eRASSt J040515. 6-745202 eleven times during eRASS3 between 2021 May 1, 01:51 (MJD 59335. 07738) and 2021 May 2, 17:52 (MJD 59336. 74441; see Fig. 1 ). For the data analysis, we used the eROSITA Standard Analysis Software System (eSASS version eSASSusers_211214_0_3;  Brunner et al. 2022). To extract source and background events for light curves and spectra, we used the eSASS task srctool (see e. g. Maitra et al. 2021; Haberl et al.
Swift Following the eROSITA discovery of eRASSt J040515. 6-745202 (Rau et al. 2021) , we initiated follow-up target of opportunity observations with the Neil Gehrels Swift Observatory  (Gehrels et al. 2004 ). We used the data obtained from the X-ray telescope (XRT) in photon counting mode with a time resolution of 2. 5 s. The observations were performed on 2021 May 12 and a total XRT exposure time of 1682 s was obtained. The XRT data were analysed using standard procedures  (Evans et al. 2007 (Evans et al. , 2009)).
XMM-Newton To investigate the nature of eRASSt J040515. 6-745202, we triggered one of our XMM-Newton anticipated target of opportunity observations (PI FH) to follow up on new hard transient sources in the Magellanic system. For this purpose, we utilised data from the European Photon Imaging Camera (EPIC), sensitive to the 0. 15-12 keV band. Two of the three XMM-Newton telescopes are equipped with Metal Oxide Semi-conductor (MOS) CCD arrays  (Turner et al. 2001 ) and the third with a pn-CCD (a predecessor of the eROSITA CCDs;  Strüder et al. 2001). The observation was performed on 2021 May 14 (observation ID 0860800401, start MJD = 59348. 0497). We used the cameras with medium optical blocking filters, EPIC-pn in large-window readout mode (48 ms time resolution), and EPIC-MOS in full-frame mode (2.
6 s). We obtained net exposure times of 25. 7 ks and 27. 9 ks for EPIC-pn and EPIC-MOS, respectively, after we removed intervals of high background flaring activity, which occurred only at the end of the observation. XMM-Newton/EPIC data were processed using the XMM-Newton data analysis software SAS, version 19. 1. 0 2. We used the SAS task evselect for event extraction from circular regions around the source (45 radius) and nearby background (60 radius). Events with PATTERN 1-4 for EPICpn  (Strüder et al. 2001)  and PATTERN 1-12 for EPIC-MOS  (Turner et al. 2001)  were selected. We created light curves in the energy bands 0. 2-2. 2 keV, 2. 2-8. 0 keV, and 0. 2-8. 0 keV, applying the standard filtering with flags #XMMEA_EP and #XMMEA_EM for EPIC-pn and EPIC-MOS, respectively. For spectra we used the conservative FLAG=0 filtering and created response files using the SAS tasks arfgen and rmfgen.
NICER The NICER X-ray Timing Instrument (XTI,  Gendreau et al. 2012 Gendreau et al. , 2016 ) is a non-imaging, soft X-ray telescope on board the International Space Station (ISS). The XTI consists of an array of 56 co-aligned concentrator optics, each associated with a silicon drift detector  (Prigozhin et al. 2012) , operating in the 0. 2-12 keV band. The XTI provides high time resolution (∼100 ns) and spectral resolution of ∼85 eV at 1 keV. It has a field of view of ∼30 arcmin 2 in the sky and an effective area of ∼1900 cm 2 at 1. 5 keV (with 52 active detectors). For the current study, we used NICER data obtained between MJD 59339 and 59346 (i. e. before the XMM-Newton observation) which were grouped into six observation intervals (observation IDs 4570010101-6). Data were analysed with HEASOFT version 6.
29c, NICER DAS version 2020-04-23_V007a, and the calibration database (CALDB) version 20200722 (redistribution matrix and ancillary response files). For event selection and background filtering, we used standard screening criteria and intervals with magnetic cut-off rigidity between 1-15 GeV/c. Since NICER is not an imaging instrument, the X-ray background must be calculated from a database of data collected from empty background fields. The 'space weather' method  (Gendreau et al. , in prep. ) contains functions which can be used to create the background spectrum based on estimates of the space weather environment. On the other hand, the 3C50 tool  (Remillard et al. 2022 ) uses a number of background proxies from each NICER observation to define the basis states of the background database.
7. The phases covered by the XMM-Newton observation are indicated at the top of the plots. For the case of a period of 78 ks, dips are expected at the end of the XMM-Newton observation as they are observed (Fig. 2 ). In particular, from the EPIC light curves, it is evident that the associated decrease in flux does not follow the pattern expected from total or partial eclipses, but it more so resembles a dipping behaviour. Finally, we investigated the NICER data for possible X-ray bursts using 5 s and 10 s time bins, but no bursts were identified. This is not unexpected since the combined duration of the NICER exposures is shorter than the XMM-Newton observation.
e. exposure of 8 ks). Data were initially fitted by an absorbed power law while a soft disk blackbody was added to account for residuals at low energies. Best-fit (PG statistics of 880 for 764 d. o. f. ) parameter values are given in Table  1 , while the best-fit model is plotted in Fig. 10. We then compared the intensity of the low states seen in the NICER light curve with the NICER background level. For that, we extracted source and background spectra using the respective time intervals. This resulted in a net count rate of 0. 66 cts s -1 and a background of 0. 57 cts s -1 , using the 3C50 model. Consequently, the low states are marginally brighter than the background level, and the source almost fades away during these intervals. We also fitted the eROSITA spectrum with the twocomponent model of a power law and disk blackbody.
The total exposure time was 20. 98 ks, which is significantly higher than the UVOT exposure. We downloaded the pipeline products generated from the OM data from the XMM-Newton science archive 7. The OM/UVW1 image is shown in Fig. 12  (right). UVOT source 1 is also detected in the OM image (source ID 44 in the OM detection list). Source 1 is ruled out in any case as the UV counterpart of eRASSt J040515. 6-745202 by the improved XMM-Newton position. A clear source (OM 42) is detected inside the 2σ XMM-Newton/EPIC error radius with a UVW1 Table  1. Results of spectral fits to the X-ray spectra of the persistent emission. Instrument N H /10 20 (a)  kT   Comparing OM and UVOT images (Fig. 12 ), UVOT source 2 might suffer from a cross-like feature, which is visible in the UVOT image. This could be caused by statistical background fluctuations due to the short exposure time or an independent transient source (at the position of UVOT 2) blended with OM 42.
We regard OM source 42 as more reliable as its shape is mostly circular, which is consistent with the point spread function of the instrument. We found no counterpart at any wavelength within 5 to OM source 42 using the VizieR catalogue access tool 8. Γ C/PG-stat χ 2 d. o. f. F obs /10 -12 (b) L/10 36 (c) R in × √ cos θ (d) (cm -2 ) (eV) (erg cm -2 s -1 ) (erg s -1 ) (km) An optical counterpart was identified in archival data from DECam  (Flaugher et al. 2015) , a wide-field CCD camera on the Blanco 4-m telescope at the Cerro Tololo Inter-American Observatory. Twenty-four exposures in g, r, i, and z filters from three programmes were used (including the DECam eROSITA Survey, DeROSITAS, an optical companion survey to the eROSITA survey). The exposures were instrumentally calibrated by the 8 http://cdsarc.
Acknowledgements. This work is based on data from eROSITA, the soft X-ray instrument aboard SRG, a joint  Russian-German  science mission supported by the  Russian Space Agency (Roskosmos) , in the interests of the  Russian Academy of Sciences  represented by its  Space Research Institute (IKI) , and the  Deutsches Zentrum für Luft-und Raumfahrt (DLR). The SRG spacecraft was built by  Lavochkin Association (NPOL)  and its subcontractors, and is operated by NPOL with support from the  Max Planck Institute for Extraterrestrial Physics (MPE). The development and construction of the eROSITA X-ray instrument was led by  MPE , with contributions from the  Dr. Karl Remeis Observatory Bamberg & ECAP (FAU Erlangen-Nürnberg) , the  University of Hamburg Observatory , the  Leibniz Institute for Astrophysics Potsdam (AIP) , and the  Institute for Astronomy and Astrophysics of the University of Tübingen , with the support of  DLR  and the  Max Planck Society.
The  Argelander Institute for Astronomy of the University of Bonn  and the  Ludwig Maximilians Universität Munich  also participated in the science preparation for eROSITA. The eROSITA data shown here were processed using the eSASS software system developed by the  German eROSITA consortium. This work used observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA. The  XMM-Newton  project is supported by the  DLR  and the  Max Planck Society. Work on NICER science at NRL is funded by  NASA  and the  Office of Naval Research. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published in Ochsenbein et al.
Discussions of outcomes The project is approaching its final stage. An impressive database of fungal organisms and insects have already been collected and partially analyzed by the research groups of the different countries involved in the project. Taxonomical results will be presented to the international scientific communities in peer review journals in the near future. Such an outcome represents a step forward in the study of non-native forest tree pests. Furthermore, it successfully tests the double approach based on In-patria and Ex-patria concept, validating its use for the early detection of insects and fungal pathogens potentially harmful for the examined trees. A wide, intercontinental use of Sentinel Plantings was extensively performed during this project, aiming to prove its reliability and efficiency as investigation method in invasion science.
Acknowledgements This study was funded by  European Commission Horizon 2020 Research and Innovation Programme 'HOlistic Management of Emerging forest pests and Diseases' (HOMED)  (grant No  771271 ). MAA, AR, LR and FJT acknowledge support from  IFOPE , the  Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia , created between  INRAE URZF and Beijing Forestry University , and funding from the  French Embassy in Beijing.
Our paper is organized as follows: In Section 2, we briefly introduce the aircraft instrumentation and sampling strategy applied during the field campaign in the Southern North Sea. We describe the mass balance method used for the calculation of CH4 fluxes and give an overview of the emission inventories. In Section 3, we discuss our measurements and compare the estimated fluxes with the annualized Global Fuel Exploitation Inventory (GFEI)  (Scarpelli et al. , 2019) , UK annually reported data (UK National Atmospheric Emissions Inventory (NAEI), UK Environmental and Emissions Monitoring System database (EEMS)), and with individual reporting by operators of the sampled Dutch platforms. Additionally, we compare our estimated fluxes with ship-based measurements, which were taken around the sampled Dutch platforms in 2018  (Hensen et al.
The DHC6 Twin Otter research aircraft, operated by the British Antarctic Survey (BAS), was equipped with several instruments to collect in situ data of atmospheric trace gases. A Picarro G2311-f 10 Hz Analyser measured dry-air CH4 and CO2 mole fractions at a response time of 0. 4 s and at a precision of 1. 2 ppb (1σ @ 1 Hz) for CH4. A tuneable infrared laser direct absorption spectrometer (TILDAS, Aerodyne Research Inc. ) was deployed to detect C2H6 (response time < 2 s; precision 50 ppt over 10 s)  (Yacovitch et al. , 2014). To assess boundary layer physics, sensors for temperature, pressure, humidity and 3D-wind were mounted at the front nose of the aircraft. A NOAA "Best Air Turbulence" probe was installed at the boom of the aircraft and provided wind measurements at a resolution of 50 Hz  (Weiss et al.
Globally gridded annual inventory of CH4 emissions from fossil fuels exploitation The Global Fuel Exploitation Inventory (GFEI)  (Scarpelli et al. , 2019 ) is a globally gridded 0. 1° x 0. 1° inventory containing CH4 emissions arising from fossil fuel exploitation for the year 2019. National emission totals, which are based on countryspecific emission factors, are reported to the UNFCCC (United Nations Framework Convention on Climate Change) and used in the inventory for a spatial downscaling to the locations of potential sources  (Scarpelli, 2020). Thereby, global data sets for oil and gas infrastructure are used. The UK UNFCCC reporting for emissions from the offshore oil and gas exploitation is based on the UK Environmental and Emissions Monitoring System (EEMS) database  (Brown et al. , 2022)  and the Dutch reporting is based on the Dutch Pollutant Release and Transfer Register  (Honig et al.
, 2022). In the UNFCCC reported data, fugitive emissions are already categorized into subsectors, whereas venting and flaring emissions are reported as totals. Thus, the latter are disaggregated by the inventory to the subsectors using IPCC Tier 1 methods  (IPCC, 2006). As a result, the inventory resolves the different fossil fuels sectors (oil, gas, coal) and associated subsectors (distribution (fugitive), exploration (fugitive + venting + flaring), processing (fugitive, flaring), production (fugitive, flaring), storage (fugitive) and transmission (fugitive, venting)). We compare our emission estimates with the GFEI v2 data set for total global fuel exploitation for gas from the Harvard Dataverse  (Scarpelli et al. , 2019). Thereby, we take the inventory data given for each grid cell (Mt/km 2 ) and calculate the emission from the grid cell area.
UK annual point-source inventories The The UK National Atmospheric Emissions Inventory (NAEI) is an emission database listing all UK point-sources and is provided by BEIS. For offshore oil and gas installations it is based on the EEMS inventory  (Tsagatakis, et al. , 2022). In the NAEI inventory, emission data is aggregated for all platforms associated with a certain oil or gas field (NAEI, 2020). Offshore emission data is available for CH4 and for CO2. The fluxes observed in this study arise from installations within a certain field and are compared to the inventory data from 2018.
Comparison of calculated and reported CH4 fluxes In the following, the top-down results of the 2019 measurements are compared to the most recent available bottom-up estimates from globally gridded and national point-source annual inventories from the years 2018 (NAEI, EEMS), 2019 (EEMS, GFEI) and to daily operator-based facility-level reporting. We also compare our results to a ship-based top-down study conducted by  Hensen et al. , 2019 , for the sampled Dutch sites. Observational based top-down methods only provide "snap-shot" emission estimates representing emissions only for the time of the measurements. This means that a) to allow for a comparison the yearly inventory data needs to be scaled to the temporal resolution of the measurement (or vice-versa), and b) a detailed oneby-one comparison is hampered, which is especially true for cases when observations are made during times of non-typical operational conditions, as well as for intermittent emissions  (Foulds et al.
Comparison to UK annual point-source inventories The annual estimates of the UK national point-source inventories NAEI and EEMS are smaller than the fluxes measured during this study. For 2018 the measurement-derived fluxes are a factor of ~6 (NAEI; 220 kg h -1 ) and ~12 (EEMS; 109. 7 kg h -1 ) higher cumulatively for all sampled facilities. However, EEMS emission data for 2019 agree slightly better with the observations taken in 2019: Top-down estimates are a factor of ~11 higher compared to the EEMS reported data (125. As discussed in section 2, EEMS data is fed into the NAEI inventory, hence an agreement of both inventories for 2018 should be expected. A comparison between NAEI data and EEMS data from 2018, however, shows that the numbers are consistent only for two (P1, P2) out of five UK platforms.
The discrepancy to UK national inventories detected in this study is higher than reported in previous airborne studies of other offshore regions. Zavala-Araiza et al. (  2021 ) estimated offshore CH4 emissions in the Sureste Basin, Mexico, to be more than an order of magnitude lower than the values given in the Mexican greenhouse gas emission inventory. Gorchov Negron et al. (2020) generated an airborne measurement-based inventory comprising offshore facilities located in the U. S. Gulf of Mexico. They showed that for shallow-water facilities CH4 emissions are more than a factor of two higher than the estimate of the U. S. Environmental Protection Agency Greenhouse Gas Inventory (EPA GHGI) and the Gulfwide Offshore Activity Data System (GOADS) inventory. ). According to additional information on emission types provided by the operators, CH4 emissions arise from venting and fugitives for 4 out of 5 installations (P7-P10).
For P11 no venting or flaring was recorded, although CH4 was detected during the measurements conducted downstream. The measured emissions might be attributed to fugitives, which are not excluded by the operator in this case. Flaring emissions are explicitly excluded only for two out of five installations (P10, P11). For P7-P9 flaring emissions could contribute, though. P9 is reported as offline on the survey day, which agrees with the measurements showing no elevated CH4. For all sampled Dutch installations together, we find that our estimated flux of (122. 7 ± 9. 7) kg h -1 deviates by a factor 0. 7 (ranging from 0. 35-12 for individual facilities) from reported values (183. 1 kg h -1 ). A comparison with operator-reported data for offshore installations in the Norwegian Sea by  Foulds et al.
Measured values range from 2. 5% to 7. 8%. We compare the measured ratios to reported values from the OGA Shell/ExxonMobil Geochemistry Database for Central North Sea (2017) for UK sites and the NLOG for Dutch sites (for all measured and reported values see Table  D1  in the Appendix D). The reported C2H6 to CH4 ratio for the UK installation P4 is 0. 3% higher than the observed value. Compared to ratios measured downstream of Dutch facilities, the reporting underestimates the measurements, except for one facility (P11). In general, the dry gas and gas condensate binary categorization matches for the observed and reported ratios.
Loss rates In this section we determine loss rates, i. e. the ratio of gas lost to the atmosphere to dry gas production rates. We calculate the amount of gas lost to the atmosphere from the determined CH4 emission rates and the CH4 mass % from the OGA Shell/ExxonMobil Geochemistry Database for Central North Sea (2017) for UK sites and from the operator data for Dutch sites. UK production rates are given as monthly values by OGA. We include production from upstream fields with only subsea wells and no platform infrastructure. Dutch production data was provided by Dutch operators for the specific survey day. For three UK facilities (P3, P5, P6) no emissions were detected, although they were producing during the month of survey. According to the Dutch operator, P9 did not produce on the survey day, and we did not detect a plume either.
C. FLEXPART Dispersion Model: Example footprint analysis for the multi-platform complex P4 (backward simulation) The model study concludes, that 9 out of 19 platforms of the complex could have contributed to the measured CH4 enhancement (flight track with color-coded CH4 in Figure  C1 ). None of the possible emitters is listed in the inventories as single platforms. Figure  E1  shows gas lost to the atmosphere, which is calculated from CH4 emission rates and the CH4 mass % (UK sites: OGA 485 Shell/ExxonMobil Geochemistry Database for Central North Sea (2017); NL sites: operator data). The determined loss rates are the ratio of gas loss and dry gas production, i. e. normalized CH4 emissions against natural gas production rates. Table  E1  shows platform production rates along with calculated loss rates.
No loss rates were determined for installations, where emissions were below detection limit and thus, no enhancements measured (abbreviation "no enh. "). Z1-Z8 are nonemitting installations from fly-bys. Individual platform production data for 2019 were taken from the UK Oil and Gas Authority 490 (OGA), the NLOG and operator reported data. UK production rates are given as monthly values by OGA. Thereby, we include production from upstream fields with only subsea wells and no platform infrastructure. Dutch production data was provided by Dutch operators for the specific survey day.
UK Environmental and Emissions Monitoring System (EEMS) database is the environmental database of the UK oil and gas industry maintained by the Offshore Petroleum Regulator for Environment and Decommissioning (OPRED) and the UK Department for Business, Energy & Industrial Strategy (BEIS). It provides annual data from measurements and calculations made for single offshore installations based on reported data from operators. According to the EEMS Atmospheric Emission Calculations (OPRED (BEIS), 2008), monitoring systems of emitted gases are rare at offshore installations. Where no direct measurement data is available, the emission is calculated by the inventory multiplying activity data (e. g. fuel consumption or flow to flare/venting stack) with locally derived or default emission factors, which are mainly taken from literature.