dataesr/scientific-paragraphs-categorization huggingface dataset:
For turbine 1, the hydraulic oil prediction difference hits 10 degrees at about 20000 data points (139 days) before the breakdown. For turbine 2, the hydraulic oil prediction difference hits 10 degrees 5000 points (35days) before the gearbox failure. It is worth noting that 10 degrees was here selected as an anomaly threshold and it can be changed by the operator. Here, the very basic steps of building a condition monitoring system for a CPS (off-shore wind turbine farm) are presented. All the tools used can be easily retrieved from MATLAB tool boxes which make the approach very easy to reproduce. When dealing with CPS, the interconnected objects can yield huge amounts of data to be treated; as it is the case here, it becomes tedious numerically to predict failure using all of the available information.
In this way the necessary knowledge can be limited only to about the system to be maintained. This local intelligence controls and conducts maintenance by collecting data through embedded sensors and by using robots as a way to deliver maintenance capabilities (such as inspection, exchanging components, adjustments, etc. ). When a CPS detects a fault, it tries to diagnose itself using embedded intelligence and to repair the fault. The repair process will be performed by various methods, including a maintenance robot that delivers physical repair methods. However, the robot does not have specific maintenance knowledge. Instead, the system issues maintenance instructions, such as opening an inspection door, topping up fluid, changing filters, measuring and inspecting the object system. These instructions may include position, specific actions, and motion for the robot arm to operate.
5 CaCl 2 , pH 7. 4, 320 mOsm) at 35° C for 1 hour. Patch-clamp recording in whole-cell configuration was performed at room temperature on lamina II neurons visualized with an Olympus BX51WI with Nomarski optics and connected to a camera (Q-imaging). Recordings were performed in current-clamp mode or voltage-clamp mode at a holding potential of -70 mV. Thick-walled borosilicate pipettes, having a resistance of 3-5 MOhm, were filled with internal solution (in mM: 120 K-methyl-sulfonate, 10 NaCl, 10 EGTA, 1 CaCl2, 10 Hepes, 0. 5 NaGTP, 5 MgATP, pH adjusted to 7. 2 with KOH, osmolarity adjusted to 305 with sucrose). Light stimulations were evoked via Lambda Tled controller at a wavelength of 530 nm (Sutter). Data were acquired using a Multiclamp 700A amplifier and pClamp9 software (Molecular Devices, USA).
Computational modeling of light propagation We adopted a simulation approach developed by Joshua Gordon  (Stujenske et al. , 2015) , modifying the MATLAB scripts available in that work. Briefly, the simulation performs a Monte Carlo simulation of photons doing a random walk through tissue after realistically modelled emission from a fiber optic cannulae. Due to the prevalence of white matter tracts in the spinal cord, we modified the stimulation parameters used by  Stujenske, et al. (absorption, scattering, and anisotropy constants)  to be those measured from ex-vivo human white matter  (Yaroslavsky et al. , 2002). This is an upper bound on the amount of scattering expected in real spinal cord tissue, and therefore our estimate of light penetration depth is a conservative one.
Acknowledgments We thank the  Stanford Neuroscience Gene Vector and Virus Core , the  Stanford Behavioral and Functional Neuroscience Laboratory  as well as members of the Deisseroth, Scherrer, and  Delp labs  for insightful discussions and help with experiments. This study was supported by the  U. S. National Institutes of Health  ( NINDS R01-NS080954 ), and the  Stanford Bio-X NeuroVentures program. A. J. C was supported by a  Texas Instruments Stanford Graduate Fellowship , S. M. I. was supported by a  Howard Hughes Medical Institute International Student Research Fellowship , and by the  Siebel Scholars Foundation. G. S. was supported by grants from  NIH  ( DA031777 ), the  Rita Allen Foundation  and the  American Pain Society.
The analyses considered data in day-long and weeklong intervals contemporaneous with the NuSTAR observation windows. The likelihood model used for all intervals included the sources from the second Fermi -LAT catalog  (Nolan et al. 2012 ) located within a 15 • regionof-interest centered on Mrk 421, as well as the standard Galactic diffuse, isotropic and residual instrumental background emission models provided by the Fermi Science Support Center 112. For all epochs, the spectrum of Mrk 421 was fitted with a power-law model, with both the flux normalization and photon index being left as free parameters in the likelihood fit. We summarize the spectral parameters for four selected epochs (discussed in detail in § 5) in Table  4. The systematic uncertainty on the flux is estimated as approximately 5% at 560 MeV and under 10% at 10 GeV and above  (Ackermann et al.
The systematic uncertainties in the spectral measurements for a Crab-like pointsource were estimated to be 11% in the normalization factor (at ≃200 GeV) and 0. 15 in the power-law slope. The systematic uncertainty in the absolute energy determination is estimated to be 15%. Further details about the performance of the MAGIC telescopes after the hardware upgrade in 2011-2012 can be found in  Aleksić et al. (2016b). After data-quality selection, Mrk 421 was observed with MAGIC for a total of 10. 8 h between 2013 January 8 and 2013 March 18. Most of these observations (8 h in total) were strictly simultaneous with the NuSTAR observations. They were performed in the "false-source tracking" mode  (Fomin et al. 1994) , where the target source position has an offset of 0. 4 • from the camera center, so that both signal and background data are taken simultaneously.
The significance of the source detection, calculated using Equation (  17 ) from  Li & Ma (1983) , varied between 8. 3 σ (MJD 56310) and 38. 3 σ (MJD 56335). Observed intranight variability is not statistically significant, so we used data integrated over complete observations for the spectral analysis. Spectra were modeled with a powerlaw function with a normalization energy of 300 GeV. The normalization energy was chosen to be 300 GeV for both MAGIC and VERITAS , in order to facilitate a direct comparison of the VHE spectral results. For observations in which the spectrum is not well described by a power-law model (MJD 56302, 56335 and 56363), we additionally fit a log-parabolic model. A summary of the observations and the spectral modeling is given in Table  5. All uncertainties quoted in the table and in the rest of the paper are statistical only.
4 7. 5 7. 6   -Count rates for NuSTAR in the 3-7 keV (blue) and 7-30 keV (green) bands. The legend given in the second panel from the top applies to all panels; for both bands FPMA count rates are plotted with a lighter color. The data are binned into 10-minute bins. The time axis of each row starts with the first day of the month and the UTC and MJD dates are printed above the light curves for each particular observation. Note that the data shown in the top panel represent two contiguous observations (broken up near MJD 56115. 15). The intervals shaded in grey show times for which simultaneous data from MAGIC and VERITAS are presented in this paper. Both the horizontal and the vertical scales are equal in all panels. in each fit varies slightly due to the different duration of the orbits, but it is typically around 10.
The hardness of the spectrum, defined here as the ratio of count rates in the hard 7-30 keV and in the soft 3-7 keV bands, changes over the course of the observations. In Figure  3  we show the general trend of the spectrum hardening when the count rate is higher. Although the observed range of hardness ratios is relatively large at any specific count rate, the overall trend is clearly present in the binned data shown with thick black lines. There are no apparent circular patterns observed in the count rate versus hardness ratio plane, as previously seen in soft X-ray observations during bright flaring periods (e. g. ,  Takahashi et al. 1996; Ravasio et al. 2004; Tramacere et al. 2009 ). We note, however, that the circular patterns might not be observable in the NuSTAR data presented here simply because the observations predominantly covered instances of declining flux, whereas the patterns arise from differences in the rising and the declining phases of a flare.
Observation-averaged Spectroscopy 114 Because these data were taken in the calibration phase of the mission in suboptimal pointing conditions, a systematic uncertainty of 4% was added to the light curve to reflect the total uncertainty in the true count rate. a Energy (in keV) at which the model sharply changes slope from Γ1 to Γ2. For some observations this parameter is unbound, so we fix it to 7. 5 keV and mark with (f). b Flux calculated from the best-fit model, in units of 10 -11 erg s -1 cm -2. Note that the 2-10 keV band requires some extrapolation below the NuSTAR bandpass, but we provide it here for easier comparison with the literature. c Formal statistical uncertainty is 0. 008, however, the NuSTAR bandpass calibration is limited to 0. 01  (Madsen et al. 2015) , so we round up the values assuming this lowest uncertainty limit for these cases.
All relevant parameters of the fits to the observation-averaged spectra are summarized in Table  7. We note that for the NuSTAR observation on 2013 January 2 (MJD 56294), the best-fit parabolic model has marginally significant negative curvature, β = -0. 2±0. 1. As this is the shortest of all NuSTAR observations and the same effect is not observed in any of the other spectra, while negative curvature is a physically possible scenario, it is likely that this anomalous result arises from the fact that the high-energy background is not sufficiently well sampled in such a short exposure. 3. 3. Time-resolved Spectroscopy We next consider spectra integrated over time intervals shorter than the complete NuSTAR observations. Separating the data into individual orbits represents the most natural although still arbitrary way of partitioning.
Flux-resolved Spectroscopy In order to verify that the spectral parameter trends we identify in the time-resolved spectra are not spuriously produced by relatively low photon statistics at the low-flux and high-energy ends, we proceed to examine stacked single-orbit spectra of similar flux. Stacking pro-vides the highest possible S/N in well-defined flux bins, and allows us to use the data up to 70 keV -where the signal is fainter by a factor of a few than the NuSTAR detector background. We combine spectra for each focalplane module separately due to intrinsic differences in response matrices. Spectra from both modules are fitted simultaneously in Xspec, just like the observation-averaged and the single-orbit ones. Note that this procedure implicitly assumes that the source behaves self-consistently, in the sense that a particular flux level corresponds to a unique spectral shape within the data-taking time interval.
1 ± 0. 2) × 10 -11 erg s -1 cm -2 (1997 May;  Massaro et al. 2004 ). averaged spectra. The analysis performed here describes the spectral changes happening between those two extremes as a smooth function of the X-ray flux. For each of the parameters of the power-law, broken power-law and log-parabolic models we parametrize their dependence on the X-ray flux as X F 3-7 keV = s log F 3-7 keV /F 0 + X 0 , (4) where X stands for any of the spectral parameters (Γ, Γ 1 , E b , Γ 2 , α, β), s is the slope of the relation, F 3-7 keV is the 3-7 keV flux, F 0 is a reference flux in erg s -1 (chosen to be the median flux of our dataset, log F 0 = -10. 1) and X 0 is the vertical offset (parameter value at the reference flux). We find that in all cases this linear function adequately describes the general trends.
already apparent from Figure  7 , and will be discussed in more detail in § 4. 2. 1. The fluxes in the UV and Fermi -LAT bands (to the extent allowed by the limited photon statistics) are consistent with a slow increase in flux between January and March, but do not show a clear short-term flux correlation. Further details regarding these bands are presented in § 4. 2. 2. The activity observed in the first three months of 2013 can be generally described as low. Note in particular that on January 10 and 20, Mrk 421 showed a remarkably low Xray and VHE flux in comparison to the historical X-ray and VHE fluxes reported in Stroh & Falcone (2013) and Measurement uncertainties are based on photon statistics and are often smaller than the data points plotted. As in Figure  7 , the vertical lines mark midpoints of the coordinated NuSTAR and VHE observations: dashed lines mark the epochs for which we discuss SED snapshots in § 4.
While the systematic uncertainties on the absolute flux measurements 116  do not directly add to the uncertainty in F var , it is important to stress that different observing sampling and, more importantly, different instrument sensitivity, do influence F var and its uncertainty: a densely sampled light curve with very small temporal bins and small error bars might allow us to see flux variations that are hidden otherwise, and hence we might obtain a larger F var. Some practical issues of its application in the context of multiwavelength campaigns are elaborated in  Aleksić et al. (2014) ;  Aleksić et al. (2015b,c). In this paper we explore two cases, as shown in Figure  9. First, we use the full January-March dataset reported in Figure  7  (which has different cadence and different number of observations in each band), and second, we use only data collected simultaneously, in narrow windows centered on observations coordinated between NuSTAR and VHE telescopes.
3-10 keV count rates of ∼25 s -1 and VHE flux of 0. 5 Crab units. In this section, we confirm the flux-flux correlation in the X-ray and VHE bands with higher confidence, during a period of even lower activity. We also study the characteristics of such a correlation Fig. 9. -Fractional variability amplitude, Fvar, as a function of frequency for the period 2013 January-March. The vertical error bars depict the statistical uncertainty, while the horizontal error bars show the energy band covered (with the markers placed in the center of the segments). We show Fvar computed in two ways: using the complete light curves acquired in the campaign (empty symbols), and using only the data taken within narrow windows centered on the coordinated NuSTAR and VHE observations (filled symbols). Note that the points overlap where only the coordinated observations are available.
The Fermi -LAT point is based on the weekly-binned light curve shown in Figure  7. in different X-ray bands using the strictly simultaneous Swift, NuSTAR, MAGIC and VERITAS observations. 117  We summarize the results in Figure  10  for three nonoverlapping X-ray bands. The flux in each band was calculated from the best-fit broadband model (power-law, or log-parabolic where needed; see § 3). In the following, we use the discrete correlation function (DCF) and the associated uncertainty as defined in  Edelson & Krolik (1989). We carried out the correlation analysis on two timescales: a ∼1-hour timescale, using strictly simultaneous observations, and a 1-day timescale, using data averaged over complete 6-10-hour observations (i. e. , averaged over one night of observations with each of the VHE observatories).
For a chosen X-ray band, the best-fit slopes of the relation (a; listed in Figure  10 ) are statistically consistent with a single value. This is in good agreement with our finding that the dominant X-ray flux variability timescale is τ var ≈9 hours (see § 3. 1). It could also be indicative of a lag between the bands which is longer than the binning of the data taken strictly simultaneously, however, such an analysis is outside of the scope of this paper. Results of  Aleksić et al. (2015b)  point to absence of any lags between the X-ray and VHE bands in a nonflaring state of Mrk 421 in 2009. An interesting result stems from our ability to broaden the search for the correlation over a very wide band in X-rays, enabled by the simultaneous Swift and NuSTAR coverage. As shown in the upper three panels of Figure  10 , the slope of the relation systematically shifts from 1.
5-month observation time. Several physical parameters in the current modeling do differ substantially from those of  Abdo et al. (2011). The characteristic electron Lorentz factors are about an order of magnitude lower ( γ brk = 2. 5-5. 2 × 10 4 versus γ brk2 = 3. 9 × 10 5 ), while the inferred emitting region radius is about a factor of 3-10 smaller (R = 0. 6-1. 7 × 10 16 cm versus 5. 2 × 10 16 cm), the inferred magnetic field is substantially higher (B = 0. 10-0. 28 versus 0. 038), and the resulting jet powers differ by a factor of 3-4. As a consequence, the current modeling yields equipartition parameters that are much closer to unity, in the range ǫ = 0. 33-0. 56 versus ǫ = 0. 1 for the  Abdo et al. (2011)  result. Given the overall similarity in the size and shape of the synchrotron and SSC components among all five datasets (i.
We thank the anonymous referee for constructive suggestions that helped to improve and clarify the paper. M. B. acknowledges support from the  International Fulbright Science and Technology Award , and from  NASA Headquarters  under the  NASA Earth and Space Science Fellowship Program , grant  NNX14AQ07H. This research was supported in part by the  Department of Energy  Contract  DE-AC02-76SF00515  to the  SLAC National Accelerator Center. G. M. and A. F. acknowledge the support via  NASA  grant  NNX13AO97G. D. B. acknowledges support from the  French Space Agency (CNES)  for financial support. This work was supported under  NASA  Contract No. NNG08FD60C , and made use of data from the  NuS-TAR mission , a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by the  National Aeronautics and Space Administration.
The MAGIC collaboration would like to thank the  Instituto de Astrofísica de Canarias  for the excellent working conditions at the  Observatorio del Roque de los Muchachos in La Palma. The financial support of the  German BMBF  and  MPG , the  Italian INFN  and  INAF , the  Swiss National Fund SNF , the  ERDF  under the  Spanish MINECO , and the  Japanese JSPS  and  MEXT  is gratefully acknowledged. This work was also supported by the  Centro de Excelencia Severo Ochoa   SEV-2012-0234 ,  CPAN CSD2007-00042 , and MultiDark  CSD2009-00064  projects of the Spanish Consolider-Ingenio 2010 programme, by grant  268740  of the  Academy of Finland , by the  Croatian Science Foundation (HrZZ)  Project  09/176  and the  University of Rijeka  Project  13. 12. 1. 3. 02 , by the  DFG Collaborative Research Centers   SFB823/C4  and  SFB876/C3 , and by the  Polish MNiSzW  grant  745/N-HESS-MAGIC/2010/0.
This work is a part of the  Projects No 176011 (Dynamics and kinematics of celestial bodies and systems ), No  176004  ( Stellar physics ) and No 176021 (Visible and invisible matter in nearby galaxies: theory and observations) supported by the  Ministry of Education, Science and Technological Development of the Republic of Serbia. This research was partially supported by  Scientific Research Fund of the Bulgarian Ministry of Education and Sciences  under grant  DO 02-137  ( BIn-13/09 ). The Abastumani team acknowledges financial support of the project  FR/638/6-320/12  by the  Shota Rustaveli National Science Foundation  under contract  31/77. T. G. acknowledges support from  Istanbul University  (Project numbers  49429  and  48285 ),  Bilim Akademisi (BAGEP program ) and  TUBITAK  (project numbers.
Methane sources and sinks Here we provide a complete review of all methane sources and sinks based on an ensemble of bottom-up approaches from multiple sources: process-based models, inventories, and data-driven methods. For each source, a description of the involved emitting process(es) is given, together with a brief description of the original datasets (measurements, models) and the related methodology. Then, the estimate for the global source and its range is given and analysed. Detailed descriptions of the datasets can be found elsewhere (see references of each component in the different subsections and tables). Methane is emitted by a variety of sources in the atmosphere. These can be sorted by emitting process (thermogenic, biogenic or pyrogenic) or by anthropogenic vs. natural origin.
Reported global inventories The main three bottom-up global inventories covering all anthropogenic emissions are from the United States Environmental Protection Agency,  USEPA (2012 USEPA ( , 2006)) , the Greenhouse gas and Air pollutant Interactions and Syner-gies (GAINS) model developed by the International Institute for Applied Systems Analysis (IIASA)  (Höglund-Isaksson, 2012)  and the Emissions Database for Global Atmospheric  Research (EDGARv4. 1, 2010; EDGARv4. 2FT2010, 2013). The latter is an inventory compiled by the European Commission Joint Research Centre (EC-JRC) and Netherland's Environmental Assessment Agency (PBL). These inventories report the major sources of anthropogenic methane emissions: fossil fuel production, transmission and distribution; livestock (enteric fermentation and manure management); rice cultivation; solid waste and waste water.
However, the level of detail provided by country and by sector varies between inventories, as these inventories do not consider the same number of geographical regions and source sectors  (Höglund-Isaksson et al. , 2015 , see their Table  5. 2). In these inventories, methane emissions for a given region/country and a given sector are usually calculated as the product of an activity level, an emission factor for this activity and an abatement coefficient to account for regulations implemented to control emissions if existing (see Eq. 5. 1 of  Höglund-Isaksson et al. , 2015; IPCC, 2006). The integrated emission models USEPA and GAINS provide estimates every 5 or 10 years for both past and future periods, while EDGAR provides annual estimates only for past emissions. These datasets differ in their assumptions and the data used for the calculation; however, they are not completely independent as they follow the IPCC guidelines  (IPCC, 2006).
While the USEPA inventory adopts the emissions reported by the countries to the UNFCCC, EDGAR and the GAINS model produced their own estimates using a consistent approach for all countries. As a result, the latter two approaches need large country-specific information or, if not available, they adopt IPCC default factors or emission factors reported to UN-FCCC  (Olivier et al. , 2012; Höglund-Isaksson, 2012). Here, we also integrate the Food and Agriculture Organization (FAO) dataset, which provides estimates of methane emissions at country level but only for agriculture (enteric fermentation, manure management, rice cultivation, energy usage, burning of crop residues and of savannahs) and land use (biomass burning)  (FAO, 2016). It will hereafter be referred as FAO-CH 4. FAO-CH 4 uses activity data from the FAO-STAT database as reported by countries to National Agriculture Statistical Offices  (Tubiello et al.
, 2013)  and mostly the Tier 1 IPCC methodology for emission factors  (IPCC, 2006) , which depend on geographic location and development status of the country. For manure, the necessary country-scale temperature was obtained from the FAO global agro-ecological zone database (GAEZv3. 0, 2012). We use the following versions of these inventories: version EDGARv4. 2FT2010 that provides yearly gridded emissions by sectors from 2000 to 2010  (Olivier and Janssens-Maenhout, 2012; EDGARv4. 2FT2010, 2013) , version 5a of the GAINS model  (Höglund-Isaksson, 2012 ) that assumes current legislation for air pollution for the future, the revised estimates of 2012 from the USEPA (2012), and fi-   1. Overall, only EDGARv4. 2FT2010 and GAINS provide gridded emission maps by sectors, and only EDGAR provides gridded maps on a yearly basis, which explains why this inventory is the most used in inverse modelling.