dataesr/scientific-paragraphs-categorization huggingface dataset:
The variation between the S C -values of the different nuclei is below 1% for HeLa cells and below 7. 5% for V79 cells, even though the volumes of the nuclei differ by a factor of 10. As shown in Figure  7 , the S Cvalues are almost independent of the nuclear volume. The cell layer height was estimated to be 10 ± 2 lm and 7. 5 ± 2 lm respectively for the HeLa and V79 cell cultures. To investigate the influence of the uncertainty of the height of the cell layer, S C -values for the HeLa and V79 nuclei were calculated using a height of 12 lm, 10 lm, 7. 5 lm and 6 lm. On average the S C -values decreased by 8% for HeLa nuclei when the height of the cell layer was changed from 12 lm to 7. 5 lm and by 10% for V79 nuclei when the height was changed from 10 lm to 6 lm (data not shown).
However, spectral measurements are not the easiest to obtain, especially if many of them are required. It has been shown that three-band Red, Green, Blue (RGB) photometric data can be used to determine spectral indices or photoreceptor stimuli from values recorded by a Digital single-lens reflex camera (DSLR)  [19, 20]. This method is much faster and easier to perform. In this paper, we developed a similar method of calculating the MSI, SLI and IPI out of four-band RGB and Clear (RGBC) photometric data detected by the newly developed LANcube system. This method relies on a cross-calibration between RGBC values of the LANcube obtained using synthetic photometry method applied on a large spectral database along with the MSI, SLI and IPI determination by the integration of the same spectra. Very few methods are available to map the measured ALAN-related parameters over large territories.
We used a monochromator, a calibrated photodiode and an integration sphere. The sensor has a Field of View that is very close to a cosine function. Aside from the light sensors, the LAN 3 contains a real-time clock, a micro-SD card to store the data, a GPS module and a temperature sensor. The raw Analog to Digital Units (ADU) recorded in each band, the integration time and the gain, are stored on the SD card along with the date, time, GPS coordinates, and temperature. The first version of the LAN 3 (LAN 3 v1) uses the Arduino open source microcontroller. All the required documentation to learn how to build the device is available online  [26]. A new version of the device (LAN 3 v2) will be released soon. It will have shorter acquisition time. That version will allow an access to the data from a mobile device.
J(λ) is the spectrum of the given light radiant flux and r(λ), g(λ) and b(λ) are, respectively, the spectral response of the Red, Green and Blue bands of the LAN 3 after removal of the infrared response. In order to establish a relationship between LAN 3 measurements and MSI/SLI/IPI values, 314 different spectra were measured with a Stellarnet's Black comet spectrometer. The spectrometer operates in the spectral range of 280 to 900 nm with a resolution of 1 nm. The sample set contains 172 street lights, indoor lamps and other types of conventional lighting commonly available. These spectra are coming from the Lamp Spectral Power Distribution Database (LSPDD; Roby et al. [27] ). The remaining 142 spectra were acquired in situ. This allowed us to have mixed spectra combining multiple sources and environments.
Calculation of the Indices from Spectral Data The calculation of the MSI, SLI and IPI is done using the method described in Aubé et al. [14]  over the LSPDD and in situ spectra. The spectral responses used to calculate the indices come from Aubé et al. [14]  for MSAS, Vos  [28]  for Scotopic (V (λ)), DIN5031-10  [18]  for PAS and Wyszecki and Stiles  [29]  for Photopic (V(λ)). These spectral responses can be downloaded from the LSPDD. The CIE standard illuminant D65 spectrum, also used in the indices calculations, was taken from the LSPDD.
Fitting the Indices 3D Surface in the b g -r g Space Once the MSI, SLI and IPI values are calculated with the spectra, they are compared with the LAN 3 colour ratios b g and r g of each spectrum as determined by the synthetic photometry method explained in Section 2. 2. This creates three 3D spaces, one for MSI (Figure  4 ), one for SLI and one for IPI, with each one having 314 data points. For each 3D space, a 3D surface is fitted to the data and the Equation (4) of this surface becomes the transformation equation between the LAN 3 colour ratios and the respective index value. The equation is the same for each index but the parameters α to ζ differ. The purpose of the division by the g value is to remove the effect of absolute luminous intensity on the radiometric measurements of the instrument.
Results The method described above was applied to a large variety of spectral power distributions and indices. The results for MSI is presented in Figure  4. In that figure, the light blue circles are the 314 spectra. The light blue transparent surface is the result of a fit of a 3D 2nd order polynomial surface to the whole dataset. The fitted parameters of Equation (  4 ) for each index are given in Table  2. For the three indices, the most important colour ratio is b g. This reflects in the higher values of β and. Nevertheless, r g cannot be neglected at all. In the search for the optimal equation, we tried lower order polynomial functions but the results were not satisfying. In order to evaluate the intrinsic errors associated with the use of the fitted equations, we used them to calculate the three indices and compared the resultant indices to the accurate indices calculations obtained with the spectra.
This is shown in Figures  5 6 7. The figures show a good correlation between the two ways of determining the indices. In these figures, the solid line is the 1:1 relation and the right panel of each figure shows the residuals. For the MSI, the standard deviation of the residuals is 0. 024 while it is 0. 056 for the SLI. The correlation is slightly lower for the IPI with a standard deviation of 0. 107. The standard deviations translate in the margin of error (95 percent confidence interval) of ±0. 05 for MSI, ±0. 1 for SLI, and ±0. 2 for IPI. The complete dataset used in the fit is available on the project github repository  [30].
Note that in Equation (  7 ), t i is expressed in seconds while the LAN 3 is recording it in units of milliseconds. The recorded values have to be divided by 1000 prior to using Equation  (7). In general, it is better to make the correction because the spectral response curves of Figure  1a  were determined for gain = 1 and t i = 1 s. However, in the present study, it is not mandatory because, for a given sensor, the gain and integration time are the same for all bands and then taking the bands' ratios cancel their effect. In the LAN 3 , the gain varies from 1 to 60 and the integration time from 2. 4 to 614 ms depending on the ambient light level. The b g and r g values were converted into MSI, SLI and IPI values using Equation (4). We excluded extrapolated data that are outside the span of values of data used for the fit.
This was done by excluding indices lower than zero and higher than two. During the calculation of an index, all recorded R, G, B and C values are subject to a threshold value set at 20. Such a threshold value ensure that the Signal to Noise Ratio is higher than 20 (i. e. , noise represents less than 5% of the signal). The two criteria excluded 410 measurements out of 14,894 raw data. It represents less than 3% of the database. In order to generate the maps from the localised measurements, we used the nearest neighbour interpolation with a maximum interpolation distance of 30 m. The resulting maps are shown in Figures  8 9 10. The relation between the lighting technologies and the map index classes are given in Tables  3 4 5. The complete dataset used to produce the maps is available on the project github repository  [30].
Acknowledgments: We want to thank  Thomas Goulet-Soucy  for his contribution to the design of the prototype version of the LAN 3. We also want to thank  Elsa Vigneau ,  Mathieu Fréchette  and  Thomas Goulet-Soucy  for their help during the initial LAN 3 testing and calibration experiments in Barcelona, Madrid, Lyon and  Toulouse. Finally , we want to thank  DH éclairage Inc. who built and graciously provided us the LAN 3 v1 systems used to map the city of Sherbrooke.
These improvements substantially reduce the T E interfrequency disagreements. We also reassessed the estimates of the polarization efficiency for the polarized channels. Comparing different databased estimates demonstrates that the ground-based polarization efficiency uncertainty estimates (of the order of a fraction of a percent) were too optimistic by a factor of 5 to 10. Correcting for the observed polarization efficiency errors (at the percent level) very significantly reduces the EE inter-frequency disagreements. This calibration correction relies on cosmological priors (using the T T best-fit cosmology). Calibrating using either the T E or the EE spectra yields generally consistent results, except at 143 Ghz where there is disagreement at more than 2σ. At this level, this discrepancy can be caused either by a statistical fluctuation, or by an unknown residual.
We constructed an empirical model of this correlated noise from our simulations, which is included in the Plik likelihood. CamSpec was the baseline for the 2013 release and was described in detail in Planck Collaboration XV (2014), and used cross-spectra formed from detector-set temperature maps using data from the nominal mission period. It was extended for the 2015 release to include both polarization and temperaturepolarization cross-spectra and to use the data from the full mission period. Similarly to Plik, CamSpec switched from detectorset cross-spectra to cross-spectra formed from frequency maps constructed from separate halves of the full mission data in order to mitigate the effects of noise correlated between detectors. In 2015, the foreground modelling was also modified and the sky fraction retained at each frequency was increased, using common masks with Plik in temperature.
Non-Planck data While the data derived exclusively from Planck observations are by themselves already extremely powerful at constraining cosmology, external data sets can provide helpful additional information. The question of consistency between Planck and external data sets is discussed in detail in Planck Collaboration VI (2018) (henceforth PCP18). Here we focus on two data sets that are particularly useful for breaking degeneracies and whose errors can be assessed reliably. We consider the measurement of the CMB B-mode polarization angular power spectrum by the BICEP2/Keck Array collaboration and measurements of the baryon acoustic oscillation (BAO) scale. The supplementary Bmode data provide independent constraints on the tensor sector, which are better than those that can be derived from the Planck data alone (based on the shape of the scalar power spectrum).
BICEP2/Keck Array 2015 B-mode polarization data Although Planck measured the CMB polarization over the full sky, its polarization sensitivity in the cosmological frequency channels is not sufficient to compete with current suborbital experiments surveying small, particularly low-foreground patches of the sky very deeply using many detectors. In PCI15, constraints on r using the joint BICEP2/Keck Array and Planck (BKP) analysis (BICEP2/Keck Array and Planck Collaborations 2015) were reported. Here we make use of the most recent B-mode polarization data available from the analysis of the BICEP2/Keck field  (Ade et al. 2018, henceforth BK15) , unless otherwise stated. The BK15 likelihood draws on data from the new Keck array at 220 GHz in addition to those already in use for the BK14  (Ade et al.
Planck 2018 results for the main inflationary observables As in PCI13 and PCI15, we start by describing Planck measurements of the key inflationary parameters. Some of the results reported in this section can be found in the Planck Legacy Archive. 5   6 ) and (  7 ). Given this broad agreement and the consistency in the values of τ derived with SimAll separately from the three cross-spectra 70 × 100, 70 × 143, and 100 × 143 (PPL18), we will mainly use the baseline low-likelihood in the rest of the paper. As anticipated in 2015, the information in the high-polarization Planck data is powerful for breaking degeneracies in the parameters and to further decrease parameter uncertainties compared to temperature data alone. The addition of high-polarization leads to a tighter constraint on n s : Simple one-parameter modifications of the cosmological model are not sufficient to reconcile a scale-invariant power spectrum with Planck data.
Constraints on the scale dependence of the scalar spectral index The Planck 2018 data are consistent with a vanishing running of the scalar spectral index. Using Planck 7 For non-flat models the power spectra encode the eigenvalues of the corresponding Laplacian operator of the spatial sections and scale invariance holds at scales much smaller than the curvature radius. The model including a scale-dependent running can produce a better fit to the low-deficit at the cost of an increase of power at small scales; this latter effect is constrained in this release. As an example of a model with suppression only on large scales, we also reconsider the phenomenological model with an exponential cutoff: P R (k) = P 0 (k)        1 -exp       - k k c λ c              , (19) which can be motivated by a short stage of inflation  (Contaldi et al.
Constraints on the tensor-to-scalar ratio This subsection updates constraints on the tensor-to-scalar ratio r assuming that the tensor tilt satisfies the consistency relation, n t = -r/8, which is the case for slow-roll inflation driven by a single scalar field with a canonical kinetic term. By combining Planck temperature, low-polarization, and lensing we obtain r 0. 002 < 0. 10 (95 % CL, Planck TT+lowE+lensing). (29) This constraint slightly improves on the corresponding Planck 2015 95 % CL bound, i. e. , r 0. 002 < 0. 11 (PCI15), and is unchanged when high-polarization data are also combined. Note that by using CAMspec instead of Plik as the high-joint temperature-polarization likelihood, we obtain a slightly looser bound, i. e. , r 0. 002 < 0. 14 at 95 % CL. By including the Planck B-mode information at 2 < < 30 in the low-polarization likelihood, the 95 % CL constraint is essentially unchanged.
2rms degeneracy in the ΛCDM+A L +CIP model and how it can be broken by the lensing data. The value A L = 1 provides a good fit to the TT+lowE data, if ∆ 2 Planck 2015 TT,TE,EE and conservative lensing data (40 ≤ L ≤ 400) than does the ΛCDM+A L model. When using the Planck 2018 conservative lensing data (8 ≤ L ≤ 400), the best-fit value of ∆ 2 rms decreases to 0. 0036. This is due to the extra term ∝ L -2 brought by CIP to the lensing power estimator, as discussed above and shown in Fig. 46. Comparison of ΛCDM+CIP, ΛCDM+A L , and ΛCDM+A L +CIP models with various Planck datasets, when using the baseline Plik likelihood at high. The first two columns ("Constraints") are the 68% CL ranges or the 95% CL upper bounds on 1000∆ 2 rms (highlighted in bold for ΛCDM+CIP) and A L. The remaining columns give the best-fit pameter values, and the difference of the best-fit χ 2 and the difference of the log of the Bayesian evidence with respect to the pure adiabatic ΛCDM model.
Acknowledgements. We are grateful to  Jan Hamann  and  Jim Zibin  for extensive help with the final editing of this manuscript. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration.
Event selection The following triggers were employed: • Zero bias: zero-bias events are selected by using either the BPTX detectors (13 TeV data) or the LHC clock signal and the known LHC bunch structure (5. 02 TeV data). Both methods provided zero-bias events. • BPTX XOR: Here XOR stands for the exclusive OR logic, where only one BPTX is fired, corresponding to an incoming proton bunch from only one direction. This trigger was used in both 5. 02 and 13 TeV data sets. • No-BPTX: There is no signal in the BPTX detectors, which means there are no incoming proton bunches. This trigger was used in both 5. 02 and 13 TeV data sets. The present analysis uses events acquired with the zero bias trigger. The BPTX XOR and No-BPTX triggers select events with no interacting bunches, which are used to estimate the electronic noise of calorimeters and possible collisions between beam particles and residual gas molecules in the CMS beampipe (beam-gas background).
The resulting uncertainty is estimated to be 3% for both the 5. 02 and 13 TeV data sets. Similarly, the ECAL and HCAL thresholds are varied by ±5%  [47, 48] , which results in a 1% uncertainty in the corrected yields at both energies. The systematic uncertainty estimation of the multiparticle background is done by varying the control region used in the background estimation procedure: 1-2, 2-9, and 5-9 extra towers. The estimate of the systematic uncertainty in the multiparticle background normalization is 10%. Additionally, a 10% uncertainty is added to this value quadratically, taking into account the deviations shown in Table  3 ; thus the final uncertainty in the multiparticle background normalization is 14%. After subtracting this contribution, this propagates to systematic uncertainties depending on the invariant mass, transverse momentum and rapidity of the pion pair.
Data sets are validated each year and are available on the EBAS database website (http://ebas. nilu. no, last access: 10 June 2020). SO 2 concentrations are measured since 2003 with a Thermo Scientific model 43i, changed for a Thermo Scientific model 43iTL in 2017. NO, NO 2 and SO 2 are calibrated every month with reference mixtures obtained from the dynamic dilution of working standards (at 5 ppm), which are checked every years with traceable standards. The NO x instrument took part in a side-by-side intercomparison held in Hohenpeissenberg in 2012 in the framework of the ACTRIS program.
Cartridges have Swagelok caps and are kept in a dark and cool room (∼ 20 • C) at the laboratory. The analytical conditions are described in other recent articles  (Dominutti et al. , 2019; Wang et al. , 2020). Since September 2018, an online GC-FID (Gas Chromatography -Flame Ionization Detector) has been running at PUY for the online monitoring of nonmethane hydrocarbons (NMHCs) from C2 to C10. Air samples collected every 2 h at 25 mL min -1 during 40 min are dried through a semipermeable membrane (Nafion, Permapure Inc. ) and introduced in a trap cooled by the Peltier effect. This system is an adaptation of the Perkin Elmer Turbomatrix coupled with the GC-FID 6890 from Agilent. VOC measurements follow the ACTRIS/GAW Standard Operating Procedures for sampling, calibrations, and data validation.
Cloud biological measurements Since 2003, microorganisms in cloud waters and their activity have also been investigated  (Amato et al. , 2005; Vaïtilingom et al. , 2012). Each cloud water sample is collected under sterile conditions, and microbiological parameters such as biomass, biodiversity, and biological activity are measured. Total cells are counted using epifluorescence microscopy (before 2010) or flow cytometry. Bioluminescence is used to quantify adenosine triphosphate (ATP) as a marker of the whole metabolic activity. Living cultivable microorganisms (bacteria and fungi) are generally investigated as well by platting samples on nutritive media and then isolated and identified against databases by 16S rRNA gene sequencing  (Amato et al. , 2007b; Vaïtilingom et al. , 2012). This allows the isolation of microorganisms from the cloud water and their interaction with atmospheric chemical compounds  (Amato et al.
, 2009). In fact, the dynamics of the planetary boundary layer, in connection with the altitude and the topography of the sites, plays a major role in the transport of both preexisting particles and gaseous precursors at high altitude and in turn significantly contributes to the differences observed among mountain stations  (Collaud Coen et al. , 2018). During the day, the boundary layer (BL) height increases until it reaches PUY, transporting at altitude the aerosols emitted from the surface. This vertical transport by convective mixing is more marked in summer than in winter. At night, https://doi. org/10. 5194/amt-13-3413-2020 Atmos. Meas. Tech. , 13, 3413-3445, 2020 the concentrations are more representative of the free troposphere (FT)/residual night layer (RL), and the database acquired for several years provides particle size spectra of background air masses.
This influence is reflected in clear daily and seasonal variations in optical properties, when considering the whole long-term data set of optical properties. A significant decrease in the daytime aerosol scattering coefficient during summer and winter was in   2020 ) however slightly balance the previous findings of Pandolfi and co-workers, as an overall decrease of -0. 147 % yr -1 over the decade 2009-2018 was observed but reported as not statistically significant. A statistically significant decreasing trend was in contrast found for the absorption coefficient over the period 2009-2017, on the order of -0. 017 % yr -1. These observations are in agreement with aerosol mass (PM) decay observed at the European level, in relation to SO 2 emission regulations. In situ optical data were also used to evaluate aerosol remote sensing retrievals.
, 2014). For this, multivariate statistical https://doi. org/10. 5194/amt-13-3413-2020 Atmos. Meas. Tech. , 13, 3413-3445, 2020 analyses were performed considering the concentrations of the main inorganic ions and pH; the back-trajectories of the air masses that reach the PUY station for each cloud event were also calculated to confirm the classification  (Bianco et al. , 2018). This cloud database was recently complemented by measurements of trace metals that help to evaluate the impact of anthropogenic and natural sources on the cloud and to better discriminate the origin of the air masses  (Bianco et al. , 2017). In parallel to cloud chemical composition, microorganisms present in clouds (bacteria, fungi) have been studied systematically at  PUY since 2003 (Sect. 5. 4. 1). This longterm observation of cloud biological composition is a unique database.
, 2017). This predominance of the westerly direction is due to upper tropospheric jet streams, strong winds at upper tropospheric altitudes which, in the most intense cases, exceed 50 m s -1 , able to be at the origin of stratosphere-troposphere exchanges. The jet stream shows a clear seasonality, with a predominance in winter (3 % to 10 % of hourly profiles) and a minimum in summer (less than 1 %), and a decadal trend (+1. 6±1. 2 % per decade). These results are corroborated by the analysis of radionuclides (beryllium-sodium report) and the analysis of the Lannemezan upper tropospheric wind data series. The presence of radionuclides in aerosol and rain has also been investigated  (Bourcier et al. , 2012a). Measurements were conducted at three sampling sites located at different altitudes during 2 years, in both the rain and aerosol phases.