dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources and preprocessing The data under scrutiny in this study consist of 10-year time series of daily means of SSI obtained from high-quality measurements performed at four different locations (Table  1  and Fig. 1 ). The measurement stations are part of the Baseline Surface Radiation Network (BSRN), a worldwide radiometric network providing accurate readings of the SSI at 1 min temporal resolution and with an uncertainty requirement at 5 W m -2  (Ohmura et al. , 1998). The four time series for the period 2001-2010 have been quality checked according to  Roesch et al. (2011). Next, daily means of SSI were then calculated from these raw time series only if more than 80 % of the data during daylight were valid. Lastly, any isolated missing daily means were completed by linear interpolation applied to the daily clearness index, K T , which is the ratio between the daily mean of SSI and the daily mean of the total solar irradiance (TSI) received on a horizontal surface at the top of atmosphere for the same geographical coordinates.
Adaptive data analysis Ideally, data analysis methods should require that no assumptions be made about the nature of the scrutinized time series, i. e. , neither linearity nor stationarity should be presumed. This is because the true character of the underlying processes that have generated the data is usually not known beforehand. Adaptivity to the analyzed data would also be a sought-after feature, in the sense of not imposing a set of www. nonlin-processes-geophys. net/25/19/2018/ Nonlin. Processes Geophys. ,  25, [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] 2018  patterns against which data would be decomposed, but rather letting the data themselves drive the decomposition. This latter criterion ensures both that the extracted components carry physical meaning and that the influence of the mathematical artifacts inherent to the method on the rendered picture of temporal variability is kept to a minimum  (Wu et al.
Results The IMFs obtained from the BOU time series from Fig. 3  have already served as an illustrative example on the operation of the EMD. The IMFs for the other datasets (not shown) are very similar and are discussed in the following. It must be noted that, like BOU, the CAR time series is decomposed into 8 IMFs, while the PAY data have 9, and 10 IMFs are obtained for TAT. Besides the IMFs, for each time series the decomposition also yields a residual, or trend (also not shown). With respect to the decennial time span of the analysis (10 years), the trend can be thought of as a lowpass approximation of the data  (Moghtaderi et al. , 2013) , but not as an oscillation. Since this work focuses mostly on the characteristic scales of temporal variability, the EMD trends along with their statistical significance and physical meaning do not fall within the scope of the study; for such discussion, see for example  Franzke (2012).
Moreover, besides the notable similarity among the medians of these modes, for all the datasets both the interquartile ranges and the total ranges of these first five modes exhibit approximately the same variability. Added to this, IMF6 for BOU, CAR, and PAY, as well as IMF7 for TAT, whose median periods are, respectively, 368. 2, 364. 3, and 356. 6, as well as 366. 6 days, can clearly be associated with the yearly cycle given by the revolution of the Earth around the Sun. This yearly component is very similar for BOU, CAR, to a lesser extent TAT, with an interquartile range concentrated around almost the same median value and with the only minor difference the slightly extended range for TAT of 300 days as opposed to days for the other two. The PAY yearly mode from those of other stations, with its interquartile range and its range being much larger, the latter even overlapping the interquartile ranges of IMF5 and IMF4.
This is result of the mode-mixing phenomenon described in Sect. 3. 1 that may arise with the EMD, i. e. , the mixing of different timescales in same IMF, mainly related to the intermittence of signal and to contamination with noise  (Huang et al. , 2003). Nevertheless, the spectral part of IMF6 which overlaps IMF5 and IMF4 has very low power  (Bengulescu et al. , 2016b) ; thus, this phenomenon does not influence the validity of the analysis. With this in mind, one notes that for BOU and CAR no spectral components are present in the 100-to 300-day band. Furthermore, TAT is the only dataset that has a transitional mode of 143. 2 days, with the median period in between the first five IMFs common to all stations and the yearly cycle. At this point, the Hilbert frequency distribution of the IMFs for BOU may be compared to the Fourier one from the PSD in Fig.
Lastly, TAT is the only dataset whose the low-frequency variability is expressed by three components, IMF8-IMF10, with mean periods of 609, 1440. 3, and 2402. 6 days. While the first quartile of IMF9 coincides with the upper range of IMF8, the upper range of IMF9 is slightly below the lower range of IMF10; hence, the last two modes do not overlap at all. By its range, IMF8 of TAT approximates IMF7 for BOU and CAR, but there is no proximity in terms of median or interquartile range. Similarly, the IMF9 of TAT resembles the IMF8 of CAR in terms of range, but their medians are not in close agreement and their interquartile ranges even less so. With the scrutiny of these low-frequency components, the discussion of the timescale distribution of the IMFs from Fig. 7  can now be concluded. However, as previously mentioned, this particular illustration, although instructive, is incomplete.
First, the box plot representation does not take the instantaneous variations in frequency into account, but renders global aggregates instead -much like the traditional Fourier methods, with the interquartile range spread in addition. This is done on purpose, with the intent of making it easier for the readership not accustomed to the HHT to create analogies with the more familiar methods (e. g. , Fourier analysis, wavelets). Second and last, this particular representation is totally devoid of any information pertaining to the local amplitude, or power, or variance, of the data. With these considerations in mind, the Hilbert spectrum, a true timefrequency representation for nonlinear and non-stationary data, is discussed next. Since the goal of this exercise is to lay the groundwork for the forthcoming discussion, only the spectrum for the BOU data is provided as an example.
For BOU, CAR, and PAY, a low-power region can be found in the 100-to 300-day band. Beyond the 1 year timescale, the low-frequency variability in the 1. 5-to 6-year band is captured by another two (BOU and CAR) or three (PAY and TAT) components. The TAT data are the only time series that has an IMF in the low-power band between the high-frequency feature and the yearly cycle (median period 143. 2 days).
(  10 ): t l = t • l, where l ∈ {1. 1, 1. 2,. , 1. 9} is the resampling rate and runs along the horizontal axis. Next, the HHT was used to decompose the resulting time series into IMFs and to compute their spectrum-weighted mean frequencies, following Eq. (  9 ). In order to emphasize the effects of the fractional resampling on the spectral contents of the IMFs, these latter frequencies were then normalized by the SWMF of the original, non-resampled data as per Eq. (  11 ). For each dataset, this ratio is indicated on the y axis, as ω k,l , with k ∈ {1. 8} indicating the IMF number. It then becomes possible to follow the evolution of the normalized SWMF of each individual IMF as a function of the fractional resampling rate (Fig. 9 ). As the EMD is an efficient wavelet-like dyadic filter, it follows that the IMFs of time series of random pro-cesses undergo a translation towards lower frequencies under fractional resampling.
Third and last, the approach is best applied only to the high-frequency modes, with respect to the data length and sampling, since by resampling spurious low-frequency oscillation may inadvertently be introduced  (Chen et al. , 2013). This is further supported by the fact that, as the IMF number progresses, the region where the influence of edge effects becomes important gets larger and larger, hence only adding uncertainty to the interpretation of the results. This is also the reason why this type of analysis was only carried out on the first eight IMFs of each dataset.
Amplitude modulation through nonlinear cross-scale coupling This section investigates whether the first five IMFs can be modeled as purely uncorrelated, random noise or whether they also contain any other form of information. To test this, the rank correlation between the yearly and sub-yearly IMFs and their envelopes, e. g. , the AM part in the middle panel of Fig. 5 , has been computed for each SSI time series. Kendall's rank correlation coefficient, τ , a statistical measure of ordinal association describing how similar the orderings of the data are when ranked  (Kendall, 1938) , is employed here to establish whether each pair of the two variables, AMx and IMFy with x, y ∈ {1. 7}, may be regarded or not as independent. for the four time series. From top to bottom: BOU, CAR, PAY, and TAT.
The black dashed diagonal depicts the behavior of a pure noise time series under an ideal dyadic filter. For all datasets, the only mode that maintains a quasi-constant frequency under fractional resampling is the IMF associated with the yearly cycle, i. e. , IMF6 for BOU, CAR, and PAY and IMF7 for TAT. In all the other IMFs quasi-stochastic behavior is apparent through frequency downshifting towards the next lower octave, approximately following the dashed line. τ = 1 indicates perfect agreement between rankings, while τ = -1 denotes perfect disagreement -i. e. , one ranking is the reverse of the other; for τ = 0 the two variables are statistically independent. The resulting rank correlation coefficients and the associated p values, are presented in Fig. 10. For each panel, the columns denote the EMD modes (IMFx) and the rows the amplitudes of their envelopes (AMy).
The background color of each cell (AMx, IMFy) indicates the rank correlation τ between IMFy and the AM part of IMFx within the same dataset. The legend of the color encoding is found on the color bar at the bottom of the figure. The associated p values are presented numerically in each cell for the sake of completeness and transparency  (Wasserstein and Lazar, 2016). For BOU, CAR, and PAY, IMF6 accounts for the yearly variability in the time series; hence, the correlation matrices are 6 × 6 in size. For TAT, the yearly mode is IMF7; thus, in this case the correlation matrix has a size of 7 × 7. Two conclusions can be drawn from Fig. 10. Values of τ significantly different from zero, shown in red, are recorded in the last column for all stations. These demonstrate a modulation of the amplitude of the components having sub-year timescales, i.
e. , AM1 to AM5 (and AM6 for TAT), by the yearly IMF, at a statistically significant level (p ∼ 0). The effect is most pronounced for PAY, as inferred from the darker red shades (larger rank correlation coefficients). For the BOU and CAR datasets the first row (AM1) exhibits blue and dark blue cells for IMF3-IMF5 at the statistically significant level. This indicates a negative rank correlation. Similar, but lighter, amplitude modulation is observed on the second row (AM2), but only by IMF4 and IMF5. For the PAY series, this negative rank correlation is greatly reduced for the first row (light blue tones) and is absent in the second row. For TAT no such correlation can be observed. At this point it is interesting to note that, in a similar way to the discussion from Sect. 5. 4, the different features of the datasets from Fig.
The intrinsic timescales of variability in the SSI Firstly, the median periods of the IMFs composing the highfrequency band are revisited. It is shown in Fig. 7  that they follow a dyadic repartition that approximates the series dyadic sequence: 3. 5, 7, 14, 28, and 56 days. Such a doubling in frequency in IMFs has been previously reported in astrowww. nonlin-processes-geophys. net/25/19/2018/ Nonlin. Processes Geophys. , 25, 19-37, 2018 physical and geophysical signals. When investigating three independent datasets of satellite observations of the (extraterrestrial) total solar irradiance,  Lee et al. (2015)  consistently find a similar dyadic-scale progression of modes at 13. 5, 27, and 54 days, statistically significant within the 95 % level, that correspond to the 27-day solar rotation period and its (sub-)harmonics.
These findings are important for the modeling and forecasting of the SSI, as follows. On the one hand, models for BOU and CAR should not contain any power in this band, or should at least filter it out. For TAT, on the other hand, any model attempting to reconstruct the SSI should ensure that the 100-to 300-day region is not a spectral void. In Sect. 5. 1, evidence is presented that the spectral band spanning from 2 to 300 days seems to be composed mostly of random realizations of stochastic background processes, which can be modeled following, e. g. ,  Flandrin et al. (2004a) ,  Rilling et al. (2005) ,  Welter and Esquef (2013) , and  Kolotkov et al. (2016). Thirdly, the median periods detected around the 1-year mark in all the datasets can be explained by the revolution of the Earth around the Sun and the associated orbital parameters.
Our findings can be thus directly used to improve models for estimating SSI from satellite images or forecasts of the SSI. We have shown that the adaptive Hilbert-Huang transform is a versatile tool in analyzing SSI datasets, exhibiting significant nonlinearity and non-stationarity. First, we have employed it to extract the intrinsic modes of variability in the SSI at distinct timescales. Second, the HHT has been used to discriminate between the deterministic yearly cycle and the quasi-stochastic high-frequency components. The same methodology could also be employed on different geophysical signals, such as wind speed time series and river discharge datasets. When modeling climate processes as dynamical systems with low-frequency oscillations and noise effects,  Chekroun et al. (2011)  have shown that "even the 'approximately right' noise can help, rather than hinder".
Figure9. The drift of normalized SWMF ω k, l (ordinate) of IMF k, with k ∈ {1. 8}, as a function of the resampling rate l (abscissa) for the four time series. From top to bottom: BOU, CAR, PAY, and TAT. The black dashed diagonal depicts the behavior of a pure noise time series under an ideal dyadic filter. For all datasets, the only mode that maintains a quasi-constant frequency under fractional resampling is the IMF associated with the yearly cycle, i. e. , IMF6 for BOU, CAR, and PAY and IMF7 for TAT. In all the other IMFs quasi-stochastic behavior is apparent through frequency downshifting towards the next lower octave, approximately following the dashed line.
Acknowledgements. The authors wish to thank  Patrick Flandrin  from  École Normale Supérieure de Lyon, France , and  Gerard Thuiller  from  Laboratoire Atmosphères, Milieux, Observations Spatiales in Guyancourt, France , for the fruitful conversations and their insightful comments that sparked the development of this study. Dmitrii Kolotkov  from the  University of Warwick ,  United Kingdom , is also acknowledged for the personal communications pertaining to the stochastic nature of the high-frequency variability band. The authors thank all ground station operators of the BSRN network for their valuable measurements and the  Alfred Wegener Institute (AWI)  for hosting the BSRN website. Edited by: Ioulia Tchiguirinskaia Reviewed by: two anonymous referees.
Code availability. The software used for this study, comprising general EMD and Hilbert spectral analysis routines, is publicly available online. -The fast EMD routine used in this study is provided by  Wang et al. (2014)  and can be downloaded at http://rcada. ncu. edu. tw/FEEMD. rar. -Methods pertaining to Hilbert spectral analysis are part of a general HHT toolkit provided by  Wu and Huang (2009)  and can be downloaded at http://rcada. ncu. edu. tw/Matlab%20runcode. zip. -The code for the ICEEMD(AN) algorithm  (Colominas et al. , 2014)  is provided by María Eugenia Torres on her personal web page and can be downloaded at http://bioingenieria. edu. ar/grupos/ldnlys/metorres/metorres_ files/ceemdan_v2014. m. Data availability. The raw BSRN datasets employed in this study are made available by König-Langlo et al.
For WASP-92b 8 nearby comparison stars were used, and 5 for WASP-93b. Aperture radii of 5. 9 arcsec, 5. 0 arcsec and 10. 2 arcsec were chosen for 2013 June 26, 2013 July 22 and 2012 August 21 respectively. Aperture sizes were chosen to minimise the RMS scatter in the out of transit data. Initial photometric error estimates were calculated using the electron noise from the target and the sky and the readout noise within the aperture. The data were normalised with a first order polynomial fitted to the out-of-transit data.
RATCam observations RATCam is an optical CCD imager, which was installed on the Liverpool Telescope until decomissioned in February 2014  (Steele et al. 2004; Mottram et al. 2004 ). The camera was a 2048 × 2048 pixel EEV CCD, set up with a 4. 6 × 4. 6 arcmin FOV. For the WASP-93 lightcurve observed on the night beginning 2008-07-10, images were taken in 10 s exposures, which were bias subtracted and flat-fielded using a recent twilight flat-field image. Aperture photometry was performed using IRAF DAOPHOT routines with three bright non-variable comparison stars in the observed field. The lightcurve was then binned into sets of three images, resulting in 144 photometric data points.
JGT observations JGT (James Gregory Telescope) is the 0. 94-m telescope at the University of St Andrews Observatory. The CCD is an 1024x1024 e2v device, and the telescope has an un-vignetted FOV of 15 arcmin diameter. The JGT data was collected using a standard R-band filter and the telescope in focus. The data collected were corrected with flat-field and bias images, and then relative aperture photometry with a single comparison star was performed using the GAIA routines of the Starlink software packages 3.
2-m telescope. CORALIE points its second spectrograph fibre on a calibration lamp, and lunar contamination is avoided by observing only when the moon is not present. The CORALIE set-up was upgraded in November 2014, thus data from before and after this upgrade are treated as separate instruments, which could have an offset in the measured RVs. For data from each of the spectrographs, radial velocities were computed from the spectra by weighted crosscorrelation (CCF)  (Pepe et al. 2002 ) using a numerical G2spectral template. WASP-118 has been further observed with the HARPS-N instrument on the 3. 6-m Telescopio Nazionale Galileo (TNG) on La Palma  (Cosentino et al. 2012 ). An attempt was made to collect spectra during a transit on 1st October 2015 of the object to determine orbital obliquity, but cloud cover prevented data collection.
The RV fit is performed by fitting a Keplerian to the RV data points, and the RM effect (when observations are available) is modelled with the equations presented by  Ohta et al. (2005). Each individual RV dataset is assigned a floating instrumental zero-point velocity of its own, which is determined at each MCMC trial by inverse variance weighted averaging of the RV residuals following subtraction of the orbital velocities derived from the current model -for the fits presented here, the zero-point differences calculated between instruments were all smaller than 0. 1 m s -1. The jump parameters used in the MCMC analysis are listed with their definitions in Table  4. The parameters used have been updated from those listed in  Cameron et al. (2007)  by  Enoch et al. (2010)  and  Anderson et al.
RESULTS AND DISCUSSION 5. 1 WASP-92 system WASP-92b is a ∼ 0. 81 MJ planet in a 2. 17 days orbit around an F7 spectral type star. The Bagemass tool presented in  Maxted et al. (2015)  was used with the observed measurements for [Fe/H], T eff and ρ * to estimate the age and mass of WASP-92, which were found to be 2. 99 ± 1. 03 Gyr and 1. 21 ± 0. 06 M. The posterior distributions of this analysis can be seen in the upper plot of Figure  6  with the associated stellar evolution tracks and isochrones for the optimal mass and age found by the Bagemass tool. The tracks used by the tool were calculated from the GARSTEC code  (Weiss & Schlattl 2008). These results are in agreement with the gyrochronological age presented in Section 3. 1, but much older than the age suggested by the lithium abundance observed in the spectra.
ACKNOWLEDGMENTS KH and ACC acknowledge support from  STFC  doctoral training grant  ST/M503812/1  and grant  ST/M001296/1  respectively. We extend our thanks to the staff of the  ING  for their continued support of  SuperWASP. WASP-South is hosted by the  South African Astronomical Observatory , and we are grateful for their ongoing support and assistance. The Euler Swiss telescope, installed at  La Silla , is supported by the  Swiss National Science Foundation. We are all grateful to  ESO  and its  La Silla  staff for their continuous support. TRAPPIST is a project funded by the  Belgian Fund for Scientific Research (Fonds National de la Recherche Scientifique, F. R. S. -FNRS)  under grant  FRFC 2. 5. 594. 09. F, with the participation of the  Swiss National Science Foundation. L. Delrez  acknowledges the support of the F.
R. I. A. fund of the FNRS. M. Gillon  and  E. Jehin  are  FNRS Research Associates. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2013-2016 ) under grant agreement number  312430  (OPTICON). The William Herschel Telescope is operated on the island of  La Palma  by the  Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofsica de Canarias.
This data article describes the anisotropy of diffraction observed for the centered monoclinic crystals of OmpF reported in "Two different centered monoclinic crystals of the E. coli outermembrane protein OmpF originate from the same building block (Chaptal et al. , 2016 [1])". The datasets intensity falloff as a function of resolution are provided along with reflections along the (h,l) and (k,l) planes. A comparison with the crystal packing in the real cell is also provided, with the correspondence to the reciprocal vectors.
Experimental design, materials and methods X-ray diffraction datasets were collected at synchrotron beamlines ID23-1  [2]  (ESRF) and XALOC  [3]  (ALBA), from flash cooled (in liquid nitrogen) single crystals and kept under nitrogen gas during data collection. Diffraction data already reduced and scaled, in the form of structure factors, were deposited in the Protein Data Bank under accession codes PDB: 4d5u and PDB: 4jfb and are freely retrievable to the community. Structure factors were submitted to the anisotropy server at UCLA for analysis (http://services. mbi. ucla. edu/anisoscale/) (Fig. 1B )  [4]. The server outputs the diffraction intensity falloff as a function of resolution for the three main direction of the reciprocal space; vectors a * in red, b* in green and c* in blue. The same structure factors were analyzed using the Phenix software package  [5]  to visualize diffraction data along the (l,k) and (l,h) planes to exemplify the diffraction anisotropy (Fig.
Acknowledgments This work was supported by the  ANR  grant  EMMA-10-049-01 ,  13-BSV5-0001-01  and the  "labellisation par la Ligue Nationale Contre le Cancer ". The authors wish to thank  Mike Sawaya  for useful discussions during the preparation of these data. We acknowledge the synchrotrons SOLEIL,  ESRF  and  ALBA  for allocation of beam time and we are thankful to the beamline staff for their support and for the extensive beam-time required for membrane-protein projects. Financial support of  Arnaud Kilburg  was received from the  Rhône-Alpes region. The crystallization work benefited from the  Protein Science Facility of the SFR Biosciences Lyon.