dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical distribution of the plasma density irregularities using the in situ measurements has been studied for many years (e. g. ,  Basu et al. 1980; Aggson et al. 1996; Burke et al. 2004; Huang et al. 2014). However, in the conditions of the decrease or lack of the actual in situ data it is important to consider additional information on the properties of the plasma density irregularities, derived from other instruments onboard LEO satellite. Thus, a new method for the ionospheric irregularities detection was successfully implemented using the highresolution magnetic field measurements onboard the CHAMP satellite  (Stolle et al. 2006; Lühr et al. 2014). Later,  Zakharenkova and Astafyeva (2015)  proposed an "additional" use of the precise orbit determination (POD) GPS measurements onboard a single LEO satellite, CHAMP, for multi-instrumental analysis of the topside ionospheric irregularities.
This GPS POD approach was further successfully developed for multi-satellite use based on an example of quasi-simultaneous Swarm and TerraSAR-X measurements  (Zakharenkova et al. 2015). Therefore, potentially we can obtain additional information on the global distribution of the plasma density irregularities in the topside ionosphere using GPS measurements from LEO missions without LP instrument, like GRACE, MetOP, TerraSAR-X, etc. ; however, additional cross-validation analysis on the same database is needed. The present paper focuses on the comparative analysis of the plasma density irregularities detected in the topside ionosphere by two independent techniques-GPS and Langmuir probe (LP) measurements. The Swarm mission gives an opportunity to study similarities and differences between these techniques, as the Swarm satellite payload includes both these instruments.
Methods The GPS POD and LP measurements onboard Swarm Alpha (A) and Swarm Bravo (B) satellites (named hereafter SWA and SWB, respectively) were processed and analyzed in this study. While choosing the time period for our analysis, we take into account that: (1) the commissioning phase for the final Swarm orbit configuration was finished on May 2014, (2) from July 15, 2014, the Swarm GPS data are released with 1-s resolution. Therefore, our analysis comprises a period of 18 months from August 2014 to January 2016. As known, the ionospheric irregularities can be characterized by measuring its impact on amplitude and phase of the received GPS signal. Pi et al. (1997)  introduced into usage for ground-based GPS observations two GPS-based indices: ROT and ROTI. ROT (rate of TEC change) is the time derivative of TEC and is considered as a measure of the phase fluctuation activity.
The detailed description of this method implementation for the Swarm GPS data is presented in  Zakharenkova et al. (2015). There are several steps of the GPS data processing. First, we calculate a relative slant TEC along all lines of sight Swarm-to-GPS from the frequency-differenced GPS phase delay using the well-known algorithms  (Hofmann-Wellenhof 2001). Initially, the Swarm GPS data were released with 10-s sampling rate, after July 15, 2014, GPS data are provided with 1-s sampling rate. Here we use Swarm GPS data with 1-s resolution. In order to determine the location of the ROT/ROTI results, we used piercing points through a thin layer at the fixed altitude of 500 and 550 km for SWA and SWB, respectively. The ROT is determined by taking the ratio of the difference between the slant TEC values at two successive times to the time interval  (Pi et al.
g. , equatorial ionization anomaly (EIA). In order to make the comparison between GPS and LP Swarm measurements possible and more evidential, we process the Swarm in situ data in a similar way. We used Swarm Ne values with 1-s rate by selection of measurements provided by Probe 1 (Swarm document 2015), then we normalized data to the value of 10 6 el/cm 3. Using the same approach as for the GPS data, we derive estimates of the ROD (rate of density) in units of 10 6 el/ (cm 3 min) and ROD index (RODI), based on the standard deviation of the ROD and calculated over 15-s period with sliding window.
Results and discussion We divide the 18-month data into four seasons related to the June/December solstices and March/September equinoxes with solstice/equinox month in the middle and one full month before and 1 month after it. We note that data accumulation even for a 3-month period is not enough to cover all 24 h of local time (LT). The average orbit altitude was ~460 and ~510 km for SWA and SWB, respectively. It is important to mention that in the Swarm constellation the upper satellite (SWB) slowly separates in local time from the tandem satellites (SWA and SWC). It is a very important issue for this study, as such satellite configuration can provide more data for adjacent LT sectors, but can lead to considerable differences in simultaneous comparison of satellites measurements. Figure  1a  presents the temporal difference between SWA/SWB satellites from August 2014 till January 2016.
However, these available LT intervals coincide well with the time of sunset and peak of the post-sunset irregularities occurrence, which could be the reason for rather high occurrence rates despite the solstice season. Obviously, 1-2 years of the Swarm mission operation is not enough for a climatology study of the occurrence rate of the equatorial plasma irregularities, as accumulated Swarm database does not allow us to get proper LT coverage for each month and, in fact, a 3-month period can depict the occurrence rate referring mostly to a separate month with better coverage of 18-21 LT period. Comparison of the occurrence rates for the postsunset and post-midnight periods (left and right panels of Figs. 4, 5 ) clearly shows that post-midnight equatorial irregularities are quite rare phenomena.
Conclusions In this paper, we present first results on the joint comparison of the ionospheric irregularities occurrence detected by two independent instruments onboard the Swarm mission. We analyze dataset consisted of GPS and LP data provided by the Swarm A and Swarm B satellites with 460 and 510 km orbit altitude from August 2014 till January 2016. We made estimation and analysis of the behavior of two indices based on TEC and in situ density derivatives. We found that the obtained ROTI results based on the Swarm GPS measurements are in a very good consistency with the concurrent in situ plasma measurements derived from the LP instrument. Our results demonstrate a high degree of similarities in the occurrence pattern of the seasonal and longitudinal distribution of the topside ionospheric irregularities.
Acknowledgements This work is supported by the  European Research Council  under the European Union's  Seventh Framework Program / ERC  Grant Agreement No. 307998. We acknowledge the  European Space Agency (ESA)  for providing the SWARM data (http://earth. esa. int/swarm) and the  International GNSS Service  for GPS orbit products (ftp://cddis. gsfc. nasa. gov). We acknowledge the  Jicamarca Radio Observatory  for providing the JULIA Radar data through Madrigal Database (jro-db. igp. gob. pe/madrigal/). The  Jicamarca Radio Observatory  is a facility of the  Instituto Geofisico del Peru  operated with support from the  NSF   AGS-1433968  through  Cornell University. We thank  Stephan Buchert  ( IRFU ) for useful recommendations on the Swarm LP data processing and two anonymous reviewers for their valuable comments and suggestions.
A total of 14 scallops (Chlamys islandica) were wired in parallel, and data were acquired 24 h, 7 days per week via the Internet. Valve activity is associated with gill filtration, which is necessary for respiration, nutrition and waste elimination. Therefore, monitoring valve gaping behaviour is a good proxy for measuring the global activity levels of bivalves in their ecosystem. This study was conducted over three years (1060 days) from 2012 to 2015. During this period, we primarily focused on the opening duration, amplitude and circadian rhythm of the valve. We recorded the valve activity during each season with a particular focus on three successive polar nights that lasted 116 days each. To improve our investigation of the valve activity and its relationship with the perception of light cues, the polar night period was divided into three well-defined and distinct sub-periods: civil, nautical and astronomical twilights.
Circadian activity during the polar night. As described earlier, the polar night at Ny-Alesund in Svalbard can be divided into different twilight periods. To refine our analysis on the behaviour of C. islandica during the polar night, we examined the strength of their circadian rhythm during the different twilight periods, which were defined according to the light irradiance. Five periods were investigated: 2 civil twilight periods, with decreasing (CV1) or increasing (CV2) light irradiance levels; 2 nautical twilight periods, with decreasing (NA1) or increasing (NA2) light irradiance levels; and 1 astronomical twilight (AS) periods, which represented the darkest period of the polar night. For details on the polar night timetable and the twilight periods, see Fig. SI4. Figure  4  shows the group and individual level data on the rhythmic behaviour of the scallops during the three polar nights covered in this study (from 2012-2015).
Conclusions For the first time, a high-frequency recording system was deployed in situ to measure the valve activity of an Arctic bivalve, the scallop C. islandica, over a continuous period of approximately 3 years. This in situ data were used to investigate their behaviour and to determine their biological rhythm during the specific light periods that occur in a recently ice-free fjord in Svalbard. We discovered that the valve behaviour of C. islandica follows a circadian pattern in the majority of the selected individuals. This pattern is maintained even during the polar night, suggesting a sustained level of activity of C. islandica. This supports the notion that the polar night does not represent an inevitably quiescent period during but could be rather a moment of intense activity for these scallops.
62 Hz. The signal was recorded using a custom acquisition card, and the data were automatically transmitted daily to a data processing centre in the Arcachon Marine Biological Station (France) using cellular and Internet networks. The data were reported daily on the Molluscan Eye website (http://molluscan-eye. epoc. u-bordeaux1. fr/). The valve behaviour endpoints included the valve opening duration (VOD) and the valve opening amplitude (VOA) of each individual and the group. A VOD equal to 100% indicated that the scallop's valve was open for the entire time studied, and a VOA equal to 100% indicated that the scallop's valve was open at its maximum gaping amplitude for the entire time studied, whereas a VOA equal to 0% indicated that the scallop's valve was closed. Data treatment and statistical analysis of the rhythmic activity.
The mean VOA was calculated to characterize the rhythmic activity. Double-plotted actograms (each line represents 2 days) were produced using Chronos-Fit 1. 06  44. Activity levels above the average for the day are represented by a black section, whereas values below the 24 h average are represented by a white section. Chronobiological analyses were performed using the Time Series Analysis Serial Cosinor 6. 3 software (http:// www. euroestech. net/mainuk. php). Different steps were performed to validate a rhythm in the scallops  20, 45, 46. Briefly, the quality of the data set was assessed by controlling for the absence of randomness using the autocorrelation diagram and the absence of a stationary character using a partial autocorrelation function (PACF) calculation  47. The periodicities in the recorded data were tested using the spectral method of the Lomb and Scargle periodogram  48.
Acknowledgements This project was founded by the  Norwegian Research Council Nordic Regional Funding for Research "Talking Clam " project, Grant number  208974 ; by  Statoil Arctos Arctic Research Program  (http://saarp. arctosresearch. net/), by the  Svalbard Science Forum , by the  Fram Centre research program  " Fjord & Coast " and by the  DEVOTES (DEVelopment Of innovative Tools  for understanding marine biodiversity and assessing  Good Environmental Status)  project funded by the  European Union  under the 7th  Framework Programme, 'The Ocean of Tomorrow' Theme  (Grant Agreement No. 308392 ), www. devotesproject. eu. The authors would like to thank  Chris Emblow  and  Hector Andrade  for their technical help.
During the last 30 years significant effort has been made to improve air quality through legislation for emissions reduction. Global three-dimensional chemistry-transport simulations of atmospheric composition changes over the past three decades have been performed to assess the impact of these measures. The simulations are based on assimilated meteorology to account for the year-to-year observed climate variability and on different scenarios of anthropogenic emissions of pollutants, which may or may not account for air quality legislation application. The ACCMIP historical emissions dataset is used as the starting point. We show that air quality legislation has been more efficient in limiting the rapid increase of air pollutants than what is traditionally deduced through comparison of present-day atmospheric composition to that calculated using anthropogenic emissions representative of the pre-industrial period, due to significant technology development.
Methodology -The global model setup The model used for this work is the global 3D chemistry-transport model (CTM) TM4-ECPL  (Kanakidou et al. , 2012; Daskalakis et al. , 2015)  that takes into account gas and multiphase chemistry  (Myriokefalitakis et al. , 2011) , gas-particle partitioning of semi volatile organics  (Tsigaridis et al. , 2006) , and computes the gas-to-particle partitioning of inorganic components and the aerosol water using the ISORROPIA II aerosol thermodynamic model  (Fountoukis and Nenes, 2007)   The oceanic emissions of aerosols and isoprene are calculated by the model based on the 3-h varying meteorology and on monthly varying chlorophyll  (Myriokefalitakis et al. , 2011; Vignati et al. , 2010). Other VOC emissions from the ocean have been taken into account in the model using the POET database  (Granier et al.
1) (Sindelarova et al. , 2014). Lightning NO x emissions are calculated online  (Meijer et al. , 2001)  and soil emissions are climatological emissions from the POET database  (Granier et al. , 2003). Monthly anthropogenic and biomass burning emissions for the hindcast current legislation (CL) simulation are from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) database  (Lamarque et al. , 2013)  until the year 2000 and RCP6. 0  (van Vuuren et al. , 2011; Fujino et al. , 2006)  projections afterwards, also provided by ACCMIP (http: //accmip-emis. iek. fz-juelich. de/data/accmip/gridded_netcdf/accmip_interpolated/README. accmip_interpolated. txt). As anthropogenic emissions in this paper we consider the sectors included in the anthropogenic emissions of the ACCMIP database  (Lamarque et al.
Simulations performed Three global chemistry-transport transient simulations of atmospheric composition changes during the past 3 decades  were here performed using assimilated meteorology, natural emissions and biomass burning emissions specific of the simulated year. All anthropogenic emissions with the exception of shipping and aircraft emissions were different between the simulations. Aircraft and shipping emissions were the same for all simulations. The Current Legislation (CL) simulation uses the historical emissions of the ACCMIP database where legislation is applied to limit air pollution  (Lamarque et al. , 2013). The Anthropogenic Emissions 1980 (AE1980) simulation uses anthropogenic emissions that are constant throughout the years and equal to those of the year 1980. This corresponds to simulations that are typically used for comparison when evaluating the efficiency of emission control scenarios.
Construction of the BA1980 anthropogenic emissions The BA1980 inventory assumes that land anthropogenic emissions per capita remained constant from 1980 until now in major geographic regions, while population and thus overall human driven emissions changed. Advances in technologies are thus ignored and the energy demand per capita as well as the way energy was produced have been assumed constant with time and per region and equal to those of 1980. To construct this anthropogenic emissions database the ACCMIP anthropogenic emissions of the year 1980 together with global population maps were used. The global population for the year 1980 (not gridded) and global gridded population maps for the period 1990-2010, available at five-year increments were obtained from the United Nations (http://www.
These per capita emissions were then applied on the gridded population maps to construct the database of annually-varying BA1980 anthropogenic emissions. Based on the population density, the anthropogenic land emissions of 1980 and the source regions as socio-economic regions, an emission inventory has been created that takes into account the per capita anthropogenic emissions of 1980, the population density increase per grid for the period 1980-2010 and the population relocation since 1990. As a result, a new anthropogenic emission inventory was constructed, which assumes non-mitigation for improvement of air quality after 1980 and does account neither for technological developments since then nor for increases in energy demand per capita but accounts for population increase. Increase in energy demand per capita is almost negligible in Europe and N.
Comparison against surface measurements TM4-ECPL simulations were performed with the different emissions scenarios. The accuracy of the model is evaluated by comparison with available observations around the globe (see locations in Fig. S4 ). Surface observations for O 3 and CO were obtained from the World Data Centre for Greenhouse Gases (WDCGG; http://ds. data. jma. go. jp/gmd/wdcgg/introduction. html). (Tsigaridis et al. , 2014). Monthly mean and standard deviation are calculated from the original datasets available at various temporal resolutions. These datasets provide adequate coverage over Europe and N. America. For the rest of the globe, the temporal and spatial variability of the measurements is scattered, resulting in a model evaluation which is highly biased towards N. America and Europe.
Surface observations of Surface concentrations and trends were calculated for each of the three scenarios to compare the computed atmospheric composition changes during the studied period. CL simulations have been compared to global observations of O 3 , CO, SO 2- 4 , BC and OC (Fig. 2 , Fig. 3  and supplementary figures S5 to S10). These comparisons are performed on monthly mean basis for all stations and all years from 1980 to 2010 where data are available. Statistical analysis of the results was conducted per model grid. For this, monthly mean of the measurements were grouped per model grid (  6 • lon. × 4 • lat. or 3 • lon. ×2 • lat. , depending on model resolution) and they are then averaged to derive the annual mean concentration. Model results are sampled at the time and location of the observations, then annual means in each grid box are computed for model results and compared to those derived from observations.
In contrast, in the fast developing economies of China and India, computed SO 2- 4 levels are higher by about 10%. However, if we account for the increase in energy demand per capita in these regions (by factors of 3 for China and 2 for India) that is not taken into consideration when constructing the BA1980 emissions database, then technological advances seem to have limited the air pollution even in these regions (Table  S2b ). The computed increase in surface SO 2- 4 levels over China until about 2007 followed by a stabilization and even a decrease in their levels, is in general agreement with the SO 2 column satellite observations (GOME/SCIAMACHY) that show increases between 1996 and 2007 and then a decrease in SO 2 tropospheric column over China (SCIAMACHY Product Handbook). For OC and BC, the global gain computed is 10% and 22% respectively.
Acknowledgements. This work has been supported by the  EU-FP7  project  PEGASOS  ( FP7-ENV-2010-265148 ), the  EU-FP7  project  ECLIPSE  ( FP7-ENV-2011-282688 ) and the  EU-FP7  project  BACCHUS  (project number  603445 ). We thank  Frank Dentener  and  Michael Gauss  for pertinent comments during early stages of this work.
O 3 , BC and SO 2- 4 over Europe and the U. S. A. were obtained from the European Monitoring and Evaluation Programme (EMEP; http://www. emep. int) and the Interagency Monitoring of Protected Visual Environments (IM- PROVE; http://vista. cira. colostate. edu/improve/) respectively. The OC measurements are from the AeroCom Phase II database.
Remind on Classical PCA Principal Component Analysis (PCA), also known as Karhunen-Loève transform, Hotelling transform, SVD transform, etc. , is without any doubt the most useful technique for data dimensionality reduction. Let us start with a set of vectors {v i } ∈ R D , 1 ≤ i ≤ n, where n represents the number of vectors; in our case it corresponds to the number of image pixels, i. e. , n = n 1 n 2 since n 1 and n 2 are the two spatial dimensions. The goal of the PCA is to reduce the dimension of this vector space thanks to a projection on the principal component space, namely F = {v i } n i=1 -→ F ′ = {v ′ i } n i=1 (9) with v ′ i ∈ R d , where d ≪ D. In our case, dataset F ∈ M n,D (R) represents the hyperspectral image F , where each column F k ∈ R n , 1 ≤ k ≤ D corresponds to a vectorized spectral band.
PCA should find a projective space such that the mean squared distance between the original vectors and their projections is as small as possible. As we just show, this is equivalent to find the projection that maximizes the variance. Let us call w j ∈ R D , where j are the principal components. The aim of PCA is to find the set of vectors {w j , 1 ≤ j ≤ D} such as arg min w j [ n -1 n ∑ i=1 ∥v i -< v i , w j > w j ∥ 2 ] , ∀1 ≤ j ≤ D ( 10 ) Developing now the distance we have: ∥v i -< v i , w j > w j ∥ = ∥v i ∥ 2 -2 < v i , w j > 2 + < v i , w j > 2 •∥w j ∥ 2 then, by adding the additional constraint that ∥w j ∥ 2 = 1, replacing in Equation (  10 ), and keeping only terms that depend on w j , we obtain the following new objective function: arg max w j ,∥w j ∥ 2 =1 n -1 n ∑ i=1 < v i , w j > 2 , ∀1 ≤ j ≤ D (11) Since var(< v i , w j >) = n -1 n ∑ i=1 (< v i , w j >) 2 -(n -1 n ∑ i (< v i , w j >)) 2 if we consider that the dataset F has been column-centered, which means that ∑ n i=1 v i = 0, then var(< v i , w j >) = n -1 ∑ n i=1 (< v i , w j >) 2.
Scale-Space Decomposition MorphPCA In the first variant, we just use the area-based nonlinear scale-space discussed in the previous section. So the grey-scale image of each spectral band F :,:,k is decomposed into residues of area openings and area closings according to the discretization into S scales for each operator, i. e. , r l (F :,: ,k ) = γ a s l-1 (F :,:,k ) -γ a s l (F :,:,k ) and r -l (F :,:,k ) = φ a s l (F :,:,k ) -φ a s l-1 (F :,:,k ), 1 ≤ l ≤ S. Thus we have increased the dimensionality of the initial dataset from a tensor (n 1 , n 2 , D) to a tensor (n 1 , n 2 , D, 2S + 1). As discussed in  [15] , this tensor can be reduced using high order-SVD techniques. We propose here to simply compute a covariance matrix as the sum of the covariance matrices from the various scales. More precisely, we introduce V Morpho-1 ∈ M D,D (R) with : V Morpho-1 = S ∑ l=1 (V(l)) + S ∑ l=1 (V(-l)) (18) where the covariance matrices at each scale l is obtained as V(l) k,k ′ = Covar ( r l (F :,:,k ), r l (F :,:,k ′ ) ) , 1 ≤ k, k ′ ≤ D We note that it involves an assumption of independence of the various scales.