dataesr/scientific-paragraphs-categorization huggingface dataset:
Red sequence fitting To describe the location of the model RS, we use linear regression 2  to fit the optimised slope, s, and zero-point, c22. 5, for the relation, c = s(m -22. 5) + c22. 5, ( 6 ) where m is the galaxy magnitude, e. g. H, and c is the corresponding colour, e. g. z-H. Since the RS is expected to be dominated by passively evolving galaxies and since GALFORM is able to provide us with values for the star formation rates of galaxies, as well as which halos they belong to, we are able to fit a RS using just those galaxies that are passively evolving and reside in halos above our specified halo mass threshold. Therefore, we stress that this is not meant to mimic observational methods for determining the RS, but simply to provide an estimate of the RS predicted by the model. To determine the passively evolving cluster galaxies, we apply a cut in instantaneous specific star formation rate (sSFR) and fit to only those galaxies that satisfy log 10 (sSFR/Gyr -1 ) -1, which provides a reasonable distinction between actively star-forming and passively evolving galaxies in observational data (e.
e. the Figure  7. GALFORM colour-magnitude relations at redshifts z ∼ 1. 2 (upper panels) and z ∼ 1. 4 (lower panels). The observed cluster galaxies are shown by the various data points. Greyscale pixels show the number of cluster galaxies per cluster as predicted by the fiducial GALFORM model. The red dashed lines show linear fits to the red sequence predicted by GALFORM. The halo mass lower limit and aperture, r, used to select the GALFORM galaxies are shown in the label at the top of each panel. number of halos above the halo mass threshold). In each case the distribution is dominated by a clear and well-defined RS, especially at faint magnitudes, with very little indication of a blue cloud. At bright magnitudes the model RS appears to display a prominent plume of galaxies with colours extending redwards above the RS.
Cluster data from  Hilton et al. (2009)  is split into galaxies with spectroscopic redshifts (spec-z) and those without spectroscopic redshifts (photo-z). As before, the greyscale pixels show the prediction for the fiducial GALFORM model and the red, dashed line shows the linear fit to the predicted red sequence. The halo mass and aperture used to select the GALFORM galaxies are shown at the top of each panel. Figure  9. Distribution of various galaxy properties across the J -K vs. K colour-magnitude space at z ∼ 1. 4. The panels, which show the distribution of star-formation rate, stellar metallicity and combined half-mass radius of the disc and bulge of the galaxies, are labelled accordingly. Each pixel is coloured according to the median value for the galaxies that lie in that pixel, with the colour scale shown at the right of each panel.
and the K-band CGLF in Fig. 4. The observational estimates are shown by the various data points, whilst the model predictions are indicated by the solid lines (with shaded regions indicating the Poisson uncertainties). 4. 1 Observational estimatesIn Fig. 2, Fig. 3and Fig. 4we show observational estimates for the CGLF from Strazzullo et al. (2006) , Strazzullo et al. (2010 ) Fassbender et al. (2014) , as well as our estimates based upon the datasets ofMei et al. (2006b), Hilton et al. (2009)  and Snyder et al. (2012). Strazzullo et al. (2006) and Strazzullo et al. (.
Fassbender et al. adopt the same selection procedure as Strazzullo et al. (2006) when estimating the z ∼ 1. 6 K-band CGLF. For the datasets of Mei et al. , Hilton et al. and Snyder et al. we have made simple estimates for the CGLF by counting all of the galaxies regarded as being cluster members by the original authors. From the Mei et al. data we estimate the z-band CGLF at z ∼ 1. 2. From the Hilton et al. data we estimate the z and K-band CGLFs at z ∼ 1. 4. From the Snyder et al. data we estimate the z and H-band CGLFs at z ∼ 1. 2 and z ∼ 1. 4.
ACKNOWLEDGEMENTS We thank the referee for a thorough report with many useful and constructive comments. Thanks go to  Rene Fassbender ,  Chris Lidman ,  Adam Stanford ,  Veronica Strazzullo  and  Gregory Snyder  for provision of their data. In addition, we thank  Begoña Ascaso ,  James Bartlett ,  Andrea Biviano ,  Stefano Borgani ,  Mark Brodwin ,  Alberto Cappi ,  Anthony Gonzalez ,  Sophie Maurogordato ,  Pierluigi Monaco ,  David Murphy  and  Adam Stanford  for many useful and productive discussions and suggestions. This work was motivated by feedback from members of the  Euclid Consortium Galaxy Clusters Science Working Group  following the provision of galaxy mock catalogues based upon the GALFORM model. VGP acknowledges support from a  European Research Council   Starting Grant  ( DEGAS-259586 ).
FBA acknowledges the support of the  Royal Society  for a  University Research Fellowship. This work carried out on the COSMA Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  ([http://]www. dirac. ac. uk). This equipment was funded by a  BIS National E-infrastructure capital  grant  ST/K00042X/1 ,  DiRAC Operations  grant  ST/K003267/1  and  Durham University. DiRAC is part of the  National E-Infrastructure.
Analytical Model of the Digital Actuator An analytical model of the actuator implemented with MATLAB software has been developed to compute the forces exerted on the MPM and its displacement along the two displacement axes. With this model, the PMs are considered as parallelepiped blocks (without geometrical and dimensional errors) placed in free space with uniform magnetizations. The calculation of the magnetic and electromagnetic forces is based on the magnetic flux density computed using the charge model  [34]. Equation  (1)  gives the expression of the three components of the magnetic flux density generated by a parallelepiped shaped PM with dimensions (x 2x 1 , y 2y 1 , z 2z 1 ) and magnetization (M) oriented along the z-axis. The origin of the reference frame is located at the center of the PM and (x, y, z) are the coordinates of the point considered for magnet flux density computation.
The variation of solar irradiance is one of the natural forcing mechanisms of the terrestrial climate. Hence, the time-dependent solar irradiance is an important input parameter for climate modelling. The solar surface magnetic field is a powerful proxy for solar irradiance reconstruction. The analyses of data obtained with the Michelson Doppler Imager (MDI) on board the SOHO mission are therefore useful for the identification of solar surface magnetic features to be used in solar irradiance reconstruction models. However, there is still a need for automated technologies that would enable the identification of solar activity features from large databases. To achieve this we present a series of enhanced segmentation algorithms developed to detect and calculate the area coverages of specific magnetic features from MDI intensitygrams and magnetograms.
The present work is the first of a series of papers that aims toward creating a new approach for the identification of solar features from SOHO/MDI intensitygrams and magnetograms, for the purpose of semi-empirical irradiance reconstruction. The motivation for this is that in the near future we aim to incorporate it into an automated system that can operate in a real-time mode to process low-latency data from space missions. As a proof of concept we start our investigations on SOHO/MDI data, and in the near future we plan to investigate SDO/HMI data and adapt our methods accordingly. One particular outcome of the current work is the provision of area coverages of different solar features, which will be used at a later stage for irradiance reconstruction, and would also be available to the community to carry out any kind of analysis for a better understanding of these features.
In an upcoming paper we will assign intensity spectra to these activity features and reconstruct the SSI for the full MDI time series. The following parts of the paper are organised as follows: The SOHO/MDI data used in this study and the pre-processing applied are briefly described in Section 2. The statistics of the intensity and magnetogram signals from the full set of the MDI data are discussed in Section 3. The methodologies adopted for the detection of features in solar images are discussed in Section 4, the results obtained and their analysis, including a simple model of TSI based on the results obtained here, are presented in Section 5, while conclusions are drawn in Section 6.
MDI Data The Solar Oscillations Investigation/Michelson Doppler Imager (SOI/  MDI Scherrer et al. , 1995)  on board of the Solar and Heliospheric Observatory (SOHO) satellite took sets of high quality full-disk images. The images were taken using five narrow-band filters through the Ni I 676. 8 nm absorption spectral line, and were used to produce, amongst others, the intensitygrams and magnetograms 1. In this work, we use level 2 intensitygrams, which have been corrected for limb-darkening, observed with a 6-hour cadence and level 1. 8 5-min magnetograms, observed with a 96-minute cadence. A total of 90,463 FITS data files were downloaded, which included 17,347 intensitygrams and 73,116 magnetograms. The downloaded data cover a period from April 21, 1996, when the first magnetogram was recorded, to April 11, 2011, when the MDI observations were terminated.
This period covers solar cycle 23 and the beginning of solar cycle 24, and includes data from two solar minima and one solar maxima periods. The downloaded data set, however, includes a significant number of corrupted intensitygrams and magnetograms, which if left included, would cause miss detection and classification of solar features. Therefore, care was taken to identify and exclude corrupted files, which have poor quality and missing values. For the purpose of the work described here, one pair of intensitygram and magnetogram is selected for each day over the complete MDI data set. These were determined, by selecting the intensitygram observed closest to 12:00 UT each day. Then the magnetogram closest in time to the intensitygram was chosen. As part of the selection process, the time difference between the selected intensitygram and magnetogram pair was not allowed to exceed three hours.
Data Analysis In this work, we take advantage of the ASAP tool for initial photospheric feature detection and grouping. ASAP was designed to operate in near real-time and so used SOHO/MDI intensitygrams and magnetograms, provided in Graphics Interchange Format (GIF) 8-bit format. However, the downloaded data files used in the current work are in the Flexible Image Transport System (FITS) format, containing data stored as signed 32-bit format as well as detailed header information. Therefore, the conversion of the FITS data from 32-bit signed to unsigned 8-bit greyscale images is necessary to enable the data visualisation and form images that are similar in nature to the GIF ones provided in SOHO archives. To implement the data type conversion, the solar disk minimum and maximum values of the investigated intensitygrams and magnetograms were determined.
We found the full range of values are between 0 and 1. 5 for the intensitygrams, and between -3,500 and 3,500 Gauss for the magnetograms. These values were therefore used in linear operations to scale the signed 32-bit data from the FITS files to unsigned 8-bit data, which were then stored as images. Subsequently, we found and recorded the solar disk mean, standard deviation, minimum, and maximum values for each of the investigated intensitygrams and magnetograms. This is a very useful step for analysing and understanding the nature of the investigated data set. The collected values are therefore analysed for the purpose of finding parameters that enable the efficient and constant detection and enhanced visualisation of the photospheric features. The collected values and their analysis are presented and discussed in the following subsections.
Sunspot Intensities We collected and analysed 350 intensitygrams from the solar minimum periods for the purpose of obtaining an initial sunspot detection threshold T h ss. For each image, we analysed the difference between I min and I. The collected data are shown plotted in Figure  3. As expected intensitygrams with the smallest differences between I min and I have no sunspots or pores. In cases where the difference between I min and I is larger, it turns out that the corresponding intensitygrams contain pores or small sunspot groups. We experimented with the collected data to empirically define the sunspot threshold, by linearly increasing the factor f ss subtracted from I of the investigated im-  ages, and found that f ss = 15 in the 8-bit range of values provides a reasonable, by visual inspection, threshold for the sunspot detection.
Magnetic Region Analysis During the solar minimum, the Sun has lower magnetic activity and flux when compared to the solar maximum period. In this context, we collected 100 samples of magnetograms from the solar minimum periods when solar activity was lowest and analysed the B , B min , and B max values, in order to get an initial estimation of the pixel values that represent magnetic regions. The collected data are shown plotted in Figure  8. At this stage, we are unable to define the threshold values for detecting magnetic regions. However, we consider the average of the minimum, min(B min ), and the maximum, max(B max ), values of the collected samples to indicate magnetic values. We found the differences between these values and B , | Bmin(B min )|, and | Bmax(B max )|, to be approximately 30 and -30.
This segmented noise has been eliminated by applying a 3×3 median filter to the magnetogram, with little effect on the segmented signal. An example of an enhanced magnetogram is shown in Figure  9. After enhancing the investigated images, we carried out an investigation on the collected data in order to determine the threshold values for the magnetic regions detection (T h mr ). We did so by investigating the most appropriate value for the variable f mr in Equation 4 that can be used to detect the magnetic regions. The value of σ B,SMin in Equation 4 is a predetermined value, fixed at the value of σ B of the solar disk at the solar minimum, which is approximately equal to 2. 4. This value was determined by calculating the σ B of the contrast stretched magnetogram samples which were collected from the solar minimum periods, when magnetic activities were the lowest.
Results The main aim of this research is the detection of solar features for the provision of useful proxies for reconstructing TSI and SSI. For the detection of photospheric features, we carried out a series    plot between the VIRGO data and the fitted data, the correlation coefficient between the two being r 2 = 0. 88. Figure  19  shows the scatter plot of the VIRGO data compared to the simple regression model. The analysis of the time series shows that the applied method to identify solar activity features from SOHO/MDI intensitygrams and magnetograms gives realistic results.
Acknowledgements. SOHO is a project of international cooperation between  ESA  and  NASA. The authors acknowledge that the research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7 2012)  under grant agreement no  313188  (SOLID). The authors thank the  SOI and MDI team  for their guidance to acquire MDI data, and thank  Dr. Mohammad Alomari  for his assistance in retrieving MDI data. The authors thank the editor and the anonymous reviewers for their careful revision and valuable comments. The editor thanks two anonymous referees for their assistance in evaluating this paper.
Backgrounds and problem statement A Trusted Execution Environment (TEE) is completely separated from the Rich Execution Environment (REE). It offers a way to isolate Trusted Applications (TAs) and provide secure functionalities such as cryptographic operations or secure PIN input. As defined by GlobalPlatform  [14] , three main TEE software components are involved: the trusted OS, the TAs and the hardware secure resources, e. g. , trusted peripherals. The TAs can access the trusted resources and exchange with Secure Elements using a private API. From the REE, mobile applications can interact with TAs using public APIs. Additional details about TEEs can be found in  [3, 5]. Before using a service of the TEE, which is provided by a service provider, several steps should occur, as shown in Figure  1.
TEE migration protocol Considering the previous requirements, attacker model, and the GlobalPlatform Card Specifications  [12] , we introduce a new approach for deploying services in a TEE where: TAs of a service provider are hosted in a Security Domain (SD) and a new actor, called TEE admin, has a special SD and implements the migration functionalities. With such a new software architecture, we build a protocol that allows to securely transfer a TEE profile from one device to another one.
(4) With the received request, the service provider checks if the TEE source (resp. target TEE) has (resp. has not) an associated SP-SD by checking if cert src (resp. cert tgt ) is registered in its database. Then, (5) the service provider inquires TEE admin to create a SP-SD within the recipient TEE via the SD -Create RQ(cert tgt ) command. (6)  The TEE admin signs the command (the signature Signature T EE Admin is performed on the command and a timestamp) and forwards it to the trusted application Create -SD tgt in order to create SP -SD tgt. (7, 8, 9, 10). The creation is acknowledged by Ack and P aram that are returned to the service provider (through the TEE Admin) in order to let him be able to personalize SP -SD tgt , as done classically when personalizing TEE security domains. (11)  Once the SP -SD tgt installed, the service provider proceeds to the update of SP -SD tgt credentials in order to have the exclusive control over SP -SD tgt  [12].
Indeed, a malicious TEE Admin can get access to service provider credentials and user's private data. However, we estimate that the assumption of a honest-but-curious TEE Admin is reasonable. This is because a malicious TEE Admin (when detected) risks not only huge financial damages but also his reputation. Knowing that this role should be played by the device manufacturer or the mobile network operator. In our opinion, this risk is far from being taken. We validated our protocol using the AVISPA  [4]  tool web interface. AVISPA is an automated tool for the validation of security protocols. It takes as input a protocol modelled in High-Level Protocol Specification Language (HLPSL). This latter is translated into Intermediate Format (IF) and forwarded to the back-end analyser tools. In Appendix A, we show (the core subset of) our migration model written in HLPSL.
All products are accessible, free of charge after registration through FTP/HTTP (http://land. copernicus. eu/global/) and through the GEONETCast satellite distribution system. The evolution of the service towards the operations at 333m resolution is partly supported by the FP7/ImagineS project which focuses on the retrieval of LAI, FAPAR, fraction of vegetation cover and surface albedo from PROBA-V sensor data. The paper presents the innovations of the 333m biophysical products, make an overview of their current status, and introduce the next steps of the evolution of the Copernicus Global Land service.
e. comparison with in-situ reference data when they exist and are available. In particular, the Global Land Service benefits from ground campaigns, conducted in the framework of the FP7 ImagineS project, which collect in-situ measurements, according to the sampling schemes and protocols defined by CEOS/LPV, and process them to retrieve reference high resolution maps. The resulting values are used to assess the accuracy of the satellite-derived biophysical products (Figure  1 ). The in-situ data and the reference maps are made available to the community through the ImagineS website at the address http://fp7imagines. eu/pages/services/ground-data. php. Three levels of quality assessment are associated with three development stages of the products (Table  1 ): 1. "Demonstration": products are provided to users with a very limited commitment on the quality or availability of the service but they have been considered useful to be disseminated in order to enable users to test them and provide feedbacks.
The demonstration phase ends with a limited quality assessment. 2. "Pre-operational": the limitations of the products are documented and can satisfy the majority of the applicable requirements and/or they have been considered useful for distribution to the users. The pre-operational phase ends with a full quality assessment. 3. "Operational": products have well documented nonrelevant limitations and largely satisfy the requirements applicable and/or have been considered mature enough for distribution to users. The operational phase regularly performs quality control. In addition to the quality assessment, a quality monitoring, applying the same approach and metrics, is also carried out, every 6 months, on the "operational" products to check that their quality keeps stable along time. Further, an across variables quality monitoring is also performed, every 6 months, integrating the variables into a land surface model using a data assimilation scheme.
The SWI and the LAI are assimilated over France (8km x 8km) into the Land Data Assimilation system (LDAS) while a passive monitoring (i. e. comparison of model output resulting from the assimilation with the satellite products) of FAPAR, Surface Albedo and LST is also carried out. Here again, the Copernicus Global Land service benefits from the achievements of the ImagineS project which achieved upgrade and consolidation of the LDAS. In parallel, an independent assessment of the service quality is organized through 2 bodies: 1) panel of external experts which give advices and recommendations on service evolution through half-yearly reviews; 2) a technical user committee which defines the product specifications.
The Distribution The Global Land website, http://land. copernicus. eu/global, is the entry point for all information related to the service and the products. Individual product pages provide more details about the product status, its main characteristics in terms of algorithm, quality, technical properties, and applications. A set of 3 documents is associated with each product/version: 1. the Algorithm Theoretical Basis Document (ATBD) which describes the retrieval methodology, 2. the Quality Assessment Report which presents the results of the validation exercise, 3. the Product User Manual which summarizes the algorithm and the validation results and includes a detailed description of technical characteristics of the product. The website gives access to the distribution data portal. The access to the products is free and open, whatever the user category (public body or private company) and whatever the application (research or commercial use).
, 2013) , which ensures the consistency between the 333m PROBA-V products and the 1km SPOT/VGT products. Step B is a compositing and gap filling module. According to the pixel identification (Evergreen Broadleaf Forest (EBF) or not, based upon the time series of daily estimates or the Globcover land cover map (http://www. esa-landcover-cci. org ) when the time series does not provide a reliable identification of the vegetation class), the temporal smoothing is performed with specific parameters. Finally, a post-processing module is applied to fill the small gaps by linear interpolation. Figure  2 : Flow chart of the LAI/FAPAR/FCover algorithm HYGEOS has translated this algorithm into a software compliant with the coding specifications and the processing performances of the Global Land service.