dataesr/scientific-paragraphs-categorization huggingface dataset:
Each functional ACSN module was attributed a score based on the average score of its genes. This score was visualized using BiNoM. 39, 40 or phosphoproteomic analysis, the phosphorylation data for a cohort of 234 lung tumour samples were obtained from PhosphoSitePlus database. 43 About 130 phosphorylated protein forms from the data set were found on the ACSN maps. If ACSN contained only a generic representation of a 'phosphorylated' protein, then all residue frequencies related to this protein were summed up. Visualization of data was performed using BiNoM. 39, 40 ta visualization and analysis in ACSN using web-based NaviCell environment ACSN uses NaviCell environment to represent the content of the ACSN map online (Kuperstein et al. 36  ). NaviCell includes a panel of tools for visualization and analysis of high-throughput data (Figure  2 ).
The current version of the tool box allows to load users' data such as gene lists, copynumber profiles, transcriptome or protein expression data sets and to visualize them on top of ACSN map. The visualization techniques can be configured by the user and include bars and heatmaps or glyphs located near each molecular entity as well as original methods of 'map staining' for visualizing transcriptome, proteome or copy-number data sets, displaying the data at the background of the map. The data visualization modes can be combined in multiple ways, providing flexibility in representing several data types simultaneously at several zoom levels of the map. The data can be visualized for separate samples or for groups of samples. These groups can be defined by the user according to a selected sample feature (for example, molecular tumour subtype), and used further in data visualization.
All functions of NaviCell and ACSN can be programmatically manipulated through NaviCell web service,  43  using Python or R languages. In ACSN, users have the possibility to perform the most frequently used types of functional data analysis, which exploit the content of ACSN pathway database, such as functional enrichment of ACSN modules in a gene list. A hypergeometric test can be performed to compute the Pvalues of intersections of a given gene list with ACSN modules (see Supplementary Figure  S9 ).
Figure1. Structure and content of ACSN resource. The scheme demonstrates the concept of ACSN construction starting from the cancer hallmarks: collecting information about molecular mechanisms underlying those hallmarks from scientific publications and manually depicting them in the global map of ACSN and further supporting by consulting the information from the external pathway databases. ACSN is hierarchically organized into three levels: the seamless global map divided into the interconnected biological process maps that are further decomposed into interconnected module maps. ACSN can be exploited through web-based NaviCell interface allowing map navigation using Google Maps engine, map commenting via associated blog system and user omics data visualization and analysis.
ACKNOWLEDGEMENTS We thank the computer team of the  Institut Curie  and, in particular,  Camille Barette  for his help to set up and maintain the ACSN web site. We also thank the many colleagues who tested ACSN and, in particular,  Philippe Hupé ,  Vonick Sibut ,  Patrick Poullet  and  Jean-Marie Ravel. We thank  Mathurin Dorel  for help with enrichment analysis of TCGA data using ACSN. We thank the experts who participated in reviewing and improving the database content (their names are listed on the website). This work is supported by  APO - SYS  EU  FP7 project  under grant agreement no. HEALTH-F4-2007-200767 (Apoptosis map); ASSET  EU   FP7 project  under grant agreement no. FP7-HEALTH-2010-259348 ;  INCA LABEL Cancèropole Ile-de-France 2011-1-LABEL-1  grant;  INVADE grant  from  ITMO Cancer  (Call Systems Biology 2012, EMT and Cell Motility map);  Servier -Institut Curie Alliance  (DNA repair map); The grant ' Projet Incitatif et Collaboratif Computational Systems Biology Approach for Cancer ' from  Institut Curie  (DNA repair map);  Agilent Thought Leader Award  no.
The primary objective of this mission is to increase space weather forecasting time by means of a near real-time information service, that is based upon insitu and remote measurements of the aforementioned CME properties. The obtained data can additionally be used for updating scientific models. This update is the mission's secondary objective. In-situ measurements are performed using a Solar Wind Analyzer instrumentation package and Birgit Ritter* et al. : A Space weather information service flux gate magnetometers, while for remote measurements coronagraphs are employed. The proposed instruments originate from other space missions with the intention to reduce mission costs and to streamline the mission design process. Communication with the six identical spacecraft is realized via a deep space network consisting of six ground stations.
They provide an information service that is in uninterrupted contact with the spacecraft, allowing for continuous space weather monitoring. A dedicated data processing center will handle all the data, and then forward the processed data to the SSA Space Weather Coordination Center which will, in turn, inform the general public through a space weather forecast. The data processing center will additionally archive the data for the scientific community. The proposed concept mission allows for major advances in space weather forecasting time and the scientific modelling of space weather.
All spacecraft are equally equipped with in-situ measurement instruments. This paper presents a new mission proposal to monitor CMEs. The mission constellation is comprised of six spacecraft placed in heliocentric circular orbits at 0. 72 AU at a separation angle of 60 degrees from each other. All spacecraft are able to perform remote and in-situ measurements. The mission serves as a continuous information-service system, providing all necessary information of the heliosphere that allow for an increased space weather forecasting time and enhancement of scientific models regarding space weather. The data will help to forecast if there will be a magnetic storm and determine its severity, and will also provide information on radiation levels for astronauts and space missions further away from the Sun.
72 AU and thereby yields a much longer forecasting time, which is the most important advantage of this mission. However, with an increase of forecasting time comes naturally a decrease of accuracy. Lindsay et al. (1999)  concluded that in-situ measurements between 1. 0 AU and 0. 7 AU in the equatorial plane within 10 degrees east to 5 degrees west of the Earth-Sun line allow substantial space weather forecasts. While the underlying model of this conclusion is a simple linear model by  Burton et al. (1975) , the authors of the paper at hand believe that advanced data-driven CME models (e. g. Luhmann et al. 2004 , Tóth et al. 2005) , calibrated by data of the early mission-phase, will allow for a reliable warning system, if at least one spacecraft is located within a range ±30 degrees apart from the Earth-Sun line.
Mission Overview The mission consists of six identical spacecraft in a heliocentric orbit at 0. 72 AU, performing insitu and remote measurements. The obtained data will be communicated periodically via a direct link between spacecraft and Earth through a dedicated deep-space network, while communication with the spacecraft located behind the Sun is naturally excluded. The deep-space network consists of six ground stations that are located in a way to ensure continuous contact with the spacecraft. Two ground stations will operate simultaneously, each alternating communication between three spacecraft. All obtained information will be collected by a single mission operations center that passes it along to a data processing center where the data will be processed and submitted to the SSA Space Weather Coordination Center 2  (SSCC), and the information will additionally be archived for the scientific community.
The two spacecraft performing remote measurements will be the ones having the best point of view for stereoscopic imaging with respect to Earth. The three spacecraft closest to the Earth (two of which perform remote sensing as well as in-situ) will downlink their data every 15 minutes whereas the three distant spacecraft (only making in-situ measurements) will downlink every 45 minutes. The processing of stereoscopic remote sensing of a CME will result in information regarding the CME's trajectory, velocity and size. These results are processed in a standardized format (in accordance with the SSCC) and will be passed on to the SSCC within approximately 45 minutes after the CME's occurrence at the Sun. The CMEs travelling towards Earth will be continuously monitored up to 15 solar radii. In-situ measurements of the solar wind and passing CMEs will be made at 0.
Payload The instruments employed for CARETAKER originate from other space missions with the intention to reduce mission costs and accelerate overall mission design. In-situ measurements are performed using the Solar Wind Analyzer instrumentation package from the Solar Orbiter mission  (Marsch et al. 2005) , together with flux gate magnetometers from Venus Express  (Titov et al. 2006) , while remote measurements are performed using coronagraphs from the STEREO mission  (Kaiser et al. 2008 ).
2 keV/q to 20 keV/q with a relative accuracy of 8% in energy and an angular resolution of less than 2 degrees. Unlike the EAS, the PAS consists of only one device and has therefore a narrower field of view. The Heavy Ion Sensor (HIS) contains an electrostatic analyzer module and a time of flight detector. It determines mass, charge, energy distribution, and direction of incidence of heavy ions up to 56 amu/q. Table  3  lists the properties of the three sensors and their location onboard the spacecraft can be seen in Figure  2. Table  3. Properties of the three sensors (Electron Analyzer System (EAS), Proton Alpha Sensor (PAS), and Heavy Ion Sensor (HIS)) that constitute the Solar Wind Analyzer (SWA). EAS PAS HIS Field of View 4π -24 • -42 • (Az) -30 • -66 • (Az) -22. 5 • -22. 5 • (El) -17 • -22.
Earth Stations Science and housekeeping data is received by two antennas in parallel at the same time from both sides of the Earth, (Figure  5 ). The uplink and downlink will use X-band. It is planned to distribute the stations equally over the Earth to ensure parallel download from the spacecraft. In order to ascertain a continuous data stream from the spacecraft, six dedicated 15 m dishes will be used to download the science and housekeeping data. Due to high priority data downloading and time consuming antenna pointing tasks, there is no possibility to reuse the existing ESTRACK Deep Space Network (DSN) 10  , NASA's DSN 11  or the Indian DSN 12. Figure  6  shows the proposed longitudinal locations of the Earth stations that are part of the DSN defined for this mission.
Operation and Ground Segment As discussed in Section 3. 1, every 19 months there is a three week window to launch this mission. The mission timeline is depicted in Figure  7 , showing the time separated launch at a nominal launch date of the spacecraft in two sets of three spacecraft, the individual cruise duration for each spacecraft, commissioning and nominal operation in the final orbits. Subsequently the optional extended mission timeline and decommissioning are displayed. The spacecraft numbering in the figure refers to the numbering in Figure  1. The mission operations concept shall minimize the costs both in the area of ground segment tools and facilities as well as in the sharing of manpower and expertise in the development and operations teams. It is important to simultaneously approach the spacecraft system-level testing between the spacecraft manufacturer and the spacecraft operations team, maximizing the synergy between spacecraft manufacturer and operators in the preparation of operational documentation, spacecraft user manual, operations database etc.
The ground segment will rely on six ground stations all around the world with two of them continuously communicating with the spacecraft (Section 4. 2). The ground stations must be built for the mission since the capacity of any existing DSN is insufficient for a continuous link. Figure  6  shows the proposed locations of such a network. The objective is to build stations able to command the spacecraft and to receive data. For a given time two of them will be absolutely dedicated to communication with CARETAKER. However there will always be four stations available for other purposes. The ground segment organization including the further data transfer and processing is presented in Figure  8.
Mission Operation Center The CARETAKER mission operation center (MOC) will be in charge of all telecommand and telemetry operations of the mission. Communication with the six spacecraft will be provided by two ground stations at a time through the CARETAKER Network 24/7. The detailed communication schedule is given in Figure  9. The two antennas that communicate with the spacecraft at a given time download data from four spacecraft every 15 minutes. For each such interval, each antenna will download data from two spacecraft, one providing imaging and in-situ data (C+IS), the second only sending in-situ (IS) data. While data from the two spacecraft sending imaging (S i+1 and S i-1 in the figure) data and the spacecraft within the Sun-Earth line (S i ) is downloaded in every interval, the other three spacecraft are not necessarily addressed every 15 minutes.
The operation center will perform 0-level data processing as well as backing up and storing the previous 30 days of the processed data. Data will be transmitted continuously to the data processing center. Data processing for in-situ measurements is likely to take less than one minute so that the requirements of the SSCC are fulfilled. Fig. 8. Illustrating data handling at Earth. Raw data is obtained through six ground stations and is collected by a mission operation center that forwards the data to a data processing center. This center processes the data and provides the SSCC Space Weather Coordination Center with data according to their format, additionally, the data is also archived for the scientific community. It is eventually the SSCC that provides the public with a forecast.
Data Products Given the extraordinary position of the spacecraft constellation, the processed data is to become a new reference for space weather event warning as well as premium scientific content. For the insitu measurement, a fifteen minutes range between measurement and data product will be ensured as required by the SSCC. For 3D-modelling of coronagraphic data and CMEs, an extended time line is proposed: those data should be available within a maximum of sixty minutes after remote measurement. Further processing will be performed later to make the data fully exploitable by the scientific community in the long range. The whole content will be available on the internet.
The most important information will contain the propagation velocity and direction of a CME, as well as the spatial resolution of the magnetic field orientation and strength. This information helps in predicting the time of impact at Earth, and the potential severity of a geomagnetic storm if the CME proves to be geoeffective. The proposed instruments to be employed on each spacecraft originate from other space missions with the intention to reduce mission costs and to accelerate overall the mission design. The spacecraft will perform in-situ measurements using the Solar Wind Analyzer instrumentation package from the Solar Orbiter mission, together with flux gate magnetometers from Venus Express, while performing remote measurements with a coronagraph from STEREO. The mission's secondary objective is to provide data helping to improve CME models.
As for the choronagraph the present limitation for the 3D-reconstruction for the CMEs is given by the two STEREO spacecraft and SOHO which are not always positioned in a useful way to provide the necessary view angles  (Mierla et al. 2010). For the in-situ measurements models exist for determining the flux rope geometry, which in the case of one spacecraft this represents a 1D local cut through a global 3D structure. Having more than one spacecraft sampling the CME could help to constrain its global structure  (Möstl et al. 2012). Communication is realized via a dedicated deep space network consisting of six ground stations equipped with 15 m dish antennas, with continuous operation of two ground stations simultaneously, each of them alternating contact between three spacecraft. Received data will be processed by a data processing center that forwards the processed data to the Space Weather Coordination Center who eventually informs the general public through a space weather forecast.
The data processing center will additionally archive the data for the scientific community, for updating scientific models. The obtained information will result in major advances in space weather forecasting time and the scientific modelling of space weather. The data provided can be combined with data from other missions running at the same time, which has the potential to further expand the solar physics understanding. Furthermore, the proposed mission will not only monitor the solar wind in the Sun -Earth direction, but it also offers the potential for 360 degrees warnings, providing information to a warning system that protects astronauts in future manned missions (e. g. to Mars). The mission also offers unprecedented possibilities to study the Sun's corona and the inner heliosphere. Any CME with an angular extent of 60 degrees or larger will be monitored in-situ and remotely, leading to a better understanding of a CME's morphology and its spatial as well as temporal distribution.
Acknowledgements. The authors acknowledge the contribution of  Daniel Schmid ,  Ioanna Patsou ,  Joseph Nguyen , and  Markus Suter , during the Alpbach summer school and are very grateful for the expertise of  Joe Zender  and  Rumi Nakamura , who proved to be invaluable during the Alpbach summer school. Furthermore the authors are greatly indebted for the advice and efforts from  Peter Falkner ,  Christian Erd , and  Markus Hallmann , as with their help this mission developed to its current shape. Nikolaos Perakis  and  Francesco Checco Gini  offered very good support regarding orbital calculations, and  Viktor Andersson  for helped with thermal aspects. The reviewing of the article by  Günther Reitz  and  Luciano Rodriguez  proved valuable and is highly appreciated. Aditionally, the authors are grateful for all the organisational effort of  Michaela Gitsch , who is of great help to young scientists by introducing them into the (new) field of space science and engineering.
INTRODUCTION Over the last few years, a clearer picture has started to emerge on how galaxies are built up with cosmic time. These developments are primarily afforded by the remarkable wealth of multi-wavelength imaging that telescopes have provided, from both space and the ground, together with optical spectroscopic surveys (e. g. , SDSS, VVDS, zCOSMOS, DEEP2) that have amassed samples ranging from several thousands to over a million galaxies. This continuously expanding database provides a map of the galaxy distribution up to z ∼ 2 (and beyond), and enables a measure of their intrinsic properties such as their stellar mass, ongoing star formation rate (SFR) and information on their stellar populations such as ages and the chemical enrichment of the interstellar medium. As a consequence, it has been recognized that at least to z ∼ 3 most galaxies are either star-forming or had their star formation quenched while subsequently evolving in a passive mode.
With 1153 FMOS spectra in hand, we present the effectiveness of our target selection as a function of key parameters and provide a catalog of emission-line properties including spectroscopic redshifts with a detailed assessment of their accuracy. With respect to the latter point, the zCOSMOS deep sample  (Lilly et al. 2007)  allows us to perform an independent check on the FMOS redshifts. The paper is organized with the following sections: survey design (Section 3), target selection (Sections 4 and 5), induced selection biases (Section 6), observations and data reduction (Section 7), emission-line fitting (Section 8), determination of spectroscopic redshifts (Section 9), description of our catalog (Section 10), and emission-line properties (Section 11). Finally, we conclude with our plans to acquire a final sample of over 1000 galaxies in COSMOS with 1.
OBSERVATIONS AND DATA REDUCTION We report on observations carried out between March 2012 and January 2013 with Subaru/FMOS that represent the first installment of data to be acquired through our program. The central square degree of COSMOS is effectively covered with four FMOS footprints, designated as HR1-HR4 (Figure  1 ), using both the highresolution H-long and J-long gratings. In Table  2 , we provide a log of the observations with exposure times and notes on the effective seeing conditions (see section 7. 2). (cf ) SFR distribution in bins of stellar mass and color excess. The solid histograms are the full sample while the dashed histograms represent the sample with the imposition of a limit on Hα emission-line flux (panels cd: f Hα > 4 × 10 -17 erg s -1 cm -2 ; panels ef : f Hα > 1 × 10 -16 erg s -1 cm -2 ) for each bin of color excess.
Knowledge of the detailed evolution of the whole charge state distribution of projectile ions colliding with targets is required in several fields of research such as material science, atomic and nuclear physics but also accelerator physics, and in particular in regards of the several foreseen large scale facilities. However, there is a lack for collisions in the non-perturbative energy domain and that involve many-electron projectiles. Starting from the ETACHA model we developed [Rozet et al. Nucl. Instrum. Meth. B 107, 67 (1996)], we present extension of its validity domain towards lower velocities and larger distortions. Moreover, the system of rate equations is able to take into account ions with up to 60 orbital states of electrons. The computed data from the different new versions of the ETACHA code are compared to some test collision systems.
The ETACHA code  [4] , we initially developed, was devoted to calculate charge state distributions of fast few-electrons ions with at most 28 electrons. Taking explicitly into account excited states effects by solving a set of 84 coupled differential equations, it was based on the calculation of cross sections for monoelectronic atomic collision processes in the independent electron approximation and at high velocities. Therefore, within this version, perturbative theories were used to account for electron capture, intra-and inter-shell excitation as well as ionization processes. However, many experiments address the non-perturbative regime where the ion stopping power is at maximum, in particular for studies of material damages  [5] , including biological material that takes advantage of the so-called "Bragg peak"  [6, 7].
Moreover, there is, at present, an active research prospective on the production of superheavy elements like at the SPIRAL2 facility  [8]  or on plasma physics to characterize warm dense matter using intense lasers or XFEL  [9]  that needs reliable predictions of the projectile charge state distribution or even of its nl substates populations after passing through solid targets. It follows that many studies refer to the ETACHA code [see e. g. 10, 11] , sometimes using its cross sections calculation routines as an input for Monte Carlo approaches  [12] , and using it in some cases outside its range of validity. There is obviously a need for an extension of ETACHA towards lower velocities and many electron ions. Similarly, extending the code towards relativistic energies, or dealing with gas targets is desirable; these issues will not be addressed in the present study and will come later on.