dataesr/scientific-paragraphs-categorization huggingface dataset:
Commandable modes include:  Science: Including user-defined energy and time, action to be taken during SAA passage and earth occultation  Engineering: Including on-the-fly-configure, diagnostic-and-calibration, pedestal, electrical-calibration  Non-operating modes: Off, Safe These modes and other operations will be implemented in accordance with ECSS-E-70-41A -ECSS service numbers in square brackets:  Housekeeping control  [3]  (rate and possibly content)  Memory management  [6]  (load, dump, checksum of absolute address)  Function management  [8]  (high voltage control, parameter loading, mode changes)  Time management  [9]  (request time from instrument software, resynchronise time)  Monitoring [12] (limit checking, reporting and actions) In addition, the on-board software is able to make autonomous decisions, for example to go to Safe mode, switch off outof-limits detectors or modules, organise data for telemetry.
Software science functionality: The software will collect and format the acquired science event data. At the lowest level it will be able to monitor and set-up the registers of each of the electronic elements of the event processing hierarchy. The data from the 6 panels will arrive at the processor board and be handled by dedicated electronics under the control of the software. The resulting data stream will have the remaining processing done by binned as necessary, reformatted ready for compression and packetised. The software will interact with the Spacecraft, sending the data over SpaceWire for transmission to the ground. The functionality includes:   210 GByte for science storage.  I. e. enough for more than 1 orbit (as ground-passes can be lost) and enough to cope with a high count rate for a short time The LAD modes diagram is illustrated in Figure  15.
has recently confirmed the intention to continue the development aiming now at the upcoming M4 ESA launch timescale (2026). The work of the MSSL-UCL and Leicester groups is supported by the UK Space Agency. The work of SRON is funded by the Dutch national science foundation (NWO). The work of the group at the University of Geneva is supported by the Swiss Space Office. The Italian team is grateful for support by ASI (under contract I/021/12/0), INAF and INFN. The.
Generation of simulated data To generate simulated data, comprehensive PALM simulation software was written in Matlab (Figure  S1 , Scripts S1), with additional features as compared to similar software developed to date (see e. g. [31, 33, 57] ). A chosen number of dye molecules are randomly placed on a defined image pattern (typically a segmented nanoscopy image), so as to control dye molecular density. The photophysical properties of the fluorescent markers (mostly phototransformable fluorescent proteins) are defined in a database containing parameters such as excitation and fluorescence emission spectra, fluorescence quantum yield, phototransformation action spectra as well as photoconversion, on-off photoswitching, photoblinking and photobleaching quantum yields. Properties of the PTFP's chromophores in their anionic and neutral states are distinguished and simulations at various pHs can be performed.
PALM data sets with a defined frametime and number of frames can then be generated. For each single-molecule emission, the spatial (throughout the microscope PSF) and spectral (throughout the emission spectrum) distributions of emitted photons are calculated based on poissonian statistics. A defined level of poissonian autofluorescence background, optionally proportional to the local illumination power density, can be input. The program outputs the stack of acquisition frames, diffraction limited images, laser illumination profiles, and for each activated molecule, its x and y coordinates, fluorescence emission trace, the number of detected photons and an estimate of the signal-to-noise ratio.
Experimental setup A home-built PALM set-up was used based on an Olympus IX81 microscope (Olympus, Japan) equipped with diode-pumped solid-state 405 nm (CrystaLaser LC, USA), 488 nm (SpectraPhysics, USA) and 561 nm (Cobolt Jena, Sweden) lasers. Widefield illumination was achieved by focusing the laser beams to the back focal plane of a 6061. 45 NA oil immersion apochromatic objective lens (Olympus, Japan). The intensity and time-sequence of laser illumination at the sample was tuned by an acousto-optical tunable filter (AOTF, Quanta Tech, USA). Near circular polarization of the laser beams was ensured by inserting a polychromatic quarterwave plate downstream the AOTF. To prevent sample drift during data acquisition, the samples were placed on an IX2-NPS ''nosepiece'' stage (Olympus) fixed directly on the objective.
Data acquisition Reference laser beam profiles were recorded before each experiment using a coverslip uniformly marked with a fluorescent pencil. PALM datasets were acquired at room temperature (20-22uC) using two illumination schemes: 1) continuous illumination with 561 nm laser, which served both to photoconvert Dendra2 fluorophores and to readout the fluorescence of the activated red form; 2) continuous illumination with 561 nm and 405 nm laser. The power density at the focal plane was measured in each experiment and was typically ,5-7 kW/cm 2 at beam center for the 561 nm laser and ,0. 1-1 W/cm 2 for the 405 nm laser. Data sets were acquired at 30 ms/frame.
Processing pipeline To determine the on-off blinking quantum yield, the off-on blinking rate, the photobleaching quantum yield as well as the photoconversion ''brightness'' (see below) of the fluorescent marker, the processing pipeline sketched in Figure  2  was developed in Matlab (Scripts S1). Single-molecule localization is first performed based on standard 2-D Gaussian fitting of the diffraction-limited fluorescence spots, after rolling ball background subtraction. In addition to the precise localization of the fluorescent molecules, attention here is focused on extracting their accurate fluorescence traces. Indeed, removing a poorly localized spot from the dataset may result in introducing a glitch in a fluorescence trace, which would later be interpreted as a blinking event. Such problems may occur in the case of low signal-to-noise ratio or when multiple spots overlap at high molecular density.
We modified our PALM localization software to carefully select spots judged appropriate for trace reconstruction (e. g. by setting criteria to allow center-ofmass based localization instead of rejection in the case of poor Gaussian fitting). The next step consists of grouping localized spots found in subsequent frames, so as to constitute initial clusters of spots from which the most probably correct number of individual molecules and their associated fluorescence time traces can be extracted based on t c splitting. To achieve initial clustering, localized spots are first connected in space and time according to coarse threshold criteria d(s 1 ,s 2 ),5s, where d(s 1 ,s 2 ) is the distance between spots s 1 and s 2 and s is the localization uncertainty, and t(s 1 ,s 2 ),5t where t(s 1 ,s 2 ) is the time separation between spots s 1 and s 2 and t is the maximum expected molecular lifetime.
First, an initial fixed t c value (e. g. 0. 2 s) is used to split all cluster traces in the dataset so as to obtain a starting set of individual molecular fluorescence traces. These individual traces are then processed to extract bleaching and blinking histograms: N bleach = h(T bleach ), N on = h(T on ), N off = h(T off ), where T bleach , T on and T off represent the time before irreversible bleaching, blinking off, and blinking on, respectively, and N bleach , N on and N off represent the number of events within given ranges of T bleach , T on and T off. Based on our photophysical model (Figure  1 ), and on the proper evaluation of the spatially-and dipole-orientation-dependent molecular excitation rates (see below), these histograms can be fitted to extract a first evaluation of bleaching and on-off blinking quantum yields, as well as a rate for thermally activated off-on blinking.
Determination of photoconversion quantum yield under 405 nm activation laser In the presence of weak 405 nm activation, the same procedure as described above can be used as long as the density of activated Extracted blinking-off quantum yields are plotted against a range of input yields used in a series of simulated PALM data sets. Those simulated data sets were generated using parameters displayed in Table  S1  (except for the varying blinking off input yield). Error bars represent fitting errors. Yield retrieval for highly blinking molecules underestimates the true values, due to the limited time-resolution in PALM experiments. For weakly blinking molecules, retrieval accuracy is superior, but precision is worsened (larger error bars) because of statistical uncertainty due to low numbers of blinking molecules.
The inset shows the same curve in an inverted manner. A polynomial fit to the data points provides a potential calibration curve allowing, in real experiments, to estimate the true yield from the extracted yield. doi:10. 1371/journal. pone. 0098362. g003 molecules in each frame can be managed. An apparent photoconversion brightness Q a,app can then be extracted (which results from photoconversion by both activation and readout lasers), from which the photoconversion quantum yield at 405 nm W a,405 can be estimated as: W a,405 & Q a,.
Simulated data To test the proposed strategy for estimating phototransformation yields, we first simulated a representative PALM data set using our Matlab-based simulation software. 30000 Dendra2 molecules were randomly spread over a microtubule-mimicking pattern (corresponding to a molecular density of 545 molecules/mm 2 ) and 6000 frames with 30 ms exposure were generated using circularly polarized 561 nm laser excitation. No 405 nm activation laser was. Effect of experimental parameters on accuracy of phototransformation yield's retrieval. In each panel, extracted phototransformation yields are plotted against a key experimental parameter (signal to noise ratio per molecule (A), frametime (B), laser power (C), molecular density per frame (D), fraction of photoconverted molecules (E) and total number of photoconverted molecules (F)).
In each case, PALM datasets were simulated using parameters of Table  S1  (except for the varying parameter). Black: bleaching yield, blue: blinking-off yield, gray: blinking-on rate, red: photoconversion yield. Yield retrieval accuracy is estimated by the logarithm of the ratio between true and extracted yields and error bars represent fitting errors (with 95% confidence). In D, the insets show representative simulated frames at molecular densities of 1 and 8 active molecules per frame per square micron, respectively. In E, only the extracted photoconversion yield is shown, as the input parameter is not relevant to bleaching and blinking. In F, conversely, only extracted bleaching and blinking yields are represented. Experimental data discussed in this work have been obtained within ranges of conditions indicated by braces and/or black arrows.
Molecular densities per frame cannot be easily assessed for experimental data and therefore reported values are only indicative. doi:10. 1371/journal. pone. 0098362. g004 used and, under readout laser activation, the green-to-red photoconversion brightness was set to 4. 3610 24 M 21 cm 21 (W a = 1. 2610 24 ; e 561 = 3. 57 M 21 cm 21 ), so that the average density of activated molecules per frame amounted to 0. 22/mm 2. The simulated sample was assumed to be representative of a live cell, so that each Dendra2 molecule was modeled as freely tumbling. Input values for the bleaching yield, the blinking-off yield and the thermally activated blinking-on rate were taken as 2610 25 , 5610 26 and 20 s 21 respectively, close to the experimental values that can be extracted from Lee et al  [8]. A complete list of parameters used to generate this PALM dataset is shown in Table  S1.
The simulated sample is shown in Figure  S1. The dataset was then submitted to the processing pipeline described above. Erroneous phototransformation yields and blinking-on rate were deliberately input to the fitting routines as starting values (with deviations by an order of magnitude relative to true values). Five concentric regions where the laser beam can be considered homogeneous were defined as shown in Figure  S5. Convergence was achieved after 3 cycles and the final retrieved values are reported in Table  S2  and compared with the ''true'' values used in the simulation. A representative t c -curve is shown in Figure  S6  and histograms of the correlation coefficients between true and retrieved single-molecule traces are presented in Figure  S7. The photobleaching yield was retrieved with high-fidelity, with an error less than 1%.
Effect of dipole orientations and laser polarization Two additional mock data sets were generated using the same set of parameters except that this time the simulated sample was assumed to be representative of a fixed cell: each Dendra2 molecule was assigned a random dipole orientation, constant over time. In the first dataset a circular polarization of the laser beam was assumed whereas a linear polarization was chosen in the second one. In both cases, using the appropriate fitting models, the quality of yield extraction followed the same trend as for tumbling molecules, although the overall accuracy was slightly lower for circular polarization and significantly lower for linear polarization (Table  S3 ). This deterioration follows from the strong fluctuation in the ability of each molecule to absorb photons.
30%). This is explained by inspecting the exponential dependence of N A (t) = N 0 [12exp(2aQ a t)] (see also Figure  S10 ). As long as N A (t) can be approximated by a linear regime N A (t)<N 0 aQ a t (aQ a t,,1, a is a constant) the unknown parameters N 0 and Q a cannot be uncoupled and unreliable values are extracted. Finally, Figure  4F  simply shows that for a fixed sample and in the presence of a heterogeneous laser beam a substantial number of activated molecules need to be recorded so that sufficient statistics allow reliable extraction of the phototransformation yields. Overall, the analysis of simulated datasets presented above demonstrates that our procedure estimates the phototransformation yields with suitable accuracy and robustness in a large range of experimental parameters.
In E, only the extracted photoconversion yield is shown, as the input parameter is not relevant to bleaching and blinking. In F, conversely, only extracted bleaching and blinking yields are represented. Experimental data discussed in this work have been obtained within ranges of conditions indicated by braces and/or black arrows. Molecular densities per frame cannot be easily assessed for experimental data and therefore reported values are only indicative. doi:10. 1371/journal. pone. 0098362. g004.
D. B. acknowledges financial support by the  ANR  ( ANR-2011-BSV5-012-01  NOBLEACH). S. T. H. and M. S. G. were supported by  U. S. National Institutes of Health  grants  K25-AI65459  and  R15-GM094713 ,  U. S. National Science Foundation  grant  CHE0722759 ,  Maine Technology Institute  grants  MTAF 1106  and  MTAF 2061 , and the  Maine Economic Improvement Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Sloan Digital Sky Survey (SDSS) has been in operation since 2000 April. This paper presents the tenth public data release (DR10) from its current incarnation, SDSS-III. This data release includes the first spectroscopic data from the Apache Point Observatory Galaxy Evolution Experiment (APOGEE), along with spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS) taken through 2012 July. The APOGEE instrument is a near-infrared R ∼ 22,500 300-fiber spectrograph covering 1. 514-1. 696 µm. The APOGEE survey is studying the chemical abundances and radial velocities of roughly 100,000 red giant star candidates in the bulge, bar, disk, and halo of the Milky Way. DR10 includes 178,397 spectra of 57,454 stars, each typically observed three or more times, from APOGEE. Derived quantities from these spectra (radial velocities, effective temperatures, surface gravities, and metallicities) are also included.
The error arrays published in DR10 currently report the estimated errors without any contribution from a systematic component. However, for the ASPCAP analysis, we impose an error floor corresponding to 0. 5% of the continuum level. Fiber crosstalk: While an effort is made not to put faint stars adjacent to bright ones on the detector to avoid excessive spillage of light from one to the other, this occasionally occurs. We flag objects (in APOGEE STARFLAG) with a BRIGHT NEIGHBOR flag if an adjacent star is > 10 times brighter than the object, and with a VERY BRIGHT NEIGHBOR flag if an adjacent star is > 100 times brighter; in the latter case, the individual spectra are marked as bad and are not used in combined spectra. Persistence in the "blue" chip: There is a known "superpersistence" in 1/3 of the region of the "blue" APOGEE data array, and to a lesser extent in some regions of the "green" chip, whereby some of the charge from previous exposures persists in subsequent exposures.
APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) The ultimate goal of APOGEE is to determine the effective temperature, surface gravity, overall metallicity, and detailed chemical abundances for a large sample of stars in the Milky Way. Stellar parameters and chemical abundances are extracted from the continuumnormalized co-added APOGEE spectra by comparing with synthetic spectra calculated using state-of-the-art model photospheres  (Mészáros et al. 2012 ) and atomic and molecular line opacities  (Shetrone et al. , in preparation). Analysis of high-resolution spectra is traditionally done by hand. However, given the sheer size of APOGEE's spectral database, automatic analysis methods must be implemented. For that purpose, ASPCAP searches for the best fitting spectrum through χ 2 minimization within a pre-computed multi-dimensional grid of synthetic spectra, allowing for interpolation within the grid.
1. Parameter Accuracies  Mészáros et al. (2013)  have compared the outputs of ASPCAP to stellar parameters in the literature for stars targeted by APOGEE in open and globular clusters spanning a wide range in metallicity. These comparisons uncovered small systematic differences between ASPCAP and literature results, which are mostly based on highresolution optical spectroscopy. These differences are not entirely understood yet, and we hope they will be corrected in future data releases. In the meantime, calibrations have been derived to bring APOGEE and literature values into agreement. With these offsets in place, the APOGEE metallicities are accurate to within 0. 1 dex for stars of S/N > 100 per half-resolution element that lie within a strict range of T eff , log g, and [M/H]. Based on observed scatter in the ASPCAP calibration clusters, we estimate that the internal precision of the APOGEE measurements is 0.
Moreover, important systematic effects may be present in [α/M] for stars cooler than T eff ∼ 4200 K. We therefore discourage use of [α/M] for stars with T eff < 4200 K or with [M/H]< -0. 5 or [M/H]> +0. 1. Figure  15  in  Mészáros et al. (2013)  shows the rootmean square scatter in [α/M] for red giants in open and globular clusters, as a measure of the uncertainty in this parameter. However, given the trends in [α/M] with other stellar parameters, care should be taken when estimating the accuracy of [α/M]. Comparison with literature values for carbon and nitrogen abundances shows large scatter and significant systematic differences. In view of the relative paucity and uncertainty of literature data for these elements, more work is needed to understand these systematic and random differences before APOGEE abundances for carbon and nitrogen can be confidently adopted in science applications.
APOGEE Data Products The APOGEE data as presented in DR10 are available as the individual 500-second spectra taken on a per-exposure basis (organized both by object and by plate+MJD+fiber), as combined co-added spectra on a per-object basis, and as continuum-normalized spectra used by the APOGEE pipeline (ASPCAP) when it computes stellar properties (Section 3. 7). The individual raw exposure files, processed spectra, and combined summary files of stellar parameters are provided as FITS 102 files  (Wells et al. 1981 ) through the DR10 Science Archive Server (SAS). The DR10 Catalog Archive Server (CAS) provides the basic stellar parameters (including the radial velocity) from the APOGEE spectra on a per-visit (SQL table apogeeVisit) and a co-added star basis (SQL table apogeeStar). The ASPCAP results are provided in the SQL table aspcapStar; the covariances between these parameters are given in a separate table, aspcapStarCovar.
DATA DISTRIBUTION All Data Release 10 data are available through data access tools linked from the DR10 web site. 104 The data are stored both as individual files in the Science Archive Server (SAS) and as a searchable database in the Catalog Archive Server (CAS). Both of these data servers have front-end web interfaces, called the "SAS Webapp" 105 and "SkyServer" 106 , respectively. A number of different interfaces are available, each designed to accomplish a specific task. • Color images of regions of the sky in JPEG format (based on the g, r and i images; see  Lupton et al. 2004 ) can be viewed in a web browser with the SkyServer Navigate tool. These are presented at higher resolution, and with greater fidelity, than in previous releases. With DR10 we also include JPEG images of the 2MASS data to complement the APOGEE spectra.
• FITS images can be searched for, viewed, and downloaded through the SAS Webapp. • Complete catalog information (astrometry, photometry, etc. ) of any imaging object can be viewed through the SkyServer Explore tool. • Individual spectra, both optical and infrared, can be searched for, viewed, and downloaded through the SAS Webapp. • Catalog search tools are available through the Sky-Server interface to the CAS, each of which returns catalog data for objects that match supplied criteria. For more advanced queries, a powerful and flexible catalog search website called "CasJobs" allows users to create their own personalized data sets and then to modify or graph their data. Links to all of these methods are provided at http:// www. sdss3. org/dr10/data_access/. The DR10 web site also features data access tutorials, a glossary of SDSS terms, and detailed documentation about algorithms used to process the imaging and spectroscopic data and select spectroscopic targets.
FUTURE The SDSS-III project will present two more public data releases: DR11 and DR12, both to be released in December 2014. DR11 will include data taken through the summer of 2013. DR12 will be the final SDSS-III data release and will include the final data through Summer 2014 from all observations with APOGEE, BOSS, MAR-VELS, and SEGUE-2. In 2014 July, operation of the 2. 5-m Sloan Foundation Telescope will be taken over by the next generation of SDSS, currently known as SDSS-IV, which plans to operate for six years. SDSS-IV consists of three surveys mapping the Milky Way Galaxy, the nearby galaxy population, and the distant universe. APOGEE-2 will continue the current APOGEE program of targeting Milky Way stars to study Galactic archaeology and stellar astrophysics. It will include a southern component, observing from the 2.