dataesr/scientific-paragraphs-categorization huggingface dataset:
2007). THEMIS colour data (typically binned to 36 m/pixel) suffers from severe stray light calibration difficulties and poor signal to noise  (McConnochie et al. 2006)  while CRISM provides excellent spectral imaging data in 545 wavelengths but at no better than 18 m/pixel scale  (Murchie et al. 2007 ). HiRISE has revealed spectacular small-scale colour diversity but has a very narrow colour swath width (<0. 5% of global coverage) and absolute radiometric calibration challenges because of highly variable focal-plane temperatures  (Delamere et al. 2010). CaSSIS uses a single focal plane covered with a segmented filter and a rotation mechanism to acquire along-track stereo with matching illumination of the surface in colour which should lead to a superior dataset for photometric purposes. Considerable effort in the on-ground calibration has been placed in support of this goal  (Roloff et al.
Framelets are acquired from pixels exposed by each filter with a repetition rate synchronised to the ground track velocity and set to obtain sufficient overlap between successive framelets to permit accurate mosaicking. The overall dimensions of the FSA from SIMBIO-SYS were conserved but the number, dimensions and positioning of the filters within the window have been modified to reflect the differences in imaging operation, optical design, and scientific goals of the SIMBIO-SYS and CaSSIS instruments. The field-of-view of CaSSIS, the ground-track velocity of TGO, the signal-to-noise budget, and internal bottlenecks in the data transmission rate resulted in selecting four different colour filters for the FSA. The simultaneous acquisition of three framelets with a maximum size of 2048 × 280 pixels, at a repetition rate of <400 ms can be achieved.
Faster repetition rates require adaptation of the swath width or the number of filters in order to provide a continuous swath along track. A broad transmission filter, PAN, was selected to provide the highest signal and will be used for stereo reconstruction. In our baseline configuration, 280 px-high framelets will be acquired through this filter in order to have a 15% overlap between successive acquisitions from the nominal 400 km circular orbit. The framelets acquired through the three other colour filters will be 256 px high, resulting in a 5% overlap. Data through the PAN filter are acquired on both observations in a stereo run to provide the stereo pair. The colour framelets (including binning) can be chosen to optimise the science return within the data volume constraints. The dimensions of the filters to be applied on the window were derived from the framelet sizes on the detector and the nominal F-number of the instrument.
Because it is commanded, the area of the detector read does not have to be exactly centred within the illuminated area but can be slightly shifted (for example to avoid areas affected by defects on the detector or on the filter). Figure  11  shows the structure of the FSA and shows its most important dimensions. The wavelength bands for the four colour filters of CaSSIS were derived from the ones used on the HiRISE/MRO instrument  (McEwen et al. 2007 ). The two first bands, BLU and PAN correspond closely to the first two bands used by HiRISE ("BG" and "RED", respectively) ensuring consistency between the CaSSIS and HiRISE datasets. The two other CaSSIS filters, RED and NIR, split the third filter of HiRISE ("IR") in two. This additional colour is designed to improve the discrimination between expected surface minerals, particularly Fe-bearing phases, as electronic transitions and crystal field effects are responsible for strong and diagnostic absorptions in the 0.
On the fault tolerant memory controller, an 8-bit EDAC is used to detect and correct single bit-flips. There are 32 data + 8 EDAC lines between the processor and the memories. There are a total of 3 memories on the DPM board. Two non-volatile MRAMs (Magnetoresistive Random Access Memory) are used, each of which has a stored image of the flight software (FSW). One of them is re-writable to allow later updates of the firmware and saving of persistent parameters. Hence, we always have two bootable FSW versions in the instrument. These MRAMs, UT8MR2M8 from Cobham have a total of 16 Mbit and, with resistance to a TID of 1 Mrad(Si), are very robust against radiation and have SEL/SEU Immunity. The drawback is that the data bus is only 8 bits wide and the read/write access is limited to 45 ns which requires the insertion of 2 wait states by the memory controller of the processor.
Hardware Environment From the viewpoint of CaSSIS's FSW, the hardware environment consists of 3 major modules: (1) The essential hardware elements for running the FSW are integrated onto the CPU board. This (CPU) module also provides connections to the other major modules, namely, the FGPA and the PE. (2) The FPGA module defines an interface (via the memory bus) for collecting telemetry data measured by the analogue sensors connected to the FPGA via ADCs, thereby controlling the thermal management system and the telescope rotation mechanism. (3) The PE module provides an interface, via a SpaceWire (SpW) link, to access the detector, control the imaging process and receive exposure data recorded by the detector. The standard interfaces necessary to connect CaSSIS to the spacecraft (main and redundant Mil-1553, main and redundant SpaceWire links) are integrated into the LEON3 processor as IP cores.
Operating System Strict timing requirements in rotation and imaging processes as well as MIL-STD-1553 communication force the use of a Real Time Operating System (RTOS). There are several compilers available for LEON3 from bare metal compilers to more complex solutions like RTEMS (Real-Time Executive for Multiprocessor Systems). The latter was selected. It includes a scalable multitasking real time operating system that provides the opportunity for developers to use its services and focus on the implementation of application specific features. RTEMS has been designed for embedded systems. As it is free open-source software it can be used on many platforms. To use RTEMS easily on a LEON3, an additional software package, the BSP (Board Support Package), is required and is provided by the hardware vendor.
FSW Details The CaSSIS FSW uses RTEMS as the RTOS, low level drivers for the 1553 and SpW links provided in the BSP and a modified boot loader based on that provided by the BSP. Almost all the various functions are organised into separate RTEMS tasks in the FSW with different priorities (Fig. 21 ). Within the CaSSIS project, we think of different "managers" dealing with specific tasks connected to the instrument. The FSW can be executed in two modes: safe and normal operation. In case of any critical failure, the FSW goes into safe mode automatically where only essential functions (1553 communication, software update, and thermal management) are available.
Compression Algorithms and Management At the time of writing, the compression is still to be implemented in the flight software and will be uplinked to CaSSIS shortly before the start of the prime mission. Test versions are however already available and the "hooks" needed in the FSW (including the manager) have already been prepared. The speed of the algorithm is one of the key points. While data can be sent quickly to TGO's on-board computer on a high speed SpaceWire link, TGO does not compress them as "image data" which places a high load on the downlink unless CaSSIS performs the compression. The constraint is that CaSSIS has only 256 MB volatile random access memory which can be used as a temporary storage of image data. Most of the capacity of this memory is allocated for image data (about 230 MB for raw and compressed image data), about 10 MB is used by the compression algorithm and about 16 MB is reserved for the flight software.
The raw exposure data received from the PE are stored in the memory with DMA. The PE sends about 3. 2 MBytes of raw science data during a nominal detector readout. Assuming a 40 exposure acquisition is commanded in a 15 s long time window, this results in around 130 MBytes of raw science data and approaching 10 MB/s bandwidth. The processor has to store, compress and then send this amount of data to the spacecraft fast enough to make room for the next image as soon as possible. The LEON3 processor has 2 cores which can be used by tasks run by the RTEMS operating system. Using the second core simultaneously with the first one reduces the time needed to compress image data. The second core is switched on only during compression which limits the average power consumption and reduces load on the first core which is committed to several other tasks.
Boot-up For reliability and maintainability, CaSSIS contains two independent flight software binary images. One is burned into a non-volatile read-only memory on ground while the other one is stored in a mission-time writeable non-volatile memory. A CaSSIS-specific bootloader software component has to initialise the LEON3 processor, validate the integrity of the FSW images, and load the appropriate one. The basis of the bootloader component was provided by Gaisler for the GR712RC environment. Because of the special requirements of the target environment, it could not be used without modification and had to be tailored to meet requirements. The major modifications covered the following areas: • Prepare the bootloader to run from a non-writeable memory. • Prepare the bootloader to meet the strict timing requirement needed by the watchdog component in CaSSIS which checks for errors.
The system effective wavelengths and equivalent bandwidths (see  Abergel et al. 1987 ) are shown in Table  2. The detector is a spare and does have some cosmetic defects. The most problematic is a quasi-linear row of bad pixels in the centre of the PAN. Attention will therefore be paid to studying the possibility of using the RED channel to "fill-in" this data loss. However, flight data suggests that the line can be calibrated when exposure times are short (<2 ms) and comparable to those needed for Mars imaging. The bias level of the FPA is also rather high and limits the total dynamic range to 13. 5 bits. The radiative cooling of the detector  Figure  24  shows an image from the (grayscale) CTX imager on MRO and compares the fields of view of CaSSIS and HiRISE. The image shows the extended colour and stereo coverage achievable with CaSSIS with respect to other imagers currently orbiting Mars.
Operations At the time of writing (∼12 months from the start of the prime mission), the detailed operational approach has not been completely defined. However, the current plan is that the Mission Operations Centre (MOC) and ESA's Flight Dynamics (FD) team located at the European Space Operations Centre (ESOC) in Darmstadt will plan reaction wheel off-loadings such that the ground-track can be predicted to an accuracy of a few seconds several months in advance. The CaSSIS team will use a targeting tool based closely on the HiWISH tool (http://www. uahirise. org/hiwish/; retrieved 29 July 2016) for HiRISE. This tool is called CaST. A database of scientifically chosen targets will be constructed using CaST (Fig. 26 ). Once the orbit is known, a long-term plan (LTP) will be generated by identifying and prioriterizing targets along the ground-track.
This will be done using a tool called PLAN-C (Fig. 26 ) which will also allow the setting of instrument parameters allowing assessment of data volumes and general feasibility. An overplanning of approximately a factor of 2 is expected at this stage. The LTP generated will then be iterated with the Science Operations Centre (SOC) at ESAC, near Madrid. The LTP iteration process is designed to identify and resolve conflicts between CaSSIS and other instruments and spacecraft operations. The medium term plan (MTP) confirms the LTP input with the latest orbit predictions. The final target parameters are tweaked in the short-term planning (STP) process, approximately 1 week before execution. Currently, the downlink rate offers 2. 9 Gb/day on average. Within the LTP skeleton, CaSSIS will be allocated one stereo pair and one non-stereo image per orbit.
For the former, the yaw motion of the spacecraft will be suspended. However, the data volume generated by CaSSIS using such an approach would be around 12 times more than can be downlinked on average. Hence, data compression and optimization of the planning will be necessary. We estimate that around 6 stereo pairs and 6 non-stereo images will be acquired per day on average assuming an average compression ratio of 6. Once an image has been successfully acquired, it will be mapped back to the database (CaST) and flagged as retired unless multiple observations of the same site have been requested.
Processing Pipeline, Storage and Archiving The data from CaSSIS will be delivered to ESA's Planetary Science Archive (PSA) for publication. The basic flow of data is shown in Fig. 27. The pipeline through to UBE Level 1B in this diagram was completed shortly after reception of the first flight data from Mars orbit and has been written in Interactive Data Language (IDL). The geometric rectification step will be completed using ISIS for which ingestion routines have already been written. The proprietary period is 6 months but it is hoped that level 0 data can be provided for ingestion after 3 months once routine operations are in place. The data will be delivered in an XML format that is similar to the PDS V4 standard and will be converted to that standard either by PSA responsibles at the Science Operations Centre or by the University of Bern.
In the latter case, software generated in support of the CaSSIS project by University of Arizona and/or USGS would be used. The final pipeline products will be images that are calibrated both radiometrically and geometrically. It is expected that images comprising framelets stitched together to make complete swaths over the target in each colour will be made available through a public web site. There are currently no resources within the CaSSIS project to provide stereo prod-Fig. 27  Data flow from the spacecraft data repository through the processing system to the archive ucts routinely as part of the public distribution. The co-I team does however include many stereogrammetry experts who will provide stereo products to the community on a best effort basis, possibly funded through their national agencies.
(2017)  sought to test the spatial and colour capabilities of CaSSIS by several methods, including the generation of rigorous CaSSIS-simulated data products using Mars Reconnaissance Orbiter datasets and a comparison with HiRISE. Figure  28  shows a region of Nili Fossae which is known to contain diverse mafic minerals (e. g. , olivine and low-calcium pyroxene) and hydrated silicates, which includes the mineral serpentine (see  Ehlmann et al. 2010). This site and the presence of olivine and serpentine are highly relevant to CaSSIS and TGO mission objectives as the formation of this mineral, via aqueous alteration of olivine produces methane as a byproduct (e. g. ,  Parnell et al. 2010). Indeed, this location was originally identified by  Mumma et al. (2009)  as one of three regions where methane appeared notably localized during northern summer in 2003.
First Flight Data The first flight data from in orbit about Mars were acquired on 22 Nov. 2017. Figure  29  shows one of the first exposures acquired by CaSSIS in orbit. It is part of an image sequence that was subsequently assembled to produce a swath of the surface in four colours. The figure shows the four colour framelets mapped back to their position on the detector. Colour reconstruction still requires some improved in-flight calibration but first colour composites are promising. A data set of the Martian moon, Phobos, has been acquired and the surface distribution of colour units is consistent with other observations  (Pajola et al. 2017 ). The stereo capability was also tested and first scientific results from these data from Noctis Labyrinthus are currently being prepared  (Massironi et al.
Acknowledgements The authors wish to thank -the  ExoMars  project and, specifically,  D. McCoy ,  A. Haldemann , and  D. Goulty , -the  ExoMars  project science team,  J. Vago ,  O. Witasse , and  H. Svedhem , -the  Swiss Space Office team ,  D. Neuenschwander ,  A. Werthmüller , and  O. Botta , -our colleagues at the  Space Research and Planetology Division of the University of Bern , and the following persons who supported the team at various times:  S. H. Bailey ,  M. Coradini ,  R. Wiesendanger ,  E. Flamini , and personnel from the company, HPS. CaSSIS is a project of the  University of Bern  and funded through the  Swiss Space Office via ESA 's  PRODEX programme. The instrument hardware development was also supported by the  Italian Space Agency (ASI)  ( ASI-INAF agreement no. I/018/12/0 ),  INAF/Astronomical Observatory of Padova , and the  Space Research Center (CBK) in Warsaw.
̃ sin 1 1 tan ∆ 1 tan ∆ (5) The DeltaPsi2 software of Horiba Jobin Yvon allows to fit the spectra of  and , I s and I c by minimizing the values of  2. The software calculates also a "global" refractive index and a "global" extinction coefficient of the samples including the substrate + layer(s) and allows to calculate the global transmittance (T%) and reflectance (R%). The individual refractive index, n, and individual extinction coefficient, k, for each layer should be extracted from the model on the basis of the dispersion formula which gives the best fit. The direct measurement of n and k on the basis of Eq. (  5 ) can be compared with the theoretical values given by the known dispersions models (Adachi-Forouhi, Cauchy, Tauc-Lorentz, Kato-Adachi, Sellmeier, etc. ) of different materials from the data basis of the DeltaPsi2 software, and good fits between the measured n and k and calculated n (n-fit) and k (k-fit) can be obtained, for more than one dispersion model.
This procedure allows to better establish the validity of the different optical models in function of the correlations obtained for different thin films characteristics such as the films thickness, transmission coefficient, optical band gap and electrical resistivity obtained by different techniques. The best dispersion models for n() and k() were hence established by the correlations of data obtained by spectrophotometry and ellipsometry. The global and individual refractive index and global and individual extinction coefficients were calculated. This approach also shows how the ellipsometric modelling can be improved, with maybe the possibility, in the future to predict, by ellipsometric simulations, the suitable device architecture in function of the desired optical and electrical properties.
Validation of the Numerical Model To give more confidence of the model, we established some quantitative and qualitative comparisons with the experimental data of  Shi et al. (2014b). The validation of the numerical model is performed by comparing the flow structure, mixing layer thickness, diameter of the central recirculation zone (CRZ) and circumferential velocities results with the experimental results of  Shi et al. (2014b). Three cases were considered: -Case 1: L=64mm; Sw=0. 34; Qinj=0. 15Nm 3 /h; -Case 2: L=64mm; Sw=0. 34; Qinj =0. 27Nm 3 /h; -Case 3: L=16mm; Sw=1. 37; Qinj =0. 15Nm 3 /h. The seeded air (air + MgO particles) is tangentially introduced into the burner by the two horizontal injectors. Or, non-seeded air was introduced by the other injectors. To simplify the flow visualization, the flow structure is determined by tracking the particles trajectories delimiting the flow injected through the horizontal slits.
Acknowledgements The authors acknowledge fruitful discussions with  Dr. Le Si Dang. This work is funded by the European  FP7 Initial Training Network "ICARUS" , the  European Social Fund and National resources  through the  THALES  project " NANOPHOS " ( MIS-377292 ) and the  KRIPIS research project  " PROENYL " ( MIS-448305 ), and the  Chair of Excellence program  of the " Laboratoire d' excellence LANEF in Grenoble  ( ANR-10-LABX-51-01 )".
Microfouling Observations The analytical procedure was used to obtain information about the microscopic colonization of paints during immersion as described previously  [33]. Paint formulations were applied onto polycarbonate panels (4 cm × 2 cm) with a dry thickness of about 80 µm. Coupons were analyzed after 6 weeks and 3 months and observed by SEM and CLSM microscopies. The data of antimicrofouling performance were ranked from the best to the poorest, and the significance test was conducted using one-way analysis of variance (ANOVA).
Molecular Weight Measurements Average molecular weights were determined by Gel Permeation Chromatography (GPC) using a Merck pump L-7110 with two columns PLgel (mixte C, 3 µm and mixte D, 5 µm) in series from Polymer laboratories, and a Sedex DEDL detector (55 • C, 2 mbar). THF was used as eluent at a flow rate of 1. 0 mL/min and the injection volume was 20 µL. A calibration curve was generated with polystyrene standards (Easical PS-2) purchased from Agilent Technologies (Santa Clara, CA, USA). Samples concentration was 1 mg/mL. Data were analyzed by Cirrus software (Agilent Technology, Santa Clara, CA, USA).
Confocal Laser Scanning Microscopy Adherent cells on paints were stained by adding Syto 9 stain (5 µM, Molecular Probes), which is a fluorescence-based assay for cell viability (excitation and emission λ, 485 and 498-550 nm, respectively). They were observed after a 10 min-incubation period. Micro-algae were observed in red by autofluorescence (excitation and emission λ, 633 and 650-670 nm, respectively). Images were captured with CLSM using a TCQ-SP2 (Leica Microsystems, Nanterre, France). The thickness of the biofilm (µm) and the biovolume (µm 3 /µm 2 ) were measured using COMSTAT software (www. comstat. dk) [35]. Results were representative of 3 observation sites. The points were randomly chosen.
Supply group. This work was carried out within the framework of the Associated Laboratory in the field of fusion between CEA/IRFM and SWIP. This work is also supported by the domestic ITER project of China (No. 2011GB104000, No. 2014GB102000), funded by Ministry of Science and Technology (MOST) and the Natural Science Foundation of China (NSFC) project (No. 11575054, No. 11261140327), funded by NSFC.
In fact, it is difficult to perform these tasks, essentially the analysis of a large amount of binary data and the making of the relevant diagnosis decision in real time, notably when there is insufficient data. In this paper, we present a synergy between the CBR approach and the DSS to propose an Intelligent CB-DSSD of an APS. The proposed system includes two phases: An offline phase in which data, describing the system behavior as a set of cases, are collected and represented. Each instance of case is labeled as a normal or faulty behavior of the system to diagnose. The subset of cases describing normal behavior of the system is listed in normal case base. However, the subset of cases, describing faulty behavior of the system, is stored in abnormal case base. An online phase takes as input the diagnosis request of a HOS and as output the diagnosis response (faulty or normal behavior).
Application on the sorting system of ITS PLC To test the effectiveness of our proposal and describe its performance, we implemented our approach under the beta version of the Interactive Training System for PLC (ITS PLC) proposed by the Portuguese company Real Games  [22]. In fact, it is a set of simulation software dedicated to automation training, writing and using scripts written in IronPyton  [23]. It allows notably simulating failures in sensors and actuators. With the failures panel (figure. 5), the user is able to induce malfunctions in the system, presenting new challenges and increasing the realism of the simulation. In our work, we developed two scripts. The first one allows controlling the APS without the need for a real API. However, in the second script, we implement our approach.