ExoMars Trace Gas Orbiter Instrument Modelling Approach to Streamline Science Operations

Transcription

1 SpaceOps Conferences 28 May - 1 June 2018, 2018, Marseille, France 2018 SpaceOps Conference / ExoMars Trace Gas Orbiter Instrument Modelling Approach to Streamline Science Operations M. Muñoz Fernández 1, D. Frew 1, Muñíz Solaz 3, M. Ashman 1, J.J. García Beteta 1, C., F. Nespoli 3 M. Aberasturi 1, A. Cardesin Moinelo 2, J, B. Geiger 1, L. Metcalfe 1, C. Muñoz Crego 1 European Space Astronomy Centre, European Space Agency, 28691, Madrid, Spain T ExoMars Trace Gas Orbiter (TGO) science operations activities are centralised at ESAC s Science Operations Centre (SOC). The SOC receives the inputs from the principal investigators (PIs) in order to implement and deliver the spacecraft pointing requests and instrument timelines to the Mission Operations Centre (MOC). The high number of orbits per planning cycle has made it necessary to abstract the planning interactions between the SOC and the PI teams at the observation level. This paper describes the modelling approach we have conducted for TGO s instruments to streamline science operations. We have created dynamic observation types that scale to adapt to the conditions specified by the PI teams including observation timing, and pointing block parameters calculated from observation geometry. This approach is considered and improvement with respect to previous missions where the generation of the observation pointing and commanding requests was performed manually by the instrument teams. Automation software assists us to effectively handle the high density of planned orbits with increasing volume of scientific data and to successfully meet opportunistic scientific goals and objectives. Our planning tool combines the instrument observation definition files provided by the PIs together with the flight dynamics products to generate the Pointing Requests and the instrument timeline (ITL). The ITL contains all the validated commands at the TC sequence level and computes the resource envelopes (data rate, power, data volume) within the constraints. At the SOC, our main goal is to maximise the science output while minimising the number of iterations among the teams, ensuring that the timeline does not violate the state transitions allowed in the Mission Operations Rules and Constraints Document. I. Introduction HE ExoMars Programme is an element of ESA s Aurora Exploration Programme that was established to investigate the Martian environment and to demonstrate new technologies paving the way for a future Mars sample return mission in the 2020's. The ExoMars Programme consists [1] of the 2016 Trace Gas Orbiter (TGO) and Relay Mission along with the Entry, Descent and Landing (EDL) Demonstrator Module (EDM), and the 2020 Rover and Surface Platform (RSP) Mission. TGO will provide communication, command and data relay support to the ExoMars 2020 Rover and Surface Platform, and to NASA assets operating on the surface. Both missions are carried out in cooperation with Roscosmos. TGO carries scientific instruments to detect and study atmospheric trace gases, such as methane. The 2020 mission includes a rover that will carry a drill and a suite of instruments dedicated to exobiology and geochemistry research. This paper is focused on TGO s science instruments modelling and planning activity at the SOC. 1 ExoMars TGO Science Operations Centre, Science Operations Development Division, Directorate of Science 2 Mars Express Science Ground Segment, Operations Department, Directorate of Science 3 MAPPS Software Development, Data and Engineering Division, Operations Department, Directorate of Science 1 Copyright 2018 by ESA. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

2 II. ExoMars Trace Gas Orbiter ExoMars Trace Gas Orbiter (TGO) was launched on 14 March 2016 from Baikonur by a Roscosmos provided Proton-M/Breeze-M Launcher into a direct injection T2 type interplanetary transfer trajectory to Mars and arrived at the Red Planet on October TGO s mission key goal is to gain a better understanding of methane and other atmospheric gases that are present in small concentrations (less than 1% of the atmosphere) but nevertheless could be evidence for possible biological or geological activity. TGO has completed a 308 million-mile journey and now orbits the planet after a year of aerobraking that ended in February 2018 using drag on its solar wings to transform its initial highly elliptical four-day orbit of about km into the final, near-circular orbit of about 400 km. It is now circling Mars every two hours and, after calibration and installation of new software, it begins routine scientific observations. Fig. 1 shows TGO inside the clean room at Thales Alenia Space (TGO s system integrator). Fig. 1 TGO in a clean room before its launch in Credit: Thales Alenia Space/ESA TGO measures approximately 3.5 meters high and two meters across. It is built around a central cylinder serving as a structural backbone designed to evenly distribute loads experienced during launch throughout the rest of the spacecraft. Four scientific instruments with a total mass of kilograms were mounted on two horizontal panels crossing the orbiter's main structure above and below the navigational star trackers. Such an arrangement aimed to ensure best alignment of the instruments with the trackers, even when severe temperature swings in space cause minute shifts in the spacecraft's structural shape. All the communications between the spacecraft and mission control are possible via the 2.2-meter, 65-watt high-gain antenna dish operating in X-band. In addition, the NASA-provided UHF-transceivers (Electra) with a single helix antenna enable TGO to perform communication relay for NASA's Opportunity and Curiosity rovers, ahead of the arrival of InSight lander on November 26, 2018 (expected), and for the ExoMars rover and surface science platform in March TGO s scientific data will reveal whether the methane that has been detected on the red planet in recent years is geological in origin or produced by living organisms. If traces of methane are found to be mixed with more complex organic molecules, it will be a strong sign that methane on Mars has a biological source and that it is being produced or was once produced by living organisms. In case the methane detected is mixed with gases such as sulphur dioxide then that would be an indication that its source is geological. TGO s instruments look at sunlight as it passes through the Martian atmosphere and study its absorption by methane molecules. TGO's instruments will be able to detect trace gases with an improved accuracy of three orders of magnitude compared to previous measurements. 2

3 III. TGO s Science Payload Overview TGO is a three-axis stabilised platform with a science payload consisting of four composite instruments that collectively provide spectro-imaging capabilities from the UV to the near- and thermal-infrared, and were developed in Europe and Russia. The Space Research Institute of the Russian Academy of Sciences (IKI) has developed two instruments that have strong heritage from previous successful experiments onboard European (Mars Express, Venus Express) and American (Mars Odyssey, Lunar Reconnaissance Orbiter, Curiosity) missions. An overview of the payload is described below. Fig. 2 TGO s Science Payload. A. Atmospheric Chemistry Suite (ACS)- Principal Investigator: Oleg Korablev, Space Research Institute (IKI), Moscow, Russia. It includes three spectrometers [2] (Echelle spectrometers for near-and mid-infrared range (ACS-NIR and ACS- MIR) and a Fourier spectrometer (ACS-TIRVIM)), and a data storing system (ACS-BE). ACS will specifically address the requirement of high sensitivity instruments to enable the unambiguous detection of trace gases of potential geophysical or biological interest. The near-infrared (NIR) channel is a versatile spectrometer covering the µm spectral range with a resolving power of 20,000. NIR employs the combination of an echelle grating with an AOTF (Acousto-Optical Tunable Filter) as diffraction order selector. This channel will be mainly operated in solar occultation and nadir, and can also perform limb observations. The scientific goals of NIR are the measurements of water vapor, aerosols, and dayside or night side airglows. The mid-infrared (MIR) channel is a cross-dispersion echelle instrument dedicated to solar occultation measurements in the µm range. MIR has been designed to accomplish the most sensitive measurements ever of the trace gases present in the Martian atmosphere. The thermal-infrared channel (TIRVIM) is a 2-inch double pendulum Fourier-transform spectrometer encompassing the spectral range of µm with apodized resolution varying from 0.2 to 1.3 cm 1. TIRVIM is primarily dedicated to profiling temperature from the surface up to 60 km and to monitor aerosol abundance in 3

4 nadir. TIRVIM also has a limb and solar occultation capability. Fig. 3 (a) The concept design of ACS showing its four blocks: The NIR channel, MIR channel, the TIRVIM channel and the electronic block. The instrument s radiators are on the upper panels of TIRVIM and MIR. (b) The ACS protoflight model mounted on the TGO spacecraft (at premises of TAS-F, Cannes, France). B. Fine Resolution Epithermal Neutron Detector (FREND)- Principal Investigator: Igor Mitrofanov, Space Research Institute (IKI), Moscow, Russia It is designed [3] to register neutrons coming from the Martian surface as a result of interactions with galactic and solar cosmic rays and to map water ice distribution with high spatial resolution. FREND includes a dosimetry module to estimate how much of radiation dose on the orbit around Mars comes from neutrons. FREND records the radiation environment in space and on the surface of Mars, useful for spacecraft radiation analysis and manned flight radiation safety analysis. Fig. 4 FREND with dosimetric module Lyulin-MO C. Nadir and Occultation for MArs Discovery (NOMAD)- Principal Investigator: Ann Carine Vandaele, Belgian Institute for Space Aeronomy, Brussels, Belgium The instrument [4] will conduct a spectroscopic survey of Mars atmosphere in UV, visible and IR wavelengths covering the and µm spectral ranges. NOMAD is composed of 3 channels: a solar occultation channel (SO) operating in the infrared wavelength domain, a second infrared channel observing nadir, but also able to perform solar occultation and limb observations (LNO), and an ultraviolet/visible channel (UVIS) that can work in all observation modes. The spectral resolution of SO and LNO surpasses previous space-based surveys in the infrared by more than one order of magnitude. NOMAD has heritage from previous ESA missions such as Venus Express. Both SO and LNO consist of an echelle grating in combination with an acousto-optic tunable filter 4

5 (AOTF): the dispersive element provides the spectral discrimination, while the filter selects the diffraction order. An infrared detector array is actively cooled in order to maximize the signal-to-noise ratio (SNR) as much as possible. UVIS is a grating spectrometer that can operate in solar occultation, limb, and nadir observational modes. Like ACS, NOMAD studies spatial and temporal variability of trace gas concentrations and key isotopes in the Martian atmosphere. Among other goals it seeks to place constraints on models for the origin of atmospheric methane, to study gases related to possible ongoing geophysical and volcanic activity on Mars, and their breakdown processes, sources and sinks, and to explore Martian climatology and atmospheric dynamics, throughout the Martian year. Fig. 5 NOMAD Flight Spare Model. Credit: BIRA-IASB. D. Colour and Stereo Surface Imaging System (CaSSIS)- Principal Investigator Nicolas Thomas, University of Bern, Switzerland CaSSIS [5] is a high-resolution (4.5 m per pixel) stereo colour imaging camera dedicated to imaging the Martian surface. CaSSIS observations may target specific locations on the surface, which requires the yaw steering of the spacecraft to be suspended while CaSSIS pointing is prime, or they may be non-targeted, in which case CaSSIS rides along with the default spacecraft power optimised nadir pointing. Stereo image pairs permit building accurate digital elevation models of the Martian surface. Stereo imaging is performed through the employment of a rotational capability that allows the instrument to alternately look forward or backward along its trajectory over the surface. Fig. 6 CaSSIS camera protoflight model. Credit: University of Bern. 5

6 IV. Science Planning Activities at the Science Operations Centre (SOC) The Science Ground Segment (SGS) for the ExoMars 2016 mission in its operational configuration consists of the Science Operations Centre (SOC), located at ESAC, Madrid, the Russian SOC (NNK), located in Moscow, Russia and the PI facilities, at their respective institutions. The main uplink tasks of the ExoMars SOC are: Science Operations Uplink Data Production, SGS development, and Science Operations covering Nominal Science Operations and Commissioning Operations. Fig. 7 Science Planning Process. The science operations activities are centralised at ESAC s Science Operations Centre (SOC) located in Madrid (Spain). The SOC receives the inputs from the principal investigators (PI) in order to deliver the spacecraft pointing requests and instrument timelines to the Mission Operations Centre (MOC) ESOC in Darmstadt (Germany). Each instrument team is responsible for the operation of their instrument. There is constant and close collaboration in monitoring the health of the instruments and reacting to anomalous or unexpected situations. ExoMars has constrained resources that require the science inputs from all four instruments to be pre-planned and coordinated to optimise science return. There is only a limited amount of power and available data rates to downlink the science data generated by the instruments to the ground. Instruments have to be operated within their power and data volume allocations. In general, the process involves creating science plans that are usually divided into 4 week-long periods, and there is a cycle to generate long, medium, and short term plans. Long term planning [6] starts nominally six months 6

7 in advance of the plan s initial execution time. It establishes the baseline science plan, and a negotiation takes place regarding each instrument s observation targets while keeping the instrument under its data and power allocations. This work is done by the Science Working Team, which consists of principal investigators and co-investigators of the instruments, science operations engineers, the spacecraft operations team, and project management. Medium term planning nominally starts a month ahead of the plan execution time, with goals to define the specific pointing strategy and the detailed payload operations. At this stage, flight rule violations have to be identified and resolved. Short-term planning is done one week in advance. During this period scientists and engineers fine-tune the timelines of operations, the amount of data each instrument produces, and any other instrument-specific commands. The final product of the science planning cycle is a week-long integrated, conflict-free, and constraint-checked command sequence from all instrument teams. The product is finally ready to be uplinked to the spacecraft for execution. During the science operations process, a mission planning and simulation tool called MAPPS (Mission Analysis and Payload Planning System) [7] is used by the scientists and engineers. MAPPS is a tool that can be used to support any planetary mission in analysing the spacecraft and its experiments for environmental conditions, the coverage of spacecraft experiments on the target body and the visualisation of experiment timelines, including the experiments internal states and any spacecraft resources used by the experiments. MAPPS can also display the trajectory and attitude of the spacecraft and acts as flight rule checker. MAPPS has been used extensively across a number of ESA planetary missions such as Mars Express, SMART-1, Venus Express and Rosetta. We can generate scenarios for each MTP that we are planning. The scenario captures a snapshot of the current user configuration, as done in MAPPS control panel and the timeline visualisation configuration panel. The scenario files are a subset of the MAPPS settings file, but only for the dynamic configuration items. Any user specific preferences, such as the colour used for experiment swaths or for any other visualisation elements, are not stored in a scenario file. This will make it possible that users can exchange their scenarios, without affecting their visualisation preferences. Typically, a scenario will contain the simulation input data, pointing request file, experiment timeline file and event file, the experiment swaths to be computed, the simulation time range definition and any simulation options, and some more visualisation independent dynamic configuration parameters. V. TGO's Instrument Modelling Activities The high number orbits per planning cycle has made it necessary to abstract the planning interactions between the SOC and the PI teams at the observation level. We will describe the modelling we have conducted for TGO s instruments to support science planning activities. We have created dynamic observation types that scale to adapt to the conditions specified by the PI teams including observation timing, and pointing block parameters calculated from observation geometry. This approach is considered an improvement with respect to previous missions where the generation of the observation pointing and commanding requests was performed manually by the instrument teams. The SOC instrument models link the high/low level commanding and the instrument behavior. For long term planning (LTP) there is a need to link the science modes to instrument resources and constraints and for medium/short term (MTP, LTP) planning such a link would be established between the instrument commands/sequences to the instrument behavior. In order to fulfill both requirements, the model has to not only check constraints at LTP level, but also the Payload Operation Requests PORs at MTP and STP levels [8]. MAPPS software assists us to effectively handle the high density of planned orbits with increasing volume of scientific data and to successfully meet opportunistic scientific goals and objectives. Our planning tool combines the instrument observation definition files provided by the PIs together with the flight dynamics products to generate the Pointing Requests and the instrument timeline (ITL). The ITL contains all the validated commands at the TC Sequence level and computes the resources envelopes (data rate, power, data volume) within the constraints. MAPPS provides users with the ability to define models of the science instruments and of the spacecraft, as well as environmental conditions such that the information necessary for validating the science plan and the associated operational products can be computed and checked. MAPPS allows the SOC to further develop and refine a high level plan, and to perform full constraint checking on the products iterated with the PI teams. 7

8 MAPPS is used by the SOC at all levels of the science planning process. At LTP level, the science events output is used as an input to MAPPS, which along with other planning inputs, allows the SOC team member to perform a high level simulation of the proposed science observation timeline. At MTP and STP planning levels, MAPPS is used to implement and operationally validate the refined and ultimately final, science plan. MAPPS is also used at MTP and STP level to generate the desired pointing timeline request file (PTR) and the final payload operations request files (POR) which are then delivered to MOC as the output of the science planning process. For the TGO science planning process, the MAPPS software development team located at ESAC have also developed additional MAPPS plug-in modules to support the specific functionalities for ACS and CaSSIS that will be explained in the next section. VI. TGO Instrument Models Each instrument is considered an experiment consisting of several modes. Each subsystem and/or sensor of the instrument is then described as a module. The instrument mode is set via the definition of conditions, each of which describe the possible combinations of module states that shall result in the given mode. The Experiment Planning System (EPS) Experiment Description File (EDF) contains the experiment sequences and command definitions including their parameters, as well as the experiment modelling including its modes, modules and module states, power consumption, data production and memory usage, and any experiment constraints. The list of experiments as defined in the EDF is used in various locations of the MAPPS application, to populate the lists of possible experiments, or to refer to a specific experiment in certain overlay plots or summary table items. Usually, experiments are used either in relation with swaths, in which case the FOVs are referenced, or in relation with modes (including any optional modules and module states), in which case the modes (and possibly the modules and the module states). MAPPS is the user interface with EPS to execute operational timeline simulations. NOMAD Instrument Modelling This instrument is a spectrometer suite that consists of three separate channels, solar occultation (SO), limb nadir and occultation (LNO), and ultraviolet and visible spectrometer (UVIS). The Spacecraft INterface and control Board for NomAD (SINBAD) is the electronic interface in charge of managing the power and communication inside NOMAD. The SINBAD Flight Software (SFS) is the flight software embedded in SINBAD that manages the communication interfaces with TGO. These interfaces are implemented by a MIL-STD-1553B bus for telecommands (TCs) and housekeeping (HK) transmissions and a Spacewire bus for science data transmission. The ExoMars TGO mission has hard restrictions in number and size of TCs. All the communications with NOMAD (command observations, send patches, etc.,) should be done through TCs and some of them need a great number of TCs. In order to reduce the number of required TCs for nominal operations, such as observations, SINBAD stores the observation parameters in the on board memory in Channel Observation Parameters (COP) tables. With these structures, just one TC is required to command a complete observation. SINBAD uses a Non- Volatile magneto resistive random access memory (MRAM). It is the first time that this kind of memory is used as main memory in a flight mission. The NOMAD experiment definition consists of the following files: 1. tgo_nomad.edf: A Top level EDF for the NOMAD experiment which includes all lower level files. 2. tgo_nomad_constraints.edf: Constraint checks specific for experiment NOMAD, for example: -Sun illumination on NOMAD +Z Radiator and NOMAD in OBSERVING Mode 8

9 -Sun illumination on NOMAD +Z Radiator 30min before NOMAD in OBSERVING Mode 3. tgo_nomad_fov.edf: Field Of View definitions for the NOMAD experiment NOMAD Field of View SO Occultation LNO Occultation LNO Nadir UVIS Occultation MAPPS EDF FOV modelling NOMAD_SO [Rectangular] NOMAD_LNO_OCC [Rectangular] NOMAD_LNO_NAD [Rectangular] NOMAD_UVIS_OCC [Circular] UVIS Nadir NOMAD_UVIS_NAD [Circular] Table 1 NOMAD Field of View Definitions. 4. tgo_nomad_model.edf: MODEL modelling for experiment NOMAD - The behavior of the instrument is described in this file. Modelling Parameters are defined which assume the values of telecommand parameters as specified in the planned Instrument Timeline (ITL). Modelling Actions then trigger the corresponding behavior of the instrument model during a MAPPS simulation. 5. tgo_nomad_modes.edf: MODES modelling for experiment NOMAD - Instrument Modes are defined in this file (OFF, SAFE, INITIALISATION, OBSERVING). Module states Conditions corresponding to each Mode are included as well in order to place the instrument in a certain Mode when certain Module states are observed during a simulation. 6. tgo_nomad_modules.edf: MODULES modelling for experiment NOMAD - Instrument Modules and their respective States are defined in this file (NOMAD_SINBAD, NOMAD_SO, NOMAD_LNO, NOMAD_LNO_FM, NOMAD_UVIS, NOMAD_PDHU). Each Module can consist of several States characterized by a certain power and data rate.. Table 2 shows in detail the different NOMAD modules used in the model: Sub- System/Feature Description LNO CHANNEL Limb, Nadir, Occultation Channel (IR spectrometer) to observe weak IR light sources as Mars limb or nadir observations. Due to mission constraints, the LNO Channel can never be ON at the same time as the SO Channel. LNO FLIP MIRROR The LNO Channel also consists of a Flip Mirror which can be set by instrument telecommanding to either a Nadir or Solar (or Launch) State accordingly. SINBAD The central processor unit which manages all electrical power and data connections between TGO and the 3 NOMAD Channels. 9

10 Sub- System/Feature SO CHANNEL Description Solar Occultation Channel (IR spectrometer). Used for the NOMAD instrument's primary solar occultation observations. Due to mission constraints, the SO Channel can never be ON at the same time as the LNO Channel. UVIS CHANNEL Ultra Violet and Visible Channel (UV-Visible spectrometer). Difference spectrometer concept to SO and LNO, but can operate in both occultation and nadir observations. PDHU The PDHU module is set by instrument telecommanding to either an Open or Closed State and determines when science data can be written to the corresponding PDHU file. Table 2 NOMAD modules description. 7. tgo_nomad_cmds.edf: Command parameter definitions according to the operational MIB version. 8. tgo_nomad_seqs.edf: Sequence parameter definitions according to the operational MIB version. One example when the NOMAD instrument is considered to be in SAFE mode would be when the NOMAD_SINBAD Module State is ON but the states of all other instrument Modules are OFF. Operations Rehearsal 8 illustrated the limitations of the previous NOMAD EDF model. Once the scenario moves to working with ITLs from Observation Definitions (second half of MTP), the modelling was lost because the EDF model in place at that time did not go down to Telecommand Sequence level or lower. The NOMAD EDF model has since been further developed down to telecommand parameter level so that the instrument behavior is now triggered in MAPPS simulations by the commanding content of the final ITL returned by the NOMAD team.. New modelling Parameters and Actions were defined that now assume the values of the relevant TC parameters for each channel as observed in the ITL, and which then trigger the corresponding Module State changes when the Actions are run against the ITL Sequences during a MAPPS simulation. All the experiments have the same types of edf definition files to be used by MAPPS so we will not repeat these details for the rest of the instruments since it has already been described for NOMAD and the other instruments follow the same convention. CaSSIS Modelling and Plugin The SOC provides to the CaSSIS Instrument Team the allocated observation time slots and the data volume allocation from the LTP per week. The CaSSIS Instrument Team uses their planning tool, called plan-c, to select the targets based on the suggestions from the public and insert them in the allocated slots. The output of the tool is the CaSSIS Targeting File (CTF). The SOC uses the CTF file to support CaSSIS operations. A dedicated plugin has been developed that can: Generate the observation timeline Generate the instrument timeline at the TC sequence level 10

11 Compute the data volume based on the TC sequence parameter values The next sections, describe the functionality provided by the plugin. Observation timeline generation: The SOC transforms the CaSSIS plan contained in the CTF into an observation timeline that can be read by MAPPS. The three types of CaSSIS observations are categorized as follows: Targeted, which requires the yaw steering of the s/c to be suspended pointing while CaSSIS pointing is PRIME. Target observations can be either single or stereo. Non targeted, where CaSISS rides along with the default s/c power optimised nadir pointing (-Y to Mars centre) The way observations are specified in MAPPS is through observation definition files. CaSSIS observation definition files are divided into blocks describing the resources (data volume), the commanding and pointing blocks information. Any block inside an observation definition can be parameterised by observation parameters that can be modified via the plugin. The plugin reads the CTF file. For each record in the file, the plugin create a new observation. The fields in the record are used to update the observation parameters and to calculate the start time and duration of the observation. The observation start time in a single image (targeted or non-targeted) is the image prediction time minus a tranquilisation time (around 3 minutes). The observation duration for a single non-targeted image is the image prediction time plus half the duration of the image. For targeted images, the duration of the observation is 5 minutes, as this is the minimum time duration for a pointing block. Fig. 8 CaSSIS Observations. The observations created from the CTF file are added to the observation timeline. The observation timeline is used by MAPPS both to visualize CaSSIS observations in the graphical timeline and to produce the pointing blocks in the Pointing Request (PTR) file. The finalized PTR file with all the pointing blocks is sent to flight dynamics team at ESOC. 11

12 Instrument timeline generation: MAPPS can expand the observation timeline into an instrument timeline (ITL). The ITL contains all commanding at TC Sequence level and TC sequence parameters based on the CTF contents. TC sequence parameters for image timing are set to zero. An example of observation timeline is shown below: The fields are the following: An event, like the Mars Ascending Node The observation start/end time The instrument name, in this case Cassis The Observation Start or End indicators The observation type (observation parameters) The observation timeline can be expanded to the TC Sequences. For a single image targeted or not, the sequence of operations is to: Prepare the imaging parameters Position the motor to Instrument can be rotated to make push frame orthogonal to ground-track Acquire image Stereo pair commanding, it is the same as two single images commanding with opposite telescope rotation angles between images. CaSSIS uses a rotation mechanism to produce the stereo pair. The telescope in this case is not nadir-pointing but at an angle of 10 with respect to nadir. Initially, the telescope is rotated so that it points 10 in front of the nadir position on the ground track. After acquisition of the first image, the telescope is rotated through 180 so that it now points 10 aft. The second image is then acquired. Set parameters for images 1 and 2 Setup rotation for image 1 to make push frame orthogonal to ground-track Image 1 Intermediate rotation of ~180 Image 2 timed to overlap with image 1 Data volume computation: The resource computation is provided in the CTF and via the observation definitions using the module states. The CaSSIS EDF (Experiment Definition File) includes the basic modes, module states and Field of View (FoV) of the instrument. An overview of the CaSSIS modes is provided in the figure below: 12

13 Fig. 9 CaSSIS modes diagram. The FPS module uses state parameters to pass the data-rate from the CTF to the STEREO1 and STEREO2 module states: The state parameter IMG_DVOL_SP is defined as follows: A modelling action has been defined to update the IMG_DVOL_SP value from an action parameter (IMG_DVOL) that is assigned the CTF value in the observation definition. 13

14 The IMG_DVOL_SP parameter is actually a data-rate rather than the data-volume passed by the CTF, as a result the SET_DVOL action is called from the observation to set the data-rate for 1 second before resetting the value back to 0. The following example from the CaSSIS stereo observation demonstrates how the data volumes from the CTF are passed to the EDF: ACS Instrument Modelling and Plugin The ACS experiment follows a similar structure for the edf files as NOMAD. ACS is composed of the following modules shown in Fig.10. with the same logic as NOMAD. Table 3 shows how each observation definition corresponds to a type of scientific observation with a certain data rate and power profile that is used by MAPPS to run the simulation. Observation definitions are used during the first two weeks of MTP, while for the last two weeks the SOC iterates with the PIs via the ITLs. The ACS (Atmospheric Chemistry Suite) instrument consists of four sub-instruments. MIR (Middle IR spectrometer), NIR (Near IR spectrometer), TIRVIM (Thermal IR spectrometer) are scientific sub-instrument, and the BE (Electronic Block) is the interface sub-instrument which provides power and data interface between TGO and scientific sub-instruments of the ACS. The ACS experiment has the following modes of operation: OFF: Neither power line is supplied. BE is OFF. SURVIVAL: ACS BE is off, the power applies only to survival heater which is activated by direct application of voltage. STANDBY: ACS has the power consumption of the electronic block and operational (orbital) heaters when NIR, MIR, TIR are not operating. To switch to STANDBY mode from OFF mode it is necessary to turn on the ACS power and wait for the ACS initialization, after that NIR, MIR and TIR turn to STANDBY by means of BE control without any additional TC. Each one of the sub-instruments are considered modules edf files that can have different states: BE ON: Always when ACS is powered. All mode of NIR, MIR and TIR are valid only with BE ON mode. BE OFF: All ACS instrument in OFF mode. 14

15 NIR observing/standby MIR observing/ standby TIRVIM STANDBY/ready/ready to cool/cooling/stirling cooled/ready to observe/observing nadir/observing Black Body/observing space The ACS experiment follows a similar structure for the edf files as NOMAD. Table 3 shows how each observation definition corresponds to a type of scientific observation with a certain data rate and power profile that is used by MAPPS to run the simulation. Observation definitions are used during the first two weeks of MTP, while for the last two weeks the SOC iterates with the PIs via the ITLs. Table 3 Observations definition files with corresponding data rate profile. The optimal mode: rapid observation with 8 single frames accumulation and high data rate of 42.3 kbyte/s The lowest possible rate: 3.7 kbyte/s, in that case there will be only one observed diffraction order (e.g. H 2 O The ACS plugin will check the consistency of the observation inputs and generates the commands using predefined observations definitions. It reads the parameter that identifies the measurement (observation) start/end and computes the timing parameters (Y and TIME) from the instrument commands based on geometrical events: [250km, 0km INGRESS] and [0km, 250km EGRESS]. The plugin must validate that the geometrical events and commands sequences are generated within the observation start and end times. Once the timing parameters for the commands are calculated, the plugin performs the commands parameters checksum. The checksum is inserted back into the commands as another parameter. The plugin will also generate a shadow timeline that, depending on the timing parameter values, will set the instrument internal mode. Each mode has an associated power and data profile that will be used to estimate the instrument resource usage. When the plugin is loaded in MAPPS, it is possible to visualise the timeline and from the program it is possible to generate the ITL that will be send later to the PIs. Fig. 10 shows a screenshot of MAPPS where we can see that the plugin has been loaded correctly and the sequences have been generated and inserted appropriately. Figure 11 describes a diagram of the plugin for the MIR channel observations. In it, we can observe how MAPPS computes the TC timing parameters (Y and TIME) based on geometric events and on the start and end times of the observations. 15

16 Fig. 10 ACS Plugin MAPPS screenshot. Figure 11 ACS Plugin diagram for MIR that shows observation start and end times. 16

17 Fig. 12 Detail of ACS MIR observation insertion with ACS Plugin. FREND Modelling In terms of instrument behaviour (mainly from the resources point of view), there are modules defined for the following FREND subsystems: FREND Subsystem MAPPS module Electronics Module FREND_EM Dosimeter FREND_DOSIMETER 3 FREND_NEUTRON He and scintillation detectors Table 4 FREND Modules Definition. The following high-level modes: -Survival (OFF): Instrument is powered off (all modeled subsystems off). -STANDBY: Electronics module and dosimeter powered on, FREND detectors powered off. -SCIENCE: Electronics module, dosimeter and any FREND detector powered on. The preferred mode transition during operations is OFF STANDBY SCIENCE STANDBY OFF The allowed transitions are the following: OFF STANDBY STANDBY SCIENCE SCIENCE STANDBY STANDBY OFF SCIENCE OFF, although this is not recommended (no harm to the instrument, but partial loss of data) The main goal is to have FREND turned on all the time and stay in the same configuration for as long as possible, pointing to nadir. FREND Standard Science Observation is: OFF STANDBY SCIENCE STANDBY OFF Science Payload Data Flow Modelling The Payload Data Handling Unit (PDHU) interfaces with the instruments of the spacecraft and the two Electra UHF radios via the 1553 data bus for commanding and housekeeping data transfer, and a high-speed SpaceWire bus for instrument data transmission. The computer brain of TGO orbiter is the Spacecraft Management Unit (SMU). Fig. 13 shows a simplified data flow diagram focused only on TGO's payload. We also model the PDHU as a module for each instrument. 17

18 Use Case for MTP001 Fig. 13 TGO s science payload simplified data flow diagram. Once we have completed all the required TGO models that are needed for the science planning and we run the corresponding scenario in MAPPS we are able to visualise and validate our science plan as shown in Fig. 17. Given the data volume allocation for MTP001, that amount is distributed among the instrument teams. For example, for ACS we can have a data volume allocation of 196 Gbits; the distribution of the data per STP for a given MTP is as follows: Fig. 14 ACS data volume distribution for MTP

19 For the first MTPs the amount of housekeeping data is almost negligible compared to the science data: Fig. 15 HK vs. science data for initial MTPs. Finally, if we look into the details of the data received on the ground for the first STP for ACS we can verify that amount with our models and simulations. Further work will ensure the fidelity of our models to ensure a perfect connection between uplink and downlink systems. Fig. 16 ACS data accumulated data volume for the first STP. 19

20 Conclusions Fig. 17 MAPPS timelines visualization. At the SOC, our main goal is to maximise the science output while minimising the number of iterations among the teams, ensuring that the timeline does not violate the state transitions allowed in the Mission Operations Rules and Constraints Document. As we have demonstrated for TGO, a new modelling approach and automation prove to be highly useful if there is a need to streamline science operations. Acknowledgments The authors would like to acknowledge the considerable contribution and support provided by all members of ESA s Science Operations Centre and Mission Operations Centre, by the Principal Investigator teams and Roscosmos. References [1] Metcalfe, L., et al., "ExoMars Trace Gas Orbiter (TGO) Science Ground Segment (SGS)", Space Science Reviews, (submitted for publication). [2] Korablev, O., Montmessin, F., Trokhimovskiy, A. et al. The Atmospheric Chemistry Suite (ACS) of Three Spectrometers for the ExoMars 2016 Trace Gas Orbiter Space Sci Rev (2018) 214: 7. [3] Mitrofanov, I., et al., FREND experiment on ESA s TGO mission: science tasks, initial space data and expected results 19th EGU General Assembly, April 2017, Vienna, Austria, EGU 2017, p

21 [4] Vandaele, A.C., et al., "Optical and radiometric models of the NOMAD instrument part I: the UVIS channel", Optics Express, Vol. 23, pp , [5] Thomas, N., et al., The Colour and Stereo Surface Imaging System (CaSSIS) for the ExoMars Trace Gas Orbiter, Space Science Reviews, Vol. 212, No. 3-4, 2017, pp [6] Geiger, B., et al., Long Term Planning for the ExoMars Trace Gas Orbiter Mission: Opportunity Analysis and Observation Scheduling SpaceOps 2018, 28 May - 1 June 2018, Marseille, France, AIAA, [7] Plas, P. van der, García-Gutiérrez, B., Nespoli, F., Pérez-Ayúcar, M., MAPPS: A science planning tool supporting the ESA Solar System missions SpaceOps 2016 Conference, AIAA, Daejeon, South Korea [8] Ashman, M., et al., ExoMars Trace Gas Orbiter Instrument Modelling Approach to Streamline Science Operations, SpaceOps 2018, 28 May - 1 June 2018, Marseille, France, AIAA,