Optimisation of Solar Orbiter Data Return

SpaceOps Conferences 16-20 May 2016, Daejeon, Korea SpaceOps 2016 Conference

10.2514/6.2016-2618

Optimisation of Solar Orbiter Data Return Daniel T. Lakey 1 SCISYS Deutschland GmbH, Darmstadt, Germany Ignacio Tanco 2, Jose Manuel Sánchez Pérez 3 and Gabriela Ravera Iglesias 4 European Space Agency, ESOC, Darmstadt, Germany Stefan Thürey 5 and D. Müller 6 European Space Agency, ESTEC, Noordwijk, Netherlands Luis Sanchez 7 and Jayne Lefort 8 European Space Agency, ESAC, Madrid, Spain

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Matthias G. Eiblmaier 9 SCISYS Deutschland GmbH, Darmstadt, Germany ESA’s Solar Orbiter mission, with NASA participation, scheduled for launch in 2018 will, after a multi-year cruise phase, enter into an elliptical orbit around the sun with a perihelion of around 0.3 AU and an increasing inclination of up to 35° to return images of the solar polar regions and probe the plasma of the inner heliosphere. It promises to deliver ground-breaking science with its extensive suite of in-situ and remote-sensing instruments in a unique orbit. As a deep space mission, Solar Orbiter has a highly constrained data downlink, which means that optimising the science data return of the mission within the constraints is of paramount importance. Data return represents one of the greatest operational challenges for the mission as data downlink rates vary dramatically, and irregularly, over the mission due to the Spacecraft-Earth distance not following the usual periodicity seen for planet-bound missions. Furthermore, the nature of the baseline orbit tends to put science generation peaks at different times to the peaks of data downlink rate. To improve the situation, the Mission Analysis department at ESA’s European Spacecraft Operations Centre (ESOC) have designed alternative trajectories that optimise the overall data downlink while retaining the overall orbit configuration to meet the science objectives. Compared to the baseline trajectory, these new options can more than double the data return within particular periods of interest. After launch it will be the responsibility of the Mission Operations Centre (MOC) and the Science Operations Centre (SOC) to optimise data return. To this end, both MOC and SOC have independently created models of data generation versus data return so as to examine the effects of different parameters. MOC has identified three parameters which can be modified: duration and method of ranging; use of redundant storage capacity; and management of the real-time data generated during the pass itself to avoid duplicating data on the real-time and playback channels. The combined effect of these optimisations alone results in an increase of data return of around 20%. SOC considered the generation of data as well as the return, and examined the detailed on-board mass memory partitioning. SOC also considered further optimisation of the time and duration of the downlink passes. As a result SOC expects to be able to increase data return potential substantially.

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Solar Orbiter Spacecraft Operations Engineer, OPS-OPS, ESA/ESOC, Darmstadt, Germany Solar Orbiter Spacecraft Operations Manager, OPS-OPS, ESA/ESOC, Darmstadt, Germany 3 Mission Analysis, OPS-GFA, ESA/ESOC, Darmstadt, Germany 4 Ground Operations Manager, OPS-ONO, ESA/ESOC, Darmstadt, Germany 5 Solar Orbiter, Avionics Manager, SRE-PSA, ESA/ESTEC, Noordwijk, Netherlands 6 Solar Orbiter Project Scientist, SRE-S, ESA/ESTEC, Noordwijk, Netherlands 7 Solar Orbiter Science Ground Segment Development Manager, SCI-ODS, ESAC, Madrid, Spain 8 Solar Orbiter Science Operations Coordinator, SCI-ODS, ESAC, Madrid, Spain 9 Rosetta Spacecraft Operations Engineer, OPS-OPR, ESA/ESOC, Darmstadt, Germany 1 American Institute of Aeronautics and Astronautics 2

Copyright © 2016 by European Space Agency. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

I. Introduction

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OLAR Orbiter is a European Space Agency mission to explore the processes that create and control the heliosphere, implemented jointly with NASA and due to launch in 2018. The heliosphere represents a uniquely accessible domain of space, where fundamental physical processes common to solar, astrophysical and laboratory plasmas can be studied under conditions impossible to reproduce on Earth and unfeasible to observe from astronomical distances. In particular, Solar Orbiter seeks to answer the following questions1: − What drives the solar wind and where does the coronal magnetic field originate from? − How do solar transients drive heliospheric variability? − How do solar eruptions produce energetic particle radiation that fills the heliosphere? − How does the solar dynamo work and drive connections between the Sun and the heliosphere? These questions represent fundamental challenges in solar and heliospheric physics. SoloHI SWA By addressing them, major breakthroughs in our understanding of how the inner solar EPD STIX system works and is driven by solar activity are expected. To answer these questions, it is EUI MAG essential to make in-situ measurements of the solar wind plasma, fields, waves, and energetic RPW METIS particles close enough to the Sun that they are PHI MAG still relatively pristine and have not had their properties modified by subsequent transport SWA and propagation processes. This is one of the SWA fundamental drivers for the Solar Orbiter RPW mission, which will approach the Sun to as close as 0.28 AU Relating these in-situ measurements back to their source regions on the Sun requires Figure 1. Instruments on Solar Orbiter simultaneous, high-resolution imaging and The remote-sensing instruments (in red) are clustered within the spectroscopic observations of the Sun in and body of the spacecraft behind the heatshield, and “peer” through out of the ecliptic plane. The resulting feed-through channels. The in-situ instruments (green) are exposed combination of in-situ and remote-sensing to the space environment. Note; The SPICE instrument is not visible instruments on the same spacecraft (Figure 1), due to being mounted underneath the top panel together with its close perihelion distance, Credit: ESA distinguishes Solar Orbiter from all previous and current missions, and promises to enable science that can be achieved in no other way. The instruments on Solar Orbiter are as follows In-Situ

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EPD MAG RPW SWA

Energetic Particle Detector Magnetometer Radio and Plasma Waves analyser Solar Wind Plasma Analyser

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EUI METIS PHI SoloHI SPICE STIX

Extreme Ultraviolet Imager Coronagraph Polarimetric and Helioseismic Imager Solar Orbiter Heliospheric Imager Extreme UV Spectral Imager Spectrometer/Telescope for Imaging X-rays

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Remote-Sensing

While the in-situ instruments will be operating Figure 2. Projection of Science Orbits continuously throughout the orbit, the use of the The progression of the orbit of Solar Orbiter during the Nominal remote-sensing instruments will nominally be Mission Phase, with increasing latitude and varying perihelion restricted to three 10-day “remote-sensing windows” distances. The Spacecraft/Earth distance does not follow as (RSW) due to data return constraints. Typically, these simple a pattern as with a planetary mission. remote-sensing windows will be centred around perihelion and the extrema of northern and southern latitude of the orbit. A series of Venus gravity-assisted manoeuvres will gradually change the inclination of the orbit with respect to the ecliptic plane, giving unique 2 American Institute of Aeronautics and Astronautics

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A. Data Return versus Data Acquisition Problem As seen in Figure 3 and Figure 4 the relationship between Figure 3. Projection of core science orbit in data acquisition and data return is complex; rarely do periods Sun-Earth rotating system of high data rates (low Spacecraft-Earth distance) coincide The 4:3 resonance of the 2018 October trajectory with periods of high data acquisition (low Spacecraft-Sun is represented. In this figure Sun and Earth are distance). Solar Orbiter has a large (63 GiByte) Solid State fixed at the yellow and blue points, respectively. Mass Memory (SSMM) into which science data is stored All 4 aphelia are far away from the Earth and only before downlink to Earth, acting as a buffer between the data 2 perihelia are close, but not below 0.7 AU. The acquisition and data return rates. It should also be noted that result is a poor overall data return. especially the remote-sensing instruments could easily produce several orders of magnitude as much scientific data than could be downlinked. This leads to three potential difficulties with optimising data return: 1. Data return latency There are periods during which the data Range Sun AU acquisition rate is (much) larger than the data 2.00 Range Earth AU return rate. Data is therefore necessarily stored 1.80 1.60 on-board for a period of time. This condition may 1.40 continue for many months, delaying the return of 1.20 science data accordingly. More than being an 1.00 annoyance, this can prevent the assessment of 0.80 instrument performance from one RSW before 0.60 the next occurs, reducing opportunity to improve 0.40 instrument calibration settings. 0.20 2. SSMM overflow 0.00 If the above condition continues too long, the SSMM fills up and can no longer buffer the data. In this situation data must either be discarded (new data lost) or overwritten (old data lost). Figure 4. Variation of Spacecraft-Sun and Spacecraft-Earth 3. SSMM underflow (“under-run”) Distances When the data acquisition rate is smaller than The Spacecraft-Earth distance is the dominant factor for the data return rate the SSMM will gradually downlink bit rates, and the Spacecraft-Sun distance is the become empty, giving the data a chance to “catch dominant factor for the data acquisition rates. Both rates are up” with real-time. If this condition continues higher at lower respective distances. until the SSMM is empty, the daily downlink passes cannot be fully utilised. While no data is lost in this condition, it can be considered a “virtual loss” of data. 2018

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views of the solar poles (Figure 2). Up to two RSWs can be contiguous, giving a single 20-day window. Each orbit will have a duration of around 160 days, with the gravity assist located in the vicinity of aphelion. A heatshield will protect the body of the spacecraft (containing the platform units and the majority of the remote-sensing instruments) from the intense heat and radiation of the Sun. In order to observe the solar disk through the heat shield, feed-through channels are installed in the shield with individual mechanical doors that can be opened or closed as required by the instruments. Remote-sensing operations (being limited to RSWs) have very different data generation profiles to that of the in-situ instruments. In-situ instruments generally produce data at a steady rate during most the orbit, although event triggered burst modes can produce sudden peaks in production. This leads to gentle changes in data storage levels outside of the windows, and then a rise during and after the remote-sensing windows as the downlink is shared with the remote-sensing instruments. 1 AU Remote-sensing stored data increases very quickly during the remote-sensing windows making the on-board memory fillVenus states “spikey”. Furthermore, the science-generating phase of Orbit the mission is split into “Cruise” and “Nominal Mission” as defined as starting at the first Gravity Assist Manoeuvre after which the perihelion is below a certain altitude. No RSWs Sun occur during Cruise Phase, however the in-situ instruments will be generating data at a constant, albeit reduced, rate. Additionally, if the Spacecraft-Sun distance is over 1.2 AU the spacecraft will be placed into a Hibernation state where no science operations are performed.

Simply increasing SSMM size leads to larger latency and more underflows and is not the simple solution it might otherwise appear. Due to a technology re-use policy applied for this mission, certain other parameters have to be considered as fixed, like given aperture of the high-gain antenna and RF output power. With data acquisition and data return being dominated by the orbital conditions, these cannot be modified in-flight (although trajectory optimisation pre-flight may improve a given orbit, see below).

II. Background - Pre-Launch Optimisations

A. Payload Optimisations On the payload side, comprehensive measures are being taken to maximise the science return of the mission given the strict constraints on data downlink. In general, science data will be compressed on-board using stateof-the-art compression schemes, e.g. JPEG2000 for image data. In case of the Polarimetric and Helioseismic Imager (PHI), measurements of the polarisation state of the Sun’s visible light will be processed on-board into maps of physical quantities on the solar surface, e.g. the magnetic field vector and line-of-sight velocities. This requires a full spectropolarimetric radiative transfer inversion code to be implemented on an FPGA and to be routinely run on-board, a challenge that has not been attempted before. In case of the Solar Wind Analyser (SWA), which will measure distribution functions of the constituents of the solar wind, the full distribution functions will only be downlinked at low temporal cadence, while moments of the distribution functions will be calculated on-board and downlinked at high temporal cadence. B. Deep Space Transponder Modification Due to limitations of the occupied bandwidth it is only possible to use Pulse Code Modulation (Split PhaseLevel) [PCM(SP-L)] modulation up to a data rate of 300 kbps in the X-band. As this was representing a significant limitation for the data return 1000 budget, it was decided to modify the baselined, existing Deep-Space-Transponder (DST) and 800 GMSK add the capability for Gaussian Minimum Shift Keying modulation (GMSK). This, together 600 with an increase of RF-transmitter power from 400 30 W to 75 W enabled the spacecraft to utilize data rates of up to 1 Mbps (Figure 5). 200 SP-L + GMSK belongs to the family of PN RNG 0 modulation schemes used to make an efficient use of the allocated bandwidth. This together 0.0 1.0 2.0 3.0 with the fact that a combined use of GMSK + Spacecraft / Earth Distance (AU) Pseudo-Noise (PN) ranging has been Figure 5. Bit Rate vs Distance vs Modulation standardized by the CCSDS in February 2016, While GMSK and SP-L provide similar performance at makes its selection compared to other distances greater than 0.75 AU, below that the performance of alternative available on-board PCM(SP-L) + GMSK is significantly improved. Originally the facility of standard code ranging, a good compromise running Ranging operations with GMSK was not supporting, between bandwidth efficiency and link forcing a drop into SP-L + PN Ranging at regular intervals. performance without dramatically increasing the implementation complexity on both onboard transmitter and on-ground receiver. Supported Bit Rate (kbps)

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In recognition of the downlink budget as a constrained resource, some optimisation of the mission has taken place at the design level, before launch. Most significantly this includes work to refine the spacecraft trajectory such that the phasing is improved between the periods of high data acquisition and high downlink rates, following the work of Y. Langevin3.

C. Trajectory Optimisation An exercise has been carried out4 to identify possible alternative trajectories for Solar Orbiter that improve the data return capability while maintaining a trajectory profile compatible with the science objectives. During the science phase Solar Orbiter will implement a series of increasingly inclined orbits in resonance with Venus. 5:4 and 4:3 resonance * typically used at the beginning of the science phase lead to orbital periods that are close to 0.5 years, meaning that the spacecraft is almost in resonance with the Earth as well. The poor data return of the 2018 October trajectory (Figure 3) is caused by an orbit geometry in which perihelion is the closest point to Earth, while the aphelions are approximately 180° apart as seen from the Earth and always far away. Geometries to favour data return are exactly the opposite: the closest point to Earth must be around *

Spacecraft-Venus revolutions around the Sun. 5:4 means Solar Orbiter completes 5 orbits for every 4 of Venus. 4 American Institute of Aeronautics and Astronautics

The following Figure 6 compares the core science orbits (first 4:3 or 5:4 of the respective science mission) of the 3 trajectory options previously presented. In all the cases the improvement of the geometry relative to the Earth with respect to the original 2018 October trajectory is evident. The potential improvement in terms of data return (considering 8 hour daily communications and 100% availability of the ground stations) that is possible during the core orbit is estimated in a factor of 2.2 for Option A1 and 2.86 for Option E with respect to the original 2018 October. For Option D the potential data return improves in a factor of 4.4, though part of the improvement is due to a core science phase that is 0.6 years longer. In any of the cases the trajectory redesign produces a dramatic impact on the data return capability. This potential increase in data return crucially relies on adapting the science operations of the mission accordingly. 1 AU 1 AU

Venus Orbit Sun

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Venus Orbit Option A1 – First 4:3

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aphelion to provide a long period with high downlink capability *. In other words, the aphelion should be located close to an inferior Solar conjunction in order to improve the data return. Actually, this is achieved at every other aphelion. The aphelion as seen from the Earth moves about 180° per orbit, thus an aphelion close to the Earth will be followed by an aphelion that is far away. The fact that downlink bit rate scales with 1/Distance² makes this configuration more favourable, because the extremely large data return of one aphelion compensates the reduced data return of the rest of the orbit and the far away aphelion. After an exhaust trajectory analysis the following three alternatives were proposed to improve the data return. The first two options, “A1” and “D”, are based on the same cruise profile as the current 2018 October trajectory, but slightly modified to reduce the relative velocity with Venus so that only one 1:1 resonance is used during the science phase. The option “E” is based on a different cruise profile that is still compatible with the same launch timeframe. • Option A1: the science phase follows a 1:1-4:3-4:3-3:2-5:3 sequence that allows reaching a solar inclination of 33.4° at the end of the 10 years mission. Both 4:3 resonances have a geometry that favours the data return. • Option D: the science phase follows a 5:4-4:3-3:2-3:2 profile that allows reaching a solar inclination of 32.4° at the end of the 10 years mission. Basically replaces the start of the science phase 1:1-4:3 of option A1 with a 5:4 resonance with a very favourable data return geometry: 3 out of 5 aphelia are very close to Earth. At the mission end the 3:2 resonance is chosen to gain more solar inclination and to stay within the lifetime requirement. • Option E: the cruise scheme for this trajectory is an EVVVEV sequence, whereas 2 1:1 resonances with Venus are implemented in the VVV part. This cruise is very short and will allow to start the remote-sensing observations from a perihelion of 0.346 AU only 2.2 years after launch. Given the short cruise, a longer sequence of Venus resonances 4:3-3:2-3:2-5:3-3:2 is used during the science phase keeping the overall trajectory duration within 10.5 years and allowing a final solar inclination of 32.9°. The 4:3 resonance provides an excellent data return with the third aphelion located at an inferior conjunction.

Option D – First 5:4

Option E – First 4:3

Figure 6. Projection of core science orbit in Sun-Earth rotating system for the 3 options

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Being in an elliptical orbit, at aphelion the spacecraft moves slowly with respect to the Sun and so maintains the data return geometry with respect to Earth for a longer time. 5 American Institute of Aeronautics and Astronautics

III. Mission Operations Optimisation

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As operators, we are limited in terms of what we can do to improve the data return, as it is bound so tightly to orbital conditions that are fixed at the time of launch. Conversely, Operations has a key role to play in ensuring that what can be done, is done. This section shall describe what, at mission-level, is envisioned to increase data return. Practically speaking this means ground station pass management in terms of number, timing and layout. A standard ground station pass will commence with the downlink of platform- and systemlevel housekeeping packets, which are stored in a separate memory unit, the On-board computer Mass Memory (OMM); the SSMM itself is used only for Science TM storage. During some phases of the mission this engineering non-science data downlink can consume the majority of a ground-station pass duration. Once the OMM data dumps are finished, data dumps from the SSMM will begin, with high-priority data downlinked through the use of a File Transfer mechanism to ensure reliability and then the bulk science transfer via a packet-by-packet read operation. The use of File Transfer, with its automatic missing part retransmission mechanism, also allows the dumps to be scheduled on-board so as to start as soon as the spacecraft is in visibility, rather than waiting to be started by Ground commanding. A. MOC Data Return Modelling Based on the skeleton pass concept, a model was built so as to examine the effects of modification to basic pass parameters (number, frequency, length) as well as any other optimisation that might be conceived of. Initially this model was based on an Microsoft Excel spreadsheet, calculating row-wise the accumulated data in the SSMM versus the data downlinked. While this produced a good overview of the data return conditions for a given operational scenario, it was not suitable for the sort of optimisations that were needed to be tested. Instead, a Java-based tool was created, based on an iterative integration model of data acquisition and data return (not in itself fundamentally different from the spreadsheet technique but offering a mechanism to go “backwards” and explore the effects of different parameters). This gave the flexibility to “plug in” more advanced features as the avenues of optimisation were considered. The output of the model is a “Data Return Factor”, normalised to 1 for the baseline scenario. 1. Basic Model Description The model takes as input the trajectory and ground-station visibility information as computed by the Mission Analysis department. A number of passes are “lost” according to a given ground-station reliability parameter. Data acquisition rates are calculated based on the orbital conditions at each step (in hibernation? in cruise? in science phase? in RSW?) as input to the mass memories, and the effects of a ground station pass are similarly calculated taking into consideration visibility, data rates, selected modulations. Each step represents one day; the results are integrated and plotted for display. The “cruise data rate” is calculated at this point, being the rate at which data can be gathered during Cruise such that no SSMM Overflow occurs yet that the SSMM is empty at the start of the Science phase. 2. Secondary Pass Allocation The Solar Orbiter project team have allocated an extra 19 ground-station passes per orbit, on top of the daily pass. The model assumes that this can be considered as a total number of pass-hours for the mission duration. A basic model run-through is performed to identify areas of SSMM Overflow and additional passes are allocated until these overflow “peaks” have been removed (if possible to do so), preferring passes that return the most efficient (considering downlink rate and available pass length). In some cases it is not possible to allocate sufficient numbers of passes to completely remove a peak; there may not be enough visible ground stations or the rate of SSMM filling may simply be greater than the available downlink. Secondary passes are not allocated if the pass time cannot be sufficiently filled (SSMM Underflow) or if they are too small to be efficient (too much pass time lost to overheads at station set-up time). The SSMM fill level is very sensitive to even small changes in pass allocation; it is conceivable that more intelligent optimisations could be performed beyond the application of these basic rules. A more detailed analysis of Pass duration optimisation has been carried out by the SOC and is reported in Section IV. The extra pass allocation serves as a reasonable approximation so as to properly consider the additional operational optimisations, and such allocated passes are kept constant across different scenarios so as to enable direct comparisons between optimisation strategies. This is justified on the basis that to-be-used passes will be given as a fixed input to the MOC from the SOC. 3. Optimisation Modelling Each optimisation strategy, or combination thereof, is applied and the results displayed in graphical form with some key statistics (see Figure 7).

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In order to better compare optimisations, the model will increase the data generation rate until the SSMM is full without overflowing; otherwise the best data return factor would always be 1. This rate increase is performed independently for the cruise phase and for the science mission phase: the “Cruise Gen. Factor” and “Mission Gen. Factor” respectively. A “Mission Generation Factor” of 1.0 represents the agreed baseline science generation rates during the nominal science mission phase, in other words, the minimum planned science return.

Nominal Mission Phase

SP-L Limit

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Cruise Phase

Hibernation

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Figure 7. “Baseline” Data Return Results The baseline scenario is able to return all acquired data within an acceptable latency. There are significant periods when the SSMM is empty or not well-filled. This represents an opportunity for further data return improvements by acquiring more data so as to have something to downlink in these periods. The irregular nature of data generation and data return periods are clearly visible. B. Ranging Optimisation The operational benefit of using GMSK+PN ranging versus PCM(SP-L)+PN ranging relies mostly on the possibility of performing continuous downlink of on-board data using GMSK simultaneously to the PN ranging measurements required by ESOC Flight Dynamics to better determine the Solar Orbiter orbit. As a consequence, maximum profit of the allocated station time will be used in favour of the overall data volume return. As originally planned, every pass during Cruise would have been taken as SPL+PN Ranging, and a 20minute dedicated ranging session three times a week during the Nominal Science phase. Due to the lower rates in SP/L this would have constrained the data return capabilities especially during Cruise. The ability to choose an optimal ranging and modulation strategy throughout the whole mission adds up to a substantial increase in data return capability. From the ESTRACK ground receiver software implementation the GMSK modulation scheme was already supported by the current Intermediate Frequency Modem System (IFMS). However, an upgrade is required to support the combined GMSK+PN ranging scheme. The new capability will be implemented in the future Telemetry and Telecommand Processor (TTCP), which is foreseen to be deployed in ESTRACK ground station by mid-2017. C. Use of Three Memory Modules One obvious method for improving data turn is to “get a bigger SSMM” such that more data can be carried through periods of low downlink rates. This can be achieved on Solar Orbiter by using all three SSMM memory modules, rather than the default two *. This has an impact on the redundancy and robustness of the unit however this drawback and risk may be considered acceptable in view of the potential gains. Assuming that the three module configuration is cleared for in-flight use, the model simply assumes a larger capacity. No packet-store level modelling is performed; this is under the purview of SOC. An effectively larger SSMM helps only with the SSMM Overflow case; underflows may still occur and the latency is necessarily longer. *

The baseline scenario considers one module to be in cold redundancy and two in-use. 7 American Institute of Aeronautics and Astronautics

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Lower Peaks

Figure 8. Optimised “Baseline Trajectory” Data Return Results While following the general trajectory-dominated trends, the operational optimisations reduce the overall height of the data storage peaks, allowing the overall data acquisition rate to be increased during the Nominal Science Phases. This leaves scope for further optimisation though science operations planning to gather more data for later downlink during the following under-run season. D. Pass Buffer An area over which MOC has control is the downlink of engineering data (Housekeeping, Command Acknowledgments, Events) on the real-time link, versus that stored in the OMM. Generally all such data is stored in the OMM such that a loss of downlink does not result in loss of engineering data, however during a downlink pass such data is received in real-time (radio link propagation delay notwithstanding). Nominally the real-time housekeeping telemetry will be generated at a rate of 2 kbps, which represents 4% of the total available downlink bandwidth at the lowest operational bit-rate. At the start of every pass the OMM data is dumped with priority on a dedicated playback virtual channel. Instead of storing data which is anyway received on the real-time link during the ground-station pass, the concept of a “pass buffer” has been developed. At the start of a ground-station pass, once the link is seen to be stable, the storage of the data received in real-time will be switched from the nominal packet store to a “pass” packet store, which is nominally never downlinked (only in case of an unexpected loss of pass will the buffer be downlinked, so as to fill in gaps in the real-time data). This represents a saving of roughly one third of the engineering data daily, assuming an 8-hour pass and its associated data not being downlinked on the playback channel in a subsequent pass. During low bitrate seasons this can have a large cumulative effect on the overall data return. Implementation of the Pass Buffer concept requires the creation on-board of dedicated packet stores to hold the data, as well as procedures to manage the packet storage routing tables. This will be achieved on-board using existing automation mechanisms such as On-Board Control Procedures. The implementation also impacts on the “Telemetry Mode” definitions for the spacecraft, such that packets of a particular on-board process are not split between data streams, as the packet sequence control counter is maintained per-process, such that splitting the stream (such that one is not downlinked) would appear to Ground as gaps in the data. E. Results The results (Table 1) of the analysis show that the baseline un-optimised trajectory and un-optimised operations approach is achievable without experiencing SSMM over-runs (although with tight margins). When considering the operational optimisations as described above, it can be seen that the GMSK+PN Ranging greatly increases the data return during Cruise, and the use of three memory modules benefits the nominal mission phases. The improvements are cumulative, in fact the scenario where all optimisations are applied achieves a return increase greater than the sum of the individual optimisations. 8 American Institute of Aeronautics and Astronautics

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Figure 8 shows the SSMM fill profile as modelled in the combined case. A notable feature is the lower “peaks” overall. This is a consequence of considering only a flat data generation rate during both the Cruise and Nominal Mission phases; it offers scope for considerable improvement with more advanced science operations planning, which is in the domain of the SOC.

October October 2018 October 2018 October 2018 October 2018 GMSK+PN Pass Buffer 3 Memory 2018 Baseline RNG Module Combined Max OMM Loading (%) 71 71 70 71 70 Max SSMM Loading (%) 99.86 99.82 99.66 97.9 98.92 Secondary Passes 157 157 157 157 157 Cruise Science Gen. Factor 0.40 0.59 0.41 0.45 0.69 Mission Science Gen. 1.0 1.0 1.01 1.07 1.11 Factor Lost Science (%) 0 0 0 0.0 0.0 Data Return Factor 1.0 1.06 1.01 1.08 1.17 Maximum Latency (days) 167 163 164 185 182 Table 1 Impact of MOC Optimisations on Science Return

IV. Science Operations Optimisation

A. SOC Data Return Modelling The SOC modelling started by first looking at optimizing the ground station passes. This involved moving station hours , swapping primary stations and adding additional passes (see Figure 9). An algorithm was developed that allowed a minimum configurable number of pass hours to be scheduled per day; any remaining hours from the daily data dump allocation of 8 hours were then used to extend the dump length of the station passes with the highest data rates (where the visibility period was sufficiently long). The pass schedule was run through the SOC science planning system which was used to model data generation,

Figure 9. Optimised Extra Pass Scheduling The above shows optimised scheduling of extra passes and the fill state percentage of an remote-sensing instrument. The 100% on the secondary axis corresponds to the store size after the SSMM has been split “prorata” according to instrument generation rates. storage and downlink. Although the overall downlink potential of the mission had clearly increased, this optimisation increased the depths of the under-runs and had only a minor effect on the fill states when the SSMM was at its fullest. For the trajectories with fewer downlink constraints (which had no overrun risk) this was an effective way of optimising downlink, but for trajectories with few high/wide downlink windows, overruns still existed. 9 American Institute of Aeronautics and Astronautics

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The primary station assumed for Solar Orbiter is the ESTRACK Malargüe 35m Antenna in Argentina, although Cebreros and New Norcia could also be used. The station visibility on Malargüe often drops below 6 hours a day and on one of the original proposed trajectories this actually falls to zero for several weeks (e.g July 2017 launch date). The algorithm allowed the swapping of stations when the visibility on the station fell below a configurable number of hours. The SOC, in addition to the MOC, looked at the potential benefits of extra ESTRACK ground station passes (19 extra per orbit). It was evident that although adding extra passes at high rate periods gives more downlink, it is only useful if data is available in the stores and if the fill state of the store is actually approaching a peak, otherwise the extra pass only brings forward the time at which the SSMM becomes empty. Adding passes when the data rate is low can seem inefficient as the overall increase in downlink volume is low, however it is often around these times when the extra downlink is most needed, as it frequently corresponds to times when the stores are full. The optimisation is hence an iterative process requiring knowledge of the data generation, and therefore the science observation plan; and the SSMM fill state. The most effective way to schedule extra passes is to look for the highest data rate in each period between an under-run and potential overrun and add the extra passes there. Overall it is possible to double the total potential downlink for the mission by using extra passes and the redistribution of stations hours. The potential increase in downlink volume should however be traded-off against the value of the science that the extra downlink capability would enable if it were used at a more interesting (but perhaps less efficient) time. B. Downlink sharing and data store sizing The data generation rates vary strongly across the instruments (science data rates vary by a factor of 40). An equal share of the downlink would result in higher latencies for the higher producers (up to ~250 days). Science goals require the coordination of observations of the different instruments and one data set can be considered as the combination of data from several instruments, for this reason the downlink share is proposed to be split (approximately ) proportionally to the expected average data generation rates, resulting in similar latencies across all science data.

Memory capacity (64GBytes)

Mbytes

35 GBytes 33 GBytes

Figure 10. Instrument Packet Stores versus Big Bucket model A stacked plot of the space needed to accommodate the instrument data for an example trajectory. The total space needed to store instruments data in packet stores exceeds the availability of SSMM, although the “big bucket” model capacity is not exceeded. All the remote-sensing stores are shown in orange and the in-situ stores are shown in blue. All original downlink analysis, including that done by MOC, assumed the “big bucket model” of the SSMM, i.e. an SSMM not partitioned into data stores (Figure 10). The use of data stores is essential to ensure, the prompt downlink of higher priority data (for example HK, and Low Latency data used for quick look validation and selection of higher resolution data for downlink), and to separate the science data from different instruments to prevent any over-producing instrument overwriting the data of another. The packets stores act as a buffer to house data when the downlink rates are lower than the generation rates; the packet stores should be sized to ensure that no overruns occur. A pro-rata division of the SSMM according to average generation rates, should ensure that the fill-state percentage is similar for all of the in-situ instrument stores and similar for all the remote-sensing instrument stores. This does not avoid overruns however, as for trajectories with short orbits, the stores cannot always be emptied before another remote-sensing spike occurs. A deviation from the pro-rata concept is therefore necessary, this could be applied either in the downlink share or in the memory split. 10 American Institute of Aeronautics and Astronautics

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Also as the profiles of in-situ and remote-sensing instruments are different, the peaks in each store will not occur at the same time. This means that although the total memory used at any one point may not be more than the SSMM capacity, the sizing of stores needed to house the peaks could require more space than available in the SSMM. A sensible decision needs to made on the packet store sizing; extreme peaks in the fill states could be massaged by adapting the downlink priorities, lowering the storage needs of instruments with spikey fill-state profiles. Changing the sizes of the stores during the science phase of the mission is an option, but this would require all stores to be empty to avoid losing data, and interrupting the writing of the data to the SSMM. Although some instruments do have internal memories, most transfer their data in near real time to the SSMM, implying the interruption of data acquisition. C. Selective Downlink Selective downlink is a concept that can be used to increase or ensure the quality of the downlinked data. It allows chosen high resolution data to be downlinked for a sub-set of time or can be used for downlinking data only after confirmation that the data is useful (e.g. ruling out of false triggers). Selective is useful on downlink constrained missions, but it requires the use of extra space in the SSMM to accommodate the data during the time needed to receive, process and check low resolution data, upload commands, and bring down the data. Solar Orbiter is at times limited in both SSMM space and downlink. Store repurposing is a concept that would Mbytes allow selective to be used during times when the SSMM space is less constrained. An instruments science data store allocation would be split into two different stores; at times when the SSMM is less constrained, data for possible selection is sent to one store and standard data which is sure to be downlinked is sent to a normal store enabled for unbound transfers. When the normal store is near to full, the standard data will be rerouted to the second store and the production of “selective data” will be stopped. D. Dealing with under-runs Because of the large variation in data rates, some SSMM under-runs will be difficult to avoid. At high data peaks the whole SSMM could be emptied in less than 3 weeks, at low rates this would take over a year. If a data peak lasts more than 3 weeks, the only way to use the under-run would be to schedule high data rate operations at the times the peak occurs, but these occasions do not necessarily correspond to interesting opportunities and may be outside of planned RSWs. Instruments with large internal memories could send extra data to the SSMM from previous opportunities; shortening passes and redistributing pass hours is another possibility. Under- runs should not be considered as a problem to solve, however, given that the mission is constrained on downlink and storage for much of the time, the use of the under-runs should be investigated, without complicating the SOC/MOC tasks. E. Results A more realistic model of data production profiles and SSMM store usage (e.g. not “big bucket”) than that identified at mission design level is useful in validating the storage and data return design. The work has also been a useful input to the design of instruments, e.g. identifying new software requirements for handling the low rate and high rate cases. Optimisation of downlink is possible, but the meaning of optimisation depends on the problem that needs to be solved (under running or overrunning stores). In either case it requires having good draft operations plan and draft data generation profile before the station scheduling exercise.

V. Conclusion In conclusion, whilst the baseline permits a 100% data return considering nominal instrument data generation rates, there is much scope for improvement. Dramatic improvements can potentially be achieved by modifying the trajectory to better phase periods of high data generation with respect to following periods of high downlink potential, although this is a decision to be taken before launch as it has implications on the mission as a whole. In general, mission operations should focus on optimising schemes that may be used in any given trajectory. Here there is no single “magic bullet” solution for data return increase, however there are many incremental improvements, both at the science planning and mission operations level, which add up to a big overall increase. This requires close coordination between instrument teams, MOC and SOC to manage these optimisations to ensure that the opportunities this provide can be fully utilised.

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Appendix A Acronym List Acronym AU CCSDS DST ESA ESAC ESOC ESTEC ESTRACK GMSK HGA HK HKTM IFMS kbps Mbps MOC OMM PCM RF RNG RSW SOC SP-L SSMM TTCP

Definition Astronomical Unit Consultative Committee for Space Data Systems Deep Space Transponder European Space Agency European Space Astronomy Centre European Space Operations Centre European Space Research and Technology Centre European Space Tracking Network Gaussian Minimum Shift Keying High-Gain Antenna Housekeeping (Telemetry) Housekeeping Telemetry Intermediate Frequency Modem System Kilo (103) bits per second Mega (106) bits per second Mission Operations Centre (at ESOC) On-board Computer Mass Memory Pulse Code Modulation Radio Frequency Ranging Remote-Sensing Window Science Operations Centre (at ESAC) Split Phase-Level Solid State Mass Memory Telemetry and Telecommand Processor

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Appendix B Acknowledgments Solar Orbiter is an ESA mission with NASA participation and this work was carried out under a programme of and funded by the European Space Agency. The view expressed herein can in no way be taken to reflect the official opinion of the European Space Agency.

References 1

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Müller, D., Marden, R.G., St. Cyr, O.C., Gilbert, H.R., Solar Orbiter: Exploring the Sun-Heliosphere Connection, Solar Physics, Volume 285, Issue 1-2, pp. 25-70, http://adsabs.harvard.edu/abs/2013SoPh..285...25M 2 ESA, “Solar Orbiter - Objectives”, URL: http://sci.esa.int/solar-orbiter/44167-objectives/ [cited 20 January 2016]. 3 Y. Langevin, "Downlink issues for Solar Orbiter, with an application to the October 2018/10 window”, "Short cruise options for Solar Orbiter from 2018 to 2020”, February 2015 (private communications) 4 Sánchez Pérez, J.M., Martens, W. and Langevin, Y., “Optimizing the Solar Orbiter 2018 October Trajectory to Increase the Data Return”, AAS 15-591, AAS/AIAA Astrodynamics Specialists Conference, Vail, Co, USA, August 2015.

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