**Onboard Science Software Enabling Future Space Science and Space
Weather Missions**[^1]

Michael L. Rilee

Emergent IT; NASA/GSFC Mailstop 931, 28/s-207, Greenbelt, MD 20771;
301-286-4743; Michael.L.Rilee.1@gsfc.nasa.gov

Scott A. Boardsen

Emergent IT; NASA/GSFC Mailstop 632, Greenbelt, MD 20771; 301-286-0837;
boardsen@xfiles.gsfc.nasa.gov

Maharaj K. Bhat

Emergent IT; NASA/GSFC Mailstop 931, 28/s-207, Greenbelt, MD 20771;
301-286-1531; mbhat@hannibal.gsfc.nasa.gov

Steven A. Curtis

NASA Goddard Space Flight Center; Code 695, Greenbelt, MD 20771;
301-286-9188; Steven.A.Curtis \@gsfc.nasa.gov

*Abstract* -- On the path towards an operational Space Weather System
are science missions involving as many as 100 spacecraft (Magnetospheric
Constellation, DRACO, 2010). Multiple spacecraft are required to measure
the macro, meso, and micro scale plasma physics that underlies Geospace
phenomea. Simultaneous multi point in situ measurements enable the
determination of electric current in space and the discrimination of
temporal and spatial phenomena. From these quantities, the structure and
dynamics of Geospace may be modeled and better understood. We work
towards the day when multi point, real time data might be gathered on
Earth to produce maps and forecasts of Space Weather conditions.

To be feasible, however, multiple spacecraft missions must be no more
costly to operate than single spacecraft missions are today.
Furthermore, communication availability places severe constraints on an
entire mission architecture and hampers the resolution, coverage,
timeliness, and hence, usefulness of spacecraft data.

To address some of these constraints, we have been studying the
possibility of performing some science data processing functions on
board a pathfinding mission in NASA\'s Solar-Terrestrial Probe Line,
Magnetospheric Multi Scale (MMS, 2008). The MMS mission involves four or
five spacecraft, operating as one, that bring full suites of high
resolution plasma diagnostic sensors to focus on Geospace phenomena
involving plasma microphysics, i.e. sub-fluid length, time, and
frequency scales. In the MMS baseline, totality of coverage, already
likely to be hampered by communication availability, is sacrificed to
obtain high resolution data in the vicinity of critical Geospace
boundaries. Onboard computation enables the return of additional reduced
data from regions immediately surrounding these important Geospace
boundaries: data which may be critical to the correct understanding of
boundary processes. Furthermore, onboard computation will enable high
resolution reduced data for entire orbits to be sent to Earth.

Going beyond onboard data reduction, sufficient onboard computation
opens the possiblity of onboard autonomous science data management. For
purposes ranging from instrument calibration, test, diagnosis, and
through scientific discovery, the most fundamental data an instrument
produces are often required. However, communications availability places
limits on when and how much of this raw data may be transmitted to
Earth. With sufficient onboard computational resources, intelligent
systems may be implemented that recognize features in sensor data that
are important for mission goals. These data may be selected for
transmission to Earth at full fidelity, providing the best possible view
of the phenomena. Data of lesser importance are more lossily compressed,
with routine observations being compressed to summary statistics before
transmission to Earth. This sort of autonomous science data management
is an important step to science driven autonomous control of spacecraft
instrumentation.

In this paper we discuss our multi level approach to developing an
onboard science analysis system that we are studying for potential use
onboard the MMS mission. This analysis system will enhance MMS science
by improving sensor coverage and by returning to Earth high-resolution
data that would otherwise be discarded or not generated. Results of our
work using Space Physics data sets from previous missions will
illustrate our approach.

[Table of Contents]{.smallcaps}

1.  [Introduction]{.smallcaps}

2.  [A Progression of Space Science Missions]{.smallcaps}

3.  [Onboard Computing Approach]{.smallcaps}

4.  [Onboard Computing Research]{.smallcaps}

5.  [Summary]{.smallcaps}

6.  [Acknowledgements]{.smallcaps}

7.  [References]{.smallcaps}

    [1. Introduction]{.smallcaps}

Space science deals with what exists and occurs in the space
environments. The enterprise of space science is nearing a threshold in
the scope of its observations of Geospace. Geospace is that dynamic
region wherein the Earth and its electromagnetic fields affect the
interplanetary medium. The nature of the interplanetary medium is
essentially dominated by the supersonic Solar Wind, except where
influences, such as the Earth\'s, interfere. A general map of Geospace
has been pieced together from observations obtained with spacecraft
since the dawn of the space age. To go beyond this general map requires
simultaneous multi spacecraft missions to resolve structures in space
and time, which is not possible with single spacecraft missions.

*Space Science Foundation*

![](media/image1.jpeg){width="7.0in" height="4.9in"}

*Geospace Observation*

The principal methods to observe the Geospace and interplanetary media
are in situ particle spectrometry and EM field measurement \[1\].
Particle spectrometers feature EM-based optics that allow the
measurement of the distribution of particles within a 6-dimensional
velocity-position phase space. EM field measurement uses sensors
developed for low and high frequency signals. The structure of the
particle phase space density and of the electromagnetic waveform and
energy spectra are the key tools we use to understand the microphysics
of a plasma. For the more fluid-like MHD behavior, high time resolution
moments of the plasma phase space density and magnetometry suffice. For
Geospace phenomena, where many physical processes are neither completely
MHD nor completely kinetic, the understanding proceeds best when a range
of time and length scales are available for study. For example,
understanding energy transfer and particle energization in magnetic
reconnection (figure 1) requires understanding the perhaps delicate
balance obtained between a narrow kinetically dominated electric current
sheet and external MHD-like, turbulent regions separating more quiescent
layers.

In situ measurement of plasma properties has been the mainstay of space
science, but remote sensing has played a role as well. Remote sensing
data often suffers ambiguities as multiple sources overlap in sensor
response. Nonetheless, remote sensing instruments have provided
information about the large-scale dynamics and processes underlying
Geospace phenomena. Space borne radio experiments such as WAVES on the
*WIND* spacecraft routinely monitor various radio emissions from the
Sun, the interplanetary medium, and Geospace \[2\]. Active instruments
such as the Radio Plasma Imager on board the *Imager for
Magnetopause-to-Aurora Global Exploration* (*IMAGE)* also return
information about the distribution of matter in their vicinity \[3\]. In
addition, *IMAGE* hosts a number of applications of remote sensing
technology to image the various portions of Geospace in UV and neutral
atom emission. Ultraviolet (UV) imaging has proven useful in the study
of the connection of magnetospheric with ionospheric phenomena. UV and
neutral atom imaging have also provided the first global views of the
matter distribution in the Earth\'s magnetosphere.

Single point sensing, either in situ or remote, cannot adequately
constrain our understanding of the space environment. Single point in
situ measurements cannot distinguish between a stationary oscillation
and a wave moving past. Single point remote measurements often lead to
ambiguities, i.e. overlapping sources, that can be accounted for only
with a priori knowledge or multi point measurement, e.g. tomography or
interferometry \[4\]. Furthermore, the physics of these systems often
deals with how system properties evolve or are correlated in space and
time. For example, electric current depends on the spatial variation of
the magnetic field through its curl via Ampère\'s law. Measuring the
curl of the magnetic field requires simultaneous, spatially distinct
measurements of the magnetic field. Therefore, to understand the physics
underlying Geospace phenomena, simultaneous, multi point measurements
are required. This is true across the multiple temporal and spatial
scales of these phenomena (figure 2).

*Space Weather Forecasting*

Space science researchers are attempting to piece together an
understanding of what the space environment is and how it works \[5\].
Another name for the dynamics of the space environment, including
Geospace, is Space Weather. Understanding these dynamics and related
hazards including the evolution of populations of energetic radiations
is important to all that use space. Many space faring agencies support
the development of a predictive capability that would allow the
structure, dynamics, and hazards to be mapped in Geospace much as they
are mapped in terrestrial meteorology \[6\]. Our current capability is
not very specific in its predictions, and users of these forecasts act
conservatively to ensure mission safety, e.g. postponing launches or
spacecraft maneuvers, which entails some expense \[7\].

Developing a predictive or forecast capability requires multiple
spacecraft missions to observe the physics of the underlying processes.
After such an understanding has been developed, there remains the
implementation of a space-based and terrestrial infrastructure capable
of regularly obtaining sufficient information and forming predictions
for Geospace phenomena that affect terrestrial and space-based systems
and the human use and development of space. Prototypical elements of
this infrastructure of sensors, communications, and predictive modeling
using ground-based supercomputers are to be developed as part of the
advance of space science, and in these elements we will see the
beginnings of a true Space Weather service or system.

*Multiple Spacecraft*

Space science is being driven towards multiple spacecraft missions
because of the demands to observe phenomena coupled across multiple
scales, from microphysical scales (\<100 km in Geospace) to the global
(many Earth radii). These competing demands for both detail and coverage
present challenges for space science. Greatly detailed observations
place great demands on onboard data storage and handling as well as for
communication resources. Coverage, i.e. through multiple spacecraft,
multiplies the demand for communications and operations resources, not
to mention the fundamental demands for launch vehicle capacity. To
address the needs of the science from which a forecasting capability
will be developed, a sequence of progressively more advanced missions
are planned as part of NASA Sun-Earth Connections theme \[8\]. These
missions are briefly described below.

*The Future*

So far we have concentrated on those aspects of space science concerned
with Geospace. However, as the human sphere of development in space
expands outward, other aspects of the space environment come more
sharply into view. A variety of solar disturbances, magnetospheres, and
planetary bow shocks and wakes exist throughout the solar system: these
provide glimpses into physics that might help us understand laboratory
and astrophysical plasmas. A wide range of solar system bodies and
surfaces exist which harbor answers and questions concerning the
formation and evolution of the solar system. For example the asteroids
hold great potential for research into the origins of our own and other
planetary systems, as well as being a source of materials for the
exploration and development of space.

In the following, we will discuss a series of missions that illustrate
the progression in capability required for progress in space science.
Important technical challenges are identified, and the key role of
onboard science software in meeting these challenges is identified. To
conclude, we summarize the current state of affairs, our own efforts to
advance these technologies, and the direction to be taken in the future.

[2. A Progression of Space Science Missions]{.smallcaps}

![](media/image2.jpeg){width="6.944444444444445in"
height="5.208333333333333in"} and *IMAGE* represent the current state of
the art in Geospace missions. *Cluster 2* is a set of four identically
instrumented independently operated spacecraft to study the small-scale
plasma structures in key regions of Geospace. The four spacecraft each
feature full suites of 11 plasma diagnostic sensors including both
particle and EM field sensors \[9\]. Regarding onboard science
processing, the Cluster Ion Spectrometry (CIS) experiment features
several control processors and a data processing unit that can sum
particle measurements onboard and form plasma moments \[10\]. This helps
retain some information that would otherwise be lost, but some data
analysis methods, e.g. iterative corrections due to spacecraft charging
and return currents, are beyond the capabilities of the rad-hard
processors utilized in the instrument.

*IMAGE* is a single spacecraft in a \~14 hour polar orbit whose mission
is to use neutral atom, UV, and radio techniques to determine the
evolution and structure of the Earth\'s magnetosphere \[11\]. Of
particular interest are behaviors occurring (1) on magnetic storm and
substorm time scales and (2) during periods of direct driving by the
solar wind. Two examples of onboard science computing on *IMAGE* involve
the Radio Plasma Imaging (RPI) experiment and the Low Energy Neutral
Atom (LENA) imager \[3,12\]. *IMAGE*/RPI is a low power radar that uses
onboard processing to improve the signal-to-noise ratios to enhance echo
detection. This onboard processing is performed using a
commercial-off-the-shelf digital signal processor (DSP). Algorithms
implemented onboard RPI come from the field of radar signal processing,
and include a variety of matched filter techniques. These are relatively
straightforward but intensive-processing steps that would stress any
system based on rad-hard electronics.

![](media/image3.pct)*IMAGE\'s* passage through the terrestrial
radiation belts. LENA autonomously monitors the strength of its response
during radiation belt passage, and uses this data along with a model of
its own response to determine the proper configuration of its high
voltage power supplies for given environmental conditions. This allows
LENA to continuously adapt its configuration to environmental conditions
and enables LENA autonomously and rapidly to return to an optimal
science-gathering configuration after leaving the radiation belts.
Command uplink to LENA, which previously had been dominated by commands
to the instrument to avoid damage during radiation belt passage, has
been reduced to nearly zero.

*Magnetospheric Multi Scale (MMS, launch 2008*) is a four spacecraft
mission to determine how microscale physics of Geospace boundaries
couples with the larger scale evolution and structure of regions in the
vicinities of these boundaries (figure 2) \[14\]. Particular emphasis is
placed on the key processes of magnetic reconnection, particle
energization, and turbulence*. Cluster 2*, in contrast, is a more
general survey of the microphysical processes in Geospace. Also, *MMS*
is to operate more as a single multiple element spacecraft including low
bandwidth radio links for ranging and alarm communications for science
event and fault triggers. At times *MMS* will be too far from Earth for
efficient science data downlink, therefore data storage for two weeks of
normal mode (three days burst) data is baselined. Though capable of
producing more data than *Cluster 2*, the science downlink budget is
currently set about the same. To rein in operations costs of this four
spacecraft mission, various approaches towards ground and space based
autonomy are of interest.

![](media/image4.pct){width="7.3in"
height="5.426377952755906in"}*Magnetospheric Constellation Mission
(MAGCON)* *Dynamic Response and Coupling Observatory (DRACO, launch \>
2010)* is a constellation of 50-100 spacecraft which is to address the
flow of energy and how physical processes and Geospace regions couple
over a hierarchy of scales in the global context (figure 3) \[15\]. In
addition to advances in low power electronics and lightweight science
sensors, the aggressive application of methods of spacecraft autonomy
are required for *DRACO*. The concept for nominal operations for this
mission involves ground contact principally when the spacecraft are at
perigee. Interspacecraft communication is not envisaged. Because the
orbital periods of *DRACO* elements range from one to five days,
spacecraft must be capable of detecting and correcting faults while
operating for perhaps weeks at a time without ground intervention.
Similarly, data acquired by a *DRACO* spacecraft stays in space until
the spacecraft nears perigee. Without real-time downlink *DRACO* data
will be suitable for analysis and diagnosis of Geospace phenomena after
the fact but will not be able to provide input for near real-time
Geospace state inference (nowcasting) or forecasting. The development of
such capabilities might be substantially aided by enhancing the current
*DRACO* concept by including onboard science reduction with a
sufficiently low power computing resource and downlink of real-time
science.

The *Solar Imaging Radio Array (SIRA, launch \> 2015)* is an array of 16
or more low frequency radio receiver elements that provide data for
synthetic aperture radio astronomy \[16\]. The objective of *SIRA* is to
provide real-time \"snapshot\" images of radio emitting disturbances
chiefly involving the Sun and the interplanetary medium. Some aspects of
the mission resemble *DRACO*, typically a mother and multiple daughter
configuration are chosen. Radio receivers and antennae must be supported
so micro-spacecraft, as opposed to nano-spacecraft, are envisaged. To
avoid interference from terrestrial radio sources, *SIRA* is best placed
in distant orbits or libration points. However, this enhances the
already difficult challenges of making its terrific data volume
available for analysis. Deep space missions capable of producing many
tens or a few hundred giga-bits of information a day with stringent
real-time throughput requirements require new approaches in which
automated data reduction and system autonomy play critical roles. High
bandwidth interspacecraft communication, the onboard cross-correlation
of radio signals, and high performance onboard computing are enabling
technologies for this mission. Automating the process of
very-long-baseline-interferometry for aperture synthesis is a worthy
grand challenge.

![](media/image6.jpeg){width="2.6in"
height="2.0263779527559054in"}![](media/image7.png){width="1.4in"
height="1.088976377952756in"}![](media/image8.png){width="1.111023622047244in"
height="0.8334645669291338in"}*Cluster 2* and *IMAGE,* which hosted a
wide variety of instruments for which ground control is still
significant, but also for which improvements in automation and even
autonomy have been made. *MMS* is an intermediate step towards a
distributed sensor platform that is to be no more difficult to operate
than a single spacecraft mission is today. *MMS* features higher
bandwidth instruments than *IMAGE* or *Cluster 2*, but these are fewer
and more specialized to mission themes. *SIRA* goes yet further,
requiring an even greater number of spacecraft, but operating as a
single instrument. This is driven of course by the nature of the
scientific questions we seek to answer, but also provides a steady
ladder on which to build our technical capabilities for yet more
challenging opportunities ahead.

The *Autonomous Nano-Technology Swarm (ANTS, launch 2020s)* addresses a
new set of challenges for space science and both breaks and furthers the
trend of specialization and autonomous operations mentioned above
(figures 4 and 5) \[17\]. The *ANTS* Main Belt Asteroid Survey is an
advanced concept that seeks to construct a resource atlas of all
asteroids of diameter greater than 1 kilometer during the decade of the
2020s. There are thousands of asteroids, with more than ten thousand
with trajectories and little else known about them. A single
well-instrumented spacecraft encountering asteroids one after another in
a serial fashion is inadequate to this task. Instead, researchers have
proposed a swarm of a thousand or more specialized spacecraft operating
as an insect colony analog to autonomously navigate the asteroid belt,
characterizing a hundred or more asteroids each month. Human control of
such a swarm is impossible using current techniques: individual
spacecraft in *ANTS* will be autonomous spacefarers with specialized
behaviors that optimize the capability of their particular function.
Most *ANTS* will be Workers specializing in the use of a particular
science sensor, e.g. an X-ray Fluorescence Spectrometer, an IR or
multispectral imager (figure 5) or other applicable remote sensing
device. Other elements of *ANTS* provide important collective functions:
e.g. Rulers for command & control functions and Messengers for
communications. The architecture for this concept is a topic of ongoing
research, but it is clear that elements of *ANTS* will have to make
decisions about the operation of science instruments, the choice of
target asteroids, and navigation on a continual basis and without human
intervention. Thus onboard science computation and complete system
autonomy are fundamental requirements for *ANTS*.

######### Trends and mission characteristics

Tables 1 and 2 illustrate some of the characteristics of the missions
discussed in this section. The trend towards larger numbers of
spacecraft and large total data production capabilities are evident. If
the attributes of these mission concepts indicate future needs, we
notice that even as the number of spacecraft increase, the number of
science instruments per spacecraft may very well decrease. This decrease
may even reach the point where a fraction of a constellation of
spacecraft are devoted to mission science sensors while a complementary
fraction are devoted to mission enabling functions, in a word,
infrastructure. For example, for SIRA a mother ship may provide high
bandwidth communications to Earth as well as high performance computing.
For ANTS, communications and compute nodes may provide similar
functions.

Table 1 Selected Space Science Missions

+------------+-----------+------------------+----------------+--------+
| ## Mission | ####      | **Trajectory     | **Number of    | **Data |
|            | ## Launch | time scale**     | spacecraft     | rate** |
|            |           |                  | /science       |        |
|            |           | **(days)**       | sensors**      | **Gbit |
|            |           |                  |                | /day** |
+------------+-----------+------------------+----------------+--------+
| #          | 2000      | \~1/2            | 1/6            | 7      |
|  **IMAGE** |           |                  |                |        |
+------------+-----------+------------------+----------------+--------+
| **Cluster  | 2000      | \~ 2             | 4/44           | 8      |
| 2**        |           |                  |                |        |
+------------+-----------+------------------+----------------+--------+
| **MMS**    | 2008      | 1,4              | 4/20           | 8      |
+------------+-----------+------------------+----------------+--------+
|            |           | 30,10            |                |        |
+------------+-----------+------------------+----------------+--------+
| ###        | 2010      | 1/2-5            | 50-100         | 50-100 |
| #### DRACO |           |                  |                |        |
|            |           |                  | / 100-200      |        |
+------------+-----------+------------------+----------------+--------+
| **SIRA**   | \>2015    | \-               | \> 16 / "1"    | 50-200 |
+------------+-----------+------------------+----------------+--------+
| **ANTS**   | 2020s     | Years            | \~ 1000        | \>     |
|            |           |                  |                | 50-100 |
|            |           |                  | / \~800        |        |
+------------+-----------+------------------+----------------+--------+
|            |           | Weeks            |                |        |
+------------+-----------+------------------+----------------+--------+

In table 2 we see a general characterization of constellations in use or
proposed for these mission concepts. There is a range of collective
behaviors identified in these missions. Most constellations feature
elements whose behaviors are not tightly synchronized and whose commands
are not likely to be formed in a symmetric manner. Instead, the elements
of most constellations do not behave in lock-step, at least as far as
can be determined from current mission documents. The column on
spacecraft coupling corresponds roughly to the capacity for data or
command transfer between elements of mission spacecraft. The difficulty
of interspacecraft communication shows strongly in this column. However,
the need to synthesize or fuse data from multiple sources is a growing
trend and is driving greater levels of cooperation and more tightly
coupled arrays of spacecraft.

The two tables together, point out these missions will be producing ever
greater amounts of scientific data. Making these data usable will
require differing levels of interaction between elements of the
constellations, e.g. SIRA requires great cooperation and strong coupling
to achieve its goals. On the other hand, DRACO's elements are
essentially independent though their data will be used to construct a
unified data set on the ground. In the missions presented here, there is
a progression from essentially independent spacecraft that may also be
elements of a constellation to spacecraft that depend on being part of a
constellation for mission resources. Early in the sequence, collective
behaviors do not really exist, but later they become fundamental.
Onboard computing will undoubtedly play an important role in the
management of the relationships between constellation elements in the
more advanced missions. Furthermore, onboard computing will play a
fundamental role in whatever data processing, data assessment, or
goal-seeking are required to enable these multi spacecraft missions.

Table 2 Nature of operations and interactions

+------------+----------------------+------------------+---------------+
| *          | **Operations**       | **Spacecraft**   | #######       |
| *Mission** |                      |                  | # Cooperation |
|            |                      | **Coupling**     |               |
+------------+----------------------+------------------+---------------+
| #          | Single spacecraft    | N/A              | N/A           |
|  **IMAGE** |                      |                  |               |
+------------+----------------------+------------------+---------------+
| **Cluster  | Unsynchronized       | Independent      | None          |
| 2**        |                      |                  |               |
|            | Asymmetric           | Coordinated      |               |
+------------+----------------------+------------------+---------------+
| **MMS**    | Synchronized         | Independent      | Minimal       |
|            |                      |                  |               |
|            | Asymmetric           | Coordinated      | Fault & Event |
|            |                      |                  | triggering    |
+------------+----------------------+------------------+---------------+
|            |                      |                  |               |
+------------+----------------------+------------------+---------------+
| ##         | Unsynchronized       | Independent      | None          |
| #### DRACO |                      |                  |               |
|            | Asymmetric           |                  |               |
+------------+----------------------+------------------+---------------+
| **SIRA**   | Synchronized         | Strongly coupled | Great         |
|            |                      |                  |               |
|            | Symmetric            |                  |               |
+------------+----------------------+------------------+---------------+
| **ANTS**   | Unsynchronized       | Varied           | Great         |
|            |                      |                  |               |
|            | Asymmetric           |                  |               |
+------------+----------------------+------------------+---------------+
|            |                      |                  |               |
+------------+----------------------+------------------+---------------+

[3. Onboard Computing Approach]{.smallcaps}

In the preceding we have discussed future missions involving large
numbers of spacecraft, large data production rates, and limited
communications and operations resources. Performing some data reductions
on board and implementing software that selects data according to
scientific interest or mission relevance are ways that we have been
studying to address future demands for science data downlink. Data
reductions, if trustworthy, can reduce data communication requirements
by factors of hundreds or thousands. However, the current practice of
ground-based data reduction is typically labor intensive featuring
repetitive expert assessment of the results of relatively well automated
data processing steps. In the following sections, we discuss our
research into how science data reduction may be performed onboard
spacecraft while retaining the scientific value of the results.

To our knowledge, most existing implementations of onboard data
reduction act as straightforward filters. On the ground more elaborate
processes are available, mainly due to the availability of a human
expert to assess the results of filters applied to the data. These
filters are often tuned in an iterative process or are adapted to
particular portions of a data set in a rather labor intensive process
(e.g. table 3). As problems are diagnosed and remedies developed, the
data sets may be re-processed and re-filtered to produce better derivate
data products. This cycle is often repeated many times and continues for
years, indefinitely so far in some cases, as instrument properties
change or are better understood. It is worthwhile to mention here, that
for many space science missions, the data is produced by one-of-a-kind
instruments whose behavior is not completely understood observing
phenomena that may not have been characterized or identified before. A
common criticism of onboard data reduction is that the information
content of the downlinked data is irreversibly altered removing the
possibility of revising the process of constructing derivate data
products, leaving one with essentially untrustworthy measurements.

Table 3 Science Processing Characterization

+--------------------+-----------------------+-------------------------+
| **Processing       | **Processing**        | **Goal of processing**  |
| style**            |                       |                         |
|                    | **strategy**          |                         |
+--------------------+-----------------------+-------------------------+
| ## Direct          | Single process        | Single endpoint         |
+--------------------+-----------------------+-------------------------+
| **Iterative**      | Multiple process      | Single endpoint         |
+--------------------+-----------------------+-------------------------+
| **Adaptive**       | Multiple process      | Multiple options        |
+--------------------+-----------------------+-------------------------+

We feel the key to addressing this issue is to improve the set of data
processing algorithms available for use onboard spacecraft. In addition
to the straightforward filters alluded to above, a means for assessing
the quality both of data and of results must be implemented. Such an
assessment function enables said filters to be tuned onboard the
spacecraft in much the same way that the ground-based expert would tweak
parameters in his analysis programs. A suitable framework for
implementing these functions onboard is that of agent oriented
programming.

Intelligent software agents developed in this framework typically
feature a means to perceive their working environment, a means to reason
about and plan interactions with their environment, and a means to
interact with the environment (figure 6). An onboard Science Agent\'s
(SA) environment would be the science computing and data system made
available to it. Its perception function could be model or neural net
based or based on ad hoc rules (e.g. thresholds). The reasoning
component of the SA could be based on fuzzy or other rule systems. In
the near term, the actuation component for SA resides mainly in science
data processing management and the management of its own portion of the
downlink budget. The most direct example would be the triage of science
data into classes for downlink with lossless compression, lossy
compression, or only after onboard reduction. For the more advanced
missions that we are moving towards in the out years, the SA\'s domain
will grow to involve more of science data operations, to instrument
operations, until finally the SA plays a central role in system
operations.

[4. Onboard Computing Research]{.smallcaps}

The Remote Exploration and Experimentation (REE) project of NASA\'s High
Performance Computing and Communications project sought ways to adapt
commercial-off-the-shelf computing technologies to the rigors of space
flight. Reasons for this approach include cost and the dramatic
enhancement in computing power obtained (table 4). Technologies studied
included the use of commodity processors for the high performance and
low power consumption and software implemented fault tolerance (SIFT)
\[18\]. With the use of commodity parts in the space environment, faults
will occur at a much greater rate than with traditional radiation
hardened space qualified equipment. However, in many cases these faults
do not affect the operation of the system or application software
\[19\]. Some faults may be detected, screened, or even corrected by
hardware or SIFT techniques. Some of these techniques date from the time
when supercomputers started to consist of such large numbers of
components that transient faults and failures became an expected part of
normal operation.

REE\'s Solar Terrestrial Probe (REE/STP) Science Application Team
researched the benefits that fault-tolerant high performance onboard
computation might provide to space science missions. To this end, and to
produce computer codes that require intensive computation for testing
REE testbed computers, REE/STP developed two applications that modeled
certain space science data reductions: (1) cross correlations for Radio
Astronomical

Table 4 Radiation Hardened vs. Commercial

+--------------+---------------+-----------+------------+------------+
| ### CPU      | ## CLOCK      | **MIPS**  | **POWER**  | **YEAR**   |
|              |               |           |            |            |
|              |               | **MOPS**  |            |            |
+==============+===============+===========+============+============+
| # RAD6000    | 33 MHz        | 35        | \~35 W     | '97        |
+--------------+---------------+-----------+------------+------------+
| RAD750       | 150 MHz       | 300       | \~35 W     | '02        |
+--------------+---------------+-----------+------------+------------+
| PowerPC      | 200 MHz       | 300       | \~13 W     | '97        |
|              |               |           |            |            |
| 604          |               |           |            |            |
+--------------+---------------+-----------+------------+------------+
| PowerPC      | 400 MHz       | 2000      | \~16 W     | '99        |
|              |               |           |            |            |
| 7400         |               |           |            |            |
+--------------+---------------+-----------+------------+------------+
| IBM          | 1000 MHz      | \~2300    | \~ 5 W     | '02        |
|              |               |           |            |            |
| PowerPC      |               |           |            |            |
+--------------+---------------+-----------+------------+------------+

Imaging (RAI) and (2) Plasma Moment Analysis (PMA). These calculations
performed only the most intensive part of the data reductions modeled,
and therefore neglected crucial steps required to construct real results
from real data.

To improve the technological readiness of these applications and study
their applicability to space science missions, REE/STP started to
increase the realism of these applications, starting with the PMA
application (figure 7). *Hawkeye* (1974-1978) data was obtained and
*Hawkeye*/LEPEDEA data was adapted for the PMA for use as a manageable
but extensive data set. We used this data set as a basis for our
research into automated data analysis. We split the full task of
autonomous onboard science reduction into three levels (figures 8-10).
The first level consists of relatively straightforward data processing
using filters and some iterative calibration steps to produce summary
data, e.g. plasma moments. On the second level exist those functions
that require reduced data onboard: these functions include pattern and
feature recognition or trend analysis to spot science events such as a
spacecraft's passage through a density cavity or magnetic reconnection
layer. On the third level exist those functions that make use of the
summary data and feature identifications of the other two levels. This
level contains the rules and reasoning facility that determines how
science data is to be managed.

![](media/image10.png){width="6.768110236220473in"
height="3.5181102362204726in"}![](media/image11.png){width="7.195275590551181in"
height="0.9216535433070866in"}![](media/image12.png){width="6.966535433070866in"
height="0.2846456692913386in"}*Hawkeye*/LEPEDEA (figure 8 with results
shown in figure 7). PMA also fits a simple Gaussian plasma distribution
function to the measurements in preparation for the application of other
corrections to the data. The results of the PMA were verified by
comparison with an independent moment analysis method, and experiments
using many processors (25 nodes of a cluster of workstations) were
performed. Remaining to be implemented are the iterative calibrations
that involve corrections such as patching holes, correcting for
spacecraft potential effects and removing Sun-pulses in plasma particle
sensor data. Also of interest was the extension of this work to EM field
data.

The second level of automation consisting of functions that use the
results of the first level, typically associated with either process
result assessment or other perception functions (figure 9 with
preliminary results shown in figure 7). For this level, REE/STP has
developed a simple ad hoc Geospace region identifier that uses plasma
moments. This region identifier straightforwardly discriminates between
solar wind, magnetosheath, and magnetospheric regions. Also implemented
was a function that identifies in plasma moments the signatures of
magnetic reconnection, a critical energy release process in Geospace and
other plasmas. Research into other perception-like functions, for
example, to identify features in 3D plasma phase space data, was being
pursued.

The final level of automation which contains the rules and the reasoning
facility that actually implements science data management functions,
including triage of science data according to data importance or mission
relevance, is currently being designed (figure 10).

*After REE: A flight demonstration*

The REE project was cancelled along with the High Performance Computing
and Communications program in early 2001. Building on what was learned
from REE, we are currently working with researchers from JPL to put
together a flight experiment to demonstrate the use of high performance
COTS computers in a flight environment operating on space science data.
The objective of this experiment is to show that COTS computers
augmented with SIFT and other REE adaptations can provide exceptional
computing power for scientific computation onboard spacecraft.

For example, we studied the benefits that an REE enabled processor might
have for the MMS mission \[20\]. We found that a High Performance
Computing System (HPCS) onboard MMS would allow the MMS mission to
continuously monitor burst mode resolution data for science events and
kinetic-scale features. Recalling the three levels of automation
functions outlined above, MMS science return could be enhanced as
follows. It could (1) return to Earth high quality plasma moments with
burst mode time resolution along the entire orbit, (2) identify
important regions or science events, and (3) choose between lossless or
lossy compression or reduction to statistics to capture the most
important data in the highest fidelity format possible.

If successful, this follow-on flight demonstration will provide the
technological trail blazed by REE with flight heritage, and we will have
a better understanding of how these COTS systems perform when deployed
on orbit. The space science applications we have discussed here are only
a fraction of the potential applications of high performance computing
in space.

[5. Summary]{.smallcaps}

In this paper we have discussed how the fundamentals of space science
are driving the field towards challenging multi spacecraft missions. A
progression of missions was outlined, and features of each were
discussed. Taken as a whole, these missions point towards pressing needs
for advances in automation and the reliable extraction of information
from space science sensors. Geospace is a vast region of space and the
instruments required to characterize its nature produce a relatively
large amount of data to telemeter to Earth. Spacecraft can physically
transport that data to Earth's vicinity for downlink, but this process
cannot provide real-time data to Earth. Real-time data is important for
some space science and Space Weather tasks. Uplink availability can also
pose problems, because we are moving toward missions where the
spacecraft must operate without ground contact for longer and longer
periods of time.

To take the measure of Geospace, monitor interplanetary space, and
survey the lands of the asteroids requires progressively more capable
missions involving increasing numbers of increasingly autonomous
spacecraft. We will have to move beyond manually issuing commands to
individual spacecraft, but will instead have to institute collective
behaviors that achieve mission goals. From this viewpoint, onboard
science software is a low-level fundamental component that resembles
onboard software already providing autonomous functions in existing
spacecraft operations. For example, an autonomous attitude control
system must monitor, say, star sensor data, and react to perceived
sensor faults or true attitude control faults. Similarly, to enable
these large-scale multi spacecraft missions, onboard data processing,
data assessment, and the automatic diagnosis and reconfiguration of data
processing must be enabled. For our work, we see these functions
enabling the perception, reasoning, and actuation components of
autonomous goal-seeking science mission agents.

[6. Acknowledgements]{.smallcaps}

This work was carried out with support from the NASA's High Performance
Computing and Communications Program's Remote Exploration and
Experimentation Project, which is managed by R. Ferraro of the Jet
Propulsion Laboratory of the California Institute of Technology. Support
from NASA/GSFC Code 588, Advanced Architectures and Automation Branch
and collaboration with W. Truszkowski is acknowledged.

[7. References]{.smallcaps}

\[1\] Pfaff, R. F., J. E. Borovsky, D. T. Young, Eds., "Measurement
Techniques in Space Plasmas: Particles", American Geophysical Union,
Geophysical Monograph 102, 1998. Pfaff, R. F., J. E. Borovsky, D. T.
Young, Eds., "Measurement Techniques in Space Plasmas: Fields", American
Geophysical Union, Geophysical Monograph 103, 1998.

\[2\] Bougeret, J.-L., M. L. Kaiser, P.J. Kellogg, et al., "WAVES: The
Radio and Plasma Wave Investigation on the WIND Spacecraft", Space Sci.
Rev., 71, 5, 1995

\[3\] Reinisch, B. W., et al, "The Radio Plasma Imager Investigation on
the IMAGE Spacecraft", Space Science Reviews, 91: 319-359, 2000.

\[4\] Angelopoulos, V. and Panetta, eds., P. V., "Science Closure and
Enabling Technologies for Constellation Class Missions", UC Berkeley,
CA, 1998; in particular R. E. Ergun, et al., "Magnetospheric
Constellation and Tomography Mission Concept", pp. 29-35, therein.

\[5\] Song, P., H. J. Singer, and G. L. Siscoe, "Space Weather",
American Geophysical Union, Geophysical Monograph 125, 2001.

\[6\] For example, see the web page of the Space Environment Center
which is jointly operated by the National Oceanic and Atmospheric
Administration and the U.S. Air Force which lists at least nine U.S. and
international partners.

\[7\] For an example of predictions note [http://www.sec .gov/rpc/msm]()
featuring the Magnetospheric Specification Model developed at the Space
Physics and Astronomy Department at Rice University. An example of a
launch postponement due to Space Weather concerns is the recent delay of
*Kodiak Star* on 21 September 2001 (KSC Release No. 106-1).

\[8\] NASA Sun-Earth Connections, web: "http://sec.gsfc .nasa.gov".

\[9\] Burke, W. R., Cluster: mission, payload, and supporting
activities, ESA SP-371, 1993.

\[10\] Rème, H., et al, "First Multispacecraft Ion Measurements in and
near the Earth's Magnetosphere with Identical Cluster Ion Spectrometry
(CIS) Experiment", Annales Geophysique, 2001; Rème, H. R., et al, "The
Cluster Ion Spectrometry (CIS) Experiment", Space Science Reviews 79:
303--350, 1997.

\[11\] Burch, J. L., "Image Mission Overview", Space Science Reviews 91:
1-14, 2000.

\[12\] Moore, T. E., et al, "The Low-Energy Neutral Atom Imager for
IMAGE", Space Science Reviews 91: 155-195, 2000.

\[13\] Johnson, M. A., M. L. Rilee, and W. Truszkowski, "Using
Model-Based Reasoning for Autonomous Instrument Operation", in the
Proceedings of The 2001 IEEE Aerospace Conference, Big Sky, MT.

\[14\] Burch, J. L., et al, "The Magnetospheric Multi-scale

Mission\... resolving fundamental processes in space plasmas," Tech.
Rep. NASA/TM-2000-209883, NASA Goddard Space Flight Center, December
1999.

\[15\] Moore, T. E. et al, "The Magnetospheric Constellation Mission
Dynamic Response And Coupling Observatory (DRACO): Understanding the
Global Dynamics of the Structured Magnetotail," NASA/TM ---2001
--209985, Report of the NASA Science and Technology Definition Team for
the Magnetospheric Constellation Mission DRACO, 2001.

\[16\] SIRA Solar Imaging Radio Array \-- Michael L. Kaiser,
NASA/Goddard Space Flight Center, web: "[http://
www-lep.gsfc.nasa.gov/sira/sira.html](http://www-lep.gsfc.nasa.gov/sira/sira.html)".

\[17\] Curtis, S., et al., "Autonomous Nano Technology Swarm", in The
51st International Astronautical Congress, October 2000;
"http://ants.gsfc.nasa.gov".

\[18\] Turmon, M., R. Granat, and D. S. Katz, \"Software Fault Tolerance
for High-Performance Space Applications\", International Conference on
Dependable Systems and Networks (FTCS-30 and DCCA-8), New York, New
York, June 2000.

\[19\] Rilee, M. L., S. A. Boardsen, M. K. Bhat, and S. A. Curtis, "The
Effect of Faults on Plasma Particle Detector Data Reduction", in the
Proceedings of the 2001 IEEE Aerospace Conference, Big Sky, MT.

\[20\] Curtis, S., et al., "Small satellite constellation autonomy via
onboard supercomputers and artificial intelligence", in The 51st
International Astronautical Congress, October 2000.

![](media/image13.jpeg){width="1.2027777777777777in"
height="1.4659722222222222in"}**Michael L. Rilee** is a scientist with
Emergent IT supporting the GSFC Laboratory for Extraterrestrial Physics
and the Science Computing Branch of the GSFC Earth and Space Science
Computing Division. For NASA's Remote Exploration and Experimentation
project he has lead development of the Plasma Moment Application and the
Radio Astronomical Imager which are science data analysis applications
designed for space borne supercomputers. He is currently researching a
High Performance Computing System that may fly on Magnetospheric Multi
Scale (launch 2007). At GSFC he has been active in Nano-Satellite
technology development and the application of parallel computing to data
analysis and astrophysical fluid simulation (PARAMESH). He received his
Ph.D. and M.S. in Astrophysics (Plasma Physics) from Cornell University,
and his B.A. in Astrophysics and Mathematics from the University of
Virginia in Charlottesville, VA.

![](media/image14.jpeg){width="1.229861111111111in"
height="1.5708333333333333in"}**Scott A. Boardsen** is a scientist with
Emergent IT at GSFC and has fifteen years of experience in Geospace
research involving data analysis and modeling and in space mission
support activities. He is currently in charge of the daily pointing
plans for the imagers on the POLAR spacecraft. He is in charge of
developing the simulation model of the radar echoes and plasma-grams for
the Radio Plasma Imager on board IMAGE (launched 2000). He plays a key
role in developing ISTP key parameter and orbit products and in making
them accessible to the Geospace research community. He has a strong
background in numerical analysis, computer modeling, and data
visualization applied to many areas of endeavor, from data analysis, to
in-strument modelling, to mission planning. He currently contributes to
NASA's Remote Exploration and Experimentation Program.

![](media/image15.jpeg){width="1.1701388888888888in"
height="1.5in"}**Maharaj K. Bhat** is a computational scientist with
Emergent IT supporting the GSFC Laboratory for Extraterrestrial Physics
and the Science Computing Branch of the GSFC Earth and Space Science
Computing Division. He has extensive experience in computational fluid
dynamics, parallel algorithm implementation, software development, and
experimental techniques. He received his Ph.D. in Aerospace Engineering
from the University of Tennessee Space Institute, Tennessee in 1988.

![](media/image16.jpeg){width="1.1708333333333334in"
height="1.5in"}**Steven A. Curtis** has been the Head of the Planetary
Magnetospheres Branch in the Laboratory for Ex-traterrestrial Physics at
Goddard Space Flight Center for the past decade. He was a Principle
Investigator in NASA's Space Physics Theory Program (Global
Magnetospheric Simulations) and in NASA's Remote Exploration and
Experimentation Program (advanced spacecraft onboard computing). He is
presently Project Scientist for the 4-5 spacecraft Magnetospheric Multi
Scale in which he has played a leading role in conceptualizing the
science and spacecraft design implementation. His research interests
include the global and mesoscale structure of the magnetosphere and its
simulation, atmosphere-magnetosphere interactions, and wave-particle
interactions in Geospace. He is particularly interested in the
multiscale closure needed between theory and observations. He received
his Ph.D., M.S., and B.S. in Physics from the University of Maryland.

[^1]: U.S. Government work not protected by U.S. copyright.