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Lessons Learnt From N3SS GNC Operations
Get PDFN3SS is a triple Cubesat launched the 9th of October 2023. This in-orbit demonstrator embeds a miniaturized Software Developed Radio-Frequency payload that measures signals received in L and S bands. CNES, the French space agency, has been developing this satellite with the support of U-Space, a French company provider of next-generation nanosatellites. After about two weeks of commissioning, the spacecraft started its mission, which has been ongoing for six months now.
This article is focusing on the lessons learnt from the design, validation and commissioning of N3SS Guidance, Navigation and Control (GNC) system.
The GNC of N3SS includes attitude and orbit determination, on-board autonomous attitude guidance and a three axis stabilized control system that were described in a previous paper presented in 4S conference in 2022. This new paper now focuses on the robustness of the control design, especially against space environment (solar activity mainly). In particular, it is shown that the spacecraft attitude control is able to cope with a much higher level of solar activity than the satellite was expected to encounter during its mission at the time of design.
Second, this paper describes the impact of magnetic perturbation on N3SS GNC, and actions taken to mitigate it. In fact, a major lesson learnt from the previous cubesat launched by CNES (Eyesat) is that magnetization could be a major perturbation to satellite pointing and stability.
Several steps, from ground to commissioning, were performed to ensure the best pointing performance for the satellite, such as: Measurement of the residual magnetic moment of the complete satellite (on-ground, CNES facility) Demagnetization of the satellite (on-ground, CNES facility) Magnetometers calibration (on-ground, CNES facility) Magnetometers calibration (commissioning, in orbit)
The calibration algorithm, based on a non-linear least square algorithm (Gauss-Newton) is described in this article, as well as the in-orbit pointing performance gain from the calibration.
This article will also focus on a method to manage a cluster of reaction wheel during satellite lifetime to increase the reaction wheels lifetime in orbit, using the degree of freedom given by a cluster of four reaction wheels.
Finally, this document highlights how the design, validation and commissioning of the GNC N3SS were made possible with few human resources, making maximum use of CNES\u27s assets and experience
Machine Learning Models for Optimisation of Satellite Laser Communication Terminals and Optical Space Networks
Get PDFLaser communication systems in optical SatCom systems are mainly used in point-to-point networks, possibly deploying standard routing solutions which perform sub-optimally when deployed in transport and access satellite networks. Optical signal acquisition is also affected by the changing satellite environment and atmospheric conditions.
In response, Craft Prospect and partners focused on upgrading optical systems by using machine learning (ML) methods to improve laser communication terminals and to simplify interconnectivity of networked laser SatCom systems. In this context we target the European OPS-SAT Versatile Optical Laboratory for Telecoms (VOLT) mission led by Craft Prospect and the European High Throughput Optical Network (HydRON) project which benefits from using machine learning systems.
Based on initial research and development work on enhancement of satellite free space laser communication systems with machine learning, two use cases and their requirements were identified. Firstly, leveraging autonomic networking principles to develop distributed ML agents for space network nodes which monitor local network state and can autonomously make rerouting decisions on impaired links in a localised way to improve the average network throughput. This enables links between optical ground stations (OGS) and the space network segments to quickly switch in a smooth and responsive way without having to have multiple paths open; searches carried out for preset alternate paths in long/overloaded flow table lists; or an over-reliance on a software defined networking (SDN) controller. Secondly, for space segment laser terminals, development of a ML model to boost detection accuracy in coarse acquisition in reduced signal-to-noise ratio (SNR) cases with strong background light conditions and varying beacon intensity. This is relevant for the scenario where a LEO satellite receives an uplink signal from OGS with strong atmospheric fluctuations or background Earth reflections and the detector is saturated. Similarly, in inter-satellite links (ISL) where satellites are undergoing relative movement and the signal intensity rate of change is rapid, the acquisition sensor quickly arrives in a region where SNR is low, or the sensor is saturated.
In this contribution, we present the developed ML models for these use cases and describe training techniques and datasets. We discuss performance results from tests carried out with a network simulator with the results compared to an SDN controller solution to demonstrate the benefits. For small network topologies, the ML solution resulted in the throughput at the network endpoint being 16.7% higher following link degradation than the SDN controller solution. The spatial acquisition detection errors and accuracy under different SNR conditions using the ML solution was compared to a benchmark Centre of Gravity detection method typically used in laser terminal beacon acquisition. Results showed that the beacon detections from the ML solution were closer in distance to the true spot than the conventional method in low SNR conditions by over an order of magnitude, in addition to having an overall accuracy of 97.77%.
With this we show how to enable autonomic routing in optical data, and laser terminals with improved acquisition rates in networks for OPS-SAT VOLT and for multitudes of future interconnected laser SatCom missions
SEE and MiniCOR: Transforming Solar Science Through Modular Small Satellite Technology
Get PDFIn recent years, the field of space exploration has witnessed a paradigm shift with the emergence of small satellites, particularly CubeSats, as cost-effective, time-efficient and versatile platforms for scientific missions. At the same time, heliophysical studies are becoming increasingly important for space weather purposes, as they shed light on the Sun\u27s interaction with the Earth, the solar system and the interstellar medium. This work delves into the study of the Sun activities through two complementary CubeSat missions, focusing on how these similar and modular platforms will face the mission\u27s technical challenges while enhancing the efficiency and effectiveness of key solar studies.
The first mission is Sun cubE onE (SEE): a pioneering project whose primary objective is to study solar flares and their impact on space weather. Employing a compact 12U design, SEE carries a suite of instruments dedicated to capturing full-disk UV images of the Sun and analyzing its Gamma and X-rays emissions. The integration of automation in this mission is exemplified by the autonomous solar flare detection, enabling SEE to dynamically adjust its acquisition cadence based on the presence of energetic solar events. This autonomous capability not only optimizes data collection, but also minimizes the need for ground-based intervention, allowing SEE to operate more independently in the challenging space environment. The SEE mission is currently under development by a consortium of academic and industrial partners led by University of Rome Tor Vergata. Argotec is responsible for the development of the platform. The mission is part of the ALCOR program, funded and supported by the Italian Space Agency (ASI).
Similarly, the Miniature Coronagraph (MiniCOR) embarks on a solar research mission to study Coronal Mass Ejections (CMEs) and their impact on space weather. The spacecraft\u27s payload consists of a state-of-the-art deployable solar occulter and a compact coronagraph telescope, all in a 12U platform. The mission is funded by NASA H-FORT and led by Johns Hopkins University Applied Physics Laboratory (JHU/APL). This mission is a prime candidate for sharing the same CubeSat platform and subsystems as SEE, where MiniCOR aims to emphasize the importance of automation in enhancing the overall performance. The payload could leverage the algorithms developed for the ArgoMoon and LICIACube missions to autonomously analyze and prioritize data, facilitating the quick identification of CME-related phenomena. This autonomous decision-making process would significantly reduce the latency in data transmission, ensuring timely delivery of critical information to Earth-based scientists.
These solar study CubeSat missions exemplify the transformative impact of incorporating automation into small satellite systems to streamline mission operations and elevate the scientific potential of these compact platforms. By addressing the challenges posed by limited resources and communication bandwidth, SEE and MiniCOR serve as valuable testbeds, paving the way for smarter, more efficient, and more effective small satellite systems that will enhance our understanding of the solar system\u27s dynamic phenomena
SmallSat End-of-Life Operations: Opportunities and Challenges
Get PDFCompact Total Irradiance Monitor (CTIM) is a 6U cubesat that deployed in July of 2022 to continue forty years of continuous total solar irradiance measurements across multiple missions. In November of 2023, CTIM’s orbit began showing signs of accelerated decay in the form of inconsistent pass start times and noisier S-band downlinks, despite intensive orbital decay analysis. This unexpected decay, along with CTIM’s lack of GPS, presented the Smallsat Operations (SMOPS) team at the University of Colorado’s Laboratory for Atmospheric and Space Physics (CU-LASP) with a big challenge: rapidly revise CONOPs, all while remaining at or under a 1.5k weekly hiatus budget. This also presented big opportunities: it provided valuable experience the team could leverage for end-of-life operations for future missions, and a chance to give a successful mission a heartfelt farewell. Once CTIM’s decay was confirmed, the SMOPS team’s main goal was to perform end-of-life calibrations and downlink remaining scientific data onboard. The approaching deadline of re-entry allowed operators to prioritize short term science over any potential hardware degradation. Due to CTIM’s highly unpredictable orbital decay, Celestrak’s daily official TLE publications became ineffective within hours. This prompted ground station operations (GSOPS) engineers to generate in-house Two Line Element (TLE) sets in real-time based on the doppler shifted downlink signals, which were cross-verified with both the official Celestrak TLE source as well as the SatNOGS amateur radio community’s observations. This in turn necessitated a shift in the cadence of the ground station contact schedule creation process from from once a week to once every two days, demonstrating the highly flexible and reactive paradigm of the GSOPS team and automation software. As atmospheric drag continued to increase on CTIM, the team observed insurmountable momentum build-up on the spacecraft as the torque rods struggled to overcome the increased effects of drag, leading to attitude excursions, safe mode transitions, and even undervoltage events. Finally, to celebrate years of development and nearly 18 months of successful operations, the SMOPS team “sung” a farewell song to CTIM, which the satellite then “echoed” back to the rest of the world (via uplink and downlink commands). This “song”, transmitted over the amateur radio waves, was captured and decoded by the enthusiastic SatNOGS community, demonstrating the effectiveness of public outreach and collaboration on small satellites like CTIM. The final weeks of a mission are challenging, but they also provide numerous opportunities: to implement new processes that ensure mission success, to discover new ways of assessing rapid orbital decay, and for a dedicated team to give a tiny mission with a big legacy a proper sendoff
Manufacturing in Microgravity and Sample Re-Entry Using a Commercial Rideshare Spacecraft Platform
Get PDFIn 2021, Varda Space Industries contracted Rocket Lab USA to design, build, and operate four spacecraft to support an in-space manufacturing payloads inside of a commercial capsule and provide the capsule with a safe return to Earth. This mission was to be the first fully commercial controlled Earth re-entry and recovery of a space-based capsule. Four spacecraft, Winnebago-1 through -4, are to be built and launched on SpaceX Falcon-9 Transporter rideshare launches into sun-synchronous low Earth orbit. Each of these satellites feature Varda’s re-entry payload capsule which will demonstrate varied types of in-space capability from pharmaceutical crystal growth to use as a hypersonic test bed, across the separate missions. Once the on-orbit mission is complete, the Rocket Lab platform places the Varda re-entry capsule into an Earth return trajectory, allowing the capsule to touch down at various global landing sites such as the Utah Test and Training Range (UTTR).
The Pioneer spacecraft for the Winnebago missions is 300kg (wet mass) and 1.2m x 1.2m x 1.8m (arrays stowed, launch configuration), based off the Rocket Lab Pioneer-class spacecraft bus architecture that features a full suite of Rocket Lab produced satellite components. Rocket Lab’s strategy of maximizing vertical integration and ownership of supply chain of mission and program-critical vehicle components (including reaction wheels, star trackers, power and compute systems, radios, propulsion systems, separation systems and software) enables entirely new in-space application business models, which depend on the aggressive schedule, low cost, and high reliability Rocket Lab can offer.
Winnebago-1, launched in June 2023 on Transporter-8, manufactured a sample of the pharmaceutical crystal ritonavir, an HIV retroviral active pharmaceutical ingredient, inside the Varda re-entry capsule. This experiment hypothesized the discovery of a new form of ritonavir in microgravity that could minimize ingredient-induced side effects due to improved crystal structure. De-orbit operations were successful for Winnebago-1 in February 2024, landing the Winnebago-1 capsule in the UTTR for recovery by the Varda team. Post-landing study of the sample found the preferred structure of ritonavir was synthesized, paving the way for additional experimentation on the sample. The following three spacecraft under contract will launch on subsequent Transporter launches, each demonstrating a unique in-space capability.
The Rocket Lab Winnebago propulsion system is chemical bipropellant-driven and rideshare launch-compatible, unlike most chemical propulsion systems. The Curie Mk 3 thruster with significant flight heritage on Electron kick stage, coupled with a custom fluidics design, and extensive expertise in flight dynamics and trajectory design, enabled execution of multiple complex orbital maneuvers to deliver the capsule to a land touchdown within an approximately 60km x 30km landing zone within the UTTR. The Winnebago mission archetype will transform commercial in-space manufacturing, enable return-to Earth, and re-invent rideshare platform design
Maximizing Autonomy on a Small Satellite Platform
Get PDFOHB Sweden\u27s future generation InnoSat microsatellite platforms will see a large upgrade in the platform\u27s functionality maximizing onboard autonomy. In this paper, three development lines will be presented each contributing to maximizing efficiency and autonomy on a small satellite platform.
The InnoSat platform is a flexible platform concept with a recurring catalog of units, recurrent onboard software and mission control software, and heritage AIT processes. Two InnoSat missions are currently flying: Swedish National Space Agency\u27s MATS and the commercial bring-into-use satellite GMS-T. Two more InnoSat derived missions will be launched in 2024: ESA\u27s Arctic Weather Satellite and Satlantis\u27 Garai-A. In 2025, Garai-B and Space Norway\u27s ADIS are expected to be launched. Several other InnoSats, such as ESA\u27s EIS IOD and ESA\u27s Aurora-D, are in phases C and B respectively, in addition to multiple phase 0/A studies.
The three autonomy development lines are focusing on satellite control (ASK), constellation control (OPTACOM), and onboard agility (OPCMG).
The Swedish National Space Agency funded study Autonomous Station Keeping (ASK) adapts the existing ground-based InnoSat mission analysis and flight dynamics tools for autonomous use onboard. A Model Predictive Control scheme is in development and will be implemented in the InnoSat AOCS software and tested in the InnoSat Satellite Simulator. The expected Eumetsat EPS-Sterna constellation of Arctic Weather Satellites is a candidate for first flight of the ASK algorithm.
The ESA funded OPTimized Autonomous Constellation Orbit Management (OPTACOM) study is a cooperation between OHB Sweden, OHB System, DLR, and Luleå Technical University (LTU). OHB Sweden provides the use case, a large constellation based on the InnoSat platform, with three test scenarios: constellation initialization, station-keeping, and constellation reconfiguration. Collision avoidance is treated as a constraint in all three scenarios. Both embedded real-time optimization and machine learning will be explored and compared with a benchmark feedback control solution. Two simulators will be developed, one for algorithm development and training, and one high-fidelity simulator for final verification with hardware characterization.
OPCMG is an ESA funded study on autonomous and optimized agile attitude control with CMGs for small satellite platform. The study is conducted by OHB Sweden and is performed in cooperation with DLR and deals with the onboard implementation of autonomous CMG guidance and control. Within the study, the use of embedded onboard optimization is compared with adaptive control techniques to solve the combined problem of optimal slew motion, and CMG guidance and control to ensure long-term efficient commandability under varying observation conditions
Feathered Cocaine: The Misleading Collective Memory of Operation Falcon
No full textOperation Falcon (OpFalco) was a seven-year sting that upended the falconry community as numerous cases exposed falcon poaching amidst the sport. At the beginning of OpFalco, the United States Fish and Wildlife Service (FWS) recruited falconer Jeffrey McPartlin as an undercover agent. With McPartlin’s assistance, the FWS compiled hundreds of case files against falconers. By the operation’s culmination in 1984, the FWS had seized hundreds of illegal falcons and indicted 79 people for crimes against wildlife. The federal government ultimately convicted 68 of them, six were acquitted, and seven remain at large to this day after going into hiding.
Although a close, objective investigation of the convictions confirms that crimes against wildlife were committed, the North American Falconers’ Association (NAFA) successfully covered up the sting and created a narrative that framed the government’s actions as an illegitimate attack on falconers. Despite the many convictions, historians have not written about the criminal acts discovered by OpFalco. The written record of OpFalco is one of a bitter battle between the FWS and falconers. Tales of conspiracies fill the pages of pro-falconer writings about OpFalco, while the crimes against the precious falcon resource are erased. OpFalco illuminates the significant impact of the rigorous wildlife management reforms stemming from environmental laws enacted in the 1970s. It provides a critical perspective for analyzing how legislation, conservation efforts, and hunting practices collided and influenced one another. OpFalco, both the operation and its legacy, reflects an era when the FWS chose to test new wildlife laws and dove into a sting, the magnitude of which the agency has never attempted since. The reaction of those targeted by the operation is an exceptional look into the mindset of hunters accustomed to an unregulated continent of bounty as they faced tightening hunting regulations in America during the last quarter of the twentieth centur
Advancing Quantitative Approaches for Estimating Avian Population Responses to Environmental Change Using a Data-Rich Species: The American White Pelican
Get PDFAdvancements in wildlife data collection technology and analysis are helping us understand how human-caused environmental change is impacting bird species. Yet data collection for many species remains challenging, and often the data are difficult to analyze. Improved methods for collecting and analyzing avian data are needed to understand how species respond to environmental change. However, before applying new methods to poorly understood species, it is crucial to test methods on well-studied species to ensure their effectiveness. The American White Pelican is a well-studied species that is ideal for testing new analysis methods. Pelicans have been studied extensively due to conservation concerns and conflicts with humans. However, gaps still exist in our understanding of pelican survival and migratory destinations, threats, and reactions to environmental change.
My dissertation used pelican data to improve methods that estimate avian survival, identify environments bird species use, and measure how likely individuals are to migrate between regions. In Chapter 2, I developed a mathematical model to estimate how many pelicans migrate between North American regions and their resulting survival probabilities. I found that pelicans often remain in the same region year-long, with substantial variation in survival depending on location. In Chapter 3, I measured environmental conditions favored by pelicans and how this varied between individuals. I found that pelicans do not rely on specific conditions as a population, and that individual use varies substantially, suggesting population resilience to environmental change. Chapter 4 investigated the feasibility of extracting radar signatures of flying birds from weather radar using location data from GPS-tagged pelicans. Using this radar signature, I predicted locations of untagged pelicans across my study area and developed a pelican-airplane collision risk index for a local airport. In Chapter 5, I used a mathematical model to estimate how environmental conditions affected pelican colony abundance, then estimated future abundance under various management scenarios. I found that land bridge formation between the colony and mainland is a likely cause of abundance declines. My research offers improved analytical methods for avian populations, and highlights that birds may respond to environmental change differently depending on the landscape and population scales examined
Global Space-Based Inter-Calibration System: An Operational Satellite Monitoring Framework
Get PDFThe Global Space-Based Inter-calibration System (GSICS) is a sixteen consortium of satellite operating agencies that have come together to monitor in-orbit satellite sensors by comparing them with stable references.
GSICS has developed and deployed algorithms that can leverage the stability of instruments such as CrIS, VIIRS, ATMS, and IASI by multiple methods. These include Simultaneous Nadir Overpass (SNO), Ray-Matching, and Indirect Double difference comparisons to monitor in-orbit geostationary satellites, including the GOES, Meteosat, FY-2/4, Himawari, and COMS geostationary series. GSICS contributors provide over 76 bias monitoring products for operational satellites. These are disseminated through its Product Catalog (Product Catalog). Each year GSICS provides a summary of operational satellite performance to the Coordination Group for Meteorological Satellites and member agencies.
Lunar calibration is one of the GSICS methods for calibrating visible and near-infrared (Vis/NIR) wavelength sensors in orbit, leveraging the stability of the Moon’s surface reflectance. A recent GSICS lunar calibration workshop included a focus session on improving the models that provide lunar radiometric reference.
Recently, GSICS has also begun handling inter-calibration of space-based space weather sensors. Most of these sensors are in-situ measurement, and this represents a new challenge.
This presentation’s goal is to introduce the range of GSICS methods and their applications in operational satellite sensor monitoring and measurement bias removal and to summarize satellite performance over past years
A Data-Efficient Model-Based Task Decomposition Approach for Massive Satellite Constellations
Get PDFThe rapid development and launch of low-cost satellites have led to constellations of hundreds of satellites, the proliferated Low Earth Orbit (p-LEO) constellation, and has changed the dynamic of space-based earth observation. The availability of massive numbers of satellites creates the opportunity to tackle larger scale problems, such as larger scale monitoring of wildlife, illegal fishing, and climate events. These opportunities must be met with increasing automation, preferably using an approach that executes and adapts on-orbit.
Optimizing distributed resources remains an astronomically challenging problem, even more so when computations are performed on the comparatively compute restricted hardware of satellites. We propose a data-efficient two-part system, modeled after the Actor-Critic architecture, that models the dynamics of p-LEO constellations and other data streams and generates optimized tasking. First, using the Koopman operator theory approach, we model an aggregate representation of a heterogenous (multi-modal sensing) satellite constellation to predict satellite availability, observation capabilities, and resource utilization. An aggregate representation of the constellation enables scalability to model hundreds to thousands of satellites, as well as being agnostic to particular identities (satellites may enter and leave the constellation). Second, we use the Hierarchical Bayesian Program Learning (HBPL) paradigm to formulate and learn a task decomposition and generation ‘program’. Tasks are constructed and defined probabilistically while guided by expert informed structure and bounds, enabling efficient search and optimization of the task space. During execution, the HBPL component proposes sets of tasks (serving as the ‘actor’) which are scored by the Koopman model. The two methods described above are notable due to their low data requirements and speed of model training or updating, making them a stellar pairing for on-orbit applications.
This data-driven learning approach to task generation was explored to solve the task decomposition problem for the BAE Systems Collective Space Tasking and Assimilation Reasoning System (CoSTARS) under the DARPA Oversight program. The full architecture includes a distributed auction mechanism for task assignment, a data assimilation component, and updates on entities or objectives. The task decomposition approach is evaluated under this system architecture for the case of monitoring a set number of entities of interest