Publications

This paper sets the foundation for a roadmap aimed at improving the efficiency of designing complex systems by leveraging generative AI. Our focus is on integrating generative AI, specifically LLMs, with semantic system models and computation engines to improve system design capabilities. We introduce a prototype assistant tool for the design of space systems, validate it and demonstrate an improvement in the efficiency of the design process with a comprehensive case study, that includes the design of Earth Observation missions benchmarked to the European Sentinel infrastructure and the EventSat mission we are developing. The tool generates, among others, typical design artifacts such as System Requirements Specifications, Product Breakdown Structures, and Interface Control Documents. We validate it through the cosine similarity of the produced text to the ground truth, a design structure matrix to compare system dependencies and strength and manual evaluation with experts. Validation results align closely with the ground truth. Design time and associated development costs are significantly reduced when using the tool, promising an improvement in efficiency. We contextualize our work in the broader context of developing generative AI for the design of complex systems, by presenting a technology roadmap with our vision of future developments. We discuss the potential of incorporating generative AI into the design of complex systems, the status of technology and the necessary developments. Generative AI integration will enable efficient complexity management, paving the way for innovative advancements in systems engineering.Keywords: generative artificial intelligence, complex adaptive systems, space systems, engineering design

In July 2024, SUNRISE completed its third successful science flight. The SUNRISE III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013, to tackle the most recent science challenges concerning the solar atmosphere. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the SUNRISE UV Spectropolarimeter and Imager (SUSI) and the SUNRISE Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet (309 – 417 nm) and the near-infrared (765 – 855 nm) regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TUMAG) simultaneously obtains maps of the full field-of-view of 46 $times$ 46 Mm2 in the photosphere and the chromosphere in the visible (525 and 517 nm). The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLID), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005”. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. SUNRISE III was launched successfully on 10 July 2024, from the Esrange Space Center of the Swedish Space Corporation near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the SUNRISE III observatory and its instruments.Keywords: Sun: magnetic fields, Sun: photosphere, Sun: chromosphere, Instrumentation: polarimeters, Stratospheric balloons, High angular resolution

The EventSat mission is a technology demonstration mission of a 6U CubeSat by Technical University of Munich to apply event cameras for resident space object detection. Event cameras are asynchronous sensors that detect changes in pixel brightness, providing data updates at a microsecond temporal resolution with low power consumption. We perform an initial analysis of the limiting apparent visual magnitude for the Prophesee EVK4, accounting for parameters like dark current, quantum efficiency, and optic aperture diameter. Initial simulations estimate that the sensor will be able to detect visual magnitudes up to 7.7. Then, we perform a parametric analysis relating the qualities of different resident space objects like their speed, brightness, and size; and the optic qualities of the sensor like its f-number and contrast threshold. This analysis constraints the types of detectable space objects for the planned mission. Finally, we discuss the development of a single-stage baffle for our event camera. Baffles reduce stray light from nearby bodies, which is important for our mission to detect dim objects within its limiting magnitude. We estimate initial baffle sizing, and conduct trade-off studies on vane dimension, placings, and exclusions angles. These results help define the mission's pointing and operational requirements.Keywords: technology demonstration, event cameras, baffle

The design of spacecraft, and more broadly of space missions, can greatly benefit from LLM-based design assistants supporting engineers in the extraction of relevant information from past missions for its reuse in the new one, as well as in the automatic generation of system parameters or requirements in text at different levels. However, due to the mathematical nature of technical systems, many of the spacecraft parameters or requirements are numerical and involve calculations or the application of first principles. While Large Language Models excel at text generation, predicting the next words in a sequence with the highest probability, they are not suited, being statistical engines, to perform deterministic computations. In this paper, we present the architecture of a computation engine to which numerical calculations are outsourced during the process of generating parameters or requirements by an LLM-based design assistant. We present the integration with the LLM, measure its performance, and benchmark and discuss the improvement in parameters generation with a space mission as a use case. In particular, the computation engine hereby developed follows an object-oriented programming paradigm, mimicking through the classes the subsystems of a spacecraft, through the class properties their parameters, and through the class functions the equations and first principles used to calculate the parameters' values, which can be then expressed by the LLM as numerical system parameters or requirements with natural language. A high-level programming language and a modular approach are followed in seek of high readability and modifiability, allowing the engine to be extended and its granularity increased in future iterations. Different alternatives for the integration of the computation engine with the LLM and in the design flow are evaluated and compared, and parameters such as the computation speed and error are measured and assessed along different computation platforms. By presenting this computation engine for spacecraft design, we aim at patching the shortcomings of LLM-based design assistants in numerical calculations, paving the way for their adoption and thus helping accelerate and simplify spacecraft design tasks for a broader range of institutions and individuals.Keywords: spacecraft design assistant, computation engine, numerical system parameters, Large Language Model, Generative AI

Event-based cameras introduce an innovation in the field of imaging by departing from the conventional frame camera paradigm. These bio-inspired sensors capture per-pixel brightness changes asynchronously, generating a continuous stream of events that encode information such as time, pixel location, and polarity alterations. This departure from traditional imaging methods results in distinctive features including high temporal resolution, a high dynamic range, low power consumption, and minimized motion blur. Additionally, event cameras are ideal for motionoriented applications as they remove superfluous information, optimizing data generation and algorithmic performance. Such characteristics position event-based cameras as useful tools in challenging scenarios as in the realm of object observation in orbit. This paper presents a comprehensive mission architecture study for our nanosatellite space mission, called EventSat, that focuses on autonomous object detection, classification, and identification in Low Earth Orbit (LEO). The main objective of the mission is to leverage the unique capabilities of event-based sensors for enhancing Space Situational Awareness and autonomous space operations. The nanosatellite mission aims to demonstrate event-based technology to address the growing challenges of monitoring and understanding objects in space, particularly in the densely populated LEO for the sustained, long-term usage of orbital resources through the technological demonstration of event cameras integrated with onboard AI. The core components of the proposed mission include the integration of event-based cameras with advanced onboard artificial intelligence (AI) systems. This integration holds the promise of significantly advancing autonomous object detection, classification, and identification techniques in space. The ability to exploit the inherent advantages of event-based cameras, such as high temporal resolution and very high dynamic range, can lead to more accurate and timely Space Situational Awareness. The autonomous onboard AI algorithms can empower data sharing between federated spacecraft for automatic maneuvering and collision avoidance, among others. Furthermore, this paper delves into preliminary payload parameters and demonstrates the viability to use an event camera payload as a proof of concept on a 6U platform, selecting an initial range of focal length for further investigations. The presented analysis shows what payload parameters will maximize the product of pixel count and number of objects, considering looking directly at known objects or looking for chance encounters. We propose how the payload could be used in space in accordance to the selected parameters and point out further work that will allow us to refine and select from the identified possibilities. EventSat aims to bridge the gap between cutting-edge sensor technology and practical space applications, ultimately fostering advancements in space exploration and surveillance capabilities, and empowering space autonomy for safer and more optimal space operations.Keywords: Event-based cameras, Nanosatellite mission, Object detection, Space Situational Awareness, Artificial intelligence in space

As Large Language Models become ubiquitous in many sectors and tasks, there is a need to reduce token usage, overcoming challenges such as short context windows, limited output sizes, and costs associated with token intake and generation, especially in API-served LLMs. This work brings the Design Structure Matrix from the engineering design discipline into LLM conversation optimization. Applied to a use case in which the LLM conversation is about the design of a spacecraft and its subsystems, the DSM, with its analysis tools such as clustering and sequencing, demonstrates being an effective tool to organize the conversation, minimizing the number of tokens sent to or retrieved from the LLM at once, as well as grouping chunks that can be allocated to different context windows. Hence, this work broadens the current set of methodologies for token usage optimization and opens new avenues for the integration of engineering design practices into LLMs.Keywords: Large Language Models, token usage optimization, context window, output tokens, Design Structure Matrix

An increasing number of satellite constellations being deployed in orbit fosters the introduction of a more efficient paradigm of distributed, interconnected orbital assets to ensure better resource exploitation in orbit. This paper aims to illustrate through quantitative modeling the effects of satellites on board available resources and inter-satellite telecommunication hypotheses on the overall operations of a federated satellite system network. Key research questions addressed by this work include the performance quantification of a one hundred CubeSats federated satellite system network in terms of network response and data volume exchanged under several onboard resource constraints. The applicability of a federated satellite system to a time-critical disaster response monitoring scenario has also been verified to assess the practical implementation of such a paradigm in reduced inter-satellite communicability conditions. A distributed simulator based on the IEEE 1516-2010 standard is employed to obtain the federated satellite system network topology according to the individual satellite operations to analyze the network performances, exploiting an approach based on the network's adjacency matrix. Despite reduced communicability, which limits the average inter-satellite link to an 8 bps time-averaged goodput due to poor onboard resources availability, the federated satellite system proves to be an auto-sufficient and decentralized paradigm, capable of responding to a time-critical scenario such as a rapid mapping request after a natural disaster in under 6 hours. As a result, this work provides the foundation for more detailed development of a federate satellite network, compliant with current radiofrequency regulations.Keywords: Aerospace simulation, communication networks, global earth observation system of systems, satellite communication, small satellite

Although showing remarkable zero-shot and few-shot capabilities across a wide variety of tasks, Large Language Models (LLMs) are still not mature enough for off-the-shelf use in engineering design tasks. Organizations implementing model-based systems engineering practices into their product development processes can leverage on ontologies, models, and procedures to enhance LLMs applied to engineering design tasks. We present a methodology to integrate an Object-Process Methodology model of a space system into an LLMbased spacecraft design assistant and show a performance improvement, as compared to a conventional LLM. The benchmark is evaluated through subjective expert-assessed and an objective cosine-similarity-based criteria. The results motivate additional efforts in integrating Model-Based Systems Engineering practice into LLMs as means to improve their performance and reduce shortcomings such as hallucinations and black-box, untraceable behavior.Keywords: Large Language Models, Systems Engineering, Model-Based Systems Engineering, Object-Process Methodology, Engineering Design, Design Assistant

This work presents the architecture of a generative design tool that supports decision-making during the conceptual design of space missions. The tool generates the technical specifications of a spacecraft given a high-level mission statement expressed in natural language by the user. The tool includes a chatbot front-end which can be employed by users to query the model and ask questions on the design of the mission of interest. The work demonstrates the utility of the tool both as means of a design aid for engineers but also for augmentation of the dataset and underlying language model themselves. The work is in the frame of the design and construction of space missions and spacecraft, which have evolved significantly over the past few decades. Space mission design processes still rely heavily on human intervention despite the availability of large flight heritage data for varied types of missions and satellites, which can be handled in unprecedented ways by machine learning models. Advancements in computational power and in such machine learning techniques -particularly in transformer architectures for language- offer now however the potential to automate and speed up the design process, reducing even more costs and barriers to access space. In this paper we demonstrate a use case of our generative design tool prototype focused on Earth Observation missions. The goal of the prototype is to select instruments and provide key technical specification ranges starting from user needs expressed in natural language. Users can interact with the tool through a chatbot implementation. The bot provides an opportunity for user feedback to further refine the answer provided by the tool and converge iteratively to a technically feasible solution. Future iterations of the tool will include broadening the scope to a wider variety of satellite missions, as well as extending the scope of the tool to additional tasks in the conceptual design process, such as translating technical specifications to a CAD configuration model of the spacecraft involved as well as orbit design and other relevant design tasks.Keywords: generative design, spacecraft design, large language models, design assistant

Since humankind began sending spacecraft to explore our Solar System around 60 years ago, navigation, and operations in general, around and on the explored bodies have evolved substantially. Knowledge on the physics and the environment, and advances in hardware and software systems have been cornerstone to allowing advanced concepts for the execution of interplanetary missions. Indeed, during the last years, new techniques for navigation in such missions have emerged. Among them, Vision-Based Navigation (VBN) has stood out as one of the most promising techniques for navigation around small bodies. This technique leverages on taking images with an on-board camera in the satellite, with the goal of extracting useful information for the relative positioning of the satellite with respect to the navigated body. In general terms, a treatment of the image given by the camera is performed to extract information. Then, this information is transformed into data that can be used by an algorithm of orbit determination, giving the satellite's state (position and velocity) as a function of time. There are different families of VBN algorithms, some with current in-flight experience (e.g. centroid determination via centre of brightness) and some being proposed in the literature as an enhancement. In this work, VBN algorithms from the family of centroid determination via ellipse fitting, which promises an increase of accuracy over current techniques (at the cost of higher required computational power) have been coded on MATLAB, generating data that is compatible with CNES' orbit determination tools. The different alternative implementations have been tested and benchmarked with synthetic images of asteroids generated by an internal CNES tool. This work confirms the higher accuracy of this family of algorithms with respect to current centre of brightness methods. It also selects the best algorithmic combination for different scenarios. Additionally, it proposes a novel correction method that diminishes the disturbing effect of large areas of shadow on the navigated body.Keywords: Vision-Based Navigation, Line-Of-Sight vector, centroid determination, ellipse fitting, small bodies, shadowing correction

Proceedings of the 4th Symposium on Space Educational Activities (SSEA 2022), Barcelona, 27–29 April 2022, which I promoted and co-organised as Vice-Chair.Keywords: space education, symposium proceedings

The high penetration of mobile services provides an ample set of data generated by users and network elements. The analysis of such data yields insights on the behaviour of users and their experienced quality, and can be used by mobile operators to improve their mobile networks. In this paper, we design methods to infer user mobility patterns, estimate their channel quality and cluster them based on their Modulation and Coding Scheme (MCS) time evolution. In detail, we propose: i) a mapping between MCS and SNR, useful to assess the quality of the transmissions, ii) a method for deriving users' approximate velocities and categorising user mobility, and iii) a hierarchical clustering algorithm using Dynamic Time Warping, able to generate meaningful user clusters according to communication length and quality. We apply those proposed methods to real traces collected from more than one week of observations of three operative base stations in Spain. We observe that our solutions successfully provide relevant information about users' mobility and their channel quality, making them suitable for improvements in the understanding and planning of LTE resources by mobile network operators.Keywords: Channel Quality Analysis, Channel Quality Time Evolution, Clustering, Data Analysis, Dynamic Time Warping, PDCCH, Time Series Analysis, Time Series Clustering, Users Mobility, Wireless Traffic Analysis, Wireless Traffic Characterization

CADMUS was a student-made experiment with the aim of measuring the flux of muons at different altitudes and experimentally verifying time dilation, one of the effects predicted by the Special Relativity Theory. It was developed in the frame of the REXUS/BEXUS Programme and flew to the stratosphere on a high-altitude balloon in October 2017. This paper discusses its development, from inception to launch, from the perspective of space educational activities.Keywords: student-made experiment, particle detector, cloud chamber, muons, Special Relativity Theory, time dilation, REXUS/BEXUS Programme, stratosphere, high-altitude balloon, education