CoDiST Controller Display and Simulation Tool

Beschreibung der Funktionalitäten des Tools CoDiS

Robust PPP-AR Using M-Estimators for Multi-Fault Scenarios

Urban GNSS positioning is severely affected by multipath propagation and non-line-of-sight (NLOS) reception, which give rise to non-Gaussian measurement errors and multiple simultaneous outliers. These effects challenge conventional Precise Point Positioning with Ambiguity Resolution (PPP-AR) techniques, whose estimation performance degrades significantly under such conditions. Existing fault detection and exclusion methods, particularly those based on multi-hypothesis solution separation (MHSS), become computationally infeasible when applied to multi-constellation, multi-frequency GNSS due to their combinatorial complexity. In this paper, we propose a robust filtering framework for PPP-AR that incorporates M-estimators into the Kalman filter update step to mitigate the impact of faulty or contaminated observations without needing to enumerate fault hypotheses. Our method improves the reliability of the float solution in the presence of outliers while remaining scalable to modern GNSS configurations. Simulation results under fault injection scenarios demonstrate that the robust filter achieves performance comparable to an ideal fault-free estimator, effectively preventing divergence and enabling consistent navigation even under degraded conditions

Lokalisation von Rissen in strukturellen Klebverbindungen mittels faseroptischer Sensoren

Die vorliegende Arbeit untersucht die Eignung von faseroptischen Dehnungssensoren (FOSS) zur Lokalisierung von Rissen in strukturellen Klebverbindungen. Der Fokus liegt dabei auf der Bestimmung von Risswachstumsmechanismen sowie der Bewertung der Verwendung von Rissstoppern in Form von Oberflächenzähmodifikation. Es wird davon ausgegangen, dass der Einsatz von FOSS eine hochauflösende Risspositionsbestimmung mit einer Auflösung von unter einem Millimeter ermöglicht. Zusätzlich wird angenommen, dass die Verwendung von Polyphenylsulfon (PPSU) und thermoplastischem Polyurethan (TPU) als Material für die Oberflächenzähmodifikation das Risswachstum über die Klebverbindung beeinflusst und als rissstoppendes Element wirkt. Zu diesem Zweck werden Double Cantilever Beam (DCB) Tests mit integrierten und auf die Oberfläche geklebten FOSS-Sensoren gemäß ASTM D5528-21 durchgeführt. Die Ergebnisse zeigen, dass faseroptische Dehnungssensoren in der Lage sind, den Rissfortschritt mit einer Auflösung von bis zu 0,65 mm und einer mittleren Abweichung von weniger als einem Millimeter im Vergleich zur konventionellen Rissfortschrittsmethode zu messen. Es kann festgestellt werden, dass PPSU einen starken Einfluss auf das Risswachstum hat, was zu instabilen und plötzlichen Risssprüngen führt und nicht für den Einsatz in Klebeverbindungen geeignet ist. TPU hingegen hat eine signifikant verlangsamende und stoppende Wirkung auf das Risswachstum und erhöht die Energiefreisetzungsrate um 6,5 %. Dies ist deutlich weniger als die hemmende Wirkung von Polyvinylidenfluorid (PVDF) auf den Rissfortschritt, die in früheren Untersuchungen festgestellt wurde. Dennoch wird davon ausgegangen, dass TPU aufgrund seiner niedrigen Glasübergangstemperatur besser für den Einsatz bei Temperaturen unter -30°C geeignet ist

Predicting High-Dimensional Chaotic Time Series by Employing Hybridized Local State Reservoir Computing

Reservoir Computing (RC) has been shown to be one of the most promising methods for the prediction of chaotic spatiotemporal systems. Recently it has been demonstrated that by combining knowledge-based models (KBMs) with fully data-driven RC, prediction performance exceeding both methods can be achieved. Additionally, this approach is compatible with a parallel prediction scheme based on local states, making forecasting of high-dimensional chaotic spatiotemporal systems of arbitrarily large extent possible. We demonstrate this using three of the most common RC techniques, namely classical RC, Next Generation RC (NGRC), and Minimal RC (MRC), as well as three hybrid methods: input hybrid (IH), output hybrid (OH), and full hybrid (FH). A find that NGRC and MRC yield equivalent prediction performance with up to two orders of magnitude less computing time and training data than classical RC. Furthermore, our implementation of these techniques in the publicly available software package “SCAN” (Software for Chaos Analysis using Networks) enables the processing of generalized system topologies. We discuss different examples for the prediction of spatially extended systems, whether on a two-dimensional plane or a network (e.g. power grid data), and outline possible applications

DRACO SCIENTIFIC RETURN CONCEPT: DETERMINING THE TRUTH OF SATELLITE DEMISE

ESA’s DRACO (Destructive Re-entry Assessment Container Object) mission will be the world’s first demonstration of recording the break-up process of a satellite in-situ while demising during a destructive reentry. DRACO is a fully representative small satellite platform that will undergo controlled re-entry from Low Earth Orbit. The platform hosts a dedicated instrument (Demise Data Collection Unit - DDCU) that will collect a variety of measurements from specific objects of interest, including the spacecraft structure itself. It also hosts a capsule designed to survive the destructive re-entry which will transmit the data collected by the DDCU back to ground via a relay satellite system. Lessons learned from previous attempts (i.e. VAST/VASP, REBR, iBall, Hayabusa, ATV-1, etc.) show that repeatable experiments are required to reduce the still too large uncertainties associated with the understanding of destructive re-entry physics; that small scale physics extrapolated from ground-testing still needs to be validated for relevant scales; and that the understanding of the physics associated with controlled and uncontrolled re-entries are at the same level of maturity. Hence, DRACO’s mission objectives are threefold: to demonstrate the break-up process of a spacecraft during re-entry enabling better scale of ground-tests to flight, to establish an understanding of destructive aerothermal break-ups not accessible from ground or by model, and to test early fragmentation design for demise (D4D) technologies. In order to demonstrate the understanding of the process and physics on large-scale systems, the objects of interest considered in the DRACO mission are: the spacecraft structure made of aluminium sandwich panels to understand the fragmentation process; Composite Overwrapped Pressure Vessels (COPV) tanks to determine the system design impact at equipment level with Design-for-Demise (D4D) techniques; and material samples to characterise their response during the re-entry process and validate ground test results. This paper describes the fundamental and unique dataset that the DRACO mission is being designed to acquire. The measurements are divided into three categories: • Contextual data of the trajectory (attitude, position and rotational rates) and local flow conditions (altitude and dynamic pressure) will be gathered by means of IMU and GNSS systems. This will provide information on when different demise processes are taking place and support the trajectory rebuilding. • Qualitative data on the general phenomenology will be obtained by means of infrared and visual cameras to improve the understanding of which processes are taking place, and which are dominant. Images are expected to show the structure fragmentation, the early stages of COPV demise and the behaviour of joints. • Quantitative data on local temperatures, deformations and separation events will be recorded by means of thermocouples and strain gauges, to understand what happens at local levels in terms of spacecraft structure break-up and critical on-board components demise. Additionally, spectral markers are included in thespacecraft with different concepts that are illustrated in the paper. With the help of an airborne observation campaign, additional data will be acquired to improve the understanding of the fragment cloud evolution and ablation signatures of specific materials, which will eventually support the break-up event and sequence characterisation

Ammonia on demand: Titanium Dioxide Aerogels for Photocatalytic Dinitrogen Reduction

The growing demand for sustainable and efficient energy solutions has accelerated the search for novel advanced materials with interesting electronic and catalytic properties. Among these materials, titanium dioxide (TiO2) has gathered significant attention due to its exceptional photocatalytic properties.[1] With a band gap typically between 3.0 and 3.5 eV, TiO2 can be excited by the UV-light fraction of the solar spectrum and can catalyze interesting reactions like water splitting and the reduction of elemental nitrogen.[1-3] Due to the outstanding charge carrier separation properties of some TiO2-materials, they can also be used to store electrons and carry out time-shifted photoreactions.[4,5] For photocatalytic applications, nanomaterials, such as nanoparticles, are often favored over bulk materials because of their large surface area and high number of reactive sites.[1] However, the catalytic efficiency of nanoparticles usually decreases when agglomeration occurs, as the incident light can only excite the outer layer of the formed agglomerates.[6] Nanostructured, porous aerogels with large surface areas are a promising approach to tackle this problem.[7] Here the material particles are arranged in a rigid 3-dimensional network that prevents agglomeration and can be designed with sufficient light transmissibility that enables all incorporated particles to be excited upon radiation.[7] The interconnected nature of the particles in aerogels can additionally improve the charge carrier separation in a material.[8,9] Aerogels are synthesized by using the sol-gel method, which allows to directly vary the material parameters by changing the synthesis parameters, followed by supercritical drying to preserve the porous nature of the wet gels.[10] We will demonstrate the development of a titanium dioxide aerogel designed for optimal photocatalytic reduction of nitrogen to ammonia and will discuss the influence of different synthesis parameters on the materials properties. This includes different solvents and solvent-mixtures, catalyst concentrations, and processing parameters. Herein we especially investigated the direct influence of acid concentration and ligand exchange at the titanium alkoxy precursor on the pore structure and crystallinity of the aerogels. We will also show our findings on the underlying mechanisms of the sol-gel synthesis of titania aerogels and their photocatalytic activity. [1] K. Nakata, A. Fujishima, Journal of Photochemistry and Photobiology C: Photochemistry Reviews 2012, 13, 169–189. [2] C. P. Lin, H. Chen, A. Nakaruk, et al., Energy Procedia 2013, 34, 627–636. [3] K. Eufinger, D. Poelman, H. Poelman, et al., Journal of Physics D: Applied Physics 2007, 40, 5232–5238. [4] Y. Gao, J. Zhu, H. An, et al., Journal of Physical Chemistry Letters 2017, 8, 1419–1423. [5] T. Berger, M. Sterrer, O. Diwald, et al., Journal of Physical Chemistry B 2005, 109, 6061–6068. [6] J. Wen, X. Li, W. Liu, et al., Chinese Journal of Catalysis 2015, 36, 2049–2070. [7] G. Li, L. Lv, H. Fan, et al., Journal of Colloid and Interface Science 2010, 348, 342–347. [8] H. Qi, X. Ji, C. Shi, et al., Journal of Colloid and Interface Science 2019, 556, 366–375. [9] T. Chen, P. He, T. Liu, et al., Inorganic Chemistry 2022, 61, 12759–12771. [10] M. A. Aegerter, M. Koebel, N. Leventis, et al., Handbook of Aerogels, 2023

Grasping Causality for the Explanation of Criticality for Automated Driving

Safeguarding automated driving systems at SAE levels 4 and 5 is a multi faceted challenge, for which classical distance-based approaches become infeasible. To alleviate this, contemporary scenario-based approaches suggest a decomposition into scenario classes combined with the statistical analysis of these classes regarding their criticality. Unfortunately, relying solely on associative statistics may fail to recognize the causalities leading to critical scenarios. These scenarios are prerequisite for the scenario-based development of safe automated driving systems. As to incorporate causal knowledge within the development process, this work introduces a formalization of causal queries. Answering these facilitates a causal understanding of safety-relevant influencing factors. This formalized causal knowledge can be used to specify and implement safety principles that provably reduce their associated criticality. Based on Judea Pearl’s causal theory, we define a causal relation as a causal structure together with a context, both related to a suitable domain ontology. The focus lies on modeling the effect of such influencing factors on criticality as measured by appropriate criticality metrics. Our main example is a causal relation for the influencing factor reduced coefficient of friction and its effect on the Brake-Threat-Number. As availability and quality of data are important to answer the causal queries, we also discuss requirements on real-world and synthetic data acquisition. Overall, this work contributes to establish formal causal considerations within the safety process for automated driving systems