arXiv subjects

Daniel Arnold

Publications and source records attributed to Daniel Arnold.

At least 19 recordsLinked to original sources

arXiv2026

NovaMoon: A Strategic Lunar Reference Station for Positioning, Timing, and Largely Enhanced Science in the Earth-Moon System

The renewed interest in lunar exploration and the development of future lunar communication and navigation services highlight the need for a precise, stable, and interoperable geodetic and timing infrastructure on the Moon. NovaMoon, proposed as a scientific and navigation payload for ESA's Argonaut lander, is designed as a lunar-based local differential, geodetic, and timing station supporting both operational needs in the Moon's south polar region and a broad range of scientific investigations. The payload integrates a lunar laser retroreflector, a Very Long Baseline Interferometry transmitter, a receiver for navigation signals compatible with LunaNet standards, high-stability atomic clocks, and direct-to-Earth radio links -- making it the first lunar station to co-locate multiple ranging, tracking, and timing techniques. NovaMoon will enable sub-metre to decimetre positioning, provide local differential corrections for lunar users, and ensure an accurate and stable realisation of position and time. Preliminary simulation studies show that this multi-technique dataset improves the lunar reference frame, orientation and ephemerides, and estimates of interior parameters like tidal response and core properties. NovaMoon will also provide the first long-duration physical realisation of a lunar time reference. Beyond its primary goals, it supports improved cartography, precise surface geolocation, and higher-resolution topography, contributing to safer landings and operations. It also enables new tests of fundamental physics, including constraints on relativity and possible deviations from classical gravity.

astro-ph.EP↗

arXiv2025

Accelerometer Measurements for Orbit and Gravity Recovery: Challenges and Benefits for the BepiColombo Mission

The European Space Agency's BepiColombo mission continues its pioneering voyage to Mercury, the innermost planet of the Solar System. Among the advanced instruments onboard the Mercury Planetary Orbiter (MPO) is the Italian Spring Accelerometer (ISA), whose scientific objectives are closely linked to the Mercury Orbiter Radio-Science Experiment (MORE). Together, these instruments aim to provide high-precision data on the spacecraft's orbit, as well as Mercury's gravity field and internal structure. This simulation study investigates how the modeling and parametrization of accelerometer measurements influence orbit and gravity field recovery and explores strategies to overcome the challenges associated with the co-estimation of all parameters. We assess the integrated retrieval accuracy of the spacecraft orbit, gravity field, and accelerometer parameters under different noise levels and varying observation geometries during the mission. The MPO orbit is propagated, and Doppler and accelerometer measurements are simulated based on available noise models. Our results indicate that postponing the estimation of accelerometer biases until a preliminary gravity field is established helps prevent gravity field mismodelings from being absorbed into the accelerometer parameters. The daily estimation of bias was found to be essential. Using one year of Doppler tracking data and applying Kaula regularization, we demonstrate the potential to recover Mercury's gravity field up to degree and order 40. Low-degree coefficients improve under more optimistic noise conditions. Errors in cross-track bias estimation rise sharply when the beta-Earth angle falls below 45{\deg}, corresponding to the degraded Doppler observability. Orbit determination achieved centimetre-level radial accuracy and metre-level along- and cross-track accuracy, with increased cross-track errors during low-observability periods.

physics.space-ph↗

arXiv2025

Differential Privacy of Network Parameters from a System Identification Perspective

This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst observations of networked states as time-series outputs of a graph filter driven by differentially private (DP) nodal excitations, with the analyst aiming to infer the underlying graph shift operator (GSO). Unlike traditional SI, which estimates system parameters, we study the inverse problem: what assumptions prevent adversaries from identifying the GSO while preserving utility for legitimate analysis. We show that applying DP mechanisms to inputs provides formal privacy guarantees for the GSO, linking the $(\epsilon,\delta)$-DP bound to the spectral properties of the graph filter and noise covariance. More precisely, for DP Gaussian signals, the spectral characteristics of both the filter and noise covariance determine the privacy bound, with smooth filters and low-condition-number covariance yielding greater privacy.

cs.CR↗

arXiv2025

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations

This work addresses a fundamental challenge in applying deep learning to power systems: developing neural network models that transfer across significant system changes, including networks with entirely different topologies and dimensionalities, without requiring training data from unseen reconfigurations. Despite extensive research, most ML-based approaches remain system-specific, limiting real-world deployment. This limitation stems from a dual barrier. First, topology changes shift feature distributions and alter input dimensions due to power flow physics. Second, reconfigurations redefine output semantics and dimensionality, requiring models to handle configuration-specific outputs while maintaining transferable feature extraction. To overcome this challenge, we introduce a Universal Graph Convolutional Network (UGCN) that achieves transferability to any reconfiguration or variation of existing power systems without any prior knowledge of new grid topologies or retraining during implementation. Our approach applies to both transmission and distribution networks and demonstrates generalization capability to completely unseen system reconfigurations, such as network restructuring and major grid expansions. Experimental results across power system applications, including false data injection detection and state forecasting, show that UGCN significantly outperforms state-of-the-art methods in cross-system zero-shot transferability of new reconfigurations.

eess.SY↗

arXiv2025

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions

The graph-based variational autoencoder represents an architecture that can handle the uncertainty of different geological scenarios, such as depositional or structural, through the concept of a lowerdimensional latent space. The main difference from recent studies is utilisation of a graph-based approach in reservoir modelling instead of the more traditional lattice-based deep learning methods. We provide a solution to implicitly control the geological realism through the latent variables of a generative model and Geodesic metrics. Our experiments of AHM with synthetic dataset that consists of 3D realisations of channelised geological representations with two distinct scenarios with one and two channels shows the viability of the approach. We offer in-depth analysis of the latent space using tools such as PCA, t-SNE, and TDA to illustrate its structure.

stat.AP↗

arXiv2023

AI-based Identification of Most Critical Cyberattacks in Industrial Systems

Modern industrial systems face a growing threat from sophisticated cyberattacks that can cause significant operational disruptions. This work presents a novel methodology for identification of the most critical cyberattacks that may disrupt the operation of such a system. Application of the proposed framework can enable the design and development of advanced cybersecurity solutions for a wide range of industrial applications. Attacks are assessed taking into direct consideration how they impact the system operation as measured by a defined Key Performance Indicator (KPI). A simulation model (SM), of the industrial process is employed for calculation of the KPI based on operating conditions. Such SM is augmented with a layer of information describing the communication network topology, connected devices, and potential actions an adversary can take based on each device or network link. Each possible action is associated with an abstract measure of effort, which is interpreted as a cost. It is assumed that the adversary has a corresponding budget that constrains the selection of the sequence of actions defining the progression of the attack. A dynamical system comprising a set of states associated with the cyberattack (cyber-states) and transition logic for updating their values is also proposed. The resulting augmented simulation model (ASM) is then employed in an artificial intelligence-based sequential decision-making optimization to yield the most critical cyberattack scenarios as measured by their impact on the defined KPI. The methodology is successfully tested based on an electrical power distribution system use case.

eess.SY↗

arXiv2023

Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow

Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the reward function. However, this approach can lead to infeasible solutions that violate physical constraints such as power flow equations, voltage limits, and dynamic constraints. Ensuring these constraints are met is crucial in power systems, as they are a safety critical infrastructure. To address this issue, existing DRL algorithms remedy the problem by projecting the actions onto the feasible set, which can result in sub-optimal solutions. This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs. We also prove the convergence of the critic and actor networks. Our case studies on IEEE standard systems demonstrate the superiority of the proposed approach in dynamically adapting to the environment while maintaining safety constraints.

eess.SY↗

arXiv2022

Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI

The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, signals are often sparse in the Fourier domain, and more informative and compact representations for them can be obtained using the complex envelope of their spectral components, as opposed to the original real-valued signals. Motivated by this fact, in this work we generalize graph convolutional neural networks (GCN) to the complex domain, deriving the theory that allows to incorporate a complex-valued graph shift operators (GSO) in the definition of graph filters (GF) and process complex-valued graph signals (GS). The theory developed can handle spatio-temporal complex network processes. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support, the bound of the transfer error and the bound of error propagation through multiply layers. Then we apply complex GCN to power grid state forecasting, power grid cyber-attack detection and localization.

cs.LG↗

arXiv2022

Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms

This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system. We first identify the graph shift operator (GSO) based on the power flow equations. Then, we develop a spatio-temporal graph ConvNet (STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal correlation of voltage phasors. The STGCN layer performs the feature extraction task for the policy function and the value function of the reinforcement learning architecture, and then we utilize the proximal policy optimization (PPO) to search the action spaces for an optimum policy function and to approximate an optimum value function. We further utilize the low-pass property of voltage graph signal to introduce an GCN architecture for the the policy whose input is a decimated state vector, i.e. a partial observation. Case studies on the unbalanced 123-bus systems validate the excellent performance of the proposed method in mitigating instabilities and maintaining nodal voltage profiles within a desirable range.

eess.SY↗

arXiv2022

Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability

Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. We provide a derivation of the adaptive control approach and validate the algorithm in experiments on the IEEE 37 and 8500 node test feeders.

eess.SY↗

arXiv2022

Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation

Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems. In the event that a subset of DER have had their Volt-VAR and Volt-Watt settings compromised as part of a cyber-attack, we propose a mechanism to control the remaining set of non-compromised DER to ameliorate large oscillations in system voltages and large voltage imbalances in real time. To do so, we construct control policies for individual non-compromised DER, directly searching the policy space using an Adam-based augmented random search (ARS). In this paper we show that, compared to previous efforts aimed at training policies for DER cybersecurity using deep reinforcement learning (DRL), the proposed approach is able to learn optimal (and sometimes linear) policies an order of magnitude faster than conventional DRL techniques (e.g., Proximal Policy Optimization).

eess.SY↗

arXiv2021

SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation

Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast-changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe VoltagE Regulator, which is composed of an RL learner and a specifically designed, computational efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages to be within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.

eess.SY↗

arXiv2021

Learning from learning machines: a new generation of AI technology to meet the needs of science

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.

cs.LG↗

arXiv2021

Structure and local charging of electromigrated Au nanocontacts

We study the structure and the electronic properties of Au nanocontacts created by controlled electromigration of thin film devices, a method frequently used to contact molecules. In contrast to electromigration testing, a current is applied in a cyclic fashion and during each cycle the resistance increase of the metal upon heating is used to avoid thermal runaway. In this way, nanometer sized-gaps are obtained. The thin film devices with an optimized structure at the origin of the electromigration process are made by shadow evaporation without contamination by organic materials. Defining rounded edges and a thinner area in the center of the device allow to pre-determine the location where the electromigration takes place. Scanning force microscopy images of the pristine Au film and electromigrated contact show its grainy structure. Through electromigration, a $1.5 \,μ$m-wide slit is formed, with extensions only on the anode side that had previously not been observed in narrower structures. It is discussed whether this could be explained by asymmetric heating of both electrodes. New grains are formed in the slit and on the extensions on both, the anode and the cathode side. The smaller structures inside the slit lead to an electrode distance below 150 nm. Kelvin probe force microscopy images show a local work function difference with fluctuations of $70$ mV on the metal before electromigration. Between the electrodes, disconnected through electromigration, a work function difference of $3.2$ V is observed due to local charging from contact to the tip. Some of the grains newly formed by electromigration are electrically disconnected from the electrodes.

cond-mat.mes-hall↗

arXiv2021

Understanding the Safety Requirements for Learning-based Power Systems Operations

Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of power systems, such as short-term emergency control, Volt/VAr control, long-term residential demand response and battery energy management. Despite the promises of providing strong representation of complex system dynamics and fast, efficient learned operation strategies, the safety requirements of such learning paradigms are less discussed. This paper explores the design requirements on both data and model side of such learning algorithms by exploiting the impacts of adversarial attacks on safety critical system operations. Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.

eess.SY↗

arXiv2020

Lyapunov Stability of Smart Inverters Using Linearized DistFlow Approximation

Fast-acting smart inverters that utilize preset operating conditions to determine real and reactive power injection/consumption can create voltage instabilities (over-voltage, voltage oscillations and more) in an electrical distribution network if set-points are not properly configured. In this work, linear distribution power flow equations and droop-based Volt-Var and Volt-Watt control curves are used to analytically derive a stability criterion using \lyapnouv analysis that includes the network operating condition. The methodology is generally applicable for control curves that can be represented as Lipschitz functions. The derived Lipschitz constants account for smart inverter hardware limitations for reactive power generation. A local policy is derived from the stability criterion that allows inverters to adapt their control curves by monitoring only local voltage, thus avoiding centralized control or information sharing with other inverters. The criterion is independent of the internal time-delays of smart inverters. Simulation results for inverters with and without the proposed stabilization technique demonstrate how smart inverters can mitigate voltage oscillations locally and mitigate real and reactive power flow disturbances at the substation under multiple scenarios. The study concludes with illustrations of how the control policy can dampen oscillations caused by solar intermittency and cyber-attacks.

eess.SY↗

arXiv2020

Deep Reinforcement Learning for DER Cyber-Attack Mitigation

The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.

eess.SY↗

arXiv2020

Influence of the vicinal substrate miscut on the anisotropic two-dimensional electronic transport in Al2O3-SrTiO3 heterostructures

The electrical resistance of the two-dimensional electron system (2DES) which forms at the interface of SrTiO3 (STO)-based heterostructures displays anisotropic transport with respect to the direction of current flow at low temperature. We have investigated the influence of terraces at the surface of STO substrates from which the 2DES are prepared. Such terraces are always present in commercially available STO substrates due to the tolerance of surface preparation which result in small miscut angles of the order of gamma ~ 0.1° with respect to the surface normal. By a controlled increase of the substrate miscut we could systematically reduce the width of the terraces and thereby increase the density of substrate surface steps. The in-plane anisotropy of the electrical resistance was studied as a function of the miscut angle gamma and found to be mainly related to interfacial scattering arising from the substrate surface steps. However, the influence of gamma was notably reduced by the occurrence of step-bunching and lattice-dislocations in the STO substrate material. Magnetoresistance (MR) depends on the current orientation as well, reflecting the anisotropy of carrier mobility. For gamma >= 2°, MR is substantially enhanced and shows the trend towards a linear field dependence which is typical for inhomogeneous conductors. From weak-antilocalization observed at small magnetic field we deduce information on inelastic scattering and spin-orbit coupling. While the field scale associated with a Rashba-type spin-orbit coupling in 2D weak-localization does not show a pronounced correlation with gamma, distinct changes of the scale are associated with inelastic scattering.

cond-mat.mtrl-sci↗