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Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is accepted and will be published in Computer Aided Systems Theory - EUROCAST 2024

As Industry 4.0 and the Industrial Internet of Things continue to advance, industrial control systems are increasingly adopting IT solutions, including communication standards and protocols. As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises. Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts. Such sessions, however, often fail to provide an exhaustive identification of assets and interfaces due to the lack of a systematic approach. This is a major issue, as it leads to poor threat modeling, resulting in insufficient mitigation strategies and, lastly, a flawed security architecture. We propose a method for the analysis of assets in industrial systems, with special focus on physical threats. Inspired by the ISO/OSI reference model, a systematic approach is introduced to help identify and classify asset interfaces. This results in an enriched system model of the asset, offering a comprehensive overview visually represented as an interface tree, thereby laying the foundation for subsequent threat modeling steps. To demonstrate the proposed method, the results of its application to a programmable logic controller (PLC) are presented. In support of this, a study involving a group of 12 security experts was conducted. Additionally, the study offers valuable insights into the experts' general perspectives and workflows on threat modeling., Comment: Under Review, 8 pages, 7 figures

This study conducts a comparative analysis of Model Predictive Control (MPC) and Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) algorithm, applied to a 1-Degree of Freedom (DOF) Quanser Aero 2 system. Classical control techniques such as MPC and Linear Quadratic Regulator (LQR) are widely used due to their theoretical foundation and practical effectiveness. However, with advancements in computational techniques and machine learning, DRL approaches like PPO have gained traction in solving optimal control problems through environment interaction. This paper systematically evaluates the dynamic response characteristics of PPO and MPC, comparing their performance, computational resource consumption, and implementation complexity. Experimental results show that while LQR achieves the best steady-state accuracy, PPO excels in rise-time and adaptability, making it a promising approach for applications requiring rapid response and adaptability. Additionally, we have established a baseline for future RL-related research on this specific testbed. We also discuss the strengths and limitations of each control strategy, providing recommendations for selecting appropriate controllers for real-world scenarios., Comment: Accepted at INDIN2024

We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and interaction exist, they are notoriously difficult to fit to data, as most prior work requires knowledge about individual motion trajectories, i.e., a requirement that is challenging to satisfy with an increasing number of entities. To evade such confounding factors, we investigate collective behavior from a $\textit{topological perspective}$, but instead of summarizing entire observation sequences (as done previously), we propose learning a latent dynamical model from topological features $\textit{per time point}$. The latter is then used to formulate a downstream regression task to predict the parametrization of some a priori specified governing equation. We implement this idea based on a latent ODE learned from vectorized (static) persistence diagrams and show that a combination of recent stability results for persistent homology justifies this modeling choice. Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model - $\textit{Neural Persistence Dynamics}$ - substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.

This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and popular libraries like Stable Baselines3, we aim to bridge the gap between the established Simulink environment and the flexibility of Python for training bleeding edge agents. Our approach is demonstrated on the Quanser Aero 2, a versatile dual-rotor helicopter. We show that policies trained on Simulink models can be seamlessly transferred to the real system, enabling efficient development and deployment of Reinforcement Learning agents for control tasks. Through systematic integration steps, including C-code generation from Simulink, DLL compilation, and Python interface development, we establish a robust framework for training agents on Simulink models. Experimental results demonstrate the effectiveness of our approach, surpassing previous efforts and highlighting the potential of combining Simulink with Python for Reinforcement Learning research and applications., Comment: Accepted at SMPS2024

Piecewise Polynomials (PPs) are utilized in several engineering disciplines, like trajectory planning, to approximate position profiles given in the form of a set of points. While the approximation target along with domain-specific requirements, like Ck -continuity, can be formulated as a system of equations and a result can be computed directly, such closed-form solutions posses limited flexibility with respect to polynomial degrees, polynomial bases or adding further domain-specific requirements. Sufficiently complex optimization goals soon call for the use of numerical methods, like gradient descent. Since gradient descent lies at the heart of training Artificial Neural Networks (ANNs), modern Machine Learning (ML) frameworks like TensorFlow come with a set of gradient-based optimizers potentially suitable for a wide range of optimization problems beyond the training task for ANNs. Our approach is to utilize the versatility of PP models and combine it with the potential of modern ML optimizers for the use in function approximation in 1D trajectory planning in the context of electronic cam design. We utilize available optimizers of the ML framework TensorFlow directly, outside of the scope of ANNs, to optimize model parameters of our PP model. In this paper, we show how an orthogonal polynomial basis contributes to improving approximation and continuity optimization performance. Utilizing Chebyshev polynomials of the first kind, we develop a novel regularization approach enabling clearly improved convergence behavior. We show that, using this regularization approach, Chebyshev basis performs better than power basis for all relevant optimizers in the combined approximation and continuity optimization setting and demonstrate usability of the presented approach within the electronic cam domain., Comment: Accepted at LION18 conference

Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it is a relatively new field, TDA has been widely and successfully applied across various domains, such as medicine, materials science, and biology. This survey provides an overview of the state of the art of TDA within a dynamic and promising application area: industrial manufacturing and production, particularly within the Industry 4.0 context. We have conducted a rigorous and reproducible literature search focusing on TDA applications in industrial production and manufacturing settings. The identified works are categorized based on their application areas within the manufacturing process and the types of input data. We highlight the principal advantages of TDA tools in this context, address the challenges encountered and the future potential of the field. Furthermore, we identify TDA methods that are currently underexploited in specific industrial areas and discuss how their application could be beneficial, with the aim of stimulating further research in this field. This work seeks to bridge the theoretical advancements in TDA with the practical needs of industrial production. Our goal is to serve as a guide for practitioners and researchers applying TDA in industrial production and manufacturing systems. We advocate for the untapped potential of TDA in this domain and encourage continued exploration and research., Comment: Work accepted for publication in the Journal of Manufacturing Systems

Building energy efficient software is an increasingly important task for mobile developers. However, a cumulative body of knowledge of techniques that support this goal does not exist. We conduct a systematic literature review to gather information on existing techniques that allow developers to increase energy efficiency in mobile apps. Based on a synthesis of the 91 included primary studies, we propose a taxonomy of techniques for improving the energy efficiency in mobile apps. The taxonomy includes seven main categories of techniques and serves as a collection of available methods for developers and as a reference guide for software testers when performing energy efficiency testing by the means of benchmark tests.

Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection., Comment: Accepted to The World Conference on eXplainable Artificial Intelligence, Lisbon, Portugal, July 2023

Industry 4.0 is driven by demands like shorter time-to-market, mass customization of products, and batch size one production. Reinforcement Learning (RL), a machine learning paradigm shown to possess a great potential in improving and surpassing human level performance in numerous complex tasks, allows coping with the mentioned demands. In this paper, we present an OPC UA based Operational Technology (OT)-aware RL architecture, which extends the standard RL setting, combining it with the setting of digital twins. Moreover, we define an OPC UA information model allowing for a generalized plug-and-play like approach for exchanging the RL agent used. In conclusion, we demonstrate and evaluate the architecture, by creating a proof of concept. By means of solving a toy example, we show that this architecture can be used to determine the optimal policy using a real control system., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Computer Aided Systems Theory - EUROCAST 2022 and is available online at https://doi.org/10.1007/978-3-031-25312-6_67

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

The four Industry 4.0 design principles information transparency, technical assistance, interconnection, and decentralized decisions pose challenges in integrating information technology (IT) and operational technology (OT) solutions in industrial systems. These different solutions have conflicting requirements, making interfaces between them problematic for both systems and organizations. An Industrial Business Process Twin (IBPT) entity, acting as an intermediary between the realms of IT and OT, has been proposed in a previous work, to effectively reduce the amount of required IT/OT interfaces in an attempt of overcoming this situation. In this work, we investigate the effects of this approach during the design phase. We argue that, by eliminating potentially conflicting direct interfaces between IT and OT stakeholders within the organizational structure, this approach effectively eliminates conflicting communication channels within the system design. In order to verify our argument, we develop a model of our IBPT concept according to the Reference Architecture Model Industrie 4.0 (RAMI4.0) using an Industry 4.0 scenario addressing the four essential Industry 4.0 design principles. Results show that the IBPT approach indeed eliminates potentially conflicting IT/OT interfaces during the system design phase., Comment: Accepted at INCOSE IS2024. Important note: In the revised version presented at the conference, our main argument is grounded in Conway's law. In this preprint, we relied on its 'inverse', which lacks sufficient evidence in the literature. Please refer to the official conference version for our updated argumentation

Reinforcement Learning (RL) is a powerful machine learning paradigm that has been applied in various fields such as robotics, natural language processing and game playing achieving state-of-the-art results. Targeted to solve sequential decision making problems, it is by design able to learn from experience and therefore adapt to changing dynamic environments. These capabilities make it a prime candidate for controlling and optimizing complex processes in industry. The key to fully exploiting this potential is the seamless integration of RL into existing industrial systems. The industrial communication standard Open Platform Communications UnifiedArchitecture (OPC UA) could bridge this gap. However, since RL and OPC UA are from different fields,there is a need for researchers to bridge the gap between the two technologies. This work serves to bridge this gap by providing a brief technical overview of both technologies and carrying out a semi-exhaustive literature review to gain insights on how RL and OPC UA are applied in combination. With this survey, three main research topics have been identified, following the intersection of RL with OPC UA. The results of the literature review show that RL is a promising technology for the control and optimization of industrial processes, but does not yet have the necessary standardized interfaces to be deployed in real-world scenarios with reasonably low effort., Comment: preprint of Paper submitted to INDIN'23

The vision of Industry 4.0 introduces new requirements to Operational Technology (OT) systems. Solutions for these requirements already exist in the Information Technology (IT) world, however, due to the different characteristics of both worlds, these solutions often cannot be directly used in the world of OT. We therefore propose an Industrial Business Process Twin (IBPT), allowing to apply methods of one world to another not directly but, instead, to a representation, that is in bidirectional exchange with the other world. The proposed IBPT entity acts as an intermediary, decoupling the worlds of IT and OT, thus allowing for an integration of IT and OT components of different manufacturers and platforms. Using this approach, we demonstrate the four essential Industry 4.0 design principles information transparency, technical assistance, interconnection and decentralized decisions based on the gamified Industry 4.0 scenario of playing the game of Nine Men's Morris. This scenario serves well for agent based Artificial Intelligence (AI)-research and education. We develop an Open Platform Communications Unified Architecture (OPC UA) information and communication model and then evaluate the IBPT component with respect to the different views of the Reference Architecture Model Industry 4.0 (RAMI4.0)., Comment: Submitted to INDIN2023 conference

In this work we present an "out-of-the-box" application of Machine Learning (ML) optimizers for an industrial optimization problem. We introduce a piecewise polynomial model (spline) for fitting of $\mathcal{C}^k$-continuos functions, which can be deployed in a cam approximation setting. We then use the gradient descent optimization context provided by the machine learning framework TensorFlow to optimize the model parameters with respect to approximation quality and $\mathcal{C}^k$-continuity and evaluate available optimizers. Our experiments show that the problem solution is feasible using TensorFlow gradient tapes and that AMSGrad and SGD show the best results among available TensorFlow optimizers. Furthermore, we introduce a novel regularization approach to improve SGD convergence. Although experiments show that remaining discontinuities after optimization are small, we can eliminate these errors using a presented algorithm which has impact only on affected derivatives in the local spline segment., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Computer Aided Systems Theory - EUROCAST 2022 and is available online at https://doi.org/10.1007/978-3-031-25312-6_68

In this paper, we revisit the application of Genetic Algorithm (GA) to the Traveling Salesperson Problem (TSP) and introduce a family of novel crossover operators that outperform the previous state of the art. The novel crossover operators aim to exploit symmetries in the solution space, which allows us to more effectively preserve well-performing individuals, namely the fitness invariance to circular shifts and reversals of solutions. These symmetries are general and not limited to or tailored to TSP specifically., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published and is available online at https://doi.org/10.1007/978-3-031-42171-6_3

On embedded processors that are increasingly equipped with multiple CPU cores, static hardware partitioning is an established means of consolidating and isolating workloads onto single chips. This architectural pattern is suitable for mixed-criticality workloads that need to satisfy both, real-time and safety requirements, given suitable hardware properties. In this work, we focus on exploiting contemporary virtualisation mechanisms to achieve freedom from interference respectively isolation between workloads. Possibilities to achieve temporal and spatial isolation-while maintaining real-time capabilities-include statically partitioning resources, avoiding the sharing of devices, and ascertaining zero interventions of superordinate control structures. This eliminates overhead due to hardware partitioning, but implies certain hardware capabilities that are not yet fully implemented in contemporary standard systems. To address such hardware limitations, the customisable and configurable RISC-V instruction set architecture offers the possibility of swift, unrestricted modifications. We present findings on the current RISC-V specification and its implementations that necessitate interventions of superordinate control structures. We identify numerous issues adverse to implementing our goal of achieving zero interventions respectively zero overhead: On the design level, and especially with regards to handling interrupts. Based on micro-benchmark measurements, we discuss the implications of our findings, and argue how they can provide a basis for future extensions and improvements of the RISC-V architecture.

In this paper we discuss the application of Artificial Intelligence (AI) to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling Salesperson Problem (TSP). We investigate the mlrose library that provides an TSP optimizer based on various heuristic optimization techniques. Our focus is on two methods, namely Genetic Algorithm (GA) and Hill Climbing (HC), which are provided by mlrose. We present improvements for both methods that yield shorter tour lengths, by moderately exploiting the problem structure of TSP. That is, the proposed improvements have a generic character and are not limited to TSP only., Comment: Journal Version of a Eurocast'22 submission

Providing computer systems with the ability to understand and generate natural language has long been a challenge of engineers. Recent progress in natural language processing (NLP), like the GPT-3 language model released by OpenAI, has made both possible to an extent. In this paper, we explore the possibility of rationalising email communication using GPT-3. First, we demonstrate the technical feasibility of understanding incoming emails and generating responses, drawing on literature from the disciplines of software engineering as well as data science. Second, we apply knowledge from both business studies and, again, software engineering to identify ways to tackle challenges we encountered. Third, we argue for the economic viability of such a solution by analysing costs and market demand. We conclude that applying GPT-3 to rationalising email communication is feasible both technically and economically.

The SuperKEKB accelerator in Tsukuba, Japan is providing e$^+$e$^-$ beams for the Belle II experiment since March 2019. To deal with the aimed peak luminosity being forty times higher than the one recorded at Belle, a pixel detector based on DEPFET technology has been installed. It features a long integration time of 20 $\mu$s resulting in an expected data rate of 20 GByte/s (160 GBit/s) at a maximum occupancy of 3 %. To deal with this high amount of data, the data handling hub (DHH) has been developed. It contains all necessary functionality for the control and readout of the detector. In this paper we describe the architecture and features of the DHH system. Further we will show the key performance characteristics after one year of operation.

In addition to the common 3-body decay of the neutron $n\rightarrow p e^-\overline{\nu_e}$ there should exist an effective 2-body subset with the electron and proton forming a Hydrogen bound state with well defined total momentum, total spin and magnetic quantum numbers. The atomic spectroscopic analysis of this bound system can reveal details about the underlying weak interaction as it mirrors the helicity distributions of all outgoing particles. Thus, it is unique in the information it carries, and an experiment unravelling this information is an analogue to the Goldhaber experiment performed more than 60 years ago. The proposed experiment will search for monoenergetic metastable BoB H atoms with 326 eV kinetic energy, which are generated at the center of a throughgoing beamtube of a high-flux reactor (e.g., at the PIK reactor, Gatchina). Although full spectroscopic information is needed to possibly reveal new physics our first aim is to prove the occurrence of this decay and learn about backgrounds. Key to the detection is the identification of a monoerergtic line of hydrogen atoms occurring at a rate of about 1 $\rm{s}^{-1}$ in the environment of many hydrogen atoms, however having a thermal distribution of about room temperature. Two scenarios for velocity (energy) filtering are discussed in this paper. The first builds on an purely electric chopper system, in which metastable hydrogen atoms are quenched to their ground state and thus remain mostly undetectable. This chopper system employs fast switchable Bradbury Nielsen gates. The second method exploits a strongly energy dependent charge exchange process of metastable hydrogen picking up an electron while traversing an argon filled gas cell, turning it into manipulable charged hydrogen. The final detection of hydrogen occurs through multichannel plate (MCP) detector., Comment: 7 pages, 18 figures, conference report

The rise of user space packet processing frameworks like DPDK and netmap makes low-level code more accessible to developers and researchers. Previously, driver code was hidden in the kernel and rarely modified, or even looked at, by developers working at higher layers. These barriers are gone nowadays, yet developers still treat user space drivers as black-boxes magically accelerating applications. We want to change this: every researcher building high-speed network applications should understand the intricacies of the underlying drivers, especially if they impact performance. We present ixy, a user space network driver designed for simplicity and educational purposes to show that fast packet IO is not black magic but careful engineering. ixy focuses on the bare essentials of user space packet processing: a packet forwarder including the whole NIC driver uses less than 1,000 lines of C code. This paper is partially written in tutorial style on the case study of our implementations of drivers for both the Intel 82599 family and for virtual VirtIO NICs. The former allows us to reason about driver and framework performance on a stripped-down implementation to assess individual optimizations in isolation. VirtIO support ensures that everyone can run it in a virtual machine. Our code is available as free and open source under the BSD license at https://github.com/emmericp/ixy, Comment: in ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS 2019), 2019

We propose a branch-and-bound algorithm for minimizing a bilinear functional of the form \[ f(X,Y) = \mathrm{tr}((X\otimes Y)Q)+\mathrm{tr}(AX)+\mathrm{tr}(BY) , \] of pairs of Hermitian matrices $(X,Y)$ restricted by joint semidefinite programming constraints. The functional is parametrized by self-adjoint matrices $Q$, $A$ and $B$. This problem generalizes that of a bilinear program, where $X$ and $Y$ belong to polyhedra. The algorithm converges to a global optimum and yields upper and lower bounds on its value in every step. Various problems in quantum information theory can be expressed in this form. As an example application, we compute Dobrushin curves of quantum channels, giving upper bounds on classical coding with energy constraints., Comment: 30 pages, 12 figures