Cellular signaling is driven by complex, dynamic phosphorylation networks that control growth and survival, and their dysregulation underlies diseases such as cancer. Although modern mass spectrometry enables large-scale quantification of phosphoproteomic responses over time, these measurements remain descriptive and cannot by themselves predict how signaling will evolve under perturbations. Here, we extend a biologically informed recurrent neural network framework (LEMBAS), to learn time-resolved phosphoproteomic trajectories. We introduce two interpretable modules; a phosphosite mapping that links signaling nodes to measured phosphorylation sites and a monotonic time mapping that aligns continuous experimental times to discrete signaling steps. Using synthetic benchmarks and an EGF-stimulation dataset with inhibitor treatments, the model accurately interpolates unseen time points and predicts drug-induced phosphoproteomic responses in a zero-shot setting, outperforming naïve and fully connected baselines. Importantly, the model identifies both canonical and non-canonical signaling effects, including modulation of the transcription factor FOXO3:S7 (from the PI3K/AKT pathway) by drugs affecting PTPN11 (from the RAS/ERK pathway). By combining mechanistic priors with deep learning, our framework provides a scalable approach to interpret and predict dynamic drug responses from phosphoproteomic data.

Indoor localization is a critical component of various applications, including assisted living, personnel monitoring, and asset tracking. Traditional localization methods relying on specialized sensors such as LiDAR, ultrasound, and 3D cameras offer high precision but suffer from high costs and limited interoperability. To address these challenges, this paper explores the Integrated Sensing and Communication (ISAC) paradigm, leveraging signal-based modalities focusing on received signal strength indication (RSSI). These measurements, inherently embedded in wireless communication packets, enable cost-effective and vendor-agnostic localization without the need for additional hardware. However, practical deployment remains challenging due to signal degradation from obstacles, multipath effects, and reflections. This paper presents an end-to-end localization framework utilizing COTS IoT devices and advanced RSSI processing techniques to enhance measurement reliability. By integrating filtering mechanisms and machine learning models, the proposed solution improves distance estimation, categorizes line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and enhances localization accuracy. Additionally, an open and extendable edge-to-cloud infrastructure supports scalability and real-time processing. Experimental evaluation demonstrate the effectiveness of this approach in various aspects such as increase of RSSI reliability (increase of up to 82 % regarding standard deviation, drastic reduction of outliers' detection and fluctuation of more than 90 % to distances up to 2 m), accurate LoS-NLoS classification up to 99 % and overall localization increased accuracy more than 83 %.

The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

Realizing desired properties “by construction” is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving.

The integration of connected and autonomous technologies in safety-critical and cyber-physical systems offers great potential in the vital application domains of transportation, manufacturing and aerospace. These technological advancements are necessary to meet the increasing demand for intelligent services, as they open doors to new business models by analysing and sharing the generated data. However, where this sharing of mix-critical data and broader connectivity brings opportunities, it simultaneously presents serious cybersecurity and safety risks due to the cyber-physical nature of these systems. Hence, delivering these intelligent services securely, safely, and reliably to its consumers is a complex engineering and design problem. One of the ways to approach this engineering problem is to consider both system functional and non-functional properties (safety, security, reliability) and systematically integrate them across system design and operational life cycle. The XANDAR project investigates this approach and aims to develop holistic software design methods and architectures for safety-critical and cyber-physical systems that guarantee functional and non-functional properties “byconstruction”. This paper focuses on the non-functional aspects of the project and discusses the preliminary work. by presenting the core cybersecurity principles and uses them as a baseline to propose a holistic cybersecurity engineering process. The tasks of the proposed cybersecurity engineering process are also map onto relevant clauses of ISO 21434. In future, proposed work will be integrated into the XANDAR software toolchain and validated for an avionics situation perception pilot assistance and automotive autonomous driving use cases.

The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, development of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems, which relies on Machine Learning (ML) and AI applications when combined with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are Advanced Driver-Assistance Systems (ADAS) that are prone to various safety/security vulnerabilities. The XANDAR project aims at developing a mature SW toolchain (from requirements analysis to the actual code integration on target including V&V) fulfilling the needs of industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel (real-time, safety-, and security)-by-Construction paradigm.

This paper introduces a Quaternion-based rotational representation of low power, low resource, commercial-of-the-shelf (COTS) IMU sensors, offering high accuracy, reliability and real time performance. A critical aspect of this work is the distributed implementation of the proposed solution that facilitates its integration with IoT platforms utilizing the MQTT communication protocol while efficiently applying a modified Madgwick's filter implementation to extract the sensor's orientation and rotation. Based on the proposed solution, sensor orientation outperforms conventional implementations based on previously widely applied methods like Euler Angles and effectively tackles important shortcomings such as a sluggish response and issues like Gimbal Lock. Comprehensive, experimental evaluation reveals that the proposed solution yields state-of-the-art accuracy, while being deployed on a typical, low-cost COTS sensor while delay measurements indicate a highly reliable and time constrained performance adequate for a wide range of real life scenarios.

Nowadays Wireless Sensor Networks are increasingly accepted as a reliable solution to highly demanding and critical application scenarios such as military and medical environments, where security support is an absolute prerequisite. However, supporting security implies the execution of cipher algorithms posing significant overheads on WSN nodes, which suffer from scarce resource availability. Moreover, the system wide overheads imposed by requirements related to network performance and behavior are not adequately addressed in current literature. In that direction, this paper intends to evaluate these effects for critical network parameters. The results presented are based on existing results reported in literature measurements concerning the performance overhead imposed by widely utilized encryption algorithms that have been developed for prominent WSN platforms. To evaluate the measurements, the execution performance of three popular cipher algorithms, has been integrated in Omnet++/MiXiM, a well known and widely utilized WSN network simulator. As part of this work, critical insights are provided concerning the system wide effect of deploying security algorithms which are not taken into account when focusing solely in the security algorithm measurements. Furthermore, important trade-offs are provided both qualitatively and quantitatively.