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Porous Carbon Materials for Electrochemical Applications
No full textThe increasing demand for energy storage leads to a high interest in battery technologies. Batteries with high specific performance, sufficient cycle stability, wide operating temperature range, light weight, high safety, and low cost are currently the subject of global research activities. The DLR & UBC collaboration benefits from high expertise of both partners and is working onadvancing the development of next-generation batteries based on aerogel materials with carbon additives.
The electrical conductivity of powdered carbon materials is one of the key factors for electrochemical applications. The study presents synthesis, characterization, and the correlation between structural, physical, mechanical and electrical properties of pure carbon aerogels, as well as aerogel composites. The influence of carbon feedstock activation on the properties of carbon aerogels, in particular on electrical conductivity, is shown. Additionally, the impact of adjusting the electrical conductivity of several aerogel-composites and their suitability as conductive additives in Li-ion batteries is explored
Multi-Sensor Vigilance Detection: Sensor Reliability and Comfort in Automated Train Operations
No full textThe increasing levels of automation in rail transport, such as Grade of Automation Level 2 (GOA2), introduce new challenges for train driver vigilance. Despite several operational advantages of partial automation, human operators are required to maintain a high level of attention to ensure safe operations. Prolonged monitoring tasks can lead to mental fatigue and reduced vigilance, increasing the risk of errors. One potential solution to this issue lies in multi-sensor systems that can monitor the driver’s physiological state and detect signs of low vigilance in real time. The HMI4Rail project addresses this challenge by investigating the reliability and feasibility of various sensor types for detecting low vigilance in train drivers.
To explore this, a study was conducted using a high-fidelity train simulator with 14 professional train drivers. Each driver completed three simulator drive sessions with the train driving fully automated. All drives required no train operation besides activation of the dead man’s button. In the second drive, a mental fatigue induction task was administered. This task involved a 55-minute n-back task with four varying difficulty levels, designed to induce mental workload and simulate the mental fatigue experienced during prolonged real-world train driving.
Throughout the study, several body-worn sensors were employed to measure physiological signals associated with mental fatigue. These included an electrocardiogram (ECG) to track heart activity, a 26-channel wet electroencephalogram (EEG) to monitor brain waves, a breathing belt to measure respiration, eye-tracking glasses to measure blink rates, and skin conductance sensors to capture changes in sweat production, a known indicator of stress and arousal. Each of these sensors was selected based on previous research demonstrating their efficacy in detecting changes in mental state, particularly under conditions of mental fatigue.
In this talk, we will present the initial findings from our study, focusing on the comparison of sensor measurements between the baseline and post-intervention simulator drives. These results will provide insights into how the drivers' physiological states changed after the mental fatigue-inducing task, offering key information on the reliability of each sensor type for monitoring vigilance in real-world applications. Additionally, we will discuss feedback from the participants regarding the comfort and practicality of wearing these sensors during their driving tasks. The wearability and comfort of these sensors are crucial factors in their potential integration into smart garments for train drivers.
Our findings will inform future developments in smart garments for train drivers, with the goal of enhancing safety in automated rail systems by providing real-time monitoring of driver vigilance. By identifying the most reliable sensors and addressing comfort concerns, the HMI4Rail project takes an important step towards implementing wearable technology that supports the performance of train operators in increasingly automated environments. In a next step, the same sensor system will be tested during a real-world automated drive
Experience Atlas: Travel experience in intermodal public transport for service innovation
No full textThe decision to travel by public transport often involves navigating intermodal journeys, requiring travelers to switch between various modes like trains, trams, buses, and walking. These intermodal journeys are frequently associated with lower travel satisfaction due to unmet needs for safety, comfort, accessibility, efficiency, reliability, or information. The project Experience Atlas (German original title: Erlebensatlas) aims to address these issues by mapping geo-tagged collective travel experiences onto the public transport map to identify locations of collective positive and negative travel experience. This can help to identify where and why pain points occur along intermodal journeys and how they might be alleviated.
The project explores various data sources for measuring travel experience. These include subjective traveler feedback during the journey, physiological data (e.g., heart rate monitoring), and contextual information like weather and traffic conditions. These datasets can offer a dynamic, real-time picture of the travel experience, enabling operators to spot potential service disruptions or traveler discomfort as they happen.
This expo session talk will present results from such geo-tagged travel experience data collected in public transport of Hamburg and Berlin. In addition, it will discuss different approaches for visualizing such spatial travel experience data as an experience atlas. By this, we aim to provide a more nuanced understanding of the travel experience by visualizing hot and cold spots of public transport experience. This offers the potential to improve service offerings, adapt information given to customers, and ultimately enhance traveler satisfaction across intermodal journeys.
One of the key users of these visualizations will be mobility providers, who currently have no feasible option to understand dynamics in travelers’ experience. To ensure the visualizations are meaningful and actionable, we conducted a series of interviews with key decision-makers responsible for customer adaptation at a major mobility provider. By this, we identified requirements for the functionality of the experience atlas. These will be presented alongside the travel experience results and provide insights into what types of data need to be captured and how best to represent them to support service innovation.
Overall, the Experience Atlas will enable mobility providers to adopt more passenger-focused innovations in public transport services. This, in turn, is expected to lead to more positive travel experiences and encourage greater adoption of public transport
EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response
No full textWith growing concerns about climate change, increasing natural hazards, and extreme weather events, monitoring Earth’s surface parameters has become a critical area of interest for both the scientific community and society. Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative and low-cost technique that exploits existing Global Navigation Satellite System (GNSS) signals after reflection from Earth’s surface. GNSS-R constellations offer unique observations with unprecedented data volume, temporal resolution, and spatial coverage across the entire globe under all-weather conditions. As the data volumes are continuously accumulating, the trend in applying Artificial Intelligence (AI) is expanding. However, current AI models rely heavily on labelled data, feature engineering, and extra fine-tuning, leading to high computational and labor costs. To address these issues, we propose the project EcoGEM: Energy-efficient Multimodal GNSS Reflectometry Models for Generalist Earth Surface Monitoring and Hazard Response.
EcoGEM develops cutting-edge Earth observation foundation models using GNSS-R measurements and integrates them with other remote sensing data. It pioneers the first general-purpose GNSS-R foundation models and curated multimodal datasets to support climate science, hazard detection, and environmental monitoring. Unlike task-specific methods, the proposed models adapt across applications such as soil moisture, vegetation water content, and ocean wind speed. Uniquely, EcoGEM emphasizes energy-efficient AI through model pruning, knowledge distillation, and dynamic architectures, enabling deployment on edge devices and small satellite platforms. This collaborative project of GFZ and DLR advances sustainable AI and promotes novel and open-access tools for Earth scientists, environmental policymakers, and global users
How to Integrate Vertiport Operations Into the Airspace? Introducing the EUREKA Project
Full text linkInnovative Air Mobility (IAM), as envisioned by the European Aviation Safety Agency, is "the safe, secure and sustainable air mobility of passengers and cargo enabled by new-generation technologies integrated into multimodal transportation system." Vertiports, new aircraft and new air traffic management systems are essential for implementing IAM. The European project EUREKA, running from 2023 to 2026, aims at enabling key solutions for vertiports. Solutions for arrival and departure for vertiports, collaborative traffic management, how to deal with emergencies and disruptions also the network flow, capacity and operational management are developed in the project. This paper introduces the EUREKA project and reports on the early progress
Optimization of Hybrid Quantum-Classical Algorithms
Full text linkQuantum computers do not run in isolation; rather, they are embedded in quantum-classical hybrid architectures.
In these setups, a quantum processing unit communicates with a classical device in near-real time.
To enable efficient hybrid computations, it is mandatory to optimize quantum-classical hybrid code.
To the best of our knowledge, no previous work on the optimization of hybrid code nor on metrics for which to optimize such code exists.
In this work, we take a step towards optimization of hybrid programs by introducing seven optimization routines and three metrics to evaluate the effectiveness of the optimization.
We implement these routines for the hybrid quantum language Quil and show that our optimizations improve programs according to our metrics.
This lays the foundation for new kinds of hybrid optimizers that enable real-time collaboration between quantum and classical devices
Hybrid Krylov-Subspace Methods for Solving Non-Linear PDEs on Quantum Computers
No full textNumerical solvers for Partial Differential Equations (PDEs) are of great interest in various domains, e.g., in aerodynamics or for transport equations in lectrochemistry [1], and the need for fine-grained solutions of on-linear PDEs is growing. While subspace methods allow for a dimensionality reduction, non-linear PDEs require linearization schemes, such as the Carleman-linearization [2], resulting in linear systems of exponential dimensionality, operating on the limits of classical methods. Motivated by Krylov-subspace methods [3], which find approximate solutions in iteratively growing subspaces, the aim of this talk is to investigate the potential of two existing methods from quantum computing to compose a NISQ-era hybrid quantum-classical algorithm. Firstly, non-linear quantum computing (QNPUs) [4,5] and secondly Quantum Subspace Expansion (QSE) [6], both promising tools towards more scalable computations. The use of QNPUs enables linearization in a tensor-product-subspace using ancilla qubits, while offering efficient gate-based implementations. On the other hand, QSE measures high-dimensional overlaps on a quantum computer. The combination of these two methods yields the possibility of encapsulating the high-dimensional steps of linearization and subspace projection on a quantum device. As a result, only a lower-dimensional subspace problem remains to be solved on classical hardware
