A generic framework for federated CDEs applied to Issue Management

This paper analyses the requirements for managing interoperable building data in a federated Common Data Environment (CDE). We discuss the need for generic (meta)data storage patterns, semantic query interfaces, decentral authentication, data aggregation, and adaptation and prove that their combination is feasible with current-day technologies. We illustrate the mechanisms of such federated CDE by considering the topic of digital Issue Management, one of the primary functions of a CDE. In an exemplary data flow process, we show how generic (federated, Semantic Web-based) data patterns for Issue Management can be aggregated and restructured to match existing industry standards like buildingSMART's BIM Collaboration Format (BCF) API. Finally, we show the methodology is compatible with current-day practice by implementing this process in a proof of concept. The main contribution of this research is a generic, federated framework for project-related, interdisciplinary collaboration for CDEs.</p

On the Capacity of Correlated Phase-Noise Channels:An Electro-Optic Frequency Comb Example

The capacity of a discrete-time channel with correlated phase noises is investigated. In particular, the electro-optic frequency comb system is considered, where the phase noise of each subchannel is a combination of two independent Wiener phase-noise sources. Capacity upper and lower bounds are derived for this channel and are compared with lower bounds obtained by numerically evaluating the achievable information rates using quadrature amplitude modulation constellations. Capacity upper and lower bounds are provided for the high signal-to-noise ratio (SNR) regime. The multiplexing gain (pre-log) is shown to be M − 1, where M represents the number of subchannels. A constant gap between the asymptotic upper and lower bounds is observed, which depends on the number of subchannels M. For the specific case of M = 2, capacity is characterized up to a term that vanishes as the SNR grows large.</p

Recent Results on Science and Innovation Related to Electrical Processes of Thunderstorms

Lightning is a highly energetic electric discharge process in our atmosphere, evolving in several complex stages. Lightning is recognized as an essential climate variable, as it affects the concentration of greenhouse gases. It also threatens electrical and electronic devices, in particular, on elevated structures like wind turbines, and it endangers aircraft built with modern composite materials with inherently low electric conductivity. During the past decades, our fundamental understanding of atmospheric electricity has continued to evolve. For example, during the past 30 years, discharge processes were discovered in the atmosphere above thunderstorms, the so-called transient luminous events (TLEs) in the stratosphere and mesosphere, and terrestrial gamma-ray flashes (TGFs), accompanied with beams of photons, electrons and positrons, were observed from low orbiting satellites passing over thunderstorms. Lightning-like discharges also appear in plasma and high-voltage technology. The SAINT network was formed to bring the different research fields together. SAINT was the “Science And INnovation of Thunderstorms” Marie Skłodowska-Curie Innovative Training Network of the European Union Horizon 2020 program. From 2017 to 2021, 15 PhD students observed lightning processes from satellites and ground, developed models and conducted laboratory experiments. The project bridged between geophysical research, plasma technology and relevant industries. The paper presents a summary of the findings of the SAINT network collaboration.</p

How unsafe was the scenario? A criticality measure for scenario-based testing of automated vehicles

Scenario-Based Testing (SBT) is the most widely used method for safety assurance of Automated Driving System equipped Vehicles (ADS-Vs). To comply with ISO 21448, a key standard for ADS-V safety assurance, SBT can be formulated as an optimization problem to identify unknown hazardous scenarios within the ADS-V’s Operational Design Domain. To identify hazardous scenarios through optimization, an appropriate cost function for scenario hazardous-ness is needed that satisfies specific requirements. Existing criticality measures in the literature do not satisfy these requirements. We interpret scenario hazardous-ness as the proximity to a collision for the ADS-V. We propose a Criticality Measure (CM), indicating the proximity to a collision using the set of alternative future trajectories feasible for a Reference Vehicle (RV) tracing the trajectory of the ADS-V. The proposed CM provides a posteriori knowledge about the criticality of a scenario already generated by the optimization where the future evolution of every scene is available. To showcase the developed CM, multiple scenarios in two road networks, an intersection, and a highway segment are evaluated for criticality. We briefly discuss how to incorporate the severity of collisions/potential future collisions in the CM. The possible extensions and limitations of the proposed CM are discussed

Electromagnetic-Thermal Analysis of an HTS Linear Motor for High-Dynamic Applications

This article presents and applies an electromagnetic-thermal model to design a double-sided coreless, conduction-cooled high temperature-superconductor (HTS) linear motor for a high-dynamic motion application, and assesses the thermal stability of the superconducting coils for continuous long term operation. The analysed motor topology contains stationary DC operated superconducting coils and conventional three-phase AC commutated mover-coils. The stator is a vacuum chamber which houses a cryogenic assembly containing the superconducting coils. The framework utilizes three computationally efficient models: a two-dimensional finite-element-method (2D FEM) model to evaluate the feasibility of superconducting coils under static conditions, a semi-analytical model to compute the motor thrust and eddy-current losses in electrically conductive structures of the cryostat during dynamic motion, and a 2D FEM full-scale model of the linear motor for overall loss calculation in the stator. The motor design, optimized for minimum volume, and an operating temperature of 20 K, produces a peak magnetic flux density of 5.43 T in the air gap in static conditions which results in a force density of 4700 kN/m3. Results show that steady-state temperature in the superconducting coils does not exceed 25 K. As such, the dynamic losses do not result in quenching of superconducting coils. This paper shows that a reliable operation of superconducting coils during high-dynamic motion condition is feasible

Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs

This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) complexity, compared to backpropagation, which achieves 88.8% accuracy with O(n2logn) complexity

Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs

This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) complexity, compared to backpropagation, which achieves 88.8% accuracy with O(n2logn) complexity

Polarization-Insensitive, Highly-Selective Metasurface-Based Filtenna for Satcom Applications

This paper presents a planar, polarization-insensitive, space-compatible, skirt-selective filtering transmitarray for satellite communications. The transmitarray employs a double-layer frequency selective surface (FSS) to enable filtering witha sharp roll-off factor. A novel hybrid square ring-modified split ring resonator (SRR) unit cell is introduced by arranging four SRRs in sequential rotation in a rectangular array. The SRRs are integrated with an internal strip and are inductively coupled to each other. Their design is optimized using both numerical techniques and a semi-analytical procedure based on equivalent circuit theory. The sequential rotation of the SRRs enables a stable wide-band response while maintaining a constant impedance to electromagnetic waves impinging on the FSS with either vertical or horizontal linear polarization. The selected unit cell shape allows for achieving a highly selective pass-band response with a significant rejection level and the synthesis of a narrow beam in an array configuration. A single FSS is capable of producing an effective filtering response on its own. This concept lends itself to a higher-order filteringconfiguration using a double-layer FSS. The double-layer FSS is designed to operate in Ku-Band (with a wideband 10 dB impedance bandwidth of 14.74 % (center frequency (fo) = 14.8 GHz), 3 dB transmission bandwidth of 19.1 % (fo = 14.9 GHz), and flat 0.2 dB transmission bandwidth of 11.7 % (fo = 14.74 GHz)), while displaying polarization insensitivity. The achieved roll-off is sharp and is larger than 29 dB/0.25 GHz across both the upper and lower passband edges. Furthermore, the antenna radiates a narrow broadside beam with half power beamwidth lower than 7-degrees and side lobe levels smaller than 12 dB at passband frequencies. Full-wave simulation results are presented and discussed

STEMS:Spatial-Temporal Mapping for Spiking Neural Networks

Spiking Neural Networks (SNNs) are event-driven bio-inspired neural networks. Recent research has trained SNN models with accuracy on par with Artificial Neural Networks (ANNs) on computer vision tasks. Due to their sparse, event-based computation, SNNs are particularly promising for energy-efficient processing, especially in event-based vision applications. However, neurons have internal states which evolve over time and keeping track of them can be costly. Hence, efficiently deploying them, especially on memory-constrained edge devices, requires careful mapping of their computation across both spatial and temporal dimensions. To address this issue, we introduce STEMS, Spatial-Temporal Mapping for SNNs. STEMS supports inter-layer mapping exploration, as well as loop tiling optimizations. By applying STEMS inter-layer exploration, we show up to 12× reduction in external memory traffic and up-to 5× reduction in energy consumption. Finally, we show that neuron states may not be needed in early SNN layers. By optimizing neuron states in one of our benchmarks, we reduced neuron states by 20x and improved energy performance by 1.4x saving without sacrificing accuracy.</p

Substituting PFAS modifiers with more sustainable alternatives in passive ice-phobic epoxy composite coating

Traditional antiicing systems, typically based on active methods, often require additional energy input or contribute to environmental pollution. Hence, there is a significant market demand for more environmentally friendly and passive solutions. This study explores the development of passive antiicing coatings with alternatives to the perfluoro compounds commonly used in epoxy composite coatings. Due to the environmental and health hazards of per- and polyfluoroalkyl substances (PFAS) compounds, this work presents an approach to exchange 3-(perfluorooctyl)-1,2-propenoxide by alternative additives such as 1,2-epoxyhexadecane, or 1-aminohexadecane, or 1-pentadecanecarboxylic acid. The influence of these additives on the properties of rough epoxy coating filled with micro- (SiO2) and nano-particles (Al2O3), such as on surface wettability and antiicing properties (e.g., ice nucleation times or ice adhesion), was investigated. FTIR, TGA, DSC, CLSM, and SEM-EDS were used to characterize obtained coatings. The coating with the acid additive could be used as a viable alternative, although the large roughness heterogeneity of the coatings studied would still require further optimization.</p