A reliable source of randomness is not only an essential building block in various cryptographic, security, and distributed systems protocols, but also plays an integral part in the design of many new blockchain proposals. Consequently, the topic of publicly-verifiable, bias-resistant and unpredictable randomness has recently enjoyed increased attention. In particular random beacon protocols, aimed at continuous operation, can be a vital component for current Proof-of-Stake based distributed ledger proposals. We improve upon previous random beacon approaches with HydRand, a novel distributed protocol based on publicly-verifiable secret sharing (PVSS) to ensure unpredictability, bias-resistance, and public-verifiability of a continuous sequence of random beacon values. Furthermore, HydRand provides guaranteed output delivery of randomness at regular and predictable intervals in the presence of adversarial behavior and does not rely on a trusted dealer for the initial setup. Compared to existing PVSS based approaches that strive to achieve similar properties, our solution improves scalability by lowering the communication complexity from $\mathcal{O}\left( {{n^3}} \right)$ to $\mathcal{O}\left( {{n^2}} \right)$ . Furthermore, we are the first to present a detailed comparison of recently described schemes and protocols that can be used for implementing random beacons.

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14% for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.

In the context of large engineering projects the effective and efficient exchange and versioning of information from different engineering disciplines is essential. Semantic data integration approaches provide the necessary means to overcome the gap between heterogeneous local engineering tool concepts and common project-level concepts which enable the mapping of engineering data coming from different disciplines. However, evaluations have shown that ontology-based stores do not satisfy versioning and query performance criteria in case of large multi-disciplinary engineering projects. In this paper, we present an architecture solution that uses ontologies to describe engineering models and a NoSQL graph database to version individuals. We evaluate the software architectures according to quality attributes, i.e., performance and scalability, in the context of an industrial scenarios, and compare it with related approaches. Main results suggest that the architecture outperforms ontology stores and match solutions relying on relational databases.