The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties

Embedded digital devices are progressively deployed in dependable or safety-critical systems. These devices undergo significant hardware ageing, particularly in harsh environments. This increases their likelihood of failure. It is crucial to understand ageing processes and to detect hardware degradation early for guaranteeing system dependability. In this survey, we review the core ageing mechanisms, and identify and categorize general working principles of ageing detection and monitoring techniques for Commercial-Off-The-Shelf (COTS) components that are prevalent in embedded systems: Field Programmable Gate Arrays (FPGAs), microcontrollers, Systems-on-Chips (SoCs), and their power supplies. From our review, we find that online techniques are more widely applied on FPGAs than on other components, and see a rising trend towards machine learning application for analysing hardware ageing. Based on the reviewed literature, we identify research opportunities and potential directions of interest in the field. With this work, we intend to facilitate future research by systematically presenting all main approaches in a concise way.