This paper considers the attitude determination problem of a three-vehicle heterogeneous formation, whose line of sight is constrained. By some reason, such as large distance, two of the vehicles, the deputies, cannot measure the direction of each other's position. The deputies can only measure the relative direction relative to the chief, which is the remaining vehicle in the formation. The special cases of the problem are described in terms of the number of solutions related to each configuration. In general, there is only one solution, but infinite or even two ambiguous solutions are possible, respectively in the degenerate and ambiguous configurations. These arise due to lack of information or symmetry in the data, respectively. Furthermore, the subset of special configurations is a zero measure set of the global set of configurations, which is an important property of the problem. Simulations that test and validate the described configurations are shown as well, which also help to understand what effects the quality of the solution obtained according to a special case proximity metric.

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).

This paper addresses a new attitude determination problem for formations. It considers a three-vehicle formation with relative and inertial measurements from sensors, where Constraints limit the relative measurements, which are not available between two of the vehicles, also known as deputies. The other vehicle is called the chief and does not have any limitation. Furthermore, each of the vehicles has an independent inertial measurement, whose references are known. The goal is to determine all attitude relations, both inertial and relative. The solution for this problem is divided into different stages. First, the relative attitude between the chief and the deputies is assessed, which results in two candidates for each of these relations. Then, each candidate yields a candidate for the inertial attitude of the chief. Next, comparing the four inertial candidates gives the solution for their respective relations and consequently for the relative relations as well. The remaining relations derive directly from those already known. The paper also provides some early insights about degeneracies, possible particular cases of the solution, and the effect of sensor noise. Finally, the solution is validated with a simulation, whose results are similar to attitude determination problems in constrained formations.