A reference density database of aqueous alkali halide solutions is presented. The solutes are the 20 alkali halides consisting of the cations Li+, Na+, K+, Rb+, Cs+, and anions F−, Cl−, Br−, I−. Experimental density data of these aqueous electrolyte solutions are extensively collected and critically evaluated. A data evaluation procedure is proposed, utilizing support vector regression (SVR) to compare the experimental datasets against each other. Data evaluation is based on agreement with data from other sources rather than accuracy claimed in the literature. Datasets with large deviation from others are identified and removed. The proposed reference database consists of 11 081 data points of 586 datasets from 309 references. Maximum deviations between the selected datasets do not exceed 1%, and are smaller than 0.5% for most of the aqueous alkali halide solutions. SVR models are also trained based on the reference database. Data distribution is visualized using a Gaussian mixture model. Applicability domains of the SVR models are analyzed using Williams plots. An executable program is provided for calculations based on the SVR models.

No abstract

No abstract

This article concerns airflow-based odometry for estimating MAV flight speed from airflow measurements provided by a set of thermal anemometers. Our approach relies on a Gated Recurrent Unit (GRU) based deep learning approach to extract deep features from noisy and turbulent measurement signals of triaxial thermal anemometers, in order to establish the underlying mapping between the airflow measurement and the flight speed. The proposed solution is validated on a multi-rotor MAV. The results show that the GRU-based model can effectively extract noise features and perform denoising, and compensate for induced velocity effects along the propellers’ rotation axis. As a consequence, robust prediction of the flight speed is performed, including during takeoff and landing that induce ground effects and strong variations of vertical airflow.