In recent years, due to the proliferation of inertial measurement units (IMUs) in mobile devices such as smartphones, attitude estimation using inertial and magnetic sensors has been the subject of considerable research. Traditional methods involve probabilistic and iterative state estimation; however, these approaches do not generalize well over continuously changing motion dynamics and environmental conditions. Therefore, this paper proposes a deep learning-based approach for attitude estimation. This approach segments data from sensors into different windows and estimates attitude by separately extracting local features and global features from sensor data using a residual network (ResNet18) and a long short-term memory network (LSTM). To improve the accuracy of attitude estimation, a multi-scale attention mechanism is designed within ResNet18 to capture finer temporal information in the sensor data. The experimental results indicate that the accuracy of attitude estimation using this method surpasses that of other methods proposed in recent years.

Here is an analysis of the tale of the marriage of al-Hadhād (of the Himiar royal dynasty) with a woman of jinn found in Arabic sources dated from the 9th to 12th centuries. In the light of archaeological data and other folklore sources collected by scholars in the last 60 years (Serjeant, Daum, Rodionof), this tale could be interpreted as a foundation myth, with its strong anthropological and political implications, for the community of Maʾrib, the capital city and the main site of Sabaic religiousness in pre-Islamic times. It could also provide some keys of interpretation of a more general religious sensitivity in Arabia encompassing polytheistic or monotheistic creeds.