Abstract: An airspace flow prediction method and device based on an ensemble learning algorithm are provided. The method includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node; and applying the predicted airspace flow of the node to manage navigation of traffic in the airspace network.

Abstract: A method and system of determining a z-distance between an optical fiber and a substrate are presented. The method can include, for instance: obtaining an image that includes an end of the optical fiber and a reflection of the end of the optical fiber from a surface of the substrate, and processing the image to determine a z-distance along a z-axis between the end of the optical fiber and the substrate.