Abstract: A sensor module assembly for a respiratory therapy apparatus may be configured for a seal-based securement within a respiratory therapy device that includes a separable pneumatic block to make assembly or replacement more efficient. The sensor module assembly may include a flow manifold. The flow manifold may have an inlet and an outlet and a main pneumatic path between the inlet and the outlet. The assembly may have a sensor module with sensor(s) on a printed circuit board. The sensor module may be pneumatically and fixedly coupled to the flow manifold. The inlet of the flow manifold may be configured for removable coupling with an outlet of the pneumatic block assembly. Moreover, the outlet of the flow manifold may be configured for removable coupling with a pneumatic path of a housing panel of the respiratory therapy apparatus. The pneumatic path is adapted for supplying breathable gas to a patient interface.

Abstract: A booming cell start distance and recommended electronic tilt is identified by retrieving a list of cells served by a first base station. A plurality of grids is generated from the first base station to a predetermined threshold distance. A plurality of selected grids is identified between an acceptable coverage limit and a threshold distance. An evaluation is made regarding whether the first base station is not the dominant cell in each of the selected grids based. The number of grids where the first base station is not the dominant cell site is determined based on a dominant carrier threshold. A column of grids where the first base station is no longer the dominant cell site is determined based on the dominant carrier threshold. A bad booming distance of the cell is determined based on the distance from the first base station and the determined column of grids.

Abstract: A cell range determination is made by determining a sector straddling an azimuth line of a first base station with a center at the coordinates of a first base station. The sector is divided into a plurality of subsectors. A nearest neighbor base station is determined in each of the plurality of subsectors. A set of coordinates is determined for the nearest neighbor base station. An average distance between the nearest neighbor base stations is determined. A bearing angle difference between the nearest neighbor base station and the first base station is determined based on the set of coordinates of the nearest neighbor base station. A gain is determined for each of the plurality of subsectors based on the bearing angle difference. A cell range is determined for the first base station based on the gain.

Abstract: A method for classifying a microseismic event, including: analyzing microseismic event files through a combination of two fault tolerant machine learning pipelines, an acoustic machine learning pipeline and a visual machine learning pipeline; and generating a classification prediction for the microseismic event files by combining predictions from the acoustic machine learning pipeline and the visual machine learning pipeline.