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Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/B5G (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a neural network (NN) with a limited amount of ray tracing (or channel measurement) data and map data and then predicting the pathloss over location with no ray tracing data with a high level of accuracy. Our proposed pathloss map prediction-oriented NN architecture, which is empowered by state-of-the-art computer vision techniques, outperforms other architectures that have been previously proposed (e.g., UNet, RadioUNet) in terms of accuracy while showing generalization capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the other baselines (trained on a 4-fold larger dataset), corroborating the potential of PMNet.1 

We compare systems with single giant planets to systems with multiple giant planets using a catalog of planets from a high-precision radial velocity survey of FGKM stars. Our comparison focuses on orbital properties, planet masses, and host-star properties. We use hierarchical methods to model the orbital eccentricity distributions of giant singles and giant multiples, and find that the distributions are distinct. The multiple giant planets typically have moderate eccentricities and their eccentricity distribution extends toe= 0.47 (90th percentile), while the single giant planets have a pileup of nearly circular orbits and a long tail that extends toe= 0.77. We determine that the stellar hosts of multiple giants are distinctly more metal rich than the hosts of solitary giants, with respective mean metallicities of 0.228 ± 0.027 versus 0.129 ± 0.019 dex. We measure the distinct occurrence distributions of single and multiple giants with respect to orbital separation, and find that single gas giants have a ∼2.3σsignificant hot Jupiter (a< 0.06) pileup not seen among multigiant systems. We find that the median mass ($M\sin i$) of giants in multiples is nearly double that of single giants (1.71M_Jversus 0.92M_J). We find that giant planets in the same system have correlated masses, analogous to the “peas in a pod” effect seen among less-massive planets.

 

Stratospheric ozone intrusions can have a significant impact on regional near‐surface ozone levels. Especially in summer, intrusions can contribute to extreme ozone events because of preexisting high ozone levels near the surface and cause serious health issues. Considering the increasing trend of surface ozone level, an understanding of stratospheric ozone intrusion is necessary. From a 19‐year Whole Atmosphere Community Climate Model, version 6 simulation and a stratospheric origin ozone tracer, we identify the global hotspots of stratospheric intrusions based on extreme tracer concentrations near the surface: North America, Africa, the Mediterranean, and the Middle East. We investigate the common underlying large‐scale mechanisms of the stratospheric intrusions over the identified hotspots from the lower stratosphere to the lower troposphere. From the trajectory analysis, we find that the upper‐level jet drives isentropic mixing near the jet axis and initiates stratospheric ozone intrusion. Subsequently, climatological descent at the lower troposphere brings the ozone down to the surface, which explains the spatial preference of summertime stratospheric intrusion events.

 

This manuscript presents airborne jet propulsion by audio sounds and ultrasounds through orifices formed by bulk-micromachining of a silicon wafer. The propeller is integrated with a small, printed circuit board (PCB) with a DC/DC converter, an oscillator, and a power amplifier, all powered by a 100F lithium-ion capacitor to make the propeller operable wirelessly. The peak propulsion force of the wireless propeller is measured to be 63.1 mg (or 618 mN) while the packaged wireless propeller’s weight is 10.6 g, including the drive electronics and adapter) when driven by 2.5kHz sinusoidal voltage with 21.4Vpp. A wired propeller (with 563 mg weight without adapter) is shown to high jump, long jump, wobbly fly, and propel objects. Also, the propeller is shown to work when driven by ultrasounds with a maximum propulsion force of 8.4 mg (82 mN) when driven by 20kHz, 20Vpp sinusoidal signal. Varying the frequency gradient of the applied sinusoidal pulses is shown to move the propeller to the left or right on demand to reach a specific location.