Abstract. Experiments were conducted to quantify the effects of entrapped air on water infiltration into a loamy sand. Transparent three-dimensional (3-D) and 2-D columns were used for experiments carried out for two infiltration conditions: (1) when air was free to move ahead of the wetting front and leave the bottom of the column (air draining) and (2) when air was confined ahead of the wetting front and hence could escape only through the soil surface (air confining). The measurement setup was composed of a tensionpressure infiltrometer, an air flowmeter, water manometers, and video-picture cameras. We applied both positive and negative water pressures at the soil surface and measured the simultaneous changes in the rates of water inflow and air outflow, the air pressure ahead of the wetting front, and the dynamic behavior and advance of the wetting front. The air pressure ahead of the wetting front for the air-confining condition was generally found to increase with time rather than reaching a constant level, as observed in other studies by other researchers. The air pressure fluctuated locally because of air escaping from the soil surface. On the basis of an analysis of the results we present two empirical equations to predict the maximum air pressure at which air begins to erupt from the soil surface and to predict the minimum air pressure at which air eruption stops. We found that the infiltration rate was always equal to, and controlled by, the rate of air outflow. The infiltration rate varied inversely with the air pressure ahead of the wetting front and with the ponding depth at the soil surface. The infiltration rate fluctuated with time rather than undergoing changes in a three-stage process, as is often characterized in the literature. The volume of residual entrapped air in the air-confining condition increased 7% on average, and the infiltration rate decreased threefold to tenfold as compared to the air-draining condition. Finally, it was shown that the air-confining infiltration flow is fingered and unstable, consistent with the predictions of an existing theory.
Bessel beams are advantageous in high aspect-ratio microhole drilling because of their immunity to diffraction. However, conventional methods of generating Bessel beams result in poor adjustability of the nondiffraction length. In this study, we theoretically describe and experimentally demonstrate the generation of Bessel-like beams (BLBs) with an adjustable nondiffraction length by using a phase-only spatial light modulator. In this method, nondiffraction lengths varying from 10 to 35 mm can be achieved by changing the designed phase profile (curvature). High-quality, high aspect ratio (560:1) and length-adjustable microholes can be drilled by spatially shaping a femtosecond laser beam.
To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). By collectively training a unified learning model on edge devices, FL bypasses the need of direct data transmission, thereby addressing problems such as latency issues and privacy concerns inherent in centralized ML. However, FL still faces some critical challenges in deployment. One major challenge called straggler issue severely limits FL's coverage where the device with the weakest channel condition becomes the bottleneck of the model aggregation performance. Besides, the huge uplink communication overhead compromises the effectiveness of FL, which is particularly pronounced in large-scale systems. To address the straggler issue, we propose the integration of an unmanned aerial vehicle (UAV) as the parameter server (UAV-PS) to coordinate the FL implementation. We further employ overthe-air computation technique that leverages the superposition property of wireless channels for efficient uplink communication. Specifically, in this paper, we develop a novel UAV-enabled overthe-air asynchronous FL (UAV-AFL) framework which supports the UAV-PS in updating the model continuously to enhance the learning performance. Moreover, we introduce a 'staleness upper bound' metric to control the asynchronous level in AFL and conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning performance. Based on this, a unified communication-learning problem is formulated to maximize asymptotical learning performance by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results reveal valuable insights into the UAV-AFL system and demonstrate that the proposed UAV-AFL scheme achieves substantially learning efficiency improvement compared with the state-of-the-art approaches.
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