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Al-20Si-5Fe melt was rapidly solidified into particles and ribbons and then consolidated to near full density by hot pressing at 400°C/250 MPa/1 h. According to the eutectic-growth and dendritic-growth velocity models, the solidification front velocity and the amount of undercooling were estimated for the particles with different sizes. Values of 0.43-1.2 cm/s and 15-28 K were obtained. The secondary dendrite arm spacing revealed a cooling rate of 6 × 10 5 K/s for the particles with an average size of 20 μm. Solidification models for the ribbons yielded a cooling rate of 5 × 10 7 K/s. As a result of the higher cooling rate, the melt-spun ribbons exhibited considerable microstructural refinement and modification. The size of the primary silicon decreased from approximately 1 μm to 30 nm while the formation of iron-containing intermetallic compounds was suppressed. Supersaturation of the aluminum matrix in an amount of ~7 at.% Si was noticed from the XRD patterns. During the hot consolidation process, coarsening of the primary silicon particles and precipitation of β-Al 5 FeSi phase were observed. Evaluation of the compressive strength and hardness of the alloy indicated an improvement in mechanical properties due to the microstructural modification.

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<p>Soil water content (SWC) is an essential climatic variable that plays a leading role in different soil-related applications in the ecosystem. Especially in agriculture, SWC controls the plants' growth and soil productivity, so its accurate estimation could improve the crops’ yields and provide necessary information to optimize water consumption in precision farming.</p> <p>Optical sensors cannot accurately measure SWC in agricultural parcels, because agricultural fields are predominantly vegetated and covered with plants with high biomass. A Ground Penetrating Radar (GPR) sensor that can penetrate the ground and record pertinent information on soil roughness and its dielectric constant could be a solution for SWC monitoring in agricultural fields.</p> <p>In this study, we investigate the potential of a GPR sensor mounted on a drone for monitoring SWC on a profile scale from the surface to a specified depth. The results demonstrated that both the GPR reflection and derivative variables associated with the amplitude could be utilized as informative input variables in an Artificial Neural Network model for the estimation of the SWC.</p>