The present work is framed inside a broader activity aimed at improving the accuracy of numerical models in predicting the crashworthiness behavior of flexible fuel tanks. This paper describes a comprehensive experimental and numerical study aimed at estimating the impact force of a test article, consisting of a soft nylon bag filled with water, subjected to crash impact tests. In order to understand and improve response predictions, the test article drops freely from different heights, and then strikes onto a rigid plate which is instrumented with different types of sensors. Strain gauges, piezoceramic sensors, and fiber optics are used to measure the strain induced by the impact force during the experiments. To tune the test matrix and the measurement chain parameters, numerical computations are carried out to predict the dynamics of drop impact through FE explicit analyses. Through analysis and comparison with experimental results, a relationship between strain and impact energy correlated with the drop height is established, and the overall accuracy of the entire measurement chain is assessed to determine the effectiveness of such a methodology in a full-scale test on a flexible fuel tank structure.

The current design practice of composite material aeronautical structures imposes the use of knock-down structural material allowables to take into account the high sensitivity to environmental exposure (i.e., moisture, temperature, damages). The “moisture derating factor” comes from specific mechanical test campaign and drastically reduces the advantage of using such materials; but the continuous monitoring of the moisture content of the structure could enable the use of higher design allowables. In the framework of FUSIMCO (Work developed within the frame of the Project FUSIMCO-FUSoliera Ibrida Metallo COmposito-co-financed by MIUR-Italian Ministry of Research with DAC-Campania Aerospace District as beneficiary and Leonardo Company-Aerostructure Division as “prime” partner) project, the aim of this study is to verify the effectiveness of the impedance measurement method as a health-monitoring tool to evaluate the moisture quantity absorbed by an aeronautical composite structure. The method is based on the idea that a composite laminate can be associated with an equivalent electric circuit (EEC). Some electrical characteristics of this EEC can be associated to the moisture content of the laminate. A simple EEC model, mainly capacitive, was used. A frequency sweep was the electric stimulus signal of some electrodes, glued onto the specimens to investigate the EEC parameters variation with respect to the induced moisture content variation (gravimetrically determined). The study confirmed the possibility of effectively using the impedance measurement method as a health-monitoring tool for moisture content evaluation of a composite laminate.

Today, the use of UASs (unmanned aerial systems) is rapidly expanding across civil, military, and scientific applications. The deployment of drones in close proximity to urban areas is becoming increasingly common, particularly during missions conducted beyond visual line of sight (BVLOS) or in fully autonomous modes. Advancements in technology have enabled the development of systems and platforms that no longer require a human operator onboard and are equipped with progressively higher levels of autonomy. Therefore, enhancing onboard systems is crucial to ensure a high level of operational safety, particularly during missions conducted in harsh and complex environments, such as urban and suburban areas, where the presence of a large number of static and dynamic obstacles, including pedestrians, vehicles, and other aircraft, is pervasive. In this context, the implementation and integration of multiple onboard devices and sensors represent the core focus of this work, with the objective of improving perception, navigation, and safety capabilities for autonomous UAV operations. In particular, communication channels, hardware integration, and data fusion techniques have been implemented and evaluated to improve system performance and situational awareness. This work presents the hardware and software integration of LiDAR and radar sensors with a Pixhawk autopilot and a Raspberry Pi companion computer, aimed at developing obstacle detection applications.

Reconfigurable intelligent surfaces (RISs) are emerging as a key enabling technology to engineer the wireless propagation environment in 5G/6G systems. This paper presents the design, characterization, and control of a high-resolution RIS targeting slowly time-varying scenarios, in which fine-grained and stable phase control is more valuable than fast reconfiguration. Starting from the OpenRIS unit-cell layout, the cell is re-optimized for single-polarization operation and continuous phase tuning through a single varactor diode, exploiting the full tuning range enabled by a high-resolution DAC infrastructure rather than multi-bit quantization. The unit cell is analyzed via full-wave 3D FEM simulation in COMSOL Multiphysics and optimized to maximize the reflection-phase excursion while limiting amplitude modulation across the 5G N78 band (3.60–3.78 GHz). The design is experimentally validated in a WR-284 waveguide fixture, exhibiting a phase excursion approaching the full ${360}^{\circ }$ near resonance, with an amplitude variation below 1 dB over the bias sweep at any given frequency, while the average reflection level decreases by about 2 dB from the center to the upper band edge. A fifth-order polynomial phase–voltage calibration feeds a lookup table driving a layered control system based on a Python HMI, a server, and STM32-driven 16-bit DACs. Experimental measurements confirm that the control chain delivers the commanded bias voltages to the addressed unit cells within measurement uncertainty; the array-level beamforming is assessed at simulation level under idealized (unit-magnitude) assumptions, while the experimental characterization of the assembled surface is left to future work.