Politecnio die Bari - Catalogo di prodotti della Ricerca
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An asymptotic model of vibroadhesion
Get PDFA compliantly fixed hemispherical indenter in adhesive contact with an elastic sample firmly bonded to a rigid base is considered under the assumption that the rigid base undergoes small-amplitude high-frequency normal (vertical) oscillations. A general law of the rate-dependent JKR-type adhesion is assumed, which relates the work of adhesion to the contact front velocity. Using the Bogoliubov averaging approach in combination with the method of harmonic balance, the leading-order asymptotic model is constructed for steady-state vibrations. The effective work of adhesion is evaluated in implicit form. A quasi-static approximation for the pull-off force is derived. The case of rigid fixation of the indenter is considered in detail
Cooperating with additives: low-cost hole-transporting materials for improved stability of perovskite solar cells
No full textBeamtest characterization of the ENUBET Demonstrator
No full textThe goal of the ENUBET project is to develop the first monitored neutrino beam, where the neutrino flux can be measured with a 1% precision, in order to carry out measurements of high precision of the neutrino cross section in the energy range of relevance for HyperKamiokande and DUNE. The systematic uncertainties are suppressed by detecting, in an instrumented decay tunnel, the large-angle leptons generated in the K+e3 three body decay (K+ -> e+pi 0 nu e). The collaboration recently completed the beamline design and tested the largest prototype of the decay tunnel: the Demonstrator. It is a sampling calorimeter composed of iron absorbers and plastic scintillators, whose light is collected by WLS fibers and readout by SiPMs. In this contribution, we discuss the characterization results obtained in the two beamtests on the PS extracted T9 beamline at CERN in 2022 and 2023. Namely the linearity and energy resolution, the effect of the optical crosstalk and a first preliminary particle identification analysis
KGUF: Simple Knowledge-Aware Graph-Based Recommender with User-Based Semantic Features Filtering
No full textThe recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph Collaborative Filtering (KGCF), which use KGs to mine hidden user intents. Nevertheless, empirical evidence suggests that computing and combining user-level intent might not always be necessary, as simpler approaches can yield comparable or superior results while keeping explicit semantic features. Under this perspective, user historical preferences become essential to refine the KG and retain the most discriminating features, thus leading to concise item representation. Driven by the assumptions above, we propose KGUF, a KGCF model that learns latent representations of semantic features in the KG to better define the item profile. By leveraging user profiles through decision trees, KGUF effectively retains only those features relevant to users. Results on three datasets justify KGUF ’s rationale, as our approach is able to reach performance comparable or superior to SOTA methods while maintaining a simpler formalization
Model of advanced recording system for application in heat-assisted magnetic recording
Get PDFHeat assisted magnetic recording (HAMR) technology is considered a solution to overcome the limitations of perpendicular magnetic recording and enable higher storage densities. To improve and understand the performance of magnetic writers in HAMR technology, it is crucial to possess a comprehensive understanding of both the magnetic field generated during the writing process and the thermal effects induced by the laser. In this work, we have developed a micromagnetic HAMR model with atomistic parameterization. To demonstrate the applicability of the developed model, it is employed to investigate the Write Current Assisted Percentage (WCAP) measurement which is characterized by the difference in laser current needed to erase a narrow data track with and without assistance of the magnetic field generated by the writer. This value allows us to subsequently consider the strength of the magnetic field from the writer, which is difficult to evaluate experimentally. We study the effect of crucial factors such as the laser current, the frequency of the writing field and the grain size distribution of the recording media on the WCAP. The results reveal that, under a high applied field, a correspondingly elevated WCAP is generated. This observation suggests that the track undergoes erasure to approximately half of its amplitude, achieved through the utilization of a low peak temperature. The comparison between simulation and experimental data demonstrates excellent agreement and acts as a validation of the underlying principle of WCAP. Additionally, we explore theoretically the impact of the writer frequency, and the results suggest that lower frequencies give rise to an increase in WCAP. This implies that lower frequencies allow for a reduction in temperature required to erase the track. The technique is valuable in evaluating and contrasting the magnetic behavior of various write pole configurations, examining the frequency responses of different designs, and comparing different media
Large-Eddy Simulations of a Laser-Ignited Subscale Rocket Combustor: Modeling Strategies and Experimental Comparison
No full textTo predict the reliability of laser ignition in a rocket combustor using large-eddy simulations (LESs), it is essential to first ensure that the pre-ignition jet statistics and the dynamics of the hot kernel generated by the energy deposition are accurately captured. In this manuscript, we compare numerical results with experimental data to evaluate the accuracy of the computational approach. First, the jet LES statistics show good qualitative agreement with the particle imaging velocimetry (PIV) data. Quantitative comparisons at several streamwise locations reveal larger differences near the injector, but with local discrepancies of less than 15 m/s in both the mean and fluctuation statistics. Second, we quantify the mean and uncertainties of the hot kernel modeling parameters through a joint analysis of experimental data and direct numerical simulation (DNS) results. This approach accounts for shot-to-shot variability in the simulations, which demonstrate good agreement with the experimental data regarding the ejecta position
Advancing powder bed fusion-laser beam technology: in-situ layerwise thermal monitoring solutions for thin-wall fabrication
Get PDFAdditive manufacturing (AM) technologies, particularly powder bed fusion-laser beam (PBF-LB/M), offer unique capabilities in producing intricate components directly, leading to streamlined processes, cost reductions, and time savings. However, inherent challenges in AM processes necessitate advanced monitoring systems for fault detection and quality assurance. This study focuses on the development and application of in-situ, layer-by-layer thermal monitoring solutions to detect defects such as localized overheating and inadequate fusion in thin-walled components produced by PBF-LB/M. An optimal setup using an off-axis IR thermal camera was devised to monitor the entire slice during processing. Thermographic data, analyzed using MATLAB, identified thermal parameters indicative of process efficiency and print quality. Micro-tomographic scans on finished products correlated defects with thermographic data. Results showed influences of sample thickness on maximum temperatures, effects of powder bed thickness on process temperatures, and identified geometric distortions in inclined walls due to high thermal stress. Three-dimensional thermograms enabled comprehensive temperature distribution analysis, crucial for quality control and defect detection during construction. Insights from this study advance thermographic analysis for PBF-LB/M processes, providing a foundational framework for future additive manufacturing monitoring and quality control enhancements
Sustainable Approaches to Environmental Design, Materials Sciences, and Engineering Technologies
No full textJamming Echoes: On the Impact of Out-of-Band Interference on Radio Frequency Fingerprinting
No full textRadio Frequency Fingerprinting (RFF) has recently emerged as a lightweight and efficient strategy for classifying wireless devices based on their Radio Frequency (RF) emissions at the physical layer. Such emissions contain device-specific distortions that, although not affecting the quality of the communication link, can be extracted from the received signals through capable hardware (Software-Defined Radios---SDRs) and be used to classify via Deep Learning (DL) techniques the specific transmitters in a pool of RF devices. Recent research has shown that, although promising, RFF is a fragile phenomenon whose performance is significantly affected by various phenomena, e.g., channel fluctuations, device reboot, and firmware reload operations.
In this paper, we shed light on yet another phenomenon affecting the reliability and robustness of RFF, i.e., interfering out-of-band signals. Through an extensive real-world experimental campaign involving seven heterogeneous SDRs and state-of-the-art DL image-based RFF systems, we demonstrate that out-of-band interfering signals emitted on neighboring frequencies (less than 5~MHz apart from the main communication channel), independently from being malicious, reduce the accuracy of RFF up to a random guess of the transmitter, while not significantly impacting the Bit-Error Rate of the communication link. These results foster further research in the design of reliable and robust DL-based RFF systems, capable of mitigating real-world deployment factors