Archivio della ricerca - Fondazione Bruno Kessler
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    18475 research outputs found

    A Modular and Compact RF-MEMS Step Attenuator for Beamforming Applications in the Evolving 5G/6G Scenario

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    The ambitious expectations of 5G/6G paradigms in terms of data rates rely on the ubiquitous coverage offered by various kinds of cells, adopting antenna arrays in massive multiple-input-multiple-output (mMIMO) configuration, and operating at high frequencies. The revolutionary choice of mMIMO systems unleash the potential of beamforming (BF) techniques, performed by hybrid digital-analog architectures, in which components like radio frequency micro-electromechanical-systems (RF-MEMS) attenuators could be an effective building block for such high-performance antenna systems employed in different kinds of cells. In this work, starting from a basic 1-bit attenuation module, different versions are optimized and simulated to be successively combined into a single compound. The development of -1 dB, -2 dB, -3 dB, and -4 dB single modules permits to achieve a compact and modular arrangement with a maximum -10 dB attenuation by -1 dB steps, operating in the 24.25 - 27.5 GHz range, the 5G frequency band to be employed in European countries. The use case is the one of Femtocells, in which the reduced number of users does not require extensive BF capabilities

    PolyGloT: A personalized and gamified eTutoring system for learning modelling and programming skills

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    The digital age is changing the role of educators and pushing for a paradigm shift in the education system. By formalizing a learning framework that considers the perspectives of both teachers and students, taking into account their unique needs and diverse experiences, we aim to define and implement an open, content-agnostic, and extensible eTutoring platform to design and consume adaptive and gamified learning experiences. We present PolyGloT, a system able to help teachers to design and implement adaptive learning paths. We demonstrate through a concrete case study the use of a mixed narrative, that is a gamified learning path mixing lessons, quizzes and modelling exercises to learn concepts of the SysML-v2 modelling language

    Bounded cohomology classes of exact forms

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    On negatively curved compact manifolds, it is possible to associate to every closed form a bounded cocycle – hence a bounded cohomology class – via integration over straight simplices. The kernel of this map is contained in the space of exact forms. We show that in degree 2 this kernel is trivial, in contrast with higher degree. In other words, exact non-zero -forms define non-trivial bounded cohomology classes. This result is the higher dimensional version of a classical theorem by Barge and Ghys [Invent. Math. 92 (1988), pp. 509–526] for surfaces. As a consequence, one gets that the second bounded cohomology of negatively curved manifolds contains an infinite dimensional space, whose classes are explicitly described by integration of forms. This also showcases that some recent results by Marasco [Proc. Amer. Math. Soc. 151 (2023), pp. 2707–2715] can be applied in higher dimension to obtain new non-trivial results on the vanishing of certain cup products and Massey products. Some other applications are discussed

    Functional surfaces for exosomes capturing and exosomal microRNAs analysis

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    Exosomes are small extracellular vesicles well-studied both as cell signaling elements and as source of highly informative biomarkers, in particular microRNAs. Standard techniques for exosome isolation are in general scarcely efficient and give low purity vesicles. New techniques combining microfluidics with suitable functionalized surfaces could overcome these disadvantages. Here, different functional surfaces aimed at exosomes capture are developed thank to the functionalization of silicon oxide substrates. Charged surfaces, both positive and negative, neutral and immunoaffinity surfaces are characterized and tested in functional assays with both exosome mimicking vesicles and exosomes purified from cell supernatants. The different surfaces showed promising properties, in particular the negatively-charged surface could capture more than 4 × 108 exosomes per square centimeter. The captured exosomes could be recovered and their biomarker cargo analyzed. Exosomal microRNAs were successfully analyzed with RT-PCR, confirming the good performances of the negatively-charged surface. The best-performing functionalization could be easily moved to microdevice surfaces for developing modular microfluidic systems for on-chip isolation of exosomes, to be integrated in simple and fast biosensors aimed at biomarker analysis both in clinical settings and in research

    A Comparative Study of Diverse RF-MEMS Switch Design Concepts Experimentally Verified up to 110 GHz for Beyond-5G, 6G and Future Networks Applications

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    Future broad application paradigms like beyond-5G (B5G), 6G and super-Internet of things (IoT) will bring significant disruption in all the segments of the physical infrastructure ensuring such services, from the core (cloud) to the edge of the network. Substantial rearchitecting will be necessary to allow proper functioning of a highly-diversified space-air-ground-sea physical infrastructure, along with operation at frequency ranges spanning from sub-GHz, to millimeter-waves (mm-Waves), again to sub-THz (100–300 GHz) and above. In this work, we focus on the radio frequency (RF) portion of the infrastructure, and in particular on micro-relays for channel commuting and reconfiguration of passive elements. To this end, we report on high-performance and highly-miniaturized micro-switches based on microelectromechanical-systems (MEMS) technology, known as RF-MEMS. A few different design concepts of RF-MEMS-based series ohmic switches are reported, discussed and compared, with the support of finite element method (FEM) modeling and RF experimental characterization up to 110 GHz

    FLAD: Adaptive Federated Learning for DDoS Attack Detection

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    Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have not been properly addressed yet. Indeed, the Federated Averaging algorithm at the core of the FL concept requires the availability of test data to control the FL process. Although this might be feasible in some domains, test network traffic of newly discovered attacks cannot be always shared without disclosing sensitive information. In this paper, we address the convergence of the FL process in dynamic cybersecurity scenarios, where the trained model must be frequently updated with new recent attack profiles to empower all members of the federation with the latest detection features. To this aim, we propose FLAD (adaptive Federated Learning Approach to DDoS attack detection), an FL solution for cybersecurity applications based on an adaptive mechanism that orchestrates the FL process by dynamically assigning more computation to those members whose attacks profiles are harder to learn, without the need of sharing any test data to monitor the performance of the trained model. Using a recent dataset of DDoS attacks, we demonstrate that FLAD outperforms state-of-the-art FL algorithms in terms of convergence time and accuracy across a range of unbalanced datasets of heterogeneous DDoS attacks. We also show the robustness of our approach in a realistic scenario, where we retrain the deep learning model multiple times to introduce the profiles of new attacks on a pre-trained model

    Estimating the potential risk of transmission of arboviruses in the Americas and Europe: a modelling study

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    Background Estimates of the spatiotemporal distribution of different mosquito vector species and the associated risk of transmission of arboviruses are key to design adequate policies for preventing local outbreaks and reducing the number of human infections in endemic areas. In this study, we quantified the abundance of Aedes albopictus and Aedes aegypti and the local transmission potential for three arboviral infections at an unprecedented spatiotemporal resolution in areas where no entomological surveillance is available. Methods We developed a computational model to quantify the daily abundance of Aedes mosquitoes, leveraging temperature and precipitation records. The model was calibrated on mosquito surveillance data collected in 115 locations in Europe and the Americas between 2007 and 2018. Model estimates were used to quantify the reproduction number of dengue virus, Zika virus, and chikungunya in Europe and the Americas, at a high spatial resolution. Findings In areas colonised by both Aedes species, A aegypti was estimated to be the main vector for the transmission of dengue virus, Zika virus, and chikungunya, being associated with a higher estimate of R0 when compared with A albopictus. Our estimates highlighted that these arboviruses were endemic in tropical and subtropical countries, with the highest risks of transmission found in central America, Venezuela, Colombia, and central-east Brazil. A non-negligible potential risk of transmission was also estimated for Florida, Texas, and Arizona (USA). The broader ecological niche of A albopictus could contribute to the emergence of chikungunya outbreaks and clusters of dengue autochthonous cases in temperate areas of the Americas, as well as in mediterranean Europe (in particular, in Italy, southern France, and Spain). Interpretation Our results provide a comprehensive overview of the transmission potential of arboviral diseases in Europe and the Americas, highlighting areas where surveillance and mosquito control capacities should be prioritised

    A Wearable Modulated Scattering Technique (MST) Sensor for Early Detection of Skin Tumours

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    There are different types of skin cancer, but the most dangerous and aggressive is the malignant melanoma (MM), because it can be easily metastasized and reaches vital organs. The survival rates for this kind of pathology are very low if it reaches the lymph nodes or other organs. The survival rate becomes considerably high if it is early detected. Therefore, a diagnostic tool able to early detect the malignant melanoma at its early stage is fundamental to save lives. The goal of this work is to develop a wearable small sensor able to detect the small changes of the electric characteristic of the skin due to the early outbreak of the malignant pathology by using miniaturized modulated scattering technique (MST) sensors. They can be easily miniaturized since they do not need a radio-frequency section. They are simply realized with a small scattering antenna, a set of loads, and an electronic switch. To improve the probe performances, the use of an efficient switch is mandatory, and a micro-switch realized in micro-electromechanical systems (MEMS) technology has been used to reach this objective. In particular, the proposed MST sensor probe is able to detect and retransmit the information related to the dielectric permittivity of the skin and consequently provide useful diagnostic information mandatory to early identify the presence of malignant tissue. The preliminary results are quite promising and confirm that the wearable MST probe could be an important diagnostic tool able to save life or improve the survival rate and the life quality of patients

    Introducing packet-level analysis in programmable data planes to advance Network Intrusion Detection

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    Programmable data planes offer precise control over the low-level processing steps applied to network packets, serving as a valuable tool for analysing malicious flows in the field of intrusion detection. Albeit with limitations on physical resources and capabilities, they allow for the efficient extraction of detailed traffic information, which can then be utilised by Machine Learning (ML) algorithms responsible for identifying security threats. In addressing resource constraints, existing solutions in the literature rely on compressing network data through the collection of statistical traffic features in the data plane. While this compression saves memory resources in switches and minimises the burden on the control channel between the data and the control plane, it also results in a loss of information available to the Network Intrusion Detection System (NIDS), limiting access to packet payload, categorical features, and the semantic understanding of network communications, such as the behaviour of packets within traffic flows. This paper proposes P4DDLe, a framework that exploits the flexibility of P4-based programmable data planes for packet-level feature extraction and pre-processing. P4DDLe leverages the programmable data plane to extract raw packet features from the network traffic, categorical features included, and to organise them in a way that the semantics of traffic flows are preserved. To minimise memory and control channel overheads, P4DDLe selectively processes and filters packet-level data, so that only the features required by the NIDS are collected. The experimental evaluation with recent Distributed Denial of Service (DDoS) attack data demonstrates that the proposed approach is very efficient in collecting compact and high-quality representations of network flows, ensuring precise detection of DDoS attacks

    Measurements of the suppression and correlations of dijets in Xe+Xe collisions at sNN=5.44 TeV

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    Measurements of the suppression and correlations of dijets is performed using 3µb−1 of Xe+Xe data at √sNN=5.44 TeV collected with the ATLAS detector at the CERN Large Hadron Collider. Dijets with jets reconstructed using the R=0.4 anti-kt algorithm are measured differentially in jet pT over the range of 32 to 398 GeV and the centrality of the collisions. Significant dijet momentum imbalance is found in the most central Xe+Xe collisions, which decreases in more peripheral collisions. Results from the measurement of per-pair normalized and absolutely normalized dijet pT balance are compared with previous Pb+Pb measurements at √sNN=5.02 TeV. The differences between the dijet suppression in Xe+Xe and Pb+Pb are further quantified by the ratio of pair nuclear-modification factors. The results are found to be consistent with those measured in Pb+Pb data when compared in classes of the same event activity and when taking into account the difference between the center-of-mass energies of the initial parton scattering process in Xe+Xe and Pb+Pb collisions. These results should provide input for a better understanding of the role of energy density, system size, path length, and fluctuations in the parton energy loss


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