Politecnio die Bari - Catalogo di prodotti della Ricerca
Not a member yet
36616 research outputs found
Sort by
Assessment of hybrid Macroscopic/State-to-State model for numerical simulation of Ice Giant orbit insertion
No full textIn the current emerging of New Space Economy, space exploration is becoming a priority for the scientific community, for Space Agencies, and for industries. In this field, while gas giants (Jupiter and Saturn) have been explored thanks to NASA space missions, the study of ice giants (Uranus and Neptune) remains an open issue. ESA and NASA are carrying out extensive researches to devise affordable strategies in order to reach these planets in a reasonable time. Specifically, a critical issue in planet exploration is the atmospheric entry phase, due to the extremely challenging flight conditions experienced by the space vehicle. This paper illustrates a numerical investigation of entry and aerocapture maneuvers for ice giant orbit insertion. A comparison between the full State-to-State kinetic model and the proposed hybrid macroscopic model, coherently derived from state-specific dynamical data, is presented in detail. Two different flow regimes are analyzed: a low enthalpy flow, characterized by molecular dissociation, and a high enthalpy flow, where ionization and radiation become relevant
Automated Prediction Models for the Seismic Vulnerability of Masonry Structures Considering Intelligence and Learning Algorithms
No full textThe assessment of the seismic vulnerability of large portfolios of existing structures is the core indicator for developing reliable risk and mitigation plans at regional and urban scales. Masonry structures are widespread in different regions worldwide, and assessing their seismic vulnerability can contribute positively to the definition of large-scale seismic zoning and risk distribution. However, traditional empirical and experimental testing approaches present several drawbacks in practice, such as that they require the analysis of a large set of data with high computational effort. This poses new challenges in terms of quickly predicting the seismic vulnerability of masonry structures by reducing manual estimations in favour of efficient approaches based on machine learning and artificial intelligence. This paper innovatively combines machine learning algorithms with probabilistic seismic hazard models, considering eight characteristic factors affecting the seismic vulnerability of masonry structures, to develop an automated model for predicting the seismic vulnerability of masonry structures. In detail, using artificial intelligence and data-driven technology, data collection and analysis were performed on 2559 masonry structures and 1913,934 acceleration records monitored by 12 seismic stations in Dujiangyan (DJY) city affected by the Wenchuan earthquake in Sichuan (SC) Province, China, on May 12, 2008. Using four developed automated learning models (K-nearest neighbor (KNN), eXtreme Gradient Boosting (XGB), decision tree (DT), and random forest (RF)), confusion matrices and receiver operating curves (ROCs) were defined, with the aim of predicting the seismic vulnerability grades of masonry structures based on different intensity zones. The results of the proposed approaches, in terms of vulnerability curves, were compared with the analogous outputs obtained by adopting existing empirical approaches and applied to the collected seismic damage dataset of masonry structures. A comparison among the four algorithms and empirical models revealed that the random forest algorithm presented the best generalizability and the highest prediction accuracy
The COASTLINE Project: Integrating Advanced Geospatial Technologies for Coastal Monitoring and Management
No full textThe COASTLINE project, funded by Horizon 2020’s Marie Skłodowska-Curie Actions, is a multidisciplinary initiative designed to advance the monitoring, evaluation, and management of European coastal areas through cutting-edge geospatial technologies. By integrating satellite data, Unmanned Aerial Vehicles, and in-situ measurements, the project aims to create high-resolution and multi-temporal datasets crucial for investigating coastal dynamics, including erosion patterns, ecological shifts, and the impacts of human activities. Therefore, COASTLINE’s main objective is to develop a user-friendly platform that enables stakeholders, policymakers, and researchers to access and visualize real-time coastal data. This platform will be populated through data from various sources, such as the Copernicus program, lidar technologies, and proprietary datasets, facilitating efficient monitoring and early detection of changes in coastal ecosystems. Additionally, the project uses advanced 2D and 3D visualization tools to illustrate coastal transformations and leverages machine learning algorithms to assess and predict future risks, such as flooding and subsidence. This paper presents the initially developed methodologies and results obtained on three case studies: i) Elounda (Greece), ii) Paphos (Cyprus), and iii) Margherita di Savoia (Italy). These locations, characterized by their diverse environmental and cultural heritage challenges, are ideal testing grounds for the project’s innovative approaches. Consequently, they provide critical insights for refining the developed tools and methods
Classifying Remote Sensing Data Through Advanced Dimensionality Reduction Approaches
No full textOne major area of interest in the field of urban studies has been the geospatial analysis of urban structures through the classification of remote sensing data. To improve the classification process, it is recommended to use supplementary data in addition to the original satellite bands. Mathematical Morphology provides a variety of tools for the generation of spatial features such as Morphological Profiles, which are employed for the optimal discrimination of pixels present in satellite images. However, these features can be redundant or irrelevant, necessitating their elimination through dimensionality reduction. This preprocessing step is crucial in hyperspectral and multi spectral image classification to remove unnecessary bands or generated features while preserving essential information, thereby improving classifier performance. The Improved F-score technique (IFS) is a feature selection method that evaluates and retains the most relevant bands/features based on computed scores. Bands/features with scores below a calculated threshold score are discarded. A significant challenge with this method is finding an appropriate threshold score. This paper proposes an improvement to the IFS technique by introducing clustering strategies. These strategies aim to separate relevant from irrelevant information without predefining a threshold score based on the obtained scores. The proposed approaches refine the selection process of the IFS while boosting classification results. The proposed methodology was evaluated using multispectral Sentinel-2 data, demonstrating its effectiveness in enhancing classification accuracy. Among the tested clustering strategies, Agglomerative Hierarchical Clustering with Average-linkage achieved the highest performance, with an overall accuracy of 98.46% and a Kappa coefficient of 0.97. These results highlight the superiority of clustering-based approaches over traditional thresholding methods for feature selection in terms of accuracy
Efficient mid-infrared light coupling into a single-mode optical fiber
No full textFor the first time, to the best of our knowledge, a mode-selective photonic lantern is demonstrated as an efficient, transverse offset-tolerant coupling mechanism in a single-mode optical fiber. The principle relies on the selective excitation of the fundamental propagation mode at the multi-mode end of the photonic lantern. It is, after the transition, unambiguously guided in one of the single-mode fibers at the other end, i.e., in the single mode fiber having the largest normalized frequency V. To validate this concept, a photonic lantern based on indium fluoride optical fibers has been designed. A prototype has been fabricated and tested at the wavelength λ = 3.34 μm, demonstrating excellent transverse offset tolerance. The experiment is in good agreement with the simulations confirming the feasibility of the device. The operation of the proposed mode-selective photonic lantern is strongly promising for various applications, including laser pigtailing and coupling between anti-resonant hollow-core fibers and standard single-mode fibers
Flexible Phased Antenna Array for Ka-Band Applications: the WONDER Research Project
No full textThe WONDER project aims to realize a novel flexible phased antenna array designed for Ka-band satellite communication. The proposed antenna features a scalable, lightweight, and low-profile architecture, integrating beamformers, a flexible PCB, and dual-polarized radiators. A key challenge is identifying the optimal manufacturing approach and design strategy to develop such a complex, high-performance system while ensuring a certain degree of flexibility. This involves multiple engineering disciplines, from electronic and electromagnetic design to the selection of technologies and materials for the final prototype fabrication. This paper presents an overview of the progress made so far in this research
A multidimensional discrete sampling method for deriving regional level seismic fragility and losses of RC existing buildings
No full textThe paper presents a framework for deriving regional seismic fragility and direct economic losses for reinforced concrete buildings based on a multidimensional discrete sampling of the available exposure data. The main challenge posed by the paper consists of considering multimodality and multidimensionality of exposure data, which at regional level assume high relevance, especially when data derive from different sources. Usually, the exposure model constitutes a high dimensional space and the approximation to a multidimensional continuous space could sensibly affect the accuracy of results (e.g., loss of modes, variability not captured). To comply with data heterogeneity, the proposed methodology consists of sampling data from one or more multidimensional discrete spaces through an iterative method to generate a Markov chain converging towards an approximated high dimensional joint distribution. To ensure a robust comparison between target and empirical distributions and to determine the sub-optimal number of representative samples, the Kullback-Leibler divergence is employed. Sampled data are used to automatically generate numerical models to investigate through nonlinear time history analyses. The results are post-processed to obtain overall fragility and losses metrics, according to the frequency of each configuration in the sample space. The proposed approach was tested on the case of Puglia region, Southern Italy, providing specific fragility and loss parameters according to the actual distribution of the available data
Effect of Antenna Radiation Pattern Variation on the Secrecy Key Generation in LoS Indoor Environment Under On-The-Shoulder Attack
No full textThe upcoming Sixth-Generation (6G) communication systems bring several challenges to systems security. The Secrecy Key Generation (SKG) is a Physical Layer Security (PLS) technique in which two trusted nodes negotiate a session key, taking advantage of the physical layer phenomena, i.e., radio propagation and/or hardware characteristics, as a source of entropy. In this contribution, we investigate the impact of the variation of the antenna radiation pattern on the SKG performance. We considered two legitimate nodes that apply the SKG to negotiate their session key, whereas a malicious node tries to eavesdrop on the communication to obtain the key by performing the on-the-shoulder attack. The results highlight how the number of antenna dipoles, the Half Power Beamwidth (HPBW) of the main lobe, and the presence of secondary lobes or minima of radiation influence the key mismatch between the legitimate nodes as well as the eavesdropper attack surface
First Measurement of the Electron-Neutrino Charged-Current Pion Production Cross Section on Carbon with the T2K Near Detector
No full textThe T2K Collaboration presents the first measurement of electron neutrino-induced charged-current pion production on a predominantly carbon target in a restricted kinematical phase space. This is performed using data from the 2.5° off-axis near detector, ND280. The differential cross sections with respect to the outgoing electron and pion kinematics, in addition to the total flux-integrated cross section, are obtained. Comparisons between the measured and predicted cross-section results using the neut, genie, and nuwro Monte Carlo event generators are presented. The measured total flux-integrated cross section is [2.52±0.52(stat)±0.30(syst)]×10−39 cm2 nucleon−1, which is lower than the event generator predictions