Archivio della ricerca della Scuola Superiore Sant'Anna
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Guidance, Navigation, and Control of a Low-Altitude Nanosatellite equipped with Air-breathing Electric Propulsion
No full textIn recent years, near-Earth space utilization has seen the emergence of air-breathing electric propulsion as an enabling technology for long-duration missions conducted at very low altitudes. This work investigates the guidance, navigation, and control of a 6U CubeSat platform orbiting at altitudes below 300 km and equipped with an electrostatic air-breathing thruster. The analysis is conducted in MATLAB-Simulink and is based upon the coupling of its built-in orbital propagator with the NRLMSISE-00 atmospheric model, the recently developed ADBSat suite for computing the aerodynamic forces and torques acting on the platform, and a simplified performance model relating the propulsive performance to the inlet atmospheric properties, spacecraft attitude, and thruster operating parameters. A thrust control law, based on the feedback from the orbital elements estimated on-board and regulating the voltage applied to the screen electrode of a reference gridded airbreathing thruster, is implemented and verifie
ChordFormer: A Conformer-Based Architecture for Large-Vocabulary Audio Chord Recognition
No full textThe impact of prudential regulation on the UK housing market and economy: Insights from an agent-based model
No full textTwo-level massive string dictionaries
Get PDFWe study the problem of engineering space-time efficient data structures that support membership and rank queries on very large static dictionaries of strings. Our solution is based on a very simple approach that decouples string storage and string indexing by means of a block-wise compression of the sorted dictionary strings (to be stored in external memory) and a succinct implementation of a Patricia trie (to be stored in internal memory) built on the first string of each block. On top of this, we design an in-memory cache that, given a sample of the query workload, augments the Patricia trie with additional information to reduce the number of I/Os of future queries. Our experimental evaluation on two new datasets, which are at least one order of magnitude larger than the ones used in the literature, shows that (i) the state-of-the-art compressed string dictionaries, compared to Patricia tries, do not provide significant benefits when used in a large-scale indexing setting, and (ii) our two-level approach enables the indexing and storage of 3.5 billion strings taking 273 GB in just less than 200 MB of internal memory and 83 GB of compressed disk space, while still guaranteeing comparable or faster query performance than those offered by array-based solutions used in modern storage systems, such as RocksDB, thus possibly influencing their future design
Commercializing technology from university-industry collaborations: A configurational perspective on organizational factors
Get PDFEnhancing complex upper-limb motor imagery discrimination through an incremental training strategy
No full textMotor Imagery (MI)-based Brain–Computer Interface (BCI) systems are a great technological advance for the recovery of lost movements in people with severe motor impairments. Different Artificial Intelligence (AI) techniques with supervised methods have been explored for MI task discrimination, especially static movements from left and right hands. Due to different factors affecting MI-based Electroencephalography (EEG) signals related to physical and cognitive conditions, the success rate of BCIs is still low. Currently, there is a need to explore the MI of complex movements associated with Activities of Daily Living (ADLs), which brings challenges to the scientific community. In this work, an incremental training methodology for Artificial Neural Networks (ANNs) is proposed for discrimination of complex MI tasks. MI-Rest and MI-Task related to the imagination of manipulating a drinking cup were discriminated by implementing an Action Observation (AO)-based protocol in a first-person 2D virtual reality. Thirty healthy individuals were recruited to evaluate our complex MI classification approach. The incremental training proposed achieves significantly higher performance () compared to the non-incremental training. Additionally, the proposed method is significantly superior compared to widely applied methods for MI task discrimination: Power Spectrum (PS), Common Spatial Patterns (CSP), Filter Bank Common Spatial Patterns (FBCSP), each using four baseline Machine Learning (ML) methods for classification. The results show an improvement between 5 and 20% according to both Accuracy (ACC) and False Positive Rate (FPR). The proposed methodology obtained promising results, useful for AI-based BCI training, which would allow the development of more robust systems for neurorehabilitation purposes
Simulation of 6lag &omposition 5esulting)rom Electric Arc Furnace)ed:ith DRI or HBI
No full textThe transformation towards new technologies or the modification of existing processes for green steelmaking has to ensure, among other things, the implementation of circular economy concepts, e.g. slag recycling. Understanding how modifications affect slag characteristics is crucial for end applications. In the European project InSGeP, different models were developed to address this demand. This paper focuses on the use of an Electric Arc Furnace flowsheet model, simulating process behavior, steel and slag compositions for a feed containing Direct Reduced Iron or Hot Briquetted Iron. The model was tuned and validated with industrial and technology provider’s data and is being used for scenario analyses
Deep Learning‐Based Detection of Cannibalism and Competition Behaviour in Asian Corn Borer, Ostrinia furnacalis Larvae
Get PDFLe asimmetrie nelle asimmetrie. Profili penali delle disuguaglianze di genere negli squilibri di potere
No full textRegulating AI to Combat Tech-Crimes: Fighting the Misuse of Generative AI for Cyber Attacks and Digital Offenses
Get PDFLooking back at the progress made in combating cybercrime and cyberattacks in the EU, significant accomplishments have been achieved. Looking to the future, this paper argues that further milestones for enhancing and protecting cybersecurity could be attained through comprehensive integration with diverse legal frameworks related to ICT technologies, and in particular by combating the proliferation and misuse of tools that have the potential to facilitate cyberattacks and cybercrime. Within this framework, the present research is primarily focused on Generative AI and the need to prevent its malicious use for cyberattacks and cybercrime: alongside the criminal prosecution of cybercrime and the established “protective” legal framework of cybersecurity regulation, the forward-looking perspective should also encompass a complementary strategy for mitigating cyber risks and cyber threats related to Generative AI, in the evolving landscape of cybersecurity