Electron collection by a charged satellite in the ionospheric plasma

The space charge region surrounding a highly charged, electron collecting, spacecraft moving in the ionospheric plasma, can be divided into an inner zone (close to the spacecraft), where electron collection is isotropic with respect to the magnetic-field direction, and an outer zone where the electrons are mainly collected along magnetic field lines. In this paper we outline a theory to obtain the current voltage characteristic of such a positive satellite. It is shown that the theoretical results compare very favorably with the experimental data obtained by the TSS-1R mission

Electrodynamic deorbiting of LEO satellites

In this paper we present realistic calculations of deorbiting times for a LEO satellite through the use of electrodynamic tethers. We refer to two possible tether systems (a bare and a conducting insulated tether) both equipped with an inflatable conducting balloon at the upper end. The calculations take into account average ionospheric properties and the electrical interaction of the wire with the ionosphere. Furthermore, they have been done for several inclination orbits and include also the deviation of the tether from the vertical direction under the combined action of the gravity gradient and the electrodynamic forces. The results obtained for the decay times, for typical constellation satellite, indicate that such tether systems are definitely of interest for the deorbiting application

Current collection by a highly positive body moving in the ionospheric plasma

In this paper we derive an interesting feature of the space charge region surrounding a positively charged body moving in a magnetoplasma and, precisely, the fact that, for potentials of the body in excess of a certain value, at least in an inner region close to the body, the electron collection (and the structure of the self-consistent potential) is isotropic. This is used to derive current-voltage characteristics for such a situation. These theoretical characteristics are then convincingly compared with those obtained from the analysis of the data obtained during the recently flown TSS-1R mission

A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions

ALICE: Physics Performance Report, Volume I

ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark-gluon plasma in nucleus-nucleus collisions at the LHC. It currently includes more than 900 physicists and senior engineers, from both nuclear and high-energy physics, from about 80 institutions in 28 countries. The experiment was approved in February 1997. The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2001 and construction has started for most detectors. Since the last comprehensive information on detector and physics performance was published in the ALICE Technical Proposal in 1996, the detector as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) will give an updated and comprehensive summary of the current status and performance of the various ALICE subsystems, including updates to the Technical Design Reports, where appropriate, as well as a description of systems which have not been published in a Technical Design Report. The PPR will be published in two volumes. The current Volume I contains: 1. a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, 2. relevant experimental conditions at the LHC, 3. a short summary and update of the subsystem designs, and 4. a description of the offline framework and Monte Carlo generators. Volume II, which will be published separately, will contain detailed simulations of combined detector performance, event reconstruction, and analysis of a representative sample of relevant physics observables from global event characteristics to hard processes

Gait Analysis for Fall Prediction Using EMG Triggered Movement Related Potentials

Abnormal gait is an usual feature in neurodegenerative disease (i.e.: Huntington Chorea, Parkinson and Alzheimer), while the capability to maintain a stable posture and fluid walking is progressive impaired in aging. Monitoring and correcting the insurgence of abnormal dynamic balance opens new scenarios in the cure of these diseases and falls prevention. In this work, we present a study based on EEG time-frequency analysis to identify the correlation between synchronized EEG and EMG signals for gait analysis. Several tools for gait analysis are developed and experimented i.e. EMG trigger generation with dynamic threshold, EMG co-contraction, EEG movement related potentials (MRPs) and EEG event related desynchronizations (ERDs). This work particularly focus on gait analysis indexes implementation and experimentally obtained results based on a large dataset, including different type of gait i.e. normal gait, perturbed gait and gait during a second cognitive task (DT). A weighted average on the calculated indexes are exploited to quantify the falling risk

The Truth Machine of Involuntary Movement: FPGA Based Cortico-muscular Analysis for Fall Prevention

Voluntary movements are managed by movement related potentials (MRPs) which are brain activity patterns detectable even 500ms before the movement itself. The cortico-muscular matching between brain (EEG) and muscles (EMG) activity allows the assessment of the intentionality of the performed movement. Basing on this knowledge, a real-time algorithm for falling risk prediction based on EMG/EEG coupled analysis is presented. The system architecture involves 8 EMG (limbs) and 8 EEG (motor-cortex) channels wirelessly collected by a FPGA (gateway) that contextually performs the real-time processing based on an event triggered time-frequency approach. The digital architecture is validated on the FPGA to determine resources utilization, related timing constraints and performance figures of a dedicated real-time ASIC implementation for wearable applications. The system resource utilization is 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory of an Altera Cyclone V FPGA. The processing latency is lower than 1ms and the output are available in 56ms, respecting the time limit of 300ms. Outputs enables decision-taking for feedback delivering

On-Line Shelf-Life Prediction in Perishable Goods Chain Through the Integration of WSN Technology With a 1st Order Kinetic Model

The improvements in sensors and wireless technology offer an effective way to enhance food safety and certification along all the perishable goods supply-chain, in order to reduce food waste and losses, while guaranteeing a high degree of quality and preventing diseases directly related to the use of expired or harmful products. In this paper, a complete system for continuous environmental parameters (i.e. temperature, light exposition and relative humidity) acquisition and real-time shelf-life prediction of monitored product is proposed. An algorithm based on a 1 st order kinetic model of the product quality decay with a variation rate evaluated accordingly to the Arrhenius law is proposed. A case study is also shown, i.e.: data during the storage phase of agricultural product (tomatoes) have been acquired through a wireless sensor networks and uploaded to a cloud service. The collected data, a sample per 15 minutes, are processed by the computation algorithm implemented on laptop: the overall delay due to data download and processing is just about 0,3 s. As consequence, the remaining shelf-life of the food can be estimated with a 5% uncertainty with a 2K temperature sensor, highlighting critical situation in the manufacturing environment and allowing timely intervention