Inactivation in ShakerB K+ channels: a test for the number of inactivating particles on each channel

Fast inactivation in ShakerB K channels results from pore-block caused by "ball peptides" attached to the inner part of each K channel. We have examined the question of how many functional inactivating balls are on each channel and how this number affects inactivation and recovery from inactivation. To that purpose we expressed ShakerB in the insect cell line Sf9 and gradually removed inactivation by perfusing the cell interior with the hydrolytic enzyme papain under whole cell patch clamp. Inactivation slows down as the balls are removed by an amount consistent with the presence of four balls on each channel. Recovery from inactivation has the same time course early and late in papain action; it does not depend on the number of balls remaining on the channel, consistent with the idea that reinactivation is not significant during recovery from inactivation. Our conclusion is that ShakerB has four ball peptides, each capable of causing inactivation. Statistically, the balls are identical and independent. The stability of N-type inactivation by the remaining balls is not appreciably affected by removing some of the balls from a channel

Feed-Forward Neural Networks to Overcome Complex Precoding Matrix Calculation in GEO Broadband Satellite Communication Systems

peer reviewe

Flexible Payload Configuration for Satellites using Machine Learning

Satellite communications, essential for modern connectivity, extend access to maritime, aeronautical, and remote areas where terrestrial networks are unfeasible. Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse. However, recent research reveals the limitations of this approach in heterogeneous traffic scenarios, leading to inefficiencies. To address this, this paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM). We treat the RRM task as a regression ML problem, integrating RRM objectives and constraints into the loss function that the ML algorithm aims at minimizing. Moreover, we introduce a context-aware ML metric that evaluates the ML model's performance but also considers the impact of its resource allocation decisions on the overall performance of the communication system.Comment: in review for conferenc

Enhanced Communications on Satellite-Based IoT Systems to Support Maritime Transportation Services

Maritime transport has become important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport to decarbonize its impact on the environment. In order to improve the economy and people’s lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from Global Positioning System (GPS), and security through the Automatic Identification System (AIS). This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo and maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented

Multi-Criteria Ground Segment Dimensioning for Non-Geostationary Satellite Constellations

Non-Geostationary Orbit (NGSO) satellite constellations are becoming increasingly popular as an alternative to terrestrial networks to deliver ubiquitous broadband services. With satellites travelling at high speeds in low altitudes, a more complex ground segment composed of multiple ground stations is required. Determining the appropriate number and geographical location of such ground stations is a challenging problem. In this paper, we propose a ground segment dimensioning technique that takes into account multiple factors such as rain attenuation, elevation angle, visibility, and geographical constraints as well as user traffic demands. In particular, we propose a methodology to merge all constraints into a single map-grid, which is later used to determine both the number and the location of the ground stations. We present a detailed analysis for a particular constellation combining multiple criteria whose results can serve as benchmarks for future optimization algorithms

GenAI-based Models for NGSO Satellites Interference Detection

peer reviewedU-AGR-7111 - C21/IS/16193290/SmartSpace - LAGUNAS Ev

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach

Satellite Adaptive Onboard Beamforming Using Neuromorphic Processors

peer reviewedU-AGR-7111 - C21/IS/16193290/SmartSpace - LAGUNAS Ev

Harnessing Supervised Learning for Adaptive Beamforming in Multibeam Satellite Systems

peer reviewedIn today's ever-connected world, the demand for fast and widespread connectivity is insatiable, making multibeam satellite systems an indispensable pillar of modern telecommunications infrastructure. However, the evolving communication landscape necessitates a high degree of adaptability. This adaptability is particularly crucial for beamforming, as it enables the adjustment of peak throughput and beamwidth to meet fluctuating traffic demands by varying the beamwidth, side lobe level (SLL), and effective isotropic radiated power (EIRP). This paper introduces an innovative approach rooted in supervised learning to efficiently derive the requisite beamforming matrix, aligning it with system requirements. Significantly reducing computation time, this method is uniquely tailored for real-time adaptation, enhancing the agility and responsiveness of satellite multibeam systems. Exploiting the power of supervised learning, this research enables multibeam satellites to respond quickly and intelligently to changing communication needs, ultimately ensuring uninterrupted and optimized connectivity in a dynamic world