The proceedings contain 62 papers. The special focus in this conference is on Wireless and Satellite systems. The topics include: A DQN-Based Routing Algorithm for Load Balancing in LEO Satellite Networks;design of Sa...
ISBN:
(纸本)9783031861956
The proceedings contain 62 papers. The special focus in this conference is on Wireless and Satellite systems. The topics include: A DQN-Based Routing Algorithm for Load Balancing in LEO Satellite Networks;design of Satellite Network Simulation Platform Supporting Distributed Controller;Direct to Cell VLEO SatCom System Provide Low E2E Latency in STIN;dynamic Beam Optimization and Interference Mitigation Methods for Multi-beam Satellite systems;federated Learning-Based Cross-layer Security design for Satellite Networks;DRL Based Secure Optimization for RIS Aided SATINs with RSMA;Research on the Signal Detection in an Uplink Ground-to-Satellite UDC-FSO System with optical Path Difference;a High Precision Satellite Beam Agility Control Method;design of an Electro-optical Hybrid Switching Architecture for Satellite Internet;genetic Algorithm-Based Inter-Satellite Link Establishment and Routing Scheme for Satellite Networks;Kolmogorov-Arnold Networks Based Signal Detection for OTFS systems in LEO Satellite Communications;Joint Computation Offloading and Resource Allocation for Low-Earth Orbit Satellites MEC Networks;key Technologies and Future Developments in the design of Spaceborne Digital Transparent Processors;A Solution to the Problem of Retail Credit Risk Pricing Problem Based on the Machine Learning XGBoost Algorithm;a Cooperative Spectrum Sensing Method Based on Feature Extraction and Fusion Clustering;An Optimized design of Golden Angle Modulation SCMA Codebook Based on Genetic Algorithms;Bidirectional Backscatter NOMA Schemefor Efficient CDRT systems;Joint Equalization and Multi-phase Tracking Based on MCC Criterion for Underwater Acoustic Communication;ISAC Beamforming in Connected Autonomous Vehicles;Model-Driven Deep Learning for MIMO Signal Detection;integrated Sensing and Communication Empowered Secure Computation Offloading in Integrated Satellite-Terrestrial Networks;Rotator-Aided RIS Beam Stabilization Method in UAV Communications;A Massive MIMO Antenna
The present work addresses design and analysis of a T-shaped defect-based 2D photonic crystal (PhC) to realize optical interconnect application. The proposed structure is designed in OptiFDTD simulation platform, wher...
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According to the increasing demand for communication bandwidth for data exchange of space communication system, more and more customers use optical transceiver module products in space systems. Spacecraft work in the ...
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To address the high rate of missed detections and low accuracy in traditional shipboard fire alarm systems, this paper proposes a ship compartment fire alarm system based on the fusion of multi-sensor data. The hardwa...
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This paper describes a new Fiber Bragg Grating (FBG) optical sensor conceived for load measurements in harsh aerospace environments. This sensor combines miniature size, durability, and precision, making it ideal for ...
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A remarkable set of designs is found when considering imaging between the vertices of two equi-curvature mirrors. An infinite number of solutions exist for different numbers of reflections in a multi-pass configuratio...
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In order to realize the unified storage of geographic information data of different structural types of land change survey, a geographic information data storage system of land change survey based on ArcGIS is designe...
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CanSats are earth bound analogues for orbital micro and picosatellites. These devices have become increasingly relevant as small-scale satellites, such as CubeSats, have grown in popularity and accessibility. With add...
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ISBN:
(纸本)9798350350319;9798350350302
CanSats are earth bound analogues for orbital micro and picosatellites. These devices have become increasingly relevant as small-scale satellites, such as CubeSats, have grown in popularity and accessibility. With additional robotic frontiers for the exploration of space also becoming increasingly accessible, analogous systems for researching planetary surface rovers may also be potentially relevant. This research expands on the emerging field of "CanBots", which refers to this kind of earth-bound analogue for planetary surface exploring robots. The novel modular CanBot developed in this research represents a unique design framework whereby mobility aspects of the vehicle form discrete and interchangeable modules. Initial characterization of the locomotion capabilities of these modules were determined independently using an optical Motion Capture system. Then the modules were combined into a single multimodal system for additional mobility testing. Vehicle trajectories for an Aerial Quadrotor CanBot, Terrestrial Quadruped CanBot and the combined Multimodal CanBot have been compiled in this research. Initial results suggest that the Multimodal CanBot can successfully reproduce the locomotion techniques for the aerial and terrestrial modalities, but at the cost of lower overall movement efficiency in either modality.
Existing micro-imaging systems are primarily designed for specific types of magnets and are not compatible with water-cooled magnets, posing significant limitations in their use. In light of this, the article proposes...
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Stray light (SL) control is an important aspect in the development of optical instruments. Iterations are necessary between design and analysis phases, where ray tracing simulations are performed for performance predi...
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ISBN:
(纸本)9781510675216;9781510675223
Stray light (SL) control is an important aspect in the development of optical instruments. Iterations are necessary between design and analysis phases, where ray tracing simulations are performed for performance prediction. This process involves trial and error, requiring to be able to perform rapid evaluation of SL properties. The limitation is that accurate SL simulations require sending many rays, which can be time consuming. In this paper, we use deep learning to improve the accuracy of SL maps even when obtained with very few rays. Two different deep learning methods are used. The training process is performed by generating a large database of artificial SL maps, with different noise levels reproduced with a Poisson distribution. Once the training completed, we show that the autoencoder performs the best and improves significantly the accuracy of SL maps. Even with extremely small number of rays, it recovers complex SL patterns which are not visible on raw ray traced maps. This method thus enables more efficient iterations between design and analysis. It is also useful for developing SL correction algorithms, as it requires tracing SL maps under large number of illumination conditions in a reasonable amount of time.
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