During the development of each National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) instrument, significant testing was performed, both in ambient and simulated orbital...
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ISBN:
(纸本)9781467311601
During the development of each National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) instrument, significant testing was performed, both in ambient and simulated orbital (thermal-vacuum) conditions, at the instrument factory, and again after integration withthe spacecraft. the NPOESS Integrated Program Office (IPO), and later the NASA Joint Polar Satellite System (JPSS) Program Office, defined two primary objectives with respect to capturing instrument and spacecraft test data during these test events. the first objective was to disseminate test data and auxiliary documentation to an often distributed network of scientists to permit timely production of independent assessments of data quality and test progress. the second goal was to preserve the data and documentation in a catalogued government archive for the life of the mission, to aid in the resolution of anomalies and to facilitate the comparison of on-orbit instrument operating characteristics to those observed prior to launch [1]. In order to meet these objectives, NPP pre-launch test data collection, distribution, processing, and archive methods included adaptable support infrastructures to quickly and completely transfer test data and documentation from the instrument and spacecraft factories to sensor scientist teams on-site at the factory and around the country. these methods were unique, effective, and low in cost. these efforts permitted timely data quality assessments and technical feedback by contributing organizations within the government, academia, and industry, and were critical in supporting timely sensor development. Second, in parallel to data distribution to the sensor science teams, pre-launch test data were transferred and ingested into the central NPP calibration and validation (cal/val) system, known as the Government Resource for Algorithm Verification, Independent Testing, and Evaluation (GRAVITE), where they will reside for the life of th
Dynamic Beam Hopping (DBH) is a crucial technology for adapting to the flexibility of different service configurations in the multi-beam satellite communications market. the conventional beam hopping method, which ign...
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ISBN:
(纸本)9781728152103
Dynamic Beam Hopping (DBH) is a crucial technology for adapting to the flexibility of different service configurations in the multi-beam satellite communications market. the conventional beam hopping method, which ignores the intrinsic correlation between decisions, only obtains the optimal solution at the current time, while deep reinforcement learning (DRL) is a typical algorithm for solving sequential decision problems. therefore, to deal withthe DBH problem in the scenario of Differentiated Services (DIFFSERV), this paper designs a multiobjective deep reinforcement learning (MO-DRL) algorithm. Besides, as the demand for the number of beams increases, the complexity of system implementation increase significantly. this paper innovatively proposes a time division multi-action selectionmethod(TD-MASM) tosolvethecurseofdimensionality problem. Under the real condition, the MO-DRL algorithm withthe low complexity can ensure the fairness of each cell, improve the throughput to about 5540Mbps, and reduce the delay to about 0.367ms. the simulation results show that when the GA is used to achieve similar effects, the complexity of GA is about 110 times that of the MO-DRL algorithm.
Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in s...
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ISBN:
(数字)9781728168876
ISBN:
(纸本)9781728168883
Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applicationsthat require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with commercial RFID devices show that RLLL achieves an ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm from adjacent tags, in which case the state-of-the-art solutions only achieve ordering accuracy of lower than 0.8 with localization latency larger than 3 seconds.
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