EO-ALERT: A Novel Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Civil Alerts

The EO-ALERT project proposes the definition and development of the next-generation Earth Observation (EO) data processing chain, based on a novel flight segment architecture that moves opti-mised key EO data processing elements from the ground segment to on-board the satellite, with the aim of delivering EO products to the end user with very low latency. EO-ALERT achieves, globally, latencies below five minutes for EO products delivery, and below 1 minute in some scenarios. The proposed archi-tecture combines innovations in the on-board elements of the data chain and the communications, namely: on-board reconfigurable data handling, on-board image generation and processing for the generation of alerts (EO products) using Artificial Intelligence (AI), on-board AI-based data compression and encryption, high-speed on-board avionics, and reconfigurable high data rate communication links to ground, including a separate chain for alerts with minimum latency and global coverage. This paper pre-sents the proposed architecture, its performance and hardware, considering two different user scenarios: ship detection and extreme weather nowcasting. The results show that, when implemented using COTS components and available communication links, the proposed architecture can deliver alerts to ground with latency below five minutes, for both SAR and Optical missions, demonstrating the viability of the EO-ALERT concept

A Novel Satellite Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Alerts

The EO-ALERT European Commission H2020 project proposes the definition, development, and verification and validation through ground hardware testing, of a next-generation Earth Observation (EO) data processing chain. The proposed data processing chain is based on a novel flight segment architecture that moves EO data processing elements traditionally executed in the ground segment to on-board the satellite, with the aim of delivering EO products to the end user with very low latency. EO-ALERT achieves, globally, latencies below five minutes for EO products delivery, and below one minute in realistic scenarios. The proposed EO-ALERT architecture is enabled by on-board processing, recent improvements in processing hardware using Commercial Off-The-Shelf (COTS) components, and persistent space-to-ground communications links. EO-ALERT combines innovations in the on-board elements of the data chain and the communications, namely: on-board reconfigurable data handling, on-board image generation and processing for the generation of alerts (EO products) using Machine Learning (ML) and Artificial Intelligence (AI), on-board AI-based data compression and encryption, high-speed on-board avionics, and reconfigurable high data rate communication links to ground, including a separate chain for alerts with minimum latency and global coverage. This paper presents the proposed architecture, its hardware realization for the ground testing in a representative environment and its performance. The architecture’s performance is evaluated considering two different user scenarios where very low latency (almost-real-time) EO product delivery is required: ship detection and extreme weather monitoring/nowcasting. The hardware testing results show that, when implemented using COTS components and available communication links, the proposed architecture can deliver alerts to the end user with a latency below five minutes, for both SAR and Optical missions, demonstrating the viability of the EO-ALERT architecture. In particular, in several test scenarios, for both the TerraSAR-X SAR and DEIMOS-2 Optical Very High Resolution (VHR) missions, hardware testing of the proposed architecture has shown it can deliver EO products and alerts to the end user globally, with latency lower than one-point-five minutes

Prognostic significance of IDH-1 and MGMT in patients with glioblastoma: One step forward, and one step back?

A group of 160 patients with primary glioblastoma treated with radiotherapy and temozolomide was analyzed for the impact of O6-methly-guanly-methyl-transferase (MGMT)-promoter methylation as well as isocitrate dehydrogenase (IDH)1-mutational status. Unexpectedly, overall survival or progression-free survival were not longer in the group with methylated MGMT-promoter as compared to patients without that methylation. IDH-1 mutations were significantly associated with increased overall survival

Robust Vector Quantization For Low Bit Rate Speech Coding

Speech coding systems for mobile communication have to cope with noisy channels. In particular, vector quantization as central data reduction scheme is highly sensitive to transmission errors due to the low redundancy in the encoded data. Here we present three methods for the design of a vector quantizer with enhanced robustness against transmission errors. First the optimization of the index assignment of a LBG vector quantizer via simulated annealing is investigated. Second a neighborhood conserving vector quantizer is designed by using a topology conserving feature map. Third we discuss a method in which the error characteristic of the channel is used in the optimization of a vector quantizer as well as in the quantization of a data vector. Simulation experiments and results for a binary symmetric channel with bit error probabilities up to 10% are presented for vector quantization of speech signals and predictor parameters. 1 INTRODUCTION Speech coding systems have to operate in fu..