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A Falcon 9 rocket launched the National Reconnaissance Office (NRO)’s NROL-149 mission from Vandenberg Space Force Base, California, at 8:19 a.m. EST on December 17, as per reports. The flight carried next-generation spy satellites designed for the United States’ intelligence needs. The reusable first-stage booster successfully landed on the drone ship “Of Course I Still Love You” in the Pacific Ocean, marking SpaceX’s 384th recovery of an orbital-class rocket. According to mission details shared by SpaceX, this was the company’s 127th launch of the year.

Details of the Mission and Payload

The NROL-149 mission is reported to be part of a broader initiative by the NRO to deploy “proliferated architecture” satellites, described in earlier mission updates as smaller, resilient spacecraft equipped for advanced reconnaissance capabilities. These satellites, believed to be based on SpaceX’s Starlink platform but modified for intelligence purposes, are intended to enhance national security operations, as per sources. The NRO confirmed in a post on X (formerly Twitter) that this launch concluded its eighth and final mission for 2024.

Booster Reuse and Flight History

According to sources, the Falcon 9 booster used in this mission had flown twice before, deploying other NRO satellites (NROL-113 and NROL-167) and NASA’s DART asteroid impact mission. SpaceX’s commitment to reusability was highlighted again with this successful recovery. Details regarding the deployment of the NROL-149 payloads remain undisclosed due to the classified nature of national security missions.

A Year of Proliferated Architecture Missions

As per reports, this marks the sixth proliferated architecture mission for the NRO this year, with previous launches — NROL-146, NROL-186, NROL-113, NROL-167, and NROL-126 — also utilising SpaceX’s Falcon 9 rockets. The series represents a shift towards deploying numerous smaller satellites to improve resilience and capability in space-based intelligence, though official specifics remain classified.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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