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SpaceX launched its ninth Starship test flight on May 27 that featured the first-ever significant reuse of Starship hardware. As planned on Flight 9, Starship’s two stages separated successfully, and the upper stage even reached space. However, both were ultimately lost before completing their objectives. Despite these setbacks, the mission yielded valuable data which inspires SpaceX’s iterative approach to innovation as it aims to create a fully reusable launch system for space missions. This test flight exhibited successful reuse of a Super Heavy booster and aimed to demonstrate improved hardware performance.

Previous test flights

According to official site of SpaceX, Starship’s two stages are one giant booster called Super Heavy and a 171-foot-tall (52 meters) upper-stage spacecraft known as Starship, or simply “Ship.” Both are powered by SpaceX’s new Raptor engine — 33 of them for Super Heavy and six for Ship.

On Flight 7 and Flight 8 the Super Heavy performed flawlessly, acing its engine burn and then returning to Starbase for a catch by the launch tower’s “chopstick” arms. But Ship had problems: It exploded less than 10 minutes after launch on both missions, raining debris down on the Turks and Caicos Islands and The Bahamas, respectively.

Advancements in flight 9

In flight 9, SpaceX reused a Super Heavy booster for the first time, swapping out just four of its 33 Raptor engines after its initial flight in January. The booster also conducted a new atmospheric entry experiment, entering at a higher angle to collect data on aerodynamic control. Meanwhile, Ship (the upper stage) was tasked with deploying eight dummy Starlink satellites.

Despite the promising advances, Flight 9 encountered several failures. Super Heavy broke apart roughly six minutes after launch during its return burn, and Ship lost control due to a fuel tank leak. The upper stage began tumbling, which prevented a planned in-space engine relight and led to a destructive reentry over the Indian Ocean. Still, SpaceX gained critical data, particularly on tile performance and active cooling systems.

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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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