Lorenz Meier

Open-Source SAR (Synthetic Aperture Radar) Data & Tools: SAR satellites are powerful tools for observing our planet (day or night), in any weather. Unlike optical sensors, SAR can penetrate clouds, track surface changes with millimeter accuracy, and provide invaluable data for disaster response, agriculture, forestry, hydrology, and more. Major Open-Source SAR Missions ~ Sentinel-1 (ESA) – C-band (5.4 GHz), 5–20 m resolution, wide 250–400 km swath. Access: Copernicus Open Access Hub (https://lnkd.in/eZJsAhvj) ~ SAOCOM (CONAE/Argentina) – L-band (1.275 GHz), 7–100 m resolution, full polarimetry. Access: SAOCOM Data Portal (https://lnkd.in/eQyjAzkE) ~ NISAR (Upcoming, NASA–ISRO) – First dual-band radar (L- & S-band), global coverage every 12 days at ~3–10 m resolution. Access: Will be available via NASA DAACs (ASF Alaska SAR Facility) ~ RISAT (ISRO, India) – C- and X-band SAR series with versatile modes (full/compact polarimetry). Access: ISRO’s MOSDAC / Bhuvan Portal ~ ALOS/ALOS-2 (JAXA, Japan) – L-band PALSAR data with fine and ScanSAR modes. Access: JAXA ALOS PALSAR Data and ASF Alaska (https://asf.alaska.edu/) Key Open-Source Processing Tools SNAP (ESA) – Free, powerful toolbox for Sentinel-1 and SAR data. PolSARPro – Specially designed for polarimetric SAR analysis. Google Earth Engine (GEE) – Cloud-based geospatial analysis with global SAR archives. Applications of SAR & InSAR ~ Monitoring natural hazards: earthquakes, volcanoes, landslides. ~ Detecting surface deformation: subsidence, glacier motion, infrastructure stability. ~ Generating digital elevation models (DEMs). ~ Flood mapping, deforestation tracking, agriculture (crop/soil moisture). With SAR’s unique ability to “see the invisible,” researchers, students, and operational analysts can unlock critical insights for science and society. #SAR #RemoteSensing #EarthObservation #Geospatial #OpenData #SatelliteImagery #GIS #SpaceTech #ClimateChange #FloodMapping #Deforestation #Agriculture #Subsidence #GeospatialAnalysis #Copernicus #Sentinel1 #NISAR

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NVIDIA Unveils Spectrum-X Silicon Photonics: A 3.5X Leap in AI Networking Efficiency Introduction At Hot Chips 2025, NVIDIA revealed details of its Spectrum-X Ethernet Photonics interconnect, a breakthrough in silicon photonics that promises to reshape how AI compute clusters scale. Unlike traditional optical interconnects, NVIDIA’s new design brings massive power efficiency gains—critical as the industry grapples with the escalating energy demands of large-scale AI. Key Details • Silicon Photonics Redefined • Traditional silicon photonics has centered on die-to-die (D2D) interconnects for CPUs and GPUs. • NVIDIA is extending the concept to Ethernet-based AI networking, making it core to scaling AI superclusters. • Performance Gains • 3.5X improvement in power efficiency compared to legacy optical interconnects. • Optimized for high-throughput, low-latency networking, essential for multi-thousand GPU clusters. • Designed to remove bottlenecks that occur when training massive large language models (LLMs) and generative AI systems. • Spectrum-X Ethernet Photonics • Integrates silicon photonics directly into Spectrum-X switches, enabling seamless AI fabric scaling. • Provides energy-efficient bandwidth to meet the needs of next-gen GPUs, including NVIDIA’s Blackwell architecture. • Allows hyperscalers and sovereign AI projects to scale clusters while containing power and cooling costs. • Strategic Implications • Moves NVIDIA beyond GPU dominance into networking infrastructure leadership. • Aims to address one of AI’s biggest challenges: data center energy consumption. • Puts NVIDIA ahead of competitors like Intel and AMD, who are focusing more narrowly on processor-level D2D photonics. Why It Matters As AI models grow into the trillion-parameter range, compute alone is no longer the bottleneck—interconnect efficiency is. NVIDIA’s Spectrum-X Ethernet Photonics addresses this challenge head-on, offering both scalability and sustainability. By cutting energy costs while boosting throughput, it not only strengthens NVIDIA’s grip on the AI ecosystem but also ensures that future AI infrastructure can scale without collapsing under its own power demands. I’ve had the privilege of reaching over 17 million views in the past year, sharing daily insights with a network of 26,000+ followers and 9,000+ professional contacts across defense, technology, and policy. If this topic resonates, I welcome you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

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Over the past 12 years, the Stanford Space Rendezvous Laboratory has developed and deployed several #algorithms to perform provably #optimal #control of the #RelativeMotion between #satellites with computational performance superior to conventional #convex optimization. The key strategy has been to use #ReachableSetTheory as an indirect optimization method. This led to both closed-form solutions and to very efficient numerical techniques. For the first time at the 2025 AAS/AIAA Astrodynamics Specialist #Conference, Boston, Massachusetts, August 10-14 2025 (https://lnkd.in/dGs4-JD4), we are presenting the application of the methodology to the restricted three body problem. The paper titled: "Optimal Impulsive Control of Cislunar Relative Motion using Reachable Set Theory" by Matthew Hunter, Walter Manuel, and Simone D'Amico is available for download as draft: https://lnkd.in/d8E5m8vN This work presents the first application of the state-of-the-art Koenig-D’Amico reachable set theory solver to cislunar, chaotic relative motion in the CircularRestricted Three-Body Problem (CR3BP). The relative motion dynamics of two spacecraft, a chief and a deputy, in the CR3BP are formulated as a Linear TimeVariant (LTV) system, allowing the solver to find an optimal impulsive control maneuver plan. This methodology demonstrates robust and accurate control performance for both small and large reconfigurations over different CR3BP orbits and control windows. These capabilities are enhanced by a Model Predictive Control (MPC) architecture to reject all sources of control, navigation, and dynamic error. The performance of the proposed approach is validated by unit testing, Monte Carlo simulations, and comparisons to baseline models for spacecraft relative motion. Overall, this work demonstrates an optimal control methodology with the computational efficiency to be used on-board spacecraft, enabling the safe, effective, and efficient operation of Distributed Space Systems in cislunar space.

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Smallsats have defined the last decade of space, but their limitations are clear: low power, limited throughput, and fragile unit economics. K2 Space Corporation is betting on the opposite. The company is building mega class and giga class satellites, platforms measured in tons rather than kilograms, that deliver unprecedented power, capacity, and resilience. On this episode of Valley of Depth, we’re joined by Neel Kunjur, cofounder and CTO of K2 Space. Neel traces the journey from early Slack messages about the promise of MEO to a 160 person team building the largest commercial satellites ever attempted. He shares how K2 has reimagined subsystems like reaction wheels, high voltage power, and 20 kW propulsion to unlock entirely new mission architectures in MEO, GEO, and beyond. We also discuss: – Why MEO is an underexplored orbital regime – The engineering breakthroughs behind large reaction wheels and high voltage power systems – How stackable satellites change constellation design – The long term vision for space infrastructure, from in space compute to energy harvesting …and more. Full episode below 👇

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🚀 Context enrichment with relevance feedback is the backbone of what I call “Context Engineering”. In modern multi-agent workflows, it’s not enough to just retrieve documents — the system must rewrite queries, critique context, and enrich gaps with external sources when relevance drops below threshold. Only then can we deliver curated responses that truly respect user intent. This architecture we’re building combines query rewriting, Qdrant-powered retrieval, critique scoring, adaptive web search, and final response curation — a self-correcting loop where precision meets recall. Everything triggered using Agno2.0 workflows. The blog with full details drops soon. Stay tuned ✨ #AI #GenAI #RAG #AdvRAG #LLM #ContextEngineering

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