Tootfinder

“The problem today is that around 80 percent of all the [space data] traffic is downlinked to a single location in Svalbard, which is an island shared between different countries, including Russia”
https://www.politico.eu/article/space-hacks-europe-ramps-up-s…

Hacking space: Europe ramps up security of satellites
Space systems are increasingly targeted with cyberattacks, as the global hybrid warfare rages.

My art, but not my video[1] for #Genuary9 #Genuary2026: Crazy Automata
C-SCAPE (2022), a piece of multiple co-evolving cellular automata, running as a massive projection as part of the ALGORYTHMS group exhibition at Collective, Oslo (August 2023).

Oil residues can travel over 5,000 miles on ocean debris, study finds #ocean

Oil residues can travel over 5,000 miles on ocean debris, study finds
When oily plastic and glass, as well as rubber, washed onto Florida beaches in 2020, a community group shared the mystery online, attracting scientists' attention. Working together, they linked the black residue-coated debris to a 2019 oil slick along Brazil's coastline. Using ocean current models and chemical analysis, the team explains in Environmental Science & Technology how some of the oily material managed to travel over 5,200 miles (8,500 kilometers) by clinging to debris.

From our #cooperative, #MajavanTila: "Membership benefits for 2026".
https://www.majavantila.fi/en/posts/…

My art, but not my video[1] for #Genuary9 #Genuary2026: Crazy Automata
C-SCAPE (2022), a piece of multiple co-evolving cellular automata, running on vertical aspect ratio LCD screen.
Also see:

MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design
S. Rodionov, A. Burguete-Lopez, M. Makarenko, Q. Wang, F. Getman, A. Fratalocchi
https://arxiv.org/abs/2511.18980 https://arxiv.org/pdf/2511.18980 https://arxiv.org/html/2511.18980
arXiv:2511.18980v1 Announce Type: new
Abstract: Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that integrates metasurface geometry and spectra within a shared latent space. MOCLIP employs contrastive learning to align geometry and spectral representations using an experimentally acquired dataset with a sample density comparable to ImageNet-1K. The study demonstrates MOCLIP inverse design capabilities for high-throughput zero-shot prediction at a rate of 0.2 million samples per second, enabling the design of a full 4-inch wafer populated with high-density metasurfaces in minutes. It also shows generative latent-space optimization reaching 97 percent accuracy. Finally, we introduce an optical information storage concept that uses MOCLIP to achieve a density of 0.1 Gbit per square millimeter at the resolution limit, exceeding commercial optical media by a factor of six. These results position MOCLIP as a scalable and versatile platform for next-generation photonic design and data-driven applications.
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