Tootfinder

This kernel vulnerability looks interesting to look at.
crypto: caam - fix overflow on long hmac keys
VLAI Severity -> High (confidence: 0.9638)
https://vulnerability.circl.lu/vuln/CVE-2026-43330

Senate defense bill seeks to attract cyber talent, limit civilian layoffs
https://federalnewsnetwork.com/congress/2026/06/senate-defense-bill-seeks-to-attract-cyber-talent-limit-civilian-layoffs/

Senate defense bill seeks to attract cyber talent, limit civilian layoffs
Senate lawmakers seek to expand cyber interagency transfer authority, provide workforce protections to civilians and reward high-performing managers.

Has anyone ever used ADIv5 as a debug interface to a SoC using AMBA buses for interconnect but *not* containing an ARM CPU (or any CPU at all for that matter)? Any gotchas or things to be aware of?
Like a JTAG-DP going to an AXI MEM-AP then a bunch of AXI peripherals, which you can connect to via openocd to peek and poke registers, but no CoreSight ROM tables or CPU registers.
Is this even a supported configuration if you're using bog-standard ARM debug IPs?

Volador 1.0: A Data-Driven Air-Sea Full-Coupling Regional Forecast Model with Submesoscale-Permitting Based on MOE-Swin-Transformer Framework
Yuhang Zhu, Jianxin Wang, Yu-kun Qian, Yineng Li, Yahui Liu, Yankun Gong, Shilin Tang, Shiqiu Peng, Tao Song
https://arxiv.org/abs/2605.24032 https://arxiv.org/pdf/2605.24032 https://arxiv.org/html/2605.24032
arXiv:2605.24032v1 Announce Type: new
Abstract: A data-driven air-sea full-coupling regional forecast model with submesoscale-permitting, named "Volador 1.0", is developed for the South China Sea (SCS). The model features a Swin-Transformer framework integrated with a Mixture-of-Experts (MoE) system, a latent space interaction architecture based on Cross-Grid Bidirectional Cross-Attention, and a fast-slow dual-branch architecture. Both the three-month hindcast test and the 15-day operational real-time forecasting demonstrate that Volador 1.0 has a very encouraging and promising performance in 0-72h forecasting of temperature and salinity in the 0-500m upper ocean as well as the sea surface height with root-mean-square-error (RMSE) or mean absolute error (MAE) smaller than or at least comparable to those from the reanalysis datasets REDOS V2.0 and GLORYS12 and the state-of-the-art regional numerical model Regional Ocean Modeling System (ROMS). In particular, Volador 1.0 demonstrates its capability of capturing/forecasting submesoscale processes including internal waves, with an energy spectrum well representing sub- to mesoscale energy cascade as expected by the classical turbulence theory. Further analysis based on ablation experiments shows that the air-sea full-coupling framework, which takes into account the dynamic exchanges of momentum and heat fluxes between the atmosphere and the ocean, indeed helps improve the model's performance compared to the non-full-coupling one. Volador 1.0, though still subject to refinement in the coming future with a large space for improvement, blazes a path for an accurate, fine and fast marine environment forecasting, and thus could help promote our capability of disaster prevention and mitigation in the SCS as well as in other coastal regions where these innovative techniques can be applied.
toXiv_bot_toot

Precipitation diffusion downscaling and application to out-of-distribution simulations with and without stratospheric aerosol injection
Cameron Dong, James W. Hurrell, Elizabeth A. Barnes
https://arxiv.org/abs/2605.23776 https://arxiv.org/pdf/2605.23776 https://arxiv.org/html/2605.23776
arXiv:2605.23776v1 Announce Type: new
Abstract: Stratospheric aerosol injection (SAI), a possible climate engineering strategy where reflective particles are injected into the stratosphere, has been explored to mitigate global warming and its associated risks, such as the intensification of extreme precipitation events. However, current Earth system models (ESMs) often used to simulate SAI and other climate change scenarios are too coarse to properly assess such risks. Traditional statistical downscaling methods, used to project higher resolution impacts, may be biased and unrealistic. To address this, we train a deep learning diffusion downscaler to generate 0.25{\deg} contiguous United States (CONUS) daily precipitation using historical and future climate simulations from the Mesoscale Atmosphere-Ocean Interaction in Seasonal-to-Decadal Climate Prediction (MESACLIP) project, then apply the diffusion downscaler to out-of-distribution CESM2 simulations with and without SAI. The diffusion model generates realistic downscaled precipitation using either MESACLIP or CESM2 inputs. It also faithfully recreates the climate change projections of extreme precipitation in MESACLIP. Diffusion-downscaled projections of the future CESM2 SAI scenarios suggest that SAI could nearly cut in half the CONUS-average increase in yearly max precipitation, compared to the non-SAI scenario. However, there is considerable regional variation and internal variability, with SAI modeled to only slightly reduce increases in extreme precipitation frequency in the Mid Atlantic and the Pacific Northwest, but mitigating most intensification in other regions. Future application of diffusion downscaling to a wider variety of SAI scenarios would provide valuable insight into how proposed SAI strategies may affect precipitation variability on fine spatial scales for regional impact assessments.
toXiv_bot_toot