Search Results for "scripts termux setup"

...It exposes a single command-line entrypoint (Th3inspector.pl) with many switches for common reconnaissance tasks — examples include website info, whois, MX lookup, geo-IP, subdomain discovery, CMS detection, port scanning, and Cloudflare real-IP resolution — so a user can chain many checks from one script. The project provides simple install wrappers for Linux, Android/Termux and even instructions for Windows (Perl + CPAN modules), and includes helper scripts to update or install dependencies automatically. The repository is actively packaged as versioned releases in the README (current noted version 1.9) and is distributed under an MIT license.

PDP-OmniSim 🧬 Scientific Overview PDP-OmniSim is an advanced computational framework for simulating parallel and distributed processing systems, with cutting-edge applications in computational neuroscience, distributed computing, and complex systems modeling. The framework provides researchers with robust tools for large-scale simulations of networked systems and their emergent behaviors. 🎯 Key Scientific Contributions 🔬 Interdisciplinary Research Domains Computational...

myFocuserPro is an ASCOM and Moonlite compatible stepper motor telescope focus controller (DIY) based on Arduino Nano/Uno. A popular DIY ASCOM focuser with more than 121,000+ downloads. (c) Copyright Robert Brown 2014-2024. All Rights reserved. Permission is granted for personal and Academic use only. Spreadsheet to calculate what stepper motor to use. https://sourceforge.net/projects/arduinoascomfocuserpro2diy/files/Documentation/Nema-Stepper-Motors.xlsx/download

...Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training.