Simulators, Feeder Systems and Plant Image Datasets

Summary of my bookmarked links and Github repositories from Sep 17th, 2023



    ARPSyndicate's Research Project, Subdomain Center, is a powerful subdomain discovery tool. It leverages Apache's Nutch, Calidog's Certstream, OpenAI's Embedding Models, and some proprietary tools to uncover an extensive list of subdomains. However, to prevent abuse, they've limited usage to 3 requests per minute. Be prepared for occasional downtime and delays, as their servers contend with high demand and a seemingly underpowered setup.

  • Building an economy simulator from scratch

    This blog post discusses the development and refinement of a simulation for building an economy from scratch, starting with one person and gradually introducing various economic concepts. The simulation covers topics such as resource consumption, production, taxation, inflation, and consumer psychology. Through multiple iterations and adjustments, the simulation aims to create a functional and balanced economy. The author explores the effects of different economic policies and supply dynamics on the community's average quality of life. The blog concludes by suggesting potential future improvements and extensions to the simulation.

Github repositories

  • sdr-enthusiasts/docker-flightradar24

    The sdr-enthusiasts/docker-flightradar24 GitHub repository offers a Docker container for running FlightRadar24's fr24feed. Compatible with various architectures, including x86_64 and ARM, it pulls ModeS/BEAST data from sdr-enthusiasts/docker-readsb-protobuf and sends it to FlightRadar24. The repository provides clear instructions for obtaining a Flightradar24 sharing key through both script and manual methods. Users can quickly deploy the container using Docker run or Docker Compose. Various environment variables allow customization, and the repository offers support and a Discord channel for assistance. Get FlightRadar24 data running smoothly with this Docker container, supporting multiple architectures and offering easy sharing key setup and customization options. Check out the GitHub repository for detailed instructions and support.

  • sdr-enthusiasts/docker-adsb-ultrafeeder

    The **sdr-enthusiasts/docker-adsb-ultrafeeder** is a versatile ADS-B data collector container. It can: - Retrieve ADS-B data from your SDR or other devices. - Display data on a local map with track, heatmap, and performance graphs options. - Forward data to aggregators using BEAST/BEAST-REDUCE/BEAST-REDUCE-PLUS formats. - Send MLAT data to these aggregators and consolidate MLAT results data (built-in mlathub). - Interface with external visualization tools like Grafana using statistics data available in InfluxDB and Prometheus format. The container uses SDR-Enthusiasts Docker Base-Image, Wiedehopf's readsb branch, tar1090 graphical interface, graphs1090, and an MLAT Client. It's compatible with various Linux architectures. To set it up quickly with Docker, follow the provided docker-compose.yml and .env files, configure the variables, and bring up the stack. Ports are used for different purposes, including data input/output and web interfaces. Make sure to configure mandatory parameters like latitude, longitude, altitude, and timezone. Optional parameters allow you to customize the setup further, including enabling timelapse1090, specifying extra parameters for readsb, enabling debug mode, and more. You can provide ADSB data to the Ultrafeeder by connecting an SDR or allowing the container to connect to an ADSB data source in various formats. For detailed instructions and additional information, refer to the provided documentation and links.

  • plantnet/PlantNet-300K

    PlantNet-300K is a repository housing code used to create the benchmark featured in the paper titled "Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution." To utilize this dataset for research, you'll need to download it from Zenodo (version 1.1), which includes updated metadata files. Pre-trained models are also available, with loading instructions provided in the file. To train a model on PlantNet-300K, execute the provided Python command with specified parameters, ensuring you specify the dataset path and save directory name. This resource relies solely on PyTorch and torchvision. Detailed options can be found in the file. Don't forget to cite the original paper if you use this dataset for your research.

  • badlogic/heissepreise

    "Heisse Preise" is a simple grocery price search app that gathers data from major Austrian grocery chains daily. It comprises a NodeJS Express server for data retrieval and a basic HTML/JS frontend. To run it, you'll need Node.js. For development, clone the GitHub repository, install dependencies, and execute 'npm run dev.' The app fetches data initially and later updates asynchronously. You can also access raw data, including historical pricing, via JSON. Credits for historical data go to Dossier and @h43z from Visit to explore the app further.