satnogs-wut

The goal of satnogs-wut is to have a script that will take an observation ID and return an answer whether the observation is "good", "bad", or "failed".

Good Observation

Bad Observation

Failed Observation

Machine Learning

The system at present is built upon the following:

• Debian
• Tensorflow
• Keras

Learning/testing, results are inaccurate.

wut?

The following scripts are in the repo:

• wut --- Feed it an observation ID and it returns if it is a "good", "bad", or "failed" observation.
• wut-compare --- Compare an observations' current presumably human vetting with a wut vetting.
• wut-compare-all --- Compare all the observations in download/ with wut vettings.
• wut-compare-tx --- Compare all the observations in download/ with wut vettings using selected transmitter UUID.
• wut-compare-txmode --- Compare all the observations in download/ with wut vettings using selected encoding.
• wut-dl-sort --- Populate data/ dir with waterfalls from download/.
• wut-dl-sort-tx --- Populate data/ dir with waterfalls from download/ using selected transmitter UUID.
• wut-dl-sort-txmode --- Populate data/ dir with waterfalls from download/ using selected encoding.
• wut-ml --- Main machine learning Python script using Tensorflow and Keras.
• wut-obs --- Download the JSON for an observation ID.
• wut-review-staging --- Review all images in data/staging.
• wut-water --- Download waterfall for an observation ID to download/[ID].
• wut-water-range --- Download waterfalls for a range of observation IDs to download/[ID].

Installation

Most of the scripts are simple shell scripts with few dependencies.

Setup

The scripts use files that are ignored in the git repo. So you need to create those directories:

mkdir -p download
mkdir -p data/train/good
mkdir -p data/train/bad
mkdir -p data/train/failed
mkdir -p data/val/good
mkdir -p data/val/bad
mkdir -p data/val/failed
mkdir -p data/staging
mkdir -p data/test/unvetted

Debian Packages

You'll need curl and jq, both in Debian's repos.

apt update
apt install curl jq

Machine Learning

For the machine learning scripts, like wut-ml, both Tensorflow and Keras need to be installed. The versions of those in Debian didn't work for me. IIRC, for Tensorflow I built a pip of version 2.0.0 from git and installed that. I installed Keras with pip. Something like:

# XXX These aren't the exact commands, need to check...
apt update
# deps...
apt install python3-pip ...
# Install bazel or whatever their build system is
# Install Tensorflow
git clone tensorflow...
cd tensorflow
./configure
# run some bazel command
dpkg -i /tmp/pkg_foo/*.deb
apt update
apt -f install
# Install Keras
pip3 install --user keras
# A million other commands....

Usage

The main purpose of the script is to evaluate an observation, but to do that, it needs to build a corpus of observations to learn from. So many of the scripts in this repo are just for downloading and managing observations.

The following steps need to be performed:

1. Download waterfalls and JSON descriptions with wut-water-range. These get put in the downloads/[ID]/ directories.

2. Organize downloaded waterfalls into categories (e.g. "good", "bad", "failed"). Use wut-dl-sort script. The script will sort them into their respective directories under:

   • data/train/good/
   • data/train/bad/
   • data/train/failed/
   • data/val/good/
   • data/val/bad/
   • data/val/failed/

3. Use machine learning script wut-ml to build a model based on the files in the data/train and data/val directories.

4. Rate an observation using the wut script.

ml.spacecruft.org

This server is processing the data and has directories available to sync.

• https://ml.spacecruft.org/

Data Caching Downloads

The scripts are designed to not download a waterfall or make a JSON request for an observation it has already requested. The first time an observation is requested, it is downloaded from the SatNOGS network to the download directory. That download directory is the download cache.

The data directory is just temporary files,mostly linked from the downloads directory. Files in the data directory are deleted by many scripts, so don't put anything you want to keep in there.

SatNOGS Observation Data Mirror

The downloaded waterfalls are available below via http and rsync. Use this instead of downloading from SatNOGS to save their bandwidth.

# Something like:
wget --mirror https://ml.spacecruft.org/download
# Or with rsync:
mkdir download
rsync -ultav rsync://ml.spacecruft.org/download/ download/

TODO / Brainstorms

This is a first draft of how to do this. The actual machine learning process hasn't been looked at at all, except to get it to generate an answer. It has a long ways to go. There are also many ways to do this besides using Tensorflow and Keras. Originally, I considered using OpenCV. Ideas in no particular order below.

General

General considerations.

• Use Open CV.

• Use something other than Tensorflow / Keras.

• Do mirror of network.satnogs.org and do API calls to it for data.

• Issues are now available here:

  • https://spacecruft.org/spacecruft/satnogs-wut/issues

Tensorflow / Keras

At present Tensorflow and Keras are used.

• Learn Keras / Tensorflow...

• What part of image is being evaluated?

• Re-evaluate each step.

• Right now the prediction output is just "good" or "bad", needs "failed" too.

• Give confidence score in each prediction.

• Visualize what ML is looking at.

• Separate out good/bad/failed by satellite, transmitter, or encoding. This way "good" isn't considering a "good" vetting to be a totally different encoding. Right now, it is considering as good observations that should be bad...

• If it has a low confidence, return "unknown" instead of "good" or "bad".

Caveats

This is nearly the first machine learning script I've done, I know little about radio and less about satellites, and I'm not a programmer.

Source License / Copying

Main repository is available here:

• https://spacecruft.org/spacecruft/satnogs-wut

License: CC By SA 4.0 International and/or GPLv3+ at your discretion. Other code licensed under their own respective licenses.

Copyright (C) 2019, 2020, Jeff Moe