mckero
10c415fabd
The waterfall showed only the model's 400-1200 Hz window, so a tone outside it was absent from the picture entirely. Measured on keyed audio, the brightest column in that narrow view swings 1.01x between key-down and key-up against 13.76x for a tone in range - it carries no keying at all, so the operator could not tell a signal was present, let alone where it was. Markers alone could not fix that: they pointed at a frequency with nothing drawn there. compute() now takes an optional bin range, defaulting to the model's own, so the decoder path is byte-identical and the golden-vector test still holds. The display asks for DC to Nyquist, 129 bins against 65. The FFT already computed every bin - this only changes which are kept - so the cost is a wider copy. The decoder window is framed and faintly lifted, since half the picture is now outside what the model reads and nothing said which half. Marker fixes found while reviewing the render: the tone marker was orange, which is a colour the inferno ramp itself passes through, so a marker sitting on the trace it pointed at was indistinguishable from the keying gaps in that trace - invisible in exactly the case it existed for. It is cyan now, and both markers are pips in a gutter above the spectrum rather than lines across it. Also from the release audit: - compute()'s bin-count guard was written as a three-term disjunction, which any custom range satisfies regardless of bin count, leaving the model invariant unenforced for the caller most able to break it. Rewritten as an implication, with a Nyquist bound so no range can index past the FFT output. - signalStrength was gated on a confirmed out-of-window tone, which is false when detection fails - and it fails for a slow fist, measured at prominence 2.5 against a 4.5 threshold for 15% duty. So the meter still read half scale beside an empty transcript. It now requires a tone confirmed decodable: 11 flow combinations, 3 wrong before, 0 wrong after. - detectedToneHz never expired, so after retuning into the band the hint kept naming the frequency the operator had left, indefinitely. It now clears after 10 s without a tone, which is clear of any real gap - the longest being 1.7 s between words at 5 WPM. - The waterfall label read estimatedPitch while the hint read detectedToneHz, two numbers up to 800 Hz apart both claiming to be the tone. Both read the latter. - Removed a redundant toFloat() that the compiler warned about. Accessibility, untouched until now: the waterfall was a bare Canvas and the AMSAT day cells bare Boxes, so both announced nothing at all - on the status page that is the entire content of the screen. Both now carry a contentDescription naming the tone or the day's worst status and report count. The AMSAT tap target goes from 28 dp to 48 dp with the coloured tile still 28 dp, so the grid keeps its density. Strings in all nine locales for both modules.
Look4Sat: Satellite tracker
Radio satellite tracker and pass predictor for Android, inspired by Gpredict
Track satellite passes with ease!
Thanks to Celestrak and SatNOGS you have access to over 9000 active satellites.
You can search the entire database by NORAD Catalog Number or the satellite's name.
Orbital positions and passes are calculated relative to your location.
To get reliable data make sure to set the station position via the app Settings.
The application is built using Kotlin, Coroutines, Jetpack Compose and Navigation.
It is now and always will be completely ad-free and open-source.
Main features:
- Predicting satellite positions and passes for up to 10 days
- Showing the list of currently active and upcoming satellite passes
- Showing the active pass progress, polar trajectory and transceivers info
- Showing the satellite positional data, footprint and ground track on the map
- Custom TLE satellite data import is available via Three Line Element .txt files
- Offline first: calculations are made offline. Weekly TLE data update is recommended.
License
The Look4Sat application code is licensed under the GNU General Public License v3.0.
The CW decoder in feature/cw bundles the DeepCW
neural decoding model, licensed under the GNU Affero General Public License v3.0 only
(AGPL-3.0-only). Because the combined work incorporates an AGPL-3.0 component, the
combined work is distributed under the
GNU Affero General Public License v3.0 — GPL-3.0 Section 13 permits the
combination, and AGPL-3.0 Section 13 applies to the combined work as a whole.
Model provenance and attribution are documented in
feature/cw/licenses/NOTICE.md; the original GPL-3.0
text is preserved at feature/cw/licenses/Look4Sat-GPL-3.0.txt. The CW model runs
locally on-device and does not provide services over a network.