If you find "wave hunting" this interesting but don't have a SDR / receiver of your own, there are plenty of (free to use) WebSDRs to get started: <a href="http://websdr.org/" rel="nofollow noreferrer">http://websdr.org/</a>
For those of you already set up to receive or send WSPR, FT8, or CW (Morse code), make sure you’re receiving and sending reports tomorrow during and around the eclipse (1200-2200 UTC)<p>More info here: <a href="https://hamsci.org/eclipse" rel="nofollow noreferrer">https://hamsci.org/eclipse</a>
All the recent ones sound like noise, of course, but this one is musical: <a href="https://www.sigidwiki.com/wiki/Inmarsat-D(D%2B)_Downlink" rel="nofollow noreferrer">https://www.sigidwiki.com/wiki/Inmarsat-D(D%2B)_Downlink</a>
The HN hug strikes again! What I saw before it went down was pretty amazing though, and definitely something I will be returning to later when the heat dies down.
Super cool!<p>Looks like all the data is in a fairly decently scrapable format. Could one, in theory at least, add auto-identificaton to an SDR app?<p>Is there an (possibly ML) algorithm that says "take this single example picture and tell me if there is a match(Which could be scaled differently) in this other picture"?
Are there any ML approaches to identifying signals? Since using a receiver that produces sound given a FM/SSB demodulation of whatever true modulation is used, or visually inspecting a waterfall certainly has limitations.