Greetings all! 

You probably know about our recent link budget work for Earth-Venus-Earth (EVE) communications. The event happened this past weekend. Congratulations to Dwingeloo and Stockert for successful signal detection! This is an achievement that has not been accomplished in the amateur service since 25 March 2009 by AMSAT-DL at Bochum Radio Telescope in Germany. Given that the anniversary of this remarkable achievement is tomorrow, then the news we are able to share is delightful indeed. 

Here are some initial reports:

Here is Dr. Wolfgang Herrmann’s Earth-Venus-Earth (EVE) report on the Society of Amateur Radio Astronomers (SARA) email list

groups.google.com/g/sara-li…

Here is Dwingeloo’s report from their website (very nice mention of ORI!)

www.camras.nl/en/blog/2…

Deep Space Exploration Society (DSES) attempted detection with a transmission, but the signal was not received. 

If you would like to join the effort to make the most of the October 2026 inferior conjunction, then the time to join is now.

The goal is to move beyond detection into communication. This challenge is very difficult. 

ORI’s link budget work can be found here: github.com/OpenResea…

ORI’s goal for this document is to continue its educational and predictive role for interplanetary communications in space, and to spark interest and activity in EME and EVE efforts. People that want to do amateur radio satellite communications need resources that explain the communications channels of space in an accessible and citizen-science friendly manner. 

If you know someone that might want to be a part of this mission, then please direct them to openresearch.institute/getting-s…

Or, file an issue or pull request to improve the link budget. We value your opinions and expertise. 

Here is a diagram from some recent link budget work for citizen science and amateur radio band Earth-Venus-Earth communications attempts.

Thank you to @radiotelescoop and Deep Space Exploration Society for the opportunity.

Budget at github.com/OpenResea…

Here is a quick result from an #ai #deeplearning project for #amateurradio that we’ve been working on at Open Research Institute, Inc. Special thank you to MathWorks for supporting our educational, personal, and professional development through #opensource mission.

This result is from a deep learning model that recognizes RTTY messages. It’s given a RTTY transmission in audio format. Each transmission is short, only 10 characters long. The characters are randomly selected, which drives up the chance that the control characters that switch between letters and figures are involved. Another additional complication is that each message gets a random amount of AWGN from 0dB to 60dB. So, the signals aren’t clean.

The model is over 90% accurate under these conditions. The image is of a “confusion chart”, which is a compact way of communicating how well a classification system performed. To use this type of chart, look up what character was actually sent, and then see on the other axis how well the model performed in identifying it.

The model type used was a “long short term memory” model (LSTM), which is useful for finding relationships developed over time. This is a reasonable match to RTTY, where the control characters, once sent, determine which lookup table one uses for many characters in the future. In RTTY the symbols are “double booked”, in that any five bit digital representation of a character has two assignments. When you want to send figures (numbers and punctuation) then you send a figures control character. The receiver then uses the figures lookup table until a letters control character is sent. After that, the receiver uses the letters lookup table. And so on. This makes it not a simple “digits in characters out” scheme.

Note the blank space for “10”. There is no “10” character in RTTY. This is possibly a repercussion of having to give the model a one-character overhead based on Mathworks documentation about this specific deep learning technique. We are going to follow-up on this and “figure” it out.

If you would like to learn more about how to learn AI/ML using the Amateur Radio bands, then please visit ORI’s getting started page today, at openresearch.institute/getting-s…