Restoring a liveatc.net pi station

I hosted the T67 radio feed to Liveatc.net.   We'd had a storm with lightning and power hits which was the demise of the SD memory card in the old Raspberry pi (I think from 2014).   A new image got the unit back on line, but it was having memory issues.   

A new pi 4 was ordered, a new RTLSDR V4 dongle, and 978 and 1090 dongles for adsbexchange.  It was a challenge to get all services running correctly since I'm a noob with this hardware and software.  After futzing for a few days, decided to split the radio traffic from ADS-B and ordered another pi 4.

One of the struggles with adsbexchange dongles is that process may grab the wrong USB dongle.  The common work around is to write or change the serial number in the dongle and specify that serial number in the config file.  A problem is that despite doing this, one of the apps was still using the index and not the serial number.  The index number stays with the port and not the dongle, so it's important to not swap ports.  Also the blue USB 3.0 port supplies more power and was recommended for the 1090 dongle.

So one project had turned into two.  Or three.  I want to try to do some things with speech to text later.

With Dave Pascoe's help from Liveatc, we got the T67 feed working and added Copeland 4T2.  The new dongle works good with good audio quality.  I moved the antenna several times and will ultimately move it to the peak of the hangar and add lightning protection.

The adsbexchange system initially went fairly well except for the issue with serializing the dongles and getting the config files to use the serial number (or the index).  Piaware was added to feed to Flightaware.  Puzzling, was that the stats at times indicated UAT (978 Mhz) traffic on the local info page, but Flightaware only showed a couple of aircraft on one day.  Peering at the local graphs, the one that shows signal strength looked pretty noisy.   Going into the adsbexchange GUI, found the 978 gain was set to max.  I backed it down, swapped antennas etc. and the graph looked better.  However, Flightaware continued showing zero aircraft.  Pinging Grok and Google's AI, found there was a process that was not running that pulled the UAT data to forward to Flightaware.   After getting it running and tweaking gain, aircraft were showing up in the stats.  The 978 and 1090 are on temporary antennas.  The 1090 is picking up aircraft more than 250 miles away.  The 978 varies.  Occasionally it picks up 60 miles away and others, loses aircraft just west of here.  Having the two antennas to close together or near our outside weather station degrades reception.

Flightaware's stats for my reciever:https://www.flightaware.com/adsb/stats/site/262213#stats-262213

Link for Liveatc.net & T67 feed (pi 4 with RTLSDR V4 BLOG dongle) https://www.liveatc.net/hlisten.php?mount=t67&icao=t67

https://www.liveatc.net/

Establising a performance baseline

After looking at EFIS data uploaded to SavvyAviation's web site, trying to see if there were speed and cooling improvements, found it was difficult to glean from different flights since the altitude, power setting, CG varied quite a bit.   

I asked Grok how to establish a baseline and got this: 

To establish a performance baseline for speed improvements on an experimental aircraft, follow a systematic, repeatable, and data-driven approach. The goal is to create a reliable reference point for the aircraft’s current speed performance under controlled conditions, which you can use to measure the impact of modifications. Here’s how to do it effectively:

1. Define Key Performance Metrics:
   • Focus on speed-related metrics such as maximum level speed, cruise speed, climb rate, and acceleration time to a specific speed (e.g., time to reach 200 knots).
   • Include related parameters like fuel consumption, engine RPM, and airspeed (indicated, true, and ground speed) to provide context.
   • Specify units (e.g., knots, mph, or km/h) and ensure consistency across measurements.
2. Standardize Test Conditions:
   • Environment: Conduct tests in consistent weather conditions (e.g., calm winds, standard temperature, and pressure). Use a day with minimal turbulence and stable atmospheric conditions (e.g., clear skies, no significant thermal activity).
   • Altitude: Test at a fixed altitude (e.g., 5,000 ft above sea level) to minimize variations in air density. Record density altitude for each test.
   • Weight and Balance: Ensure the aircraft is at a consistent weight, including fuel, payload, and pilot. Use a standard configuration (e.g., 50% fuel, solo pilot) and document the center of gravity.
   • Configuration: Test with a fixed aircraft configuration (e.g., flaps up, gear retracted if applicable, and a specific propeller setting for variable-pitch props).
3. Use Precise Measurement Tools:
   • Airspeed: Use calibrated onboard instruments (e.g., pitot-static system) and cross-check with GPS-derived ground speed, adjusted for wind.
   • Data Logging: Employ a flight data recorder or avionics system (e.g., Garmin G1000, Dynon SkyView) to log airspeed, altitude, engine parameters, and GPS data at regular intervals (e.g., 1 Hz).
   • External Validation: If possible, use ground-based radar or a chase plane with calibrated instruments to verify speeds.
   • Time Trials: For specific tests (e.g., acceleration), use a stopwatch or automated timing system synced with GPS.
4. Conduct Controlled Flight Tests:
   • Test Plan: Develop a detailed flight test plan with specific procedures for each test. For example:
     • Max Speed Test: Fly level at full power for a set distance (e.g., 10 nautical miles) in two opposing directions to average out wind effects.
     • Cruise Speed Test: Fly at a fixed power setting (e.g., 75% power) and record stabilized speed over a set time or distance.
     • Climb Rate Test: Measure time to climb between two altitudes (e.g., 3,000 to 5,000 ft) at a constant airspeed or power setting.
   • Repetition: Perform each test at least three times to ensure repeatability and calculate average values. Discard outliers only if there’s a clear error (e.g., instrument malfunction).
   • Flight Path: Use GPS to maintain a consistent flight path (e.g., straight and level or a specific climb angle). Fly in areas with minimal air traffic to avoid deviations.
5. Document and Normalize Data:
   • Record all relevant data, including airspeed, ground speed, altitude, temperature, pressure, wind speed/direction, and aircraft configuration.
   • Normalize data to standard conditions (e.g., sea-level equivalent speed) using formulas like true airspeed (TAS) calculations:
     \text{TAS} = \text{IAS} \times \sqrt{\frac{\rho_0}{\rho}}
     where

     \text{IAS}

     is indicated airspeed,

     \rho_0

     is sea-level air density, and

     \rho

     is actual air density at test altitude.
   • Account for wind by averaging speeds from reciprocal headings or using GPS ground speed with wind vector corrections.
6. Establish the Baseline:
   • Compile the averaged, normalized results into a performance baseline. For example:
     • Max speed: 180 knots TAS at 5,000 ft, 100% power, 50% fuel.
     • Cruise speed: 160 knots TAS at 75% power, 8,000 ft.
     • Climb rate: 1,200 ft/min from 3,000 to 5,000 ft.
   • Document the baseline in a detailed report, including test conditions, equipment used, and any deviations from the plan.
7. Validate and Refine:
   • Cross-check results with theoretical performance models (e.g., drag polar, engine power curves) or manufacturer data (if available).
   • Conduct a second set of tests on a different day to confirm consistency.
   • If results vary significantly, investigate sources of error (e.g., instrument calibration, pilot technique, or unaccounted wind).
8. Use the Baseline for Comparisons:
   • After modifications (e.g., aerodynamic fairings, engine upgrades), repeat the same test procedures under identical conditions.
   • Compare new results to the baseline to quantify improvements (e.g., “Max speed increased by 5 knots after installing wingtip fairings”).
   • Use statistical methods (e.g., t-tests) to confirm that changes are significant and not due to random variation.

Additional Tips:

• Safety First: Ensure all tests comply with FAA (or relevant authority) regulations for experimental aircraft. Use a qualified test pilot and conduct a thorough pre-flight inspection.
• Calibration: Verify instrument accuracy before testing. For example, perform a pitot-static system check or calibrate GPS units.
• Incremental Changes: When testing modifications, change one variable at a time (e.g., propeller pitch) to isolate its effect on speed.
• Real-Time Monitoring: If possible, use telemetry to monitor performance live and abort tests if parameters (e.g., engine temps) exceed safe limits.

By following this approach, you’ll create a robust, repeatable baseline that allows you to accurately measure speed improvements and make data-driven decisions for your experimental aircraft. If you have specific details about the aircraft (e.g., type, engine, or mods planned), I can tailor the advice further.