They're not accounting for exposure levels anywhere that I can see, but to be honest I just skimmed it quickly looking for the expected formulas.
It's easy to write garbage in the style of a scientific paper. Use LaTeX, the right fonts, layout, and tone and most people will immediately stop questioning the content. Pepper it with formulas and it starts to look like wisdom delivered by serious men in white lab coats.
I studied physics with a particular focus on optics because I wanted (and achieved) a career in computer graphics. I wanted a good solid background on light transport and physically accurate rendering techniques.
This paper covers about 5% of what you would expect to see in a serious publication, if that. The calibration "technique" they use is hilariously bad. The assumptions are invalid. Obvious instrument limitations aren't even mentioned let alone corrected for.
Compare to the last link in my previous post. The difference is night and day.
Similarly, for a vaguely related recent example, take a look at how the JWST telescope calibration is done: https://jwst-pipeline.readthedocs.io/en/latest/jwst/linearit...
Take a look at the menu on the left! There's section after section after section that covers every aspect of this instrument! Fundamentally, it's "just" a fancy camera with a CCD/CMOS sensor, optics, and similar limits. It's the same problem space, so it's a good example of how this can be done properly.
Admittedly, JWST has a huge budget, but even amateurs do similar things when performing "image stacking", and that's just for making pretty pictures, not for scientific publication: http://deepskystacker.free.fr/english/theory.htm#Calibration
If I were given a budget of just a few thousand dollars and a year to write a PhD or something, I would:
- Get a few identical cameras and lenses, ideally prime lenses. Budget permitting, the best kind are the B&W digital ones, like this one: https://leica-camera.com/en-AU/photography/cameras/m/m10-mon...
- Mount three or more of them on something sturdy facing the same patch of sky, at least a hundred meters apart in a large triangle or other similar shape. Ideally, several at each spot aligned in parallel but using different, well-characterised filters. Or a single camera with rapidly swappable filters, which would be within reach of a student's budget using 3D printing.
- At each location have a GPS receiver as a timing source. Handheld receivers suffice, and many Arduino-type boards have modules for this. Also have a "weather station" measuring wind speed, humidity, and temperature. Also have a thermal probe stuck to the sensor of the camera, or very near it.
- Perform detailed lab calibration of each camera and lens pair. Repeat for each filter if using filters. Use a proper instrument like a spectrophotometer, which every physics lab will most likely have lying around somewhere. This need to be performed at the same focus ("infinity") and across a range of intensities. Ideally at different temperatures also.
- Calibrate in the field. E.g.: align the photos using stars or perform similar cross-checks between each camera. Use GPS data relayed from airliners to verify the altitude computation from stereopsis. Use aircraft that you know are "white" to cross-check atmospheric conditions. Fly drones up a few kilometres with blinking LED lasers on them of various wavelengths. Etc...
- Use manual focus, synchronized camera settings, and take photos every second or so using GPS timing for accurate sync. Ensure to stay well within the "middle" of the dynamic range of the sensors.
- Collect the information for analysis, ideally over weeks or months. Multiple cameras with different perspectives allows massively more accurate estimates of a range of parameters, especially height, speed, size, etc... It simultaneously helps eliminate spurious sensor problems, bugs, birds, raindrops, and other confounding factors.
This could be done with a few thousand dollars and the data could keep many students busy publishing good papers for years. You could write papers on cloud formation, bird migration, raptor hunting statistics, accuracy of aircraft transponders, lightning frequency, meteors, satellites, and even cover military topics such as spotting drones! Computer Science students could get involved with AI analysis of the data, efficient storage, real-time analysis and tracking with longer telephoto lenses, etc...
This is what real science looks like, even if done on a budget. There are teams out there basically doing this or some variant right now!