Notices by spinflip (spinflip@qoto.org)

Meta-post: I think this is the third time I've made an account and tried to commit to mastodon. The others times lasted for about as long as it took for the novelty of tooting about mastodon to wear off: once we all got that point, it turned out that nobody really had much in common and engagement slowly dropped to zero.

This time around, I'm actually genuinely enjoying being here. There's a lot more activity, and a far smaller proportion of it seems to be of the boring "mastodon is good and better than twitter" ilk. Maybe the critical mass to keep people around has finally arrived?

I'm working on a project that involves retrieving large (~2-8 GB) .zip files through HTTP and storing them for later processing. I've written a script that uses an API to lookup and generate URLs for a series of needed files, and then attempts to stream each file to storage using requests.get().iter_content.

The problem is, my connection isn't perfectly stable (and I'm running this on a laptop which sometimes goes to sleep). When the connection is interrupted, the transfer dies and I need to restart it.

What would be the best way to add a resume capacity to my file transfer? So that if the script stalls or the connection drops, it would be possible to resume the download from where it failed?

Anyway, that's all background. Today I spent a few hours trying to set up python scripts to find the most recent Sentinel passes over a given geographical location and download the associated imagery products. This would almost certainly be trivial for anyone with a proper computing background, but for this procrastinating chemist the steps involved learning how to:

• Find the relevant API and options: ✔️, not too bad
• Make an authenticated HTTP call to the constructed URI ✔️
• Parse the XML returned into a pandas dataframe containing imaging modes/acquisition time and date/unique ID for each frame containing the specified lat/lon location ✔️
• Download each ~1.8 GB image into a folder for processing. Still in progress: I've found out how to make an authenticated http GET request that *should* be streaming the retrieved file to storage, but the connection seems unstable and I can't get the download to finish. Sorting this out is the next thing I need to do: when I've got a way to download and archive the specified files, it'll be time start looking into automated processing and analysis. Still not even really sure what I'm trying to achieve, but I'm having fun and learning stuff so far!

SAR imagery does not have amazing spatial resolution, but is often good enough to do things like identify shipping. Water is a uniquely flat surface, so metal objects floating on water give a good return against a low background signal*. Some computationally demanding image processing later, and you can pick out ship locations. I've got a vague idea that it could be interesting to find ships in the territorial waters of North Korea, correlate against AIS tracks, and try to find some sanction-busting shipping running dark without AIS.

*This makes me wonder: the USSR really struggled with power requirements for the radar on its RORSAT ocean-monitoring satellites, to the point that it ended up having to power them using the only nuclear reactors to be launched into space. Why is SAR so much more efficient?

I've got a hobby-interest in remote sensing (satellite imagery). Over the past couple of days, I've been playing around with data from the ESA's Sentinel-1 mission. The ESA (being cool and European Union-y) makes most of the data from Sentinel series of satellites freely accessible to the public, and provides some decent software for processing and analysing the data.

Sentinel-1 is a synthetic aperture radar (SAR) satellite. I don't fully understand the physics behind SAR, but it's basically an active radar measurement of the ground track the satellite passes over. Different surfaces give different sorts of radar returns (measured as a change in polarisation), and so SAR can be used to classify different terrains (crops, forests, grasslands, rock, etc), like in the false-colour image of Flevoland I've attached. Resolution is moderate: for Sentinel-1, each pixel ends up being about 4x4 m on the ground.

#introduction to the above: forgot it the first time because I've been at work for too long

Hey all! I'm an Australian PhD student working in supramolecular chemistry (mainly photoswitch related, with lots of supporting NMR studies). Alongside the chemistry I play music, dance, and have interests in politics/international relations/global security (any open-source analysts hanging around here?).

I've tried mastodon on and off a few times before this account, but never really managed to find a critical mass of enough others with shared interests to keep me here. With all the interest M's been picking up recently, it seemed like it might be worth trying again!