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6 Ways to Clean Up Space

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A fascinating article in this month's Wired magazine says we're not only screwing up our own environment, we're also polluting space (big surprise, right?). Apparently there's so much garbage (some 15,000 pieces of debris) stuck in Earth's orbit that some Scientists think future space travel might be a problem if something isn't done about it soon. According to Wired, there are 6 ways to clean up the mess, which I'll reprint after the jump. But seeing as you all always have such amazing ideas, I thought it would be fun to see what you come up with? Got a better idea about how to collect a 10-ton rocket stage? Calling all back-seat rocket scientists...

6 Ways to Clean Up Space

1. Aerogel
NASA already uses this superlight, polystyrene-like material to capture space dust for study. So, some scientists suggest, why not send massive, multipaned panels of aerogel into orbit to accumulate smaller pieces of space waste like bugs on a windshield? Once saturated with crap, the swaths of goo could be rocketed into the atmosphere.

2. Lasers
Orbiting light cannons are possible but probably decades off (plus, there are diplomatic concerns). But scientists offer as a viable option ground-based lasers to disrupt the orbit of errant objects, causing them to plummet. Targeting would be handled by new supersensitive radar capable of tracking orbiting debris as small as 1 centimeter in diameter.

3. Collector Barge
Engineers at the Uni versity of Arizona propose that an unmanned barge use radar and cameras to home in on objects, then deploy robot arms to snatch them up. Once clutched, items could be decimated by gold-plated mirrors that focus sunlight. Unless, that is, the scraps are shiny, in which case they would be added to the reflective array.

4. Nets A system called Grasp (grapple, retrieve, and secure payload) would use a large, tightly meshed net strung between long, inflatable booms to ensnare objects. According to aerospace firm TUI, which is testing the system, a fleet of GRASP-equipped micro-satellites could fly into new debris clouds and trap the rubble before it wreaks additional havoc.

5. Foam
The fastest-growing category of debris is the small stuff — paint flecks, titanium bits, and other artifacts from collisions or explosions of large objects (such as rocket stages). NASA says a massive panel of highly porous foam (think Nerf football) could be placed in the path of a debris field. Junk passing through would quickly decelerate and fall to Earth.

6. Tethers
Tethers of copper and other highly conductive materials could be installed on outgoing satellites or attached to older ones by delivery vehicles. Once unfurled, TUI's theory goes, they would react with Earth's electromagnetic field and become a sort of super conductive drag anchor, slowing an object until gravity pulled it into the inferno of reentry.

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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