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Actually Sante and me just reviewed their paper. Although (some of) the scientists in the paper seem to have good track records their experimental techniques are by far not the best to determine the excess amount of energy produced. Even though their methods may introduce fairly large errors they would not be able to negate the cited power output - so they either are super-sloppy (i.e. they lie) or there is TRULY new physics involved... A big problem is that they are basically verifying somebody else's experiment - however because this guy is paranoid he does not tell them exactly what he did. In fact they went to his lab and used a setup that HE put together. All they do is do a measurement on it and it seems like they try to be thorough. There is quite a chance that the guy behind it all (Rossi) is setting them up - personally I would think >95%. However, the implications of this being new physics are so big that I think further research should be conducted.

and here comes my standard question: how can we use this for space? fast detection of natural disasters onboard?

Even on ground. You could for example teach it what nuclear reactors or missiles or other weapons you don't want look like on satellite pictures and automatically scan the world for them (basically replacing intelligence analysts).

In fact, I think this could make a nice ACT project: counting seals from satellite imagery is an actual (and quite recent) thing: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092613 In this publication they did it manually from a GeoEye 1 b/w image, which sounds quite tedious. Maybe one can train one of those image recognition algorithms to do it automatically. Or maybe it's a bit easier to count larger things, like elephants (also a thing).

In HiPEAC (High Performance, embedded architecture and computation) conference I attended in the beginning of this year there was a big trend of CUDA GPU vs FPGA for hardware accelerated image processing. Most of it orbitting around discussing who was faster and cheaper with people from NVIDIA in one side and people from Xilinx and Intel in the other. I remember of talking with an IBM scientist working on hardware accelerated data processing working together with the Radio telescope institute in Netherlands about the solution where they working on (GPU CUDA). I gathered that NVIDIA GPU suits best in applications that somehow do not rely in hardware, having the advantage of being programmed in a 'easy' way accessible to a scientist. FPGA's are highly reliable components with the advantage of being available in radhard versions, but requiring specific knowledge of physical circuit design and tailored 'harsh' programming languages. I don't know what is the level of rad hardness in NVIDIA's GPUs... Therefore FPGAs are indeed the standard choice for image processing in space missions (a talk with the microelectronics department guys could expand on this), whereas GPUs are currently used in some ground based (radio astronomy or other types of telescopes). I think that on for a specific purpose as the one you mentioned, this FPGA vs GPU should be assessed first before going further.