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It can be readily seen that all of the solutions generated in the first generation, and thus the best solution of this generation too, are worse than the baseline solution of the repeated random draws based method (as witnessed by their relative fitness being lower than unity).
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Before going through the details of each of those techniques, we first present our baseline solution for the diversification of multiple sequential queries, namely: Greedy Construction.
Thereafter, the scheme of the GPS baseline solution based on the EMD is suggested.
Thereafter, the scheme of the GPS/PLs baseline solution based on the EMD is suggested.
As final part of our research, Section 6 validates the simulation results by comparing the performance of the baseline solution and SDDV on the iMinds w-iLab.t wireless testbed [20].
In this baseline solution, the Greedy Construction algorithm (or Greedy for short), is directly applied for the diversification of results.
In case of weak models (i.e., low precision of data), however, the success rate of AR may be low, which may consequently introduce large errors to the baseline solution in cases of wrong fixing.
It can be observed that the scalability issues which were clearly visible in the evaluation of the baseline solution in Section 3 have completely disappeared in SDDV.
In the baseline solution, no problems occur due to the high level of rebroadcasting redundancy of the simple flooding technique.
Similar to the approach taken to validate SDDV on the iMinds w-iLab.t wireless testbed, a theoretical comparison of the baseline solution and SDDV would be vary valuable.
The baseline solution is a calorimeter composed of two disks of BaF2 crystals read by UV extended, solar blind, Avalanche Photodiode (APDs), which are under development from a JPL, Caltech, RMD consortium.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com