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Resource usage fuzzing samples and related data. Contains samples from Pythoin, random data, GPT-3.5, GPT-4, Gemini-1.0, Claude Instant, and Claude Opus. These samples are generated for 50 Python functions. Also included are resource measures for CPU time, instruction count, function calls, peak RAM usage, final RAM allocated, and coverage. These values were collected on an isolated system and account for interference from other processes.
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Sea ice concentration is important because it helps in determining important climate variables. Together with sea ice thickness, important fluxes between air and sea as well as heat transfer between the atmosphere can be determined. We designed an adapted bootstrap algorithm called SARAL/AltiKa Sea Ice Algorithm (SSIA) with some tunings and segregated the algorithm into winter and summer algorithms to estimate daily sea ice concentration (SIC) in the Arctic.
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This file include Instrument lookup tables for EV8TOz algorithm use. It also inlcude sensor soft calibration tables.
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The concentration of sea ice is essential for determining crucial climate factors. Together with sea ice thickness, it is possible to determine significant air-sea fluxes and atmospheric heat transfer. In this study, the SARAL/AltiKa Sea Ice Algorithm is used to determine the monthly sea ice concentration (SIC) in the Arctic (SSIA). For the period from April 2013 to December 2020, data from the dual-frequency microwave radiometer (23.8 GHz and 37 GHz) on the SARAL/AltiKa satellite are used to compute SIC.
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Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" published at IEEE TIFS https://ieeexplore.ieee.org/document/8396276.
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