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  2. Googolplex - Wikipedia

    en.wikipedia.org/wiki/Googolplex

    If each book had a mass of 100 grams, all of them would have a total mass of 10 93 kilograms. In comparison, Earth's mass is 5.97 × 10 24 kilograms, [5] the mass of the Milky Way galaxy is estimated at 1.8 × 10 42 kilograms, [6] and the total mass of all the stars in the observable universe is estimated at 2 × 10 52 kg. [7]

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  4. Random number generation - Wikipedia

    en.wikipedia.org/wiki/Random_number_generation

    Dice are an example of a mechanical hardware random number generator. When a cubical die is rolled, a random number from 1 to 6 is obtained. Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols is generated that cannot be reasonably predicted better than by random chance.

  5. Diceware - Wikipedia

    en.wikipedia.org/wiki/Diceware

    Diceware is a method for creating passphrases, passwords, and other cryptographic variables using ordinary dice as a hardware random number generator. For each word in the passphrase, five rolls of a six-sided die are required. The numbers from 1 to 6 that come up in the rolls are assembled as a five-digit number, e.g. 43146. That number is ...

  6. Googol - Wikipedia

    en.wikipedia.org/wiki/Googol

    To put in perspective the size of a googol, the mass of an electron, just under 10 −30 kg, can be compared to the mass of the visible universe, estimated at between 10 50 and 10 60 kg. [5] It is a ratio in the order of about 10 80 to 10 90 , or at most one ten-billionth of a googol (0.00000001% of a googol).

  7. A Million Random Digits with 100,000 Normal Deviates

    en.wikipedia.org/wiki/A_Million_Random_Digits...

    Columns and lines are grouped in fives, and the lines are numbered 00000 through 19999. The standard normal deviates are another 200 pages (10 per line, lines 0000 through 9999), with each deviate given to three decimal places. There are 28 additional pages of front matter. [1] [2] [3]

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  9. CryptGenRandom - Wikipedia

    en.wikipedia.org/wiki/CryptGenRandom

    The weaknesses in the paper all depend on an attacker siphoning the state bits out of the generator. An attacker in a position to carry out this attack would typically already be in a position to defeat any random number generator (for instance, they can simply sniff the outputs of the generator, or fix them in memory to known values).

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