def ms_next(seed, seed_len):
    seed = seed * seed
    seed = str(seed).zfill(seed_len * 2)
    mid = int(seed_len / 2)
    seed = seed[mid:-mid]
    return int(seed)

def ms_numbers(seed, seed_len):
    while True:
        seed = ms_next(seed, seed_len)
        yield seed

for i, num in zip(range(5), ms_numbers(123456, 6)):
    print(i, num)

0 241383
1 265752
2 624125
3 532015
4 39960

import os

def rand_digit():
    while True:
        d = os.urandom(1)[0]
        if d > 250:
            continue
        return d % 10

seed_len = 50
seed = ""

for _ in range(seed_len):
    seed += str(rand_digit())

seed = int(seed)

seed

7378710975714809271419972422814068416462491488115

def ms_bits(seed, seed_len):
    for num in ms_numbers(seed, seed_len):
        yield num & 1

def ms_bytes(seed, seed_len):
    bits = ms_bits(seed, seed_len)
    while True:
        b = 0
        for _ in range(8):
            b <<= 1
            b |= next(bits)
        yield b

bits = ""
for i, bit in enumerate(ms_bits(seed, seed_len)):
    if i == 40:
        break
    
    bits += str(bit)

print(bits)

1100101111000110001001111100001001101011

import subprocess

ent = subprocess.Popen(["ent"], stdin=subprocess.PIPE, stdout=subprocess.PIPE)

buf = b""

for i, byte in enumerate(ms_bytes(seed, seed_len)):
    buf += bytes([byte])
    
    if i == 500_000:
        break
ent.stdin.write(buf)
ent.stdin.close()

print(ent.stdout.read().decode('ascii'))

Entropy = 7.999652 bits per byte.

Optimum compression would reduce the size
of this 500001 byte file by 0 percent.

Chi square distribution for 500001 samples is 241.47, and randomly
would exceed this value 71.91 percent of the times.

Arithmetic mean value of data bytes is 127.6098 (127.5 = random).
Monte Carlo value for Pi is 3.138780555 (error 0.09 percent).
Serial correlation coefficient is -0.002272 (totally uncorrelated = 0.0).