A short excursion – although the program had been improved, computation took still way too long. Thus, the main analysis string has been shortened slightly. Prog runs way better now, even allows to search for an additional string simultaneously (!). That means after some slight pre-settings for + and IoFBc, the main string can be analyzed for e.g. three words as well as an additional word at some completely different location of the cipher.
Hypothesis is therefore:
If you choose the letter S for the + symbol as well as the letters THERE for the IoFBc symbols and find three words in the section Z274 to Z298, the words TENTH, EARTH, TEETH, MONTH, DEMON, NINTH, EFFORT, WANTS, EXERT, REFER are some of only few more (approx. 25) fitting into the section Z189 to Z198.
A small illustration:
One may claim that it is not too hard to find four words in the cipher – true. Nevertheless, in this case we are talking about a complete (!) list, with approximately 3,300 combinations, covering this specific S/THERE-presetting. There are no other solutions/results. And the previous were computed out of approximately 17,727,153,600,000 potential variations of how letters can be placed onto those cipher sections..
Now let this be for multiple IoFBc’s (e.g. ENTHE, ENTER, THAND etc.) and +’s (L, S, O,…) and we may see that a few thousands of variations are actually (hopefully) not too many to proceed with the computation process. Still a long way to go but a 1.86 x e(-10) or 0.0000000000186 to one cracking ratio should (meanwhile) be enought to sooner or later show Z that he is not crack proof.
Regarding the haystack: It’s comparable to remove approximately 1,000 tons of straw with 3,300 straws left over, the needle somewhere inside.
QT
Tried out one of the results in ZDK:
PRESSING
STERNLY
DEAL
YIELDED
SALES AGENT WAS SWELLED TO
all found in five lines only. Guess it still might be accidential, though.
QT
*ZODIACHRONOLOGY*
Another update.
Of course, the decision is crucial, which location of the 340 cipher is analyzed first. To check a simple string of length 8, as an example, might require 26^8 or 208,827,064,576 variations to be computed. All of these variations cross-checked with a dictionary of approximately 5,000 different words, leading the computer to easily getting a task of one trillion different variations of how such a word may or may not be placed into such string. Not even having mentioned the position of the word inside that string.
Currently, a string with 26 letters/homophones is analyzed.
Based on Bernoulli (+), frequent overlapping trigrams (IoFBc) as well as additional components, all realistic scenarios are on our map. This involves at least the most frequent 1,000 trigrams, etc.. Thus, as we don’t know the cleartext, yet, we are searching for any presettings that allow us to place e.g. three words of length >4 into such a string of 26 letters. Then, there even is a cross-check useful with another string of the cipher, of length 10, if there is also a word to be found.
Based on an overall of 23 different homophones (26^23 or 3.5 +e32), computation has meanwhile proceeded to a level that all presettings for +, IoFBc as well as a specific trigram (starting from the most frequent one downwards) can be identified.
So to say, instead of 26^14 (minus the six pre-defined homophones) or 64,509,974,703,297,150,976 variations to compute, a group of only approximately 1,000 presettings can be selected for further computation (for additional strings of the cipher).
As finding words is the main criteria, three words have already been found in our first, an additional word in our second string selected. Although that list is only presetting but not complete, it cracks down the amount of 26^14 variations to approximately 1,000 only (0.000000000000000155%).
Below is a simplified list of the first triple word combinations found (e.g. S-THE-ERERE), with its presetting of a total of 23 homophones (not all shown here but already representing 47% of all cipher letters..), as well as the presettings which did not deliver three words in combination with one word in the second string (e.g. S-THE-ENTHE).
Obviously, interesting words can be found (e.g. ‘second’, ‘seeing’, ‘useful’ etc.), to no surprise (regarding the amount of variations analyzed). Further computation shall then exclude ‘fruitless’ presettings and will focus only on the ‘groups’ or ‘arrays’ with ‘successful’ presettings >> saving a lot of computation time.
QT
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EESEAMENSEVEREHESSHEETEE seamen severe sheet THE ERERE EESEXPENSEVEREHESSHEETEE expense severe sheet THE ERERE EESEQUELSEVEREHESSHEETEE sequel severe sheet THE ERERE ESSENCERSEVEREHESSHEETEE essence severe sheet THE ERERE EPSEUDERSEVEREHESSHEETEE pseud severe sheet THE ERERE EESETHERSEVEREHESSHEEPEE ether severe sheep THE ERERE EESENTERSEVEREHESSHEEPEE enter severe sheep THE ERERE EESEAGERSEVEREHESSHEEPEE eager severe sheep THE ERERE EESENSERSEVEREHESSHEEPEE sense severe sheep THE ERERE EESEIZERSEVEREHESSHEEPEE seize severe sheep THE ERERE EESERIESSEVEREHESSHEEPEE series severe sheep THE ERERE EESERVESSEVEREHESSHEEPEE serve severe sheep THE ERERE EESEDGESSEVEREHESSHEEPEE edges severe sheep THE ERERE EESEAMENSEVEREHESSHEEPEE seamen severe sheep THE ERERE EESEXPENSEVEREHESSHEEPEE expense severe sheep THE ERERE EESEQUELSEVEREHESSHEEPEE sequel severe sheep THE ERERE ESSENCERSEVEREHESSHEEPEE essence severe sheep THE ERERE EPSEUDERSEVEREHESSHEEPEE pseud severe sheep THE HATER THE HATER RESENTLISTSAEHHESSHALTAR resent lists shalt THE HATER ROSESELISTSAEHHESSHALTAR roses lists shalt THE HATER RISENALISTSAEHHESSHALTAR risen lists shalt THE HATER RISESELISTSAEHHESSHALTAR rises lists shalt THE HATER RUSEFULISTSAEHHESSHALTAR useful lists shalt THE THING THE THING GESETINTSITENTHESSHENCEG tints tenth hence THE THING GESEHINTSITENTHESSHENCEG hints tenth hence THE THING GESETENTSITENTHESSHENCEG tents tenth hence THE THING GESECENTSITENTHESSHENCEG cents tenth hence THE THING GESEMINESITENTHESSHENCEG mines tenth hence THE THING GESELINESITENTHESSHENCEG lines tenth hence THE THING GESEVENESITENTHESSHENCEG seven tenth hence THE THING GESEVINESITENTHESSHENCEG vines tenth hence THE THING GESEWINESITENTHESSHENCEG wines tenth hence THE THING GESETONESITENTHESSHENCEG tones tenth hence THE THING GESECONDSITENTHESSHENCEG second tenth hence THE THING GESEHANDSITENTHESSHENCEG hands tenth hence THE THING GESETENDSITENTHESSHENCEG tends tenth hence THE THING GESEMINDSITENTHESSHENCEG minds 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elect shell THE THECO OESETELLSELECTHESSHELLEO tells elect shell THE THECO OESEWELLSELECTHESSHELLEO wells elect shell THE THECO OESEBILLSELECTHESSHELLEO bills elect shell THE THECO OESEROLLSELECTHESSHELLEO rolls elect shell THE THECO OESEBELLSELECTHESSHELLEO bells elect shell THE THECO OESECELLSELECTHESSHELLEO cells elect shell THE THECO OUSEFULESELECTHESSHELLEO useful elect shell THE THECO OESETALESELECTHESSHELFEO tales elect shelf THE THECO OESEMILESELECTHESSHELFEO miles elect shelf THE THECO OESEHOLESELECTHESSHELFEO holes elect shelf THE THECO OESEMALESELECTHESSHELFEO males elect shelf THE THECO OESEPOLESELECTHESSHELFEO poles elect shelf THE THECO OESETTLESELECTHESSHELFEO settle elect shelf THE THECO OESESALESELECTHESSHELFEO sales elect shelf THE THECO OESEFILESELECTHESSHELFEO files elect shelf THE THECO OESECALLSELECTHESSHELFEO calls elect shelf THE THECO OESEHILLSELECTHESSHELFEO hills elect shelf THE THECO OESEMILLSELECTHESSHELFEO mills elect shelf THE THECO OESETELLSELECTHESSHELFEO tells elect shelf THE THECO OESEWELLSELECTHESSHELFEO wells elect shelf THE THECO OESEBILLSELECTHESSHELFEO bills elect shelf THE THECO OESEROLLSELECTHESSHELFEO rolls elect shelf THE THECO OESEBELLSELECTHESSHELFEO bells elect shelf THE THECO OESECELLSELECTHESSHELFEO cells elect shelf THE THECO OUSEFULESELECTHESSHELFEO useful elect shelf THE THECO OESETHERSELECTHESSHEETEO ether elect sheet THE THECO OESENTERSELECTHESSHEETEO enter elect sheet THE THECO OESEAGERSELECTHESSHEETEO eager elect sheet THE THECO OESENSERSELECTHESSHEETEO sense elect sheet THE THECO OESEIZERSELECTHESSHEETEO seize elect sheet THE THECO OESEPRESSELECTHESSHEETEO press elect sheet THE THECO OESEBLESSELECTHESSHEETEO bless elect sheet THE THECO OESERIESSELECTHESSHEETEO series elect sheet THE THECO OESEDRESSELECTHESSHEETEO dress elect sheet THE THECO OESEDGESSELECTHESSHEETEO edges elect sheet THE THECO OESEIDEASELECTHESSHEETEO ideas elect sheet THE THECO OESERVEASELECTHESSHEETEO serve elect sheet THE THECO OESEAMENSELECTHESSHEETEO seamen elect sheet THE THECO OESEOPENSELECTHESSHEETEO opens elect sheet THE THECO OESEXPENSELECTHESSHEETEO expense elect sheet THE THECO OESEITEMSELECTHESSHEETEO items elect sheet THE THECO OESESEEMSELECTHESSHEETEO seems elect sheet THE THECO OESEQUELSELECTHESSHEETEO sequel elect sheet THE THECO OESEFEELSELECTHESSHEETEO feels elect sheet THE THECO OPSEUDERSELECTHESSHEETEO pseud elect sheet THE THECO OESETHERSELECTHESSHEEPEO ether elect sheep THE THECO OESENTERSELECTHESSHEEPEO enter elect sheep THE THECO OESEAGERSELECTHESSHEEPEO eager elect sheep THE THECO OESENSERSELECTHESSHEEPEO sense elect sheep THE THECO OESEIZERSELECTHESSHEEPEO seize elect sheep THE THECO OESEPRESSELECTHESSHEEPEO press elect sheep THE THECO OESEBLESSELECTHESSHEEPEO bless elect sheep THE THECO OESERIESSELECTHESSHEEPEO series elect sheep THE THECO OESEDRESSELECTHESSHEEPEO dress elect sheep THE THECO OESEDGESSELECTHESSHEEPEO edges elect sheep THE THECO OESEIDEASELECTHESSHEEPEO ideas elect sheep THE THECO OESERVEASELECTHESSHEEPEO serve elect sheep THE THECO OESEAMENSELECTHESSHEEPEO seamen elect sheep THE THECO OESEOPENSELECTHESSHEEPEO opens elect sheep THE THECO OESEXPENSELECTHESSHEEPEO expense elect sheep THE THECO OESEITEMSELECTHESSHEEPEO items elect sheep THE THECO OESESEEMSELECTHESSHEEPEO seems elect sheep THE THECO OESEQUELSELECTHESSHEEPEO sequel elect sheep THE THECO OESEFEELSELECTHESSHEEPEO feels elect sheep THE THECO OPSEUDERSELECTHESSHEEPEO pseud elect sheep THE THECO OESETALESEXACTHESSHALLAO tales exact shall THE THECO OESEMILESEXACTHESSHALLAO miles exact shall THE THECO OESEHOLESEXACTHESSHALLAO holes exact shall THE THECO OESEMALESEXACTHESSHALLAO males exact shall THE THECO OESEPOLESEXACTHESSHALLAO poles exact shall THE THECO OESETTLESEXACTHESSHALLAO settle exact shall THE THECO OESESALESEXACTHESSHALLAO sales exact shall THE THECO OESEFILESEXACTHESSHALLAO files exact shall THE THECO OESECALLSEXACTHESSHALLAO calls exact shall THE THECO OESEHILLSEXACTHESSHALLAO hills exact shall THE THECO OESEMILLSEXACTHESSHALLAO mills exact shall THE THECO OESETELLSEXACTHESSHALLAO tells exact shall THE THECO OESEWELLSEXACTHESSHALLAO wells exact shall THE THECO OESEBILLSEXACTHESSHALLAO bills exact shall THE THECO OESEROLLSEXACTHESSHALLAO rolls exact shall THE THECO OESEBELLSEXACTHESSHALLAO bells exact shall THE THECO OESECELLSEXACTHESSHALLAO cells exact shall THE THECO OUSEFULESEXACTHESSHALLAO useful exact shall THE THECO OESEVERESEXACTHESSHAREAO severe exact share THE THECO OESEWARESEXACTHESSHAREAO wares exact share THE THECO OESECARESEXACTHESSHAREAO cares exact share THE THECO OESEFIRESEXACTHESSHAREAO fires exact share THE THECO OESECURESEXACTHESSHAREAO secure exact share THE THECO OESEDARESEXACTHESSHAREAO dares exact share THE THECO OESEWORDSEXACTHESSHAREAO words exact share THE THECO OESECORDSEXACTHESSHAREAO cords exact share THE THECO OESELORDSEXACTHESSHAREAO lords exact share THE THECO OESEPARTSEXACTHESSHAREAO parts exact share THE THECO OESEPORTSEXACTHESSHAREAO ports exact share THE THECO OESECARTSEXACTHESSHAREAO carts exact share THE THECO OESESORTSEXACTHESSHAREAO sorts exact share THE THECO OESEFORMSEXACTHESSHAREAO forms exact share THE THECO OESETERMSEXACTHESSHAREAO terms exact share THE THECO OESEWORMSEXACTHESSHAREAO worms exact share THE THECO OESEFARMSEXACTHESSHAREAO farms exact share THE THECO OESECOMESEXACTHESSHAMEAO comes exact shame THE THECO OESETIMESEXACTHESSHAMEAO times exact shame THE THECO OESEHOMESEXACTHESSHAMEAO homes exact shame THE THECO OESENAMESEXACTHESSHAMEAO names exact shame THE THECO OESESIDESEXACTHESSHADEAO sides exact shade THE THECO OESETALESEXACTHESSHALTAO tales exact shalt THE THECO OESEMILESEXACTHESSHALTAO miles exact shalt THE THECO OESEHOLESEXACTHESSHALTAO holes exact shalt THE THECO OESEMALESEXACTHESSHALTAO males exact shalt THE THECO OESEPOLESEXACTHESSHALTAO poles exact shalt THE THECO OESETTLESEXACTHESSHALTAO settle exact shalt THE THECO OESESALESEXACTHESSHALTAO sales exact shalt THE THECO OESEFILESEXACTHESSHALTAO files exact shalt THE THECO OESECALLSEXACTHESSHALTAO calls exact shalt THE THECO OESEHILLSEXACTHESSHALTAO hills exact shalt THE THECO OESEMILLSEXACTHESSHALTAO mills exact shalt THE THECO OESETELLSEXACTHESSHALTAO tells exact shalt THE THECO OESEWELLSEXACTHESSHALTAO wells exact shalt THE THECO OESEBILLSEXACTHESSHALTAO bills exact shalt THE THECO OESEROLLSEXACTHESSHALTAO rolls exact shalt THE THECO OESEBELLSEXACTHESSHALTAO bells exact shalt THE THECO OESECELLSEXACTHESSHALTAO cells exact shalt THE THECO OUSEFULESEXACTHESSHALTAO useful exact shalt THE THECO OESEEXPOSEXACTHESSHAPEAO expose exact shape THE THECO OESEIMPOSEXACTHESSHAPEAO impose exact shape THE THECO OESEREPOSEXACTHESSHAPEAO repose exact shape THE THECO OESEOPPOSEXACTHESSHAPEAO oppose exact shape THE CTION THE WHICH THE THESE THE THESE EESETINTSEXISTHESSHINEIE tints exist shine THE THESE EESEHINTSEXISTHESSHINEIE hints exist shine THE THESE EESETENTSEXISTHESSHINEIE tents exist shine THE THESE EESECENTSEXISTHESSHINEIE cents exist shine THE THESE EESEMINESEXISTHESSHINEIE mines exist shine THE THESE EESELINESEXISTHESSHINEIE lines exist shine THE THESE EESEVENESEXISTHESSHINEIE seven exist shine THE THESE EESEVINESEXISTHESSHINEIE vines exist shine THE THESE EESEWINESEXISTHESSHINEIE wines exist shine THE THESE EESETONESEXISTHESSHINEIE tones exist shine THE THESE EESECONDSEXISTHESSHINEIE second exist shine THE THESE EESEHANDSEXISTHESSHINEIE hands exist shine THE THESE EESETENDSEXISTHESSHINEIE tends exist shine THE THESE EESEMINDSEXISTHESSHINEIE minds exist shine THE THESE EESELANDSEXISTHESSHINEIE lands exist shine THE THESE EESESENDSEXISTHESSHINEIE sends exist shine THE THESE EESEKINDSEXISTHESSHINEIE kinds exist shine THE THESE EESEFINDSEXISTHESSHINEIE finds exist shine THE THESE EESEWINDSEXISTHESSHINEIE winds exist shine THE THESE EESEBANDSEXISTHESSHINEIE bands exist shine THE THESE EESEFUNDSEXISTHESSHINEIE funds exist shine THE THESE EESERINGSEXISTHESSHINEIE rings exist shine THE THESE EESESINGSEXISTHESSHINEIE sings exist shine THE THESE EESESONGSEXISTHESSHINEIE songs exist shine THE THESE EESEKINGSEXISTHESSHINEIE kings exist shine THE THESE EESEEINGSEXISTHESSHINEIE seeing exist shine THE THESE EESEVENESEXESTHESSHENCEE seven sexes hence THE THESE EESECONDSEXESTHESSHENCEE second sexes hence THE THESE EESEEINGSEXESTHESSHENCEE seeing sexes hence THE THESE EESETTLESEXESTHESSHELLEE settle sexes shell THE THESE EUSEFULESEXESTHESSHELLEE useful sexes shell THE THESE EESETTLESEXESTHESSHELFEE settle sexes shelf THE THESE EUSEFULESEXESTHESSHELFEE useful sexes shelf THE THESE EESETHERSEXESTHESSHEETEE ether sexes sheet THE THESE EESENTERSEXESTHESSHEETEE enter sexes sheet THE THESE EESEAGERSEXESTHESSHEETEE eager sexes sheet THE THESE EESENSERSEXESTHESSHEETEE sense sexes sheet THE THESE EESEIZERSEXESTHESSHEETEE seize sexes sheet THE THESE EESERIESSEXESTHESSHEETEE series sexes sheet THE THESE EESERVESSEXESTHESSHEETEE serve sexes sheet THE THESE EESEDGESSEXESTHESSHEETEE edges sexes sheet THE THESE EESEAMENSEXESTHESSHEETEE seamen sexes sheet THE THESE EESEXPENSEXESTHESSHEETEE expense sexes sheet THE THESE EESEQUELSEXESTHESSHEETEE sequel sexes sheet THE THESE ESSENCERSEXESTHESSHEETEE essence sexes sheet THE THESE EPSEUDERSEXESTHESSHEETEE pseud sexes sheet THE THESE EESETHERSEXESTHESSHEEPEE ether sexes sheep THE THESE EESENTERSEXESTHESSHEEPEE enter sexes sheep THE THESE EESEAGERSEXESTHESSHEEPEE eager sexes sheep THE THESE EESENSERSEXESTHESSHEEPEE sense sexes sheep THE THESE EESEIZERSEXESTHESSHEEPEE seize sexes sheep THE THESE EESERIESSEXESTHESSHEEPEE series sexes sheep THE THESE EESERVESSEXESTHESSHEEPEE serve sexes sheep THE THESE EESEDGESSEXESTHESSHEEPEE edges sexes sheep THE THESE EESEAMENSEXESTHESSHEEPEE seamen sexes sheep THE THESE EESEXPENSEXESTHESSHEEPEE expense sexes sheep THE THESE EESEQUELSEXESTHESSHEEPEE sequel sexes sheep THE THESE ESSENCERSEXESTHESSHEEPEE essence sexes sheep THE THESE EPSEUDERSEXESTHESSHEEPEE pseud sexes sheep THE AFTER THE EOFTH THE ABOUT THE ERTHE THE IONAL THE FIRST THE WOULD AND ERERE AND HATER AND THING AND WITHA AND WITHI AND ITHER AND WITHE AND ENTHI AND ITHAT AND THALL AND HATHI AND ITHIS AND THITH AND OFTHE AND ATION AND INGTH AND INGTH HESDWARESGSATINDSSNAREAH wares satin snare AND INGTH HESDCARESGSATINDSSNAREAH cares satin snare AND INGTH HESDFIRESGSATINDSSNAREAH fires satin snare AND INGTH HESDDARESGSATINDSSNAREAH dares satin snare AND INGTH HESDWORDSGSATINDSSNAREAH words satin snare AND INGTH HESDCORDSGSATINDSSNAREAH cords satin snare AND INGTH HESDLORDSGSATINDSSNAREAH lords satin snare AND INGTH HESDPARTSGSATINDSSNAREAH parts satin snare AND INGTH HESDPORTSGSATINDSSNAREAH ports satin snare AND INGTH HESDCARTSGSATINDSSNAREAH carts satin snare AND INGTH HESDSORTSGSATINDSSNAREAH sorts satin snare AND INGTH HESDFORMSGSATINDSSNAREAH forms satin snare AND INGTH HESDTERMSGSATINDSSNAREAH terms satin snare AND INGTH HESDWORMSGSATINDSSNAREAH worms satin snare AND INGTH HESDFARMSGSATINDSSNAREAH farms satin snare AND INGTH HESDWARESGLATINDSSNAREAH wares latin snare AND INGTH HESDCARESGLATINDSSNAREAH cares latin snare AND INGTH HESDFIRESGLATINDSSNAREAH fires latin snare AND INGTH HESDDARESGLATINDSSNAREAH dares latin snare AND INGTH HESDWORDSGLATINDSSNAREAH words latin snare AND INGTH HESDCORDSGLATINDSSNAREAH cords latin snare AND INGTH HESDLORDSGLATINDSSNAREAH lords latin snare AND INGTH HESDPARTSGLATINDSSNAREAH parts latin snare AND INGTH HESDPORTSGLATINDSSNAREAH ports latin snare AND INGTH HESDCARTSGLATINDSSNAREAH carts latin snare AND INGTH HESDSORTSGLATINDSSNAREAH sorts latin snare AND INGTH HESDFORMSGLATINDSSNAREAH forms latin snare AND INGTH HESDTERMSGLATINDSSNAREAH terms latin snare AND INGTH HESDWORMSGLATINDSSNAREAH worms latin snare AND INGTH HESDFARMSGLATINDSSNAREAH farms latin snare AND OTHER AND ANDTH AND ANDTH HESDCOMESDESTANDSSNSMASH comes stand smash AND ANDTH HESDTIMESDESTANDSSNSMASH times stand smash AND ANDTH HESDHOMESDESTANDSSNSMASH homes stand smash AND ANDTH HESDNAMESDESTANDSSNSMASH names stand smash AND ANDTH HESDRAMASDESTANDSSNSMASH drama stand smash AND ANDTH HESDWARESDSATANDSSNAREAH wares satan snare AND ANDTH HESDCARESDSATANDSSNAREAH cares satan snare AND ANDTH HESDFIRESDSATANDSSNAREAH fires satan snare AND ANDTH HESDDARESDSATANDSSNAREAH dares satan snare AND ANDTH HESDWORDSDSATANDSSNAREAH words satan snare AND ANDTH HESDCORDSDSATANDSSNAREAH cords satan snare AND ANDTH HESDLORDSDSATANDSSNAREAH lords satan snare AND ANDTH HESDPARTSDSATANDSSNAREAH parts satan snare AND ANDTH HESDPORTSDSATANDSSNAREAH ports satan snare AND ANDTH HESDCARTSDSATANDSSNAREAH carts satan snare AND ANDTH HESDSORTSDSATANDSSNAREAH sorts satan snare AND ANDTH HESDFORMSDSATANDSSNAREAH forms satan snare AND ANDTH HESDTERMSDSATANDSSNAREAH terms satan snare AND ANDTH HESDWORMSDSATANDSSNAREAH worms satan snare AND ANDTH HESDFARMSDSATANDSSNAREAH farms satan snare
*ZODIACHRONOLOGY*
I now know how to crack the Z340 (as a homophone cipher).
STEP1 – Search for words in line 17 (26 letters..use + letter, alphabet, 1,000 trigrams, most frequent 5-grams – only)
STEP2 – Use that list of line 17 variations (which shall contain at least three words from a dictionary) to search for additional words in other parts of the cipher.
263,640,000,000,000 variations can be covered in approximately 3 hours. Out of those, a list of only ~3,000 variations from STEP1 occurs.
Please tell me that this is not a good approach for STEP2..
We will f…ing see. Some examples below from that area which is currently running,
Z, winter is coming – get your clothes on (the orange one will fit best..).
QT
ENT ERETH E TEN O ERS ILE (EOS) HOSTILERSEETTENTSSNTENTH [{'Pos': 0, 'Word': 'hostile'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tenth'}] ENT ERETH E TEN O EAS ILE (EOS) HOSTILEASEETTENTSSNTENTH [{'Pos': 0, 'Word': 'hostile'}, {'Pos': 5, 'Word': 'lease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tenth'}] ENT ERETH E TEN O ENS STE (EOS) HOSTSTENSEETTENTSSNTENTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'tense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tenth'}] ENT ERETH E TEN O ENS SSE (EOS) HOSTSSENSEETTENTSSNTENTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'sense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tenth'}] ENT ERETH E CAT E ASS GRA (EOS) HESTGRASSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'grass'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ASS CLA (EOS) HESTCLASSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'class'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ASS BRA (EOS) HESTBRASSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'brass'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ANS PLA (EOS) HESTPLANSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'plans'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ANS MEA (EOS) HESTMEANSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'means'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ALS RIA (EOS) HESTRIALSEECTENTSSNCATCH [{'Pos': 3, 'Word': 'trial'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ALS DEA (EOS) HESTDEALSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'deals'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ALS SEA (EOS) HESTSEALSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'seals'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E AUS ICA (EOS) HESTICAUSEECTENTSSNCATCH [{'Pos': 5, 'Word': 'cause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E AUS CLA (EOS) HESTCLAUSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'clause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E AUS MPA (EOS) HESTMPAUSEECTENTSSNCATCH [{'Pos': 5, 'Word': 'pause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ARS STA (EOS) HESTSTARSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'stars'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ARS HEA (EOS) HESTHEARSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'hears'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ARS YEA (EOS) HESTYEARSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'years'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E ARS TEA (EOS) HESTTEARSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'tears'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT E AYS PLA (EOS) HESTPLAYSEECTENTSSNCATCH [{'Pos': 4, 'Word': 'plays'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT A ASS ERA (EOS) HASTERASSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT A ASS YEA (EOS) HASTYEASSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'hasty'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT A AUS ECA (EOS) HASTECAUSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'cause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT A AUS EPA (EOS) HASTEPAUSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'pause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT O ASS STA (EOS) HOSTSTASSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E CAT O AUS SPA (EOS) HOSTSPAUSEECTENTSSNCATCH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'pause'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'catch'}] ENT ERETH E ERV E RES FOR (EOS) HESTFORESEEETENTSSNERVEH [{'Pos': 4, 'Word': 'foresee'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES HER (EOS) HESTHERESEEETENTSSNERVEH [{'Pos': 3, 'Word': 'there'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES WAR (EOS) HESTWARESEEETENTSSNERVEH [{'Pos': 4, 'Word': 'wares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES CAR (EOS) HESTCARESEEETENTSSNERVEH [{'Pos': 4, 'Word': 'cares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES FIR (EOS) HESTFIRESEEETENTSSNERVEH [{'Pos': 4, 'Word': 'fires'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES AIR (EOS) HESTAIRESEEETENTSSNERVEH [{'Pos': 2, 'Word': 'stair'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RES DAR (EOS) HESTDARESEEETENTSSNERVEH [{'Pos': 4, 'Word': 'dares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RDS WOR (EOS) HESTWORDSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'words'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RDS COR (EOS) HESTCORDSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'cords'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RDS LOR (EOS) HESTLORDSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'lords'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E ROS EAR (EOS) HESTEAROSEEETENTSSNERVEH [{'Pos': 5, 'Word': 'arose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E ROS PPR (EOS) HESTPPROSEEETENTSSNERVEH [{'Pos': 5, 'Word': 'prose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RTS PAR (EOS) HESTPARTSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'parts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RTS POR (EOS) HESTPORTSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'ports'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RTS CAR (EOS) HESTCARTSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'carts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RTS SOR (EOS) HESTSORTSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'sorts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RMS FOR (EOS) HESTFORMSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'forms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RMS TER (EOS) HESTTERMSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'terms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RMS WOR (EOS) HESTWORMSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'worms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV E RMS FAR (EOS) HESTFARMSEEETENTSSNERVEH [{'Pos': 4, 'Word': 'farms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV A RES EAR (EOS) HASTEARESEEETENTSSNERVEH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV A ROS EAR (EOS) HASTEAROSEEETENTSSNERVEH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'arose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV A ROS EPR (EOS) HASTEPROSEEETENTSSNERVEH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'prose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E ERV O RES STR (EOS) HOSTSTRESEEETENTSSNERVEH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 18, 'Word': 'nerve'}] ENT ERETH E TRU E RES FOR (EOS) HESTFORESEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'foresee'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES HER (EOS) HESTHERESEETTENTSSNTRUTH [{'Pos': 3, 'Word': 'there'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES WAR (EOS) HESTWARESEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'wares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES CAR (EOS) HESTCARESEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'cares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES FIR (EOS) HESTFIRESEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'fires'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES AIR (EOS) HESTAIRESEETTENTSSNTRUTH [{'Pos': 2, 'Word': 'stair'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RES DAR (EOS) HESTDARESEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'dares'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RDS WOR (EOS) HESTWORDSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'words'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RDS COR (EOS) HESTCORDSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'cords'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RDS LOR (EOS) HESTLORDSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'lords'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E ROS EAR (EOS) HESTEAROSEETTENTSSNTRUTH [{'Pos': 5, 'Word': 'arose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E ROS PPR (EOS) HESTPPROSEETTENTSSNTRUTH [{'Pos': 5, 'Word': 'prose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RTS PAR (EOS) HESTPARTSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'parts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RTS POR (EOS) HESTPORTSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'ports'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RTS CAR (EOS) HESTCARTSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'carts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RTS SOR (EOS) HESTSORTSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'sorts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RMS FOR (EOS) HESTFORMSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'forms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RMS TER (EOS) HESTTERMSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'terms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RMS WOR (EOS) HESTWORMSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'worms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU E RMS FAR (EOS) HESTFARMSEETTENTSSNTRUTH [{'Pos': 4, 'Word': 'farms'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU A RES EAR (EOS) HASTEARESEETTENTSSNTRUTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU A ROS EAR (EOS) HASTEAROSEETTENTSSNTRUTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'arose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU A ROS EPR (EOS) HASTEPROSEETTENTSSNTRUTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'prose'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E TRU O RES STR (EOS) HOSTSTRESEETTENTSSNTRUTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'truth'}] ENT ERETH E AUS A URS ECU (EOS) HASTECURSEEATENTSSNAUSAH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'curse'}, {'Pos': 10, 'Word': 'eaten'}, {'Pos': 12, 'Word': 'tents'}] ENT ERETH E TOO E OUS PRO (EOS) HESTPROUSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'rouse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OUS THO (EOS) HESTTHOUSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'house'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OUS SPO (EOS) HESTSPOUSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'spouse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OUS EMO (EOS) HESTEMOUSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'mouse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E ORS THO (EOS) HESTTHORSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'horse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E ORS TWO (EOS) HESTTWORSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'worse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E ORS EMO (EOS) HESTEMORSEETTENTSSNTOOTH [{'Pos': 5, 'Word': 'morse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E ORS ENO (EOS) HESTENORSEETTENTSSNTOOTH [{'Pos': 3, 'Word': 'tenor'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OSS GRO (EOS) HESTGROSSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'gross'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OSS CRO (EOS) HESTCROSSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'cross'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OES SHO (EOS) HESTSHOESEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'shoes'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OWS SHO (EOS) HESTSHOWSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'shows'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OWS GRO (EOS) HESTGROWSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'grows'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OWS KNO (EOS) HESTKNOWSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'knows'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OWS HRO (EOS) HESTHROWSEETTENTSSNTOOTH [{'Pos': 3, 'Word': 'throw'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO E OWS BRO (EOS) HESTBROWSEETTENTSSNTOOTH [{'Pos': 4, 'Word': 'brows'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO A ONS ECO (EOS) HASTECONSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO A OUS EMO (EOS) HASTEMOUSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'mouse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO A OUS ERO (EOS) HASTEROUSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'rouse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO A ORS EMO (EOS) HASTEMORSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'morse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO O ONS SIO (EOS) HOSTSIONSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO O OUS SHO (EOS) HOSTSHOUSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'house'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TOO O ORS SHO (EOS) HOSTSHORSEETTENTSSNTOOTH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'horse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'tooth'}] ENT ERETH E TEE E ERS ATE (EOS) HESTATERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'state'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS ARE (EOS) HESTARERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stare'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS IVE (EOS) HESTIVERSEETTENTSSNTEETH [{'Pos': 5, 'Word': 'verse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS ONE (EOS) HESTONERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stone'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS ORE (EOS) HESTORERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'store'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS AGE (EOS) HESTAGERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stage'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS AKE (EOS) HESTAKERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stake'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS OWE (EOS) HESTOWERSEETTENTSSNTEETH [{'Pos': 3, 'Word': 'tower'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ERS ALE (EOS) HESTALERSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stale'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS PRE (EOS) HESTPRESSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'press'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS OVE (EOS) HESTOVESSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stove'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS BLE (EOS) HESTBLESSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'bless'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS RIE (EOS) HESTRIESSEETTENTSSNTEETH [{'Pos': 3, 'Word': 'tries'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS IME (EOS) HESTIMESSEETTENTSSNTEETH [{'Pos': 3, 'Word': 'times'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS REE (EOS) HESTREESSEETTENTSSNTEETH [{'Pos': 3, 'Word': 'trees'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ESS DRE (EOS) HESTDRESSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'dress'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E EAS NCE (EOS) HESTNCEASEETTENTSSNTEETH [{'Pos': 5, 'Word': 'cease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E EAS IDE (EOS) HESTIDEASEETTENTSSNTEETH [{'Pos': 4, 'Word': 'ideas'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E EAS BLE (EOS) HESTBLEASEETTENTSSNTEETH [{'Pos': 5, 'Word': 'lease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS NTE (EOS) HESTNTENSEETTENTSSNTEETH [{'Pos': 5, 'Word': 'tense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS IDE (EOS) HESTIDENSEETTENTSSNTEETH [{'Pos': 5, 'Word': 'dense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS USE (EOS) HESTUSENSEETTENTSSNTEETH [{'Pos': 5, 'Word': 'sense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS OPE (EOS) HESTOPENSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'opens'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS OLE (EOS) HESTOLENSEETTENTSSNTEETH [{'Pos': 2, 'Word': 'stolen'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ENS OKE (EOS) HESTOKENSEETTENTSSNTEETH [{'Pos': 3, 'Word': 'token'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E EMS ITE (EOS) HESTITEMSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'items'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E EMS SEE (EOS) HESTSEEMSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'seems'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE E ELS FEE (EOS) HESTFEELSEETTENTSSNTEETH [{'Pos': 4, 'Word': 'feels'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A ERS ERE (EOS) HASTERERSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A ERS EVE (EOS) HASTEVERSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'verse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A EAS ELE (EOS) HASTELEASEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'lease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A EAS ECE (EOS) HASTECEASEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'cease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A ENS ESE (EOS) HASTESENSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'sense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A ENS ETE (EOS) HASTETENSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'tense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE A ENS EDE (EOS) HASTEDENSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'dense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE O ERS STE (EOS) HOSTSTERSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE O ERS ILE (EOS) HOSTILERSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'hostile'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE O EAS ILE (EOS) HOSTILEASEETTENTSSNTEETH [{'Pos': 0, 'Word': 'hostile'}, {'Pos': 5, 'Word': 'lease'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE O ENS STE (EOS) HOSTSTENSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'tense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E TEE O ENS SSE (EOS) HOSTSSENSEETTENTSSNTEETH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'sense'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'teeth'}] ENT ERETH E SMA E MOS ORM (EOS) HESTORMOSEESTENTSSNSMASH [{'Pos': 2, 'Word': 'storm'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MOS EEM (EOS) HESTEEMOSEESTENTSSNSMASH [{'Pos': 1, 'Word': 'esteem'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MES COM (EOS) HESTCOMESEESTENTSSNSMASH [{'Pos': 4, 'Word': 'comes'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MES TIM (EOS) HESTTIMESEESTENTSSNSMASH [{'Pos': 4, 'Word': 'times'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MES HEM (EOS) HESTHEMESEESTENTSSNSMASH [{'Pos': 3, 'Word': 'theme'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MES HOM (EOS) HESTHOMESEESTENTSSNSMASH [{'Pos': 4, 'Word': 'homes'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MES NAM (EOS) HESTNAMESEESTENTSSNSMASH [{'Pos': 4, 'Word': 'names'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA E MUS SAM (EOS) HESTSAMUSEESTENTSSNSMASH [{'Pos': 5, 'Word': 'amuse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA A MOS ERM (EOS) HASTERMOSEESTENTSSNSMASH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA O MOS SOM (EOS) HOSTSOMOSEESTENTSSNSMASH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E SMA O MUS SAM (EOS) HOSTSAMUSEESTENTSSNSMASH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'amuse'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 19, 'Word': 'smash'}] ENT ERETH E XAM A AUS ECA (EOS) HASTECAUSEEXTENTSSNXAMXH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'cause'}, {'Pos': 10, 'Word': 'extent'}, {'Pos': 12, 'Word': 'tents'}] ENT ERETH E XAM A AUS EPA (EOS) HASTEPAUSEEXTENTSSNXAMXH [{'Pos': 0, 'Word': 'haste'}, {'Pos': 5, 'Word': 'pause'}, {'Pos': 10, 'Word': 'extent'}, {'Pos': 12, 'Word': 'tents'}] ENT ERETH E XAM O AUS SPA (EOS) HOSTSPAUSEEXTENTSSNXAMXH [{'Pos': 0, 'Word': 'hosts'}, {'Pos': 5, 'Word': 'pause'}, {'Pos': 10, 'Word': 'extent'}, {'Pos': 12, 'Word': 'tents'}] ENT ERETH T ARE E RES HER (EOS) HESTHERESETATENTSSNAREAH [{'Pos': 3, 'Word': 'there'}, {'Pos': 12, 'Word': 'tents'}, {'Pos': 17, 'Word': 'snare'}]
*ZODIACHRONOLOGY*
Meanwhile my program has evaluated about 20,000 (partial) cleartext results as a list. The previous, currently based on the most frequent 72 trigrams – continuously adding more – out of at least 1,000 trigrams that should be considered.
Thus, besides statistical outliers, the program requires approximately 150 hours of pure computing to find all (!) triple word combinations in the 26 letter string (starting in line 17). Then having already solved ~40% of the cipher, as a list of est. 250,000 to 300,000 different partial solutions. To compute the rest should then become possible.
If that fails, however, the symbol + might be modified into another letter (e.g. ‘L’), the procedure then be repeated.
QT
*ZODIACHRONOLOGY*
Fascinating QT, very exciting.
Here is a screenshot of what is currently going on:
as well as the area covered by the computation process:
In the first picture you can see the given results according to the pre-settings, shown in the first seven columns, followed by the note EOS (End of settings). Instead of now continuing the computation process, only the ‘successful’ presettings will be used for further computation. This makes the program way slimmer, thus faster. You can see, for example, that for the first
being S
being ENC
being ATION
no resuts occur while for
being INGTH
a total of ten results occur.
This said, in theory, there is no need to follow the setting with S, ENC, ATION anymore while the setting S, ENC, INGTH is ‘promising’ to find additional strings containing (additional) cleartext on other positions of the cipher. Cutting off useless computation, so to say.
There even have been found strings of length 26 letters which fully consisted of cleartext words. However, only to solve the complete cipher is what counts.
QT
*ZODIACHRONOLOGY*
Some of the findings so far:
RESETHARSHNEEDLESSLEASER – reset harsh needless lease r(ates)
ROSESMARSHNEEDLESSLEASER – roses marsh. Needless lease r(ates)
RESERMARSHNEEDLESSLEADER – ..reser marsh. Needless leader
EESEADVISECARELESSLAVEAE – ..eese advise careless slave a e..
SESUBANDSHRUINOUSSOUNDUS – ..sesu bands h ruinous sound us..
Just few, most likely none the right one, out of so far ~25,000 to proceed with.
QT
*ZODIACHRONOLOGY*
After a few years of programming and about 10 days computation (on this run), the first solid results are now available:
Considering the string’s inner structures (e.g. homophones occurring twice), out of roughly 1,511,536,000,000 variations, a list of 42,680 strings has showed up. All of these strings contain at least three different words of length >4. Z’s cleartext might but not necessarily must be amongst those results.
Used were the most likely settings, only one out of ~34.5m possible letter combinations has been selected (the ones with words in it).
Needless to say this is a good approach for further computation, now focussing on other areas of the cipher. Based on these intermediary results.
Below is one example of such results, already continued to other parts of the cipher. The words found contain plenty of identical homophones, also it was fully computer generated (not manual ‘trial and error’). It also shows, however, that there is still a long way to go.
QT
*ZODIACHRONOLOGY*
RESETHERSHAREDRESSRRESRR_ERE_DTHER_A_RES_E_ERS_THE_A_E_B_A_STHERERE_SASHESHER_EBASEDEES [{‘Pos’: 6, ‘Word’: ‘draws’}] [{‘Pos’: 0, ‘Word’: ‘tears’}] [{‘Pos’: 1, ‘Word’: ‘heart’}]
One result (example, with parameters different sections, third consecutive computation); the program is now able to find up to nine words from different partial strings:
RESET SHARE DRESS THERE ASHES BASED DRAWS TEARS HEART
Special is, that this is not only one accidential result but delivers the program all (!) of such potential solutions (forcing the cleartext to the surface..). Thus, the whole cipher is computed, according to various settings (at least the sections analyzed).
It should be considered, that all of the strings – one longer, six shorter ones – consist of at least some identical homophones (overlapping), making it pretty hard to simply ‘place’ any words into it. Nevertheless, problems of the pre-setting become obvious:
– 3 words of length>4 in a 24-letter string could be too ambitious
– potentially long words in the 24-letter string cleartext (e.g. ‘crossroad’, ‘passport’) might have been skipped due to lack of space for additional two words to be found
– 8-11 letter strings could consist of shorter words (e.g. ‘as soon as she did this’ has 18 letters but no word of length >4).
Thus, plan is now to find at least one longer word, e.g. 6-8 letters, plus one word of length >4 in the longer string, instead of searching for three words, to get linguistically more realistic results. Furthermore, a dictionary starting from length >3 should be considered for the shorter sections. In those, after all, there is no risk of getting overlapping results (e.g. ‘walk’, ‘walked’) because search is going on there for one word only anyway.
Then there was Indish guy.
A guest of mine, from India, is a programmer. It was interesting to talk to him about Python and how to speed up the computation process. FIrst of all he mentioned that I should switch into ASCII. I knew that this would be better but still hesitated to write the letter ‘A’ as 065. It makes the program pretty abstract to read, from now on. Nevertheless, there do exist ASCII translators. Accordingly, I will have to transform the whole dictionary, trigram lists etc into ASCII, too. The Indian guy estimates at least a 20% gain in computation speed. He mentioned other things (‘recursion’) by which e.g. after finding the first word, the longer string should be cut in half, promising 1:4 computation speed improvement. I will keep that in mind but it might be some effort to prog it.
Finally he recommended me to switch from Python IDLE to Visual Studio Code. It’s just a more comfortable tool to write in. Below you can see the first computation step (without setting of variables, Aho-Corasick search algorithm etc., in fact it delivers a better overview.
QT
*ZODIACHRONOLOGY*
Small update:
Using Visual Studio Code instead of the standard Python Idle pays off. Although both use Python in the background, Visual Studio Code is way more comfortable to scroll up/down, hide already working code structures etc. And it’s pretty fast, too:
After finishing 2019 without cracking the 340, I changed my focus from line 17/18 to other areas of the cipher. With line 17/18 still being very promising, due to lots of repeating symbols, those symbols are not very much present, at least not clusteredd, in other parts of the cipher. One still might conclude, however, the homophone W might represent the letter ‘E’, or at least a very frequent letter (as it occurs three times in a string of only 12 homophones, representing a local frequency of 25%…not many letters can do that usually).
Focussing on such other areas, I was able to find a four string combination of the Z340, of length 7, 10, 9, 8 letters, which has plenty of identical homophones (‘overlapping’). And: It is extremely hard to find even one word in only the three first strings, simultaneosly.
According to this, I rewrote the program, which currently looks like this:
As you can see, the program creates three strings simultaneously before checking for words, instead of creating each string step-by-step. Although it is not logical to me at all, this appears to speed up the whole program somehow.
You also can see the letter S (‘+’ symbol) as well as THAND (‘IoFBc’ section) as well as a bigram eg. ON (‘UZ’) which are used as the presetting for the computation process. Currently, this computation does not show any positive results but did I have had some results in the past, with the following presettings:
ENTER SE
ERERE EA
THING AN
THING IN
HATER SE
THERE IS
..
Each step with positive results shows up with approximately 100 nearly identical, iterated (partial) solutions. Each step, e.g. the current S-THAND-ON presetting, takes approximately three to five minutes to be completed. To do so, an additional bigram (hoping it’s one of the most 30 frequent ones) as well as four alphabetical letters have to be added, to set up the strings later checked against the dictionary.
Leading to 13,709,280 different letter variations every few minutes. Each of those variations is then checked with 4,500 words on about 14 potential positions inside the three strings.
Therefore, a total of 863,684,640,000 words/variations is performed in compliance with the cipher’s homophone structure – every few minutes. IMO this is a pretty nice speed and can I only guess that Visual Studio Code transforms the letters into ASCII code in the background automatically?
With not 30 but 676 different bigrams as well as not only one quintgram but 11,881,376 and it not being a 100% sure that the + symbol really represents the letter S, however, it actually shows how far we still are from really solving the cipher. But it is a complete (!) computation of the selected strings, therefore even ruling out the presettings shown in the picture as potential solutions.
QT
*ZODIACHRONOLOGY*
Just uploaded a short clip of the FCCP program running:
https://workupload.com/file/kuYvqVfD
(download, just a video)
In the lower part of the video you can see the results ‘popping up’ according to the chosen set-up. As you can see, it searches and finds at least four words simultaneously in four different strings of the cipher. The program delivers all results, according to an English dictionary with ~4,500 entries.
Currently, the program covers 22 (!) different homophones (out of 63). Thus 26^22 or 13,471,428,653,161,560,586,981,973,426,176 different variations as how those homophones can be replaced by alphabetical letters. Therefore, good luck if someone tries to solve the cipher with a paper and a pen only, like Harden successfully did (with a less complex cipher).
Still assuming a homophone substitution, it is needless to say why the cipher has not yet been cracked. It also is needless to mention, that the program – while running for an hour or so – did not complete the full computation but only covered ‘lousy’ 2,481,152,873,203,736,576, so far. All repeating homophones in the cipher strings always considered reciprocally, of course.
Currently, I see only one way to reduce the amount of computation, which is searching for less results (e.g. only analyzing two strings of the cipher). However, to find two words ‘somewhere’ in the cipher, is indeed an easy task. More than 12 words or so to be found simultaneously would be interesting, however.
QT
*ZODIACHRONOLOGY*
Below you find some of the results currently generated by the (new) FCCP model.
All of those results are (still) incorrect, however, and only part of the output received so far. But they show some ‘successful’ permutation over letters, even words (‘seaman, season, series, edges, excess, useless etc., not necessarily in an alphabetical order); thus: Potential cleartext. All of these results, btw, were found with almost no pre-setting (just guessing the + symbol and focussing on one section of the cipher).
One main problem, however, is that we do not know how many words Z had actually used in this specific section. Take the following text as an example:
If she had not done so lazy we now had more food
Such sentence, moreless, might be suitable for any part of cipher but has too many variations amongst itself (e.g. ‘he now had more food‘ instead of ‘we now had more food‘). This causes trouble, especially when looking for words of length >4 only. But we have to try somehow.
Besides the + symbol, the program considers a list of the most frequent 5-grams (‘IoFBc’) as well as nine additional alphabetical letters from A-Z. Accordingly, only 30 of such 5-grams have been computed, so far. This computation covered
30 x 26^9 or
30 x 5,429,503,678,976 or
162,885,110,369,280 or
162.9 Trillion
different variations of letters; simultaneiously placing at least three words from a dictionary into that specific string.
Because of the latter (simultaneious lingual cross-check with the dictionary), one may confidently multiplicate the amount of variations with ~4,500^3 or 91,125,000,000 to get some idea of what the program actually does (14,842,905,682,400,640,000,000,000). Individual positions of those words, however, not even considered, yet.
Still more 5-grams than 30. And not even sure if the + symbol has been ‘guessed’ right. Nevertheless, these are the most likely variations (regardless any frequency analysis). To compute those 162.9 Trillion variations, my computer took its time (roughly 2-3 weeks), too.
But potential cleartext solutions show up: In this context, it may be noted that the program covers all solutions but only some of those. Meaning, if the pre-setting is correct, the real cleartext somewhat will show up sooner or later (still assuming homophone substitution; having the right dictionary etc.).
So far, about 2,000 potential cleartext results have been found out of those 162.9 Trillion variations (a ‘readable’ amount of results). You might want to compare it with picking up some 2,000 pre-determined objects from all households over the world – a very difficult but not necessarily impossible task (if you know where to find these objects).
Thus, the attack on the cipher is both, pretty strong as well as set-up quite ‘tightly’ (not allowing many results to show up.
My worries, however, is not if the program is able to crack the 340 but to find the right pre-setting. IMO it is more likely that a combination of short, medium and longer words is present in that string than three medium words of length>4 (covering 15 out of 21 letters or 71%). In the current setting, there almost is no space left for shorter words, as in the example text above.
Still some work to do, though.
QT
*ZODIACHRONOLOGY*
Small update:
The newly invented FCCP method is getting into shape. Since implementation of some Windows Net.Frame stuff, computation got faster. Also, the program itself has been slimmed to the extent that now the focus is mostly on performance, not only the cipher anymore. Of course, for each computation, some specific cipher criteria are still considered in advance.
Depending on where the method starts to find cleartext in the Z340, the following string being analyzed might have only two or even more additional homophones to compute. To not simply shuffle any alphabetical letters without cipher structure, our next string should require as few additional symbols as possible. A hard task, not knowing the cleartext at all, having different homophones mostly everywhere.
Currently, up to twelve different strings are identified as containing identical, overlapping homophones to each other. Those I’d like to call from now on PARTNER STRINGS; defined as strings containing identical homophones to one or multiple other strings.
Because it makes a difference if 26^2 or 26^4 alphabetical letters are computed, it is required that the right partner strings are chosen. Each modification in this procedure requires new programming of the main FCCP program. Otherwise, millions of partial results without any connection to each other might lead to no result at all – or never ending computation / lists.
The following shows some results computed from a total of three different partner strings. Some pre-set criteria required, of course, such as for the homophone as well as the section. In addition to that, frequent bigrams have been chosen for the which actually occurs three times in the cipher.
The following picture shows a complete list of results:
a) pre-setting
b) number (for my overview)
c) string1……string2
d) results found in string2
e) number (for my overview)
f) string3
As you can see, some results appear to be promising while others do not at all. Because the correct setting is not yet found. Fully aware, that it is still a long way to go but the ‘goal’ is to find the string, eg. the one with number 10, which makes common sense in English language. With additional partner strings complying with our dictionary, too.
To get a string longer thatn just ‘one word’ is somewhat a small breakthrough as it is the first computation ever leading to not only potential cleartext words but even one longer string, which is actually required to identify the correct setting.
In this case, based on finding
– one word (dictionary) of length >3 in string1
– two words of length >4 in string2
– one word of length >4 in string3
the program identified exactly 24 result groups out of 3,594,116,240 or 3.6B different variations (homophones>letters). This, however, can be modified, e.g. into ‘one word of length >5 in string 2‘ or comparable – depending on what we want to search for; longer or shorter words.
In the previous case, with a dictionary of 4,500 words, the program thus considered a total of 4,500^4 or 410,062,500,000,000 different possibilities of how any text may or may not have occurred in those three partner strings.
The intensity of this procedure is, ignoring the large numbers above so far, approximately 1 : 150,000,000. One out of 150m different setting is identified as promising (because containing words from the dictionary).
THIS run, however, is going with even one additional letter in the first string as well as looking for a longer word in the second string:
The good part of this: The program can do one symbol after another (L, S, T, E, M, O,…). It can also do one bigram after another. Even can do all n-grams. Thus, if there is no transposition etc. added, the cleartext – sooner or later – will show up. Not in a second, not necessarily in a year but if it is there, covered by the dictionary…well.
QT
*ZODIACHRONOLOGY*
Quick thought – is there a way to put promising results (from across different solvers) into a database along with their respective keys / transpositions / etc. and then cross reference them all for commonalities?
Maybe some kind of patterns could emerge and guide further testing.
Quick thought – is there a way to put promising results (from across different solvers) into a database along with their respective keys / transpositions / etc. and then cross reference them all for commonalities?
Maybe some kind of patterns could emerge and guide further testing.
Thanks for asking…I started to store the results, even showed it sometimes. So far they were too far away from ‘readable’ cleartext. This should change now, yes it will be available. But we are still looking into one symbol under few bigrams only. Think it is a better idea to first collect a complete group of such results (from now on) or improve the method, if possible, for further proceedings / trying transpositions then. Because I mostly interrupt the computation process to get the program / settings ‘better’.
QT
*ZODIACHRONOLOGY*