The Infinite Crucible
26-07-2006, 20:22
Jehoel Satellite Observation Network
Background:
The JSON development program began in 1998 after certain high level officials had noticed military possibilities for the red tide detection network used to predict the location of red tide outbreaks on Crucible shores. The original civilian system utilized satellites to analyze the amount of light that was reflected from the oceans below to detect the presence of large amounts of algae. This analysis was accomplished using Sea WiFS (Sea Wide Field-of-View Sensor) and MODIS (Moderate Resolution Imaging Spectroradiometer). While the system was not perfect it saved the fishing industry millions as they were able to better divert their resources to non infected waters.
After a full year of successful operations the head scientists of the Red Tide Detection Network were approached by the more covert sections of the military and government and offered government contracts to develop a far more powerful and versatile system. Despite the refusal of a few scientist based on ethical grounds the majority agreed to work. Following the induction the whole project was made classified at the highest level, with each scientist not really knowing the full extent of their own progress.
Over the next nine years the scientists developed multiple sensors and systems, land and space based, that together would form the JSON. The MODIS system was made far more accurate and sensitive to light waves than before, capable of picking up the regular growth of algae, not just the massive blooms of red tide. However, this was simply not enough. It was further enhanced to be able to pick out the specific light absorbed and reflected by the different phylogenetic groups of water going algae. This enabled it to “switch lenses” so to speak and pick out specific species of algae individually. There are limits, however, as the system can not differentiate between algae that reflects and absorbs the same wavelengths. Beyond that, the deeper the growth of algae the less accurate its detection. Of all the different phylogenetic groups, Dinoflagellata was detectable at the deepest depth due to its phosphorescent qualities. Finally it was programmed to recognize particularly common species that appeared on ship and submarine hulls, such as Ulva.
After the analysis of light is complete the results are sent to a land based supercomputer network which sifts through the data looking for peculiar shapes of algae formation, rapid movement of blooms, and other key features that are characteristic of algae on a moving man made object. For example, a bloom of red algae at one hundred feet below sea level in the middle of the deep ocean that seems to be moving in a quick linear fashion ignoring predominate ocean currents could represent a submarine.
The system is not perfect, however. Particularly small unmanned subs and ships are simply too difficult to detect with any certainty. Multiple different forms of algae must be scanned for to notice an anomaly amongst the “noise” of free floating algae. While the system retains powerful sea penetration abilities, a sub at deep depths can evade the system entirely.
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Overview of Network Process:
Detection:
Satellites use spectroratiometers among other tools to detect specific algae blooms and formations:
Phylogenetic groups scanned for:
Chlorophyta – Green Algae, common so it is likely to be on a hull, but also in the water
Dinoflagellata – Detectable at deepest depths, but less likely to attach to hulls and is rare
Phaeophyta– Brown Algae, rugged and likely to stick to hulls, can survive at deep depths, limited by natural habitat i.e. colder waters
Rhodophyta – Red Algae, can survive at the deepest depths (almost 300 meters), not free floating, limited by natural habitat i.e. warmer waters
Cyanobacteria – Guaranteed to be on the hull, also guaranteed to be in the surrounding water
Following a multi phylum scan of the water, the data is sent back to the supercomputer network
Analysis:
The computers works through the data and look for patterns that are characteristic of ships and subs. Some characteristics are:
1. Inappropriate depth of formations
2. Inappropriate movement of formations
3. Inappropriate species in local waters
4. Odd shapes of formations
5. Inconsistencies
After working though those criteria and more, possible anomalies are reported to trained technicians for review. If everything works as planned, as it does about 87% of the time, a submarine or stealth ship is identified and its location roughly determined.
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The government plans to spend three trillion over the next year launching the necessary satellites for the completion of the JSON. At the moment the information is classified at the highest level, and the government has not yet announced the network to the populous of world community.
Should it become available in the future, a full network would retail for about five to seven trillion dollars.
Background:
The JSON development program began in 1998 after certain high level officials had noticed military possibilities for the red tide detection network used to predict the location of red tide outbreaks on Crucible shores. The original civilian system utilized satellites to analyze the amount of light that was reflected from the oceans below to detect the presence of large amounts of algae. This analysis was accomplished using Sea WiFS (Sea Wide Field-of-View Sensor) and MODIS (Moderate Resolution Imaging Spectroradiometer). While the system was not perfect it saved the fishing industry millions as they were able to better divert their resources to non infected waters.
After a full year of successful operations the head scientists of the Red Tide Detection Network were approached by the more covert sections of the military and government and offered government contracts to develop a far more powerful and versatile system. Despite the refusal of a few scientist based on ethical grounds the majority agreed to work. Following the induction the whole project was made classified at the highest level, with each scientist not really knowing the full extent of their own progress.
Over the next nine years the scientists developed multiple sensors and systems, land and space based, that together would form the JSON. The MODIS system was made far more accurate and sensitive to light waves than before, capable of picking up the regular growth of algae, not just the massive blooms of red tide. However, this was simply not enough. It was further enhanced to be able to pick out the specific light absorbed and reflected by the different phylogenetic groups of water going algae. This enabled it to “switch lenses” so to speak and pick out specific species of algae individually. There are limits, however, as the system can not differentiate between algae that reflects and absorbs the same wavelengths. Beyond that, the deeper the growth of algae the less accurate its detection. Of all the different phylogenetic groups, Dinoflagellata was detectable at the deepest depth due to its phosphorescent qualities. Finally it was programmed to recognize particularly common species that appeared on ship and submarine hulls, such as Ulva.
After the analysis of light is complete the results are sent to a land based supercomputer network which sifts through the data looking for peculiar shapes of algae formation, rapid movement of blooms, and other key features that are characteristic of algae on a moving man made object. For example, a bloom of red algae at one hundred feet below sea level in the middle of the deep ocean that seems to be moving in a quick linear fashion ignoring predominate ocean currents could represent a submarine.
The system is not perfect, however. Particularly small unmanned subs and ships are simply too difficult to detect with any certainty. Multiple different forms of algae must be scanned for to notice an anomaly amongst the “noise” of free floating algae. While the system retains powerful sea penetration abilities, a sub at deep depths can evade the system entirely.
--------------------------------------------------------------------------
Overview of Network Process:
Detection:
Satellites use spectroratiometers among other tools to detect specific algae blooms and formations:
Phylogenetic groups scanned for:
Chlorophyta – Green Algae, common so it is likely to be on a hull, but also in the water
Dinoflagellata – Detectable at deepest depths, but less likely to attach to hulls and is rare
Phaeophyta– Brown Algae, rugged and likely to stick to hulls, can survive at deep depths, limited by natural habitat i.e. colder waters
Rhodophyta – Red Algae, can survive at the deepest depths (almost 300 meters), not free floating, limited by natural habitat i.e. warmer waters
Cyanobacteria – Guaranteed to be on the hull, also guaranteed to be in the surrounding water
Following a multi phylum scan of the water, the data is sent back to the supercomputer network
Analysis:
The computers works through the data and look for patterns that are characteristic of ships and subs. Some characteristics are:
1. Inappropriate depth of formations
2. Inappropriate movement of formations
3. Inappropriate species in local waters
4. Odd shapes of formations
5. Inconsistencies
After working though those criteria and more, possible anomalies are reported to trained technicians for review. If everything works as planned, as it does about 87% of the time, a submarine or stealth ship is identified and its location roughly determined.
--------------------------------------------------------------------------
The government plans to spend three trillion over the next year launching the necessary satellites for the completion of the JSON. At the moment the information is classified at the highest level, and the government has not yet announced the network to the populous of world community.
Should it become available in the future, a full network would retail for about five to seven trillion dollars.