of the TLE format. At the time there were two formats designed for punch cards, an "internal format" that used three cards encoding complete details for the satellite (including name and other data), and the two card "transmission format" that listed only those elements that were subject to change. The latter saved on cards and produced smaller decks when updating the databases. Cranford continued to work on the modelling, eventually leading Lane to publish Spacetrack Report #2 detailing the Air Force General Perturbation theory, or AFGP4. The paper also described two simplified versions of the system, IGP4 which used a simplified drag model, and SGP4 (Simplified General Perturbations) which used IGP4's drag model along with a simplified gravity model. The differences between the three models were slight for most objects. One year later, Spacetrack Report #3 was released, included full FORTRAN source code for the SGP4 model. This quickly became the de facto standard model, both in the industry as well as the astronomy field. Shortly after the publication of Report #3, NASA began posting elements for a variety of visible and other well known objects in their periodic NASA Prediction Bulletins, which consisted of the transmission format data in printed form. After trying for some time to convince NASA to release these in electronic form, T.S. Kelso took matters into his own hands and began manually copying the listings into text files which he distributed through his CelesTrak bulletin board system. This revealed a problem in NASA's checksum system, which traced back to the lack of the plus character (+) on the teletype machines used at NASA, which ultimately turned out to be a problem from the punch card era that occurred when NORAD updated from the BCD to EBCDIC character set on the computer sending out the updates. This problem went away when Kelso began to receive data directly from NORAD in 1989. The SGP4 model was later extended with corrections for deep space objects, creating SDP4, which used the same TLE input data. Over the years a number of more advanced prediction models have been created, but these have not seen widespread use. This is due to the TLE not containing the additional information needed by some of these formats, which makes it difficult to find the elements needed to take advantages of the improved model. More subtly, the TLE data is massaged in a fashion to