Two-Stage Federated Compression for Lossless Satellite Telemetry
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Solution Overview
Problem
Existing data compression methods for satellite telemetry and command systems lack efficient, low-latency, and lossless compression techniques that preserve information integrity.
Innovation Solution
A system and method for federated two-stage compression using a persistent cognitive machine, integrating neural networks with probability prediction and long short-term memory systems for efficient lossless data compression, utilizing probability prediction driven arithmetic coding and a plurality of codebooks to store codewords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression is used to preserve information integrity, then information loss is minimized, but compression ratio is reduced
Solution Approach 1:
The patent applies segmentation by dividing the compression process into two distinct stages: a first compression stage and a second compression stage. Each stage uses different codebooks and compression techniques, allowing the system to achieve both high compression ratios and lossless compression. The first stage handles general compression while the second stage optimizes for specific data patterns, resolving the contradiction between information preservation and compression efficiency.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting compression parameters and switching between different codebooks based on data characteristics. The system modifies compression parameters adaptively to maintain lossless compression while optimizing compression ratios for different data types and patterns encountered in satellite telemetry and command systems.
2Quantity of substance
If complex compression algorithms are used to achieve higher compression ratios, then compression efficiency is improved, but processing time increases
Solution Approach 1:
By segmenting the compression process into two stages with different codebooks, the patent avoids using a single overly complex algorithm. Each stage is optimized for specific purposes, reducing overall processing time while maintaining high compression ratios. This segmentation prevents the need for excessively complex single-stage algorithms that would increase processing time.
Solution Approach 2:
The first compression stage performs preliminary compression on the data before the second stage applies additional optimization. This preliminary action reduces the data size early in the process, making subsequent compression steps more efficient and reducing total processing time compared to applying a single complex algorithm to the entire data set.
3Productivity
If multiple codebooks are used to improve compression efficiency, then device complexity increases
Solution Approach 1:
The patent segments the codebook structure into multiple specialized codebooks (first codebook, second codebook) each optimized for different compression stages and data patterns. This segmentation improves compression efficiency by matching codebooks to specific data characteristics while keeping each individual codebook relatively simple and manageable.
Solution Approach 2:
The multiple codebooks are designed to work together in a unified two-stage compression system that handles various data types efficiently. The system provides universal functionality across different satellite telemetry and command data formats, reducing the need for separate specialized systems for each data type and thereby managing overall complexity.
Data Source
AI summary
A system and method for federated two-stage compression with federated joint learning. The system and method proposed allow for fast and efficient lossless data compression of a large variety of data types. The system and method have a variety of real-world applications, including deep learning solutions for telemetry, tracking, and command subsystems for satellites. Satellites and their control centers are incredibly spaced apart which makes data compression an extremely important process to transmit large sets of information in a low-latency, high-efficiency environment. The proposed system and method utilize probability prediction driven arithmetic coding which provides faster encoding times and higher compression ratios when paired with a long short-term memory system for data compression.