Compression and Energy-Efficient Archival of Spiral-Domain Encoded Time-Series Trajectories: Discretization, Codec Composition, and Deployment-Scale Storage Economics

This revised deposit contains the Paper 4 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Storage (under preparation). Coverage. 9 pre-registered studies (Studies 54-62, Phase IX + Phase X of the spiral-domain encoder validation campaign). 27 hypotheses, 17 SUPPORTED (63%), 10 honest bounded negatives substantively interpreted. Substantive findings. 4x structural compression via discretization; 46.75x cumulative compression with delta-encoding + zstd-9; 71.72x maximum with zstd-22; 33.02x for 90%-idle daily-living monitoring; linear storage scaling R-squared > 0.999 across four duration tiers (1 min, 1 hour, 24 hours, 1 year); 1,000-device smart-factory deployment fits 3 TB/year approximately $830/year AWS S3 budget. Contents. Manuscript (PDF and DOCX of the paper itself); supplementary reproducibility archive containing: README.md (submission-package map and reproduction instructions); preregistrations/ (frozen pre-registration .md documents with literal-threshold decision rules); reports/ (per-study .md verdict reports against frozen rules + phase summaries); runners/ (deterministic Python runners under PYTHONHASHSEED=0); raw_data/ (per-study CSV outputs and JSON verdict blocks); figures/ (manuscript figures at 300 DPI + figure-build script); code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+, SciPy, and the python-zstandard binding. Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 5, 6, 7, 8 archives in this same Zenodo collection (Paper 8 at 10.5281/zenodo.20466035).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-30
DOI
https://doi.org/10.5281/zenodo.20467218
Primary Topic
Network Time Synchronization Technologies
Type
preprint
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preprint

Compression and Energy-Efficient Archival of Spiral-Domain Encoded Time-Series Trajectories: Discretization, Codec Composition, and Deployment-Scale Storage Economics

Randolph James Ferlic, Kimberly Kate Ferlic
Zenodo (CERN European Organization for Nuclear Research)
Network Time Synchronization Technologies
preprint

Compression and Energy-Efficient Archival of Spiral-Domain Encoded Time-Series Trajectories: Discretization, Codec Composition, and Deployment-Scale Storage Economics

Randolph James Ferlic, Kimberly Kate Ferlic
preprint en

Abstract

This revised deposit contains the Paper 4 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Storage (under preparation). Coverage. 9 pre-registered studies (Studies 54-62, Phase IX + Phase X of the spiral-domain encoder validation campaign). 27 hypotheses, 17 SUPPORTED (63%), 10 honest bounded negatives substantively interpreted. Substantive findings. 4x structural compression via discretization; 46.75x cumulative compression with delta-encoding + zstd-9; 71.72x maximum with zstd-22; 33.02x for 90%-idle daily-living monitoring; linear storage scaling R-squared > 0.999 across four duration tiers (1 min, 1 hour, 24 hours, 1 year); 1,000-device smart-factory deployment fits 3 TB/year approximately $830/year AWS S3 budget. Contents. Manuscript (PDF and DOCX of the paper itself); supplementary reproducibility archive containing: README.md (submission-package map and reproduction instructions); preregistrations/ (frozen pre-registration .md documents with literal-threshold decision rules); reports/ (per-study .md verdict reports against frozen rules + phase summaries); runners/ (deterministic Python runners under PYTHONHASHSEED=0); raw_data/ (per-study CSV outputs and JSON verdict blocks); figures/ (manuscript figures at 300 DPI + figure-build script); code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+, SciPy, and the python-zstandard binding. Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 5, 6, 7, 8 archives in this same Zenodo collection (Paper 8 at 10.5281/zenodo.20466035).

Zenodo (CERN European Organization for Nuclear Research)
EP Analytics (United States) (US)
Affordable and clean energy
Network Time Synchronization Technologies
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