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Paper Citation Record · LEDGER

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins

As of 20 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2512.15432.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2512.15432 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:50:20.572239Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

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  • verified fuzzy0
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b628202-4280-4aee-8c8d-de49a9b3cc19 · outbound

This paper cites Digital twins: A survey on enabling technologies, challenges, trends and future prospects,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Digital twins: A survey on enabling technologies, challenges, trends and future prospects,

Reference 1

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Unavailable: canonical work link unavailable.

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Observation 5b5b1c05-e52b-4cd6-866d-2670c3e1828b · outbound

This paper cites Network digital twin: Concepts and reference architecture,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Network digital twin: Concepts and reference architecture,

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:50:19.269872Z digest=sha256:84d0870e72a3b41eea9c5df10e7cc5ce8238d66d94eacc2102a6b25e7cbef680

Observation de0fe292-107b-4be1-8bba-9cc49386d441 · outbound

This paper cites Self-similarity in world wide web traffic: evidence and possible causes,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Self-similarity in world wide web traffic: evidence and possible causes,

Reference 3

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Unavailable: canonical work link unavailable.

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Observation f42d17f5-6a45-459f-aaa2-7db3bf7abbb8 · outbound

This paper cites Feasibility of state space models for network traffic generation,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Feasibility of state space models for network traffic generation,

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:50:19.421848Z digest=sha256:91216fcbdecb7a9677c2a0cdaf30e31cc3a4d2971c89ed581f07c12ba77d22e9

Observation c8ab1ac4-0b9f-447f-a200-2aa5c1a5d841 · outbound

This paper cites Using GANs for sharing networked time series data: Challenges, initial promise, and open questions,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Using GANs for sharing networked time series data: Challenges, initial promise, and open questions,

Reference 5

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source=pdf_text observed=2026-08-03T15:50:19.513033Z digest=sha256:969f8d581baecfed9b0e06ee73c49542fbe5c745c19fa7531dab8b12017afcb5

Observation f9a6e4f6-c822-467c-a1e4-ab7a28908934 · outbound

This paper cites Mobile user traffic generation via multi-scale hierarchical GAN,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Mobile user traffic generation via multi-scale hierarchical GAN,

Reference 6

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verified exact
doi, observed 2026-08-03T15:53:32.350514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9c061e61-4b1b-46fd-8539-c56f9c2ad02d · outbound

This paper cites Generative deep learning for internet of things network traffic generation,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Generative deep learning for internet of things network traffic generation,

Reference 7

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source=pdf_text observed=2026-08-03T15:50:19.659244Z digest=sha256:777e0fbbfcee5b298563d724af4b0063a4812212ae5622af6eda1eb075220e1e

Observation 340e7405-f1a8-49fa-b692-cdec3f3cd4d5 · outbound

This paper cites NeCSTGen: An ap- proach for realistic network traffic generation using deep learning,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins NeCSTGen: An ap- proach for realistic network traffic generation using deep learning,

Reference 8

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source=pdf_text observed=2026-08-03T15:50:19.726682Z digest=sha256:f657f34be2ce2b03e97ffd79456c6608e59d58647d6ace8dfc636b93b9ac7ba6

Observation 0af14b72-a77e-443c-83a6-bf306f577e86 · outbound

This paper cites TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

Reference 9

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source=pdf_text observed=2026-08-03T15:50:19.777788Z digest=sha256:cd0d09aca654357382c809a91580aeed49da93e1dec35266a287301d00c974c6

Observation 03ebdb0d-3ca7-4d97-a758-8f2cb778921c · outbound

This paper cites IP traffic generator based on hidden Markov models,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins IP traffic generator based on hidden Markov models,

Reference 10

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source=pdf_text observed=2026-08-03T15:50:19.843701Z digest=sha256:d79a888f5f15e6d1e9621ca84982f8b0600ec8e37de9ce412f5f0c46153f4cf1

Observation 3f663a96-ba66-4c83-bffa-f2ba1fc51267 · outbound

This paper cites Characterization of encrypted and VPN traffic using time-related,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Characterization of encrypted and VPN traffic using time-related,

Reference 11

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Unavailable: canonical work link unavailable.

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Observation e98b8198-aad1-45de-96a3-7a9918ad180c · outbound

This paper cites A tutorial on hidden markov models and selected applica- tions in speech recognition,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins A tutorial on hidden markov models and selected applica- tions in speech recognition,

Reference 12

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source=pdf_text observed=2026-08-03T15:50:19.985800Z digest=sha256:3c95a6f82300df72a722221ac0ad1c3c0caf48630b1913991d8bf88f6fac0eb9

Observation 27f5517b-4101-43e3-b2ac-19bced850d7c · outbound

This paper cites hmmlearn: Hidden markov models in python,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins hmmlearn: Hidden markov models in python,

Reference 13

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Observation c6f2b3bd-9565-424b-9bb5-05a66d098ec1 · outbound

This paper cites On the self-similar nature of ethernet traffic (extended version),.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins On the self-similar nature of ethernet traffic (extended version),

Reference 14

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source=pdf_text observed=2026-08-03T15:50:20.143741Z digest=sha256:d661e0d4b6d50428b1043b8182f73bccb99a6942adbd1c5704af099dd41656ab

Observation 40024c3e-ad41-4e06-bad9-588715819ea3 · outbound

This paper cites State aware traffic generation for real-time network digital twins,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins State aware traffic generation for real-time network digital twins,

Reference 15

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Observation 4cc666f9-c41d-4e43-a71b-98b3bd973a7c · outbound

This paper cites Robust statistical modeling using the t distribution,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Robust statistical modeling using the t distribution,

Reference 16

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source=pdf_text observed=2026-08-03T15:50:20.288431Z digest=sha256:70ae0e3f77e45cb273857ae32d2bb4f5af108effd5569dc268a58e2f006521e1

Observation 39999520-6152-4a1c-aa1a-3bad49ac7d66 · outbound

This paper cites Time-series generative adversarial networks,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Time-series generative adversarial networks,

Reference 17

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Observation f73eb890-eccb-4252-a15e-22ffd2d604f3 · outbound

This paper cites Wide area traffic: the failure of poisson modeling,.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Wide area traffic: the failure of poisson modeling,

Reference 18

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source=pdf_text observed=2026-08-03T15:50:20.509484Z digest=sha256:988ad2abb3cd328e267231d1433fdff5fc27511abbf96edff24da2507e21392f

Observation 211a92f9-6c49-44d6-ba4b-8e23aa5f9380 · outbound

This paper cites Computational Optimal Transport.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Computational Optimal Transport

Reference 19

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source=pdf_text observed=2026-08-03T15:50:20.572239Z digest=sha256:1110ab07c13e9a3d3560ccb1a88c2d8e6f3efde13d46edad98127ac5b9b61836

Observation 7b260597-f4ec-46ce-a3b6-74bad75fcb1a · outbound

This paper cites Available: https://proceedings.neurips.cc/paper files/ paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf.

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins Available: https://proceedings.neurips.cc/paper files/ paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf

Reference 2019

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Pith citing papers

No inbound Pith citation observations are available.