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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 9 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 2026-08-09T06:31:02.800959+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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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.

source=pdf_text observed=2026-08-03T15:50:19.328178Z digest=sha256:4023ae3c63ab0cc0985652c4029c3afa37ed8676775eab62958ce045fb0146bc

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=sha256:720e6ec0d8a6609096609b7aa567c5e725a4668a82510e1aa91603ab37085e19

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:50:19.513033Z digest=sha256:97dd0d74dffd5819737d72fad9a250298b5ebb4ec9cfe6c6aff87bf9a95c1b93

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-03T15:50:19.602815Z digest=sha256:fe857a4deb6702445b4e0cadcc56744d7fd670a86190b9c18e2cebc802d1659f

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:50:19.659244Z digest=sha256:32475e4b026e6bb67b25243496700d297c8d8f3714c26b5a95654f19a9c01123

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=sha256:50eb98947fb3a6db5e4d3e6da5e0612f3a747c03e93e1e07b4b5907ebfa5454a

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=sha256:67a73bf80bb9c52caac436e7b4f106f16c03a8e007077f074591a84ca5beda4b

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=sha256:1b67f6d6ccf47c82d4b94bd4caff34dee015735b9a0dcc4833c50feba47ad8f4

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.

source=pdf_text observed=2026-08-03T15:50:19.927765Z digest=sha256:0b2cde0e89537afc48d0e83dac6f9be3b3a121ef8215c761f248f0fc878f4cca

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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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:fa41b2bf9db038893a4a29e66310ff68d1610b1d0e0c0ff1f07399b1ca221379

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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source=pdf_text observed=2026-08-03T15:50:20.202817Z digest=sha256:243aa19683722c532f237dcb01acdf38cb5d28beddb332059c35dce2b9a8827d

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=sha256:29684deb004202d328a7cfca846ca0a55df89b4ad98ff2a89a50c8ccaeddb322

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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source=pdf_text observed=2026-08-03T15:50:20.343738Z digest=sha256:59f706453fede6f3a0dee6fb3a1851683e24943c2216e047e2dd34c1ad1b9e41

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=sha256:18fc8dd98800079dd4369a6e426b03fac27a1252eabde1904307568eb7df369d

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=sha256:1ffce03b3d213e5adcf1be29215aec3f2f7c5bd8109e1db2e88f7ab22ad071c2

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.