Paper Citation Record · LEDGER

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.15802.

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

pith.paper-citation-record.v1
2505.15802 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:16:33.635476Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

33 of 33 outbound references displayed

verified exact1
verified fuzzy26
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9354d9c6-1b8c-4f34-981a-81d4eb1c0f17 · outbound

This paper cites A note on a simple transmission formula,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation A note on a simple transmission formula,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.513653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ee8a1c8a-6228-43e8-a62f-0248da95f965 · outbound

This paper cites A terrain parabolic equation model for propagation in the troposphere,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation A terrain parabolic equation model for propagation in the troposphere,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.503166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5fa1a86-dae6-4b60-8c0c-bb4ee0d32cb6 · outbound

This paper cites User’s guide for the vtrpe (variable terrain radio parabolic equation) computer model,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation User’s guide for the vtrpe (variable terrain radio parabolic equation) computer model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.489648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 21d3085e-c451-4003-b270-a62a2161c187 · outbound

This paper cites Simulated microwave propagation through tropospheric turbulence,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Simulated microwave propagation through tropospheric turbulence,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.480257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 77598c61-da03-49c8-b8b3-4e74b8df38b5 · outbound

This paper cites Radio meteorology. national bureau of standards monogr,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Radio meteorology. national bureau of standards monogr,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.469514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5476d08a-bec8-42cd-ab0d-12f249717a40 · outbound

This paper cites Path loss prediction based on machine learning: Principle, method, and data expansion,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Path loss prediction based on machine learning: Principle, method, and data expansion,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.458930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f494272b-9173-4a12-8164-9017571f359f · outbound

This paper cites Machine learning in the air,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Machine learning in the air,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.447998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c7b9d65a-b2cd-4870-8b3a-691b9dea9fbc · outbound

This paper cites Deep learning-based channel estimation for doubly selective fading channels,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Deep learning-based channel estimation for doubly selective fading channels,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.435488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fc01395-72fa-46bd-ba27-d56c8210a47c · outbound

This paper cites Regression of large-scale path loss parameters using deep neural networks,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Regression of large-scale path loss parameters using deep neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.424114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f687c945-9139-42da-af89-4530b75d669a · outbound

This paper cites Deepray: Deep learning meets ray-tracing,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Deepray: Deep learning meets ray-tracing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.412586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c2e7f25-d51f-4e59-bc92-9f64affb623c · outbound

This paper cites Generalizable physics-guided convolutional neural network for irregular terrain prop- agation,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Generalizable physics-guided convolutional neural network for irregular terrain prop- agation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.393409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3079ec29-8623-4f64-b52b-9ebef16ba932 · outbound

This paper cites A study on radar target detection based on deep neural networks,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation A study on radar target detection based on deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.374654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1edd7062-b918-47c7-8206-89546bd7bce1 · outbound

This paper cites An evaporation duct height prediction method based on deep learning,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation An evaporation duct height prediction method based on deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.362859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f951d36-dadd-4694-a5c8-46f0ad81328f · outbound

This paper cites Refractivity inversions from point-to-point x-band radar propagation measurements,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Refractivity inversions from point-to-point x-band radar propagation measurements,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.351664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e186ef8b-e534-4ade-ac9b-be2b9928775b · outbound

This paper cites The com- parison of long short-term memory neural network and deep forest for the evaporation duct height prediction,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation The com- parison of long short-term memory neural network and deep forest for the evaporation duct height prediction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.325840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b7874b5-d884-418d-9726-0c99b814bfaa · outbound

This paper cites Path loss prediction in evaporation ducts based on deep neural network,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Path loss prediction in evaporation ducts based on deep neural network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.309626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b37dfbd-018f-4ca4-8a49-58cb9310b9c3 · outbound

This paper cites Determination of neural network parameters for path loss prediction in very high frequency wireless channel,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Determination of neural network parameters for path loss prediction in very high frequency wireless channel,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.298375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation df457adf-40e5-4377-81d5-274a2f74d95d · outbound

This paper cites A deep neural network-based multi- frequency path loss prediction model from 0.8 ghz to 70 ghz,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation A deep neural network-based multi- frequency path loss prediction model from 0.8 ghz to 70 ghz,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.286888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b392b3e-88af-4795-b634-4bcda3e849d2 · outbound

This paper cites Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 ghz,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 ghz,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.274650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5f67a63c-208d-4a8d-bb72-1cd3fec968f7 · outbound

This paper cites Deep learning method for path loss prediction in mobile communication sys- tems,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Deep learning method for path loss prediction in mobile communication sys- tems,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.262040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49344a0d-11fa-44bc-80ac-5c91e4272715 · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Image-to-image translation with conditional adversarial networks,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:32.505903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef0b4ee-6401-4b54-a84e-ac900669278f · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:32.551407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0993963-f526-4a09-bdd5-d0cf7b506365 · outbound

This paper cites Image-to-image translation: Methods and applications,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Image-to-image translation: Methods and applications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.238335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 132f15dd-51b4-4c0a-8726-a237e523bb6a · outbound

This paper cites Deep generative adversarial networks for image-to-image translation: A review,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Deep generative adversarial networks for image-to-image translation: A review,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.226993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c37113d-d4cb-470b-9fe6-2f359afa3cf0 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation U-net: Convolutional networks for biomedical image segmentation,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:32.833880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4fad66-c6f6-4d8e-bf89-5150ddcbb81a · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:32.899268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 023c7380-f831-41d6-b7a0-5f177fffab04 · outbound

This paper cites U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:32.998967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deda231d-b439-4c45-aff5-8b6dd7d7ab85 · outbound

This paper cites Rethinking cyclegan: Improving quality of gans for unpaired image-to-image translation,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Rethinking cyclegan: Improving quality of gans for unpaired image-to-image translation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.200886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aff430a8-6fb8-4203-9456-92e6eede9b49 · outbound

This paper cites UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:16:33.785238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e14cf54-7f8a-4230-96da-01d17c0ec4f6 · outbound

This paper cites Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with trans- former,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with trans- former,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.187463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81516aa9-5fd8-40db-845d-e9da8ecec38a · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Image quality assessment: from error visibility to structural similarity,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:33.396709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1dc9baa-c361-484a-ab76-de257bdf7feb · outbound

This paper cites Frechet inception distance (fid) for evaluating gans,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Frechet inception distance (fid) for evaluating gans,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.168515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bb3a2187-c3ba-4467-9be0-2034cad31ec5 · outbound

This paper cites Global sensitivity of parabolic equation radar wave propagation simulation to sea state and atmospheric refractivity structure,.

A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Global sensitivity of parabolic equation radar wave propagation simulation to sea state and atmospheric refractivity structure,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:16:34.020900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.