Pith. sign in

Paper Citation Record · LEDGER

Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.04547.

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

pith.paper-citation-record.v1
2310.04547 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:41:09.849914Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T09:25:21.892885Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c132b4b4-e031-4372-a9ae-781fafe9efee · inbound

Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning cites this paper.

Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-04T11:41:09.849914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T11:41:09.849914Z digest=sha256:2cde0f3938ad55ba17e78025f4fda1cecb7c1f4c0794320abc7c44a503648970

Observation d5ebd6bb-0f2d-4248-9cb2-30510ca7e24e · inbound

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness cites this paper.

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

Reference 119

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:25:21.897431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-15T09:24:16.570453Z digest=sha256:57b2e13f20db2fdeb0851c37de376f971856b88e4a1c52164f4b957e6783142c

Observation a532134a-af73-4933-8e3a-666bb2cd508c · inbound

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness cites this paper.

A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

Reference 130

Resolution
unresolved
no resolver link, observed 2026-07-13T23:08:19.232619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:08:19.232619Z digest=sha256:f06bd25b2201634f6afdd86159f9c5bc42d0078ea9cba8c802a72d60e4558b71