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Paper Citation Record · LEDGER

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.17684.

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

pith.paper-citation-record.v1
2505.17684 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:45:28.378330Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

41 of 41 outbound references displayed

  • verified exact0
  • verified fuzzy36
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 71348910-474d-475e-bce4-f4be433755e3 · outbound

This paper cites A Comprehensive Framework for 5G Indoor Localization,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization A Comprehensive Framework for 5G Indoor Localization,

Reference 1

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raw_fallback, observed 2026-08-07T14:45:35.582514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.200187Z digest=sha256:584cd8c65c3a248abacc7386383168e307ce856937c128af000444637d42b963

Observation 75747a5d-c4ac-4825-9d45-28b19ab0c0fd · outbound

This paper cites Transfer Learning to Adapt 5G AI-based Fingerprint Localization Across Environments,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Transfer Learning to Adapt 5G AI-based Fingerprint Localization Across Environments,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.271625Z digest=sha256:a1217cc3f419b088ea8e856b3381d2f94f75ae94349c577f0185c37d5a487cf4

Observation e35095b8-fbad-47a7-855d-b14a580e0db5 · outbound

This paper cites Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation,

Reference 3

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raw_fallback, observed 2026-08-07T14:45:35.160723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.345762Z digest=sha256:d22cdefef7c1d1d777c20f1d3f8631ea4037959e0630f7d8dfedd645c8d6aebf

Observation de4d1db2-8aa3-427f-8279-8549dad59423 · outbound

This paper cites 5G Positioning Advancements with AI/ML.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization 5G Positioning Advancements with AI/ML

Reference 4

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no resolver link, observed 2026-08-07T14:45:25.419094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:25.419094Z digest=sha256:3851e8730f2f1c0bb6eb4e1ca8233d4bf4ea240613dc3b62075e57a0b35b0f55

Observation ca9ac08e-c93a-41bc-9181-8af77e493a98 · outbound

This paper cites Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.503614Z digest=sha256:ef734cc4e34cb693518658fde9e280ae039d9552898e9a9eff57e9aa69201815

Observation 5e6e52a4-65ba-4b84-bac9-bd5d6d47ec39 · outbound

This paper cites FeMLoc: Federated Meta- learning for Adaptive Wireless Indoor Localization Tasks in IoT Net- works,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization FeMLoc: Federated Meta- learning for Adaptive Wireless Indoor Localization Tasks in IoT Net- works,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.591397Z digest=sha256:56534e927f162cea21f65b4347af3392e545788131d688f6f951ac4dc1437532

Observation 6d6f68b2-75f1-40b4-9ea0-ea4f4850a5af · outbound

This paper cites Mitigating Catastrophic Forgetting in Deep Transfer Learning for Fingerprinting Indoor Posi- tioning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Mitigating Catastrophic Forgetting in Deep Transfer Learning for Fingerprinting Indoor Posi- tioning,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.657935Z digest=sha256:b761ac1a79e2e1183954b0a3f08252902aef9e2ba8ea93c083a813dadfd122a7

Observation 8b7a6d90-af45-4aeb-b3b3-29304d5f051b · outbound

This paper cites Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning,

Reference 8

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raw_fallback, observed 2026-08-07T14:45:34.378343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.726356Z digest=sha256:7c6e3d6be2f2c472023f80c0c6598dc0ecb08b26771c1cbd3d9a4f40e0eb2521

Observation 935e9086-5e59-4fe1-98a2-b832dbd4a18a · outbound

This paper cites Indoor Localization in Commercial 5G Environment with Single BS,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Indoor Localization in Commercial 5G Environment with Single BS,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.822743Z digest=sha256:8406b59a58c5657b6b387843535fcb0f11772bc6fc1b2f11182faa6d1fe7600f

Observation 23d058c5-7364-464b-a20e-e602da65f438 · outbound

This paper cites Deep Learning-based Positioning with Multi-task Learning and Uncertainty-based Fusion,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Deep Learning-based Positioning with Multi-task Learning and Uncertainty-based Fusion,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:25.912512Z digest=sha256:1e77b999452d7e314c82fb1cec990be2a611656f515b5ca580b231f736c17f18

Observation 0b347804-6af9-4e27-8a2f-54ad557ea9ee · outbound

This paper cites A Tutorial on Terahertz-Band Localization for 6G Communication Systems,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization A Tutorial on Terahertz-Band Localization for 6G Communication Systems,

Reference 11

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raw_fallback, observed 2026-08-07T14:45:33.867626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.000640Z digest=sha256:458eb20020bd38e0a5ef2718aef12e207ae1b0c58a9c3308eae5cd9b2e277d8d

Observation 846b9f53-c7fb-40c8-8bde-64f1d182c636 · outbound

This paper cites A CSI- Based Data-Driven Localization Framework Using Small-Scale Training Datasets in Single-Site MIMO Systems,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization A CSI- Based Data-Driven Localization Framework Using Small-Scale Training Datasets in Single-Site MIMO Systems,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.089137Z digest=sha256:408357dba8becb4bab2e1f51d742e7e10f23be22cd7fd4879b642978c62b1423

Observation 3a8bc8c0-29c4-4b26-84c7-31054fda9f26 · outbound

This paper cites Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:26.159914Z digest=sha256:2674d940cd15800386318534a30f50da800a06a63fd7fdea315c8b3192b0e0d2

Observation da9ea18a-b460-4b7f-8698-92a58b8ed4fe · outbound

This paper cites 5G1M: Indoor Fingerprint Positioning Using a Single 5G Module,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization 5G1M: Indoor Fingerprint Positioning Using a Single 5G Module,

Reference 14

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raw_fallback, observed 2026-08-07T14:45:33.515224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.260852Z digest=sha256:3d9db105bcabda27366b8b2e6f5b3fab78f58d044acbf0a5cdefddeb67648106

Observation ead94bc1-f54d-4eb8-b7a9-2a7bcb7b8796 · outbound

This paper cites RSSI-based Fingerprint Localization in LoRaW AN Networks Using CNNs with Squeeze and Excitation Blocks,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization RSSI-based Fingerprint Localization in LoRaW AN Networks Using CNNs with Squeeze and Excitation Blocks,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.328842Z digest=sha256:1eb14276733adee5c35b525afc6a1baa0682ff639530721c28e735c0e35597da

Observation b9a53760-09bc-46b2-9ee5-5a2c914e07d8 · outbound

This paper cites SNWPM: A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization SNWPM: A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable,

Reference 16

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.407236Z digest=sha256:8057650cf3d4a6a878bf89695dfd5211025ff0a70e44b34e7323a61839938beb

Observation ef0e19fd-82e6-47bf-910f-fc56cb4313b7 · outbound

This paper cites Im- plementation of a Transfer Learning and Fingerprint-Based Positioning System,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Im- plementation of a Transfer Learning and Fingerprint-Based Positioning System,

Reference 17

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raw_fallback, observed 2026-08-07T14:45:32.789920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.465149Z digest=sha256:067a8ea0d2e36ca4e44c086fd7885fc5e2f7dc67607c20d65014139e8fd11c9c

Observation d33bb613-099e-4b34-b69e-d083faad74a0 · outbound

This paper cites An Attention Auxiliary Network- Based Method for WiFi Fingerprint Indoor Localization,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization An Attention Auxiliary Network- Based Method for WiFi Fingerprint Indoor Localization,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.516826Z digest=sha256:491e94755bbec39ae6b51288338ff74ecf4327a2fa64b54071de35f5c3b0f75c

Observation 734b165d-1b04-4e60-babe-a6dec19b0a09 · outbound

This paper cites Progressive Neural Networks.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Progressive Neural Networks

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:26.586639Z digest=sha256:f0b3bc22900f2549e1111db09b26f867f18fcf940f3eba3642768a745f2b5932

Observation 510fad91-193e-4143-9d9a-a48746c1e431 · outbound

This paper cites Continual Learning Through Synaptic Intelligence,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Continual Learning Through Synaptic Intelligence,

Reference 20

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raw_fallback, observed 2026-08-07T14:45:32.208080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.686079Z digest=sha256:19152a0f0270e8fd72bf571bbfe9081c0cb5b33c0a79ea97952a189c07a85873

Observation 7f952189-3997-4940-8f23-3935ad06b3b9 · outbound

This paper cites Experi- ence Replay for Continual Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Experi- ence Replay for Continual Learning,

Reference 21

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raw_fallback, observed 2026-08-07T14:45:32.055593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.762282Z digest=sha256:2156ca56f12b3eaaefa111194761485c7af03d637f1472975e3424ab2ea3ebc0

Observation 8b90604b-5ae5-42ed-ad95-0b758f2ec18d · outbound

This paper cites Overcoming Catastrophic Forgetting in Neural Networks,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Overcoming Catastrophic Forgetting in Neural Networks,

Reference 22

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raw_fallback, observed 2026-08-07T14:45:31.835642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.838499Z digest=sha256:0ca4d281c3bf293ffd63a74a56c840f31b3aad74048f011d6aa3dab951e919b2

Observation 414e1af6-5990-4739-be6c-a84014feee07 · outbound

This paper cites Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:26.913598Z digest=sha256:7adfc23f01cbf0726bc4e91ae2ceef145d285f0bf943af441b66a915efa0ea87

Observation 8eeab774-05cf-42c3-9aec-c5a5f195c2f9 · outbound

This paper cites Learning Without Forgetting,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Learning Without Forgetting,

Reference 24

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raw_fallback, observed 2026-08-07T14:45:31.563174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:26.976444Z digest=sha256:884bb6e24875e18512a3b67eb9610c6982ca539ea9f5a5748afc3c8a4f947f87

Observation bcc6bec9-dba8-4472-8127-4b2f39eda1e6 · outbound

This paper cites DTL-5G: Deep Transfer Learning-based DDoS Attack Detection in 5G and Beyond Networks,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization DTL-5G: Deep Transfer Learning-based DDoS Attack Detection in 5G and Beyond Networks,

Reference 25

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raw_fallback, observed 2026-08-07T14:45:31.343418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.053763Z digest=sha256:18e58f5abb91e1c6060cc5ad3fca804baf47be0362ea7b28bf52a91212dc1d2c

Observation 55ea61c2-499f-4ac5-849e-bb5531a880ff · outbound

This paper cites Dynamic Anomaly Detection in 5G-Connected IoT Devices using Transfer Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Dynamic Anomaly Detection in 5G-Connected IoT Devices using Transfer Learning,

Reference 26

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raw_fallback, observed 2026-08-07T14:45:31.157096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.133848Z digest=sha256:7b03d9ecb1efc793baa60720384c2f51aadae07a470fa82f1f8e48d416ab73f8

Observation db8c4810-82ae-464a-9002-d910c4570fb7 · outbound

This paper cites Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning,

Reference 27

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raw_fallback, observed 2026-08-07T14:45:31.030606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.228024Z digest=sha256:f8b1236fefebb5302edbf0953c6ccd3f465c617ce3738240730bbf94cb90274b

Observation 048bbcd4-c25c-4b78-a7c1-ec82fdc08938 · outbound

This paper cites One-Shot Domain Incremental Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization One-Shot Domain Incremental Learning,

Reference 28

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raw_fallback, observed 2026-08-07T14:45:30.864176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.298122Z digest=sha256:93c70891c3042e8a736847e987009fcffa9f9a237baaff1028fda02df89a963c

Observation e6f2d5d9-b729-42f1-bc32-567460374efd · outbound

This paper cites An Efficient Domain-Incremental Learning Approach to Drive in All Weather Condi- tions,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization An Efficient Domain-Incremental Learning Approach to Drive in All Weather Condi- tions,

Reference 29

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raw_fallback, observed 2026-08-07T14:45:30.731713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.365556Z digest=sha256:5d55678af44f121d419aa67bc108b79815c22524d7ca7d0c7953be01d3bf814c

Observation 89a38f7a-bc69-4836-9846-102ac0e5309c · outbound

This paper cites Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning,

Reference 30

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raw_fallback, observed 2026-08-07T14:45:30.532373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.451476Z digest=sha256:7aead1e16a02e3ca805de17adf052849be8652602b45253b0c0566774b0b2a4e

Observation fc78aa3c-b5ce-44c5-9c48-805dbc46d125 · outbound

This paper cites Graph-Based vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MA V Indoor Pose Estimation,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Graph-Based vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MA V Indoor Pose Estimation,

Reference 31

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raw_fallback, observed 2026-08-07T14:45:30.400255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.542360Z digest=sha256:ff8cdde97ddd7cb81b576801c1c7679c2391fc9b3159ba6b9cf11149aaa915dc

Observation 6b10189e-6f70-404d-9847-550024d03d99 · outbound

This paper cites Neural 5G Indoor Localization with IMU Supervision,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Neural 5G Indoor Localization with IMU Supervision,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:30.222304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.620528Z digest=sha256:956ebc4f24b6c8ec5406d73212ac6bfc9749bec48115b009d8d5a00f52eafa94

Observation 37f75a8d-7d64-4960-bfa9-52a12a473501 · outbound

This paper cites Non- Exemplar Domain Incremental Learning via Cross-Domain Concept Integration,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Non- Exemplar Domain Incremental Learning via Cross-Domain Concept Integration,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:30.046533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.732346Z digest=sha256:bae062b5d13c1ec615c0aae116e3078e942b063a21b35aac89176ca6881e59d0

Observation 872b2045-7173-4495-a92f-d5918015ed89 · outbound

This paper cites A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.858014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.794642Z digest=sha256:af7505fba290d3957b89d4ec76e8f3796c0163d6c378e80240528b96438db1b8

Observation 07d7c756-6c1f-4ba0-b302-c87cdc4d3259 · outbound

This paper cites Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation,

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T14:45:27.899395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:45:27.899395Z digest=sha256:42f75cc128a20a0d98018404b22bc201b6f82e0c5514351800278d2fda422eeb

Observation f86be39c-3ffe-49a9-977d-b0727e150f4f · outbound

This paper cites Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.673648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:27.974241Z digest=sha256:7239b747fbca4f6a7a1a60d478d3ec4a8d9df1e12522aed3ac724b425c8eb22a

Observation 2352c020-9098-4bb1-b09a-f5c1cd6de5b6 · outbound

This paper cites Indoor Positioning in 5G-Advanced: Challenges and Solution Toward Centimeter-Level Accuracy with Carrier Phase Enhancements,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Indoor Positioning in 5G-Advanced: Challenges and Solution Toward Centimeter-Level Accuracy with Carrier Phase Enhancements,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.524856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:28.057910Z digest=sha256:8dc5afbf9ea722fe269d4c6d20d446eeb70f695fcbfd00786c397c262312f367

Observation a9825f06-1015-4ce4-9bd9-11535f993d1d · outbound

This paper cites Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.363545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:28.142041Z digest=sha256:53a5fb082f1a8ed2c090e60f95a1c8b6ae5dd0ceb2277bd0d6edc0ca7f45bbbc

Observation a3c8fcc8-aaba-4292-bd27-196949e21f84 · outbound

This paper cites Prediction Error- based Classification for Class-Incremental Learning,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Prediction Error- based Classification for Class-Incremental Learning,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.208675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:28.245271Z digest=sha256:631ea3807410fd00b6e2919739baa5b72cb0269561d22afa27173d5c0e1054ec

Observation 94dfd548-1b17-4d6b-8860-7b2c0922f550 · outbound

This paper cites [Online].

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization [Online]

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:29.037698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:28.298545Z digest=sha256:c8df932d48da56f966e5bc1c2d8e1ff6c3446808b81859f4ed364bd8cd80d5e5

Observation 29a7ff74-3dfd-4e53-84a9-eff731d3a3ff · outbound

This paper cites Indoor Positioning Technologies,.

5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization Indoor Positioning Technologies,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:45:28.843845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-07T14:45:28.378330Z digest=sha256:ac4ed281338cdbcd694b9a534984258b905f324145322086e4fcb8e73aeac921

Pith citing papers

No inbound Pith citation observations are available.