Paper Citation Record · LEDGER

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation

As of 17 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2412.03968.

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

pith.paper-citation-record.v1
2412.03968 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:03:27.955448Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:19:58.533293Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T16:19:58.570883Z

Reference resolution

60 of 60 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6475e9bb-4932-4e99-a5d2-24f5a964e81c · outbound

This paper cites Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation

Reference 1

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Observation 0f9f939b-85fe-4134-831e-c5cdff9154da · outbound

This paper cites Weakly su- pervised learning of instance segmentation with inter-pixel relations.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Weakly su- pervised learning of instance segmentation with inter-pixel relations

Reference 2

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Observation f223a9a4-33ce-46e9-b395-2cbd43997d58 · outbound

This paper cites Single-stage semantic segmentation from image labels.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Single-stage semantic segmentation from image labels

Reference 3

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Observation ec580af6-7e61-4104-8f5c-bdcb7a9e4554 · outbound

This paper cites Self-labelling via simultaneous clustering and repre- sentation learning.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Self-labelling via simultaneous clustering and repre- sentation learning

Reference 4

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Observation 74566824-3b8f-4548-9839-8cd18ece9dac · outbound

This paper cites Omnisat: Self-supervised modality fusion for earth observation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Omnisat: Self-supervised modality fusion for earth observation

Reference 5

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Observation d7b76b24-eb1a-4295-86ed-a49208d0a541 · outbound

This paper cites Multi- modal learning for geospatial vegetation forecasting.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Multi- modal learning for geospatial vegetation forecasting

Reference 6

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Observation 79c0fefd-220d-434b-8ba1-321f2e789f63 · outbound

This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 8

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Observation 63356484-fcca-44db-b204-d2f47ebdf611 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 9

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Observation 3dba36f7-975f-4b01-ba36-30ce38092b2c · outbound

This paper cites Fpr: False positive rectification for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Fpr: False positive rectification for weakly supervised semantic segmentation

Reference 10

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Observation 79dee626-03d0-4f3e-b844-46b4d9a5634b · outbound

This paper cites Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation

Reference 11

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Observation ecf4b5ac-7301-4b67-99d5-d2dcc8619ce9 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation A simple framework for contrastive learning of visual representations

Reference 12

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Observation 94e09e6b-87ff-4877-b2db-78e86a450912 · outbound

This paper cites Knowledge transfer with simulated inter-image erasing for weakly supervised semantic segmen- tation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Knowledge transfer with simulated inter-image erasing for weakly supervised semantic segmen- tation

Reference 13

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Observation f4a3b273-d9ad-47bb-8e0b-fefe85d02730 · outbound

This paper cites Extracting class activation maps from non-discriminative features as well.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Extracting class activation maps from non-discriminative features as well

Reference 14

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Observation caf10350-30ef-4da8-8ccd-1c0c63a58572 · outbound

This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery

Reference 15

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Observation 61bbc4c7-2792-46b5-a6a1-4ebb89ff0168 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Sinkhorn distances: Lightspeed computation of optimal transport

Reference 16

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Observation 57e58c18-86cf-428c-b5bd-58fdb7bd9c13 · outbound

This paper cites An image is worth 16x16 words: Transform- ers for image recognition at scale.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation An image is worth 16x16 words: Transform- ers for image recognition at scale

Reference 17

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Observation b3bf348a-6b5c-4dce-b444-0cdb62651b58 · outbound

This paper cites Sentinel-2: Esa’s optical high-resolution mission for gmes operational services.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Sentinel-2: Esa’s optical high-resolution mission for gmes operational services

Reference 18

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Observation abeabe97-0dbc-434a-b414-c305307bfcc1 · outbound

This paper cites Weakly supervised semantic segmentation by pixel-to-prototype con- trast.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Weakly supervised semantic segmentation by pixel-to-prototype con- trast

Reference 19

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Observation 3bf6bea2-b9ee-4a08-9e30-f97cf6ba92b8 · outbound

This paper cites Ts-cam: Token semantic coupled attention map for weakly supervised object localization.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Ts-cam: Token semantic coupled attention map for weakly supervised object localization

Reference 20

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Observation 9f3e058a-9187-4595-82b0-7f4bea4633a5 · outbound

This paper cites Panoptic seg- mentation of satellite image time series with convolutional temporal attention networks.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Panoptic seg- mentation of satellite image time series with convolutional temporal attention networks

Reference 21

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Observation 9b5785e4-ea0f-4075-9156-a719f5460c3c · outbound

This paper cites Satellite image time series classi- fication with pixel-set encoders and temporal self-attention.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Satellite image time series classi- fication with pixel-set encoders and temporal self-attention

Reference 22

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Observation 12a08195-6d6d-49af-8939-900587f2b13c · outbound

This paper cites Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery

Reference 23

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Observation 381b4a48-76d7-48d9-a0b1-843864de4909 · outbound

This paper cites Feature selection of time series modis data for early crop classification using random forest: A case study in kansas, usa.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Feature selection of time series modis data for early crop classification using random forest: A case study in kansas, usa

Reference 24

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Observation a07eb4ea-3bec-4609-b562-c522215686ac · outbound

This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Efficient inference in fully connected crfs with gaussian edge potentials

Reference 25

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Observation bc790c23-cd93-4e8a-b72e-724cc67ab2b8 · outbound

This paper cites Label-efficient segmenta- tion via affinity propagation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Label-efficient segmenta- tion via affinity propagation

Reference 26

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Observation bb6fd931-8b51-4a55-b0fd-c8ffe4ffd244 · outbound

This paper cites S2mae: A spatial-spectral pretraining foundation model for spectral remote sensing data.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation S2mae: A spatial-spectral pretraining foundation model for spectral remote sensing data

Reference 27

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Observation b5a70a00-b660-4278-9a61-df1164a726dc · outbound

This paper cites Feature pyramid networks for object detection.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Feature pyramid networks for object detection

Reference 28

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Observation e217a3aa-a50e-4395-9bda-b31f1b6de13a · outbound

This paper cites Decoupled Weight Decay Regularization.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Decoupled Weight Decay Regularization

Reference 29

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Observation d5e48f37-69ec-4516-a1f5-a8beaec221d1 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Sgdr: Stochastic gradient descent with warm restarts

Reference 30

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This paper cites Semantic segmen- tation of crop type in africa: A novel dataset and analysis of deep learning methods.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Semantic segmen- tation of crop type in africa: A novel dataset and analysis of deep learning methods

Reference 31

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Observation c7ab4a36-5f45-46b2-8f16-c647dbb01650 · outbound

This paper cites Transfer learning in environmental remote sensing.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Transfer learning in environmental remote sensing

Reference 32

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Observation 9ac2f1b2-1694-4f39-ae53-0f942afe7e1a · outbound

This paper cites Fully convolutional recurrent networks for multi- date crop recognition from multitemporal image sequences.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Fully convolutional recurrent networks for multi- date crop recognition from multitemporal image sequences

Reference 33

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Observation dc936953-2556-40a6-a54f-2baed1255799 · outbound

This paper cites Im- provement in crop mapping from satellite image time series by effectively supervising deep neural networks.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Im- provement in crop mapping from satellite image time series by effectively supervising deep neural networks

Reference 34

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Observation 8fa7aaa8-5aa4-4e72-b036-5861807b4044 · outbound

This paper cites Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas

Reference 35

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Observation 21a896dc-f372-4819-b723-c8542372dae2 · outbound

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

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 36

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This paper cites Max pooling with vision transform- ers reconciles class and shape in weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Max pooling with vision transform- ers reconciles class and shape in weakly supervised semantic segmentation

Reference 37

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Observation 45b37a11-679f-4d09-9dc4-bf00f6b9c70a · outbound

This paper cites Token contrast for weakly-supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Token contrast for weakly-supervised semantic segmentation

Reference 38

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Observation 4d0b7fb4-f60d-4fd3-827c-fb96154c8709 · outbound

This paper cites Multi-temporal land cover classification with sequential recurrent encoders.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Multi-temporal land cover classification with sequential recurrent encoders

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:29.144809Z

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Observation 21fda08f-73a5-47af-83c8-b5caf7084562 · outbound

This paper cites Grad- cam: Visual explanations from deep networks via gradient- based localization.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Grad- cam: Visual explanations from deep networks via gradient- based localization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:29.116732Z

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Observation 622f3a68-0d79-49de-9bda-2cc6a2ad3112 · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation now- casting.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Convolutional lstm network: A machine learning approach for precipitation now- casting

Reference 41

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Observation 8bef89cf-3d47-437b-96d2-6e942baafe1c · outbound

This paper cites A hidden markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation A hidden markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data

Reference 42

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Observation d46e3b7e-5d74-47e7-a74e-01adbda6a721 · outbound

This paper cites Hunting attributes: Con- text prototype-aware learning for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Hunting attributes: Con- text prototype-aware learning for weakly supervised semantic segmentation

Reference 43

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Observation 94af8125-1f8d-4590-99c6-8edf8454a797 · outbound

This paper cites Context-self contrastive pretraining for crop type semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Context-self contrastive pretraining for crop type semantic segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.793784Z

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Observation 2dadae70-1e8e-4adb-a770-53951fbd0684 · outbound

This paper cites Vits for sits: Vision transformers for satellite image time series.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Vits for sits: Vision transformers for satellite image time series

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.780313Z

Source-reported events for the cited work

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Observation c2d6d00e-af27-4a0d-a29a-3c7a0810c139 · outbound

This paper cites Gmes sentinel-1 mission.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Gmes sentinel-1 mission

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.766555Z

Source-reported events for the cited work

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Observation e03c5c11-9c70-40cb-bfb8-478c02e2b27c · outbound

This paper cites Visualizing data using t-sne.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Visualizing data using t-sne

Reference 47

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Observation b75415e7-f429-4ab4-ba42-8bab7c20bd05 · outbound

This paper cites Weakly supervised deep learning for segmentation of remote sensing imagery.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Weakly supervised deep learning for segmentation of remote sensing imagery

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.710885Z

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This paper cites Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation

Reference 49

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This paper cites Free access to landsat imagery.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Free access to landsat imagery

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.582748Z

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This paper cites Dupl: Dual student with trustworthy progressive learning for robust weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Dupl: Dual student with trustworthy progressive learning for robust weakly supervised semantic segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.569796Z

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Observation bced9459-99cd-4a99-b17e-d018f2fd0092 · outbound

This paper cites Multi-class token transformer for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Multi-class token transformer for weakly supervised semantic segmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.555317Z

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Observation e8df9c70-9437-4de2-8de1-5c9b471a8f84 · outbound

This paper cites Mctformer+: Multi-class token transformer for weakly supervised semantic segmen- tation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Mctformer+: Multi-class token transformer for weakly supervised semantic segmen- tation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.541052Z

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Observation 9b5afb3d-2bc5-4a22-95c1-1ca4e85ddc01 · outbound

This paper cites Self correspondence distilla- tion for end-to-end weakly-supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Self correspondence distilla- tion for end-to-end weakly-supervised semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.527235Z

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Observation 2d0a42d6-0139-4bed-a8d2-055949720414 · outbound

This paper cites Separate and conquer: Decou- pling co-occurrence via decomposition and representation for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Separate and conquer: Decou- pling co-occurrence via decomposition and representation for weakly supervised semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.359228Z

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Observation 0d18764f-37b5-44a1-bfbd-3bf81c99a356 · outbound

This paper cites Class tokens infusion for weakly supervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Class tokens infusion for weakly supervised semantic segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.344530Z

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Observation 68656a4c-766a-4a78-ab93-9576c3dd592e · outbound

This paper cites Frozen clip: A strong backbone for weakly su- pervised semantic segmentation.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Frozen clip: A strong backbone for weakly su- pervised semantic segmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.325512Z

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Observation f1f21256-ebdb-4043-8d8d-7a448809fbb4 · outbound

This paper cites Deep learning based multi-temporal crop classification.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Deep learning based multi-temporal crop classification

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.231478Z

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Observation ab83e1b5-62ef-4dc2-aa0f-0fe631d1148c · outbound

This paper cites Learning deep features for discriminative localization.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Learning deep features for discriminative localization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.173686Z

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Observation 8162c77a-3f0b-40db-bbef-9048a9bd52cd · outbound

This paper cites Rethinking semantic segmentation: A prototype view.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation Rethinking semantic segmentation: A prototype view

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.158643Z

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Observation 2ca95e12-5945-4ae5-acee-ef680a094eee · outbound

This paper cites 81.9 71.6.

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation 81.9 71.6

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:03:28.129190Z

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Pith citing papers

Observation 42dbd553-bae7-40e3-af74-6ddd65ff8865 · inbound

Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer cites this paper.

Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:19:58.577882Z

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