Evidence map›Paper›PMID 34883871›Full record

ArticleSensors (Basel, Switzerland)2021

Simulation of RSO Images for Space Situation Awareness (SSA) Using Parallel Processing.

Ryan Clark, Yanchun Fu, Siddharth Dave, Regina Lee

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
7.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Ryan ClarkDepartment of Earth and Space Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.
Yanchun FuDepartment of Earth and Space Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.ORCID 0000-0002-5491-505X
Siddharth DaveDepartment of Earth and Space Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.
Regina LeeDepartment of Earth and Space Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.
York University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid increase in resident space objects (RSO), there is a growing demand for their identification and characterization to advance space simulation awareness (SSA) programs. Various AI-based technologies are proposed and demonstrated around the world to effectively and efficiently identify RSOs from ground and space-based observations; however, there remains a challenge in AI training due to the lack of labeled datasets for accurate RSO detection. In this paper, we present an overview of the starfield simulator to generate a realistic representation of images from space-borne imagers. In particular, we focus on low-resolution images such as those taken with a commercial-grade star tracker that contains various RSO in starfield images. The accuracy and computational efficiency of the simulator are compared to the commercial simulator, namely STK-EOIR to demonstrate the performance of the simulator. In comparing over 1000 images from the Fast Auroral Imager (FAI) onboard CASSIOPE satellite, the current simulator generates both stars and RSOs with approximately the same accuracy (compared to the real images) as STK-EOIR and, an order of magnitude faster in computational speed by leveraging parallel processing methodologies.

Indexed as

artificial intelligence (AI)parallel processingresident space objects (RSOs)space situational awareness (SSA)

Identifiers

PMID34883871
PMCPMC8659528
OpenAlexW3216769124

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.