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Record W2484465561 · doi:10.1109/aero.2016.7500765

Radiation environment onboard spacecraft at LEO and in deep space

2016· article· en· W2484465561 on OpenAlexaff
Lembit Sihver, Satoshi Kodaira, Iva Ambrožová, Y. Uchihori, Vyacheslav Shurshakov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCosmic rayHealth threat from cosmic raysSpacecraftIonizing radiationRadiation protectionPhysicsSpace environmentElectromagnetic shieldingInterplanetary spaceflightRadiationVan Allen radiation beltAstrobiologySpace explorationEnvironmental scienceAstronomyMagnetosphereIrradiationNuclear physicsSolar windCoronal mass ejection

Abstract

fetched live from OpenAlex

It is well known that outside the Earth's protective atmosphere and magnetosphere, the environment is very harsh and unfriendly for any living organism, due to the micro gravity, lack of oxygen and protection from high energetic ionizing cosmic radiation, as well as from powerful solar energetic particles (SEPs). The space radiation exposure leads to increased health risks, including tumor lethality, circulatory diseases and damages on the central nervous systems. In case of SEP events, exposures of spacecraft crews may be lethal. Space radiation hazards are therefore recognized as a key concern for human space flight. For long-term interplanetary missions, they constitute a limiting factor since current protection limits might be approached or even exceeded. Better risk assessment requires knowledge of the radiation quality, as well as equivalent doses in critical radiosensitive organs, and different risk coefficient for different radiation caused illnesses and diseases must be developed. The use of human phantoms, simulating an astronaut's body, provides detailed information of the depth-dose distributions, and radiation quality, inside the human body. In this paper we will therefore review the major phantom experiments performed at Low Earth Orbits (LEO) [1]. However, the radiation environment in deep space is different from LEO. Based on fundamental physics principles, it is clear that hydrogen rich, light and neutron deficient materials have the best shielding properties against Galactic Cosmic Rays (GCR) [2,3]. It has also been shown [4,5] that water shielding material can reduce the dose from Trapped Particles (TP), the low energetic part of GCR, and from low energetic SEP events. However, the total dose from GCR, for moderate shielding thicknesses, is actually increasing when increasing the shielding thickness due to the buildup of secondary fragments, protons and neutrons [5]. Examples of promising shielding materials are polyethylene and hydrogen rich carbon composite materials. Nevertheless, not even these shielding materials have been proven to significantly reduce the radiation health risks compared to e.g. aluminum shielding due to the high energetic GCR particles, the created fragments, and the large radiobiological uncertainties in the GCR risk projection [6,7]. A better understanding of the radiobiological effects of GCR are therefore needed, as well as better cancer risk models, and models for estimating the risks for circulatory diseases and damages on the central nervous systems. To reduce the health risks, a combination of passive and active shielding might be a realistic option for long term interplanetary missions, in combination with means to minimizing the time in deep space and to perform the missions during solar maximum to minimize the flux of GCR. Suitable radioprotectors, e.g. agents that act directly to protect cellular component and oppose the action of radiation induced free radicals, and reactive oxygen species, as well as radiomitigators, e.g. agents that accelerate post-radiation recovery and prevent complications, could also be developed. There might also be a need to accept an increased risk for carcinogenesis than what is stated by current dose limits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2016
Admission routes1
Has abstractyes

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