| 1. |
- Pöntinen, M., et al.
(författare)
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Euclid: Identification of asteroid streaks in simulated images using deep learning
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 679
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Tidskriftsartikel (refereegranskat)abstract
- The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition of asteroids, but we currently have such data, especially in the near-infrared wavelengths, for only a limited number of asteroids. This is a significant limitation considering the complex orbital structures of the asteroid populations. Up to 150 000 asteroids will be visible in the images of the upcoming ESA Euclid space telescope, and the instruments of Euclid will offer multiband visual to near-infrared photometry and slitless near-infrared spectra of these objects. Most of the asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-machine-learning approach based on the Streak Det software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample. The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0.25–0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.
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| 2. |
- Ion, John, et al.
(författare)
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Laser surface modification of a 13.5% Cr, 0.6% C steel
- 1991
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Ingår i: Journal of Materials Science. - 0022-2461 .- 1573-4803. ; 26:1, s. 43-48
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Tidskriftsartikel (refereegranskat)abstract
- A 13.5% Cr, 0.6% C steel, with an initial microstructure of chromium carbides in a ferrite matrix, was heat-treated by scanning a high-power laser beam over the surface. The aim was to compare the physical and chemical properties produced by this type of selective surface treatment with those resulting from a conventional furnace desensitization and quench-hardening heat treatment. Surface heating homogenized the carbon originally bound in the carbides sufficiently to produce martensite, giving hardening to levels comparable with a conventional heat treatment. Chromium-rich zones, carbides and retained austenite were also detected in the heated microstructure. Surface melting produced complete homogenization of both carbon and chromium, which resulted in the retention of large amounts of austenite in the microstructure on cooling to room temperature. Subsequent refrigeration at - 196 °C transformed some of the austenite to martensite. Pitting corrosion and local reductions in hardness were observed adjacent to treated areas under certain conditions, due to precipitation of secondary carbides and elevated tempering, respectively.
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| 3. |
- Pedersen, Kristine B., et al.
(författare)
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Impacts of climate change on metal leaching and partitioning for submarine mine tailings disposal
- 2022
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Ingår i: Marine Pollution Bulletin. - : Elsevier Ltd. - 0025-326X .- 1879-3363. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- At present, there are no standardised tests to assess metal leaching during submarine tailings discharge. In this study the influence of variables known to affect metal mobility and availability (dissolved organic carbon (DOC), pH, salinity, temperature, aerated/anoxic conditions) along with variables affected by the discharge conditions (flocculant concentration, suspension) were studied in bench-scale experiments. The leaching tests were developed based on the case of a copper mine by Repparfjorden, northern Norway, which is planned to re-open in 2022.The experiments, which had three week duration, revealed low (<6 %) leaching of metals. Multivariate analysis showed that all variables, apart from DOC, highly influenced leaching and partitioning of at least one metal (Ba, Cr, Cu, and/or Mn). The high quantity of the planned annual discharge of mine tailings to the fjord (1–2 million tonnes) warranted estimation of the leached quantity of metals. Multivariate models, using present-day conditions in the fjord, estimated leaching of up to 124 kg Ba, 154 kg Cu and 2400 kg Mn per year during discharge of tailings. Future changes in the fjord conditions caused by climate change (decreased pH, increased temperature) was predicted by the multivariate models to increase the leaching up to 55 %, by the year 2065.The bench-scale experiments demonstrated the importance of including relevant variables (such as pH, salinity, and temperature) for metal leaching and -partitioning in leaching tests. The results showed that metal leaching during discharge is expected and will increase in the future due to the changed conditions caused by the foreseen climate change, and thereby underline the importance of monitoring metal concentrations in water during operations to determine the fate of metals in the fjord.
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