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Sensors and Extreme Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 3094

Special Issue Editor


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Guest Editor
Associate Professor, School of Future Environments, Auckland University of Technology, Auckland, New Zealand
Interests: animal computer interaction; tangible computing; assistive computing; nature; conservation; animals; futures thinking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme environment research is critical for understanding the impacts of climate change, assessing ecosystem resilience, ensuring human safety and adaptability, and advancing planetary exploration. This research provides valuable insights into the effects of climate change on extreme environments, facilitating the development of effective mitigation and adaptation strategies. By studying these environments, researchers gain a deeper understanding of unique ecosystems’ resilience and can develop strategies to conserve and manage them. Additionally, investigating extreme environments helps ensure the safety of humans in these challenging conditions and informs the development of technologies and guidelines for protection.

This Special Issue aims to collate original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the deployment of sensors in extreme environments.

Potential topics include, but are not limited to:

  • Climate change impacts in polar regions and sensors for temperature, ice dynamics, and glacial retreat.
  • Desert adaptations and sensors for temperature, humidity, and soil moisture.
  • High-altitude physiology and sensors for oxygen levels and atmospheric pressure.
  • Deep sea biodiversity and sensors for temperature, pressure, salinity, and chemical composition.
  • Underwater monitoring and sensors for currents, pH levels, and dissolved oxygen.
  • Extraterrestrial exploration and sensors for atmospheric composition, radiation levels, and geological properties.
  • Sensor development for extreme temperature resistance, corrosion resistance, and pressure tolerance.
  • Sensor networks and data transmission systems for remote monitoring in extreme environments.
  • Autonomous sensors and robotics for data collection and exploration feasibility in extreme environments.
  • Sensor data integration for understanding extreme environment dynamics.

Dr. Ann Morrison
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme environments
  • harsh conditions
  • challenging terrains
  • severe climates
  • unforgiving settings
  • sensor resilience
  • extreme sensing

Published Papers (2 papers)

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Research

30 pages, 74562 KiB  
Article
Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI
by Damini Raniga, Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Johan Barthelemy, Krystal Randall, Sharon A. Robinson, Felipe Gonzalez and Barbara Bollard
Sensors 2024, 24(4), 1063; https://doi.org/10.3390/s24041063 - 6 Feb 2024
Cited by 1 | Viewed by 1413
Abstract
Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation [...] Read more.
Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications. Full article
(This article belongs to the Special Issue Sensors and Extreme Environments)
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19 pages, 39258 KiB  
Article
Simulation and Experimental Verification of Magnetic Field Diffusion at the Launch Load during Electromagnetic Launch
by Yuxin Yang, Qiang Yin, Changsheng Li, Haojie Li and He Zhang
Sensors 2023, 23(18), 8007; https://doi.org/10.3390/s23188007 - 21 Sep 2023
Viewed by 868
Abstract
The unique magnetic field environment during electromagnetic launch imposes higher requirements on the design and protection of the internal electronic system within the launch load. This low-frequency, Tesla-level extreme magnetic field environment is fundamentally distinct from the Earth’s geomagnetic field. The excessive change [...] Read more.
The unique magnetic field environment during electromagnetic launch imposes higher requirements on the design and protection of the internal electronic system within the launch load. This low-frequency, Tesla-level extreme magnetic field environment is fundamentally distinct from the Earth’s geomagnetic field. The excessive change rate of magnetic flux can readily induce voltage within the circuit, thus disrupting the normal operation of intelligent microchips. Existing simulation methods primarily focus on the physical environments of rails and armatures, making it challenging to precisely compute the magnetic field environment at the load’s location. In this paper, we propose a computational rail model based on the magneto–mechanical coupling model of a railgun. This model accounts for the dynamic current distribution during the launch process and simulates the magnetic flux density distribution at the load location. To validate the model’s accuracy, three-axis magnetic sensors were placed in front of the armature, and the dynamic magnetic field distribution during the launch process was obtained using the projectile-borne-storage testing method. The results indicate that compared to the previous literature methods, the approach proposed in this paper achieves higher accuracy and is closer to experimental results, providing valuable support for the design and optimization of the launch load. Full article
(This article belongs to the Special Issue Sensors and Extreme Environments)
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