scholarly journals REAL-TIME WILDFIRE DETECTION FROM SPACE – A TRADE-OFF BETWEEN SENSOR QUALITY, PHYSICAL LIMITATIONS AND PAYLOAD SIZE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 209-213
Author(s):  
S. B. Shah ◽  
T. Grübler ◽  
L. Krempel ◽  
S. Ernst ◽  
F. Mauracher ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Forecasting ◽  
Low Earth Orbit ◽  
Ground Support ◽  
Ground Station ◽  
Thermal Output ◽  
Surface Location ◽  
Physical Limitations ◽  
Internet Networks

<p><strong>Abstract.</strong> Wildfires cause large scale devastation to human settlements and forests every year and their frequency and severity is on the rise. A major reason for this devastation is the significant delay in their detection due to their remote locations in forests. To mitigate this, a constellation of nanosatellites in Low Earth Orbit (LEO) equipped with multi-spectral visible to Infrared (IR) cameras is proposed leveraging the modular and affordable architecture of CubeSats. Coupled with the payload design, meticulously planned constellation and a ground support system, all surface points on the planet will be revisited at least once in an hour. Capturing a surface location with a high resolution in MidWavelength Infrared (MWIR) and LongWavelength Infrared (LWIR) allows a precise estimation of thermal output of the surface. Simulations indicate that a fire of about four hundred square meters can be easily detected from this satellite payload. Through onboard data processing, wildfires can be already detected in space, minimizing bandwidth requirements for real-time alerts. This enables an early wildfire warning within 30 min by utilizing existing satellite internet networks. Additionally, compressed raw images will be transmitted on fixed ground station passes to provide a global thermal data updated every 90&amp;thinsp;min. The near real-time multi-spectral data provides opportunity for several other applications like weather forecasting besides wildfire detection.</p>

scholarly journals Cloud-Based Experimental Platform for the Space-Ground Integrated Network

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Xiaofeng Wang ◽  
Xinyu Chen ◽  
Haiyang Ye ◽  
Yuan Liu ◽  
Guizhu Zhang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Random Number Generation ◽  
Long Distance ◽  
Integrated Network ◽  
Experimental Platform ◽  
Orbital Maneuvers ◽  
Ground Station ◽  
Bit Error Ratio ◽  
Time Varying Networks

The space-ground integrated network (SGIN) is an important direction of future network development and is expected to play an important role in edge computing for the Internet of Things (IoT). Through integration with an SGIN, IoT applications can provide services with long-distance and wide-coverage features. However, SGINs are typical large-scale and time-varying networks for which new network technologies, protocols, and applications must be rigorously evaluated and validated. Therefore, a reliable experimental platform is necessary for SGINs. This paper presents a cloud-based experimental platform for the SGIN context named SGIN-Stack. First, the architecture of SGIN-Stack, which combines the Systems Tool Kit (STK) and OpenStack, is described. Based on this architecture, a seamless linkage between OpenStack and STK is achieved to realize synchronous, dynamic, and real-time network emulation for an SGIN, and the dynamic differential compensation technology and a random number generation algorithm are applied to improve the emulation accuracy for satellite links. Finally, an emulation scenario is constructed that includes six space-based backbone nodes, sixty-six space-based access nodes, and a ground station. Based on this emulation scenario, experiments concerning the satellite link delays, bit error ratio (BER), and throughput are carried out to prove the high fidelity of our SGIN-Stack platform. Emulation experiments involving satellite orbital maneuvers and attitude adjustments show that SGIN-Stack can be used for dynamic and real-time SGIN emulation.

scholarly journals Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts

2021 ◽  
Vol 11 (4) ◽  
pp. 1607
Author(s):  
John A. Karasinski ◽  
Isabel C. Torron Valverde ◽  
Holly L. Brosnahan ◽  
Jack W. Gale ◽  
Ron Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Emerging Technologies ◽  
Space Station ◽  
Low Earth Orbit ◽  
Task Completion ◽  
Human Spaceflight ◽  
Control Center ◽  
New Era ◽  
Ground Support ◽  
Long Duration

NASA’s human spaceflight efforts are moving towards long-duration exploration missions requiring asynchronous communication between onboard crew and an increasingly remote ground support. In current missions aboard the International Space Station, there is a near real-time communication loop between Mission Control Center and astronauts. This communication is essential today to support operations, maintenance, and science requirements onboard, without which many tasks would no longer be feasible. As NASA takes the next leap into a new era of human space exploration, new methods and tools compensating for the lack of continuous, real-time communication must be explored. The Human-Computer Interaction Group at NASA Ames Research Center has been investigating emerging technologies and their applicability to increase crew autonomy in missions beyond low Earth orbit. Interactions using augmented reality and the Internet of Things have been researched as possibilities to facilitate usability within procedure execution operations. This paper outlines four research efforts that included technology demonstrations and usability studies with prototype procedure tools implementing emerging technologies. The studies address habitat feedback integration, analogous procedure testing, task completion management, and crew training. Through these technology demonstrations and usability studies, we find that low- to medium-fidelity prototypes, evaluated early in the design process, are both effective for garnering stakeholder buy-in and developing requirements for future systems. In this paper, we present the findings of the usability studies for each project and discuss ways in which these emerging technologies can be integrated into future human spaceflight operations.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

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