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Telemetry

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Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method.

Sensors (Basel, Switzerland)
Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse...

Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model.

Sensors (Basel, Switzerland)
Most of the land use land cover classification methods presented in the literature have been conducted using satellite remote sensing images. High-resolution aerial imagery is now being used for land cover classification. The Global Learning and Obse...

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach.

Sensors (Basel, Switzerland)
In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites' high mobility and resultant insufficient time for ...

A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images.

Sensors (Basel, Switzerland)
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large ...

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach.

Sensors (Basel, Switzerland)
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To ...

HyFormer: Hybrid Transformer and CNN for Pixel-Level Multispectral Image Land Cover Classification.

International journal of environmental research and public health
To effectively solve the problems that most convolutional neural networks cannot be applied to the pixelwise input in remote sensing (RS) classification and cannot adequately represent the spectral sequence information, we propose a new multispectral...

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis.

The Journal of toxicological sciences
Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in...

STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea-Land segmentation.

Journal of environmental management
Extraction of coastline from optical remote sensing images is of paramount importance for coastal zone management, erosion monitoring, and intelligent ocean construction. However, nearshore marine environment complexity presents a challenge when capt...

Detecting DoS Attacks through Synthetic User Behavior with Long Short-Term Memory Network.

Sensors (Basel, Switzerland)
With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential ...

Design of wireless web-based multiplatform system for thermal environmental control of broiler facilities using fuzzy set theory.

Anais da Academia Brasileira de Ciencias
The control and monitoring process for broiler facilities needs to be improved to mitigate or eliminate birds' thermal stress. Thus, the objective was to develop of a fuzzy controller embedded in a microcontroller and a multiplatform web application ...