Note: Intro -- Preface -- Contents -- Health Monitoring of Artificial Satellites -- Tensor-Based Anomaly Detection for Satellite Telemetry Data -- 1 Introduction -- 2 Satellite Telemetry Data Anomalies Detection -- 3 Tensor-Based Anomaly Detection (TAD) -- 3.1 Supervised Models -- 4 Tensor Decomposition -- 5 Pervious Anomaly Detection Techniques for Satellite Telemetry Data -- 6 Tensor-Based Anomaly Detection Technique for Satellite Telemetry Data -- 7 Conclusions -- References -- Machine Learning in Satellites Monitoring and Risk Challenges -- 1 Satellite Orbit -- 1.1 Different Orbits of Satellites -- 1.2 Different Uses of Satellites -- 2 Satellites Monitoring -- 2.1 Satellite Remote Sensing -- 2.2 Data Characteristics -- 3 Risk Challenges -- 3.1 Space Weather Impacts -- 3.2 Debris -- 4 Importance of Machine Learning and Applications -- 5 Conclusion -- References -- Formalization, Prediction and Recognition of Expert Evaluations of Telemetric Data of Artificial Satellites Based on Type-II Fuzzy Sets -- 1 Introduction -- 2 Creation of Expert Evaluation Models Based on Type-I Fuzzy Sets -- 3 Creation of Generalized Expert Evaluation Models Based on Interval Type-II Fuzzy Sets -- 4 Weighted Intervals for Interval Type-2 Fuzzy Sets -- 5 Prediction of Expert Evaluations Based on Linear Regression with Initial Interval Type-II Data -- 6 Prediction of Expert Evaluations Based on Linear Regression with Initial Special Case of Interval Type-2 Fuzzy Sets -- 7 Prediction of Expert Evaluations Based on Nonlinear Regression with Initial Interval Type-II Data -- 8 Prediction of Quantitative Parameters Values Based on Linear Regression with Interval Type-II Coefficients -- 9 Conclusions -- References -- Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques -- 1 Introduction -- 2 Satellite Telemetry Data. , 2.1 Characteristics of Satellite Telemetry Data -- 3 Health Monitoring System -- 3.1 Health Monitoring Based on Conventional Techniques -- 3.2 Intelligent Health Monitoring Based on Data Mining Techniques -- 3.3 Intelligent Health Monitoring Applications -- 4 Conclusion -- References -- Design, Implementation, and Validation of Satellite Simulator and Data Packets Analysis -- 1 Introduction and Basics -- 2 Satellite Simulator Design Phase -- 2.1 The Output of Communication Subsystems -- 2.2 Parameters of Simulator Inputs -- 2.3 Communication Subsystem Simulator GUI -- 2.4 Data Packets -- 3 Satellite Communications System Segments -- 3.1 The Ground Segment (GS) -- 3.2 The Space Segment (SS) -- 3.3 The Control Segment (CS) -- 4 Satellite Applications -- 5 Satellite Functions -- 6 Satellite Orbits and Pointing Angles -- 7 Satellite Links -- 7.1 The Basic Satellite Link -- 7.2 Design of the Satellite Link -- 7.3 Quantities for a Satellite RF Link -- 7.4 Digital Links -- 8 Satellite Communication Advantages -- 9 Satellite Communication Disadvantages -- 10 Conclusion -- References -- Telemetry Data Analytics and Applications -- Crop Yield Estimation Using Decision Trees and Random Forest Machine Learning Algorithms on Data from Terra (EOS AM-1) & -- Aqua (EOS PM-1) Satellite Data -- 1 Introduction -- 2 Related Work -- 2.1 Machine Learning-A Brief Overview -- 2.2 Machine Learning in Agriculture -- 2.3 Crop Yield Estimation -- 3 Methodology Adopted -- 3.1 About DSSAT v4.6 Crop Simulation Model -- 3.2 Study Methodology Flow chart -- 3.3 Dataset Used -- 4 Results and Discussions -- 4.1 Decision Tree Interpretation -- 4.2 Random Forest Interpretation -- 4.3 Normalized Difference Vegetation Index as a Performance Measure -- 5 Future Scope -- 6 Conclusion -- References -- Data Analytics Using Satellite Remote Sensing in Healthcare Applications. , 1 Introduction and Historical Perspective -- 1.1 Working of Satellite -- 1.2 Artificial Satellites Classification -- 2 Change Detection -- 3 Data Pre-processing -- 4 Data Mining and Its Techniques -- 4.1 Bayesian Framework -- 4.2 Data Visualization Tools -- 4.3 Data Mining Tools -- 5 Literature Review of Related Work in Visual Data Mining -- 6 Proposed Model for Remote Sensing Using Data Mining -- 7 Data Visualization and Outcomes -- 8 Conclusion -- References -- Design, Implementation, and Testing of Unpacking System for Telemetry Data of Artificial Satellites: Case Study: EGYSAT1 -- 1 Introduction -- 2 The Unpacking System -- 2.1 Unpacking Module -- 2.2 Limit Checking Module -- 2.3 Mining Module -- 3 Case Study: EGYSAT1 -- 3.1 EGYSAT1 Unpacking Module Design -- 3.2 Test Data -- 4 Conclusion -- References -- Multiscale Satellite Image Classification Using Deep Learning Approach -- 1 Introduction -- 2 Related Work -- 3 Convolutional Neural Networks -- 4 Proposed Methodology -- 5 Experimental Results -- 5.1 Remote Sensing Datasets -- 5.2 Results -- 6 Conclusion -- References -- Security Issues in Telemetry Data -- Security Approaches in Machine Learning for Satellite Communication -- 1 Introduction -- 2 Cognitive Satellite Communication Issues Using Machine Learning -- 2.1 Satellite Communication Channel from Earth to GEO Orbit -- 2.2 Land Cover Prediction from Satellite Imagery Using Machine Learning -- 2.3 Performance Analysis of LEO Satellite Networks -- 2.4 An Adaptive Routing Based on an Improved ACO Technique in Leo Satellite Networks -- 2.5 Rainfall Estimation Using Carrier to Noise of Satellite Communication -- 2.6 Deep Learning for Amazon Satellite Image Analysis -- 2.7 Satellite Super Resolution Images Using Deep Learning -- 3 Security and Prevention from Cyber Attacks in Satellite Communication. , 3.1 Non-reliable Data Source Identification Using Machine Learning Algorithm -- 3.2 Deep Learning and Machine Learning for Interruption in Network -- 3.3 Security Protected Procedures Using Machine Learning -- 3.4 Reinforcement Learning -- 3.5 Extreme Learning Machine -- 3.6 Malware Detection Using Machine Learning -- 4 Conclusion -- References -- Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems -- 1 Introduction -- 2 Background -- 2.1 Aerospace Cyber-Physical Systems (CPS) -- 2.2 Internet of Things -- 2.3 Security Overview in IoT -- 2.4 Machine Learning Techniques -- 3 The Proposed Detection Method -- 3.1 Module 1: Dataset Generation -- 3.2 Module 2: Data Pre-processing -- 3.3 Module 3: Data Classification -- 4 Implementation -- 4.1 Evaluation Metrics -- 4.2 Experimental Setup -- 4.3 Experimental Results and Evaluations -- 5 Conclusion and Future Works -- References.