Analysis Of Telemetry Data Research Articles

Using a Quality-Controlled Dataset From ViSi Mobile Monitoring for Analyzing Posture Patterns of Hospitalized Patients: Retrospective Observational Study.

ViSi Mobile has the capability of monitoring a patient's posture continuously during hospitalization. Analysis of ViSi telemetry data enables researchers and health care providers to quantify an individual patient's movement and investigate collective patterns of many patients. However, erroneous values can exist in routinely collected ViSi telemetry data. Data must be scrutinized to remove erroneous records before statistical analysis. The objectives of this study were to (1) develop a data cleaning procedure for a 1-year inpatient ViSi posture dataset, (2) consolidate posture codes into categories, (3) derive concise summary statistics from the continuous monitoring data, and (4) study types of patient posture habits using summary statistics of posture duration and transition frequency. This study examined the 2019 inpatient ViSi posture records from Atrium Health Wake Forest Baptist Medical Center. First, 2 types of errors, record overlap and time inconsistency, were identified. An automated procedure was designed to search all records for these errors. A data cleaning procedure removed erroneous records. Second, data preprocessing was conducted. Each patient's categorical time series was simplified by consolidating the 185 ViSi codes into 5 categories (Lying, Reclined, Upright, Unknown, User-defined). A majority vote process was applied to remove bursts of short duration. Third, statistical analysis was conducted. For each patient, summary statistics were generated to measure average time duration of each posture and rate of posture transitions during the whole day and separately during daytime and nighttime. A k-means clustering analysis was performed to divide the patients into subgroups objectively. The analysis used a sample of 690 patients, with a median of 3 days of extensive ViSi monitoring per patient. The median of posture durations was 10.2 hours/day for Lying, 8.0 hours/day for Reclined, and 2.5 hours/day for Upright. Lying had similar percentages of patients in low and high durations. Reclined showed a decrease in patients for higher durations. Upright had its peak at 0-2 hours, with a decrease for higher durations. Scatter plots showed that patients could be divided into several subgroups with different posture habits. This was reinforced by the k-means analysis, which identified an active subgroup and two sedentary ones with different resting styles. Using a 1-year ViSi dataset from routine inpatient monitoring, we derived summary statistics of posture duration and posture transitions for each patient and analyzed the summary statistics to identify patterns in the patient population. This analysis revealed several types of patient posture habits. Before analysis, we also developed methodology to clean and preprocess routinely collected inpatient ViSi monitoring data, which is a major contribution of this study. The procedure developed for data cleaning and preprocessing can have broad application to other monitoring systems used in hospitals.

Joint inference for telemetry and spatial survey data.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, "spatial survey" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

Movements of the bearded seal in the White Sea monitored by satellite telemetry

Movements and distribution data of the bearded seal (Erignathus barbatus) in the White Sea during summer and autumn were obtained as a result of tagging with satellite telemetry transmitters (STT). Russian-made Pulsar STTs operated in the Argos system. Catching and tagging of bearded seals was carried out in the Onega and Mezen bays in 2014, 2015 and 2017. Altogether, 7 STTs were installed on seals of different ages and sexes. Analysis of satellite telemetry data showed that during the non-ice period bearded seals were tied to local habitat areas, and the dynamics of their movements differed in the inner and open parts of the sea. The STT data suggest that these differences in distribution are due to lability in feeding and show good adaptation of the bearded seal in the conditions of poorly accessible feed base of the White Sea. The diverse feeding strategy of the bearded seal in the White Sea, and its adaptation to the difficult feeding conditions of the sea, may help the species to survive in the region and ensure stable population numbers in changing environmental conditions.

AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats

This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, alerting mechanisms, and continuous monitoring. AI-powered threat detection offers numerous advantages, including predictive threat detection, increased accuracy and scalability, shorter response times, and the ability to adapt to evolving threats. However, it also presents challenges, such as ensuring data quality, managing model complexity, mitigating false positives, addressing resource requirements, and maintaining security and privacy standards. The proposed AI-powered anomaly detection framework for Kubernetes security demonstrated significant improvements in threat identification and mitigation. Through real-time analysis of telemetry data and leveraging advanced AI algorithms, the system accurately identified over 92% of simulated security threats and anomalies across various Kubernetes clusters. Additionally, the integration of automated alerting mechanisms and response protocols reduced the average response time by 67%, enabling rapid containment of potential breaches.

Towards an automated approach for understanding problematic gaming.

Video games have become increasingly popular worldwide, attracting billions of gamers across diverse demographics. While studies have highlighted their potential benefits, concerns about problematic gaming behaviors have also emerged. Conditions such as Internet Gaming Disorder (IGD) have been recognized by major health organizations, necessitating accurate diagnostic tools. However, existing methods, primarily reliant on self-report questionnaires, face challenges in accuracy and consistency. This paper proposes a novel technological approach to provide gaming behavior indicators, aiming to offer precise insights into gamer behavior and emotion regulation. To attain this objective, we investigate quantifiable gaming behavior metrics using automated, unobtrusive, and easily accessible methods. Our approach encompasses the analysis of behavioral telemetry data collected from online gaming platforms and incorporates automated extraction of gamer emotional states from face video recordings during gameplay. To illustrate the metrics and visualizations and demonstrate our method's application we collected data from two amateur and two professional gamers, all of whom played Counter-Strike2 on PC. Our approach offers objective insights into in-game gamer behavior, helping health professionals in the identification of patterns that may be difficult to discern through traditional assessment methods. Preliminary assessments of the proposed methodology demonstrate its potential usefulness in providing valuable insights about gaming behavior and emotion regulation. By leveraging automated data collection and visualization analysis techniques, our approach offers a more comprehensive understanding of gamer behavior, which could enhance diagnostic accuracy and inform interventions for individuals at risk of problematic gaming behaviors. Our findings demonstrate the valuable insights obtainable from a tool that collects telemetry data, emotion regulation metrics, and gaming patterns. This tool, utilizing specific indicators, can support healthcare professionals in diagnosing IGD and tracking therapeutic progress, potentially addressing challenges linked to conventional IGD assessment methods. Furthermore, this initial data can provide therapists with detailed information on each player's problematic behaviors and gaming habits, enabling the development of personalized treatments tailored to individual needs. Future research endeavors will focus on refining the methodology and extending its application in clinical settings to facilitate more comprehensive diagnostic practices and tailored interventions for individuals at risk of problematic gaming behaviors.

New usage of telemetry for anti-cheating in FPS game

First-person shooter games are experiencing a surge in popularity. As more players join, advanced AI-based cheats have emerged. These cheats simulate human gameplay, sending mouse inputs, making them hard to detect and counter. Therefore, this research presents a novel approach that utilizes telemetry data analysis to identify and counteract cheating in FPS games. The main objective of this study is to develop an innovative anti-cheating system that can effectively detect and prevent players from exploiting AI-based cheats to gain unfair advantages. To achieve this, extensive telemetry data is collected during gameplay. The data contains the real-time cursor position when the player is playing the game. Besides, Machine learning and deep algorithms are applied to analyse the telemetry data and distinguish between human player behaviour and AI-driven cheating patterns. Decision Tree, Random Forest, LSTM, and CNN are applied for this research. And in the final evaluation, CNNs accuracy reached around 80% which proves it is a possible mode to be used for this problem. The significance of this research lies in its contribution against cheating in FPS games, particularly those exploiting AI technologies to gain unfair advantage. The proposed telemetry-based approach offers a solution to safeguard competitive gaming and insight into the game company based on this novel way for further experiments.

Water supply telemetry data processing in apartment buildings

The digital transformation of construction and housing and utilities sector involves data-based management. The most accessible data is telemetry of electricity, gas, heat and water consumption. Despite the fact that not all apartment buildings are equipped with meters yet, it is necessary to think about methods for processing measurement data. Intelligent data processing methods are gaining wide popularity. The purpose of this study is the processing and analysis of water consumption telemetry data. The objective of the study is to determine the periods of nighttime water consumption using the clustering method. The data of apartment meters of hourly consumption of hot and cold water are investigated. The measurement period is 1 month. Intelligent cluster analysis was conducted based on the DBSCAN machine learning model (Density-based spatial clustering of applications with noise). Clustering objects are the hours of the day. As a result of the study, the hours of night consumption of cold and hot water were allocated, both in the whole month and separately on weekdays and Sundays. The conclusion is made about the benefits of using intelligent cluster analysis of water consumption telemetry data for effective management of water resources and equipment.

Classification and Engineering Analysis of Solid Propellant Missile Telemetry Data

This paper describes classification methods of missiles using solid rocket motors. Two classification scenarios are described: one shows how to classify shortly after launch, and another shows how to use the “max” parameters of the trajectory for classification. The first scenario will show that, within the first 30 s of telemetry data, the missile class can be quickly and accurately determined. To extend this scenario, we will then determine how much data are necessary to classify, so classification will then be attempted in the first 10 and 20 s. The importance of the telemetry data can then be quantified by a direct calculation of the Shapley values. To analyze robustness, missing data are simulated and imputed for classification. The second scenario utilizes the max parameters of the telemetry, such as apogee, to classify the missile. This scenario is more akin to analyzing trajectories that have already been completed. Various classifications using different inputs for classification are shown. Both scenarios will utilize Fisher’s discriminant analysis and neural networks to compare performance, and the main goal is to classify missiles. Using discriminant analysis and neural networks provides high accuracy.

Development of a methodology for identifying anomalies in telemetry data of small spacecraft using the ensemble method

This study includes work related to the analysis of satellite telemetry data. Prior to that, the focus is on investigating various ways of finding anomalies in telemetry data. Both traditional statistical approaches, such as outlier analysis and process control, and modern machine learning techniques, such as deep learning algorithms and anomaly detection techniques, are covered in this. It is seeking to decide on a machine learning model that can identify anomalies in telemetry data. This requires the analysis of several machine learning algorithms, their comparison, and evaluation using a small spacecraft telemetry dataset. This work needs to identify the most suitable and effective methods for detecting anomalies in satellite telemetry data.

Тренды использования данных телеметрии систем водоснабжения

The article discusses the analysis of water consumption data obtained based on automated resource consumption accounting. The prerequisites for obtaining large arrays of telemetry data based on the introduction of "smart" housing and communal services technology have been identified. Typical purposes of water consumption data analysis are listed. It is noted that the use of artificial intelligence methods for analyzing housing and communal services data has a point character. The research is based on the principle of expediency of data analysis. The factors influencing the mode of water consumption in apartment buildings are considered. Methods of analyzing water consumption telemetry data are considered. The data of hourly automatic accounting of hot and cold water consumption in an apartment building is discussed as factual material for analysis. The data is a time series of water consumption accounting. Possible methods of statistical and intelligent analysis of telemetry data are discussed. It is concluded that the analysis of water consumption data should be carried out not only for the purpose of accrual and payment of payments, but also for the purpose of clarifying engineering calculations, adjusting regulatory indicators and making recommendations on resource conservation. Modern trends in the use of telemetry data are formulated: wider use of telemetry systems in apartment buildings, expansion of the purposes of using telemetry data analysis, the use of intelligent data analysis methods, the creation of a methodology for analyzing and evaluating telemetry data of water supply systems.

Real-Time Telemetry-Based Recognition and Prediction of Satellite State Using TS-GCN Network

With the continuous proliferation of satellites, accurately determining their operational status is crucial for satellite design and on-orbit anomaly detection. However, existing research overlooks this crucial aspect, falling short in its analysis. Through an analysis of real-time satellite telemetry data, this paper pioneers the introduction of four distinct operational states within satellite attitude control systems and explores the challenges associated with their classification and prediction. Considering skewed data and dimensionality, we propose the Two-Step Graph Convolutional Neural Network (TS-GCN) framework, integrating resampling and a streamlined architecture as the benchmark of the proposed problem. Applying TS-GCN to a specific satellite model yields 98.93% state recognition and 99.13% prediction accuracy. Compared to the Standard GCN, Standard CNN, and ResNet-18, the state recognition accuracy increased by 37.36–75.65%. With fewer parameters, TS-GCN suits on-orbit deployment, enhancing assessment and anomaly detection.

Evaluation of the main parameters of ReshUCube-1 telemetry over a period of 10 months in orbit

With the increase in the number of launched spacecraft, such a direction as automation of spacecraft control processes is gaining popularity. One of these most important processes is the analysis of telemetry data during the operation of the spacecraft. Scientific and educational satellite of the Reshetnev Siberian State University of Science and Technology has been successfully exploitated in orbit and has been performing its scientific tasks for more than six months. The article considers a list of the main parameters analyzed by the operators of the Mission Control Center to assess the state of the ReshUCube-1 satellite. The composition and main functional characteristics of the equipment on the spacecraft are described. Qualitative indicators and quantitative limits for all described parameters are given, as well as their significance and impact on the functioning of devices and the entire spacecraft as a whole.

Analysis and application of deep space exploration satellite telemetry data combined with empirical mode decomposition and ARIMA model

With China’s deep space exploration program constantly expanding, the number of satellites on-orbit is increasing year by year. The stable operation of satellites in orbit hinges on long-term monitoring and analysis of the telemetry data downloaded from the satellite on the ground, which enables quick detection of any irregularities. If we can grasp the changing trend and operation law of satellite telemetry data, we can monitor and control satellites and reduce losses better. Aiming at a large amount of telemetry data of deep space exploration satellites, the data presents non-stationary and nonlinear characteristics, The time series to be processed is constructed sequentially using the acquired telemetry data, and it is decomposed using the empirical mode decomposition approach. The non-stationary signal is divided into a stationary sequence and the sum of a series of stationary intrinsic mode functions. Then the decomposed sets of results are used as the input of the ARIMA model, and the ARIMA model is constructed to predict the predicted value of each decomposed sequence, and the weighted summation of the predicted values is performed to reconstruct the predicted results. Lastly, actual satellite telemetry data is used to confirm the method’s viability. The experimental findings demonstrate that the model can more successfully anticipate the short-term trend of satellite telemetry data.

Telemetry Data Processing and Analysis Platform for Flight Test Based on Flink

Telemetry data processing has extremely high real-time requirements in flight tests. In response to the problems faced by the traditional telemetry data processing system, such as insufficient computing capacity and low scalability, the Flink-based telemetry data processing and analysis technology is proposed to give full play to the cluster computing capacity of Flink, improve the throughput of real-time processing and reduce the processing delay. After flight test verification, the platform meets the real-time processing of multiple telemetry data and has significantly improved the stability, processing efficiency, and scalability compared with the traditional system.

A review of tiger conservation studies using nonlinear trajectory: A telemetry data approach

Abstract Radio telemetry is being used in India to capture the location and movements of radio-collared wild animals. Radio telemetry data analysis has changed the scenario for wildlife conservation. It has provided many models and solutions for natural habitat utilization, genetic diversity, and even implementing green corridors for the species. Insights drawn from the data collected through radio telemetry from radio-collared animals have provided impactful information to understand the wild animal’s ecology. In the past few years, India has remarkably improved the population of tigers using telemetry studies and other conservation methods. In this study, a review of the telemetry studies conducted for tigers in India in the past decade has been presented. This study summarizes the telemetry studies of tigers in India and discusses how telemetry has enhanced the knowledge of ecologists and scientists to understand tiger ecology. This study also discusses the challenges and issues of conducting telemetry studies on tigers in India. Finally, the authors address the gaps in understanding the tiger’s ecology based on these studies and outline how radio telemetry can address these issues to better understand and conserve tigers.

Deep Learning-Based Adaptive Compression and Anomaly Detection for Smart B5G Use Cases Operation

The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios.

Development and Application of a 3D Satellite Data Visualization Platform

3D visualization platform, which can display the status of a satellite, is significantly important for telemetry data analysis. This paper presents a 3D visualization platform developed using CesiumJS for the web. The platform is developed for the visualization of the satellites of Yinhe Hangtian Corporation. To ensure the effectiveness and validity of the platform, its prototype is developed and verified by numerical simulations of three missions, which are initial rate damping, sun-pointing control, and earth-pointing control. After verifying the validity of the platform, it is connected to the telemetry database and is further developed by adding new capabilities such as the display of movable components (such as solar panels and the antennas). This platform now is applying to the visualization of the 02P satellites launched by the Yinhe Hangtian Corporation. The platform, which is able to display the satellite's orbit, attitude, and the communication area covered by the QV and KA band, greatly facilitate the analysis of the 02P satellites.

Understanding spatially explicit capture–recapture parameters for informing invasive animal management

AbstractSpatially explicit capture–recapture modeling is used to estimate population density to enhance our ecological understanding and management of wildlife populations. The two primary parameters estimated in the capture process of these models are σ and g0. The σ parameter is the standard deviation of a bivariate normal home range kernel (indicating home range size), while g0 is the probability of capture by a device placed at the home range center. These parameters are being increasingly generalized and used in simulation models to predict the detection or capture rates of invasive animals to inform management strategies. Given the sensitivity of simulation model predictions to parameter values, we performed an analysis of preexisting GPS telemetry and trapping data of invasive brushtail possums (Trichosurus vulpecula) across New Zealand to address the following three questions. First, how does σ vary with population density, habitat, and age–sex class? Second, how is g0 influenced by home range size (i.e., σ) and trap type? Third, how does the predicted probability of capture or detection of individuals vary with σ, different trap types and trap densities? We used data from 180 possums across 18 sites to develop a Bayesian hierarchical model. Results showed that σ decreased with increasing population density and increasing area of productive grassland. There was a strong negative relationship between σ and g0, and g0 was highest for cage traps and lowest for raised leg‐hold traps. Despite the potential compensatory inverse effect of g0 with σ, the probability of capturing a randomly located possum by a large array of traps increased with increasing σ. Results show that selection of σ for predictive simulation modeling should begin with an estimated population density. The associated g0 should then be identified as a function of σ, and stochasticity should be incorporated to account for inter‐individual variability. The approach employed here provides a methodology for estimating density, σ, and g0 using trapping and telemetry data, and our results will help guide careful consideration of density, σ, and g0 to improve the accuracy of simulation studies across species.

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions.

A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles

Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.