Telemetry System Research Articles

Intervention Methods in Control and Communication Systems of Unmanned Aerial Vehicles, Analysis and Evaluation of Signal Quality

This research investigates the methods of radio-electronic warfare (REW) against the control and communication systems of unmanned aerial vehicles (UAVs) and analyzes the performance of their telemetry systems. The growing use of UAVs in both military and civilian applications makes it essential to prevent REW interventions targeting their communication and control systems. The study analyzes the effects of GPS jamming, spoofing, electromagnetic pulses (EMP), cyberattacks, and artificial intelligence-based interference technologies on UAVs. REW interventions are used to disrupt UAV communication, cause loss of control, or render them functionally inoperative. In particular, blocking or falsifying GNSS signals can cause UAVs to operate with incorrect coordinates or completely lose control. The distance-dependent changes in communication parameters such as Path Loss (PL) and Signal-to-Noise Ratio (SNR) in UAV communication systems were modeled using the Python programming language. Graphs showing the relationship between distance and Path Loss, and distance and SNR were generated using the Matplotlib library. The diagrams indicate that high-frequency modules (XBee PRO S2C – 2.4 GHz) experience greater Path Loss and a faster decline in communication quality. In contrast, low-frequency modules (RFD900+ – 900 MHz and TBS Crossfire – 868 MHz) provide more stable communication and allow signals to propagate over longer distances. The findings of this study show that enhancing the resilience of UAVs to REW attacks requires the use of low-frequency communication modules, implementation of anti-jamming technologies, and development of defense strategies such as adaptive frequency hopping. This research provides a scientific foundation for improving the effectiveness of UAV control and communication systems against REW interference.

Water Depth Monitoring based on Affordable Modem to Prevent Flooding and Wildfires, Study Case in Teluk Seruo Lake, Indonesia

A cutting-edge telemetry system for water depth monitoring is essential for achieving effective and reliable measurements through bidirectional communication. This system is designed to operate at any time requested by the user, enhancing the relevance of the collected data while promoting significant energy and memory savings. We propose implementing this innovative design at a Teluk Seruo Lake, featuring advanced flood and drought warning functions to proactively address the challenges of seasonal natural disasters. Using an Arduino board paired with an ultrasonic sensor and equipped with a Wi-Fi modem, users can seamlessly communicate with their smartphones to initiate measurements and access real-time water depth data via a dedicated website. Our laboratory tests confirm that timely alerts were successfully sent to users’ phones whenever water levels exceeded critical thresholds. Furthermore, during field tests, continuous monitoring of the lake’s water depth was efficiently conducted through the website, powered by 8400 mAh batteries that supported up to 75 data transmissions over a 12.5-hours period. This remarkable performance establishes a solid baseline for modem power consumption, underscoring the system’s effectiveness and reliability.

Driving innovation through experimentation: Empowering human-AI collaboration in multi-tenant customer care platforms

Generative AI is rapidly transforming enterprise systems, particularly in multi-tenant customer care platforms, creating an urgent need for systematic evaluation methodologies. This article introduces a reusable framework for conducting GenAI experimentation in cloud-native environments, addressing the limitations of traditional A/B testing when applied to non-deterministic AI systems. The framework extends conventional approaches by incorporating business-relevant metrics, deterministic cohort assignment strategies, and tenant-aware analysis capabilities that capture the multidimensional impact of GenAI implementations. Architectural requirements for implementing such frameworks are examined, including real-time testing methodologies, custom telemetry systems, and cloud-native considerations. The framework specifically addresses the critical challenge of understanding and justifying GPU computational costs against target success metrics, enabling organizations to optimize resource allocation while maximizing business value. Through detailed case studies across financial services, healthcare, retail, and insurance sectors, the article demonstrates how structured experimentation reveals nuanced performance patterns and unexpected insights about human-AI collaboration models. The framework enables organizations to make evidence-based decisions about GenAI investments by quantifying business impact across efficiency, quality, and customer experience dimensions while addressing ethical considerations in AI-augmented workflows.

Water Depth Monitoring based on Affordable Modem to Prevent Flooding and Wildfires, Study Case in Teluk Seruo Lake, Indonesia

A cutting-edge telemetry system for water depth monitoring is essential for achieving effective and reliable measurements through bidirectional communication. This system is designed to operate at any time requested by the user, enhancing the relevance of the collected data while promoting significant energy and memory savings. We propose implementing this innovative design at a Teluk Seruo Lake, featuring advanced flood and drought warning functions to proactively address the challenges of seasonal natural disasters. Using an Arduino board paired with an ultrasonic sensor and equipped with a Wi-Fi modem, users can seamlessly communicate with their smartphones to initiate measurements and access real-time water depth data via a dedicated website. Our laboratory tests confirm that timely alerts were successfully sent to users’ phones whenever water levels exceeded critical thresholds. Furthermore, during field tests, continuous monitoring of the lake’s water depth was efficiently conducted through the website, powered by 8400 mAh batteries that supported up to 75 data transmissions over a 12.5-hours period. This remarkable performance establishes a solid baseline for modem power consumption, underscoring the system’s effectiveness and reliability.

Assessment of Conveyor Telemetry System in a Manufacturing Firm: A Case Study of a Brewery in Eastern Nigeria.

Assessment of Conveyor Telemetry System in a Manufacturing Firm: A Case Study of a Brewery in Eastern Nigeria.

A Miniaturized Implantable Telemetry Biosensor for the Long-Term Dual-Modality Monitoring of Core Temperature and Locomotor Activity.

Implantable telemetry biosensors have become powerful tools for continuous physiological monitoring with minimal animal perturbation. However, commercially available implants are relatively oversized for small animals such as mice and have limited transmission range, leading to concerns about animal welfare, experiment scenarios, and the reliability of the data. In this study, we designed a telemetry system that tracks the animals' body temperature and locomotor activity in real time. The implant integrates a temperature sensor with a 3-axis accelerometer and is capable of wirelessly transmitting data over a 40 m mesh network. The implant's temperature performance was evaluated in bench tests, showing a response rate of 0.2 °C/s, drift ≤ 0.03 °C within 31 days, and a standard deviation of 0.035 °C across three identically designed implants. Meanwhile, the in vivo implant's locomotion recordings showed strong agreement with computer vision analysis with a correlation coefficient of r = 0.95 (p < 0.001), and their body temperature recordings were aligned to differential states of rest, exercise, or post-exercise recovery. The results demonstrate stable and highly accurate performance over the 30-day implantation period. Its ability to minimize behavioral interference while enabling long-term continuous monitoring highlights its value in both biomedical and animal behavior research.

Crop Harvesting Performance Analysis via Telemetry: Fuel and Environmental Insights

Telemetry systems are increasingly finding applications in agriculture for a variety of tasks. These systems assist farmers in optimizing farm processes. By leveraging these technologies, energy resources can be used more efficiently, leading to reduced environmental pollution. The primary objective of this research is to analyze telemetry data and explore ways to enhance the efficiency of combine harvesters (CHs). For this study, data from the Lexion 750 TT CH equipped with a crawler chassis was selected. Harvesting operations were conducted across fields growing popular plant types in Lithuania, including wheat, barley, rapeseed, oats, corn, and beans. The selected CH was also equipped with a remote monitoring system for tracking machine parameters. During the research, the structure of the time distribution of the work and the consumed fuel was analyzed. The highest operational efficiency—defined as the proportion of time spent on productive harvesting tasks—was 78%, observed during the oat harvest, when the unloading while harvesting, unloading while idle, harvesting, and headland turns were 3%, 2%, 64%, and 9%, respectively. The lowest efficiency, 56%, occurred during wheat harvesting. It was found that harvesting 899.32 ha of six different plant species with the tested CH produces 46.11 t of GHG emissions in CO2eq. The largest part of the emission in CO2eq was released during direct harvesting, with the engine operating at 1800–1900 min−1. However, as much as 30% of the time and 11.2% of fuel was consumed by the CH for non-harvesting activities. In conclusion, attention should be paid to reducing the inefficient use of CH time. In this way, technological operations would not only be carried out more rationally, but also environmental pollution would be reduced, and in the case of this study, we could potentially reduce CO2eq emissions by more than 10%.

A Review on Recent Advances in Assessment of Myocardial Toxicity

Myocardial toxicity is a significant challenge in drug development, chemotherapy, and environmental exposure assessments. Current techniques to evaluate cardiac damage uses multiple parameters like functional, biochemical, and molecular domains. Traditional markers like cardiac troponins and creatine kinase-MB remain valuable indicators of acute injury, while newer biomarkers including natriuretic peptides, galectin-3, and ST2 provide information about chronic remodeling processes. Advanced imaging techniques such as strain echocardiography and cardiac magnetic resonance provide detailed structural and functional information. In vitro platforms utilizing human induced pluripotent stem cell-derived cardiomyocytes and organ-on-chip technologies enable high-throughput screening and mechanistic studies. Animal models, particularly rodent systems, continue to offer irreplaceable insights into integrated cardiovascular responses. The combination of oxidative stress markers, inflammatory mediators, and tissue-specific molecular signatures enhances our ability to detect subclinical injury and predict long-term outcomes. Recent developments in telemetry systems and real-time monitoring have improved temporal resolution in toxicity assessment. Usage of these parameters has led to more sensitive and specific evaluation strategies, crucial for early detection and intervention in cardiac injury. The aim of this review is to outline the main parameters and methodologies in myocardial toxicity assessment, emphasizing their complementary roles in providing information about mechanisms involved in cardiac damage

MODERN TECHNOLOGIES OF NATURAL SCIENCE-BASED SUPPORT FOR ADAPTIVE SPORTS AND PHYSICAL ACTIVITY IN THE PROFESSIONAL PRACTICE OF FUTURE EXPERTS IN SPORTS SCIENCE AND PHYSICAL EDUCATION

The modern advancement of adaptive physical culture (APC) is characterized by the active integration of cutting-edge natural science technologies aimed at personalizing training and rehabilitation processes. This article provides a theoretical justification for the role of natural science support in APC within the professional training of future specialists in physical culture and sports. The study identifies current trends in the development of APC in the context of digital transformation, highlighting the integration of biomedical sciences, information technologies, artificial intelligence, and sensor biometrics into physical rehabilitation systems and sports performance enhancement. The significance of fundamental natural science disciplines – such as biomechanics, physiology, sports medicine, biochemistry, and age-related physiology – in optimizing corrective and rehabilitation measures is examined. The article outlines key technological innovations applied in APC, including kinematic and dynamic movement analysis methods, electromyography monitoring, digital platforms for functional status assessment, mobile applications, and VR/AR systems for motor learning and recovery. Particular attention is given to the rationale for integrating these technologies into the professional training of specialists in physical culture and sports. The study proposes methodological approaches for incorporating biomedical and digital technologies into the educational process, including the digitalization of the learning environment, the implementation of project-based learning, and the gamification of rehabilitation programs. The article also explores future directions for enhancing natural science support in APC, focusing on the implementation of artificial intelligence, telemetry systems, biometric sensors, neurointerfaces, and automated platforms for analyzing physical parameters and predicting adaptive responses. Strategic priorities for the digital transformation of APC are summarized, aiming to improve the efficiency of physical activity for individuals with special needs, optimize rehabilitation programs, and enhance the professional training of specialists in physical culture and sports.

Ability of Head Impact Measurements to Predict Sports Concussions: A Review.

Provide a review of the current landscape of motion sensor-based analyses of mild traumatic brain injury (concussion) and shed light on avenues for further investigation. A review of the literature on motion sensor-based concussion studies was conducted using search terms "concussion prediction sensor," "concussion prediction motion," "concussion diagnosis sensor," and "concussion diagnosis motion" in PubMed (between January 2000 and March 2024). In total, 207 publications were initially identified. However, only 14 studies were ultimately included, due to lack of requisite measurement variables, focus on different outcomes, or participant overlap with included studies. Consolidation of mean and standard deviation of measurement variables was performed using the application of Cochrane formula. Across 14 studies, most used head impact data from football (85.7%) and the Head Impact Telemetry system for data acquisition (92.9%). Most of the studies used data sets from collegiate athletes (71.4%). A minority of studies included female athletes (14.3%). On average, male athletes experienced higher linear and rotational accelerations during concussive vs nonconcussive impacts (97.6 ± 33.8 g and 4614.9 ± 2568.7 rad/s2 vs 24.4 ± 16.2 g and 1641.9 ± 1216.6 rad/s2). Moreover, male athletes experienced higher linear accelerations, but similar rotational accelerations, compared with female athletes specifically during concussive impacts (97.6 ± 33.8 g and 4614.9 ± 2568.7 rad/s2 vs 43.0 ± 11.5 g and 4030 ± 1435 rad/s2). Notably, studies that predicted concussion probability using multivariate regression methods (26.7%) demonstrated challenges with accuracy due to low positive predictive values (ranging from 0.3%-0.9%) and high false-positive rates (up to 39.4%). Despite the statistical differences in head impact measurements between concussive and nonconcussive impacts, they have limited clinical utility as a stand-alone concussion identification tool. Head impact measurements may be most useful when used as an adjunct to other clinical and physiological markers.

Stable Flight Technology for Spying, Disaster Management, Flower Dropping, Flag Carrying

This project explores the integration of drone technology for efficient data collection, offering innovative solutions across various domains. By combining advanced sensors and imaging capabilities, methods have been developed to capture critical environmental data with high accuracy. To reduce costs, a custom telemetry system was designed, offering a cost-effective alternative to commercial options. This system enables seamless communication and autonomous control of the drone, allowing it to operate independently without human intervention. Additionally, the drone is equipped with an advanced GPS system that ensures stability and precise navigation in terms of altitude, latitude, and longitude, enhancing reliability during data collection missions. The system is designed to assign specific missions to the drone through the android application tailored for the drone, enabling it to autonomously complete tasks and return to its designated launch zone. Furthermore, the drones are capable of performing many functions, including flower dropping, flag raising, and flag flying, thereby broadening their range of applications. Overall, this project represents a significant advancement in Unmanned Aerial Vehicle (UAV), offering comprehensive solutions for environmental monitoring and extending its usability across various fields. Key Words: Drone technology, Imaging Systems, Custom Telemetry System, GPS System, Unmanned Aerial Vehicle (UAV)

0085 Extended Amygdala Neural Circuits Linking Hormonal Influences on Body Temperature to Sleep and Circadian Rhythms

Abstract Introduction Sleep is key for socioemotional function, and the neuronal mechanisms underlying sleep and emotional regulation remain understudied, especially in women. Body temperature fluctuations are a key feature of sleep/circadian rhythms and are modulated by gonadal steroid hormones. Several brain structures are implicated in interactions between emotional behaviors, hormonal status, and sleep/wake regulation, including the bed nucleus of the stria terminalis (BNST), a core component of the extended amygdala. The BNST regulates emotional responses to stress and consists of distinct cellular populations, including neurons expressing tachykinin2 (Tac2), the gene encoding Neurokinin B (NKB). Tac2/NKB signaling drives the hypothalamic-pituitary-gonadal reproductive axis and is implicated in social stress as well as hot flashes. Thus, we hypothesized that Tac2-BNST neurons regulate hormonal influences on body temperature, sleep, and corresponding emotional arousal states. Methods We used wireless telemetry systems for monitoring locomotor activity and core body temperature, and EEG/EMG monitoring for characterizing sleep/wake states in female mice across the estrous cycle, examining changes due to Tac2-BNST chemogenetic stimulation using the excitatory Gq-DREADD (hM3Dq-mCherry). Additional studies assessed sex differences in Tac2-BNST circuit organization using anterograde viral tracing of neuronal output pathways. Results We identified estrous cycle effects on core body temperature, which was increased during high estradiol states (estrus and proestrus). Intriguingly, this effect was reversed by Tac2-BNST Gq stimulation. Additionally, we identified a trend toward increased REM and NREM sleep following activation of Tac2-BNST neurons, relative to viral controls. Anterograde tracing revealed greater Tac2-BNST innervation of the lateral hypothalamus (LH) in male mice compared to females, providing a potential circuit basis for sex differences in emotional and thermoregulatory influences on sleep/wake. Conclusion Overall, these studies add to our understanding of sex differences in hormonal influences on sleep and will advance the development of therapeutic strategies for treating disorders of stress and sleep by manipulating the Tac2/NKB system. Support (if any) Stanford School of Medicine Propel Postdoctoral Scholars Program (BJB), NIH R00 AA025677 (WJG)

FORMATION TECHNOLOGY DEVELOPMENT MATHEMATICAL MODELS FOR DIGITAL TWINS OF OIL AND GAS COMPANIES

The paper addresses a range of issues related to the development of mathematical models for a Digital Twin of an oil and gas production company, aimed at managing technological processes at its gas field facilities within the framework of the fourth industrial revolution, based on the Industry 4.0 concept.Considering the specific characteristics of gas field facilities, the primary purpose of the Digital Twin from the perspective of technological process management is formulated. The most preferred tool — predictive analytics—is presented for data analysis and interpretation, enabling the forecasting of parameters for technological processes occurring at gas field facilities.A six-layer structure of the Digital Twin is proposed, including: regulatory and reference information, electronic design and estimate documentation, a graphical representation layer of objects, engineering data, mathematical models, and real-time data obtained from information management systems and automated process control systems integrated with telemetry systems that manage the operation of production wells.A structural diagram of the management system for an oil and gas production company is presented. The applicability of known modeling methods for describing technological processes at gas field facilities is analyzed, and the choice of modeling methods for each management level is justified, taking into account the purpose and technological characteristics of the facility.It is demonstrated that this modeling ideology, applied in practice, has led and continues to lead to the development of specific technical and technological solutions for managing technological processes at various stages and modes of hydrocarbon field development, protected by Russian invention patents.The proposed ideas and solutions for creating the Digital Twin serve as a foundation for defining requirements for the development and implementation of design solutions for information management systems. These systems ensure the most efficient management of technological processes across all aspects of the integrated operations of oil and gas production companies.

HIGH CORROSION INHIBITOR AVAILABILITY CONCEPT: MYTH OR REALITY?

In the article, we consider the successful experience in achieving consistently high corrosion inhibitor availability in the organization of inhibitory protection of oil and gas pipelines at the fields Kondaneft Oil Company JSC. Depending on the activities carried out, we have shown the dynamics of changes in the corrosion inhibitor availability over time. Practical recommendations are given for achieving the corrosion inhibitor availability of more than 90 % when working with standard dosing equipment (in the absence of flowmeters and telemetry systems).In addition to the currently generally accepted term «minimal effective concentration», we have developed, tested and proposed additional technical and terminological concepts for the implementation of effective protection. These terms are «general protective concentration», «equivalent protective concentration», «unavailability level».The current «conservative» and «liberal» concepts of the corrosion inhibitor availability standards are considered. The requirements imposed by some Russian Oil Companies to the standards for the corrosion inhibitor availability and corrosion standards, as well as the necessary activities to control inhibitory protection, depending on the required availability, are given.It has been established that when working with standard field equipment, the current «conservative» concept is the most optimal solution for the successful organization of inhibitory protection. Within the framework of it, it is possible to achieve a corrosion inhibitor availability of 90–95 %. At the same time, it must be understood that, in an effort to achieve each additional percentage of corrosion inhibitor availability over 95 %, significant organizational and economic resources will need to be spent. In this regard, the main contribution to further improving the effectiveness of corrosion inhibitor availability should be realized by working with the term of «unavailability level». This term implies 5 main levels for achieving a given inhibitor corrosion concentration: К1 ≤ 5 %; 5 % &lt; К2 ≤ 15 %; 15 % &lt; К3 ≤ 40 %; 40 % &lt; К4 ≤ 90 %; К5 &gt; 90 %. At the same time, it is necessary to strive to maintain «unavailability» at the K1 level for the rest of the time when the specified corrosion inhibitor concentration can not be achieved.The developed technical concepts and proposed activities will make it possible to ensure a high level of corrosion inhibitor availability of oil and gas gathering pipelines of Oil Companies over a long period of time.

Validation of the SoHo™ Telemetry System for Continuous Body Temperature Monitoring in Socially Housed Mice

Monitoring body temperature is critical in pharmacological studies to evaluate the physiological effects of test compounds, including their potential to induce hypothermia or fever. Short-term body temperature changes, such as reductions and subsequent recoveries, provide valuable insights into compound safety and pharmacodynamics. This study aimed to validate a novel telemetry system, SoHo™, for continuous temperature monitoring in group-housed mice (3-5 mice per cage). Healthy adult (C57BL/6NTac (75%) / 129S6SvEvTac (25%)) mice were subcutaneously implanted with SoHo transmitters (model SoHo-X00N), and body temperature was continuously monitored using the SoHo™ telemetry system and as specified timepoints with a rectal themometer. The studies showed that the probes do not affect the mouse body weights (day 1: 32.7 ± 0.74 g, day7: 33.3± 0.62). The data dropouts were minimal (0.35 %). Temperatures measured after the implantation with the probes showed similar results to rectal measurements. First, we evaluated the effects of the surgical placement of the device on body temperature measurements. No significant differences were observed in temperature recordings between dorsal and flank implantations on day one (dorsal 34.8 ± 0.28 °C; flank 35.1 ± 0.14 °C) or day 7 (dorsal 35.6 ± 0.34 °C; flank 36.55 ± 0.30 °C) post-surgery. The delta temperatures from flank between rectal and probe were smaller than dorsal during baseline (7 days after implantation). Therefore, flank implantation was selected for subsequent studies. Next, IgE mediated anaphylaxis was induced with administration of MAR-1 and body temperature responses were asseses. Body temperature reductions were dose- and time-dependent: at 30 minutes post-challenge, reductions were 1.2 ± 0.22 °C for the vehicle group (N = 10), 4.3 ± 1.2 °C at 0.25 mg/kg (N = 5), 6 ± 0.27 °C at 0.50 mg/kg (N = 11), and 5.1 ± 0.3 °C at 0.75 mg/kg (N = 10). Temperature nadirs were between 30 and 60 minutes for all doses and normalized within 240 minutes. Lastly, we studied recovery from anesthesia-induced hypothermia, revealing time-dependent recovery. After two minutes of anesthesia (N = 32 mice), the temperature decrease was 2.5 ± 0.2 °C and 0.4 ± 0.1 °C at 20- and 60-minutes post-anesthesia, respectively. The data shown suggest that probes are more sensitive to small changes than rectal thermometer. These findings confirm the SoHo telemetry system as a reliable tool for continuous temperature monitoring in socially housed mice. This validation supports its application in preclinical compound testing and safety evaluations. This study was supported by DSI and Regeneron Pharmaceuticals This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Study on precise identification of remote bacterial species using multi-temporal LIBS optimized by plasma electron temperature coefficient of variation.

Study on precise identification of remote bacterial species using multi-temporal LIBS optimized by plasma electron temperature coefficient of variation.

Particle algorithms for animal movement modelling in receiver arrays

Abstract Particle filters and smoothers are sequential Monte Carlo algorithms used to fit non‐linear, non‐Gaussian state‐space models. These algorithms are well placed to fit process‐oriented models to animal‐tracking data, especially in receiver arrays, but to date they have received limited attention in the ecological literature. We introduce a Bayesian filtering–smoothing algorithm that reconstructs individual movements and patterns of space use from animal‐tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a simulation‐based analysis, we compare the performance of our algorithm to the prevailing heuristic methods used to study movements and space use in passive acoustic telemetry systems and analyse algorithm sensitivity. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle‐based maps represent simulated movements more accurately, even in dense receiver arrays, and are better suited to analyses of home ranges, residency and habitat preferences. This study sets a new state‐of‐the‐art for movement modelling in receiver arrays. Particle algorithms provide a robust, flexible and intuitive modelling framework with potential applications in many ecological settings.

Patter: Particle algorithms for animal tracking in R and Julia

Abstract State‐space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state‐space models to animal‐tracking data can be difficult and computationally expensive. Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state‐space models to tracking data, with a focus on data from aquatic animals in receiver arrays. patter is written in R, with a performant Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping. In two examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle algorithms represent an individual's possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual's (unobserved) location every 2 min during 1 month and use particles to visualise movements, map space use and quantify residency (Example Two). patter facilitates robust, flexible and efficient analyses of animal‐tracking data. The methods are widely applicable and enable refined analyses of space use, home ranges and residency.

Intelligent detection and recovery on satellite telemetry loss

Abstract The article takes the high-orbit satellite telemetry system as an example. Firstly, the general system architecture of the satellite telemetry system is summarized. Then, starting from the system design characteristics, the recovery strategy analysis method for satellite in orbit telemetry loss faults is studied. We have established a tree decomposition diagram of telemetry loss fault reason classification and countermeasures, consisting of a three-layer model of fault reason classification, fault root localization, and response and recovery strategy. At the level of fault root localization, specific recovery strategies are formulated based on the classification of telemetry loss fault causes, including satellite software FDIR (Fault Detection, Isolation, and Recovery, FDIR) intelligent autonomous fault detection, diagnosis, and recovery methods. The logical flow of recovery strategies is analyzed for each method. Finally, the development direction of countermeasures for satellite telemetry loss faults is discussed, and it is pointed out that improving the intelligence and autonomy of onboard software is the future development direction. The article provides reference and guidance for the analysis method of in-orbit telemetry loss faults of GEO satellites in this field, and can also be applied to other application fields such as medium/low orbit satellites of different orbit business types, mobile communication constellation networking satellites, etc.

Object Detection and Tracking Using Deep Learning for Missile Applications

This report details the development and implementation of an object detection model using the You Only Look Once (YOLO) algorithm, specifically tailored for detecting missiles. The primary objective of the internship project was to enhance the capabilities of telemetry systems by accurately identifying and tracking missiles in real-time. The project was conducted in two phases: the creation of the YOLO-based object detection model and the development of a distance estimation module. We collected and annotated a comprehensive dataset of missile images from various sources, ensuring a diverse range of missile types and environments. The YOLO algorithm was then trained on this dataset, achieving a high detection accuracy and rapid inference speed, essential for real-time applications. Performance metrics such as precision, recall, and mean Average Precision (map) were used to evaluate the model. The results demonstrated significant improvements in detection accuracy and processing speed, showcasing the potential of YOLO for military applications.