Telemetry Data Research Articles

Mort: An R package to conservatively identify mortalities and shed tags in passive telemetry arrays.

Telemetry is commonly employed to track animals and support ecological inferences, yet the possibility that tagged animals have died or shed their tags is often not fully considered. Neglecting to consider mortality and/or tag shedding can lead to biases in the interpretation of results. We introduce the R package mort, developed to identify potential mortalities or shed tags in passive telemetry arrays. mort was designed for aquatic acoustic receivers with primarily non-overlapping detection radii, but the methods can be applied to any telemetry study that uses passive tracking (i.e. stationary receivers). We describe the primary functions and key options that are supported, provide guidance on the general use of the package, and demonstrate use with three case studies. The thresholds to identify mortalities are either user-defined or derived from the dataset itself (using observed durations of residences of animals that are known to be alive). This flexibility, along with numerous customizable options, allows application to multiple species and systems. mort fills an important gap in standardized workflows when processing and analysing passive telemetry data. The R package is a useful tool and will improve reproducibility in ecological research and management decisions that rely on results from passive telemetry.

Current and future habitat suitability of northern fur seals and overlap with the commercial walleye pollock fishery in the eastern Bering Sea

BackgroundUnderstanding the abiotic and biotic drivers of species distribution is critical for climate-informed ecosystem management. We aimed to understand habitat selection of northern fur seals in the eastern Bering Sea, a declining population that is also a key predator of walleye pollock, the target species for the largest U.S. commercial fishery.MethodsWe developed species distribution models using random forest models by combining satellite telemetry data from lactating female fur seals tagged at different rookery complexes on the Pribilof Islands in the eastern Bering Sea with regional ocean model simulations. We explored how data aggregation at two spatial scales (Pribilof-wide and complex-specific) impacted model performance and predicted distributions. Spatial predictions under hindcasted (1992–2018) and projected (2050–2059) physical and biological conditions were used to identify areas of core habitat, overlap with commercial fishery catches, and potential changes in future habitat suitability.ResultsThe most important environmental predictor variables across all models were bathymetry, bottom temperature, and surface temperature. The Pribilof-wide model both under- and overrepresented the importance of specific areas, while complex-specific models exhibited considerable variability in transferability performance. The majority of core habitat occurred on the continental shelf in areas that overlapped with commercial catches of walleye pollock during the “B” season (June – October), with an average of 76% of the total percentage of the catch occurring in core fur seal habitat within the foraging range of lactating females. Projections revealed that considerable changes in fur seal habitat suitability may occur in the coming decades, with complex-specific variation in the magnitude and direction of changes.ConclusionsOur results illustrate the need to sample multiple sites whenever possible and consider spatial scale when extrapolating species distribution model output for central-place foragers, even when terrestrial sites are < 10 km apart. The high overlap between suitable fur seal habitat and commercial fishery catches of pollock, coupled with projected changes in habitat suitability, underscore the need for targeted studies investigating fisheries impacts on this declining population.

Online Estimation Method and Verification of Sampling Mass for Lunar Drilling in the Chang’E-6 Mission

The Chang’E-6 lunar mission successfully collected the lunar back surface and subsurface lunar regolith by excavating and drilling and returned the lunar regolith samples to the earth. Drilling–sampling system exhibits highly nonlinear characteristics due to the stratified structure of lunar regolith and unknown physical property parameters, making it prone to abnormal operating conditions and sampling disturbances. Furthermore, constrained by extraterrestrial environmental limitations, the system can only obtain health parameters, operational protocol parameters, and drilling status parameters while lacking direct measurement data on sampling mass. The development of online estimation methods for sampling mass under nonlinear and under-sensing characteristics poses significant technical challenges. Based on the mechanism of machine–regolith interaction and the experimental data of ground drilling and sampling, this paper constructs a sampling status identification model and a fuzzy pre-judgment model of sampling mass based on the downhole WOB based on the response characteristic parameters of the drilling–sampling stage. According to the telemetry data of Chang’E-6 lunar surface drilling–coring operation, the drilling–sampling mass is predicted to be 292.4 g, and the error between the predicted result and the actual sampling mass of 320 g is within 10%. This estimation method provides a new idea for the prediction of the fidelity sampling efficiency of extraterrestrial objects.

Observability for AI-Enabled Cloud-Native Networks: A Unified Framework Integrating OpenTelemetry, CortexDB, Loki, GenAI, and RAG

Modern cloud-native networks built on ephemeral microservices generate massive volumes of telemetry data across disparate sources, making comprehensive observability increasingly challenging. This article presents a unified framework that integrates OpenTelemetry, specialized time-series databases, and advanced artificial intelligence techniques to address these challenges. The framework establishes a robust telemetry foundation by leveraging OpenTelemetry for standardized data collection while elevating events to first-class citizenship alongside traditional metrics, logs, and traces. This foundation is anchored by scalable storage solutions CortexDB for time-series metrics and Loki for log aggregation providing cost-effective persistence for historical analysis. The framework transforms raw telemetry into actionable intelligence through Generative AI, which automatically analyzes multi-modal observability data, identifies significant patterns, and proactively flags anomalies. To enhance contextual awareness, Retrieval-Augmented Generation (RAG) incorporates historical operational data and domain knowledge, significantly improving the accuracy and relevance of generated insights. The intelligent alerting system transcends traditional threshold-based approaches by implementing pattern-based, predictive, and contextual alerting through an AI-enhanced alert manager. Automated response capabilities range from diagnostic data gathering to fully autonomous remediation for well-understood issues. The integrated article dramatically reduces mean time to detection and resolution, decreases false positives, improves proactive issue identification, and enables significantly more efficient resource utilization, ultimately transforming observability from a reactive troubleshooting aid to a proactive operational intelligence platform for cloud-native networks.

Building operator-centric digital twins for optical transport networks: from data models to real applications

The complexity and heterogeneity of modern optical transport networks (OTNs) demand advanced solutions to enhance their operation and maintenance. This paper presents lessons learned from the design and implementation of a digital twin network (DTN) tailored to network operators’ requirements, integrating hybrid models that combine physical representations with data-driven analysis. By leveraging telemetry data, the DTN can facilitate network operators to perform predictive maintenance, resource optimization, and risk analysis. Through a case study on soft failure detection across 454 optical sections, we analyze the value of transforming theoretical concepts coupled with collected data into actionable insights, reducing operational overhead and enhancing network resilience. This work underscores the potential of hybrid modeling in the context of DTN to create operator-centric solutions for optical transport networks.

One‐stage spatial mark–resight analysis reveals an increasing grizzly bear population with declining density near roads

AbstractWildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture–recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark–resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio‐collared animals required a two‐staged analysis. We developed a one‐stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one‐ and two‐stage gSMR models. We then applied the one‐stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest that one‐ and two‐stage models performed equally well. One‐stage models are more dependable as they use exact likelihoods, whereas two‐stage models have shorter computation times for large data sets. Both methods had &gt;95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates, and ≥10 marked animals were required to obtain coefficients of variation &lt;20% in most scenarios. The grizzly bear population increased slightly (growth rate λmean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km2. Population density was highest within protected areas, within high‐quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and the importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry‐based SMR models for monitoring spatiotemporal trends in abundance.

Time series forecasting for multidimensional telemetry data based on Generative Adversarial Network in a Digital Twin

Time series forecasting for multidimensional telemetry data based on Generative Adversarial Network in a Digital Twin

A Power-Efficient Envelope-Detector-Less Amplitude-Shift-Keying Forward Telemetry for Wirelessly Powered Biomedical Devices.

This paper proposes an envelope-detector-less (EDL) amplitude-shift-keying (ASK) forward telemetry (FT) demodulator for wireless power/data transfer (WPDT) systems. The EDL ASK FT demodulator can substitute bulky and power-hungry components, which are an envelope detector and an analog comparator in the conventional ASK FT demodulator, with a digital controller, reducing both power dissipation and chip area. The proposed demodulator shares the gate control signals of pass transistors, which are used in an ac-dc regulator for wireless power reception, to maintain a constant load voltage while efficiently demodulating the forward telemetry data. Also, a proposed digital cleaner in the EDL demodulator refines this control signal into a wide pulse without suffering from resonant frequency noise, while a synchronizer can align its frequency with the data rate and resonant frequency. The 0.25-µm CMOS prototype chip of the proposed power-path-less EDL ASK FT demodulator, equipped with the ac-dc regulator, demonstrates a significant 38.2% reduction in power dissipation compared to the conventional ASK FT demodulator. Moreover, the EDL ASK FT demodulator occupies only 0.023-mm2 silicon area and achieves a low bit error rate (BER) less than 10-4 while maintaining a regulated voltage of 4.5 V on the load.

Long-Term Changes in Survival of Eurasian Lynx in Three Reintroduced Populations in Switzerland.

For conservation or management programs, basic data on vital rates are important but often hard to acquire for long-lived and elusive wildlife species such as large carnivores. In this study, we analyzed long-term changes in survival rates for different sexes and age classes (juvenile, subadult, adult) in three reintroduced Swiss lynx populations (Alps, Jura, Northeastern Switzerland). A novel modeling approach allowed us to combine picture data from camera trapping and lynx pictures resulting from chance observations, telemetry data, and dead recoveries over a monitoring period of 25 years (1997-2022). Mean annual survival of adult lynx varied between 0.71 and 0.81 for males and between 0.70 and 0.85 for females. Mean survival of subadults ranged between 0.59 and 0.89 among populations. Juvenile survival was highly variable and low on average (< 0.4). Our findings highlight that unknown sources of mortality exist in some populations and that future studies on mortality causes and potential effects of inbreeding on survival are needed to ensure long-term conservation of the lynx in Switzerland. Our study can serve as a basis for future studies on population viability and conservation threats to the species in human-dominated landscapes and demonstrates the complexity and high variation of survival between different age and sex classes in space and time, potentially leading to source-sink dynamics.

Leveraging Cloud-based ai and zero trust architecture to enhance U. S. cybersecurity and counteract foreign threats

The increasing sophistication of cyber threats targeting U.S. national security, critical infrastructure, and financial systems necessitates a proactive, AI-driven cybersecurity strategy. Traditional security models relying on perimeter-based defenses are insufficient against state-sponsored attacks, ransomware, and advanced persistent threats (APTs). This paper explores the transformative potential of cloud-based artificial intelligence (AI) and Zero Trust Architecture (ZTA) in fortifying U.S. cybersecurity and mitigating foreign threats. Cloud-based AI enhances threat detection, real-time anomaly identification, and automated incident response by leveraging machine learning (ML), deep neural networks, and behavioral analytics. These models analyze vast amounts of network telemetry data, endpoint activities, and encrypted communications to detect evolving attack vectors with unprecedented accuracy. By incorporating federated learning and AI-driven deception techniques, cybersecurity frameworks can proactively predict and neutralize cyber threats before they materialize. Zero Trust Architecture (ZTA) further strengthens national security by enforcing continuous authentication, micro-segmentation, and least-privilege access controls. Unlike traditional models, ZTA operates under the assumption that no entity—internal or external—should be inherently trusted. By integrating cloud-native security solutions with identity-centric AI models, organizations can mitigate insider threats, secure critical infrastructure, and ensure compliance with federal cybersecurity directives. This paper examines real-world applications of AI and ZTA in national defense, critical infrastructure protection, and supply chain security, addressing implementation challenges, ethical concerns, and future research directions. The findings highlight how cloud-driven AI and Zero Trust policies are essential in safeguarding the U.S. against cyber warfare, foreign espionage, and next-generation cyber threats.

Data Acquisition and Transmission System for Energy Boat

This paper presents the design, development, and deployment of a reliable, real-time data acquisition and telemetry system for monitoring parameters aboard Team Sea Sakthi's electric catamaran. The Design is to monitor and log parameters like current, voltage, , efficiency of the propulsion system, and navigation metrics like speed and geolocation. These real-time measurements are instrumental in maintaining operation reliability, optimizing power efficiency, and enhancing fault diagnostics of the vessel. The system combines a variety of sensor modalities and employs hybrid data transmission methods involving Radio Frequency (RF) communication for near-shore relays and cloud- enabled IoT solutions for long-range operation insights. Keywords—data acquisition, telemetry, electric propulsion, real-time monitoring, CAN protocol, RF communication, IoT

Driver violation analysis: IETT example

This article discusses the importance of driver behavior analysis in improving public transportation safety. It specifically focuses on the case of Istanbul Metropolitan Municipality Istanbul Electricity Tram and Tunnel Operations General Directorate (IETT), which operates a large number of public vehicles in Istanbul, including buses, Metrobus, Beyoglu nostalgic tram, and Karakoy tunnel funicular system. The article highlights various technologies IETT uses to analyze driving behavior, such as GPS data analysis, bus telemetry data analysis, and driver behavior data analysis. It also presents an analysis of driver violation data obtained from a driver motion analysis system used by IETT to detect violations. The data shows that the most violated rule is seat belt fastening, followed by distraction, drowsiness, and other violations. The article suggests that analyzing driver behavior data can help identify areas where driver behavior can be improved, reduce the risk of accidents, and improve overall safety.

Real-Time Telemetry Transmission for CubeSat Mission using KL AP – APRS

This paper describes the design and implementation of a ground station to receive Automatic Packet Reporting System (APRS) signals from the KLAP transmitter module designed for the KLSAT 1U CubeSat. The KLAP module enables fast transmission of telemetry data using the APRS protocol, specifically operating in the AX.25 standard. The ground station includes the use of software to determine APRS signals, a Terminal Node Controller (TNC) integrated with the antenna for signal processing, and an increase in the operating frequency to 144.390 MHz (APRS frequency). The system works with the PINPOINT APRS application and APRS.fi, allowing continuous monitoring and analysis of telemetry data from CubeSats. The test demonstrated that the KLAP module provides reliable and efficient data transmission from space to ground, demonstrating the potential of APRS technology in the development of small satellite communications. The design ensures data integrity and low cost, making it suitable for small satellite missions. We conducted extensive practical tests to evaluate the performance of the KLAP module and ground station and demonstrated that the system can instantly and reliably identify and analyse telemetry data from CubeSats. The results highlight the effectiveness of APRS technology in improving small-scale radio communications, providing high-resolution and efficient field telemetry solutions. This study demonstrates the potential for APRS to be more widely used in satellite communications, especially in the field where limited use requires new methods of information transmission and resception.

AI-Powered DevOps: Enhancing Cloud Automation with Intelligent Observability

This article explores the transformative impact of AI-powered observability on cloud operations and DevOps practices. It examines how intelligent monitoring systems are revolutionizing infrastructure management, deployment strategies, and incident response through advanced anomaly detection, predictive resource allocation, and automated remediation workflows. The integration of technologies like OpenTelemetry, Prometheus, and commercial AIOps platforms enables organizations to shift from reactive to proactive operational models, significantly enhancing system reliability and performance. The article analyzes how AI capabilities extend beyond monitoring to enhance continuous integration and deployment pipelines through automated validation and intelligent rollback mechanisms. Through examination of implementation case studies across financial services, SaaS, and healthcare sectors, the research demonstrates tangible benefits in operational efficiency, deployment success rates, and incident management. The article also addresses implementation challenges, including data quality requirements, alert optimization needs, skills gaps, and integration complexities. By combining telemetry data with artificial intelligence, organizations can achieve unprecedented levels of reliability, efficiency, and agility in their cloud operations.

Augmented Intelligence for Cloud Architects: AI-Powered Tools for Design and Management

Augmented intelligence represents a transformative paradigm for cloud architects, enhancing their capabilities through AI-powered tools across the entire cloud lifecycle. The integration of these technologies addresses the growing complexity of modern cloud environments, where performance isolation issues, multi-cloud deployments, and dynamic workloads create significant challenges. Through strategic implementation of machine learning algorithms, cloud architects gain substantial advantages in architecture design, cost management, security posture, and operational monitoring. The augmented intelligence approach maintains human judgment as the central decision-making authority while leveraging computational capabilities to process vast quantities of telemetry data, identify optimization opportunities, predict resource requirements, detect security vulnerabilities, and troubleshoot complex issues. This synergistic relationship between human expertise and artificial intelligence creates measurable improvements in resource utilization, cost efficiency, security posture, and operational stability. The transformative impact extends beyond mere efficiency gains to enable fundamentally more resilient and adaptive cloud architectures that respond dynamically to changing conditions while maintaining consistent performance under variable loads. By embracing these AI-powered tools, cloud architects can navigate increasingly complex environments with greater confidence while delivering enhanced business value through optimized cloud investments.

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating a 256×256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices

In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.

Estimating Spatially Explicit Survival and Mortality Risk From Telemetry Data With Thinned Point Process Models.

Mortality risk for animals often varies spatially and can be linked to how animals use landscapes. While numerous studies collect telemetry data on animals, the focus is typically on the period when animals are alive, even though there is important information that could be gleaned about mortality risk. We introduce a thinned spatial point process (SPP) modelling framework that couples relative abundance and space use with a mortality process to formally treat the occurrence of mortality events across the landscape as a spatial process. We show how this model can be embedded in a hierarchical statistical framework and fit to telemetry data to make inferences about how spatial covariates drive both space use and mortality risk. We apply the method to two data sets to study the effects of roads and habitat on spatially explicit mortality risk: (1) VHF telemetry data collected for willow ptarmigan in Alaska, and (2) hourly GPS telemetry data collected for black bears in Colorado. These case studies demonstrate the applicability of this method for different species and data types, making it broadly useful in enabling inferences about the mechanisms influencing animal survival and spatial population processes while formally treating survival as a spatial process, especially as the development and implementation of joint analyses continue to progress.

Demographic disparities in video visit telemetry: understanding telemedicine utilization.

To investigate demographic disparities in failed episodes of telemedicine utilization. The primary hypothesis was that certain demographic groups, including older adults and specific racial or ethnic groups, would experience disparate amounts of failed video visits. A retrospective review was conducted using electronic health record-integrated scheduled telehealth video visit telemetry data gathered for all video visits at a California academic health center from September1, 2020, to November 30, 2020. For each visit, we collected demographics including age, sex, ethnicity, primary language, and race. Outcomes were categorized as successful or failed based on review of telemetry data. Successful visits were defined as simultaneous connections and completion of video visit, whereas failed visits were defined as provider-reported failure or lack of simultaneous connections for the telemedicine visit. Binomial generalized logistic regression using a generalized estimating equation approach was used to assess the impact of demographic factors on video visit success. Of 47,065 scheduled telemedicine video visits, telemetry data were available for 30,996; the 16,069 visits excluded from the study were due to no-shows, cancellations, or a nonintegrated solution being utilized. Of 30,996 visits included in the study, 27,273 were successfully completed. Analysis of the 3723 failed visits revealed that older adults and African American/Black patients were more likely to experience failed video visits, with ORs of 2.02 and 1.56, respectively. This study highlights the significant demographic disparities in failed video visit occurrence caused by technical failure as demonstrated by telemetry data. These findings highlight the need for targeted interventions and opportunity for improved outcomes.

Climate and Dispersal Ability Limit Future Habitats for Gila Monsters in the Mojave Desert.

Describing future habitat for sensitive species can be helpful in planning conservation efforts to ensure species persistence under new climatic conditions. The Gila monster (Heloderma suspectum) is an iconic lizard of the southwestern United States. The northernmost range of Gila monsters is the Mojave Desert, an area experiencing rapid human population growth and urban sprawl. To understand current and potential future habitat for Gila monsters in the Mojave Desert, we fit ensemble species distribution models using known locations and current environmental variables known to be important to the species' biology. We then projected future suitable habitat under different climate forecasts based on IPCC emission scenarios. To ensure that Gila monsters would be able to disperse to newly suitable habitat, we fit Brownian Bridge movement models using telemetry data from two locations in Nevada. This model indicated that Gila monsters prefer to move through areas with a moderate slope and higher shrub cover. Modeled current suitable habitat for Gila monsters in Nevada was primarily in rugged bajadas and lower elevations at the bases of mountain ranges. Predictions of potential future habitat suggested that overall habitat suitability through 2082 would remain relatively stable throughout the study area in the lower emissions scenario, but in the high emissions scenario potential habitat is greatly reduced in many lower-elevation areas. Future habitat areas at higher elevations under the high emissions scenario showed moderate increases in suitability, though occupancy would likely be limited by Gila monster dispersal capabilities. Finally, we determined how well the protected area network of our study area encompassed future Gila monster habitat to highlight potential opportunities to protect this important species.