Utilisation of this data is subject to European Space Agency's Earth Observation Terms and Conditions. Read T&C here

This is Dataset Version 3 - Updates may be done following feedback from the machine learning community.

Dataset Description

This dataset contains 327 time series corresponding to the temporal values of 327 telemetry parameters over the life of the real GOCE satellite (from March 2009 to October 2013). It consists both the raw data and Machine-Learning ready-to-use resampled data: - The raw values (calibrated values of each parameter) as {param}_raw.parquet files (irregular) - Resampled and popular statistics computed over 10-minutes windows for each parameter as {param}_stats_10min.parquet files. - Resampled and popular statistics computed over 6-hours windows for each parameter as {param}_stats_6h.parquet - metadata.csv list of all parameters with description, subsystem, first and last timestamp where a value is recorded, fraction of NaN in the calculated statistics and the longest data gap. - mass_properties.csv: provides information relative to the satellite mass (for example the remaining fuel on-board).

Why is it a good dataset for time series forecasting?

• Real-world: the data originates from a real-world complex engineering system
• Many variables: 327 allowing for multivariate time series forecasting.
• Variables having engineering values and units (Volt, Ampere, bar, m, m/s, etc...). See the metadata
• Different and irregular sampling rates: some parameters have a value recorded every second, other have a value recorded at a lower sampling rate such as every 16 or 32s. This is a challenge often encountered in real-world systems with sensor records that complexity the data pipelines, and input data fed into your models. If you want to start easy, work with the 10min or 6h resampled files.
• Missing Data and Large Gaps: you'll have to drop many parameters which have too much missing data, and carefully design and test you data processing, model training, and model evaluation strategy.
• Suggested task 1: forecast 24 hrs ahead the 10-min last value given historical data
• Suggested task 2: forecast 7 days ahead the 6-hour last value given historical data

About the GOCE Satellite

The Gravity Field and Steady-State Ocean Circulation Explorer (GOCE; pronounced ‘go-chay’), is a scientific mission satellite from the European Space Agency (ESA).

Objectives

GOCE's primary mission objective was to provide an accurate and detailed global model of Earth's gravity field and geoid. For this purpose, it is equipped with a state-of-the-art Gravity Gradiometer and precise tracking system.

Payloads

The satellite's main payload was the Electrostatic Gravity Gradiometer (EGG) to measure the gravity field of Earth. Other payload was an onboard GPS receiver used as a Satellite-to-Satellite Tracking Instrument (SSTI); a compensation system for all non-gravitational forces acting on the spacecraft. The satellite was also equipped with a laser retroreflector to enable tracking by ground-based Satellite laser ranging station.

The satellite's unique arrow shape and fins helped keep GOCE stable as it flew through the thermosphere at a comparatively low altitude of 255 kilometres (158 mi). Additionally, an ion propulsion system continuously compensated for the variable deceleration due to air drag without the vibration of a conventional chemically powered rocket engine, thus limiting the errors in gravity gradient measurements caused by non-gravitational forces and restoring the path of the craft as closely as possible to a purely inertial trajectory.

Thermal considerations

Due to the orbit and satellite configuration, the solar panels experienced extreme temperature variations. The design therefore had to include materials that could tolerate temperatures as high as 160 degC and as low as -170 degC.

Due to its stringent temperature stability requirements (for the gradiometer sensor heads, in the range of milli-Kelvin) the gradiometer was thermally decoupled from the satellite and had its own dedicated thermal-control system.

Mission Operations

Flight operations were conducted from the European Space Operations Centre, based in Darmstadt, Germany.

It was launched on 17 March 2009 and came to and end of mission on 21 October 2013 because it ran out of propellant. As planned, the satellite began dropping out of orbit and made an uncontrolled re-entry on 11 November 2013

Orbit

GOCE used a Sun-synchronous orbit with an inclindation of 96.7 degree, a mean altitude of approximately 263 km, an orbital period of 90 minutes, and a mean local solar time at ascending node of 18:00.

Resources

• [Data Source](https://earth.esa....

OPSSAT-AD - anomaly detection dataset for satellite telemetry

This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.

It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.

The included files are:

segments.csv with the acquired telemetry signals from ESA OPS-SAT aircraft,
dataset.csv with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.
code files for data processing and example modeliing (dataset_generator.ipynb for data processing, modeling_examples.ipynb with simple examples, requirements.txt- with details on Python configuration, and the LICENSE file)

Citation Bogdan, R. (2024). OPSSAT-AD - anomaly detection dataset for satellite telemetry [Data set]. Ruszczak. https://doi.org/10.5281/zenodo.15108715

Bearded seals (Erignathus barbatus) are one of the most important subsistence resources for the indigenous people of coastal northern and western Alaska, as well as key components of Arctic marine ecosystems, yet relatively little about their abundance, seasonal distribution, migrations, or foraging behaviors has been documented scientifically. Ice-associated seal populations may be negatively...

Satellite Telemetry Sensor Market Outlook

According to our latest research, the global satellite telemetry sensor market size reached USD 2.14 billion in 2024, with a robust growth trajectory supported by increasing satellite launches and the evolution of advanced remote sensing technologies. The CAGR for this market is projected at 7.8% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to attain a value of approximately USD 4.23 billion. The primary growth factor driving this expansion is the accelerated deployment of Low Earth Orbit (LEO) satellite constellations for communications, Earth observation, and scientific research, which demand highly reliable and sophisticated telemetry sensor systems.

One of the key growth drivers for the satellite telemetry sensor market is the exponential rise in satellite launches, particularly for commercial and governmental applications. The proliferation of mega-constellations aimed at providing global internet coverage, such as Starlink and OneWeb, has significantly increased the demand for telemetry sensors that can monitor satellite health, status, and performance in real time. Advancements in sensor miniaturization and the integration of artificial intelligence for predictive analytics have further contributed to market growth. These innovations enable satellites to operate autonomously and efficiently, reducing the need for ground-based interventions and enhancing mission success rates.

Another significant factor fueling the expansion of the satellite telemetry sensor market is the growing adoption of satellite-based remote sensing across various industries. Sectors such as agriculture, environmental monitoring, disaster management, and defense increasingly rely on high-resolution, real-time data provided by satellite telemetry sensors. These sensors are critical for collecting precise information about environmental conditions, crop health, and natural disasters, supporting timely decision-making and resource allocation. The ongoing trend of digitization in industries and the increasing reliance on geospatial intelligence are expected to sustain the demand for advanced telemetry sensors in the coming years.

The market is also benefitting from increased investments in space exploration and scientific research. National space agencies and private companies are launching missions to study planetary bodies, monitor atmospheric changes, and conduct experiments in microgravity. These initiatives require telemetry sensors with enhanced sensitivity, reliability, and durability to withstand harsh space environments. As a result, manufacturers are focusing on developing sensors with advanced materials, improved power efficiency, and greater resistance to radiation. The rising collaboration between public and private entities in the space sector is anticipated to further boost the satellite telemetry sensor market throughout the forecast period.

From a regional perspective, North America currently leads the global satellite telemetry sensor market, owing to the presence of major satellite manufacturers, a robust space industry ecosystem, and significant government funding for space programs. Europe and Asia Pacific follow closely, with both regions experiencing rapid growth due to increased satellite launches and expanding commercial space activities. The Asia Pacific region, in particular, is expected to witness the highest CAGR during the forecast period, driven by burgeoning investments in satellite infrastructure, technological advancements, and supportive government policies. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as they continue to develop their space capabilities and invest in satellite-based applications.

Sensor Type Analysis

The satellite telemetry sensor market, segmented by sensor type, includes temperature sensors, pressure sensors, position sensors, motion sensors, and others. Temperature sensors play a pivotal role in monitoring the the

The Global component of the OOI includes arrays at critical, yet under-sampled, locations such as within the Southern Ocean. The Global Southern Ocean Array includes two types of gliders that provide simultaneous spatial and temporal sampling capabilities. Open-Ocean Gliders follow track lines around the triangular mooring array and are equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring.

File List

 Files

for running analyses from the WinBUGS gui (Windows or Linux)

 DCRW.txt
 WinBUGS code for

"DCRW" model, a first-difference correlated random walk 2 KB

 DCRWS.txt
 WinBUGS code for

"DCRWS" model, a first-difference CRW with switching 2 KB

 hsdata.txt
 Hooded seal

movement data 12 KB

 hsDCRWinits.txt
 Hooded seal

initial values for DCRW model 3 KB

 hsDCRWSinits.txt
 Hooded seal

initial values for DCRWS model 5 KB

 gs617data.txt
 Grey seal 617

movement data 59 KB

 gs617DCRWinits.txt
 Grey seal 617

initial values for DCRW model 3 KB

 gs617DCRWSinits.txt
 Grey seal 617

initial values for DCRWS model 4 KB

 gs2986data.txt
 Grey seal 2986

movement data 71 KB

 gs2986DCRWinits.txt
 Grey seal 2986

initial values for DCRW model 3 KB

 gs2986DCRWSinits.txt
 Grey seal 2986

initial values for DCRWS model 5 KB

 Files

for running analyses by calling WinBUGS from within R (Linux)

 DCRW.rbugs.R

 R script for

fitting DCRW model to hooded seal data

 1

KB

 DCRWS.rbugs.R

 R script for

fitting DCRWS model to hooded seal data 1 KB

 dat4bugs.R
 R code required

by DCRW.rbugs.R and DCRW.rbugs.R 4 KB

 hseal.dat

 Hooded seal data

for analysis from within R (Linux)
6 KB

 gs617.dat

 Grey seal 617

data for analysis from within R (Linux)
27 KB

 gs2986.dat

 Grey seal 2986

data for analysis from within R (Linux)
35 KB

 allfiles.zip
 All files

together 48 KB

DescriptionWinBUGS version 1.4.1 was used to run the code and is freely available at http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml. In Windows, to run the DCRW model on the hooded seal data, open DCRW.txt in WinBUGS and paste in the data file hsdata.txt and hsDCRWinits.txt. For all models we ran 2 chains for 40 000 samples with a 20 000-sample burn-in and retained every 5th sample to reduce autocorrelation. See the WinBUGS manual, included with the software, for further details on running the models. Follow the same procedure to run models on the other datasets. Summary statistics and plots can be generated directly in WinBUGS or from other software by exporting results as text files. Alternatively, one can call WinBUGS from within R, a freely available statistical computing environment, using the R2WinBUGS package (see http://www.stat.columbia.edu/~gelman/bugsR/ for details). In Linux, WinBUGS can be run via Wine (available at http://www.winehq.com/). We recommend building Wine from sourcerather than installing the binary files (see documentation included in Wine download for details). We have found that WinBUGS works flawlessly using the 20050524 release of Wine compiled on Ubuntu Linux. In Linux, WinBUGS can be run via the gui (as above) or called from within R using the rbugs package (available on the CRAN site at http://www.r-project.org). We provide sample R scripts and associated R code (in File list) to illustrate this approach for both the DCRW and DCRWS models. Note that in addition to the rbugs package, these scripts also require the chron package (available on the CRAN site at http://www.r-project.org). The following commands in R will fit the DCRW model to the hooded seal data: source('~/pathtofile/dat4bugs.R') hseal.dat

Telemetry Data Lake for Space Missions Market Outlook

According to our latest research, the global Telemetry Data Lake for Space Missions market size reached USD 1.82 billion in 2024, and it is expected to grow at a notable CAGR of 13.7% from 2025 to 2033. By 2033, the market is projected to attain a value of USD 5.38 billion. This robust growth is primarily driven by the increasing complexity and volume of data generated by modern space missions, necessitating advanced data management, storage, and analytics solutions for mission success and operational efficiency.

The exponential rise in satellite launches, deep space missions, and earth observation projects is a significant growth factor for the Telemetry Data Lake for Space Missions market. As space missions become more sophisticated, the volume of telemetry data collected from various sensors, instruments, and subsystems has surged dramatically. This data is invaluable for mission planning, real-time monitoring, anomaly detection, and post-mission analysis. Traditional data management systems are proving inadequate to handle such scale and complexity, prompting agencies and commercial space companies to invest in telemetry data lakes. These platforms offer scalable, high-performance storage and advanced analytics capabilities, enabling organizations to extract actionable insights from massive datasets, optimize mission outcomes, and reduce operational risks.

Another key driver is the increasing adoption of cloud-based telemetry data lake solutions. Cloud deployment offers unparalleled scalability, flexibility, and cost-efficiency, making it an attractive choice for both established space agencies and emerging commercial space companies. The cloud enables seamless integration of disparate data sources, supports collaborative mission planning across geographies, and facilitates access to advanced analytics tools such as artificial intelligence and machine learning. As a result, cloud-based telemetry data lakes are becoming central to digital transformation strategies in the space industry, supporting a wide range of applications from spacecraft health monitoring to deep space exploration. This trend is expected to accelerate further as more organizations embrace cloud-native architectures to drive innovation and operational agility.

The growing emphasis on real-time data analytics and artificial intelligence is also shaping the future of the Telemetry Data Lake for Space Missions market. Modern space missions demand rapid decision-making capabilities, especially in scenarios involving spacecraft anomalies or mission-critical events. Telemetry data lakes equipped with AI-driven analytics empower mission operators to detect patterns, predict failures, and automate responses, thereby enhancing mission reliability and safety. Furthermore, the integration of telemetry data lakes with digital twins, predictive maintenance, and autonomous mission control systems is opening new avenues for value creation in the space sector. As space missions evolve toward greater autonomy and complexity, the demand for advanced telemetry data management and analytics platforms will continue to surge.

Regionally, North America remains the dominant market for telemetry data lakes in space missions, driven by substantial investments from NASA, private space enterprises like SpaceX, and a vibrant ecosystem of technology providers. However, Asia Pacific is emerging as a high-growth region, fueled by ambitious space programs in China, India, and Japan, as well as increasing collaboration between government agencies and private players. Europe also commands a significant share, supported by the European Space Agency and a strong focus on earth observation and scientific missions. Latin America and the Middle East & Africa, while smaller in market size, are witnessing gradual adoption as regional space programs expand and seek advanced data management solutions to support their missions.

Component Analysis

The Telemetry Data Lake for Space Missions market

The Global component of the OOI includes arrays at critical, yet under-sampled, high-latitude locations such as within the Gulf of Alaska in the Northeast Pacific. The Global Station Papa Array includes two types of gliders that provide simultaneous spatial and temporal sampling capabilities. Open-Ocean Gliders follow track lines around the triangular mooring array and are equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring.

Solar Array Degradation Analytics Using Satellite Telemetry Market Outlook

According to our latest research, the global Solar Array Degradation Analytics Using Satellite Telemetry market size reached USD 1.87 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.2% projected through 2033. By the end of 2033, the market is expected to attain a value of approximately USD 5.42 billion. This impressive growth trajectory is driven by increasing investments in solar energy infrastructure, the need for efficient asset management, and the integration of advanced analytics solutions leveraging satellite telemetry to monitor and predict solar array performance and degradation.

A primary growth factor for the Solar Array Degradation Analytics Using Satellite Telemetry market is the escalating global shift toward renewable energy sources, particularly solar power. As governments and private entities intensify efforts to achieve sustainability goals and reduce carbon emissions, the deployment of large-scale solar arrays has surged. However, maximizing the return on these investments necessitates sophisticated monitoring and predictive tools to ensure optimal performance and longevity of solar assets. Satellite telemetry, in conjunction with advanced analytics, provides continuous, real-time, and highly granular data, enabling asset managers to detect early signs of degradation, optimize maintenance schedules, and minimize operational expenditures. This technological advancement is particularly vital for utility-scale solar farms, where even marginal efficiency gains can translate into significant financial benefits.

Another critical driver is the increasing complexity and scale of solar installations, which often span vast geographical regions and challenging terrains. Traditional ground-based monitoring systems are often insufficient or cost-prohibitive for such expansive assets. Satellite telemetry overcomes these limitations by offering wide-area coverage, frequent data acquisition, and the ability to monitor remote or inaccessible sites. This capability is further enhanced by the integration of predictive analytics and machine learning algorithms, which can analyze historical and real-time data to forecast potential faults and degradation patterns. As a result, stakeholders can make data-driven decisions to extend asset life, reduce downtime, and improve overall energy yield, further fueling the adoption of satellite telemetry-based analytics solutions across the solar energy sector.

Furthermore, the growing emphasis on digital transformation and the adoption of cloud-based analytics platforms are accelerating the market’s growth. Cloud deployment enables seamless integration of satellite telemetry data with other data sources, such as ground-based sensors and aerial imagery, providing a comprehensive view of solar asset health. This holistic approach enhances the accuracy of degradation analytics, supports proactive maintenance, and facilitates collaboration among diverse stakeholders, including energy companies, asset managers, and research institutes. Additionally, advancements in artificial intelligence, data visualization, and IoT connectivity are further expanding the capabilities of solar array degradation analytics, making them indispensable tools for modern energy infrastructure management.

From a regional perspective, North America and Europe currently dominate the Solar Array Degradation Analytics Using Satellite Telemetry market, owing to their mature solar energy sectors, high adoption rates of advanced analytics technologies, and supportive regulatory frameworks. The Asia Pacific region, however, is poised for the fastest growth, driven by rapid solar capacity additions in countries such as China, India, and Australia, alongside increasing investments in digital infrastructure and satellite technology. Latin America and the Middle East & Africa are also witnessing growing adoption, supported by favorable climatic conditions for solar power and expanding renewable energy initiatives. The regional dynamics highlight the global nature of this market, with each region presenting unique opportunities and challenges for stakeholders.

The Global component of the OOI includes arrays at critical, yet under-sampled, high-latitude locations such as within the Gulf of Alaska in the Northeast Pacific. The Global Station Papa Array includes two types of gliders that provide simultaneous spatial and temporal sampling capabilities. Open-Ocean Gliders follow track lines around the triangular mooring array and are equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring.

The Global component of the OOI includes arrays at critical, yet under-sampled, high-latitude locations such as within the Irminger Sea in the North Atlantic. The Global Irminger Sea Array includes two types of gliders that provide simultaneous spatial and temporal sampling capabilities. Open-Ocean Gliders follow track lines around the triangular mooring array and are equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring.

The Global component of the OOI includes arrays at critical, yet under-sampled, locations such as within the Argentine Basin in the South Atlantic Ocean. The Global Argentine Basin Array includes two types of gliders that provide simultaneous spatial and temporal sampling capabilities. Open-Ocean Gliders follow track lines around the triangular mooring array and are equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring.

File List CodeSupplement_model.txt -- WinBUGS code CodeSupplement_data and inits.txt -- sample data and initial values Description “CodeSupplement_model.txt” contains WinBUGS code for running the stage-2 state-space model in: S. Eckert, J. Moore, D. Dunn, R. Sagarminaga, K. Eckert, P. Halpin "Hierarchical state-space models of loggerhead sea turtle (Caretta caretta) movement in relation to turtle size and oceanographic features in the western Mediterranean Sea". Code includes prior specification for all estimated parameters and hyper-parameters (stochastic nodes), including parameters describing covariate effects on behavioral switch probabilities and parameters describing movement characteristics (rates and variance of turn angles) for two behavioral states. Code includes normal and wrapped Cauchy likelihoods for mode-specific movement parameters and a Bernoulli likelihood for behavioral state. Model is based on a hierarchical switch model from: J. Morales, D. Haydon, J. Frair, K. Holsinger, and J. Fryxell. Extracting more out of reloction data: building movement models as mixtures of random walks. Ecology 85:2436–2445. “CodeSupplement_data and inits.txt” contains sample data and initial values in WinBUGS format. All data were standardized as z-scores for analysis.

GeoEye's OrbView-3 satellite was among the world's first commercial satellites to provide high-resolution imagery from space. OrbView-3 collected one meter panchromatic (black and white) and four meter multispectral (color) imagery at a swath width of 8 km for both sensors. One meter imagery enables more accurate viewing and mapping of houses, automobiles and aircraft, and makes it possible to create precise digital products. Four meter multispectral imagery provides color and near infrared (NIR) information to further characterize cities, rural areas and undeveloped land from space. Imagery from the OrbView-3 satellite complements existing geographic information system (GIS) data for commercial, environmental and national security customers. OrbView-3 orbits 470 km above the Earth in a sun-synchronous polar orbit while collecting imagery of the Earth's surface at one meter resolution in the Panchromatic (black and white) mode, or at four meter resolution in the Multispectral (color) mode with a three day repeat cycle.

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center received 179,981 OrbView-3 image segments from GeoEye with no restrictions. The data were delivered in Basic Enhanced (Level 1B) radiometrically corrected format. The product files include satellite telemetry data, rational functions, post-processed Ground Sample Distance (GPS) at nadir data, and sufficient metadata for rigorous triangulation.

The data in this collection were acquired between September 2003 and March 2007, both multispectral (MS) and panchromatic (Pan) sensor.

Snapshot img

Preclinical Animal Telemetry Market 2024-2028

The global preclinical animal telemetry market size is estimated to grow by USD 21.55 million at a CAGR of 5.69% between 2023 and 2028.

As a part of organic growth strategies, companies are increasingly focusing on new feature integration, as well as product development and launches. The launch of new products is allowing companies to remain competitive in the market by offering the latest available technologies to their customers. Additionally, companies can efficiently meet the specific demands of end-users by bringing innovative changes in their product lineup as per customers' expectations. Some of the recent product launches have been discussed below: Since 2021, the US Animal Telemetry Network (ATN) has been attempting to integrate ocean profiles obtained from animal-borne satellite telemetry tags, including the Wildlife Computers SCOUT-CTD, into the World Meteorological Organizations Global Telecommunication System. Such constant improvements and upgrades in features have brought significant differentiation in products and have helped companies lower the competition and increase product penetration. Therefore, an increase in product launches of preclinical animal telemetry is driving the market growth during the forecast period.

Technavio has segmented the market into End-user, Type, and Geography

The end-user segment is classified into industrial laboratories and CROs, academic government, and other research laboratories
The type segment is classified into small animal telemetry and large animal telemetry
The geography segment includes key regions such as North America, Europe, Asia, and Rest of World (ROW)

It also includes an in-depth analysis of drivers, trends, and challenges. Our report examines historical data from 2018-2022, besides analyzing the current market scenario.

What will be the Size of the Preclinical Animal Telemetry Market During the Forecast Period?

To learn more about this report, Download Report Sample

Preclinical Animal Telemetry Market Segmentation by End-user, Type and Geography Analysis

End-user Analysis

Industrial laboratories and CROs

The market share growth by the industrial laboratories and CROs segment will be significant during the forecast period. Industrial laboratories (including pharmaceutical and biotechnology companies) and CROs are among the key organizations involved in drug discovery, research, design, and development. These organizations spend a considerable number of resources and time in preclinical and clinical research for advancing new therapeutic candidates.

Get a glance at the market contribution of various segments Download PDF Sample

The Industrial laboratories and CROs were the largest segment and were valued at USD 32.41 million in 2018. Further, the increase in patent expiries of blockbuster drugs and biologicals has led to an increased focus on R&D activities. As a result, pharmaceutical and biotechnology companies are increasingly promoting the outsourcing of clinical research to CROs. This led to the outsourcing of R&D activities for developing new formulations. To reduce the risk of adverse events during clinical trials of these new formulations, the demand for preclinical animal testing, including toxicology testing and cosmetic testing, has increased. Animal telemetry solutions are finding extensive use for collecting and analyzing a large amount of preclinical safety and efficacy data. This preclinical safety and efficacy data is required to be submitted to drug regulatory authorities before permission for further studies in humans is granted. As a result, significant demand for preclinical animal research and testing can be observed across industrial laboratories and CROs globally, which is driving the adoption of animal telemetry systems, which, in turn, will drive the growth of the market during the forecast period.

Type Analysis

Small animal telemetry

The small animal telemetry segment includes animal telemetry solutions that are used in preclinical research with small animals such as mice, rabbits, ferrets, rats, hamsters, and Guinea pigs. Small animal telemetry solutions are usually miniaturized telemetry implants that are small in size and lightweight and can be custom-configured and modified as per the research and testing requirements. Furthermore, the availability of cost-effective, minimally invasive, wireless battery charging-based, flexible telemetry systems is adding to the growing sales of small animal telemetry systems. The market is expected to witness swift growth owing to stringencies in regulations for maintaining pharmacological safety and the growing availability of funding for research related to neuroscience, cardiology, and oncology.

Regional Analysis

For more insights about the market share of various regions Download PDF Sample now!

North America is estimated to contribute 44% to the growt

Satellite NTN for Telemetry Backhauls in CLaaS Market Outlook

According to our latest research, the global Satellite NTN for Telemetry Backhauls in CLaaS market size reached USD 2.18 billion in 2024, reflecting robust adoption across multiple industries. The market is projected to expand at a CAGR of 17.1% from 2025 to 2033, reaching a forecasted value of USD 8.01 billion by 2033. The primary growth driver for this market is the increasing need for reliable, real-time data connectivity in remote and harsh environments, where terrestrial networks are either infeasible or cost-prohibitive. As per our latest research, advancements in satellite technology, the proliferation of IoT devices, and the growing demand for mission-critical telemetry and backhaul solutions are propelling this market’s expansion at a remarkable pace.

One of the central growth factors for the Satellite NTN for Telemetry Backhauls in CLaaS market is the dramatic evolution in satellite technologies, particularly the deployment of Low Earth Orbit (LEO) constellations. These networks provide low-latency, high-throughput connections, making them ideal for telemetry backhauls in applications such as aerospace, defense, and remote industrial operations. The integration of satellite Non-Terrestrial Networks (NTN) with Cloud-as-a-Service (CLaaS) platforms enables organizations to leverage scalable, cloud-based data analytics and storage solutions, significantly enhancing operational efficiency and decision-making capabilities. This synergy is especially critical in sectors where real-time monitoring and control are paramount, such as oil & gas, maritime, and transportation, further fueling market growth.

Another key growth driver is the rapid adoption of IoT and M2M (machine-to-machine) communications, which require ubiquitous connectivity for telemetry data transmission. Traditional terrestrial networks often fall short in providing coverage for remote or mobile assets, making satellite NTN solutions indispensable for continuous data backhaul. The convergence of satellite NTN with CLaaS platforms allows enterprises to centralize telemetry data, apply advanced analytics, and automate responses, thereby reducing operational costs and improving asset utilization. The flexibility and scalability offered by cloud-based models are also encouraging small and medium-sized enterprises (SMEs) to adopt these solutions, further widening the market base.

A third significant growth factor is the increasing regulatory and safety requirements across critical industries such as aerospace & defense, oil & gas, and transportation. These sectors are mandated to maintain stringent monitoring and reporting standards, often in locations beyond the reach of conventional networks. Satellite NTN for telemetry backhauls provides a reliable and secure means to meet these compliance requirements, ensuring uninterrupted data flow for mission-critical applications. Moreover, the trend towards digital transformation and automation in these industries is driving investments in advanced telemetry and backhaul solutions, positioning the market for sustained long-term growth.

From a regional perspective, North America currently leads the Satellite NTN for Telemetry Backhauls in CLaaS market, driven by significant investments in aerospace, defense, and energy infrastructure. Europe and Asia Pacific are also witnessing rapid growth, with increasing adoption of satellite-enabled telemetry solutions in maritime, agriculture, and logistics sectors. The Middle East & Africa and Latin America are emerging markets, benefiting from governmental initiatives to enhance connectivity in remote regions and support industrial development. The global landscape is characterized by a diverse set of market drivers, including technological innovation, regulatory frameworks, and industry-specific requirements, ensuring a dynamic and competitive market environment.

Component Analysis

The component segment of the Sat

Context

This data set includes the data recorded during the pre-flight tests of the RHEA Model Satellite Team competing in the 2023 Turksat Model Satellite Competition organized by Teknofest.

Contents

Some data may be missing or incorrect. The reason is either there was a malfunction, we could not save the data, or we could not run it at all.

You can access the contest specifications from this link. So you fully understand the task and what they mean. https://cdn.teknofest.org/media/upload/userFormUpload/T-MUY_2023_Yar%C4%B1%C5%9Fma_K%C4%B1lavuzu_HE4XU_V4hvY.pdf

• paket_numarasi - package number. It is the sequential number assigned to each telemetry packet generated at the time of the competition and sent to the ground station. The first packet starts with "1" and continues sequentially. In case of a restart of the processor, the packets should continue from the last left number.

• uydu_statusu - satellite status. It is the information to be specified numerically, showing the status of the model satellite during the mission. It is obligatory to create the following statuses numerically. 0: Ready-to-Fly (Before the Rocket is Fired) 1: Ascension 2: Model Satellite Landing 3: Separation 4: Payload Landing 5: Recovery (Payload Ground Contact) 6: Package Video (500 KB) Received 7: Package Video (500 KB) Sent (Bonus Quest)

• hata_kodu - It is a 5-digit telemetry data consisting of 0 or 1 to be created according to the specified error conditions.

• gonderme_saat - It is real-time clock data in the form of Day/Month/Year, Hour/Minute/Second.

• basinc1 - It is the atmospheric pressure value measured by the sensor on the payload. Its unit is Pascal.

• basinc2 - It is the atmospheric pressure value measured by the sensor on the carrier. Its unit is Pascal.

• yukseklik1 - It is the height of the payload from the starting point of flight. Height configuration; The starting point of the flight should be set to 0 meters. Its unit is a meter.

• yukseklik2 - It is the height of the carrier from the starting point of the flight. Height configuration; The starting point of the flight should be set to 0 meters. Its unit is a meter.

• irtifa_farki - The absolute difference between HEIGHT1 and HEIGHT2 is the value. Its unit is meter.

• inis_hizi - Descent velocity data. Its unit is m/s

• sicaklik - It is the measured temperature data. Its unit is degrees C.

• pil_gerilim - Indicates the voltage of the battery. Its unit is V.

• gps_latitude - It is the latitudinal position of the payload.

• gps_longitude - It is the longitudinal position of the payload.

• gps_altitude - It is the altitude data of the payload received from GPS.

• pitch - It is the tilt angle on the pitch axis. The unit is degrees.

• yaw - It is the tilt angle on the yaw axis. The unit is degrees.

• roll -It is the tilt angle on the roll axis. The unit is degrees.

• takim_no - Teams applying to the competition are given a team number after the application process is completed. It is a 5-digit number. The team number of each team is different from the number of other teams.

• video_aktarim_bilgisi - Informs whether the camera image is recorded or not.

In addition to the data set, you can also visit these reviews to visualize this data and examine the codes of the ground station we recorded. https://github.com/SHaken53/Yer_Istasyonu_06

Satellite NTN for Maritime Telemetry Market Outlook

According to our latest research, the global Satellite NTN for Maritime Telemetry market size reached USD 2.14 billion in 2024, driven by the increasing need for robust connectivity and real-time data solutions across maritime sectors. The market is set to expand at a CAGR of 11.8% from 2025 to 2033, with the forecasted market size projected to hit USD 5.97 billion by 2033. This robust growth is largely attributed to the rising adoption of satellite Non-Terrestrial Networks (NTN) for vessel tracking, cargo monitoring, and environmental data collection, as well as the growing demand for reliable communication solutions in remote and offshore maritime environments.

One of the primary growth drivers of the Satellite NTN for Maritime Telemetry market is the escalating need for uninterrupted communication and telemetry in the maritime industry. With global shipping routes becoming increasingly complex and international trade volumes surging, there is a critical demand for advanced telemetry solutions that ensure real-time tracking and monitoring of vessels, cargo, and environmental conditions. Satellite NTN enables seamless data transmission far beyond the reach of terrestrial networks, which is essential for ships operating in remote oceanic regions. This technological advancement not only improves operational efficiency and safety but also supports regulatory compliance regarding vessel tracking and environmental reporting, thereby fueling market expansion.

Another significant growth factor is the integration of advanced IoT and AI-driven analytics within maritime telemetry solutions. The convergence of satellite NTN technology with IoT sensors and AI platforms allows for granular, real-time data collection and predictive analytics, transforming how shipping companies and port authorities manage fleets and cargo. These innovations facilitate proactive decision-making, optimize route planning, enhance asset utilization, and reduce operational costs. Additionally, the growing focus on environmental sustainability and the need to monitor emissions, ballast water, and other ecological parameters are pushing maritime operators to adopt state-of-the-art telemetry systems powered by satellite NTN, further accelerating market growth.

The market is also benefitting from increasing investments by governments and private players in satellite infrastructure and maritime digitalization. Several global initiatives aim to enhance maritime safety, security, and efficiency through better connectivity and data sharing. For instance, defense and government agencies are leveraging satellite NTN solutions for border surveillance, anti-piracy operations, and emergency response. Simultaneously, offshore oil & gas companies are deploying these systems to ensure the safety and efficiency of remote operations. The growing collaboration between satellite service providers, maritime technology firms, and regulatory bodies is fostering innovation and expanding the reach of satellite NTN solutions, thus stimulating further market growth.

From a regional perspective, Asia Pacific and North America are leading the adoption of satellite NTN for maritime telemetry, driven by large merchant fleets, significant offshore energy activities, and proactive investments in maritime digitalization. Europe is also witnessing substantial growth due to stringent environmental regulations and a strong focus on maritime safety. The Middle East & Africa and Latin America are emerging as promising markets, supported by expanding trade routes and increasing focus on port modernization. Regional disparities in infrastructure development and technology adoption, however, remain a challenge, influencing the pace and scale of market growth across different geographies.

Component Analysis

The Component segment of the Satellite NTN for Maritime Telemetry market is categorized into hardware, software, and services, each

This project aimed to take initial steps towards producing a physical representation of an ethically and legally sound drone-based system intended as a safer method to generate large cetacean related satellite telemetry, biopsy and photogrammetry data streams. Specifically we aimed to compile pertinent information to inform our design process: physical measurements (velocity, flight trajectories) for all currently employed projectiles (biopsy darts, satellite tags) from all current deployment devices by way of ballistics testing recorded using a high-speed camera with projectiles propelled horizontally.

We undertook ballistics testing at an indoor shooting range, firing biopsy darts and satellite tags (both the LIMPET and Type C implantable) at a foam target while filming their flight with a high frame rate camera, the Sony Cybershot RX100 VII set to record at 500 frames per second. The high frame rate video files were processed in the Tracker software (https://tracker.physlets.org/) and we derived both the velocity and vertical displacement of the projectiles over various distances (10 m and 15 m for biopsy darts ; 6.4 m for satellite tags,) and shot pressures (15 and 25 on the Paxarm rifle dial for biopsy darts; 10, 15 and 20 bar for LIMPETS; 8, 12 and 16 bar for Type C implantable tags). Average flight speeds ranged from 55.45 ± 1.74 ms-1 (shot distance of 10 m, Paxarm dial set at 15) to 61.77 ± 1.03 ms-1 (shot distance of 15 m, Paxarm dial set at 25) for biopsy dart flight trajectories. For LIMPET flight trajectories, average speeds ranged from 26.13 ± 0.57 ms-1 (shot distance of 6.38 m, 10 bar pressure), 32.01 ± 0.34 ms-1 (shot distance of 6.38 m, 15 bar pressure) and 38.32 ± 0.79 ms-1 (shot distance of 6.38 m, 20 bar pressure). For Type C implantable satellite tags, average speeds ranged from 21.96 ± 0.48 ms-1 (shot distance of 6.38 m, 8 bar pressure), 26.97 ± 0.58 ms-1 (shot distance of 6.38 m, 12 bar pressure) and 32.63 ± 0.41 ms-1 (shot distance of 6.38 m, 16 bar pressure).

We provide: 1. A spreadsheet (filming_metadata.xlsx) that provides details of the flight trajectory setup and recording. There are two sheets describing the recordings from the camera positioned at the firearm and the camera positioned at the target. The ‘Name’ column refers to the video file name, size is the video file size, 'DateModified' is the video file date, 'Projectile' describes the whale sampling device filmed, 'Video start time' provides the time at which the projectile was released, 'Dist to target' is the flight distance in metres, 'Dial pressure' is the deployment pressure as registered on the Paxarm dial or pressure gauge (ARTS and DanInject), 'Notebook time' for cross reference refers to the physical notes recorded, 'Replicate' is the flight trajectory replicate number, 'Notes' provides any additional information needed to interpret the data.

1. The videos are MP4 format and are contained in folders that indicate their date of collection in yyyymmdd (either 20210726 or 20210727) and camera position (FIREARM or TARGET). These are the videos that were processed in the Tracker software to obtain flight metrics.

2. The Speed-and-Trajectories.html file provides a detailed description and photos of the setup of firearms and cameras at the indoor shooting range and also plots an calculations of flight speeds and displacement for all the data points generated in Tracker for all combinations of projectile, flight distance and deployment pressure.

The global component of the OOI design includes a network of moorings at critical, yet under-sampled, high-latitude locations such as Station Papa in the North Pacific. Moorings located at Station Papa support sensors for measurement of air-sea fluxes of heat, moisture and momentum, and physical, biological and chemical properties throughout the water column. The Global Station Papa Array is a combination of fixed platforms (moorings) with moored profilers to address the requirement to sample the full water column and mobile platforms (gliders) that provide simultaneous spatial and temporal sampling capabilities. The array is composed of a subsurface Global Profiler Mooring made up of two wire-following profilers, one operating from ~300 m to 2200 m and the second from ~2200 m to 4000 m. Two Flanking Moorings form a triangular array ~40 km on a side. These flanking Moorings have their uppermost flotation at ~20 m depth and instruments at discrete depths along the mooring line to a depth of 1500 m. Open-Ocean Gliders sample within and around the triangular array equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring. The array at Station Papa is occupied in coordination with the National Oceanic and Atmospheric Administration (NOAA), which maintains a surface mooring there. As one of the oldest oceanic time series sites, with surveying conducted in the area since 1949, Station Papa is an important location within the global network of OceanSITES.The global component of the OOI design includes a network of moorings at critical, yet under-sampled, high-latitude locations such as Station Papa in the North Pacific. Moorings located at Station Papa support sensors for measurement of air-sea fluxes of heat, moisture and momentum, and physical, biological and chemical properties throughout the water column. The Global Station Papa Array is a combination of fixed platforms (moorings) with moored profilers to address the requirement to sample the full water column and mobile platforms (gliders) that provide simultaneous spatial and temporal sampling capabilities. The array is composed of a subsurface Global Profiler Mooring made up of two wire-following profilers, one operating from ~300 m to 2200 m and the second from ~2200 m to 4000 m. Two Flanking Moorings form a triangular array ~40 km on a side. These flanking Moorings have their uppermost flotation at ~20 m depth and instruments at discrete depths along the mooring line to a depth of 1500 m. Open-Ocean Gliders sample within and around the triangular array equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring. The array at Station Papa is occupied in coordination with the National Oceanic and Atmospheric Administration (NOAA), which maintains a surface mooring there. As one of the oldest oceanic time series sites, with surveying conducted in the area since 1949, Station Papa is an important location within the global network of OceanSITES.The global component of the OOI design includes a network of moorings at critical, yet under-sampled, high-latitude locations such as Station Papa in the North Pacific. Moorings located at Station Papa support sensors for measurement of air-sea fluxes of heat, moisture and momentum, and physical, biological and chemical properties throughout the water column. The Global Station Papa Array is a combination of fixed platforms (moorings) with moored profilers to address the requirement to sample the full water column and mobile platforms (gliders) that provide simultaneous spatial and temporal sampling capabilities. The array is composed of a subsurface Global Profiler Mooring made up of two wire-following profilers, one operating from ~300 m to 2200 m and the second from ~2200 m to 4000 m. Two Flanking Moorings form a triangular array ~40 km on a side. These flanking Moorings have their uppermost flotation at ~20 m depth and instruments at discrete depths along the mooring line to a depth of 1500 m. Open-Ocean Gliders sample within and around the triangular array equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring. The array at Station Papa is occupied in coordination with the National Oceanic and Atmospheric Administration (NOAA), which maintains a surface mooring there. As one of the oldest oceanic time series sites, with surveying conducted in the area since 1949, Station Papa is an important location within the global network of OceanSITES.The global component of the OOI design includes a network of moorings at critical, yet under-sampled, high-latitude locations such as Station Papa in the North Pacific. Moorings located at Station Papa support sensors for measurement of air-sea fluxes of heat, moisture and momentum, and physical, biological and chemical properties throughout the water column. The Global Station Papa Array is a combination of fixed platforms (moorings) with moored profilers to address the requirement to sample the full water column and mobile platforms (gliders) that provide simultaneous spatial and temporal sampling capabilities. The array is composed of a subsurface Global Profiler Mooring made up of two wire-following profilers, one operating from ~300 m to 2200 m and the second from ~2200 m to 4000 m. Two Flanking Moorings form a triangular array ~40 km on a side. These flanking Moorings have their uppermost flotation at ~20 m depth and instruments at discrete depths along the mooring line to a depth of 1500 m. Open-Ocean Gliders sample within and around the triangular array equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring. The array at Station Papa is occupied in coordination with the National Oceanic and Atmospheric Administration (NOAA), which maintains a surface mooring there. As one of the oldest oceanic time series sites, with surveying conducted in the area since 1949, Station Papa is an important location within the global network of OceanSITES.