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Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of data meshes and data products in response to user requests, ensuring that stakeholders have access to specific portions of the data mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates the secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of data meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory. [ABSTRACT FROM AUTHOR]

Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice. [ABSTRACT FROM AUTHOR]

Ocean salinity is an important variable that affects the ocean stratification. We compared the salinity and ocean stratification in the tropical Pacific derived from the Argo (Array for Real-time Geostrophic Oceanography data), EN4 (Ensemble 4 analysis), SODA (the Simple Ocean Data Assimilation reanalysis), IAP (Institute of Atmospheric Physics data), and ORAS4 (Ocean Reanalysis System 4) over 2005–2017. Results show that the spatial distribution of climatological mean of sea surface salinity (SSS) in all the products is consistent, and the low salinity region showed large deviation and strong dispersion. The Argo has the smallest RMSE and the highest correlation with the ensemble mean, while the IAP shows a high-salinity deviations relative to other datasets. All the products show high positive correlations between the sea surface density (SSD) and SSS with respect to the deviations of climatological mean from ensemble mean, suggesting that the SSD deviation may be mainly influenced by the SSS deviation. In the aspect of the ocean stratification, the mixed layer depth (MLD) climatological mean in the Argo shows the highest correlation with the ensemble mean, followed by EN4, IAP, ORAS4, and SODA. The Argo and EN4 show thicker barrier layer (BL) relative to the ensemble mean while the SODA displays the largest negative deviation in the tropical western Pacific. Furthermore, the EN4, ORAS4, and IAP underestimate the stability in the upper ocean at the depths of 20–140 m, while Argo overestimates ocean stability. The salinity fronts in the western-central equatorial Pacific from Argo, EN4, and ORAS4 are consistent, while those from SODA and IAP show large deviations with a westward position in amplitude of 0°–6° and 0°–10°, respectively. The SSS trend patterns from all the products are consistent in having ensemble mean with high spatial correlations of 0.95–0.97. [ABSTRACT FROM AUTHOR]

The Rover-mounted Subsurface Penetrating Radar (RoSPR) is one of the scientific payloads onboard China's first independent Mars exploration mission, Tianwen-1. The radar aims to characterize the thickness of the upper Martian soil and investigate the subsurface stratigraphy by collecting and processing the data. This article is mainly divided into two parts, the introduction of data pre-processing and analysis of pre-processed radar signals, aiming at helping scientists make more effective use of radar data. The first part describes the operating principle of the RoSPR and the procedure of radar data pre-processing at all levels. Data pre-processing is mainly designed to transfer the raw data format to a common PDS (Planetary Data System) and eliminate the influence of the instrument. In the signal analysis part, the performances of both self-check signals and echo signals of low- and high-frequency channels are analyzed, which indicate a stable radar system and are useful for background removal. Phase and time calibration is of great importance for improving data quality and making the radar data more accurate. Moreover, further processing is required to obtain clear radar images, such as filtering, background removal and gain setting. [ABSTRACT FROM AUTHOR]

The Tianwen-1 mission is China's first Mars exploration mission. To complete the scientific objectives of "orbiting, landing, and patrolling" in one mission, thirteen instruments for acquiring multi-disciplinary data are configured. The data products with rich ancillary data and flexible structures will facilitate the data application. To integrate the requirements of experts from disparate disciplines and leverage new information technologies, a complete redesign of data products is promoted in the Tianwen-1 mission. We discuss the whole process of data product generation from the data pipeline design to data validation. We design the various data products according to the application requirements of each data type, including radar echo data, spectrum data, image data, energy spectrum data, magnetic field data, meteorological data, and acoustic data. Since various error sources will exist in the generation chain of the data products, data validation is essential before release to the public; the validation activities are discussed at the end. We deliver these scientific products to the community in a timely manner, with ancillary information and quality information. This paper can provide practical reference for Tianwen-1 data application. [ABSTRACT FROM AUTHOR]

SCATSAT-1 carries a Ku-band scatterometer with a scanning pencil beam configuration. It deploys two beams, a vertically polarized outer beam and a horizontally polarized inner beam, to cover a swath of 1800 km. The mission mainly caters to oceanographic applications and weather forecasting, with the data being extensively used for cyclogenesis predictions across the globe and specifically, the tropical region. Since the launch of SCATSAT-1 in September 2016, the satellite and payload performances as well as mission and ground segment operations have been found to be nominal and satisfactory. This article highlights various levels of operational data products as well as algorithms used for deriving radar backscatter and retrieving wind vector data from scatterometer measurements. [ABSTRACT FROM AUTHOR]

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment. [ABSTRACT FROM AUTHOR]

The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of ∼100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SDC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept. [ABSTRACT FROM AUTHOR]

The payload on board the FengYun 3 (FY-3) satellite series has been providing critical observations to the Earth, and the observation data have been processed and distributed to various users by the corresponding ground application system. In this paper, the FY-3 payload and its future development plan are presented, and its ground system is introduced. The major characteristics of the FY-3 instruments and the requirement on the instruments for major applications are discussed as well. There are nine new instruments, including the Microwave Temperature Sounder (MWTS), Microwave Humidity Sounder (MWHS), Infrared Atmospheric Sounder (IRAS), Medium-Resolution Spectral Imager (MERSI), Microwave Radiation Imager (MWRI), Solar Backscatter Ultraviolet Sounder, Total Ozone Unit, Solar Irradiation Monitor, and Earth Radiation Measurement. The data quality from the IRAS, MWTS, MWHS, and MWRI has proved to be good, and according to our evaluation at the European Centre for Medium-Range Weather Forecasts (ECMWF), the data have a very high availability. The MERSI has 20 visible (VIS) and infrared (IR) channels, of which three are VIS channels with a 250-m spatial resolution, one is a near-IR channel with a 250-m spatial resolution, and one is a long-wave IR channel with a 250-m spatial resolution. The global data at a 250-m spatial resolution are very useful in weather, disaster, and environmental monitoring. Compared with the ground observations, the ozone products derived from the FY-3 series are highly accurate. We have completed the calibration and validation of these sensors and most products. [ABSTRACT FROM AUTHOR]

Abstract: Global Monitoring for Environment and Security (GMES) is the most ambitious operational Earth Observation programme to date and will provide global, timely and easily accessible information in application domains such as land, marine, atmosphere, emergency response, climate change and security. To accomplish this, the European Union (EU)-led GMES programme comprises three components namely the space component, the in-situ component and the service component. The space component, led by ESA, is in its pre-operational stage, serving users with satellite data acquired by the so called “GMES Contributing Missions” already available today or planned at European, national and international level. It will become operational once the dedicated space infrastructure, comprised by the “Sentinel” missions and their corresponding ground segments are operational. The first of these satellite series will be launched in 2013. The Sentinel missions will provide a unique set of observations utilising different techniques spanning C-band SAR, mid to medium resolution optical and thermal observations with increased spectral resolution, altimeter and dedicated spectrometers for atmospheric chemistry. This data, combined with in-situ data, some assimilated into models, will then be turned into services for monitoring the environment, the climate and for security related issues. The GMES Space Component (GSC) is organised in two overlapping phases: the development phase and the operational phase, the latter planned to start in 2014. The main challenge is now to ensure the programme''s long-term sustainability. [Copyright &y& Elsevier]

The functions of KAGUYA(SELENE) Operation and Analysis Center (SOAC) are to operate three satellites: the main orbiter KAGUYA and two small satellites, Relay satellite OKINA and VRAD (VLBI (Very Long Baseline Interferometry) RADio source) satellite OUNA; and to process, archive and provide mission data. SOAC has two main functional areas, 'Tracking and Control system' and 'Mission Operation and Data Analysis system.' The former is for operational planning of bus and mission instruments including satellite navigation, and for the implementation of those plans and for the evaluation of satellite conditions. The latter is the system that processes, archives and provides mission data, and which principal investigators use to generate higher-level data products. Data up to the end of the operation in June 2009 have been processed and the total amount of Level-2 data products reaches about 50 TB. The data products have been released to the public since November 2009. [ABSTRACT FROM AUTHOR]

The MESSENGER Science Operations Center (SOC) is an integrated set of subsystems and personnel whose purpose is to obtain, provide, and preserve the scientific measurements and analysis that fulfill the objectives of the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission. The SOC has two main functional areas. The first is to facilitate science instrument planning and operational activities, including related spacecraft guidance and control operations, and to work closely with the Mission Operations Center to implement those plans. The second functional area, data management and analysis, involves the receipt of science-related telemetry, reformatting and cataloging this telemetry and related ancillary information, retaining the science data for use by the MESSENGER Science Team, and preparing data archives for delivery to the Planetary Data System; and the provision of operational assistance to the instrument and science teams in executing their algorithms and generating higher-level data products. [ABSTRACT FROM AUTHOR]

The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection. Plain Language Summary: Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance. Key Points: A new laser altimeter system design consisting of a laser altimeter, two laser footprint cameras, and a laser optical axis surveillance camera equipped with the Gao Fen 7 satellite is introducedThe application of the Gao Fen 7 laser altimeter system including ground control point measurement, polar ice sheet monitoring, and forest biomass estimation is presentedScientific experiments on the Gao Fen 7 laser altimeter system to be performed in November 2019 are given. Six levels of data products are listed and will be provided to users worldwide [ABSTRACT FROM AUTHOR]