Data Access Methods Research Articles

The Ebola Data Platform: A prospective, standardised, clinical dataset collected during the 2013-2016 West African Ebola outbreak.

The Ebola Data Platform (EDP) was developed to strengthen knowledge and capacity across health, research, and humanitarian communities to reduce the impact of Ebola through responsible data use. This collaborative initiative was established by West African governments, NGOs, academic organisations, and intra-governmental health organisations directly involved in the 2013-2016 West African Ebola outbreak. The platform was established to provide a centralised, standardised dataset of individual patient data collected during the outbreak for the purpose of research to improve Ebola treatment and control, and includes over 13,600 patient records of individuals infected and treated from 22 different Ebola treatment centres across Guinea, Sierra Leone, Liberia, and Nigeria. Patient data are available from treatment centre triage and admission, inpatient clinical observations, and outcomes, with outpatient follow-up available for some datasets. Data include signs and symptoms, pre-existing comorbidities, vital signs, laboratory testing, treatments, complications, dates of admission and discharge, mortality, viral strains, and other data. This publication describes characteristics of the EDP dataset, its architecture, methods for data access and tools for utilising the dataset.

The Royal Marsden BRIDgE TRE Transparency Project

BackgroundThe BRIDgE platform is the Royal Marsden’s Trusted Research environment, which aims to transform clinical practice and improve outcomes for patients by enabling data analytics and AI development using real-world cancer data within secure, collaborative, cloud-based workspaces. This project evaluated the BRIDgE website against the new UK Health Data Research UK Transparency Standards, leading to changes to implement the following: Open Access Application form and Guidance (Standard 1), Transparent Access Process and Criteria (Standard 2), Clear website navigation (Standard 3), Consider Target Audience (Standard 4), Regular Review of Website Content (Standard 5), and Transparency of Data Use and Auditing (Standard 6). Our primary aim was to improve the content and sign posting so that patients and researchers would be better informed, resulting in increased website traffic and requests via our Contact Us page. MethodsInformation about the transparency project and the updates to the website were presented to a patient panel for feedback and a leaflet was co-designed with patient representatives. ResultsChanges included a new BRIDgE home page, which was re-designed to make navigation simpler and clearer, improved signposting, so both patients and researchers were aware of the sections meant for their consumption, such as the new Researchers’ Frequently Asked Questions (FAQ) page, which contained detailed instructions on how to request data, the governance workflow and costing structure. Finally, we expanded the Research Projects and Publications page, and added a Case Study from one of the Researchers, detailing their personal experience on the platform. ConclusionsWe anticipate that the impact of this project will be that patients will be more informed about Trusted Research Environments in general and what BRIDgE specifically enables in terms of innovative research and data security, leading to greater assurance for patients that their data is being used securely for their benefit. Researchers will get the benefit of the new FAQ page, which includes a great deal more information than was previously available about the platform, the data access methods, and the governance processes. We envisage that this will lead to more traffic on the site and a greater number of requests for information and workspaces via the Contact Us page. Finally, the patient leaflet will be the first time that patients in our waiting areas will be able to read about research informatics at the Royal Marsden and the work of our data scientists, in paper form. The resources developed using the funding from the transparency project are the revamped BRIDgE website https://www.cancerbrc.org/BRIDgE and (soon to be published) patient leaflet.

A survey on semantic data management as intersection of ontology-based data access, semantic modeling and data lakes

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Such approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Conflict-Free Parallel Data Access Technology for Matrix Calculation in Memory System of ASIP of 5G/6G Macro Base Stations

Among the physical layer baseband algorithms in macro base stations, the matrix processing has the dom-inant computing cost, large data access overhead and com-plicated addressing mode. The existing data access methods are not the best solution for memory system of ASIP (Ap-plication Specific Instruction Set Processor) in 5G/6G macro stations. We hence proposed a parallel conflict-free data access method for matrix. Moreover, we proposed a non-redundant access method for positive definite matrix that stores only trigonometric part, and the corresponding parallel addressing method with low storage overhead. Our method solves the problem of data conflict and minimizes the memory space of ASIP, and supports ASIP to approach the performance limit of architecture to the maximum ex-tent under the constraint of architecture and data parallel-ism. We implemented the proposed method as a static memory optimizer, which provides a tool for the design and optimization of memory system of ASIP in 5G/6G macro base stations with low overhead. Experimental results show that for 64W64 positive definite matrix inversion algorithm based on Cholesky decomposition, our addressing method can save 32% of the execution time, and the cost of memory space is reduced by half.

Adaptive Access and Tracking Method for Supply Chain Data Based on Sharding Blockchain

Adaptive Access and Tracking Method for Supply Chain Data Based on Sharding Blockchain

DATA_SPHERE

This paper presents a comprehensive overview of Database Management Systems (DBMS) and their significance in modern information management. DBMS technology plays a crucial role in the storage, organisation, retrieval, and manipulation of vast amounts of data in various domains, ranging from business operations to scientific research. This abstract highlights the key aspects covered in the paper, including the evolution of DBMS, its architectural components, and the challenges and advancements in the field. The paper begins by discussing the historical development of DBMS, tracing its origins from file-based systems to the emergence of relational databases and the subsequent rise of object-oriented and NoSQL databases. We explore the motivations behind these advancements and their impact on data management. Next, we delve into the fundamental architectural components of a DBMS. We examine the storage layer, which encompasses data structures and access methods, and discuss different indexing techniques for efficient data retrieval. The query processing and optimization module are explored, focusing on query execution plans and cost-based optimization strategies. Additionally, we analyse the transaction management component, highlighting concepts such as ACID properties, concurrency control, and recovery mechanisms. The abstract also highlights the challenges faced by modern DBMS. With the proliferation of big data and the advent of cloud computing, scalability, availability, and performance have become critical concerns. We examine techniques such as parallel and distributed databases, replication, and sharding to address these challenges. Furthermore, we discuss the integration of DBMS with emerging technologies like machine learning and blockchain to leverage their capabilities in data analytics and secure data transactions. Lastly, the abstract touches upon recent advancements in DBMS, including the rise of graph databases for managing interconnected data, the adoption of in-memory databases for high-performance applications, and the exploration of new database models to handle unstructured and semi-structured data. In conclusion, this paper provides a comprehensive overview of DBMS, covering its historical evolution, architectural components, challenges, and recent advancements. By understanding the principles and advancements in DBMS, researchers and practitioners can effectively harness the power of data management systems to tackle the complexities of modern data-driven applications.

High-resolution calibrated and validated Synthetic Aperture Radar Ocean surface wind data around Australia

The dataset consists of ocean surface wind speed and direction at 10 m height and 1 km spatial resolution around the wider Australian coastal areas, spanning 4 years (2017 to 2021) of measurements from Sentinel-1 A and B imaging Synthetic Aperture Radar (SAR) platforms. The winds have been derived using a consistent SAR wind retrieval algorithm, processing the full Sentinel-1 archive in this region. The data are appropriately quality controlled, flagged, and archived as NetCDF files representing SAR wind field maps aligned with satellite along-track direction. The data have been calibrated against Metop-A/B Scatterometer buoy-calibrated, wind measurements and examined for potential changes in calibration over the duration of the data. The calibrated data are further validated by comparisons against independent Altimeter (Cryosat-2, Jason-2, Jason-3, and SARAL) wind speeds. Several methods for data access are also listed. The database is potentially useful for offshore industries (oil and gas, fisheries, shipping, offshore wind energy), public recreational activities (fishing, sailing, surfing), and protection and management of coasts and natural habitats.

DIRAC at JINR as a general purpose system for massive computations

Joint Institute for Nuclear Research (JINR) has several large computing facilities: Tier1 and Tier2 grid clusters, Govorun supercomputer, cloud, and LHEP computing cluster. Each of them has different access protocols, authentication and authorization procedures, data access methods. With the help of the DIRAC Interware, we were able to integrate all these resources to provide a uniform access to all these facilities. Right now, it is possible to perform basic workflows on all resources. The main use-cases covered by the DIRAC service in JINR: centralized Monte-Carlo simulation for the MPD experiment, Monte-Carlo for the Baikal-GVD neutrino telescope, as well as running jobs for the Folding@HOME project.During the pre-production stage, it is important to estimate the characteristics of user jobs. That information is crucial for planning of the execution process on different resources. An approach was elaborated to collect data about RAM, CPU, and network consumption by each user job. This helped to tune the mass production algorithms before the production actually starts. Since the system processes tens of thousands of similar jobs during one particular production, it appeared to be possible to collect from DIRAC data about these jobs and perform analysis of their execution parameters. We collect data related to the CPU model, wall-time, CPU benchmark DB12, hostname, username, and resource name. An approach was elaborated to extract meta-data about job execution and visualize it. With this visualization, it became possible to compare different computing resources, study the CPU and worker node performance. This approach does not require submitting special jobs, so the resources are not wasted for this analysis.

Similarity matching method of power distribution system operating data based on neural information retrieval

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy. Therefore, improvement of the ability of data-driven operation management, intelligent analysis, and mining is urgently required. To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation, maintenance experience, and knowledge by rule and line, a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology. Based on the processing flow of the operating data of the power distribution system, a technical framework of neural information retrieval is established. Combined with the natural graph characteristics of the power distribution system, a unified graph data structure and a data fusion method of data access, data complement, and multi-source data are constructed. Further, a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed. The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set. The model is verified on the operating section of the power distribution system of a provincial grid area. The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

Napredne tehnike u analizama saobraćajnih nezgoda

Modern vehicles in road traffic are equipped with mutually integrated electronic devices used for managing systems. A chip in the airbag module identifies collisions and, at the same time, memorizes key parameters prepared for the analysis of traffic accidents. After the transfer of data, it is possible to make an undoubted temporal-spatial analysis and give answers to all those questions which the previous practice had no answers for and instead relied on the principle of in dubio pro reo. The paper describes the current cognitive methods, presents modern digital forensics devices, its history, organization of digital forensics systems, highlights the formal and factual dilemmas of this novelty, describes the technique and method of data access as well as different perspectives. A list of questions that have not been answered by previous practice is provided, as well as several examples from practice, demonstrating the advantages that digital forensics achieves in relation to the traditional way of working. Applying the results of digital forensics will undoubtedly have a substantial effect on eradicating false accusations, erroneous verdicts, and unnecessary lawsuits. It will completely change the work of traffic-technical experts by eradicating all known weaknesses, reduce the number of lawsuits, and largely shorten the existing ones, which will affect the economy of proceedings in this area.

개방형BIM기반 BF인증 평가 룰 연동 모듈 개발

With the development of BIM technology, one of the smart construction technologies, the government and public institutions are applying the goal of increasing productivity and maximizing work efficiency in the form of supporting existing work in construction related fields. In particular, the need for BIM appeared for an inefficient human resource-oriented administrative processing system that works based on 2D drawings, and research on it is ongoing. In this study, in order to build an openBIM-based BF certification evaluation system, a module that performs essential functions for system implementation was created by utilizing the evaluation items of Barrier Free certification among the currently implemented design certification system. Through a module that can utilize objects used in evaluation items and related data, the results were derived through data access methods and rules. Through this, it is expected that the module will be used in the development of similar design certification system to broaden the scope of application of the openBIM-based evaluation system and increase the possibility of using the module according to the diversity of evaluation items.

Blockchain-enabled efficient distributed attribute-based access control framework with privacy-preserving in IoV

Aiming at solving the challenges of the Internet of Vehicles (IoV), such as privacy sensitive data exposing, data being vulnerable to unauthorized access and tampered with and the single point of failure of the cloud server, this paper introduces a blockchain-enabled data access method based on attribute-based encryption. To enhance the privacy protection, the attributes are hidden and all generated transactions are recorded on the blockchain for auditing. Even if it is untrusted to a third party, it can guarantee the user’s control over his own data. To reduce the computation cost, decentralized edge nodes jointly maintain the blockchain and assist users with limited resources to complete most of the calculations. Meanwhile, the use of smart contracts not only reduces the verification workload, but also evaluates multiple factors such as the communication range, communication time, and calculation overhead of the roadside unit (RSU) to ensure that the vehicle can receive messages in a timely manner. In addition, timely updating the attributes can reduce the user’s computational burden and increase the flexibility of the system. Numeral experiments to show the proposed scheme has many advantages of over others such as the storage cost, the whole time consume of the proposed algorithms and the proxy computation cost.

Application-oriented Transformation and Development of Private Undergraduate Universities Based on Fuzzy Clustering

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the rapid development of our country's economy, the society's demand for talents is also increasing. As the cradle of cultivating professional technical talents, private schools have gradually become an indispensable education base in the higher education system. Among the many types of courses, there are many subjects with application as the main characteristic in our country. For example, computer and information engineering, electronic information technology, etc. Then, for how to transform and develop applied courses in private undergraduate colleges and universities, this paper conducts certain research based on fuzzy clustering. This article mainly uses survey questionnaire method, expert analysis method, comparative research method and data access method to conduct research on the application-oriented transformation of private undergraduate colleges and universities. Through investigation and analysis, it is found that there are indeed some problems in the application-oriented transformation of private undergraduate colleges and universities in our country. 86.52% of people think that there are still big problems in the construction of the teaching team.</span>

An efficient dynamic access control and security sharing scheme using blockchain

This study seeks to understand the role of institutions and organizations that have used cloud service providers to store and share data as ensuring third-party access to storage is a major challenge to avoid data theft and unwanted access. Hence, in this paper, Blockchain-Based Data Access and Secure Sharing Method (BDASS) is introduced to enhance security processes related to personal data through data access control and secure sharing method, the proposed method uses blockchain aggregation, file system (IPFS), dynamic access control (DAC), and ciphertext-attribute-based encryption (CP-ABE) to enhance the security of personal data. To keep the owner safe, a blockchain-based DAC is designed. To keep data storage and sharing secure, the blockchain-based CP-ABE is designed. In this proposed methodology, the data owner encrypts the data they have stored in IPFS, thus enhancing data security, which has been improved with the help of CP-ABE regarding detailed access policy and data owner. Policy parameters are managed by the DAC. In the proposed methodology, the data owner uses the blockchain to control security and access to the data. Finally, the paper has come up with a set of findings in order to achieve data security and access control for the data owner through the blockchain-based approach. To evaluate the performance of the proposed method, MATLAB was used. The proposed technology also contrasts with existing technologies, such as the Blockchain-Based Security Sharing Scheme for Personal Data (BSSPD) as well as the Rivest-Shamir-Adleman Algorithm (RSA) and Elliptic Curve Cryptography (ECC).

Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.

The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications-by which time essential details are no longer accessible. Thus, we propose an approach to research informatics in which FAIR principles are applied continuously, from the inception of a research project and ubiquitously, to every data asset produced by experiment or computation. We suggest that this seemingly challenging task can be made feasible by the adoption of simple tools, such as lightweight identifiers (to ensure that every data asset is findable), packaging methods (to facilitate understanding of data contents), data access methods, and metadata organization and structuring tools (to support schema development and evolution). We use an example from experimental neuroscience to illustrate how these methods can work in practice.

Sweet tweets! Evaluating a new approach for probability-based sampling of Twitter

As survey costs continue to rise and response rates decline, researchers are seeking more cost-effective ways to collect, analyze and process social and public opinion data. These issues have created an opportunity and interest in expanding the fit-for-purpose paradigm to include alternate sources such as passively collected sensor data and social media data. However, methods for accessing, sourcing and sampling social media data are just now being developed. In fact, there has been a small but growing body of literature focusing on comparing different Twitter data access methods through either the elaborate firehose or the free Twitter search or streaming APIs. Missing from the literature is a good understanding of how to randomly sample Tweets to produce datasets that are representative of the daily discourse, especially within geographical regions of interest, without requiring a census of all Tweets. This understanding is necessary for producing quality estimates of public opinion from social media sources such as Twitter. To address this gap, we propose and test the Velocity-Based Estimation for Sampling Tweets (VBEST) algorithm for selecting a probability based sample of tweets. We compare the performance of VBEST sample estimates to other methods of accessing Twitter through the Search API on the distribution of total Tweets as well as COVID-19 keyword incidence and frequency and find that the VBEST samples produce consistent and relatively low levels of overall bias compared to common methods of access through the Search API across many experimental conditions.

Building the Sierra Leone Ebola Database: organization and characteristics of data systematically collected during 2014–2015 Ebola epidemic

PurposeDuring the 2014–2016 Ebola outbreak in West Africa, the Sierra Leone Ministry of Health and Sanitation (MoHS), the US Centers for Disease Control and Prevention, and responding partners under the coordination of the National Ebola Response Center (NERC) and the MoHS's Emergency Operation Center (EOC) systematically recorded information from the 117 Call Center system and district alert phone lines, case investigations, laboratory sample testing, clinical management, and safe and dignified burial records. Since 2017, CDC assisted MoHS in building and managing the Sierra Leone Ebola Database (SLED) to consolidate these major data sources. The primary objectives of the project were helping families to identify the location of graves of their loved ones who died at the time of the Ebola epidemic through the SLED Family Reunification Program and creating a data source for epidemiological research. The objective of this paper is to describe the process of consolidating epidemic records into a useful and accessible data collection and to summarize data characteristics, strength, and limitations of this unique information source for public health research. MethodsBecause of the unprecedented conditions during the epidemic, most of the records collected from responding organizations required extensive processing before they could be used as a data source for research or the humanitarian purpose of locating burial sites. This process required understanding how the data were collected and used during the outbreak. To manage the complexity of processing the data obtained from various sources, the Sierra Leone Ebola Database (SLED) Team used an organizational strategy that allowed tracking of the data provenance and lifecycle. ResultsThe SLED project brought raw data into one consolidated data collection. It provides researchers with secure and ethical access to the SLED data and serves as a basis for the research capacity building in Sierra Leone. The SLED Family Reunification Program allowed Sierra Leonean families to identify location of the graves of loved ones who died during the Ebola epidemic. ConclusionsThe SLED project consolidated and utilized epidemic data recorded during the Sierra Leone Ebola Virus Disease outbreak that were collected and contributed to SLED by national and international organizations. This project has provided a foundation for developing a method of ethical and secure SLED data access while preserving the host nation's data ownership. SLED serves as a data source for the SLED Family Reunification Program and for epidemiological research. It presents an opportunity for building research capacity in Sierra Leone and provides a foundation for developing a relational database. Large outbreak data systems such as SLED provide a unique opportunity for researchers to improve responses to epidemics and indicate the need to include data management preparedness in the plans for emergency response.

Database isolation by scheduling

Transaction isolation is conventionally achieved by restricting access to the physical items in a database. To maximize performance, isolation functionality is often packaged with recovery, I/O, and data access methods in a monolithic transactional storage manager. While this design has historically afforded high performance in online transaction processing systems, industry trends indicate a growing need for a new approach in which intertwined components of the transactional storage manager are disaggregated into modular services. This paper presents a new method to modularize the isolation component. Our work builds on predicate locking, an isolation mechanism that enables this modularization by locking logical rather than physical items in a database. Predicate locking is rarely used as the core isolation mechanism because of its high theoretical complexity and perceived overhead. However, we show that this overhead can be substantially reduced in practice by optimizing for common predicate structures. We present DIBS, a transaction scheduler that employs our predicate locking optimizations to guarantee isolation as a modular service. We evaluate the performance of DIBS as the sole isolation mechanism in a data processing system. In this setting, DIBS scales up to 10.5 million transactions per second on a TATP workload. We also explore how DIBS can be applied to existing database systems to increase transaction throughput. DIBS reduces per-transaction file system writes by 90% on TATP in SQLite, resulting in a 3X improvement in throughput. Finally, DIBS reduces row contention on YCSB in MySQL, providing serializable isolation with a 1.4X improvement in throughput.

An Efficient Dual-Channel Data Storage and Access Method for Spaceborne Synthetic Aperture Radar Real-Time Processing

With the development of remote sensing technology and very large-scale integrated circuit (VLSI) technology, the real-time processing of spaceborne Synthetic Aperture Radar (SAR) has greatly improved the ability of Earth observation. However, the characteristics of external memory have led to matrix transposition becoming a technical bottleneck that limits the real-time performance of the SAR imaging system. In order to solve this problem, this paper combines the optimized data mapping method and reasonable hardware architecture to implement a data controller based on the Field-Programmable Gate Array (FPGA). First of all, this paper proposes an optimized dual-channel data storage and access method, so that the two-dimensional data access efficiency can be improved. Then, a hardware architecture is designed with register manager, simplified address generator and dual-channel Double-Data-Rate Three Synchronous Dynamic Random-Access Memory (DDR3 SDRAM) access mode. Finally, the proposed data controller is implemented on the Xilinx XC7VX690T FPGA chip. The experimental results show that the reading efficiency of the data controller proposed is 80% both in the range direction and azimuth direction, and the writing efficiency is 66% both in the range direction and azimuth direction. The results of a comparison with the recent implementations show that the proposed data controller has a higher data bandwidth, is more flexible in its design, and is suitable for use in spaceborne scenarios.