Data Link Research Articles

Modeling and analysis for group delay mismatch effect on wideband adaptive spatial interference cancellation

The adaptive interference cancellation technique has been widely utilized in radar, GPS, data link, etc., systems to address challenges from external interference, such as co-site and hostile interference. Since the anti-jamming performance of the adaptive interference cancellation technique is sensitive to group delay mismatch between channels, the group delay mismatch becomes one of the main factors that limit the system’s anti-jamming capability. However, the traditional adaptive interference cancellation system’s mathematical model cannot quantitatively characterize the group delay mismatch effect on the wideband interference cancellation performance. In this paper, the mathematical model of the wideband adaptive spatial interference cancellation (ASIC) system is established, which considers the group delay mismatch, to quantitatively analyze the impact of group delay mismatch on the hostile interference cancellation. The mathematical model utilizes the weighted multi-tone signals to fit the wideband interference, and then, delay differences are attached to each tone signal to simulate the group delay mismatch. Then, the analytic expressions of weight and interference cancellation ratio are derived, which consider the interference bandwidth and group delay mismatch, to quantitatively analyze the group delay mismatch effect on the anti-jamming performance of the wideband ASIC system. Simulation results indicate that the theoretical analysis based on the mathematical model of wideband ASIC system are accurate, which can achieve the quantitative analysis of the group delay mismatch effect on the WIC performance.

Designing a Cloud-Based MES-SaaS Platform Model in Precision Machining

Smart manufacturing environments are spreading at home and abroad. As a result, SMEs are also recognizing the need for digital transformation. However, they are facing difficulties in introducing MES/ERP software due to the initial cost burden and lack of resources. Under these circumstances, this paper aims to design a cloud-based subscription MES platform that enables SMEs specializing in the precision machining industry to operate efficient production processes and improve productivity and competitiveness. To build a cloud-based subscription MES system, we collect and store manufacturing interlocking data based on the AAS standard and utilize data link sockets such as the EDC standard, which is an important factor in securing compatibility and integration between various systems. Therefore, we aim to design an MES-SaaS platform that enables SMEs to utilize MES and ERP software in line with the growth of the smart manufacturing environment.

On understanding and predicting issue links

Stakeholders in software projects use issue trackers like JIRA or Bugzilla to capture and manage issues, including requirements, feature requests, and bugs. To ease issue navigation and structure project knowledge, stakeholders manually connect issues via links of certain types that reflect different dependencies, such as Epic-, Block-, Duplicate-, or Relate- links. Based on a large dataset of 16 JIRA repositories, we study the commonalities and differences in linking practices and link types across the repositories. We then investigate how state-of-the-art machine learning models can predict common link types. We observed significant differences across the repositories and link types, depending on how they are used and by whom. Additionally, we observed several inconsistencies, e.g., in how Duplicate links are used. We found that a transformer model trained on titles and descriptions of linked issues significantly outperforms other optimized models, achieving an encouraging average macro F1-score of 0.64 for predicting nine popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic- links, the model achieves top F1-scores of 0.89 and 0.97, respectively. If we restrict the task to predict the mere existence of links, the average macro F1-score goes up to 0.95. In general, the shorter issue text, possibly indicating precise issues, seems to improve the prediction accuracy with a strong negative correlation of -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document} 0.73. We found that Relate-links often get confused with the other links, which suggests that they are likely used as default links in unclear cases. Our findings particularly on the quality and heterogeinity of issue link data have implications for researching and applying issue link prediction in practice.

RETRACTED: A 6U CubeSat Platform for Low Earth Remote Sensing: DEWASAT-2 Mission Concept and Analysis

This paper presents an in-depth analysis of DEWASAT-2, a 6U CubeSat designed for low Earth remote sensing applications. DEWASAT-2 is equipped with two payloads: a high-resolution camera for Earth observation and a spectrometer for detecting greenhouse gases. This paper describes the mission analysis and design of DEWASAT-2 as well as the link budget, power budget, and data budget. Additionally, the paper includes simulations and plots that illustrate the access times, lifetime, and other important parameters of the CubeSat. The outcomes presented in this article emphasise that DEWASAT-2 will contribute to fulfilling and enhancing various use cases of the Dubai Electricity and Water Authority (DEWA) network such as weather monitoring and forecasting and the detection of seawater salinity.

Data conversion control of virtual network devices in cloud computing: A deep reinforcement learning approach

As the response time of virtual space task requests is affected by different data conversion processes, it becomes more serious to solve the load imbalance problem between the underlying nodes and the underlying data links. The traditional least square method needs to distinguish each node for the remapping of the original virtual space. After the trajectories in different spaces and times are mapped again, it is easier to cause new resource bottlenecks. Aiming at the necessary virtual space remapping cycle of cloud service nodes, this paper selects the nodes with the highest upper and lower limits of load rate according to the dynamic critical value of each node's load rate. It migrates part of the virtual network space on the overloaded service nodes to the non-important nodes with a lower load rate, which is to avoid the gradual formation of new resource bottlenecks after remapping. The data transformation model based on reinforcement learning can further improve the acceptance rate of virtual space requests and reduce the computational overhead caused by space remapping. The experiments compare the performance evaluation of different algorithm models on the simulation platform data conversion tasks, including complex instructions test, virtual network performance test, average remapping link overhead, and root mean square error of the underlying node load rate test. The test results indicate that the calculation accuracy of the proposed method is 7.25% higher than other comparison methods, and the computational overhead is reduced by 15.22%. The overall performance is improved by 10.74%. It is verified that the technology in this paper improves the data conversion control method of virtual network devices in the cloud computing environment, and has practical significance for virtual network remapping and data services in cloud computing.

The Prospect Forecast of the Market Development of Chinese Elderly Care Institutions

With the aggravation of population aging and economic development, China is not only facing more and more serious pension pressure, but also has greater capacity to support pension services, which has brought about the vigorousdevelopment of pension services. Obviously, it can be expected that there may be some internal relations among these indicators. For this reason, this paper selects relevant data from China's civil affairs statistical yearbook, takes the number of beds used by China's pension institutions as the main development indicator of the pension industry, establishes data links for different indicators such as GDP, the number and proportion of China's aging population, and uses linear regression with the least square method to establish mathematical models. After testing the regression coefficients, strong linear results are obtained. It can be concluded that there is a linear relationship between the development of pension services and the aging of the population and the development of GDP.

Ultra-long haul high speed 96 × 40 GB/s radio over IsOWC system employing advanced modulation scheme

Abstract This paper successfully demonstrates that point-to-point (P2P) superdense wavelength division multiplexing (SDWDM) based on the radio over intersatellite optical wireless communication (Ro-IsOWC) system of 96 channels separated by a 100 GHz bandwidth with 40 GB/s data rate on each stream at a 10,000 km link range has been designed and investigated. The performance of the proposed work has been evaluated by considering the comparison of advanced modulation schemes such as carrier-suppressed return-to-zero (CSRZ), duobinary return-to-zero (DRZ), and modified duobinary return-to-zero (MDRZ) with an optical preamplifier on the bases of bit error rate (BER), quality factor, eye height, and optical signal-to-noise ratio (OSNR) in the clear weather condition. Moreover, the effects of internal parameters such as transceiver aperture diameter and gain of an optical amplifier also have been analyzed with an optimized modulated system. Apart from this, the system performance also has been assessed under varying data rates and OWC frequency ranges with the best-suited modulation format. It has been found that the CSRZ modulation format has shown robust performance and can achieve BER as better as 10−09 with a 5.5 dB quality factor for all wavelengths. It also has been observed that the quality of the received signal deteriorates below an unacceptable value for data rate and link distance beyond 40 GB/s and 10,000 km, respectively.

Cross-Layer Based QoS Aware Load-balancing Multi-Path Routing Protocol over Wireless Multimedia Sensor Networks

Abstract: Currently, the utilization of WMSNs in different real-time and non-real-time applications requires an excessive amount of bandwidth for reliable data delivery. The unique features of WMSNs are significantly challenging in satisfying the QoS requirements in such application-specific environments and balancing the traffic load among the devices. The provision of reliable multipath routing is a cornerstone in fulfilling the QoS requirements of WMSNs. Selecting multiple optimal paths between a source and destination based on peculiar routing metrics enhances the performance of QoS routing. Generally, routing protocols exploit several routing metrics, such as delay, remaining energy of nodes, hop count, available bandwidth, and packet loss rate in path selection to attain high reliability in data delivery. Many existing routing protocols only consider the network layer parameters, whereas it lacks focus on the data link and physical layer parameters, which creates a severe impact on the degradation of QoS. In addition to that, varying bandwidth channels create interference in multimedia data delivery and degrade the network performance. Designing a multipath routing protocol by considering cross-layer parameters offer a promising solution to optimize the WMSN performance. In cross-layer design, diverse protocol layers support the routing decisions adaptively by perceiving the dynamic characteristics of the wireless medium, resulting in fair use of scarce resources with high QoS. A Cross-Layer Based QoS Aware Load-Balancing Multipath Routing Protocol over Wireless Multimedia Sensor Networks was the goal of the study's five design objectives. The study and analysis of QoS and cross-layer-based routing algorithms for WMSNs was the initial goal. Secondly, a Deep Learning prioritization-based packet classifier to divide traffic according to priority. To ensure fair resource consumption and distribution of multimedia traffic, the third goal was to design and create a cross-layer optimizer model for optimal multiple disjoint route selection using machine learning techniques. The development of a cutting-edge channel-scheduling algorithm was goal four. It was designed to efficiently assign low-interference channels to communication devices in order to lower the packet drop rate in real-time packet delivery. Last but not least, a security method for Wireless Multimedia Sensor Networks' Cross-Layer based multipath routing protocol.

Analysis Performance of FSO System Using RZ and NRZ Technique at Various Data Rate and Link Distance

Analysis Performance of FSO System Using RZ and NRZ Technique at Various Data Rate and Link Distance

Electrical programmable multilevel nonvolatile photonic random-access memory

Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase-Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material (Ge2Sb2Se5, GSSe) with ultralow absorption in the amorphous state. A zero-static-power and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100× improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual microheaters validating performance tradeoffs. Experimentally we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example.

Research on Transmission Control of Airborne Communication Data Link System Based on Artificial Fish Swarm Algorithm

Research on Transmission Control of Airborne Communication Data Link System Based on Artificial Fish Swarm Algorithm

Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

PurposeMany libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.Design/methodology/approachThe authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.FindingsThe authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.Originality/valueThe authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

A Survey on Time-Sensitive Networking Standards and Applications for Intelligent Driving

Stimulated by the increase in user demands and the development of intelligent driving, the automotive industry is pursuing high-bandwidth techniques, low-cost network deployment and deterministic data transmission. Time-sensitive networking (TSN) based on Ethernet provides a possible solution to these targets, which is arousing extensive attention from both academia and industry. We review TSN-related academic research papers published by major academic publishers and analyze research trends in TSN. This paper provides an up-to-date comprehensive survey of TSN-related standards, from the perspective of the physical layer, data link layer, network layer and protocol test. Then we classify intelligent driving products with TSN characteristics. With the consideration of more of the latest specified TSN protocols, we further analyze the minimum complete set of specifications and give the corresponding demo setup for the realization of TSN on automobiles. Open issues to be solved and trends of TSN are identified and analyzed, followed by possible solutions. Therefore, this paper can be an investigating basis and reference of TSN, especially for the TSN on automotive applications.

Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques.

In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 9 ( (2), ), eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.

Applications of Nanosatellites in Constellation: Overview and Feasibility Study for a Space Mission Based on Internet of Space Things Applications Used for AIS and Fire Detection.

In some geographically challenging areas (such as deserts, seas, and forests) where direct connectivity to a terrestrial network is difficult, space communication is the only option. In these remote locations, Internet of Space Things (IoST) applications can also be used successfully. In this paper, the proposed payload for IoST applications demonstrates how an Automatic Identification System (AIS) and a fire detection system can be used effectively. A space mission based on efficient and low-cost communication can use a constellation of nanosatellites to better meet this need. These two applications, which use a constellation of nanosatellites, can provide relevant university-level data in several countries as an effective policy for the transfer of space technology in an educational initiative project. To enhance educational participation and interest in space technology, this paper shares the lessons learned from the project feasibility study based on an in-depth design of a nanosatellite with several analyses (data budget, link budget, power budget, and lifetime estimation). Lastly, this paper highlights by experiments the development and application of a cost-effective sensor node for fire detection and the use of GPS to enable AIS capabilities in the IoST framework.

AL-aided AMC in a multi-user white-light OFDMA VLC system over a light-diffusing fiber loop.

To develop an adaptive modulation scheme for flexible high-speed multi-user visible light communication (VLC), automatic modulation classification (AMC) is adopted for monitoring the modulation formats of different subcarrier groups. An AMC scheme based on a joint convolutional neural network (CNN), active learning (AL), and data augmentation (DA) is demonstrated over an orthogonal frequency division multiplexing access (OFDMA) VLC system. The configuration of the diffuse white-light VLC system is combined with a pair integrated transceiver module, a light-diffusing fiber (LDF), and a wireless channel, which can provide white-light illumination and ubiquitous access. Within the forward error correction (FEC) threshold, the data rates of the white-light VLC links can reach 325.5 Mbps with a bit error rate (BER) of 2.163 × 10-3. An experiment with two-user access via the proposed VLC link with an unequal bandwidth allocation was demonstrated. The performance of the AL-aided CNN AMC scheme also shows a classification accuracy rate of 95.48% for the constellation diagrams of different subcarriers of the OFDMA signal over 240 training samples and faster convergence than a CNN-based AMC.

Massively scalable Kerr comb-driven silicon photonic link

The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limited ‘chip-escape’ bandwidth densities, is perhaps the singular factor determining the scalability of future systems. Using light to send information between compute nodes in such systems can dramatically increase the available bandwidth while simultaneously decreasing energy consumption. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. Although previous high-bandwidth demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data-centre interconnects require a compact on-chip form factor for these operations. Here we demonstrate a massively scalable chip-based silicon photonic data link using a Kerr comb source enabled by a new link architecture and experimentally show aggregate single-fibre data transmission of 512 Gb s−1 across 32 independent wavelength channels. The demonstrated architecture is fundamentally scalable to hundreds of wavelength channels, enabling massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

AKAASH: A realizable authentication, key agreement, and secure handover approach for controller-pilot data link communications

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

INVESTIGATION OF THE APPLICATION OF CPDLC TO AERODROME AIR TRAFFIC CONTROL PROCEDURES

This paper analyzes the application of Controller Pilot Data Link Communication (CPDLC) technology to different air traffic control sectors. It also presents the analysis of the performed research and tests. A CPDLC program is being developed that is adapted for aerodrome flight control. The program takes into account the advantages and disadvantages of other software, as well as the recommendations of other authors. The program is developed using JAVASCRIPT and HTML programming languages. The tests of the developed CPDLC program are performed in the Expert NITA flight control simulator. The CAPAN method is used for data analysis. Analysis of changes in workload, language errors, and time saved using different communication methods during simulations is also performed.

Analysis on BDS-3 Autonomous Navigation Performance Based on the LEO Constellation and Regional Stations

The global navigation satellite system (GNSS) is developing rapidly, and the related market applications and scientific research are increasing. Studies based on large low Earth orbit (LEO) satellite constellations have become research hotspots. The global coverage of the LEO constellation can reduce the dependence of navigation satellites on ground-monitoring stations and improve the precise orbit determination (POD) accuracy of navigation satellites. In this paper, we simulate various LEO satellite constellations (with 12, 30, and 60 satellites), along with ground stations’ observation data, to examine the impact of LEO satellites on the precision of the BeiDou-3 Global Navigation Satellite System (BDS-3) in terms of its POD accuracy. Using the simulated observation data of both LEO satellites and ground-monitoring stations, we analyze the integrated orbit determination for the LEO and BDS-3 satellites. The findings reveal that the 3D orbital accuracy of BDS-3 is 9.51 dm by using only seven ground-monitoring stations, and it is improved to a centimeter level after adding the LEO constellations. As the number of LEO constellation satellites increases, the impact on improving accuracy gradually diminishes. In terms of time synchronization accuracy in the BDS-3, compared to the results of clock offset using only ground stations, the addition of 12 LEO satellites resulted in an improvement of 49% for RMS1(root mean square) and 52% for RMS2 (standard deviation), the addition of 30 LEO satellites resulted in an improvement of 66% for RMS1 and 70% for RMS2, and the addition of 60 LEO satellites resulted in an improvement of 87% for RMS1 and 90% for RMS2. The integrated orbit determination of the LEO and BDS-3 satellites constellation greatly improves the accuracy of time synchronization. In addition, we also use simulated inter-satellite link (ISL) data to perform enhanced BDS-3 satellites POD and time synchronization experiments. The experiments showed that the orbit determination accuracy of the seven sta (seven stations) and ISL scheme is comparable to that of the seven sta and LEO12 scheme, and that the time synchronization accuracy of the seven sta and ISL scheme is slightly worse. The preliminary experiments showed that the LEO satellite could enhance the orbit determination accuracy of BDS-3 and obtain a higher time synchronization accuracy.