Data Communication Research Articles

SIMULATING SUBJECTIVITY - BAUDRILLARD AND THE POLITICAL ECONOMY OF LLMS

Despite the rise in commercial applications of LLMs, scholarship has neglected an in-depth appreciation of the free contribution of subjects communicative social action as the engine of training data production as a necessary moment in digital processes of valorisation. This issue was popular in the analyses of the post-operaist tradition of free labour, but have since been missing in examinations of more recent technological developments, specifically in what concerns AI. Although the work of Baudrillard is semi-frequently evoked in descriptive critical assessments of new technologies, there is little integration of Baudrillard's work in contemporary studies of AI. This paper aims at contributing in this direction, by showcasing the utility of Baudrillard’s concepts of simulation, subject function, masses, and the social, for an understanding of immaterial free labour in the context of Large Language Models (LLMs). Drawing from the recent phenomena of the sale of Reddit communications content to OpenAI as training data, I propose the notion of digital common as the pre-trained collected and recorded data of actual human communication through digital systems. I propose the framework of the subject function as expounded by Baudrillard, in both its individual and collective aspects, as a necessary conjuncture to understand how commercial applications of conversational LLMs fit into the broader landscape of digital political economy. I suggest that the role played in this specific application derives from the appropriation of freely generated user-data as constituting the digital common and as carrying a specific conception of subjectivity as functional.

1380 nm VCSELs using surface-activated bonding of GaAs-based DBRs on a Ge substrate

Abstract A wafer-bonded Vertical-Cavity Surface-Emitting Laser (VCSEL) was demonstrated by surface-activated bonding (SAB) of a GaAs-based distributed Bragg reflector (DBR) on a Ge substrate. Current injection lasing was achieved at the 1380-nm band under continuous- wave operation with AlGaInAs-based multi-quantum wells and a buried tunnel junction for current and optical confinement. The thermal conductivity of the VCSELs bonded by SAB was superior to that of InP VCSELs owing to high heat dissipation of GaAs DBR. This bonding method is suitable for short-wavelength infrared VCSELs and various other applications, including sensing, data communication, and silicon photonics.

Data Communication Challenges of Connected and Automated Vehicles in Rural Areas

Data Communication Challenges of Connected and Automated Vehicles in Rural Areas

Optimized transmission of multi-path low-latency routing for electricity internet of things based on SDN task distribution.

With the continuous development of 5G network, how to further improve the routing and transmission efficiency of electric power IoT has become a popular research at present. Facing the current problems of high data transmission delay, low efficiency, and large task volume in the electric power IoT, this research combines software-defined networking to design a multi-path low-latency routing and transmission model under the concept of task allocation. First, the grid data communication network model and network slicing technology in 5G power IoT are introduced. On this basis, considering the data transmission in the core network in the power IoT, a multi-path low-latency routing optimization transmission model based on software-defined network task allocation is designed by combining the software-defined network controller and task allocation concept. The results indicated that the average delay of the designed model is only 15.78ms when the transmission task size is 10KB and 23.38ms when the transmission task size is 50KB. In addition, the designed model was able to achieve a throughput of 298bps in the local area network and the lowest jitter and packet loss in the wide area network, which are 0.13ms and 0.001%. It can be concluded that the constructed multi-path low-latency routing and transmission model can not only provide theoretical guidance for the optimization of data transmission in the power IoT, but also lay the foundation for the in-depth application and development of software-defined networking in the power IoT and other fields.

The Retention, Access and Re-Use of Communications Data: An Unbreakable Legal Chain?

The Retention, Access and Re-Use of Communications Data: An Unbreakable Legal Chain?

Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.

A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments

Cloud computing has advanced significantly alongside the growth of communication technology and data exchange. Many businesses and organizations now adopt cloud computing solutions and services to enhance flexibility and scalability. However, despite its numerous advantages, cloud computing remains increasingly susceptible to various security threats that can disrupt services and business operations. This highlights the critical need to strengthen the security of cloud environments. In this context, implementing robust protection measures, such as Intrusion Detection Systems (IDS), is essential to mitigate potential threats and safeguard sensitive data. To effectively counter the ever-evolving cyber threats landscape, IDS must possess adaptive capabilities. Hence, integrating Machine Learning (ML) technologies is imperative for the detection of a broad and diverse range of cyber threats, thereby enhancing the overall bolstering the security posture of the environment. This research explores the integration of ML technologies in IDS and examines the application of feature selection methods to identify the key and most significant indicators for attack detection. The study conducts a comparative analysis of five ML techniques, employing two distinct feature selection methods to evaluate their effectiveness in strengthening the security of cloud environments. Using a recently developed, reputable dataset and concentrating on attack types that pose significant threats to cloud environments, our experimental results offer a comprehensive evaluation of these techniques, including a variety of machine learning algorithm performance metrics.

Development and desing for a vehicular care based on a embedded system to prevent car heist

Globally, a common trouble is the inefficiency of security systems in cars, several vehicles are stolen for this reason every year. The purpose on this paper is to make an embedded system for vehicular security. This system has direct communication with a smartphone, adding some control systems manipulated by user. It has an electronic card, as embedded system, that makes communication work, control, data process and voltage source. The system has microcontrollers and satellite communication modules to improve its communication characteristics, making high efficiency. This way, we look to do a social work in the local area to prevent and recover vehicles with this trouble.

Congestion boundary approach for phase transitions in traffic flow

This study aims to understand the phase transitions, between congested and free-flow states, by estimating the congestion boundary. Since the congestion transition process occurs rapidly, the probability density distributions of traffic flow parameters, i.e. speed and density, show bimodal shapes. The congestion boundary is defined as the threshold that decomposes the bimodal distribution. The dedicated short-range communication data and toll collection system data were used to estimate congestion boundaries on the highway section from Seoul tollgate to Shingal junction. The results show that the congestion boundaries for speed, density, and flow rate were estimated to be 66.9 km/h, 22.8 veh/km, and 341.3 veh/5 min, respectively. With the flow rate at the congestion boundary, the capacity was estimated to be between 71.4% and 82.7% of traditional capacities. The congestion boundary approach provides critical thresholds for each dynamic transition phase on highways, offering crucial insights for effective congestion management.

Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.

COMPARATIVE ANALYSIS OF THE LEVEL OF DIGITAL COMPETENCES OF FUTURE TEACHERS IN POLAND AND UKRAINE

Digital transformation requires continuous development from the individual, including the development of media competences. Effective education of the young generation requires teachers to have digital competences. On the other hand, teachers are prepared to work in the era of new technologies at universities, where students gain important knowledge and skills. Therefore, the author focused on the digital/media competences of pedagogy students. According to the Digital Competence of Educators [1], these include information and data skills, communication and collaboration, digital content creation, safety, and problem-solving. The article presents the results of its own research, which showed the level of media competence of pedagogy students in Poland and Ukraine and possible differences in this area noticeable in future teachers from the two European countries. 84 students from Ukraine and 102 from Poland participated took part in the survey. The questionnaire was developed on the basis of relevant sources and tools for examining the level of digital competence of teachers: Digital Competence of Educators [1], Simons, Meeus, Sas [2], Selfie for Teachers [3]. The questionnaire covered three areas: media use, media understanding and use, and media creation and transmission. A Likert scale from 1 to 5 was used. In the analysis of the collected data, the following tests were used: Kolmogorov-Smirnov, Mann-Whitney U. Statistical analysis of the obtained data allowed to confirm the hypothesis regarding the lack of statistically significant differences in the level of digital competences of future teachers. The results of the research indicated certain tendencies - a slightly higher level of media competence of students from Ukraine in terms of, m.in knowledge, knowledge of online communication tools with students, netiquette of the use of tools to communicate with colleagues. Therefore, future teachers from both countries are prepared for rational and selective use of media in their didactic and educational work. It is important to constantly supplement the study programs with content corresponding to technological, economic and socio-cultural development.

An Efficient Steganography Method Based on Chaotic Functions and XOR Operation for Data Hiding

The advancing technology and digitalizing world have increased the importance of secure data transmission. Steganography, a technique that ensures secure data communication, is a critical component of data security. Derived from the term meaning "hidden writing" in Turkish, steganography is based on the principle of embedding the data to be hidden into a carrier medium. While historically applied using primitive methods, steganography has transitioned to the use of modern techniques and methods in today’s digitalized era. In this study, a steganography method based on chaotic functions and the XOR operation is proposed. The proposed method consists of two stages. In the first stage, data embedding, the data to be hidden is first converted into binary format. This binary data is then subjected to an XOR operation with a tent map sequence. The resulting final data is embedded into a grayscale image by determining its embedding positions using a logistic map. In the second stage, data extraction, the embedded message is retrieved using the logistic map, and the extracted message is XORed with the tent map to recover the original data. The effectiveness of the proposed method was evaluated using commonly employed metrics such as PSNR, MSE, and SSIM on images in the literature. The results demonstrate that the proposed method offers a robust structure against steganalysis techniques while ensuring critical security parameters.

Soft Wireless Passive Chipless Sensors for Biological Applications: A Review.

Soft wireless passive sensors have been applied in biological, engineering, and other fields due to their advantages in powerless supply and remote data transmission. External information is obtained by soft wireless passive sensors via the external coils based on electromagnetic induction. The purpose of this review paper is to outline the biological applications of soft wireless passive chipless sensors and provide a classification of wireless passive sensors and an overall explanation of the main work. Three kinds of soft wireless sensors, soft wireless passive LC-resonant sensors, soft wireless radio frequency (RF) sensors, and soft wireless surface acoustic wave (SAW) sensors, are introduced with their working principles, equitant circuits, and biological applications. Soft wireless passive sensors with integrated LC-resonant units are applied to physical quantity measurements for denoting the mapping relationship between the frequency resonance and the monitored object. Utilizing the electromagnetic field principle, RF sensors enable wireless measurements and data exchange of physical parameters. SAW sensors with piezoelectric substrates are applied to physical parameter monitoring using guided waves in monitoring objects. Soft wireless passive sensors aim to monitor biological health without an external power supply or wired data communication, which would bring increased convenience to the lives of the people who use them.

Application of WebGIS in Managing and Monitoring Greenery in Schools: A Case Study at the Thu Dau Mot University

This research developed a WebGIS system for managing green spaces at the Thu Dau Mot University. The system utilizes PostgreSQL for data storage and GeoServer for spatial data sharing, enabling efficient communication of field data. The platform's user-friendly interface, combined with QGIS tools and PostGIS/PostgreSQL, allows for easy data visualization and management, even for users without GIS expertise. This cost-effective solution facilitates quick information sharing, fosters a shared understanding of the data, and enables more efficient and sustainable management of the university's green spaces.

Prototype‐Based Collaborative Learning in UAV‐Assisted Edge Computing Networks

ABSTRACTContextThe rise of artificial intelligence of things (AloT) has enabled smart cities and industries, and UAV‐assisted edge computing networks are an important technology to support the above scenarios. UAV‐assisted refers to leveraging UAVs as a dynamic, flexible infrastructure to assist edge network data processing and communication tasks. Multiple UAVs can use their own resources, and collaborate edge servers to train artificial intelligence (Al) models.ObjectiveCompared with cloud‐based collaborative computing scenarios, UAV‐assisted edge collaborative learning can reduce training and inference delays and improve user satisfaction. However, UAV‐assisted edge networks scenario brings new challenges in terms of transmission burden and energy consumption.MethodThis paper proposes a prototype‐based joint optimization and training software system. The system consists of an optimization module and a training module. The optimization module first models an optimization problem including energy consumption and prototype error. Then it solves the optimization problem by problem transformation and plans the location of each UAV given the objects' position. After UAVs fly to the designated area and complete data collection, UAVs and the edge server train a model according to the proposed prototype‐based collaborative training module. Our training module enables multiple UAVs and an edge server to collaboratively train a model by lightweight prototype transmission and prototype aggregation. We also prove the convergence of the proposed collaborative training method.ResultsResults show our method reduces prototype error and energy consumption by at least 12.31% and improves model accuracy by 3.62% with a little communication burden.ConclusionFinally, we verify system performance through experiments.

Research on Waveform Adaptability Based on Lunar Channels

In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments.

Measures of Information Visualizations in Design Studio Education: Designing Climate Change Visualizations

The climate crisis and sustainability are priority issues that are increasingly integrated into all fields of study in universities. For the field of communication design, there is a challenge to deliver effective designs on the climate crisis due to the complexity of the issue and the large volume of scientific data involved. To overcome this challenge, information visualizations are designed to help communicate complex data. Information visualizations, being data-driven, necessitate specific design principles in addition to fundamental graphic design skills. This study investigates the communication of the climate crisis through information visualizations, with the aim of determining the prominent measures and relevant design language that can be incorporated into design studio education. We conducted three focus groups with academics teaching communication design studios and professionals who have work experience in information visualization design and experts who are doing projects on sustainability and the climate crisis. The findings highlight the importance of actionability, enlightening, evoking curiosity, evoking emotions, localization, personification and positivity in designing effective climate change visualizations. This study contributes to the fields of information visualization and communication design by offering measures to enhance climate change visualizations and providing insights for teaching these principles in communication and graphic design studios.

Dion-Jacobson Perovskites with a Ferroelectrically Switchable Chiral Nonlinear Optical Response.

Electrically switchable second harmonic generation (SHG) is highly valuable in electro-optic modulators, which can be deployed in data communication and quantum optics. Coupling circular dichroism (CD) with an electrically controlled SHG process is advantageous because it enhances the signal transmission bandwidth and security while enabling multiple modulation modes for optical logic. However, ferroelectrically switchable chiral second-order nonlinearity is rarely reported. Here, we demonstrate the electro-optic modulation of SHG based on the multipolar effect in a chiral ferroelectric two-dimensional hybrid perovskite s-(FBDA)CdCl4 (s-FBDA = (2s)-2-fluorobutane-1,4-diamine). s-(FBDA)CdCl4 (s-FCC) crystals are highly stable in air, and its SHG response exhibits circular dichroism (i.e., SHG-CD) that can be ferroelectrically switched. The ferroelectrically switchable SHG-CD arises from the reconfiguration of distinct chirality-induced multipolar moments during the ferroelectric switching process. This reconfiguration induces a π/2 circular difference in SHG, leading to a reversal of circularly polarized light sensitivity. As a proof of concept, we demonstrate that the electrically controlled SHG-CD of s-FCC can be used in the transmission of encrypted optical communications.

Enhancing Autonomous Vehicle Safety with Blockchain Technology: Securing Vehicle Communication and AI Systems

In recent years, the rapid development of autonomous vehicles (AVs) has brought new challenges in terms of data security, privacy, and communication integrity. Our research investigates the potential of blockchain technology to improve the security of AVs by securing vehicle communication systems. By integrating blockchain with AI-based predictive algorithms, this approach aims to secure vehicle peer-to-peer communication, reduce traffic congestion, and improve safety for drivers and pedestrians. Blockchain’s decentralized ledger ensures the integrity of data exchange between vehicles and smart city infrastructure and mitigates the risks of cyberattacks such as data manipulation and identity forgery. This paper also examines recent advances in vehicular ad hoc networks (VANETs) and vehicular social networks (VSNs), and it demonstrates how the immutability and cryptographic security of the blockchain can strengthen AV systems. The proposed architecture not only protects user privacy but also decentralizes access to critical data needed for AI-driven decisions, ultimately promoting a safer and more reliable environment for autonomous vehicles.

Unlocking Healthcare Data Potential: A Comprehensive Integration Approach with GraphQL, openEHR, Redis, and Pervasive Business Intelligence

This paper investigates the transformative potential of integrating technical and methodological tools such as GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare. Modern healthcare systems face data silos, interoperability, and efficient data communication challenges. The integration of these technologies offers innovative solutions to address these challenges. GraphQL, known for its flexible data retrieval capabilities, simplifies data communication and integration. openEHR, a standards-based approach to healthcare data management, fosters interoperability through a unified data model. Redis, a scalable data storage and caching system, enhances application performance and real-time data processing. Pervasive Business Intelligence empowers healthcare analytics, aiding data-driven decision-making by enabling an integrated Electronic Health Record. This paper explores these technologies’ benefits, integration possibilities, and synergies. The practical implications of this integration are demonstrated through a real-world case study. The findings underscore the potential to revolutionize healthcare data management, communication, and analysis, improving patient care and operational efficiency.