Access Technologies Research Articles

Transformational Healthcare Leadership in Improving Tele-Health System and Safety Performance Sustainability in University Teaching Hospitals

In developed countries, telehealth system applications are available in hospitals, hospital-based telemedicine centers, and rehabilitation centers. However, in contrast to developing countries, the telehealth system has not been implemented properly. Countries that implement telehealth systems experience different benefits, especially in developed countries. In developing countries, implementation remains challenging due to the requirement for literacy about the telehealth system itself in addition to using smartphone and tablet cellular technology for access. This research is motivated by concerns about the role of transformational leadership in providing a telehealth system to ensure the safety of medical personnel. The study adopts a quantitative approach with primary data collected at three major Indonesian university teaching hospitals—Gajah Mada Hospital, Diponegoro National Hospital, and Sebelas Maret Hospital—with medical personnel and administrative staff as the observation units. Hypothesis testing is conducted using Smart PLS 3.0. The study’s results reveal that traditional health services remain problematic despite efforts to make long-term changes. This study aligns with previous research, emphasizing that strategic flexibility serves as a proactive and reactive strategy for effecting long-term changes in key aspects of a company fostering innovation processes that contribute to sustainable business growth. Received: 21 November 2023 / Accepted: 12 March 2024 / Published: 5 May 2024

Mapping of school teachers’ digital competency in the context of digital infrastructure: a systematic review and empirical study of India

PurposeThis research paper investigates the theoretical frameworks encompassing a nuanced analysis of the digital divide in the Indian educational context, recognising that it is not merely a matter of technology access but also the ability to effectively use the resource for enhancing learning outcomes. This research provides valuable insights for fostering a more equitable and digitally inclusive learning environment by integrating conceptual insights with empirical evidence. The New Education Policy (NEP), India 2020 firmly emphasises the appropriate integration of technology into the teaching-learning process to develop relevant competencies. The pertinent question is, for India to conquer the second digital divide challenge, is the pace of technology accessibility and skill development sufficient?Design/methodology/approachThe paper is a desk research, using secondary data from the Unified District Information System for Education (UDISE+), the Indian Government database of schools. A structured dataset has been created for all years, where states are grouped in descending ranking order of availability of infrastructure and teachers trained. A colour key segregates the States into three zones demonstrating their different levels of performance – high (green), moderate (blue) and low (yellow). The purpose is to identify state/s that have moved from one zone to another and, thereafter, analyse the reasons behind the movement.FindingsAlmost all states remained in the same digital resource availability zone for the four years studied, except for a limited few. Despite government interventions through higher budget allocation and targeted policies, growth rates of teacher training in computer usage slowed down post-COVID-19. A high positive correlation between Teachers' training in computer usage and the availability of computer and Internet facilities in schools indicates that an increase in digital infrastructure in schools is highly linked to teachers' training in computer usage and would ultimately translate into better use of digital resources to impart equitable education opportunities.Research limitations/implicationsPrimary data collection through interviews might have added to the critical findings. Therefore, researchers are encouraged to test the proposed propositions further on a case-by-case basis for any state under consideration.Practical implicationsEnhancing digital infrastructure in schools and building digital competence in teachers must be understood in the context of the learning organisation and the beneficiaries' attitudes at the meso-level to expand stakeholder motivation towards digital internalisation. This requires continuous engagement with education institutions as professional learning organisations, which will thereby help develop a decentralised context for teacher competency building. Collaboration, continuous monitoring of the outcomes of professional development programs, and sharing best practices are crucial in improving teacher readiness for digital education.Social implicationsAccess to tangible resources, such as computers, Internet connectivity and educational software, and developing intangible resources, such as teacher digital competencies, will play a pivotal role in shaping students' learning experiences. By studying the discrepancies in digital resource accessibility and teacher technology adoption, this research endeavours to add to the efforts towards enhancing the educational landscape.Originality/valueThis paper seeks to address a critical issue in the Indian education system and contribute to the ongoing effort to prevent the widening of the second and third digital divide in schools, and help achieve UN SDG Goals 4 and 10.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Iot Based Electric Meter Tampering Detector

Abstract: IoT based smart and multifunctional energy meter reading of automatic meter using ATmegha kit. It has the ability to connect to the public database maintained by the energy supplier through computing and energy meter tamper detection. It can prevent theft detection for unregistered users and save losses as a result. A unique feature of this meter is its Internet of Things based functionality. Metmegha. In this case, the system will use the wireless system communication protocol. Wireless is used because the application requires high data rate, low power consumption and low cost. This article shows the ATmegha remote meter reading system. This is to solve the shortcomings of traditional ATmega Meter Reading technology by combining the features of wireless technology with Microcontroller ATMega16. The hardware design was implemented and then the usage of the ATmegha meter was analyzed. There are many opportunities for errors, processing delays, meter confusion, and misuse of electricity and other resources. More than one worker is needed; One group of workers must record the readings, and others must reduce electricity if payment is not made on time.

Wireless Temperature Measurement for Curved Surfaces Based on AlN Surface Acoustic Wave Resonators.

In this paper, we propose a novel method for temperature measurement using surface acoustic wave (SAW) temperature sensors on curved or irregular surfaces. We integrate SAW resonators onto flexible printed circuit boards (FPCBs) to ensure better conformity of the temperature sensor with the surface of the object under test. Compared to traditional rigid PCBs, FPCBs offer greater dynamic flexibility, lighter weight, and thinner thickness, which make them an ideal choice for making SAW devices working for temperature measurements under curved surfaces. We design a temperature sensor array consisting of three devices with different operating frequencies to measure the temperature at multiple points on the surface of the object. To distinguish between different target points in the sensor array, each sensor operates at a different frequency, and the operating frequency bands do not overlap. This differentiation is achieved using Frequency Division Multiple Access (FDMA) technology. Experimental results indicate that the frequency temperature coefficients of these sensors are -30.248 ppm/°C, -30.195 ppm/°C, and -30.115 ppm/°C, respectively. In addition, the sensor array enables wireless communication via antenna and transceiver circuits. This innovation heralds enhanced adaptability and applicability for SAW temperature sensor applications.

Frequency migration challenges and strategic decisions-making in broadband wireless access networks

Purpose: The impetus for this study lies in the pressing need for telecommunication companies to adapt to the rapidly evolving technological landscape and regulatory environment to maintain competitiveness and meet increasing consumer demands. Method: This study conducted a comprehensive analysis using SWOT analysis, Qualitative Data Analysis, and the Analytical Hierarchy Process (AHP) to determine the best course of action. Results: This study navigates through the initial stages of identifying the strategic, operational, and financial implications of PT. XYZ Frequency Migration. It delves into the background of Broadband Wireless Access (BWA) technology, the significance of the 3.3 GHz and 10 GHz bands in telecommunications, and the broader context of industry trends necessitating this shift. Methodologically, this research integrates SWOT, stakeholder, qualitative data analysis (QDA), and the Analytical Hierarchy Process (AHP) to provide a holistic understanding of the complexities of migration. This multi-faceted approach facilitates a comprehensive evaluation of the technological, operational, and strategic aspects of migration and offers nuanced insights into the challenges and opportunities inherent in the transition to the 10 GHz band. The study proposes that the convergence of various factors necessitates a strategic realignment that encompasses operational efficiency, technical excellence, financial prudence, regulatory compliance, and customer focus to effectively adapt to the 10 GHz spectrum. Contributions: This study contributes to the academic and practical understanding of network migration within the telecommunications sector, offering nuanced insights into the complexities of transitioning to higher frequency bands. It provides a framework for other telecommunications entities considering similar migrations, emphasizing the importance of a multifaceted strategic approach to navigating challenges and leveraging opportunities presented by such technological advancements.

Artificial intelligence driven cognitive optimization and predictive analysis using blockchain privacy-based machine learning model

According to the cognitive radio paradigm, spectrum sensing, decision-making, sharing, and mobility phases can be integrated to enable both authorised and unauthorised users to coexist in the radio-electric spectrum to the fullest extent possible. Using a blockchain privacy model and ML approaches in a B5G network, this study suggests a new approach to cognitive network optimisation and predictive analysis which stands for blockchain privacy-based transfer encoder neural networks. One further step is to optimise the network using binary swarm optimisation (BSMO). Bandwidth efficiency, throughput, forecast accuracy, and quality of service are the experimentally measured variables for a given number of channels and users. An innovative QoS-based optimisation phase and two separate decision-making procedures are proposed for usage in a proactive approach. One use of artificial neural networks (ANNs) is the prediction of future traffic loads for different radio access technologies (RATs), which employ different bands of the electromagnetic spectrum.

Reducing Non-Revenue Water

Non-Revenue water (NRW) refers to water produced and lost before reaching consumers. The summary focuses on the importance of reducing Non-Revenue water (NRW), as it increases water availability and the revenues of drinking water. This reduction is achieved when we install the flow meters and water pressure devices at water sources or inlets and exits of isolated areas. We can calculate the amount of water supplied to the area, comparing it with the calculated amount and determining the volume of water not subject to accounting. To solve this, actions like leak detection to determine the location of hidden connections and installation of effective water meters. Supervisory Control and Data Access (SCADA) systems have a role in water management, providing control and real-time monitoring, early problem detection, improved efficiency, and support decision making. When we use Supervisory Control and Data Access (SCADA) technology, we can reduce water losses, ensure reliable water supplies, and contribute to city water management practices. Reducing inaccurate billing or theft can increase revenue. Also, reducing physical/real losses, like leakages or inefficient distribution systems, engage water and wastewater companies to postpone investment in water resource development. To achieve the challenge, cooperation between departments such as network operations, hydraulic analysis, and geographic information systems is required. The commercial sector is important to evaluate and identify the different components that contribute to reducing water loss (NRW).

Access in all areas? a round up of developments in market access and health technology assessment: part 4.

In this latest update, we look at recent developments in market access including the pricing agreement of Libmeldy® by the Beneluxa Initiative, the financial impact of managed entry agreements in Italy and the restructuring of Agenzia Italiana del Farmaco (AIFA). We also highlight the collaboration between FINOSE and the New Expensive Drug (NED) section of the Nordic Pharmaceutical Forum.

GvdsSQL: Heterogeneous Database Unified Access Technology for Wide-Area Environments

GvdsSQL: Heterogeneous Database Unified Access Technology for Wide-Area Environments

An intelligent load balancing algorithm for 5G‐satellite networks

SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

SilentInterpreter: Analysis of Lip Movement and Extracting Speech Using Deep Learning

Lip reading presents a captivating avenue for advancing speech recognition algorithms, leveraging visual cues from lip movements to recognise spoken words. This paper introduces a novel method employing deep neural networks to convert lip motions into textual representations. The methodology integrates convolutional neural networks for visual feature extraction, recurrent neural networks to capture temporal context, and the Connectionist Temporal Classification loss function for aligning lip features with phonemes. Additionally, dynamic learning rate scheduling and a unique callback mechanism for training visualization are incorporated into the process. Post-training on a sizeable dataset, the model demonstrates notable convergence, showcasing its ability to discern intricate temporal correlations. Comprehensive evaluations, combining quantitative metrics and qualitative assessments, validate the model's effectiveness. Visual inspections of lip reading capabilities and standard speech recognition criteria evaluation highlight its performance. The study delves into the impact of various model topologies and hyperparameters on performance, providing valuable insights for future research directions. This research contributes a deep learning framework for accurate and efficient speech recognition, expanding the landscape of lip reading technologies. The findings open paths for further refinement and deployment across diverse domains, including assistive technologies, audio-visual communication systems, and human-computer interaction. Keywords: lip reading, deep learning, convolutional neural networks, recurrent neural networks, CTC loss, speech recognition

Component analysis for energy-efficient multimedia networks utilizing 5G radio access technologies

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches.

Abstract CT053: Exploration of potential biomarkers correlated with efficacy of ociperlimab (anti-TIGIT) plus tislelizumab (anti-PD-1) in 1L PD-L1+ non-small cell lung cancer (NSCLC)

Abstract Background: PD-L1 expression was associated with anti-immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) + anti-PD-(L)1 treatment, in which a high PD-L1 subgroup showed improved efficacy. We investigated if anti-TIGIT mechanism of action (MOA)-related markers were associated with the efficacy of ociperlimab + tislelizumab in Cohort 3 (1L PD-L1+ NSCLC) of the phase 1/1b AdvanTIG-105 trial (NCT04047862) and evaluated a potential patient-enrichment strategy based on tumor tissue gene expression profile (GEP). Methods: Tumor tissue GEP was tested using TruSeq RNA Access technology. Ventana SP263 PD-L1 immunohistochemistry (IHC) assay was used to evaluate PD-L1 expression. Median progression-free survival (mPFS) by investigator was calculated descriptively by Kaplan-Meier methodology. 95% confidence intervals for mPFS were generated using the Brookmeyer method. The primary inferential PFS comparison used unstratified log-rank test with 2-sided descriptive P-values. Results: At data cutoff (Feb 2, 2023), 24 of 45 patients had GEP results. Anti-TIGIT MOA-related genes and signatures correlated with ociperlimab + tislelizumab treatment response. Patients with high (H) vs low (L) expression of TIGIT, CD226, CCR8, or a tumor-associated macrophage (TAM) signature had significantly longer mPFS (Table). Dual biomarkers combining both anti-PD-L1 (PD-L1 IHC) and one of the anti-TIGIT MOA-related factors (TIGIT, CCR8, TAM signature GEP) identified subgroups of PD-L1 H + TIGIT MOA-related factor H patients with improved PFS vs other subgroups (Table). A highly overlapped PD-L1 H + TIGIT H + CCR8 H + TAM signature H patient population was observed in dual biomarker analyses. Conclusions: Anti-TIGIT MOA-related genes and signatures correlated with efficacy in ociperlimab + tislelizumab-treated 1L PD-L1+ NSCLC. Combining anti-TIGIT MOA-related factors with PD-L1 expression identified a subgroup of patients with improved efficacy. TABLE 1. NAND Table. Efficacy Analyses in Patient Subgroups TIGITa CD226b CCR8b TAMb Subgroup H L H L H L H L n 8 16 12 12 12 12 12 12 mPFS, months (95% CI) NR (2.6, NR) 5.26 (2.07, 11.86) NR (4.21, NR) 4.68 (1.41, 15.05) 15.21 (2.6, NR) 4.7 (1.71, 7.16) 15.21 (4.21, NR) 4.17 (1.71, 5.45) PFS, P-value 0.0326 0.0327 0.0131 0.0153 PD-L1c + TIGITb PD-L1c + CCR8b PD-L1c + TAMb Subgroup TIGITHPD-L1H TIGITHPD-L1L TIGITLPD-L1H TIGITL PD-L1L CCR8HPD-L1H CCR8HPD-L1L CCR8LPD-L1H CCR8L PD-L1L TAMHPD-L1H TAMHPD-L1L TAMLPD-L1H TAML PD-L1L n 8 4 8 4 11 1 5 7 9 3 7 5 mPFS, months (95% CI) NR (1.41,NR) 4.76 (2.6,NR) 8.62 (2.07,NR) 2.94 (1.25,NR) NR (1.41,NR) 2.6 (NR,NR) 5.19 (2.07,NR) 4.21 (1.25,7.16) 23.89 (1.41,NR) 5.32 (4.21,NR) 5.29 (2.07,NR) 2.6 (1.25,NR) Cutoff: aTop 1/3; bMedian; cTC≥25% Citation Format: Se Hyun Kim, Jiayuan Zhang, Wei Tan, Han Yan, Tian Tian, Hao Zheng, Ziqi Zhou, Ruihua Wang, Yun Zhang, Zhirong Shen, Hye Ryun Kim, Diansheng Zhong, Shun Lu. Exploration of potential biomarkers correlated with efficacy of ociperlimab (anti-TIGIT) plus tislelizumab (anti-PD-1) in 1L PD-L1+ non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT053.

Enhancing Lip Reading: A Deep Learning Approach with CNN and RNN Integration

This research introduces an innovative approach to enhance lip reading-based text extraction and translation through the integration of a double Convolutional Neural Network (CNN) coupled with Recurrent Neural Network (RNN) architecture. The proposed model aims to leverage the strengths of both CNN and RNN to achieve superior accuracy in lip movement interpretation and subsequent text extraction. The methodology involves training the double CNN+RNN model on extensive datasets containing synchronized lip movements and corresponding linguistic expressions. The initial layers of the model utilize CNNs to effectively capture spatial features from the visual input of lip images. The extracted features are then fed into RNN layers, allowing the model to grasp temporal dependencies and contextual information crucial for accurate lip reading. The trained model showcases its proficiency in extracting textual content from spoken words, demonstrating an advanced capability to decipher nuances in lip gestures. Furthermore, the extracted text undergoes a translation process, enabling the conversion of spoken language into various target languages. This research not only contributes to the advancement of lip reading technologies but also establishes a robust foundation for real-world applications such as accessibility solutions for individuals with hearing impairments, real-time multilingual translation services, and improved communication in challenging acoustic environments. The abstract concludes with a discussion on the potential impact of the double CNN+RNN model in pushing the boundaries of human-computer interaction, emphasizing the synergy between deep learning, lip reading, and translation technologies

Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA

Wireless and power line communication hybrid relay technology can realize complementary advantages and comprehensively improve the communication coverage and performance of power Internet of Things. In order to study the mechanism of the physical layer and Media Access Control (MAC) layer algorithm that affects the performance of hybrid relay systems, the cross-layer performance modeling, optimization, and simulation analysis are carried out for the non-orthogonal multiple access (NOMA) technology. Firstly, a two-hop NOMA media access control protocol is designed based on the CSMA algorithm. Considering the effects of non-ideal channel transmission at the physical layer and competitive access at the MAC layer on the system performance, a cross-layer performance analysis model of hybrid wireless and power line communication relay system under NOMA is established. Finally, a cross-layer optimization model based on multi-objective programming is established for the hybrid relay system. By analyzing the relationship between transmitting power and performance index, the joint optimization of transmitting power and power distribution factor between users is realized. Simulation results verify the validity and reliability of the proposed cross-layer model. The results show that the hybrid relay algorithm combined with NOMA and CSMA can effectively improve the performance of the system throughput, packet loss probability, and delay.

Performance of hybrid transmission in a reconfigurable intelligent surface assisted D2D using NOMA

Recent research on Reconfigurable Intelligent Surface (RIS) aided communication and Non-Orthogonal Multiple Access (NOMA) technology shows enhanced spectral efficiency for 5 G and beyond wireless network systems. This paper evaluates the performance of a Device-to-Device (D2D) network where RIS is deployed in a suitable position and NOMA is employed to accommodate more number of users in the spectrum simultaneously. The D2D users exploit cellular network resources. Analytical models for outage, throughput, and energy-efficiency are developed considering spectrum sensing and a transmit power constraint. This work investigates the network performance in underlay, overlay, as well as hybrid modes through extensive simulations. The simulation model is designed based on the analytical models. Outage probability, network throughput, and energy efficiency are analysed to evaluate the D2D network performance. The simulation results shows incorporation of RIS and NOMA, network performance improves substantially. Increase in number of RIS elements also improves the network performance.

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

LEO Satellite Access Network (LEO-SAN) Toward 6G: Challenges and Approaches

With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network. Aside from terrestrial base stations, satellite nodes, especially the low-earth-orbit (LEO) satellites, can also serve as base stations for Internet access, and constitute the LEOsatellite access network (LEO-SAN). LEO-SAN is expected to provide seamless massive access and extended coverage with high signal quality. However, its practical implementation still faces significant technical challenges, such as high mobility and limited budget for communication payloads of LEO satellite nodes. This paper aims at revealing the main technical issues that have not been fully addressed by the existing LEO-SAN designs, from three aspects namely random access, beam management and Doppler-resistant transmission technologies. More specifically, the critical issues of random access in LEO-SAN are discussed regarding low flexibility, long transmission delay, and inefficient handshakes. Then the beam management for LEOSAN is investigated in complex propagation environments under the constraints of high mobility and limited payload budget. Furthermore, the influence of Doppler shifts on LEO-SAN is explored. Correspondingly, promising technologies to address these challenges are also discussed, respectively. Finally, the future research directions are envisioned.

Resisting Normality with Cultural Accessibility and Slow Technology

Abstract Although the COVID-19 virus continues to circulate, there is an increasing insistence that the world “return to normal.” In this paper the authors resist this pull to normalcy and the way it devalues the knowledges, vitality, and livelihoods of disabled people. They examine the crip technoscience practices used during the 2022 digital gathering Practicing the Social: Entanglements of Art and Social Justice, situating them as examples of cultural accessibility that engage with slow technology to provoke crip(ped) ways of being in time. They argue that sustained engagement with cultural accessibility offers a different path through the pandemic, one that centers access and resists the way necropolitics devalues disabled life.