Ground Communication Research Articles

EXPRESS: Unpacking the Role of Ethical Leadership in the Era of Sustainable Development Goals and Values-based Marketing

Tackling sustainable development goals (SDGs) has become a key strategic marketing activity for multinational enterprises (MNEs) to build their reputation and ensure their brand value. However, SDGs can cause goal conflicts for MNEs, resulting in firms not being equally responsive to all stakeholders. Therefore, this study aimed to explore how ethical leadership, underpinned by a corporate purpose, enables MNEs to pursue SDGs in foreign markets. The researchers argued that ethical leadership based on a corporate purpose can provide strategic direction for MNEs, thus enabling them to focus on selected SDGs that can be integrated into their values-based marketing agendas. Further, the findings revealed that a corporate purpose can offer consistency and perseverance in the exertion of ethical leadership at the local market level; however, ethical leadership may require organizational members to disassociate from structural inconsistencies and goal conflicts in pursuit of aspired SDGs. Thus, the study makes a significant contribution to the literature on international marketing by adopting an ethical perspective to explain how MNEs can strategically position themselves in relation to SDGs in international markets. The findings suggest that ethical leadership can build a foundation for credible marketing communication while serving as a driver of values-based marketing programs.

Indeterministic Data Collection in UAV-Assisted Wide and Sparse Wireless Sensor Network

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%.

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

Self-Presentational Communication in Context Digital Realities of Modern Society

Introduction. In the age of the digital economy and hyper-competition, self-presentation communication as a type of self-promotion, as the ability to present oneself to a communication partner, employer, business partner or target audience, is considered by experts as a key skill of the 21st century. It is also considered as one of the significant factors of competition both in the modern highly competitive labor market and in the consumer market, both global and local. The article discusses the theoretical foundations of self-presentational communication in its historical retrospective, traditions, innovations and development trends in the conditions of new realities.Methodology and sources. The methodology of philosophical, sociological and interdisciplinary approaches is used. The article uses the following sources: specialized literature from domestic and foreign authors, scientific research, publications and websites that allow one to explore the specifics of digital self-presentation, its new formats and current practices.Results and discussion. Digital society is a society whose distinctive feature is the electronic-digital method of receiving, storing and distributing information, as well as the electronic-digital mediation of social relations and communicative interaction. One of the ways of such interaction is digital self-presentation, which combines the traditions of this content and innovations caused by the development of new technologies.Conclusion. Self-presentational communication, like the entire communicative component of modern society, including the «human-AI» interface, is carried out in three spatial dimensions: real/offline/Physics, virtual/online/Digital, as well as in their interaction offline online/Phygital. In each dimension, self-presentational communication has its own specifics and could can be considered both as a separate phenomenon and in the process of continuity and development.

Computer Vision Target Detection-Aided High-Frequency Satellite–Ground Communications

Computer Vision Target Detection-Aided High-Frequency Satellite–Ground Communications

Fog-assisted de-duplicated data exchange in distributed edge computing networks

The Internet of Things (IoT) generates substantial data through sensors for diverse applications, such as healthcare services. This article addresses the challenge of efficiently utilizing resources in resource-scarce IoT-enabled sensors to enhance data collection, transmission, and storage. Redundant data transmission from sensors covering overlapping areas incurs additional communication and storage costs. Existing schemes, namely Asymmetric Extremum (AE) and Rapid Asymmetric Maximum (RAM), employ fixed and variable-sized windows during chunking. However, these schemes face issues while selecting the index value to decide the variable window size, which may remain zero or very low, resulting in poor deduplication. This article resolves this issue in the proposed Controlled Cut-point Identification Algorithm (CCIA), designed to restrict the variable-sized window to a certain threshold. The index value for deciding the threshold will always be larger than the half size of the fixed window. It helps to find more duplicates, but the upper limit offset is also applied to avoid the unnecessarily large-sized window, which may cause extensive computation costs. The extensive simulations are performed by deploying Windows Communication Foundation services in the Azure cloud. The results demonstrate the superiority of CCIA in various metrics, including chunk number, average chunk size, minimum and maximum chunk number, variable chunking size, and probability of failure for cut point identification. In comparison to its competitors, RAM and AE, CCIA exhibits better performance across key parameters. Specifically, CCIA outperforms in total number of chunks (6.81%, 14.17%), average number of chunks (4.39%, 18.45%), and minimum chunk size (153%, 190%). These results highlight the effectiveness of CCIA in optimizing data transmission and storage within IoT systems, showcasing its potential for improved resource utilization and reduced operational costs.

TECHNOLOGIES FOR FORMING TEAMWORK SKILLS IN CHILDREN OF PRESCHOOL EDUCATIONAL ORGANIZATIONS

Teamwork skills are essential in preparing children for future social and academic environments. In preschool educational organizations, fostering these skills at an early age provides a solid foundation for collaboration, communication, and problem-solving. This article explores the use of various educational technologies to develop teamwork skills in preschool children. These include interactive learning tools, digital applications, play-based learning, and structured group activities. The goal is to identify and assess effective strategies that enhance children's ability to work together, resolve conflicts, and achieve shared goals.

Mapping Medical Context: Workshop-Driven Clustering of Influencing Factors on Medical Decision-Making.

Clinical decision support systems (CDSS) can efficiently support doctors in coping with ever-increasing amounts of data by providing evidence-based recommendations for medical decisions. To integrate the systems into the medical workflow and provide patient-specific recommendations for action in the context of personalized medicine, it is essential to tailor the systems to the context of use. This study aims to present an overview of factors influencing medical decision-making that CDSS must consider. Our approach involves the systematic identification and categorization of contextual factors relevant to medical decision-making. Through extensive literature research and a structured card-sorting workshop, we systematized 774 context factors and mapped them into a model. This model includes six primary entities: the treating physician, the patient, the patient's family, disease treatment, the physician's institution, and professional colleagues, each with their relevant context categories. The developed model could serve as a foundation for communication between developers and physicians, supporting the creation of more context-sensitive CDSS in the future. Ultimately, this could enhance the utilization of CDSS and improve patient care.

A soft actor–critic reinforcement learning approach for over the air active beamforming with reconfigurable intelligent surface

Combining reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) provides ubiquitous connectivity for 6G air–ground communications, effectively enhancing coverage. However, due to the ”multiplicative fading” effect, passive RIS can only offer weak capacity gain. In addition, the mobility of UAVs may lead to imperfect channel state information (CSI), making it difficult to perform accurate beamforming. To address these issues, this paper adopts active RIS to actively amplify the reflected signals to overcome the high path loss caused by ”multiplicative fading”. To adapt to the randomness of channel changes, this paper employs the soft actor–critic (SAC) algorithm, which is based on the maximum entropy strategy. This approach jointly optimizes the precoding of the base station (BS) and the beamforming of the aerial RIS (ARIS), aiming to maximize the multi-user transmission rate. Simulation results show that when active ARIS is employed, the proposed algorithm achieves similar sum-rate results in imperfect CSI and perfect CSI scenarios and realizes 71% and 74% performance improvement compared to the traditional passive RIS, respectively. Moreover, the sum-rate remains stable within a certain range when the UAV hovers at any position between the BS and the user.

Compact Extended Formulations for Low-Rank Functions with Indicator Variables

We study the mixed-integer epigraph of a special class of convex functions with nonconvex indicator constraints, which are often used to impose logical constraints on the support of the solutions. The class of functions we consider are defined as compositions of low-dimensional nonlinear functions with affine functions. Extended formulations describing the convex hull of such sets can easily be constructed via disjunctive programming although a direct application of this method often yields prohibitively large formulations, whose size is exponential in the number of variables. In this paper, we propose a new disjunctive representation of the sets under study, which leads to compact formulations with size exponential in the dimension of the nonlinear function but polynomial in the number of variables. Moreover, we show how to project out the additional variables for the case of dimension one, recovering or generalizing known results for the convex hulls of such sets (in the original space of variables). Our computational results indicate that the proposed approach can significantly improve the performance of solvers in structured problems. Funding: This work was supported by the National Science Foundation Division of Computing and Communication Foundations [Grant 2006762].

Resource and trajectory optimization for UAV-assisted MEC communication systems over unlicensed spectrum band

The new radio unlicensed (NR-U) technology is proposed by 3GPP to extend NR to the unlicensed spectrum because of the shortage of the licensed spectrum. Different from the ground and fixed communication equipment-based unlicensed spectrum access scheme, the unmanned aerial vehicle (UAV) mobile platform-based unlicensed spectrum access scheme is not only related to incumbent users but also its trajectory and resource allocation. Therefore, this paper proposes a hybrid unlicensed spectrum access scheme for the UAV-assisted unlicensed mobile edge computing (MEC) communication (UAUM) system, where each flight time slot of the UAV is divided into two parts: power free (PF) and power controlled (PC) stages. In the PF stage, the transmit power is only restrained by the unlicensed spectrum regulations, and thus the UAV can provide high-rate services for real-time downlink users (RDUs) and uplink computing users (UCUs). In the PC stage, the transmit power of the UAV is mainly restrained by the interference to WiFi devices, and thus UAV can be allowed to provide low-rate services for non-realtime downlink users (NDUs) without affecting WiFi users. Based on the proposed scheme, a multi-variable optimization problem regarding trajectory, bandwidth, transmit power, and duty cycle is formulated to maximize the total offloaded computing bits on the premise of ensuring the quality of experience of RDUs, NDUs, and WiFi users under the maximum energy budget. To solve this problem efficiently, we propose an iterative algorithm based on the block coordinate descent method and successive convex approximation technique to decompose the original problem into four optimization subproblems of trajectory, bandwidth, transmit power and duty cycle, which are then solved alternatively in an iterative manner. A large number of simulation results demonstrate that in terms of spectrum efficiency and total offloaded computing bits, the proposed algorithm outperforms other unlicensed spectrum access schemes and optimization algorithms. The other performances of the proposed algorithm are deeply evaluated to prove its effectiveness and feasibility.

An implementation manual for an interprofessional enhanced recovery after surgery protocol in cardiac surgery following international established frameworks.

Enhanced Recovery After Surgery (ERAS) protocols represent a paradigm shift in perioperative care, aim to optimize patient outcomes and accelerate recovery. This manual presents findings from implementing the INCREASE study, a bicentric prospective randomized controlled trial focusing on ERAS in minimally invasive heart valve surgery. Utilizing the Consolidated Framework for Implementation Research (CFIR) and the Template for Intervention Description and Replication (TIDieR), the study examined contextual factors, intervention components, and implementation strategies. Key findings from the CFIR analysis revealed critical domains influencing implementation success. These included innovation characteristics, external and internal settings, and individual dynamics. The study showcased ERAS's adaptability to diverse healthcare systems, emphasizing its potential for successful integration across varying contexts. Furthermore, the importance of interprofessional collaboration emerged as a foundation of practical implementation, fostering teamwork, communication, and patient-centered care. Utilizing the TIDieR framework, this manual comprehensively describes ERAS intervention components, detailing preoperative counseling, intraoperative management, and postoperative care strategies. The manual underscored the importance of tailored, patient-centered approaches, highlighting the role of an academic ERAS nurse, early mobilization, and psychosomatic interventions in promoting optimal recovery outcomes. In conclusion, the INCREASE study provided valuable insights for creating an implementation manual for ERAS in cardiac surgery, emphasizing adaptability, collaboration, and ongoing evaluation as key drivers of successful implementation. These findings have broad implications for improving patient care outcomes and advancing perioperative practices in cardiac surgery settings.

ATC-SD Net: Radiotelephone Communications Speaker Diarization Network

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets.

Energy efficiency and interoperability through O-RAN Rapid Transition Protocol (ORTP)

Mobile network traffic is increasing accompanied by increased energy consumption. Traditional Radio Access Networks (RANs) have been used for decades as the foundation of mobile communications, providing mobile customers with reliable voice and data services. However, they encounter obstacles in terms of scalability, adaptability, and cost-effectiveness. Next-generation wireless networks need efficient Radio Access Network (RAN) solutions to achieve low latency and high throughput. The Open RAN (O-RAN) architecture is a potential solution for 5G and beyond networks due to its open interfaces, disaggregated network entities and services, network hardware and software virtualization, and intelligent control. Traditionally standard RAN accounts for the bulk of mobile network energy consumption, leading towards higher Operational expenditure (OPEX) costs. In the context of cellular networks. Handover strategies need careful development to cater for efficient resource usage as well as overall system energy efficiency. O-RAN Rapid Transition Protocol (ORTP) presented in this paper employs minimum value of handover margin (HOM) and is aimed at increased energy efficiency. 5G based System level simulations have been performed to investigate the efficacy of ORTP for key performance indicators including handover probability, Radio Link Failure, connection density, ping pong effect and dynamic power consumption. It is found that the usage of lower HOM values provide noticeable achievement in terms of power consumption, thereby rendering ORTP around 20 % more efficient compared to 5G networks, while keeping it interoperable.

Hybrid Detection Method for Multi-Intent Recognition in Air–Ground Communication Text

Hybrid Detection Method for Multi-Intent Recognition in Air–Ground Communication Text

Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits

Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

Exact and Approximation Algorithms for Sparse Principal Component Analysis

Sparse principal component analysis (SPCA) is designed to enhance the interpretability of traditional principal component analysis by optimally selecting a subset of features that comprise the first principal component. Given the NP-hard nature of SPCA, most current approaches resort to approximate solutions, typically achieved through tractable semidefinite programs or heuristic methods. To solve SPCA to optimality, we propose two exact mixed-integer semidefinite programs (MISDPs) and an arbitrarily equivalent mixed-integer linear program. The MISDPs allow us to design an effective branch-and-cut algorithm with closed-form cuts that do not need to solve dual problems. For the proposed mixed-integer formulations, we further derive the theoretical optimality gaps of their continuous relaxations. Besides, we apply the greedy and local search algorithms to solving SPCA and derive their first-known approximation ratios. Our numerical experiments reveal that the exact methods we developed can efficiently find optimal solutions for data sets containing hundreds of features. Furthermore, our approximation algorithms demonstrate both scalability and near-optimal performance when benchmarked on larger data sets, specifically those with thousands of features. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported in part by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 224614], the Division of Computing and Communication Foundations [Grant 2246417], and the Office of Naval Research [Grant N00014-24-1-2066]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0372 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0372 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

THE PHILOSOPHY OF FRATERNITY AND THE FOUNDATIONS OF MORAL SOLIDARITY

Analyzing the historical forms of moral solidarity, one of which is the phenomenon of fraternity and trying to see in this form the foundations of constructive cooperation in modern conditions, we should pay close attention to the underestimated conceptual provisions and mechanisms. We believe that these factors play an important role in overcoming disintegration processes, both at the level of social communication and at the level of the mental foundations of intercultural and intercivilizational communication. The article is devoted to the philosophical foundations of the phenomenon of fraternity, mainly based on the material of European and domestic socio-philosophical reflection. The relevance of this topic is determined by the fact that the current international situation presents new challenges and that Russia is looking for constructive solutions to complex contradictions that have arisen in the international community. On the other hand, the axiological foundations of Russian civilization, which have been its genetic basis for many centuries, require constant philosophical reflection. The article discusses the essential importance of the phenomenon of fraternity for overcoming social disharmony, clarifies the advantages of this form of social interaction over formal cooperation. The methodology of the work is based on a socio-philosophical analysis of the axiological and ethical foundations of various forms of joint human activity. The article attempts to identify the structure-forming foundations of the system of social relations by the example of the phenomenon of fraternity.

Controlled remote implementation of operations for many systems

Quantum communication plays a key role in the next generation of information transfer and security schemes, by using quantum entanglement and measurements from quantum mechanics. We introduce the mathematical and physical foundations of quantum communication, such as the CNOT gate and Kronecker product. Then we propose two quantum communication protocols, namely dense coding and stealth coding. These protocols offer unique advantages that are theoretically unbreakable. Then we extend the protocols to the quantum communication protocol allowing for third-party supervision to ensure information security. Based on this, a communication protocol involving four parties is designed, enabling them to exchange information while being supervised.

Protecting stable biological nomenclatural systems enables universal communication: A collective international appeal.

The fundamental value of universal nomenclatural systems in biology is that they enable unambiguous scientific communication. However, the stability of these systems is threatened by recent discussions asking for a fairer nomenclature, raising the possibility of bulk revision processes for "inappropriate" names. It is evident that such proposals come from very deep feelings, but we show how they can irreparably damage the foundation of biological communication and, in turn, the sciences that depend on it. There are four essential consequences of objective codes of nomenclature: universality, stability, neutrality, and transculturality. These codes provide fair and impartial guides to the principles governing biological nomenclature and allow unambiguous universal communication in biology. Accordingly, no subjective proposals should be allowed to undermine them.