Unstable Communication Research Articles

A Model-Based Reinforcement Learning Algorithm for Multi-Agent Cooperation Nash Equilibrium With Unstable Communication

Solving Nash equilibrium is important to multi-agent systems, in which the communication is an important factor. However, many proposed reinforcement learning (RL) based algorithms take into account the communication factors by assuming stable communication conditions, which does not hold in the real environment. In this paper, we analyze the effect of a typical RL algorithm in the cases of unstable communication and communication failure, which causes information loss between agents, leading to isolation of agents and affecting algorithm convergence. Then, we propose a model-based RL algorithm to solve Nash equilibrium for multi-agent systems when agents are isolated, and prove its convergence and rationality through mathematical proofs. The simulations results show the effectiveness of the proposed algorithm.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

The Data Value Based Asynchronous Federated Learning for UAV Swarm Under Unstable Communication Scenarios

The Data Value Based Asynchronous Federated Learning for UAV Swarm Under Unstable Communication Scenarios

APPLICATION OF FUZZY MODELS TO CONTROL THE STREAM OF MESSAGES IN DATA TRANSMISSION INFRASTRUCTURES

The report presents one of the problems that arises in distributed data transmission infrastructures - node overload in data transmission when there is unstable communication between nodes. As a solution, a mechanism for controlling the message flow intensity based on a fuzzy model is proposed. The developed fuzzy control model has demonstrated its effectiveness in the network for collecting and transmitting quotes of financial instruments from various trading platforms to the information-analytical system of a financial company. Message queues at the sources have significantly decreased, while minimizing losses of quotes on unstable communication channels. Additionally, the possibility of more flexible adjustment of fuzzy control by expanding the number of metrics of input parameters of the fuzzy model has been explored.

Investigation of navigation information correction techniques for master-slave AUV formations in unstable communication environments

Cooperative navigation within master–slave Autonomous Underwater Vehicles (AUVs) necessitates the periodic transmission of high-precision positional data from the master AUV, endowed with advanced navigational instrumentation, to the slave AUV characterized by limited positional accuracy. This data exchange is facilitated through an acoustic sensor mechanism, enabling the quantification of relative spatial separation between the two vehicles. Employing an Extended Kalman Filter (EKF), the positioning error of the slave AUV can be estimated and corrected, enhancing its positional accuracy. However, this collaborative navigation methodology encounters dual challenges. Firstly, the intricate underwater acoustic environment introduces the potential for data loss or outlier measurements in submarine acoustic communication, thereby jeopardizing the efficacy of collaborative navigation. Secondly, the current approach solely rectifies the slave AUV's positional error upon the receipt of data from the master AUV, neglecting the fundamental divergence in positional error that accrues between successive data receptions, stemming from the inherent imprecision in the dead reckoning process. To contend with these complexities, this study presents a novel approach to rectifying navigation data within master–slave AUV formations, particularly within the context of unstable communication conditions. The proposed method harnesses an outlier adaptive discriminant EKF framework, proficiently addressing the slave AUV's positional error while simultaneously estimating and compensating for azimuthal angular errors during underwater acoustic communication between the master and slave AUVs. This method establishes a pivotal groundwork for heightening the accuracy of independent autonomous navigation within the slave AUV during subsequent operational phases. The accuracy and applicability of the proposed method are rigorously validated through simulation-based verification and lake-water field experiments. The empirical results unequivocally highlight the efficacy of the underwater acoustic communication approach put forth in this study, with an outlier recognition rate exceeding 80%. Furthermore, the method adeptly rectifies azimuthal angular errors and positional inaccuracies within the AUV, thus effecting a comprehensive enhancement of overall positioning precision. This research bears significant implications for advancing autonomous underwater navigation, providing a robust framework to bolster the precision and reliability of master–slave AUV formations, particularly under challenging communication circumstances.

Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.

Robust Permissioned Blockchain Consensus for Unstable Communication in FANET

Robust Permissioned Blockchain Consensus for Unstable Communication in FANET

Analysis of options for the complex of transport and technological means interaction for transmitting information to the control system

The article describes the organization of interaction of a complex of autonomous intelligent transport and technological means (ITTS) for the transmission of information to the controlling system. The need for this solution is caused by the fundamental impossibility of implementing a cloud solution based on cellular communications and mobile Internet, due to unstable communication in the airport area, which is random and unpredictable. A variant of interaction is described in which ITTS transmits critical information about its condition and the results of technological operations to other ITTS using them as couriers to deliver information. Possible variants of such interaction and consequences are analyzed. A mechanism is proposed for synchronizing a set of ITTs containing redundant information, both among themselves and with the central system. The mechanism of the synchronization horizon is described. The criteria for choosing the most suitable ITTS are considered on the basis of various information available at each specific time. The concept of zones of stable transmission of information is introduced and a tool for creating a map of such zones is described.

Enabling Space-Air integration: A Satellite-UAV networking authentication scheme

One of the goals of sixth-generation mobile networks (6G) is to achieve a larger network coverage area. Satellite networks enable global coverage and aerial nodes such as Unmanned Aerial Vehicle (UAV) can serve as a supplement to ground networks in remote environments. Therefore, 6G networks are gradually evolving towards Space-Air-Ground integrated networks. The combination of UAV networks and satellite networks is a research hotspot in the field of Space-Air integrated networks. However, the combination of UAV networks and satellite networks currently faces many challenges in terms of security. The characteristics of large propagation delay and unstable communication links in satellite networks make them vulnerable to various attacks, including eavesdropping, tampering, and impersonation. Meanwhile, existing research on UAV networks mainly focuses on UAV-Ground networking authentication mechanisms, which are not suitable for resource-constrained nodes in the Space-Air integration scenario. Therefore, based on elliptic curve public key cryptography and Chebyshev polynomial, we propose a secure networking authentication scheme for satellite nodes and UAV nodes in the Space-Air integration scenario. The security analysis indicates that our scheme possesses security attributes such as mutual authentication, key agreement, identity anonymity, unlinkability, perfect forward-backward security, and resistance against various protocol attacks, among other security properties. Performance analysis also indicates certain advantages of our scheme over existing schemes in terms of signaling, bandwidth, and computational overhead.

Task Assignment of UAV Swarms Based on Auction Algorithm in Poor Communication Environments

Poor communication environments always lead to unstable communication in unmanned aerial vehicle swarms. To solve the problem of task assignment in poor communication environments, this study proposed an information fusion strategy (IFS) based on information integrity and authenticity. The proposed IFS was embedded into the classical sequential and the Prim assignment and its generalization (G-Prim) decentralized task assignment algorithms, and these two improved variants with the proposed IFS were denoted as sequential auction with IFS (Seq-IFS) and G-Prim-IFS, respectively. The Bernoulli and Gilbert–Elliott models, which can model communication delay and packet loss, were adopted to describe unstable communication channels. A series of test instances with different swarm sizes and levels of communication channel reliability was used to test the performances of Seq-IFS and G-Prim-IFS in their original forms. Numerical experimental results demonstrated that the proposed Seq-IFS and G-Prim-IFS significantly outperformed their original versions in most test instances, particularly in cases with low communication environments.

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

A Novel Access and Handover Authentication Scheme in UAV-Aided Satellite-Terrestrial Integration Networks Enabling 5G

As an outlook of the terrestrial network, the flexible Unmanned Aerial Vehicle (UAV)-aided satellite-terrestrial integration network would have numerous prospective applications such as service enhancement, maritime communication, military, and emergency communication, which is getting significant attention. However, the public and open-access network must result in various imperative risks including impersonation, sensitive data, and privacy disclosure. Due to several unique characteristics including long transmission distance, unstable communication environment, resource-limited and highly dynamic characteristics of UAVs, diversified terminals that lack the ability of accessing the satellite and may be resource-limited, there are new challenges for the access authentication process in the UAV-Aided Satellite-Terrestrial Integration Networks with 5G. In this paper, we present a novel Physically Unclonable Function (PUF)-based access authentication scheme consisting of two access authentication protocols for the UAV and the ground terminals like 5G User Equipments (UEs), respectively. Two access authentication protocols for the drone and the terminals can both achieve mutual authentication, key agreement, and privacy protection with distinct advantages of no need to store secret key and supporting physical attack resistance. Finally, we propose an efficient handover authentication protocol executed by the ground terminals when the UAV is required to switch. We employ different security analysis tools to analyze the security of all proposed protocols, as well as present informal security analysis on various security properties. The performance comparison and evaluations show our protocols have better advantages.

A Task-Driven Scene-Aware LiDAR Point Cloud Coding Framework for Autonomous Vehicles

LiDAR sensors are almost indispensable for autonomous robots to perceive the surrounding environment. However, the transmission of large-scale LiDAR point clouds is highly bandwidth-intensive, which can easily lead to transmission problems, especially for unstable communication networks. Meanwhile, existing LiDAR data compression is mainly based on rate-distortion optimization, which ignores the semantic information of ordered point clouds and the task requirements of autonomous robots. To address these challenges, this article presents a task-driven <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> cene- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> iDAR <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> louds <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> oding (SA-LPCC) framework for autonomous vehicles. Specifically, a semantic segmentation model is developed based on multi-dimension information, in which both 2D texture and 3D topology information are fully utilized to segment movable objects. Further, a prediction-based deep network is explored to remove the spatial-temporal redundancy. The experimental results on the benchmark semantic KITTI dataset validate that our SA-LPCC achieves state-of-the-art performance in terms of the reconstruction quality and storage space for downstream tasks. We believe that SA-LPCC jointly considers the scene-aware characteristics of movable objects and removes the spatial-temporal redundancy from an end-to-end learning mechanism, which will boost the related applications from algorithm optimization to industrial products.

Distributed cooperative search method for multi-UAV with unstable communications

In search-attack and search-rescue tasks performed by multiple unmanned aerial vehicles (multi-UAV), cooperative search plays an important part. The majority of approaches in use today assume that the UAV swarm’s communication network is complete. However, these links are susceptible to environmental changes or adversary interference. Aiming at cooperative search in search-attack and search-rescue tasks, we propose a distributed cooperative search method for multi-UAV with unstable communications (DCS-UC), which is developed based on ant colony optimization (ACO). The proposed method presents three algorithms to enable the UAV swarm to conduct online cooperative search efficiently and safely. First, a pheromone matrix consensus update technique for usage in ACO is designed under switching and connected topology graphs. This approach can help each UAV’s pheromone matrix achieve consensus, improving the UAV swarm’s cooperative efficiency. Then, a position consensus update algorithm is presented where the position vector, including the positions of all the UAVs in each UAV, can achieve consensus under switching and connected topology graphs. Finally, a collision avoidance algorithm is developed using the determined consistent positions of all the UAVs to address the issue of collision avoidance for all the UAVs. This approach enables the UAV swarm to conduct cooperative search safely. Results from extensive physical simulations performed in Gazebo confirm the benefits of the proposed multi-UAV cooperative search method.

Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network

&lt;p&gt;As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

높은 전력밀도의 무선전력전송 시스템용 새로운 수신측 통합 레귤레이터 구조 및 제어 방법

The wireless power transfer (WPT) systems have to supply a stable output voltage to the battery. However, due to its physically separated structure and unstable communication control between transmitter-side (Tx) and receiver-side (Rx), there should be a battery control converter in front of the battery, causing a large number of components and low power density. To relieve these drawbacks, a new receiver-side post regulator (RSPR) with a simple control method and high power density is proposed in this paper. The proposed RSPR structure can help the WPT system to supply stable output to the battery. Besides, the proposed one can remove complex phase detection and unstable communication control method between Tx and Rx. Moreover, through the integrated single-switch into the rectifier diodes, high power density can be obtained. The effectiveness of the proposed structure is analyzed and verified experimentally by a prototype under 380VDC input and 250W output.

Peer-to-Peer Federated Learning for COVID-19 Detection Using Transformers

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-I.I.D.) data, combined with the unstable communication network environment, causes a bottleneck that slows convergence and degrades learning efficiency. Additionally, the majority of weight averaging-based model aggregation approaches raise questions about learning fairness. In this paper, we propose a peer-to-peer federated learning (P2PFL) framework based on Vision Transformers (ViT) models to help solve some of the above issues and classify COVID-19 vs. normal cases on Chest-X-Ray (CXR) images. Particularly, clients jointly iterate and aggregate the models in order to build a robust model. The experimental results demonstrate that the proposed approach is capable of significantly improving the performance of the model with an Area Under Curve (AUC) of 0.92 and 0.99 for hospital-1 and hospital-2, respectively.

The Efficiency of Tailored Systems for Language Education: An App Based on Scientific Evidence and for Student-Centered Approach

&lt;p style="text-align: justify;"&gt;In the vulnerable and unstable contexts that characterize populations in mobility between countries, especially immigrants and refugees, the second language learning has assumed new traits of difficulty. To help the new generation of learners, mainly with origin in forced immigration, the mobile-assisted learning helps motivation for language learning and reduces anxiety related to language acquisition. Attending to this challenge educational scenario, this review study presents a literature systematic analysis and a concrete technology tool that advocates the student-centered approach. A mobile pedagogical plan was developed for the learning of European Portuguese as a Second Language and as a Foreign Language. A critical review of 38 studies was conducted to understand how the mobile-assisted learning responds to the inclusion and education, especially concerning minorities. Based on previous empirical data with 108 immigrants, we understand the type of tasks that new immigrants have more difficulty learning in Portuguese. It was developed as a mobile app for Android, IOS, computers and tablets: the GoGenius app. In mobile format, individuals can access fourteen themed units with a symmetrical game architecture. These games focused the tasks and themes with priority for new language learners who recently arrived in a hosting country. These units involve a consistent number of working hours that intentionally intend to complement to the contact hours that the subjects have in classroom contexts or in unstable communication contexts (daily communication). This technology project aims to match “tailored” psychological and technological resources. Flipped classroom approach showed how mobile-assisted learning reinforces the educational goals worldwide, specifically for language learning. However, mobile tools should be well structured and centered on students’ needs, especially with migration backgrounds.&lt;/p&gt;

An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses

The objective of this manuscript was to present the e-Synch system, integrating an intravaginal electronically controlled hormone delivery and sensing device with an IoT platform for remote programming and monitoring. Secondary objectives were to demonstrate system functionality and cow responses to e-Synch. External components of e-Synch include a 3D-printed case with retention wings, a flexible wideband antenna, and silicone membrane for pressure balancing. Internal components include a central control board, battery, wireless charging coil, and two silicone hormone reservoirs connected to individual peristaltic pumps. An accelerometer and a high-accuracy temperature sensor are integrated in the custom printed circuit board (PCB). The IoT platform includes a gateway consisting of Raspberry PI 3 and a CC1352 radiofrequency module that collects sensor data at 915 mHz. Data is transferred to the Google Cloud utilizing the IoT Core service through TCP/IP, and then is pulled by the Pub/Sub service. After routing to a BigQuery table by the Dataflow service, data visualization is provided by Data Studio. Drug delivery protocols are selected using an IOS device app that connects to e-Synch through Bluetooth. Experiments with lactating Holsteins cows were conducted to demonstrate proof-of-concept system functionality and evaluate cow responses. Despite unstable communication and signal discontinuity because of signal strength attenuation by body tissue, devices (n=6) communicated with the IoT platform in 89% (24/27) of use instances. Temperature and accelerometer data were received for at least one 15 min period during an 8 h insertion period from all devices that communicated with the IoT platform. Variation in accelerometer data (± 8.565 m/s2) was consistent with cow activity during experimentation and mean vaginal temperature of 39.1 °C (range 38.6 to 39.5 °C) demonstrated sensor functionality. Hormone release was confirmed in all instances of device use except for one. Cow behavior evaluated through signs of discomfort and pain, and tail raising scores was mostly unaltered by e-Synch. Vaginal integrity and mucus scores also remained unaltered during and after device insertion. In conclusion, the e-Synch device integrated with a controlling app and IoT platform might be used to automate intravaginal hormone delivery and sensing for controlling the estrous cycle of cattle.

ПРОПАГАНДА ТА МЕДІАРЕАЛЬНІСТЬ ЯК ІНСТРУМЕНТИ ВЕДЕННЯ ІНФОРМАЦІЙНОЇ ВІЙНИ: ЛОКАЛЬНИЙ ВИМІР

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The purpose&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; of the study is to &lt;/em&gt;&lt;em&gt;to consider the local dimension of propaganda as a component of media reality in modern public discourse and to clarify its functions and role in conducting information warfare.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Research methodology&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;.&lt;/em&gt; &lt;em&gt;The following methods were used in the research process: systematization, analysis and synthesis, comparison. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;The analyzed source base reveals that the toolkit for constructing media reality in Russian propaganda materials includes the following elements: stereotypes, framing, selection method, manipulation of opinions, use of conflicts, storytelling, use of actions/actors, emphasis on violation of norms, propaganda methods, preferences, genre specificity, reporting elements, visual tools, etc. The study indicates that, unfortunately, there are many elements of Russian propaganda in the modern local dimension of media discourse. On the one hand, the messages of these propaganda materials are aimed at an audience with a lack of critical thinking and a low level of media literacy. However, the toxic content in the distribution of such messages in the temporarily occupied territories of the Zaporizhzhia region is also realized to a wider audience, since the residents of these cities and villages have no or almost no access to Ukrainian sources of information, have unstable communication, and are in an information vacuum. All these are favorable conditions for the effective spread of Russian messages in the minds of our fellow citizens.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Practical significance&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;. &lt;/em&gt;&lt;em&gt;The results of the research can be used during courses related to the topics of propaganda, construction of media reality, modern history of Ukraine, journalism and social communications, in particular, for the specialties «Journalism», «Advertising and public relations», «Information business». In addition, they can be used as materials of informational and educational significance, a source base for further scientific and practical developments, as well as in the work of media workers to counter enemy propaganda in conditions of information warfare. Equally important is that they can form the basis of the formation of Ukraine’s strategic narrative.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key words&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;:&lt;/em&gt;&lt;em&gt; propaganda, media reality, fakes, narratives, information war, media literacy, key messages, temporarily occupied territories of Zaporizhzhia region, manipulation, fact-checking.&lt;/em&gt;&lt;/p&gt;