Search Results (565)

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article

This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model—with conventional convolutional layers replacing the separable convolutional layers—can be adopted as a more precise alternative. Full article

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

With the increasing scale of low Earth orbit (LEO) satellite networks, leveraging non−terrestrial networks (NTNs) to complement terrestrial networks (TNs) has become a critical issue. In this paper, we investigate the issue of handover satellite selection between multiple terrestrial terminal groups (TTGs). To support effective handover decision-making, we propose a long short-term memory (LSTM)-network-based traffic prediction mechanism based on historical traffic data. Building on these predictions, we formulate the handover strategy as a Markov Decision Process (MDP) and propose an attention-enhanced rainbow-DQN-based joint traffic prediction and handover design framework (ARTHF) by jointly considering the satellite switching frequency, communication quality, and satellite load. Simulation results demonstrate that our approach significantly outperforms existing methods in terms of the handover efficiency, service quality, and load balancing across satellites. Full article

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. Full article

This paper presents the design of a wideband circularly polarized crossed-dipole antenna for multi-GNSS applications, covering the frequency range of 1.16–1.61 GHz. The proposed antenna employs orthogonally placed dipole elements fed by a three-branch quadrature hybrid coupler for broadband and wide gain/axial ratio beamwidth. The design is carried out using CST Studio Suite for a single dipole antenna followed by a crossed-dipole antenna, a feed network, and the entire antenna structure. The designed multi-GNSS antenna shows, at 1.16–1.61 GHz, a reflection coefficient of less than −17 dB, a zenith gain of 3.9–5.8 dBic, a horizontal gain of −3.3 to −0.2 dBic, a zenith axial ratio of 0.6–1.0 dB, and horizontal axial ratio of 0.4–5.9 dB. The proposed antenna has a dimension of 0.48 × 0.48 × 0.25 λ at the center frequency of 1.39 GHz. The proposed antenna can also operate as an LHCP antenna for L-band satellite phone communication at 1.525–1.661 GHz. Full article

In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the foundation for evaluating its performance within a digital beamforming architecture. This architecture is implemented in a system-level simulator to evaluate the performance of the transmitter and receiver chains. This study advances the analysis of the digital antennas by incorporating both the RF front-end and digital sections non-idealities into a digital-twin framework. This approach enhances the designer’s ability to optimize the system with a holistic approach and provides insights into how various impairments affect the transmitter and receiver performance, identifying the subsystems’ parameter limits. To achieve this, we analyze several subsystems’ parameters and impairments, assessing their effects on both the antenna radiation and quality of the transmitted and received signals in a real applicative context. The results of this study reveal the sensitivity of the system to the impairments and suggest strategies to trade them off, emphasizing the importance of selecting appropriate subsystem features to optimize overall system performance. Full article

Chemometrics has emerged as a powerful approach for deciphering complex environmental systems, enabling the identification of pollution sources through the integration of faunal community structures with physicochemical parameters and in situ analytical data. Leveraging advanced technologies—including satellite imaging, drone surveillance, sensor networks, and Internet of Things platforms—chemometric methods facilitate real-time and longitudinal monitoring of both pristine and anthropogenically influenced ecosystems. This review provides a critical and comprehensive overview of the foundational principles underpinning chemometric applications in environmental science. Emphasis is placed on identifying pollution sources, their ecological distribution, and potential impacts on human health. Furthermore, the study highlights the role of chemometrics in interpreting multidimensional datasets, thereby enhancing the accuracy and efficiency of modern environmental monitoring systems across diverse geographic and industrial contexts. A comparative analysis of analytical techniques, target analytes, application domains, and the strengths and limitations of selected in situ and remote sensing-based chemometric approaches is also presented. Full article

THz communication stands as a pivotal technology for 6G networks, designed to address the critical challenge of data demands surpassing current microwave and millimeter-wave (mmWave) capabilities. However, realizing Tbps and kilometer-range transmission confronts the “dual attenuation dilemma” comprising severe free-space path loss (FSPL) (>120 dB/km) and atmospheric absorption. This review comprehensively summarizes our group′s advancements in overcoming fundamental challenges of long-distance THz communication. Through systematic photonic–electronic co-optimization, we report key enabling technologies including photonically assisted THz signal generation, polarization-multiplexed multiple-input multiple-output (MIMO) systems with maximal ratio combining (MRC), high-gain antenna–lens configurations, and InP amplifier systems for complex weather resilience. Critical experimental milestones encompass record-breaking 1.0488 Tbps throughput using probabilistically shaped 64QAM (PS-64QAM) in the 330–500 GHz band; 30.2 km D-band transmission (18 Gbps with 543.6 Gbps·km capacity–distance product); a 3 km fog-penetrating link at 312 GHz; and high-sensitivity SIMO-validated 100 Gbps satellite-terrestrial communication beyond 36,000 km. These findings demonstrate THz communication′s viability for 6G networks requiring extreme-capacity backhaul and ultra-long-haul connectivity. Full article

High-speed communication can be achieved using laser inter-satellite links. However, laser terminals are highly sensitive to environmental conditions, which can lead to link disconnections. Therefore, an acquisition method capable of determining pointing errors is essential. In this study, a fast space–time fusion acquisition method was developed. This method establishes the relationship between satellite position, capture time, and azimuth and elevation angles. The performance of the proposed acquisition time optimization method was verified in a practical engineering application. Experimental results show that the pointing error was reduced by five times, the acquisition rate increased by 40%, the acquisition speed improved by 300 times, and multiple interference factors were effectively addressed. Full article

Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning. Full article

In next-generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through strong authentication is essential due to the network’s dynamic and distributed structure. Traditional authentication systems often struggle in these environments, leading to the adoption of Zero-Trust Security (ZTS) models. However, current ZTS protocols typically introduce high computational overhead, especially as the number of satellite nodes grows, which can impact both security and network performance. To overcome these challenges, a new bio-inspired ZTS framework called Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS) has been introduced. This approach uses data-driven learning methods to enhance security across satellite communications. It continuously evaluates access requests by applying a cost function that accounts for risk level, likelihood of attack, and computational delay. The Manta Ray Foraging Optimization (MRFO) algorithm is used to minimize this cost, enabling effective classification of nodes as either trusted or malicious based on historical authentication records and real-time behavior. MRFCO-ZTS improves the accuracy of attacker detection while maintaining secure data exchange between authenticated satellites. Its effectiveness has been tested through numerical simulations under different satellite traffic conditions, with performance measured in terms of security accuracy, latency, and operational efficiency. Full article

Geostationary Earth orbit (GEO) satellite communication systems have become increasingly significant in global communication networks and national strategic infrastructure, owing to their advantages of extensive coverage, high capacity, and robust reliability. Constructing accurate and reliable simulation systems is essential to support the design, evaluation, and optimization of GEO satellite communication systems. This article first reviews the current developments and application prospects of GEO satellite communication systems and highlights the critical role of simulation technologies in system design and performance assessment. Subsequently, a systematic analysis is conducted on two core modules of simulation systems, i.e., coverage analysis and resource management and scheduling. Moreover, this article provides a comprehensive comparison and evaluation of mainstream satellite communication simulation platforms and tools. This review aims to offer valuable insights and guidance for future research and applications in GEO satellite communication simulation, thereby promoting technological innovation and advancement in related fields. Full article