Search Results (170)

Phosphorus (P) limitation is prevalent in terrestrial ecosystems. Plants can improve soil P availability through the exudation of organic acids and symbiotic interactions with microorganisms. However, associations between different plant functional groups and phosphorus cycling in P limited karst ecosystems remain poorly understood. To investigate this, the exudation rates of oxalic, citric and acetic acids from fine roots, the contents of carbon, nitrogen, and P in leaves and fine roots, and the contents of oxalic, citric and acetic acids, total P, available P (AP), and microbial biomass P in rhizosphere soils were measured across different plant functional groups in a karst ecosystem in southwestern China. Additionally, the activities of acid and alkaline phosphatases were also analyzed, as well as the relative abundance, community structure, diversity, and co-occurrence network patterns of arbuscular mycorrhizal fungi (AMF) and alkaline phosphatase-encoding (phoD) gene-harboring bacteria. The results showed that both the exudation rates and the contents of organic acids and AP were highest in the tree group, followed by the shrub and grass groups. The AP content of the legume group was significantly higher than that of the non-legume group. The exudation rates of oxalic acid were significantly greater than those of citric and acetic acids. AMF diversities were highest in the shrub and legume groups. The diversities of phoD-harboring bacteria decreased from the tree group to the shrub group and then to the grass group, yet there were no significant differences between the legume and non-legume groups. The communities of both AMF and phoD-harboring bacteria exhibited significant differences among these plant functional groups. The prevalent genera of phoD-harboring bacteria across all groups were Pseudomonas and Halomonas, with Halomonas being particularly prevalent in the legume group. The AMF community was dominated by Glomus, which attained its highest relative abundance in the tree and legume groups. Furthermore, the increased exudation rate and content of oxalic acid were associated with higher relative abundances of Glomus in AMF and Pseudomonas and Bacillus among phoD-harboring bacteria. Structural Equation Model (SEM) analysis demonstrated that plant-exuded organic acids, especially oxalic acid, were positively associated with P availability indirectly through their linkages with the diversity and abundance of AMF and phoD-harboring bacteria. The crucial role of oxalic acid was particularly prominent in the tree and legume groups. Our findings suggest that screening AMF and phoD-harboring bacteria with highly efficient P transformation activity and inoculating them into the rhizosphere of plants with high oxalic acid exudation could help improve plant resilience to P limitation and support sustainable restoration in karst ecosystems. Full article

The rapid growth of Internet of Things (IoT) deployments in hybrid terrestrial/non-terrestrial networks (TN/NTN) faces a major bottleneck: the verbosity of standard data formats like JSON. This is critical for large-scale M2M systems tracking and monitoring multimodal dry containers, where devices must comply with the strict message-size limits of commercial satellite IoT (around 160 bytes per message). We present a comparative evaluation of four device-friendly binary serialization protocols (CBOR, MessagePack, Protocol Buffers, and a custom Struct+Zlib hybrid) targeted at battery-powered microcontrollers. Using a horizontally scalable testbed with up to 2000 concurrent devices and the oneM2M standard framework, we assess payload efficiency, throughput, latency, and maintainability. Only Protocol Buffers and Struct+Zlib meet NTN message-size limits, with Protocol Buffers providing the best trade-off between performance and long-term maintainability. Real-world validation with the Astrocast LEO satellite platform and the oneM2M Mobius framework confirms these results. Cost analysis suggests potential savings exceeding €62,000 per month for a 10,000-device maritime fleet, demonstrating both technical feasibility and economic viability. This study provides a methodological framework for designing efficient, scalable IoT systems in hybrid TN/NTN networks, offering practical guidance for global container tracking and monitoring deployments. Full article

Satellite–terrestrial integrated networks provide seamless global coverage, especially in remote areas where terrestrial deployment is costly. Integrated sensing and communications (ISAC) enhances spectral efficiency by merging both functions on a single platform. This paper proposes a novel integrated satellite–terrestrial ISAC architecture, where a satellite performs simultaneous communication and sensing. The satellite transmits communication signals and sensing waveforms to an Earth Station, which then relays them to a terrestrial base station to serve multiple users. We formulate a joint beamforming design problem to maximize the sum rate of users under quality-of-service constraints, backhaul capacity limits, beampattern requirements for sensing, and power budgets. With perfect channel state information, the non-convex problem is transformed into a difference-of-convex form and solved via the convex–concave procedure. For imperfect channel state information, a robust method combining successive convex approximation and the S-procedure is developed. Simulations show the proposed design outperforms benchmarks and is suitable for low-Earth orbit satellite systems. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

This work proposes a fiber-tethered UAV-enabled adaptive aerial passive optical network (AA-PON) framework for rapid extension of optical access and backhaul in hard-to-reach or temporarily disrupted environments. The proposed architecture supports two distinct operating modes: (i) an aerial base station (ABS) mode for wide-area service extension and (ii) a ground-to-air-to-ground (G2A2G) mode for targeted high-speed optical bridging to ground terminal units. Unlike conventional UAV relay approaches, the proposed framework is developed as a network-level optical access/backhaul architecture based on tether-assisted aerial nodes and reconfigurable optical topology formation. In the ABS mode, representative Bus, Ring, and Star topologies are analyzed to evaluate serviceability, outage, deployment latency, and scalability as the number of UAV nodes increases. In the G2A2G mode, a stochastic-geometry-based analysis is used to characterize blockage-limited optical serviceability and infrastructure-density trade-offs. To complement the analytical study, a 2 Gb/s proof-of-concept FSO link between two fiber-tethered UAVs is demonstrated as an initial feasibility validation of the end-to-end optical link. The results show that the proposed AA-PON provides a flexible aerial optical networking framework that combines reconfigurable topology support with localized high-capacity optical access extension. Full article

Abiotic stresses severely constrain crop productivity by disrupting cellular redox homeostasis and hormone signaling. Although individual stresses differ in origin, plant responses converge on a conserved regulatory system centered on reactive oxygen species (ROS) and phytohormone crosstalk. Controlled ROS production in chloroplasts, mitochondria and the apoplast functions as a signaling mechanism that interacts dynamically with abscisic acid, auxin, ethylene, jasmonate and cytokinin pathways through shared regulatory nodes, including nicotinamide adenine dinucleotide phosphate (NADPH) oxidases and redox-sensitive transcriptional cascades. Endogenous metabolites, including phenolics, terpenoids, carotenoids, alkaloids, polyamines, glutathione and signaling peptides, are embedded within this network and modulate its amplitude and sensitivity. In parallel, non-microbial biostimulants derived from seaweeds, higher plants, protein hydrolysates and humic substances have been widely reported to enhance crop performance under abiotic stress. However, mechanistic integration between biostimulant research and plant stress signaling remains limited. In this review, we propose that terrestrial plant- and algal-derived biostimulants act not as external substitutes for hormones or antioxidants but as modulators of endogenous ROS–hormone signaling hubs. We first synthesize the current understanding of redox–hormone integration under abiotic stress, then examine endogenous metabolites as intrinsic regulators of this network, followed by an analysis of biostimulants in relation to shared regulatory nodes. By positioning biostimulant action within the established redox–hormone network, we provide a mechanistic framework that links stress biology with agronomic application and supports rational strategies to enhance crop resilience. Full article

The increasing demand for global connectivity has accelerated the integration of non-terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite constellations, into next-generation position navigation and time (PNT) systems. While LEO-based PNT offers low-latency and high-accuracy potential, challenges such as high path loss and limited ground-level signal diversity remain. Reconfigurable intelligent surfaces (RISs) have emerged as a cost-effective solution to enhance localization performance by providing controllable reflections with minimal infrastructure. Building on prior work in single-RIS NTN scenarios, this paper investigates RIS-aided localization in a single-LEO PNT setting with multiple RISs. We introduce a detailed signal model and multi-stage processing framework that estimates both the satellite and RIS-assisted paths, enabling accurate receiver localization. Simulations assess the trade-offs in coverage and accuracy, providing insights into the feasibility and optimization of RIS-assisted NTN PNT solutions as a complementary alternative to global navigation satellite system (GNSS). Full article

In maritime non-terrestrial networks, long-distance links and resource heterogeneity give rise to significant system transmission delays. As an effective mechanism for alleviating link pressure and enhancing efficiency, caching has been widely employed in this scenario. However, existing studies have predominantly focused on the separate optimization of caching or beamforming, often overlooking the joint potential of the physical and network layers as well as the impact of dynamic channel variations. This paper develops a system model for cache-assisted maritime non-terrestrial networks that incorporates hit status, two-hop link characteristics, and a dynamic channel switching mechanism to characterize link instability. Accordingly, an optimization framework for the joint design of caching and beamforming is presented with the aim of minimizing average delay. The proposed algorithm decomposes the mixed non-convex problem into two sub-problems through a hierarchical alternating strategy, and integrates semidefinite relaxation, successive convex approximation, and a greedy mechanism to attain an efficient solution. Numerical results verify the rapid convergence of the joint design of caching and beamforming algorithm and demonstrate that the semidefinite relaxation relaxation achieves a rank-one recovery probability of over 92.8%, ensuring near-optimal beamforming design. Simulation results demonstrate that the joint design robustly outperforms various state-of-the-art benchmarks in delay performance and effectively circumvents physical-layer bottlenecks as network scales expand or satellite antenna resources become constrained. More importantly, the proactive caching design maintains a significant “robustness gap” against isolated optimization schemes, thereby offering solid theoretical support and a practical pathway for cross-layer collaborative optimization in integrated air-ground-space communication systems. Full article

Accurate time and frequency acquisition is essential for deploying Orthogonal Time–Frequency Space (OTFS) modulation in non-terrestrial networks (NTNs), where severe Doppler shifts and low-SNR conditions are common. We propose a practical synchronization method that inserts an m-sequence-based pilot (illustrated using the 5G NR PSS) periodically in the delay–Doppler grid. Leveraging OTFS mapping properties, the method enables robust matched-filter detection for joint coarse time and frequency acquisition and continuous phase-drift tracking without increasing transmission redundancy. Numerical simulations show that the proposed method achieves a slightly lower PAPR and approximately a 3 dB improvement in detection threshold compared to a recent practical baseline. The algorithm is suitable for 5G/6G NTN links such as LEO constellations and operates reliably at low and negative SNR values. Full article

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice link performance. In this paper, we propose the Hybrid Handover Strategy (HHS), a low-complexity algorithm that addresses this trade-off. The HHS utilizes a multi-attribute utility function that integrates the signal-to-interference-plus-noise ratio (SINR), satellite elevation angle, and network load with a novel logistic-decay stability bonus mechanism. We provide a formal mathematical analysis of the algorithm’s stability and performance trade-offs. To ensure industrial relevance, the strategy is validated using a high-fidelity simulator driven by real-world two-line element (TLE) data from the Starlink constellation. Results demonstrate that the HHS reduces the handover frequency by 64% compared to SINR-based benchmarks while maintaining service availability of 90.2%. The proposed algorithm delivers these improvements with significantly smaller computational overhead than machine learning approaches, making it suitable for resource-constrained on-board processing and ground terminals. Full article

In the Non-Terrestrial Networks (NTNs) formed by the integration of fifth-generation mobile communication systems and Low Earth Orbit (LEO) satellites, Doppler frequency offsets can severely degrade the performance of OFDM signal detection. Particularly for the Physical Uplink Control Channel (PUCCH), conventional detection algorithms suffer significant performance degradation due to the difficulty of accurately estimating and compensating for Doppler frequency offsets at the receiver. Consequently, achieving robust signal detection under conditions with high Doppler frequency offsets becomes particularly critical. To address this challenge, we propose a maximum-likelihood detection algorithm robust to both Doppler frequency and time offsets. In the first step, we derive the frequency-offset matrix, which directly affects the detection peaks. Subsequently, we develop a novel two-dimensional search algorithm that jointly considers UCI and frequency offset. Finally, based on the sparse characteristics of the dominant elements in the frequency offset matrix, we simplify the implementation of the frequency offset matrix, reducing computational complexity to 9% of the original algorithm while achieving negligible performance loss. This approach satisfies the requirements for onboard implementation. Full article

Limited terrestrial network coverage in rural and remote areas constitutes a significant barrier to the digital transformation of the agricultural sector. Smart and precision farming applications, ranging from conventional environmental monitoring systems to advanced Digital Twin solutions, rely on the reliable transmission of sensor data, images, and video streams from geographically isolated farms. Such data-intensive services cannot be effectively supported without a robust communication infrastructure. Non-Terrestrial Networks (NTNs), particularly satellite systems, offer both narrowband and broadband connectivity, enabling the transmission of low-rate sensor measurements, as well as high-throughput multimedia data from the field. This paper presents an experimental performance evaluation of two satellite backhauling solutions: a Geostationary Earth Orbit (GEO) system provided by SES and a Low Earth Orbit (LEO) system from Starlink. The networks were first deployed and tested in a laboratory environment and subsequently validated in an operational agricultural field setting. Their performance is benchmarked against a terrestrial cellular network to assess their suitability for supporting advanced agricultural applications. The performance assessment results indicate that both satellite backhauling solutions are reliable and capable of meeting the bandwidth and latency requirements of delay-tolerant agricultural applications. In addition to the technical evaluation, this work presents a cost–benefit analysis that further underscores the advantages of NTN-based solutions. Despite higher initial expenditures, they provide extended coverage in remote areas and enable cost sharing across multiple users, improving overall economic viability. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

Accurate network traffic forecasting is essential for the proactive management of modern communication systems, particularly for emerging satellite constellations. However, forecasting network traffic is a non-trivial task, as the data exhibit complex statistical properties such as self-similarity, spatial–temporal long-range dependence, and non-stationarity, which challenge even the recent surge of deep learning models like iTransformer and DLinear. To address these limitations, this paper proposes MDETST, a novel Transformer-based architecture designed to more effectively capture and disentangle these complex, multi-scale spatio-temporal dependencies. Recognizing the scarcity of public satellite data, we conduct a comprehensive evaluation on the large-scale CESNET-TIMESERIES24 dataset, a challenging real-world benchmark that shares the critical statistical properties of both satellite and terrestrial systems. Extensive experiments demonstrate that our proposed MDETST model achieves state-of-the-art performance, consistently and significantly outperforming strong baselines, especially in demanding long-term forecasting scenarios. This work provides a robust and effective solution for complex traffic forecasting, showing strong potential for application in both satellite and terrestrial network management. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article