Search Results (287)

Animals feature regularly on children’s television, exposing children to implicit and explicit messages about the natural world. Although animal representation and anthropomorphism have been examined across children’s media, film, literature, and popular culture, less is known about how animals are portrayed in preschool television, particularly in relation to biological and ecological accuracy. This study examined animal representations in programs aired on ABC Kids, the Australian Broadcasting Corporation’s free-to-air children’s television channel, using quantitative and qualitative content analysis. A total of 947 episodes from 92 programs were analyzed, with a subset of 265 episodes (43 h and 45 min) from 39 programs examined in greater detail. Animals were prominently featured, appearing on screen for 88% (171 h and 40 min) of total programming time, with programs featuring animals as central characters accounting for 41% (81 h). Nearly all central animal characters exhibited anthropomorphic traits, such as walking, talking, dressing, or behaving like humans. Inaccuracies in species depiction were common, including portrayals of animals in human-like social structures, unrealistic environments, and mixed predator–prey groupings, alongside a hierarchy based on roles and relationships. These findings indicate that realistic portrayals of animals and the natural world were uncommon. The study highlights the need for greater consideration of how animal characters are represented in preschool television, and how imaginative animal portrayals might be balanced with biologically and ecologically grounded depictions. Further research should examine how such portrayals influence children’s understanding of animals and their connection with nature. Full article

Televised accountability can be understood as a socio-technical urban governance arrangement in which media exposure, evidentiary inputs, staged interaction, and bureaucratic response are tied together in a structured oversight process. Drawing on Nanning’s long-running “Commitment to the People—TV Accountability” program, the analysis covers 73 episodes broadcast between 2014 and 2023, including 327 issue chains and 3675 official responses. Rather than collapsing episodes into aggregate cases, the design preserves issue chains and response sequence. Pre-response pressure is measured through factual specificity, emotional intensity, accountability directness, and evidence type, while official outputs are coded as acknowledgement, deflection, generic commitment, and specific commitment. Row-level logit models indicate that emotional intensity is associated with a broader immediate visible response: higher odds of acknowledgement, deflection, and generic commitment, but lower odds of specific commitment. Accountability directness is associated with higher odds of acknowledgement and deflection. Non-clip evidence is associated with specific commitment, whereas negative exposure clips are associated with lower odds of generic commitment. Terminal unit-chain models and sequence analyses further show that visible concession emerges early, whereas checkable commitment appears later and in fewer chains. Rather than serving as a simple transparency device, televised oversight operates here as a systemic accountability interface. It tends to channel official response toward low-cost, publicly legible adjustment while making high-cost commitment more selective, evidence-dependent, and stage-dependent. The findings speak to ongoing discussion of socio-technical governance, smart-city governance, and public-value production in data-mediated urban accountability settings. Full article

The Automatic Identification System (AIS) is a maritime radio system that regularly broadcasts vessel data, such as the vessel’s identification, position, course and speed. For modulation, the AIS standard defines Gaussian minimum shift keying (GMSK) as an easy to implement modulation scheme with constant envelope, meaning that a GMSK complex baseband signal carries information solely in its phase. AIS does not use any forward error correction (FEC) mechanism. In this paper we propose to extend GMSK with amplitude modulation, leading to multi-amplitude Gaussian minimum shift keying (MA-GMSK). The additional modulation of the amplitude increases the spectral efficiency so that additional information, i.e., additional bits can be transmitted. We use the increased spectral efficiency to implement FEC, where we transmit the redundancy bits of a systematic channel code via the additional amplitude modulation in the proposed MA-GMSK scheme. With this approach, the proposed MA-GMSK signal can be processed by off-the-shelf AIS receivers, thus demonstrating empirical standard compatibility with the tested receivers. Based on simulations and experimental results, we propose a suitable MA-GMSK modulation parameter setting and evaluate the packet error rate (PER) performance accordingly. To verify standard compatibility, we examine the performance of commercially available AIS receivers fed with MA-GMSK signals. Using the proposed modulation and coding scheme, an advanced MA-GMSK receiver including FEC provides performance improvements up to 3 dB in the required signal-to-noise ratio (SNR) compared to state-of-the art AIS using uncoded GMSK. Full article

To address the limited communication capacity of the traditional time-division half-duplex (TDHD) systems in BDS-3 inter-satellite links (ISLs), this paper proposes a cooperative design of ranging and communication based on an in-band full-duplex (IBFD) architecture. By utilizing BDS broadcast ephemeris to assist signal acquisition and selecting the serial acquisition strategy with the lowest computational complexity, a 100% acquisition success rate can be achieved within milliseconds. This completely releases the 250 ms preamble originally used for acquisition in the traditional time slot. Adopting the IBFD system, the ISL time-slot structure is optimally redesigned: the preamble is used for signal acquisition and tracking to accomplish inter-satellite ranging, while the original measurement period is used for QPSK dual-channel parallel data transmission. This design extends the effective communication duration from 1 s to 2.5 s, expands the communication from single-channel to dual-channel, and theoretically achieves a 5-fold improvement in communication efficiency. Simulation analysis shows that, while the communication efficiency is significantly improved, the ranging accuracy remains essentially unchanged compared with the traditional TDHD system. Without altering the existing 3 s time-slot duration, this method achieves cooperative optimization of ranging and communication, providing a feasible technical solution for enhancing the communication capacity of BDS-3 ISLs. Full article

Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks. Full article

Ensuring physical layer security for low-altitude economic networking (LAENet) is critical due to the broadcast nature of wireless channels. In dense urban environments, multi-antenna LAENet systems are often impaired by the keyhole effect, which induces rank deficiency and poses significant security challenges. This paper investigates the secrecy performance of a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system in LAENet with keyhole channels. Depending on the availability of channel state information (CSI) at the transmitter, three wiretap scenarios are considered: (i) broadcasting, (ii) passive eavesdropping, and (iii) spoofing. For each scenario, the optimal precoder is designed to maximize the secrecy transmission rate. Based on these designs, we derive closed-form expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR). To provide insights into the effect of keyholes on secrecy diversity order and array gain under this severe rank-deficiency structure, we also obtain asymptotic expressions for SOP and ASR in the high signal-to-noise ratio (SNR) regime using the Mellin transform. Numerical results validate the analytical expressions and illustrate the influence of key parameters on secrecy performance. These findings provide meaningful guidance for the secure design of future LAENet deployments. Full article

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article

The continuous growth of traffic in metro networks is increasing the need for cost-effective, scalable, and power-efficient optical solutions. Filterless optical networks (FONs) have emerged as a promising architecture for metro-aggregation and metro-access domains, thanks to their low complexity and reliance on passive optical components. However, their inherent broadcast nature introduces key challenges, including spectrum waste, limited power equalization, and significant noise accumulation, particularly when coherent pluggable transceivers are employed. This work provides a detailed assessment of FON performance using state-of-the-art multi-source agreement (MSA)-compliant coherent modules, evaluating both point-to-point (P2P) and digital subcarrier multiplexing (DSCM)-based point-to-multipoint (P2MP) architectures. A novel optical amplifier (OA) optimization algorithm is proposed to balance expressed and added signal power in FON, accounting for optical power saturation effects and optical performance degradation due to limited power at the receiver input. The analysis highlights the substantial impact of transmitter out-of-band (OB) noise in FONs and its detrimental accumulation during multi-channel colorless aggregation, which can limit network capacity. In scenarios with lower capacity requirements, P2MP architectures demonstrate superior performance, benefiting from reduced insertion loss and lower OB noise accumulation while offering enhanced scalability compared with P2P solutions. Overall, the study highlights that FONs combined with coherent pluggables can support cost-efficient and scalable metro solutions, provided that OB noise, power imbalance, and amplifier operation are properly addressed through optimized design strategies. Full article

Geothermal exploration and monitoring often require dense temperature observations in terrains where wired networks are impractical and battery replacement for in situ sensors is costly. This paper proposes an infrared-assisted, temperature-aware access scheme for a UAV-enabled backscatter IoT network tailored to geothermal hotspot sensing. A rotary-wing UAV equipped with a thermal infrared camera and an RF transceiver first surveys the area to construct a surface temperature map and identify candidate hotspots, and then hovers above a selected hotspot to perform periodic frames consisting of wireless energy transfer followed by backscatter uplink collection. Ground sensors harvest RF energy, measure their local temperature, and autonomously activate only when both the harvested energy exceeds a threshold and the measured temperature falls within a target interval broadcast by the UAV, thereby concentrating channel access on thermally relevant nodes. We develop a system model that couples a geothermal-like thermal field, RF energy harvesting, and framed slotted backscatter access, and introduce hotspot-oriented performance metrics including effective hotspot throughput, task completion time, and energy per hotspot report. The simulation results show that the proposed temperature–energy-gated access significantly increases the fraction of successfully decoded packets originating from hotspot regions and improves the energy efficiency of geothermal monitoring compared with full activation and purely energy-based activation. Full article

Real-time object detection in soccer videos presents significant challenges due to the dynamic nature of matches, varying object scales, and the stringent requirement for efficient processing. In this work, we define real-time detection as that which achieves inference speeds of at least 30 frames per second (FPS), which is the minimum requirement for smooth video processing and live broadcast applications. While transformer-based detectors have achieved remarkable accuracy, their quadratic computational complexity limits their real-time applications. In this paper, we propose SoccerDETR, a novel real-time detection framework that integrates MobileMamba-based visual state space models with an efficient transformer encoder for soccer object detection. Our approach introduces four key innovations: (1) a MobileMamba backbone leveraging selective state space modeling to achieve linear computational complexity while maintaining global receptive fields; (2) a Semantic-aware Dynamic Feature Fusion Module (SDFM) that adaptively aggregates multi-scale features through progressive semantic injection; (3) a Spatial-Channel Synergistic Attention (SCSA) mechanism that explores the synergistic effects between spatial and channel attention for enhanced feature representation; and (4) a Separable Dynamic Decoder that employs dynamic convolution attention to replace traditional cross-attention, significantly reducing computational overhead. Additionally, we design a Scale-Aware Focal Loss (SAFL) that addresses the class imbalance and scale variation problems inherent in soccer scenarios. Extensive experiments on the Soccana and SoccerNet datasets demonstrate that SoccerDETR achieves state-of-the-art performance with 94.2% mAP@50 on Soccana and 91.8% mAP@50 on SoccerNet, while maintaining real-time inference speed of 78 FPS on a single NVIDIA RTX 4090 GPU with a batch size of 1 and an input resolution 640 × 640. Our method outperforms existing approaches by 2.3–5.7% in mAP while being 1.5–3.2× faster, demonstrating the effectiveness of state space models for efficient sports video object detection. Comprehensive ablation studies validate the effectiveness of each proposed component, and cross-dataset experiments demonstrate strong generalization capability. Full article

Adaptive coding and modulation (ACM) is a key feature in satellite broadcasting; it allows the dynamic selection of modulation and coding (MODCOD) schemes based on channel conditions. The selection is based on the quasi-error-free (QEF) threshold and additional margins. We introduce three distinct types of margins for improved robustness. One of these margins, impairment margin (IM), depends on the nonlinearities of different components in the satellite channel. Current IM selection methods require expert intervention; are costly and prone to errors; and only allow a discrete set of environments. We aim to develop a low-complexity algorithm that converges fast and is quasi-error-free on user traffic due to a non-intrusive exploration method. For this, we propose a Q-learning-based solution that uses passive exploration, with fill frames, to allow error-free IM optimization. Our solution shows a higher average spectrum efficiency compared to expert and default IMs, with fewer low efficiency test cases and more high-efficiency cases. Full article

The spoofing of Automatic Identification System (AIS) messages presents a hazard to safe maritime navigation. To prevent such spoofing, we present an authentication system based on Public Key Cryptography (PKC) that is both fully open source and backwards compatible with mariners’ existing use of the AIS. Using this, we have successfully demonstrated the ‘live’, over-the-air broadcast of authenticated AIS messages in a busy radio environment. The technique used is an improvement upon earlier work in that digital signatures are carried using the terrestrial VHF Data Exchange (VDE-TER) component of the VHF Data Exchange System (VDES). This prevents additional channel loading on the AIS and offers greater flexibility. Full article

Cross-medium acoustic–radio collaborative networks enable integrated communication among underwater, surface, and aerial nodes for marine observation and detection. However, heterogeneous propagation characteristics of acoustic and radio channels significantly degrade the performance of conventional single-medium routing protocols, resulting in excessive control overhead, a low packet delivery ratio (PDR), and high latency. To address these challenges, this paper proposes an optimized AODV protocol for Cross-medium Acoustic–Radio Collaborative Networks (CACN-OAODV). The proposed protocol incorporates a medium-aware routing initiation mechanism to reduce unnecessary broadcasts, a link stability factor that jointly considers hop count and channel quality for reliable path selection, and a lightweight control optimization scheme to limit routing overhead in acoustic environments. Extensive simulations conducted in NS-3 with realistic multi-channel propagation models demonstrate that CACN-OAODV significantly outperforms the standard AODV protocol, achieving improved PDR, higher throughput, and reduced end-to-end delay. These results indicate that CACN-OAODV provides an effective routing solution for heterogeneous cross-medium marine communication networks. Full article

This paper investigates the performance of a multi-user multiple-input single-output (MU-MISO) broadcast channel under the practical constraints of imperfect, delayed, and quantized channel state information at the transmitter (CSIT). Conventional interference alignment (IA) strategies—classified into spatial (SIA), temporal (TIA), and message-domain (MIA) techniques— typically designed for specific, idealized CSI regimes and often rely on successive interference cancellation (SIC) at the receiver. However, the iterative structure of SIC is highly susceptible to error propagation, particularly under CSI uncertainty and high-order modulation. We propose Deep-STMIA, a novel end-to-end deep learning framework that jointly optimizes interference management across the space, time, and message domains. Using a neural network-based autoencoder architecture with structural message-domain regularization, Deep-STMIA learns to mitigate the catastrophic effects of error propagation and adapts to a continuum of CSIT conditions. Simulation results demonstrate that Deep-STMIA matches the performance of degrees-of-freedom (DoF) optimal benchmarks in extreme CSI regimes and significantly outperforms state-of-the-art baselines, such as rate-splitting multiple access (RSMA), in practical imperfect CSIT scenarios. Full article