Search Results (18)

Indonesia is a country of vast geographical and cultural diversity, hosting numerous cultural festivals annually, such as Sekaten, Labuhan, and the Lembah Baliem Festival. However, as the world’s largest archipelago country, Indonesia faces geographical challenges in terms of ensuring the reliability of communication networks, particularly in maintaining user experience in high-density, short-duration traffic burst environments, such as festivals. The nation’s network connectivity relies heavily on satellite networks and Palapa Ring, a national fibre-optic backbone network that comprises a combination of inland and underwater networks, connecting major and remote islands to the global internet. Although this solution can provide a baseline for broadband connectivity, an adaptive intelligent mobile edge-based solution is needed to complement the existing network infrastructure in order to meet the dynamic demands of localised and transient traffic surges across multiple temporary, geographically dispersed festival sites in both urban and rural areas. In this paper, we present a multimodal study that combines network connectivity measurements during a festival with an extensive user analysis of festival participants and organisers to investigate reliability gaps in user experience regarding network connectivity. Our findings show that internet connectivity was intermittently disrupted during the festival, and our user analysis revealed a gap between customer expectations and perceptions of network service quality and the provision of application services in a heterogeneous festival environment. To address this challenge, we propose a novel next-generation intelligent festival mobile edge framework, MobiFest, which integrates the multi-layer Cognitive Cache which has geospatial–temporal edge intelligence for localised service provisioning to improve the delivery of application services in both urban and rural festival environments. In our extensive experiments, we employ smart garbage as our use case and demonstrate how our complex, multimodal intelligent network protocol SmartGarbiC, designed based on MobiFest for garbage management services, outperforms state-of-the-art and benchmark protocols. Full article

Reciprocating compressors, which serve as core equipment in the petrochemical and natural gas transmission sectors, operate under prolonged variable loads and high-frequency impact conditions. Critical components, such as valves and piston rings, are prone to failure. Existing fault diagnosis methods suffer from inadequate spatio-temporal feature extraction and neglect spatio-temporal correlations. To address this, this paper proposes a spatio-temporal feature fusion-based fault diagnosis method for reciprocating compressors. This method constructs a spatio-temporal feature fusion model (STFFM) comprising three principal modules: First, a spatio-temporal feature extraction module employing a multi-layered stacked bidirectional gated recurrent unit (BiGRU) with batch normalisation to uncover temporal dependencies in long-term sequence data. A graph structure is constructed via k-nearest neighbours (KNN), and an enhanced graph isomorphism network (GIN) is integrated to capture spatial domain fault information variations. Second, the spatio-temporal bidirectional attention-gated fusion module employs a bidirectional multi-head attention mechanism to enhance temporal and spatial features. It incorporates a cross-modal gated update mechanism and learnable weight parameters to dynamically retain the highly discriminative features. Third, the classification output module enhances the model’s generalisation capability through multi-layer fully connected layers and regularisation design. Research findings demonstrate that this approach effectively integrates spatio-temporal coupled fault features, achieving an average accuracy of 99.14% on an experimental dataset. This provides an effective technical pathway for the precise identification of faults in the critical components of reciprocating compressors. Full article

The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is challenging due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness. Full article

The practical deployment of Orthogonal Frequency Division Multiplexing Passive Optical Networks (OFDM-PONs) is hindered by the lack of a Medium Access Network (MAC) protocol capable of managing their flexible, distance-dependent data rates, despite their high spectral efficiency. This paper proposes and validates a novel rate-adaptive, Time Division Multiplexing (TDM)-based MAC protocol for OFDM-PON systems. A key contribution is the design of a three-layer header frame structure that supports multi-ONU data scheduling with heterogeneous rate profiles. Furthermore, the protocol incorporates a unique channel probing mechanism to dynamically determine the optimal transmission rate for each Optical Network Unit (ONU) during activation. The proposed Optical Line Terminal (OLT) side MAC protocol has been fully implemented in hardware on a Xilinx VCU118 FPGA platform, featuring a custom-designed ring buffer pool for efficient multi-ONU data management. Experimental results demonstrate robust upstream and downstream data transmission and confirm the system’s ability to achieve flexible net data rate switching on the downlink from 8.1 Gbit/s to 32.8 Gbit/s, contingent on the assigned rate stage. Full article

To address the issue of heavy reliance on manual intervention in substation maintenance tasks, this paper proposes an end-to-end hierarchical intelligent inference method for collaborative operation of grid switches. The method constructs a unified knowledge environment that can simultaneously describe the operational characteristics of both the power grid and the substation, and combines Dueling Double Deep Q-Network (D3QN) with Multi-Task Dueling Double Deep Q-Network (MT-D3QN) algorithms for interactive training, achieving hierarchical inference. The upper layer uses bays as the base nodes to reflect the power flow, designing a reward and penalty function under N-1 power flow constraints and ring-current impact constraints, optimizing the load transfer plan for the power outages caused by maintenance tasks. The lower layer uses switches as the base nodes to reflect the main wiring status of the substation, introduces a multi-task learning mechanism for parallel training of bays with the same tasks, designs the reward and penalty function according to the five protection rules, and optimizes the switching operations within the bay. The experimental results show that the trained model can quickly deduce the switching operation sequence for different maintenance tasks. Full article

This paper introduces a novel design of a circularly polarized (CP) beamforming antenna that is capable of shaping the original beam into a flat-top configuration. Upon loading a metallic ring, the beamforming pattern can transition into an isoflux pattern. The proposed compact lens antenna comprises a multi-layer honeycomb-like unit lens structure, with a patch and support platform situated beneath the lens. Positioned above the lens, a loadable metallic ring is employed to assist in beamforming. Through a specially designed dielectric lens structure, the lens can control the radiation of electromagnetic waves to achieve the desired beam pattern, while the loadable metallic ring plays a role in optimizing the field across the aperture plane of the lens. This work utilizes a multi-port feed network to drive the patch. To validate the proposed antenna design method, a prototype is fabricated for measurement. The measured result is nearly identical to the simulated result. Within the frequency range spanning from 4.8 GHz to 5.2 GHz (which represents a 10% bandwidth), the antenna demonstrates effective beamforming ability and achieves effective pattern switching. This renders it a promising candidate for scenarios where uniform signal strength coverage is required. Full article

Understanding the atomistic basis of multi-layer mechanisms employed by broadly reactive neutralizing antibodies of the SARS-CoV-2 spike protein without directly blocking receptor engagement remains an important challenge in coronavirus immunology. Class 4 antibodies represent an intriguing case: they target a deeply conserved, cryptic epitope on the receptor-binding domain yet exhibit variable neutralization potency across subgroups F1 (CR3022, EY6A, COVA1-16), F2 (DH1047), and F3 (S2X259). The molecular basis for this variability is not fully understood. Here, we employed a multi-modal computational approach integrating atomistic and coarse-grained molecular dynamics simulations, binding free energy calculations, mutational scanning, and dynamic network analysis to elucidate how these antibodies engage the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and influence its function. Our results reveal that neutralization efficacy arises from the interplay of direct interfacial interactions and allosteric effects. Group F1 antibodies (CR3022, EY6A, COVA1-16) primarily operate via classic allostery, modulating flexibility in RBD loop regions to indirectly interfere with the ACE2 receptor binding through long-range effects. Group F2 antibody DH1047 represents an intermediate mechanism, combining partial steric hindrance—through engagement of ACE2-critical residues T376, R408, V503, and Y508—with significant allosteric influence, facilitated by localized communication pathways linking the epitope to the receptor interface. Group F3 antibody S2X259 achieves potent neutralization through a synergistic mechanism involving direct competition with ACE2 and localized allosteric stabilization, albeit with potentially increased escape vulnerability. Dynamic network analysis identified a conserved “allosteric ring” within the RBD core that serves as a structural scaffold for long-range signal propagation, with antibody-specific extensions modulating communication to the ACE2 interface. These findings support a model where Class 4 neutralization strategies evolve through the refinement of peripheral allosteric connections rather than epitope redesign. This study establishes a robust computational framework for understanding the atomistic basis of neutralization activity and immune escape for Class 4 antibodies, highlighting how the interplay of binding energetics, conformational dynamics, and allosteric modulation governs their effectiveness against SARS-CoV-2. Full article

Traditional cryptographic systems face critical vulnerabilities posed by the rapid advancement of quantum computing, particularly concerning key exchange mechanisms and the quality of entropy sources for random number generation. To address these challenges, this paper proposes a multi-layered, quantum-resistant hybrid cryptographic architecture. First, to ensure robust data confidentiality and secure key establishment, the architecture employs AES-256 (Advanced Encryption Standard-256) for data encryption and utilizes the Kyber Key Encapsulation Mechanism (KEM), which is based on the Learning With Errors (LWE) problem, for secure key exchange. Second, to further bolster overall security by establishing a high-quality cryptographic foundation, we design a TRNG (true random number generator) system based on a multi-level Ring Oscillator (RO) architecture (employing 5, 7, 9, and 11 inverter stages), which provides a reliable and high-quality entropy source. Third, to enable intelligent and adaptive security management, we introduce FA-Kyber (Flow-Adaptive Kyber), a dual-trigger key exchange framework facilitating dynamic key management strategies. Experimental evaluations demonstrate that our implementation exhibits robust performance, achieving an encrypted data transmission throughput of over 550 Mbps with an average end-to-end latency of only 3.14 ms and a key exchange success rate of 99.99% under various network conditions. The system exhibits excellent stability under network congestion, maintaining 86% of baseline throughput under moderate stress, while adaptively increasing the key rotation frequency to enhance security. This comprehensive approach strikes an optimal balance between performance and post-quantum resilience for sensitive communications. Full article

Oil seals are vital components in industrial production, necessitating high-precision 3D reconstruction for automated geometric measurement and quality inspection. High-quality point cloud completion is integral to this process. However, existing methods heavily rely on large datasets and often yield sub-optimal outcomes—such as distorted geometry and uneven point distributions—under limited sample conditions, constraining their industrial applicability. To address this, we propose a point cloud completion network that integrates a dynamic weighted fusion of multi-scale features with Transformer enhancements. Our approach incorporates three key innovations: a multi-layer perceptron fused with EdgeConv to enhance local feature extraction for small-sample oil seal rings, a dynamic weighted fusion strategy to adaptively optimize global feature integration across varying missing rates of oil seal rings, and a Transformer-enhanced multi-layer perceptron to ensure geometric consistency by linking global and local features. These innovations collectively enable high-quality point cloud completion for small-sample oil seal rings, achieving significant improvements at a 25% missing rate, reducing CD by 46%, EMD by 49%, and MMD by 74% compared to PF-Net. Experiments on the ShapeNet-Part dataset further validate the model’s strong generalizability across diverse categories. Experimental results on the industrial oil seal ring dataset and the small-sample ShapeNet sub-dataset show that our approach exhibits highly competitive performance compared to existing models. Full article

Understanding the concept of dynamic aperture provides essential insights into nonlinear beam dynamics, beam losses, and the beam lifetime in circular particle accelerators. This comprehension is crucial for the functioning of modern hadron synchrotrons like the CERN Large Hadron Collider and the planning of future ones such as the Future Circular Collider. The dynamic aperture defines the extent of the region in phase space where the trajectories of charged particles are bounded over numerous revolutions, the actual number being defined by the physical application. Traditional methods for calculating the dynamic aperture depend on computationally demanding numerical simulations, which require tracking over multiple turns of numerous initial conditions appropriately distributed in phase space. Prior research has shown the efficiency of a multilayer perceptron network in forecasting the dynamic aperture of the CERN Large Hadron Collider ring, achieving a remarkable speed-up of up to 200-fold compared to standard numerical tracking tools. Building on recent advancements, we conducted a comparative study of various deep learning networks based on BERT, DenseNet, ResNet and VGG architectures. The results demonstrate substantial enhancements in the prediction of the dynamic aperture, marking a significant advancement in the development of more precise and efficient surrogate models of beam dynamics. Full article

This paper presents a compact stacked RF energy harvester operating in the WiFi band with multi-condition adaptive energy management circuits (MCA-EMCs). The harvester is divided into antennas, impedance matching networks, rectifiers, and MCA-EMCs. The antenna is based on a polytetrafluoroethylene (PTFE) substrate using the microstrip antenna structure and a ring slot in the ground plane to reduce the antenna area by 13.7%. The rectifier, impedance matching network, and MCA-EMC are made on a single FR4 substrate. The rectifier has a maximum conversion efficiency of 33.8% at 5 dBm input. The MCA-EMC has two operating modes to adapt to multiple operating conditions, in which Mode 1 outputs 1.5 V and has a higher energy conversion efficiency of up to 93.56%, and Mode 2 supports a minimum starting input voltage of 0.33 V and multiple output voltages of 2.85–2.45 V and 1.5 V. The proposed RF energy harvester is integrated by multiple-layer stacking with a total size of 53 mm × 43.5 mm × 5.9 mm. The test results show that the proposed RF energy harvester can drive a wall clock (30 cm in diameter) at 10 cm distance and a hygrometer at 122 cm distance with a home router as the transmitting source. Full article

The topology structure of multi-layer networks is highly correlated with the robustness of consensus. This paper investigates the influence of different interlayer edge connection patterns on the consensus of the two-layer ring networks. Two types of two-layer ring network models are first considered: one is a kind of two-layer ring network with two linked edges between layers (Networks-a), and the other is a kind of two-layer ring network with three linked edges between layers (Networks-b). Using the Laplacian spectrum, the consensus of the network model is derived. The simulation experiments are used to demonstrate the influence of different interlayer edge connection patterns on the consensus of networks. To determine the best edge connection pattern for Networks-a and Networks-b, the number of nodes in a single-layer ring network is denoted by n. The best edge connection pattern for Networks-a is 1 & $\left[\right(n+2)/2]$. Furthermore, n is subdivided into $3k,3k+1,3k+2$, and the best edge connection patterns of Networks-b are near 1 & $k+1$ & $2k+1$. Full article

The rapid development of remote sensing image sensor technology has led to exponential increases in available image data. The real-time scheduling of gigabyte-level images and the storage and management of massive image datasets are incredibly challenging for current hardware, networking and storage systems. This paper’s three novel strategies (ring caching, multi-threading and tile-prefetching mechanisms) are designed to comprehensively optimize the remote sensing image scheduling process from image retrieval, transmission and visualization perspectives. A novel remote sensing image management and scheduling system (RSIMSS) is designed using these three strategies as its core algorithm, the PostgreSQL database and HDFS distributed file system as its underlying storage system, and the multilayer Hilbert spatial index and image tile pyramid to organize massive remote sensing image datasets. Test results show that the RSIMSS provides efficient and stable image storage performance and allows real-time image scheduling and view roaming. Full article

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations. Full article

Determining the amount of void fraction of multiphase flows in pipelines of the oil, chemical and petrochemical industries is one of the most important challenges. Performance of capacitance based two phase flow meters highly depends on the fluid properties. Fluctuation of the liquid phase properties such as density, due to temperature and pressure changes, would cause massive errors in determination of the void fraction. A common approach to fix this problem is periodic recalibration of the system, which is a tedious task. The aim of this study is proposing a method based on artificial intelligence (AI), which offers the advantage of intelligent measuring of the void fraction regardless of the liquid phase changes without the need for recalibration. To train AI, a data set for different liquid phases is required. Although it is possible to obtain the required data from experiments, it is time-consuming and also incorporates its own specific safety laboratory consideration, particularly working with flammable liquids such as gasoline, oil and gasoil. So, COMSOL Multiphysics software was used to model a homogenous regime of two-phase flow with five different liquid phases and void fractions. To validate the simulation geometry, initially an experimental setup including a concave sensor to measure the capacitance by LCR meter for the case that water used as the liquid phase, was established. After validation of the simulated geometry for concave sensor, a ring sensor was also simulated to investigate the best sensor type. It was found that the concave type has a better sensitivity. Therefore, the concave type was used to measure the capacitance for different liquid phases and void fractions inside the pipe. Finally, simulated data were used to train a Multi-Layer Perceptron (MLP) neural network model in MATLAB software. The trained MLP model was able to predict the void fraction independent of the liquid phase density changes with a Mean Absolute Error (MAE) of 1.74. Full article