Search Results (643)

In this paper, we propose a novel deep learning-based framework for the joint design and decoding of linear block codes, the end-to-end deep coding cross-attention message-passing Transformer (E2E DC-CrossMPT). To improve linear block code design and decoding, we redesign the conventional error correction code (ECC) decoder, CrossMPT, to fit within an end-to-end framework. The redesigned decoder separately utilizes magnitude and syndrome vectors obtained from the received signals as inputs. It further employs one-hot encoding based syndrome embedding and incorporates a parity-check matrix into the output layers. Experimental results demonstrate that, across various code lengths and code rates, E2E DC-CrossMPT consistently outperforms both traditional decoding algorithms and a conventional end-to-end deep coding model in terms of decoding performance. Moreover, the codes designed by E2E DC-CrossMPT achieve superior error-correction capability compared with both traditional linear block codes and those designed by the conventional end-to-end deep coding model, while requiring lower computational complexity. Full article

The severe susceptibility of qubits to environmental noise remains the primary obstacle to practical quantum computing. To overcome this, we introduce a purification-assisted quantum error-correction (QEC) framework that embeds a symmetric subspace projection module between the encoding and physical layers. Acting as an information-theoretic noise-entropy filter, it compresses von Neumann entropy before encoding. Under depolarizing noise, a three-copy scheme elevates the surface-code threshold from 1.1% to a 2.0% noiseless bound (~1.6% at circuit level). Our iterative purification-assisted error-correction (IPEC) algorithm dynamically modulates purification depth via syndrome feedback, delivering a 46-fold logical error reduction for surface codes (d = 7) at a 1.0% physical error rate. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

The problem of constructing binary array codes capable of correcting criss-cross has attracted more attention due to such errors appearing in racetrack memories and DNA-based storage systems. In this paper, we investigated the constructions of binary array codes that can correct a single criss-cross edit error and the constructions of binary array codes that can correct a single $(t,s)$-burst criss-cross edit error, respectively. Specifically speaking, we first present a family of binary array codes that can correct a single criss-cross edit whose redundancy is $2nlogn+2n$ by using the Levenshtein codes capable of correcting a single edit error. Then, based on the above codes and array representation, we construct a class of binary array codes that can correct a single $(t,s)$-burst criss-cross edit error. The redundancy of such binary array codes is $2n(t+s)logn+2n(t+s)$. The decoding methods are incorporated in the proof of the corresponding theorem and the complexities of the decoding methods of both binary array codes are all $O\left(n^2\right)$. Moreover, the proposed binary array codes and decoding methods are validated through illustrative examples. Full article

The rapid growth of scientific spatiotemporal data poses increasing challenges for efficient storage and transmission while preserving sufficient reconstruction fidelity for downstream analysis. Existing compression methods remain limited in this setting: lossless approaches often yield low compression efficiency, conventional lossy methods lack flexible local fidelity control, and learning-based schemes may introduce oversmoothing in reconstructed results. To address these issues, we propose a hybrid machine-learning framework for error-bounded compression of gridded scientific data. The proposed framework integrates MPEG-based temporal coding and transformer-based super-resolution reconstruction to exploit temporal correlation and spatial redundancy, and it introduces an error-bounded correction module to explicitly control local reconstruction errors. In addition, a lightweight Dense Residual Swin Transformer (DRCT)-based reconstruction model is employed to enhance long-range dependency modeling and multi-scale feature recovery. Experimental results on Copernicus Marine Service (CMEMS) gridded sea-level data demonstrate that the proposed framework achieves a favorable balance between compression efficiency and reconstruction quality. For $192\times 192$ ADT data, the method reaches a peak PSNR of 38.14 dB with a compression ratio of 594×. With the error-bounded correction module enabled, the reconstructed values are first de-normalized using the recorded normalization parameters, and the local reconstruction error in the original floating-point physical-value domain can then be explicitly controlled by the prescribed absolute error threshold while maintaining a compression ratio of 295×. Additional experiments on SLA further indicate that the proposed framework is not restricted to a single sea-level variable. These results indicate that the proposed framework is a practical and effective solution for compressing large-scale scientific spatiotemporal data with controllable error and high-fidelity reconstruction. Full article

Automating system-level nuclear thermal-hydraulic (T-H) model construction remains challenging because platform-specific API syntax, graph connectivity, parameter dependency ordering, and solver admissibility must be satisfied simultaneously. This study develops a closed-loop modeling framework on the SAFRI platform by combining supervised fine-tuning (SFT), a Plan-and-Act agent with retrieval-grounded parameter completion, and reinforcement learning based on group relative policy optimization (GRPO). The SFT stage uses a 6003-record domain corpus derived from expert-authored or expert-verified SAFRI modeling exemplars, while system-level generalization is evaluated on a held-out 50-case in-house evaluation set separated at the case-template level. At the component level, LoRA-adapted Qwen3-8B achieves 100% code accuracy, compared with 50% for zero-shot and 74% for one-shot prompting. At the system level, the SFT agent attains a 100% syntax success rate (SSR), 90% topology success rate (TSR), and 72.4% physical convergence rate (PCR), showing that local API correctness is insufficient for solver-valid model assembly. After GRPO training with schema, topology, physics, and sequence rewards, the full SAFRI-SFT-RL agent reaches a 100% SSR, 100% TSR, and 88.8% PCR on the in-house evaluation set, while an error self-healing loop resolves execution-time failures in an average of 2.3 corrective iterations. These results show that solver-grounded reinforcement learning is effective for closing the gap between syntactically correct script generation and physically convergent nuclear T-H model construction. Full article

Despite significant improvements in vehicle management efficiency achieved through the integration of VANET and Internet of Things technologies, Internet of Vehicles (IoV) networks remain vulnerable to cyberattacks. This is because the wireless data exchange channel in IoV has several vulnerabilities that are exploited to carry out cyberattacks. The article suggested using the symmetric block cipher GOST 34.12-2015 (SBCG) to combat a variety of cyberattacks. This cipher was chosen because it can be efficiently implemented on low-power platforms and offers high cryptographic strength and encryption speed. Furthermore, implementing SBCG in polynomial modular codes (PMCs) enables detection of encryption errors caused by faults in encoder/decoder operation. The scientific novelty of the proposed solution is that it is the first method to increase the fault tolerance of an SBCG encoder, enabling real-time, effective countermeasures against faults caused by both Differential Fault Analysis (DFA) attacks and natural faults. The originality of the solution lies in the integration of cryptographic theory and the theory of constructing correcting modular codes. The goal of this study is to improve the resilience of SBCG encryptors/decoders to faults by using polynomial modular codes. Imparting fault-tolerant properties to SBCG encryption systems implemented in PMC will enable them to effectively mitigate real-time faults arising from both Differential Fault Analysis (DFA) attacks and natural faults. Full article

Quantum error correction (QEC) is indispensable for suppressing noise in near-term and future quantum processors. Most neural decoders proposed for the surface code exploit predominantly local syndrome neighborhoods, which limits their ability to capture lattice-wide correlations. To overcome this limitation, we develop a two-stage learning-based decoding framework that leverages transformer self-attention to provide a global receptive field. In the first stage, a low-level decoder (LLD) based on a feedforward neural network predicts physical corrections from measured syndromes. In the second stage, a high-level decoder (HLD) performs logical-level verification by classifying the logical equivalence class and checking logical consistency; the HLD is instantiated using either a convolutional network or a transformer encoder. Monte Carlo experiments under depolarizing noise demonstrate clear threshold gains: for code distance d = 3, 5 and 7, the threshold improves from 0.03339 ± 0.00072 (LLD only) to 0.04110 ± 0.00085 with a CNN-based HLD, and further to 0.05350 ± 0.00112 when the HLD is implemented with a transformer; comparable improvements are observed for larger code distances. These results indicate that explicit logical-level discrimination mitigates decoding failures caused by degeneracy, and that global attention better captures long-range topological structure than convolutional baselines. Full article

Voltage transformers (VTs) are part of the power quality (PQ) measurement system in renewable energy installations where harmonic distortion (HD) exists. Although they are designed for fundamental-frequency operation, VTs exhibit frequency-dependent behaviour that causes ratio and phase errors at harmonic frequencies. These errors decrease measurement accuracy and impact compliance verification under South African grid code standards. International standards such as IEC TR 61869-103 and IEEE 519 do not specify harmonic-frequency accuracy classes or correction methods. This paper examines published research on VT frequency response and considers its effects on harmonic measurement in South African renewable networks. The review highlights technical and regulatory challenges that affect the reliability of harmonic measurements and emphasises the need for structured frequency-response testing under local operating conditions. A complementary methodological study addressing this need has been submitted for publication. Full article

This paper presents an extended combined experimental and numerical study on the vibration comfort assessment of a modern timber-framed public utility building. The research focuses on a lightweight skeleton floor system, representing a typical high-frequency floor. In situ vibration measurements were conducted under various walking excitations (single and multiple pedestrians) to determine key vibration parameters. Post-processing, which yielded root mean square accelerations and velocities, was performed using a custom-developed code in the Mathematica package. A finite element model was prepared in Dlubal RFEM 6 using shell and beam elements with offsets. The dynamic characteristics obtained from the FE modal analysis showed high consistency with the experimental data, with a relative error of approximately 5 % for the fundamental frequency. The vibration comfort was assessed using two distinct methodologies: the JRC report and the SCI P354 guide. Both approaches positively verified the floor’s vibration comfort, confirming its suitability for the intended use. The study demonstrates that the JRC methodology is more straightforward and unambiguous for engineering practice. Furthermore, the results indicate that simplified FE models provide a reliable basis for predicting vibration modes and calculating mode shape factors, which are essential for the correct interpretation of local measurements in existing buildings. Full article

This paper extends cyclic permutation coding, previously applied for error correction in power-line communications (PLC), to synchronization-oriented sequence design by introducing a novel class of Non-Binary Cyclic Permutation Sequences (NCPS) for low-correlation multiuser synchronization. Unlike conventional Zadoff–Chu (ZC) and constant-amplitude zero-autocorrelation (CAZAC) sequences that rely on complex-valued phase laws, NCPS employ discrete modular permutations mapped to complex exponentials. Autocorrelation properties were analytically derived where tractable, while general correlation behavior was characterized through structural analysis and confirmed via simulation. Results demonstrated that NCPS achieved near-orthogonal cyclic correlation performance comparable to ZC sequences while preserving optimal Hamming distance, beneficial for error correction, and offering reduced implementation complexity. These characteristics highlight the potential of NCPS as synchronization preambles in PLC systems and other low-complexity or quantized communication platforms, including Internet of Things networks. Full article

Cracking in reinforced concrete beam bridges severely compromises their durability and structural integrity. Although external prestressed CFRP plate reinforcement technology has emerged as an effective repair solution, current design codes primarily rely on idealized crack-free or simplified single-crack assumptions, leading to inadequate precision in prestressing application for real-world structures with complex crack networks. This study investigated the reinforcement effectiveness of externally prestressed CFRP plates on three pre-cracked reinforced concrete T-beams with varying reinforcement ratios (1.20%, 2.41%, and 3.61%). A comprehensive experimental program was conducted to monitor crack closure behavior, strain distributions, and deflection changes during tensioning and loading phases. A three-dimensional finite element model was developed using Midas FEA NX 2022, and theoretical formulas for crack closure prestressing were derived under the plane-section assumption, supplemented by engineering correction factors. Results demonstrated that calculation errors for both crack closure prestressing and secondary cracking loads were below 5%, while correlation coefficients between finite element simulations and experimental data ranged from 0.93 to 0.99. External prestressing significantly enhanced the stiffness of cracked beams, with stiffness recovery rates reaching up to 156.2%, and exhibited excellent synergistic performance among CFRP plates, steel reinforcement, and concrete. These findings provide a theoretical foundation and technical support for the precision design of external prestressing reinforcement in cracked reinforced concrete beams. 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

As a structural material characterised by low density, high strength, excellent corrosion resistance and recyclability, aluminium alloy tubes are finding increasingly widespread application in the construction sector. However, there is currently a lack of research on the prediction of the bearing capacity of aluminium alloy square tube columns. To investigate the failure behaviour of aluminium alloy square tube columns under axial and eccentric compression, this paper first designed 10 thin-walled aluminium alloy square tube column specimens with varying lengths, cross-sectional dimensions and wall thicknesses. Axial and eccentric compression tests were conducted, and the loading process and failure modes were analysed. Building on this, a hybrid load-bearing capacity prediction model combining Multi-Objective Particle Swarm Optimisation (MOPSO) with the Gaussian process regression (GPR) algorithm was proposed. This model is capable of automatically learning and capturing 236 sets of experimental data. Subsequently, using the established prediction model, the contributions of high-sensitivity parameters and cross-sectional influence parameters to the load-bearing capacity were determined. Based on the prediction results, a correction factor for the diameter-to-thickness ratio was introduced into the eccentric compression bearing capacity formula of the Chinese code to establish an improved calculation formula. Compared with the implicit formula provided by machine learning models, the explicit formula proposed in this paper is more suitable for practical engineering design. The results show that the prediction results agree well with the experimental results and can accurately predict the ultimate bearing capacity of aluminium alloy square columns. Compared with the bearing capacity calculation methods in existing codes, the proposed formula reduces the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²) of the dataset by 70.91%, 70.85% and 64.27%, respectively, whilst increasing the coefficient of determination (R²) from 0.8107 to 0.9830 (a relative improvement of 21.25%). Full article

Plant mitochondrial genomes are difficult to analyze because of their structural dynamism and frequent annotation errors. To address these challenges, we first constructed a high-confidence mitochondrial reference library for Rhodiola by integrating transcriptomic evidence, public sequence resources, and experimental validation. This curated resource defined 30 mitochondrial protein-coding genes (PCGs), including corrected exon–intron boundaries and validated 5′-terminal variants in ccmC, ccmFn, and nad9. Leveraging this curated dataset, we developed the RhoMitoAnnotator, which integrates three novel algorithms, EBAnno, REAnno, and NCAnno, to accurately annotate trans-splicing, RNA editing, and non-canonical start/stop codons. Using long-read sequencing guided by the RhoMitoAnnotator, we completed the mitogenomes of R. rosea, R. crenulata, and R. sacra, systematically re-annotated seven publicly available mitogenomes, revealing cross-chromosomal gene arrangement, and widespread structural misannotations. To enable scalable analysis with short-read data, we built Polypods, an integrated pipeline that successfully assembled mitochondrial PCGs from 108 samples across 39 Rhodiola species, and identified variant genes, stop codon-lacking regions in nad6, and internal stop codons in rpl16. Phylogenetic analyses based on mitochondrial and chloroplast PCGs showed a lineage pattern consistent with the hypothesis of an evolutionary transition from hermaphroditism to dioecy in Rhodiola, and consistently supported six species as monophyletic lineages. Overall, this study provides a curated mitochondrial gene atlas for Rhodiola and a reference-guided analytical framework for mitochondrial PCG annotation and recovery in this genus, with potential adaptability to other plant lineages after lineage-specific database construction and parameter optimization. Full article