Search Results (4,855)

Robotic Aerial Work Platforms (RAWPs) are subjected to multi-source excitations including wind gusts and inertial loads, which result in strongly nonlinear and time-varying coupled triaxial attitude vibrations. Conventional recurrent architectures such as LSTM and GRU suffer from gradient vanishing when processing long sequences, while Transformer utilize self-attention mechanisms to learn simple periodic correlations; however, the vibrations in RAWPs exhibit a complex time-series pattern composed of low-frequency oscillations superimposed with high-frequency impacts and accumulates errors through autoregressive decoding. To address these limitations, this paper proposes an improved Transformer model featuring dual-channel periodic positional encoding and global–local hybrid multi-head attention for one-shot multi-step long-sequence prediction of RAWPs attitude vibrations. The proposed method designs a dual-channel independent sine–cosine positional encoding with a tunable periodic modulation factor to explicitly embed the multi-scale periodicity priors of vibration signals and introduces a global–local hybrid attention mechanism that parallelly extracts transient amplitude impact features in the time domain and periodic fluctuation features in the frequency domain. A full-scale aerial experimental platform is established to collect triaxial vibration data under two operating conditions at a sampling frequency of 20 Hz. The results determine the optimal periodic modulation factor and input window length, and ablation studies validate the synergistic gains of the two proposed modules. Comparative results demonstrate that the proposed model achieves substantially reduced prediction errors. In terms of pitch angle, the proposed model achieves a performance improvement of 53.89% over Transformer, 52.11% over LSTM, and 57.67% over GRU. The proposed model effectively provides a reliable data-driven prediction framework for attitude monitoring and active vibration suppression of aerial work platforms. Full article

In reconfigurable array transmissions, a phased-array transmitter changes its characteristics, yet must still be able to control its transmission while optimizing its performance. To enable full reconfiguration while transmitting predictably, performing accurate, real-time measurements within the transmit chain is useful. This recently developed in-situ measurement approach, shown in multiple previous contributions, is summarized in this paper. It serves two purposes: (1) informing the real-time optimization algorithm whether changes in transmitter characteristics improve or worsen performance, and (2) updating the array calibration to obtain the desired transmit array pattern. This will enable real-time, “on the fly” optimizations of transmitters to coexist with other wireless devices in an increasingly congested spectral environment. A four-port coupler, with two monitoring outputs, is used to monitor the total voltage and current between a reconfigurable impedance tuner and the antenna in each element of a transmit array chain. Experimental work from the different prior contributions shows the overall trajectory, reliability, and proposed applications of this in-situ measurement technique. Less than 1 mV of error vector magnitude is shown in vector network analyzer methods compared with simulations using the in-situ coupler approach. The integration and calibration of a software-defined radio to perform antenna input current in-situ measurements has been implemented, with an average current error vector magnitude of 258 µA when comparing the software-defined radio measurements with simulations. Simulation results have shown that in-situ measurements can successfully correct input voltage waveforms for accurate directionally modulated transmissions, lessening reliance on fixed transmitter array pre-calibrations. Full article

To reduce aggregate gradation counting errors caused by over-segmentation and under-segmentation of stacked and clustered aggregates with mixed types and diverse spatial distributions in hydraulic concrete, this study proposes BC-GECO2, a coarse and fine aggregate segmentation and counting method. Firstly, a BAHiera feature extraction network is designed to extract multi-scale deep features through edge-aware attention. In addition, a DFG-Edge module is developed to enhance the boundary features of densely distributed aggregates by integrating wavelet transform with a gated fusion mechanism, thereby alleviating the loss of small aggregate features during downsampling. Secondly, a CSFM-GFFCA module is constructed, in which a dual-branch structure is employed to adaptively fuse adjacent-scale features, strengthen the edge responses of densely distributed small aggregates, and enhance cross-layer feature interaction. Finally, a joint optimization function combining Focal loss and counting loss is established to guide the model toward hard-to-classify pixels, especially boundary pixels, thereby improving segmentation integrity and counting accuracy. Experiments conducted on an aggregate dataset collected from practical construction sites show that, compared with the baseline GECO2 model, the proposed method improves the average segmentation IoU, Dice, and BIoU by 2.92%, 5.04%, and 2.83%, respectively, while reducing the average counting MAE and RMSE by 6.92 and 15.65, respectively. Moreover, BC-GECO2 exhibits superior robustness and generalization capability under different stacking densities and blurred-boundary scenarios, providing technical support for the intelligent development of rapid concrete gradation detection. Full article

With the widespread adoption of cloud computing technology, modern cloud platforms have become increasingly complex and dynamic, posing significant challenges for efficient resource management. Accurate forecasting of cloud resource loads has therefore become essential for improving service quality, optimizing resource utilization, and reducing operational costs. To address the intrinsic characteristics of cloud load time series, including nonlinear fluctuations, multi-scale temporal dependencies, and redundant high-dimensional features, this paper proposes the MST-iTransformer model, which integrates multi-scale temporal encoding, sparse attention, and adaptive feature selection mechanisms. Specifically, a multi-scale temporal encoding module is developed to capture and fuse temporal dependencies across multiple periodic scales. Furthermore, an adaptive feature selection module is introduced to dynamically assign importance weights to resource features, enhancing informative variables while suppressing redundant ones. Meanwhile, a sparse attention mechanism is incorporated to reduce computational overhead while maintaining forecasting accuracy. The proposed model is evaluated on the Alibaba Cluster Trace dataset. Experimental results demonstrate that MST-iTransformer achieves MSE, RMSE, and MAE values of 0.5559, 0.7456, and 0.4968, respectively. Compared with the original iTransformer, the proposed model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency. These improvements provide reliable prediction support for resource scheduling and elastic scaling in cloud data centers. Full article

Orthogonal time-frequency space (OTFS) modulation is a promising waveform for integrated sensing and communication (ISAC) in high-mobility environments, where both large Doppler shifts and target delays must be accurately handled. Existing CP-OTFS radar analyses often assume CP-preserving target delays, for which the received echo remains inside the protected interval and CP-induced interference is avoided. However, practical sensing scenarios may involve large Doppler shifts and large target delays exceeding the cyclic-prefix duration, leading to CP violation and interference. This paper extends the analytical performance evaluation of cyclic-prefix OTFS (CP-OTFS) to this large-delay regime while also accounting for Doppler shifts. Starting from the CP-OTFS transmit signal, point-target channel, time-frequency demodulation, and delay-Doppler matched filtering, closed-form expressions are derived for the radar matched-filter output statistics, including the mean response, average delay-Doppler energy, main-lobe energy, peak sidelobe level ratio (PSLR), and integrated sidelobe level ratio (ISLR). For the communication function, an analytical error vector magnitude (EVM) expression is derived to quantify the degradation induced by large delays. The analytical expressions are evaluated as functions of target delay and Doppler shift and validated through end-to-end CP-OTFS ISAC simulations. The results demonstrate the accuracy of the analytical expressions and quantify the reduction in useful energy, the increase in interference, the degradation of the PSLR and ISLR, and the increase in EVM. Full article

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) develop over decades, yet their earliest pathogenic drivers remain poorly understood. Epidemiological and experimental animal studies suggest that disturbances in oral sensorimotor regulation, particularly within trigeminal proprioceptive pathways, may contribute to neural dysfunction long before clinical symptoms emerge. The mesencephalic trigeminal nucleus (MesV), the only primary sensory neuron population located entirely within the central nervous system (CNS), links oral proprioception with brainstem and forebrain networks. Chronic occlusal mismatch, impaired mastication, sleep bruxism, and sleep-disordered breathing may generate persistent sensorimotor prediction errors that destabilize MesV-centered circuits and subsequently recruit the locus coeruleus (LC), the brain’s principal noradrenergic stress nucleus. This review proposes an oral–brain axis model in which chronic MesV-related prediction error signaling engages LC-dependent stress systems, leading to neuroimmune activation, locus coeruleus–asparagine endopeptidase (LC-AEP) pathway engagement, and downstream proteinopathic processes. Sustained LC activity may facilitate microglial priming, reactive astrocytosis, and neuroinflammatory signaling, creating conditions that favor LC-AEP pathway activation and downstream tau pathology. Epidemiological studies associate tooth loss, reduced occlusal support, and impaired mastication with increased dementia risk, while experimental models of prodromal PD demonstrate early trigeminal sensory-processing abnormalities preceding motor symptoms. Together, these findings support the hypothesis that chronic disturbances in oral sensorimotor homeostasis may increase neurodegenerative vulnerability. This framework identifies potential biomarkers and preventive targets, suggesting that modulation of oral function and neuroimmune pathways may help reduce neurodegenerative risk before irreversible neuronal loss occurs. Full article

The configuration of AUTOSAR Basic Software (BSW) modules relies on verbose AUTOSAR XML (ARXML) files that are complex, error-prone, and costly to produce manually—particularly for safety-critical modules governed by ISO 26262. This paper presents a two-stage approach to automating BSW configuration generation that generalizes to any ECU Configuration (ECUC)-based module: a fine-tuned large language model (LLM) generates a compact JSON intermediate representation capturing only semantically meaningful parameters, which a deterministic expansion function reconstructs into schema-conformant ARXML. We fine-tune three open-weight models (Qwen3-8B, Ministral-3-8B-Instruct, Llama 3.1 8B) with Quantized Low-Rank Adaptation (QLoRA) on 6050 compositionally generated Watchdog Manager (WdgM) samples spanning five complexity tiers with 30+ prompt templates, and introduce a hierarchical evaluation pipeline combining schema validation with referential integrity, structural completeness, parameter accuracy, and semantic constraint satisfaction. The compact representation reduces output tokens by approximately 8–10× compared to full ARXML. All three models achieve closely comparable performance (0.815–0.836 overall score), with Llama 3.1 8B scoring highest (0.836) and every model reaching ≥93% schema validity and ≥72% parameter accuracy—an 8.4× improvement over zero-shot baselines. Decomposing generation into LLM-driven semantic capture and deterministic expansion is an effective strategy for verbose, schema-governed configuration formats, extensible to other AUTOSAR modules beyond WdgM. Full article

Accurate short-term wind-power forecasting supports renewable-energy integration. This study presents a reproducibility and implementation audit of MoRAM, an integrated architecture that combines feature-token attention, a residual Mamba layer, dense two-expert blending, and a one-dimensional convolution (Conv1D)–linear residual output path to forecast 12 h of normalized turbine power from 48 h multivariate histories in Dataset A (200 turbines; 8760 hourly records). Source reconstruction found that the historical PyTorch attention layer used its default sequence-major interface on batch-major data, thereby mixing samples; the dense-window record is therefore retained only as an implementation audit. We corrected the layer to batch-first feature-token semantics and ran an attention-only ablation using all turbines, a 12 h window-start stride, and five paired seeds. Corrected MoRAM achieved mean absolute error (MAE) 0.2428 ± 0.0029 per unit (p.u.) and root-mean-square error (RMSE) 0.3035 ± 0.0059 p.u.; bypassing only attention achieved MAE 0.2298 ± 0.0027 p.u. and RMSE 0.2839 ± 0.0028 p.u. (mean ± sample standard deviation). The attention-free condition had lower overall MAE and RMSE in every seed and, after averaging across seeds, at every forecast horizon. The principal validated contribution is therefore an auditable reconstruction—complete data protocol, source/log mapping, corrected tensor semantics, and a reproducible five-seed negative ablation—rather than evidence that every constituent module or the integrated architecture is superior. Full article

The transition from early non-standalone 5G deployments to 5G Standalone and, more recently, 5G-Advanced has turned mobile networks into flexible, programmable infrastructures capable of supporting private, industrial, and research-oriented deployments for the development of beyond-5G applications and architectures. Evaluating these networks’ capabilities, however, remains challenging because commercial platforms often provide limited access to internal interfaces, radio parameters, and network measurements. This paper presents an open-source private 5G SA testbed for beyond-5G application validations built using Open5GS, srsRAN, Ettus USRP N310 software-defined radio, programmable SIM cards, and commercial 5G customer-premise equipment. The platform is deployed in a semi-anechoic chamber. End-to-end operation is validated through subscriber registration, authentication, PDU session establishment, and external data connectivity. The performance of the implemented 5G network is evaluated using throughput, block error rate, modulation and coding scheme, and gNB trace logs. Unlike previous open-source 5G testbeds that primarily use RF waveguides, individual network components, or a limited set of radio configurations, the proposed platform combines COTS SIM-based UE operation with a controlled over-the-air evaluation of FDD/TDD and multiple antenna configurations and correlates application-level throughput with internal gNB radio metrics. For FDD downlink operation, the average throughput increased by approximately 74% from 1 × 1 to 2 × 2 and by a further 57% from 2 × 2 to 4 × 4, although the additional peak-throughput gain from 2 × 2 to 4 × 4 remained limited. The platform provides a reproducible environment for validating beyond-5G mechanisms, comparing network configurations, and studying the behavior of future open-source 5G SA systems under controlled conditions. Full article

Warm-season precipitation over Sichuan, China, is jointly modulated by complex terrain, monsoon water vapor transport, and local convective activities, leading to significant spatiotemporal heterogeneity. However, radar observations over mountainous areas are frequently impaired by terrain blockage, beam shielding, and insufficient network coverage, which cause missing data and spatial discontinuity, thereby restricting the accurate monitoring of precipitation systems. To alleviate these problems, this study develops an Efficient Multi-Scale Attention (EMA) U-Net model integrated with Digital Elevation Model (DEM) information, termed EMA-U-Net-DEM, to retrieve radar composite reflectivity by utilizing multi-channel observations from the Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI). In the experiments, FY-4A AGRI multi-spectral measurements were used as model inputs, while radar composite reflectivity products from the Severe Weather Automatic Nowcasting (SWAN) system were applied as reference labels. The modeling and validation were carried out using warm-season (June–August) datasets over Sichuan Province. The results indicate that the proposed EMA-U-Net-DEM exhibits better performance than the traditional U-Net and several typical attention-based benchmark models. Quantitatively, the model achieves a root mean square error (RMSE) of 6.728 dBZ, a mean absolute error (MAE) of 4.788 dBZ, a coefficient of determination $R^2$ of 0.656, a peak signal-to-noise ratio (PSNR) of 25.243 dB, and a structural similarity index measure (SSIM) of 0.793. Categorical verification further reveals that the model yields the highest critical success indices (CSI) of 0.850, 0.560, and 0.364 in the reflectivity ranges of 0–25 dBZ, 25–45 dBZ, and 45–70 dBZ, respectively, demonstrating its superior ability in characterizing weak precipitation backgrounds, moderate precipitation structures, and intense convective cores. The performance enhancements are mainly attributed to the strengthened multi-scale feature extraction by the EMA module and the effective topographic constraints introduced by DEM data. This study confirms that the fusion of FY-4A multi-spectral observations and topographic information can effectively improve radar composite reflectivity retrieval over complex terrain, providing a feasible solution for precipitation monitoring, quantitative precipitation estimation, and severe weather nowcasting in mountainous regions with limited radar coverage. Full article

The evolution from 4G Long-Term Evolution (LTE) to 5G New Radio (NR) has increased cell density in cellular deployments, thereby increasing inter-cell interference (ICI), which is most significant in cell-edge scenarios. The Minimum Mean Square Error–Interference Rejection Combining (MMSE-IRC) receiver was widely adopted in LTE systems around Release 11 as a practical implementation for advanced interference rejection. Although not standardized in 3GPP specifications, it was commonly used in 3GPP studies and has been effectively used in 5G-NR environments. In this paper, we propose new versions of the Alternating Projections Hard Successive Interference Cancellation (AP-HSIC) equalizer algorithm that combine the Minimum Mean Square Error (MMSE) and MMSE-IRC equalizers (denoted MMSE-AP-HSIC and MMSE-IRC-AP-HSIC, respectively). The effectiveness of the new approach is demonstrated by Block Error Rate (BLER) analysis for Quadrature Phase Shift Keying (QPSK), 16-QAM, and 64-QAM modulations. Physical Uplink Shared Channel (PUSCH) 5G-NR receiver simulations demonstrate the differences between the proposed algorithms and the classical AP-HSIC, MMSE, and MMSE-IRC equalizer algorithms. Simulation results show that the proposed scheme, MMSE-IRC-AP-HSIC, outperforms the conventional MMSE-IRC scheme in cell-edge scenarios. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

To address large fluctuations in fertilizer flow and the difficulty of real-time quantitative blockage monitoring during fertilization by an integrated tillage, land preparation, and seeding machine, a fertilization monitoring system combining real-time detection and intelligent alarm functions was designed and developed. The system uses an STC32G12K128 microcontroller as the core control unit and integrates fiber-optic sensors, fiber-optic amplifiers, and associated peripheral hardware. Supporting host computer software was also developed on the Python3.13 platform. Based on the light-blocking principle, the optical signal generated by fertilizer particles passing through the sensing area is converted into a digital signal by the fiber-optic amplifier. A quantitative correlation model between the amount of blocked light and the fertilizer discharge rate was then established, enabling indirect and non-contact measurement of the fertilizer discharge rate. Indoor bench tests demonstrated a highly significant positive linear correlation between the amount of blocked light and the fertilizer discharge rate. The overall mean absolute percentage error of the fitted model was below 10%. Based on this detection system, a fertilization monitoring and alarm module was further developed for indoor bench conditions, together with discrimination logic for fertilizer blockage and fertilizer shortage. At rotational speeds of 30–50 r/min, the system achieved an average blockage detection rate of 98%, an average false alarm rate of 4.4%, and an alarm response time of no more than 3 s. Full article