Search Results (1,604)

Conventional network-only short-circuit ratios (SCRs) do not quantify converter-control dependence or the reliability of a measurement-derived dynamic model. This paper combines an EMS network prior with structured converter identification, a return-difference margin, conditional uncertainty propagation, and event-dependent reporting. The unsigned margin is interpreted only with an independent stability check and a specified port normalization. The original IEEE 39-bus study is extended to include 1800 independent matched-model records, 1400 deliberate-mismatch trials, 1200 threshold-test records, repeated control changes, and coupled models at all three converter buses. Matched-model ellipsoid projections show 99–100% scalar-margin coverage, whereas direct scalar intervals show 91.5–97%, including one undercovered condition. A 0.1 ms relative measurement-channel delay gives zero ellipsoid coverage despite a median point error of 0.502%, establishing a measurement-calibration limitation. Three-converter coupling reduces the baseline margin from the independent-model value of 0.676 to 0.580. Under the same disturbance, two nominally stable PLL tunings produce voltage norms of 0.00818 and 0.01184, distinguishing their compliance with a specified 0.01 response limit. An event-triggered shorter-memory update combined with a complete post-event observation-window restart recovers all 24 tested changes within 12 s. The results support reliability-aware small-signal monitoring across the stated topologies and controller variants, while retaining explicit limitations on model mismatch, finite-band interpretation, and transfer to field or nonlinear converter behavior. Full article

Child Sexual Abuse Material (CSAM) constitutes one of the most serious forms of child sexual abuse in the digital environment, causing not only primary harm at the moment of abuse but also prolonged and repeated victimization due to the continued storage, dissemination, and accessibility of such material. Although international and European Union legal frameworks provide extensive mechanisms for the protection of children and the prosecution of offenders, questions remain regarding the effectiveness of these mechanisms in preventing the repeated digital victimization of child victims. The aim of this study is to evaluate the criminal justice response to CSAM and to analyse the implementation of the best interests of the child principle in the protection of children who are victims of online sexual abuse. This article argues that the prevention of digital re-victimization should be recognised as an integral component of the best interests of the child principle in CSAM cases. The research employs doctrinal legal analysis, comparative legal methods, and normative analysis, examining the United Nations Convention on the Rights of the Child, European Union legal instruments, the case law of the European Court of Human Rights, and recent academic literature. The analysis indicates that substantial emphasis within the existing criminal justice framework remains on the investigation of offences and the punishment of offenders, while the continuing digital harm that may persist after the conclusion of criminal proceedings requires broader child-protection measures. The article concludes that, in CSAM cases, the best interests of the child principle should be interpreted as imposing a state obligation not only to ensure child-friendly criminal proceedings but also to establish effective mechanisms for the identification, removal, and prevention of the further dissemination of child sexual abuse material, thereby reducing the risk of repeated digital victimization. Full article

Automated vision-based structural health monitoring (SHM) of catenary support components is critical for ensuring railway operational safety. However, achieving reliable structural damage identification in practical engineering scenarios is hindered by complex environmental and operational variations (EOVs) in aerial imagery, the multi-scale nature of structural degradation, and the scarcity of damage samples. To address these SHM challenges, this paper proposes a high-precision unsupervised damage detection framework named Mask-Guided Asymmetrical Flow (MGAF). First, to mitigate the impact of EOVs, a SAM-guided preprocessing strategy is introduced to explicitly suppress background interference and extract effective structural Regions of Interest (ROIs). Second, an Asymmetrical Parallel Flow architecture is designed to balance damage detection sensitivity and processing latency. By optimizing the flow depth for high-resolution features, this architecture prevents overfitting to high-frequency environmental noise while preserving deep semantic information. Furthermore, a Cross-Scale Feature Fusion (CSFF) module is developed to enhance the detection capability for early-stage structural damages (e.g., fatigue micro-cracks and fastener looseness) by integrating global structural semantics with local textural damage indicators. Finally, a Morphological Edge Suppression (MES) mechanism is employed to eliminate boundary artifacts, thereby reducing the false alarm rate in pixel-level damage localization. Extensive experiments on the self-constructed CSCUD dataset demonstrate that the proposed system achieves competitive anomaly detection performance compared with representative unsupervised methods with an Image-level AUROC of 98.9% and a Pixel-level AP of 40.1%. During online inference, MGAF achieves a processing time of 0.077 s per frame on an NVIDIA RTX 4090 GPU, excluding the offline GSI-Net localization and SAM-based structural ROI extraction stages. This demonstrates the efficiency of the proposed anomaly detection module for practical railway inspection scenarios. Additional experiments on selected categories from the MVTec AD benchmark provide preliminary evidence of the transferability of MGAF to visually similar industrial anomaly detection scenarios. Full article

Joint detection-and-embedding (JDE) trackers avoid per-detection crop inference by reading identity features for re-identification (ReID) from the detector. The detection-center readout, however, does not use a track prediction when forming the appearance descriptor. This is restrictive in unmanned aerial vehicle (UAV) video, where small targets, camera motion, and localization jitter can displace the detection center from stable identity features. We propose the Motion-Guided Multi-Offset Detector-Native ReID Readout. It samples shared feature pyramid network (FPN) identity maps at the current detection and an assigned reference box obtained from globally compensated track predictions. A 12-D detection-level motion prior conditions the Motion-Guided Dense Branch. The Multi-Offset Token Branch retains source, pyramid-level, and box-relative offset information before the two branches produce one normalized appearance descriptor. Controlled ablation on the VisDrone2019-MOT Val split shows that the full dual-branch readout improves identity association compared with the detection-center readout. On VisDrone2019-MOT test-dev, the complete system achieves an Identification F1 score (IDF1) of 67.16 with HybridSORT. Its Multiple Object Tracking Accuracy (MOTA) is 53.15, with a throughput of 16.98 frames per second (FPS). With Deep OC-SORT, it achieves 66.32 IDF1, 52.19 MOTA, and 17.07 FPS. A candidate-conditioned pair residual provides a HybridSORT application extension, further raising IDF1 to 67.53. Full article

The widespread adoption of unmanned agricultural machinery has transformed modern agricultural production. In unstructured farmland scenarios, variations in soil conditions and operational loads induce large perturbations to system dynamic parameters, leading to degraded tracking accuracy and insufficient robustness in fixed-parameter path tracking controllers. Additionally, conventional single-structure observers cannot simultaneously achieve fast convergence and smooth steady-state output. This study constructs a mixed preview error state-space equation, which aggregates parameter perturbations, unmodeled dynamics, and external disturbances into a unified lumped disturbance term of the system. A composite observer architecture is designed by parallelly combining an adaptive generalized super-twisting observer and a nonlinear extended state observer, where observation weights are continuously and smoothly scheduled via online identification of field operation conditions. Furthermore, a gain-power dual-adaptive super-twisting sliding mode control strategy is proposed, and the closed-loop stability of the system is rigorously proven. Co-simulation and field experiments covering powered rotary tillage, high-speed unloaded transfer, and variable tire pressure conditions verify that, under parameter perturbation, the increase in tracking error remains within 10%. Compared with the conventional PID controller used as a benchmark, the proposed controller reduces the root mean square error (RMSE) of lateral deviation by 40–50%, and maintains centimeter-level tracking accuracy in field operations. The proposed method provides technical support for high-precision operation of agricultural machinery in unstructured farmland environments. Full article

Radio frequency (RF)-based drone identification enables passive low-altitude sensing, but its performance degrades under low signal-to-noise ratio (SNR) and long-range propagation. Existing deep models mainly learn spectrogram amplitude textures and underuse the second-order structure and cross-channel correlations of multi-channel RF signals. We propose the Multi-resolution Riemannian-Spherical Network (MRS-Net), a robust identification framework centered on Riemannian Structure Preservation (RSP). RSP derives noise-referenced Riemannian distance, local geometric variation, log-determinant, and multi-scale statistics from local symmetric positive definite (SPD) covariance matrices. Pairwise similarity alignment transfers these structural relationships into the convolutional neural network (CNN) embedding space as a training-stage teacher signal. RSP is removed at inference and therefore adds no online manifold computation. Multi-resolution short-time Fourier transform (STFT) representations provide complementary weak-signal observations, while CosFace enlarges inter-class angular margins. Experiments on DroneRFa and Noisy Drone RF evaluate propagation attenuation and additive noise, respectively. With four-channel DroneRFa input, MRS-Net achieves 90.30% overall balanced accuracy. Compared with SR-CNN-4ch, it improves the 80–150 m result from 61.30% to 78.30%; compared with MR-Spherical-4ch, it reduces the 5-fold standard deviation from 16.78% to 1.46%. Ablations show that RSP is most effective when cross-channel covariance is informative and the backbone preserves local time-frequency structure. Full article

With the growing penetration of renewable energy, accurately characterizing the frequency support performance of grid-following wind and photovoltaic (PV) units has become increasingly important. However, conventional methods for assessing frequency support parameters often overlook practical dynamic effects, making it difficult to determine the support parameters actually realized during disturbances. To address the time-domain coupling, differential noise amplification, and parameter distortion problems in the online identification of virtual primary frequency regulation and virtual inertia coefficients, this paper establishes a frequency response model for wind and PV units that incorporates these support mechanisms together with practical physical constraints. On this basis, a two-stage forgetting-factor recursive least squares (FF-RLS) method is proposed to identify realized frequency support parameters. Exploiting the difference in response time scales between primary frequency regulation and inertial support, a quasi-steady-state frequency regulation window and a transient inertia window are constructed to decouple the two parameters. Meanwhile, Tustin phase compensation and band-limited differentiation are introduced to mitigate measurement noise and the phase mismatch between frequency and power responses. Finally, a stable window criterion is developed to adaptively extract reliable identification intervals. Simulation studies on a modified IEEE 24 bus system, together with comparisons against conventional identification methods, demonstrate the effectiveness and accuracy of the proposed method. Full article

To address the high labor intensity, low level of automation, and insufficient uniformity of spray coverage in plant protection operations for trellised crops in multi-span greenhouses, an autonomous spraying system based on magnetic navigation and RFID node identification was developed. The system mainly includes a differential-drive chassis module, a control module, and a spraying module, with an STM32 microcontroller serving as the core controller. The CAN bus was used for information exchange between modules. Magnetic navigation was employed for continuous path tracking between crop rows, while RFID was used to identify key operation nodes. A fuzzy PID algorithm was adopted to correct path deviations by adjusting the motion states of the differential-drive wheels online. The system thereby achieved autonomous path tracking, steering, obstacle avoidance, and point stopping. To evaluate the performance of the system, tests were conducted on the trellised watermelon in a multi-span greenhouse. The results showed that, at an angular velocity of 0.6 rad/s, the average actual turning angle was 88.48°, corresponding to the smallest steering error. At a travel speed of 0.5 m/s, the average obstacle-avoidance braking distance and RFID point-stopping error were 7.50 cm and 14.44 cm, respectively. The spraying tests showed that, within the tested ranges, spray coverage and coverage uniformity exhibited clearer changes with travel speed than with spray pressure. Among the nine tested operating conditions, the highest observed mean canopy coverage was 40.98% at 0.8 m/s and 0.4 MPa, with a coverage coefficient of variation of 26.56%. These results demonstrate that the developed system can perform autonomous navigation, node-based control, and spraying operations in multi-span greenhouse environments. The findings provide a reference for automated spraying operations of trellised crops in greenhouse environments. Full article

This paper presents a virtual sensor for the in-process prediction of cutting forces from feed drive current measurements in milling processes via inverse filtering. Components of the feed drive current in ball-screw drives that are related to inertia, friction, and gravity are separated from the cutting-force-related component via models or air-cutting experiments. Impact hammer tests are then used to identify the transfer function between forces at the tool tip and the corresponding response at the feed drive. The proposed inverse filtering approach completes the virtual sensor for online monitoring of cutting forces based on feed drive current signals. Compared to existing approaches, which are mainly based on Kalman-filter or deep learning models, this method avoids the additional effort for modeling, parameter identification and generation of training data. Experimental results are presented for cutting tests on a three-axis turn-milling center. The prediction error between the virtual sensor and the measured cutting forces lies between 6% and 17%, depending on the cutting parameters. In general, the virtual sensor can be implemented in any feed drive system with a minimal effort for parameter identification. Full article

Increasing soil salinisation challenges food production, since most crops are highly sensitive to salinity. The identification of wild halophytes adapted to saline environments with nutritional value represents a promising strategy for food production on salt-affected farmlands. Assessing the nutritional potential of such species requires evaluating their proximate composition, mineral profile, and antioxidant properties. In parallel, citizen-science approaches complement and enhance scientific research by actively engaging potential final consumers in the research process, thereby improving the societal relevance, dissemination, and potential impact of scientific findings. In this study, we characterised the nutritional profile of the facultative halophyte Cakile maritima and found it to be comparable, and in some respects superior, to that of other conventional leafy vegetables, particularly regarding its mineral composition and bioactive compounds. Moreover, similar though not identical nutritional characteristics were observed in two C. maritima leaf morphotypes analysed, since differences in dry matter, ashes, total proteins and carbohydrates were identified. Interestingly, vitamin C accumulation was organ- and morphotype-dependent. Finally, the biochemical characterisation was complemented with an online survey that showed a clear predisposition of consumers towards the incorporation of wild edible plants into their diet, together with a sensory evaluation in which 32 participants assessed the acceptability of up to 11 dishes prepared using C. maritima. Sensory evaluation revealed a prominent bitter flavour amongst dishes containing C. maritima, with weighted scores for negative perceptions slightly exceeding those for positive ones, suggesting that its sensory profile may limit its acceptance by the general public while offering potential for specific consumer segments. Overall, our findings highlight the nutritional potential of C. maritima and support its valorisation as a sustainable species for saline agriculture. Full article

Online social networking platforms provide communication channels for users worldwide. Meanwhile, the behavior of spammer groups quickly spread worldwide and caused serious harm to cyberspace. Currently, spammer identification research mainly focuses on the unimodal modeling of user behavior. However, spammer behavior usually includes multimodal evidence to support their misleading statements. Therefore, we propose a spammer-detection model that integrates multimodal behavioral features and key time node analysis. First, a pre-trained model is used to mine unimodal user behavior features. Second, the association relationship between multimodal behavioral features is deeply mined by integrating the multi-head attention component (MHA) and the auto-encoder component. Finally, considering the indirect temporal coherence and suddenness of user group behavior, historical behavioral sequences are encoded using absolute position encoding (APE). Moreover, the attention mechanism is combined to analyze temporal behavioral features to capture the key time nodes and identify the spammer accounts. Experiments on the public WEIBO and TWITTER datasets show that MACSD achieves competitive and, in many settings, superior performance compared with strong baseline methods. Full article

Selective catalytic reduction (SCR) systems in coal-fired power plants must maintain low NOx emissions during increasingly frequent load changes. Variations in flue gas temperature and flow complicate ammonia-injection control and can cause NOx overshoot or excessive NH₃ slip. This study evaluates an implicit generalized predictive self-tuning controller using a coupled dynamic model of a 660 MW ultra-supercritical coal-fired power plant and its SCR system. The controller combines recursive least-squares identification with generalized predictive control (GPC) and is compared with proportional–integral–derivative (PID) control between 50% and 75% turbine heat acceptance (THA), at load-cycling rates of 0.5–2.0% $P_{\mathrm{e}0}$ min⁻¹. GPC improves NOx set-point tracking and reduces NH₃ slip over the conditions examined. During loading-down, the maximum outlet NOx concentrations are 48.43 mg m⁻³ with GPC and 65.78 mg m⁻³ with PID. During loading-up at 1.0% and 2.0% $P_{\mathrm{e}0}$ min⁻¹, GPC reduces the cumulative NH₃-slip index by 46.52% and 75.56%, respectively. The identified model coefficients vary more strongly at higher ramp rates, while the loading-down response also depends on the transient SCR inlet temperature. These results indicate that online model adaptation can improve ammonia-injection control during load-cycling. Full article

Maintaining stable dissolved oxygen (DO) and pH conditions is critical for reliable operation of bioreactor systems used in cell culture and bioprocess manufacturing. However, accurate regulation of DO and pH remains challenging due to nonlinear process dynamics and variations in operating conditions. In particular, temperature fluctuations can affect gas solubility, gas–liquid mass transfer, and CO₂ buffering equilibrium, resulting in deviations in DO and pH. Existing control methods often rely on predefined mechanistic models or reactor-specific parameter identification, which may limit adaptability under changing operating conditions. This paper proposes a disturbance-compensated data-driven adaptive predictive control framework for DO and pH stabilization in bioreactor systems under dynamic temperature disturbances. Based on dynamic linearization, the proposed framework establishes an online input–output representation using measured gas composition, temperature disturbance, and environmental responses. An adaptive gain adjustment mechanism and pseudo-partial-derivative estimation method are developed to update the control relationship online without requiring an explicit process model or iterative optimization. Furthermore, temperature variations are incorporated as measurable disturbances to achieve real-time compensation of their effects on DO and pH dynamics. The proposed framework was evaluated through simulations and experiments using a 3 L bioreactor platform. Compared with a PID controller with temperature feedforward and conventional model-free adaptive predictive control, the proposed method reduced DO and pH tracking errors and improved recovery performance under temperature disturbances. The results demonstrate that the proposed data-driven adaptive predictive control strategy provides an effective approach for DO and pH stabilization in bioreactor systems under temperature-varying conditions. Full article

Traditional fault diagnosis methods for gas-insulated switchgear (GIS) equipment primarily rely on offline detection and periodic maintenance, which suffer from limitations such as poor real-time performance and localization difficulties, thereby compromising the safe and stable operation of ultra-high-voltage power grids. To enhance the accurate identification and localization capabilities of defects within GIS equipment, this study first establishes a multi-physics coupled simulation model integrating temperature field, flow field, and concentration field to analyze gas diffusion characteristics under varying conditions of fault source locations, decomposition product types, and initial concentrations. Subsequently, a GIS busbar gas chamber experimental platform is constructed to validate the simulation model. Finally, a response time matrix, a peak concentration matrix, and a fault coverage index are developed, and a weighted comprehensive evaluation method is employed to optimize sensor placement schemes. The findings reveal that fault source location significantly influences concentration response speed and spatial distribution patterns; SO₂, HF, H₂S, and SOF₂ exhibit distinct diffusion characteristics due to their differing physical properties; and initial concentration primarily affects the non-uniformity during the early diffusion stage. The simulation results demonstrate good agreement with experimental data, with a maximum root-mean-square error of 3.936 × 10⁻⁴. Monitoring point M4 achieves the highest comprehensive score, making it the preferred location for single-sensor deployment. These results provide a theoretical foundation and technical guidance for GIS online monitoring and fault diagnosis. Full article

Sustainability is increasingly recognised as a key priority in tourism development. Based on stakeholder theory, sustainable tourism implicates the involvement of diverse destination stakeholders. However, the roles of local residents and tourism providers as stakeholders who play an important role in achieving sustainability have largely been examined separately. This study addressed the dual role of local resident tourism providers operating in rural areas by exploring their perceptions, implementation, and recommendations related to sustainable tourism practices (STPs). By integrating stakeholder theory with systems theory, the study considered local resident tourism providers as embedded within a complex destination system. Data were collected through an online and offline survey of local resident tourism providers in the rural area of Istria County, Croatia, with 82 valid responses obtained. A qualitative approach based on an interpretive research paradigm was applied, and reflexive thematic analysis (RTA) was used to analyse the data. The findings indicate that respondents have an understanding of the concept of STPs and implement them, mainly in an environmental context, in their tourism activities. A focus on the quality of tourism services, presentation of local identity and heritage, and relationships and communication with guests emerged as important to respondents, while stakeholder cooperation between providers was limited in their responses. These findings suggest that resident tourism providers may be unaware of the importance of stakeholder collaboration, including their own role, in achieving sustainable tourism development. However, the findings also highlight the importance of adopting a systems approach: respondents’ identification of STPs and recommendations for improving their implementation point to the need for appropriate enabling conditions at destination system level. The findings are intended to be of interest to researchers, destination management organisations, local authorities, and tourism policymakers working in the field of sustainable tourism. Full article