Engineering

Abstract: UHF RFID technology is widely used for automatic identification and material-flow monitoring, but read reliability depends on the configuration of the reading system and transport conditions. This study evaluated RFID read reliability during passage of a transport trolley through an identification gate. Six factors were considered: antenna system configuration, identification target, tagged-item arrangement, load configuration, the presence of additional tags, and transmit power. Ninety-six experimental scenarios were tested in three repetitions, giving 288 trials. Complete system-level reads were obtained in 69.1% of trials. Generalized estimating equations (GEE) showed significant effects of antenna system configuration (p = 0.0132), transmit power (p = 0.0292), and load configuration (p = 0.0081). Relative to A1_1, configuration A1_2 increased the odds of a complete read 7.67-fold (95% CI: 1.78–33.08), whereas L2 reduced the odds to OR = 0.24 (95% CI: 0.084–0.691). Higher transmit power increased the odds approximately threefold (OR = 3.06; 95% CI: 1.12–8.35). Repetition agreement was high. The results indicate that RFID-gate performance should be evaluated at the system level because incomplete reads from individual antennas may complement one another.

Abstract: Digital twin synchronization is central to reliable real-time control in distributed production networks, yet current implementations remain constrained by latency, state inconsistency, heterogeneous data structures, limited scalability, cybersecurity exposure, and weak industrial validation. This review critically examines the conceptual foundations, architectures, performance metrics, synchronization strategies, control mechanisms, applications, and research gaps associated with physical–virtual alignment across geographically dispersed manufacturing systems. The reviewed strategies comprise time-driven, event-driven, hybrid, edge–cloud coordinated, AI-driven predictive, and blockchain-based synchronization. Their suitability depends on the required balance among update frequency, state accuracy, communication delay, computational burden, data consistency, security, and control criticality. Evidence from manufacturing, robotics, logistics, energy-aware production, and multi-plant coordination shows that effective synchronization can support closed-loop control, adaptive scheduling, predictive maintenance, resource allocation, energy optimisation, and disturbance response. However, no single strategy performs optimally under all operating conditions. Fixed updates may waste resources, event-triggered methods may overlook gradual drift, predictive approaches remain vulnerable to model error, and secure consensus mechanisms may introduce unacceptable delay. The review therefore positions synchronization as a first-class control and systems-engineering problem rather than a secondary data-management function. Future research should prioritise self-adaptive and uncertainty-aware synchronization, deterministic low-latency communication, causal and federated twin architectures, energy-efficient computing, secure governance, standardised metrics, shared benchmarks, and long-term industrial field validation. These advances are required to move digital twins from passive monitoring tools to dependable operational intelligence systems for distributed manufacturing.

Abstract: Background: Keirin races are often decided by the "makuri," a final-lap sprint in which a racer from the rear overtakes the leader at approximately 75 km/h. Because direct measurement during races is extremely difficult, its mechanisms remain unclear. This exploratory, hypothesis-generating study used musculoskeletal simulation to examine how pedaling posture may reduce its metabolic cost. Methods: A modified AnyBody BikeModel was checked for plausibility against one professional racer's pedaling force (approximately 150 rpm, 1,000 W). Model-estimated metabolic power–cadence curves were simulated at constant mechanical loads, and only relative differences were interpreted. On these curves, a racer in the group (Point A: seated, 1,200 W, 147 rpm, 73 km/h) changes posture at the same load and cadence (Point A′), then accelerates (Point B: 1,255 W, 151 rpm, 75 km/h). Results: At Point A′, a non-seated posture with the hips anterior to the bottom bracket increased ankle dorsiflexion (possibly promoting stretch-shortening cycle utilization) and reduced metabolic power by 67 W (4%); it also raised the optimal cadence but recruited more fatigue-prone muscles. At Point B, despite a 55 W higher load, metabolic power remained comparable to that at Point A, assuming a slightly smaller drag area; under idealized conditions, the resulting speed difference (0.55 m/s) would close a 5 m gap in approximately 9 s. A seated "makuri" with deliberate ankle dorsiflexion reduced metabolic power by 59 W (4%). Idealized trunk stabilization representing strong core stability (relative pelvic rotation restricted from 5° to 0.5°) reduced it by a further 86–109 W (5–7%). These differences changed little when the scaling factor of the metabolic model was reduced from 1.5 to 1.0. Conclusions: Hip position, deliberate ankle dorsiflexion, and trunk stabilization are experimentally testable candidates for an energy-saving "makuri." Drawing on previous studies, we also discuss hybrid training, in which voluntary agonist contraction is resisted by electrically stimulated antagonists, and the potential of this simulation framework as a "digital trainer" for individualized training and bicycle configuration.

Abstract: This paper develops a Deep Operator Network (DeepONet) approach to accelerate observer-gain computation for thermal single-particle battery state-of-charge (SOC) estimation. The offline network maps complete sampled diffusion trajectories to a continuous-time, boundary-constrained backstepping kernel; observer gains follow by differentiating the same kernel. Lipschitz continuity is established on a uniformly analytic input class. A perturbed Volterra transformation proves exponential observer and SOC error decay under uniform residual bounds and a positive decay margin.After all six models had completed training and their weights had been fixed, we evaluated the three independently trained full-feature models and their corresponding feature-ablation models on a newly generated common test set of 72 trajectories. Complete-feature models attain mean gain relative RMSE of 0.3936%, versus 0.6070% after masking. Across 500 public-parameter inversion and perturbation tests, paired SOC RMSE remains below 0.000229 percentage points. After training, DeepONet computes 10,100 gains per trajectory in 2.70–3.11 ms on CPU (0.54–0.63 ms on GPU), versus 4.93–5.18 s for the CPU implementation of the six-term numerical solver, giving approximately 7871–9645-fold speedups. The results demonstrate accurate offline gain replacement and reduced gain-generation cost relative to the specified numerical implementation.

Abstract: The increasing adoption of hydrogen as a green energy carrier has raised the need for reliable, battery-less sensing solutions for leak detection in storage, distribution, and industrial applications. In this context, radio-frequency identification (RFID) technology offers a promising approach to wireless sensing, enabling battery-free operation and seamless integration with standard RFID infrastructure. This work presents a versatile sensing platform based on passive ultra-high-frequency (UHF) RFID technology, designed for continuous wireless monitoring in safety-critical environments. The platform is specifically conceived to interface with a wide range of DC-operated chemiresistive sensing elements, independently of their sensing materials and functionalization, providing a flexible and scalable solution for wireless chemical sensing. The platform relies on a commercial RFID chip (Asygn AS3212), fully compliant with EPC Gen2 and RAIN RFID standards, whose analog interface enables direct integration of the chemiresistive sensing element. The chip features an integrated switched-capacitor amplifier and a 10-bit analog-to-digital converter, enabling the acquisition and digitization of the sensor response. The system achieves a maximum passive read distance of 1.7 m. As a proof-of-concept demonstration of the platform’s versatility, a chemiresistive sensor based on graphene flakes functionalized with platinum nanoparticles (Gr-Pt) is employed for hydrogen detection. The resulting system enables quantitative hydrogen sensing upon exposure to 25,000 ppm, with a limit of detection (LOD) of approximately 500 ppm. The proposed platform operates in fully passive mode by harvesting energy from the reader’s RF field, eliminating the need for an onboard battery, while also supporting battery-assisted operation to extend the read range.

Abstract: Quadrotor unmanned aerial vehicles (UAVs) are widely utilized for various low-altitude tasks. However, precise attitude tracking performance is restricted by external interferences and internal model uncertainties, including unmodeled aerodynamic characteristics, parameter drifts, and time-varying environmental loads. To address the piecewise discontinuous lumped disturbances caused by low-altitude turbulence, this paper proposes improved schemes based on the mature dual-layer adaptive high-order sliding mode framework. A dual-layer nested adaptive strategy is constructed, where the first layer adjusts the amplitude of switching gain in control laws, and the second layer adaptively tunes time-varying parameters to limit the gain variation rate. This design eliminates the requirement for prior upper bound information of disturbances and their derivatives. Sufficient conditions for sliding mode existence under piecewise discontinuous lumped disturbances are derived in this work. The considered disturbances are piecewise discontinuous with uniformly bounded second derivatives in each interval, which better matches the real low-altitude gust flight scenarios of quadrotors. A unified composite Lyapunov function is constructed to simultaneously analyze the finite-time convergence of sliding variables, adaptive deviations, and disturbance observation errors, which simplifies the stability deduction process of the closed-loop system. Multiple segmented composite gust disturbances and parameter perturbation simulation cases are established. Combined with two-dimensional and three-dimensional phase trajectory visualization methods, multi-dimensional quantitative evaluation of the controller is realized. Based on homogeneity theory and Lyapunov analysis, the finite-time stability of the closed-loop system is proven, and MATLAB numerical simulations fully verify the comprehensive control performance of the proposed controller.

Abstract: This paper presents a combined fractional-order control strategy for stabilizing a cart–inverted pendulum system at its upright equilibrium while regulating the cart position. The proposed strategy combines a fractional-order direct model reference adaptive controller (FODMRAC) for cart-position regulation with a fractional-order proportional–integral–derivative (FOPID) controller for pendulum-angle stabilization. Particle swarm optimization (PSO) is used to tune the FO-PID controller gains and the FODMRAC gains, orders of integration and adaptive controller parameters respectively. The control problem is challenging because the system is underactuated and unstable, and the cart and pendulum dynamics are coupled through a single control input. The combined strategy is evaluated through numerical simulations using a model of the Feedback Instruments inverted pendulum platform. The reported results demonstrate better upright stabilization and cart-position regulation performance. Also, the control effort is evaluated to characterize the actuation requirements. These findings support the feasibility of combining fractional-order adaptive control and FO-PID control for the stabilization of an underactuated mechanical system.

Abstract: This paper is a continuation of the first preprint on the two-layer conflict model, where the model was formulated and validated on a highway HDV stream from the UTE dataset. Here the same Layer 2 is applied to a fundamentally different environment: a signalized urban intersection with a mixed vehicle-class stream, namely the DLR Urban Traffic (DLR UT) dataset recorded at the AIM Research Intersection in Braunschweig, Germany. The public fragment contains 96 files covering 24 September 2023 (one day). We compute the three-level conflict metrics (Ψ, Ψ_frame, C), the state parameters (M, Δ, r, ρ), and analyse the contribution of different pair types (car-to-car, car-to-truck, truck-to-car, car-to-motorbike, etc.). The results on this single day show that (i) car-to-car dominates both by absolute contribution and per pair (Ψ = 0.0416 vs. <= 0.0037 for all other types), while among non-car types car-to-truck has the highest per-pair Ψ (0.0037), followed by truck-to-truck (0.0033); (ii) the NE direction is the most conflict-prone (r = 4.35%), consistent with the left-turn-with-oncoming scenario reported in the literature, and the SE direction is the least conflict-prone (r = 1.89%); (iii) the ratio M/Δ ≈ 7 in all four directions, and the co-occurrence index ρ = (C - M)/Delta is close to the right edge of the inequality band (ρ = 0.88-0.97), indicating that the structural composition of conflict does not change across directions and only the intensity changes; different pair types co-occur in the same conflicting frames rather than replacing each other. The danger_report tool introduced in the first preprint is reused here without changes to its format; the urban instantiation only adds vehicle-class and direction labels, and it is used below to illustrate two aggregation pitfalls that are specific to short urban encounters. The analysis uses the open-source repository dlr-ut-conflict-metrics available at https://github.com/andreychernyshov5-beep/dlr-ut-conflict-metrics, which builds on the conflict-metrics library of the first preprint.

Abstract: Thermal interaction between oil and gas wells and permafrost rocks leads to their thawing which can cause infrastructure deformation, greenhouse gas emissions, and landscape degradation. This paper presents a simplified engineering method for estimating the thaw radius around a cylindrical well with a constant wall temperature. Based on the Stefan condition and a quasi-steady-state temperature distribution in the thawed zone, an analytical formula (Equation 9) is derived. This formula explicitly relates the thaw radius to the well radius, thermal conductivity of the thawed soil, ice content, and latent heat of fusion, wall temperature, and time. The method accounts for the heterogeneity of the geological profile by identifying intervals with average thermophysical properties. Calculations for typical permafrost conditions (loam, ice content of 0.3, initial temperature - 2 °C, wall temperature - 5 °C) show that the thaw radius reaches 4.3 m after three years and exceeds 14 m after ten years at a wall temperature of 6 °C. Ice content slows down thawing, while high thermal conductivity accelerates it. The proposed formula has been verified by comparison with a finite-difference numerical model and the Kutasov–Eppelbaum method; the discrepancy does not exceed 15 %, which is acceptable for conservative engineering estimates. The method is suitable for rapid calculations to account for depth-dependent variations in permafrost properties. The results contribute to sustainable infrastructure design in permafrost regions by enabling quantitative assessment of safe well spacing, selection of operational regimes, and well thermal protection methods.

Abstract: This paper investigates the influence of phase inductance identification accuracy on the modelling and control performance of switched reluctance motors (SRMs). Fundamental motor equations and representative operating waveforms are presented to highlight the importance of inductance profiles in electromagnetic torque prediction and control strategy development. Experimental investigations reveal that the identified phase inductance depends on the excitation frequency used during measurement. This phenomenon is examined using a parameter-fitted Jiles–Atherton hysteresis model incorporating the influence of the magnetic circuit air gap. The experimentally observed relationship is reproduced in the model and approximated using a power-law function, enabling the recalculation of inductance profiles to improve their consistency with torque-based measurements. The original and recalculated inductance profiles are subsequently evaluated using five control strategies, including three model predictive control (MPC) approaches. Control performance is assessed using three quality indices describing torque tracking accuracy, peak-to-peak torque ripple, and maximum torque deviation. The comparative results support the hypothesis that the proposed inductance recalculation substantially improves SRM control performance, demonstrating the importance of accurate magnetic characteristics in advanced control strategies.

Abstract: Trimming-based consensus rules such as weighted mean-subsequence-reduced (W-MSR) consensus protect multi-agent networks against a bounded number of faulty or malicious agents, but most of their guarantees assume synchronous, delay-free communication. Real networks deliver delayed, reordered, and replayed messages, and timestamps cannot be trusted when adversaries can forge them. For resilient consensus with bounded delays, prior work shows that (F+1, F+1)-robustness is necessary and (2F+1)-robustness is sufficient. To our knowledge, whether the gap between the two conditions is real has remained open. We study a timestamp-free W-MSR rule in which every normal agent trims received values relative to its own current state while neighbor values arrive with arbitrary time-varying delays bounded by τ. First, we prove a safety property that holds for every adversary and every delay pattern: the hull of normal states over the most recent τ+1 steps never expands. Second, we prove that (2F+1)-robustness suffices for exponential consensus, with a contraction horizon of |N|(τ+1)+τ steps that is linear in τ+1. Third, we show that (F+1, F+1)-robustness is not sufficient once delays can be scheduled adversarially: with τ=1, a broadcasting adversary can present different recent values to different receivers and block consensus on a family of graphs that, by exhaustive enumeration, is (F+1, F+1)-robust for F ≤ 2 and 2F-robust for F ≤ 3. We also specify a reproducible simulation protocol built on certified robust graphs, three delay schedules, and four adversary models including stale replay. This preprint presents the model, the analysis, and the evaluation protocol. It does not report empirical outcomes.

Abstract: This paper demonstrates that a power grid may be fully protected from E₃ high-altitude magnetic pulses (HEMP) using E₃ arrestor devices placed at substations that represent any vertex cover of the grid. This serves as a simple linear time check to see if pre-existing E₃ arrestor-type assets are sufficient for the mitigation of E₃ HEMP waveforms. The algorithm to determine where to place new arrestor devices is computationally inexpensive, with a runtime of 8 ms for an 18-bus test case and a runtime of 486 ms for a 150-bus test case in MATLAB to determine the bounding case approximation. The algorithm accomplishes this by finding the minimum vertex cover of the grid and placing E₃ arrestor devices in those locations. Since computing the minimum vertex cover is NP-Hard, the algorithm approximates this by computing the solution of a linear program combined with a heuristic based on the four color theorem and knowledge of the permissible substations to upgrade (i.e. substations with E₃ arrestors). If the solution exists, it guarantees the near zeroing out of flux across all transformers. If some flux can be tolerated, there may exist an even smaller set of substations to protect that allow system specifications to be met. Additionally, if there already exist devices that make up a vertex cover on the grid, then it is guaranteed that the grid is already protected from E₃ disturbances. The check for this distribution of devices is linear in time. The vertex cover solution may be used with other optimization techniques as a homotopy to find the minimum number of required E₃ arrestors. The minimum vertex cover is the bounding case solution for the E₃ arrestor device.

Abstract: This paper establishes exponential convergence to zero, rather than to a residual neighborhood, for a first-order serial differentiator and its higher-order cascade. If the acceleration of the input is locally absolutely continuous with known acceleration and jerk bounds, appropriate gain conditions yield an exponential estimate that is uniform on every prescribed initial energy sublevel. The Lyapunov function is the energy of the error system plus a quadratic correction that makes it strict. For a nominal input observed through a continuous perturbation with a persistent amplitude, a practical exponential estimate with residual holds, without any smallness condition, and exact exponential convergence is retained without perturbation. This amplitude-only transfer applies to the nonsmooth interstage error of a serial differentiator. With an exact first input it closes an exact exponential induction over any fixed finite number of stages under stagewise rate conditions.

Abstract: In loss-minimizing model predictive control of induction motor drives, the torque reference is often enforced by a quadratic penalty. Since every newton-meter costs loss, it leaves a steady torque error, and its weight has no physical basis. The error is shown to equal \( λ_M/(2q_M M^* ) \), where \( λ_M \) is the marginal loss per unit torque of the loss-optimal steady state, and a penalty on the absolute torque error removes it once its weight, in W/(N·m), exceeds the largest \( λ_M \)below the reference. The closed form of \( λ_M \) is derived from a temperature-dependent machine and inverter loss model. For a 7.5 kW drive at 36 simulated operating points, the closed-loop threshold equaled the marginal loss at fixed flux, which coincides with \( λ_M \) except on the voltage-limit boundary (up to 15% lower). The nominal quadratic weight left errors of 0.46–3.88%; with exact measurements the absolute-value penalty kept them below 10^-4%, also on pump and conveyor cycles, while the loss per unit of work changed by at most 0.6%. Without flux reservation it lowers and delays the torque during a current-limited flux rebuild, unlike a combined absolute-value and quadratic term. Field-orientation mismatch, measurement noise and dead time cause errors that the penalty does not remove.

Abstract: Campus heat recovery depends on the coincidence of computing output, thermal demand and required delivery temperature. This study develops an hourly energy–exergy model with bounded storage, boiler backup and an explicit investment threshold expressed as recovered-source utilisation. A synthetic annual campus profile is evaluated at 45, 55 and 70 °C, with three storage capacities and 100 computing-load realisations. Without storage, raising delivery temperature from 45 to 70 °C increases the supplied heat share from 83.1% to 87.2%, yet reduces recovered-source utilisation from 60.6% to 52.5% because compressor electricity contributes more heat. Corresponding net present values are £333,374 and −£347,066 under stated assumptions. At 55 °C, adding 2.4 MWh storage raises supplied heat from 1,696.2 to 1,817.5 MWh but changes investment value from £57,349 to −£13,933. An equal-demand calendar counterfactual changes the direction of the schedule effect as storage increases. The results distinguish thermal feasibility, operating savings and capital recovery, providing conditional screening thresholds rather than validated campus forecasts.

Abstract: Background: Mexico reports collecting 64% of its PET bottles against targets of 70% (2025) and 80% (2030), and informal collectors carry much of that work. A deposit-refund system (DRS) could raise recovery but could also take material and income from them; Mexico’s 2026 Circular Economy Law recognizes base recyclers but creates no DRS. Methods: We build a stock-and-flow model of containers, consumer returns and collector entry and exit, anchor inputs to Mexican sources where they exist, and add a calculation of collector income from six quarters of the national ENOE employment survey. Results: Across simulated systems, a 1.5 MXN deposit with collectors receiving 30% of it on the containers they hand in reaches 80% recovery while keeping at least 90% of collectors in 68–95% of systems, the upper end assuming an optimistic reading of collector income; a 3.0 MXN deposit reaches 92–99%. Failures are almost always consumer-response failures; displacement appears when collector access is low. Consumer hassle cost, collector income margin, entry sensitivity and the accessible share of discards drive most of the uncertainty. Conclusions: A pilot should measure consumer returns at three or more deposit levels, collector access and income, and audited recovery, with decision thresholds we derive.

Abstract: This paper presents the design and implementation of an integrated electronic system based on the open-source Arduino Uno platform for real-time speed measurement of different moving objects. The system integrates two infrared (IR) sensors, an Arduino Uno microcontroller, a liquid crystal display (LCD), light-emitting diodes (LEDs), and an acoustic buzzer, providing a functional, low-cost, and extensible solution. The proposed measurement method employs two IR sensors positioned alongside the object’s movement trajectory at a known separation distance. The microcontroller records the detection times at both sensors and calculates the object’s average speed over the sensing interval using the measured time difference and the predefined distance. The calculated speed is displayed on the LCD, while exceeding a configurable speed threshold activates visual and acoustic warning signals. A functional prototype was developed and tested under laboratory conditions using a fixed sensor separation of 0.1 m and different moving objects, including battery-powered toy vehicles, moving spheres, and human passage. Laboratory demonstrations confirmed the operation of the detection, speed calculation, and warning functions for the tested scenarios. The primary contribution of this work is the design and integration of accessible electronic components and open-source technologies into a reproducible speed-monitoring system. Its modular architecture and modifiable software provide opportunities for further development and adaptation to educational, laboratory, industrial, and motion-monitoring applications.

Abstract: The organization of the lower limb exoskeleton robot (LLRER) is a highly nonlinear system and there is key difficulties including the stability issue and the complex control design. This article uses the feedback linearization method to robustly stabilize LLRER that takes the place of nonlinear function approximator, such as the fuzzy control and the neural network control. The current work “firstly” proposes the difficult nonlinear exponential convergence rate formula of LLRER. The effective operating range of presented algorithm is “global”, whereas the Jacobian linearization method is “locally” valid. The proposed approach simultaneously possesses some features including the almost disturbance rejection feature that replaces the singular perturbation approach without the sufficient requirement that original system should have “complete” condition, and the system stability feature without addressing the difficult Hamilton-Jacobin equation for the H-infinity method. To rigorously verify the effectiveness of the proposed approach, this study uses the AI software “Python” to build a controller-design human–machine interaction package of general nonlinear system. Finally, a comparative example, that cannot be solved for the first paper of almost disturbance decoupling problem, is used to show that the effectiveness of proposed algorithm is better than the singular perturbation method.

Abstract: National geospatial infrastructures increasingly connect Earth Observation (EO), geospatial artificial intelligence (GeoAI), interoperable services, and public-sector decision processes. However, an explicit method for assessing whether these technical functions operationalize the nine pathways of the United Nations Integrated Geospatial Information Framework (UN-IGIF) remains underdeveloped. This Communication develops a pathway-to-function-to-evidence assessment framework and applies it illustratively to the Rwanda Space Agency (RSA) GeoHub case. Using the UN-IGIF pathway definitions as normative criteria and the available GeoHub description as case material, we classify reported functions, map their institutional and technical dependencies, and identify the records required to substantiate operational performance and decision uptake. Worked mappings cover EO data stewardship, GeoAI processing, interoperable delivery, and sectoral decision workflows. The contribution is a transparent analytical procedure that distinguishes reported capabilities, proposed applications, and independently verified outcomes; it does not constitute an audit or quantitative impact evaluation. The framework offers a transferable basis for planning and evaluating EO-enabled geospatial infrastructures in emerging space ecosystems.

Abstract: Industrial production plants in sectors such as chemicals, life sciences, metals and mining, pulp and paper, and power generation operate with a high degree of automation. During steady-state operation, control systems maintain process stability with little need for human intervention, while operators primarily supervise plant behavior and intervene during startup, shutdown, product transitions, or abnormal situations. At the same time, modern plants must monitor multiple domains simultaneously, including process performance, asset conditions, cybersecurity threats, and physical security. These developments significantly increase the number and diversity of events generated by monitoring systems. Advances in sensing technologies, connectivity, and data analytics have greatly improved the ability to detect deviations from normal operation. However, the increasing volume of detected anomalies and notifications raises the challenge of determining which events should be brought to the attention of human supervisors. Since direct human intervention becomes less frequent but more critical in highly automated plants, the effective use of operator attention becomes an essential aspect of system design. This paper examines monitoring within the broader framework of supervisory control in industrial systems. Monitoring is viewed as the continuous determination of system state across multiple domains, with anomaly detection forming the basis for subsequent supervisory decisions. Building on this perspective, the paper analyzes how detected deviations are translated into events and discusses criteria for deciding which events require human attention. A structured view of monitoring and supervision is proposed that integrates process operation, asset health, cybersecurity, and physical security. The goal is to outline design principles for event generation that support effective human oversight in increasingly autonomous industrial environments.