Engineering

Abstract: This review looks at cloud–IoT edge middleware for analyzing user behavior in smart cities, with a particular focus on linking behavioral analytics to energy-aware and mission-critical urban services. The topic sits at the crossroads of smart-city platforms, IoT sensing, edge/fog/cloud computing, service-oriented architectures, machine learning, explainable AI, privacy-aware governance, and energy-critical applications such as EV charging, energy demand, environmental exposure, crowd safety, and the operational resilience of urban infrastructure. As such, this paper maps out the main categories of user-behavior data, examines five application domains, compares several case studies, and puts forward a new XAI-EdgeSOA Middleware Framework called X-EdgeCity Middleware. This framework brings together IoT sensing, edge preprocessing, fog coordination, event-driven communication, cloud analytics, XAI explanations, privacy-by-design, and benchmark logging. The goal is to support not just general smart-city applications, but also energy-sensitive or mission-critical services, where latency, reliability, explainability, and accountability have a direct impact on urban safety and continuity of operations.

Abstract: This paper develops RF interconnection computing as a deliberately narrow architectural concept: a transmission network that is already required to route coherent radio-frequency signals is co-designed to perform a restricted linear transformation during propagation, rather than being treated as an electrically neutral connection followed by a separate computing block. The proposal does not claim that electromagnetic waves performing matrix operations are new. Coupling-matrix filter synthesis, passive beamforming and sum–difference networks, computational metamaterials, programmable wave-based computers, and recently demonstrated microwave matrix processors already establish that interference and multiport networks can realize linear transformations [1]–[15]. The open question addressed here is instead whether an interconnection itself can be treated as an application-specific operator and evaluated jointly for routing and computation. To place that concept on a physically consistent basis, the paper first separates input and output port groups in a reciprocal multiport network and defines the forward operator as the transmission submatrix H = S_OI, rather than identifying an entire scattering matrix with an input–output computation. A general temporal coupled-mode formulation is then connected to an idealized single-frequency bipartite coupling model. In this special model, a real target contraction W can be mapped analytically to a normalized coupling block G, while the complete 2N-port scattering matrix remains reciprocal and lossless. Three externally selected three-channel operators—through transmission, common-mode extraction, and differential-subspace projection—are used as reproducible numerical examples. The calculations expose both the opportunity and the limitation of the concept: useful modal transformations can be embedded algebraically in a passive interconnection, but rejected modal energy is reflected rather than destroyed, bandwidth and loss are absent from the ideal model, and abstract coupling coefficients are not yet a manufacturable transmission-line layout. The paper therefore frames RF interconnection computing as a falsifiable co-design hypothesis and specifies the electromagnetic, circuit, and system-level evidence required before claims of reduced hardware, latency, or data movement would be justified.

Abstract: To address the transmission capacity constraints and high reserve offer costs associated with cross-regional reserve sharing under high renewable energy penetration, this paper proposes a method for designing cross-regional virtual reserve services based on spatially transferable loads in data centers. First, a data center task migration model that considers task queues, transmission bandwidth, processing capacity, and deadline constraints is established to quantify the relationship among load reduction on the task-exporting side, additional electricity consumption on the task-importing side, and the reserve capacity of supporting generating units. Second, a novel Cross-Regional Virtual Reserve Consortium (CRVRC) business model is proposed to incentivize data centers and supporting generating units to jointly provide cross-regional reserve through joint bidding and revenue allocation. Then, a robust joint clearing model for energy and reserve that considers uncertainty in renewable energy output is established and solved using the Benders decomposition algorithm. Finally, case studies based on a modified IEEE 14-bus system show that, compared with the baseline, the proposed method reduces the joint clearing cost by 5.02% and the reserve offer cost by 3.60%, increases data center net profit by 89.73%, and maintains transmission margins on all cross-regional tie lines under wind power forecast deviations.

Abstract: Low-inertia power systems require scenario-dependent assessment of additional frequency support. This study develops a computational-intelligence-based framework for minimizing missing virtual inertia with conventional inertia fixed as an external input. Following a five-metaheuristic comparison, a whale optimization algorithm (WOA)-based search is coupled to real-time electrical simulation on RTDS NovaCor Light through RSCAD FX. Acquired trajectories determine candidate feasibility under event-direction rate of change of frequency (RoCoF), nadir, and zenith limits. A 70-case experiment combines conventional inertia of 1.0–4.0 s with signed demand changes of 5–25 MW. Of these cases, 56 yielded feasible outputs: 24 required no addition and 32 required 0.155–7.349 s on the system base. The remaining 14 cases at ±25 MW had no feasible candidate within the tested 0–10 s additional-inertia interval and budget. For positive-support cases, the nearest constraint shifted from RoCoF at 10–15 MW to nadir or zenith at 20 MW, revealing the need to assess frequency extrema and both disturbance directions. Their median optimization time was 28.95 min, supporting prospective scenario assessment. Virtual support was represented by an ideal equivalent generator-inertia increment; consequently, the best-found outputs inform battery energy storage and virtual synchronous machine design without demonstrating physical converter delivery.

Abstract: Radio detectors operating in complex electromagnetic environments face distribution shifts between controlled laboratory and field measurements. This study proposes a Structure-Aware and Physics-Constrained Generative Adversarial Network (SA-PCGAN) for cross-domain time–frequency data generation. The model combines self-attention for global feature association with an L1-norm alignment constraint to preserve dominant signal structures while learning field background characteristics. Training uses 10,000 pairs of measured time–frequency images, and downstream classification is evaluated using a separate test set of 1,500 measured field samples. In the internal ablation study, the complete model achieves a time–frequency Pearson correlation coefficient of 0.7544 and a Fréchet Inception distance (FID) of 109.76. In the external benchmark comparison, the FID is 273.41 and the distributional maximum mean discrepancy is 0.181606. A ResNet50 classifier trained with SA-PCGAN-augmented data achieves 93.00% accuracy; in the separate ResNet18 ablation setting, accuracy reaches 92.27%. These results indicate that, under the datasets and experimental conditions considered, SA-PCGAN improves generated data quality and their utility for downstream recognition. The method provides a basis for cross-environment data augmentation and performance evaluation of radio detectors.

Abstract: In some applications of voltage-source inverters (VSI) in UPS systems, a lower switching fre-quency (about 10 kHz) is advisable, e.g., to reduce power losses. The cheapest control solution is Single-Input-Single-Output (SISO) control when only output-voltage measurement is required. However, such a SISO control does not work perfectly at low switching frequencies because of the dynamic characteristics of the measuring trace, which can be modelled in the frequency range up to the output filter's resonant frequency as a delay equal to some switching periods. The lower the frequency, the higher the delay. In the case of the SISO, the traditional solution could be, e.g., the Smith Predictor. But in this case, the accuracy of the control depends strongly on the accuracy of the inverter model. The previously shown results of using the Smith Predictor were not spectacu-lar. The new approach is the application of the Equivalent Input Disturbance (EID) when the control SISO system works independently, and the EID (using the inverter model) just compen-sates the disturbances, decreasing output voltage distortions – in our case, the output current is treated as the independent disturbance. The disadvantage of this method is the requirement of a second measurement trace of the inductor current what increases the VSI cost. Three versions of EID control law were presented – the continuous control based on the VSI continuous model, the discretized continuous control, and the discrete control based on the VSI discrete model. The sim-ulations of the SISO Coefficient Diagram Model (CDM) with the chosen EID version were shown for the standard nonlinear rectifier RC load (Power Factor = 0.7). Finally, the measurements of the experimental VSI model with the chosen CDM&EID control were presented.

Abstract: Modern electricity markets are complex socio-technical systems in which price formation results from interactions among physical, economic, operational, and institutional mechanisms. Under the European Target Model, the sequential coupling of Day-Ahead, Intraday, and Balancing Markets, together with renewable variability, cross-border exchanges, system imbalances, reserve activation, and settlement mechanisms, creates an interconnected nonlinear system. This study investigates the interaction structure underlying Market Clearing Price (MCP) formation in the Greek electricity market using variance-based Global Sensitivity Analysis. A Sparse Polynomial Chaos Expansion (PCE) surrogate represents MCP as a function of sixteen variables describing generation technologies, demand, cross-border exchanges, balancing-energy activation, system imbalances, reserves, and Uplift Accounts. Sobol sensitivity analysis decomposes MCP variance into individual, pairwise, and higher-order contributions. Natural-gas generation emerges as the dominant first-order driver, whereas hydropower constitutes the principal nonlinear interaction hub, linking domestic flexibility with renewable generation and cross-border exchanges. Individual effects account for approximately 74.5% of MCP variance, pairwise interactions for 19.1%, and higher-order effects for 6.4%. The strongest nonlinear interactions involve hydropower with exports, imports, and renewable generation, demonstrating that variables with moderate individual importance may nevertheless play structurally important roles through their interactions. These findings show that MCP variability cannot be explained solely by isolated market fundamentals but reflect the coupled operation of scheduling, flexibility, European market integration, renewable variability, and balancing mechanisms. The Sparse PCE–Sobol framework therefore provides an interpretable computational approach for revealing the interaction architecture of interconnected electricity markets as complex systems.

Abstract: Open-source IEC 61850 software is widely used in smart-grid research, but it has mostly been validated against software-emulated servers. This paper integrates OpenPLC61850, an open-source programmable logic controller (PLC) featuring an integrated Manufacturing Message Specification (MMS) client, with the Siemens SIPROTEC 5 distance (7SX85) and backup overcurrent (7SJ82) relays of a 150 kV transmission-line bay. The client runs in a virtual machine on the station bus and is engineered through a reproducible two-stage workflow that combines offline tagging of Substation Configuration Language (SCL) files with online data-set and report engineering. Seven interoperability gaps were identified and resolved, among them a firmware-specific suffix in report names, data-set granularity, double-point decoding, device-side switching authority and setting-group control through the Setting Group Control Block, which SCL-based tools do not expose. The client was extended with select-before-operate breaker control and remote setting-group switching. Protocol traces and secondary injection with an OMICRON CMC 356 test set showed that, for both relays, an identical fault trips the relay in one setting group and not in the other, with the appropriate group selected by the PLC, while the operation times agree with the manufacturer’s specifications. The results establish a reproducible path for using open-source clients with commercial protection devices.

Abstract: Semiconductor devices are pivotal technologies for almost all existing industrial sectors, as demonstrated by the recent chips shortages. The limits in scaling, performance and power consumption of electronic components, combined with the strong growth of ICT share in global electrical energy consumption, have put forward the need for alternative solutions. In this respect, innovative and disruptive device architectures have played a decisive role over the past 15 years and will continue to do so for future electronic circuits and systems. In this regard, FDSOI, multi-gate devices, nanowires, 2D devices, and 3D device architectures have shown promising electrical properties for many applications. This paper will review and compare innovative MOSFET architectures which are and will be needed for advanced electronic systems, like FDSOI, FinFET, Gate-All-Around, Nanosheets, Forksheet, CFET, and will address potential novel MOS Transistors with other disruptive architectures recently proposed for future high performance and/or low power ICs, such as the GAIA (Gate-All-Inside-and-Around) MOSFET.

Abstract: Directly coupling high-speed generators to turbines or expanders enables compact conversion of low-grade thermal energy from industrial waste heat, geothermal resources, and small-scale renewable sources. For this application, this paper presents a multiphysics comparative assessment of six candidate topologies: three-phase and six-phase surface-mounted permanent magnet, NdFeB and ferrite interior permanent magnet, synchronous reluctance, and induction machines, all evaluated within a common 30 krpm, 750 V DC-link, water-cooled IEC 132 frame envelope. Two-dimensional transient finite element simulations are used to obtain electromagnetic performance maps, torque-speed characteristics, power factor, and efficiency, while thermal and mechanical analyses are used to assess continuous operating capability and rotor integrity. At 30 krpm, the ferrite IPM and induction machines are predicted to deliver continuous outputs of 100.8 kW and 100.7 kW, respectively. Their main design concerns are winding temperature and rotor-lamination stress margin for the ferrite IPM, and rotor heating for the induction machine. The comparisons show that peak electromagnetic performance alone is insufficient for topology selection, particularly at high speeds. For this reason, the results provide a basis for selecting high-speed generator topologies before detailed optimization and prototype development.

Abstract: A wearable hip airbag must recognize a balance disturbance early enough to inflate before the body reaches the ground. Unexpected postural perturbations evoke a cortical response recordable behind the ear. We developed a detector that uses one mastoid electroencephalography (EEG) channel and a network small enough for a microcontroller. EEG and platform acceleration were recorded from 40 young and older adults during 34 translations each; no falls occurred. A 1570-parameter EEGNet was evaluated with leave-one-subject-out cross-validation, running continuously over entire recordings, with the accelerometer only gating classification. The detector recognized 94.1% of the unexpected perturbations, rejected 92.7% of the unlabeled movements that reached the classifier and decided a median of 364 ms after the detected acceleration onset, within 400 ms for 90.9% of the perturbations. On an ESP32-S3 microcontroller, one inference took about 25 ms, leaving 78.4% of the decisions within 400 ms. Starting the analysis window 60 ms before the detected onset instead of 80 ms after it raised the area under the receiver operating characteristic curve from 0.898 to 0.962 without adding latency. This leaves time for airbag inflation before impact in typical falls; the main barrier, 37–46 false alarms per hour, sets the target for inertial confirmation.

Abstract: We propose a conditional policy-design framework for renewable small distributed generator (PMGD) adoption, informed by Chile. We hypothesize that stabilization with reversible access sustains investment, while restitution moderates that incentive. A least squares Monte Carlo model links deferral and remuneration choice for 4 MW photovoltaics and a project integrating 1 MW/2 MWh storage. Five configurations distinguish fixed-regime deferral, switching, and restitution. Common paths isolate time-block comparisons; historical flat stabilization uses a separate calibration. Without restitution, switching raises simulated exercise from 42.6% to 51.9% for photovoltaics and from 72.9% to 76.0% for the integrated project, relative to fixed stabilization. Twelve following-year monthly restitution installments reduce exercise to 16.3% and 30.2%, close to spot. A positive-NPV price sensitivity without restitution shows immediate investment under fixed stabilization but waiting under spot, with exclusive deferral premiums of USD 1.3269 and 0.6921 million for photovoltaics and the integrated project, respectively. Uncertainty also motivates waiting; restitution removes much support-induced acceleration. Policy choices require local calibration, transfer budgets, and connection safeguards. Annual accrual, real indexation, omitted guarantee and liquidity costs, and in-sample valuation limit the findings, which establish neither optimal deployment nor international welfare superiority.

Abstract: Load growth is pushing utilities to add transformer, bus and feeder capacity at existing substations, yet most of that work requires de-energizing equipment that customers depend on. This article reviews the main alternatives for doing capacity-expansion work without interrupting service: load transfer through the distribution network, mobile substations, temporary transformers, temporary generation and storage, temporary buses and bypass arrangements, transfer-bus schemes, staged construction, closed-transition paralleling and energized work. For each, it explains how the method works, when it fits, its limits, and the protection and safety checks it requires. A decision matrix, a selection roadmap and a planning checklist help project teams choose and execute the right combination. The goal is practical: a construction outage that customers never see.

Abstract: Fault identification from measured scattering parameters is complicated by manufacturing offsets, environmental drift, frequency-correlated measurement noise, and structural indistinguishability. This paper studies a baseline-referenced pipeline for detecting changes in complex two-port scattering responses and classifying resonator detuning, coupling reduction, and excess internal loss. A whitened local model separates environmental nuisance directions from candidate fault signatures. We derive the exact relationship between nuisance projection and a profile generalized likelihood-ratio test (GLRT) in the linear Gaussian regime, demonstrate the condition under which a fault is locally unidentifiable, and report indistinguishable nodes as symmetry-aware diagnostic regions. Reproducible synthetic experiments on branched and inline five-resonator models compare uncorrected change energy, projected residual energy, and a one-sided matched GLRT. Under moderate simulated drift, the GLRT achieves 51.9% and 60.1% detection on deliberately weak faults, compared with 10.8% and 11.9% for uncorrected energy, while false-alarm rates are 5.3% and 4.3%, respectively. Strong out-of-calibration drift increases false alarms to 12.5% and 17.0%, showing that nuisance projection is not a universal remedy. These findings support an explicit three-way reporting policy: identified region, ambiguous/weak evidence, or suspected model mismatch. No physical electromagnetic simulation or vector-network-analyzer data are claimed.

Abstract: This paper proposes a pre-chirp shift keying-aided affine frequency division multiplexing (PCSK-AFDM) scheme for secure and low peak-to-average power ratio (PAPR) high-mobility communications. Motivated by the fact that existing secure AFDM designs mainly rely on modifying pre-chirp parameters, we exploit legitimate channel state information to construct a secure permutation between information indices and cyclic shifts of the pre-chirp matrix, thereby conveying additional information without altering the conventional chirp-parameter configuration. To reduce PAPR while introducing additional waveform uncertainty at the eavesdropper (Eve), we further incorporate affine Fourier-domain phase rotations and develop an efficient gradient-screened refinement-aided quantized projected gradient descent (GSR-QPGD) algorithm. We derive the bit error ratio (BER) performance of both the legitimate receiver (Bob) and Eve, along with their mutual information and secrecy mutual information. For scalable detection, a candidate-screened orthogonal approximate message passing (CS-OAMP) detector is proposed. Simulation results show that Eve's BER approaches 0.5, GSR-QPGD effectively reduces PAPR while mitigating nonlinear power amplifier (PA) distortion and increasing Eve's information entropy, and CS-OAMP achieves improved BER performance with a favorable complexity trade-off.

Abstract: The article focuses on investigating the amount of energy radiated under real-world operating conditions of a controlled modern power supply and describing the phenomena arising from the nonlinearity of the electrical load. The theoretical description of the resulting electromagnetic field (EMF) is supported by experimental measurements of a nonlinear load powered by a conventional low-frequency switched-mode power supply operating at a frequency of 1,400 Hz and employing pulse-width modulation (PWM). For the sake of improved measurement accuracy, a commercially available LED strip used for decorative lighting was selected as the nonlinear load. However, the experimental approach allows any type of load to be used. The first part of the article provides a theoretical description of the phenomena occurring in the circuit and focuses on verifying the fundamental energy balance equation. For the investigated system, an alternative form of this equation is derived. The second part describes the procedure for calculating the parameters of an equivalent DC power supply delivering the same effective power, which is subsequently used to power the nonlinear load. An experiment is then conducted to investigate the conversion of the supplied energy within the circuit. With the aid of Fourier transform analysis, higher-order harmonic components are visualised. In the third part, the nonlinear load is powered by an actual non-sinusoidal power sup-ply, while the amount of energy delivered to the load is kept constant. The experimental measurements enable the quantification of the supplied energy converted into Joule heat and the energy radiated into the surrounding environment in the form of electromagnetic radiation.

Abstract: Counting how many vehicles crossed an intersection produces a report; finding which vehicles move together, and through which cameras, reveals the structure of urban traffic. License plate recognition cameras make that structure observable. Sets of cameras frequently shared by the same vehicles expose the corridors that traffic actually uses, and groups of vehicles repeatedly seen together at the same cameras support automated convoy detection for traffic enforcement. Both are frequent itemset mining problems, and they are precisely where relational databases lose ground as data grow. We quantify that loss for the corridor formulation, comparing a self join in indexed PostgreSQL with distributed FP-Growth on a four-worker Apache Spark cluster over 100 million simulated detections from 500 cameras on the road network of Pereira, Colombia, at eight nested scales from 50 thousand to 15 million rows. For reports, histograms, lookups and alerts, PostgreSQL wins at every scale by up to five orders of magnitude, and Spark never leaves a fixed overhead of two to four minutes. Structure mining behaves differently: the relational advantage collapses from 328 to 1.6 times, because the self join grows super-linearly while FP-Growth grows almost linearly. Extrapolated growth exponents place the crossover between 30 and 73 million detections, four to nine days of operation, which tells a city when structural analysis must move off the relational engine.

Abstract: Recent advances in ambient sensing, speech processing, and smart-home technologies have enabled the passive monitoring of human activities in domestic environments. Existing systems primarily focus on detecting speech presence, social interaction occurrence, emotional states, or individual behavioral indicators. However, limited work has explored how continuous in-home sensing can support reflection on family-level social dynamics while preserving privacy and avoiding judgmental or diagnostic interpretations. We present and evaluate a privacy-preserving, locally processed household sensing architecture that integrates distributed audiovisual sentinels, speech analytics, structured interaction abstractions, and longitudinal evidence retrieval. The study evaluates selected components under controlled and household deployment conditions and identifies the limitations of automated relational interpretation, particularly in the presence of incomplete observations and generative language-model errors. This work aims to gradually bring AI agents at home towards a more intimate human-machine relationship. We initiate this co-existence through a privacy-preserving platform that utilizes distributed room-level audiovisual sentinels and on-premises speech analytics to derive longitudinal indicators of household relational dynamics. These indices include Warmth, Joy, Tension, and Withdrawal. Rather than attempting to infer subjective constructs such as happiness, depression, or marital satisfaction, it focuses on observable conversational behaviors including participation balance, responsiveness, conflict intensity, repair after conflict, and social inclusion. The system introduces a smartwatch-like visualization framework that communicates family-level and dyadic social dynamics through ambient and interpretable visualizations. Weekly reports summarize patterns of connection while maintaining a non-judgmental approach. Our aim is to establish an evidence-grounded relational representation of the family system, in which software agents coexist with human residents as tools for observation, communication, and reflection that will follow them for decades. In this work, we narrow our aim as to whether the system can identify observable interactional behaviors under clearly defined conditions. We have the ambition to initiate a trend where everyone is allowed to design their personal in-home agent, therefore we provide a large open codebase that enables researchers and developers to implement, examine, and extend the proposed framework within their own homes.

Abstract: Wind integration involves numerous interacting factors and time-varying transient responses, making it difficult to represent their effects consistently and distinguish their contributions using equipment parameters directly. This study develops a framework linking wind integration factors, nodal channel variables, and system assessment indices. Five nodal channels summarize wind integration effects, and node contribution mapping relates them to the indices to identify dominant channels and their directions of influence across nodes. Five channels represent pre-fault synchronous-generator active power, equivalent inertia, equivalent damping, dynamic synchronizing torque, and reactive power amplification. Batch total least squares converts transient responses into scenario-level equivalent values. The machine synchronization dispersion index (MSDI) and reactive power mismatch recovery rate (QMRR) provide mechanical- and electrical-domain outputs under fixed-fault conditions; QMRR does not require mechanical rotor-angle measurements. A nonlinear mapping multiplies channel factors within each node and sums contributions across nodes, enabling sensitivity-based analysis of dominant channels and their directions of influence. Source comparisons and parameter perturbations in an extended IEEE 9-bus system reveal differences in factor-to-channel effects. Comparisons of the two indices and response trajectories under two fault durations illustrate their assessment behavior. Across 225 identification scenarios in an IEEE 39-bus system, the mean relative fitting errors are 0.0191% for the MSDI and 0.8518% for QMRR, demonstrating in-sample reproduction of index variation by the channel representation and mapping structure. In a representative scenario, the equivalent-inertia and reactive-power-amplification channels have MSDI sensitivity weights of 62.51% and 36.61%, respectively, while the latter has a QMRR weight of 78.15%. The framework connects factor-to-channel effects with channel-to-index contributions for quantitative analysis within the investigated scenario domain.

Abstract: Microwave breast tumor detection relies on the dielectric contrast between healthy and malignant tissue, and the antenna bandwidth affects the cost, complexity and sensitivity of the system. This paper compares an ultra-wideband (UWB) and a narrowband (NB) circular patch antenna that share the same FR4 substrate, 30 × 30 mm² footprint and radiator but differ in their ground plane and feed. Both antennas were fabricated and measured; the simulated UWB antenna covers 3.56–14.73 GHz, and the NB antenna resonates at 7.79 GHz in its TM21 mode; a finite-difference time-domain (FDTD) model with the measured patch dimensions explains the measured NB resonance shift. Sensing is evaluated with finite-element and FDTD solvers, with each antenna 10 mm from a homogeneous healthy or malignant phantom. Both solvers predict the same 140 MHz tumor-induced resonance shift for the NB antenna and a similar peak reflection-coefficient contrast for the two antennas (19.2 and 21.9 dB in the finite-element model; 7.1 and 9.7 dB in the FDTD model), while the UWB antenna is sensitive over a three to seven times wider spectrum and directs 5.5–6 dB more gain toward the phantom. The NB antenna therefore suits low-cost single-frequency detection, whereas the UWB antenna suits imaging systems that exploit frequency diversity.