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This paper describes novel method of path loss modeling for radio communication channels in container port area. Multi-variate empirical model is presented, based on multidimensional regression analysis of real path loss measurements from container terminal environment. The measurement instruments used in propagation studies in port area are also described.

Recently, Semtech has released a Long Range (LoRa) chipset which operates at the globally available 2.4 GHz frequency band, on top of the existing sub-GHz, km-range offer, enabling hardware manufacturers to design region-independent chipsets. The SX1280 LoRa module promises an ultra-long communication range while withstanding heavy interference in this widely used band. In this paper, we first provide a mathematical description of the physical layer of LoRa in the 2.4 GHz band. Secondly, we investigate the maximum communication range of this technology in three different scenarios. Free space, indoor and urban path loss models are used to simulate the propagation of the 2.4 GHz LoRa modulated signal at different spreading factors and bandwidths. Additionally, we investigate the corresponding data rates. The results show a maximum range of 133 km in free space, 74 m in an indoor office-like environment and 443 m in an outdoor urban context. While a maximum data rate of 253.91 kbit/s can be achieved, the data rate at the longest possible range in every scenario equals 0.595 kbit/s. Due to the configurable bandwidth and lower data rates, LoRa outperforms other technologies in the 2.4 GHz band in terms of communication range. In addition, both communication and localization applications deployed in private LoRa networks can benefit from the increased bandwidth and localization accuracy of this system when compared to public sub-GHz networks.

This study presents a measurement-driven comparison of LoRa communication performance in two tropical deployment scenarios at 923.2 MHz: an open line-of-sight (LOS) path and a forest-obstructed path. To ensure a controlled comparison, both scenarios were evaluated over the same transmission distance of 1.2 km using identical radio configuration, antenna heights, and hardware settings. Field measurements were conducted from 50 m to 1.2 km in 50 m increments, with three repeated measurements at each distance point. The measured RSSI decreased from −60.52 dBm to −89.48 dBm in the LOS case and from −77.62 dBm to −114.62 dBm in the forested case. Using a bandwidth of 125 kHz and a receiver noise figure of 6 dB, the corresponding estimated SNR at 1.2 km was 27.55 dB for the LOS path and 2.41 dB for the forested path. Relative to the free-space baseline, the measured LOS link showed a deviation of 31.14 dB at 1.2 km, while the forested link showed a deviation of 56.28 dB. The additional attenuation specifically associated with the forested environment was approximately 25.14 dB, with a mean excess loss of 24.70 dB over the full route. Regression analysis further yielded effective path-loss exponents of 2.31 for the LOS case and 3.22 for the forested case. Based on these results, a site-specific empirical correction approach and an approximate 25 dB first-order design margin are suggested for preliminary LoRa link-budget planning in similar tropical vegetated environments. The findings indicate that free-space-only prediction may be insufficient for practical deployment and that measurement-driven correction can improve the realism of wireless sensor network design in vegetation-rich environments.

Magnetic hysteresis strongly influences energy dissipation and efficiency in power magnetic components under time-varying excitation. This work proposes a compact dynamic hysteresis model using a Hammerstein structure, consisting of a closed-form arctangent static operator followed by a first-order relaxation dynamic stage. The formulation enables direct datasheet-based parameterization and avoids iterative differential solvers or distributed hysteron representations, resulting in low calibration effort and computational cost. The static hysteresis behavior is characterized using four static parameters directly identified from manufacturer B-H datasheets, while dynamic effects are captured using two global calibration parameters derived from datasheet loss curves. This formulation enables accurate reconstruction of major and minor hysteresis loops, while introducing frequency-dependent phase lag and dynamic loop opening. Model performance is evaluated under diverse excitations, including sinusoidal, amplitude-modulated, FORC and chirp signals, showing waveform deviations below 7.2% peak-to-peak NRMSE relative to classical hysteresis models. Energy-loss predictions are validated against manufacturer datasheet curves for ferrite material 3C90 across multiple frequencies, yielding a root-mean-square relative error of 8.3% with 89% of operating points within ±20% deviation. The proposed model provides a datasheet-driven framework for hysteresis and energy-loss prediction in power magnetic components.

In this paper, a theoretical methodology is proposed for the systematic analysis of the power conversion efficiency of a full-bridge converter. Using the steady-state analysis results derived from the equivalent circuit, we introduced the concept of “loss coefficients” by categorizing parameters based on their correlation with load power and current. In particular, the loss factors for key components were organized in a tabular format according to their respective contributions to the load current. Furthermore, this study presented the contribution of the rectifier’s internal loss and overall power conversion efficiency through graphical representations to facilitate intuitive understanding. Finally, to verify the validity of the efficiency analysis incorporating the proposed loss factors, a loss and efficiency analysis was conducted on a 300 W-class full-bridge converter, and the results were compared with measured data from an experimental prototype. The results demonstrate that, at maximum load power, the discrepancy between the experimental and theoretical power conversion efficiency values ranged from a maximum of 1%. Consequently, it was confirmed that the proposed analysis method based on effective currents of key components and loss coefficients reflecting internal parasitic elements provides a valid approach for characterizing internal power loss and conversion efficiency relative to load current.

Due to the nonlinear material behavior and contradicting application requirements, the selection of a specific electrical steel grade for a highly efficient electrical machine during its design stage is challenging. With sufficient knowledge of the correlations between material and magnetic properties and capable material models, a material design for specific requirements can be enabled. In this work, the correlations between magnetization behavior, iron loss and the most relevant material parameters for non-oriented electrical steels, i.e., alloying, sheet thickness and grain size, are studied on laboratory-produced iron-based electrical steels of 2.4 and 3.2 wt % silicon. Different final thicknesses and grain sizes for both alloys are obtained by different production parameters to produce a total of 21 final material states, which are characterized by state-of-the-art material characterization methods. The magnetic properties are measured on a single sheet tester, quantified up to 5 kHz and used to parametrize the semi-physical IEM loss model. From the loss parameters, a tailor-made material, marked by its thickness and grain size is deduced. The influence of different steel grades and the chance of tailor-made material design is discussed in the context of an exemplary e-mobility application by performing finite-element electrical machine simulations and post-processing on four of the twenty-one materials and the tailor-made material. It is shown that thicker materials can lead to fewer iron losses if the alloying and grain size are adapted and that the three studied parameters are in fact levers for material design where resources can be saved by a targeted optimization.

Underwater wireless power transfer (UWPT) operates under special conditions, where the conductivity of seawater introduces eddy current losses, thereby reducing system efficiency. Meanwhile, the design parameters of magnetic couplers significantly influence their transmission characteristics. This paper proposes a fast and accurate neural network prediction model for mutual inductance and losses of magnetic couplers based on mirror-method prior knowledge within a prior knowledge input (PKI) framework. The proposed model integrates a low-fidelity analytical model with data-driven learning to achieve high prediction accuracy while maintaining computational efficiency. Based on the developed model, the transmission characteristics of unipolar rectangular and bipolar DD magnetic couplers are systematically investigated. The results indicate that the rectangular couplers exhibit higher overall efficiency than the DD couplers, with a more monotonic variation in efficiency under design constraints. Owing to its structural characteristics, the DD couplers present an optimal current-carrying area ratio, which is approximately 0.85 within the parameter range. Experimental validation is conducted at a 1 kW power with outer dimensions of 200 mm × 250 mm. The optimal transfer efficiencies of the rectangular and DD couplers reach 97.33% and 96.19%, respectively. The experimental results show good agreement with both simulations and model predictions, demonstrating the reliability of the proposed method for UWPT magnetic coupler analysis.

Reliable wireless connectivity in precision agriculture requires accurate propagation models that remain robust across varying orchard corridor geometries within a given vegetation canopy type. This study characterizes radio propagation at 18 GHz (FR3 band) through an extensive measurement campaign (N = 17,269 samples) in a custard apple orchard, systematically varying corridor width  m and transmitter height  m across nine geometric configurations. The standard Close-In (CI) model captures the distance-dependent path loss (global fit ) but leaves substantial geometry-dependent variability unexplained (RMSE = 3.91 dB). When the CI model is fitted per configuration, the parameters vary markedly ( and  dB), indicating that a single averaged exponent should not be interpreted as geometry-invariant. To address this limitation, we propose a Hybrid Linear+XGBoost framework that combines physically interpretable geometric corrections with nonlinear residual learning. Under leave-one-scenario-out cross-validation, the hybrid model achieves RMSE = 2.97 dB, a 24.0% improvement over the CI baseline. Crucially, while pure ensemble methods (Random Forest, Gradient Boosting, XGBoost) exhibit performance degradation up to 1.05 dB when extrapolating to unseen geometric configurations, the hybrid architecture demonstrates a 0.35 dB improvement, achieving better accuracy on novel corridor configurations than on interpolated data. Corridor width emerged as the dominant factor influencing shadow fading (SF) within the studied custard apple canopy. These findings establish that geometric parameters must be explicitly incorporated into channel models and that hybrid physics-ML architectures offer robust methodological generalization across corridor geometries, whereas the fitted numerical coefficients are expected to depend on vegetation descriptors such as leaf area index, leaf orientation, and moisture content.

A model for the dynamic pressure loss in standard oleo-pneumatic shock absorbers without gas-oil separator for avionics applications is introduced. The dynamics of such a device under load variations are primarily determined by the throttle between the oil chamber and the gas chamber. During operation, gas is absorbed by the oil upon compression and desorbed upon expansion, which are processes that extend over time and entail hysteresis. It is found that the assumption of isothermal conditions is sufficient. An excellent alignment is achieved by the model and the measured hysteresis across diverse drop test scenarios, adjusting a single parameter to a value that is physically reasonable. The standard deviation of the error in pressure is$${\\sigma _{p}=0.49\\,\\textrm{bar}}$$σ p = 0.49 bar . Moreover, the model rests on thermodynamic considerations and experimental gas solubility data, while it is consistent with other laboratory data from the literature.

The single axis solar tracker based on flat panels is used in large solar plants and in distribution-level photovoltaic systems. In order to achieve this, the solar tracking systems generally need to work by tracking the sun’s position with dozens, maybe hundreds of movements along the day with a maximal known tracking error within the specifications. A novel model is proposed along this work based on the control of the angle deviation within a (polar) single axis configuration. This way an optimization of the harnessing of solar energy can be achieved with as few panel displacements as possible in order to decrease the wear in the mechanical parts of the equipment and the energy consumed by it. This tracking approach was implemented with as few as seven positions along the day and got an estimated theoretical value of 99.27% of the total collected energy in a continuous tracking system. Regarding an annual average basis, it would be about 96.5% of a dual axis system according to the proposed model. The novelty of the model is related to a tradeoff between the gain with the simplicity of a single axis n-position tracking and the solar energy loss associated.