Search Results (632)

IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart wireless sensor that employs a small photovoltaic module simultaneously as sensing element and energy harvester. The device integrates an ESP32 microcontroller, precision ADC (Analog-to-Digital converter), and programmable load to sweep the PV (photovoltaic) I–V (Current–Voltage) curve and compute irradiance from electrical power and solar-cell temperature via a calibrated third-order polynomial. Supporting Modbus RTU (Remote Terminal Unit)/TCP (Transmission Control Protocol), MQTT (Message Queuing Telemetry Transport), and ZigBee, the sensor operates from batteries or supercapacitors through sleep–wake cycles. Validation against industrial irradiance meters across 0–1200 W/m² showed average errors below 5%, with deviations correlated to irradiance volatility and sampling cadence. All hardware, firmware, and data-processing tools are released as open source to enable reproducibility and distributed PV monitoring applications. Full article

The accurate determination of the equilibrium moisture content (EMC) of gel-related powdery samples requires strictly controlled conditions and a long time period. In this study, the adsorption and desorption isotherms of two fructooligosaccharide (FOS) powders and three inulin powders were determined using a dynamic moisture sorption analyzer at 0.1–0.9 water activity (a_w) and 20–35 °C, respectively. The adsorption and desorption isotherms all exhibited type IIa sigmoidal curves; the desorptive isotherm was smooth, the FOS adsorption curves had three inflection points, and the inulin adsorption curves had five inflection points. Large hysteresis between the adsorption and desorption isotherms occurred at 0.1–0.7 a_w for FOS and 0.1–0.6 a_w for inulin. Seven equations, Boquet, Ferro–Fontan, Guggenheim–Anderson–de Boer (GAB), Generalized D’Arcy and Watt (GDW), modified GAB (MGAB), Peleg, and our developed Polynomial, were found to fit the isotherms of the FOS and inulin samples; for adsorption, the best equations were Ferro–Fontan and GDW, and for desorption, the best equations were Polynomial and MGAB. The GDW and MGAB equations could not distinguish the effect of temperature on the isotherms, while the Polynomial equation could. The mean adsorptive monolayer moisture content (M₀) values in FOS and inulin samples were predicted as 7.29% and 7.94% wet basis, respectively. The heat of moisture sorption of FOS and inulin approached that of pure water at about 32.5% and 22.5% wet basis (w.b.) moisture content (MC), respectively. Fourier Transform Infrared Spectroscopy (FTIR) showed that the peaks in inulin with absorbance values above 0.52 and in FOS with absorbance values above 0.35 were at 1020, 1084, and 337 cm⁻¹; these could represent the amorphous structure (primary alcohol C-OH), C-O group, and hydroxyl functional group, respectively. Microscopic structure analysis showed that inulin powder particles were more round-shaped and adhered together, resulting in hygroscopic and sticky characteristics, with a maximum equilibrium moisture content (EMC) of 34% w.b. In contrast, the FOS powders exhibited irregular amorphous particles and a maximum EMC of 60% w.b. As hydrogels, 3–10% FOS or inulin addition reduced the peak, trough, final, breakdown, and setback viscosities of rice starch pasting, but increased the peak time and pasting temperature. FOS addition gave stronger reduction in the setback viscosity and in amylose retrogradation of rice starch pasting than inulin addition. The differential scanning calorimeter (DSC) showed 3–10% FOS addition reduced the amylopectin aging of retrograded paste of rice starch, but 5–7% inulin addition tended to reduce. These results suggest that FOS and inulin have strong hygroscopic properties and can be used to maintain the freshness of starch-based foods. These data can be used for drying, storage, and functional food design of FOS and inulin products. Full article

This study develops a multi-stage, Arrhenius-type reaction rate model for exothermic heat release during thermal runaway (TR) that depends on the local active material temperature, T_Cell, and the remaining reactant fraction, Y. Model parameters are identified from an accelerating rate calorimetry (ARC) test on an NMC721 pouch cell. Validation across other cell formats (cylindric and prismatic) and cathode chemistries (LCO, LMO, NCA, LFP) is left for future work. Model performance is evaluated in a 3D CFD (AVL FIRE™ M 2021.2) representation of the ARC assembly and benchmarked against Gaussian and polynomial one-step TR formulations that depend solely on T_Cell. The three TR models are further applied to a generic 4S4P pouch cell module under stagnant and actively cooled conditions to assess thermal propagation. In the ARC test, the Arrhenius-type model shows improved agreement with measured cell skin temperatures for the NMC721 cell; in the 4S4P module, it exhibits a trend toward higher thermal propagation rates relative to the Gaussian and polynomial models. Full article

Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10⁻¹¹ K m⁻²; AdvT < −1 × 10⁻⁴ K s⁻¹), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences. Full article

We develop algorithms for computing permutation polynomials (PPs) using normalization, so-called F-maps and G-maps, and the Hermite criterion. This allows for a more efficient computation of PPs for larger degrees and for larger finite fields. We use this to improve some lower bounds for $M(n,D)$, the maximum number of permutations on n symbols with a pairwise Hamming distance of D. Full article

High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors’ rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to <2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments. Full article

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

Let ${\chi }_{n-1}\left(\sigma \right)$ denote the irreducible character of the symmetric group $S_n$ corresponding to the partition $(n-1,1)$. For an $n\times n$ matrix $M=\left(m_{i,j}\right)$, we denote its $(n-1)$-th immanant by $d_{n-1}\left(M\right)$. Let G be a simple connected graph and let $L\left(G\right)$ and $Q\left(G\right)$ denote the Laplacian matrix and the signless Laplacian matrix of G, respectively. The $(n-1)$-th Laplacian (respectively, signless Laplacian) immanantal polynomial of G is defined as $d_{n-1}(xI-L\left(G\right))$ (respectively, $d_{n-1}(xI-Q\left(G\right))$). In this paper, we partially resolve Chan’s open problem by establishing that the broom graph minimizes $d_{n-1}\left(L\left(T\right)\right)$ among all trees with given diameter. Furthermore, we give combinatorial expressions for the first five coefficients of the $(n-1)$-th Laplacian immanantal polynomial $d_{n-1}(xI-L\left(G\right))$. We also investigate the characterizing properties of this polynomial and present several graphs that are uniquely determined by it. Additionally, for the $(n-1)$-th signless Laplacian immanantal polynomial $d_{n-1}(xI-Q\left(G\right))$, we show that the multiplicity of root 1 is bounded below by the star degree of G. Full article

Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R² values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R² of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R² of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping. Full article

Extreme rainfall events are frequently associated with regions of complex topography, where terrain-induced convergence and uplift enhance storm development. Understanding the interaction between surface relief and atmospheric dynamics is essential for improving severe weather forecasting and hazard mitigation. Storm “Daniel”, which affected Greece from 4–7 September 2023, produced extreme rainfall and widespread flooding in the Thessaly region—a landscape characterized by significant elevation gradients. This study investigates the spatial relationship between lightning activity and terrain elevation, aiming to assess whether deep convection was preferentially triggered over mountainous regions or followed specific orographic patterns. High-resolution elevation data (SRTM 1 Arc-Second Global DEM) were used to calculate the mean elevation around each lightning strike across four spatial scales (2 km, 5 km, 10 km, and 20 km). Statistical analysis, including correlation coefficients and third-degree polynomial regression, revealed a non-linear relationship, with a distinct peak in lightning frequency at mid-elevations (~200–400 m). These findings suggest that topographic features at local scales can significantly modulate convective initiation, likely due to a combination of mechanical uplift and favorable thermodynamic conditions. The study integrates geospatial techniques and statistical modeling to provide quantitative insights into how terrain influences the formation, location, and intensity of thunderstorms during high-impact weather events. Full article

The power-law time plotting for reliability prediction needs to be reexamined. Until now, most degradation plots in microelectronics reliability analysis assume that the data follow a power-law change in time. The plot is the change in a measured parameter versus the log of time, based on the principle that one can calculate exactly the initial indicator value, S₀, and from that, extrapolate any change in that parameter, ΔS(t), as a power-law with time, t^1/m. The normalized change, ΔS(t)/S₀, relies heavily on a precise value for S₀ such that the calculated power-law exponent, m, may be exaggerated such that extrapolated time-to-fail calculations will be optimistic, even by many orders of magnitude. Also, the extrapolated lifetime may be pessimistic, also by orders of magnitude in time. We show that by transforming the x-axis as the time to a power of 1/m, choosing m by setting the second order of a polynomial curve fit to zero, a more accurate prediction can be achieved with a realistic time to fail given the accelerated testing conditions. We also show how to determine what the correct power of time is using a linear fit to a second-order polynomial. The plotting principles presented here are independent of any physics, rather an empirical focus on how to plot the data according to a power-law in time assumption. Full article

This study investigates the behavior of large-scale wind turbines operating under high wind speed conditions. A particular emphasis is placed on power output limitations and the dynamic adjustment of rotor blade pitch angles to ensure system stability and prevent structural or operational damage. The novelty of this work lies in integrating real operational data with simplified empirical models, C_P(ω) and P_WT(β, V), to identify an energy-efficient operating zone that minimizes curtailment losses. Using experimental data from a 2.5 MW wind turbine located in the Dobrogea region of Romania, the power curves and mechanical behavior under variable pitch control were analyzed. At a wind speed of 16.5 m/s, the theoretical available power exceeded 12 MW, while the measured output was curtailed to 2.52 MW, corresponding to an ≈80% loss due to pitch regulation. The recalculated power coefficient C_P decreased from ≈0.48 at V = 10 m/s to ≈0.28 at V = 16.5 m/s. Polynomial fitting achieved R² = 0.982 and RMSE = 0.014, ensuring accurate representation of experimental data. Results demonstrate significant losses in extractable wind power when the turbine is operated in a curtailed mode due to pitch regulation. Strategies for maintaining maximum power point (MPP) operation are discussed, along with potential implications of coupling turbines with energy storage systems to reduce curtailment effects. The findings contribute to improved wind turbine control strategies in variable and extreme wind environments. The theoretical models developed in this study were validated using real-world data recorded from a GEWE-B2.5-100 wind turbine located in Dobrogea, Romania. Full article

In the present work, we define certain families, ${\mathbb{M}}_{\Sigma }\left(\mu ,{\rm Y},\gimel ,q; x\right)$ and ${\mathbb{N}}_{\Sigma }\left(\mu ,{\rm Y},\gimel ,q; x\right)$, of normalized holomorphic and bi-univalent functions associated with Bazilevič functions and $\gimel$-pseudo functions involving the $q$-Bernoulli polynomial, which is defined by the symmetric nature of quantum calculus in the open unit disk $U$. We determine the upper bounds for the initial symmetry Taylor–Maclaurin coefficients and the Fekete–Szegö-type inequalities of functions in the families we have introduced here. In addition, we indicate certain special cases and consequences for our results. Full article

This paper offers a lattice-based digital signature construction, optimized for the provision of post-quantum security in resource-constrained environments, such as Internet of Things (IoT) devices. The offered scheme is built upon structured hardness assumptions, defined over polynomial rings that exhibit inherent algebraic symmetry. By exploiting the cyclic properties of these ring structures and implementing efficient Number Theoretic Transforms (NTTs), the construction achieves compact signatures that are under 3 KB and a runtime feasibility that uses less than 10 KB of RAM. The signature generation process incorporates balanced rejection sampling and carefully designed polynomial encodings that preserve structural regularity and computational efficiency. The security of the offered scheme is proven. The benchmark results generated from using the ARM Cortex-M4 platform demonstrate its practical usability. This study highlights how symmetric algebraic frameworks based on lattice-based cryptography can be leveraged to achieve both theoretical security and real-world performance in the post-quantum era. Full article

This study investigates the mechanical behavior of water-washable photosensitive resins used in masked stereolithography (mSLA) 3D printing, evaluating the effect of post-curing time (0, 5, 10, 30, and 60 min) and printing orientation (Flat [XY], Vertical [Z], and On-edge [XZ]) on the material characteristics. Specimens were manufactured according to ISO 527-2 type 1B and ISO 178 standards for tensile and bending tests, respectively. A Matlab algorithm was developed to automate the processing of experimental data. This tool enabled the extraction of parameters to fit distinct mathematical models for the elastic (linear) and nonlinear (polynomial) regimes, allowing the material response to be characterized at different curing times and print orientations. These models were implemented in Ansys Workbench for comparison with experimental results. The results show that increasing the post-curing time from 0 to 60 min raises the elastic modulus from 964.5 to 1892.4 MPa in the Flat [XY] orientation and from 774 to 1661.2 MPa in the Vertical [Z] orientation for tensile testing. In bending testing, the Flat [XY] orientation presented the best mechanical properties, while the Vertical [Z] and On-edge [XZ] orientations showed similar behavior. The numerical simulations adequately reproduced the experimental results, validating the developed constitutive models. Finally, a stress–strain correlation model is presented that enables estimation for any post-curing time between 0 and 60 min. This study provides essential data for optimizing 3D printing processes and developing structural applications with photopolymer resins. Full article