Grzegorz Kozieł

This article presents a new method for measuring the angle of rotation of the light polarisation plane (LPPR) using a dual-channel power-based approach with tilted Bragg gratings (TFBG) and Bragg gratings (FBG), and optical power measurement. Unlike previous solutions based on the analysis of the...

The objective of this study was to identify vibration frequency bands that most effectively differentiate vehicle operating conditions in real driving. Vehicle vibration signals acquired along three measurement axes and as a resultant signal were analyzed together with synchronized CAN bus parameters. The vibration signals were divided into 5-s segments and analyzed using the fast Fourier transform (FFT). Mean spectra and spectral variability were evaluated for different RPM classes. To quantitatively assess spectral differences, one-way analysis of variance (ANOVA) with False Discovery Rate correction and frequency-dependent Cohen’s d effect-size analysis were applied. The results demonstrated that engine operating conditions significantly influence the vibration spectrum across a broad frequency range, with the most diagnostically relevant changes occurring within the 8–12 Hz and 25–35 Hz bands. The strongest spectral separation was observed during the transition from idle to low-RPM operation, yielding very large effect sizes. An additional assessment of the usefulness of the selected descriptors was conducted using a Random Forest classifier, accounting for group-based temporal partitioning and five-fold cross-validation. The classifier achieved an accuracy of 88.28% and a balanced accuracy of 74.51% on a separate test set. The results indicate high recognition rates for the idle state and greater difficulty in distinguishing between low and medium engine speeds. Global spectral descriptors demonstrated the greatest predictive power, whereas the energies of selected narrow frequency bands did not provide significant additional information to the classification model.

This study presents a comparative analysis of two concurrency paradigms in modern Java applications: reactive programming and Java virtual threads. Traditional operating system threads consume excessive resources under high concurrency, which has driven the adoption of non-blocking, event-driven frameworks such as Spring WebFlux, as well as lightweight JVM-managed virtual threads introduced as part of Project Loom in Java 21. While prior research highlights the benefits of both approaches, direct empirical comparisons under controlled conditions remain limited. To address this gap, this study evaluates the performance, scalability, and resource utilization of both paradigms through controlled experiments conducted on the Google Cloud Platform. The analysis investigates their behaviour under varying levels of concurrent load and examines the impact of different CPU and memory configurations on system efficiency. The results aim to support developers and architects in selecting appropriate concurrency models for Java applications.

This paper presents the results of studies on the synthesis of nitrogen oxides (NOx) in a three-phase GlidArc plasma reactor operating at atmospheric pressure. The objective was to determine the influence of electrical power and gas flow rate on NOx concentration, process productivity, and energy efficiency. The analysis was performed over a wide range of power levels and air flow rates, using global energy efficiency (GEE), specific energy consumption (E), and specific energy input (SEI) as evaluation metrics. The results demonstrate that discharge intensification initially leads to an increase in NOx concentration; however, at higher power levels a deterioration of energy efficiency is observed due to increasing thermal losses and partial destruction of nitrogen oxides. Gas flow rate was also found to play a significant role by determining the gas residence time in the plasma zone and the degree of reactant conversion. Based on the analysis of the median GEE and E maps, a recommended operating window for the reactor was identified, within which high energy efficiency and high NOx productivity are simultaneously achieved. The obtained results provide a basis for further optimization and scaling of GlidArc technology in the context of decentralized nitrogen compound production.

This study presents a comparative analysis of five machine learning classification algorithms: support vector machine (SVM), multilayer perceptron (MLP), classification and regression tree (CART), k-nearest neighbors algorithm (K-NN), and naive Bayes classifier (NB) across four datasets from various domains. Using nested cross-validation, the research evaluated classifier performance on Heart Disease, German Credit, Spambase, and Online Shoppers Purchasing Intention datasets. Results demonstrated that no single classifier consistently outperformed others across all datasets and selection should be based on dataset characteristics and application requirements. Dataset characteristics emerged as the primary factor influencing performance, with class imbalance proving particularly problematic. Training efficiency analysis revealed that simpler algorithms can maintain competitive performance with lower computational costs.

This paper introduces a new method for designing inverted gradient multilayer optical fibres to generate flat-top laser beams with specific propagation features. Using numerical simulations in OptiFibre software, over 700 three-layer optical fibre configurations were modelled, and their output beam profiles were assessed based on beam flatness, edge steepness, and full width at half maximum (FWHM). A unique set of parameters describing the refractive index profile was proposed to improve the estimation of beam properties. The random forest algorithm was employed to predict beam characteristics based on fibre geometry, and a feature importance analysis identified key parameters that influence these characteristics. The model showed high prediction accuracy, particularly when the proposed descriptors were combined with layer refractive indices. Additionally, two multi-criteria optimisation methods – trade-off and weighted objectives – were used to find optimal fibre configurations that simultaneously maximise beam flatness, edge steepness, and FWHM. The findings offer practical guidelines for the inverse design of beam-shaping optical fibres and demonstrate the potential of machine learning to accelerate fibre design by reducing dependence on lengthy simulations.

Sensors using tilted fibre Bragg gratings (TFBG) are now widely used and intensively developed due to their high precision, interference immunity, small size and low price. One of the parameters that can be measured with them is the angle of light polarisation plane rotation (LPPR) propagating in...

Tilted fibre Bragg gratings (TFBG) are used as sensors in the measurement of many physical and chemical quantities. Measurement is performed indirectly by analysing the power of light transmitted at a specific wavelength or in a selected range of light wavelengths. The wavelengths to be used are...

Sensors using tilted fiber Bragg gratings (TFBGs) are of great interest for researchers because of their high precision, compact size and resilience in harsh environments. However, the precision and reliability of the measurements made with them often depend on the polarization orientation. It is, therefore, necessary to control the rotation angle of the light polarization plane. Existing methods of measuring such an angle of light propagating in an optical fiber offer low measurement precision, require a long calibration process involving a human operator, or allow measurements only in narrow ranges of the rotation angle. In this study, we introduce a method of measuring the polarization plane rotation angle of light propagating in an optical fiber using two parallel TFBGs rotated relative to each other. The proposed method offers fully automatic calibration without human intervention and a greatly simplified calculation method. Our findings indicate that our method outperforms other methods in precision and reliability of measurement. The proposed method allows for measurements in the 0°–180° range with a mean absolute error of 0.064°.