Institute of Electronics and Computer Science

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region.

Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability.

The development of bio-based polyurethane foams has become a key direction in polymer materials research, driven by the need to replace petrochemical raw materials with renewable alternatives. This study investigates the synthesis and characterization of open-cell polyurethane foams produced using mixed bio-polyols derived from radish seed oil and tall oil in various mass ratios. For comparison, reference foams based on a radish seed oil polyol, tall oil-based polyol and a petrochemical polyol were also prepared. The influence of the polyol composition on the foaming behavior, cell structure, apparent density, mechanical properties, and thermal conductivity of the resulting foams was analyzed.

There are various methods of determining the parameters of transmission lines. Some of them use the geometric dimensions of the line, others are based on modern synchronization measurements, and still others use recalculation. The more accurate and get-at-able methods are necessary in some cases, especially in tropical regions with warm humid climate and increased leakage power losses. Those losses, directly recalculated to line conductance parameter, fail to yield the adequate results compared to those determined by ABCD coefficients. The proposed method of transmission line parameter determination takes into account the losses measured in the sending ends of the idle line.

This study evaluates the impact of different flame retardants on the mechanical and thermal properties of rigid polyisocyanurate (PIR) foams, focusing on formulations with isocyanate indexes of 335 and 400. The flame retardants tested include triethyl phosphate (TEP), ammonium polyphosphate (APP), aluminium hydroxide (Al(OH)3), and a combination of APP and Al(OH)3. FOAMAT® was used to analyse the foaming kinetics, while further tests assessed density, thermal conductivity, and compression strength. TEP, a liquid flame retardant, was found to reduce peak heat release rate (pHRR) and total heat release significantly, outperforming solid flame retardants. Although solid flame retardants like APP slightly increase start times and gel times due to their non-reactive, filler role, they increase the foam’s density and somewhat limit the effectiveness in reducing flammability. The uneven dispersion and lower compatibility of solid additives may lead to suboptimal improvements in fire resistance. APP displayed dual-phase decomposition, aiding char formation to a degree. Overall, TEP proved most effective in enhancing PIR foam’s fire resistance, demonstrating the advantage of liquid over solid flame retardants in achieving uniform distribution and better integration with the foam matrix, thus optimising thermal insulation and mechanical performance.

The study focuses on creating symbol-shaped flame designs by optimizing flame dynamics, using machine learning techniques integrated with advanced combustion modelling. This research addresses the limitations of traditional pyrotechnic systems, which lack precise control over flame shapes, by introducing a method that uses differentiable physics simulations for shaping flames. The method uses the optimization framework PhiFlow and includes radiative heat loss and chemical kinetics for improved accuracy. Machine learning is used not only to train and adapt the model but also to determine the physical parameters that correspond to the actual observed behaviour of the flames. However, it is also important to mathematically and analytically evaluate the results obtained to ensure their accuracy and relevance. Results demonstrate the successful formation of custom flame shapes in a 2D simulated environment, with promising alignment to real-world flame stage projector behaviour. This approach enables control over flame shapes and dynamics, expanding creative possibilities in stage design and interactive art while proving valuable for applications requiring detailed control of dynamic systems, such as pyrotechnics, performance art, and educational demonstrations.

Accurate classification of EEG signals is crucial for applications such as brain-computer interfaces (BCI). In this study, we examined the effectiveness of long short-term memory (LSTM) networks for classifying EEG signals recorded during six different motor imagery tasks. The dataset included recordings from 16 electrodes and consisted of actions such as forearm supination, elbow flexion, forearm pronation, hand closing, hand opening, and elbow extension. Our model achieved an average F1 score of 0.87 on the real-world EEG data. To evaluate the feasibility of training the model on similar synthetic data, we generated synthetic EEG signals for hand opening and closing tasks using EEG-GAN. When tested on synthetic data, the LSTM model trained solely on real data achieved the average F1 score of 0.76. These results highlight the potential of generative models for augmenting EEG datasets and the robustness of LSTM-based classifiers.