Military University of Technology

This paper presents the Tykhonov well-posedness of variational–hemivariational inequality with history-dependent operators. Some theorems are deduced to illustrate the continuous dependence of the solution with respect to the data. Next, concrete example of the model from contact mechanics is presented for which the tools presented in this paper can be applied.

Trends of essential climate variables are often estimated from climate data records to quantify changes in the Earth system. An understanding of the uncertainty in a trend is essential for accurately determining the significance of a trend and attributing its causes. Despite this importance, trend-uncertainty estimates rarely account for all known sources of uncertainty. Common approaches neglect measurement-system instability or neglect the impact of natural variability on trend uncertainty. Such neglect can result in over-confidence in trend estimates. This study addresses trend-uncertainty assessment, particularly the need to account for the combined effects of measurement instability and natural variability on the trend uncertainty. The study presents a novel, unified framework for trend estimation that combines available measurement uncertainty information with empirical modelling of natural climate variability to achieve a more accurate uncertainty estimate. The framework is demonstrated for a time series of global mean sea level observations, obtaining more realistic trend-uncertainty values. The framework is applicable to most other climate data records. Adopting this approach will enhance confidence in climate change analysis through more accurate trend-uncertainty assessment in climate studies.

The growing demand for digital content protection has significantly increased the importance of image watermarking, particularly in light of the rising vulnerability of multimedia content to unauthorized modifications. In recent years, research has increasingly focused on leveraging deep learning architectures to enhance watermarking performance, addressing challenges related to transparency, robustness, and payload capacity. Numerous deep learning-based watermarking methods have demonstrated superior effectiveness compared to traditional approaches, particularly those based on Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformers, and diffusion models. This paper presents a comprehensive survey of recent developments in both conventional and deep learning-based image watermarking techniques. While traditional methods remain prevalent, deep learning approaches offer notable improvements in embedding and extraction efficiency, particularly when facing complex attacks, including those generated by advanced AI models. Applications in areas such as deepfake detection, cybersecurity, and Internet of Things (IoT) systems highlight the practical significance of these advancements. Despite substantial progress, challenges remain in achieving an optimal balance between invisibility, robustness, and capacity, particularly in high-resolution and real-time scenarios. This study concludes by outlining future research directions toward develop robust, scalable, and efficient deep learning-based watermarking systems capable of addressing emerging threats in digital media environments.

The search for innovative solutions in the field of construction materials used in aircraft manufacturing has led to the development of composite materials, particularly CFRP polymer composites. Composite airframe components, which are required to have high strength, are joined using mechanical fasteners. Considering that the composite consists of a polymer matrix, which is a material susceptible to rheological phenomena occurring rapidly at elevated temperature, there is a high probability of significant changes in the strength and performance properties. Coupled thermal and mechanical loads on composite material joints occur in everyday aircraft operation. Experimental tests were conducted using a quasi-isotropic CFRP on an epoxy resin matrix with aerospace certification. The assessment of changes in the strength parameters of the material itself showed a decrease of approx. 40% in its short-term strength at 80 °C compared to the ambient temperature and a decrease in the load-bearing capacity of single-lap bolted joints of over 25%. Even more rapid changes were observed when assessing the fatigue life of the joints assessed at ambient and elevated temperature. In addition, the actual glass transition temperature of the resin was determined using the DSC technique. Analysis of the damage mechanisms showed that at 80 °C, the main degradation mechanisms of the material are accelerated creep processes of the CFRP and softening of the matrix, increasing its susceptibility to damage in the joint area.

Non-graphitizable carbonaceous materials containing iron and sulfur are subjected to pyrolysis at 1150, 1300, and 1450 degrees C under a dynamic vacuum. Three batches of samples are prepared with initial iron-to-sulfur molar ratios (in the starting mixture of reagents) of 1, 10, and 100. After the vacuum pyrolysis, iron-based phases are removed from the resulting carbon materials by high-temperature heat treatment with Cl2, followed by H2. This research examines how the initial sulfur content in the carbon-rich carbon-iron-sulfur (C-Fe-S) ternary system influences the graphitization process within a moderate temperature range, particularly focusing on the effectiveness of the catalytic graphitization. A key observation is that the system with the highest sulfur content exhibits the greatest extent of graphitization. In contrast, the system with the lowest sulfur content shows the poorest conversion yield to the partly graphitized carbon phase. This study provides evidence and elucidates why sulfur-rich C-Fe-S mixtures produce greater amounts of graphitized phases than S-deficient mixtures, irrespective of the vacuum pyrolysis temperature.