Software and technological complex of identification of sea vessels based on the use of radar space images Sentinel 1

The paper considers the problem of using images from SAR satellites for the identification of seagoing vessels. It describes the main functions of software and technological complex of the automated monitoring. The system is operated with utilizing space images of SAR satellites Sentinel 1A (B). The algorithmic part, which implements the detection on the sea surface the marks associated with ships, is described in details. To reduce the impact of speckle-noise, the image is pre-processed with the improved Lee-filter. Further processing lies in using an adaptive threshold algorithm that provides detection for each local background fragment of the image the unusually bright pixels, at the same time the algorithm provides a constant probability of error. By solving a nonlinear equation, for each position of the background window the algorithm finds the threshold brightness value and then all pixels above this value are considered vessels. In advance the evaluation of parameters of statistical distribution of pixels’ brightness is performed for each position of the background window. K-mean is used for such distribution. The selected bright pixels are combined into compact groups and their size and coordinates are being determined. The obtained results are compared with the data of the AIS, Automatic Identification System of ships, and the results are displayed on a cartographic basis.

Analysis of Structures’ Orientations in Archaeoastronomy

In the scope of archaeoastronomy, the analysis of a large number of structures through the frequency histograms for their azimuths and declinations can identify singular patterns of orientation. Conclusions often rely on qualitative assessments. Quantitative assessments have been proposed by using as null hypothesis a pure random distribution of azimuths over the 360º horizon. In some cases, such as orientation of Christian churches, the histograms or spectra are composite, with peaks overlapping a continuous and not uniform background. This paper presents a methodology for assessing the statistical significance of the net area of a peak in the histogram in relation to the local background level. The spectra use Normal kernel functions. The background contribution is estimated from the area of the trapezoidal polygon under the peak, and it is interpreted as the probability parameter for a Binomial distribution. The methodology is illustrated with a real case study which includes the azimuth and declination histograms for a set of churches from southern Spain dedicated to the Virgin of the Assumption. The method is more restrictive than previous approaches.

Spatiotemporal Distribution Patterns and Local Driving Factors of Regional Development in Java

Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development level should, theoretically, vary over space. Therefore, in this study, we aim to investigate the local factors that influence the development level of Java’s regions. We used the spatiotemporal pattern analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR), utilizing the regional development index as the predicted variable, and the social level, economy, infrastructure, land use, and environmental barriers as predictors. As per our results, it was found that the level of development in Java has improved over the past two decades. Metropolitan areas continued to lead this improvement. All the predictors that we examined significantly affected regional development. However, the spatial pattern of the local regression coefficients of Human Development Index (HDI), landslide, paddy conversion, and crime shifted due to changes in the spatial concentration of development activities.

Quantum Dot-like Plasmonic Modes in Twisted Bilayer Graphene Supercells

We derive a material-realistic real-space many-body Hamiltonian for twisted bilayer graphene from first principles, including both single-particle hopping terms for $p_z$ electrons and their long-range Coulomb interaction. By disentangling low- and high-energy subspaces of the electronic dispersion, we are able to utilize state-of-the-art constrained Random Phase Approximation calculations to reliably describe the non-local background screening from the high-energy $s$, $p_x$, and $p_y$ electron states which we find to be independent of the bilayer stacking and thus of the twisting angle. The twist-dependent low-energy screening from $p_z$ states is subsequently added to obtain a full screening model. We use this modeling scheme to study plasmons in electron-doped twisted bilayer graphene supercells. We find that the finite system size yields discretized plasmonic levels, which are controlled by the system size, doping level, and twisting angle. This tunability together with atomic-like charge distributions of some of the excitations renders these plasmonic excitations remarkably similar to the electronic states in electronic quantum dots. To emphasize this analogy in the following we refer to these supercells as \emph{plasmonic quantum dots}. Based on a careful comparison to pristine AB-stacked bilayer graphene plasmons, we show that two kinds of plasmonic excitations arise, which differ in their layer polarization. Depending on this layer polarization the resulting plasmonic quantum dot states are either significantly or barely dependent on the twisting angle. Due to their tunability and their coupling to light, these plasmonic quantum dots form a versatile and promising platform for tailored light-matter interactions.

Biomimetic chameleon soft robot with artificial crypsis and disruptive coloration skin

Development of an artificial camouflage at a complete device level remains a vastly challenging task, especially under the aim of achieving more advanced and natural camouflage characteristics via high-resolution camouflage patterns. Our strategy is to integrate a thermochromic liquid crystal layer with the vertically stacked, patterned silver nanowire heaters in a multilayer structure to overcome the limitations of the conventional lateral pixelated scheme through the superposition of the heater-induced temperature profiles. At the same time, the weaknesses of thermochromic camouflage schemes are resolved in this study by utilizing the temperature-dependent resistance of the silver nanowire network as the process variable of the active control system. Combined with the active control system and sensing units, the complete device chameleon model successfully retrieves the local background color and matches its surface color instantaneously with natural transition characteristics to be a competent option for a next-generation artificial camouflage.

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon in human exposure profiling and mobile air quality monitoring. Due to it being relatively new on the market, the field lacks a refined assessment of the instrument's performance under various settings and data post-processing approaches. This study assessed the mobile real-time performance of the MA200 to determine a suitable noise reduction algorithm in an urban area, Augsburg, Germany. Noise reduction and negative value mitigation were explored via different data post-processing methods (i.e., local polynomial regression (LPR), optimized noise reduction averaging (ONA), and centred moving average (CMA)) under common sampling interval times (i.e., 5, 10, and 30 s). After noise reduction, the treated data were evaluated and compared by (1) the amount of useful information attributed to retention of microenvironmental characteristics, (2) the relative number of negative values remaining, (3) the reduction and retention of peak samples, and (4) the amount of useful signal retained after correction for local background conditions. Our results identify CMA as a useful tool for isolating the central trends of raw black carbon concentration data in real time while reducing nonsensical negative values and the occurrence and magnitudes of peak samples that affect visual assessment of the data without substantially affecting bias. Correction for local background concentrations improved the CMA treatment by bringing nuanced microenvironmental changes into view. This analysis employs a number of different post-processing methods for black carbon data, providing comparative insights for researchers looking for black carbon data smoothing approaches, specifically in a mobile monitoring framework and data collected using the microAeth® series of Aethalometer.

BIOGEOCHEMICAL EVALUATION OF THE VEGETATION STATUS OF REINDEER PASTURES OF THE NADYM DISTRICT OF THE YAMAL-NENETS AUTONOMOUS DISTRICT

The chemical elemental composition of the most widespread species of wood (Betula pubescens, Larix sibirica, Pinus sylvestris, Salix lanata), shrubs (Vaccinium vitis-idaea, Ledum palustust sl), herbs (Eriophorum angustifolium, Equisetum arvense) and lichens (Cladonia stellaris, Cladonia stygia). The concentrations of Cr, Co, Ni, Cu, Zn, Ga, As, Y, V, Na, Mg, Si, P, S, K, Ca, Ti, Mn, Fe, S obtained using the method of retgenofluorescence energy dispersion analysis. The features of the local biogeochemical background of plants are revealed and their geochemical specialization is studied. The greatest difference in the level of accumulation between different plant species was found for Ni, Zn, Ca, Mn, S, and Si. The analysis of the accumulation coefficients of chemical elements in plants relative to the local background level is carried out. Statistical significant differences in the elements accumulation by plants in different bioclimatic zones were revealed for Cu, Fe, Co, Cr, As, Mg, V, Y. The studied plants according to environmental safety criteria and the content of normalized micro- and macrocells mainly meet the requirements for fodder plants. The exception is the low content of nutrients Co, Na and K. For the prevention of animal diseases associated with a deficiency of essential elements, it is necessary to optimize the diet of deer by enriching feed with biologically active substances and macro- and microelements.