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Abstract

An important step in solving the problem of classification and segmentation of 2D images is the extraction of local geometric features. Convolutional neural networks were widely used in recent years to solve problems in this field. Typically, the neighborhood of each pixel in an image is used to collect local geometric information. A convolutional neural network is used to extract the underlying geometric features of the neighborhood. In this work, we propose a neural network based on descriptor concatenation for two well-known neural networks to solve the problem of extracting local geometric features of mammographic images. To improve the accuracy of mammogram classification, feature filtering is used based on the calculation of joint information. Results of computer simulation are presented to illustrate the performance of the proposed method.

Abstract—The main task of the study is to improve the performance of existing computer diagnostic systems using new methods for classification of benign and malignant tumors using digital mammograms. Methods and algorithms for systems of computer diagnostics are being actively developed using deep neural networks. To achieve better results on the selected data set, we transform the data using autoencoders to obtain features with low intraclass and high interclass variance. The entire working cycle of the system consists of the following stages: extraction of features using a segmented part of the pathology, division of the data into two clusters, and feature transformations using linear discriminant analysis for minimization of intraclass variance and classification of pathologies. The results of this study show that the classification of pathologies using deep learning methods makes it possible to achieve high results.

Abstract—The paper addresses capacitated clustering problems: (a) basic capacitated clustering problem, (b) capacitated centered clustering problem, (c) multicapacity clustering problem, and (d) related problems. The paper is based on the combinatorial clustering viewpoint. A survey on the problems, solving approaches, and some applications are presented. The optimization models of the basic capacitated clustering problem and multicriteria capacitated clustering problem are considered. An application of capacitated clustering as the handover minimization problem in mobile wireless networks is briefly described. Numerical examples illustrate problems and applications.

Abstract—The rapid growth in the number of smart devices capable of exchanging data within a single network leads to the emergence of mechanisms that allow adapting data transmission technologies to the Internet of Things networks. One of them is the mechanism of the periodic restricted access window (PRAW) presented in the 802.11ah standard. A competent choice of parameters of the PRAW mechanism allows a large number of sensors to transmit data quickly and energy-efficiently, but the 802.11ah standard itself does not give recommendations on their choice. This article solves the following optimization problems: minimizing (a) the average delay, (b) the average energy consumption per transmitted packet when the average delay limit is met, and (c) the share of channel time consumed by the PRAW mechanism when the restrictions on both metrics are met. Based on the results of solving these problems, we give recommendations on the choice of PRAW parameters for different network loads determined by the intensity of packet generation and the number of stations.

Abstract—

Multi-User Multiple Input Multiple Output (MU-MIMO) technology allows you to increase the channel throughput. However, its efficiency is reduced by overhead induced by frequent channel sounding and transmission of channel feedback frames. This article examines the problems of channel state information (CSI) compression in Wi-Fi networks using MU-MIMO with channel aging. The research aims to experimentally test the effectiveness of MU-MIMO technology in real use cases, considering the channel sounding procedures and CSI feedback transmission. Using an experimental setup, the channel has been recorded to analyze its behavior and the evolution of the signal received power under different conditions. In addition, the limits of the applicability of the TGax MU-MIMO channel model in the WLAN Toolbox have been investigated by comparing it with experimental results. The findings of this study are particularly useful in optimizing MU-MIMO performance under channel time evolution and CSI compression.

Abstract—Reconfigurable intelligent surfaces (RISs) are a promising technology for increasing the information capacity and coverage of future wireless networks. Various available types of these devices consist of different elements that can be used for signal absorption, increasing the information capacity, and phase shift keying. This causes a lack of a single concept and misunderstanding of functionality of specific RISs. The aim of this study is to eliminate this gap by describing one of the most widely used RIS structures and its operating principles, which make it possible to formulate the main functionalities of the RIS.

Abstract—Hyperspectral analysis is a powerful tool in the precision agriculture arsenal that becomes increasingly accessible. The number of hyperspectral images obtained near the Earth surface is constantly growing. It is important to consistently use this data along with conventional data of multispectral monitoring. In this work, problem of harmonization of hyperspectral survey data obtained at the surface of the Earth and satellite multispectral monitoring data is investigated. The problem of spectral harmonization, which is insoluble in general case, is further complicated in this case by the heterogeneity of the available data. In this regard, a simplified formulation of the harmonization problem is considered, aimed at calculation of vegetation indices. A novel method has been developed that does not require pixelwise matching or calibration panels. The experimental part of the work shows that the proposed method allows significant compensation for shifts of the NDVI and WBI, observed in the absence of harmonization.

Abstract—Reconfigurable intelligent surfaces (RISs) are promising devices capable of increasing the information capacity and coverage of new and available wireless networks. By now, most of these devices have been presented in the form of prototypes that have no environmental protection and are not adapted for use in real communication systems. Meanwhile, a protective coating can significantly affect the characteristics of a RIS and reduce its efficiency. This study considers the effect of thickness, permittivity, and dissipation factor of the most common protective coating materials on the frequency and phase responses of a RIS unit cell (UC). Recommendations on choosing a material and its thickness and on correcting UC sizes at constant parameters of a protective coating are provided.

Abstract—Reconfigurable intelligent surface (RIS) is a promising technology that can increase the capacity and coverage of wireless networks. The effectiveness of a RIS is determined by its configuration, which can be made based on the information about location of the transceiver devices. In practice, there are two main types of RIS configurations: focusing the signal reflected from the RIS at the receiver location and redirecting the signal towards the receiver. Both types of RIS configuration become outdated in time due to changes in environmental parameters caused by the movement of transceiver devices and other objects in the surrounding environment. This paper examines outdating of RIS configurations made by focusing and redirection procedures in a system with spatially static transceiver devices. The paper shows that the difference in signal-to-noise ratio for the two types of configurations can reach up to 8 dB and has nonmonotonic features that can be explained by considering the near-field region of a RIS.

Abstract

To increase throughput of Wi-Fi networks, the IEEE 802.11be standard has introduced a multi-link operation feature that enables devices to transmit and receive data in multiple channels. The article studies an adaptive waiting control algorithm for channel access by multi-link devices (MLDs) incapable of simultaneous transmission and reception (NSTR) at different channels. The algorithm features accounting for the channel capacities, the number of single-link devices in the network, and the traffic intensity in the channels. The simulation shows high performance of the proposed algorithm.

Nature Geoscience, Published online: 29 July 2024; doi:10.1038/s41561-024-01516-z

Author Correction: Uncertain response of ocean biological carbon export in a changing world

Abstract

Noble gases are accreted to the giant planets as part of the gas component of the planet-forming disk. While heavier noble gases can separate from the evolution of the hydrogen-rich gas, helium is thought to remain at the protosolar H/He ratio \(Y_{\mathrm{proto}}\sim 0.27\) –0.28. However, spacecraft observations revealed a depletion in helium in the atmospheres of Jupiter, Saturn, and Uranus. For the gas giants, this is commonly seen as indication of H/He phase separation at greater depths. Here, we apply predictions of the H/He phase diagram and three H/He-EOS to compute the atmospheric helium mass abundance \(Y_{\mathrm{atm}}\) as a result of H/He phase separation. We obtain a strong depletion \(Y_{\mathrm{atm}}<0.1\) for the ice giants if they are adiabatic. Introducing a thermal boundary layer at the Z-poor/Z-rich compositional transition with a temperature increase of up to a few 1000 K, we obtain a weak depletion in Uranus as observed. Our results suggest dissimilar internal structures between Uranus and Neptune. An accurate in-situ determination of their atmospheric He/H ratio would help to constrain their internal structures. This is even more true for Saturn, where we find that any considered H/He phase diagram and H/He-EOS would be consistent with any observed value. However, some H/He-EOS and phase diagram combinations applied to both Jupiter and Saturn require an outer stably-stratified layer at least in one of them.

Abstract

Accurate orientation of geodetic instruments is fundamental for understanding deformation processes within the Earth's interior. Misalignment can lead to significant errors in data interpretation, affecting various geophysical applications. However, accurate alignment of standalone instruments like seismometers, strainmeters and tiltmeters remains a challenge in field geodesy. While numerous seismic-wave-based orientation methods have been successfully applied to seismometers, they are often inapplicable to tiltmeters due to their high-frequency filtering behavior and the requirement for a neighboring, pre-oriented instrument. In response to these challenges, we propose a novel orientation calibration method for borehole tiltmeters based on maximizing the correlation between recorded tilt data and theoretical tides by adjusting azimuthal angles. Our study encompasses two kinds of borehole tiltmeters and four datasets from three different field sites. Using solid and ocean tides modeling together with local topography and cavity disturbances, we obtain coefficient correlations ranging between 0.831 and 0.963, and 95% confidence intervals of azimuthal angles below 3.3°. The correlation-based method demonstrates robustness across various tidal-signal extraction techniques, including different averaging window sizes and band-pass filters. Moreover, it yields azimuthal results in agreement with direct compass measurements for known orientations, while exhibiting a moderate sensitivity to factors such as ocean tides and site-specific topography for the studied cases. This method appears to be advantageous when direct measurements are either unavailable or challenging, and emerges as an accurate tool for determining borehole tiltmeter orientation. Its potential applicability may extend beyond tiltmeters to other instruments that can also record tidal phenomena, such as strainmeters and broadband seismometers. Additionally, its utility could be extended to environments like the seafloor, in order to refine the precision of azimuthal angle estimation and simplify methods for azimuthal angle determination.

Abstract

The global navigation satellite system’s signals are frequently obstructed in complex environments, and the carrier phase is prone to experiencing large cycle slips; hence, phase prediction becomes necessary. However, the current phase prediction theory presents flaws in the prediction preparation stage. The existing prediction methods merely resort to the error threshold to determine the start time of prediction, which may give rise to significant initial prediction errors. We tackle this problem and its corresponding solution for deeply coupled systems: the multistage threshold discrimination method. This method analyzes the phase error information of the cache in the prediction preparation stage to accurately determine the start time of prediction and minimize the initial prediction error. The performance of the proposed method is evaluated with static and dynamic data. In predicting for 20 s under static conditions and 10 s under dynamic conditions, the predicted pass rates are 92.6% and 52.4%, respectively, 10.4% and 11.0% higher than those of the original method. The average prediction error is reduced by 36.6% and 33.9% under static and dynamic conditions. In the scenario where the signal is interrupted multiple times, the root mean square of positioning error is reduced by 30.2%, 53.0%, and 58.7% in the east, north, and up directions. These results suggest that the proposed method is effective and constitutes a complement to the phase prediction theory.

Nature Geoscience, Published online: 29 July 2024; doi:10.1038/s41561-024-01503-4

Author Correction: Regional stratification at the top of Earth’s core due to core–mantle boundary heat flux variations

This paper describes the performance of a seabed benchmark system for measuring ground deformation associated with underwater volcanism. The system is installed in the center of the Aira Caldera, Japan, on the...

The southern Kuril Trench is one of the most seismically active regions in the world. In this study, marine surveys and observations were performed to construct fault models for possible outer-rise earthquakes...

Abstract

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m−2 (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m−2 dec−1 (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.

Abstract

Convective dynamics in a supercell thunderstorm, a volcanic eruption, and two pyrocumulonimbus (pyroCb) events are compared by computing cloud-top divergence (CTD) with an optical flow technique called Deepflow. Visible 0.64-μm imagery sequences from Geostationary Operational Environmental Satellites (GOES)-R series Advanced Baseline Imager (ABI) are used as input into the optical flow algorithm. CTD is computed after post-processing of the retrieved motions. Analysis is performed on specific image times, as well as the full time series of each case. Multiple CTD-based parameters, such as the maximum and the two-dimensional area exceeding a specified CTD threshold, are examined along with the optical flow-retrieved wind speed. CTD is shown to accurately and quantitatively represent the behavior and magnitude of different deep convective phenomena, including distinguishing between convective pulses within each individual event. CTD captures updraft intensification as well as differences in convective activity between two pyroCb events and individual updraft pulses occurring within a single pyroCb event. Finally, the characteristics of high-altitude smoke plumes injected by two separate pyroCb pulses are linked to CTD using ultraviolet aerosol index and satellite imagery. Optical flow-derived parameters can therefore be applied to individual pyroCbs in real-time, with potential to characterize pyroCb smoke source inputs for downstream smoke modeling applications and to facilitate future tools supporting air quality modeling and firefighting efforts.

Abstract

Iron (Fe) has profound impacts on Earth's ecosystem and global biogeochemical cycles. Fe deposited onto glacier surfaces reduces snow and ice albedo, thereby accelerating glacier melting, and supplying downstream ecosystems with dissolved Fe. However, the origins of atmospheric Fe deposition in glacier regions of western China remain unclear. This study presents novel insights into Fe isotopic composition (refer to δ56Fe) and origins, gained from geochemical analysis of large-scale cryoconite samples collected from glaciers in western China, which encompass the Tibetan Plateau (TP) and the Tianshan Mountains. Results showed that cryoconite δ56Fe ranged from −1.06 ± 0.07‰ to 0.33 ± 0.04‰, regardless of their concentration. Moreover, anomalous δ56Fe values deviating significantly from the upper continental crust values (with an average of 0.09‰) were detected, indicating a significant impact of anthropogenic Fe materials on the investigated glaciers. This impact was particularly prominent in the margin regions of the TP and its surroundings, but was less apparent in the interior and southern of the plateau. Using MixSIAR isotope mixing model, we determined that coal combustion and other anthropogenic combustion sources (such as liquid fuel combustion and steel smelting) contributed to cryoconite Fe in the range of 6.9%–43.1% and 0.8%–23.4%, respectively. Among these, coal combustion was the predominant anthropogenic source of cryoconite Fe in western China's glaciers. Compared with other sink areas in the Northern Hemisphere, glaciers in western China are obviously affected by anthropogenically sourced Fe. This study has significant implications for understanding glacier-fed downstream ecosystems and the regional biogeochemical cycle.