Abstract Total solar eclipses (TSEs) provide a unique opportunity to observe the large-scale solar corona. The solar wind plays an important role in forming the large-scale coronal structure, and magnetohydrodynamic (MHD) simulations are used to reproduce it for further studying coronal mass ejections (CMEs). We conduct a data-constrained MHD simulation of the global solar corona including solar wind effects of the 2024 April 8 TSE with observed magnetograms using the message-passing interface adaptive mesh refinement versatile advection code (MPI-AMRVAC) within 2.5 R ⊙ . This TSE happened within the solar maximum, hence the global corona was highly structured. Our MHD simulation includes the energy equation with a reduced polytropic index γ = 1.05. We compare the global magnetic field for multiple magnetograms and use synchronic frames from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager to initialize the magnetic field configuration from a magnetofrictionally equilibrium solution, called the outflow field. We detail the initial and boundary conditions employed to time-advance the full set of ideal MHD equations such that the global corona is relaxed to a steady state. The magnetic field, the velocity field, and distributions of the density and thermal pressure are successfully reproduced. We demonstrate direct comparisons with TSE images in white light and Fe XIV emission augmented with quasi-separatrix layers, the integrated current density, and the synthetic white-light radiation, and find a good agreement between simulations and observations. This provides a fundamental background for future simulations to study the triggering and acceleration mechanisms of CMEs under solar wind effects.

The serial concatenation of Bose-Chaudhuri-Hocquenghen (BCH) and Low-Density-Parity-Check (LDPC) codes results in an efficient channel coding framework which is used in the next generation of advanced communication, such as video broadcasting and satellite applications, for transmitting data on non-ideal communication channels with constrained bandwidth. The Next Generation Communication System's high-speed necessities and adaptive coding approach create significant design issues for an effective codec hardware implementation. This manuscript proposes an efficient Forward Error Correction (FEC) Transceiver (TR) architecture by concatenating the BCH and LDPC codes on the hardware platform to overcome the design issues in the next-generation communication system. The FEC TR system utilizes <1 % chip area, operates at 343.5 MHz, and obtains a Throughput of 1.202 Gbps with a Bit Error Rate (BER) of 10-8 on the Artix-7 chip. The FEC transmitter and receiver use only 22.5 and 18 Clock Cycles (CC), obtaining the Throughput of 1.44 Gbps and 1.202 Gbps, respectively. Lastly, the FEC TR, BCH, and LDPC modules are compared with existing approaches, with enhanced improvement in the performance parameters.

Reversible Data Hiding in Encrypted Image via Block Classification and Adaptive Coding

The technique of reversible data hiding in encrypted images (RDH-EI) has experienced significant interest as it allows for precise extraction of embedded data without compromising the confidentiality of the original image. This research introduces a novel RDH-EI technique designed to accommodate multiple data hiders. To tackle this challenge, we propose a sophisticated RDH-EI method that integrates secret sharing Founded on the Learning With Errors (LWE) problem alongside adaptive coding strategies. On the content owner’s side, the original image is first distributed to multiple data hiders using a method Founded on the Learning With Errors (LWE) problem. Then, block permutation along with stream cipher encryption are performed to completely preserve the spatial correlation between image blocks. The proposed method benefits from the robust security provided by LWE. Initially, we examine the spread of the most significant bit planes to detect segments that are suitable for data embedding. Next, the data hider produces extra data and embeds errors within the encrypted image to guarantee precise image reconstruction. To increase data storage capacity, the most significant bits (MSB) The blocks suitable for embedding are adaptively compressed based on their frequency of occurrence. The extra data may be inserted into the (MSB) of the encrypted image, where it is combined with inverse Huffman codewords and supplementary auxiliary information. At the receiving side, the initial image can still be completely restored Losslessly, even if some shares happen to be damaged or missing, provided that a sufficient number of valid shares are available. Experimental findings demonstrate that the RDH-EI approach exceeds the performance of various cutting-edge methods, including those employing secret sharing (SS), in terms of embedding capacity.

Satellite communication has become a fundamental component of global information exchange. Advances in high-capacity geostationary satellites (HTS systems), clusters of low-orbitingsatellites (LEOconstellations), adaptive coding, digital beamforming, and AI-driven network management have created new possibilities for high-speed, low-latency communication systems. Misra et al. (2013) and Fourati and Alouini (2021) highlight how innovations in satellite architecture and modulation have significantly improved bandwidth efficiency. Meanwhile, integration with 5G and future 6G technology is reshaping global communication infrastructure, though challenges such as cybersecurity, regulation, and space debris remain serious concerns (Tarek et al., 2024; Abdelsalam et al., 2023). India’s experience demonstrates how satellite communication supports rural development, education, telemedicine, and national security (Manjunath et al., 2007; Annadurai, 2018). This report synthesizes findings from existing literature—including contributions by Ibim (2025), Saeed et al. (2021), Makam (2023), and Hashima et al. (2025)—to provide a comprehensive overview of current advancements, persistent challenges, and future prospects in satellite communication

Medical imaging has now become an everyday part of hospital care systems, thus helping doctors look inside the human body without having to perform any surgery. However, as imaging technologies like CT, MRI, and PET have shown advancements, the amount of data produced has been growing enormously. Such type of images are now larger, more detailed, and also much harder to manage. This scenario has created a clear and urgent need for smarter ways to process and analyze them quickly without losing crucial and fine details that matter most in diagnosis. In this work, a complete framework that combined the methods of image fusion, image compression, and texture-based feature extraction to identify whether the tissue regions are benign or malignant has been proposed. The approach has been built on our earlier research work where images from multiple modalities were fused using an Undecimated Discrete Wavelet Transform (UDWT) and compressed with Context-Based Adaptive Binary Arithmetic Coding (CABAC) without loss of image quality. After the compression process, we extract texture features using an improved Gray Level Co-occurrence Matrix (GLCM) model along with deep learning-based descriptors so as to capture the subtle variations in the tissue texture. Experiments that we have were carried out on medical image datasets show that proposed system is able to maintain the strong diagnostic quality and it has achieved an accuracy of 94.3% even after image compression. The results have highlighted its potential for faster, more reliable diagnosis in telemedicine and smart healthcare environments.

Source code plagiarism identificatio requires a system capable of identifying semantic similarity rather than mere textual resemblance. This study utilized a dataset of 1,000 source code files, which after cleaning resulted in 996 individual code samples collected from GitHub repositories. The dataset included various programming languages (Python, Java, JavaScript, TypeScript, C++), divided into 697 training data, 149 validation data, and 149 testing data. The model employed was CodeBERT, configured with a hidden size of 768, 12 layers, and 12 attention heads. CodeBERT generated vector embeddings for each code sample, which were then projected by a Siamese Network to calculate cosine similarity between code pairs. Testing used a threshold of 0.80 to classify plagiarism. The identification results achieved an accuracy of 96.4%, precision of 95.2%, recall of 97.8%, F1-score of 96.4%, and an error rate of 4.6%. The system produced similarity scores and status labels of “plagiarism detected” or “not detected,” demonstrating the effectiveness of the CodeBERT-based approach for adaptive and intelligent code similarity identificatio.

Setting the stage for statistical learning? Sensitivity to environmental statistics in early sensory processing.

Adaptive Coding and Modulation in Direct A2G Communication Link for Inflight Broadband Connectivity

Abstract To address the security vulnerabilities encountered during the transmission of image data in Internet of Things (IoT) devices, this study introduces a novel encryption framework that integrates an enhanced four-dimensional hyperchaotic system with adaptive DNA coding. The proposed chaotic model is developed by augmenting the traditional Chen system with nonlinear feedback and coupling terms, resulting in improved dynamic behavior, which is validated via phase portraits, bifurcation diagrams, and NIST randomness assessments. A hybrid key initialization mechanism, driven by the SHA-256 hash of the plaintext image and external parameters, enables image-dependent key generation. A fourth-order Latin square is utilized to construct a dynamic DNA substitution matrix, while chaotic sequences guide the scrambling and cross-segmentation operations. Furthermore, a DNA-based XOR logic combined with bidirectional weighted diffusion enhances the confusion and diffusion strength of the encrypted image. Experimental evaluation indicates that the information entropy of the ciphertext image is 7.9993, while the Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) reach 99.61% and 33.46% respectively. All test indicators demonstrate excellent performance, which proves that the encryption algorithm has high robustness and is applicable to the image encryption module for secure image transmission in the Internet of Things (IoT) environment.

In this paper, a novel framework to enhance the reliability of wireless sensor networks (WSNs) by addressing the high probability of outage (OP) resulting from limited energy resources and unreliable channels. The framework integrates three techniques: half-duplex two-way relaying (HD-TWR), digital network coding (DNC), and rateless codes. Although these techniques have been extensively studied in isolation, a comprehensive analysis of their joint performance is provided as the main contribution. The proposed scheme leverages the energy efficiency of HD-TWR, the transmission reduction capability of DNC, and the retransmission-free resilience of rateless codes. Simulation results show that the integrated framework significantly reduces OP, offering a robust and practical solution to enhance the reliability enhancement. Furthermore, the impact of optimal relay node placement is investigated through parameter adjustments in the simulation stage to maximize performance gains.

Wireless communication system has found worldwide acceptability as a backbone of modern digital transformation, enhancing connectivity, efficiency and transformation. However, the system is characterized with severe multipath propagation effects that degrade its performance. Maximal Ratio Combiner (MRC), as one of the techniques being used to address this problem is associated with poor performance in a time-varying channel due to increase in delay time as transmission data rate increases. Hence, in this paper, enhancement of MRC is carried out to improve its performance in a time-varying channel by reducing error rate using Adaptive Coding and Modulation (ACM) transmission technique and the Bit Error Rate (BER) of the enhanced MRC scheme is analytically derived and employed as performance metric. The multiple copies of the received signals at varying paths „L‟ (2, 4) are combined using Conventional MRC (CMRC). The transmitter then selects the appropriate coding rate and constellation size for another transmission slot based on the obtained channel gain through ACM transmission technique. Mathematical expressions using Probability Density Function (PDF) of a time-varying Nakagami fading channel for Bit Error Rate (BER) is also derived. The proposed technique is simulated using MATLAB R2021a. The results obtained revealed that, the enhanced MRC scheme shows a lower BER, which indicate a significant gain in sound quality. Therefore, the proposed technique suggests a practical application in improving next-generation wireless network.

Abstract Moldova’s geopolitical position, caught between Russia and the West, presents a critical, yet often oversimplified, lens through which to understand its post-Soviet development. This article problematizes the assumption, arguing that Moldovan party politics demonstrates a more fluid and contested landscape than commonly portrayed. Through a qualitative analysis of 31 party electoral programmes between 2001 and 2024, we map the evolution of ‘geopolitical codes’ – how parties articulate foreign policy – and examine their impact on consensus-building and strategic choices. The findings reveal nuanced ideological distinctions within both pro-Russian and pro-European factions, and adaptive codes shaped by both domestic competition and transnational pressures. Crucially, we demonstrate how inter-party dynamics – beyond simple geopolitical alignment – mediate external influences and shape Moldova’s foreign policy. This research contributes to the literature by moving beyond deterministic geopolitical frameworks, highlighting the agency of domestic actors in peripheral states, and offering a deepened understanding of how party competition shapes geopolitical orientation and consensus formation.

Improved tightly-coupled PPP/MEMS-INS integration with adaptive code pseudorange weighting and LSTM-based smoothing for urban canyon navigation

Altered posterior mid-cingulate cortex activation during adaptive coding in individuals with schizotypal traits, subthreshold depression and autistic traits.

The rapid development of telemedicine and intelligent healthcare technologies necessitates high efficiency and accuracy in medical image ‎compression methods for effective remote diagnostics and precise treatment. This paper introduces a new Context-Based Adaptive Binary ‎Arithmetic Coding (CABAC) framework designed to specifically compress sensitive healthcare images, such as MRI, CT, and X-ray ‎images. Like conventional techniques such as JPEG and JPEG2000, which can corrupt important diagnostic information through lossy ‎compression, the proposed CABAC-based algorithm leverages the statistical and distinctive nature of medical images to adaptively model ‎the context and optimize binary arithmetic coding of the images. Therefore, it leads to increased compression ratios while maintaining ‎diagnostically important essential image quality. The CABAC framework combines preprocessing, binarization, statistical context modeling, ‎and binary arithmetic coding to achieve more compression efficiency. Quantitative analyses of conventional datasets at The Cancer Imaging ‎Archive (TCIA) demonstrate that the proposed method achieves a compression ratio of up to 15:1, surpassing the capacity of JPEG and ‎JPEG2000. Moreover, the technique also guarantees large Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) ‎values, which reveal the high visual and structural quality of the decomposed medical images. Designed with computational efficiency in ‎mind, the model is well-suited for integration into real-time telemedicine technologies, such as innovative healthcare systems featuring AI-capable diagnostics and IoT-enabled medical devices. This method provides a viable remedy for bandwidth optimization, as well as ‎addressing storage needs and improving the accuracy of diagnostic tests, especially in technically limited environments‎.

Context . Exascale supercomputing unleashes the potential for simulations of astrophysical systems with unprecedented resolution. Taking full advantage of this computing power requires the development of new algorithms and numerical methods that are GPU friendly and scalable. In the context of multi-fluid dust-gas dynamics, we propose a highly accurate algorithm that is specifically designed for GPUs. Aims . We developed a multi-fluid gas-dust algorithm capable of computing friction terms on GPU architectures to machine precision, with the constraint for the drag-time step to remain a fraction of the global hydrodynamic time step for computational efficiency in practice. Methods . We present a scaling-and-squaring algorithm tailored to modern architectures for computing the exponential of the drag matrix, enabling high accuracy in friction calculations across relevant astrophysical regimes. The algorithm was validated through the Dustybox, Dustywaye, and Dustyshock tests. Results . The algorithm was implemented and tested in two multi-GPU codes with different architectures and GPU programming models: Dyablo, an adaptive mesh refinement code based on the Kokkos library, and Shamrock, a multi-method code based on Sycl. On current architectures, the friction computation remains acceptable for both codes (below the typical hydro time step) up to 16 species, enabling a further implementation of growth and fragmentation. This algorithm might be applied to other physical processes, such as radiative transfer or chemistry.

Introduction. Earth observation by low Earth orbit (LEO) satellites plays a critical role in supporting various sectors of the national economy. To increase the efficiency of this technology, optimizing the video data downlink —particularly with respect to the satellite’s elevation angle relative to the ground station — is essential.Problem Statement. The communication link margin varies depending on the selected signal waveform, whilechanges in the satellite’s elevation angle alter the propagation path length and, consequently, the energy characteristics of the downlink. For small satellites such as CubeSats, which have limited onboard power, the potential to transmit high-data-rate video within a brief communication window — depending on modulation mode — has not been sufficiently studied.Purpose. This study aims to enhance the performance of Earth remote sensing systems by improving the energy efficiency and throughput of satellite-to-ground video transmission in the X-band.Materials and Methods. The analysis applies microwave communication theory to evaluate the energy budget of the downlink, incorporating Adaptive Coding and Modulation (ACM) techniques supported by the DVB-S2/S2X standard. The study considers various modulation and coding (MODCOD) schemes and output power levels at diff erent satellite elevation angles.Results. The energy margin of the LEO satellite downlink has been calculated, enabling an assessment of thefeasibility of using DVB-S2X for video transmission from Earth observation satellites. The findings have shownthat at low elevation angles, a connection can be established using the most robust mode (QPSK 1/4), supporting a data rate of 38 Mbps. At elevation angles exceeding 50 degrees, higher-order modulation such as 32APSK 9/10 becomes feasible, achieving data rates up to 384 Mbps.Conclusions. The study has demonstrated that applying the DVB-S2(X) standard to CubeSat-class Earth observation missions enables more efficient and adaptive use of the X-band downlink channel. This approach has improved flexibility and throughput of video data transmission, especially when tailored to satellite elevation angles.

Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that encode differential (i.e., incremental) changes to a given variable. The accuracy of this computation hinges on a precisely tuned integration gain. Experiments have shown that the brain can recalibrate this gain using ground-truth sensory information, yet existing CBAN models rely on biologically implausible or currently unknown plasticity rules for recalibration. Here, we demonstrate that ring-type CBANs can recalibrate their integration gain through two mechanisms that rely on well-established, biologically plausible forms of plasticity. In the first mechanism, the spatially distributed synapses conveying incremental information to the attractor are plastic, allowing the integration gain to become transiently inhomogeneous during recalibration. In the second, plasticity is implemented in other components of the network, keeping the gain homogeneous during recalibration. Both mechanisms require explicit error signals that drive plasticity. We instantiate each mechanism within a CBAN, demonstrating their potential for biologically plausible, adaptive coding of continuous variables.

Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expansive domains. Today’s supercomputers’ extreme heterogeneity presents a significant challenge for dynamically adaptive codes, highlighting the importance of achieving performance portability at scale. Our research focuses on astrophysical simulations, particularly stellar mergers, to elucidate early universe dynamics. We present Octo-Tiger, leveraging Kokkos, HPX, and SIMD for portable performance at scale in complex, massively parallel adaptive multi-physics simulations. Octo-Tiger supports diverse processors, accelerators, and network backends. Experiments demonstrate exceptional scalability across several heterogeneous supercomputers including Perlmutter, Frontier, and Fugaku, encompassing major GPU architectures and x86, ARM, and RISC-V CPUs. Parallel efficiency of 47.59% (110,080 cores and 6880 hybrid A100 GPUs) on a full-system run on Perlmutter (26% HPCG peak performance) and 51.37% (using 32,768 cores and 2048 MI250X) on Frontier are achieved.