This paper addresses the challenge of wireless power transfer to implantable endovascular devices, which have been used for pressure monitoring and tissue stimulation. However, a thorough analysis and optimization of the wireless link remain unexplored, leaving the maximum power efficiency and power delivery unknown. Methods: Theoretical analysis, computer simulations, and experimental measurements with biological tissues were combined. Results: Different coils were designed and optimized for endovascular devices and compared. With proper geometry, material, and frequency selection for coils, power efficiency over 10% and power delivery over 100 mW was achieved at 15 mm under human safety limits. Conclusion: This work demonstrates an optimized results and provides some key insights for endovascular coil design. Significance: The designed inductive link enables a minimally invasive, durable, and powerefficient solution for future endovascular applications.

This paper presents a sensor-package co-simulation, design, and optimization approach for a multi-channel pressure scanning valve. The methodology combines finite element analysis to design both the sensor chip and its packaging, focusing on the interactions between these components to improve overall sensor performance. By systematically simulating and optimizing the structural characteristics of the chip-such as the thin-film diaphragm-and analyzing various packaging materials and configurations, we aim to mitigate the adverse effects of packaging-induced stress. Results demonstrate that careful selection of packaging materials and design parameters can effectively reduce packaging-related measurement errors, thereby improving the accuracy of the sensor.

The United States housing market has historically exhibited regional imbalances in housing supply and demand, which have contributed to reduced housing affordability and market volatility. The application of big data technology enables the utilisation of data to enhance comprehension of these imbalances, thereby informing the formulation of policy. We put forth a model for analyzing the housing supply and demand based on big data, which employs a comprehensive approach to examine both the demand and supply sides. With regard to the demand side, the model incorporates a multitude of data sources to ascertain and delineate the pivotal elements influencing housing demand. These include population growth rate, household income level, employment opportunity distribution, migration and flow trends, and cost of living. By constructing cubes, the model is capable of capturing the characteristics of dynamic demand changes in different regions. With regard to the supply side, the model assesses land use, building materials and labor costs, the timeliness of building permitting and approval processes, and the impact of regional policies and regulations. By means of a quantitative analysis of the aforementioned factors, the model is able to identify housing supply bottlenecks in different regions. The model's efficacy in identifying significant imbalances between supply and demand in the United States housing market was validated through experimental analysis of historical data.

This paper presents a lightweight modified rod antenna with tapered feeding, designed for high-power microwave (HPM) applications. The proposed antenna is capable of transmitting narrowband high-power pulses centered at 2.45 GHz. Numerical simulations were conducted to assess the antenna's performance, followed by prototype fabrication and experimental validation. The manufactured antenna achieved a bandwidth of 3.07 GHz (3.52 GHz-6.59 GHz) and a total gain of 5 dBi. The absence of dielectric materials and the designed 50ohm input impedance facilitate direct connection to an HPM generator, making the proposed design highly suitable for defense and radar applications.

This paper analyzes the performance of Alamouti coded short-packet non-orthogonal multiple access (NOMA) systems with hybrid multicast and unicast transmission over Nakagami-m fading, where only the statistical channel state information is available at the transmitter, and the multicast and unicast signals are intended for all users and a particular user, respectively. Due to some practical limitations, channel estimation errors (CEE) caused by a linear minimum mean-square error estimator and residual hardware impairments (RHI) caused by imperfect hardware implementation are taken into account. We first derive the approximate closed-form expressions for the average block error rate (BLER) and the corresponding asymptotic expressions at all users. Using such expressions, we analyze the diversity performance including conventional diversity order and finite-SNR diversity order. After this, we quantify the relationship among the common blocklenth, power allocation, and pilot sequence length under users' reliability constraints. Finally, numerical and simulation results verify our theoretical analysis and show that i) CEE and RHI greatly affects the transmission blocklength, ii) RHI leads to the error floor phenomenon at high SNRs and the finite-SNR diversity order is the effective performance metric in the presence of RHI, iii) there exist the optimal power allocation coefficients and pilot sequence length to minimize the transmission blocklength in the considered hybrid multicast-unicast system, and iv) the NOMA scheme is superior to the OMA counterpart by achieving low-latency transmission.

The intricate challenge of maintaining semantic coherence and contextual alignment in text generation has driven the development of innovative embedding recalibration methodologies. Contextual Embedding Recalibration introduces a dynamic mechanism that iteratively adjusts token representations to reflect evolving semantic requirements within extended sequences. The recalibration framework leverages hierarchical token encoding and adaptive weight computation to balance localized fidelity and global coherence, addressing limitations of traditional static embedding strategies. Empirical evaluations across diverse tasks demonstrate substantial improvements in perplexity reduction, contextual consistency, and robustness under varied conditions, including noisy inputs and cross-domain generalization. A key feature of the recalibration layer is its seamless integration with existing transformer architectures, achieved through non-linear transformations and entropy-based regularization mechanisms. Quantitative analyses reveal that the recalibrated model excels in maintaining long-range dependencies, reducing semantic drift, and aligning tokens effectively in multi-turn dialogues and narrative completions. The ability to process out-of-vocabulary tokens with improved accuracy further highlights its adaptability to real-world linguistic variability. By incorporating computationally efficient techniques, the recalibration process achieves scalability while preserving performance gains across large datasets. The presented results demonstrate the efficacy of embedding recalibration in enhancing the representational depth and contextual reasoning capabilities of large-scale transformer models. Cross-domain evaluations confirm the robustness of the recalibrated model, indicating its versatility for applications requiring context-sensitive natural language generation. The findings not only address fundamental challenges in token representation but also offer a foundational framework for advancing machine learning methodologies in broader linguistic contexts. The proposed recalibration approach establishes a benchmark for future explorations into adaptive embedding mechanisms within artificial intelligence frameworks.

Electrical modeling of photovoltaic devices has important real-life applications including system design, power forecasting, and degradation analysis. In this context, the singlediode model is widely used to simulate the electrical behavior of photovoltaic cells, modules, and arrays under various operating conditions. A popular adaptation commonly used in the field is the De Soto et al. model, which updates the five parameters of this onediode model to the operating conditions, particularly for silicon technologies. Alternative methods also exist, but they have not been thoroughly evaluated to see if they can keep up with modern technologies. In this study, we use current-voltage curve measurements of commercial crystalline silicon and thin-film solar panels to evaluate the accuracy of twelve techniques found in the literature to adjust the single-diode model parameters to temperature and irradiance. We find that these approaches have overall comparable performances, but that the optimal choice among them should be based on the prevailing operating conditions and cell technology. Although the De Soto model can potentially be improved by accounting for the resistances' dependence on irradiance, it still offers a good tradeoff between simplicity and accuracy.

A nonlinear electromagnetic inversion scheme incorporating a deep-learning-based plug-andplay (PNP) regularization approach is proposed. Unlike conventional regularization techniques that rely on fixed priors, the PNP method decouples the data fidelity and regularization terms, enabling the integration of a learned prior via a state-of-the-art denoiser. The recently introduced Swin-Conv-UNet (SCUNet), known for its superior image denoising capabilities by leveraging swin transformer blocks and residual convolutional blocks in its architecture, is employed as the PNP denoiser within a multi-frequency Gauss-Newton nonlinear inversion framework. The resulting PNP-SCUNet inversion scheme is evaluated on both synthetic and experimental data, demonstrating superior performance compared to traditional inversion methods, including Tikhonov and total variation, and previously developed PNP regularization with block-matching and 3-D filtering (BM3D) denoiser.

This letter proposes a novel impedance domain nonorthogonal multiple access (ID-NOMA) to overcome problems on conventional power domain NOMA (PD-NOMA). The proposed ID-NOMA integrates impedance-matching circuits (IMCs) with impedance based power attenuation reinforcement (I-PAR). The IMC values are derived from source and load IMCs while I-PAR attenuates user' signals by using I-PAR coefficients. The numerical results show that the proposed ID-NOMA outperforms PD-NOMA in terms of ergodic capacity (EC) and outage probability (OP) owing to the effectiveness of I-PAR.

Parametric Semantic Lattices (PSL) offer a new approach to enhancing semantic representation and contextual processing in advanced language modeling architectures. By embedding hierarchical structures that capture both local and global linguistic dependencies, PSL addresses key limitations observed in traditional transformer-based frameworks. The adaptive nature of the lattice configurations enables the dynamic interpretation of syntactic and semantic relationships across diverse linguistic inputs. Quantitative analysis reveals significant reductions in perplexity and marked improvements in semantic coherence when compared to baseline models. Through leveraging multi-dimensional encoding, the PSL framework enhances interpretability while maintaining computational efficiency across long text sequences. Experiments demonstrate its scalability across extensive datasets and various hardware configurations, validating its application to resource-intensive natural language processing tasks. Evaluation under conditions of noisy input highlights its robustness in maintaining contextual fidelity and accuracy. Practical applications span areas such as automated translation, sentiment analysis, and advanced query resolution, showcasing its versatility. The integration of PSL into transformers introduces minimal computational overhead while yielding substantial performance gains. Results further highlight its ability to retain long-range dependencies, a critical factor for tasks requiring deeper contextual understanding. By redefining structural modeling within transformers, PSL opens new avenues for the development of contextually aware artificial intelligence systems. The findings collectively underscore its transformative potential in advancing the state of language modeling and representation.

Quantum computing has made significant strides, with hardware platforms now advanced enough to tackle the challenges of scaling up to larger and more robust systems. Central to this progression is the interface between quantum processors, comprising isolated qubits, and the classical systems required for their control and readout. As these systems transition from few-qubit devices to fault-tolerant architectures, the complexity of this quantum-classical interaction poses significant engineering challenges. Field-Programmable Gate Arrays (FPGAs) have emerged as an adaptable and efficient solution, offering realtime control, low-latency operations, and seamless integration. This review delves into current FPGA-based platforms, including commercial systems, as well as open-source solutions developed by academic research groups. The architecture, features, and contributions of these platforms to addressing scalability and integration issues are thoroughly examined. Additionally, the review highlights innovations in cryogeniccompatible controllers and explores the potential of Cryo-FPGAs to reduce noise and improve fidelity in quantum systems. By providing a comprehensive analysis of current technologies and identifying opportunities for advancement, this review offers valuable insights for researchers and engineers working to develop scalable quantum computing solutions.

This paper introduces DynamicRenderNet, a novel neural network architecture designed to optimize graphics rendering in real-time Augmented Reality (AR) and Virtual Reality (VR) games. By adaptively adjusting rendering parameters based on scene complexity and user interaction, DynamicRenderNet significantly improves frame rates and reduces latency. Our model utilizes a deep learning approach to analyze real-time input data, such as camera feed, user head movements, and scene geometry, to predict optimal rendering settings. This enables dynamic resource allocation and prioritization, leading to a more efficient and responsive rendering pipeline. Experimental results demonstrate substantial improvements in frame rates and reduced latency, resulting in a more immersive and visually appealing AR/VR experience.

Potential for subsynchronous interactions (SSI) involving inverter-based resources is often screened for using small-signal impedance scans. However, these impedance scans are often slow to compute and it is often desired to study a wide range of operating conditions. This paper presents modeling practices and a new scanning approach for performing impedance-based SSI screening studies that can reduce the required computation time by orders of magnitude with negligible effect on accuracy. Relevant principles are illustrated using a type3 wind turbine model, using two different reactive power control strategies that have significantly different SSI behavior. The discussion of modeling practices includes per-unitization, mechanical dynamics, timestep selection, PLL design, and negative-sequence behavior and covers both time-saving simplifications and pitfalls or unsuitable simplifications to avoid. The new scanning approach involves identifying sets of input frequencies that do not interfere with one another’s outputs and using crest factor minimization to develop multisine test signals.

A fundamental challenge in contemporary computational linguistics lies in the ability of language models to adaptively capture and maintain complex contextual relationships within diverse and dynamically shifting textual inputs. Transformer-Based Cascade Synthesis offers a pioneering solution to this problem through a cascading, multi-layered framework that progressively refines contextual embeddings at each stage, creating a more flexible, responsive model architecture capable of complex context handling across extended text sequences. Employing a structured series of transformations, the synthesis approach recalibrates embedding layers to achieve a balance between context sensitivity and computational efficiency, facilitating coherent, syntactically accurate language generation even in complex linguistic scenarios. Experimental results demonstrate substantial improvements in contextual coherence, lexical diversity, and syntactic precision, suggesting that Transformer-Based Cascade Synthesis achieves a marked enhancement in model adaptability and response fidelity compared to traditional static embedding methods. This methodology's effectiveness across diverse language tasks highlights its potential to set a new standard for adaptability in large language model architectures, contributing meaningfully to the broader pursuit of human-like language comprehension in artificial intelligence.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem's nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

We present the reconfiguration of a circular feed horn operating in bandwidth from 1.4 GHz to 1.8 GHz for radio astronomical applications, adapting it to the 21 cm band (1.42 GHz), and performing observations of the hydrogen line. The proposed model was implemented using cylindrical structures. The design, simulation, measured performance, and reconfiguration are detailed. This feed horn is used at the prime focus of a parabolic reflector antenna. A prototype was designed and characterized, and the simulated and measured results were in good agreement. The reflection coefficients (S 11) of the feed horn was below-15 dB, and the voltage standing wave ratio (VSWR) remained below 1.43. The gains obtained from simulation and measurement were 12 dBi and 12.3 dBi, respectively. To determine the suitability of the proposed design in a practical environment, spectral measurements were performed using an FPGA-based spectrometer. The results showed that the intensity of the signal reached 3.5 dB more than that of the original feed horn. The overall performance of the proposed feed horn suggests that it can be applied to the optics of the 4.9 m reflector antenna.

Big Data analytics has become an essential tool for industries to make informed decisions, enhance operational efficiency, and gain deep insights from diversified vast amounts of data. This paper explores the transformative power of Big Data analysis, focusing on four core types: Exploratory, Descriptive, Sentiment, and Predictive analysis. These methods are critical in sectors such as healthcare, retail, finance, and transportation, enabling businesses to identify trends, improve customer satisfaction, and forecast future outcomes. The paper discusses the methodology, required resources, and enabling technologies for effective Big Data utilization, offering a comprehensive framework for implementation. A discussion on challenges and strategies for overcoming them is included, alongside real-world applications that demonstrate Big Data's tangible benefits. Finally, a forward-looking conclusion emphasizes the importance of continuous adaptation and innovation to leverage Big Data's full potential.

In this work, a comparison between three broadband methods used to estimate the propagation constant of planar transmission lines is presented. The goal of this comparison is to study how possible random measurement errors can affect the use of the aforementioned methods commonly used, since in ideal conditions the same solution is obtained from all of them. For this purpose, a sensitivity analysis is carried out in order to study the similarities and differences and how errors in measured Sparameters and in line lengths affect the attenuation and the phase constant obtained from each method. Subsequently, a minimization approach that consists of a least-square estimation using a criteria to choose the optimal line lengths is proposed to minimize measurement errors. Finally, an experiment has been designed, manufactured using microstrip transmission lines, and measured to validate the developed theory. Results corroborate the proposed theory and show an excellent agreement with electromagnetic simulations in the 0.1-to 50-GHz frequency band, therefore assessing the suitability of the proposed error analysis.