The first of Calderón identities indicates that the operator of the electric field integral equation (EFIE) can potentially act as a preconditioner for itself. However, the discretization of this preconditioning operator via the boundary element method (BEM) requires constructing a dual space commonly defined on a barycentric refinement of the orginal mesh. This contribution introduces a novel Calderón strategy that leverages high-order quasi-Helmholtz projectors. Unlike the standard strategies, which are based on barycentric refinements, a dual space is obtained by combinating functions of higher order. This strategy allows the development of a discretization of the Calderón identity that is suitable for arbitrary order. The well-conditioning of the proposed formulation is investigated, and numerical results are presented to corroborate the theory.

The diverse range of Internet of Things (IoT) devices available today offers great potential for home automation and Smart Home (SH) development, whether partial or complete. However, for research in this area to advance, it is essential to overcome the scarcity of data, as data collection in SHs faces significant challenges, such as the complexity of devices and environments, privacy and security concerns, and restrictions imposed by proprietary devices. Furthermore, freely available datasets often have limitations, such as incomplete annotations, restricted device usage, and insufficient spatial and temporal coverage. To address this limitation, we developed a novel SH simulator that supports multi-state devices and captures detailed information on device usage and inhabitant activities, including metrics like lamp brightness and air conditioning settings (temperature, mode, etc.). The primary goal is to provide researchers in SHs, the SH, and related fields with a tool for generating datasets that effectively represent an inhabitant or group of inhabitant activity patterns in simulated environments. Home Environment Simulator Targeting Inhabitant Activities (HESTIA) resolves the inconsistency in message exchanges between devices, which often stems from differences in device manufacturers, while also eliminating the time-consuming and costly process of collecting real-world data by generating synthetic datasets. The simulator HESTIA offers rich, dynamic information that can be tailored to specific user profiles. To validate our method, we collected data over two weeks from a real environment using four devices and compared it to a synthetic version of the same setup generated by HESTIA. The results show that the simulator accurately replicates real-world environments, even for devices with inconsistent status changes. Additionally, we evaluated the computational complexity of this simulator in terms of time.

This paper presents a framework for training a Gaussian process (GP) to estimate a steady urban wind field from a sparse set of wind measurements by leveraging training data collected from computational fluid dynamics (CFD) simulations. Gaussian process models for spatial estimation often use measurement locations as the input space with proximity-based covariance functions. This work investigates including building morphology features into the GP model that are defined by the signed distance field (SDF) and its gradient evaluated at a pattern of points around each sample location. Augmenting the measurement locations with different subsets of building morphology features leads to unique feature spaces. Several different GP models are trained using various feature spaces and covariance functions, including with a coregion covariance function that allows simultaneous training over multiple CFD datasets for different urban geometries. A framework is developed to generate CFD wind field data for a set of randomized geometries, build various feature spaces, and perform the estimation with the proposed GP models. The framework is evaluated with a simple environment that consists of two buildings with randomized position and geometry in a wind field with constant inflow magnitude and direction. Results are presented comparing the estimation performance across different GP models with an increasing number of optimization iterations. The computation versus accuracy trade-off of using hyperparameters trained over multiple similar prior CFD datasets, rather than hyperparameters that are optimized on-the-fly, over a single dataset is also demonstrated.

This article provides a research viewpoint on the training procedure of a Bidirectional Encoder Representations from Transformers (BERT) model starting from the beginning, employing the Tensor Processing Unit (TPU), with a particular emphasis on the Spanish language. This article focuses on the techniques and strategies, such as the whole word masking (WWM) technique, that are essential for achieving successful BERT training in multiple languages, including English. The goal is to provide efficient development of natural language processing applications for understanding the Spanish language. The BEThiz model exhibits exceptional proficiency in comprehending contextual cues and producing accurate responses for masked inputs, surpassing other models within the same category and language. This characteristic sets our model apart and solidifies its role as a particularly potent tool in the field of natural language processing.

An effective passive source localization technique is proposed as essential for industrial applications. Extensive research has been conducted on multi-station passive localization using two-dimensional angle of arrival (2D AOA), time difference of arrival (TDOA), and their combination. However, certain practical factors limit the observation to only a restricted set of one-dimensional angles of arrival (1D AOA). Additionally, there may be perturbations in the stations' locations. To address these issues, this paper proposes a localization model that hybrid a single 1D AOA with multiple TDOA measurements, taking into account the perturbations in station locations. For clarity, we refer to this combination as hybrid measurements. Two types of localization algorithms are proposed for the hybrid measurement model. The first type approximates the localization problem as a non-convex constrained weighted least squares optimization problem. We introduce two solution algorithms: the two-step weighted least squares algorithm (TWLS) and the semi-infinite relaxation method (SDR), both of which do not require an initial guess. The second type directly transforms the localization problem into a maximum likelihood estimation (MLE) minimum optimization problem. Several iterative algorithms are introduced, including iterative weighted least squares based on Taylor expansion (IRLS-TE), Gaussian Newton (GN), adaptive moment estimation (Adam), and simplification adaptive moment estimation (SAdam). Unlike the first type, these algorithms require an initial guess. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for hybrid measurements and analyze the computational complexity and minimum station configuration needed for each solution algorithm. Simulations and the SWellEx96-S5 ocean acoustic experiments demonstrate the effectiveness of the proposed methods. The results indicate that the proposed algorithms can achieve CRLB performance at low noise levels, with the TWLS algorithm showing the lowest complexity, while the Adam algorithm achieves the highest accuracy. These proposed solution algorithms can be selected based on computational complexity, localization accuracy and source of interest location.

Quantum sensors often consist of packaging, such as dielectric-based vapor cells and metallic electrodes, that reduces and spatially alters the locally observed electromagnetic fields. These effects have been well studied in the optical regime, and even in the RF regime over a few GHz. However, there have been few studies in the electrically small regime below 1 GHz. In order to account for or remove the effects of the packaging, more studies are needed across a broad range of frequencies. This paper reports on the complex permittivity and conductivity of several commercially available vapor cells used for Rydberg electric field sensing from 10-300 MHz. A new method using a stripline transmission measurement was performed and full wave electromagnetics modeling was used to extract the effective dielectric constitutive parameters from the vapor cells. Additionally, the field reduction inside the vapor cell is reported, and published atomic measurements of the electric field are used to further validate the results presented here. Several observations were made from the measurements, such as the frequency dependencies of the RF dispersion and absorption. Applications of this technique include making precise numerical field corrections or physically designing a more optimal vapor cell via coatings, material changes, or geometric changes to improve field strength and uniformity.

Power cycling reliability is one of the most rigorous methods to evaluate the durability of power semiconductor devices against real-life varied electrical and thermal stresses. The corresponding test results can generate lifetime models to predict the service life in operating conditions. This paper provides an overview of the power cycling reliability testing for power semiconductor devices, focusing on both legacy Si and new wide bandgap (WBG) devices. It begins by presenting the power semiconductor packaging trends, their evolving failure mechanisms, and the power cycling test methods. Different testing standards, lifetime models, and their challenges are then discussed. Special attention is given to WBG devices, examining various testing methods and their impacts. A generic lifetime model for different semiconductor materials and packaging technologies is provided based on above 200 samples collected from the existing literature, which can serve as a useful tool for reliability evaluation. Finally, the limitations and associated open questions are discussed to identify future research opportunities.

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference. In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way.

This paper introduces a novel technique based on a planar microwave sensor for contactless monitoring of hydrocarbon contamination in groundwater. Short-chain hydrocarbons are more toxic and volatile, which makes their detection vital for the management of Petroleum Hydrocarbons (PHCs) pollution in aquatic environments. The sensor consists of a tag and reader, which are wirelessly connected; hence, non-contact and highly sensitive sensing is enabled. The tag is fabricated on a flexible substrate for ease in the monitoring of the alkanes. This non-contact nature, coupled with high resolution, makes this sensor ideal for monitoring hazardous and toxic hydrocarbons in groundwater. The detection of Hexane has been studied by varying its concentration in water up to 30% (v/v).

5G wireless networks harness the extensive spectrum available in the millimeter-wave (mmWave) frequency bands which set them apart from current wireless systems in terms of directivity, propagation loss, and susceptibility to blockages. Sub-6 GHz systems can attain omni-directional coverage, displaying limited sensitivity to physical obstacles. Still, they are incapable of achieving the same level of service quality as systems outfitted with electronically steerable directional antennas offering reduced propagation loss and higher gains due to the beam directionality. In our framework, we investigate the utilization of directional, steerable device-to-device (D2D) mmWave antennas as integral components. The nodes communicate by manipulating the orientation of their antennas, i.e., steering their beams. To minimize dependence on a base station, the D2D nodes are categorized into primary and secondary ones and they communicate in three phases: Uplink, Downlink, and PAPA. We delve into the impact of optimal steering of the main lobe beams transmitted by these D2D antennas as well as optimizing the time sharing among three phases. Within each phase, we assume that the nodes follow a random transmission scheduling scheme and derive the achievable rates accordingly. Through meticulous design of polynomial-time heuristics, we maximize the overall network capacity.

The capacity for sustained contextual coherence in language model outputs remains a critical factor in achieving natural, relevant, and semantically rich responses across extended interactions. Traditional context-handling methods, often limited to token-level attention and basic memory retention mechanisms, fall short when confronted with the intricate, hierarchical nature of real-world discourse. Multi-Stage Latent Contextual Synthesis presents a novel solution, employing a structured, multi-tiered synthesis approach to refine context integration within an LLM framework. Through a sequential layering process, latent knowledge is synthesized at progressively deeper levels, yielding a more nuanced understanding of semantic dependencies, thematic continuity, and response relevance across multi-turn conversations. The experimental results reveal substantial improvements in contextual retention, thematic alignment, and token stability, underscoring the method's efficacy in enhancing complex context management without adding prohibitive computational demands. Comprehensive evaluations demonstrated that Multi-Stage Latent Contextual Synthesis not only enhances memory efficiency and reduces thematic error rates but also promotes embedding stability, thereby supporting a more structured and responsive approach to contextual synthesis in high-stakes applications, including conversational AI and automated support systems. These findings illustrate the potential of hierarchical, latentcontextual strategies to redefine contextual processing standards within LLMs.

Vehicle-to-vehicle (V2V) communications require highly accurate and real-time channel modeling techniques. The dynamic nature of V2V scenarios demands continuous scene updates while ray-tracing (RT) algorithms typically require substantial computational time to achieve accurate predictions. This letter proposes an OptiX-based GPU-parallel dynamic shooting and bouncing ray tracing (DSRT) method that combines the advantages of shooting and bouncing ray (SBR) and RT approaches. The method employs an enhanced adaptive ray tube splitting mechanism to improve accuracy. A level of detail (LOD)-based dynamic scene update method further optimizes performance, while OptiX and CUDA acceleration enable efficient RT. Channel measurements in urban environments demonstrate the high accuracy of our algorithm. Performance evaluation results show that DSRT achieves a parallelization degree exceeding 98.8% and delivers exceptional speedup ratios.

Adders are the most important element in the ALU. Speeding the addition improves the performance of the ALU in particular and the CPU in general. Pipelining is used to process tasks in parallel with minimal adjustment of the hardware needed to process the same tasks sequentially. In this paper, a pipelining procedure is presented to enable the half adder to perform the addition of two numbers in parallel. The resulted speed up factor is guaranteed to perform the same addition in half the clock cycle of full adder.

New challenges in designing scalable and adaptable neural architectures require innovative approaches to semantic representation and processing. The Semantic Entanglement Modeling (SEM) framework introduces a paradigm shift through its integration of modular semantic units, hierarchical attention mechanisms, and tensor-based operations, all configured to enhance coherence and task adaptability across diverse linguistic benchmarks. Its hybrid encoding scheme, combining static and contextual embeddings, demonstrates significant versatility in handling varied input complexities while ensuring resourceefficient operation. Experimental evaluations reveal substantial improvements in contextual alignment, with perplexity and entropy metrics highlighting the framework's robustness in managing high-dimensional data. Noise reduction through gating mechanisms enhances the precision of semantic outputs, particularly in tasks requiring minimal error tolerance. Fewshot learning experiments emphasize the model's adaptability, achieving high accuracy rates with limited task-specific data. Resource utilization analyses underline its scalability, showcasing consistent performance across multiple computational environments. The framework also addresses key inefficiencies in multi-modal integration, ensuring coherent semantic alignment across textual and non-textual inputs. Comparative analyses with existing architectures confirm that SEM not only reduces computational overhead but also extends applicability to specialized and high-complexity tasks. Its contributions are further evidenced in the context of generalization capabilities, where robust performance was observed even under variable input distributions. The SEM framework exemplifies a transformative approach to optimizing neural models for diverse linguistic challenges, delivering significant advancements in performance, efficiency, and scalability.

Passive synthetic aperture radar (SAR) has attracted research in recent years because of its advantages in exploiting opportunistic signals for imaging. Hence, it minimizes the need for active radar transmitters, significantly reducing system costs. In addition, the massive proliferation of telecommunication satellites in recent years motivates the idea of using the signal for the integrated sensing and communication (ISAC) concept. This paper explores the feasibility and opportunity of exploiting communication signals, specifically the common waveform of orthogonal frequency division multiplexing (OFDM), as a source of SAR illumination. The reflected signal is captured using a flying uncrewed aerial vehicle (UAV) at a much lower altitude than the satellite. To perform this evaluation, we developed an extensive signal-level emulator that considers many realistic aspects, such as orbital parameters, pulse repetition frequency (PRF), RF radio bandwidth, and antenna beamwidth. Point targets were placed within the imaging swath using regular spacing, and then the backscattered signal was collected using the UAV. Accordingly, the SAR image was formed using the range-Doppler (RDA) processing algorithm. The integrated sidelobe ratio (ISLR) was evaluated for ground reference point (GRP) cross-sections in order to provide a numerical assessment for performance. The results indicate the feasibility of exploring the direction of ISAC in passive SAR image formation.

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

Modern academic research encompasses a broad spectrum of subjects, reflecting the intricate and evolving challenges and opportunities that societies encounter globally. This study delves into the role of literature in shaping cultural identities and the profound impact of technology on traditional structures. Current scholarly contributions offer deep insights into the blend of humanistic concerns and technological advancements, underscoring their combined relevance in tackling contemporary societal issues.

In this paper, bikeshare data from Chicago on weather-friendly days in 2019 and 2020 were analyzed to investigate the variation in bikeshare travel before and during the pandemic. Our results show that bikeshare trips during the pandemic were much longer than prior to the pandemic. The increased rate of bikeshare usage was unbalanced spatially and varied significantly for different user types. Bikeshare was used remarkably more by casual users than by subscribers, and the increase occurred much more in the outskirts of the city. The increase in bikeshare travel was associated with a reduction in travel by ridehailing and public transit, especially in the urban periphery. The correlation of bikeshare use with the bus system was much less significant than with the rail system. Bike lanes/facilities had a mixed effect on bikeshare travel. Weekend bike trips increased in areas where there was no bike lane. Weekday trips, on the contrary, increased in the vicinity of bike greenways.