Search Results (351)

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

In recent years, the large-scale deployment of Low Earth Orbit (LEO) constellations has made autonomous time synchronization and reference maintenance within constellations a critical enabling technology. Achieving high-precision synchronization with low cost and low power consumption, without relying on onboard atomic clocks or Global Navigation Satellite System (GNSS) signals, remains a significant challenge. This paper proposes an autonomous time synchronization method for LEO constellations that relies solely on high-stability crystal oscillators as local oscillators. By leveraging satellite-to-ground and inter-satellite measurement links, the proposed approach enables constellation-wide time synchronization without external timing references. A satellite-to-ground link visibility time model is established based on orbital parameters and ground station visibility geometry. On this basis, a discrete state-space model is constructed, incorporating temperature-induced frequency perturbation compensation, frequency offset estimation, and control voltage regulation. A combined Kalman filtering and Linear Quadratic Regulator (LQR) control framework is employed to achieve precise time offset synchronization and long-term maintenance. Experimental results demonstrate that, under a Walker-Delta constellation configuration with an orbital altitude of 800 km and an inclination of 55°, the proposed method introduces a time synchronization performance better than 5 ns (1$\sigma$), with a peak-to-peak error below 30 ns. This level of performance satisfies the timing requirements of typical LEO constellation applications, including communication scheduling, high-rate modulation, and critical infrastructure timing services. Moreover, the proposed scheme supports decentralized deployment and provides local physical time signal outputs, making it well suited for large-scale satellite networks requiring high-precision autonomous time synchronization. Full article

Background: Non-classical MHC class I molecules MICA and MICB are stress-inducible NKG2D ligands that contribute to immune surveillance, non-HLA antibody formation, and alloreactivity in solid organ and hematopoietic stem cell transplantation; population-level data for Southern Europe remain limited. Methods: High-resolution MICA and MICB genotyping was performed in 1364 unrelated individuals from southern Portugal using a hybrid-capture next-generation sequencing workflow, and allele calls were analyzed with standard population-genetic metrics (allele and genotype frequencies, heterozygosity, Hardy–Weinsberg equilibrium, and LD-like D, D′, r²) and multilocus allele presence/absence encodings explored by k-means clustering, spectral clustering, principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Results: Forty-two MICA and twenty-two MICB alleles were identified; MICA*002:01, MICA*004:01, MICA*008:01, MICA*008:04 and MICB*002:01, MICB*004:01, MICB*005:02, MICB*008:01 were most frequent, and most individuals carried at least two distinct MICA and two distinct MICB allotypes. Co-occurrence and LD-like analyses revealed conserved MICA–MICB combinations, including a strong association between MICA*009:02 and MICB*005:06, while unsupervised analyses identified partially overlapping multilocus genotype backgrounds and recurrent four-allele constellations. Conclusions: These findings provide a detailed non-classical MHC reference for southern Portugal and a multilocus framework to support interpretation of non-HLA antibodies and MICA/MICB-aware donor evaluation in selected clinical scenarios, as well as the development of machine learning-based immunologic risk models. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

High-speed electrical wireline links, spanning Serializer/Deserializer backplanes and cables, chip-to-chip and die-to-die interfaces, wide-parallel single-ended (SE) buses, and simultaneous-bidirectional (SBD) buses, increasingly operate under severe insertion loss, long channel memory, and strong multi-lane interference. Equalization is therefore a central enabler for reliable symbol recovery in the presence of inter-symbol interference (ISI), echo, and near-/far-end crosstalk. This review synthesizes recent principles, architectures, and silicon-proven implementations of wireline equalizers with an emphasis on practical hardware constraints. It further organizes key research trajectories in high-speed wireline communications across three domains: (i) Time-domain equalization and detection for ISI-limited channels, spanning feed-forward equalizers, latency-relaxed decision-feedback equalization architectures that mitigate stringent feedback-loop constraints, and partial-response signaling combined with reduced-complexity maximum-likelihood sequence detection to enhance resilience against extended channel memory. (ii) Advanced modulation and frequency-domain processing, marking the transition from conventional 4-level pulse-amplitude modulation toward higher-order constellations and multicarrier techniques, notably discrete multitone and orthogonal frequency-division multiplexing, which necessitates modulation-aware frequency-domain equalization and adaptive bit- and power-loading algorithms. (iii) Crosstalk and echo mitigation for dense SE and SBD systems, including cancellation filtering in a multiple-input multiple-output framework and coding-aided interference suppression approaches. Across these domains, we present the fundamental trade-offs between equalization performance, algorithmic convergence, power-area efficiency, and latency. Full article

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

The exponential rise in microsatellite constellations offers unprecedented temporal resolution for urban monitoring. However, ensuring the radiometric integrity of these sensors over heterogeneous built environments remains a critical challenge due to low signal-to-noise ratios and spectral uncertainties. Traditional vicarious calibration relies on homogeneous pseudo-invariant calibration sites (PICS) in deserts, which fail to represent the spectral complexity and adjacency effects of urban landscapes. This study presents a novel triple-platform validation framework integrating ground (Hyperspectral), UAV (Multispectral), and satellite (Sentinel-2) data to bridge the “Point-to-Pixel” scale gap. We introduce a physics-informed “Double Calibration” protocol—combining the empirical line method with spectral response function convolution—and a block kriging spatial upscaling technique to mathematically model intra-pixel heterogeneity. Results from a 2025 campaign in a complex urban environment (Cheongju, Republic of Korea) demonstrate that simple point-averaging introduces significant representation errors ($R^2\approx 0.46$ with time lag). In contrast, our UAV-based block kriging approach recovered high correlations even with a 1-day time lag and dramatically improved the coefficient of determination (R²) under simultaneous acquisition conditions: from 0.68 to 0.92 in the blue band and to 0.96 in the NIR band. Furthermore, quantitative spatial analysis identified artificial grass as the most stable “Urban PICS” ($\mathsf{\sigma }\approx 0.020$), whereas asphalt exhibited unexpected high spatial heterogeneity ($\mathsf{\sigma }> 0.09$) due to surface aging and challenging conventional assumptions. This framework establishes a rigorous, scalable standard for validating “New Space” data products in complex urban domains. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

Reconfigurable intelligent surface (RIS)-aided index modulation (IM) shows great potential for next-generation wireless communications. Nevertheless, obtaining channel state information (CSI) for RIS-based IM incurs high pilot overhead, particularly for multi-domain IM. In this paper, we integrate orthogonal frequency division multiplexing into RIS-aided differential reflecting modulation (DRM) communications, introducing the differential reflecting frequency modulation (DRFM) system. In DRFM, information bits are jointly conveyed through the activation permutations of reflecting patterns, grouped carriers, and constellation symbols. The transmitter combines the differentially coded reflecting-time block and the time–frequency block using the Kronecker product. This allows DRFM to operate without relying on CSI at the transmitter, RIS, or receiver. Moreover, we design a novel high-rate quadrature amplitude modulation (QAM) scheme for DRFM. Compared to PSK-based DRFM, this QAM scheme can boost either the throughput or the performance of DRFM. Simulation results illustrate the superiority of the DRFM system, along with an acceptable SNR penalty, compared to non-differential modulation with coherent detection. At the same spectral efficiency, the proposed QAM-aided DRFM outperforms schemes using traditional PSK, amplitude phase shift keying (APSK), and star-QAM constellation modulations. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

This article offers a comparative study of two closely linked constellations of early Ṣūfī thought: the ascetic–mystical program of al-Fuḍayl ibn ʿIyāḍ (d. 187/803) and that of his renowned disciple Bishr al-Ḥāfī (d. 227/841). Moving beyond hagiographic anecdote, the study advances the thesis that the pair articulate two complementary modalities of tawba (repentance) that generate distinct ascetic habitus and pedagogical lineages: al-Fudayl’s “ethic of awe” (fear, juridical redress, and renunciation of patronage) and Bishr’s “aesthetics of reverence” (beauty-induced modesty, evident humility, and fame avoidance). Drawing on primary sources (Ḥilyat al-Awliyāʾ, al-Sulamī’s Ṭabaqāt al-Ṣūfiyya, al-Qushayrī’s Risāla, al-Sarrāj’s Lumaʿ), the article reconstructs each thinker’s core concepts, practices (e.g., returning wrongs, ḥafāʾ/barefoot humility), and teaching styles and maps how the teacher–disciple nexus transmits, adapts, and ritualizes these ethics into durable Ṣūfī dispositions. Methodologically, the article combines close textual analysis with practice theory to show how emotions—such as fear and modesty (ḥayāʾ)—are choreographed into public, socially legible acts, thus reframing repentance as embodied discipline rather than interior feeling alone. A prosopographic appendix traces transmission from al-Fudayl to Bishr to Sarī al-Saqaṭī and al-Junayd, clarifying how each modality survives in later Baghdad sobriety and Malāmatī self-effacement. The contribution is twofold: first, it supplies a granular typology of early Ṣūfī repentance that explains divergent stances toward money, publicity, and power; second, it models how to read early Ṣūfī biography as anthropology of practice, recovering the lived grammar by which “conversion stories” become social programs. In doing so, the article nuances standard narratives of early Ṣūfism, showing that Bishr is not merely al-Fuḍayl’s echo but a creative reframer whose “reverential” path complements—rather than imitates—the awe-driven ethic associated with al-Fuḍayl. Full article

Current passive microwave satellites like SMAP still face limitations in observational frequency and responsiveness in regions with frequent cloud cover, dense vegetation, or complex terrain, making it difficult to achieve continuous global monitoring with high spatio-temporal resolution. To enhance global high-frequency monitoring capabilities, this study utilizes global reflectivity data provided by the Tianmu-1 (TM-1) constellation since 2023, combined with multiple auxiliary variables, including NDVI, VWC, precipitation, and elevation, to develop a 9 km resolution soil moisture retrieval model. Several spatial clustering and temporal partitioning strategies are incorporated for systematic evaluation. Additionally, since the publicly available TM-1 L1 reflectivity data does not provide separable polarization channels, this study uses DDM/specular point reflectivity as the primary observable quantity for modeling and mitigates non-soil factor interference by introducing multi-source priors such as NDVI, VWC, precipitation, terrain, and roughness. Unlike SMAP’s “single orbit daily fixed local time” observation mode, TM-1, leveraging multi-constellation and multi-orbit reflection geometry, offers more balanced temporal sampling and availability in cloudy, rainy, and mid-to-high latitude regions. This enables temporal gap filling and rapid event response (such as moisture transitions within hours after precipitation events) during periods of SMAP’s quality masking or intermittent data loss. Results indicate that the model combining LC-cluster with seasonal partitioning delivers the best performance at the cluster level, achieving a correlation coefficient (R) of 0.8155 and an unbiased RMSE (ubRMSE) of 0.0689 cm³/cm³, with a particularly strong performance in barren and shrub ecosystems. Comparisons with SMAP and ISMN datasets show that TM-1 is consistent with mainstream products in trend tracking and systematic error control, providing valuable support for global and high-latitude studies of dynamic hydrothermal processes due to its more balanced mid- and high-latitude orbital coverage. Full article

The rapid advancement of low-Earth-orbit (LEO) satellite constellations and unmanned aerial vehicles (UAVs) has positioned space–air–ground integrated networks as a key enabler of large-scale IoT services. However, ensuring reliable end-to-end operation remains challenging due to heterogeneous IoT–UAV link conditions and rapidly varying satellite visibility. This work proposes a two-stage optimization framework that jointly minimizes UAV energy consumption during IoT data acquisition and ensures stable UAV–LEO offloading through a demand-aware satellite association strategy. The first stage combines gradient-based refinement with combinatorial path optimization, while the second stage triggers handover only when the remaining offloading demand cannot be met. Simulation results show that the framework reduces UAV energy consumption by over 20% and shortens flight distance by more than 30% in dense deployments. For satellite offloading, the demand-aware strategy requires only 2–3 handovers—versus 7–9 under greedy selection—and lowers packet loss from 0.47–0.60% to 0.13–0.20%. By improving both stages simultaneously, the framework achieves consistent end-to-end performance gains across varying IoT densities and constellation sizes, demonstrating its practicality for future SAGIN deployments. Full article

Background and Clinical Significance: Visual agnosia and speech production deficits are well-described sequelae of neurosurgical interventions, but their selective dissociation remains rare. This report presents an unusual combination of postoperative deficits following awake resection of a left frontal low-grade glioma. Case Presentation: We present the case of a right-handed female with left hemisphere language dominance who had a left frontal low-grade glioma. Preoperatively, she exhibited anomia and dysexecutive syndrome, including difficulty completing everyday goal-directed tasks such as sending emails and paying for parking. Following awake tumor resection, she developed two rare, dissociated deficits: (1) speech restricted to infinitive verb forms and (2) selective visual agnosia for static images, with preserved recognition of dynamic stimuli. Conclusions: This uncommon clinical constellation highlights the vulnerability of left frontal language and ventral visual processing networks during surgery and supports the dual-stream model of vision and language production. We describe a selective form of static visual agnosia affecting static images with relative preservation of dynamic and object recognition, for which we use the descriptive label “astatopsia”. This peculiar clinical condition is rarely documented in this particular combination and has not, to the best of our knowledge, previously been denominated in such a manner in the literature. Full article