Search Results (82)

Radio Frequency Interference has emerged as a growing challenge for aviation safety and system integrity due to the increasing spectral overlap between communication technologies and aviation systems. This paper investigates the sources, types, and consequences of RFI in Global Navigation Satellite Systems, Instrument Landing Systems, and altimeters used in civil aviation. A detailed examination of both intentional and unintentional interference is presented, highlighting real-world incidents and simulated impact models. The study analyzes technical mechanisms such as receiver desensitization, intermodulation, and cross-modulation, and further explores UAV-based interference detection frameworks. Mitigation strategies are reviewed, including regulatory practices, spectrum filters, shielding architectures, and dynamic UAV sensing systems. Comparative insights into simulation results, shielding techniques, and regulatory gaps are discussed. The paper concludes with recommendations for enhancing current aviation standards and suggests a hybrid validation model combining in-flight measurements with simulation-based assessments. This research contributes to the understanding of electromagnetic vulnerabilities in aviation and provides a basis for future mitigation protocols. Full article

Reliable Position, Navigation, and Timing (PNT) is becoming more and more important, considering the proliferation of highly autonomous safety- and liability-critical systems. Due to their vulnerability to various threats such as deliberate Radio Frequency Interference (RFI), including jamming, spoofing, and others, there is significant research into finding backup/fallback solutions that allow safe mission completion or termination. This work compares two such systems: one based on Angle of Arrival (AoA) measurement and one based on cellular (4G and 5G) signals. The results are generated using simulations, which are substantiated by real-world performance measurements. It is shown that both systems have the potential to serve as backup navigation solutions and that the cellular system outperforms the AoA-based solution, albeit at a much higher price and with higher computational requirements. Full article

The U.S. range sheep industry uses estimated breeding values (EBVs) as part of their breeding objectives to increase post-weaning weight. The study objective was to quantify the relationship between lamb growth EBVs, feed intake, and feed efficiency. Eighty-one range ewe lambs were enrolled in the study to measure residual feed intake (RFI) over two 42-d periods at both the weaning and yearling stages. The ewe lambs’ post-weaning weight EBVs (PWWT EBVs) were linearly associated with their phenotypic traits. Preliminary genome wide associations (GWAs) were also performed with Dry Matter Intake (DMI), RFI, mid-test body size, and average daily gain (ADG) and Ovine 50K SNP genotypes. Post-weaning weight EBVs were associated with dry matter intake (DMI) (p < 0.05) but had no association with residual feed intake (RFI) (p > 0.05) in both experimental periods. However, PWWT EBV was predictive of mid-test body weight in both periods (p < 0.05). A single SNP at Oar2:68,812,505, located within DMRT2, was associated with DMI and RFI in the second experimental period (Bonferroni corrected p <0.05). While selecting for higher post-weaning weight range ewes may increase feed consumed due to a larger body size, it was not associated with feed efficiency. Full article

In this study, we combined genomic and gut microbiome data to evaluate 13 economically important growth and feed efficiency traits in 407 Dahen broilers, including body weight (BW) at four, six, nine, and ten weeks of age (BW4, BW6, BW9, and BW10), as well as the average daily gain (ADG6, ADG9, and ADG10), feed conversion ratio (FCR6, FCR9, and FCR10), and residual feed intake (RFI6, RFI9, and RFI10) for the three growing ages. The highest ADG and lowest FCR were observed at nine and six weeks of age, respectively. We obtained 47,872 high-quality genomic single-nucleotide polymorphisms (SNPs) by sequencing the genomes and 702 amplicon sequence variants (ASVs) of the gut microbiome by sequencing the 16S rRNA gene, both of which were used for analyses of linear mixed models. The heritability estimates (± standard error, SE) ranged from 0.103 ± 0.072 to 0.156 ± 0.079 for BW, 0.154 ± 0.074 to 0.276 ± 0.079 for the ADG, 0.311 ± 0.076 to 0.454 ± 0.076 for the FCR, and 0.413 ± 0.077 to 0.609 ± 0.076 for the RFI traits. We consistently observed moderate and low negative genetic correlations between the BW traits and the FCR and RFI traits (r = −0.562 to −0.038), whereas strong positive correlations were observed between the FCR and RFI traits (r = 0.564 to 0.979). For the FCR and RFI traits, strong positive correlations were found between the measures at the three ages. In contrast to the genomic contribution, we did not detect a gut microbial contribution to all of these traits, as the estimated microbiabilities did not confidently deviate from zero. We systematically evaluated the contributions of host genetics and gut microbes to several growth and feed efficiency traits in Dahen broilers, and the results show that only the host genetics had significant effects on the phenotypic variations in a flock. The parameters obtained in this study, based on the combined use of genomic and gut microbiota data, may facilitate the implementation of efficient breeding schemes in Dahen broilers. Full article

Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS. Full article

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R²) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made. Full article

eHealth literacy (eHL) is directly linked to disease prevention, health promotion, and improved healthcare outcomes. The objectives of this study are to assess undergraduate university students’ knowledge and perceived skills of finding, appraising, and applying electronic health information to health-related problems, as well as to assess the association of eHL with physical, psychological, and emotional self-care. Methods: The measurement model, comprising four correlated factors based on the 28 valid items from two reliable and valid tests, the ‘eHealth literacy scale (eHEALS)’ and ‘the self-care assessment tool (SCAT)’, was estimated using confirmatory factor analysis (CFA) among a sample of 1557 undergraduate university students in Pakistan. Results: The mean value of the eHEALS ranges between 2.90 and 3.33, indicating that the majority of the respondents had moderate levels of eHL skills. Female respondents and respondents from urban areas have greater levels of perceived eHL skills compared with their male and rural counterparts. The CFA model fit indices show that the goodness of fit values are acceptable: x² = 7.727, p = 0.000; RMSEA = 0.065; TLI = 0.930, CFI = 0.936, IFI = 0.936, GFI = 0.890, NFI = 0.928, RFI = 0.920, PGFI = 0.754. Conclusion: Electronic health (eHealth) literacy has a strong positive association with physical, psychological, and emotional self-care. However, perceived eHL skills among undergraduate university students are moderate, making them potentially susceptible to health risks. Implications: Our study has several practical implications. Its findings can be used to devise eHealth literacy programs for developing relevant skills among undergraduate university students based on their identified needs. Full article

Radio frequency interference (RFI) analysis is crucial for ensuring the proper functioning of a radio telescope and the quality of astronomical observations, as human-generated interference can compromise scientific data collection. The aim of this study is to present the results of an RFI measurement campaign in the frequency range of 4–5.8 GHz, a portion of the well-known C-band, for the Sardinia Radio Telescope (SRT), conducted in October–November 2023. In fact, this Italian telescope, managed by the Astronomical Observatory of Cagliari (OAC), a branch of the Italian National Institute for Astrophysics (INAF), was recently equipped with a new C-band receiver that operates from 4.2 GHz to 5.6 GHz. The measurements were carried out at three strategically chosen locations around the telescope using the INAF mobile laboratory, providing comprehensive coverage of all possible antenna pointing directions. The results revealed several sources of RFI, including emissions from radar, terrestrial and satellite communications, and wireless transmissions. Characterizing these sources and assessing their frequency band occupation are essential for understanding the impact of RFI on scientific observations. This work provides a significant contribution to astronomers who will use the SRT for scientific observations, offering a suggestion for the development of mitigation strategies and safeguarding the radio astronomical environment for future observational campaigns. Full article

This study was conducted to evaluate associations of blood variables and urine variables with different residual feed intakes (RFIs) in growing Chuanzang black (CB) pigs. A total of 228 growing CB boars from 99 days were used. The same basal diet was offered ad libitum and individual feed intake and body weight were measured over a period of 181 d. The CB pigs were categorized based on their residual feed intake values, with six individuals each from the high and low ends selected and divided into two groups: the low residual feed intake group (LS) and the high residual feed intake group (HS). Serum and urine samples were collected at the end of the experiment for determination of metabolomics profiling. Results showed that there were significantly different metabolites in serum and urine of different RFI groups (fold-change, FC > 2.0 or FC < 0.5, and p < 0.05), and 21 metabolites were identified in serum and 61 in urine. Cluster analysis showed that 20 metabolites were up-regulated and one metabolite was down-regulated in serum; 44 metabolites were up-regulated and 17 metabolites were down-regulated in urine. Kyoto Encyclopedia of Genes and Genomes analysis showed that the differential metabolites of serum were enriched in linoleic acid metabolism, and the differential metabolites of urine were enriched in steroid hormone biosynthesis, taurine and hypotaurine metabolism, and primary bile acid biosynthesis. The correlations between serum metabolites and urine metabolites indicated a significant positive correlation between all fatty acyls in serum metabolites and L-glutamate in urine. However, no compelling genetic or blood biomarkers have been found to explain the differences in RFI, suggesting multiple approaches to effective feed use in pigs. This study provides new insights into the subsequent assessment of RFI by metabolomics profiling, as well as the development of novel feed additives for the factors that will facilitate future research directions in CB pigs. Full article

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. Full article

Brangus cattle are gaining popularity in the Southeast U.S. due to the desirable heat tolerance from their Brahman influence combined with the superior carcass merit aspects of Angus genetics. However, little is known about the optimal evaluation conditions for this hybrid breed when placed on test for Residual Feed Intake (RFI), a heritable measure of feed efficiency that allows improvement in performance without altering carcass traits. To address this, dry matter intake (DMI) was measured on Brangus heifers for 70-d to determine the optimal days on feed required to estimate feed intake and ADG and assess if inclusion of ultrasound measures of carcass merit into the model impact RFI rankings for this breed. The 56-d test period had a regression coefficient of 0.96 (p < 0.0001), R² = 0.94, rp = 0.97 (p < 0.0001), and rs = 0.97 (p < 0.0001), indicating little change in rank of cattle for DMI compared to a 70-d test. ADG was the limiting factor in determining test duration. Based upon examining only heifers that calved, ultrasound backfat measures should be included in the RFI model to normalize for differences in heifer maturity. Results from this study indicate that a test duration of 56-d is sufficient to accurately estimate DMI in this population. This data indicates on-test duration can be shortened, enhancing the rate of genetic change by reducing cost and increasing the number of animals that can be tested annually. Full article

Background: This pilot study aimed to evaluate the Retinal Function Imager (RFI) for visualizing retinal vasculature and assessment of blood flow characteristics in patients with retinal vasculitis. The RFI is a non-invasive imaging device measuring the blood flow velocity (BFV) in secondary and tertiary retinal vessels using hemoglobin as an intrinsic motion-contrast agent. Methods: To test the feasibility of the RFI for patients with retinal vasculitis, capillary perfusion maps (nCPMs) were generated from 15 eyes of eight patients (five females; mean age: 49 ± 12 years) with a mean uveitis duration of 74 ± 85 months. Five of these patients had birdshot chorioretinopathy, and three had primarily non-occlusive venous retinal vasculitis of unknown origin. To reflect that the BFV may be more reduced in patients with prolonged disease, patients were classified into a short-term (uveitis duration: 8–15 months) and a long-term uveitis group (uveitis duration: 60–264 months). Data were compared with healthy controls (16 eyes of 11 patients; mean age 45 ± 12 years; 8 females). Results: The mean BFV in the controls was 3.79 ± 0.50 mm/s in the retinal arteries and 2.35 ± 0.44 mm/s in the retinal veins, which was significantly higher compared to the retinal vasculitis group. Patients revealed an arterial BFV of 2.75 ± 0.74 mm/s (p < 0.001) and a venous BFV of 1.75 ± 0.51 mm/s (p = 0.016). In the short-term group, a trend towards a decreased venular and arteriolar BFV was seen, while a significant reduction was observed in the long-term group. The patients’ microvasculature anatomy revealed by the nCPMs appeared unevenly distributed and a lower number of blood vessels were seen, along with a lower degree of complexity of their branching patterns, when compared with controls. Conclusions: This study demonstrated a reduction in venular and arteriolar BFVs in patients with retinal vasculitis. BFV alterations were already observed in early disease stages and became more pronounced in progressed disease. Additionally, we showed that retinal microvasculature changes may be observed by nCPMs. Retinal imaging with the RFI may serve as a diagnostic and quantifying tool in retinal vasculitis. Full article

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver’s bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system. Full article

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the “zero trap” region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the “zero trap” region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. Full article

This study focused on exploring the metabolomic profiles of crossbred beef cattle with varying levels of residual feed intake (RFI), a measure of feed efficiency in beef cattle. Sixty-seven crossbred growing beef steers (BW = 277 ± 29.7 kg) were subjected to a high-forage total mixed ration for 64 days to determine their RFI phenotypes. At the end of the 64d feeding trial, beef steers were divided into two groups based on their RFI values: low (or negative)-RFI beef steers (n = 28; RFI = −1.08 ± 0.88 kg/d) and high (or positive)-RFI beef steers (n = 39; RFI = 1.21 ± 0.92 kg/d). Blood samples were collected, and plasma samples were analyzed using Nuclear Magnetic Resonance spectroscopy, resulting in the identification of 50 metabolites. The study found a distinct metabolomic signature associated with RFI status. Eight metabolites, including amino acids (tyrosine, glycine, valine, leucine, and methionine) and other compounds (dimethyl sulfone, 3-hydroxy isovaleric acid, citric acid, creatine, and L-carnitine), showed differential abundance between low- and high-RFI groups. Specifically, tyrosine, glycine, and dimethyl sulfone exhibited significant specificity and sensitivity, which produced a discriminatory model with an area under the receiver operating characteristic (ROC) curve of 0.7, making them potential markers for RFI. A logistic regression model incorporating these biomarkers effectively distinguished between high- and low-RFI steers, with a threshold cutoff point of 0.48, highlighting a distinctive metabolite profile associated with efficient nutrient utilization in low-RFI cattle. The logistic regression model, incorporating these biomarkers, holds promise for accurately categorizing RFI values, providing insights into the metabolic basis of feed efficiency in beef cattle. Full article