Search Results (19)

As a critical infrastructure, the transportation network impacts health, safety, comfort, and the economy, making it highly vulnerable to disruptions that significantly affect social and economic well-being. To maintain optimal service during such disruptions, the critical links that are vulnerable to disruptions must be identified and their impact on network performance must be understood. This study proposes a method for identifying network vulnerabilities by targeting critical links based on topological parameters, assessing worst-case scenarios under severe conditions. These parameters serve as proxies for performance and are utilized to generate critical-link attacks to assess the network vulnerability. In addition, this study proposes a straightforward and simplistic modeling framework using topological parameters to assess the impact of such attacks on traffic flow changes. To characterize network performance and traffic volume changes under critical-link attacks, this study utilizes the complementary cumulative distribution function (CCDF), which highlights the upper tail of the distribution where extreme or rare events occur. The proposed method was applied to a real network in the Colombo Municipal Council (CMC) area in Sri Lanka. The findings of this study will help us understand the impact of critical-link attacks on transportation network performance and traffic flow and develop proactive policies to address vulnerabilities and improve overall network performance. Full article

N⁶-methyladenosine (m⁶A) is one of the most abundant chemical modifications on mRNA in eukaryotes. RNA-binding proteins containing the YT521-B (YTH) domain play crucial roles in post-transcriptional regulation of plant growth, development, and stress response by reading the m⁶A mark. However, the YTH domain-containing RNA-binding protein family has not been studied in a valuable and medicinal tree such as Cinnamomum camphora (C. camphora) yet. In this study, we identified 10 YTH genes in C. camphora, located on eight out of 12 chromosomes. Phylogenetic analysis revealed that these genes can be classified into two major classes, YTHDF (CcDF) and YTHDC (CcDC). Closely related CcYTHs within the same class exhibited a similar distribution of conserved motifs and domain organization, suggesting functional similarities among these closely related CcYTHs. All CcYTH proteins possessed a highly conserved YTH domain, with CcDC1A containing an additional CCCH domain. The liquid–liquid phase separation (LLPS) predictions indicate that CcDC1A, CcDF1A, CcDF1C, CcDF3C, CcDF4C, and CcDF5C may undergo phase transitions. Quantitative expression analysis revealed that tissue-specific expression was observed fo CcYTHs. Notably, there were two genes, CcDF1A and CcDF5C; both exhibited significantly higher expression levels in various tissues than other genes, indicating that the m⁶A-YTH regulatory network in C. camphora might be quite distinct from that in most plants such as Arabidopsis thaliana (A. thaliana) with only one abundant YTH protein. According to the analysis of the up-stream cis-regulatory elements of these YTH genes, these genes could be closely related to stress, hormones, and development. The following stress response experiments further verified that their expression levels indeed changed under both PEG and NaCl treatments. These findings not only provide a foundation for future functional analysis of CcYTHs in C. camphora, but also provide insights into the functions of epigenetic mark m⁶A in forest trees. Full article

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos. Full article

The equatorial post-sunset ionospheric irregularities induce rapid fluctuations in the phase and amplitude of global navigation satellite system (GNSS) signals which may lead to the loss of lock and can potentially degrade the position accuracy. This study presents a new analysis of L-band scintillation from a low latitude station at Guntur (Geographic 16.44°N, 80.62°E, dip 22.18°), India, for the period of 18 months from August 2021 to January 2023. The observations are categorized either in the medium Earth-orbiting (MEO) or geosynchronous orbiting (GSO) satellites (GSO is considered as a set of the geostationary and inclined geosynchronous satellites) for L1, L2, and L5 signals. The results show a higher occurrence of moderate (0.5 < S4 ≤ 0.8) and strong (S4 > 0.8) scintillations on different signals from the MEO compared to the GSO satellites. Statistically, the average of peak S4 values provides a higher confidence in the severity of scintillations on a given night, which is found to be in-line with the scintillation occurrences. The percentage occurrence of scintillation-affected satellites is found to be higher on L1 compared to other signals, wherein a contrasting higher percentage of affected satellites over GSO than MEO is observed. While a clear demarcation between the L2/L5 signals and L1 is found over the MEO, in the case of GSO, the CCDF over L5 is found to match mostly with the L1 signal. This could possibly originate from the space diversity gain effect known to impact the closely spaced geostationary satellite links. Another major difference of higher slopes and less scatter of S4 values corresponding to L1 versus L2/L5 from the GSO satellite is found compared to mostly non-linear highly scattered relations from the MEO. The distribution of the percentage of scintillation-affected satellites on L1 shows a close match between MEO and GSO in a total number of minutes up to ~60%. However, such a number of minutes corresponding to higher than 60% is found to be larger for GSO. Thus, the results indicate the possibility of homogeneous spatial patterns in a scintillation distribution over a low latitude site, which could originate from the closely spaced GSO links and highlight the role of the number of available satellites with the geometry of the links, being the deciding factors. This helps the ionospheric community to develop inter-GNSS (MEO and GSO) operability models for achieving highly accurate positioning solutions during adverse ionospheric weather conditions. Full article

Orthogonal frequency division multiplexing (OFDM) inherits multi-carrier systems’ inevitable high peak-to-average power ratio (PAPR) problem. In this paper, a novel alternating companding technique is proposed to combat the harassment of high PAPR. The sequential $\mu$-law companding (SULC) and a tone with a lower PAPR result in only partial tones needing companding. The SULC scheme’s PAPR and bit error rate (BER) performance has been balanced and improved. However, the computational complexity is still too high to be implemented. Therefore, this study sorted the transmission signals according to their amplitudes. Then, all the tones are divided into two groups by estimating the rough companding amount (around 54% of the subcarriers), using traditional parallel companding for the first group and the other group only by partial $\mu$-law companding. This alternating $\mu$-law companding (AULC) is proposed to improve the PAPR performance and simultaneously reduce complexity. Simulation results show that the proposed AULC method appreciably reduces the PAPR by about 5 dB (around 45%) compared with the original $\mu$-law at complementary cumulative distribution function (CCDF) equal to ${10}^{-4}$. Moreover, it only requires a moderate complexity to outperform the other companding schemes without sacrificing the BER performance in the OFDM systems. Full article

To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure. Full article

The increase in channel bandwidth and peak-to-average power ratio (PAPR) of modern communication standards poses a serious challenge to performing channel power (CP) and complementary cumulative distribution function (CCDF) measurements in real-time using standard measurement solutions based on spectrum analyzers (SA). Recently, Software Defined Radio (SDR) technology has become a viable alternative to the conventional real-time spectrum monitoring approach based on SA due to its reduced cost. Therefore, in this paper, we propose a novel, innovative, agile and cost-effective solution to enable both CP and CCDF measurements on a state-of-the-art SDR platform. The proposed solution exploits the ability of the SDR equipment to access signal samples in the time domain and defines both CP and CCDF-type measurements. The two measurement functions are software implemented in GNU Radio by designing customized blocks and integrated into a graphical user interface. The proposed system was first tested and parameterized in a controlled environment using emitted signals specific to the IEEE 802.11ax family of wireless local area networks. After parameterization, the SDR-based system was used for over-the-air measurements of signals emitted in the 4G+, 5G and 802.11ax communication standards. By performing the measurement campaign, we have demonstrated the capabilities of the measurement system in performing real-time measurements on broadband channels (up to 160 MHz for IEEE 802.11ax). Altogether, we have proved the usability of CP and CCDF measurement functions in providing valuable insights into the power distribution characteristics of signals emitted by the latest communication standards. By exploiting the versatility of SDR technology, we have enabled a cost-effective solution for advanced real-time statistical measurements of modern broadband signals. Full article

In direct current optical orthogonal frequency division multiplexing (DCO-OFDM) systems, the high peak-to-average power ratio (PAPR) has been a significant challenge. Recently, lexicographical symbol position permutation (LSPP) using random permutations has been introduced as an efficient solution to reduce high PAPR. In this paper, we aim to evaluate the effectiveness of LSPP by comparing both adjacent and interleaved lexicographical permutation sequences with random lexicographical permutation sequences. Our findings demonstrate that random permutation yields superior PAPR reduction performance results when compared to adjacent and interleaved permutation. However, in scenarios with a limited number of sub-blocks, the use of adjacent and interleaved permutation becomes more favorable, as they can eliminate the possibility of generating identical permutation sequences, a drawback of random permutation. Additionally, we propose a novel algorithm to determine the optimal number of candidate permutation sequences that can achieve acceptable PAPR reduction performance while adhering to computational complexity constraints defined by the system requirements. Full article

Orthogonal frequency division multiplexing (OFDM) has the characteristics of high spectrum efficiency and excellent anti-multipath interference ability. It is the most popular and mature technology currently in wireless communication. However, OFDM is a multi-carrier system, which inevitably has the problem of a high peak-to-average power ratio (PAPR), and s signal with too high PAPR is prone to distortion when passing through an amplifier due to nonlinearity. To address the troubles caused by high PAPR, we proposed an improved tone reservation (I-TR) algorithm to alleviate the above native phenomenon, which will pay some modest pre-calculations to estimate the rough proportion of peak reduction tone (PRT) to determine the appropriate output power allocation threshold then utilize a few iterations to converge to the near-optimal PAPR. Furthermore, our proposed scheme significantly outperforms previous works in terms of PAPR performance and computational complexity, such as selective mapping (SLM), partial transmission sequence (PTS), TR, tone injection (TI), etc. The simulation results show that in our proposed scheme, the PAPR is appreciably reduced by about 6.44 dB compared with the original OFDM technique at complementary cumulative distribution function (CCDF) equal to ${\mathbf{10}}^{-3}$, and the complexity of I-TR has reduced by approximately 96% compared to TR. Besides, as for bit error rate (BER), our proposed method always outperforms the original OFDM without any sacrifice. Full article

A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities. Full article

With the acceleration of urbanization, urban multi-energy systems (UMESs) generate more and more carbon emissions, causing severe environmental issues. The carbon generated by UMESs includes not only emissions from the consumption of fossil fuels for electricity generation during operation phases, but also those from the transportation, extraction, and recycling of materials during construction phases. Meanwhile, as carbon emissions are delivered with the energy flow among devices in the UMES, they are distributed differently across devices. Under this background, analyzing the carbon emissions of UMESs considering different life-cycle phases (i.e., operation and construction) and carbon flow characteristics is essential for carbon reduction and environmental protection. Considering that, a novel framework for tracing and evaluating life-cycle carbon emissions of UMESs is proposed in this paper. Firstly, the carbon emission models of different devices in UMESs, including energy sources and energy hub (EH), are established considering both the construction and operation phases. On this basis, the carbon flow matrixes of EHs coupled with the energy flow model are formulated to trace the distribution of life-cycle carbon emissions in UMESs. Moreover, different evaluation indices including the device carbon distribution factor (DCDF) and consumer carbon distribution factor (CCDF) are proposed to quantify the carbon emissions of devices and consumers in UMESs. The case study results based on a typical test UMES are presented to verify the effectiveness of the proposed framework. The analysis results of the test system show that about 60% of carbon emissions are delivered to electricity loads and the construction-produced carbon emissions of energy sources and EH devices account for nearly 35% of total carbon emissions at some periods. Full article

The partial transmit sequence (PTS) technique is a fairly suitable scheme to mitigate the high peak-to-average power ratio (PAPR) problem inherent in 5G multicarrier systems, especially considering a high-order QAM modulation design. However, the high computational complexity level and the speed of the convergence for optimizing the phases of the transmitting signal restrict this technique in practical applications. In this paper, a low-complexity frequency-domain-evaluated PTS (F-PTS) based on a spacing multiobjective (SMO) processing algorithm is proposed to reduce the PAPR values. The PAPR performance are accurately predicted in terms of modifying relative dispersion in the frequency domain. As a result, the complexity of searching the optimal phase factors and IFFT computing is simplified. Moreover, a frequency-domain- and time-domain-evaluating PTS (FTD-PTS) is employed to search the optimal solution with a reasonable complexity. Simulation results verify that the operation rate of F-PTS is significantly improved after transferring the exhaustive search strategy of PTS into the SMO algorithm, and the F-PTS PAPR reduction performance is just 0.3 dB away from theoretical optimal performance. The FTD-PTS spends an acceptable operation rate to obtain optimal PAPR reduction performance, which subtracts 0.5 and 0.6 dB more than PSO-PTS and conventional PTS at $CCDF={10}^{-3}$, respectively. Full article

Nowadays, the demand for communication multi-carriers’ channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases $U=8,$ the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10⁻², which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10⁻¹, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM. Full article

The signal conditioning treatment to achieve good relation of power with radio-frequency (RF) conversion in conventional transceiver systems require precise baseband models. A developed framework is built to provide a demonstration of the modeling figures of merit with orthogonal frequency division multiplexing (OFDM) support under signal conditioning and transmission restrictions to waveforms with high peak to average power ratio (PAPR) in practical applications. Therefore, peak and average power levels have to be limited to correct high PAPR for a better suited correction power from the amplifier that can lead to compression or clipping in the signal of interest. This work presents an alternative joint crest factor reduction (CFR) algorithm to correct the performance of PAPR. A real-time field-programmable gate array (FPGA) testbed is developed to characterize and measure the behavior of an amplifier using a single-carrier 64–QAM OFDM based on long-term evolution (LTE) downlink at $2.40$ GHz as stimulus, across wide modulation bandwidths. The results demonstrate that the CFR accuracy capabilities for the signal conditioning show a reliable clipping reduction to give a smooth version of the clipping signal and provide a factor of correction for the unwanted out-of-band emission validated according to the adjacent channel power ratio (ACPR), PAPR, peak power, complementary cumulative distribution function (CCDF), and error vector magnitude (EVM) figures of merit. Full article

Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes. Full article