Signal Power Research Articles

Comparison of Brain Function Between Medication-Naïve ADHD with and without Comorbidity in Chinese Children Using Resting-State fNIRS.

This study used functional near-infrared spectroscopy (fNIRS) to investigate brain activation patterns in children with attention deficit hyperactivity disorder (ADHD) with and without additional comorbidities to identify disease-related biomarkers by the neuroimaging that will facilitate to make a diagnosis decision. In this study, 165 medication-naive children aged 7 to 15 years were recruited and categorized into four groups: ADHD, ADHD with learning disabilities (ADHD&LD), ADHD with oppositional defiant disorder (ADHD&ODD), and healthy controls. A multichannel fNIRS system was used to monitor hemodynamic changes at rest state in the prefrontal and temporal lobes of the brain. The amplitude of a low-frequency fluctuation (ALFF) matrix was calculated by summation and averaging of the square root of the signal power spectrum. One-way analysis of variance was used to identify statistical differences between channels. All ADHD children presented significantly higher ALFF values in different brain regions when compared with the healthy controls. Patients with ADHD&LD exhibited higher ALFF values in the medial prefrontal cortex (P Ch38 = .01, P Ch48 = .01), temporal cortex (P Ch22 = .04, P Ch41 = .002, P Ch51 = .001), and the left ventrolateral prefrontal cortex (P Ch39 = .0009, P Ch50 = .001), whereas ADHD&ODD children were not significantly different to those diagnosed with ADHD. ADHD with learning disabilities (LD) possessed a different pathogenesis from ADHD, manifested as lower functional brain activity in the medial prefrontal cortex, temporal cortex, and the left ventrolateral prefrontal cortex, while ADHD&ODD did not present significant changes compared with ADHD. ODD-related symptoms may be part of ADHD symptoms rather than being an independent disorder.

Charisma and Humanity: The Powerful Signals of Effective Leadership in Times of Uncertainty

Charisma and Humanity: The Powerful Signals of Effective Leadership in Times of Uncertainty

Digital mobile fronthaul scheme based on diff-delta-sigma modulation and OCT precoding.

In this Letter, we experimentally demonstrate digital mobile fronthaul (MFH) for 65536 quadrature amplitude modulation (65536-QAM) signals based on a diff-delta-sigma modulation (D-DSM) scheme with orthogonal circulant matrix transform (OCT) precoding. By combining the D-DSM scheme with OCT precoding, we successfully solved the problem of uneven distribution of in-band quantization noise (IBN) while bringing about a quantization SNR gain of about 1.5 dB. In addition, we compare two signal combination schemes, Gray coding and power superposition, in the D-DSM scheme. The results show that the power superposition scheme can achieve similar performance to Gray coding with lower computational complexity. At the same time, the power superposition scheme is less affected by the saturation effect. Therefore, the D-DSM solution combined with OCT precoding and power superposition provides an effective solution for future 6 G digital mobile fronthaul.

Sparsity-Based Nondestructive Evaluations of Downhole Casings Technique Using the Uniform Linear Array

Borehole pulsed eddy-current (PEC) systems based on uniform linear multicoil arrays (ULMAs) perform efficient nondestructive evaluations (NDEs) of metal casings. However, the limited physical space of the borehole restricts the degrees of freedom (DoFs) of ULMAs to be less than the number of constraints, which leads to the difficulty of compensating for the differences in signals acquired by different receivers with different transmitting-to-receiving distances (TRDs), and thus limits the effectiveness of the ULMA system. To solve this problem, this paper proposes sparse linear constraint minimum variance (S-LCMV) for NDEs of downhole casings employing ULMAs. By transforming and characterizing the original PEC signal, it was observed that the signal power dramatically decreased with increasing Legendre polynomial stage, confirming that the signal was sparsely distributed over the Gauss–Legendre stages. Using this property, the S-LCMV cost function with reduced constraints was constructed to provide enough DoFs to accurately calculate the weight coefficients, thus improving the detection performance. The effectiveness of the proposed method was verified through field experiments on an 8-element ULMA installed in a borehole PEC system for NDEs of oil-well casings. The results demonstrate that the proposed method could improve the weighting effect by reducing the number of constraints by 70% while ensuring the approximation accuracy, which effectively improved the signal-to-noise ratio of the measured signals and reduced the computational cost by about 87.9%.

A MEPS-UHPLC-MS/MS analytical platform to detect isoprostanoids and specialized pro-resolving mediators in the urinary extracellular vesicles of mountain ultramarathon runners

Oxylipins are powerful signalling compounds derived from polyunsaturated fatty acids (PUFAs) and involved in regulating the immune system response. A mass spectrometry-based method was developed and validated for the targeted profiling of 52 oxylipins (e.g., isoprostanoids, prostaglandins, epoxy- and hydroxy-fatty acids, specialized pro-resolving mediators) and 4 PUFAs in small urinary extracellular vesicles (uEVs). Ultrasound-assisted extraction using a 50:50 v/v MeOH:H2O mixture ensured optimal analytical performances. Limits of detection ranged between 10 and 400 pg/mL for oxylipins and 0.10–3 ng/mL for PUFAs. Satisfactory recoveries (85–116 %) and good intra- and inter-day precisions (RSD ≤15 %) were obtained for all the analytes. The reliability of the procedure was tested in a real case scenario by monitoring ultramarathon runners during the world Tor des Géants® (TDG) race. Both F2- and E2-isoprostanes were detected in small uEVs of the ultramarathon runners, suggesting the onset of an oxidant insult. 5-F2t-IsoP exhibited significant pre- to post-race variations, thus potentially representing a non-invasive marker of in-vivo lipid peroxidation. The presence of specialized pro-resolving mediators suggests the activation of pro-resolution signalling cascade resolving inflammation. These outcomes may help manage post-exercise recovery and improve training.

Acquisition and Analysis of Facial Electromyographic Signals for Emotion Recognition.

The objective of the article is to recognize users' emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

Comparative Analysis of Digital Self-Interference Cancellation Methods for Simultaneous Transmit and Receive Arrays

This paper provides a comprehensive theoretical derivation of the residual noise power and effective isotropic isolation (EII) signal models in baseband digital cancellation, aperture-level digital cancellation, and beamforming spatial cancellation models in simultaneous transmit and receive (STAR) arrays. We simulated and analyzed the isolation performance and self-interference (SI) noise cancellation of STAR systems using digital SI cancellation (SIC) and beamforming in a 32-element planar array. The simulation results show that in the absence of SIC or baseband digital SIC, the EII obtained by receive adaptive beamforming (ABF) is 20 dB higher than that obtained by transmit ABF. On the basis of aperture-level digital SIC, the EII obtained from transmit ABF and receive ABF is basically the same. The EII obtained by baseband digital SIC is 22.5 dB and 41.1 dB lower than that of transmit ABF and receive ABF, respectively. Therefore, baseband digital SIC technology is not required when using transmit ABF and receive ABF.

High-performance power spectral/bispectral estimator for biomedical signal processing applications using novel memory-based FFT processor

This research paper proposes a novel robust high-performance power spectrum estimator, and bispactral power density analyzer that has outstanding capabilities in estimating noisy biomedical signal's power spectrum, and bispectrum. Biomedical signals usually are exposed to several sources of noises such as electrical noise from environmental noise from external sources, electrical equipment, and biological noise from the body. Therefore, accuracy and reliability are the most important feature of these systems in processing non-stationary biomedical signals. The proposed computationally-efficient architecture is based on a radix-8 memory-based 1024-point Blackman-Tuckey method power spectral density (PSD) estimator. The proposed nonparametric estimator uses a novel shared-resource CORDIC-Ⅱ unit to avoid multiplications in FFT computation, as well as filtering operations implemented in folded architectures. In order to merge two FFTs, the module uses bidirectional fractional delay filters to estimate half delay samples. By using modified safe-scaling, valid final output would be achieved, without any averaging operation. The proposed and competing designs are implemented on Artix-7 FPGA which is an ideal option for DSP applications. As final results demonstrate, the hardware has a remarkable capability in operating in short word-lengths which allows high-performance in low-power applications to compute the power spectrum and bicoherence of a vital signal.

The Carrier Proteome Should Be Reassessed for Each Mass Analyzer Architecture.

A clever utilization of classic proteomics reagents now allows the effective amplification of the peptide sequencing potential in shotgun proteomics. The application of this method has helped usher in the exciting new field of single cell proteomics. While it was easy to first think that this approach was finally the answer for the polymerase chain reaction in protein chemistry, limitations were carefully described by the authors and others. A study by Cheung et al. systematically identified the consequences of higher concentration carrier proteomes and defined the "carrier proteome limit" [Cheung et al. Nat. Methods 2021, 18, 76]. While this work has been replicated by others, every analysis published to date has used a variation of the same mass analyzer. When the same analysis is performed on alternative instruments, these limits appear to be very different and can be attributed to defined characteristics of each mass analyzer. Specifically, in mass analyzers with a higher relative intrascan linear dynamic range, increased carrier channels appear less detrimental to quantitative accuracy. As such, we may be limiting the power of isobaric peptide signal "amplification" by restricting ourselves to traditional mass analyzer options for shotgun proteomics.

Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.

Posttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that occurs following traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for PTSD and their potential implications. Over four weeks, we measured PTSD symptoms using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings that lasted approximately 180 seconds, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the randomness or unpredictability of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL-5 measured closest to the EEG session, using age, sex, and the grouping variable 'batch' as covariates. We observed a significant negative correlation between Shannon entropy in fronto-temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 (r = -0.456, FDR-corrected p = 0.01), AF4 (r = -0.362, FDR-corrected p = 0.04), and T7 (r = -0.472, FDR-corrected p = 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 (r=-0.429, FDR-corrected p=0.04). Our findings suggest that EEG data acquired using a mobile EEG device is associated with PTSD symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD.

Integrated erbium-doped waveguide amplifier on lithium niobate on insulator

The erbium-doped lithium niobate on insulator (Er:LNOI) has garnered significant attention as it combines outstanding gain property with integration capabilities, making it a promising solution for active devices on the versatile LNOI platform. This also paves a way for the development of appealing erbium-doped waveguide amplifiers. Here, we demonstrate an efficient integrated Er:LNOI micro-waveguide amplifier directly compatible with the lens fiber. In a 5.6-cm-long Er:LNOI micro-waveguide, we demonstrate a maximum internal net gain of 18.8 dB for 1531-nm signal light with 1460-nm pumping. Moreover, the maximum output signal power surpasses 20 mW, thereby confirming the practicality of the Er:LNOI micro-waveguide amplifier.

Modeling of multi-kW in-band pumped triple-clad thulium-doped fiber architecture.

We present a model and design for a high-power triple-clad thulium doped fiber specifically for in-band or tandem pumping. This fiber is designed to maximize the output signal power while taking into consideration common effects that limit high-power operation. In-band pumping will allow for thulium fiber lasers to reach multi-kW power levels.

Architecting CubeSat constellations for messaging service, Part I

In today’s modern and globalized world, connectivity is a key factor for businesses, production facilities, sensor networks, and ordinary people. However, there are still populated areas which are not covered by ground-based telecommunications infrastructure. This is where telecommunication satellite constellations come in, as they can provide coverage to remote and uninhabited regions and fill existing connectivity gaps to ensure data transfer.LoRa is one of the technologies designed for data transmissions over long distances with low power consumption. Alongside with other technologies of the Low-Power Wide Area Network family, it is widely used for Internet of Things applications. LoRa chirp spread spectrum modulation is robust against the Doppler frequency shifts encountered in low earth orbits, and it has already been used in IoT satellite communications. Due to the low transmitted signal power, the achieved data rate is not high, making it a suitable technology for telecommunications payloads on CubeSat platforms for messaging services. As compared to existing traditional communication satellite systems, CubeSat constellations are low-cost and may offer an affordable connectivity service to developing regions.This study is divided in two parts. In Part I the demand model is built based on the population distribution not covered by cell towers. The LoRa link performance is analyzed, considering the impact of LoRa channel parameters variation, such as spreading factor and channel bandwidth, while satellite orbital height, transmission antenna beamwidth, and transmitter peak power have a direct impact on the payload mass. Among thousands of possible configurations, 73 feasible payload designs have been downselected. In Part II of the study, the satellite mass and the total system cost are estimated based on the payload parameters obtained. Messages transmission simulation via a constellation is conducted in order to identify optimal constellation architectures for messaging service, as well as the main drivers of the system economic profitability.The presented analysis results provide a deeper understanding of LoRa connectivity advantages and limitations together with the performance drivers, which will support the optimization of future LoRa-based satellite communication systems and other IoT satellite constellations.

Analyzing the Impact of Accumulated Training Shots on Electromyography Parameters in Trained Archery Athletes: Exploring Fatigue and Its Association with Training Practices

Background: Accumulated training shots throughout a session may induce changes in electromyography (EMG) parameters of the primary muscles involved in movement in archery athletes. Thus, the aim of this study was two-fold: (i) analyze the impact of 50 and 100 archery shots on a single session on the EMG parameters of trained archery athletes; and (ii) explore the effects of training routines of the athletes to cope with fatigue induced by the accumulated shots on the EMG parameters. Methods: They were divided into two groups: those who regularly performed ≤100 shots per training session (n = 13) and those who performed >100 shots per session (n = 7). The participants were exposed to a condition involving 100 archery shots, with measurements taken at baseline, after 50, and after 100 shots. EMG was used to measure the electric potential of the deltoid (middle and posterior), trapezius (upper, middle, and lower), and infraspinatus during isometric contraction. The collected outcomes included the mean and maximal amplitude of EMG root mean square (EMGRMS, µV) and the median frequency of the raw surface EMG signal power spectrum (EMGMED, Hz). Results: The results showed significant differences for most of the analyzed muscles analyzed, specifically in the deltoid, infraspinatus, and trapezius (p < 0.05). Conclusions: Our study suggests that in most of the muscles analyzed, EMG parameters—particularly mean and maximal EMGRMS—tend to increase from baseline to 50 shots, with significant declines observed after 100 shots, indicating muscle fatigue. The training routines of the athletes do not appear to significantly influence their response to fatigue conditions.

An ultrasensitive dual-mode stagey for 17β-estradiol assay: Photoelectrochemical and colorimetric biosensor based on a WSe2/TiO2-modified electrode coupled with nucleic acid amplification

BackgroundThe abuse of 17β-estradiol(E2) has aroused wide concern in environmental and biomedical fields, which severely affects the endocrine function of human and animals. Therefore, an ultrasensitive and accurate assay of E2 is critically important. Traditional chromatography or immunoassay techniques exhibited good sensitivity and selectivity, but expensive instruments and antibodies may pose cost and stability issues, as well as difficulties in meeting on-site detection requirements. Ultrasensitive, reliable, and on-site detection of E2 at trace level remains a challenge. Hence, developing a simple, ultrasensitive assay to simultaneously achieve accurate detection and rapid visual analysis of E2 is extremely crucial. ResultsWe developed a versatile dual-mode photoelectrochemical (PEC) and colorimetric biosensor based on isothermal nucleic acid amplification strategy for the ultrasensitive and accurate detection of E2. The method modified titanium dioxide (TiO2) with tungsten selenide (WSe2) nanoflowers to synthesize WSe2/TiO2 heterostructures as a substrate for signal amplification and nanoprobe modification. Isothermal nucleic acid amplification strategy has been proven to be a powerful tool for strong signal amplification. The presence of a target triggered the nucleic acid amplification reaction, and produced a large amount of tDNA that competed with G-quadruplex immobilized on the electrode surface. The remaining G-quadruplex/hemin catalyzed the 4-chloro-1-naphthol (4-CN) to form biocatalytic precipitation (BCP) and ABTS-H2O2 chromogenic reaction, thus, the dual-mode platform was capable of achieving PEC-colorimetric ultrasensitive detection based on the catalytic activity of G-quadruplex/hemin DNAzyme. Within optimal conditions, the dual-mode biosensor exhibited a remarkable detection limit as low as 0.026 pM. SignificanceBenefiting from the superior performance of WSe2/TiO2 and the power signal amplification of isothermal nucleic acid amplification strategy, this aptasensor achieved the ultrasensitive detection of E2. The independent transmission paths of photoelectrochemical and colorimetric provide mutual support and flexible switching, significantly enhancing the overall sensitivity and accuracy of the detection strategy, which can meet the needs for E2 precise quantification and rapid on-site detection.

Allosteric probe-initiated double rolling circle amplification in tandem for sensitive whole-cell detection of E. coli O157:H7

The development of facile, reliable nucleic acid amplification assays for pathogenic bacteria is paramount but still poses a challenge. Here we present a molecular amplification system that integrates the whole-cell recognition of allosteric probes with cascaded rolling circle amplification. Allosteric probe-initiated double rolling circle amplification (AID-RCA) assay involves a three-step process that utilizes allosteric probes, dumbbell templates, and circular templates to recognize, amplify, and detect target bacteria, E. coli O157:H7. The allosteric probes are designed to recognize E. coli O157:H7 without bacterial isolation and nucleic acid extraction. Subsequently, the two-stage in-tandem amplification, that is, dumbbell template-based exponential-rolling circle amplification (DT-RCA) and circular template-based linear-rolling circle amplification (CT-RCA), is initiated and yields powerful fluorescence signals using molecular beacon as a signal probe. The symmetric structure of the dumbbell template endows the AID-RCA assay with good specificity. Cascaded signal amplification boosts its sensitivity. The AID-RCA assay demonstrated high sensitivity in the quantitative detection of E. coli O157:H7(limit of detection 1.6 CFU·mL−1). Compared to real-time PCR, the AID-RCA system exhibited higher sensitivity for low concentrations of E. coli O157:H7. Furthermore, the AID-RCA system can selectively differentiate E. coli O157:H7 from other pathogenic bacteria in complex samples such as drinking water and milk. In a blind validation study using milk samples, the AID-RCA system effectively distinguished between contaminated and pasteurized milk samples. Our method is expected to hold great potential for developing precise, reliable, and efficient analytical platforms to combat pathogenic bacterial pollution and safeguard human health.

Experimental study on improving effect of cooling garment on thermal comfort and EEGs

Phase change cooling garments (PCCGs) utilize phase change materials to store and release thermal energy, which can be used to reduce thermal sensation in hot environments. This study aims to investigate the effects of PCCGs on thermal comfort and electroencephalography (EEG) signals for laborers in hot conditions. Experiments were conducted in a climate chamber at 33 °C with 50 % relative humidity, with subjects having a metabolic rate of 2.7 met. EEG signals from 20 participants were recorded using a 32-channel EEG system after completing laboring task wearing the PCCGs or normal garment (NG), and the mean power of EEG and topological features of the brain network were analyzed. The results indicate that the use of PCCGs significantly reduce the mean power of the EEG signals, with notable changes in the θ, α, and β-bands. Additionally, wearing PCCGs alters the topological structure of the brain network, and the inter-regional information exchange was enhanced, as evidenced by the increased clustering coefficient, global efficiency, local efficiency, and decreased characteristic path length of the brain network. In this study, the effects of PCCGs on thermal comfort, EEG activity and brain network of laborers in hot environment were explored, which provides a possible indicator for assessing the improvement of thermal comfort by PCCGs.

A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model

The impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns. Monitoring and prediction of electric and magnetic fields have become critical tasks for researchers. In this paper, we propose a granularity data method based on T–S (Takagi–Sugeno) fuzzy model, named fuzzy rule-based model, which utilizing finite rules that are determined by the deviations between the predicted values and the true values after the data goes through a granulation-degranulation mechanism, to predict the intensity of power frequency electric field and electromagnetic field. A series of experiments show that fuzzy rule-based models have better robustness and higher prediction accuracy in comparison with several existing prediction models. The improvement of the performance of the fuzzy rule-based model quantified in terms of Root Mean Squared Error is 20.86%, 51.91%, 62.28%, 65.10%, and 71.92% in comparison with that of the Ridge model, Lasso model, and a family of support vector machine model with different kernel functions, including linear kernel (SVM-linear), radial basis function (SVM-BRF), polynomial kernel (SVM-polynomial) respectively, on the electromagnetic field testing data, and 37.42%, 55.16%, 58.79%, 59.28%, 64.27% lower than that of the Ridge model, Lasso model, SVM-linear model, SVM-BRF model and SVM-polynomial model on the power frequency electric field testing data.

Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm

Establishing a national comprehensive PNT (Positioning, Navigation, and Timing) system has become a consensus among major countries worldwide. As a crucial component in completing the entire PNT system, the 5G NR (new radio) time service signal plays a vital role. This paper proposes a 5G NR time service signal that uses a spread spectrum system, shares the 5G signal frequency band, but does not occupy the bandwidth of the 5G communication signal. This timing service signal has relatively low power, making it appear “submerged” within the power of the 5G communication signal. The spread spectrum code for this timing signal employs the chaotic orthogonal composite sequence proposed in this paper. Compared to traditional spread spectrum sequences, this sequence offers better security than m-sequences, improved autocorrelation than Walsh sequences, and an effective suppression of the short-period characteristics exhibited when the Skew Tent-Map chaotic sequence takes special values. This paper simulates the capture of the 5G NR time service signal in an environment with a signal-to-noise ratio of 10 dB using an FFT-based parallel code phase search algorithm, successfully capturing the 5G NR time service signal and verifying the feasibility of the proposed chaotic orthogonal composite sequence as a spread spectrum code.