Search Results (77)

Since the early development of Fluorescence Correlation Spectroscopy (FCS), it has been recognized that background intensity can lead to artifacts in the amplitude of the autocorrelation function (ACF) and, consequently, to inaccurate estimates of particle numbers. Here, we present a protocol for quantitative background evaluation and amplitude correction in FCS experiments, applicable to different sources of background such as detector noise, autofluorescence, and light scattering. We demonstrate the performance of our approach through three representative case studies: (i) FCS measurements of a bright fluorophore at low concentration, (ii) FCS of dim nanoparticles affected by solvent Raman scattering, and (iii) FCS performed using a confocal setup equipped with a SPAD array, where background originates from detector hot pixels. These examples represent typical experimental conditions in which background signals compromise quantitative interpretation, illustrating how our protocol restores accuracy and reproducibility in FCS analysis. By systematically identifying and correcting these effects, the proposed protocol addresses a long-standing limitation of FCS and provides a robust framework for improving the accuracy and reproducibility of quantitative fluorescence measurements. Full article

Understanding the seasonal behaviour of sewer failures is essential for infrastructure reliability and sustainable asset management. This study presents a seasonality-centred, data-driven analysis of monthly sewer failures over a 15-year period (2010–2024) in a major city in south-eastern Poland. The assessment is based exclusively on operational failure records, allowing intrinsic temporal regularities to be extracted without the use of external meteorological covariates. Seasonal Decomposition of Time Series by LOESS (STL), Autocorrelation Function (ACF), Seasonal Index (SI) and the Winter–Summer Index (WSI) were applied to quantify periodicity, seasonal amplitude and long-term variability. The results confirm a pronounced annual cycle, with failures peaking around March and reaching minima in September, supported by a strong autocorrelation at a 12-month lag (r ≈ 0.45). The mean WSI value (1.05) indicates a nearly balanced but still winter-sensitive pattern, while annual WSI values ranged from 0.71 to 1.51. The STL seasonal amplitude remained structurally stable at ≈61 failures throughout the study period, while annual values showed a modest but statistically significant increasing tendency. Trend analysis showed no significant monotonic trend in the deseasonalized series (Z ≈ 0.89, p = 0.37), whereas the raw series exhibited a weak but significant upward trend (τ ≈ 0.33, p < 0.001), largely attributable to short-term operational variability rather than to changes in intrinsic failure rate. The study demonstrates that long-term operational data alone are sufficient to capture seasonal and long-term dynamics in sewer failures. The presented framework supports utilities in integrating seasonality diagnostics into preventive maintenance, resource allocation and resilience planning, even in the absence of detailed climatic datasets. Full article

This paper proposes an evolutionary modeling method of scattering clusters based on Geometric-Based Stochastic Modeling (GBSM). In the single-bounce scenario of vacuum pipeline maglev train communication, the dynamic generation and extinction process and statistical behavior of multiple clusters at high speed are analyzed. The model abstracts the multipath component into a cluster structure. By iteratively updating the channel state and the birth and death cluster information after initialization, a dynamic model of the evolution process of scattering clusters in time-varying channels is constructed, which depicts the time evolution process of multipath clusters. Under the framework of GBSM, the correlation statistical characteristics of the scattering cluster birth and death process are further derived, and analytical integral form expression of the channel time autocorrelation function (ACF) is theoretically solved. The simulation results reveal the inherent law of channel time-varying characteristics under the joint action of high-speed train operation and closed pipe structure, and the results show that the proposed method can effectively capture the transient dynamic characteristics and long-term statistical trends of multipath clusters. The proposed model provides a practical basis for channel modeling in vacuum tube maglev wireless communication systems. Full article

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER). Full article

In this article, we conduct a statistical analysis of the autocorrelation functions (ACF) of multi-scale logarithmic returns computed over maximal monotonic uninterrupted trends (runs) in financial indices’ daily data. We analyze the Dow Jones Industrial Average (DJIA) and the Mexican IPC (Índice de Precios y Cotizaciones) over a period from 30 October 1978 to 19 May 2025. We examine how deterministic alternation of signs shapes the ACF of multi-scale returns, and we evaluate covariance stationarity via formal tests (e.g., Augmented Dickey–Fuller and Phillips–Perron). We conclude that, despite the persistent long-memory oscillations in the ACF, multi-scale return series pass the stationarity tests, an outcome with interesting implications for econometric modeling of financial time series. Full article

This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive (iAR) and moving average (iMA) models separately, and moving average autoregressive processes (iARMA) for positive autoregressions. The objective of this work is to generalize the iARMA model to include negative correlations. A first-order moving average autoregressive model for irregular discrete time series is presented, being an ergodic and strictly stationary Gaussian process. Parameter estimation is performed by Maximum Likelihood, and its performances are evaluated for finite samples through Monte Carlo simulations. The estimation of the autocorrelation function (ACF) is performed using the DCF (Discrete Correlation Function) estimator, evaluating its performance by varying the sample size and average time interval. The model was implemented on real data from two different contexts; the first one consists of the two-week measurement of star flares of the Orion Nebula in the development of the COUP and the second pertains to the measurement of sunspot cycles from 1860 to 1990 and their relationship to temperature variation in the northern hemisphere. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

Terahertz (THz) wireless communications can support ultra-high data rates and secure wireless links with miniaturized devices for unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic channel model (GSCM) is proposed for multiple-input multiple-output (MIMO) communication links between the UAVs in the THz band. The proposed channel model considers not only the 3D scattering and reflection scenarios (i.e., reflection and scattering fading) but also the atmospheric molecule absorption attenuation, arbitrary 3D trajectory, and antenna arrays of both terminals. In addition, the statistical properties of the proposed GSCM (i.e., the time auto-correlation function (T-ACF), space cross-correlation function (S-CCF), and Doppler power spectrum density (DPSD)) are derived and analyzed under several important UAV-related parameters and different carrier frequencies, including millimeter wave (mmWave) and THz bands. Finally, the good agreement between the simulated results and corresponding theoretical ones demonstrates the correctness of the proposed GSCM, and some useful observations are provided for the system design and performance evaluation of UAV-based air-to-air (A2A) THz-MIMO wireless communications. Full article

With the advancement of sixth-generation (6G) wireless communication technology, new demands have been placed on maritime communications. In maritime environments, factors such as evaporation ducts and sea waves significantly impact signal transmission. Moreover, in multi-user communication scenarios, interactions between different users introduce additional complexities. This paper proposes a novel channel model for maritime unmanned aerial vehicle (UAV) to multi-unmanned surface vehicle (USV) communications, which incorporates massive multiple-input–multiple-output (MIMO) antennas at both the transmitter (Tx) and receiver (Rx), while also accounting for the effects of evaporation ducts and sea waves on the channel. For the USV-single-user maritime model, the temporal auto-correlation function (ACF) and spatial cross-correlation function (CCF) are analyzed. For the UAV-to-multi-user channel model, key channel characteristics such as channel matrix collinearity (CMC) and channel capacity are examined. Finally, the accuracy and effectiveness of the proposed model are validated through a comparison between the measured and simulated data under a single-link environment. Meanwhile, a comparison between the CMC obtained from the proposed model and that derived from Ray-Tracing further verifies the model’s accuracy in multi-link environments. This model provides essential theoretical guidance for future 6G maritime communication systems. Full article

With its unique environment and strategic value, the near space (NS) has become the focus of global scientific and technological, military, and commercial fields. Aiming at the problem of communication interruption when the aircraft re-enters the atmosphere, to ensure the needs of communication, navigation, and telemetry, tracking, and command (TT&C), this paper proposes an overall integration of communication, navigation, and TT&C (ICNT) signals scheme based on the K/Ka frequency band. Firstly, the K/Ka frequency band is selected according to the ITU frequency division, high-speed communication requirements, advantages of space-based over-the-horizon relay, overcoming the blackout problem, and the development trend of high frequencies. Secondly, the influence of the physical characteristics of the NS on ICNT is analyzed through simulation. The results show that when the K/Ka signal is transmitted in the NS, the path loss changes significantly with the elevation angle. The bottom layer loss at an elevation angle of 90° is between 143.5 and 150.5 dB, and the top layer loss is between 157.5 and 164.4 dB; the maximum attenuation of the bottom layer and the top layer at an elevation angle of 0° is close to 180 dB and 187 dB, respectively. In terms of rainfall attenuation, when a 30 GHz signal passes through a 100 km rain area under moderate rain conditions, the horizontal and vertical polarization losses reach 225 dB and 185 dB, respectively, and the rainfall attenuation increases with the increase in frequency. For gas absorption, the loss of water vapor is higher than that of oxygen molecules; when a 30 GHz signal is transmitted for 100 km, the loss of water vapor is 17 dB, while that of oxygen is 2 dB. The loss of clouds and fog is relatively small, less than 1 dB. Increasing the frequency and the antenna elevation angle can reduce the atmospheric scintillation. In addition, factors such as the plasma sheath and multipath also affect the signal propagation. In terms of modulation technology, the constant envelope signal shows an advantage in spectral efficiency; the new integrated signal obtained by integrating communication, navigation, and TT&C signals into a single K/Ka frequency point has excellent characteristics in the simulation of power spectral density (PSD) and autocorrelation function (ACF), verifying the feasibility of the scheme. The proposed ICNT scheme is expected to provide an innovative solution example for the communication, navigation, and TT&C requirements of NS vehicles during the re-entry phase. Full article

This study analyzes the series of annual emissions of greenhouse gases (GHGs) from agriculture in the European Union countries for 32 years. The outliers, autocorrelation, and change points were detected for each series and the Total one using the boxplot, autocorrelation function (ACF), and Pettit, Hubert, and CUSUM tests. The existence of a monotonic trend in the data series was checked against the randomness by the Mann–Kendall test; further, the slope of the linear trend was determined by Sen’s nonparametric approach and classical regression. The best distribution was fitted for each data series. The results indicate that most series present aberrant values (indicating periods with high emissions), are autocorrelated, and have a decreasing tendency over time (showing the diminishing of GHG emissions from agriculture during the study period). The distributions that best fit the individual series were of Wakeby, Johnson SB, Burr, and Log-logistic type. The Total series has a decreasing trend, presents a second-order autocorrelation, and is right-skewed. An ARIMA(1,1,2) model was built and validated for it and was used for the forecast. Full article

The identification of the orders of time series models plays a crucial role in their accurate specification and forecasting. The Autocorrelation Function (ACF) is commonly used to identify the order q of Moving Average (MA(q)) models, as it theoretically vanishes for lags beyond q. This property is widely used in model selection, assuming the sample ACF follows an asymptotic normal distribution for robustness. However, our examination of the sum of the sample ACF reveals inconsistencies with these theoretical properties, highlighting a deviation from normality in the sample ACF for MA(q) processes. As a natural extension of the ACF, the Extended Autocorrelation Function (EACF) provides additional insights by facilitating the simultaneous identification of both autoregressive and moving average components. Using simulations, we evaluate the performance of q-order identification in MA(q) models, which is based on the properties of ACF. Similarly, for ARMA($p,q$) models, we assess the ($p,q$)-order identification relying on EACF. Our findings indicate that both methods are effective for sufficiently long time series but may incorrectly favor an ARMA($p,q-1$) model when the $a_q$ coefficient approaches zero. Additionally, if the cumulative sums of ACF (SACF) behave consistently and the Ljung–Box test validates the proposed model, it can serve as a strong candidate. The proposed models should then be compared based on their predictive performance. We illustrate our methodology with an application to wind speed data and sea surface temperature anomalies, providing practical insights into the relevance of our findings. Full article

The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric $\rho$, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR. Full article

To investigate the non-stationary characteristics of the wind field at the U-shaped canyon bridge site and its impact on fluctuating wind characteristics, a wind observation tower was installed near a cable-stayed bridge. The Augmented Dickey–Fuller (ADF) test was employed to assess the stationarity of wind speed series, while the discrete wavelet transform (DWT) was applied to reconstruct the time-varying mean wind and analyze its effect on fluctuating wind characteristics. Results indicate that wind speeds in this region exhibit bimodal distribution characteristics, with the Weibull-Gamma mixed distribution model providing the best fit. The proportion of non-stationary samples increases with height. Autocorrelation function (ACF), partial autocorrelation function (PACF) tests, and power spectral density (PSD) analysis determined the optimal wavelet decomposition level for wind speed in this region. Analysis of non-stationary samples using db10 wavelet reconstruction reveals that the stationary wind speed model overestimates turbulence intensity but underestimates the turbulence integral scale. The downwind spectrum deviates from the Kaimal spectrum in both low- and high-frequency bands, whereas the vertical spectrum aligns well with the Panofsky spectrum. The findings demonstrate that the wavelet reconstruction method more accurately captures fluctuating wind characteristics under the complex terrain conditions of this canyon area. Full article

The pollution and congestion caused by the shortage of parking spaces are threatening the sustainable development of cities. Smart parking platforms are one of the major tools to solve the problem by providing the efficient usage of parking resources. However, current platforms can only realize limited functions, and shared parking is far from being implemented on a large scale. Since off-street parking provides the majority of potential shared parking spaces, this paper takes periodic off-street parking lots as the starting point for opening the shared parking market. Based on data from the Ningbo Yongcheng parking platform, power spectral density (PSD) and the autocorrelation function (ACF) are used to identify periodic parking lots. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based method is applied to clustering the occupancy time series. Land use, user type, parking duration, and parking patterns are then analyzed to study shared parking supply characteristics. The results show that (1) 31.3% of off-street parking lots are periodic parking lots, and 90.3% of them have regular users exceeding 50%. (2) Periodic parking lots are classified into four types. Most parking lots show convex flat peak, double peak, or triple peak characteristics. (3) The shared parking spaces demonstrate spatial and temporal imbalances. But in a small area, even considering the concentration of land use and the peak period, there are still enough spaces available. The above research is of significance for the large-scale implementation of shared parking, which can promote the sustainable development of a city. Full article