The increase in flight volumes in the aviation industry has significant socioeconomic implications that affect different aspects of our communities and economies. Although it has great economic benefits, it also causes annoyance and disturbance to communities living near airports. The latter requires understanding and prediction of the varying noise levels generated by various aircraft types. Noise assessment on a fleet level is traditionally achieved by using prediction models such as the DOC29. Such models need to be validated using real measurements. For Amsterdam Schiphol Airport, the so-called NOMOS (Noise Monitoring System) with 39 measurement stations is used for this purpose. We analyze the time series of these stations, collecting annual data for the period from 2006 to 2023. The main objective is to determine how the aircraft-generated noise at these stations can be assigned to 13 different aircraft types, taking into account the different noise levels produced by each aircraft type. This is performed by time series analysis of individual stations and the averaged time series over all stations. The results from two least-squares methods, namely unconstrained least squares (LS) and a proposed bounded least squares subject to weighted constraints (BLS + WC), are compared. The constraints are based on certification data as prior information in the least squares method, which is expected to enhance the model's performance. Based on the above two least squares methods, predictions are performed for 2022 and 2023. The results clearly demonstrate the superiority of the BLS + WC over the LS method. We further extend our analysis to predict noise levels for a hypothetical future year with more newer aircraft models. The results indicate a substantial reduction in the noise level compared to 2023. These findings can thus underscore the effectiveness of the proposed method in outperforming the LS and highlight the model's capability to forecast the impact of fleet modernization on noise reduction.

Seabed backscatter data acquired by the multibeam echosounder (MBES) have been identified as a valuable indicator of sediment properties and benthic community characteristics. However, developing robust change detection models with MBES backscatter remains challenging due to the high costs and limited spatial coverage of seabed ground truth data. Lack of absolute backscatter calibration also hinders the comparison between repeated MBES measurements. To mitigate these issues, we propose an unsupervised method to detect seabed changes by fitting a Gaussian Mixture Model to the backscatter difference between two datasets. A relative calibration is conducted based on a stable reference area to eliminate the impact of possible drifts in echosounder characteristics on the backscatter difference. We then model the unchanged class as a zero-mean Gaussian distribution, with its variance constrained by the backscatter uncertainty estimated from the reference area. By processing each incident angle individually, the angular range with the greatest ability for seabed change detection can also be investigated. We demonstrate the effectiveness of the proposed method through two case studies in the Dutch North Sea. The detected changes reveal seasonal and temporal variations in benthic communities, such as sand mason worms, and are consistent with the sediment movement in one of the study areas. This research highlights the value of MBES backscatter data for seabed change detection and provides a cost-effective solution for seabed habitat monitoring with acoustic measurements.

Highlights: What are the main findings? The trained ANN algorithm simultaneously retrieves soil moisture and vegetation optical depth from CYGNSS observations, showing strong agreement with reference satellite products (SMAP SM: R = 0.83, RMSE = 0.063 m ³/m ³; SMOS VOD: R = 0.89, RMSE = 0.088). ANN-derived VOD shows strong correlation with independent vegetation indicators—biomass (R~0.77), canopy height (R~0.95), Leaf Area Index (R = 0.96), and vegetation water content (R~0.90)—confirming reliable sensitivity to vegetation structure. What are the implications of the main findings? The combination of GNSS-R data with environmental variables enables reliable dual retrieval of soil moisture and vegetation optical depth, serving as a cost-effective, higher-resolution alternative/complement to SMAP and SMOS. Joint retrieval of SM and VOD enables improved characterization of land–atmosphere interactions, supporting hydrological, ecological, and climate applications. Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m ³/m ³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks.

This study presents a comprehensive mathematical framework for modeling Bluetooth Low Energy (BLE) beacon Received Signal Strength Indicator (RSSI) measurements to improve the estimation of RSSI-distance model parameters through the integration of stochastic and functional modeling. Although such approaches are well established in geodesy, they have been only sparsely applied to indoor positioning, particularly for characterizing RSSI dependencies arising from multipath propagation. Experiments were conducted using two types of BLE beacons in a controlled static outdoor environment to isolate signal behavior and facilitate reliable RSSI–distance modeling. Outliers were mitigated using a median filter based on local neighborhood comparisons. Stochastic modeling was then performed using the autocorrelation function (ACF) and partial autocorrelation function (PACF), revealing that RSSI noise exhibits short-memory, time-correlated behavior that is well represented by a second-order autoregressive AR(2) model. Measurement weights were estimated using Least Squares Variance Component Estimation (LS-VCE), followed by functional modeling for the estimation of environmental and beacon transmit-power parameters together with their uncertainties. The estimated candidate AR models were further compared using their maximized log-likelihood values, which confirmed that the AR(2) model provides the most appropriate representation of the RSSI noise process. The reconstructed RSSI achieved root mean square errors (RMSEs) of 1.12dBm and 0.50dBm for the IT002 and IT008 beacons, respectively, at a distance of 1m, increasing to 2.19dBm and 2.40dBm at 10m. These errors correspond to relative distance uncertainties of approximately 6–13% at 1m and 15–25% at 10m, demonstrating the effectiveness of the proposed framework for accurate RSSI modeling and parameter estimation.

Given the importance of predicting the total ionospheric electron content (TEC), many studies have attempted to predict its spatiotemporal nature. In this study, a patch-based convolutional neural network with long short-term memory (CONVLSTM) (with patch sizes of 5 and 15), simple CONVLSTM, and 3D-U-Net models were used to predict the spatiotemporal nature of the next day's TEC data (next 12 samples). The proposed models use the spatiotemporal nature of the previous day's TEC data (previous 12 samples) along with temporal data such as AP, KP, DST, SN, and F10.7 to predict the next day's TEC data. The results showed that the 3D-U-Net model and then the model with patch size 5 had a higher generalization ability than the classical CONVLSTM architectures, while reducing the RMSE and MAE. The execution time of the program in the 3D-U-Net model has been significantly reduced compared to other models and it has also been able to better extract the microstructural features of TEC maps.

This paper presents the implementation of the single-layer least-squares-based deep learning (LSBDL) model, optimized using the steepest descent method. As a showcase, the work numerically validates LSBDL’s performance in complex non-linear applications, such as surface fitting. LSBDL is proposed as a transparent deep learning solution, uniquely merging the theoretical robustness and quality control capabilities of the least squares (LS) method with the flexibility of deep learning (DL) models. Unlike conventional black-box DL architectures, the LSBDL framework naturally provides statistical quality assessment metrics, including the covariance matrix of estimated parameters and precision of predicted outcomes. This enables seamless model mis-specification and outlier detection using established reliability theory. The key focus of this study is the model’s demonstrated efficiency, accuracy, and performance in complex non-linear applications. In a complex surface fitting application, the implemented LSBDL model achieved a root mean square error (RMSE) of 0.0021, which is significantly lower than the simulated noise level. Furthermore, the estimated LS residuals are consistent with the simulated (and also estimated) standard deviation of σ = 0.01. The implemented model offers an effective, statistically grounded, and numerically efficient solution for handling complex non-linear problems, particularly those involving heterogeneous and correlated observations. All hyperparameters, initialization steps, optimization, and validation procedures are thoroughly discussed. The Matlab and Python code is freely available at: https://github.com/tud-dasaa/lsbdl.v1.

The multibeam echosounder (MBES) has been widely used in seabed mapping, considering its ability to collect continuous and broad-scale seabed measurements efficiently. The presence of shellfish or dead shell material can alter the geophysical properties of the sediment and thus affect the MBES backscatter intensity, making acoustic surveys with the MBES a potential non-invasive solution for regularly monitoring the benthic habitats of shellfish aggregations. Although there exists an increasing interest in mapping marine benthos with MBES measurements recently, the use of multi-spectral backscatter data is still limited. Thus, this research aims to enhance the acoustic mapping of benthic habitats using multi-spectral MBES data, with a focus on a shell bed region in the Dutch North Sea. With backscatter measurements from three frequencies, 90, 300, and 450 kHz, we achieved seabed classification in two steps. First, a semi-supervised backscatter completion was conducted to generate full-coverage backscatter data for each incident angle, mitigating the limited overlap between adjacent survey lines. We then classified the multi-Angle backscatter data from each individual frequency using the Gaussian Mixture Model. Our results indicate an improved seabed classification performance compared to the classical Bayesian method. Comparisons of classification maps across frequencies also show their different abilities to distinguish the shell bed region from other coarse sediments, demonstrating the value of leveraging multi-spectral backscatter data in seabed habitat mapping.

Indoor positioning systems based on Bluetooth technology have gained significant attention with the widespread adoption of Bluetooth Low Energy (BLE). The rapid growth of BLE-enabled devices—now exceeding eight billion worldwide—has enabled the development of cost-effective, location-aware applications. The precision of BLE indoor positioning systems depends on several factors, among which the quality of received signal strength indicator (RSSI) measurements and the spatial deployment of sensors are most critical. Existing research has largely focused on improving RSSI accuracy through techniques such as multichannel measurements, outlier detection, Kalman filtering, and regression modeling. In this work, we examine two primary components that govern positioning precision: 1) the uncertainty in the RSSI-distance model parameters, including the path-loss exponent, reference power, and raw RSSI values; and 2) the geometry of sensor deployment, which directly affects estimation precision through the geometric dilution of precision (GDoP). We demonstrate that optimizing sensor placement using centroidal Voronoi tessellation (CVT) reduces GDoP and substantially improves positioning precision. Comparative experiments across two deployment scenarios, one based on CVT, confirm that CVT-based sensor configurations yield significantly higher precision in BLE RSSI-based indoor positioning.

The study of long-term GNSS time series provides valuable insights for researchers in the field of earth sciences. Understanding the trends in these time series is particularly important for geodynamic researchers focused on earth crust movements. Functional and stochastic models play a crucial role in estimating trend values within time series data. Various methods are available to estimate variance components in GNSS time series. The least squares variance component estimation (LS-VCE) method stands out as one of the most effective approaches for this purpose. We introduce an innovative method, which streamlines calculations and simplifies equations, and therefore significantly boosting the processing speed for diagonal(ized) cofactor matrices. The method can be applied to the GNSS time series of linear stochastic models consisting of white noise, flicker noise and random walk noise. Moreover, unlike the conventional approaches, our method experiences high computational efficiency even with an increase in the number of colored noise components in time series data. For GNSS time series, this variable transformation has been applied to both univariate and multivariate modes, preserving the optimal properties of LS-VCE. We conducted simulations on daily time series spanning 5, 10, 15, and 20 years, employing two general and fast modes with one and two colored noise components plus white noise. The computation time for estimating variance components was compared between the two modes, revealing a notable decrease in processing time with the fast mode.

To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. This research evaluates the noise–power–distance (NPD) tables employed in the European Doc 29 noise model using the noise measurements taken around Amsterdam Airport Schiphol. Thrust estimation is based on extracting the blade passing frequency from acoustic measurements and converting it to the engine rotational speed indicator N1%. The N1% estimates are validated with onboard flight data. Even with accurate input parameters (thrust and distance to the observer), discrepancies are observed between modelled and measured noise levels, which can be attributed to the inaccuracies in the NPD tables. To further investigate this, empirical thrust-noise relations are derived from the measurements. These derived relations are found to differ from those in the original NPD tables. When the empirical thrust-noise relations are used, the agreement between the modelled and measured mean noise levels improves. The standard deviation of the differences gets reduced by 25% for departure operations. This finding is subsequently confirmed using independent measurements around Oslo Airport Gardermoen. Beyond improving current best-practice noise modelling, the methodology presented in this research offers insight into the development and validation of NPD tables.

Fitting a smooth curve to 2D, a surface to 3D, and a manifold to 4D irregular point cloud data is becoming a common practice in many engineering and science applications. Piecewise-polynomial spline functions provide a powerful tool applicable to interpolation and approximation problems. This study presents the least squares B-spline approximation (LSBSA) theory, which is a generalized version of the spline interpolation and can be applied to any irregularly scattered point cloud data at knots specified by the user. The formulation allows to apply the well-established body of knowledge of least squares theory to the B-spline approximation. This for example has the benefit of embedding quality control measures such as hypothesis testing and proper error propagation to assess the quality of the approximation problem. The method is applicable to many 1D curve, 2D surface and 3D manifold fitting problems of which both simulated and real data are used to illustrate the efficacy of the proposed theory. In particular, its real-world applications to multi-beam echo-sounder bathymetric data, digital terrain modeling and Greenland ice sheet deformation monitoring will be highlighted. The performance of the method for linear, quadratic, cubic and quartic spline functions will be investigated. The primary application of LSBSA lies in its ability to perform 3D manifold fitting for deformation monitoring. This capability provides the possibility of monitoring changes in continuous spatial and temporal domains. The Python and Matlab source codes of LSBSA are freely accessible at https://github.com/tudelft4d/lsbsa.

Purpose – This study aims to automate the visual inspection of piling sheets in water channel construction using artificial intelligence (AI). By employing image classification and object detection techniques, the research focuses on extracting and analysing geometric features to enhance the accuracy and efficiency of the inspection process. It also addresses key challenges associated with the unique characteristics of construction materials and the limited variability of available inspection datasets. Design/methodology/approach – Convolutional neural networks (CNNs) with varying complexities are employed for image classification, across four and six classes, and for object detection of piling sheets in water channel environments. A dataset provided by Witteveen + Bos is preprocessed to generate training sets, and the CNN architectures are optimized for enhanced performance. The accuracy and efficiency of the proposed models are evaluated and compared against traditional manual inspection methods. Findings – The AI-driven approach significantly reduces processing time, evaluating 40, 000 images in just 11.9 h, compared to approximately one month using manual assessment. The 4-class classification model achieves an accuracy of 96%, while the 6-class model attains 72%. The object detection model produces a mean average precision (mAP) of 79%. These results meet the performance standards set by the Dutch company Witteveen + Bos, which demonstrate the effectiveness of AI in automating the inspection of piling sheets. Originality/value – This study introduces a novel AI-based approach for assessing piling sheets, demonstrating substantial improvements over traditional inspection methods. It introduces a systematic evaluation of various CNN architectures and hyperparameters to optimize the models specifically for piling sheet inspection rather than relying on off-the-shelf solutions. The use of CNNs for both image classification and object detection adheres to relevant Dutch engineering standards. Notably, the reduction in processing time, from one month to around 12 h, represents a major advancement in the efficiency of civil engineering inspections.

Identifying the correct stochastic model in GNSS time series is essential to study geophysical parameters such as site velocities, and hence enhancing their accuracy. The rate uncertainty is a critical aspect in GNSS time series analysis. The variance component estimation (VCE) methods commonly utilize unconstrained estimation principles. Simulating 1000-time series for 4 different noise combinations with 10 years’ time span, we have investigated the performance of non-negative least squares VCE (NNLS-VCE) method for identifying an appropriate noise model. Our results are provided for both univariate and multivariate analysis. As the noise model's complexity increases, the significance of employing multivariate analysis is prominent in contrast to univariate analysis. After thorough analysis, we have determined that treating the false-positive model as a stochastic model in time series yields significant insights. Specifically, if the accumulative spectral index is lower than the true value, it results in an underestimation of the rate uncertainty. Conversely, if the index is higher than the actual value, it leads to an overestimation. Additionally, we observed that as the noise model complexity increases, the number of false-positive models also increases. However, the implementation of multivariate analysis mitigates this increase, offering a more realistic and reliable approach. In case of four distinct noise models, the detection power percentages of 98.5%, 90.5%, 69.5%, 29.3% of univariate analysis increased to 99.5%, 99.8%, 88.4% and 83.7% for multivariate analysis.