Scalar Data Research Articles

Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite

The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM2.5 retrieval. We identified the optimal conditions for the effective estimation of PM2.5. The quantitative results indicated that, under these optimal conditions, the PM2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R2 of 0.9272 and an RMSE of 7.38 µg·m−3. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R2 of 0.9099 and RMSE of 7.42 µg·m−3, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R2 of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM2.5 retrieval and providing support for PM2.5 estimation through multi-angle polarized data.

A Non-Contact AI-Based Approach to Multi-Failure Detection in Avionic Systems

The increasing electrification and integration of advanced controls in modern aircraft designs have significantly raised the number and complexity of installed printed circuit boards (PCBs), posing new challenges for efficient maintenance and rapid failure detection. Despite self-diagnostic features in current avionics systems, circuit damage and multiple simultaneous failures may arise, compromising safety and diagnostic accuracy. To address these challenges, this paper aims to develop a fast, accurate, and non-destructive, multi-failure diagnosis algorithm for PCBs. The proposed method combines a self-attention mechanism with an adaptive graph convolutional neural network to enhance diagnostic precision. A convolutional neural network with residual connections extracts features from scalar magnetic field data, ensuring robust input diversity. The model was tested on a typical dual-phase amplitude boosting circuit with up to four different simultaneous failures, achieving the experimental results of 99.08%, 98.50%, 98.78%, 98.01%, 98.93%, 98.25%, 97.03%, and 99.77% across metrics including overall precision, per-class precision, overall recall, per-class recall, overall F1 measure, and per-class F1 measure. The results demonstrated its effectiveness and feasibility in diagnosing complex PCBs with multiple failures, indicating the algorithm’s potential to improve failure diagnosis performance and offer a promising PCB diagnosis solution in aerospace applications.

A framework for visual comparison of scalar fields with uncertainty

AbstractScientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

Algorithm XXXX: The Delaunay Density Diagnostic

Accurate approximation of a real-valued function depends on two aspects of the available data: the density of inputs within the domain of interest and the variation of the outputs over that domain. There are few methods for assessing whether the density of inputs is sufficient to identify the relevant variations in outputs—i.e., the “geometric scale” of the function—despite the fact that sampling density is closely tied to the success or failure of an approximation method. In this paper, we introduce a general purpose, computational approach to detecting the geometric scale of real-valued functions over a fixed domain using a deterministic interpolation technique from computational geometry. The algorithm is intended to work on scalar data in moderate dimensions (2-10). Our algorithm is based on the observation that a sequence of piecewise linear interpolants will converge to a continuous function at a quadratic rate (in \(L^{2}\) norm) if and only if the data are sampled densely enough to distinguish the feature from noise (assuming sufficiently regular sampling). We present numerical experiments demonstrating how our method can identify feature scale, estimate uncertainty in feature scale, and assess the sampling density for fixed (i.e. static) datasets of input–output pairs. We include analytical results in support of our numerical findings and have released lightweight code that can be adapted for use in a variety of data science settings.

Graphical model for mixed data types

With the development of data collection technologies, data types have become more diverse. Additionally, graphical models, as tools for describing variable network relationships, have become increasingly popular in recent years. Previous studies have focused on graphical models tailored to specific types of data. However, these existing methods fail to identify graphical models for mixed data types. The difficulty of constructing graphical models for mixed data types lies in the fact that each type of data has its own space, which challenges the estimation of network relationships in a graphical model when the data are combined. To address this issue, this study presents a novel method that utilizes a vectorization and alignment strategy developed particularly for mixed data types, including scalar, interval-valued, compositional, and functional data, to estimate a graphical model. By iteratively employing a block-sparse graphical lasso method on aligned data, the method can achieve satisfactory results, as shown by numerous simulation experiments. The results also validate the superiority of our proposed method over potential competing methods. Furthermore, this method was applied to an engine damage propagation network as an illustrative example. Our method provides a novel modeling approach for graphical models in the case of mixed data types.

A Practical Solver for Scalar Data Topological Simplification.

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of "signal" persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of "non-signal" pairs, while (ii) preserving the "signal" pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

Comparing Leach protocol and its descendants on transferring scalar data

In the last years, The CMOS was developed and miniaturized rapidly, which, made sensors very fast, small and accurate. Hence, the creation of wireless sensor network (WSN) which are a network of nodes that exchange the data between them until it reaches the sink (base station). It is responsible for treating the data and transfer them to other servers linked to the internet for further treatment or storage. Therefore, everything related to WSN is a big topic of research for scientific community, especially transferring scalar data. In fact, many factors enter into account when it comes to send data like a radio, range of transmission, energy consumption and routing protocol. Routing protocols are very important in transferring data. They also have a big impact on energy consumption by nodes. Many categories of routing protocols exist: planning and level routing. Each type has its strength and weakness points. So, using a routing protocol in high-density environments is very challenging in energy consumption and data delivery. In addition, since level routing protocols like Leach are known for their energy efficiency. We choose three level routing protocol (Leach, MLD-Leach and MRE-Leach) to put them in a harsh environment to test their energy consumption and data transferring. We found that MLD-Leach has better energy consumption and data delivery.

Multi-Output Prediction Model for Basic Oxygen Furnace Steelmaking Based on the Fusion of Deep Convolution and Attention Mechanisms

The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use multi-variable input and single-variable output approaches, neglecting the coupling relationships between different property indicators, making it difficult to predict multiple outputs simultaneously. Consequently, a multi-output prediction model based on the fusion of deep convolution and attention mechanism networks (FDCAN) is proposed. The model inputs include scalar data, such as the properties of raw materials and target molten steel, and time series data, such as lance height, oxygen supply intensity, and bottom air supply intensity during the blowing process. The FDCAN model utilizes a fully connected module to extract nonlinear features from scalar data and a deep convolution module to process time series data, capturing high-dimensional feature representations. The attention mechanism then assigns greater weight to significant features. Finally, multiple multi-layer perceptron modules predict the outputs—final carbon content, temperature, and phosphorus content. This structure allows FDCAN to learn complex relationships within the input data and between input and output variables. The effectiveness of the FDCAN model is validated using actual BOF steelmaking data, achieving hit rates of 95.14% for final carbon content within ±0.015 wt%, 84.72% for final temperature within ±15 °C, and 88.89% for final phosphorus content within ±0.005 wt%.

A permittivity imaging method in the polar coordinates: Electrical capacitance tomography based on the electromagnetic momentum

Electrical capacitance tomography (ECT) is widely used in petroleum, chemical, and other industrial detection. The ECT equations are nonlinear, and their corresponding inverse problems are ill-posed, so the imaging resolution is low. The ECT technique detects scalar data. The permittivity imaging method based on the electromagnetic momentum reciprocity theorem detects vector data, which carries more information than scalar data. To improve the ability of the imaging system to recognise permittivity boundaries, we propose a permittivity imaging method in the polar coordinate system, which is centred on electrical capacitance tomography based on the electromagnetic momentum (ECT-EMM). ECT-EMM principle with the electrodes-moving scheme and the object-moving scheme is proposed. We reconstruct the permittivity gradient from the capacitance gradient. We adopt the object-moving scheme and use the Tikhonov regularisation algorithm to reconstruct the permittivity through the permittivity gradient. The image reconstruction results and image evaluation metrics show that the proposed method clearly recognises the object boundaries compared to ECT. The proposed method performs better when using threshold filtering.

A computational topology-based spatiotemporal analysis technique for honeybee aggregation

A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales, at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.

Feasibility of reservoir monitoring in the Groningen gas field using ghost reflections from seismic interferometry

SUMMARY Seismic interferometry (SI) retrieves new seismic responses, for example reflections, between either receivers or sources. When SI is applied to a reflection survey with active sources and receivers at the surface, non-physical (ghost) reflections are retrieved as well. Ghost reflections, retrieved from the correlation of two primary reflections or multiples from two different depth levels, are only sensitive to the properties in the layer that cause them to appear in the result of SI, such as velocity, density and thickness. We aim to use these ghost reflections for monitoring subsurface changes, to address challenges associated with detecting and isolating changes within the target layer in monitoring. We focus on the feasibility of monitoring pore-pressure changes in the Groningen gas field in the Netherlands using ghost reflections. To achieve this, we utilize numerical modelling to simulate scalar reflection data, deploying sources and receivers at the surface. To build up subsurface models for monitoring purposes, we perform an ultrasonic transmission laboratory experiment to measure S-wave velocities at different pore pressures. Applying SI by autocorrelation to the modelled data sets, we retrieve zero-offset ghost reflections. Using a correlation operator, we determine time differences between a baseline survey and monitoring surveys. To enhance the ability to detect small changes, we propose subsampling the ghost reflections before the correlation operator and using only virtual sources with a complete illumination of receivers. We demonstrate that the retrieved time differences between the ghost reflections exhibit variations corresponding to velocity changes inside the reservoir. This highlights the potential of ghost reflections as valuable indicators for monitoring even small changes. We also investigate the effect of the sources and receivers’ geometry and spacing and the number of virtual sources and receivers in retrieving ghost reflections with high interpretability resolution.

Functional linear model with prior information of subjects’ network

In many modern applications, data samples are interconnected by a network, and network information is a crucial factor in forecasting. However, existing network data analysis methods, which are designed for scalar data, are not effective for infinite-dimensional function data, particularly when functional predictors are observed on an irregular sampling design. In this paper, we propose a functional linear model for network-linked data. To improve the estimation and prediction, the network cohesion is enforced using the Laplace quadratic penalty function. The statistical properties of the proposed model are studied, and an extension to high-dimensional functional data is developed to simultaneously select relevant functional predictors and estimate the coefficient functions. Simulation results and real data application demonstrate the satisfactory performance of the proposed methods.

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

The current reverse-time migration, which is based on wave equations for imaging wavefields, employs an imaging formula derived from Claerbout’s imaging principle. This imaging formula is only valid for plane waves with small incident angles on the perfectly flat reflecting surface. However, the complexity of seismic wave propagation may lead to situations that do not meet this requirement. Therefore, this paper divides the subsurface into local scattering and reflecting bodies. It proposes linear forward representations for scattering and reflection data based on perturbations in the physical parameters and wave impedance, respectively. To further describe the effect on the reflecting body boundary, the local reflection coefficient is defined and the linear forward representation for the reflection data based on it is obtained. After that, the proposed linear forward representations are used as the forward equations for the linear inverse of the seismic data, and the seismic data waveform imaging method is developed based on linear inversion theory. At the same time, the specific waveform imaging calculation formulas for scalar wave scattering data and scalar wave reflection data are provided and validated via numerical experiments. Compared with the current reverse-time migration, waveform migration not only has the correct phase and higher resolution in theory but also does not increase the computational complexity. To some extent, it improves the deficiencies of the current structural imaging and provides a basis for subsurface lithological imaging.

Adjusting for bias due to measurement error in functional quantile regression models with error-prone functional and scalar covariates

Wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data with poorly characterized errors. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise with mean zero and constant variance. Viewing this noise as homoscedastic and independent ignores potential serial correlations. Our preliminary studies indicate that failing to account for these serial correlations can bias estimations. In dietary assessments, epidemiologists often use self-reported measures based on food frequency questionnaires that are prone to recall bias. With the increased availability of complex, high-dimensional functional, and scalar biomedical data potentially prone to measurement errors, it is necessary to adjust for biases induced by these errors to permit accurate analyzes in various regression settings. However, there has been limited work to address measurement errors in functional and scalar covariates in the context of quantile regression. Therefore, we developed new statistical methods based on simulation extrapolation (SIMEX) and mixed effects regression with repeated measures to correct for measurement error biases in this context. We conducted simulation studies to establish the finite sample properties of our new methods. The methods are illustrated through application to a real data set.

An Energy Efficient Tree-Based Routing Protocol for Wireless Multimedia Sensor Networks

The recent technological advances in micro electromechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. Routing in WMSN is very challenging task. Multipath establishes several path for data transmission rather than single path. In this paper we proposed An Energy Efficient Tree Based Multipath Routing for WMSN’s (EETMRP) technique that will intend to provide a reliable transmission of data for data synchronization at the destination node with low energy consumption. This is done by efficiently utilizing the energy availability and the received signal strength of the nodes to identify multiple routes to the destination. EETMRP discusses that all of the packets of audio, image and scalar data are spread over the nodes lying on different possible paths between the source and the sink, in proportion to their residual energy and received signal strength. The objective of EETMRP is to assign more loads to highest energetic paths and fewer loads to lower energetic paths so that uniform resource utilization of all available paths can be ensured. It uses multiple paths between source and the sink which is intended to provide a reliable transmission environment with low energy consumption, by efficiently utilizing the energy availability of the nodes to identify multiple routes to the destination and efficiently balanced the loads of all sensor nodes. We also focused on node failure and it recovery process during packet transmission and during the start of packet transmission. Finally we proposed an algorithm based on our proposal to simulate and analysis our proposed routing protocol.

Pribadi: A decentralized privacy-preserving authentication in wireless multimedia sensor networks for smart cities

Wireless Multimedia Sensor Network (WMSN) is a network of interconnected sensor nodes equipped with multimedia devices capable to retrieve multimedia as well as scalar sensor data in smart city scenarios. With the progress of smart city development, WMSNs are becoming points of interest. The existing research works on the security of WMSNs show some significant centralized solutions; they are prone to denial of service attacks, central failure, and privacy breaches. Also, these solution are having limitations because of their centralized nature.Therefore, the the proposed scheme adopted the concept of decentralization to achieve more effective and ubiquitous results. As per our best knowledge, we introduce the first decentralized privacy-preserving authentication framework for WMSNs to address the energy and storage challenges. We call our solution PRIvacy-preserving Blockchain-based Authentication for multimeDIa networks (PRIBADI). PRIBADI uses a blockchain framework for authenticated communication between sensor nodes and base stations (as a trusted authority) by using certification and access authentication schemes. We elect trustworthy cluster heads based on weight metrics and these cluster heads forward the information to the Trusted Authority (TA). Subsequently, TA forward large data to the cloud for storage, and the information relevant to certificates and hashes are recorded on the distributed blockchain. Moreover, TA revokes the certificates of malicious sensor nodes and eliminates them from the blockchain. We run a set of experiments on PRIBADI and compare the results with the state-of-the-art models. We use the evaluation criteria of detection accuracy, operation, and energy consumption. We find that PRIBADI is 31.7% better than the existing models.

Inversion of Target Magnetic Moments Based on Scalar Magnetic Anomaly Signals

As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.

Tadpole cosmology: Milne solution as a cosmological constant hideout

Dynamical cancellation frameworks present a potential means of mitigating the effect of a large vacuum energy, that would otherwise ruin the late-time, low energy dynamics of the Universe. Certain models in the literature, such as the Fab Four and Well Tempering, realize this idea by introducing some degeneracy in the dynamical equations. In this paper, we introduce a third potential route to self-tuning, and infer the existence of a new, exact Milne solution in the simplest tadpole plus cubic-Galileon scalar-tensor theory. We study the dynamics of the scalar field and metric in the vicinity of the Milne coordinate singularity, and find that the vacuum solution belongs to a more general family of Milne-like metrics. By numerically evolving the field equations for a range of initial conditions, we show that the Milne solution is not an attractor, and varying the initial scalar field data can lead to completely different asymptotic states; exponential growth of the scale factor, a static non-spatially flat metric or a severe finite-time instability in the scalar field and metric. We generalise the Milne solution to a class of FLRW spacetimes, finding that the tadpole-cubic Galileon model admits perfect-fluid-like solutions in the presence of matter. Finally, we present a second Horndeski model which also admits an exact Milne solution, hinting at the existence of a larger undiscovered model space containing vacuum-energy-screened solutions.

Rainbow Colormaps: What are they good and bad for?

Guidelines for color use in quantitative visualizations have strongly discouraged the use of rainbow colormaps, arguing instead for smooth designs that do not induce visual discontinuities or implicit color categories. However, the empirical evidence behind this argument has been mixed and, at times, even contradictory. In practice, rainbow colormaps are widely used, raising questions about the true utility or dangers of such designs. We study how color categorization impacts the interpretation of scalar fields. We first introduce an approach to detect latent categories in colormaps. We hypothesize that the appearance of color categories in scalar visualizations can be beneficial in that they enhance the perception of certain features, although at the cost of rendering other features less noticeable. In three crowdsourced experiments, we show that observers are more likely to discriminate global, distributional features when viewing colorful scales that induce categorization (e.g., rainbow or diverging schemes). Conversely, when seeing the same data through a less colorful representation, observers are more likely to report localized features defined by small variations in the data. Participants showed awareness of these different affordances, and exhibited bias for exploiting the more discriminating colormap, given a particular feature type. Our results demonstrate costs and benefits for rainbows (and similarly colorful schemes), suggesting that their complementary utility for analyzing scalar data should not be dismissed. In addition to explaining potentially valid uses of rainbow, our study provides actionable guidelines, including on when such designs can be more harmful than useful. Data and materials are available at https://osf.io/xjhtf.

Generalized Partially Functional Linear Model with Unknown Link Function

In existing models with an unknown link function, the issue of predictors containing both multiple functional data and multiple scalar data has not been studied. To fill this gap, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and responses, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and establish the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study, functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020, and scalar predictors are GDP and the number of beds in hospitals. The experimental results indicate that the unknown link function model has a smaller prediction error and better performance than both the model with the known link function and the model without a link function.