esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Conventional Machine Learning
#### 2.1.1 Support Vector Machine (SVM)  
Support vector machines (SVMs) are a common choice for binary classification tasks, including AD classification from MRI images. SVM works by finding the hyperplane that best separates two classes of data in a high-dimensional space [5]. The equation of an SVM can be represented as [42]:  
\[F(x)=w^{l}x+b \tag{1}\]  
Figure 2: Conventional Machine Learning vs. DL. Conventional machine learning requires manual feature extraction before making predictions, while DL automatically extracts features from raw data.  
where \(w\) is the weight vector, \(x\) is the input feature vector, and \(b\) is the bias term. The goal is to find the optimal values for \(w\) and \(b\) such that the distance between the hyperplane and the closest data points (support vectors) is maximized. SVM is particularly effective when the feature space is clear and well-defined, making it useful for early studies in detecting AD. An SVM classifier has been used to diagnose early stages of dementia in [34]. The power spectral density (PSD) and temporal data were combined for feature extraction. The work proposed a new method to differentiate between three categories--AD, MCI, and HC based on electroencephalographic (EEG) signals. The classification accuracy achieved after data augmentation using the VAE model increased by 3% compared to before augmentation. In [36] six basic models were used in addition to the SVM. The proposed models achieved the highest performance with selected features against regular ML classifiers and stacking models using full modality. In the study [43] the potential of RTOP/RTAP/ was evaluated and SVMs based on different discriminant metrics for early detection of AD. An ML model was trained to classify patients with AD and healthy controls based on their genetic data in article [44], the best-performing model was SVM, achieving 89% accuracy.
## Materials and Methods  
### Substrate Fabrication  
Textured substrates were obtained by replication in polydimethylsiloxane (PDMS; Sylgard 184 Silicone Elastomer Kit; Dow Corning, USA MI,; 1:10) using polycarbonate (PC) multi-scaled molds micromachined by a projection laser ablation system equipped with a KrF excimer laser (ExitechPPM-601E Gen6 Tool, Oxford, UK), (20 ns pulse, 248 nm wavelength, 50 Hz repetition rate, average fluence at the substrate level 400 mJ cm\({}^{-2}\) per pulse), as described in [6, 28]. Briefly, the laser beam was collimated on a grey level mask, focused on the PC substrate, with the de-magnification factor of 5, and the mask was moved in a controlled manner under the beam to lead to a design on a 1 cm\({}^{2}\) area. The residual ablated particles were aspired during the texturing process. Before being used for replication, the PC molds were cleaned by ultrasonication for 10 min, sequentially, in isopropyl alcohol (IPA)(Chemical Company, Iasi, Romania) and Millipore ultrapure water (\(R\) = 18 M\(\Omega\)), followed by their subsequent drying with an air gun.  
The liquid PDMS obtained from mixing the two above components was placed onto the PC mold for 48 h at room temperature, and then heated at 80 \({}^{\circ}\)C for 1 h [29, 30]. The resulting PDMS samples were subsequently removed from the mold and cleaned by ultrasonication, twice, with each 10 min cleaning step being performed in Millipore ultrapure water. Three degrees of densities were created using the mold design, with 4 \(\upmu\)m\({}^{2}\) units and varying the spacing between the features and their disposal at multiscale levels; specifically, irregular stairs/lines in low, medium and high densities were created [28]. Such structures were further referred to as low-density square unit-LDSU, medium density square unit-MDSU and high-density square unit-HDSU, respectively (Supplementary Materials Figure S1).
## 3 Numerical Modelling in Single Pipelines
### Mathematical Model of Single Pipelines (Cases 1 and 2)  
The mathematical model of [PERSON] et al. [18] was used as reference, which represents a model of single pipe that contains an air pocket and a water column. This model considers the same assumptions mentioned in the mathematical model of [PERSON] et al. [17] concerning the mass oscillation and emptying column equations, as shown in Table 6.  
To analyse the physical behaviour of trapped air in Case 1, an equation was added as a function of air pocket length (\(L_{ap}\)) and polytropic coefficient (\(\kappa\)), as shown in Equation (11):  
\[p_{a}{L_{ap}}^{\kappa}=p_{atm}{L_{iap}}^{\kappa} \tag{11}\]  
On the other hand, for the simulation of trapped air considering an air admission orifice in the single pipe (Case 2), [PERSON] et al. [18] defined three (3) equations: (i) a mass conservation equation (Equation (12)), (ii) a polytropic equation considering mass oscillations over time (Equation (13)) and (iii) a mass flow equation for the air inlet orifice simulation, as shown in Equation (14):  
\[\frac{dm_{a}}{dt}=\frac{d\rho_{a}}{dt}V_{a}+\frac{dV_{a}}{dt}\rho_{a} \tag{12}\]  
\[\frac{dp_{a}}{dt}=-\kappa\frac{p_{a}}{V_{a}}\frac{dV_{a}}{dt}+\frac{p_{a}}{V_{ a}}\frac{\kappa}{\rho_{a}}\frac{dm_{a}}{dt} \tag{13}\]  
\[Q_{a}=C_{d,atm}A_{adm}\sqrt{7p_{atm}\rho_{a}}\Bigg{[}\bigg{(}\frac{p_{a}}{p_{atm }}\bigg{)}^{1.4286}-\bigg{(}\frac{p_{a}}{p_{atm}}\bigg{)}^{1.714}\Bigg{]} \tag{14}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline Mass oscillation & \(\frac{dt_{a}}{dt}=\frac{p_{a}-p_{atm}}{\rho_{a}V_{a}}+g\frac{\Delta\rho_{a}}{ L_{a}}-f\frac{u_{a}|u_{a}|}{2p}-\frac{R_{a}\kappa^{2}u_{a}|u_{a}|}{L_{a}}\) \\ Water column length & \(\frac{dL_{a}}{dt}=-u_{a}(L_{a},0-f\int_{0}^{0}\!\!u_{a}\,dt)\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fundamental equations of the mathematical model.  
Figure 8: A single pipeline with Case \(1\) (without air valves), and Case 2 (with air valves).  
This mathematical model by [PERSON] et al. [18] used a similar computational setup to the mathematical model presented by [PERSON] et al. [17] described in Section 2.1.
## 2 Research Methodology
### Cluster Classification and Feature Matching
#### 2.2.2 Category 2: One-to-Many  
This category addresses scenarios of merging defects, where a cluster contains a single feature in set X\({}_{Q}\) (X\({}_{q_{0}}\)) and multiple features in set X\({}_{P}\). Any feature X\({}_{P_{k}}^{\sim}\) in set X\({}_{P}^{\sim}\) is considered a match if its distance to feature X\({}_{q_{0}}\) is within the merging distance threshold (\(\lambda\)), denoted as \(d\left(X_{q_{0}},X_{\widetilde{P}_{k}}\right)\leq\lambda,\forall k=1,2,\ldots,2n_{X}\). Features that fail to meet this criterion are classified as outliers.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 6 Discussion  
In the field of exploration geophysics, the solution of partial differential equations is one of the most important topics. However, most current algorithms for solving partial differential equations cannot consider accuracy and efficiency, so it is necessary to propose an efficient and high-precision solution. In this paper, we propose an arbitrary sampling Fourier transform algorithm based on quadratic interpolation of the shape function. After combining the AS-FT algorithm and the spatial wavenumber domain algorithm, we applied them to the magnetic method numerical simulation to achieve high-efficiency and high-accuracy imaging of the magnetic field [33].  
We compared AS-FT, standard-FFT, and Gauss-FFT with the space-wavenumber domain algorithm to realize magnetic field numerical simulation.  
Numerical simulation based on standard-FFT and Gauss-FFT does not require wavenumber selection, and the simulation process is relatively simple [24; 26; 33]. The AS-FT algorithm in this paper can achieve high-precision simulation only when the wavenumber is selected accurately. However, the process is relatively simple and greatly improves the algorithm's efficiency, which is very valuable for research.  
Numerical simulation based on standard-FFT has a strong edge effect, and it often requires edge expanding to reduce the edge effect and improve the calculation accuracy. Still, the edge expanding constantly sacrifices calculation efficiency [33]. Numerical simulation based on Gauss-FFT weakens the influence of the edge effect, but it demands more memory with lower efficiency [33]. However, the numerical simulation based on AS-FT has almost no edge effect. The computational efficiency is superior to the standard-FFT and Gauss-FFT, and it can therefore achieve high-efficiency and high-precision numerical simulation research.  
Numerical simulation based on standard-FFT and Gauss-FFT can only achieve uniform sampling. In contrast, numerical simulation based on the AS-FT algorithm can achieve non-uniform sampling, so it can flexibly sample according to the sampling requirements, and achieve the highest accuracy with the fewest sampling points.  
Therefore, the AS-FT algorithm based on shape function interpolation proposed in this paper provides an efficient and high-precision method of solving partial differential equations. This algorithm does not need to expand the edge, can effectively avoid the influence of edge effects, and can achieve non-uniform sampling. It is significant to the development and perfection of exploration geophysics theory.
## 1 Introduction  
The power energy sector is growing fast, with more large substations than ever. Keeping these substations running smoothly and safely is crucial for the power grid's stability [1]. Staff violations in operations significantly increase the likelihood of accidents [2]. Statistics show that from 2011 to 2021, 967 electrical workers in the United States died in accidents [3]. From 2020 to 2021, there were 70 power-related personal injury accidents in China, resulting in 82 deaths and over 1 million yuan in direct economic losses [4; 5; 6]. Substation workers' limited safety awareness jeopardizes both their safety and the reliability of business operations and urban energy provision. Therefore, timely detection of PPE is of utmost importance.  
As the electric power industry develops, the importance of safety management in substations is increasingly emphasized. The correct use of PPE is fundamental to ensuring the safety of workers in the substation environment. However, despite the significant risk reduction provided by wearing PPE, challenges persist in PPE recognition and detection within substations. First, the complex and variable working conditions in substations can impair the effectiveness of PPE detection. Additionally, in emergencies, workers might overlook essential gear like insulated gloves or helmets, raising the risk of violations. Furthermore, current PPE detection technologies lack precision and speed, particularly in complex situations. Therefore, it is vital to develop effective PPE detection models that quickly and accurately evaluate PPE use in diverse conditions, improving substation safety management.  
Although PPE detection technology has significantly improved, detecting various scale targets in complex and dynamic substation environments remains challenging due to numerous model parameters. Therefore, this study proposes the lightweight MEAG-YOLO model for detecting PPE. The model incorporates several key innovations:  
* This study integrated MSCA into the backbone network. This integration enhances the efficiency of target feature extraction. As a result, it improves the overall detection performance.
* The EC2f structure was developed to streamline the model. It reduces the number of parameters. This structure also enhances feature fusion efficiency. Furthermore, it simplifies calculations by using one-dimensional convolution. This is used for cross-channel interactions.
* The PAN was optimized. This bolstered the model's feature fusion capabilities. It ensures a more effective integration of multi-scale features.
* YOLOv8n was incorporated with GhostConv. This merger significantly reduces the amount of calculations. It has a slight impact on precision.  
These innovations collectively make the MEAG-YOLO model effective for detecting various scales of PPE in substation environments.
## 2 Voltage Regulator on a Power Line  
The structure of the conventional under load tap changer (ULTC) is shown in Figure 2. As shown in Figure 2, the ULTC consists of a transformer with a tab, a tap-switching device with a motor driving element, a dead band, a time delay, an automatic voltage regulator, and a voltage and current measuring device for line voltage compensation. The ULTC switches the tab of the transformer winding during loading and maintains a constant transformer output voltage during load fluctuations. Furthermore, two types of nonlocal tab-switching devices (NLTC/No Load Tap Changer), load tab switching devices (OLTC/On Load Tap Changer), and ULTCs are typically applied to large-capacity transformers [10; 11].  
To eliminate the arcing voltage caused by the load current when switching the taps of the NLTC, a specific tap of the transformer is selected with a tap selector in the no-load condition, and the changeover switch is then moved to the center. As shown in Figure 3, in the ULTC, both taps are connected simultaneously when the taps are switched, and the internal circulating current flows, owing to the potential difference. A transition resistor (current-limiting resistor) or a reactor is typically installed to suppress the circulating current [12; 13].  
Figure 4 shows the tap-switch circuit with two taps using a static switch. In the figure, R1 and R2 are resistors that limit the short-circuit current that occurs during the tap switching. In this topology, the RMS voltage is measured in a half-cycle unit, and when switching is required, the voltage is compensated using S1-S8 [14].  
Figure 1: Power quality compensation method by power system network.  
Figure 3: ULTC tap change operation principle.  
Figure 2: The structure of a typical ULTC. (**a**) Auto transformer type. (**b**) Serial transformer type.
# Distributed Uniform Streaming Framework:
## 5 Experiments
### Use Case 3: Global Optimisations  
The final use case shows that the current auction implementation of the DUST Coordinators is not always able to optimize the resource consumption to global minima. Figure 6 shows the configuration of this experiment. The sensor is sending two event streams to the actuator. This data is first filtered before sending it to the corresponding consumer component. Producer 1 will send 200 messages per second while producer 2 only send 100 messages per second. The sensor and actuator node are only allowed to run 3 different components at the same time, due to limited computational resources. Both nodes are executed on two Raspberry Pi 3 B+ with only a single core enabled.  
Table 5 is a summary of the experiment results. The network usage shows to be the most optimal in the first two experiments when Filter 1 is placed on the sensor node and Filter 2 on the actuator node. The last experiment however shows that the DUST Coordinator was not able to migrate the components as only one component can be moved at a time. These results show that the DUST Coordinators cannot always find the global minima in resource consumption.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Managed by** & **Device** & **Running Components** & **CPU Usage** & **Messages** & **Device Network** \\
**DUST** & & **on the Device** & **of Device (\%)** & **Received** & **Usage (MB)** \\ \hline ✗ & Sensor 1 & Source & 59.9 & n.a. & 33.16 \\ ✗ & Sensor 2 & Source & 19.2 & n.a. & 11.54 \\ ✗ & Actuator & Processor, Sink & **77.9** & **237,045** & 25.18 \\ ✗ & Cloud & Average, Sink & 37.8 & 88 & **20.56** \\ \hline ✓ & Sensor 1 & Source & 61.2 & n.a. & 33.97 \\ ✓ & Sensor 2 & Source, Average, & 191.5 & n.a. & 49.90 \\  & & Processor & & & \\ ✓ & Actuator & Sink & **57.1** & **291,620** & 25.75 \\ ✓ & Cloud & Sink & 20.3 & 87 & **8.240** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fog computing results of use case 2.  
Figure 6: The streaming configuration from sensor to actuator. The filters can be placed on the sensor or on the actuator.
## 1 Introduction
This innovative approach not only improves the timeliness and reliability of emission reporting, but also significantly reduces operational costs and resource consumption through the automation of the estimation process. These advancements are vital for power generation companies navigating stringent environmental regulations and seeking cost-effective compliance strategies. Moreover, the enhanced accuracy and efficiency of our model provide governments and enterprises with improved tools for developing scientific and feasible emission reduction strategies, which are crucial for alignment with global carbon neutrality goals and supporting the practical implementation of these policies in the power industry. The rest of this paper is organized as follows: Section 2 introduces related research. Section 3 describes materials and our method. Section 4 describes the performance evaluation of the proposed method. Finally, Section 5 presents the conclusions.
## 3 Radar Micro-Doppler Analysis of Vibrating Structures
### Radar Micro-Doppler Modeling  
Mathematical expressions have been developed to describe the micro-Doppler shifts caused by various types of micro-motions [25]. In the case of vibrational motion of a target, the micro-Doppler frequency \(f_{\mu D}\) is given by [25]  
\[f_{\mu D}=\frac{4\pi f_{0}f_{v}D_{v}}{c}[cos(\alpha-\alpha_{P})cos\beta cos\beta _{P}+sin\beta sin\beta_{P}]\cos(2\pi f_{v}t), \tag{2}\]  
where \(f_{0}\) is the radar transmit frequency, \(f_{v}\) is the target vibration frequency, \(D_{v}\) is the amplitude of the target displacement, \(\alpha\) and \(\beta\) are the azimuth and elevation angle of the target center with respect to the radar boresight axis, respectively, and \(\alpha_{P}\) and \(\beta_{P}\) are the azimuth and elevation angle of the target vibration feature with respect to its center, respectively. When the target as well its vibration features are directly along the line-of-sight of the radar boresight, the angles \(\alpha\), \(\beta\), \(\alpha_{P}\), and \(\beta_{P}\) are all zero, and the micro-Doppler frequency \(f_{\mu D}\) can be simplified as  
\[\left.\begin{array}{c}f_{\mu D}\big{|}_{\begin{array}{c}\alpha=0\\ \beta=0\\ \alpha_{P}=0\\ \beta_{P}=0\end{array}}\right.=\frac{4\pi f_{0}f_{v}D_{v}}{c}\cos(2\pi f_{v}t). \tag{3}\]  
From Equation (3), it is evident that the micro-Doppler shift is proportional to the carrier frequency. Therefore, operating at millimeter or sub-millimeter wave frequenciesenhances the ability to resolve fine Doppler features, particularly for slower-moving targets such as humans.  
Coherent Doppler radars can detect extremely small displacements in a target's position, often to a fraction of a wavelength. This sensitivity can be leveraged to uncover characteristic signatures of targets, such as those arising from internal vibrations or modulations, including engine vibrations, breathing, heartbeat, and speech [26]. The displacement error \(\sigma_{x}\) is related to the phase error of the measurement \(\sigma_{\phi}\) and the wavelength \(\lambda\) as  
\[\sigma_{x}=\frac{\lambda\sigma_{\phi}}{4\pi}. \tag{4}\]  
The phase error, in turn, is related to the signal-to-noise ratio \(SNR\) as [26]  
\[\sigma_{\phi}=\frac{1}{\sqrt{2\cdot SNR}}. \tag{5}\]  
In realistic operational scenarios, there are numerous factors that cause or affect the radar micro-Doppler characteristics of stationary engine-running vehicles. Some of the more important factors are depicted in the fishbone diagram in Figure 2.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion
### Variable Importance and Partial Dependence Plots
This matrix presents PDPs between variable pairs in a format that aids in assessing slope stability: By knowing the values of two slope features, one can pinpoint corresponding points on the matrix and gauge the stability likelihood based on their location. To validate the effectiveness of the matrix, eight slope cases from the study by [PERSON], et al. [54] were examined, with the slope stability factors ranging from 0.824 to 2.448. Figure 6 highlights the slope angle and angle of internal friction as key factors, leading to a two-dimensional PDP analysis of these variables in the cases. The safety factor for slopes is a crucial parameter used to assess the stability of a slope in civil  
Figure 8: Partial dependence matrix. engineering. It is defined as the ratio of the resisting forces to the driving forces acting on the slope. A safety factor greater than 1 indicates a stable slope, where the resisting forces are greater than the driving forces, while a safety factor less than 1 suggests an unstable slope, where failure is likely due to the dominance of driving forces. In Figure 8, the size of the blue bubbles represents the magnitude of the safety factor for each case. As shown in Figure 8, slopes with higher stability coefficients cluster in the upper right quadrant, contrasting with lower stability coefficients in the lower left quadrant, aligning with the slope case's safety coefficient and confirming the matrix's reliability.
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 5 Discussion  
This study introduces a flexible and interpretable framework for multidimensional data visualization, placing the user at the center of the exploratory process. The core innovation lies in the semantic division of variables: users can assign the most meaningful interpretable features (such as quality, popularity, or sensory attributes) to avatar characteristics, while more abstract or technical variables (e.g., chemical or behavioral parameters) are mapped to spatial coordinates. Unlike traditional approaches, such as PCA or parallel coordinates--which treat all variables equally and often obscure critical features--our method allows for user-driven, context-sensitive mapping that supports practical interpretability.  
Spatial avatars further extend classical glyph-based visualization by embedding expressive, user-configurable avatars in a fully interactive 3D environment. Users can dynamically explore the data cloud, rotate and zoom into clusters, and rapidly test hypotheses by reassigning features to avatars or axes. This interactive capacity enables both the detection of structural patterns and the immediate comparison of individual profiles--capabilities not readily available in static projection-based visualizations.  
Empirical results across synthetic and real-world datasets--including fictional politicians, synthetic soft drinks, and the Vinho Verde wine collection--demonstrate that spatial avatars make group structure, outliers, and archetypes directly observable and interpretable. In contrast, classical PCA-based projections often mix multiple technical variables into principal components, rendering key features invisible or ambiguous from the user's perspective.  
By empowering users to control the mapping of features and explore data interactively, the spatial avatar framework bridges the gap between statistical rigor and practical, human-centered interpretation. The system, implemented as an open-source plugin for the dpVision platform, is adaptable to various analytical goals, data types, and user preferences.