esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Eligibility Criteria  
Studies were eligible for inclusion if they met the following Population, Concept, and Context (PCC) criteria:  
Population (P): The population targeted in the included studies encompassed individuals of any age and health status, including both healthy individuals and those with specific medical conditions. Specifically:  
* General populations: Healthy individuals, including men and women across different age groups.
* Specific conditions: Individuals with medical conditions relevant to foot health, such as:
* Diabetes: At risk of developing plantar ulcers.
* Gout: Experiencing foot pain and functional limitations.
* Down syndrome: Where pediatric deformities and limited physical activity can be impacted by footwear.
* Elderly individuals: Particularly vulnerable to issues caused by inadequate footwear, such as foot pain and difficulties in walking.  
Concept (C): The key concept of interest was shoe fit, which included various factors such as shoe size, width, support, and comfort, as well as the adaptability of shoes to the specific needs of the individual. The studies examined different aspects of footwear:  
* Shoe size: The appropriateness of shoe size in fitting the individual's foot.
* Shoe width and fit: The width of the shoes and how well they match the foot's shape.
* Support: The level of support provided by the shoes in preventing foot problems (e.g., calluses, ulcers, deformities).
* Comfort: The individual's perceived comfort, which is crucial for preventing chronic pain and injuries.
* Educational interventions: The use of informational materials or educational programs to raise awareness among individuals and healthcare professionals about the importance of proper footwear.
* Custom-made shoes: The effects of custom-fitted shoes for individuals with specific needs, such as children with Down syndrome or people with gout.  
Context (C): The context in which the studies were conducted varied widely, encompassing clinical as well as community settings. The studies covered different scenarios where footwear choice plays an important role:  
* Clinical settings: Medical or hospital environments, such as diabetic care units or podiatry clinics, where the focus is on preventing and managing foot problems.
* Community settings: Studies conducted in community or home environments, where the impact of footwear choice is assessed in daily life, particularly for elderly individuals or those with limited mobility.
* Sport and recreational settings: Studies exploring the use of footwear during physical or sports activities, focusing on injury prevention due to inappropriate footwear.
## 2 Basic Theory of Signal Noise Reduction Method
### Wavelet Noise Reduction Principle
#### 2.1.2 Wavelet Multi-Scale Decomposition  
The discrete wavelet basis function [18] is shown as Equation (1)  
Figure 1: Steel cord conveyor belt break scene.  
\[\psi_{j,k}(t)=a_{0}-\frac{i}{2}\psi(a_{0}-i-kb_{0}) \tag{1}\]  
where \(a_{0}\) represents the extension scale and \(b_{0}\) represents the time translation, usually \(a_{0}=2\), \(b_{0}=1\).  
Through the discrete wavelet transform, the original signal \(x(t)\) including noise is shown as Equation (2)  
\[d_{j,k}(t)=\int_{-\infty}^{\infty}x(t)2^{-\frac{j}{2}}\psi(2^{-j}t-k)\mathrm{d}t \tag{2}\]  
The Mallat forward transform is shown as Equation (3) [19]  
\[\left\{\begin{array}{l}c_{j,k}=\sum\limits_{n}h_{k-2n}c_{j-1,n}\\ d_{j,k}=\sum\limits_{n}g_{k-2n}d_{j-1,n}\end{array}j=1,2,\cdots,J\right. \tag{3}\]  
where \(c_{j,k}\) is the smoothing signal of original signal, \(d_{j,k}\) is the detail signal of original signal, \(g_{n}\) is the impulse response of band-pass filter concerned with wavelet function, \(h_{n}\) is the impulse response of low-pass filter concerned with scaling function.
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### System Evaluation Standard
#### 3.1.2 Equal Error Rate  
Before introducing EER, we have to define some terms first. The proportion of correctly detected positives among all positive samples is the true positive rate (TPR), as in (14). The proportion of misclassified negative samples among all negative samples is the false positive rate (FPR), as in (15).  
\[\text{True positive rate }=\frac{\text{True positives}}{\text{True positives}+\text{False negatives}} \tag{14}\]  
\[\text{False positive rate }=\frac{\text{False positives}}{\text{False positives}+\text{True negatives}} \tag{15}\]  
By adjusting the threshold of the detection model, we can use TPR (\(Y\) axis) and FPR (\(X\) axis) to draw the ROC curve, as shown in Figure 23. The blue line is the result, derived by randomly guessing the classifier. If the threshold of the classifier is higher, all samples are classified as negative samples. Then, the FPR and TPR are both 0, as in the case of point (a) (0,0) in Figure 23. If the threshold of the classifier is low, all samples are classified as positive samples, and then the FPR and TPR are both 1, as in the case of point (b) (1,1) in Figure 23. This is the way to adjust the threshold and draw the ROC curve between (0,0) and (1,1). If the ROC curve is closer to (0,1), the classifier is better as point (c) in Figure 23, and all the predictions are correct. If the ROC curve is closer to (1,0), as in the case of point (d) in Figure 23, all the predictions are wrong.  
Figure 23: ROC Curve.  
Figure 22: High precision and low recall.  
If we draw a diagonal line between (1,0) and (0,1) on the ROC curve, the intersection of this line and ROC curve is the Equal Error Rate, as in Figure 24. This Equal Error Rate is around 0.73. The setting of this threshold makes the system have the most balanced performance. Therefore, the Equal Error Rate is usually used to determine the performance of the system. In the following experiments, we also use it to perform comparisons with different classifiers.
## 3 Results
### Relationship between the Serum 25(OH)D Level and MetS

Table 3: Anthropometric and biochemical measurements of the participants by categories of serum 25-hydroxyvitamin D (25(OHD) concentration and gender. The connection between the individuals' serum 25(OH)D and each component of MetS was analyzed using complex sample logistic regression (Table 4). As the serum 25(OH)D concentration of all individuals increases, abdominal obesity and hypertriglyceridemia decrease significantly. A higher serum concentration of 25(OH)D was linked with lower abdominal obesity and a significant decrease in hypertriglyceridemia in men, which is consistent with Table 3's findings regarding lower blood TG levels in higher serum 25(OH)D levels. In women, however, no association existed between serum 25(OH)D levels and any factor of MetS.
## 3 Results and Discussion
### Volatile Analysis
#### 3.3.2 Chilled Samples
When taken together, the most important components that contributed to the overall sensory flavour perception of PEF treated chilled lamb samples were the flavour volatiles (dimethyl disulphide, pyridine, heptanal, hexanal, 2-ethylfuran, pyrrole, dimethyl trisulphide, 2-heptanone, 3,5-diethyl-2-methyl pyrazine, 2,3-octanedione, thiophene, methyl pyrazine, 2-pentylfuran, 2-nonanone, 2-methylbutanal, and 2-ethyl-3-methyl pyrazine) and amino acids (histidine, proline, glycine, glutamic acid, tryptophan, isoleucine, tyrosine, threonine, methionine, phenylalanine, and aspartic acid) (Figure 5d). In terms of PEF treated chilled lamb samples, amino acids predicted the sensory quality of meat better than fatty acids, because of their role in the Maillard reaction and Strecker degradation. These reactions are important pathways that contribute to the formation of volatile compounds in cooked meat [22]. These volatile compounds, in turn, contribute to meaty, savoury, roast, and boiled flavours [16]. For instance, sulphur compounds produced from reaction with ribose and cysteine are specifically critical for a characteristic meat aroma [16].
## 2 Materials and Methods
### Numerical Simulation by FEM
#### 2.3.1 Elasto-Dynamic Equilibrium Equations and Approximation with Finite Elements
In summary, the procedure for determining the natural frequencies and the associated vibration modes consists of the following:  
* the mass matrix \(M\) and the stiffness matrix \(K\) are calculated with the finite element method;
* the matrix is found with \(H=M^{-1/2}KM^{-1/2}\);
* by the spectral decomposition of the matrix \(H\), the eigenvalues \(\lambda_{i}\) and the corresponding eigenvectors \(r^{(i)}\) of \(H\) are calculated;
* the natural frequencies \(\omega_{i}\) are related to the eigenvalues by \(\lambda_{i}=\omega_{i}^{2}\);
* and the deformations are associated with the vibration modes through \(U^{(i)}~{}=~{}M^{-1/2}r^{(i)}\). The deformations calculated in this way are not uniquely determined, having a random magnitude, for this reason a corresponding normalization is made for each vibration mode. The calculation effort is high for determining the eigenvalues, i.e., for the spectral decomposition of the matrix \(H\). In practice, it is enough to find the first smallest eigenvalues, respectively, the corresponding eigenvectors. A useful method for this purpose is the method of projections on subspaces: the eigenvectors are approximated and the size of the matrices \(M\) and \(K\) is reduced to the size of the obtained projection subspace.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 1 Introduction
For the case with multiple influencing factors, the commonly used test methods include comprehensive test and orthogonal test. Compared with comprehensive test, the orthogonal test can reduce test times and improve efficiency, which is essential for the cases with multiple influencing factors and levels. Therefore, in view of this problem, this paper firstly analyzes the effect of SBS content on the high and low temperature performance of modified asphalt, and then establishes the calibration curve of SBS content and characteristic absorption peak absorbance in modified asphalt according to the infrared spectrum. Finally, the orthogonal test is designed, and the results are analyzed by the multivariate analysis of variance to explore the influence of additives on the determination results of SBS in modified asphalt.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.7 Handling Temporal Information  
For dynamic biomedical imaging, such as videos or time-series data, DDPM's applicability might be limited. The model's architecture is primarily designed for static image processing and might not capture temporal dependencies effectively [103; 104].
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### Problem Description
#### 3.2.3 Problem Formulation  
This paper addresses the challenge of deploying edge nodes with the objective of optimizing response time and cost. In the scenario involving fixed devices, the aim is to minimize both response time and cost. In the mobile scenario, the goal is to optimize and minimize response time. The objective function can be expressed as follows:  
\[min\ T_{max} \tag{11}\]  
\[min\ C_{all} \tag{12}\]  
s.t. (c1): \(\sum_{j\in E}\alpha_{i,j}\geq 1,\forall i\in T\)  
(c2): \(\sum_{i\in T}\alpha_{i,j}\geq 1,\forall j\in E\)  
(c3): \(T_{max}\leq MAXT\)  
(c4): \(0<C_{all}\leq MAXC\)  
c1: All tasks are handled by at least one edge node. c2: Each edge node serves at least one device; otherwise, the placement of the node is meaningless. c3: The maximum response time in edge computing cannot exceed the maximum service time defined by the edge node. c4: The cost of placing edge nodes should be a nonnegative number and not exceed the expected cost.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
###### Abstract  
The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations in RDA necessitates high bandwidth and lacks data reuse, leading to bottlenecks. This paper introduces a synthetic aperture radar dual in-line memory module (SARDIMM), which executes RDA operations near memory via near-memory processing (NMP), thereby effectively reducing memory accesses, execution time, and energy consumption. The embedded NMP module in SARDIMM optionally supports a combination of FFT, IFFT, and matched filter operations of the RDA for range and azimuth compression. The operator within the NMP module accelerates the FFT by performing two radar-2 single butterfly operations in parallel. The NMP module was implemented and validated on a Xilinv UltraScale+ field-programmable gate array (FPGA) using Verilog-HDL. The acceleration performance of RDA for images of various sizes was evaluated through a simulator modified with gem5 and DRAMSim3 and achieved a 6.34-6.93\(\times\) speedup and 41.9-48.2% energy savings.  
synthetic aperture radar; range-Doppler algorithm; fast Fourier transform; memory bottleneck; near-memory processing; acceleration
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 2 Proposed Method
### Different Methods for Anomaly Score Calculation
#### 2.4.2 K-Means  
K-Means [34] is a widely used unsupervised learning algorithm [43]. It partitions a dataset into \(k\) non-overlapping clusters, ensuring that samples within the same cluster are highly similar, while samples in different clusters are distinctly different. Each cluster is represented by its centroid, which is computed as the geometric mean of the samples within the cluster. The similarity between data points and centroids is typically measured using Euclidean distance.  
The process of calculating anomaly scores using K-Means consists of two main steps: first, performing K-Means clustering on the normal latent features of a specific class to obtain the cluster centers; second, calculating the distance between the latent features of the test samples and the cluster centers to determine the anomaly scores. The method for obtaining the cluster centers is shown in Algorithm 1, and the visualization of the clustering results is presented in Figure 3.  
For anomaly detection, the distance between the test sample and the cluster centers can also be measured using cosine similarity. Specifically, we calculate the anomaly score by measuring the cosine similarity between the feature vector \(z_{i}\) of the test sample and the centroids \(\mu_{c,j}\) of all clusters in its corresponding category \(c\), computed as follows:  
\[\cos(z_{i},\mu_{c,j})=\frac{z_{i}\cdot\mu_{c,j}}{\|z_{i}\|\|\mu_{c,j}\|} \tag{10}\]  
Subsequently, the cluster with the highest cosine similarity to \(z_{i}\) is identified as the closest cluster, and the calculation is given by the following:  
\[\text{best\_cos}=\max_{j}(|\cos(z_{i},\mu_{c,j})|) \tag{11}\]  
Finally, the anomaly score is derived from the best similarity value best_cs using the following formula:  
\[A_{K\_Means}=-\ln(\text{best\_cos}) \tag{12}\]  
Here, \(A_{K\_Means}\) represents the anomaly score, and the higher the value, the more likely the data point is an anomaly.  
```
1:\(\{z_{1},z_{2},z_{3},\dots,z_{n}\}\in Z\): set of latent features extracted from the training data \(Z\)\(\{C_{1},C_{2},C_{3},\dots,C_{k}\}\in C\): set of clusters \(C\)\(max\)\(iter\): number of allowed maximum iterations
2:Steps:
3: Assign initial centers \(\mu_{1},\mu_{2},\mu_{3},\dots,\mu_{k}\) randomly
4:repeat
5:\(C_{1},C_{2},\dots,C_{k}\leftarrow\emptyset\)
6:for each \(z\in Z\)do
7:\(i=\arg\min_{i=1,\dots,k}\|z-\mu_{i}\|^{2}\)
8:\(C_{i}\gets C_{i}\cup\{z\}\)
9:endfor
10:for\(i=1,\dots,k\)do
11:if\(C_{i}\
eq\emptyset\)then
12:\(\mu_{i}=\frac{1}{|C_{i}|}\sum_{z\in C_{i}}z\)
13:endif
14:endfor
15:until the cluster centers \(\mu_{1},\mu_{2},\mu_{3},\dots,\mu_{k}\) do not change or \(max\)\(iter\) is reached
```  
**Algorithm 1** K-Means algorithm
## 1 Introduction
This inspection cannot be obtained from the scheme presented in the previous study [7; 8; 9; 19]. In this paper, Gaussian noise is used to model measurement uncertainty. The effect of Gaussian noise on the accuracy of the azimuth estimate is rigorously derived. Furthermore, an explicit expression of the MSE of the azimuth estimate is also derived. In comparison with the previous studies on the performance analysis of the maximum likelihood algorithm [7; 8; 9; 19], a more explicit representation of the MSE of the azimuth estimate is proposed in this paper. Many previous studies on the ML DOA estimation algorithm focused on how the performance of the ML DOA estimation algorithm can be improved by proposing new algorithms or by modifying the ML DOA estimation algorithm [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Note that our contribution in this paper does not lie in how much improvement can be achieved by proposing an improved ML DOA algorithm. Our contribution lies in a reduction in computational cost in getting the MSE of an existing ML DOA algorithm by adopting an analytic approach, rather than the Monte Carlo simulation-based MSE under measurement uncertainty which is assumed to be Gaussian distributed. That is, the scheme described how analytic MSE can be obtained with much less computational complexity than the Monte Carlo simulation-based MSE. In this paper, the derivation is based on the Taylor series expansion of the sample covariance matrix since the cost function of the ML DOA estimation algorithm can be explicitly written in terms of the sample covariance matrix. The difference between the sample covariance matrix associated with noisy measurement and that associated with noiseless sample covariance matrix is explicitly expressed in terms of additive noises on the antenna arrays. Azimuth estimation error is explicitly expressed in terms of the additive noises. Finally, the MSE of the azimuth estimate is given in terms of the statistics of an additive noise. To the best of our knowledge, no previous study presented these explicit expressions of the azimuth estimation error and the MSE of the azimuth estimate in terms of the statistics of an additive noise. The proposed scheme can be used in predicting how accurate the estimate of the ML DOA estimation algorithm is without a computationally intensive Monte Carlo simulation. The performance of the ML algorithm depends on various parameters including the number snapshots, the number antenna elements in the array, inter-element spacing between adjacent antenna elements, and the SNR. Therefore, Monte Carlo simulations for different values of the various parameters can be computationally intensive. Therefore, the scheme presented in this paper can be adopted to predict the accuracy of the ML DOA algorithm for different values of various parameters.
## 3 Application and Validation of MuLiWEA
### Real Dual-Doppler Lidar Wake Measurements  
A case study on a measurement scenario executed in the German offshore wind farm \"alpha ventus\" is provided. Two Leosphere Windcube WLS200S long-range pulsed lidars (Orsay, France) were used to measure the wake of the AV10 (alpha ventus, number ten) Adwen AD 5-116 wind turbine (Adwen GmbH, Bremerhaven, Germany) by means of single elevation PPI scans. The lidars were installed on offshore platforms, resting on their four adjustable legs and mounted with additional tensioning belts. The turbine has a rated power \(P_{r}\) of 5 MW, rotor diameter \(D\) of 116 m and hub height \(h_{h}\) of 90 m. The same two lidars were previously used in a dual-Doppler configuration during an onshore measurement campaign. During that experiment, [PERSON] et al. [34] compared the wind vector evaluated from a cup anemometer and a wind vane installed on a meteorological tower close to the lidar measurements with the horizontal wind vector computed from the radial speed measured by two lidars. This validation yielding goodness of fit values \(R^{2}\) of 0.986 and 0.998 for the wind speed and wind direction in a free inflow sector, respectively.  
In Figure 5, a sketch of the measurement setup of the dual-Doppler lidar PPI scan scenario is depicted. These single PPI scans have elevation angles of 2.0\({}^{\circ}\) (WLS2) and 2.8\({}^{\circ}\) (WLS3), respectively. They cover an azimuth sector of approximately 30\({}^{\circ}\) with a resolution of 0.3\({}^{\circ}\). For the application of MuLiWEA, measurements were selected with a \(C_{h}\) value of 0.3, corresponding to an altitude range of (72.6, 107.4) m around the wind turbine hub height. The wind field resolution is 20 m, and the radius of influence is chosen to be 30 m. The measurements were filtered by their CNR values, by selecting measurements within a (\(-22\), \(-8\)) dB range. This omitted both measurements with a high uncertainty because of a minor backscatter energy in the spectrum, thus a low CNR value, as well as measurements compromised by hard targets such as wind turbines, which can be distingu  
Figure 4: Side view of the simulated measurement setup with the six PPI scans with different elevation angle from lidar 1 (see Figure 3) relative to the simulated wind turbine.  
Figure 3: Top view of the simulated PPI scan measurement setup of the four modeled lidars.
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 2 Real-Time 3D Reconstruction Method
### Texture Mapping  
Texture mapping can help produce a visually appealing model by applying high-quality textures on a 3D mesh with minimal geometric complexity, as claimed by [29]. Additionally, it is necessary to consider that voxel-based 3D reconstruction methods generate, on average, fewer triangles and vertices than the depth map's initial 2D resolution in pixels, depending on the number of voxels [29]. Therefore, using a colour-per-vertex approach results in colour aliasing, leading to insufficient quality. We use complete texture mapping to address this matter, which entails projecting vertex points onto colour images to get the UV coordinates. The texture is then assigned per triangle as suggested in [30].  
A process involving transformation from 3D mesh vertices \(v\) = (\(x\),\(y\),\(z\))\({}^{\rm T}\)\(\triangleright\)\(\in\)\(V\) is relation to an object space into a texture coordinate t = (u,\(v\))\({}^{\rm T}\)\(\in\)\(\Omega\)_T on a 2D texture image T: \(\Omega\)_T \(\rightarrow\) R\({}^{3}\) is known as texture mapping. Using UV coordinates to define a triangle or fragment permits the storage of many texels (texture elements) t \(\in\)\(\Omega\)_T, each of which holds an RGB colour value. After we had the reconstructed 3D model, the model's vertices were projected onto the colour image planes to acquire colour correspondences in the neighbouring colour frame, resulting in a textured 3D model.  
Figure 3: The modified Marching Cube triangulation cases lookup-table. (**a**) The original Marching Cube triangulation cases. (**b**) The modified Marching Cube triangulation cases.