esa-sceva/satcom-chunk-collection huggingface dataset:
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 3 Proposed System
### Segmentation Module  
The Segmentation Module process is performed in two steps: SSIM-MAP generation and human segmentation. The SSIM-MAP represents the similarity for each point having the same coordinates in two images, with higher similarity displayed by a bright color and lower similarity displayed by a dark color.  
The proposed system measures the SSIM values in two directions. The first direction is to compare the geometrically transformed image of Figure 4 with the color-transformed image of Figure 5b. This measurement produces an output defined as the channel SSIM-MAP, as shown in Figure 6. The second direction is to examine the similarity between the past and the current images, which are taken by the camera with a short interval, to catch the differences caused by the speaker's movement. We have defined the output of this measurement as the time SSIM-MAP, as illustrated in Figure 6. Finally, those two SSIM-MAPs are merged into a single SSIM-MAP containing all the areas that should be considered as the dimming areas controlled by the Output Module.  
The Channel SSIM-MAP enables the detection of the appearance of an unexpected object. If an object that does not exist in the original image appears in the camera image, the object will be displayed as an area with low SSIM values. This means that if an obstacle in front of the screen (such as a person) impedes light travel, the channel SSIM-MAP will show that obstacle. SSIM-MAP is used to detect moving objects. Since the image on the wallpaper is stationary, areas of low similarity could be where the speaker's movement has occurred when comparing the previous and current camera images. Finally, those channel  
Figure 5: Original image (**a**) and color-transformed image (**b**) with CTP.  
Figure 6: SSIM-MAP generation process.  
SSIM-MAP and time SSIM-MAP images are merged into a single SSIM-MAP so that it can be used to determine the areas for regional brightness control.  
As shown in Figure 7, to extract a human body area for the brightness control, the merged SSIM-MAP image (Figure 7a) is first inverted into grayscale to be used as a mask to filter non-human areas in the camera-captured image. Since the mask (Figure 7b) indicates a transparent area when it overlaps with the captured image, only the human body area keeps the original color while the other areas are covered with the black color (Figure 7c). Finally, human body segmentation is performed on the composite image to remove all non-human areas (Figure 7d).
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 3 Bending Properties
### Bending Failure Properties  
The choice of the used experimental set-up was due to the fact that the bending strength is considered critical as it involves the combination of both the tensile and compression performances of the samples during the impact. As shown in this set-up, the front facesheet undergoes tension while the back face predominantly faces compression. Three active failure modes with governing laws described in [22; 23] were used to analyse the exposed and unexposed samples to determine the effect of moisture uptake on the failure modes. As discussed, the application of these theoretical approaches was necessary to validate the experimental results obtained in the pendulum tests (single and multiple impact regimes) as well as to predict the predominant damage modes for further verification with image processing (see below). For the failure prediction, the goal was to identify the main damage modes and to understand the effects of moisture on them. Furthermore, since catastrophic damage was considered here, only the single impact loading regime was utilised. It was established in [24] that the main failure modes for sandwich structures subjected to low-velocity impacts are facesheet rupture, indentation, and core shear. These failure modes can be calculated as follows:  
Facesheet fracture:  
\[P_{f}=\frac{4}{3}bt_{1f}\sqrt{\sigma_{fp}\sigma_{c}}, \tag{3}\]  
Indentation failure:  
\[P_{i}=\frac{4}{3}bt_{1f}\sqrt{\frac{\tau^{2}dE_{fp}\sigma_{c}^{2}}{3L}}, \tag{4}\]  
Shear failure of core:  
\[P_{c}=2 bd\tau_{c}, \tag{5}\]  
where \(E_{fp}\), \(\sigma_{fp}\), \(\sigma_{c}\), and \(\tau_{c}\) are the bending modulus, bending strength of the facesheets, compression, and shear strengths of the core, respectively, while \(b\) and \(t_{1f}\) are the sample width and facesheet thickness, respectively, with \(d=c+t_{1f}\), where \(c\) is the core thickness.
# A New Method for Detecting Weld Stability Based on Color Digital Holography
###### Abstract  
Weld stability is directly related to the safety and reliability of engineering, and continuous improvement of its detection technology has great research significance. This paper presents a novel method for weld stability detection based on color digital holography. A color digital holography optical path was designed to capture the holograms of welds under varying loads. Several common denoising algorithms were used to deal with speckle noise in the wrapped phase, among which the 4- of optical simulation integrated cycle-consistent generative adversarial network (4f-CycleGAN) denoising algorithm based on deep learning was more suitable for the color digital holographic detection system. The three-dimensional deformation fields of three samples (lap-jointed, butt-jointed, and defective butt-jointed aluminum alloy plates) under different loads were calculated. The center profile of the deformation field in the direction of load and holographic reconstruction images are used to determine the position of the weld. The coefficient of variation near the weld was used to evaluate the weld stability. The coefficient of variation for lap-jointed, butt-jointed and defective butt-jointed plates are 0.0335 (<0.36, good stability), 0.1240 (<0.36, good stability) and 0.3965 (>0.36, poor stability), respectively. The research results of this paper provide a new strategy for detecting weld stability.  
color digital holographic; weld stability; deep learning; the coefficient of variation +
Footnote †: journal: Medical Stability Based on Color Digital Holography
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 3 Materials and Methods
### Filtering Area  
The sensitivity of the expected output to changes in the input layer depends on the location of the filter layer in the network. In fact, it is feasible to insert the filter layer anywhere in the network. The most straightforward placement of the filter layer is just after the input layer, which is equivalent to adding noise to individual pixels of the image.  
Image filtering technology is mainly used to suppress noise in the target image while preserving image details as much as possible. This is an indispensable operation in image preprocessing, and the quality of its processing directly affects the effectiveness and reliability of subsequent image processing and analysis. Common image filtering methods include linear filtering (such as mean filtering and Gaussian filtering) and nonlinear filtering (such as median filtering and bilateral filtering). Each filtering method has its specific application scenarios and advantages. For example, median filtering can effectively remove salt and pepper noise, while bilateral filtering can preserve edge information while smoothing the image.  
The area where image filters calculate pixels is usually referred to as the \"filtering area\" or \"neighborhood\". The filter will perform operations on the pixels in this area, and the result of the operation is used to determine the pixel value of the new image. The filtering area is designed as an \(a\times b\) rectangle. The filter scans the entire image in order from left to right and from top to bottom, as shown in Figure 4.  
By using a filtering layer, the size of the output image is the same as the size of the input image. Each pixel of the output image is influenced by its neighboring pixels. Given the input image size \(n\times n\) and filtering area size \(a\times b\), the output image of size \(n\times n\) is calculated from all pixels in the filtering area. Let \(S_{x,y}\) denote the set of coordinates of the input image affected by the filtering area of the filtered image at point \((x,y)\). Specifically, the filtering area of the image filter is defined as Formula (3), as follows:  
\[\begin{split} S_{x,y}&=\{(u,v)|u\in[x-\frac{a}{2},x +\frac{a}{2})\wedge[0,n),\\ v&\in[y-\frac{b}{2},y+\frac{b}{2})\wedge[0,n),u,v \in N\}\end{split} \tag{3}\]  
Figure 4: Scanning methods of image filters: the filtering area scans the entire picture in the order from left to right and top to bottom.  
where \((u,v)\) is the coordinate of the filtering area. The region of the filtering area is a rectangle of size \(a\times b\) formed by the center pixel (x, y) and its neighbor pixels.  
When processing the edges of the input image, it is necessary to expand the image boundary to ensure the completeness of the filtering area. The padding method of the image boundary affects the effect of the image filter. Ideally, we expect the image filter to be unaffected by the padding value. During the experiment, it is feasible to fill the border of the input image with 0.
# Structural Assessment of Endodontic Files via Finite Element Analysis
## 2 Materials and Methods
### Finite Element Modeling of Physical Tests
#### 2.2.2 Finite Element Mesh Development for ProTaper Universal F2 Endodontic File  
The process of acquiring the endodontic file's geometry began with a 3D scan of the file within a micro computed tomography (MicroCT) scanner Skyscan 1275, accompanied by the utilization of 3D reconstruction software NRecon (V1.7.4.6) [21]. The scanner generated images of the endodontic file's geometry in TIFF format, and subsequently, the NRecon software was employed to transform these TIFF images into BMP format. The next stage involved the utilization of open-source software 3D Slicer (V4.11.20210226) [22] to combine the BMP images into a single STL geometry data file that defined the resultant 3D surface (Figure 6).  
The final STL file contained the 3D surface of the endodontic file as defined by triangular elements. Once the STL geometry file was generated, it was imported into the finite element pre-processor software and subjected to the following examination:  
Identification of free surface edges arising from potential imperfections in the scanning process.  
Assessment of geometric details that could be omitted from the analysis to reduce the model's size and reduce the analysis time.  
After the geometry was reviewed and corrected, AutoDesk Fusion 360 [23] was employed to derive surface data from the STL geometry file. In the final phase, the resultant surface geometry of the endodontic file was partitioned into 40 segments by parallel planes perpendicular to the endodontic file's axis, and the cross sections were defined. Finally, a 2D surface mesh was constructed on each cross section and swept along the endodontic file's axis to form solid elements (Figure 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Quality of Brick and Penta Elements**} \\ \hline Warpage & 95\% \textless{} 40\({}^{\circ}\) & 5\% \textless{} 50\({}^{\circ}\) \\ \hline Aspect Ratio & 95\% \textless{} 10 & 5\% \textless{} 20 \\ \hline Skew & 95\% \textless{} 70 & 5\% \textless{} 80 \\ \hline \multirow{2}{*}{Minimum angle} & \textgreater{}45\({}^{\circ}\) & \\  & (min. 90\% \textgreater{} 45\({}^{\circ}\) and 10\% \textgreater{} 20\({}^{\circ}\)) & \\ \hline \multirow{2}{*}{Maximum angle} & \textless{}145\({}^{\circ}\) & \\  & (min. 90\% \textless{} 135\({}^{\circ}\) and 10\% \textless{} 165\({}^{\circ}\)) & \\ \hline Jacobian & 95\% \textgreater{} 0.4 & 5\% \textgreater{} 0.25 \\ \hline \multicolumn{3}{c}{Quality of tetra elements} \\ \hline Tetra Collapse & 0.13 (minimum 95\% \textgreater{} 0.3 and 5\% \textgreater{} 0.2) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Element quality criteria.  
Figure 6: F2 File Geometry Scanning Steps: (**a**) An illustration of geometry stored as a TIFF image; (**b**) An illustration of geometry stored as a BMP image; (**c**) STL-triangulated surface geometry stored as a single STL geometry.
# Treatment Selection for Early to Intermediate Hepatocellular Carcinoma
## 2 Application of RFA in HCC Treatment Guidelines  
The most widely used criterion in the treatment HCC algorisms is Barcelona clinic liver cancer (BCLC) staging. Both the European Association for the Study of the Liver (EASL) and the American Associations for the Study of Liver Disease (AASLD) have adopted BCLC staging in their guidelines [6; 9]. In both guidelines, RFA is recommended for the treatment of both very early-stage HCC (BCLC-0) and early-stage HCC (BCLC-A). Similar applications for the use of RFA are recommended in the guidelines of Japanese, Korean, Chinese, Taiwanese, and Asian Pacific associations for the study of the liver [4; 5; 7; 10; 11]. All these associations, except Chinese and Taiwanese, adopted the standard application criteria for RFA, namely, HCC less than or equal to 3 cm in diameter and limited to three nodules. Chinese and Taiwanese guidelines accepted the use of RFA for the treatment of tumors larger than 3 cm in diameter.  
Minimally invasive RFA involves passing high-energy radio waves through a probe inserted into a tumor. The radio waves heat the tumor and destroy the tumor cells without severe deterioration of liver function [12]. Another approach, transcatheter arterial chemoembolization (TACE), involves embolization of the feeding artery of the specific tumor and is a standard therapy for intermediate HCC. Although the curability of nodules treated with RFA is higher than that in nodules treated with TACE, none of the guidelines recommend RFA for the treatment of intermediate HCC.
# Effect of Baffle Board on Aerodynamic and Stealth Performance of Double S-Duct Caret Intake
## 2 Computational Methodology  
By employing applicable methods, the RCS and aerodynamic characteristics of the caret intake were analyzed initially. To suppress the radar echo intensity of intake, four curved baffle boards with the same area were installed at different locations of the S-duct diffuser. In order to further improve the stealth performance, four corrugated baffle boards were designed to replace the curved board with vertical orientation in the front diffuser. By investigating the effects of the corrugated baffle boards with different configurations, the RCS and aerodynamic characteristics of the caret intake were optimized.
# Real-Time Circuit Breaker Availability Assessment in the Transmission Network
## 2 State Estimation Model  
### Conventional Model  
In conventional state estimation, the topology processor determines the bus/branch network model based on topological measurements (statuses of switching devices). Afterwards, analog measurements are processed to estimate the bus voltages [29].  
The conventional state estimation model is built by merging bus sections with closed circuit breakers. For an \(N\)-bus system and an \(m\) measurement set, the model is given as [29; 30],  
\[z=\mathbf{h}(\mathbf{x})+\mathbf{v} \tag{1}\]  
\[E(\mathbf{v})=0;\textit{and}\ E\Big{(}\mathbf{v}\mathbf{v}^{\mathrm{T}}\Big{)}=\mathbf{R} \tag{2}\]  
where \(\overset{\wedge}{x}\) is a \(n\times 1\) nodal state vector or the set of state variables with \(n\) variables. In power system representations, it generally consists of node voltage magnitudes and phase angles. The function \(\mathbf{h}(\mathbf{x})\) is a nonlinear function relating measured quantities and states, and \(\mathbf{v}\) is a random vector representing normally distributed measurement errors. Each error is supposed to have zero mean, represented in (2). The set z represents m measured variables that generally consist of node active and reactive power flows in nodes and branches and node voltage magnitudes and phase angles (the set \(\overset{\wedge}{x}\)). \(E\) represents the expectation operator and \(\mathbf{R}\) diagonal covariance matrix (\(m\times m\)) of the measurement errors. The diagonal terms of \(\mathbf{R}\) represent the square of the standard deviation of the measurements. The exponent T represents the transpose of the given sent.  
\(Z\) consists of bus voltages, branch power flows, and injections. The estimate of the error measure \(\overset{\wedge}{x}\) is obtained by  
\[\mathbf{J}(\mathbf{x})=[z-\mathbf{h}(\mathbf{x})]^{\mathrm{T}}\mathbf{R}^{-1}[z-\mathbf{h}(\mathbf{x})] \tag{3}\]  
\[\frac{\partial\mathbf{f}(\overset{\wedge}{x})}{\partial\mathbf{x}}=0 \tag{4}\]  
where \(\mathbf{R}\) minimizes the weighted least-square function \(\mathbf{J}\). The minimization of the scalar \(\mathbf{J}\) represents the optimum of \(\overset{\wedge}{x}\).  
The estimate \(\overset{\wedge}{x}\) is computed by solving  
\[\mathbf{G}(\mathbf{x}^{k})\Delta\mathbf{x}^{k}=\mathbf{H}^{\mathrm{T}}(\mathbf{x}^{k})\mathbf{R}^{-1} \Delta z^{k},\ k=0,1,2,\ldots \tag{5}\]  
\[\Delta\mathbf{x}^{k}=\mathbf{x}^{k+1}-\mathbf{x}^{k} \tag{6}\]  
\[\Delta z^{k}=z-\mathbf{h}(\mathbf{x}^{k}) \tag{7}\]  
\[\mathbf{H}=\frac{\partial\mathbf{h}}{\partial\mathbf{x}} \tag{8}\]  
where \(\mathbf{H}\) is the \(m\times n\) Jacobian matrix of \(\mathbf{h}(\mathbf{x})\).  
The \(n\times n\) gain matrix is given by  
\[\mathbf{G}(\mathbf{x}^{k})=\mathbf{H}^{\mathrm{T}}(\mathbf{x}^{k})\mathbf{R}^{-1}\mathbf{H}(\mathbf{k}^{ \mathrm{T}}) \tag{9}\]  
where \(n\) is the number of state variables \(n=2N-1\). The state estimator outputs a database from which any number of other devices and functions can be fed.
## 1 Introduction
### Contributions  
The Dragon_Pi, Dragon_Slice framework extends our previous work [6]. Indeed, this work can be considered as part two of our recent work [4], where we discuss and analyse the signatures of both normal and attack behaviours within the Dragon_Pi dataset. Thisrecent work seeks to illustrate the value of power data in cybersecurity and discuss possible usages. Dragon_Pi and Dragon_Slice seek to fulfil some of the possible works that we said were possible based on the data in [4; 6]. The specific contributions are as follows:  
1. Dragon_Pi: The first contribution is a fully labelled device power behaviour dataset containing normal and attack scenarios from two popular IoT devices. Dragon_Pi explores various attack scenarios and, to the authors' knowledge, is the only publically available IoT Security power dataset. Dragon_Pi focuses on IoT devices, and the insights gained from studying this dataset can be applied to the entire cybersecurity field. Dragon_Pi serves as a step forward for IoT security, encouraging security professionals to consider as many data sources as possible rather than focus on internet traffic.
2. Dragon_Slice: The second contribution is Dragon_Slice for anomaly detection within linear time series. Importantly, Dragon_Slice is a single model trained only on the normal power behaviours of both the DragonBoard and Raspberry Pi. Using the Dragon_Slice framework, a single IDS model can be trained on the normal power consumption data of different devices. This model can then detect unseen anomalies across the different devices. This is a massive contribution to the field of IoT, which is filled with heterogeneous devices. In this way, a single model can serve as many devices as desired, rather than requiring specifically trained models per device type.
3. Explainability: The third contribution involves explaining the AI used in this paper. To this end, we explain Dragon_Slice from the perspective of inputs and outputs, and, thus, why the AI is making particular decisions. In AI research, many works under-explain the presented AI. This hinders readers from effectively understanding what the AI is doing or from reconstructing similar AI for themselves. Another key issue with under-explained AI is the lack of verification of the AI functionality. Readers can only confirm the plausibility of the presented results if they are able to understand the AI demonstrated.
## 6 Conclusions  
The paper provides and overview of different aspects of 5G network slicing to improve 5G networks' energy efficiency. Different types of 5G network slice use cases, defined as 5G service set types, have been presented and analyzed in terms of the energy consumption of specific use cases. Also, KPIs standardized as the metrics for 5G NSs' energy efficiency are defined and elaborated for each 5G network slice use case. The paper further sheds light on ideas for future research on topics related to improving the energy efficiency of 5G networks utilizing network slicing. A plethora of options for the development of new techniques and algorithms that could impact the reduction in the energy consumption of 5G networks through the implementation of the network slicing concept are proposed. The discussed options include techniques such as limiting network slice energy consumption by mobile network operators and implementing dynamic changes in the operational states of NSs and corresponding resources according to variations in the traffic in an NS. Additionally, comprehensive analyses dedicated to possibilities for improving energy efficiency in the RAN part of 5G network slices through the implementation of methods for achieving energy savings by exploiting network resource adaptation techniques in the time, frequency, and space domains have been presented. The paper also overviews the AI-based processes' ability to optimize 5G sliced RAN energy efficiency. Case studies exploiting the use of AI-based techniques in network slicing to improve mobile network energy efficiency are discussed. Lastly, the impact of the spatial arrangement of baseband unit elements in the cloud data centers for the improvement of 5G network energy efficiency through the implementation of 5G RAN network slicing has been elaborated.  
It is shown that each of the techniques exploiting network slicing analyzed in the paper can contribute to the improvement in 5G network energy efficiency. Therefore, the improvement in 5G network energy efficiency through the application of one or a combination of the analyzed techniques based on network slicing represents an area with significant research potential, which can contribute to the further energy consumption optimization of energy-demanding 5G networks. This improvement is particularly important as energy is mostly derived from non-renewable sources. We ultimately aim to not only reduce the operational costs of mobile network operators, but also to reduce their negative impact on the environment.  
Conceptualization, J.L. and A.K.; methodology, J.L.; validation, J.L. and Z.B.; formal analysis, A.K. and J.L.; investigation, J.L. and A.K.; resources, A.K. and J.L.; data curation, A.K. and J.L.; writing--original draft preparation, A.K., J.L. and Z.B.; writing--review and editing, J.L. and A.K.; visualization, A.K.; supervision, J.L. and Z.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.