esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Response Topology Optimization with Condensed Localized Mortar Method  
Topology optimization is widely applied as a conceptual design that can be used to derive well-matched structures without any intervention from the designer [6; 7; 8]. The SIMP method is the most widely used approach among the various topology optimization techniques [9; 10; 11; 12]; it is used to gradually find an optimum layout by determining the material existence or absence of each element in a given design domain. The SIMP method uses a relative density function to determine the existence of a material \(\eta\).  
\[\eta(x)=\left\{\begin{array}{l}1\text{ if }x\in\Omega\\ 0\text{ if }x\
otin\Omega\end{array}\right. \tag{13}\]  
where \(x\) indicates the location of the finite element in the design domain, \(\Omega\). From Equation (13), it can be inferred that the material exists when the relative density function \(\eta\) is 1, and the material does not exist when the relative density function \(\eta\), is 0. In the modified SIMP method [13], material properties such as Young's modulus and density of each element are defined based on the function of relative density \(\eta\) as given below:  
\[\begin{array}{l}E_{e}=E_{0}\Big{(}\alpha\eta_{e}^{p}+(1-\alpha)\eta_{e} \Big{)}\\ \rho_{e}=\rho_{0}\eta_{e}\end{array} \tag{14}\]  
where \(E_{0}\) and \(\rho_{0}\) are the Young's modulus and density of the material, respectively, \(\alpha\) is a positive constant that is typically defined as \(15/16\), and \(p\) is a penalization parameter that is typically set as 3.
## 4 Evaluation Methodology
### Experiment Results of Feature Selection with Binary Decoding
#### 4.5.1 Analysis of Fitness Curve  
Figure 12 shows the fitness curves of the proposed algorithm for binary decoding. In this experiment, the GA used a population size of 100. Our algorithm found the optimal model for approximately 15 generations. The fitness value converged from approximately 0.45 to 0.55. Therefore, our proposed algorithm solves the detection model optimization problem and achieves good results.  
\begin{table}
\begin{tabular}{l l l l} \hline Sub Feature Set & 10\% & 15\% & 20\% \\ \hline ETS (Equitable Threat Score) & & & \\ \hline Proposed & 0.9160 & 0.9279 & 0.9318 \\ Wrapper-DR & 0.8973 & 0.9042 & 0.9318 \\ PCA & 0.5498 & 0.4068 & 0.6024 \\ \hline CSI (Critical Success Index) & & & \\ \hline Proposed & 0.9920 & 0.9929 & 0.9933 \\ Wrapper-DR & 0.9900 & 0.9903 & 0.9933 \\ PCA & 0.9330 & 0.8860 & 0.9454 \\ \hline PAG (Post Agreement) & & & \\ \hline Proposed & 0.9940 & 0.9950 & 0.9951 \\ Wrapper-DR & 0.9917 & 0.9921 & 0.9934 \\ PCA & 0.9344 & 0.8868 & 0.9470 \\ \hline ACC (Accuracy) & & & \\ \hline Proposed & 0.9927 & 0.9935 & 0.9942 \\ Wrapper-DR & 0.9909 & 0.9913 & 0.9933 \\ PCA & 0.9389 & 0.8960 & 0.9501 \\ \hline \end{tabular}
\end{table}
Table 5: Result of ETS/CSI/PAG/ACC.  
Figure 10: ETS/CSI/PAG/ACC result in binary: proposed vs. wrapper-DR.
# IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
## 7 Conclusions  
This paper focuses on studying the intermittent fault diagnosability of the \(n\)-dimensional augmented cube \(AQ_{n}\) under the PMC model. By thoroughly investigating and making full use of the structural characteristics of \(AQ_{n}\), we have successfully proved that its intermittent fault diagnosability is \(2n-2\). Moreover, we put forward an intermittent fault diagnosis algorithm named _IFDA_, which draws inspiration from the subpath-based network decomposition strategy. We then evaluate the performance of this algorithm  
Figure 4: Experimental evaluation of IFDA performance in augmented cubes of different dimensions.  
Figure 5: (**a**) Evaluation of the impact of parameter \(Ratio\) on experimental results. (**b**) Evaluation of the impact of parameter \(Rounds\) on experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ratio** & **20.0\%** & **30.0\%** & **40.0\%** & **50.0\%** & **60.0\%** & **70.0\%** & **80.0\%** & **90.0\%** & **95.0\%** \\ \hline Accuracy & 98.24\% & 98.24\% & 98.26\% & 98.38\% & 98.74\% & 99.29\% & 99.75\% & 99.97\% & 100.0\% \\ Precision & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ Recall & 0.00\% & 0.04\% & 0.98\% & 7.93\% & 28.29\% & 59.36\% & 85.74\% & 98.13\% & 99.73\% \\ Accuracy’ & 98.42\% & 98.30\% & 98.24\% & 98.21\% & 98.19\% & 98.19\% & 98.18\% & 98.19\% & 98.19\% \\ Times (s) & 13.49 & 14.09 & 15.05 & 16.76 & 19.20 & 21.86 & 23.89 & 25.06 & 25.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the impact of parameter \(Ratio\) on experimental results (\(AQ_{10}\), \(Rounds=3\)).  
through simulation experiments. The experimental results clearly show that the algorithm proposed in this paper can effectively determine the status of intermittent fault nodes.  
Conceptualization, H.F. and J.W.; methodology, C.Y.; software, C.Z. and C.Y.; validation, C.Y. and J.L.; resources, H.F. and J.L.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored by the Hainan Provincial Natural Science Foundation of China under Grant No. 624 RC481, the specific research fund of The Innovation Platform for Academicians of Hainan Province under Grant No. YSPTZX202410, and the Joint Fund for Basic and Applied Basic Research in Guangdong Province under Grant No. 2024A1515110008.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Search Strategy  
An electronic search was performed relating to the effects of laser-activated dental bleaching. Databases used were PubMed, Cochrane, Scopus, ScienceDirect, and Google Scholar with the following MeSH terms and keywords: (bleaching OR whitening) AND laser AND (tooth OR dental OR enamel OR dentine) published after 2010. The last search for possible recently published papers was performed in July 2023.  
The initial articles retrieved numbered 1001 and were allocated per database as follows:  
* Pubmed n = 259.
* Cochrane n = 85.
* Scopus n = 238.
* Science Direct n = 204.
* Google Scholar n = 215.  
After removing 764 duplicate reports and ineligible clinical trial registrations, 237 studies remained.  
Subsequently, following the exclusion of 114 irrelevant studies, titles and abstracts of the articles were independently screened by two reviewers ([PERSON] and V.M.) via application of the inclusion and exclusion criteria listed below. In the case of any disagreements arising, these were resolved by discussions between the researchers involved.  
Inclusion criteria:  
* Articles in English language.
* Laser activation by wavelengths in the range of 445-980 nm.
* Clinical studies on vital teeth of adult patients.
* In vitro studies using human teeth.
* Control group; conventional non-activated in-office bleaching.
* At least 10 patients/samples per group.
* Sufficient protocol description.
* Exclusion criteria:
* Review papers or systematic reviews.
* Conference papers.
* Book chapters.
* Editorials.
* Short notes.
* Articles in press.
* Language other than English.
* Experimental studies not focused on teeth.
* No conventional bleaching control group.
* No laser applied as activation medium.
* Laser applied out of wavelength range 445-980 nm.
* Non-vital teeth/internal bleaching.
* Animal studies.
* Incomplete parameters described.
* Case series/pilot studies/less than 10 samples.
* Use of bovine teeth.
* Studies not retrievable.  
After implementation of these criteria, the remaining 123 articles were further reduced to 38.  
Additionally, one study was included via manual citation searching from the above articles.  
Eventually, 39 studies were included in this systematic review.  
Regarding the nature of the articles, these studies could be further sub-divided as shown:* Clinical studies: 19.
* In vitro studies: 20.  
In accordance with the PRISMA 2020 statement [50], details of the selection criteria are presented in Figure 1.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Attribute-Based Access Control Implementation  
The ABAC model constitutes a pivotal element within the integrated system of the proposed risk-aware RBAC and ABAC model to provide a critical role in governing access decisions. It collects attributes related to users, resources, and environments, along with the evaluated risk score from the risk engine. These attributes are then incorporated into the access control process, allowing for more granular and adaptable access control decisions.  
The ABAC model is implemented based on the eXtensible Access Control Markup Language (XACML) architecture, incorporating three key components: Policy Enforcement Point (PEP), Policy Decision Point (PDP), and Policy Information Point (PIP). XACML is an XML-based access control language standardized by the OASIS Alliance Technical Committee [27; 38]. Its architecture outlines a framework for deploying necessary software modules within the infrastructure to allow effective policy enforcement. Figure 4 shows the interaction in the designed architecture through the ABAC sequence diagram. Using XACML allows for flexibility and enables highly fine-grained control and context awareness in the proposed model. This reflects the healthcare system, which can handle complex environments and adapt to changing conditions in healthcare domains, providing enhanced protection of sensitive resources through a dynamic access control approach.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods
### Sampling  
Few samples should be chosen for GNN model training. It is an operation that manually assigns several superpixels their true ground labels (water or non-water). It is important to note that the non-water label probably contains several different ground features, such as bare soil, grass, and buildings. More seriously, some ground with a high-water content has a gray tone, which is easily confused with water in SAR images. Thus, samples should carefully include all features that probably affect the extraction of water. Any sample errors or deficiencies reduce the model's precision. An unsupervised method could be optionally used to assist sample selection, the specific operation of which is as follows.  
First, the feature vector of each superpixel is reduced to two dimensions using the t-SNE method [32], and the aggregation of the two-dimensional feature scatterplot is drawn after dimensionality reduction. Different cluster centers are regarded as different categories, other scattered points are ignored, and the amount of cluster centrality is recorded. Next, the K-Means clustering method is used to cluster the features of all superpixels. The number of cluster centers is the number of clusters recorded in the previous step using t-SNE. Using K-Means results as a reference, some representative water and non-water objects are selected by hand as samples (train sample data). It is important to note that K-Means results cannot be regarded as final segmentation results because they still have many errors that are removed later in the GNN network.
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 4 I/O Detection Scheme
### Machine Learning Approaches
#### 4.2.5 Neural Network  
Artificial neural networks (ANN) are computing systems that are including many different machine learning techniques to work together and process complex data inputs. Such systems \"learn\" to perform tasks using examples, and they increase performance gradually without being programmed according to any task-specific rules. Instead, they automatically generate identifying characteristics from the learning material they process. The original goal of the ANN approach was to solve problems in the same manner a human brain would. However, ANNs have been developed over time to perform specific tasks, leading to deviations from biology. ANNs have been used in a variety of fields, including computer science, security, medicine, social network science, board and video games, and military purposes.  
In this study, the neural network model was designed as follows: nine for input layer (each receiving a GPS signal), 18 hidden neural, and two output neural (Figure 10).
## 3 Results  
The analysis was carried out on a 64 bit Windows XP-based PC with two dual-core 2.1 GHz Intel(R) Pentium(R) CPU B950 processors and 6 GB of RAM. Because of the computer memory limitation, the simulation running time was set to 10 s. ABAQUS solver took around 5 wall-clock hours to complete the analysis.  
Acceleration was one of the parameters used to characterize the vibration. As this research work aimed to focus on the vibration in the vertical direction, the vertical acceleration responses at head, chest, upper arm, lower arm, waist, thigh, and legs were evaluated from the results. The chosen points for extracting the vertical accelerations, along with the obtained values, are shown in Figure 4.  
Figure 3: (**a**) Interconnected portions and (**b**) meshed body.  
Figure 5 shows the vertical acceleration responses of all the points of interest.  
Vertical displacement plots were obtained in similar fashion and are shown in Figure 6.  
Figure 4: Points of interest and vertical acceleration.  
Figure 5: Vertical accelerations of the points of interest.  
Simple co-relation between the frequency, time, mass, acceleration, damping, velocity, stiffness, and displacement was  
\[\omega=\frac{1}{t}Sin^{-1}\bigg{(}\frac{m\times a+c\times v+k\times d}{F}\bigg{)} \tag{10}\]  
\(m=\) mass;  
\(a=\) acceleration;  
\(c=\) damping coefficient;  
\(v=\) velocity  
\(k=\) stiffness;  
\(d=\) displacement;  
\(F=\) force;  
\(\omega=\) frequency;  
\(t=\) time.  
For a multi-mass system, this co-relation in Equation (10) can be represented through matrix format, and the set of equations inside the solver system can be expressed as  
\[[M]\ddot{[X]}+[C]\dot{[X]}+[K]\{[X]=\{f\} \tag{11}\]  
\([M]\), \([C]\), \([K]\) are mass, damping, and stiffness matrices, respectively, and \(\{f\}\) is the external force acting on the system. Taking the Fourier transformation of Equation (11), complex displacement response of a multi-mass system can be obtained as a function of frequency \(\omega\) and displacement \(\{X\}\):  
\[\{X(j\omega)\}=\Big{[}[K]-\omega^{2}[M]+j\omega[C]\Big{]}^{-1}\{F(j\omega)\} \tag{12}\]  
where \(\{F(j\omega)\}\) is the complex Fourier transformation vector of \(\{f\}\).  
The frequencies at points of interest on the human body were plotted and are shown in Figure 7.  
Figure 6: Vertical displacements at the points of interest.  
The average frequency values of all the human portions were extracted from the frequency plot and are shown in Table 1.
## 3 The LBP-FFT-CNNs Model
### Data Preprocessing
#### 3.3.2 The Circular Local Binary Pattern Operator  
The Circular Local Binary Pattern (CLBP) [19] operator extends the concept of LBP by incorporating circular neighborhoods, which can capture texture information more effectively. Let \(I(x,y)\) denote the grayscale intensity of pixel at coordinates \((x,y)\). Define a circular neighborhood around center pixel \(I(x,y)\) with radius \(R\). The circular neighborhood consists of \(P\) points equally spaced around a circle of radius \(R\) centered at \((x,y)\). These points can be represented as \((x_{i},y_{i})\) for \(i\)\(=0,1,\ldots,P-1\) as follows:  
\[\begin{pmatrix}x_{i}=x+\text{ {Rcos}}\binom{2\pi i}{p}\\ y_{i}=y+\text{ {Rcos}}\binom{2\pi i}{p}\end{pmatrix} \tag{4}\]  
where \(i\) ranges from \(0\) to \(P-1\), and \((x_{i},y_{i})\) represents the coordinates of \(i\)-th point in circular neighborhood. For each pixel \(I(x,y)\), compute the \(CLBP\) value \(CLBP_{P,R}(x_{i},y_{i})\) as shown in Equation (5).  
\[CLBP_{P,R}(x_{i},y_{i})=\sum_{i=0}^{p-1}s(i)\times 2^{i} \tag{5}\]  
where \(s(i)\) is defined as follows:  
\[s(i)=\begin{cases}1,\text{ {if}}\text{ }I(x_{i},y_{i})\geq I(x,y)\\ 0,\text{ {if}}\text{ }I(x_{i},y_{i})\leq I(x,y)\end{cases} \tag{6}\]  
Here, \(I(x_{i},y_{i})\) represents the intensity value of \(i\)-th point in circular neighborhood, and \(I(x,y)\) is the intensity value of center pixel.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 4 Simulation and Evaluation  
In the simulation experiment, 100 nodes are randomly deployed in a 100 \(\times\) 100 m\({}^{2}\) area, and the base station is located in the center of the monitoring area. The server mentioned in the initial stage of key establishment generates a pair of public key \(P\) and private key \(S\) and will be appointed as the base station. In fact, the common sensor nodes in the network do not have enough computation and storage capacity, and the mobile sink node mainly takes on the tasks of sending, receiving and storage of data, and also the communication management with the cluster heads. Most of the computation in the network is undertaken by the server. In this implementation circumstance, the server is mainly implemented in the base station. [PERSON] _et al._[PERSON] et al. (2013) proposed the scheme of using the wireless communication model. If we transmit \(k\) bits message and the distance is \(d\), the node's energy consumption can be calculated as follows:  
\[\begin{array}{ll}E_{Tx}\left(k,d\right)&=E_{Tx\_dec}\left(k\right)+E_{Tx\_ amp}\left(k,d\right)\\ &=\left\{\begin{array}{ll}kE_{ellec}+ke_{fs}d^{2}&d<d_{0}\\ kE_{ellec}+k_{mp}d^{4}&d\geq d_{0}\end{array}\right.\end{array} \tag{16}\]  
where, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are energy consumption coefficients of power amplification circuit, and \(d_{0}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\).  
When receiving \(k\) bits data, the energy consumption of data transmission can be calculated as follows:  
\[E_{Rx}(k)=E_{Rx\_dec}(k)=kE_{elec} \tag{17}\]  
where \(E_{elec}=50\) nJ/bit, \(\varepsilon_{fs}=10\) pJ/bit/m\({}^{2}\), and \(\varepsilon_{mp}=0.0013\) pJ/bit/m\({}^{4}\). The energy consumption of the data aggregation is set as: \(E_{DA}=5\) nJ/bit. The parameters in the experiment are set up as in Table 1.
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 2 Advantages of the Gyrotrons as Powerful Radiation Sources for Sensing and Imaging
In the gyrotrons, the mechanism of frequency modulation is based on the relativistic dependence of the cyclotron frequency on the energy of the electrons (as it has been explained by Equation (1)) and is being realized by alternating the body potential of the tube and therefore by varying the accelerating voltage [64]. Although the variation of the frequency \(\Delta f\leq f/2Q\) is limited by the quality factor Q of the resonator, a modulation depth sufficient for many practical purposes can be obtained. A good example of a radiation source with such capabilities is the 0.46 THz gyrotron FU CW GVI (developed at FIR UF and used in the 700-MHz DNP-NMR spectrometer at the Osaka University under the name Gyrotron FU CW GOI) [65]. It has the following modulation characteristics, which are appropriate for this particular measuring system but more generally illustrate this eminent feature and advantage of the gyrotrons. A modulation amplitude of up to \(\pm\)50 MHz is obtained with the cavity potential variation of \(\pm\)0.5 kV with a modulation frequency of 300 Hz. In these experiments, both sinusoidal and triangular modulations of the beam voltage have been used. At some modulating parameters (frequency and amplitude of the modulating signal), a linear dependence of the frequency variation on the voltage variation has been observed. Analogously, by sweeping the anode voltage in a frequency tunable 0.26 THz gyrotron and sweep rates up to14 kHz, a 60% gain in the signal enhancement has been observed [65]. Illustrative Applications of Gyrotrons to Advanced Spectroscopic Techniques, Imaging, and Inspection
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
## 2 Difference Pattern Hom Array Antenna Concept
Consequently, a difference pattern is generated. Although this configuration is intricate, it was selected based on its compatibility with the chosen manufacturing process, the details of which will be subsequently explained. Further optimization of the twisted sections' lengths ensures smooth transitions between dividers without impacting impedance matching or degrading the antenna's radiation performance. Adding these twist segments has a minimal effect on the antenna's overall length, but they allow a simplified feeding network by enabling the reuse of identical H-plane dividers across the entire network. Furthermore, Figure 2 shows an exploded view of the difference pattern horn array antenna, featuring two twist sections in the clockwise orientation and two in the counterclockwise orientation from the reference point of the antenna's aperture. The array is engineered for operation within the 34 to 36 GHz range, aligning with mm-Wave application requirements. As an initial design choice, a standard WR28 waveguide is considered. However, the spacing of the array elements has been optimized to reduce both the antenna's total dimensions and its weight. This is achieved by implementing a tapered modification to the input waveguide, adjusting its dimensions progressively along the length of the antenna. These tapered changes are visible at the antenna input in the overall views in Figure 2. The WR28 waveguide's standard dimensions are 7.112 mm in height and 3.556 mm in width, while the tapered waveguide ultimately measures 5 mm in height and maintains a width of 3.556 mm, producing a taper specifically in the \(H\)-plane. A more detailed design process is reported in [33]. The antenna's input design remains compatible with the WR28 rectangular waveguide standard, allowing easy integration with standard transitions. From the antenna's input port, the waveguide width is progressively reduced to align with the width of the \(H\)-plane power dividers and the horn antenna inputs. It is noteworthy that the electric field at the input is oriented parallel to the \(x\)-axis. For the mechanical design of the differential pattern horn array, once again, thanks to the manufacturing possibilities, an envelope conforming to the vacuum design was created. This envelope typically has a thickness of 1.5 mm while adding extra thickness in the critical areas, i.e., those that could be affected by thermal deformation during manufacturing. Finally, for measuring purposes, the standard WR28 flange was added. Overall, the total length of the difference pattern horn array antenna is 80 mm with an aperture of 41.5 \(\times\) 41.5 mm\({}^{2}\).