esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 3 Materials and Methods
### Principal Component Analysis (PCA)
The Pseudo Code of PCA is presented in Algorithm 1. ```
Input: Data matrix X of size \((n\times p)\)  where \(n\) is the number of samples and p is the number of features  Number of components k (k <= p) Steps: 1. Standardize the data:  Compute the mean of each feature \(mean\_X=mean(X,\ axis=0)\)  Subtract the mean from each feature value to center the data \(X\_centered=X-mean\_X\)
2. Compute the covariance matrix of the centered data:  Covariance matrix \(C=(1\ /\ (n-1))\times(X\_centeredT\times X\_centered)\)
3. Compute the eigenvalues and eigenvectors of the covariance matrix:  Eigenvalues \(\lambda\) and eigenvectors V of C such that \(C\times\ V=\lambda\times V\)
4. Sort the eigenvalues and their corresponding eigenvectors in descending order:  Sort eigenvalues \(\lambda\) in descending order  Reorder the eigenvectors V accordingly
5. Select the top k eigenvectors to form a new matrix W:  W = (v1, v2,..., vk) where vi is the i-th eigenvector
6. Transform the original data to the new subspace:  Principal components \(Z=X\_centered\times W\)
7. Compute the explained variance:  Total variance = sum(\(\lambda\))  Explained variance for each principal component = (\(\lambda\) / total_variance)
8. Extract the most informative features:  Calculate the contribution of each original feature to the principal components:  feature_contributions = \(|\)W \(|\)
9. Output the principal components return \(Z\), explained variance, and feature contributions. ```  
**Algorithm 1** The Pseudo Code of Principal Component Analysis (PCA) Algorithm
# Recycled Eco-Concretes Containing Fine and/or Coarse Concrete Aggregates. Mechanical Performance
## 2 Materials and Methods
### Aggregate Characterisation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Property [Standard]** & **CS-F** & **RCF** & **CS-M** & **CS-C** & **RCG** & **RCC** & **EN 12620** \\ \hline Density (Mg/m\({}^{3}\)) [46] & 2.82 & 2.79 & 2.78 & 2.77 & 2.72 & 2.73 & - \\ Sorptivity (wt%) [46] & 1.18 & 4.42 & 0.88 & 0.78 & 5.40 & 3.63 & \textless{}5 \\ Fine equivalent (wt%) [47] & 73 & 61 & - & - & - & - & \textgreater{}70 * \\ LA coefficient (wt%) [48] & - & - & 16 & 18 & 27 & 27 & \textless{}40 \\ Flakiness index (wt%) [49] & - & - & 20.36 & 24.79 & 16.08 & 20.85 & \textless{}55 \\ Water-soluble chlorides (wt%) [50] & & & \textless{}0.01 & & & \textless{}0.05 \\ Acid soluble sulphates (wt%) [50] & & & \textless{}0.002 & & & \textless{}0.80 \\ Total sulphates (wt%) [50] & & & \textless{}0.001 & & & \textless{}1 \\ \hline \hline \end{tabular}  
* * Aggregates intended for concrete elements exposed to exposure class X0 or XC. Note: CS-F: natural sand, RCF: recycled sand, CS-M: natural gravel—medium, CS-C: natural gravel—coarse, RCG: recycled gravel—medium, RCC: recycled gravel—coarse.  
\end{table} 
Table 2: Physical, chemical and mechanical properties of the aggregates. Finally, the fine content (<0.063 mm) of the natural aggregate (CS-F) is 11.59%, slightly over the limit established for concrete with non-limestone aggregates subject to general exposure classes X0 or XC and not subject to any specific XA, XF or XM exposure class. In the case of recycled sand (RCF), the fine content is 7.78%.
## 3 Flow Field Calculation
### Grid Partition and Sensitivity Analysis  
In the numerical simulation, the quality of grids directly affects the accuracy and efficiency of computation, as well as the validity and reliability of computing results. Thus, a hexahedron-structured grid partition is conducted with the integrated computer engineering and manufacturing code (ICEM) software (ANSYS 15.0 2013, Canonsburg, PA, USA), where an H-shaped grid topology is applied to the front and rear guide vanes. In addition, considering that the wrap angles of impeller blades are large, J-shaped grid topology was applied for impellers. Also, boundary-layer grid division and local refinement near the walls of the overflow components were carried out. A sensitivity analysis of partitioned grids of different sizes was conducted, where \(2.31\times 10^{6}\), \(3.24\times 10^{6}\), \(4.21\times 10^{6}\) and \(4.75\times 10^{6}\) grids were selected. The relative variation ratio of the torque and flow rate was less than \(1.7\%\) when the grid number exceeded \(4.21\times 10^{6}\). Therefore, \(4.21\times 10^{6}\) units of grids were chosen for the fluid calculation domain of the whole pump unit, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Diameter of impeller/m & 1.7 \\ Number of impeller blades/- & 4 \\ Number of inlet guide vanes/- & 5 \\ Number of outlet guide vanes/- & 7 \\ Blade rated angle/\({}^{\circ}\) & \(-6\)-4 \\ Design head/m & 2.5 \\ Design speed/r/min & 250 \\ Design discharge/m\({}^{3}\)/s & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic parameters of the pump flow system.  
Figure 3: _Cont_.  
Figure 2: Geometric model of the whole flow system for the axial-flow pump.
## 5 Experimental Results
### Scalability of the Proposed Mechanism in IoT Environments  
The proposed mechanism demonstrates strong scalability, as each IoT device independently derives encryption keys using the lightweight D-H protocol. In high-density IoT environments, the computational overhead on microcontrollers remains minimal, ensuring the feasibility of large-scale deployments.  
To further validate the mechanism's scalability, additional experiments were conducted by simulating the connection of five additional devices in a test environment. Each device established a secure communication channel with the server using the proposed mechanism. The results showed that the average latency per device increased by only 3 milliseconds during key negotiation, even with the added device density. This negligible increase in latency highlights the mechanism's practicality for real-world implementations, ensuring secure and efficient communication as the number of connected devices grows.
## 3 Experiments and Configuration Detail  
### Experimental Environment for DCGAN Training  
This paper used the DCGAN model to synthesize realistic tunnel crack images based on the features learned from actual data. First, the model was pretrained on a concrete dataset to obtain initial weights and then fine-tuned on a base dataset of tunnel crack images. The experiments were conducted on a computer with an Intel I5-9600 KF CPU (Santa Clara, CA, USA), 16 GB of memory and an NVIDIA GeForce GTX3060 (12 GB) GPU  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Output Size** & **KSize** & **Stride** & **Repeat** & **Output Channels** \\ \hline Image & 224 \(\times\) 224 & & & & 3 \\ Conv1 & 112 \(\times\) 112 & 3 \(\times\) 3 & 2 & 1 & 24 \\ Maxpool & 56 \(\times\) 56 & 3 \(\times\) 3 & 2 & 1 & 116 \\ Stage2 & 28 \(\times\) 28 & & 1 & 3 & 116 \\ Stage3 & 14 \(\times\) 14 & & 2 & 1 & 232 \\  & 14 \(\times\) 14 & 1 & 7 & 2 & 1 \\ Stage4 & 7 \(\times\) 7 & 1 & 3 & 464 \\ Conv5 & 7 \(\times\) 7 & \(1\times\) 1 & 1 & 1 & 1024 \\ GlobalPool & 1 \(\times\) 1 & 7 \(\times\) 7 & & & 2 \\ FC & & & & & 2 \\ \hline \hline \end{tabular}  
* Notes: KSize: Using the size of the convolutional kernel; Stride: The step size of the convolution operation. In the ”Stage” phase, the downsampled blocks are used for the first operation and the standard blocks are repeated operation. Repeat: Number of times the operation is repeated at that stage.  
\end{table}
Table 2: ShuffleNetV2-1.0-SE model architecture [24].  
Figure 8: Images of concrete cracks.  
(Santa Clara, CA, USA) with CUDA support. Python 3.7 and Pytorch 1.7.1 were used as the programming environment and tools. The hyperparameters were set as follows: the number of epochs was 300 [29]; the initial learning rate was 0.002 [30] with adaptive decay; the batch size was 128 [32]; and the optimizer was Adam with a momentum of 0.9 [34].
_Applied_  
_sciences_  
_Review_  
**Review of Rock-Mass Rating and Tunneling Quality**  
**Index Systems for Tunnel Design: Development,**  
**Refinement, Application and Limitation**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 1,**  
**[PERSON] 2 and [PERSON] 1,***  
Footnote 1: Department of Civil and Environmental Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea; [EMAIL_ADDRESS] (H.R.); [EMAIL_ADDRESS] (A.M.N.); [EMAIL_ADDRESS] (J.K.)  
Footnote 2: Department of Geotechnics & Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS] (W.A.); [EMAIL_ADDRESS] (R.A.A.)  
Footnote 3: Correspondence: [EMAIL_ADDRESS]; Tel.: +82-31-400-5147; Fax: +82-31-409-4104  
Received: 2 July 2018; Accepted: 23 July 2018; Published: 28 July 2018  
Although rock-mass rating (_RMR_) and tunneling quality index (_Q_) systems are used in different rock engineering projects as empirical design tools, their application in tunnel design is widely accepted as these systems were developed and updated for this purpose specifically. This paper reviews the work conducted by various researchers since the development of these two systems with respect to tunneling only. Compared to other empirical classification systems, these systems received international acceptance and are still used as empirical design tools in tunneling due to their continuous updates in the form of characterization and support. As the primary output of these systems is the initial support design for tunnel, however, their use in the calculation for rock-mass properties is an essential contribution of these systems in rock engineering design. Essential for the tunnel design, these rock-mass properties include the modulus of deformation, strength, Poisson's ratio, Mohr-Coulomb parameters and Hoek-Brown constants. Other application for tunneling include the stand-up time and rock load. The uses and limitations of these systems as empirical tunnel design tools are also included in this review article for better results. Research to date indicates that if the ground behavior is also taken into account, the application of these empirical systems will be more beneficial to the preliminary design of tunnels.  
_RMR_; \(Q\); tunnel support; rock-mass properties; rock load; stand-up time; limitations
# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 4 Vehicle Interactive Control Strategy
### Driving Cost Function
#### 4.3.2 Driving Safety Cost Function of HSCV  
The collision threat faced by an HSCV in the lane changing scenario mainly comes from the relative distance and relative speed between NV and the HSCV. The lateral threat and lane change threat are used to design the driving safety cost function of the HSCV, where the lateral threat and lane change threat are expressed as \(J_{s-y}^{e}\) and \(J_{s-lc}^{e}\), respectively. The driving safety cost function is defined as follows:  
\[J^{e}=J_{s-y}^{e}+J_{s-lc}^{e} \tag{26}\]  
The cost function of lateral threat \(J_{s-y}^{e}\) is related to the distance between the HSCV and a NV. It is similar to Equation (23) and is defined as:  
\[J_{s-y}^{Ve} =\beta_{\sigma}\varpi_{s-y}^{e}\eta_{\sigma}^{e}\Big{(}X^{e}-X_{ \sigma}^{NV}-L_{y2}\Big{)}^{2} \tag{27a}\] \[\eta_{\sigma}^{e} =0.5+0.5\,\mathrm{sgn}\Big{(}X^{e}-X_{\sigma}^{NV}-L_{y2}\Big{)} \tag{27b}\]  
where \(X^{e}\) and \(X_{\sigma}^{NV}\) represent the longitudinal positions of the HSCV and NV in lane \(\sigma\), respectively. \(\beta_{\sigma}\) is a lane change intention judgment, and the value of \(\beta_{\sigma}\) is 1 only if one of the vehicles wants to change lanes into the lane of the other vehicle; otherwise, it is 0. \(\varpi_{s-y}^{e}\) are the weighting coefficients. \(\eta_{\sigma}^{e}\) is a switch function. \(L_{y2}\) represents the lateral safety threshold.  
The calculation of the lane change threat of an HSCV is based on the potential field mode, which is defined as follows:  
\[J_{s-lc}^{e}(X,Y)=E^{LV}(X,Y)+\sum_{i=1}^{n}E^{NV(i)}(X,Y) \tag{28}\]
# User Experience in VR Fashion Product Shopping: Focusing on Tangible Interactions
## 2 Background
### VR Environment: Immersive and Non-Immersive  
VR refers to a computer environment that allows users to experience immersion in the virtual world generated by one or more types of virtual sensation synthesis feedback, such as sight and hearing [42]. In an artificially created world of senses, it refers to a cyberspace where users can feel a sense of presence and immersion and feel that they are actually in that space. Virtual reality is divided into non-immersive and immersive types, according to the method of experience. Non-immersive VR experiences the virtual world through an image output device, such as a monitor or projector, without wearing a separate device. Non-immersive virtual reality, using a monitor to allow users to easily experience virtual reality, is widely applied in 3D games and simulations. In early studies on virtual reality, non-immersive virtual reality using a monitor was often used under the title of desktop virtual reality [43].  
Immersive virtual reality is a technology in which a user wears and experiences a head-mounted display (HMD) connected to a computer or mobile device [44]. Immersive virtual reality and non-immersive virtual reality can be clearly distinguished in terms of visuals. By wearing a separate device in an immersive virtual reality, the field of view of the user is controlled, the visual stimulus of the real world is blocked, and the user will only receive a 3D image output from the HMD [45; 46]. In this way, it becomes possible to experience a virtual world as if it is the real world, and an intense sense of reality and immersion occur. Therefore, it can be expected that immersive virtual reality will make the users experience a higher sense of presence and immersion than non-immersive virtual reality, which will have a positive effect on a user experience evaluation.  
However, in the case of immersive virtual reality, some users experience VR sickness and complain of dizziness, headaches, or nausea, among other issues [19], which hinder the sense of immersion or presence and will have a negative impact on a user experience evaluation in a real VR shopping environment [47]. In addition, the need to purchase special VR devices such as HMDs can negatively affect the intention to use and convenience in using VR fashion product shopping. Although an immersive VR environment simply provides a higher sense of presence and immersion, it is difficult to predict whether it can provide an improved user experience in a VR shopping environment compared to a non-immersive type. It is therefore necessary to compare immersive and non-immersive user experiences and discuss a method for providing virtual reality, which is advantageous for application to a real virtual VR shopping environment.
# Fault Sensing Using Fractal Dimension and Wavelet
## 2 Description of Short-Circuit Fault Components in Online Power Cable  
The main classes of short-circuit faults in the three-phase power cable system and their voltage and current characteristics are shown in Table 1.  
If a short-circuit fault of the three phases occurred, the voltage of each phase would be reduced. If a single-phase fault occurred, the fault phase current would increase, and the currents of the non-fault phases would decrease. The same class of faults has the same features, and different classes of faults have different features [25; 26]. The power cable fault state (PCFS) can be denoted by the sum of the normal state (NS) and the fault component state (FCS), as indicated in Equation (1) and Figure 1.  
\[\text{FCS}\ =\ \text{PCFS}\ -\ \text{NS} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Schematic Diagram of Fault** & **Characteristics** \\ \hline Three-phase grounded & & \\ fault of \(A\), \(B\) and \(C\) & & \\ \hline Phase-between fault of \(B\) and \(C\) & & \\ \hline Single-phase grounded fault of \(A\) & & \\ \hline Two-phase grounded & & \\ fault of \(B\) and \(C\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classes of short-circuit faults and their characteristics.  
The fault components exist in the added abnormal state of the power cable. The voltage of the fault components can be obtained by the differences between the cable voltage after fault occurrence and the voltage of the normal state. Similarly, the current of the fault components can be obtained by the differences between the cable current after fault occurrence and the current of the normal state.  
In a practical system of an online power cable, the \(n\) periods of voltage or current before fault occurrence are regarded as the voltage or current of the normal state. The voltage or current of the fault components \(S_{g}(t)\) can be calculated as follows [27].  
\[S_{g}(t)=S(t)-(-1)^{n}S(t-nT/2) \tag{2}\]  
where \(S(t)\) is the voltage or the current after fault occurrence, \(T\) is the power frequency period, and \(n\) is a positive natural number (usually, \(n=2\)).  
The fault component is the voltage difference or current difference of a period. The fault component should also satisfy the time requirement for fault recognition.  
The fault component of the current was taken as the initial signals of the power cable fault in this paper. We used Equation (1) to obtain the fault components of the power cable in the 10 types of short-circuit states. It includes three types of single-phase faults, three types of two-phase faults, three types of two-phase grounded faults, and one type of three-phase faults. The three phase currents and their fault components of the single-phase \(A\) earth faults are shown in Figure 2. The blue curves are for phase \(A\), the red curves are for phase \(B\), and the green curves are for phase \(C\).  
Figure 1: Power cable states of (a) fault component state (FCS); (b) power cable fault state (PCFS); and (c) normal state (NS).  
Figure 2: _Cont._
## 3 Materials and Methods
### Breaking News Discovery
```
1:whileforeverdo
2:\(\triangleright\) get the articles of the past \(h\) hours
3:get articles[\"timestamp >= now-h\"]
4:for article in articlesdo
5:int counter
6:title \(\leftarrow\)get_title(article)
7:title_vector \(\leftarrow\)get_vec(title)
8:for tmp_article in articlesdo
9:tmp_title \(\leftarrow\)get_title(tmp_article)
10:tmp_title_vector \(\leftarrow\)get_vec(tmp_title)
11:s \(\leftarrow\)similarity(title_vector, tmp_title_article)
12:ifs >= tthen
13:increment counter
14:endif
15:endfor
16:if counter >= mthen
17:classify article as breaking
18:endif
19:endfor
20:endwhile
```  
**Algorithm 1** Breaking news detection \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Survey Question** & **Empirical Results, Subject to Test** \\ \hline In your experience, how many common words should the title of two articles contain to be considered to refer to the same event? & One-four or over five words, depending on the length of the title. \\ \hline In your experience, what is the maximum time difference of the articles under comparison to being considered as referring to the same extraordinary event? & From thirty minutes to over three hours, with the shortest duration to provide stronger indications. However, this does not matter if it is an extraordinary event it will be constantly updated. \\ \hline When developing an algorithmic system for automated comparison between different articles, would it make sense to use? & Multiple sequential or sliding time-windows, with a degree of overlapping. \\ \hline What criteria would you use to determine the importance of an article? & Number of sources that simultaneously appear, number of “reliable” sources, specific thematic classification (e.g., natural disaster), number of sharing posts and/or reactions (likes, comments, etc.). \\ \hline What extra fields would you consider important for the purpose? & Author’s name listed, images attached/enclosed, sources and their reliability. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Empirical rules regarding the criteria for announcing an event as emerging. The detection algorithm is now at the proof of concept stage while it continues to be improved. We are aiming to introduce more modules that will be part of the \"detector\" super module, each one contributing to the process differently. A topic detection model will also be integrated to improve the filtering of the \"candidate\" events. The problem of topic detection is not solved, but there have been promising works that introduced automated classification methods [55] that can fit in the proposed framework. On top of this, given that classification algorithms require continuous testing and training using data from reliable databases, focus is given to the continuous enrichment of the designed database, which will help in improving the detection methods.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 2 Complexity Assumptions
#### 3. Proposed ITP-AKA Protocol  
This section demonstrates our proposed identity-based three-party authenticated key agreement (ITP-AKA) protocol. Detailed descriptions of all these phases are shown in Sections 3.1-3.3, respectively. Login and authentication phase was shown in Figure 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Elucidation** \\ \hline \(\mathcal{U}_{k}\) & \(k_{th}\) user of the system \\ \(ID_{k}\) & Identity of \(\mathcal{U}_{k}\) \\ \(\mathcal{TPD}_{k}\) & Tamper proof device issued to each specific \(\mathcal{U}_{k}\) \\ \(TID_{k}\) & Temporary identity of \(\mathcal{U}_{k}\) \\ \(\mathcal{FN}_{f}\) & Fog-node \\ \(ID_{f}\) & Identity of \(\mathcal{FN}_{s}\) \\ \(TID_{f}\) & Temporary identity of \(\mathcal{FN}_{s}\) \\ \(\mathcal{TPD}_{f}\) & Tamper proof device of \(\mathcal{FN}_{s}\) \\ \(\mathcal{CSP}\) & Cloud service provider \\ \(ID_{CSP}\) & Identity of \(\mathcal{CSP}\) \\ \(x\) & Private key of \(\mathcal{CSP}\) \\ \(\mathcal{SK}_{k-f-\mathit{csp}}\) & Session key between \(\mathcal{U}_{k}\), \(\mathcal{FN}\) and \(\mathcal{CSP}\) \\ \(\|\) & Concatenation operator \\ \(\oplus\) & Xok operator \\ \(\mathcal{A}_{att}\) & Attacker \\ \(h(.)\) & One-way hashing function \\ \(IBC\) & Identity-based cryptography \\ \(AKA\) & Authenticated key agreement \\ \hline \hline \end{tabular}
\end{table}
Table 2: Commonly used notations.
## 5 Experimental Results
### Training and Testing of Dataset  
During training, the SVM uses the data to learn and predict the same behavior for the future. The goal was to give necessary training data (\(x_{t}^{i}\)) for every observation (\(i\)) at time (\(t\)) on traffic of HETVNETs, to predict whether or not the packets were received, by \(x_{t}^{i}+\Delta\), as depicted in Figure 3. Using the induction learning technique, it withheld two stage-processing agendas. During the initial stage of the training phase, the SVM acquired knowledge from the training set. In the later stage, the testing phase, it evaluated and associated the members with their respective classes accordingly.