esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
### Research Contributions  
In light of these limitations, this paper makes two significant contributions:  
1. _Survey of Research-informed EH Tools_: This study surveys 100 research-informed EH tools developed in the last decade. It highlights key areas such as licensing, release dates, source code availability, development activity level, and peer review status. This analysis aims to provide insights into the state-of-the-art EH tools developed by the research community.
2. _Alignment with Recognised Frameworks_: This study categorises the tools into _process-based_ frameworks, such as the _Penetration Testing Execution Standard_ (PTES) [9], and the _Mitre ATT&CK framework_[10] and _knowledge-based_ frameworks like the _National Cyber Security Centre's Cyber Security Body Of Knowledge_ (CyBOK) [11] and the _Association for Computing Machinery's Computing Classification System_ (ACM CCS) [12]. Combining these four classifications offers an informative view of the landscape of novel and research-informed ethical tools, their functionality, and application domain for the benefit of scholars, researchers, and practitioners.  
This comprehensive approach not only bridges the gap between industry and academia but also ensures that Ethical Hacking tools evolve in tandem with the ever-changing cyber threat landscape.
## 4 Q-Deformed Trigonometric Functions Method
### Kerr Law \(H(\psi)=\psi\)  
From Type 3, we suppose the solution to NPSEs in Equation (14) in the form in Equation (92). The formula in Equation (92) is a solution to Equation (14) when \(n=1\), hence  
\[\psi(\xi)=A_{0}+A_{1}sec_{q}\xi+B_{1}\tan_{q}\xi. \tag{96}\]  
Substituting Equation (96) into Equation (14) and setting the coefficients of \((\sec_{q}\xi)^{j}(\tan_{q}\xi)^{i}\), \(\j=0,1,\i=0,1,2,\dots\), to be zero, we acquire the system equations which can be solved to have:  
**Set 1.**  
\[A_{0}=0,\ \ A_{1}=\sqrt{\frac{2q(\varepsilon-\sigma v)}{\eta s_{0}-\varsigma}}, \ \ B_{1}=0,\ w=\frac{\varepsilon s_{0}^{2}+\alpha s_{0}-\sigma v+\varepsilon}{ \sigma s_{0}-1}\,\ q>0,\ \frac{\varepsilon-\sigma v}{\eta s_{0}-\zeta}>0. \tag{97}\]  
In this set, the solution to NPSEs in Equation (13) is  
\[Q_{36}(x,t)=\sqrt{\frac{2q(\varepsilon-\sigma v)}{\eta s_{0}-\varsigma}}\ \ e^{i(-s_{0}x+\omega t+\rho)}\ \sec_{q}(x-vt) \tag{98}\]  
**Set 2.**  
\[A_{0}=0,\ \ A_{1}=0,\ \ B_{1}=\sqrt{\frac{2(\varepsilon-\sigma v)}{\eta s_{0}- \varsigma}},\ \ w=\frac{\varepsilon s_{0}^{2}+\alpha s_{0}+2\sigma v-2\varepsilon}{ \sigma s_{0}-1},\ \frac{\varepsilon-\sigma v}{\eta s_{0}-\zeta}>0. \tag{99}\]  
In this set, the solution to NPSEs in Equation (13) is  
\[Q_{37}(x,t)=\sqrt{\frac{2(\varepsilon-\sigma v)}{\eta s_{0}-\varsigma}}\ \ e^{i(-s_{0}x+\omega t+\rho)}\ \tan_{q}(x-vt) \tag{100}\]  
**Set 3.**  
\[A_{0}=0,\ A_{1}=\sqrt{\frac{q(\varepsilon-\sigma v)}{2(\eta s_{0}-\varsigma)}},\ B_{1}=\sqrt{\frac{(\varepsilon-\sigma v)}{2(\eta s_{0}-\varsigma)}},\ w= \frac{\sigma v+2\varepsilon s_{0}^{2}+2\alpha s_{0}-\varepsilon}{2(\sigma s_{ 0}-1)}\,\ q>0,\ \frac{(\varepsilon-\sigma v)}{(\eta s_{0}-\varsigma)}>0. \tag{101}\]In this set, the solution to NPSEs in Equation (13) is  
\[Q_{38}(x,t)=e^{i(-s_{0}x+wt+\rho)}\Bigg{[}\sqrt{\frac{q(\varepsilon-\sigma v)}{2( \eta s_{0}-\varsigma)}}\sec_{q}(x-vt)+\sqrt{\frac{(\varepsilon-\sigma v)}{2( \eta s_{0}-\varsigma)}}\tan_{q}(x-vt)\Bigg{]} \tag{102}\]  
**For the type 4.** The solutions of the NPSEs in Equation (13) are given by:  
\[Q_{39}(x,t)=\sqrt{\frac{2q(\varepsilon-\sigma v)}{\eta s_{0}-\varsigma}}\ e^{i(-s_{0}x+wt+\rho)}\ \csc_{q}(x-vt) \tag{103}\]  
where \(w=\frac{\varepsilon s_{0}^{2}+\alpha s_{0}-\sigma v+\varepsilon}{\alpha s_{0} -1},\ q>0\), and \(\frac{\varepsilon-\sigma v}{\eta s_{0}-\varsigma}>0\)..  
\[Q_{40}(x,t)=\sqrt{\frac{2(\varepsilon-\sigma v)}{\eta s_{0}-\varsigma}}\ e^{i(-s_{0}x+wt+\rho)}\ \cot_{q}(x-vt) \tag{104}\]  
where \(w=\frac{\varepsilon s_{0}^{2}+\alpha s_{0}+2\sigma v-2\varepsilon}{\alpha s_{ 0}-1},\ q>0\), and \(\frac{\varepsilon-\sigma v}{\eta s_{0}-\varsigma}>0\)..  
\[Q_{41}(x,t)=e^{i(-s_{0}x+wt+\rho)}\Bigg{[}\sqrt{\frac{q(\varepsilon-\sigma v) }{2(\eta s_{0}-\varsigma)}}\csc_{q}(x-vt)+\sqrt{\frac{(\varepsilon-\sigma v) }{2(\eta s_{0}-\varsigma)}}\cot_{q}(x-vt)\Bigg{]}, \tag{105}\]  
where \(w=\frac{\sigma v+2\varepsilon s_{0}^{2}+2\alpha s_{0}-\varepsilon}{2(\alpha s _{0}-1)}\) and \(\frac{q(\varepsilon-\sigma v)}{(\eta s_{0}-\varsigma)}>0\).
# Low-Temperature Direct Bonding of SiC to Si via Plasma Activation
## 1 Introduction  
Silicon carbide (SiC) has superior physical and thermal properties, including a large energy band gap, high breakdown voltage, high thermal conductivity, high electron saturation velocity, and excellent stability [1]. The combination of SiC with Si technologies ouldn have promising applications in high-temperature, high-voltage, and high-power systems [2]. In particular, SiC/Si heterojunction diodes have the potential to present superior electrical properties over Schottky barrier diodes (SBDs) [3; 4; 5]. By introducing a Si layer on top of the SiC substrate, gate oxide can be grown on the Si layer, which can improve the reliability of the gate oxide of SiC MOSFETs [6; 7; 8]. Furthermore, based on the SiC/Si structure, more applications would be possible in microelectronics and microelectromechanical systems (MEMS) and photoelectric devices [9; 10].  
Since the SiC/Si heterostructure was proposed, much research has been conducted. Due to the large lattice mismatch between Si and SiC, the SiC/Si heterostructure obtained by epitaxy remains problematic [3; 10; 11]. Neither Si films grown on SiC substrates nor SiC films on Si can overcome the lattice mismatch issues [9]. Direct wafer bonding, as a unique monolithic integration technique, can achieve the combination of two materials in spite of their crystal difference [12]. [PERSON] et al. succeeded in creating SiC/Si heterojunctions with improved electrical properties through surface-activated bonding (SAB) [2; 13; 14], in which the amorphous layer in the interface was eliminated after annealing at 1000 \({}^{\circ}\)C. However, there is a high-cost complicated system required for SAB, which has restrained its further investigation and application to the SiC/Si heterojunction. As a practical method of wafer bonding, plasma-activated bonding (PAB) can achieve low-temperature waferbonding at low cost [15]. However, the study of PAB for SiC/Si wafer bonding has not been completed, and the bonding efficiency remains low. A high bonding area ratio of over 90% can hardly be obtained by low-temperature PAB [16]. In addition, a reasonable mechanism of low-temperature direct bonding of SiC to Si via plasma activation has not been proposed.  
In this paper, we investigated the low-temperature direct bonding of SiC/Si via plasma activation. Reactive ion etching (RIE) O\({}_{2}\) plasma was used as the plasma source to activate the bonding surfaces. The parameters of plasma activation and annealing were investigated and optimized. To characterize the bonding interface and study the bonding mechanism, specific testing methods were employed in our experiment. In particular, the comparison of the TEM results of the SiC/Si interface under different annealing temperatures was made to investigate the impact on the amorphous layer. After a series of measurements, the rational bonding mechanism of SiC/Si PAB was discussed.
# Adaptive Bit-Labeling Design for Probabilistic Shaping Based on Residual Source Redundancy
## 3 Analysis and Design of Bit-Labeling
### Effects of Bit-Labelings  
With an aim to illustrate the effects of bit-labelings on the JSCCM system, we compare the BER performance of two different Gray labelings. Figure 2 shows a 16-QAM constellation with \(L\) and \(L^{\prime}\) labelings, which are both under the rule of one-bit discrepancy between adjacent binary labels.  
Figure 3 plots the simulated BER results of those two labelings with different source codes (\(\mathbf{B}^{s,1}\)[15] and \(\mathbf{B}^{R4/A}\)[19]) and different source probabilities (\(p=0.04\) and \(p=0.96\)). Note that the two source probabilities contribute to the same source entropy according to (1). It can be found that the labeling \(L\) with a competitive advantage at \(p=0.04\) is outperformed by \(L^{\prime}\) at \(p=0.96\) when \(\mathbf{B}^{s,1}\) is utilized. Meanwhile, it can be observed that the better labeling for \(\mathbf{B}^{s,1}\) results in worse performance for \(\mathbf{B}^{R4/A}\) when \(p=0.96\). Therefore, the good bit-labelings for different source probabilities or different source codes can be different. An adaptive design of bit-labeling according to the source probability and the source code is essential for the JSCCM system.  
Figure 2: A 16-QAM constellation with two labelings: \(L\) and \(L^{\prime}\)(in parentheses).
## 3 Simulation Environment and RMWBI System  
### Breast Model  
Several types of tissue make up the female breasts. They consist of tissue types such as muscle, ligament, glandular, lymphatic, fat, nipple, and skin. At microwave frequencies, breast tissues have different electrical properties. The theoretical basis for detecting breast cancer with RMWBI is based on the high contrast between the dielectric constant and conductivity between healthy tissue and cancerous tissue. Figure 5 shows the dielectric properties of healthy and tumor tissues obtained in the study [91].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. No.** & **Size (mm2)** & **Material** &
\begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} & **Gain (dB)** & **Directivity (dB)** \\ \hline
[32] & 90 \(\times\) 95 & Rogers RO3010 & 1–4 & - & - \\
[40] & 96.5 \(\times\) 51.5 & FR-4 & 3.87–4.49 & 6.06 & 6.44 \\
[48] & 88 \(\times\) 75 & RT/Durooid 5870 & 1.54–7 & 9.8 & - \\
[49] & 60 \(\times\) 60 & FR-4 & 2.4–14 & 8.1 & - \\
[50] & 51 \(\times\) 42 & RT/Durooid 5870 & 2.8–7 & 7.8 & - \\
[51] & 39.2 \(\times\) 44 & RT/Durooid 6010 & 3.1–10.6 & - & - \\
[52] & 40 \(\times\) 40 & FR-4 & 2.5–11 & 7.2 & 7.2 \\
[53] & 36 \(\times\) 36 & FR-4 & 3.05–12.2 & 8.2 & 10.5 \\
[74] & 73.4 \(\times\) 42 & FR-4 & 5–10 & 10 & - \\
[76] & 45 \(\times\) 40 & FR-4 & 2.79–16.66 & 4.77 & 5.84 \\
[77] & 25 \(\times\) 20 & Polyimide flexible & 4–9.4 & 2.33 & 4.06 \\
[78] & 67 \(\times\) 46 & Rogers RO3010 & 1–8 & 5.8 & - \\
[79] & 88 \(\times\) 44.75 & Rogers RO4003C & 2.42–11.52 & 8.14 & - \\
[80] & 84 \(\times\) 48 & RT/Durooid 5870 & 5.3–9.6 & 12 & - \\
[85] & 40 \(\times\) 45 & FR-4 & 2.9–12 & 8.2 & - \\
[86] & 48 \(\times\) 60 & FR-4 & 2.4–14 & 10 & - \\
[87] & 56 \(\times\) 50 & FR-4 & 2.0–12 & 5.2 & - \\
[88] & 96 \(\times\) 86.2 & RF35 & 2.6–11 & 9 & - \\
[89] & 37 \(\times\) 21 & FR-4 & 3.6–12 & 4.5 & - \\
[90] & 60.77 \(\times\) 34.98 & FR-4 & 3.04–3.30 & 4 & 4.44 \\ Proposed & 34.6 \(\times\) 33 & FR-4 & 3.6–13 & 9.27 & 11 \\ \hline \end{tabular}
\end{table}
Table 2: Vivaldi antenna studies in the literature.  
With the RMWBI technique, data are obtained using two different methods. This includes monostatic and multistatic measurement methods [93]. In multistatic measurement, antennas are fixedly placed around the breast phantom. Each antenna in the array transmits signals sequentially while the other antennas collect the reflected signals. Therefore, it is a complex and time-consuming measurement method [94]. However, the proposed monostatic measurement method is a simple, fast, and effective method used for the detection of breast cancer. It also offers high data rates to find the tumor using DMAS [95].
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 4 RL Based Selection of TRxPs for RA
### Design
#### 4.4.1 States  
A single state in our design is further composed of three entities. These are the initial criteria, system parameter criteria, and set of TRxPs. We further elaborate on the entities as follows:  
1. Initial criteria: In the RA selection problem, user nodes are allowed to participate in the RA selection problem if they are in the RRC_IDLE RA mode and have a full buffer to send data in the uplink, or a request from a TRxP to receive data in the downlink. Mathematically, we use an indicator variable as follows. \[I=\left\{\begin{array}{ll}1&if\quad\text{user has full buffer},\\ 0&else.\end{array}\right.\] (14)
2. System parameters criteria: Once users pass the above criteria, the next step is to pass the ACB system parameter criteria that they receive from the serving cell. The ACB flag indicates whether users are barred from performing RA in case of insufficient resources. Initially, this parameter is represented as a real number. We use another indicator variable to check whether a user has passed this parameter as follows. \[A=\left\{\begin{array}{ll}1&if\quad 0<a<t<1\;,\\ 0&else,\end{array}\right.\] (15)where \(a\) is the ACB factor the user randomly selects and sends to the serving cell, and \(t\) is a threshold factor for admission control by the cell. Finally, the user is allowed to perform RA if the above two criteria are successfully met, which is mathematically presented as  
\[E=\left\{\begin{array}{ll}1&if&I\text{=1\&}A\text{==1},\\ 0&else.\end{array}\right. \tag{16}\]  
* Set of TRxPs: We encode the set consisting of the TRxPs from which user nodes can obtain a pilot signal and measure the RSRP representing potential candidates for RA channel selection. This is given as follows. \[A=<A_{1},A_{2},...,A_{n}>,\] (17) where \[A_{i}=\left\{\begin{array}{ll}1&if&T_{i}\\ 0&else.\end{array}\right.\] (18)  
Finally, the state is represented by the vector as:  
\[S=[E,A]. \tag{19}\]
## 3 Results and Discussion
### Initial Fuel Temperature
This process with intensive heating of a composite fuel droplet makes it possible to achieve ignition conditions faster due to a multiple increase in the free heat exchange surface, the evaporation of liquid components, and the release of gaseous products during the thermal decomposition of the solid components. At the same time, composition No. 4 is the least sensitive to changes in the initial fuel temperature: with an increase in the initial temperature from minus 60 \({}^{\circ}\)C to plus 60 \({}^{\circ}\)C, the ignition delay time decreases by 60% at \(T_{g}\) = 700 \({}^{\circ}\)C and by 55% at \(T_{g}\) = 1000 \({}^{\circ}\)C.  
In turn, fuel composition No. 2, based on FC with the addition of turbine oil, is the most sensitive both to changes in the initial fuel temperature and to changes in the heating source temperature (Figure S2). Increasing the ambient air temperature from 700 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C reduces the ignition delay time by 75% at \(T_{0}\) = \(-\)60 \({}^{\circ}\)C and by 65% at \(T_{0}\) = 60 \({}^{\circ}\)C. With identical values of \(T_{g}\), an increase in the initial fuel temperature leads to a decrease in the ignition delay time by 70% at \(T_{g}\) = 700 \({}^{\circ}\)C and by 55% at \(T_{g}\) = 1000 \({}^{\circ}\)C.  
It can be concluded that it is possible to achieve a significant intensification of the fuel ignition process by cumulatively increasing the heating source temperature \(T_{g}\) (from 700 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C) and the initial fuel temperature \(T_{0}\) (from minus 60 \({}^{\circ}\)C to plus 60 \({}^{\circ}\)C). Moreover, the reduction in the ignition delay time will be about 7.5 times. The result obtained is important for practice, for example, in the case of switching coal-fired boilers to composite fuel or when designing furnaces for new boilers, when minimizing the duration of ignition and fuel burnout processes is necessary to reduce the dimensions of furnaces and combustion chambers, as well as to increase the completeness of fuel components burnup. Droplet dispersion under its intense heating conditions is one of the promising ways to intensify the combustion process of multicomponent fuels [34; 37].
## 1 Introduction
Meanwhile, SWET is induced by two oppositely polarized AC electrode terminals and usually behaves as a series of micro-vortices on top of the electrode array within the fluid channel [30]. In the present numerical investigation, a unique electroconvection method driven by multiple-frequency electrothermal flow (MET) is proposed, which delicately combines the advantages of TWET and SWET fluid motion. In our numerical model, by coupling the Joule heating source of the dual-frequency electric field, these two electrothermal flow effects will interact actively, thereby resulting in the patterns of twisted pumping and stirring streamlines of MET above a discrete electrode array. Our group has previously studied the phenomenon of MET actuated by multiple-frequency idealized sinusoidal voltage waves in the literature [28]. On the contrary, we propose in the current study to induce MET fluid motion on a four-phase discrete electrode array. Note that the specific flow behaviors of MET differ from each other under the condition of an ideal sine wave and the condition of a discrete-phase AC voltage. For instance, the flow velocity of SWET driven by an ideal sine wave vanishes to zero in the high-frequency limit due to a delicate compensation of a hydraulic pressure gradient in the vertical direction, while that caused by the four-phase AC powering persists well at high field frequencies. So, it is quite necessary to test the feasibility of utilizing a practical discrete electrode structure in driving active MET fluid motion in microfluidic channels. What is more, we further utilize the MET technology to enable simultaneous pumping and mixing of incoming nanoparticle samples, to accelerate the binding of free antigens to antibody probes fixed on the functionalized electrode surface (Please refer to the movie in the Supplementary Materials). This improves the efficiency and sensitivity of microfluidic immunoassays by activating active synthetic electrothermal flows, as demonstrated by systematic and comprehensive simulation studies. This paper aims to address the diffusion limitation issue in microfluidic immunoassays by introducing the technology of MET in the form of numerical simulation, further promoting the application of microfluidic chips in the biomedical field.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.1 Supervised
[77] used a two-branch network to learn the integration model of the vision language. Of the two branches of the network, one receives the text features and the other the visual features. The triple loss based on the margin trains the network by combining a neighborhood-preserving term and two-way ranking terms. * _Hierarchical self-attentive network:_ The hierarchical self-attentive network (HSAN) is able to understand the consistent connection between video content and its associated semantic data at both the frame and segment levels. This ability enables the generation of a comprehensive video summary [78]. A hierarchical self-attentive network was presented by [PERSON] et al. [78]. First, the original video is divided into segments, and then, using a pre-trained deep convolutional network, the visual feature is extracted from each frame. To record the semantic relationship at the section level and at the context level, a global and a local self-care module are proposed. To learn the relationship between visual content and caption, the self-attention results are sent to a caption generator, which is enhanced. An importance score is generated for each frame or segment to produce the video summary. * _Weight learning:_ The weight learning approach was proposed by [PERSON] et al. [54], in which using maximum-margin learning, it can automatically learn the weights of different objects. Learning can occur for processing styles that are not the same or different types of product, as these videos contain annotations that are highly relevant to the domain expert's decisions. For different processing styles or product categories, there may be different weightings of audio annotations built directly with domain-specific processing decisions. For efficient user exploration of the design space, there may be default storage of these weights. * _Gaussian mixture model:_ A Gaussian mixture model is a clustering method used to estimate the likelihood that a specific data point is part of a cluster. In the user preference learning algorithm proposed by [PERSON] et al. [46], the most representative are initially selected as temporary keyframes from the extracted frames. To indicate a scene change, temporary frames are displayed to the user. If the user is not satisfied with the selected temporary keyframes, they can interact by manually selecting the keyframes. A Gaussian mixture model (GMM) is modeled by learning user preferences. The parameters of the GMM are automatically updated based on the user's manual selection of keyframes. Production of the personalized summary is performed in real time as the personalized frames update the selected frames from the temporary base. Personalized keyframes represent user preferences and taste.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 1 Introduction
### Outline  
In this paper, we investigate the question on how to merge separated ring-based overlays once the underlying networks are reconnected again. Any merger should operate independently, automatically, and without administrative restrictions. Specifically, we investigate how to identify, from a single node's point of view, the existence of further overlay networks, how to initiate the merging, and how to terminate the merging process.  
Therefore, we present in Section 2 a taxonomy of design decisions for merging algorithms and put related work in context. Motivated through this overview, we identify the weaknesses of current merging algorithms and present our proposal, the Ring Reunion Algorithm, in Section 3. In Section 4, we evaluate our proposed approach in comparison to Chord-Zip [6], the simple and Gossip-based Ring-Unification Algorithm [7], and the unmodified stabilization algorithms of Chord.  
Figure 1: Ring composed of nodes from different regions is corrupted during isolation of the regions.