esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Autonomous Vehicles (AVs)
### Essential Security Principles  
**Definition 1** (Data Processing).: _Data processing, which is at the core of computer security, has an intricate connection with information security as an entire area, which is an even broader area of study._  
**Description 1** (Security Measures).: _Antivirus software, which must carefully evaluate sensor data in order to allow for proper responses, is an excellent example of this connection as a result of the manner in which it works. In the process of performing their primary tasks, such systems run the risk of accidentally turning into attractive targets for attackers who seek to take advantage of the data-rich features they provide._  
As a consequence of this, it is very necessary to apply the concepts of privacy and security to autonomous vehicles in order to strengthen their defensive systems against possible attacks. The basic triangle is the driving force behind these fundamental principles. Figure 2 illustrates a visual representation of the way in which the triangle of confidentiality, integrity, and availability (CIA) applies to the data security principles of autonomous vehicles.  
* Confidentiality : This principle underscores the importance of preventing unauthorized data access. Upholding confidentiality is crucial to preempt potential misappropriation or exploitation of sensitive information, which, if mishandled, could compromise the safe and reliable operation of AV systems.
* Integrity: Integrity ensures the authenticity, accuracy, and consistency of data over their entire life cycle. This involves not only detecting unauthorized data access but also thwarting unsanctioned data modification. Preservation of data integrity is critical to maintaining the trustworthiness of a system and its decision-making abilities.
* Availability: A system's effectiveness is contingent upon the consistent availability of its functionalities and its ability to perform as expected. In the context of AVs, any compromise in availability could have immediate and severe safety implications.  
Besides these fundamental concepts, AV security may also attempt to accomplish additional attributes, depending on the specific circumstances and application, including privacy, authenticity, accountability, non-repudiation, and reliability. These characteristics, when considered together, help to provide an integrated and all-encompassing security framework for autonomous vehicles, which helps to protect such vehicles from a wide variety of possible threats.  
Figure 2: Triangle of confidentiality, integrity, and availability for AVs.
# Shading on Photovoltaic Collectors on Rooftops
## 5 Sloped Rooftops  
Photovoltaic modules may be installed in multiple rows on sloped rooftops. Rooftops may be oriented at any azimuth. Figure 11 shows a PV system installed on a rooftop facing west with a slope \(\varepsilon\) and roof width \(W\) (one side). The distance between the rows is denoted by \(D\) and the row inclination angle is \(\beta\) with respect to the roof plane. To fully utilize the roof width, the modules are rotated with an angle \(\tau\) with respect to the roof length, creating a shift \(S\) between the rows, the amount of which is given by:  
\[S=(H\cos\beta+D)\tan\tau \tag{19}\]  
where \(H\) is the width of the PV modules.  
Figure 10: The row distance for an inclined plane \(\varepsilon(^{0})\), see Figure 9.  
Figure 9: Stepped collectors.  
Given \(H\) and \(D\), one may calculate the optimal angles \(\beta\) and \(\tau\) to obtain the maximum annual incident irradiation per unit area on the PV module, based on the solar radiation data at a given location. The optimization was performed for \(W=30\,m\), \(K=20\) rows, \(H=1.62\,m\), latitude \(\phi=32^{0}N\), and for roof slopes \(\varepsilon=0^{0}\) to \(25^{0}\) with increments of \(5^{0}\) for two cases: (a) optimal \(\beta\) and \(\tau\), and (b) optimal \(\beta\) for \(\tau=0^{0}\) (rows pointing due south for comparison). Mutual shading between rows was taken into account. The results are shown in Table 1.  
The left column indicates the roof slope \(\varepsilon\), the second column is the optimal inclination angle \(\beta\) of the PV modules with respect to the roof plane, the third column indicates the rotation angle \(\tau\) of the rows relative to the roof length, and the fourth row is the annual global incident irradiation per square meter \(G_{\tau=\text{var}.}\). For comparison, the optimization was also carried out for PV rows facing south (\(\tau=0\)). The fifth column is the optimal inclination angle, the six column is the annual global incident irradiation per unit area, and the last column is the percentage difference between the two cases calculated by \(100(G_{\tau=\text{var}.}-G_{\tau=0})/G_{\tau=0}\). Table 1 reveals that almost no gain in incident irradiation is obtained by rotating the rows on sloped roofs for \(\varepsilon<10^{0}\); however, a gain of about 2% may be obtained by pointing the rows south-east with an angle \(\tau=-38^{0}\).  
Remark: Because of asymmetry in the solar radiation at the location, the optimal azimuth angle \(\tau\) of the PV rows on a horizontal plane is positive, i.e., pointing to the south-west by \(+4^{0}\).
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## 1 Introduction  
Quantum Key Distribution (\(QKD\)) represents a new cryptographic method to distribute a key between two remote users, usually called [PERSON] and [PERSON] [1]. \(QKD\) systems require legitimate users to authenticate each other through a public channel. The purpose of a \(QKD\) system is to generate secret bits that can be used to encrypt plaintext messages according to a simple xor function between the message and the secret key.  
If an eavesdropper, commonly called [PERSON], tries to intercept the quantum channel to get the key, she will be detected. In such a case, [PERSON] and [PERSON] will discard the process before a key could be established. However, if they do not detect any eavesdropping activity, the quantum measurements can be used to derive the secret key [2]. After the transmission, [PERSON] and [PERSON] can compare a fraction of the exchanged key to see if there are any transmission errors caused by eavesdropping. \(QKD\) systems have been carried out experimentally through dedicated optical fibers, across free space, weak laser pulses or single photons, entangled photon pairs or continuous variables [3].  
Ideally, the security of \(QKD\) protocols is supported by the properties of quantum mechanics, which make eavesdropping in the middle of the quantum channel detectable [1, 4]. However, serious concerns arise at the technological level. Unfortunately, most of the promising \(QKD\) systems are vulnerable to quantum hacking due to loopholes in the optical detection system [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Under this scenario, new \(QKD\) methods must be developed to resist attacks related to such vulnerabilities as the Photon Number Splitting (\(PNS\)) and the Intercept and Resend with Faked States (\(IRFS\)) attacks [16, 17].  
In [18], we introduced a novel \(QKD\) protocol that uses weak coherent states and active basis measurement, capable of detecting the \(PNS\) eavesdropping activity. The strengths of the \(ack\)\(state\) (in [18] referred to as \(ack\)\(QKD\)) against \(PNS\) attack were discussed in [19]. In this paper, we extend the \(ack\)\(state\) protocol to the dual protocol \(nack\)\(state\) protocol to analyze its security against \(IRFS\) attack. One of the main advantages of the proposed protocol is that it protects against \(IRFS\) attack without requiring any changes in the hardware; only software changes are required. We do not discuss other attacks nor pretend to perform a general formal demonstration of its security.
# The UV Plasmonic Behavior of Rhodium Tetrahedrons--A Numerical Analysis
## 3 Results and Discussion  
### Comparison of Rlt, Pt and Pd in Simple Geometries  
Palladium (Pd) and platinum (Pt) are also materials that have shown photocatalytic behavior Pd has been presented as key piece in the looming hydrogen economy due to its capability to dissociate hydrogen and store it [19]. Also, along with Pt and Rh, has been used in heterometallic antenna-reactor complexes. In these systems, metallic nanoparticles with strong optically resonant properties (i.e., Al) behave as nanoscale optical antennas that are coupled to weakly light-absorbing catalytic metallic NPs (i.e., Pd, Pt, or Rh) to trigger a light-induced generation of hot carriers within the catalyst nanoparticles [20; 21].  
As a first step, we consider tetrahedron monomers of Rh, Pd and Pt with an edge of 20 nm. This size has been chosen to mimic real photocatalysis experiments as reported in Reference [13]. Moreover, other authors have reported the possibility of synthesizing Pd and Pt tetrahedral NPs [22; 23]. The nanostructure is illuminated with a monochromatic plane wave under normal incidence and \(x\)-polarization (see Figure 1a). To explore the relative contributions of the geometry and optical properties, calculations for a sphere and a cube with equivalent volume are also presented. The radius of the equivalent sphere is \(R=6\) nm. The equivalent cube has a side length of \(L=10\) nm.  
Figure 3 plots the absorption cross-section \(C_{abs}\) (top row) and spectral electric field intensity averaged (\(\langle|\mathbf{E}|^{2}\rangle\)) over the NP's closed surface (bottom row) for equivalent spheres, cubes and tetrahedra made of Rh, Pt and Pd. It can be seen how the tetrahedron presents the LSPR at lower photon energies than the other two geometries. In fact, it shows a broad peak that covers from 3.5 to 6 eV. In general the values of \(C_{abs}\) and \(\langle|\mathbf{E}|^{2}\rangle\) are the highest for Rh followed by Pd and Pt as expected by the values of \(Q_{LSP}\): 2.25 for Rh, 0.35 for Pt and 0.66 for Pd. In addition, it can be observed how the shape has a strong influence on the LSPRs and their spectral position. The LSPRs in the tetrahedra are produced at lower energies than those of the nanocubes and spheres, the latter being those appearing at higher energies.  
Figure 3: Absorption cross-section (\(C_{abs}\)) (**top row**) and spectral electric field intensity (\(|\mathbf{E}|^{2}\)) averaged over the nanoparticle’s closed surface (**bottom row**) for spheres (\(R=6\) nm, first column), cube (\(L=10\) nm, second column), and tetrahedron (\(L=20\) nm, third column) composed of Rh (blue), Pt (yellow) and Pd (red) illuminated under normal incidence, and embedded in air.
## 3 Graph Clustering Methods
### Graphsage for Community Detection
Essentially, it aims to distribute the samples into n groups with uniform variances while minimizing the inertia or intra-cluster sum of squares, as defined by the following equation:  
\[\sum_{i=0}^{n}\left(\min_{\mu_{j}\in\mathbb{C}}\left(\left|x_{i}-\mu_{j} \right|^{2}\right)\right) \tag{7}\]  
The k-means algorithm partitions a set of \(n\) samples \(x\) into \(k\) distinct clusters \(C\), with each cluster characterized by its mean \(\mu_{j}\); commonly known as the cluster centroids. Primarily, the experimental setup involves determining the optimal number of clusters to retain. Various methods exist for this purpose, including analyzing the percentage of variance explained relative to the number of clusters [49]. This typically entails solving the clustering problem for different values of \(k\) and then employing suitable criteria to select the most suitable value. Notably, the proposed method directly furnishes clustering solutions for all intermediate values of \(k\), thereby eliminating the need for additional computational efforts. The selection of the number of clusters hinges on ensuring that the addition of another cluster does not substantially improve data modeling. Specifically, as the percentage of variance explained by clusters is plotted against the number of clusters, the initial clusters contribute significantly to explaining variance. However, there comes a point where the marginal gain diminishes. Thus, the number of clusters is chosen at this juncture, where the addition of another cluster yields little improvement. This section has presented benchmark algorithms for graph clustering such as statistical methods from Louvain to deep learning methods such as GraphSAGE. To compare the performances of these methods an experimental setup on reference datasets was implemented using a systematic and rigorous approach. These reference algorithms serve as a basis for evaluating the effectiveness and performance of clustering methods, with the aim of improving industrial diversification recommendations using the Product Space graph.
# Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
## 2 Principle of the Heat Sink Equivalent Thermal Test Method
The satellite was placed in a vacuum container for thermal tests, and the test state is shown in Figure 3.  
Ignoring the shielding of the heat exchange between the satellite and the heat sink exhibited by the test equipment, the thermal balance equation of the test can be expressed as:  
\[\frac{A_{S}\sigma\big{(}T_{S}^{4}-T_{0}^{4}\big{)}}{\frac{1}{\epsilon_{S}}+ \frac{A_{S}}{A_{0}}\Big{(}\frac{1}{\epsilon_{0}}-1\Big{)}}=Q_{n} \tag{7}\]  
Figure 2:0 Schematic of satellite thermal balance test. where \(T_{S}\) is the surface temperature of the satellite in the test, \(\epsilon_{0}\) is the surface emissivity of the heat sink, and \(A_{0}\) is the superficial area of heat sink. According to Formulas (6) and (7), we can deduce Formula (8). Figure 2:1  
It is considered that the surface temperature of the satellite in the test and in orbit is the same, \(T_{a}\approx T_{S}\). When the difference between the surface area of the satellite and that of the heat sink is large enough and the emissivity of the heat sink is high enough, it can be considered that \(\frac{A_{S}}{A_{0}}\Big{(}\frac{1}{\epsilon_{0}}-1\Big{)}\to 0\). Then, the temperature at which the heat sink should be set in the heat sink equivalent heat balance test method can be obtained from Equation (8). Figure 2:2  
The error caused by approximation of \(\frac{A_{S}}{A_{0}}\Big{(}\frac{1}{\epsilon_{0}}-1\Big{)}\to 0\) can be expressed as:  
Figure 2:3  
In the formula, \(\delta_{1}=\frac{A_{S}(1-\epsilon_{0})}{A_{0}\epsilon_{0}}\), \(\delta_{2}=\sqrt[4]{1+\frac{A_{S}\epsilon_{S}(1-\epsilon_{0})}{A_{0}\epsilon_ {0}}}\). According to Formula (9), when \(A_{S}/A_{0}<0.1\), \(\epsilon_{0}>0.9\), the principle error of the heat sink equivalent test method is less than 1 \({}^{\circ}\)C.  
Finally, the heat flow simulation method and control temperature are simulated and checked. In step S7, after setting the temperature of the heat sink in the test, it is necessary to carry out the simulation verification of the test model and compare the temperature result of the test model with the predicted result in orbit to ensure it meets the allowable error \(\Delta T\). Otherwise, the area of the heat sink equivalent method applied needs to be re-divided; that is, step S2 needs to be re-performed.
# BN-RA: A Hybrid Model for Risk Analysis of Overload-Induced Early Cable Fires
## 4 Numerical Analysis of the Fire Risk Assessment
### Simulation
#### 4.3.2 Risk-Level Classification Based on the Risk Evolution  
By virtue of the flexible inference of the BN and the ability to update the node evidence data, the probability of each root node in the BN was updated, to simulate the change in risk. The probabilities of the sub-node states were obtained by Bayesian reasoning based on the simulated root node states, and then the risk indices were obtained using Equation (4). Figure 8 shows the evolution curve of the risk indices based on the current, aging time, mechanical damage degree, and environment temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cable Heat Source** & **Risk Level of Insulating** & **Risk Level of Insulation** \\
**Temperature/\({}^{c}\)C** & **Materials** & **Breakdown** \\ \hline
34.8908 & 3 & 1 \\
33.2958 & 1 & 1 \\
30.2914 & 2 & 1 \\
33.503 & 1 & 1 \\
8.01759 & 1 & 1 \\
35.0763 & 1 & 1 \\
10.5014 & 1 & 1 \\
7.81303 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Node states and risk factors generated by Netica.  
The division of the overall risk level depends on the risk evolution of the risk factors. The change trends of the risk indices for the different factors can be seen as follows:  
1. When the total risk index is below 0.35, the risks of the three factors are relatively small, and the current indicator is in the state of normal or slight overload; the overload fire risk is relatively low.
2. When the total risk index is in the 0.35-0.5 range, the factors in this range are often in the state of intermediate risk. For example, for currents in the 15-20 A range, the risk index of the cable heat source risk is \(R_{1}\) = 0.4342. According to the explanation of the current overload degrees in Table 4, the current is in the medium-overload state.
3. When the total risk index is in the 0.5-0.65 range, the factors are often in a high-risk state. For example, for 20-25 A currents, the risk index of the cable heat source is \(R_{1}\) = 0.6128, and when the damage rate of mechanical damage is in the 50-75% range, the risk index of insulation breakdown is \(R_{2}\) = 0.694.
4. When the total risk factor is above 0.65, the basic node states, such as the overcurrent, cable damage, and aging, are close to the edge of fire, the states of all the risk factors are above high risk, and the fire risk of the cable overload is extremely high.  
Based on the above rules of risk evolution, the risk index can be categorized into the following four categories, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Risk Level** & **1** & **2** & **3** & **4** \\ \hline Interpretation & Low risk & Medium risk & High risk & Extremely high risk \\ Index interval & 0–0.35 & 0.35–0.5 & 0.5–0.65 & 0.65–1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Risk classification table.  
Figure 8: Risk evolution.
# A 112 Gb/s DAC-Based Duo-Binary PAM4 Transmitter in 28 nm CMOS
###### Abstract  
To reduce the high bit error rate of serial transceivers under strong channel attenuation, a low-power 112 Gb/s SerDes transmitter was designed using a duo-binary PAM4 modulation technology. By adopting duo-binary PAM4 modulation technology, the problem of the low bandwidth utilization of a high-speed PAM4 (pulse amplitude modulation 4) signal was improved. The problem of high jitter caused by charge sharing and the limited bandwidth of a 4:1 high-speed MUX was improved by using precharging auxiliary transistors. The system power consumption of the transmitter was reduced by using a 7-bit weighted voltage-driven digital-to-analog converter (DAC). The transmitter was designed with a 28 nm CMOS process and powered by a voltage of 0.9 V. The simulation results showed that when the channel attenuation was 20.9 dB, the transmitter could work at 112 Gb/s, the power consumption was 2.02 pJ/bit, and the linearity was 96.7%.  
strong channel; duo-binary PAM4 modulation; transmitter; charge in advance; digital-to-analog converter (DAC) 2022112486. [[https://doi.org/10.3390/electronics11162486](https://doi.org/10.3390/electronics11162486)]([https://doi.org/10.3390/electronics11162486](https://doi.org/10.3390/electronics11162486))
# Helix-Free Ferroelectric Liquid Crystals: Electro Optics and Possible Applications
## 5 Theoretical Model of the Ferroelectric Liquid Crystal Deformation and Director Reorientation
### Ferroelectric Liquid Crystal Director Reorientation
This is defined through the transformation  
\[\psi_{0}=\Phi\big{(}y^{\prime}-Vt^{\prime}\big{)}\exp[i(V/2)(y^{\prime}-(V/2)t ^{\prime})]. \tag{17}\]Then, the function \(\psi\) is  
\[\psi=\sqrt{\frac{2}{\alpha}}\frac{\sqrt{\alpha\Theta^{2}-V^{2}/4}k}{\sqrt{1+k^{2}} }\,sn\left[\frac{\sqrt{\alpha\Theta^{2}-V^{2}/4}y^{\prime}}{\sqrt{1+k^{2}}},k \right]\exp\left(i\frac{V}{2}(y^{\prime}-Vt^{\prime})-i\omega t^{\prime}\right), \tag{18}\]  
where \(\omega=\xi-V^{2}/4\) is the frequency in the system of reference moving with a soliton, and \(\xi\) is the frequency at the fixed (laboratory) system of reference. From Equation (18) at \(k\)\(\rightarrow\)1, we finally obtain a spatially localized solution--the wave of the stationary profile moving with velocity \(V\) along the coordinate \(y\):  
\[\psi=\sqrt{\frac{\alpha\Theta^{2}-V^{2}/4}{\alpha}}th\left[\sqrt{\frac{\alpha \Theta^{2}-V^{2}/4}{2}}(y^{\prime}-Vt^{\prime})\right]\exp\left(i\frac{V}{2}( y^{\prime}-Vt^{\prime})-i\omega t^{\prime}\right). \tag{19}\]  
So, the spatially localized solution of Equation (8) is a two-parameter soliton: the first parameter is the velocity \(V\) of its center motion, and the second one is its eigenfrequency \(\omega\) in the system of reference moving with the soliton. Such a solution is presented in graphical form in Figure 7.  
As seen from Figure 7, the transition to the Maxwellian mechanism of the energy dissipation when the dissipative coefficient is the shear viscosity \(\gamma_{\psi}\) results in the appearance of a soliton, which is a wave packet with a periodic wave localized therein. The maximum speed of soliton motion found from Equations (9) and (19) is  
\[V=2\Theta_{0}\sqrt{\alpha}K/(\gamma_{\psi}d)=(\Theta_{0}/\gamma_{\psi})\sqrt{2K( M+P_{S}E\cos\varphi_{0})}, \tag{20}\]  
and the time of director reorientation due to the movement of the orientation bend (Figure 7b) is  
\[\tau_{C}=\frac{\gamma_{\psi}d^{2}}{K\xi}=\frac{2\gamma_{\psi}}{\Theta_{0}^{2}( P_{S}E\cos\varphi_{0}+M)}. \tag{21}\]  
If \(\varphi_{0}=30^{\circ}\), \(P_{S}=50\) nC/cm\({}^{2}\), \(M=4\times 10^{3}\) erg/cm\({}^{3}\), \(K=5\times 10^{-12}\) N, \(E=3\) V/um, \(\Theta_{0}=23^{\circ}\), and \(\gamma_{\psi}=0.2\) P, then the speed of the soliton center motion is \(V=0.65\) cm/s, and the director reorientation time \(\tau_{C}\) is of about \(\approx\)150 us. Light Modulation in an Electro-Optic Cell with Helix-Free Ferroelectric Liquid Crystal (Experimental Results)
## d Comparison of Different Protocols with Node Mobility  
In Figure 12, the PDRs of different protocols were examined by changing the number of mobile nodes (_nMNodes_) from 10% to 40% with \(K\) fixed to six. It was shown that the PDR of Flooding was not affected by the increase of _nMNodes_. The DPFNI protocol was also considerably reliable against node mobility. However, note that SSMAb also achieved high reliability comparable to DPFNI overall against node mobility, while it consumed low energy, as shown in Figure 10(c). As expected, it is proven that RSBP does not respond well to node mobility.