esa-sceva/satcom-chunk-collection huggingface dataset:
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
###### Abstract  
Multipartite quantum key distribution (QKD) is a promising area of quantum networks that provides unconditional secret keys among multiple parties, enabling only legitimate users to decrypt the encrypted message. However, security proofs of existing multipartite QKD typically assume perfect state preparation devices of legitimate users and neglect the relative rotation of reference frames. These presumptions are, nevertheless, very difficult to meet in practice, and thus the security of current multipartite QKD implementations is not guaranteed. By combining the idea of a loss tolerant technique, introduced by [PERSON] et al. ([PERSON] et al., Phys. Rev. A, 90, 052314, 2014), and the concept of a reference frame-independent protocol, we propose a three-party QKD protocol that considers state preparation flaws and the slow drift of reference frames. Through a numerical simulation, the influence of misaligned reference frames on the protocol's stability was examined by drifting reference frames through angles \(\beta=\pi/5\), \(\beta=\pi/6\) and \(\beta=\pi/7\). In addition, the performance of the proposed protocol was examined for the encoding flaws set at \(\delta=0.35\), \(\delta=0.20\), and \(\delta=0.10\). The results show that the protocol is robust against state preparation flaws, and is insignificantly impacted by misalignment of the reference frames because the achieved transmission distances and secret key rates are comparable to the perfect scenarios. This work dramatically contributes toward the realization of practical and secure multipartite QKD. The proposed protocol has direct applications in quantum communication network environments that involve unknown and slowly varying reference frames, web conferences, and online communications.  
refence frame-independent; three-party; quantum key distribution +
Footnote †: journal: Physics Letters A
## 3 Numerical Model of Viaduct and Foundation Excavation
### Calculation Model  
A large three-dimensional numerical model was built as shown in Figure 6. The total length of deep excavation model of subway station is 304.8 m, the width is 70.5 m, and the maximum excavation depth is 26.42 m. To reduce the influence of boundary size on the calculation results, the relative position of the foundation excavation and the adjacent elevated station is fully considered. The distance between the boundary and the excavation in the calculation model is greater than three times the maximum excavation depth [23]. The final calculation model is 700 m long along the X-axis, 450 m wide along the Y-axis and 100 m deep along the Z-axis.  
For the boundary condition of the model, horizontal constraints were selected on the four lateral side surfaces, vertical constraints were applied to the bottom surface, and the top surface was a free boundary. The soil parameters used in calculation were obtained by field tests and laboratory tests. Soil weight, cohesion, internal friction angle, elastic modulus, Poisson's ratio, and initial pore ratio were all obtained through field tests. The slopes of the critical state line \(M\), normal consolidation line \(\lambda\), and rebound line \(\kappa\) were obtained through triaxial tests and isotropic consolidation tests.
## 4 Multi-Objective Scheduling Optimization Model
### Multi-Objective Scheduling Model
#### 4.1.1 Objective Functions
\[\text{(P3)}\ \text{min}\ F_{3}=\ \left\{\sum\limits_{k=1}^{K}\gamma_{k}\left\{ \sum\limits_{t=1}^{T}\left[g_{WPP,t}+g_{PV,t}-\left(g^{dis}_{ESD,t}-g^{ch}_{ESD,t}\right)-g_{av}\right]^{2}\right\}_{k}\right\}^{1/2}/T \tag{17}\]  
\[g_{av}=\sum\limits_{t=1}^{T}\left[g_{WPP,t}+g_{PV,t}-\left(g^{dis}_{ESD,t}-g^{ ch}_{ESD,t}\right)\right]/T \tag{18}\]  
where \(g_{av}\) is the average value of the MECS power output fluctuations. For the MECS, the uncertainties of WPP and PV will bring a great level of uncertainty in the net power output. Therefore, determining how to measure the power output fluctuations is key to guaranteeing the stable operation of the MECS. In the MECS, ESD could charge the power of WPP and PV in the valley period and discharge their power in the peak period. Therefore, the standard deviation of the total net power output of WPP and PV is taken as the measure indicator. \(g^{dis}_{ESD,t}\) and \(g^{ch}_{ESD,t}\) are the power levels of ESD discharging-charging at time \(t\), respectively. \(F_{3}\) is the standard deviation of the power output fluctuations of the MECS. When \(F_{3}\) is small, the output fluctuations of the WPP and PV are small, which is more favorable for the safe operation of the MECS.
## 2 Compatibility between Indoor Radon Exposure and Buildings' Energy Efficiency Improvement
### Radon Level Measurement Units  
The radon decay speed in the air is measured in becquerels per cubic meter (Bq/m\({}^{3}\)) or picocuries per liter (pCi/L), where 1 pCi/L equals 37 Bq/m\({}^{3}\) and 1 m\({}^{3}\) equals 1000 L:  
* Becquerels per cubic meter corresponds to the International System (SI) of Units for measuring the rate of radioactive decay, being widely adopted in Europe. One becquerel per cubic meter corresponds to 1 nuclear disintegration per second within a cubic meter of air;
* Picocuries per liter is a measure of the rate of radioactive decay of radon. One pCi is one trillionth of a Curie, 0.037 disintegrations per second, or 2.22 disintegrations per minute.  
Depending on the country's national law, acceptable radon levels vary. A generally accepted action level, established by the WHO, is 100 Bq/m\({}^{3}\), or 2.7 pCi/L. If this level cannot be implemented under the prevailing country-specific conditions, then the upper limit should not exceed 300 Bq/m\({}^{3}\), or 8 pCi/L, as required for most European countries. For indoor radon concentrations higher than 300 Bq/m\({}^{3}\), it is advised to take remedial action to lower the radon level [2].  
Required by federal law, the pCi unit is used in the United States of America (USA). According to the US Environmental Protection Agency (EPA), an action level of 4 pCi/L is the standard, above the WHO specified level [10; 11]. A value of 4 pCi/L corresponds to approximately 12,672 radioactive disintegrations in one liter of air for 24 h [12].  
Another important unit is the Sievert (Sv) that represents the radiation exposure dose that the human body receives. The larger the value expressed in sieverts, the larger the effects of radiation to which the human body is exposed [6].
## 3 Challenges
### Security
#### 3.11.1 Physical Layer Attack  
A jamming attack is one type of attack on a physical layer (PHY). In this type of attack, the adversary transmits radio signals on the wireless channels to interfere with CR wireless sensors normal operations. These adversaries may have powerful and sophisticated hardware and software. If the adversary blocks the entire network, it constitutes DoS attack at PHY. Another type of attack on PHY is a tampering attack. In this, the attacker may damage the CR wireless sensors, replace the entire nodes, or part of its hardware.
## 3 Domain Decomposition and Application  
### Domain Decomposition  
The main idea of DD is to decompose a domain into subdomains with small covering and use the obtained solutions on neighbouring contact nodes as boundary conditions for the domain that one is considering [25], as shown in Figure 6.  
For initialisation, the system in each cell is written as  
\[\mathbf{K}_{j}V_{j}=f_{j}, \tag{13}\]  
where \(\mathbf{K}_{j}\) is the matrix \(\mathbf{DCG}\) in the cell \(j\) without adding the contributions of the nodes in contact with neighbouring cells.  
In order to take into account the current between cells, after initialisation, the contact nodes in neighbouring cells are considered in the new system. For cell \(j\), its neighbouring cells are denoted as cell \(n_{1}\), cell \(n_{2}\), cell \(n_{3}\), cell \(n_{4}\), cell \(n_{5}\), and cell \(n_{6}\). The new system in cell \(j\) is written as  
\[\mathbf{K}_{jj}V_{j}+\mathbf{K}_{jn_{1}}V_{n_{1}}+\mathbf{K}_{jn_{2}}V_{n_{2} }+\ldots\mathbf{K}_{jn_{6}}V_{n_{6}}=f_{j}. \tag{14}\]  
where \(\mathbf{K}_{jj}\) and \(\mathbf{K}_{jn_{i}}\) have the same definition as in the Equation (12).  
In DD solving, the system (14) will be solved plenty of times until convergence. The values in neighbouring cells are imposed by the solution obtained in the previous iteration. Therefore, for iteration \(i\), system (14) can be rewritten as  
\[\mathbf{K}_{jj}V_{j}^{i}=f_{j}-(\mathbf{K}_{jn_{1}}V_{n_{1}}^{i-1}+\mathbf{K} _{jn_{2}}V_{n_{2}}^{i-1}+\ldots\mathbf{K}_{jn_{6}}V_{n_{6}}^{i-1}). \tag{15}\]  
If the covering between subdomains increase, DD converges faster [26]; however, the covering argument increases the number of unknowns. For our case, it is difficult to define a larger covering than adjacent nodes due to the random position of fibres in cross-section. In this paper, model order reduction is coupled to domain decomposition to artificially increase the overlapping without a complex algorithm to search fibres, which are in adjacent subdomains. Indeed, the POD allows for projecting the influence of global nodes onto adjacent nodes. In the next part, the coupling between DD and POD is shown.
## 6 Conclusions  
We have performed a brief analysis on the interference scenarios and techniques to mitigate the interference for underlay D2D communications in cellular networks. To mitigate the uplink interference and improve the overall system throughput, we have proposed an optimal resource management and power control scheme with cell sectorization method. We also considered the FFR technique, which allows fractional reuse of resources between the traditional CUs and DPs in a non-orthogonal fashion. In the proposed scheme, the resource reuse factor is analyzed by considering a binary random variable. Then, we formulated an optimization problem for multiple DPs simultaneously sharing the same cellular resources to fulfill the required QoS of both CUs and DPs. We analyzed the proposed scheme in two steps. We first solved a greedy heuristic algorithm with a local search scenario to overcome the complexity of the resource reuse pairing phenomenon. Then, we proposed a binary power control scheme to maximize the system capacity for large scale networks. We conducted extensive simulations with different parameters such as SINR, system capacity, spectral efficiency, and transmit power of DPs. The results indicated that our proposed technique achieves the highest capacity and spectral efficiency, and reduces the co-channel interference generated by the integration of D2D communications. As future work, our proposed method can be extended by considering the downlink resource reuse scenario for D2D communications.  
The main concept to solve the problem was developed by [PERSON]. Moreover, the mathematical analysis and performance evaluation was performed by [PERSON]. Overall concept of the paper was guided and approved by S.S.  
This study was supported by research funds from Chosun University, 2018.  
The authors declare no conflict of interest.
## 3 Design and Development of the Measuring System
### System Calibration Platform  
A calibration platform has been designed and manufactured to obtain the distance between the two sensors of each group. The platform shown in Figure 9 is made up of two standard metal plates. They are connected by long bolts. The spacing and parallelism between the fixed panels could be adjusted by the nuts and finally fixed. The plates are thick enough and have high structural stiffness. Accurate abrasive machining has been used to grind the working surfaces of the plates, resulting in a flatness of no more than 5 \(\upmu\)m. As with the measuring principle, for each group, the distance between two sensors is the difference value between the spacing of two plates and the measured displacements of the two sensors.  
It should be noted that the probe itself must have been calibrated on the same material of the measured surface.  
Figure 8: Photographs of the sensor array component (a) and the integrated controlling component (b).  
Figure 9: The calibration platform of the measuring system.
###### Abstract  
This paper presents a novel control algorithm for optimising operational costs of a combined domestic micro-CHP (combined heat and power), battery and heat storage system. Using a minute by minute basic time-step, this work proposes a simple and computationally efficient rule based whole-system management, developed from empirical study of realistic simulated domestic electricity and heat loads. The CHP availability is considered in two binary states which, together with leveraging storage effectively, maximises CHP efficiency, and gives the algorithm increased real world feasibility. In addition, a novel application of a dual battery system is proposed to support the micro-CHP with each battery supplying just one of the distinctive morning and evening electrical load peaks, and thus inherently improving overall battery system lifetime. A case study is presented where the algorithm is shown to yield approximately 23% energy cost savings above the base case, almost 3% higher savings than that of the closest previous work, and 96.8% of the theoretical minimum cost. In general, the algorithm is shown to always yield better than 88% of the theoretical minimum cost, a ratio that will be considerably higher when real-world CHP limitations are factored into the theoretical minimum calculation.  
multi-energy system; system optimisation; dual battery; binary CHP control; rule based control; domestic building +
Footnote †: journal: Applied Sciences
## 3 Numerical Example
The displacement sensitivity method [39] is a damage assessment method to acquire damage extent through static displacement sensitivity analysis. The main formulas of the static sensitivity method are briefly reviewed as follows. From Equations (1)-(3), one has the following formulas:  
\[u=K^{-1}\cdot l \tag{26}\]  
\[u_{d}=K_{d}^{-1}\cdot l=(K-\Delta K)^{-1}\cdot l \tag{27}\]  
where the superscript \(-1\) denotes the matrix inversion. Subtracting Equation (26) from (27) yields the following:  
\[\Delta u=u_{d}-u=\left[\left(K-\Delta K\right)^{-1}-K^{-1}\right]\cdot l \tag{28}\]  
where \(\Delta u\) is the displacement change before and after damage. Using Neumann series expansion, Equation (28) can be approximated as the following:  
\[\Delta u=K^{-1}\cdot\Delta K\cdot K^{-1}\cdot l \tag{29}\]  
Substituting Equation (10) into (29) yields the following:  
Figure 4.0  
Figure 4.1  
where \(s_{j}\) is the \(j\) th elementary displacement sensitivity. Equation (30) can be rewritten as the following:  
Figure 4.2  
Figure 4.3  
From Equation (32), the damage parameters can be calculated with the following:  
Figure 4.4  
where the superscript + denotes the Moore-Penrose inverse of a matrix. Using the above displacement sensitivity method, the calculation results for the three damage cases of the numerical example are presented in Figure 4.5  
From Table 3, one can see that the calculation results obtained by the proposed method are more accurate than those of the displacement sensitivity method. As stated before, the dynamic method is also often used to detect the damage in cables. From the existing literature, the static method usually has higher detection accuracy compared with the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Cases** & **True Values** & \
\begin{tabular}{c} **Calculation Results of the** \\ **Displacement Sensitivity Method** \\ \end{tabular} &
\begin{tabular}{c} **Calculation Results of the** \\ **Proposed Method** \\ \end{tabular} \\ \hline
1 & \(\alpha_{31}=0.15\) & \(\alpha_{31}=0.1518\) & \(\alpha_{31}=0.15\) \\ \hline
2 & \(\alpha_{15}=0.15\) & \(\alpha_{15}=0.1518\) & \(\alpha_{15}=0.15\) \\ \hline
3 & \(\alpha_{10}=0.2\) and \(\alpha_{30}=0.2\) & \(\alpha_{10}=0.2033\) and \(\alpha_{30}=0.2023\) & \(\alpha_{10}=0.2\) and \(\alpha_{30}=0.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of the calculation results obtained by both the displacement sensitivity method and the proposed method. dynamic method, but the operation process is more complex and requires a longer time. Note that the proposed method is applied for the first time to cable damage identification by this numerical example. In future studies, it is necessary to carry out a comparative study between the proposed method and the dynamic method for cable damage detection.
## 2 Optimization Methods and Design Procedure
### Principle of Particle Swarm Optimization Algorithm
#### 2.2.3 Optimization Procedure  
After calculating the initial state of cable force by traditional methods, the optimal design process of cable-stayed cable by SOM is as follows:  
1. The main program of the M-MOPSO algorithm in MATLAB is established and control parameters of particle swarm are input.
2. The influence matrix is solved and built into the main algorithm program.
3. The initial cable forces and constraints obtained by conventional methods initialize the particle swarm and update the corresponding structural response using the built-in influence matrix. The fitness value of the target function is calculated, and the particles are evaluated accordingly. The excellent particles are temporarily stored in a nondominant solution set.
4. The velocity of particles is updated based on their evaluation. Variation is also performed from the particles selected in the proportions. The structural response, adaptive function, and particle evaluation are updated again using the built-in influence matrix.
5. We judge whether the conditions for termination have been met and, if so, we terminated. Otherwise, we return to renew particle location and velocity to produce the next generation of population.
6. Steps (4) and (5) are repeated until the Pareto front solution set of the cable force is obtained.  
The optimization flow chart shown in Figure 5 summarizes all the above optimization steps.  
Figure 4: Comparison of Pareto front solution sets for the test functions.
# Improving Vulnerability Management Through Process Mining
## 4 An Exemplary Use Case
### Problem Definition and Goals  
The company has an existing vulnerability management process. The current process is highly time-sensitive since every open vulnerability is a risk to the company. The process should therefore be as fast as possible while making sure that all vulnerabilities can be handled effectively.  
The process is supported by the tool ServiceNow. This tool provides some basic KPIs about the process, such as the number of open vulnerabilities per priority or the average throughput time of vulnerabilities. Using this tool, an overview is given of how well the process is running. It does not, however, provide a way to analyze the process in more detail. While a KPI shows that the process is not running as it should be, there is currently no straightforward way to find the reason for that and how to improve it. Furthermore, each new analysis that is of interest to the security team needs to be individually developed by the ServiceNow team. This greatly hinders quick analysis of the process when a new question emerges.  
The goal of the project is to help the security team fix these problems by using process mining. Firstly, it should enable the security team to deep dive into the vulnerability management process and improve it. Secondly, it should enable the security team to analyze the process in a flexible way whenever necessary. While it is part of the project to find some process improvements to figure out whether process mining is a suitable tool, it is not part of the project to deep dive into every finding and redesign the entire vulnerability management process. This will be an ongoing task of the security team once the process mining pipeline has been set up.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.4 Data Redundancy  
OR intentionally permits data redundancy so as to make the data transmission reliable and robust. However, many uncontrolled redundant data packets, due to many uncontrolled forwarder nodes, causes a network to become congested, which leads to energy waste. In order to solve this issue, it is required to keep a good degree of data redundancy. Since the redundancy is affected by the forwarder set, which may be affected by the scheduling of the wakeup times of ADC, it is going to be interesting to investigate the effect of ADC on controlling the degree of data redundancy. For example, the references [11; 12; 13; 14; 15; 16; 17] have used sensor node ADCs in order to reduce the number of redundant packets.
## 4 Discussion and Analysis
### Performances of Friction Pairs with Single Texture Patterns
#### 4.2.2 Surface Temperature of Friction Pairs  
Figure 7 shows the temperature changes of the friction pairs under 250 rpm. The surface temperatures of friction pairs and temperature rises are listed in Table 6 for 2-B1, 2-B2, and 2-B3 groups under 200 rpm and 200 N. The comparison of the temperature variation trends of the triangular, elliptical, and conventional spiral grooves indicated that the temperature rises of the 2-B1, 2-B2, and 2-B3 groups were 4.1, 12.3, and 15.3\({}^{\circ}\)C, respectively. In the initial testing stage, the temperature trend of the 2-B1, 2-B2, and 2-B3 groups were similar. However, as the test progressed, the surface temperature of the specimen in the 2-B1 group tended to reach equilibrium first, and it reached a maximum temperature of only 27.7 \({}^{\circ}\)C. Meanwhile, the surface temperature of the 2-B2 and 2-B3 groups increased slowly, which indicates that the higher frictional heat per unit of time was produced by the 2-B2 and 2-B3 groups and that the temperature dynamic equilibrium was delayed. Comparing Figure 5 with Figure 7, the temperature rise of the 2-B1 group  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load** & **Rotating Speed** & **Friction Coefficient** & **2-B1** & **2-B2** & **2-B3** \\ \hline \multirow{6}{*}{200 N} & \multirow{6}{*}{250 rpm} & Real time & 0.044–0.082 & 0.107–0.146 & 0.07–0.1 \\  & & Average & 0.060 & 0.129 & 0.081 \\ \cline{1-1}  & & Surface temperature & 2-B1 & 2-B2 & 2-B3 \\ \cline{1-1}  & & Real time & 23.5–27.7 \({}^{\circ}\)C & 24.6–37 \({}^{\circ}\)C & 24.1–39.4 \({}^{\circ}\)C \\ \cline{1-1}  & & Rise & 4.1 \({}^{\circ}\)C & 12.3 \({}^{\circ}\)C & 15.3 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data of friction coefficient and surface temperature of friction pairs for 2-B1, 2-B2, and 2-B3 groups.  
Figure 6: Real-time friction coefficient of friction pairs with single texture patterns under 250 rpm.  
(4.1 \({}^{\circ}\)C) was less than that of the 1-A1 group (12.3 \({}^{\circ}\)C), and the temperature rise of the 2-B3 group (15.3 \({}^{\circ}\)C) was higher than that of the 1-A1 group. Therefore, through the changes of the surface temperature of the friction pairs, it was determined that the friction heat can be affected by the surface texture. The temperature rise of the friction pairs could be well controlled and improved by the triangular texture.