esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Energy Management of Electrical Power Systems
## 1 Introduction  
Energy transition encourages the development of renewable energy sources. However, the growth of decentralized energy production reveals an increasing complexity for power grid managers, implying more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The smart grid concept, defined as an electricity supply network that uses communications technology to detect and react to local power changes, represents the solution to such technical problems. To build key smart grid blocks and to overcome these problems, decentralized renewable energy production would allow intelligent energy management, local regulation including ancillary services as well as self-consumption, and therefore, fewer constraints for the public grid. Hence, the concept of multi-source energy systems, i.e., microgrids based on renewable energy sources, storage, controllable loads, and public grid connection, aims to actively participate in the smart grid implementation balance of electricity production and consumption. Microgrids are reliable and effective options for increasing the penetration of renewable energy while minimizing the cost of energy and the negative impact on the public grid.  
Faced with the emergence of the smart grid and the imperative of implementing of intelligent energy management microgrids must be able to optimally manage power flows, take into account energy strategies, and imply scheduling and design optimization. On the other hand, microgridsloads, in particular buildings as well as charging stations for electric vehicles (EVs), require a specific intelligent energy management controller.  
This Special Issue presents the latest research on relevant topics and addresses challenging issues with the integration of intelligent energy management systems. There were 27 papers submitted to this Special Issue, and 13 papers were accepted, i.e., 48% acceptance rate. Various topics in intelligent energy management of electrical power systems for a sustainable and resilient power system have been addressed. The contributed articles belong to three broad groups: (i) multi-source systems optimization: scheduling, design, and system; (ii) power management of multi-source systems: applications for buildings and EVs charging station; (iii) required electric power intelligent components.
## 3 Results and Discussion
### Analysis of Flow and Heat Transfer Characteristics Under Sloshing Conditions
#### 3.4.2 Effect of Different Sloshing Period
In contrast, at periods close to the natural period (8 s, 8.6 s, and 10 s), the temperature gradient is significantly reduced, with a difference of only about 0.1 K. This indicates that heat exchange is more intense near the natural period due to enhanced fluid motion and interfacial mixing. Specifically, at 8.6 s, the thermal stratification at the gas-liquid interface is more pronounced, and the temperature near the tank wall is relatively higher. However, near the natural period, the temperature near the wall is lower, suggesting that sloshing near the natural period promotes more efficient heat exchange.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation
#### 5.1.1 Throughput Performance Analysis  
Figure 10 shows the throughput for both sinkhole and framework scenarios. The y-axis shows the throughput in megabits/s (Mbps) and x-axis shows the time in milliseconds (ms). In the sinkhole scenario, there was a 2000 ms lag time at which the application starts. The next 14,000 ms showed a positive hike up to 0.74 Mbps in throughput followed by a steady increase until the end of the simulation, unlike the framework scenario which grew with time. The highest network throughput achieved by the framework was 0.77 Mbps at 68,806.37 ms of simulation time. Although the framework scenario's throughput grew with time, it was not by a big margin, nevertheless it was better than that of the malicious scenario from 2000 ms moving forward.
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 2 The Beam-Like Model
### Eigen-Problem of the Non-Uniform Beam-Like Model  
As anticipated in Section 2.2, the stiffness matrix terms given by Equation (10) are affected, in view of Equation (4), by the three correction coefficients \(k_{x},k_{y},k_{c}\) that are evaluated by taking into account the modal properties, such as frequencies and modal displacements. In this view, the modes of vibration in the generalized space, denoted as \(\hat{\mathbf{\psi}}\), and the vibration frequencies \(\omega\) of the equivalent beam are obtained solving the following standard eigen-problem:  
\[\big{[}\mathbf{K}-\omega^{2}\mathbf{M}\big{]}\hat{\mathbf{\psi}}=0, \tag{15}\]  
Consequently, the modal shape in the geometric space can be obtained by means of Equation (6), where the \(N\) displacement shape functions of a uniform beam, of the type given in Equation (5), are considered.  
Therefore, since the solution of the multi-stepped beam is approximated with \(N\) shape functions, the mass and stiffness matrices have a \((3\times N)\times(3\times N)\) dimension and the solution of the eigen-problem, represented by Equation (15), is a \((3\times N)\times 1\) vector of natural frequencies and a \((3\times N)\times(3\times N)\) matrix of eigen-vectors. Figure 3 depicts the eigen-vector matrix in the generalized space and the components of the generic \(j\)-th eigen-vector.  
Consequently, the \(j\)-th modal shape in the geometric space of the equivalent non uniform multi-stepped beam, denoted as vector function \(\boldsymbol{\varphi}_{j}(\zeta)\), is obtained, in view of Equation (6), by multiplying each displacement shape function of the uniform beam \(\psi_{i}(\zeta)\), \(i=1,2,\ldots,N\), considered for the discretization, times the corresponding eigen-vector components.  
\[\begin{split}\varphi_{u,j}(\zeta)&=\psi_{1}(\zeta) \hat{\mathbf{\psi}}_{1j_{x}}+\psi_{2}(\zeta)\hat{\mathbf{\psi}}_{2j_{x}}+\, \ldots\,+\,\psi_{N}(\zeta)\hat{\mathbf{\psi}}_{Nj_{x}}\\ \varphi_{v,j}(\zeta)&=\psi_{1}(\zeta)\hat{\mathbf{ \psi}}_{1j_{y}}+\psi_{2}(\zeta)\hat{\mathbf{\psi}}_{2j_{y}}+\,\ldots\,+\,\psi_ {N}(\zeta)\hat{\mathbf{\psi}}_{Nj_{y}}\\ \varphi_{g,j}(\zeta)&=\psi_{1}(\zeta)\hat{\mathbf{ \psi}}_{1j_{g}}+\psi_{2}(\zeta)\hat{\mathbf{\psi}}_{2j_{g}}+\,\ldots\,+\,\psi_ {N}(\zeta)\hat{\mathbf{\psi}}_{Nj_{g}}\end{split}, \tag{16}\]  
Equation (16) provides the three spatial components in the actual geometric space (Figure 2) of the \(j\)-th modal shape to be adopted to perform a suitable dynamic analysis of the equivalent non-uniform beam by means of a suitable modal superposition procedure, as shown later.
## 1 Introduction
The applicability of the proposed methodology is demonstrated in two particular scenarios, an ICT gateway used in smart grid infrastructures and an Artificial Intelligence (AI) Investments platform that highlights the methodology's relevance in assessing security for critical infrastructure components in different environments. The contributions of this paper are threefold:  
* A flexible and scalable security evaluation framework that integrates risk assessment with test-based evaluation, allowing for continuous monitoring, mitigation, and recertification of ICT systems. * A dynamic security labeling system, which provides transparent and real-time security status indicators, making it easier for both experts and non-experts to assess and compare the security levels of different products or systems. * Validation of the methodology through two case studies, demonstrating its adaptability across different types of ICT systems, including both hardware-centric infrastructures and AI-driven platforms.
## 4 Interleaver Communication System Analysis in the Presence of Fading
### N-Correlator Receiver  
Following the procedure explained in Section 2.2 for fading coefficient \(\alpha\
eq 1\), the output of the \(n\)-th correlator can be expressed as  
\[w_{n}=\sum_{i=1}^{2\beta}z_{i}c_{in}=\sqrt{E_{c}}\sum_{i=1}^{2\beta}\alpha_{i} c_{i1}c_{in}+\sqrt{E_{N}}\sum_{i=1}^{2\beta}n_{i}c_{in}, \tag{47}\]  
which is a realization of a random variable having zero mean and variance  
\[\sigma_{wn}^{2}=E_{c}E\{c_{i1}^{2}\}E\{c_{in}^{2}\}\sum_{i=1}^{2\beta}\alpha_ {i}^{2}\ +2\beta E_{N}\sigma_{ni}^{2}E\{c_{in}^{2}\}. \tag{48}\]  
In the case of binary chips the variance is  
\[\sigma_{wn}^{2}=\sum_{i=1}^{2\beta}\alpha_{i}^{2}\ E_{c}+2\beta E_{N}. \tag{49}\]  
Following the procedure that was presented in Section 3.2, the probability of bit error can be expressed as  
\[\begin{split} p_{e}(\alpha\ )&=\frac{N/2}{N-1}\frac{1}{ \sqrt{2\pi\sigma_{wn}^{2}}}\sum_{i=1}^{N-1}\binom{N-1}{i}\,(-1)^{i+1}\int_{- \infty}^{\infty}[\frac{1}{2}\,erfc\,\frac{w_{1}}{\sqrt{2\sigma_{wn}^{2}}}]^{ t}\\ &\qquad\qquad\cdot e^{(-w_{1}^{2}/2\sigma_{wn}^{2})}dw_{1}\end{split} \tag{50}\]  
In this expression, the probability of bit error depends on the sum of fading coefficient and their squared values. Therefore, the probability of error needs to be calculated as the mean value of this random function.
## 3 The Jointed Rock Slope Models
### Establishment of Open-Pit Slope  
The east slope is characterized by biotite granulite. The particular flow slope model of the slope profile has been established, as depicted in Figure 5. The slope model is established between 0\({}^{\circ}\) to 90\({}^{\circ}\) based on two structural plane models. The inclination angle of the structural plane will be slightly random to complete the establishment of the jointed characteristic rock slope model. Table 4 shows some model grain data.  
Perform a probability and statistics analysis based on on-site observation of the slope structural plane and mapping results. The significant parameters of the linear RDFN model are determined to be the following: an area length of 10, a joint density of 0.5, and single group inclination angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). A digital image model of the rough RDFN slope is established and simulated in PFC2D, displayed in Figure 6i(a). Furthermore, it is transformed into derivative structural planes, with the same spectrum but varying shapes, using the Fourier transform principle in the MATLAB code. The crucial parameters of the Fourier transform are as follows: an amplitude height of 0.02 m, a frequency of 2, a Fourier series of 20, a trapezoidal wave value of 0.2, a region length of 10 m, a joint density of 0.5 traces/m, and single dip angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). A digital image model of the linear RDFN slope is established and presented in Figure 6 ii(b). The model was horizontally and vertically servo-loaded, and the particles were fully contacted through 2000-2500 cycles of loading. Last, we generated a mechanical model of the open-pit slope rock mass that considers its rough shape.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Numerical Models**} & \multirow{2}{*}{**Dip Angle**} & **Total** & **Number of** & **Number of** & **Number of** & **Joint** \\  & & **Number of** & **Rock** & **Joint** & **Element** \\  & & **Elements** & **Elements** & **Elements** & **Proportion** \\ \hline Rough joint & 30\({}^{\circ}\) & 9147 & 7978 & 1169 & 12.7\% \\ Linear joint & 30\({}^{\circ}\) & 9109 & 7924 & 1185 & 13.0\% \\ Rough joint & 60\({}^{\circ}\) & 9703 & 8735 & 968 & 9.9\% \\ Linear joint & 60\({}^{\circ}\) & 9554 & 8578 & 976 & 10.0\% \\ \hline \end{tabular}
\end{table}
Table 4: Particle data of different PFC particle models.  
Figure 5: The slope model for PFC simulation.
## 2 Principle and Structure of the In-Situ Simulation Device for Deep Coring Tools
### Principle of Temperature and Pressure Application  
(1) Analysis of the stress state of underground rock  
In the deep earth environment, rocks are mainly subjected to multidirectional compressive stress. In the actual drilling and coring process, the pressure on bottom-hole rock in a high-temperature environment is due to four compressive stresses: overburden pressure, lithostatic pressure, pore pressure, and drilling fluid column pressure. The stress state of rock is illustrated in Figure 2[31].  
The overburden pressure is caused by the vertical downward pressure exerted by rocks in the upper strata. Lithostatic pressure is the lateral pressure exerted by rocks in the surrounding strata. Due to the presence of pores in the rock, the fluid in the pores provides pore pressure. Fluid column pressure is caused by the unavoidable use of drilling fluid during drilling, which exerts pressure on the rock.  
(2) Temperature and pressure application method  
The principle for applying temperature and pressure is shown in Figure 3. To apply overburden pressure on rock samples, a pressure oil cylinder is added in the lower part of the rock cabin, the pressurized hydraulic oil enters the lower end face of the piston, and the piston applies the overburden pressure on the upper face of the rock sample. The overburden pressure can be adjusted by adjusting the oil pressure on the lower end of the piston, and finally, an overburden pressure of 140 MPa is applied to the rock sample.  
Figure 2: Stress state of rock at the bottom of the hole.  
A special rubber sleeve is used to wrap the rock sample, and the outer cylindrical surface of the rock sample and the inner wall of the rock cabin form a closed pressurized space. Then, the high-pressure pump forces high-temperature and high-pressure water into the space. The rock sample rubber sleeve applies pressure perpendicular to the sample surface, which provides lithostatic pressure on the rock sample. To apply pore pressure, the high-temperature and high-pressure water added by the high-pressure pump is injected into a hole through a pore in the middle of the upper cover of the piston, flows to the porous bottom plate, and then penetrates upward from the bottom of the rock sample. The drilling fluid column pressure is added by regulating the back pressure with a throttle valve installed in a high-pressure line in the return hole of the first drill pipe cabin.  
The outer layer of the rock sample chamber is wrapped with a layer of heating material to apply heat to the rock sample. Although the pressurized oil in the bottom oil cylinder is at normal temperature, the lithostatic pressure, pore pressure, and drilling fluid column pressure are established with a high-temperature and high-pressure water with a temperature of 150 \({}^{\circ}\)C, which also applies heat to the whole test cabin. Additionally, the use of clear water as a heating and pressurizing medium reflects the actual drilling process.
# An Experimental and Numerical Study on Reduction of Generated Axial Force
## 5 Results and Discussion
### GAF Reduction
#### 5.2.2 GAF Measurement and Comparison  
Table 2 shows the experimental conditions for the GAF measurement and Figure 13 shows the estimation and measurement results according to the contact angle. As seen in Figure 12a, GAF is more sensitive to the contact angle than the GAF measurement.  
Figure 11: Estimated GAF with respect to design parameters.  
Figure 12: GAF changes with respect to (**a**) track curvature (**b**) contact angle.  
from the estimation results in Figure 12a, contact angle variation does not make noticeable changes to GAF. The measurement and the estimation have the same trend with the change of contact angle in that GAF slightly decreases as contact angle increases. The measured GAF change is from 36.52 to 38.16 N whereas the estimated GAF change is from 37.13 to 37.50 N. The minimum and maximum differences between the measurement and the estimation (measurement-estimation) are \(-0.62\) and \(0.67\) N, respectively. From the verification results with respect to contact angle, it is obvious that contact angle does not have a large influence on GAF.  
Figure 14 shows the estimation and measurement results according to track curvature. Like the estimation, track curvature is controlled from \(-20\) to \(20\%\) with \(10\%\) increments because track curvature has a large influence on normal pressure. As shown in Figure 14, the measurement and the estimation show the same trend with the change of track curvature in that both results increase with the track curvature. The measured GAF change is from 34.56 to 39.56 N whereas the estimated GAF change is from 33.97 to 37.62 N. The minimum and maximum differences between the measurement and the estimation (measurement-estimation) are \(0\) and \(1.94\) N, respectively. The verification results show that track curvature is a key factor for the GAF.  
Based on the results, the optimized design parameter that causes the maximum GAF reduction is \(+20\%\) contact angle and \(-20\%\) track curvature. Figure 15 shows the GAF comparison results between the existing and optimized design parameters. According to  
\begin{table}
\begin{tabular}{c c} \hline \hline Torque (Nm) & 400 \\ Rotational speed (rpm) & 200 \\ Articulation angle (deg) & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GAF verification conditions.  
Figure 14: GAF verification results with respect to track curvature.  
Figure 13: GAF verification results with respect to contact angle.  
the measurement results, the GAF with the optimized design parameter is 34.05 N whereas it is 37.77 N with the existing parameter. The GAF reduction is 3.72 N, which is \(-9.85\%\).
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 4 Analysis
### Analysis of Throughput Efficiency  
Next, we discuss the throughput efficiency which is defined as the ratio of the average number of information digits successfully accepted by the receiver per unit of time to the total number of digits that could be transmitted per unit of time. Suppose the simple case of the communication protocol, and the sender continuously sends codewords to the receiver and only resends those negatively acknowledged codewords. First, three probabilities are defined: \(P_{c},P_{d}\), and \(P_{e}\), where \(P_{c}+P_{d}+P_{e}=1\). \(P_{c}\) is the probability of receiving no error message, \(P_{d}\) is the probability of receiving detectable error pattern, and \(P_{e}\) is the probability of receiving undetectable error pattern. Then, the probability of receiving vector being accepted by the receiver is \(P=P_{c}+P_{e}\).  
The average number of retransmissions (including original transmissions) for a codeword to be successfully received by the receiver is  
\[t_{AV}=1\cdot P+2\cdot P\cdot(1-P)+\cdots+l\cdot P\cdot(1-P)^{l-1}+\cdots= \frac{1}{P} \tag{17}\]  
Finally, the throughput of sending n codewords successfully is \(T=\frac{n}{t_{AV}}=nP\). Thus, the throughput efficiency depends on the channel error rate only.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 1 Introduction
A new detour-aware mobile sink tracking method via analysis of movement angle changes is proposed, which is beneficial for establishing energy efficient data transition path from a RA to a mobile sink without detours, and decreasing overhead for normal nodes and the mobile sink. 2. A routing algorithm based on route deviation angles is designed to construct a shortest path between two unconnected adjacent useful footprints, which can promote the data transition speed and further reduce the overhead. Moreover, in DAMST, it is not necessary for normal sensor nodes to be equipped with GPS devices to track mobile sinks, and a novel packet format is defined for the mobile sinks to conveniently define data type and data collection scope. The performance of DAMST on different trajectory patterns is simulated and explored. The simulation results verify the significant advantages of DAMST in obviously reducing the network energy consumption and overhead. The rest of this paper is organized as follows: In Section 2, related works are introduced. Design details of DAMST are presented in Section 3. The performance of DAMST is analyzed and investigated in Sections 4 and 5 concludes our work.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 3 Analysis of Outage Probability and Throughput of NOMA-V2X System
### Outage Probability of \(D_{1}\)  
The successful communication through \(\mathrm{D}_{2}\rightarrow\mathrm{RSU}\rightarrow\mathrm{D}_{1}\) link is possible only if the following conditions are all met: i) RSU decodes \(x_{1}\) correctly, ii) RSU decodes \(x_{2}\) correctly by SIC, iii) \(\mathrm{D}_{1}\) decodes \(x_{1}\) correctly, and iv) \(\mathrm{D}_{1}\) decodes \(x_{2}\) correctly by SIC. Based on the category of SIC in NOMA, i.e., pSIC and ipSIC, different events of outage for \(\mathrm{D}_{1}\) can occur. Thus, the outage probability for detecting \(x_{1}\) at the vehicle \(\mathrm{D}_{1}\) can be expressed in two distinct forms depending on which SIC scheme is used. In particular, ipSIC is considered as a main reason for degraded performance in term of outage probability.  
First, we consider a worse case resulting in higher outage probability, in which the first vehicle \(\mathrm{D}_{1}\) utilizes ipSIC. This corresponds to the case of \(\varpi=1\), and the outage probability of \(\mathrm{D}_{1}\), denoted by \(\mathcal{P}_{1}^{ipSIC}\), is defined and expanded as  
\[\begin{split}\mathcal{P}_{1}^{ipSIC}&=1-\Pr\left\{ \gamma_{\mathcal{D}_{1}\to R}>\gamma_{1},\gamma_{\mathcal{D}_{2}\to R }>\gamma_{2},\gamma_{\mathcal{D}_{1},x_{1}}>\gamma_{1},\gamma_{\mathcal{D}_{1},x_{2}}>\gamma_{2}\right\}\\ &=1-\Pr\left\{\left|{{{h_{{n^{\prime}}}}}_{1}}\right|^{2}>\delta \left({{{\left  
whose detailed derivation is provided in Appendix B.  
Second, we consider an ideal case resulting in a lower outage probability, in which the receiver of \(\mathrm{D}_{1}\) utilizes pSIC. This corresponds to \(\varpi=0\), and the outage probability of the first vehicle, denoted by \(\mathcal{P}_{1}^{PSIC}\), is defined and expanded as  
\[\begin{split}\mathcal{P}_{1}^{PSIC}&=1-\Pr\left\{ \gamma_{D_{1}\to R}>\gamma_{1},\gamma_{D_{2}\to R}>\gamma_{2},\gamma_{D_{1}x_{ 1}}>\gamma_{1},\gamma_{D_{1}x_{2}}>\gamma_{2}\right\}\\ &=1-\Pr\left\{\gamma_{D_{1}\to R}>\gamma_{1},\gamma_{D_{2}\to R}> \gamma_{2}\right\}\times\Pr\left\{\gamma_{D_{1}x_{1}}>\gamma_{1},\gamma_{D_{1}x _{2}}>\gamma_{2}\right\}\\ &=1-\Pr\left\{\left|h_{n^{*},1}\right|^{2}>\delta\left(\left|h_{ k^{*},2}\right|^{2}\rho+1\right),\left|h_{k^{*},2}\right|^{2}>\delta\right\} \times\Pr\left\{\left|g_{1,n^{*}}\right|^{2}>\Lambda\right\},\end{split} \tag{16}\]  
where \(\Lambda=\max\left(\delta X_{\ell}\tilde{\phi}\right)\). We further expand (16) as  
(17)  
whose detailed derivation is given in Appendix C.  
**Remark 1**.: _Based on above analytical results of outage performance at each vehicle, it is inferred that the system performance depends on what kind of SIC (i.e., ipSIC or pSIC) is used at the receiver. The higher outage probability is obtained for V2X-NOMA with ipSIC due to the existence of residual interference in SINR._