esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G V2X Testbeds
#### 3.2.1 5 GVINNI Munich Experimentation Facility Site  
The 5 GVINNI project [40] includes the 5 GVINNI Munich testbed as the facility's design solution. The testbed supports the eHealth use case named \"Remote interventional support for emergency care application--mobile ultrasound\". The testbed infrastructure supports MEC functionalities, 5G RAN, and the 5G core of Huawei. The SDN component is based on the floodlight controller, and Huawei deploys the MANO and NFVI. For the NFVI deployment, Docker container and Mininet are used. Mininet enables SDN capabilities that allow NFs inter-connectivity running on Docker through OpenvSwitch. The 5G site is connected to the core network using the Ethernet link for a high data rate. An ambulance is the UE where the 5G modem is mounted, which moves within the coverage area. The MEC functionalities are not fully deployed; Docker is used for the deployment of the MEC services and applications. The MANO includes an NFVO that enables VNF instantiation and termination. The VNFM compiles a software module to initiate and manage the UPF Docker container [41].
## 4 Materials and Methods
### Simulated Annealing Approach for the Optimal Location of the New Fire Station
#### 4.2.2 Iterative Optimisation Process  
The iterative optimisation process is shown in Figure 2.  
The process begins by establishing the initial value of the parameter \(T\) of the simulated annealing method (step 1 in Figure 2). This is the highest value of the cost function for a number of previously calculated alternatives and is modified by 10% every ten interactions [44].  
The process then continues with the generation of alternative \(V_{0}\) in the first iteration (step 2 in Figure 2). The locations of the new \(TotNFS\) FS are randomly selected among all the municipalities except the ones with old stations. The set of the locations of the new FS is \(NS_{0}\), so \(V_{0}=[OS\bigcup NS_{0}]\); this solution must fulfil the restrictions described in Section 4.2.1.  
For the generation of the rest of alternatives \(V_{i}\) (step 2 in Figure 2), ten intermediate neighbouring alternatives, \(V_{i,j}\), are obtained, while only one location (municipality) changes with respect to \(V_{i-1}\), considering that old fire stations will not be replaced. This location is chosen randomly from among the closest municipalities of alternative \(V_{i-1}\), except the municipalities of the old fire stations. This new municipality will substitute the closest new one in \(V_{i-1}\).  
Figure 2: Optimisation process chart.  
The cost function is calculated for each alternative (step 3 in Figure 2). First, it is determined in which incidents the resources of the new FS are going to intervene. If the arrival time of the new vehicle to an incident is less than the arrival time of any vehicle assigned to the incident, this vehicle will replace the old one.  
Next, the variables of the cost function \(\overline{CT_{ji}}\) and, \(CT_{ji}\) are updated, a new collapse index map is obtained, and the restrictions are checked.  
In the next step (step 4 in Figure 2), the cost functions of alternatives \(V_{i,j}\) and \(V_{i-1}\) are compared according to the simulated annealing method.  
If \(E(V_{i,j})\leq E(V_{i-1})\), alternative \(V_{i,j}\) is better than \(V_{i-1}\); this alternative is not discarded, and the information is saved.  
If this is not the case, and \(E(V_{i,j})>E(V_{i-1})\), a random number \(q\) with \(q\in[0;\,1]\) is generated; if \(P(\Delta E)=exp\left(-\frac{E(V(i-1))-E\left(V_{i,j}\right)}{T}\right)>q\), then this new alternative is not discarded and data are saved, while in the other case it is discarded.  
The new alternative \(V_{i}\) is the one with the lowest cost function value among these \(V_{i,j}\) and with a cost function value less than \(E(V_{i-1})\), otherwise the alternative that fulfils the probability condition is the next alternative \(V_{i}\).  
The process ends when the cost function becomes stable (step 5 in Figure 2).
# Study on Noise Model of an Automotive Axial Fan Based on Aerodynamic Load Force
## 3 Noise Model of Automotive Axial Fan
### Time-Domain Solution of Fan Noise Model
#### 3.2.1 Coordinate Conversion  
Assuming that the position of a point source on the blade surface in the blade coordinate system is \(\xi=\left(a^{\prime},\ b^{\prime},\ c^{\prime}\right)^{\mathrm{T}}\), the position of the point source in the fixed coordinate system can be described by the coordinate transformation formula [6]:  
\[y=\begin{bmatrix}1&0&0\\ 0&cos\theta&-sin\theta\\ 0&sin\theta&cos\theta\end{bmatrix}\begin{bmatrix}a^{\prime}\\ b^{\prime}\\ c^{\prime}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ h\end{bmatrix} \tag{38}\]  
where \(y\) is the position vector of the point source on the blade surface in the fixed coordinate system.  
The coordinate of the receiving point is described as \(x=\left(x_{1},\ x_{2},\ x_{3}\right)^{\mathrm{T}}\) and combined with Equation (38), the radiation vector can be written as  
\[r=x-y=\begin{bmatrix}x_{1}-a^{\prime}\\ x_{2}-b^{\prime}cos\theta+c^{\prime}sin\theta\\ x_{3}-b^{\prime}sin\theta-c^{\prime}cos\theta-h\end{bmatrix}=\begin{bmatrix} r_{1}\\ r_{2}\\ r_{3}\end{bmatrix} \tag{39}\]
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 3 Results  
Employing the described test setup and following the presented test plan, several parameters that allow us to assess the TTFF robustness of the different GNSSs are derived. Firstly, two of the main factors that affect the TTFF, namely the number of tracked satellites and the power of the signals, are analysed. Afterwards, the TTFF itself, which is achieved with the different GNSSs, is assessed. Finally, the horizontal positioning error is also studied, as it strongly influences the usefulness of the solution. All test results are derived as the average of the 97 signals and their 10 iterations. For this purpose, data obtained from the NMEA messages (version 4.11) provided by the u-blox ZED-F9P receiver through 11 test runs, one per attenuation level, are used.
## 1 Introduction
However, Q-routing-based routing protocols have the following limitations: (1) they cannot be adapted to large-scale network scenarios because learning a large-scale Q-value table is time-consuming and convergence is not guaranteed; (2) the scalability of the protocols is insufficient, therefore, when new nodes join or leave the network, the state space and action space changes, requiring the entire network to be retrained; (3) they are unable to handle continuous state spaces. Therefore, the authors of [19] proposed a routing system based on a deep Q-network (QNGPSR). However, it did not consider the increase in delays and packet losses caused by heavy-load scenarios. In [20], the authors proposed a traffic-aware routing protocol to adapt congestion routing based on a Q-network (TQNGPSR). In [21], the researchers considered the link stability and predicted the resident energy when selecting the next-hop node. However, these routing protocols did not consider the link quality and total waiting time for packets in the neighboring node's buffer before propagating over the channel. Motivated by the above research, we propose a routing protocol that utilizes a dueling double-deep Q-network that considers the link quality and total waiting time. The main contributions of this study are the following:  
* First, we evaluated the link quality that combined the physical layer metric, the signal-to-noise ratio (SNR), and the data link layer metric, the expected transmission count (ETX). The long distances between nodes and the nodes' high mobility could lead to a significant path loss and Doppler shift, respectively. However, the SNR considered the path loss and Doppler shift. The ETX metric was used to reflect the link quality degradation caused by bursty link anomalies or malicious link attacks. * Secondly, as opposed to the method described in [20], which used the number of packets in a candidate forwarding node's buffer as a penalty factor, we considered the total waiting time of packets in the candidate forwarding node's buffer before they were propagated over the communication channel, which was more accurate because the SNR influenced the transmission rate. The transmission rate affected the transmission delay. * Lastly, we proposed a dueling DQN-based routing protocol that trained nodes to make routing decisions intelligently and automatically. The packet-forwarding process was modeled as a Markov decision process. Furthermore, the reward function was designed to accelerate the learning process. The experiments proved our proposed routing protocol resulted in better network performance. The remainder of our paper is organized according to six sections. In Section 2, we describe the related work. Section 3 presents the system model. The proposed routing protocol based on a dueling DQN is detailed in Section 4. Section 5 describes the parameter settings and analyzes the experimental results of different routing protocols. Section 6 provides the conclusions drawn based on this study.
## 3 Experimental Tests and Results  
The following research was conducted to validate the MIIM methodology for evaluating errors by large-scale MTs caused by variations in the ambient temperature and to generalise its application to other MT architectures beyond those employed in previous studies. Prior to this work, all experiments had been restricted to a single machine within a single environment. In contrast, this research involves extensive testing on two different machines in two distinct environments, providing a broader scope for analysis.
## 3 Method
### Sliding-Window Method  
In the defect detection phase, each image to be detected needs to extract several patches (64 \(\times\) 64 pixels) using the sliding-window method. Then the patches are detected one by one using the CNN networks. Using the sliding window can comprehensively extract image features, and combined with the proposed decision strategy it can effectively solve the problem of background interference. Figure 8 shows the segmentation method using the sliding-window. The size of the window is 64 \(\times\) 64 pixels. In the DAGM2007 dataset, the window moves along the rows and columns over the image with a 32-pixel stride. In this way, 225 patches can be extracted from the original image (512 \(\times\) 512 pixels), and 49 patches can be extracted from the low-frequency sub-image in the wavelet domain (256 \(\times\) 256 pixels). In the micro-surface defect database, the stride is set to 16 pixels. Therefore the patch number of the low-frequency sub-image in the wavelet domain (240 \(\times\) 320 pixels) is 204.
# Redesigning the Wheel for Systematic Travelling Salesmen
## 2 Methods  
### Basics of k-Opt Permutations  
Suppose a tour consists of ten points numbered from zero to nine. Their current order can be represented by a permutation vector  
\[\boldsymbol{\pi}=(0\;1\;2\;3\;4\;5\;6\;7\;8\;9)\;.\]  
The order of points is cyclic, that is, points 0 and 9 are direct neighbours and the order of points could be alternatively represented as follows:  
\[\boldsymbol{\pi}=(2\;3\;4\;5\;6\;7\;8\;9\;0\;1)\qquad\text{or}\qquad\pi=(5\;4 \;3\;2\;1\;0\;9\;8\;7\;6)\;.\]  
A so-called \(k\)-opt operations changes the order of points such that the sequence of points is cut at \(k\) positions. For example, a 2-opt operation might cut the entire sequence between points 2 and 3 and between 6 and 7:  
\[\boldsymbol{\pi}=(0\;1\;2\;|\;3\;4\;5\;6\;|\;7\;8\;9)\;.\]  
The only operation (or permutation) that can now be performed is an inversion of the cut section leading to \(\boldsymbol{\pi}=(0\;1\;2\;|\;6\;5\;4\;3\;|\;7\;8\;9)\)[45]. In terms of tour length, this is equivalent to turning the outer segment around: \(\boldsymbol{\pi}=(9\;8\;7\;|\;3\;4\;5\;6\;|\;2\;1\;0)\). The higher \(k\) is chosen, the more different permutations can be performed. For example, for \(k=3\) there are five new possibilities:  
\[(0\;1\;|\;2\;3\;4\;|\;5\;6\;|\;7\;8\;9)\quad\text{original order}\] \[1:\;(0\;1\;|\;4\;3\;2\;|\;6\;5\;|\;7\;8\;9)\quad\text{flip of both segments}\] \[2:\;(0\;1\;|\;5\;6\;|\;2\;3\;4\;|\;7\;8\;9)\quad\text{exchange of segments}\] \[3:\;(0\;1\;|\;6\;5\;|\;2\;3\;4\;|\;7\;8\;9)\quad\text{}\] \[4:\;(0\;1\;|\;5\;6\;|\;4\;3\;2\;|\;7\;8\;9)\quad\text{.}\]For many \(k\)-opt permutations, the same result can be achieved by a subsequent application of permutations with fewer cuts. For example, permutation 3 from the above list of 3-opt permutations could be performed by two 2-opt permutations:  
\[(0\;1\;|\;2\;3\;4\;|\;5\;6\;|\;7\;8\;9)\] original order \[(0\;1\;|\;6\;5\;|\;4\;3\;2\;|\;7\;8\;9)\] flip of segment 2... 6 \[3:\;(0\;1\;|\;6\;5\;|\;2\;3\;4\;|\;7\;8\;9)\] flip of segment 2... 4.  
In theory, to find a permutation that can shorten the tour, all points must be considered as intersection positions. The complexity of a pure 2-opt approach would be \(O(N^{2})\) and grows to about \(O(N^{3})\) for 3-opt permutations. It becomes obvious that such an approach becomes infeasible for higher values of \(k\) when focusing on large instances with \(N>\) 10,000.
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## 3 Results
### Parametric Study
#### 3.1.2 Concrete on the Bottom  
Table 5 shows that several parameters (Daytime of the \(T_{\text{floor\_surf}}\) max, FST-at-8:00 p.m., Daytime of operative temperature above 20 \({}^{\circ}\)C, the energy consumption and the maximal operative temperature) can be considered as constant between 8 and 14 cm of concrete on the bottom of the EHF (with a constant 10 cm of concrete on the top of the EHF). Thus, having a concrete thickness below the EHF higher than 8 cm (total concrete thickness of 18 cm) does not improve the performance of the thickness.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Thickness of** \\ **Insulation on** \\ **the Bottom (m)** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multicolumn{3}{c}{**FST**} & \multicolumn{3}{c}{**Operative Temperature**} & \multirow{2}{*}{
\begin{tabular}{c} \(E\) \\ **(kWh/Day)** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } \\ \cline{2-2} \cline{4-7}  & **Max** & **Daytime** & **\(T_{\text{floor\_surf}}\)** & **\(T_{\text{floor\_surf}}\)** & **at 8:00** & **Max \(T_{\text{op}}\)** & **Daytime** & **Daytime** \\  & **(\({}^{\circ}\)C)** & **Max (\(h:m\))** & **pm (\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)**C)** & **(\({}^{\circ}\)**C)** & **(\({}^{\circ}\)**C)** & **(\({}^{\circ}\)**C)** \\ \hline
0.075 & 28.02 & 6:33 am & 23.64 & 23.08 & 10:52 am & 10:25 pm & 102.88 \\ \hline
0.100 & \multirow{2}{*}{28.16} & \multirow{2}{*}{6:19 am} & \multirow{2}{*}{23.85} & \multirow{2}{*}{23.22} & \multirow{2}{*}{10:39 am} & \multirow{2}{*}{10:50 pm} & \multirow{2}{*}{98.71} \\ Reference case & & & & & & & \\ \hline
0.125 & 28.22 & 6:03 am & 23.99 & 23.30 & 10:34 am & 10:59 pm & 95.78 \\
0.150 & 28.28 & 6:01 am & 24.09 & 23.36 & 10:28 am & 11:09 pm & 93.56 \\
0.175 & 28.30 & 5:53 am & 24.17 & 23.41 & 10:24 am & 11:15 pm & 91.86 \\
0.200 & 28.33 & 5:48 am & 24.24 & 23.44 & 10:25 am & 11:20 pm & 90.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the parametric study: Impact of the lower layer Insulation thickness.  
Table 5 also indicates that between concrete thickness of 2 and 8 cm on the bottom, the maximal FST decreases while the FST-at-8:00 p.m. increases, the maximal FST and operative temperature appear later, and the duration with a good TC is extended: the storage capacity has increased. Thus, between 2 and 8 cm of concrete on the bottom, one can observe a slight improvement of the performance of the system (for example, the period of time with an operative temperature staying above 20 \({}^{\circ}\)C is increased of more than one hour). This is due to an increase of the storage capacity of the system. Thus, one can conclude that a small increase of the concrete thickness increases the storage capacity, and thus provides the required TC for a longer period of time. However, from 8 cm, it appears that, with already 10 cm of concrete layer on the top of EHF, increasing the concrete thickness below the EHF does not impact the system performance.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 3 Effects of Si Content on NGO Electrical Steels  
Silicon (Si) content of NGO electrical steels has a strong effect on magnetic properties [17]. Higher Si content increases the material resistivity, which leads to a decrease in the eddy current loss. Furthermore, the addition of Si content also led to a decrease in magnetocrystalline anisotropy [18].  
Magnetocrystalline anisotropy is the property that defines when ferromagnetic material takes more energy to magnetize in some directions than others. Decreasing magnetocrystalline anisotropy leads to higher permeability. Magneto-striction and saturation induction are also lower when the Si content increases.  
However, it is also observed that steel brittleness increases when the Si content is higher than 3 wt.%, which has a significant effect on cold deformability [19, 20, 21].  
[PERSON] et al. [22] found that in NGO electrical steels, a higher optimum grain size is obtained as the Si content increases. For instance, 1.85 wt.% Si steels have an optimum grain size of approximately 100 \(\upmu\)m, and 3.2 wt.% Si steels have an optimum grain size of approximately 150 \(\upmu\)m [23].
# MCCM: An Approach for Connectivity and Coverage Maximization
## 5 Validation, Simulation and Comparison with Existing Algorithms
### Comparative Study
#### 5.1.2 Energy Consumption  
As the energy consumption is proportional to the displacement, we adopt the formula in Equation (7). It measures the energy depleted by a moving device, where \(H_{e}\) is the energy required to move one meter \((J/m)\) and \(d\) is the displacement \((m)\).  
\[E=H_{e}*d \tag{7}\]  
\(CMHN\) and \(ABSDC\) are both centralized algorithms. However, \(ABSDC\) operates only in homogeneous environments. For fair comparison we run both in scenarios composed of 150 homogeneous devices. The results were as follows: \(ABSDC\) required an average displacement of 47.3 m to maximize the coverage. Consequently, the energy consumed by the devices in \(ABSDC\) was: \(E_{ABSDC}=2.8\times 47.3=132.44\) J. \(CMHN\) required 39.06 m displacement to maximize the coverage. The energy consumed is \(E_{CMHN}=2.8\times 39.06=109.368\) J. Hence, the centralized version of MCCM algorithm reduced the displacement and improved the coverage by saving 17.4% of energy in the network:  
\[\frac{E_{(CMHN)}-E_{(ABSDC)}}{E_{(ABSDC)}}=\frac{109.368-132.44}{132.44}=-0.174\]  
\(MCCM\) and \(DCTC_{k}\) are distributed algorithms and can operate on different network environments (homogeneous and heterogeneous). \(DCTC_{k}\) requires higher computational resources than \(MCCM\) (as shown in Table 2). The comparison of the two algorithms in a homogeneous environment of 150 devices depicted the following:  
\(DCTC_{k}\) required an average of 19.87 m displacement to maximize the coverage. The consumed energy is \(E_{DCTC_{k}}=2.8\times 19.87=55.636\) J. Whereas the \(MCCM\) required 18.21 m displacement to maximize the coverage, the energy is \(E_{MCCM}=2.8\times 18.21=50.988\) J. We can notice that \(MCCM\) algorithm reduced the nodes displacement by saving 8% of energy over \(DCTC_{k}\) algorithm:  
\[\frac{E_{(MCCM)}-E_{(DCTC_{k})}}{E_{(DCTC_{k})}}=\frac{50.988-55.636}{55.636}= -0.08\]  
\(MCCM\) behaves similarly in other scenarios. Displacements results in Table 3 showed an energy saving up to 35.5% in \(MCCM\) compared to \(DCTC_{k}\) and \(DCHGA\).
# A High-Conversion Ratio Multiphase Converter Realized with Generic Modular Cells
## 4 Extension for Higher Order Converters
An example of a three-phase converter response to non-equal duty cycle among the phases is demonstrated in Figure 6. Similar conversion ratios can be achieved by applying a different combination of \(D_{1}\), \(D_{2}\), and \(D_{3}\) (Figure 6a). Since, as mentioned earlier, the \(n\)-1 phase always carries double the current of the rest of the phases, in three-phase converter it makes sense to separate its duty cycle from the rest of the phases. This case is demonstrated in Figure 6b, where \(D_{1}\) and \(D_{3}\) are equal and being changed together (represented by the \(x\) axis). The solid traces represent the resulting conversion ratio of several selected duty cycles of the \(n\)-1 phase. Horizontal dashed red lines demonstrate three conversion ratios, and as can be observed, there are many intersections with the solid black traces, which are the operation points of different duty cycles that result in the same conversion ratio. Using this degree of freedom, the inherent current of the \(n\)-1 phase could be dialed down to be equal to the rest of the phases. This is beneficial since a current mismatch between phases and between modules can lead to a higher output current ripple. Taking advantage of these expansion possibilities offers three degrees of freedom in the converter architecture, allowing the design objectives to remain independent of one another. For example, going with a higher conversion ratio does not penalize the efficiency or phase balance of the converter. The general expression for maximum voltage stress as a function of \(V_{in}\) across each of the converter switches, relative to the converter order, is summarized in Table 2. A higher converter order results in a lower voltage stress on both low-side and high-side switches. It is worth noting that low-side switches (\(S_{\text{XL}}\)) conduct for much longer periods of time and as a result carry a much higher average current during the discharge states. They are exposed to twice as low a voltage stress as their high-side counterparts, allowing them to select a lower resistance switch to further reduce the losses. High-side switches (\(S_{\text{XLH}}\))--except for \(S_{\text{1H}}\) and \(S_{\text{(n-1)}\text{(n)}}\)--experience higher voltage stresses during one of the switching states.