esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 6 Experimental Results
### Outdoor Environment  
Table 4 shows instead the values obtained outdoors with no obstacles between the devices and in the line of sight. In this case, the RSSI index does not differ much from that of the previous case; it continues to reflect the ideal trend, as reported in Figure 13. The difference can be seen in the standard deviation, which tends to have smaller values. This reflects the fact that the effects of reflections and environmental effects are less pronounced, as a smaller standard deviation value means that there is less variability in the RSSI calculation, i.e., the individual measurements are closer to the mean value.  
Therefore, it can be said that the received signal strength indicator is reliable and usable for estimating distances; moreover, it is more suitable for outdoor environments as expected. If it was not usable, the localization algorithms would work with false values, and the results obtained would be meaningless.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(d\) **(m)** & **RSSI (dBm)** & \(\sigma\) \\ \hline
0 & \(-2\) & 0.898 \\
1 & \(-51\) & 0.644 \\
2 & \(-56\) & 0.871 \\
3 & \(-59\) & 1.358 \\
4 & \(-60\) & 1.480 \\
5 & \(-66\) & 2.013 \\
6 & \(-69\) & 2.135 \\
7 & \(-72\) & 2.259 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RSSI in outdoor environment.  
Figure 13: Real RSSI trend graph in outdoor environment.
# Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks
## 5 Improvements to the Charging Procedures
### Scenario 4: Combinations of the Solutions from Scenario 2 and Scenario 3  
In Scenario 4, the combination of connecting a 30 kVA D-STATCOM unit, explained in Scenario 3, along with the electric vehicle load control, corresponding to Scenario 2, has been analyzed. Tables 5 and 6 as well as Figure 11 compare the results obtained in the four scenarios with EV. The chosen case for Scenario 3 was that which included a D-STATCOM with a rated power of 60 kVA. Scenarios 2, 3, and 4 manage to limit voltage drop within the desired limit of 0.9 p.u. The main difference is noticeable from 20:00 onwards, when the most significant voltage drop occurs along with the highest increase in maximum load. Scenarios incorporating load control through voltage monitoring yield the best results in this regard. Furthermore, as expected, the best balancing data is obtained in scenarios including the D-STATCOM. It can be observed from Tables 5 and 6 that voltage control in Scenario 2 limits voltage value but worsens current unbalance. In Scenario 4, the combination of a D-STATCOM with half the power of that considered in Scenario 3, along with load control from Scenario 2, clearly achieves the best overall results. Table 6 shows the improvement of the analyzed indices in percentage. It can be seen that Scenario 4 exhibits the best overall performance. Only Scenario 3 slightly enhances current unbalance results at the cost of doubling the equipment's power. Finally, it is important to indicate that the voltage imbalance limit of 2% indicated by the regulations is not reached in all the proposed solutions.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Total** \\ **Losses** \\ **(kWh/Day)** \\ \end{tabular} & \begin{tabular}{c} **Minimum** \\ **Voltage** \\ **(pu)** \\ \end{tabular} & \begin{tabular}{c} **Max. Neutral** \\ **Current** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **Max.** \\ **Loading** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Max.** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Max.** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **CUF0** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **VUF** \\ **(\%)** \\ \end{tabular} \\ \hline Scenario 1 & 18.910 & 0.838 & 152.1 & 85.06 & 39.97 & 54.9 & 2.64 & 3.65 \\ Sr = 30 kVA & 14.860 & 0.878 & 87.47 & 72.58 & 17.66 & 26.0 & 1.43 & 2.11 \\ Sr = 45 kVA & 14.050 & 0.896 & 68.52 & 66.56 & 13.37 & 18.4 & 1.14 & 1.62 \\ Sr = 60 kVA & 13.990 & 0.903 & 68.87 & 64.71 & 12.45 & 17.1 & 1.05 & 1.47 \\ Sr = 75 kVA & 13.990 & 0.903 & 68.87 & 64.71 & 12.45 & 17.1 & 1.05 & 1.47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results depending on the D-STATCOM rated power.  
Figure 10: Comparison of the results of Scenario 3 with different rated power values for the D-STATCOM regarding Scenario 1.
# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
## 7 Conclusions and Future Work  
This paper introduces an innovative approach for maximizing the cache hit ratio of CDNs through an MADQN-based cache replacement policy. We consider a mesh CDN network with edge servers that cooperatively learn efficient cache replacement, aiming to maximize cache hit ratios. We compare the performance of our algorithm with that of heuristic caching policies like LFU, LRU, and random cache replacement policies. Moreover, the proposed algorithm is compared with non-cooperative reinforcement algorithms, such as SADQN and the actor-critic method. This study shows that our MADQN approach surpasses existing caching policies and achieves higher cache hit ratios and lower average delays. It is shown that, with a higher number of cooperative agents, our MADQN achieves higher cache hit ratios. Furthermore, it is shown that the proposed algorithm performs better than existing policies when the content item sizes are different in the network. Our experiments validate that using an MADQN reinforcement learning approach is more effective than traditional methods for improving cache replacement in CDNs by maximizing the cache hit ratio.  
Figure 8: Training accuracy vs. request time span.  
Figure 7: Convergence of the proposed algorithm.  
**Author Contributions:** Methodology, J.K.D., M.W. and M.Z.H.; software, J.K.D., M.W. and M.Z.H.; validation, J.K.D., M.W. and M.Z.H.; formal analysis, J.K.D., M.W. and M.Z.H.; investigation, J.K.D., M.W. and M.Z.H.; resources, J.K.D., M.W. and M.Z.H.; data processing, J.K.D., M.W. and M.Z.H.; writing--original draft preparation, J.K.D., M.W., M.Z.H. and N.Y.; visualization, J.K.D., M.W. and M.Z.H.; supervision, N.Y.; writing--review and editing, N.Y.; project administration, N.Y.; funding acquisition, N.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the US National Science Foundation under grant CC-2018919.  
Our work concentrates on the development and evaluation of the MADQN algorithm, specifically highlighting its application for cache replacement in CDNs. To demonstrate the performance of the MADQN algorithm, we employed a simulation environment modeled. Consequently, no actual dataset was used, as our approach relies solely on simulations.  
The authors declare no conflicts of interest.
###### Abstract  
The integration of Distributed Generation, Electric Vehicles, and storage without compromising the quality of the power delivery requires the deployment of a communications overlay that allows monitoring and controlling low voltage networks in almost real time. Power Line Communications are gaining momentum for this purpose since they present a great trade-off between economic and technical features. However, the power lines also represent a harsh communications medium which presents different problems such as noise, which is indeed affected by Distributed Generation, Electric Vehicles, and storage. This paper provides a comprehensive overview of the types of noise that affects Narrowband Power Line Communications, including normative noises, noises coming from common electronic devices measured in actual operational power distribution networks, and noises coming from photovoltaic inverters and electric vehicle charging spots measured in a controlled environment. The paper also reviews several techniques to mitigate the effects of noise, paying special attention to passive filtering, as for being one of the most widely used solution to avoid this kind of problems in the field. In addition, the paper presents a set of tests carried out to evaluate the impact of some representative noises on Narrowband Power Line Communications network performance, as well as the effectiveness of different passive filter configurations to mitigate such an impact. In addition, the considered sources of noise can also bring value to further improve PLC communications in the new scenarios of the Smart Grid as an input to theoretical models or simulations.  
Advanced Metering Infrastructure (AMI); Distributed Energy Resources (DER); Distributed Generation (DG); Electric Vehicle (EV); Narrowband Powerline Communications (NB-PLC); noise; PoweRline Intelligent Metering Evolution (PRIME); Smart Grids; supraharmonics +
Footnote †: journal: _Energies_  
_Article_
## 5 Key Issues of BGB Top Plate Thickening Design in Acoustic Vibration Control
### Exploration of the Key Influence of Vibration Distribution of BGB on Its Sound Radiation
From the above analysis, it can be seen that thickening the top plate changes the low-frequency oscillation distribution of the BGB, and so resonance can be avoided to a certain extent through the transfer of vibration frequency, thus weakening the resonance and the consequential sound radiation. The modal acoustic radiation efficiency of BGBs at lower frequency bands has a significant effect on their acoustic radiation capacity. According to the literature [11], when the local vibration in the top plate dominates the BGB's overall vibration, the plate would ideally be thin. Therefore, by changing the (_m_, _n_) order vibration pattern of the top plate of the BGB, the modal acoustic radiation efficiency of the BGB is suppressed. Thus, the overall acoustic radiation efficiency of the BGB is weakened, as shown in Figure 13. Therefore, the vibration of the two kinds of top plate-thickened BGBs, as a result of small-vibration-peak sound radiation, can be limited to the lower frequency by changing the vibration distribution of the BGB in the noise control design. Changing the vibration distribution is the most critical factor in controlling the lower-frequency band noise of the BGB. In addition, changing the vibration distribution of the BGB will also greatly impact the propagation of sound radiation, and changing the sound radiation pattern of the BGB is very significant, as shown in Figure 14. The vibration characteristics of the whole top plate and the remaining three plates of the BGB when under the three operating conditions and subjected to 27.1 Hz show that the local bending vibration of the top plate is significantly changed at this time. The number of bending waves in the top plate is reduced under both top plate-thickening schemes. Analyses of the remaining three plates against the same benchmark show that, compared with the initial BGB, the vibration in most of the remaining plates is significantly enhanced, and the vibration area increases, resulting in a gradual increase in the area of radiated acoustic waves, but a narrowing of the area of concentrated acoustic radiation. The amplitude of the acoustic radiation in the area above the BGB is thus significantly weakened, but no obvious pattern of acoustic radiation arises as a result. This shows that the vibration distribution of the BGB also has a significant effect on its sound radiation pattern. When subjected to the severe vibration band, the vibration of the BGB will be more complex. The sound radiation distribution will show no obvious pattern because vibration damping reduces the overall sound radiation amplitude, as shown in Figure 15.  
Figure 13: Comparison of sound radiation efficiencies of the three types of BGBs.
## 5 Influence of Major Factors on the Performance of Building Settlement Prediction
### Input Number  
Using the proposed LSTM-RA-ANN model to predict the settlement of different buildings, the results of the evaluation index calculation when the number of input points is different are shown in Figure 11. From Figure 11a,b, the trends of RMSE and MAE are basically the same when inputting single-point and multi-point monitoring values, with the average values of RMSE being 0.50 and 0.31, and the average values of MAE being 0.46 and 0.28, respectively, From Figure 11c, the mean value of \(R^{2}\) is 0.89, and the mean value of \(R^{2}\) is 0.97. The mean values of RMSE, MAE, and \(R^{2}\) are 0.92 times, 0.90 times, and 1.1 times higher than those of the single-point values, so the prediction accuracy of the multiple-point values is higher than that of the single-point values, but the difference is not significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyperparameter Type** & **Hyperparameter Size** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline  & 0.06 & 0.14 & 0.13 & 0.86 \\ Initial learning rate & 0.08 & 0.16 & 0.19 & 0.85 \\  & 0.10 & 0.27 & 0.20 & 0.73 \\ \hline  & 15 & 0.20 & 0.26 & 0.65 \\ Maximum iteration batch & 20 & 0.14 & 0.14 & 0.79 \\  & 25 & 0.30 & 0.38 & 0.60 \\ \hline  & 400 & 0.13 & 0.15 & 0.80 \\ Maximum iteration period & 600 & 0.14 & 0.16 & 0.79 \\  & 800 & 0.12 & 0.14 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model evaluation indices under various hyperparameters for monitoring point F1.
## 5 Experimental Study and Result Analysis
### Analysis of Image Recovery  
The lossless restoration of the original image is the critical concern of any RDH scheme. The recovery of the original image can be analyzed using reference image quality measures such as peak-to-signal noise ratio (PSNR) [44] and the structural similarity index (SSIM) [44]. The PSNR between the original image \(I\) and the restored image \(G\) of size \(R\times C\) can be defined as follows:  
\[E=\frac{1}{RC}\sum_{X=0}^{R}\sum_{Y=0}^{C}\left[I_{X,Y}-G_{X,Y}\right]^{2} \tag{11}\]  
where \(E\) is the mean square error between two images. If both the original image \(I\) and the restored image \(G\) are the same, then \(E\) will be 0. Otherwise, it will be a positive value. To find the PSNR value \(P\) between two images, we have to use \(E\) in the following way:  
\[P=10.Log_{10}\left(\frac{255^{2}}{E}\right) \tag{12}\]  
The \(P\) value will be \(\infty\) if the original image \(I\) and the restored image \(G\) are the same. Note that if both the images are the same, the \(E\) value will be 0, which will lead to \(P=\infty\).  
Similarly, SSIM is another measure that we can use to analyze the quality degradation between the original image \(I\) and the restored image \(G\)[44].  
To ensure the image recoverability of the proposed scheme, we have analyzed the PSNR and SSIM. The PSNR and SSIM measure observed during the recovery analysis of the proposed scheme is given in Table 3.  
The results shown in Table 3 demonstrate that the proposed scheme losslessly restores the image without any recovery error.
## 2 Method
The optimization procedure can be summarized as follows:  
Firstly, give a slight deviation from the nominal dimensions of the investigated sample by calculating the signature difference \(\Delta\mathbf{y}_{i}=[\Delta y_{i1},\,\Delta y_{i2},\,\ldots,\,\Delta y_{il}]^ {\mathrm{T}}\) (\(i=1,2,\,\ldots,N\)) under \(N\) measurement configurations according to Equation (5);  
Secondly, for each element \(\Delta y_{ij}\) (\(j=1,2,\,\ldots,\,l\)) in \(\Delta\mathbf{y}_{i}\), carry out the eigen-analysis according to Equations (6)-(9) to identify and eliminate the measurement configurations with redundant information one-by-one; since at least \(m\) measured data points are required to determine the \(m\) unknown parameters for any mathematical inverse problem in theory [21; 22; 23; 24], the achieved set \(S_{j}\) will contain \(m\) non-redundant measurement configurations;Thirdly, considering that different \(S_{j}\) may contain identical measurement configurations, the union of \(S_{j}\) for all \(j=1\), \(2\),..., \(l\) is the final set of non-redundant measurement configurations, namely,  
\[S_{opt}=\underset{j=1}{\overset{l}{\cup}}S_{j}. \tag{10}\]  
After that, the inverse problem described in Equation (2) should be solved under the above achieved measurement configurations, namely, \(\textbf{a}\in S_{opt}\). It is noted that the above refinement of measurement configurations involves the calculation of the Jacobian matrix \(\textbf{J}(\textbf{x}^{\star}\), \(\textbf{a}_{i})\), which is typically only valid at the vicinity of **x**. To ensure that the refinement results of measurement configurations are valid in the changes of profile parameters, it is necessary to repeat the above refinement process of measurement configurations at \(k\) different values of **x**. In this case, the final set of non-redundant measurement configurations will be the union of the achieved set at each value of **x**. Here, the value of \(k\) depends on the types of measured signatures as well as the complexity of the nanostructure under measurement. As a rule of thumb, \(k\) should take a relatively large value for a complicated nanostructure with a simple type of measured signature, such as reflectance.
## 3 Generic Model of the 3 RRR PPM
### Kinematic Model of the 3 RRR PPM in Fully Cartesian Coordinates  
The fixed platform is an equilateral triangle defined by three points: \(A_{1}\), \(A_{2}\), and \(A_{3}\). The mobile platform is also an equilateral triangle defined by points: \(C_{1},C_{2}\), and \(C_{3}\). The three limbs, with two links each, are defined by points \(A_{1},B_{1},C_{1}\); \(A_{2}\), \(B_{2}\), \(C_{2}\); and \(A_{3},B_{3}\), \(C_{3}\), respectively. As it can be seen, all \"basic points\" in the model--\(A_{1},A_{2}\), \(A_{3}\), \(B_{1},B_{2},B_{3}\), \(C_{1}\), \(C_{2}\), and \(C_{3}\)--are shared among the bodies. Point \(H\) represents the end-effector and is not part of the \"basic points\".  
These points introduce a vector, \(\mathbf{q}\), of 18 Cartesian coordinates:  
\[\mathbf{q}=\begin{bmatrix}\mathbf{q}_{A1}^{T}&\mathbf{q}_{B1}^{T}&\mathbf{q}_{C1 }^{T}&\mathbf{q}_{A2}^{T}&\mathbf{q}_{B2}^{T}&\mathbf{q}_{C2}^{T}&\mathbf{q}_{A3 }^{T}&\mathbf{q}_{B3}^{T}&\mathbf{q}_{C3}^{T}\end{bmatrix}^{T} \tag{29}\]  
where \(\mathbf{q}_{p}=[x_{p}\ y_{p}]^{T}\), \(p=\{A_{1},A_{2},A_{3},B_{1},B_{2},B_{3},C_{1},C_{2},C_{3}\}\). Note the way that the coordinates are organized. This is very important in the assemblage procedure of the mass matrix of the system. Any order is possible, but once selected, it must be maintained throughout all the calculation. Vector \(\mathbf{q}\) constitutes a set of dependent Cartesian coordinates related by a set of constraint equations.
## 4 Experimental Results
### Datasets
#### 4.1.2 AVD8 Dataset  
AVD8 dataset [10] consists of 8 categories including 400 videos with augmented virtual objects and holograms taken on real environment. Each category comprises 50 video clips. The categories are Dog, Tiger, Gorilla, Santa-Claus, Spider-Man, Wolf, Man Dance and Monkey. It also includes videos with large variation in scale, object appearance, object motion and illumination. Furthermore, it contains poorly textured virtual object in Marker-less Augmented Reality environment. Figure 5 represents an example of an AVD8 dataset.  
Figure 4: An example of UCSB dataset.  
Figure 5: An example of AVD8 dataset [10].
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 2 Materials and Methods  
### Selection of Terrains  
Several types of terrains, both natural and man-made, exist around us. The goal of this study was to classify such terrains which humans encounter on daily basis. In this context, we chose six different types of terrains having variations in hardness and friction. The hard terrains include concrete floor, asphalt, and tiles, whereas the soft terrains include carpet, grass, and soil (Figure 1).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Category** & **Year** & **Sensor** & **Classifier** \\ \hline [PERSON] et al. [11] & Vision & 2015 & Camera & SVM \\ [PERSON] et al. [6] & Vision & 2017 & Camera & Random Forest \\ [PERSON] et al. [9] & Vision & 2017 & Camera & SRC \\ [PERSON] et al. [22] & Acoustic & 2016 & Microphone & SVM \\ [PERSON] et al. [15] & Acoustic & 2018 & Microphone & Deep Learning \\ [PERSON] [13] & Robotics & 2006 & IMU, Motor & ANN \\ [PERSON] et al. [4] & Robotics & 2011 & Tactile & ANN \\ [PERSON] et al. [3] & Robotics & 2016 & Tactile & SVM \\ [PERSON] et al. [1] & Autonomous off-road driving & 2005 & Ladar \& Camera & Gaussian Process \\ [PERSON] et al. [20] & Autonomous off-road driving & 2009 & Laser & PNN \\ [PERSON] et al. [33] & Human Gait & 2018 & IMU & LSTM \\ [PERSON] et al. [34] & Human Gait & 2018 & Camera, IMU & BoW model \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the main approaches in terrain classification. A variety of sensors are used for data collection including camera, microphone, inertial measurement units (IMU), laser, tactical and ladar sensors. For classification, different types of classifier are proposed including Random Forest, Support Vector Machine (SVM), Sparse Representation Based Classification (SRC), Artificial Neural Network (ANN), Probabilistic Neural Network (PNN) and Long Short-Term Memory Units (LSTM).  
An indoor environment was used to record data for concrete floor, carpet, and tiles, whereas an outdoor environment was used for the rest of the terrains (Table 2).
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 2 Effect of Horizontal Two-Layer Soil Characteristics on Grounding System Performances  
### Depth of Grid Buried in a Two-Layer Soil  
Apart from grounding grid design parameters, the depth of the grid buried in the soil, especially in the two-layer soil model [19; 38; 39; 40; 41] has a significant impact on the protection of a grounding system. Grid burial depths are generally in the range of 0.5 m to 1.5 m, or 2.0 m to 2.5 m in some cases, according to IEEE 80 [42]. Generally, increasing the depth of the grounding grid buried into soil reduces the grid impedance and ground potentials in homogeneous soil. On the other hand, the behavior of a grounding system would be different when the depth of the grid increases in a two-layer soil structure, which will be discussed in this section.  
An example has been adapted from [40] to show the Ground Potential Rise (GPR) performances as the depth of the grid is increased. The GPR is obtained for 30 m \(\times\) 35 m area, the top layer's soil resistivity is 37.82 \(\Omega\)m (\(\rho_{1}\)) with 5.15 m depth (h), the deep layer soil resistivity is 120.42 \(\Omega\)m (\(\rho_{2}\)) with infinite depth, the grid is attached with 3 m vertical rod length as shown in Figure 1. The figure shows 4 different scenarios (A, B, C, D); with the presence of 3 m ground rods and without the rods. Cases A and C show the grounding grid with vertical rods; whereas in the case of A, the top layer soil is less resistive than the bottom layer and vice versa in case C. Cases B and D show the grounding grid without vertical rods; wherein case B, the top layer soil is less resistive than bottom layer and vice versa in case D.  
Table 1 summarizes the findings such that increasing the depth of the grid in high resistive bottom layer soil increases the GPR value [40]. The bottom layer's high resistivity allows more current flow towards the upper layer's lower resistivity, raising the ground surface potential. In the low resistive top layer soil, however, increasing the grid depth  
Figure 1: Visual representation of cases A, B, C, and D.  
decreases the GPR value. A number of vertical rods attached to the main grounding grid help to enhance a grounding grid's safety by reducing the GPR value. Therefore, it is also evident that the GPR is lower for a grounding system with vertical rods than a grounding system without vertical rods.