esa-sceva/satcom-chunk-collection huggingface dataset:
# Make predictions from new data. y_pred = clf.predict(X_test),
## 7 Results
### Comparison with Compound Parameters  
Table 12 shows the values of the correlation coefficient between the blows of the penetrometers and the results of both the analytical formulas and the algorithms used. The values obtained by linear regression are also shown, in order to compare with the simplest possible ML algorithm.  
As can be seen, the ML algorithms show a higher correlation with the penetrometers than the compound parameters in all sites except for site 4, where similar values of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Size** & **Set** & **Accuracy** & **Correlation Coefficient** & **MAE** & **MSE** & **R\({}^{2}\)** \\ \hline \multirow{2}{*}{1} & Test & 0.18 (\(\pm\)0.02) & 0.76 (\(\pm\)0.02) & 3.45 (\(\pm\)0.02) & 26.81 (\(\pm\)0.02) & 0.45 (\(\pm\)0.02) \\  & Train & 1.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) \\ \hline \multirow{2}{*}{2} & Test & 0.31 (\(\pm\)0.03) & 0.54 (\(\pm\)0.06) & 2.15(\(\pm\)0.13) & 12.82(\(\pm\)1.84) & 0.10(\(\pm\)0.11) \\  & Train & 1.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) \\ \hline \multirow{2}{*}{3} & Test & 0.38 (\(\pm\)0.03) & 0.57 (\(\pm\)0.07) & 2.60 (\(\pm\)0.24) & 20.03 (\(\pm\)3.05) & 0.04(\(\pm\)0.13) \\  & Train & 1.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 0.01 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) \\ \hline \multirow{2}{*}{4} & Test & 0.18 (\(\pm\)0.02) & 0.28(\(\pm\)0.07) & 4.83 (\(\pm\)0.36) & 54.30 (\(\pm\)7.00) & \(-\)0.26(\(\pm\)0.16) \\  & Train & 1.00 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) & 0.00 (\(\pm\)0.00) & 0.02 (\(\pm\)0.00) & 1.00 (\(\pm\)0.00) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Correlations obtained for RFs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound Parameter** & \multicolumn{4}{c}{**Site**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline Penetration resistance & \(-\)0.45 & \(-\)0.09 & \(-\)0.40 & \(-\)0.24 \\ Somerton index & 0.40 & \(-\)0.07 & 0.42 & 0.12 \\ Drilling specific energy & 0.52 & 0.13 & 0.44 & 0.34 \\ Specific energy & 0.44 & 0.02 & 0.48 & 0.32 \\ Alteration index & 0.49 & 0.14 & 0.39 & 0.29 \\ DT & 0.62 & 0.42 & 0.57 & 0.24 \\ DT (GridSearch CV) & 0.62 & 0.49 & 0.34 & 0.24 \\ RF & 0.76 & 0.54 & 0.57 & 0.28 \\ Linear regression & 0.56 & 0.39 & 0.49 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Correlation coefficient for the compound parameters.  
Figure 10: Accuracy as a function of the number of trees considered for site 1.  
correlation coefficient are obtained. DT and RF outperform linear regression in three of the four sites.
# HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
## 2 Relevant Knowledge
### Elliptic Curve Cryptography--ECC  
Public key cryptography algorithms, as a cornerstone of modern cryptography, address the challenges of key management and distribution inherent in symmetric cryptography algorithms. Elliptic curve cryptography (ECC) [13] stands as a prominent public-key encryption technique. First introduced by [PERSON] and [PERSON] in 1985, ECC boasts distinct advantages and holds vast potential for a wide range of applications. Compared to traditional asymmetric cryptography algorithms such as RSA and Diffie-Hellman, ECC provides higher security with shorter key lengths, making it well-suited for encrypting  
Figure 1: AES algorithm encryption process.  
large data volumes, as seen in blockchain medical scenarios. However, the ECC algorithm is more complex, and it is less time-efficient as an asymmetric cryptographic algorithm.  
The ECC algorithm is grounded in the mathematical theory of elliptic curves, characterized by the functional equation \(y^{2}=x^{3}+ax+b\), which defines an approximate elliptic curve on the projective plane coordinate system. In ECC, a point at infinity, denoted as \(O_{\infty}\), serves as the zero element. A base point \(G\) is defined, and after \(k\) scalar multiplications, it generates the final point \(K\), where \(k\) acts as the private key of the ECC cipher, and \(K\) serves as the public key. ECC leverages the computational difficulty of the elliptic curve discrete logarithm problem for encryption and decryption: given \(k\) and \(G\), it is straightforward to compute \(K=kG\); however, computing \(k\) when \(K\) and \(G\) are known is challenging, as division is more computationally intensive for computers.  
Currently, ECC is emerging as the dominant choice in the new generation of public key cryptography. Much like RSA, ECC is typically employed for data encryption. In our blockchain medical application scenario, we specifically use ECC to enhance AES key management. By encrypting the AES key with ECC, we not only bolster the security of medical data but also elevate the overall trustworthiness of the blockchain-based medical application framework.
## 3 Experiment
### Experimental Process  
As shown in Figure 5, based on the PhySO project, the experimental process of this article is mainly divided into the following steps:  
1. Input the measured channel data. In this experiment, the data was sampled from the free space path loss Formula (7);
2. Initialize the environment, including batch size, library, priority of operations, dataset, etc.;
3. Initialize the deep neural network, including its size, learning rate, etc.;4. Initialize the observations which are composed of parent nodes and their units, sibling nodes and their units, previous node and its unit, dangling nodes and the unit of the current node;
5. Input the observations into the neural network model to calculate the action, and use the action to form the channel model expression;
6. According to the reward Formula (2), calculate the reward using the sampled dataset;
7. After receiving the reward, use the risk-seeking policy gradient [14], and entropy regulation [15] method to calculate the loss;
8. Backpropagation loss, using Adam optimizer to optimize neural network and update the observations;
9. Repeat 5-8 until the reward value reaches the set value or the maximum number of iterations is reached.  
Figure 5: Experimental Process.
## 2 Materials and Methods
### Preliminary Interviews for the Two-Way Propulsion System
#### 2.2.2 Expert Interviews  
Expert interviews were conducted three times with two professors in mechanical engineering and one specialist in wheelchair manufacturing to assess the feasibility of the two-way propulsion system (Figure 2). The first and second interviews involved one professor each. The third interview involved the specialist. These expert interviews aimed to determine whether the two-way propulsion system could be practically implemented and gather opinions on the most suitable mechanical structure for its realization.  
Experts explained that a wheel can rotate in the opposite direction of the applied rotational force. However, they noted that implementing a mechanism in which the wheel consistently rotates forward, even when receiving either a forward or backward rotational force--an essential function of the two-way propulsion system--could be relatively challenging. Various power transmission systems, including a four-bar linkage, gears, ratchet gears, and a planetary gear train, were considered to control wheel rotation for the implementation of the two-way propulsion system. Considering the characteristics of each system, a planetary gear train was expected to maximize the feasibility of the two-way propulsion system.  
Findings from the expert interviews suggested that implementing the two-way propulsion system could be relatively challenging due to the complexity of designing a mechanism that consistently converts both forward and backward rotational forces into forward wheel movement. However, within the field of mechanical engineering, its practical implementation was deemed feasible, and the most suitable power transmission system was identified. Drawing from the interview findings, a planetary gear train was determined to be the optimal mechanism for designing the two-way propulsion system, as it allows for efficient torque transmission and directional control while maintaining mechanical stability. Experts also noted that, particularly for individuals with high-level spinal cord injuries who have limited trunk strength, supplementary chest pads or backrest supports should be considered [23; 24]. Adding these additional support devices may make it more feasible to perform pulling motions in a stable manner.  
Therefore, the two-way propulsion system was designed utilizing a planetary gear train to enable efficient two-way propulsion while maintaining mechanical stability.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### VANETs Characteristics
#### 1.2.4 Dynamic Network Topology  
VANET's network configuration continuously develops due to the mobility of vehicles as some nodes join and leave the network. Malicious nodes could benefit from these dynamic configurations, hiding their routes after compromising a particular network [6].
# SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems
## 4 Proposed Solutions for Locality Problem in P2P Cloud System
### Generalized Hypercube Connected Cycle Overlay Network
For example, given the two design parameters \(D\) and \(\delta\), it is easy to find the appropriate values for \(m\) and \(l\) to form a network with the size of \(N=l\times m^{l}\). As an other instance, we can fix the values of \(N\) and \(\delta\), and find appropriate values for \(l\) and \(m\) in such a way that \(l\times m^{l}\geq N\) and the degree is \(\delta\). The diameter of this graph is then calculable as the following [46]. \[D=\lfloor\frac{5l}{2}\rfloor-2,for\,l\geq 4,m\
eq 1\,and\,D=\lfloor\frac{5l}{2 }\rfloor-1,for\,l\leq 3,m\
eq 1\]  
SLoPCloud algorithm on the GHCC topology is implemented the same way as on the Hypercube and CCC topologies. Network administrator chooses the appropriate values for parameters \(l\) and \(m\). The starting nodes then placed close to each other according to the RTT in between. The network then starts to work. A new node to join the network should start to broadcast a join request message limited by the TTL. After receiving the first response, the new node tries to find an empty position in the neighborhood of the responder node, in the overlay network. Then, the new node calculates the RTT among itself and the neighbors to update the positions. Similar to Hypercube and CCC topologies, \begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{d}\) & **Number of Overlay Topology Dimensions** \\ \hline \(\aleph\) & The set of starting nodes \\ \(\theta\) & The timeout threshold \\ \(N\) & Number of network nodes \\ \(D\) & Diameter of the graph \\ \(\delta\) & Degree of the graph \\ \(l\) & GHCC parameter representing number of levels \\ \(m\) & GHCC parameter \\ \((p;u_{l-1}u_{l-2}\cdot\cdot\cdot u_{1}u_{0})\) & Representation of a node in GHCC \\ \(p\) & Level number of the node in GHCC \\ \(u_{l-1}u_{l-2}\cdot\cdot\cdot u_{1}u_{0}\) & String node index in level \(p\) \\ \((p,n)\) & Another representation of a node in GHCC \\ \(n\) & Decimal node index in level \(p\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of notations. there also exists an optimal routing algorithm for GHCC topology. Describing the optimal routing of GHCC is out of the scope of this paper, hence we refer the interested researchers to read the detailed optimal routing algorithm of GHCC in [49].
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 4 Experiment
### Ablation Experiment  
Figure 16a shows the comparison of the loss with GC and without GC in 50 rounds of training. The original network only takes Batch Normalization (BN) as optimization. As shown in Figure 16a, the training loss of BN+GC is reduced faster than BN, so GC can further speed up convergence of the model during training. Figure 16b compares SmoothL1 Loss with the manual combination of L2 loss and L1 Loss. Using SmoothL1 Loss is significantly lower than the one using the manual combination of L2 loss and L1 Loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **F1** & **Sensitivity** \\ \hline AnoGAN & 0.878 & 0.749 & 0.852 & 0.827 \\ Zhao’s method & 0.869 & 0.887 & 0.899 & 0.931 \\ GANomaly & 0.908 & 0.892 & 0.929 & 0.952 \\ f-AnoGAN & 0.912 & 0.930 & 0.930 & 0.950 \\ ppFDetector & **0.929** & **0.945** & **0.944** & **0.960** \\ \hline \hline \end{tabular} Bold shows the best performance between the four models.  
\end{table}
Table 4: Performance comparison with semi-supervised and unsupervised models.  
Figure 15: ROC curve comparison chart. (**a**) semi-supervised and unsupervised methods; (**b**) fully supervised methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Accuracy** & **F1** & **Sensitivity** \\ \hline Pre-trained Vgg16 (10) & 0.324 & 0.265 & 0.378 & 0.453 \\ Pre-trained Vgg16 (100) & 0.746 & 0.750 & 0.795 & 0.852 \\ Pre-trained Vgg16 (200) & **0.930** & **0.960** & 0.936 & 0.941 \\ ppFDetector & 0.929 & 0.945 & **0.944** & **0.960** \\ \hline \hline \end{tabular} Bold shows the best performance between the four models.  
\end{table}
Table 3: Performance comparison with fully supervised model.  
This proves that SmoothL1 Loss can make the model more accurate. The GC is applied on conditional GAN of ppFDetector to regularize weight space and output feature space to prevent overfitting, so as to improve the generalization performance in the deep convolution network. Figure 16c is a comparison of the loss with and without the CBAM module. CBAM first extracts features based on the attention of the spatial domain, then extracts features based on the attention of the channel domain, and finally achieves the optimal attention extraction effect, thereby improving the performance of the model.
## 5 Experimental Verification and Results
### Experimental Verification at Various Speed  
(1) Experimental verification at 80% rated speed  
Figure 33: The observed speed using VPDPI at rated speed. (**a**) The overall response diagram of observed speed at 1000 r/min. (**b**) Observed speed under no load. (**c**) Observed speed under 2 N-m load.  
Figure 32: The observed speed using PI at rated speed. (**a**) The overall response diagram of observed speed at 1000 r/min. (**b**) Observed speed under no load. (**c**) Observed speed under 2 N-m load.  
When the speed is set to 800r/min, Figures 34 and 35 show the observed speed response of the two regulators under no-load and 2 N\(\cdot\)m load, respectively.  
From Figures 34 and 35, the observed speed under the traditional PI regulator has a large overshoot in the dynamic response process, and the result also exists a large speed ripple when the response is stable. In contrast, the maximum overshoot of the VPDPI regulator is about 0.3% of the maximum overshoot of the traditional PI. Compared with the traditional PI regulator, the speed pulsation of the proposed method is reduced about 90% under no-load condition. Moreover, the traditional PI speed pulsation becomes more serious under changing load condition, and the pulsation is up to \(\pm\)25 r/min. Fortunately, the proposed method can control the pulsation within \(\pm\)5 r/min, which reflects a more higher stability.  
(2) Experimental verification at 120% rated speed:  
When the speed is set to 1200 r/min, the observed speed response of the two regulators under no-load and 2 N\(\cdot\)m load are presented in Figures 36 and 37.  
From Figures 36 and 37, it can be seen clearly that the overshoot of the traditional PI regulator is about 64 r/min. However, the overshoot of the VPDPI regulator is only 1.6% of the traditional PI regulator, which indicates a significant overshoot suppression effect. Meanwhile, the observed speed pulsations using two regulators are obviously reduced than others operation conditions, especially the VPDPI regulator. Nevertheless, it can still be detected that the pulsation of the traditional PI regulator is as high as 25 r/min with 2 N\(\cdot\)m load condition. It is worth noting that the pulsation of VPDPI regulator is less than 0.15 r/min. In brief, the experimental results under this condition are similar to the performance reflected before.  
The experimental results in this section verify the effectiveness of the proposed method to suppress integral saturation, which further improve the control performance and observation accuracy of the traditional sensorless system.
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 2 Database  
### CIPIC Database  
In the process of designing and implementing personalized HRTF, the HRTF database publicly provided by the Center for Image Processing and Integrated Computing (CIPIC) [24] of the University of California is used. The database contains the head-related impulse response (HRIR) of 45 subjects at 25 azimuths and 50 elevations, with a sampling length of 200 and a sampling rate of 44.1 kHz. The spatial sampling is roughly evenly distributed on a sphere with a radius of 1 m. The horizontal azimuth ranges from \(-80^{\circ}\) to \(+80^{\circ}\), and the elevation ranges from \(-45^{\circ}\) to \(230.625^{\circ}\). The sampling points are shown in Figure 1.  
The database also provides anthropometric parameters and ear images of each subject, including 17 head and torso parameters and 10 pinna parameters. The specific measurement parameters are shown in Figure 2.
# Optimal Base Station Density of Dense Network:
## 5 Numerical Results and Discussion
### The Throughput of an SmBS and the Optimal Deployment Scheme  
Although the areal throughput of HetNets can indicate the performance of the networks, there is another metric for optimization, which is the throughput of each SmBS. This metric focuses on the efficiency of the SmBS and aims to deploy the base stations in a cost-effective way. This metric also attracts attention of some telecom operators. Thus, we also investigate the throughput of an SmBS and find the optimal density ratio of SmBSs and MaBSs.  
With Equation (16), Figure 9 illustrates the impact of SmBS density on the throughput of an SmBS. From the picture, there is always an optimal SmBS density under different user densities. As the user density increases, more SmBSs are needed to avoid overload. In the network that is not too busy, i.e., \(\lambda_{\rm u}/\lambda_{\rm M}=300\) or \(\lambda_{\rm u}/\lambda_{\rm M}=700\), denser SmBSs can offload more data traffic, so the maximum throughput increases with the user density. However, in busy network, i.e., \(\lambda_{\rm u}/\lambda_{\rm M}=2000\) or \(\lambda_{\rm u}/\lambda_{\rm M}=5000\), since the optimal SmBSs densities are already very dense, higher SmBS density can lead to greater interference that degrades the link rate and further reduces the throughput of an SmBS, so the maximum throughput of an SmBS decreases with the user density in such situation. Nevertheless, as depicted in Figure 9, when \(\lambda_{\rm u}/\lambda_{\rm M}=700\), the optimal SmBS density is \(150\lambda_{\rm M}\), and if it increases to \(\lambda_{\rm u}/\lambda_{\rm M}=5000\), the SmBS density should also be enhanced to \(400\lambda_{\rm M}\) in order to achieve the maximum throughput of an SmBS. It indicates that network densification is reasonable.  
Figure 10 has compared the performance of the optimal deployment and other suboptimal ones. The optimal deployment is implemented by selecting the optimal SmBS density under different network conditions of user density. We take \(\lambda_{\rm m}/\lambda_{\rm M}=10\), \(\lambda_{\rm m}/\lambda_{\rm M}=200\) and \(\lambda_{\rm m}/\lambda_{\rm M}=400\) as examples of the suboptimal deployment, which indicate low density, medium density and relative high density. As the figure shows, our deployment scheme outperforms other ones and achieves the best performance of an SmBS under any user density.  
Figure 9: The impact of SmBS density on the throughput of an SmBS. \(P_{\rm m}/P_{\rm M}=0.003\).
###### Abstract  
At present, sensor-based E-Healthcare systems are attracting more and more attention from academia and industry. E-Healthcare systems are usually a Wireless Body Area Network (WBANs), which can monitor or diagnose human health by placing miniaturized, low-power sensor nodes in or on patient's bodies to measure various physiological parameters. However, in this process, WBAN nodes usually use batteries, and especially for implantable flexible nodes, it is difficult to accomplish the battery replacement, so the energy that the node can carry is very limited, making the efficient use of energy the most important problem to consider when designing WBAN routing algorithms. By considering factors such as residual energy of node, the importance level of nodes, path cost and path energy difference ratios, this paper gives a definition of Optimal Path of Energy Consumption (OPEC) in WBANs, and designs the Optimal Energy Consumption routing based on Artificial Bee Colony (ABC) for WBANs (OEABC). A performance simulation is carried out to verify the effectiveness of the OEABC. Simulation results demonstrate that compared with the genetic algorithm and ant colony algorithm, the proposed OEABC has a better energy efficiency and faster convergence rate.  
E-Healthcare system; wireless body area networks; energy consumption; Artificial Bee Colony Algorithm 2018  
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