esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based Multi-Class Seizure Type Classification System
## 2 Materials and Methods
### Proposed Technique
#### 2.2.1 Preprocessing  
All the data in TUSZ do not have the same montage and sampling rate, as those recordings were collected from real hospital data (see Section 2.1). As a result, we performed some initial procedures to generalize the input data prior to feature extraction. Firstly, the EEG segments which are exclusively responsible for seizures were extracted from the dataset. This was achieved using the annotation file provided with the dataset, including the start and the stop time of each seizure event. After extracting the seizure events, we used the transverse central parietal (TCP) montage to accentuate spike activity [9]. Montage is a differential view of the data, which consists of differencing the signals collected from two electrodes (e.g., Fp1-F7, F7-T3) [32]. In fact, neurologists are very particular about the type of montage used when interpreting an EEG because it helps in noise reduction [32; 33]. Different experiments on montage selection have been done in [32; 34], and TCP was found to be the most efficient montage that helps different machine learning algorithms to detect seizures. Secondly, we re-sampled all recordings at 250 Hz, as the EEG recordings in the TUSZ have various sampling rates ranging from 250 Hz to 512 Hz [8]. Finally, we cropped each extracted signal into equally non-overlapped segments such that each segment is of the length of two seconds. This choice was influenced by [9], where the authors investigated different window lengths, and they reported that the two second window length of the signal is the most optimal choice to achieve the best classification results. In summary, we took the following preprocessing steps in sequence to generalize the input data for processing:  
1. Used the transverse central parietal (TCP) montage to accentuate spike activity. Table 4 presents the EEG channels considered in our study.
2. Re-sampled all recordings at 250 Hz.
3. Cropped the signal into equally non-overlapped segments such that each segment is of 2 seconds, resulting in 500 data points.  
After the initial preprocessing steps, the input data were generalized and ready to be processed for transformation.  
Figure 1: Overall architecture of the proposed technique.
## 3 Optimization Problems in Vehicular Networks
### Optimization Challenges in Traffic Improvement Application of Vehicular Networks
\begin{table}
\begin{tabular}{|l l l l l l l|} \hline \hline
**Article** & **Year** & **Objective** & **Methods** & **Type** & **Whicle Communication** & **Case Study** \\ \hline \multirow{2}{*}{[75]} & \multirow{2}{*}{2013} & Evaluate inter-communication system & SLMO & \multirow{2}{*}{Clustering communication} & AODV, \multirow{2}{*}{80211p} & Manhattan map \\  & in cambaring & & NS-2, CHR & & \\ \hline \multirow{2}{*}{[76]} & \multirow{2}{*}{2020} & Improve service delivery in ensarning & \multicolumn{2}{l}{Mutual authentication scheme} & \multirow{2}{*}{Car sharing security and privacy} & \multirow{2}{*}{Cyptopography operations} & \multirow{2}{*}{-} \\  & & minimizing waiting time & & two-way parameter exchange technique & & \\ \hline \multirow{2}{*}{[78]} & \multirow{2}{*}{2002} & Find the optimal place for UAVs to & PSO algorithm & \multirow{2}{*}{Infrastructure development in VANET} & V2I, RSU, AOOV & \multirow{2}{*}{Daegu, South Korea} \\  & & minimize the usage of UAVs service time & OpenStreMap, SUMO & & \\ \hline \multirow{2}{*}{[79]} & \multirow{2}{*}{2002} & Minimize the number of RSUs deployed & Genetic algorithms & \multirow{2}{*}{Infrastructure development in VANET} & V2I, V2I, RSU, AOOV & \multirow{2}{*}{Daegu, South Korea} \\  & & find the best location & & & \\ \hline \multirow{2}{*}{[80]} & \multirow{2}{*}{2021} & MaxMin, MaxSum, MaxMinSum, MaxMinSum, MaxDiff problem & \multirow{2}{*}{Mathematical formulation} & \multirow{2}{*}{Diversity problem} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & &
## 2 Related Work and Our Contributions
### Related Work  
Dependability is extremely important for future 6G communications, mainly due to the integration of critical infrastructures through wireless networks. There exists research that focus on each individual topic, such as reliability, availability, safety, and security. However, dependability as whole has received little research attention. There also exist research on specific topics, such as dependability of industrial IoT [14], where the focus is on real-time and reliability requirements of industrial IoT networks. Similarly, the authorsof [15] discuss the dependability of software-defined networks. The article argues the need for secure and dependable SDNs by-design by first highlighting the threat vectors that can be used by adversaries. Then, the article sketches the design of a secure and dependable network architecture, mainly focusing on the control platform. A survey on heterogeneous dependable wireless networks, focusing on industry, is presented in [16]. The article elaborates on the heterogeneous nature of the next generation factories, where diverse technologies are interconnected through a diverse set of wired and wireless connectivity technologies. However, the main focus is on industrial systems, where dependability in terms of availability and latency of existing technologies is critically discussed.  
The related work on dependability can be divided based on the four categories discussed in this article, i.e, reliability, availability, safety, and security. Articles, surveys, and review articles can be found discussing each of these four aspects in detail. For instance, the authors of [17] discussed deep learning-based techniques that can increase reliability of 6G networks. A survey on deep learning techniques for improving reliability in 6G is presented in [18]. UAVs can also be used to increase the reliability of 6G networks, as surveyed in [19]. Even though all the dimensions of dependability are highly dependent, reliability and availability are highly intertwined. Increasing the reliability of communication networks increases availability. Increasing the reliability and availability of 6G networks, therefore, is discussed in [20], where the focus in mainly on the IoT. The safety feature of dependability is mainly discussed from use-case points of view, and general discussion on theme of safety in 6G networks is lacking. For example, huge research efforts are dedicated to safety in autonomous vehicles and UAVs [21]. Security in 5G and beyond is discussed in [22], in which a detailed description of the security landscape of 5G with potential research direction in 6G is presented. Security and trust in 6G are also elaborated in [23]. The authors discuss the main dimensions of security and trust in 6G, ranging from the physical layer up to the application layer.  
The main lesson learned from the existing research is that most articles are focused on a specific dimensions of dependability.
## 1 Introduction
Due to the huge cost of data collection and data labeling, another alternative solution is to construct a compact data representation, reducing the dependence on data volume. Therefore, redundancy removal is crucial. Some classical redundancy reduction methods, such as principal component analysis (PCA) [17] do not take feature separability into consideration. For a pattern recognition problem, the high feature separability and low redundancy are both important. To address the aforementioned issues, we propose a novel framework of redundancy removed DTDWT (RR-DTDWT) to reduce global redundancy introduced by DTDWT and achieve a compact signal representation through low-redundancy features in the wavelet domain. First, DTDWT was employed to represent EEG signals in dural tree wavelet domain and at the same time, to overcome the drawbacks of DWT at the cost of increasing information redundancy. Then, energy and complexity features were both extracted for classification in our method. The energy features refer to the mean absolute values and variance-like statistic metrics. To measure the complexity property of EEG signals and reduce the influence of the short length of time sequence on entropy calculation, we used modified multi-scale entropy (MMSE) [18] to represent the complexity of EEG signals. MMSE can overcome the shortcoming of MSE by replacing the coarse-graining procedure with a moving-average procedure. To the best of our knowledge, this is the first study to evaluate the effectiveness of MMSE for seizure detection. We constructed a complete picture to represent mental electrophysiological activity in wavelet domain with both a wealth of useful information and redundancy. The redundancy in this work was introduced from three levels: (1) redundancy between adjacent EEG channels, (2) redundancy between dual wavelet trees and (3) redundancy between entropy in different scales. Therefore, in the next step, we aimed to minimize the global redundancy, and meanwhile retain the feature separability. In our work, auto-weighted feature selection via global redundancy minimization (AGRM) [19] was used to reduce the information redundancy. AGRM can take both feature redundancy and separability into account. Moreover, the optimization problem involved in AGRM is convex, so that a global optimum instead of local optimum can be obtained. In our work, a compact representation of the EEG signal can be obtained after removing information redundancy by AGRM. We validated the proposed RR-DTDWT framework on two benchmark databases (Bonn database and CHB-MIT database). The results on both databases demonstrate that RR-DTDWT can yield competitive results compared with previous studies.
# Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding
## 2 Experiment Setup
### Experiment Design  
Welding parameters in this study are listed in Table 1. To quantify the joint condition in microlap welding, a peeling test was conducted for each welded sample. Moreover, peeling force was obtained by breaking the joint with self-designed equipment, as displayed in Figure 1. The peeling force required to break the joint was measured using a Kistler 9217A load cell, and the level was recorded during the entire testing process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Power(W)** & 105 \\ \hline
**Scan Speed(mm/s)** & 200 \\ \hline
**Pulse Frequency (kHz)** & 0.01 \\ \hline
**Processing Time (ms)** & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Welding parameters.  
In a practical welding situation, the loss of appropriate contact between two layers of welded metal sheets might be due to the deformation of thin workpieces or the lack of clamping force caused by improper clamper design. Partial loss in contact reduces joint strength or does not form a joint. To simulate the aforementioned conditions, welding location, the implementation of a central clamp, and the installation of thin paper between two metal-sheet layers were controlled to generate different contact conditions between the metal-sheet layers. Welding quality was then confirmed by conducting the peeling test before signal analysis. Figure 2 displays the fixture design used for generating proper and improper contacts. To generate improper contact between two metal sheets, the central clamp bar was removed, and low-value torque was applied to the screw. Moreover, the welding location was chosen to be close to the central part of the fixture, and a paper sheet that did not cover the welding point was installed between the metal sheets to increase the chance of gap generation. Conversely, to generate proper contact between the metal sheets, the welding location was moved close to the clamp screw, high-value screw torque was applied, and a central clamp bar was implemented. The summary of torque and setup is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proper Contact** & **Loss of Contact** \\ \hline
**Torque for screw** & 6N & 1.5N \\ \hline
**Extra Central Clamp** & Yes & No \\ \hline
**Thin paper between workpiece** & No & Yes \\ \hline
**Welding Location** & Close to the corner of clamp & Close to central Line \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter design for generating proper and improper contacts.  
Figure 1: Peeling test.  
Figure 2: Setup for various contact conditions during welding: (a) proper contact; (b) contact with a gap between metal sheet layers.
# Emotion-Driven Music and IoT Devices for Collaborative Exer-Games
## 4 Integrating Music into Exer-Games
### Problem Description and Requirements  
When a participant is playing, the sensors in their environment acquire data that describe their actions and physiological responses to physical activity. These data are processed by the game application to modify the conditions of the current activity or to determine the next activities the player will complete. As part of these decisions, the application also selects the music to be played during activities to influence the player's emotions. Typically, this music is intended to increase motivation and thereby improve the outcomes of the physical tasks the player is performing. However, it could also be used for other purposes, such as creating distractions that lead to overexertion.  
Figure 1: High-level architecture of a collaborative game.  
In collaborative games, the actions and achievements of one player may influence other participants. This principle also applies to music: the application can select or modify the music that a player hears based on the performance of other players. For example, it may increase the motivation of players who are lagging behind in their activities or create a distracting atmosphere for those who are ahead. These decisions are more complex than those affecting individual players, but both types of decisions share the same technological requirements.  
The requirements related to emotion-based music in exer-games are as follows (these are listed to facilitate reference in subsequent sections):  
* R1: Recognize players' emotions during physical activities.
* R2: Recognize the emotions that songs are likely to evoke in listeners in order to create a catalog of songs labeled from an emotional perspective.
* R3: Make music recommendations based on emotional criteria.
* R4: Personalize emotion-based music recommendations for each player, considering their tastes and musical preferences.
* R5: Integrate decision mechanisms that use music as a stimulus to influence game progress.  
These requirements are translated into a set of software components that work in conjunction with sensors and devices available in players' environments to provide the necessary functionality. These components are organized and orchestrated based on the architectural model presented above. Additionally, cloud computing principles are adopted to address the technical and integration challenges of the proposed solution.
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 4 Parameter Analysis
### The Influence of Seafloor Parameters  
The seabed environment is complex and changeable, so the influence of the seafloor medium parameters is considered on the sound propagation characteristics. The characteristics of seafloor parameters are chiefly manifested in compressional wave velocity and shear wave velocity. The sound source frequency is 50 Hz, the sound source and the sound receiver are at the depth of 30 m and 50 m, respectively, the thickness of the ice is 5 m, the following groups are selected for the sound velocity of a compressional wave and shear wave: one is that the compressional wave velocity and shear wave velocity are 3800 m/s and 1800 m/s, respectively, and the other is that the compressional wave velocity and shear wave velocity are 2400 m/s and 1200 m/s, respectively.  
It can be seen from Figure 5 that the propagation loss of the first seafloor is lower than that of the other seafloor, and the attenuation laws of the two lines are different. The sound field environment is different under different medium parameters. Under the first seabed (compressional wave velocity and shear wave velocity are 3800 m/s and 1800 m/s), a Scholte surface wave is excited, and there is a waveguide normal wave in the water. Under the other seabed (compressional wave velocity and shear wave velocity are 2400 m/s and 1200 m/s), no Scholte surface wave is excited, and there is only an attenuated normal wave in the water. Obviously, different dielectric parameters can lead to the change of sound field, which leads to different attenuation modes. With the decrease of shear wave velocity, the seabed medium behaves as the fluid medium, and the ability of the absorbing surface wave energy decreases. When the shear wave velocity is 0, it is equivalent to the infinite deep seafloor, and the propagation loss reaches the maximum.
## 3 Proposed Protocol: Co-DNAR
### Cooperation of Relay Nodes  
The Algorithm 1 shows the data cooperation in Co-DNAR protocol. Due to data broadcasting, all the neighbour nodes received the data sent by a source \(S\). In the critical situation, \(S\) follows cooperation in data forwarding. Figure 11 shows a simple model of cooperation in which \(S\) transmits the same signal to relay \(R1\), \(R2\) and destination \(D\). The signal received by \(D\), \(R1\) and \(R2\) is modeled as [47]:  
\[Y_{sd}=X_{s}h_{sd}+n_{sd}, \tag{30}\]  
Figure 10: Flow chart showing the working mechanism of Co-DNAR.  
where \(X_{s}\) is the original transmitted signal. The symbols \(h_{sd}\) and \(n_{sd}\) are the channel gain and noise from \(S\) to \(D\), respectively. \(Y_{sd}\) is the output signal at \(D\). The signals transmitted from \(S\) to \(R1\) and \(R2\) are presented, in a respective manner, as:  
\[Y_{sr1}=X_{s}h_{sr1}+n_{sr1} \tag{31}\]  
\[Y_{sr2}=X_{s}h_{sr2}+n_{sr2}, \tag{32}\]  
where \(Y_{sr1}\) and \(Y_{sr2}\) are the output signals at relay 1 and relay 2, respectively. The \(h_{sr1}\)\(n_{sr1}\) are the gain of the channel and noise over the link from \(S\) to \(R1\), respectively. The channel gain and channel noise from \(S\) to \(R2\) are represented by \(h_{sr2}\) and \(n_{sr2}\), respectively. The signals received from \(R1\) and \(R2\) at \(D\), in a respective manner, are modeled as:  
\[Y_{r1d}=\beta Y_{sr1}h_{r1d}+n_{r1d} \tag{33}\]  
\[Y_{r2d}=\beta Y_{sr2}h_{r2d}+n_{r2d}, \tag{34}\]  
where \(Y_{r1d}\) and \(Y_{r2d}\) are the signal received at \(D\) from \(R1\) and, respectively. The channel gains and channel noise from \(R1\) to \(D\) are \(h_{r1d}\) and \(n_{r1d}\), respectively. The channel gains and channel noise from \(R2\) to \(D\) are \(h_{r2d}\) and \(n_{r2d}\), respectively. When the relay has data for transmitting, it first amplifies the data. The factor by which the data is amplified is denoted by \(\beta\) and mathematically is modelled as [48]:  
\[\beta=\sqrt{\frac{1}{|h_{sr}|^{2}+\sigma^{2}}}, \tag{35}\]  
where \(\sigma^{2}\) is the Gaussian random variable with zero mean and unity variance.  
Figure 11: A simple model of cooperation.  
```
\(BR\Leftarrow\) The best relay node \(SS\Leftarrow\) Sink Surface \(SN\Leftarrow\) Sender node \(RN\Leftarrow\) Receiving node \(D_{i}\Leftarrow\) Depth of the sensor node \(i\) \(N_{i}\Leftarrow\) Neighbour of the source node \(i\) for\(i=1:1:5\)do  BR forwards a data packet while(data packet not reached(to the SS)) do if\(SN.Nexthop=SS\)then if\(\text{BER}<\text{Threshold}\)then  packet accepted  find neighbours \(N\)  Sort the depth of \(N\) neighbours in ascending order  else if  Make \(N_{1}\) as 1 st relay node  Make \(N_{2}\) as 2 nd relay node  Packets reached to the \(SS\) = True  while packets not reached to the \(SS\)  else  Packets drop endif else  Nodes consume all of the energy and have no battery power endwhile endif endfor
```  
**Algorithm 1**Data Cooperation.
## 4 Discussion
### Actual Test Environment Setup  
The physical structure of the layers of a machined 6 \(\times\) 6 unit cell prototype is shown in Figure 8a, with an overall size of 128 \(\times\) 128 \(\times\) 2.794 mm\({}^{3}\). The DC power supply and the MS feed setup are illustrated in Figure 8b. Two standard feed horns were used as transmitter and receiver antennas, respectively. The transmitter horn was used to feed the vertical LP wave to the MS under test (MUT), while the receiver horn collected the reflected EM wave signals. It is important to note that during the actual testing, after adjusting the DC voltage, a waiting period was required before the testing could commence. This delay is due to the hysteresis effect in the electro-optical response of the liquid crystals (LCs), meaning the electric field generated by the new voltage cannot be uniformly distributed immediately, which may lead to an inconsistent arrangement of the LC molecules, resulting in an unstable tuning mechanism. Furthermore, to prevent leakage of the LCs, UV-curable adhesive was used for interlayer bonding, and the injection holes were sealed after the LC injection. The test environment was maintained at a temperature of approximately 20 \({}^{\circ}\)C. Figure 8c depicts the actual test setup in the OTA microwave anechoic chamber. The MS to be tested and the transmitting horn were fixed on a wooden frame to minimize errors.