esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures
#### 2.4.1 Body Composition Assessment  
This study utilized the Inbody 270 body composition analyzer (Inbody Co. Ltd., Seoul, Republic of Korea), a specialized medical device employing the quantitative bioelectric impedance method for body composition assessment. This method relies on the conductivity of muscle tissue. Body height was measured using a Soehnle electronic ultrasonic height measuring device (Soehnle, Backnang, Germany), which utilizes ultrasound measurements facilitated by a built-in tilt sensor for precision. Height data from the measuring device is transmitted to the computer program Lookin'Body 120, included in the package with the Inbody 270 device. This mat, measuring 700 \(\times\) 500 \(\times\) 5 mm, is equipped with 2304 pressure sensors on its active surface and is a diagnostic tool for evaluating feet under static and dynamic conditions. The sensors on the mat collect measurements for 20 s and transfer them to the computer via the Biomech Studio program (Biomech Studio 2.0 Manual, Letsens Group, Letsens S.R.L., Castel Maggiore, Italy). The mat allows for the measurement of various stabilometric variables, including height (cm), mass (BM [kg]), and body fat percentage (BFP [%]) [32].  
Reliability: Body composition was measured using the Inbody 270 analyzer (Inbody Co. Ltd., Seoul, Republic of Korea), which utilizes a multi-frequency bioelectrical impedance method known for its high reproducibility. Height was measured via a Soehnle ultrasonic device (Soehnle, Backnang, Germany) with automated data transfer to Lookin'Body 120 software. Reliability for these devices is supported by previous studies indicating high test-retest reliability (ICC > 0.90) in athletic populations.
## 1 Introduction
Finally, a part for ML-based IRS communication and some direction integrating IRS with other technologies were provided. The key technologies that can be integrated with IRS are non-orthogonal multiple access, physical layer security, simultaneous wireless information and power transfer, unmanned aerial vehicles, and autonomous driving vehicles. The authors in [24] provided a survey of IRS technology that was focused on hardware design and implementation. In addition, basic concepts, the channel model, physical layer design implementations issues, AI-based solutions for IRS, and system deployment challenges were provided. The study in [25] conducted a survey on the IRS operation principle, metasurface fundamentals, IRS implementation structure, field-programmable gate-array-based IRS, and future research directions for IRS. Another study in [26] focused on a state-of-the-art survey of IRS-assisted technology below a 10 GHz frequency. The authors surveyed the IRS reflecting antenna array operation principle using microstrip patch antennas and adaptation in a wireless communication system. In addition, the authors discussed metasurface properties and reflections for metasurfaces. ML methods are considered to be a powerful tool in solving different problems in wireless communication, and will dominate the future research trend. Thus, this survey is entirely dedicated to reporting ML-based IRS works for the readers. The contributions of the paper can be summarized as follows:  
* In the beginning, we give a complete insight into IRS technology. We provide a comprehensive introduction of the IRS technology, including its structure, working principle, and advantages. * ML-based approaches are provided in a systematic way to understand the state-of-the-art research. We categorize different ML techniques as DL, RL, SL, UL, and FL. Each related paper is described in a comprehensive way by placing them into one of the categories. The problems that are related to IRS system using ML are described as channel state information (CSI), phase shift estimation, signal detection, beamforming, optimization, spectral efficiency, and privacy protection or security. Finally, we give some of the future research scopes that are further needed to be investigated by combined IRS and ML approaches. The organization of the paper is as follows. Section 2 describes the IRS technology fundamentals. Section 3 describes the different ML methods used in the IRS communication system, some future research directions are described in Section 4, and Section 5 concludes the paper.
## 1 Introduction
In some cases, an inaccurate friction model would likely induce an additional control payload. Therefore, in this work, the LuGre model [26] is considered, but no extra estimator [23; 24], friction observer [27] or adaption law [28] is applied. Designers only have to deal with a pair liner gain and a robust gain. To relax the implementation effort, only the upper bound of the variation on the lumped perturbation is needed to adjust the control gain \(w\). Therefore, based on the robust stability property, one can gradually increase the value of the robust gain to achieve a satisfactory result. From the end-user point of view, less control parameters are able to achieve a more user-friendly environment for control tuning. To preserve the approaching/sliding conditions in the traditional sliding mode control (SMC), as well as ease the control discontinuity, this paper develops an extended backstepping sliding mode control (EBSMC) algorithm, which provides global stability of the system without including an additional compensator or observer. Moreover, it will be shown that the proposed controller turns into a robust PD controller once a robust gain is set to be zero. Under this circumstance, the robust stability issue is going to be addressed by using some linear matrix inequalities (LMIs). Finally, the effectiveness of the developed control strategy is verified through numerical and experimental studies.
## 3 MOS-Aware Measurement Scheme in Mobile Networks
### Video Conference MOS Testing and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**RSRP Range**} & \multicolumn{4}{c}{**Downlink Throughput (Kbps)**} \\ \cline{2-5}  & **Q\({}_{\text{DL}}\)=5\%** & **Q\({}_{\text{DL}}\)=1** & **Q\({}_{\text{DL}}\)=20\%** & **Q\({}_{\text{DL}}\)=50\%** \\ \hline \((-80,-75)\) & 81,108.99 & 69,941.47 & 58,038.26 & 42,717.76 \\ \hline \((-75,-70)\) & 84,024.21 & 73,216.94 & 61,404.49 & 45,849.86 \\ \hline \((-70,-65)\) & 84,040.74 & 73,226.51 & 61,409.52 & 45,851.77 \\ \hline \((-65,-60)\) & 88,638.70 & 78,669.06 & 67,211.11 & 51,452.32 \\ \hline \((-60,-55)\) & 94,461.86 & 86,211.75 & 75,755.57 & 60,218.17 \\ \hline \((-55,-50)\) & 101,295.20 & 99,657.95 & 94,443.59 & 83,012.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Since this experiment is based on LTE telecom network architecture, assuming that the network throughput can meet the Skype video conferencing demand for users. The experiment aims at analyzing the impact caused by packet loss rate on MOS values of video conferencing. Since there are eight users using video conferencing services at the same time, they compete with each other for throughput, as shown in Figure 6. It can be found that when the RSRP is greater than \(-122.6\) dBm, the packet loss rate is smaller than \(10\%\). When the RSRP is lower than \(-122.6\) dBm, the packet loss rate increases sharply due to the long distance from the eNB. Figure 7 shows the relationship between MOS and RSRP. The MOS value of the Skype video conference service is calculated according to Equation (7) based on the packet loss rate and the relative video bitrate. In Figure 7, when the RSRP is greater than \(-122.6\) dBm, the MOS value is about \(4.25\). When RSRP is smaller than \(-122.6\) dBm, the packet loss rate rises to more than \(10\%\), and the MOS value drops below \(4\), lower than the MOS acceptable threshold. Based on the explanations above, the eNB needs to determine whether the current eNB can satisfy the users' demand for video conferencing services based on the received RSRP from UE. \begin{table}
\begin{tabular}{c c} \hline Band & 2.1 GHz \\ \hline Channel Bandwidth & 15 MHz \\ \hline Resource Block & 75 \\ \hline eNB’s transport range & 4200 M \\ \hline eNB’s typower & 43 dBm \\ \hline eNB’s antenna gain & 18 dB \\ \hline UE’s typower & 18 dBm \\ \hline UE’s moving speed & 20 m/s \\ \hline \end{tabular}
\end{table} 
Table 4: The network parameters used in the simulation. Figure 6: The relationship for packet loss rate and RSRP from UE.
## 2 Related Works
### DoH Tunneling Detection Methods  
Most previous researchers focused on the applicability and performance aspects of DoH [25; 26; 27]; only a few papers about DoH security have been published so far. Two of the most important objectives studied in DoH security are privacy and data exfiltration.  
Refs. [28; 29] studied the privacy of DoH protocol, and the results show that it is technically possible to defer private information about one's web browsing activity by capturing and analyzing their DoH traffic. Refs. [30; 31] also studied DoH privacy, and the authors also agreed that despite the advantage in privacy, DoH could be a double-edged sword.  
Since DoH is a very recently developed technology, there is little research detecting DoH tunnels. In [29], the authors surveyed the effectiveness of traffic analysis attacks on DoH traffic. They planned a new set of features and showed that the proposed DoH traffic analysis effectively identifies malicious websites. However, their work shows that training a model in a different environment to the one deployed causes a negative impact on the performance. Ref. [30] proposed a novel traffic analysis method that combines size and timing information to classify the encryption DNS traffic. A new encrypted DNS traffic detection was proposed in [32]. In this work, the captured traffic was analyzed based on temporal features and packet sizes to detect DoH traffic. SANS Institute published a white paper [33], clearly explaining DoH detection by applying the Real Intelligence Threat Analysis (RITA) framework.  
The best-known example of malicious DoH attacks is the Godlua Backdoor [34]. In this attack, the attacker used DoH as a command-and-control (C2) communication channel between the attack command servers and malware or botnet in the victim network. Another cruel method of attackers is using DoH to trigger a redirected web page as a part of a spam campaign.  
The author in [15; 16; 17] presented a two-layered binary classification approach, where the first layer distinguishes DoH traffic from non-DoH traffic and the second layer detects the malicious DoH. In their works, some traditional machine learning algorithms, such as Random Forest, Gradient Boosting, and K-Nearest Neighbors were applied for classification tasks. Eventually, these algorithms showed some limitations in term of accuracy and execution time, and required a large labeled dataset. Table 1 illustrates the previous research methods and their drawbacks.
## 2 Dynamic Model and Parameters Optimization  
Since many vibration control devices, such as air springs and metal rubber, have viscoelastic characteristics, the Maxwell three-element model is a typical model describing the viscoelastic characteristics. Therefore, this paper proposes a Maxwell-type DVA with lever, inerter, and grounded stiffness, as shown in Figure 1. The model has a fixed support lever frame between the primary system and the DVA, and its fulcrum is \(O\). In a real installation, the lever fulcrum \(O\) can be fixed to the ground or other inertial frame of reference. The resistance arm and the power arm of the lever are \(r_{1}\) and \(r_{2}\), respectively, which represent the distance between the lever fulcrum \(O\) and the articulated points \(M\), \(N\). The masses of the primary system and DVA are \(m_{1}\) and \(m_{2}\), respectively. The stiffness coefficients of the primary system, DVA, the viscoelastic Maxwell model, and the grounded spring are \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\), respectively. The inerter coefficient is \(b\). The damping coefficient of viscoelastic Maxwell model is \(c\). The displacements of the primary system and the DVAare \(x_{1}\) and \(x_{2}\), respectively. The split point of the spring and damping in Maxwell model is \(x_{3}\). \(F\) and \(\omega\) represent the excitation amplitude and frequency, respectively.  
It should be noted that the influence of the inerter depends significantly on the terminal locations. The amplification ratio of the lever increases means that the distance between the inerter position and the primary system increases, and then the force provided by the inerter will be amplified. We define \(L=r_{2}/r_{1}\) as the amplification ratio of the lever mechanism. Ignoring the mass of the lever and the frictional resistance in the movement, according to [PERSON]'s second law and lever principle, the governing equation of the system is established as  
\[m_{1}\ddot{x}_{1}+k_{1}x_{1}+k_{2}L(Lx_{1}-x_{2})=F\cos{(\omega t)}\] \[(m_{2}+b)\ddot{x}_{2}+k_{2}(x_{2}-Lx_{1})+k_{4}x_{2}+c(\dot{x}_{ 2}-\dot{x}_{3})=0\] \[k_{3}x_{3}+c(\dot{x}_{3}-\dot{x}_{2})=0 \tag{1}\]
# A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks
## 5 Result Analysis
### UiT to HAW  
In this scenario, we examined an inter-national transmission between UiT and HAW in the same continent (see Table 1). The distance between the site of UiT and the site of HAW is around 1847 km, both are located in different European countries. In particular, UiT with 2 ISPs is located in Tromso, a city in northern Norway, and HAW with 1 ISP is located in Hamburg, Germany. For FullMesh and PCDC, using Equation (3), the total number of subflows is two, and the names of subflows are PowerTech-DFN and Telenor-DFN for every CC. Table 2 gives the IF values, which show that all subflows can be used for transmission in PCDC. Therefore, in this scenario, the _SP_* used in PCDC is equivalent with the _SP_ used in FullMesh.  
By using the same method to handle this scenario as above two scenarios, we obtained Figure 8, Equation (15) and Table 4. The R-squares and _p_-values in Table 4 show that Equation (15) is a good model.  
\[tp=\left\{\begin{array}{l}0.567+0.002 bs+0.098 CC_{1}+0.054 CC_{2}+0.114 CC_{3}+0. 093 CC_{4}+0.12 CC_{5}+0.088 CC_{6}+\\ 0.002 bs*CC_{2}+0.098 CC_{1}{}^{2}+0.054 CC_{2}{}^{2}+0.114 CC_{3}{}^{2}+0.093 CC_{4}{ }^{2}+0.12 CC_{5}{}^{2}+0.088 CC_{6}{}^{2}\\ 0.568+0.002 bs+0.098 CC_{1}{}^{2}+0.056 CC_{2}{}^{2}+0.114 CC_{3}{}^{2}+0.093 CC_{4}{ }^{2}+0.12 CC_{5}{}^{2}+0.088 CC_{6}{}^{2}\\ \end{array}\right\}\quad\text{if }PM=\text{FullMesh} \tag{15}\]  
From Table 4, we find the maximum throughput is _tp_max = 0.87 Mbit/s, and the corresponding BS is _bs_ = 30 MiB when CC is Scalable, and PM could be either FullMesh or PCDC. Actually, from Table 1, we can see that the two ISPs of UiT almost have the same bandwidth, and therefore the networks between UiT and HAW are homogeneous networks, where the IFs of all subflows are positive, see Table 2. In this case, PCDC is equivalent to FullMesh. In other words, both PM algorithms can achieve almost the same performance. Since PCDC is more complicated than FullMesh, we can directly choose to use FullMesh in a homogeneous network. According to Table 4, when _bs_ decreases, _tp_ does not change too much. Therefore, considering Equation (10), by sacrificing \(\delta=7\%\) throughput, the final output of MPCOA algorithm is OBS = 0.5 MiB, OPM = FullMesh, OCC = Scalable and OTP = 0.81 Mbit/s. Comparing BS = 30 MiB and OBS = 0.5 MiB, it can be seen that a lot of buffer resource is saved by using Equation (10).  
Figure 8 shows that for scenario UiT-HAW, the best choice is FullMesh and Scalable with throughput of 0.82 Mbit/s. The actual test results are consistent with the output results given by the MPCOA algorithm.  
Figure 8: Throughput test results on NorNet Core testbed for UiT to HAW with different PM and CC, without MPCOA.
# Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value
## 2 Methodology  
The data on which a GIS runs are structured in layers containing information in vector or raster formats. Vector data represent geographic objects or entities such as points, nodes, lines or polygons. The values of the features of interest of such geographic objects are stored in the attribute table of the vector layer. Raster data are stored in an array of cells or pixels (each one representing an elementary geographic area) organized into rows and columns. The spatial resolution of a raster dataset determines the level of detail represented and depends on the size of the cell or geographic area it depicts. Both types of data are referenced to a geographic coordinate system.  
In the proposed methodology, the main objective is to obtain a set of GIS maps that facilitate the identification of those places where the perception time of new PV power plants would be more reduced. The observers considered are visitors or tourists travelling on roads, paths or trails (we will refer to them as routes throughout the article) of the geographical area studied. GIS tools enable the creation of such maps working with data in raster format. These maps represent, for each geographic area or cell, the values of the variable called Global Accumulated Perception Time (_GAPT_). This variable corresponds to the accumulated value of the number of hours per year that a proposed PV plant can be seen by all possible observers in motion, considering all the observation points in the area under study. That is, the _GAPT_ variable is the sum of the values of another variable, the Accumulated Perception Time (_APT_), for all the possible observation points. Considering a single observation point, the value of the variable _APT_ in any cell represents the cumulative value of the possible hours per year in which the proposed PV plant, located in such a cell, can be seen by all observers passing through the observation point. It should be noted that, throughout this article, when we say that a PV plant is located in a particular cell, it really means that the PV plant is located in the elementary geographic area represented by that cell in a GIS.
## 3 Material and Methods
### Proposed Work
#### 3.2.4 Proposed CNN Models  
We have proposed two CNN models; the first one (Figure 7a) starts with 6 blocks. Each block has a convolutional layer, an activation function, and a Max Pooling step. Then we find the Flatten layer. This layer consists in converting the data into a one-dimensional table which will be considered as input in the next layer. We flatten the output of the convolution layers to create a single long feature vector. And it is connected to the final classification model, called the fully connected layer. In other words, we put all the discrete data on a single line and make connections with the 6 dense layers.  
The convolutional layer is the key component of CNN and always constitutes at least their first layer. Its goal is to identify the presence of a set of features in the images received as input. The Max pooling operation consists in reducing the size of the data, while preserving their important characteristics. The Flatten layer makes it possible to flatten the tensor and reduce its dimension. We have used four activation functions: ReLU, SELU, ELU, and Tanh.  
Our second CNN model (Figure 7b) has 4 blocks (like the first model) followed by an LSTM layer. The LSTM is a type of RNN.  
The idea behind this choice of neural network architecture is to divide the signal between what is important in the short term, through the hidden state (analogous to the output of a simple RNN cell), and what is important in the long term, through the cell state, which will be explained below. Thus, the global functioning of an LSTM can be summarized in 3 steps:  
1. Detecting relevant information from the past, taken from the cell state through the forget gate.
2. Select, from the current input, those that will be relevant in the long term via the input gate. These will be added to the cell state, which acts as a long memory.
3. Draw from the new cell state the important short-term information to generate the next hidden state through the output gate. The LSTM layer is followed by a Flatten layer and 3 Dense layers. CNNs are commonly used to solve problems with spatial data. LSTMs are best suited for analyzing temporal and sequential data. However, it is useful to think of our CNN 2 architecture as defining two sub-models: the CNN model for feature extraction and the LSTM model for interpreting features across time steps.  
The proposed CNN1 model consists of 23,767,300 parameters and 696,324 parameters for the proposed CNN2 model. We have used for the two CNN, the ADAM optimizer which for weight updates and categorical cross-entropy loss. Then, we trained the network by using a batch size of 16 and 1000 training epochs.  
The ADAM optimizer is a gradient descent method used for the minimization of an objective function which is written as a sum of differentiable functions. Categorical cross-entropy is a loss function that is used in multi-class classification tasks.
## 3 Results and Discussion
### Strain Behavior of Transverse Bar Resulting from the Corrosion of Longitudinal Bar  
Figure 11 shows the relationship between transverse bar strain (shown as the maximum strain in each specimen) and the amount of longitudinal bar corrosion. According to previous studies [5], internal cracking can be estimated by evaluating the strain behavior of horizontal rebar, as crack width and transverse bar strain are strongly correlated. A comparison of the specimens with different compressive strengths (F20-D32-C40, F40-D32-C40, and F60-D32-C40) showed that the maximum  
Figure 10: Comparisons of corrosion crack behaviors over transverse bars.  
strain of the transverse bar tended to be higher in higher strength concrete. This was again likely a result of corrosion by-products becoming concentrated at the crack surfaces due to the relative scarcity of pores and the formation of dense micro-structures in the cement paste associated with a lower water/cement ratio. In specimen F60-D32-C40, the transverse bar strain was more than 400 \(\upmu\), which approaches 25% of the rebar yield stain. However, as the corrosion by-products could flow out through the cracks in the concrete cover, the strain did not exceed 400 \(\upmu\) by much. In contrast to our findings, strain values above 800 \(\upmu\) have been recorded in experiments where transverse bar corrosion cracking did not occur. This can be explained by the fact that, unlike in our experiments, corrosion products could not flow out through the transverse bar corrosion cracks.  
For the specimens with different longitudinal bar diameters (F40-D25-C40, F40-D32-C40, and F40-D38-C40), the largest longitudinal bar diameter was associated with higher transverse bar strain values with a relatively low amount of corrosion (<10 mg/cm\({}^{2}\)). This might reflect the occurrence of greater ring tension at similar expansion pressures due to the higher longitudinal bar diameter, which would lead to cracking even with a relatively low amount of corrosion. However, when the amount of corrosion increased, the transverse bar strain values tended to converge at approximately 300 \(\upmu\).  
In the specimens with different concrete cover thicknesses (F40-D32-C20, F40-D32-C40, and F40-D32-C60), corrosion cracking occurred later in those with thicker covers. However, there is a high possibility of increased damage to concrete around a longitudinal bar in these cases as the transverse bar strain increases rapidly. Therefore, caution must be taken when evaluating durability, even when a thick concrete cover is used.