esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles and Methodologies
### Simulation Methods for Cracks, Contraction Joints, and Degradation of Elastic Modulus  
When simulating the dynamic characteristics of concrete arch dams, cracks, contraction joints, and the degradation of elastic modulus are key factors affecting the dynamic behavior of the arch dam. To accurately capture the impact of these characteristics on the dynamic response of the arch dam, this study employs the numerical calculation methods described in detail from Sections 2.2.1-2.2.3. The combined use of these methods provides an integrated numerical simulation framework for the cracks, contraction joints, and material performance degradation of the arch dam, thereby providing the necessary foundation for the subsequent optimal sensor placement. Below is a detailed introduction to these methods.
## 5 Field Tests
### Initial Calibration for Acceleration-Based BWIM Analysis on Field Data  
A BWIM analysis is carried out using the derived acceleration responses from a year of traffic passing over the bridge. A total number of 398,445 multi-axle vehicles crossed the bridge in 2014. As the vehicle velocity has a strong impact on the BWIM analysis [46], the velocity range of data needs to be selected. Thus, the traffic data are filtered by using only 5-axle trucks passing within the velocity range of 23.5 m/s to 25.5 m/s, which covers 70% of vehicle velocities, and the GVW range of 15.75 t to 16.75 t. As a result, a dataset of 5208 trucks across the full year is used for the analysis.  
To implement the BWIM concept, an influence line response is required for calibration, i.e., the acceleration response corresponding to the passage of an axle of unit weight. In this section, the influence line is back-calculated from acceleration responses similar to that shown in Figure 13b using the matrix approach described by [47]. In a process similar to that used to estimate masses [19], [PERSON] et al. found the influence curve which gives a least square fit of calculated to measured accelerations [47].  
The acceleration responses to 10 trucks (all 5 axle trucks) passing the bridge with a constant internal bridge temperature are used to calculate 10 influence line responses, as shown in Figure 14. A sample of 10 trucks from the 5208 trucks is used as any greater sample size made little difference to the accuracy of the influence line. All 10 runs took place when the measured internal bridge temperature was 20 \({}^{\circ}\)C, to keep the bridge environmental conditions consistent. The influence line response for each truck passage is adjusted by shifting the phase, i.e., aligning the highest peak points in the response. Finally, the influence line used for the analysis is taken as the mean of the curves. It is shown in Figure 14 as a solid black line.  
Figure 13: Measured response of the Séntvid bridge to the passage of a typical 5-axle truck: (**a**) Strain; (**b**) Derived acceleration.  
Figure 15. shows the typical measured acceleration response to a 5-axle truck and the corresponding calculated response, calculated using the mean influence line. It can be seen from Figure 15 that the calculated response matches the measured bridge response to a 5-axle truck fairly well, and the BWIM system can be developed using the mean influence line (also shown in Figure 14).
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 1 Introduction
Firstly, the AUV should adaptively select the best communication technology according to the specific marine operational data requirements (e.g., data importance and packet size). For example, high-quality data collection (e.g., high-resolution images with 4K size) with UOC can prolong the network lifetime of the UWSN by sacrificing the energy consumption of the AUV, and when the volume of data is relatively less, UAC can be used for remote collection to reduce the energy consumption and travel time of the AUV. Furthermore, the AUV should complete the data collection operation quickly to guarantee the freshness of data as the data value usually decays over time [19]. Generally, the age of information (AoI) can be used to measure data freshness in mobile data collection scenarios [5; 20; 21]. By optimizing AoI, the requirement of the network for timely data delivery can better be satisfied. Therefore, in a multi-modal AUV-enabled mobile data collection scheme, how to optimize the trajectories of AUV and select a communication option to minimize both AoI and energy consumption based on the size and the importance of the packets is a critical issue. To solve the aforementioned issues, in this paper, we propose an acoustic-optical multi-modal mobile data collection scheme. Based on the type and the size of data, the AUV intelligently searches for the optimal trajectory and communication options using the deep reinforcement learning (DRL) approach, thereby minimizing the AoI and extending the lifetime of the sensor network. To the best of our knowledge, this is the first study which focuses on integrating an acoustic-optical multi-modal option with optimal AUV path planning for reliable and timely mobile data collection leveraging the DRL approach. The main contributions of this study are listed as follows. * We investigate an AUV-assisted underwater trajectory planning problem for data collection by integrating the complementary advantages of both acoustic and optical communication with data diversity to perform reliable and timely mobile data collection. * We propose a DRL-based AUV-assisted multi-modal mobile data collection scheme in which we consider several key factors, such as data importance, packet size and data collection option, to minimize AoI and reduce energy consumption. * We propose an optimal angle steering algorithm for AUV navigation to reduce energy consumption, in which the steering angle of the AUV is determined based on the AUV and sensor positions as well as the data collection option. The rest of the paper is structured as follows. We briefly review the related works in Section 2. In Section 3, we introduce the network model with necessary background. In Section 4, we analyze the problem of the multi-modal data collection. In Section 5, we describe the proposed scheme of DRL-based multi-modal data collection in detail. We evaluate the performance of the proposed scheme in Section 6. Finally, we conclude the paper in Section 7.
## 2 Materials and Methods
### Elastography  
Liver stiffness was determined by two methods: 2D-SWE and TE. The 2D-SWE method was performed using Logiq S8 General Electric equipment (General Electric Healthcare, Wauwatosa, WI, USA) and a LOGIQ Shear Wave Elastography S8 XDclear 2.0 software. The investigations were performed using a C1-6-D XDClear convex probe (General Electric Healthcare Company). For each child, 12 measurements were recorded and the final value was represented by their median (E\({}_{\text{median}}\)--median elasticity measured in kilo Pascals--kPa, V\({}_{\text{median}}\)--median velocity). The measurements were performed at 1-2 cm below [PERSON]'s capsule, in a homogenous, free from blood vessels region of interest with a size of 0.5 cm, using an intercostal approach. An interquartile range of <0.3 was used for validation of the measurements.  
TE was performed with FibroScan module IPX0 (Echosens, Paris, France) using the M probe. The measurements were performed by placing the probe in the seventh or eighth intercostal space in the right anterior axillary line. Investigations with ten valid shots and an interquartile range <0.3 were included, and the stiffness results were reported in kPa and the velocity in m/s.  
Fasting duration before the investigations ranged from 4 h in infants to 6 h in adolescents. Children old enough to cooperate were placed with the right arm in abduction in a supine position, whereas infants were held in the supine position by an assistant. The right and left liver lobe lengths as well as the spleen length were measured and compared to normal length of subjects of the same age [25]. SWE was performed in 13 [PERSON] patients and 20 [PERSON] patients, and TE in 7 [PERSON] patients and 14 [PERSON] patients.  
Liver elastography and ultrasonography were performed by a highly skilled physician with over 10 years' experience in pediatric echography and 5 years in hepatic elastography.
###### Abstract  
The historic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.  
mMIMO; 5G; augmented reality; virtual reality; wireless body area networks; IoT applications; beyond 5G
# Parameter Analysis on Seismic Response of Long Lined Tunnel by 2.5D Substructure Method
## 5 P-SV Wave Scattering of Long Lined Tunnel
### Influence of Soil-Rock Interface on Structural Seismic Response  
When the P-SV wave is inclined to incidence, the incidence angle is 30\({}^{\circ}\) and the conversion angle is 45\({}^{\circ}\). The Kobe wave in Section 5.1 is selected for the seismic wave time history record. The tunnel inner diameter \(R=5\) m, and the lining thickness \(r=0.1\)\(R\). The parameters of soil and rock materials are given in Table 3. The location of soil-rock interface is shown by the red lines in Figure 14a-c, which respectively represent that the tunnel is above the soil-rock interface, the tunnel is on the soil-rock interface and the tunnel is below the soil-rock interface. The longitudinal seismic response of the tunnel at different locations of the soil-rock interface is focused on.  
Figure 13: Maximum bending moment of lining under different incident angles of P-SV wave.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**The First Lam\(\hat{e}\)**} & \multicolumn{2}{c}{**The Second Lam\(\hat{e}\)**} \\  & **Constant \(\lambda\) (GPa)** & **Constant G (GPa)** & **Density \(\rho\) (kg/m\({}^{3}\))** \\ \hline \hline Lining & 4.44 & 6.67 & 2240 \\ Soil & 2.14 & 0.24 & 2665 \\ Rock & 2.592 & 1.728 & 2700 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Site conditions.  
The method proposed in this paper is applied to analyze the influence of different soil-rock interface positions on the tunnel seismic response. When the P-SV wave is incident, the maximum bending moment values on the inner surface of the lining are shown in Figures 15 and 16, respectively. According to Figure 15a, when the tunnel is located at the soil-rock interface, the in-plane bending moment \(M_{c}\) reaches the maximum value. For the longitudinal bending moments \(M_{v}\) and \(M_{h}\) in Figure 15b,\(c\), when the tunnel passes through the soil-rock interface, the longitudinal bending moments reach the maximum.  
Figure 14: The location of soil–rock interfaces: (**a**) soil layer thickness 5 m, (**b**) soil layer thickness 15 m and (**c**) soil layer thickness 25 m.  
Figure 15: The maximum bending moment (**a**) \(M_{c}\), (**b**) \(M_{v}\) and (**c**) \(M_{h}\) of tunnel lining at different soil–rock interfaces when P wave is incident.  
However, as shown in Figure 16a,b, when the SV wave is incident, the in-plane bending moment value \(M_{c}\) and the longitudinal bending moment value \(M_{v}\) are close to the maximum value when the soil layer thickness is 5 m and the tunnel passes through the soil-rock interface. When considering the longitudinal bending moment value \(M_{h}\), as shown in Figure 16c, the bending moment reaches the maximum value when crossing the soil-rock interface. Compared with numerical examples of different soil-rock interfaces, the internal force value of tunnel crossing soil-rock interface is still close to or reaches the maximum when the SV wave is incident.
## 3 Proposed Protocol
### Authentication Mechanisms
#### 3.4.2 Other Authentication Mechanisms  
Other authentication mechanisms that can be used in our proposed framework include password-based authentication, challenge-response authentication, and biometric authentication. These mechanisms can be used in situations where the overhead of PKI is not feasible, such as in resource-constrained environments. A challenge-response authentication mechanism is the hash-based message authentication code (HMAC), which can be represented using the following Equation (2).  
\[\text{HMAC(K,M)}=\text{H((K\_0 Xor opad)}\mid\mid\text{H((K\_0 Xor ipad)}\mid \mid\text{M))} \tag{2}\]  
where \(K\) is the shared secret key. \(M\) is the message to be authenticated. \(K\_0\) is the key after padding zeros to the block length of the hash function. _opad_ and _ipad_ are the outer and inner padding values, respectively. \(H\) is the hash function.
# Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
## 2 Dataset Preparation
### Generating Training and Testing Data for the Machine Learning Model  
After generating the peptides, we prepared training and testing datasets to build our machine learning model in the following manner. We first calculated the affinity of the peptides to interact with proteins and scored each peptide with a number between 0 and 1. After that, we defined a conservative threshold to select protein-binding and non-binding peptides. In this way, we made sure that the protein-binding peptides in our dataset are actual binding and positive samples, while non-binding peptides are actual negative samples. In this way, the model can effectively learn the patterns. Based on the corresponding scores, the number of protein-binding peptides was much smaller than that of non-binding peptides.  
The number of non-binding peptides was four times more than the number of binding peptides. We considered this ratio for building our training and testing datasets.  
We selected those peptides with over 0.8 (out of 1) affinity of binding to proteins as positive and those peptides with less than 0.2 (out of 1) affinity of binding to proteins as negative samples. In this way, our dataset could be more robust and have clear-cut positive and negative samples. Peptides with binding affinity between 0.2 and 0.8 cannot be accurately categorized as binding or non-binding. Therefore, to make sure that our true positive and true negative samples were accurate, we discarded them. The aim of this study is to accurately identify protein-binding and non-binding peptides. These thresholds were selected experimentally and by studying the similarity of samples with each other. As a result, we generated a training dataset containing 1600 samples. This set was balanced to contain an equal representation of classes, with 800 samples labeled as positives and 800 as negatives. Such a balanced dataset aids in preventing model bias toward the more dominant class. However, to make sure that our model performs well on the actual data, we considered the actual imbalance when we built the testing dataset. For the independent testing, a dataset comprising 1000 samples was prepared. This dataset includes 200 positive samples and 800 negative samples, intentionally skewed to reflect a real-world scenario where negative instances are more prevalent than positive ones.
## 2 Materials and Methods
### Accuracy Evaluation  
For model performance evaluation, \(R^{2}\) and mean absolute percentage error (\(MAPE\)) are selected as the evaluation index.  
The calculation for \(R^{2}\) is shown in Equation (3).  
\[R^{2}=1-\frac{\sum\limits_{i=0}^{m}{\left(y_{i}-\widehat{y}_{i}\right)}^{2}}{ \sum\limits_{i=0}^{m}{\left(y_{i}-\widehat{y}\right)}^{2}} \tag{3}\]  
where \(y_{i}\) is the real result of the data, \(\hat{y}_{i}\) is the predicted result of the model and \(\overline{y}_{i}\) is the mean value of the data. In \(R^{2}\), the numerator is the difference between the real value and the predicted value, which is the total amount of information not captured by the established model. The denominator is the amount of information carried by the real tag, so the numerator divided by the denominator measures the proportion of the amount of information not captured by the model to the amount of information carried by the real tag. Therefore, the closer \(R^{2}\) is to 1, the better this model will be. Generally, when \(R^{2}\) is greater than 0.6, the model can be considered to have a wonderful effect.  
The calculation for \(MAPE\) is shown in Equation (4).  
\[MAPE=\frac{100\%}{m}\sum\limits_{i=0}^{m}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{4}\]  
\(MAPE\) reflects the relative error of the model, and \(MAPE=0\) means that the model has no error. If \(MAPE\) is greater than 100%, the model is too ineffective. The larger \(MAPE\) is, the larger the error the model will have.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 5 Methods and Results
### Genetic Algorithm for Solving the DM-CVRP
#### 5.2.2 Solving Phase
Therefore, the list of stations considered in the time \(t_{l}\) such that \(l\in\{2,\ldots,b\}\) is \(N^{\prime}_{t_{l}}=N_{t_{l}}\cup\ A_{t_{l}}\setminus\{0_{l}^{v}\}\). At each time slice, the depot considered is the last station visited in the previous time slice. Selection  
Computed fitness allows the selection of members for the next generation; this can be done using different selection types, like Roulette, Tournament, and Random. The tournament selection is adopted to choose some chromosomes to undergo genetic operations. The most common tournament method is the binary tournament, where we select two individuals randomly in the population. We choose the best one (which has the smallest fitness value). This process is repeated until the obtained required number of individuals is reached. On the other hand, after applying genetic operators, selection for replacement determines which individuals will disappear from the population in each generation and which strong individuals survive in the next generation population. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline _Types_ & _Stations_ & \(\mathbf{0_{1}^{1}}\) & 2 & \(\mathbf{5}\) & \(\mathbf{1}\) & \(\mathbf{0}\) \\ \hline \(T_{1}\) & / & \(q_{61}\) & 0 & 0 & \(q_{91}\) & / \\ \hline \(T_{2}\) & / & 0 & \(q_{72}\) & \(q_{10,2}\) & \(q_{92}\) & / \\ \hline \(T_{3}\) & / & \(q_{63}\) & 0 & 0 & 0 & / \\ \hline \(T_{4}\) & / & \(q_{64}\) & \(q_{34}\) & 0 & \(q_{94}\) & / \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._For each individual, we associate a fitness function that corresponds to a compromise between the two objectives, namely the total distance traveled cost and the cost of the penalty caused by exceeding the bin capacity. We represent this function as follows:  
\[\alpha_{1}C\sum_{v\ \in\ V}\sum_{i\in N^{\prime}_{t_{1}}}\ \sum_{\bigcup\{0,0^{ \prime}_{i},\pi+1\}}\ \sum_{j\in N^{\prime}_{t_{1}}}\ \sum_{\cup\{0,0^{\prime}_{i},\pi+1\}}\ d_{ij}x_{ij}^{v}+\ \alpha_{2}\sum_{w\in W}\sum_{i\in N^{\prime}_{t_{1}}}\ \ p_{wi}\ u_{wi}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) represent the importance of the two objectives considered.
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
## 2 Architecture of the Active Phased Array Antenna  
Figure 1 presents the architecture of the proposed phased array antenna. In order to evaluate each of the components individually before their total integration within the antenna, the complete system was divided into several parts: antenna, beamformer, control circuit and power supply circuit, as can be seen in Figure 1. In this figure, the antenna is connected to the beamformer board by 8 matched cables; the control and power circuit are behind the beamformer, as shown in Figure 2. Each part is explained in more detail in the following sections.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 4 Numerical Experiment
### MRI Reconstruction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Template** & **Image** & **Model** & **RE** & **PSNR (dB)** \\ \hline \multirow{2}{*}{Variable Density Sampling} & \multirow{2}{*}{Image1} & \(\ell_{1}\) & 0.0458 & 35.6637 \\  & & \(\ell_{1/2}\) & 0.0356 & 37.8626 \\  & & GMC & **0.0277** & **40.0481** \\ \hline \multirow{2}{*}{Variable Density Sampling} & \multirow{2}{*}{Image2} & \(\ell_{1}\) & 0.0912 & 31.3563 \\  & & \(\ell_{1/2}\) & 0.0797 & 32.5274 \\  & & GMC & **0.0648** & **34.3238** \\ \hline \multirow{2}{*}{Cartesian Sampling} & \multirow{2}{*}{Image3} & \(\ell_{1}\) & 0.1076 & 29.8075 \\  & & \(\ell_{1/2}\) & 0.0952 & 30.8666 \\  & & GMC & **0.0866** & **31.6867** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of different regularizations. The best results are highlighted in bold. Figure 2: (**a**), Original image; (**b**), undersampling template with 30% sampling rate; (**c**–**e**), reconstructed images using \(\ell_{1}\) regularization, \(\ell_{1/2}\) regularization and GMC regularization, respectively; (**f**), difference between (**a**) and (**c**); (**g**), difference between (**a**) and (**d**); (**h**), difference between (**a**) and (**e**).