esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Approach
### Grid-Based Partition of Congestion Data  
Let \(P_{t}\) be an original traffic congestion matrix with \(M\) rows and \(N\) columns representing traffic congestion levels for a road network at time \(t\) as shown below:  
\[P_{t}=\begin{bmatrix}p_{t,1,1}&p_{t,1,2}&\cdots&p_{t,1,N}\\ p_{t,2,1}&p_{t,2,2}&\cdots&p_{t,2,N}\\ \vdots&\vdots&\ddots&\vdots\\ p_{t,M,1}&p_{t,M,2}&\cdots&p_{t,M,N}\\ \end{bmatrix}\]  
where each element of \(P_{t}\) is a numerical value representing one traffic congestion level for a corresponding pixel of a raw traffic congestion map snapshot captured at time \(t\).  
After grid-based partition of \(P_{t}\) using a grid size of \(g\ \times g\), \(P_{t}\) now is divided into \(R\ \times C\) grids. As an intuitive visual illustration, Figure 1c shows this segmentation process applied to a network-only image corresponding to \(P_{t}\). For a grid located at \((i,j)\) where \(1\ \leq\ i\ \leq\ R\), and \(1\ \leq\ j\ \leq\ C\), \(\{\ p_{t,i,j}^{k}\ |1\leq k\leq g^{2}\ \}\) denotes the set of values for congestion levels as represented by all pixels in that grid at time \(t\).
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 7 Simulation Results  
We have successfully constructed the DRL model for solving the VNF-FGE problem with and without the nodal outages. Currently, in this paper, we considered two distinctive topologies as our environment: Netrail topology with 7 Nodes and 10 links and BtEurope topology with 24 Nodes and 37 links [22]. The performance of the model is evaluated based on the SAR, and Runtime, under diverse conditions: (1) Variation in Nodal capacity, (2) Variation in VNF-FG complexity, and (3) Nodal outages. A diagrammatic representation of these expansions is shown in Figure 12.  
We assumed that the link capacity of the substrate network is between 1 Gbps to 10 Gbps, and the link delays of 0 to 10 ms, which is initialized randomly among the links. Note that, when we embed the VNF-FGs into the substrate network, we need to ensure that a path between any two VNFs should have a total delay of less than 30 ms, which is a sum of these randomly initialized link delays. We have started by analyzing the performances of the networks based on the different nodal capacity. For each run, we have considered one scenario out of four: 2, 4, 8, and 12 core CPUs per node where each core can accommodate 2, 4, 8, and 16 VNFs. Therefore, we have evaluated the network performance for 16 different combinations of nodal capacity.  
To advance our work, we have investigated the performance of the model for various degrees of complexity of the VNF-FGs. That is 2, 4, 6, 8, and 10 VNFs per VNF-FG. These VNF-FGs are generated using the Erdos-Renyi model [23] with the \(\epsilon\) value of 0.3. The probability of connectivity among the VNFs depends upon the \(\epsilon\) value and number of VNFs per VNF-FG. Thus, these VNF-FG's degree of complexity varies. However, our investigation did not end here; we have examined the efficiency of the Intelligent topology under the compromised situation by assuming the occurrence of nodal outage due to disasters or vulnerability. The above experiment is analyzed for 30 ms delay with 30% and 50% of the nodal outages.  
Each run consists of 100 episodes, and each episode comprises 100 time-steps. Each time-step generates one VNF-FG with a unique combination of resources. The DRL model is built using the PyTorch library written in the Python language. The results are generated using the Intel Core i7 processor with 64 GB RAM. The following Table 2 provides the parameters used to construct the DRL model. The constructed codes for the DRL model are available in a repository [24].
## 2 Optimisation Methods and Modelling  
### The Energy Hub (EH) Model  
An Energy Hub is a conceptual model of any bounded area's energy infrastructure, where different forms of energy can be converted, transmitted and stored [29]. It connects inputs and outputs of multiple energy carriers. Figure 2 gives an example of an Energy Hub.  
Figure 1: Energy Hub optimisation algorithm proposed in this paper.  
There are two common ways to establish a matrices equation for an Energy Hub. The first one uses forward-coupling matrices to determine for a set of given inputs, how the dispatch factors and conversion efficiencies determine system outputs [30]. In this way, an Energy Hub model can be mathematically expressed as:  
\[\mathbf{L}=\mathbf{C}\mathbf{P}-\mathbf{M_{E}}=[\mathbf{C}-\mathbf{S}]\ [\ \ \ \ \mathbf{P}\ ] \tag{1}\]  
where \(\mathbf{C}\) represents the coupling matrix. Normally the terms in this matrix are the product of the energy converters efficiencies and the dispatch factor that denotes the proportion of energy flowing into the converter. \(\mathbf{L}\) and \(\mathbf{P}\) are the different energy carrier outputs and inputs power matrix in an Energy Hub respectively. \(\mathbf{M_{E}}\) is an equivalent storage power exchange vector. \(\mathbf{S}\) and \(\mathbf{\dot{E}}\) represent the storage coupling matrix and steady-state energy storage vector respectively.  
\[\mathbf{P}\ =\ \mathbf{DL}\ +\ \mathbf{M_{E}} \tag{2}\]  
However, in order to reduce energy consumption, it is often a requirement to get the minimum input power for a given output power (demand) [31; 32]. Therefore, in this way, the EH model can be mathematically where the supplies are explicitly expressed in terms of the loads:  
In Equation (2), \(\mathbf{D}\) represents the backwards coupling matrix. Energy Hubs can be modelled in either way, and both approaches are effective in optimising energy consumption. For a house holder, the daily energy cost optimisation problem can then be formulated as:  
\[\textit{Minimise}\ F(x) \tag{3}\]  
\[\textit{Subject to}\ g(x)\ =\ 0 \tag{4}\]  
\[h(x)\ \leq\ 0 \tag{5}\]  
where \(F(x)\) is a scalar-valued objective function, \(g(x)\) normally comes from the conservation of power and \(h(x)\) normally comes from limitations, e.g., maximum output power, or number of converters.  
To optimise the Energy Hub model, it is important to predict the demands (heat and electricity) precisely, because the predicted demands can be used to inform on control of energy generation systems and energy storage system over a considered time horizon. In Section 2, part 2, short term prediction will be introduced to control power generation system (CHP system control) and in Section 2, part 3, long term prediction will be used to control the energy storage system.  
Figure 2: An example of Energy Hub model that contains a combined heat and power (CHP), a boiler, a hybrid electrical storage system and a water tank heat storage system.
## 3 Results and Discussion
### Optimization of Deff, Sp and dE
#### 3.3.2 Drying at Optimal Mode  
To evaluate the accuracy of the proposed optimization model, drying at optimal mode (optimal input parameters) was performed. The experimental results were then compared with the theoretical results. The results are presented in Table 2.  
From Table 2, the percentage error between experimental results and predicted theoretical value is calculated. This error is less than 5%, which means it is still in the acceptable percentage error range. Therefore, the model can be used for the optimization of the drying process of Bo Chinh ginseng by the ultrasound-assisted heat pump drying method.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
###### Abstract  
Device-to-Device (D2D) communication is one of the critical technologies for the fifth-generation network, which allows devices to communicate directly with each other while increasing transmission rate, but this communication is vulnerable to interference. When video transmission is carried out in an environment with interference, problems such as high packet loss rate, poor quality of the video, and blurred screen may exist. These problems can be effectively solved by redundant coding operations at the source node, but the extra coding operation imposes a heavy computational burden on the source node. In order to alleviate the computational overhead of the source node, reduce transmission delay, and guarantee transmission quality, this paper proposes an efficient video multicast transmission scheme based on Random Linear Network Coding (RLNC) in D2D networks. In the scheme, the receiving devices in the transmission participate in the process of generating repair packets that are used to remedy the loss of encoded packets during transmission. The source node multicasts the encoded video file. The receiving nodes re-encode the received data packets with RLNC and then send them to the network again. The nearby nodes can decode the original data through the encoded or re-encoded data packets. The performance of the proposed scheme is evaluated through both simulation and real experiments. The experimental results show that compared with the traditional RLNC scheme, this scheme could balance the computation overhead of the mobile devices and reduce the encoding and decoding delay by about 8%. When the packet loss rate is high, the proposed scheme can obtain better video quality than the traditional replication-based scheme.  
Network Coding; Video Distribution; Reliability; Multicast; Load Balancing +
Footnote †: journal:
# Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network
## 5 Conclusions  
Considering the increasing use of the steel plate shear wall as a lateral load-bearing system in high-rise buildings, investigating the behavior of this system is important. The opening in the infill plate of SPSW can be made due to functional and/or architectural purposes. The effect of opening existence along with the influence of opening area on the behavior of the SPSW needs to be understood. In this paper, 110 steel shear walls specimens were modeled and analyzed using the finite element method. The results were used to develop a simple, fast and accurate machine learning method, called RBF, in order to estimate the seismic performance of the SPSWs. The result was normalized so that it can be used for any SPSW with arbitrary dimensions.  
The outputs of the network are normalized ultimate load, absorbed energy, and stiffness. The results showed that the presence of rectangular openings decreases the load capacity, stiffness and, energy absorption of SPSWs. An opening with the ratio of opening area to infill-plate area, i.e., A\({}_{0}\)/A\({}_{s}\), up to 15% will not have much impact on reducing the load capacity and energy absorption. The shear wall stiffness is dramatically affected by the thickness of the infill plate. The results show that locating an opening in the thicker infill plates will result in a further reduction in stiffness. In addition, the results indicated that the proposed neural network could precisely predict the effects of rectangular opening on the behavior of the steel shear walls.  
Conceptualization, M.J.M.; methodology, M.J.M., M.M.R., and A.S.; software, M.J.M.; validation, M.J.M.; formal analysis, M.J.M.; investigation, M.J.M., and A.S.; resources, M.J.M., M.M.R., and A.S.; data curation, M.J.M.; writing--original draft preparation, M.J.M.; writing--review and editing, M.J.M., A.S., and M.K.; visualization, X.X.; supervision, M.K.; funding acquisition, A.S., and M.K.\" All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Actual Tracking Test and Verification of AUV
### RBF-Based MRAC  
Two time-varying functions are used as the input of the AUV control terminal. They are, respectively, the surge force \(F_{u}\) and the yaw moment \(F_{r}\) generated by the actuator.  
\[\begin{array}{l}F_{u}=10\sin(3t+0.1)\\ F_{r}=6 sin(0.4t+2)-0.2\end{array} \tag{34}\]  
This simulation mainly considers the tracking performance in the surge direction. For a more intuitive understanding, the environmental disturbance in the nonlinear continuity equation is set as:  
\[\begin{array}{l}f_{1}(u,t)=1000 sin(0.02t)\\ f_{2}(r,t)=0\end{array} \tag{35}\]  
The underwater robot used for testing is shown in Figure 1, with mass \(m=20.32\) kg and inertia \(I_{z}=2.3\) kg \(\cdot\) m\({}^{2}\). The 3D model of the underwater robot is imported into the hydrodynamic analysis software to obtain the hydrodynamic additional mass, \(X_{H}=-0.62\) kg/s, \(Y_{v}=-23.6667\) kg/s, \(N_{r}=-23.6667\) kg \(\cdot\) m\({}^{2}\)/s. The speed and acceleration of the robot can be obtained through its sensors, which are convenient for real-time control. The following  
Figure 4: Typical AUV Model.  
Figure 3: Electronic warehouse for underwater robots.  
nonlinear dynamic model is used as the actual model of the MRAC controller. Of course, the reference model is to remove the nonlinear disturbance \(f_{1}(u,t,)\) and \(f_{2}(r,t)\) in (9):  
\[\begin{array}{l}\dot{u}=M_{1}(5.871u+43.9867 vr+f_{1}(u,t)+F_{u})\\ \dot{v}=M_{2}(43.6971v-20.94 ur)\\ \dot{r}=M_{3}(23.6667r-23.0467 uv+f_{2}(r,t)+F_{r})\end{array} \tag{36}\]  
where  
\[M_{1}=0.0478,M_{2}=0.00.0227,M_{3}=0.0385\]  
Experiments are carried out according to the above conditions, and the nonlinear disturbance given by Formula (35) is applied. The initial conditions are \(x_{0}=1m\) and \(y_{0}=0.6m\). The experiment is carried out in a pool that can provide waves, which can effectively simulate the complex underwater environment and increase the uncertainty of the model. The data obtained by the built-in acceleration sensor of the AUV is compared with the expected trajectory value for analysis. The experimental results are shown in Figures 5 and 6. Figure 6 shows the output of the sway direction controller and the output of the reference model. Figure 7 shows the controller output and desired output for the yaw rate. It can be found that the proposed RBF and model reference adaptive controller can achieve the desired output within a certain period when the AUV has disturbance (underwater flow, etc.) uncertainty. It can make \(\underset{t\rightarrow\infty}{lim}error=0\). Using the controller designed in this article, the underwater robot is controlled for trajectory tracking experiments. It is verified that the AUV can effectively track the preset trajectory in the presence of water flow (Figure 7).  
Figure 5: (a) Surge speed control output and desired output. (b) Error.
## 1 Introduction  
As a core link between inter-city transportation (e.g., airplane and rail, etc.) and intra-city transportation (e.g., vehicle and urban rail transit, etc.), hubs play an irreplaceable role in city mobility. Passengers board vehicles in the pick-up zone and depart the hub by a connected roadway. A better understanding of the unique traffic characteristics of the connected roadway can help to reveal the collecting and dispersing capacity of the hub.  
One key element for exploring the traffic characteristics is a consideration of the queues generated in the pick-up zone. Generally, the pick-up zone contains three main parts: the vehicle service area, vehicle queueing area and passenger waiting area. After arrival at the hub by inter-city transportation, passengers walk to the passenger waiting area. Meanwhile, the vehicles leave the vehicle storage park and enter the vehicle service area to pick passengers up using the connected roadway. Because the interactions between passengers and vehicles cost time, delays and queues of vehicles are generated on the roadway. This process is illustrated by Figure 1. The traffic characteristics led by the queuing in the pick-up zone can be seen in the data. In Figure 2, when the vehicle flow density increases over the peak regime, the flow drops sharply along with capacity drops [1]. The capacity of the roadway linking to a hub is thus hard to measure.  
Motivated by this, this paper aims to describe the process of human-vehicle interaction in the pick-up zone through a threshold queueing theory model, and from this, to reason about the queuing of vehicles on the connected road and thus assess its capacity. [PERSON] [2] explained this phenomenon as the transition from non-congested traffic to congested traffic, which inspired our research. Specifically, a fundamental diagram (FD) model derived from M/M/1 threshold queueing theory with two service conditions is used for capturing the traffic characteristics of a roadway connecting to a large-scale hub.
# Negative Stiffness, Incompressibility, and Strain Localisation in Particulate Materials
## 4 Incremental Incompressibility in 2D and 3D Simulations
### 2D Simulations  
For the 2D case, the critical value of incremental Poisson's ratio is equal to 1. In order to verify that critical value, we performed 2D simulations of the uniaxial (unconfined) compressive test. The 2D sample consisting of 3786 particles was generated as explained in Section 2. The linear parallel bond model was used to simulate particle bonds, as described in Section 2.  
In order to determine the incremental Poisson's ratio, we need to use a suitable time interval, as plotting the Poisson's ratio timestep by timestep will demonstrate considerable fluctuations. A natural choice of the time interval is the characteristic time of the computational stabilisation. In order to obtain this characteristic time, we applied a step loading to our sample. We then counted the timesteps required for the force on the wall to stabilise. As shown in Figure 4, it took about 4000 computational timesteps for the average force on the wall to be stabilised. Hence, the time that corresponded to 4000 steps was used as the time interval to compute the incremental Poisson's ratio.  
The incremental Poisson's ratio is calculated as  
\[\
u=-\frac{L_{n+1}-L_{n}}{A_{n+1}-A_{n}}, \tag{13}\]  
where \(L_{n}\) and \(A_{n}\) are the lateral and axial strain for timestep \(n\).  
Figure 5 shows the incremental Poisson's ratio and axial stress vs. the timestep. It can be seen from the figure that the critical value of 1 of incremental Poisson's ratio was reached in the peak region.  
The localisation pattern of the 2D virtual sample is shown in Figure 6. It can be seen that the localisation pattern does not exactly incline at 45\({}^{\circ}\). The difference between the simulated inclination angle and 45\({}^{\circ}\) was due to the fact that the material after microfracturing and dilatancy is no longer isotropic (as assumed) due to stress-induced anisotropy, although the anisotropy and the difference between the localisation angle and 45\({}^{\circ}\) are small.  
Figure 4: Characteristic time (in computational timesteps) of a full-sized 2D sample. The horizontal axis numbering starts with actual loading, skipping the stages of sample generation and relaxation.  
Figure 5: Incremental Poisson’s ratio and axial stress vs. timestep in 2D simulations.
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
## 3 Governing Equations
### Wave Equation  
The incident electromagnetic wave on the plasma satisfies Maxwell equations:  
\[\
abla\times\mathbf{H}\ =\ i\omega\varepsilon_{0}\varepsilon_{\mathrm{r}}\mathbf{E} \tag{1}\]  
\[\
abla\times\mathbf{E}\ =\ -i\omega\mu_{0}\mathbf{H} \tag{2}\]  
where \(\mathbf{E}\) and \(\mathbf{H}\) represent the electric and magnetic field strength vectors, respectively. In addition, \(i\) is \(\sqrt{(-1)}\), \(\omega\) signifies the angular wave frequency, and \(\varepsilon_{0}\) and \(\mu_{0}\) denote the permittivity and permeability of the vacuum, respectively. The symbols \(\varepsilon_{\mathrm{r}}\) express the relative electric permittivity [30]. The substitution of Equation (1) into Equation (2) provides Equation (3) as follows:  
\[\
abla\times\
abla\times\mathbf{E}\ =\ \omega^{2}\mu_{0}\varepsilon_{0}\varepsilon_{ \mathrm{r}}\mathbf{E} \tag{3}\]  
The wavenumber in a vacuum is given by \(k_{0}\ =\ \omega/c\), \(c\ =\ \varepsilon_{0}/\mu_{0}\) thus, Equation (3) can be rewritten as Equation (4):  
\[\
abla\times\
abla\times\mathbf{E}\ =\ k^{2}\varepsilon_{\mathrm{r}}\mathbf{E} \tag{4}\]  
The relative permittivity of the plasma in Equation (4) is given by Equation (5):  
\[\varepsilon_{\mathrm{r}}\ =\ 1-\frac{\omega_{\mathrm{p}}^{2}}{\omega(\omega- iv)} \tag{5}\]  
where \(v\) is used to express the electron elastic collision frequency; \(\omega_{\mathrm{p}}\ =\ \sqrt{\frac{n_{\mathrm{e}}e^{2}}{\varepsilon_{0}\mathrm{m }_{\mathrm{e}}}}\) denotes the angular frequency of the plasma; and the terms \(n_{\mathrm{e}}\), \(m_{\mathrm{e}}\), and \(\mathrm{e}\) stand for the electron density, electronic mass, and fundamental charge, respectively. The electronic conductivity is given by Equation (6).  
\[\sigma\ =\ \frac{n_{\mathrm{e}}e^{2}}{m_{\mathrm{e}}}\frac{1}{v+i\omega} \tag{6}\]Now Equation (4) is rewritten as Equation (7):  
\[\
abla\times\
abla\times\mathbf{E}\ =\ k^{2}\varepsilon_{\rm r}\bigg{(}1-i\frac{ \sigma}{\varepsilon_{0}\omega}\bigg{)}\mathbf{E} \tag{7}\]  
When the microwave directly incidents into plasma, according to Equation (5), we find that the real part of \(\varepsilon_{\rm r}\) becomes negative for the plasma, which is collisionless when \(\omega_{\rm p}^{2}>\omega^{2}\). Thus, the electron density \(n_{\rm e}\) has a critical number density of approximately \(7.6\times 10^{16}\) m\({}^{-3}\), which corresponds to \(\omega_{\rm p}^{2}\ =\ \omega^{2}\) and \(\varepsilon_{r}\ =\ 0\).  
When a medium is present between the plasma and the incident wave, the plasma frequency is expressed by Equation (8) [37].  
\[\omega_{\rm p}^{2}\ =\ (1+\varepsilon_{\rm d})\omega^{2} \tag{8}\]  
In the model, the effective permittivity of the quartz tube is 4.0, and the incident wave operating frequency is 2.45 GHz. Therefore, the cutoff electron density value of the collisionless plasma is calculated to be \(4\times 10^{17}\) m\({}^{-3}\).
## 4 Experimental Results of the Proposed Systems
### Results of ANN Classifier Based on Fusion of Features
#### 4.5.2 Best Validation Performance  
The cross-entropy, or mean square error, is one of the most important tools for evaluating the ANN classifier on the two X-ray datasets for tuberculosis. Mean square error values between actual and expected values are recorded in all epochs during all phases of splitting of the dataset. The grid generates the mean squared error in different colors, where each color represents a phase; a green color represents the evaluation of the network during validation, a blue color represents its performance during the training of the dataset, and a red color represents its performance during the testing of new samples, while the best performance of the network is represented by the dashed lines between the x and y axes. Figure 10 shows the cross-entropy produced using an ANN classifier for diagnosing the two tuberculosis datasets. For the first dataset, it is noticed that the ANN network with the features of ResNet-50, GLCM, DWT and LBP reached the best performance when the minimum error was 0.0051026 at epoch 10, while with the features of GoogLeNet, GLCM, DWT and LBP, it reached the best performance for the network when the minimum error was 0.010765 at epoch 20. For the second dataset, note that ANN with ResNet-50, GLCM, DWT and LBP features obtained the best performance when the minimum error was 0.041676 at epoch 27, whereas with GoogLeNet, GLCM, DWT and LBP features, it achieved the best network performance when the minimum error was 0.0041272 at epoch 28.  
Figure 10: Display validation performance of the ANN to classify the two datasets based on the hybrid features. (**a**) ResNet-50, GLCM, DWT first dataset and LBP. (**b**) GoogLeNet, GLCM, DWT and LBP first dataset. (**c**) ResNet-50, GLCM, DWT and LBP second dataset. (**d**) GoogLeNet, GLCM, DWT and LBP second dataset.