esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Updating Method for FE Model of Antenna
### Parameter Optimization  
We update the original FE model of the antenna by establishing an optimization function. Similar to Section 3.3, the surrogate model for parameter optimization is fitted before parameter optimization is performed. We used the \(d\)' sensitivity parameters obtained from the sensitivity analysis as input variables for parameter optimization. Then, we use the cubic polynomial interpolation method to interpolate the displacements of the 424 upper chord nodes of the backup structure to obtain the displacements matching the 1640 marker points on the main surface of the actual antenna.  
\((u_{k}^{x},u_{k}^{y},u_{k}^{z})\) represents the displacement of the actual antenna primary reflector surface node measured by the photogrammetry method, and \((v_{k}^{x},v_{k}^{y},v_{k}^{z})\) represents the node displacement of the FE model obtained by the interpolation method relative to the actual antenna measurement point node; the relative displacement error between the antenna's node and the original FE model's corresponding nodes is calculated as  
\[\Delta_{k}=\sqrt{(u_{k}^{x}-v_{k}^{x})+(u_{k}^{y}-v_{k}^{y})+(u_{k}^{z}-v_{k}^ {z})} \tag{27}\]  
to minimize the relative displacement error's RMS as the optimization objective, and the objective function is  
\[f=min\sqrt{\frac{\sum_{k=1}^{M}\Delta_{k}}{M}} \tag{28}\]  
The optimization problem for the antenna primary reflector surface deformation is expressed as follows:  
\[\left\{\begin{array}{ll}find&[E_{1},\ldots,E_{d^{\prime}}]\\ \\ \text{min}&f=\sqrt{\frac{\sum_{k=1}^{M}\Delta_{k}}{M}}\\ \\ \text{s.t.}&\underline{E}<E<\overline{E}\\ &f<f<\overline{f}\end{array}\right. \tag{29}\]where \(\underline{E}\) and \(\overline{E}\) represent the elastic modulus parameter \(E\) upper and lower bounds, and \(\underline{f}\) and \(\overline{f}\) represent the objective function \(f\) upper and lower bounds. Finally, the established optimization model is solved using the genetic algorithm.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results
### VOCs Dependency
As stated earlier, substantial formation of HONO on the asphalt surface under low light conditions and then rapid saturation in typical daylight indicate that the asphalt surface has a light dependency similar to that of the humic acid surface [28], in contrast to photocatalytic mineral surfaces where HONO formation is linear for a wide range of light intensities [36]. These distinct characteristics of VOCs dependency on HONO formation observed in our experiment strongly suggest that the HONO production mechanism on hydrophobic asphalt surfaces is different with that on typical hydrophilic humic acid surfaces. In this condition, hydrocarbons and its oxidized derivatives may act as hydrogen donors on the hydrophobic asphalt surface and enhance the reduction of NO\({}_{2}\) to HONO by a process  
Figure 4: HONO production rates as a function of toluene and hexane mixing ratios. NO\({}_{2}\) and TUV were fixed as 100 ppbv and 11.3 Wm\({}^{-2}\), respectively. similar to that proposed by [PERSON] et al. [42] (R5). Consequently, the saturated HONO production on the asphalt surface at higher VOCs concentrations can be attributed to the limited oxidation of VOCs under fixed conditions of light intensity and NO\({}_{2}\) concentration. \[NO_{2}(g)\underset{\text{VOCs as extra hydrogen source}}{\overset{ asphalt surface}{\to}}HONO(g)\] (R5)  
Given that our experiments were not designed to analyze the detailed mechanism of HONO formation on the asphalt surface, our postulate cannot be fully proven at this stage and needs to be tested in further studies. The purpose of this study was to assess the contributions of HONO flux from asphalt surface to daytime HONO concentrations in typical urban conditions.
# Design of Optical Tunnel Switching Networks for Big Data Applications
## 6 Conclusions  
A large-scale optical tunnel switching network using Torus topology networks and wavelength selective switches for future big data applications has been presented. All nodes of a large-scale optical tunnel switching network use WSS switching modules, and communication between nodes uses multiple wavelengths to establish a tunnel, and its wavelength can be reused. Because using a wavelength to establish a tunnel not only simplifies the complexity of the network, but also reduces the latency of the network. When the traffic of one node is increased, the number of tunnels for the node can be increased in time to balance the load traffic to avoid traffic loss. Therefore, many applications of the Internet of Things with big data in the future can be realized. The simulation results show that the Torus topology optical switching network with 25 nodes needs two planes to provide approaching to 100% throughput, and the Torus topology optical switching network with 49 nodes needs three planes to provide approaching to 100% throughput. The advantages of the optical tunnel switching network is that it does not require high-speed switching elements and flexible channel scheduling to avoid data loss when traffic volume is changed.  
Conceptualization, Y-J.C., B-T.C., C-P.W. and Y-Y.L.; methodology, Y-J.C., B-T.C., and C.-P.W.; software, Y-J.C., B-T.C., and C-P.W.; validation, Y-J.C., B-T.C., C-P.W. and Y-Y.L.; formal analysis, Y-J.C., B-T.C., and C-P.W.; investigation, Y-J.C., B-T.C., and C-P.W.; resources, Y-J.C., B-T.C., C.-P.W and Y-Y.L.; data curation, Y-J.C., B-T.C., C-P.W. and Y-Y.L.; writing-original draft preparation, Y-J.C., B.-T.C., C.-P.W. and Y.-Y.L.; writing--review and editing, Y-J.C., B.-T.C., C.-P.W. and Y-Y.L.; visualization, Y-J.C.; supervision,  
Figure 20: Application of Torus topology optical tunnel switching network.  
Y.-J.C. and Y.-YL.; project administration, Y.-J.C. and Y.-YL.; funding acquisition, Telecommunication Laboratories, Chunghwa Telecom Co., Ltd. All authors have read and agreed to the published version of the manuscript.  
No external funding was obtained for this research.  
There are no conflicts of interest in this research.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Correlation Coefficient Generation
To evaluate the performance of the proposed LW-SSM and SW-SSM algorithms, the decoding performance for each participant was computed as the percentage of correctly decoded NOL windows. To evaluate the delay to detect a cued attention switch of the proposed LW-SSM and SW-SSM algorithms, the delay was computed as the time takes for the LW-SSM and SW-SSM algorithms to detect an attention switch after the moment the arrow on a screen cued to switch attention. #### 2.5.2 Adaptive Gain Controller (AGC)

The probabilistic attention measure of the attended speaker \(\hat{p}_{a,i}\) in window \(i\), either obtained using the GLM in (30) or using the SSM in (46), is then used to drive the AGC (see Figure 2). The speech signal \(s_{1,t}\) of speaker 1 and the speech signal \(s_{2,t}\) of speaker 2 are first segmented into non-overlapping windows of length \(K_{\text{AGC}}\), i.e., for window \(i\)

\[\mathbf{s}_{1,i}=\Big{[}s_{1,(i-1)K_{\text{AGC}}+1}\ \mathbf{s}_{1,(i-1)K_{ \text{AGC}}+2}\ \cdots\ \mathbf{s}_{1,iK_{\text{AGC}}}\Big{]}^{T}, \tag{47}\]

\[\mathbf{s}_{2,i}=\Big{[}s_{2,(i-1)K_{\text{AGC}}+1}\ \mathbf{s}_{2,(i-1)K_{ \text{AGC}}+2}\ \cdots\ \mathbf{s}_{2,iK_{\text{AGC}}}\Big{]}^{T}. \tag{48}\]

Based on the AAD result for window \(i\), the attended speech vector \(\hat{\mathbf{s}}_{a,i}\) and the unattended speech vector \(\hat{\mathbf{s}}_{u,i}\) are determined as

\[\left\{ \begin{array}{ll}\hat{\mathbf{s}}_{a,i}=\mathbf{s}_{1,i},&\hat{ \mathbf{s}}_{u,i}=\mathbf{s}_{2,i}&\text{if the identified attended speaker is speaker 1}\\ \hat{\mathbf{s}}_{a,i}=\mathbf{s}_{2,i},&\hat{\mathbf{s}}_{u,i}=\mathbf{s}_{1,i }&\text{otherwise}.\end{array} \right. \tag{49}\]By multiplying the attended speech vector \(\mathbf{\hat{s}}_{u,i}\) with the gain \(\lambda_{a,i}\) and multiplying the unattended speech vector \(\mathbf{\hat{s}}_{u,i}\) with the gain \(\lambda_{u,i}\), the objective of the AGC is to achieve a desired signal-to-interference-ratio (SIR) between the identified attended and unattended speakers in window \(i\). The desired SIR in window \(i\) is defined as a linear function of the probabilistic attention measure \(\hat{p}_{a,i}\), i.e.,

\[\text{SIR}_{i}^{des}=\text{2 SIR}_{max}\hat{p}_{a,i}-\text{SIR}_{max}, \tag{50}\]

such that \(\hat{p}_{a,i}=1\) corresponds to \(\text{SIR}_{max}\), i.e., the maximum desired SIR, and \(\hat{p}_{a,i}=0.5\) corresponds to \(\text{SIR}=0\) dB.
## 4 Mobility Aware Duty Cycling Algorithm (MADCAL)
### Initial Calculation of Threshold  
With circular mobility, this threshold is calculated based on the angle of the closest point to the circular path in relation to the static node--the circlePoint. Taking into account the interference range of the node and the radius of the circle, an initial maximum threshold before and after the circlePoint can be calculated. This is demonstrated in Figure 4 in relation to node 15. However, a simplistic approach such as this would result in a significantly large threshold if the static node is close to the path of the MSN. In this event, this node could monopolise communication with the sink for a considerable time, to the detriment of other significant nodes. To negate this, a more dynamic approach to calculating the threshold is required. Firstly, node distance to the sink path is taken into consideration and divided by the interference distance, in order to create a factor by which the threshold angle shall be multiplied. Secondly, in order to now avoid an extremely small threshold for nodes closer to the path, a factor check is utilised. This lessens how much the threshold is reduced based upon the speed of the sink node, which is constant throughout each scenario. It was found, via simulation, that as the sink speed increases a smaller threshold is more efficient, given that the sink shall pass through this threshold more often. For example, in our experiments, if the sink node is travelling at 40 mps it shall pass through the threshold of any significant node 40 times in each scenario, giving each significant node many opportunities to communicate with the sink. However, if the sink node is travelling at just 2 mps it shall only pass through the threshold of each significant node two times in each scenario. In this case, it would make sense for thresholds to be larger to give each significant node as much chance as possible to communicate with the sink.  
Therefore, as a first approach, the factor check is utilised such that for speeds less than 10 mps the threshold cannot be reduced by less than a factor of 0.5, for less than 20 mps this factor reduces to 0.35, reducing again to no less than 0.25 for less than 40 mps. If the speed is exactly 40 mps we take no action to use a factor check, allowing the threshold to be reduced based upon the initial calculation of node distance over interference distance. It is envisaged that in future work an extension to this algorithm could be developed to ensure the factor is completely dynamic based on the speed of the sink node.  
Figure 4: Illustration of initial threshold calculation.
# Neural Network Based Country Wise Risk Prediction of COVID-19
## 3 Proposed Model
### Network Search  
Let \(\phi_{1},\phi_{2},...\phi_{n}\) be different hyperparameters of a learning algorithm and \(\mu_{1},\mu_{2},...,\mu_{n}\) be domains of the parameters as defined earlier. The dataset (\(D\)) is divided into train (\(D_{train}\)) and test (\(D_{test}\)). The hyperparameter space is \(\theta=\mu_{1}\times\mu_{2}...\times\mu_{n}\). Training data is trained on \(\phi\in\theta\). The test error \(E(\phi,D_{train},D_{test})\) is the error on \(D_{test}\) of the parameter \(\phi\). The hyperparameter is optimized for a given dataset (\(D\)) by minimizing:  
\[f^{D}(\phi)=E(\phi,D_{train},D_{test}) \tag{8}\]  
We have considered root-mean-square error (RMSE) on validation set to chose best architecture. Hence, the problem can be defined as:  
\[\phi^{*}=\operatorname*{arg\,min}_{\phi\in\theta}(f^{D}(\phi)) \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Distribution/Selection** & **Values** \\ \hline Learning rate & Minimum learning rate & Log uniform & \(1\times 10^{-1}\) to \(1\times 10^{-7}\) \\ \hline Hidden layers & Number of layers in the network & Discrete numeric & 1 to 20 \\ \hline Hidden state & Number of memory cell in each layer & Discrete numeric & 1 to 200 \\ \hline Activation & Activation in each layer & Category & \{ReLU, sigmoid, tanh\} \\ \hline Batch size & Batch size during training & Discrete numeric & 2 to 10 \\ \hline Dropout & Dropout size before dense layer & Log uniform & 0 to 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in optimum network search for novel coronavirus (COVID-19).  
Figure 3: The architecture of LSTM module. Here, \(i\) is the input gate, \(f\) is the forget gate and \(o\) is the output gate. \(c\) is the cell state and \(\widetilde{C}\) is the update cell.  
In general, the problem of hyper-parameter search can be very expensive as we need to train and evaluate the dataset for each combination of parameters. Searching algorithms such as random search and grid search are better than manual setup but computationally expensive when we have a large volume dataset and a wide hyper-parameter search space. These methods do not consider the previous outcome to choose the next set of parameters, hence they spend most of the time evaluating bad parameters. In our case, the RMSE of a set of parameters (\(f^{D}(\phi_{next})\) is estimated by the conditional probability \(P(f^{D}(\phi_{next})f^{D}(\phi_{previous}))\). The method selects the set of hyper-parameters that performs the best according to the probability. First, individual COVID-19 trend is used to automatically design the desired neural network. Next, the network optimized for an individual country is used to predict the number of cases (\(\kappa\)), the number of deaths (\(\delta\)), and the number of recoveries (\(\rho\)). These data are used in the next module to decide the risk of the country.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.4.1 Ablation Experiment
Resnet50 [18] backbone, Densenet121 [17] backbone, DNET without FPN and our DNET backbone. employing the improved loss function successfully captures the depth of distant windows and objects on the table in the foreground. In contrast, the algorithm that relies on the original cross-entropy loss function fails to preserve these specific details. In Scene 5, during the process of depth estimation, the enhanced algorithm extracts more precise object edge details, outperforming the original algorithm. In Scene 6, the effects of the ablation experiment are notably prominent, especially in the upper-left and lower-right corners of the image. The enhanced algorithm, which considers class imbalance, dynamically adapts penalty terms across various categories. This approach ensures accurate estimation of depth for distant objects and improves estimation for nearby targets.
## 4 The Proposed DSB Workflow Scheduling Algorithm
### Proposed Algorithm
#### 4.4.5 Elastic Resource Provisioning
In this way, if a task finishes earlier, the remaining tasks will have more time to run and hence they can either be assigned to a cheaper VM or delayed, to be scheduled in subsequent cycles. If a task finishes later than expected, adjusting the deadline of the remaining tasks may prevent the deadline from being missed. The VM scaling-down strategy is used by the scheduler during execution to shut down any leased VMs that are idle for a long time and approaching their subsequent billing interval. An overview of resource scaling down technique is shown in Algorithm 6. ```
Input: Workflow \(W\), Deadline \(DL_{W}\), Runtime of tasks on VMs \(RT^{r}_{i}\)
1 Add \(v_{entry}\) and \(v_{exit}\) and their corresponding edges to the workflow \(W\)
2 forall\(v_{i}\in V\)do
3 Calculate upward rank \(urank_{i}\)//according to Equation (6)
4 Calculate degree of dependency \(dd_{i}\)//according to Equation (7)
5 endfor
6 Sort the tasks in descending order of priorities
7 Calculate level of each task \(lv(v_{i})\)//according to Equation (8)
8 Identify all the BoTs in the workflow
9 Call DD(\(W\), \(DL_{W}\), \(RT^{r}_{i}\))
10 forall\(bot_{q}\in BoT\)do
11 Put BoTs in priority queue and sort them based on the rules mentioned in Section 4.4.4
12 endfor
13 Put ready BoTs in execution queue \(ExeQ\)
14 Call ERP(\(ExeQ,VM\))
```
**Algorithm 4 Resource Scaling Up**

The algorithm of the proposed method is shown in Algorithm 5.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Full Duplex D2D  
Although FD communications double the network capacity, FD D2D communications result in severe interference and complex resource management problems. Further FD-D2D research is required for real future implementation.
## 8 Simulation and Evaluation of the Employed Control Algorithm  
The performance of the controller performing the trajectory-tracking maneuver was evaluated by defining a trajectory using (55)-(57) as follows:  
Figure 10: Real-time experiments with the proposed localization algorithm.  
Figure 9: Driving scenario from Malaga and localization algorithm simulation result with the Malaga dataset. (**a**) Short avenue loop closure driving scenario dataset from Malaga; (**b**) performance of employed visual-inertial odometry in simulation with the Malaga dataset.  
\[X=10*e^{\cos{(t)}} \tag{55}\] \[Y=15*e^{\sin{(t)}}\] (56) \[\Psi_{0}=1.36\ (\text{rad}) \tag{57}\]  
where vectors of \(x\) and \(y\) coordinates in the trajectory plane are shown with \(X\), \(Y\). Heading at the starting point is shown with \(\Psi_{0}\). Simulations were conducted using MATLAB and GAZEBO simulators. The CasADi package [49] was employed in order to solve the optimization problem. The package provides an open-source software framework for numerical optimization. The package is a general-purpose tool that can be used to model and solve optimization problems. The core of the package has been written in C++, but the creators made some interfaces for Python, MATLAB, and Octave. The package is widely using for academic purposes as well as in industrial applications in different fields, including optimal control, robotics control, and the aerospace industry. In this study, the optimizer used the IPOPT class inside the package. A primal-dual interior-point method was applied, which uses line search based on the filter method [50]. The software simulation architecture for evaluating the controller is shown in Figure 11.  
Figure 12a depicts the desired trajectory and the performance of the trajectory tracking of the simulated vehicle. The vehicle heading angle error is shown in Figure 12b. The control inputs including velocity and steering angle are shown in Figure 13a,b, respectively. Lateral and longitudinal trajectory tracking performance (with respect to their occurrence time) are shown in Figure 14.  
Figure 11: Software simulation architecture for evaluating the vehicle controller.  
Figure 12: Results of simulation for trajectory-tracking and heading error. (**a**) Desired trajectory Vs the trajectory made by the simulated vehicle; (**b**) heading angle error.  
The simulation results in Figure 12 show that NMPC is able to control the car properly while tracking the trajectory. Simulation results, also, show that NMPC has optimized the controller effort during the simulation. In addition, the controller managed to steer the car towards the desired heading at the destination.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 6 MZAP Android Application--Adoption and the Effects of Digitalization
### Adoption Rate and System Usability Scale  
The adoption rate among officials aged 20 to 40 was 96.26%, with a 95% confidence interval (CI) ranging from 93.72% to 98.80%. For officials aged 40 and above, the adoption rate was 82.28%, with a 95% CI of 73.86% to 90.70%. These results, calculated from data presented in Table 2, show that younger officials (20 to 40 years old) were significantly more likely to adopt the MZAP app compared to older officials (40+ years). A chi-square test [41] confirmed this difference was statistically significant with following values: \(\chi^{2}\) (1, \(n=293\)) = 14.29, \(p=0.0002\).  
This disparity is expected, as younger officials are generally more accustomed to the use of digital technologies. Nevertheless, older officials have demonstrated commendable effort in adapting to the MZAP application's functionalities and workflow.  
To evaluate the usability of the basketball endgame reporting application, we conducted a structured assessment using the System Usability Scale (SUS) as described and shown in papers by [PERSON] [42] and [PERSON] [43], a widely recognized instrument for the rapid and reliable evaluation of system usability.  
The usability test involved five distinct user groups: coaches (\(n=27\)), delegates (\(n=48\)), referees (\(n=47\)), statisticians (\(n=94\)), and other basketball officials (\(n=55\)), each representing varying levels of technological proficiency. Following a training session on the use of the MZAP application, each participant was asked to complete an endgame report for a full basketball match. Immediately upon task completion, participants filled out the SUS questionnaire, which comprises 10 statements rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).  
The SUS scores ranged from 62.5 to 90.1, with a mean score of 79.12 and a standard deviation (SD) of 7.34, aggregated across all user groups, thus falling into the \"Good\" category. Table 3 presents the SUS scores disaggregated by user group.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Age Group** & **Adopted** & **Not Adopted** & **Total** \\ \hline
20–40 & 206 & 8 & 214 \\
40+ & 65 & 14 & 79 \\ \hline Total & 271 & 22 & 293 \\ \hline \end{tabular}
\end{table}
Table 2: User age groups and adoption of the MZAP application.  
The reported SUS scores fall within the \"Marginal,\" \"Good,\" and \"Excellent\" usability categories. The Other group, which primarily includes recorders, timekeepers, and other supporting basketball officials, reported usability ratings within the Marginal range. In contrast, all other user groups rated the usability of the MZAP application as Good or Excellent.