esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lifestyle-Based Perspective on the Mobility Choices and Preferences of Urban Travelers
## 4 Results
### Mobility Characteristics and Related Beliefs/Perceptions/Feelings of Attitudinal Clusters  
To have a better understanding of the attitudinal clusters, Table 3 presents the modal split per trip purpose and attitudinal cluster. Private car dominance is evident across most trip purposes and clusters with the exception of education trips. This is further validated by the overall modal split percentages of car (Figure 3) and the high percentages of access to car among respondents. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multirow{2}{*}{**Value**} & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & \multirow{2}{*}{**No. of**} \\  & & **(Eco-Conscious** & **(Digitally** & **(Cautious** & **(Socially Driven** & **No. of** \\  & & **Social** & **Engaged Conventionalists)** & **Green** & **Digital** & **Replies** \\  & & **Adapters)** & **tionalists)** & **Conservatives)** & **Enthusiasts)** & \\ \hline \multirow{8}{*}{Monthly budget *} & \textless{}250 & 10.5\% & 4.8\% & 2.8\% & 5.1\% & \multirow{8}{*}{**Replies**} \\  & \textless{}250\textless{}500 & 5.8\% & 4.8\% & 5.6\% & 7.6\% & \\ \cline{1-1}  & \textless{}500\textless{}750 & 8.1\% & 6.3\% & 1.9\% & 8.9\% & \\ \cline{1-1}  & \textless{}750\textless{}1000 & 17.4\% & 9.5\% & 6.5\% & 13.9\% & \\ \cline{1-1}  & \textless{}1000\textless{}1500 & 29.1\% & 34.1\% & 36.4\% & 20.3\% & \\ \cline{1-1}  & \textless{}1500\textless{}2000 & 22.1\% & 19.0\% & 24.3\% & 22.8\% & \\ \cline{1-1}  & \textgreater{}62000 & 7.0\% & 21.4\% & 22.4\% & 21.5\% & \\ \cline{1-1}  & Cluster population & 86 & 126 & 107 & 79 & 398 \\ \hline \hline \end{tabular}  
* Significance of variable \(p<0.05\).  
\end{table} 
Table 2: _Cont._Some differences between the clusters emerge regarding the second most preferred mode and when grouping motorized transport modes (car, motorcycle, taxi) and sustainable modes (public transport, walk, micromobility) together. The highest percentages of motorized mode use among all clusters are observed for Digitally Engaged Conventionalists (cluster 2) in work and leisure trips, Socially Driven Digital Enthusiasts (cluster 4) in shopping and education trips and Eco-Conscious Social Adapters (cluster 1) in personal obligations trips. On the other hand, the highest percentages of sustainable mode use among all clusters are observed by Cautious Green Conservatives (cluster 3) in work trips, Eco
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 7 Implementation  
The described scheme is intended for its use in areas related to the Internet of Things. It has been implemented for Android and Android Wear platforms, which are two examples of the proliferation of devices in this new dimension of the Internet. Android is the most popular smartphone operating system, with over 80% market share worldwide. Android Wear is the operating system based on Android for wearable devices of the same company and has more than 90% of the market share in the devices of its kind. Therefore, all of the results presented here are the result of the implementation of the scheme in these two platforms belonging to the Android Open Source Project. The source code is open source under a Git repository on the GitHub Platform [38].  
Figure 4 shows a screenshot of the Android application that was created to analyze the performance of the scheme in smartphones.  
Despite the implementation described in this section having been focused on IoT devices where it is possible to have an Android operating system, like current smartphones and smartwatches, this does not mean that the necessary devices have to be extremely powerful devices. For instance, there are sensor platforms, like Odroid, that, despite their small size, are able to run an operating system like Android, so consequently, they can be used to run the proposed system. Besides, Google has recently introduced a new operating system for IoT, called Brillo, which is a fork of Android,  
Figure 4: Android application screenshot.  
so the implementation described in this paper is easily adaptable to future devices with the Brillo operating system.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction
### Recursive Feature Elimination  
For testing any further model complexity relaxation, a Recursive Feature Elimination (RFE) method was adopted. To sort the five features (SOG, SWT, DWS, WD, and Trim) by their importance in predicting the MEP, the RFE involved an XGBoost-based feature importance test. Specifically, independently for each ship model, an XGBoost regressor was fitted to the training data and the \(F\)-values of the correlation statistical tests (feature vs. target variable) were extracted to indicate the feature importance. XGBoost was preferred against other feature importance methods, given its fast execution time and superiority against linear models. All ship models showed the same sorted-by-importance version  
Figure 2: Model performance as a function of the learning rate \(a\) for each local ship model. All models have their optimal ANN deepness of 4 hidden layers.  
of features as follows: [Trim, SOG, WD, STW, DWS] (from highest- to lowest-importance features). As expected, Trim (depends on the cargo load) and SOG (depends on fuel consumption) have a high degree of association with MEP, with the WD being the next most important feature (see Figure 3). Having the sorted feature list, we recursively applied the steps included in Algorithm 1 below.  
Figure 4 depicts the Normalized Percentage Accuracy bars for all feature-specific models of each ship. This metric was calculated as \(A_{norm}\) (%) to concretely demonstrate the percentage accuracy decrease as features were eliminated. As shown in Figure 4, there is a gradual performance decrease with the number of features included in the model. This is attributed to the fact that each feature related (highly or slightly) to the MEP can improve the model performance at the expense of increasing the model complexity (performance-complexity trade-off). Given that four features do not significantly reduce the model accuracy (lower than 10% decrement than accuracy with all features), we finally eliminated the number of features used for input to four (Trim, SOG, WD, STW), so as to mitigate the model complexity.  
Figure 3: Sorted importance scores of features derived by XGBoost model for all ship models.
## 5 Simulation
### Verification of the Initial Trajectory Generation  
In order to verify the effectiveness of the initial trajectory generation method proposed in this paper, the three types of relationship between initial point and target point are selected to be tested, respectively, to analyze the guidance effects and compare with the iterative trajectories. Where the initial states of all trajectories are set as \([r_{0},\varphi_{0},\varphi_{0},\varphi_{0},\theta_{0},\psi_{0}]=[(\text{R}_{e}+ 70\text{ km})/R_{e},0^{\circ},0^{\circ},6000/v_{c},-2^{\circ},45^{\circ}]\). Then, three guidance location are selected to generate trajectories on different sides in the velocity direction of the initial state \([r_{f1},\phi_{f1},\varphi_{f1},\varphi_{f1}]=[(R_{e}+30\text{ km})/R_{e},30^{\circ},15^{\circ}]\), \([r_{f2},\phi_{f2},\varphi_{f2}]=[(R_{e}+30\text{ km})/R_{e},34^{\circ},24^{\circ}]\), \([r_{f3},\phi_{f3},\varphi_{f3}]=[(R_{e}+30\text{ km})/R_{e},24^{\circ},34^{\circ}]\). It can be seen from Figure 5a,b that the longitudinal and lateral landing point of the initial trajectory can basically reach the target point by the initial trajectory generation algorithm in this paper. Then, target point 2 was taken as the example to compare the iterative trajectory. As can be seen from Figure 5c,d, by our method the vehicle could effectively converge to the optimal trajectory after solving for five iterations with a total time of 18.46 s. When the maximum attack angle strategy method was adopted as the initial trajectory in this paper, the number of iterations of the algorithm increased to nine. The total time is 40.21 s, which indicates our initial trajectory generation method has a better effectiveness for the optimization algorithm.
## 3 Proposed Work
### CH Selection Fuzzy ANP Mode  
In this section, FANP method has been developed for CH selection to improve the network lifetime and to minimize the energy consumption in the remote SC environment. In FANP, the pair wise comparison matrix is performed by the linguistic variables for both criteria and the alternatives, which are represented by triangular numbers.  
Figure 2 shows in the interdependency relationship between the criteria. Here, four main criteria have been selected: residual energy (RE), node density (ND), distance to the sink (DS) and the average distance between nodes and neighbor (ADN).  
**Step 1:** Calculate the inner dependency matrix for CH selection criteria with respect to the RE, ND, DS and ADN. Table 15 shows the inner dependency matrix of all criteria with respect to RE. The normalized non-fuzzy relative weights of each alternative for each criterion are shown in Table 15.
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### Definition of DC Resistances  
All resistances in this work are presented on a per-unit-length basis (\(\Omega/\text{m}\)) to allow for meaningful comparisons across different cable configurations and geometries. First, we define \(\text{R}_{\text{DC-2D-cal}}\) as the _numerically calculated_ direct-current resistance per meter using the cable's cross-sectional area and copper resistivity from Table 1. In other words, this is an idealized value that excludes any particular cable length, yielding an \(\Omega/\text{m}\) parameter based purely on the total section area and material properties.  
Next, \(\text{R}_{\text{DC}}\) is the _obtained total_ DC resistance in ohms (\(\Omega\)) from the finite-element analysis when the model is driven at \(0\,\text{Hz}\). This value comes directly from the total power dissipation and injected current under the DC condition, reflecting any small effects of the mesh or numerical solution.  
Finally, \(\text{R}_{\text{DC-2D}}\) is the _obtained per-meter_ resistance, determined by dividing \(\text{R}_{\text{DC}}\) by the effective cable length \(l_{\text{twist}}\). This measure incorporates the actual DC resistance from the simulation and provides a convenient \(\Omega/\text{m}\) metric, enabling direct comparisons across different cable models or designs.
## 4 Experiments and Results  
### Experimental Setup  
#### 4.1.1 Dataset  
We evaluated our method on the EMOPIA dataset [33], which consists of both audio and corresponding symbolic music files for music emotion recognition. In this paper, we focused on the emotion recognition of the audio files. The dataset contains 1087 music clips from 387 pop piano music songs. These clips were labeled into four emotion classes using [PERSON]'s 4Q model [8], including HVHA (high valence, high arousal), HVLA (high valence, low arousal), LVHA (low valence, high arousal), and LVLA (low valence, low arousal). The clips in the EMOPIA dataset are divided into train-validation-test splits with a ratio of 7:2:1.
###### Abstract  
The concept of the nanosatellite comes into play in launching miniaturized versions of satellites or regarding payloads with minimizing cost and building time. The economic affordability of nanosatellites has been promoted with a view to launching various nanosatellite missions. The communication system is one of the most important aspects of a satellite. The antenna is a key element for establishing a communication link between the earth and the nanosatellite. The antenna and solar panel of the nanosatellite are two of the most vital components that profoundly impact antenna type and design. This paper proposes a non-deployable lower ultra-high frequency (UHF) antenna, strategically mounted on the satellite body, to address the constraints of deployment complexity and solar panel integration. The antenna was fabricated and performances measured with a 1U monosatellite structure, which achieved resonance frequency at 401 MHz frequency bands with 0.672 dBi realized gain. The overall antenna size is 0.133 \(\times\) 0.133 \(\times\) 0.006\(\lambda\). The major challenges addressed by the proposed antenna are to design a nanosatellite-compatible lower UHF antenna and to ensure solar irradiance into the solar panel to minimize input power scarcity.  
antennas; lower UHF; nanosatellite; omnidirectional Article
# Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds
## 4 Experiment and Discussion  
### Data Collection  
In the experimental verification stage, the unmanned aerial vehicle lidar system (including M600 Pro UAV, RIEGL VUX-1 UAV lidar, etc., which has completed integrated verification and data calibration) was used to collect point cloud data from the Zhangjiakou Wanlong Ski Resort and the National Cross-Country Skiing Center for the Beijing Winter Olympics. The unmanned aerial vehicle lidar system is shown in Figure 4a, and the collected point cloud data of the Wanlong Ski Resort and the National Cross-Country Skiing Center are shown in Figure 4b,c.  
In order to illustrate and verify the effectiveness of the proposed method, the point cloud data of the ski resort in Figure 4b,c are selected as experimental objects and the differences between the classical Graham algorithm and the method presented in this paper for the processing of dense and complex point clouds and sparse and simple point clouds are compared.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 5 Optimal Input Parameter Derivation
### Optimal Number of Stations in One RAW Slot
This has been demonstrated through simulation in our own previous work [1] and by the analytical model presented by [PERSON] et al. [33]. In this article, we use \(\sigma^{r}_{opt}\) to maximize throughput in both saturated and unsaturated scenarios. Obtaining a larger \(\sigma^{r}_{opt}\) based on traffic load and AID re-assignment to further optimize latency as well as throughput is considered future work. Even though the derived optimal value of \(\sigma^{r}_{opt}\) depends on the packet size, TAROA does support packet size variability, due to the use of cross boundary slots. An average packet size can be assumed, \begin{table}
\begin{tabular}{l r} \hline \hline \multicolumn{3}{c}{**(a) Default PHY Parameters**} \\ \hline \hline
**Parameters** & **Values** \\ \hline Frequency (MHz) & 868 \\ TX power (dBm) & 0 \\ TX gain (dB) & 0 \\ RX gain (dB) & 0 \\ Noise Figure (dB) & 6.8 \\ Coding method & BCC \\ Propagation model & Outdoor, macro [9] \\ Error rate model & YansErrorRate \\ \hline \multicolumn{3}{c}{**(b) Default MAC Parameters**} \\ \hline \hline
**Common Parameters** & **Values** \\ \hline Duration of AIFS (\(\mu\)s) & 316 \\ RTS/CTS & not enabled \\ Beacon interval (ms) & 100 \\ Cross slot boundary & enabled \\ Station distribution & random \\ Rate control algorithm & constant \\ Size of transmit queue (packets) & 10 \\ Max/min traffic ratio between stations & 20 \\ \hline
**High-Throughput (HT) Parameters** & **Values** \\ \hline Wi-Fi mode & MCS8, 2 MHz \\ Payload size (bytes) & 256 \\ Non-hidden node topology radius (m) & 50 \\ Hidden node topology radius (m) & 100 \\ \hline
**Low-Throughput (LT) Parameters** & **Values** \\ \hline Wi-Fi mode & MCS1, 1 MHz \\ Payload size (bytes) & 64 \\ Non-hidden node topology radius (m) & 200 \\ Hidden node topology radius (m) & 450 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Default parameter values used in the simulation experiments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Packet Payload Size (Bytes)**} \\ \hline \hline
**Data Rate (Mbps)** & **16** & **64** & **256** & **1024** \\ \hline
**0.15** & 180 & 128 & 32 & 6 \\
**0.60** & 5 & 5 & 3 & 1 \\
**2.6** & 5 & 5 & 5 & 1 \\
**7.8** & 2 & 2 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal number of stations in one RAW slot \(\sigma^{r}_{opt}\) for different data rates and packet payload sizes. and some degree of variability in packet size (and thus transmission rate) is averaged out over multiple slots.
## 2 Materials and Methods
### Analytical Methods  
#### 2.4.1 Determination of sCOD, sBOD\({}_{5}\), and Biodegradability Index  
The concentration of soluble COD (sCOD) was spectrophotometrically measured (at 600 nm) by dichromate method according to Standard Methods [29], using a DR2500 spectrophotometer (Hach Company). Soluble biochemical oxygen demand (sBOD\({}_{5}\)) was determined using a manometric respiratory measurement system (Lovibond Water Testing BD 600) and biodegradability index (BI) was calculated as the ratio of sBOD\({}_{5}\) over sCOD.
## 3 Auxiliary Problem Principle for MAED Problem
### Auxiliary Problem Principle  
In this section, we take advantage of auxiliary problem principle to cope with the non-separable quadratic term \(\frac{c}{2}\left\|{Ax_{1}+Bx_{2}}\right\|^{2}.\) The iterative scheme APP for solving problem (8) can be expressed as follows [14; 15].  
Step1. Compute  
\[x_{1}^{k+1}=\arg\min\left\{f(x_{1})-\lambda^{T,k}Ax_{1}+\frac{c}{2}\left|{Ax_{1 }+Bx_{2}^{k}}\right|^{2}+\frac{\beta-c}{2}\left|{Ax_{1}-Ax_{1}^{k}}\right|^{2} \left|{Ax_{1}\in\Omega_{1}}\right\}\right. \tag{11}\]  
Step2. Compute  
\[x_{2}^{k+1}=\arg\min\left\{g(x_{2})-\lambda^{T,k}Ax_{2}+\frac{c}{2}\left|{Ax_{1 }^{k}+Bx_{2}}\right|^{2}+\frac{\beta-c}{2}\left|{Ax_{2}-Bx_{2}^{k}}\right|^{2} \left|{Ax_{2}\in\Omega_{2}}\right\}\right. \tag{12}\]  
Step3. Lagrange multiplier updating  
\[\lambda^{k+1}=\lambda^{k}-\varepsilon\left(Ax_{1}^{k}+Bx_{2}^{k}\right) \tag{13}\]  
Step4. Check the stop criterion. If  
\[\max\left(\left\|{\lambda^{k}-\lambda^{k+1}}\right\|,\left\|{Ax_{1}^{k}-Ax_{ 1}^{k+1}}\right\|,\left\|{Bx_{2}^{k}-Bx_{2}^{k+1}}\right\|\right)<\eta \tag{14}\]  
then stop. If not \(k\)=\(k\)+1, go to Step1.  
where \(\beta\) is given APP parameter and \(\varepsilon\) denotes step size. The sufficient condition for convergence of APP iterative scheme is given as follow [13].  
\[\varepsilon<2c<\beta \tag{15}\]  
Furthermore, experience on applications has illustrated that, for the given penalty parameter \(c\), the selection of parameters \(\beta\) and \(\varepsilon\) has a significant influence on convergence performance of APP iterative scheme. [PERSON] has pointed that APP iterative scheme can get the best convergence performance when \(\beta\to 2c\) and \(\varepsilon=c\)[16]. Moreover, [PERSON] has pointed that convergence performance of APP was reliable with the choice [13]:  
\[\varepsilon=\frac{\beta}{2}=c \tag{16}\]  
As a result, for the best convergence performance, we choice the APP parameters satisfying \(\varepsilon\)=\(c\)=\(\beta/2\) in this paper.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 2: Coupling of Subsystems with Lumped Parameters  
In the previous case, the receptance coupling method using frequency-based substructuring (RCFBS) is validated for a dynamic structure with continuous characteristics. In this case study, the same approach is applied to a system with lumped specifications.
# A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
## 4 Experiments
### Experimental Results
#### 4.3.1 Grayscale Images Denoising
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(\mathbf{\sigma=15}\) & \(\mathbf{\sigma=25}\) & \(\mathbf{\sigma=50}\) & **Avg** \\ \hline BM3D & 31.15/0.93 & 28.57/0.88 & 25.58/0.80 & 28.43/0.87 \\ DnCNN & 31.62/0.94 & 29.14/0.90 & 26.18/0.82 & 28.98/0.89 \\ FFDNet & 31.65/0.94 & 29.17/0.90 & 26.24/0.83 & 29.02/0.89 \\ DIP & 30.03/0.92 & 27.47/0.87 & 24.26/0.78 & 27.25/0.86 \\ N2V & 29.38/0.85 & 27.81/0.87 & 25.37/0.67 & 27.52/0.80 \\ VDNet & 28.65/0.89 & 27.90/0.87 & 26.45/0.84 & 27.67/0.87 \\ WINNet & 31.70/— & 29.27/— & 26.36/— & 29.11/— \\ BCDNet & 31.81/— & 29.37/— & 26.47/— & 29.22/— \\ DAGL & 31.81/0.94 & 29.33/0.90 & 26.37/0.83 & 29.17/0.89 \\ DeamNet & 31.83/0.94 & 29.35/0.91 & 26.42/0.83 & 29.20/0.89 \\ DRUNet & 31.86/0.94 & 29.39/0.91 & 26.49/0.84 & 29.25/0.89 \\ IRCNN & 31.65/0.94 & 29.13/0.90 & 26.14 /0.82 & 28.97/0.89 \\ Restormer & 31.88/0.94 & 29.41/0.91 & 26.49/0.84 & 29.26/0.90 \\ SwinIR & 31.91/0.94 & 29.41/0.91 & 26.47/0.84 & 29.26/0.89 \\ TripleDIP & 32.24/0.94 & 30.22/0.92 & 26.92/0.84 & 29.79/0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average PSNR and SSIM values of competitive methods on BSD68 with different noise levels. Figure 6: The average performance of the competitive methods on BSD68. output content. Therefore, our proposed model brings noticeable improvement to denoising performance.
# Dynamic Network Formation for FSO Satellite Communication
## 2 Problem of Interest
One cannot have more than three to four such modules on a single satellite. Thus, it is impossible for such a satellite to establish a connection with more than three to four satellites (the number of modules). It is important to mention that inter-satellite laser communication is performed in the vacuum of space, and therefore the main parameter that effects such FSO links is the distance between every two neighboring satellites, which is represented by the weights of the edges. Therefore, it is mandatory to limit (bound) the number of edges from each vertex. This modified \(MST\) version is called the Bounded-Degree \(MST\), and this is a NP Hard problem [16]. We address the first problem (the dynamic scenario) in the next section and the bounded-degree constraint in the following section.