esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Methodology
The block diagram for the proposed filter pruning approach is presented in Figure 2. The overall procedure for the proposed filter pruning is as follows:  
1. Train the original unpruned model \(M\) from scratch and calculate its loss \(E(F)\). 2. Add importance weights to each convolutional filter using any of the weight initialization strategies such as random uniform initialization, chaotic initialization, i.e., logistic map, He initialization, LeCun uniform initialization, and Xavier initialization. 3. Sort the filters based on the additional importance weights. 4. Prune the filters whose weight values (additional importance weights) are less than a pre-defined threshold. Eliminate the filters in the next convolutional layer corresponding to pruned feature maps (pruned filters), and obtain a compact model \(M^{\prime}\) using the remaining filters. 5. Compute \(E(F^{\prime})\) of the pruned model \(M^{\prime}\). 6. Check if \(\abs{E(F)-E(F^{\prime})}<\epsilon\), where \(\epsilon\) denotes the error tolerance level: 1. If the loss difference is less than \(\epsilon\), update the importance weights using Equation (1) and go to Step 3. 2. Else, stop the process. 7. Obtain the final pruned model (or compact model). 8. Fine-tune the pruned model to recover the loss. Figure 2: The proposed filter pruning approach. Here, \(w_{t}\) represents the weight of the filter at the \(t^{th}\) iteration. In this way, after pruning and fine-tuning, a compact and lightweight model will be generated, which would show an approximately similar performance as the unpruned model.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 3 Performance Evaluation and Results
### D2C Performance According to Changes in Ambient Lighting  
Figure 11 shows the BER performance of the proposed D2C system under ambient lighting. Experiments were performed inside a closed room with \"normal\" and \"ambient\" lighting. To achieve normal lighting, the room lights were turned off and only the natural light was provided. On the other hand, under the ambient lighting condition, all room lights were turned on. For all data-embedding mechanisms, under the similar PSNR levels, we can observe that the BER performance is relatively poorer when images are captured with \"ambient\" lighting compared to \"normal\" lighting. This is because the captured images under \"ambient\" lighting were overexposed to light and their intensities were thus much greater than usual. This lowers the probability of successful decoding when data is extracted from the captured image. It is also clear from the figure that of the three data-embedding mechanisms, M1 can provide an optimal BER performance of 0.06 with a PSNR of approximately 35 dB when tested under \"ambient\" lighting conditions. Therefore, regardless of the transmission environment under unfavorable lighting conditions, it is certainly possible to choose data embedding with M1 in order to achieve robust communication performance and higher image reconstruction quality. Note that the ultimate BER performance of the proposed system under frequently changing lighting conditions is significantly dependent on the amount of variation or alteration in the pixel intensities of the captured image.  
Figure 10: The BER performance of the proposed system (**a**) by distance (\(D\)) and (**b**) angle of capture (\(AOC\)).
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 3 'T-Node' Map Representation
The T-node map can be generated by maneuvering the robot in the environment and setting points of turns (where the robot turns by around 90 degrees) as nodes. Automatic generation of T-node map is also possible if a map is available. For example, if there is a grid-map with obstacles (black), open (white) and unknown (grey) areas, the first step is to generate a binary image of the grid-map which is done by turning all unknown cells to blocked (black) value. This is shown in Figure 6a. Noise is removed by successively applying morphological erode and dilate operations [32; 33]. The next step is to apply skeletonization algorithm [34; 35]. Many skeletonization and thinning algorithms generate unnecessary tentacles which needs to be removed using pruning algorithm [36]. Result of showing skeletonization on binary map of Figure 6a is shown in Figure 6b. Line segments are then detected using techniques like SVD and Hough Transform [1]. The end-points of segments which are within a small threshold distance (\(\delta\)) can be clustered [37] using k-means [38; 39], fuzzy c-means [40] or density based clustering methods [41] into a single node as shown in Figure 6c,d. A graph '\(N\)' of these nodes \(\{n_{1},n_{2},\cdots,n_{m}\}\) form the T-node map of the environment. The pseudo-code is given in Algorithm 1. ```
Data: m : Gridmap, m_height : map height, m_width : map width Function node_mapping(m) for each row in m_height do

for each col in m_width do

if cell m(row)[col] is unknown then

\(m[row][col]\gets l_{\text{occupied}}\)

Successively erode and dilate binary image multiple times [32; 33]

Apply skeletonization algorithm [34; 35]

Apply prunning algorithm [36]

Detect lines segments and their endpoints using algorithm [1]

Cluster nearby endpoints in range \(\delta\) with k means algorithm. [37]

Mark clustered points as nodes N \(\leftarrow\)\(\{n_{1},n_{2},\cdots,n_{m}\}\)return(N)
```  
**Algorithm 1**T-node-map Generation  
It should be noted that a 'node' is merely a point of turn in the navigational graph. It does not include any feature information (e.g., corners, line, color, etc.) of the map. Hence, map-merger on T-Node map is not possible. However, traditional methods [42; 43] can be used to first merge feature-rich maps and thereby T-node map. Moreover, since the characteristics of the navigational paths are different for unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), this work does not consider the case of heterogeneous robots. Only ground robots are considered and the proposed method will work well for both differential drive robots and skid-steer drive robots. Figure 5: Scale and rotation effects on the T-node map. In all cases, meaningful information can still be shared between robots. (**a**) Original section \(n_{9}n_{3}\) of Figure 4b. (**b**) Figure 5a scaled down. (**c**) Figure 5a rotated by angle \(\theta_{1}\). (**d**) Figure 5a scaled up and rotated by angle \(\theta_{2}\).
# An Improved Convergence Analysis of a Multi-Step Method with High-Efficiency Indices
## 5 Basins of Attraction  
To verify the numerical stability of the method, we analyze the dynamics of the method (3). The set of all initial points that converge to a specific root is called the Basin of Attraction (BA) [22].  
**Example 3**: \(\begin{cases}a^{5}-b=0\\ b^{5}-a=0\end{cases}\)  
**Example 4**: \(\begin{cases}3a^{2}b-b^{3}=0\\ a^{3}-3 ab^{2}-1=0\end{cases}\)  
**with roots \((-\frac{1}{2},-\frac{\sqrt{3}}{2}),(-\frac{1}{2},\frac{\sqrt{3}}{2}),\,(1,0)\).**  
**Example 5**: \(\begin{cases}a^{2}+b^{2}=4\\ 3a^{2}+7b^{2}=16\end{cases}\)  
**with roots \((\sqrt{3},-1),\,(-\sqrt{3},-1),\,(\sqrt{3},1),\,(-\sqrt{3},1)\).**  
The BA for the roots of the given nonlinear equations is given (Figure 2) in a \(401\times 401\) equidistant grid points within a rectangular domain \(D=\{(a,b)\in\mathbb{R}^{2}:-2\leq a\leq 2,-2\leq b\leq 2\}\). Each initial point is given a color corresponding to the root, which the iterative method converges. If the method fails to converge or diverges, the point is marked as black. The BA is shown with a tolerance of \(10^{-8}\), and a maximum of 45 iterations is considered.
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 4 Experiments and Analysis
### Results and Analysis
#### 4.3.4 Planar Localization of pylons  
Some of the localization results are shown in Figure 17. Compared with the ground truth, all were positioned correctly at the center of the pylon. This proves that the algorithm is robust to regions with few ground points. In fact, the localization error of Dataset 1 was slightly higher than that of Dataset 2 due to its mountainous terrain, where the grass points blended under the tower and affected the center calculation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset 1** & **Dataset 2** & **Average** \\ \hline Precision & 0.93 & 1 & 0.96 \\ Recall & 0.95 & 1 & 0.97 \\ F1 & 0.94 & 1 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Extract accuracy. Overall accuracy: 96.9%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset 1** & **Dataset 2** & **Average** \\ \hline Precision (\%) & 96.3 & 96.7 & 96.5 \\ Recall (\%) & 95 & 98 & 96 \\ F1 (\%) & 95.6 & 97.3 & 96.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assessment of the PL point cloud extraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset 1** & **Dataset 2** & **Average** \\ \hline  & **Proposed Method** & **Sohn’s Method**3  & **Proposed Method** & **Sohn’s Method** \\ \hline Precision (\%) & 96.3 & 95.4 & 96.7 & 95.9 \\ Recall (\%) & 95 & 96.6 & 98 & 96.5 \\ F1 (\%) & 95.6 & 96 & 97.3 & 96.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of PL point cloud extraction.  
Figure 18 shows the deviation of the center coordinates. In comparison to the reference, the coordinate precision was sufficient for the modeling of pylons, as the overall positional accuracy assessed by the root-mean-square error (RMSE) of the horizontal coordinates was less than 0.25 m.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 2 Background  
### Satellite Video Single-Object Tracking  
As mentioned previously, the main challenges of SOT in SVs are object rotation and tracking drift. A few methods have been proposed to solve the object rotation problem in SVs. [PERSON] et al. [37] detect the orientations of objects by using slope information and output rotating bounding boxes. [PERSON] et al. [40] rotate the extracted patch with a fixed-angle pool to deal with the object rotation issue and obtained axis-aligned bounding boxes. These methods may be numb to a slight rotational issue. To address tracking drift, some approaches [29; 30; 31; 36; 37; 41; 42] have built motion models based on the relatively stable motion patterns of objects in SVs. In [30; 36; 37; 41; 42], the authors use the properties of the Kalman filter [43] to predict the object position at low tracking confidence, which attenuates the tracking drift. In [29; 31], the motion smoothness and centroid inertia models are embedded into the tracking framework to reduce tracking drift. However, most of them [29; 31; 36; 37; 41; 42] place high demands on positioning accuracy during the initial stage. Other methods [30; 38; 39; 44] extract the motion features contained in adjacent frames to prevent tracking drift. [PERSON] et al. [44] combine the three-frame-difference approach and the KCF tracker to obtain the object's position. [PERSON] et al. [30] construct a refining branch modeled on Gaussian mixture models (GMM) to reduce the risk of drifting. In [37; 38], the authors use the Lucas-Kanade sparse optical flow [45] feature for SOT in SVs. However, they ignore the directional information of the optical flow. And the sparse optical flow makes it difficult to represent pixel-level motion information [46].  
To achieve precise angle estimation and localization of SOT in SVs, this study designs a rotation-adaptive tracking framework with motion constraint. It decouples the rotation and translation motion patterns by decomposing the rotation issue into a translation solution. In addition, it further synergizes the appearance and motion information to enhance the localization performance of SOT in SVs. It guarantees that the proposed method can estimate slight angle differences of objects and prevent tracking drift.  
Figure 2: Visualization of different kinds of bounding boxes. The red area in the original frame (**a**) is enlarged; (**b**) shows an axis-aligned bounding box marked in yellow, whereas (**c**) shows a rotating bounding box marked in green.
# Extended Reality in Applied Sciences Education: A Systematic Review
## 3 Results  
The analysis of Figure 3 reveals that among the 56 selected studies, 32 (53%) utilized virtual reality (VR) ([5, 11, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), 25 (42%) employed augmented reality (AR) ([7, 12, 13, 16, 36, 38, 45, 46, 47, 48, 49, 50, 51, 57, 59, 60, 61, 62, 63]), and 3 (5%) implemented mixed reality (MR) technologies ([9, 64, 65]). VR, known for its immersive teaching experiences, has been extensively applied in high-presence educational settings such as medical simulations or industrial training. In contrast, AR, which integrates real-world environments with virtual information, has been utilized in courses requiring field observation or hands-on activities, such as engineering and natural sciences. MR, due to its higher technological and equipment requirements, appears to be primarily adopted in advanced courses with significant interactive demands, such as design and architectural simulations. Notably, four studies explored the simultaneous application of VR and AR technologies in educational contexts.  
The regional distribution of XR-related research exhibits significant disparities, with varying degrees of emphasis on different types of XR technologies across regions. Europe serves as a major hub for XR research, particularly excelling in studies on VR and AR technologies. The region accounts for 13 studies on VR ([22, 23, 25, 26, 31, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]) and 10 on AR ([7, 36, 38, 46, 47, 50, 51, 57, 59, 62]), making it the only area that includes MR-related research ([9, 64, 65]), with a total of three studies. Asia, recognized as a critical region for emerging technological development, contributes 11 studies on VR ([5, 11, 13, 16, 17, 19, 20, 24, 30, 33, 41]) and 8 on AR ([13, 16, 49, 52, 55, 58, 60, 61]). The Middle East has four studies each on VR ([27, 29, 32, 42]) and AR technologies ([12, 48, 53, 54]). Oceania has only two studies focusing on AR ([56, 63]). In contrast, the Americas show relatively limited contributions to XR research, with just four studies on VR ([18, 21, 28, 43]). This trend may be attributed to differences in research focus or data collection criteria, warranting further investigation.  
Figure 2: PRISMA flow diagram.  
Africa has a single study on AR, highlighting the region's limited resources for advancing educational technologies.
## 3 Controllers Design
### Case A: Zero Initial Velocity  
The controllers (16) have parameters \(P_{i}=1.535\) and \(D_{i}=2.5\) for the simulation. The initial velocity of all links is set to zero.  
Figure 5 shows the simulation results for each link with zero initial velocity. From Figure 5a-c, it can be seen that at \(t=7.62\) s, the PL rotates at a steady speed while the first two links are stabilized at the desired angle. Finally, at \(t=50\) s, the PL stabilizes at \(q_{3d}=1.8507\) rad. As can be seen in Figure 5d, the endpoint of the PL already reached the desired positional coordinates, indicating that the control objective of the planar AAP system was realized. The simulation results show that the control method is effective when all the link velocities are zero at the beginning.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Link \(i\)** & \(m_{i}\) (kg)** & \(L_{i}\) (m) & \(I_{i}\) (m) & \(J_{i}\) (kg \(\cdot\) m\({}^{2}\)) \\ \hline \(i=1\) & 1.258 & 0.34 & 0.17 & 0.0121 \\ \(i=2\) & 5.686 & 0.29 & 0.145 & 0.0398 \\ \(i=3\) & 2.162 & 0.52 & 0.26 & 0.0487 \\ \hline \end{tabular}
\end{table}
Table 2: Structural parameters for the planar AAP system (second group).  
Figure 4: The planar AAP system’s simulation results in case C (first group). (**a**) Joint angle; (**b**) joint angular velocity; (**c**) joint torque; (**d**) endpoint coordinates.  
**Case B: Non-zero Initial Velocity**  
The controllers (16) have parameters \(P_{l}=1.52\) and \(D_{l}=2.30\) for the simulation. The initial velocities are chosen to test the efficacy of the control approach proposed in this paper, as follows:  
\[[q_{10}\;q_{20}\;q_{30}]=[0\;0\;-0.01]\;\text{rad}/\text{s} \tag{53}\]  
The simulation results for a PL with non-zero initial velocity are shown in Figure 6. As shown in Figure 6a,b, the last link rotates at a steady speed at \(t=7.05\) s. In the second stage, as shown in Figure 6c, the PL reaches the desired position at \(t=50\) s with \(q_{3d}=1.8507\) rad, while the angular velocities of all links converge to zero, as shown in Figure 6d. The simulation results show that the control method is effective when the PL velocity is not zero at the beginning.  
Figure 5: The planar AAP system’s simulation results in case A (Second group). (**a**) Joint angle; (**b**) joint angular velocity; (**c**) joint torque; (**d**) endpoint coordinates.  
Figure 6: The planar AAP system’s simulation results in case B (second group). (**a**) Joint angle; (**b**) joint angular velocity; (**c**) joint torque; (**d**) endpoint coordinates.
## 2 System Overview
### Power Supply Module  
Since the internal motor of the ROV uses a 12 V DC power supply, the UAC modem uses a 24 V DC power supply, and the operational amplifier and comparator are powered by \(\pm\)5 V voltage, the hardware system needs to convert the power supply. The system is powered by a 24 V lithium battery, and an EVEPS switching power supply module wa selected to realize power isolation and 12 V regulated output. In the visible light receiving circuit part, the EVISUN IA1205S-1W switching power supply module was selected to realize the dual output of power isolation and voltage regulation. The nominal output ripple noise is 30 mV\({}_{pp}\), and the nominal switching frequency is 100 kHz. Moreover, in order to reduce the ripple noise, an LC filter circuit is attached to its 12 V voltage input terminal and \(\pm\)5 V voltage output terminal, as shown in Figure 7.
## 3 Materials and Methods
### Datasets of Speech Audios and Background Noises  
#### 3.1.1 Audio Features Decision  
The first step consists of selecting the audio file format and the signal processing characteristics: sampling rate, bit-depth, and the number of channels. As a file format, we use WAV or WAVE (apocope of Waveform), launched in 1991 by Microsoft based on the RIFF (Resource Interchange File Format) specification. It has the advantage of being in an uncompressed format, in which the user hears what is stored. It also supports different quality characteristics such as sampling frequency and bit depth [22], which are the most important features of audio management [23]. We have decided to use 22.5 kHz, a bit-depth of 16 bits, and only one audio channel corresponding to monaural sound reproduction. These characteristics are sufficient for the problem we want to solve.  
During the second step, we need to create the pairs of audios. As no dataset solves the presented use cases, we have created our own. For that, we need speech audios and background noises that represent the four use cases.  
For the former, we are using the TED-LIUM 3 dataset, which includes audio recordings of the well-known TED-TALK series. The dataset contains 2351 audio files in a NIST Sphere format (SPH), which is very common in audio speech files as it includes the audio alongside a transcription of the speech. It uses a sampling rate of 16 kHz and a bit-depth of 16 bits. The total length of the dataset reaches more than 452 h. In the audios, we can find a diverse range of people with different types of tones and quality recordings under other  
Figure 1: Workflow for training a model for background noise suppression.  
circumstances, which augments the problem's difficulty. The dataset is freely available to download from Open Speech and Language resources.  
For the latter, we have obtained the different background noises from two websites. They used a WAV format, a sampling rate of 16 kHz, and a bit-depth of 16. The number of audios per use case differs, as the availability was not the same. This problem will be, lately, solved during the preprocessing stage. Also, the length of the audio varies depending on the use case. Table 1 summarizes this information for the four use cases.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 4 Mathematical Analysis of Atmospheric Turbulence  
### Refractive Index Structure Parameter  
To determine the strength of atmospheric turbulence, the key aspect is the refractive index of the air, \((C_{n}^{2})\). However, the estimation of \(C_{n}^{2}\) is an intensive process, owing to the specific hardware and high computation costs involved in this process [40]. Several models, such as the Hufnagel, Ai-Valley, and Greenwood models are commonly used to predict the refractive index. However, these models are appropriate over a vertical path only [40; 41]. Moreover, the atmospheric turbulence varies with height and local conditions, such as the terrain type, geographical location, and meteorological values [42; 43]. Consequently, it is essential to establish and enhance the \(C_{n}^{2}\) prediction models using meteorological parameters, such as the temperature, humidity, and wind speed. In this study, we adopt a macro-meteorological model to estimate \(C_{n}^{2}\) for the following reasons:  
* The existing studies demonstrated that the macro-meteorological model can be successfully used to estimate the values of \(C_{n}^{2}\) in a coastal area with a high correlation (up to 90%) compared with the measured values [43; 44; 45; 46; 47];
* This model has been validated for a similar coastal area called Negev area, as shown in Figure 2. Negev is approximately 400 km from the study area, NEOM, and has a similar landscape;
* This model can help correlate the changes in the atmospheric turbulence strength \(C_{n}^{2}\) with the meteorological parameters.  
The macro-meteorological model can be mathematically expressed as [48]:  
\[C_{n}^{2}=3.8\times 10^{-14}W+f(T)+f(v)+f(H)-5.3\times 10^{-13} \tag{1}\]  
\[f(T)=2\times 10^{-15}T \tag{2}\]  
\[f(v)=-2.5\times 10^{-15}v+1.2\times 10^{-15}v^{2}-8.5\times 10^{-17}v^{3} \tag{3}\]  
\[f(H)=-2.8\times 10^{-15}H+2.9\times 10^{-17}H^{2}-1.1\times 10^{-19}H^{3} \tag{4}\]  
where \(W\) is the weight function, \(T\) is the air temperature (\({}^{\circ}\) K), \(H\) is the relative humidity (%), and \(v\) is the wind speed (m/s). This model is valid under specific limits of macroscale parameters, specifically, the temperature (from 9 to 35 \({}^{\circ}\)C), relative humidity (from 14% to 92%), and wind speed (from 0 to 10 m/s) [48]. The weight function \(W\) is calculated based on a temporal hour that relates the actual time to sunrise and sunset, as indicated in Table 1.  
Figure 1: Comparison of Turbulent Cell Size with (**a**) Scintillation and (**b**) Beam Wander.  
\[H_{T}=12\frac{H_{actual}-H_{sunrise}}{H_{sunset}-H_{sunrise}} \tag{5}\]  
where \(H_{T}\) is the temporal hour, \(H_{actual}\) is the actual time, \(H_{sunrise}\) is the sunrise time and \(H_{sunset}\) the sunset time. The typical values of \(C_{n}^{2}\) are \(C_{n}^{2}=0.5\times 10^{-14}\) m\({}^{-\frac{3}{2}}\) for weak turbulence, \(C_{n}^{2}=2\times 10^{-14}\) m\({}^{-\frac{3}{6}}\) for moderate turbulence, and \(C_{n}^{2}=5\times 10^{-14}\) m\({}^{-\frac{3}{6}}\) for strong turbulence [49; 50].