esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### BER versus Transmit Power  
In this test, the measurement of BER versus transmit power was conducted. In all measurements receiver's power was kept at 10 dB while the transmitter's power was varied from \(-36\) dBm to \(-16\) dBm. From the results that are depicted in Figure 12, it can be seen that, in all FM deviation and detector combinations, threshold effect is present, which could be related to the use of FM. Smaller FM deviation provides better communication quality with lower BER in both detector types. The system with an ED allows for achieving much lower BER than one with SD.  
Figure 13 contains power spectra obtained from the signal logged immediately after the Adalm Pluto block in the receiver. These power spectra allow approximately estimate SNR of the receiver at transmit power \(-26\) dBm and receiver gain 10 dB. This, in turn, allows recalculating the horizontal axis of Figure 12 into SNR using the approximate formula:  
\[SNR=G_{\rm s}-G_{\rm n}+10\log_{10}(K_{\rm s})-10\log_{10}(K_{\rm n})\;, \tag{13}\]  
where \(G\) is power spectral density reading from the power spectrum and \(K\) is a count of frequency bins occupied by the signal (subscript s) or noise (subscript n). For example,  
Figure 11: BER versus SNR for data transmission with (1023, 1013) and (7, 4) Hamming codes in FM-ACSK system. SD—strobe-based detector; ED—energy-based detector. FM deviation is 10 kHz.  
in Figure 13 for spectrum with deviation 100 kHz, \(G_{\rm s}\approx-85\), \(G_{\rm n}\approx-95\), \(K_{\rm s}\approx 3\), \(K_{\rm n}\approx 2\). Conversely, SNR at transmit power level -26 dBm is approximately \(-85+95+5-3=12\) dB. Therefore, to approximately covert the horizontal axis of Figure 12 into SNR, it is necessary to add 38.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 6 Markov Decision Policy Topology
### Action  
When the vehicle moves, the macrocell base station allocates the response resource according to current state \(S\). In this paper, Action A could be presented by:  
\[A=\{\{M\},\{F\}\} \tag{9}\]  
where \(M\) means that the system selects the macrocell base station as the communication base station, while \(F\) indicates that the system selects the femtocell base station as the communication base station.
## 4 Electromagnetic Underwater Propagation Model  
Electromagnetic waves in EM bands generally suffer from a high tangent loss in seawater (Equation (1)) [34]. These high dielectric losses are closely related to the medium's conductivity \(\sigma_{sea}\) (which depends on temperature and salinity) and the behavior of the electrical permittivity \(\varepsilon_{sea}\) with frequency, which is generally described using [PERSON]'s relaxation model (Equation (2)).  
\[\tan\delta=-\frac{\text{Im}\left(\varepsilon_{sea}\right)}{\text{Re}\left( \varepsilon_{sea}\right)} \tag{1}\]  
\[\varepsilon_{sea}=\varepsilon_{\infty}+\frac{\varepsilon_{s}-\varepsilon_{ \infty}}{1+j\omega\tau}-j\frac{\sigma_{sea}}{\omega\varepsilon_{0}}. \tag{2}\]  
\(\varepsilon_{s}\) and \(\varepsilon_{\infty}\) are the static and optical-range electric permittivity, respectively. \(\tau\) is the relaxation time, \(\varepsilon_{0}\) is the dielectric constant of vacuum, and \(\omega\) denotes angular frequency.  
EM-URFC can be carried out using two main approaches. On the one hand, terrestrial RF equipment can be adapted to the underwater medium using an appropriate enclosing and adjusting the antennas. This technique relies on the electric field and is highly affected by the aforementioned effects, allowing only short range links [35]. On the other hand, Magneto-Inductive URFC (MI-URFC) relies on the magnetic field, which suffers from a lower attenuation in seawater although its intensity is _a priori_ lower than the electric field. [PERSON] et al. identified MI-URFC as an enabling technology for UWSN [1].  
In this work, a MI-URFC-based UWSN is proposed. The parametric model described in Equation (3) [36] has been used in order to determine the received power associated to each transmission event in the _software-in-loop_ simulator.  
\[L(d)=L(d_{0})+\eta_{1}\frac{d}{d_{0}}+10\eta_{2}\log\left(\frac{d}{d_{0}} \right)+X, \tag{3}\]  
where \(L(d)\) is the estimated loss at distance \(d\) (in dB), \(d_{0}\) is a reference distance, \(\eta_{1}\) is a coefficient related to the medium's absorption, \(\eta_{2}\) is a coefficient related to the exponent of the geometrical loss, and \(X\) is a random variable which follows a zero-mean normal distribution with empirically-determined standard deviation \(\sigma_{X}\). The parameters used in this study are the same as the ones used in Reference [36], and can be observed in Table 2.
## 3 Materials and Methods
### Dynamic Model Proposed for Android Malware Detection
#### 3.2.5 Voting and Routing  
As the name suggests, this process performs a two functions: voting and routing. The voting function produces a score of 0 or 1 according to the numerical status of the outputs of the six classifiers included in the multi-classification process with a score of 0.5 or more. Below is the equation for the function that produces the output for this process.  
\[Vt_{out}=\begin{cases}1&3\leq n_{clf}\leq 6\\ 0&0\leq n_{clf}<3\end{cases} \tag{10}\]  
Here, \(n_{clf}\) represents the number of classifiers that produce an output score of 0.5 or more. If this number is 3 or more, the result of the voting function is 1, otherwise, this result is equal to 0. According to experimental studies on the six classifiers, the NN, SVM and DF methods successfully detected the true positive rate (TPR), while LR, BPM and BDT effectively detected the true negative rate (TNR). The voting and routing processed are used to take advantage of the good performances of various classifiers, as well as to enable a simpler design by reducing the number of FIS entries that must be employed in the process of combining the scores. Therefore, if the voting function produces a score of 1, which means that the application is believed to be malicious according to the classifiers, then the NN, SVM and DF algorithms are selected to combine the scores due to having a high TPR ratio. Otherwise, classifiers LR, BPM and BDT with high TNR ratios are chosen, and their outputs are used in the following process. The values that must be sent to the FIS entries are obtained by multiplying the classifier scores selected over the routing function by their weights. Equation (11) shows the formula for the routing process.  
\[I_{FIS}(i)=Vt_{out}.O_{clf}(i).w(i)+[1-Vt_{out}].O_{clf}(j).w(j),\begin{array}[] {l}i=1,2,3\\ j=4,5,6\end{array} \tag{11}\]  
In this equation, \(I_{FIS}(i)\) represents the \(i_{th}\) input that is used to the FIS, \(Vt_{out}\) shows the result of the voting function, \(O_{clf}(i)\) represents the output of \(i_{th}\) classifier and \(w(i)\) shows the weight of the \(i_{th}\) classifier on the input. The voting function (\(Vt_{out}\)) result determines which classifiers' output will be used in the combining scores process (FIS) based on its weight, as shown in the formula provided in Equation (11). The weights are calculated using the false positive rate (FPR) and the false negative rate (FNR) obtained from the experimental studies of the classifiers in the positive and negative sample classifier groups. With the experimental studies involving classification errors, both the positive and negative sample classifiers are determined, and the FNR and FPR error rates are determined for each classifier, and the necessary parameters for calculating the weights are obtained. The process of finding the weights for the positive sample classifiers is given in Equation (12), and the process of finding weights for the negative sample classifiers is given in Equation (13).  
\[w_{i}=(1-FNR_{i})\ i=1,2,3 \tag{12}\]  
\[w_{j}=\left(1-FPR_{j}\right)\ j=4,5,6 \tag{13}\]
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 4 Experiments
### Ablation Study
#### 4.3.3 Experiments of Opacity Update Module  
We visualized the trend in the number of Gaussian points before and after incorporating the opacity update strategy into the baseline 3D Gaussian Splatting model, as shown in Figure 8. This figure presents the results from the flower subset of the \(\text{nerf\_LLFF\_data}\) dataset, demonstrating the positive impact of the opacity update strategy on pruning. In  
Figure 7: Compression ratio and visualization at different stages on the Horse dataset.  
this experiment, we used contribution ratio pruning twice. With the addition of the opacity update strategy, the change in the number of Gaussian points becomes smoother.  
In this regard, we analyze that while the opacity update part is more consistent with the principle of Gaussian point replication, it may result in a slightly lower overall opacity compared to the original 3 DGS model. The introduction of a fixed decrement to prevent fluctuations in the Gaussian point count curve also helps accelerate the change in the opacity parameters across the entire model. Redundant Gaussian points can be quickly captured and masked, thereby achieving effective compression of the number of Gaussian points.  
To further verify the opacity update strategy, we subtracted the opacity update strategy represented by OpaU from the final compression model and compared the experimental results when only ConP and GMask modules were effective. The results on the flower sub-dataset are shown in Table 6. It can be seen from the table that the opacity update strategy has little impact on visual indicators but can effectively reduce the size of the model.  
We conducted a series of comprehensive ablation experiments to demonstrate the impact of each module on visual indicators and demonstrated their respective performance on compression effects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **LPIPS** & **Size** \\ \hline (-OpaU) Ours & 26.487 & 0.826 & 0.237 & 58.17M \\ Ours & 26.336 & 0.824 & 0.247 & 36.49M \\ \hline \hline \end{tabular}
\end{table}
Table 6: Qualitative results with opacity update strategy removed.  
Figure 8: The influence curve of the opacity update strategy on the number of Gaussian points.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Method\(\backslash\)Dataset**} & \multicolumn{3}{c}{**Flower**} \\ \hline
**OpaU** & **ConP** & **GMask** & **PSNR** & **SSIM** & **LPIPS** & **Size** \\ \hline  & 3 DGS & & 26.971 & 0.845 & 0.207 & 150M \\ ✓ & & & 26.932 & 0.844 & 0.211 & 98M \\ ✓ & ✓ & & 25.446 & 0.815 & 0.246 & 64.60M \\ ✓ & ✓ & ✓ & 26.336 & 0.824 & 0.247 & 36.49M \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment of Gaussian point number reduction module on nerf_LLFF_data dataset. OpaU denotes the opacity update strategy, ConP denotes Gaussian proportional contribution pruning, and GMask denotes the learnable Gaussian mask.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 3 System Model and Problem Description  
### Network Scenario  
Consider the emergency scenarios for 6G, where natural disasters such as earthquakes cause severe damage to ground infrastructure and prevent the provision of computing services to IoT devices. Due to the easy deployment characteristics of UAVs, we consider providing computing offload services for ground devices with the help of UAVs to meet the execution needs of computationally intensive and latency-sensitive tasks.  
The UAVs collect tasks from ground-based IoT devices and communicate with multiple low-orbiting satellites simultaneously. In order to minimise execution latency, multiple LEOs with a relatively good channel state are selected to share the computational load. Selecting too few LEOs may result in an inability to carry the entire task load, leading to high computational latency and energy consumption. Conversely, selecting too many LEOs can take up satellite resources and other IoT devices are allocated fewer satellite resources, reducing overall system performance.  
The ground-based IoT devices \(\{1,\cdots,N\}\) are denoted as \(i\), UAVs \(\{1,\cdots,M\}\) are denoted as \(m\) and low-orbiting guard \(\{1,\cdots,V\}\) is denoted as \(j\). The locations of IoT devices, drones and satellites are represented using 3D coordinates. The access bandwidth of the satellite is divided into \(K\) sub-channels, each with bandwidth \(B\), and the satellite storage capacity is \(C_{j}\). Where \(S_{i}\) denotes the size of the device computational task, we used \(S_{ij}\) to denote the task allocation of the IoT devices and the size of the allocation; \(\beta\) denotes necessary computing cycles to complete a bit of the computational task.
###### Abstract  
Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.  
mining vehicle; articulated vehicle; path tracking; nonlinear model predictive control; nonlinear control systems +
Footnote †: journal: Applied  
_Article_
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 3 Preliminaries  
### Channel State Information  
CSI, or Channel State Information, is a sampled version of the Channel Frequency Response (CFR) that captures the properties of a wireless channel. Specifically, CSI can be expressed as the ratio of the frequency responses of the transmitted and received signals, denoted as \(X(f)\) and \(Y(f)\), respectively, such that \(H(f)=Y(f)/X(f)\). Raw CSI data are complex numbers that convey two essential pieces of information: amplitude and phase angle. The amplitude component reflects the power attenuation of the signal along the propagation path, while the phase angle corresponds to the time delay caused by the propagation.  
Assuming that there are \(K\) samples in the frequency domain (subcarrier extracted) and \(T\) samples in the time domain (packets received), a CSI matrix of \(K\)-by-\(T\) can be obtained on a single antenna. Each element of this matrix can be expressed as  
\[H(f_{k},t)=|H(f_{k},t)|\cdot\mathrm{e}^{j\angle H(f_{k},t)}. \tag{1}\]  
where \(H(f_{k},t)\) represents the CSI at subcarrier frequency \(f_{k}\) and time \(t\). Here, \(|H(f_{k},t)|\) denotes the magnitude (i.e., amplitude) of the CSI and \(\angle H(f_{k},t)\) represents its phase angle. We use the tool developed by [40] in this paper to extract raw CSI from the commodity Network Interface Card (NIC) Intel 5300. There are 30 subcarriers available in the 20 MHz channel.
## 4 Results and Discussion
### Simulation Analysis of Energy Harvesting Circuit  
Assuming that the generator was installed in the middle of the span, the energy collection analysis was carried out. In the energy harvesting phase, the four coil closing switches of the acquisition circuit were disconnected, and the calculated value of the electromotive force was input into the four voltage generators. The simulation results are shown in Figure 15. The output voltage of each coil is E1, E2, E3, E4. The output current of each coil is I, I2, I3, I4. The input current of the storage capacitor is I and the voltage of the storage capacitor is U.  
It can be seen from Figure 15 that the voltage of the capacitor increased gradually. The alternating current signal in each coil was always at a small value, indicating that the voltage dropped on the internal resistance was very small. The voltage of the energy storage capacitor was also much higher than that of a single rectifier circuit, indicating that the system had a good boost function and energy collection effect. The voltage on the capacitor was much smaller than the input signal.  
The instantaneous power of the circuit energy collection was obtained by multiplying the voltage and current values of the capacitor. The calculated instantaneous power is  
Figure 14: The coil electromotive force at the nodes.  
Figure 15: Array energy harvesting circuit simulation.  
shown in Figure 16, and the average power is 1.093 \(\times\) 10\({}^{-9}\) W. Based on the current value in each coil, the damping force provided by each coil in the system energy acquisition phase can be calculated, as shown in Figure 17.  
If the four coil closing switches are closed, the output power is 0. At this time, the array circuit of the system becomes four independent circuits, and the electromagnetic damping force of a single coil can be directly calculated. As shown in Figure 18, the current in the circuit increased significantly, and the electromagnetic damping force was significantly enhanced. Compared with the applied moving surface load, the damping force cannot be ignored, so the approximate calculation is no longer applicable.  
Figure 16: Energy harvesting power.  
Figure 17: Coil electromagnetic damping force during the energy harvesting.
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
## 4 Numerical Models
### Equation of the Compressive Stress-Strain Curve
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mix** & **Parameters** & **25 \({}^{\circ}\mathbb{C}\)** & **200 \({}^{\circ}\mathbb{C}\)** & **400 \({}^{\circ}\mathbb{C}\)** & **600 \({}^{\circ}\mathbb{C}\)** & **800 \({}^{\circ}\mathbb{C}\)** \\ \hline \multirow{3}{*}{ NWC} & \(n\) & 3.702 & 8.378 & 9.106 & 7.392 & 5.457 \\  & \(\varphi\) & 2.342 & 2.945 & 5.328 & 4.393 & 3.867 \\  & \(\alpha\) & 1.000 & 1.045 & 1.000 & 1.205 & 1.243 \\ \multirow{3}{*}{ AL} & \(\delta\) & 5.155 & 7.701 & 3.820 & - & - \\  & \(\varphi\) & - & - & - & 4.981 & 10.040 \\  & \(\alpha\) & 1.000 & 1.000 & 1.000 & 1.156 & 1.104 \\ \multirow{3}{*}{ SL} & \(\delta\) & 4.299 & 4.794 & 1.846 & - & - \\  & \(\varphi\) & - & - & - & 3.282 & 5.289 \\ \multirow{3}{*}{ CFAL} & \(\alpha\) & 1.065 & 1.186 & 1.026 & 1.270 & 1.364 \\  & \(\varphi\) & 8.657 & 8.548 & 3.423 & 4.731 & 6.809 \\ \multirow{3}{*}{ CFSL} & \(\alpha\) & 1.262 & 1.247 & 1.219 & 1.363 & 1.286 \\  & \(\varphi\) & 5.402 & 3.513 & 2.107 & 3.398 & 4.451 \\ \multirow{3}{*}{ HFAL} & \(\alpha\) & 1.112 & 1.130 & 1.041 & 1.449 & 1.333 \\ \multirow{3}{*}{ HFSL} & \(\varphi\) & 4.127 & 2.067 & 3.504 & 4.762 & 5.877 \\ \multirow{3}{*}{ HFSL} & \(\alpha\) & 1.237 & 1.288 & 1.300 & 1.375 & 1.208 \\  & \(\varphi\) & 2.853 & 3.014 & 2.508 & 3.323 & 3.514 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Regression parameters \(n\), \(\delta\), \(\alpha\), and \(\varphi\).
## 3 YAMNet: An Efficient CNN for Sound Event Detection
### Mel Spectrogram Features  
The feature maps that input YAMNet are constructed on the basis of coefficients drawn from time-frequency representations of sounds. However, human perception of sound in terms of frequency content is not linear. It is widely known that humans perceive pitch with better resolution at lower frequency ranges. Indeed, provided a fixed frequency gap between two sounds, our hearing would differentiate tones at lower frequency ranges better than in higher ranges, where tones would seem quite similar. Consequently, artificial intelligences that learn sound perception tasks should account for this aspect. To properly label auditory events, deep learning strategies for speech recognition, music recognition, and SED evolved by exploiting feature spaces pursuant to the human perception of sound. In particular, YAMNet employs a Mel spectrogram feature map.  
The Mel (root of the word Melody) scale [60] and the Mel Spectrogram [61] take into account the logarithmic nature of human hearing. The Mel scale is empirically designed on the base of the psychoacoustic perception of pitch. The expression of Equation (3) shows how frequencies \(f\) can be converted in \(Mel\):  
\[Mel=\left\{\begin{array}{rl}f,&f\leq 1000\ Hz\\ 2595\ \cdot\log_{10}\Bigl{(}1+\frac{f}{\sqrt{0}}\Bigr{)},&f>1000\ Hz\end{array}\right. \tag{3}\]  
In accordance with human experience, the points that are equally spaced on the Mel scale are not linearly resolved in the Hz scale and vice versa. The Mel spectrogram is a time-frequency representation of sounds, produced by applying Mel scale filter banks to classic spectrograms [61]. Figure 4 shows the Mel conversion curve and reports an example of traditional Hz and Mel scale spectrograms for a linear sine sweep.  
Figure 5 shows some of the features extracted from different layers of YAMNet. Compared with AlexNet (Figure 2), the extracted features are distinctive of spectrograms attributes. As previously discussed, this is due to the fact that the weights of YAMNet are specifically optimized to detect spectrograms features. For this reason, the authors of this work hypothesize that some of the knowledge needed to classify REB spectrograms may be already included in the layers of SED networks. This actually means that only the last layers would require learning adjustments, whereas the feature extraction layers and the corresponding weights could be frozen without requiring further training. Then, a small dataset would be sufficient for fine-tuning the net weights, complying with limited data availability.  
Figure 4: Mel scale (**top**). Traditional spectrogram (**bottom left**). Mel spectrogram (**bottom right**).  
Figure 5: Examples of features extracted from YAMNet trained with AudioSet data: (**a**) low-level features; (**b**) medium-level features; (**c**) high-level features.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### LoRa Tests
#### 5.2.2 Indoor Tests  
Another series of tests was performed indoors to understand the limits of the LoRa technology when used inside a building. These tests were carried out in Torre Archimede, the building housing the Department of Mathematics of the University of Padova. As shown in Figure 7, this building has seven floors, the ground floor, and two underground floors.  
Figure 4: PDR as a function of the distance between nodes and spreading factor (7, 10, 12).  
Similarly to the outdoor experiment, we recorded PDR, RSSI, and SNR for this series of indoor tests. When the first test was carried out, the first device was placed on the ground floor, and the second one was on the seventh floor, and both used a spreading factor of 7.  
Figure 5: PDR as a function of SNR in LoRa outdoor test.  
Figure 6: PDR as a function of RSSI in LoRa outdoor test.  
Figure 7: Torre Archimede, Mathematics Department of the University of Padova.  
The first iteration of the test showed, as can be seen in Table 2, that the two nodes could communicate rather well, but some data loss occurred with a spreading factor of 7 (SF 7, in the table).  
To guarantee perfect communication between the two devices, meaning a 100% PDR, two options are possible:  
1. Moving the nodes closer together;
2. Using a higher spreading factor (more than 7).  
For option 1, we moved the second device closer to ground level at the 5 th floor. With this configuration, the nodes were able to communicate without errors. Similarly, with option 2, the nodes had 100%.