esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Propagation of Ultrashort Pulses  
To follow the effect of the linear dispersion of the medium on the temporal shape of a pulse, a laser pulse with a Gaussian temporal envelope of electric field strength is considered, having a transform limited temporal duration (_FWHM_) of \(\tau_{0}\) at the position \(z=0\). The amplitude of the laser pulse is denoted by \(E_{0}\), its carrier wave has a frequency of \(\omega_{0}\) and--for the sake of simplicity--zero initial phase. In mathematical form, the pulse can be described as  
\[E(t,0)=E_{0}\exp\Biggl{[}-2\ln(2)\frac{t^{2}}{\tau_{0}^{2}}\Biggr{]}\cdot\exp \bigl{[}io_{0}t\bigr{]}. \tag{1}\]Fourier transform provides a relationship between the spectral and the temporal representation of the laser pulse. In the spectral domain, the complex field strength of the pulse from Equation (1) can be written as  
\[\tilde{E}(\omega)=\frac{E_{0}\tau_{0}}{2\sqrt{\ln(2)}}\exp\Biggl{[}-\frac{\tau_{ 0}^{2}}{8\ln(2)}\bigl{(}\omega-\omega_{0}\bigr{)}^{2}\,\Biggr{]}. \tag{2}\]  
The shape of the pulse after travelling a distance of \(z\) in a medium with an index of refraction \(n(\omega)\) is given by  
\[E(t,z)=\frac{1}{\sqrt{2\pi}}\int\limits_{-\omega}^{\omega}\tilde{E}(\omega) \exp\bigl{[}i\omega(t-n(\omega)\cdot z/c)\bigr{]}\,do. \tag{3}\]  
A simplified representation can be achieved if we introduce the _spectral phase_  
\[\varphi(\omega,z)=\omega\cdot n(\omega)\cdot z/c \tag{4}\]  
corresponding to a geometrical distance \(z\). The function of the spectral phase describes the phase evolution of a light wave through any optical system. In the case of optical bulk material, its expression seems relatively simple (see Equation (4)), but in many cases, calculations with the refractive index functions can be rather complicated. The spectral phase expression of sophisticated optical elements or assemblies, like a compressor or stretcher, may become pretty cumbersome. In the experimental practice, moreover, the determination of the full spectral phase function is not possible. Hence, the usual way of handling spectral phase functions is to describe them through their Taylor series as  
\[\varphi(\omega,z)=\varphi(\omega_{0},z)+\frac{\mathrm{d}\varphi(\omega,z)}{ \mathrm{d}\omega}\Biggl{|}_{\omega_{0}}\cdot(\omega-\omega_{0})+\frac{1}{2} \frac{\mathrm{d}^{2}\varphi(\omega,z)}{\mathrm{d}\omega^{2}}\Biggl{|}_{\omega _{0}}\cdot(\omega-\omega_{0})^{2}+\frac{1}{6}\frac{\mathrm{d}^{3}\varphi( \omega,z)}{\mathrm{d}\omega^{3}}\Biggl{|}_{\omega_{0}}\cdot(\omega-\omega_{0} )^{3}+\ldots \tag{5}\]  
Once the coefficients of the series is determined to the required (or experimentally feasible) order, the spectral phase function can be constructed in a semi-empirical way, _i.e._, with the use of fitting algorithm to find the coefficients of the required function (e.g., Sellmeier-type function for bulk dielectrics, _etc._). It has to be noted that the Taylor coefficients should be always used with caution, since they are only approximations: the reconstructed spectral polynomial may diverge from the actual spectral phase function at frequencies far from \(\omega_{0}\).
# Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization
## Extended Particle-Aided Unscented Kalman Filter
### Particle Filter Initialization  
Initialization provides the initial guess of the ego vehicle's position in global coordinates via GPS. Once an algorithm knows the initial guess, it randomly generates some particles based on the initial position. If the algorithm is run in an indoor environment, an initial guess of the global map-based position should be given. In the PAUKF algorithm framework, an initial guess is not necessary per se, since features in the HD map provides a relative global position, but having one can make the estimated result converge to the ground truth more rapidly.
## 3 Earthquake Prediction Models
### The Deep Neural Network Model
This is significant for analyzing complex time-series data, such as earthquake signals. Using convolution kernels of different sizes can help the model capture features at different scales. Smaller kernels (e.g., 5 \(\times\) 5) can capture local, detailed features in the signal, while larger kernels (e.g., 7 \(\times\) 7) can capture a broader spatial range, thus identifying larger-scale patterns in the signal. This multi-scale feature extraction method helps improve the model's performance and generalization capabilities. Additionally, we used a technique called \"dilated convolution\" to increase the receptive field range. By increasing the dilation rate, the model can expand the receptive field range without increasing the kernel size. This helps capture more extensive contextual information while maintaining relatively low computational complexity. Finally, the feature maps after the convolution operation are concatenated through the concatenate layer, combining the different scale features captured by the two groups of convolutional layers. In summary, by using multi-scale convolution kernels and feature fusion strategies, the 2D-CNN branch can simultaneously capture rich features in the time and frequency domains, providing strong support for earthquake prediction tasks. * Feature Concatenation Layer. After processing the outputs of the 1D-CNN and 2D-CNN branches, we concatenate them, achieving feature fusion. This design aims to combine the advantages of 1D-CNN and 2D-CNN to fully extract the features of the earthquake signal data. * Dense Layer. After the feature fusion, we added a fully connected layer with 128 neurons and a ReLU activation function for further processing of the feature information. * Outputs Layer. The model has three outputs, which are used to predict the time to failure (TTF), time since failure (TSF), and a binary classification result (to determine whether TTF is less than 0.5 s). The TTF output uses a fully connected layer with one neuron and a ReLU activation function. The TSF output also employs a fully connected layer with one neuron. For the binary classification output, a fully connected layer with one neuron and a sigmoid activation function is used.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 5 Results and Discussion  
In this section, we provide simulation results along with the relevant discussion. The link-level simulations were performed in MATLAB to validate the expressions. Moreover, we have performed Monte Carlo simulations for both LOS and non-LOS conditions. We have fixed the value of \(\alpha,\eta,\kappa,\mu,p,q\) throughout the simulations. Specifically, the value of \(\alpha=2.2,\eta=73,\kappa=5.7,\mu=1.01,p=1.05,q=1\) for LOS outdoor channel, whereas, for non-LOS outdoor channel \(\alpha=2.545,\eta=0.006,\kappa=2.5,\mu=1.98,p=1.5,q=1.05\). Besides this, although there are infinite summations in \(P_{e}\) and \(C_{erg}\), these summations easily converge for small values of \(k\), \(n\) and \(l\). Furthermore, the values of other simulation parameters are \(k=10\), \(n=10\), \(l=15\), and \(\psi=2\) bits/s/Hz.  
Figure 2 shows \(P_{e}\) against increasing values of \(\bar{\lambda}_{c}\). It can be seen that the probability of receiving erroneous packets decreases when \(\bar{\lambda}_{c}\) increases, whereas, it increases with an increase in \(\bar{\lambda}_{i}\). Moreover, the PEP is higher for non-LOS conditions as compared to the LOS communication scenario which shows the impact of blockage at higher frequencies. Due to these blockages, the signal cannot penetrate through the object which results in high PEP for non-LOS conditions. The increasing values of \(M\) also increase the \(P_{e}\) which shows that smaller coherence time increases the probability of receiving erroneous packets.  
In Figure 3, ergodic capacity curves are plotted as a function of \(\bar{\lambda}_{c}\). Here, it can be noted that the analytical curves closely corroborate with the simulation results. Additionally, an increase in the \(\bar{\lambda}_{c}\) increases the overall ergodic capacity for both LOS and non-LOS conditions. The smallest ergodic capacity is achieved when non-LOS communication takes place under a high value of \(\bar{\lambda}_{i}\). Furthermore, the gap between the non-LOS and LOS curves increases at higher values of \(\bar{\lambda}_{c}\). This shows the signal power greatly impacts of LOS and non-LOS conditions for vehicular networks.  
Figure 2: Probability of receiving erroneous packets versus \(\bar{\lambda}_{c}\).
## 2 Results and Discussion
### Sensory Evaluation  
The icewines treated with different clarification treatments was assessed by sensory analysis to evaluate whether there were any perceivable differences among clarification treatments. As can be seen from Figure 4, clarification treatments could increase limpidity and decrease the color intensity of wine samples. Some researchers suggested that phenol compounds could be absorbed by fining or filtration, thus influence the color intensity of wine samples [17; 37], which was in agreement with this study. For balance and persistence, there was no observed differences in the wine samples. Among all treated wines, BT showed the most significant influence on acidity, sweetness and complicacy.  
Figure 3: Total OAV of the aroma series after different clarification treatments. Data are means \(\pm\) SD (\(n=3\)). Different letters represent significant differences at a significant level of 0.05.  
Moreover, the aroma intensity, fruity and floral for BT and MF wines were lower than other treatments, indicated that these treatments may have a negative effect on the aroma quality of the wine samples. However, there was no observed difference between BT and MF (Figure 4). In contrast, SP and CF treatments showed little impacts on wine aroma quality (the influence was not significant) which could be alternative method in icewine clarification.
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 5 Conclusions  
This paper proposes the development, integration and use of a system to generate a georeferenced point cloud based on integrating a LiDAR sensor into a UAV platform. Furthermore, we designed and built a support structure to facilitate coupling the sensor to the drone platform. Finally, the ROS packages and the Python programming language were used to process data collection.  
The proposed LiDAR system captured the shape of the object studied and yielded a maximum error of 0.347 m (34.7 cm) and an RMSE of 0.171 m (17.1 cm) (Table 10). In terms of accuracy, the results revealed that the georeferenced point cloud generated by the system implemented herein evidenced an RMSE of 0.766 m (76.6 cm). Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure, in which a presentation scale of 1/2000 or more is required due to the accuracy obtained in work presented and in line with the recommended scales for topographic work.  
Future research studies may consider using high-precision GNSS/INS sensors with an error of fewer than 0.1 degrees in orientation measurements and 5 cm in position measurements to improve the accuracy of the point cloud. Path generation can also be enhanced by processing sensor position and orientation data through a particle filter to better model sensor uncertainty. This would be supported by developing a particle filter framework for solving positioning, navigation and tracking issues (the Monte Carlo sequential methods), which numerically illustrates its advantages over classical algorithms based on Kalman filters in airborne applications. This is because of the use of non-linear models and non-Gaussian noise. In addition, registration algorithms, such as Iterative Closest Point (ICP) variants, can be incorporated into the workflow to improve the adjustment and optimize  
Figure 22: Photogrammetry point cloud (**upper image**) and LiDAR point cloud (**lower image**).  
the system recalibration process, thereby offsetting systematic measurement errors and leveraging the redundant information contained in the strip overlay areas that comprise the point cloud and terrain data obtained with support equipment [34]. ICP proposes solving the registration issues in point clouds by addressing the local minimum problem through direct georeferenced points and feedback bias in GNSS/INS systems [38].  
The authors declare no conflict of interest.
## 4 Simulation Results
### Comparison of the Information Integrity  
Information integrity reflects the proportion of information required to meet the monitored area as a percentage of the total required information. The decline in information integrity, to a certain extent, means that the obtained information has become less desirable. As shown in Figure 17, similar to the network lifetime and coverage, the information integrity of LEACH is the first to decline among the five protocols, followed by the DEEC and the GSEN, while the EECRP-HQSND only experienced a decline in information integrity in the 1332 nd round. Finally, the ICRM in this paper can greatly reduce the CHs' energy load, especially for the CHs that are remotely located, resulting in the EECRP-HQSND-ICRM postponing the decline of information integrity to the 1365 th round, thus ensuring that the network can obtain more complete data during this period.  
Figure 16: The network coverage varies with the iteration round.  
Figure 17: The information integrity varies with the iteration round.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 3 Dataset  
The dataset for this study was derived from Xeno-canto [33]. It comprises 263 audio recordings from 16 bird species belonging to 7 orders, 9 families, and 15 genera. The dataset was divided into a training set and a test set in a ratio of 8:2. The details are provided in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ID** & **Latin Name** & \
\begin{tabular}{l} **Number of Audio** \\ **Recordings** \\ \end{tabular} &
\begin{tabular}{l} **Duration of Bird** \\ **Vocalization (s)** \\ \end{tabular} \\ \hline
1 & _Elachura formosa_ & 13 & 409.97 \\
2 & _Leioltrix lutea_ & 15 & 604.88 \\
3 & _Coturnix coturnix_ & 14 & 925.56 \\
4 & _Asio otus_ & 32 & 1001.03 \\
5 & _Grus grus_ & 19 & 835.76 \\
6 & _Numenius plaapous_ & 30 & 860.62 \\
7 & _Larus canus_ & 11 & 497.83 \\
8 & _Accipher nisus_ & 20 & 989.37 \\
9 & _Accipher gentilus_ & 17 & 592.73 \\
10 & _Falco timunculus_ & 14 & 527.57 \\
11 & _Plasianus coldcticus_ & 11 & 701.19 \\
12 & _Lagopus muta_ & 10 & 536.17 \\
13 & _Lyrurus tetrix_ & 8 & 784.16 \\
14 & _Cygnus cygnus_ & 15 & 619.89 \\
15 & _Philloscopus trochiloides_ & 21 & 1516.88 \\
16 & _Francolinus pintadeanus_ & 13 & 612.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed information on the bird dataset.  
As the rarity and various vocal habits of different bird species can influence the robustness of a model, we focused on extracting the energy-dense segments of the Mel spectrum from each audio sample. This resulted in approximately 1000 samples per species, as illustrated in Figure 1.
## 5 Discussion
### Comparison with Other Methods
As shown at the red boxes marked in the last two columns of Figure 15, it is noted that some sheds and trees contained in the cropland will have holes, which can be filled by our method to make the cropland more homogeneous inside. Table 4 presents the performance comparison of different methods based on the GID dataset, in which HRNet-CFR+ is a method to increase morphological post-processing. Compared with no post-processing, the overall accuracy was increased by 1.81%. The overall accuracy of our approach can reach 92.03%, which is 3.39% higher than Deeplabv3+ and 5.12% higher than UperNet. In Sections 4 and 5, the Jilin-1 high-resolution remote sensing dataset and the public GID dataset with multiple time phases and multiple regions were selected for testing. The extraction results of cropland in the growing season were better than those in the non-growing season. Regions with large geographic differences need to re-add sample training, and the model transformation ability was not strong. The HRNet-CFR method had a certain improvement in the extraction accuracy, and the overall accuracy was improved by about 1.2%. Compared with the Deeplabv3+ and UPerNet algorithms, it was improved by 3.39-5.12%, and the overall accuracy can reach 92.03%. From the qualitative and quantitative evaluation, it shows the superiority of our method in high-resolution image cropland extraction.
# Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
## 5 Conclusions  
In this paper, for the first time, we designed and investigated an analytical model of the energy detector for ultra-wideband chaotic radio pulses with a large base.  
Ultra-wideband nature of the chaotic signal allowed us to model it with normally distributed random values. The large base (number of freedom degrees) \(B\ =\ 2T\Delta F\gg 1\) of chaotic radio pulses justified application of the central limit theorem to the statistic of the radio pulse energy, so it was also described with a normal distribution. These approximations allowed us to get closed-form expressions for the performance of the energy detector (the detection and false alarm probabilities, and the optimal threshold).  
The area of application of the proposed model is, first of all, ultra-wideband communications based on chaotic radio pulses. For engineering problems, the applicability bound of this model can be expressed as \(B\geq 100\), because in this case for the error probability \(P_{\mathit{err}}=10^{-4}\), the numerical and analytical SNR estimates differ by less than 0.5 dB, which is quite sufficient. This bound can also be expressed through the data rate \(R\) since the pulse duration \(T\) is related to the transmission rate \(R\) as:  
\[R\ =\ 1/T\ =\ \frac{2\Delta F}{B}. \tag{19}\]hence, the model better suits the relatively low-rate communications. For example, for \(\Delta f\) = 500 MHz and \(B\) = 100 the lower bound of the data rate is \(R_{min}\) = 10 Mbps, whereas \(\Delta f\) = 2000 MHz and \(B\) = 100, \(R_{min}\) = 40 Mbps. (Note that this bound is not for the energy detector, but only for the applicability of the discussed model. The energy detector is certainly functional at the frequencies below this bound, but the model describes it less accurately.) Of the modern wireless communication systems, this model most closely matches the systems of the standards 802.15.4a (optional solution) or 802.15.6.  
The obtained analytical solution can facilitate the design of communication systems with a chaotic carrier, in particular, of multi-element systems, by means of replacing tedious numerical simulation with direct calculation.  
This research was done in terms of the State Assignment of the Kotelnikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences.  
Not applicable.  
The author declares no conflict of interest.
## 3 Results and Discussions
### LET Difference between Layer Structures with and without SiGe  
The LET spectra for the LANSCE neutron and 63 and 230 MeV protons with and without SiGe were plotted in Figure 7. The difference in the overall spectra between the structures with and without SiGe is insignificant except for the LET value greater than 20 MeV-cm\({}^{2}\)/mg. LANSCE neutron and 63 MeV proton give lower differential fluence when the SiGe layer is added, but 230 MeV proton gives a larger differential fluence. This is because with high-energy protons, the yields of alpha and light ions correlate with the Z of an incident target. Since Ge has a larger Z than most of the materials in our structures, being four times larger than the Z of O and 2.3 times larger than the Z of Si, we observed more counts for the structure with SiGe in the lower LET. With low-energy protons, however, it is difficult to have spallation and generate secondary ions [24]. In addition, Ge has a larger neutron absorption coefficient than silicon [25; 26], hence some neutrons were absorbed by the SiGe layer. Therefore, in the case of the LANSCE neutron, the events with LETs less than 0.1 MeV-cm\({}^{2}\)/mg are somewhat lower when SiGe is added.
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 2 Materials and Methods
### Risk Estimation
To facilitate the spatial analysis calculations, all layers were rasterized into 10 \(\times\) 10 m pixels. Figure 5: Neighborhoods located on the colluvium and residual soil coverage and their classification. The total value of losses \(L\) for each precipitation event \(i\) was obtained by adding the loss values at the pixel level with the following formula. \[L|Event_{i}=\sum_{j=i}^{n}L_{pxji} \tag{18}\]  
The loss values for each return period and their annual exceedance probabilities were integrated through a loss exceedance curve, following which the area under the curve was calculated to estimate the expected annual loss for the entire study area, using the following formula (adapted from [PERSON] et al. [46]):  
\[AAL=\sum_{i=n}^{n}(L|Event_{i})F_{a}(Event_{i}), \tag{19}\]  
where \(AAL\) is the average annual loss, \((L|Event_{i})\) is the value of the loss for precipitation event\({}_{i}\), and \(F_{a}\) (\(Event_{i}\)) is the annual frequency of precipitation event \(i\). Estimation of the AAL as the area under the exceedance curve was performed by applying the interval sum method [49; 50]. The AAL is a key metric for risk analysis and is calculated in catastrophic risk models for different hazards such as hurricane, floods, and earthquakes. However, it had not been previously used in landslide disaster risk studies, due to difficulties in estimating the losses associated with different return periods. The methodology developed in the present study overcomes these limitations. The proposed risk assessment is focused on quantifying direct losses or damages caused by slow-moving translational and rotational landslides. This may result in underestimation of the total risk associated with those hazards, as it does not consider social, environmental, or indirect economic losses. It is important to note that, as the risk is associated with slow-moving landslides, the likelihood of injuries and loss of human lives will be practically non-existent. Additionally, the areas exposed to slow-moving landslides are housing areas and, so, the economic activity is minimal, limited to small micro-enterprises located within homes. The interruptions of communication routes and their economic impact are also expected to be limited, as road closures due to landslides will be very localized events. Furthermore, the damage approach aligns with other widely disseminated catastrophic risk analysis models, such as Hazus [51; 52] or CAPRA [46], contributing to the methodological comparability of the model.