esa-sceva/satcom-chunk-collection huggingface dataset:
# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 3 Experiments
### Dataset  
The dataset created for this study consists of recordings from two distinct sound sources: ambient noise and UAV sounds. As summarized in Table 2, the dataset includes four ambient noise recordings totaling 1.19 GB with a combined duration of 416 s. In contrast, the UAV recordings--captured using a single DJI Mavic 3 Cine drone--are more extensive, with 128 recordings amounting to 14.61 GB and a total length of 5120 s.  
In addition to the previously described recordings, we collected 18 audio files totaling 16.15 GB using the Zoom H4 essential recorder. These recordings have a total duration of 33,090 s, sampled at a 96 kHz frequency with a 32-bit depth, ensuring high-resolution audio capture.  
We performed recordings at varying heights and distances to capture UAV acoustic signatures under controlled conditions. The UAV was positioned and recorded under the following configurations:  
* Height: 20 m; distance: 10 m;
* Height: 20 m; distance: 20 m.  
At each position, recordings were conducted with an azimuth resolution of 45 degrees, covering eight orientations around the acoustic array.  
Furthermore, for each UAV position and orientation, we recorded four separate sessions where the UAV was facing different directions relative to the acoustic measurement system:  
* On the left side, towards the acoustic array;
* On the back side, towards the acoustic array;
* On the right side, towards the acoustic array;
* On the front side, towards the acoustic array.  
Each recording session lasted 40 s, with 75% of the data (30 s) used for training and the remaining 25% (10 s) used for testing. This setup resulted in 64 files per height-distance combination, 16 files per azimuth angle, and 32 files per UAV orientation.
## 3 Results
### Best Model Further Results  
The final experiment in this stage consisted of comparing DualUNet vs. DualUNet100. Table 3 shows the segmentation results for these two models regarding Dice score and Hausdorff distance.  
It can be seen that the myocardium segmentation Dice score is consistently higher after applying the data augmentation strategy, and the standard deviation is also less. This difference was proven to be statistically significant under a Wilcoxon test with \(p_{value}<10^{-5}\). Regarding the Hausdorff distance since the region of interest is bordered by two contours, namely, endocardium and epicardium, they both have to be taken into account. It can be observed that the Hausdorff distance is slightly higher for the endocardium and slightly lower for the epicardium. However, the increase is lower than the decrease. This might indicate that the segmentation performance at the contour level is approximately maintained.  
In a final stage, the segmentation performance exclusively on MI cases was analyzed. If the proposed data augmentation worked as expected, it should reduce the outliers for the cases classified as myocardial infarction.  
Figure 10 shows the distribution of the Dice score for the myocardium segmentation before and after applying the data augmentation algorithm, with the DualUNet architecture trained with 100% data augmentation. As expected, the overall distribution of the metric went up, and two of the three outliers were eliminated. Although the worst one was not removed, its score improved significantly, which gives evidence of an increase in the robustness of the model, better delineating the myocardium despite the presence of MI.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DualUNet DSC** & \begin{tabular}{c} **DualUNet** \\ **(100\% DA) DSC** \\ \end{tabular} & \
\begin{tabular}{c} **DualUNet HD (mm)** \\ \end{tabular} &
\begin{tabular}{c} **DualUNet** \\ **(100\% DA) HD (mm)** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(0.845\pm 0.044\)} & \multirow{3}{*}{0.856 \(\pm\) 0.040} & Endocardium & \(\mathbf{5.73\pm 2.12}\) & Endocardium & 5.77 \(\pm\) 2.23 \\ \cline{2-4}  & & Epicardium & \(6.08\pm 1.75\) & Epicardium & \(\mathbf{5.90\pm 1.93}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean Dice score and Hausdorff distance of the best model (DualUNet) after applying the best data augmentation strategy (DualUNet100). Best results in bold.  
Figure 9: Example of segmentation performance under single and multi-modality approaches (highlighted by the black arrows). The left ventricle cavity and myocardium are in blue and red, respectively.
## 3 Energy-Efficient aDaptive hiEarchical and RobusT Architecture
### Fault Tolerant Mechanisms  
Given the fact that UWSNs are prone to errors and that they are even more erroneous than WSN due to the harsh environment, it is mandatory to provide the protocol with some fault tolerant mechanisms to allow it recover from different node malfunctioning.  
One of the biggest problems of this protocol resides in a malfunction of a CH. If a CH falls down, all the nodes in the cluster will not be able to send their data. In addition, the CH below them in the tree will never reach the sink.  
To reduce the impact of this kind of troubles, the substitute node role has been introduced. A substitute node is a node within the cluster that shares the schedule with the CH and takes its place if it falls down.  
The substitute node is selected by the CH in the initialization phase, based on the energy of the node and the proximity between them. To allow normal nodes to be in a low power state as much as they can, the normal node will be notified in the first round of the normal operation phase. This way, if a CH falls down, its substitute node will detect it and will take its place acting as the CH.  
Another possible problem resides on a sink fall down, for which there is no solution in single-sink networks. As previously mentioned, the protocol allows multiple sinks in the network. Each sink is the root of its CHs tree. If a sink falls down, its tree becomes root-less. In this case, the CHs in the root-less tree have to integrate within the other trees. In order to do so, the CHs at the first level of the tree (which directly communicated with the sink) will detect that the sink is down as messages are not answered. In that moment, they have to inform all the CHs about this fact waking them up, using a low-power radio triggered wake-up [29; 30]. By sending a wake-up signal, all nodes in the network will wake up and the CHs within the root-less tree can send their association request to join the remaining trees.  
There is one more fault tolerant mechanism to be exposed. When a node has a temporally malfunction and performs a reset (or even when a new node is deployed), it has to be reinserted in the network. As previously mentioned, if this node was a CH the network will continue to work thanks to the substitute node role.  
As this node is starting from the beginning, it has to perform the role election mechanism. On one hand, if it becomes a normal node, it will start listening to the channel in order to learn the CHs around it and, when it realizes that the network activity is not the expected one, it has to use the wake-up signal to wake up its nearby CHs and to join them by the usual mechanism. On the other hand, if it becomes a CH it will start sending HEAD messages but no one will respond to them. So, it will use the wake-up signal explained for the normal nodes.
## 4 Discussion
Future works should emphasize on multi-modal data fusion, so as to improve the spatial and spectral details of the objects that impact on the shoreline extraction task. The validation of shoreline/ coastline positions is also of paramount importance, yet very few studies [81; 84; 85; 93; 104] quantify positional errors through other in situ data. The widespread availability of in situ data can facilitate the development of new machine learning models, as well as the calibration and validation of algorithms by the research community. In the context of cases that need to investigate coastal erosion due to climate change, sea level rise, or other phenomena, the monitoring of coastlines is more appropriate, whilst shorelines seem to be more suitable for monitoring tidal-datum indicators. Last but not least, it should be stated that machine learning techniques have been also applied to the coastal environment in the context of other application focuses, such as the monitoring of marine litter and the extraction of wave and tide parameters from multimedia (i.e., videos). Litter classification tasks can be achieved by applying similar approaches with the ones investigated in this paper [111], whilst typical examples of the latter require different convolutional neural network architectures, in which each tide class is classified to a tide height in each video frame, and optical flow techniques can be applied for the calculation of waves flows and heights [30].
## 5 Enhancement of Brillouin Stokes Signal (II): Influence of Core Diameter and Fiber Length  
In this Section, we describe the characterization on the BGS in PFGI-POFs with 62.5-\(\upmu\)m core diameter [32]. First, using 5-m PFGI-POFs, we show that extremely high Stokes power can be obtained compared to that of a PFGI-POF with 120-\(\upmu\)m core, and estimate the Brillouin threshold power to be 53.3 W. Then, we experimentally show that using a PFGI-POF longer than \(\sim\)50 m is not an effective way to enhance the Stokes signal. We also theoretically predict that it is difficult to decrease the Brillouin threshold power of PFGI-POFs at 1.55-\(\upmu\)m wavelength down to that of km-order-long silica SMFs even when their core diameter is sufficiently reduced to satisfy the single-mode condition. Finally, we investigate the Brillouin linewidth as a function of pump power, and confirm the linewidth narrowing effect.  
Figure 5: Dependence of (**a**) BGS and (**b**) BFS on temperature in PFGI-POF.
## 2 Transmission Packet Structure and Channel Model  
To reduce the synchronization complexity, we used multiple equispaced LFM signals in one packet, which is also widely used in UWA communications [19; 20]. The LFM signal in the analytic signal form is written as:  
\[s_{\text{Sync}}(t)=e^{j2\pi f_{0}t(1+\frac{f_{1}-f_{0}}{2f_{0}^{\text{T}} \text{Sync}})},\quad 0\leq t<T_{\text{Sync}}, \tag{1}\]where \(f_{0}\) and \(f_{1}\) are the start and end frequencies, respectively, and \(T_{\text{Sync}}\) is the duration of the synchronization signal. A whole packet consists of \(N_{\text{Frm}}+1\) LFMs, with the occurrence period of \(T_{\text{Frm}}\) and the transmitted synchronization given by:  
\[s(t)=\sum_{k=0}^{N_{\text{Frm}}}s_{\text{Sync}}(t-kT_{\text{Frm}}). \tag{2}\]  
The \(N_{\text{Frm}}\) data frames are transmitted among the synchronization signals.  
The acoustic channel was modeled as the cascade of the time-invariant linear filter and the motion-induced time-varying Doppler compression or dilation. The CIR is represented by \(h(t)\), and its convolution with \(s(t)\) is written as:  
\[r_{h}(t)=\int s(t-\tau)h(\tau)d\tau. \tag{3}\]  
The received waveform in the passband is then given by:  
\[r_{\text{PB}}(t)=r_{h}\bigg{(}t-\frac{d(t)}{c}\bigg{)}+w(t), \tag{4}\]  
where \(d(t)\) is the time-varying distance between the transmitter and the receiver, \(c\) is the speed of the sound, and \(w(t)\) is the additive noise. During the vertical communication between the submersible and the mother ship, the position of the mother ship rapidly fluctuates with the surface wave height. Therefore, in this scenario, the displacement is modeled by the time-varying surface wave height. The Pierson-Moskowitz model was adopted, whose spectrum is given in [8] as:  
\[\psi_{\text{SW}}(f)=\frac{2\pi\alpha g^{2}}{(2\pi f)^{5}}e^{-\beta(\frac{g}{2 \pi f(t)})^{4}}, \tag{5}\]  
where \(U\) is the wind speed, and the other constant parameters take the following values: \(\alpha=0.0081\), \(\beta=0.74\), and \(g=9.81\).
###### Abstract  
With the rapid development of communications technology, the space-based information network (SBIN) is increasingly threatened by the outside world. Dynamic changes in any part of its interior can cause the collapse of the entire network. Therefore, research on the invulnerability of SBIN has become an urgent need to promote the economic development of our country and improve the living standards of our people. To this end, this paper has carried out research on the node-attacked invulnerability of SBIN based on the complex network theory. First, based on the model of SBIN, the internal parameters of the network are analyzed theoretically based on complex networks. Second, the paper proposes an improved tree attack strategy to analyze the invulnerability of SBIN, which constitutes a problem where the traditional attack strategy has a low invulnerability and the connected edge cannot fully realize the network function. Then, based on the improved tree attack strategy algorithm, this paper optimizes the invulnerability of SBIN by constructing four different edge-increasing strategies. Through the research, the LDF edge-increasing strategy makes the entire network flatter and can effectively improve the network's ability to resist destruction. The research of invulnerability based on the complex network has a certain technical support and theoretical guidance for the construction of a reasonable and stable SBIN.  
Keywords:space-based information network (SBIN); node attack; invulnerability; complex network +
Footnote †: journal:
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 2 Vibration Signal Data Preprocessing and Feature Extraction Modelling  
### Variational Modal Decomposition (VMD)  
Variational mode decomposition (VMD) mainly deals with the problem of resolving K amplitude-frequency modulation sub-signals from the original vibration signal [25]. VMD can decompose transformer vibration signal data non-recursively into mode components with a specified number of levels, which not only has good adaptability but also reduces the instability of the original signal sequence. The algorithm uses Wiener filtering to eliminate the high- and low-frequency components of the signal and initialises the related parameters to obtain K estimated center angular frequencies \(\omega\)k. Finally, the alternating direction method of multipliers is used to update each mode function and its center frequency, and each mode is demodulated to the relevant frequency band to minimise the total estimated bandwidth to achieve optimal mode decomposition. The formula is as follows:  
\[\begin{cases}\underset{\{u_{k}\}\{v_{k}\}\{w_{k}\}}{\min}\left\{\sum_{k}\| \partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t)\right] \mathrm{e}^{-j\omega_{k}t}\|_{2}^{2}\right\}\\ \hskip 14.226378 pts.t.\sum_{k}u_{k}=X(t)\end{cases} \tag{1}\]  
In Equation (1), \(\{u_{k}\}=\{u_{1},u_{2},\cdots,u_{k}\}\) and \(\{\omega_{k}\}=\{\omega_{1},\omega_{2},\cdots,\omega_{k}\}\) are the K modal components and frequency centres obtained from the decomposition, respectively. For problems with restricted variables, to find \(u_{k}\) and \(\omega_{k}\) for the optimal solution, updating occurs as follows:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{X}(\omega)-\sum_{i\
eq k}\hat{u}_{i}( \omega)+\hat{\lambda}(\omega)/2}{1+2\alpha(\omega-\omega_{k})^{2}} \tag{2}\]\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\abs{\hat{u}_{k}(\omega)^{2}}\abs{d \omega}}{\int_{0}^{\infty}\abs{\hat{u}_{k}(\omega)^{2}}\abs{d\omega}} \tag{3}\]  
where \(\lambda\) is the Lagrangian operator and \(\alpha\) is the quadratic penalty factor.  
The steps to create a VMD model are as follows:  
1. Initialise \(\{\hat{a}_{k}^{1}\},\ \{\omega_{k}^{1}\},\ \{\hat{\lambda}^{1}\},\) and n.
2. Update \(u_{k}\) and \(w_{k}\) according to Equations (2) and (3).
3. Update \(\lambda\) using \(\lambda^{n+1}(\omega)\leftarrow\lambda^{n}(\omega)+\tau\Big{[}\hat{X}(\omega) -\sum_{k}\hat{n}_{k}^{n+1}(\omega)\Big{]}\).
4. Set the judgment accuracy e > 0. If the judgment condition \(\sum_{k}\frac{\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\|_{2}^{2}}{\|\hat{u}_{k}^{n }\|_{2}^{2}}<e\) is not met, then go back to step 2; terminate the iteration if the judgment condition is met. However, the VMD algorithm is somewhat subjective in determining the decomposition parameters K and \(\alpha\) during signal processing, which requires a lot of practical experience and repeated attempts. Therefore, this paper introduces the positive cosine algorithm to adaptively determine the above parameters.
## 3 Design of Mobile Unit (MU)
### GNSS (GPS) Subsystem Design
#### 3.2.2 Dynamic Positioning  
A typical scenario is to locate and identify moving objects. The system analyzes all movements and makes a decision about the status of the objects. Therefore, in-motion  
Figure 8: Static positioning error: external microstrip antenna upwards.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **mod\_1** & **mod\_4** & **mod\_5** \\ \hline \multirow{3}{*}{Positioning error [m]} & min. & 0.25 & 0.09 & 5.44 \\  & avg. & 8.31 & 11.7 & 7.13 \\  & max. & 55.8 & 36.1 & 273 \\ \hline \multicolumn{2}{c}{N of measurements} & 8535 & 3489 & 7115 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Static location errors for external active antenna heading upwards.  
Figure 9: Static positioning error: external active antenna upwards.  
positioning is much more important than static positioning. Based on our experiments, the positioning accuracy decreases with increasing object speed. Nevertheless, the cross-tracking error of the selected GPS modules was investigated. Measurements were taken with the antenna pointing upwards (i.e., typical operating position). A measurement was taken every 5 s at a distance of 195.5 m at walking speed (i.e., 5 km/h), since most of the tracking objects are expected to be devices carried by airport personnel walking by foot.  
Both mod_5 and mod_2 have a tendency to remember and \"hold\" the last position before power off. This is probably an effect of the internal motion prediction algorithm but is not accessible at the user level.  
The mod_1 module works well with the external active antenna. Modules mod_7 and mod_8 have the best performance in static and dynamic (cross-tracking) measurements, outperforming module mod_4. However, their advantage is only visible under clear and open-sky conditions (Figure 10).  
In general, the best behavior for static (or low speed) operation (e.g., wearable) is mod_3, mod_4 (both used for the prototypes), and low-power mod_1, but equipped with an additional active antenna. For automotive use (where higher power consumption is not an issue), mod_4, mod_7, and mod_8 receivers are preferred.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Quantum-Inspired AI  
Quantum-inspired Reinforcement Learning (QiRL) is a heavily researched application of quantum theory. It is inspired by the collapse phenomenon and amplitude amplification properties of quantum computing [268]. [PERSON] et al. [268] used QiRL to optimize the uplink transmission rate to an Unmanned Aerial Vehicle (UAV) with little context about the clients' geographical location. Another proposal by [PERSON] et al. [269] used QiRL to control the navigation of autonomous mobile robots. Simulation results of the system demonstrated good results as compared to other models.  
Another proposal for use of quantum concepts in reinforcement learning comes from [PERSON] et al. [270]. In this proposal, quantum concepts were used to adaptively choose experiences from the replay buffer in reinforcement learning models according to the complexity and the replayed times of each experience. This effectively achieves a balance between exploration and exploitation.  
Quantum concepts also see much use in neural networks. [PERSON] et al. [271] proposed Quantum-inspired Multidirectional Associative Memory (QMAM). This model allows the neural network to progressively develop a resonance state, from inputs to outputs. In another proposal by [PERSON] et al. [272], a Quantum-inspired Fuzzy-based Neural Network (Q-FNN) was introduced. Quantum computing concepts were used to adjust the fuzzy parameter to optimize the network. Testing on 15 benchmark datasets demonstrated superior results.
## 2 Results and Discussion
### Influence of the Amount of the Catalyst  
To optimize the catalyst amount, the model reaction was performed with different quantities of the catalyst and according to obtained results (Table 1, entries 2-8). An amount of 0.05 g (20%) of the nanostructured diphosphate Na\({}_{2}\)CaP\({}_{2}\)O\({}_{7}\) was chosen as the optimal catalyst amount; with this amount, the reaction can be performed in 30 min, providing a 94% yield of **5a** (Figure 6). With an increased amount of Na\({}_{2}\)CaP\({}_{2}\)O\({}_{7}\), there was no improvement in the product yields (Table 1, entries 7 and 8). This may be due to the attainment of the maximum conversion efficiency of the catalyst. No target product was observed without the catalyst. This result suggests that our catalyst plays an important role in this transformation (Table 1, entry 1).  
Figure 5: TEM micrographs of Na\({}_{2}\)CaP\({}_{2}\)O\({}_{7}\) nanopowder.
# Bat Optimized Link State Routing Protocol for Energy-Aware Mobile Ad-Hoc Networks
## 5 Simulation and Result
### Experimental Results and Discussion
As the previous results, the performance of the BOLSR is better than the CAABO, EBDLSR, and OLSR protocols with regards to the ROR. The BOLSR set's up robust and steadier routes as is shown in the ROR and EC measures in which there are less likelihoods of route failure, and hence, reducing the need for the route discovery process. The effect of the node speed on the EC for the BOLSR, CAABO, EBDLSR, and OLSR are shown in Figure 12. The results show that the EC of the nodes is dramatically increased when the node speed is increased as high node speed increases the probability of the link failure between nodes. Again, the EC of the BOLSR routing protocol is lesser than the CAABO, EBDLSR, and OLSR for the same mentioned reasons. The effect of the node speed on the E2E delay for the BOLSR, CAABO, EBDLSR, and OLSR are shown in Figure 13. The node speed causes a higher probability of link failure which dramatically increases the E2E delay of the nodes. The SD results of Figure 12 clearly show that the BOLSR outperforms the other protocols as it optimizes routes based on various parameters. In the third scenario, the protocols were evaluated against the simulation time. Generally, the increase in the simulation time exhausts the energy of the nodes which increases the ROR and E2E of the network. The results of this scenario are shown in Table 5.  
Figure 10.0 ROR vs. node speed. Figure 10.1 EC vs. node speed. Based on Table 5, the effect of the simulation time on the routing overhead of the four protocols is shown in Figure 10.2 In general, when the simulation time is increased (10-70 s), the ROR is tremendously increased. The lowest ROR is recorded in the BOLSR and the highest ROR is recorded in the OLSR protocol while the EBOLSR and CAABO maintain adjacent ROR results. The effect of the simulation time is shown in Figure 10.3 Like the first and second scenarios, the EC of the nodes increases when the simulation time is increased. The BOLSR routing protocol consumes less energy compared to the CAABO, OLSR, and EBOLSR. The BOLSR protocol chooses a stable path with the highest energy for the same reasons mentioned previously. Figure 10.4 The ROR vs. simulation time. Figure 10.5 E2E delay vs. node speed. Figure 10.6 The EC vs. simulation time. The effect of the simulation time of the four protocols on the E2E delay is shown in Figure 10.7 Whilst, the simulation time increases the E2E delay increases too. Consequently, the BOLSR protocol has less E2E delay when compared with CAABO, OLSR, and EBOLSR as it selects a steady path with the maximum energy which increases the network lifetime and ultimately decreases the E2E.