esa-sceva/satcom-chunk-collection huggingface dataset:
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 1 Introduction
This is the case, but there are VoIP servers wherein the voice and video traffic are passed/processed by the VoIP server. This study is about those VoIP servers, wherein the voice and video traffic are passed/processed by the server. In this work, a simple model consisting of an IP network composed of several users, a VoIP server, and an attacker located in an external network has been created. The GNS3 IP network-modeling platform, Asterisk Free PBX, Kali Linux and network-monitoring tools have been used for the purpose of the study. Asterisk Free PBX is one of the VoIP servers described above, wherein voice and video traffic are passed/processed by the server. The research was carried out in two parts. Initially, only voice traffic was exchanged between subscribers in the network, and in the second part, video traffic was exchanged between two subscribers. During all the conversations (voice and video calls), Kali Linux continuously attacked Asterisk with various TCP DoS attacks. Finally, an analysis of the obtained results was carried out. This article has the following structure: Section 2--Related work; Section 3--Modeling platform, tools and research methodology; Section 4--Results and discussion; and Section 5--Conclusions.
## 3 Dual-Band Doherty Power Amplifier
### Impedance Modification for Dual-Band DPA
[19] modified the structure of a DPA so that they eliminated the effect of the impedance inverter at the output of classical DPA, and in addition used four transmission line sections (two in series and two in shunt), as shown in Figure 10, in order to make the proposed DPA works in dual-band according to the equations proposed by [PERSON] [33]. They suggested matching the main amplifier to 50 \(\Omega\) and the auxiliary amplifier to 100 \(\Omega\). In this case, at low input power, the main and auxiliary amplifiers will see 50 \(\Omega\) and \(\infty\)\(\Omega\) respectively, whereas at the high-power region, they will see 25 \(\Omega\) and  
Figure 9: Modified Doherty amplifier proposed by [31]. 100 \(\Omega\) respectively. Two simplified designs for dual-Band DPA were done by [34], where the designs were tested, the main advantage of these new structures is the impedance inverter at the output power of DPA is eliminated as shown in Figure 11[34]. This was done by changing matching the auxiliary output impedance to 100 \(\Omega\) instead of 50 \(\Omega\). In this case, only one quarter-wavelength impedance inverter will be needed at the output. Monolithic microwave integrated circuit (MMIC) was used to design dual-band Doherty amplifier [14; 35; 36; 37], because the power ratio between the main and the auxiliary amplifier which determines the back-off of the DPA depends on the availability of devices in the market; however, this can be ignored when MMIC technology is used. The linearity of the DPA was improved in [35] by cancelling the effects of the third-order intermodulation (IM3) by using a tunable capacitor at the input to make sure that the phase difference between IM3 of the main amplifier and IM3 of the auxiliary amplifier is equal to 180\({}^{\circ}\), and their IM3 magnitudes are same. In addition, MMIC dual-band multi-mode power amplifier was designed by [36], where at the lower band (3.5 GHz), the amplifiers work as Doherty amplifier; whereas at the upper band (5.8 GHz) both amplifiers biased as class AB. It should be noted that the load seen by both amplifiers are modified  
Figure 11: Simplified structure of DPA [34]. Figure 10: Dual-band matching structure used in [19]. to be \(Z_{0}\) instead of \(Z_{0}/2\) for the purposing of reducing the transformation ratio. At the same time, high and low pass filters were used at the input side of both the main and peaking amplifier respectively to compensate the phase difference between the amplifiers due to the frequency band change.
## 2 Study Area and Data  
### Overview of the Study Area  
The study area is located within the Liaohe River Estuary National Nature Reserve, approximately 30 km southwest of Panjin City, Liaoning Province. It spans a geographic range from 121\({}^{\circ}\)28\({}^{\prime}\)24.58\({}^{\prime\prime}\) to 121\({}^{\circ}\)58\({}^{\prime}\)27.49\({}^{\prime\prime}\) east longitude and from 40\({}^{\circ}\)45\({}^{\prime}\)00\({}^{\prime\prime}\) to 41\({}^{\circ}\)05\({}^{\prime}\)54.13\({}^{\prime\prime}\) north latitude, covering a total area of 80,000 hectares (Figure 1). The Liaohe River Estuary National Nature Reserve is a sedimentary plain at the lower reaches of five rivers: Liaohe, Hunhe, Taizihe, Raoyanghe, and Dalinghe. The terrain is flat, with an elevation ranging from 0 to 6.5 m. Situated at the confluence of the Liaohe River and Liaodong Bay, the area consists of tidal wetlands, where freshwater, rich in nutrients, mixes with seawater, creating an ideal environment for diverse biological reproduction. The dominant ecosystem in this region is wetland, primarily comprising reed marshes, river water bodies, and mudflats. Coastal wetland vegetation, such as reeds (_Phragmites australis_) and _Suada salsa_, is widely distributed. The reserve serves as a crucial habitat and migratory corridor for numerous rare species and supports a rich diversity of marine, terrestrial, and aquatic life. Among its endangered inhabitants are the red-crowned crane (_Grus japonicus_), black-headed gull (_Chroiccephalus suundersi_), and harbor seal (_Phoca largha_). These ecological attributes contribute to the area's distinctive \"Red Beach\" landscape, a unique and vibrant coastal wetland ecosystem. The extraction of oil and gas resources, along with the construction of internal roads, not only occupies natural wetlands and destroys surface vegetation and wetland landscapes, but also poses a risk of oil and gas leakage during development. Such leaks can contaminate wetland soils and, through surface runoff, infiltrate nearby water sources, exacerbating pollution and severely impacting the wetland ecosystem.  
Figure 1: Location of the study area and overall layout of monitoring points. (The map of China in Figure 1 is from the Ministry of Natural Resources of the People’s Republic of China.)
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods  
### Case Study and Google Earth Engine Data  
Leyte province, located in the Eastern Visayas, is the seventh largest island in the Philippines (Figure 1). With a population of approximately 250,000, Tacloban is the biggest city and the capital of Leyte, which in total houses ca. 1.7 million people. The region has a tropical climate with two slightly different rainfall patterns during a year. While most of Leyte island has an even annual rainfall distribution, some eastern parts have a pronounced maximum rainfall from November to January. Typhoon Haiyan (also known as Typhoon Yolanda in the region) passed over Leyte island close to Tacloban City on 8 November 2013. Tacloban was hit by the full force of the typhoon, which causedmassive destruction in the city. A state of emergency was declared in Tacloban and the typhoon caused a storm surge of up to 5 m [44].  
Since Leyte island is a tropical region and is covered by clouds most of the time, we first aimed at using Synthetic Aperture Radar (SAR) data. However, only Landsat images were available and found suitable for our study in the GEE data sets for the time of the disaster and recovery processes. Accordingly, atmospherically corrected surface reflectance Landsat 7 and 8 satellite images with 30 m spatial resolution were used to map the selected land cover types and implement the developed approach for post-disaster recovery assessment (Table 1). In total, 446 Landsat images were employed in this study. In particular the potential of big geospatial time-series data (i.e., Landsat 7 and 8 images) available on the cloud was used to generate the best-available-pixel image composites (explained in the methodology section) for the pre-disaster, event time, and post-disaster time steps over Leyte island to minimize/remove the cloud and shadow areas.
# Soft Fuzzy Reinforcement Neural Network Proportional-Derivative Controller
## 4 Results and Discussion
### Complexity Analysis  
#### 4.3.1 Space Complexity Analysis  
The space complexity of each layer in the model is as follows:  
* For the input layer, the space required to store the input data \(x\), where there are \(n\) input features, is \(O(n)\).
* In the fuzzification layer, each input \(x_{i}\) computes \(j\) fuzzy membership values (since there are \(j\) fuzzy membership functions per input). The total space required for these fuzzy membership values is \(O(i\cdot j)\).
* In the multiplication layer (fuzzy inference), we store the fuzzy inference result \(\alpha\) for each rule. With \(m\) rules, the space complexity is \(O(m)\).
* The normalization layer generates a new vector \(\beta_{j}\), requiring \(O(m)\) space to store the values.
* The defuzzification layer calculates the output \(y_{i}\), and for \(r\) outputs, the space required is \(O(r)\).  
Thus, the overall space complexity is:  
\[O(n)+O(i\cdot j)+O(m)+O(m)+O(r) \tag{26}\]  
Simplified, this becomes:  
\[O(n+i\cdot j+m+r) \tag{27}\]  
As the system's input dimensionality \(i\) increases, the number of fuzzy rules \(m\) grows exponentially (i.e., \(m=j\)), leading to a rapid increase in space requirements. Moreover, when increasing the number of fuzzy membership functions \(j\), space complexity increases exponentially. Therefore, as the system complexity grows, the number of fuzzy rules increases significantly, potentially making real-time implementation more challenging due to the high space demands.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Initialization
#### 2.1.5 Triangulation of the Feature Points  
When two images are known to have the same name, matching point, and camera matrix, the 3D coordinates of the same name point can be solved. Here, using \(C_{1}\) as the reference coordinate system, the camera matrices corresponding to \(I_{1}\) and \(I_{2}\) were as follows:  
\[\mathbf{P_{1}}=\mathbf{K}[\mathbf{I}|\mathbf{0}]\;\;\text{and}\;\mathbf{P_{2} }=\mathbf{K}[\mathbf{R}_{C_{2}}^{C_{1}}|t_{C_{2}}^{C_{1}}]\,. \tag{8}\]  
Equation (8) was substituted into (9) to obtain the 3D coordinates, \(X_{k}^{W}\), in the world coordinate system corresponding to the \(k\)-th feature point, as follows:  
\[x_{k}^{C_{1}}=\mathbf{P_{1}}X_{k}^{W}\;\text{and}\;x_{k}^{C_{2}}=\mathbf{P_{2}} X_{k}^{W}. \tag{9}\]  
The least squares solution for the 3D coordinates \(X_{k}^{W}\) was obtained by solving the system of chi-square equations using SVD.
## 3 Results and Discussion
### Effects of pH and Temperature on Lac Enzyme Activity  
A schematic illustration of Lac immobilization on the P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) nanocomposites is shown in Figure 4. Under the optimized immobilization conditions, the maximum enzyme load was about 120 mg Lac/g nanocomposite and the maximum retention was about 80% of the original Lac activity. The effect of pH on the activity of the free and immobilized Lac is shown in Figure 5a. According to the experimental results, the optimum reaction pH of free Lac was approximately 4.0, which was consistent with the results of [PERSON] et al. [28]. However, the optimal pH of immobilized Lac showed a slight shift to 3.5. The activity of the immobilized Lac was higher than that of the free Lac in the pH range of 2.0-6.0, which was attributed to the unique electronic properties of P_p_PD. It was previously reported that P_p_PD is a conducting polymer and that the electrochemical process of P_p_PD involves proton transfer [29]. Under acid conditions, the protonation of P_p_PD improved its conductivity property, which was propitious for electron transfer between the enzyme, carrier, and substrates. This resulted in an enhancement of the activity of immobilized Lac, compared with that of free Lac.  
The effects of temperature on the activities of free and immobilized Lac are shown in Figure 5b. The relative activities of both forms of Lac clearly increased with the initial increases in temperature, and then decreased with the further increases in temperature. The optimum temperature for the activity of free Lac was 60 \({}^{\circ}\)C, while that for the activity of immobilized Lacs was 65 \({}^{\circ}\)C. Immobilized Lac exhibited higher stability than free Lac did at higher temperatures. The reason may be that  
Figure 3: FT-IR (fourier transform infrared spectroscopy) spectra of (**a**) P_p_PD/Fe_\({}_{3}\)O_\({}_{4}\) nanocomposites, (**b**) P_p_PD and (**c**) Fe_\({}_{3}\)O_\({}_{4}\).  
the \(\text{P}p\text{PD}/\text{Fe}_{3}\text{O}_{4}\) nanocomposite support limited the conformational changes in enzyme molecules, protecting them to some degree from deactivation at high temperatures.
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 4 Performance Comparison  
Table 2 summarizes the comparative analysis of proposed flexible filtenna with other relevant works reported recently in the literature. The comparison is provided against various performance metrics, including size, impedance bandwidth, gain, flexibility, and filtering characteristics. It is clear from the table that the proposed antenna offers compactness, wide bandwidth, and significant gain for the resonant band, while filtering capability is also attained for undesired bands. In addition, confirmability is also achieved for the proposed filtenna. Overall, this developed filtenna has significant advantages over other works for application in radio frequency front-end platforms with the inevitable future requirements of compactness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Electrical Size** & **Bandwidth** & **Gain** & \multirow{2}{*}{**Conformability**} & \multirow{2}{*}{**Filtering**} \\  & **(mm\({}^{2}\))** & **(GHz)** & **(dBi)** & & \\ \hline
[15] & 32 \(\times\) 27 & 2.27–2.72 & 2.25 & \(\times\) & ✓ \\
[19] & 50 \(\times\) 78 & 2.5–2.7 & – & \(\times\) & \(\times\) \\
[20] & 46.67 \(\times\) 53 & 2.45–2.48 & – & \(\times\) & \(\times\) \\
[21] & 48 \(\times\) 48 & 2.3–2.6 & 3.5 & \(\times\) & \(\times\) \\
[28] & 32 \(\times\) 25 & 1.6–3.8 & 3 & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ISM band antennas with proposed filtering antenna.  
Figure 16: S-parameter of the proposed filtenna bend over the y-axis. (**a**) Simulated and (**b**) measured.  
Figure 17: Gain and efficiency of the proposed filtenna bend over the y-axis. (**a**) Simulated and (**b**) measured.
## 4 The DCPVP Protocol
### Phase A: Exploration Phase  
In this phase, each node explores the network topology and gathers some information which includes the node distance from the BS, the number, and distance from its own neighbors. Distances are calculated by the RSSI equation [31, 32]:  
\[d(v)\;=\;10^{\frac{\left|RSSI-RSSI_{0}\right|}{10\cdot\eta}} \tag{7}\]  
where _RSSI_ is the received signal strength, _RSSI0_ is the signal attenuation for one meter distance from the source node and \(\eta\) is the path loss exponent [33].  
When the exploration phase begins, the BS broadcasts a packet (Start-Packet) to inform nodes of the beginning time and to let nodes calculate their distances from the BS. Then every node sequentially (based on ID) sends a packet to its neighbors during its own time slot (Hello-Packet) which contains its ID. All the other nodes monitor the channel during this time slot, so the neighbor nodes can receive the packet and store the information of the packet and the distance which is measured by RSSI in their local memory (Table 1). The nodes' information about the network topology depends on the neighbors ID, so if a node dies, the neighbor node should only know its ID and modify its calculations as the network is considered as being stationary. This phase is a significant preparation for the next phases and is done once for a network. After the exploration phase, DCPVP operates in rounds. In every  
Figure 3: The performance of DCPVP in dense areas.  
round, the clusters' construction should be rebuilt and new CHs should be elected. After clustering, nodes generate Data-Packets and the network works normally, so the remaining phases repeat in every round. At the end of this phase, each node forms a table such as Table 1, and then updates and uses it later.  
Table 1 is updated only when a node dies in its neighborhood. When the node's energy level falls to less than a predefined threshold, the node is considered dead and it notifies its neighbors by sending a corresponding packet (Death-Packet). Every node that is in its neighborhood receives the packet and removes its information row from the Table 1. Figure 4 shows the flowchart of this phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Neighbors** & **ID** & **Distance (m)** \\ \hline
1 & 32 & 0.56 \\
2 & 57 & 4.41 \\... &... &... \\ M & 4 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary nodes’ information from the network topology.  
Figure 4: Phase A: Exploration phase.
## 3 Results
### Different Backfill Thickness  
Cases 1, 5, and 6 considered influences on ground subsidence at the bottom of the foundation pit with different backfill thicknesses at the top of the underground structure. The results are plotted in Figure 7.  
As is shown in Figure 7, the thicker the backfill thickness before tunnel excavation, the greater the settlement of the foundation pit bottom after tunnel excavation. In Figure 6(a), it can be seen that the ground surface at the bottom of the foundation pit directly above the tunnel has basically subsided. However, when the twin tunnel has been excavated, the ground surface at the bottom of the foundation pit directly above the tunnel has basically moved upward. This phenomenon is likely due to the excavation of a large diameter tunnel which produced a large unloading, meaning that the soil moved upward instead of subsiding.  
Figure 7: Pit bottom subsidence with different backfill thicknesses.  
Different backfill thicknesses have different effects on the settlement control of foundation pit bottom. This is related to the thickness of backfill as well as to the tunnel diameter, because a large amount of unloading in super-large diameter tunnels may cause the soil to move upward.
# AutoFL: Towards AutoML in a Federated Learning Context
## 5 Evaluation
### Impact of Differential Privacy  
Privacy-enhancing techniques for federated learning, such as differential privacy or secure aggregation, may impact the accuracy of the model or the computational cost. In this experiment, we set up a federated learning configuration using MNIST and ten clients with homogeneous resource characteristics and with both the i.i.d. and non-i.i.d. datasets. The baseline did not have any privacy-enhancing techniques applied, and this baseline was compared with a similar setting with differential privacy applied. This privacy-enhancing technique bounds a client's model update by clipping the coefficient via a cap on the L2 norm of the update. Additionally, Gaussian noise is added to the federated average computed by the coordinating server. For the sake of convenience, we implemented the clipping of each client's updates within the coordinating server, but an alternative is that each client clipped their coefficients themselves. However, by implementing this strategy on the coordinating server, we expected the impact of applying this step for each client update to be more noticeable compared to a scenario where this step is carried out in parallel on each client.  
The results of this benchmark are depicted in Figure 7. While there is a difference in accuracy between the i.i.d. and non-i.i.d. configurations of the training dataset, as discussed earlier, there is no significant difference for the wall clock time of the coordinating server when differential privacy is applied in this particular MNIST experiment. Hence, if the clipping of the model updates were executed by the clients themselves in parallel, the impact would be even less outspoken. For more complex datasets and with a higher number of clients, the outcome may be different.  
However, one must be careful with differential privacy in an automated federated learning setting. Traditionally, the privacy budget spent is incremented with each federated training round to compute the total privacy budget after the training process has ended. When Bayesian optimization is used to identify and evaluate different model configurations, this privacy budget must also be accounted for across these model configurations. Furthermore, if there is a maximum privacy budget, the Bayesian optimization process must stop if this threshold is reached; otherwise, there is a risk of information leakage. Currently, the Bayesian optimization does not take this privacy budget into consideration when identifying the next best model to evaluate.  
Figure 7: Benchmarking MNIST in a federation with and without differential privacy.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
###### Abstract
In non-dynamic systems, the occasional appearance of areas with high user concentration can severely degrade the channel quality and the system's ability to serve its users. In this work, the term hotspot is used to describe this region with a high density of users and it does not mean a new Access Point (AP). Besides the increase on traffic demand, due to a high number of users, there is also a higher probability of increased interference, especially on systems with a reuse factor of 1. In such scenarios, the users in the borders of the coverage area are the most affected by interference. The paper is organized as follows. Section 2 presents the problem of high user concentration and its impact on system performance, along with some classical solutions for this problem. Sequentially, Section 3 introduces Fractional Frequency Reuse (FFR), an important component of the proposed solution, and Section 4 shows how 3 rd Generation Partnership Project (3 GPP) has developed Inter-Cell Interference Coordination (ICIC) techniques over the years. Section 5 presents relevant related works and Section 6 indicates how the system is modeled for simulations. Finally, Sections 7-9 show results from preliminary analysis and Section 10 presents our proposed solution, as well as the proof-of-concept results and discussion. In this paper, our key contributions are:  
* Performance comparison of classic FFR-based ICIC algorithms that take into account the number of users and their distributions along the cell (edge and center);
* Statistical analysis of the strict frequency reuse scheme that identifies which parameters do not need to be dynamically controlled;
* Analysis of the strict frequency reuse algorithm's performance in a mixed scenario with hotspot and homogeneous user distribution;
* Q-Learning algorithm that continually operates in the network to dynamically mitigate the performance loss (SINR) that results from the appearance of hotspots (densely populated areas).