esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Losnus
### Transmitter Identification  
Transmitter coding is realized as a fixed frequency coding with multiple time slots. As the starting time of the signal is known due to pulse compression of the preceding chirp, coherent N-FSK demodulation can be performed. Again, computational complexity is low because identification only needs to be performed on detected echoes and the signals are again 1-bit quantized. Furthermore processing can be performed on offline data if received frames are stored either on detection of a correlation peak or by the lead-in signal. For example, in the test setup with a total frame length of 740 \(\upmu\)s (100 \(\upmu\)s lead-in, 256 \(\upmu\)s chirp and 384 \(\upmu\)s transmitter encoding), storage requirements for a single frame are 740 Bit or 93 Byte. In case of six transmitters and assuming up to 5 NLOS echoes for each signal total storage requirements are 2.79 kByte. This is reasonable even on very low cost microcontrollers.  
The basic decoding structure is shown in Figure 6a. The binary input signal is multiplied by the different frequencies and low-pass filtered. The minimum length required by the filter in case of coherent demodulation is 1 divided by the frequency spacing. Knowing the starting time of the frame it suffices to evaluate the filter output at discrete multiplies of the symbol length. An example from a real measurement where six coding frequencies and two symbols have been used is shown in Figure 6b. This already allows up to 36 transmitters sufficient for covering large rooms.  
The computational complexity again is low and it requires for a sequence of length \(M\) and \(n\) transmitter coding frequencies \(n\cdot M\) multiplications and additions at most. As the input sequence again is binary with values between \(+1\) and \(-1\) multiplication reduces to either a positive or negative summation.  
Figure 5: **(a)** Received (analog) waveforms for burst with f\({}_{0}\) = 52.5 kHz and chirp with settings from Figure 3; **(b)** Binary autocorrelation function showing pulse compression properties of the chirp compared to a burst.
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## Appendix B  
We provide here (summarized in Table 11) the main parameters that were used throughout the paper, in order to facilitate the reading.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Significance** \\ \hline \(\mathbf{X}(n)\) & zero-mean multiple-input signal matrix \\ \hline \(\overline{\mathbf{x}}(n)=\mathrm{vec}[\mathbf{X}(n)]\) & input signal vector \\ \hline \(y(n)\) & output signal at time \(n\) (i.e., the bilinear form) \\ \hline \(d(n)\) & zero-mean desired signal at time \(n\) \\ \hline \(v(n)\) & zero-mean additive noise, of variance \(\sigma_{v}^{2}\) \\ \hline \(\mathbf{h}(n)\), \(\mathbf{g}(n)\) & true impulse responses of the system, of lengths \(L\) and \(M\), respectively \\ \hline \(\eta\) & arbitrary scaling parameter \\ \hline \(\mathbf{f}(n)\) & true spatiotemporal (i.e., global) impulse response of the system, of length \(ML\) \\ \hline \(\widehat{\mathbf{h}}(n)\), \(\widehat{\mathbf{g}}(n)\) & estimated impulse responses of the system \\ \hline \(\widehat{\mathbf{f}}(n)=\widehat{\mathbf{g}}(n)\otimes\widehat{\mathbf{h}}(n)\) & estimated spatiotemporal impulse response of the system \\ \hline \(\overline{\mathbf{x}_{\mathbf{\tilde{h}}}}(n)\), \(\overline{\mathbf{x}_{\mathbf{\tilde{g}}}}(n)\) & the input signals from Figures 1 and 2 \\ \hline \(\widehat{y}(n)\) & estimated output signal at time \(n\) \\ \hline \(e(n)\) & error signal between the desired and estimated signals at time \(n\) \\ \hline \(\overline{\mathbf{w}_{\mathbf{h}}}(n)\), \(\overline{\mathbf{w}_{\mathbf{g}}}(n)\) & zero-mean white Gaussian noise vectors, with correlation matrices \\ \(\mathbf{R_{\overline{\mathbf{w}_{\mathbf{h}}}}}(n)=\sigma_{\overline{\mathbf{ w}_{\mathbf{h}}}}^{2}\mathbf{I}_{L}\) and \(\mathbf{R_{\overline{\mathbf{w}_{\mathbf{g}}}}}(n)=\sigma_{\overline{ \mathbf{w}_{\mathbf{g}}}}^{2}\mathbf{I}_{M}\), respectively \\ \hline \(\mathbf{k_{h}}(n)\), \(\mathbf{k_{g}}(n)\) & Kalman gain vectors \\ \hline \(\mathbf{c_{h}}(n)\), \(\mathbf{c_{g}}(n)\) & a posteriori misalignments corresponding to the two impulse responses, with correlation matrices \(\mathbf{R_{\mathbf{c_{h}}}}(n)\) and \(\mathbf{R_{\mathbf{c_{g}}}}(n)\), respectively \\ \hline \(\mathbf{c_{h_{h}}}(n)\), \(\mathbf{c_{g_{\mathbf{g}}}}(n)\) & a priori misalignments corresponding to the two impulse responses, with correlation matrices \(\mathbf{R_{\mathbf{c_{h_{g}}}}}(n)\) and \(\mathbf{R_{\mathbf{c_{g}}}}(n)\), respectively \\ \hline \(m_{\mathbf{g}}(n)=\mathbb{E}\left[\|\mathbf{c_{g}}(n)\|^{2}\right]\), & squared norms of the a posteriori misalignments \\ \(m_{\mathbf{h}}(n)=\mathbb{E}\left[\|\mathbf{c_{h}}(n)\|^{2}\right]\), & \\ \hline \(\mu_{\mathbf{k},\partial^{\prime}}^{2}\mu_{\mathbf{g},\partial}\) & optimal step-size parameters corresponding to the two adaptive filters \\ \hline \hline \end{tabular}
\end{table}
Table 11: The main parameters used throughout the paper.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 4 Incentives
### Malevolent Botnets  
In the world of malevolent botnets, there exist two main types of incentives for the development of a botnet. These two incentives are:  
* A desire to harm a designated target or group of targets.
* A desire to better one's (often the C&C master) monetary situation.  
Concerning the first driving force, harming a designated target, a great many tools can be utilised to cause harm. One such method, as described in [PERSON] et al.'s paper [41], is through a Distributed Denial of Service attack (DDoS). This is showcased in the Mirai botnet back in 2016. Mirai, Japanese for \"uture\", was not the first botnet to emerge. As touched on in [PERSON] et al.'s paper [50], several botnets had already emerged, dating all the way from the late 1980s and early 1990s. It was, however, capable of performing an excessively powerful attack against the French webservice provider, OVH, with a peak throughput of 1.1 Tbps [51]. The reasoning for this attack, as it turned out, was based on the fact that OVH hosted a popular tool for Minecraft Server hosts [52]. Ironically, this tool helps to mitigate DDoS attacks against servers.  
References [44; 45; 46] present some of the possibilities within the scope of monetary gain from botnets, either via actively cracking user credentials through various means or by cracking entire pieces of encrypted data. Another example would be barring the user from accessing a service or device they own or rely on, as documented by [53].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Motivation** & **Type of Attack** & **Target** & **Vector of Attack** & **Papers** \\ \hline Disruption & & Hosting Service & IoT devices. & [41] \\ Denial of service. & & Provider. & & \\ \hline Political affiliation & & Military complex & C\&C-based botnet. & [42] \\ Censorship. & & & & \\ \hline Disruption and & & Metering infrastructure. & Internet-connected & [43] \\ Power grids. & & infrastructure. & computers and IoT devices. & [43] \\ \hline Sensitive data & & Cracking/Brute-forcing. & Common people. & Various. & [44; 45; 46] \\ Password cracking. & & & & \\ \hline Cyber espionage. & (Sear-)phishing and & Multiple victims listed. & Various. & [31; 47] \\ Security patching & & & & \\ Vulnerability scanning (benevolent). & Security patching & Unsecure IoT devices. & Other IoT devices. & [48] \\ \hline Botnet spoofing (fighting other botnets). & Mitigation. & Malicious botnets. & Existing botnets. & [49] \\ \hline Miscellaneous pieces of work & & Various types discussed. & & \\ detailing motivations. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of motivations behind botnet-based attacks. The columns describe the motivation, type of attack, known affected targets, attack vector(s) and the case study/paper describing the attack.
# Data-Driven Control Algorithm for Snake Manipulator
## 3 Simulation of DDPG Control Based on 2-Link Model
### Overall Design Scheme of Control System  
The DDPG-based control system is shown in Figure 2. According to the given parameters of the target trajectory point, the angular value, angular velocity value, and angular acceleration value of each joint moving to the target point are calculated by using the ridge method. These parameters are used as input to the DDPG controller. The DDPG agent is trained, saving the agent that meets the conditions, and finally the state value with the lowest reward value is the output. The position of the actual trajectory point is obtained by the forward kinematics of the manipulator. The control precision value is obtained from the error between the actual position and the target position.
## 1 Introduction
In this paper, we propose robust and unbiased estimation of robot pose and pipe diameter (RU-EPD). RU-EPD is based on our novel error-based residual function which estimates robot pose and pipe diameter robustly and unbiasedly. We first formulate an estimation of robot pose and pipe diameter as a non-linear least-square fitting problem with the canonical ellipse residual function (C-EPD). It transforms sensor measurements from the sensor coordinate system to the pipe coordinate system and fits the transformed points to an elliptic cylinder model evaluated by the canonical ellipse residual function. We analyze and prove that C-EPD produces an estimation bias in pipe diameter estimation with this residual function and the presence of random depth errors. The canonical ellipse residual function is more sensitive to positive depth errors than negative depth errors which leads to the overestimation of pipe diameter. To overcome this limitation, we propose a novel error-based residual function for RU-EPD. This function directly estimates depth errors and eliminates the asymmetry inherent in the canonical residual function. We mathematically prove that the error-based residual function ensures an unbiased estimation of pipe diameter. Extensive simulations and prototype pipeline experiments validate that RU-EPDsuccessfully corrects the estimation bias of C-EPD. Furthermore, RU-EPD demonstrates significantly higher consistency in both robot pose and pipe diameter estimation, proving its robustness against sensor measurement noise. The contributions of this paper are summarized as follows:  
* We identify and mathematically prove the estimation bias introduced by C-EPD due to its canonical ellipse residual function. * We propose a novel error-based residual function for RU-EPD, which eliminates asymmetry to depth errors and ensures robust and unbiased performance. * Using both simulated and experimental results, we demonstrate that RU-EPD outperforms C-EPD, achieving higher accuracy and consistency in robot pose and pipe diameter estimation. The remainder of this paper is organized as follows: Section 2 briefly summarizes related works on the estimation of robot pose and pipe diameter, as well as the depth error in 3D ToF sensors. Section 3 presents the RU-EPD scheme, including theoretical analysis and the proposed error-based residual function. The numerical results are presented and discussed in Section 4. Finally, we conclude this paper in Section 5, summarizing key findings and discussing potential directions for future research.
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 5 Simulation Experiments
### Quadrotor with Bias Fault  
A scenario with actuator bias faults was constructed for the variable-load quadrotor: actuators 1 and 2 developed bias faults when \(t>5\) s, with bias parameters \(b_{1}^{F}=b_{2}^{F}=10\) N. The simulation results are shown in the figures. Figure 10 shows the error of the quadrotor trajectory tracking. Figure 11 shows the instant when the observer event was triggered, while Figure 12 shows the instant when the controller was triggered.  
Figure 10 demonstrates that the PPC-CADRC exhibited superior disturbance rejection after fault occurrence. During the post-fault period, the RMSEs were 0.05 (PPC-CADRC), 0.06 (CADRC), and 0.09 (ADRC) for the respective control systems. The designed controller showed better performance with smaller overshoot and faster settling time. When actuator bias failure occurred at \(t>5\) s, the PPC-CADRC maintained smaller oscillations and demonstrated enhanced robustness.  
Figure 10: The trajectory tracking errors of the quadrotor.  
Figure 9: Trajectory tracking errors under various loss of effectiveness conditions.  
Figure 11 shows the event-triggered instant and sampling error for the \(z\)-axis observation channel. The event-triggered mechanism reduced 83% of the data transfer compared to the continuous triggering mechanism. Figure 12 shows the event-triggered instant and sampling error for the control variable \(U_{1}\). This time-varying event-triggered mechanism reduced 94.65% of data transfer. From the figures, we can see that when the system was in the time period of high error of \(t=\) 5-7 s, both the observer and controller event-triggered mechanisms transmitted data more frequently, ensuring the effectiveness of the control system and saving the computational resources of the quadrotor at the same time.  
Simulations were performed on the quadrotor with bias faults of 5 N, 10 N, 15 N, and 20 N applied to actuators 1 and 2. The results are presented in Figure 13. As the bias fault magnitude increased from 5 N to 20 N, tracking error amplitudes showed significant growth. During the post-fault period, the RMSEs were 0.04 (5 N), 0.05 (10 N), 0.06 (15 N), and 0.10 (20 N), respectively. All bias conditions induced a transient overshoot. Under mild fault conditions, the overshoot remained small with rapid recovery. In contrast, the 20 N fault case exhibited a prolonged overshoot duration. However, the trajectory-tracking capability was ultimately recovered in these scenarios.  
Figure 11: The instant and sampling error of the observation event-triggered mechanism.  
Figure 12: The instant and sampling error of the control event-triggered mechanism.  
Data Availability Statement: The original codes presented in this study are openly available in Github at [[https://github.com/StudyWang214/quadrotor](https://github.com/StudyWang214/quadrotor)]([https://github.com/StudyWang214/quadrotor](https://github.com/StudyWang214/quadrotor)) (accessed on 18 June 2025).
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 4 Numerical Results  
In this section, we present the numerical results of the coverage probability and average rate for the proposed LA-CTC scheme, which can provide guidelines for the practical system design. Furthermore, we compare the proposed LA-CTC scheme with the other three user association schemes, which are the conventional MRP scheme [32], the CRE scheme [33] and the LA-CTCT scheme [23].  
The transmit powers of MBSs and FBSs were \(P_{1}=46\) dBm and \(P_{2}=20\) dBm. The densities of the two tiers were \(\lambda_{1}=(\pi 500^{2})^{-1}\) and \(\lambda_{2}=5\lambda_{1}\) with \(\alpha_{1}=\alpha_{2}=4\). The user density was \(\lambda_{u}=20\lambda_{1}\). The thermal noise power was assumed to be \(\sigma^{2}=-104\) dBm (i.e., the system bandwidth was \(10\) MHz). For the evaluation of the coverage probability, \(\tau\) was assumed to be \(0\) dB.
## 2 Proposed Feed-Forward NN Structure-Based Estimation Methods
### Proposed NN-Based Estimation Method 1: FFNN Structure
#### 2.2.3 Integration into a Learning Algorithm: Back-Propagation  
NN-based estimation requires a learning algorithm, such as error correction, perception learning, Boltzmann learning, Hebbian rules, or back-propagation (BP). Among these, BP is one of the most popular network training algorithms because it is both simple and generally applicable [55]. The back-propagation algorithm trains a NN by applying the chain rule method. The weights of the network are randomly initialized, then repeatedly adjusted through minimization of the cost function, which is calculated by measuring the difference between the actual output \(\hat{y}_{\_}{out}\) and the desired value \(y_{\_}{out}\). One of the most common error measures is the root mean square (RMS) error, defined by  
\[E=\frac{1}{2}\sum(y_{\_}{out}-\hat{y}_{\_}{out})^{2} \tag{10}\]During the training process, the RMS error (cost function) is minimized as much as possible. The iterative step of the gradient descent algorithm then changes the weights \(w_{j}\) according to  
\[w_{j}\to w_{j}+\Delta w_{j}\text{ where }\Delta w_{j}=-\eta\frac{\partial E(w)}{ \partial w_{j}}. \tag{11}\]  
Here, \(\eta\) is the learning rate, which can determine the effect of the gradient. There are a few alternative optimization techniques for finding the local minimum, such as conjugate gradient, steepest descent, and [PERSON]'s method.  
1. Steepest descent is an iterative method that finds the local minimum by moving in the direction opposite to the one implied by the gradient; the learning rate (such as traingda or traingdx) indicates how quickly it moves. Usually, the smaller the learning rate, the slower the process of convergence to a local minimum. In standard steepest descent, the learning rate remains constant throughout the training stage while the weight and bias parameter values are interactively updated.
2. The resilientBP training algorithm (i.e., trainrp) is a local adaptive learning scheme for supervised batch learning in an FFNN. Resilient back-propagation is similar to the standard BP algorithm, but it is capable of training the NN model faster than the regular method without the need to specify any free parameter values. Additionally, since trainrp merely considers the size of the partial derivatives, the direction of the weight update is only affected by the sign of the derivative.
3. The Levenberg-Marquardt algorithm (i.e., trainlm) is a standard method for solving nonlinear least-squares minimization problems without computing the Hessian matrix. It can be thought of as a middle ground between the steepest descent and Gauss-Newton methods.
## 4 Discussion of the Findings
### Code Validation  
The code validation of RestRho through various software metrics reveals significant insights into its quality and maintainability. Specifically, the cyclomatic complexity and Halstead metrics indicate a system with low complexity and high maintainability. The average per-function cyclomatic complexity (CNN) for RestRho is well below the threshold, suggesting that the system is easy to understand and maintain.  
Halstead metrics further demonstrate RestRho's superiority, showing the lowest difficulty (HD), effort (HE), and error estimate per module (HB) among the components analyzed. This comparison with other components and frameworks, such as those in RhoArchitecture (CRA) and the frameworks used by RestRho (FRR), underscores RestRho's superior productivity and ease of maintenance.  
Additionally, the documentation quality of RestRho is notable, with comment lines (CLOC) averaging 34%, surpassing the 25% threshold, which ensures that the codebase is well documented and easier for developers to navigate. This is particularly evident in the ServerRestRho component, which has the highest documentation rate at 43%. The maintainability index (MI) for all components and frameworks analyzed shows a \"good maintainability\" score (>85), with RestRho's components averaging higher than both CRA and FRR, demonstrating its robust and reliable nature. Overall, these findings confirm that RestRho is a well-structured, maintainable system, positioning it favorably compared with other components and frameworks analyzed.
## 3 Uncertainty Analysis Model  
### Characteristics of the Uncertainties  
To conduct an uncertain analysis of the temperature and thermal stress for the receiver tube, the first step is to select the uncertainties. For the boundary conditions of the receiver tube, the peak incident heat flux on the tube wall is a typical uncertain parameter, which is influenced by the level of direct normal irradiance. For the heat loss of the receiver tube, the proportion of the reflection heat loss is obviously larger than that of the radiation and convection heat loss. Consequently, the uncertainty of the tube coating absorptivity is taken into account, while the tube coating emissivity, the convective heat transfer coefficient outside the tube wall, and the ambient temperature are given specific values. For the thermophysical properties of the heat transfer fluid and the tube material, the measurement  
Figure 2: Computation meshes for the numerical procedure. (**a**) Meshes for temperature analysis. (**b**) Meshes for thermal stress analysis.  
error of the density is smaller than that of other properties. Therefore, the uncertainties of the thermophysical properties of the tube material and the heat transfer fluid are all considered except for their densities. The central values and uncertainty probability distributions of nine uncertain input parameters are given in Table 1.
## 2 Methods
### CFD Method
#### 2.1.3 Boundary Conditions and Solution Method Setup  
To ensure the accuracy of numerical calculation results, it is crucial to define boundary conditions appropriately. In this study, the inlet of the jet was set as a pressure inlet, while the outlet was set as a pressure outlet. The wall surface was designated as a standard non-slip wall surface. Furthermore, the ambient pressure was established at 50 MPa, and the simulated inundation depth was set at 5000 m.  
The numerical simulation's parameter settings are as follows: the simulation focuses on pressure calculation and utilizes pressure-velocity coupling. The SIMPLE method is chosen for the solution system [24; 25], the pressure term of the control equation is treated in the standard format, the gradient is treated using the least squares method, the volume  
Figure 1: Computational domain.  
transport equation is treated with a one-section windward difference, and the momentum, turbulent kinetic energy, turbulent dissipation, and continuity equations are treated with a second-order windward difference, with default settings for the rest of the parameters. The residual accuracy was set to \(10^{-5}\), and 5000 iterations were performed after global initialization to enhance computational accuracy and convergence [26; 27].  
Figure 2 illustrates the entire computational flow, which aids in better understanding the CFD solution process.  
According to [PERSON]'s principle, the relationship between fluid velocity and pressure can be simplified as follows:  
\[\mathrm{v}=w\sqrt{\frac{2P}{\rho}} \tag{3}\]  
where \(\mathrm{v}\) is the fluid velocity, \(w\) is the flow coefficient, \(P\) is the inlet pressure, and \(\rho\) is the fluid density. Under a 10-50 MPa injection pressure, Figure 3 compares the theoretical and simulated velocities of nozzles with different injection pressures and simulation plots of the jet velocity distribution at 50 MPa. The results reveal that theoretical and simulated velocities agree well, and numerical simulation results are accurate.  
Figure 2: CFD solution process.