esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[18] used centrifugal model tests to evaluate the impact of ground vibration near elevated transportation systems on the safety of tunnel structures, analyzing the vertical vibration attenuation trend of vibration load propagation and the vibration response of the tunnel model. According to the scale and principle of the test model, the existing test methods can be divided into centrifugal model tests, 1 g model tests, similar model tests, scale model tests, etc. While existing research has offered invaluable insights into tunnel excavation and support, particularly regarding the instability mechanisms and support strategies for tunnel faces across various soil conditions, certain limitations persist [19; 20; 21]. The majority of studies concentrate on experiments involving a singular stratum type, with a notable deficiency in exploring the dynamic response of shield excavation and the stability of tunnel faces under unique geological circumstances, such as those presented by loess with significant loose collapsibility [22; 23]. Moreover, the preponderance of current research is either static or reliant on simplified model tests, lacking a profound examination of the tunnel system's response to the dynamic loading imposed by high-speed trains. The principal aim of this study is to meticulously investigate the influence of high-speed train dynamic loads on the stability of tunnel faces and ground response, with a specific emphasis on underpass high-speed shield tunnel excavation in loess regions. By employing a sophisticated amalgamation of high-precision, similar model testing and numerical simulation, the research undertakes a thorough analysis of the dynamic response elicited by high-speed train vibration loads during tunnel excavation. Additionally, it offers an exhaustive dissection of the damage characteristics and ultimate support forces associated with tunnel faces.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Accuracy Calculation  
#### 3.6.1 K-Nearest Neighbor Queries Accuracy Calculation  
To calculate the accuracy of the Euclidean distance method for each category of query point and each number of k in the kNN search method, the following formula was used:  
\[\text{Accuracy}=\left(\frac{\text{Total number of correct results}}{\text{Total number of retrieved results}}\right)\times 100\% \tag{1}\]  
The correct results were defined by comparing the set of POIs obtained from the Euclidean distance method with the collection of POIs obtained from the road network distance method. If the set of POIs obtained from the Euclidean distance method is different in any way from the set obtained from the road network distance method, the result is marked as incorrect.
## 3 Guidance Law and Analysis
### Guidance Law Design  
Based on the high-gain extended state observer in (19), the guidance law is designed as  
\[\psi_{d}=\text{atan}\Big{(}-\frac{y_{e}}{\Delta}-\hat{\alpha}\Big{)}, \tag{27}\]  
where Delta is the designed look ahead distance, and  
\[\hat{\alpha}=\frac{\hat{\delta}}{u\cos\psi}. \tag{28}\]  
Noting that  
\[\left\{\begin{array}{c}\sin\psi_{d}=\sin\bigl{(}\text{atan}\bigl{(}-\frac{y_ {e}}{\Delta}-\hat{\alpha}\bigr{)}\bigr{)}=-\frac{y_{e}+\Delta\cdot\hat{\alpha }}{\big{\langle}\Delta^{2}+\left(y_{e}+\Delta\cdot\hat{\alpha}\right)^{2}}, \\ \cos\psi_{d}=\cos\bigl{(}\text{atan}\bigl{(}-\frac{y_{e}}{\Delta}-\hat{\alpha} \bigr{)}\bigr{)}=\frac{\Delta}{\sqrt{\Delta^{2}+\left(y_{e}+\Delta\cdot\hat{ \alpha}\right)^{2}}}.\end{array}\right. \tag{29}\]  
It is assumed that \(\psi=\psi_{d}\). The cross-track error dynamics is denoted as  
\[=\frac{\dot{y}_{e}=u\sin\psi+u\cos\psi\tan\beta}{\sqrt{\Delta^{2}+\left(y_{e} +\Delta\cdot\hat{\alpha}\right)^{2}}}+\frac{u\Delta\cdot\widetilde{\alpha}}{ \sqrt{\Delta^{2}+\left(y_{e}+\Delta\cdot\hat{\alpha}\right)^{2}}}, \tag{30}\]  
where \(\widetilde{\alpha}=\alpha-\hat{\alpha}\).  
**Lemma 6**.: _The system (30), with the state being \(y_{e}\) and the input being \(\widetilde{\alpha}\), is ISS._  
**Proof.** Consider a Lyapunov function candidate  
\[V_{2}=\frac{1}{2}{y_{e}}^{2}. \tag{31}\]  
Taking the time derivative of \(V_{2}\), it follows from Equation (30):  
\[\dot{V}_{2}=\frac{-{u}{y_{e}}^{2}}{\sqrt{\Delta^{2}+\left(y_{e}+\Delta\cdot \hat{\alpha}\right)^{2}}}+\frac{u\Delta\cdot\widetilde{\alpha}\cdot y_{e}}{ \sqrt{\Delta^{2}+\left(y_{e}+\Delta\cdot\hat{\alpha}\right)^{2}}}. \tag{32}\]  
Let  
\[c=\frac{u}{\sqrt{\Delta^{2}+\left(y_{e}+\Delta\cdot\hat{\alpha}\right)^{2}}}>0. \tag{33}\]  
It follows:  
\[\begin{array}{l}\dot{V}_{2}=-c\cdot{y_{e}}^{2}+c\cdot\Delta\cdot\widetilde{ \alpha}\cdot y_{e}\\ =-c\cdot(1-\theta_{2})\cdot{y_{e}}^{2}-c\cdot\theta_{2}\cdot{y_{e}}^{2}+c \cdot\Delta\cdot\widetilde{\alpha}\cdot y_{e},\end{array} \tag{34}\]  
where \(0<\theta_{2}<1\), then it gives  
\[|y_{e}|\geq\frac{\Delta\cdot\widetilde{\alpha}}{\theta_{2}}, \tag{35}\]  
leading to  
\[\dot{V}_{2}\leq-c\cdot(1-\theta_{2})\cdot{y_{e}}^{2}\leq 0. \tag{36}\]Choosing \(\alpha_{11}(s)=\alpha_{12}(s)=\frac{1}{2}s^{2}\), \(\rho_{1}(s)=\frac{\Lambda_{1}s}{\theta_{2}}\), by Definition 2 and Lemma 2, it follows from Equations (36) that there exists a class KL function \(\zeta_{1}(\cdot)\) and a class K function \(\gamma_{1}(\cdot)\) such that  
\[\|y_{e}(t)\|\leq\zeta_{1}(\|y_{e}(0)\|,t)+\gamma_{1}(\|\widetilde{\alpha}\|). \tag{37}\]  
This concludes the proof. \(\square\)
## 3 Experimental Results and Analysis
### Results
#### 3.3.2 Second Group of Experiments (Images with Combined Attacks)
The cause of such a trend or behavior of the results is attributed to the change in the geometry of the texture and features of the forged images, which eventually lead to the decrease in the classification performance to some extent. For BER, the error rates increased gradually with the increasing value of the scaling factor, with average value of 8%. Finally, we have to mention that the performance of the forgery detection approach under this attack, with the values in Table 4, can be regarded as sufficiently good and competitive. They refer to the resistance of the proposed approach for such a kind of challenging attack. Table 5 presents the results of the application of a rotation geometrical attack to the forged images. The results show that the trend of precision, recall and F1 score classification metrics monotonically decreases with the increasing of the rotation angle, with average values of 94%, 95% and 94% for these metrics, respectively. Such behavior in the classification measure values is attributed to the influential effects of rotation on the texture and features of the forged images, which lead to difficulty in the classification of the forged blocks. Now, if we look at the watermark image quality measures, the trend of these measures is similar to the trend of the forgery classification metrics. In other words, their values decrease with increase of the rotation angle, with average values of NC (92%), TAF (92%), PSNR (10.79%) and SSIM (56%). Finally, the error rates represented by BER increase  
gradually with the increasing value of the rotation angle, and such behavior is expected for the reasons mentioned earlier in this paragraph.
## 3 Materials
### Cybersecurity in IoT

Table 3: Hardware and operating systems used in the laboratory. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Description** \\ \hline TP-Link Archer C20 & WiFi router that deploys the IoT Lab private network and provides access to the corporate network \\ Mikrotik LR8 Kit & LoRaWAN gateway that enables the connection of LoRa devices to long-range IoT networks \\ Dragino LHT65 & Temperature and humidity sensor with LoRaWAN connectivity \\ Dragino LGT-92 & GPS tracker with LoRaWAN connectivity \\ Dragino LPS8 & LoRaWAN Gateway \\ Dragino LDS02 & Door sensor with LoRaWAN communication for detecting openings and closings \\ ESP32 WiFi & WiFi/Bluetooth microcontroller widely used in IoT applications \\ DHT11 & Low-cost temperature and humidity sensor for environmental applications \\ KY-038 & Sound sensor with a condenser microphone \\ HC-SR501 & PIR motion sensor for presence detection \\ Raspberry Pi 3 Model B & Used as a gateway for ESP32 devices to capture network traffic \\ Raspberry Pi OS & Optimized operating system for Raspberry Pi \\ Linux Virtual Machine & Virtual machine with Docker for application management and deployment \\ Debian 12 & Linux Debian operating system installed on the virtual machine \\ PC & Used for writing code and uploading it to Arduino boards \\ Windows 11 & Operating system for the PC \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Software used in the laboratory. monitor and react to incidents, increasing the risk of data breaches, unauthorized access, and service interruptions. The Smart Rural IoT Lab addresses this by incorporating secure communication protocols. LoRaWAN includes AES-128 encryption for long-range transmissions [18], while MQTT [38] and CoAP can be secured using TLS or DTLS, enabling both secure and non-secure network configurations for experimentation. In addition to communications security, anomaly detection is essential for identifying attacks or system failures. In this context, artificial intelligence models trained on network traffic datasets, such as ToN-IoT and UNSW-NB15, have shown they can support automated anomaly detection, identifying patterns associated with intrusion attempts or system anomalies [12; 52]. While the current work does not implement specific AI models, the infrastructure has been designed to facilitate future research in AI-assisted intrusion detection based on real traffic data by capturing network traffic and processing it with tools like Zeek, thereby generating datasets that will be useful in these types of tasks. Finally, securing the IoT in rural environments requires an approach that integrates secure communication, access control, and anomaly detection, among others. In summary, the laboratory is composed of affordable, energy-efficient devices and open-source tools. The combination of WiFi, LoRaWAN, and mesh technologies provides flexibility for a wide range of rural IoT applications, while the modular software and hardware design ensures replicability and future expansion.
## 3 Background  
### Q-Learning  
Reinforcement learning is a type of machine learning algorithm that establishes a policy to determine the behavior in which the sum of the total compensation values is at maximum in the current state. It is composed of the environment and agent, as shown in Figure 1. The agent, which is the subject of determining the next action to perform, interacts with the environment through actions, and the environment responds with the status and rewards. The agent repeatedly interacts with the environment to establish an optimal policy.  
Reinforcement learning is suitable for solving problems that have trade-off relationships. In particular, the reward value of short- and long-term behaviors is clear. Thus, it is used in various fields, such as robot control [11] and gaming. It is well known that Atari [12] and DOTA 2 [13] are well trained through deep reinforcement learning.  
There are many algorithms used to implement reinforcement learning, among which, we apply the Q-learning algorithm. The agent of Q-learning has a Q-table that records the Q-value, the value of all actions that can be taken in each state, to solve the problem. The Q-learning algorithm works as follows. When the agent starts Learning for the first time,  
Figure 1: Reinforcement learning structure.  
all values of Q-table are initialized to 0. If the Q-value is 0 in each state, the agent randomly chooses an action and updates the Q-value in the Q-table. If the Q-value is not 0, then the agent selects the action with the largest Q-value in order to maximize the reward. The agent repeats these steps until finding the best policy.  
Q-learning is a type of model-free reinforcement learning which uses a Bayesian equation [14] to find the action with the highest total reward. In reinforcement learning, a model predicts the state change and compensation of the environment. Model-free reinforcement learning has the advantage of being easy to implement or tune.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.2 Sensitivity to the Period Range  
In this simulation, 500 queries were uniformly imposed on a network of 50 nodes with different period ranges. Figure 15 is the graph of the AEC for each period range. Without merging, the AEC is inversely proportional to the period range because greater query response periods indicate less query execution frequencies. Figure 16 shows the energy efficiency rate for each period range. As expected, the query result merging mechanism becomes less efficient as queries are executed less frequently without much overlapping. Therefore, although the proposed mechanism still proves efficient than the normal query processing methods, its efficiency diminishes as query periods are distributed on a larger period scale.  
Figure 17(a) shows the sensitivity of the network lifetime to the period range. For a normal network, an increase in the period range decreases the number of query executions during the same time interval, so network lifetime in enhanced as the period range is increased. When query results are merged, however, the network lifetime shows only slight variations since each node always produces one tuple  
Figure 16: Sensitivity of the EER to the period range.  
Figure 15: Sensitivity of the AEC to the period range.  
at a time. Hence, the NLR is inversely proportional to the number of queries. Nonetheless, query result merging still proves to be efficient in terms of network lifetime, as shown in Figure 17(b).
# Algorithms for Calculating Generalized Trigonometric Functions
## 5 Algorithms for Calculation Space Transform Based on _Ateb_-Functions
### Orthogonal Ateb-Transform with Two Parameters  
Let \(x(t)\) be a real function; then, we construct a generalization of the known Fourier transform _Ateb_-transform in the form  
\[X(m,n,\omega)=A(m,n,\omega)-iB(n,m,\omega), \tag{36}\]  
where  
\[A(m,n,\omega)=\int_{-\infty}^{\infty}x(t)\cdot ca^{m}(m,n,\omega t)dt, \tag{37}\]  
\[B(n,m,\omega)=\int_{-\infty}^{\infty}x(t)\cdot sa^{n}(n,m,\omega t)dt. \tag{38}\]  
where \(ca(m,n,\overline{\omega})\) is the _Ateb_-cosine and \(sa(m,n,\overline{\omega})\) is the _Ateb_-sine function. Taking into account the expression (14), we obtain the formula for the inverse transform.  
\[x(m,n,t)=\frac{1}{\Pi}\int_{-\infty}^{\infty}\{A(m,n,\omega)ca(m,n,\omega t)+ B(n,m,\omega)sa(n,m,\omega t)\}d\omega, \tag{39}\]  
where \(\Pi(m,n)\) are half-period _Ateb_-functions.  
In the case where \(n=1\) and \(m=1\), the introduced Formulas (36)-(38) for _Ateb_-transform become well-known orthogonal Fourier transform formulas. And expression (39) becomes the inverse Fourier transform.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASE & Amplified Spontaneous Emission \\ BER & Bit Error Rate \\ COI & Channel of Interest \\ DBP & Digital Back-Propagation \\ FEC & Forward Error Correction \\ IC & Interfering Channel \\ ISI & Inter-Symbol Interference \\ LDPC & Low density parity check \\ LMS & Least Mean Squares \\ MAP & Maximum a-posteriori \\ NLIN & Nonlinear Interference Noise \\ OFDM & Orthogonal Frequency Division Multiplexing \\ PPRN & Phase and Polarization Rotation Noise \\ RLS & Recursive Least Squares \\ SMF & Single mode fiber \\ SNR & Signal to Noise Ratio \\ SSFM & Split-step Fourier method \\ WDM & Wavelength Division Multiplexing \\ \end{tabular}
## 2 Methods and Experiment Details
The reaction temperature was 180 \({}^{\circ}\)C, and the reaction time was 10 h. After cooling, Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) nanoparticles were washed alternately with absolute ethanol and 1% NaOH solution and magnetically separated. In order to study the photocatalytic performance of Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) and TiO\({}_{2}\), dye wastewater was simulated by AR73 solution. The photocatalytic degradation effect of TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) on AR73 was studied. Photocatalytic experiments were carried out in a photocatalytic workstation. The power of the high-pressure mercury lamp used in the experiment was 300 W and the main radiation wavelength was 365 nm. The lamp was 50 mm above the sample. Quantities of 50 mg Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) and 50 mg TiO\({}_{2}\) were weighed and placed at the bottom of two quartz tubes, respectively. Then, 25 mL AR73 solution with a concentration of 20 mg L\({}^{-1}\) was added to the two quartz tubes mentioned above. The mixtures in the two quartz tubes were first placed in a dark chamber for 25 min and then in a photocatalytic reaction chamber. Fockability of photocatalysts is also important in practical applications. After each photocatalytic degradation experiment, the Fe\({}_{3}\)O\({}_{4}\)@TiO\({}_{2}\) particles were collected by a permanent magnet, washed alternately with ultrapure water and absolute ethanol three times, and dried at 40 \({}^{\circ}\)C in a vacuum drying oven for 24 h.  
The rate of dye degradation (\(\eta\)) in wastewater was measured according to the following equation:  
\[\eta=\frac{\text{C}_{0}-\text{C}_{\text{t}}}{\text{C}_{0}}\times 100\% \tag{1}\]where C\({}_{0}\) (mg L\({}^{-1}\)) and C\({}_{t}\) (mg L\({}^{-1}\)) are the initial concentration of dye and the concentration of dye at time t (min), respectively.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Conversion between the Proposed Line Representation and the Plucker Line  
Plucker lines have no singularities in representing spatial line and can be converted into other types of representation conveniently. Line transformation between different coordinate systems can also be easily represented in Plucker coordinates. These are the reasons why Plucker lines are widely employed. In this section, we derive the conversion between the proposed representation with Plucker lines, then we can also convert it into other types of representations easily.  
In Plucker coordinates, a line is represented by six dependent variables; \(\mathbf{d}\) corresponds to the direction part and \(\mathbf{m}\) corresponds to the position part. As shown in Figure 5, \(\mathbf{L}_{1}\), \(\mathbf{L}_{2}\), and \(\mathbf{L}_{3}\) correspond to \((\mathbf{m}_{1};\mathbf{d})\), \((\mathbf{m}_{2};\mathbf{d})\), and \((\mathbf{m}_{3};\mathbf{d})\), respectively. \(\mathbf{L}_{1}\), \(\mathbf{L}_{2}\) and \(\mathbf{L}_{3}\) have the same direction \(\mathbf{d}\). Since \(\mathbf{m}_{1}\) and \(\mathbf{m}_{2}\) are collinear, \(\mathbf{L}_{1}\), \(\mathbf{L}_{2}\) and the origin \(\mathbf{O}\) are coplanar. As \(|\mathbf{m}_{1}|=|\mathbf{m}_{3}|\), then \(\mathbf{L}_{1}\) and \(\mathbf{L}_{3}\) are tangents of the same 3D circle with a radius of \(|\mathbf{m}_{1}|\). Accounting for the orthogonality between \(\mathbf{m}\) and \(\mathbf{d}\), and by forcing \(|\mathbf{d}|=1\), a 3D line actually has four degrees of freedom. Similar to the Plucker line representation, the proposed spatial line representation has two parts; \(\theta\) and \(\phi\) correspond to the directional part, \(m_{l}\) and \(\alpha\) correspond to the positional part.  
Figure 4: The variation of line position(with fixed direction \(\mathbf{d}\)) with the fluctuation of \(m_{l}\) and \(\alpha\). The fluctuation \(\Delta m_{l}\) is added to \(m_{l}\) and \(\Delta\alpha\) is added to \(\alpha\).  
Figure 3: The variation of line direction with the fluctuation of \(\theta\) and \(\phi\). The fluctuation \(\Delta\theta\) is added to \(\theta\) and \(\Delta\phi\) is added to \(\phi\). The cone indicates the fluctuation of direction with \(\Delta\theta\) and \(\Delta\phi\).
## 2 Materials and Methods
### CFD Model
#### 2.2.2 Turbulence Model  
The standard \(k-\varepsilon\) model was applied as a turbulence model. The transmission equation adopted was as follows:  
\[\frac{\partial k}{\partial t}+\overline{u_{j}}\frac{\partial k}{\partial x_{ j}}=\frac{\partial}{\partial x_{j}}(\frac{v_{t}}{\sigma_{k}}\frac{\partial k }{\partial x_{j}})+v_{t}(\frac{\partial\overline{u_{i}}}{\partial x_{j}}+ \frac{\partial\overline{u_{j}}}{\partial x_{i}})\frac{\partial\overline{u_{i }}}{\partial x_{j}}-\varepsilon \tag{4}\]  
\[\frac{\partial\varepsilon}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}( \frac{v_{t}}{\varepsilon_{\varepsilon}}\frac{\partial\varepsilon}{\partial x _{j}})+\frac{\varepsilon}{k}\bigg{[}C_{\varepsilon 1}v_{t}(\frac{\partial\overline{u_{i}}}{\partial x_{j}}+\frac{\partial \overline{u_{i}}}{\partial x_{i}})\frac{\partial\overline{u_{i}}}{\partial x_ {j}}-C_{\varepsilon 2}\varepsilon\bigg{]} \tag{5}\]  
where \(v_{t}=C_{\mu}\frac{k^{2}}{\varepsilon}\), \(C_{\mu}=0.09\), \(C_{\varepsilon 1}=1.44\), \(C_{2}=1.92\), \(\sigma_{k}=1.0\), and \(\sigma_{\varepsilon}=1.3\). The simple algorithm was used for pressure-velocity coupling, and the discrete format was set to second-order upwind to minimize numerical diffusion and improve accuracy [27; 28; 29]. The convergence criteria for all residuals were set to \(10^{-6}\). Scalar diffusion was performed using the same number of iterations for all simulations, ensuring consistent conditions throughout the diffusion process [30].  
Figure 2: Diagram of the computational domain and boundary conditions under different wind directions. (**A**) Parallel wind; (**B**) Oblique wind; (**C**) Perpendicular wind.
## 5 Conclusions  
This paper provides a comprehensive summary and review of some of the research that has been carried out by our research group in the area of fiber-wireless systems; in particular focusing on wireless signal transport schemes and power consumption. The main focus of these research activities has been directed towards the realization of high-performance and energy-efficient fiber-wireless links. The study shows that digitized transport scheme based on bandpass sampling has the potential to simplify the base station architecture while maintaining high link performance. We have demonstrated the transport of 1 GHz bandwidth OFDM signal at 6-10 GHz RF frequency via digitized transport. Error-free reception was achieved even at 4-bit ADC resolution and it outperformed the analog transport counterpart. We have also investigated the power consumption of the base station under different transport schemes for both rural and urban coverage. The study showed that although a digitized transport scheme resulted in better link performance, analog transport is more energy-efficient for urban areas using micro base stations with a transmitting power of \(<\)25 dBm. Digitized transport is more energy-efficient for rural environments with a transmitting power of \(>\)40 dBm.