esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Simulation Results  
To model a magnetic core from the datasheet of ETD 49 core, which is constructed using 3C90 material with the following parameters (effective area \(A_{e}\) = 211 mm\({}^{2}\), Area product = 57,600 mm\({}^{4}\), magnetic conductance \(A_{L}\) = 4200 nH, with \(L\) = \(A_{L}\).\(N^{2}\)). Our inductor was calculated by taking \(d\) = 0.5 and \(I\) = 3 A. We have to wire a coil of \(N\) = 28 turns for \(L_{1}\) and \(N\) = 12 turns for \(L_{2}\) using a wire of 2 mm and density of (2.5 A/mm\({}^{2}\)), and the PSpice Model Editor was used [21]. A co-simulation between Orcad and Simulink was used to simulate the system by using the SLPS block, as shown in Figure 12. SIMULINK was used to implement control part (P&O algorithm) and the PV panel of the system, while PSpice was used to model the conventional and proposed boost converter in Figures 3 and 4, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline DE\(\backslash\)E & NB & NS & Z & PS & PB \\ \hline NB & PB & PS & PS & NS & NB \\ \hline NS & PB & PS & Z & NS & NB \\ \hline Z & PB & PS & Z & NS & NB \\ \hline PS & PB & PS & Z & NS & NB \\ \hline PB & PB & PS & PS & NS & NB \\ \hline \end{tabular} Where NB: Negative Big, PB: Positive Big, PS: Positive Small, NS: Negative Small, and Z: Zero.  
\end{table}
Table 2: Rules of the fuzzy controller.  
Figure 11: Current through 2 nd coil and its reference.  
Figure 13a shows the simulated input solar radiation of the PV panel while Figure 13b,c reports the simulated output power and power conversion efficiency of the conventional and proposed converter with the following element values (\(C_{1}\) = 200 \(\mu F\), \(C_{2}\) = 400 \(\mu F\), and \(L_{1}\) = 3500 \(\mu H\)), which are chosen corresponding to the used PV panel characteristics (SANYOHP). It can be observed that the power conversion efficiency of the proposed converter is about 9% higher than that of the conventional one when the radiation is equal to 1000 W/m\({}^{2}\) (i.e., when the input current value is maximum), whereas the two converters show similar performance for values of radiation lower than 300 W/m\({}^{2}\).  
Figure 12: Simulation block diagram.  
Figure 13: _Cont._
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 4 Analysis and Application of Engineering Case
### Calculation Results of Buckling of DS  
By introducing the boundary conditions of the pilot hole and calculating the DS dynamics, the movement and deformation of the DS were clearly obtained. When the thrust force is 10T, 20T, and 30T, the displacement cloud diagram of DS is shown in Figure 10a-c. In Figure 10a, the DS is in a stable state immediately below the borehole, has no buckling. In Figure 10b, the DS is below the borehole and showed sinusoidal buckling. In Figure 10c, the DS shows obvious helical buckling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Construction** & **Position** & **Drilling Tool** & **Weight (Kg/m)** & **Inner Diameter** & **Outer Diameter** \\
**Stage** & **(m)** & **Drilling Tool** & **Weight (Kg/m)** & **(mm)** & **(mm)** \\ \hline \multirow{4}{*}{Pilot hole} & Entrance-800 & 7-5/8 in Drill pipe & 63.75 & 171.83 & 193.7 \\  & 800-2300 & 6-5/8 in Drill pipe & 58.42 & 146.46 & 168.3 \\  & 2300-3180 & 5-1/2 in Drill pipe & 33.57 & 139.7 & 118.6 \\  & 3180-3190 & 8 in Drill collar & 223.5 & 71.40 & 203.2 \\  & 3190-3200 & Mud motor & & & \\  & 3200 & 12-1/4 in Drill bit & & & \\ \hline \multirow{4}{*}{First stage} & Entrance-1000 & 7-5/8 in Drill pipe & 63.75 & 171.83 & 193.7 \\  & 1000–2500 & 6-5/8 in Drill pipe & 58.42 & 146.46 & 168.3 \\  & 2500 & 24 in Rock reamer & & & \\ \hline \multirow{4}{*}{Secong stage} & Entrance-1000 & 7-5/8 in Drill pipe & 63.75 & 171.83 & 193.7 \\  & 1500–3000 & 6-5/8 in Drill pipe & 58.42 & 146.46 & 168.3 \\ \cline{1-1}  & 3000 & 34 in Rock reamer & & & \\ \hline \multirow{4}{*}{Third stage} & Entrance-1000 & Drill pipe (A) & 63.75 & 171.83 & 193.7 \\  & 1500–3000 & Drill pipe (B) & 58.42 & 146.46 & 168.3 \\ \cline{1-1}  & 3000 & 42 in Rock reamer & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bottom hole assembly.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object** & **Poisson’s Ratio** & \begin{tabular}{c} **Elasticity** \\ **Modulus (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Dilation Angle** \\ **(°)** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion Stress** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Frictional Angle** \\ **(°)** \\ \end{tabular} \\ \hline Rock & 0.111 & 31,200 & 25.02 & 39.24 & 27.61 \\ Drill pipe & 0.30 & 210,000 & & & \\ Drill collar & 0.30 & 210,000 & & & \\ Bit and reamer & 0.26 & 206,000 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material Properties.
## 3 Methodology
### Track Management and Zero-Shot ReID Integration
#### 3.3.3 Zero-Shot ReID Integration  
The zero-shot ReID module is crucial for maintaining object identity continuity, especially when objects exit and re-enter the scene or when tracks are temporarily lost. Unlike traditional ReID methods that require extensive training on labeled data [42], our zero-shot ReID module uses pre-trained VLMs to match reappearing objects to their previous identities without additional fine-tuning. We treat the confident track storage as a dynamic gallery, comparing new queries from the current frame against this gallery using pre-trained embeddings from the VLM. This allows for seamless identification of potential matches based on stored descriptors and VLM similarities, eliminating the need for specialized training on additional ReID datasets.  
**Feature Extraction and Embedding Comparison:** In the zero-shot ReID module, the appearance features of each detected object are extracted using the same embedding structure described in earlier sections. The embedding vector \(e_{d}\) for a new detection is compared against the stored embeddings \(e_{t}\) of all previously tracked objects in the database. The similarity between the embeddings is computed using a cosine similarity measure as given in Equation (16):  
\[S_{cos}(e_{d},e_{t})=\frac{e_{d}\cdot e_{t}}{||e_{d}||||e_{t}||} \tag{16}\]  
where \(e_{d}\) is the embedding vector of the current detection, and \(e_{t}\) is the embedding vector of a previously tracked object. A high similarity score indicates that the detection is likely to correspond to a previously tracked object, facilitating the re-assignment of the same identity.  
**Zero-Shot Matching Process:** When a detection \(z_{i}\) cannot be matched to an existing track during the data association step (as described in Section 3.3.2), the zero-shot ReID module is activated. The module searches for the highest similarity score \(S_{cos}\) between the current detection and the stored embeddings of lost tracks. If the similarity score exceedsa predefined threshold \(\tau_{ReID}\), the detection is reassociated with the corresponding track, effectively recovering the identity of the object as given in Equation (17) :  
\[ReIDMatch=\arg\max_{l}S_{cos}(e_{d},e_{t})\quad\text{if}\quad S_{cos}(e_{d},e_{t })>\tau_{ReID} \tag{17}\]  
**Integration into Track Management:** The zero-shot ReID module is integrated into the track management system, offering a fallback for identity preservation when standard data association fails. This allows the tracking system to maintain consistent object identities during occlusions, exits, and re-entries without the need for manual labeling or retraining. By leveraging the generalization capabilities of VLMs, the zero-shot ReID module enhances the overall robustness and reliability of the tracking pipeline.
## 5 Various Fabricating Methods for Flexible Antennas
### Inkjet Printing  
Because of its dependability and the speed with which it can design, inkjet printing has become a preferred and cost-effective approach for the fabrication of flexible antennas [84]. Using an additive process, the design is transferred directly onto the substrate without the use of masks, which contributes to a reduction in material waste. It is the primary method of production for polymeric substrates such as polyimide, PET, and paper [85] because of its ability to produce precise prototypes rapidly. The figure below provides an overview of the printing process as well as the assembly of the printer. When it comes to printing, the quality of the ink is dependent on factors such as its viscosity, surface tension, and particle size [86]. The topology of the substrate surface, the temperature of the platen, and the properties of the print head are all especially important.  
Unlike photolithography, which is a subtractive approach since it entails eliminating unwanted patterns from the metallic or conductive side of the substrate, inkjet printing, on the other hand, deposits a regulated amount of ink droplets from the nozzle to the designated place. As a result, there is no waste or by-product produced, which results in a solution that is not only affordable but also clean and quick. Figure 10 illustrates the process of printing using an inkjet printer.  
Figure 9: Screen printing fabrication.
## 2 Methodology
### Survival Analysis  
#### 2.2.1 Kaplan-Meier Method  
In order to explore the effects of driving style and distraction state on drivers' lane departure behavior, a survival analysis model was conducted based on LDD. Survival analysis is a probabilistic method which is well suited for analyzing time-related data where the duration of the study is required until the occurrence of an event. Survival models have been widely used in various fields including bioscience, public health, and social sciences [27; 28]. Recently, they have also become popular in the transportation field, such as traffic accident duration analysis [29; 30] and braking duration analysis [31]. In this study, Kaplan-Meier (K-M) non-parametric survival analysis was used to directly estimate the survivor function and hazard function of LDD under the influence of specific factors. \(F(t)\) represents the probability of the occurrence when the time duration is smaller than a specific time \(T\leq t\), and \(f(t)\) is the probability density function of the duration. The distribution function \(F(t)\) of the duration can be presented as:  
\[F(t)=\mathrm{P}(T\leq t)=\int_{0}^{t}f(x)\mathrm{d}x \tag{2}\]  
The survival function \(\mathrm{S}(t)\) means the probability that the duration variable \(T\) was not less than the specified ending time \(t\)[32]. There are s groups of lane departure behavior events collected during the driving simulator test, and the LDDs of these groups arearranged in ascending order as \(t_{1}\leq t_{2}\leq\ldots\leq t_{s}\). The lane departure behavior survivor function \(S(t)\) can be expressed as:  
\[S(t)=\mathrm{P}(T>t)=\int_{t}^{\infty}f(x)\mathrm{d}x=1-F(t) \tag{3}\]  
\[S(t_{i})=\prod_{j=1}^{i}\left[1-N_{j}/n_{j}\right] \tag{4}\]  
where \(N_{j}\) is the number of events that the departure behavior ended in the interval \([t_{j},t_{i})\), \(n_{j}\) is the total number of events just prior to \(t_{j}\). \(\left[1-N_{j}/n_{j}\right]\) represents the conditional probability during each observation interval.  
The hazard function for the departure behavior \(h(t)\) is the probability of a departure event that can end within a short period of time, \((t,t+\triangle t)\):  
\[h(t)=f(t)/\left[1-F(t)\right]=f(t)/S(t)=\lim_{\Delta t\to 0}\mathrm{P}(t\leq T \leq t+\triangle t|t\leq T)\ /\ \triangle\ t \tag{5}\]
## 4 Numerical Simulations and Results
In the multi-group MC simulation, the total number of samples per group is set to be 4000, the propagation time is chosen as \(\mathbf{t}=\{0.5,1,\ldots,5\}\) days, the position standard deviation is \(\mathbf{\sigma}_{r}=\{40,80,\ldots,1000\}\) m, and the velocity standard deviation is \(\mathbf{\sigma}_{v}=\{0.04,0.08,\ldots,1\}\) m/s. There are a total of 625 groups in each case and are parallel computed. \begin{table}
\begin{tabular}{c c} \hline
**Elements** & **Values** \\ \hline \(a\) & 6921 km \\ \(e\) & 0.0001 \\ \(i\) & 53 deg \\ \(\Omega\) & 10 deg \\ \(\omega\) & 10 deg \\ \(M\) & 60 deg \\ \hline \end{tabular}
\end{table} 
Table 1: Initial mean elements. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Values** & **Explanation** \\ \hline \(J_{2}\) & 1082.62668355 \(\times 10^{-6}\) & zonal harmonic \\ \(n_{e}\) & 7.2921158553 \(\times 10^{-5}\) rad/s & Earth spin rate \\ \(R_{e}\) & 6378.137 km & Earth’s equatorial radius \\ \(\mu\) & 3.98600436 \(\times 10^{14}\) m\({}^{3}\)/s\({}^{2}\) & Earth’s gravitational constant \\ \(\
u\) & 0.1 & variability of scale height \\ \(\varepsilon_{e}\) & 1/298.257 & Earth flattening \\ \(H\) & 68.7 km & scale height \\ \(\rho_{0}\) & \(2.34\times 10^{-13}\) kg/m\({}^{3}\) & atmospheric density \\ \(C_{D}\) & 2.2 & drag coefficient \\ \(m\) & 227 kg & mass of satellite \\ \(S\) & 1.2 m\({}^{2}\) & cross-sectional reference area of satellite \\ \hline \end{tabular}
\end{table} 
Table 2: Analytical model coefficients [32]. Figure 3: Position distribution and 3-\(\sigma\) error ellipse projection on the \(x\)-\(y\) plane. Figure 2: Initial uncertainty distribution before (**a**–**c**) and after (**d**–**f**) conversion from osculating elements to mean elements. The standard deviation surfaces of the relative phase in two cases are shown in Figure 4, and the least-squares surface fitting parameters are shown in Table 4. Taking \(\alpha=0.01\), the \(z\) value of the two-sided test is 2.58, and the acceptance rate of hypothesis \(H_{0}\) reaches 96% in both two scenarios, indicating that our assumption is logical. Since the fitting relationship of the initial standard deviation to the terminal standard deviation has been obtained, we can easily make deviation forecasts for controllers with known error magnitudes, or infer the control and observation error of a constellation backward from its deviation evolution. For example, if two co-plane satellites have an initial uncertainty of 100 m standard deviation in three-dimensional position, after 20 orbits, the relative phase appears an uncertainty of 0.438\({}^{\circ}\) standard deviation, which could be also be caused by 0.109 m/s three-dimensional velocity errors.
# Towards Large-Scale Fast Reprogramable SOA-Based Photonic Integrated Switch Circuits
## 6 InP MRR-Based Switching Cross-Point Matrices
Moreover, the combination of high precision lithography for shorter couplers, higher efficiency quantum well phase modulators, and the use of all-deep ring resonators, which avoid the need for mode converters, is believed to enable further scaling up to the same order as for SOI ring-resonators based cross-point matrices [45]. Ten and twenty billion bits per second (10 Gbps and 20 Gbps) data routing is performed across a combination of four different paths in the circuit with error-free operation with a maximum power penalty of 2.6 dB [47]. The on-state electrical power consumption is recorded to be only 79 uW for the single switch element, which leads to a minimum energy consumption of 8 fJ/s for 10 Gb/s operation, identifying a potential route for energy efficient optical switching. Fast switching of a few nanoseconds for the rise and the falling time is also verified.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 3 Machine Learning Based Botnet Detection  
### Introduction to Machine Learning and Its Techniques  
#### 3.1.1 Introduction to Machine Learning  
Machine learning is a field of computer science, involving the study and construction of techniques that enable computers to self-study based on the input data to solve specific problems [15; 16]. Based on the learning methods, machine learning techniques are usually divided into three main categories: supervised learning, unsupervised learning and semi-supervised learning [16]. Supervised learning is a form of learning in which training data is labeled. The machine will \"learn\" from the labeled patterns to build the classifier and use it to predict labels for new data. In contrast, unsupervised learning is a form of learning in which training data has not been labeled. In this form, the machine will \"learn\" by analyzing the data characteristics to construct the classifier. Semi-supervised learning is the hybrid approach between supervised and unsupervised learning. In semi-supervised learning, the input training dataset will contain both labeled and unlabeled data. Each method has its own advantages and disadvantages and has its own application domain. In this paper, we only examine the effectiveness of supervised learning techniques in botnet detection and the next subsection briefly describes some of the common supervised machine learning algorithms, including k-nearest neighbor (kNN), decision tree, random forest, and Naive Bayes. Interested readers may find the full details of these machine learning algorithms in [15; 16].
## 3 Materials and Methods
### Methods
#### 3.2.4 Belief Distance Measure  
To compare two BS models, a belief distance must be defined. Since each BS model \(S\) is composed of \(N\) evaluation grades, it can be represented as a vector \(B=v(S)=[\beta_{1},\beta_{2},\ldots,\beta_{N}]\). In this form, comparing two BS models becomes a comparison between their respective vectors. Let \(S_{1}\) and \(S_{2}\) be two BS models, with their corresponding vectors denoted as \(B_{1}\) and \(B_{2}\). The distance between these two models is defined as follows [46]:  
\[d_{BS}(S_{1},S_{2})=d_{BS}(B_{1};B_{2})=\bigg{(}\frac{1}{2}(B_{1}-B_{2}) \widetilde{S}(B_{1}-B_{2})^{T}\bigg{)}^{\frac{1}{2}}. \tag{7}\]  
Here, \(\widetilde{S}=\Big{[}\widetilde{s}_{ij}\Big{]}\) is the similarity matrix that captures the differences among the evaluation grades \(H_{i}\) and \(H_{j}\), \(i,j=1,\ldots,N\) determined using Formula (6).
## 1 Introduction
To address these analytical gaps, the present study introduces two complementary methods: the Fisher-Shannon information framework [19; 20; 21] and the Visibility Graph (VG) method [22; 23; 24]. These techniques, though widely applied in other disciplines, remain largely underutilized in the analysis of atmospheric pollution time series. Their application offers a novel perspective to uncover hidden patterns, quantify degrees of disorder, and understand the structural evolution of pollutant concentrations. The Fisher-Shannon method has proven effective in various domains, including geosciences [19] and heavy metal dynamics [25]. Notably, a study in Switzerland used this framework to analyze hourly time series of \(NO_{2}\), \(O_{3}\), and \(PM_{10}\), revealing a clear relationship between pollutant concentration disorder and the spatial characteristics of monitoring stations--insightful for distinguishing land-use types and human activity patterns [26]. In parallel, the Visibility Graph method has gained attention as a powerful tool for mapping time series into complex networks. By leveraging the topological features of these networks, VG analysis allows for the extraction of meaningful structural information about the dynamics of the original signal. Applications span diverse fields: from seismic activity analysis [27; 28] and sea surface temperature fluctuations during El Nino events [29], to the study of \(PM_{10}\) dynamics in Guadeloupe [30] and the differentiation of urban vs. rural ozone behaviors [31]. To better understand these dynamics, the present study applies both the Fisher-Shannon and Visibility Graph methods to \(CO\) and \(O_{3}\) time series in the MCMA from 2019 to 2023, including the COVID-19 lockdown period. These tools allow us to identify temporal patterns, assess the degree of disorder in pollutant concentrations, and provide insight into how human activity and meteorological variability influence the atmospheric behavior of key pollutants. By comparing findings within the MCMA and contextualizing them with patterns from other urban regions, this study contributes to a broader understanding of pollution dynamics in complex urban environments. Although there is a substantial body of research in Mexico focusing on air pollutants--including their concentrations, health impacts, and source attribution, no study to date has applied informational or topological approaches such as the Fisher-Shannon or Visibility Graph methods to explore the underlying dynamical complexity of air pollution time series. This study addresses that gap and proposes a novel methodological perspective to analyze urban air quality in the Mexican context.
## 4 Experiment
### Indoor Experiment 2  
Indoor experiment 2 aimed to show the effectiveness of HSDLIO in the indoor environment with a certain height information. In this experiment, the backpack was carried up and down four flights of stairs. Figure 11a shows the stairs scene, while Figure 11b-d shows the overall mapping of LeGO-LOAM, LIO-SAM, and HSDLIO algorithms. The white points are backpack localization trajectories. LeGO-LOAM (Figure 11b) cannot obtain the structure of the stairs. Because the vertical FOV of the horizontal lidar is relatively narrow, the mapping result of LIO-SAM (Figure 11c) does not show the height information. With the help of the vertical lidar and the tight coupling of the IMU, the mapping of HSDLIO (Figure 11d) is the most complete and accurate, which is closer to the real stairs' scene.  
Figure 12 shows the trajectories obtained by LeGO-LOAM, LIO-SAM, and HSDLIO. The red, black, and green lines represent the trajectories of HSDLIO, LeGO-LOAM, and LIO-SAM, respectively, with the trajectory of HSDLIO approximating the real trajectory. LeGO-LOAM has no IMU correction, which causes the drift of the trajectory. Although the trajectory of LIO-SAM does not drift, due to the narrow vertical FOV of the horizontal lidar, a significant error occurs in the \(\mathbf{z}\) direction information. Comparing the height  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **LeGO-LOAM** & **LIO-SAM** & **HSDLIO** \\ \hline \(\mathbf{x_{o}}\) & Fail & 0.079 & 0.077 \\ \hline \(\mathbf{y_{o}}\) & Fail & 0.092 & 0.090 \\ \hline \(\mathbf{z_{o}}\) & Fail & 0.121 & 0.001 \\ \hline RTE & Fail & 0.171 & 0.118 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative translational error when the backpack returns to the starting point (meters).  
Figure 10: Comparison of LeGO-LOAM, LIO-SAM, and HSDLIO trajectories. The red line is the trajectory of HSDLIO, the blue line is the trajectory of LIGO-SAM, and the black line is the trajectory of LeGO-LOAM, with the latter having drifted.  
information of LIO-SAM and HSDLIO in the \(z\) direction in Figure 13, it can be seen that when going up and down four floors of stairs, the localization results of LIO-SAM provide no height information and also indicate a great drift, while HSDLIO provides accurate height information. Therefore, the tight coupling of dual lidars and IMU makes the advantages of HSDLIO clearly apparent. Table 2 shows that the error of HSDLIO in the \(x\) and \(y\) direction is smaller than LIO-SAM, but the accuracy in the \(z\) direction is much higher than LIO-SAM and the RTE of HSDLIO is much smaller than LIO-SAM.