esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Preprocessing
* Apply zero-padding or truncation to the spectrum to adjust it to the requested resampling rate. Zero-padding involves adding zeros to the spectrum to increase its length, while truncation involves removing unnecessary frequency components to reduce its length. * After adjusting the spectrum, process the modified spectrum using the inverse Fast Fourier Transform (IFFT) to obtain the resampled signals in the time domain. Figure 4: Three-phase current waveform of a single line to ground fault (CG). Following these steps, the signal is effectively resampled to the desired rate while preserving its essential information. This approach allows the model to handle signals with non-integer sampling rates, leading to more accurate fault classification and improved performance in transmission line fault classification applications. An example of data with a sampling rate of 188.5 samples per cycle, which have been resampled to 4 samples per cycle, is illustrated in Figure 6.  
Figure 7 provides examples of data that have been resampled to different sampling rates of 16 and 4 samples per cycle. The downsampling process from a higher to a lower sampling rate can introduce challenges in accurately representing the original signal, leading to potential distortions. With a lower sampling rate, the data may experience a loss of high-frequency information, resulting in a waveform that may not perfectly resemble a smooth sine wave. This distortion is evident in the resampled data with a sampling rate of 4 samples per cycle, as illustrated in Figure 7b. Such distortions can impact the accuracy of fault classification and introduce artifacts in the signal representation, potentially affecting the performance of the fault classification model. Furthermore, the phase issue becomes evident in the resampled data of 4 samples per cycle. Our approach addresses this problem by training the model with various inception angles, representing the phase angles at which the data are captured or sampled. By incorporating different inception angles during training, the model becomes more adept at handling diverse phase conditions, enhancing its robustness and generalization capabilities. Figure 5: Normalization of the three-phase current waveform. Figure 6: Resampling from 188.5 to 4 samples per cycle of a single line to ground fault (AG). By addressing these preprocessing steps, including window selection, normalization, and resampling, the input data are appropriately prepared for subsequent analysis and fault classification tasks, leading to more accurate and reliable results.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 4 Dichotomy Graph Sketch Mechanism  
A detailed description of the Dichotomy Graph Sketch (DGS) mechanism is presented in this section. DGS is able to mitigate the performance problem caused by hash collisions between heavy edges and light edges. The proposed framework is illustrated in Figure 4.  
To begin with, we represent the edges in the current session by a sub-graph stream \(C=\{e_{i},e_{i+1},\ldots,e_{j}\}\). Then, an edge classifier is fed sequentially with these edges. The edge will be stored in the _heavy sketch_ it is classified as a heavy edge; otherwise, it will be stored in the _light sketch_. Due to the lack of training of the edge classifier, all edges will be stored in the light sketch during the first session. To generate training samples for the edge classifier, a _sample generator_ is applied. During the current session, it queries the min-heap to find all heavy edges, and then it randomly chooses the same number of light edges. Combined with the chosen light edges, the heavy edges serve as training samples for the edge classifier. Upon completion of each session, the sample generator is reset to null, and DGS proceeds to build the graph sketches incrementally in the next session.  
DGS consists of four main components, namely the heavy sketch, the light sketch, the sample generator, and the edge classifier.  
Figure 4: The framework of our proposed Dichotomy Graph Sketch.  
Figure 3: An example of using two graph sketches to summarize a graph stream.
# Electrochemical Degradation of Crystal Violet Using Ti/Pt/SnO\({}_{2}\) Electrode
## 3 Results and Discussion
### Comparative Study  
Table 3 lists the treatment of various dyes by anodic oxidation. It appears that even if the discoloration is generally complete, the COD abatement differs according to the operating conditions used. It can be seen that both BDD and PbO\({}_{2}\) anodes show good removals. As for the supporting electrolytes, Na\({}_{2}\)SO\({}_{4}\) and NaCl are the most used. On one hand, Na\({}_{2}\)SO\({}_{4}\) is usually chosen for the following reasons: (1) it is relatively cheap, (2) it can be oxidized to the strong oxidant S\({}_{2}\)O\({}_{8}\)\({}^{2-}\), and (3) it would not produce hazardous compounds during processing. On the other hand, a low concentration of NaCl is sufficient, of about a few mM. For electrochemical processes, the current density is one of the most important parameters. As shown in Table 3, in the range of 4 to 500 mA cm\({}^{-2}\), a higher current density could lead to a better treatment performance for the removal of pollutants, COD and TOC, i.e., removing the contaminants in less time or achieving a higher removal rate at the same time. For example, using malachite green as the model pollutant and G/\(\beta\)-PbO\({}_{2}\) as the anode, it would take 80 min for complete degradation, when the applied current density was 4 mA cm\({}^{-2}\)[50]. Similar findings could be observed for the degradation of purple blue on BDD, where the removal efficiency in COD was 82.61% and the current density was 500 mA cm\({}^{-2}\)[6]. In this work, with Ti/Pt/SnO\({}_{2}\), a COD removal of 80% was reached after 120 min of treatment at 50 mA cm\({}^{-2}\).  
However, it is the comparison of energy consumption that highlights the interesting properties of SnO\({}_{2}\). Indeed, this electrode makes it possible to obtain great removals while maintaining a lower voltage than with the BDD for example. In addition, this material is cheaper than BDD and less toxic than PbO\({}_{2}\). For all these reasons, SnO\({}_{2}\) is a good material to treat organic pollutants.  
Figure 13: Elemental mapping images of Ti/Pt/SnO\({}_{2}\) electrode by SEM: **(1)** before electrolysis, **(2)** after electrolysis, **(C: Carbon, O: Oxygen).
# Composition Diversity and Expression Specificity of the _Tps_ Gene Family among 24 _Ficus_ Species
## 3 Discussion
### Sequence Characteristics and Subfamily Categories of the FTPS Genes
_TPS-g_ genes are often believed to show prevalence in catalyzing acyclic products [21; 46]. Here, these _TPS-g_ genes were categorized into three genes (_FTPS5_, _FTPS10_, and _FTPS21_) and two singletons (_ficha23769_ and _ffill14859_). According to annotation by the nonredundant protein database, all five genes were matched to two acyclic monoterpenes, linalool synthase and (35, _6E_)-nerolidol synthase 1. _TPS-e_/_ and _TPS-c_ are small subfamilies in the _Ficus_ species. The _FTPS-e_/_ genes are present in six species and involved in only two orthologous genes (_FTPS15_ and _FTPS32_). The genes encode a diterpene (ent-kaur-16-ene synthase) and a monoterpene \(S\)-linalool synthase, respectively. The ent-kaurene synthases often serve as precursors to plant growthregulators, and they exhibit a variety of useful pharmacological properties [47]. In other words, the role of the compound is more focused on pest defense. However, in sunflowers, [PERSON] et al. [48] showed that kaurane diterpenes can be used as oviposition stimulants of insects. By contrast, \(S\)-linalod is often used as an attractive volatile that some flowers emit to please pollinators [49]. Despite their variability in function, in the fig-fig wasp symbiosis, terpen are always considered to be chemical attractants to fig wasps. Therefore, the exact functions of the two _TPSs_ in _Ficus_ deserve further exploration. In the _TPS-c_ subfamily, we only found two paralogous sequences that encoded for _FTPS25_. The two sequences were annotated as ent-copalyl diphosphate synthases and were highly specifically expressed in _F. hitta_. This obvious gene expansion event implies that the _FTPS25_ may function during the receptive phase of male syconia of _F. hitta_. In addition, the presence of the classic DXDD motif in the gene suggests they function as Class II _TPS_. Figure 2: The R(R)X8W motif of the _FTPS_ genes in the _TPS-b_ subfamily. (**A**) The R(R)X8W motif logo of all _FTPS-b_ subfamily genes. (**B**) Some representative sequences of _FTPS-b_ subfamily genes, showing the R(R)X8W motif without the tandem RR domain.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 4 Numerical Results and Analysis
### Simulation Discussion and Comparison
The convergence and stability of the proposed DDPG-based UAV-RIS framework are critical for reliable deployment in dense urban environments. Figure 10 shows the cumulative reward convergence under varying hyperparameters. The baseline setting (\(\kappa=0.001,\tau=0.001\)) achieved stable convergence after 1500-2000 episodes. Increasing the learning rate (\(\kappa=0.002\)) accelerated the convergence but increased the reward variance due to aggressive updates. Lowering the learning rate (\(\kappa=0.0005\)) yielded slower but smoother convergence. A high learning rate (\(\kappa=0.01\)) resulted in instability and oscillations. Reducing the soft update coefficient (\(\tau=0.0005\)) further stabilized convergence by smoothing target network updates. In contrast to the above works, the proposed DDPG-based UAV-RIS framework jointly optimized the UAV trajectory, RIS phase shifts, and user mobility management via modified K-means clustering to address HOFs, LoS connectivity, and energy-efficient deployment in dense urban scenarios. Figure 8: OP vs. distance for UAV-to-ground communication. The proposed DDPG-based UAV-RIS framework demonstrated performance across multiple key metrics. Specifically, the integration of UAV mobility with RIS phase adaptation significantly reduced HOF probability and OP while improving throughput and energy efficiency in dense urban environments. The simulation results confirm the robustness of the proposed approach in dynamically handling user mobility, LoS blockages, and urban propagation impairments, validating the effectiveness of joint UAV trajectory and RIS phase shift optimization under practical deployment conditions.
# A Quantum Approach for Exploring the Numerical Results of the Heat Equation
## 2 Methodology
### Basic Equations and Discretization  
The heat conduction equation usually describes the mathematical modeling of the process of heat propagation in a medium. The mathematical models that describe one-dimensional thermal boundary value problems with Dirichlet boundary conditions are presented below. The heat equation is used to determine the change in temperature over time \(t\). For the one-dimensional case, the heat equation can be approximated as in Equation (1):  
\[\frac{\partial T(x,t)}{\partial t}=k\frac{\partial^{2}T(x,t)}{\partial x^{2}}, \tag{1}\]  
where \(T(x,t)\) is the temperature at point \(x\) at time \(t\), and \(k\) is the thermal conductivity coefficient. The thermal conductivity coefficient (\(k\)) is a parameter that describes the ability of a material to conduct heat. To address the impact of the thermal conductivity parameter \(k\) on the accuracy of quantum simulations, we chose a low value of 0.01. Higher values of \(k\) can lead to decreased accuracy and smoothness in the temperature distribution [21]. The Dirichlet boundary conditions are applied at the initial point, \(x=0\), and at the end point, \(x=1\), as given by the formula:  
\[T(0,t)=0,\;\;t\geq 0\;\text{and}\;T(1,t)=1,\;t\geq 0. \tag{2}\]  
The initial conditions at t = 0 is  
\[T(x,0)=x,\;\;0\leq x\leq 1. \tag{3}\]  
These mathematical models of Equations (1)-(3) describe the heat conduction in a one-dimensional uniform rod of length one unit with no internal heat sources, thermal diffusivity one, perfect lateral insulation, and initial condition \(x\) when \(0\leq x\leq 1\). Both the left end and the right end are insulated and kept at 0.  
To solve this equation numerically, various discretization methods are applied [23]. One approach is to use central differences to approximate the second derivative with respect to the spatial variable \(x\), as in Equation (4):  
\[\frac{T_{i}^{n+1}-T_{i}^{n}}{\Delta t}=k\frac{T_{i+1}^{n}-2T_{i}^{n}+T_{i-1}^ {n}}{\left(\Delta x\right)^{2}}, \tag{4}\]  
where \(T_{i}^{n}\) represents the temperature at node \(i\) at time level \(n\). Here, \(\Delta t\) is the time step, and \(\Delta x\) is the spatial step.  
In its original form, the heat conduction equation represents a classical model that describes the propagation of heat in a medium. However, a direct transition to quantum circuits from this model is impossible. Instead, by using discretization methods, a difference equation was obtained that could already be adapted for quantum computing. This difference equation was used for each time step, using the temperature values at the previous time level to obtain the evolution of temperature in space and time.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 1 Introduction
Hence, these two penalties can be viewed as low-level and high-level feature selection, respectively. At last, the fault-cause identification is carried out using ML classifiers and integrated features. The contributions of this paper are highlighted as follows:  
* To the best of our knowledge, this is the first time that multiview learning is introduced for transmission line fault-cause identification in view of the nature of multiview fault data. * We propose a novel approach, HMVFS, based on the \(\varepsilon\)-dragging and two regularization terms to select the discriminative features across views. We also develop an iterative algorithm to solve the optimization problem and prove its convergence theoretically. * The performance of HMVFS is evaluated on field data and compared with classical feature selection methods. Experimental results prove the effectiveness of combining waveform and contextual features and demonstrate the feasibility and superiority of HMVFS. The rest of this paper is organized as follows: Section 2 presents the proposed HMVFS algorithm and its convergence analysis. Section 3 outlines the real-life line fault dataset and extracts features in terms of waveform and nonwaveform. The empirical study is provided and discussed in Section 4. Section 5 presents concluding remarks.
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 3 Discussion
### Summary of Suitable Data Characteristics for the Included Models
RF has a good capability of avoiding overfitting by limiting the generalization error with an important requirement of a large dataset with less noise. However, when estimating the linear data, RF might have lower performance as compared to SVM and ARIMA due to its random behavior, and the prediction is computed based on the average values. DL models such as ANN and RNN are both neural network models inspired by the human neuro system. They have a few pros and cons in common, such as good generalizability and good performance on nonlinear and non-stationary data modeling. However, these models are also black box in nature, which results in low model transparency. Both DL models have shown better results compared to ML models when the dataset size is sufficient for training. Among ANN and RNN, RNN has demonstrated a better result than ANN in forecasting data with high volatility. Moreover, the hybrid model has demonstrated its robustness as a model that combines two or more models, complementing the advantages of the single model. Therefore, a hybrid model can better model the underlying relationship and obtain a more accurate result than any single model. In the case that any single model cannot take care of the different data characteristics, the application of the hybrid model is more appropriate. The discussed models are generally being applied widely in different areas and have achieved outstanding results. Moreover, different comparative studies have pointed to the suitability of a particular model on certain data characteristics. In conclusion, this section has comprehensively shown the suitable data characteristics for each model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Linear** & **Nonlinear** & **Stationary** & **Non-Stationary** & **Volatile** & **Non-Volatile** & **Large** & **Small Dataset** \\ \hline ARIMA & ✓ & & ✓ & & & ✓ & & ✓ \\ \hline GARCH & & ✓ & & ✓ & & & ✓ \\ \hline HMM & ✓ & & ✓ & ✓ & & ✓ & \\ \hline SVM & ✓ & & ✓ & & ✓ & & ✓ \\ \hline RF & ✓ & & ✓ & ✓ & & ✓ & \\ \hline ANN & ✓ & & ✓ & & ✓ & ✓ & \\ \hline RNN & ✓ & & ✓ & ✓ & & ✓ & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Optimal model selection with its suitable data characteristics.
## 5 Performance Evaluation
### QoS Provisioning  
Experiment III looks at the received video information after setting various constraints of delay and reliability. Figure 8a gives the frame delivery under reliability requirements for different deadlines. For each reported image frame, we count the number of received packets within the deadline. If the percentage of received packets for a frame is above the frame decodable threshold (DT), we show that it can successfully be decoded at the sink. Based on the number of decoded frames, we can obtain the percentage of successfully decoded video frames (frame delivery ratio). From Figure 8a, CDCS can meet reliability requirements in most cases.  
Figure 6: Reconstruction.  
Figure 7: Energy consumption.  
Figure 8b shows the result of the average packet delay with different reliability requirements. CDCS can provide a lower delay for video transmissions than M3 SPEED. Multimedia is resource-consuming applications. The multi-path transmission of M3 SPEED brings extra traffic to a network, resulting in network performance degradation. CDCS utilizes a geographic mechanism to map an end-to-end requirement to a local delay requirement, which ensures its average delay is under delay constraint. The average delay of DCSR is lower than that of CDCS. The reason for this is that the former has less compressed data than the latter does.  
Comparing Figures 7 and 8b, DCSR has less energy consumption and average delay than CDCS, but it satisfies energy efficiency and delay requirement at the cost of sacrificing image quality. Thus, CDCS can provide a more effective way to improve the quality of visual information received.
## 3 Robot Functionalities: People Perception, Human Following and Pick and Placing
### Pick and Placing
#### 3.4.4 Pick and Place Pipeline  
The complete Pick and Place pipeline is implemented as a state machine, and its pseudo-code is presented in Algorithm 3:  
Figure 5: End-effector localization through ALVAR tags (M: marker pose, h: hand pose).  
Figure 6: Diagram of the visual servoing architecture for reaching and grasping objects.  
The pipeline is triggered by calling an ROS service, using two fields as input: the type of object to grasp and the target pose where the object should be placed. The position of the object is tracked by the YOLO CNN and depth estimator, and its position (_object_tf_) is published as an ROS tf transform. The sweep_head procedure turns mbot's head left and right to look for the object and allows it to build an octomap of the scene, which is used for collision avoidance in the arm's motion planning. The head is then turned so that the object is centered in the frame. The select_pregrasp_pose procedure is then executed, with the goal of computing an end-effector pose that positions the wrist in the frame, enabling the use of visual servoing. The MoveIt! framework is used to calculate and execute the pregrasp motion plan. With the end-effector in frame, the final approach is performed through visual servoing, minimizing the error in the camera frame until it reaches a threshold, at which point the gripper is closed, and the object is lifted. The end-effector is moved to place the object on the target position relative to the table, and it is then lowered, the gripper is opened and the arm returns to the resting position. Figure 7 shows the robot executing the pipeline, and more examples can be seen in a video available at [[https://youtu.be/CZaLNTZ_ITU](https://youtu.be/CZaLNTZ_ITU)]([https://youtu.be/CZaLNTZ_ITU](https://youtu.be/CZaLNTZ_ITU)) (accessed on 30 October 2020 ).
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 3 Vibration Control Method Based on the Stiffness-Adjustable Connector  
A vibration control method is proposed to suppress VLFS motion through adjusting the stiffness of the connectors using actuators. According to different wave conditions, the output forces of the actuators can be determined by the state feedback law. The coefficients of the feedback law, which are as the adjustable stiffness of the system, are derived through an optimizing process by means of the minimization of the spectral response of the VFLS. As such, the mathematical model of the VLFS needs to be formulated first.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 2 Materials and Methods
### The Test Setup and Signal Propagation Environment  
All results presented here were recorded from the authors' offices in Strindvegen 4, Trondheim, Norway, from which multiple base stations were available for measurements, as documented in Table 2 Within this study, we present the data from a subset of these visible base stations, which could be reliably tracked over the extended periods required for extended stability analysis without loss of lock.  
Unfortunately, due to national security concerns [17], the locations of these base stations are no longer public information, and the authors are therefore unable to conclusively say which signal was transmitted from which position. Consequently we also cannot say if there are any shared characteristics such as base station equipment or antennas between any of the cells. We visually identified at least five base station clusters less than 300 m from the reception antenna location and believe that all of the signals discussed here emit from one of those base stations.  
Figure 1: Conceptual overview of the experimental test setup.  
The RF propagation environment is urban and contains several large concrete buildings that are expected to produce a complex but mostly static propagation environment. Two of the five mentioned base stations are directly visible from the authors' office, while three of them are obstructed by a building. Only moving vehicles on the adjacent roadway were suspected as significant non-static reflectors.