esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Traffic Data Analysis  
Traffic data analysis is of great significance to traffic management. Therefore, research on traffic data has gradually become a research hotspot. [PERSON] et al. used mobile sensor data for traffic jam speed prediction [6]. [PERSON] et al. used LTE data to predict road traffic [7]. [PERSON] et al. used the smart card data of the Nanjing Metro to analyze the spatiotemporal changes of passenger flow and residents' commuting characteristics [8]. [PERSON] et al. analyzed the changes of urban community structure based on taxi GPS trajectory data [9]. [PERSON] et al., based on urban road intersection data, analyze the evolution characteristics of traffic accidents and identify spatiotemporal hotspots [10].  
In traffic data visualization analysis, the interaction in the view is an important research direction. Interactive operations include editing, filtering data, and adjusting the parameters of the algorithms and models involved. Single attribute filtering is usually done using a slider or a double slider [11] and multiple attribute filtering is done using a parallel coordinate system or a scatter plotcite [12]. In the study of historical traffic data, the study of trajectory data has always been the focus of traffic data visualization [1; 2]. The trajectory data is usually combined with geographic information and is represented by a line connecting two locations [3; 13], the attributes of which can represent different trajectory information [14]. [PERSON] et al. [15] designed a 3D view in which the trajectories were placed on a base map, each trajectory is represented by a colored bar and the color is used to represent the attribute value. 3D stacked wall maps provide a good display of historical information [16]. [PERSON] et al. used smart card data to analyze the travel situation in Beijing's work area and living area [17; 18]. [PERSON] et al. proposed a visualization method that combines traffic data with social network data [19]. [PERSON] et al. proposed a method to analyze normal traffic data and abnormal traffic data separately [20]. [PERSON] et al. proposed a visual analytics method to analyze large-scale taxi O/D (Origin/Destination)data [21].  
In the research on traffic data, most use road state data, trajectory data and IC card (smart card) data to study vehicle trajectories, urban hotspots, citizen travel patterns, and traffic state detection and prediction. Some of them use interactive visualization technology to explore traffic data and display the analysis results more intuitively. In mega cities, public transportation has gradually become the mainstream mode of travel. However, there is very little research on the visual analytics of public transportation travel data.
# A Distributed Terahertz Metasurface with Cold-Electron Bolometers for Cosmology Missions
## 2 Numerical Model
Vertical wires seen by mode #1 have ohmic contacts to the rings, while horizontal wires seen by mode #2 with horizontal polarization have a thin SiO\({}_{2}\) layer between the wire pad and the ring in the overlapping area that creates series capacitance \(Cw\), such that \(Cw>>Cr\). Impedance of wire inductance \(Lw\) is shunting large impedance of \(Cr\) and reduces its reactance to the value comparable to the reactance of the ring with the four CEBs. Both equivalent networks shown in Figure 4b and representing \(Z_{FSS}\) are equivalent to a series resonant network, which impedance around the resonant frequency is real and equal to the resistance \(R=100\)\(\Omega\) of the CEB. Figure 5 indicates calculated \(Z_{FSS}\) for both modes. As is shown, both modes can see the DC-biasing wires (the horizontal and vertical wires) and capacitive coupling between two adjacent unit cells can be tuned out at around 350 GHz for mode#1 and 750 GHz for mode#2. Series capacitance \(Cw\) shifts the absorption frequency for mode#2 and increases the absorption bandwidth (see Figure 6). Figure 6a shows absorption of the waves coming from the silicon half-space for both orthogonal modes, Absorption of arbitrary polarization \(Pabs\) can be calculated as total absorption of different waves with amplitude \(a\) and random uniform distribution of polarization angle \(\theta\). The inset in Figure 6b shows vectors P1 and P2 corresponding to polarizations of mode #1 and mode #2. \(Pabs\) can be found by taking a simple integral:  
\[Pabs=\frac{Pabs^{(1)}+Pabs^{(2)}}{2}=|a|_{2}\int\limits_{0}^{\pi/2}\cos^{2} \theta d\theta+|a|_{2}\int\limits_{0}^{\pi/2}\sin^{2}\theta d\theta \tag{1}\]  
where \(Pabs^{(1)}\) is absorbed power of mode#1 and \(Pabs^{(2)}\) is absorbed power of mode#2. Figure 2: Unit cell of the CEB array: (**a**) 3D model with lumped elements representing CEBs [14], (**b**) SEM-image. Figure 4: (a) Unit cell layout for dual mode sensing. (b) Equivalent circuits for \(Z_{FSS}\) for two orthogonal modes of the Floquet port. Figure 5: Real and imaginary impedances of \(Z_{FSS}\) calculated for mode#1 and mode#2. Figure 3: Computed _Pabs_ for the FSS unit cell: (a) mode #1, (b) mode #2. From Equation (1) the calculated absorbed power of both modes for the pattern of array with the optimized unit cell design as functions of frequency are provided and shown in Figure 6, which illustrates the results of a wideband of frequency range from 100 GHz to 1000 GHz. Figure 6a shows absorption for each mode separately and it can be seen for two modes the absorption is from 70% to more than 95%. According to Equation (1) the absorption power of the two modes can be concluded (see Figure 6b), which shows absorption more than 75% (from 75% to 95%).
## 2 Intelligent Identification of Structural Surfaces  
As a critical geometric input for surrounding rock modeling, accurate identification of structural surfaces is fundamental to achieving digital reconstruction of tunnel geology. To improve both recognition efficiency and accuracy, this study proposes an automatic structural surface identification method based on deep learning, which integrates YOLOv5 and DeepLabV3+ models. The overall process consists of three stages: (1) object detection to localize the structural surface region; (2) semantic segmentation to extract precise boundaries; and (3) skeletonization to obtain the central axis of the surface, providing geometric input for subsequent 3D reconstruction.
## 6 Simulations Results and Discussions
### Critical Discussion
In our proposed EEUCH protocol, the SNs are randomly deployed, and thus our focus shifts to the information coverage of the UAV, which represents the number of SNs covered by the UAV. Our goal is to maximize the information coverage by strategically deploying 200 SNs within the FoI. As mentioned in Table 2, the protocol ensures that the UAV covers a 100 \(\times\) 100 m\({}^{2}\) area by utilizing these 200 deployed SNs in a single sweep.
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Vulnerabilities  
Scanning of the entire IPv4 address range back in 2012 found that 450 million devices were wide open and accessible [82]. This is clearly a major vulnerability issue that needs to be closed if IoT devices are to be made secure and illegal access to them denied. Recent (2017) demonstration of breaking of the Wi-Fi Standard WPA2 security protocol by key reinstallation attacks (KRACKs) forcing nonce reuse [83], proves that Wi-Fi-based IoT devices are at risk too. The ways to increase protection of Wi-Fi security are explained in [84]. Mobile Internet connectivity being more secure than Wi-Fi and with the deployment of 5G, the IoT industry may move away from Wi-Fi to 5G for better connectivity and security. The use of the Blockchain [76] also offers the potential to implement a more secure and private IoT ecosystem. Furthermore, the use of statistical fingerprinting may aid in developing a more secure IoT world--which is essential for the healthcare sectors [22].  
Figure 12: The Five Stages of the Industrial Revolution. Adapted from: [81].  
The hacking of most IoT devices is currently relatively straightforward due to their complete lack of any security features. By utilizing literally a myriad of insecure devices, a DDOS (distributed denial of service) attack can be executed to bring down complete infrastructures. This can have disastrous consequences for human society. Another example of an attack is using just one weak IoT device to gain entry as a gateway deep into a network to possibly access sensitive, critical and valuable data. The largest DDOS attack in human history so far occurred in October 2016 using an IoT botnet to spread the Mirai malware, which caused large sections of the Internet to go down. The Internet service provider Dyn was targeted and major services including Netflix, Twitter, CNN, Reddit and The Guardian all experienced service disruptions. The Mirai malware searched for those devices whose usernames and passwords had not been changed from their default values to infect them. Digital cameras and Digital Video Recorders (DVRs) were not immune from this attack either. The lessons that may be learnt from this particular incident are these [85]:  
* Do not use any devices that cannot have their usernames, passwords, drivers, software and firmware updated.
* Change the default login details immediately on acquisition for any Internet connected device.
* Each IoT device must be assigned a unique password.
* All IoT devices must execute the latest firmware, driver and software to protect against security vulnerabilities.
# Orderly Charging and Discharging Group Scheduling Strategy for Electric Vehicles
###### Abstract  
To address the challenge of optimizing the real-time scheduling for electric vehicles on a large scale, a day-ahead-intraday multi-timescale electric vehicle cluster division strategy is proposed based on the different expected charging completion times of the accessed electric vehicles. In the pre-day phase, historical travel statistics are used to model and determine the moments when the electric vehicles are on-grid and off-grid. In the intraday phase, the EV clusters are carefully divided by real-time data collection, taking into full consideration the response willingness and ability of vehicle owners. For each scheduling period, a real-time optimal scheduling model for EV clusters based on the V2G mode is established by taking into account the constraints of the power grid, vehicle owners, batteries, and other parties. The model is divided into two layers to find the charging and discharging plans: the upper model aims to determine the aggregate charging and discharging power of the cluster during the current time period by targeting the distribution grid's minimum variance load curve within the scheduling interval; the lower model takes the lowest cost to the EV owner as the goal to find the charging and discharging plan of a single EV and, at the same time, introduces the scheduling penalty factor to adjust the difference with the cluster charging and discharging plan. The simulation outcomes indicate that the suggested approach successfully mitigates load fluctuations and has a good optimization effect and fast solution speed for dealing with large-scale EV access problems. The simulation results show that the proposed strategy can effectively smooth load fluctuations and can significantly reduce the difficulty of optimal real-time scheduling for electric vehicles on a large scale, and it has a better optimization effect and faster solution speed for dealing with large-scale electric vehicle access problems.  
electric vehicles; vehicle-to-grid (V2G); orderly charging and discharging; real-time optimization; grouping control; optimal scheduling; multiple timescales Article
## 3 Results and Discussion
### AICc  
A comprehensive comparative analysis was conducted to evaluate three tortuosity estimation models using the AICc as the primary statistical metric. AICc not only assesses the goodness of fit between the model predictions and the reference data but also penalizes model complexity. This balance is critical for preventing overfitting and ensuring that models capture the underlying physical processes in a generalizable and parsimonious manner. Tortuosity values derived from streamline-based calculations, obtained via pore-scale fluid flow simulations, were used as the reference or ground truth to reflect the actual fluid transport pathways through complex pore networks, providing the most physically representative benchmark for model validation.  
The boxplot visualization of the relative AICc values for the three models (Figure 16) reveals distinct differences in model performance. The empirical model described by Equation (17) consistently achieves the lowest AICc value (\(-113.11\)), indicating the best balance of accuracy and parsimony in representing flow tortuosity. This model's integration of coordination number and throat length effectively captures key structural and topological features of the pore space, enabling it to approximate flow behavior more closely than the other models.  
The Dijkstra model ranks second with an AICc value of 99.74. Despite its purely geometric basis and absence of fitted parameters, it provides a robust estimation of tortuosity by explicitly considering the shortest flow paths within the discretized pore network. Its strength lies in capturing spatial heterogeneity and anisotropy critical for modeling transport phenomena, though it may miss some subtle dynamic flow effects accounted for by the empirical descriptors in Equation (17).  
Figure 16: Boxplots of the AICc distribution.  
In contrast, the poorly performing Equation (13) had a markedly higher AICc value of 202.23. This model's sole reliance on porosity, a scalar bulk property, limits its ability to reflect the complex geometry and connectivity inherent in porous media. Consequently, it deviates significantly from the streamline tortuosity reference, emphasizing the insufficiency of bulk porosity alone in capturing transport pathways.  
This hierarchy of model performance emphasizes the fundamental principle in porous media transport modeling--that accurate tortuosity prediction must incorporate pore connectivity and topological characteristics rather than relying solely on bulk properties. Geometrically informed and structurally descriptive models outperform simpler empirical relations by more faithfully representing the complex fluid pathways in real rock samples. Additionally, the use of AICc as a quantitative criterion balances model complexity and fit quality, supporting the empirical model of Equation (17) as the most efficient and reliable among those tested.
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Ic-Pls  
Influence coefficients-partial least squares (IC-PLS) is based on a combination of traditional IC method and PLS multivariate regression. The idea is to get rid of the limitation associated with IC that the number of parameters taken into account should be smaller than the number of available standard samples. For this purpose, a PLS algorithm is employed to calculate the regression coefficients in the IC equation instead of the traditional ordinary least squares procedure. At the first stage in IC-PLS, a matrix is constructed where the columns contain XRF signal intensities of analyzed and interfering elements, their products and ratios. Next, this matrix is employed for PLS modeling. The resulting model can be further applied for the quantification of elements in the new samples. A detailed explanation of the IC-PLS procedure can be found in [12]. In our study, the IC-PLS models were calculated in R environment [15] using an _mdatools_ package [16].
# Wideband UHF Antenna for Partial Discharge Detection
## 5 Conclusions  
In this work, a wideband UHF antenna for PD detection was proposed. This antenna could provide a wide bandwidth of 760 MHz (740-1500 MHz). The proposed antenna was an offset structure to the conducting ground wires so that it did not have to be clamped to the conducting ground wires to detect PD. Such a simple structure makes it easy to carry and use for the PD detection system of HV power equipment nearby conducting ground wires. The proposed UHF antenna can be used for PD detection on conducting ground wires in any power equipment systems such as electric vehicles.  
The presented work was carried out in collaboration of all authors. [PERSON] performed the simulations. [PERSON], [PERSON] and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper, which was edited by all co-authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2015R1A6A1A03031833 and No. 2017R1D1A1B0304537).  
The authors declare no conflict of interest.  
Figure 14: Simulation and measurement results (S\({}_{21}\)).  
Figure 13: Actual measurement picture.
## Appendix A MVSS Beamforming Methodology
### Superposition with Weight Calculation
The critical value \(L=M/2\) is taken to achieve the highest possible imaging resolution. Diagonal loading (DL) [34] is adopted to improve the robustness of the algorithm by adding spatial white noise to the recorded wavefield and to adjust the weight calculation [25] using:  
\[R_{dl}=\hat{R}(t)+\delta\text{-trace}\big{\{}\hat{R}(t)\big{\}}\cdot\text{I} \tag{12}\]  
to replace \(\hat{R}(t)\), where \(\delta\) represents the DL factor and I represents the identity matrix. The DL factor should satisfy \(\delta\ll 1/L\). Then, we replace \(R\) in (8) with \(R_{dl}\), and the weight of the beamformer becomes:  
\[w_{MV}=\frac{R_{dl}\!\!\!^{-1}a}{a^{H}R_{dl}\!\!\!^{-1}a} \tag{13}\]  
The beamformer can be written as:  
\[b(t)=\frac{1}{M-L+1}\sum_{l=0}^{M-L}w_{MV}^{H}\overline{y_{l}}(t) \tag{14}\]A weighting factor modified from the CF map is presented as:  
\[\text{CF}(t)=\frac{\left|\sum_{t=0}^{M-1}y_{t}(t)\right|^{2}}{M\sum_{t=0}^{M-1} \left|y_{t}(t)\right|^{2}} \tag{11}\]  
CF ranges from 0 to 1: when \(\text{CF}(t)=1\), it is a perfect focused point at time \(t\), and when \(\text{CF}(t)=0\), the signals of different receivers are not coherent. After multiplication by the amplitude beamformer \(b(t)\), the final image of the Nth shot \(I_{t}(N)\) is obtained as:  
\[I_{t}(N)=b(t)\cdot\text{CF}(t) \tag{12}\]
## 3 Results and Discussion
### Model Validation Using ROC Curve
#### 3.5.1 Cross-Validation Using Coordinate-Based Approach  
To evaluate model robustness under spatial heterogeneity, we employed a 10-fold cross-validation strategy based on X and Y coordinates, rather than relying on conventional random K-fold approaches. The dataset used in this approach was well-balanced between landslide and non-landslide classes. This balance helped prevent bias during model training and contributed to the stable and accurate performance observed across all folds.  
We evaluated the performance of three machine learning classifiers: LR, RF, and XGBoost. As shown in Figure 13, the RF and XGBoost models outperformed LR in terms of both average AUC and consistency across folds. The mean AUC values achieved were 0.944 for RF, 0.940 for XGBoost, and 0.875 for LR. This demonstrates the superior ability of ensemble tree-based models to capture the complex, non-linear relationships inherent in landslide processes and their robustness in capturing complex and non-linear landslide-triggering mechanisms under spatially heterogeneous conditions. While LR still maintained a relatively high performance, its lower average AUC and greater variability suggest a reduced ability to generalize in the presence of spatial heterogeneity. These findings highlight the importance of adopting cross-validation strategies that account for geographic autocorrelation. Unlike traditional random K-fold methods, coordinate-based partitioning ensures that model performance reflects real-world deployment scenarios. The consistent superiority of RF and XGBoost in this setting validates their application for operational landslide susceptibility mapping in mountainous and spatially complex regions such as the central Nepal Himalayas.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 6 Conclusions  
The results presented in this paper prove that the combination of the Cayley-Tustin time-discretization scheme and the general regulation theory in the state space setup led to an effective design procedure for a discrete-time controller, which can be directly used in digital satellite attitude control systems with zero-order hold. The advantage of the developed controller is that it only requires the satellite's sampled attitude angle to be measured, such that no angular velocity measurement is required. The controller matrices involve the feedback gain \(F\) and the output injection gain \(L\), which can be computed using LQR algorithms to enable satisfactory behavior of the plant output and tracking  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(\bar{k}=0.9k\left[\begin{array}{c}\mathrm{N}\cdot\mathrm{m}\\ \hline\mathrm{rad}\end{array}\right]\) & 675 \\ \(\bar{b}=1.1b\left[\begin{array}{c}\mathrm{N}\cdot\mathrm{m}\cdot\mathrm{s} \end{array}\right]\) & 0.011 \\ \(\bar{I}=0.45I\left[\mathrm{kg}\cdot\mathrm{m}^{2}\end{array}\right]\) & 0.765 \\ \(\bar{p}=1.1p\left[\begin{array}{c}\mathrm{kg}\cdot\mathrm{m}^{2}\end{array}\right]\) & 0.11 \\ \hline \end{tabular}
\end{table}
Table 3: The perturbed parameters of the satellite with a solar panel.  
Figure 10: The actual error \(e(t)\) (**left**) and the control torque \(\tau(t)\) (**right**) for \(h=0.05\,\mathrm{s}\).  
error signals. In order to control the behavior of these signals, the scalar weights \(w_{0}\), \(u_{0}\), and \(w_{1}\), \(u_{1}\) are used. Instead of the simple scalar weights in Functionals (142) and (147), one can introduce matrices to weigh the state variables selectively and match additional quantitative design objectives such as the overshoot, rise time, and settling time.  
The developed procedure is general and easily extendable to more complex multi-variable plants with various time-varying reference signals and disturbances. It follows from the literature (see, e.g., [14]) that the general regulation theory is also successfully applicable in the design of asymptotic tracking controllers for non-linear plants. The author's plan for future work covers the development of a proper robustness analysis of discrete-time controllers designed for both linear and non-linear plants.  
This research received no external funding.  
All data related to the results of computations presented in this paper are available on request from the author.  
The author declares no conflicts of interest.