esa-sceva/satcom-chunk-collection huggingface dataset:
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 3 Traditional Algorithm
### Optimization Based on Projective Geometric Distance  
After an initial \(F\) is achieved by the methods as that in Section 3.1, an optimization always follows as the last step. One goal of optimization is to minimize the re-projection error--the (summed squared) distances in projective space. Usually the cost function is like Equation (8), which minimizes the distance of a point from its corresponding projected point, computed in each of the images.  
\[\epsilon^{proj} =\frac{1}{N}\sum_{i=1}^{N}\|u_{i}-\bar{u}_{i}\|+\|u_{i}^{{}^{ \prime}}-\bar{u}_{i}^{\prime}\| \tag{8}\] \[=\frac{1}{N}\sum_{i=1}^{N}\|u_{i}-PX_{i}\|+\|u_{i}^{{}^{\prime}}- P^{\prime}X_{i}\|\]  
\[P=\left[\begin{array}{cc}I&|&0\end{array}\right],P^{\prime}=\left[\begin{array}[ ]{cc}\epsilon^{\prime}\end{array}\right]\times Fe^{\prime} \tag{9}\]  
With projection matrices of cameras denoted by \(P,\ P^{\prime}\), the feature coordinates on image should be \(\bar{u}=PX\), \(\bar{u}^{\prime}=P^{\prime}X\); however, there must be some error for \(\bar{u},\ \bar{u}^{\prime}\). \(|u_{i}-PX_{i}|\) and \(|u_{i}^{{}^{\prime}}-P^{\prime}X_{i}|\) represent this re-projection error, which means the projective distances between the projections of \(X\) to the measured image points \([u,u^{\prime}]\). Obviously, \(P,P^{\prime}\) are the projective matrices under projective coordinate but not the metric reconstruction under Euclidean coordinate. \(e^{\prime}\) is the epipole on the second image with respect to \(F\).  
The initial \(X\) can be computed by \(P,P^{\prime}\) and \([u,u^{\prime}]\) by methods such as linear or nonlinear triangulation. Then, the Levenberg-Marquardt method is applied to minimize cost of the Equation (8), which has \(3N+12\) variables: \(3N\) for \(N\) 3D points \(X_{i}\), and \(12\) for the camera matrix \(P^{\prime}=[M|t]\), with \(F=[t]_{\times}M\). After optimization, a better result is produced most of the time. It is worth noting that sometimes it has only better numerical index on the average projective distance rather than reasonable 3D metric reconstruction.
## 5 MPPT Algorithms  
Table 6 presents the classification of the MPPT algorithms implemented in this study. Conventional techniques such as Perturb and Observe (P&O) and Incremental Conductance (InC) were included as baseline methods due to their simplicity and widespread use in PV applications.  
Additionally, a control logic-based approach using the fuzzy logic controller (FLC) was implemented for its ability to handle system non-linearities through rule-based decision-making. An optimization-based algorithm, the genetic algorithm (GA), was also tested for its global search capability in identifying the maximum power point, particularly under partial shading conditions.  
Hybrid algorithms were explored to combine the advantages of different methods, including InC combined with P&O (InC+P&O), GA combined with InC (GA+InC), and GA combined with P&O (GA+P&O), aiming to enhance convergence speed and tracking accuracy in dynamic environments.  
However, particular emphasis in this work was placed on artificial intelligence (AI)-based algorithms due to their superior adaptability and learning capabilities. Specifically, the Artificial Neural Network (ANN) was employed for its powerful pattern recognition and non-linear mapping ability, enabling accurate prediction of the optimal operating voltage under varying irradiance and temperature conditions. Furthermore, the Deep Q-Network (DQN) algorithm, which integrates reinforcement learning with deep neural networks, was implemented to achieve autonomous learning and optimal decision-making  
Figure 6: Partial shading conditions: (**a–d**) corresponding to Cases 1–4 in Table 5.  
without requiring an explicit PV system model, demonstrating good performance even under complex partial shading scenarios.  
Overall, the integration of AI-based algorithms, namely ANN and DQN, formed a key focus of this study, aiming to improve MPPT accuracy, adaptability, and robustness beyond traditional approaches.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 5 Conclusions and Future Work
### Outlook
#### 5.2.2 Learning Discontinuity in Patterns  
Traffic patterns, especially congested traffic, are highly driven by underlying physics, thus, how to learn patterns around shockwaves, which correspond to discontinuities in which gradients do not exist, remains an active research area in PIDL. Ref. [115] introduces a convolutional neural network (CNN) for TSE, using a customized kernel, i.e., the anisotropic kernel, based on kinematic wave theory. Their anisotropic kernel specifies the influence region regarding the forward and backward traffic wave propagation characteristics. Fewer parameters are to be learned, making the training less computationally intensive. There is a small amount of literature that has discussed such a limitation using PIDL for nonlinear PDEs with solutions containing shocks and waves. One solution is to add viscosity terms [28; 31; 116] to smoothe the discontinuity.  
Because the state of the art primarily focuses on the \"soft\" method, which imposes the physics constraints as part of the loss function, the fulfillment of physics constraints cannot be guaranteed, leading to poor prediction in shocks and waves. Thus, \"hard\" methods that enforce physics could be a radical remedy.
## 2 Materials and Methods  
The calculations were performed based on density functional theory (DFT) with exchange-correlation interaction described by the projector-augmented wave method (PAW) [31; 32] in generalized gradient approximations (GGA) [33] with PW91 form [34], implemented in VASP software [35]. The employed cut-off energy of the plane waves was 500 eV. The convergent criterion of total energy was set as \(10^{-5}\) eV per supercell. The force convergence criterion was smaller than 0.02 eV/A. For all calculations, Monkhorst-Pack k-point sampling grids of \(8\times 8\times 2\) were used [36]. The supercell (seen Figure 1a) contained 20 carbon and 40 hydrogen atoms with a length of 25.38 A in the \(z\) direction [37]. The lattice constants of the supercell in the \(x\) and \(y\) axes were set to be a = b = 20 A, which was large enough to minimize the unwanted interactions between the adjacent chains. Three initial adsorption sites were considered, labeled as s1, s2, and s3, as shown in Figure 1a. For site s1, the H atom was located in the plane perpendicular to the \(z\)-axis of the hydrocarbon chain. For site s2, the H atom was in the same plane but above the middle point of the C-H bond (bridge site). While for site s3, the adsorbed H atom was in one of the hydrogen columns along the \(z\)-axis. All these sites represent the configurations for the possible transition site. The initial distance from the H atom to the nearest hydrogen atom in the hydrocarbon chain for all the three cases was set to be 0.74 A, i.e., the typical bond length of a H\({}_{2}\) molecule. The positions of the atoms were fixed for the calculations of the three initial configurations.  
The stability of the three typical adsorption sites for H and H\({}_{2}\) were explored by the calculations of the adsorption energy, defined as:  
\[\mathrm{E_{a}=E_{hc+adatom}-E_{hc}-E_{adatom}} \tag{1}\]  
where E\({}_{hc+adatom}\) is the total energy of a hydrocarbon chain with one H or H\({}_{2}\) adsorbed in the supercell. E\({}_{hc}\) and E\({}_{adatom}\) are the corresponding total energies of the hydrocarbon chain and an isolated H or H\({}_{2}\), respectively.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Modulation Parameter Analysis  
Figure 12 shows the S-curves of several navigation signal code loops when the receiver bandwidth is 36 MHz and the time delay between the immediate branch and the delay branch is 20 ns. According to the S-curve, we can analyze the ranging performance of the navigation signal. The steeper the curve between the two main peaks, the higher the ranging accuracy; the lower the other side peaks, the lower the probability of false lock. We find that the curve between the two main peaks of OFDM(128) is the steepest, thus it has the highest code loop phase resolution and the highest ranging accuracy. Although the curve between the two main peaks of BOC(10,5) is relatively steep, there are many secondary peaks with similar peaks on both sides of the main peak, which make false lock of the code tracking loop. Therefore, the OFDM(128) modulation has a high ranging accuracy and low probability of false locks, and is a relatively good modulation method.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Power Line Carrier Communication (PLCC)  
Power Line Carrier Communication (PLCC) uses the mains power line, i.e., electricity grid, radio, etc., to transmit and receive data, typically by injecting a high-frequency carrier onto the AC power line and modulating the carrier with the data to be sent. It is a cost-effective communication technology as it can be installed using the present infrastructure, i.e., there is no need for any rewiring or any network modification costs and it is more commonly used in remote locations where cellular coverage is not readily available. Despite its cost-effectiveness, traditional PLCC networks generally have low bandwidths (in the range of 20 kbps) and are prone to noise and data distortion [87], which limits them to low-bandwidth applications, e.g., turning lights and appliances ON and OFF, etc. To overcome these limitations, a variety of PLCC variations such as Insteon (data rate and range of up to 13 kbps and 3000 m, respectively), IEEE 1901--HomePlug AV and HomePlug PV (data rate and range of up to 200 Mbps and 300 m), CE bus (data rate and range of up to 10 kbps and 3000 m) and LonWorks (data rate and range of up to 1.25 Mbps and 3000 m) have also been brought to the market in recent years [88; 89].
## 2 Principle of VMD
### Construction of the Variational Constrained Model  
The number of Intrinsic Mode Function (IMF) components, denoted as \(K\), is predetermined, with the objective of minimizing the bandwidth. The finite bandwidth and central frequency of each IMF are iteratively optimized to achieve the optimal decomposition of the signal [13; 14; 15]. The model is expressed as follows:  
\[\left\{\begin{array}{l}\min\limits_{\{u_{k}\},\{\omega_{k}\}} \left\{\sum\limits_{k=1}^{K}\left\|\partial_{t}\left[\left(\delta(t)+\frac{ \mathrm{j}}{\pi t}\right)\otimes u_{k}(t)\right]\mathrm{e}^{-\mathrm{j}\omega _{k}t}\right\|_{2}^{2}\right\}\\ \mathrm{s.t.}\sum\limits_{k=1}^{K}u_{k}(t)=f(t)\end{array}\right. \tag{1}\]  
Here, \(u_{k}\) represents the \(k\)-th IMF component. \(\omega_{k}\) represents the corresponding central frequency of the IMF component. \(\delta(t)\) is the Dirac delta function. \(\partial_{t}\) represents the partial derivative with respect to time \(t\). \(\otimes\) represents the convolution operator. Additionally, \(f(t)\) represents the original signal and \(j\) represents the imaginary unit.
# The Generalized Skin Depth for Polarized Porous Media Based on the Cole-Cole Model
###### Abstract  
In the field of frequency-domain electromagnetic detection, skin depth is an important parameter for electromagnetic data interpretation and imaging. The classic skin depth formula is calculated based only on conductivity; the induced-polarization effect in real earth is not considered, so the imaging results have obvious errors. To solve these problems, based on plane wave theory and the Cole-Cole conductivity model, a generalized skin depth formula of polarized media is derived in the frequency domain. The accuracy of the generalized skin depth is verified through comparison with the classical skin depth. To show the practicability of this study, the theoretical data with induced polarization (IP) effects are used to explain the generalized skin depth for polarized porous media. The generalized skin depth calculation for a typical porous polarization model is related not only to conductivity, but also to polarization parameters, such as chargeability, characteristic time constant, and frequency dependence. At low-frequency excitation, the generalized skin depth formula can be used to calculate the propagation depth of electromagnetic waves relatively accurately for porous polarized media. This method can be applied to the calculation of electromagnetic wave propagation depths in complex dispersive media. Compared with non-polarized media, in porous polarized media, under low-frequency excitation, the electromagnetic wave propagates deeper, allowing the detection of deeper objects. The data interpretation and imaging of polarized porous media by the generalized skin depth formula have higher accuracy.  
chargeability; Cole-Cole model; frequency dependence; generalized skin depth Article  
The Generalized Skin Depth for Polarized Porous Media Based on the Cole-Cole Model
## 8 Results and Discussion
### Selection of APs with Fuzzy C-Means  
In this experiment, three types of selection APs approaches were also conducted on each cluster at the same time: random, strongest, and maximum spatial variance. Figure 15 shows the CDF of distance error for the selection approaches with q = 20, and AP reduction is 50% (number of APs = 17). The maximum spatial variance exhibits superior performance as compared with the other selected APs approaches. In addition, the performance of maximum spatial variance with 17 APs (50% reduction) is similar to the performance of all APs (34 APs). Table 6 summarizes the accuracy of the proposed C-means-based system with various kinds of selection APs.  
Figure 14: Fuzzy C-means clustering. (**a**) CDF of Error; (**b**) mean error versus number of clusters.  
Figure 16 shows the comparison between C-means and the proposed K-means-based system with maximum spatial variance selection AP. The C-means clustering-based system performance exhibits a slight improvement in accuracy. The reason is the average number of FPs at each cluster in C-means is more than the average number of FPs in K-means.  
Figure 17 shows the compression between the proposed system and traditional technique that considers each SSID as a single AP. The superior performance of the proposed system with 17 SSID  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Selection Technique** & \begin{tabular}{c} **Strongest APs** \\ **50\% Reduction** \\ \end{tabular} & \begin{tabular}{c} **Random APs** \\ **50\% Reduction** \\ \end{tabular} & \
\begin{tabular}{c} **Max. Spatial Var.** \\ **50\% Reduction** \\ \end{tabular} &
\begin{tabular}{c} **Whole APs** \\ **Wide APs** \\ \end{tabular} \\ \hline
**Single SSID** & 1.32 & 1.51 & 1.06 & 1.01 \\ \hline
**Multiple SSIDs** & 1.1 & 1.25 & 0.84 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The distance error of the C-means-based proposed system versus a single SSID.  
Figure 16: The proposed technique performance with the Fuzzy C-mean and K-mean approaches versus number of APs.  
Figure 15: The proposed technique with the Fuzzy C-mean approach.  
selection versus the traditional technique performance is clear. The MSSID-based technique selects 17 SSIDs (17 distinct APs), while the traditional technique selects 17 SSIDs, which means approximately 6 distinct APs (each AP transmits 3 SSIDs). The shortcoming of the traditional technique comes from exploiting the similar SSIDs in the same fingerprinting vector.  
Table 7 summarizes the percentage of calculation reduction versus the number of SSIDs when q = 20. Choosing informative APs leads to a reduction in computational costs as well as the reduction that comes from fusing and the clustering technique.
## 2 The \({}^{3}\)ColStar Satellite Mission
This device is designed to fit within the CubeSat's 1U platform, offering advanced particle tracking capabilities with minimal power consumption and mass. Its main role is to monitor particles generated by solar storms, which is crucial for evaluating potential impacts on essential infrastructure such as power grids, communication networks, and satellites [58]. The MiniPIX TPX3 SPACE, provided by ADVACAM, excels in high-resolution particle characterization and real-time data analysis. These capabilities are central to meeting the mission's scientific objectives. Specifically, the device enables detailed observations of particle behavior and dynamics during space weather events, which is vital for understanding the interactions between solar particles and the Earth's magnetic environment. By delivering precise and timely data, the MiniPIX TPX3 SPACE not only ensures that the mission's objectives are achieved but also enhances the mission's effectiveness in addressing critical scientific questions and practical challenges associated with space weather [57]. In this context, \"fulfilling the objectives\" refers to achieving the specific goals of data collection and analysis, while \"enhancing an objective\" means improving the quality and impact of these efforts, thereby elevating the overall success and utility of the mission. Additionally, the CubeSat will incorporate a secondary payload comprising an IoT device designed for data transmission to a mobile ground station. This facilitates analysis and risk control. Furthermore, the satellite features an in-house developed Fine Sun Sensor, and a proof-of-concept for the research and development of a reaction wheel and magnetorquers [57]. These in-house developments are the evidence of the proof of the collaboration between academia, industry and government. As the structure, IoT and magnetorquers stem from engineering models developed by SpaceTech AESS Uniandes and its start-up NovaOrigin and COCSA-SPACE acceleration scheme.
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
## 3 EN Model of the OTCS
### Traction System and the OTCS in EMU
#### 3.1.2 OTCS Equipment  
The core function of the OTCS equipment is to supervise the speed of EMU in real-time and automatically control the braking system to prevent the train from over-speeding. A brief introduction of the CTCS-3 (Chinese Train Control System Level 3) level OTCS is given as an example below [23; 24].  
The subsystems and devices of the OTCS equipment cooperate with the wayside equipment to achieve real-time control. Figure 6 shows the structure diagram and locations of the OTCS, the main functions and features of the equipment are as follows:  
* The roof of the EMU  
The location and line data in the movement authority (MA) are sent and received by the antenna on the roof of the EMU between the OTCS and the wayside equipment via wireless communication. GSM-R is the most widely used and LTE-R (Long-Term Evolution for Railway) will be applied in the future.  
* The bottom of the EMU  
The speed sensor is used to acquire the speed and the over-speed protection is realized by the speed and distance processing unit (SDU) in OTCS with the acceleration data from the radar. The parts of the MA information from the track circuit and balise are acquired by the track circuit receiver (TCR) and compact antenna unit (CAU) in the balise transmission module (BTM). BTM can also calculate and calibrate the location in real-time.  
* The insides of the EMU  
The driver-machine interface (DMI) is used to show the interaction information to the driver and the driver can execute some instructions through DMI to control the EMU. The train interface unit (TIU) is used to provide an interface between the train and OTCS. The kernel of the OTCS and BTM are installed in the ATP cabinet with power and ground connection with the EMU.  
Figure 6: Structure diagram of the on-board train control system and layout.
## 2 Materials and Methods
### Method  
To predict tool wear accurately, this study employed a systematic approach utilizing the NUAA dataset, which encompasses a wide range of inputs reflecting the complex relationships influencing tool wear. This comprehensive dataset facilitated the implementation of models specifically designed to learn and model nonlinear relationships between the input features and tool wear. The use of these models allowed us to capture intricate dynamics that would otherwise be overlooked, thus enhancing the accuracy of our predictions.  
The analysis proceeded as follows:  
1. **Data Selection and Preprocessing**: The dataset was carefully curated and preprocessed to ensure consistency and relevance. This included cleaning the data, handling missing values, and normalizing features to prepare them for analysis.
2. **Pipeline Implementation**: An AI pipeline consisting of ensemble models (Random Forest and Gradient Boost) and a Long Short-Term Memory network was used to compare the accuracy of tool wear prediction.
3. **Evaluation Metrics**: The models were evaluated using appropriate metrics, such as the mean squared error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 Score), to assess their predictive accuracy and generalization capabilities.
4. **Visual Analysis**: Different plots were generated to visually assess the distribution of predictions against actual values, providing insights into the best model's performance and calibration. Additionally, a correlation matrix was implemented to explore relationships between variables within the dataset. This algorithm analyzed how different input features correlate with tool wear, aiming to uncover patterns that can inform predictive modeling and enhance our understanding of tool wear mechanisms.
## 6 Results and Discussion
Their values in Figure 5b are kept nearby zero and prevent the systems from flipping over. Figure 5: Cont. Figure 5: Motion performance of the proposed designs. (**a**) Tracking performance in three-dimensional geometry; (**b**) Time responses of translations and rotations; (**c**) three-dimensional partial motion of the water-conveying hose; (**d**) Control inputs. Figure 6: _Cont_. Figure 5a,b and Supplementary Video S1 also show that the effects of the dynamics of the water hoses on the motions of the heads are noteworthy. In particular, the tension of the hoses leads to large overshoots in horizontal motions when the systems take off. The tension also results in the fluctuations between the 20 th [s] and 30 th [s] when the systems' heads reach their furthest positions. Moreover, the postures of the water hoses are plotted for every second in Figure 5c. The \(Y^{*}\) axis represents the Y-coordinate being spread out with time. One can see that the water hose of the latest system fluctuates the most. This contributes to the worst performance of this system. The hose in the flow rate control design swings less than this, but more than the hose in the nozzle rotation control system. The flow rate control system, as its name, regulates the flow rates of the nozzle to maneuver, thus causing the flow to flutter inside the hose. This phenomenon can be seen in Figure 5d and results in the hose movement. Then, more disturbances act on the motion of the system head. That explains why its roll and pitch rotations oscillate more than those of the nozzle rotation control system. Similar comparative conclusions are drawn in the second scenario. The first two designs provide adequate performance while the latest design performs worst. The nozzle rotation control system remains the better one. The influence of the firefighting action and the robustness of the control system are also revealed, as shown in Figure 6 and Supplementary Video S2. Whenever a fire extinguishing nozzle changes its operating status, the corresponding system initially deviates from its current position due to the reaction force from this nozzle. The control inputs then quickly compensate it and bring the head to the desired reference. However, the impact of disturbances on motion performances is still considerable. The designed control law can preserve the systems' stability, but cannot effectively reject the disturbances acting on the system.