esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Layers
#### 1.1.1 Stakeholders Layer  
In this layer stakeholders cover a diverse group of entities, ranging from individual end-users, communities, network operators, and aggregators to (local) governmental organizations. Stakeholders interact with smart grids through an interest or concern. Some features of smart grids, such as demand side management (DSM) and exchange of energy with other end users (peer-to-peer trading) involve individual end-user type stakeholders of the type of individual end users, whereas  
Figure 1: Three-layer research model for smart grids environments (adapted from ERA-Net Smart Grids Plus [1,2]).  
other features--such as a high penetration of renewable energy at a local level, as well as electric mobility [2; 4; 5; 6]--involve stakeholders of a more organizational character such as network operators and governmental organizations.
## 5 Mechanism for Lithium-Ion Storage in Si-Based Anode Materials
### Phase Transition Behavior  
In polymer-derived ceramic anode materials, the intercalation and de-intercalation processes of lithium-ions induce significant phase transition behavior. [PERSON] and colleagues [27] suggested that SiOC materials undergo a transition from SiC\({}_{4}\) to SiO\({}_{4}\) mixed bond configurations during lithium-ion intercalation, significantly impacting the material's reversible capacity. Additionally, [PERSON]'s doctoral thesis indicated that during the second cycle of charging in Si/Si-O-C anode materials, the de-intercalation process of nano-silicon and the Si-O-C glass phase overlaps but can be distinguished by the oxidation peaks in the potential range.  
[PERSON] and colleagues pointed out that the carbon phase is the main lithium-ion storage site, with lithium-ions mainly accommodated at the edges and defect sites of graphene sheets, as well as at the interfaces of graphite nanocrystals. This phase transition behavior manifests as significant potential hysteresis phenomena during lithium-ion intercalation and de-intercalation, due to the kinetic and thermodynamic limitations of lithium-ion diffusion at the electrode and electrolyte surface.  
In summary, polymer-derived ceramic anode materials exhibit complex phase transition behavior during lithium-ion storage, which not only affects the material's electrochemical performance but also has significant implications for its cycling stability.
## 3 Methodology
### Time Series 2D Transformation
#### 3.1.1 Determination of Frequency and Period Length  
To accurately capture the intra-cycle variations in time series data, it is essential to utilize expert knowledge to uncover the hidden periodic characteristics within the data. This method employs the fast Fourier transform (FFT) to analyze the frequency domain features of the time series, effectively identifying the underlying periods. FFT is an efficient algorithm used to compute the discrete Fourier transform (DFT), facilitating the conversion of signals from the time domain to the frequency domain. By leveraging the extracted frequency components, it becomes possible to analyze the periodic characteristics of the signal comprehensively.  
Specifically, for a time series data segment of length \(T\), denoted as \(x[n]\in X_{1D}\), where \(n\) is the time index representing the observation time points, the components of the time series at different frequencies can be obtained using the following equation:  
\[X[k]=\sum_{n=0}^{N-1}x[n]e^{-2\pi kn/N} \tag{8}\]  
where \(X[k]\) is the \(k\)-th frequency component in the frequency domain given by the formula, \(N\) is the total number of samples in the signal, \(n\) is the index of the current sample, \(k\) represents the frequency index corresponding to the frequency at time point \(n\), \(e\) is the base of the natural logarithm, and \(i\) is the imaginary unit.  
Once the frequency domain representation is obtained, the main periods of the time series can be identified by finding the frequency components with the highest amplitudes. If \(f\) is one of the indices of the highest amplitude frequencies, the corresponding period \(p\) can be calculated using the following formula:  
\[p=\frac{N}{f} \tag{9}\]  
where \(N\) is the total number of samples in the signal, and \(f\) denotes the index of the selected frequency component.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 5 Implementation
### Access Point Implementation  
As has already been mentioned, this work was focused on implementing a hybrid network supporting VLC, LoRa, and ZigBee technologies. Both ZigBee and LoRa subsystems were constructed based on components-off-the-shelf (COTS) devices, while the VLC interface of the access point was custom-designed.  
Libelium Waspmote kits were selected as main platforms because they contain a microcontroller, integrated sensors, secure digital memory (to store digital contents), and external ZigBee communication modules. The ZigBee module installed on the access point acted as coordinator of a ZigBee subnetwork, and in this work it had a hard-coded MAC address table. The ZigBee modules plugged into the sensor/actuator nodes had to be configured accordingly.  
Low power consumption is a capital aspect that IoT networks must take into account. The technologies selected for this work comply with this requirement, and the remote nodes can be easily powered by batteries or even by solar panels. In addition, remote nodes can be set to sleep mode (or even deep sleep mode) in order to further increase energy saving.  
Besides the initial configuration of the ZigBee coordinator module, the LoRaWAN module connected to the ZigBee coordinator (Figure 1) also requires some configuration. In this case, over the air activation (OTAA) was enabled in the LoRaWAN nodes. This type of easy configuration is similar to BlueTooth pairing, and is an easy, secure (is made at a very short distance prior to deployment), and fast way to establish the network.  
Regarding the protocol-abstraction layer implementation, it has been carried out into the waspnotes' embedded microcontrollers, using the frame formats previously described.
# Characterization of Prints Based on Microscale Image Analysis of Dot Patterns
## 3 Measurement, Modelling and Analysis at the Microscale of Printed Dots
### Computation of Dot Descriptors (Step 6)
* Circular equivalent dots (see example shown in Figure 11) belonging to category ID5 are defined by a roundness value higher than 0.90 (and an eccentricity upper than 0.85). * Elliptic equivalent dots (see example shown in Figure 11) belonging to category ID5 are defined by a roundness value between 0.8 and 0.9 (and an eccentricity between 0.6 and 0.85). * Elongated dots (belonging to category ID6) are defined by an eccentricity lower than 0.6 and a roundness value lower than 0.8 (and an elongation upper than 1.3). * Any other shape which does not satisfy any of the above rules belongs to category ID6. These rules are based on threshold values experimentally set from a statistical analysis of 4467 dots from 18 print samples. These thresholds are only valid for the test samples considered in this study case. These threshold values and rules must be adapted to each study case considered. For example, ID1, ID2, ID3 and ID4 patterns may occur only in few study cases, for example when the viscosity of the ink is low, as shown in images corresponding to samples number 1, 2, 3, 4 and 5 (see Table 1 and Figures 12 and 13). These thresholds are dependent of the quality of the print (noise, contrast, physical dot gain, rate of ink covering, overlap of inks, etc.) and, therefore, of the printing parameters, such as the ink viscosity or the ink temperature. The setting of these descriptors results in a trade-off between the accuracy of the descriptors and their robustness against print noise from one dot to another one. Figure 11: Examples of descriptor values for various patterns. The elliptic bounding box corresponds to the red ellipse. The inscription corresponds to the green ellipse. Figure 12: Example of results for two print samples printed with a cell angle of 60\({}^{\circ}\). (**a**) % of dots belonging to category ID1 (0%), ID2 (92%), ID3(2%), ID4 (less than 1%), ID5 (0%), ID6 (5%, most of them are at right of the figure). There are two missing dots. In total, 92% of dots have a circular shape—see statistical values report in Table 1. (**b**) % of dots belonging to category ID1 (less than 1%), ID2 (0%), ID3 (less than 1%), ID4 (40%), ID5 (49%, all have a circular shape), ID6 (6%, most of them are the top of the figure). There is no missing dot. In total, 94% of dots have a circular shape. From this study we claim that: several dot descriptors must be used to properly evaluate the shape of the dots printed; several dot descriptors must be analyzed conjointly to classify properly the dots in predefined dot patterns; the robustness of these thresholds and rules greatly depends on the print parameters.
## 2 Theoretical Analysis of the Directivity Response
### Standing Waves versus Traveling Waves  
As shown in the previous section, undamped eigenmodes or standing waves tend towards a negative DI. In order to prevent individual sources from being perfectly canceled, it is essential to change the panel's vibration pattern. The presented solution uses edge damping to reduce the proportion of standing waves and to increase the amount of traveling waves.  
Figure 6 visualizes the wave pattern at 5 kHz for a high-pressure laminate (HPL) panel in the cases of low damping (left) and high damping (right). If the panel exhibits low damping, then the traveling waves reflect from the boundaries and form a standing wave pattern. In the case of high damping, the traveling wave is damped while propagating from the excitation point to the boundaries, and the reflected wave has an almost negligible amplitude. As a result, the distribution of the deflection corresponds to a traveling wave with the maximum at the point of excitation.  
Increasing the damping does not directly minimize the acoustic output. [PERSON] [17] calculated the total panel radiance, taking into account both the standing waves and the point force radiation component, for various damping measures. He concluded that damping has no effect below the first eigenmode and above the coincidence frequency. Furthermore, below coincidence, an increase in modal damping also yields an improvement in the radiation efficiency.  
In a real panel, the superposition of standing and traveling waves is present. Figure 7 shows the deflection distribution on the HPL panel for the weak damping case (left) and moderate damping case (right). Increasing the damping increases the proportion of traveling waves relative to standing waves, depending on the degree and spatial distribution of the damping [12]. An amplitude redistribution occurs, and the deflection in the center of the panel is significantly higher than that at all other locations.  
Figure 6: Comparison of (**a**) a high-pressure laminate (HPL) panel with a very low damping loss factor of \(\eta=0.5\%\), which represents the standing wave pattern; and (**b**) a HPL panel with a high damping loss factor of \(\eta=20\%\), which represents the traveling wave pattern.  
Figure 7: HPL panel with different damping loss factors showing an overlay of standing and traveling wave patterns: (**a**) represents a panel with a low damping loss factor; and (**b**) represents a panel with a mid-damping loss factor.
### Related Works, Research Gaps, and Questions
#### 1.4.2 Research Gap  
Despite the substantial advancements in applying RL to portfolio optimization, A significant gap persists in systematically comparing different RL configurations. The existing body of research tends to focus on specific agent architectures or a narrow range of investment instruments, leading to heterogeneity in the methodologies and results.  
Furthermore, existing studies often do not account for the transaction costs and slippage that affect real-world trading. For example, there has been little exploration into the comparative impact of using continuous rebalancing versus less frequent rebalancing on agent performance, particularly in the context of transaction costs and cumulative reward [21; 29]. Additionally, the lack of standardized investment instruments across studies limits the ability to systematically compare the results. While some studies employ indices or ETFs, others focus on more volatile assets like cryptocurrencies, making it challenging to evaluate the consistency of RL approaches in varying asset classes.  
While RL agents like DDPG and SAC have been successfully applied, there is limited research on the influence of varying environmental signals, reward structures, and feature extractors across diverse financial assets [13; 31]. The heterogeneity in data sources, agent configurations, and feature extraction methods makes it difficult to draw generalizable conclusions about the best approaches for portfolio optimization under various market conditions. To date, no study has conducted an exhaustive analysis of how different rebalancing frequencies, investment instruments, feature extractors, and reward functions affect portfolio optimization performance under RL. As a result, there is a gap in understanding the trade-offs involved in choosing one configuration over another in varying market conditions [32].
## 4 Plasma Interaction with Surfaces
### Chemical Reactions with Surfaces  
As discussed in the introduction to this section, chemical reactions between a plasma and a surface can either result in the plasma species moving into the surface or particles from the surface leaving into the plasma [69].  
Plasmas induce chemical reactions on surfaces through interactions with reactive species such as radicals, excited molecules, and ions. These species drive oxidation, reduction, or other surface chemistry processes without relying on physical bombardment. For example, oxygen plasmas generate reactive oxygen species, such as O, O\({}_{2}^{+}\), and O\({}_{3}\), that react with organic contaminants, breaking them down into volatile by products like CO\({}_{2}\) and H\({}_{2}\)O, which are easily removed [23]. Similarly, hydrogen plasmas facilitate the reduction of oxides or the removal of surface hydrocarbons by forming volatile compounds, such as H\({}_{2}\)O or CH\({}_{4}\). The chemical selectivity of plasma-surface interactions depends on the gas composition, energy levels of the reactive species, and the nature of the lead frame, allowing precise control over surface cleaning, functionalization, and modification processes [14].  
These chemical reactions occur primarily through the adsorption and reaction of reactive species generated within the plasma phase. Atomic radicals, such as O, H, and Cl, metastable molecules, and molecular fragments diffuse into the surface and participate in heterogeneous reactions dictated by thermodynamic and kinetic constraints. For instance, in oxygen plasma cleaning, atomic oxygen (O) oxidizes hydrocarbons, to form volatile CO, CO\({}_{2}\), and H\({}_{2}\)O. The reaction rate depends on factors such as the surface energy, temperature, and residence time of the species [14,72].  
Plasma-induced surface chemistry is further influenced by secondary reactions, such as radical recombination and competitive adsorption, which impact the efficiency of material removal or functionalization. By tailoring process parameters such as pressure, gas composition, and power input, plasma systems can be optimized to achieve highly selective and controlled chemical surface modifications [73].  
The next section will review the particular characteristics of each of the gases, which can potentially be used in plasma cleaning processes.
## 3 Experimental Results
### Results of IEEE 69-Bus System
The total power loss decreases to 4.44 kW by reducing 98.03%. The obtained results are compared to the other algorithms and are given in Table 10. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Bus No.** & **CB Size (kVAR)** & **Power Loss (kW)** & **Loss Reduction (\%)** \\ \hline \multirow{3}{*}{CSA [20]} & 9 & 600 & \multirow{3}{*}{147.95} & \multirow{3}{*}{34.21} \\  & 21 & 250 & & \\  & 62 & 1200 & & \\ \hline \multirow{3}{*}{SA [18]} & 58 & 900 & \multirow{3}{*}{155.45} & \multirow{3}{*}{30.91} \\  & 11 & 450 & & \\  & 59 & 600 & & \\ \hline \multirow{3}{*}{GSA [18]} & 26 & 150 & \multirow{3}{*}{145.9} & \multirow{3}{*}{35.16} \\  & 13 & 150 & & \\ \cline{1-1}  & 15 & 1050 & & \\ \hline \multirow{3}{*}{AA [16]} & 11 & 368 & \multirow{3}{*}{145.21} & \multirow{3}{*}{35.46} \\  & 21 & 231 & & \\ \cline{1-1}  & 61 & 1196 & & \\ \hline \multirow{3}{*}{AOA} & 11 & 410 & \multirow{3}{*}{145.096} & \multirow{3}{*}{35.51} \\  & 21 & 233 & & \\ \cline{1-1}  & 61 & 1234 & & \\ \hline \end{tabular}
\end{table} 
Table 8: The results of optimum CBs allocation for IEEE 69-bus system. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Bus No.** & **DG Size (kW)** & **Bus No.** & **CB Size (kVAR)** & **Power Loss (kW)** & **Loss Reduction (\%)** \\ \hline \multirow{3}{*}{WCA [22]} & 17 & 0.5408 & 2 & 1187.9 & \multirow{3}{*}{33.339} & \multirow{3}{*}{85.18} \\  & 61 & 2.0 & 62 & 1237.3 & & \\  & 69 & 1.1592 & 69 & 269.7 & & \\ \hline \multirow{3}{*}{BSA [23]} & 19 & 0.294 & 7 & 450 & \multirow{3}{*}{7.6047} & \multirow{3}{*}{96.61} \\  & 22 & 0.219 & 2 & 300 & \\ \cline{1-1}  & 61 & 1.768 & 3 & 150 & \\ \hline \multirow{3}{*}{SA [39]} & 19 & 0.358 & 11 & 600 & \multirow{3}{*}{4.837} & \multirow{3}{*}{97.85} \\  & 10 & 0.518 & 48 & 600 & & \\ \cline{1-1}  & 60 & 1.6735 & 60 & 1200 & & \\ \hline \multirow{3}{*}{AOA} & 61 & 1.6511 & 61 & 1282 & \multirow{3}{*}{4.6243} & \multirow{3}{*}{97.94} \\ \cline{1-1}  & 11 & 0.521 & 11 & 414 & & \\ \cline{1-1}  & 21 & 0.334 & 21 & 234 & & \\ \hline \end{tabular}
\end{table} 
Table 9: The results of optimum DGs and CBs allocation for IEEE 69-bus system.
## 4 Field Monitoring Setting  
To investigate the influence of shield tunnels of Line 12 passing through the diaphragm wall on Yuanlin Road station of Line 4, the deformation monitoring, diaphragm wall top deformation, horizontal displacement monitoring, and axial force monitoring are arranged to evaluate the deformation of Yuanlin Road station and diaphragm wall. Figure 7 shows the layout of monitoring points in Yuanlin Road station.  
Figure 6: Flowchart of the ground heave calculation.  
Figure 7: Layout of field monitoring points. Red circle represents deformation monitoring point, and red triangle represents diaphragm wall top deformation and horizontal displacement monitoring points.  
The arrangement of deformation monitoring points aimed to monitor the overall deformation of Yuanlin Road station. DINI03 electronic level, Tianbao, China and indium steel ruler are used in deformation monitoring. A total of 10 ground observation piers are installed on different positions of Yuanlin Road station, as denoted DB-1 to DB-10 in Figure 7. DB-1, DB-2, DB-3, DB-4, and DB-5 points are arranged on monitoring section A-A\({}^{\prime}\), which located upward side of left tunnel. In addition, DB-6, DB-7, DB-8, DB-9, and DB-10 are arranged on monitoring section B-B', which located upward side of right tunnel. These points are installed by sleeve burying method, as shown in Figure 8a.  
TCA2003 total station and DINI03 electronic level are employed to monitor the vertical deformation (points ZL-1, ZL-2, ZL-3, and ZL-4) and horizontal displacement (points CX-1, CX-2, CX-3, CX-4, CX-5, and CX-6) of wall top, respectively. Monitoring points are installed by screw-thread steel in the top of diaphragm wall with a length of 100 mm and diameter of 16 mm, as plotted in Figure 8b. The buried depth of screw-thread steel is 100 mm, and master prism is installed on steel top. The layout of monitoring points is shown in Figure 7.
# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 4 Fault Diagnosis in Railway Traction Drives
### Fault Diagnosis in Sensors
Another data-driven approach is presented in [74], where Independent Component Analysis (ICA) in combination with Kullback-Leibler divergence (KLD) is presented to detect incipient faults in motor current sensors. ICA, in contrast to PCA, provides satisfactory results for non-Gaussian measurement signals. Furthermore, this approach does not require the probability density function (PDF) calculation, reducing the computational cost and allowing an online implementation. Recently, a new approach based on PCA and called probability relevant PCA [75], presents a comparison among different PCA-based approaches. It reduces the  
Figure 12: Sliding mode observer (SMO) proposed in [71] for sensor fault diagnosis. Figure 13: (**a**) Real and measured DC-link voltage; (**b**) DC-link voltage sensor fault reconstruction. missing alarm ratio and false alarm rate in case of incipient faults in current and voltage sensors of an electrical drive for a high-speed train.
## 4 Discussion
### Effect of Observation Frequency Bands on Fault Imaging  
The inversion results (Figure 4a,b) indicate that, in Scenario 1, the fault zone location and resolution from regular inversion bands are inferior to those from broadband survey data, mainly due to the absence of low-frequency data. The inversion depth of the F3 fault is limited to 2 km, failing to fully capture the depth of the fault. This limitation is not observed in any other survey designs discussed in this paper. Additionally, the image area of the F2 and F3 faults appears significantly broader than their actual dimensions, suggesting an overestimation of fault zone width. The fault depth resolution is notably better in Scenario 2 compared to Scenario 1, particularly for the deeper imaging of the F3 fault, which closely matches the broadband survey data. However, the width of the deeper imaging of the F1 and F3 faults is significantly broader than in the geological model, indicating poor resolution of narrow structures. Among all the survey designs, the F2 fault exhibits the poorest imaging quality, with inversion depths significantly diverging from those in the geological model.  
Figure 4: Inversion resistivity profile at different observation frequency bands. (**a**) The observation frequency band is 0.01–100 Hz. (**b**) The observation frequency band is 0.001–1000 Hz.
## 5 Numerical Experiments
### Base Instance Sets Generation and Description
#### 5.1.2 Demand between Warehouses and Delivery Stations  
Demands of the customer nodes are given in the 2E-CVRP, which are combined in origin (warehouses)-destination (delivery stations) pairs for our problem. In this subsection, three rules corresponding to different scenarios are provided to generate demand between warehouses and delivery stations. Random Nodes rule (RN), the demand of each delivery station is fully supplied by only one warehouse and the warehouse is chosen randomly. Equal Proportion rule (EP), the demand of each delivery station is supplied by all warehouses with equal proportions. Random Proportion rule (RP), the demand of each delivery station is supplied by all warehouses with random proportions.