esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Studies
### Locomotion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terrain Type** & **Recommended** & **Subtype/Example** & **Justification** \\ \hline Flat Indoor Surfaces & Wheeled Locomotion & Differential Drive, Ackermann & Efficient, fast, low-energy use on smooth, predictable surfaces \\ \hline Confined/ & Omni-Directional & Mecanum Wheels, Swedish & Enhanced maneuverability, zero \\ Cluttered Areas & Wheels & Wheels & turning radius \\ \hline Sandy/ & & & \\ Uneven Outdoor & Tracked Locomotion & Flexible Belt, Flipper Track & Greater ground contact, better traction, \\ \hline Rocky/ & Hybrid (Wheel-Leg, & Rocker-Bogie, Transformable & Combines mobility and terrain \\ Rugged Terrain & Tracked-Leg) & Track-to-Leg Mechanism & adaptability \\ \hline Pipeline/ & Magnetic Wheeled, & Multi-link Magnetic Wheels & Adaptability to curved/narrow environments, wall climbing capability \\ \hline Stairs/ & Articulated or & Triangular Track with & Designed to overcome vertical height \\ Elevations & Triangular Tracked & Stair-Climbing Kinematics & changes and obstacles \\ \hline Urban Debris/ & Legged or Wheel-Leg & Biped, Hexapod, Scorpio (rolling & Ability to navigate unpredictable, \\ Disaster & Hybrid & + crawling + climbing) & obstructed, or destroyed environments \\ \hline High-Slip/ & Skid-Steering with & & Tolerant to wheel slip, simple \\ Slippery & Sensor Feedback & 4-Wheel Skid-Steer Robot & mechanism, suited with proper slip \\  & & & control logic \\ \hline Cleanroom/ & Wheeled or & Synchro-Drive with Sterile Design & Precise control, low noise, clean-surface \\ Laboratories & Synchro-Drive & Synchro-Drive with Sterile Design & compatible \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complete robot locomotion system suited for different terrain types. Figure 7: Example of different drive-type locomotion. (**A**) Slithering robot, sourced from [36]. (**B**) Crawling, inching/peristaltic robots (Sub-figures (**a**–**I**) represent various shapes and applications in soft robotics), sourced from [37]. (**C**) Rolling, crawling robot, sourced from [38]. (**D**) Bipedal, sourced from [39]. (**E**) Hybrid (Wheel-Legged), sourced from [40]. (**F**) Quadruped, sourced from [41].
# Low-Frequency Sound Absorption in Air Pipelines and Water-Filled Pipelines Based on Metamaterials
## 2 Design Paradigm and Theoretical Method
### Structure of the Low-Frequency Composite Absorption Metamaterials for Water-Filled Pipeline  
When dealing with sound absorption in water, there is a challenge with impedance mismatch between the incident sound and the HR pairs. Because of the huge modulus difference between water and air, the weak monopole resonance of chambers and low viscoelastic coefficients contribute to low energy dissipation efficiency. Both impedance matching and dissipation capacity indicate that directly applying sound-absorbing metamaterials designed for air pipelines to underwater environments is impractical. To overcome this issue, we introduce the viscoelastic rubber layer into the system to provide additional impedance. Figure 3 shows a cutaway view of the composite metamaterial formed by HRs with rubber coating. The rubber is bonded to the inner bottom surface of the cavity with the thickness of \(l_{\text{r}}\). The neck connects the sound field inside and outside of the cavity. Due to the abrupt change in cross-section, the cylinder of water and visco-thermal effects provide acoustic mass and acoustic resistance for HR. Additionally, the coated rubber layer and cavity contribute to the acoustic resistance and partial capacitance.  
Figure 3: Schematic diagram of the composite absorption metamaterials for water-filled pipelines.
## 1 Introduction
### Paper Organization  
The paper structure progresses as follows: Section 2 provides a detailed state-of-the-art comparison between DRCF and microstrip filter technologies, thoroughly analyzing their respective advantages and drawbacks for space applications. Section 3 describes the characterization algorithms developed for input-output coupling, inter-resonator coupling optimization, mechanical adjustment procedures, and transmission/equalization zeros' analysis. Building on these foundations, Section 4 presents the complete design methodology for two filter topologies (4-2-0 and 4-0-2), covering theoretical principles, simulation approaches, and performance trade-offs. Section 5 then documents the full manufacturing process and experimental validation, including measurement techniques, simulation-to-hardware correlation, and performance verification against specifications. Finally, Section 6 concludes with key findings and recommendations for space-grade filter design.
## 3 Results and Discussion
### Sample PM Capability  
The performance in PM trapping of SUNSPACE and SUNSPACE synthesized by using BA was evaluated in previous work [20]. In this paper, these values were compared with samples 3, 4, 5 and 6.  
To evaluate the adsorption capacity of these porous materials, three samples of each material were exposed to a TiO\({}_{2}\) suspension with size dimension of 300 nm, 3 g/L. The suspension was prepared according to [20] and then sprayed using Grimm aerosol nanoparticle generator. The same experimental set up of [20] was performed. Samples were exposed for 4 min, after 1 min of stabilization.  
To evaluate the amount of trapped TiO\({}_{2}\) by each specimen, pristine and exposed samples were compared. They were scraped to obtain 0.25 g of powder from each sample for chemical analysis. For this aim, samples were digested according to the procedure reported in the experimental section. The Ti concentration (mg/L) was evaluated by TXRF analysis and then converted to TiO\({}_{2}\) expressed in mg/kg.  
Figure 6 shows the comparison between pristine and exposed samples synthetized by BA (Figure 6a) (sample 3 and 4) and SF (Figure 6b) (sample 5 and 6). The results show that pristine materials always present a titania amount lower than that of corresponding materials, which were subjected to titania flux.  
Figure 6: Amount of TiO\({}_{2}\) concentration on SUNSPACE samples synthetized by (**a**) bottom ash (samples 3 and 4) and (**b**) silica fume (samples 5 and 6). Pristine samples were not in contact with titania nanoparticles. The other samples were exposed to TiO\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Accessible** & **Average Pore** & **Median Pore** & **Modal Pore** \\  & **Porosity** & **Radius (\(\upmu\)m)** & **Radius (\(\upmu\)m)** & **Radius (\(\upmu\)m)** \\ \hline \(1\) & 41.1 & 0.0356 & 0.0777 & 0.0849 \\ \(2\) & 63.2 & 0.0282 & 1.7808 & 1.7938 \\ \(3\) & 3.4 & 0.0141 & 9.6896 & 5.6504 \\ \(4\) & 58.1 & 0.0387 & 0.6153 & 0.6127 \\ \(5\) & 51.6 & 0.0418 & 0.0954 & 0.1058 \\ \(6\) & 49.1 & 0.0455 & 0.0911 & 0.1063 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results from mercury intrusion porosimetry.  
It is also apparent that the pristine samples synthetized with BA are characterized by a higher concentration of titania than those generated with SF. Indeed, as reported in the literature, BA is characterized by titania (about 1%wt) [27; 28; 44; 45].  
Using both using BA and SF, samples 4 and 6 (characterized by a lower hydrogen peroxide concentration) obtained good results despite their different pore structure. Sample 4 is characterized by a bimodal pore size distribution. The presence of interconnected pores with sizes more than 1 \(\upmu\)m suggests an open structure that favors the uptake of PM. Sample 6 shows a uniform structure, with pores of a lower dimension (about 0.1 \(\upmu\)m). The graph shows that sample 4 seems to have the best performance. High concentrations of TiO\({}_{2}\) (about 300 mg/kg) penetrated into its porous surface.
## 3 System Architecture
### Overall Architecture  
The architecture is illustrated in the Figure 1 below. The architecture is built upon an evolutionary type of ICN that ensures compatibility with the current TCP/IP network, as outlined in Section 2.2. The architecture comprises several key entities, including users, network nodes, computing resource management gateway nodes, computing clusters, and the ICN name resolution system. Each entity within the architecture is assigned a unique identifier (ID) and a corresponding network address (NA, such as an IP address), typically in a one-to-many relationship. The ICN name resolution system is responsible for maintaining the mapping between these identifiers and network addresses.  
Figure 1: System architecture overview.  
Within this architecture, network nodes can be divided into three layers based on the scale of computing clusters deployment and the actual computing application scenario requirements. This hierarchical division is primarily driven by the need to address the varying delay requirements of different service requests. Low-delay services, such as virtual reality, vehicular networks, and financial transactions, require stringent delay guarantees and are assigned to edge computing clusters located close to users. Medium-delay services, such as smart city, government applications, and video on demand, with slightly relaxed delay constraints but higher computational demands, are processed by metro computing clusters. Finally, high-delay services, like data backup, AI training, and scientific computing, which can tolerate longer delays and demand significant computational resources, are handled by distant, large-scale data centers or cloud infrastructures to avoid resource competition.  
Within each layer, nodes are subdivided into domains (such as E1, M1, L1) based on transmission delay to ensure timely processing of services. Each domain selects the upper-layer node with the lowest average delay as the main node. The primary reason for this approach is that clusters with sufficient computing resources but poor network conditions are irrelevant for delay-sensitive services. This partition filters out unnecessary cluster information, conserves network resources, and reduces the controller's load when scheduling tasks. The following is a detailed description of each entity.
## 5 Case Study: Cryptographic Hardware Coprocessor for the Secure Subsystem of Rhea GPP
### Security Services
All the tests were successful, satisfying the security requirements defined by the NIST and the Bundesamt fur Sicherheheit in der Informationstechnik (BSI) [19]. To evaluate the performance of the Crypto-Tile and its cryptographic engines, we also implemented a demoboard on a VCU128 board by Xilinx/AMD. The implemented system was aimed at emulating a Security Domain (Figure 6) by integrating the Crypto-Tile together with a 64-bit RISC-V processor CVA6 [87] as Secure MCU, a DDR controller to exploit the DDR4 memory onboard and implement the SRAM, and an AXI4 bus as system bus. The results showed that the Crypto-Tile is able to accelerate the security services and functions up to 400 times [14].
## 4 Simulation Results and Discussion
### Frequency Stability Analysis with/without EVPC
The results reveal that the MG operator's profit (F\({}_{1}\)) is 324.2 \(\mathcal{E}\), which is 30.1 \(\mathcal{E}\) more than the case without EVPC. Moreover, MG emission is decreased about 205.849 kg with the proposed strategy. Figure 17: Frequency variation at the maximum load. Figure 18: Frequency variation (**a**) at 8:00 and (**b**) at 18:00. Furthermore, since the frequency deviations in the case with EVPC remain in the allowable range, the cost corresponding with load frequency control is zero. However, in the case without EVPC, cost of frequency control (F\({}_{3}\)) raises up to 11.7 \(\epsilon\). Finally, the objective function that is equal to the total MG operator's profits with and without the implementation of EVPC are obtained as 282.1 and 237.6 \(\epsilon\), respectively. Thus, the assessment of the results shows that, with the application of EVPC, the total MG operator's profit is increased considerably.
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Learning Rules of the Multi-Armed-Bandit Algorithms
#### 4.2.4 Modified Tug-of-War  
As the channel quality changes dynamically owing to the emergence of mobile nodes or the deployment of new networks, a forgetting parameter \(\alpha\)\((0<\alpha\leq 1)\) is introduced to reduce the influence of past experiences. Subsequently, Equation (5) is modified to (16). \(X_{k}(t)\) is calculated and selects the channel \(k^{*}\) is selected using Equation (15), after it wakes up at the next cycle.  
\[Q_{k}(t)=\alpha Q_{k}(t-1)+\Delta Q_{k}(t). \tag{16}\]  
The smaller \(\alpha\) is, the lesser the effect of past experiences on the current learning state. Algorithm 3 describes the learning process for TOW dynamics and MTOW-based channel selection.  
```
1:Initialize \(Q_{k}(0),R_{k}(0),N_{k}(0)\),
2:while wake time \(t=1,2,\dots\), is not expired do
3: Select channel \(k^{*}=\text{arg max}X_{k}(t)(\forall k\in K)\)
4: Transmit the data frame
5:if the data frame is transmitted and the corresponding ACK frame is received then
6: Data transmission succeed
7: Update \(R_{k}(t)+1\)
8: Set \(\Delta Q_{k^{*}}(t)=+1\)
9:else
10: Data transmission failed
11: Update \(p_{k}(t)\) given by Equation (1)
12: Update \(\omega(t)\) given by Equation (14)
13: Set \(\Delta Q_{k^{*}}(t)=-\omega(t)\)
14:endif
15: Update \(Q_{k}(t)\) given by Equation (6) in TOW or Equation (16) in MTOW
16: Update \(X_{k}(t+1)\) given by Equation (8)
17:\(t=t+1\)
18:endwhile
```  
**Algorithm 3** TOW dynamics or MTOW-based channel selection algorithm.
## 6 Analysis and Results
### EMCR Evaluation
#### 6.2.2 EMCR over EHMR and CLWPR
The delay of EMCR with 2 s cache time is 5.8% higher, the EMCR with 5 s cache time is 11.8% is higher, and the EMCR with 10 s cache time is 17.2% higher than EMHR over a range of vehicle density.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 3 Materials and Methods
### Mutual Information  
From a perspective grounded in information theory, mutual information (MI) offers a distinct approach to understanding correlations by measuring how much information the knowledge of one variable provides about another. In other words, mutual information quantifies the reduction of uncertainty about one variable given information about the other variable (Equation (9)). The minimum value of MI is 0, indicating that two variables are completely independent, or that the knowledge of one variable provides no information about the other. It does not have a fixed upper limit; the maximum value can be as large as allowed by the entropy of each of the variables.  
\[I(\mathbf{x};\mathbf{y})=\sum_{i=1}^{n}\sum_{j=1}^{m}P(x_{i},y_{j})\log\left( \frac{P(x_{i},y_{j})}{P(x_{i})P(y_{j})}\right) \tag{9}\]  
where \(P(x_{i},y_{j})\) is the joint probability that \(\mathbf{x}\) takes the value \(x_{i}\) and \(\mathbf{y}\) takes the value \(y_{j}\), while \(P(x_{i})\) and \(P(y_{j})\) are the marginal probabilities of \(\mathbf{x}\) and \(\mathbf{y}\), respectively. Mutual information is generally calculated over discrete probability distributions. Therefore, if the numerical variables are continuous, they may need to be discretized into intervals or categories. Sincediscretization can affect the accuracy of the calculation and lead to the loss of important information, in this work, we will use the scikit-learn implementation based on the work of [PERSON] (2014) [31], which avoids discretization by using an entropy estimation based on K-nearest neighbor distances.
## 2 Grid Forming and Small-Signal Modeling
### Network Frequency Perturbation Visualization Method
For example, increasing the damping coefficient (corresponding to lower \(m_{p}\) values) enhances system damping, as evidenced  
Figure 4: NFP Bode and different regions for a system with the same inertia constant (H = 2.5 s) and different droop values with (\(L_{g}\) = 0.1 pu). by the phase curve moving closer to 180\({}^{\circ}\). The inertial region can be identified using the inertia asymptote, given by \(\Delta P/\Delta\omega_{g}=\tau_{p}/m_{p}\)= 2H, Equation (3). High-Frequency damping or synchronizing power:  
At higher frequencies, the NFP plot reveals the device's response to rapid changes in grid frequency or phase-angle disturbances. As the modulation frequency increases, the amplitude response steadily decreases. For ideal synchronous generators (SGs) or synchronous condensers (SCs), the phase response drops and eventually stabilizes around 90\({}^{\circ}\) near the fundamental frequency. This phase shift reflects a reduction in the damping power component and an increase in the synchronizing power component. The latter is characterized by active power variations that are in phase with changes in the load angle--corresponding to a 90\({}^{\circ}\) phase lead relative to frequency variations. This behavior ensures a positive synchronizing power contribution and contributes to stable dynamic performance. For devices designed to exhibit grid-forming (GFM) behavior, it is essential to replicate these distinctive SG characteristics. Moreover, any additional control loops must preserve, rather than alter, this inherent dynamic behavior.
## 4 Experiment
From the perspective of the real-time SNR of the SOTM system, it can be concluded that when the SOTM antenna remains connected to the satellite despite the high maneuvering state of the carrier, the proposed scheme in this paper can achieve better communication quality than the conventional scheme, with relatively high SNR and small fluctuations; this indicates that the multi-beam fast-tracking scheme proposed in this paper outperforms the conventional scheme. From the results of Experiment 1, it could be concluded that when the carrier is in a high maneuvering moving state, and the SOTM system does not experience communication interruption, the multi-beam tracking scheme designed in this paper is superior to the conventional method. As for the real-time AGC level value, the results of the two schemes are close because the AGC level data retains two decimal places, and the real-time AGC level changes less when the SOTM system communication is normal. Figure 17: AGC of the conventional method in Experiment 1. Figure 20: Signal quality of the multi-beam method in Experiment 1. Figure 19: Signal quality of the conventional method in Experiment 1. Figure 20: SNR of the multi-beam method in Experiment 1. Figure 19: Signal quality of the conventional method in Experiment 1. Experiment 2: SOTM antenna experiences satellite loss  
In this experiment, physical obstruction was given to the SOTM antenna during its operation, causing the SOTM antenna to experience satellite loss. It should be noted that this physical obstruction was artificially given, and the time of the obstruction could not be accurately determined. Figure 22 illustrates the real-time AGC level value of the SOTM system when using the multi-beam fast-tracking scheme proposed in this paper, while Figure 23 illustrates the real-time AGC level value of the SOTM system when adopting the conventional scheme. It could be seen that the proposed method in this paper can reacquire the communication satellite in a significantly shorter time than the conventional method after the SOTM antenna experiences satellite loss; this indicates that the satellite acquisition capability of the method proposed in this paper is significantly better than that of the conventional method. Figures 24 and 25 show the signal quality when using the multi-beam fast tracking scheme designed in this paper and the traditional scheme, respectively, while Figures 26 and 27 show the SNR when using the multi-beam fast tracking scheme designed in this paper and the traditional scheme, respectively. Similarly, it can be concluded that the satellite acquisition  
Figure 23: AGC of the conventional method in Experiment 2. Figure 22: AGC of the multi-beam method in Experiment 2. From the experimental results above, it can be seen that when the SOTM antenna remains connected to the satellite despite the high maneuvering state of the carrier, the proposed scheme in this paper can achieve better communication quality than the conventional scheme. When the SOTM experiences satellite loss and communication interruption, the proposed scheme in this paper can reacquire the satellite faster than the conventional scheme.
# Performance Analysis of NR-DCSK Based Copper Cable Model for G.fast Communication
## 4 System Parameters Justification
### Noise Reduction Parameters  
The first NR-DCSK system, described in [27], was intended to reduce the noise variance in the received sequence. The design requires regulating two critical parameters: the spreading factor \(\beta\) and the reduction factor \(P\). The value of \(\beta\) is an integer number, in this study, we utilize examples of 200 and 600. The \(P\) values are chosen based on \(\beta\) and range from 1 to the value of \(\beta/P\), which is an integer bigger than 1. For example, if \(\beta\) = 200, the appropriate values for \(P\) are 1, 2, 5, and 20, which correspond to \(R\) values of 200, 100, 40, and 10, respectively. When \(P=1\), the system reverts to the traditional DCSK system, resulting in no reduction in noise variance.
# Abnormal Ship Behavior Detection Based on AIS Data
###### Abstract  
With the development of navigation globalization and ship dehumanization, the contradiction between the increasing demand for ship behavior supervision and limited traffic service resources is obvious, and the frequent occurrence of accidents at sea is a problem. The monitoring of abnormal ship behavior is an important link in maritime transportation. With the popularization of the automatic identification system and increasing research in the maritime field, the AIS is widely used in the management of ship static information and the real-time sharing of dynamic information. The generated moving ship trajectory data provide a new opportunity for research into abnormal ship behavior and its detection. In light of the current situation of abnormal ship behavior research, we detected abnormal ship behavior from the point of view of spatial information and thematic information based on moving ship trajectory data. Therefore, this study first modeled the cognition of abnormal ship behavior. Then, based on the cognition of group ship behavior rules, we used a method based on graph structure learning to mine maritime routes from the perspective of ship spatial information. Next, we used [PERSON]'s criterion to detect the anomalous behavior of ships in space. Then, based on the isolation forest algorithm, we detected and described the abnormal behavior shown by ship thematic information. The experimental results show that the framework proposed in this paper can effectively detect the abnormal behavior of ships.  
AIS data; maritime trajectory; trajectory data mining; anomaly detection Article Abnormal Ship Behavior Detection Based on AIS Data  
[PERSON], [PERSON], [PERSON], M. Abnormal Ship Behavior Detection Based on AIS Data. _Appl. Sci._**2022**, _12_, 4635. [[https://doi.org/10.3390/app12094635](https://doi.org/10.3390/app12094635)]([https://doi.org/10.3390/app12094635](https://doi.org/10.3390/app12094635))