esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Challenges in Achieving Cyber Resilience for Space Infrastructure
#### 1.2.2 SWaP Constraints and Legacy Systems  
Space assets are highly constrained by size, weight, and power (SWaP) limitations, which impose significant design trade-offs [27; 28]. For instance, terrestrial systems often incorporate robust hardware security modules, redundant communication channels, and other resource-intensive security mechanisms [29]. In contrast, the physical constraints of space systems necessitate lighter, lower-power solutions that may not provide the same level of resilience [30]. As a result, space systems often employ legacy technology that is more susceptible to modern cyber threats, as these systems cannot be easily upgraded or replaced [1]. This persistence of legacy systems, coupled with the inability to modify or upgrade hardware after launch, significantly increases the difficulty of maintaining cyber resilience over time. Unlike terrestrial systems, where software patches and hardware refresh cycles are frequent, space systems must rely on pre-launch resilience measures to mitigate future threats [30].
## Case Study
### A Practical Distribution System with 173 Nodes
#### 4.1.5 Results, Comparisons and Analysis of the 173 Nodes System  
The proposed method EWOSM is compared with conventional reactive power optimization method to verify the validity and effectiveness. Genetic Algorithm (GA) and SQP method are chosen as the representatives of the existing artificial intelligence algorithms and traditional mathematical methods respectively to compare with EWOSM. The optimal results of the three methods under three different load levels are shown in Table 4. GA method based on literature [37; 38] is used as the solution of conventional reactive power optimization, and the specific parameters are as follows: crossover rate is 0.8, mutation rate is 0.2, population size is 20, the maximum number of iterations is 100, and the algorithm will stop with no evolution for more than 50 continuous generations. And SQP method is based on literature [36].  
It can be seen from Table 4 that the differences of network loss of GA, SQP and EWOSM under the three different load levels are less than 1%, and the differences of minimum module-eigenvalue are less than 1.5%, which proves the validity and effectiveness of the proposed EWOSM. Besides, the three methods are also applied to several different scales of test systems, including the standard distribution system of IEEE 33 nodes [39], PG & E 69 nodes [40] and a practical distribution system with 292 nodes, and the results are listed in Appendix A (Table A1) due to length of the article, which can further proof the effectiveness of EWOSM.
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 4 Simulation Analysis and Results
### FOP in-Plane Displacements: Rotation and Translation
#### 4.2.1 Rotation  
We set the image sensor pixel spacing to 10 \(\upmu\)m. For the two FOP schemes obtained in Section 4.1, we rotate the FOP counterclockwise from 0 to \(\pi\) with the rotation step value of \(\pi/12\). We set the FOP rotation angle to \(\theta\), then when the FOP rotates counterclockwise by \(\theta\), the coordinates of the fiber are expressed as:  
\[\begin{split} x^{\prime}&=x_{0}+Rx_{f}\cos\theta -\sqrt{3}Ry_{f}\sin\theta\\ y^{\prime}&=y_{0}+Rx_{f}\sin\theta+\sqrt{3}Ry_{f} \cos\theta\end{split} \tag{9}\]  
where \(x^{\prime}\), \(y^{\prime}\), \(x_{f}\), \(y_{f}\), \(x_{0}\) and \(y_{0}\) are known by Formula (5). Every time the FOP was rotated by one step, we performed one PTOIS simulation in MATLAB. We established the relationship between the CLESD and the rotation angle. The simulation results were shown in Figure 6.  
Combined the above two FOP schemes, we can get a comprehensive analysis. We observed that FOP rotation periods of two FOP schemes were both \(\pi/3\). No matter which FOP scheme we chose, when FOP was rotated counterclockwise by 0, \(\pi/3\), \(2\pi/3\), \(\pi\), the monocentric imager had a highest CLA, at this time, the arrangement of FOP was shown in Figure 7a. When FOP was rotated counterclockwise by \(\pi/6\), \(\pi/2,5\pi/6\), the monocentric imager had a lowest CLA, at this time, the arrangement of FOP was shown in Figure 7b. When we chose FOP scheme one, the lowest CLA that the monocentric imager can withstand is 0.035 pixel. When we chose FOP scheme two, the lowest CLA that the monocentric imager can withstand is 0.041 pixel. In conclusion, no matter which FOP scheme is selected, the loss of CLA generated by FOP rotation is within 0.004 pixels, and when the FOP is arranged as shown in Figure 7a, the monocentric imager has the higher CLA.  
Figure 6: The FOP was rotated \(\pi\) counterclockwise with the relative initial position as the center. The connection between the rotation angle \(\theta\) and the CLESD of the two FOP schemes was established separately. Scheme one: \(d_{f}=\)4 \(\upmu\)m, \(K=0.7\); scheme two: \(d_{f}=6\) \(\upmu\)m, \(K=0.7\).
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 2 Experimental Programs
### Samples Preparation
#### 2.3.2 Typical Load-Displacement Curves
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Anchorage Length (Mm)** & **300** & **200** & **150** \\ \hline
1:0 & 14.0 & 8.1 & 4.7 \\
1:1 & 20.4 & 9.8 & 12.0 \\
1:3 & 48.9 & 41.9 & 21.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Failure loads of samples (kN). Figure 7: Typical load-displacement curve of splitting failure (sample 2-3). and shear failure at the bolt-grout interface and bond strength is enhanced significantly, which is the reason why the bond strength of thread steel bolt is higher. A large number of studies show that the radial confining pressure increases with the increase of applied load because of the lateral deformation resistance provided by the surrounding rock stiffness [1; 14]. The soft rock with low radial stiffness will not produce enough radial constraint, which will lead to the development of radial crack. On the contrary, the hard rock will produce relatively high radial stiffness, thus inhibiting the development of radial crack, which will significantly improve the bearing capacity of anchorage structure [7]. Based on the above analysis, it is not difficult to find that the radial stress has a significant impact on the bearing capacity of anchorage structure, and its improvement on the bearing capacity of anchorage structure should not be neglected in practical engineering applications.
## 2 Proposed Method
### Label Classifier Based on Local Minimum Mean Discrepancy
#### 2.3.3 Pseudo-Label Loss Function  
The pseudo-label loss function is utilized during the process of calculating the weights \(w_{j}^{Tc}\) for samples in the target domain. It employs a label classifier \(G_{y}\), which has been trained on samples from the source domain, to forecast the labels of the target domain samples \(x_{j}^{t}\)[23]. However, this approach unavoidably introduces additional losses. To address this issue, pseudo-label learning is implemented. The definition of the pseudo-label loss function is detailed below [24]:  
\[L_{\text{pseudo}}=-\frac{1}{M}\sum_{j=1}^{M}\sum_{m=1}^{C}p(\hat{y}_{j}^{t}=m \mid x_{j}^{t})\log p(\hat{y}_{j}^{t}=m\mid x_{j}^{t}) \tag{4}\]Finally, by combining Equations (1), (2) and (4), the loss function of the label classifier in RASAN-W proposed in this article is obtained:  
\[L_{y}=L_{\text{cls}}+\lambda L_{lmnd}+\mu L_{pseudo} \tag{5}\]  
where, \(\lambda\) and \(\mu\) are the trade-off parameters of various losses.
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 3 Controller Design  
In this section, a robust adaptive controller based on the desired trajectory is designed to meet the control tasks of the universal six-axis manipulator. In summary, the controller consists of four parts. The first is the nominal dynamics feedforward term calculated by the desired trajectory. The second is the robust term to correct the perturbation of the inertial parameters of the links. Then, the adaptive term is used to learn the friction coefficients of the joints online. Lastly, a compensation term is designed to satisfy the Lyapunov stability of the system. The control scheme is shown in Figure 1.
###### Abstract  
Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts' confidence in the results of the information system.  
compressed state machine learning, multi-agent approach, short-term power consumption forecast; SHapley Additive exPlanation Article  
Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 3 Energy Efficiency Analyses of Radio-and-Fiber Networks
### Power Saving Techniques in the Wireless Domain of FiWi Networks
The approach utilizes the contextual degrees of traffic interaction in the background for ML classifier applications. The output traffic is then classified to optimize context-aware listen interval PSMs. The study evaluates the performance of several ML classifiers using a real-world dataset of several smartphone applications that enable the reflection of various types of network interactions and behaviors. In addition to the aforementioned PS methods in the wireless domain of the FiWi network, methods for powering the locations of wireless network devices (AP and BS) using renewable energy sources have also been proposed [42; 43]. These methods represent an effective way to save energy and reduce OPEX for network owners [42]. Due to the lack of ability for BSs to access the electricity grid, having limited access to daily power supply or simply having large expenses for consumed electricity, mobile network operators have a significant interest in using renewable energy sources for powering remote BS sites. Therefore, using renewable energy sources can reduce OPEX and improve the energy efficiency of the wireless domain in FiWi networks. Furthermore, the authors in ref. [44] proposed an algorithm that aims to conserve energy and maintain connectivity in mobile ad-hoc networks (MANETs). The algorithm aims to conserve energy in nodes that have limited battery life and maintain connectivity between nodes, which is crucial for route discovery. The proposed algorithm is a generic algorithm that can be applied in various situations and operates at layer 2 of the Open Systems Interconnection (OSI) model, making it independent of routing algorithms. The simulation results showed that the proposed algorithm significantly reduces energy consumption while maintaining good connectivity over time.
## 4 Results and Discussion
### Spatial Analysis
From the above, it can be seen that the choice of a site for the establishment of a new operation with well-determined requirements (quality and/or geotechnical class) for specific use (industrial exploitation, civil engineering, or aesthetic works...), has become very easy thanks to this geotechnical mapping. The latter requires a combination of field data, laboratory analysis, and spatial analysis. On the other hand, these results remain directly related to the characterization data applied to the various samples of the study area and must be validated by other geological data.
## 2 Related Work
### Autoencoder  
An autoencoder [31] is a neural network that compares input and output as shown in Figure 3. It makes it a difficult neural network by limiting the network in many ways. For example, the number of neurons in the hidden layer is smaller than that of the input layer, thereby compressing (reducing) data. There are various types of autoencoders, and there is also a model that learns a network to restore the original input after adding noise to the input data. Because the under complete autoencoder cannot copy the input into the output as it is by a low-dimensional hidden layer, the output must learn to include some details from the input. Through this learning, the under complete autoencoder allows to learn the most important characteristics from input data. These constraints prevent the autoencoder from simply copying the input directly into the output and allow it to learn how to efficiently regenerate data.  
The encoder, also referred to as a cognitive network, converts an input into an internal expression. The decoder, also referred to as a generative network, converts an internal expression into an output. The encoding operation maps the input data \(x_{i}\) to and the decoding operation reconstructs the input at the latent layer \(z\). It may appear impractical to simply reconstruct input data, however, practical useful characteristics can be obtained by applying some restrictions to autoencoders to obtain \(x_{i}^{\prime}\) so that input data can only be approximate.  
In the encoding step, a hidden layer representation of the mapping \(q_{\theta}(x)\) for a given input data set \(x\) can be obtained, and the details are as follows.  
\[z=q_{\theta}(x)=S(Wx+b) \tag{6}\]  
In the decoding step, the output data \(x_{i}^{\prime}\) can be reconstructed using the \(g_{\theta}(z)\) function for the hidden layer, and the specific expression is as follows:  
\[x^{\prime}=g_{\theta}(z)=S(W^{\prime}z+b^{\prime}) \tag{7}\]  
Here, S is the activation function, \(\theta=W,b\) is the parameter set of the encoder, and \(\theta^{\prime}=W^{\prime},b^{\prime}\) is the parameter set of the decoder. In addition, autoencoder completes learning by minimizing the reconstruction error \(L(z,g_{\theta}(x))\) between \(x_{i}\) and \(x_{i}^{\prime}\).
# Effect of Injecting Epoxy Resin Adhesive into Cement Mortar on Tile Adhesion Performance
## 3 Results and Discussion
### Mechanical Properties  
Figure 9 shows the flexural and compressive strengths with different types of tile mortar. The average flexural strength of the ERM was 4.08 MPa. This indicated that the ERM demonstrated the highest flexural and compressive strengths compared to those of the widely used DM and AM. As shown in Figure 10, the epoxy resin adhesive improved the flexural tensile performance and resistance to lateral loads by penetrating and curing inside the pores of the mortar, thus significantly increasing the flexural and compressive strengths.  
Figure 8: Permeability of epoxy resin based on the solvent mixing ratio.  
Figure 9: (**a**) Flexural and (**b**) compressive strength based on the mortar type.  
Figure 10: Penetration status of epoxy resin inside the mortar: (**a**) 60 \(\times\), (**b**) 150 \(\times\), and (**c**) 20k \(\times\).  
In particular, previous studies have postulated that the epoxy resin transformed into polymer films (i.e., telechelic polymers) and combined with a cement matrix to achieve enhanced strength [13; 14]. Here, we confirmed the structure of the telechelic polymer, which may be linked to the strength enhancement.  
Moreover, the microstructural analysis directly confirmed the degree of penetration of the epoxy resin adhesive that had been previously visually inspected.
## 5 Conclusions  
Effective route generation and CH selection are regarded as challenging problems in WSN. In this research, a combination of ICOA and JILFA is proposed to optimize the overall energy consumption and the network lifetime is also increased. Based on five varying metrics such as node degree, base station distance, node centrality, distance to neighbors, and node residual energy. ICOA is used to choose the precise CH. With the help of the fitness values function, the best CH is selected from the cluster nodes. The energy-efficient routing is created by combining three factors such as distance, node degree, and residual energy, to improve JILFA. Throughout the simulation of the proposed algorithms, the base station is moved from inside to outside. When compared to current methods, the proposed scheme performed better than PEGASIS and TEEN in terms of sustained network usage. Additionally, the proposed methodology is contrasted with other routing approaches. In terms of network performance, it is found that the proposed scheme performed better than PEGASIS and TEEN.  
Conceptualization, K.S.; Supervision; Original draft and review& editing, I.A.; validation, K.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data will be shared for review based on the editorial reviewer's request.  
The authors declare no conflict of interest.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Environmental Impact Assessment Methods  
#### 3.3.1 Composition Analysis of Infiltration Fluid  
As a concomitant of steelmaking, SL contains a variety of metal elements, leading to some concerns about whether its infiltration fluid will pollute surrounding soil and groundwater. An environmental assessment of SL thus contributes to encouraging the use of SL in China. This section aims to evaluate the influence of infiltration fluid on nearby surface water and groundwater after the subgrade is covered with water. The composition analysis results of infiltration fluid for these test soils are listed in Table 5. From the 11 trace elements shown in Table 5, for SL, the contents of arsenic, selenium, and hexavalent chromium were 0.62 \(\mu\)g/L, 0.54 \(\mu\)g/L and 0.009 mg/L, respectively, and the contents of other trace elements were lower than the detection limits. When the soil was mixed with cement (i.e., SSLC samples), the concentrations of these three trace elements decreased by 0.08 \(\mu\)g/L, 0.07 \(\mu\)g/L and 0.001 mg/L, which was mainly caused by the reduction of SL content in the improved soil. It is worth noting that all trace elements in SSLC were lower than the detection limits, reflecting the consolidation effect of cement on trace elements. However, the test results were very vulnerable to the influence of material homogeneity. Uneven mixing of cement may cause degradation of the solidification effect of trace elements, but this positive effect cannot be ignored.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-5}  & \(A\) & \(B\) & \(\mathbf{R^{2}}\) & \(E_{\text{max}}\) (MPa) \\ \hline S & 4.5394 & 0.00846 & 97.89 & 118.20 \\ SSL & 3.4846 & 0.00710 & 98.48 & 140.85 \\ SSLC & 1.6921 & 0.00391 & 98.74 & 255.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The fitting parameters of dynamic modulus.  
Figure 12: The volume stability test results: (**a**) volume expansion curve; (**b**) final volume expansion rate.  
According to the test results in Table 5, all trace elements in the test soils could meet the class I standard of surface water (GB 3838-2022), indicating that the infiltration fluid of subgrade soil mixed with SL meets the environmental requirements. The addition of SL had no impact on the use of common domestic water for drinking and bathing. The standard for groundwater is strict, and the requirements for barium, cadmium, chromium, and nickel in the class I standard are much higher than those for surface water. In general, except for nickel, the infiltration fluid of all test soils conformed to the class II standard for groundwater (GB/T 14848-2017), that is, there was no impact on centralized drinking, industrial and agricultural production, breeding, etc. Therefore, based on the infiltration fluid content, SSL has the potential to be a green subgrade filler. In order to ensure full safety, edge wrapping can be incorporated with SL in the subgrade.
## 5 Results and Discussion
In the proposed algorithm NR-MDEC (also a modified MDEC), the first node died earlier, as expected, which was due to building a balanced network. However, once it all set up, 60% nodes died only after 2560 rounds, which was quite impressive and far better than the previous algorithms. Similarly, it could also be said for the Last Node Dead in NR-MDEC as it ran out of energy only after 2641 rounds. It can be observed that the proposed method NR-MDEC protocol increases the network lifetime by the number of rounds as it improves the performance of the network. Simulation results of comparisons for the number of 60% died nodes are shown for DEC, MDEC, and NR-MDEC protocols in Figure 13.  
Figure 12: Comparison of dead nodes for different methods. Figure 13: Comparison of the number of Dead nodes for standard DEC, MDEC, and NR-MDEC protocols. This is a follow-up illustration of Figure 12 and gives a more comparative view of the aforesaid. Three colored lines, Grey, Blue, and Orange represent DEC, MDEC, and NR-MDEC, respectively. Here, data are also sectioned into three categories: FND, LND, and 60% dead. The \(y\)-axis denotes the number of rounds. It could be observed that MDEC and NR-MDEC, although have variations in between the transmission patterns, both have a similar slope as compared to NR-MDEC. As seen in Figure 13, the first node dies out (FND) by around 60% and the last node dies out (LND) at around 65% of the 3000 rounds for DEC and MDEC. Initially, the performance of NR-MDEC was not good because it tried to balance the network and sort out nodes as per their residual energy and switching priorities to make them cluster heads accordingly. However, it improved linearly and bypassed DEC and MDEC and stabilized itself. This also implicated how well NR-MDEC worked over the other two. Additionally, the network of NR-MDEC survived longer, and it is visible from the results that the proposed NR-MDEC has outperformed the existing methods of DEC and MDEC.