esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization Considering Porosity Defects in Metal Additive Manufacturing
## 4 Constraint Function Considering Porosity Defects
### Formulation of Porosity Constraints  
Assuming \(B_{x,r}=x+rB\), \(x\)\(\in\)\(\Omega\) represents a small pore with radius \(r\) at \(x\) in the design domain \(\Omega\), the design domain where the pore appears can be expressed as \(\Omega_{0,x,r}=\Omega_{0}-B_{x,r}\). The objective function \(J(\Omega_{0})\) does not change significantly due to the appearance of pores in the design domain \(\Omega_{0}\), so the change of structural performance before and after the appearance of pores can be expressed as:  
\[c_{0}=J(\Omega_{0,x,r})-J(\Omega_{0}) \tag{33}\]  
Introduce the definition of topological sensitivity in Section 3.2:  
\[J(\Omega_{0,x,r})=J(\Omega_{0})+D_{T}J(\Omega_{0,x,r})M(B_{x,r})+(M(B_{x,r})) \tag{34}\]  
where \(M(B_{x,r})\) is the measure of the pore \(B_{x,r}\) and \(\lim\limits_{r\to 0}\frac{\left[\omega(M(B_{x,r}))\right]}{M(B_{x,r})}=0\).  
By substituting (34) into (33), the result is:  
\[c_{0}=D_{T}J(\Omega_{0,x,r})M(B_{x,r})+(M(B_{x,r})) \tag{35}\]  
After considering the porosity constraints, assuming that \(\Omega_{p}\) is set as the structural design domain of the optimal design, and \(J(\Omega_{p})\) is the corresponding objective function to characterize the structural performance, the change of structural performance can be obtained:  
\[c_{p}=J\big{(}\Omega_{p,x,r}\big{)}-J\big{(}\Omega_{p}\big{)}=D_{T}J\big{(} \Omega_{p,x,r}\big{)}M(B_{x,r})+(M(B_{x,r})) \tag{36}\]  
According to the above analysis, the structural performance will decrease after considering the porosity constraint, so the inequality can be established:  
\[c_{p}-\eta c_{0}\leq 0 \tag{37}\]  
where \(\eta\)\(\in(0,1]\) is a user-defined tolerance that indicates the admissible change of objective.  
In level set topology optimization, the inequality is not conducive to solving by Lagrange method, so inequalities need to be transformed. According to the work of [PERSON] et al. [49; 50], assuming \([F]^{+}\equiv\max(F,0)\), the inequality (37) satisfies:  
\[\big{[}c_{p}-\eta c_{0}\big{]}_{+}\equiv 0 \tag{38}\]  
Equation (38) are constraints defined at various points in the design domain. This type of constraint is inconvenient for implementation in topology optimization and to solve shape sensitivity. Addressing the issue, the domain integral is established:  
\[\int_{\Omega}\Big{(}\big{[}c_{p}-\eta c_{0}\big{]}_{+}\Big{)}^{2}dx=0 \tag{39}\]  
Therefore, the topology optimization problem considering porosity constraints is expressed as:  
\[\begin{array}{c}\min\;\;J(u)=\int_{\Omega}f(u)d\Omega=\int_{\Omega}E\epsilon( u)\cdot\epsilon(u)d\Omega\\ s.t.\;\;\int_{\Omega}d\Omega=V_{max}\\ C_{p}(\Omega)=\frac{1}{2}\int_{\Omega}\Big{(}\big{[}c_{p}-\eta c_{0}\big{]}_{+ }\Big{)}^{2}dx=0\end{array} \tag{40}\]
## 2 Materials and Methods
### Materials and Cell Fabrication  
We used ITO as a glass substrate because ITO was used considering its transparency and conductivity as in the preceding research of light-driving LCPS [19; 20]. We chose the HTAB as a surface-active agent because it has a high affinity with ITO [23]. HTAB is an organic compound containing hydrophilic groups at both ends of a prolate molecule-amphophilic molecule (Figure 1a). HTAB has a bromide anion, which has a high affinity with ITO, and finally adheres to the ITO glass substrate. Owing to the polar nature of HTAB, the cyano groups of the LCs are expected to point towards HTAB [33]. Finally, LC molecules can be aligned vertically by the HTAB on the ITO glass substrates (Figure 1b). Some previous studies have considered LCPSs that use (4-cyano-4\({}^{\prime}\)-pentylbiphenyl) (SCB) LCs, and some studies have used HTAB and 5 CB to achieve VA; these studies guarantee the validity of our experimental conditions [8; 34; 35].  
We used a 2 cm \(\times\) 3 cm sized superstrate and substrate, and we mixed 5 CB with a surface-active agent HTAB. The materials were mixed in a glass vial by stirring continuously for 4 h at 70 \({}^{\circ}\)C, 200 rpm. We also mixed HTAB with 8 CB to test whether other liquid crystals have similar responses to phase-change temperature and [HTAB]. These results will be discussed with the DSC results.  
We fabricated the cells as follows (Figure 1c). First, we prepared empty cells and used spacers and UV-cured epoxy to establish a different cell gap. For a large cell gap \(\geq\) 100 \(\upmu\)m, we used 3M tapes to maintain the cell gap. To avoid degradation of optical transmittance (OT), we ensured that the spacer scattered region did not overlap the region in which the OT was measured. After preparing the empty cell, we injected the LC mixture into a 2 cm \(\times\) 2 cm area by exploiting capillary action, then sealed the samples with UV-cured epoxy to prevent leakage of LCs, then cured it using UV to stiffen the samples.
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 6 Conclusions  
In this paper, we proposed a new attack that exploits OpenFlow protocol vulnerabilities, named the packet injection exploiting attack. We proved the possibility of the attack through experiments and assessed its impact. In order to protect against the packet injection exploiting attack, we designed PIEDefender, an efficient, protocol-independent component to detect and mitigate attacks effectively. We implemented the PIEDefender prototype in the Floodlight controller and evaluated the effectiveness in the software environment. The evaluation shows that PIEDefender could effectively mitigate the packet injection exploiting attack against SDN with limited overhead. However, our research is inapplicable to wireless networks. This is because all hosts connect through the access point (AP) in a wireless network, and the AP is a single port. In this case, the proposed injection detection mechanism will fail because we cannot effectively identify every terminal. We will consider more scenarios to make our scheme more universal in future work.  
Conceptualization, J.L.; methodology, J.L.; software, J.L.; validation, J.L., H.Z., and Y.L.; formal analysis, J.L. and T.T.; investigation, J.L. and S.Q.; resources, S.Q., T.T. and H.Z; data curation, J.L. and S.Q.; writing--original draft preparation, J.L.; writing--review and editing, S.Q. and T.T.; visualization, J.L., H.Z. and Y.L.; supervision, H.Z. and Y.L.; project administration, S.Q. and T.T.; funding acquisition, S.Q. and T.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 6 Results
### Relative Error (RE) between Predicted Value and Actual Value  
The maximum RE values using XGBoost, SVR and XGBoost are 5.0%, 9.55% and 20.14% (see Table 1), respectively. From Figures 12-17, we note that the REs of predicted values obtained using the XGBoost method are less than 5.0% for all monitoring points. From Figures 12-16, we note that the REs using the SVR method also are mostly less than 5.0%. The REs using the RNNs method are mostly more than 6.0% for all monitoring points, vary greatly, and their data are very discrete.  
Figure 16: Variation of RE with slope displacement of D10.  
Figure 14: Variation of RE with slope displacement of D8.  
Figure 15: Variation of RE with slope displacement of D9.  
For large displacement values and small numbers of samples, the displacement-prediction effect of the XGBoost algorithm is better than that of the other two prediction methods in the sliding process of landslides. It is consistent with existing research results [46; 47].  
Both XGBoost and SVR can predict the displacement of landslides accurately in the sliding process, and RNNs is not suitable in this condition. In further research work, we will investigate the comparison of the predictive abilities of XGBoost and SVR with other deep-learning algorithms.  
If these displacement-prediction methods are used for other landslides, the accuracy of the automatic collection of displacement data should be guaranteed. XGBoost, SVR and RNNs should be selected in order, according to the number of data samples from small to large and the nonlinear degree from large to small. In order to improve the early warning level in the critical slip state, it is recommended to evaluate the reliability of the prediction scheme by combining the three-dimensional numerical simulation test of nonstationary rheological parameters and the multivariate Bayesian update inverse analysis theory.  
J.X.: conceptualization and algorithm development; Y.J.: data curation and processing, investigation; C.Y.: software; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China-Yalong River Joint Fund, grant number U1765110, National Natural Science Foundation of China, grant number 40872179.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflict of interest.
# Grouping Sensors for the Key Distribution of Implicit Certificates in Wireless Sensor Networks
###### Abstract  
As sensor nodes communicate via wireless channels, information security is essential for wireless sensor networks. Efficient protection mechanisms to ensure data security among nodes are critical. This study developed the bi-directed grouping (top-down grouping (TDG) and bottom-up grouping (BUG)) methods. In this study, we propose a group-based key distribution method, \"aggregator-based grouping\" (ABG), which combines the advantages of TDG and BUG to address the security issues of nodes. It employs horizontal and vertical searches, which are based on breadth-first and aggregator searches, respectively. A node performs encryption and decryption only when it requires either data aggregation or inter-group communication. The secure aggregation method can be applied to key-grouping management. We compared the proposed method with TDG and BUG using the same number of groups and network structure. For a network with maximum group members of 50 (total sensor nodes = 1000), compared with TDG and BUG, ABG reduced the number of encryption and decryption operations by \(\sim\)36%. ABG avoids unnecessary encryption and decryption in the network.  
aggregator-based grouping; wireless sensor networks; encryption and decryption operations +
Footnote †: journal: Electronics Electronics
# Failure of Rock Slope with Heterogeneous Locked Patches: Insights from Numerical Modelling
## 1 Introduction  
Rock slopes usually contain geological discontinuities, such as flaws, joints, fissures, weak surfaces and even faults [1]. The rock slope is generally controlled by intact unfractured rock between discontinuities [2, 3, 4, 5, 6]. Intact unfractured rock provide shear resistance along the potential sliding surface and a complex interaction between existing natural discontinuities and brittle fracture propagation through intact rock is required to bring the slope to failure [7]. Experimental and numerical studies have been conducted to explore the failure process of intact rock, in which most are simplified as linear rock bridge ignoring thickness and emphasising the crack penetration [8, 9, 10, 11, 12, 13, 14]. The investigations on large rock slides indicate that the failure of actual rock slope is complicated. The Touzhai large-scale rock slide which occurred in Yunnan Province of China produced a deposit of 2400 m in length, 130 m in average width, and 10 m in thickness [15]. Strongly weathered basalt of 1 m thick was found in the source area and the intact basalt was inferred to control the rock slide. For the Xikou rock slide that occurred in Sichuan Province of China, it was postulated that the cemented fault breccia acted as a locking section to prevent thedeformation of the sliding body [16]. The field investigation revealed that the stability of Yinjiangyankou rock slide which took place in Guizhou of China is controlled by the intact rock patches located at the lower portion of the interlayer [17]. The intact hard structure along the possible sliding surfaces instead of linear rock bridge governs the rock slides. Some studies have focused on the locked rock slope [18; 19; 20] and summarised different types of locked rock slope in terms of engineering geology [21; 22]. However, the failure process of intact rock patches remains to be revealed and interactions between different intact rock patches are still vague. We broadly define locked patch to represent the rock patches with higher strength along the potential sliding surface of a rock slope in this study (Figure 1) [23]. Only when all the locked patches fail, the rock mass starts to slide macroscopically. Therefore, failure of the locked patch possibly provides precursory information and thus early warning of rockslides [21].  
In our study, based on the self-developed, FEM-based code, RFPA (Rock Failure Process Analysis), we simulate the failure of two locked patches sandwiched by bedrock and slide body subjected to direct shear, analogous to the evolution of a rockslide. The effects of locked patch features on model stability and interactions between locked patches were investigated. The failure process and associated acoustic emission characteristics were analysed. Similar laboratory tests were employed to validate the numerical results. We also propose a simplified mechanical model to elucidate the failure process of the rock slope with locked patches. Our findings greatly advance our understanding on the failure of the rock slopes with locked patches.
## 4 Electrical Tomography Applications
### Examples of Applications of Electrical Tomography to Chemical Engineering Processes
#### 4.1.1 Operating Apparatus  
Pipes, storage and mixing systems are among the most common devices described in the literature in which Electrical Tomography has been applied. The possession of multidimensional information about flow features and settings in fluid (either liquids or gases) transportation systems (which would require being able to see inside the pipes) composed of pipes and piping machinery would provide numerous benefits in process design, monitoring and control. This is why the Electrical Tomography applications described in the literature have without doubt focused on diverse pipes situations. Vertical, horizontal, and inclined straight pipes, helical pipes, and so on are Among the various reported configurations that can be found in the literature. When the focus was on vertical straight pipes, diameters between 50 and 125 mm have been investigated [92; 93]. There are more numerous publications of case studies using horizontal straight pipes addressing diverse flow regimes than for any other configuration, with pipe diameter varying in the range 35 to 85 mm [94; 95; 96; 74]. Reports on studies using ERT in inclined pipes with different inclination angles [97] are scarce; the same can said for its application in studies with helicoidal pipes [70]. In contrast, storage and mixing containers, which can be found in industrial processes in large number, and which vary with respect to their size, purpose, etc., are the second most studied application area using ET. In particular, researchers have focused on stirred containers possessing various baffles and mixing characteristics with diameters in the interval 15-40 cm [98; 99], and on plant-scale containers with diameters of around 150 cm [100; 101].
###### Abstract  
In this paper, we demonstrate an active switching of extremely high Q-factor Fano resonances using vanadium oxide (VO\({}_{2}\))-implanted THz asymmetric double C-shaped metamaterial (MM) structures. The simulation results indicate the highly temperature-sensitive nature of the double Fano resonances that can be switched at very low external thermal pumping (68 \({}^{\circ}\)C), which is only slightly higher than room temperature. We employ the surface current and electric field distributions of the structure to analyze the physical mechanism of the observed switching behavior in the thermally excited Fano MMs. More importantly, by optimizing the asymmetric parameter (offset length), the linewidth of the Fano resonance can reach only 0.015 THz and the Q-factor is as high as 98, which is one order of magnitude higher than that of the traditional MMs. The findings of this work would enable a thermally-induced high-Q Fano resonance MMs for ultra-sensitive sensors, modulators, low threshold switching in metadevices.  
Keywords:terahertz; metamaterials; vanadium dioxide; thermal control; switching; Fano resonances +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Geometric Modeling  
#### 2.1.1 Patient-Specific Aorta Model  
A patient-specific aorta model was reconstructed from a chest multi-slice computed tomography (CT) image set by using MIMICS software (Materialise, Leuven, Belgium). The scanning was performed on a 16-detector CT with the following parameters: 0.5 mm slice thickness, 120 kV tube voltage and 400 mA tube current. The reconstructed three-dimensional (3D) aorta model included ascending aorta (AAo), aortic arch, descending aorta (DAo), abdominal aorta, brachiocephalic artery (BA), left common carotid artery (LCCA) and left subclavical artery (LSA) (Figure 1a). The diameter of the aorta at the proximal end, the top of the aortic arch and the distal end were 25 mm, 26.7 mm and 17 mm, respectively. The diameters of BA, LCCA and LSA at the distal ends were 13.2 mm, 7.5 mm and 8.7 mm, respectively.
## 3 Results and Discussion
### CO Concentration Experimental Results
The MBE and RMSE of the MSCC and mean XCO for the DA, SIM experiments, and the MOPV8] data [75; 76], relative to the external CAM-Chem results, [48] are listed in Tables 6 and 7, respectively. The MBE between the posterior XCO, estimated by the four DA experiments, and the CAM-Chem results [48] were 0.38 ppb, 0.86 ppb, \(-\)0.43 ppb, and \(-\)0.59 ppb, respectively. The DA experimental results were, on average, 0.05-ppb higher than those of the external CAM-Chem results in the four-year experiments. The MBE between the posterior surface-CO concentrations, estimated by the four DA experiments and the CAM-Chem results [48], were 33.84 ppb, 20.06 ppb, 14.71 ppb, and 14.53 ppb, respectively. The surface-CO concentrations of the DA experiments were, on average, 20.79-ppb higher than those of the external CAM-Chem results [48]. In the SIM experiments with the same initial and boundary conditions as the DA experiments, all the mean winter seasonal XCO, simulated by the WRF-Chem model and using the prior CO fluxes listed in Table 3, were lower than those mean posterior XCO results of the DA experiments, and also lower than those from the external CAM-Chem results [48]. During the four-year experiments, the Sim experimental XCO results were 2.11-ppb, 1.24-ppb, 0.57-ppb, and 0.45-ppb lower than those of the DA experiments, and were 1.73-ppb, 0.38-ppb, 1.00-ppb, and 1.04-ppb lower than those from the external CAM-Chem results [48], respectively. This fact implied that the prior CO fluxes used in the DA and SIM experiments might underestimate the actual CO flux values in the winter months. Actually, the annual anthropogenic CO emissions of western Europe in 2010, provided by EDGAR V5.0 [104], is 38.15% higher than that provided by EDGAR V4.3.2 [80].
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.9 Adaptive EMF Exposure Control Using AI-Driven Approaches  
AI-driven solutions offer dynamic control of EMF exposure by optimizing network parameters such as power levels, beamforming, and antenna configurations in real time. Machine learning models, including deep reinforcement learning, can adapt to traffic patterns and user density to balance performance and safety. Federated learning enhances privacy, while explainable AI improves transparency for regulatory compliance. Future research should focus on scalable, energy-efficient AI frameworks for intelligent EMF management in 5G networks and beyond.
# Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
###### Abstract
Although the _RUL_ prediction of liquid lithium-ion batteries has achieved a high accuracy, the _RUL_ of solid lithium batteries is rarely reported due to the different degradation mechanisms, challenging performance variability, complicated interfacial reactions, and the lack of open-source test datasets [25]. In the following paragraph, some recent studies are reviewed to introduce the different degradation mechanisms between liquid and solid-state lithium batteries. Many physical and chemical battery models have been proposed for diverse degradation mechanisms such as the growth of the solid electrolyte interphase, the fracture of lithium dendrites, active material loss, and interfacial polarization, which are listed in Figure 1 [26; 27; 28; 29; 30]. In general, the causes of battery degradation can be divided into two categories, capacity fading and impedance increases. Both have some related measurable physical quantities, while impedance information is more expensive to test and difficult to analyze. For commercial lithium-ion batteries, impedance does not change too much, because the liquid electrolyte wets the gaps inside electrode particles, as well as the gaps between electrodes and electrolytes [31]. In contrast, impedance increases take up a major degradation factor for solid-state batteries [6]. Delicate point contact is expected for solid-solid interfaces in solid-state batteries. These solid electrolyte/electrode interfaces significantly govern the electrochemical properties and cycle lifetime, as unwanted reaction products at the interface cannot dissolve and diffuse in the solid electrolyte [32; 33]. Hence, it is necessary to employ both features to capture the electrochemical impedance evolution during degradation and to capture the capacity fading during cycling. In this work, we chose symbolic regression (SR) as the prediction method considering the lack of a mature quantitative mapping formula between battery features and cycle life, as well as the strong nonlinear fitting ability of symbolic regression. The results showed that symbolic regression can accurately predict the _RUL_ using the features derived from the voltage-time curves. To the best of our knowledge, the largest open-source solid-state lithium polymer battery charging and discharging dataset (LFP/Li or NCM/Li) was generated and made publicly available. After the extraction of features and parameter optimization, our models reached an 88% test accuracy for the quantitative prediction of the remaining cycle life. This work highlights the potential of data-driven methods to estimate the evolution of complex material systems, and it lays the foundation for the commercial application of solid-state lithium batteries in the future.