esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Full-scale intervention
Based on the literature,  
Figure 12: (**a**) PCE, (**b**) TCE, (**c**) 1,2-DCE, and (**d**) vinyl chloride (VC) concentrations detected in shallow aquifers in the last intervention area. there is still uncertainty regarding how subsurface heterogeneity may affect the design, implementation, and monitoring of this technology [10]. The remedial investigation phase primarily focuses on characterizing subsurface geology and contaminant distribution [61]. From the implementation perspective, field applications should continuously stress adequate site characterization for remedial design. Verifying the amendment distribution should be part of performance monitoring. The distribution of activated carbon-based amendments is a key factor determining the success of the remedy [10]. The multidisciplinary geodatabase and integrated data modeling support the design, sizing, and configuration of the intervention strategy. In fact, the injection mode strongly depends on geological, hydrogeological, and operational characteristics, and particularly critical is the possible presence of preferential flow pathways. The geophysical model obtained from the three ERT profiles strengthened the geological model and provided the necessary high-resolution details. Electrical resistivity tomography measurements indicated that the geological site structure, hydrogeology, and the injected product can be discriminated based on altered electrical properties [22; 23; 24]. The realization of injection stations and the injection of PlumeStop(tm) were done based on the most suitable configuration. The pilot test, appropriately coordinated through the multidisciplinary and multitemporal data management model, was checked in the implementation phase in terms of yield through ERT, soil, and groundwater sampling. Geophysical surveys and monitoring activities were conducted in near real time to control the effectiveness of the intervention, in terms of the capacity of product diffusion. The field test provided an impressive picture, illustrating how in situ remediation actions affected different portions of the subsurface, as an effect of subsoil hydraulic heterogeneity. The pilot test provided the elements for verifying efficiency, optimizing the intervention layout, and designing an optimized full-scale intervention. The data-driven model comprises, collects, and establishes a connection between environmental variables, to optimize the contribution of each aspect supporting the design, implementation, and validation of the remediation technique. The hydrogeophysical model and thematic database act as integrated and continuously updated tools that can manage and calibrate the progress of the intervention modality according to innovative approaches during the remediation phases, from pilot site to full-scale intervention. The multisource model, data fusion, and integrated approach demonstrate the biodegradation process in conjunction with contaminant adsorption for in situ subsurface remediation of chlorinated solvents with activated carbons and illustrate how geological heterogeneity affects reagent distribution.
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 4 Modeling Reconnaissance Activity
### Modeling Intruder's Behavior  
An important parameter for the modeling of reconnaissance activity is the intruder's decisions in different steps of the process. In this subsection, a scheme to model the decisions of an intruder with respect to reconnaissance activities, such as host discovery and port scanning in a network, is described. As mentioned in Section 3, regarding host discovery, the different options of the intruder are (i) ICMP Echo Request, (ii) ICMP Timestamp Request, (iii) ICMP Information Request, (iv) ICMP Address Mask Request, (v) TCP SYN Ping, (vi) TCP ACK Ping, and (vii) UDP Ping. Regarding port scanning, the different options are (i) TCP SYN Scan, (ii) TCP Connect Scan, (iii) TCP NULL Scan, (iv) TCP FIN Scan, (v) TCP XMAS Tree Scan, (vi) TCP ACK Scan, (vii) TCP Window Scan, and (viii) UDP Scan.  
Therefore, all possible actions/decisions of an intruder can be represented as elements of two distinct sets.  
\[HD=\{hd_{1},hd_{2},...,hd_{n}\} \tag{1}\]  
and  
\[PS=\{ps_{1},ps_{2},...,ps_{m}\} \tag{2}\]  
The behavior \(B_{i}\) of an intruder \(i\) is represented by a tuple of subsets of the sets HD and PS.  
\[B_{i}=\{HD_{i},PS_{i}\} \tag{3}\]  
where \(HD_{i}\subseteq HD\) includes all host discovery techniques that intruder \(i\) uses, while \(PS_{i}\subseteq PS\) includes all port scanning techniques that intruder \(i\) uses.  
An example of a representation of the knowledge about the behavior \(B\) of a specific intruder is shown in the following:  
B = {  {  ICMP Echo Request,  ICMP Information Request  }  },  {  {  TCP SYN Scan  }  } } For the specific intruder, there is no complete knowledge about their preferences regarding host discovery but it is known that he uses either ICMP Echo Request or ICMP Information Request. Regarding port scanning, it is explicitly known that they are using a TCP SYN Scan. As a result of the incomplete knowledge of the host discovery approach of the intruder, it is expected that during the analysis of the state diagrams, more than one probable path to be followed by the intruder will be extracted.
###### Abstract  
With the construction and development of the smart grid, the power business puts higher requirements on the communication capability of the network. In order to improve the energy efficiency of the space-air-ground-integrated power three-dimensional fusion communication network, we establish an optimization problem for joint air platform (AP) flight path selection, ground power facility (GPF) association, and power control. In solving the problem, we decompose the problem into two subproblems, one is the AP flight path selection subproblem and the other is the GPF association and power control subproblem. Firstly, based on the GPF distribution and throughput weights, we model the AP flight path selection subproblem as a Markov Decision Process (MDP) and propose a multi-agent iterative optimization algorithm based on the comprehensive judgment of GPF positions and workload. Secondly, we model the GPF association and power control subproblem as a multi-agent, time-varying K-armed bandit model and propose an algorithm based on multi-agent Temporal Difference (TD) learning. Then, by alternately iterating between the two subproblems, we propose a reinforcement learning (RL)-based joint optimization algorithm. Finally, the simulation results indicate that compared to the three baseline algorithms (random path, average transmit power, and random device association), the proposed algorithm improves an overall energy efficiency of the system of 16.23%, 86.29%, and 5.11% under various conditions (including different noise power levels, GPF bandwidth, and GPF quantities), respectively.  
space-air-ground-integrated network (SAGIN); Low Earth Orbit (LEO) satellites; dynamic resource allocation; multi-agent reinforcement learning (RL); Markov Decision Process (MDP); K-armed bandit +
Footnote †: journal: Electronics Electronics
## 5 Analysis of the Effects of Artificial Boundary Parameters on Computational Accuracy  
To investigate the optimal static artificial boundary parameter values, a computational model of the IRDM accounting for far-field medium effects is established. The lateral and bottom boundaries are set at 0.5 times the structural width and height (\(R_{S}=R_{B}=0.5\)), respectively. The artificial boundary parameter \(\alpha_{T}\) is assigned values of 0.35, 0.5, 0.75, and 1.0, with the parameter \(\alpha_{N}\) set to twice \(\alpha_{T}\) on the basis of [PERSON] et al.'s study. By calculating the internal forces (axial force \(N\), shear force \(Q\), and bending moment \(M\)) and lateral deformations of vertical structural members and comparing these results with those of dynamic time history analyses, the relative errors under different \(\alpha_{T}\) conditions are systematically evaluated, as shown in Figure 11.  
The analysis results indicate that as \(\alpha_{T}\) increases from 0.35 to 1.0, the computational errors of the proposed method initially decrease and subsequently increase. When \(\alpha_{T}\) increases from 0.35 to 0.5, the errors transition from positive to negative. This phenomenon arises because insufficient artificial boundary stiffness at lower \(\alpha_{T}\) values prevents static foundation reactions from adequately balancing non-free-field displacements caused by soil-structure kinematic incompatibility. The unbalanced residual displacements are superimposed onto free-field displacements and are converted into equivalent seismic loads, leading to overestimation of internal forces and deformations. As \(\alpha_{T}\) further increases from 0.5 to 1.0, non-free-field displacements are effectively balanced; however, excessive boundary stiffness introduces overconstraint effects on the SSI model, gradually amplifying errors. A comprehensive evaluation demonstrates that the optimal parameters \(\alpha_{T}=0.5\) and  
Figure 10: Schematic diagram of the IRDM computational model considering far-field effects.  
\(\alpha_{N}\) = 1.0 minimize computational errors while achieving an ideal balance between accuracy and model complexity, which is a recommended configuration.
## 5 Conclusions and Future Works  
In this paper, we presented a distributed task redirection method among MECSs to improve the resource efficiency of MEC systems. We proved that our method drives the MEC system to the state where the loads of the MECSs are lexicographically minimal. Through simulation studies, we showed that compared with other conventional load balancing methods, the proposed method achieves the smallest load difference between the load of the MECS whose load is the highest and that of the MECS whose load is the smallest. By obtaining the leximax minimal load vector, the proposed method improves the rate of tasks completed within their delay constraints. In addition, our method decreases the average task blocking rate when the task offload rate is high.  
We are planning the following future works. Firstly, we will devise a task selection method that selects tasks to redirect from the waiting queue of a highly loaded MECS. By considering the delay requirements of the tasks in \(O_{i}(t)\) when constructing the set \(\Phi_{ij,i^{\prime}}\), we expect that the rate of tasks completed within the delay constraints will improve. Secondly, we will inspect the influence of the delay required to redirect a task from one MECS to another MECS. We will also scrutinize the optimal number of redirection per task. Finally, we will extend the proposed method so that it can be used in the environment where only a subset of MECSs in the system can exchange their load information.  
Conceptualization, J.P. and Y.L.; methodology, J.P.; software, Y.L. Both authors have read and agreed to the published version of the manuscript.  
This research was supported by stage 4 BK21 project in Sookmyung Women's Univ of the National Research Foundation of Korea Grant. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1047113).  
The authors declare no conflict of interest.
## 4 Fretting Wear Analysis Considering Internal Contact Pressure
### Finite Element Model of Fretting Wear  
The wear model is established with reference to the Archard equation [18], and based on the contact characteristics of parallel wires, reciprocating relative displacements of two semi-cylinders is used to simulate the fretting wear. As Figure 21 shows, the contact wear model of two wires is established in ABAQUS 6.10., and relevant parameters are provided in the Table 2 below.  
The process of fretting wear fundamentally involves the formation and evolution of wear debris. The contact surfaces undergo continuous sliding friction, leading to adhesion and plastic deformation, accompanied by work hardening to make the material more brittle. In this process, the white layer formed undergoes disruption, peeling, and detachment during reciprocating motion, resulting in the loss of material at the contact interface [3]. Neglecting the influence of wear debris on the fretting wear process, the wear coefficient can be employed to quantify the rate of material loss during this process, enabling the computation of the wear depth at each point on the contact interface. Finite element model is effective in simulating the development of contact parameters during the wear process, providing relevant parameters for fretting wear in the assessment of the service life of stay cable.  
Based on the contact model of two wires, this study has developed a finite element subroutine that facilitates real-time updates of node coordinates and interface conditions, enabling an accurate analysis of the wear process. In the finite element software, geometric parameters, material properties, normal loads, and contact characteristics are defined. The subroutine takes input parameters such as sliding distance, total cycle count, and wear coefficient. After completing the calculations, the exported variables CPRESS, CSLIP, and DEPTH can be used to accurately calculate the contact stress and wear depth.
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 4 Implementation and Measurement
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**C (pF)** & \(f_{\mathrm{H}}\) **(GHz)** & \(\left|\mathrm{S_{11}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{21}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{31}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{41}}\right|_{\mathrm{dB}}\) \\ \hline
2.67 & 5.44 & & \(-17\) & \(-1.6\) & \(-17.3\) \\ \hline
1.4 & 5.54 & & \(-20\) & \(-1.57\) & \(-17\) \\ \hline
1.12 & 5.64 & \(<\!\!-20\) & \(-19\) & \(-1.56\) & \(-18.7\) \\ \hline
0.88 & 5.74 & & \(-16\) & \(-1.58\) & \(-18.5\) \\ \hline
0.63 & 5.84 & & \(-15\) & \(-1.59\) & \(-17.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measurement results for the high-frequency band of the proposed crossover. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**C (pF)** & \(f_{\mathrm{L}}\) **(GHz)** & \(\left|\mathrm{S_{11}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{21}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{31}}\right|_{\mathrm{dB}}\) & \(\left|\mathrm{S_{41}}\right|_{\mathrm{dB}}\) \\ \hline
2.67 & 2.06 & & \(-20\) & \(-1\) & \(-17\) \\ \hline
1.4 & 2.15 & & \(-16.5\) & \(-0.95\) & \(-17.2\) \\ \hline
1.12 & 2.23 & \(<\!\!-15\) & \(-19\) & \(-1.02\) & \(-17.3\) \\ \hline
0.8 The overall performance of the fabricated prototype is in agreement with what was anticipated from the theoretical analysis and HFSS simulation. Also, the measurement results are approximately the same as those obtained from the simulation. Figure 8 shows a comparison between the S-parameters found from simulation and those obtained from measuring the fabricated prototype for five different capacitance values. The small discrepancies between the results can be due to fabrication and measurement errors, material loss, and the soldering of the varactor diodes, which can lead to additional losses. Table 5 shows a comparison between the aforementioned published designs and the proposed dual-band frequency-reconfigurable crossover. The table compares insertion loss, return loss, isolation, covered frequency bands, tuning range, size, and the ability to resonate at single or dual frequencies. It can be noted that [25; 26; 27; 28] presented a tunable crossover design, but it is valid for only a single frequency. While in [22; 23; 24], their designs show a dual-band response achieved using CLs, the two frequencies are fixed. Accordingly, the only design that can realize both frequency reconfigurability and dual-band response is the proposed design. Figure 8: Comparison between the simulated and measured S-parameters of the proposed crossover: (**a**) S\({}_{31}\) at the low-frequency band; (**b**) S\({}_{31}\) at the high-frequency band; (**c**) all S-parameters for case 3 (C = 1.12 pF).
## 5 Experimental Setup  
We set up an experimental environment to implement our proposed framework using Fog Node (FN) for loMT networks with the corresponding throughput elapsed time or intervened time. When a cloud-based ordering system is established using a virtual machine (VM), the time of the associated bypass and the number of nodes is assessed. For performance measurement, 16.04 LTS Core i5 CPU Ubuntu is a Linux distribution 2.50 GHz 2.71 GHz (VirtualBox). The RAM capacity is 16 GB. Each follower peer virtualization scenario has 30 vCPUs and 8 gigabytes of RAM. It repeats the following process 30 times. The number of transactions handled per subsequent is referred to as a second transaction--several transactions per second (TPS). The response time was 226 milliseconds, with a minimum of milliseconds and milliseconds. Figure 6 shows the hyperledger-based fog architecture for intervened time. As the thread group starts and pauses a demo application, we notice network latency (NL). We also managed threads in a blockchain (BC) network successfully. The response times to the blockchain (BC) network are depicted in Figure 7 and show the intervened time and active thread in the fog computing environment. We moved the ordering service to the cloud to determine if the network is steady. Despitethe low minimum (LM), the ordering instance 233 generates a reasonably stable network. It was proven to work in situations where 234 high throughput and a real-time environment are required. The performance graph of a 235 fog network based on Hyperleader Fabric (HF) over time is shown in Figure 7. Blockchain, IoMT (Fog-BC-IoMT), and FC technologies all utilized the proposed architecture (Fog-IoMT). To record transactions, the BC is utilized to create a legal public, hyperdistributed EMR. Several IoMT-NODES are utilized in the architecture testing and implementation. The outcomes were estimated satisfactorily. This study suggests an architecture for preventing data fraud by converting existing centralized database systems to block-based distributed databases. It divides the system into four parts: cloud, fog, blockchain, and IoMT. The IoMT system is self-contained. We also examined whether the network convention method could assist with public cloud resources more effectively. The ordered migrates to the cloud for stability, security, and scalability and avoids performance issues by not directly connecting IoMT devices to forbidden networks. Hyperledger, a chain block solution, handles IoMT validation and safety. To reduce network latency (NL) and output, smart contracts (SC) and transaction checking on fog nodes (FN) is recommended. The network architecture will challenge cost reductions in the cloud and optimize cloud and FN instancing performance to boost the efficiency of the hyperledger BC network [50; 51; 52; 53; 54; 55; 56; 57; 58].
## 5 Results and Discussion
### Performance of Data-Driven Models under Study
Additionally, the value of SI for MARS was indicative of the most successful performance. M5 MT with IOA of 0.9435, SSE of 0.1581, and MAE of 0.1356 came in second. The performance of GEP model (Equation (A3)) with IOA = 0.9329 and SSE = 0.1892 was comparatively at the same accuracy level as for EPR (Equation (A6)) (IOA = 0.9356 and SSE = 0.1816). Additionally, MAE and SI values proved similarity of performance between GEP and EPR models. Moreover, MARS model had more significant underprediction (ADR = 0.9578) than GEP (ADR = 1.0020) with very marginal overestimation. ADR analysis also demonstrated that M5 MT and EPR provided low underprediction and fairly relative overprediction, respectively. In terms of illustrative comparisons in training phase, Figure 7a indicated that GEP and MARS techniques underpredicted, with an observed value of \(V_{R}^{*}=1.11\). As it can be seen in Figure 7a, the scatter plot for training phase shows highly prosperous performance with almost all data inside \(\pm\)25% error ranges. Relatively important overprediction for M5 MT and GEP was seen in Figure 8b at \(V_{R}^{*}=2.22\). MARS and GEP techniques underpredicted for \(V_{R}^{*}=1.35\), whereas EPR provided significant overprediction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**AI Models**} & \multicolumn{6}{c}{**Training Stage**} \\ \cline{2-6}  & **IOA** & **SSE** & **MAE** & **SI** & **ADR** \\ \hline M5 MT & 0.9801 & 0.0788 & 0.0728 & 0.1046 & 1.0163 \\ GEP & 0.9760 & 0.0953 & 0.1199 & 0.1147 & 1.0294 \\ EPR & 0.9913 & 0.0345 & 0.1030 & 0.0692 & 1.0113 \\ MARS & 0.9831 & 0.0670 & 0.1030 & 0.0964 & 1.0050 \\ \hline \hline \multicolumn{6}{c}{**Testing Stage**} \\ \cline{2-6}  & **IOA** & **SSE** & **MAE** & **SI** & **ADR** \\ \hline M5 MT & 0.9435 & 0.1581 & 0.1356 & 0.1601 & 0.9955 \\ GEP & 0.9329 & 0.1892 & 0.1525 & 0.1755 & 1.0020 \\ EPR & 0.9356 & 0.1816 & 0.1500 & 0.1713 & 1.0503 \\ MARS & 0.9670 & 0.0855 & 0.1086 & 0.1601 & 0.9578 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical performances of the AI models in the estimation of \(V_{R}^{*}\). Figure 7: Performance of AI models in the prediction of \(V_{R}^{*}\) for: (**a**) Training and (**b**) testing stage. Figure 8: Variations of \(V_{H}^{*}\) against \(e/D\) at: (**a**) \(KC=8.7\) and \(\alpha=0^{\circ}\), (**b**) \(KC=15.8\) and \(\alpha=0^{\circ}\), (**c**) \(KC=18.0\) and \(\alpha=0^{\circ}\), (**d**) \(KC=15.8\) and \(\alpha=30^{\circ}\), (**e**) \(KC=18.0\) and \(\alpha=30^{\circ}\), (**f**) \(KC=18.0\) and \(\alpha=15^{\circ}\), and (**g**) \(KC=15.8\) and \(\alpha=45^{\circ}\).