esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The current design is a result of many iterations to optimize available space, and components are placed to allow for dynamic stability of the spacecraft. The physical properties and theoretical performance parameters of MIMPS-G500 mN utilizing state-of-the-art green monopropellants are presented in Table 11. Although HNP225 has the lowest \(I_{tot}\) and \(\Delta V\), it allows for the greatest payload mass onboard the spacecraft while still complying with the design requirements and constraints mentioned in Figure 4 (i.e., \(\sim\)1.5 kg BOL mass and \(I_{tot}\) = 858.027 N s). If HNP225 is considered for MIMPS-G500 mN it will allow for the use of metal 3D printed relatively low-cost thruster that would impact positively the propulsion system inert mass and thermal control due to the propellant low adiabatic flame temperature. The latter, along with the high solubility and non-detonating nature of this propellant, can be a point of advantage over other considered propellants in the first prototypes of the propulsion system with respect to management and control of the autogenous pressurization and feed cycle. A relatively low-cost COTS micro electric pump is used [35], that has a mass of only 75 g and cylindrical dimensions of \(\mathcal{O}\)22.0-70.60 mm, and provides propellant mass flow rate (\(\dot{m}\)) and output pressure up to 30 mL min\({}^{-1}\) and 2.2 MPa, respectively, at nominal 12 VDC and 7 W with viscous fluids similar to the used propellants, which makes this model a candidate for MIMPS-G. The \"Tuna Can\" protrusion volume existing within the CubeSat deployer springs differs from one deployer model to another which depends on the manufacturer. A deployer design allowing for protrusion volume of \(\mathcal{O}\)86.0-78.0 mm offered by a European manufacturer [39] was considered. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Part** & **Materials/Comments** & **Mass (g)** \\ \hline Tank Heater & Polyimide ThermofoilTM Heaters (4 W; 6–12 VDC) & 4 \\ PMD \$ & Titanium alloys and Acetal (Delrin®) Sponge and Vanes & \\  & [49,50] (no steel, no CFRP) _“Rough estimate”_ & \\ Microtube/Piping & Titanium alloy Grade 1 & \\  & \(\phi_{\text{in}}\) = 3 mm; t = 0.5 mm; total length = 363.6 mm & \(\leq\)10 \\ Computer, Sensors, and Interfaces & SBC *; Driver; 1 Pressure, 1 Temp. Sensors; Wiring & \(\leq\)120 \\  & Total Inert Mass & 1093 \\ \hline \hline \end{tabular}  
* Delrin® acetal homopolymer (Polyoxymethylene POM). \$ Propellant Management Device.
* Single Board Computer. FCV: Flow Control Valve.  
\end{table} 
Table 10: _Cont._
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 4 Our Proposed Scheme
### Authentication for Delegation Phase  
If \(DU_{A}\) wants to delegate their aggregate key, \(DU_{A}\) and \(DU_{B}\) conduct mutual authentication using \(HGID_{l}\). If they have same \(HGID_{l}\), they compute the same session key \(SK\). After that, \(DU_{A}\) can send their own aggregate key and keyword using \(SK\). In this case, \(DU_{A}\) can delegate limited access rights by sending only some of the keywords which \(DU_{A}\) have. The detailed steps are illustrated in Figure 6 and are as follows.  
**Step 1: \(DU_{A}\)** generates a random nonce \(r_{A}\) and timestamp \(T_{2}\). Then, \(DU_{A}\) computes \(R_{A}=PK_{a}^{r_{A}}\), \(V_{A}=PK_{B}^{h_{a}r_{A}}\), and \(L_{AB}=h_{1}(V_{A}||T_{2})\). \(DU_{A}\) sends \(R_{A},L_{AB},T_{2}\) to \(DU_{B}\).  
**Step 2:** After receiving messages from \(DU_{A}\), \(DU_{B}\) computes \(V_{A}=R_{A}^{B_{b}}\), and checks if \(L_{AB}=h_{1}(V_{A}||T_{2})\). If it is valid, \(DU_{B}\) generates a random nonce \(r_{B}\) and computes \(R_{B}=PK_{B}^{h_{B}}\), \(V_{B}=PK_{A}^{h_{b}r_{B}}\), \(L_{BA}=h_{1}(V_{A}||HGID_{l}||T_{3}||V_{B})\), \(AGID_{l}=h_{1}(V_{A}||HGID_{l})\), and session key \(SK=h_{1}(V_{A}||V_{B}||AGID_{l}||T_{3})\). After that, \(DU_{B}\) sends \(R_{B}\), \(L_{BA}\), \(T_{3}\) to \(DU_{A}\).  
**Step 3: \(DU_{A}\)** computes \(V_{B}=R_{B}^{h_{B}}\) and checks if \(L_{BA}=h_{1}(V_{A}||HGID_{l}||T_{3}||V_{B})\). If it is same value, \(DU_{A}\) computes the session key \(SK\). At the end, \(DU_{A}\) and \(DU_{B}\) authenticate each other and compute the same \(SK\) for their secure communication.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 3 Simultaneous Target Localization and Unmanned Aerial Vehicle (UAV) Trajectory Optimization
### Simultaneous Target Localization and UAV Trajectory Optimization
#### 3.2.2 UAV Trajectory Optimization
```
0: Initial state \(\hat{\mathbf{x}}_{0}\) and covariance matrix \(\mathbf{P}_{0}\); initial position of UAV \(\mathbf{y}_{0}\); Process noise covariance: \(\mathbf{Q}_{k}\); Measurement noise covariance: \(\mathbf{R}_{k}\),\(\forall k=0,1,2\dots\)
0: UAV waypoints \(\mathbf{y}_{k}\), and target position estimate \(\hat{\mathbf{x}}_{k}\); \(\forall k=0,1,2\dots\)
1:\(\hat{\mathbf{y}}_{0}\leftarrow\mathbf{P}_{0}^{-1}\hat{\mathbf{x}}_{0};\mathbf{Y}_{0} \leftarrow\mathbf{P}_{0}^{-1}\);
2:for\(k=1\); \(k\leq T\)do
3:\(\mathbf{Y}_{k|k-1}\leftarrow\left(\mathbf{\Phi}_{k|k-1}\mathbf{Y}_{k-1}^{-1}\mathbf{ \Phi}_{k|k-1}^{\top}+\mathbf{\Gamma}_{k|k-1}\mathbf{Q}_{k}\mathbf{\Gamma}_{k| k-1}^{\top}\right)^{-1}\);
4:\(\widehat{\mathbf{Y}}_{k|k-1}\leftarrow\mathbf{Y}_{k|k-1}\);
5: Establish the objective: \(\widehat{\mathbf{Y}}_{k}\leftarrow\widetilde{\mathbf{Y}}_{k|k-1}+\overline{ \mathbf{\Gamma}}_{k}\); \(y_{k}\leftarrow\) Solve the optimization problem (31) using interior points method. 6:\(\mathbf{B}_{k|k-1}\leftarrow\mathbf{Y}_{k|k-1}\mathbf{\Phi}_{k|k-1}\mathbf{Y}_{k}^ {-1}\);
7:\(\hat{\mathbf{y}}_{k|k-1}\leftarrow\mathbf{B}_{k|k-1}\hat{\mathbf{y}}_{k-1}\);
8:\(\mathbf{I}_{k}\leftarrow\mathbf{H}_{k}^{\top}\mathbf{R}_{k}^{-1}\mathbf{H}_{k}\);
9: Wait our new measurement \(\mathbf{z}_{k}\);
10:\(\mathbf{i}_{k}\leftarrow\mathbf{H}_{k}^{\top}\mathbf{R}_{k}^{-1}\mathbf{z}\);
11:\(\mathbf{I}_{k}\leftarrow\mathbf{H}_{k}^{\top}\mathbf{R}_{k}^{-1}\mathbf{H}_{k}\);
12:\(\mathbf{Y}_{k}\leftarrow\mathbf{F}_{k|k-1}+\mathbf{I}_{k}\);
13:\(\mathbf{\hat{y}}_{k}\leftarrow\hat{\mathbf{y}}_{k|k-1}+\mathbf{i}_{k}\);
14:\(\hat{\mathbf{x}}_{k}\leftarrow\mathbf{Y}_{k}^{-1}\mathbf{y}_{k}\);
15:\(k\gets k+1\);
16:endfor
```  
**Algorithm 1**Simultaneous target localization and UAV trajectory optimization.
# Vulnerability Analysis of Column-Supported Reinforced Concrete Silo Structures
## 1 Introduction
Both Hussein A H [13] and Benkhellat S [18] investigate the complex interaction mechanism of the soil-pile-silo structure with granular solid, and the latter quantify and evaluate the damage of the studied silo by utilizing two damage indices by IDA curves. On this basis, some progress in the systematic evaluation of reinforced concrete silos has been made through vulnerability analyses. However, although the above studies mainly focused on cylindrical reinforced concrete silos and floor silos, there was no consideration of the damage evaluation, damage states, and seismic vulnerability from a probability perspective. A seismic vulnerability analysis is an effective probability-based method to evaluate structural seismic performance and earthquake disaster risks. The engineering demand parameter (EDP) and damage limit states (LSi) both play an important role in determining the accuracy of the seismic vulnerability analysis. The IDA is an analytical method commonly used in the seismic vulnerability analyses of structures [19]. It can help perform an elastic-plastic dynamic analysis of structures through a series of seismic records with multiple intensities, finally simulating the entire dynamic instability process. In fact, the reliability of the vulnerability analysis results is closely related to the damage state division of the structure. The column-supported reinforced concrete silo is different from the conventional frame structure, and no study has focused on the damage status and the corresponding classification criteria of this structure. The determination of the damage state of the silo structure and the evaluation of the seismic performance need a large amount of data, and it is obviously impossible to carry out a large number of tests on it. The numerical simulation is an effective method for evaluating structural seismic performance and the numerical modeling of seismic analysis for frame structure [20; 21] has been developed. In addition, the seismic properties of complex structures such as water towers [22], geodesic domains [23], and nuclear power plant structures [24] have also obtained a lot of conclusions and engineering guidance through finite element modeling. In this study, the IDA was applied to a column-supported reinforced concrete silo as the research subject, and 12 IDA curves of ground motions with varying intensities were plotted. Subsequently, the damage and limit states of the silo were defined for the first time under distinct damage levels, and vulnerability curves under different storage conditions were plotted.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 3 Design of QCORS Algorithm
### Packet-Redirection Strategy Model
This article only studies the routing in the network, and we can simplify the problem as follows:  
\[ \begin{split} min& Q_{n}(t)-x_{n}^{t}(b,f)-VU(t)\\ s.t.& 0\leqslant x_{n}^{t}(b,f)\leqslant C_{n}^{t}(b), \forall b\in O(n)\\ & 0\leqslant x_{n}^{t}(b,f)\leqslant Q_{n}(t),\forall n\in N\\ & 0\leqslant Q_{n}(t)\leqslant Q_{max},\end{split}  \tag{18}\]  
where \(Q_{max}\) represents the maximum queue length of the node. Because the utility function \(U(t)\) is a concave and derivative function, the throughput issue is a convex optimization problem. Let \(U^{*}(t)\) be the optimal solution of this problem, based on convex optimization theory [53], and the \(U^{*}(t)\) is achieved by  
\[U^{*}(t)=[U^{t-1}(\frac{Q_{n}^{t}(t)}{V})]_{0}^{Q_{max}}, \tag{19}\]  
where \(U^{t-1}(. )\) represents the first derivative of the inverse function of the utility function \(U(. )\) and \([x]_{a}^{b}=min(max(x,a),b)\). The process of QCOSR is illustrated in the Algorithm 1. ```
0: Input the Network topology \(G\), initialize the parameters of differentiating packets and their priority, set the congestion level threshold value \(C(P_{sd})_{low}\), \(C(P_{sd})_{high}\), and \(V\). 0: Output the path from \(s\) to \(d\) with optimal routing. 1: Calculate the link congestion \(\overline{C(P_{sd}^{t})}\) between node \(s\) and node \(d\)
2:while\(\overline{C(P_{sd}^{t})}\geq C(P_{sd})_{low}\)do
3: Check a new topology \(G^{\prime}\)
4: Obtain the current optimal path based on the Equation (18)
5:if\(C(P_{sd})_{low}\leqslant\overline{C(P_{sd}^{t})}\leqslant C(P_{sd})_{high}\)then
6: Select the packet with the highest priority for the new route
7:endif
8:if\(\overline{C(P_{sd}^{t})}\geqslant C(P_{sd})_{high}\)then
9: Select a new routing transmission except for video packets
10:endif
11:endwhile
```  
**Algorithm 1** QCORS
## 3 Results and Discussion
### Landslide Susceptibility Mapping and Comparison of Models
#### 3.8.3 Landslide Area-Based Verification of Susceptibility Classes  
Figure 21 presents the model verification based on the spatial match between predicted susceptibility classes and the actual landslide area. The RF and XGBoost models both assigned over 85% of the total landslide area to the High and Very High zones, with RF slightly outperforming XGBoost at 88.7% versus 85.5%. In contrast, the LR model only assigned 76% of the landslide area to these critical zones, indicating a tendency toward underestimation. Furthermore, LR misallocated 10.3% and 7.1% of the landslide area to the Medium and Low classes, respectively, which values are considerably higher than those of RF and XGBoost, which confined these misclassifications to less than 7% each. These differences indicate a tighter spatial correspondence between ensemble model predictions and actual landslide areas, reinforcing their enhanced mapping accuracy. Overall, the RF and XGBoost models demonstrate superior capacity for accurately classifying high-resolution landslide-prone zones in earthquake-affected terrain. Their robust alignment with both landslide counts and areas confirms their operational utility for seismic hazard zoning and landslide risk mitigation in complex mountainous regions.
# Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems
## 4 Experiments
### Ablation Analysis
#### 4.4.2 Effect of Hyperparameters  
We follow the work of [25] to conduct a sensitivity analysis under different learning rates and random seed configurations. Table 7 presents the experimental results for Random Seeds 1234 and 1235 under two learning rate strategies. The results show that, across TSPs, CVRPs, and OPs of varying scales, the variations in average tour lengths and optimality gaps are minimal, demonstrating the robustness and reliability of our method under different hyperparameter settings. Taking CVRP100 as an example, the difference in the optimality gap for E-MTAM across different settings does not exceed **0.19%**.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{**Components of Proposed E-MTAM**} & \multicolumn{2}{c}{**OP20**} & \multicolumn{2}{c}{**OP50**} & \multicolumn{2}{c}{**OP100**} \\ \hline
**EDMHA Block** & **Dynamic Graph** & **JS Loss** & **Prize** & **Opt.gap** & **Prize** & **Opt.gap** & **Prize** & **Opt.gap** \\ \hline  & & & 5.23 & 2.97\% & 15.77 & 2.59\% & 31.92 & 3.83\% \\ \hline ✓ & & & 5.29 & 1.86\% & 15.87 & 1.98\% & 32.24 & 2.86\% \\  & ✓ & & 5.26 & 2.41\% & 15.82 & 2.29\% & 32.11 & 3.25\% \\  & & ✓ & 5.27 & 2.23\% & 15.84 & 2.16\% & 32.19 & 3.01\% \\ \hline ✓ & ✓ & & 5.33 & 1.11\% & 16.02 & 1.05\% & 32.69 & 1.51\% \\ ✓ & & ✓ & 5.32 & 1.30\% & 15.96 & 1.42\% & 32.53 & 1.99\% \\  & ✓ & ✓ & 5.32 & 1.30\% & 15.99 & 1.24\% & 32.61 & 1.75\% \\ \hline ✓ & ✓ & ✓ & **5.35** & **0.74\%** & **16.06** & **0.80\%** & **32.85** & **1.02\%** \\ \hline \end{tabular}
\end{table}
Table 6: Ablation analysis of different components of proposed E-MTAM on OPs.
## 3 EM Techniques for CH Diagnosis
### Microscopy
#### 3.1.1 Light Microscopy  
Light microscopy is a low-cost, user-friendly technique widely used to investigate CH artifacts. It involves illuminating the object with light reflected or transmitted, depending on the mode--reflectance, transmission, or both. Images depend on the surface and in transmission mode, as well as on internal structures, as light passes through the sample. Magnification decreases with sample thickness, so microscopy works best on flat surfaces. Using specialized lighting, such as raking or polarized light, enhances contrast. Sample preparation varies: in reflectance mode, no extraction is needed, whereas in transmission mode, samples are often extracted and may require reagent treatment.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## Abbreviations  
The following abbreviations are used in this manuscript:  
SSH sea surface height GNSS global navigation satellite system GNSS-R global navigation satellite system reflectometry iGNSS-R  interferometric global navigation satellite system reflectometry BDS  BeiDou Navigation Satellite System GPS  Global Positioning System GAL  Galileo Satellite Navigation System DM  delay mapping SWOT  Surface Water and Ocean Topography CYGNSS  Cyclone Global Navigation Satellite System SPIR  software-defined interferometric receiver UPC  Technical University of Catalonia MIR  microwave interferometric reflectometer PARIS-IOD  Passive Reflectometry and Interferometry System In-Orbit Demonstration ESA  European Space Agency ISS  International Space Station GEROS-ISS  GNSS Reflectometry, Radio Occultation, and Scatterometry on the International Space Station G-TERN  GNSS Transport Earth Reflectometry exploriNg
## 4 Results and Discussion
### Performance of Proposed DBN Model  
Table 4 presents the performance results from the proposed DBN model using \(R^{2}\) and \(RMSE\) indicators. It is worth noting that the closing value of \(R^{2}\) to 1, the better the predicted model would be. As can be seen, the proposed DBN model performed excellence for both training and testing datasets with the value of \(R^{2}\) were 0.9687 and 0.9734, respectively. With regard to the \(RMSE\) indicator, the proposed model can produce the output with a high level of accuracy with the value of \(RMSE\) for the entire database only at 0.1274.  
Figure 8: Sensitivity of input parameters to the output.  
Figure 7: Effect of chloride contamination level (**a–c**) and rebar corrosion status (**d–f**).  
To gain a better understanding of the ability of the proposed DBN model in predicting the output, scatter plots were employed to visualize the performance results. Figure 9a-c presents the experimental GPR data versus the prediction values generated from the proposed model for training, testing, and the entire database, respectively. As can be observed, the fitting line of the data was roughly coincident with the diagonal line, meaning that the proposed DBN model can yield very reliable outputs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \(\mathbf{R^{2}}\) & _RMSE_ & **Records** \\ \hline Training & 0.9687 & 0.1299 & 244 \\ Testing & 0.9634 & 0.1388 & 44 \\ Overall & 0.9681 & 0.1274 & 288 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the selected DBN model.  
Figure 9: _Cont._
# Implementation of a Blood Cold Chain System Using Blockchain Technology
## 2 Related Work
### Limitations of the Existing Research  
Currently, the blood cold chain system has problems with supply time and information management due to its centralized structure. Most of the studies conducted to solve the supply time problem have focused on mathematical models with objective functions and determine the number, location, or delivery route of blood sources. However, adjustment of the blood supply region may cause conflicts of interest between blood sources, avoidance of roles of medical institutions, accountability, and cost issues.  
This study aims to design and implement a new blood cold chain system based on blockchain technology to improve the above problems. Blockchain technology makes it difficult to forge and falsify information, allowing the system to be managed transparently in real time. In addition, blockchain-based smart contracts can save time and effort related to document processing, and have a feature of speeding up execution. To solve the supply time problem by using these advantages, blood transactions between hospitals are designed and executed as smart contracts in emergency situations. In addition, in the case of disposing of blood in the hospital, which is the final consumption place of donated blood, a method in which the cause can be recorded in real time can be provided. Therefore, the proposed system overcomes the limitations of the existing system and prior research by considering both the aspects of the existing blood information system and the supply time.
## 3 Background
### Edge Computing  
\"Edge\" in our context literally means geographic distribution. Edge computing refers to the computing that is taking place near the data source [18]. Although computing is no longer done at the cloud, this does not exclude it from the architecture, it actually means that the cloud is coming closer to the users. One of the great advantages that the edge computing came up with is the speed and reduced latency.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 2 Related Work
### Selfish Node Detection  
Selfish node detection techniques in wireless ad hoc networks have attracted many researchers recently. [PERSON] et al. [6] reviewed several different selfish nodes detecting methods including Watchdog and Pathrater approaches in MANETs. Although the Watchdog approach can identify the misbehavior node at the forwarding level, it is not able to detect the misbehavior node in collision situations. The Pathrater approach eliminates the route containing misbehavior nodes from the routing protocol. [PERSON] et al. [9] proposed a detection scheme for selfish nodes in MANETs based on game theories. The proposed protocol uses the battery as a limited resource in routing and identifies selfish nodes with their battery consumption. However, the energy generation and consumption are not adequately discussed. [PERSON] et al. [10] proposed a selfish node monitoring and detecting system with reduction in energy consumption and without lowering detection performance. [PERSON] et al. [11] proposed an Attacked Packet Detection Algorithm (APDA) in vehicular ad hoc networks (VANETs) that uses node position, velocity, frequency, and the number of packets broadcast per second, attached to road side unit (RSU), to detect DoS attacks before verification time. [PERSON] et al. [12] further developed Enhanced Attacked Packet Detection Algorithm (EAPDA) in VANETs, upgraded the algorithm by the improvement of throughput. [PERSON] et al. [13] proposed a collaborative security attack detection mechanism based on multi-class support vector machine (SVM) in a software-defined vehicular cloud (SDVC) environment. However, the mechanism assumed that all vehicles in the network have sufficient resources for analyzing incoming flow data. [PERSON] et al. [14] concluded machine learning based solutions to detect distributed denial of service (DDoS) attacks on VANET systems. Furthermore, [PERSON] et al. [15] analyzed various state-of-the-art approaches for DoS attack detection in VANETs. However, in the abovementioned literature, the impact of selfish nodes, the focus of this paper, is not discussed adequately in neither MANETs nor VANETs.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Impact of Varying Network Radius on Transmission Loss  
Transmission loss for EEBET, EBET, BTM and the UWDAR techniques is shown in Figure 9. Form this figure, it is clear that the transmission loss of BTM and UWDAR is relatively high as compared to EBET and EEBET. Since transmission loss depends on spreading loss and distance, both BTM and UWDAR schemes use direct transmissions which leads to high path loss. The EBET and the EEBET minimize the communication distance to achieve relatively low transmission loss. This figure also shows the impact of network radius increase on the transmission loss. In this regard, EBET and EEBET exhibit less increase whereas BTM and UWDAR show very high increase. Thus, our proposed techniques are less affected by the network radius as compared to the selected existing ones.