esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis of Results
### Analysis of Duplicate Packets  
The total number of duplicate packets forwarded in the network is plotted by varying the transmission range and the network size, as shown in Figures 16 and 17. The total forwarded copies in the sparse network are lesser than those in the denser network, as shown in Figure 16. The reason is that most of the packets do not reach their destinations and are dropped earlier from traversing even four hops from the source node. For bigger networks and transmission range, a large number of duplicate packets is forwarded in the network due to the reason that the pipeline radius increases and the number of PFNs in the potential forwarding zone also increases. The second reason is that the propagation delay between the two forwarders is greater than the holding time difference.  
The number of copies forwarded in ESEVBF is greater than that in ESEVBF, which is due to the hidden terminal problem, as discussed in Section 3. Although the holding time is expanded in ESEVBF in such a way that the holding time difference between the two forwarder nodes is greater than the propagation delay between them; however, due to the hidden terminal problem, the reproducing nodes initiate duplicate packets. The proposed protocol suppresses the instantiation of the duplicate packets from the reproducing nodes by using _Container_ and _Announcement Packets_. For the transmission range  
Figure 21: Number of Nodes vs. End-to-End delay.  
Figure 22: Number of Nodes vs. End-to-End delay.  
of 900 m, it forwards, on average, 13.5 lesser copies than the ESEVBF. For lower transmission range and small network size, ESEVBF performs better in terms of forwarding copies than the EESEVBF (see Figure 16). The reason is that the source node has the very least number of forwarding nodes in the transmission zone, and according to the holding mechanism of ESEVBF, the holding time difference is greater than the propagation delay between them, which causes the suppression of more data packets. In this case, the proposed protocol is not much effective due to the small number of nodes and the higher probability that all nodes are in the transmission range of one another, thus overcoming the occurrence of the hidden terminal problem. In contrast, for higher transmission, by solving the hidden terminal problem, the proposed protocol suppresses duplicate packets more than those by ESEVBF. The similar trends can be seen from Figure 17, when the transmission range increases for the same number of nodes, the difference between the forwarded copies of the two protocols is also increased. The effectiveness of the proposed protocol increases as the transmission range increases and the reason is clear, i.e., the suppression of duplicate packets initiated by the reproducing nodes.
## 4 Methodology
### Optimization Objectives and Constraints  
The core focus of this work lies in constructing and optimizing the topology structure of satellite-UAV-integrated networks to minimize the average end-to-end communication delay for nodes such as satellites and drones while ensuring a high level of network resilience. Assuming a consistent transmission rate of electromagnetic waves in a vacuum, the internode distances can be considered as the end-to-end delay.  
To describe the problem abstractly, in time slice \(s_{i}\), an adjacency matrix \(\mathcal{A}(s_{i})\) is given to represent the graph with \(\mathcal{N}\) nodes, and a subgraph should be found to minimize the objective function under some restrictions. Suppose the selected visible matrix to be \(C(s_{i})\) in time slice \(s_{i}\), ensuring that  
\[\min\tau(s_{i})=\frac{2}{\mathcal{N}(\mathcal{N}-1)}\sum_{i=1}^{\mathcal{N}-1 }\sum_{j=1}^{i-1}Delay_{\text{C}}\big{(}s_{i},v_{i},v_{j}\big{)} \tag{9}\]  
An algorithm should be designed to find a subgraph \(C\) with distance sum \(\tau(s_{i})\) as small as possible in the time slice \(s_{i}\), and the running time should be as short as possible. In addition to the dynamic nature of networks, there are other limitations that contribute to the complexity of satellite-UAV-integrated network optimization:  
* For invisible node pairs, it is required that they cannot have links.
* Due to the limitation of the number of transmitters and receivers on satellites, the number of satellites that can be linked simultaneously with other satellites/drones is limited.
* Considering the load balancing of the overall network, the total number of links cannot exceed a given value.
* The internode communication is assumed to be bidirectional by default. For a link from \(v_{i}\) to \(v_{j}\), there must be a corresponding edge from \(v_{j}\) to \(v_{i}\).  
The aforementioned constraints can be formalized as follows:  
\[C_{i,j}=\begin{cases}compare\big{(}\mathcal{A}_{ij},\infty\big{)}&,\mathcal{ A}_{i,j}<\infty\\ \infty&,\mathcal{A}_{i,j}=\infty\end{cases} \tag{10}\]  
\[Degree_{\text{C}}(v_{i})=\sum_{i=1}^{\mathcal{N}}[C_{v,i}<\infty]\leq Max_{ degree},\forall v_{i}\subseteq[1,\mathcal{N}] \tag{11}\]\[Edgesum_{C}=\sum_{i=1}^{\mathcal{N}}\sum_{j=1}^{i}[C_{\texttt{D},i}<\infty]\leq Max _{edge} \tag{12}\]  
\[C_{i,j}=C_{j,i},\forall i,j\in[1,\mathcal{N}] \tag{13}\]
## 4 Experiment
### Randomized-Map Experiment
Figure 18: Comparison of the number of path nodes, total path length, and total path-turning angle. (**a**) Comparison of the number of path nodes based on 20% obstacle-occupied maps. (**b**) Comparison of the number of path nodes based on 30% obstacle-occupied maps. (**c**) Comparison of the number of path nodes based on 40% obstacle-occupied maps. (**d**) Total path length based on 20% obstacle-occupied maps. (**e**) Total path length based on 30% obstacle-occupied maps. (**f**) Total path length based on 40% obstacle-occupied maps. (**g**) Total turning angle of the path based on 20% obstacle-occupied maps. (**h**) Total turning angle of the path based on 30% obstacle-occupied maps. (**i**) Total turning angle of the path based on 40% obstacle-occupied maps. From the perspective of the number of path nodes, the length of generated paths, and the total number of path turns, the Theta* algorithm has a definite advantage. The any-angle path planning strategy of the Theta* algorithm causes it to ignore most of the redundant nodes generated by the A* algorithm during the search process, as shown in Figure 18a-c. Also, because the Theta* algorithm is no longer limited by the expansion angle during the search process, it results in a shorter generated path, as shown in Figure 18d-f, and a smaller total path turning angle, as shown in Figure 18g-i. On the contrary, the RRT algorithm is limited by the design of the step size, which leads to an inevitable significant increase in the number of nodes as the map size increases. Additionally, the random-search nature of RRT can lead to too many path-turning angles and poor smoothness. The 3D JPS algorithm was similar to the 3D A* algorithm in terms of the number of path nodes, the total length of the path, and the total turn angle of the path in the comparison experiments of the four algorithms. The 3D JPS algorithm increased the path length by 0.583% to 5.9% and the total path-turning angle by 27% to 98.2% compared to the Theta* algorithm. The 3D JPS algorithm is not optimal in terms of path quality because its search angle is limited by the discretized grid map. There are still redundant turn points in the paths generated by the 3D JPS algorithm that can be optimized, and these unnecessary turn points cause the 3D JPS algorithm to be higher than the Theta* algorithm, which supports any-angle path planning, in terms of path length and total turn angle. Through the comparative analysis of five sets of metrics for the 3D spatial path-planning algorithm, it can be seen that the 3D JPS algorithm performs better in terms of time complexity and space complexity, but its generated result paths still need further optimization and improvement in terms of length and smoothness.
## 2 Theoretical Background  
### Electrode System of Submerged Arc Furnaces  
Figure 1 shows the principle of the three-phase electrode system of submerged arc furnaces. The electrical energy is delivered into the furnace through the electrode. The arc heat at the electrode end and the resistance heat between the material or slag are used to melt and reduce the ore [2]. During the smelting process, the molten metal is increased by the melting of the ore, and the electrodes are consumed by corrosion. Therefore, it is necessary to raise and lower the electrode to adjust the position of the electrode ends so that the furnace maintains the proper smelting temperature [24]. However, because of the extremely high temperature in the furnace, there still needs to be suitable measurement techniques to determine the position of the electrode's working end [25]. In addition, because of mechanical stress and thermal deformation, cracks may occur in the electrode during the smelting process. If these hidden cracks are not found in time, serious accidents can easily be caused. In this paper, the method we considered was the use of ultrasonic-guided waves to detect the position of the electrode's working end and the hidden cracks in the electrode. However, the critical basis for using ultrasonic-guided wave technology is that there is a channel suitable for ultrasonic propagation in the electrode.
# Model Modification of the Soil-Water Characteristic Curve of Unsaturated Weak Expansive Soil
###### Abstract  
This study evaluates the impact of compaction on the soil-water characteristic curve of unsaturated remododeled weakly expansive soils by assessing changes in soil pore structure resulting from variations in compaction. The remododeled weakly expansive soil in the Xinjiang Hami area is taken as the research subject to investigate how compaction affects microscopic pore structure using mercury intrusion testing. Subsequently, mercury intrusion porosimetry is employed to examine pore structure and distribution patterns at different dry densities. Based on the capillary principle and experimental methods (filter paper method and pressure plate method test), modified soil-water characteristic curves are obtained by fitting them with a three-parameter model law. The results indicate that higher dry density leads to an increased air intake value and significantly reduces the total volume of large pores within samples. Both the Fredholm and Xing model and the three-parameter model effectively capture the influence of initial dry density on the development pattern of the soil-water characteristic curve.  
expansive soil; soil-water characteristic curve; matric suction; pore struct 2024 14 7498 1
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP@50/\%** & **mAP@50:90/\%** & **FPS** \\ \hline Faster R-CNN & 52.1 & 50.6 & 53.7 & - & \\ SSD & 61.2 & 59.3 & 62.8 & - & \\ YOLOv3-tiny & 81.8 & 78.4 & 84.5 & 47.6 & 62.9 \\ YOLOv5 & 90.5 & 84.0 & 90.6 & 59.4 & 77.8 \\ YOLOv8n & 90.6 & 86.2 & 91.2 & 60.1 & 79.5 \\ YOLOv9t & 90.1 & 83.0 & 89.1 & 58.3 & 38.2 \\ YOLOv10n & 89.1 & 82.8 & 90.3 & 59.0 & 65.0 \\ YOLO-SAR (ours) & 92.9 & 88.6 & 93.8 & 61.6 & 51.7 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison results for the Lowship dataset. In terms of experimental setup, the same hardware platform and hyperparameter configurations as the main experiment were adopted. The real-world dataset was partitioned using an 8:2 split into a training set (6823 images) and a validation set (1706 images). All baseline models were trained from zero, with consistent evaluation and testing procedures. The experimental results on the real-world dataset are summarized in Table 2. YOLOSAR achieves substantial improvements across all key detection metrics, notably achieving an accuracy of 96.4%, recall of 93.6%, and [EMAIL_ADDRESS] of 94.5%, outperforming state-of-the-art detectors such as YOLOv5, YOLOv8n, and YOLOv10n. These results underscore its robustness and generalization capability in real-world low-light conditions. Benefiting from the synergistic integration of an image enhancement module, receptive-field attention mechanism, and multi-scale feature fusion strategy, YOLO-SAR demonstrates superior perception of low-texture targets, along with enhanced fine-grained detection and adaptability to complex environments, making it well-suited for nighttime maritime surveillance tasks in challenging inland waterway scenarios.
# An Internet of Things Model for Improving Process Management on University Campus
## 2 Materials and Methods
### Architecture of a University Campus  
In general, university campuses have a layered network architecture that guarantees scalability and modularity. Figure 1 identifies the layers of the architecture of a university campus, the first of which is the access layer that is responsible for providing services to end-users [23]. The distribution layer provides policy-based connectivity and controls the boundary between the access and the core layers. The core layer provides fast transport between distribution switches within the campus [24]. The benefit of dividing a flat network into smaller and easier to manage blocks is that local traffic remains local. Only traffic destined for other networks is moved to a higher layer.  
The proposal for the IoT model applied to a university is based on the one proposed in [25] that starts from a network architecture, as presented in Figure 2. It should be considered that any increase in IoT devices will affect the performance of the network. Certainly, in a traditional campus network, the performance of wireless networks suffers. With the inclusion of devices that use the wireless medium as the main communication channel, the problems increase. However, this problem can be overcome by updating the network or even integrating a network that specifically handles the connectivity of IoT devices [26].  
The proposed IoT solution for the management of certain processes and resources of a university necessarily responds to a distributed technological architecture, where the devices can interact with each other in a scalable, flexible, robust, efficient, and secure way [18]. For this purpose, it is proposed  
Figure 1: The layered network architecture of a university campus [24].  
Figure 2: IoT architecture composed of a simple three-layer model.  
to apply the basic IoT architecture adapted to the requirements of a university environment. These requirements relate to connectivity and communication, the management and control of devices, as well as data collection and analysis [27]. This architecture responds to the needs of the university that is considered in this paper, with the requirements of each of the three layers being easily solved by the infrastructure owned by the university. Figure 2 shows the IoT architecture based on the three-layer model.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Self-Organizing Control Using Artificial Potential Functions
#### 2.2.1 Scale-Independent Relative Orbital Elements and Bound Formulations
\begin{array}{l}-B_{3}s^{6}+(6A_{3}-2C_{3})s^{5}+(5B_{3}-3D_{3})s^{4}+(4C_{3 }-4E_{3})s^{3}+(3D_{3}-5F_{3})s^{2}\\ \frac{\partial w}{\partial s}=\frac{+(2E_{3}-6G_{3})s^{3}+F_{3}}{\left(1+s^{2} \right)^{4}}\end{array}  \tag{26}\]  
Let \(\partial w/\partial s=0\), there exists  
\[ \begin{array}{l}-B_{3}s^{6}+(6A_{3}-2C_{3})s^{5}+(5B_{3}-3D_{3})s^{4}+(4C_{3 }-4E_{3})s^{3}+(3D_{3}-5F_{3})s^{2}\\ +(2E_{3}-6G_{3})s^{3}+F_{3}=0\end{array}  \tag{27}\]  
or  
\[s=\pm\infty \tag{28}\]  
The real roots of Equation (27) together with \(s=\pm\infty\) constitute a set of \(s\) values that perhaps make the squared relative distance take the maximum or minimum value, which are relative motion bounds. Note that when \(s=\pm\infty\), \(\psi=\pm\pi\), the squared relative distance takes \(w=A_{3}\). Assuming that \(s_{\max}\) is the \(s\) value that makes the squared relative distance \(w\) take the maximum value and \(s_{\min}\) is the one that makes \(w\) take the minimum value, the maximum bound \(\widetilde{d}_{ij}\) and the minimum bound \(\widetilde{d}_{ij}\) for the current time can be written as  
\[\left\{ \begin{array}{l}\widetilde{d}_{ij}=\sqrt{\frac{A_{3}s_{\max}^{6}+B_{3 }s_{\max}^{5}+C_{3}s_{\max}^{4}+D_{3}s_{\max}^{3}+E_{3}s_{\max}^{2}+F_{3}s_{ \max}+G_{3}}{\left(1+s_{\max}^{2}\right)^{3}}},\text{if}\quad s_{\max}\
eq\pm \infty\\ \widetilde{d}_{ij}=\sqrt{A_{3}},\text{if}\quad s_{\max}=\pm\infty\\ \end{array} \right. \tag{29}\]  
\[\left\{ \begin{array}{l}\widetilde{d}_{ij}=\sqrt{\frac{A_{3}s_{\min}^{6}+B_{3 }s_{\min}^{5}+C_{3}s_{\min}^{4}+D_{3}s_{\min}^{3}+E_{3}s_{\min}^{2}+F_{3}s_{ \min}+G_{3}}{\left(1+s_{\min}^{2}\right)^{3}}},\text{if}\quad s_{\min}\
eq \pm\infty\\ \end{array} \right. \tag{30}\]  
Relative motion bounds are complicated functions of SIROEs, and SIROEs are functions about translational states, given in Equation (23). Therefore, \(\widetilde{d}_{ij}\) and \(\widetilde{d}_{ij}\) are functions about translational states \(x,y,z,x,\dot{y},\dot{z}\).
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Information System and Attributes
#### 2.1.2 Abstraction Level  
Developers use hardware description language to describe the designed IC at different levels of abstraction [20]. Correspondingly, an HT can be designed with four levels of abstraction, which are, in decreasing order, the _Register Transfer_, the _Gate_, the _Layout_, and the _Physical_ level. At the register transfer level, each module of the IC is described in terms of signals, registers, and Boolean functions. An adversary has full access to the functionality as well as the implementation of the modules, and can easily change them at this level. A design is represented as a list of gates and their interconnections at the gate level. Here, an adversary can insert HTs as the relevant gates and their interconnections. At the layout level, the impact of HTs on power consumption or delay characteristics can be planned. HTs can be realized even by changing the parameters of the original circuit's transistors. All circuit components s well as their dimensions and locations are determined at the physical level, which is the lowest level of abstraction. Attackers may insert HTs by modifying the size of wires or the distance between circuit components. It can be seen that as the abstraction level is reduced, attackers tend to implant HTs more specifically and carefully. This makes such HTs more difficult to detect, and thus more threatening.
## 4 Discussion
### Three-Dimensional Surface Deviation Analysis and Geometric Parameters  
The areas with the largest surface deviations were found on the convex side at the level of the CLAV, STRN, and superior of the iliac crest (ILIAC) in the medial/lateral direction (Figure 7f). The smallest surface deviations were located at the pelvis in the medial/lateral direction on the convex and concave sides (Figure 7f,g) and on the posterior side (Figure 7i). These results indicate that most of the rectification occurs on the convex side at the thoracic level, as it was observed in the ALP and centroid results, and at the iliac crest marker level corresponding to the location of the iliac crest pads. Deviations at the iliac crest and R-ASIS increased, corresponding to removal of material from the mould in the abdominal area, indicating a large anterior rectification in the sagittal plane (Figure 7h).  
Rectification strategies were observed from lateral contours of the patient's body in the coronal plane, in terms of medial/lateral deviations and a torso tilt correction (Figure 8). The medial/lateral deviations at the waist level with respect to the pelvis width (L-ASIS to R-ASIS) showed a mean rectification ratio at the level of the iliac crest of 0.23 \(\pm\) 0.04 on the convex side and 0.11 \(\pm\) 0.02 on the concave side (Figure 8i), therefore, demonstrating that this ratio can be used as a parameter for digital rectification. The lateral tilt angles from the external contours of the post-rectification mould were 81 \(\pm\) 3.8\({}^{\circ}\) and 83.3 \(\pm\) 2.6\({}^{\circ}\) on the convex and concave sides, respectively (Figure 8j). Moreover, the analysis of the contours in the sagittal plane showed mean tilt angles of 82.4 \(\pm\) 2.4\({}^{\circ}\) (anterior) and 85.2 \(\pm\) 2.3\({}^{\circ}\) (posterior) on the post-rectification moulds (Figure 8k). The tilt angle results found in our study indicate rectification parameters that can be used to define the initial geometry of the rectified mould and the rectification distance at the iliac crest region, preceding the addition/removal of material corresponding to the pressure pad areas.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 5 Numerical and Simulation Results
### Simulation Results
#### 5.3.1 Intra-Gateway Mobility with X2 Handover  
As described in the handover section above, Intra-H.O. occurs when U.E. enters a new domain outside the coverage area of the current base station. In the first simulation scenario, U.E. moves from source to target eNB with speed ranging from 18 km/h to 120 km/h. The total execution time is composed of time for attachment/re-attachment and data delivery. The distance between eNBs is 60 m. After the handover procedure finishes successfully, the value delaySum is collected from the flow monitor module. Figure 15 shows the proposed 5G reduces by 40% of total delay time than the legacy 4G architecture for different U.E. speeds. This reflects the ability of the proposed framework to save time and get better results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Speed of UE & From 5 to 120 km/h \\ eNB Tx Power & 46 dBm \\ Distance between eNB & 100 m \\ EPS Bearer type & NGBR-VIDEO-TCP \\ QCI & 9 \\ Bandwidth & 5 MHz \\ Data rate & 100 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key parameters for simulation.  
The second simulation scenario, study the performance while U.E. moves across several eNB in the same gateway's domain. The number of eNB is varied from 2 to 6. The distance between eNBs is 60 m. All eNBs connect to only one S-GW/ P-GW (in the case of 4G) and one M-GW (proposed 5G). U.E. moves across eNB's domain with a speed of 108 km/h. After 2 s since U.E. has re-attached to the new eNB, the value delaySum is collected from the flow monitor module. The result is displayed in Figure 16. As expected, the proposed 5G model reduces more than 30% in terms of the total delay time.  
Figure 14: Comparison of 4G and proposed 5G in terms of latency of initial attachment.  
Figure 15: Comparison of 4G and proposed 5G in terms of latency of Intra-Handover with varied UE speed.