esa-sceva/satcom-chunk-collection huggingface dataset:
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 2 Related Works
In [25], a protocol called the Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) protocol was presented based on WDFAD-DBR, the work presented in [23]. The protocol handles the void area problem by choosing the forward candidates among the total distributed nodes that have a large residual energy and have several nodes in their transmission range (neighbors). To obtain this useful node information, each node in the network topology exchanges its information with its one-hop neighbors through the neighbor request and neighbor acknowledgment packets, and each node has to maintain its neighbor table. EDOVE considers energy level as one of its parameters to help in reducing energy consumption and avoiding energy holes. However, exchanging neighbor information and maintaining the neighbor table consumes node resources. The protocol also suffers from duplicate packet transmissions, which increases energy consumption. Moreover, the void area is not handled completely since the protocol only addresses energy void holes. Most recently, an Energy Efficient Depth-Based Opportunistic Routing protocol (EE-DOR) was presented in [15]. EEDOR is a hybrid forward set selection procedure where the forwarding candidate set is selected in a cooperative way between the current forwarder node and its neighbors. In EEDOR, the current forwarder node's forwarding set contains only the candidate neighbors with less depth than the current forwarder. Moreover, the nodes in the forwarding candidate sets are given priorities based on their depths. These priorities are used in the novel holding time proposed by the protocol to determine the transmission time for each node in the forwarding set candidates. The proposed holding time reduces the collisions and retransmissions issue caused by multiple nodes having equal transmission times. The protocol enhances the lifetime of UWSNs, decreases the total energy consumption and reduces the number of nodes involved in the packet forwarding procedure. In contrast, EEDOR suffers from a low packet delivery ratio in the simulated network topology. In addition, the protocol does not address the void area problem. Advantages and disadvantages of the OR protocols for UWSNs related to our work discussed above are summarized in Table 1.
## 3 Network Model and Problem Statement
### Network Model  
We model the cloud data center as an undirected graph \(G=\) <V, E>, where V is the collection of all switches \(s\) and hosts \(h\) in the network, and \(E\) is the collection of links connecting these switches and hosts. All links adopt a full-duplex communication mode, which means that data can be simultaneously transmitted on the same link.  
Considering the actual application and the simulation of network performance, we adopt the following two models accepted by most researchers [31; 32; 33; 34].  
The first one is the network center model with three-tier architecture. h1-h8 are clients and nodes that generate data transmission tasks. s1-s10 are switches, forming a hierarchical inter-networking model with core layer, convergence layer, and access layer. This three-layer non-blocking fully populated network is at the center of today's network. It has a wide range of applications [35], shown in Figure 1.  
In the second type of network topology shown in Figure 2, we use a two-layer architecture, where S1-S9 are the core layer switches, and each switch has its own access layer and client. For example, S1 and its corresponding components are a group of networks; S11 and S12 are access layers; and h1-h4 are clients. In real life, this network topology covers fewer networks and ranges but reduces the workload of deployment and maintenance.  
All the switches in this article support the OpenFlow protocol, which means that the packets in the flow have sequence numbers to record their position in the flow. If the flow changes the corresponding path during transmission, the packet will arrive at a random time node. However, there is no need to retransmit the packets that have been transmitted before; instead, after all the flows are transmitted, the packets are reordered according to the sequence number.  
Figure 1: Three-layer non-blocking fully populated network model.
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Performance in Complex Scenarios  
To investigate how different methods perform in complex scenarios, we analyzed images containing multiple densely distributed instances of the same target category (person), as shown in Figure 9. The figure reveals notable issues with Seg-XRes-CAM. For example, in the first scene, weak positive responses appear along the edges of umbrellas and at the boundaries of the crowd, causing the activation regions (blue areas) to extend into non-target regions such as umbrellas and the ground. This validates our previous discussion, highlighting how local pooling mechanisms can overestimate the influence of features at boundaries, leading to excessive activation in irrelevant areas.  
In contrast, our method achieves more accurate target localization. In the same scene, it clearly distinguishes between relevant and non-relevant regions, generating heatmaps with more concentrated activation regions primarily focused on the human figures. This indicates that our approach is capable of preserving semantic consistency even in visually cluttered environments. A similar trend is observed in the basketball court scene, where our method maintains robust identification performance even under lower image retention rates, effectively filtering out irrelevant visual information and producing more precise and concise explanations.  
The experimental results confirm the importance of precise spatial information utilization in enhancing the interpretability of the model in complex scenes. Using a more refined feature processing approach, our method effectively mitigates background interference commonly observed in gradient-based methods and generates more detailed and reliable explanations.  
Figure 8: Visualization comparison of local interpretation of different methods.The red boxes indicate the parts which users’ focus on, and different colors reveal the heat map which generated by XAI method.  
Figure 9: Visualization comparison of different methods in complex scene.
## 2 Materials and Methods
### Local Feature Matching  
After the feature descriptors extracted by SURF-BRISK are 512-bit binary bit strings consisting of 0 and 1, the Hamming distance is used to measure similarity. Assuming that there are two descriptors of \(S_{1}\) and \(S_{2}\), the Hamming distance is determined as  
\[D_{kd}(S_{1},S_{2})=\sum\
olimits_{i=1}^{512}(x_{i}\otimes y_{i}), \tag{9}\]  
where \(S_{1}=x_{1}x_{1}\dots\ x_{512}\), \(S_{2}=y_{1}y_{2}\dots\ y_{512}\), \(x\), \(y\) and the value of x and y is 0 or 1. The smaller the value of \(D_{kd}\), the higher the matching rate, and vice versa. Therefore, the matching point pairs are obtained using the nearest-neighbor Hamming distance in the matching process.  
Here, three descriptors, SURF, BRISK, and SURF-BRISK, are compared. Two tile images have been chosen for matching tests to compare the real-time and matching rate of these descriptors, as shown in Table 1 and Figure 5. Obviously, the SURF algorithm has the most matching points, the BRISK algorithm has the fastest matching, and the SURF-BRISK algorithm combines the advantages of both. The algorithm is faster than SURF and gets more matching points than BRISK.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 4 Design of Experimental Scenario and Experiment Results
### Design of Experimental Scenario
#### 4.1.2 STUXNET Case-Based Network Topology and Scenario Data Configuration  
A scenario was constructed using the attack graph designed based on the STUXNET case, as shown in Figure 10, out of the cases [11; 12] introduced in Section 4.1.1. The goal of the operation and the commander's goal were to neutralize the target industrial process at the bottom of Figure 10, and it was assumed that the operation was performed in cyberspace. After that, it was assumed that the targets analyzed according to the cyber target processing procedure in Figure 3 were composed of the network topology structure shown in Figure 11.  
Figure 10: Attack Graph designed based on STUXNET case.  
Each vertex in Figure 10 is defined as one node in Figure 11, which shows the network structure. In addition, assuming that there can be multiple nodes such as a printer server, employee workstation, and laptop, 2-5 nodes were placed. The cluster consisted of each tier in Figure 10, except for the initial asset layer and the print server. Each cluster was also assumed to be connected to one switch, and the switches were assumed to be connected to each other in one direction.  
To perform the TIR proposed in this study, it needed to be converted into a logical network topology as shown in Figure 9. It could be converted as shown in Figure 12 through the following process:  
1. It is assumed that nodes clustered based on the switch (clotted with a green background) may communicate internally with each other because of the switch;
2. Connect the nodes in one cluster to each other.  
Among the nodes in Figure 12, the common vulnerabilities and exposures (CVE) of the hosts were selected by combining the contents of case [11] and Figure 10. In the case of an engineering station, as shown in Figure 10, there was only network share in the call related to the vulnerability. After that, the Windows shell LNK (CVE-2010-2568)--a network share  
Figure 11: STUXNET case-based network topology (Physical).  
Figure 12: STUXNET case-based network topology (Logical).  
vulnerability introduced in the cases in [11]--was selected as a vulnerability, and common vulnerability scoring system (CVSS) v2 [13] parameter scores were inserted. There were a total of two neighboring hosts (other hosts in the same cluster), and this operation was repeated to generate data to be used in the experiment, as listed in Table 12.
## 3 Proposed Algorithm
### Definition of Opposite Path using the Current Optimum
For instance, the second node O\({}_{2}\) of opposite path \(P_{i}^{o}\) and node B\({}_{2}\) of \(P_{i}\) have the same/similar distance from nodes A\({}_{2}\) and A\({}_{3}\) of the optimal path \(P_{co}\). The third node O\({}_{3}\) of opposite path \(P_{i}^{o}\) and node B\({}_{3}\) of \(P_{i}\) have the same/similar distance from nodes A\({}_{3}\) and A\({}_{4}\) of the optimal path \(P_{co}\), and so on. For opposite path (Circle), the ellipse \(E_{k}\) is degenerated into a circle \(C_{k}\), in which the center is the \(k\)th node of the optimal path \(P_{co}\) and the radius is the distance between the center and the \(k\)th node of \(P_{i}\). For instance, the node B\({}_{2}\) of \(P_{i}\) is located at the boundary of the circle \(C_{2}\) and the second  
Figure 4: Component elements of opposite path (Backward Ellipse). (**a**) Optimal path in the current population. (**b**) Candidate path. (**c**) Cities in the graph. node O\({}_{2}\) of opposite path \(P_{\ell}{}^{\rho}\) is selected around the boundary. The node B\({}_{3}\) is located at the boundary and the node O\({}_{3}\) is selected around the boundary of the circle \(C_{3}\), and so on. Without a doubt we introduce the information of the optimum solution in the current population, including the Euclidean distances and the node sequences, into all definitions of opposite path in this paper. The path and its corresponding opposite path have the similar distance to the current optimum in a sense. In addition, the node sequences of opposite path are nearly kept with the original path and optimal path in order, although the direction of opposite path may be slightly different with the original path. In a word, our definition method of opposite path, which is significantly different from [28], considers both the node sequences of candidate paths and the distances between adjacent nodes at the same time. In my opinion, it may be a novel and promising attempt to applying the opposition-based soft computing in discrete domain.
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 2 Materials and Methods
### Refik Cesur Intersection Signalization Operation Types
#### 2.2.2 Fully Adaptive Signaling Management  
Refik Cesur Intersection fully adaptive signaling system works with a camera and loop-sensor-based smart signaling system. The number of vehicles at the intersection, vehicle classes, queuing, average speed, and vehicle directions are determined by image processing technology through the fisheye camera placed at the intersection. In addition, detected data are supported by loop sensors placed at intersection directions. Both sensor types work in a hybrid manner. According to the detected traffic data, signal times for intersection directions are determined dynamically. The minimum and maximum times determined for Refik Cesur Intersection traffic flows are shown in Table 2.  
Phase patterns and signal times change automatically according to the traffic data obtained through the image-processing-based sensor and loop sensor at Refik Cesur Inter  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fixed-Time Signaling Program** & **First Phase (45 s)** & **Second Phase (10 s)** & **Third Phase (10 s)** & **Fourth Phase (20 s)** & **Fifth Phase (15 s)** \\ \hline Q5 & & & & \\ \hline Q6 & & & & \\ \hline Q7 & & & & \\ \hline Q8 & & & & \\ \hline Q9 & & & & \\ \hline Q10 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fixed-time signaling management program and fixed times.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Flows** & **Minimum Green Time (s)** & **Maximum Green Time (s)** \\ \hline Q5 & 25 & 70 \\ Q6 & 25 & 70 \\ Q7 & 10 & 20 \\ Q8 & 10 & 20 \\ Q9 & 8 & 25 \\ Q10 & 10 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic flows minimum and maximum green times.  
section. The image-processing-based sensor located at Refik Cesur Intersection is shown in Figure 3.  
Flexible structures are available in fully adaptive signaling operation. The phases formed by traffic flows change dynamically. The intersection control device at the intersection works in a matrix structure. Traffic flows that can work together are programmed into the intersection control device. As a result of the algorithms in its structure, the intersection control device automatically decides the phase structures according to the number of vehicles, vehicle classes, queuing, average speed, and neighboring intersection data coming from the sensors. Matrix phase structures of Refik Cesur Intersection according to traffic flows are shown in Table 3. For example, when Q6 traffic flow is turned on, Q5 or Q8 traffic flow can operate simultaneously. In other words, there are two different phase options: Q5-Q6 or Q6-Q8 phases. The intersection controller evaluates both options based on traffic data at the intersection.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 3 TCP in Dynamic Blockage Environments
### TCP Throughput  
Using the geometric argument, see Figure 1, the TCP throughput during the blocked and non-blocked states can be represented as \(R_{i}=C_{i}S_{R_{i}}/T_{i}\), \(i=0,1\), where \(S_{R_{i}}\) is the mean area under the rate line in blocked (\(i=0\)) and non-blocked (\(i=1\)) states, \(C_{i}\) is the link rate in blocked and  
Figure 1: Example Transmission Control Protocol (TCP) throughput in the presence of dynamic blockage.  
non-blocked states, determined by the system parameters [12] and propagation model [13]. Hence, the time-averaged TCP throughput can be defined as  
\[R=\frac{C_{0}S_{R_{0}}+C_{1}S_{R_{1}}}{(T_{0}+T_{1})}, \tag{1}\]  
and the task at hand reduces to determining \(S_{R_{0}}\) and \(S_{R_{1}}\).  
Observing Figure 1, one may notice that \(S_{R_{0}}\) and \(S_{R_{1}}\) can be found by using a similar approach. In what follows, we utilize \(S_{R_{1}}\) as an example. We proceed by differentiating between two cases, \(T_{1}<\tau_{1,0}\) and \(T_{1}\geq\tau_{1,0}\), where \(\tau_  
The throughput during the blocked interval is characterized similarly. Omitting the intermediate calculations, we have  
\[R_{0}=\left\{\begin{aligned} &\frac{T_{0}^{2}+T_{0}C_{0}D}{2D},& T_{0}<\tau_{0,0},\\ &\frac{T_{0}^{2}+T_{0}C_{0}D}{2D}+\frac{3(T_{0}-\tau_{0,0})C_{1} \tau_{0,1}}{4\tau_{0,1}},& T_{0}\geq\tau_{0,0},\end{aligned}\right. \tag{6}\]  
where \(T_{0}\) is the mean duration of the blocked interval, \(C_{0}\) is the maximum rate during this interval, \(\tau_{0,0}=DC_{0}/2\) is the time to achieve the rate of \(C_{0}\) upon entering the blocked interval estimated similarly to (3), and \(\tau_{0,1}\) is time to achieve the rate of \(C_{0}\) in the blocked interval.  
Combining the results in (4), (5) and (6) we obtain an approximation for TCP throughput. The only unknowns are the means of blocked and non-blocked intervals, \(T_{0}\) and \(T_{1}\).
# An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator
## 2 Design and Working Principle of MRE Isolator  
Figure 1 shows a cross section view of the proposed variable stiffness MRE isolator. It includes MREs layer, magnetic coil, and iron cores. MREs work in compression mode and shear-compression mixed mode. It is known that MRE working in mixed mode may present better vibration mitigation effects than in a single mode. The size of MREs in compression mode is outer diameter 110 mm and inner diameter 90 mm. The size of MREs in shear mode is a cone-like sheet with outer diameter 45 mm, inner diameter 37 mm, and conical degree 60\({}^{\circ}\). By magnetic field analysis, the total winding number of the coil is determined as 600. Other detailed information can be found in [18]. As shown in Figure 1, the perpendicular magnetic field is in the same direction as the particle chains in MREs working in compression mode. Due to the special geometry of the MRE layer working in shear-compression mixed mode (conical shape), it is cured without a magnetic field, and it is an isotropic MRE. The MRE layers, magnetic coil, and iron cores form a complete magnetic circle.  
By magnetic analysis in finite element method [18], the corresponding values between applied currents (I) and magnetic field strength (B) are developed, as listed in Table 1. When the applied current is 2 A, MRE achieves a magnetic field saturation, 0.65 T.  
Silicone MRE was synthesized using silicon rubber, silicon oil, and iron particles with average diameters of 2-4 \(\upmu\)m. The three components were mixed at a volume ratio as 15%, 77.3%, and 7.3%, respectively, and they were mixed completely for 120 min. Then, the pre-cured mixer was degassed at room temperature for 180 min to remove the bubbles. MRE layer in compression mode in proposed isolator is cured under an applied magnetic field. Since the formation of chainlike structures of iron particles within MRE is significant to its performance, the pre-cured silicone MRE in a permanent magnet in 1.0 Tesla for 120 min. Thus, the chain-like structure is formed. By size limitation, MRE layer in shear-compression mode (the conical sheet) is not cured within an applied magnetic field, and it is an isotropic MRE.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods  
To map flooded areas, different remote sensing data and methodologies were used considering the following criteria: (I) type of data (SAR, multispectral); (II) cost (i.e., we used only free-of-charge data); (III) availability of data in relation to the flood phase (pre-flood, co-flood and post-flood); (IV) spatial resolution; and (V) availability of support data (DEM or other ancillary data).  
We collected (a) pre-flood data acquired before the inundation and used as a reference for the change detection analysis; (b) co-flood data, collected around the time of maximum inundation and used for mapping flooded areas with limited post-processing; (c) post-flood data, taken after the flood event. Co-flood data is relative to the segment of river considered; downstream or upstream time and magnitude of flood peak could be different, particularly for large river basins. For instance, the flood wave of the Po or Ebro River takes several days to transit from the upper to the lower part of the catchment.  
In the case of the Ebro valley and Po basin floods, we used river stage data to select the satellite data most suitable for our analysis. Figure 2 shows the evolution of hydrometric levels during and after the flood events for Po River at Carignano gauge [60] (P_Ca in Figure 1C), T  
Figure 2: Hydrometric data recorded by gauge river stations in (**A**) Zaragoza (Ebro River valley); (**B**) Alessandria (Tanaro River); and (**C**) Carignano (Po River) versus the satellite data used in our analysis [53; 60; 61]. The location of river gauges is reported in Figure 1.  
gauge [61] (T_AL in Figure 1B) and Ebro River at Zaragoza gauge [53] (E_ZA in Figure 1A) versus the satellite passes.  
In the case of the Po and Tanaro Rivers, the number of useful images acquired during the co-flood phase (approximately 12 h) is limited, and most of the analysis was performed by using post-flood data. In the case of the Ebro River valley, the long co-flood phase of March 2015 (2-3 days) allowed us to collect more co-flood data.  
In the Supplementary Materials, a list of the acquisition dates used in this work is provided.  
Tables 1 and 2 summarizes the revisit time of the satellite, the bandwidth, the spatial resolution and the data provider of the satellite images used for this study. Two types of satellite are exploited: Active SAR sensors operating in the microwave domain and passive multispectral sensors ranging from visible to thermal infrared. Among the multispectral sensors, the spatial resolution ranges from low (500 m/pixel) to medium-high (10 m/pixel).