esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
## 6 Case Study  
We simulated the risk propagation process on an SCN existing in real life [70]. This SCN has 577 nodes and 2662 edges, and we visualized this SCN as shown in Figure 14. Nodes with larger degrees are larger after visualization. In Figure 14 the blue circles represent node firms and the black edges represent links between firms.  
We sorted this SCN according to the degree of nodes, as shown in Figure 15. The curve in Figure 15 has a power-law distribution pattern. The largest node degree in this SCN is 204, and there is only one node. The average clustering coefficient of the actual SCN used in this study was calculated to be so small that it was approximately equal to zero and the  
Figure 14: Visualization of a real-life SCN.  
scale index was equal to approximately 0.905. The average degree value of this SCN was 7.841. In the meantime, we also analyzed the distribution of degree distribution in this SCN and drew its histogram of degree distribution as shown in Figure 16. Observing Figure 16, we found that a large number of nodes have small degrees and a small number of nodes have a large number of connections in the SCN. Among them, there are 201 nodes with a node degree of three.  
The results of the experiments under two metrics on the instances are shown in Figure 17. The horizontal coordinate in Figure 17 is the time step and the vertical coordinate is the SCNR at this time step. The blue curve represents the use of resilience metric \(y\), and the red curve represents the use of resilience metric \(s\). Both curves in Figure 17 go through an initial phase of plummeting and maintain a steady trend after reaching the steady state. When risk propagation occurs, the system has reached a steady state at \(T=20\), and we begin our analysis at this time step.  
After calculation, when the SCN risk propagation reaches a steady state, the resilience of this actual SCN when measured by resilience metrics \(Y\) and \(S\) is 0.0191 and 0.6700, respectively. The experimental results of this case study show that our proposed SCN resilience metrics can be used to measure the resilience of real SCNs, which provides a richer choice for supply chain managers to measure the resilience of real SCNs. Our study  
Figure 16: The degree distribution of a real-life SCN.  
Figure 15: A real-life SCN node degree ranking.  
can provide theoretical guidance for supply chain managers to adjust the SCN structure according to the demands of different SCNs in real life, and to build more resilient SCNs. In addition, our study may allow supply chain managers to utilize our proposed resilience metrics for a more rational measurement of the SCNs that exist in real life.
## 5 Analysis and Discussion of the Results
It should also be noted that in Dimension 2, the \"Blockchain\" technological category in the Present, although with a moderate weight in this dimension and negative, is in contrast to the \"Mobility\", \"Robotics/Automation\" and \"Cloud Solutions\" categories with positive weights. Figure 6 shows the evolution of the results for the ten technological categories in present and future. Associating the information from Table 6 with Figures 5 and 6, we can state that, in Dimension 1, the technological categories identified as preponderant in the Present are those that the respondents consider to be less prepared in their organizations. On the other hand, the technological categories identified as predominant in the Future, in this dimension, are those considered by them as less important than for the implementation of DT. Regarding Dimension 2, we verified that the technological categories with the greatest weight in the dimension were those that the respondents pointed out as being the best prepared categories in their organization regarding digital adoption in the Present. In the opinion of the respondents regarding the degree of priority of implementation in the Future, it was verified that the technological categories with greater weight, in this dimension, are those evaluated by them as being of priority. More explicitly, we can conclude that, in relation to the technological categories that constitute the four pillars of DT (categories 1, 2, 3 and 8), the respondents evaluated their organizations as:  
* well prepared in relation to digital adoption in the Present for all categories, except for the \"Agile tools\" category (only with moderate preparation--category 8);
* the categories are all priorities in relation to the implementation of DT in the Future, with particular relevance in the \"Agile tools\" category where the largest increase occurred. Finally, there was a significant decrease in the degree of priority in the future implementation of \"Robotics\", \"VR/AR\" and \"3D printing\" categories. Summarizing, Dimension 1 can be designated by the technological categories with \"Less preparation (P)/priority (F)\" and Dimension 2 by \"Greater preparation (P)/priority (F)\". In order to verify if there are significant differences in the organizations' evaluations, before and after (P/F), for each of the technological categories, we used Wilcoxon nonparametric tests (suitable for paired samples). We found that, in the \"Mobility\", \"3D printing\" and \"Blockchain\" categories (categories 1, 5 and 10), there were no significant differences (\(p\)-values \(>\) 0.5) in the evaluations of organizations in Present and Future, conclusions which confirm the previous exploratory analysis.
## 2 Results and Discussion
### Phenolic Profile in Egyptian Dukkah and Syrian Za'atar (ug/g)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Rt (min)**} & \multicolumn{2}{c}{**EDT**} & \multicolumn{2}{c}{**EDN**} & \multicolumn{2}{c}{**SZT**} & \multicolumn{2}{c}{**SZS**} \\ \cline{3-10}  & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Gallic acid & 4.1 & 0.9 D & 0.00 & 2.0 C & 0.03 & 9.6 B & 0.03 & 21.3 A & 0.09 \\ Protocatechuic acid & 7 & 13.6 A & 0.22 & 5.2 B & 0.10 & 5.2 B & 0.00 & 2.8 C & 0.06 \\ \(p\)-Hydroxybenzoic acid & 10.3 & 11.3 A & 0.24 & ND & ND & 1.9 C & 0.04 & 5.6 B & 0.05 \\ (Epi)Catechin & 11.9 & 14.7 A & 0.16 & ND & ND & 14.8 A & 0.04 & 3.9 B & 0.53 \\ Chlorogenic acid & 12.8 & 2.37 B & 0.04 & ND & ND & 1.3 C & 0.00 & 2.5 A & 0.02 \\ Caffe acid & 13.9 & 194.4 A & 0.19 & 31.0 D & 0.07 & 89.4 B & 0.28 & 63.6 C & 1.61 \\ Syringic acid & 14.9 & 23.1 A & 0.14 & 0.3 C & 0.00 & 1.5 B & 0.02 & 1.4 B & 0.09 \\ Vanillic acid & 16.7 & 2.8 B & 0.01 & 0.6 D & 0.02 & 3.9 A & 0.07 & 1.2 C & 0.05 \\ Ferulic acid & 21 & 325.8 A & 0.23 & 18.9 D & 0.07 & 91.3 B & 0.14 & 31.7 C & 0.26 \\ Sinapic acid & 21.8 & 28.8 A & 0.11 & 2.6 D & 0.05 & 5.4 B & 0.01 & 3.8 C & 0.11 \\ Rutin & 24.4 & 302.1 A & 0.89 & 12.5 D & 0.59 & 68.6 B & 0.43 & 29.0 C & 0.13 \\ \(p\)-Coumaric acid & 27.1 & 2.4 C & 0.06 & 4.1 B & 0.11 & 2.7 C & 0.09 & 10.5 A & 0.32 \\ Apigenin-7-O-glucoside & 28.8 & 7.8 & 0.29 & ND & ND & ND & ND & ND & ND \\ Rosmarinic acid & 30.2 & 338.3 A & 1.62 & 22.6 D & 0.38 & 149.6 B & 0.06 & 34.7 C & 0.33 \\ Daidzein & 34 & 26.8 A & 0.22 & 1.4 C & 0.11 & ND & ND & 5.1 B & 0.13 \\ Cinnamic acid & 35.6 & 11.0 A & 0.03 & 1.1 C & 0.02 & 1.3 B & 0.02 & 0.3 D & 0.03 \\ Quercetin & 36.4 & 3.9 C & 0.00 & 4.4 B & 0.04 & 5.6 A & 0.17 & 5.7 A & 0.13 \\ Genistin & 39 & 9.7 A & 0.04 & 3.2 C & 0.08 & 4.9 B & 0.01 & ND & ND \\ Kaempfer & 40.8 & 2.8 A & 0.07 & ND & ND & 1.1 B & 0.06 & 1.1 B & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Phenolic compound contents (ug/g) of Egyptian dukkah and Syrian za’atar.
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 4 Results and Evaluation
### Metrics  
Daily activity recognition for smart homes is a classification problem. Therefore, we used evaluation metrics of accuracy, precision, recall and F1 score as shown in Equations (1)-(4), respectively. TP is the number of true positives which are correctly classified. Additionally, TN is the number of true negatives correctly classified. FP is the number of false positives which are incorrectly classified, whereas FN is the number of false negatives which are incorrectly classified.  
\[\text{Accuracy}=\text{TP}+\text{TN}/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{1}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{2}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{3}\]  
\[\text{F1-score}=2\times\text{Precision}\times\text{Recall}/(\text{Precision}+\text{Recall}) \tag{4}\]
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 3 Joint Optimization Algorithm
### UAV Position Optimization
```
Input:\(\mathbf{\omega}_{k}^{\sigma},\forall k,\mathbf{\omega}_{n}^{\tau},\forall n,\sigma_{k}^ {2},\sigma_{e}^{2},P_{\text{max}},\mathbf{u}_{k},\mathbf{e}_{j},\Gamma_{k}^{\text{ conn}},\Gamma_{\ell}^{\text{en}}\) Output:\(\mathbf{q}\)
1. Initialize iteration count \(l=1\) and \(\mathbf{q}^{(0)}\), setting the minimum trust region radius \(R_{\text{min}}\). 2. Repeat. 3. Solve the optimization problem P7.\(l\) to obtain \(\mathbf{q}^{(l)*}\). 4. If the objective function value decreases, then
5.\(\mathbf{q}^{(l)}=\mathbf{q}^{(l)*}\), \(l=l+1\). 6. else
7.\(R^{(l)}=R^{(l)}/2\). 8. end if
9. Until \(R^{(l)}<R_{\text{min}}\)
```  
**Algorithm 2** UAV position optimization
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols
### Congestion Control Algorithms
#### 3.3.2 Bbr  
The Bottleneck Bandwidth and Round-trip time (BBR) congestion control is based on the fact that the slowest link or bottleneck of the network path bounds transport performance. It determines the connection's maximum data-delivery rate and it is where persistent queues build up. In principle, BBR tracks the path's BDP by estimating the bottleneck bandwidth and the round trip propagation delay. Then, it tries to operate at the optimum BDP point without causing an excessive queue in the bottleneck, hence reducing the delay. This is in sharp contrast to loss-based algorithms such as Cubic which try to fill the bottleneck queue to deliver full link bandwidth at the cost of high delay and frequent packet loss [24,26].  
BBR probes the maximum bottleneck bandwidth (BtlBW) and the minimum round-trip time (minRTT) and uses them as efficient unbiased estimators of the bottleneck bandwidth and RTT of the network path. The minRTT is estimated from RTT samples obtained from ACKs while BtlBW is estimated by tracking the delivery rate at the receiver which can be inferred from ACKs and the corresponding amount of data delivered. It then paces the packet-arrival rate at the bottleneck queue to its departure rate. This keeps the bottleneck queue near its minimum, maximizing delivered bandwidth while minimizing delay and loss. The primary control parameter in BBR is the pacing gain, which determines the sending rate as a function of BtlBW [24,26].
## 3 Result and Analysis
### Vertical Flow Characteristics  
This paper only shows the vertical flow characteristics of the representative sections of \(Z=10\) cm, 40 cm, 120 cm, and 190 cm.  
* Velocity distribution: The distribution of the velocity \(w\) in the middle and lower part of the tank was similar. The flow velocity changed sharply in the FAA and the front and the back of the flow adjustment board, while it was uniform in the SSA. Affected by the friction of the tank bottom, the velocity \(u\)-\(v\) near the bottom was low and only significantly increased near the baffle. Influenced by the high-velocity flow from the inlet, when \(Z=40\) cm, the velocity \(u\)-\(v\) in the FAA was extremely high. After the water flowed through the flow adjustment board, the velocity \(u\)-\(v\) significantly reduced, but it was still higher than others at other heights which were not beneficial to the sediment settling, which was caused by the water flowing into the ISA through the gap between the baffle and the tank bottom. In the ISA, because the tubes blocked the longitudinal movement of the middle and lower part of the flow, the surface flow velocity \(u\)-\(v\) was high. In the latter half of the area, affected by the suction effect of the overflow weir, the direction of the velocity \(u\)-\(v\) was gradually biased toward the left side of the tank (i.e., the side of the overflow weir), indicating the great influence of the overflow weir on the flow pattern in the tank (see Figure 6a).
* Vertical dimensionless turbulence intensity \(I_{Z}\) distribution: The distribution of the \(I_{Z}\) was low at the bottom and the upper part of the tank and high in the middle part (see Figure 6b). In the three dimensions, the \(I_{Z}\) was the lowest. The lower turbulence intensity was favorable for the collision and adhesion of the viscous sediment particles and the formation of the floc. It also helped to speed up sediment settling at the tank bottom in a shorter time.
* Vertical dimensionless Reynold shear stress (\(R_{\rm YZ}\)) distribution: \(R_{\rm YZ}\) represents the dimensionless Reynolds shear stress in the direction \(Z\) and normal to \(Y\), within a range of 0-12% (see Figure 6c). The \(R_{\rm YZ}\) distribution was high in the FAA and the lower part of the SSA, and the \(R_{\rm YZ}\) decreased with the decrease of the water depth. This might because of the high flow velocity at the tank  
Figure 5: Distribution of transverse flow characteristics: (**a**) velocity (the contour plot represents the transverse velocity \(v\), and the vector represents velocity \(u\)-\(v\)); (**b**) dimensionless turbulence intensity \(I_{Y}\); and (**c**) dimensionless Reynolds shear stress \(R_{XY}\). (The unit of velocity is m/s, and the dimensionless turbulence intensity and dimensionless Reynolds shear stress is dimensionless).  
bottom while the low velocity in the upper part. The \(R_{YZ}\) ranged from 6% to 12% in the upper part of the ISA, which was the result of the high upward velocity of the water flowing out of the inclined tubes.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 5 Conclusions  
This paper introduces a 2D/3D medical image registration method based on normalized cross-correlation. It analyzes the advantages and disadvantages of normalized cross-correlation. It is only suitable for small-angle rotation. The registration accuracy is relatively low to solve the normalized cross-correlation algorithm only using global gray information. An improved normalized cross-correlation algorithm is proposed. Based on the LOG operator's improvement, based on the original normalized cross-correlation, the cosine similarity of linear combination Laplacian of Gaussian operators is proposed. The operator increases the edge information and the internal details such as outliers and lines. Based on the improvement of the Sobel operator, the angle of the gradient vector is introduced. The sum of the gradient vector angle's weight function is multiplied by normalized cross-correlation to increase the rotation transformation's sensitivity to improve the accuracy and the convergence region. The multiresolution algorithm is adopted to aim at the computational efficiency problem caused by the normalized cross-correlation algorithm. The experimental results show that the improved normalized cross-correlation has higher registration accuracy. The multiresolution strategy can reduce the registration algorithm's initial value sensitivity and reduce the algorithm's probability of falling into the local optimum.  
This experiment can be improved in the future. In the optimization process of iterative 2D/3D medical image registration, DRR images need to be generated continuously through projection rendering, which will lead to a lot of time consumption. Furthermore, the DRR projection belongs to graphics rendering, so we can consider using a GPU to accelerate the DRR projection process and improve the algorithm's calculation speed. This study assumes that only rigid body transformation is performed on 3D images. However, in clinical application, some tissues of the human body and between bones will deform to a certain extent. Therefore, 2D/3D registration based on rigid body transformation cannot fully meet the clinical needs. More complex deformation registration problems can be studied in the future.  
Conceptualization, W.Z. and B.Y.; methodology, B.Y. and S.L.; software, Y.W. and J.T.; validation, Y.W., J.T. and B.Y.; formal analysis, W.Z.; investigation, J.T.; resources, W.Z.; data curation, Y.W.; writing--original draft preparation, Y.W. and L.Y.; writing--review and editing, W.Z. and S.L.; visualization, Y.W. and S.L.; supervision, S.L.; project administration, B.Y.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.
# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 2 Materials and Methods
### Components
#### 2.1.3 Embedded Device Structure  
The programming of ESP32-CAM was carried out utilizing ESP-IDF (version 5.1.2), which is the development framework offered by Espressif, the system-on-chip (SoC) manufacturer. This framework, based on C++, provides a hardware abstraction layer and incorporates the _FreeRTOS_ real-time operating system, drivers, and supplementary modules that streamline the development of embedded systems. Figure 3 illustrates the deployment of the most pertinent hardware and software modules within the project. The uppermost layers correspond to the developed code.  
The ESP32 SoC features two 32-bit Xtensa microcontrollers with LX6 cores. To harness the process-level parallelism inherent in this architecture, the \"State Machine Abstraction Library (SMAL)\" was devised, a collection of thread-safe methods and structures that leverage the FreeRTOS scheduler to define the system in terms of states and transitions. This programming approach fosters loose coupling, high-level abstraction, and eases error handling, thereby enhancing the robustness of a system that must operate continuously without interruption.  
Using the SMAL Library, the system defines a series of tasks, which are independent units of programming that can run concurrently or in parallel, depending on whether they share a processor or not. The following tasks make use of the SMAL library and collaborate in the operation of the embedded system:  
* Main Task: As the initial task of the system, it is responsible for configuring the state machine states, setting up event handlers for the communication driver, allocating memory for necessary data structures, and launching the initial state of the system. While the SMAL library does not directly control this task, its continuous execution is imperative for the system's proper functioning.
* Configuration Task: This task, when active, runs periodically and is responsible for requesting the current device configuration from the remote service. The remote reconfiguration mechanism is implemented within this task.  
Figure 3: Trap-embedded device structure.  
* Perception Task: This cyclic task accesses the integrated camera to capture images of the trap. It collaborates with the Communication Task to transmit the images to the remote server. Given its computational demands, this task is exclusively scheduled on one of the processors.
* Communication Task: This task handles the transmission of images captured by the Perception Task to the server.
* WebServer Task: This task initiates an embedded web server designed to host a remotely accessible web interface for camera configuration and visualization.
## 2 Security and Privacy Challenges in IoE
### Next-Generation Attack Vectors in IoE
#### 2.2.1 AI-Based Cyber Attacks: Adversarial AI, Model Poisoning  
Although AI systems are widely used in IoE security, AI systems themselves can also be vulnerable to attacks [61]. IoE systems utilize machine learning and AI models to analyze data, make decisions, and optimize user interactions. However, malicious actors can manipulate these systems to exploit security vulnerabilities. In particular, Adversarial AI and Model Poisoning attacks pose significant threats to the reliability of smart systems in IoE environments.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Attack Type** & **Targeted Systems** & **Threat Mechanism** & **Possible Consequences** & **Recommended Defense Methods** \\ \hline \multirow{3}{*}{Adversarial AI [29; 30]} & Edge Devices, AI Models, Machine Learning Systems & Manipulation of Models Through Internally Misleading Inputs & Incorrect Decision-Making, System Failures & Robust Adversarial Training, Secure Model Training \\ \hline \multirow{3}{*}{Model Poisoning [57]} & AI Models, AI-Based IoE Devices & Manipulation of Training Data & Faulty Training, Malicious Model Behavior & Data Sanitization, Federated Learning \\ \hline \multirow{3}{*}{Quantum Attacks ([PERSON], Grover) [52; 53; 54; 55; 56]} & Cryptographic Systems, Blockchain, IoE Security & Breaking Classical Encryption Algorithms & RSA/ECC Encryption Becomes Obsolete & PQC, Lattice-Based Encryption \\ \hline \multirow{3}{*}{Edge/Cloud Vulnerabilities [58; 59; 60]} & Edge Devices, IoE Gateways, Cloud Infrastructures, Data Storage Systems & Physical Interventions, Network Vulnerabilities, Malware Infections, Firmware Exploitation, Unauthorized Access, Data Leakage, Multi-Tenancy Issues & Data Manipulation, Device Security Breaches, Theft of Sensitive Data Encryption \\ \hline \hline \end{tabular}
\end{table}
Table 3: Threat vectors and targeted components in the IoE ecosystem.  
Figure 2: Threat vectors in the IoE ecosystem.  
In Adversarial AI attacks, misleading data is used to cause IoE devices to make incorrect decisions [62, 63]. For example, if the facial recognition algorithm of a smart security camera is subjected to an adversarial attack, it may identify an unauthorized person as an authorized one. Similarly, model poisoning attacks inject malicious data into a machine learning model, causing the model to learn incorrectly over time [64, 65]. These types of attacks target IoE devices, creating serious security vulnerabilities in automation processes. In Model Poisoning, AI systems are deliberately fed with incorrect data to manipulate training, leading to the misguidance of IoE devices
## 3 Results and Discussion  
### FTIR Spectroscopy  
The functional groups present in the sol-gel silica materials (obtained as powders) were identified by FTIR spectroscopy (see Figure 1).  
The spectra of all samples showed a prominent characteristic band situated at \(\sim\)1039 cm\({}^{-1}\), which was attributed to stretching vibrations of the Si-O-Si group. The C-H bonds from -CH\({}_{3}\) and -CH\({}_{2}\)- were noted in the sol-gel silica materials and identified in the 2960-2850 cm\({}^{-1}\) range (stretching vibration) and at 719 cm\({}^{-1}\) (rocking vibration). The peak at \(\sim\)950 cm\({}^{-1}\) was attributed to the bending vibration of the Si-O-Si group. The band observed at \(\sim\)833 cm\({}^{-1}\) could be assigned to Si-O stretching vibration. The peak at 1258 cm\({}^{-1}\) indicated the presence of Si-C [28]. The peaks identified at \(\sim\)1408 cm\({}^{-1}\) and 1460 cm\({}^{-1}\) were attributed to the CH\({}_{2}\) group (in-plane bending deformation for SiCH-CH\({}_{2}\)) [29].  
The presence of these peaks indicates that the hydrolysis and condensation reactions between TEOS and silane precursors occurred successfully inside the silica materials.