esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Topology Aware Routing Protocols
#### 4.1.1 Distributed Priority Tree-Based Routing Protocol (DPTR)  
In [36], the author considers the problem of network segmentation issues between aero-ground ad hoc networks, the basic structure of r-b tree is modified and a routing protocol (DPTR) suitable for transmission in a coordinated system is proposed according to certain rules. This protocol not only solves the problem of topology formation, but also solves the problem of routing two self-organizing nodes running simultaneously. The protocol is divided into three parts: ground node identification, air node identification, and the interfaces using neural network architecture. The author utilizes the operability of the neural network framework [37] to perceive the formation of a tree, forming a routing table consisting of air nodes and ground nodes.  
**Advantages:** It is easier to identify the appropriate nodes and prioritize them based on the different parameters of the operation and evaluation of a given protocol. Experimental analysis shows that DPTR can support highly dynamic hybrid network formation and is fully applicable to FANET.  
**Disadvantages:** As more nodes continue to join or leave the network, it needs to maintain more logs to support more node movement, so more memory and computation are required for processing operations. The formation of routing tree will be affected when the number of nodes exceeds the set threshold.
###### Abstract  
To reduce the environmental impact of concrete, recycled aggregates are of significant interest. Recycled concrete aggregate (RCA) presents a significant resource opportunity, although its performance as an aggregate in concrete is variable. This study presents a meta-analysis of the published literature to refine the understanding of how the moisture content of RCA, as well as other parameters, affects the compressive strength of concrete. Seven machine learning models were used to predict the compressive strength of concrete with RCA, including linear regression, support vector regression (SVR), and k-nearest neighbors (KNN) as single models, and decision tree, random forest, XGBoost, and LightGBM as ensemble models. The results of this study demonstrate that ensemble models, particularly the LightGBM model, exhibited superior prediction accuracy compared to single models. The LightGBM model yielded the highest prediction accuracy with R\({}^{2}\) = 0.94, RMSE = 4.16 MPa, MAE = 3.03 MPa, and Delta RMSE = 1.4 MPa, making it the selected final model. The study, employing feature importance with LightGBM as the final model, identified age, water/cement ratio, and fine RCA aggregate content as key factors influencing compressive strength in concrete with RCA. In an interaction plot analysis using the final model, lowering the water-cement ratio consistently improved compressive strength, especially between 0.3 and 0.4, while increasing the fine RCA ratio decreased compressive strength, particularly in the range of 0.4 to 0.6. Additionally, it was found that maintaining moisture conditions of RCA typically between 0.0 and 0.8 was crucial for maximizing strength, whereas extreme moisture conditions, like fully saturated surface dry (SSD) state, negatively impacted strength.  
machine learning; recycled concrete aggregate; moisture content +
Footnote †: journal: Applied Sci. 2024, 14, 3512. [[https://doi.org/10.3390/app14083512](https://doi.org/10.3390/app14083512)]([https://doi.org/10.3390/app14083512](https://doi.org/10.3390/app14083512))
# Frechet-Derivative-Based Global Sensitivity Analysis and Its Physical Meanings in Structural Design
## Appendix A Proof of Some Properties of the Fre-GSI
### Link of the Fre-GSI with the Failure-Probability-Based GSI  
Integrating the Fre-GSI \(\mathcal{F}_{i,j}\) in Equation (19) in the failure domain leads to  
\[\begin{split}\int_{\Omega_{\mathbb{F}}}\mathcal{F}_{i,j}\mathrm{ d}x&=\int_{\Omega_{\mathbb{F}}}\frac{\partial p_{X}(x;\mathbf{\xi})}{ \partial\xi_{i,j}}\mathrm{d}x\Big{/}\frac{\partial p_{\mathbf{\Theta}}(\mathbf{ \theta};\mathbf{\xi})}{\partial\xi_{i,j}}\Big{\|}\\ &=\frac{\partial}{\partial\xi_{i,j}}\int_{\Omega_{\mathbb{F}}}p_{X} (x;\mathbf{\xi})\mathrm{d}x\Big{/}\frac{\partial p_{\mathbf{\Theta}}(\mathbf{\theta}; \mathbf{\xi})}{\partial\xi_{i,j}}\Big{\|}\\ &=\frac{\partial p_{\mathbb{F}}(x;\mathbf{\xi})}{\partial\xi_{i,j}} \Big{/}\left\|\frac{\partial p_{\mathbf{\Theta}}(\mathbf{\theta};\mathbf{\xi})}{ \partial\xi_{i,j}}\right\|\end{split} \tag{107}\]  
or equally, there is  
\[\mathcal{S}_{i,j}^{\mathrm{F}}=\frac{\partial p_{\mathbb{F}}(x;\mathbf{\xi})}{ \partial\xi_{i,j}}=\left\|\frac{\partial p_{\mathbf{\Theta}}(\mathbf{\theta};\mathbf{\xi}) }{\partial\xi_{i,j}}\right\|\cdot\int_{\Omega_{\mathbb{F}}}\mathcal{F}_{i,j} \mathrm{d}x. \tag{108}\]Moreover, denote the safety domain as \(\Omega_{\rm S}\), which satisfies \(\Omega_{\rm F}\cup\Omega_{\rm S}=\Omega_{X}\) and \(\Omega_{\rm F}\cap\Omega_{\rm S}=\emptyset\). Then, it is obvious that  
\[\int_{\Omega_{X}}\mathcal{F}_{i,j}\mathrm{d}x=\int_{\Omega_{\rm F}}\mathcal{F} _{i,j}\mathrm{d}x+\int_{\Omega_{\rm S}}\mathcal{F}_{i,j}\mathrm{d}x=0, \tag{101}\]  
which means  
\[\left|\int_{\Omega_{\rm F}}\mathcal{F}_{i,j}\mathrm{d}x\right|=\left|\int_{ \Omega_{\rm S}}\mathcal{F}_{i,j}\mathrm{d}x\right|. \tag{102}\]  
Hence,  
\[\begin{split}\left|\mathcal{S}_{i,j}^{\rm F}\right|& =\left\|\frac{\partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i,j}} \right\|\cdot\left|\int_{\Omega_{\rm F}}\mathcal{F}_{i,j}\mathrm{d}x\right|\\ &=\left\|\frac{\partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i,j }}\right\|\cdot\frac{1}{2}\left(\left|\int_{\Omega_{\rm F}}\mathcal{F}_{i,j} \mathrm{d}x\right|+\left|\int_{\Omega_{\rm S}}\mathcal{F}_{i,j}\mathrm{d}x \right|\right)\\ &\leq\left\|\frac{\partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i, j}}\right\|\cdot\frac{1}{2}\left(\int_{\Omega_{\rm F}}\left|\mathcal{F}_{i,j} \right|\mathrm{d}x+\int_{\Omega_{\rm S}}\left|\mathcal{F}_{i,j}\right| \mathrm{d}x\right)\\ &=\left\|\frac{\partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i,j} }\right\|\cdot\frac{1}{2}\int_{\Omega_{X}}\left|\mathcal{F}_{i,j}\right| \mathrm{d}x=\left\|\frac{\partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i,j}} \right\|\cdot\mathcal{S}_{i,j}^{F}\end{split} \tag{103}\]  
along with  
\[\left\|\mathcal{S}_{i}^{\rm F}\right\|_{\infty}\leq\max_{j}\left\|\frac{ \partial p_{\Theta}(\theta;\xi)}{\partial\xi_{i,j}}\right\|\cdot\left\| \mathcal{S}_{i}^{F}\right\|_{\infty}. \tag{104}\]
# Analysis of Vehicle-Bridge Coupling Vibration Characteristics of Curved Girder Bridges
## 1 Introduction
Combined with the vehicle dynamic analysis model and considering the roughness of the bridge top deck, the vibration equations of two subsystems of the bridge and the vehicle were solved simultaneously via the coupling relationship; the direct integration method was used to iteratively solve the equations of the vehicle-bridge system to obtain the dynamic response, thereby deriving a theoretical analysis method for the highway curved girder bridge vehicle-bridge coupling vibration.
## 4 Results and Analysis
### 3D Structure Reconstruction  
The multi-thresholding segmentation method was used for this thresholding. Multi-thresholding is a threshold segmentation algorithm for CT scan image data; the method is also applicable to binary segmentation of other grayscale images. In this experiment, five different regions were extracted by four different thresholds; two regions, external and internal, were predefined and the image segmentation of CT scan images could be performed by adjusting the size of the thresholds. The specific yield of the loess sample was experimentally measured to be 10.1%. Then, the threshold value was adjusted several times in AVIZO software to select the threshold value corresponding to a relatively small error between the model porosity and the measured specific yield. The final threshold value selected was 170 and the model porosity was 10.08%. The process of threshold selection is shown in Figure 6a. The 308 th slice along the Z-direction after the threshold segmentation is shown in Figure 6b.  
In this study, based on the volume reconstruction of porous media technique in AVIZO software, the 3D reconstruction of the threshold segmented image was carried out; the 3D pore structure model of the intact Malan loess sample obtained is shown in Figure 7. The pore identification could identify 56,837 pores; the volume of the largest pore was 0.576142 cm\({}^{3}\), the volume of the smallest pore was \(2.92\times 10^{-6}\) cm\({}^{3}\), and the average volume of the pores was 0.001777 cm\({}^{3}\).  
Through the reconstruction of the 3D structure, we could extract the coordinates of the center of gravity of each pore and conduct the statistics of the center of gravity of pores in different coordinate intervals in the X-direction (vertical), Y-direction, and Z-direction (horizontal), respectively, and set a statistical interval every 0.0456 cm in different coordinate axes. As shown in Figure 8, it could be found that the number of pores in the same pore center of gravity coordinate interval in the X-direction was relatively uniformly distributed. From Figure 9, it could be found that the number of pores in the Y-direction was normally distributed with different coordinate intervals; more than 95% of the pores were concentrated between 1.3 cm and 9.3 cm. From Figure 10, the number of pores in the Z-direction were also normally distributed, with different coordinate intervals; 95% of the pores were concentrated between 1 and 8.9 cm.  
Figure 6: (**a**) is the process of threshold selection; (**b**) after the threshold segmentation, slice 308 along the Z-direction.  
Figure 8: Coordinates of the center of gravity in the X-direction.  
Figure 7: Three-dimensional model of pore structure of sample.
## 4 Conclusions  
In this paper, the physical-layer security for a three-node wiretap system model with multiple eavesdroppers over cascaded \(\kappa\)-\(\mu\) fading channels is investigated. The secrecy was assessed in terms of the secrecy outage probability, the probability of non-zero secrecy capacity, and the intercept probability. Two scenariosw were examined for the way in which eavesdroppers tap and analyze the information: colluding and non-colluding eavesdroppers. Results reveal that the cascade level has a significant impact over the privacy of the shared information. In addition, the results show that the security can be improved by enhancing the average received SNR at the main channel. However, confidentiality is reduced by the increasing number of antennas for the two eavesdroppers' scenarios. Additionally, the effect of the distances between the nodes is investigated, suggesting that the privacy deteriorates significantly as the eavesdropper draws closer to the transmitter. Moreover, our results prove that having colluding eavesdroppers in the network is more threatening and challenging to combat than having non-colluding eavesdroppers. This is maintained even when the density of non-colluding eavesdroppers is larger.  
**Author Contributions:** Formal analysis, D.T.; Supervision, W.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 1 Introduction
[33; 34] developed an efficient and rapid equivalent layer technique for processing gravity and magnetic data by using an equivalent source to carry the calculations of the forward problem of iterative methods to estimate the physical property distribution through a 2D discrete convolution. This method reduces the memory RAM usage taking advantage of the symmetry in Block-Toeplitz Toeplitz-block (BTTB) contained in the sensitivity matrices. However, it is important to note that neither [33] nor [34] explicitly mentioned that this specific structure, the BTTB form, emerges only when the sensitivity matrix is defined in the coordinate system of the observed data. Consequently, all computational operations in this study were performed in the data reference frame, which is aligned with the acquisition geometry axes. We demonstrate an efficient equivalent layer technique for jointly processing gravity-gradient data by exploiting the block Toeplitz with Toeplitz blocks (BTTB) structure of the sensitivity matrices. Our approach combines the conjugate gradient least squares (CGLSs) algorithm with 2D FFT, following a structure similar to the method proposed by [34]. By estimating a single equivalent layer, our method is capable of handling all available gravity-gradient components simultaneously. Although inspired by the same general objective as [34], our formulation differs in key aspects. In [34], the sensitivity matrix is constructed as a weighted sum of directional components derived from magnetic data. In contrast, our method uses gravity-gradient observations and constructs the sensitivity matrix by stacking contributions from each measured component. Additionally, unlike the method proposed in [34], which assumes acquisition geometries aligned with north-south and east-west directions, our approach is designed to remain effective even when the flight lines follow rotated geometries. Furthermore, it can handle data distributed over irregularly spaced points or uneven observation surfaces. Tests on both synthetic and real datasets confirm the robustness and flexibility of the proposed method under a variety of acquisition configurations.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 4 Experiment
### Results for Insider Threat Detection
However, the proposed method is an unsupervised anomaly detection method that does not rely on prior knowledge from domain experts to design and extract user behavior feature vectors. Based on the experiments conducted, it can be concluded that the proposed method achieves favorable ROC curves and AUC values for all three types of abnormal behaviors. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Anomaly Type**} \\ \cline{2-5}  & **Type 1** & **Type 2** & **Type 3** & **Avg** \\ \hline BAIT & 51.29 & 54.60 & 49.04 & 51.64 \\ \hline Isolation Forest & 82.09 & 95.68 & 71.08 & 82.95 \\ \hline Random Forest & 89.58 & 85.94 & 96.35 & 90.62 \\ \hline Deep Autoencoder & 90.25 & 99.48 & **94.10** & 94.61 \\ \hline Proposed method & **98.01** & **99.84** & 87.50 & **95.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Recall_ rates of existing comparative methods for detecting three types of abnormal behaviors. This validates the capability of the proposed method to simultaneously detect multiple types of abnormal behaviors without relying on prior knowledge from domain experts to extract user behavior feature vectors. Furthermore, when comparing the experimental results with existing detection methods, both the proposed method and the Deep Autoencoder demonstrate commendable \(Recall\) rates. This finding confirms that deep neural networks possess superior learning capabilities when contrasted with traditional machine learning methods. However, the detection method of the Deep Autoencoder requires prior knowledge for designing and extracting feature vectors, as well as supervised training of the classifier using samples of abnormal behavior. In contrast, the proposed method utilizes the constructed encoder-decoder model to automatically learn user behavior feature representations, while performing clustering and anomaly detection. This unsupervised anomaly detection approach is more suitable for practical detection environments.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 3 BEAST System Model
### System Process
(2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Making MA-ABE fully accountable: A blockchain-based approach for secure digital right management. _Comput. Netw._**2021**, _191_, 108029. [CrossRef]
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* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Enforcing dynamic spectrum access with spectrum permits. In Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, Bellevue, WA, USA, 16-19 October 2012; pp. 195-204. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Specguard: Spectrum misuse detection in dynamic spectrum access systems. _IEEE Trans. Mob. Comput._**2018**, _17_, 2925-2938. [CrossRef]
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## 3 Results and Discussion  
### Ft-Ir  
FT-IR investigation was performed to obtain a profound view of the chemical structure of the prepared films [34]. The FT-IR results are shown in Figure 2. The bonding of papaya puree, gelatin, and defatted soy protein composite films is demonstrated by these transmittance peaks [25]. FT-IR spectra of edible films showed that the addition of gelatin and soy protein to papaya did not change the molecular interactions of the resulting films, but it decreased the intensity of the peak slightly. All spectra showed three primary regions, and the results are under the work conducted by [PERSON] et al. [35]: (i) A wide asymmetric band in the range of 3500 and 2800 cm\({}^{-1}\): Peaks around 2931 cm\({}^{-1}\) encompassing the characteristic -C-H vibration peaks are associated with the methylene hydrogen molecules. A wide peak in the range of 3000-3500 cm\({}^{-1}\) is the result of stretching vibrations of the -OH groups [36]. (ii) An area somewhere in the range of 1700 and 1100 cm\({}^{-1}\) is normally for the amide groups: All films displayed trademark ingestion peaks at around 1637, 1457, and 1240 cm\({}^{-1}\), which relates to -C=O stretching. (iii) An absorption region somewhere in the range of 800 and 1200 cm\({}^{-1}\): The groups at 1020-1036 cm\({}^{-1}\) bands are based on -C-O extending vibrations. Peak intensities are quite comparable with maize starch-gelatin composite films [37]. In addition, for the G/PP/SP-2 film, the highest intensity of peaks is present in the region of the -OH group at the level of 3298 cm\({}^{-1}\). This is because the interaction between papaya, soy protein, and gelatin can form hydrogen bonds in the film. G/PP/SP-2 increased bond energy, resulting in increased maximum strength [38].
# M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application
###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**No**} & \multicolumn{3}{c}{**Risk Factor [13; 14]**} \\ \cline{2-3}  & \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Description**} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Obesity (overweight)} & There is a significant link between obesity and blood sugar levels, and the degree of obesity with a body mass index (BMI) \(>\) 23, which can lead to an increase in blood glucose levels of up to 200 mg\%. \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Hypertension} & An increase in blood pressure beyond the normal range of hypertensive patients is closely associated with the improper storage of salt and water, or increased pressure in the body of the peripheral vascular system. \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Dyslipidemia} & Dyslipidemia is a condition characterized by elevated blood fat levels (triglycerides \(>\) 250 mg/dl). There is a relationship between an increase in plasma insulin and low high-density lipoprotein (HDL) (\(<\)35 mg/dl). \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Age} & Individuals aged \(>\) 40 years are susceptible to DM, although it is possible for individuals aged \(<\) 40 years to avoid DM. The increase in blood glucose occurs at the age of about 45 years and the frequency increases with age. \\ \hline \end{tabular}
\end{table} 
Table 2: Risk factors. \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**No**} & \multicolumn{3}{c}{**Diabetes Mellitus**} \\ \cline{2-3}  & \multicolumn{1}{c}{**Type**} & \multicolumn{1}{c}{**Description**} \\ \hline
1 & Type 1 [6] & Beta cell damage, generally leading to absolute insulin deficiency [3]. \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Type 2 [7]} & It varies from dominant insulin resistance with relative insulin deficiency to dominant insulin secretion defect as a result of insulin resistance [7]. \\ \multirow{3}{*}{3} & \multirow{2}{*}{Other Types [8]} & \(\bullet\) & Diabetes mellitus is caused by a disease of the exocrine pancreas. \\  & & \(\bullet\) & Diabetes mellitus due to drugs (e.g., HIV and AIDS therapy or after kidney transplantation, etc.), chemicals, or infections. \\  & & \(\bullet\) & Diabetes mellitus is caused by immunological disorders. \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Gestational [9]} & In gestational diabetes mellitus, the diagnosis of DM is made at the time that a patient’s pregnancy is in progress. \\ \hline \end{tabular}
\end{table} 
Table 1: Classification of diabetes mellitus.