esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Security Analysis
Table 5 presents a comprehensive analysis of the security attributes of the proposed ECC-based model, emphasizing its effectiveness in mitigating critical cryptographic threats within IoT environments. Specifically, the table delineates the model's resilience against replay attacks, man-in-the-middle attacks, and key compromise impersonation-threat vectors that are particularly relevant in distributed and resource-constrained networks. The findings, derived from the implementation phase, substantiate the practical applicability and robustness of the proposed approach. Notably, the results demonstrate that the ECC-based framework surpasses conventional cryptographic schemes, such as RSA and Diffie-Hellman, by achieving superior energy efficiency, reduced computational complexity, and enhanced resistance to adversarial attacks. These attributes make the proposed model a viable and scalable security solution for IoT architectures characterized by heterogeneous device capabilities and stringent resource constraints. In addition to its demonstrated resilience, the model's flexibility and adaptability make it a viable solution for evolving IoT security challenges. By integrating optimized elliptic curve operations with tailored parameterizations, the proposed method provides a scalable and secure foundation for future enhancements. This includes potential integration with hybrid cryptographic systems that combine the strengths of ECC with quantum-resistant algorithms to address emerging threats posed by advancements in quantum computing. Such extensions would further solidify the model's position as a cornerstone for secure and efficient communication in IoT networks. In conclusion, the construction methodology of the proposed model exemplifies a deliberate and comprehensive approach to addressing the unique security and performance challenges of IoT environments. Its resilience is deeply rooted in the carefully defined subspaces of elliptic curves and the robust parameterization strategies employed. The results obtained during implementation not only validate the model's theoretical underpinnings but also highlight its practical relevance in securing heterogeneous IoT networks against a wide array of cyber threats.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 3: Generate a Time-Frequency Spectrogram by STFT  
STFT is used to determine the frequency and phase of a local-region sine wave of a time-varying signal. For the original signal sequence \(f(k)\quad k=1,2,\ldots,K\), a time-frequency localized window function is selected, and the analysis window function sequence \(g(k-m)\) is assumed to be stationary (pseudo-stationary) over a short interval. Moving the window function \(g(k-m)\) makes the product of sequence \(f(k)\) and sequence \(g(k-m)\) be stationary in different window widths, so as to calculate the power spectrum at different times. In simple terms, the process of calculating the STFT is to divide a longer time signal into shorter segments of the same length and to calculate the Fourier transform on each of the shorter segments. The formula is as follows:  
\[STFT_{f(k)}(e^{i\frac{2m}{N}})=\sum\limits_{m=0}^{K}f(m)g(k-m)e^{-i\frac{2m}{ N}m},0\leq n\leq N-1, \tag{2}\]  
where \(K\) is the number of sampling points, and \(N\) is the sampling rate of the discrete signal.  
By comparing the time-frequency spectrogram generated by selecting different window functions, the window function of STFT is chosen as Hamming (\(W/4\)), where \(W\) is the number of sampling bins and is set to 64.
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 4 Cross-Section Design and Case Analysis for Minimum Bending Moment Shield Tunnel in Soft Soil Area
### Analysis for Cross-Section Key Parameters of Zero Bending Moment Shield Tunnel
The axial forces for the zero bending moment tunnel are shown as Figure 13 (for the vertical symmetry of the axial forces, only axial forces at the range between 0\({}^{\circ}\) and 180\({}^{\circ}\) are given). The difference values of the horizontal diameters between any cross-sections of the zero bending moment tunnel are smaller than any difference value between the horizontal diameters of the zero bending moment tunnel and circular tunnel. So, any cross-section of the zero bending moment tunnel is adopted for the metro shield tunnel, and its maximum bending moment is smaller than the circular cross-section. Figure 12: Zero bending moment shield tunnel cross-section with vertical diameter of 6 m: (**a**) Different coefficients of lateral earth pressure; (**b**) Different thickness of overlaying soil. Figure 13: Axial force for zero bending moment shield tunnel with vertical diameter of 6 m: (**a**) Different coefficients of lateral earth pressure; (**b**) Different thickness of overlaying soil. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**H/m** & \(k\) & **\(P_{1}\)/kPa** & **\(P_{2}\)/kPa** & **\(P_{3}\)/kPa** & \(a\)/m & **\(b\)/m** & \(c\)/m & **\(N_{1}\)/kN** & **\(N_{2}\)/kN** & **\(N_{3}\)/kN** & **\(\Delta\)/m** \\ \hline
6 & 0.46 & 111.00 & 51.06 & 51.06 & 6.00 & 4.984 & 4.992 & 204.24 & 255.30 & 277.03 & 0.1652 \\
9 & 0.46 & 166.50 & 76.59 & 51.06 & 6.00 & 4.699 & 4.703 & 280.83 & 331.89 & 391.52 & 0.1244 \\
12 & 0.46 & 222.00 & 102.12 & 51.06 & 6.00 & 4.550 & 4.552 & 357.42 & 408.48 & 505.30 & 0.0997 \\
18 & 0.46 & 333.00 & 153.18 & 51.06 & 6.00 & 4.395 & 4.397 & 510.60 & 561.66 & 732.05 & 0.0713 \\
28 & 0.46 & 518.00 & 238.28 & 51.06 & 6.00 & 4.282 & 4.282 & 765.90 & 816.96 & 1109.14 & 0.0483 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key parameters for no bending moment shield tunnels with different buried depths.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.4 Olympus V103 Transducer  
This transducer is a nominally 12.7 mm diameter, 1 MHz design for ultrasonic applications from Olympus NDT, Waltham, MA, USA, but is also used as a broadband AE sensor [18]. Results of bar-wave displacement calibration tests are given in Figure 12. The blue curve is from the present test, while the red curve was reanalyzed from the previous test data for Figure 12b in reference [8] (using the equivalent time period in conducting FFT). These two spectra showed large dips at 400 kHz, and at 900 \(\pm\) 100 kHz. While these two curves matched reasonably well overall (except below 120 kHz, near 600 kHz and near 1.1 MHz), the low frequency data diverged. This discrepancy was also found in other sensor data (pages 15 and 18) and apparently resulted from a large effect of transmitter radial resonance that was present in the previous set-up. Higher displacement output below 120 kHz in the previous interferometer data did not produce higher sensor output. Exact cause of this effect is still unclear, however.  
In Figure 12, the dashed green curve is the normally incident wave displacement sensitivity for V103 (given in Figure 9a in reference [4]). This is smoother than the bar-wave data and peaked at 1.1 MHz. This shows no dip unlike the bar-wave sensitivity curves.  
Figure 11: Receiving sensitivity of PAC R6a (AI06). The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves. Green curve is the velocity calibration for surface waves from PAC.  
The observed frequency of 400 and 900 kHz for the two major dips in Figure 12 are compared to the predictions from the nulls of J\({}_{1}\) function. The first dip matches with the first null if the velocity is assumed to be 4.2 mm/\(\upmu\)s. According to the bar-wave dispersion curves (Figure 5c), this velocity is 13% higher than the peak velocity of 3.7 mm/\(\upmu\)s calculated at 400 kHz. At 900 kHz, the needed velocity is 5.1 mm/\(\upmu\)s for the second null, while a few modes have velocity of 4.7 mm/\(\upmu\)s, at 6% difference. These are acceptable deviations as two theoretical predictions have to be reconciled. Alternately, a combination of 2.3 mm/\(\upmu\)s at 400 kHz and 3.6 mm/\(\upmu\)s at 900 kHz provides a good matching for the second and third nulls. However, this choice requires another spectral dip at a lower frequency of around 200 kHz. No such dip is evident in Figure 12 and the alternate set is not viable. While the predictions are not exact matches to the bar-wave calculations, the dips can be attributed to the aperture effects.
## 2 Materials and Methods  
### Plant Collection and Processing  
This work targeted four halophytic species included in the eHaloph database, namely, _Calystegia soldanella_ ([PERSON]. 1810 (Convolvulaceae), _Medicago marina_ L. 1753 (Fabaceae), _Plantago coronopus_ L. 1753 (Plantaginaceae), and _Limoniastrum monopetulum_ ([PERSON]. 1848) (Plumbaginaceae). It also focused on the following five salt-tolerant plants: _Pistacia lenticus_ L. 1753 (Anacardiacaeae), _Cladium mariscus_ ([PERSON] [PERSON] 1809 (Cyperaceae), _Inula crithmoides_ L. (syn. _Limharda crithmoides_ [PERSON]. 1827; Asteraceae), _Helichrysum italicum_ subsp. _picardii_ ([PERSON] 1984 (Asteraceae), and _Crucianella maritima_ L. 1753) (Rubiaceae) [22]. From an initial list of plants identified along the Algarve coastline in previous projects (XtremeBio, PTDC/MAR-EST/4346/2012), these nine selected species were selected based on combined available knowledge on their (1) traditional veterinary uses [10], (2) phytochemical available data, and (3) abundance in wild settings during the harvesting years, aiming at their nutraceutical or phytotherapeuticutical application within the context of gastrointestinal nematode infections in small ruminants [23].  
Fresh aboveground plant organs, including leaves, stems, flowers, and fruits, whenever present, were harvested by hand in spring (Sp), summer (Su), autumn (Au) and winter (Wi) seasons during 2017 and 2018 (Appendix A, Table A1), covering four districts of Algarve, Southern Portugal (Figure 1). The voucher specimens of each plant species were identified based on morphological features by Dr. [PERSON] (CCMAR), and a record was maintained at the herbarium of the XtremeBio group (CCMAR; Appendix A, Table A1). Before solvent extraction, the biomass was reduced to dry powder after lyophilization for three days (Lyoalfa 15, Telstar, Barcelona, Spain) by firstly using a coffee grinder and, afterwards, using a ball miller to reduce particle size (Retsch PM 100, VWR, Carnaxide, Portugal). To avoid compound degradation by light, samples were stored in the dark in a desiccator. This protocol was followed in compliance with the standard procedures recommended by \"Instituto da Conservacao da Natureza e das Florestas (ICNF)\", the national regulatory body, and mandatory licenses for wild collections were obtained appropriately.
###### Abstract  
The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of \(-4.21\), compared to 1.22 for standard GWO, \(-2.81\) for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs.  
energy efficiency; grey wolf optimization; intra-cluster communication; swarm algorithms; wireless sensor networks +
Footnote †: journal: Applied system  
0000-0001-4000-0001-4000][PERSON] 2
## 6 Conclusions  
In this paper, an aeronautical communication system based on multi-UAV relays is proposed, considering the hybrid FSO/RF link transmission and the mobility of platforms. Furthermore, we propose four relay selection modes and derive the exact closed-form expressions for the end-to-end outage probabilities. Moreover, the influence of the velocity variance of the AWACS and the mobile GS on the outage probability and BER of the system is simulated, followed by an analysis of the applicability of the relay selection modes under different mobile characteristics. The numerical results analyzed include the optimal number of UAVs and the deployment altitude. Finally, guidelines were provided for the design of a multiple UAV-based hybrid FSO/RF aeronautical communication system.  
**Author Contributions:** X.Z.: methodology, software and writing; S.Z.: conceptualization and supervision; Y.W.: software and validation; H.H.: funding acquisition.; G.Y.: investigation; X.S.: visualization; X.L. and J.L.: data curation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Shaanxi Province Natural Science Basic Research Program (2024 JC-YBMS-514).  
**Data Availability Statement:** The data are only available upon request to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Mobile System Integration Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Experiments, Results, and Discussion
### Performance Evaluation
#### 4.4.1 Intrusion Detection Using Centralized Methods  
To evaluate the effectiveness of the proposed model, we first applied two traditional centralized ML approaches for cyber-attack detection, namely, CNN and RNN.  
The values that were used for the various classifiers' parameters in our suggested approach are listed in Table 3.  
Table 4 shows the results of ML approaches for a centralized model in terms of Accuracy, Precision, Recall, and F1-score, which indicates how well the model detects benign and attack classes from the dataset. As shown in the table, the Accuracy and F1-Score values can reach as high as 94% and 93% for RNN and CNN approaches, while the Precision and Recall values can reach as high as 95% (RNN) and 94% (CNN) for identifying benign and malicious attacks, respectively. Despite the fact that centralized models performed pretty well, they are nevertheless susceptible to Single Point of Failure concerns.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 4 Network Architecture  
Similar to the traditional sender-initiated handshaking MAC protocols, the receiver-initiated handshaking methods can be applied to a wide range of network applications. The parallel reservation in the receiver-initiated handshaking works particularly well in networks with specific data flow, e.g., data gathering networks. Figure 2 illustrates a data gathering network in which data from underwater sensors or vehicles is gathered to a surface buoy or a control center. This type of network has a wide range of applications, such as underwater environment monitoring as well as scientific exploration [28; 29].  
With parallel reservation, node \(r_{2}\) in Figure 2 is able to collect information from its neighbor nodes. This data gathering scheme helps to reduce the channel competition and thus decreases the number of collisions among data packets. The receiver-initiated mechanism also enables packet merging at the link layer which further reduces the network traffic load. So, the receiver-initiated handshaking MAC protocols with parallel reservation are especially preferred in data gathering applications.  
In this paper, we assume the sensor nodes monitor the underwater environment (e.g., temperature, pressure, salinity) with periodical sensing. Because of the limited power supply and channel capacity, not every piece of data is reported, but only when some event is detected [30], i.e., temperature change, infusion target detection, and so on. This filtered information results in a random traffic generation rate in the underwater sensor network. We assume that the data packets on network nodes are generated independently with a given probability \(p_{g}\) in each sensing period. For all nodes that act as relays, the total traffic, which includes its self-generated data and the relayed data, follows a Bernoulli Distribution. The traffic prediction problem is not trivial, since it is further affected by the behaviors of the transport and network layer protocols as well as the collisions and retransmissions on the link layer. Figure 3 shows that the resulting traffic has a much heavier tail than the traffic pattern on the application layer. For this reason, we study the link layer traffic from a probability point of view. The traffic estimation algorithm here updates the probability distribution of the number of packets, which supports the data polling for the receiver-initiated MAC protocols.
## 3 KIDE4I: A Generic Semantic-Based Task-Oriented Dialogue System
### Semantic Repository  
The semantic repository stores all the information that allows the dialogue manager to function. It contains both knowledge at class level--commonly known as terminological box or _TBOX_--and the individuals that belong to those classes--assertional box or _ABOX_[33].  
The core of this semantic repository--and, hence, the dialogue system--that corresponds to the _TBOX_ in this framework is the Task-Oriented Dialogue management Ontology (TODO) [8]. This ontology consists of different modules that allow to model the dialogue management (TODODial) and the domain (TODODom) knowledge areas of the dialogue system.  
Additionally, the _ABOX_ stores, on the one hand, for the dialogue management dimension, (i) the requests and responses that the dialogue system can output to the user, (ii) the processing functions that the dialogue manager must perform given the key elements obtained from a user command and (iii) the implications of the different outputs of these functions. The advantage of this approach is that most instances and relations of this dimension can be reused through use cases and even languages, only requiring translations for requests and responses.  
On the other hand, for the domain-related knowledge, the \(ABOX\) contains (i) the modelling of all the world elements of the domain and their relations, (ii) the different actions that can be performed by the target system, along with their arguments, (iii) the world elements that could belong to those arguments, (iv) the different variants to refer to both domain elements and actions in the target language and (v) the target-system-readable equivalents for world elements and actions. Finally, the \(ABOX\) is also the destination for the traces generated in each interaction with the dialogue system.  
To sum up, the semantic repository determines the outputs the dialogue system will show to the user, the flow of the dialogue and allows the dialogue manager to interpret user commands to obtain a readable input for the target system. This means that the total control of the dialogue process depends on semantic-technology-based resources.
## 3 Results
### Comparative Experiment
As shown in Figure 5, MCANet generates the most accurate and visually appealing alignment results on cross-sectional  
Figure 4: Box plot of dice score, SDlogJ, \(|J_{\theta}|\leq 0\), and sensitivity for different methods on Mindboggle101 and Neurite-OASIS datasets. Dice score measures the accuracy of registration. SDlogJ indicates the smoothness of the deformation field. \(|J_{\theta}|\leq 0\) measures the folding rate of the deformation field, while sensitivity measures the sensitivity of registration. slices of the brain, effectively reconciling the trade-off between accuracy and computational efficiency. In summary, MCANet demonstrates exceptional registration performance across both the Mindboggle101 and Neurite OASIS datasets. Compared with traditional algorithms and other deep-learning-based methods, MCANet has obvious advantages in terms of registration accuracy and time efficiency and can generate more accurate and natural deformation results.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 6 Example Analysis
### Effect of Cohesion  
When the shallow-tunnel burial depth of the slope section \(H=20\) m, rectangular tunnel height \(h=10\) m, span \(b=10\) m, bulk density of surrounding rock \(\gamma=20\) kN/m\({}^{3}\), uniaxial tensile strength \(\sigma_{1}=30\) kPa, cohesion of surrounding rock \(\epsilon_{0}=7\)-\(15\) kPa, angle of internal friction in the surrounding rock \(\varphi=18^{\circ}\), slope surface inclination \(\delta=18^{\circ}\), ratio of horizontal support force to vertical support force of surrounding rock \(K=1.0\), nonlinear coefficients \(m=1.0\), \(1.1\), \(1.2\), \(1.4\), and slope top load \(p=100\) kPa, the variations in the pressure and damage patterns of the surrounding rock in shallow-buried tunnels are presented in Figures 8 and 9.  
Figure 8 indicates that when the nonlinear coefficient \(m\) is certain, the surrounding rock pressure shows a significant decreasing trend with the increase in cohesion. When the cohesion \(\epsilon_{0}\) is certain, the surrounding rock pressure increases significantly with the increase in the nonlinear coefficient. It can be seen that the surrounding rock pressure calculated by the nonlinear criterion is very sensitive to the change in the cohesive force. Analyzed from the perspective of the principle of virtual work, the cohesive force increases, the internal energy consumption also increases, and the support reaction force required by the surrounding rock will decrease. As can be seen from Figure 9, with the decrease in cohesive force, the damage surface has an obvious tendency to extend outward, and the area of the shear zone increases. Therefore, in the case of poor surrounding rock conditions, the tunnel cross-section construction is prone to forming a large plastic zone. Based on the monitoring data obtained during the construction stage and the characteristics of the surrounding rock, measures such as the use of spraying anchors and grouting reinforcement can be employed to protect shallow-buried, eccentrically loaded tunnels, thereby enhancing the stability of the surrounding rock.  
Figure 8: Influence of cohesive force on surrounding rock pressure.  
Figure 9: Influence of cohesive force on failure mode of surrounding rock (\(m=1.2\)).