esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case Study
### Management Suggestion  
Based on the analysis results in Sections 6.2 and 6.3, the following management suggestions were obtained. First, focus should be placed on protecting the resource nodes with a larger degree and larger betweenness (i.e., the important nodes in the CMS). The structural robustness and process robustness of the CMS decreased rapidly in the failure modes based on the node degree (i.e., ID and RD) and node betweenness (i.e., IB and RB), indicating that nodes with a larger degree and betweenness are crucial to maintaining system robustness. More specifically, nodes with a large degree are closely connected with other nodes, so they can play an important role in maintaining system connectivity, and nodes with a large betweenness have greater control over the logistics and information flow in the system, so they can play an important role in maintaining the information transmission rate of the system. Second, alternative resources must be provided to ensure that when the original resources fail, alternative resources can replace them to complete their tasks. These alternative resources can be set up within (1) the same service provider, (2) other external service providers, or (3) a combination of both, and all these methods can protect the robustness of the CMS to a certain extent.
## 5 Numerical Results and Discussion
The variation of the skin friction coefficient \(C_{f}\), against the nanoparticle volume fraction \(\varphi\), is presented in Figure 4, for the same nanofluids at different values of \(\alpha\) and \(Nr\), equal to zero or one. The variation of the skin friction coefficient \(C_{f}\), against the nanoparticle volume fraction \(\varphi\), is presented in Figure 4, for the same nanofluids at different values of \(\alpha\) and \(Nr\), equal to zero or one. In both cases, i.e., in the absence or presence of radiation, the skin friction coefficient is a decreasing function with respect to the fractional parameter \(\alpha\). Considering its variation with respect to \(\varphi\), two different situations appear. In the absence of radiation (Figure 4b), it is an increasing function with respect to \(\varphi\), and its values are always greater for Ag-water fractional nanofluid compared to CuO-water fractional nanofluid. In the presence of radiation, when \(Nr=1\) (Figure 4a), the shear stress on the plate decreases up to a critical value of \(\varphi\) (about 0.1), and then increases. Furthermore, up to this value of \(\varphi\), it is smaller for Ag-water fractional nanofluid in comparison to CuO-water fractional \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{y}\)} & \multicolumn{4}{c}{**CuO**} & \multicolumn{4}{c}{**Ag**} \\ \cline{2-10}  & \(\boldsymbol{\alpha=0.1}\) & \(\boldsymbol{\alpha=0.5}\) & \(\boldsymbol{\alpha=0.85}\) & \(\boldsymbol{\alpha=1}\) & \(\boldsymbol{\alpha=0.1}\) & \(\boldsymbol{\alpha=0.5}\) & \(\boldsymbol{\alpha=0.85}\) & \(\boldsymbol{\alpha=1}\) \\ \hline
0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\
0.1 & 0.86341 & 0.84348 & 0.84816 & 0.87689 & 0.86675 & 0.84694 & 0.85013 & 0.87864 \\
0.2 & 0.74363 & 0.72536 & 0.75925 & 0.77086 & 0.74937 & 0.73083 & 0.76448 & 0.7768 \\
0.3 & 0.64057 & 0.62803 & 0.65475 & 0.6609 & 0.648 & 0.63504 & 0.66273 & 0.6691 \\
0.4 & 0.55187 & 0.54341 & 0.55664 & 0.55868 & 0.56041 & 0.55166 & 0.55628 & 0.56886 \\
0.5 & 0.47551 & 0.46851 & 0.46887 & 0.46476 & 0.48472 & 0.47766 & 0.47961 & 0.47635 \\
0.6 & 0.40976 & 0.40218 & 0.39111 & 0.38035 & 0.4193 & 0.4119 & 0.4025 & 0.39275 \\
0.7 & 0.35313 & 0.34379 & 0.32302 & 0.30608 & 0.36274 & 0.35376 & 0.33461 & 0.31872 \\
0.8 & 0.30436 & 0.29277 & 0.26415 & 0.24213 & 0.3183 & 0.30272 & 0.27556 & 0.25448 \\
0.9 & 0.26234 & 0.2485 & 0.21388 & 0.18821 & 0.27154 & 0.25821 & 0.22481 & 0.19985 \\
1 & 0.22613 & 0.2103 & 0.17148 & 0.14371 & 0.23496 & 0.2196 & 0.1817 & 0.15432 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of the dimensionless temperature for \(\varphi=0.2\), \(t=0.5\), and different values of the fractional parameters \(\alpha\) and \(y\).
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows: a background of routing and rerouting solutions in SDNs are given in the next section. Section 3 presents the system architecture and outline of our work. Section 4 presents the system model, problem formulation in our work. In Section 5, we detail the proposed method. Section 6 includes the simulation and metric descriptions. In Section 7, the performance of the proposed method is analyzed and validated in terms of the presented metrics. Finally, Section 8 presents conclusions and future outlook.
## 3 Materials and Methods
### Technical Evaluation  
The technical evaluation consisted of three sections. Firstly, the input and output of each of the ZenTBI modules were evaluated using an Oscilloscope. Secondly, the power consumption was calculated by measuring the voltage over a 1-ohm resistor placed in series between the battery and the ZenTBI supply input. According to [PERSON]'s law, the voltage measured with a digital multimeter corresponded to the current consumption (I = V/R).  
Finally, a quality evaluation of the optical signals was conducted in MATLAB to demonstrate the functionality of all the components of the system. A head phantom was used to simulate pulsations on the cerebral arteries that could be measured by the optical sensor [24], as is shown in Figure 5. The use of a head phantom rather than a human head allows the control of multiple parameters such as the oxygen levels and the intracranial pressure. Moreover, the lack of scalp or extracerebral circulation ensures the acquisition of signals only from the cerebral arteries, reducing this confounding factor. Moreover, Table 2 presents the parameters of the setup.  
The analysis included a pre-processing step, where the signals from all wavelengths and both proximal and distal photodetectors were filtered using a Butterworth bandpass filter of order 3 (fc: 0.5 to 8 Hz). Then, a 60-s recording was segmented into 5-s windows, where nine different signal quality indices were calculated based on the literature [25; 26]. The list of indices is perfusion, kurtosis, skewness, kurtosis in the frequency domain, skewness in the frequency domain, entropy, zero-crossing rate, signal-to-noise-ratio (SNR),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Parameter** & **Value** & **Units** \\ \hline \hline \multirow{3}{*}{Sensor} & Recording time & 60 & Sec \\  & LEDs currents & 30 & mA \\  & Sample frequency & 2000 & Hz \\  & Probes distance & 5 & mm \\  & Location & Phantom’s forehead & \\ \hline \hline \multirow{2}{*}{Phantom} & Circulating fluid & Artificial blood & 65\% \\  & Heart rate & 60 & bpm \\  & Stroke & 20 & cc/stroke \\  & Intracranial pressure & 15 & mmHg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quality evaluation setup parameters.  
Figure 5: The head phantom consists of a brain, skull, cerebrospinal fluid, and blood circulation. The sensor on top of the forehead shines light to reach the pulsatile vessels of the brain and receives light that correlates to oxygenation and ICP.  
and relative power. Outliers were replaced by the median in all the indices. Finally, the comparison of proximal and distal signal quality at all wavelengths was made by a non-parametric ANOVA (Kruskal-Wallis), considering a confidence level of 95%.
## 8 Conclusions  
This research paper proposed a multi-hop routing protocol for Underwater Acoustic Sensor Networks. The study examines various existing routing protocols and addresses  
Figure 7: Network performance (**a**) energy consumption, (**b**) packet delivery ratio, (**c**) end-to-end delay.  
the triple challenges of reliability, energy conservation, and void hole mitigation. Through a novel way of strategic node deployment, optimal clustering, and void-aware routing path selection, our findings demonstrate a significant improvement in network efficiency, achieving reduced energy consumption while enhancing the packet delivery ratio. This study offers an energy-efficient and reliable UASN network, promoting sustainable oceanographic data collection and monitoring. The results suggest that our proposed methodology can contribute to prolonging the network lifespan of UASNs in challenging underwater environments, offering practical solutions for real-world applications where data timeliness is less critical. Future work may explore the improvement toward reducing the longer delays, which may significantly improve the network performance, especially for the applications where quicker transmissions are needed to make time-critical decisions.  
Conceptualization, methodology, mathematical model, validation, and original draft preparation M.U.K.; writing--review and editing, M.A. and P.O.; supervision, M.A. and P.O.; All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Institute of Oceanic Engineering Research of the University of Malaga, Malaga, Spain. This research was funded by the Universidad de Malaga, funding number is UMA-06.34.  
Not applicable.  
Data Availability Statement: Data are contained within the article.
# Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation
## 6 Conclusions  
This paper has introduced a supervised neural-based WSD method. Previous works have noticed the bottleneck of poor performance on rare and unseen senses and achieved  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **MFS** & **LFS** \\ \hline WordNet S1 & 100.0 & 0.0 \\ Lesk+ext,emb & 92.7 & 9.4 \\ Babelfy & 93.9 & 12.2 \\ EWISE & 93.5 & 31.2 \\
**ours** & **94.0** & **60.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-score (%) on words labeled with most frequent sense (MFS) and words labeled with less frequent senses (LFS) of the ALL set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Ablation** & **Total** & **MFS** & **LFS** \\ \hline BERT-base & 68.4 & **94.7** & 36.9 \\ BERT-base+gloss & 78.9 & 94.1 & 51.7 \\ BERT-base+gloss,SR & 79.2 & 93.5 & 57.2 \\ BERT-base+gloss,HR & 79.1 & 93.4 & 57.3 \\ BiCapAtt & **79.5** & 94.0 & **60.2** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on the ALL set.  
inspiring results through making use of gloss knowledge. We manage to obtain better presentations while encoding context and sense glosses of target ambiguous word in the same space to improve LFS from a novel perspective. We leverage the advantage of capsule network to improve multi-head attention, thus obtain deeper contextual presentation. The experimental results on benchmark datasets prove that our method is effective and encouraging.  
In this paper, we use the neural network architecture to encode the context and sense definition, which has room for improvement. In the future, we consider incorporating more relation such as hypernym and hyponym to enrich sense embeddings. Multilingual resources for improving sense embeddings will be another way taken into consideration. We also plan to apply our WSD model to downstream NLP tasks such as machine translation.  
Conceptualization, J.C. and W.T.; Methodology, J.C.; software, W.T.; validation, J.C., W.T., W.Y.; formal analysis, J.C.; investigation, J.C.; resources, W.T.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, W.T., W.Y.; visualization, J.C.; supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Committee of Science and Technology, grant number 19511121002 and grant number 19 DZ2252600, and by the Natural Science Foundation of Shandong Province, grant number ZR2019 LZH002.  
The authors declare no conflict of interest.
# Mechanically Reconfigurable, Beam-Scanning Reflectarray and Transmitarray Antennas: A Review
## 4 Feed Movement
### Feed Displacement on Fully Illuminated Apertures
#### 4.1.2 Arrays with Multi Focal points
Instead of simply averaging the phase compensation of two feeds, the bifocal function is defined as a weighted average of the phase compensations. The values of weights were determined by the illumination intensity on each unit cell, giving greater weights to higher intensities. Reference [67] shows the superior scanning performance of bifocal designs compared to that of the unfocal designs of the same size. The side lobe level of bifocal design is \(-13.8\) dB, while that of the unfocal design is \(-8.1\) dB. Moreover, the bifocal design provides less than \(3\) dB scan loss, which is significantly less than \(6\) dB scan loss of the unfocal design over the similar scan range.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 2 Error Correction Scheme (ECS)  
In the literature, there are various types of ECS presented to support various types of reliability, delay constrained, and data rates in different types of communication systems. Here, we present a brief overview of various types of ECS used in the communication. There are several types of ECSs available to maintain and enhance the reliability and delay-constraints of a communication link. These ECSs were loosely grouped in to four categories. Figure 1 illustrates a broad classification of ECS for WBAN error correction context.
## 2 Materials and Methods
### Frameworks and Tools  
In this section, the hardware components of the workstation used to run the experiments are introduced, as well as a detail of the software packages used to develop this work.  
Geospatial data from Copernicus EMS was processed using the GDAL software library [49] to determine AoIs' coordinates and to identify the precise regions affected by forest wildfires and the corresponding severity levels. Sentinel-Hub services were used to collect Sentinel-2 data.  
Satellite imagery was processed through Python with OpenCV and scikit-image [50] libraries. Data analysis was performed through scikit-learn [51], whereas neural network models were developed and trained using PyTorch framework [52]. All the software packages and versions are specified in Table 2.  
The experiments were run on a workstation with an Intel Core i9-7940X @ 3.10 GHz with 128 GB of RAM and 4x GTX 1080 Ti.
## 4 Conclusions  
This study conducted a parametric analysis of the static-dynamic characteristics of adjacent tunnels for super-large-diameter twin tunnels under train-induced vibrations using the DEM. Three sets of models were developed to investigate the effects of internal walls, absolute and relative spacing, and water pressure on the static-dynamic responses of adjacent tunnel structures and surroundings. The main conclusions are as follows:  
1. The NW condition (no internal walls) of T2 exhibits significantly greater static-dynamic responses in both liners and surroundings compared to other conditions. Internal walls in super-large tunnels markedly reduce these responses, with their stabilizing effectiveness ranked as wall2 > wall1 > wall3 = wall4. Wall2 plays a particularly decisive role. \(D_{\text{R}}\) and \(V_{\text{R}}\) in liners and surroundings correlate strongly with the burial depth and distance to the vibration source.
2. Under equivalent relative spacing, super-large twin tunnels show smaller static-dynamic responses in liners and surroundings than small twin tunnels. However, under equivalent absolute spacing, super-large twin tunnels exhibit significantly enhanced dynamic responses and larger discrepancies in liner and surroundings displacements and velocities. The static-dynamic characteristics of super-large twin tunnels fundamentally differ from those of small twin tunnels under any spacing.
3. Optimal water pressure (\(P_{2}\) = 149 kPa) effectively suppresses displacements and velocities in liners and surroundings, while excessive pressure (\(P_{3}\) = 223 kPa) amplifies static-dynamic responses. Critical monitoring points requiring attention include the central tunnel region (180\({}^{\circ}\)), vault (90\({}^{\circ}\)), and 135\({}^{\circ}\), where responses are most pronounced due to proximity to the vibration source and the surroundings easily sliding.  
The static-dynamic characteristics of super-large-diameter twin tunnel liners and surroundings differ fundamentally from those of small twin tunnels, highlighting the complex interactions between their liners and surroundings. Future research will employ the scale model to explore the dynamic behavior of super-large twin tunnels under train-induced vibrations.  
Conceptualization, L.W. and X.B.; methodology, L.W.; software, Z.C.; validation, L.W., X.B. and Z.C.; formal analysis, L.W.; investigation, X.B.; resources, H.G.; data curation, H.G.; writing--original draft preparation, Z.C.; writing--review and editing, L.W.; visualization, Z.C. and J.W.; supervision, H.G.; project administration, X.B.; funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript.
# Design and Implementation of Industrial Accident Detection Model Based on YOLOv4
## 1 Introduction
The data standardization for the video data was based on the guidelines for building a learning dataset provided by the Korea National Information Society Agency. You Only Look Once (YOLO)v4 was used as the object detection model, and the accuracy exceeded 95%. Thus, this approach could create safe working environments for workers, including those outside the manufacturing sector.
## 4 Shear Instability Analysis
### Shear Instability Mechanism
It should be noted that the post-peak soft modulus (\(S_{p}\)) is different from the post-peak modulus (\(S\)), because \(S_{p}\) only describes the weakening behavior of the local damage shear zone (e.g., asperities providing shear resistance) while \(S\) describes the average weakening behavior of the whole shear zone. Therefore, \(S_{p}\) is more suitable than \(S\) to describe the shear instability behavior of rock joints, which is also called the shear instability parameter. At present, there are few shear instability parameters that can specifically consider the influence of the damage zone alone. According to Equation (1), the shear stress-plastic shear displacement experimental curves of marble joints are obtained by eliminating  
According to Figure 10, the relationship between _CNL_, \(d\), and _S_\({}_{p}\) can be obtained, as shown in Figure 11. Figure 11a shows that _S_\({}_{p}\) first increases and then decreases linearly with the increase in d, which is different from the exponential relationship between \(S\) and \(d\) shown in Figure 7c. Figure 11b shows that _S_\({}_{p}\) first increases linearly with the increase in CNL and then tends to be stable or decreases. However, the exponential increase in S with the increase in CNL in Figure 8c is not reasonable because the marble specimen changes from brittleness to ductility when _CNL_ is very large. Therefore, _S_\({}_{p}\) is more suitable for describing the shear weakening behavior of the local shear zone, while \(S\) is more suitable for describing the average shear weakening behavior of the whole shear zone.
## 1 Introduction
Despite the fact that the limitations of Cmax as an absorption rate parameter have been recognized since its inception, Cmax has been used in BE studies for more than 30 years. The aim of this study is to use the modern tool of machine learning in order to address, from a new perspective, the old problem of setting the most appropriate rate of absorption for use in BE assessment. Two machine learning algorithms (principal component analysis and random forest) are utilized to explore the relationships among the PK parameters and to identify the most suitable metric for absorption rate. Moreover, among the PK metrics evaluated, a novel metric, the Cmax/Tmax ratio, is introduced and explored for its utility in describing absorption rate alongside the other measurements. In order to accomplish this task, the computation approach included three steps: (a) First, the two ML algorithms are applied to the actual bioequivalence dataset of donepezil. To validate the findings obtained in the previous step and to address additional questions, simulated bioequivalence datasets were generated. (b) Since a population pharmacokinetic model would be necessary for the simulations, non-linear mixed effect modeling was applied to the actual concentration-time (C-t) data of donepezil. (c) Simulated BE datasets were then generated for three different types of absorption kinetics (slow, intermediate, and fast). (d) Machine learning was applied again to the simulated data.
# Design and Optimization of a Compliant Morphing Trailing Edge for High-Lift Generation
## 6 Refined Model and Result Evaluation
The displacements illustrated in these plots were suitably magnified to better highlight the details of the modal shapes. Finally, a buckling analysis was also carried out, yielding an eigenvalue of \(-\)17.3. The maximum stress level, the 1 st modal frequency, and the 1 st buckling eigenvalue are compliant with the constraints assumed for the optimization process. A comparison was organized among the outcomes of the simplified and refined models. All the values, relevant deviations, and % errors are reported in Table 5. The simplified model proved to be slightly more conservative from a structural point of view: the stress level, the buckling eigenvalues, and the frequency were a little bit closer to the thresholds discussed in the previous section. In terms of displacement, the refined model yielded a slightly smaller prediction (2 mm less) of the vertical displacement of the trailing edge. The two models, in any case, appear in good agreement with an exception on the buckling eigenvalue, where the tridimensional feature makes the difference in terms of global absorption of the loads. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Value** \\ \hline Number of nodes & 125,055 \\ Number of elements & 524,666 \\ Extension along the chord & 0.53 m \\ Width & 0.5 m \\ Upper skin material & 7075-T6 aluminum alloy7075-T6 \\ Lower skin material & aluminum alloy7075-T6 aluminum \\ Web materialKinematics bars & alloySteel \\ Thickness distribution for the upper skin & \(\bullet\) 2.84 mm, front portion \\ Thickness transition on the upper skin & 34\% of skin length in chord \\ Thickness distribution for the lower skin & \(\bullet\) 2.84 mm, front portion \\ Thickness transition on the lower skin & 40\% of skin length in chord \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main features of the refined FE model. Figure 16: Results of the static analysis: displacement field (morphed shape and maximum displacement printed in the caption) and stress distribution (contour map and relevant color bar) under the actuation of the loads alone (**a**) and in combination with the aerodynamic loads (**b**). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Comparison Term** & **Simplified Model** & **Refined Model** & **Deviation** & **Error** \\ \hline Trailing edge vertical displacement & 0.140 m & 0.138 m & \(-\)0.002 m & \(-\)1.45\% \\ \hline Max [PERSON] stress & 200 MPa & 193 MPa & \(-\)7 MPa & \(-\)3.63\% \\ \hline
1 st buckling eigenvalue & \(-\)17.3 & \(-\)30 & \(-\)12.7 & +42\% \\ \hline
1 st modal frequency & 45.2 Hz & 46.7 Hz & +1.5 & +3.21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the outcomes of the simplified and refined FE models.