esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Performing Clustering for Supporting Routing in Multi-UAVs  
Due to ultra-densification in the multi-UAVs and multi-UAV swarm scenarios, essential collaborative tasks, such as data aggregation, load distribution, resource allocation, and local synchronization, are performed to cater to the massive amount of data generated by UAVs. Clustering (i.e., hierarchical routing) segregates nodes with similar characteristics and behaviors into logical groups in order to improve network stability and scalability; hence, it is suitable to support the collaborative tasks [138]. Since clustering can increase network lifetime, it can increase route stability and lifetime [139]. While clustering schemes have been proposed for FANETs [103], there is a lack of focus on scenarios under ultra-densification. Since the network densification changes with geographical locations and time, further investigation could be pursued to perform clustering using context-aware approaches, such as artificial intelligence approaches and bio-inspired algorithms [140].
## 3 Methodology
### Validation Methods and Evaluation Indicators  
In this paper, the 10-fold cross-validation method [52] is applied to verify the validity of the five point-surface fusion methods. The basic idea is to divide the original datasets randomly into 10 equal-sized parts. Nine parts are then used as the training set for model fitting, and the remaining part is used as the validation dataset for model testing. We then repeat the process 10 times so that every part is tested. Finally, the 10 results can be averaged to produce the final estimation called the \"cross-validation results\", and the model with the maximum correlation coefficient is selected as the best fitting model for the later prediction. To verify the effectiveness of each model, the training sets and the test sets are quantitatively evaluated. The indicators are R and the RMSE.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 6 Wear Rate Expectation Model  
### Model Structure  
It is hypothesized that the wear rate expectation can be modeled with low-order polynomial functions. The hypothesis is supported by the initial insights into experimental data given in Figure 9 and the preliminary modeling results published in [26]. More general models, e.g., those based on advanced machine learning techniques, are less appealing in this application due to the low amount of recorded data available for model training (only 121 points, Section 3). The following general three-input full cubic model structure is selected:  
\[\begin{array}{ll}\hat{y}=\beta_{0}+\beta_{1}x_{1}+&\beta_{2}x_{2}+\beta_{3}x _{3}+\beta_{12}x_{1}x_{2}+\beta_{13}x_{1}x_{3}+\beta_{23}x_{2}x_{3}+\beta_{11} x_{1}^{2}+\beta_{22}x_{2}^{2}\\ &+\beta_{33}x_{3}^{2}+\beta_{123}x_{1}x_{2}x_{3}+\beta_{112}x_{1}^{2}x_{2}+ \beta_{113}x_{1}^{2}x_{3}+\beta_{122}x_{1}x_{2}^{2}\\ &+\beta_{133}x_{1}^{3}+\beta_{223}x_{2}^{2}x_{3}+\beta_{233}x_{2}x_{3}^{2}+ \beta_{111}x_{1}^{3}+\beta_{222}x_{2}^{3}\\ &+\beta_{333}x_{3}^{3},\end{array} \tag{10}\]  
where there are 20 free (\(\beta\)) parameters and the total number of \(2^{20}-1=\) 1,048,575 submodels contained (each with unique combination of included terms). The general model inputs \(x_{1}\), \(x_{2}\), and \(x_{3}\) (i.e., independent predictor variables) are used to form the input features \(z_{i}\), \(i\in\{1,\ 2,\ldots,\ 20\}\), which are multiplied by the model parameters \(\beta_{i}\). The general model output, i.e., the dependent response variable is denoted as \(\hat{y}\). In the context of the wear rate model, the three inputs correspond to operating parameters averaged over the \(n\) cycles of each operating point (see Section 4 and Figure 9) the surrogate distance \(x_{1}=\overline{a}_{s}=\overline{\omega_{0}^{*}}\overline{a}_{2}^{t}\) the clutch temperature \(x_{2}=\overline{T}_{d}\), and the clutch torque \(x_{3}=\overline{M}_{z}\), while the output is the wear rate expectation \(\hat{y}=w_{\mu}\). The process of averaging the time-varying parameter inputs introduces a modeling error, because the wear dependencies on parameter inputs are  
Figure 11: Construction of run-in weighting function \(g(E_{c})\).  
not linear. However, as demonstrated in Appendix D, the error is very small, and thus negligible, because of relatively small parameter inputs variations.  
The optimal wear rate expectation model is found by the best subset selection method [29], i.e., by iterating over all 1,048,575 submodels of the full cubic model (10) and assessing them according to a predefined criterion. It should be noted that if four inputs were considered, i.e., the initial slip speed \(\omega_{0}\) and the closing time \(t_{2}\) instead of the surrogate distance \(d_{s}\), the full cubic model would have 35 free parameters and the total number of \(2^{35}-1=34\),359,738,367 submodels, and the best subset selection approach would not be feasible. In that case, another model structure optimization method should be used [30].
## 2 M2M Service Networks
### Discussion  
The M2M standardization approaches demonstrate the heterogeneity of the M2M service networks area. The area is very large; our aim is to review the available technological solutions at least from the following perspectives: M2M Information and services, M2M communication and M2M security. In this review we categorize the available technologies into these perspectives. Within each category, the key contribution of each technology is analyzed. After the review, the potential contributions of the technologies to support horizontal and autonomic M2M are discussed.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 1 Introduction
The cloud agent aggregates knowledge from multiple patients or longer time spans to form broader wisdom and coordinates the overall healthcare decisions (Wisdom \(\rightarrow\) Purpose). We define a semantic communication protocol for secure data fusion such that only essential information or knowledge (rather than raw sensor data) is transmitted upstream, dramatically reducing network load and exposure of sensitive data [14]. Moreover, we implement a semantic task orchestration mechanism that allows healthcare workflows (e.g., an alert or intervention protocol) to be flexibly defined via high-level purpose-driven rules rather than low-level programming logic. This can be seen as a software-defined approach to configuring intelligent behavior in the IoT network, improving the adaptability and manageability of the system. * Prototype Implementation and Evaluation: We develop a prototypical scenario focusing on wearable health monitoring for chronic disease management and early warning of acute events. In our simulated environment, patients wear devices measuring vital signs (e.g., heart rate, SpO\({}_{2}\), temperature), which connect to a smartphone-based edge node and onward to a cloud service--an approach reflective of modern IoT health monitoring systems [3, 4]. We implement DIKWP logic such that the wearable and phone collaboratively transform sensor data into information (e.g., computing heart rate variability), detect anomalies using learned knowledge (e.g., a model of normal vs. abnormal patterns), and make preliminary decisions (wisdom) like issuing an alert or adjusting the monitoring frequency. The cloud component collects anonymized summary information from edges to update global knowledge (e.g., refining risk models across the population) and can send purpose-driven directives back to devices (for example, instructing a device to watch more closely if a patient is at high risk). We evaluate this system on synthetic datasets that emulate realistic vital sign fluctuations and health events. Key metrics include anomaly detection accuracy, communication overhead (data transmitted), decision explainability (whether the system can provide understandable reasons for an alert), and robustness to network disruptions. The experimental results show that the DIKWP-enhanced approach can achieve accuracy on par with cloud-centric AI while using only a fraction of the bandwidth [4]. Additionally, each decision comes with a traceable semantic explanation (e.g., which threshold or rule was triggered, under what knowledge/purpose context), and the system continues functioning even if cloud connectivity is intermittent, thanks to local autonomy at the edge. To assist readers in understanding the technical terms and abbreviations used throughout this article, we have provided a glossary (see Abbreviations part) listing all abbreviations along with their corresponding descriptions.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 2 Related Work
### Learning Sentence Embeddings Directly
#### 2.3.1 Paragraph Vectors  
The method presented in [162], and sometimes called Doc2 Vec, is one of the first successful Word2 Vec [16] adaptations to sequences of tokens. The authors presented two methods of how document representation could be trained.  
In the Distributed Memory model of Paragraph Vectors (PV-DM), every paragraph is mapped to a unique vector, which, together with context words, participates in predicting the next word. This way, such a unique vector acts as a memory that stores the topic of the paragraph and bears its representation. The second is the Distributed Bag Of Words version of the Paragraph Vector (PV-DBOW) model. It leaves only a unique paragraph vector in the input and predicts words randomly sampled from that paragraph in the output. However, unlike the first method, it does not account for word ordering.  
In the original article [162], authors represented sentences as a concatenation of DM and DBOW vectors, as they saw such a setup as more consistent. Later works [163; 164] also reported that individual model performance had only marginal differences, yet deviations were usually task-dependent.  
The authors of [165] proposed a modification to Paragraph Vectors and called it Doc2 VecC. Differently from Doc2 Vec, a document vector is derived not as a unique vector, but as an average of sampled constituent word embeddings. The other notable contribution is the added data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. At the time, the authors showed Doc2 VecC to match state-of-the-art in multiple tasks.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Auxiliary for External Circuits  
#### 3.4.1 Increase the Bandwidth by Applying the Base Auxiliary Signal  
The unique three-electrode structure of TL offers the potential to control and improve the light output characteristics through external measures. Because carriers are injected into the electrical collector (i.e., QW) at a longer distance than that of the optical collector during transport, it leads to a phenomenon wherein the electrical gain is slower than the optical gain. In the case of a certain number of injected carriers, if the probability of carrier recombination in the QW is increased by some means, the time to reach the peak light intensity and the overall response time will be shortened [74], which will lead to a decrease in electrical gain. [PERSON] et al. proposed the use of a low-frequency AC base auxiliary signal to enhance the carrier transport to the QW. More carriers are guided into the QW as the applied AC signal suppresses the electrical gain in the collector, which improves the differential gain of the laser, accelerates the optical response, and increases the bandwidth. Figure 12 shows the proposed circuit diagram by [PERSON] et al. After applying an AC auxiliary signal of 9 MHz, the modulation bandwidth of the TL was increased to 22 GHz, which is 11.5 GHz higher than the response bandwidth without the AC signal.  
[PERSON] et al. demonstrated that the electrical and optical gains are coupled in TL; thus the differential optical gain, photon density, and modulation bandwidth can be improved by the photon-electron \"trade-off\" [75], which provides a new way to improve the TL modulation rate. This trade-off is somewhat similar to the previously mentioned trade-off of bandwidth and electrical gain, but the regulation mechanisms different. Whereas the \"trade-off\" mentioned in Section 3.1.2 is achieved by changing the QW position, the \"trade-off\" here increases the bandwidth by suppressing the current gain by the AC signal.
## 4 Uniform Load Test Results and Discussion
### Influence of Cold-Bending Curvature on Coupling Stress  
By comparing the maximum coupling stress of specimens in Table 3 with the same interlayer thickness but different cold-bending curvatures, it was found that the maximum coupling stress was positively correlated with the cold-bending curvature. This is because the cold-bending stress was the initial stress on the glass panel during the uniform load test, and a greater cold-bending curvature would lead to greater cold-bending stress and consequently greater coupling stress. More specifically, when the cold-bending curvature increased by 0.2%, the maximum coupling stress would also grow by 4.5% on average.  
Based on the data in Table 3, the relationship between the coupling stress and the uniform load was plotted in Figure 9. In this regard, Figure 9a shows SJ1 to SJ4 with an interlayer thickness of 1.14 mm, and Figure 9b shows SJ5 to SJ8 with an interlayer thickness of 1.52 mm. Judging from the two figures, when the interlayer thickness was identical, the relation curves of each specimen were arranged according to the cold-bending curvature in a descending order, and the curves with larger cold-bending curvatures were located higher. Besides, the cold-bending curvature is shown to be positively correlated with the coupling stress. Thus, the bearing capacity of the glass panel clearly decreased after the reverse coupling of cold bending and uniform load. Meanwhile, the relationships between the coupling stress and uniform load across different cold-bending curvatures were not parallel. More specifically, at the initial stage of uniform load, the distances between the relation curves were small, but they grew bigger at later stages of uniform load, indicating that both the inclination angle of the relation curves and the growth rate of the coupling stress dropped as the uniform load rose, a phenomenon that is due to the geometrical nonlinearity effect of cold bending.  
The relationship between the coupling stress and the cold-bending curvature at testing point S22 was drawn in Figure 10, demonstrating a non-linear but positive correlation between the two. The coupling stress increased faster under a greater cold-bending curvature, which again reflected the geometrical nonlinearity effect. The intensity of the geometrical nonlinearity effect is positively correlated to the cold-bending curvature. Hence, when calculating the bearing capacity of cold-bent SGP laminated glass panels, this effect is a crucial factor to consider.  
Figure 8: Coupling stress versus uniform load curves. (**a**) SJ1; (**b**) SJ2; (**c**) SJ3; (**d**) SJ4; (**e**) SJ5; (**f**) SJ6; (**g**) SJ7; (**h**) SJ8.
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 5 Simulation Results
Figure 4: BER versus antenna noise variance \(\sigma_{\mathrm{a}}^{2}\) for the PS-DF, PS-DF-STC, and PS-AF schemes (conversion noise variance \(\sigma_{\mathrm{c}}^{2}=0.01\) and power splitting ratio \(\rho=0.5\)). Figure 5: BER versus conversion noise variance \(\sigma_{\mathrm{c}}^{2}\) for the PS-DF, PS-DF-STC, and PS-AF schemes (antenna noise variance \(\sigma_{\mathrm{a}}^{2}=0.01\) and power splitting ratio \(\rho=0.5\)). Figure 6: BER versus antenna noise variance \(\sigma_{\rm a}^{2}\) for the TS-DF, TS-DF-STC, and TS-AF schemes (conversion noise variance \(\sigma_{\rm c}^{2}=0.01\) and energy harvesting time \(\alpha=0.5\)). Figure 7: BER versus conversion noise variance \(\sigma_{\rm c}^{2}\) for the TS-DF, TS-DF-STC, and TS-AF schemes (antenna noise variance \(\sigma_{\rm a}^{2}=0.01\) and energy harvesting time \(\alpha=0.5\)). To get a further insight into the impact of \(M,\rho\), and \(\alpha\), we plot the BER curves of the multiple-antenna relaying schemes with optimal power allocation (i.e., using the optimal \(\rho\) or \(\alpha\)) in Figures 10-13. Comparing these results to those in Figures 4-7, we find that for all considered  
Figure 8: BER versus power splitting ratio \(\rho\) for the PS-DF, PS-DF-STC, and PS-AF schemes (antenna noise variance \(\sigma_{\mathrm{a}}^{2}=0.01\) and conversion noise variance \(\sigma_{\mathrm{c}}^{2}=0.01\)). Figure 9: BER versus energy harvesting time \(\alpha\) for the TS-DF, TS-DF-STC, and TS-AF schemes (antenna noise variance \(\sigma_{\mathrm{a}}^{2}=0.01\) and conversion noise variance \(\sigma_{\mathrm{c}}^{2}=0.01\)). Figure 12: BER versus antenna noise variance \(\sigma_{\mathrm{a}}^{2}\) for the TS-DF, TS-DF-STC, and TS-AF schemes (conversion noise variance \(\sigma_{\mathrm{c}}^{2}=0.01\) and optimal energy harvesting time). Figure 13: BER versus antenna noise variance \(\sigma_{\mathrm{c}}^{2}\) for the TS-DF, TS-DF-STC, and TS-AF schemes (antenna noise variance \(\sigma_{\mathrm{a}}^{2}=0.01\) and optimal energy harvesting time).
# Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction
## 4 Results and Discussion
### Device Model Parameter Extraction  
The trained NN model is used for parameter extraction. Measured results include data of various device sizes. The gate length included 56 nm, 100 nm, 200 nm, 300 nm, 400 nm, and 500 nm, and the gate width included 654 nm, 700 nm, 800 nm, 900 nm, and 1000 nm. The devices were subjected to different operating temperatures. We used Equation (3) to calculate the error between the measured result and simulation result using the extracted model parameters. The errors of the five sets of measured data are 4.88%, 5.71%, 5.62%, 5.78%, and 5.18%. Figures 13 and 14 show part of the fitting curves of the first two sets of  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **RR-All** & **RR-Sub** & **RR-Nonsub** \\ \hline
1 & 4.59\% & 6.83\% & 4.01\% \\
2 & 3.05\% & 4.02\% & 2.54\% \\
3 & 3.13\% & 3.67\% & 2.87\% \\
4 & 4.54\% & 6.58\% & 3.39\% \\
5 & 5.20\% & 6.06\% & 4.81\% \\
6 & 6.68\% & 7.30\% & 6.41\% \\
7 & 2.95\% & 3.15\% & 2.91\% \\
8 & 3.01\% & 5.08\% & 2.43\% \\
9 & 2.98\% & 3.63\% & 2.83\% \\
10 & 5.91\% & 6.11\% & 5.87\% \\ \hline \end{tabular}
\end{table}
Table 4: Errors of ten sets of inputs.  
Figure 12: IV fitting curves for the third set of inputs: (**a**) I\({}_{\text{ds}}\)–V\({}_{\text{ds}}\) curves at different V\({}_{\text{gs}}\) when V\({}_{\text{bs}}\) = 0; (b) I\({}_{\text{ds}}\)–V\({}_{\text{gs}}\) curves at different V\({}_{\text{bs}}\) when V\({}_{\text{ds}}\) = 1.0 V.  
measured data, where the curves indicate the measured results, and the dots indicate the simulation results of the extracted model parameters. The results show a good fit.  
We used SPICE and the NN model to extract parameters from the measured data of a single device. The extraction error of the SPICE was 3.27%, and it took 4.4 h. The extraction error of the NN model was 4.28%, and it took 8.6 s. Both the SPICE and the NN model worked on the same CPU processor.
## 6 Case Study
### Data Gathering

Table 3: Summary of the system usability scale (SUS) and designated usability questionnaire. The trust level expressed by men in both hospitals was almost identical. Experience with the system did not change their perception. On the other hand, among female nurses, we found differences between those who had experience with the system ([PERSON]) and those who only received the training ([PERSON]). Kaplan's nurses expressed a higher level of trust. It seems that the factors affecting the women's trust are different; some of these can be inferred from the interviews that we conducted, in which two main topics were raised. First is understanding the system mechanism. Participants were asked to explain how the AnapnoGuard system works. It seems that all the male nurses understood the system's rationale; all of them were familiar with the CO2 sensor, as opposed to the female nurses who did not know how to explain what was happening. Perhaps this is the reason they found it difficult to trust the system. One of the men also pointed this out when he was asked if he trusted the system, saying: \"At first, I was skeptical, but after understanding its rationale, I trust it.\" The level of trust is possibly related to the level of understanding. Another possible explanation for the different results between males and females is their perception of the human factor. In our interviews, we asked the participants if they could imagine a future where autonomous systems would replace human nurses. While all said no, their reasoning was very different. Women spoke in terms of compassion and contact: \"The patient needs to have someone to hold his hand.\" The word \"communication\" was repeated in all three interviews: \"Without a doubt, patients need to communicate with a person.\" Among male nurses, the reasons were related to the human brain: \"Nothing can compare with the human mind in understanding the overall picture\"; \"There is a need for a person to integrate all the information.\"  
Figure 14: Correlation between gender and trust. Figure 15: Correlation between SUS score and users’ trust level. These results could help improving user trust by incorporating changes in the system itself, such as adding communication, written or verbal, or informing the user regarding its actions in real-time. Another adjustment could be to reach a higher level of transparency by explaining the reason for the system's actions and revealing the next move. Second, user trust could also be improved by designing a slightly different training program to make sure all users fully comprehend the way the system operates. The nature of our case study, however, is subject to several limitations. The research tools utilized to evaluate the system are subjective; therefore, respondents' answers may be biased. In addition, due to staff turnover, we ended up with a relatively small sample size. This is a common situation due to the fact that we were evaluating a product for the first time with real users, employing a small group of participants.