esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Lifetime of Blue TADF Emitters  
Device lifetime is also a crucial point for a further commercialization of OLEDs. If this parameter is of importance, this point is often not mentioned in the different publications. Faced with the difficulty to produce a deep blue emission and before examining the stability of devices; the first parameter to consider is to produce this blue emission. Examination and elaboration of strategies to improve the device stability come only in a second step. This point was however examined with a series of TADF emitters with EL emissions ranging from 458 nm (blue OLEDs) to 490 nm (sky blue OLEDs) [108]. In this work, authors demonstrated that the device stability could be greatly improved by substituting the emitters with _tert_-butyl groups. While comparing the device lifetimes of OLEDs that were fabricated with **32-35** (Figure 10), a significant improvement could be evidenced for the two complexes substituted with _tert_-butyl groups (167 and 770 h for **33** and **35**, respectively), whereas lifetimes of only 67 and 176 h could be determined for the parent structures **32** and **34**, respectively. Introduction of a steric hindrance around the emitter is thus an efficient strategy to enhance the device stability without sacrificing the color purity. Interestingly, even if the _tert_-butyl groups are electron-donors by nature, influence on the EL emission was only marginal, the emission only shifting from 458 for **32** to 463 nm for the _tert_-butyl substituted emitter **33**. While comparing **34** and **35**, EL emission was invariant (490 nm), irrespective of the substitution. This beneficial effect can be assigned to the limited electronic communication in TADF emitters, enabling the introduction of electron-donating groups onto the donor without drastically affecting the emission wavelength.  
The chemical structure of the electron donors/acceptors used to form the TADF light-emitting materials is also of crucial importance. On the basis of the structures that are reported in the literature, lifetime of devices comprising a carbazole-based emitter seems to be longer than for the other structures. Thus, **36** (Figure 10) could retain 80% of its initial luminance of 52 h, the initial luminance being of 500 cd/m\({}^{2}\)[109]. On the opposite, a lifetime of only one hour was found for a 9,10-dihydroacridine-based emitter **37** (Figure 10) (the luminance of devices being 500 cd/m\({}^{2}\)) [110]. Based on these different elements, it is still difficult to rationalize the molecular structure of a long-living deep blue TADF emitter [107; 108; 110].  
Figure 9: Materials used to initiate an exciplex TADF emission.
# Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites
## 6 Conclusions  
This paper presents an experimental study on detecting and monitoring of evolution of fatigue damage in composites under cyclic loads by using guided waves. Piezoelectric wafers are used to excite and receive guided wave signals to monitor the evolution of fatigue damage after different fatigue cycles. The guided wave signals are processed by WPT to extract features based on WP component energies to characterize the internal fatigue damage accumulation which is reflected by measured stiffness degradation. A statistical multivariate outlier analysis is then performed with consideration of uncertainties from amplitude fluctuations caused by environmental effects and measurement noise. It is used to determine the existence of fatigue damage in composite specimens and to characterize their evolution using MSD. Experimental results have demonstrated the effectiveness and potential application of the guided wave-based monitoring technique: the extracted WP component energies of the guided wave signals can be used as effective signal features to characterize the internal fatigue damage in composites, and the statistical outlier analysis is an effective tool to detect the damage with consideration of uncertainties, while the merged MSDs of the signal features have a similar trend to evolution of fatigue damage in composites.  
Conceptualization, G.Y.; Methodology, G.Y., X.L., and J.T.; Investigation, G.Y., X.L., and J.T.; Writing--Original Draft preparation, G.Y.; Writing--Review and Editing, G.Y., X.L., and J.T.; Supervision, G.Y.  
Figure 12: Outlier analysis results for specimen S_B with Level 2 contamination: (**a**) PDF of baseline MSD and (**b**) MSDs for different fatigue states.  
**Funding:** This research is supported by the National Nature Science Foundation (Grant No. 11602104) and the Fundamental Research Funds for the Central Universities (Grant No. NS2016011).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Optimized Scheduling of IECPS
#### 5.3.2 Robust Optimization Scheduling  
Unlike stochastic optimization, robust optimization does not rely on specific probability distributions for uncertainty parameters such as deliberate attacks and system failures. Instead, it is an uncertainty decision-making method based on interval perturbation information. Because it does not rely on the assumption of probability distributions, it is more suitable for deliberate attack defense optimization studies with scarce historical data. Moreover, this method considers the optimal solution under the worst-case scenario as the final defense strategy, resulting in a defense strategy with high feasibility. However, by focusing on the defense strategy under the worst-case scenario, this method compromises some level of optimality. To reduce the conservatism of robust optimization scheduling, it can be mitigated using distributionally robust optimization techniques. In addition, robust optimization also faces the same model complexity issues as stochastic programming. Data-driven distributionally robust optimization can be explored as an alternative.
## 6 Challenges and Opportunities
### Future Research Directions
#### 6.4.4 Human-Centered Design and User Acceptance  
The achievement of widespread adoption depends on public trust and acceptance [131]. Future efforts should explore collaborative design strategies that involve community stakeholders, advocacy groups, and representatives of vulnerable populations (e.g., the elderly and individuals with disabilities). This approach ensures that technologies are developed with empathy for user needs and sociocultural factors. Pilot programs, focus groups, and living labs can provide insight into how citizens view these systems and what improvements could increase their comfort and engagement.  
User-friendly interfaces, from mobile and web dashboards to AR/VR platforms, can clarify the data obtained from IoPVT systems and allow citizens to make informed decisions about their safety. Research can investigate innovative ways to visualize complex environmental data or activity metrics, potentially gamifying community participation. By offering intuitive control panels, citizens and policymakers can work together to establish thresholds, customize alerts, and actively shape their local safety landscape.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 7 Discussions of Results and Research Findings
### Other Tangible System Benefits of Presented Method  
It is worth noting that in cases studied, the derived peaking at the Tx is about 70% of the total compensation required. Therefore, the remaining 30% is assigned--by the adaptive algorithm--to the Rx. Besides the gain in ROSNR as presented in Sections 5 and 6, splitting the compensation (i.e., relaxing the required peaking at the Tx) offers many system advantages. We will discuss briefly seven of them below:  
1. The ability to do pre-compensation, i.e., applying peaking to high frequencies, is dependent on the effective number of bits (ENOB) of the DAC. Per example, each 6 dB to pre-compensation requires 1 bit of ENOB, which requires four times more power consumption [48]. Therefore, splitting the compensation helps to relax the ENOB requirement of the DAC.
2. With next-generation transponders aiming for 600 Gb/s--and beyond--line rate, the case of 84 GBaud requiring a high-sampling rate was presented in [49], the analog driver of the optical  
Figure 9: Impact on jitter with pre-compensation (i.e., more amplitude peaking at Tx) illustrated.  
modulator requires to have a large bandwidth. Our method helps to relax the requirement of the design since the frequency response does not have to be wide to let through all the peaking at high frequencies.
3. The decrease in PAPR results in processing the signal, at the output of the DAC, in the linear operation region of the optical modulator and its driver.
4. Lower PAPR at DAC output manifests itself as a reduction in voltage swing at modulator' driver. In a current mode driver, the peak output voltage directly determines the driver power. Therefore, lower PAPR means a reduction in power dissipation at the transmitter [50; 51].
5. As shown in Equation (16), allows system to be more resilient to jitter for the PLL driving the DAC clocking. It is known that high-frequency components are more sensitive to jitter [52; 53], therefore by reducing peaking, higher system SNR can be achieved.
6. Continuously moving the compensation of the static portion of the channel, such as chromatic dispersion as in [22], from the adaptive filter to the static filters of the Tx and Rx, allows some of the adaptive filters taps to deal with more dynamic impairments of the optical link such as PMD and PDL. In other words, the saving in ROSNR can be used to tolerate an increase in PDL or more tracking of SOP.
# SA-SGRU: Combining Improved Self-Attention and Skip-GRU for Text Classification
## 3 The Proposed Method
### Skip-GRU Network Module
#### 3.2.2 GRU Network  
GRU, a version of the conventional RNN, is a widely used gated recurrent neural network. The gradient disappearance or explosion issue can be reduced with GRU, much like LSTM, which can also successfully capture the semantic link between lengthy sequences. GRU has two gates: a reset gate and an update gate. The reset gate controls how new input data is integrated with old memory, while the update gate determines how much of the past memory is saved to the current time step.  
The standard GRU reads each word sequentially and uses the update function to refresh the hidden state. The unit structure of the GRU is shown in Figure 4. The status update for the GRU network is as follows:  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{3}\]  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{4}\]  
\[\widetilde{h}_{t}=tanh(W\cdot[r_{t}*h_{t-1},x_{t}])\] (  
The skip probability value \(\pi_{t}\) in 3.1.1 determines whether the word can be sent to the GRU network, and 0.5 is chosen as its threshold value. When \(\pi_{t}\) < 0.5, the input word is skipped, and the hidden layer is not updated:  
\[h_{t}=h_{t-1} \tag{7}\]  
when \(\pi_{t}\) > 0.5, it means that the word is more important to the classification result and will be sent to the GRU network. At this time, the hidden state of the GRU network will update as Equations (3)-(6). Algorithm 1 shows the construction of the skip network.  
```
Input: The text sequence \(T\) Output: The probability of \(x\)
1. Do convolution operations on \(x\) in \(T\) to obtain word vector \(S\)
2. for vector \(S\) in \(T\)do
3.\(S_{t}=RELU(W_{1}S+b_{1})\)
4.\(\pi_{t}=softmax(W_{2}S_{t}+b_{2})\)
5.if\(\pi_{t}\) < 0.5 then
6. skip this word
7.else
8. put \(x\) into GRU network
9.endif
10.endfor
```  
**Algorithm 1** Construction of The Skip Network
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion
### Watermark Robustness
#### 5.2.2 Combined Distortions: JPEG Quality Factor (QF) = 70 + Signal Processing Distortions  
Considering 1000 color images extracted from the COCO dataset [30], and a binary watermark of \(L\) = 32 bits, in Figure 12 we summarize the average BCR obtained by PSO-DFT and conventional DFT watermarking respectively, against combined distortions composed by JPEG compression with quality factor 70+ several signal processing distortions given in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Processing Distortion** & **Average** & **Average** \\  & **BCR PSO-DFT** & **BCR Conventional DFT** \\ \hline JPEG lossy compression QF = 20 & 0.79 & _0.73_ \\ JPEG lossy compression QF = 30 & 0.90 & 0.81 \\ Gaussian noise \(\mu\) = 0 and \(\sigma^{2}\) = 0.05 & 0.80 & _0.74_ \\ Box Filter 3 \(\times\) 3 & 0.98 & 0.92 \\ Box filter 5 \(\times\) 5 & 0.92 & _0.76_ \\ Median filter 3 \(\times\) 3 & 0.96 & 0.90 \\ Motion filter 5 \(\times\) 5 & 0.97 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relevant improvements obtained by PSO-DFT in terms of average BCR against signal processing distortions.  
Figure 11: Average bit correct rate (BCR) against signal processing distortions.  
As shown in Figure 12, the optimized 2D DFT-based watermarking by PSO algorithm outperforms the conventional 2D DFT image watermarking in terms of watermark robustness against several combined distortions in Table 7. The relevant improvements are when the watermarked image is distorted by the combined attacks in Table 8. Italics in Table 8 indicate that the detection has failed.  
From Figure 12 we show that against the rest of combined distortions in Table 7, the PSO-DFT watermarking keeping the robustness obtained by the conventional 2D DFT watermarking, obtaining average BCR values over the threshold value \(Th=0.78\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID: JPEG70 + Signal Processing Distortion** \\ \hline
1: Gaussian noise \(\mu=0\) and \(\sigma^{2}=0.01\) & 9: Gaussian filter \(7\times 7\) \\
2: Gaussian noise \(\mu=0\) and \(\sigma^{2}=0.05\) & 10: Sharpening \\
3: Impulsive noise density \(=0.09\) & 11: Brightness increase \\
4: Speckle noise \(\mu=0\) and \(\sigma^{2}=0.2\) & 12: Brightness reduction \\
5: Speckle noise \(\mu=0\) and \(\sigma^{2}=0.09\) & 13: Median filter 3 \(\times\) 3 \\
6: Box Filter \(3\times 3\) & 14: Motion filter \(5\times 5\) \\
7: Box filter \(5\times 5\) & 15: Gamma correction \(\gamma=2\) \\
8: Histogram equalization & 16: Gamma correction \(\gamma=0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of combined attacks composed by JPEG QF = 70 + signal processing distortions applied to watermarked images.  
Figure 12: Average BCR against combined attacks composed by JPEG QF = 70 + signal processing distortions.
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 2 Materials and Methods  
### Refrigerated Wind Tunnel  
Testing was performed in the Anti-icing Materials International Laboratory's (AMIL) Icing Wind Tunnel (IWT), shown in Figure 1, which is a closed-loop low-speed refrigerated and thermally-isolated wind tunnel designed to operate at subzero temperatures at sea level pressure. A complete description of AMIL's IWT can be found in [20; 21]. The refrigeration system is able to vary and maintain total air temperature between \(-\)48 \({}^{\circ}\)C and 22 \({}^{\circ}\)C. The IWT has two test sections. The larger test section is 0.91 m wide by 0.76 m high, and tests can be performed at airspeeds up to 50 m/s at room temperature. The wind tunnel meets the conditions [22] of the Society of Automotive Engineers (SAE) Aerospace Recommended Practice for icing wind tunnel ARP5905 [23] and Aerospace Information Report for droplets sizing AIR4906 [24]. A water spray system was used to generate the icing cloud. It comprises up to three spray ramps of eight to nine air atomizing nozzles composed of pneumatic sprinklers. A cooling unit controls the water temperature used to produce a cloud of supercooled water droplets with median volumetric diameters (MVD) ranging from 20 to 100 \(\upmu\)m and liquid water contents (LWC) ranging from 0.1 to 3.0 g/m\({}^{3}\). The water is filtered and cleaned to obtain osmotic de-mineralized distilled water. The supercooled droplets impinge on the test model in the center of the test section. During a test, the water flows continually, the air pressure is turned on when the test begins, and the simulated cloud is formed in the IWT test section.
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 3 Results  
### Hydrodynamic Force on Fine-Mesh Nettings in Waves  
The experimental and numerical values of the horizontal wave forces on the nettings under various wave conditions are presented in Figure 6. The horizontal wave forces increase with longer wave periods for all nettings, reaching a maximum at a wave period of 2.3 s. Similarly, horizontal wave forces increase with greater wave heights, with the maximum force observed at a wave height of 0.19 m. For Net 1, the experimental and numerical mean peak wave force values at a wave height of 0.19 m were as follows: 9.90 N and 9.80 N at a wave period of 1.3 s, 16.61 N and 15.48 N at a wave period of 1.8 s, and 28.12 N and 27.47 N at a wave period of 2.3 s, respectively. For Net 2, the corresponding values were 5.28 N and 3.82 N at a wave period of 1.3 s, 7.73 N and 7.67 N at a wave period of 1.8 s, and 15.66 N and 14.90 N at a wave period of 2.3 s, respectively. Overall, the mean peak wave force of Net 1 was consistently greater than that of Net 2 across all wave conditions. Furthermore, the experimental and numerical wave force values for the nettings showed good agreement under the tested wave conditions, validating the accuracy of the numerical model.
# Inclence of Chronic Pain following Oral Soft Tissue Biopsies: A Retrospective Study
## 1 Introduction  
Biopsies of the mucosal tissues of the oral cavity are often used for histopathological evaluation and the diagnosis of abnormalities such as persistent non-healing lesions; suspected malignant lesions; rapidly expanding lesions; mucosal color changes; and the evaluation of systemic conditions with oral manifestations such as vesiculobullous diseases [1]. Incisional and excisional biopsies are the most prevalent procedures in the mouth, whereas fine-needle biopsy, core needle biopsy and punch biopsies [2] are used less often.  
Orofacial pain following injury has been divided into acute and chronic pain by the International Classification of Orofacial Pain (ICOP) [3]. Chronic pain lasts more than 3 months and occurs on at least 15 days per month. The International Classification of Headache Disorders (ICHD) [4] states that chronic pain due to traumatic injury (following trauma) occurs within 7 days of the event and persists for more than 3 months.  
Chronic neuropathic pain occurs due to damage or injury to peripheral nerves and presents as dull, deep, aching pain, in conjunction with episodic sharp pain [5, 6].  
The term painful post-traumatic trigeminal neuropathy (PPTTN) was recently proposed by the International Headache Society [4], and ICOP [3] to reflect neuropathic pain oftraumatic origin affecting the trigeminal nerve and resulting in long-lasting pain. PPTTN, like other post-traumatic neuropathies, is very resistant to therapy, including extensive pharmacotherapeutic protocols [6].  
Considerable research has evaluated the incidence of PPTTN following various minor oral procedures including root-cansals, extractions or implants [7]. There is no clear evidence regarding the incidence of long-term neuropathic pain or long-term neuropathic changes such as dysesthesia following a biopsy of the oral mucosa.  
The aim of our study was to investigate the incidence of chronic pain after oral soft-tissue biopsies and determine whether there is a correlation with lesion site or pathology. This study may provide insights into the potential risks associated with oral soft-tissue biopsies and aid healthcare professionals and patients in making informed decisions regarding these procedures.
## 4 Engineering Applications and Verification  
### Engineering Example  
The Qiyueshan tunnel is located between the Shaba Village Group and Land Mouth Village in Lichuan City, Enshi Tujia, and Miao Autonomous Prefecture, Hubei Province.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grade** & **I** & **II** & **III** & **IV** \\ \hline Total water inrush (10\({}^{2}\)m\({}^{3}\)/d) & \textgreater{}100 & 100–30 & 30–5 & \textless{}5 \\ Economic loss (billion) & \textgreater{}1 & 1–0.5 & 0.5–0.1 & \textless{}0.1 \\ Number of death (quantity) & \textgreater{}30 & 30–10 & 10–3 & \textless{}3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification of water inrush risk.  
Figure 8: Tunnel water inrush evaluation system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & \multicolumn{5}{c}{**Risk Level**} \\ \cline{2-5}
**Index** & **I (Very High Risk)** & **II (High Risk)** & **III (Medium Risk)** & **IV (Low Risk)** \\ \hline I\({}_{1}\) & \textgreater{}0.254 & 0.104–0.254 & 0.042–0.104 & \textless{}0.042 \\  & \textgreater{}75 & 75–50 & 50–25 & 25–0 \\ I\({}_{2}\) & \textgreater{}75 & 75–50 & 50–25 & 25–0 \\ I\({}_{3}\) & \textgreater{}35\({}^{\circ}\) & \(35^{\circ}\)–\(20^{\circ}\) & \(20^{\circ}\)–\(10^{\circ}\) & \(10^{\circ}\)–\(0^{\circ}\) \\ I\({}_{4}\) & \textgreater{}60\% & 40–60\% & 20–40\% & 20–0 \\ I\({}_{5}\) & \textgreater{}75 & 75–50 & 50–25 & 25–0 \\ I\({}_{6}\) & \textgreater{}75 & 75–50 & 50–25 & 25–0 \\ I\({}_{7}\) & \textgreater{}60 & 30–60 & 10–30 & 10–0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Grading standards of seven indices.  
Unfavorable geology includes fault zones, high ground stress, natural gas, karst, and underground rivers in this area. The site area has a humid subtropical continental monsoon climate. Additionally, the average annual rainfall is about 1400 mm. The precipitation is mostly concentrated in summer. Groundwater is mainly fractured water and pore water. Groundwater is mainly recharged by atmospheric rainfall and is injected into the underlying karst aquifers through waterfall holes or inclined caves. The karst formations are mainly waterfall holes, shafts, karst depressions, karst troughs, etc. The normal and maximum water inflow of this tunnel is 67225m\({}^{3}\)/d and 95078m\({}^{3}\)/d, respectively. There are 15 larger-scale faults, all of which may lead to water intrusion during tunnel construction. The topographic profile and location of this tunnel are shown in Figure 9.