esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.1 Potassium Salts  
Many DH-reducing toothpastes contain potassium salts such as potassium chloride (KCI), potassium citrate (K\({}_{3}\)C\({}_{6}\)H\({}_{5}\)O\({}_{7}\)), and potassium nitrate (KNO\({}_{3}\)) [65]. Through penetration into dentin tubules, these salts numb nerve fibers, consequently diminishing tooth sensitivity. More specifically, they can increase the pain threshold of nerve fibers in response to specific stimuli [66; 67]. Toothpastes containing KNO\({}_{3}\) and fluoride have demonstrated positive impacts on DH reduction [66; 68; 69; 70]. For optimal outcomes, these toothpastes should be used in conjunction with soft toothpasthes and minimal water, two times daily following tooth brushing for at least 14 days [66]. Moreover, mouthwashes containing potassium oxalate (K\({}_{2}\)C\({}_{2}\)O\({}_{4}\)), potassium nitrate, and fluoride are also deployed for daily use, significantly contributing to DH alleviation [71].
# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
## 4 Discussion
### Clinical Retrospective Analysis  
Observing the integrity of all 25 locations is of paramount importance, however, we found that only 19.28% of patients were observed at all locations and nearly five locations had a leak rate of more than 20%. This suggests that the quality of endoscopy needs to be improved.  
Studies have shown that spending more time on EGD improves the diagnostic rate, so we recorded the total procedure time during EGD and counted the procedure time per anatomical location based on model analysis. This helps the endoscopist to control the duration of each examination procedure, thereby reducing variability in the level of examination due to competent factors such as experience and fatigue examinations. This study concluded that \"slow\" endoscopists (who take on average more than 7 min to perform a normal endoscopy) are more likely, or even up to two times more likely, to detect high-risk gastric lesions [61]. However, in a retrospective analysis of experimental data results, the total time of the procedure was lower than the recommended time. Therefore, we recommend that endoscopists be able to increase the examination time further.  
Among the various sites, the esophagus was the only one that was not missed in all videos, and had the longest examination time. On the one hand, this is because the esophagus has a certain length in space and is the entrance to the EGD. On the other hand, we have patients with [PERSON]'s esophagus [62] in our videos. Studies have shown that the examination time of [PERSON]'s esophagus is related to the detection rate of the associated tumor [63]. The less time spent on the lower body curvature also contributes to its high rate of missed diagnoses. The effective examination time is only 23%, so the mucosal visibility of UGI is not high enough during most EGD examinations, which is due to invalid frames when the endoscopist performs operations such as flushing and insufflation, or when the lens shakes and fails to focus. This value can be used as a reference indicator. For endoscopists with a high percentage of invalid frames, further demands can be made on the operation level.  
With these data, we can clearly see the behavioral habits of Chinese doctors in gastroscopy and the possible blind spots. It is beneficial for the system to achieve quality monitoring, improve the quality of gastroscopy, and further improve the detection rate of diseases. All the indicators mentioned in this paper can reflect the details of the gastroscopy process to some extent. These indicators prove that our model has great potential value for application to improve the quality of examination.
## 1 Introduction
However, there are still two problems in the fourth method: (1) Since the communication process causes the WPT system to deviate from its normal operating states, the output power may fluctuate, and the power transmission performance is affected by the information transmission [26; 27]; (2) The data-frequency-ratio is limited by the non-differential modulation method, and the operating frequency of power carrier can not be fully utilized. Figure 1: Diagram of four basic methods for bidirectional communication. (**a**) Radio frequency link. (**b**) Additional communication coils. (**c**) Coupling with compensation circuit. (**d**) Modulating with power converter. To address these problems, this paper proposes a novel differential phase shift keying (DPSK) modulation method for simultaneous wireless power and information transmission. The main advantages of proposed method are summarized as follows:  
* By using the DPSK modulation method, the power transmission is quasi-independent of information transmission and remains stable during communication process. * Compared with the non-differential modulation, the operating frequency of power carrier is fully utilized, the date-frequency-ratio is increased. * Not only the inductive link but also the power/data modulator is shared, which greatly reduces the complexity, cost and volume of the SWPIT system. The remainder of this paper is organized as follows. In Section 2, the proposed DPSK scheme is briefly introduced, and the steady-state and transient characteristics of the capacitor-compensated SWPIT system are analyzed. In Section 3, the communication system is described in detail, which includes modulator integrated with power converter, demodulators and half-duplex communication protocol. The simulation and experiment are performed in Section 4. Finally, the conclusion is drawn in Section 5.
## 3 Case Study
### TEP Dataset Analysis
#### 3.1.3 Comparative Study on Few-Shot Fault Classification  
To underscore the efficacy of the ATASML framework, this study compares it with renowned deep learning models such as VGG-11 [43] and ResNet-18 [44], which are benchmarked for their ability to generalize from extensively pretrained features to new, unseen fault conditions. These models are thoroughly adapted to specific fault-diagnosis tasks by fine-tuning on targeted datasets. Our experiments concentrate on demonstrating that the ATASML framework, when subjected to comprehensive network fine-tuning, achieves superior accuracy compared to merely fine-tuning the classifiers, particularly under few-shot learning conditions. Moreover, to ensure a fair comparison across all evaluated methods, an N-way K-shot setup was employed. This setup not only maintains the integrity of the experimental comparisons but also aligns with the research paradigms of few-shot learning,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module Number** & **Configuration** \\ \hline
1 & TimeSeriesConvLSTM\_Module\_TEP \\
2 & Repeats Module 1 \\
3 & Repeats Module 1 \\
4 & Repeats Module 1 \\
5 & Dense Layer (Output) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structure of the Base Learner for the TEP Dataset Using TimeSeriesConvLSTM_Module_TEP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Configuration** & **Output Shape** \\ \hline Input & - & (None, T, F) \\ Conv1D (TimeConv) & 64 filters, kernel size 3 & (None, T-2, 64) \\ BatchNorm & - & (None, T-2, 64) \\ MaxPooling1D & Pool size 2 & (None, (T-2)/2, 64) \\ LSTM & 100 units & (None, 100) \\ Dense (Output) & Softmax activation & (None, num\_classes) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of the Convolutional and LSTM Layers for the TEP Dataset (TimeSeriesConvLSTM_Module_TEP).  
allowing for a precise assessment of the ATASML framework's performance in complex fault-diagnosis tasks.  
As illustrated in Figure 6, a performance comparison between ATASML and conventional deep learning architectures, VGG-11 [43] and ResNet-18 [44], was conducted on the augmented TEP dataset. The results underscore that ATASML significantly outperforms both VGG-11 and ResNet-18 across all few-shot scenarios, particularly excelling in the 3-way 6-shot setting with a diagnostic accuracy of 98.47%. This marked improvement can be attributed to ATASML's integration of meta-learning and adversarial training strategies, enhancing the model's generalization capabilities to new, unseen fault conditions. In the more challenging 8-way 5-shot and 8-way 6-shot configurations, although a performance decline is observed for all models, ATASML maintains superior stability and higher diagnostic accuracy. This validates ATASML's efficacy in managing high-dimensional fault classification tasks, demonstrating robust performance even as the number of categories increases.
## 4 Experimentation
### Input Signals  
Shaking table tests consisted of both dynamic identification and seismic tests. The input signals utilized were: (i) white noise tests (WN), (ii) increasing and decreasing sine sweep (SS) tests, (iii) natural earthquake (NE) records considering two near-fault and two far-field earthquakes. The description of each signal with the range of frequency and intensity investigated are reported in detail in Tables 2 and 3. For the earthquake signals, the time axes were scaled by the factor \(\lambda_{T}=\sqrt{1/\lambda_{L}}=0.45\).
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Performance Evaluation
#### 3.3.2 The Encoding Parameters and Normalization Scheme  
In the proposed hybrid feature vector, the number of nearest neighbors and value of beta(\(\beta\)) (used for regularization) parameter for locality constraint coding have been evaluated empirically while fixing the size of the codebook as mentioned previously. The number of neighbors were varied between 1 to 10 and the best results were achieved at five nearest neighbors and the value of \(\beta\) was set to 0.2. Normalization influences the recognition performance of the proposed scheme as learned during the experimentation. Five different normalization schemes have been evaluated, namely: L1, L2, Power Normalization (PN), PN+L1, PN+L2. The \(\alpha\) parameter for Power Normalization has been set to 0.5. The results for these normalization methods are given in Figure 6.  
The results are computed on HMDB51-split 1, where L1 and PN performed poorly. The best results are computed using L2 normalization. The combination of PN+L2 provides almost similar results but we rely only on L2 to avoid extra processing.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 7 Conclusions  
In this paper, we proposed a novel RCR scheme to exploit an on-demand number of cooperative nodes to facilitate the prediction of personalized end-to-end (ETE) reliability. In the proposed RCR scheme, we consider energy-efficient cooperative node sleeping and clustering problems in cooperative sensor networks where clusters of relay nodes jointly transmit sensory data to the next hop. Specific to the cooperative node clustering problem, we propose an optimal duty cycle for a given number of cooperative nodes when the network state is dynamic, which enables part of the cooperative nodes to switch into idle status for further energy saving. Simulation results show that the proposed RCR scheme controls the energy consumption while guaranteeing ETE reliability. In future research, we will continue to improve the relay node selection and data transmission mode while considering a real environment and new wireless communication technologies.  
Figure 10: CNs select in simulation.  
**Acknowledgments:** The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia, for funding this research group No. (RG-1437-042).  
**Author Contributions:** [PERSON] proposed the concept of reliability-aware cooperative routing (RCR) protocol and designed energy-efficient cooperative node sleeping, clustering, selecting and delivering algorithm; [PERSON] conceived and performed the experiments; [PERSON] improved this article in terms of architecture, design, experiments as well as theories, meanwhile polished the article in language and expression; [PERSON] analyzed the data; [PERSON] investigated the background and related work of different routing schemes in WSN.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method
## 3 Materials and Methods
### Retrieving Similar Objects  
The method of retrieving similar objects is presented in a pseudo-algorithmic form in Figure 4. It involves downloading both the key (query) and the image database. A sketch of the image is prepared in line with the procedure described in the previous chapter. Then the algorithm performs a segmentation of the pore space for the image database. The values of selected parameters are then calculated both for the query and all images in the database. These values are used to create a set of parameters describing each object. These parameters can be a shape, orientation, size, etc. There exist many approaches which use different types of descriptors. Unfortunately, they are often determined by numerous internal parameters, which significantly hampers their optimal selection in regard to  
Figure 2: An example of a sketch which serves as a search query for porosity in images, when retrieving a similar object (**a**) and images with a similar distribution of porosity (**b**).  
universal image retrieval in geology. In the literature, simple parameters are often used, which do not require internal parameters to be provided but are based directly on the geometric representation of the object. These include the surface area, side length ratio, Feret diameter, etc. The paper suggests that the feature space is founded on the following parameters:  
* Area of the object,
* Equivalent Diameter (diameter of a circle with the same area as the object)
* Major axis of the fitted ellipse,
* Minor axis of the fitted ellipse,
* Orientation (angle between the x-axis and the major axis of the best fitted ellipse).  
To avoid the excessive impact of the scale of the sketch, it is possible to remove dimensionality through parameter scaling. In the research, the Area parameter was divided by the surface area of the entire sketch, while Equivalent Diameter, Major axis of the fitted ellipse and Minor axis of the fitted ellipse parameters were divided by the element from the surface area of the sketch (or appropriately, the database image). The Orientation parameter was left unscaled. The parameters obtained were then normalized from the set minimum to the set maximum.  
The similarities between the query feature vector and the image feature vectors for the images in the database were then determined. There are many ways of determining the similarity between objects. For example, it is possible to use distance analysis methods in feature spaces (e.g., Euclidean, Minkowski, etc.). For the purpose of this study, the Sum of Squared Differences (SSD) was used as a measure of similarity. The lowest results of the SSD values indicate the closest location in the feature space, which may indirectly translate into considerable similarity.
## 3 Results and Discussion
### Thermal Properties
According to previous published works, carbon-based nanomaterials can significantly improve cement's thermal conductivity [37; 38; 39]. Also, [PERSON] et al. [33] reported the increment of cement board's thermal conductivity by adding expanded perlite-stabilized RT28 HC; specifically, the 40 % w.t. additive reached 0.149 W/(m\(\cdot\)K) from 0.11 W/(m\(\cdot\)K). Octadecane exhibits 0.2 W/(m\(\cdot\)K) [32], which also can improve, in our case, the lower thermal conductivity of perlite cement boards. Figure 8: (**a**) Thermal conductivity, denoted as ‘K’, and (**b**) specific heat capacity, abbreviated as ‘Cp’, for gypsum and cement boards with varying percentages of OD@CCF additive. In comparing the building boards (GB and CB), the inclusion of OD@CCF boosts thermal conductivity and specific heat capacity by approximately 33% and 10%, respectively, for CB, and by 193% and 25% for GB (Figure S3b,c).
## 4 Results  
### Power Spectral Graph  
The power spectral plot represents the distribution of signal energy as a function of frequency and provides insight into the frequency components of the signal. Figure 14 shows the spectral power graphs and the spectral power of the dominant frequencies for the three cases.  
In the spectral analysis of a corrosion signal, the unit of measurement \(\mathrm{N/Hz}\) for spectral power provides information about the frequency distribution of energy or force in our study system. The physical interpretation of spectral power expressed in the unit \(\mathrm{[N/Hz]}\) implies the amount of force or energy distributed per unit frequency. More precisely, for each frequency range, expressed in hertz, the spectral power in the unit \(\mathrm{N/Hz}\) gives us a measure of the intensity of force or energy present in that specific frequency range. In the context of corrosion, this interpretation has significant implications for how the phenomenon affects the structure or system being monitored. Higher spectral power at certain frequencies may indicate a more intense response of the system to corrosion at those specific frequencies, suggesting a potential vulnerability of the respective material or components to this destructive phenomenon.  
Figure 14: _Cont._
# Coordination and Private Information Revelation
## Appendix A Correlated Equilibrium  
We demonstrate that there is full revelation of private information in the correlated equilibrium of the one-shot game discussed in Section 2. This bolsters our intuition that non-revelation is driven by uncertainty in coordination. Had there been an impartial third party acting as a correlating device, there would have been complete information revelation in our one-shot game.  
Suppose that following complete information revelation in Stage 1, a third party tosses an unbiased coin at the beginning of Stage 2. In case of Heads, the third part instructs each agent to select technology \(A\), and to play \(B\) otherwise. Consider strategies whereby in Stage 1, agent A reveals her type for all \(\theta\). Subsequently in Stage 2, both agents follow the recommendations made by the third party. Whereas in case of no information revelation, the agents play the completely mixed strategy Stage 2 equilibrium.  
In the equilibrium following information revelation, agent A gets \([\frac{(\theta+c)}{2}+\frac{c}{2}]=c+\frac{\theta}{2}\) while agent B gets \([\frac{(b+c)}{2}+\frac{c}{2}]=c+\frac{b}{2}\). This payoff for agent A is strictly greater than the non-revelation payoff from Stage 2, as \(c>\theta\).  
\[\pi_{A}(\text{correlated})-\pi_{A}(NR)=\left\{\begin{array}{ll}c+\frac{\theta }{2}-[\theta+\frac{c-\hat{\theta}}{2}]>0,\text{ if }\theta>\hat{\theta},\\ c+\frac{\theta}{2}-[\frac{c+\hat{\theta}}{2}]>0,\text{ otherwise.}\end{array}\right.\]  
Please note that any correlated equilibrium which randomizes among the strict pure strategy equilibria (A,A) and (B,B) with a probability greater than or equal to \(\frac{1}{2}\) on (A,A) will be consistent with full disclosure in stage 1, as this will give agent A an expected payoff greater than the non-revelation payoff.
# Differential Signalling in Free-Space Optical Communication Systems
## 4 Accounting Atmospheric Turbulence  
### Weak Turbulence  
In FSO under weak turbulence, the fading of the optical signal can be modelled as \(h=h_{0}\exp(2X)\), where \(X\) is a distributed normal random variable (RV) with mean and variance of \(m_{h}\) and \(\sigma_{h}^{2}\), respectively [32]. This way, \(h\) has a lognormal probability distribution function (PDF) given as [32]  
\[f_{h}(h)=\frac{1}{2h}\frac{1}{\sqrt{2\pi\sigma_{h}^{2}}}exp\left(-\frac{\left( ln(h/h_{0})-2m_{h}\right)^{2}}{8\sigma_{h}^{2}}\right). \tag{15}\]  
For the log-normal distribution, we have  
\[Mean(h_{i})=exp\Big{(}2m_{h,i}+2\sigma_{h,i}^{2}\Big{)}, \tag{16a}\] \[Var(h_{i})=\Big{(}exp\Big{(}4\sigma_{h,i}^{2}\Big{)}-1\Big{)}\times exp\Big{(} 4m_{h,i}+4\sigma_{h,i}^{2}\Big{)}, \tag{16b}\]  
where \(m_{h,i}=-\sigma_{h,i}^{2}\)[32]; therefore,  
\[Mean(V_{thresh})=0, \tag{17a}\] \[Var(V_{thresh})=(\eta S_{thresh})^{2} \tag{17b}\]  
where \(\rho_{AT}\) is the correlation coefficient between the turbulence influenced channels. To determine the strength of the turbulence, Rytov variance \(\sigma_{R}^{2}\) can be used [33]. For weak turbulence \(\sigma_{R}^{2}<1\)[34], the simplified form of Label (17b) can be used (see Table 1). In the case of a plane wave propagation through a turbulence channel, we have [33]  
\[\sigma_{h,i}^{2}=\sigma_{R}^{2}/4=0.3075(2\pi/\lambda)^{7/6}C_{n}^{2}L^{11/6}, \tag{18}\]  
where \(C_{n}^{2}\) (in m\({}^{-2/3}\)) is the refractive index structure parameter.  
The summation of two lognormal variables can be approximated by a lognormal variable [35; 36]; therefore, \(h_{\text{DSS}}=h_{0}\exp(2X_{\text{DSS}})\), where \(X_{\text{DSS}}\) is a Gaussian RV with mean \(m_{\text{DSS}}\) and variance \(\sigma_{\text{DSS}}^{2}\). Wewill adopt the same procedure as in [PERSON]'s method [35] to estimate the required parameters of \(h_{\text{DSS}}\). To further normalize \(h_{\text{DSS}}\), we set the mean value to [27]  
\[m_{DSS}=-\sigma_{DSS}^{2}=ln\bigg{(}1+\frac{Var[h_{DSS}]}{{h_{0}}^{2}}\bigg{)}, \tag{19}\]  
where \(\text{Var}(h_{DS})\) is given by Label (17b) replacing \(V_{thresh}\) by \(h_{DSS}\) and letting \(\eta S_{thresh}=1\). Once \(\sigma_{DSS}^{2}\) is determined, it is possible to specify the PDF of a DSS based FSO system using Label (8). The simplified BER expression for weak turbulence is summarised in Table 1. To obtain the closed form equations in weak turbulence, the Gauss-Hermite quadrature formula was used [37]; for details, see Appendix A.