esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### SVM Model  
The SVM model is a machine learning method based on statistical learning theory [29]; it can handle high-dimensional data, solve nonlinear problems, exhibit excellent generalization ability, and achieve high accuracy.  
Figure 2: A network architecture of the UASN with multiple sink nodes.  
The core idea of the SVM model is to use a kernel function that satisfies the Mercer condition to replace nonlinear mapping. Hence, the sample points in the input space can be mapped to a high-dimensional feature space to facilitate linear separation, and an optimal hyperplane is constructed to approximate the ideal classification result. Hence, the SVM model is specifically used for small data samples and is a learning machine with optimal classification and generalization capability.  
Given a set of training data samples, \(\mathcal{F}=\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{n},y_{n})\}\), where \(y_{i}\in\{-1,1\}\), the hyperplane can be expressed as  
\[\omega^{\mathrm{T}}x+b=0. \tag{8}\]  
If the hyperplane \((\omega,\ b)\) can correctly classify the training data samples, we have  
\[\begin{cases}\omega^{\mathrm{T}}x_{i}+b\geq 1,\ \ y_{i}=1,\\ \omega^{\mathrm{T}}x_{i}+b\leq-1,\ \ y_{i}=-1.\end{cases} \tag{9}\]  
An optimization problem supporting the vector machine model can be constructed as  
\[\begin{split}\underset{\omega,b}{\min}&\frac{1}{2}\|\ \omega\ \|^{2}+C\sum_{i=1}^{n}\xi_{i},\\ &\text{s.t.}\ \ y_{i}\big{(}\omega^{\mathrm{T}}x_{i}+b\big{)}\geq 1 -\xi_{i},\ \ i=1,2,\cdots,n.\end{split} \tag{10}\]  
where \(\xi_{i}\) represents the degree of misclassification for sample \(i\). In order to control the degree of misclassification, an error penalty factor \(C\) is introduced.  
As a convex quadratic programming problem, the problem formulated in (10) can be solved by transforming to the dual problem using the Lagrange multiplier method. That is,  
\[L(\omega,b,\xi,\alpha,\beta)=\frac{1}{2}\|\ \omega\ \|^{2}+C\sum_{i=1}^{n}\xi_{i}- \sum_{i=1}^{n}\alpha_{i}\times[y_{i}(\omega x_{i}+b)-1+\xi_{i}]-\sum_{i=1}^{n }\beta_{i}\xi_{i}, \tag{11}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) are Lagrange multipliers, \(\alpha_{i}\geq 0\) and \(\beta_{i}\geq 0\).  
The dual problem of (11) is obtained as  
\[\begin{split}\underset{\alpha}{\max}&\sum_{i=1}^{n }\alpha_{i}-\frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n}\alpha_{i}x_{j}y_{j}y_{j}k \big{(}x_{i},x_{j}\big{)},\\ \text{s.t.}&\sum_{i=1}^{n}\alpha_{i}y_{i}=0,\ 0\leq \alpha_{i}\leq C,\ i=1,2,\cdots,n.\end{split} \tag{12}\]  
The decision function, denoted as \(f(x)\), can be expressed as  
\[f(x)=\omega^{\mathrm{T}}x+b=\sum_{i=1}^{n}\alpha_{i}y_{i}k(x_{i},x)+b, \tag{13}\]  
where \(k(\cdot,\cdot)\), the kernel function, can be a linear function, a radial basis function (RBF), or a polynomial function.
## 4 System Evaluation and Results
### Accuracy of the Model within the Application  
After integrating the CNN model to the Windows application, we ran the application while playing the game and we calculated the time to recognize each captured image and its accuracy as illustrated in Table 4.  
As the results show, the recognition of the image takes a few seconds, which makes the application running in almost real time. To release the scents, the accuracy was set to be at least 90% or higher.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Recognition Time** & **Class and Accuracy** & **Total Time to Recognize the Image and Release the Scent** \\ \hline  & 2073 ms & Grass with 99.99\% & 123 ms \\  & & Ocean with 99.98\% & 2 ms \\  & 1793 ms & Grass with 99.99\% & 2 ms \\  & 1560 ms & Ocean with 97.76\% & 63 ms \\  & 1839 ms & Dirt with 98.74\% & 81 ms \\  & 1823 ms & Grass with 99.99\% & 28 ms \\  & Fire with 97.42\% & & 67 ms \\  & 1529 ms & Fire with 99.58\% & 67 ms \\  & 1369 ms & Grass with 99.73\% & 84 ms \\  & Middev with 96.76\% & & 65 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the model within the application.  
Figure 6: A sample of the testing images used to test the accuracy of the model in classifying new images. (**a**) Testing image for dirt class; (**b**) Testing image for fire class; (**c**) Testing image for grass and fire classes; (**d**) Testing image for grass and ocean class; (**e**) Testing image for grass class; (**f**) Testing image for mildew(zombie) class; (**g**) Testing image for mildew and grass classes; (**h**) Testing image for ocean class; (**i**) Testing image for snow.  
Then we applied the micro-average method and macro-average method and we produced the results in Table 12.  
The overall accuracy was 97% with individual accuracies reaching as high as 99%. This overall accuracy is satisfactory to the needs of the olfactory display. Furthermore, statistical results of Precision and Recall are also closer to 0.9 in most cases. Thus, validating the acceptance of this approach.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 2 Related Work
### Contrastive Learning  
In recent years, several powerful contrastive learning methods have emerged, achieving excellent performance across various computer vision tasks, thanks to the optimization of contrastive learning loss parameters. A popular loss function, infoNCE [24], works by drawing pairs of positive samples closer together while pushing negative samples apart. Recently, contrastive learning has been applied to enhance semantic segmentation using various design strategies. Traditionally, contrastive learning has been employed primarily for self-supervised pre-training, which leverages unlabeled data to learn useful visual representations [25]. This approach provides the model with robust initial parameters, effectively initializing it with a small number of labels for downstream tasks [26; 27].  
However, this strategy heavily relies on data augmentation to generate positive pairs and a suitable method for selecting positive and negative sample pairs. SIMCLR [28] achieved good results using training techniques such as larger batch sizes and effective image augmentation. However, these methods require significant hardware resources. Recent studies [29] have demonstrated the advantages of cross-image comparison learning in medical image segmentation.  
However, a major drawback of contrastive learning in this context is the class conflict problem [30], where semantically similar patches are compared due to uninformed negative selection. Our method requires significantly less memory for contrastive learning through active sampling. Unlike these methods, which apply to stored feature libraries, our method focuses on on-the-fly feature sampling. While active sampling in the existing literature often relies on learnable, class-specific attention modules, our approach utilizes only relationship graphs and prediction confidence for sampling features, avoiding additional computational overhead and ensuring a simpler, more memory-efficient implementation.
# Beam Propagation Factor of a Cosh-Airy Beam
## 2 Analytical Expression of the Beam Propagation Factor
The premise of the \(\cosh\)-Airy beam with a small \({M_{x}}^{2}\) factor is that these two Airy beams both have small \({M_{x}}^{2}\) factors. The outcome is that \(a+\gamma\) and \(a-\gamma\) should both be large enough. In other words, \(a\) should deviate from \(\gamma\). From this perspective, it might be easier to understand the physical meaning of the effects of the parameters \(a\) and \(\gamma\) on the \({M_{x}}^{2}\) factor. To further understand the beam propagation factor, the normalized intensity distribution in the \(x\)-direction of the \(\cosh\)-Airy beam is shown in \[E(s,0)=\frac{1}{2}\Bigg{[}Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{ a_{+}s}{w_{0}}\Bigg{)}+Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{a_{-}s}{w_{0}} \Bigg{)}\Bigg{]} \tag{3}\]5 \(w_{0}=0.8~{}\upmu\)m, \(\gamma=0.2\), and \(\lambda=0.628~{}\upmu\)m in \[E(s,0)=\frac{1}{2}\Bigg{[}Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{ a_{+}s}{w_{0}}\Bigg{)}+Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{a_{-}s}{w_{0}} \Bigg{)}\Bigg{]} \tag{3}\]6 \(z_{0}=kw_{0}{}^{2}\) represents the Rayleigh length of the Airy part. When \(a=0.4\), \(0.5\), and \(0.6\), the \({M_{x}}^{2}\) factor was \(2.0376\), \(1.3379\), and \(1.1703\), respectively. In the source plane, the main lobe of the \(\cosh\)-Airy beams with different decay factors was nearly the same. Only the position of the main lobe was different. However, side lobes in the source plane were more apparent with the decrease of the decay factor. In the observation plane \(z=5z_{0}\), the side lobes disappeared. Moreover, the \(\cosh\)-Airy beam expanded with the decrease of the decay factor. With the decrease of the decay factor, therefore, the \({M_{x}}^{2}\) factor increased. Figure 8 can be interpreted by Figures 2, 4 and 6. Through this example, one can use the beam propagation factor of the \(\cosh\)-Airy beam to evaluate its propagation behavior. \[E(s,0)=\frac{1}{2}\Bigg{[}Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{ a_{+}s}{w_{0}}\Bigg{)}+Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{a_{-}s}{w_{0}} \Bigg{)}\Bigg{]} \tag{3}\]7 By giving the decay factor \(a\), the square of the divergence in the \(x\)-direction of the \(\cosh\)-Airy beam as a function of the \(\cosh\) parameter \(\gamma\) is shown. \[E(s,0)=\frac{1}{2}\Bigg{[}Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{ a_{+}s}{w_{0}}\Bigg{)}+Ai\Bigg{(}\frac{s}{w_{0}}\Bigg{)}\exp\Bigg{(}\frac{a_{-}s}{w_{0}} \Bigg{)}\Bigg{]} \tag{3}\]8 By giving the \(\cosh\) parameter \(\gamma\), the square of the divergence in the \(x\)-direction of the \(\cosh\)-Airy beam as a function of the decay factor \(a\) is shown.
## 5 Solution Methodology Based on NSGA-II and TOPSIS
### Optimization Based on NSGA-II
#### 5.1.6 Optimization Process  
The steps of applying the above NSGA-II to solve the proposed OPP model are as follows:  
(1) Input the information of distribution network: the connectivity matrix \(A\), locations of ZIBs, SCADA measurements and so on;  
(2) Compress the network and rearrange bus number;  
(3) Create the initial population which has \(Q\) individuals and choose this population as the parent population;  
(4) Set the total number of iterations _MaxGen_ and set iteration number as \(Gen=1\);  
(5) Perform crossover operation and mutation operation to generate the offspring population, and merge the offspring population with the parent population;  
(6) Calculate the rank and crowding distance of each individual;  
(7) Select \(Q\) individuals according to the rank and crowding distance to generate the parent population, and set \(Gen=Gen+1\);  
(8) Whether the maximum number of iterations is reached? Yes, end; No, go to step (5).
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions
### Characterization of Ground Granulated Blast Furnace Slag (GGBFS) and Alkali-Activated Slag (AAS)
#### 3.1.2 X-ray Diffraction Analysis  
Figure 1b depicts X-ray diffractions of raw GGBFS and AAS paste material with a Na\({}_{2}\)O concentration of 8% after 28 d of curing. The XRD pattern of GGBFS exhibited a typical hump at approximately 23-35\({}^{\circ}\) 20, which was reported to represent the amorphous aluminosilicates phase [5]. Additionally, gypsum CaSO\({}_{4}\cdot\)2H\({}_{2}\)O was also detected in the crystalline phase composition of GGBFS. It is noted that the phase composition was observed to show a significant transformation when GGBFS was activated by the alkaline solution. Tobermorite (Ca\({}_{5}\)Si\({}_{6}\)O\({}_{16}\)(OH)\({}_{2}\)), fukalite (Ca\({}_{4}\)Si\({}_{2}\)O\({}_{6}\)(CO\({}_{3}\))(OH)\({}_{2}\)), and calcium silicate hydrate (1.5 CaO-SiO\({}_{2}\)-xH\({}_{2}\)O) appeared to be the dominant crystalline phases of hydrated AAS. In addition, an amorphous hump at 23-35\({}^{\circ}\) 20 for aluminosilicates of GGBFS was detected and became a smaller hump at approximately 28-32\({}^{\circ}\) 20 in XRD patterns, indicating the formation of C-S-H in hydrated AAS [27].
## 3 Atmospheric Conditions
### Upstream Vertical Profile
This \(N\) and the surface-to-mountaintop mean wind speed \(U\) are used to compute the non-dimensional mountain height \(h\) [\(h=NH/U\), where H is the height of the mountain above the upwind plains]. For the first sounding, the modeled and observed \(h\) values are close to 1, but for the other two soundings, the observed \(h\) becomes smaller than 1 (suggesting unblocked flow), while the modeled \(h\) becomes larger than 1 (suggesting more blocked flow). High \(h\) values tend to imply higher-amplitude gravity waves and more significant departures from linear behavior in stratified flow (p. 14 in [45]). The high \(h\) values in the model are due to an underestimation of the low-level wind speed in the last two soundings (Table 2). That in turn may be related to the fact that the modeled flow over Saratoga is less affected by the Sierra Madres: at 03:00 UTC, the observed low-level flow is from 271\({}^{\circ}\) and thus downwind of the northern part of the Sierra Madres, while the modeled wind is from 301\({}^{\circ}\) (Figure 3c,f), a direction lacking significant upwind terrain (Figure 1b). Other discrepancies in the WRF simulations, compared to radiosonde data, include excessively cold conditions in the first sounding (before the UWKA observations), suggesting that the model advected the colder airmass too early, and the lower tropopause than observed. The main model discrepancy is the lack of elevated inversion (around 5.5-6.1 km MSL) and the concomitant lack of wind reversal in the upper troposphere in all three soundings (Figure 3). The latter is one of the seven conditions for downslope windstorms mentioned before. While condition (f) is not satisfied in the model, conditions (b) [regarding cross-MBM wind speed] and (d) [regarding change of stratification with height] are satisfied more convincingly in the last two model soundings (during UWKA observations) (Figure 3). Thus upstream conditions in the WRF simulation appear conducive to a downslope windstorm throughout the 10-hour period, except for the lack of a critical level and significant reverse shear (condition f).
## 3 Discussion
### Theoretical Prediction of Solubility of Wild Thyme Compounds in Green Solvents
#### 3.2.4 Effect of HVED on the Content of Volatile Compounds in Wild Thyme Extracts  
Results of HS-SPME/GC-MS analysis of volatile compounds from wild thyme extracts are given in Table 5. Carvacrol and thymol were already measured by UPLC-MS/MS, but very low concentrations were found by this methods (\(<\) 3 ng/mL), so these volatile compounds were measured also by HS-SPME/GC-MS. Results showed that monoterronoid phenol carvacrol (0.47-21.86%) was the predominant volatile compounds found in wild thyme extracts, followed by linalool, thymol, and finally, 1,8-cineole that was found in small amounts (0.96-3.46%). A similar trend was found in a study where wild thyme essential oil was extracted by supercritical carbon dioxide as another green extraction method, and carvacrol and thymol were found as main compounds in the extract. That study demonstrated that oil rich fraction of the CO\({}_{2}\) extract yields were 0.3-0.5% [35]  
All measured compounds were found in higher concentrations in HVED extracts, compared to CE. 1,8-cineol and linalool were higher in HVED extracts obtained with nitrogen, while thymol and carvacrol were higher in extracts where argon was used. The differences between CE and HVED treated extracts in the same extraction conditions are presented in Figure S3a-c. The influence of ethanol content was difficult to assess, since overlapping of peaks profile happened with ethanol peak in chromatograms. For that reason, most extracts containing ethanol do not have available data for compound concentration. However, a traceability of results is visible in HVED extracts with nitrogen (RN2-RN6 and RN9-RN10) and argon (RA2-RA6 and RA9-RA10), except for linalool that was found in extracts MA5 and MA6, while it was not found in extracts MN5 and MN6. Similar results were presented in study by [PERSON] et al., where thymol and carvacrol were found to be the main components of essential oil of wild thyme, assessed by GC-MS [36].  
Results of thymol, carvacrol and linalool were compared with theoretical prediction models where low solubility was expected in water measured with both methods (HSPs and COSMO-RS) and in ethanol when calculated by HSPs. However, very good solubility of carvacrol and thymol (100%) was expected in ethanol, while medium solubility was expected for linalool (52.48%) evaluated by COSMO-RS software. Since it was difficult to evaluate experimentally extracts containing ethanol, no clear correlations can be drawn with theoretical results. On the other hand, COSMO-RS results showed lower probability of solubility of linalool, compared to thymol and carvacrol, which was proven with experimental results.
# Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
## 3 Simulation Results
### Simulation Results of CPA-EEHG
An important factor for analyzing the influences of such tunability on the final FEL pulses is the diffraction efficiency of the grating at different azimuth angles. The simulation results indicated that the diffraction efficiency of the grating remains virtually unchanged (varying from 43% to 46%) at the altitude angle of 3\({}^{\circ}\) when the azimuth angle is tuned within \(\pm\)3\({}^{\circ}\). The GDD at variable azimuth angles is shown in Figure 8, which provides significant flexibility for the performance of the compressor. When tuning the azimuth angle, the FEL pulses will experience a process from under-compression to over-compression. This leads to the variation of the peak power and pulse duration of the compressed FEL pulses. As can be seen from Figure 8, the pulse duration features a declining trend at the beginning and rising up in later period, while the peak power shows an opposite tendency. The critical point is found at the designed azimuth angle, with the final compressed pulse shown in Figure 7. The spatial chirp when the gratings are rotated is also demonstrated in Figure 8. This introduced spatial chirp is significantly smaller than the spot size of the FEL pulse at the entrance of the compressor and is, therefore, negligible. The peak power can still remains above 20 GW even when the incidence azimuth angle is tuned from \(\sim\)26\({}^{\circ}\) to 29\({}^{\circ}\). In comparison to the previous CPA-HGHG method, there are noticeable advantages of the proposed CPA-EEHG scheme. On the one hand, the larger harmonic up-conversion  
Figure 8: The variation of the GDD, spatial chirp introduced by the compressor (**top**), as well as the peak power and pulse duration of the compressed pulses (**bottom**) when tuning the azimuth angle within \(\pm\)3\({}^{\circ}\). Figure 7: The Wigner distribution of the FEL pulse after the compressor. number in CPA-EEHG leads to a shorter wavelength and smaller FEL pulse duration according to Equation (3). On the other hand, the compressor in the OPM geometry with much higher diffraction efficiency guarantees an increase of the peak power by orders of magnitude. It is worth mentioning that extending the wavelength of the output FEL pulses to the X-ray regime is achievable without any compromise in the FEL peak power at CPA-EEHG.
## 6 Extension to Discontinuous Time Series  
At present, when drilling oil wells, the MWD system utilizes the mud pulse to send the data from underground to the monitoring equipment on the ground. Its transfer rate and reliability are low while its bit error rate is high. Consequently, the data received on the ground often appears to be abnormal, such as with unreadable words or showing a loss of data in a certain period of time. So the data are discontinuous in the timeline. However, when meeting the discontinuous gyroscope data, both the classical DAVAR and the fast DAVAR are invalid. It is critical to further improve the algorithm of the DAVAR to deal with discontinuous time series. Based on the recursive character of the fast DAVAR algorithm, we make a further advance to extend the fast DAVAR to discontinuous time series.  
Figure 5: Flowchart of fast dynamic Allan variance.  
Before computing \(\sigma_{\omega}^{2}[n+1,k]\), we need to compute the discrete second-order difference \(\Delta_{k}^{2}[n+N_{w}/2-2k]\) and \(\Delta_{k}^{2}[n-N_{w}/2]\). For the given \(n\) and \(k\), \(\Delta_{k}^{2}[n+N_{w}/2-2k]\) and \(\Delta_{k}^{2}[n-N_{w}/2]\) can be obtained by Equations (21) and (22):  
\[\Delta_{k}^{2}[n+\frac{N_{w}}{2}-2k]=\left(\theta[n+\frac{N_{w}}{2}]-2\theta[n +\frac{N_{w}}{2}-k]+\theta[n+\frac{N_{w}}{2}-2k]\right)^{2} \tag{21}\]  
\[\Delta_{k}^{2}[n-\frac{N_{w}}{2}]=\left(\theta[n-\frac{N_{w}}{2}+2k]-2\theta[ n-\frac{N_{w}}{2}+k]+\theta[n-\frac{N_{w}}{2}]\right)^{2}. \tag{22}\]  
At this time, we can judge whether \(\theta[n+\frac{N_{w}}{2}]\), \(\theta[n+\frac{N_{w}}{2}-k]\) and \(\theta[n+\frac{N_{w}}{2}-2k]\) are unreadable words or not. If one of them is an unreadable word, we set \(\Delta_{k}^{2}[n+N_{w}/2-2k]\) to zero and then continue the computation. \(\Delta_{k}^{2}[n-N_{w}/2]\) could be obtained in the same way.  
The recursion characteristic of the fast DAVAR will judge whether the data contains unreadable code or not. So the fast DAVAR could be extended to deal with discontinuous time series. We call this modified algorithm improved fast DAVAR.
## 2 Methods  
### Strength Reduction Method  
With the development of computer technology, the application of finite element-based Strength Reduction Method (SRM) in slope stability analysis is becoming more and more common [28; 29; 30; 31]. Compared with the limit equilibrium method, the finite element strength reduction method does not need to assume the location and shape of the sliding surface first, and integrates the stress, strain and displacement field characteristics of the slope. It can automatically obtain the critical slip surface of arbitrary shape and the corresponding safety factor, and can also simulate the whole process of slope instability and plastic zone expansion [32].  
The basic principle of the finite element strength reduction method is to divide the shear strength parameters of the geotechnical body, i.e., the cohesion \(c\) and the internal friction coefficient \(f\) (\(f=\tan\varphi\), \(\varphi\) is the internal friction angle), by a reduction factor FOS to obtain a new set of cohesion and internal friction angle values (Equations (1) and (2)). Then, they are used as new calculation parameters for finite element calculation; when the calculation converges exactly, the corresponding FOS is the minimum factor of safety of the slope. At this time, the slope reaches the critical state and destabilization damage occurs. If FOS is further increased, the calculation will not converge [33].  
\[c^{\prime}=\frac{c}{\text{FOS}} \tag{1}\]  
\[\varphi^{\prime}\!=\arctan\!\left(\frac{f}{\text{FOS}}\right)\!=\arctan\!\left( \frac{\tan\,\varphi}{\text{FOS}}\right) \tag{2}\]  
The finite element strength reduction method cannot directly calculate the safety factor with the formula, but needs to determine whether the system enters the ultimate equilibrium state according to some damage criterion [32]. Therefore, the key to the analysis of slope stability by the finite element strength reduction method is the determination of instability-discriminant criteria. At present, there are three main types of slope instability criteria: non-convergence of the numerical iterative process of finite element, abrupt change in displacement in a characteristic part, and generalized plastic strain or equivalent plastic strain penetration [12; 33]. [PERSON] et al. [34] analyzed how the above three types of criterion are theoretically uniform and consistent and, among these three types of criterion, the finite element numerical iterative process non-convergence criterion is the most convenient to use, true, and reliable. Therefore, this criterion is chosen as the criterion for slope instability in this calculation.