esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Set-Up  
A test set-up was built to investigate the crack localization capability on the basis of the NPW. For the ease of practical implementation, the capillaries are represented by 3/4\" tubes (internal diameter of 16.9 mm) and with a length of 462 mm. Along the longitudinal direction of the tube, multiple connections are foreseen to simulate different leak positions and to pull vacuum. A detailed drawing of the tube can be seen in Figure 4 and a graphical presentation of the test-setup is given in Figure 5. The circular leak had a diameter of 1 mm. The initial pressure level is set at approximately 20 kPa (absolute) while the outside environment is at ambient conditions (101.3 kPa). The temperatures are expected to be all in equilibrium at room temperature (293.15 K).  
Figure 3: Computational fluid dynamics analysis of negative pressure waves.  
Along the length of the tube, multiple holes were foreseen to simulate different leak positions (called L1 to L5 from left to right in Figure 4). During a test, all but one were closed while the remaining hole was connected to a solenoid on-off valve to simulate the opening of the crack with a known diameter of 1 mm. The last hole, on the right along the length direction of the tube, was used to pressurize at the required initial pressure level before the measurement.  
The tube was closed at both ends and each side was equipped with a pressure sensor (Endevco MEGGIT 8540-200). The sensor was selected such that it could measure static pressure changes with a sufficiently high sensitivity (noise level about 10-20 Pa, much below the expected amplitude of the NPW (see Equation (6))). The sensors were connected to a LMS Scadas III mobile data acquisition system with a maximum sampling rate of 204.8 kHz. Such a high sampling rate is needed to lower the theoretical uncertainty on the location of the leak to a value of 0.84 mm (see Equation (4)).
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 4 The Proposal of the Enhanced B-REP Mechanism
Then, the router S2 looks inside its EB-REP Backup Table and selects its precalculated BS value according to the destination address of the original packet. This BS value is then inserted as a new BS value into the EB-REP packet header. In principle, there are two cases where, in addition to our first, other failures can occur. The first case is when there is another outage in another part of the network and outside the currently used alternative EB-REP route. In this case, the EB-REP mechanism behaves the same as when a new error is detected (a router becomes the new S router). That is, the EB-REP finds a new precalculated Bit-String of an alternative path, creates an EB-REP packet, and inserts the BS value into the EB-REP header. The second situation is that an existing alternative EB-REP route fails, which means that packets are already routed (switched) based on the BS value and not their destination IP address. In this case, the question is, can failure occur on the precalculated alternative path for D, where the router acts as R? The alternative path calculated by each router (including S1) is the second shortest possible path for D. That means that for other routers on this alternative path (including S2), an alternative path will be the shortest and primary for a specific D. Therefore, if there is a second outage on our main alternative path calculated  
Figure 9: The principle of EB-REP.  
from S1 to D, and the N router (new S2) detects a failure on the next-hop according to the Bit-String-based routing (decided by the S1), it is an outage on the primary unicast next-hop route to the destination from the S2 point of view. Therefore, S2 has already precalculated an alternative path to D. This alternative path from S2 will replace the alternative path from S1. If we apply this behavior to our situation, router R6 detects an outage on its primary path toward D (R7), which is part of the alternative path decided by R3 (S). Therefore, R6 must reroute the EB-REP packet to an alternative next-hop. For this purpose, the R6 uses its precalculated EB-REP backup path and modifies the existing Bit-String with a value of \"... 0101 0000\" and forwards the packet toward the new next-hop, router R5. R6 becomes the new router S (S2). This adjustment of Bit-String values ensures the process of multi-failure protection. Finally, the EB-REP packet is routed through R5 and to destination D, router R7. R7, as the last router on the path, receives the EB-REP packet with a Bit-String value of \"0100 000\". After processing, the router sets the last bit in the Bit-String to zero, which means that all bits are now zero. This indicates to the EB-REP router that the decapsulation process needs to be performed. Therefore, the R7 router decapsulates the IP packet by removing the added BIER (EB-REP) header. Packets are then routed forward using unicast routing.
## 3 Results and Discussion
### Selectivity of Molecularly Imprinted Magnetic Stir Bar for Adsorption of Bisphenol A  
Relevant research has shown that molecularly imprinted stirring rods have a certain adsorption effect on compounds that are similar in chemical structure and properties to the template molecules [29; 30; 31]. In order to further investigate the selective adsorption properties of the bisphenol A molecularly imprinted magnetic stirring rod, three phenolic substances, phenol, 2,4,6-trichlorophenol (2,4,6-TCP), and tetrabromobisphenol A (TBBPA), were selected as interfering substances in the experiment. The competitive adsorption of the three phenols on BPA was analyzed in single system, binary mixed system, and ternary mixed system. The adsorption effects of bisphenol A molecularly imprinted stir bar (BPA-MIPs-SBSE) and non-molecularly imprinted stir bar (BPA-NIPs-SBSE) on four phenols (BPA, phenol, 2,4,6-TCP, and TBBBPA) with the same initial concentration were tested, as shown in Figures 12 and 13. Under the same conditions, the removal rates of BPA, phenol, 2,4,6-TCP, and TBBPA by BPA-MIPs-SBSE were 86.2%, 15.34%, 36.78%, and 34.7%, respectively, and the adsorption capacities were 3.48 \(\upmu\)mol/g, 0.58 \(\upmu\)mol/g, 0.15 \(\upmu\)mol/g, and 1.19 \(\upmu\)mol/g. The removal rates of BPA, phenol, 2,4,6-TCP, and TBBPA by BPA-NIPs-SBSE were 24.7%, 7.88%, 36.09%, and 21.6%, respectively, and the adsorption capacities were 1.89 \(\upmu\)g/g, 0.49 \(\upmu\)g/g, 1.83 \(\upmu\)g/g, and 0.49 \(\upmu\)g/g, respectively. By comparing the adsorption effects of BPA-MIPs-SBSE and BPA-NIPs-SBSE on four phenols, it was found that the removal rate and adsorption capacity of BPA-MIPs-SBSE for BPA were much higher than those of the other three phenols, and also higher than those of BPA-NIPs-SBSE for the four phenols. This suggests that there are sites on the surface of BPA-MIPs-SBSE that can recognize the template molecules, thereby improving its adsorption effect for BPA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Parameters** & **BPA-MIPs-SBSE** & **BPA-NIPs-SBSE** \\ \hline q\({}_{\text{e}}\) (\(\upmu\)mol/g) & 3.545 & 1.963 \\ q\({}_{\text{m}}\) (\(\upmu\)mol/g) & 35.19 & 18.92 \\ K\({}_{\text{L}}\) (L/\(\upmu\)mol) & 0.031 & 0.015 \\ R\({}^{2}\) & 0.960 & 0.941 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters and correlation coefficients of the Langmuir adsorption isotherm model.
## 3 Industrial Jet Cooler Module
### CFD Simulation of the Module
A symmetry condition is imposed on the lower surface of the metal strip, since in the real system, the jet coolers are located on both sides (upper and lower) of the strip, to ensure uniform cooling. Based on the previous experience with the test cases, the turbulence model used is the realizable k-\(\epsilon\), with the option of enhanced wall treatment. The discrete ordinate (DO) radiation model is activated to take into account the radiative heat transfer. The structured mesh has 7*106 elements with a grid refinement to get y\({}^{+}<1\) at the wall. The metal band is also meshed and the conjugate heat transfer is considered to simulate the heat conduction mechanism into the band. In Figure 16, the streamlines through the modeled volume show the overall flow structure and highlight a correct repartition induced by the periodicity condition towards the suction mouth. The distortion of the jets, in the streamwise direction, closest to the suction mouth can be clearly noticed in the side view shown in Figure 17. This situation affects the heat exchange in the area closest to the suction, with a reduction because the deflected jets do not correctly impinge on the band surface. In Figure 18, the surface temperature of the band is shown. The result obtained is comparable to the distribution due to a steady analysis; the temperature decreases from the inlet to the outlet of the computational volume. A higher temperature gradient can be seen in the area below the jets, as expected. The predicted \(\Delta\)T between inlet and outlet is approximately 35 \({}^{\circ}\)C and matches the design intent for this specific jet cooler; this confirms that the simulation approach is representative of the actual solution. Considering the length of the jet cooler and the speed of the band, a cooling rate equal to \(-\)39.4 \({}^{\circ}\)C/s is obtained (see Figure 19), which is the main design parameter for the cooling process. Figure 16: Streamlines in jet cooler model. Figure 17: Side view of streamline development. The contours of the heat transfer coefficient along the band are shown in Figure 11:0 From this image, the difference between the central jets and those closest to the suction is evident; the former are not particularly affected by the adjacent ones due to the rather high values of the jet spacing p\({}_{\text{jet}}\)/D and H/D ratios (where p\({}_{\text{jet}}\) is the pitch distance between the jet centers in the rake). The above contours give a regular distribution of the heat transfer coefficient that decreases when moving away from the stagnation area of each single jet. The above structure is no longer valid for the jets close to the suction, whose flow deflection strongly affects the thermal coefficient. These sets of jets are less efficient, as shown by the low values of h in the contours of Figure 11:1 The distortion of the jet is reflected in a non-regular distribution of the heat coefficient.
## 2 Materials and Methods
### Hyperspectral Data Transformations  
Prior to classification, we conducted experiments to transform the hyperspectral data into more informative feature representations. Two key feature extraction techniques were employed: wavelet transformation and Gabor-like filtering. The wavelet transformation enabled us to capture multi-scale frequency information from the input data, while Gabor-like filtering, which is widely used in signal processing, was employed to capture both spatial (or temporal) and frequency information from the signal. This approach is particularly effective for analysing localized frequency variations in spectral data. These preprocessing steps enhanced the discriminative power of the input data, potentially improving the model's ability to differentiate between various levels of flavonoid content.
# Optically Switchable MeV Ion/Electron Accelerator
## 2 Experimental Setup  
We performed the experiments using the NePTUN 20 TW laser system [35] at Tel Aviv University. A schematic drawing of the setup is shown Figure 2. Laser pulses of 29 fs at reduced energy of 140 mJ were focused using an f/2.5 off-axis parabolic mirror having an effective focal length of 12.7 cm unto 800 nm-thick Au foil targets. A measurement showed 70% of the laser energy to be contained within a circle of 4.1-um diameter, which corresponds to an intensity of 1.2 \(\times\) 10\({}^{19}\) W/cm\({}^{2}\). The energy stability of the laser was measured to be 1.3% (RMS). The study relied on our automatic target system [21], which delivered the targets to the laser focus at a rate of 0.2 Hz.  
We employ the residual energy from the frontend pump laser as the light source for the engineered pre-pulses. These \(\lambda\) = 532 nm, E = 30 \(\mu\)J, \(\tau\) = 20 ps laser pulses are optically synchronized with the main pulse. The optical period and pulse duration of these pre-pulses are much shorter than the plasma expansion time, so their exact values should not affect the plasma heating in a significant manner. Our measurements found that 70 percent of the pre-pulse energy was contained within a circle of 8.1 \(\upmu\)m diameter, which corresponds to an intensity of 9.3 \(\times\) 10\({}^{11}\) W/cm\({}^{2}\). The energy stability of the pre-pulse was measured to be 1.5% (RMS). The temporal jitter between the pre- and main-pulses was measured be shorter than 20 ps. These properties correspond to a contrast ratio of 8 \(\times\) 10\({}^{-8}\) between the pre- and main-pulses. Before focusing, the pre-pulses are spatially filtered, collimated, and delayed in a variable delay line. A relative delay of 0-90 ns between the pre- and main pulses is achieved using our multi-plane \"Cat\"s cradle\" [36] delay line.  
We measured the emitted electron and ion spectra for irradiation with or without auxiliary pre-pulses preceding the main pulse by 4 ns to 30 ns. Electrons were measured using a magnetic spectrometer with a field strength of 0.15 T and an angular acceptance of 0.12 msr. Ions were measured with a Thomson parabola ion spectrometer (TPIS) with a similar design to that of [PERSON] et al. [37], operating with an electrode voltage difference of 1 kV and having an angular acceptance of 0.10 msr. For both spectrometers, spectra were recorded by a charge-coupled device imaging a CsI(Tl) scintillator at the focal plane of the spectrometer. We obtained the absolute charge calibration of the electron spectrometer by acquiring the scintillation signal of a \({}^{90}\)Sr calibration beta emitter placed behind the scintillator, using the same imaging system.
## 2 Feature Selection Using Radial Chebyshev Moment Invariants
### Radial Chebyshev Moments  
Radial moments are generally expressed in polar coordinates \((r,\theta)\), and combine a one-dimensional polynomial basis with a circular-harmonic function to construct moments of an image \(f(r,\theta)\). From the mathematical point of view, moments are \"projections\" of a function onto a polynomial basis [35]. However, in digital image processing, the radial moment has to be converted from the continuous to the discrete domain. For an image of size \(N\times N\) pixels, the parameter \(r\) is allowed to vary from 0 to \(N/2\), and the angle \(\theta\) is allowed to vary from 0 to \(2\pi\). Consequently, the radial moment of an image of order \(p\) and repetition \(q\) is generally constructed in the following expression:  
\[R_{pq}=n_{pq}\sum\limits_{r=0}^{m-1}\sum\limits_{l=0}^{n-1}\varphi_{pq}(r, \theta)f(r,\theta) \tag{2}\]  
where \(n_{pq}\) is the normalizing factor; \(p\), \(q\) are non-negative integers, \(0\leq p,q\leq N-1\); \(m=N/2\); \(\theta=2\pi l/n\) with \(0\leq l\leq n-1\); \(n\) is the maximum resolution used in the angular direction, and \(\varphi_{pq}(r,\theta)\) is a polynomial basis function.  
Image moments are generally defined and named by the polynomial basis used. For the radial Chebyshev moments, their polynomial basis is defined as follows:  
\[\varphi_{pq}(r,\theta)=c_{p,N}(r)e^{-jq\theta}=c_{p,N}(r)e^{-jq\frac{2\pi l}{ n}} \tag{3}\]  
where the scaled discrete Chebyshev polynomials \(c_{p,N}(r)\) are given by [36]:  
\[c_{p,N}(r)=\frac{p!}{N^{p}}\sum\limits_{k=0}^{p}\left(-1\right)^{p-k}\left( \begin{array}{c}N-1-k\\ p-k\end{array}\right)\left(\begin{array}{c}p+k\\ p\end{array}\right)\left(\begin{array}{c}r\\ k\end{array}\right) \tag{4}\]  
and \(e^{-jq\theta}\) is an angular part of the polynomial.  
From the above, the radial Chebyshev moments of order \(p\) and repetition \(q\) of an \(N\times N\) image are defined as [37]:  
\[C_{pq}=\frac{1}{n\rho(p,m)}\sum\limits_{r=0}^{m-1}\sum\limits_{l=0}^{n-1}c_{ p,m}(r)e^{-jq\frac{2\pi l}{n}}f(r,\theta) \tag{5}\]  
where the squared-norm \(\rho(p,N)\) is given by:  
\[\rho(p,N)=\frac{N\Big{(}1-\frac{1}{N^{2}}\Big{)}\Big{(}1-\frac{2^{2}}{N^{2}} \Big{)}\cdots\Big{(}1-\frac{p^{2}}{N^{2}}\Big{)}}{2p+1} \tag{6}\]  
In terms of real-valued components, the above equation can be rewritten as:  
\[C_{pq}=C_{pq}^{(c)}-jC_{pq}^{(s)} \tag{7}\]  
where the real-valued radial Chebyshev moments are given by the following equation:  
\[\begin{array}{l}C_{pq}^{(c)}=\frac{1}{n\rho(p,m)}\sum\limits_{r=0}^{m-1} \sum\limits_{l=0}^{n-1}c_{p,m}(r)\cos\Big{(}\frac{2\pi ql}{n}\Big{)}f(r, \theta)\\ C_{pq}^{(s)}=\frac{1}{n\rho(p,m)}\sum\limits_{r=0}^{m-1}\sum\limits_{l=0}^{n-1} c_{p,m}(r)\sin\Big{(}\frac{2\pi ql}{n}\Big{)}f(r,\theta)\end{array} \tag{8}\]
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Comprehensive Satisfaction of Tour Itinerary
#### 3.2.4 Reward Based on Scenic Category Diversity  
The monotonous categories of scenic spots in an itinerary may lead to tourist aesthetic fatigue. Considering that tourists may have preferences for the categories of scenic spots included in their itinerary, we have designed two types of diversity rewards \((R_{div})\) for scenic categories. They are described as follows:  
\[R_{div1}(I)=|IS_{cat}\cap Cat(I)|*\gamma_{1}+(|IS_{cat}|-|IS_{cat}\cap Cat(I)| )*\gamma_{2} \tag{5}\]  
\[R_{div2}(I)=R_{div1}+(|Cat(I)|-|IS_{cat}\cap Cat(I)|)*\gamma_{3} \tag{6}\]  
where \(IS_{cat}\) represents the set of user interest scenic spot categories and \(Cat(I)\) represents the set of scenic spot categories included in the itinerary. The first diversity reward \(R_{div1}\) consists of two parts: a bonus for including the interest categories of scenic spots and a negative reward for missing interest categories in the itinerary, applicable when the user has selected interest categories. The second diversity reward \(R_{div2}\) consists of three parts:it builds upon \(R_{div1}\) by adding a bonus for the diversity of categories beyond the selected interest categories, and it applies even when the user has not selected interest categories. In this paper, \(\gamma_{1}=1,\gamma_{2}=-1\) and \(\gamma_{3}=0.5\).
## 3 Performance Analysis  
MATLAB 2021b was used to implement the ExMPLPQ model algorithms. The implementation was structured with a set of modular functions (main, pre-processor (exemplar division function), Inception local phase quantization (ILPQ), INCA, and kNN). The model was trained and tested with three study data subsets comprising axial images only, sagittal images only, and combined axial and sagittal images. The model performance was compared against 19 pre-trained models, in which kNN was deployed as a classifier, and  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Step** & **Parameter** \\ \hline \multirow{2}{*}{Exemplar division} & 32 \(\times\) 32 sized patches have been used. To generate features from local areas. \\ \hline \multirow{6}{*}{LPQ} & This step was used to extract textural features with variable parameters (we have used 3, 5, and 7 parameters). Therefore, this feature extractor is named MPLPQ. The prime purpose of this feature extractor is to use the effectiveness of the LPQ by using variable parameters. \\ \hline \multirow{6}{*}{Feature concatenation} & The proposed model extracts 768 features from each patch and raw image. In our architecture, 50 (=49 + 1) feature vectors have been created, and each feature vector has 768 features. Therefore, the created feature vector has 38,400 (=768 \(\times\) 50) features. \\ \hline \multirow{2}{*}{INCA} & Loop range: from 100 to 1000 \\  & Loss vector: kNN \\ \hline \multirow{2}{*}{Classification using kNN} & k: 1 \\  & Distance: Spearman \\ \multicolumn{2}{l}{Voting: None} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The used parameters.  
the classification was repeated 100 times for each pre-trained model. The ExMPLPQ algorithm possessed low computational complexity and was executed on a desktop personal computer with Windows 10.1 pro-OS, 11 th generation Intel i9 processor, 32 GB memory, and 1.5 TB hard disk without parallel processing or graphics processing. Standard performance metrics--precision, sensitivity, specificity [35,36], F1-score, accuracy, [PERSON]'s correlation coefficient (MCC) [35,36]--were used to evaluate ExMPLPQ as well as the 19 pre-trained models.
# Time Series Classification with Shapelet and Canonical Features
## 6 Conclusions  
The continuous expansion of data scale puts forward higher requirements for the efficiency of data mining algorithms. In order to perform accurate and efficient time series classification, a random Shapelet forest algorithm (RSFCF) embedded with typical time series features is proposed in this paper. RSFCF randomly selects Shapelet and limits the scope of Shapelet to improve efficiency, and embeds typical time series features in Shapelets to compensate for the loss of accuracy caused by random selection of Shapelet. The classification results on the 112 UCR time series datasets show that the accuracy of RSFCF surpasses that of multiple advanced time series classification algorithms and reaches the current leading level. The meta-integrated method HIVE-COTE is more accurate than RSFCF, but experiments have shown that its efficiency is much lower than that of RSFCF, so HIVE-COTE is difficult to apply to large-scale datasets. In summary, the RSFCF algorithm proposed in this paper takes into account both accuracy and efficiency, and has higher practicality. Future work includes studying a fusion strategy to embed RSFCF into the meta-integration method HIVE-COTE for a more precise classification of scenarios where real-time requirements are not high.  
Conceptualization, H.-Y.L.; methodology, Z.-H.W.; software, Z.-Z.G.; writing--original draft preparation, H.-Y.L.; writing--review and editing, Y.-H.D. All authors have read and agreed to the published version of the manuscript. This research was funded by Fundamental Research Funds for the Central Universities, grant number 2019 RC052, Beijing Natural Science Foundation, grant number 4214067 and National Key Research and Development Program of China, grant number 2021 ZD0113002. The authors declare no conflict of interest.  
Figure 10: Feature contribution comparisons on three UCR datasets CBF, ShapesAll and FaceFour.