esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.1 Recognition Accuracy  
For each person, one image containing gesture \"Five\" was used as the template for identifying all the 100 images from this person. Notice there were ten images of gesture \"Five\". Thus, we applied a cross-validation method by repeating the recognition for ten times, each time using a different gesture \"Five\" as the template, and then calculated the mean accuracy. One thing to mention that some experimenters found it hard to unbend the ring finger when making the gesture \"Eight\", thus we deem both \(Y=[0,1,0,0,1]\) and \(Y=[0,1,0,1,1]\) to be \"Eight\".  
The result is shown in Table 1, including the confusion matrix, accuracy, and time cost of each gesture. Our algorithm achieves a high accuracy of 99.38%, where there is no classification mistake for half of the gesture types including gesture \"Zero\", \"One\", \"Four\", \"Five\", and \"Nine\". Wrong samples are mainly from gestures with three fingers. The two gestures with most mistaken times are gesture \"Three\" and \"Six\", whose number of fingers are the same and finger angles are near. The misclassifications are mostly due to the variation of hand and fingers' posture. When a finger is not totally straight, such as the gesture \"Eight\" in Figure 8, its length in the camera view turns smaller. Also, when a finger gets too close to the camera, its width increases which may result in a false estimation of the finger number in the region. Another major reason is the swing of the hand. Left and right swings bring error to the estimation of hand's direction, while the forward and backward swing changes the angle gap between fingers.  
Figure 8: Ten gestures labeled from zero to nine from ten different subjects, and their respective recognition results.
## 5 Iterative Process to Obtain the Pressure Loss Coefficients per Unit Length
### Different Trends for the Behavior of the Value for \(\varphi_{z\ ext}\)  
The iterative process was completed for the 81 cases in Table 2. Once the values for the coefficients per unit length were obtained for all cases, it was possible to distinguish three different trends in the values of the pressure loss coefficients among the 81 cases. The first trend includes only two cases, for which a correct adjustment between the compact and the detailed models was obtained using a value for \(\varphi_{z\ ext}\) that was higher than 0 and lower than \(\varphi_{z\ int}\). An example of this is presented in Figure 4, where the results for case 40 (\(\varphi_{z\ ext}=3533\) and \(\varphi_{z\ int}=100,000\)) from Table 2 are shown. As the figure illustrates, the approach of the velocity field that was obtained presents a satisfactory adjustment with that obtained by the detailed modeling from [PERSON] et al. [17].  
In the second trend, negative values for \(\varphi_{z\ ext}\) are needed to achieve a correct adjustment. Using a negative value for this coefficient does not make any physical sense, as it means a momentum is generated instead of a sink, which is what it should be. However, there is some similarity with what actually happens in the detailed models, as they show that the flow by the hub zone is deviated towards the annulus side when crossing a screen in a certain way (as can be seen in Figure 5). In other words, the annulus side receives  
Figure 4: Axial and tangential velocity patterns (m/s) for the outlet cross-section obtained for case 40 in Table 2 via compact modeling and detailed modeling.  
momentum from the hub side. The only way to generate this effect for compact modeling through porous media is by using a negative value for \(\varphi_{z\ ext}\).  
The rest of the cases belong to the third trend, which includes all those cases where a very good adjustment is not achieved for the minimum and maximum axial velocity. As the \(\varphi_{z\ ext}\) coefficient is reduced further, a correct adjustment is achieved for the maximum velocity but not for the minimum one, for which the value of \(\varphi_{z\ ext}\) should be even smaller. An example of this trend is shown in Figure 6, which shows the second case in Table 2. The adjustment presented between both models in the figure indicates that the approach is good even for the difference in the minimum velocity value. The maximum axial velocity zone and the tangential velocity field are well approximated.  
It must be highlighted that the values for the coefficients were obtained by comparing the flow magnitudes at the outlet cross-sections of the domain for all cases. However, the approaches of the flow pattern obtained by this compact modeling were verified as being reliable once the jets produced by the holes disappeared.  
Figure 5: Velocity magnitudes contour plots (m/s) at different distances from the outlet of the screen for case 2 in Table 2.
## 3 Results and Discussion
### Audio Analysis Method
#### 3.4.3 Performance Metrics  
In classification tasks, different metrics can be used as figures of merit, with some showing improved robustness to unbalanced datasets. Table 8 lists the metrics used in the papers that focused on machine-learning-based classification. As can be seen, the most common metric is accuracy, followed by area under the curve (AUC). The majority of the other metrics rely on the classification confusion matrix, and include, e.g., Precision, recall and F1-score. For cases with class imbalance, measures such as F1-score and AUC are most appropriate for binary classification task.
## 4 Experiments and Analysis  
### Experiment Settings  
#### 4.1.1 Datasets  
We adopt four primary benchmarks to assess the CIL capabilities of CLIP. The ImageNet-R [48] benchmark involves randomly dividing 200 classes into 5, 10, 20 distinct tasks, with each task consisting of 40, 20, and 10 classes. Similarly, the CUB-200 is established on the CUB-200-2011 [49] dataset, which focuses on fine-grained bird classification. In this benchmark, the 200 classes are randomly split into 5, 10, and 20 tasks, with each task comprising 40, 20, and 10 classes. Meanwhile, the CIFAR-100 [50] benchmark involves the random partitioning of the original CIFAR-100 dataset into different separate tasks, denoted as T10, T20, and T50, each containing different classes. For the TinyImageNet benchmark with 200 classes, we follow a similar implementation as [15], initially trainingon 100 classes. The remaining 100 classes are randomly split into 5, 10, and 20 tasks, with each task comprising 20, 10, and 5 classes.
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 4 Solution Algorithms  
As most of the multi-objective problems have to meet several objectives, it is impossible to obtain a unique solution that satisfies all the objectives simultaneously. Usually, Simulated Annealing (SA) [21], Ant Colony Optimization (ACO) [26], Multi-objective Heuristic Algorithms [27], Discrete Evolutionary Multi-objective Optimization (DEMO) [28], Niched Genetic Algorithm [5], Non-dominated Sorting Genetic Algorithm (NSGA) [29; 30] are often chosen to solve multi-objective problems. One of the widely used algorithms proposed to solve multi-objective problems is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II).  
The scheduling of different bus lines will affect the transfer efficiency. Moreover, there are many schemes for each bus line and the two objectives conflict with each other. Therefore, it is necessary to design an algorithm that can deal with multi-objective public transportation scheduling quickly. In this section, a two-step algorithm is employed in order to solve this problem. The first step is to obtain the Pareto solutions, and the second step is to obtain a satisfactory scheme from the Pareto solution set on the basis of the entropy weight-TOPSIS method.
## 1 Introduction
This offers a fresh approach to developing the SLODAR method. [PERSON] and [PERSON] [22] proposed the S-DIMM+ method for measuring atmospheric optical turbulence intensity distribution in daytime solar observations. In 2012, [PERSON] et al. [23] employed this method to measure turbulence intensity distribution at the Big Bear Solar Observatory (BBSO). In 2018, [PERSON] et al. [24] further extended this method and conducted turbulence measurements at the 1-m New Vacuum Solar Telescope (NVST) site. Most of the above methods are for the study of algorithms. Most methods use the AO system open-loop state and offline data to measure atmospheric turbulence parameters. Long-term turbulence monitoring of large-aperture telescope sites cannot be realized. This study introduces a real-time measurement system of atmospheric optical turbulence intensity and distribution based on the GLAO system on NVST at Fuxian Lake Solar Observatory (FSO) [4]. The DIMM method is used to measure the Fried parameter. As the Shack-Hartmann wave-front sensors (SHWFS) have multiple sub-apertures, and any two sub-apertures in a pair can be worked as a DIMM, all sub-apertures are used to improve the measurement accuracy of atmospheric coherence length. The measurement system is run on the upper computer of the GLAO system; it acquires slope and voltage data from the real-time controller (RTC) of the GLAO system and reconstructs the turbulence information using the pseudo-open-loop method, then measures the atmospheric turbulence intensity and distribution and displays the final results. This measurement system should be the first real-time atmospheric turbulence measurement system that does not require hardware costs and works simultaneously with the GLAO system. The configuration, principles, simulation, indoor experiments, and real-time measuring results of the turbulence distribution measurement system are given in this article. The subsequent sections of this paper are organized as follows. Section 2 details the components and measurement principles of the system; Section 3 presents the results based on simulation data; Section 4 highlights the real-time measurement results of the measurement system, including results for indoor hot air turbulence and real atmospheric turbulence; and finally, in Section 5, we offer a concise summary of the measurements system.
## 4 Result Discussion and Analysis
### Comparative Analysis of Existing Drag Calculation Formulas and Experiments
When the towing speed is greater than 1.5 m/s, the difference factor sharply decreases, and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Water Flow** & **Navigational** & **Navigation Tunnel** & **Zwankov Formula** & **Zhongqi Yu** & **Chuanjiang Barge** \\
**Velocity (m/s)** & **Speed (m/s)** & **Resistance (kN)** & **(kN)** & **Formula (kN)** & **Formula (kN)** \\ \hline \multirow{4}{*}{0} & 0.5 & 71.69 & 2.42 & 1.34 & 0.23 \\  & 1.0 & 108.18 & 8.43 & 4.77 & 0.78 \\  & 1.5 & 161.60 & 17.87 & 10.01 & 1.61 \\  & 2.0 & 240.75 & 30.98 & 16.95 & 2.71 \\  & 2.5 & 332.63 & 48.34 & 25.50 & 4.07 \\ \hline \multirow{closest value only differs by a factor of six. In dynamic water conditions, the difference factor between the [PERSON] fitting formula calculation value and the experimental value is within 21 times, and the difference factor between the Zwankov formula calculation value and the experimental value remains stable at around 10 times. 3. The calculated value of the barge formula in the Chuanjiang River is much smaller than the experimental value, with a multiple of 81-157 times.
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 3 Model
### Algorithm Implementation  
This section provides a summary of the algorithmic process, with the caveat that the algorithm is predicated on the subgraph \(G^{\prime}_{r}\) remaining connected when \(r=1/3\). The overall algorithm can be divided into three main steps:  
1. Execute the blossom algorithm on subgraph \(G^{\prime}_{r}\) to obtain matched node pairs.
2. From the matched node pairs, calculate the virtual nodes to form the virtual graph \(G^{*}\), and then run the XTC algorithm on this virtual graph to obtain the virtual subgraph \(G^{*^{\prime}}\).  
Figure 2: \(u\), \(v\) represent a node in \(G\), and \(m\), \(m^{\prime}\) are virtual nodes.  
Map the virtual subgraph back to the original graph \(G\) to obtain the resulting graph.  
For a detailed description of the algorithmic computation process, refer to Algorithm 1.  
```
Input:\(G\) Output:\(G_{result}\)  compute \(G_{r}^{\prime}\)  match = blossom(\(G_{r}^{\prime}\)) \(virtual=\{\}\) for\((u,v)\) in matchdo \(m\) = midpoint of \((u,v)\) \(virtual=virtual\cup\{m\}\) endfor  compute virtual graph \(G^{*}\) with radius of \(2r(r^{*})\) \(G^{*^{\prime}}\)=XTC(\(G^{*}\)) \(E=\{\}\) \(G^{{}^{\prime}}=(V,E)\) for\(edge=(u^{\prime},v^{\prime})\) in \(G^{*^{\prime}}\)do \(u_{1}\),\(u_{2}\)=match\([u^{\prime}]\) \(v_{1}\),\(v_{2}\)=match\([v^{\prime}]\) \(u_{m}\)=min\(\{degree_{G^{\prime}}(u_{1}),degree_{G^{\prime}}(u_{2})\}\) \(v_{m}\)=min\(\{degree_{G^{\prime}}(v_{1}),degree_{G^{\prime}}(v_{2})\}\) \(E=E\cup\{(u_{m},v_{m}),(u_{1},u_{2}),(v_{1},v_{2})\}\) \(G^{{}^{\prime}}=(V,E)\) endfor
```  
**Algorithm 1** VNTC Algorithm
## 3 Results and Discussion
### Water Absorption (WA), Solubility, and Swelling Degree  
Table 2 presents the fundamental characteristics of the films, encompassing their water absorption (%), solubility (%), and swelling degree (%). Water content (WC) showed a decrease of up to 25.15% when coffee silverskin was added to the matrix at the lowest concentration. Subsequently, with higher incorporations, water absorption increased, compared with the WFF-2.5 ss film. The water absorption of the WFF-20 ss film was 5.22% lower than that of the control WFF film. The addition of CS aqueous extract in the matrix showed a decrease in WA values up to 9.14-38.68%, with respect to the control WFF. Wheat filter flour films with glycerol and sorbitol as plasticizers, containing carvacrol as additive, exhibited comparable levels of WA [32]. On the contrary, films made from wheat flour by-products, when combined with glycerol and sorbitol, exhibited higher levels of WA [43]. The water resistance of biodegradable films can be greatly impacted by two essential attributes, i.e., solubility and swelling. The solubility and swelling degree values are presented in Table 2. By increasing the addition of coffee silverskin to wheat flour films, the swelling degree displayed an increase from 4.21% (WFF-2.5 ss) to 42.57% (WFF-20 ss) in relation to the control WFF. Initially, the inclusion of CS extracts led to elevated values of almost up to 50% (WFF-a1) and then reduced by up to 8.47% (WFF-2%B). Water solubility did not show much difference among the films. At first, water solubility showed a reduction of up to 11.93% and then exhibited an increase until WFF-10 ss, with the same values as the control WFF. At two higher concentrations, the WFFs showed a decrease of up to 8.14% compared with the control WFF. This decrease in solubility values may be related to the presence of coffee silverskin, which has a hydrophobic character and bad solubility in water. In addition, the lower solubility of certain flour films can be attributed to the presence of lipids and proteins distributed throughout the wheat flour matrix as a result of adding coffee silverskin, which seems to improve hydrophobicity and reduce solubility [29]. Higher values of solubility were observed in films with CS extracts, as they are aqueous. Our films had less water resistance in comparison with films based on wheat filter flour incorporated with carvacrol and sorbitol/glycerol [32]. Similar trends were observed in amaranth flour-based films [44]. Wheat Flour/PBAT active films incorporated with oregano oil microparticles had lower solubility values, possibly because of the high concentration of PBAT, which has a hydrophobic character [45].
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Topology  
Topology is an important factor that affects both the time synchronization protocols and other protocols in the network (e.g., routing protocol). The researchers that proposed FTSP and GTSP claimed that both protocols operate well in different topologies. In this scenario, we wish to demonstrate how different topologies affect the performance of FTSP and GTSP.
## 2 Materials and Methods
### Total Fatty Acids and TAG Profiles  
Cell pellets for total fatty acid analysis were submitted to direct transesterification with freshly prepared methanol sulfuric acid (97.5:2.5, \(v/v\)) at 70 \({}^{\circ}\)C for 60 min [31]. Subsequently, fatty acids methyl esters (FAMEs) were recovered using petroleum ether and the solvent evaporated under a constant N\({}_{2}\) flow in a dry bath at 30 \({}^{\circ}\)C [20; 32]. FAMEs were resuspended in hexane and 1 \(\mu\)L was injected in a gas chromatograph (Varian 430-GC gas chromatograph, Middelburg, The Netherlands), equipped with a hydrogen flame ionization detector set at 300 \({}^{\circ}\)C. The temperature of the injector was set to 270 \({}^{\circ}\)C, with a split ratio of 50. The fused-silica capillary column (50 m \(\times\) 0.25 mm; WCOT Fused Silica, CP-Sil 88 for FAME; Varian, Middelburg, The Netherlands) was maintained at a constant N\({}_{2}\) flow of 2.0 mL min\({}^{-1}\) and the oven set at 190 \({}^{\circ}\)C. Fatty acids identification was achieved by comparison of retention times with standards (Sigma-Aldrich, St. Louis, MO, USA), and chromatograms analyzed by the peak surface method, using the Galaxy software (Varian, Inc., Palo Alto, CA, USA). The internal standard used was pentadecanoic acid (15:0) to identify losses during preparation.  
Two indexes are commonly used to infer and predict the potential health benefits associated with the ingestion of a certain food: indexes of atherogenicity (_IA_) and thrombogenicity (_IT_) [33]:  
\[IA=\frac{4\times C14:0+C16:0}{\sum M\dot{U}FA+\sum PUFA-n6+\sum PUFA-n3}\]\[IT=\frac{C14:0+C16:0}{0.5\times MUFA+0.5\times PUFA-n6+3\times PUFA-n3+\frac{ PUFA-n3}{ PUFA-n6}}\]  
where _MUFA_ and _PUFA_ correspond to relative concentrations of the monounsaturated fatty acid (_MUFA_) and _PUFA_. The _IA_ is related to the plaque formation and to the decrease in the levels of esterified fatty acid, cholesterol, and phospholipids, thereby preventing the appearance of micro- and macro-coronary diseases [33]. The \(IT\) is related to the tendency to form clots in the blood vessels [33].  
For neutral lipids' separation pellets were boiled in water for 5 min to inactivate lipolytic enzymes. The extraction of lipophilic compounds was performed using a mixture of chloroform/methanol/water (1:1, \(v/v/v\)), as previously described [34]. Neutral lipid separation was achieved by thin layer chromatography (TLC) on silica plates (G-60, Merck, VWR) using as solvent a mixture of petroleum ether/ethyl ether/acetic acid (70/30/0.4, \(v/v/v\)) [35]. Lipids bands were visualized with 0.01% primitive in 80% acetone (\(v/v\)) under UV light, and the lipid band correspondent to TAG scraped off and transesterified as above-mentioned for total fatty acids, using the scrapped portion as sample [36].
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 5 Computer Simulations
### Many Tags with Random Time Slots
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Data** \\ **Raleibility** \\ **Rate** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **Minimum SNR (dB) for TD** \\ **Method at Distance (m)** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **Minimum SNR (dB) for Gold** \\ **Code Method at Distance (m)** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Minimum SNR (dB) for** \\ **Conventional Method at** \\ **Conductance (m)** \\ \end{tabular} } \\ \cline{2-13}  & **0.5 m** & **0.6 m** & **0.7 m** & **0.8 m** & **0.5 m** & **0.6 m** & **0.7 m** & **0.8 m** & **0.5 m** & **0.6 m** & **0.7 m** & **0.8 m** \\ \hline
90\% & \textless{}0 & \textless{}0 & 4.0 & 18.8 & \textless{}0 & \textless{}0 & 4.4 & 19.3 & N/A & N/A & N/A & N/A \\
95\% & \textless{}0 & \textless{}0 & 6.0 & 21.0 & \textless{}0 & \textless{}0 & 6.5 & 21.7 & N/A & N/A & N/A & N/A \\
97\% & \textless{}0 & 0.1 & 7.1 & 22.3 & \textless{}0 & 0.7 & 7.9 & 22.8 & N/A & N/A & N/A & N/A \\
99\% & \textless{}0 & 2.0 & 9.0 & 24.0 & \textless{}0 & 2.4 & 9.6 & 24.5 & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SNR analysis at different distances in the presence of 510 interference tags. Figure 13: BER curves of the proposed system, conventional system without anti-collision technique, and anti-collision system based on the gold-code sequence, for four tags of interest with different distances in the presence of 510 interference tags.
## 2 Methodology
### Calculation
#### 2.2.2 Vapor/Liquid Equilibrium (VLE)  
The vapor/liquid equilibrium (VLE) theory is used to investigate phase change behavior of the heat transfer fluid. The binary VLE relation is summarized in Equation (11) [22]. This relation combines the vapor phase fugacity coefficients (\(\dot{\phi}_{i}\)) and the liquid phase activity coefficients (\(\gamma_{i}\)) to provide non-ideal gas and non-ideal solution. The fugacity coefficients and the activity coefficients of ethylene-glycol and water are obtained by using SRK  
Figure 2: Arrangement of the tips in in-line in flow direction.  
cubic equation of state and UNIQUAC functional-group activity coefficients (UNIFAC), respectively [22].  
\[y_{i}\Phi_{i}P=x_{i}\gamma_{i}P_{i}^{sat} \tag{11}\]  
where \(\Phi_{i}\) is a ratio of fugacity coefficient, which can be expressed as:  
\[\Phi_{i}\equiv\frac{\hat{\Phi}_{i}^{\
u}}{\phi_{i}^{sat}}\exp\left[-\frac{V_{i }^{l}\left(P-P_{i}^{sat}\right)}{RT}\right] \tag{12}\]  
The saturated pressure of pure specie, \(i\) can be calculated by using Antoine equation as shown below:  
\[\ln P_{i}^{sat}=A-\frac{B}{T+C} \tag{13}\]  
where \(A\), \(B\) and \(C\) are parameters corresponding to each substance.  
Finally, the binary VLE relation is applied to calculate T-xy diagram for investigating the phase change behavior.