esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
### Evaluation Metrics  
There are different metrics for the evaluations of the recommender systems that have been used in the literature [47; 50; 51; 52; 53; 54]. Some of them are veracity measures, i.e., they are used to measure accuracy. The higher the value of the measure, the better the result. On the other hand, some of them are fallacy measures, i.e., the minimum resulting value indicates better performance. We have used four veracity measures and four fallacy measures in our results to decide the proposed mechanism's performance. For measures of precision, we have used p@10, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and Modified Spearman's Rank Correlation Coefficient (MSRCC). Whereas, for the parameters to be termed as fallacy measures, we have used FPR@10, FNR@10, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The list and details of the evaluation metrics are given below.  
1. P@10
2. FPR@10
3. FNR@10
4. Mean Average Precision (MAP)
5. Mean Absolute Error (MAE)
6. Mean Reciprocal Rank (MRR)
7. Root Mean Square Error (RMSE)
8. Modified Spearman's Rank Correlation Coefficient (MSRCC)  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Sequence** & **Course Title** & **Univ\_1** & **Univ\_2** & **Univ\_3** & **Univ\_4** & **Univ\_5** & **Univ\_6** & **Univ\_7** \\ \hline
1. & Artificial Intelligence & \(\phi\) & \(\phi\) & \(\gamma\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
2. & Compiler Design & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
3. & Computer Networks & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
4. & Discrete Mathematics & \(\phi\) & \(\phi\) & \(\gamma\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
5. & Data Structure & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
6. & Graphics & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
7. & Operating Systems & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
8. & Principles of Database & \(\phi\) & \(\phi\) & \(\phi\) & \(\gamma\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
9. & Software Engineering & \(\phi\) & \(\phi\) & \(\phi\) & \(\gamma\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline
10. & Theory of Computation & \(\phi\) & \(\phi\) & \(\gamma\) & \(\phi\) & \(\phi\) & \(\phi\) & \(\phi\) \\ \hline \end{tabular}
\end{table}
Table 2: List of computer science courses that have been included in the syllabus at top universities.
## 1 Introduction
With the development of laser technology, ultra-short pulse lasers (e.g., picosecond lasers, femtosecond lasers) have been used in the microprofiling of stents. Ultra-short pulse laser cutting could minimize defects, such as dress, recast layer, and HAZ, for the vaporization mechanism. [PERSON] et al. [PERSON] et al. (2017) described the fabrication of NiTi and platinum-iridium stents using a picosecond laser. The average roughness was \(1.34\)\(\upmu\)m and \(0.49\)\(\upmu\)m, respectively, and negligible HAZ was detected on the platinum-iridium stent. Different material properties (e.g., transformation temperature and thermal conductivity) were shown as the main causes for the differences in cutting-kerf characteristics. [PERSON] et al. [PERSON] et al. (2017) also investigated the effects of femtosecond lasers cutting of NiTi tubes under wet cutting conditions, which resulted in no HAZ, debris, or recast. Ultra-short pulse lasers have many excellent advantages; however, the disadvantages of low cutting efficiency, expensive procurement costs, and high maintenance costs make it difficult for ultra-short pulse lasers to be commercialized for microprofiling of metal vascular stents [PERSON] et al. (2017). Long-pulse lasers, especially fiber lasers, are widely used for their higher beam quality, reliability, and efficiency. Microcutting thin tubes into mesh-like devices is a conventional method for manufacturing stents. To reduce production time and improve geometrical flexibility, [PERSON] et al. [PERSON] et al. (2013) successfully produced a CoCr stent using selective laser melting (SLM), which provided a new method for manufacturing vascular stents. SLM productivity can outperform the conventional stent manufacturing scheme. The additive manufacturing process merges microtube manufacturing and laser microcutting phases into a single step method. The mechanical performance, in terms of tensile strength, recoil, and fatigue resistance, should be studied further. The present work aims to: first, investigate the cutting-kerf characteristics of fiber laser microprofiling of CoCr tubes and study the influences of the assisting gas type on cutting quality; second, research the influences of process parameters on cutting quality; and third, explore the guidelines for optimizing the laser cutting process of CoCr vascular stents.
## 10 Open Challenges and Future Directions
### Collective Navigation  
Collective navigation is a commonly used practice for swarm mobility. It consists of flying UAVs in a tight formation to maintain network connectivity. Difficulties arise with a large and highly mobile swarm, where the positions and directions of the UAVs change relative to each other. The most frequent issues to address are collision and crash avoidance, obstacle detection, and the connectivity of the swarm [24]. A better way to control and harmonize the movements of the swarm is to use the \"divide-and-conquer\" technique [56]. It consists of dividing the swarm into several sub-swarms. Each sub-swarm is assigned a static formation with a well-defined polygon shape (regular, irregular, concave, convex, or complex). The UAVs inside the sub-swarms move and maintain a distance from each other without leaving the boundary of their formation, while the sub-swarms move in relation to their delimitation while remaining two by two disjoint, as shown in Figure 20.
## 5 Performance Evaluation  
Extensive simulations are carried out using NS3 [34] on Ubuntu 16.04 on a 64-bit operating system, Intel Core i5 Processor, 1.80 GHz frequency, and 16 GB RAM, in order to assess the performance of the modified ring routing. The simulation setup, the settings, and the metrics utilized for assessing the proposed routing, in comparison to the existing cutting-edge protocols [35], are examined in this section. To remove the random nature of some simulation events, each scenario is also simulated 20 times with different seeds, yielding  
Figure 8: Data dissemination from the cluster head to the mobile sink node.  
Figure 10: Pseudo code of the ring change mechanism.  
Figure 9: A ring change scenario.  
approximately 3600 simulations for the network density graphs and 1920 simulations for the mobility graphs.
## 5 Extensions of the Architecture
### Parallel Pattern Classification  
According to Figure 4, each input image is correlated with one filter of the bank. The setup can be modified so as to perform parallel classifications of the input images. Each input image \(g_{m}\) must be compared to all filters \(f_{m}\) of the reference bank. For this purpose, the Fourier transform of each one-dimensional structure \(\widetilde{g}_{m}\) must be replicated in the filter plane. In Figure 5, this operation is performed by a one-dimensional Fourier transform noted by \(\mathrm{FT_{x}}\). Each one-dimensional distribution \(\widetilde{G}_{m}\), extended over the y-axis, is Fourier-transformed with respect to the x-axis. The distribution is then replicated to form a two-dimensional structure.  
Figure 5: Parallel image classification using a bank of one-dimensional patterns, \(\mathrm{TF_{x}}\) one-dimensional Fourier transform with respect to the x-axis.  
After compressing the input images by means of a multi-faceted diffractive compressing element (DCE), a two-dimensional Fourier transform is performed by means of a spherical lens L1 (Figure 5). Then, the resulting spectra are replicated by a subsystem similar to that of Figure 4. Each replica of one spectrum is multiplied by one one-dimensional pattern from the filter bank \(\widetilde{F}_{1}^{*}\) to \(\widetilde{F}_{N}^{*}\). The correlation product, observed in the focal plane of the spherical lens L2, contains the Fourier transforms of all the products: \(\widetilde{G}_{1}\)\(\widetilde{F}_{1}^{*}\), \(\widetilde{G}_{1}\)\(\widetilde{F}_{2}^{*}\),..., \(\widetilde{G}_{M}\)\(\widetilde{F}_{N}^{*}\).  
The setup of Figure 5 allows for the parallel classification of \(M\) input images \(g_{m}\), where a bank of \(N\) references, \(f_{m}\), is used.  
Rotation invariance is ensured, and scale invariance can be added by enlarging the reference bank. To add shift invariance, the intensity distribution of the Fourier transforms of the objects is taken as the input of the classification system.
## 3 Results and Discussion
### Distribution of Volatile Compounds in the Four Wine Grape Varieties
#### 3.3.2 Bound Volatile Compounds of the Four Wine Grape Varieties  
Bound volatile compounds of the four wine grape varieties were also analyzed using PCA to investigate the effect of varieties on aroma profile. As shown in Figure 3b1, the first (31.11%) and second (29.98%) components explained 61.09% of the total variance. The over-ripen samples of different varieties (D35 and D42) separated well from the samples in earlier stages by PC1. Bound volatile compounds of benzyl alcohol, \(\alpha\)-Terpineol and other compounds contributed to the positive aspect of PC1, while octanal, methyl myristate and other compounds contributed to the negative aspect of PC1.  
Different varieties picked at various dates were well separated by PC1 and PC2 of bound volatile compounds. The samples of Cabernet Sauvignon were distributed in the third and fourth quadrants. Compared with other varieties, the number of volatile compounds that contribute significantly to the aroma of Cabernet Sauvignon berry was larger. Among them, D0 was far away from other samples and was located in the third quadrant. According to Figure 3b2, it can be seen that the contribution of (E)-2-hexenal, terpinolene and other compounds in the third quadrant to the berry aroma of Cabernet Sauvignon during the D0 period was larger than that of other samples. More significantly, Cabernet Sauvignon D14-D42 was mainly located at the intersection of the third and fourth quadrants, which contributed to positively by geranyl acetone, ethyl laurate, \(\alpha\)-cedarene, ethyl myristate and methyl heptenone. Cabernet Franc samples were mainly distributed in the negative semiaxis direction of PC1, which was characterized by the bound volatile compounds of 2,4-di-tert-butylphenol, octanal, methyl myristate, (E)-2-hexenal, etc. Different from free volatile compounds, Cabernet Gernischet samples were well separated from Cabernet Franc by bound volatile compounds. Cabernet Gernischet was more related to methyl hexadecanoate, methyl myristate, 2-ethylhexanol and 2,4-dimethylbenzaldehyde. The distribution of Merlot samples in Figure 3b1 is located in the first quadrant, on the positive semiaxis of both PC1 and PC2, with contributors of major terpen ((E)-ocimene, d-limonene, pseudolimonene, etc.).
## 2 Materials and Methods
### Statistics  
In this context, variables are named following a specific convention to denote the method used for their calculation. Variables appended with \"_RQA\" are those calculated using the RQA-based method. This suffix indicates that the analysis and values of these variables are derived from RQA techniques. On the other hand, variables with the suffix \"_Gex\" are calculated using the Gas Exchange (Gex) method. This naming convention helps in differentiating the source and methodological background of each variable, ensuring clarity and precision in our analysis and discussions.  
The Kolmogorov-Smirnov Test of Normality [20] confirmed the normal distribution of VO2 and HR values at the AerT. The outcomes were as follows: VO2_RQA (D = 0.09, \(p\) = 0.26) and HR_RQA (D = 0.07, \(p\) = 0.64).  
Repeated measures analysis of variance (RM ANOVA) was utilized to assess the main effects of methods (RQA vs. Gex) and the interaction effects.  
Agreement between the RQA-based and Gex methods was assessed using an Ordinary Least Products (OLP) regression analysis [21].  
The regression's coefficients of determination (R2), regression parameters (slope and intercept), and 95% confidence intervals (CI) were calculated to identify fixed and proportional biases. The following criteria were adopted to interpret the magnitude of the correlation r between the estimates: <0.1 trivial, 0.1-0.3 small, 0.3-0.5 moderate, 0.5-0.7 large, 0.7-0.9 very large, and 0.9-1.0 almost perfect [20]. The hypothesis of proportional and fixed bias was rejected when the 95% CI for the slope included 1 and the intercept included 0.  
Percentage differences between the RQA-based and Gex methods at AerT for HR and VO2 were calculated using Typical Percentage Error (TE(%)) and Mean Percentage Difference (Meandiff (%)).  
Intraclass Correlation Coefficients (ICC) were employed as measures of criterion validity for the RQA-based method compared to the Gex method at AerT [22].  
The magnitude of differences between the RQA-based and Gex methods was assessed using [PERSON]'s d effect size statistics. Effect sizes were classified as follows: d < 0.2 was considered trivial, 0.2 < d < 0.6 as small, 0.6 < d < 1.2 as moderate, 1.2 < d < 2.0 as large, and d > 2.0 as very large. Additionally, the interpretation of ICC ranges was defined as excellent (ICC > 0.9), moderate (0.75 < ICC < 0.90), fair (0.50 < ICC < 0.75), and poor (ICC < 0.5) [23].  
Bland-Altman plot analyses were employed to visualize the agreement between RQA-based and Gex methods [24].  
Statistical significance was set at \(p\) < 0.05 in both studies, and all statistical analyses were conducted using SPSS version 24.0 software (SPSS Inc., Chicago, IL, USA).
# Vibration Prediction of Space Large-Scale Membranes Using Energy Flow Analysis
## 3 Results  
For the purpose of method validation, a large-scale tensioned membrane in space is chosen as the model. The edges of the membrane are connected to a support frame with tension \(T=10\) N (see Figure 2). It is assumed that the membrane structure is excited by a transverse harmonic point force at a corner and the membrane is made of polyimide film with length \(a=100\) m, width \(b=100\) m, thickness \(h=0.025\) mm, elastic modulus \(E=3\) GPa, material damping \(\eta=0.005\), and mass density \(\rho=1400\) kg/m\({}^{3}\).  
Figure 6 shows the energy response intervals in frequencies at the point by assuming the length \(a\) as a Gaussian random variable and its relative standard deviation \(\sigma=0.05,\ 0.1,\ 0.15\). The relative standard deviations of the energy responses are shown Figure 7. It is observed that, due to the random effect of the length, the energy responses are no longer deterministic. Potential energy responses form confidence intervals and the practical responses can be any value in the corresponding interval. However, the mean responses are equal to the results from deterministic parameters without deviations. The width of intervals and the relative standard deviation of energy responses decrease with increasing excitation frequency and decreasing standard deviation which indicates that the uncertainty of responses becomes more significant for lower frequencies and greater uncertainty of structures.  
Figure 8 shows the standard deviation of the energy responses in space at \(\omega=50\ \mathrm{rad/s}\) with the relative deviation of the length is \(0.05\). It can be observed that the standard deviation decreases with increasing distance from the excitation. However, Figure 9 shows the relative standard deviation remains unchanged in space. It is indicated that, although the absolute uncertainty of the energy response changes at different locations, the relative uncertainty stays at a steady level at a fixed frequency regardless of the coordination.  
Figure 5: Energy response in space field from the exact displacement solution. The reference energy is \(10^{-12}\ \mathrm{J/m^{2}}\).  
Figure 6: Energy response intervals of the membrane with a random length in frequency field. The reference energy is \(10^{-12}\ \mathrm{J/m^{2}}\).  
Figure 8: Standard deviation of energy response of the membrane with a random length in space field. The reference energy is \(10^{-12}\) J/m\({}^{2}\).  
Figure 7: Relative standard deviations of energy response of the membrane with a random length in frequency field. The reference energy is \(10^{-12}\) J/m\({}^{2}\).  
Figure 9: Relative standard deviation of energy response of the membrane with a random length in space field.
## 3 Proposed Approach
### Ldr2 edr  
To produce a 12-bit EDR image from an 8-bit LDR image (LDR2 EDR), we first multiply the intensity of each pixel by 24. That is, the dynamic range extends from [0, 255] to [0, 4095]. To fill the empty bins in the histogram of the EDR image resulting from direct scaling, the image is upsampled using bicubic interpolation to obtain a continuous histogram distribution. The bicubic interpolation technique considers 22 \(\times\) 22 pixels (16 samples) nearest to a new pixel to be determined and computes the new pixel value through the weighted average of the pixel values of these 16 samples. The weight of each sample is determined by the distance to the new pixel. We use the following interpolation weight kernel to perform the convolution operation.  
\[W(i)=\begin{cases}\begin{aligned} (\alpha+2)|i|^{3}-(\alpha+3)|i|^{2}+1,\end{aligned}&\text{for }|i|\leq 1\\ \alpha|i|^{3}-5\alpha|i|^{2}+8\alpha|i|-4\alpha,& \text{for }1<|i|<2\\ 0,&\text{otherwise}\end{aligned}. \tag{1}\]  
where \(i\) denotes the generic sample, \(\alpha\) is \(-0.5\) or \(-0.75\), and \(\alpha\) determines the performance of the kernel. Note that \(W(0)=1\) and \(W(n)=0\) when \(n\) is a nonzero integer. The pixel intensity \(f(x,y)\) of the new image is then given by  
\[f(x,y)=\sum_{i=0}^{3}\sum_{j=0}^{3}f\big{(}x_{i},y_{j}\big{)}W(x-x_{i})W\big{(} y-y_{j}\big{)}. \tag{2}\]  
where \(f(x,y)\) is the interpolation function, \(W\) is the interpolation kernel, and \(f\big{(}x_{i},y_{j}\big{)}\)/s are the samples.  
By upsampling the image, the empty bins of the histogram are filled, but the image resolution is increased by a factor of \(2^{4}\). To restore the original image size, a downsampling operation is performed by the pixel intensity average over a \(4\times 4\) region. This reduces image size while avoiding the appearance of moire pattern. After the proposed LDR2 EDR calculation, the image histogram maintains the original distribution, and the image dynamic range is expanded to \(2^{12}\). As shown in Figure 2, the histogram obtained by LDR2 EDR can eliminate the saturation that appeared in the original image. This is key to further extending the dynamic range in the intensity saturation regions.
## 2 System Model
### Physical Coding
The measurements performed on \(|\varphi_{i}\rangle\) and \(|\tilde{\varphi}_{i}\rangle\) result in raw data units \(X_{i}\in\mathbb{N}\big{(}0,\sigma_{X}^{2}\big{)}\) and \({X_{i}}^{\prime}\in\mathbb{N}(0,\sigma_{X}^{2}\big{)}\). The noise of the first channel changes the Gaussian random distribution of the quadratures from \(\mathbb{N}\big{(}0,2\sigma_{\omega}^{2}\big{)}\) to \(\mathbb{N}\Big{(}0,2\sigma_{\omega}^{2}+\sigma_{N_{1}}^{2}\big{)}\) in the combined phase space \(\mathcal{S}_{A\times B}\), with mean \(\mu=0\), and results \(X\) raw data level variance \(\sigma_{X}^{2}=\left(2\sigma_{\omega}^{2}+\sigma_{N_{1}}^{2}\right)\), and where noise variance \(\sigma_{N_{1}}^{2}\) arises from the first channel use. The quadratures of the second mode of the coupled beam are also characterized by the same variance, i.e., \(|\varphi_{i}\rangle\in\mathcal{CN}\Big{(}0,\sigma_{\phi_{i}}^{2}\Big{)}\). The noise of \(\mathcal{N}_{2}\) transforms \(|\varphi_{i}\rangle\in\mathcal{S}_{A\times B}\) into \(|\xi_{i}\rangle\in\mathcal{S}_{A\times B}\) and further modifies the distribution, so finally [PERSON]'s received quadratures will follow a Gaussian distribution \(\mathbb{N}\Big{(}0,2\sigma_{\omega}^{2}+\sigma_{\mathcal{N}_{1}}^{2}+\sigma_ {N_{2}}^{2}\Big{)}\). The \(X^{\prime}\) raw data level variance is evaluated as \(\sigma_{X^{\prime}}^{2}=\left(2\sigma_{\omega}^{2}+\sigma_{\mathcal{N}_{1}}^{2 }+\sigma_{N_{2}}^{2}\right)\), which, in fact, arises from the cumulated Gaussian random noise of \(\mathcal{N}_{1}\) and \(\mathcal{N}_{2}\). One can recognize that on the raw data level, only the difference of the variance of [PERSON]'s and [PERSON]'s raw data \(\sigma_{X}^{2}\) and \(\sigma_{X^{\prime}}^{2}\) has relevance and \(\sigma_{\mathcal{N}_{1}}^{2}\) vanishes from the picture. This difference is, indeed, \(\sigma_{\mathcal{N}_{2}}^{2}\). In the level of raw data manipulations [PERSON]'s \(X_{i}\) will serve as a reference unit to correct [PERSON]'s noisy unit, \({X^{\prime}}_{i}\). In other words, the first channel use will have no relevance in the raw data-level calculations, hence the noise of \(\mathcal{N}_{1}\) can be excluded from the error-correction process. Precisely, the use of \(\mathcal{N}_{1}\) has only one consequence: it increases the initial variance \(2\sigma_{\omega}^{2}\) by \(\sigma_{\mathcal{N}_{1}}^{2}\), which finally results in \(\mathbb{N}\big{(}0,\sigma_{X}^{2}\big{)}\) on the level of raw data blocks. In particular, only \(\mathcal{N}_{2}\) will have significance, and, in fact, only the noise of the second channel use has to be corrected in the reconciliation phase. (Note: Throughout the manuscript, the noise will be modeled on the quadrature-level via a real vector). In the reconciliation phase, our task is to share an error-free secret key between the parties. This requires the raw data-level error-correction of the noise that arises from the quantum-level transmission. First, we review the background of the multidimensional reconciliation, and then we introduce our solution.
## 3 Results and Discussion
### Flow Field
#### 3.1.1 Effect of \(L_{0}\) on the Flow Field  
Three types of \(L_{0}\) (distances between the wind duct outlet and the closed end of the model) were considered, which were 1 m, 2 m, and 3 m, respectively. Figure 7 shows the variation in the cross-section average wind speed along the model for different digging lengths \(L_{d}\) at different \(L_{0}\) values. The positive and negative values of the wind speed represent the direction of the speed. As depicted in Figure 7, the average wind speed profiles under varying \(L_{0}\) influences showed significant differences within a distance of 4 m from the closed end. This part of the region is conventionally defined as the vortex zone. Then, a stable return flow developed behind this zone, which is often referred to as the return zone. The curves of \(u_{\text{return}}\) for different \(L_{0}\) conditions are in good overall agreement. This phenomenon indicates that the influence of \(L_{0}\) on the flow field in the V-shaped tunnel mainly lies in the vortex zone near the closed end, and \(L_{0}\) does not have a significant effect on whether or not a stable returning wind is formed or the size of \(u_{\text{return}}\) in this experiment. This conclusion is consistent with [PERSON] et al. (2013).
## 2 Materials and Methods
### Prediction Models Workflow
#### 2.2.6 Evaluating Prediction Models Performance  
The evaluation of the prediction models was conducted using the remaining 30% of the hand-labeled data that were assigned as hold-out test set. For the rule-based prediction model evaluation, the collaborative behaviors were predicted using the defined rules from Section 2.2.4 As for the HMM prediction model evaluation, we selected the transition and emission matrices from the HMM with the highest accuracy in the \(k\)-fold cross-validation and used the MATLAB function hmmviterbi to generate the predicted collaborative behaviors using the observations in the hold-out test set. We then compared the predicted collaborative behaviors to the hand-labeled collaboration behaviors. The results are presented in the next section.