esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Research Methods
### Robustness
#### 2.3.2 Robustness Measurement  
Initially, scholars typically used network performance parameters to evaluate network robustness. These parameters included the number of connected components, the maximum component size, the network diameter, the average shortest path length, the network efficiency, the clustering coefficient, and numerous centrality indicators. These indicators provide different perspectives for measuring network robustness and therefore necessitate tailored measures to be taken based on the specific problems that need to be addressed. Network efficiency and the maximum connected sub-graph proportion were used as measures of robustness.  
In case a network is subjected to continuous attack, the size of the maximum connected sub-graph becomes an indispensable measure of network performance. It displays the maximum connected component that separates from the network after the attack and reflects the network connectivity. The size of the maximum connected sub-graph relative to the original network determines the proportion of retained nodes after an attack. A smaller value indicates more substantial damage to the network. The relative size can be evaluated using the following relation:  
\[S(t)=\frac{N_{\text{live}}}{N} \tag{11}\]where \(N_{\text{live}}(t)\) stands for the number of nodes preserved in the maximum connected subgraph following the network attack at time \(t\), while \(n\) symbolizes the total nodes in the original network at time \(t\).  
[PERSON] [33] defined global efficiency, also known as network efficiency in this reference, as the average value of the reciprocal distances between all node pairs in a network. It indicates the convenience or difficulty of accessing one node from another, making it a fundamental measure of network connectivity. The calculation formula for global efficiency is as follows:  
\[E(t)=\frac{1}{N(N-1)}\sum_{i\
eq j}\frac{1}{l_{ij}(t)} \tag{12}\]  
where \(n\) refers to the number of nodes in the network at time \(t\), and \(l_{ij}(t)\) signifies the shortest distance between node \(i\) and node \(j\) at time \(t\).
## 3 Results and Discussions  
A total of 143 measured TSP data points were retrieved and subsequently used to calculate visibilities using the equations in Table 1. In the first sub-section, the results are presented for general observations and analysis; these are followed by another sub-section highlighting the statistical significance of the results by suggesting optimistic scenarios and their probabilities.
## 3 Method
### Homography Warping  
In deep learning-based multi-view stereo (MVS) methods [27; 29; 30], the construction of the cost volume often involves the use of differentiable homographic warping, drawing inspiration from traditional plane sweep stereo. Homographic warping leverage camera parameters to establish mappings between each pixel on the source view and different depths under the reference view, within a depth range of \([d_{min},d_{max}]\). The procedure entails warping source image features to the \(d_{j}\) layer of the reference view's viewing frustum. This process is mathematically expressed as shown in Equation (1).  
\[p_{s_{i},j}=K_{i}\cdot\left(R_{0,j}\cdot\left(K_{0}^{-1}\cdot p_{r}\cdot d_{j} \right)+t_{0,i}\right) \tag{1}\]  
The pixel feature in the reference view is denoted as \(p_{r}\). We use \(\{K_{i}\}_{i=0}^{N-1}\) to represent the camera intrinsic parameters and \(\{[R_{0,j}|t_{0,i}]\}_{i=1}^{N-1}\) to represent the motion transformation parameters from the source views to the reference view. By embedding camera parameters into features and performing mapping transformations, we establish the mapping relationship between the pixel feature \(p_{s_{i},j}\) in the i-th source view corresponding to \(p_{r}\). The features \(p_{s_{i},j}\) are distributed along the epipolar line in the source view, and the depth features of layer \(d_{j}\) can be represented as \(\widetilde{F}_{src,j}=\left\{\widetilde{F}_{ij}\middle|i=1\dots N-1\right\}\). Simultaneously, \(N-1\) feature volumes \(\{V_{i}\}_{i=1}^{N-1}eR^{H\times W\times C\times D}\) are generated, where \(D\) is the total number of depth hypotheses. This process accomplishes the conversion of features from two-dimensional to three-dimensional, thereby restoring depth information.  
Due to the absence of calibration in the input images, directly performing uniform sampling in depth space may result in spatial sampling points not being evenly distributed along the epipolar lines when projected onto the reference view. This is particularly evident in regions farther from the camera center, where the mapped features may be very close, leading to a loss of depth information, as illustrated in Figure 3. To address this issue, inspired by references [29; 34], in the first stage of depth sampling, this paper employs the inverse depth sampling method to initialize depth. The specific operation involves uniformly sampling in inverse depth space, ensuring equidistant sampling in pixel space, as shown in Equation (2).  
\[d_{j}=\left(\left(\frac{1}{d_{min}}-\frac{1}{d_{max}}\right)\frac{j}{D-1}+ \frac{1}{d_{max}}\right)^{-1} \tag{2}\]  
Employing this depth sampling method effectively avoids the loss of depth information, thereby significantly enhancing the reconstruction results.  
Figure 3: Schematic diagram of depth sampling. When sampling points are evenly spaced in depth space, they may not uniformly map to the epipolar lines corresponding to the source view. This leads to sampling errors and the loss of certain depth information. (In the figure, the depth space and epipolar lines are scaled to the same scale).
## 3 Results and Discussion
In the present study, emissions of saturated aliphatic hydrocarbons were reduced by 3.3% (Z0.3-135) up to 58.9% (Z0.6-R30-145); unsaturated aliphatic hydrocarbons by 0.1% (Z0.3-135) up to 45.3% (Z0.6-R30-145); and aromatic compounds by 17.5% (Z0.6-R20-135) up to 62.3% (Z0.6-R30-145) in warm mix asphalts and zeolite-amended WMAs. On the other hand, WMAs produced greater emissions of chlorinated compounds (Figure 3) than the hot mixtures, from 21.3% (Z0.6-R30-145) to 1256.0% (Z0.3-135), probably because the competitive adsorption of water significantly decreased the adsorption by zeolites of chlorinated compounds such as tetrachloroethylene [103; 104]. However, all the asphalt mixtures studied produced fewer chlorinated compounds compared with the literature [16; 84]. Thus, considering the lower production of toxic aromatic and chlorinated compounds compared with the literature, and the fact that PAHs were not detected, the WMAs prepared in the present study should be less harmful to paving workers and the environment than HMA.
## 5 Course Control Based on ANF-FOID Controller  
### FO PI\({}^{\lambda}\)D\({}^{\mu}\) Controller  
There are four main types of representative fractional-order controllers: the TID controller [63], the CRONE controller [64], the fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controller and the lead lag compensator [65]. This paper adopts the fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controller proposed by [PERSON] Compared with the traditional PID controller, the fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controller has two more parameters, namely the integral order \(\lambda\) and differential order \(\mu\) make the control system more flexible and robust. The fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controller transfer function is  
\[G_{c}(s)=K_{p}+\frac{K_{i}}{s^{\lambda}}+K_{d}s^{\mu} \tag{39}\]  
\(\lambda>0\), \(\mu>0\) is the order of the controller, taking any real number.  
In order to apply the theory of integer-order controllers to fractional-order controllers, the fractional-order calculus needs to be approximated to integer order. This paper uses a  
Figure 6: Flow of the approximation process.  
modified Oustaloup approximation [66] to construct a continuous rational transfer function model for the fractional order calculus operator \(s^{\alpha}\), in the fitted frequency range \((\omega_{b},\omega_{h})\).  
\[G(s)=K\bigg{(}\frac{ds^{2}+b\omega_{h}s}{d(1-\alpha)s^{2}+b\omega_{h}s+d\alpha} \bigg{)}\prod_{k=-N}^{N}\frac{1+s/\omega_{k}^{\prime}}{1+s/\omega_{k}} \tag{40}\]  
where \(K=\left(\omega_{b}\omega_{h}\right)^{\alpha}\), \(\omega_{k}\) and \(\omega_{k}^{\prime}\) are given by  
\[\omega_{k}^{\prime}=\bigg{(}\frac{b}{d}\bigg{)}^{\frac{2k+\alpha}{2N+1}}\omega _{h}^{N+k+\frac{1}{4}(1-\alpha)}\omega_{b}^{N-k+\frac{1}{4}(1+\alpha)} \tag{41}\]  
\[\omega_{k}=\bigg{(}\frac{b}{d}\bigg{)}^{\frac{2k+\alpha}{2N+1}}\omega_{h}^{N+k +\frac{1}{4}(1+\alpha)}\omega_{b}^{N-k+\frac{1}{4}(1-\alpha)} \tag{42}\]
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Clothing Sales Forecasting: Case Study
Figure 4 provides a detailed comparison of our proposed forecasting method with other models in the task of apparel sales forecasting. Upon comparative analysis, it is evident that our method demonstrates a significant advantage in overall predictive accuracy. Particularly when dealing with long-term forecasting tasks, our approach more accurately approximates the actual apparel sales data, indicating that our model possesses a stronger capability in capturing the long-term dependencies and seasonal patterns of time series data. Figure 4: Prediction results of different models when the prediction time step is 60. (**a**) MD–Autoformer; (**b**) Autoformer; (**c**) Informer; (**d**) Reformer; (**e**) DLinear. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**MD–Autoformer**} & \multicolumn{2}{c}{**Autoformer**} & \multicolumn{2}{c}{**Informer**} & \multicolumn{2}{c}{**Reformer**} & \multicolumn{2}{c}{**DLinear**} \\ \hline
**Metric** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Apparel \\ Sales \\ 60 \\ \end{tabular} } & 24 & \(\pm\)0.014 & \(\pm\)0.007 & \(\pm\)0.013 & \(\pm\)0.010 & \(\pm\)0.037 & \(\pm\)0.020 & \(\pm\)0.039 & \(\pm\)0.031 & \(\pm\)0.020 & \(\pm\)0.027 \\  & 36 & \(\pm\)0.019 & \(\pm\)0.017 & \(\pm\)0.025 & \(\pm\)0.016 & \(\pm\)0.027 & \(\pm\)0.012 & \(\pm\)0.059 & \(\pm\)0.043 & \(\pm\)0.011 & \(\pm\)0.010 \\  & 48 & \(\pm\)0.038 & \(\pm\)0.030 & \(\pm\)0.010 & \(\pm\)0.023 & \(\pm\)0.022 & \(\pm\)0.013 & \(\pm\)0.026 & \(\pm\)0.023 & \(\pm\)0.004 & \(\pm\)0.011 \\  & 60 & \(\pm\)0.018 & \(\pm\)0.016 & \(\pm\)0.025 & \(\pm\)0.014 & \(\pm\)0.023 & \(\pm\)0.008 & \(\pm\)0.019 & \(\pm\)0.008 & \(\pm\)0.028 & \(\pm\)0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation of each model in apparel sales forecasting experiment. The experimental results highlight the better performance of our method in mitigating the fluctuations that may occur in long-term forecasting and the resultant extreme prediction errors. This is especially crucial for datasets with pronounced seasonality and trends, such as those in apparel sales forecasting, where higher demands are placed on the precision and robustness of the forecasting models. The experimental outcomes further substantiate the effectiveness of our method in practical applications, particularly in commercial environments where accurate predictions are essential for guiding inventory management and the formulation of sales strategies.
## 2 Materials and Methods
### Isostatic and Decompensative Gravity Anomalies
#### 2.3.2. Decompensative Correction  
The isostatic correction computed from the adjusted topography (Figure 3b) is shown in Figure 5a. Mostly mid-wavelength are dominated in this field as the effect of small-scale compensation is largely reduced by the relatively large compensation depth and, especially by the high values of EET (Figure 4). Adding of this correction to the Bouguer gravity anomalies with the removed effect of sediments (Figure 2b) results in the isostatic gravity anomalies. At the long wavelengths these anomalies are still dominated by the dynamic effects induced by the mantle convection [52,53]. However, it was demonstrated in the above papers that this effect can be easily reduced by applying a gauss-type filter with the boundary wavelength (half amplitude) about 2500 km. The short wavelengths are not biased by this procedure since the spectrum of the isostatic anomalies usually has relative minimum in the transition wavelengths. The residual isostatic anomalies are shown in Figure 4b.  
Relatively small-scale anomalies are generally presented in the residual isostatic field. This is a result of partial isostatic compensation of near-surface density anomalies as was suggested above. We further apply the decompensative correction to restore the full effect of the shallow subsurface structures.  
The decompensative correction for the isostatic gravity anomalies is shown in Figure 6a. It is relatively insignificant in the areas with high values of EET exceeding 50 km since most of the isostatic compensation is provided by long-wavelength elastic deformations of  
Figure 4. (**a**) Depth to the Moho from the sea level; (**b**) effective elastic thickness (EET) of the lithosphere [48].  
Figure 5. (**a**) Isostatic correction; (**b**) isostatic residual gravity anomalies. See text for detailed explanations.  
the lithosphere. However, in other areas (e.g., in the Black Sea and Dnieper-Donetsk Rift) it is significant and reaches the amplitudes \(\pm\)50 mGal (Figure 6a).  
The final decompensative gravity anomalies are shown in Figure 6b. Their amplitude varies within the range \(\pm\)135 mGal, which is already comparable with the effect of the initial density model of the sedimentary cover. In further chapters these anomalies are interpreted with respect to correction of this model.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Wind Field Reconstruction Methods
#### 2.2.2 Predicted Residual Error Sum of Squares (PRESS)  
The _PRESS_ method [27; 28] can be used to make the _CLS_ procedure more robust. _PRESS_ detects outliers and excludes them from the evaluation using leave-one-out cross validation. The _PRESS_ method has been used for comparable applications such as cross-validation of samples [29], but this paper is the first known application of the _PRESS_ method to lidar wind data.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Acceptable Range**} \\ \cline{2-3}  & **Minimum** & **Maximum** \\ \hline Lidar measurements & & \\ CNR (min, mean) [dB] & \(-22\) & \(10\) \\ \hline Weather conditions & & \\ Wind direction [\({}^{\circ}\)] & 245 & 315 \\ Temperature [\({}^{\circ}\)] & \(>\)2 & - \\ \hline Mast function & & \\ Std. cup anemometer & \(>\)0.01 & - \\ \hline Data availability & & \\ lidar [\%] & 90 & 100 \\ met mast [\%] & 100 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection criterion used in Figure 5.  
In _PRESS_, the initial record--containing \(N\) data points--is reduced by one value and the reduced record \(N\)-1 is used to calculate the underlying model. The calculation scheme is represented in Figure 6 and repeats itself \(N\) times for each time step. The new estimated reconstructed line-of-sight velocity \(\hat{v}_{los}\) are calculated from this solution and the inverted model. Then the coefficient of determination \(R^{2}\) (Equation (4)) is calculated to quantify the goodness of fit. The \(R^{2}\) is calculated as:  
\[R^{2} =\frac{SS_{reg}}{SS_{tot}} \tag{4}\] \[SS_{reg} =\sum_{i}{(\hat{v}_{los,i}-\overline{v}_{los})^{2}}\] (5) \[SS_{tot} =\sum_{i}{(v_{los,i}-\overline{v}_{los})^{2}} \tag{6}\]  
where \(SS\) is the sum of the squares for the regression (\(v_{reg}\)) and the total (\(v_{tot}\)); \(\overline{v}_{los}\) represents the mean values and \(\hat{v}_{los}\) is the reconstructed line-of-sight velocity. In each time step a different point is omitted. From these \(N\) iterations, the variant with the highest \(R^{2}\) is selected for further post-processing.
## 3 Simulation and Results
### Simulation and Performance Measurements of the PF Algorithm  
The objective of this section is to measure the performance of the PF algorithm compared with the original datasets, _i.e._, the EUR-USD exchange rate using the same methods employed by Section 3.2.  
Similar to Figure 7, Figure 10 shows three plots, _i.e._, the original datasets (Y2), the \"original datasets \(+\) noise\" (Y2) and PF (Y1). We analysed the plots and found that the PF curve did not agree with the curve of \"the original datasets \(+\) noise\" and the original dataset's, inasmuch as fluctuations of those three plots were not the same. The loss estimations emerged by varieties of noise parameters, in terms of estimation, Tables 1-6 indicate that all loss estimators, namely; MSE, MAE, MAPE are relatively low ranging from 4.43079-4.52658, 1.64377-1.67868 and 139.488-141.868, respectively. The R\({}^{2}\) is unacceptable with the ranging of 0.0049-0.096, whereas, AIC and BIC are relatively too low _i.e._, \(-\)1067.39 and \(-\)1046.02, respectively, whereas the Accuracy count showed 49.76%-50.58%. As a result, the performance of the PF algorithm is also unacceptable.
# Study on the Carbon Emissions in the Whole Construction Process of Prefabricated Floor Slab
## 5 Analysis of the Results  
### Comparative Analysis and Evaluation of Three-Stage Carbon Emissions  
Based on the LCA method, the whole PFS construction process is used to analyze the previous case, and the total carbon emissions are 564.12+3.49+11.136 = 578.746 kg/m\({}^{3}\). As shown in Figure 6, the carbon emissions during the three stages of production, transportation, and construction accounted for 97.5%, 0.6%, and 1.9% of the total carbon emissions, respectively. According to the Activity Based Classification analysis method, carbon emissions in the production stage are the main factors affecting the total carbon emissions.  
In the production stage, the carbon emissions are 564.12 kg/m\({}^{3}\), and the emissions of raw materials, energy consumption, and artificial consumption are 481.97, 78.09, and 4.06 kg/m\({}^{3}\), respectively, which account for 85.4%, 13.8%, and 0.7% of the total, respectively, as shown in Figure 7. By comparison, the use of raw materials is the main factor that generates a large amount of carbon emissions in the production stage. After further detailed analysis of the consumption of raw materials, the production of 1 m\({}^{3}\) of HB2022-1 prestressed laminated slab requires 34.76 kg of hot-rolled rebar and 1 kg of C40 concrete. The produced carbon emissions are 90.97 and 391 kg, respectively.  
In this study, in terms of reducing carbon emissions of PFS, waste of raw materials should be avoided in the production stage, especially concrete and rebar. The materials with lower carbon emission factors should be selected and used for the production of similar floor slabs. At the same time, we should also pay attention to the recycling of templates in the production stage.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Total Power** & **Carbon Emission Factor** & **Total Carbon Emissions** & **Carbon Emission per** \\
**Consumption (kWh)** & **(kg CO\({}_{2}\) eq/kWh)** & **(kg)** & **m\({}^{3}\) (kg)** \\ \hline
30,096 & 0.928 & 27,929.08 & 11.136 \\ \hline \end{tabular}
\end{table}
Table 12: Carbon emissions of energy consumption in the construction stage.  
Figure 6: Proportion of carbon emissions in each stage.
## 5 Deployment Challenges
### Streaming Media Delivery  
Existing virtual desktop platforms have little or no support for multimedia applications, especially for delivering streaming media content [31]. Recall that in the proposed model HTTP is being used as the virtual client protocol. Since HTML 5's \"video\" and \"audio\" tags are protocol agnostic, RTP/RTSP can be used for streaming. However, the HTML 5 compatible browsers do not yet (at the time of this writing)support efficient RTP/RTSP-based streaming. Another issue to be resolved is the choice of codecs to be supported by the browsers because of patent-related restrictions.  
Some applications support/require the exchange of capabilities (e.g., codecs supported by the user's terminal) between the user's client and the media server before delivering media content. One such example is IP Multimedia Subsystem (IMS) [32] based applications that require end-to-end capability negotiation and resource reservation between the IMS client and the Media Resource Function (MRF). Given that the surrogate is responsible for establishing and maintaining sessions (that deliver media content) on behalf of the user's client, the surrogate should have prior information on the capabilities of the user's terminal before negotiating with the remote media server. In the case the user's terminal does not have the necessary hardware/software support to playback any audio/video type media, the media might be received by the surrogate first, and then be transmitted to the user's platform after suitable transcoding.
# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## 1 Introduction
Mutation mechanism in Gaussian bare-bones can increase the diversity of the population and avoid falling into local optimum. In addition, in view of the shortcomings of SVM, we propose a new CBBOA-SVM model, which can substantially improve the classification accuracy of the original SVM by optimizing the parameters. In order to verify the effectiveness of the proposed CBBOA, we conducted a series of experiments based on benchmark functions. Simulation results illustrate that the algorithm showed better performance than the original BOA. In order to better study the career decision factors of college students, it is necessary to conduct comparative experiments between CBBOA-SVM and other algorithms. The results show that CBBOA-SVM can produce more accurate classification results as well as greater stability in terms of the four indicators studied when compared to all other comparison methods. The following are some of its most significant contributions:  
* An enhanced BOA (CBBOA) is proposed, where a communication mechanism is used to boost the exploitation ability of the convergence accuracy, as well as a Gaussian bare-bones mechanism is utilized to raise the diversity of the population and the capacity of avoiding falling into local optimum. * A new CBBOA-SVM with a feature selection model is developed to predict the future career decisions of college students, which can aid jobless college students in selecting acceptable occupations for themselves but also help the government's macro-management of college students' employment market. * The performance of CBBOA is experimentally verified by comparing the high-quality algorithm with CBBOA, which shows that the performance of CBBOA is better than other peers. * To further validate the performance of CBBOA-SVM with feature selection, we compared it with five other similar methods, which indicates that it has a better performance than other similar methods and can be used to study the impact of career decisions. The remainder of the paper's structure is depicted below. The proposed CBBOA model and the CBBOA-SVM model are described in detail in Sections 2 and 3, respectively. Section 4 is primarily concerned with the introduction of the data source and simulation settings. On the real-world dataset, the experimental results of CBBOA on benchmark functions and CBBOA-SVM on the benchmark functions are discussed in Section 5. Section 6 is devoted primarily to the discussion of the improved algorithm and its implications. Finally, there is a section dedicated to summaries and advice.