esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 4 Conclusions  
In this paper, we considered both the topology structure of networks and nodes' attribute information to define the degree ratio and the similarity ratio, then the trust-value was proposed. Combined with PageRank, this paper proposed the trust-PageRank algorithm to identify influential nodes based on trust-value. In the experimental part, the TPR was first applied to the Kite and Karate networks to verify the effectiveness of the algorithm. To evaluate the accuracy and infection ability of the nodes identified by the TPR algorithm, eight real networks were selected, and the SIR model and Kendall correlation coefficients were used to evaluate the propagation rate and ability of to identify influential nodes in the networks. The influential nodes of each network identified by DC, BC, PageRank, HITS, and TPR algorithms were compared with the results obtained by the SIR model, and the correlation coefficients of ranking between the selected algorithms and the SIR model for the top 30% of nodes were calculated. The results showed that the TPR algorithm can effectively identify the influential nodes, and has advantages in the initial propagation of SIR and the identification of nodes with similar influence.  
In general, we proposed an algorithm to identify influential nodes, not only considering the structure of network but also the attribute information of nodes. The accurate experimental results suggest that it should receive more attention and it may be helpful for controlling information dissemination in real-world networks, the spread of infectious diseases, etc. In addition, in some networks, nodes have certain features. For example, in social networks, people, as nodes, have features such as age, gender, and behaviors that may also affect trust-value, which are worthy of further attention.  
**Author Contributions:** Conceptualization, J.S. and Y.W.; methodology, J.Z.; supervision, J.S. and B.W.; validation, J.Z. and Y.W.; writing, J.S. and Z.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key Research and Development Program of China under grant no. 2018 YFB1003602.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Sets** & **DC** & **BC** & **PageRank** & **HITS** & **TPR** \\ \hline PolBooks & 0.687 & 0.283 & 0.646 & 0.517 & 0.674 \\ PDZBase & 0.269 & 0.462 & 0.283 & 0.41 & 0.466 \\ Football & 0.114 & 0.133 & 0.216 & 0.19 & 0.307 \\ Propro & 0.411 & 0.411 & 0.4 & 0.14 & 0.543 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Kendall correlation coefficients.
###### Abstract
Removing \({}^{66}\)Ga and \({}^{67}\)Ga impurities is difficult through separation techniques due to similar chemical properties and would be impractical as \({}^{68}\)Ga has a short half-life. However, these impurities can be limited during irradiation with the proton irradiation energy range Ep = 14 \(\rightarrow\) 5 MeV, consequently preventing \({}^{68}\)Zn(p,2n)\({}^{67}\)Ga and \({}^{68}\)Zn(p,3n)\({}^{66}\)Ga nuclear reactions [24]. Co-production of \({}^{66}\)Ga and \({}^{67}\)Ga can also exist through \({}^{66}\)Zn(p,n)\({}^{66}\)Ga and \({}^{67}\)Zn(p,n)\({}^{67}\)Ga nuclear reactions. Thus, a highly enriched \({}^{68}\)Zn [25] and proton irradiation energy below 14 MeV can produce high \({}^{68}\)Ga radioactivity with below limit \({}^{66}\)Ga, and \({}^{67}\)Ga [26]. Figure 1: (**A**) Number of documents by year related to \({}^{68}\)Ga-based on Scopus search; (**B**) Illustration of \({}^{68}\)Ga radionuclide elution from \({}^{68}\)Ge/\({}^{68}\)Ga generator using hydrochloric acid (HCI). Arrow indicating flow of eluate. Depending on the method, \({}^{68}\)Ga production using cyclotrons can reach up to 37.5 GBq per production with a nominal proton energy of 12.5 MeV cyclotron [27]. The large \({}^{68}\)Ga production would allow large-scale labeling of peptides. The problem of the possible occurrence of radiolysis can be solved by adding radical scavengers, such as sodium ascorbate and ethanol [28; 29]. Furthermore, the apparent molar activity (AMA) which accounts for the amount of radioactivity per unit mole of non-radiolabeled, radiolabeled impurities, and remaining precursor for cyclotron production, is on par with or improved from the \({}^{68}\)Ge/\({}^{68}\)Ga generator production [30; 31]. The progress in \({}^{68}\)Ga radionuclide production with cyclotrons encourages further studies that could lead to the establishment of a routine route for \({}^{68}\)Ga-labeled peptide preparation. This review discusses the production of \({}^{68}\)Ga with cyclotron using solid and liquid targets and the prospects and issues associated with cyclotron production.
## 4 Evaluation and Test Cases
### Test-2  
In the second test case, a separate (compute node) physical host of the cloud is used. Hyperthreading is enabled in this case with five different virtual machine and container configurations, as shown in Table 3. The configurations may seem identical with the previous case, but the difference is that each virtual machine or container has double vCPU cores. The HEPSPEC06 benchmark is used to calculate starting performance on the physical host. Each configuration is individually evaluated and all VM or container configurations execute with the same benchmark.  
The main goal of our test-2 is to compare the improvement in the performance of multiple machines vs. HT on/off. Therefore, all of the input parameters for both experiments were the same. Since the HT enabled machines will give more vCPU cores, these features help us to assign more cores to VMs, which should be evaluated whether or not such features provide meaningful performance improvements.
## 4 Numerical Tests of the Operation of the Algorithm
Root-mean-square error (RMSE) of tracking the desired rotation angle: \[\mathrm{\varepsilon_{i}}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}\mathrm{e_{ik}^{2}}}[ \mathrm{rad}]\] (33) where n is the number of discrete measurements. 4. RMSE of tracking the desired angular velocity: \[\mathrm{\dot{\varepsilon}_{i}}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}\mathrm{\dot{e}_{ ik}^{2}}}[\mathrm{rad/s}]\] (34)
4. The maximum distance between the desired and implemented position of the characteristic point S of the robot on the xy plane: \[\mathrm{d_{max}}=\mathrm{maxd_{k}}[\mathrm{m}]\] (35) where \(\mathrm{d_{k}}=\sqrt{\left(\mathrm{x_{Sk}-x_{dSk}}\right)^{2}+\left(\mathrm{y_ {Sk}-y_{dSk}}\right)^{2}}\), \(\mathrm{x_{S}}\), \(\mathrm{y_{S}}\) are the coordinates of point S in a stationary coordinate system xyz, and \(\mathrm{x_{dS}}\), \(\mathrm{y_{dS}}\) are the desired coordinates of point S. 6. RMSE of the distance between the desired and implemented position of the robot's characteristic point S: \[\mathrm{\rho}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}\mathrm{d_{k}^{2}}}[\mathrm{m}]\] (36)
5. The distance between the desired and implemented point S position after the end of the motion: \[\mathrm{d_{n}}=\sqrt{\left(\mathrm{x_{Sn}-x_{dSn}}\right)^{2}+\left(\mathrm{y_ {Sn}-y_{dSn}}\right)^{2}}[\mathrm{m}]\] (37)  
During the implementation of the desired motion path, the orientation of the robot's frame remained constant \(\mathrm{\beta}=0[\mathrm{rad}]\). Point S of the robot, in the first stage, moved along a straight-line path according to the direction of the instantaneous velocity vector \(\mathrm{\overline{v_{S}}}\), deviating from the x-axis by an angle \(\mathrm{\alpha}=\frac{\mathrm{\overline{\alpha}}}{2}[\mathrm{rad}]\). In the second stage, point S moved along a circular path, traversing \(\frac{3}{4}\) of a full circle (vector \(\mathrm{\overline{v_{S}}}\) changed its direction by an angle \(\frac{\mathrm{\overline{\alpha}}}{2}\leq\mathrm{\alpha}\leq 2\mathrm{\pi}[ \mathrm{rad}]\)). The movement along a curvilinear path of the robot began at \(\mathrm{t_{op}}\approx 4\) [s] and ended when \(\mathrm{t_{ok}}\approx 11\) [s] (the mean time of the start and the end of vector direction change). The shape of the angular velocity profile \(\dot{\alpha}\), defining the change in the motion direction of the point S, was analogous to the shape of the velocity profile of point S (Equation (30)). In the last stage, the robot moved along a straight-line path again, at angle \(\alpha=2\pi[\text{rad}]\), as shown in Figure 4.
## 4 Conclusions  
We have presented results on the PER performance of PPM in pre-amplified OWC systems with diversity. To this end, we derived an exact expression for the binary PPM PER and combined it with an existing approximation to extend it to \(Q\)-ary PPM. We demonstrated that the combination of the new and existing formulas is accurate for \(\chi^{2}\) noise statistics, which model the amplified optical receiver operation, and tested its accuracy in weak, moderate, and strong fading. The approximated results were in good agreement with the exact ones in all fading scenarios, and it was shown that the approximation becomes more accurate with the diversity order and the OSNR. These conditions are typically expected in any practical OWC system that operates in low PERs, and therefore this work may prove of use in designing high performance systems.  
All authors contributed equally to the work presented in this manuscript. This research received no external funding. The authors declare no conflict of interest.  
Figure 4: Average PER vs \(\overline{E_{b}}/N_{0}\) for \(Q=2,16,256\) in strong fading. The plot lines and points correspond to the exact and approximated results, respectively. \(k=2\) (**a**) and \(k=8\) (**b**).
# Study on Cold Resistance Performance of Composite Subgrade Structure in Seasonal Frozen Regions
## 3 Results and Discussion
### Temperature Variation Right beneath the XPS Plates  
To illustrate the cold resistance performance of the cold resistance layer with temperature distribution inside the subgrade more precisely, the temperature at the bottom center of the XPS plates was compared with the temperature at the corresponding position of the common subgrade, as depicted in Figure 8.  
Figure 8 depicts that the temperature beneath the XPS plates reduced slowly and tended to eventually stabilize at 4 \({}^{\circ}\)C, which resulted in the subgrade soil not being frozen. The temperature beneath the XPS plates decreased by only 3.1 \({}^{\circ}\)C during the whole monitoring period. The temperature at the corresponding position of the common subgrade decreased sharply by about 7.7 \({}^{\circ}\)C in the beginning. Then, it tended to stabilize between \(-2\)\({}^{\circ}\)C and \(-3\)\({}^{\circ}\)C, and the soil began to freeze. After the stable period, the soil temperature fluctuated because of the fluctuation of air temperature. The difference between the temperature at the bottom center of the XPS plates and that at the corresponding position of the common subgrade at the same time is illustrated in Figure 9.  
Figure 8: Temperature variation diagram of the subgrade with and without XPS plates.  
Figure 9: Temperature difference diagram between the bottom center of the XPS plates and the corresponding position of the common subgrade.  
As evident from Figure 9, the temperature difference curve increased rapidly by about 6.4\({}^{\circ}\) C in the first 20 days and then tended to stabilize between 7.5 \({}^{\circ}\)C and 8.5 \({}^{\circ}\)C. After the 80 th day, the temperature interpolation fluctuated because the change in air temperature led to variation of the temperature distribution inside the subgrade. In other words, the application of a cold resistance layer can preserve the temperature at 55 cm under the atmospheric contact surface by about 8 \({}^{\circ}\)C.
# Mechanical Behaviors and Numerical Simulation Analysis of a New Isolation System
## 3 Numerical Analysis of a Based Isolated System and Conventional Structure
### Model Results Analysis  
#### 3.3.1 Basement Shear  
Based on the friction coefficient of 0.05 obtained from previous tests, Figure 12 shows the base shear curves of the composite isolation structure and the seismic-resistant structure under the same working conditions. It can be seen from the figure that the base shear of the structure varies under different earthquake waves and increases with an increase in seismic intensity.  
Compared with traditional seismic-resistant structures, the composite isolation structure exhibits good isolation effects. Regardless of the input ground motion, the base isolation model can significantly reduce the structure's shear force.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 2 Materials and Methods
### Satellite Based Crop GPP and Yield Modeling
#### 2.2.2 Cropland GPP Estimation
_NDVI\({}_{max}\)_ and _NDVI\({}_{min}\)_ represent the NDVI values corresponding to 98% and 2% of the NDVI frequency distributions for all seven MT crop types each year (April to September) over the entire study period. Tower eddy covariance CO\({}_{2}\) flux measurement-based daily GPP observations were used to evaluate the satellite-based GPP estimates from this study. However, since no available flux tower sites were located in MT cropland areas, an alternative tower site representing a natural grassland from Fort Peck, MT (48.3077\({}^{\circ}\)N, 105.1019\({}^{\circ}\)W; US-Fpe), was selected for the comparison. The US-Fpe tower measurement-based daily GPP observations were compared against overlying satellite-based GPP estimates representing a cereal crop (Table 2) for this location. The US-Fpe tower and model data records represented different time periods (2000 to 2006 vs. 2008 to 2015), so we compared GPP 8-day climatology records derived from the model (GPP\({}_{\text{M}}\)) and flux tower observations (GPP\({}_{\text{F}}\)). We also compared GPP\({}_{\text{M}}\) at the US-Fpe tower location with the MODIS GPP Version 6 (MOD17H2) product, which has 500-m resolution and 8-day temporal fidelity. The GPP\({}_{\text{M}}\) and MOD17H2 8-day climatology records were compared for randomly selected locations representing different MT crop types over the 2008 to 2015 period. Both the GPP\({}_{\text{M}}\) and MOD17H2 products were extracted as the mean values within 1 \(\times\) 1 km\({}^{2}\) windows centered over selected locations representing uniform crop type conditions to minimize potential geolocation errors.
## 2 Passive Wake-Up Radio
### Passive Wake-Up Radio Hardware Architecture
#### 2.2.1 Antenna  
The passive wake-up radio's antenna collects energy from the various frequency band of ambient RF source [54; 55; 56]. The received RF signal is converted into an electrical signal by electromagnetic induction and passed to the matching network for further processing. The antenna can be broadband [57], single-band [58; 59], dual-band [60], triple-band [61], and so on depending on the frequency bands in which the antenna operates. For the RF energy harvesting, high radiation efficiency antennas operating at desired frequencies and polarization states, with omnidirectional/hemispherical radiation patterns are preferred, due to the considerably low-power densities.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 3 Test Methods and New Approach
### Simulation Procedure  
The single-delay scenario was simulated as an event-driven process that is triggered by the arrival of each new sample. The process reconstructs the signal for the time interval between the current and the previously received input sample. The newly reconstructed samples are appended to the stored reconstruction from the previous process runs.  
The simulation procedure is shown as pseudo-code in Figure 3. After receiving a new sensor sample, the input window for the resampling algorithm is selected, containing the current and several past samples. If upsampling is required, i.e., multiple output samples must be calculated per input sample, code execution time can be largely reduced for Kriging and Margolis. The output points use all the same input samples with identical time differences between them. The left side matrix in (11) for Kriging remains constant, as well as the denominator in (6) for Margolis. The inversion of the Kriging matrix can be pre-calculated. An explicit call of QR-decomposition resulted in the same RMSE as calling the standard matrix inversion method 'numpy.linalg.pinv' in Python. Both options required similar processing time. For Margolis, the denominator values were pre-calculated and stored in a table.  
The output samples were appended to a vector. The RMSE was calculated after all input samples were processed. Lagrange and Akima were tested by a similar procedure. The latter two algorithms with lower complexity did not require pre-calculation.  
All simulations were programmed in Python. Simulation results were stored as JSON files and plotted in Matlab (See data attachment) or Supplementary Materials. The average required CPU time to reconstruct one sample was calculated from the total CPU time for reconstructing the whole reference signal with 2000 samples.  
Figure 3: Pseudo code for processing one input sample in the single-delay scenario.
## 3 Analysis of Active Earth Pressure in Circular Shafts
### Validation and Comparison with Other Theoretical and Experimental Results
(**a**) Active earth pressure distributions corresponding to different radius-to-height ratios; (**b**) active earth pressure distributions corresponding to different wall-soil friction angle (2021). However, it should be noted that the assumption of a linear sliding surface extending to the bottom of the shaft results in a sudden drop in the calculated earth pressure to 0 at the base. As this discontinuity is distinctly localized and has a minimal effect on the overall distribution, a practical adjustment can be adopted in engineering applications by extrapolating the earth pressure value computed at 95% of the shaft depth to the bottom 5%.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 2 Kalman Filtering Algorithm
\[\mathbf{K}_{k}=\widetilde{\mathbf{P}}_{k}\mathbf{H}_{k}^{\mathrm{T}}\bigg{(} \mathbf{H}_{k}\widetilde{\mathbf{P}}_{k}\mathbf{H}_{k}^{\mathrm{T}}+\mathbf{R} _{k}\bigg{)}^{-1} \tag{10}\]  
\[\mathbf{\hat{x}}_{k}=\widetilde{\mathbf{x}}_{k}+\mathbf{K}_{k}(\mathbf{z}_{k}- \mathbf{H}_{k}\widetilde{\mathbf{x}}_{k}) \tag{11}\]  
\[\mathbf{\hat{P}}_{k}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\widetilde{ \mathbf{P}}_{k}, \tag{12}\]  
where \(\overset{\wedge}{\mathbf{x}}_{k}\) is the state estimate matrix, \(\widetilde{\mathbf{x}}_{k}\) is the state prediction matrix, \(\overset{\wedge}{\mathbf{P}}_{k}\) is the estimate error covariance matrix, \(\widetilde{\mathbf{P}}_{k}\) is prediction error covariance matrix, \(\mathbf{K}_{k}\) is KG at that time, and \(\mathbf{I}\) is identity matrix. KF time update (prediction), this system uses the previous state to predict the state of next system. The covariance matrix \(\mathrm{P}\) is used to represent the uncertainty of the estimated state at this moment, which means noise. The state transition matrix \(\Phi\) can be used to pass the uncertainty to the next moment. In addition to pass the uncertainty of the previous moment, there will also be in the prediction model. Therefore, its uncertainty at each moment in the transitive relationship needs to be modified, as shown in (13). Process model of system predicts the next state. Assuming the current system state is k, according to the process model of the system, based on the current state of the system, the next state can be estimated, as shown in (14) [10; 30]. \[\widetilde{\mathbf{P}}_{k+1}=\Phi_{k}\hat{\mathbf{P}}_{k}\Phi_{k}^{\mathrm{T}}+ \mathbf{Q}_{k} \tag{13}\]  
\[\widetilde{\mathbf{x}}_{k+1}=\Phi_{k}\hat{\mathbf{x}}_{k} \tag{14}\]
## 4 Experiments
### Baseline  
1. TextCNN [34] extracts the local key features using convolutional operations and filters the salient features using the maximum pool. Then, this information is used for classification;
2. Bi-LSTM [35] is used to extract contextual semantic and sequential information from text;
3. Bi-LSTM-Attention [36] adds an attention mechanism to the Bi-LSTM model, which enables the model to adjust the weight parameters according to different levels of data importance and increase the weight of key information;
4. RCNN [37] first extracts global semantic information using Bi-LSTM and then extracts salient features using max-pooling. Then, this information is used for classification;
5. BERT-Bi-LSTM [38] is a classification model that uses Bert to generate dynamic feature vectors and then uses the Bi-LSTM network for feature extraction;
6. BERT-Bi-LSTM-atten [39] uses Bert to generate dynamic feature vectors, then uses the Bi-LSTM network for feature extraction, and finally uses the attention mechanism for feature optimization.  
The results of the general dataset experiment are shown in Table 4. The experimental data of the COVID-19 Weibo dataset are shown in Table 5. The experiments show that the SMP2020-EWECT general dataset contains 80 pieces of data per classification, and the SMP2020-EWECT COVID-19 Weibo dataset contains 20 classifications per classification. None of the model's Acc and F1 values reached 0.6 when the amount of data per classification was less than \(10^{2}\), defined as a small dataset; if the data per classification was less than 10, this was defined as an ultra-small dataset. The performance of our model on large datasets is comparable to that of the current mainstream sentiment analysis models, with both the general dataset and the COVID-19 Weibo dataset outperforming the other models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{8} & \multicolumn{2}{c}{**80**} & \multicolumn{2}{c}{**800**} & \multicolumn{2}{c}{**800**} \\  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline RCNN & 0.312 & 0.248 & 0.489 & 0.449 & 0.656 & 0.640 & 0.702 & 0.673 \\ TextCNN & 0.321 & 0.279 & 0.593 & 0.532 & 0.721 & 0.695 & 0.750 & 0.722 \\ BiLSTM & 0.287 & 0.112 & 0.334 & 0.315 & 0.604 & 0.588 & 0.715 & 0.664 \\ BiLSTM-Attention & 0.273 & 0.153 & 0.373 & 0.345 & 0.628 & 0.590 & 0.727 & 0.694 \\ Bert-BiLSTM & 0.467 & 0.426 & 0.586 & 0.534 & 0.690 & 0.654 & 0.738 & 0.703 \\ Bert-BiLSTM-Attention & 0.425 & 0.389 & 0.544 & 0.508 & 0.701 & 0.677 & 0.734 & 0.703 \\ BBME & 0.466 & 0.035 & 0.599 & 0.484 & 0.715 & 0.666 & 0.746 & 0.708 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of the SMP2020-EWECT general dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Experimental Setting** \\ \hline Operating System & Ubuntu 9.4 \\ Programming Language & Python 3.8.10 \\ Deep Learning Framework & Torch 1.13.1 cu117 \\ Graphics Card & RTX 3090 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental environment.