esa-sceva/satcom-chunk-collection huggingface dataset:
# Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal
## 3 Results
### Human Breast Data  
Our research projects the full view DBT images from the data set of Breast Cancer Screening [22] to obtain original human breast data.  
The compressed thickness of the selected breast was 70 mm, the patient with larger breasts having been selected. This choice allowed a better observation of the influence of TPA on the images. These data were reconstructed by SART, our proposed algorithm and the diffusion method, respectively. Figure 8a-c shows the breast section with 35 mm high on the \(Z\)-axis, and Figure 8d-f shows the breast section with 51 mm high. In each iterative reconstruction, we used the same parameters as the simulated data. Since the artifact area on the right side of the image does not contain breast information, there is no artifact on the right side of the image. By visual observation, it can be found that both our algorithm and the diffusion method reduce artifacts on the left side of the image. We zoomed in on the artifact area and found that the image reconstructed by the diffusion method still contained artifacts on the left side of the image, and our algorithm can remove the artifacts more effectively, resulting in a clearer reconstructed image in Figure 8c,f. At the same time, by comparing images of different heights in Figure 8a,d, it can be found that as the height of the breast section increases, the artifacts extend from the ends of the image to the middle. With the increase of the artifact, the ability of the diffusion method to eliminate the artifact gradually weakens, resulting in the artifact in Figure 8b appearing more serious than in Figure 8e. Our algorithm overcomes this difficulty, and it can be seen that there are no obvious artifacts both in Figure 8c,f. Data analysis is performed on the dashed line in Figure 8d, and the results are shown in Figure 8g,h. It can be found from Figure 8g that no matter what method we use for reconstruction, the pixel values of non-artifact regions  
Figure 7: (**a**) The real image. (**b–d**) Image reconstructed by SART; our proposed algorithm; diffusion-based method. (**e**) The selected line and its position are marked in (**a**). (**f**) The gradient information of the artifact area box in (**e**).  
remain unchanged. Zoom in on the artifact area, and it can be seen from Figure 8h that our algorithm can beat smooth the voxel values, thus our algorithm can better remove artifacts and reconstruct breast images. At the same time, since the diffusion method continuously spreads the value from the updated area to the un-updated area, the voxel value at the edge of the image will become abnormal, which will affect the iteration of the image.
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery
### Intelligent Algorithms for Vital Signs Monitoring
At the Mayo Clinic, researchers examined the heart's electrical activity using AI with ECG signals and identify asymptomatic left ventricular dysfunction (ALVD). The research shows that using the ECG data, CNN works on classify ejection fraction \(\leq\)35% for patients with ventricular dysfunction [92]. The results showed that AI applications using ECG data are a low-cost approach that allows identifying ALVD in asymptomatic individuals easily\".
## 4 Materials and Methods
### QRT-PCR Analysis of the Expression Files of LncRNAs and Targets  
RNA purity, concentration and integrity of each sample treated by DNase I were examined using a NanoDrop 2000c bioanalyzer (Thermo Fisher Scientific, Waltham, MA, USA). Then, RT was carried out using 1 \(\upmu\)g total RNA and 200 U M-MLV Transcriptase (TaKaRa, Dalian, China) in a 20 \(\upmu\)L volume. The reaction was conducted at 70 \({}^{\circ}\)C for 10 min, 42 \({}^{\circ}\)C for 60 min and 70 \({}^{\circ}\)C for 15 min. The RT product was diluted 40 times with sterile water. The qPCR was conducted on the BIO-RAD CFX system in a 20 \(\upmu\)L volume containing 4 \(\upmu\)L diluted cDNA, 250 nM forward primer, 250 nM reverse primer, and 1 \(\times\) SYBR Premix Ex Taq II (TaKaRa, Dalian, China) using the following conditions: 95 \({}^{\circ}\)C for 3 min, 40 cycles of 95 \({}^{\circ}\)C for 15 s, 59 \({}^{\circ}\)C for 15 s and 72 \({}^{\circ}\)C for 15 s. Each test was performed in triplicate. The 40S ribosomal protein S8 (T_00004056) was used as an internal reference because its expression level is relatively stable in different tissues. Melting curves were analyzed to verify the specificity using the Bio-Rad CFX Manager software. The relative expression levels were calculated using the 2\({}^{-\Delta\Delta\)CT method [117]. The primer sequences are listed in Table S20.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/ijms222111989/s1](https://www.mdpi.com/article/10.3390/ijms222111989/s1)]([https://www.mdpi.com/article/10.3390/ijms222111989/s1](https://www.mdpi.com/article/10.3390/ijms222111989/s1)).  
**Author Contributions:** Conceptualization, S.C. and B.W.; Data curation, B.W., Y.L., J.L., Z.X., M.L., C.G. and Y.S.; Formal analysis, Y.L.; Methodology & Software, Y.L. and B.W.; Project administration, S.C. and B.W.; Validation, Y.L.; Supervision, S.C. and B.W.; Writing--original draft and review and editing, Y.L., J.L. and B.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Natural Science Foundation of China and Karst Science Research Center of Guizhou Province (U1812403-1) and National Natural Science Foundation of China (81274015).  
**Data Availability Statement:** All the raw data of the transcriptome and small RNAs are deposited in the NCBI short read archive (SRA) under the accession numbers SRR12904745 and SRR12886428.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Effects of Acoustic Wave Field on Particle Number Concentration
Based on the orthokinetic agglomeration mechanism in the acoustic field, acoustic agglomeration is mainly embodied in the carrying effect of acoustic waves on particles. Furthermore, particles vibrate with the acoustic waves to generate the aggregate volume and relative motion among particles. If the particles are extremely small, then the frequency of the acoustic wave that can completely carry the particles is high, the volume of submicron particles is minuscule, the movement range is considerably limited, and there are fewer particle collisions.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 4 Parametric Analysis  
### Effect of \(\rho\) on the Resistance Factor and the Factor of Safety  
This section intends to study the effect of \(\rho\) on the resistance factor and the factor of safety for driven piles. \(\beta_{\text{T}}\) is designed as 2.33 (corresponding failure probability is 0.01),and the increase factor \(M_{\rm setup}\) is accepted as a value of 1, which is the mean value of the recommended range between 0 and 2. According to [PERSON] et al. (2018) point out that for the relationship of the ratio of \(\rho=Q_{\rm D}/Q_{\rm L}\) and the bridge span length, the values in the range of 0.5 to 4 for \(\rho=Q_{\rm D}/Q_{\rm L}\) are used. It should be noted that the correlation between \(R_{0}\) and \(R_{\rm setup}\) is not considered in this section. Based on these proposed values of key parameters, the resistance factor and factor of safety can be calculated using Equations (17) and (19). The variations of the calculated resistance factor and factor of safety with \(\rho=Q_{\rm D}/Q_{\rm L}\) for driven piles in clay and sand are shown in Figure 1.  
\(\phi\) slightly decreases as \(\rho\) increases from 0.5 to 4 in the distribution curve of \(\phi\) versus \(\rho\). In particular, the resistance factor decreases obviously when \(\rho\) is between 0.5 and 2, but the resistance factor tends to be constant when \(\rho\) is between 2 and 4. Meanwhile, Figure 1 also can verify driven piles considering setup effects have greater uncertainties in sand, because the resistance factor of driven piles in clay is larger than the resistance factor of driven piles in sand for the same value of \(\rho\), the average difference is about 22%. Particularly, for \(\rho=2.0\), the recommended resistance factor of driven piles in clay and sand are estimated as 0.26 and 0.20, respectively.  
The value of FOS decreases obviously as \(\rho\) increases from 0 to 4 in the distribution curve of FOS versus \(\rho\), but the decreasing rate decreases gradually, and the value of FOS approaches a constant when \(\rho\) tends to infinity. Meanwhile, when \(\rho\) increases from 0 to 4, the value of FOS nearly decreases from 7.98 to 1.6 in clay, with a decrement of about 79.9%; when \(\rho\) increases from 0 to 4, the value of FOS nearly decreases from 9.47 to 1.89 in sand, with a decrement of about 80%. Therefore, the value of \(\rho\) is one of the most important factors for the factor of safety.  
By comparing the two figures in Figure 1, it can be concluded that the resistance factor and factor of safety for driven piles in sand and clay both decrease with the increase in \(\rho\), but varying \(\rho\) is not largely sensitive to the resistance factor for reliability-based design of driven piles, which is consistent with other research in this field ([PERSON] et al., 2019; [PERSON] et al., 2019). On the contrary, the factor of safety for driven piles in clay and sand decreases greatly with \(\rho\).
## 4 Simulation Analysis
### Performance Metrics  
Following is the brief description of the performance metrics that are analyzed through simulations.  
* _Total forwarded copies of data_: represents the number of copies forwarded in the network for all data packet transmissions initiated by the source nodes.
* _Number of dead nodes_: is the total number of nodes that could not participate in the data forwarding process because they have residual energy less than the transmission energy.
* _End-to-end delay_: is the cumulative delay experienced by the data packet between its source and the sink node.
* _PDR (Packet Delivery Ratio)_: is the ratio of successfully received data packets by sink over the total number of generated data packets.
* _Energy consumption_: total energy consumption in the network during the whole simulation time.
* _Hop count_: represents the average number of hops that the data packets have traversed between source and sink node.  
Following is the brief discussion about each performance metric that is estimated through simulations in the static Sink network scenario.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 5 Experimental Results
### Environment Setup  
For evaluation purpose, we utilize the Precision (\(P\)), Recall (\(R\)), and \(F1\) scores as metrics in the experiment. The formula is shown as follows.  
\[\begin{split} P&=\frac{TP}{TP+FP}\\ R&=\frac{TP}{TP+FN}\\ F1&=\frac{2*P*R}{P+R}\end{split} \tag{20}\]  
For the hyperparameter settings for NER, we tune it on the basis of the performance of the development set. We set the character embedding size \(d_{e}\) to 100, the dimension of the LSTM hidden state \(d_{h}\) to 120, the initial learning rate to 0.001, the loss weight coefficient \(\lambda\) to 0.06, and the dropout rate to 0.3, the number of projections h is set to 8. For the initialization of trainable parameters, use an initializer to initialize the parameters. In terms of entity disambiguation and predicate mapping, a BERT-base model has 12 layers, 768 actual states, 12 heads, and a total number of parameters of 110 M. For fine-tuning of BERT, all hyperparameters are tuned on the development set. For the dataset, the maximum  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tagging Content** & \\ \hline Question & Who wrote the novel Journey to the West? \\ Named entity recognition & Journey to the West \\ Triple & Journey to the West \(\mid\) \(\mid\) Author \(\mid\) \(\mid\) [PERSON] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset sample annotation sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Data Set** & **The Training Set** & **The Validation Set** & **The Test Set** \\ \hline NLPCC 2016 & 13,609 & 1000 & 9870 \\ Named entity recognition & 13,609 & 1000 & 9870 \\ Entity disambiguation & 24,985 & 4744 & 36,208 \\ Relational prediction & 76,009 & 10,600 & 121,958 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset statistics.  
sequence length is set to 60, and the batch size is set to 32. ADAM (Adaptive Moment Estimation) is used for optimization, \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), the dropout rate is set to \(1\times 10^{-5}\), and the initial learning rate of BERT-Softmax is set to \(5\times 10^{-5}\) by applying a learning rate warm-up strategy. The training epoch of BERT-Softmax is 3. In the answer selection module, the hyperparameter \(\alpha\) is set to 0.6. For all baseline models, the word embeddings in the experiments are pre-trained by the word2 vec toolkit on the Baidu Encyclopedia corpus, where the size of embedding is set to 300.
## 8 Implementation  
### Experimental Setup  
Wireless devices: In our work, the TP-Link wireless router (TP-Link WR 1043 ND) and smart device with wireless hotspot functionality (iPhone 6 plus) are used as wireless transmitters, respectively, and the desktop with Intel 5300 NIC (Network Interface Controller) is used as a receiver. The transmitter and receiver both have three antennas. The receiver needs to be deployed with the Linux 802.11n CSI Tool [47], which is needed to open two terminals to ping data packets and collect data packets. The receiver continuously pings packets, and stores and processes the collected packets. The work of the transmitter follows the IEEE 802.11n protocol, and the firmware reports CSI to the upper layers. The collected packets are a sequence of data, and each packet contains the received signal strength indicator (RSSI) value of three antennas, the value of noise, CSI, and so on. Each CSI represents the phase and amplitude on a set of 30 OFDM subcarriers.  
Figure 6: Influence of packet rate loss on calculated DTW distances between two samples.  
Scenarios: We implement WiGuard on current TP-Link wireless routers and smart devices with hotspot functionality in a corridor with a size of 2 m \(\times\) 5 m and in a room in an indoor environment with a size of 5 m \(\times\) 10 m.  
Configurations: The safe wireless transmitters are placed at 0.5 meters to 7 meters away from the target public APs. Six conditions for safe wireless transmitters and target public AP are given, and they are separately \(\{S_{1},A_{6}\}\), \(\{S_{6},A_{1}\}\), \(\{S_{1},A_{11}\}\), \(\{S_{11},A_{1}\}\), \(\{S_{6},A_{11}\}\) and \(\{S_{11},A_{6}\}\). \(\{S_{1},A_{6}\}\) represents that the safe wireless transmitter works on Channel 1 and the target public AP works on Channel 6, while the other five configurations are in the same manner.
## 2 Signal Analysis and Neural Network  
### Hilbert-Huang Transform (HHT)  
According to the mathematical theory of modern well-known mathematician [PERSON], HHT proposed by [PERSON] in 1998 [13]. The original signal is decomposed into IMF by EMD [14; 15] to obtain the instantaneous frequency of the analysis data. HHT conversion has a good analysis effect on the analysis of nonstable or nonlinear signals.
# Is Malware Detection Needed for Android TV?
## 4 Performance Results
### Performance Metrics  
The outcomes of the malware detection models are predictions about the classification of the given application as malicious or benign. The accuracy of the predictions is deter mined using the confusion matrix shown in Table 4. In the confusion matrix, the Positive (P) value represents the number of malware, while the Negative (N) value represents the number of benign applications. Applications predicted by the model to be malicious are considered to be Predicted Malware (PP), and those predicted to be benign are considered to be Predicted Benign (PN). The True Negative (TN) value shows the number of applications that are actually benign and classified as benign by the model, and the True Positive (TP) value shows the number of applications that are actually malicious and classified as malicious. The False Negative (FN) value shows applications that are actually malicious but classified as benign, and the False Positive (FP) value shows applications that are actually benign but classified as malicious by the model.  
The performance of the malware detection models is evaluated using the F1-Score and Matthews Correlation Coefficient (MCC) performance metrics, as presented in Equation (3) and Equation (4), respectively.  
\[\begin{split} Precision&=\frac{TP}{TP+FP}\\ Recall&=\frac{TP}{TP+FN}\\ F1\text{-}Score&=2\times\frac{Precision\times Recall }{Precision+Recall}\\ \end{split} \tag{3}\]  
\[\begin{split} Negative(N)&=TN+FP\\ Positive(P)&=FN+TP\\ PredictedNegative(PN)&=TN+FN\\ PredictedPositive(PP)&=FP+TP\\ MCC&=\frac{TP\times TN-FP\times FN}{\sqrt{N \times P\times PN\times P\overline{P}}}\\ \end{split} \tag{4}\]  
We would like to demonstrate the calculation of the performance metrics with an example. For a dataset containing 182 benign (N) and 188 malicious (P) applications, a model detected 178 benign applications (PN) and 192 malware (PP). When we compared the predictions with the samples, we observed that 186 malicious and 176 benign applications were detected successfully, which represented True Positive (TP) and True Negative (TN) results, respectively. It misclassified two malware as benign applications, which were False Negative (FN) results. It also misclassified six benign applications as malicious, which were False Positive (FP) results. In this case, we calculated the Precision as \(\frac{186}{186+6}=0.9688\) and the Recall as \(\frac{186}{186+2}=0.9894\) according to Equation (3). Finally, we obtained the F1-Score as \(2\times\frac{0.9688\times 0.9894}{0.9688\times 0.9894}=0.9789\). According to Equation (4), we calculated the MCC metric as \(\frac{186\times 176-62}{182\times 188\times 178\times 192}=0.9570\).
## 1 Introduction
This can be used as a means for structural health detection, detecting damage such as cracks, monitoring or positioning the damage of materials or structures, and characterizing their damage [28; 29]. [PERSON] et al. [28; 29] utilized the acoustic emission approach to investigate the damage fracture characteristics of porous asphalt mixture or pervious asphalt with different additives under the compression or splitting loading, respectively. [PERSON] et al. [30] studied the application of rejuvenators in aged asphalt mixtures and evaluated their low-temperature performance by using acoustic emission. [PERSON] et al. [31] used three-point bending tests and AE tests to study the continuous damage evolution characteristics of asphalt mixtures. The dynamical damage evolution was evaluated based on the wavelet transform and fractal theory. [PERSON] et al. [32] studied the warm mix asphalt prepared by using reclaimed asphalt pavement instead of aggregates and evaluated the high-temperature rutting, low-temperature cracking resistance, and moisture stability of asphalt mixtures with four additives combining acoustic emission test. Therefore, based on the above research, the following studies were conducted and described in this paper: First, styrene-butadiene-styrene (SBS)-modified SMA-13 specimens with basalt fiber were prepared. Then, a high-temperature compression test and a low-temperature splitting test were conducted. At the same time, the fracture characteristics under compression and splitting loadings were studied using the AE test. Meanwhile, the AE signal parameters including amplitude and count were recorded and analyzed to evaluate the fracture damage under various F-T cycles. The purpose was to correlate the fracture damage with AE signal and study the effect of F-T cycles on SBS-modified asphalt mixture specimens with basalt fiber through a high-temperature compression test and low-temperature splitting test, and to study the fracture damage for the seasonal frozen area, which is affected by seasonality, i.e., freezing in winter and melting in summer.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Experimental Settings
#### 4.1.2 Evaluation Indicators  
\(\bullet\) MR (Miss Rate)  
The Miss Rate (MR) is a metric employed to assess the accuracy of predicted trajectories. It is typically computed by determining whether the Final Displacement Error (FDE) between the predicted trajectory and the ground truth trajectory surpasses a predefined threshold. The mathematical formula for MR (k = i) is as follows:  
\[MR=\frac{1}{N}\sum_{i=1}^{N}II(FDE_{best}^{(i)}>d) \tag{20}\]Here, N represents the total number of samples.  
\(FDE_{best}^{(i)}\) represents the final displacement error (FDE) of the \"best predicted trajectory\" for the \(i\)-th sample. The definition of \"best\" depends on the selection criteria:  
\(II(\cdot)\) is the indicator function, which equals 1 if the condition inside the parentheses is true and 0 otherwise.  
\(d\) represents the threshold for determining a miss (Argoverse 2.0 defaults to \(d\) = 2.0 m).  
MR (k = 1) indicates that only the top predicted trajectory is considered. If the deviation between the predicted trajectory and the ground truth exceeds the threshold, it is classified as a miss.  
MR (k = 6) denotes that the optimal trajectory is selected from six predicted trajectories. If the FDEs of all six trajectories exceed the threshold, it is classified as a miss.  
* Brier-minFDE (k = 6)  
The Argoverse 2.0 [24] motion prediction challenge adopts brier-minFDE (k = 6) as the evaluation metric. Brier-minFDE is computed by multiplying the squared difference (1.0 - p)\({}^{2}\) by the endpoint L2 distance, where p represents the probability associated with the optimal predicted trajectory. This metric is designed to assess the quality of probabilistic predictions and is widely utilized to evaluate the reliability of multimodal trajectory prediction models. It integrates both the accuracy of probabilistic predictions (calibration) and the trajectory error (FDE). The mathematical formula for brier-minFDE (k = 6) is as follows:  
\[\text{Brier}-\text{minFDE}(\text{k}=6)=\frac{1}{N}\sum_{i=1}^{N}\,(\sum_{k=1} ^{6}p_{k}^{(i)}{\cdot}(FDE_{k}^{(i)})^{2}) \tag{21}\]  
Here, \(N\) represents the total number of samples.  
* \(p_{k}^{(i)}\) denotes the normalized probability of the \(k\)-th trajectory for the \(i\)-th sample  
\(FDE_{k}^{(i)}\) is the Final Displacement Error (FDE) of the \(k\)-th trajectory for the \(i\)-th sample, computed as the Euclidean distance between the predicted endpoint and ground-truth endpoint (unit: meters).  
The squared term \((FDE_{k}^{(i)})^{2}\) penalizes large errors more severely, encouraging the model to reduce significant deviations.