esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Palladium Coating  
The thickness of the palladium coating, defined by FIB-SEM, is \(\approx\)240 nm (Figure 1), and the mass fraction of Pd is at the level of 0.01%.  
The EDX analysis was used to confirm the progress of the palladium deposition process. The presence of palladium on the surface of the HEA fibers was established (Figure 2); however, the SEM resolution used does not allow us to characterize the morphology of a very thin formed coating. The TOF-SIMS was used to analyze the chemical composition of the deposited layer which allowed us to conclude that the process of deposition was carried out. The obtained Pd\({}^{+}\) ions' distribution after TOF-SIMS measurements is shown in Figure 2d. In order to estimate the depth of the Pd coating, this map was averaged across the \(x\)-axis as illustrated in Figure 2e. It was confirmed that the Pd coating is quite homogenous and has the same thickness of \(\sim\)240 nm.  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **Set Composition, at. \%** & **Defined Composition, at. \%** \\ \hline Ti & 20 & 18.1 \\ Zr & 20 & 19.7 \\ V & 20 & 19.2 \\ Nb & 20 & 21.5 \\ Ta & 20 & 21.5 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of TiZrVNbTa after EBM-PDME.  
Figure 1: SEM image visualizing thickness of the palladium coating.  
Figure 2: SEM images of **(a)** palladium-coated TiZrVNbTa fibers; **(b)** multilayer EDX image; **(c)** element mapping; **(d)** thickness distribution of Pd (red); **(e)** signal intensity dependence on thickness of Pd.  
According to the XRD analysis, the alloy obtained by EBM-PDME is single-phase (Figure 3a) with a bcc lattice (Table 2). We have to note that the palladium coating does not affect the XRD patterns; the palladium phase is not detected because of its very small amount. The unit cell parameter of the alloy is 0.3301 nm, which agrees with the literature data on the similar alloy (0.3304 nm [2]).  
Figure 3: X-ray diffraction patterns of the EBM-PDME fibers TiZrVNbTa alloy obtained by: (**a**) as prepared; (**b**) after hydrogenation; (**c**) after dehydrogenation; (**d**) palladium-coated fibers after hydrogenation; (**e**) palladium-coated fibers after dehydrogenation.
# Using Graph Neural Networks for Social Recommendations
## 3 Methodology
### RelationalNet Algorithm Architecture
#### 3.2.5 Graph Attention Layer  
The graph attention layer, depicted in Figure 1 with blue boxes labeled Graph ATT, generates the latent embeddings for each user and for each item using node attention at every layer by a combination of suited graph embeddings.  
* For user \(a\), using the graph attention layer at the \((k+1)^{th}\) layer to combine the latent embeddings learned from neighboring users \(h_{a}^{k+1}\) and from item consumed users \(\tilde{l}_{i}^{k+1}\) for each item \(i\) and item latent embeddings from the \(k^{th}\) layer, i.e., \(\tilde{v}_{i}^{k}\), is \[u_{a}^{k+1}=MLP_{2}([h_{a}^{k+1},MLP_{1}([\tilde{l}_{i}^{k+1},v_{i}^{k}])])\] (12) where \(MLP_{1},MLP_{2}\) are the multi-layer perceptrons used to learn the complex relationship between social relations and item embeddings. Figure 6 shows an example of this step.
* For item \(i\), using the graph attention layer at the \((k+1)^{th}\) layer to combine the latent embeddings learned from neighboring items \(l_{i}^{k+1}\) and from users consumed item \(\tilde{h}_{a}^{k+1}\) for each user \(a\) and item latent embeddings from \(k^{th}\) layer, i.e., \(u_{a}^{k}\), is \[v_{i}^{k+1}=MLP_{2}([l_{i}^{k+1},MLP_{1}([\tilde{h}_{a}^{k+1},u_{a}^{k}])])\] (13) where \(MLP_{1},MLP_{2}\) are the multi-layer perceptrons used to gain an understanding of the intricate connections between item relations and user embeddings.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Prediction Model Performance Results  
To comprehensively evaluate the performance of the LSTM traffic conflict prediction model, based on test-set samples, the classification effect of the model was evaluated from multiple perspectives, such as accuracy, recall rate, F1 value, and ROC curve. Table 10 shows the evaluation indexes of the model at four conflict levels. It can be seen that the performance of the model at safety, minor, and general levels is relatively balanced; the accuracy and recall rate are generally above 80%, and the F1 value of the comprehensive evaluation index is also high. However, the recall rate of the model is relatively low in the case of a small number of serious grade samples. Overall, the model achieved a classification accuracy of 83.7% on 4795 samples in the test set, and the overall performance was excellent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conflict Level** & **Sample Size** & **Recall Rate** & **F1 Value** \\ \hline Security & 4309 & 0.942 & 0.934 \\ Slight & 267 & 0.835 & 0.820 \\ General & 175 & 0.811 & 0.822 \\ Severe & 44 & 0.750 & 0.798 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification performance evaluation of LSTM traffic conflict prediction model.  
Figure 12: Comparison of the risk of traffic conflict for key driving behaviors.  
An ROC curve and PR curve were drawn, as shown in Figure 13. The area under the ROC curve is 0.912, indicating that the model can balance the TPR (true-positive rate) and FPR (false-positive rate) well and that it has strong classification and discrimination ability. The area under the PR curve is 0.836, indicating that the model can accurately identify conflict samples but also better cover all conflict samples, which has high practical value.  
In summary, the traffic conflict prediction model based on LSTM can effectively warn of possible traffic conflict risk during driving by comprehensively considering the heterogeneous characteristics of vehicle movement, driving behavior, road condition environment, and other sources, and it has high accuracy and practicality. The model has a stable training process and excellent test performance, which provides reliable tool support for assisting driving decision-making and indicating hidden safety risks.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Analysis of Experimental Results
#### 5.5.5 Denoising Performance Analysis  
To assess the denoising performance of the model, we introduced noise with various signal-to-noise ratios into the target domain data. The signal-to-noise ratio is formulated as Equation (10), where \(P_{signal}\) is the signal power and \(P_{noise}\) is the noise power. We selected a task from the DCDF scenario with the source domain operating conditions as Load 0, Load 2, and Load 3, and fault types being normal state, ball fault (B), and outer ring fault  
Figure 8: Average accuracy under different SNRs.  
(O). The target domain operated under Load 1 conditions, with the fault types being normal state and inner ring fault (I).  
\[SNR=10 log_{10}\frac{P_{signal}}{P_{noise}}. \tag{10}\]  
Figure 9 illustrates the experimental results of noise in the performance of various diagnostic methods. In particular, the accuracy of each method declines gradually as noise levels increase from right to left. This reduction in accuracy aligns with our expectations, as noise inherently introduces uncertainty and complexity into the diagnostic process. However, FedCDAE-MN exhibits a slower decrease in accuracy compared to other methods. Moreover, FedCDAE-MN maintains consistently high accuracy levels even under varying signal-to-noise ratios. Therefore, FedCDAE-MN has significant implications for practical fault-diagnosis applications. In real-world scenarios, noise is often an unavoidable aspect of data acquisition, stemming from various sources such as sensor inaccuracies, environmental conditions, or data transmission issues. Consequently, an algorithm's ability to maintain high accuracy in noisy conditions is of paramount importance. In addition, these findings also inspire further research into strategies for enhancing noise tolerance in diagnostic methodologies and highlight the pivotal role of FedCDAE-MN in addressing the challenges posed by noise in real-world scenarios.
## 7 Findings and Limitations  
The main findings of this paper are as follows:  
* integrating entity clues into the MR model better helps the model to represent an entity completely.
* MR depends on syntax to clarify the sentence structure. Under these circumstances, GCN inputs syntactic knowledge into the model completely and efficiently. Furthermore, soft pruning strategy helps to remove the noise in syntactic dependency relations.
* the proposed method can be generalized for different word-level NLP tasks, such as event extraction and relation extraction, indicating the great prospects of this research. However, there are also some limitations need to be solved in the future:
* dependency directions and types (such as nsubj, rmod) should be added to the model to make the model more robust.
* the constraints mined in this paper all come from sentence. However, many metonymies are proper nouns that denote existing well-known works or events, suffering from the limitations of lacking \"real world\" and common sense knowledge to access the referents. External knowledge bases could be an appropriate choice to overcome this problem.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 2 FTN Signaling with Turbo Equalization
### Experiment with 5G LDPC Code and Turbo Equalization in FTN Signaling  
We simulated 5G LDPC code and turbo equalization in FTN signaling with MATLAB. The diagram is depicted in Figure 1. The LDPC code used here is a (7680, 3840) code based on BG2 [24]. We used shortened \(\mathbf{v}\) as in (5) and used BPSK modulation for the baseband system model. We used turbo equalization containing an M-BCJR detector and BP decoding for demodulation. Considering complexity and latency, we chose a maximum turbo equalization count of 3 to lower the number of whole iterations and \(M=4\) states stored at each stage of the trellis in BCJR detection, i.e., 4-BCJR detection, to reduce memory usage. We considered maximum iteration limits of 10, 20, and 50 for BP decoding. Therefore, in total, three iterations of the 4-BCJR algorithm and 30, 60, and 150 iterations of BP decoding were performed at most for three turbo iterations. Unfortunately, an error floor was found near \(10^{-5}\) as in Figure 2. It can be eliminated through many detection and decoding iterations, but this is unacceptable considering the low latency and storage. Accordingly, few turbo iterations and states of FTN detection can be adopted. With sufficient SNR, the error floor of the BP decoder will disappear independently. It appears in the BER region (\(10^{-5}\)) of most concern in wireless communication, which is why we must lower or eliminate the error floor.
# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
## 5 Conclusions  
To investigate the disaster and clarify the causes of the disaster, a field survey and numerical simulation were conducted. A risk assessment method for tunnel water inrush was proposed to evaluate the risk. The main conclusions are as follows:  
A funnel-shaped formation makes the groundwater converge towards the tunnel. The thickness of strongly weathered tuff at the tunnel vault in the collapsed area is only 0.7 m. At the same time, heavy rainfall increased the groundwater level and the water pressure. These geological conditions became the trigger factors of the accident.  
This disaster was significant because of the poor surrounding rock and seepage pressure. When the tunnel was excavated from the slightly weathered area to the strongly weathered area, deformation increased abruptly. As the excavation proceeded, the excavation-affected area gradually expanded to the surface. The monitoring and simulation data showed that when the tunnel was excavated in the strongly weathered area, the surface settlement approached the warning value quickly. In addition, after the disaster, a negative pore water pressure zone formed around the tunnel, and increased the scope and scale of the accident as the water converged here.  
A risk assessment index system for tunnel water inrush in Qingdao area is proposed. An improved RBF neural network was established by combining gray correlation analysis and a PAM clustering algorithm, and its reliability was verified. The water inrush risk level of the Jing-sha section was very high risk at level IV, which corresponds to the actual situation (water inrush). Making a proper risk assessment and taking the corresponding measures can effectively reduce engineering risks.  
Conceptualization, Y.Z. and B.G.; methodology, Y.Z. and B.G.; investigation, Y.Z., W.Z. and H.X.; formal analysis, H.X. and F.L.; validation, J.Z. and K.L.; data curation, Y.Z.; funding acquisition, Y.Z.; supervision, Y.Z. and B.G.; writing--original draft, Y.Z., W.Z. and H.X.; writing--review and editing, B.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Demonstration Project of Benefiting People with Science and Technology of Qingdao, China (Grant No. 23-2-8-cspz-14-nsh), the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2022 QD102) and the National Natural Science Foundation of China, China (Grant No. 51674151).  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# IoT Blockchain Data Vercity with Data Loss Tolerance
## 3 Results
### Result of IoT Blockchain Strength Monitoring
The relationship of IoT blockchain strength increases in proportional to the number of nodes, which are presented in the charts shown respectively in Figure 21.The relationship between the number of the capacitor-cluster (node), capacitor (transaction) and their inductance currents are presented in the chart depicted in Figure 21a. There are two observations shown by the results. First, with a similar number of transactions from one to 10, the inductance current (strength) increased with the increment of more capacitor-clusters (node). Secondly, this result also demonstrates that the inductance current (strength) increased  
Figure 20: Description of capacitor-cluster (node), transaction and inductance current. with the increment of more nodes and transactions. The strength increment uptrend is indicated by the blue colored dotted line. Figure 21: LTSPIC Results (**a**) Number of nodes vs. inductance current clustered chart; (**b**) Number of nodes vs. cycle time clustered chart; (**c**) Number of nodes vs. inductance difference (1 st & 2 nd cycle) clustered chart. The relationship between the number of capacitor-clusters (node), capacitors (transaction) and their cycle time are presented in the chart of Figure 21b. The result showed that their cycle time increased with the increment of more nodes. The cycle time increment uptrend is indicated by the blue colored dotted linear line. It means that more nodes will allow longer time to crash if it happens. The relationship between number of capacitor-cluster (node), capacitor (transaction) and their inductance current difference between the 1 st and 2 nd peak are presented in the chart in Figure 21c. The result showed that their inductance current (strength) difference increased with the increment of more nodes. The increment uptrend is shown by the blue colored dotted linear line. Summary of these three clustered charts shows that with 10 transactions, more capacitor-clusters (node) can generate more capacitance (strength). The capacitance (strength) shall create a long cycle time of the capacitor charges to release. Therefore, the strength is enhanced. IoT blockchain strength measurement is thus tested and proven here. This strength is quantifiable and can be used to facilitate monitoring and control, thus further strengthening the IoT blockchain data veracity. Results showed that the IoT blockchain can be measured with its capacitance. Hence IoT blockchain strength monitoring can be based on its capacitance measurement.
# Classification of Heart Sounds Using Convolutional Neural Network
## 1 Introduction
Deep learning methods have been combined with the traditional feature engineering method in some fields recently [22]. A CNN has been used to extract features automatically in many recent studies. However, the features that are extracted by a CNN are abstract and have low explanatory power. Therefore, in this study, we classified normal and abnormal heart sounds using a combination of the feature engineering method and a compact CNN model. We also aimed to explore the feature selection and classification capabilities of the CNN. First, 497 features were extracted from eight domains in accordance with our previous work. Then, the designed CNN was used to select features and distinguishing abnormal heart sounds from normal heart sounds. We introduced dropout and global average pooling strategies into the proposed model to avoid overfitting and class weights were set in order to solve the class imbalance problem. Finally, we evaluated the performance of the proposed method using mean accuracy, sensitivity, specificity and Matthews correlation coefficient (MCC) based on stratified five-fold cross-validation. To demonstrate the validity of the designed CNN model, we further tested several machine learning algorithms (support vector machine [23], random forest [24] and adaboost [25]). The performances of the proposed method were more remarkable than those machine learning classifiers. Compared with recurrent neural network (RNN) [26], which is said to be better for sequential tasks, CNN can reduce the computation overhead [27]. The results indicate that the proposed method efficiently classifies normal and abnormal heart sounds and demonstrate the feasibility of the CNN as a feature selector and classifier.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 3 Results
### Efficiency Analysis: Execution Time and Parameters  
Table 3 compares the performance of four Transformer-based models based on their execution time and the number of parameters. The Transformer model has the longest execution time, at 2779 s, and the largest number of parameters, at 2282. The Transformer (decoder only) records an execution time of 1731 s with 1174 parameters, while the Transformer (encoder only) records an execution time of 1863 s with 1030 parameters. Our LTE model demonstrates the best performance, achieving the fastest execution time of 1093 s, which is a 36.86% improvement compared to the Transformer (decoder only), which achieves the second-fastest execution time of 1731 s. In terms of the number of parameters, the LTE model has 1006 parameters, the lowest among all models, representing a 2.33% reduction compared to the Transformer (encoder only) model, which has the second-lowest number of parameters at 1030. These results indicate that our LTE model not only offers excellent prediction performance but is also more efficient, with both shorter execution times and fewer parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Execute Time (s)** & **Num. of Parameters** \\ \hline Transformer & 2779 & 2282 \\ \hline
**Transformer (encoder only)** & 1863 & **1030** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Execution time and number of parameters for Transformer-based models and the LTE model. The best model is indicated in bold with a checkmark, and the second-best model is indicated in bold without a checkmark. “Improvement” is the percentage of accuracy improvement between the best-performing model (bold with a checkmark) and the best-performing model except ours (bold). In terms of execution time, our model is 36.86% faster than the second-fastest model, Transformer (decoder only). Regarding the number of parameters, our model achieves a 2.33% reduction compared to the second-best model, Transformer (encoder only).
## 4 Development of a Damage Constitutive Model for Argillaceous Silitstone
### Constitutive Correction  
For Equation (12), when the stress state of the clayey siltstone microelement is at a relatively low stress level, the relationship between \(\sigma_{1}\) and \(\varepsilon_{1}\) is linear. This linearity does not align with the initial compressibility phase observed in the stress-strain curve of clayey siltstone obtained from experiments. Therefore, it is necessary to revise the derived constitutive model. [PERSON] et al. proposed that the total axial void strain of the rock is equal to the intercept of the fitted curve in the elastic deformation phase on the horizontal axis (strain axis) [27]. Additionally, an exponential relationship exists:  
\[\varepsilon_{\text{lg}}=\frac{c}{b}(1-e^{-a\varepsilon_{1}}) \tag{17}\]  
In the expression, \(\varepsilon_{1\text{g}}\) represents the total axial void strain, defined as \(c/b\); \(\varepsilon_{1}\) denotes the axial strain; and c and b are fitting parameters in the elastic deformation phase of the rock's fitting formula. The coefficient \(a\) can be determined by fitting the experimental data to this equation \((\varepsilon_{1}-(\sigma_{1}-2\mu\sigma_{3})/E_{i}=\varepsilon_{\text{lg}}(1 -e^{-a\varepsilon_{1}}))\). Consequently, the revision of Equation (12) can be expressed as follows:  
\[\sigma_{1}=\Big{(}E_{0}\Big{(}\varepsilon_{1}-\varepsilon_{\text{lg}}(1-\text {exp}(-a\varepsilon_{1}))\Big{)}+2\mu\sigma_{3}\Big{)}(1-D_{1})(1-D_{2}) \tag{18}\]  
By substituting Equations (16) and (17) into Equation (18), we obtain the expression for the triaxial compressive damage constitutive model of the argillaceous siltstone. This model captures the intricate interactions between stress, strain, and damage evolution in the material under various loading conditions, allowing for a comprehensive understanding of its mechanical behavior during triaxial compression tests.  
When \(\varepsilon_{1}<\varepsilon_{1\text{d}}\),  
\[\sigma_{1}=(E_{0}(\varepsilon_{1}-\varepsilon_{1\text{g}}(1-\text{exp}(-a \varepsilon_{1})))+2\mu\sigma_{3})*\frac{E_{i}}{E_{0}} \tag{19}\]When \(\varepsilon_{1}\geq\varepsilon_{\rm Id}\),  
\[\sigma_{1}=\left(E_{0}\Big{(}\varepsilon_{1}-\varepsilon_{\rm lg}\Big{)}+2\mu \sigma_{3}\right)\times\frac{E_{i}}{E_{0}}\times\exp[-(\frac{\varepsilon_{1}- \varepsilon_{\rm Id}}{\lambda})^{m}] \tag{20}\]  
This experiment is a uniaxial compression test; therefore, \(\sigma_{3}=0\).
## 2 Technological Background and Related Work
### Multi-Core Systems  
A Symmetric Multi-processing (SMP) system is a multi-core system in which cores can share different levels of cache and the cost to access any location in the main memory is the same for all cores, as shown in Figure 0(a). For a Non-Uniform Memory Access (NUMA) [1] system, as shown in Figure 0(b), system resources such as cores, RAM, and memory controllers are divided into several nodes, and interconnection links connect these nodes. Cores can access the memory located on the same node with it, which is called local memory access. Accessing memory on other nodes via interconnection links is called remote memory access. This requires a longer time. The NUMA factor is the ratio between the remote memory access latency and the local memory access latency.  
Figure 1: The load balancing mechanism affects SMP and NUMA systems differently. (**a**) SMP system; (**b**) NUMA system.  
Though a NUMA system is more scalable than an SMP system and more memory accesses can occur simultaneously, operating system design must reduce costly remote memory access. Nevertheless, for load balancing, modern operating systems migrate tasks from an overloaded core's runqueue to the runqueue of a core with a lesser load. The load-balancing mechanism affects SMP and NUMA systems differently, as shown in Figure 1. Unlike an SMP system that features a uniform cost to access memory, migrating a task across nodes can involve costly remote memory access after migration. The benefit gained from load balancing thus is reduced by the need for remote memory access.