esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study evaluated the effect of spacing of the disposal hole and tunnel on the thermal conductivity of the buffer material through FEM analysis. The main focuses and findings of this paper are as follows:  
* A high-performance buffer material with improved thermal conductivity was used in the study. We analyzed the reduction in the distance between the disposal tunnels and disposal holes from the R-SNF of KRS\({}^{+}\) conditions in 3D using the FEM model.
* When a buffer material with a thermal conductivity of approximately 1.2 W/(m\(\cdot\)K) added with graphite was used, the R-SNF condition of 40 m distance between the dis posal tunnels and 7.5 m distance between the disposal holes could be reduced to 30 m and 6 m between the disposal tunnels and between the disposal holes, respectively.
* Additionally, the thermal conductivity test results conducted by [PERSON] et al. (2013) [17] for bentonite mixed with graphite matched well with the results obtained from the DEM numerical analysis verification.  
Although a validated 3D numerical model was applied in this study, it only considered the thermal analysis, indicating that the numerical results are likely to be affected by the fully coupled hydraulic-mechanical conditions in a deep geological repository. These coupled effects will be considered in future studies. In addition, in the future, enhanced buffer materials could meet all the requirements for high thermal-hydraulic-mechanical-chemical performance. Thus, considerations of all the processes must be developed. Nevertheless, this paper shows how much the spacings between disposal tunnels and between disposal holes can be narrowed when using enhanced buffer material with improved thermal conductivity well, and the results of this paper can be very useful when designing a high-level radioactive waste repository.  
Conceptualization, S.Y.; numerical analysis, M.-J.K., G.-J.L.; writing-original draft, G.-J.L.; writing-review and editing: M.-J.K., S.Y.; revision, G.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Nuclear Research and Development Program of the National Research Foundation of Korea (2021M2E3A2041351), and Basic Research Projects of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science and ICT of Korea (GP 2020-010).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Performance Evaluation Using Randomized Search CV  
Table 4 presents the optimal combinations of hyperparameters obtained using randomized search CV. These were selected based on their superior performance using tenfold randomized search CV, with 50 iterations. Table 5 presents the impact of these hyperparameters on model performance. Among all of the algorithms, the extra tree classifier showcased exceptional results, achieving accuracy of 97.54%, precision of 96.27%, recall of 98.72%, F1-score of 97.48%, and Cohen's kappa of 95.07%. To further validate our results,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Cohen’s Kappa (\%)** \\ \hline Random Forest & 96.00 & 95.00 & 96.80 & 95.90 & 91.99 \\ \hline Extra Tree Classifier & 97.23 & 95.68 & 98.72 & 97.18 & 94.46 \\ \hline XGBoost & 96.31 & 95.03 & 97.45 & 96.22 & 92.61 \\ \hline CatBoost & 96.31 & 94.48 & 98.09 & 96.25 & 93.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance metrics for the algorithms using the default hyperparameter settings.  
Figure 3: The area under the ROC curve and the PR curve of the algorithms using the default hyperparameter settings.  
we applied an ROC curve and PR curve, as demonstrated in Figure 4. The extra tree classifier performed better than the random forest, XGBoost and CatBoost models.
## 2 Materials and Methods
### Problem Modelisation
#### 2.1.5 Optimization Objective  
We model a multi-objective SFC deployment optimization: At each simulation time-step \(t\), we measure the accomplishment of three objectives:  
* Our first goal is to maximize the network throughput as defined in (10), and we express such objective as \(\max(\chi_{T}^{t})\),
* Our second goal is to minimize the hosting costs as defined in (11), and we express such objective as \(\min(\chi_{H}^{t})\),
* Out third goal is to minimize the DT cost as defined in (13), and such objective can be expressed as \(\min(\chi_{D}^{t})\).  
We tackle such a multi-objective optimization goal with a weighted-sum method that leads to a single objective function:  
\[\max(w_{T}\cdot\chi_{T}^{t}-w_{D}\cdot\chi_{D}^{t}-w_{H}\cdot\chi_{H}^{t}) \tag{16}\]  
where \(w_{T}\), \(w_{H}\), and \(w_{D}\) are parametric weights for the network throughput, hosting costs, and data transfer costs, respectively.
## 4 Security Challenges and Solutions in 5G Network and LPWAN-IoT
### Security Challenges and Solutions in LPWAN-IoT and 5G
#### 4.2.1 Security Challenges and Solutions in LPWAN-IoT
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threats/Attacks** & **Concerned Security Requirements** & **Applicable** & **Applicable LPWAN** \\ \hline Replay attack & Confidentiality, Authenticity, Availability & Replay protection & LoRaWAN, Sigfox, and Weightless \\ \hline Spoofing attack & Authenticity, Confidentiality, and Integrity & Authentication & Most LPWAN devices \\ DoS attack & Availability and Authenticity & Network Monitor & Most LPWAN devices \\ \hline Wormhole & Availability and Authenticity & E2E security & Most LPWAN devices \\ \hline Signal jamming & Availability and Integrity & Secured credential & LoRaWAN, Weightless, and NB-IoT \\ Eavesdropping & Confidentiality and Authenticity & Data Confidentiality & LoRaWAN, Weightless, and DASH \\ Man in the mid & Confidentiality and Availability & Reliable delivery & Most LPWAN devices \\ Floods attack & Authenticity, Availability, and Integrity & E2E security/monitor & Most LPWAN devices \\ \hline Session hijacking & Availability and Authenticity & Certified equipment & LoRaWAN \\ Injections attack & Authenticity, Confidentiality, and Integrity & Updatability & Most LPWAN devices \\ Sniffing attack & Confidentiality & Network Monitor & Weightless and DASH \\ Sybil attack & Confidentiality, Authenticity, Availability & Updatability & Most LPWAN devices \\ \hline Sinkhole attack & Availability and Authenticity & E2E security & Most LPWAN devices \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Security threats and the applicable countermeasures in LPWAN-IoT. Session hijacking: This is a method an attacker uses to take control of another user's session and gain illegal access to data or resources. Certified equipment and encryption can be used to mitigate this threat;
* Injections attack: An attacker provides malicious code to an application and changes the operation of the application by manipulating it to perform certain commands. This can lead to a DoS attack and can be avoided with firewall, network monitoring, and filtering;
* Sniffing attack: An attacker intercepts data by capturing the network traffic using a packet sniffer tool, then analyzes the network to gain information and eventually disrupt the network. This can be mitigated by using firewalls, network monitoring, and filtering;
* Sybil attack: It is a type of attack in which an attacker disrupts the service's reputation system by creating many pseudonymous or false identities and uses them to gain extreme access. This can produce wrong reports and loss of privacy. Updating the application and password authentication can mitigate this threat;
* Sinkhole attack: It is a type of attack in which a compromised node attracts network traffic by advertising its false routing update. This can lead to DoS and can be avoided by E2E security and network filtering.
# An Anchor-Free Location Algorithm Based on Transition Coordinates
## 3 Algorithm Description
It can be expressed by mathematical formula as:  
\[\Delta\mathbf{p}_{i}^{k+1}=\left(d^{\prime}{}_{ij}-d_{ij}^{k}\right)\mathbf{e}_{ij} \tag{6}\]  
When you decompose \(\mathbf{e}_{ij}\) along the \(x\)-axis and \(y\)-axis, there is:  
\[\mathbf{e}_{ij}=cos\alpha_{ji}\bullet\mathbf{e}_{x}+sin\alpha_{ji}\bullet \mathbf{e}_{y} \tag{7}\]  
\(\mathbf{e}_{x}\) and \(\mathbf{e}_{y}\) represent unit vectors in the \(x\)-axis and \(y\)-axis directions, and their modulus is equal to 1. Substituting Equation (7) into Equation (6) gives:  
\[\Delta\mathbf{p}_{i}^{k+1}=\left(d^{\prime}_{ij}-d_{ij}^{k}\right)cos\alpha_{ji }\bullet\mathbf{e}_{x}+\left(d^{\prime}{}_{ij}-d_{ij}^{k}\right)sin\alpha_{ji }\bullet\mathbf{e}_{y} \tag{8}\]  
The algorithm proposed in this article can ensure reliable convergence in the vast majority of scenarios, except for some extreme scenarios such as nodes on a straight line or overlapping node coordinates where convergence may not be possible. However, the possibility of the above extreme situations occurring is relatively small, so it can be said that the algorithm proposed in this article has good convergence. As is shown in Algorithm 1, an anchor-free location algorithm based on transition coordinates is given to realize the specific steps of positioning. ```
1: Set the real position coordinates of the moving member \(A_{i},1\leq i\leq N\). 2: Set the assumed position coordinates the moving member \(P_{i}^{0},1\leq i\leq N\) and the starting position of the transition coordinates. 3: Calculate the real distance \(d^{\prime}_{ij}\) between each moving member through the distance formula between two points. 4: Calculate the distance between the transition coordinates of each moving member in the same way, and use formula (3) to calculate the offset of the transition coordinates of each moving member on its horizontal and vertical axes. 5: Formula (1) is used to obtain the new position coordinates of the transition coordinates of each moving member, and formula (9) is used to find the error proportion of the distance between the transition coordinates of each moving member and the actual coordinates of each moving member. 6: The new position coordinate of the obtained transition coordinate of the moving member is used as the starting position of the next iteration update, which is returned to step 2, and the iteration is stopped until the set distance error ratio is met. ```  
**Algorithm 1** An anchor-free location algorithm based on transition coordinates
## 4 Results
### Models' Comparative Analysis
#### 4.4.2 Residual Analysis  
Table 7 provides descriptive statistics of the residuals for models GA, RNN, and KNN with a confidence level of 95% for the PINC value. From the table, we can see that GA model is the one with the smallest standard deviation compared to other models, which tells us that it has a smaller error distribution. So, GA makes it the best model compared to others. For the GA, RNN, and KNN, the error distributions are approximately normal because their skewness is close to zero. The values for kurtosis are greater than 3 for all the models, except for GA model.  
Figure 6 shows the box plots of the forecast errors for the entire fitted models ResGA, ResRNN, and ResKNN, where ResGA, ResRNN, and ResKNN are the residuals from GA, RNN, and KNN, respectively. The box plot for the prediction errors from the GA model shows that the distribution has shorter tails an indication of a smaller error distribution compared to those of the other two models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **PICP** & **PINAW** & **PINAD** \\ \hline GA & 98.00\% & 11.81\% & 0.07\% \\ RNN & 98.60\% & 11.47\% & 0.05\% \\ KNN & 98.12\% & 15.09\% & 0.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of the best models with the confidence interval (CI) ( PICP, PINAW and PINAD) at 95%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mean** & **Median** & **Min** & **Max** & **StDev** & **Skewness** & **Kurtosis** \\ \hline GA & \(-\)26.74 & \(-\)19.25 & \(-\)83.12 & \(-\)0.10 & 23.36 & \(-\)0.54 & \(-\)1.14 \\ RNN & 3.78 & \(-\)0.24 & \(-\)688.63 & 764.30 & 56.76 & 0.48 & 41.14 \\ KNN & \(-\)5.44 & \(-\)4.33 & \(-\)747.01 & 796.95 & 57.22 & 0.08 & 43.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Residual comparison of the models.  
Figure 6: Box plots of the residuals from ResGA, ResRNN, and ResKNN.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method
### Steinberg Method  
Generally, the stress amplitude and average stress of the random response vary randomly. [PERSON] [14] proposed a three-interval method based on Gaussian-distribution and Miner linear cumulative damage theory to analyze the fatigue life from the perspectiveof probability. The stress can be divided into three ranges in terms of probability, as shown in Figure 2 and Table 1.  
Most of the time, the stress is (\(-1\sigma\), \(+1\sigma\)), and the probability is 68.3%. Outside (\(-3\sigma\), \(+3\sigma\)), the probability is 0.27%, which is very small. Thus, the time greater than \(3\sigma\) will cause almost no damage to the structure. Therefore, the overall fatigue damage can be simplified from Equation (1) to Equation (13) by using the Miner linear cumulative damage theory:  
\[D=\frac{n_{1\sigma}}{N_{1\sigma}}+\frac{n_{2\sigma}}{N_{2\sigma}}+\frac{n_{3 \sigma}}{N_{3\sigma}}=\frac{0.6831V_{a}^{+}T}{N_{1\sigma}}+\frac{0.271V_{a}^{ +}T}{N_{2\sigma}}+\frac{0.0433V_{a}^{+}T}{N_{3\sigma}} \tag{13}\]  
When \(D\) = 1, the fatigue life of structural components can be expressed as follows:  
\[T=\frac{1}{V_{a}^{+}\Big{(}\frac{0.6831}{N_{1\sigma}}+\frac{0.271}{N_{2\sigma} }+\frac{0.0433}{N_{3\sigma}}\Big{)}} \tag{14}\]  
Fatigue damage to structural components when \(D\geq 1\). \(N_{i\sigma}\) is the number of cycles under the \(i\sigma\) stress value obtained according to the S-N curve (\(i\) = 1, 2, 3).
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 5 Experiments
### Ablation Study
#### 5.4.1 Component Adjustment  
The first experiment observes the changes in the performance of the various approaches by adjusting the model's components. The HGE-CRec proposed in this paper uses GCN and GAT to improve the meta-path embedding after generating the original meta-path embedding. The first source of improvement is realized by using GCN to generate the simulated meta-path embedding, which enhances the meta-path embedding and expands the range of sample feature distribution. The second improvement is to use GAT to aggregate the various kinds of meta-path embedding based on neighbor weights. In order to explore the effect of these two improvements on the model, an ablation study of the component was set up using the following models for comparison:  
* HGE-CRec\({}_{GCN}\) Only the first part of the HGE-CRec model's meta-path embedding is improved, that is, while GCN is used to generate analog meta-path embedding, GAT is not used to aggregate various kinds of meta-path embedding based on neighbor weight.
* HGE-CRec\({}_{GAT}\) Only the second part of the HGE-CRec model's meta-path embedding is improved, that is, while GAT is used to aggregate the original meta-path embedding based on the neighbor weights, GCN is not used to generate simulated meta-path embeddings.  
The training ratio was uniformly 80%. The experimental results of the above models on the four datasets are shown in Table 8.  
It can be seen from the experimental results that HGE-CRec\({}_{GCN}\) has the weakest effect, which means that only the first part of the improvement has no significant impact on the model; slight jitter is observed during the training process, with a slight increase or decrease. This further indicates that, based on the premise of there being sufficient of original meta-path embeddings, more analog meta-path embeddings are generated by aggregating neighborhood information, and there is little improvement on the model. That is, when meta-path embedding reaches a sufficient amount, saturation occurs and it is of  
Figure 6: Comparison of RMSE with the change in the training rate on four datasets.  
little significance to add meta-path embedding. The effect of HGE-CRec\({}_{GAT}\) is significantly improved, showing that the improvement in the second part has a good impact on the model. Furthermore, this shows that using GAT to aggregate the neighbor embedding information of each node based on weight can effectively improve the performance of the model. That is, by learning the influence weight of different neighbors on nodes in the meta-path, the node representation can be enriched and the accuracy of prediction can be improved.
## 1 Introduction
The objectives were: (1) to propose two pixel-based deep learning models named PB-Conv1D and PB-BiLSTM, comparing them with PB-Transformer; (2) to introduce Snapshot and SWA techniques to enhance model accuracy in deep learning; (3) to develop the PB-CB model through ensemble learning techniques from PB-Conv1D and PB-BiLSTM; (4) to generate multiple-crop distribution maps in North China using the time series Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data from MODIS satellite imagery combined with the PB-CB model for the years 2005, 2010, 2015, and 2020; (5) to compare the accuracy of various deep learning methods.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 5 Performance Comparison of Conventional Methods and Deep-Learning-Based Spectrum Sensing
So they generally do not need to know the a priori information of the detected signals, thus having a wider scope for application. In terms of performance, the three conventional single-node spectrum-sensing methods have detection performance ranging from 40% to 100% at an SNR ranging from \(-\)20 dB to 0 dB, and the energy detection method has a detection performance of only 20% at \(-\)20 dB [18; 112]. This shows that the traditional methods perform poorly at a low SNR and are susceptible to noise variations. We reviewed the performance of deep-learning-based spectrum-sensing methods in the text above and summarized them in Tables 4-8. By reviewing the previous section, we can understand that the deep-learning-based methods also perform well at low SNR ratios, with a detection probability of 85% to 90% at \(-\)20 dB and 98% to 100% at 0 dB SNR [53; 57; 73; 78]. It can be seen that the deep-learning-based spectrum-sensing method shows a great improvement in the detection level at a low SNR and has strong robustness to noise variations. In CSS, the introduction of deep reinforcement learning can better solve decision-making and security problems and can achieve better spectrum monitoring for more efficient spectrum utilization [103; 104; 106]. In summary, compared with conventional spectrum-sensing methods, deep-learning-based spectrum-sensing methods can not only improve the detection probability at low SNRs but also apply to a wider range of application scenarios and, thus, help researchers solve more complex problems. Of course, current deep-learning-based spectrum-sensing methods are not without drawbacks. For example, deep learning can be limited by the computational resources of the hardware, and multiple deep learning models may be required in a wide range of spectrum-sensing environments, which can increase complexity. As well, most deep learning methods require large-scale labeled data for learning and training, which is expensive and difficult to obtain in the real world. Therefore, the development of this technology is dependent on the innovation of researchers as well as the development of hardware devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectrum Sensing** & **References** & **Method/Model** & **Performance** & **Application Scenario** \\ \hline \multirow{8}{*}{Based on conventional methods} & \multirow{8}{*}{Energy detection} & \multirow{8}{*}{Performs well in high-SNR and stable noise environments.} & Suitable for scenarios where the main user signal information is not known, the phase requirement is low, and the SNR is not low. \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of conventional methods and deep-learning-based spectrum sensing.
## 4 Proposed Metrics
### Results and Discussion of the Proposed Metrics  
The metrics discussed in this section and used in our evaluations are important to study the network performance. The analysis of these metrics is intended to give a measure of quality in the delivery of information in the network. The particular parameters of robustness that have to be taken into account depend on the type of application of the network. For example, if a network is designed to be deployed in a large area, it is essential that the number of hops and the interference of the environment be measured rigorously and that the channel conditions be observed closely. Likewise, if a network is to be implemented in an indoor or outdoor environment, it is essential to know the roll that the routing protocol will play. Now, we classify the metrics of interest into three categories: MAC layer metrics, network layer metrics and a global metric.
## 5 Classification  
Classifications play a crucial role in the field of orthopedic surgery, as they assist surgeons in accurately diagnosing fractures and making informed decisions regarding appropriate treatment strategies [71]. Proximal femoral fractures can be classified based on their relationship with the hip joint capsule. These fractures are categorized into two main types: extracapsular fractures (also known as trochanteric fractures) and intracapsular fractures (which include femoral neck fractures) [72]. The type of fracture significantly influences the treatment approach, with each requiring a tailored surgical intervention. This paper focuses specifically on intracapsular fractures, which are commonly associated with more complex management due to their involvement with the hip joint capsule and blood supply.  
One of the most widely used classification systems for femoral neck fractures is [PERSON]'s classification, which is based on the appearance of the fracture on the anteroposterior (AP) X-ray [73] (Table 1). The classification includes the following types:  
**Type 1:** Incomplete and undisplaced fracture, where the bone is not fully fractured and remains in proper alignment;  
**Type 2:** Complete, undisplaced fracture, where the bone is fully fractured but maintains its original position without displacement (Figure 1);  
**Type 3:** Partial displacement with misalignment of the trabecular bone, where the fracture fragments begin to shift out of alignment but are not fully displaced;  
**Type 4:** Complete displacement, where the fracture results in significant separation and displacement of the bone fragments.  
Another commonly used classification is [PERSON] classification [74], which categorizes femoral neck fractures based on the angle of the fracture line relative to the horizontal plane (Table 1). The angle is measured between a tangential line drawn to the most superior aspect of the femoral head and the fracture line. The classification includes three types:  
**Type 1:** The fracture angle is less than 30\({}^{\circ}\), typically representing a stable fracture;  
**Type 2:** The fracture angle ranges between 30\({}^{\circ}\) and 50\({}^{\circ}\), indicating an intermediate level of stability;  
**Type 3:** The fracture angle is greater than 50\({}^{\circ}\), which is associated with an unstable fracture and a higher risk of complications.  
These classifications provide important information that guides treatment decisions, surgical planning, and the management of potential complications in patients with femoral neck fractures [75] (Table 2).  
Figure 2: AP view of undisplaced PFF.