esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Modeling
Previous studies have also proven that ADINA performs well in simulating temperature development and distribution. [PERSON] (2018) used a three-dimensional finite element method to study the development and spatial distribution of the temperature field during the construction of an artificial ground freezing technique in the context of the reinforcement construction of a liaison channel of the Nanning Metro. The finite element results were first verified against the measured results to verify the feasibility of the numerical method. The formation of the permafrost curtain around the connection channel with time was then discussed, and a series of influencing factors, such as thermophysical parameters and soil condition factors, were systematically and rationally investigated in depth with the help of numerical models. This assisted us in understanding the behavior of the temperature field in the permafrost around the metro connection channel during artificial freezing. [PERSON] (2018) developed a three-dimensional finite element model based on ADINA-TMC that considers both thermal coupling and seismic loading. In this model, the laws of heat transfer to the temperature field, seismic loading, and fault movement of the soil are elaborated. Based on the numerical results, the stress-strain under temperature loads, gravity, and seismic loads were compared, providing a theoretical approach to the failure analysis of buried thermal pipes. [PERSON] (2018) built a numerical model based on ADINA to investigate the temperature distribution patterns generated during the grout sealing of tiles by a high-power diode laser (HPDL). The analysis involved a simulation of a three-dimensional transient temperature field generated by a laser beam that was scanned with constant power at a constant speed across a glazed enamel surface. Latent heat effects due to the melting and solidification of the glaze were considered in the finite element model, thus facilitating a more realistic thermal analysis.
[PERSON] [29] used RF algorithm to urban mapping with long time series Landsat images, and identified the urban development boundary. Although the number of trees can determine whether the RF is over-fitted, the split also affects the number of nodes in a single tree and the final classification result. [PERSON] [36] employed RF to explore the effect of multi-temporal Sentinel-2 data on crop identification in small areas, but the research did not involve the research on RF algorithm mechanism, and the default parameters were directly applied to classification, which will have an impact on the final classification accuracy. [PERSON] [37] used a single classification algorithm RF to monitor land-cover changes based on GEE p, but uses fixed RF parameters for classification, and does not pay attention to the impact of RF algorithm parameters on the final classification results. Another simple and effective decision tree algorithm is CART, which has been applied to many classification studies. [PERSON] [26] used CART and RF algorithms to map woody plants in Queensland and Australia, and achieved good classification accuracy. However, the research did not involve the algorithm mechanism and parameter setting. The default parameters were directly used for classification, which affects the classification performance to a certain extent. [PERSON] [15] employed CART, RF and other algorithms to map crops with 30 m Landsat-8 Operational Land Imager (OLI) images, and the results showed that the highest overall accuracy (OA) was 0.75. Although a variety of algorithms have been applied to crop mapping research, almost all algorithms have not involved in the discussion of classification mechanism and parameter changes, which may be an important factor causing the low classification accuracy of algorithms. Previous studies have shown that for many machine algorithms, the algorithm parameters have an important impact on the classification results. Considering these problems, this paper employed SVM, RF, and CART to identify and map winter wheat with Sentinel-2 10 m resolution multi-spectral data, and these algorithms built in GEE are fast but effective for crops classification. Finally, in order to further research the sensitivity and performance of SVM, RF, and CART, we employed the grid search method to iterate (C and gamma), (tree and split), and (maxD and minSP), respectively. The effects of these parameters changes on the algorithm's accuracy scores and classification residuals were analyzed in detail.
## 7 Conclusions  
The proposed methodology utilizes PMSM's three-phase currents and speed measurements for online, non-invasive, and cost-effective condition monitoring of the PMSM. To extract fault-related features from the measurements, d-q transform was used. Distortions in time waveforms and several eccentricity-related frequencies in the power spectral density were observed for different speed and load conditions of the PMSM. Then, to extract useful indicators of fault conditions, statistical measures in time and frequency domain were used. The extracted statistical features were used for outlier detection by means of fault detection and severity estimation through Isolation Forest, One Class Support Vector Machine (SVM), and Robust Covariance Ellipse.  
First, Isolation Forest was investigated for different isolation trees and contamination parameters. The best evaluation metrics were extracted for 100 Isolation Trees and a contamination parameter in the range of 0.4-0.5. The accuracy of Isolation Forest reached 0.82. One Class SVM was employed for the same task. Radial Basis Function was selected as the kernel and different gamma and fraction parameters were investigated. The best  
Figure 19: Mean Ensemble anomaly scores.  
Figure 20: Max Ensemble generated anomaly scores.  
evaluation metrics were extracted for gamma equal to 0.2 and fraction parameter equal to 0.6. The accuracy reached 0.97. Lastly, Robust Covariance Ellipse fitting was tested. The highest accuracy achieved was 0.91 for gamma and contamination parameters equal to 0.2 and 0.3, respectively. One Class SVM was the best candidate in terms of Accuracy, Recall, Precision, F1-Score, and ROC AUC. For Severity Estimation, the extracted Outlier Anomaly Scores from the above methods were used. Comparing the three methods, increasing fault severity was better observed in Outlier Scores generated by Robust Covariance Ellipse fitting, then Isolation Forest, and lastly, One Class SVM. To combine the predictions and outlier scores, and so the advantages of each method, Independent Ensemble approaches are proposed. Majority Voting and Averaging Ensemble of the predictions led to Accuracy equal to 0,94 and 0,97, respectively. Max and Mean Ensemble of the Outlier Scores led to better observability of the increasing severity by each sample of the tested dataset.  
Conceptualization, K.K. and E.M.; methodology, K.K.; software, K.K.; validation, K.K., investigation, K.K.; resources, K.K.; data curation, K.K. and E.M.; writing--original draft preparation, K.K and E.M.; writing--review and editing, K.K and E.M.; supervision, E.M.; project administration, E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The present work was financially supported by the \"Andreas Mentzelopoulos Foundation\" (Corresponding Author: K.K.).  
The authors declare no conflicts of interest.
## 4 Discussion
### Performance Evaluation of Land Surface Temperature Predictive Modeling
The issue of data overestimation in the MYD11A1 day model highlights the practical challenges encountered when applying theoretical models. This phenomenon can be attributed to the impact of solar radiation on the thermal infrared signal during daytime, thereby introducing complexity to the relationship between \(T_{\text{air}}\) and LST [116]. Ref. [38] suggested that utilizing both Terra and Aqua satellites enabled the development of consistent indices suitable for analysis during both day and night. This methodology revealed that anomalies in LST effectively mirror the climatic trends of the area and reliably signal the occurrence of heatwaves, especially the more intense ones, which can be further employed in future studies. Adjusting hyper-parameters in the RF model can lead to overfitting, particularly when the rules are overly complex and tailored to the training data. Employing unseen test data and cross-validation methods can help mitigate this risk and ensure improved performance on new datasets [119, 120]. Furthermore, regarding the use of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI) for downscaling the TIR bands, an improvement in performance accuracy should be further considered in modeling LST [121, 122].
## 2 System Model
As in [42], with the same beamwidth for transmission and reception, the maximum transmission range \(R\) in the data transmission phase is given by  
\[R=R_{0}\left(\frac{2\pi}{\Phi}\right)^{2/\alpha}, \tag{2}\]  
where \(\alpha\) is the path loss exponent and \(R_{0}\) is the reference transmission range depending on the transmit power with \(\Phi=2\pi\). We assume that all the nodes operate with the same maximum transmit power and beamwidth. We make this assumption for analytical simplicity to gain design insights, as in [31; 42]. However, in practice, MTC devices may have different transmission ranges and beamwidths. We leave the investigation of mmWave M2M networks consisting of devices with non-homogeneous transmission ranges and beamwidths to future work. In the actual data packet transmission, with the given next-hop node, the source and the relays adapt their transmit power to be just strong enough to reach the next-hop node to save energy consumption, as in [13; 43]. Therefore, the short-hop routing takes less transmit energy compared to the long-hop routing. We will investigate the transmit energy in more detail in Section 5.2. Moreover, to reflect the blockage effects in the mmWave systems, we use the blockage model based on the stochastic geometry proposed in [29]. Let \(r_{i}\) be the distance from a transmitting node (e.g., source or a relaying node) to Node \(i\) towards the destination. From [29; 30; 31], the probability of no blockage to Node \(i\) is  
\[P_{LoS,i}=e^{-\beta r_{i}}, \tag{3}\]  
where \(\beta\) is the blockage parameter that depends on the density and sizes of obstacles blocking LoS paths from the transmitting node to Node \(i\). As in [29; 44; 45; 46; 47], we assume that an outage occurs if the LoS path is blocked. In other words, even if Node \(i\) is within the transmission area \(\mathcal{A}\), the link to Node \(i\) is unavailable with the probability of \(1-P_{LoS,i}\).
# Si Photonics for Practical LiDAR Solutions
## 3 Si Photonics for LiDAR
### Implementations of Si Photonics in LiDAR  
The simple idea behind Si photonics is to replace a complex free space optical system made of many discrete optical components with a chip scale solution on Si platform. A complex optical system for LiDAR is apparently a perfect target to tackle. Among all the mapping approaches described earlier, the OPA may be the mostly suited to leverage of the benefit of Si photonics. The concept of an OPA is not new and chip-scale OPA research activities started with electro-optic medium such as liquid crystal in early 1990s [15; 16] but lead to no commercial success. With the help of Si photonics researchers and entrepreneurs resurge the adoption of the OPA which is analyzed in details in Section 4.  
Besides OPA, Si photonics might find other uses in a LiDAR system. The FMCW method requires a coherent receiver for mixing a local optical oscillator with the optical signal reflected from objects. It is nearly identical to a phase-shift keying (PSK) coherent receiver that is widely used for long-haul optical networks. Si photonics has been adopted by both new and incumbent optical companies to make such PSK coherent receivers with increasing market shares. Adopting Si photonics for such receivers is naturally considered for FMCW LiDAR and is discussed in Section 5.1. Si photonics can also be used for making narrow linewidth tunable lasers as the light source for FMCW LiDAR which is discussed in Section 5.2.
## 2 Materials and Methods
### Benchmark Methods  
We here introduce the benchmark algorithms compared in this paper, including degree centrality, k-shell algorithm, WL algorithm, ego betweenness centrality and LLS algorithm.  
1. Degree centrality Degree centrality is a very simple ranking algorithm. The node degree \(k_{i}\) represents the number of neighbours of node \(i\), namely \[k_{i}=\sum_{j=1}^{N}a_{ij}.\] (2)
2. K-shell algorithm The implementation of the k-shell decomposition method is as follows: firstly, continuously remove the nodes with degree one until all nodes' degrees are larger than one. All of these removed nodes are assigned 1-shell. Then, keep removing the existing nodes until all nodes' degrees are larger than two and add the removed nodes to 2-shell. Repeat this procedure until all nodes have been assigned to one of the shells.
3. WL algorithm WL algorithm holds the opinion that the importance of nodes in the network is closely related to the importance of edges the nodes connected. The weight of edge \(ij\) is expressed as \[w_{ij}=k_{i}\times k_{j},\] (3) where \(k_{i}\) is the degree of node \(i\). The weight of node is expressed as \[w_{i}=\sum_{j\in\Gamma_{i}}w_{ij},\] (4)where \(\Gamma_{i}\) is the set of neighbors of node \(i\). Thus, the importance of the node is expressed as \[w(i)=\frac{w_{i}}{\sum\limits_{j\in N}w_{j}}.\] (5)
4. Ego betweenness (Abbreviated as EgoBet) Ego network consists of a target node and the nodes (1-hop neighbors) which are connected to the target node and all the edges between those nodes. The standard measure of betweenness considers all the shortest paths of node pairs across the target node, while ego betweenness just takes into account the shortest paths of node pairs within the ego network. Ego betweenness can be expressed as \[C_{EB}(i)=\sum\limits_{s\
eq i\
eq t,s,t\in\Gamma_{1}(i)}\frac{\sigma(s,t|i)} {\sigma(s,t)},\] (6) where \(\sigma(s,t|i)\) is the number of shortest paths passing through node \(i\) between node \(s\) and node \(t\), \(\sigma(s,t)\) is the total number of shortest paths between node \(s\) and node \(t\), and \(\Gamma_{1}(i)\) represents the set of 1-hop neighbors of node \(i\).
5. LLS algorithm LLS algorithm is a method to evaluate the importance of nodes based on similarity of node neighbors. The similarity of node neighbors is calculated by Jaccard index when nodes b and c are not connected, while the value of similarity is 1 when nodes b and c are connected, namely \[sim(b,c)=\left\{\begin{array}{ll}\frac{|n(b)\cap n(c)|}{|n(b)\cup n(c)|}&b,c \ \ \text{not connected};\\ 1&b,c\ \ \text{connected},\end{array}\right.\] (7) where \(b(i)\) is the set of one hop and two hop neighbors of node \(i\). Thus, the node importance is denoted as \[LLS(i)=\sum\limits_{b,c\in n(i)}{(1-sim(b,c))}.\] (8)  
For the five benchmark algorithms, k-shell algorithm makes use of the information of the whole network and all of the other five algorithms just utilize the local information.
# Anodic Oxidation of 3D Printed Ti6 Al4V Scaffold Surfaces: In Vitro Studies
## 3 Results and Discussion
### Surface Characterization
Deconvolution analysis revealed the presence of Ti in the oxidation states of Ti\({}^{3+}\) (458.6 eV and 463.8 eV) and Ti\({}^{4+}\) (460.0 eV and 465.5 eV). These states are associated, respectively, with Ti2O3 and TiO2 oxides that are formed during the anodization process [25; 26; 27].Deconvolution of the O 1s peak resulted in three components (Figure 7f,g): O\({}^{2-}\) (529.5 eV), which is present in the formation of oxides with Ti; hydroxyl groups (-OH) (531.9 eV); and adsorbed water molecules (533.6 eV) on the surface [25; 26]. Its deconvolution (Figure 7b,c) revealed peaks at 284.6 eV (C-C, C-H), 286.6 eV (C-O), and 288.6 eV (O-C=O). After the growth of TiO\({}_{2}\) nanotubes, a fourth component at 290.0 eV (C=O) was observed [25; 27]. The carbon present on the surface originated from contamination, as verified in other studies involving anodic oxidation. No interference with cell adhesion and proliferation was observed [28]. According to the literature, analyzing samples without carbon carbonates that can be deposited on the surface of TiO\({}_{2}\) in a few seconds is a significant challenge [29]. Figure 7: _Cont._  
surfaces before and after anodization indicated a significant decrease from 23.4 to 1.98. This value is consistent with the expected ratio for the formation of TiO\({}_{2}\) oxide. Therefore, the XPS data indicates that the surface treatment promoted the formation of TiO\({}_{2}\). Additionally, an increase in hydroxyl content and a peak related to adsorbed water molecules on the oxide layer were observed. Hydroxyl groups may be present as functional groups attached to the surface or within the oxide layer, such as Ti-OH [25, 26]. The presence of hydroxyl groups and water in the nanotube layer may enhance the hydrophilicity of the scaffolds.
## 3 Results and Discussion
### Impact of the Angle of View on the Defect Detection  
The other factor that may affect the accuracy of the defect detection in IRT is the angle of view, defined here as the angle offset from perpendicular to the tank surface. Figure 13 shows a set of experiments ranging from 0\({}^{\circ}\) to 75\({}^{\circ}\) relative to the perpendicular to the tank surface. The defects detected in the directions 0\({}^{\circ}\) to 30\({}^{\circ}\) produce the most reasonable thermograms for defect detection, so the angle of view for inspection should not surpass 30\({}^{\circ}\).
## 5 Numerical Results
### Weight and Parameter Setting of Our Reward Components
The remaining SAC hyperparameters, including the learning rate and MLP structures, were meticulously fine-tuned through extensive experimentation to achieve an optimal balance between beacon interval costs and congestion costs. Specifically, we tested different learning rates, starting with 0.005 and refining it to 0.0003, to find a balance between convergence speed and stability. Additionally, we evaluated various MLP configurations consisting of 64 and 256 neurons to identify the best performance in terms of learning efficiency and model accuracy. After extensive testing, we ultimately selected a configuration with 512 hidden units, as discussed in Section 5.3, which provided the most robust performance across multiple scenarios. These adjustments were crucial for ensuring that our model effectively manages network resources, particularly under varying traffic conditions, without compromising overall system performance.
## 2 Passive Wake-Up Radio
### Passive Wake-Up Radio Hardware Architecture
#### 2.2.5 Energy Storage  
A capacitor is connected at the output of the voltage multiplier to store the output DC voltage. The stored power is used as a reserve source when the external energy source is inaccessible or inadequate. This will ensure continuous power delivery to wake-up the main radio whenever it is needed, hence offer smoother and continuous network operation.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 3 Fault Diagnosis of the High-Speed Train Traction System and Brake System
### Fault Diagnosis of Train Braking System  
The braking system of trains mainly consists of brake units, brake cylinders, and related brake components. The complex service environment of high-speed trains, coupled with long-distance driving, leads to various early failures that inevitably occur. If these early failures are not detected in time for maintenance, safety accidents may occur. Therefore, it is important to carry out fault diagnosis research on the train braking system, and its main fault diagnosis methods are shown in Figure 8.  
Combining the concepts related to inter-variate variance (IVV) and the idea of four-stage division, [PERSON] et al. [82] proposed a fault detection method based on refined IVV (R-IVV) and stage monitoring indicators, which can improve the accuracy of early fault diagnosis of the train's braking system. To address the problem of data imbalance in train brake system fault detection, [PERSON] et al. [83] presented a diagnostic framework based on a support vector machine (SVM), which improved the computational efficiency from the aspects of feature brush selection, feature selection, and model construction, and verified that the method had better results through experiments. [PERSON] et al. [84] advanced a method based on data domain description and a variable control limit, which can detect three kinds of early failures of high-speed train braking systems and effectively reduce the false alarm rate. [PERSON] et al. [85] designed an improved convex packet vertex-based method to diagnose multiple faults in the train's braking system accurately.  
Therefore, the fault diagnosis of train braking systems is mainly made by failure mode identification, and the main methods include IVV, feature selection, SVM, etc.  
Figure 7: Fault diagnosis method of traction system: (**a**) independent component analysis (ICA) [73]; (**b**) relevant vector machine (RVM).
## 5 Conclusions  
This study has proposed a lightweight security-enhanced authentication key-agreement solution to address the security risks of eavesdropping, counterfeiting, and tampering in UAV communication scenarios. Compared with existing representative protocols, the proposed protocol can ensure the security of session keys when temporary session random number information is leaked and can provide the same level of security with less time-consuming calculations. Security analysis and security simulation verification show that the proposed protocol can satisfy all the security attributes in Section 2. The performance analysis results showed that the proposed protocol has obvious advantages in terms of performance. The authentication-key negotiation protocol is suitable for one-to-one communication between UAVs and ground stations. The protocol needs to be optimized and improved in the future to meet the requirements of one-to-many communication between ground stations, UAVs, and wireless security communication between UAVs.  
Conceptualization, Z.H., Y.Z. and K.Z.; methodology, Y.Z. and S.C.; software, S.L.; validation, Z.D. and Y.Z.; formal analysis, Y.Z. and S.C.; writing--original draft preparation, Y.Z. and Z.H.; supervision, K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (61972318,61601 346), the Shaanxi Provincial Science and Technology Project (2023-GHZD-47) and the Shanghai Aerospace Technology Research Institute Industry University Research Cooperation Fund Project (SAST2024G007).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned Aerial Vehicle \\ ECC & Elliptic-Curve Cryptography \\ DH & Diffie-Hellman \\ RC & Key-Generation Center \(RC\) \\ PK & Public key of the key-generation center \\ sk & Private key of the key-generation center \\ TS & Timestamp information \\ SSK & Session keys of protocol entities \\ \end{tabular}