esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Model  
In this study, a novel CSRO-MHDT method was developed for the optimal choice of routes for data transmission in a UWSN. Clustering is a well-known energy-saving strategy in sensor networks. The least-cost-clustering steering procedure (MCCP) is one of the clustering-based routing protocols (UWSNs). This parameter is composed of three important values: the total energy consumed by member nodes during data transfer to the \(\mathbb{C}H\), the total energy residual on the bunch head node and its associate bulges, and the distance between the cluster head node and the originating base station. The major intention of the CSRO-MHDT technique is to the reduce energy dissipation and enhance the lifetime of a UWSN. Primarily, the CSRO-MHDT technique involves WCA for the effectual choice of \(\mathbb{C}H\)s. In addition, the CSRO-MHDT technique derives a fitness function and can effectually select the set of routes in a UWSN. Figure 1 presents the general procedure of the CSRO-MHDT method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Published Year** & **Approach** & **Advantages** & **Disadvantages** \\ \hline [26] & 2019 & Adaptive node clustering & \begin{tabular}{c} Throughput and reliability \\ are high in clustering process \\ \end{tabular} & Network life time \\ \hline [27] & 2020 & LOCAN methodology & Energy consumption & Packet loss \\ \hline [28] & 2020 & Bee algorithm & Reliable transmission & \begin{tabular}{c} Not suitable for deep \\ UWSNs \\ \end{tabular} \\ \hline [29] & 2021 & \begin{tabular}{c} Grey wolf \\ optimization algorithm \\ \end{tabular} & \begin{tabular}{c} Reducing packet loss and \\ traffic systems \\ \end{tabular} & Sensor failure \\ \hline [30] & 2019 & \begin{tabular}{c} Threshold and energy \\ level partition \\ \end{tabular} & \begin{tabular}{c} Reducing delay and high \\ throughput \\ \end{tabular} & \begin{tabular}{c} Maximizing energy \\ Resources \\ \end{tabular} \\ \hline [36] & 2021 & Path selection strategy & High throughput & Time delay \\ \hline [37] & 2020 & \begin{tabular}{c} Fuzzy C means and moth–flame \\ optimization method \\ \end{tabular} & \begin{tabular}{c} Reducing energy \\ consumption \\ \end{tabular} & \begin{tabular}{c} Time delay \\ Packet loss \\ \end{tabular} \\ \hline Proposed & 2022 & \
\begin{tabular}{c} Chaotic search and \\ rescue optimization \\ \end{tabular} &
\begin{tabular}{c} Reliable transmission, \\ Reducing time delay and \\ high throughput \\ \end{tabular} & Packet loss in high \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing approaches.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 4 Experiments
### Performance Comparison (Rq1)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Recall@5** & **Recall@10** & **Recall@20** & **Recall@50** & **MAP@5** & **MAP@10** & **MAP@20** & **MAP@50** \\ \hline NeuMF & 0.0368 & 0.0610 & 0.0981 & 0.1727 & 0.0235 & 0.0241 & 0.0271 & 0.0310 \\ NGCF & 0.0390 & 0.0627 & 0.0980 & 0.1688 & 0.0249 & 0.0250 & 0.0278 & 0.0314 \\ DGCF & 0.0435 & 0.0669 & 0.1028 & 0.1764 & 0.0291 & 0.0288 & 0.0316 & 0.0354 \\ LightGCN & 0.0469 & 0.0721 & 0.1076 & 0.1796 & 0.0317 & 0.0312 & 0.0341 & 0.0378 \\ SGL & 0.0452 & 0.0707 & 0.1080 & 0.1852 & 0.0300 & 0.0299 & 0.0330 & 0.0371 \\ NCL & 0.0463 & 0.0723 & 0.1083 & 0.1839 & 0.0313 & 0.0310 & 0.0338 & 0.0378 \\ STACP & 0.0274 & 0.0450 & 0.0700 & 0.1275 & 0.0187 & 0.0186 & 0.0206 & 0.0235 \\ LGIMF & 0.0284 & 0.0459 & 0.0729 & 0.1284 & 0.0192 & 0.0190 & 0.0212 & 0.0242 \\ GPR & 0.0316 & 0.0502 & 0.0763 & 0.1272 & 0.0183 & 0.0205 & 0.022
###### Abstract  
An oscillator is a key component of a satellite communication measurement and control system, performing symmetry precisely as a time frequency reference. At the same time, the phase noise index has a close coupling relationship with the overall performance of the entire system, while persistently breaking the symmetry property of the oscillator during work. It is very important to study and reasonably allocate the phase noise index. According to the theoretical formula of phase noise, this paper analyzes the power law spectral model in the frequency domain and the noise jitter characteristics in the time domain. Using the carrier tracking loop in the measurement system, the frequency domain transfer model of phase noise is established, and typical analysis results are given. A discrete fractional integration algorithm is proposed, which can generate the phase noise time domain sequence under the given power law spectral model coefficients. The proposed algorithm is more realistic compared with the previous numerical calculation method, and has sufficient accuracy compared with the results of the instrument. After frequency domain conversion, the RMS deviation between the simulated noise sequence in the frequency domain and the measured single sideband power spectral density is less than 2.5 dB, indicating that the phase noise sequence can reflect the frequency domain characteristics more completely. A communication measurement simulation system is built, and a discrete sequence simulation analysis method combining frequency domain and time domain is provided, and the coupling relationship of key indicators such as phase noise, thermal noise, communication data rate, modulation method and bit error rate is synthesized. The results show that the BER of the QPSK/BPSK communication system will not be significantly reduced if the phase jitter RMS caused by the phase noise is less than 5 degrees, so 5 degrees can be used as a reference for the decomposition of the carrier SSB phase noise index. The simulation results have been successfully applied to a satellite inter-satellite link system, which has universal practical application value.  
phase noise; noise jitter characteristics; oscillator; QPSK; BPSK 7 October 2023  
Revised: 1 November 2023  
Accepted: 7 November 2023  
Published: 12 November 2023
## 3 Experiments and Results
### Electro-Thermal Coupling Problem
The activation function we select is Tanh, the optimization algorithm is Adam [38], and the initial learning rate of both networks is \(lr=1\times 10^{-2}\). For training data, we utilize full batch size. Moreover, we set the cut-off threshold of the loss function as \(1\times 10^{-5}\) and the maximum number of epochs at \(20,000\), that is, when the neural network training meets any of these two conditions, the training ends. Weights and biases of the neural network usually need to be initialized by some initialization methods. Here, we choose a widely used method: Xavier initialization [39]. Based on the experiment results, the number of epochs need for the two field problems are \(1108\) for the steady electric field,  
Figure 4: Sample points: (**a**) for the electric field problem; (**b**) for the thermal field problem. and 1182 for the heat conduction field. The lowest loss function values and the spent time of each neural network training are displayed in Table 1. We can notice that because of the non-homogeneous distribution of the thermal field, the learning of the temperature distribution is more difficult than the electric field. Our training results are shown in Figure 5. We use the FEM results as the reference solutions to evaluate the training accuracy. The FEM results, shown in Figure 6, are obtained with a mesh of 6592 points and the CPU time of 0.87 s.  
In order to further compare the difference between our training results and the reference solutions, Figure 7 shows the absolute error distributions between the PINNs solutions and the reference solutions. The relative errors are below \(4\times 10^{-4}\) for both problems. We can conclude that DNN's training results are in good agreement with those calculated by FEM; however, the large computation time of DNN as compared to that of FEM is an apparent shortcoming that needs to be improved. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Steady Electric Field** & **Thermal Field** \\ \hline Lowest Loss & \(9.97\times 10^{-6}\) & \(9.84\times 10^{-6}\) \\ Time(s) & 101 & 109 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The lowest loss values and the time consumed in each training. Figure 5: Results of our PINN networks: (**a**) the electric potential (V); (**b**) the temperature (K). Figure 6: Result of the FEM: (**a**) the electric potential (V); (**b**) the temperature (K).
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 3 Methodology
### Formal Concept Extraction Module  
According to the formal concepts definition (see in Section 3.1.1), SFCA automatically generate formal concepts from arbitrary KGs without external annotated knowledge. To get high-quality formal concepts, we developed a FCE module to generate massive corresponding formal concepts from KGs in mining valuable information. All generated formal concepts contain corresponding entity set and relation set.  
Each fact triple set will first be encoded into a two-dimensional tabular as the formal context. Then, we produce formal concepts by the entity and relation binary relations in these formal contexts. The formal representation of \(KG\):  
\[KG=\big{\{}(e_{i},r_{j},e_{k})|e_{i}\in E,e_{k}\in E,r_{j}\in R\big{\}} \tag{3}\]  
where \(E\) is the set of entities, \(R\) is the set of relations from \(E\) to \(E\).  
In this paper, entity \(e_{i}\) is regarded as the object and relation \(r_{j}\) as the attribute of \(e_{i}\). The formal context can be obtained by a \(KG\) and then the concept lattice \(K\) can be induced by the formal context.  
For every \(e_{i}\in E\), let \(\text{C}_{e_{i}}^{temp}=\{(A_{l},B_{l})|e_{i}\in A_{l}\}\). Where \((A_{l},B_{l})\) is the concept in the concept lattice \(K\).  
The mapping from the formal concepts to the entity is formulated as:  
\[\begin{split}& f:\text{C}_{e_{i}}^{temp}\to\{e_{i}\}\\ &\text{for every }(A_{l},B_{l})\in\text{C}_{e_{i}}^{temp},\ f((A_{l},B_{l }))=& e_{i}.\end{split} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Score Function** & **Parameters** \\ \hline TransE & \(-\|\mathbf{h}+\mathbf{r}-\mathbf{t}\|_{1/2}\) & \(\mathbf{h},\mathbf{r},\mathbf{t}\in\mathbb{R}^{k}\) \\ DistMult & \(\mathbf{h}^{\top}diag(\mathbf{M}_{r})\mathbf{t}\) & \(\mathbf{h},\mathbf{r},\mathbf{t}\in\mathbb{R}^{k}\) \\ ComplEx & \(Re(\mathbf{h}^{\top}diag(\mathbf{M}_{r})\mathbf{f})\) & \(\mathbf{h},\mathbf{r},\mathbf{t}\in\mathbb{C}^{k}\) \\ RotatE & \(-\|\mathbf{h}\circ\mathbf{r}-\mathbf{t}\|_{2}\) & \(\mathbf{h},\mathbf{r},\mathbf{t}\in\mathbb{C}^{k},\left|r^{i}\right|=1\) \\ HAKE & \(-\|\mathbf{h}_{m}\circ\mathbf{r}_{m}-\mathbf{t}_{m}\|_{2}-\lambda\big{\|}\text {sin}((\mathbf{h}_{p}+\mathbf{r}_{p}-\mathbf{t}_{p})/2)\big{\|}_{1}\) & \(\mathbf{h}_{m},\mathbf{t}_{m}\in\mathbb{R}^{k},\) \\  & \(\mathbf{h}_{p},\mathbf{r}_{p},\mathbf{t}_{p}\in[0,2\pi)^{k},\lambda\in\mathbb{ R}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of several knowledge graph embedding models.
## 6 Conclusions and Future Work  
### Conclusions  
This study proposed a PCA-IWOA-XGBoost model for earthquake fatality prediction, aiming to address challenges such as small sample sizes, feature redundancy, and parameter tuning in disaster forecasting. By integrating Principal Component Analysis (PCA) for dimensionality reduction, an Improved Whale Optimization Algorithm (IWOA) for intelligent hyperparameter tuning, and XGBoost as the core predictive engine, the model achieved superior prediction accuracy and robustness. The key findings are summarized as follows:  
1. Baseline XGBoost Performance: The XGBoost model achieved an average prediction accuracy of 88.2% on the test set, with an \(R^{2}\) of 0.936 and \(RPD\) of 6.155, demonstrating strong regression capability but limited optimization of complex settings.
2. Effectiveness of PCA Dimensionality Reduction: The PCA-XGBoost model reduced the original 11 features to 6 principal components, retaining 87% of the data information. This reduction effectively alleviated feature coupling and multicollinearity, improving the prediction accuracy (\(R^{2}\) = 0.966, \(RMSE\) = 25.578, \(MAE\) = 15.133, \(RPD\) = 8.354) and reducing the \(RMSE\) by 26.3% compared to the baseline.
3. IWOA Optimization Contribution: The IWOA-XGBoost model intelligently tuned key hyperparameters (learning rate, tree depth, and estimators), enhancing the prediction performance (\(R^{2}\) = 0.984, \(RMSE\) = 15.847, \(MAE\) = 9.791, \(RPD\) = 13.486) and reducing the RMSE by approximately 54.3% relative to the baseline
4. Synergistic Advantage of PCA-IWOA-XGBoost: The integrated PCA-IWOA-XGBoost model exhibited the optimal performance with \(R^{2}\) = 0.997, \(RMSE\) = 8.751, \(MAE\) = 5.366, and \(RPD\) = 24.422. Compared to the baseline XGBoost, the RMSE and MAE were reduced by 74.8% and 73.8%, respectively. These results confirmed the synergy of the PCA and IWOA in enhancing the model accuracy, stability, and generalization ability.
5. Model Generalization and Limitations: Cross-validation (k-fold and LOOCV) and statistical tests ([PERSON] and Nemenyi) confirmed the model's robustness under different partitions. However, external validation using Japanese earthquake samples revealed a significant drop in performance (\(R^{2}\) = 0.55), indicating regional differencesin impact factors. Retraining the model on localized data restored a high accuracy (\(R^{2}\) = 0.97), highlighting the necessity of region-specific modeling in seismic scenarios.
6. Interpretability via SHAP Analysis: SHAP values indicated that the most influential components (\(F_{1},F_{3}\)) were mainly driven by epicentral intensity, magnitude, fortification level, and building codes. Both the global and local SHAP interpretations verified the physical relevance and consistency of model behavior with seismic knowledge.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods  
The analyzed fault detection algorithms include the following: overcurrent (OC), undervoltage (UV), rate of change of current (ROCOC), rate of change of voltage (ROCOV), differential and limiting inductor voltage ratio derivative (LIVRD) algorithms.  
OC and UV are classic yet fast detection methods that rely on local measurements, which is advantageous in MVDC grids, where immediate detection is critical. Although these methods face challenges regarding their selectivity, their simplicity and speed make them essential benchmarks for comparing more advanced techniques in medium-voltage systems.  
The ROCOC and ROCOV enable the detection of abrupt changes in current and voltage, which are characteristic indicators of faults. These methods are particularly suited to the rapid state changes typical of DC systems. Their potential for faster and more sensitive detection compared to absolute current or voltage methods makes them valuable in MVDC systems, despite their susceptibility to signal noise.  
LIVRD is a more advanced and less-studied technique for MVDC grids. It is considered promising due to its capability to discriminate faults based on the ratio of voltage derivatives across the limiting inductor. Evaluating this algorithm in the context of MVDC networks could offer valuable insights into the feasibility of novel fault detection approaches.  
Selecting these algorithms for analysis in MVDC protection research is advantageous due to their broad applicability, compatibility with MVDC systems, efficiency, reliability, and established research support. These methods play a pivotal role in fault detection for medium-voltage DC systems, ensuring the timely activation of protection devices to prevent potential system damage.
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
## 3 Results
### Analysis of Previous Accidents and Incidents
#### 3.1.3. Contingency Analysis and Three-Way Associations  
Results were obtained at a 95% confidence level, for permutations of two or three of the parameters in Table 5. These were then recoded and converted into binary variables, as shown in Table 5, to help understand the meanings of the associations. In some cases, such as for the \"Time\" parameter, which had many possible outcomes, these were grouped to reflect the part of the day (morning and evening, daytime and night-time) and therefore have only three possible outcomes. In other cases, such as for the \"Phase of Flight\" parameter, some possible outcomes were grouped into an \"Other\" binary variable, so that the focus could be on the phases of flight deemed to be of interest during the trend analysis. This was generally done for the least frequent outcomes, to avoid binary parameters with a low expected count, as those would not provide accurate results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Binary Variables** & **Count** & **Description/Reasoning** \\ \hline \multirow{3}{*}{Year} & 1990–2000 & 117 & \multirow{3}{*}{Separated the years into 3 groups of equal number of years.} \\ \cline{3-4}  & 2001–2011 & 186 & \\ \cline{2-4}  & 2012–2022 & 163 & \\ \hline \multirow{3}{*}{Month} & May–October & 432 & \multirow{3}{*}{Grouped into flying season (May–October) and outside flying season (November–April).} \\ \cline{3-4}  & \multirow{2}{*}{November–April} & \multirow{2}{*}{34} & \multirow{2}{*}{Grouped into flying season (May–October) and outside flying season (November–April).} \\ \cline{3-4} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters used for the contingency analysis and three-way associations. Figure 11: Numbers of injuries and fatalities per year, for private recreational seaplane operations on water in Canada. The results of this analysis are presented in Figure 12, which represents a heatmap of the relevant associations with a 95% confidence level. The blue shades indicate moderate relationships, while the red shades indicate strong relationships. Some findings that are similar to the findings already presented in the previous sections were excluded. Moreover, the associations where one of the parameters was daytime, May-October, or non-amateur-built were excluded, as it was previously determined that the majority of accidents and incidents happen then. Moreover, the \"Other\" categories were also removed, as they include combinations of less frequent variables. Taking these factors into account, the main findings for strong relationships with a 95% confidence level are shown in Figure 12. As an example, there is a strong association of amphibious aircraft being destroyed after occurrences during take-off or landing, or having substantial damage. However, occurrences of amphibian seaplanes during taxi are likely to not cause serious damage to the aircraft.
## 3 Experimental Validation  
To demonstrate the working principle of the proposed OW-DCN and to evaluate the transmission performance, a fully functional 4 \(\times\) 4-rack DCN prototype with a control plane and data plane was built. First, the FPGA-based switch scheduler with the label system was validated with a testbed comprised of an FPGA-based switch scheduler and four FPGA-based ToR switches to demonstrate the system's switch-scheduling ability for contention-solving. Furthermore, the SDN control plane based on ODL and Openstack was implemented to reconfigure the network for supporting the dynamic transmission traffic patterns. The data plane network based on a 4 \(\times\) 4-port AWGR and SWS at each ToR was assessed to validate the transmission and switching performance for all the combination links. Moreover, a packet switching operation for ToR-to-ToR true packetized payloads was experimentally assessed.
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 3 Traffic Monitoring
### Coordination of UAVs  
With the sensing techniques in hand, another issue that practitioners are often concerned about is the motion control of a single UAV and the coordination of multiple UAVs. The authors of [55] investigated the path-planning problem for a single UAV. The UAV is used to survey a given set of road segments, and the authors developed a real-time routing method by considering the time-varying traffic-monitoring demand. Still focusing on road patrolling, the authors of [56] paid attention to the scheduling problem of multiple UAVs, and the charging issue has been accounted for in the formulated model. Moreover, in the context of multiple UAVs, Reference [57] formulated a multi-objective optimization problem for a group of UAVs to patrol some road segments. The overall flight distance of the UAVs, the number of UAVs, and the satisfaction level of customers were jointly optimized. The authors of [58] proposed a decentralized autonomous navigation algorithm for the surveillance UAV to detect traffic blockage and to then effectively gather to the blocked area and monitor the majority of targets. The proposed algorithm consists of stages: initial, searching, accumulating, and monitoring. The UAVs only need to share measured information and their positions with their neighbours. A limitation of [58] is that the proposed navigation laws are for a planar motion only, and 3D mobility of the UAVs was not utilized. Another study on road-traffic monitoring by UAVs can be seen in [59], in which the authors proposed a distributed navigation algorithm based on Voronoi partition for a UAV network to maximize the quality of surveillance of a group of targets moving along a curvy road with unknown time-varying speeds. The convergence of the UAVs  
Figure 5: An example illustrating traffic-monitoring UAVs, where the blue dots stand for groups of monitoring targets (pedestrians or vehicles) and the red circles stand for the sensing range of UAVs. With the targets merged and then separated, the UAVs navigate to the locations with better coverage of the targets. A video recording how these UAVs move is available here: [[https://youtu.be/OEGr5-0](https://youtu.be/OEGr5-0) zavg]([https://youtu.be/OEGr5-0](https://youtu.be/OEGr5-0) zavg), accessed on 1 July 2021.  
positions to the local optimal locations was also proved in [59], but the global optimality was not guaranteed.