esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Success Rate** & **Precision** & **FPS** \\ \hline Ours & 0.699 & 0.872 & 42.6 \\ SiamGAT & 0.637 & 0.857 & 27.8 \\ TransT & **0.641** & 0.823 & 36.8 \\ SiamCAR & 0.623 & 0.830 & 78.6 \\ STARK & 0.596 & 0.822 & 64.5 \\ SiamBAN & 0.584 & 0.845 & 71.3 \\ MHT & 0.512 & 0.789 & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The success rate and precision rate of all sequences in VOT-NIR. The highest and second-highest values are marked in _red italics_ and blue bold, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Challenges** & **MHT** & **SiamBAN** & **SiamCAR** & **SiamGAT** & **STARK** & **TransT** & **Ours** \\ \hline BC & 0.529 & 0.505 & 0.592 & 0.504 & 0.531 & 0.565 & _0.602_ \\ CM & 0.422 & 0.501 & 0.537 & 0.521 & _0.646_ & **0.596** & 0.540 \\ DEF & 0.662 & 0.664 & 0.719 & 0.688 & 0.701 & 0.724 & **0.723** \\ FM & 0.544 & 0.614 & 0.662 & 0.675 & 0.613 & 0.631 & **0.674** \\ IPR & 0.666 & 0.649 & 0.676 & 0.627 & 0.691 & **0.725** & 0.743 \\ IV & 0.368 & 0.409 & 0.441 & 0.464 & 0.490 & **0.488** & 0.463 \\ LC & 0.036 & 0.165 & 0.176 & 0.181 & _0.397_ & **0.337** & 0.219 \\ LL & 0.093 & 0.152 & 0.123 & 0.147 & 0.130 & 0.172 & **0.161** \\ LR & 0.452 & 0.535 & 0.553 & 0.562 & **0.569** & 0.550 & 0.594 \\ MB & 0.564 & 0.672 & 0.665 & _0.687_ & 0.655 & **0.679** & 0.666 \\ OCC & 0.449 & 0.472 & 0.510 & 0.531 & 0.547 & **0.551** & 0.558 \\ OPR & 0.617 & 0.639 & 0.667 & 0.628 & 0.680 & **0.694** & 0.715 \\ OV & 0.326 & 0.397 & 0.409 & 0.391 & 0.602 & **0.559** & 0.531 \\ REF & 0.594 & 0.733 & **0.726** & 0.713 & 0.709 & 0.605 & 0.722 \\ SC & 0.642 & **0.722** & 0.679 & 0.729 & 0.595 & 0.666 & 0.669 \\ ST & 0.427 & 0.671 & 0.624 & 0.660 & 0.594 & 0.515 & 0.649 \\ SV & 0.555 & 0.577 & 0.590 & 0.579 & 0.610 & **0.663** & _0.646_ \\ TLO & 0.213 & 0.336 & 0.321 & **0.381** & 0.212 & _0.511_ & 0.229 \\ TOI & _0.273_ & 0.178 & **0.261** & 0.177 & 0.189 & 0.224 & 0.222 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The AUC comparison on hyperspectral/false-color videos. The highest and second-highest values are marked in _red italics_ and blue bold, respectively.
## 5 Conclusions  
A port detection method in polarimetric SAR images has been proposed by extracting the special interference water in ports using the volume scattering power and the ratio of double-bounce to volume scattering power based on the improved three-component decomposition. Port water is an important component of a port in polarimetric SAR images. Due to a special scattering component of the water in a port, the water appears differently from other water. Thus, water can be separated from land using the volume scattering power, and the interference water in a port can then be separated from normal water by the power ratio parameter using the multi-scale thresholding segmentation method. By extracting the special scattering component based on polarization decomposition, port water is easy to detect. Because the detection steps are independent of the accuracy of the coastline extraction and the geometric feature extraction of the contours of the port, the proposed method has a low time complexity and a high accuracy. The experimental results of the seven polarimetric data acquired by RADARSAT-2 over the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America demonstrated the effectiveness of the proposed method. All ports can be fast and accurately detected by the proposed method. Compared with the method based on jetty scanning, no false alarms are detected, and the IoU indexes of the detected port are much higher in the proposed method.
## 3 Communication at Ground Level: Radio Channel Modeling  
As discussed above, several applications require sensors to be deployed at ground level. However, the existing WSN platforms were never evaluated in such conditions.  
Different metrics can be used to measure the quality of a communication link. Taking a networking point of view, we adopted the packet reception ratio (PRR) as the main metric in our study, as this allows a straightforward derivation of application layer metrics; however, we also provide extensive Received Signal Strength Indicator (RSSI) results in the following, as a complementary metric. As well established metrics, the PRR and RSSI can also be computed theoretically, using standard communication models. Therefore, before detailing our experimental results, we use a similar reasoning to the authors of [16; 17] and we derive analytically the PRR and RSSI, to allow a fair comparison between experimental and theoretical results in Section 6.
## 3 Experiments and Results
### Experimental Hyperparameter Settings  
To ensure a fair and comprehensive comparison, we carefully configure hyperparameters for each method while maintaining a consistent experimental framework. All methods share a baseline setting: 20 or 50 clients participate in the training (100 in synthetic data), 25% of which are selected per round (10% in synthetic data), balancing communication efficiency and model updates. Local training uses a batch size of 20 and runs for 20 local epochs per round, with a learning rate of 0.01 applied via stochastic gradient descent (SGD) without momentum. For FedAvg, these common hyperparameters suffice, as it relies solely on local SGD and global averaging without additional tuning parameters. FedProxintroduces a proximal term weight (\(\mu\)) set to 0.01, which controls the regularization strength towards the global model. PerFedAvg incorporates a beta parameter (\(\beta\)) set to 0.002. For pFedMe, the regularization parameter \(\lambda\) is configured as follows: \(\lambda=5\) for MLR model and \(\lambda=15\) for MLP model on MNIST data, whereas \(\lambda=30\) is utilized for synthetic data. For FedGKD, we set the temperature in knowledge distillation to 1, the penalty weight on Kullback-Leibler divergence to 0.001, and the buffer length for historical models to 5. Our pFedKD method uses a knowledge distillation weight \(\gamma=0.1\) in Equation (3), weighting the KL-divergence loss between local and global model predictions. All models are initialized randomly, and client data is pre-partitioned to ensure identical non-IID distributions across methods. Evaluation metrics differ by algorithm design: FedAvg and FedProx report global model accuracy, while PerFedAvg, pFedMe, FedGKD and pFedKD-WCL provide personalized metrics after local adaptation, reflecting their focus on client-specific performance.
## 4 Proposed Work
### Smart Contract  
The smart contract used in this protocol (Algorithm 1) is a set of functions facilitating key exchange and encrypted data storage between IoT devices. Deployed on the blockchain, it utilizes the Solidity language. It enables device registration, public and shared key storage, and the management of encrypted data (CID). Its key functions include the following: device registration (registerDevice), public key retrieval (getPublicKey), shared key definition (setSaS), and encrypted data storage (setEncryptedData) and retrieval (getEncryptedData). These operations ensure secure, transparent, and confidential data exchanges between IoT devices.
# C-MWCAR: Classification Based on Multiple Weighted Class Association Rules
## 5 Experimental Evaluation  
### Experimental Setup  
We evaluate the performance of the proposed C-MWCAR on the hypertension diagnosis task based on a real BCG dataset of 128 subjects. In particular, 61 of them are hypertensive and the rest are normotensive. For each subject, one night of BCG is collected while sleeping, since at that time physiological indexes remain relatively stable. To achieve robust evaluation results, 10-fold cross-validation is utilized. Concretely, the dataset is randomly divided into ten portions, and nine of them are used as the training set and the last one is used as the test set. Repeat this process ten times until each portion has already been selected as the test set just once. Finally, ten groups of classification results are obtained, and the averaged values are regarded as the final classification results. The number of subjects in each portion is shown in Table 5.  
When conducting the experiments, _userSup_ and _userConf_ are initialized to 0.25 and 0.75, respectively. Additionally, the length of the same prefixes (i.e., the \(k\) in BCSA), the coverage threshold (i.e., the C in BCSA), the redundancy threshold (i.e., the \(R\) in BCSA), and the number of similar CARs (i.e., the \(K\) in MWCC) are initialized to 4, 0.6, 0.85, and 3, respectively. The selection process of these parameters is detailedly shown in Section 5.2.3.  
Three widely used metrics, i.e., accuracy (ACC), sensitivity (SEN), and specificity (SPE), are also utilized in this study [20].  
The experiments are conducted in Matlab 2014a and Eclipse Neon on an Intel Core i5-4590 PC with an 8 GB RAM running Windows 7 operating system.
# Beam-Plasma Stabilizer for the New Type of Nuclear Power Energy Systems
## 4 Discussion  
In the considered conditions, the problem of high voltage stabilizing is solved by creating LVDD directly between the cathode and the control electrode through the main anode hole as well as via the involvement of thermal electrons in the processes of current transport. The process of stabilized voltage control can be performed in two ways. The first  
Figure 4: Angular structure of thermal electrons’ velocity distribution function near the anode hole: **(a)** For different values of helium pressure; **(b)** For various currents at the CE i\({}_{\text{ee}}\) and fixed pressure P\({}_{\text{He}}\) = 0.5 Torr. The horizontal axis coincides with the discharge symmetry axis.  
Figure 5: Current-voltage curves of the different gaps, registered at different helium pressure values P\({}_{\text{He}}\), Torr: 1–0.9; 2–0.3; 3–0.1; 4–1; 5–0.8; 6–0.6; 7–0.5.  
method includes changing the helium pressure. With its decrease, the discharge voltage grows and reaches the values of U\({}_{\text{stab}}\approx 110\) V at P\({}_{\text{He}}\) = 0.1 Torr (Figure 6a).  
Another possibility requires changing the negative potential of the main anode U\({}_{\text{a}}\) in the range between 2 and 14 V. This solves the problem of controlling the voltages up to U\({}_{\text{stab}}\) = 90 V (Figure 6b). A further increase in the negative potential results in the deterioration of stabilization quality (the curve at \(-\)16 V, Figure 6b). The distortion of the I-V characteristic at large negative MA potentials is apparently associated with an imbalance in the processes of generation and the escape of charged particles from the plasma. To confirm this hypothesis, further research is required.  
The effectiveness of the proposed plasma stabilizer is illustrated by the stabilized voltage dependence on helium pressure (Figure 7a) and on the negative potential of the main anode (for three helium pressure values (Figure 7b)).  
In all cases of a decrease in energy (and, accordingly, the degree of anisotropy) of thermal electrons that is caused by an increase in the helium pressure or a decrease in the current values at CE, the possibilities of voltage stabilization deteriorate, which is confirmed by the data that is presented in Figures 5-7.  
Further investigation of the EVDF formation process mechanisms near the narrowing of the discharge channel (i.e., the anode hole) and their influence on the energy characteristics of plasma devices require an extensive theoretical analysis at the kinetic level.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Train \%** & **Test \%** & \multicolumn{4}{c}{**Test Data**} \\ \hline
**First** & **Last** & **F1** & **RMSE** & \multicolumn{2}{c}{**No. of Transitions**} & \multicolumn{2}{c}{**No. of Transitions Detected**} & \multicolumn{1}{c}{**R-Squared \({}^{*}\)**} \\ \hline
10 & 90 & 0.37 & 0.87 & 73 & 85,971 & 0.87 \\
20 & 80 & 0.65 & 0.61 & 65 & 70,808 & 0.93 \\
30 & 70 & 0.78 & 0.49 & 58 & 43,039 & 0.95 \\
40 & 60 & 0.88 & 0.38 & 51 & 26,973 & 0.97 \\
50 & 50 & 0.91 & 0.35 & 42 & 14,400 & 0.97 \\
60 & 40 & 0.95 & 0.31 & 32 & 6369 & 0.98 \\
70 & 30 & 0.97 & 0.27 & 24 & 3948 & 0.98 \\
80 & 20 & 0.96 & 0.32 & 16 & 2469 & 0.97 \\
90 & 10 & 0.99 & 0.21 & 8 & 457 & 0.98 \\ \hline \hline \end{tabular} \({}^{*}\) R-squared for test data.  
\end{table} 
Table 10: Forecasting efficacy of the CNN model for different training and testing partitions. to model the transition a short time after the transition in mental effort actually occurred. This was where the efficacy of the CNN may have come into play--its less strict temporal dependency meant that it was less dependent on immediately previous data, including transitions in the data, and so it provided predictions that were relatively independent of the immediate transitions that were occurring. Although the CNN's predictions indicated many transitions in the data that did not actually occur, the high R-squared value, low RMSE, and high F1 scores indicated that the predictions were relatively close to the actual reported level of mental effort, reaching the best performance when 50% or more of the data were used for training. A next step in the research will be to develop a way to combine the CNN and LSTM models such that both smoothness of the predictions and the ability of the system to capture transitions when they occur can be optimized.
# Non-Classic Atmospheric Optical Turbulence: Review
## 6 Influence in Applications
### Wireless Optical Communications
#### 6.1.3 Mean-Signal-to-Noise Ratio  
Due to turbulent fluctuations, the received irradiance must be treated as a random variable, over long detection intervals. Hence, in the case of a shot-noise limited system and under the assumption of sufficiently small beam spreading due to turbulence, the mean SNR at the output of the detector \(\langle SNR\rangle\) assumes the following general form [95]:  
\[\langle SNR\rangle=\frac{SNR_{0}}{\sqrt{1+\sigma_{I}^{2}SNR_{0}^{2}}}, \tag{49}\]  
where the scintillation flux is developed in [100] and \(SNR_{0}\) is the free-space SNR.  
We show in Figure 19 the mean SNR, \(\langle SNR\rangle\), keeping the structure function at the Fresnel distance fixed, as shown in [100] and previously proposed in [83]. The two anisotropic factors are kept fixed (shown as \(z_{x}\)eff and \(z_{y}\)eff in the figure title). The impact of anisotropy on the mean SNR is noticeable: in general, the decrease in scintillation due to anisotropy leads to an increase in the SNR, implying better performance.  
Figure 19: Mean SNR with fixed structure function for fixed values of anisotropic factors (shown as \(z_{x}\)eff and \(z_{y}\)eff). Reprinted with permission from Reference [100].  
Figure 18: Probability of fade as a function of \(\alpha\) for several cases of anisotropy and fixed structure function (shown as \(z_{x}\)eff and \(z_{y}\)eff in the legend). Reprinted with permission from Reference [100].
## 6 Experiment
### Experimental Data Analysis
As shown in Figure 12a, the increase in bottom noise due to rainfall reaches 2-7 dB when the target distance is between 10 and 30 m. Figure 12b depicts that the bottom noise of the beat signal gradually increases with increasing rainfall and operating frequency. At a rainfall rate of 50 mm/h and an operating frequency of 60 GHz, the increase in bottom noise reaches up to 25 dB. The bottom noise in the spectrum is caused by multiple scattering of MMW signals in the rain field. The greater the rainfall, the more raindrops per unit volume, resulting in a more pronounced multipath effect. Owing to the beam width of the fuze in the simulation being 120\({}^{\circ}\), which is significantly wider than the beam width of the MMW detector used in Reference [8], the increase in bottom noise in the simulation is greater than the test result in Reference [8]. The simulation results and actual test results of the bottom noise are compared to verify the simulation conclusion: rainfall will increase the bottom noise of the MMW fuze echo signal under rainy conditions.
# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
###### Abstract  
This paper discusses the possible early crack detectability performances of low contact ratio (LCR) and high contact ratio (HCR) spur gears. CAD models of the LCR and HCR gears are created using the rack cutter type tool. Static bending stress analyses are performed to define the starting point of the cracks and crack propagation analyses are conducted to define the realistic crack paths. Using the healthy and cracked gear geometries, the single tooth stiffness (STS) and the time-varying mesh stiffness (TVMS) of the LCR and HCR gears are calculated numerically. A six degrees of freedom dynamic model of the single-stage gear system is proposed. Dynamic force variation of the LCR and HCR gears are calculated by using the dynamic model. Statistical failure indicators, which are kurtosis, RMS, crest factor, mean, standard deviation, and variance, are used for the determination of the crack in the early stage. The results show that all statistical indicators increase with the increase in the crack length. The statistical indicators increase more for LCR gears than for HCR gears. Although HCR gears have many advantages over LCR gears, it has been determined that their crack detection capacity is lower than that of LCR gears.  
dynamic analysis; LCR and HCR gears; mesh stiffness; crack propagation; gear health diagnostics and monitoring +
Footnote †: journal: Applied Sciences
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 3 The Proposed Method
### The Implementation of StructSim
#### 3.4.1 Node-Composition Operator  
In Figure 2, we describe the detailed process of subgraph expansion. However, the expansion is different in different layers of the meta structure. For instance, we can easily find the difference between the first layer expansion and the second layer expansion by carefully observing Figure 2. At the first layer expansion, only a single node is expanded, as shown by the subgraph expansion from (1)a to (2)a in Figure 2. However, at the second layer expansion, two nodes need, meanwhile, to be expanded, as shown by the subgraph expansion from (2)a to (3)a in Figure 2. It is not easy to calculate the expansion probability in the situation of the second complicated expansion. Therefore, an effective solution is needed to solve this problem.  
Inspired by the literature [11], we designed the Node-Composition Operator to facilitate the expansion of the complex structure and computation of the expansion probability matrix with bias. To be specific, we combine two nodes into a composition node. The relation connecting the composition node can also be called the composition edge. Based on the operation of Node-Composition, we regard the composition node as a whole to create subgraph expansion in a way similar to single node expansion. Then, we can easily compute the subgraph expansion probability with bias in this situation.  
Figure 2 is an example to clearly illustrate the Node-Composition Operator. From the process of subgraph expansion based on the meta structure in Figure 2, we can see that, in the second layer of subgraph expansion, we need expand two nodes of Person type at the same time from the node with Movie type. The process from 2(a) to 3(a) in Figure 2 shows the expansion from [PERSON] (film) to [PERSON] (film editor) and [PERSON] (director). After the Node-Composition operation, we obtain subgraph (3)a' in Figure 3. The relation connecting [PERSON] (film) and composition node ([PERSON] (film editor) and [PERSON] (director)) is also adjusted to be a composition edge, which is revealed by the bold arrow in (3)a' of Figure 3. Then, in the third layer expansion, it is the expansion from a composition node to a single node. As illustrated by the process from 3(a) to 4(a) in Figure 2, we generate subgraph expansion from [PERSON] and [PERSON] (a composition node) to [PERSON] (a single node). After the operation of Node Composition, we obtain the subgraph exhibited by (4)a' in Figure 3.  
Based on the Node-Composition Operator, the node sets in every layer of the meta structure can be regarded as a whole. Then, we can adopt the matrix to conveniently calculate the expansion probability, which is similar to the transition probability matrix in random walk. Finally, we can apply the matrix multiplication to the implementation of StructSim.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Datasets  
The proposed method is evaluated using the development and additional training datasets from the DCASE 2020 Challenge Task 2 [7], which include parts of two datasets: ToyADMOS [44] and MIMI [45]. These datasets consist of recordings of both normal and anomalous operating sounds from six types of machines: ToyCar, ToyConveyor, Valve, Pump, Fan, and Slide. Except for ToyConveyor, which contains six machine IDs, the remaining datasets contain seven machine IDs each. Anomalous sounds were intentionally generated by deliberately damaging the target devices. Each recording is a 10 s audio clip that captures both the operational sound of the machine and the surrounding environmental noise. All signals have been downsampled to a sampling rate of 16 kHz.
## 2 Materials and Methods
### Framework of a River Health Warning System and Tool Development
#### 2.4.2 Structure and Interface of RHP-EWS  
RHP-EWS is very structured (Figure 3), consisting of a data module, pre-processing module, assessment module, forecasting module and DSS module, as well as an input and output module. The data module manages data that are sourced from real-time field monitoring, user input, other public databases, etc. The preprocessing module is responsible for geographic map digitalization, indicator selection, and data normalization to prepare and preprocess data, and to render data applicable for data fusion. The assessment module exists to compute the values of HI, WQI, and IBI and grade the current health status. Using the forecasting module, HI, WQI, and IBI in future periods can be forecasted with pre-stored predictive models to explore the changing trends of river health under normal or emergency conditions. The DSS module is useful for diagnosing major causal factors that influence the river health conditions. Additionally, the timely response suggestions on treatment measures, times, and locations, which are presented in the form of warning reports, can be imposed with the DSS module based on information from the assessment module and forecasting module. After that, managers and decision-makers can be warned. In this system, GIS has been used to spatially analyze and visualize thematic information (e.g., health grades, health trends, and spatial difference) with dotdensity, bar, line, or category thematic maps.  
The visualized tool has been developed with Visual Basic 6.0 integrating MapX control (developed by Mapinfo Ltd., North Greenbush, NY, USA). The inputs and outputs of each module operate through a geographic user interface (GUI), which has four function blocks: the menu bar, tool bar, parameter setting block, and map operation, and data addressing block (Figure 4). In menu bar, there are seven function menus: File, Data, Models, Assessment, Prediction, EW-DSS (early warning and decision supporting system), and Help. There is a functional button bar through which users can remotely perform the functions of file management and map management. In the map operation and data addressing block, original maps or thematic maps can be visualized as well as original input data and computation or analysis data. Additionally, the functions of zooming in/out on a map and searching for information can be carried out through a pop menu.  
Figure 3: Structure of the river health prediction and early warning system (RHP-EWS) integrated with the three-phase approach.  
Figure 2: Conceptual framework of a river health early warning system based on the three-phase approach.