esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 5 Discussion
### Feature Visualization  
According to Tables 4-6, it is obvious that the IATN performs better than the other methods in extracting domain-invariant features. To demonstrate the superiority of the IATN more intuitively in extracting domain-invariant features, this paper performs feature visualization of the D2\(\rightarrow\)D3 task. Specifically, the source domain features and target domain features extracted by each model are reduced to two dimensions for visualization with t-distributed stochastic neighbor embedding (t-SNE) [65]. The results are shown in Figure 6.  
Figure 5: Training curves. (**a**) D0\(\rightarrow\)D1, (**b**) D0, D1\(\rightarrow\)D2, (**c**) D0, D1, D2\(\rightarrow\)D3.  
Figure 6: t-SNE feature visualization. (**a**) CNN. (**b**) DANN. (**c**) ISAE-CSDF. (**d**) CDGATLN. (**e**) IATN. D2-0 indicates data collected for category “0” fault bearings in D2 working conditions.  
In Figure 6, there are five subfigures. Figure 5(a) is the feature visualization of the CNN, Figure 5(b) is the feature visualization of the DANN, Figure 5(c) is the feature visualization of the ISAE-CSDF, Figure 5(d) is the feature visualization of the CDGATLN and Figure 5(e) is the feature visualization of the IATN. In each subfigure, there are 18 clusters; 9 belong to D2 tasks and 9 belong to D3 tasks. There are two criteria for judging model performance. One is the degree of separation of the nine fault clusters in each WD, and the other is the degree of overlap of the same fault clusters in the two working conditions.  
According to Figure 6, it is obvious that there is significant overlap between different fault clusters of source domain features and target domain features extracted by the CNN, DANN, ISAE-CSDF and CDGATLN, which initially have completely separate clusters, but there is still aliasing at the boundaries of some categories. In Figure 5(e), all the clusters of each WD are separated entirely, and all the same fault clusters of D2 and D3 are basically overlapped, which indicates that the IATN has strong domain-invariant feature extraction capabilities.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 2 Preliminaries and Related Works
### Federated Reinforcement Learning  
FRL, a combination of FL and RL, was first introduced in [32]. In this study, the authors showed that FRL techniques can utilize observations from various environments together for RL. Furthermore, the study verified that FRL performs better than general DQNs based on partial observations of the same environment. FRL are applied to various technical fields including autonomous driving and robot system control. As explained in Section 2.3, FL can be applied to UAV systems well and effectively. Thus, applying FRL to UAV systems can make comprehensive use of various observations collected from different environments with performing efficient communication, which allows UAVs to learn better on their own. Motivated by this, we proposed the system which utilizes FRL for UAVs to be able to consider required traffic on the ground, move to more proper locations, and provide network services autonomously.
# Animal Sound Classification Using Dissimilarity Spaces
## 4 Clustering
### K-Means  
K-means is one of the most popular clustering algorithms. It partitions patterns into \(k\) clusters by placing each observation into a cluster based on the nearest centroid (according to the Euclidean Distance measure).  
The standard k-means algorithm involves four steps:  
1. Randomly select a set of centroids from among the data points.
2. For each data point \(x\) remaining in the training set, compute the distance \(d(x)\) between it and the nearest centroid.
3. Recalculate new centroids via a weighted probability distribution.
4. Repeat Steps 2 and 3 until convergence.
## 2 Methodology
### Feature Extraction
#### 2.4.2 Global Features Extraction  
Since the skeleton lines represent the main and convex features of the areal settlement, the features between adjacent skeleton lines in residential areas cannot be ignored and offer great advantages in the classification of complex shapes. As shown in Figure 6a, the midpoint D of the boundary line segment AB is connected to the center point of the areal settlement. The global features are extracted as follows: The length of the line OD is \(L_{2}\). The turning angle of the line segment OD to OE is \(\beta\). It represents the angle from OD to OE, which is positive in the counterclockwise direction and negative otherwise. Area S1 is denoted by the line connecting the center point of the areal settlement to the points on either side of the skeleton line segment (Figure 6b).  
The global features need to be normalized. The length \(L_{2}\) is normalized by dividing the sum of \(L_{\text{sum}}\) the angle \(\beta\) is normalized by dividing 360\({}^{\circ}\), and the area \(S_{1}\) by dividing the sum of the local area \(S_{\text{sum}}\), as follows:  
\[\overline{L_{2}}=\frac{L_{2}}{L_{sum}} \tag{3}\]  
\[\overline{\beta}=\frac{\beta}{360} \tag{4}\]  
\[\overline{S}=\frac{S_{1}}{S_{sum}} \tag{5}\]  
Figure 5: Local features extraction of node D.  
Figure 6: Global features extraction of node D: (**a**) length and turning angle; (**b**) local area.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 4 Results
### Data Files to Support the Findings  
A number of pcap files (listed in Table 5) of the data traffic generated for our experiments were captured using tcpdump and then analysed using tshark to provide evidence for the results.  
Figure 8: \(P_{f}\) values for each client as new flows are observed by the Attacker. With a static address (red), \(P_{f}\) decreased consistently. With temporary addresses used only a few times (blue), the change in \(P_{f}\) depended on how many times the address was reused. With ephemeral NIDs (a new address for each flow—green), \(P_{f}\) was constant and maximal (Lines connecting points are a visual aid.).  
The Testbed pcap files are all 'raw' captures--they contain the TCP flows created, plus the normal control-plane traffic for IPv6, such as Router Advertisements (RAs). As noted in Section 3.3.1, the Testbed experiment involved:  
* emulating three servers, and these can be seen as the three separate IPv6 addresses: 2001:0 db8:0011:00 cc:0000:000:00 dd:00 XX where XX is 11, 22, or 33.
* emulating three clients, and these can be seen as three separate IPv6 prefixes: 2001:0 db8:0022:00 YY/64 where YY is dd, ee, or ff.  
The Global pcap files have been filtered to include only the relevant packets for the relevant TCP flow from the IETF118 Hackathon venue to the St Andrews server. The client addresses all had prefix 2001:67c:1232:eee1/64, and the server address was 2001:630:35:0:138:251:30:221.
## 4 Experiment
### Experimental Results
Table 5 shows \(k\)-NN evaluation results for each dataset after fine-tuning using the proposed method. Overall, it is observed that the choice of each hyperparameter does not have a noticeable impact on performance, which confirms that the proposed method is robust to these hyperparameters. \begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Method** & **Aircraft [16]** & **Car [15]** & **Flower [39]** & **Food [40]** & **CUB [14]** & **Dog [41]** & **Avg. Rank** \\ \hline MoCov v3 [20] & 26.61 & 16.94 & 78.45 & 61.60 & 44.06 & 64.71 & 3.83 \\ iBoT [21] & 35.88 & 20.84 & 85.02 & **68.70** & 66.86 & **78.39** & 2.17 \\ DINO [1] & 36.66 & 22.52 & 86.45 & 67.65 & **69.45** & 64.45 & 2.17 \\ Ours & **37.32** & **23.22** & **87.54** & 66.25 & 64.36 & 75.20 & 1.83 \\ \hline \end{tabular}
\end{table} 
Table 3: Fine-grained recognition performance of ImageNet pretrained models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Flower [39]** & **CUB [14]** \\ \hline \hline \multicolumn{2}{c}{DINO [1]} & 29.33 & 22.02 \\ \multicolumn{2}{c}{Ours} & **44.87** & **37.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation performance comparison between DINO and our model. We demonstrate results using a pretrained ViT-S/16 with ImageNet and evaluate the Jaccard index between ground truth and predicted masks by applying a 60% self-attention map threshold. The result for the best-performing head is shown. Figure 5: Visualization of attention maps from ImageNet pretrained models. The left column indicates the original images from each FGVC dataset. The middle and the right columns represent the attention maps of DINO and the proposed method, respectively. Six attention maps from the last block are summed and L2-normalized for visualization. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\lambda\) & **Pooling Size** & **Aircraft [16]** & **Car [15]** & **Flower [39]** & **Food [40]** & **CUB [14]** & **Dog [41]** & **Avg.** \\ \hline
0.5 & \(3\times 3\) & **39.2** & 26.8 & **90.9** & **75.9** & **60.8** & 74.2 & **61.3** \\  & \(1\times 1\) & \(38.1\) & **27.6** & 89.9 & **75.9** & 60.6 & **74.3** & 61.1 \\ \hline
1 & & 38.9 & 26.5 & 90.5 & 75.5 & **60.9** & **74.6** & 61.1 \\
0.5 & \(3\times 3\) & 39.2 & 26.8 & **90.9** & **75.9** & 60.8 & 74.2 & **61.3** \\
0.1 & & **39.6** & **26.9** & 90.5 & **75.9** & 60.8 & 74.0 & **61.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(k\)-NN accuracy of models trained with different \(\lambda\) and pooling size.
## 4 Results and Discussion
### Microwave Frequency
#### 4.1.2 Experimental Research  
According to the controlling variable method, the ice thickness of the test piece is controlled to be 50 mm, the microwave emission power is 1000 W, and the distance from the test piece to the waveguide port is 50 mm. The specimens are placed in the test device and tested at microwave frequencies of 2.45 GHz and 5.8 GHz. Place the beaker filled with water on both sides of the specimen to absorb the microwave energy not absorbed by the specimen. A thermocouple between the asphalt concrete and the ice layer records the temperature change of the concrete surface, and the influence of different microwave frequencies on the heating efficiency is obtained, thereby reflecting the effect on the efficiency of the microwave deicing and snow removal.  
The ice after microwave irradiation is shown in Figure 8. It can be clearly seen that a large hole appeared in the ice layer, which resembles a bowl-shaped depression, indicating that ice melts first from the part near the concrete surface. The ice layer on the ice surface and the concrete surface melt into water. The water absorbs microwave energy in a large amount, accelerating the melting of the ice layer at the interface, and reducing the bond stress at the joint of the ice surface layer. Therefore, the ice layer is easily removed by mechanical means. This phenomenon also shows that the microwave absorption capacity of the ice layer is weak, and microwaves can penetrate the ice layer to directly heat the concrete.  
Figure 8: Ice after microwave irradiation.  
Figure 7: Temperature field distribution along the vertical direction of concrete.  
A thermocouple between the asphalt concrete and the ice layer records changes of the temperature in the concrete surface. The results are shown in Table 7. The average temperature rise rate at 2.45 GHz is 0.032 \({}^{\circ}\)C S\({}^{-1}\), the average temperature rise rate at 5.8 GHz is 0.0149 \({}^{\circ}\)C S\({}^{-1}\), and the average temperature rise rate at 5.8 GHz is 4.6 times that of the 2.45 GHz microwaves.  
Comparison of deicing rates between indoor tests and simulations of different frequencies is shown in Figure 9. The results also show that there is no correlation between the initial temperature and the temperature rise rate, but it will affect the ice melting time. The higher the initial temperature, the shorter the ice melting time. The test results are very close to the results of the simulation model, demonstrating the reliability of the simulation model. However, the comparison shows that the deicing efficiency obtained by the test is slightly lower than the simulation results. This is because in the simulation model, assuming that the analysis area is insulated on all four sides, the microwave heat loss is all used to melt the ice. In actual experiments, this cannot be completely achieved.
# Effect of Surface Mass Loading on Geodetic GPS Observations
## 2 Materials and Methods
### Mass Loading  
For the non-tidal atmospheric loading deformation, we adopted the provisional product provided by the International Earth Rotation and Reference Systems Service (IERS) Global Geophysical Fluids Center (GGFC)-German Research Centre for Geosciences (GFZ), which is available at [[http://www.gfz-potsdam.de/en/esmdata](http://www.gfz-potsdam.de/en/esmdata)]([http://www.gfz-potsdam.de/en/esmdata](http://www.gfz-potsdam.de/en/esmdata)). Non-tidal atmospheric mass loads with 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution and 6-hourly temporal resolution were estimated by using the surface pressure from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model [15]. For non-tidal ocean loading deformation, we also adopted the provisional product provided by the GFZ. Non-tidal ocean loads with 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution and 6-hourly temporal resolution were derived from non-tidal ocean bottom pressure from the Ocean Model for Circulation and Tides (OMCT) [15]. The GFZ processes the consistent deformation fields from the daily means of the ECMWF atmospheric surface pressure and the OMCT non-tidal ocean bottom pressure to derive the total non-tidal deformation signal. For hydrological loading (HYDL) deformation, we used the provisional product provided by the GFZ. The HYDL with 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) spatial resolution and the daily temporal resolution was predicted from the Global Land Surface Discharge Model (LSDM) [20]. The LSDM was found to be composed of the masses of soil moisture, snow and runoff from glaciers, especially water in rivers and lakes. To maintain consistency with GPS heights, the mass loading displacements were all calculated in the center of the figure frame (CF).
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 4 Numerical Simulation and Results
### The Simulation Scenario  
In this section, the OneWeb system model is used as input to design the interference avoidance scheme based on the genetic algorithm. The OneWeb system is used as a reference system. We have given a detailed description of the parameters of the system. In addition, the settings of the relevant parameters of the genetic algorithm are also given here. The specific parameters are summarized in Table 2.  
To avoid the unacceptable interference with the GSO satellite system, the off-axis angle threshold needs to be determined. Based on the beamwidth and frequency of the OneWeb  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline LEO satellite orbit height (Km) & 1200 \\ GSO orbit height (Km) & 36,000 \\ The radius of earth (Km) & 6371 \\ Minimum downlink frequency (GHz) & 10.7 \\ Maximum downlink frequency (GHz) & 12.7 \\ Number of beams & 16 \\ Bandwidth (MHz) & 250 \\ EIRP (dBW) & 34.6 \\ Beamwidth in minor-axis (deg) & 2.98 \\ Beamwidth in major-axis (deg) & 47.6 \\ Downlink EPFD limit (dBW/m\({}^{2}\) 40 KHz) & \(-\)160 \\ Generations & 200 \\ Population size & 100 \\ Length of gene & 72 \\ Mutation rate & 0.02 \\ Crossover rate & 0.5 \\ Election rate & 0.2 \\ Off-axis angle threshold (deg) & 11.5 \\ Coverage overlap threshold (deg) & 1 \\ Max pitch angle (deg) & 18 \\ Slow adjustment value of pitch angle \(\Delta\chi\) (deg) & 0.5 \\ Fast adjustment value of pitch angle \(L\Delta\chi\) (deg) & 1.5 \\ Sampling interval in latitude \(\Delta\psi\) (deg) & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of genetic algorithm.  
system, we are able to obtain the antenna gain of the OneWeb satellite beam in the minor axis. Then according to expression (6), we can obtain the off-axis angle threshold \(\mu\)th, which corresponds to an antenna gain that is 27.1 dB lower relative to the maximum antenna gain. The relative antenna gain attenuation of 27.1 dB corresponds to an off-axis angle of 11.5\({}^{\circ}\), which has been given in Table 2.  
The number of satellites in each orbital plane \(N\) highly affects the seamless coverage of the LEO constellation. The seamless coverage is ensured by the overlap between adjacent satellites. The relationship between coverage overlap and the number of satellites has been shown in Table 3, without considering the harmful interference into the GSO satellite system. The overlap denotes the geocentric angle of the shared coverage between adjacent satellites in the same orbital plane, which increases with the number of satellites.  
In the above table, the seamless coverage in the orbital plane could be ensured. While the co-frequency interference could not be ignored, it is very important to ensure the seamless coverage under progressive pitch. In order to ensure there exists a progressive pitch scheme enabling the global coverage, we study the progressive pitch under 48 satellites per orbit plane.
## 2 Data and Methodology
### Data Sets
#### 2.2.3 In Situ Measurements  
In this research, in situ experiments were designed on 28 April 2019 and 29 April 2021. The soil volumetric moisture at a 0-5 cm depth was obtained by handheld TDR. A total of 172 experimental points (91 points were used for model building, and 81 points were used for validation) were set up on both dates, with a reasonable spatial distribution. A detailed distribution of the experimental points is displayed in Figure 2a. The soil volumetric moisture content on every experimental point was obtained from the average of three measuring points, which were arranged within a radius of 3 m from the experimental point. A portable global positioning system (GPS) receiver was used for positioning measurement. Figure 3 shows the statistics of the 172 in situ measured SSM values. As shown in Figure 3a, the in situ measured SSM values range between 0.05 and 0.32 cm\({}^{3}\)/cm\({}^{3}\). The mean, maximum, and minimum values of the in situ measured SSM were 0.169, 0.32, and 0.05 cm\({}^{3}\)/cm\({}^{3}\), respectively. The measurements collected for wheat, corn, vegetables, soybean, and sweet potato were 82, 50, 24, 10, and 6, respectively.  
Figure 3: Statistics of in situ measured SSM: (**a**) distribution of in situ measured SSM; (**b**) in situ measurements for different crop types.  
Figure 2: Basic information of in situ measurements: (**a**) distribution of experimental plots in the study area; (**b**) photograph of the scene.
## 2 Stimulus Generation Based on Noise-Vocoded Speech
### Noise-Vocoded Speech  
The process flow of the speech analysis/synthesis method we utilized to generate NVS as noise-vocoded stimuli is shown in Figure 1. In the NVS scheme, a speech signal can be re-synthesized by replacing the temporal fine structure of speech in the sub-bands with band-limited noise carriers while preserving the TAE. This scheme is therefore suitable for investigating the role of the TAE in urgency perception.  
First, we reduced the effect of the average intensity by normalizing the active speech levels of all speech signals to \(-26\) dBov with a P.56 speech voltmeter [14]. We then implemented band-pass filters (BPF) (essentially functioning as a band-pass filterbank) to divide the signals into several frequency bands. Next, we defined the bandwidth and center frequencies of the band-pass filterbank utilizing equivalent rectangular bandwidth (ERB\({}_{\text{N}}\)) and ERB\({}_{\text{N}}\)-number scales in Cam [17]. The ERB\({}_{\text{N}}\)-number scale is similar to a distance scale from the basal side to the apical side of the cochlea, so it is fairly straightforward to replicate the frequency selectivity of the auditory system by simply decomposing the frequency bands on the basis of the ERB\({}_{\text{N}}\)-number.  
We respectively define the ERB\({}_{\text{N}}\) and ERB\({}_{\text{N}}\)-number as  
\[\text{ERB}_{\text{N}}=24.7(4.37f/1000+1), \tag{1}\]  
\[\text{ERB}_{\text{N}}-\text{number}=21.4\log_{10}(4.37f/1000+1), \tag{2}\]  
where \(f\) refers to the center frequency in Hz. We define the center frequencies of the band-pass filterbank from 3 to 35 Cam with the bandwidth set to \(2\times\) ERB\({}_{\text{N}}\), resulting in a band-pass filterbank with 16 channels. The band-pass filterbank was implemented using 6 th-order Butterworth infinite impulse response (IIR) filters.  
We extracted the TAE of each channel signal from the output of the filterbank by utilizing the Hilbert transformation and a low-pass filter (LPF). The LPF was implemented using a 2 nd-order Butterworth IIR filter and then, on the basis of its cut-off frequency, we set the upper limit of the modulation frequency to 64 Hz. This upper limit is related to the temporal resolution in that the higher the upper limit, the higher the obtained temporal resolution.  
In the final step, the TAE in each channel was utilized to modulate the temporal amplitude with the narrow band-limited noise (NBN) generated by band-pass filtering white  
Figure 1: Process flow of speech analysis/synthesis method utilized to generate noise-vocoded stimuli (BPF: band-pass filter, LPF: low-pass filter, and NBN: narrow-band noise).  
noise at the same channel (band-frequency). The noise-vocoded stimuli were generated by summing up all amplitude-modulated NBNs.
## 4 Experimental Results
### Feature Importance Analysis  
The most affected and significant features for personal authentication are listed in Table 1, in which the ranking of the dynamic pattern recognition (behavioral biometrics) based on the TOPSIS methodology is presented. As illustrated in the Table, the three top-ranked values of the affected and valuable features were the touch force (pressure), abscissa (X coordinate), and duration (velocity). Note that these features have been chosen based on their highest essentiality. Then, they were fed as a primary input into each of the implemented ML models instead of considering all of the entered features by the users. This helps to further refine the performance of the system and optimize the entire framework.