esa-sceva/satcom-chunk-collection huggingface dataset:
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 6. Results
### Social Telepresence  
The feeling of being in the same room with another person while listening (\(t(11)=3.022\), \(p<0.05\), \(r=0.674\)) and speaking (\(t(11)=2.421\), \(p<0.05\), \(r=0.590\)) decreased in the auto condition compared with the tele condition. As with the sense of agency, these feelings did not statistically decrease in the semi condition compared with the tele condition (listening: \(t(11)=1.393\), \(p=0.191\), \(r=0.387\); speaking: \(t(11)=-0.561\), \(p=0.586\), \(r=0.167\)). These results mean that robot conferencing performed through an autonomous robot decreases social telepresence, but that autonomous behavior mixed into teleoperation does not decrease social telepresence (Hypothesis 3).
# Advanced Deep Learning Methods to Generate and Discriminate Fake Images of Egyptian Monuments
## 1 Introduction  
The breathtaking heritage of ancient Egyptian monuments continues to face deterioration from natural weather elements, air pollution, and recurrent resource shortages while battling damage from the continuous heavy foot traffic of visitors [1, 2].  
Recent technological developments have allowed for the generation of high-quality images that have enabled the evolution of electronic preservation systems that address modern cultural heritage needs, facilitate global access to cultural heritage through virtual monuments, create educational learning materials, and provide a valuable tool to guide monument and artifact restoration programs [3, 4, 5].  
In particular, Generative Adversarial Networks (GANs) have been instrumental in image synthesis within multiple domains, with a style-based generator architecture for Generative Adversarial Networks (StyleGAN) as the leading architecture for controlling high-resolution image creation [6]. Past studies have illustrated the capacity of GANs to contribute to cultural heritage preservation and dissemination through visual training enhancement and Chinese calligraphy generation [7] alongside artifact virtual restoration and interactive historical scene development [8]. However, existing applications have limitations in generating the unique characteristics of Egyptian monuments because they incorporate properties such as extensive spatial dimensions along with highly geometric structures, ornate decorations, and centuries of natural weathering patterns. Moreover, the application of StyleGAN for generating accurate imagery of Egyptian monuments remains underexplored in computational creativity research and digital heritage conservation.  
Thus, this research aims to fill this gap by developing StyleGAN architectures to generate photorealistic Egyptian monument imagery, focusing on architectural optimization and image manipulation while evaluating the performance of these architectures. Specifically, this research follows five main objectives:  
1. To develop a specialized StyleGAN framework while evaluating its ability to generate realistic representations of Egyptian monuments that preserve cultural heritage site characteristics and visual appeal;
2. Regarding the primary objective, the goal is to evaluate the original discriminator design against enhanced versions that add noise injection, a squeeze-and-excitation block, as well as improved statistical layer discriminators;
3. To implement and evaluate sigmoid loss for language image pre-training (SigLIP) when configuring semantic alignments between text descriptions and visual representations of Egyptian monuments;
4. To explore the use of Differential Evolution (DE) for latent-space optimization in monument generation and check its success rate against proven methods;
5. To investigate how different truncation values affect the quality-diversity relationship while generating Egyptian monument imagery for noise control.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 3 Alternative Categorization
### Frequency  
Frequency has a direct relation with the bandwidth of an RFPA. The bandwidth of an RFPA refers to the frequency range in which the amplifier can work efficiently. In this context, the analysis of the frequency domain becomes relevant. The term \(\sum_{n=2}^{\infty}P_{out,hf}\) in Equation (4) governs the harmonic efficiency of the RFPA and it is obtained by summing over the output power at frequencies which are integer multiples of the fundamental.  
However, considering only three harmonics [24] is typically sufficient for designing RFPAs in all practical cases. Based on the voltage and current equations presented in Equations (9) and (10), the fundamental and harmonic components of voltage and current are determined though Fourier integrals, resulting in the following frequency representation of voltage and current:  
\[V_{n}=U_{n}-jV_{n} \tag{11}\]  
\[I_{n}=W_{n}-jX_{n} \tag{12}\]  
With \(n=1\), \(2\),..., \(N\). The load impedance at the fundamental as well as at the harmonic frequencies is given by Equation (13):  
\[Z_{n}=-\frac{V_{n}}{I_{n}}=-\frac{U_{n}-jV_{n}}{W_{n}-jX_{n}} \tag{13}\]  
Figure 5: Time domain representation waveform of J/B continuum (\(\alpha=1\) and \(\delta=[-1\) to \(1]\)).  
High efficiency in RFPAs is often achieved through techniques that optimize the amplifier's performance at a specific frequency or within a narrow frequency range, employing resonant circuits that maximize power transfer at a particular frequency. Consider, for example, the Class B amplifier, which requires an _L-C_ resonant tank to short all harmonics, ensuring that the output network favors efficiency over bandwidth. While these methods are effective in enhancing efficiency, they naturally restrict the amplifier's bandwidth because the optimizations are frequency specific.  
On the other hand, expanding the bandwidth of an RFPA involves design considerations that can compromise efficiency or linearity. For instance, broadband design techniques, like using non-resonant or wideband matching networks and employing feedback mechanisms, allow the amplifier to operate over a wider range of frequencies. However, these approaches tend to introduce losses or require compromises in the load impedance seen by the transistor at different frequencies, which can reduce the overall efficiency of the amplifier.  
These arguments point to a key limitation of RFPAs, namely their limited performance outside the narrow frequency band for which they were originally designed. In this paper, the primary methods to address these issues can be further sub-categorized into two approaches: the step approach and the continuous approach.
## 4 Implementation and Evaluation
### Evaluation
#### 4.2.1 Blockchain Latency and Throughput Analysis  
Figure 8 shows the delay and throughput of this solution under different numbers of nodes. During the experiment, we observed that the delay increases almost linearly as the number of nodes increases, from 6 s for 25 nodes to 23 s for 40 nodes, and the delay results are better than the DDS-based scheme proposed by [PERSON] et al. [25]. For the same number of nodes, the overall performance improvement averaged 4.5 s. We took the PoW algorithm used in Bitcoin and adjusted the difficulty accordingly, while also reducing the redundancy of the block information, which helps reduce the time it takes the system to reach consensus. Because [PERSON] et al.'s scheme is tested on Ethereum, the block composition is more than our scheme with state tree, receipt tree, and other structures, which affects the efficiency of a single node when synchronizing blocks. At the same time, it can be seen from the throughput test of this scheme that the throughput result of our proposed system is better. There is not much change in the number of nodes. This depends on the data processing performance of the experimental equipment, the network environment where the equipment is located, and the communication protocol used. In our experiment, we had an average throughput of 50.75 kb/s for RPC communication using the TCP protocol.  
Figure 8: The latency and throughput of transactions under the BDIDA-IoT and [PERSON]’s [25] solution when the number of nodes is different. The abscissa is the number of nodes participating in the system, and the ordinate is the time it takes to reach consensus. (**a**) Delay Time shows the time it takes to reach consensus under different numbers of nodes. (**b**) Throughput shows the throughput data of the system under different numbers of nodes.  
We have observed that the transaction completion delay rises proportionally with an increase in the number of nodes, and the extent of this increase becomes more pronounced. This phenomenon is attributed to variations in the volume of data [26]. Given that the throughput of each node remains relatively constant, it implies a limitation in the data processing capacity of individual nodes. As the number of nodes within the blockchain system grows, the data influx experienced by each node also escalates. Consequently, this surge in data has repercussions on the overall consensus achievement, necessitating a certain amount of time during the data transmission and reception processes.
## 4 Object-Oriented Classification Process
### Feature Selection Results
From the remote sensing images of the research area, the vegetation and water areas were larger than other land features, and NDVI and NDWI can effectively extract vegetation and water bodies. Secondly, there were many geometric features. Ground objects are usually characterized by large area and complex spectral features. It is difficult to distinguish objects such as roads and construction land from other ground objects only using spectral features, but they can be effectively classified using geometric features.
## 2 Materials and Methods
### Processing and Collection of Rainfall Data: Trigger Factors
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Event** & \multicolumn{2}{c}{**0601 Heavy Rain in 2017**} \\
**Station** & **Maximum Daily Rain (mm)** & **Maximum Hourly Rainfall (mm)** \\ \hline Alishan & 622.5 & 61 \\ Ruili & 423 & 71.5 \\ Shipanlong & 297 & 44.5 \\ Fenqihu & 421 & 51 \\ Da-an & 490.5 & 75.5 \\ Dapu & 0 & 0 \\ Shenmu Village & 611.5 & 60.5 \\ Zhongxinlun & 504.5 & 89.5 \\ Tongtou & 588.5 & 90 \\ Caoling & 539 & 87.5 \\ Feng Shan & 0 & 0 \\ Meishan & 362 & 85.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Rainfall data from the heavy rain of 1 June 2017. Figure 15: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & \multicolumn{2}{c}{**Typhoon Morakot in 2009**} & \multicolumn{2}{c}{**0503 Heavy Rain in 2012**} \\
**Station** & **Maximum Daily Rain (mm)** & **Maximum Daily Rain (mm)** & **Maximum Daily Rain (mm)** \\ \hline Alishan & 1165.5 & 123 & 481.5 & 49.5 \\ Ruili & 675.5 & 105 & 208.5 & 26.5 \\ Shipanlong & 1183 & 106 & 247.5 & 35.5 \\ Fenqihu & 1187 & 110.5 & 304 & 43 \\ Da-an & 238.5 & 20 & 164 & 30.5 \\ Dapu & 159.5 & 20.5 & 73.5 & 22.5 \\ Shenmu Village & 910.5 & 95 & 411 & 39.5 \\ Zhongxinlun & 334 & 33.5 & 152.5 & 28 \\ Tongtou & 513.5 & 65 & 150.5 & 43 \\ Caoling & 643 & 88 & 191.5 & 32.5 \\ Feng Shan & 705 & 121.5 & 311.5 & 39.5 \\ Meishan & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Rainfall data for Typhoon Morakot and the heavy rain of 3 May 2012.
## 2 GNSS/IMU Sensors Data Characteristics Analysis
### GNSS Measurements
Compared with the PDOP values observed on the playground in Figure 3b, the STD of PDOP values observed by geodetic receiver and Mate 10 in Figure 5b increased by 201.16% and 247.05%, respectively. Meanwhile, there are 148 and 285 epochs for which the satellite signals of GPS PRN 19 and BDS PRN 05 cannot be detected in 500 epochs, with unlock rates of up to 29.60% and 57.00%, respectively. Figure 4: Carrier-to-noise power density ratio values of GNSS satellites observed by the Huawei Mate 30 on the playground. (**a**) Some GPS and BDS satellites whose signals are degraded due to the trees, and the red box shows the C/N\({}_{0}\) value in the area marked by the yellow box in Figure 4b. (**b**) Left: reference trajectory and sky plot of GPS and BDS. Right: realistic environment marked by the yellow box. In addition, the average C/N\({}_{0}\) value of GPS PRN 19 in the first 50 epochs is 33.55 dB-Hz, and the average C/N\({}_{0}\) value in the second 50 epochs is 15.06 dB-Hz, which decreases by 55.11%. Some studies indicated that the number of carrier phase cycle slips increased significantly with C/N\({}_{0}\) below 30 dB-Hz [8], while some researchers even suggested that the GNSS measurements with C/N\({}_{0}\) below 30 dB-Hz observed by smartphone can be rejected [7; 25]. The cutoff C/N\({}_{0}\) value is a convenient method that is suitable for static positioning using smartphones; however, it may not be directly applicable in dynamic mode. In Figure 6a, the average C/N\({}_{0}\) values of GPS and BDS are 28.53 dB-Hz and 27.55 dB-Hz in 500 epochs, respectively, and more than 74.43% of the GPS PRN 19 observations had the C/N\({}_{0}\) below 30 dB-Hz in 500 epochs. Moreover, by comparing GPS PRN 19, PRN 17, and BDS PRN 27, the GNSS satellites with low elevation are more affected by signals being blocked. Figure 5: Number of GNSS satellites and PDOP values on the sidewalk in area 2. **(a)** GPS and BDS measurements observed by Mate 30; **(b)** GPS measurements observed by the geodetic receiver and Mate 10. Figure 6: The C/N\({}_{0}\) values of GNSS satellites observed by the Huawei Mate 30 on the sidewalk in area 2. **(a)** The same GPS and BDS satellites as in Figure 4a, and the red box shows the C/N\({}_{0}\) values of the satellites decrease significantly. **(b)** Reference trajectory and sky plot of GPS and BDS.
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Comparison of the Results Obtained by MLP, Two Other ML Models, and the Weather Service
#### 4.6.3 Forecast of Mean Daily Atmospheric Pressure  
Table 19 in Appendix E contains example values of the mean daily atmospheric pressure as measured, predicted by the MLP, and forecasted by the weather service. Figure 5 presents a comparison of the effectiveness of the MLP model and the weather service in forecasting the mean daily atmospheric pressure. Table 11 includes quantitative values of the effective measures of the MLP, two other predictive models, and the weather service in forecasting the mean daily atmospheric pressure.  
The effectiveness of the MLP model was higher than that of the weather service when forecasting this parameter. The MAE value was 0.373 less for the MLP model, and the MSE value was 8.3269 less for the MLP model than for the weather service. The value of the determination factor R\({}^{2}\) score was 0.0478 higher for the MLP model than for the weather service, which shows the higher effectiveness of the MLP in predicting the atmospheric pressure.  
However, the calculated value of R\({}^{2}\) score is not as high as the value of this factor calculated for the MLP when forecasting the maximum and minimum temperatures.This observation indicates that the accuracy of the forecasting of atmospheric pressure by the MLP was lower than that of the forecasting of temperatures. For the prediction of the daily atmospheric pressure values, the MLP model enabled slightly higher accuracy compared with the LSTM and SVR. A higher effectiveness in forecasting the atmospheric pressure was observed for the MLP model compared with for the weather service overall in the months in the surveyed year.  
Figure 4: Comparison between the observed values and those predicted by the MLP and weather service minimum temperature.
## 3 Auction-Based Cooperative Rescue Plan
### The Adjustment in Task Allocation  
When bidder \(B_{j}\) is participating in task \(T_{i}\), the utility \(u_{i}\) it could gain from task \(T_{i}\) varies as time goes by. Assuming that a new task \(T_{k}\) is auctioned around \(B_{j}\), and there exist few available rescue teams for task \(T_{k}\) within the scope of cooperation \(s_{c}\). Then \(B_{j}\) would evaluate task \(T_{k}\). If the net utility \(g_{kj}\) of task \(T_{k}\) were much higher than \(g_{ij}\) of task \(T_{i}\), i.e., \(g_{kj}-g_{ij}\geq Q\) (\(Q\) is pre-defined), the current allocation of tasks would be revised. The number of teams required to complete task \(T_{i}\) would be updated according to Equation (4), for the buried depth \(v_{rubble}\) is changing. The updated number of required teams for \(T_{i}\) is \(n^{\prime}_{i}(n^{\prime}_{i}\leq n_{i})\), then auctioneer \(A_{i}\) would allow up to \(n^{b}_{i}=n_{i}-n^{\prime}_{i}\) bidders to withdraw from task \(T_{i}\). Assuming task \(T_{k}\) still lacks \(n^{c}_{k}\) available rescue teams within the scope of cooperation, then the number of rescue teams that withdraw from task \(T_{i}\) would be \(\min(n^{b}_{i},\ n^{c}_{k})\). The rescue teams that withdraw from \(T_{i}\) would bid on new task \(T_{k}\).
## 2 System and Channel Models
### Foggy Channel  
When considering the mobile platform-based fog effect in terrestrial environments, it is difficult to directly apply the foggy channel characteristics studied in [7] because the channel model is based on the form of the vertical or horizontal link. However, as the mobile platform considered in maritime environments has a model in the form of the horizontal link, the statistical characteristics of the foggy channel in [7] can be directly applied. Therefore, in this study, we assume that the random foggy channel follows the Gamma distribution by applying the statistical model of the foggy channel given in [7], and that it has isotropic properties. Based on the relation between the link length and the related signal attenuation (i.e., the Beer-Lambert law) in [7], the state of the foggy channel is given as  
\[I_{a}=\exp(-\alpha l/4.343), \tag{1}\]  
where \(l\) and \(\alpha\) represent the length of the propagation link (km) and the related signal attenuation random variable (dB/km), respectively. Based on [7], the PDF of \(I_{a}\) can be written as  
\[f_{I_{a}}(I_{a})=\frac{z^{k}}{\Gamma(k)}\bigg{[}\ln\bigg{(}\frac{1}{I_{a}} \bigg{)}\bigg{]}^{k-1}I_{a}^{z-1}, \tag{2}\]  
where \(0<I_{a}\leq 1\), \(z=4.343/\beta\), \(\beta>0\) is a continuous scale parameter, and \(\Gamma(\cdot)\) is the Gamma function for \(k>0\), which is a continuous shape parameter that defines four types of fog (dense, thick, moderate, and light) according to the visible range in meters (\(V\)) [6]. The corresponding values of \(k\) and \(\beta\) are listed in Table 1.
## 4 Dynamics and Isolation Performance of the Vibration Isolator
### Vibration Isolation Performance under Different Excitations
#### 4.4.2 Vibration Isolation Performance under Multi-Frequency Wave Excitation  
The non-dimensional multi-frequency wave in Equation (28), referring to [25], is used as the input displacement excitation to analyze the vibration isolation performance of the QZS isolator when \(\overline{a}=0.45\), \(r=1\) and \(\xi=0.10\).  
\[\begin{array}{ll}\overline{Z}_{\rm m}=&0.25\sin(0.11\varepsilon\times\tau)+0.175\sin(0.32\varepsilon\times\tau)\\ &+0.10\cos(0.18\varepsilon\times\tau)+0.20\cos(0.36\varepsilon\times\tau)\\ \end{array} \tag{28}\]  
where \(\varepsilon\) is the coefficient of excitation frequency.  
Figure 12: Effects of damping ratio \(\xi\) on displacement transmissibility \(T\).  
Figure 13: Effects of excitation amplitude \(\overline{Z}\) on displacement transmissibility \(T\).  
Figure 14 shows the time history of the steady-state displacement responses of the QZS vibration isolator and its equivalent linear isolator, at \(\varepsilon\) = 10. When only linear isolation is performed, the displacement response is greater than the excitation, showing the deterioration of vibration. After the NSC is introduced, the vibration is attenuated, and the maximum of the displacement response drops to 36.7% of the maximum excitation amplitude. Then, the spectrum results with respect to the non-dimensional response frequency \(\overline{f}\) are given in Figure 15. The linear vibration isolator can reduce the vibration displacement at high excitation frequency, but that of the lowest excitation frequency is amplified to 213.1%, which leads to poor final isolation performance in the above time history. On the contrary, the QZS isolator reduces excitation amplitude at each frequency component by more than 73.7%.  
Figure 16 shows the effects of \(\varepsilon\) on the root mean square (RMS) of the displacement response. The variation of \(\varepsilon\) means that the excitation frequency is changed, which causes multiple peaks on the RMS curves. When \(\varepsilon\) is small, neither the QZS vibration isolators nor the equivalent linear isolator can  
Figure 14: Time history under multi-frequency wave excitation. (**a**) displacement excitation; (**b**) displacement responses of QZS and equivalent linear isolators.  
Figure 15: Spectrums under multi-frequency wave excitation. (**a**) displacement excitation; (**b**) displacement response of QZS isolator; (**c**) displacement response of equivalent linear isolator.  
isolate the multi-frequency wave excitation. In this region where the excitation vibration is amplified, although the QZS isolator has lower peak of RMS, its vibration around \(\varepsilon=1\) and \(\varepsilon=7\) is worse than that of the equivalent linear isolator. With the increase of \(\varepsilon\), it can be seen that adding the NSC makes the peaks move left, and the QZS vibration isolator plays the isolation function firstly. When \(\varepsilon=10\), its value of the displacement RMS is much smaller than the corresponding values of the excitation and equivalent linear isolator.