esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis  
The experimental evaluation of the proposed Fed-DTB framework aims to demonstrate its effectiveness in addressing the unique challenges of FL in IoV networks. We investigate the framework's robustness, adaptability, and efficiency through extensive simulations under realistic and adversarial conditions. This section presents the experimental setup, dataset distribution, evaluation metrics, and analysis of the obtained results.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 3 Performance Evaluation  
In the performance evaluation, to show the QoS performance and derive the factors to be considered to the QoS policy in the satellite network, we analyzed the performance in the application layer for various environments and parameters in Table 1[25; 27; 41; 42; 43; 44]. We implemented an event-driven simulator in the Riverbed modeler Version 18.6. In the performance evaluation, source UTs send the data to destination UTs with the mesh connection using the MF-TDMA. We assumed that the total bandwidth and the control channel was 10 MHz and 3 MHz, respectively. The available bandwidth to transmit the service traffic can thus be calculated as:  
\[B_{\rm A}=(B_{\rm T}-B_{\rm C})\times(1-R_{\rm G})\times R_{\rm P}, \tag{1}\]  
where \(B_{\rm T}\) and \(B_{\rm C}\) mean the total bandwidth and the bandwidth to be allocated to transmit the control traffic, respectively. \(R_{\rm G}\) is the guard band ratio. \(R_{\rm P}\) is the MF-TDMA packing ratio. Thus, the available bandwidth is 6.044 MHz in the environment of Table 1. For example, if the spectral efficiency was assumed to be 1 bps/Hz, the maximum transmission capacity was 6.044 Mbps in the simulation. To evaluate a more realistic environment, we considered various simulation environments by varying each parameter such as the resource allocation method, the number of UTs, the number of service flows, the total traffic load, the data rate for each service flow, the burstiness, the spectral efficiency, and the delay constraint, as shown in Table 2. In this paper, the QoS requirement by users was the delay constraint. To evaluate the QoS performance, we measured the throughput, the 95 th percentile end-to-end delay, the satisfaction ratio, and the goodput in the application layer. The throughput means the total throughput in the satellite network. The satisfaction ratio is defined as the ratio that all packets of transmitted data have successfully arrived at the destination UT while satisfying the delay constraint in the satellite network. The goodput means the total data volume that successfully has been received at the destination terminal within the delay constraint in the satellite network.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Total bandwidth & 10 MHz \\ Guard band ratio & 10\% \\ Control channel & 3 MHz \\ MF-TDMA packing ratio & 0.95 \\ Frame length & 0.5 s \\ Resource allocation delay & 4 frames \\ Propagation delay in the satellite link & 125 ms \\ Maximum data rate & 45 Mbps \\ The number of UTs & 50–150 \\ Spectral efficiency & 1, 2, 3 bps/Hz \\ The number of service flows & 50–150 \\ Ave.data TX rate for each service flow & 33–400 kbps \\ Ave. data size (application layer) & 2–500 KB \\ Ave. inter-arrival time & 0.5–10 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.
Remote Sens._**2013**, \(5\), 4280-4303; doi:10.3390/rs5094280  
[1]_Remote Sensing_**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
[2]_Article_  
[3]**Hub Height Ocean Winds over the North Sea Observed by the NORSEWInD Lidar Array: Measuring Techniques, Quality Control and Data Management**  
[4]**[PERSON] 1,*, [PERSON] 2, [PERSON] 1, [PERSON] 1,**  
[5]**[PERSON] 1, [PERSON] 3 and [PERSON] 4**  
[5]**1** Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark; E-Mails: [EMAIL_ADDRESS] (M.C.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (T.M.)  
[6]**2** GL Garrad Hassan, Brooktorkai 18, D-20457 Hamburg, Germany; E-Mail: [PERSON]SS]  
[7]**3** Department of Mechanical Engineering, University of Strathclyde, Glasgow G1 1 XJ, UK; E-Mail: [EMAIL_ADDRESS]  
[8]**4** Oldbaum Services, Stirling FK9 4 NF, UK; E-Mail: andy@oldbaum Services.co.uk  
[9]***** Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +45-2132-7328; Fax: +45-4677-5970.  
[10]_Received: 28 July 2013; in revised form: 3 September 2013 / Accepted: 3 September 2013 / Published: 5 September 2013_  
[1]**Abstract:** In the North Sea, an array of wind profiling wind lidars were deployed mainly on offshore platforms. The purpose was to observe free stream winds at hub height. Eight lidars were validated prior to offshore deployment with observations from cup anemometers at 60, 80, 100 and 116 m on an onshore met mast situated in flat terrain. The so-called \"NORSEWInD standard\" for comparing lidar and mast wind data includes the criteria that the slope of the linear regression should lie within 0.98 and 1.01 and the linear correlation coefficient higher than 0.98 for the wind speed range 4-16 m\(\cdot\)s\({}^{-1}\). Five lidars performed excellently, two slightly failed the first criterion and one failed both. The lidars were operated offshore from six months to more than two years and observed in total 107 months of 10-min mean wind profile observations. Four lidars were re-evaluated post deployment with excellent results. The flow distortion around platforms was examined using wind tunnel experiments and computational fluid dynamics and it was found that at 100 m height wind observations by the lidars were not significantly influenced by flow distortion. Observations of the vertical wind profile shear exponent at hub height are presented.  
keywords:lidar; offshore winds; remote sensing; wind energy; wind resources +
Footnote †: journal: Journal of Sound Energy
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 2 Materials and Methods
### Inference Workflow for Volumetric Data-Sets
#### 2.3.3 Prompt Selection and Accumulator Integration  
As mentioned above, SAM allows queries using various prompts such as point prompts (seed points and bounding boxes) and dense prompts (masks and brushes). Multiple studies [24; 30] have shown that, depending on the input data, higher segmentation quality can be achieved by using multiple prompts, such as point prompts distributed evenly over the segment region or negative point prompts, which are not considered part of the segment. Additionally, the use of rectangular prompts consisting of two anchor points often leads to adequate segmentation results.  
Given that the main objective of this study is to evaluate the applicability of SAM in the automated NDT domain, we have opted to solely assess single point prompts and dense prompts as they can be easily automated.  
We placed a single point prompt at the exact centre of the tile. The centre point of a tile was either chosen by a seed point or deemed highly likely to belong to the current segment, due to the iterative processing of the tiles.  
For dense prompts, we utilized the SAM output stored in the accumulator, which was shifted by the relative position of the current point prompt. This requires SAM to complete the segmentation proposal at the edge of the current tile. Since our tile step size was \([1,20]\) voxels, the overlap between the tiles and the dense prompt with the expected segmentation proposal was high, allowing SAM to only predict a relative slim border of new voxels. Figure 8 illustrates an idealized schematic of such an operation. In the case of dense prompts, we also include a corresponding point prompt at the centre of the tile as more prompts tend to increase the segmentation performance [24].  
Figure 8: Schematic view of two subsequent inference steps, denoted as \(n\) (represented by \(\blacksquare\)) and \(n+1\) (represented by \(\blacksquare\)), which use the modified accumulator volume from the previous step to create a dense SAM prompt. In step \(n\), the content of the accumulator volume of the previous step \(n-1\) is used to generate a dense SAM prompt \(n+1\). This prompt, along with the point prompt \(n\) and the extracted input volume tile \(n\), is used by SAM to compute prediction \(n\). Subsequently, the accumulator volume is updated to the state \(n\) based on this prediction. In the subsequent step \(n+1\), the accumulator volume \(n\) is used to determine the movement \(n+1\) to the tile \(n+1\). Tile \(n+1\) significantly overlaps with tile \(n\). SAM is parametrized with the extracted input volume tile \(n+1\), point prompt \(n+1\), and dense prompt \(n+1\) to compute prediction \(n+1\), which is used to update the accumulator volume \(n+1\).
## 4 Results
### Correlation of the Reanalysis, Wave Recorder and OBS Data
The remarkable increases in the amplitudes of seismic noise level within the period when the sea was covered with ice, highlighted in Figure 2 as yellow rectangles 3-5, are also visible on the spectrograms in Figure 3 (see the red arrows). In addition, the spectrogram shows a time-constant signal amplification in a narrow frequency band in the vicinity of 10 Hz (indicated by the white arrows), and in some time periods also in narrow frequency bands in the vicinity of 18 and 30 Hz (indicated by the orange arrows). In the time period after 9 December, the same constant signal amplifications appear in narrow frequency bands in the vicinity of 8 and 15 Hz (indicated by the gray arrows). Figure 4 shows on a larger scale the time history of wind speed at the Typ2 site and its correlation with the increase of the seismic noise level shown on the spectrogram obtained for the horizontal seismometer at the Typ2 site. It can be seen that the signal amplification at 10 Hz is present all the time at any wind strength (indicated by the white arrows), while with an increase in wind speed this signal becomes more pronounced. In this case, additional maxima appear at 18 and 30 Hz (indicated by the orange arrows). Figure 5 demonstrates the correlation between sea level fluctuation in the 30 s-5 min spectral periods range (related to infragravity sea waves [40]) obtained by the wave recorder RBR virtuoso3 and its spectrogram, the horizontal component of ground velocity, obtained by the OBS at the Typ2 site, and the wind speed. The green rectangle 1 corresponds to the period of time when the sea was ice-covered and seismic noise levels were low. At the same time, there was a low level of infragravity waves amplitudes and wind speed. In the time period after 8 December, indicated by the purple rectangle 2, there was ice either, and short but very frequent peaks in the level of seismic noise were observed, as well as an increase in the level of amplitudes of infragravity waves and wind speed. The energy level of infragravity waves and wind speeds fluctuate almost identically. Figure 4: Time history of wind speed at the Typ2 site obtained from the ERA5 reanalysis [26] and its correlation with the seismic noise level shown on the spectrogram obtained for the horizontal sensor CME-3311 at the Typ2 site. Colored arrows indicate the features of the spectrogram, described in the text.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 5 Actuation Methods  
The continuous growth of MEMS devices enables the rapid growth of numerous industry sectors that support the batch fabrication of a wide variety of miniaturized components, including optical devices. MEMS-based actuators are largely used in optical scanners due to their small dimensions, low power consumption, light weight, fine performance characteristics, and easy integration with other devices. In optical scanners, MEMS actuators permit scanning by sweeping a light beam in one direction for 1-D scanners, or over a solid angle for 2-D scanners.  
The MEMS actuators can mainly be classified into five categories based on their working principle, which are: electrostatic, electrothermal, piezoelectric, electromagnetic, and shape memory alloy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Resonant Scanner** & **Semi-Resonant Scanner** & **Non-Resonant Scanner** \\ \hline Scan area & & & \\ Power consumption & & & \\ Operating frequency & High & Intermediate & Low \\  & & Large scanning amplitude & Operable at very low \\  & Large displacement & than non-resonant & frequencies, stable to \\ Advantages & amplitude, low power & scanners, variable imaging & small variations of \\  & consumption & field, stable working & operating conditions, \\  & & conditions & image field is variable \\  & Offsetting the image field & Performance highly & Small scanning \\ Disadvantages & requires complex & depending on the working & amplitude, high power \\  & systems, Instability can & frequency & consumption \\  & lead to whirling motion & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary table for different scanners.  
actuators. The performance of these actuators is shown in Table 4 along with their strengths and limitations for one use as compared to others.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Correlation of Errors  
An important aspect of model error analysis is examining the potential correlation between input configurations and the magnitude of absolute or relative errors. This is particularly relevant because, if the largest errors are concentrated in specific input patterns, the model's overall accuracy could be overestimated. A detailed understanding of error distribution allows for the implementation of varying error tolerances across amplifier models based on their respective operating ranges. To address this, the mean absolute error of the noise figure is analyzed as a function of each input parameter, considering the cumulative distribution of absolute errors discussed in previous sections. By plotting absolute error against each parameter, distinct trends and behaviors across different device brands can be identified. A notable trend observed across nearly all devices is the increase in error near the frequency band edges. As illustrated in Figure 16, restricting the analysis to central channels results in lower overall errors. This effect may stem from overfitting, as polynomial models typically exhibit reduced accuracy at the boundaries of their fitting intervals.  
The total input power seems to have a negligible impact on the error distribution, with varying trends observed across datasets of the same device type. However, Cisco EDFA-17, EDFA-35, and Juniper devices consistently exhibit lower errors at higher target gain values, as shown in Figure 17. Juniper devices also exhibit a reduction in errors as the target tilt increases, as depicted in Figure 18. In summary, the noise figure model demonstrates varying degrees of error across different input data intervals. These variations can be quite substantial, as shown in Figure 16b, where the 99%-threshold error decreases from 0.46 to 0.28 dB, and in Figure 17c, where it drops from 0.43 to 0.27 dB. However, these intervals are highly device-dependent, preventing the establishment of a universal guideline for narrowing the input parameter range to minimize errors, except for the consistent influence of channel frequency, which remains a common trend across all devices.  
Figure 16: Cumulative distribution of absolute errors and their correlation with the frequency range. (**a**) EDFA-35, low-gain range; (**b**) L-band EDFA, low-gain range. Green and red dashed lines represent 99% and 90%-threshold values, respectively, with green and red bullets highlighting minimum and maximum values obtained through these curves.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 5 Results and Discussion
### Multiply Scattered Acoustic Waves  
Acoustic wavefields (represented by the particle velocity) for the 1% volume fraction case for a single realization and the ensemble-averaged wavefield with 60 realizations are shown in Figure 4a,b, respectively. Figure 4a represents \(\mathrm{s}_{m}(x_{i},t)\) and Figure 4b denotes \(\langle\mathrm{s}(x_{i},t)\rangle\). In these figures, wave rays with a positive slope indicate forward-propagating wave components and those with a negative slope indicate backward-propagating wave components. To clearly visualize incoherent wave components, the amplitude range (particle velocity) was limited to \(-1\) to \(1\) m/s in Figure 4a,b. The single realization case (Figure 4a) displays a mixture of coherent forward-propagating waves and incoherent noise-like wave components that are set up by the network of fine dust particles. For the ensemble-average case (Figure 4b), the incoherent wave components that are distinct in Figure 4a are suppressed. Coherent forward-propagating waves are dominant in the ensemble-averaged wavefield and coherent back-scattering components are also observed. Acoustic wave signals at the first sensor position for the single realization and ensemble-average cases are depicted in Figure 4c,d, respectively. The two signals also demonstrate incoherent wave suppression by ensemble averaging.  
The coherent wave attenuation for the 1% volume fraction, computed using the coherent wavefield data presented in Figure 4b, is illustrated in Figure 5. As a reference, the ISA-based analytical attenuation computed by multiplying the analytical total scattering cross-section and the known number density value of 123 is also displayed. Overall, the coherent wave attenuation obtained using the simulation wavefield data exhibits good agreement with the analytical attenuation in the increasing trend over frequencies.  
Figure 4: Acoustic wavefields for the 1% volume fraction case: (**a**) wavefield for a single realization and (**b**) ensemble-average wavefield. Acoustic wave signals at the first sensor position for the two cases are shown in (**c**) and (**d**), respectively.  
However, as the frequency increases, the deviation between the two attenuation curves (data and analytical) becomes more distinct, revealing the possibility of number density estimation error.
## 2 Materials and Methods
### Effect of Allelopathic Compounds on Callus Growth  
To assess the effect of allelopathic compounds on callus growth, identified phenolic compounds including salicylic acid, \(p\)-coumaric acid, vanillic acid, \(p\)-hydroxybenzoic acid, ferulic acid, and syringe acid at concentrations of \(10^{-2}\), \(10^{-3}\), and \(10^{-5}\) M were prepared. These compounds were mixed with MS medium supplemented with 1 mgL\({}^{-1}\) 2,4-D, upon which six to eight young and fresh calli were placed and maintained at \(25\pm 1\)\({}^{\circ}\)C for 40 d. The fresh weight of the calli was recorded after 40 d.
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 2 Related Work
### Starting with Feature Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & **Year** & **Dataset** & **Classification** & **News** & **Social** & **Emotion** & **Results** \\  & & **Approach** & **Content** & **Context** & **Feature** & & **Results** \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{2019} & PHEME labeled & Different & & & & The use of emotional words & \\  & & Twitter dataset & classification & \(\surd\) & & \(\surd\) & is beneficial in \\  & & & approaches & & & & sentiment-aware & \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{2019} & Politifact-1 & \multirow{3}{*}{EmoCred} & \multirow{3}{*}{\(\surd\)} & & & \multirow{3}{*}{\(\surd\)} & & \multirow{3}{*}{\(\surd\)} \\  & & Politifact-2 & & & & & & \\ \hline \multirow{3}{*}{[22]} & \multirow{3}{*}{2017} & Twitter & Conditional random & & & & & \\  & & (five events) & fields & \(\surd\) & \(\surd\) & & & F1 score of 0.607 \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{2020} & News Articles & Emotionally Infused & & & & & Superior results with F1 \\  & & and Twitter & Network & \(\surd\) & & & \(\surd\) & value of nearly 96\% \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{2020} & RumourEval & & & & & & Boosts accuracy by more \\  & & PHEME & & & & & & than 2.05\% and 1.90\% \\ \hline \multirow{3}{*}{[25]} & \multirow{3}{*}{2021} & RumourEval-19 & & & & & & Outperforms state-of-the-art \\  & & Weibo-16 & BiGRU & & \(\surd\) & \(\surd\) & \(\surd\) & task-related \\  & & Weibo-20 & & & & & & emotional features \\ \hline \multirow{3}{*}{[26]} & \multirow{3}{*}{2023} & \multirow{3}{*}{PHEME} & Emotion-Enriched & & & & & \\  & & and Psycholinguistic & & & & & \\ \cline{1-1}  & & Feature-Based & & \(\surd\) & \(\surd\) & Superior performance \\ \cline{1-1}  & & Approach & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of existing detection approaches for fake news. Figure 3: Emotional feature analysis diagram.
## 4 Discussion  
It is important to highlight that the proposed methodology and the results obtained are not intended to replace the design spectra established by regulations, which would be complex from a regulatory standpoint. However, the methodology is useful as an early warning tool for potential local effects. This can allow, for instance, the improvement of exploration programs or the adjustment of structural design criteria to better adapt to the specific site conditions.  
The methodological approach used in this study could be replicated in other cities, adapting it to local geological characteristics. This adaptability is one of the strengths of  
Figure 12: Comparison between response spectra obtained for all zones of the microzonation campaign and the design spectrum for the city of Cončn. (**a**) West Zone; (**b**) Center Zone; (**c**) North Zone; (**d**) South Zone. In this Figure, gray lines represent the response spectrum in each measurement point.  
the approach, as it would not only be viable under the conditions of the city of Concon, but also utilizes a modeling framework that can be applied to areas with similar geological conditions, such as coastal terraces, dune deposits, or alluvial plains. For example, regions with coastal dune formations or areas prone to seismic activity could benefit from a similar integration of seismic microzonation and 3D FEM modeling, improving local risk assessments.  
In the case of Concon, the model and the results obtained will be used as complementary inputs for the generation of seismic response information layers, which can be integrated into geographic information systems (GISs). Moreover, this procedure does not produce static models; as geotechnical exploration is enhanced, continuous adjustments can be made to develop a regional model that becomes a regular working tool for seismic design in the city.  
Although this study focused on a 3D model to capture the regional seismic response in Concon, it is important to note that the implementation of 2D models could be a valuable complementary tool in future research. 2D models allow for greater local refinement of the FEM mesh, which is particularly useful in areas with complex geometries or specific geotechnical characteristics. These models could focus on critical areas identified by the 3D model, providing a more detailed analysis of soil-structure interaction and allowing for more precise adjustments in structural design. The integration of 2D models in subsequent studies could therefore enhance the ability to predict seismic response in localized areas, further optimizing seismic risk mitigation strategies in the region. In summary, the use of the generated 3D model will allow microzonation data to be used to simulate the response of the soil and structures in various hypothetical earthquake scenarios. This could include different magnitudes and focal depths, providing a broader range of data for seismic risk management planning in the locality.