esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fatty Acids Profile  
The percentage of fatty acids in the extracted oils from _C. edulis_ was evaluated by GC-FID, and the results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Essential Trace Minerals** \\ \hline Fe (\(\mu\)g/g) & 32.20 \(\pm\) 0.42 \\ Cu (\(\mu\)g/g) & 5.89 \(\pm\) 0.17 \\ Zn (\(\mu\)g/g) & 25.06 \(\pm\) 0.41 \\ Mn (\(\mu\)g/g) & 7.05 \(\pm\) 0.36 \\ Mo (\(\mu\)g/g) & n.d. \\ Co (ng/g) & \textless{}LoD \\ Cr (ng/g) & n.d. \\ Se (\(\mu\)g/g) & 0.016 \(\pm\) 0.010 \\ \hline \hline \multicolumn{3}{c}{Non-essential and toxic trace elements} \\ \hline Al (\(\mu\)g/g) & 10.4 \(\pm\) 2.0 \\ As (ng/g) & 12.8 \(\pm\) 5.1 \\ B (\(\mu\)g/g) & \textless{}LoD \\ Ba (ng/g) & n.d. \\ Be (ng/g) & \textless{}LoD \\ Bi (ng/g) & n.d. \\ Cd (ng/g) & 0.55 \(\pm\) 0.06 \\ Cs (ng/g) & \textless{}LoD \\ Li (ng/g) & 11.2 \(\pm\) 3.1 \\ Ni (\(\mu\)g/g) & n.d. \\ Pb (ng/g) & \textless{}LoD \\ Rb (\(\mu\)g/g) & 14.20 \(\pm\) 0.29 \\ Sb (\(\mu\)g/g) & n.d. \\ Sn (ng/g) & 101 \(\pm\) 14 \\ Sr (ug/g) & 20.12 \(\pm\) 0.11 \\ Te (ug/g) & \textless{}LoD \\ Ti (\(\mu\)g/g) & n.d. \\ V (\(\mu\)g/g) & \textless{}LoD \\ W (\(\mu\)g/g) & \textless{}LoD \\ Zr (ng/g) & 24.39 \(\pm\) 1.2 \\ \hline \hline \multicolumn{3}{c}{Macro-elements} \\ \hline Ca (mg/g) & 12.44 \(\pm\) 0.89 \\ K (mg/g) & 1.24 \(\pm\) 0.40 \\ Mg (mg/g) & 2.98 \(\pm\) 0.25 \\ Na (ug/g) & \textless{}LoD \\ \hline \hline \end{tabular} Results expressed in \(\mu\)g/g (Fe, B, Al, Ti, V, Mn, Ni, Cu, Zn, Se, Rb, Sr, Mo, Te, and Na), ng/g (Li, Be, Cr, Co, As, Zr, Cd, Sn, Cs, Ba, Bi, W, and Pb), and mg/g (K, Ca, and Mg) of dw. Values are presented as mean \(\pm\) standard deviation (\(n=3\)). LoD—Level of Detection. n.d.—Not Detected.  
\end{table}
Table 2: Total content of essential, non-essential, and toxic trace elements of _C. edulis_ fruits.  
Our investigation discovered 11 fatty acids, with \(\alpha\)-linoleic being the most abundant essential fatty acid in _C. edulis_ fruits.
## 3 Fault Diagnosis Method Based on CASTSR and EMD  
To solve the mode-aliasing problem of EMD decomposition, improve the accuracy of EMD decomposition and make the extracted characteristic frequency more prominent, it is essential to carry out pre-noise reduction before the signal is measured. Therefore, a cascade system needs to select the appropriate series so that the characteristic frequency obtained by EMD decomposition of the signal after denoising is in IMF1. For this purpose, Hilbert transform is adopted to propose a rolling bearing fault diagnosis method based on CASTSR and EMD. The specific steps of the proposed fault diagnosis method are described as follows:  
* **Step 1.** The signal was transformed into an envelope signal by Hilbert transformation, which is passed into a high-pass filter to filter interference components in the low-frequency region. The simulation signal is directly passed into the high-pass filter.
* **Step 2.** The filtered signal is input into the CASTSR system for noise reduction pretreatment. Then, EMD is used to process output results of the first-stage stochastic resonance system. If the characteristic frequency is in IMF1, stop this method.
* **Step 3.** If the characteristic frequency is not in IMF1, EMD is used to process the results of the stochastic resonance system at the next level until the characteristic frequency is in IMF1.
* **Step 4.** According to the extracted characteristic frequency, the fault diagnosis of rolling bearing is carried out.  
The fault diagnosis flow based on CASTSR and EMD is shown in Figure 4.  
Figure 3: The detailed fault diagnosis process based on CASTSR for rolling bearings.
## 3 Results
### SWOT Data Filtering
Looking at the \(\lambda_{\rm c}\) corresponding to the different minimum points, for both boxes, the minimum of the curve for SSH and the absolute geostrophic velocity (Vg) is found for a 30 km (29) \(\lambda_{\rm c}\). It is slightly higher, 40 km (41), for relative vorticity. This directly relates to the amplification of fine scale structures, and thus the effect of the noise, in the computation of second-order derivatives. It is also in accordance with what is found in the SNR in Figure 6, which shows that we cannot recover the signal at wavelengths lower than 40-50 km. With the qualitative (Figures 7-12) and the spectra (Figures 13 and 14) plots, we saw that, for box 2, as the signal is lower than in box 1, the effect of the noise is greater, and larger \(\lambda_{\rm c}\)s are necessary. The RMSE plots show us another point of view. As the signal is not as intense in box 2 as in box 1, the over-smoothing (signal lost) due to the Laplacian diffusion filter is lower, and thus we observe lower RMSE values in Figure 7:0 Moreover, the improvement of the RMSE values is greater for box 2 than box 1. For SSH, the RMSE reduces by 0.05 and 0.07 m from the no filter (0 \(\lambda_{\rm c}\)) to the minimum RMSE, for box 1 and box 2, respectively. For Vg, the RMSE reduces by 0.42 and 0.455 m/s, and for \(\zeta/f\) by 3.2 and 3.35. For SSH, we could say that with a 40 km \(\lambda_{\mathrm{c}}\) it is sufficient, but if we do not want to see the effect of the noise in vorticity, we need a greater \(\lambda_{\mathrm{c}}\) of 60 km. Therefore, together with what is observed in Figures 4-15, we consider that, with this filtering technique applied to this region, SWOT will be able to resolve wavelengths down to a 40-60 km wavelength range. This is the \(\lambda_{\mathrm{c}}\) range where we found that there is a compromise between filtering out the noise of SSH and its derived variables (Vg and \(\zeta/\mathfrak{f}\)), and over-smoothing the original image as little as possible. It is important to note that \(\lambda_{\mathrm{c}}\) depends on the signal-to-noise ratio between SSH signals and instrument noise at fine scales. As such, it can be expected to change from region to region (and from season to season) depending on the energy levels at fine scales and on the noise level.
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for Protocol Stack Layers
#### 6.2.4 Application Layer  
ML can be used in the application layer in several ways. As an example, internet traffic classification can be difficult due to the encrypted traffic streams. Traditionally, the feature extraction phase is performed by field experts, and the classification is made on the base of the obtained features. ML can streamline the process by performing both feature extraction and classification in one system. In [50], the authors presented a system which first characterizes the traffic and then identifies the specific application, obtaining a precision and recall for plain and VPN-encapsulated traffic greater than 90%. In NTNs,traffic classification may be used to properly select the NTE, when more of them are available in the area. As an example, when a VoIP call is identified, it may be better to route the traffic over UAV or HAPS, and leave GEO satellites to fewer sensitive applications, such as unidirectional video streaming.  
Another interesting use of ML at application layer consists in optimizing the data rate at application level by estimating the end-to-end round trip time (RTT). Popular media streaming services (e.g., YouTube or Netflix) suffer from high RTT because of the TCP congestion control strategies (i.e., higher RTT values lower end-to-end data rate [43]). Predicting future values of users' RTT can help in directly improving the throughput, filling the buffer of the user that is expected to experience a larger RTT or properly modify in advance the coding strategy in order to avoid video quality worsening. Due to the air and space elements, instantaneous RTT readings may not be representative because NTEs are subject to RTT fluctuations. In this case, having an estimation in advance is even more important to properly implement the most suitable countermeasure.  
Table 5 reports some of the ML techniques that can be applied to each layer in the NTN protocol stack based on the implementations in Section 4.2.
# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 2 Materials and Methods
### Improving Firefly Optimization Algorithm
#### 2.4.1 Firefly Algorithm  
In the FA algorithm, each firefly represents a candidate solution to the optimization problem, with its brightness serving as an indicator of solution quality--greater brightness denotes a superior solution. The FA operates by simulating mutual attraction among fireflies, enabling the swarm to progressively converge toward the optimal solution. Specifically, the attractiveness of a firefly is proportional to its brightness and diminishes with increasing distance, reflecting the natural attenuation of light. During each iteration, fireflies adjust their positions by moving toward individuals exhibiting higher brightness, with the movement's direction and magnitude governed by both the brightness differential and their spatial proximity. The formula is as follows:  
\[x=x_{i}+\beta e^{-\gamma r^{2}}(x_{j}-x_{i})+\alpha\varepsilon \tag{3}\]  
where \(x_{i}\) and \(x_{j}\) represent the positions of fireflies \(i\) and \(j\), respectively, \(\beta\) is the attraction coefficient, \(\gamma\) is the light intensity decay factor, \(r\) is the distance between fireflies, \(\alpha\) is the random disturbance coefficient, and \(\varepsilon\) is a random variable that follows a normal distribution. The firefly algorithm (FA) demonstrates robust global search capabilities; however, it is susceptible to becoming trapped in local optima, particularly within complex, high-dimensional search spaces. This limitation is intensified when the light intensity decay factor is substantial, as it significantly impedes the algorithm's convergence speed. To address these challenges, this study proposes three enhancements to the classical FA.
## 2 Modeling of the PV System  
As illustrated in Figure 1, the photovoltaic system under investigation comprises three major components: a photovoltaic module, a direct current solar pump, and a MPPT control system. The MPPT system includes a look-up table to indicate the maximum possible power at any irradiance and temperature. It also comprises a controller that compares the desired power with the actual one and takes corrective measures by specifying the duty cycle of a buck-boost converter. The MPPT control systems are composed of an MPPT algorithm and a DC-DC buck-boost converter. The photovoltaic panel comprises photovoltaic cells, which operate via the photoelectric effect, arranged in series and parallel, as the potential generated by a single photovoltaic cell is small. The output voltage-current relationship in a photovoltaic cell is nonlinear. The equivalent circuit is based on a single-diode circuit model, as illustrated in Figure 2. The BP SX 150S solar module was chosen to power several pumps. Its parameters are listed in Table 1[14; 15; 16].  
where \(I\): overall output current from the cell; \(I_{ph}\): photocurrent generated by light; \(I_{D}\): current through the diode; \(I_{Sh}\): current through the shunt resistor; \(I_{S}\): diode saturation current; \(K_{1}\): Boltzmann constant; \(q\): electron charge; \(T_{c}\): actual cell temperature; \(R_{Sh}\): shunt resistance; \(I_{SC}\): short-circuit current; \(T_{r}\): reference temperature; \(\lambda\): irradiance; and \(R_{Sh}\): series resistance.  
Figure 3A,B illustrates the I-V and P-V characteristics at various irradiations and constant temperatures (T = 25 \({}^{\circ}\)C). Figure 4A,B shows the I-V and P-V relationships at different temperatures and constant irradiance levels (1000 Watts/meter\({}^{2}\)).
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Incentives  
One of the significant challenges in MASs involving deceptive agents is to motivate agents to bestow accurate information. In this paper, we are inspired by the credit mechanism to achieve this operation. Each of the agents in MASs has a fixed number of credits, no matter whether it acts as a resource client, a witness, or a resource provider, which are administered and displayed by a blackboard. However, credits only make sense when an agent acts as a resource client or a witness. A witness is only allowed to contribute its testimony if the resource client has credits. Simultaneously, a witness might receive or lose credits according to the interactive feedback of the resource client. As a resource client, an agent can no longer solicit others for any testimonies once it runs out of credits because it acts as a witness. Of course, it has the opportunity to encourage information by presenting convincing information and accumulating credits.  
We suppose that gaining or losing credits is affected by interactive feedback. The specific amount is determined by its original credit and the confidence value decided by credibility value and consistency. In order to explain the credit mechanism in detail, we apply \(CR_{i}\) to represent the credit of agent \(c_{i}\). We force the value to be in the range \([0,2]\), and it is initialized to \(1\). It is capable of demanding information unless \(CR_{j}\) is bigger than the threshold, for example, \(CR_{j}>0.2\). After the interaction, the credits of all witnesses are updated as follows:  
1. If the interactive feedback matches the testimony, then \[CR_{i}=CR_{i}[1+Cred(c_{i},w_{k})*Cer(c_{i},w_{k})]\]
2. If the interactive feedback does not match the testimony, then \[CR_{i}=CR_{i}[1-Cred(c_{i},w_{k})*Cer(c_{i},w_{k})]\]  
It is necessary to explain what \"match\" means here. Namely, the witness supports the resource provider if the feedback was a success and the other way around. We set two bounds, as the witness is not allowed to accumulate or lose too many credits in a dynamic MASs. A high-credit agent loses credits rapidly if it keeps lying. Another advantage of our trust estimation model is that another \"second chance\" mechanism is not required. As soon as a dishonest agent reaches the minimum credit level, it understands that it can no longer demand information for trust estimation. Thus, to maximize its own social welfare, it has to provide honest information to gain credit.
## 4 Experiments and Results
### Dataset  
For comparison with other models, the benchmark dataset RESIDE [55] was used for experiments, which consists of four subsets, two training sets, i.e., the indoor training set (ITS) and the outdoor training set (OTS), and two testing sets, i.e., the synthetic objective testing set (SOTS) and the hybrid subjective testing set (HSTS). Our experiments used OTS as the training set, which consists of 8447 clear images and 296,695 haze images, and 1500 clear images and haze images were randomly selected as the training set. SOTS was used as the test set, which consisted of 500 indoor images and 500 outdoor images, and the synthetic images in HSTS were also used as the supplementary test set. To verify the dehazing effect of the proposed model in real scenes, real images from HSTS were used as the real haze image test set.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 2 IEEE 802.11 ax Use Cases in Fifth-Generation Radio Access Network on Unlicensed Bands (5G-U)
### Improved Network Capacity Using Multi-User Multiple-Input and Multiple-Output (MU-MIMO)  
HEW-enabled base stations (BSs) typically have more radio antennas than WEs. Therefore, a BS can utilize downlink MU-MIMO (the 802.11 ax working group is also proposing uplink MIMO [3]), which allows a single BS to transmit parallel beam-formed transmission streams to different WEs, such as WE 1, WE 2, WE 3 and WE 4 in Figure 1b, on the same frequency. Beam-forming has previously been used in single-user (SU) transmissions to achieve higher data rates, and can now be used to increase overall WLAN network capacity. As a result, a WE equipped with a smaller number of antennas (that is, only one) does not affect network performance by occupying the whole radio channel with its lower transmission rate. Moreover, since MU-MIMO transmissions are realized in parallel and possibly with different transmission rates, the distance from BS is less important than in legacy SU networks in which WEs on the edge of the coverage cell (using low rates) could severely affect the performance of others by deferring their transmissions.
## 3 Results
### Configuration of the GCP  
Scenario B was selected to analyse the optimal number and most suitable placement of ground control points (GCPs) within the block, aiming to achieve the lowest mean errors (\(\mu_{\text{xyz}}\)) in the check points (CPs). An analysis was conducted by varying the number of GCPs while keeping four CPs fixed, in order to determine the most efficient configuration and to produce photogrammetric outputs with the lowest RMSE. Six different configurations were assessed, and a total of 19 GNSS points were surveyed in the field. Figure 6 illustrates the spatial distribution of the GCPs and CPs across the study area.  
The distribution and number of CPs, specifically points 12, 15, 16, and 19, remained constant across all six configurations, in order to ensure objective and comparable results during the evaluation process.  
In Configuration 1, GCPs were assigned to points 1, 2, 9, and 10, located at the corners of the block, along with point 17 positioned at the centre. Configuration 2 included points 1, 2, 5, 6, 9, and 10 as GCPs, covering the four corners and two points along the central edges of the block. For Configuration 3, points 3, 4, 7, 8, 11, 13, 14, 17, and 18 were used as GCPs. In Configuration 4, GCPs were assigned to points 1 through 10, all located along the perimeter of the block. Configuration 5 included the same ten perimeter points, along with points 13 and 17. Finally, Configuration 6 comprised a total of 15 points distributed across the block as GCPs.  
Figure 7 displays the root mean square error values in the horizontal plane (RMSExy), in elevation (RMSEz), and in three dimensions (RMSExy) for the check points (CPs) corresponding to each configuration. The results obtained were as follows:  
Configuration 1: 19.8 cm, 24 cm, and 31 cm; Configuration 2: 36 cm, 20 cm, and 41 cm; Configuration 3: 21 cm, 26 cm, and 33 cm; Configuration 4: 4 cm, 9.7 cm, and 10.4 cm; Configuration 5: 3.9 cm, 9.7 cm, and 10.4 cm; Configuration 6: 3.9 cm, 9.7 cm, and 10.4 cm. Configurations 1 and 3 exhibited the highest RMSE values. In contrast, Configurations 4, 5, and 6 yielded the best results, with low and nearly identical error values. In Configuration 3, all of the GCPs were positioned along the block's edges, within the standard overlap zone used for the georeferencing of each flight.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Scenario** & \(\mu_{\text{xy}}\)**CP** & \(\mu_{\text{z}}\)**CP** & \(\mu_{\text{xyz}}\)**CP** \\ \hline A & 1.0 & 1.5 & 1.8 \\ B & 1.0 & 0.9 & 1.3 \\ C & 0.9 & 1.4 & 1.7 \\ D & 1.1 & 1.5 & 1.9 \\ mean & 1.0 & 1.3 & 1.7 \\ std & 0.1 & 0.3 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 7: Weight coefficients for the CP in horizontal (xy) and vertical (z) directions. The best results are shown in green and the worst in red (dimensionless).  
Figure 6: Scenario B distribution of GCPs and CPs for the six configurations in the study area.
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 4 Results
### Comparison of Guided Wave Mixing Technique and Phase Matching Mode Experimental Setup and Results  
To examine the feasibility of the guided wave mixing technique, the results were compared with phase matching mode results for the same specimen. Figure 14,b show the experimental diagrams for the phase matching mode technique and guided wave mixing technique. \(b\) means the mixing wave propagation distance (the scan range). As the distance is changed, nonlinearity is also measured (\(a=125\) mm, \(b=50\) mm, \(55\) mm, \(\cdots\), \(85\) mm). Tables 4 and 5 give information about the experimental setup. Even though the two experimental setups are based on different mode and frequency, it is worthwhile to compare the results, because the data type that is used for study is not absolute data, but relative data (nonlinearity variation). Through nonlinearity variation, sensitivity information is estimated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Type** & **Frequency** & **Wedge Angle** \\ \hline Pulser & 3.5 MHz & \(\omega_{1}=3.41\) MHz & \(25^{\circ}\) \\ Receiver & 7.5 MHz & \(\omega_{2}=6.82\) MHz & \(25^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental conditions for phase matching mode technique.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Type** & **Frequency** & **Wedge Angle** \\ \hline Pulser & 10 MHz & \(\omega_{1}=7.83\) MHz & \(25^{\circ}\) \\ Receiver & 2.25 MHz & \(\omega_{2}=2.06\) MHz & \(37^{\circ}\) \\ Receiver & 7.5 MHz & \(\omega_{1}-\omega_{2}=5.77\) MHz & \(52^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental conditions for guided wave mixing technique.  
Figure 15 shows the nonlinearity variation as the receiver distance (\(b\)) is increased. Both graphs show the nonlinearity increment. The same tendencies are observed for the different techniques. To compare the degree of variation and sensitivity, a graph with the same vertical axis range of the two graphs is inserted in Figure 15a. The results for the guided wave mixing technique represent a big nonlinearity variation difference. This means that the technique is more appropriate for evaluating nonlinearity variation than the phase matching mode technique. The reasons for the good nonlinear sensitivity are as follows:  
A mixing signal is not generated directly from the equipment, but is instead newly generated and mixed in the specimen by two primary waves. Therefore, it has less influence from the system. This means the mixing signal contains reduced system nonlinearity.  
Figure 14: Experimental Setup: (**a**) Phase matching mode; (**b**) Guided wave mixing technique.  
Figure 15: Nonlinearity variation depending on the receiver position with the same vertical range: (**a**) Phase matching mode; (**b**) Guided wave mixing.  
In the case of the difference frequency mixing component, the system nonlinearity commonly included in the primary waves is canceled out, so the system nonlinearity is reduced.
## 3 Coherent Feature Extraction and Evaluation  
In a complex electromagnetic environment, if the coherent feature is extracted from the time domain waveform of the radar signal directly, it is easily affected by noise [23]. The autocorrelation function reflects the correlation degree between the two signals. Considering that the correlation of noise is poor, while the correlation of signals is strong and the noise is not related to the signal, instantaneous autocorrelation processing can strengthen the signal and weaken the noise; therefore, the instantaneous autocorrelation function can be used to extract the coherent feature [24].  
Supposing \(s(n)\) denotes the received signal, its instantaneous autocorrelation function can be expressed as follows.  
\[Y(n,m)=s(n)\times s^{*}(n+m)\qquad m=0,\pm 1,\cdots \tag{12}\]  
\[Y(n)=\sum_{m=-\infty}^{+\infty}Y(n,m)=\sum_{m=-\infty}^{+\infty}s(n)\times s^ {*}(n+m) \tag{13}\]  
Where \(s^{*}(\cdot)\) is the conjugate function of \(s(\cdot)\).  
From Formula (13), it can be seen that the biggest advantage of instantaneous autocorrelation is that there is no time integration. In other words, the instantaneous information of correlation processing is well preserved.