esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analyzing of the Applicability of Sunflower Husk Ashes Granules as Bio-Fertilizer
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**PAH Concentration, \(\mu\)g/kg**} & \multicolumn{3}{c}{**PAH Concentration, \(\mu\)g/kg**} \\ \hline ANA & Acenaphthenene & \textless{}10.0 & CHR & Chrysene & \textless{}10.0 \\ ANY & Acenaphthylene & \textless{}10.0 & DBA & Dibenzo(a,h)anthracene & 41.5 \(\pm\) 8.3 \\ ANT & Anthracene & \textless{}10.0 & FA & Fluoranthene & \textless{}10.0 \\ BaA & Benzo(a)anthracene & \textless{}10.0 & FLU & Fluorene & \textless{}10.0 \\ BaP & Benzo(a)pyrene & \textless{}10.0 & IPY & Indeno(1,2,3-cd)pyrene & \textless{}10.0 \\ BbF & Benzo(b)fluoranthene & \textless{}10.0 & NAP & Naphthalene & 15.7 \(\pm\) 3.1 \\ BPE & Benzo(g,h,i)perylene & \textless{}10.0 & PHE & Phenanthrene & \textless{}10.0 \\ BkF & Benzo(k)fluoranthene & \textless{}10.0 & PYR & Pyrene & \textless{}10.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Polyccl aromatic hydrocarbon concentrations of granulated sunflower husk ash. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Non-Dioxin-like PCBs**} & \multicolumn{2}{c}{**Mono-Ortho PCBs**} & \multicolumn{2}{c}{**Non-Ortho PCBs**} \\ \multicolumn{2}{c}{\(\mu\)g/kg**} & \multicolumn{2}{c}{**ng/kg**} & \multicolumn{2}{c}{**ng/kg**} \\ \hline PCB 28 & \textless{}0.10 & PCB 123 & \textless{}0.66 & PCB 81 & \textless{}0.73 \\ PCB 52 & \textless{}0.10 & PCB 118 & 15.95 \(\pm\) 3.99 & PCB 77 & \textless{}0.72 \\ PCB 101 & \textless{}0.10 & PCB 114 & \textless{}0.65 & PCB 126 & \textless{}0.98 \\ PCB 138 & \textless{}0.10 & PCB 105 & 5.51 \(\pm\) 1.38 & PCB 169 & \textless{}1.39 \\ PCB 153 & \textless{}0.10 & PCB 167 & 7.02 \(\pm\) 1.76 & & \\ PCB 180 & \textless{}0.10 & PCB 156 & 3.98 \(\pm\) 1.0 & & \\ PCB 157 & \textless{}0.96 & & & \\ PCB 189 & \textless{}1.08 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Polychlorinated biphenyl concentrations of granulated sunflower husk ash. authors. Researchers have investigated the nutrient retention of different granulated agricultural waste ashes and proved that such ashes exhibit complex nutrient release properties compared to the raw materials. Authors have experimentally demonstrated the direct retention of nutrient compounds by plants and their negligible release to the soils. The heavy metals do not leave the granule structure during the fertilizing processes [42,43].
## 3 Overview of 31P qNMR for Phosphorus Compounds
qNMR relies on chemical shifts and peak areas for quantification. It does not require standard curves, minimizes sample destruction, and is particularly effective for the quantification of complex natural products. Therefore, it usually requires the use of certified reference materials as standards to achieve quantification [23]. Quantitative nuclear magnetic resonance (qNMR) encompasses two primary methodologies: the internal standard method and the external standard method. In the internal standard method, a reference compound is added directly to the sample solution, facilitating accurate quantification by comparing the integrals of the analyte and the standard within the same spectrum. Conversely, the external standard method involves preparing the reference standard and the analyte in separate solutions, with quantification achieved by comparing their respective spectra. Each method has its advantages; the internal standard method often offers higher precision, while the external standard method is beneficial when sample recovery is essential, such as when analyzing expensive materials (Figure 4) [24]. As mentioned in Figure 4, in the resulting spectrum of the internal standard method, the reference material and sample produce distinct peaks at different positions along the ppm (parts per million) scale. This method allows for direct comparison within a single spectrum by using a known internal reference within the same sample solution. In the external standard method, separate test tubes are used for the sample (green star) and reference material (yellow dot), indicating independent preparation. This method requires a separate analysis of the sample and reference, with results combined by referencing the separate spectra. The diagram compares how each method handles sample and reference material positioning and peak interpretation in ppm spectra. The internal standard method involves direct comparison within a single solution, while the external standard method analyzes each separately. Figure 4: Internal standard and external standard method of quantitative NMR. Copyright (2021) MDPI.
## 2 Review of Sample Entropy (SE)  
The sample entropy is obtained from [PERSON]'s improved approximate entropy [15], which is obtained by the following algorithm [16]:  
1. Reconstruct the time series \(x(i)\) in phase space in m dimensions: \[X_{m}(i)=\{x(i),\cdots,x(i+m-1)\}\ \ (i=1,\cdots N-m+1)\] (1)
2. Define the maximum in the absolute value as the Chebyshev distance between the two vectors \(d[X_{m}(i),X_{m}(j)]\): \[d[X_{m}(i),X_{m}(j)]=\underset{k=0,\cdots,m-1}{max}[|x(i+k)-x(j+k)|]\ \ (i,j=1,\cdots N-m,i\
eq j)\] (2)
3. Preset the value of similarity tolerance \(r\), count the number \(B_{i}\) of all vectors whose Chebyshev distance \(d[X_{m}(i),X_{m}(j)]\) is less than \(r\), and ratio this number of vectors to the total number of distances \(N-m-1\) to obtain the ratio \(B_{i}^{m}(r)\): \[B_{i}^{m}(r)=\frac{1}{N-m-1}B_{i}\ \ \ \ \ (i=1,\cdots N-m)\] (3)
4. Calculate \(B_{i}^{m}(r)\) the average of \(B^{m}(r)\): \[B^{m}(r)=\frac{1}{N-m}{\sum_{i=1}^{N-m}B_{i}^{m}(r)}\] (4)
5. Increase the reconstruction dimension to \(m+1\) and repeat the above steps, thus obtaining \(B^{m+1}(r)\): \[B^{m+1}(r)=\frac{1}{N-m}{\sum_{i=1}^{N-m}B_{i}^{m+1}(r)}\] (5)
6. Finally, calculate the sample entropy of the time series:\[SampEn(m,r)=\underset{N\rightarrow\infty}{lim}\bigg{\{}-ln\bigg{[}\frac{B^{m+1}(r)}{ B^{m}(r)}\bigg{]}\bigg{\}} \tag{6}\]  
Whereas, in practice, \(N\) is finite in value, so the sample entropy as:  
\[SampEn(m,r,N)=-ln\bigg{[}\frac{B^{m+1}(r)}{B^{m}(r)}\bigg{]} \tag{7}\]  
The sample entropy exhibits noise immunity and can suppress interference from amplitude noise below a threshold value of \(r\). SE mitigates the issue of self-similarity by omitting self-matches from its counting scheme. During computation, Equation (3) quantifies the number of template vectors with distances less than the similarity tolerance \(r\). The template vector would be excluded from this quantification to avoid self-comparison. This exclusion ensures non-overlapping when comparing two template sequences, thus solving the self-similarity problem that exists in ApEn [22]. When there are large oscillation points in the signal, this type of data constitutes a large inter-vector Chebyshev distance from neighbouring samples, which is also removed in the threshold check. While practical applications find that sample entropy is still disturbed by significant background noise, making it difficult to obtain comprehensive information on time series changes and reducing the accuracy of entropy calculation. Analysis of the sample entropy algorithm, where Equation (2) represents the plane metric Chebyshev distance, reflecting the characteristics of high-dimensional time series individually, indicates that plane space to deal with non-linear relationships is limited and will interfere with the time series characterization accuracy. The introduction of a high-dimensional vector space is proposed to reflect the characteristics of the data samples and achieve a non-linear transformation of the data samples from a low-dimensional to a high-dimensional space. Consequently, the direct solution is a valuable attempt from another dimension.
## 2 Hierarchical Control for Frequency Regulation
### Secondary Level  
From the above droop control formula, it is evident that when \(P^{*}\) maintains a constant value and both \(P\) and the coefficient \(m\) are positive, when \(P\) and \(P^{*}\) are not equal, \(\omega\) will not equal the rated value \(\omega^{*}\), resulting in a frequency deviation. To address these deviations,  
Figure 1: AC microgrid topology.  
the secondary level employs compensation \(\delta\omega\) to adjust and normalize the value to its rated value. This adjustment is executed through the following formulas, utilizing a PI controller as the general method [25, 26]:  
\[\omega=\omega^{*}-mP+\delta\omega \tag{3}\]  
\[\delta\omega=K_{p\omega}\;(\omega^{*}-\omega)+K_{i\omega}\int{(\omega^{*}- \omega)\;dt} \tag{4}\]  
where \(K_{p\omega}\) and \(K_{i\omega}\) are PI control coefficients.  
However, this implementation of the secondary level is achieved by generating additional compensation. That is, through the PI controller, the offset caused by the primary level is restored to the rated value through quantitative addition.
## 3 Algorithms
### Single-Air-Route Planning Based on A* Algorithm
#### 3.1.3. Single-Air-Route Planning Flow  
The processes of single-air-route planning can be described as follows:  
Step 1: Obtain the starting point \(S\) and ending point \(E\), establish the OPEN and CLOSE list, and add the starting point \(S\) to the OPEN list.  
Step 2: Judge whether the OPEN list is empty. If it is not empty, perform the following steps; otherwise, turn to Step 7.  
Step 3: Pop up the grid point with the smallest \(f(x)\) value in the OPEN list, take this grid point \(P\) as the next air-route point, and put it in the CLOSE list.  
Step 4: Judge whether point \(P\) is the ending point. If it is the ending point, turn to step 7; otherwise, turn to step 5.  
Step 5: Check the grid points adjacent to point \(P\). If the adjacent grid points are not in the OPEN list, add them to the OPEN list, and take point \(P\) as the parent node of the new grid. If the adjacent grid points are in the OPEN list, check whether the cost of the current path is minimum.  
Step 6: Recalculate \(g(x)\), \(h(x)\), and \(f(x)\) according to the cost function, and return to step 3.  
Step 7: Judge whether the ending point \(E\) is reached. If the endpoint has been reached, turn to Step 8; otherwise, the planning fails.  
Step 8: Smooth the air-route planning result and output the air route.
## 4 Handover and Mobility Process in 5G HetNet
There are several conditions for triggering HO events, including A1, A2, A3, A4, and A5, but the most commonly used HO event in 5G is A3, where the T-gNB's received signal strength (RSS) is higher than the S-gNB's by a specified offset value. Table 4 provides a description of the various HO events and their triggering conditions. The three-tier 5G HetNet's HO and mobility process is illustrated in Figure 6. This network consists of macro, micro, and picocells, which are deployed to provide coverage and manage UE mobility. UEs located at L1, L2, and L3 are connected to the macrocell. However, due to strong RSS from other cells, UEs at L4 and L7 perform HO from the macrocell to the microcell and from the microcell to the picocell, respectively. L5 and L6 are  
Figure 5: The 5G HO process. locations where UEs are connected to the microcell, while L8 and L9 are locations where UEs are connected to the picocell.
## 5 Numerical Examples
### Measurement Verification
#### 5.2.1 Static Test  
The main goal of this test is to evaluate the bias of least-squares parameter estimator under a static situation. The locations of test points and anchors and the experimental scene are shown in Figure 6. The positions of test points were calibrated ahead of time. In the test, 300 epoch data was sampled at each test point.  
Figure 7 represents the cumulative distribution function (CDF) of the ranging error of all the measurements collected. It can be observed that the random error of these observations are small and differ slightly. However, all of their systematic errors are very large. The distance between T1 to A1 perfectly matches the distance between T1 and A3, but the range estimation accuracy is different due to the hardware. Although the hardware devices adopt the principle of TWR without a common time reference, the clock drift and offset still affect the ranging error. We can also see that the systematic error based on anchor A1 and A3 becomes larger than that based on anchor A0 and A2.  
The frequency histograms of the parameter estimator bias are given in Figure 8. The average PDOP, HDOP and VDOP for two test points are shown in Figure 9. The anchors are uniformly distributed on a planar ground. The two test points constitute two different positioning configurations with anchors. The three kinds of DOP values of T1 and T2 have minor difference. It indicates that their positioning configuration is close. Meanwhile, it can be seen that the coordinate components are all biased. It indicates that the corresponding component does not satisfy the orthogonal condition with the zero bias.  
Figure 6: The locations of test points and anchors (**a**) and the experimental scene (**b**).  
Figure 7: The cumulative distribution function (CDF) of the ranging error.  
As is shown in Table 2 and Figure 10, the hypothesis test is implemented to check the parameter estimator bias. The bias of two test points in the coordinate component z is much larger than x and y. Because the anchors are distributed in a plane. It makes known that the positioning configuration is symmetrical in the x and y direction, but it is ill-conditioned in component z. The bias in the coordinate components x and y and the Mahalanobis distance at T1 are larger than that of T2. In the x-y plane, the geometry of T2 is closer to the orthogonal condition with the zero bias. It indicates that the test point T2 and anchors constitute a better positioning geometry. Meanwhile, we can see that the test statistic of two tests are both very small compared with the threshold value. It shows that the bias is small enough to be neglected and the linearized model is a sufficiently approximate for UWB positioning. Figure 11 illustrates the parameter estimators in x-y plane with 9
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies
### Real-Time Remote Maintenance Support Based on Augmented Reality (AR)  
Cutting-edge digital technologies in the field of extended reality (XR), such as augmented reality, have entered the mainstream (AR). Furthermore, cloud computing, and recently 5G technology, have made it possible to provide high-quality services, particularly in the field of maintenance. However, as modern machines become more complex, maintenance must be performed by experienced and well-trained personnel, while overseas support is both timely and costly. Although augmented reality (AR) is a backbone technology that enables the development of robust maintenance support tools, it is limited to the provision of predefined scenarios that cover a limited number of scenarios. This research work aims to address this emerging challenge by designing and developing a framework for the support of remote maintenance and repair operations based on AR, by establishing appropriate communication channels between shop-floor technicians and expert engineers who use real-time feedback from the operator's field of view. The applicability of the developed framework has been tested in a lab-based machine shop and in a real-life industrial scenario. As illustrated in Figure 17 we have elaborated the proposed framework and the added layers and infrastructure towards 5G enabled tactile internet are highlighted in green.  
Figure 16: Integrating 5G and TI applications for integrated and adaptive AR maintenance and shop-floor rescheduling (adapted from [67]).
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
###### Abstract  
With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race \"fighting fire with fire\", where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.  
deepfake attacks; Audio and Video Systems (AVS); Internet of Video Things (IoVT); adversarial machine learning; environmental fingerprint; Electrical Network Frequency (ENF) signals; spectral estimation; Singular Spectrum Analysis (SSA) +
Footnote †: journal:
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 2 Problem Formulation and Preliminaries
### WUNT TR Algorithm
#### 2.3.1 NT and CNT  
The most recent studies on the type-reduction of interval type-2 fuzzy set have shown that the continuous version of NT (CNT) algorithms is an accurate method for defuzzifing [34]. Intuitively, we can feel that if the sampling size approaches infinity, random sampling will become an accurate TR method. The proof is as follows.  
**Theorem 1**.: _When the number sampling is infinite, the random sampling algorithm will calculate the accurate centroid of IT2 FS._  
Proof.: Suppose there is a discrete IT2 FS \(\overset{\sim}{A}\), whose domain is X. Let M represent the number of vertical slices along the y-axis, and L represents the number of horizontal slices along the \(\upmu\)-axis. Therefore, the total number of T1 FS embedded in \(\overset{\sim}{A}\) is LM, because T1 FS contains M vertical slices [35].  
Let \(\overline{\mu_{\overset{\sim}{A}}(x)}\) and \(\underline{\mu_{\overset{\sim}{A}}(x)}\) denote the LMF and UMF of FOU, respectively. Select N embedded T1 FS of \(\overset{\sim}{A}\) randomly. Let \(\mu_{i}(x)\) be the MF of the i-th embedding set. Then, we summarized N randomly embedded T1-FS,  
\[\sum_{i=1}^{N}\mu_{i}(x)=\sum_{j=1}^{M}\sum_{i=1}^{N}\mu_{i}\big{(}x_{j}\big{)} \tag{12}\]  
Hence,  
\[\lim_{N\to\infty}\frac{1}{N}\sum_{i=1}^{N}\mu_{i}(x)=\lim_{N\to\infty}\frac{1 }{N}\sum_{j=1}^{M}\sum_{i=1}^{N}\mu_{i}\big{(}x_{j}\big{)} \tag{13}\]  
Since \(\mu_{i}\big{(}x_{j}\big{)}\) is a random value uniformly distributed on \([\underline{\mu_{\overset{\sim}{A}}(x)},\overline{\mu_{\overset{\sim}{A}}( x)}]\), the right side of the equation can be transformed into  
\[\lim_{N\to\infty}\frac{1}{N}\sum_{j=1}^{M}\sum_{i=1}^{N}\mu_{i}\big{(}x_{j} \big{)}=\frac{1}{L}\sum_{j=1}^{M}\sum_{k=1}^{L}\mu_{k}(x_{j}) \tag{14}\]  
Hence,  
\[\lim_{N\to\infty}\frac{L}{N}\sum_{i=1}^{N}\mu_{i}(x)=\sum_{j=1}^{M}\sum_{k=1} ^{L}\mu_{k}(x_{j}) \tag{15}\]  
For Equation (14), the left side is a random sampling of \(N\to\infty\), and the right side is exactly the set of all \(\overset{\sim}{A}\) embedded MFs. Note that, since \(\frac{L}{N}\) is only a constant, it does not affect calculating the centroid of \(\overset{\sim}{A}\).  
When \(\overset{\sim}{A}\) is a continuous IT2 FS form, it is easy to get Equation (15) by replacing integral with summation  
\[\lim_{N\to\infty}\frac{L}{N}\sum_{i=1}^{N}\mu_{i}(x)=\sum_{j=1}^{M}\sum_{k=1 }^{L}\mu_{k}(x_{j}) \tag{16}\]  
End of proof. \(\square\)  
Therefore, when the step size of random sampling satisfies certain conditions, the NT algorithm in the discrete domain approximates the CNT algorithm, which is sufficient to meet the accuracy of the reduction.
## 2 Materials and Methods
### Streptococcus mutans and Candida albicans Strains and Growth Conditions
albicans_ in saline, and was adjusted to 0.5 McFarland turbidity standard (\(\sim\)1.5 \(\times\) 10\({}^{8}\) CFUs/mL). Cell suspensions were further diluted with RPMI 1640 medium (Biosera, Cholet, France), in a 1:20 ratio. HSs were diluted in RPMI 1640 at different concentrations ranging from 100% to 10% (\(v/v\)), decreasing by 5% \(v/v\) each time. EOssamples and carvacrol were diluted in sterile and distilled water with Tween-20 (Serva, Heidelberg, Germany) at concentration 0.5% \(v/v\), then diluted in RPMI 1640 at different concentrations ranging from 0.5% to 0.0175% (\(v/v\)). A total of 10 \(\upmu\)L of cell suspension was added to 190 \(\upmu\)L of the tested diluted hydrosols, EO, and carvacrol, and incubated at 35 \({}^{\circ}\)C for 48 h. Positive control wells containing RPMI 1640 were inoculated only with _C. albicans_. Similarly, control wells containing 0.5% \(v/v\) Tween-20 and RPMI 1640 broth inoculated with bacteria tested the growth of the pathogen in the presence of the detergent. Negative control wells contained hydrosols, EO, carvacrol and Tween-20 dilutions in RPMI 1640 without inoculated fungus. Furthermore, control wells containing only RPMI 1640 were used to assess possible contamination. MIC was the lowest concentration of our tested sample that inhibited visual growth of _C. albicans_[41]. MICs were determined in triplicate in at least two independent experiments. The lowest concentration of any antifungal agent that might kill tested fungus is considered the Minimum Fungicidal Concentration (MFC). MFC was determined by transferring a small quantity of sample contained in each replicate well of the microtiter plates to Sabouraud dextrose agar plus chloramphenicol plates by using a microplate replicator. The plates were incubated at 35 \({}^{\circ}\)C for 48 h. The MFC was determined as the lowest concentration of tested sample at which no fungal growth was observed.