esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
PCA, a potent tool for dimensionality reduction, serves the technical purpose of revealing distinctive correlation patterns [48] and preserving a substantial portion of data to evaluate the overall influence of diverse treatments on total phenolic contents, total flavonoid content, anthocyanin contents, volatile groups, and free amino acid profiles. Additionally, it aims to identify potential clusters among bicultures and the unfermented sample. The output (Figure 3) demonstrated that PCs 1, 2, and 3 collectively accounted for an impressive 97.73% of the total variance, affirming the robustness of the applied methods [18; 38]. The samples are categorized into four distinct components, each associated with specific attributes. Sample JLAp exhibited close associations with elevated anthocyanin contents and a moderate amount of ester groups. The control displayed high volatiles: ketones, alcohols, and free amino acids: proline, aspartic acid, alanine, glycine, isoleucine, and arginine. Sample JLhAp was heavily clustered with most attributes--predominantly free amino acids: phenylalanine, tyrosine, valine, threonine, leucine, lysine, serine glutamic acid--and relatively characterized by a total phenolic content and total flavonoid content. Puree JLpAp showcases elevated levels of methionine, predisposing it to have an advantage of full essential amino acid content at the expense of the other bicultures (JLLeAp and JLhAp) and unfermented sample (JCON). In summary, principal component analysis (PCA) elucidated that the lactic-acetic acid co-fermented jujuibe purees significantly impacted various functional indices. This effect is attributed to the distinct strains that noticeably influenced the phenolic contents, free amino acids, and volatile groups of the bicultured purees, microscopically modified by SEM.  
Figure 3: Three-dimensional loading plot for PC1-PC2-PC3 of bicultured Junzao jujuibe purees and control showing linkages between variables: free amino acids, phenolic profile groups, and volatile groups. **Note:** TPC—total phenolic acid content; TPC—total flavonoid content; TAC—total anthocyanin content; ALC—alcohol group; ACD—acid group; EST—ester groups; ALD—aldehyde group; KET—ketone groups. JLcAp—_Lactaciseobacillus case_ Lc 122—_Acetobacter pasteaurians_ Ap-As.141 HuNiang 1.01 puree; JLhAp—_Lactobacillus heleticus_ Lh 43—_A. pasteaurians_ Ap-As.141 HuNiang 1.01 puree; JLpAp—_Lactiplantiobacillus plantarum--A. pasteaurians_ Ap-As.141 HuNiang 1.01 puree; (JCON)—pure with no bacteria inoculations (unfermented).
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
## 4 Results
### Optimized Results  
On the basis of the best features described in Table 4 and the optimal values of the hyper-parameters defined in Table 3, the ER, FPR and FNR have been calculated using the Decision Tree algorithm. It was also evaluated the impact of the size of the data set. From the whole data set, a partitions of the whole data set have been selected and the ER, FPR and FNR have been calculated. The results are presented in Figure 9 and related subfigures where 'All' means the whole data set and 'All/x' is a partition by the factor x. The size of 'All' can be calculated from the values presented in Table 1. The partition is created by extracting randonmly 'All/x' elements from the whole data set. To mitigate the risk of bias, the selection of the partition and the calculation of the results is repeated 100 times and the results are averaged.  
Both for the PortScan and the DDoS attacks, it can be seen that the performance of the detection of the attack is lower for smaller partitions of the data set because it is more difficult for the algorithm to discriminate the legitimate traffic from the traffic related to the attack. This trend is coherent for all the three metrics (ER, FPR and FNR) and the two attacks.  
Figure 8: Error Rate (ER) relation with all features with \(W_{S}=100\). The features related to 2 DDE are highlighted with a red bar for improved visualization. DT algorithm is used.  
To complete the previous results, the ROCs for the DDoS and the PortScan attacks are presented respectively in Figure 10a,b. Since the FPR is relatively limited (because the data set is quite unbalanced), a more detailed figure of the same ROCs (i.e., zoom of the previous figures) is presented in Figure 10a,b respectively for the DDoS and the PortScan attacks. The values of the \(EER\) for each value of \(W_{S}\) are also reported. The results from the ROCs confirm the previous result that the optimal value of the window size is \(W_{S}=100\) because an increase of \(W_{S}\) produces slightly worst results in terms of ROCs and EER. It can also been seen that the detection of the PortScan attack is slightly worse than the DDoS attack. This may be due to the reason that PortScan attacks are more difficult to distinguish from legitimate traffic than the DDoS attacks when the entropy measures are applied (especially in the CIC-IDS2017 data set). The structure of the sequences of network flows features in the DDoS attacks can be quite different from legitimate traffic (e.g., since a flooding of messages is implemented) while the PortScan attack traffic may resemble legitimate traffic. The weakness of the proposed approach in achieving an optimal FPR is also discussed in the comparison with the literature results in Section 4.3. We note that the proposed approach manages to achieve a very competitive FNR instead.  
Figure 9: ER, FPR and FNR for the PortScan and DDoS attack for different sizes of the whole data set.
## 3 Metrics for Jamming Attack Detection
### Selected Metrics for the Proposed System--SNR and BPR  
We select SNR and BPR as the jamming attack metrics for our system. However, we prefer to call the BPR as Packets Dropped per Terminal (PDPT) because our PDPT is the average BPR of a node during a simulation cycle. The reasons for this choice have been discussed above, and the same are summarized as follows:  
* The received radio power at a node is easily measurable as nodes are/can be provided with RF power meter.
* In our system, the node simply keeps the base station informed about the received radio power, at a time interval as decided by the base station. The base station calculates the jammer (noise)power by subtracting the average legitimate signal power of the node from the current power. The ratio of the two powers is then calculated by the base to get the SNR. Thus there are no overheads involved at the node level.
* The node keeps the base station informed about the number of good packets and total packets received by it during a time interval, as decided by the base station, in a normal routine way. The base station calculates the BPR (or, PDPT) for each node. Thus, the nodes are not burdened additionally.
* The combination of SNR and BPR (or, PDPT) is capable of detecting any form of jamming attack, as discussed in the previous sections.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 4 Antennas for mmWave Industry 4.0 and beyond Communication
### On-Chip Antennas  
As a result of unprecedented development in integrated circuit (IC) design due to the unmatched level of integration and aggressive scaling of transistors in the last few decades, on-chip antennas have become part of the chip. Earlier, the system-in-package (SiP) approach was mainly used to include antenna design along with the chip. However, with this approach, the antennas, which have diameters on the order of wavelengths, are still outside the package and are still the bottleneck for true on-chip system realization [102]. More advancements and improvements in complementary metal oxide semiconductor (CMOS) and silicon germanium (SiGe) technologies have paved the way for the design of small-scale packaged antennas on silicon wafers using the system-on-chip (SoC) approach [59; 102]. Moreover, for PCB-based antennas, the bond wires are used to connect antennas to ICs because they are typically implemented on PCBs. As a result, because these bond wires are not well characterized, matching can suffer greatly, especially at higher frequencies GHz band. In this way, on-chip antennas helped to alleviate this problem [102].  
On-chip antenna configuration is governed by foundry-specific constraints, whereas on-chip antenna characterization necessitates the use of particular text fixtures. In recent years, the majority of on-chip antennas have been implemented in bulk silicon-based technologies such as CMOS and SiGe (with low resistivity of 10 ohm-cm), as opposed to other semiconductor technologies (with high resistivity) such as gallium arsenide (GaAa) [59; 102].  
Owing to smart manufacturing and integration techniques, these on-chip antennas can be integrated with moving objects, smart robots, moving machines, unreachable points in a factory scenario, and/or human head-mounts to provide indoor seamless connectivity at 60 GHz. Various on-chip antennas have been reported in the literature [59; 101; 102], among which monopole, dipole, loop, and Yagi-Uda antennas are fundamental types. An insightful demonstration of on-chip antennas, their design rules, characterization, limitations, challenges, and solutions are presented in [102]. The 60 GHz on-chip antennas, as well as the complete RF front end, are reviewed in [59]. The advantages and disadvantages of different types of 60 GHz mmWave antennas are summarized in Table 3. A comparison of various 60 GHz antennas is given in Table 4.
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
## 2 Exposure Standard Limits  
The guidelines of the ICNIRP are the most widely adopted and used for controlling non-ionizing radiation exposure in many countries [19; 20; 21]. This standard has set maximum limits based on baseline values directly related to the adverse effects produced by EMF radiation. The ICNIRP defined two groups of people for radiation exposure [7], occupational workers (OWs) and the general public (GP). Similarly, the USA FCC [8] defined the maximum permitted exposure (MPE) for the general public and occupational workers. Table 1 lists the exposure limits for both the ICNIRP and FCC.  
The ICNIRP also specified the total reference limits from instantaneous concurrent exposures from multiple sources, which are aggregated using Equation (1) shown below [6]:  
\[\begin{split}\sum\limits_{\text{i=100 kHz}}^{\text{30 MHz}}\left\{\Big{(}&\frac{E_{inc,f_{i}}}{E_{inc,RL,f_{i}}}\Big{)}^{2}+ \Big{(}\frac{H_{inc,f_{i}}}{H_{inc,RL,f_{i}}}\Big{)}^{2}\right\}\\ &+\sum\limits_{\text{i>30 MHz}}^{\text{2 GHz}}\text{MAX}\bigg{\{}\Big{(}\frac{E_{inc,f_{i}}}{E_{inc,RL,f_{i}}}\Big{)}^{2}, \Big{(}\frac{H_{inc,f_{i}}}{H_{inc,RL,f_{i}}}\Big{)}^{2},\Big{(}\frac{S_{inc, f_{i}}}{S_{inc,RL,f_{i}}}\Big{)}\bigg{\}}\\ &+\sum\limits_{\text{i>2 GHz}}^{\text{300 GHz}}\Big{(}\frac{S_{inc,f_{i}}}{S_{inc,RL,f_{i}}}\Big{)}\ \leq 1\end{split} \tag{1}\]  
where \(f_{i}\) is the frequency range, \(E_{inc,f_{i}}\) is the electric incident field level, \(E_{inc,RL,f_{i}}\) is the electric incident field reference level at \(f_{i}\). \(H_{inc,f_{i}}\) and \(H_{inc,RL,f_{i}}\) are the magnetic field and  
Figure 2: MORAN and MOCN active sharing between three operators.  
reference levels at \(f_{i}\), respectively. \(S_{inc,f_{i}}\) and \(S_{inc,RL,f_{i}}\) are the power density and its reference level at \(f_{i}\), respectively.
## 3 Proposed System Model  
The system model of the proposed modulation recognition framework is presented in Figure 1. The expression of the received signal is in Equation (1):  
\[v(t)=h(t)*x(t)+\eta(t) \tag{1}\]  
where \(x(t)\) and \(v(t)\) are the transmitted and received signals, respectively. \(\eta(t)\) is the additive white Gaussian noise (AWGN), and \(h(t)\) represents the Rayleigh fading coefficients.  
The input signal is modulated, i.e., BPSK, QPSK, QAM, 16-QAM, and 64-QAM, and transmitted over the Rayleigh fading channel in addition to AWGN. The main reason for choosing these modulation formats for the problem under consideration was their application in commonly used wireless technologies. The recognition of modulation formats consisted of three phases:  
* Parameter extraction (HOCs);
* Super features (optimal weight finder);
* Recognizer (K-nearest neighbor).  
The proposed AMR module is presented in Figure 2. The HOCs were selected as features and represented by \(\zeta\). The genetic algorithm (GA) was used to find the optimized weights \(\gamma\). Super feature cumulants \(L\) were formed by the linear combination of \(\zeta\) and \(\gamma\) and are represented as:  
\[L_{i}=\gamma_{m*1}^{T}.\zeta_{m*1} \tag{2}\]  
where \(L\) is be computed for all five modulation scenarios: (BPSK, QPSK, QAM, 16-QAM, 64-QAM). The KNN recognizer computes the Euclidean distance (ED) to decide the modulation format of the received signal.  
Figure 1: Proposed system model.
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c} \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
0.879 & Acetic acid, cyano- & 56,772 \\
1.040 & Pentanal, 2,4-dimethyl- & 57,277 \\
1.071 & 1-Propen-2-ol, formate & 167,675 \\
2.285 &.alpha.-Pinene & 170,241.5 \\
2.328 & 1,3,6-Octatriene, 3,7-dimethyl-, (Z)- & 363,519 \\
2.385 & (R)-(+)-Citronella acid & 85,004 \\
2.460 & 3-Undecen-1-yne, (E)- & 7687 \\
3.225 & Hexanal & 28,897 \\
3.965 & Methanone, 1,3-dithian-2-ylphenyl- & 64,181 \\
6.013 & D-Limonene & 44,500,934 \\
6.715 & 4-Pentenal, 2-methyl- & 50,578 \\
7.642 &.gamma.-Terpinene & 6,561,102 \\
8.201 &.beta.-Ocinene & 328,361 \\
8.607 & Benzene, 1-methyl-3-(1-methylethyl)- & 2,692,739.5 \\
9.068 & 2-Careene & 3,351,302 \\
9.078 & (+)-4-Careene & 3,540,274 \\
10.465 & Octane, 2,3,3-trimethyl- & 44,003 \\
10.752 & Undecane, 3,8-dimethyl- & 74,779 \\
11.947 & 5-Hepten-2-one, 6-methyl- & 273,176.5 \\
13.846 & 1-Octanol, 3,7-dimethyl-, (S)- & 180,675 \\
14.722 & Nonanal & 92,985 \\
15.668 & Tridecane & 139,294 \\
16.709 & Benzene, 2-ethenyl-1,3-dimethyl- & 191,674 \\
16.740 & o-Isopropenyltoluene & 194,054 \\
18.399 & Cyclohexan-1,4,5-triol-3-one-1-carboxylic acid & 361,809 \\
18.408 & Glycidol & 717,548 \\
18.670 & Furfurral & 621,339 \\
18.970 & Copenae & 551,737 \\
18.986 &.alfa.-Copaeene & 507,260 \\
19.180 & Decane, 5-propyl- & 88,193 \\
19.434 & 6-Octenal, 3,7-dimethyl-, (R)- & 837,850 \\
19.699 & 3-Octen-2-ol, (Z)- & 451,255 \\
19.722 & Cyclopropane, trimethylmethylene- & 866,370 \\
20.190 & E-1,5,9-Decatriene & 64,911 \\
20.665 & 1-Methoxy-3-hydroxymethylheptane & 126,181 \\
21.051 & 1,2,4-Triroxolamine, 3-methyl-5-phenyl- & 59,002 \\
21.430 & Octane, 5-ethyl-2-methyl- & 168,365 \\
21.683 & (Z,Z)-alpha.-Farnesene & 330,622 \\
21.813 & Nonane, 5-(2-methylpropyl)- & 161,847 \\
22.189 & 3-Ethyl-1,5-octadecine & 224,260 \\
23.256 & trans-p-mentha-1(7),8-dien-2-ol & 842,313 \\
23.272 & cis-p-mentha-1(7),8-dien-2-ol & 736,608 \\
24.139 & 1-Bromo-3,7-dimethyl-2,6-octadecine & 8,422,457 \\
24.146 & Jete-Myrence & 7,426,440 \\
24.450 & Caryophyllene & 2,412,339 \\
24.803 & Cyclohexane, 1-ethenyl-1-methyl-2,4-bis(1-methylethyl)- & 1,661,763 \\
24.828 & cis-alpha.-Bisabolene & 1,997,395 \\
25.130 & Dodecanal & 957,818 \\
25.148 & trans-2-Dodecen-1-ol, trifluoroacetate & 522,026 \\
26.458 & Cyclohexane, 4-methyl-2-methylene-1-(1-methylethylidene)- & 236,924 \\
27.055 & Gentadecane & 615,120 \\
27.122 & Heptadecane, 2,6,10,14-tetramethyl- & 369,320 \\
27.928 & 3-Tetradecyn-1-ol & 964,333 \\
27.958 & trans-2-Dodecen-1-ol, trifluoroacetate & 840,881 \\
28.359 & 1,4,7,-Cycloundecatriene, 1,5,9,9-tetramethyl-, _Z,Z,Z-_ & 245,910 \\
28.761 & 2,2-Dimethylpropanoic acid, 2-adamantly ester & 255,092 \\ \hline \end{tabular}
\end{table} 
Table 3: Results for the SGL sample fingerprint determination with indication of the retention time (RT) and abundance mean for every identified compound. **Table A3. **_Cont._
## 3 Noise Characteristics According to Vehicle Maintenance Conditions
In summary, suspension system components affecting the vehicle's dynamic performance are managed within specific material property limits according to maintenance regulations, resulting in minimal differences in material properties before and after maintenance. Other inspection tasks are limited to exterior inspections, vehicle cleaning, and consumable replacements, with maintenance related to interior noise primarily involving cleaning. Additionally, both the wheels of Case 2 and Case 3 were re-profiled according to design specifications, and despite differences in wheel diameters, the difference in interior noise was minimal. This suggests that wheel diameter has a negligible effect on interior noise. Case 4 showed the highest noise levels, with an average of 81.8 dB and a maximum of 89.5 dB among the four test conditions. Although this vehicle is relatively new and in good condition, the high noise levels are likely due to differences in body noise characteristics resulting from variations in manufacturing practices of the current manufacturer compared to previous ones. Therefore, vehicle maintenance conditions, particularly wheel re-profiling, have a measurable effect on interior noise. However, in sections with rail corrugation, the influence of maintenance conditions on interior noise is not significant enough to provide a fundamental solution to abnormal noise issues.
# An Intelligent Condition Monitoring Approach for
## 2 Theoretical Background
### Hidden Markov Model  
The HMM method is a doubly stochastic model. A Markov chain is used to describe the transition between states, and the other stochastic process is applied to describe the statistical relation between state and observed variables. In the HMM, the state is not directly visible, but the output, by which the existence and characteristics of the state can be perceived, is visible. Therefore, the method is called the \"hidden\" Markov model [30; 38]. The HMM can be divided into two layers: the hidden and the observation layer. The Markov chain exists in the hidden layer, and the observation layer is seen as the output of the hidden layer. An HMM can be expressed as:  
\[\lambda=(N,M,\pi,A,B), \tag{5}\]  
where \(N\) is the number of states, \(M\) the number of possible observation symbols, \(\pi=(\pi_{1},\pi_{2},\ldots,\pi_{N})\) the initial-state probability vector, \(A=(a_{ij})_{N\times N}\) the transition probability matrix, and \(B\) the observed value probability matrix (for continuous HMM, \(B\) is the observed value probability density function). The model can describe various random processes by considering different \(\pi\), \(A\), and \(B\). The composition of HMM is shown in Figure 2, where \(S=\{q_{1},q_{2},\ldots,q_{N}\}\) is the state sequence, \(O=\{o_{1},o_{2},\ldots,o_{T}\}\) the observed sequence.  
HMM can be used in the fields where it is necessary to recover a data sequence that is not directly observed (but some other data that depend on the sequence are), especially for the fields of requiring high dynamic-modeling ability, such as speech recognition. When used for classification, the probability distribution generated by HMMs gives some information about the states. The data are categorized according to the model producing the highest likelihood. In practice, there are three classical algorithms for HMM: forward-backward (known model parameters; calculating the probability of a particular output sequence), Viterbi (known model parameters, looking for a hidden state sequence that can most likely produce a particular output sequence), and Baum-Welch (known output sequences, looking for the most likely state transfer and output probability).
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 2 Swarm Intelligence
### Bat Algorithm (BA)  
BA [25] is a nature-inspired optimization algorithm inspired by the echolocation behavior of bats. Bats use echolocation to navigate and locate prey in the dark, emitting sound waves and listening for the echoes that bounce back from obstacles. The BA leverages this behavior to find optimal solutions by mimicking the bats' ability to adjust their position and velocity based on the feedback from their environment [23]. The steps of BA are as follows:  
1. Initialize the population of bats (solutions) \(\mathbf{x}_{i}\) and their velocities \(\mathbf{v}_{i}\). Assign random values to pulse frequency \(f_{i}\), pulse rate \(r_{i}\), and loudness \(A_{i}\).
2. Update the frequency \(f_{i}\), velocity \(\mathbf{v}_{i}\), and position \(\mathbf{x}_{i}\) of each bat: \[f_{i}=f_{\text{min}}+(f_{\text{max}}-f_{\text{min}})\beta\] (17) \[\mathbf{v}_{i}^{(t+1)}=\mathbf{v}_{i}^{(t)}+(\mathbf{x}_{i}^{(t)}-\mathbf{x}_ {\text{best}})f_{i}\] (18) \[\mathbf{x}_{i}^{(t+1)}=\mathbf{x}_{i}^{(t)}+\mathbf{v}_{i}^{(t+1)}\] (19)where:  
* \(f_{i}\) is the frequency of the bat.
* \(\beta\in[0,1]\) is a random number.
* \(\mathbf{x}_{\text{best}}\) is the current global best solution.
3. Generate a local solution around the best solution if a random number \(r\) is greater than the pulse rate \(r_{i}\): \[\mathbf{x}_{\text{new}}=\mathbf{x}_{\text{best}}+\epsilon A_{i}^{(t)}\] (20) where \(\epsilon\) is a random number drawn from a uniform distribution.
4. Update the loudness \(A_{i}\) and pulse rate \(r_{i}\) of each bat: \[A_{i}^{(t+1)}=\alpha A_{i}^{(t)}\] (21) \[r_{i}^{(t+1)}=r_{i}^{0}[1-\exp(-\gamma t)]\] (22) where \(\alpha\) and \(\gamma\) are constants.
5. Accept the new solutions if they improve the objective function and update the best solution.