esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Non-Negative Matrix Factorization  
For each subject and each condition, a separate data matrix was constructed. Each data matrix contained 16 rows with each row corresponding to one muscle. Three repetitions of the perturbed step cycle were concatenated end to end to construct the data matrix of size \(m\) muscles \(\times n\) samples. Each row was normalized to the corresponding row's maximum value for unperturbed walking, and each row was normalized to unit variance to weight variations in each muscle equally in the factorization by dividing all entries in the row by this row's standard deviation [12]. The normalization to unit variance was undone after factorization by multiplying with the standard deviation again.  
Muscle synergy theory suggests that humans control muscle synergies (i.e., muscle groups) instead of individual muscles to perform movements, assuming that a set of measured muscle activation signals (EMG signals) is a linear combination of underlying patterns [3,12]. A frequently used technique for the decomposition of a set of measured data (\(\mathbf{M}\)) is NNMF, which factorizes \(\mathbf{M}\) into so-called muscle synergies (\(\mathbf{W}\)) and weighting curves (\(\mathbf{C}\)):  
\[\mathbf{M}_{m\times n}=\mathbf{W}_{m\times s}\mathbf{C}_{s\times n}+\mathbf{E}, \tag{1}\]  
where \(m\) is the number of muscles, \(n\) the number of samples, \(s\) the number of synergies, and \(\mathbf{E}\) is the error. Muscle synergies represent the relative activation level of the muscles, i.e., the spatial patterns, and weighting curves represent the time-varying weighting coefficients of the muscle synergies, i.e., the temporal patterns. Other common names for muscle synergies and weighting curves are motor modules and motor tuning curves, respectively. Each muscle synergy (column vector \(\mathbf{W}_{i}\)) is thus fixed across time and is recruited (i.e., multiplied) by the corresponding weighting curve (row vector \(\mathbf{C}_{i}\)). Reconstructed muscle activation patterns decomposed in \(s\) synergies, could thus be represented by:  
\[\mathbf{M}_{\text{reconstructed}}=\mathbf{W}_{1}\mathbf{C}_{1}+\mathbf{W}_{2} \mathbf{C}_{2}+...+\mathbf{W}_{s}\mathbf{C}_{s}. \tag{2}\]  
Factorization was performed using the NNMF Matlab function (version 2020a). This function uses a multiplicative update algorithm that starts with random initial values for the non-negative factors \(\mathbf{W}\) and \(\mathbf{C}\) for every repetition. Factorization was repeated 50 times to avoid local minima. The function minimizes the mean square residual between matrix \(\mathbf{M}\) and matrix \(\mathbf{W}\cdot\mathbf{C}\). The termination tolerance of the change in size of the residual was \(10^{-6}\), the termination tolerance of the relative change in the factors \(\mathbf{W}\) and \(\mathbf{C}\) was \(10^{-4}\), and the maximum number of iterations was 1000.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Terahertz Antennas for 6G Applications  
#### 3.7.1 General Description  
Terahertz (THz) antennas, characterized by their compact size, wide frequency bandwidth, and high data rate capabilities, play a crucial role in transmitting and receiving THz electromagnetic waves in emerging THz systems. However, there are many challenges to enable the THz wireless communication systems in the future due to very high path loss and molecular absorption loss that lies in the nature of the specific frequency band. Specifically, the high path loss of the THz band presents a significant limitation on the communication distances range. The following is a summary of the main details of the design, operation, and use of terahertz antennas. References to specific antenna examples are made, as well as the difficulties encountered when dealing with these procedures.
# A Wireless Covert Channel Based on Dirty Constellation with Phase Drift
## 5 Simulation Analysis of Dirty Constellations  
Based on simulation studies, we compared our solution with the dirty constellation method presented in [30]. In this case, we assumed that the number \(K\) of averaging in the DCM modulator corresponds to the number of covert symbols per carrier symbol (SPS) in [30], the cover is the 4 QAM signal, and \(M=4\). For easier comparison, we additionally assumed that the centroid distance \(A_{m}\) and the radius \(r\) of covert information dispersion in [30] remain constant. Figure 12 depicts the obtained comparison results of the transmission capabilities of the covert channels and the effect of the covert channels on the carrier QAM signals for EVM = 3%.  
The transmission rate obtained for the covert channel is directly related to the number of symbols transmitted by the cover. Assuming the same value of the cover and steganographic information, \(K\)-fold averaging or given SPS brings about a \(K\)-fold reduction in the number of symbols transmitted over the covert channel. EVM at the level of 3% does not cause any noticeable deterioration of cover properties. In the proposed solution, compared to [30] higher numbers of averages need to be used to obtain similar BER levels for a covert channel.  
Introducing random \(A_{m}\) reduces the level of energy per symbol of transmitted covert information, which means that it is necessary to increase the number of averages to provide the assumed BER level. An example for such a case (i.e., random \(A_{m}\)) was presented in Figures 13-16. The cover is provided by 4 QAM, and \(M=4\). In Figure 13, we show BER versus SNR for different \(K\) and EVM = 3%. Figure 14 illustrates EVM versus SNR for the carrier signal and covert channel (\(K=30\)), whereas Figures 15 and 16 depict the constellations and histograms for \(K=30\), EVM = 3%, SNR > 50 dB and SNR = 38 dB, respectively.  
EVM < 8%. The obtained constellations and histograms show that for good transmission quality (i.e., SNR = 38 dB and SNR > 50 dB), the randomness of the centroid distance ensures the undetectability of the proposed method using statistical steganalysis.  
Figure 12: Comparison of dirty constellation methods based on bit error rate (BER) versus signal-to-noise ratio (SNR) graphs for EVM = 3%: (**a**) proposed solution; (**b**) solution presented in [30].  
Figure 14: Error vector magnitude (EVM) versus SNR for cover and covert channel (\(K\) = 30), and random \(A_{m}\).  
Figure 13: BER versus SNR for different \(K\), random \(A_{m}\), and error vector magnitude (EVM) = 3%.  
Figure 16: (a) Constellation, (b) in-phase and (c) quadrature histogram for SNR = 38 dB, \(K=30\), random \(A_{nn}\), and EVM = 3%.  
Figure 15: (a) Constellation, (b) in-phase and (c) quadrature histogram for SNR > 50 dB, \(K=30\), random \(A_{nn}\), and EVM = 3%.
## 2 Background and Related Work
### Related Work  
In terrestrial broadcast systems, IP systems have already been used in ATSC-M/H [16], HbbTV [17], and DVB-H [18]. However, the IP system transmits nonreal-time data or plays a role in interactive service. In addition, it plays the role of transmitting media data or nonreal-time data using MPE [19], unidirectional lightweight encapsulation (ULE) [20], generic stream encapsulation (GSE) [21], and so on to maintain compatibility with the existing broadcasting system. The reason the IP system simply transmits in the broadcasting system is that sufficient broadcasting systems exist (see Table 1). For example, it is known that service is possible with a bandwidth of about 5 to 6 Mbps for FHD. In addition, multi-mode service, which provides multiple media services to a single channel, is provided under the terms multiplex in the UK, multicasting in the US, and multi-program in Japan.  
In addition, some studies have analyzed the DASH performance in the internet and 5G networks where the speed varies according to time [23] and studies that propose a QoE model considering DASH's ARG algorithms [24]. Various studies exist on transmitting DASH over the internet and communication networks. However, it is difficult to find similar studies that propose a transmission structure and a delay model in a terrestrial broadcasting system. Therefore, the proposed delay model in this paper is valuable for the IP-based terrestrial broadcasting system.
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
## 3 QKD Protocols  
Utilizing the photon of light as a foundation, it is possible to develop protocols in discrete-variable or continuous-variable formats, each regarding light either as discrete or continuous photons. Discrete-variable QKD protocols capitalize on the particle aspect of light, encoding information within single photon states. Conversely, continuous-variable QKD protocols leverage the wave nature of light, encoding information within its amplitude and/or phase.  
Discrete-variable QKD schemes come in two main types: Prepare and Measure (PaM) protocols including BB84 [10] and SARG04 [33] and Entanglement-Based (EB) protocols, including E91 [34] and BBM92 [35]. The earliest QKD protocols employed the PaM method, where a qubit state is generated and sent to the recipient party. It is worth noting that, in quantum computing, a quantum bit (qubit) represents a fundamental departure from classical bits and exists in a superposition of both 0 and 1 states simultaneously. Subsequently, EB protocols were introduced, allowing two parties to establish a secret key by performing measurements on a shared quantum state [36]. Unlike PaM protocols, EB protocols do not necessitate one communicating node to possess the joint state source or trust it. Instead, quantum correlations between measurements made by legitimate parties on the joint states can be examined using [PERSON]'s theorem inequalities. While EB protocols offer enhanced security by eliminating the need for a trusted quantum source, PaM protocols remain more prevalent due to their simplicity. In this study, the SARG04 protocol in comparison with the BB84 one is effectively utilized.
## 13 Conclusions  
This research suggests that wind tunnel tests are still reliable in investigating the aerodynamics of long-span bridges, but the design of the experimental setup is crucial to the results. In this study, Setup 1 achieved SDOF heave and pitch but suffered from a lot of damping and noise. Setup 2 reduced the damping but introduced an additional mode, affecting results. The nonlinearity of the SDOF response was due to large initial displacements. The filtration process should be carefully considered, as a wrong filter can remove important information. The system identification methods showed good results except for Logarithmic-Decrement Method, with Hilbert Transform Method being the most effective due to its capability of extracting both damping and frequency from the nonlinear response.  
Direct flutter derivatives closely match the experimental results, except for \(H_{4}^{*}\), which is difficult to identify accurately due to small frequency shifts at higher wind speeds. The HTM shows a stable decreasing trend in \(H_{4}^{*}\). Modal frequencies and system parameters extracted using the ITD and MITD methods with Setup 1 were more reasonable than those of Setup 2, which had an additional dominant mode, causing poor-quality responses. Setup 1 can be improved with modifications without allowing unrestricted motion. The MITD parameter quality depends on the quality of the simulated signal. Separating heave and pitch components improves signal and parameter quality, with MITD converging faster than ILS in up to seven iterations versus up to eighty iterations, respectively.  
The accuracy of system parameters and flutter derivatives can be improved by repeating wind tunnel tests. The flutter derivatives obtained from numerical CFD simulations showed good agreement with the flat plate results. Despite limitations, valuable results were still obtained from the noisy data. Overall, the experimental and numerical results are comparable to flat plates, which confirms the streamlined behavior of the Great Belt section from an aerodynamic perspective.  
Wind tunnel tests are reliable for investigating the aerodynamics of long-span bridges, but the experimental setup is crucial. The Hilbert transform method is effective for system identification. The results confirm the streamlined behavior of the Great Belt section from an aerodynamic perspective.  
The accuracy of the system parameters and flutter derivatives can be improved by repeating wind tunnel tests with multiple sensors. Techniques such as stochastic subspace identification (SSI) and enhanced frequency domain decomposition (EFDD) could also be utilized for the identification of modal parameters for more comparison options.  
Conceptualization, M.S.A. and L.F.N.V.; methodology, M.S.A., A.J. and L.F.N.V.; analysis of results, M.S.A.; writing--original draft preparation, A.J. and M.F.U.D.A.; writing--review and editing, A.J. and A.M.; supervision, L.F.N.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix C Multiple MDFs at a Time  
In this section, the behavior of our proposal is evaluated in presence of multiple mobile elements. Each group of MDFs \(u\in U\) takes off sequentially from the base station \(\zeta\). When all the MDFs of the group return to the \(\zeta\), the next batch of MDFs will lift from the base station, until all regions in the operational environment have pheromones. The MDFs do not have previous knowledge of the deposited pheromones by others MDF; thus, the base station must communicate this information to them. There are two cases to be considered: a) overlapping regions and b) disjoint regions.  
1. **Disjoint regions.** At any time that a MDF \(u_{q}\) is sampling a region \(r_{i}\), denoted as \(<u_{q},r_{i}>\) then \(\forall u\in U|<u,r_{i}>\), then \(u_{q}\
eq u\) given that there are multiple choices from the adjacent regions.
2. **Overlapping regions:** The mobile elements may share a region at any time.  
Disjoint regions. When no overlap exists at any given time, every MDF will explore different regions. Figure 11 shows an example of this. MDF \(u_{q}\) is represented by blue while MDF \(u_{q-1}\) is represented by red. In the base station there is no knowledge about the operational environment. At step 0, since there is only one region, both MDFs must share a region. The MDFs follow Rules 1 and 2 from Section 4.5. At step 1, the MDFs have chosen and move to new regions and select new regions following the rules. After several steps, the MDFs must return to the base station.  
Each one communicates to the base station the deposited pheromones as shown in Figure 12. Next, the base station combines the information of the two MDFs into one snapshot of the DFG. In the next cycle, two new MDFs will continue the reconnaissance task, however each one of them will have the snapshot from the previous task. Each time new regions are visited, the overall time required to do DFORE will reduce by a certain amount; however, the algorithm for DFORE stays the same. This amount is bounded by the depth of the DFG.  
Overlapping regions. Similarly, when overlap occurs, the information can be shared among the MDFs trough the base station. In the worst case scenario, every MDF will visit the same region at the same time. Thus, every region will be visited multiple times. This is equivalent to the single MDF scenario where the only difference is the amount of pheromones deposited in each region. The number of trips in this scenario is bounded between the single case and the disjoint scenarios. Since the number of trips in the single case is greater than any of the multiple cases, Restriction 1 is satisfied.
## 4 Evaluation
### Testing of Harmonic Interference Suppression Effects
This results in a backscatter communication system comprising three tags, while all other configurations remain unchanged. The simulated BER under varying signal-to-noise ratio (SNR) conditions is depicted in Figure 13b. The results presented in Figure 13b indicate that the adaptive pulse-width square wave can still provide a certain degree of harmonic interference suppression under specific configurations. This suggests that as the scale of the backscatter communication system increases, the introduction of the adaptive pulse-width square wave effectively controls harmonic interference. Figure 12: Illustration of the BER under various tag configurations and distance parameters. (**a**) BER under different square wave conditions and distance parameters. (**b**) BER under different adaptive pulse-width square wave conditions and distance parameters.
## 4 Finite Element Numerical Simulation of Casing Damage Well
### Von Mises Stress Analysis of Casing under Mudstone Creep
#### 4.2.2 Finite Element Analysis of Casing under Mudstone Creep
The vertical stress contour under different injection production ratios is shown in Figure 14, which can reflect the stress variation of the casing when it is stretched by the rock layer after mudstone expansion. It can be seen that as the injection production ratio increases, the vertical stress S33 on the casing gradually increases. When the injection production ratio is greater than 1.6, the vertical \begin{table}
\begin{tabular}{c c c} \hline \hline
**Producing Pressure** & **Injection Pressure** & **Injection-Production Ratio** \\
**Difference (MPa)** & **(MPa)** & **Injection-Production Ratio** \\ \hline
10 & 15.5 & 1.0 \\
10 & 15.5 & 1.3 \\
10 & 15.5 & 1.6 \\
10 & 15.5 & 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Process parameters of injection production ratio scheme. stress on the casing exceeds its yield limit, and the casing gradually undergoes longitudinal tensile deformation. Figure 12: Normal stress contour at different injection rates (left: maximum horizontal normal stress component; right: minimum horizontal normal stress component): (**a**) 1.0; (**b**) 1.3; (**c**) 1.6; (**d**) 1.9. Figure 13: Shear stress contour at different injection rates (left: maximum horizontal normal stress component; right: minimum horizontal normal stress component): (**a**) 1.0; (**b**) 1.3; (**c**) 1.6; (**d**) 1.9.  
wall thickness casing. From the perspective of the bearing stress capacity of the casing, increasing the wall thickness appropriately can effectively delay the deformation and damage time of the casing.
## 4 Solar Pumping System Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline Daily Productivity at Different Angles: & & \\ \hline
**Group1** & **Group2** & **Group3** & **Angle** \\ \hline
2.7 \(\times\) 10\({}^{6}\) & 8.1 \(\times\) 10\({}^{5}\) & 8.1 \(\times\) 10\({}^{5}\) & 0 \\ \hline
2.3383 \(\times\) 10\({}^{6}\) & 7.0148 \(\times\) 10\({}^{5}\) & 7.0148 \(\times\) 10\({}^{5}\) & 30 \\ \hline
1.9092 \(\times\) 10\({}^{6}\) & 5.7276 \(\times\) 10\({}^{5}\) & 5.7276 \(\times\) 10\({}^{5}\) & 45 \\ \hline
1.35 \(\times\) 10\({}^{6}\) & 4.05 \(\times\) 10\({}^{5}\) & 4.05 \(\times\) 10\({}^{5}\) & 60 \\ \hline
0 & 0 & 0 & 90 \\ \hline \hline Monthly Productivity at Different Angles: & & & \\ \hline
**Group1** & **Group2** & **Group3** & **Angle** \\ \hline
3.24 \(\times\) 10\({}^{7}\) & 9.72 \(\times\) 10\({}^{6}\) & 9.72 \(\times\) 10\({}^{6}\) & 0 \\ \hline
2.8059 \(\times\) 10\({}^{7}\) & 8.4178 \(\times\) 10\({}^{6}\) & 8.4178 \(\times\) 10\({}^{6}\) & 30 \\ \hline
2.291 \(\times\) 10\({}^{7}\) & 6.8731 \(\times\) 10\({}^{6}\) & 6.8731 \(\times\) 10\({}^{6}\) & 45 \\ \hline
1.62 \(\times\) 10\({}^{7}\) & 4.86 \(\times\) 10\({}^{6}\) & 4.86 \(\times\) 10\({}^{6}\) & 60 \\ \hline \hline Yearly Productivity at Different Angles: & & & \\ \hline
**Group1** & **Group2** & **Group3** & **Angle** \\ \hline
3.24 \(\times\) 10\({}^{7}\) & 9.72 \(\times\) 10\({}^{6}\) & 9.72 \(\times\) 10\({}^{6}\) & 0 \\ \hline
2.8059 \(\times\) 10\({}^{7}\) & 8.4178 \(\times\) 10\({}^{6}\) & 8.4178 \(\times\) 10\({}^{6}\) & 30 \\ \hline
2.291 \(\times\) 10\({}^{7}\) & 6.8731 \(\times\) 10\({}^{6}\) & 6.8731 \(\times\) 10\({}^{6}\) & 45 \\ \hline
1.62 \(\times\) 10\({}^{7}\) & 4.86 \(\times\) 10\({}^{6}\) & 4.86 \(\times\) 10\({}^{6}\) & 60 \\ \hline
0 & 0 & 0 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The table shows the overall solar water pumping system production rate daily, monthly, and annually. Figure 15: Total daily output for each angle across all groups. Irregular solar panels, also known as non-standard solar panels, offer a viable solution for harnessing solar energy in agricultural areas and large-scale industrial projects. These panels are designed to optimize solar ray utilization and achieve maximum efficiency. However, several challenges are associated with their installation. For instance:  
1. Maintenance poses a significant hurdle. Accessing and cleaning the solar panels can be a cumbersome task, ultimately impacting long-term production efficiency;
2. The concentration of land used for solar panel installation can have repercussions on other activities, such as agriculture. Moreover, excessive exposure of the soil to sunlight must be balanced against the benefits of utilizing solar panels while preserving the land for other purposes;
3. The visual impact and environmental harmony of this installation should be considered. The presence of such a system could potentially disrupt the natural landscape and impact local biological life.
## 5 Experimental Verification
Figure 10: Comparison between the measured results of the balancing system and estimated results of the loss model. in Figure 1 for three different balancing processes. To obtain the trend of the voltage varieties in the battery module, Figures 11b, 12b and 13b show the processed results for the significant changes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{V_{B1}}\) & \(\mathbf{V_{B2}}\) & \(\mathbf{V_{B3}}\) & \(\mathbf{V_{B4}}\) & \(\mathbf{V_{B5}}\) & \(\mathbf{V_{B6}}\) \\ \hline
3.56 V & 3.63 V & 3.27 V & 3.24 V & 3.33 V & 3.59 V \\ \hline \hline \end{tabular} \(V_{B_{\text{H}}}\) – the voltage of nth cell.  
\end{table} 
Table 4: Cells’ voltage of the unbalanced 6S1P battery module for the testing condition. Figure 11: Measured voltage of each cell using cell-to-pack procedure (**a**) actual hardware measurements (**b**) processed results. Figure 12: Measured results of each cell’s voltage using pack-to-cell procedure (**a**) actual hardware measurements (**b**) processed results. Figure 13: _Cont._  
After the collation of the data shown above, this research gives a table, as shown in Table 5, to compare the balancing duration and maximum error among three balancing processes. The maximum voltage error is the voltage difference between the cell with the highest voltage and the cell with the lowest voltage. The cell-to-pack control takes 2783 s to complete the balancing process with a 100 mV voltage difference left. The pack-to-cell control spends 5175 s to balance the battery module, but its maximum voltage error can not be reduced anymore at 130 mV. The bidirectional control has the least balancing duration as 2727 s and it is able to decrease the maximum voltage error to 50 mV. It can be obtained that the implemented bidirectional control strategy, which will always balance the cell having the most voltage difference to the average cell voltage \(V_{avg}\) in the battery module, has a less balancing duration than the pack-to-cell and a less maximum voltage error between the cell with the highest voltage and the one with the lowest voltage.