esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Biologically Active Polysaccharides--Structure and Function
### Fungal polysaccharides
The main chain of PSK is made of \(\beta\)-(1,4)-glucopyranoside, with \(\beta\)-(1,6)-glucopyranoside side-chains at every fourth glucose unit [103; 104]. Another source of therapeutic (immunomodulatory and antidiabetic) polysaccharides is the edible medicinal _Tremmella_ mushrooms. _Tremella_ polysaccharides are composed of a linear backbone of \(\alpha\)-(1,3)-D-rhamnose, with side groups of xylose and glucuronic acid. These polysaccharides are acidic, with a pH of 5.1-5.6 in aqueous solutions [105; 106; 107]. Extracellular acidic heteroglycans from filtrates of _Tremella_ species, in turn, are characterized by an \(\alpha\)-(1-3)-mannan backbone with \(\beta\)-linked side chains. They contain xylose, arabinose, mannose, galactose, glucose, and glucuronic acid residues [106]. Other examples are highly branched galactomannans, which have been purified from fruiting bodies of _Cordyceps sinensis_. Their main chain contains predominantly (1,2)-\(\alpha\)-D-mannopyranose-groups, with branches of (1,3)-, (1,5)-, and (1,6)-linked d-galactofuranose and (1,4)-D-galactopyranose residues [108]. A list of so-called \"medicinal mushrooms\" capable of biosynthesis biologically active polysaccharides is long, and includes nearly 270 species [96]; therefore, it is impossible to list all types of active polysaccharides of fungal origin in a short paragraph in this review.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 3 Treatment Methods for AMR
### Multi-Technology Coupling
#### 3.4.3 Other Coupling Techniques  
Therrochemical technology coupled with other approaches represents a widely adopted strategy. Beyond conventional hydrothermal methods, calcination processes integrated with activation techniques can overcome the application bottlenecks of single-technology systems. Research indicates that calcination effectively removes moisture from biomass and reduces the oxygen-to-carbon (O/C) ratio, significantly enhancing its physicochemical properties [140]. When applied to AMR treatment, this method efficiently eliminates residual antibiotics and improves thermal stability, yielding structurally stable biochar [141]. However, biochar produced via calcination typically exhibits a low specific surface area, limiting its material-oriented applications [142]. By coupling chemical activation technologies and introducing chemical activating agents, such as KOH/ZnCl\({}_{2}\), pore structure development can be induced. The resulting biochar not only demonstrates enhanced specific surface area and porosity but also forms abundant defect sites and diverse oxygen-containing functional groups. These augmented physicochemical characteristics broaden its application prospects in fields such as materials science and carbon-based technologies [143].  
Currently, multi-technology coupling provides a comprehensive solution for AMR treatment, yet its large-scale implementation still faces significant challenges. The primary constraint lies in the stringent requirements for cross-system parameter collaborative optimization and equipment compatibility design, leading to a concurrent escalation in system operational complexity and costs. Secondly, uncertainties persist regarding the ecological safety of resource-oriented products. For instance, the environmental residual effects of biochar and similar outputs necessitate systematic toxicological studies and long-term environmental behavior monitoring to establish a scientifically robust risk assessment framework. Future research should prioritize low energy consumption-coupled processes, utilizing intelligent control algorithms to optimize reaction parameters and reduce costs. Concurrently, advancing life cycle assessments and refining environmental health risk frameworks are critical for transitioning AMR treatment toward green, low-carbon, circular models.
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 7 Conclusions  
In this paper, a downlink beamforming technique was proposed for the secure communication in a two-cell MIMO-NOMA-based CRN. There were no other limitations for the number of antennas at the BS. This was achieved by the signal alignment matrix that aligns the interference channel matrix, before the implementation of the proposed method. The proposed technique is very effective in enlarging the total secrecy rate of the CRN systems.  
For the future work, the proposed method may be used in IoT-based cognitive radio networks. The IoT users in the CRN are sensed as per the RF spectrum slot to give the logistic and disaster response. So, there is a need for the accurate spectrum decision framework in 5G engineering.  
The spectrum decision, which is taken by an unlicensed SUs of a CRN, holds an important role in CR-based IoT in 5G network. The proposed method can be very useful for taking the optimistic spectrum decision in IoT-based cognitive radio networks.  
Conceptualization, H.S.M.A.; methodology, H.S.M.A.; formal analysis, H.S.M.A. and T.L.; investigation, H.S.M.A. and T.L.; data curation, H.S.M.A.; writing-original draft preparation, H.S.M.A.; writing-review and editing, H.S.M.A. and T.L.; software, H.S.M.A. and T.L.; supervision, T.L.; project administration, H.S.M.A. and T.L.  
This research received no external funding.  
The authors declare no conflict of interest.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
###### Abstract  
Accurate prediction of metro passenger flow helps operating departments optimize scheduling plans, alleviate passenger flow pressure, and improve service quality. However, existing passenger flow prediction models tend to only consider the historical passenger flow of a single station while ignoring the spatial relationships between different stations and correlations between passenger flows, resulting in low prediction accuracy. Therefore, a multi-scale residual depthwise separable convolution network (MRDSCNN) is proposed for metro passenger flow prediction, which consists of three pivotal components, including residual depthwise separable convolution (RDSC), multi-scale depthwise separable convolution (MDSC), and attention bidirectional gated recurrent unit (AttBiGRU). The RDSC module is designed to capture local spatial and temporal correlations leveraging the diverse temporal patterns of passenger flows, and then the MDSC module is specialized in obtaining the inter-station correlations between the target station and other heterogeneous stations throughout the metro network. Subsequently, these correlations are fed into AttBiGRU to extract global interaction features and obtain passenger flow prediction results. Finally, the Hangzhou metro passenger inflow and outflow data are employed to assess the model performance, and the results show that the proposed model outperforms other models.  
metro passenger flow prediction; spatiotemporal dependencies; graph convolutional network; residual network +
Footnote †: journal: Applied  
doi:10.3390/appl32011272
## 2 IEEE 802.15.4 Main Characteristics  
This section gives a short summary of the main IEEE 802.15.4 characteristics. IEEE 802.15.4 is the de-facto family of standard radio interfaces for low-cost, low-power, and low-rate wireless communications. IEEE 802.15.4 specifies Physical (PHY) and Medium Access Control (MAC) layer functionality [22].  
The widely implemented IEEE 802.15.4-2003 specification defines three physical layers in the 868 MHz, the 915 MHz and the 2.4 GHz bands, which offer raw data rates of 20 kbit/s, 40 kbit/s and 250 kbit/s, respectively. The latter physical layer, which uses an Industrial, Scientific and Medical (ISM) band available worldwide, is the most commonly used one.  
At the MAC layer, IEEE 802.15.4 networks may be configured in two modes: (i) on the basis of a superframe structure delimited by beacons, or (ii) in a beaconless mode, which is generally preferred due to its simplicity, whereby nodes use unslotted CSMA/CA for medium access. In this mechanism, the process for transmitting a data frame requires the sender to wait for an initial random backoff time. Subsequently, the sender performs Clear Channel Assessment (CCA) to check the state of the medium. If the medium is idle, the sender carries out the transmission. Otherwise, the sender performs a new backoff, of a duration randomly chosen from a greater window of possible values, and evaluates the medium state again. This procedure is repeated several times if the medium is always found to be busy. If the number of unsuccessful channel access attempts reaches a maximum limit, the frame transmission fails and the frame is discarded.  
IEEE 802.15.4 offers optional reliability for unicast frames by allowing the request of acknowledgment frames. Broadcast frames are not acknowledged. Network layer multicast packets (see subsection 3.1) are encapsulated in broadcast frames in IEEE 802.15.4.
# Extended L-Band InAs/InP Quantum-Dash Laser in Millimeter-Wave Applications
## 3 Results and Discussion
### WL and SMF Transmission Performance Analysis
Therefore, the performance variation between the WL and hybrid-WL channel systems, considering both sources, is attributed to the noise-induced \(P_{OR}\) degradation. This may be qualitatively understood from the third term of Equation (2), which is the MMW signal that includes \((\phi_{IL_{0}}(t)-\phi_{IL_{2}}(t,\tau_{t}))\) depicting the chromatic-dispersion-induced phase decorrelation between the optical carrier and the sideband while transmitting along the fiber with length \(L\). In general, this phase decorrelation is related to the optical sources' linewidth, and in this case, the CL-DWL optical source linewidth is expected to be better than that of the unoptimized QD-DWL counterpart, and the same argument discussed above holds true. Besides, the last term of Equation (2) also underlines that RIN and phase noise are coupled; in other words, chromatic dispersion influences RIN by the decorrelation effect when propagating through the fiber. Hence, these arguments support the observation of \(\sim\)1.7 dB \(P_{OR}\) degradation in the hybrid-WL channel compared to in the WL transmission case and better performance from the CL-DWL. Moreover, an increase in the noise of the MMW beat-tone signal after SMF transmission could also be inferred by comparing the SNR values at a fixed received optical power of \(\sim\)0.5 dBm by subtracting the received SNRs for the WL system from those of the hybrid-WL system at a fixed WL sub-link distance and considering both sources. In this case, an inferior SNR of \(\sim\)1.4 dB was noted from the hybrid-WL channel's transmission results compared to from the WL counterpart. Besides, a visible difference in the QPSK constellations of WL and hybrid-WL cases was also observed by comparing the insets of Figures 4 and 5, thus indicating this additional SNR penalty appearing from the phase decorrelation of the optical tones induced by fiber chromatic dispersion.
# Mechanical Analysis of Ground Sleeve
## 5 Mechanical Analysis of Grouted Sleeve Lapping Connector
### Approximate Mechanical Model  
Figure 24 shows an approximate mechanical model of the grouted sleeve lapping connector. In the model, the two bars cling to each other, and the line connecting their centers passes though the center of the sleeve. Assuming the tensile stress of the grout has a triangular distribution at the ultimate load, the equilibrium equation can be written per unit length as follows.  
\[\sigma_{u}A_{t}+\sigma_{d}A_{t}+\frac{1}{2}R_{t}(d_{i}-2d)S=2\sigma dS \tag{9}\]  
where \(S\) is the effective length, known as the unit length. \(\sigma_{u}\) and \(\sigma_{d}\) are the stresses of the sleeve wall far from the overlapped bars and close to the overlapped bars, respectively, where the values of \(\sigma_{u}\) and \(\sigma_{d}\) are obtained by multiplying the elastic modulus (\(E\)) by the strain of SG2 (\(\varepsilon_{2}\)) and the strain of SG1 (\(\varepsilon_{1}\)), respectively. \(A_{t}\) denotes the cross-sectional area of the sleeve wall and is equal to the product of \(t\) and \(S\). \(t\) is the thickness of the sleeve. \(R_{t}\) is the nominal tensile strength of the grout. \(d_{i}\) is the sleeve internal diameter. \(d\) is the bar diameter and \(\sigma\) is the radial stress acting on the grout that is exerted by the bar ribs.  
Figure 25 shows the bond stress distribution on the bar surface. Assuming that the bond stress, \(\tau\), is uniformly distributed on the bar surface, Equation (10) is as follows.  
\[P=\tau\tau dL \tag{10}\]  
where \(P\) is the pulling force, \(\tau\) is the bond stress, and \(L\) is the lap length.  
Assuming that the radial stress (\(\sigma\)) equals the longitude, bond stress (\(\tau\)) yields:  
\[\sigma=\tau \tag{11}\]  
Figure 24: Mechanical model of a grouted sleeve lapping connector.  
Figure 25: Distribution of bond stress along the steel bar.  
Combining Equations (9)-(11) yields Equation (12).  
\[L=\frac{2P}{\pi\big{[}\sigma_{u}t+\sigma_{d}t+\frac{1}{2}R_{t}(D-2d)\big{]}} \tag{12}\]  
To evaluate the reliability of the mechanical model, the maximum pulling force (\(P_{u}\)), the corresponding stresses \(\sigma_{u}\) and \(\sigma_{d}\), and other relevant data for the specimens that failed by bar-grout bond failure were substituted into Equation (10), and the calculated lap length (\(L_{\text{cal}}\)) was compared to the actual lap length (\(L\)). The results are shown in Table 5. It was found that the average calculated lap length (\(\overline{L_{\text{cal}}}\)) was near the actual lap length (\(L\)), which indicated that the approximate mechanical model was highly reliable.
# Robust Control of Electric Tail Reduction System: Uncertainty and Performance Index
## 5 Numerical Simulation
### Sinusoidal Response under Constant Load Condition  
We set the tracking speed (system constraint) \(\dot{\theta}_{d}=85\pi+5\pi\cos(2t)(rad/s)\) and initial speed \(\dot{\theta}_{m0}=55\pi(rad/s)\) of the electric tail reduction system. The tracking speed error in the simulation was defined as \(\dot{\theta}_{e}=\dot{\theta}_{m}-\dot{\theta}_{d}\). In this section, when \(V=V_{h}\), the tail rotor load was \(T_{tr}=19.4692(Nm)\).  
Taking the first-order derivative of the above constraint with respect to \(t\), in accordance with (23), then \(A=1\), \(b=-10\pi\sin(2t)\). In (29), we chose \(n=4\), then the robust control gain can be expressed as  
\[\gamma(||\mu||)=\frac{(1+\dot{\rho}_{E})^{-1}||\mu||^{2}}{||\overline{\mu}||( ||\mu||^{3}+||\mu||^{2}\epsilon+||\mu||\epsilon^{2}+\epsilon^{3})}\]  
We chose \(\epsilon=1\), \(P=1\). By choosing five different \(\hat{\alpha}\)'s, their values and the corresponding \(\kappa_{opt}\)'s and \(J_{\min}\)'s are given in Table 3. In (25), \(\Delta J_{tr}=0.002\sin(t)\), \(\Delta B_{tr}=0.001\cos(2t)\), and \(\Delta T_{tr}=0.05\cos(t)\) are used to simulate uncertainty and load disturbance of the system. Furthermore, according to (32), we chose the bounding function \(\rho{=}\) 15.  
Figures 7-9 show the simulation results under the maximum rotor load at low speed flight conditions including the effects of five different optimization schemes on system  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\hat{\alpha}\) & \(\kappa_{opt}\) & \(J_{min}\) \\ \hline
500/10 & 0.0088 & 0.0116 \\
100/10 & 0.0150 & 0.0068 \\
50/10 & 0.0189 & 0.0054 \\
10/10 & 0.0324 & 0.0031 \\
5/10 & 0.0408 & 0.0025 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weighting factor/optimal parameter/minimum performance index.  
Figure 6: The step tracking control cost curve of the electric tail reduction system.
## 3 Results  
### Characterization of YAG Nanocrystals  
To correctly determine the size of Er:YAG nanocrystals several structural methods were used. Figure 2 shows an XRD pattern of Er:YAG nanocrystals with denoted (hkl) indices. A sample calcined at 950 \({}^{\circ}\)C exhibited a set of well pronounced peaks which corresponded to pure YAG crystallizing in the cubic structure with a lattice parameter \(a=12.00\) A. The peak intensities proved that orientation of nanocrystals in the analyzed samples is random. No traces of impurities were detected in the XRD record. The mean nanocrystal size, calculated from XRD peak broadening according to Scherrer's equation, was 140 \(\pm\) 20 nm.  
To verify the size of nanocrystals and their stability in the colloidal solution, the size distribution of YAG nanocrystals was measured by dynamic light scattering. Figure 3 shows that the nanocrystal sizes followed Gaussian's distribution ranging from 160 to 310 nm and that the effective diameter of the nanocrystals was 234 nm. A value of the polydispersity index equal to 0.162 indicated that the colloidal solution exhibited a moderately polydisperse character.  
Figure 3: Size distribution of YAG nanoparticles recorded by dynamic light scattering.  
Figure 2: XRD pattern of YAG nanocrystals with denoted (hkl) indices.  
Shapes and morphology of Er:YAG nanocrystals were examined by transmission electron microscope (TEM) and the selected results are shown in Figure 4. The TEM images allowed us to distinguish two main nanocrystal fractions in the sample (Figure 4a,b). To the first fraction belong the nanocrystals larger than 110 nm. They occurred either as individual nanocrystals or as larger nanocrystal clusters with a size up to 400 nm. The selected area electron diffraction (SAED) performed on these nanocrystals (Figure 4c) and the local EDX analysis confirmed that their crystalline structure and chemical composition corresponded to YAG. To the second fraction belong nanoparticles smaller than 5 nm. These nanoparticles are aggregated into clusters smaller than 30 nm. The related SAED pattern (Figure 4d) shows only three diffraction rings which are not as sharp as for typical crystalline structure, and fourth diffusive ring. It can be concluded that these nanocrystals are amorphous rather than crystalline. The local EDX analysis confirmed that their chemical composition corresponded to aluminum oxide.
# Deep Neural Network-Based Modeling of Multimodal Human-Computer Interaction in Aircraft Cockpits
## 4 Experimental Design and Analysis of Results
### Analysis of Results
#### 4.2.3 Results and Analysis of Intention Classification  
We also train the 14,400 sets of EEG data samples in groups using 5-fold cross-validation. The model needs to be cross-validated five times. For each cross-validation, the sample size of the training set is 11,520, and the sample size of the testing set is 2880. The final classification accuracy of the intention classification model in this paper is the average of the classification accuracies obtained from 5-fold cross-validations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Parameters** & **Accuracy** \\ \hline SVM [23] & Kernel Type: RBF & 70.2\% \\ \hline \multirow{4}{*}{AlexNet [24]} & num\_convolution: 5 & \\  & Batch size: 64 & \\  & The initial learning rate: \(10^{-3}\) & 78\% \\  & Loss function: cross-entropy & \\ \hline \multirow{4}{*}{GoogLeNet [25]} & num\_layers: 22 & \\  & Batch size: 64 & \\  & The initial learning rate: \(10^{-3}\) & 83.6\% \\  & Loss function: cross-entropy & \\ \hline \multirow{4}{*}{RESNET [26]} & Convolution kernel size: 3 \(\times\) 3 & \\  & Stride: 2 & \\  & Padding: 1 & 88.4\% \\  & Batch size: 64 & \\  & The initial learning rate: \(10^{-3}\) & \\ \hline \multirow{4}{*}{OURS} & num\_lstm\_layers: 2 & \\  & Dropout rate: 0.5 & \\ \cline{1-1}  & Batch size: 64 & \\ \cline{1-1}  & The initial learning rate: \(10^{-3}\) & \\ \cline{1-1}  & Loss function: cross-entropy & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results of target classification.  
The intention classification module in the human-machine interaction model determines whether the subject intends to select the aircraft target by extracting features from the EEG signals. Previous studies have used both single-channel and multi-channel EEG data for classification. Therefore, we compare the difference in classification accuracy between inputting single-channel EEG data and 32-channel EEG channel data. Figure 9 shows the accuracy of the model with different numbers of channels. 'All' represents 32 channels. From Figure 10, we can see that when using 32 channels of EEG data, the model has a classification accuracy of up to 98.5%. Therefore, we send all 32-lead EEG data recorded into the model for training.  
Figure 10: Accuracy comparison of different models.  
Figure 9: (**a**). Accuracy comparison of Fp1-C3 channel. (**b**). Accuracy comparison of Cz-All channel.  
To evaluate the performance of the EEG intention classification models, we use the DCNN [27], DRCNN [28], FCNN [29], and BERT [30] for EEG classification on this paper's dataset as well. All models use 32-channel EEG data. Figure 10 demonstrates the classification accuracy of the compared models on this paper's dataset. The results in Figure 10 show that our proposed method obtained the highest classification accuracy. It is better than the other models.
## 4 An Experimental Case Study
First, we apply the Hilbert transform algorithm to the original FRF, and then we compare both of the FRFs to find possible distortions. The resulting transformation is shown in Figure 9, where the original FRF is shown in a continuous blue line and the transformation in bold black. The zoom in the three-dimensional chart in Figure 9 shows the low distortion level for the range of the second resonant frequency. A visual inspection of Figure 9 confirms that the original FRF does not present an important distortion after applying the Hilbert transformation; therefore, we can consider the blade as a linear system under these excitation and deflection amplitudes. The proposed non-linearity index expressed in Equation (27) is:  
\[\sigma_{\mathcal{H}}=\left\|R_{\mathcal{H}\mathcal{R}}(0)\right\|^{2}=0.93 \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Point** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** \\ \hline
1 & \(-0.0189\) & \(-0.0480\) & \(-0.1040\) & \(0.3852\) & \(-0.2265\) & \(0.3477\) \\
2 & \(-0.0487\) & \(-0.1956\) & \(-0.1342\) &  
The Argand diagram of the FRF and its Hilbert transformation is shown in Figure 10, where the original response is depicted in a blue and continuous line. In contrast, the corresponding Hilbert transformation is represented in a bold black line. The averaged modal parameters estimated by the traditional rational fractional polynomials and peak picking techniques are reported in Table 2 for the first six bending vibration modes. The modal parameters reported in Table 2 were obtained by applying the traditional and experimental modal analysis techniques that are inherently offline and frequency domain. Those experimental techniques are reported in [2; 31; 32; 33]. Those parameters are used as a comparative reference for evaluating the performance of the online and algebraic approach for the first six bending vibration modes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Frequency [Hz]** & **Damping Ratio \%** \\ \hline
1 & 9.15 & 1.5 \\
2 & 32.59 & 0.9 \\
3 & 74.75 & 1.43 \\
4 & 141.0 & 2.8 \\
5 & 175.1 & 2.0 \\
6 & 213.24 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Offline modal parameters estimation, averaged peak picking, and rational fractional polynomial (RFP) methods. Figure 10: Argand diagram of the original FRF (blue continuous line) and its Hilbert transformation (black marked line). Figure 9: Original FRF (blue line) and its corresponding Hilbert transformation with first mode detail. Moreover, based on the results obtained with the application of the non-linearity index, whose numerical value is \(\sigma_{\mathcal{H}}=0.93\), it is perfectly valid to use the velocity measurements of the analyzed output for the synthesis of the algebraic identifier (21), given that the dynamic behavior of the blade is dominantly linear and, as a consequence, the condition \(\Phi(x,\dot{x})\approx 0\) is satisfied.