esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussion
### Parameter Analysis
#### 3.3.5 Effectiveness of Attention Mechanism
This configuration allows for the simultaneous extraction of spectral and spatial features while effectively reducing overfitting due to model complexity. Compared to the 3D CNN, the SSRN enhances accuracy by stacking multiple spectral residual blocks and spatial residual blocks for feature extraction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **SVM** & **2D CNN** & **3D CNN** & **SSRN** & **HybridSN** & **Proposed Model** \\ \hline
1 & 83.21 & 76.3 & 100 & 100 & 100 & 100 \\
2 & 74.83 & 82.5 & 77.92 & 98.79 & 99.30 & 99.50 \\
3 & 81.05 & 86.9 & 91.25 & 100 & 99.83 & 99.83 \\
4 & 78.72 & 63.54 & 91.84 & 98.96 & 100 & 98.81 \\
5 & 74.65 & 89.63 & 98.92 & 99.20 & 99.11 & 100 \\
6 & 92.5 & 99.03 & 97.99 & 99.31 & 99.80 & 100 \\
7 & 95.31 & 77.41 & 100 & 100 & 100 & 100 \\
8 & 84.7 & 100 & 96.97 & 100 & 100 & 100 \\
9 & 96.83 & 65.31 & 100 & 100 & 100 & 100 \\
10 & 72.04 & 81.93 & 80.6 & 99.36 & 99.85 & 99.85 \\
11 & 77.51 & 90.65 & 86.44 & 99.80 & 99.59 & 99.88 \\
12 & 85.6 & 84.25 & 90.74 & 98.54 & 98.33 & 99.04 \\
13 & 84.61 & 99.36 & 97.62 & 94.25 & 98.59 & 100 \\
14 & 97.54 & 98.68 & 97.64 & 99.12 & 99.66 & 99.89 \\
15 & 93.61 & 88.29 & 94.44 & 97.77 & 99.26 & 98.18 \\
16 & 73.4 & 99.63 & 100 & 100 & 95.31 & 100 \\ OA & 85.45 \(\pm\) 2.43 & 90.36 \(\pm\) 0.33 & 89.31 \(\pm\) 0.38 & 99.20 \(\pm\) 0.39 & 99.42 \(\pm\) 0.36 & 99.64 \(\pm\) 0.08 \\ AA & 78.33 \(\pm\) 3.11 & 88.62 \(\pm\) 0.92 & 91.68 \(\pm\) 0.55 & 98.85 \(\pm\) 0.54 & 98.93 \(\pm\) 0.41 & 99.26 \(\pm\) 0.32 \\ \(\kappa\) & 83.21 \(\pm\) 2.51 & 89.92 \(\pm\) 0.59 & 87.82 \(\pm\) 0.53 & 99.1 \(\pm\) 0.47 & 99.36 \(\pm\) 0.35 & 99.59 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracy results on IP dataset. Figure 7: My Dataset classification results: (**a**) false-color image, (**b**) ground-truth image, (**c**) SVM, (**d**) 2D CNN, (**e**) 3D CNN, (**f**) SSRN, (**g**) HybridSN, and (**h**) My Net. As seen in Table 10, the SVM, which has a simple structure, takes less time than the other classification methods, which use neural networks. Compared with the 3D CNN, the 2D CNN requires less time to train or test data. Unlike the 3D CNN, My Net uses both 3D and 2D convolutional layers to reduce model complexity. Compared with HybridSN, My Net needs a slightly shorter training time on IP and My Dataset but requires slightly more time on PU.
## 3 Results
### Analysis of Surface Roughness  
To further explore the impact of different coal volatiles on surface roughness, this study selected six coal samples with varying volatiles and calculated the surface roughness parameters (R\({}_{\rm{A}}\), R\({}_{\rm{q}}\)) for each sample to quantitatively describe the structural characteristics of the coal surface. Meanwhile, to deeply analyze the evolution of coal surface properties, the roughness parameters (R\({}_{\rm{sk}}\), R\({}_{\rm{ku}}\)) for all samples were also calculated, as shown in Table 2.  
Since the kurtosis of a Gaussian height distribution is 3, if R\({}_{\rm{ku}}<3\), the surface is relatively flat. If R\({}_{\rm{ku}}>3\), it indicates that the coal surface has more peaks and valleys, and the surface is relatively steep, suggesting a higher degree of pore and fissure development in the coal structure, with a more uneven distribution of pore sizes and shapes [33]. Since the R\({}_{\rm{ku}}\) of all samples is greater than 3, it indicates that the surface profiles of the samples are not flat and contain many peaks and valleys, with the height values distributed near the reference plane and a relatively concentrated pore size distribution in the coal samples. Simultaneously, it was found that as the volatiles increase, the R\({}_{\rm{ku}}\) value also gradually increases, reflecting a further increase in the complexity of the pore structure. The R\({}_{\rm{sk}}\) value represents the asymmetry of the coal surface roughness profile. When R\({}_{\rm{sk}}=0\), the surface has an equal number of peaks and valleys, forming a flat profile. R\({}_{\rm{sk}}>0\) indicates that peaks are more prominent than valleys, whereas R\({}_{\rm{sk}}<0\) indicates that valleys are more prominent than peaks on the coal surface [34]. According to the data in Table 2, the R\({}_{\rm{sk}}\) values of most samples are less than 0, indicating that the number of valleys is greater than the number of peaks on the surfaces of most samples, while the R\({}_{\rm{sk}}\) value of sample SX-4 is greater than 0, indicating that the number of peaks is greater than the number of valleys on its surface.  
The results of the surface roughness evaluation are presented in Table 2. The R\({}_{\rm{a}}\) values of the coal samples ranged from 1.54 to 4.55 nm, while the R\({}_{\rm{q}}\) values varied between 1.98 and 5.83 nm. Additionally, the study found that the R\({}_{\rm{q}}\) values of all samples were greater than their corresponding R\({}_{\rm{a}}\) values. [PERSON] et al. [15] and [PERSON] et al. [35] also observed the same phenomenon, attributing it to the different sensitivities of these two indices to surface roughness. Specifically, R\({}_{\rm{q}}\) is more sensitive to surface roughness than R\({}_{\rm{a}}\). When changes occur on the coal surface to a certain extent, R\({}_{\rm{q}}\) undergoes a greater degree of alteration, resulting in higher R\({}_{\rm{q}}\) values compared to R\({}_{\rm{a}}\) for all samples.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 1 Introduction
The proposed user grouping solution is analyzed in detail in Section 4. Section 5 presents the simulation's parameters used to test the performance of the proposed algorithm. The clustering of users and throughput performance of the MU-MIMO system are then evaluated in Section 6 for various user distributions and required angle separations. Finally, Section 7 presents the conclusion and research directions for future works.
# An Overview of Smart Composites for the Aerospace Sector
## 4 Discussion
### Keywords Trends
In this regard, self-sensing nanocomposites incorporate conductive fillers like graphene platelets or carbon nanotubes into polymer matrices, enabling damage detection through changes in electrical properties [46; 47] and they can detect various types of damage, including impact and delamination [48; 49]. Self-sensing nanocomposites in smart carbon fiber-reinforced polymer composites hold promise for structural health monitoring and early damage detection in aerospace applications. Finally, the red cluster consists of less frequently co-occurring keywords, such as shape memory polymers and scanning electron microscopy, which indicate emerging research areas focused on material characterization and adaptive capabilities. It is important to note that shape memory polymers offer the ability to change shape in response to stimuli like temperature or light. They are being explored for use in morphing aircraft structures, deployable space components, and self-healing materials for aerospace [50; 51].
## 2 Design Concepts
### Antenna Configuration  
Based on the above design considerations, we propose a novel design--a dual-polarized octagonal cavity-backed antenna with two orthogonal pairs of differential feedlines that excite a radiating open prism in the center of the cavity. We call this design an \"octagonal cavity-backed open radiating prism,\" or OCROP. Our antenna consists of four parts: (1) a conductive octagonal-shaped cavity, (2) a conductive radiating open prism, (3) two orthogonal pairs of differential feedlines, and (4) a supporting dielectric substrate. The geometry is shown in Figure 1a. The term \"open prism\" comes from the radiating element at the center of the cavity. The radiating element is a hollow metallic prism that is open at the bottom side. As an example, the case with a cubic prism essentially uses a hollow box with open bottom. The copper feedlines are placed on the top side of the supporting substrate. For high mechanical stability and low mass, the substrate should be a relatively thin woven dielectric with low permittivity. Here, we used a 0.762 mm thick Rogers CuClad 217 (\(\epsilon_{r}\) = 2.17 and \(\tan\delta\) = 0.0009). The initial value of the aperture length (\(L_{g}\)) was 165 mm, the cavity height (\(H_{sw}\)) was 80 mm, the gap between the lower dielectric layer and the ground plane (\(g\)) was 11 mm, and the spacing between the feedline and the radiating prism (\(s\)) was 3 mm. The feedline lengths and widths (\(l\) and \(w\)) were 53 mm and 30 mm, respectively. The transition feedline lengths and widths (\(l^{\prime}\) and \(w\)) were 1 mm and 3.6 mm, respectively. These values were selected to make the antenna operate in the L-band (1-2 GHz) with a resonance merging effect.  
We considered numerous design configurations and performed parametric simulation studies to test the concepts. Since the antenna was designed for dual-polarized operation, the length and the width of the open prism needed to be the same, i.e., \(L=W\), to allow geometrical symmetry and identical performance in two orthogonal polarizations of interest. For the antenna to operate in the L-band (1-2 GHz) around a center frequency of 1.5 GHz, the dimensions of the open prism were set to \(L=W=H=51\) mm, forming a cube with no bottom face (thus, the name \"open\" prism, as it has an open side). This value was found through simulation studies that are not included here for brevity.
# A Real-Time Sound Field Rendering Processor
###### Abstract  
Real-time sound field renderings are computationally intensive and memory-intensive. Traditional rendering systems based on computer simulations suffer from memory bandwidth and arithmetic units. The computation is time-consuming, and the sample rate of the output sound is low because of the long computation time at each time step. In this work, a processor with a hybrid architecture is proposed to speed up computation and improve the sample rate of the output sound, and an interface is developed for system scalability through simply cascading many chips to enlarge the simulated area. To render a three-minute Beethoven wave sound in a small shoe-box room with dimensions of 1.28 m \(\times\) 1.28 m \(\times\) 0.64 m, the field programming gate array (FPGA)-based prototype machine with the proposed architecture carries out the sound rendering at run-time while the software simulation with the OpenMP parallelization takes about 12.70 min on a personal computer (PC) with 32 GB random access memory (RAM) and an Intel i7-6800K six-core processor running at 3.4 GHz. The throughput in the software simulation is about 194 M grids/s while it is 51.2 G grids/s in the prototype machine even if the clock frequency of the prototype machine is much lower than that of the PC. The rendering processor with a processing element (PE) and interfaces consumes about 238,515 gates after fabricated by the 0.18 \(\upmu\)m processing technology from the ROHM semiconductor Co., Ltd. (Kyoto Japan), and the power consumption is about 143.8 mW.  
sound field rendering; FPGA; FDTD 2018 8, 35; doi:10.3390/app8010035
## 3 Proposed Methodology
### The Architecture of the Framework Based on HMLC-SBELM  
The structure of the proposed diagnostic framework based on HMLC-SBELM is shown in Figure 4.  
Figure 3: The procedure of probability output fusion.  
The steps of the proposed diagnostic framework are summarized below:  
(1) Collect a large number of single failure mode samples and a small number of simultaneous failure mode samples from the actual environment. The vast majority of single failure samples are extracted to generate a training set, and the remaining single failure samples and simultaneous failure samples are divided into two subsets to generate the validation set and testing set. All the samples were preprocessed to increase the signal-to-noise ratio and extract typical features.  
(2) The diagnostic model training module: use the training set to train the HMLC-SBELM-based model which can efficiently solve the classification problem with multiple classes benefiting from the advantage in a sparse representation based on probability inference.  
(3) The optimal threshold determination module: use the validation set and adopt the F1-measure index as the standard measurement of diagnostic accuracy. The particle swarm optimization algorithm is utilized to optimize a suitable discriminative threshold which is critical for simultaneous fault recognition. With the discriminative threshold, the probability output vector of the diagnostic model trained in Step (2) is transformed into the final failure modes.  
Figure 4: The proposed simultaneous fault diagnostic framework.  
(4) Performance evaluation module of the diagnostic model: the testing set and the optimal threshold determined in Step (3) is used to evaluate the performance of the diagnostic model.
## 3 Results and Discussion
### Physicochemical Quality
It is also important to mention that the changes in TTA had a positive correlation with changes in pH values, which increased significantly (\(p<0.05\)) from 5.9 in fresh-untreated carrots on day 0 to 6.12 on day 21, whereas the treatment with MJ and ABA resulted in values comparable to that of the fresh-untreated samples on day 0 (i.e., 6.01 and 6.04, respectively). These results are in accordance with those elsewhere reported [28]. Since acidity and pH are inversely proportional to each other, it is believed that protons are  
Figure 3: Moisture content (%) and juiciness (%) of fresh-untreated and treated purple carrot samples. released from organic acid hydrolysis in such a way that the lower the acid content in the sample, the higher the hydrogen concentration. Figure 5 depicts the maturity index of purple carrot samples. Since maturity index is a sugar-acid (TSS:TTA) ratio, it decreased significantly (\(p<0.05\)) in fresh-untreated samples during cold storage (i.e., 62% on day 0 vs. 49% on day 21). This patter is a direct result of the marked increase in TTA measured in fresh-untreated samples throughout the storage period. It is also interesting that the decrease in maturity index was not affected by MJ elicitation. In fact, no significant (\(p>0.05\)) differences between MJ-treated samples and fresh-untreated carrots on day 21 were measured (i.e., 49% vs. 51%, respectively). On the contrary, the exposition of purple carrots to ABA resulted in a preservation of their maturity index over storage as compared with that of fresh-untreated carrots on day 0 (i.e., 75% vs. 62%, respectively). In view of these results, the post-harvest elicitation of purple carrots by using ABA as a natural phytoregulator was effective in preserving moisture content, juiciiness, sugar content and organic acid content throughout cold storage for 21 days. Considering that sugar and organic acid contents determine the tasting quality of a product, elicitation with ABA seems a recommendable approach to preserve physicochemical quality of purple carrots during 21-day cold storage. Additionally, ABA treatment also provided satisfactory results in terms of visual appearance. Although studies in this respect have not been performed with purple carrots, a comparison with bibliographic reports on traditional orange carrots [6,7] suggests that ABA treatment in conjunction with low temperatures is able to overcome the limitations of freezing, drying and canning as a preservation method in terms of sensorial quality. On the other hand, the exogenous application of ABA on the roots during the post-harvest period is a simple, economic and reproducible approach with no need for an exhaustive control, as is the case for the use of refrigeration alone [8]. Figure 4: TSS (%) (a) and TTA (°Brix) and pH (b) of fresh-untreated and treated purple carrot samples.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 5 Experiment and Results Analysis
### Experimental Parameter Settings  
For the selection of parameters \(\alpha\), \(\beta\) and \(\mu\) in the ant colony algorithm, there is no general method to determine their optimal combination. It should be noted that these parameters have a great impact on the performance of the ant colony algorithm. The smaller the value of \(\mu\), the smaller the influence of the previously searched path on the current search, making the algorithm difficult to converge. The larger the value of \(\mu\), the greater the influence of the previous search path on the current search, but this also increases the risk of falling into a local optimum. So, the \(\mu\) value is generally set at 0.5. In the parameter selection experiment, the \(\alpha\) and \(\beta\) values were set as \(\mu\) = 0.5 and \(\mu\) = 0.6. The simulation program was cycled 1000 times, the best result was selected as one experiment, taking the average value of 100 experiments. The parameters are substituted into the algorithm for the simulation test. The simulation experiment data are shown in Table 3.  
Figure 4: Topology diagram of the SWIM task scheduling simulation experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & \(\mu\) & \(\alpha\) & \(\beta\) & **Optimal Path** & **Running Time (S)** \\ \hline
1 & 0.5 & 1.0112 & 2.7375 & 568.10969 & 39.98 \\
2 & 0.5 & 1.0351 & 2.6856 & 568.19446 & 40.37 \\
3 & 0.5 & 1.0323 & 2.7685 & 568.19446 & 40.56 \\
4 & 0.5 & 1.0289 & 2.7386 & 568.10969 & 40.33 \\
5 & 0.5 & 1.0156 & 2.7235 & 568.10969 & 40.25 \\
6 & 0.5 & 4.3589 & 1.5195 & 568.19446 & 41.79 \\
7 & 0.5 & 5.2368 & 2.4978 & 568.10969 & 41.65 \\
8 & 0.5 & 5.3646 & 2.5568 & 568.10969 & 41.15 \\
9 & 0.5 & 6.3567 & 4.2556 & 568.19446 & 42.48 \\
10 & 0.5 & 6.3551 & 4.2550 & 568.19446 & 42.68 \\
11 & 0.6 & 1.0253 & 2.7526 & 568.10969 & 39.86 \\
12 & 0.6 & 1.0362 & 2.6898 & 568.19446 & 40.29 \\
13 & 0.6 & 1.0387 & 2.7365 & 568.19446 & 40.53 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter combination test results.  
From Table 3, we can see that the optimal path is 568.10969, the running time is 39.86 s, \(\mu=0.6\), \(\alpha=1.0253\) and \(\beta=2.7526\). After the comparative study and multiple experiments, the parameters in this paper simulation experiment were set to \(\alpha=1.0\), \(\beta=2.7\) and \(\mu=0.6\). In the experiment, we found that when the same parameters were used for experiments of different scales, the results obtained were different. The weight parameters of the resource node load impact indicator in SWIM were set to \(\delta_{1}=0.4\), \(\delta_{2}=0.3\) and \(\delta_{3}=0.3\). The population size was 50, and the maximum number of iterations was 150. The simulation experiment test parameters are shown in Table 4.
## 6 Verification and Results
### Episode 3: Security Integrity (req: ii, viii)
#### 6.3.1 Eavesdropping  
In order to demonstrate flow-based isolation and encryption (req: ii), we defined one SFC with multiple inner encrypted flows. The SFC is defined as SF1 \(->\) SF2 \(->\) SF3 where the flows are defined as email traffic and video traffic (over HTTP). SF1 is a v CPE that contains multiple network services. However, in this episode of the scenario, we define it to handle email traffic. Hence, SF1 is set up to handle all email traffic (port 25,110), while SF2 handles video traffic over HTTP (port 80,443). SF3 is a firewall that handles both flows. Due to the risk of eavesdropping, we encrypt the email traffic between SF1 and SF3 and we encrypt the HTTP traffic for SF1 \(->\) SF2 and SF2 \(->\) SF3.  
We ran both traffic types simultaneously by running two instances of Iperf on two different ports, namely flow 1 (port 25) and flow 2 (port 80) (Figure 10). Hence, we classified the traffic into two different types, in total, three pairs of encrypted links. Flow 1 was defined as SF1 (v CPE) \(->\) EF1 \(->\) EF2 \(->\) SF3 (vFW). Flow 2 was defined as SF1 (v CPE) \(->\) EF3 \(->\) EF4 \(->\) SF2 (vVideo) \(->\) EF5 \(->\) EF6 \(->\) SF3 (vFW). By using tcpdump, we observed both encrypted and non-encrypted traffic flows at ISP-B. This confirmed that the architecture was supporting flow-based encryption.
## 3 Results and Discussion
### Calibration Models Using Full Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compositions**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{Calibration} & \multicolumn{4}{c}{Prediction} \\ \cline{3-10}  & & & **R\({}^{2}_{\rm c}\)** & **RMSEC** & **Biasc** & **R\({}^{2}_{\rm p}\)** & **RMSEP** & **RPD** & **Biasp** \\ \hline \multirow{3}{*}{chlorogenic acid} & PLS & 9 & 0.89 \({}^{\bullet\bullet}\) & 0.022 & \(-2.25\times 10^{-7}\) & 0.85 \({}^{\bullet\bullet}\) & 0.026 & 2.58 & 0.0027 \\  & ELM & 17 & 0.90 \({}^{\bullet\bullet\bullet}\) & 0.022 & \(1.79\times 10^{-9}\) & 0.86 \({}^{\bullet\bullet\bullet}\) & 0.025 & 2.68 & 8.47 \(\times 10^{-4}\) \\  & LS-SVM & 144.6281, 201.9949 & 0.93 \({}^{\bullet\bullet\bullet}\) & 0.018 & \(6.80\times 10^{-15}\) & 0.83 \({}^{\bullet\bullet\bullet}\) & 0.029 & 2.31 & 0.0049 \\ \hline \multirow{3}{*}{luteolin-7-O-glucose} & PLS & 7 & 0.82 \({}^{\bullet\bullet\bullet}\) & 0.019 & \(1.35\times 10^{-7}\) & 0.77 \({}^{\bullet\bullet}\) & 0.022 & 2.09 & 0.0046 \\  & ELM & 31 & 0.86 \({}^{\bullet\bullet\bullet}\) & 0.017 & \(5.32\times 10^{-10}\) & 0.81 \({}^{\bullet\bullet}\) & 0.020 & 2.30 & 0.0037 \\  & LS-SVM & 2783.4589, & 0.85 \({}^{\bullet\bullet\bullet}\) & 0.018 & \(-3.21\times 10^{-13}\) & 0.77 \({}^{\bullet\bullet}\) & 0.023 & 2.00 & 0.0056 \\ \hline \multirow{3}{*}{3,5-O-dicaffeoylquinic acid} & PLS & 9 & 0.84 \({}^{\bullet\bullet}\) & 0.050 & \(1.66\times 10^{-7}\) & 0.83 \({}^{\bullet\bullet\bullet}\) & 0.054 & 2.41 & \(-0.0017\) \\  & ELM & 23 & 0.85 \({}^{\bullet\bullet\bullet}\) & 0.050 & \(-1.62\times 10^{-9}\) & 0.83 \({}^{\bullet\bullet\bullet}\) & 0.053 & 2.45 & \(-2.07\times 10^{-4}\) \\ \cline{1-1}  & LS-SVM & 49.789.1255, & 0.86 \({}^{\bullet\bullet\bullet}\) & 0.049 & \(2.02\times 10^{-11}\) & 0.83 \({}^{\bullet\bullet\bullet}\) & 0.055 & 2.36 & \(7.07\times 10^{-4}\) \\ \hline \hline \end{tabular}  
* \({}^{\bullet}\): The ** symbols indicate that the R2 is significant at the significance level of 0.01.  
\end{table} 
Table 3: Results of calibration models for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in dry flowers using full spectra (unit: % DW, DW means dry weight). models for chlorogenic acid. However, models for 3,5-O-dicaffeoylquinic acid in fresh flowers obtained worse results than models for 3,5-O-dicaffeoylquinic acid in dry flowers. Most of the models in Tables 1 and 2 had R\({}^{2}\) over 0.8 and RPD over 2.00. The overall results showed the feasibility of using hyperspectral imaging to determine chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in fresh and dry _Chrysanthemum morifolium_ flowers. In general, among all models, ELM models obtained the best performances, followed by LS-SVM and PLS models. Selection of optimal models would help to improve the detection performances.