esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 7 Conclusions  
Every channel estimation method based on neural networks found in the literature needs multiple stages to estimate the whole channel. In this paper, we presented a convolutional neural network for channel estimation which has the LS applied at DMRS signals as an input and generates the whole channel as an output. With the purpose of a future hardware implementation, the performance of the proposed CNN is evaluated using two terms of comparison: computational cost and the mean square error. Our CNN has the  
Figure 10: MSE analysis varying the SNR. The comparison is between the implemented designs and the FCDNN postinterpolated proposed in [3].  
lowest complexity among the analyzed neural networks such as FCDNN, LSTM, and other CNN architectures. Additionally, Tables 2 and 3 present the mathematical expressions to compute the complexity, not only for the neural networks included in this study, but with the computational cost divided by stages, for any model which uses these types of layers. Moreover, the results of the error analysis prove competitive MSE performance to the MMSE channel estimation applied to a 2 \(\times\) 2 MIMO system, having a maximum discrepancy of 0.018 at 5 dB SNR. On the basis of these analyses, the proposed CNN technique gives a deployable solution outperforming earlier approaches reported in the literature. In this vision, a further study on the integration of the real and imaginary parts could lead to additional resource savings. Subsequently, quantizing the proposed CNN and implementing it on VERSAL technology can provide a definitive validation for the CNN-based channel estimation method.  
**Author Contributions:** Formal analysis, S.S. and C.S.; funding acquisition, G.G.; investigation, S.S.; methodology, S.S. and C.S.; project administration, G.G.; supervision, G.G.; writing, S.S. and C.S.; review and editing, S.B.S., Y.D., S.S., C.S. and G.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Characterization of Driver Dynamic Visual Perception Under Different Road Linearity Conditions
## 3 Data Analysis
### Patterns of Saccade Amplitude in Drivers Under Different Road Conditions  
Saccadic amplitude refers to the spatial distance traveled by the eye when shifting from one fixation point to the next [29]. This metric was used to measure a driver's eye tracking amplitude in a visual task and reflected the visual search strategy and task complexity. Saccade amplitude of drivers under different driving conditions was shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of Difference** & **SS** & **df** & **MS** & **F** & \(p\)**-Value** & **F Crit** \\ \hline Between-Group & 16.23766 & 3 & 5.412553 & 3.109245 & 0.030969 & 2.718785 \\ Between-Group & 139.2634 & 80 & 1.740793 & & & \\ Sum & 155.5011 & 83 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: One-way ANOVA of average pupil diameter.  
Figure 4: Average pupil diameter of drivers in different driving conditions.  
From the figure, it can be observed that the saccade amplitude was the smallest during straight driving. This was primarily due to the simplicity of the driving task and the low environmental dynamics in straight sections. The driver's fixation point tended to focus on the center of the road or distant targets, resulting in a relatively fixed fixation point and a smaller range of saccadic movement. In contrast, during the turning section, drivers needed to dynamically scan multiple targets, such as the inner and outer edges of the curve as well as road signs. The frequent shifts in fixation, particularly towards distant targets on the outer side of the curve, led to a significant increase in saccade amplitude. In climbing sections, drivers' fixation was primarily focused on the vehicle ahead and the target at the top of the slope. Due to the relatively static environment and concentrated targets, the range of saccadic movement remains at a moderate level. The downhill section presented the most complex driving task, as drivers need to frequently monitor vehicle speed, obstacles ahead, and distant road targets. The rapid shifts in fixation within a dynamic environment and the broader coverage of visual attention result in the largest saccade amplitude.  
The data were further analyzed by using one-way analysis of variance (ANOVA) to analyze the variability of drivers' average saccade amplitude under different driving sections as shown in Table 3. According to the results in the table, F = 2.803535 \(>\) F crit = 2.718785, \(p\)-value \(<\) 0.05, and it can be seen that the average saccade amplitude of the drivers under different driving conditions shows significant variability.
## 5 Discussion
### Drought Monitoring Capability of TVMPDI in Southeastern Tibet
In addition, it is worth mentioning that in the eastern part of the study area, the monitoring effect of TVPDI, which has better correlation with meteorological index, was worse than that of TVMPDI for the areas with excess vegetation moisture. This is because precipitation inhibits vegetation growth and land-air coupling effect makes atmospheric composition unable to reflect soil moisture changes (this will be explained in more detail in Section 5.3). TVMPDI with adding soil moisture could better represent the changes, which can be verified by Figure 6c,e.
# Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
## 3 Results
### Salinity and Moisture Characteristics
Spectral signatures of mixed salts (Figure 9) were similar to the spectra of single salt-contaminated soils. Soil mixtures with Na\({}_{2}\)CO\({}_{3}\) salt had absorbances from 1240 to 1480 cm\({}^{-1}\) and multiple absorbance/transmittances at different wavelengths depending on the moisture and salt content. An additional absorbance occurred between 1364 and 1430 cm\({}^{-1}\) (Figure 9).
## 3 Experiments and Results
### Performance Comparison Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{\(\boldsymbol{\alpha=0.5}\) with Top-2 Classes} & \multicolumn{2}{c}{\(\boldsymbol{\alpha=0.05}\)} \\ \cline{3-6}  & & **N = 20** & **N = 50** & **N = 20** & **N = 50** \\ \hline FedAvg & MLR & 89.45 \(\pm\) 0.10 & 78.94 \(\pm\) 0.40 & 81.05 \(\pm\) 0.30 & 85.51 \(\pm\) 0.06 \\ FedProx & MLR & 89.57 \(\pm\) 0.10 & 79.94 \(\pm\) 0.40 & 81.60 \(\pm\) 0.30 & 85.90 \(\pm\) 0.07 \\ PerFedAvg & MLR & 96.62 \(\pm\) 0.01 & 95.30 \(\pm\) 0.01 & 95.25 \(\pm\) 0.02 & 95.23 \(\pm\) 0.01 \\ pFedMe & MLR & 96.25 \(\pm\) 0.01 & 94.72 \(\pm\) 0.01 & 96.13 \(\pm\) 0.01 & 96.56 \(\pm\) 0.01 \\ FedGKD & MLR & 83.85 \(\pm\) 0.30 & 78.21 \(\pm\) 0.40 & 79.39 \(\pm\) 0.30 & 85.06 \(\pm\) 0.07 \\ pFedKD-WCL & MLR & **98.45 \(\pm\) 0.01** & **97.97 \(\pm\) 0.01** & **97.72 \(\pm\) 0.01** & **98.14 \(\pm\) 0.01** \\ \hline FedAvg & MLP & 91.11 \(\pm\) 0.20 & 95.57 \(\pm\) 0.06 & 95.70 \(\pm\) 0.04 & 97.54 \(\pm\) 0.02 \\ FedProx & MLP & 89.82 \(\pm\) 0.20 & 95.44 \(\pm\) 0.06 & 95.46 \(\pm\) 0.05 & 97.41 \(\pm\) 0.02 \\ PerFedAvg & MLP & 96.94 \(\pm\) 0.01 & 94.85 \(\pm\) 0.02 & 94.84 \(\pm\) 0.02 & 95.34 \(\pm\) 0.01 \\ pFedMe & MLP & 96.75 \(\pm\) 0.01 & 95.37 \(\pm\) 0.01 & 95.52 \(\pm\) 0.02 & 96.04 \(\pm\) 0.01 \\ FedGKD & MLP & 97.40 \(\pm\) 0.05 & 98.24 \(\pm\) 0.04 & 96.46 \(\pm\) 0.05 & **98.79 \(\pm\) 0.01** \\ pFedKD-WCL & MLP & **98.70 \(\pm\) 0.01** & **98.50 \(\pm\) 0.01** & **98.33 \(\pm\) 0.01** & 98.71 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparisons of the average test accuracy on MNIST with different levels of heterogeneity and numbers of clients. Figure 3: Performance comparisons of the average test accuracy of different algorithms on MNIST generated by Dirichlet distribution (\(\boldsymbol{\alpha=0.05}\)): (**a**) Multinomial logistic regression (MLR) with 20 clients. (**b**) Multinomial logistic regression (MLR) with 50 clients. (**c**) Two-layer multilayer perceptron (MLP) with 20 clients. (**d**) Two-layer multilayer perceptron (MLP) with 50 clients.
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
###### Abstract  
Long term evolution-advanced (LTE-A) system introduces carrier aggregation (CA) technique to improve the user throughput by aggregating multiple component carriers (CCs). Previous research works related to downlink radio resource allocation with carrier aggregation have not considered the delay factor and the error probability. Therefore, the previous methods are unable to provide better quality of service (QoS) compared to the LTE-A standard. This paper considers the radio resource management problem by zooming into the head of line delay, probability of packet loss, and the delay threshold for different types of data. In doing this, several constraints are imposed following the specifications of LTE-A system. Hence, an improved method is developed in this study to enhance the system throughput and to maintain the computational complexity. Extensive simulations were carried out with other well-known methods to verify the overall performance of the proposed method. The result obtained indicates that the proposed method outperforms the previous methods in the measurement of average user throughput, average cell throughput, fairness index, and spectral efficiency.  
LTE-advanced; carrier aggregation; component carrier; scheduling algorithm; greedy method +
Footnote †: journal: Applied Sciences  
_Article_  
An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced  
[PERSON], [PERSON], [PERSON], and [PERSON] (2017), 7, 394; doi:10.3390/app7040394
## Theory Description
### Adaptive Signal Decomposition Based on Reassignment Vector
**(a)** TFR obtained by STFT. **(b)** TFR obtained by the proposed method. Figure 2: The researched numerical simulation signal. **(a)** Numerical signal shown in time-domain. **(b)** Numerical signal shown in frequency-domain. Subsequently, the multi-component signal decomposition and reconstruction operation on the basis of TF characteristics is performed. The computation result is shown in Figure 5 and we can draw a conclusion that the performance of the raised method has obvious superiority in signal decomposition. To compare performance of signal decomposition, the commonly used method of EMD is employed to decompose the simulated signal and the corresponding frequency spectrum analysis result is drawn in \[\hat{\omega}_{f}(t,\xi)=\xi-\frac{1}{2\pi}\Im\left\{\frac{V_{f}^{\delta^{ \prime}}(t,\xi)}{V_{f}^{\delta}(t,\xi)}\right\} \tag{7}\]0 Only the constant frequency \(f_{1}=35\) Hz, which has been marked by a red circle, can be found in IMF3 and IMF4. The other signal components such as modulated signal \(x_{2}\) and impulse signal \(x_{3}\) still remain unidentified. Numerical simulation results show that the presented method is more suitable for the mode decomposition of different non-stationary components. It should be noted that the signal components must meet strict separation conditions in the TF domain described in [19], and thus the proposed method cannot fully separate close and overlapped signal components. \[\hat{\omega}_{f}(t,\xi)=\xi-\frac{1}{2\pi}\Im\left\{\frac{V_{f}^{\delta^{ \prime}}(t,\xi)}{V_{f}^{\delta}(t,\xi)}\right\} \tag{7}\]1 Performance of signal decomposition and reconstruction by proposed method. (**a**) multi-components signal decomposition. (**b**) signal reconstruction. \[\hat{\omega}_{f}(t,\xi)=\xi-\frac{1}{2\pi}\Im\left\{\frac{V_{f}^{\delta^{ \prime}}(t,\xi)}{V_{f}^{\delta}(t,\xi)}\right\} \tag{7}\]2 Performance of signal decomposition by Empirical Mode Decomposition (EMD).
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 3 Risk Assessment Methodology
### Determination of Weight Vectors
#### 3.2.1 Calculation of Subjective Weight Using AHP  
Step 1. Constructing the judgment matrix (G):  
The scale's interpretation from 1 to 9 is shown in Table 2. Based on the established risk assessment index system for drilling and blasting construction, the 1-9 scale method is used to compare the importance of each indicator factor within the system's subordinate levels, obtaining the initial judgment matrix as follows:  
\[G=\left(g_{ij}\right)_{n\times n} \tag{3}\]  
where \(g_{ij}\) represents the element in the i-th row and j-th column of the judgment matrix \(G\), and n is the matrix order.  
Step 2. Solve the judgment matrix to obtain its maximum eigenvalue and corresponding eigenvector.  
\[G_{n\times n}\cdot M=\lambda_{\max}\cdot M \tag{4}\]  
where \(G_{n\times n}\) represents the n-th order judgment matrix; \(M\) represents the eigenvector corresponding to the maximum eigenvalue of the judgment matrix; \(\lambda_{\max}\) represents the maximum eigenvalue of the judgment matrix.  
Step 3. Consistency check.  
\begin{table}
\begin{tabular}{c c} \hline
**Scale** & **Explanation** \\ \hline
1 & Both elements have the same level of contribution to the goal. \\ \hline
3 & One element has a slightly higher contribution compared to the other. \\ \hline
5 & One element has a significantly stronger contribution than the other. \\ \hline
7 & One element overwhelmingly dominates the other in terms of contribution. \\ \hline
9 & One element’s contribution is so dominant that the other’s role is almost insignificant. \\ \hline
2, 4, 6, 8 & Take the median value between the two adjacent degrees of the above comparison. \\ \hline \end{tabular}
\end{table}
Table 2: Relative comparison scale.  
When constructing the judgment matrix for drilling and blasting construction risk assessment, consistency testing is required to eliminate the influence of subjective factors on the judgment matrix. The specific testing steps include the following:  
Consistency index  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{5}\]  
where CI is the consistency index.  
Consistency ratio  
\[CR=\frac{CI}{RI} \tag{6}\]  
where CR is the consistency ratio, and RI is the average random consistency index, with specific values shown in Table 3. Generally, if CR < 0.1, the judgment matrix passes the consistency test.  
Step 4. Repeat steps 1 to 3 for each level, and combine the calculated weights to form the global weight vector W.
# Secure Localization in the Presence of Colluders in WSNs
## 6 Experimental Results  
We present in this section some numeric simulation results. We implemented our algorithms and scenarios under the OMNeT++ framework [34]. Two different set of simulations are reported hereinafter. First, we assume scenarios where the sensor nodes are uniformly deployed. Second, we assume a random deployment of nodes. We assume that a neighbourhood of over 30 active nodes creates a major interference condition and makes communications ineffective. The performance of the SCC algorithm is evaluated as follows. We measure and compare the successful detection colluders with regard to the performance and cost of the _cross-check_ (CC) algorithm presented in [16]. The CC algorithm is reproduced in our work as Algorithm 2, for the convenience of readers (see [16] and citations thereof, for the correctness and complexity analysis of Algorithm 2).  
```
1:repeat
2://sensor \(A\) requests the location of all its neighbours and computes the point of
3:// intersection \((x_{t},y_{t})\) for each triple \(t\) of nodes \(B_{i}\), \(B_{j}\), \(B_{k}\) in its neighbourhood;
4:until a consistent position is derived from the majority of triplets;
5://sensor \(A\) accepts \((x_{t},y_{t})\) as its position in the system, adds every triplet
6://(\(B_{i}\); \(B_{j}\); \(B_{k}\)) in agreement with \((x_{t},y_{t})\) to its cross-check list (CCL), and
7:// broadcasts both its location \((x_{t},y_{t})\) and its CCL to all its neighbours;
8:repeat
9://sensor \(A\) requests the CCL of its neighbours;
10:until consistency of \(A\)'s CCL and two CCLs from two different neighbours
11:for each node \(B_{i}\) in \(A\)'s neighbourhood do
12:if the position of \(B_{i}\) is consistent in all three CCLs then
13:\(B_{i}\) is labelled as a truth-teller and is added to the neighbourhood list of \(A\);
14:else
15:\(B_{i}\) is labelled as a colluder and is excluded from \(A\)'s neighbourhood list;
16:endif
17:endfor
```  
**Algorithm 2** Cross-Check (CC)
## 2 Design and Analysis
### MIMO Assessment
Similarly, at 35 GHz, the MIMO antenna achieves a gain of 7.8 dBi in contrast to 7.3 dBi for the single-element antenna. Furthermore, at 38 GHz, the MIMO antenna exhibits a gain of 13.8 dBi compared to 11.1 dBi for the single-element antenna. The higher gains of the MIMO antenna over the single-element antenna come from advantages like spatial diversity, spatial multiplexing, beamforming, interference mitigation, increased channel capacity, and frequency diversity. These features collectively enhance signal quality, spectral efficiency, and interference suppression, resulting in a superior gain performance across various frequencies. The radiation pattern analysis shown in Figure 7 depicts the MIMO antenna's radiation characteristics across frequencies as follows:  
Figure 5: (a) ECC and (b) DG versus frequency of 4-port MIMO antennas. Figure 6: Peak gain versus frequency of single-element and MIMO array antennas. Figure 7: Radiation patterns of 4-port MIMO antenna; (**a**) E-plane, (**b**) H-plane at 28 GHz, (**c**) E-plane, (**d**) H-plane at 35 GHz, (**e**) E-plane, and (**f**) H-plane at 38 GHz. 28 GHz
* E-plane: 7.7 dBi gain, main lobe: \(\theta\) = 16\({}^{\circ}\), beamwidth: 60.7\({}^{\circ}\). * H-plane: 9.8 dBi gain, main lobe: \(\theta\) = 7\({}^{\circ}\), beamwidth: 25\({}^{\circ}\). * 35 GHz
* E-plane: 6.7 dBi gain, main lobe: \(\theta\) = 42\({}^{\circ}\), beamwidth: 19.6\({}^{\circ}\). * H-plane: 5.2 dBi gain, main lobe: \(\theta\) = 19\({}^{\circ}\), beamwidth: 26\({}^{\circ}\). * 38 GHz
* E-plane: reduced gain performance. * H-plane: 13.8 dBi gain, main lobe: \(\theta\) = 26\({}^{\circ}\), beamwidth: 12\({}^{\circ}\). Frequency-dependent directivity variations are evident, particularly in beamwidth narrowing at higher frequencies. The H-plane maintains superior gain characteristics compared to the E-plane across all frequencies. Notable beam steering occurs with frequency changes (main lobe shifts from 7\({}^{\circ}\) to 26\({}^{\circ}\) in the H-plane). This pattern analysis enables precise antenna performance prediction, the identification of optimal operational frequencies, and the validation of design objectives for MIMO applications.
## 3 Experiment and Analysis
### Model Comparison  
In this section, models such as DNN, Convolutional Neural Network (CNN) [70], and Mixed Logistic Regression (MLR) [71] were integrated with MFE and applied to the same hazmat transportation dataset to serve as comparisons for GRU-DNN with MFE. ACC was selected as the performance measure. The ACCs of DNN with MFE, CNN with MFE, MLR with MFE and GRU-DNN with MFE are illustrated in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Level** & **PR (\%)** & **RE (\%)** & **F1 (\%)** \\ \hline Risk level I & 94.3 & 83.2 & 88.4 \\ Risk level II & 95.8 & 85.6 & 90.4 \\ Risk level III & 90.5 & 82.5 & 86.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction result of real-time risk levels of road hazmat transportation.  
Figure 10: ACCs of training and validation sets with 0.001 learning rate.  
As shown in Figure 11, the curves were fitted according to the results such that they became smoother and easier to interpret. The GRU-DNN with MFE converges better than the other three models. It has better performance and generalization ability. Figure 12 is the PR-RE (P-R) curve of the model of GRU-DNN with MFE. This curve completely covers the P-R curves of the other three models, indicating that the GRU-DNN with MFE offers the best performance among the four models.  
This study also applied the following models to the prediction of the real-time risk of hazmat road transportation to serve as comparisons with our model: K-Nearest Neighbor (KNN) [72], Support Vector Machine (SVM) [73], Naive Bayes (NB) [74], Decision Tree (DT) [75], and Random Forest (RF) [64]. MFE was integrated with these models as well. A performance comparison of the nine models is presented in Table 7. AUC is the area under the receiver operating characteristic (ROC) curve 7 [76]. If the ROC curve of a model encircles that of another one, the former model is definitely better than the latter one. However, AUC is more appropriate for the comparison among models if the ROCs of these models intersect with each other. AUC values often fall between 0.5 and 1. The closer the AUC gets to 1.0, the more accurately the model captures reality. When it is equal to 0.5, the model only poorly reflects reality reality.  
Figure 11: ACCs of four models in training and validation sets. (**A**) training set; (**B**) validation set.  
Figure 12: P-R curves of four models.  
Even though the ACCs of CNN, KNN and RF with MFE are slightly higher than that of GRU-DNN with MFE, the AUC of GRU-DNN is significantly higher than that of the other three models. After the analysis of ACC, AUC and F1, presented in Table 7, the performance of GRU-DNN with MFE was found to be better than that of any of the other models on the basis of the comparison in this paper.