esa-sceva/satcom-chunk-collection huggingface dataset:
# Determinants of Memory Encoding of Altruistic Messages: M-Delphi and F-DEMATEL Approach
## 7 Limitations and Future Research Directions  
This study has several limitations which future studies could address. First, the data used were collected from participants based in one country. Furthermore, the non-profit organization and television station the participants were based on are in the same region. However, cultural differences across countries might imply differences in people's perceptions of determinants of effective communication. This is mainly because communication dynamics differ across countries [84; 85]. Future studies could, therefore, examine the determinants of memory encoding in altruistic communication in other contexts. Secondly, different media platforms have different affordances [86; 87]. Thus, the determinants of memory encoding may differ depending on the platform on which they are communicated. Future studies could examine the determinants of the memory encoding of altruistic messages communicated through other platforms, such as social media. This could clarify how effectively altruism can be communicated across different platforms.  
Furthermore, the study used data collected from experts. However, the individuals in the audience are the ones who are involved in the encoding of the messages. Therefore, examining this framework using data collected from the audience could provide a richer understanding of the determinants of memory encoding of altruistic messages. Finally, the study could not examine the intervening mechanisms and indirect mechanisms through which the factors affect memory encoding. Future studies could use other approaches to examine possible moderating effects and mediating effects in the relationships between the factors and memory encoding, hence providing a more comprehensive understanding of the effects of the factors on memory encoding.
## 4 EMD and DCNN Partial Discharge Type Recognition
### Comparison with Other Algorithms  
In order to verify the effect of the model and algorithm, the random forest and EMD are compared with the DCNN algorithm, in which the former uses the random forest as the classifier. The Random Forest (RF) model is a supervised machine learning algorithm based on decision tree construction. The IMF information obtained after EMD decomposition is input into the RF model, and the recognition results are shown in Figures 16 and 17.  
Figure 15: Charts of IMF components decomposed by EMD.  
The identification of the two methods is shown in Table 5. It can be seen from Table 5 that the EMD-DCNN recognition method can better identify the three types of defects, and the average recognition rate is 95.3%. The average recognition rate of the traditional algorithm is only 89.7%. The EMD-DCNN recognition method is higher than the traditional mechanical learning algorithm, RF, in the recognition accuracy of each defect.  
Figure 16: Recognition results of EMD and DCNN.  
Figure 17: Recognition results of EMD-SVD-RF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defect Type** & \
\begin{tabular}{c} **EMD-SVD-RF** \\ **Accuracy/\%** \\ \end{tabular} &
\begin{tabular}{c} **EMD-DCNN** \\ **Accuracy/\%** \\ \end{tabular} \\ \hline Joint looseness & 82 & 96 \\ Casing fouling & 85 & 96 \\ Insulation scratch & 86 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compares the results of the two algorithms.  
SVD is a linear transformation that has a limited ability to express the characteristics of nonlinear signals. SVD can classify and recognize low-dimensional features. When dealing with high-dimensional data, training errors are easy to accumulate, resulting in distortion of training results, which cannot maintain the accuracy of recognition. In addition, SVD also needs to construct feature parameters manually, which has a great subjective influence. When the feature dimension is high, the training and inference time of RF is significantly increased, which makes the RF not ideal in scenes with high real-time requirements. In addition, the EMD-DCNN recognition method is also superior to the traditional mechanical learning algorithm, RF, in the recognition speed.  
In this paper, EMD-DCNN is used as an improved CNN deep learning network model, which can directly process the image and retain the original information of the data structure. After multi-layer processing of the network and deep extraction of features, without human interference, the deep and shallow feature information of the data is automatically extracted layer by layer, and finally, a higher recognition rate and recognition speed are obtained.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Algorithm
#### 3.4.2 Graph Generation  
Following the clustering of the sensor nodes, a graph representing the virtual sensor nodes is constructed. This graph, denoted by \(\mathbf{G}=(\mathbf{V},\mathbf{E})\), comprises the set of virtual sensor nodes \(\mathbf{V}\) and the edges E. The graph enables the computation of an optimized trajectory using a traveling salesman problem (TSP)-based approach.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 7 Hydrological Application
### Urban Drainage System Scheduling  
It is important to predict the ability of an urban catchment to respond to rainfall-generated runoff, which is essential for effective management and control of urban drainage systems. [PERSON] et al. [45] verified that the application of CML-based rainfall measurement data in the urban hydrological model can better capture the spatial and temporal dynamic distribution of rainfall, and thus better capture the temporal dynamic changes of drainage pipe flow, especially in heavy rainfall, which can better reflect the changes of outlet and peak flows. [PERSON] et al. [110; 111] studied the effects of the characteristics and locations of CMLs on the runoff forecast. The experimental results show that the quantitative precipitation estimates (QPEs) of shorter CMLs located within or near the catchment boundary reproduce the runoff dynamics better than the QPEs of longer CMLs located outside the catchment boundary.
## 2 Sound Sampling and Data Transmission
### Node Implementations  
Different types of nodes are deployed using distinct strategies. For instance, sensor and relay nodes are battery-powered, allowing for flexible and rapid deployment. At the same time, the central processing unit, responsible for sound classification tasks, relies on a direct power source due to its higher computational demands. Additionally, we enhanced the communication range and signal quality of the ESP32 modules by installing external antennas. This Section will provide a detailed overview of the deployment strategies for sensor nodes, relay nodes, the central processing unit, and the cloud server in real-world environments.
## 5 Simulation Results
### Simulation Results
#### 5.2.5 System Throughput  
System throughput is defined as the total number of data packets delivered from the source to the destination within a certain period of time. Figure 14 shows the throughput comparison between two MAC protocols with the percentage of link asymmetry. The proposed protocol improved the system throughput by 15% when the network link asymmetry is 10%. However, in 90% link asymmetry, the proposed protocol improved the system throughput by almost 10%. For 54 nodes (Figure 14b), however, the overall throughput improved by 10%. Considering all situations, COASYM-MAC improves performance over ASYM-MAC. In case of link asymmetry, COASYM-MAC uses a cooperative node to send data or changes its parents rather than sending a control packet If a node changes its parent in run time due to link asymmetry, it solves the link asymmetry problem permanently for the next time. Since it sends data without using any control packets, it increases throughput. Figure 14c shows the results of same metrics for 90 nodes which agree with the previous characteristics.  
\[\textit{System Throughput}=\frac{\text{Number of packet received}}{\text{Overall network operation time}} \tag{10}\]  
Figure 13: Average delay per packet: (**a**) for 27 nodes; (**b**) for 54 nodes; and (**c**) for 90 nodes.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 3 Proposed Method for Range Extension
### Effect of CP  
For \(y_{3}(k)\), during delay-shifting stage of the proposed method, the last part of the window, which is selected in step 1 of the proposed method, occupies the complete CP part of the OFDM symbol, and a peak with processing gain of \(20\text{log}_{10}(N_{\text{cp}})-10\text{log}_{10}(N-N_{\text{cp}})-\text{Mod} _{\text{noise}}\) dB appears at delay zero (defined by (27)). Where \(\text{Mod}_{\text{noise}}\) dB is the raise in noise due to digital modulation, e.g., 16-QAM causes a raise of \(\approx\)2.7 [36]. For \(M\) number of OFDM symbols, an additional \(10\text{log}_{10}(M)\) dB is added to CP peak. Appearance of peaks due to CP, for echoes with delay longer than OFDM symbol duration, which are exactly \(N\) samples behind the target, can be eliminated from the observations.
## 3 Results
### Responses of the SIF-IWUE Relationship to the Environmental Factors
#### 3.3.4 Effects of LAI on IWUE-SIF Correlations  
An increase in LAI may result in more loss of SIF emission due to reabsorption and scattering processes [29], which may deteriorate relationships between SIF and WUEs. Our previous studies have shown that a dense canopy tends to negatively affect SIF-GPP/\(T\) relationships at a canopy level [32; 40]. Therefore, it is important to evaluate how different LAI values affect linkage between SIF signals and IWUE. For this purpose, we calculated the SIF-IWUE correlations from hourly data under three different LAI groups: (1) low: 0 \(<\) LAI \(\leq\) 2.0, (2) medium: 2.0 \(<\) LAI \(\leq\) 4.5, and (3) high: LAI \(>\) 4.5.  
The SIF-IWUE correlations decline when LAI values are in the low and high groups (Figure 6). In the low LAI group, all the selected single SIF bands explain less than 35% of variance in IWUE for the GPR models. The SIF band combinations provide marginal improvements, and the combination of SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\) shows the correlations with IWUE with \(R^{2}\) of 0.29 and 0.45 for the LR and GPR when LAI is lower than 2.0 (Figure 6). When the LAI is the highest (LAI \(>\) 4.5), the SIF-IWUE correlations are considerably weakened, as all the single SIF bands show \(R^{2}\)\(<\) 0.04 and the use of GPR only provided very limited improvements (Figure 6). Although the combinations of SIF bands generally provide a better performance in estimating IWUE, the combination of SIF\({}_{687}\), SIF\({}_{720}\),and SIF\({}_{761}\), the best predictor, only shows \(R^{2}\) of 0.06 and 0.04 for LR and GPR, respectively (Figure 6).  
The SIF-IWUE correlations are highest when LAI values are in the medium group (Figure 6). For example, the \(R^{2}\) between SIF\({}_{761}\) and IWUE was 0.37 and 0.41 in the group for the LR and GPR model, which are higher than those when LAI are in the low and high groups (Table 1). The SIF band combinations obtain further improvements, particularly, the combination of SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\)  
Figure 6: The effect of leaf area index (LAI) on the coefficients of determination (\(R^{2}\)) between inherent water use efficiency (IWUE) and the selected single SIF bands and their combinations by using (**a**) linear regression analysis and (**b**) Gaussian processes regression, respectively. The LAI ranges are included in the legends. All data have an hourly time resolution and belong to the period between 7 a.m. and 5 p.m. local time.  
shows the best correlations with IWUE with \(R^{2}\) of 0.49 and 0.58 for the LR and GPR, respectively (Figure 6a,b).
## 3 Data Processing  
The merged TLS point cloud with overlapped scans was unified by spatial filtering to 10 mm resolution in the preprocessing data phase with the use of Trimble Realworks(r) software. To verify the quality of the scanned point cloud of RK, CloudCompare was used (function Cloud to Cloud was applied, with local modeling enabled (Table Local modeling, Local model option 2D1/2 Triangulation)). RMSD 2 mm with a maximum 4 mm characterizes a sufficient match of TS and TLS measurements. The RK shell has a cylindrical shape, so this comparison compares by matching the shell radius to the match of its position in the Y, Z axes. Therefore, we consider TLS data to be correctly localized for this study. The general equation for the RMSD is following:  
\[RMSD=\sqrt{\frac{\frac{\sum\limits_{1}^{n}(r_{i}-r)^{2}}{n}}} \tag{4}\]  
where \(r_{i}\) are the determined radiuses, and \(r\) is the nominal, or approximate radius of the RK.
## 5 Experimental Study  
The RDH scheme can secure both the cover image and the secret information. To validate the proposed \(MPSA\) scheme, we implemented the proposed algorithms using Matlab R2019a. The standard image dataset available from the University of Southern California, which is popularly known as the USC-SIPI images [39], and the BOWS-2 [40] database are used for experimentation. All the images were prepared into a \(512\times 512\) pixel image, before being given as input to the proposed \(MPSA\) system. The secret information used for this study is a series of pseudo-random binary bits generated using a pseudo-random generator, and the length of the bit sequence is fine-tuned based on the number of blocks used during data hiding. The following parameters are considered for validation of the newly introduced scheme:  
1. Embedding rate (ER)
2. Bit error rate (BER)
3. Peak signal-to-noise ratio (PSNR)
4. Structural similarity index (SSIM)
5. Entropy
6. Histogram analysis
7. Image recovery analysis
8. Computation time  
In the next sub-sections, we discuss all the above-mentioned results in detail.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## Appendix B  
Given a unit vector \(\mathbf{u}=\left(u_{x},u_{y},u_{z}\right)\), where \(u_{x}^{2}+u_{y}^{2}+u_{z}^{2}=1\), the matrix [28] of a rotation by an angle of \(\theta\) about an axis in the direction of \(\mathbf{u}\) is  
\[R=\left[\begin{array}{ccc}\cos\theta+u_{x}^{2}(1-\cos\theta)&u_{x}u_{y}(1- \cos\theta)-u_{z}\sin\theta&u_{x}u_{z}(1-\cos\theta)+u_{y}\sin\theta\\ u_{y}u_{x}(1-\cos\theta)+u_{z}\sin\theta&\cos\theta+u_{y}^{2}(1-\cos\theta)&u_ {y}u_{z}(1-\cos\theta)-u_{x}\sin\theta\\ u_{z}u_{x}(1-\cos\theta)-u_{y}\sin\theta&u_{z}u_{y}(1-\cos\theta)+u_{x}\sin \theta&\cos\theta+u_{z}^{2}(1-\cos\theta)\end{array}\right] \tag{12}\]  
When plane B is rotated around the rotation axis \(\mathbf{u}\) with the rotation angle \(\theta\), as shown in Figure 11, the original point \(P_{1}=(x_{1},y_{1},z_{1})\) on plane B will be rotated to the target point \(P_{2}=(x_{2},y_{2},z_{2})\) on the fixed plane A. Based on Equation (12), we can derive that  
\[{P_{2}}^{T}={R{P_{1}}^{T}} \tag{13}\]  
Now considering the specific situation, point \(P_{1}\) represents a bolt hole on the base plate, and point \(P_{2}\) represents the corresponding bolt hole on the frame. Ignoring the x and y directional error, the necessary and sufficient condition of the contact between part A and B is  
\[z_{2}=\left[\begin{array}{cc}u_{z}u_{x}(1-\cos\theta)-u_{y}\sin\theta&u_{z}u_ {y}(1-\cos\theta)+u_{x}\sin\theta&\cos\theta+u_{z}^{2}(1-\cos\theta)\end{array} \right]\cdot{P_{1}}^{T} \tag{14}\]  
where the coordinate \(z_{2}\) is determined already since the frame is fixed, and the coordinate \(P_{1}\) is known well in Step 1.  
The rotation angle \(\theta\) for assembly can be then determined by solving Equation (14).  
For each bolt hole on part B, one can calculate the corresponding rotation angle for contact, the minimum rotation angle determines the first contact point.
# RFID Technology as a Low-Cost and Passive Way to Digitize Industrial Analog Indicators
## 3 Materials and Methods
### Design and Tuning of RFID Tags
#### 3.2.1 Analog Manometer Digitization Concept  
Analog manometers are widely used in industry to measure various physical parameters. Despite the diverse applications for which these manometers are designed, they all have similar mechanical mechanisms. The basic concept of this study was to implant small RFID tags on a single-side printed circuit board (PCB) FR4 substrate in analog manometers. The FR4 layer is placed on the manometer dial layer and covered with a metal cover to rotate with the manometer's pointer. The impact of cover addition weight on the manometer performance was neutralized by two offset adjustment screws inside the manometer. A circular opening in line with the pointer direction in metallic cover let EM waves pass to turn on the red or green zone RFID tag. A typical commercial RFID reader determines the manometer zone (green or red) by reading the tag's electronic product code (EPC) numbers.
### Research Questions  
This study aims to answer the following research questions (RQs):  
RQ1: How does a hybrid CNN-TCN architecture improve automatic modulation classification accuracy compared to standalone CNN or TCN models?  
RQ2: What are the contributions of CNN and TCN components in handling local signal patterns and long-term temporal dependencies, respectively, in dynamic wireless environments?  
RQ3: How robust is the proposed CNN-TCN model under varying signal-to-noise ratio (SNR) conditions, particularly in low-SNR scenarios?RQ4: What is the computational efficiency of the CNN-TCN model in real-time signal processing applications, and how does it compare to other deep learning-based AMC methods?  
RQ5: How does the proposed model perform in comparison to state-of-the-art AMC techniques (e.g., CNN, ResNet, LSTM, or CLDNN) in terms of classification accuracy and robustness?
# Traffic Noise Reduction Strategy in a Large City and an Analysis of Its Effect
###### Abstract
To achieve this, noise mapping can serve as a useful tool for evaluating different noise mitigation strategies or for proposing optimized soundscape designs to reduce the impact of noises emitted by major sources [25]. Nowadays, noise barriers are globally applied in residential areas lying adjacent to major roads, such as highways or expressways, to reduce the traffic noise emissions [35]. However, there are few studies using strategic noise mapping to characterize the performance of noise barriers implemented in the areas in megacities of developing countries that suffer from noise. Hence, in the present study, a noise map of Panyu District, which is one of the central parts of Guangzhou, China, was first constructed using a commercial numerical platform based on field traffic flow measurements and GIS. Then, noise barriers were suggested and applied along the traffic arteries in the areas exposed to high traffic noise levels in the numerical model. The qualities of the sound environment before and after the implementation of noise barriers were finally compared, and the effects of the noise barriers were discussed. The motivation of this study is to provide scientific references for the consideration of sound environment quality in the urban and urban renewal plans of Guangzhou, which has a history of more than 28 centuries and a total population of about 18.8 million. According to its 14 th Five Year Plan (2021-2025), Guangzhou will be undergoing a mega urban renewal plan, which is expected to have large infrastructural projects, such as the construction of new expressways and high-speed railways and the widening of the main roads in its core areas, in the coming years. The future traffic noise nuisance will potentially be a big issue if it is not well considered before the start of these infrastructural projects.
## 2 Materials and Methods
### Improvement of the Optimized Structures Gain  
Although the bandwidth was increased along the studies, a final modification is made in the structure aimed at the gain increase. Thus, in Figure 14, the new structure which has 2 microstrip lines along the directors was created. The structure has the characteristics of the optimum determined across all the studies so far; thus, it is made on a Rogers RO3003 substrate of 0.51 mm thickness, with 5 directors with an increased length and a grounding of 8 mm. The measured S parameters in the 1-3 GHz frequency interval are presented in Figure 15a. The results obtained through numerical modeling using the software program HFSS are in accordance with the measured ones obtained using the NanoVNA SAA-2N, but it can be observed that the antenna is functioning immediately after the 3 GHz limit. Thus, in HFSS the frequency sweep was performed between 1 and 5 GHz, and it was seen that indeed, the antenna is functioning on two bandwidths which together give us a 19% bandwidth (Figure 15b).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**2 mm** & **4 mm** & **6 mm** & **8 mm** & **10 mm** \\ \hline
18.32\% & 18.99\% & 19.66\% & 23.31\% & 17.96\% \\ \hline \end{tabular}
\end{table}
Table 6: The bandwidths obtained for the grounding variation.  
Figure 14: The optimized structure: (**a**) modeled with HFSS; and (**b**) constructed on a Rogers RO3003 dielectric.  
The gain of the structure increased (Figure 16), reaching a value of 4.84, thus increasing the directivity of the original structure by 30%. The dielectric at this thickness is also flexible and thus easy to integrate in close or other systems used for communication at this frequency, but the user must be careful not to fold it because the characteristics of the antenna could be easily changed.  
Figure 16: Gain of the optimized structure.  
Figure 15: S11 values obtained for the optimized structure: (**a**) measured; (**b**) obtained through numerical analysis.