esa-sceva/satcom-chunk-collection huggingface dataset:
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion
### Replacing the ReLU Activation Function  
In this study, the ReLU activation function utilized in the basic unit, downsampling unit, Conv1 structure layer, and Conv5 structure layer was substituted with the PReLU activation function. The experimental findings indicated that the PReLU activation function outperformed the ReLU function in our network model. Figure 9 illustrates the superior performance of the PReLU function in terms of accuracy, demonstrating a consistent and rapid improvement. This suggested that the PReLU function effectively mitigated the issues related to nonupdatable weights caused by inputs in the hard saturation zone, thereby more efficiently preventing \"neuron death\". The analysis of the results presented in Table 2 suggested that the PReLU activation function achieved higher accuracy, precision, and recall rates of 92.42%, 92.32%, and 92.25%, respectively. These rates were 1.06%, 0.95%, and 1.11% higher than those achieved by the ReLU activation function, respectively. These findings further validated the suitability of the PReLU activation function in our model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activation Function** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline ReLU & 0.9136 & 0.9137 & 0.9114 & 0.9125 \\ PReLU & 0.9242 & 0.9232 & 0.9225 & 0.9229 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of activation functions based on experimental results.  
Figure 9: Accuracy comparison.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Problem Definition and Formulation  
#### 3.2.1 Environmental Modeling  
In this study, three types of environments were modeled. The models were created based on the environment scale and the presence of obstacles and no-fly zones. All three scenarios were generated based on real-world environments in a suburban environment in Bangi, Malaysia. Table 2 lists the considered parameters in modeling the environments. The considered scenarios are expanded from small-scale scenario (Scenario I) to large-scale scenario (Scenario II) and, finally, to Scenario III, which represents a large-scale scenario with more realistic assumptions related to BVLOS UAV operations. The terrain is considered flat to reduce the computation complexity and maintain this paper's main objective. However, the terrain also can be simulated by numerical methods, where the train profile can be presented in the form of a matrix, in which matrix elements contain the terrain elevation in their respective coordinates.  
Figure 4 depicts a simulated model for Scenario III, where the partial hemispheric represents the reliable cellular coverage zone, the red cylinder represents the obstacles (such as high-rise buildings, hills, water reservoirs, etc.), and the violet cylinder represents the no-fly zone.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 3 System Model
### Traffic Model  
In a dense urban vehicle environment, vehicles at the same moment tend to travel at similar speeds. However, as time changes, changes in traffic conditions will affect the number of vehicles on the lane; for example, changes in traffic lights will lead to changes in vehicle density. In this paper, we assume that there exists a set of continuous time periods set, denoted as \(T=\{T_{1},\cdots,T_{x},\cdots,T_{x}\}\), and the vehicles in each time period travel at the same uniform speed and the vehicle users in different time periods travel at different speeds. Use \(\left\{n_{1}^{t},\cdots,n_{x}^{t},\cdots,n_{X}^{t}\right\}\) and \(\left\{n_{1}^{r},\cdots,n_{x}^{r},\cdots,n_{X}^{r}\right\}\) to denote the number of vehicles entering the roadway from the left and right, respectively, at each time period. Then, the total number of vehicle users coming in from the left side of the roadway is \(N_{l}=\sum\limits_{x=1}^{X}n_{x}^{l}\) and the total number of vehicle users coming in from the right side of the roadway is \(N_{r}=\sum\limits_{x=1}^{X}n_{x}^{r}\).  
For the movement of the UAV, the road is divided into several rectangular blocks and the side length of each rectangular block is the coverage diameter of the UAV. Taking each time period Tx as a trajectory optimization cycle, the UAV stays in a block during a trajectory optimization cycle to serve the vehicle users in that block and selects an adjacent block at the end of the current trajectory optimization cycle to fly to that block in the next cycle, i.e., whenever the vehicle density changes, the UAV's position also changes. Choosing different flight trajectories results in different gains and time delays. It is worth noting that the caching decision of frequent node replacement will incur a large overhead,  
Figure 1: A content caching system comprising a CP and an MNO with cache-enabled RSUs and vehicular users.  
so the optimization of caching decision is a long-time optimization, while the UAV has mobility and needs to plan the flight trajectory for each time slot for it, which is a short-time optimization; the relationship between the two is shown in Figure 2.
## 3 Experimental Results
### Varying Infrastructure Density Scenario  
A comparison of the results presented in the previous subsection with the results of the study published in [16] has indicated that the infrastructure density might have a rather remarkable impact on the performance of C-V2X technologies, especially the 4G-based one, in the investigated context. Therefore, we present here the simulation results obtained using the same traffic scenario and communication technology-specific settings as applied in the previous section, i.e., the fixed infrastructure density scenario, but with a variable number of cells, i.e., 4 and 8, in order to shed light on this issue. Due to space constraints, the results obtained for the lowest and highest simulated value of the traffic intensity, i.e., 250 vehicles per hour and 1500 vehicles per hour, are only reported in the following subsections. Regardless, the trends remain the same across all the traffic intensity values.
## 5 Discussion
### Interpretation of the Results  
The experimental findings demonstrate that FP-Growth consistently outperforms Apriori in terms of the execution time and number of rules generated, particularly as minimum support thresholds decrease. This efficiency stems from FP-Growth's ability to bypass exhaustive candidate generation and leverage a compact tree-based structure. In contrast, Apriori's performance degrades sharply in more permissive configurations due to its iterative scans and exponential growth in candidate itemsets.  
Statistical tests further support these observations. The one-way ANOVA confirmed that FP-Growth execution times vary significantly across different support levels, reflecting its sensitivity to rule complexity. The Kruskal-Wallis test revealed similar trends for Apriori, although its performance degradation was more severe. The Pearson correlation and regression models demonstrated a strong linear relationship between the rule counts of the two algorithms, suggesting that FP-Growth scales more aggressively when rule thresholds are relaxed.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
###### Abstract
This paper details an experiment where the researched subjects were presented in a visual stimuli in the form of a single colour image on the screen followed by a state of relaxation. The output EEG brain signals were measured and classified to detect a state of inactivity and an exposure to a visual stimulus state. The classifiers used were SVM and random forest. The result of the classification of the state of inactivity and the state of exposure to the image stimulus was on average 94.6%. A precursor work to this research was carried out in 2017 [8]. This work was aimed at the recognition of the commands from the EEG signal, however only low level of accuracy was achieved. The data set of EEG signals was compiled using 20 subjects who pronounced 50 different commands and a cross-subject Hidden Markov model was trained to recognize the spoken commands. The best recognition accuracy was more than 5%, which is more than the chance level, but many false alarms occurred during the recognition. However, this work had paved the way to the idea of improving the speech recognition from EEG signals by adding a speech detector that can increase the overall accuracy. The above work was followed by a research summarised in Reference [9], This research was focused on the speech activity detection from EEG using the Feed-Forward Neural network with a single-subject model. The research has achieved a considerable improvement in the detection accuracy averaging at 77% for F1 results.
# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
## 4 Results and Analysis
### Physical Parameters (Unchanging Parameters)  
Length per junction (Figure 3): the green areas are DMA 6 (Al Faya) and DMA 4 (Al Barashi). They are rural areas with less population density, and the WDN in these areas has more continuous pipelines with fewer junctions compared to the old dense areas that are highlighted in orange (DMA 10 (Al Ghafia)) and red (DMA 8 (Al Butina) and DMA 7 (Al Guwair)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Score** & **1** & **2** & **3** & **4** & **5** \\ \hline Category & V. Low & Low & Med & High & V. High \\ \hline Color & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vulnerability maps color scheme.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Weight** \\ \hline Flow drops (Q in m\({}^{3}\)) & 0.25 \\ Pressure drops (P in bar) & 0.25 \\ Chlorine drops (Chl in ppm) & 0.20 \\ No of fittings per length (FL) & 0.14 \\ Length per junction (LJ in m) & 0.08 \\ Friction factor (f) & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters assigned weights.  
Fittings per length (Figure 4): the highlighted red area represents DMA 7 (Al Guwair), an old area with old souks with a high number of fittings and customer connections compared to the other areas in the WDN. The orange area represents DMA 1 (Al Rahmaniya 1), another old area with high fittings and customer connections. The yellow area represents DMA 4 (Al Barashi) and DMA 5 (Al Maysaloon), areas with average fittings and customer connections. The green area represents DMA 6 (Al Faya), DMA 9 (Al Sabkha), and DMA 10 (Al Ghafia), which are areas with low fittings and customer connections.  
The friction factor (Figure 5) represents both pipe diameter and pipe material combined. DMA 8 (Al Butina) has the lowest minimum and maximum, implying that its pipes' pressure experienced the lowest loss. DMA 7 (Al Guwair) had the highest minimum and maximum, implying that its pipes experienced increased pressure loss.
# Hybrid Resonant Converter with Three Half-Bridge Legs for Wide Voltage Operation
## 3 Circuit Characteristics
According to Equation (5), the DC load voltage \(V_{o}\) can be further rewritten as  
\[V_{o}=V_{in}/m\sqrt{\left[1+\frac{f_{n}^{2}-1}{l_{n}f_{n}^{2}}\right]^{2}+x^ {2}\left(f_{n}-\frac{1}{f_{n}}\right)^{2}}, \tag{6}\]  
where \(m=n_{1}\), if \(50~{}\mathrm{V}\leq V_{in}<100~{}\mathrm{V}\); \(2n_{1}\), if \(100~{}\mathrm{V}\leq V_{in}<200~{}\mathrm{V}\); or \(4n_{1}\), if \(200~{}\mathrm{V}\leq V_{in}<400~{}\mathrm{V}\). On the basis of the actual input voltage value, the switching states of \(Q_{1}\)-\(Q_{6}\) and \(S\) can be properly controlled. From Equation (6), the developed circuit has high voltage gain under low input voltage range. On the other hand, the converter has low voltage gain under high input voltage range.
# Quantum Modular Adder over \(GF(2^{n}-1)\) without Saving the Final Carry
## 1 Introduction
Using the RCA, [PERSON]'s quantum adder also has the carry propagation problem, which has to use additional qubits for carrying or saving all carries--the same problem as the RCA [12]. These existing adders support modular addition over the Galois Field \(GF(2^{n})\). However, since the Galois Field \(GF(2^{n}-1)\) contains special numbers that play an important role in a public cryptographic system, there is a need to develop an efficient modular addition over \(GF(2^{n}-1)\). In particular, the \(GF(521)\) is one of the recommended numbers for elliptic curve cryptography (ECC), the \(GF(31)\) is for multivariate quadratic-based post-quantum cryptography (MQ-PQC), such as Rainbow and MQDSS, and large prime numbers are for RSA [25; 26]. Since the speed of the adder affects the performance and analysis of these public key cryptographic algorithms with specific secure parameters, it is essential to design an optimized modular adder for particular numbers, such as the \(GF(31)\) for improving the performance of and analyzing MQ-PQC. In this paper, we propose a new quantum modular adder over \(GF(2^{n}-1)\) without saving the final carry. The main contributions of this paper are as follows:  
* We propose a lightweight quantum modular adder over \(GF(2^{n}-1)\) using one full adder based on RCA and one carry-truncated adder. In contrast, the general modular adder usually uses multiple dividers or multipliers. The final carry in the carry-truncated adder affects the decision to add one or not for completing the modular operation and is not included in the results. * We designed the algorithm of the proposed quantum modular adder as a quantum circuit, called a referenced quantum circuit. Then, we optimized the circuit as a more efficient circuit, called an optimized quantum circuit, by an equivalence rule of quantum circuits. * We simulated the referenced quantum circuit and the optimized quantum circuit to add two numbers over \(GF(2^{n}-1)\) via IBM's ProjectQ when \(n=5\) with 16 qubits, and compared them to other RCA-based quantum modular adders. The organization of this paper is as follows. Section 2 introduces the representative quantum modular adder based on the RCA; Section 3 describes the quantum circuit of the proposed quantum modular adder over \(GF(2^{n}-1)\) in quantum computing; Section 4 presents the analyzed results in terms of the number of qubits, the number of gates, and the depth, and compares the differences from the existing quantum modular adder. Finally, Section 5 presents our conclusions.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
###### Abstract  
In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.  
oil spill; SVM; real-time monitoring; shipborne radar; remote sensing; image processing; GIS +
Footnote †: journal:  
_Article_  
Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussions  
### Physico-Chemical and Morphological Characterization  
Figure 2 presents the FTIR spectra of liposomes loaded and unloaded with azelaic acid, along with the FTIR spectrum of powder azelaic acid (AA).  
Azelaic acid possesses two carboxylate groups with the fingerprints at 724, 929, 1200, 1460, 1705 (very strong), and a pair at 2865/2962 cm\({}^{-1}\), with similar FTIR peaks reported by other studies [6; 35]. The fingerprints of phosphatidylcholine-based liposomes are visible as prominent bands of phosphate groups P-O-C asymmetric and symmetric stretching bands near 1200 and 1064 cm\({}^{-1}\), and the (CH3)3N stretching band at about 970 cm\({}^{-1}\), while the band at 1720 cm\({}^{-1}\) corresponds to a strong C=O vibration due to the ester groups present in the phospholipid molecule. In the high wavenumber region, CH\({}_{2}\) symmetric and asymmetric stretching vibrations are visible at 2860 and 2925 cm\({}^{-1}\)[36; 37]. The loaded liposomes preserve the characteristic features of AA and blank liposomes, but some peaks are shifted or broadened, such as the 1700 cm\({}^{-1}\) and 1064 cm\({}^{-1}\) ones, which also decreased in intensity. These findings demonstrate the successful encapsulation of azelaic acid in phosphatidylcholine-based liposomes.
## 4 Seismic Performance of a Masonry Structure with RC Frames on the First Story with CFST Dampers
### Result Analysis of Three Structures JG-1, JG-2, and JG-3
#### 4.2.5 Energy Dissipation Capacity Analysis of Three Structures JG-1, JG-2, and JG-3  
Investigating the seismic energy input and dissipation of structures under the action of seismic waves is conducive to understanding the energy dissipation and damping effect of CFST ductile columns in masonry structures with RC frames on the first story.  
The nonlinear dynamic response equation of the structure under earthquake action is as follows:  
\[Mx^{\prime\prime}+Cx^{\prime}+F_{s}=-Mx_{g}^{\prime\prime} \tag{16}\]  
Left multiplication of both sides of Equation (16) yields:  
\[dx^{T}=(dx/dt)^{T}dt=\big{(}x^{\prime}\big{)}^{T}dt\]  
The following energy balance equation can be obtained after integrating the time in the whole duration \(t_{0}\) of the earthquake:  
\[\begin{array}{l}\frac{1}{2}(x^{\prime})^{T}Mx^{\prime}+\int_{0}^{t_{0}}(x^{ \prime})^{T}Cx^{\prime}dt+\\ \int_{0}^{t_{0}}(x^{\prime})^{T}F_{s}dt=-\int_{0}^{t_{0}}(x^{\prime})^{T}Mx_{ g}^{\prime\prime}dt\end{array} \tag{17}\]  
For Equation (17), the first part on the left is the kinetic energy \(E_{K}\) during the earthquake; the second part represents the energy \(E_{C}\) dissipated by the damping of the structure itself and the energy \(E_{D}\) dissipated by the energy dissipation device; and the third part is the accumulated plastic deformation energy stored by the structural frame and the energy dissipation device and the elastic deformation energy during the earthquake. The right side of the above equation is the total energy \(E_{I}\) of the seismic wave input to the structure during the entire seismic action time. Seismic energy input and dissipation can be expressed as:  
\[E_{I}=E_{K}+E_{S}+E_{H}+E_{C}+E_{D} \tag{18}\]  
where \(E_{I}\) is the total seismic energy input to the structure; \(E_{K}\) is the kinetic energy of the structure; \(E_{S}\) is the elastic deformation energy of the structure; \(E_{H}\) is the hysteretic energy dissipation of the structure; \(E_{C}\) is the damping energy dissipation of the structure itself; \(E_{D}\) is the energy dissipated by the energy dissipation device.  
As can be seen from Figure 22, the energy time-history curves of the three structures first increase and then tend to be stable with the increase in time. From the numerical point of view, the energy dissipated by the CFST dampers in JG-1 is about 1/4 of the total input energy. The energy dissipation device yields earlier than the frame column, which takes the lead in dissipating the earthquake energy and plays an excellent role in energy dissipation. Compared with the other two structures, the energy dissipation capacity of the masonry structure with RC frames on the first story with CFST dampers is significantly increased by 1.25-1.5 times. The CFST dampers can dissipate more seismic energy, thus protecting the main structure from serious damage and improving the seismic performance of the building.
## 5 False Call Analysis
#### Trade-Off between PFP and POD  
From the developed approach, it is possible to analyze the trade-off between PFP and POD. To introduce the novel approach, a single probability point is analyzed. Here, the 90% probability is used and drawn to intercept POD and confidence curves (see Figure 11). At the point of intersection, the POD, PFP, decision threshold, and the process parameter (here: contrast values) are known. These values are considered for every point on the drawn 90% probability line. A graph depicting the relationship between the POD, PFP, DT, and contrast is shown in Figure 14.  
The Figure 14 makes it possible to visualize the relationship between the POD, PFP, DT, and process parameter, which is not possible using the ROC curve. To analyze other probabilities required for evaluating the specific points, as is performed for the 90% probability, the introduced method presents a novel and significant approach to concurrently examine all properties affecting the classification results.
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 5 Results and Discussion
Together, these findingsprovide an important insight, namely that pre-training the model for longer training steps is not necessary to achieve optimal performance. Finally, the results reported in Table 11 show that the within-task adaptation approach has a positive impact on the final results of the emotion detection task. Accordingly, pre-training the QARiB language model with unlabeled emotion datasets and fine-tuning it with labelled emotion datasets improves emotion detection task performance. In addition, further pre-training of the model for longer training steps is unnecessary to get the highest performance. Exp-II: Experiment to Investigate the Influence of the Cross-Task Adaptation Approach on Sentiment and Emotion Classification Performance  
Table 12 summarises the results of fine-tuning the BERT-base-QaRiB, QE3, and QE6 models using the sentiment datasets SS2030, Twitter-AB, 40k-Tweets, ArSentiment, and MASC. Table 13 shows the results of fine-tuning the BERT-base-QaRiB, QST, QSR, and QSRT models using EATD and ExaAEC datasets. The results are discussed, compared, and analysed specifically based on the macro-F1 score for the partition of the test set. In addition, the results of each dataset of the original studies [75, 76, 77, 78, 79, 80] are presented in Tables 12 and 13. The results that showed an improvement over those obtained by the baseline model (i.e., BERT-base-QaRiB model) are highlighted in bold. The results that are in bold and underlined represent the highest results that have been obtained for each dataset and according to which model. In total, 16 experiments were carried out using various sentiment and emotion datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline Datasets & Models & Precision & Recall & Accuracy & Macro-F1 \\ \hline \multirow{5}{*}{EATD} & SVM [80] & 69.67\% & 69.04\% & - & 68.52\% \\  & BERT-base-QaRiB & 85.31\% & 86.47\% & 85.64\% & 85.46\% \\  & QST & 91.58\% & 89.92\% & 91.09\% & **90.18\%** \\  & QSR & 88.18\% & 88.89\% & 88.61\% & **88.27\%** \\  & QSRT & 89.74\% & 90.26\% & 90.10\% & **89.78\%** \\ \hline \multirow{5}{*}{ExaAEC} & BERT-base-QaRiB & 65.87\% & 62.84\% & 74.26\% & 63.81\% \\  & QST & 67.98\% & 63.58\% & 76.79\% & **65.05\%** \\ \cline{1-1}  & QSR & 67.19\% & 63.83\% & 75.53\% & **64.98\%** \\ \cline{1-1}  & QSRT & 71.33\% & 64.25\% & 75.74\% & **64.38\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of fine-tuning sentiment models on emotion datasets (all-metrics).