esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The ABM presented in this research will use mathematical epidemiology to determine the states of agents in each slice of time and choose the maximally connected component (MCC) as the measure to evaluate the connectivity robustness of WSN. The SEIRS-F ABM proposed in this study considers system characteristics such as the reliability of sensor nodes, malware, topology, environment, and maintenance. The main contributions of this work are listed as follows. * We propose a cross-domain ABM. The model integrates the physical domain (component failure) and information domain (malware propagation) and can model the cascading failures between the physical and information domains. * We consider a cross-domain description and classification of agents, which includes not only sensors but also other aspects such as malware, network topology, environment, and maintenance. * We carry out a series of simulations based on NetLogo with different scenarios and topologies. Scenarios include military, industry, and Smart Home scenarios. Topologies include star, lattice-2d, random, and complex networks. The real-time display of the discrete states of the node and the MCC at each step of time \(t\) is shown. * We compare the robustness of each scenario and topology based on the simulation results. We also compared with an existing model; the results show that there is a significant difference in the robustness of the model considering malware propagation compared to the control model. Noting that malware is already one of the important factors affecting WSN reliability, our model is more realistic. The rest of the paper is organized as follows. Section 2 gives an overview of recent research in the field of component reliability and mathematical models that have been proposed to model malware propagation in WSN. In Section 3, the SEIRS-F agent-based model to simulate node failure and malware propagation in WSN is presented. For simulations, the model parameters obtained according to the application scenarios are given in Section 4. Section 5 discusses the simulation results. Finally, in Section 6, the conclusions are presented.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 6 Results and Discussion
### Tuning the Dropout Relay  
The dropout relay is a crucial mechanism in preventing the over-complexity of neural networks by regularising it through approximation. It allows for parallel exploration of diverse network architectures during the training process, thereby enhancing the general-isability of the model. To the best of our knowledge, previous studies on static malware detection using deep learning did not explicitly consider the impact of dropout-relay tuning on model performance. Our empirical experimentation found that a range of values between 0.1 and 0.5 for the dropout relay resulted in an optimal increase of 1 to 3% in model accuracy. However, exceeding this range could decrease the accuracy, as evidenced by a drop from 93% accuracy to 88% when the value was set to 0.6. The placement of the dropout relay within the hidden layers also plays a significant role in determining the performance of the network, as noted by [PERSON] and [PERSON] in their studies [3; 13]. Thus, our study sheds light on the importance of dropout-relay tuning for improving the performance of deep learning models for static malware detection.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 2 Basic Theory
### Range Estimation by Sound Intensity of The Pressure Difference
#### 2.2.1 Radial Velocity Derivation  
According to the normal wave model theory, the sound pressure at a certain receiving depth \(z\) and range \(r\) in the sound field can be expressed as [20]:  
\[p(r,z)  
Thus, Equation (10) can be further expressed as:  
\[I_{\Delta}(R,\Delta r)=|A|^{2}\sum_{m}\sum_{n}A_{m}A_{n}\Big{[}\text{cos}\Big{(} \overline{k}_{r}\Delta r\Big{)}\Big{]}\cdot\text{cos}[(k_{m}-k_{m})R] \tag{11}\]  
where the range interval is also expressed by \(\Delta r(t)=v_{s}(t)\Delta t\), and \(v_{s}(t)\) stands for the radial velocity of the moving target varying from time \(t\). From Equation (10), we find that the interference pattern is mainly determined by the term \(\text{cos}\Big{(}\overline{k}_{r}v_{s}(t)\Delta t\Big{)}\). When meeting the following relationship:  
\[\overline{k}_{r}v_{s}(t)\Delta t=2n\pi \tag{12}\]  
\(I_{\Delta}(R,\Delta r)\) will achieve the maximum.  
The sound intensity of the pressure difference can be calculated in another form. From Equation (9), we find that the intensity can be described as:  
\[\begin{array}{ll}I_{\Delta}(R,\Delta r)&=\big{[}p(r+\Delta r)-p(r)\big{]} \cdot\big{[}p(r+\Delta r)-p(r)\big{]}^{*}\\ &=I_{r}+I_{r+\Delta r}-p(r+\Delta r)\cdot p(r)^{*}-p(r+\Delta r)^{*}\cdot p(r) \end{array} \tag{13}\]  
where \(I_{r}\) and \(I_{r+\Delta r}\) stand for the intensity of \(p(r)\) and \(p(r+\Delta r)\), respectively. The real part of the cross term \(p(r+\Delta r)^{*}\cdot p(r)\) has the same interference pattern as Equation (11) [7]. Thus, we can conclude the following relationship:  
\[\text{Re}[p(r+\Delta r)^{*}\cdot p(r)]\propto\text{cos}\Big{(}\overline{k}_{r} v_{s}(t)\Delta t\Big{)} \tag{14}\]  
The average wave number \(\overline{k}_{r}\) is related to the frequency \(f\) and satisfies the expression:  
\[\overline{k_{r}(f)}=2\pi f/\overline{v_{p}} \tag{15}\]  
where \(\overline{v_{p}}\) represents the average phase speed. In this paper, we only consider the single-frequency condition. Substituting Equation (15) into Equation (12), we obtain the radial velocity of the moving target as:  
\[v_{s}(t)=\frac{\overline{v_{p}}}{d_{t}\cdot f} \tag{16}\]  
where \(d_{t}\) is the peak interval in the \((\Delta t,t)\) plane and can be obtained from the interference fringes. Generally speaking, \(\overline{v_{p}}\) approximates the speed of the sound field.
## 4 Implementation and Deployment
### Software Modules and Prototyping
Whenever the routes have to be updated due to modifications of the status of the network, EGC reads the position of each vehicle in the fleet and re-arranges a new instance of the pickup and delivery with time windows model by incorporating the occurred changes into the initial model. In particular, in this case, a new complete graph is built with as many vertices as current vehicle positions, and without considering a depot vertex. Other vertices in the graph are the locations of users not yet visited. Distances between vehicles and users' locations are computed from scratch with the [PERSON]'s algorithm implementation of the SUMO module. New routes are then distributed to the vehicles via the broker system.
# Assessment of Road Noise Pollution in Urban Residential Areas--A Case Study in Pitesti, Romania
## 2 Materials and Methods
The percentiles of the noise level \(\mathrm{L_{10}}\) [dB], \(\mathrm{L_{50}}\) [dB], \(\mathrm{L_{90}}\) [dB] are physical indicators and shows the sound levels exceeded by 10%, 50% and 90% of the total measurement time, so these indicators are of statistical type. Figure 2: The sound-level meter installation and use for measuring noise in the Maior Sontu intersection: (**a**) measurement characteristics particularization; (**b**) road noise measurement. The noise climate \(c\) characterizes the change in sound level and is defined as the difference between 10% and 90% percentiles of the sound level:  
\[c=\mathrm{L}_{10}-\mathrm{L}_{90}\;\mathrm{[dB]}. \tag{1}\]  
The psychophysical index of the level of noise pollution \(L_{NP}\) expresses the degree of recognition in relation to the subjective reaction of a person to noise in each period, based on 3 values of the noise level (\(\mathrm{L}_{10}\), \(\mathrm{L}_{50}\), \(\mathrm{L}_{90}\)), or based on 50% noise level (average noise level), \(\mathrm{L}_{50}\), and the noise climate already calculated:  
\[L_{NP}=\mathrm{L}_{50}+c+\frac{c^{2}}{60}\;\mathrm{[dB]}. \tag{2}\]  
The psychophysical index of traffic noise \(TNI\) [dB(A)] expresses the discomfort degree caused by random noise and is determined statistically based on the distribution of the measured noise level, with a certain sampling frequency, in a certain period, with the following calculation relation [42]:  
\[TNI=4\cdot(\mathrm{L}_{10}-\mathrm{L}_{90})+\mathrm{L}_{90}-30. \tag{3}\]  
It should be noted that the sound climate, which characterizes the variability of the sound level, has a significant weight in the calculation (since noise with a significant change in level is more disturbing than stationary noise). The psychophysical index \(L_{eq}\) [dB(A)], which represents the evaluated average energy (weight A) of the sound level in each period, is calculated from the following context:  
\[L_{eq}=10\frac{q}{3}\Bigg{|}log\frac{1}{T}\sum_{i=1}^{\mathrm{k}}10^{0.3L_{i} \frac{t_{i}}{i}}\Bigg{|}, \tag{4}\]  
where: \(T=\sum_{i=1}^{\mathrm{k}}t_{i}\)-duration of a complete recording (10 min); \(t_{i}\), \(L_{i}\) (\(i=1,\,2,\,\ldots,\,n\))-time intervals (duration for the noise level \(L_{i}\), which means, in fact, the frequency of this level), respectively, noise levels (in fact, values that correspond to half time intervals); \(k\)-number of intervals used for histograms; \(q\)-weighting constant, \(q=4\) for traffic noise. Based on these analyzes, conclusions will be drawn about how urban noise in the proximity of road intersections is determined by how road traffic is organized at those intersections.
## 4 Results  
### Validation of the PT-JPL-Thermal ET  
The daily PT-JPL-thermal \(ET\) was aggregated into monthly estimates (mm/day) and compared against the in-situ calibrated hydrological model WATEN \(ET\) and MOD16 \(ET\). Annual \(ET\) values are shown in Figure 2c. The 10-year \(ET\) average from 2003 to 2012 was 739 mm for PT-JPL-thermal and 352 mm for MOD16, which represented a 7% overestimation and 49% underestimation compared to WATEN (\(ET\) = 691 mm), respectively. The \(ET\) time series resulting for the period of 2003 to 2012 is shown in Figure 2a. The average seasonality derived from the PT-JPL-thermal\(ET\) and WATEN \(ET\), which is shown in Figure 2b, presented maximum \(ET\) values in June while MOD16 \(ET\) maximum values were in April/May. The PT-JPL-thermal \(ET\) showed a high agreement with WATEN results after a lag of one month. Figure 3 shows the relationship between the PT-JPL-thermal and MOD16 monthly average \(ET\) values versus WATEN \(ET\) over the study period between 2003 to 2012. Table 3 illustrates the performance of the models based on the estimators proposed (\(\rho\) coefficient, \(e_{2}\), \(MAE\), bias, and \(RMSE\)) for monthly and annual average values. In general, the PT-JPL-thermal showed higher performance than MOD16. The PT-JPL-thermal vs. WATEN correlation remarkably increased when considering the one-month lag between both models although MOD16 accuracy was even lower in this case. Figure 3: Scatterplot of monthly \(ET\) (mm/day) from the PT-JPL-thermal and MOD16 versus WATEN \(ET\) in the mixed-irrigation BXII. Figure 2: (**a**) Monthly \(ET\) (mm/day), (**b**) average \(ET\) seasonality (mm/day), (**c**) annual \(ET\) (mm/year) derived from WATEN, PT-JPL-thermal, and MOD16 in the mixed-irrigation BXII for the period between 2003 to 2012. The Theii's inequality components distribution of the \(ET\) estimations derived from the PT-JPL-thermal and MOD16 \(ET\) models versus WATEN \(ET\) is illustrated in Table 4.  
Potential evapotranspiration \(ETp\) from MOD16 did, however, significantly correlate with the on-site \(ETp\) at the agro-climatic station Lebrija I (\(\rho\) = 0.98, bias = 0.2 mm/day) and \(ETp\) derived from the PT-JPL-thermal also resulted in high correspondence with the \(ETp\) at Lebrija I (\(\rho\) = 0.99, bias = 0.17 mm/day) (Figure 4). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monthly \(ET\)** & \multicolumn{3}{c}{**Theii’s Inequality Components**} \\ \hline  & \(\mathbf{u_{m}}\) & \(\mathbf{u_{s}}\) & \(\mathbf{u_{c}}\) \\ \hline PT-JPL-thermal vs. WATEN & 0.02 & 0.02 & 0.96 \\ MOD16 vs. WATEN & 0.34 & 0.48 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Theii’s inequality components distribution for monthly \(ET\) derived from PT-JPL-thermal and MOD16 vs. WATEN. Ideal Theii’s inequality components distribution: \(u_{m}\cong u_{s}\cong 0\) and \(u_{c}\cong 1\). Figure 4: (**a**) Monthly \(ETp\) (mm/day) derived from the agro-climatic station Lebrija I versus \(ETp\) from the PT-JPL-thermal and MOD16 over 2003 to 2012. (**b**) Average monthly correlation of \(ETp\) (mm/day) from PT-JPL-thermal and MOD16 against \(ETp\) at Lebrija I.
## 4 Conclusions  
### Comparative Analysis of the Models  
Manual screening of tea tree seeds (e.g., visual inspection and dissection) suffers from issues such as low efficiency, reliance on experience, and difficulty in identifying early-stage mildew and internal damage. The combination of MIR spectroscopy and deep learning has provided an efficient solution for maize seed identification.  
The ECA-DenseNet model has shown significant performance improvements. Specifically, its accuracy has been enhanced from 95.20% to 99.42%, representing a notable increase of 4.43%. Meanwhile, the F1-score, precision (P), and recall (R) have, respectively, increased by 3.09%, 3.60%, and 3.16%. Notably, the misjudgment rate has been reduced from a maximum of 3.7% to 0%. When compared with machine learning algorithms, ECA-DenseNet outperforms the best-performing SVM (91.42%) by 8% in accuracy. Its Kappa value (99.01%) and Matthews Correlation Coefficient (MCC, 99.11%) are also 8.12% and 7.99% higher than those of SVM, respectively. In the comparison with deep learning models, ECA-DenseNet achieves a 3.32% improvement in accuracy over InceptionV3 (96.10%), while only requiring 31% of its parameters and reducing the training time by 66.3%. Ablation experiments further indicate that the synergistic effect of the three modules increases the accuracy by 4.57% compared to the model without these modules. These results collectively demonstrate ECA-DenseNet's remarkable advantages in classification accuracy, generalization ability, and computational efficiency, highlighting that each module plays a critical complementary role in enhancing the model's performance.  
Therefore, the constructed ECA-DenseNet model has advanced the technological progress in tea tree seed identification. Its core advantage lies in the integration of the molecular-level detection capability of MIR spectroscopy and the automated feature learning capability of deep learning, providing an accurate screening technical pathway for agricultural production practices.
## 5 Practical Applications  
The findings of this study have important implications for both researchers and practitioners in the field of exercise science, specifically in the context of designing effective exercise programs for physically active older women. Based on the results of this study, it is recommended that exercise programs for independent older women incorporate a multicomponent training (MCT) approach. Specifically, the order of exercises should begin with aerobic training followed by resistance training. This exercise order has been shown to be more effective in improving or maintaining muscle strength, which is crucial for maintaining autonomy and functional independence in aging individuals. Practitioners working with older women should consider individual differences in terms of physical fitness levels, exercise preferences, and health conditions when designing MCT programs. By tailoring the exercise program to each individual's needs and capabilities, practitioners can optimize benefits and ensure a safe and enjoyable exercise experience. The study's findings underscore the importance of focusing on knee extensor (KE) and knee flexor (KF) strength training. Improving KE and KF strength is associated with enhanced functionality, mobility, and reduced fall risk in daily activities. Thus, practitioners should include exercises that specifically target these muscle groups to maximize the functional benefits for older women. Future research should explore the effects of the different intensities of MCT and varying exercise orders. This will provide valuable insights into the optimal training parameters for improving muscle strength and functional capacity in older women. Researchers and practitioners can use this information to refine exercise prescription guidelines and develop evidence-based interventions. While this study did not control dietary factors, future research should consider the impact of nutrition on the outcomes of MCT interventions. Researchers should explore the potential synergistic effects of exercise and dietary interventions in improving muscle strength and functional outcomes in older women. The study highlights the negative consequences of prolonged periods of physical inactivity, such as decreases in muscle strength and increased risks of sarcopenia, fragility, and falls. This emphasizes the importance of promoting continuous physical activity engagement among older women in order to maintain their quality of life, functional independence, and overall well-being.
## 4 Simulation Test Analysis
### Simulation Results of Low Frequency Part of Power Data  
Decomposed by the Mallat algorithm, the frequency of the A3 layer is 1-400 Hz, including the fundamental wave and the 3, 5 and 7 low frequency steady-state harmonics. Therefore, the A3 layer is used as the low frequency part, and the A3 layer is reconstructed. The low frequency steady-state part of the A3 layer is shown in Figure 6.  
The original method is based on the Hanning window function interpolation DFT and DWT mixed harmonic detection method, and the improved method is based on the DFT and DWT using the Bartlett-Hann window function mixed harmonic detection method. In order to compare the accuracy of low frequency part detection between the two, the low frequency steady state portion of the A2 layer after reconstruction is analyzed by the Hanning window function interpolation DFT algorithm and Bartlett-Hann window function interpolation DFT algorithm, respectively. The corresponding frequency spectrum is shown in Figure 7.  
According to the proposed Bartlett-Hann window interpolation optimization and the literature [21], Hanning window interpolation optimization is compared with the theoretical value. Combining the spectrum analysis result in Figure 7, the DFT analysis frequency and amplitude of the low frequency steady state harmonic component fundamental wave, 3 rd, 5 th and 7 th harmonics are obtained. The comparison is shown in Table 3 and the data obtained in the table are the average of three sets of data.  
Figure 6: Reconstructed waveform of A3 layer.  
Figure 7: A3 layer windowed interpolation DFT spectrum comparison chart.  
Table 3 presents the comparison between the proposed low frequency part using the Bartlett-Hann window interpolation DFT and the original method Hanning window interpolation with theoretical values. The steady-state harmonic component results obtained by the two detection methods are very close to the theoretical values. In order to make the test results more intuitive, amplitude accuracy and relative error are used to illustrate the accuracy of the two methods, as shown in Table 4.  
As can be seen from Table 4, the amplitude accuracy of the interpolation DFT algorithm of the Bartlett-Hann window function is about 95.3375%, and the relative error of the amplitude is less than 6%. It can be seen from the accuracy and relative errors that the Bartlett-Hann window function's interpolation DFT algorithm has higher accuracy in detecting harmonics in the low frequency part, which is improved by about 1.5175%. Compared with the data in Table 2, the data in Table 2 directly analyzed the original signal, and the data in Table 4 analyze the data after hierarchical reconstruction, so the relative errors and accuracy are different.
# Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality
## 3 Construction of Risk Traceability System
### Traceability System Design
#### 3.2.3 System Database Design  
To build the system and realize the functional characteristics, the relational database MySQL was used to compare and analyze the system data. Based on the data flow generated by the functional business to be accomplished by the system, the system database was standardized and designed in accordance with the steps of database construction.  
According to the actual research and the analysis of the realized functional requirements, the traceability system of agricultural product quality and safety designed by the research of this paper divides the agricultural product hazards into three major categories, including heavy metal, pesticide, and mycotoxin contamination. The occurrence of the risk of exceeding the standards for all three hazards is a systematic process, and the level of risk of agricultural products as affected bodies is determined by the sources of risk and exposure  
Figure 5: System function module diagram.  
pathways, i.e., by the impact factors. Therefore, the following database tables were created: user information table, soil heavy metal effective state content table, crop heavy metal uptake coefficients table, heavy metal content from other sources table, pesticide degradation coefficients table, pesticide usage table, other factors influence coefficients table, storage and transportation schedules table, storage and transportation temperatures table, and moisture activity table, and the tables were instantiated by creating MySQL relational databases in Navicat software. The specific form name design and system database table design diagram is shown in Figure 6.  
The above data sheet records the detailed information required for the evaluation of agricultural products in the process of agricultural products from planting to marketing, and establishes an electronic file for the traced agricultural products. The use of information technology means to trace the monitoring data of agricultural products has the following advantages: more efficiently completes the business needs of the traceability of agricultural product quality and safety; according to the results of the system evaluation, the data can be further excavated; and more effective improvement measures can be put forward, which will achieve the unified management of the traceability information on the production of agricultural products.