esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 2 System Models and Problem Formulations
Due to different task requirements, the scheduling cycles of each UAV can vary. However, all UAVs will be scheduled repeatedly within a period determined by the least common multiple (LCM) of all UAV scheduling periods. Let the scheduling cycle of \((i,j)\) be denoted as \(T_{j}^{i}\), \(i=1,\ldots,N_{c}\), \(j=1,\ldots,M\). The system performance can then be evaluated over a period given by \(N=LCM\left(T_{j}^{i}|i=1,\ldots,N_{c},j=1,\ldots,M\right)\). Since each UAV is scheduled repeatedly, the time slots in which a UAV is scheduled are determined by its initial time slot. Let \(s_{j}^{i}\) represent the initial scheduling time slot of UAV \((i,j)\). The initial time slot of each UAV is restricted by its required cycle, i.e.,  
\[1\leqslant s_{j}^{i}\leqslant T_{j}^{i},s_{j}^{i}\in\mathbb{Z}^{+},i=1, \ldots,N_{c},j=1,\ldots,M. \tag{8}\]  
The UAV \((i,j)\) is scheduled in the time slots given by  
\[ \begin{split}&\quad s_{j}^{i}=\mathbf{1}_{N}\left(s_{j}^{i}+zT_{j }^{i}\right),\\ &\quad i=1,\ldots,N_{c},j=1,\ldots,M,z=1,\ldots,\frac{N}{T_{j}^{i}},\end{split}  \tag{9}\]where \(\mathbf{1}_{N}(\mathbf{x})\) is a vector of length \(N\), where elements specified by indices \(\mathbf{x}\) are equal to 1, while the remaining elements are equal to 0. The UAV \((i,j)\) is scheduled in the time slots that correspond to the 1 elements of its scheduling vector. Assuming each RIS-UAV possesses \(L\) RIS arrays, it is capable of scheduling a maximum of \(L\) UAVs within a single time slot. The scheduling time slots of UAVs must satisfy the constraint  
\[\sum_{j=1}^{M}\mathbf{s}_{j}^{i}\prec(L+1)\mathbf{1}_{N},i=1,\ldots,N_{c},j=1, \ldots,M, \tag{10}\]  
where \(\mathbf{1}_{N}\) is an all 1 vector of length \(N\), and \(\prec\) indicates that all elements of \(\mathbf{s}_{j}^{i}\) are less than \(L+1\). In the time period \(N\), we can express the total interference received by UAV \((i,j)\) from beam \((i^{\prime},j^{\prime})\) as  
\[I\left(\mathbf{s}_{j}^{i},\mathbf{s}_{j^{\prime}}^{i}\right)=c_{i,i^{\prime}}\left(j, j^{\prime}\right)\left(\mathbf{s}_{j}^{i}\right)^{\mathsf{T}}\mathbf{s}_{j^{\prime}}^{i}. \tag{11}\]  
We aim to minimize the overall interference level, and this optimization problem is mathematically formulated as  
\[ \begin{split}\operatorname*{arg\,min}_{s_{j}^{i}}\gamma& =\sum_{i=1}^{N_{c}}\sum_{j=1}^{M}\sum_{j^{\prime}=1}^{N_{c}}\sum_{j ^{\prime}=1}^{M}I\left(\mathbf{s}_{j}^{i},\mathbf{s}_{j^{\prime}}^{i}\right)\\ s.t.&\quad\eqref{eq:11},\eqref{eq:12},\eqref{eq:13}.\end{split}  \tag{12}\]  
The constraint condition (8) ensures that the initial slot is smaller than the scheduling periods of UAVs. Moreover, (9) stems from the periodic scheduling restriction that requires each UAV to be allocated on periodic time slots according to its period. Furthermore, constraint condition (10) is imposed to limit the quantity of RIS of each cluster.
## 4 Proposed Method
### Coverage Hole Repair Based on Mobile Node Redeployment
#### 4.2.1 Repair Path Search Method Based on MEP-ISSA
The algorithm can avoid getting stuck in local optima by using Cauchy mutation to perturb individual positions with a certain probability. The Cauchy mutation operator can improve the optimization ability of the algorithm. Applying the reverse learning strategy to the sparrow search algorithm can broaden the search area and improve optimization speed. The premise for the algorithm to execute Cauchy mutation and reverse learning strategy is that the fitness value after perturbation is better than the original position. 3. Dynamic adjustment of security threshold The search capability of the SSA is closely related to the size of the security threshold. When the value of ST is large, all sparrows are in a safe state, and the algorithm performs a global search. When the value of ST is small, sparrows can only forage in a smaller area, and the algorithm performs a local search. In the optimization problem of this paper, it is required that the algorithm combines the characteristics of both. Therefore, we designed a dynamic adjustment strategy to ensure that the algorithm performs well in both global and local searches. After introducing this strategy, the speed and accuracy of the algorithm are improved. Figure 2 is a schematic diagram of using ISSA to search for the MEP. The white sector in the figure represents the stationary sensor, the blue sector represents the mobile sensor, and the red curve represents the searched MEP. From the figure, it can be seen that the path searched by ISSA has the worst coverage. The pseudocode of the proposed MEP-ISSA method is shown in Algorithm 2. This method can be used to determine the MEP with the minimum exposure value. The obtained MEP is the path for executing repair tasks. ```
Input: L, H, S, \(n_{s}\), \(n_{m}\), P Output: MEP
1: Initialize monitoring area and sensing model
2: Initialize population parameters
3: Calculate fitness values and sort them by size. 4:while failure to meet termination conditions do
5:if\(f_{t+1}-f_{t}\leq\varepsilon\)then
6: Calculate the attenuation function of the security threshold
7:else
8: Update population
9:end
10: Update the position of sparrows
11:if\(rand<P_{s}\)then
12: Implement reverse learning strategy
13:else
14: Using Cauchy mutation operator
15:endif
16: Determine whether to update the location
17:endwhile
18: Output: MEP
```  
**Algorithm 2** Repair path search method based on MEP-ISSA  
Figure 2: The results of executing the ISSA algorithm.
## 3 Results
### Characterization of Purified Cutinase
#### 3.7.2 Effect of Metals Ions  
Effects of the various monovalent and divalent metals on the cutinase activity were evaluated at 2 and 15 mM (Table 2). Ca\({}^{2+}\), Zn\({}^{2+}\), K\({}^{+}\), and Mg\({}^{2+}\) enhanced the enzyme activity at both concentrations (_p_ < 0.005), whereas Hg\({}^{2+}\), Ni\({}^{2+}\), Cd\({}^{2+}\), Ba\({}^{2+}\), and Co\({}^{2+}\) strongly inhibited the cutinase-like activity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Metals**} & \multicolumn{2}{c}{**Residual Activity (\%)**} \\ \cline{2-3}  & **15 mM** & **2 mM** \\ \hline Control & 100 \(\pm\) 0.0 & 100 \(\pm\) 0.0 \\ Ca\({}^{2+}\) & 111.32 \(\pm\) 0.6 & 102.34 \(\pm\) 0.5 \\ Fe\({}^{2+}\) & 98.82 \(\pm\) 0.7 & 100.39 \(\pm\) 0.6 \\ Cu\({}^{2+}\) & 107.81 \(\pm\) 1.0 & 100.78 \(\pm\) 0.2 \\ Co\({}^{2+}\) & 55.85 \(\pm\) 0.2 & 58.98 \(\pm\) 0.3 \\ Ni\({}^{2+}\) & 100.39 \(\pm\) 0.4 & 100 \(\pm\) 0.1 \\ Zn\({}^{2+}\) & 114.45 \(\pm\) 0.5 & 107.42 \(\pm\) 0.4 \\ K\({}^{+}\) & 115.23 \(\pm\) 0.5 & 111.71 \(\pm\) 0.3 \\ Cd\({}^{2+}\) & 1.95 \(\pm\) 0.2 & 3.51 \(\pm\) 0.3 \\ Na\({}^{+}\) & 85.54 \(\pm\) 0.1 & 91.79 \(\pm\) 0.2 \\ Mg\({}^{2+}\) & 107.42 \(\pm\) 0.4 & 105.07 \(\pm\) 0.3 \\ Ni\({}^{2+}\) & 71.48 \(\pm\) 0.3 & 66.01 \(\pm\) 0.5 \\ Hg\({}^{2+}\) & 0 \(\pm\) 0.00 & 0 \(\pm\) 0.00 \\ Ba\({}^{2+}\) & 54.29 \(\pm\) 0.1 & 55.46 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of metal ions on the activity of the cutinase-like enzyme from the _S. maltophilia_ PRS8 strain.  
Figure 9: Effect of the temperature and pH on the activity and stability of the purified cutinase-like enzyme from the _S. maltophilia_ PRS8 strain. (**A**) Temperature profile. (**B**) Profile of the pH.
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## 3 Results  
### Spatial Distribution of Cotton GPP in Three Sentinel-2 Tiles  
One challenge to accurately simulate cotton GPP using BEPS is to determine the irrigation information. We developed an irrigation module in the GPP simulation; when this module is turned on, the soil moisture in each layer is increased to 0.42 m\({}^{3}\)/m\({}^{3}\) (volumetric soil water content) if it is below this level every few days (e.g., 14 days). We chose 0.42 m\({}^{3}\)/m\({}^{3}\) because this is the maximum field capacity for all soil textures [84]. In this study, there is no ideal way to identify which and when a pixel is irrigated. According to the NASS statistics, the ratios of irrigated cotton area are 32%, 34%, and 51% for Texas (TX), Georgia (GA), and Mississippi (MS), respectively; therefore, the irrigation module was turned on for tile 15 SYT (MS) only.  
Another challenge is to determine the start and end of growing season for cotton. In MS, the cotton is usually planted in later April or May, and the harvest begins in October and November. In the southern part of the Great Plains (covering 14 SKB), the plant date can be in June. Since both cotton plots in tiles 14 SKB and 15 SYT are single-cropping systems, the LAI out of the growing season is low; therefore, the annual GPP is a good proxy for cotton GPP in the whole growing season. This is not true for the tile 17 RKQ in GA. In the coastal plain, especially in lower south Georgia where the growing season is long, double cropping is popular. GA is characterized by a wheat-cotton cropping system, and the cotton is often planted in June; therefore, the cotton growing season for BEPS simulation was limited from June 1 to October 31.  
Mainly rainfed, the tile 14 SKB in TX is characterized by low GPP values (726 \(\pm\) 74 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\)) from the BEPS simulation, although some high GPP values (\(\sim\)1500 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\)) appear to be associated with the center-pivot irrigation (Figure 2). The GPP in the cotton growing season for tile 17 RKQ is 1482 \(\pm\) 74 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\) (Figure 3). Owing to the long growing season, the annual GPP for tile 15 SYT is 2005 \(\pm\) 177 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)year\({}^{-1}\) (Figure 4). The statistics of average GPP values for each county are listed in Table 2.  
Figure 2: The simulated annual gross primary production (GPP) for cotton by the Boreal Ecosystems Productivity Simulator (BEPS) in 2017 for Sentinel-2 tile 14 SKB.  
Figure 4: The simulated annual GPP for cotton by BEPS in 2017 for Sentinel-2 tile 15 SYT.  
Figure 3: The simulated annual GPP for cotton by BEPS in 2017 for Sentinel-2 tile 17 RKQ.
# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
###### Abstract  
Grain quality changes during the storage period, and an important grain quality indictor is the free fatty acid (FFA) content. Understanding real-time change of FFA content in stored grain is significant for grain storage safety. However, the FFA content requires manual detection with time-consuming and complex procedures. Thus, this paper is dedicated to developing a method to estimate FFA content in stored grain accurately. We proposed a machine learning approach--multiple-kernel support vector regression--to complete this goal, which improved the accuracy and robustness of the FFA estimation. The effectiveness of the proposed approach was validated by the grain storage data collected from northeast China. To show the merits of the proposed method, several prevailing prediction methods, such as single-kernel support vector regression, multiple linear regression, and back propagation neural network, were introduced for comparative purposes, and several quantitative statistical indexes were adopted to evaluate the performance of different models. The results showed that the proposed approach can achieve a high accuracy with mean absolute error of 0.341 mg KOH/100 g, root mean square error of 0.442 mg KOH/100 g, and mean absolute percentage error of 2.026%. Among the four models tested, the multiple-kernel support vector regression model performed best and made the most robust forecasts of FFA content in stored grain.  
food security; free fatty acid; grain storage; support vector regression; multiple-kernel learning +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Limitations and Future Research  
Despite these results, questions remain. Table 2 indicates that the SL IST-cube is not characterized by false detections for change years in which meteorological disasters occurred but does contain several missed detections. There is no rule found in these missed detections, and more studies and experiments are needed to explore. Furthermore, we were unable to investigate changes in the vertical structure of the forest, as this study did not include information on these changes. This computationally expensive task results in the small size of the study area, which indicates that capturing large-scale changes in the spatiotemporal domain is not possible. Further studies should attempt to achieve a higher detection accuracy, include more forest structure data, and optimize the algorithm. Long-term earth observation data are limited, and their spatial resolution and temporal resolution depend on sensors. The reason why we chose Landsat as the data source is that Landsat can provide the longest stable earth observation images [89; 90]. However, the geographic phenomena observed by a sensor with a determinate spatiotemporal resolution are limited by spatiotemporal scale. Geographic phenomena captured by cell cube are also limited by the spatiotemporal resolution of the sensor. Thus, more studies should focus on using different remote sensing data for the IST-cube model [31].
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 3 Research on Mechanical Performance of Porous Asphalt Mixture
### Research on Mechanical Performance of High-Viscosity Modified Asphalt Mixtures
#### 3.2.4 Fatigue Resistance  
According to the Chinese specification JTG E20-2011, in the actual use of asphalt mixtures, repeated exposure to ambient temperature and vehicle loads can cause internal cracking within the mixture, leading to pavement fatigue cracking [52]. This study adopted a two-point fatigue test to investigate the fatigue performance of porous asphalt mixtures. The test results are presented in Figure 17.  
From Figure 17, the fatigue lifetimes of porous asphalt mixtures were 10-A > 10-B in a descending order. Because of the large void ratio of porous and the amount of asphalt used, excellent asphalt properties could reduce internal cracking in the mixtures. In addition, at the joints of aggregate particles, the high-viscosity modified bitumen could exert greater adhesion to bond the coarse aggregate as a single unit, resulting in the excellent fatigue resistance of 10-A [53]. [PERSON] et al. observed that the production of stabilizers and antioxidants in modifiers could be significantly influenced by their respective dosages [54]. This finding aligns with previous research by [PERSON] et al. [55], which indicated that a high dosage of stabilizers coupled with a low dosage of antioxidants might reduce asphalt fluidity, thereby increasing the risk of aging. However, this study did not clarify the causal relationship between antioxidant content and aging resistance, an issue that warrants further exploration through long-term longitudinal studies.  
It is also important to note that the effect of stabilizer content in modifiers may depend on the type and concentration of the stabilizer used. Future investigations should take into account the diversity of stabilizers, including tin-based stabilizers, co-stabilizers, and heat stabilizers.  
Figure 17: Two-point fatigue test results.
###### Abstract  
This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization algorithms, leverage biological behaviors for feature selection, they are limited by their inability to synergize diverse strategies, resulting in suboptimal performance and scalability. To address this, we introduce the Hybrid Predator Algorithm for Classification (HPA-C), a novel hybrid feature selection algorithm that uniquely integrates the framework of a nature-inspired feature selection technique with position update equations from other algorithms, harnessing diverse biological behaviors like echolocation, foraging, and collaborative hunting. Coupled with a custom convolutional neural network (CNN), HPA-C achieves superior classification accuracy (98.6-99.8%) on agricultural datasets (Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten) and demonstrates exceptional adaptability across diverse imagery applications.  
s mart agriculture; feature extraction; feature selection; classification; deep neural networks +
Footnote †: journal:  
0000-0001-9399-2231][PERSON]  
1 Department of Computer Engineering, COMSATS University Islamabad, Wah Campus,  
Wah Cantonment 47040, Pakistan; [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (S.A.H.); [EMAIL_ADDRESS] (T.A.)
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 4 Experimental Setup
### Comparative Analysis of Parameter's Influence
PS detection and analysis should not be affected by the mentioned spectral shift, since the theoretical response of a stable target is represented by the Dirac impulse, hence its spectrum should have a constant value. Dispersion of perpendicular baselines values associated with the ERS dataset is much higher compared to the Sentinel-1 images--553.54 m compared to 53.39 m. This represents a direct advantage for the latter dataset, since the aforementioned spectral shifts are lower. Higher dispersion is desirable if PS-InSAR [13] and will be exploited on the network of detected PS, since a more precise estimation of atmospheric phase screen [25] can be carried out.
## 1 Introduction
In this way, considering that H-D chaotic maps are difficult to implement, 1-D chaotic maps have been the most used in different applications [14; 15; 57], but in order to avoid their security weaknesses the following issues must be considered: (i) existence in the chaotic map of chaos annulling conditions, which are not identified and therefore are not avoided, (ii) a high degradation rate of the dynamic behavior when digital maps are used as quantization functions to approximate the true chaotic maps, (iii) low complexity of the chaotic map, (iv) strong correlation between the data set and the number sequences produced by the chaotic map, and (v) non-uniform statistical distribution of the number sequences produced by the chaotic map. Thus, PRNGs based on a single chaotic system are potentially insecure systems since the produced number sequences expose information related to the initial condition of the chaotic system. In such case, an intruder can be able to decrease the computational complexity to find that initial condition. However, in order to avoid this condition PRNGs based on a single chaotic system, the following approaches should be used: higher finite precision [47; 58], methods reducing the dynamical degradation of digital sequences [50], cascading multiple chaotic systems [47; 59; 60; 61], combining chaotic maps by using modular operations [62; 63], and coupled chaotic systems [64; 65; 66]. In this way, it is more difficult to obtain information about the initial condition of the system, since the number sequences it produces will be determined by different conditions, configurations, and mixed chaotic orbits. Under these considerations, we propose and analyze a function composition (FC) that couple the sine function and skew tent map (STM) to include three FCs as core in a PRNG. In this way, we also propose a PRNG that uses three modular operations to increase the precision in the scaling and discretizing procedures used to translate the real number sequences produced by FCs to binary number sequences, and it uses a modular operation to combine the pseudorandom binary sequences. Through this strategy we overcome the disadvantages of using a single chaotic system. To guarantee the effectiveness of the FC during the operation of the proposed PRNG, we avoid in each FC the chaos annulling conditions; and in order to evaluate the proposed PRNG, the following tests have been considered: correlation coefficient, key sensitivity, entropy analysis, statistical analysis, linear complexity, key space analysis, pseudorandomness, and speed analysis. It is important to emphasize that in this work, we use the word _key_ **of the PRNG** to identify what other authors call the _seed_ or _initial condition_ of the PRNG. The rest of the paper is organized as follows. Section 2 shows the definition, the sensitivity analysis, and a basic sensitivity test of the FC. Section 3 provides design details of the proposed PRNG. Section 4 shows the results of performance tests applied to the number sequences produced by the proposed PRNG. Finally, Section 6 is devoted to conclusions.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 8 LoRa/LoRaWAN/Wi-Fi Use Cases
### USV: Monitoring Water and Environment Quality  
The architecture can be expanded to monitor the quality of water and the parameters of the environment by the aggregation of the data from the sensors on the ground, as well as from sensors that can be placed on the boats of local people. In this use case, the Danube Delta administration or the associations of fishermen can be beneficiaries. The boats of local people can thus become nodes for collecting data regarding the environment.
## 5 The Proposed Hybrid Scheme
### Mobility Model  
Mobility models are commonly used to analyze newly designed schemes or protocols in both cellular and ad-hoc networks. In FANET, the choice of suitable mobility model is essential to obtain results with maximum precision and conformity due to the agile motion of UAVs. In this context, mobility models are classified into five classes: purely randomized, time-dependent, path planned, group, and hybrid [45]. In order to patrol a specific region, there are many situations where a group of UAVs move together following a common point in the FANET system. For this reason, we used reference point group mobility (RPGM) [46] to simulate a group of UAVs in the proposed scheme. In the RPGM model, UAVs are grouped to achieve a collective task by moving together around a logical reference point, i.e., a group leader. The behavior of the reference point's motion defines the entire group's motion including location, altitude, speed, and direction. Usually, UAVs are uniformly distributed within the geographic scope of a group. The group leader follows a random waypoint (RWP) mobility model, and other UAVs will be moving around the center with their own mobility pattern (see Figure 4b) [47]. This mobility model has numerous variants, such as column (CLMN) [47], nomadic community (NC) [47], and purse (PRS).  
In the proposed scheme, BM-UAV of each group is located at the reference point. The altitude, speed, and direction of BS-UAV depend on such a reference point. Moreover, the location of BS-UAVs is updated according to the BM-UAV.