esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Fruit's Sweetness Using Artificial Intelligence--Case Study: Orange
## Materials and Methods
### Image Acquisition  
As part of the preparations for the acquisition of the oranges' images, a measurement and research platform was designed and constructed (Figure 2). The aim of the project was to obtain graphical data containing information that is encoded in the form of RGB values.  
In the first step, the orange fruit was attached to a skewer. Pictures of each fruit were acquired from 6 side views at an angle of 90\({}^{\circ}\). The next stage of the experiment was to adjust the distance of the camera (iPhone 12) in order to obtain the highest image quality, but also the smallest dimensions of the terminal. An iPhone 12 Pro camera was used to take the digital photos, as it features a quad camera system (\(12+12+12\) MP and TOF 3D LiDAR scanner) with a field of view of 120 degrees. In order to maintain the repeatability of the results of the pictures that were taken, the device had to be calibrated. The iPhone 12 Pro was equipped with the Camera+ application for the advanced control of the image  
Figure 1: Diagram of the sweetness prediction methodology.  
Figure 2: A diagram of pictures taking platform. (**a**) Side view; (**b**) arm rotation angle; (1) the base; (2) servo motor; (3) iPhone mobile; (4) orange fruit; (5) fruit stabilizer.  
sharpness in manual mode. The following image exposure parameters were used in the research: sensitivity--ISO 320--and exposure time--2/3 s.  
The device was mounted on a movable arm that was controlled by the torque of a servo motor. The distance between the camera and the fruit was 20 cm. The servo motor rotated the device through an angle of 90\({}^{\circ}\) and took the pictures. As a result of the rotation, a series of four photos was taken horizontally. The fruit was then manually turned to take two pictures vertically. As a result, six photos were obtained for one fruit (Figure 2b).  
When studying the outer color of the fruit, a good lighting system is required that should provide uniform radiation, avoiding glare or shadows. Four side lamps were installed at an angle of 45 degrees perpendicular to the fruit. With this apparatus the reflection was reduced, thus avoiding unwanted glare. A similar lighting system was used by [PERSON] et al. and [PERSON] et al. [47,48].
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 7 Penultimate Layer Activations with Adversarial Images
### How Are the CNNs' Classification Layers Affected by Adversarial Images? For each CNN \(\mathcal{C}_{k}\), we obtain the following. The aforementioned weights are extracted, and for both \(c_{a}\) and \(c_{t}\), they are separated into positive and negative weights. Then, we compute the difference in activation values in the penultimate layer between each adversarial \(\mathcal{D}_{k}^{\textit{attk}}(\mathcal{A}_{a}^{p})\) and its ancestor \(\mathcal{A}_{a}^{p}\). Since our intention is to measure the proportion of units, relevant to a class, that are increased or decreased by the adversarial noise, we compute the average percentage of both positively and negatively related units--Table 5 for \(c_{a}\) and Table 6 for \(c_{t}\) (see Tables 10 and 11 in Appendix B.3 for an individual outcome)--in which the activation increased, stagnated, or decreased. For \(c_{a}\) and \(c_{t}\), Table 7 presents the average change in penultimate layer activation for both the positively and negatively related units. Note that \(\mathcal{C}_{9}\) and \(\mathcal{C}_{10}\) have different behaviors than the other CNNs as far as the values of \(W_{pos}\Delta_{0}\) and \(W_{neg}\Delta_{0}\) are concerned. The EA and BIM change the activations of \(\mathcal{C}_{9}\) and \(\mathcal{C}_{10}\) much less frequently than with the other CNNs. Indeed, between 50.28% and 74.85% of the activations of these two CNNs are left unchanged, and this is valid for \(c_{a}\), for \(c_{t}\), and for both attacks. Observe that the group of units that contribute to the values taken by \(W_{pos}\Delta_{0}\) and by \(W_{neg}\Delta_{0}\) for \(c_{a}\) coincides with the group of units that contribute to the values taken by \(W_{pos}\Delta_{0}\) and \(W_{neg}\Delta_{0}\) for \(c_{t}\). Overall, Tables 5 and 6 show that neither the EA nor BIM increase the activation of all positively \(c_{t}\)-related penultimate layer units; the percentages where such an increase happens is similar between the two attacks and varies between 31.40% and 77.68% throughout the different CNNs. However, in all cases, more positively \(c_{t}\)-related units are increased rather than decreased in activation. Meanwhile, for \(c_{a}\), this preference for increasing rather than decreasing the activation is present for the negatively \(c_{a}\)-related units.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Air Traffic Network Situation Dataset in East China  
We selected 191 significantly good and significantly poor air traffic operation conditions in 2019 as the training dataset. A total of 90 samples were selected as the test dataset. In Table 3, we refer to the method in Section 2.1 to calculate the adjacency matrix of the air traffic weighted network in East China. Four typical operation indexes of air traffic in East China were used to calculate the edge weight \(w_{i}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Distance** & **Situation Level** & **Explanation** \\ \hline (1.5, 2.0] & Safety & The air traffic is stable and safety. There is no security risk. \\ (1.0, 1.5] & Potential Risk & The air traffic is basically stable. Air traffic robustness is weakened. \\  & & The risk of unsafe incidents increased. \\ (0, 1.0] & General Risk & There are systemic risks in air traffic. The stability of air traffic \\  & & operation decreases. A few unsafe incidents are happening. \\ \((-1.0,0]\) & Moderate Risk & The air traffic security situation is deteriorating. The probability of \\  & & seriously unsafe incidents is greatly increased. The number of flight \\ \([-2.0,-1.0]\) & Significant Risk & The robustness and safety of air traffic are weak. Many unsafe incidents \\  & & are happening. The normal operation order is seriously damaged. \\ \hline \end{tabular}
\end{table}
Table 2: Air traffic operation state.  
Figure 4: Process of air traffic situation assessment.  
In Table 4, the adjacency matrix of the air traffic network was used to calculate seven typical evaluation indexes based on the method in Section 2.2. The air traffic network situation dataset in East China was obtained by calculating seven complex network evaluation indexes of each day. The training dataset consists of samples with significantly good and significantly poor air traffic operation conditions. These samples were labeled to calculate the classification boundary.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 3 Problem Statement and Methodology  
### The Fusion Tree Generation Problem  
The computing power of the local fusion nodes and the network bandwidth are limited. Therefore, the fusion tree should balance local tracks across local fusion nodes to increase the number of processed tracks in a fusion cycle.  
Problem overview:  
* Electing a set of local fusion nodes that can cover the other radars via one hop;  
Figure 2: Fusion process model at local fusion nodes.  
* Electeting an appropriate number of local fusion nodes by considering the number of tracks; and
* Electeting more local fusion nodes in the area where there are more tracks.  
Requirements:
* No predefined tree structure, such as the number of local fusion nodes and child radars, is used;
* All radars should be connected to one of the local fusion nodes in one hop, or become local fusion nodes; and
* Fully-distributed, radars need to know the information of their neighbors, only.
## 2 Materials and Methods
### Methods
#### 2.3.2 Trend Analysis  
The Theil-Sen median trend analysis is a non-parametric method for estimating time series trends, and it is immune to outliers [44; 45]. The Mann-Kendall (M-K) test is a non-parametric method for assessing the significance of time series trends [46; 47]. The two methods are often combined to determine the change trends of vegetation in time series data. The Theil-Sen median trend analysis is expressed as Equation (2):  
\[\text{Sen}=\text{Median}\bigg{(}\frac{\text{X}_{\text{j}}-\text{X}_{\text{i}} }{\text{j}-\text{i}}\bigg{)} \tag{2}\]  
Sen \(>0\) denotes an increasing trend in X; Conversely, it denotes a decreasing trend.  
The Mann-Kendall test is expressed as Equation (3):  
\[\text{S}=\sum_{\text{i}=1}^{\text{n}-1}\sum_{\text{j}=\text{i}+1}^{\text{n}} \text{sgn}\big{(}\text{X}_{\text{j}}-\text{X}_{\text{i}}\big{)} \tag{3}\]  
where, n is the time series length, \(\text{X}_{\text{i}}\) and \(\text{X}_{\text{j}}\) are the data values in the time series i and j (j > i), respectively. The \(\text{sgn}\big{(}\text{x}_{\text{j}}-\text{x}_{\text{i}}\big{)}\) is calculated as Equation (4):  
\[\text{sgn}\big{(}\text{X}_{\text{j}}-\text{X}_{\text{i}}\big{)}=\begin{cases} +1,&\text{if }\text{X}_{\text{j}}-\text{X}_{\text{i}}>0\\ 0,&\text{if }\text{X}_{\text{j}}-\text{X}_{\text{i}}>0\\ -1,&\text{if }\text{X}_{\text{j}}-\text{X}_{\text{i}}>0\end{cases} \tag{4}\]  
The standard normal test statistic Z is calculated using Equation (5):  
\[Z=\begin{cases}\frac{\text{S}-1}{\sqrt{\text{s}(\text{S})}},\ S>0\\ 0,&\text{S}=0\,\ \text{s}(\text{S})=\frac{\text{n}(\text{n}-1)(2\text{n}+5)} {18}\\ \frac{\text{S}+1}{\sqrt{\text{s}(\text{S})}},\ S<0\end{cases} \tag{5}\]  
in which, a positive or negative Z indicates an increase or decrease. \(|z|>1.96\) indicates a significant increase (or decrease) in the time series at a confidence level of \(\alpha\) = 0.05.
# Climatic Indices over the Mediterranean Sea: A Review
## 3 Large-Scale Atmospheric Forcing: Climatic Indices  
Large-scale atmospheric patterns strongly influence the weather and climate of wide regions. Climatic indices are defined in order to quantify the variability of these general patterns and their effects on local climatic systems. They provide information on how the overall physical variability is linked to the evolution of the local climatic parameters. Hence, they can be used as a proxy to monitor the long-term variability of climatic variables that influence the Mediterranean circulation. For this reason, the improvement of our knowledge of the interrelation between the climatic indices and the Mediterranean climate and circulation is essential in order to understand their variability and evolution under climate change. The most outstanding climatic indices that influence the Mediterranean region, namely the North Atlantic oscillation (NAO), East Atlantic (EA) pattern, East Atlantic-West Russia (EA-WR) pattern and the Mediterranean oscillation (MO), will be reviewed in the following sections. The Scandinavian (SCAN) pattern produces weak variations in the sea level pressure anomaly field and plays a secondary role in the large-scale atmospheric forcing over the Mediterranean [28; 29].  
The spatial structure of the large-scale atmospheric patterns can be described in numerous ways and, therefore, there are several accepted definitions of the climatic indices that represent their modes of variability and temporal evolution. Two approaches have been historically followed: the combination (usually difference) between the sea level pressure (SLP) anomalies measured at different stations over the region of influence of the atmospheric patterns (e.g., see [29; 30; 31; 32; 33; 34; 35] for MO index; see [33; 34; 35; 36; 37] for a complete review of station-based NAO indices) or from the principal components (PC) time series of the first empirical orthogonal function (EOF) of SLP or some other climate variable (see [38; 39] for MO index; see [40] for a summary of several NAO definitions).  
The definition based on the PC has the advantage of representing the variability of the atmospheric patterns in its whole extension, not being affected by the noise introduced in the SLP measurements by the short-term meteorological events present in the station-based indices (see [40; 41; 42] or [43] for MO). A widely used analysis of the most outstanding modes of atmospheric variability is carried out at the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC). They identify the main modes by a rotated PC analysis [44] of the measured monthly mean 500 mb height anomaly fields in the area 20\({}^{\circ}\) N-90\({}^{\circ}\) N. Their database provides monthly index values for each mode computed by this method (see details in [45]).
# Social Internet of Vehicles for Smart Cities
## 4 Social Network Analysis in SIoVs
### Social Clustering of Vehicles
Using the social behaviour of vehicles as a basic parameter in the clustering formation procedure, the method presented in [85] manages to increase cluster stability. The social behaviour of drivers is derived from historical data that are collected from RSUs, which are deployed at critical points of the road network. To implement the proposed clustering method, the path that vehicles are likely to follow is added to every beacon message. Both methods use the routes that the vehicles follow or tend to follow as an additional parameter to form stable clusters. A special category of clusters is platoons. Platooning describes the automated coupling of vehicles by electronic means, with the first vehicle taking control [86]. The lead vehicle decides the speed with which the platoon will move, while the remaining vehicles regulate their speed to follow automatically. The advantages of this scheme are higher security and improved use of resources. Large transport vehicles naturally have a high air resistance, which can be reduced through slipstream effects within a platoon. This saves fuel and helps to make better use of road space [87]. A platoon can consist of an arbitrary number of vehicles, and as vehicles become members of the platoon, they can also form a social network on the fly [88]. After the creation of the platoon, the leading vehicles can serve as the clusterhead and take over most of the communications between the platoon and the outer network (see Figure 4).
## 4 Conclusions  
In this paper, the Yang-Church model is used to simulate the nonlinear vibration of bubbles in thermally viscous media. The effects of multifrequency ultrasonic combination mode, peak negative pressure and its duration, phase angle difference, and polytropic index on the transient cavitation threshold in different media are analyzed. The simulation results show that:  
1. On the premise of the same frequency difference and initial bubble radius, the transient cavitation threshold of the triple-frequency combination with higher frequency is higher than that of the triple-frequency combination with lower frequency. With the increase of the initial bubble radius, the threshold difference between the single-frequency ultrasound and the multifrequency combination increases, and the influence of the frequency difference on the transient cavitation threshold of the triple-frequency combination with higher frequency is more obvious.
2. When the lowest frequency of triple frequencies is the same, the larger the frequency difference is, the higher the corresponding transient cavitation threshold is. When the bubble radius is small, the frequency difference has little effect on the transient cavitation threshold of the triple-frequency combination.
3. The level of transient cavitation threshold is related to the peak negative pressure and duration of ultrasound. Compared with the single-frequency ultrasound, the introduction of a low-frequency component in the multifrequency combination can significantly reduce the transient cavitation threshold in the medium. However, the introduction of a high-frequency component in the multifrequency combination can significantly increase the transient cavitation threshold in the medium.
4. When the phase angle difference of the multifrequency excitation is zero, the corresponding transient cavitation threshold in the medium is the lowest. The change of the polytropic index has little effect on the transient cavitation threshold of different frequency combinations.  
The results of this study can provide guidance for multifrequency ultrasound in frequency combination, frequency difference, and phase angle selection, so as to better reduce the transient cavitation threshold in the medium, and provide reference for the design of tissue ablation treatment schemes in clinical application.  
[PERSON] conceived and designed the research idea and the framework; [PERSON] and [PERSON] performed the simulations; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON], and [PERSON] analyzed the data, [PERSON] modified the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by National Natural Science Foundation of China (Grant No.11774088, 11474090), the Hunan Provincial Natural Science Foundation of China (Grant No.2018 JJ3557), and the key Program of Changsha Normal University (Grant No. 2019 XJZK12).  
The authors declare no conflict of interest.
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 2 Related Work
### AD-Based Test Case Generation  
Previously, ADs have been mostly regarded as a modeling tool for business processes and workflows. However, ADs have a reach symbolic vocabulary that plays a significant role in specifying low-level system behaviors, which can be manipulated by automatic means for generating test cases. Currently, an extensive body of literature describes different strategies for using the ADs of the SUT for automated test case generation [6; 7]. However, most of the studies have investigated mainly by identifying abstract test scenarios (the sequence of activities) without clearly specifying their test data generation method [25; 26; 27; 28]. The most important aspects of identifying test cases are the decision on what behavior of the system to test with what data values. According to a systematic mapping study [7] on MBT using UML ADs, some of the commonly adopted methods for test data generation are category-partition, combinatorial logic coverage, data-object trees, and condition-classification trees. For instance, a gray-box method was proposed [29] to use a UML AD to generate test cases directly from ADs without the cost of creating an extra test model. That study adopted the category-partition method to generate a rational combination of input values and output values. Another UML AD-based test-case generation technique [30] transformed an AD into software success and fault trees using combinatorial logic coverage for test data generation. Data-object trees were also proposed for test data generation [31], along with a condition-classification tree method [10]. Another study on semantics and verification of data flow in activities was performed by [32]. The study defines formal semantics of ADs in UML 2.0 covering control flow, concurrency, and data flow.  
Most previous papers are poorly stated and do not precisely specify the test input generation methods. Furthermore, the support for those proposed approaches used very simple examples. Moreover, the results reported by those studies are mainly concerned with control flow-based testing issues from the HLBP. In this study, we examine the ADs from a lower level (system level behavior rather than business processes and workflows) to explicitly analyze the flow of data among activities. We also perform an experimental investigation using relatively extensive open-source systems.
## 4 Results and Discussion
### Vegetation Type Productivity Response to the Potential Environmental Variables
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Explanatory Variables** & **Estimated Coefficient** & **Standard Errors** & \(p\)**-Value** \\ \hline Fall precipitation & \(-0.17\) & \(0.03\) & \(1.5\times 10^{-6}\) \\ \hline Spring temperature & \(-1.06\) & \(0.39\) & \(0.007\) \\ \hline Aspect & \(6.57\times 10^{-5}\) & \(2.71\times 10^{-5}\) & \(0.016\) \\ \hline \multirow{4}{*}{_Whole model_} & \multicolumn{3}{c}{_Intercept = 0.32_} \\  & \multicolumn{3}{c}{_RMSE = 0.03_} \\  & \multicolumn{3}{c}{_Adjusted R-Square = 0.23_} \\  & \multicolumn{3}{c}{_p-value = 8.31 \(\times\) 10\({}^{-8}\)_} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of the stepwise regression model for the response variable (vegetation productivity for grasslands) showing the estimated coefficient, standard error, and \(p\)-value for only significant explanatory variables as well as for the whole model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Estimated Coefficient** & **Standard Errors** & \(p\)**-Value** \\ \hline Fall precipitation & \(-0.13\) & \(0.04\) & \(0.002\) \\ \hline Aspect & \(-1.32\) & \(0.39\) & \(0.007\) \\ \hline Elevation & \(6.57\times 10^{-5}\) & \(3\times 10^{-5}\) & \(1.9\times 10^{-5}\) \\ \hline \multirow{4}{*}{_Whole model_} & \multicolumn{3}{c}{_Intercept = 0.31_} \\  & \multicolumn{3}{c}{_RMSE = 0.036_} \\ \cline{1-1}  & \multicolumn{3}{c}{_Adjusted R-Square = 0.28_} \\ \cline{1-1}  & \multicolumn{3}{c}{_p-value = 3.091 \(\times\) 10\({}^{-10}\)_} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical results of the stepwise regression model for the response variable (vegetation productivity for pinon-juniper woodlands) showing the estimated error, standard error, and \(p\)-value for only significant explanatory variables as well as for the whole model.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.3.3 Random Forest  
RF belongs to the bagging algorithm in ensemble learning, and its basic unit is the decision tree. As shown in Figure 4, RF utilizes multiple decision trees for prediction or classification, and the final result is the average or voting result of all decision trees. In constructing each decision tree, RF employs two randomization methods: bagging and random feature selection. Random feature selection is to randomly select a subset of features in the feature set for tree node partitioning, which can reduce the correlation between decision trees and further improve the performance of the model [35].  
The general steps of RF are as follows:  
1. Bag a certain amount of data from the original database as a data subset. Then, randomly select a feature subset from the original feature set.
2. Build a decision tree for the data and feature subsets. At each tree node, select the optimal features from the feature subset for splitting.
3. Repeat steps (1) and (2) to construct multiple decision trees. Each decision tree is constructed from a different subset of data and a subset of features.
4. For classification problems, the results of all decision trees are voted to obtain the final classification result. For regression problems, the results of all decision trees are averaged to obtain the final value.  
RF is considered as an ensemble algorithm with the advantages of high accuracy, resistance to overfitting, parallelized training, flexibility, and ease of use. Therefore, RF is widely used in various areas to solve classification and prediction problems [36].