esa-sceva/satcom-chunk-collection huggingface dataset:
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 7 Experiments
### Statistical Analysis  
We perform experiments to verify if the results obtained by the heuristics using the proposed algorithm are statistically better than the results obtained by the original heuristics proposed by [12].  
We performed the paired \(t\)-test on the data of each graph presented in the previous section to verify if the means of \(T_{total}\) obtained by Incremental-Move are statistically smaller than the means obtained by Incremental and the same with Decremental-Move and Decremental. The \(t\)-test is a type of statistical test used to compare the means of two groups of values and evaluate if they are significantly different from each other [25]. The \(t\)-tests can be divided into two types: independent and paired. The independent \(t\)-test is used when the two groups under comparison have no relation to each other. The paired \(t\)-test is used when the two groups under comparison are dependent on each other [26]. In our analyses, we used the paired \(t\)-test because both individuals in each pair used the same heuristic to create the tour and the same network topologies.  
Since the \(t\)-test can be used only to analyze samples with normal distribution, we first applied the Shapiro-Wilk test [27] on each sample that generated a mean to plot in the graphs. All samples presented normal distribution. Then, we applied the \(t\)-test on each pair of samples with the same heuristic and the same value in the X-axis. For example, in the Scenario 1 (Section 7.1), we calculated all the values of the t observed for Incremental vs. Incremental-Move, when \(X=20\), 40, 60, 80, 100, and 120 Kbps. The same was made for Decremental vs. Decremental-Move. These values are in Table 3. The values of the observed t for the Scenarios 2, 3, and 4 (Section 7.2) are presented in Tables 4-6, respectively. However, in Scenario 1, we analyzed only the samples plotted in the Figure 11. It is because this graphs presents the main metric (\(T_{total}\)). The other graphs in this subsection were created only to explain the curves' behavior in the first graph.  
Figure 16: Varying the drone speed (m/s)—Larger Monitored Area.  
The number of degrees of freedom of these experiments is \(N-1=29\), where \(N\) is the number of executions with different network topologies. We set the significance level to 0.001 (0.1%). The critical \(t\), obtained from the table, is 3.659.  
In all scenarios, we can verify that the observed value \(t\) is greater than the critical value \(t\). Hence, we can affirm with 99.9% confidence that the proposed algorithm reduces the overall data gathering time substantially.
## 3 Experiment
### Experimental Set-Up  
The experimental system consisted of the stress-loading system (Figure 2a, the maximum load was 2000 tons), the acoustic emission acquisition system (Figure 2b, the maximum sampling rate was 100 MHz, this experiment used 25 MHz), and the stress and strain monitoring system (Figure 2c, the resolution was 16 bit).  
Figure 1: (a) Rock sample; (b) the sample coated with silicone sealant; (c) cylindrical expansion view of the installation position of transducers and strain gauges.  
Figure 2: Schematic diagram of the experimental system (citing from [49]).  
As shown in Figure 3, the experiment lasted 7.5 h and could be roughly divided into five stages: hydrostatic loading stage (L0), water-saturation stage (L1), axial loading stage (L2), hydraulic fracturing stage (L3), and rock fracture stage (L4). In the L0 stage, the axial (\(P_{\rm a}\)) and confining pressure (\(P_{\rm c}\)) were simultaneously increased to 35 MPa at a loading rate of 2 MPa/min. At the L1 stage, we kept \(P_{\rm c}\) at 35 MPa and increased the axial pressure to 132 MPa at a loading rate of 2 MPa/min. Then, distilled water was injected into the borehole continually. About half an hour later, the water injection pressure (\(P_{\rm in}\)) reached 4.7 MPa, and the flow rate was stabilized at 0.001 mL/min. In the L2 stage, we kept \(P_{\rm c}\) and \(P_{\rm in}\) constant and increased \(P_{\rm a}\) to 176 MPa (65% of the strength of dry rock under the same confining pressure). In the L3 stage, we kept \(P_{\rm a}\) and \(P_{\rm c}\) constant, and water was injected into the sample gradually. After about half an hour, the flow rate was stabilized at 0.02 mL/min, and after about 100 min, the water pressure was increased to 16.25 MPa. In the L4 stage, the water injection pressure began to decline slowly, and about 13 min later, the water injection pressure and axial pressure suddenly decreased rapidly, and the rock ruptured. Finally, axial loading stopped automatically.  
Figure 3: The loading history of the sample. The temporal variations of **(a)** stress; **(b)** water injection rate and volume; **(c)** strain and acoustic emission count. The experiment can be roughly divided into four stages: hydrostatic loading stage (L0), water-saturation stage (L1), axial loading stage (L2), hydraulic fracturing stage (L3), and rock fracture stage (L4). The black dotted line marks the demarcation points for each stage.
## 2 Related Work
### Fractal Pattern Generation  
At present in the reprinting and dyeing industry, pattern generation based on fractal geometry is a research hotspot.  
[PERSON] [21] has used the Fourier transform to directly generate as a batik master pattern a pattern with fractal features. [PERSON] [22] has proposed a fractal pattern generated by an L-system as a decorative pattern. In the literature [23; 24; 25], starting from the fractal characteristics of the batik pattern, the above fractal technique has been used to generate various fractal patterns similar to traditional batik patterns. The fractal pattern is similar to the traditional batik pattern in spirit but not in shape. [PERSON] [26] and his team, the \"Pixel People Project\", developed the software jBatik for the simulation of Indonesian-style digital wax patterns based on fractal algorithms; the software is not universal for printing and dyeing patterns in other areas such as China and Africa, however.  
[PERSON] [27] has used Visual Basic language to study single-rule and multi-rule algorithms in a deterministic L-system and has obtained a rich and varied L-system graph by transforming the parameters in the algorithm. However, only a few pattern generation methods can be implemented, so it is necessary to study the generation characteristics and rules of other types of maps. [PERSON] [28] has proposed using an iterated function system to generate a bionic butterfly pattern as a batik pattern. However, this method can only generate butterfly patterns and is not universal. [PERSON] [29] has improved the shape grammar and applied it to the formal expression of pattern configuration and at the same time has proposed the manual and automatic extraction of configuration based on this method. However, this method cannot extract fractal patterns, and the efficiency of the algorithm has room for improvement.  
[PERSON] [30] has proposed a fractal pattern generation method based on the matrix Kronecker product and has applied it to the design of Wangjiang cross stitch. It is necessary, however, to establish a pattern Boolean matrix model and carry out multilevel fractal processing. [PERSON] [31] has used the programming software Visual Basic6.0 to study the generation method of the Mandelbrot set and Julia set on a complex plane to realize its visualization. A series of varying fractal maps were obtained by changing the form of the iterative function and the fractal pattern was applied to the textile using printing techniques. However, the fractal image needed to be selected and processed with the help of Photoshop which is an image processing software. [PERSON] [32] has used the C++ language to write a fractal pattern based on generator and escape time algorithms and has conducted a secondary design of the generated pattern in pattern design software. Finally, the designed pattern program was woven.  
In this paper, fractal geometries using a combination of an iterated function system and curve function can not only automatically generate batik flower patterns but also can automatically generate a series of different flower patterns merely by modifying the parameter values.
## 4 Redundant Transmission Control Clustering Approach
### Data Collection at Cluster Heads
#### 4.4.2 Redundancy Elimination at Cluster Heads  
To eliminate the data redundancy, our designed algorithm checks the correlated data sets through the HSD test and yields a logical value. Primarily, this designed Algorithm 1 computes the value of \(S_{out}\) and the threshold value \(T_{DOF}\) by applying the HSD test. Finally, it yields a Boolean value if the threshold \(T_{DOF}\) is greater than the variance between their measures. When the tests are completed the CH splits up and erases the similar data sets from the existing data sets. Our algorithm 1 intelligently decides which data is conveyed toward the final destination from the available data sets. Instead of forwarding all the information, only the selected information is conveyed to the RH with higher measures to increase the system efficiency and resources.  
```
Require: set of Cluster Heads \(CHs=\{CH_{1},CH_{2},...,CH_{l}\}\). Ensure: list of the selected sets, I. \(l\leftarrow\emptyset\) for all for each Cluster Head \(CH_{i}\in CHs\)do  consider the set \(CH_{j}\) has the longest cardinality in \(CH_{i}\), (i.e., \(|CH_{j}|>|CH_{j=i}|\); where \(CH_{j=}\in CH_{i}\) ) \(l\gets l\bigcup CH_{j}\) endfor  return 1
```  
**Algorithm 1** Data-redundancy Elimination Algorithm
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
## 2 Materials and Methods
### Meteorological Data  
Meteorological data for Palermo, Bari, Ferrara, Padua, and Venice during a period running from 2012 to 2021 were used to analyze the effect of weather on the crops.  
Rainfall data from an online database of the Italian weather station were considered [25]. The site took into consideration the monthly in rain millimeters, then an average was made for all the years considered to determine the effect on crops according to the crop's life cycle--April/November for corn and October/June for wheat (Tables 8 and 9).  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Durum Wheat**} \\ \cline{2-4}  & **Production, t** & **Area, ha** & **Yield, t/ha** \\ \hline
2012 & 133,021.2 & 17,054 & 7.8 \\
2013 & 170,040 & 22,672 & 7.5 \\
2014 & 100,599.4 & 17,642 & 5.7 \\
2015 & 108,071 & 16,130 & 6.7 \\
2016 & 127,375 & 19,215 & 6.6 \\
2017 & 104,326 & 15,599 & 7.1 \\
2018 & 136,075 & 21,887 & 6.2 \\
2019 & 108,176 & 19,550 & 5.5 \\
2020 & 113,666 & 18,099 & 6.3 \\
2021 & 145,019 & 21,553 & 6.7 \\ \hline \end{tabular}
\end{table}
Table 6: Agricultural production of durum wheat in Padua in the period 2012–2021.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**Corn**} \\ \cline{2-6}  & \multicolumn{3}{c}{**Padua**} & \multicolumn{3}{c}{**Venice**} \\ \cline{2-6}  & **Production, t** & **Area, ha** & **Yield, t/ha** & **Production, t** & **Area, ha** & **Yield, t/ha** \\ \hline
2012 & 302,900.4 & 68,841 & 4.4 & 240,500 & 84,100 & 2.8 \\
2013 & 504,823 & 53,371 & 9.5 & 419,144 & 48,012 & 8.7 \\
2014 & 635,500 & 50,840 & 12.5 & 535,937.5 & 42,875 & 12.5 \\
2015 & 374,620 & 37,460 & 10.0 & 316,690 & 31,602 & 10.0 \\
2016 & 394,248 & 37,460 & 10.5 & 346,694 & 31,602 & 11.0 \\
2017 & 300,631 & 36,356 & 8.3 & 294,046 & 30,702 & 9.5 \\
2018 & 270,009 & 27,847 & 9.7 & 277,139 & 25,965 & 10.7 \\
2019 & 304,395 & 34,881 & 8.7 & 293,023 & 31,376 & 9.3 \\
2020 & 355,479 & 32,559 & 10.9 & 354,154 & 30,569 & 11.6 \\
2021 & 301,495 & 30,438 & 9.9 & 310,534 & 30,588 & 10.1 \\ \hline \end{tabular}
\end{table}
Table 7: Agricultural production of corn in Padua and Venice in the period 2012–2021.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Rain, mm**} \\ \cline{2-5}  & **Palermo** & **Bari** & **Ferrara** & **Padua** \\ \hline
2012/2013 & 588.0 & 464.3 & 878.1 & 1147.8 \\
2013/2014 & 529.1 & 581.3 & 669.1 & 1049.0 \\
2014/2015 & 649.7 & 454.6 & 596.9 & 746.0 \\
2015/2016 & 508.2 & 526.2 & 651.5 & 828.4 \\
2016/2017 & 363.8 & 366.3 & 437.8 & 541.8 \\
2017/2018 & 126.1 & 133.4 & 314.3 & 466.4 \\
2018/2019 & 356.5 & 247.5 & 411.8 & 389.0 \\
2019/2020 & 166.6 & 124.0 & 423.8 & 336.8 \\
2020/2021 & 135.4 & 147.5 & 375.4 & 547.0 \\
2021/2022 & 190.8 & 140.5 & 214.1 & 297.5 \\ \hline \end{tabular}
\end{table}
Table 8: Millimeters of rain measured in the considered Italian cities in the period October–June 2012–2021.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 2 Background
### Material Data Fusion  
Data fusion can be generally accomplished in three layers, namely data layer, feature layer, and decision layer. One problem that often arises in the data layer fusion is the data conflicts in multi-source heterogeneous data. These conflicts are difficult to be accurately resolved by regular computer programming methods. At the same time, multi-source material data are often with multiple formats. Most material data do not have a uniform representation standard. Therefore, it is necessary to unify the data representation of material data before data fusion. In addition, when dealing with multi-modal material data, there will be more troubles, e.g., chart data, text data and image data are difficult to be integrated perfectly. Material data includes, not only static data such as text and XML, but also dynamic data such as functional graphs that describe how a variable is constantly changing in a certain process. Another problem comes from the fusion of dynamic data, such as the fusion of functional curves from multiple data sources. Since the function represents the relationship between the dependent variable and the independent variable over a time period, the curve contains the numerical pairs corresponding to the infinite number of time points. Therefore, it is very difficult to judge which function curve can most accurately describe the process. In addition, the data describing the 3D model of the molecular composition [8] of the material is also difficult to be fused, and the multimodal data contained in the multi-source heterogeneous material data is also difficult to integrate. In addition to the above problems, feature extraction of material data is also a difficult task. Meanwhile, when judging the results of the fusion at the decision-making level, certain expert support is needed due to the high professionalism of material science.
## 2 Materials and Methods
### Dynamic Vapor Soprtion Tests
If the further evolution of the mass does not exceed this limit value and reaches the total length of time required to achieve stability, the system assumes the equilibrium point has been reached and moves onto the next point. If the maximum value of the derivative is exceeded during this time, the device resets the timer and when the derivative falls below the limit, the counting of time starts again. The minimum duration indicates the time from which the derivative  
Figure 3: (**a**) Sample pan with a sample in the chamber; (**b**) detail of dynamic vapor sorption (DVS) manifold; (**c**) DVS device with computer. is calculated. It is not always possible to realize the DMDT criterion, and the maximum duration of the step is then limited by the last parameter, indicating the maximum step duration. If the criterion is still unattained, the measurement moves to the next point after the maximum time is reached. Sometimes it is appropriate to use a combination of both fixed-duration and DMDT modes for the measurement. The Surface Measurement Systems DVS Resolution was used to measure the sorption parameters. Each bulk density was measured several times. The weight of the samples ranged from 36 to 55 mg. Initially, the samples were placed in the desiccator with a potassium sulphate solution with a relative humidity at 97% for two weeks to saturate the samples naturally. Subsequently, the cycle of sequential desorption and sorption was measured twice for each sample. Detailed steps of the method used are shown in Table 4, in the column of the first and second measurement. After measuring the first two cycles, the samples were stored in the laboratory for about a month. They were then placed in the desiccator again, with a potassium sulphate solution, for one week. After conditioning, one cycle was repeated, including desorption and sorption, with a four-hour drying period in between. This procedure is captured in Table 4.  
The desorption curve included eight relative humidity points of 95, 90, 85, 80, 60, 40, 20 and 0%. The sorption curve contains the same values in reversed order. Prior to the measurement itself, the sample in the DVS system was conditioned in a 97% relative humidity environment to eliminate moisture loss during the sample transfer from the desiccator to the DVS. The individual points on the curve used the DMDT criterion. For relative humidity points up to 80%, the DMDT derivation value was set to 0.0005%/min as a criterion for reaching the steady state. The criterium value for humidity from an 80% relative humidity was lower (0.0002%/min) to achieve a higher accuracy. The DMDT criterion has the advantage of saving a measurement cycle time at a low relative humidity, where rapid stabilization occurs. The DMDT criterion monitors the change in the weight of the sample over time. However, there is a risk of a slower sorption response of the material, which
## 4 Materials and Methods
### Model Selection Approach
#### 4.3.6 Boosting Algorithms  
Boosting algorithms are a useful option when working with weak estimators. Boosting hierarchically builds a model, attempting to minimize the error over time. The three most popular ensemble learning models that implement gradient boosting algorithms are XGBoost ([[https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)]([https://xgboost.readthedocs.io/en/stable/](https://xgboost.readthedocs.io/en/stable/)) (accessed on 28 March 2023)), LightGBM ([[https://lightgbm.readthedocs.io/en/v3.3.2/](https://lightgbm.readthedocs.io/en/v3.3.2/)]([https://lightgbm.readthedocs.io/en/v3.3.2/](https://lightgbm.readthedocs.io/en/v3.3.2/)) (accessed on 28 March 2023)) and Catboost ([[https://catboost.ai/](https://catboost.ai/)]([https://catboost.ai/](https://catboost.ai/)) (accessed on 28 March 2023)). While the first two algorithms utilize asymmetrical trees--with XGBoost growing vertically and LightGBM horizontally, Catboost relies on symmetrical trees.  
These algorithms have performed well on many different prediction tasks so far. For our task, we selected XGBoost due to it relying on asymmetrical trees that expand level-wise rather than leaf-wise, as well as the splitting method it uses. Additionally, recent works in personality computing have reported promising results when using XGBoost for prediction of the MBTI personality types [111]. However, it should be noted that boosting algorithms are not advised for smaller sets of data or in cases where the features outnumber the data samples as this can lead to overfitting.  
These factors can potentially pose an issue in the case of the PANDORA dataset, as the amount of Big Five labels is relatively small. In order to minimize the risk of overfitting, we used 100 early stopping epochs [112] as well as performed five-fold cross validation during the training process.  
Since XGBoost uses many different hyperparameters, it is difficult to tell which combination would lead to most optimal results. For this purpose, we relied on the Optuna ([[https://github.com/optuna/optuna](https://github.com/optuna/optuna)]([https://github.com/optuna/optuna](https://github.com/optuna/optuna)) (accessed on 28 March 2023)) package for searching the hyperspace, in an effort to find the best possible combination of parameters for our experiments. Table 3 lists the parameters and their values as calculated by the optimization package used in our work.
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 2 Methods
### Vehicle Model  
This study takes the CRH3 vehicle (a vehicle which widely operates in the HSR system in China) as the modeling target, which is made up of one car body, two bogies, four wheelsets, suspension systems, etc. The vehicle model was developed according to multibody dynamic theory, and a rigid body with six degrees of freedom (DOF), which are in the vertical, lateral, longitudinal, pitching, yawing, and rolling directions, was employed to simulate the car body, bogies, and wheelsets. As a result, the elastic deformations of the components were ignored, and the total number of DOF was 42. Further, the suspension systems, which consist of the primary and secondary suspensions, were regarded as nonlinear springs connecting the car body, bogies, and wheelsets. This rigid vehicle model can be adopted to investigate the vibration of the vehicle-track-bridge system. However, due to neglect of the elastic deformation of the wheels, the rigid vehicle mode is deficient for investigating rail corrugation, wheel polygons, etc. A topological graph of the vehicle model is shown in Figure 1.  
Figure 1: A topological graph of the vehicle model.  
The dynamic formula of the vehicle motion is presented in Equation (1):  
\[\mathbf{M}_{v}\cdot\ddot{\mathbf{Z}}_{v}+\mathbf{C}_{v}\cdot\dot{\mathbf{Z}}_{v}+ \mathbf{K}_{v}\cdot\mathbf{Z}_{v}=\mathbf{F}_{v}, \tag{1}\]  
where \(\mathbf{M}_{v}\), \(\mathbf{C}_{v}\), and \(\mathbf{K}_{v}\) denote the mass, damping, and stiffness matrices of the vehicle model; \(\ddot{\mathbf{Z}}_{v}\), \(\ddot{\mathbf{Z}}_{v}\) and \(\mathbf{Z}_{v}\) refer to the acceleration, velocity, and displacement vectors; and \(\mathbf{F}_{v}\) represents the force vector, which includes the external and internal force of the system. The parameters of the CRH3 vehicle are presented in Table 1.
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 2 Tubular Linear Eddy Current Damper with Halbach Array (TLECD-HA)  
### Configuration of the TLECD-HA  
The TLECD's longitudinal cross-section is shown in Figure 1. The Halbach permanent magnet array, which has been demonstrated to improve the performance of ECDs, has been employed [32; 34]. The Halbach array has been widely used in the fields of permanent magnet motors [35], nuclear magnetic resonance [36], and eddy current braking [37]. However, for the application of the TLECD, the form of a combination of PMs and pole shoes is usually a better choice because of the difficulty to manufacture magnetic rings with radial magnetization. Figure 1 depicts that the tubular linear eddy current damper with Halbach array (TLECD-HA) is mainly made of PMs, a conductor cylinder, back irons for the conductor cylinder and PMs, and two end connections. One connection is connected to the conductor by an end cap, and another is connected to the PMs by a shaft. As a result,the relative motion between the structures results in the relative motion between the PMs and the conductor cylinder, which generates the damping force.