esa-sceva/satcom-chunk-collection huggingface dataset:
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 2 Dynamic Modeling for Morphing Aircraft with SJA
### Equations of Motion  
While the synthetic jet-based control can improve the aerodynamic performance effectively, the fluctuation of the aerodynamic force induced by the periodic excitation of SJA is inevitable, as shown in Figure 4. Notice that the main frequency component of the unsteady response is close to the forcing frequency \(f\), which is equal to \(f_{\text{s}}\) in this work [28; 30]. Based on Equations (3), (6) and (9), the following equations of motion arrive at  
\[\left\{\begin{array}{ll}M_{x}&=M_{x}^{\text{jet}}+\Delta M_{x}^{\text{morphing }}\\ &=-47.07\omega_{x}+60.54\delta_{\text{jet}}-60.54\delta_{\text{jet}}+45.78 \delta_{\text{morphing}l}-45.78\delta_{\text{morphing}}+804.25\sin 2\pi f_{ \text{s}}t\\ \dot{\gamma}&=\omega_{x}\\ \delta_{\text{jet}}&=-44.10e^{-0.1766\overline{U}_{\text{jet}}}+44.10\\ \delta_{\text{morphing}}&=(\Delta L)^{3}\end{array}\right., \tag{10}\]where \(M_{x}\) is the total roll moment. It is obvious that Equation (10) can be expressed in state space:  
\[\dot{x}=Ax+B_{1}u_{\text{jet}}+B_{2}u_{\text{morphing}}+D\sin 2\pi f_{s}t, \tag{11}\]  
with \(A=\left[\begin{array}{cc}-47.07/J&0\\ 1&0\end{array}\right]\), \(B_{1}=\left[\begin{array}{cc}60.54&-60.54\\ 0&0\end{array}\right]/J\), \(B_{2}=\left[\begin{array}{cc}45.78&-45.78\\ 0&0\end{array}\right]/J\), \(x=\left[\begin{array}{c}\omega_{x}\\ \gamma\end{array}\right]\), \(u_{\text{jet}}=\left[\begin{array}{c}\delta_{\text{jet}}\\ \delta_{\text{jet}}\end{array}\right]\), \(u_{\text{morphing}}=\left[\begin{array}{c}\delta_{\text{morphing}}\\ \delta_{\text{morphing}}\end{array}\right]\), and \(D=\left[\begin{array}{c}804.25/J\\ 0\end{array}\right]\), where \(J\) = 50 kg\(\cdot\)m\({}^{2}\) is the moment of inertia.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
###### Abstract
The contributions of this study are as follows:  
* In a TSCH network, a method for minimizing the delay time by constructing a static path for downlink traffic from the root node to the mobile node is proposed, while the effect on the schedule between the existing stationary nodes is minimized. * In the process of supporting node mobility in a TSCH network, we propose a technique to reduce the control overhead incurred in a contention-based existing shared cell by allocating a dedicated cell for an RPL control message. * A technique to quickly select an alternate parent node in response to the change in link quality of the mobile node and supplement the path to minimize the omission of request packets, and to transmit packets generated from the root node without delay. * When forming the routing path of the mobile node in the RPL protocol through the dedicated control cell, the possibility of collision of control messages is reduced by omitting the DIS packet propagation process. The remainder of this paper is organized as follows: In Section 2, we examine the 6 TiSCH network stack proposed by the IETF, and the background knowledge and operation process of the IEEE 802.15.4 TSCH, IETF 6 top [5], and IETF RPL [6]. Section 3 discusses the participation of nodes in the network, the process of synchronization and topology formation, the problem of the network in which the mobile node exists, and the fast cell allocation scheduling method of the mobile node. Section 4 presents the results of the experiments using the 6 TiSCH simulator. Section 5 presents the conclusions.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Management Based on the Pixel-Level Grid
#### 2.2.4 Data Ingestion  
The width of the imagery data of specific types and sensors is fixed. For example, the GF-1 WFV sensor data has achieved an imaging width greater than 800 km. When the study area is much smaller than the coverage of one imagery data scene, cropping and mosaicking are required, which add to the heavy data processing for specific tasks. The proposed framework has a data ingestion process that subdivides the original scene data into data blocks with customized volumes, thus enabling fast and flexible access to and operations on small study area data.  
During data ingestion, the proposed framework created and executed a YAML document containing information about custom slice sizes, specific projection formats, and specific latitude and longitude range so that the user-imported imagery data could be sliced, resampled, and compressed to generate easily computable data slices in the NetCDF (Network Common Data Form) [42] format and store them in the local file system. As a computer- and application-independent self-describing file format, NetCDF enables efficient storage and management of matrix data in heterogeneous network environments and is widely used in research areas such as meteorological sciences and geophysics [43]. In the meantime, we created dynamic B-tree-based indices for data slices in the PostgreSQL database to facilitate data query and analysis. Due to the insecurity of network storage and transmission, especially for the high-resolution GF-1 imagery data, local file system storage was adopted for the proposed framework.
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 4 Application to OpenSSL AES
### Experimental Results of General SOCPA and SIV-Based SOCPA  
In this section, we demonstrate that the correlation coefficient of the SIV-based SOCPA is considerably higher than that of General SOCPA. The PC and AD are utilized as preprocessing functions, and the results for AD are presented in Appendix A. The experimental environment is the same as that described in Section 4.1. We analyze the power consumption that occurs while performing OpenSSL AES on ChipWhisperer UFO STM32F (32-bit processor) [16].  
To perform SOCPA, the analyst must combine two intermediate values concealed by the same mask. The inputs of the MixColumns share the same mask by row, as shown in Figure 1 [Step 4]. Our attack scenario performs minimum times of the attacks to recover the entire secret key. We analyze each row of the MixColumns by dividing it into two pairs. Thus, the combination of byte indexes of intermediate value for analysis is \((00,04),\ (01,05),\ \cdots,\ (11,15)\). Recall that the state of AES is a column-major order array.  
Figure 6 shows the bytewise peak correlation of two versions of the SOCPA. The correlation of the SIV-based SOCPA for every combination is higher than that of the General SOCPA, and the average correlation of the SIV-based SOCPA is approximately 1.7 times higher than the General SOCPA. The correlation of the SIV-based SOCPA is not 1.00 because the remaining 24 bits behave similar to noise.  
Furthermore, we find the minimum trace to disclose (MTD) to illustrate that the SIV-based SOCPA is more effective, i.e., it can reveal the secret key with less information than the General SOCPA. Figure 7 shows the MTD for a combination of 1 st and 5 th bytes. In this figure, the SIV-based SOCPA can disclose the secret key with only about 34% of the power trace than that required by the General SOCPA.  
Figure 8 shows the MTD of every combination of intermediate values; the maximum MTD of the SIV-based SOCPA is 1717, and that of the General SOCPA is 6643. Therefore, SIV allows the disclosure of the entire secret key using only a quarter of the trace required by the General SOCPA.  
Figure 6: Peak correlation coefficient of the two versions of SOCPA on MixColumns (Product-Combining).
## 5 Results and Discussion
### ANFIS Simulation Results  
Sixty experimental data records were used to verify the performances of the ANFIS models. To improve the ANFIS model, approximately 75% of the data were used for training and the remainder for testing the performance. In Table 4, the training and testing experimental data are specified at the different run numbers. Figure 8 shows that the training optimized structure was selected as ANFIS, based on the percentage error minimum at 25 epochs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Runs** & **Type of Material** & **Depth of Crack** & **Width of Crack** &
\begin{tabular}{c} **Percentage of** \\ **Signal** \\ \end{tabular} \\ \hline Training data & & & & \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Training and experimental testing data.  
Figure 7: Predicted and actual values of the signal percentage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Runs** & **Type of Material** & **Depth of Crack** & **Width of Crack** &
\begin{tabular}{c} **Percentage of** \\ **Signal** \\ \end{tabular} \\ \hline  
5 & 0.6 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._The relative error results of ANFIS model are shown in Figure 9 for training data, where the percentage error (\(ER\)) for the input test variable and the average error (\(AER\)) are estimated as follows:  
\[ER\%=\frac{\left|Y_{Ex}-Y_{pre}\right|}{Y_{Ex}}*100 \tag{5}\]  
\[AER\%=\frac{1}{N}\sum_{i=1}^{N}(ER\%) \tag{6}\]  
where \(ER\%\), \(Y_{EX}\), and \(Y_{Pre}\) are the error percentage values, experimental test data, and ANFIS predicted values, respectively.  
The ANFIS models for training and testing data are shown in Figure 10. The numerical and predicted values of the average percentage of the signal using the ANFIS model are in good agreement with the \(R\)-squared value of 0.9983. Table 5 shows that the maximum percentage error and average percentage error were approximately 0.000998 and 0.000493, respectively.  
Figure 8: ANFIS training.  
Figure 9: Percentage error of ANFIS models for training data.
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 3 Materials and Methods
### Data Processing
#### 3.2.2 Correlation of Dependent Variables  
Using copula-based multivariate modeling requires the correlation between dependent variables to be evaluated. When historical data are lacking, a subjective evaluation of the correlation from experts is acquired. Here, the Spearman correlation (\(\rho\)) is used to measure the association between random variables because of its ability to capture relationships through a pairwise measure of dependence [25; 33]. While the Spearman correlation coefficient varies between \(-1\) and \(+1\), most correlations between cost and schedule in construction are positive, limiting the values in this application to between 0 and 1 [34]. Correlations are classified into three categories, as proposed by [PERSON] [34], to reflect the linguistic representation of correlations often used by experts, namely weak (0-0.3), moderate (0.3-0.6), and strong (0.6-1). The midpoint of each interval is chosen to represent the interval (i.e., 0.15 for weak, 0.45 for moderate, and 0.8 for strong). Consequently, the correlation matrix (\(R\)) will take one of the following forms:  
\[R_{\text{weak}}=\left[\begin{array}{cc}1&0.15\\ 0.15&1\end{array}\right], \tag{12}\]  
\[R_{\text{moderate}}=\left[\begin{array}{cc}1&0.45\\ 0.45&1\end{array}\right],\text{ and } \tag{13}\]  
\[R_{\text{strong}}=\left[\begin{array}{cc}1&0.8\\ 0.8&1\end{array}\right] \tag{14}\]
## 2 Synthesis of Cyclodextrin-Based Adsorbents
### Immobilization  
Immobilization involves immobilizing material on the solid supporter by physical or chemical methods. Besides the preparation concept, immobilization also refers to the different types of adsorbents with a macromolecular structure used as a supporter. Immobilization is dependent on the kind of supporter for CDs and is categorized into two separate parts: fiber composites and magnetic materials.  
Fiber composites possess a stable property with several functional groups. CDs immobilized by the chemical cross-linking process primarily produce fibers. To prepare polymer fibers, N-isopropyl acrylamide (NIPAM) could settle cyclodextrin molecules to obtain a porous material, and it has been found to be helpful to remove more than a gram of crystal violet dye by a gram of adsorbent [70]. Moreover, electrostatic force has been a widely utilized method for immobilization. An environmentally-friendly composite fibrous adsorbent, synthesized from gelatin and \(\beta\)-CD molecules by the electrospinning method, has been used for Methylene Blue (MB) [71]. Carbon nanofibers have been used as adsorbents in several applications and have been used as carriers for decades. They have also shown a higher adsorption capacity when CD molecules are introduced. Furthermore, [PERSON] and co-authors prepared CD-based carbon nanoparticles via electrospinning. They gained a larger specific surface area, more porous structure, and larger pore volume than typical carbon nanofibers [72].  
Natural fibers refer to materials derived from natural resources, like straw, cotton, and wood. CD was introduced onto cotton fibers by chemical interaction to enhance the adsorption capacity for Methyl Blue and Congo Red (CR) [73]. For dyes, macromolecules have good properties and have higher adsorption capacities when connected with \(\beta\)-CDs.  
However, magnetic adsorbents have more practical applications, compared to fibrous composites. Iron oxide (Fe\({}_{3}\)O\({}_{4}\)), a standard magnetic material, has been extensively used as an excellent solid supporter [74] in the last decade. CD introduced with Fe\({}_{3}\)O\({}_{4}\) has been extensively used to remove heavy metals and dyes. By chemical reaction, \(\beta\)-CD is modified on Fe\({}_{3}\)O\({}_{4}\) NPs when reacted with CS (CDCM) [75]. Similarly, CD immobilized EPI cross-linked with Fe\({}_{3}\)O\({}_{4}\) could be effective for dyes and heavy metals [76]. Therefore, graphene oxide (GO) has also been utilized as a magnetic support for the adsorbent, and this offered excellent removal of atrazine (ATZ) [77]. By using the magnetic properties of iron oxide, the adsorbent can be rapidly and easily extracted from water through magnetic attraction.
## 5 Experimental Results
### Preprocessing: Time Series Representation  
#### 5.2.1 Preprocessing of the Companies News Data: Sentiment Analysis  
For sentiment analysis of the companies' news, firstly we have chosen randomly twenty one texts of the news. Then, we evaluated the results of two APIs, AYLIEN API [14] and SentiWordNet [13], to select the one that gives the best results. As we can see in the Supplementary Material file, in Table S1, the two APIs have no difference in the total percentage of the right results of sentiment analysis.  
We have chosen AYLIEN API to continue with our experiments. This API provides the sentiment score (positive, negative and neutral). For the creation and the representation of the time series of news, we matched these scores with the values 1, \(-\)1 and 0, respectively. Then, the sum of sentiment analysis results about each day was calculated. For instance, Table S2 of the Supplementary Material file shows a short excerpt of the sentiment analysis results of Apple news on 7 August 2015. In this excerpt, the sentiment score of this day's news is equal to four. In the same way, the sentiment score was calculated for the other days of Apple and for the other four companies. Using these sentiment scores, we created the time series of news. We called these time series sentimentScore time series. Table S3 of the Supplementary Material file shows all sentiment scores per day for the period 20 April 2015 to 30 October 2015 for the five stocks.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **AAPL** & **GE** & **IBM** & **MSFT** & **ORCL** \\ \hline
**Number of news** & 4282 & 1506 & 1549 & 2577 & 575 \\
**Number of tweets** & 310,503 & 46,237 & 56,804 & 67,107 & 16,494 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total data for 137 days (20 April 2015 to 30 October 2015).
# PFAS: The Journey from Wonder Chemicals to Environmental Nightmares and the Search for Solutions
## 4 Remediation and the Future Use of PFAS  
### Remediation Methods  
As the toxicological effects increase on wildlife and humans, due to the bioaccumulation and extreme persistence of the environment, remediation of PFAS has grown concerns rapidly. Effective 8 th of July 2024, the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund), the Environmental Protection Agency (EPA) is designating PFOS and PFOA, two of the oldest and most used PFAS molecules are hazardous including their salts and structural isomers. But over the last decades, there are many PFAS that have been produced including PFOA, PFOS, and their precursors in large quantities and require more regulatory attention [89].  
The current PFAS remediation techniques for water treatment can be categorized as _separation_ and _degradation_. Figure 4 illustrates the different techniques in each category adopted from the existing remediation techniques for the other contaminants. _Adsorption_ is an effective, eco-friendly, and economical separation method designed to be used to treat PFAS contaminants. It is important to select a suitable absorbent with an appropriate adsorption capacity and efficiency based on the sorbent properties such as pore structure. Solution pH is also an important factor in adsorption as pH can change the surface charge. The widely used adsorption technique in PFAS removal is using activated carbon, ion exchange resins, or polymers. But this concentrates PFAS from the liquid phase to the solid phase and it requires suitable treatment approaches. This has been considered as a disadvantage of this process. Other commonly used separation methods are filtration technologies including nanofiltration and _reverse osmosis_[90]. These filtration processes are effective for removing all PFAS chains but are more expensive than adsorption methods. _Foam fractionation_ is also one of the emerging technologies, that introduces gas bubbles into the liquid to concentrate surface-active PFAS molecules at the gas-liquid bubble interface and separate for further treatment or disposal. _Ozor fractionation_ combines foam fractionation with ozone, which is widely used in the aquaculture industry. Even though it can be used to remove a wide range of contaminants with different concentrations of PFAS, it will not be a suitable technique to achieve low regulatory limits in ng/L and need other treatment processes to enhance the destruction effect.  
Figure 3: Direct PFAS exposure pathways in humans in different stages of human lifetime.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
###### Abstract  
This paper proposes a phase angle-modulated bat algorithm (P-AMBA) for high-dimensional binary optimization. The idea was to reduce the optimization time by introducing angle modulation technology to reduce the optimization dimensions. Different from the original angle-modulated bat algorithm (AMBA), the control of the trigonometric generating function cosine wave is by introducing new parameters, thereby improving the perturbation ability of the function curve near the \(x\)-axis. P-AMBA can explore more 0/1 solutions, and it has advantages in optimizing convergence speed and global search capabilities. The numerical results of the 0-1 knapsack problem tests show that P-AMBA is superior to the contrast algorithms on optimization ability and optimization time. Finally, the experimental result of a compact dual-band planar monopole antenna design showed the effectiveness of P-AMBA in engineering applications.  
bat algorithm; binary optimization; angle modulation; P-AMBA; high-dimensional optimization +
Footnote †: journal: Applied
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 2 System Design Procedure
### Electromagnet Design
Another factor to be considered for the coil height optimization is the levitation effort. The vertical force was mainly considered in this design. To consider the Fabry factor and the vertical force together, the maximum height must be determined since the force continues \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Parameters** & **Value** & **Unit** \\ \hline Outer width & \(D_{o}\) & 75.00 & mm \\ Inner width & \(D_{i}\) & 31.75 & mm \\ Outer radius * & \(R_{o}\) & 27.625 & mm \\ Inner radius * & \(R_{i}\) & 6.00 & mm \\ Height & \(H\) & 50.435 & mm \\ Number of turns & \(N\) & 560 & turns \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation (I): magnetic flux density comparison between square and cylindrical coils. Figure 1: Round-corner square coil with key dimensions: (**a**) top view; (**b**) side view. to increase with the increment of the coil height or the number of vertical winding turns. With the optimal height and the range of the coil height from the previous step, the coil height was preferred to be higher than the optimal height to provide more force. Therefore, the range of coil height was from 50.48 mm to 133.98 mm. The range of the number of vertical winding turns was from 35 turns to 90 turns. The parametric simulation of the force between a square coil and a single disc magnet was conducted in ANSYS Maxwell with the parameters in Table 2. The simulation model consisted of a disc magnet grade N50, a 101.60 mm (4-inch) diameter \(\times\) 12.70 mm (1/2-inch) thickness, suspended with a 20 mm gap above the center of the square coil. The coil had inner and outer widths as designed. The coil height was a function of the number of vertical winding turns \(N_{vert}\). The coil was supplied with a current of 1 A. Ideally, only the z-component of the force on the magnet can be observed whereas other force components are zero. The normalized values of the Fabry factor and the vertical force are plotted in Figure 4. From the plot, the optimal number of vertical winding turns was close to 60 turns or an estimated height of 86.50 mm. Hence, the number of vertical winding turns of 60 turns was selected. All designed parameters of the square coil are summarized in Table 3.  
Figure 3: Percentage differences of the magnetic flux density z-component \(B_{z}\) between square and cylindrical coils along the diagonal distance from center to corner at different levels. Figure 2: Magnetic flux density z-component \(B_{z}\) comparison between square and cylindrical coils along the diagonal distance from center to corner at different levels.