esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis
### Analysis of Complexity Estimation  
The Mackey-Glass time series is a typical chaotic time series. It can be obtained from the time delay differential equation:  
\[\frac{dx(t)}{dt}=\frac{\alpha x(t-\tau)}{1+x^{10}(t-\tau)}-\beta x(t) \tag{21}\]  
where \(\alpha\) = 0.2 and \(\beta\) = 0.1, when \(\tau\) > 17, the chaotic sequence is generated. Different values of \(\tau\) correspond to different complexity of time series. For \(\tau\) = 18, 24, 30, and 36, the chaotic time series is obtained by applying the fourth-order Runge-Kutta method, taking \(x\) as the observed variable, and the time domain signals corresponding to different values of \(\tau\) are shown in Figure 4, respectively.  
In order to demonstrate the superiority of RSDE in distinguishing signals of different complexity, SE, FE, and PE are selected for comparative study. Using the above method, a time series of length 10,000 is generated. The length of the initial signal is taken to be 500, and it varies from 500 to 10,000 with an interval of 500. To make a fair comparison, the embedded dimensions in RSDE, SE, FE and PE are set to equal \(m=3\) and \(r=0.15\) in SE and FE. In addition, for RSDE, two symbolic numbers, \(c=6\), \(\varepsilon=10\), and the fuzzy power \(n\) of FE is set to 2. The entropy values of sequences of different complexity are calculated by using four entropy calculation methods, and the obtained curves with the length of the sequences are shown in Figure 5.  
Figure 4: The Mackey–Glass series for different \(\tau\) values: (**a**) \(\tau\) = 18; (**b**) \(\tau\) = 24; (**c**) \(\tau\) = 30; (**d**) \(\tau\) = 36.  
From Figure 5a,c,d, with the increase of \(\tau\) value, the values of RSDE, FE and PE show a decreasing trend. When \(\tau\) = 18, the calculated results obtained by the three entropy methods are maximum. Additionally, the results reach a minimum when \(\tau\) = 36. These three methods display a kind of consistency. However, the SE values in Figure 5b display inconsistency with the change of \(\tau\) value. In addition to \(\tau\) = 36, the entropy curves of the other three cases appear to cross and overlap. The results demonstrate that using SE to estimate the complexity of chaotic time series is invalid. Besides, when the signal length is below 5000, except for RSDE, the calculation results of the remaining entropy methods present incorrect estimates, indicated by the overlap between the curves in Figure 5b-d. Although the value of RSDE also fluctuates slightly when the sequence length is shorter, among the four entropy methods, the fluctuation range of RSDE is the smallest compared with the other three methods. In the face of sequence length variations, the RSDE has the most stable estimation performance. The above analysis results indicate that the RSDE can achieve an effective differentiation for chaotic sequences with different complexity. Additionally, it is superior to other methods based on entropy.
# Motion Characteristics of Self-Sensing Piezoelectric Actuator for Yarn Micro-Gripper
## 2 Structure and Modeling of the Self-Sensing Micro-Gripper
### Design and Modeling of Self-Sensing Circuits  
#### 2.3.1 Circuit Design and Modeling  
Figure 5 shows the self-sensing circuit designed for the BPBA. The drive voltage \(V_{in}\) of the circuit is applied to the equivalent capacitance of the piezoelectric ceramic cantilever after being amplified by a signal amplifier circuit consisting of resistor \(R_{1}\), voltage amplifier \(U_{1}\), and resistor \(R_{2}\). A voltage divider circuit consisting of resistors \(R_{3}\) and \(R_{4}\) is required to prevent the operational amplifier chip from being damaged by excessive voltage and current. Voltage follower \(U_{2}\) has a buffer, isolation, improved load carrying capacity, and other characteristics, while maintaining the original voltage to make the next level of amplification circuit operate more effectively. The self-sensing measurement circuit composed of resistors \(R_{5}\), \(R_{6}\), \(R_{7}\), feedback capacitor \(C_{0}\), and operational amplifier \(U_{3}\) is the key to detect the sensing signals of the piezoelectric cantilever beam actuator, which is improved by the current integral amplifier circuit, which can integrate the current flowing through the feedback capacitor according to the change of external force and applied voltage. The output voltage \(V_{out}\) is the target quantity of the self-sensing circuit, and its expression is:  
\[V_{out}=-\frac{R_{4}^{\prime}}{(R_{3}+R_{4})}\cdot\frac{1}{C_{0}}\cdot\int \limits_{0}^{T}i(t)dt=-\frac{R_{4}^{\prime}}{(R_{3}+R_{4})}\cdot\frac{1}{C_{0 }}\cdot Q \tag{15}\]  
Figure 4: Equivalent electrical schematic of a piezoelectric actuator.  
Considering the bias current of the operational amplifier and the effect of the internal leakage resistance of the piezoelectric ceramic [29], the output voltage of the free cantilever is given by the following equation:  
\[\begin{array}{l}V_{out}\quad=-\frac{R_{4}^{\prime}}{C_{o}(R_{3}+R_{4})}\cdot \left(\frac{1}{\alpha}\cdot\delta_{z}+\int\frac{V_{in}}{R_{FP}}dt+\int i_{BIAS}dt\right) \\ \quad=-\frac{R_{4}^{\prime}}{C_{o}(R_{3}+R_{4})}\cdot\left(\frac{1}{\beta}\cdot F _{z}+\int\frac{V_{in}}{R_{FP}}dt+\int i_{BIAS}dt\right)\end{array} \tag{16}\]  
The resulting estimates of output displacement and output force are given as  
\[\delta_{z}=-\frac{C_{o}(R_{3}+R_{4})}{R_{4}^{\prime}}\cdot\alpha\cdot V_{out}- \alpha\cdot\int\frac{V_{in}}{R_{FP}}dt-\alpha\cdot\int i_{BIAS}dt \tag{17}\]  
\[F_{z}=-\frac{C_{o}(R_{3}+R_{4})}{R_{4}^{\prime}}\cdot\beta\cdot V_{out}-\beta \cdot\int\frac{V_{in}}{R_{FP}}dt-\beta\cdot\int i_{BIAS}dt \tag{18}\]
## 3 Results and Discussion
### X-Ray Diffraction (XRD) Analysis  
XRD performed the structural analysis of AuNPs prepared from the alkaline-treated sample. The XRD pattern of synthesized AuNPs showed pure crystalline gold nature (Figure 2). Reflection peaks appeared at 38.10\({}^{\circ}\), 44.13\({}^{\circ}\), 64.43\({}^{\circ}\), and 77.32\({}^{\circ}\) which correspond to (111), (200), (220), and (311) Miller indices, respectively. Taking into account the angular positions of the Bragg peaks, the face-centered cubic (FCC) structure was assigned to the gold nanostructures. The majority of the gold nanostructured samples were preferentially oriented along the (111) plane. Another less intense, board peak was observed, oriented along the (200) plane. The observed 'd-values' of the samples (from XRD patterns) correlate well with the standard 'd-values' from the JCPDS data card 04-0784 for Au and confirming the formation of AuNPs. The characteristic of FCC structure and the broadening of peaks confirmed the formation of NPs. The Scherrer equation was used to calculate average particle size and the size was found to be 28 \(\pm\) 1.5 nm. Similar results were observed by [PERSON] et al. [34], who reported that the XRD pattern was crystalline in nature and noted reflection peaks indicating the high purity of AuNPs synthesized by an aqueous extract of cypress leaves.  
Figure 1: UV-vis spectral analysis and color intensity of biosynthesized NPs at various time intervals of AuNPs (gold nanoparticles) at 45 min. The inset shows the color change from ruby-red to dark black color.  
Figure 2: X-ray diffraction (XRD) patterns of biosynthesis of AuNPs using an aqueous extract of _Eclipta prostrata_.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 7 Saving the iptables rules' configuration:
#### 3.2.6 Prototype Structure  
With the necessary elements for the implementation of the prototype, a simple scheme was elaborated that illustrates the layout and location of the different components, including both the IP telephony communication system and the power system, shown in Figure 22. The UAV elements, such as the GPS, controller, and radio telemetry, are at the drone's top. In contrast, the Raspberry Pi, its battery, and the voltage regulator are at the bottom. The UAV is connected by a 10 m long cable linked to a power supply on the ground. This power supply features a plug for connecting the system to the mains, providing the necessary power to operate the system. The components have been strategically arranged to optimize space and ensure efficient system operation.
## 2 Materials and Methods
### Covariate Shift Detection  
The Isolation Forest [58] was the method investigated to detect the presence of covariate shift in new coming EEG data. In particular, it is a machine learning algorithm used for anomaly detection, which means it is designed to identify unusual or anomalous data points within a dataset. It is based on the concept of isolating anomalies rather than explicitly modeling normal data points.  
The isolation forest algorithm works by randomly selecting a feature and then randomly selecting a split value within the range of that feature. By repeating this process recursively, the algorithm partitions the data points into individual trees called isolation trees. Anomalies are expected to require fewer splits to be isolated compared to normal data points.  
The algorithm follows these steps:  
1. Random Selection: Randomly select a feature from the d available features.
2. Random Split Point: Randomly select a split point between the minimum and maximum values of the selected feature.
3. Recursive Partitioning: Split the data based on the selected feature and split point, such that data points with feature values less than the split point go to the left branch, and those with values greater than the split point go to the right branch. Repeat this step recursively until all data points are isolated, or a predefined maximum tree depth is reached.
4. Tree Construction: Construct the isolation tree by repeating steps a-c until a specified number of isolation trees are created.
5. Anomaly Score Calculation: For a new data point, calculate its average path length (number of edges traversed) across all the isolation trees. The anomaly score is determined by comparing the average path length with the expected average path length of normal data points. Smaller average path lengths indicate anomalies.  
We have used the isolation forest method since we hypothesized that the presence of a covariate shift could represent an anomaly with respect to the data recorded since that moment. So, we expected that the method could well fit the experimental problem.  
In the current study, we employed the Isolation Forest model from the [PERSON] [56] library. We tuned the \"contamination\" parameter of this method to adjust the expected proportion of anomalous data within the training condition. Multiple simulations were conducted each time using a different value (1%, 5%, 10%, 15%, 20%, 30%, 40%), with the aim of investigating the relationship between this percentage and the identified covariate shift occurrences in the test condition.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 7 Open Research Issues
Another issue that is worth mentioning is that the vast majority of research works focuses on homogeneous WSNs, meaning that all the sensor nodes are presumed to incorporate identical operational and structural characteristics. This assumption simplifies considerably the research needed for the study of the relevant research issues, but it may lead to unrealistic results because in real-life scenarios heterogeneous WSNs are too common to ignore [27]. In order to cope with this issue, novel research works are proposed [203; 204; 205]. Likewise, in most of the routing protocols that are proposed for WSNs, there is lack of mobility considerations. However, mobile wireless sensor networks (MWSNs), thanks to their enhanced capabilities, outperform traditional WSNs in terms of various performance metrics and thus are involved in more and more applications. On the other hand, routing in a mobile network by default is particularly complex and thus initiates notable research issues. For these reasons, a lot of research interest in energy efficient routing is expected to be attracted by MWSNs [206; 207; 208]. Almost 75% of the surface of planet Earth is covered by water. This fact, by itself, is adequate to highlight the importance of Underwater Wireless Sensor Networks (UWSNs). However, conventional mediums used in terrestrial communications are not able to support underwater communications because they are associated with high attenuation, long propagation delay, and limited transmission capacity. For these reasons, novel research works are required to handle with issues of such type [209; 210; 211]. Last but not least, the authors of this research work believe that WSNs will keep on being in the center of scientific interest and hope that this survey will be helpful for scientists that devote their research efforts to this very challenging domain. **Author Contributions:** Conceptualization, D.K. and C.N. ; methodology, D.K. and C.N. ; formal analysis, D.K., and C.N. ; investigation, D.K., C.N., and G.V. ; resources, D.K., C.N., and G.V. ; writing--original draft preparation, D.K., C.N., and G.V. ; writing--review and editing, D.K. and C.N. ; visualization, D.K. and C.N. ; supervision, D.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Spectral Data Extraction and Analysis  
All the spectral images were processed through the following procedures using MATLAB 8.3 and ENVI 5.1. Dark correction and white correction were carried out on the raw sample images. The dark image was acquired by covering a dark cap with very low reflectance to the lens of the CCD camera. The white reference image was acquired with a white Teflon board. Then, the corrected reflectance images were calculated by the following Equation 1:  
\[R=\frac{I_{0}-B_{\mathrm{r}}}{I_{\mathrm{r}}-B_{\mathrm{r}}} \tag{1}\]  
where \(R\) is the corrected reflectance image of the sample, \(I_{0}\) is the raw spectral image of the samples, \(B_{\mathrm{r}}\) is the dark reference image, and \(I_{\mathrm{r}}\) is the white reference image. Then, the region of interest (ROI) and segmentation of the images were implemented at the same time to extract the spectra of the samples by setting three thresholds in the reflectance image--two of them were set for all pixels at 651 nm as 0.50 for maximum value and 0.25 for minimum value, the last one was set with the ratio value of 651 nm and 550 nm and 1.20 for minimum value. Figure 2a shows a grayscale image at band 651 nm, and the segmentation was confirmed relatively well as shown in Figure 2b, which was chosen to be the ROI at the same time. Then, the mean reflectance spectrum of the ROI was extracted to be the spectrum of the sample. The reflectance spectra of all samples were finally obtained by the above procedures.  
Figure 2: Grayscale image at 651 nm and segmentation of ROI of meat sample: (**a**) Grayscale image at band 651 nm; (**b**) Segmentation of ROI. ROI: region of interest.  
In this study, two methods were used to establish the prediction model of total bacterial count. Support vector machine regression (SVR) [15] could make the regression problem simpler by mapping the low dimensional nonlinear problem into a high dimensional linear problem based on kernel function. At the same time, SVR can account for both the training accuracy and generalization ability by the maximum distance method based on structural risk minimization principle. The method has been widely used in many fields, such as classification, regression, and face recognition, etc. Partial least squares regression (PLSR) [16] is a linear and supervised multivariate calibration method that attempts to find fewer factors that can predict the microbial counts from the reflectance values at various wavelengths. PLSR is particularly suited when the matrix of predictors has more variables than number of samples, and when there is a high correlation among the original predictors. Therefore, these two mathematical approaches were adopted as modeling methods for comparison. The better one was used to predict the microbial contamination in pork samples and for spatial visualization of the results within the sample.
## 4 Discussion
### Strengths and Limitations
The rigorous requirements of SCED, particularly the need for complete data across all measurement points, led to the exclusion of participants with incomplete data. This attrition could potentially introduce selection bias. However, the strength of SCED lies in its focus on within-participant effects, allowing for strong inferences about individual responses to the intervention, even with smaller sample sizes. Consequently, while we can demonstrate changes within individuals over time, attributing these changes solely to the PERSIST platform and generalizing the findings to a broader population requires careful consideration. Finally, interoperability across different data types and clinical centers remains a significant hurdle in healthcare research. This challenge requires further investigation to facilitate seamless data exchange and collaboration.
## 3 Results and Discussion
### Areal Surface Texture Analysis
Therefore, roughness topography (Figure 10c) contains the complete irregularities due to balling melts not exceeding 250 \(\upmu\)m in size, and the shape of balling melts can be seen. There is no presence of a staircase or any other defect in the obtained topography. Figure 10: Surface topography of as-built EBM surface after filtering. Figure 9: Surface topography of as-built DMLS surface after filtering. The same methodology for filtering the surface is applied to the LENS surface, and the resulting topographies are shown in Figure 11. Similar trends are observed in the LENS surface for all the filtering conditions. The LENS surface consists of only partially melted and some loosely attached particles. The roughness topography (Figure 11c) gives the best results after filtering all other wavelengths from the surface. The above investigations show that selecting a proper \(\lambda_{\mathrm{c}}\) plays a significant role in determining the resulting waviness and roughness. For an AM component, if the size distribution of each type of asperity is known, the above results show that the textural variations arising from each type of asperity present on the surface can be identified and quantified separately.
## 2 Materials and Methods  
### Collection, Isolation, and Morphological Identification of Alternaria Species  
Infected potato leaves with brown spots were collected from different governorates in Egypt, as indicated in Table 1. All fungal isolates were obtained by cutting a portion of the infected leaf tissue, putting it on potato dextrose agar (PDA), and incubating for 3-4 days at \(28\pm 2\,^{\circ}\)C until the fungal mycelium appeared [17]. The pure hyphae were then picked and transferred onto a new PDA plate. The examined _Alternaria_ isolates were identified by their morphological traits, including conidial length and width (\(\upmu\)m), and the L/W ratio was determined using an ocular and stage micrometer or, later, by molecular methods [18].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Collection Sites**} & \multirow{2}{*}{**Geographical Data**} & \multirow{2}{*}{**Fungal Code**} & \multicolumn{2}{c}{**Myototoxin Detection**} \\ \cline{3-4}  & & & **Tenuazonic Acid** & **Alternariol Monomethyl** \\  & & & **(TeA) ng/g** & **Ether (AME) ng/g** \\ \hline Beban, Kom Hamada, El Beheira Governate & \(30\,^{\circ}46\,^{\circ}04.4\,^{\prime\prime}\,{\rm N}\) & & KH3 & 133,200 a & 138,320 c \\  & \(30\,^{\circ}39\,^{\circ}46.8\,^{\prime\prime}\,{\rm E}\) & & KH1 & 22,560 b & 94,340 b \\  & \(30\,^{\circ}47\,^{\circ}07.0\,^{\prime\prime}\,{\rm E}\) & & & \\ \hline An Nubartiyah, Gharb El Noubareya, El Beheira Governate & \(30\,^{\circ}40\,^{\circ}13.5\,^{\prime\prime}\,{\rm N}\) & NO1 & 24,000 b & 114,000 f \\  & \(30\,^{\circ}07\,^{\circ}53.0\,^{\prime\prime}\,{\rm E}\) & & & \\ \hline Al Naton Valley, El Beheira Governate & \(30\,^{\circ}33\,^{\circ}12.0\,^{\prime\prime}\,{\rm N}\) & & W2 & 27,768 b & 118,800 e \\  & \(30\,^{\circ}18\,^{\circ}04\,^{\circ}\,{\rm E}\) & & & \\ \hline Quora Kheriguine, Gharb El Noubareya Quora Kheriguine, & \(30\,^{\circ}42\,^{\circ}36.7\,^{\prime\prime}\,{\rm N}\) & NO3 & 49,270 ab & 212,000 a \\  & El Beheira Governate & \(29\,^{\circ}54\,^{\circ}32.7\,^{\prime\prime}\,{\rm E}\) & & Alnam1 & 38,160 b & 147,278 b \\ \hline Al Hamam Desert, Matrouh Governate & \(30\,^{\circ}44\,^{\circ}56.2\,^{\prime\prime}\,{\rm N}\) & & & \\ \hline Al Naton Valley, El Beheira Governate & \(30\,^{\circ}34\,^{\circ}40.6\,^{\prime\prime}\,{\rm N}\) & & W3 & 43,680 b & 131,560 d \\  & \(29\,^{\circ}84.31.^{\prime\prime}\,{\rm E}\) & & & \\ \hline L.S.D.0.05 & & & & 40.2411 & 68.04136 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Screening of mycotoxins produced by _Alternaria_ isolates isolated from infected potato plants from different areas.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.1 Channel Model  
In the UAV swarm ad hoc network, the main wireless communication links are air-to-air (A2A) links. For A2A communication, the free-space propagation model is often utilized, since A2A links are mainly dominated by line-of-sight (LOS) links [3]. Thus, the channel power gain depends mainly on the distance between UAVs for simplicity. The path loss between UAV-\(i\) and UAV-\(j\) can be expressed by (1):  
\[L_{ij}=\eta_{los}\bigg{(}\frac{4\pi f}{c}\bigg{)}^{2}d_{ij}^{2} \tag{1}\]  
where \(\eta_{los}\) represents the attenuation factors corresponding to the LOS link, while \(f\) is the carrier frequency and \(c\) denotes the speed of light.  
Given the bandwidth \(B\), the maximum transmission rate between UAVs \(i\) and \(j\) can be calculated by (2):  
\[C_{ij}=B\log_{2}\bigg{(}1+\frac{P_{t}}{L_{ij}\sigma^{2}B}\bigg{)} \tag{2}\]  
where \(B\) is the bandwidth of the network, \(P_{t}\) is the transmit power of each UAV node, and \(\sigma\) is the Gaussian noise distributed with zero mean. We assume that the gain of omnidirectional antennas is one.  
Figure 1: An illustration of the multi-UAV network, showing the interference of UAV communication slots.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Feature Ranking Methods
#### 7.1.2 Kruskal-Wallis  
The Kruskal-Wallis test [45; 46] is a nonparametric method to test whether multiple independent samples originate from the same distribution. It is often used for feature ranking in data sets where the assumptions of parametric tests, such as normality and homogeneity of variances, may not hold. The test assesses whether there are statistically significant differences between the medians of groups for a given feature. Mathematically, for a feature \(X\) with \(k\) groups and \(n\) total observations, the Kruskal-Wallis test computes a test statistic \(H\) as  
\[H=\frac{12}{n(n+1)}\sum_{i=1}^{k}\frac{\left(\sum R_{i}\right)^{2}}{n_{i}}-3(n+1) \tag{4}\]  
where \(n_{i}\) is the number of observations in group \(i\), \(R_{i}\) is the mean rank of group \(i\), and \(n\) is the total number of observations, given by \(n=\sum_{i=1}^{k}n_{i}\). To compute \(H\), the data are ranked in all groups, and these ranks are used to calculate the mean rank \(R_{i}\) for each group. Under the null hypothesis that all groups come from the same distribution, the test statistic \(H\) approximately follows a chi-square (\(\chi^{2}\)) distribution with \(k-1\) degrees of freedom, provided the sample size is large. A higher \(H\) statistic indicates that the feature effectively differentiates between groups, making it more relevant for the selection of features. The features are ranked according to their corresponding \(H\) statistics, with higher values indicating greater discriminatory power.