esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Data Augmentation  
A computational diagnostic model that achieves and produces good performance requires a large amount of training data to prevent overfitting problems. ECG recordings are limited in data acquisition for modeling, so time series data augmentation techniques, such as random transformation, pattern mixing, generative models, and decomposition, have commonly been applied [27]. This study selected a random transformation method to generate datasets segmented in 2.5-min intervals. Datasets were tested for sufficiency showing convergence in each epoch during training.
## 4 Biocontrol Methods
### Postbiotics
Pediocin is marketed as a part of different preparations, such as Alta 2341 TM, and is used in fermentation processes to prevent contamination of animal-derived foods, especially cheeses. Studies have shown that a combination of bacteriocins with other natural preservatives, such as reuterin, can be more effective in preventing the growth of pathogens than their individual use [122]. This approach has the potential to significantly extend the shelf life of food products and improve their safety. The application of bacteriocins in food is, however, limited by their narrow spectrum of action and loss of activity in the presence of enzymes or unfavorable pH. Encapsulation of bacteriocins may provide a solution, protecting them from degradation and ensuring a controlled release into food. Microencapsulation technology of bacteriocins allows their efficient application in food products, such as milk and cheese. For example, nisin-loaded zein microcapsules have been shown to be effective in reducing the population of _L. monocytogenes_ in treated milk [5]. Encapsulation protects bacteriocins from interactions with food composition and ensures their slow release, maintaining antimicrobial activity. Edible polymer films can be used to evenly distribute bacteriocins on food surfaces, preventing aggregation and disruption of lipid membranes. Edible coatings containing nisin have also been shown to be effective against pathogens, extending the shelf life of food products [24,123]. Therefore, bacteriocins represent a promising solution, especially since they are effective against antibiotic-resistant bacteria. With the identification of an increasing number of bacteriocins, there is a major interest in studying them to explore their applicability in the food industry [124,125]. Recently, a nanoencapsulation technique has been developed that improves the antimicrobial activity of bacteriocins, allowing them to be applied more effectively in the food industry [117]. Nanoencapsulation of bacteriocins is an effective technique that uses nanomaterials to improve the antimicrobial potential of these peptides. This method allows the application of bacteriocins in food matrices, where they interact effectively with pathogenic microorganisms. Although nanoencapsulated bacteriocins are already used in the food industry, further research is needed to better explore their potential [124,126,127]. In conclusion, bacteriocins are promising antimicrobial compounds for food preservation. Although nisin is the only bacteriocin approved by the FDA for use in food products, there is a growing interest in the discovery and use of other bacteriocins in the food industry. In addition, the development of encapsulation technologies and the combination of bacteriocins with other bioactive agents can contribute to the efficiency of their use in food products, providing safe and effective solutions for natural food preservation. Future studies should focus on expanding the spectrum of application of bacteriocins and optimizing encapsulation methods to ensure better stability and antimicrobial efficacy [128].
## 2 Overview of Whirlpool Hash Function
### Comparison between Whirlpool and SHA-1  
#### 2.3.1 Computation Speed  
To compare the calculation speed between Whirlpool and SHA-1, we generate random strings in different lengths (10,000 strings for each length). Let these two algorithms calculate the hash value of these strings respectively, then compare the average time of them during one time of calculation procedure. The test is performed under Inter Pentium Dual E2180 2.00 GHz and 2 GB memory environment. Figure 2 shows the comparison of these two algorithms.  
From this figure we can learn that when the length of string is short, these two algorithms have a similar calculated performance, even SHA-1 is a bit better than Whirlpool. However, when the string is long (longer than 256 bytes), the calculation speed of Whirlpool is faster than SHA-1, and with the increment of length, the gap between them in calculation speed is bigger and bigger.
## 3 Results and Discussion
### Limitations of the Study  
The sparse PCE method is founded on the assumption of independence among the random input parameters. This assumption, while simplifying the mathematical framework and computational implementation of the PCE, is a limitation when dealing with systems or processes where input parameters are not strictly independent. However, the independence assumption can still provide valuable insights and reasonably accurate results for systems where correlations among input parameters are not highly significant. Moreover, PCE can be employed as a tool for sensitivity analysis, helping to identify which input parameters have the most influence on the system's output, even under the independence assumption. In situations where strong correlations among input parameters exist, more advanced techniques, such as Copula-based modeling or surrogate modeling methods, that can handle correlated inputs may be considered. These approaches can provide a more accurate representation of the system's behavior when independence among inputs cannot be assumed. Therefore, while the independence assumption of PCE is a limitation, its applicability and usefulness persist in various scenarios, making it a valuable tool in uncertainty quantification and sensitivity analysis.
## 3 Results  
### Aroma-Active Compounds in Strong-Aroma Baijiu  
For the purpose of facilitating GC-O/MS analysis and compound identification, the samples were further separated into AF and NBF fractions to reduce complexity. Following this procedure, a total of 72 aroma compounds were identified through the combination of liquid-liquid extraction and GC-O analysis, including 34 esters, nine alcohols, nine acids, nine aldehydes, four ketones, two furans, two aromatic compounds, two pyrazines, and one sulfide (Table 3). The results showed that acids, alcohols and high content esters were more abundant in AF, which mainly provide cheesy, sweaty, alcoholic and fruity aromas. Aldehydes and heterocyclic compounds were mainly detected in NBF, which provide pungent, fruity and roasted odors. Among the 34 aroma-active esters, ethyl hexanoate, as the dominant ester compound characteristic of strong-aroma baijiu, consistently exhibited an odor intensity exceeding 4 across all four samples. This compound imparts a strong fruity note to the baijiu. Furthermore, several other esters were present at relatively high concentrations, such as ethyl acetate, ethyl butyrate, isoamyl acetate, ethyl valerate, isoamyl butyrate, and ethyl octanoate (each with an odor intensity \(\geq\)3). A total of nine acid compounds were identified in this study. Their odor descriptors were predominantly characterized as acidic or sweaty. Among these, acetic acid and butyric acid exhibited odor intensities greater than 3. Of the nine aroma-active alcohols, isoamyl alcohol demonstrated the highest odor intensity, reaching 3.8, indicating its highest odor contribution. Furthermore, n-propanol, isobutanol, n-butanol, and n-hexanol consistently displayed odor intensities exceeding 2 across nearly all four samples. Among the seven aroma-active aldehydes, isobutyraldehyde and phenylacetaldehyde (floral note) both exhibited odor intensities greater than 2. Analysis of odor descriptors revealed that aldehydes predominantly possess pungent aroma characteristics, frequently described as grassy or green. Beyond the aforementioned esters, acids, alcohols, and aldehydes, other chemical classes also make significant contributions to the aroma profile of strong-aroma baijiu, including ketones, phenols, furans, and pyrazines. Specifically, 2,3-butananedione (odor intensity > 1) and acetoin (odor intensity > 3) impart sweet, buttery notes. 4-Methylphenol (odor intensity > 1.8) contributes smoky aromas. Furfurral (odor intensity > 1.8) and 5-methylfurral (odor intensity > 1.5) provide caramel-like and baked notes. 2-Acetylfuran (odor intensity > 1.5) exhibits almond and nutty characteristics. Dimethyl trisulfide (odor intensity > 2) contributes a sulfurous off-note.
## 4 Experimental Results  
### Experimental Platform and Projects  
In order to verify the performance of the proposed distribution control method, experiments were implemented as described in this section. The experimental platform used for the validation is shown in Figure 6.  
The studied vehicle can be regarded as a combination of six wheel corners, a body, and other components. Each wheel corner consists of an independent electrical drive motor system and a suspension system. Each wheel cannot steer, and the platform steers by skidding. Therefore, the experimental platform is a 6 WD skid-steering EUGV. The tire model configured in the platform is 205/55 R16. The other components include a GPS/IMU, computer, and wireless receiver module. The model of the GPS/IMU is XW-GI7660. The platform parameters are listed in Table 1.  
Figure 6: The experimental platform.  
Figure 7 shows the details of the hardware of the platform. The desired control signals are sent by the operator via the operating remote control and received by the computer via the wireless receiver module. The motion status of the vehicle, including the longitudinal and lateral velocity, heading, yaw rate, and accelerations, was measured with the GPS/IMU system in the vehicle. The proposed distribution and control algorithm was employed in the computer, while the computer sent the control signals to the motor controller and received the feedback signals from the independent drive motors through the CAN bus.  
In order to demonstrate the excellent performance of the algorithm proposed in this paper, three control methods for experiments were used:  
Method 1: Platform under the rule-based driving force even distribution method in [23], which is marked by Ref1.  
Method 2: Platform under the optimal driving force vectoring distribution method in [15], which is marked by Ref2.  
Method 3: Platform under the proposed distribution control.  
The 6 WD skid-steering EUGV platform requires straight-line and steering tracking capabilities. Thus, two types of testing scenarios were considered:  
Scenario 1: Platform motion in straight conditions.  
Scenario 2: Platform motion in curved conditions.  
The figures were drawn by using MATLAB R2018b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameters** & **Value and Units** \\ \hline \(m\) & Vehicle total mass & 2020 kg \\ \(l_{2}\) & Yaw moment of inertia of the vehicle & 1897 N\(\cdot\)m \\ \(a\) & Distance from center of gravity to the middle axle & 0.2 m \\ \(B\) & Vehicle width & 2.2 m \\ \(H\) & Height of center of gravity & 0.68 m \\ \(l_{1}\) & Distance from the front axle to the middle axle & 1.2 m \\ \(l_{2}\) & Distance from the middle axle to the rear axle & 1.206 m \\ \(J_{w}\) & Rotation moment of the wheel & 0.85 kg\(\cdot\)m\({}^{2}\) \\ \(R\) & Wheel effective radius & 0.308 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the experimental platform.  
Figure 7: The hardware of the six-wheel-drive (6 WD) skid-steering electric unmanned ground vehicle (EUGV) platform.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Electronic Neutralizers  
Electronic neutralizers allow instant actions, can easily aim at the target and are not affected by environmental agents (for example wind and gravity). They can be implemented with different techniques, such as cyber-attacks, high-powered electromagnetics and lasers. Cyber-attacks include jamming and spoofing techniques, which constitute the vast majority of neutralizers used in the context of UAVs, protocol-based attacks (for example, de-authentication and address resolution protocol (ARP) cache poisoning in the case of Wi-Fi networks) and replay attacks. Below a survey is reported.
## 3 Results and Discussion  
### Experimental Condition  
The experimental condition in this paper mainly included two aspects: hardware and software. In terms of hardware, a high-performance computing server was used to ensure the efficient computing and data processing capabilities during the model training process. Specifically, the CPU model was an Intel Xeon Gold 6248, with 20 cores and 40 threads, the memory was 512 GB DDR4 ECC REG, and the GPU model was an NVIDIA Tesla V100 with 32 GB of video memory.  
In terms of software, an open-source framework suitable for deep learning and graph convolutional network development was selected. TensorFlow ([[https://www.tensorflow.org](https://www.tensorflow.org)]([https://www.tensorflow.org](https://www.tensorflow.org))) and PyTorch ([[https://pytorch.org](https://pytorch.org)]([https://pytorch.org](https://pytorch.org))) are mainly used as deep learning frameworks, which provide rich APIs and tools to support the efficient construction and training of complex neural network models. The CuDA and cuDNN libraries were utilized to accelerate the computing process on the GPU, thus further improving the training efficiency of the model.  
The experimental condition was configured with the Python programming language and its related scientific computing libraries (NumPy, Pandas, SciPy, etc.) to support data preprocessing and results analysis and other work.
# Jupyter Notebooks in Undergraduate Mobile Robotics Courses: Educational Tool and Case Study
## 5 Results and Discussion  
### Learning Performance According to Students Grades  
As previously discussed, a valuable resource for objectively measuring students learning performance is their final exam grades. Figure 6a illustrates the obtained grades by the students in the group formed by the 2017/2018 and 2018/2019 courses (that is, previously to the implementation of the Jupyter Notebook technology), and in the 2019/2020 one. In such a figure, for each group, the lowest, horizontal line represents the minimum value in the data distribution (Q1-1.5 xIQR [interquartile range]), the lower part of the box the first quartile (Q1/25 th Percentile), its inner horizontal line the median (Q2/50 th Percentile), the higer part the thrid quartile (Q3/75 th Percentile), and the highest horizontal line the maximum value (Q3 + 1.5 xIQR). At a first glance, we can observe that the grades obtained improved with the usage of the notebooks.  
To statistically underpin this, we have resorted to the Welch one-way analysis of variance (ANOVA) [77]. Although standard ANOVA is the de facto method to compare two means and decide if they are equal, i.e., statistically they are not differences between them, it is not suited in scenarios with unbalanced data (the number of students in the two groups differ) and heteroskedastic (non-homogeneous variances among the groups) [78]. This test returned an equivalent \(p\)-value of 0.068; hence confirming that considering a significance of \(\alpha=0.1\), the two groups show significant statistical differences regarding their means. This is, the null hypothesis stating that both means are the same can be safely rejected.  
These results support the fact that the academic results of students are positively correlated with the application of the Notebook technology in the subject. In this way, they help to positively answer research questions _RQ1_ and _RQ2_, that is, notebooks enhance learning in mobile robotic undergraduate courses, and also represent an improvement over traditional problem posing approaches. This also motivates their usage in STEM education programs [14; 15].  
\begin{table}
\begin{tabular}{p{34.1 pt}} \hline \hline
**Id** **Question** \\ \hline \(Q1\) & Indicate your degree of understanding about the topics in the subject after theory sessions. \\ \(Q2\) & Indicate your degree of understanding about the topics in the subject after _lab_ sessions. \\ \(Q3\) & I consider that the utilization of Jupyter notebooks in hands-on sessions empowered my learning to a greater extent that following a traditional approach (e.g., statement-solution). \\ \(Q4\) & I consider that the provided Jupyter notebooks have helped me to pass the final exam. \\ \(Q5\) & I consider that the Jupyter Notebook learning curve in the subject is appropriate. \\ \(Q6\) & I consider that the Python programming language is suited for completing the practical sessions. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Survey for retrieving students’ subjective perception about items of interest.
## 4 Experiments and Results
### Accuracy Evaluation Metric: mIoU  
In this study, we define shade house extraction as a semantic segmentation task. For semantic segmentation tasks, the mean Intersection over Union (mIOU) is an important evaluation metric widely used to measure the model's classification accuracy at the pixel level, particularly for extracting two target classes: background and shade houses. mIoU effectively reflects the model's accuracy in extracting target regions and provides a quantifiable measure of the model's ability to distinguish between different classes. Given that the primary goal of this study was to achieve high-precision shade house extraction, mIoU is particularly suitable as the performance evaluation metric, as it comprehensively assesses the model's ability to distinguish between these two classes and ensures its precision and stability in practical applications.  
The mIoU calculation relies on binary confusion matrices, with the specific formula:  
\[\text{mIOU}=\frac{1}{2}\bigg{(}\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP }}+\frac{\text{TN}}{\text{TN}+\text{FN}+\text{FP}}\bigg{)}. \tag{3}\]  
The detailed expression of the confusion matrix is listed in the Table 1. In the binary confusion matrix used in this experiment, the meanings of TP, FN, FP, and TN are as follows: TP: true positive, i.e., the actual class of the sample is a shade house, and the model predicts it as a shade house; FN: false negative, i.e., the actual class of the sample is a shade house, but the model predicts it as background; FP: false positive, i.e., the actual class of the sample  
Figure 10: Overall process diagram of shade house extraction.  
is background, but the model predicts it as a shade house; TN: true negative, i.e., the actual class of the sample is background, and the model predicts it as background.
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 3 Optimization Methods
### Variable Number of Dimensions MOPSO
#### 3.1.1 Conventional MOPSO
If \(r<r_{\rm g}\), then a random member of the external archive, \(\mathcal{E}\), is selected for the \(p\)-th particle to serve as the \(\mathbf{g}_{p}\). Contrarily, if \(r\geq r_{\rm g}\), then the closest member from \(\mathcal{E}\) is selected as \(\mathbf{g}_{p}\). When \(r_{\rm g}=1.0\), the global best positions are selected randomly from \(\mathcal{E}\) for all particles. Combining these two approaches is crucial to avoid the MOPSO getting stuck in the local optimum (local Pareto front) and to enable the best possible convergence. ```
1:Input :Probability of random global best \(r_{\text{g}}\), set of DSVs \(\mathcal{U}\), external archive \(\mathcal{E}\)
2:Set of global best positions \(\mathcal{G}\)
3:Find \(n=\text{length }\mathcal{U}\)
4:Find \(m=\text{length }\mathcal{E}\)
5:for\(p=1\), \(p\leq n\), \(p++\)do
6: Generate random value \(r\in\langle 0;1.0\rangle\)
7:if\(r<r_{\text{g}}\)then
8: Select random index \(i\) from list \(1,2,\ldots,m\)
9:else
10: Find Eucl. distances \(\mathbf{d}_{p}\) from \(\mathcal{U}[p]\) to all members of \(\mathcal{E}\)
11: Find index \(i\) of minimal Eucl. distance \(\mathbf{d}_{p}\)
12:endif
13: Set \(\mathcal{G}[p]=\mathcal{E}[i]\)
14:endfor
15: Return \(\mathcal{G}\)
```  
**Algorithm 1**Global best selection algorithm used in MOPSO  
After the position update (6), the particles' locations can reside outside the limits of the feasible decision space, \(\Gamma\). Then, the so-called boundary conditions can be applied to make the particles feasible again [36]. The possible boundary conditions are: (1) reflecting (the particle is reflected back to \(\Gamma\) by the violated limit), (2) absorbing (the particle resides on the violated limit), and (3) invisible (the particle is left outside of \(\Gamma\) but its objective function values are worsened artificially). At the end of every iteration, the new positions of particles are evaluated using the objective functions. Then, the external archive is updated. The iterative process of MOPSO continues until the termination condition is met. Usually, a combination of several termination conditions is used, e.g., maximum number of iterations, \(N_{\text{I}}\), or limits for the values of objective functions are reached, etc.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Time Domain Methods
#### 2.1.2 Travel Time between Peaks of Input and Output Signals  
The 'peak-to-peak' method is based on the assumption that the output signal bears high similarity to the input signal. The time lapse between the peak of the input signal (point B in Figure 1) and the first major peak in the output signal (point F) is taken as the travel time of the shear wave [26]. This method is very simple and popular, and it has been adopted by many researchers [8; 12]. However, the identification of the first major peak may be difficult, since the output signal may have several consecutive peaks of similar amplitudes.