esa-sceva/satcom-chunk-collection huggingface dataset:
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 3 Methodology
### Naive Bayesian Classifier(NBC)  
NBC is a discrimination technique derived from the probability model, which is a special form of Bayesian Network. It acquires the conditional probability of each attribute given a category label from the training data. Given an instance of a specific attribute, the Bayesian rule is used to calculate the category probability, and the category prediction is implemented by recognizing the category with the highest posterior probability [41]. The reason why this classifier is naive is that it assumes that the attributes are independent of each other. This assumption means that the conditional probability that an object to be classified belongs to a specific category given some attribute values has nothing to do with the values of other attributes [42]. Although this assumption is inconsistent with the reality, research by [PERSON] et al. [43] shows that it has no significant impact on the prediction performance of NBC. Moreover, a small amount of abnormal data will not greatly affect the results [44]. So, NBC has many applications, including tunnel face stability prediction [45], traffic accident duration prediction [42], and the classification of rockbursts in underground projects [46].
# Comparative Analysis of Skeleton-Based Human Pose Estimation
## 5 Discussion  
The main findings of this study are summarized in this section. MediaPipe Pose had the lowest overall performance in the image dataset. However, it performed well in the video dataset with an overall performance slightly lower than the top overall performer, MoveNet. It achieved the best performance in 7 out of 14 actions.  
MoveNet achieved the highest overall performance for keypoint detection in the image and video datasets. For the video dataset, the PDJ values for all actions were greater than 50%, which was the best performance compared to the other HPE libraries. Additionally, it achieved the best performance in 6 actions, compared to 7 actions in MediaPipe Pose. However, its overall average PDJ was the highest (69.85%).  
OpenPose achieved the second highest performance in the image dataset. However, the weakness of OpenPose was in detecting the keypoints in continuous video frames. Its overall performance in the video dataset was the lowest, approximately 30% lower than the other HPE libraries. Overall, PoseNet had the third highest performance in both the image and video datasets.  
The performance of HPE libraries is reduced when they are constrained by challenges such as inappropriate camera position and the self-occlusion effect. OpenPose was the least robust in detecting video frames when facing these challenges. This is because OpenPose always loses track when self-occlusion occurs in the video frames. Figure 17 illustrates this point in the line graph of PDJ values for OpenPose in each frame in Action 1 (baseball pitch). Based on the results, the PDJ values decreased from frame 84 to 102 (refer to the green box). Due to the bottom-up method used by OpenPose, the detected keypoints cannot be grouped into a human instance when there is self-occlusion in these frames, resulting in tracking failure. The original video frames and results of the OpenPose detection from frame 78 to 103 are shown in Figure 18 for illustration. OpenPose obviously performed poorly in frames with self-occlusion (frames 84 to 103).  
Figure 16: PDJ for each body part in Action 7 (jump rope).  
Based on the results of this experiment, MoveNet is suitable for detecting both images and videos. On the other hand, OpenPose is more suitable for detecting images while MediaPipe Pose is more suitable for detecting videos. The performance of PoseNet is mediocre.
## 5 Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD)
### LNR-RCTD Algorithm Illustration
After constructing the new path, the flow \(f^{1}_{6,1}\)is redirected on this new path and residual capacities over both the newly selected path \(NUP_{f^{1}_{6,1}}\) and the unused portion of the old path \(UNUP_{f^{1}_{6,1}}\)are recomputed. The underutilized link \(e_{7,4}\) is added to the set of extra links \(F_{extra}\) if its used capacity becomes zero. Finally, the \(TOPOLOGY\)-\(OPTIMIZATION\) algorithm is being activated. It simply removes all the links in the set of extra links \(F_{extra}\) and then tests the isolation of all network nodes. If a node is found to be isolated, it will be added to the set of extra nodes \(V_{extra}\) and finally \(TOPOLOGY\)-\(OPTIMIZATION\) also removes all nodes in the set \(V_{extra}\).The final result of \(TOPOLOGY\)-\(OPTIMIZATION\) is an optimized connected network topology \(G^{{}^{\prime}}\left(V^{{}^{\prime}},E^{{}^{\prime}}\right)\subseteq G(V,E)\).
## 1 Introduction
### HPAi Epidemiology  
HPAI strains like H5N1 are highly contagious influenza A viruses that occur naturally in wild birds, particularly the order _Anseriformes_ (geese, ducks, and swans). The transmission of HPAI strains occurs via contact with infectious organic material from infected birds, including saliva, feces, blood, mucus, or feathers. Humans become infected when they are exposed to these materials while working closely with infected birds or birds that have died because of HPAI infection [4]. Wild birds infected with HPAI viruses can recover, but these viruses can wipe out entire domestic flocks if introduced on poultry farms with domestic poultry chickens, ducks, and turkeys [10]. Infected birds transmit the virus within their flocks and to other susceptible waterfowl species that roost in the same areas, lakes, and ponds. Infected birds do not follow anthropogenic boundaries and can fly if asymptomatic, so they effortlessly deposit infectious material as they move to areas where resources are present (farms, inland water bodies, open agricultural fields) [3]. This results in H5N1 exposure to any susceptible animal, domestic bird, or human that lives or works in an area where waterfowl resources are present.  
Depending on environmental temperature, HPAI H5N1 can be transmitted even when infected birds leave the area or die. A 2013 study that focused on the resilience of the H5N1 in dry and wet feces reported that the virus remained active in wet and dry feces for 18 h at 42 \({}^{\circ}\)C, 24 h at 37 \({}^{\circ}\)C, and for up to 5 days at 24 \({}^{\circ}\)C [5]. Another study conducted in 2017, focusing on H5N1 resilience in tissue from dead chickens, reported that the virus remained viable at 4 \({}^{\circ}\)C for up to 240 days in feather tissues, 160 days in muscle tissue, and 20 days in liver tissue in laboratory conditions. The study also reported that at 20 \({}^{\circ}\)C, in laboratory conditions, H5N1 was maintained for up to 30 days in feather tissues, 20 days in muscle tissue, and 3 days in liver tissue [6]. These results suggest that H5N1 is resilient in the external environment, making it important to evaluate environmental risk factors, including species abundance, to identify waterfowl species that increase the risk of H5N1 infection in domestic poultry. HPAI viruses now pose constant threats, as H5N1 outbreaks have repeatedly occurred since 2021 [3]. HPAI risk reduction will require the cooperative expertise of numerous researchers working together to increase knowledge of viral circulation and identify viral diversity [11]. Our study aims to contribute to the knowledge of viral circulation by evaluating whether Canada goose abundance in Iowa is associated with increased H5N1 risk.
## 4 Experimental and Results Analysis
### Experimental Results and Analysis
#### 4.3.2 Experiment 2  
The results of Experiment 1 demonstrate that the model's performance on Dataset A2 is superior, which led to its selection for further evaluation in Experiment 2. The findings from Experiment 2, as shown in Table 7, involve three ablation experiments: the UniTS_SAF model using only the sinusoidal activation function, the UniTS_SAM model using only the spectral attention mechanism, and the combined UniTS-SinSpec model, which integrates both components. The results reveal that both the UniTS_SAF and UniTS_SAM models show significant improvements over the baseline UniTS model, with the UniTS_SAF model achieving an increase of X% and the UniTS_SAM model improving by Y%. The combined UniTS-SinSpec model reached the highest average validation accuracy of 96.24%, demonstrating that integrating both components leads to greater performance gains compared to using either individually. However, this improvement also resulted in an increased training time due to the added complexity.
## 1 Introduction
These high expenses and cumbersome testing processes pose significant challenges to research endeavors and considerably impede the rapid advancement of practical engineering applications. This study proposes the utilization of electromagnetic compatibility characteristics to conduct antenna communication testing. If important sampling points in the anechoic chamber can be determined through preliminary simulations, testing costs and complexity can be significantly reduced. This paper proposes an electromagnetic compatibility testing method for communication antennas under actual working conditions. The first step is to select the antenna model and simulate the gain radiation coupling between antennas through their far-field radiation pattern, comparing it with radiation values from relevant standards to verify feasibility. By indicating the levels of coupling gain values between the radiation patterns, we can demonstrate the extent of interaction between these two antennas, leading to conclusions about the strength of wireless communication capabilities between antennas. This allows for precise debugging and optimization of S-parameters through simulation software, thereby enhancing the stability of communication systems and achieving cost reduction, along with repeatability and comparability of testing results. Currently, communication testing in automobiles still relies on methods designed for small-scale electrical components. When these methods are applied to vehicles, the testing process becomes considerably more complex and often requires substantial financial investment. The high costs and cumbersome procedures not only pose significant challenges to research endeavors, but also substantially impede the rapid advancement of practical engineering applications. The testing method proposed in this study ensures the integrity and reliability of electromagnetic signal transmission at the very first step--signal reception--while also enabling long-distance propagation. Compared to traditional testing methods, the approach presented in this study simplifies the testing process and reduces associated costs.
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy Studies
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cellulose Composition** & **Standard Method** & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Method 5** \\ \hline Lignin (\%) & TAPPI T 15 os-80 oo80 & 0.56 & 5.20 & 4.58 & 10.26 & 3.60 \\ \(\alpha\)-Cellulose (\%) (\%) & TAPPI T203 om-93 & 93.60 & 70.40 & 90.80 & 92.60 & 87.80 \\ ISO brightness & ISO 3688-1999 & 94.61 & 90.10 & 86.85 & 88.77 & 80.03 \\ Intrinsic Viscosity (mL/g) & Viscosity measurement & 1400–1650 & 525–600 & 600–850 & 550–610 & 700 \\ Crystallinity(20) & X-ray diffraction & 0.63 & 0.59 & 0.64 & 0.63 & 0.45 \\ Total yield of cellulose \% & HPLC & 5.38 & 10.17 & 7.09 & 3.53 & 8.80 \\ \hline \end{tabular}
\end{table} 
Table 3: Chemical composition, physical and chemical properties of cellulose extracted by different methods.
## 2 Background and Related Work
### WSN Deployment
#### 2.4.3 Coverage and Connectivity
From the experiment, it is concluded that the reliable communication range within the mine is up to 35 m.  
Regarding the coverage range, some authors assume approximate values without any basis, while others do not consider this value. In this work, a sensing range of 12 m is assumed, taking into account the results obtained in [39; 40; 41].
## 3 Test Results and Analyses  
### Shift Diameter Sensitivity Test Results  
The load-displacement curves for the shaft-to-plate diameter ratio are shown in Figure 6. The load-displacement curve can be divided into the following three stages: (1) The initial elasticity stage, where the load-displacement curve is linear; (2) the nonlinear slow-change stage, where the curve is nonlinear, and the slope gradually increases (the ultimate bearing capacity of the helical anchor is achieved during this stage); and (3) the smooth stage, where the load no longer increases with displacement, and the slope of the curve stabilizes at a fixed value.  
As shown in Figure 6, the four different shaft-to-plate diameter ratios exhibit a consistent pattern: as the shaft-to-plate diameter ratio increases, the uplift bearing capacity of the helical anchor gradually decreases. This indicates that the shaft-to-plate diameter ratio has a significant impact on the uplift bearing capacity of the helical anchor. A larger helix pitch reduces the support area and contact friction between the helical anchor and the soil, thereby decreasing their interaction and the uplift bearing capacity. Conversely, a smaller anchor helix pitch increases the support area and contact friction, leading to a higher uplift bearing capacity.
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 2 Database and Data Collection
### Headset Selection  
Careful selection of the EEG data collection headset plays an important role in maintaining data integrity and accuracy during EEG analysis. The EMOTIV EPOC+ headset was selected for our data collection. The highlights of the Epoc+ headset selection for the process of data collection include its collection process method using 14 sensors, its user-friendly interfaces, its light weight, and its ease of use [43]. With a high sampling rate of 128 Hz, the EMOTIV EPOC+ is well suited to our study objectives as it captures a broad range of frequency bands that are essential for in-depth brainwave pattern analysis. With this configuration, precise tracking of brain activity is made possible by accurate EEG signal mapping to designated brain areas.  
Another advantage of the EPOC+ is its strong software support, particularly the proprietary software from EMOTIV (version 3.8) and the accessible Python Software Development Kit (SDK), known as CORTEX V2. The SDK, along with the software suite, provides an easy-to-use interface for data collection and analysis, accommodating researchers with varying degrees of experience in EEG data processing. The software's support for exporting raw data in standard formats (e.g., EDF, CSV) streamlines integration with diverse data analysis tools [21; 43].  
From an economic standpoint, the EMOTIV EPOC+ strikes a judicious balance between cost and performance. In comparison to other high-end, research-grade EEG systems, the EPOC+ delivers a similar functionality at a more budget-friendly price point, enhancing its appeal for large-scale database collection initiatives while acknowledging the EPOC+'s susceptibility to noise and artifacts. Our protocol systematically addresses these issues through meticulous electrode placement and sufficient saline use, ensuring a reported signal strength of 100% from all 14 electrodes to guarantee that the collected data have a high fidelity.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Dataset Validation  
To ensure the quality and appropriateness of our dataset, we implemented the preprocessing techniques and visualized the skewness levels [23; 24]. Figure 5 shows the distribution of URL lengths in the dataset. The tallest bar falls within the 0-50 character range, indicating a significant concentration of shorter URLs. This suggests that the majority of URLs in the sample are relatively short. Only a small number of URLs exceed 200 characters, and very long URLs (over 400 characters) are rare, with the frequency declining sharply as URL length increases. These longer URLs likely originate from specific types of web pages, such as those generated by content management systems or containing complex query parameters. Investigating these outliers could reveal distinctive patterns or behaviors. Overall, the distribution demonstrates that the dataset is predominantly composed of short URLs, displaying a right-skewed pattern. This skewness could adversely affect the model's detection performance. To mitigate this, we applied the Box-Cox  
Figure 4: The transformer-encoder model [22].  
transformation, a technique effective for outlier detection and skewness reduction, helping to normalize the dataset and improve model training.
# Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping
## 3 Data Acquisition and Processing
### Data processing
Additionally, experiments demonstrated that boundary condition 1 yields more accurate and realistic results in handling key-point drift and gap filling during fast movement, without introducing errors or unnatural changes. Its simplicity and computational efficiency further make it an ideal choice, especially when dealing with large data. Therefore, boundary condition 1 is chosen for segmented cubic spline interpolation in this study. 2. Data filtering: this paper adopts the method of moving average to remove noise from the key-point data. The filter window size is set to 1, 3, 5, and 7, and a certain key-point data in the \(X\)-axis direction are mean-filtered; the filtering effect is shown in Figure 5.  
As shown in the figure, with the increase in the window size, the data noise points are gradually reduced, and the smoothness of the curve is gradually increased, but when the window size is 7, the original data are changed to a large extent, and the accuracy of the experimental results cannot be guaranteed. In order to eliminate the influence of data noise, drift, etc., without changing the accuracy of the original data, it is appropriate to select the window size of 5 for the moving average.
# Real-Time Kinematic Positioning Using Multi-Frequency Smartphone Measurements +
## 5 RTK Tuning Discussion  
The smartphone measurements are noisier than the ones of standard GNSS receivers. Therefore, the results presented in this paper were obtained by ad hoc tuning the Topcon RTK engine to take into account the peculiarities of the smartphone observations and the specific test scenarios. As mentioned above, a routine was developed to find the best setting parameters for the specific test. The best parameters were considered the ones which maximised the horizontal/vertical accuracy and percentage of fix solutions and minimised the TTFF. The set of parameters included the following:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TTFF** & **FIX \%** & **TTFF** & **Fix \%** \\  & **[s]** & & **Reference [s]** & **(Reference)** \\ \hline Car (inside) & 49 & 75.34 & 0 & 97.76 \\ \hline Car (outside) & 0 & 78.5 & 3 & 98.34 \\ \hline Tractor & 41 & 81.95 & 1 & 99.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistic of the kinematic test.  
Figure 8: (a) and (b) Additional path sections of the kinematic tests with the smartphone placed inside the car (green: reference solution, blue: smartphone solution).  
Figure 9: (a) Path of the kinematic tests with tractor; (b) section of the path in correspondence of a curve (green: reference solution, blue: smartphone solution).  
* Satellite elevation mask;
* C/N\({}_{0}\) cut-off values for the different signals;
* Observation weighting approach;
* Code and phase noise.  
For all tests, the code and phase noise were assumed to be ten times larger than the ones set for the standard GNSS receiver cases. Moreover, for the different test cases, the selected elevation cut-off values ranged between 15 and 20 degrees for both static and kinematic tests. These high values helped to reject the measurement of bad quality, which decreased the final performance. Finally, for all cases, an observation weighting scheme depending on the signal C/N\({}_{0}\) was selected. Indeed, smartphone measurement can also be deteriorated for high satellite elevations. Therefore, it is necessary to take into account the signal power to give less weight to the lower-quality measurements. It should be considered that, for the automotive tests, the environment was quite variable, ranging from an open sky section to a section with tall trees and buildings. However, a unique set of best parameters was also selected in this case. An a priori identification of the scenario type could assist in further optimising the selection of the RTK setting parameters.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 4 Experimental Method and Result Analysis
### Experiment Method  
The experimental procedure is as follows. The prediction performance of each ANFIS model is compared according to four different input space division methods.  
First, in the experiment using the ANFIS1 model, influential input variables were selected from the input variables, and the input variables were recombined and used in the experiment. In the input variable recombination method, the number of input variables to be used was specified, and the combination of input variables was used in which the verification RMSE (root means square error) value was the lowest. The first input combination used two influential input variables among the total input variables, the second input combination used three influential input variables among the total input variables, and finally, all the input variables were used. In the grid partitioning method, the membership function (MF) increased from 2 to 5 by 1, and the estimation performance was confirmed.  
Figure 8: Correlation of input variables of CCPP (combined cycle power plant) data.  
In the ANFIS2 model, the radius of the SC was increased from 0.2 to 0.9 by 0.1. In the experiment using the ANFIS3 model, the number of clusters and the fuzzification coefficient were set in the FCM clustering method. The number of clusters was increased from 5 to 50 by 5, and the fuzzification coefficient was fixed at 1.5 to confirm the prediction performance. Finally, in the experiment using the ANFIS4 model, the number of contexts (p), number of clusters (c), and the fuzzification coefficient were set in the CFCM clustering method. First, the number of contexts was fixed at 6 and 10, the number of clusters was increased from 2 to 20 by 1, and the fuzzification coefficients were fixed at 1.5. Each experiment was repeated 10 times.  
The predictive performance was expressed as the root mean square error (RMSE). The RMSE can be expressed as Equation (19) as a prediction measure used to represent the difference between the estimated value or the value obtained from the prediction model and the observed value in the actual environment. The RMSE values are rounded to two decimal places.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{19}\]  
Here, \(y_{i}\) represents a result value, and \(\hat{y_{i}}\) represents an actual value. When \(y_{i}=\hat{y_{i}}\), the prediction performance is 100% and the RMSE value becomes zero. Therefore, if the RMSE value is large, it indicates that the prediction performance is poor, and when it is small, it indicates that the prediction performance is good. Figure 9 shows how to select two input variables out of four input variables in the CCPP data.