esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Modal Parameters Analysis
### Comparisons of the Measured Vibration Modes and Those Simulated by Finite Element Method
Figure 15: Correlation verification of modes at the first three orders along the X axis, Y axis and torsional direction: (**a**) correlation verification of modes at the first three orders along the X axis; (**b**) correlation verification of modes at the first three orders along the Y axis; (**c**) correlation verification of modes at the first three orders along the torsional direction. Figure 16: The finite numerical simulation results of the first three-order vibration modes along the X and Y axes and torsional direction: (**a**) the finite numerical simulation results of the first-order vibration mode along the X axis; (**b**) the finite numerical simulation results of the second-order vibration mode along X axis; (**c**) the finite numerical simulation results of the third-order vibration mode along the X axis; (**d**) the finite numerical simulation results of the first-order vibration mode along the Y axis; (**e**) the finite numerical simulation results of the second-order vibration mode along the Y axis; (**f**) the finite numerical simulation results of the third-order vibration mode along the Y axis; (**g**) the finite numerical simulation results of the first-order vibration mode along torsional direction; (**h**) the finite numerical simulation results of the second-order vibration mode along torsional direction; and (**i**) the finite numerical simulation results of the third-order vibration mode along torsional direction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & \multicolumn{3}{c}{**Mode**} & \multicolumn{3}{c}{**Natural Frequency/Hz**} \\ \cline{2-6}  & \multirow{2}{*}{**Measured**} & **Finite Element** & \multirow{2}{*}{**Measured**} & **Finite Element** & \multirow{2}{*}{**Relative Deviation**} \\  & & & **Simulation** & & **Simulation** & **(\%)** \\ \hline \multirow{4}{*}{1} & The first-order & The first-order & \multirow{4}{*}{
\begin{tabular}{c} The first-order \\ translational mode along \\ the Y axis \\ The first-order \\ torsional mode \\ The first-order \\ translational mode along \\ the X axis \\ The second-order \\ translational mode along \\ the Y axis \\ The second-order \\ torsional mode \\ The second-order \\ translational mode along \\ the X axis \\ The third-order \\ Translational mode along \\ the X axis \\ The third-order \\ Translational mode along \\ the X axis \\ The third-order \\ Translational mode along \\ the Y axis \\ The third-
## 2 Theoretical Background
### ML Models
#### 2.1.1 Support Vector Machines (SVMs)
Using the non-linear vector function \(K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\big{(}\mathbf{\Phi}^{T}(\mathbf{x}_{i})\ \mathbf{\Phi}(\mathbf{x}_{j})\big{)}\), the decision function can be written in the form of Equation (7):  
\[f(x)=\text{sign}\left(\sum\limits_{i,j=1}^{N}\alpha_{i}y_{i}(\mathbf{\Phi}^{T}(\bm {x}_{i})\ \mathbf{\Phi}(\mathbf{x}_{j}))+b\right) \tag{7}\]  
The kernel function of the following Equation (8) returns the dot product of the feature space mapping from the original data points:  
\[K\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}=\left(\mathbf{\Phi}^{T}(\mathbf{x}_{i})\ \mathbf{\Phi}(\mathbf{x}_{j})\right) \tag{8}\]  
There are many types of kernel functions, such as linear, polynomial, and Gaussian RBF (radial basis function). The selection of the most suitable kernel is crucial, because it defines the feature space in which the training set data will be classified [20; 82].
## 4 Findings and Discussion
### Qualitative Study Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Rule Ranking** & **Rule ID** & **Consequent** & **Antecedent** & **Support (\%)** & **Confidence (\%)** & **Instances** & **Rule Support** & **Lift** & **Deployability** & **Rule** \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{14} & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Cleanliness = Yes & & & & & & Cleanliness = Yes and \\  & & and Customer & & & & & & & Customer Service \(\times\) No \\  & & Star-Rating = & Service \(\times\) No and & 11.46 & 70.563 & 231 & 8.069 & 1.635 & 3.366 & & and Value for Money \(\times\) \\  & & Lower & Value for Money = & & & & No and Hotel & & No and Hotel Facility \(>\) \\  & & Star-Rating & No and Hotel & & & & & No \(\sim\) Lower & Star-Rating \\  & & Facility = & & & & & & & & \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{14} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Note: We selected only \(76^{\circ}\) best priority rules due to the space limitation, Note: Rule ID, Instances, Support, Confidence, Rule Support, Lift and Deployability are defined by IBM SPSS, Modeler 18.0 Modelling 2005 [26], pg 251. Rule ID displays the rule ID assigned during model building. A rule ID enables you to identify which rules are being applied for a given prediction. Instances displays information about the number of unique IDs to which the rule applies-that, is for which associations are true. Support displays the proportion of IDs in the training data for which the associations are true. Confidence: displays the percentage of IDs where a correct prediction is made, out of all the IDs for which the rule makes prediction. It is calculated as the number of IDs for which the entire exposure is found divided by the number of IDs for which the author’s account, based on the training data. Rule Support for Sequence models is based on instances and displays the proportion of training records for which the entire rule, antecedents, and consequently, are true. It displays the ratio of confidence for the rule to the prior probability of having the consequent. Deployability: is a measure of what percentage of the training data satisfies the conditions of the antecedent but does not satisfy the consequent. \\ \cline{1-1} \cline{2-10}  & & & & & & & \\ \cline{1-1} \cline{2-10}  & & & & & & & & \\ \cline{1-1} \cline{2-10}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cont. The academic experts expressed their views that the results obtained from the algorithm analysis were both practical and useful, not only for hoteliers but also for those working in related literature. For those reasons, more research applying machine learning to the field of hospitality is encouraged. In sum, the qualitative portion of the study corroborates the results of the algorithm work and affirms its practical application to the industry. Below is one of the academic professor's comments:  
\"This kind of algorithm technique is practical and useful both for industry and academics. I hope to see more research applying this approach to the hospitality industry... \" (Acad. 01, Acad. 02)
## 5 Discussion
What is also worth noting is that in the solution presented in [33], the authors recorded the data locally on an SD card, and they trained the SVM model in the MathWorks environment. Although the authors mention the possibility of remote data transmission with the GSM module, there is no information that the solution, unlike ours, uses any Cloud or Edge processing. Additionally, for kNN and LDA classifiers and the same method of data acquisition, [PERSON] et al. [34] achieved a bit worse results of _V. destructor_ detection than we did. In terms of detection quality, [PERSON] et al. [44] reached the F1 score at a maximum level of 0.91 when using a CNN classifier for _Varroa destructor_ detection based on image analysis. Although this is a better result than ours (F1 = 0.8), their solution affected bees' natural environment--they used a passageway for bee illumination and recording. The main differences between various solutions for bee monitoring, including detection of the _Varroa destructor_ mite, and our approach are summarized in Table 5. We compared the approaches according to the capability of detecting the _Varroa destructor_, the type of analyzed data, the algorithm used for the analysis, the device platform, and Cloud- and Edge-based processing capabilities. While analyzing four different camera resolutions in our research, we concluded that the most accurate results were obtained with 5 Mpx as image resolution. The detection and classification results are the best, and the average time needed for processing a single image is similar to two lower resolutions. Measured processing times are also satisfactory and do not differ significantly for both healthy and infected bees.
# Autonomous Environment Generator for UAV-Based Simulation
## 3 Experimental Setup
### Frontend (Angular)  
The frontend portion of the process pipeline implements the Angular open-source framework to allow client-side web applications. This project expands upon the cloud platform built by PDMPC [8] that allows for remote control of UAVs on maps of satellite images. The decision to continue using the Angular framework allows for overall consistency with components and models, as well as Angular's ability to handle asynchronous data for decreased latency between the backend and the UAV [9].  
Angular was used instead of other popular frameworks, like Vue.js and React, for data-rending purposes and overall framework support. The Vue's reactivity system would not be ideal for this project, as data is rerendered in chunks which could be problematic for efficient data transfer [10]. Furthermore, while React requires further libraries for model and controller properties, Angular has these functionalities built-in. Angular's support of dependency injections, as well as a TypeScript language consistency with our chosen backend, made Angular our ideal frontend choice.  
For this project, the ability to screenshot the current satellite image map was implemented through the EasyPrint plugin [11] through Leaflet, a JavaScript library for interactive maps [12]. This plugin allows the user to click on a button to take a screenshot of the current satellite map view and send it to the backend for further processing. The latitude and longitude of the corners of the satellite image are forwarded to the backend for use in future simulations. Running on a laptop with an i7-8550U Intel CPU with 4 cores, the maximum scale that the simulation can accurately handle without crashing is approximately 1000 square meters, with a density of 9 objects per 100 square meters (900 total objects). Figure 2 is a screenshot from the said user interface and button.  
Figure 1: Leaflet to Gazebo Pipeline Fluxogram.
## 2 Methodology of Time-Frequency Separation Neural Network
### Enhanced Defect Detection with a Time-Frequency Separation Neural Network
#### 2.3.3 Overall Procedure of the Proposed TFSNN Method  
After defining the forward structure and training options of the TFSNN, such as layers, neurons, learning rate, optimizer criterion, etc., the flow chart of the TFSNN employed in separation of AE defect signals is shown in Figure 3. Note that the true source number, \(n\), should be first given in this analysis. The entire procedure can be divided into the following three main steps: (1) pre-processing of the observations, (2) self-organizing or adaptive learning of the trainable network parameters, and (3) separation of the estimated sources after the network is trained. Detailed operations for the main steps are shown in the flow chart. Theoretically, the proposed method has the following advantages or novelties in the source separation task:  
* Precise representation of the time-frequency spectrogram for the mixture signals. Firstly, a superior time-frequency analysis, namely SPWVD, is combined as a pre-processing technique. SPWVD is an improved version of TFA, which can both suppress the cross-item interference and retain a high-resolution spectrogram. In this way, the characteristics of sources can be represented in a more accurate manner.
* Sufficient exploitation of the inherent time-frequency characteristics for the AE signals: To cooperate with the STFDs, 1D-CNN and GRU structures were introduced in the TFSNN to extract and exploit the dominant characteristics from AE signals' TF representation.
* High accuracy and stable acquisition of the separation weights with the self-organizing network: The above learned features were provided to the separation regressor, and the optimization process can be achieved using powerful network optimizers such as Adam rather than a traditional quasi-Newton method. Hence, it can avoid oscillation and provide a faster convergence and higher accuracy.  
In summary, combined with the adaptive separation network, it promises outstanding performance in the separation and extraction of impulsive rail defect signals. The proposed method will be utilized and subsequently compared with the typical BSS methods in the source separation of simulation and experimental railway AE signals. The mentioned superiorities of the proposed method will also be verified in detail.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
###### Abstract  
To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis.  
3D point cloud facets; facet-abstracted downsampling; facet growth; point cloud segmentation; 3D point cloud analysis +
Footnote †: journal: Applied Sciences  
Article
## 3 Results and Discussions  
### Influence of Different Geometrical Parameters on Characteristics of the Machine  
In this section, firstly the influence of PM thickness and rotor disk thickness on the axial component of magnetic flux density is calculated by using simplified FEM. Additionally, the influence of PM span and stator thickness on torque and induced voltage of the machine was analyzed by using the 3D FEM.  
Figure 4 shows the influence of PM thickness on the \(B_{\rm z}\) size in the middle of the stator for the machine described in Table 1. This analysis was carried out using simplified FEM.  
It can be seen in Figure 4 that the PM thickness has a significant influence on the size of the axial component of magnetic flux density and on its degree of changing with the increase in the distance between PMs on opposite rotor disks.  
Figure 5 shows the influence of rotor disk thickness on the \(B_{\rm z}\) size in the middle of the stator for the machine described in Table 1. This analysis was also carried out using the simplified FEM.  
Figure 4: Influence of PM thickness on the size of \(B_{\rm z}\).  
Figure 5: Rotor disks thickness influence on the size of \(B_{\rm z}\).  
Figure 5 shows that rotor disks thickness also influences the size of the axial component of magnetic flux density up to a specific thickness. This is the reason that the rotor disk thickness has to be carefully selected. When designing the machine, namely rotor disks, the thickness has to be carefully selected in order to withstand the pull forces from the PMs (to keep the air gap constant and prevent the deflection of rotor disks) on one hand and on the other hand it influences the total weight of the machine and its efficiency.  
Since some characteristics of the machine, namely waveforms, cannot be analyzed by the simplified FEM, a regular 3D FEM analysis was performed where PM span and stator thickness influence were analyzed.  
Figures 6 and 7 show the influence of PM span on the torque and induced voltage for the machine described in Table 1 and Figure 8 shows the influence  
Figure 6 shows that PM span has a positive influence of the torque size of the machine, since the torque value increases with the increase in PM span. As we can see in Figure 7, the PM span increase has a different influence on the induced voltage of the machine. Its value does not change significantly, but its waveform does. We can see that for higher PM spans the induced voltage waveform has a flat top and for lower PM spans it has a more triangle-shaped waveform. It can be noticed that for 24\({}^{\circ}\) PM span the induced voltage has a sinusoidal waveform. That is the reason for all the input data for the derivation of the mathematical model being obtained with the simplified FEM calculations of a model with a 24\({}^{\circ}\) PM span (it covers 120\({}^{\circ}\) electrical).  
When we derived the mathematical model, we also stated that the ratio between stator and PM thickness can be up to 1:4. Figure 8 shows the reason for this limitation, since the torque value increase rate reduces with the increase in the stator thickness.
# The Use of Correlation Features in the Problem of Speech Recognition
## 3 Materials and Methods
The normalized covariance (correlation \(R\)) has no units, and the interval k is equal to the sampling time step (discretization frequency is 48 kHz). Figure 6 shows all 20 reference correlation functions of speech commands. All of them are distinguishable either in form or in correlation values. Values depend on source voice message. Such signals can already be used for classification. On the curves in Figure 6, it is not difficult to notice both the fading nature of the connection in speech signals, and some quasi-periodicity, which is present in speech and leads to several extrema on the graphs. However, the shape is already much better visually distinguished, which can be used to train neural networks. As another preprocessing method for feature extraction, let us consider the construction, not of cross-correlation portraits, but autocorrelation portraits. To do this, it is necessary to import the original time series for a voice message. Then, knowing the total number of samples \(N\) and the desired sweep size \(M\) (it is necessary that the remainder of dividing \(N\) by \(M\) is 0), it is possible to obtain a correlation portrait using a sliding window:  
Figure 1:2  
where \(\sigma^{2}(i)\) is the signal variance in the region of the local window. In this case, it is good idea to vary the different sizes of portraits. Figure 7 shows a representation of the voice message \"Pre-flight check\" with different portrait sizes. Figure 1:3 Examples of radio exchange messages correlation functions. It is easy to see that width of the image may show correlation interval. If brightness along X-axis is changing slowly the width should be bigger. Height of the portrait should be chosen such way that provides integer number of rows according to length of discrete message signal. Figure 8 shows the autocorrelation portraits of two phrases for comparison. Analysis of Figure 8 shows that very different images are generated for different messages. As a result, after building such portraits it is possible to use convolutional neural networks for recognition. Figure 9 shows the final diagram of the preprocessing of a speech message to prepare for recognition. Figure 1:4 Comparison of autocorrelation portraits. Figure 1:5 Autocorrelation portraits for the phrase “Pre-flight check”. In the next section, we will consider the results of comparing the proposed methods for extracting features of speech messages with methods of deep and machine learning.
## 1 Introduction
The performance evaluation was carried out again by ns-3 simulations, over a scenario consisting of 100 nodes arranged in a 10 \(\times\) 10 regular grid, where both the gateways (nine nodes) and the traffic generating nodes were randomly chosen. Results showed the improvements obtained in terms of average end-to-end delay and packet delivery ratio. As already mentioned, this paper presents a new proposal for improving performance in smart grid NANs when using IEEE 802.11 mesh network technology. Although the modification of the routing metrics is a good idea to differentiate the service offered to different traffic in the network, we preferred to maintain the basic airtime link metric and focused our efforts on the modification of the mechanism used by the HWMP protocol for the selection of the most appropriate path each time a data packet must be (re)transmitted. By its own nature, the default metric informs about the congestion state of the different network areas, which is the most relevant measure for our approach. Moreover, it is important to keep in mind that working with more complicated metrics usually leads to higher CPU and memory requirements in the network nodes, as well as to protocols that generate more network control traffic. In this way, a modification of the HMWP protocol is proposed and implemented, to allow an efficient selection of paths among multiple possibilities, depending on the service quality needs of the different traffic flows. The proposed mechanism is complemented with the assignment of different frequency channels to each available path. In addition, to avoid packet losses due to the formation of unwanted loops, the proposed technique is combined with a criterion of the minimum number of hops when choosing the paths. This technique reduces the number of selectable paths, but avoids the need to use packet hop counters (which are used by the nodes to discard packets after a given number of hops, with the added disadvantage of using network resources for a certain number of retransmissions in a completely useless way). On the other hand, as will be seen in the Results Section, we considered it of great importance to provide not only the average values of the performance parameters under study, since this way, the real network performance is not obtained and would probably lead us to an erroneous network planning. The rest of the paper is organized as follows. The next section presents the modifications proposed for the HWMP protocol. Details about the multi-path and multi-channel mechanisms implementation are provided, as well as the route selection and assignment algorithms. Section 3 presents and analyzes the results obtained through the simulations, and finally, the conclusions, as well as the future lines of our research are summarized in Section 4.