esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fabric Physical Property  
#### 3.2.1 Fabric Thickness  
The measured values of thickness of fabric samples are presented in Figure 9. For the comparison between different fabric sample sets, when comparing fabric sample set A and set E to set B and set F, respectively, the effect of weft-baked structure, with and without regulating layer, can be examined. It is seen that the fabric samples with the regulating layer possess a slightly higher thickness than that of fabrics without the regulating layer. To examine the effect of weft density on the fabric thickness, fabric sample sets C and G are compared to fabric sample sets D and H, respectively. Figure 9 illustrates that fabric sample sets D and H with a higher weft density are thicker than that of fabric sample sets C and G, respectively. Figure 9 also exhibits that the fabric sample sets A, E, B, and F have significantly higher fabric thickness than that of fabric sample sets C, G, D, and H, with the average percentage increase of 46.52%. It shows the effect of weft yarn count on the fabric thickness wherein the fabric sample sets A, E, B, and F were fabricated by using a higher weft yarn count (150 denier) while the fabric sample sets C, G, D, and H were produced with 75 denier weft yarns. Through the comparison of the fabric sample set A to D without the mixing component of black yarn with their corresponding fabric sample set E to H with the mixing component of black yarn shown in Figure 9, the effect of face structure, with or without the mixing component of black yarn, can be examined. It is easy to see that each comparison group does not show a significant difference, which indicates that the fabric thickness is not significantly affected when the black yarn is added into the fabric face layer for color mixing.
# Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method
## 5 Conclusions  
This results of this study illustrate the possibility of using the AE method to detect diffusion interlayers in dissimilar welded joints of pearlitic and austenitic steels. Diffusion interlayers, including carbon and diffusion interlayers, are specific structural defects, the presence of which can adversely affect the mechanical properties of welded joints. Diffusion interlayers are usually microscopic in size and cannot be detected by conventional non-destructive testing (NDT) methods. Therefore, the possibility of using AE testing to detect diffusion interlayers is of great importance for practical applications.  
As a result of this research, we found that diffusion interlayers under cyclic loading are an active source of AE and emit acoustic signals of a similar specific waveform. Therefore, the presence of diffusion interlayers can be detected not only by an increase in the frequency of AE hits, but also by the presence of such deterministic ordered signals with a certain waveform. These signals can be detected using cluster analysis or by a rise time parameter that is constant for signals of similar waveform. The appearance of AE hits set with the same rise time leads to structuring of scattering diagrams (rise time vs. amplitude) and a change in the AE hits' rise time distribution. The reliability of diffusion interlayers detection is confirmed by the AE event location distributions--the AE source is detected in the area of a dissimilar welded joint.  
The results obtained can be used to determine the diagnostic criteria for the detection of defects and the subsequent development of an industrial NDT technique for dissimilar welded joints.  
Conceptualization, V.B. ([PERSON]) and A.M.; methodology, [PERSON] ([PERSON]); software, M.K.; validation, V.B. ([PERSON]) and T.B.; formal analysis, M.K.; investigation, V.B. and A.M.; resources, S.E.; data curation, T.B. and D.Z.; writing--original draft preparation, V.B. ([PERSON]); writing--review and editing, V.B. ([PERSON]); visualization, D.Z.; supervision, S.E.; project administration, S.E. and A.M. All authors have read and agreed to the published version of the manuscript.  
The research was carried out within the framework of the project \"Diagnostics of dissimilar welded joints of pearlit and austenitic steels by the acoustic emission\" with the support of a grant from NRU \"MPEI\" for the implementation of scientific research programs \"Energy\", \"Electronics, Radio Engineering and IT\", and \"Industry 4.0, Technologies for Industry and Robotics\" in 2020-2022 (project no. 20/22-0000028/44).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Data and Methodology
### AdaBoost  
AdaBoost, or Adaptive boosting, was the first really successful boosting algorithm developed for binary classification [75]. It is an approach to minimize the error of a weak learning algorithm. Theoretically, any algorithm can be a weak learning algorithm if it can produce classifiers that need to be slightly more consistent than random guessing [76]. AdaBoost helps to combine multiple \"weak classifiers\" into a single \"strong classifier\" [77]. The most common algorithms used with AdaBoost are DTs. A weak classifier (decision stump) is prepared using weighted samples from the training data. Only binary (two-class) classification problems are supported; thus, each decision stump makes a decision on one input variable and outputs a value of +1 or -1 for the first or second class. Weak models are sequentially added and trained with weighted training data. The method progresses until a predetermined number of weak learners has been generated or no more improvements can be achieved on the training dataset.
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 5 Discussion
### Limitations and Future Works  
The current work presents some important limitations that will be addressed in future studies. Initially, the result metrics were plotted from all networks over time to give a general insight into how the entire system works.  
From the statistical analysis in Figure 6, it appears evident that the models have an important improvement margin from a precision point of view. As is also clear in Figure 5, false positive events are frequent throughout the test frame. In the same way, the high accuracy levels suggest that there is a tendency to overfit. Despite the shared network architecture, the results vary significantly between all the models. It can be assumed that many factors contribute to this limitation:  
* The nature of the incoming data and the large number of classes (as discussed in Section 4.3);
* The limited number of data chunks used for training and testing (as shown in Table 5);
* As the neural networks are trained to catch the error occurrence, instead of the entire error frame, final metrics might be underestimated.  
Despite these challenges, tests on the entire dataset present highly variable results among both the models and the prediction time, generally corresponding to the AUROC values reported in the table metric for each model. The main aspect that causes confusion is the presence of many false positives and false negatives, as reported in Figure 5, but this problem could be mitigated by improving both the quality and quantity of the data through more accurate class handling.  
All the limitations cited bring us to many possible future scenarios to overcome them and improve the reported results. First, a more detailed and complete dataset should be arranged, along with a cleaner error configuration and description. The high number of classes/errors reported in Figure 3 is considered to have highly polluted the behaviour of the models, as the features drastically change the overall classification process. This implies setting not only to \"0\" the value classes, but also the level of the features at a low excitation, to support the model classification process. Second, the current model classification process does not consider some of the \"drag\" that the machine has during the stopping process. In fact, a problem with the same error might have a different stopping time and speed. Thismight lead to a different type of classification and/or data extraction. Third, the continuing collection of data to feed the neural networks will be positive for the overall performance; however, this is a time-consuming process, as errors rarely occur.  
In conclusion, it is also acknowledged that LSTM networks have long been employed in similar scenarios. However, in this particular case, as reported in Figure 6, the system lacks robustness. Therefore, a possible improvement involves the implementation of hybrid models that combine different predictive methods to classify error classes, or the usage of advanced techniques like transfer learning.
## 2 Spatiotemporal Characteristics of Diversified Loads in Multiple Functional Areas
### Characteristics of the Loads Suitable for Transmission across Functional Areas  
#### 2.2.1 Characteristics of the Electric Loads  
Unlike heating/cooling loads, which have natural energy storage characteristics, the electric loads have low elasticity and have no inertia. Generally, the flexibility of power supply systems can only be increased through demand response such as interruptible loads.  
For the different functional areas, the electric loads also show great differences, and there is a clear phenomenon of peak-valley staggering. Because the electric loads have the advantages of fast transmission and low losses in long-distance transmission, it is possible to establish tie lines between functional areas and realize the complementary balance of electric loads in the whole area, which can utilize the different characteristics of the electric loads in each functional area to improve the consumption of renewable energy.
## 3 Results
### Statistical Tests
#### 3.4.8 Non-Parametric Friedman Test
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Accuracy** & **Precision** & **Recall** & **f1** & **roc\_auc** \\ \hline RF & 4.23 & 5.05 & 2.83 & 3.81 & 4.46 \\ ANN & 3.53 & 2.58 & 4.70 & 4.01 & 2.77 \\ LR & 6.93 & 6.17 & 6.92 & 6.95 & 6.85 \\ SVM & 3.52 & 4.97 & 1.70 & 2.92 & 5.97 \\ LGBM & 4.17 & 4.34 & 3.58 & 4.00 & 3.99 \\ CB & 2.71 & 2.39 & 4.01 & 3.02 & 1.90 \\ XGB & 2.90 & 2.50 & 4.25 & 3.28 & 2.06 \\ \hline \end{tabular}
\end{table} 
Table 15: Rank aggregation table on 100 estimates across multiple metrics. Figure 5: Critical difference diagrams from Friedman Test with Nemenyi post-hoc analysis by metrics (accuracy, precision, recall, F1, ROC-AUC). It is important to note that the black lines connecting models in the CD diagrams indicate that no statistically significant difference exists between the grouped models for the selected metrics. For example, in the precision metric sub-chart, CB, XGB, and ANN are not statistically different in performance, just as RF, LGBM, and SVM form a statistically indistinguishable group.
# Hierarchical Concept Learning by Fuzzy Semantic Cells
## 4 Experiments
### Initialization Method  
The dataset needs to be preprocessed before starting the classification experiment. To ensure the rationality of the dataset segmentation, the data of each category is firstly extracted proportionally and then spliced together as the training set. Finally, we shuffle the samples in the training set, and the remaining data is regraded as the test set.  
For the initialization of the parameter \(P\), we try a variety of methods.  
* The first one is to perform K-means on each type of training set and get the clustering centers as the initial prototypes.
* The second way is to get the clustering centers by K-means in the entire training set to get the initial prototypes.
* The third way is to select samples in each category as prototypes randomly.
* The last way is to take the average of each category of data as initialization.  
Along with our paper, we assume the number of clusters in initialization is equal to the number of prototypes. The first three methods take a proportional number of prototypes \(M\) to the number of categories \(K\). Notice that we define \(K\) is the number of categories, and \(K\) is not related to K-means in our scenario. The final method takes the mass centers of the samples from each class as the prototypes, and it assumes the number of prototypes equal to the number of categories. In the experimental section, we will compare these four initialization methods to investigate the effects of different prototype initialization methods.  
For the parameter \(\sigma\), as a parameter for controlling the bell-shaped distribution width, experiments show that the initialization of \(\sigma\) will affect the convergence speed and the classification result of the algorithm. Empirically we discovered that a better classification result can be obtained when the value of \(\sigma\) is one-third of the average distance between training set samples and the corresponding prototype. However, no additional theoretical support for this phenomenon has been discovered. We will leave it for future work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Instances** & **Attributes** & **Classes** \\ \hline Synthetic dataset & 750 & 2 & 3 \\ Forest & 523 & 9 & 4 \\ Pendigits & 10,992 & 16 & 10 \\ MNIST & 70,000 & 784 & 10 \\ MIT face & 3240 & 4096 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description of five datasets.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 4 Receivers Design
### IB-DFE Receiver Combined with MRC  
A receiver for mMIMO scenarios was provided in [18] and it reduces the complexity of matrix inversion since it only occurs on the first iteration, achieving performance values very close to MFB at the end of the second iteration. This receiver, in the first iteration, behaves as an IB-DFE receiver and, in the remaining iterations, as the MRC receiver.  
The feedforward coefficients are given by Equations (8) and (11), which can be simplified to:  
\[\mathbf{\Lambda}_{k,p}=\left(\mathbf{H}_{k,p}^{H}\mathbf{H}_{k,p}+\frac{\sigma_{N }^{2}}{\sigma_{S}^{2}}\mathbf{I}_{R}\right)^{-1}, \tag{14}\]  
In the remaining iterations, the feedforward and feedback coefficients are given by Equations (9) and (13), respectively.  
In Figure 2, the structure of this receiver is depicted.
## 5 D-MSR Management Phases
### Defining Initial Communication Links with Neighbors (Phase-2)  
After the new device joins the network, it needs to find the route toward the other nodes in the network or the gateway. The _neighbor connection manager_ module in each network device, uses a handshaking mechanism (explained in Section 4.2) in order to define one Tx and one Rx link with each of its neighbors. Those links and a typical superframe will be added in the data link  
Figure 12: Receiving activation command (**a**) and defining initial communication links with neighbors (**b**).  
layer communication tables. These links enable a node to communicate with all its neighbors. Afterwards, the routing layer can be run to find the path between the endpoints. This procedure is shown in Figure 12(b).
# Stiffness Estimation and Equivalence of Boundary Conditions in FEM Models
## 3 Equivalence Method Based on Finite Element Model Updating
### Experimental Modal Analysis of the Beam  
The proposed equivalence method is presented on a similar beam model as used in the previous case. The rectangular steel beam was fixed to the rigid base on one side by means of two bolt connections (Figure 4). Its natural frequencies and mode shapes were determined by a modal test, in which the beam was excited by impact hammer (Bruel and Kjaer type 8206) and its responses were measured by laser vibrometer (Polytec PDV100). Data acquisition was provided by the Pulse LAN-XI system. Modal parameters were extracted using Pulse Reflex software. The experimental setup is shown in Figure 5. In the frequency range up to 3.2 kHz, seven modes were identified, five of which are bending and two torsional (see Figure 6). The natural damped frequencies and damping ratios of these modes are listed in Table 4.  
Since NX allows only one objective function to be defined, the frequency of the first mode was set as the objective. Other frequencies were defined as optimization constraints with a tolerance of \(+/-5\%\). It should be noted that the lateral mode shapes of vibration were not identified by the modal test; however, they occur in FEM analysis (mode 3 and mode 8). For this reason, these modes have not been included among the optimization constraints. The coefficients \(k_{x}\), \(k_{y}\), \(k_{z}\) representing the equivalent translational stiffness of the bushing in x-, y-, z- direction, respectively, were considered as the design variables. The optimization parameters were defined as follows:  
* Design Objective: Target Model Frequency Mode 1, Target value = 47.000 Hz
* Design Constraints: Model Frequency, Mode 1, Upper limit = 49.000 Hz Model Frequency, Mode 1, Lower limit = 45.000 Hz Model Frequency, Mode 2, Upper limit = 304.00 Hz Model Frequency, Mode 2, Lower limit = 275.00 Hz Model Frequency, Mode 4, Upper limit = 694.00 Hz Model Frequency, Mode 4, Lower limit = 628.00 Hz  
Since not all optimization constraints were met in any cycle, it was necessary to choose a solution where the estimate was closest to meeting them. In the 14. design cycle, five of the seven conditions were met; therefore, this cycle was considered to be the result of the optimization. Stiffness coefficients in this cycle were \(k_{x}\) = 18,654.71 N/mm, \(k_{y}\) = 2428.06 N/mm, \(k_{z}\) = 1068.86 N/mm. These values were used in the definition of bushing in the final equivalent FE model.
# Blind Deconvolution with Scale Ambiguity
## 5 Conclusions and Future Work  
In this paper, we consider the relative scale ambiguity between the latent image and the blur kernel based on an existing hyper-Laplacian regularization. We impose \(L^{p}\) normalization constraint on the blur kernel instead of the arbitrary \(L^{1}\) normalization constraint, which has been selected in most existing deblurring methods. We present that the hyper-Laplacian regularization can be transformed to a joint prior. Statistical results demonstrate that the joint prior favors ground-truth sharp solutions over trivial solutions when \(p>1.2\), which facilitates our deconvolution formulation to avoid trivial solutions. To determine the parameter \(p\), we carry out numerical experiments and find that \(p=2\) can be a good choice. Both quantitative and qualitative experiments on benchmark datasets and real images demonstrate that the proposed method is able to achieve the state-of-the-art results without any heuristic filtering strategies to select salient edges or kernel sparsity prior. Our future work will focus on focus on considering the scale ambiguity by imposing the \(L^{p}\) normalization constraint on the blur kernel associated with other image priors and improving the computational efficiency to implement realtime processing on the embedded platform.  
Conceptualization, W.F. and H.W.; methodology, W.F. and H.W.; software, W.F.; validation, W.F. and Y.W.; formal analysis, W.F. and H.W.; investigation, W.F.; data curation, W.F.; writing--original draft preparation, W.F.; writing--review and editing, H.W. and W.F.; visualization, W.F.; supervision, Z.S.; project administration, Z.S.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. This work has been partially supported by National Natural Science Foundation of China under Grant 61976041.  
The authors declare no conflict of interest.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 5 Results and Analysis
### Sample Experiment  
Based on the experimental results, we successfully simulated a USV in static and dynamic environments using both the baseline method and the proposed method. Figures 11 and 12 show examples of the experimental results in the static and dynamic environments for the baseline method and the proposed method, respectively.  
Figure 11 shows how paths in a static environment are formed. The experimental image was produced using a USV with three sensor inputs and a mutation rate value of 0.025. The experimental results show how the proposed method can produce paths that converge faster than those of the baseline method. The proposed method only requires a total of 120 genomes to be able to converge, but the baseline method requires a total of up to 369 genomes. Although, for one full round, the proposed method produces a longer path, the proposed method produces a faster travel time with 41.60-time units (around two times faster) compared to that of the baseline method, which is 80.82-time units.  
Figure 12 shows the results of path planning in a dynamic environment. The experimental image was produced using a USV with five sensor inputs and a mutation rate value of 0.255. These experiments show how the proposed method can produce paths that converge more quickly with 162 genomes (around 3.2 times faster) when compared to those of the baseline method with 522 total genomes. In this figure, it can also be seen that for experiments conducted in a dynamic environment, it is possible for a USV to create or generate different paths between the first round and the next round. For the baseline  
Figure 11: Examples of experimental results in static environments for the baseline method (**left**) and the proposed method (**right**) with three sensor inputs and a mutation rate value of 0.025 (this figure is based on Tables 1 and 2 in Appendix A).  
method, the USV produces different paths when turning to the left and right twice in a row (in the area to the left of the center). However, the experimental results with the proposed method produce a significantly different path with every right turn. This does not happen in a static environment (where there is no water wave factor), where the path is almost the same for the first and subsequent rounds.