esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 2 Mixed Signal Dereverberation
### Optimization
\begin{cases}\sum\limits_{l,n}W_{k,l}U_{l,n-h}\frac{\left(X_{k,n }^{[t-1]}\right)^{\beta-1}}{M_{k,h}^{[t-1]}}&,\hskip 28.452756 pt\beta>1\\ \sum\limits_{l,n}\frac{W_{k,l}U_{l,n-h}}{M_{k,h}^{[t-1]}}&,\hskip 28.452756 pt \text{other}\end{cases}  \tag{13}\]  
The vector \(\boldsymbol{b}^{(k)}=\left(b_{h}^{(k)}\right)_{H\times 1}\), \(b_{h}^{(k)}\in\mathbb{R}_{0,+}\) is defined as:  
\[b_{h}^{(k)}= \begin{cases}\sum\limits_{l,n}W_{k,l}U_{l,n-h}Y_{k,n}\left(X_{k,n}^{ [t-1]}\right)^{\beta-2}&,\hskip 14.226378 pt\beta\leq 2\\ \sum\limits_{l,n}W_{k,l}U_{l,n-h}Y_{k,n}&,\hskip 28.452756 pt\text{other}\end{cases}  \tag{14}\]  
Similar to Formula (12), Formula (15) is used to solve for \(\mathbf{M}_{k}^{[t]},k=1,\ldots K\), which is combined to obtain the optimal \(\hat{\mathbf{M}}\):  
\[[\mathbf{A}^{(k)}+2\eta_{k}^{\text{m}}\mathbf{U}^{{}^{\prime}}\mathbf{U}] \mathbf{M}_{k}^{[t]}=\boldsymbol{b}^{(k)} \tag{15}\]Therefore, we can obtain the optimal \(\hat{\mathbf{W}}\), \(\hat{\mathbf{V}}\) and \(\hat{\mathbf{M}}\). Then, a time-varying gain coefficient \(Z_{k,n}\) is utilized to avoid the error caused by the simple definition \(\hat{\mathbf{S}}=\hat{\mathbf{W}}\hat{\mathbf{V}}\), where \(\hat{\mathbf{S}}\) is the estimated spectrogram of the sound source signal. \[Z_{k,n}=\frac{\sum_{l}\hat{W}_{k,l}\hat{V}_{l,n}}{\sum_{l,h}\hat{W}_{k,l}\hat{ V}_{l,n-h}\hat{M}_{k,h}} \tag{16}\]  
Eventually, the estimated spectrogram of the sound source signal \(\hat{\mathbf{S}}\) is obtained by:  
\[\hat{S}_{k,n}=Z_{k,n}Y_{k,n} \tag{17}\]  
To obtain the estimated spectrum of the dereverberated signal, the phase information of the recorded signal is used to reconstruct that of the dereverberated signal. Thereafter, the spectrum of the dereverberation signal shown in Figure 4 is taken as an example to show the effect of the dereverberation module, while the spectrum of the source signal and the recorded signal are shown in Figure 2a,b, respectively. It can be observed that compared to the recorded signal, the harmonic structure in the TF spectrum of the dereverberated signal is clearer and the energy between adjacent harmonics is obviously reduced. In addition, the spectrum of the dereverberated signal is more similar to that of the sound source signal.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 3 Results
### Image Transmission
The constants \(c_{1}\), \(c_{2}\), and \(c_{3}\) are used to avoid division by zero. \(\alpha\), \(\beta\), and \(\gamma\) represent the weighting factors for different features in the SSIM measurement, here customarily set to 1. \(\Delta E\) as an important indicator was proposed by the International Commission on Illumination (CIE) to evaluate color accuracy, with the \(\Delta E\)2000 defined as:  
\[\Delta E=\sqrt{\left(\frac{\Delta L^{\prime}}{K_{L}S_{L}}\right)^{2}+\left( \frac{\Delta C^{\prime}}{K_{C}S_{C}}\right)^{2}+\left(\frac{\Delta H^{\prime} }{K_{H}S_{H}}\right)^{2}+R_{T}\frac{\Delta C^{\prime}\Delta H^{\prime}}{K_{C} S_{C}K_{H}S_{H}}} \tag{26}\]  
The equation includes parameters \(K_{L}\), \(K_{C}\), \(K_{H}\) for luminance, chroma, and hue factors; \(\Delta L\), \(\Delta C\), \(\Delta H\) for their respective differences and weighting factors \(S_{L}\), \(S_{C}\), \(S_{H}\) for each. \(R_{T}\) is the rotation factor. By calculating the \(\Delta E\) between the original image and the image restored after echo cancellation, color fidelity can be evaluated. Typically, a smaller \(\Delta E\) value indicates a higher quality of image restoration. Table 3 shows the computed evaluation indexes of image restoration for different echo cancellation methods. As can be seen, both the LMS filter and DPRNN model remarkably enhance the image restoration level, and the DPRNN model is far more efficient in echo cancellation than the LMS filter. SSIM in the DPRNN case is up to 0.9471 high, which is quite favorable from the view of image restoration.
## 4 Discussion  
### Sample Data  
In relation to the sample data, one of the most remarkable results was the high rejection rate, which was 33.3%. This result aligns with other studies in the literature [3, 4, 40]. The participants who rejected prosthetics mainly indicated that they were accustomed to performing ADLs without using a prosthesis, or they believed that a prosthesis would not confer them additional personal autonomy given their degree of amputation (as reported by the individual with shoulder disarticulation and a damaged brachial plexus). Although the lack of need is a recurring justification for prosthesis rejection [38, 40, 41], personal and contextual factors are critical determinants of prosthesis acceptance [39]. However, in this case, the degree of amputation did not seem to have an apparent effect on the rejection rates, except when explicitly mentioned by the user, as in the individual with shoulder disarticulation. This may be interpreted as a need for prosthetic devices that are easier to use by individuals who are unable to activate their arm muscles, who commonly perform myoelectric prosthesis control via their torso muscles, which is complex and involves a hard adaptation period. Another significant observation is the used prosthesis type, with 84.62% of individuals using electrical/myoelectrical prosthetics. This aligns with recent studies and denotes a rising use in such prostheses over the past decade, which was reported in previous studies to be 34.2% by the participants in a study in 2012 [42] and 92.86% in 2022 [43]. Furthermore, the study of 2012 reports that 19.9% of users use cosmetic prostheses, which were reported neither in the present study nor in that of 2022 [43]. These results denote the current trend of moving toward the use of functional and active prostheses and highlight the importance of considering 3D-printed upper limb socket prototypes that accommodate myoelectrical sensors.  
Figure 13: A satisfaction–importance plot tailored to correlate answers to the first survey sections with the obtained importance of the specific socket properties in this section. The correlated questions are specified in the plot legend. The questions are labeled as detailed in the entire manuscript.
# Magnetic Self-Healing Composites: Synthesis and Applications
## 4 Applications
### Stretchable Electronics
Due to their light weight, low cost, corrosion resistance and processability, flexible polymeric conductive and magnetic nanocomposites have found a spot in the next generation of advanced EMI materials, outperforming the conventionally used metallic rigid shields [134; 135; 136]. For dynamic systems where flexible EMI shields are exposed to potentially aggressive environments, self-healing capacity can provide additional reliability to conventional protective systems. [PERSON] et al. proposed a self-healing nanocomposite based on a dynamic covalent network crosslinked with imine reversible bonds, filled with a Fe\({}_{3}\)O\({}_{4}\)@MWCNTs hybrid filler as an EM wave-absorbing component. The proposed system exhibits decent EM wave-absorbing properties and recyclable performance when dissolving the material in an acidic solution [137]. [PERSON] et al. initiated the polymerization of dissociative covalent networks by grafting reversible covalent bonds at the interface of MWCNTs and a polymeric crosslinker using Diels-Alder [138] and disulfide [139] reversible crosslinks. Magnetic NPs are added to the network to enhance the EMI shielding effect. In addition, the same research group reported a similar system but with shape-memory properties, promoting the SMASH capacity to enhance the healing performance [140]. Additional research is still needed on novel processing approaches in order to finely disperse magnetic and/or conductive nanofillers in the matrix to improve EMI radiation scattering and boost the EMI shielding effect while lowering the fillers concentration.
# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
###### Abstract  
**Background:** Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refacored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level.  
**Objective:** This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. **Methodology:** At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). **Result:** In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. **Conclusions:** Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.  
Software Refactoring prediction; software metrics; ensemble classifier; multi-phased feature extraction +
Footnote †: journal: Applied Sciences
# Autonomous Balloon Controls for Protection against Projectiles with Known Destinations
## 1 Introduction
The center of the net is controlled so that the net center moves while not rotating the line-of-sight connecting the pair (the net center and the UAV). The trajectory control of balloons poses a great challenge, given their importance in scientific explorations and military applications [12; 13]. The authors of [12] addressed a balloon trajectory control system, which was under development for use on NASA's Ultra Long Duration Balloon Project. The use of a control device tethered to a balloon is required to provide sufficient lateral forces to counteract the air drag on the balloon [13]. In our paper, we assume that once a designated point of a balloon is set, the balloon can be located at the point using the controls in [12; 13]. The control of a balloon is not within the scope of this paper. The proposed defense strategy is to deploy multiple balloons on the EP's viable paths so that they block the incoming projectile effectively. Because the projectile is launched to reach its destination, the balloons can effectively intercept the projectile using this blocking strategy. As far as we know, this article is novel in using multiple balloons for protection against an EP. The effectiveness of our defense strategy is further verified utilizing MATLAB simulations. The organization of this article is as follows. Section 2 presents the preliminary information of this article. Section 3 discusses definitions and assumptions. Section 4 discusses the defense strategy utilizing multiple balloons. MATLAB simulations are addressed in Section 5 in order to demonstrate the performance of the proposed strategy. Section 6 addresses the discussion on balloon deployment positions. Section 7 provides conclusions.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans  
The main well-developed communication systems used in oceans include maritime radio and satellite systems. Mobile cellular systems are occasionally exploited, while cabled underwater observatories are designed for special applications. Meanwhile, there are also some new technologies under research and development, typically including high-altitude platform (HAP) and wireless ad hoc networks (WANETs). These systems are briefly reviewed in the following.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Data Processing  
#### 3.3.1 Data Labeling  
To construct the dataset for the SVM, experimental data were split into two categories: \"damaged\" and \"undamaged\", with labels 1 and 0, respectively. Based on the physical definition of the data, two types of key vectors were defined:  
Critical Vectors:  
Critical vectors are the acceleration data when the recorder transitions from an \"undamaged\" to a \"damaged\" state. For example, in the data shown in Table 6 (Data 01), the complete vector [24,439, 38,164, 50,529, 61,137, 72,144, 85,871] is labeled as \"damaged\" (label 1), while the vector obtained by removing the last data point, [24,439, 38,164, 50,529, 61,137, 72,144], is labeled as \"undamaged\" (label 0). Extracting critical vectors is crucial for the accuracy of the model because they contain key information about the device in its extreme state, making it easier for SVM models to identify them as support vectors.  
Non-Critical Vectors:  
Non-critical vectors are data points where the recorder is still in an \"undamaged\" state and can withstand more than one impact. For instance, from Table 6 (Data 01), the complete data [24,439, 38,164, 50,529, 61,137, 72,144, 85,871] can be used to extract shorter vectors, like [24,439], [24,439, 38,164], [24,439, 38,164, 50,529], and [24,439, 38,164, 50,529, 61,137], all of which are non-critical vectors and are labeled as \"undamaged\" (label 0). These vectors provide additional training samples, helping the model better learn the characteristics of the \"undamaged\" state.  
As a result, from the 10 sets of experimental data in Table 6, a total of 20 critical vectors and 53 non-critical vectors were extracted.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Experimental Conditions  
All the experiments were run using the classification scheme and network architecture described in Figure 6. The figure shows, on the left, a patch extracted from the HSI. The black boundaries represent the superpixel edges. The extraction process is explained in Section 2.1. The data augmentation technique of choice was then applied to the patch. The data augmentation techniques based on DWS are explained in Section 2.3. The patches resulting from this data augmentation process were then fed to the CNN.  
Figure 5: Proposed dual-flip 16\(\times\) data augmentation technique based on the DWS framework. The red orientation marks (dots) are shown to indicate the orientation of the data relative to the original image.  
Figure 6: Classification scheme and CNN architecture.  
The network consists of two blocks of 2D-convolutional layers coupled with 2D-max-pooling layers, and two dense layers. With the aim of reducing overfitting, two dropout layers were added to the network, both of them using an aggressive dropout ratio of 0.5. Table 1 details the parameters for each layer. ELU activations were used for all layers due to the advantages this function has over others such as the ReLU family. Namely, ELU provides more robust training and faster learning rates [25]. All trainings were run for 112 epochs using a NADAM [26] optimizer with \(\text{learning\_rate}=0.0001\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), \(\epsilon=1\times 10^{-7}\).  
In the experiments, data augmentation was performed online for the first epoch and then cached for the rest of the training. The techniques considered are a set of commonly used data augmentation techniques from the literature and the four proposals introduced as part of this work. In the first group we have: rotate, the standard 90 degree rotation applied four times; flip, the standard flip over both axes of the image; \(\text{PVS}(+/-)\) or pixel-value shift augmentation, where pixel values of the input image are shifted relative to the average band [12]; and random occlusion, which removes rectangular regions of the patch on up to 50% of the input patches in a batch [13]. The new proposals to be studied include inner-rotate, inner-flip, dual-rotate and dual-flip, as described in Section 2.3. The results are compared in terms of classification accuracy after the application of the different data augmentation techniques.  
All the tests were run on a 6-core Intel i5 8400 CPU at 2.80 GHz and 48 GB of RAM, and an NVIDIA GeForce GTX 1060 with 6 GB. All experiments were run under Ubuntu Linux 16.04 64-bits, Docker 19.03.5, Python 3.6.7, Tensorflow 2.0.0 and CUDA toolkit 10.0. All the training instances were performed on Tensorflow with GPU support enabled, using single-precision arithmetic.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Comparison Experiment Results
Similarly, by comparing the prediction results of our GCN-GRU model with the GRU model at the time points 5, 10, 15 and 30 min, the _RMSE_ of the GCN-GRU model is reduced by 28.68%, 24.36%, 32.54% and 35.43%, and the _MAE_ is reduced by 33.47%, 32.39%, 30.2%, and 29.5%, respectively. The prediction results indicate that the GCN-GRU model can also efficiently extract temporal features. In Figure 14, the predicted performance of the GCN-GRU model is consistent with the average traffic flow in the mean speed capacity. The errors of _RMSE_ and _MAE_ are reduced to a certain extent, then remain relatively stable in (0.2, 0.3) and (0.1, 0.3), respectively. Therefore, the significant error reductions of the proposed GCN-GRU in _EMSE_ and _MAE_ reveal that fusing the spatiotemporal features is effective in accurate short-term traffic flow prediction. Figure 14: Comparison results of the mean speed capacity. (**a**) The comparative experiment of the _RMSE_; (**b**) The comparative experiment of the _MAE_. Figure 13: Comparison results of the average traffic flow prediction. (**a**) The comparative experiment of the _RMSE_; (**b**) The comparative experiment of the _MAE_.
## 3 System Design
### System Scenarios
#### 3.2.3 Uploading a File on CEA System  
Figure 6 illustrates the scenario of uploading a file on CEA system. Users employ the CEA DApp interface to upload a file. The file is stored as an object on a local storage IPFS client node. Then, the object is pinned recursively in each client's local storage node to provide file availability. It pins  
Figure 4: Sequence diagram of registration on IPFS private network.  
Figure 5: Sequence diagram of registration on the CEA Network.  
all links to the object to be stored locally. After the object is successfully pinned in local storage, IPFS leverages DHT, to distribute the object to other nodes in the private network. In this distribution, the closest peer ID among the nodes has the highest priority. IPFS returns a hash to identify the file. The system calls Metamask to sign the transaction containing the hash of a file. However, the user must unlock the account to enable the signing of the transaction on CEA Network. When the transaction is committed, CEA Network returns a transaction ID as its identity.
## 2 Related Works
### Data Augmentation
Therefore, the over-sampling method is needed to increase the number of samples in an appropriate manner. Furthermore, appropriate data augmentation methods should be applied to over-sampling methods to prevent overfitting. [PERSON] et al. created a synthetic wafer map with a specific defect pattern. The authors showed that a CNN model trained only on synthetic wafer maps for network training could classify real wafer maps with a high accuracy. The authors showed that the network was not significantly overfitted by using real wafer map images as a validation data set [28]. [PERSON] et al. proposed automatic techniques using a policy search strategy to reduce the human effort required to augment WBM data. They considered rotation, flipping, shifting, shearing range, and zooming as data augmentation methods to effectively solve data imbalance problems [29]. [PERSON] proposed a rotation-based augmentation method as a way to mitigate the decrease in the accuracy of the classifier due to the lack of WBM defect patterns [30]. The author augmented wafers by seven times compared to the original wafer by rotating it by 90 degrees and flipping the rotated wafer horizontally. From the experimental results, the author argued that CNN models that learn augmented data through rotational augmentation methods can achieve a higher classification performance by providing consistent predictions of rotational variations. [PERSON] and [PERSON] proposed a DCGAN-based augmentation model to generate imbalanced WBM defect patterns. The authors showed that a classifier that has learned wafers generated from a DCGAN-based augmentation model exhibited a higher classification performance than a classifier that has learned wafers generated from rotation-based augmentation techniques [31]. As such, several studies have been conducted on data augmentation methods to improve the classification performance of WBM defect patterns. In addition, because it is difficult to collect abundant industry data, predicting the appropriate sample size of the training data required for training a classifier is the most common limitation to consider in reality. [PERSON] et al. proposed a 'proxy approach' to measure the degree of convergence of other functions related to the final error probabilities of the classifiers to predict the optimal learning sample size required for learning the classifiers [32]. Instead of calculating the actual error probability of the classifier according to the training set size, they calculated a proxy curve represented only by the training set size and a function represented by feature space dimension to obtain the optimal training sample size. However, the results of studies on numerical analysis related to the efficiency of the methods applied to solve the data imbalance problem remain unclear.