esa-sceva/satcom-chunk-collection huggingface dataset:
# RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
## 2 Materials and Methods
### Signal Denoising  
ECG signals are susceptible to various noises, making accurate analysis challenging, even for experts. The original signal is corrupted by two main types of noise:  
Figure 1: Number of available recordings per class (blue) vs. the number of used recordings per class (orange).  
Figure 2: Original signal/recording (65,535 features) vs. one segmented signal (500 features).  
* _Low-frequency noise_[32]: This noise distorts the signal, making it difficult to interpret.
* _Baseline wander_[33]: This low-frequency noise causes the baseline of the signal to drift up and down, obscuring the heartbeats.  
These noises can significantly impact prediction accuracy. To address this problem, we implemented two stages of infinite impulse response (IIR) Butterworth filters to remove noise and to improve the ECG signal for analysis. ECG signals are vulnerable to low-frequency noise from various sources. This noise can vary depending on the recording equipment and the patient's condition. In our study, we identified a low-frequency noise range between 0.15 and 0.3 Hz. To eliminate this noise, we applied a Butterworth filter with a cut-off frequency \(w_{c}\) of 0.39 Hz. This specific value effectively removed low-frequency artifacts without compromising the ECG waveform itself. Baseline wander is another type of low-frequency noise that causes the baseline of the signal to drift. This noise typically occurs above 1 Hz. To address this, we employed a 4 th-order bandpass Butterworth filter. The order of 4 was chosen because it provides a good balance between reducing the noise and preserving the core ECG information. The specific cut-off frequencies for the bandpass filter were normalized as \(0.01<w_{c}<0.78\).  
As depicted in Figure 3, we could observe the transformation of the signal. The original signal was cluttered with noise, making it difficult to analyze the underlying ECG data. However, after applying the filters, the signal became significantly cleaner. The filters effectively removed unwanted artifacts, resulting in a signal that was much closer to the zero axis (zero crossing). This cleaner signal allowed for a more accurate analysis of the heart's electrical activity.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Theoretical Analysis of the Improved BPA
#### 3.3.1 Range Compression  
As the ranging code of the navigation satellite signal has good autocorrelation characteristics, the range compression is accomplished by autocorrelating the echo signal with the reference signal. Taking the C/A code of GPS L1 signal as an example, its autocorrelation peak is 24 dB higher than the sidelobe and cross-correlation results. Under a Gaussian white noise background, the autocorrelation gain is about 30 dB. In this paper, the calculation speed is accelerated by converting to the frequency domain, due to which the range autocorrelation is relatively computationally intensive. As shown in Figure 3, firstly, due to the actual collected echo data of GNSS-R BSAR consisting of hundreds of gigabytes, its capacity greatly exceeds the size of the video memory (tens of gigabytes) [30]. The proposed solution is to equally divide the echo data into multiple data segments and process them sequentially according to time. Secondly, the direct signal and the echo signal are converted to frequency domain for range compression, the specific implementation process is described in [25]. At a given azimuthal moment, the signal after range compression is expressed as:  
\[F(\eta,\tau)=P[\tau-\frac{R(\eta)}{c}]\exp[-j2\pi\frac{R(\eta)}{\lambda}] \tag{7}\]  
\(P(\tau)\) represents the autocorrelation function of the ranging code.
## 4 The Role of the Sandstone Carrier in Controlling Oil and Gas
### The Control Effect on the Lateral Migration and Accumulation of Oil and Gas  
The control effect of sandstone carriers on oil and gas is mainly reflected in the influence of sandstone facies change and the difference in conduction performance on the lateral migration and accumulation of oil and gas. The updip pinch-out of sandstone develops on the slope of the nose-like structure. The lithology changes from permeable sandstone to dense impermeable mudstone of the caprock. Oil and gas migrate along the pinch-out line of sandstone to the high part of the structure. When encountering a trap, it may accumulate and form a reservoir. For example, well HD171 encounters an oil layer, while well HD171-H is a water well (Figure 9).  
The Category I conductor in sandstone has good permeability. A match with the structural morphology leads to a greater likelihood of the formation of a dominant migration path for oil and gas. The Category III conductor has a dense lithology, poor physical properties and relatively developed interlayers. Drilling reveals that it is a dry layer that blocks fluid entry and changes the migration path of oil and gas (Figure 10).  
Figure 9: Superimposed map of carrier type and preferential migration path of layer 2 of Donghe sandstone. The area represented by the frame in the figure is the scope of the area represented by Figure 11, but their stratigraphic positions are not the same.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model  
### System Description and RA Procedure  
We consider a cluster-tree-based WSN that consists of multiple noncooperative SNs and an HS. SNs measure certain (local, ambient) information and send it to the HS through an RF channel called a physical RACH (PRACH is a well-known terminology used in 3 GPP standards. In this paper, however, PRACH does not imply a specific one used in current standards, but is a generic term that just indicates some physical-layer definitions for RACH described in the following). Every node is assumed to be equipped with a single omnidirectional antenna. We assume that a frame of PRACH is 20 ms long, which consists of 15 access slots, as in Figure 1. When an SN has information to send, a frame-long message (referred to as MSG) is assumed to encapsulate the information wholly and to be sent to the HS using PRACH. To send MSG successfully, the SN should completes three steps: transmitting a preamble on PRACH, receiving the AI of the preamble from the HS and sending MSG, which is referred to as a transmission cycle (TC) in this paper.
## 2 Fundamental Theories
### Support Vector Data Description  
Support vector data description (SVDD) [25] maps data set into a high-dimensional feature space and deduces a minimum hypersphere which contains as many target objects as possible. Given a target object set \(x_{i}\), \(i=1,2,\ldots,n\), a hypersphere of SVDD can be established. Accordingly, the sphere of the hypersphere can be described by center \(a\) and radius \(R\) of the sphere. The expression for the hypersphere is as follows:  
\[\begin{array}{l}\min f=R^{2}+C\sum\limits_{i}\xi_{i}\\ s.t.\ \ \left\|x_{i}-a\right\|_{2}\leq R^{2}+\xi_{i},\,\xi_{i}\geq 0,\forall i \end{array} \tag{17}\]  
where \(\xi_{i}\) is the slack variable, \(C\) is the penalty factor to reach a compromise between the size of hypersphere and number of misclassified samples. By introducing Lagrange multipliers, Equation (17) can be transformed into the following form:  
\[L=R^{2}+C\sum\limits_{i}\xi_{i}-\sum\limits_{i}\alpha_{i}(R^{2}+\xi_{i}- \left\|x_{i}-a\right\|_{2})\ -\sum\limits_{i}\beta_{i}\xi_{i} \tag{18}\]  
where \(\alpha\) (\(\alpha\geq 0\)) and \(\beta\) (\(\beta\geq 0\)) are Lagrange multipliers. Finally, a Gaussian radial basis function is applied to replace the inner product, transforming the optimization problem into the following dual problem:  
\[\begin{array}{l}\max L=\sum\limits_{i}\alpha_{i}K(x_{i},x_{i})-\sum\limits_{ i,j}\alpha_{i}\alpha_{j}K(x_{i},x_{j})\\ K(x_{i},x_{j})=\exp(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{\sigma^{2}})\\ s.t.\ \sum\limits_{i}\alpha_{i}=1,\ \ \ a=\sum\limits_{i}\alpha_{i}x_{i},\ 0\leq\alpha_{i}\leq C,\ \ \forall i\end{array} \tag{19}\]  
where \(\sigma\) is the kernel parameter.  
Solving programming problem (19), \(\alpha_{i}\) is obtained. To be specific, if \(0<\alpha_{i}<C\), the target sample is on the hypersphere, which is called the support vector (SV); if \(a_{i}=0\), the sample is in the hypersphere; if \(\alpha_{i}=C\), the sample is outside the hypersphere, which is called bounded support vector (BSV). For a certain SV \(x_{p}\), radius of the hypersphere can be calculated as \(R\). For any sample \(y\) to be tested, its distance from the center of the hypersphere is \(D\):  
\[R^{2}=K(x_{p},x_{p})-2\sum\limits_{i=1}^{n}\alpha_{i}K(x_{i},x_{p})+\sum\limits _{i,j=1}^{n}\alpha_{i}\cdot\alpha_{j}K(x_{i}\cdot x_{j}) \tag{20}\]  
\[D^{2}=K(y,y)-2\sum\limits_{i=1}^{n}\alpha_{i}K(x_{i},y)+\sum\limits_{i,j=1}^{ n}\alpha_{i}\cdot\alpha_{j}K(x_{i}\cdot x_{j}) \tag{21}\]  
Fault Diagnosis Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 4 Results  
### Experimental Setup  
In this paper, we implemented the proposed adaptive Laplacian mesh smoothing algorithm for tetrahedral mesh with CUDA11.0 on a workstation computer. The specifications and details of the workstation used for the experiments are shown in Table 1.  
In order to evaluate the performance of the proposed parallel adaptive algorithm, we conducted five groups of experimental tests, which are implemented on CPU and GPU, respectively. And the CPU version is implemented serially. The test data includes five sets of tetrahedral meshes: 5k, 10k, 59k, 77k, and 109k vertices; among them, the test of 77k vertices is supplemented by a slope model. The experimental data are shown in Table 2. The input mesh is created as follows. First, the equidistant nodes were distributed in a user-specified multidimensional data set. Second, random nodes were created in this space. Third, Delaunay meshes for these discrete point sets were established by using the TetGen library [37; 38]. As shown in Figure 8, we displayed the surface and internal structure of experimental mesh models with 59k nodes and 77k nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Details** \\ \hline Compiler & VS2017 Community \\ CUDA version (GHz) & V11.0 \\ CPU & Intel Xeon Gold \\ CPU Frequency & 2.30 GHz \\ CPU RAM & 128 GB \\ CPU cores & 48 \\ GPU & NVIDIA Quadro P6000 \\ GPU core & 3840 \\ GPU Memory & 24 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications and details of the workstation computer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Number of Nodes** & **Number of Tetrahedrons** \\ \hline
1 & 5000(5k) & 32,978 \\
2 & 10,000(10k) & 66,542 \\
3 & 59,261(59k) & 391,220 \\
4 & 76,928(77k) & 388,428 \\
5 & 109,260(109k) & 727,088 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental data.  
Figure 7: Illustration of single block and multiple blocks iteration.
## Main Design of DERM
### Design Overview  
As analyzed in Section 3.2, DERM can be abstracted into the energy-optimal dynamic destination region anycast problem named EO-DRA.  
To address this problem, the design of DERM mainly includes three components, as shown in Figure 2. Firstly, a _geographic routing protocol tailored for EO-DRA_ is proposed. Each node estimates the current sink location and destination region by the sink mobility pattern. Then, it delivers the newly generating packet to any node in that region in a greedy manner based on a comprehensive metric considering both the location information and the link quality. To handle the communications void, we adapt the traditional perimeter routing for EO-DRA and demonstrate the efficacy. Secondly, we observe that the estimated location may not be accurate due to some factors such as pauses and velocity fluctuations. Therefore, an efficient _sink location calibration method_ is adopted, with which each node updates the sink location when its route length dilation exceeds a certain limit caused by the outdated information. The method has low control overhead, and can still ensure high routing energy efficiency. Thirdly, for each node, there still exist unexpected sink location errors incurred by delayed updating or unpredicted movement deviations in future. Thus, we further propose a fault-tolerant mechanism named _track routing_. If a packet has been transferred to and buffered in an RN, but finds that the sink has not arrived by the deadline, it will exploit a reliable forwarding approach to track the sink along the path \(P\) and complete the last-mile delivery.  
To facilitate the subsequent descriptions of DERM, we introduce the network initialization operation here. In the initial phase, the sink broadcasts a message including its ID, mobility pattern (\(P\), \(v_{M}\), \(tp\)), initial location \(P_{init}\), and the time to start moving \(t_{init}\). Each node receiving the message stores this information in the sink list. All RNs will launch a procedure to preliminarily construct a  
Figure 2: DERM framework overview.  
route along the sink path, which will be elaborated in Section 4.4. Furthermore, each node exchanges its status information (i.e., node ID, PRR, and location) with the 1-hop neighboring nodes, and the graph planarization required for void handling is completed in a distributed manner.
## 1 Introduction
[PERSON] et al. [30] proposed an enhanced technique of skin cancer classification using a deep convolutional neural network with transfer learning models. Network pruning [31] and knowledge distillation [32] are also model compression techniques that are currently popular and have generated many studies and applications. Network pruning methods involve the removal of irrelevant weight connections in a network to increase inference speed and decrease model size. Knowledge distillation approaches transfer knowledge from a heavy network to a compact network so that the lightweight model retains the performance of the massive one as much as possible. They have proven effective in compressing most deep-learning models. For instance, [PERSON] et al. [33] provided a pruning approach for reducing model parameters to shorten the computation overhead and overall training time in federated learning on edge devices. [PERSON] et al. [34] used a knowledge-distillation framework to reduce the model weight and floating-point operations in compressing a deep neural network for the prediction of a machine's remaining useful life. According to the literature reviews mentioned above, YOLOv5s is expected to achieve highly accurate recognition, while network pruning and knowledge distillation may contribute greatly to its computing efficiency. However, it remains unclear how they could be correctly integrated and utilized in the automated identification of surface defects on arc magnets. To this end, we proposed a lightweight transfer learning model with pruned and distilled YOLOv5s and applied it to identify multiple surface defects on various arc magnets. The contributions of this paper can be mainly summarized in three aspects:  
(1) We developed a transfer learning model based on the frozen and fine-tuned YOLOv5s to achieve high accuracy in identifying surface defects of arc magnets through small-sample training. (2) We presented a YOLOv5s compression strategy of network pruning followed by knowledge distillation to minimize the loss of recognition accuracy when maximizing the reduction of model parameters and size. (3) We introduced a newly defined \(\lambda\) weight factor in the confidence loss function of the student model during knowledge distillation to improve the sensitivity of identifying image information regarding the surface defects. The remainder of this paper is structured as follows: the proposed method for identifying surface defects on arc magnets is described in Section 2. The details of the experiments for our method are presented in Section 3. The experimental results are analyzed and discussed in Section 4. Finally, our conclusions with topics for future research are given in Section 5.
## 4 Experiment
### Performance Evaluation Criteria  
Since the purpose of the classification model is to identify faulty wafers correctly, the confusion matrix can be summarized as Table 3. As performance measures, we used recall, precision, simple accuracy, balanced correction rate (BCR), and the F1-measure, all of which can be computed based on the confusion matrix.  
Figure 13: Multilayer perceptron architecture used in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Class**} \\ \cline{3-4}  & & **Bad (Positive)** & **Good (Negative)** \\ \hline \multirow{2}{*}{**Predicted Class**} & Bad (Positive) & True Positive (TP) & False Positive (FP) \\  & Good (Negative) & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for wafer bin map classification.  
Recall is the ratio of the number of correctly classified bad wafers and the total number of bad wafers, whereas precision is the ratio of the number of correctly classified bad wafers and the total number of wafers classified as bad by the classification model, as shown in Equations (4) and (5), respectively:  
\[Recall=\frac{TP}{TP+FN}, \tag{4}\] \[Precision=\frac{TP}{TP+FP}. \tag{5}\]  
A good classification model can achieve both high recall and precision. If recall is low, then many faulty wafers are missed by the model. If precision is low, then false alarms occur frequently. Simple accuracy is the ratio of correctly classified wafers regardless of the original class, as shown in Equation (6):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{6}\]  
Accuracy is not an appropriate performance measure when the class imbalance is significant, as in our case, since it yields high accuracy if the model classifies all test examples to the majority class, which is practically useless. Hence, BCR and the F1-measure could be more appropriate for an imbalanced dataset. BCR is the geometric mean of class-wise accuracy and the F1-measure is the harmonic mean of recall and precision, as shown in Equations (7) and (8), respectively:  
\[BCR=\sqrt{\frac{TP}{TP+FN}\times\frac{TN}{TN+FP}}, \tag{7}\] \[F1-measure=\frac{2}{\frac{1}{Precision}+\frac{1}{TPR}}. \tag{8}\]  
The five performance evaluation criteria described above depend on the cut-off values. To compare the intrinsic ability of classification models, cut-off independent performance evaluation criterion is needed. The receiver operating characteristic (ROC) curve shows the relationship between the false positive rate (\(x\)-axis) and true positive rate (\(y\)-axis) by varying the cut-off from tightest value to the loosest value. The ROC curve always starts from \((0,0)\) and ends with \((1,1)\). The area under the ROC curve (AUROC) is shown in Figure 14, and is commonly adopted as a cut-off independent performance metric. The AUROC ranges between 0.5 (random model) and 1 (ideal model); the larger the AUROC, the better the classification model.  
Figure 14: Receiver operating characteristic curve.
## 3 Results and Discussion
### Accuracy Assessment  
Three resolutions were validated using the confusion matrix (Table 7). In fact, the higher spatial resolution (5.42 mm) gave a higher accuracy level of 92%. The weed detection was underestimated at 20.31 mm spatial resolution when the producer accuracy (PA) was lower than the user accuracy (UA). Overall, KHAT accuracies were high for all spatial resolutions, indicating accurate classification, except for the 20.31 mm resolution (KHAT <80%). A KHAT accuracy of more than 70% is adequate for classification [44].  
Weed mapping was performed using the object-based image analysis (OBIA) method at different spatial resolutions. Kappa statistics in the range of 0.61-0.80 indicate good classification [44]. They are deemed comparable to 61-80% KHAT values. In this study, the classification accuracy remained higher than 80% for the overall and KHAT accuracies, except for _Cyperus rotundus_ (NG) at 10.83 mm spatial resolution. Meanwhile, at 5.42 mm spatial resolution, the classification accuracy increased to 92% and 97% for overall and KHAT accuracies, respectively, as shown in Table 7. This shows that higher spatial resolutions will provide higher accuracy. A previous study recommended the use of very high spatial resolution for weed detection [51]. Larger crop rows can help in detecting weeds using UAV. At least four pixels should be used for 15 cm crop rows to detect small objects via aerial images [52, 53]. The same approach was used in another study, where 2 cm spatial resolution was used to detect weeds in maize fields [50]. The researchers obtained 90% accuracy for weed detection using a combination of normalized difference vegetation indexes (NDVI) and OBIA. Fewer band combinations could be tested in the future. There may also be a trade-off between the number of bands and the spatial resolution of the images.
## 3 Results: Prediction of Wear Characteristics in a Mining Pump  
### Wear Model  
According to the existing literature, the wear empirical models proposed by the researchers are summarized, and as they have similar laws, they can all be summarized into the following equations [38].  
\[R_{erosion}=\sum_{n=1}^{N_{particles}}\frac{m_{p}C(d_{p})f(\theta)v^{b(v)}}{A_ {face}} \tag{3}\]  
where:  
\(R_{erosion}\): erosion rate;  
\(C\big{(}d_{p}\big{)}\): particle size function of the solid-phase particles;  
\(\theta\): impact angle between the particle and the wall;  
\(f(\theta)\): function of the impact angle;  
\(v\): relative velocity between the particle and the wall, m/s;  
\(b(v)\): function of the relative velocity between the particle and the wall;  
\(A_{face}\): unit surface area of the wall, mm\({}^{2}\);  
\(N\): the number of particles impacting the cell surface area;  
\(m_{p}\): mass of a single particle;  
Figure 4: Flow-head curve of mining pump.  
\(d_{p}\): particle size;  
\(n\): number of particles.  
After combining the above formula and the particle motion trajectory for analysis, the wear on the surface of the pump's various through-passage components caused by the movement of solid-phase particles in the computational domain can be quantitatively predicted. The impact angle function, \(f(\theta)\), can be expressed using a piecewise polynomial as follows:  
When \(\theta\leq 15^{\circ}\),  
\[f(\theta)=b\theta^{2}+c\theta \tag{4}\]  
When \(\theta>15^{\circ}\),  
\[f(\theta)=x\cos^{2}\theta\sin(w\theta)+y\sin^{2}(\theta)+z \tag{5}\]  
The values of the constants in the above formula are shown in Table 3.