esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The deep learning model solves the pattern recognition problem faster without human involvement [11]. Deep learning is utilized to create new business models increasingly in more companies. Deep learning applications are in language translation [12], image classification [13], speech recognition [14], asteroseismology [15], neuroscience [16], and many more. The artificial neural networks [17] are based on the layers of nodes. The nodes in the individual layers are connected to adjacent layers, making a fully connected network graph [18]. The information signals travel between nodes and assign corresponding weights. The final output layer compiles the weights to produce an output. Our research study uses a deep-learning-based approach for classification in asteroseismology. Our research study in the context of asteroseismology has the following prominent contributions:  
1. A novel bidirectional-gated recurrent unit and recurrent neural network (BiGR)-based deep learning model is proposed to classify the RGB (red-giant branch) and HeB (helium burning);
2. The Asteroseismology Exploratory Data Analysis (AEDA) is conducted to find the dataset feature patterns disorders and obtain a fruitful data visualization analysis for a better understanding of asteroseismology norms;
3. A comparative analysis of the past approaches in the context of asteroseismology classification with our novel approach is conducted and examined in this research study. The proposed technique outperforms other state-of-the-art studies;
4. The layered architectural analysis of our novel deep-learning-based BiGR model is conducted to analyze the working layers stack involved in model building and classification tasks;
5. The hyperparameter tuning is applied to obtain the best-fit parameters for our proposed model to classify the RGB and HeB in asteroseismology;
6. The effects of the number of iterations per epoch during the training of our proposed deep learning model are analyzed by its each epoch accuracy, data loss, complete training time, and data validation;
7. The time-series analysis of the proposed model in terms of data loss and accuracy score among each epoch utilized in the model building;8. The ROC (receiver operating characteristic) curve analyses our novel deep-learning-based model at different threshold levels of the asteroseismology target category. The rest of the research study is organized as follows: Section 2 is based on the related literature summary and comparison with our proposed approach. The research methodology is examined in Section 3. The novel proposed BiGR approach for classification in asteroseismology is examined in Section 4. The research results validation and evaluations are examined in Section 5. Section 6 is based on our research conclusions and study limitations.
# Sliding Mode Control of Electro-Hydraulic Position Servo System Based on Adaptive Reaching Law
## 5 Conclusions  
In this paper, the dynamic models of the valve-controlled asymmetrical hydraulic cylinder were studied, and a simplified mathematical model of the electro-hydraulic position servo system was given by ignoring the nonlinear factors of the servo valve. The SMC method was studied in this paper. The SMC method based on the exponential reaching law is frequently used to control the displacement and velocity of the piston. However, this method leads to a large sliding mode chattering. Therefore, a novel SMC method based on the adaptive reaching law was designed in the paper. At the same time, a comparative analysis was conducted on the SMC controller using the traditional exponential reaching law and the adaptive reaching law. The simulation results showed that the method could effectively suppress the sliding mode chattering of the electro-hydraulic position servo system.  
Figure 10: Tracking trajectory of SMC algorithm using adaptive reaching law. (**a**) Displacement buffeting curve and (**b**) velocity buffeting curve.  
Figure 9: Tracking trajectory of SMC algorithm using exponential reaching law. (**a**) Displacement buffeting curve and (**b**) velocity buffeting curve.  
**Author Contributions:** Conceptualization, X.D.; Methodology, C.S.; Writing--original draft, C.S. and X.D.; Writing--review & editing, M.W. and J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Introduced Talents Foundation of Kunming University of Science and Technology (no. KKSY201901014).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Applicable on request.  
**Conflicts of Interest:** The authors declare that there is no potential conflict of interest.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 5 Analysis on _p-y_ Curve
### Modification of \(p\)-\(y\) Curves for P-BF
#### 5.3.1 Modification of Lateral Ultimate Soil Reaction  
According to Equation (2), when the pile is above the turning point of the lateral limit soil reaction, the lateral ultimate soil reaction of piles is affected by undrained shear strength \(S_{u}\), soil weight \(\gamma\), depth \(Z\) and pile diameter \(d\). As verified above, the diameter of the pile in P-BF has little effect on the reinforcement section in the \(p\)-\(y\) curve; thus, the \(d\) in P-BF can be considered to be the same as the diameter of the bucket. When the soil parameters and the calculated width are known, the API specification recommends that the lateral ultimate soil resistance in the curve can be transformed into a one-variable function related to depth \(Z\). Equation (3) is obtained by adding a modification coefficient to Equation (2).  
\[\left\{\begin{array}{l}P_{ult}=(es_{u}+f\gamma Z)d_{s}+gs_{u}Z\\ d_{s}=\alpha d\\ \alpha=\frac{D}{d}\end{array}\right. \tag{3}\]  
where  
\(d_{s}\) = calculated width, m;  
\(D\) = Bucket diameter, m;  
Other parameters are the same as above.  
[PERSON] [26] et al. found that the calculation of geotechnical problems using the M-C failure model within the limited stress range is in good agreement with the measured results. Above the turning point of the lateral ultimate soil reaction, the stress is relatively small. Thus, the undrained shear strength above the turning point is calculated by the shear strength formula, which has a linear relationship with depth.  
\[s_{u}=c_{u}+\gamma Z\tan\varphi \tag{4}\]  
The parameters in the formula are consistent with the above.  
Figure 15: \(p\)-\(y\) curve at different depths. (**a**) Depth of 2.25 m. (**b**) Depth of 3.75 m. (**c**) Depth of 5.25 m.  
When Formula (4) is inserted into Formula (3), the following expression is obtained.  
\[\left\{\begin{array}{l}P_{ult}=A+BZ+CZ^{2}\\ A=ecd_{s}\\ B=e\gamma d_{s}tant\varphi\\ C=g\gamma tant\varphi\end{array}\right. \tag{5}\]  
According to the results of the soil reaction distribution, the turning point of the lateral limit soil reaction should be below the wall of the bucket in engineering design. Under the same load, the distribution of the soil reaction outside the bucket section of the P-BF bucket agrees well with that of PF [27]. Based on the results of the ABAQUS simulation, the undetermined coefficients \(A\), \(B\) and \(C\) in Formula (5) are solved by the least square method. Then, the formulas of \(e,f\) and \(g\) are inversely solved, and the formula of the lateral ultimate soil reaction above the turning point is established.  
\[\left\{\begin{array}{l}P_{ult}=(9.059s_{u}+1.605\gamma Z)d_{s}-10.402s_{u}Z \,Z\leq Z_{r}\\ P_{ult}=9s_{u}\,Z>Z_{r}\end{array}\right. \tag{6}\]  
Other parameters are the same as above.  
The revised results are consistent with the fitting trend of the finite element simulation results, which are shown in Figure 16.
## 3 Experiments
### Results
#### 3.3.1 Onset Detection  
For comparative purposes, the DNN and RNN were used for onset detection. In the training of DNN and RNN, we performed a grid search over sets of parameters to find an architecture with the best performance. The uncertain parameters of neural networks are: number of layers \(L\in\{1,\,2,\,3,\,4\}\)number of hidden units \(H\in\{32,\,64,\,128,\,256,\,512\}\). The hidden unit activation is a ReLU function and the output unit activation is sigmoid. In the architecture of RNN, LSTM [41] units are used, and the length of sequence was set to 10. The other parameters and methods in training are same as them in the CNN, such as dropout and early stopping.  
All the results of onset detection are presented in Table 1. As shown in Table 1, the CNN performs best and the RNN outperforms DNN on all evaluation metrics. For example, the CNN yields a relative improvement of 2.84% over the RNN, and the RNN outperforms the DNN by 4.48% on F-measure. Both the CNN and RNN take a sequence of spectrums as input, which utilize the context information over time. Additionally, the spatial structure of the spectrogram is preserved by the CNN, which is useful for onset detection.  
Figure 6 shows the outputs of neural networks for a music excerpt along with the corresponding ground truth. The excerpt is the first 10 s of track MAPS_MUS-bk_xmas5_ENSTDkCl. It is a typical example for transcription, and it is analyzed in each of the following experiments. In the ground truth (Figure 6d), there are two values: zero represents non-onset, and one stands for onset. We can also observe that the onset is sparse in the excerpt's first 8.8 s, and it is dense in the last 1.2 s. As shown in Figure 6, the DNN's output is far away from the ground truth, which cannot detect the dense onset and bring many false positives. This example explains why the DNN yields low recall and precision in Table 1. RNN and CNN are more suitable for onset detection than DNN. This is largely due to the context information over time. The evolution of a note can be modeled using the sequence information, so the false positives will not be detected in the sustain or decay stage of the note. Compared to RNN, CNN's output is closer to the ground truth--especially for the dense onset. When two adjacent onsets have small time difference, their detection is difficult through change along the time axis. In this case, we can identify the onset using the pitch information. CNN is such a method, which learns a feature along both the time and frequency axes through its convolutional layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Recall** & **Precision** & **F-Measure** \\ \hline CNN & 0.9731 & 0.9590 & 0.9660 \\ DNN & 0.9319 & 0.8683 & 0.8990 \\ RNN & 0.9530 & 0.9259 & 0.9393 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance on onset detection using different neural networks. DNN: deep neural network; RNN: recurrent neural network.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 3 System Model
### Problem Illustration  
To estimate and detect the multiuser NOMA signal, a Bi-LSTM model is proposed. The different UEs signals interfere with each other conventionally. Using the SIC technique, the cancelation of the stronger signal is imperfect. To address this limitation, joint detection is proposed in order to learn the Rayleigh channel and multiuser signal detection. The Rayleigh fading channel \(H_{i}(t)\) for the multipath environment can be expressed as follows:  
\[H_{i}(t)=\sum_{r=1}^{R}e^{i(2\pi f_{r}t+\phi_{r})}v_{i,r}\eta(t-\tau_{i,r}), \tag{13}\]where \(f_{a}\) is the Doppler frequency (DF) shift and \(\phi_{a}\) is the DF phase of the \(r\)th path, respectively. The mathematical expression of DF shift can be written as \(f_{a}=(v/c)f_{c}sin\theta_{a}\), where \(v\) is the speed of UE, \(c\) is the light speed, \(f_{c}\) is the carrier frequency, and the angle between UE and incident signal is represented by \(\theta_{a}\). The transmitted symbols are considered the true values, and the received signals are treated as the input to the training model.
## 4 Experiments and Analysis
### Performance Analysis for the GPS System  
Figure 2 demonstrates the results of the mean satellite repeat shift time for the GPS system by three methods during DOY (Day of Year): 161-181, 2018. The blue, red, and green line denote the results calculated by the BEM, CCM, and ARTM methods, respectively. From Figure 2, it can be seen that the mean shift time of the three methods are almost same for most satellites, and the average of the difference between the three methods for all satellites is only 2 s. In addition, the maximum difference between the CCM and the ARTM methods appeared in G32, which is about 4 s. Moreover, for the same method, the mean shift time of each satellite is also different, and the difference between the maximum and minimum reached about 10 s. Statistically, the STD (Standard Deviation) of the satellite repeat shift time between all the satellites for the three algorithms is 2.74 for BEM, 2.68 for CCM, and 2.67 for ARTM.  
In order to assess the performance of the fluctuation of the satellite repeat shift time, the STD of the three methods for each satellite is also presented in Figure 3. It is obvious that the STD of the three methods for each satellite is less than 0.5, which is very small. These results indicate that, for the same satellite and calculation method, although the satellite repeat shift time is different every day, the fluctuation is very small. However, the STD of the satellite repeat shift time varies for different satellites. For example, the STD of the G05 satellite is slightly higher than other satellites, and the G08 satellite is more stable than other satellites for all three methods. In addition, for some satellites, the STD performance of the different methods is significantly different, such as the G11 and G27 satellites. Generally, in terms of fluctuation, the performance of the BEM and ARTM methods is more similar for all the satellites.  
Figure 1: Flowchart of the aspect repeat time method.  
It should be noted that in both Figures 2 and 3, the G04 and G20 satellite information is not included. Since G04 is invalid during this period, there is no observation. Additionally, G20 is in the state of maneuvering during this period and the mean repeat shift time is markedly different from other satellites, which is about 187 s during DOY:161-172 and is about 246 s during DOY:173-181 for the BEM method. The STD of these two periods is about 0.56 for the period DOY:161-172 and nearly 0.81 for DOY:173-181, respectively. This phenomenon indicates that the satellite repeat shift time fluctuates more obviously when the satellite is undergoing an orbit maneuver.
## 3 Proposed Method
### Saliency-Based Patch Selection  
Visual attention is one of the selective mechanisms of our human visual system that involves an attractiveness towards some regions of the image. These attractive regions highly influence our subjective judgement and therefore impact the subjective quality of an image. In this study, we exploited this perceptual mechanism to select the most relevant patches that have high perceptual impact. To do so, we employed the scanpath predictor described in [75], which aims to mimic the behavior of our human visual system when it faces a real image. It predicts fixation points of the scanpath via a given saliency map. Figure 2 shows an image (Figure 2a), its corresponding saliency map (Figure 2b), its highlighted image (Figure 2c), and the predicted scanpath (Figure 2d). The predicted fixation points of the scanpath are represented by the blue points.  
For each predicted fixation point, a small patch was extracted. In [45; 76], they examined the impact of the patch size and found that a size of \(32\times 32\times 3\) constitutes a decent trade-off between the performance and the time computation. The same size, \(32\times 32\times 3\), was used in our approach. As found by [PERSON] et al. [77] observers at visual angles up to \(60^{\circ}\) reach the same salient region, indicating that saliency is the same at different distances. The saliency map was computed using the Graph-Based Visual Saliency (GBVS) method [78]. GBVS has shown to be very good for fixation location and scanpath predictor [79], and is therefore used. The number of fixation points and its impact on the performance will be discussed in Section 4.1. For more details about the saliency-based patch section, readers are referred to [80].  
Figure 1: Flowchart of the proposed IQM.
# Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles
## 2 Modeling and Energy Consumption Analysis on an Electric Sanitation Vehicle
### The Systematic Modeling
#### 2.2.4 Modeling the Garbage Distribution on the Road Surface  
Conventionally, garbage density with the unit as \(\text{kg}/\text{m}^{2}\) is used to describe the amount of garbage, which is a broad category including waste and dust. The target street where the sanitation vehicles work was a typical one stretching from downtown to the suburb. Through field observation, it was concluded that the garbage density distribution was significantly influenced by the green belts, which periodically appeared along the roadside, bringing fallen plant tissues, and commercial regions with strong fluidity. Thus, the garbage distribution model was established as the following steps:  
Step 1: cleaning levels ranging from 1 to 4 were defined to preliminarily classify the different segments of the road. Segments labeled with the high cleaning level had more garbage. Specifically, level 1 aimed at relatively clean segments without green belts alongside; level 2 was applied to the segments with some fallen tissues or garbage; then, segments marked level 3 had more garbage or leaves that were simple to clean. However, there were more or less some segments covered by materials such as oil stains or squashed dirt, making it tougher to wash thoroughly, for which the highest level 4 was defined. Some examples are shown in Figure 11.  
Figure 10: Power requirement model of the working motor.  
Step 2: The garbage density distribution was a result of multiple factors involving both natural and artificial influence. However, a qualified model could be established based on the fact that garbage density was probably continuous and followed historical patterns. Therefore, statistically, the garbage density on a certain level of segment tended to be normally distributed, whose mean value and standard deviation could be assumed reasonably.  
Step 3: A certain garbage density value generated from the normal distribution model in step 2 was probably maintained for tens of meters on the segment, which could similarly be estimated based on the actual condition.  
Step 4 resulted from multiple uncertain factors, and the fluctuation of the garbage density was inevitable. Hence, different sets of white Gaussian noise were added to the garbage model to reflect the randomness of the real scenario.  
The modeling process is illustrated by a flow chart shown in Figure 12.  
Figure 13 displays one of the possible garbage distribution models. It is worth noting that, apart from segment levels 1 to 3 that were affected by regular factors such as artificial green belts, the segments marked level 4 were almost unforeseeable, occurring less but more randomly and continuing for a much shorter distance in practice.  
Figure 11: Examples for segments marked with different levels: (**a**) Level 1, (**b**) Level 2, (**c**) Level 3, and (**d**) Level 4.  
Figure 12: Modeling the process of the garbage distribution.
# Wearable Voice Dosimetry System
## 3 Materials and Methods
### System Design
#### 3.1.1 Accelerometer  
The motivation behind the choice of using an accelerometer over a microphone has been two-fold. First, unlike the microphone, which picks up signals from its surroundings that are not the object of measurement (including the voice of other persons), the accelerometer is not affected by other signals than the vibrations of the skin, providing a high signal-to-noise ratio [58]. Second, there is the question of privacy. A person is less willing to wear a system that is recording their voice. Similarly, other people who are around or talk with the user may not be willing for a device to record their voices even if it is not the goal of the device. We used the BU-27135-000 accelerometer from Knowles Electronics because of its small form factor (7.92 mm \(\times\) 5.59 mm \(\times\) 2.24 mm), and unidirectional and flat frequency response between 20 and 2000 Hz. The small size, the key to achieving a wearable device, allows correct positioning between the upper part of the sternum and the lower part of the larynx. It is important that it is in this area as it is the closest to the vocal cords without being directly over the larynx, which translates into good reception of the vibrations generated by the vocal cords. The small size will also provide greater comfort for the patient. For the measurement of relative intensity, it is essential that the response of the device be linear in the range of measured frequencies. This allows the same amplitude to be correlated with the same relative intensity independent of the frequency.
## 2 State of the Art and Theoretical Framework
### Principles of Operation and Functionality  
The proposed device allows for the measurement of filtration indices without the need for a pressurized tank. To achieve this, the propulsion system is powered by compressed gas, integrated with a pneumatic control system housed within a control panel. Through flexible connections, the system is linked to both the analyzed liquid and the gas supply. The pneumatic system operates by using compressed air or gas to generate mechanical energy, which drives the liquid through the device. This process is controlled by valves and regulators that ensure constant pressure, while the resulting flow is delivered in pulses due to the cyclical design of the pneumatic mechanism. This configuration eliminates the need for electrical equipment or pressurized tanks, simplifying operation and enhancing safety [29]. In this context, it is important to consider that the use of a pneumatic system introduces variables related to gas pressure and flow conditions, as well as the hydraulic properties of the liquid, which can influence the measurements. To ensure accurate resultsand minimize flow variability, a control system is incorporated, and the necessary and sufficient conditions are established to prevent potential deviations that could affect the interpretation of the filtration indices. In this context, the hydraulic design was based on the selection and use of a pumping system capable of operating efficiently at a flow rate exceeding 80 mL/s under open flow conditions, maintaining a working pressure of 30 psi. This range falls within the operational limits of the system, which supports a minimum pressure of 20 psi (1.4 bar) and a maximum pressure of 80 psi (5.5 bar).  
The system consists of several components. Figure 3 shows the diagram with the involved components. The system includes a membrane gas pressure regulator (a), which adjusts the gas pressure to the required level through the activation of a valve, allowing the value to be displayed on the control pressure gauge (b). The gas supply (c) can be a pressurized cylinder or a compressed gas line, which contains a primary pressure regulator (d) set at a pressure higher than the one required for the test. The compressed gas used may be nitrogen, CO\({}_{2}\), compressed air, or another gas that can be safely released into the environment without posing a risk to the operator.  
The pressure regulator (a) controls the working pressure of the pneumatic pump (e) through hoses or pipes. The pump is connected to a container (f) holding the liquid to be analyzed, using both flexible and rigid hoses (g). The flow rate of the fluid is controlled by a mechanical ball valve (h), ensuring quick opening and closing. Once the valve (h) is opened, the fluid flows into the filter holder (i) and is collected in a graduated container (j).