esa-sceva/satcom-chunk-collection huggingface dataset:
# Inhibited Channel Potential of 3D NAND Flash Memory String According to Transient Time
## 1 Introduction  
Recently, efforts have been made to enhance the competitiveness of NAND flash memory in the semiconductor-based memory market by producing high-density and low-power memory technology [1]. However, the existing 2D structures have limitations. Because the 2D structure of NAND flash memory does not have a high degree of integration, and as the device size becomes smaller, problems such as cell-to-cell interference and limitations of lithography patterning occur [2; 3]. As a result, it was difficult to store a large amount of data compared to the area. To solve this problem, 3D NAND flash memory can be used [4]. The structure of a 3D NAND flash memory uses an ultrathin-body structure in which the channel of the main cell is not connected to the body [5]. The floating state of the structure in which the channel is not connected to the body indicates that the body bias is unaffected by the channel of the main cell [6]. Because of this structural characteristic, the inhibited string channel is not directly connected to the body. Therefore, it can easily achieve a floating state [7; 8]. As a result, when the program pulse is applied to the selected word line (WL) to proceed with the program, a phenomenon occurs in which the cell of the selected WL is localized in the inhibited string channel. This is called natural local self-boosting (NLSB). NLSB is a beneficial effect that can prevent program disturb [9; 10]. However, the channel potential increased by NLSB is not maintained.  
In this study, the channel potential for NLSB in a 16-layer 3D NAND flash memory was analyzed according to the transient time and threshold voltage (\(V_{th}\)) value change of adjacent cells. Through our analysis, it was confirmed that the reason why the channel potential is not maintained is related to the electrons of the nitride. The 3D-technology computer-aided design (TCAD) tool ATLAS (Silvaco) [11] was used to analyze the channel potential and electron concentration.
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### Summary and Future Challenges  
As a result, from the above experiment, we can observe that the classification of seismic patch data can be improved by means of hybridizing CNN-features with existing classifiers. The summary of the experimental results and future tasks are as follows.  
In this approach, CNN models were first trained to extract CNN-features. To this end, we first prepared a CNN model using synthetic data and then used it as a pretrained model when learning models for the feature extraction of real-life data. Therefore, it is necessary to analyze the extent to which such transfer learning has affected the CNN-feature extraction process.  
More specifically, the task of analyzing the impact of the learning results of the source domain (i.e., CNN models learned from the synthetic data) on the classification performance of the target domain is a future challenge. We also experimented with a very limited number of real-world data here. Therefore, the task of investigating the optimal cardinality of the training set required for the target domain is also an open problem.  
It is also interesting to see what has happened when using enhanced CNNs to extract CNN-features with the hybrid method. Here, however, only CNN-sum and CNN-O2P were implemented in one simple CNN and its weakly enhanced CNN, respectively, and were compared to each other. Therefore, experiments on strongly enhanced CNNs, including LesNet [43] and VGG-Net [44], remain open.  
In order to find out why this hybrid approach leads to improved results, we measured the data complexity of the CNN-features and compared it with that of the existing extractors. However, complexity measurements were limited here, using only a limited number of measurements. In addition, the feature-extraction performance was compared using only a few traditional extractors. Therefore, introducing more diverse complexity metrics and including handcrafted extractors in comparison, such as the LBP, the HOG, etc., is a future challenge.
## 3 Development of the Aerodynamic Field Measurement Platform
### Pressure Distribution Measurement  
In order to measure pressure with higher accuracy, the range of pressure sensor should be determined firstly. A better method is to evaluate the maximum flow velocity and the corresponding pressure. The relative inflow speed at blade tip is about 90 m/s when the rotor runs at the rated revolving speed. The corresponding inflow dynamic pressure is about 5000 Pa, according to the deductive Bernoulli equation (Equation (1)):  
\[q=\frac{1}{2}\rho V^{2} \tag{1}\]  
Therefore, the range of pressure sensor should be lower than 10,000 Pa. In addition, the length of pressure tube has a damping effect on the dynamic response of pressure signal. It has been shown [31] experimentally that acoustic damping is small for frequencies up to 40 Hz for a pressure tube with length of 1.1 m and diameter of 1 mm. It requires that location of pressure sensor should be close enough to the pressure taps. With both the above considerations, pressure scanners, ESP-64 with full scale of 1 PSI and measuring accuracy of \(\pm\)0.05% full scale, manufactured by Measurement Specialties,  
Figure 4: (**a**) Geometry of seven-hole probe, and (**b**) photo of the probe at the leading edge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Probe Name** & **P1** & **P2** & **P3** & **P4** & **P5** \\ \hline \hline Span Location (m) & 1.798 & 3.085 & 4.008 & 5.122 & 6.905 \\ Length beyond the leading edge (m) & 0.55 & 0.4 & 0.345 & 0.293 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Locations of probe tip.  
were purchased and installed in the chamber of a blade, as shown in Figure 5. To facilitate field maintenance and repairs of pressure scanner, three installation windows were manufactured on the pressure side of the blade.  
It is difficult to fabricate pressure tap in the large composite wind turbine blades. A method to embed pressure tubes into composite blades during the vacuum assistant resin infusion was developed and applied on the tested blade in this paper. The method has obtained two patents in China. Finally, pressure taps were manufactured at five airfoil sections. The parameters of pressure measurement sections, including airfoil, span location, pressure tap number, and chord, are shown in Table 6. The photo of finished aerodynamic measurement blade is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Section Name** & **S1** & **S2** & **S3** & **S4** & **S5** \\ \hline Airfoil & CAS-W2-450 & DU00-W-350 & DU97-W-300 & DU91-W2-250 & DU93-W-210 \\ Span Location (m) & 1.498 & 2.785 & 3.708 & 4.822 & 6.605 \\ Pressure Tap Number & 53 & 57 & 49 & 51 & 46 \\ Chord (m) & 0.758 & 0.658 & 0.558 & 0.467 & 0.369 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of five pressure measurement sections.  
Figure 5: (**a**) Sketch diagram, and (**b**) photo of surface pressure measurement system on blades.  
Figure 6: Photo of the tested blade.
# Study of High-Frequency Pulsating Flow Control Valves
## 3 Mathematical Modeling
### Servo Motor Mathematical Model  
The spool movement of the sinusoidal flow valve is driven by the eccentric wheel shaft, which is connected to the servo motor with a keyway, and the servo motor drives the eccentric wheel shaft to rotate together. The structure of the servo motor is relatively complex, and it has more nonlinear factors; therefore, when deriving the mathematical model of the servo motor, some non-important factors that do not have much influence on the nature of the system are ignored.  
The servo motor in this paper uses the Id = 0 vector control method; therefore, the torque equation for EM can be simplified as:  
\[T_{e}=\frac{3}{2}p_{n}\psi_{f}i_{q} \tag{6}\]  
where \(T_{e}\) is the internal torque of the motor; \(P_{n}\) is the number of pole pairs; \(\psi_{f}\) is the magnetic chain of the permanent magnet; and \(i_{q}\) is the component of the current in the stator in the \(q\)-axis.  
The balance equation for torque on the servo motor shaft is as follows:  
\[T_{e}-T_{L}=J_{L}\frac{d\omega_{m}}{dt}+D\omega_{m} \tag{7}\]  
where \(T_{L}\) is the load torque; \(D\) is the damping coefficient of the motor; \(\omega_{m}\) is the mechanical angular velocity; and \(J_{L}\) is the moment of inertia converted to the top of the rotating shaft.  
Both \(P_{n}\) and \(\psi_{f}\) in Equation (6) are constants, so by making \(K_{t}=1.5P_{n}\psi_{f}\), Equation (6) can be changed to:  
\[T_{e}=K_{t}i_{q} \tag{8}\]  
where \(K_{t}\) is the torque coefficient.  
In addition, the speed, angle of rotation, and rotor angular velocity have the following relationships:  
\[\begin{cases}N_{r}=K_{b}\omega_{m}\\ \omega_{e}=n_{p}\omega_{m}\\ \theta_{e}=\int\omega_{e}dt\end{cases} \tag{9}\]  
where \(K_{b}\) is the speed factor; and \(\omega_{e}\) is the rotor angular velocity.  
The voltage equation for the equivalent armature circuit of a motor in operation is as follows:  
\[U_{q}=L_{q}\frac{di_{q}}{dt}+R_{s}i_{q}+K_{m}\omega_{e} \tag{10}\]  
where \(K_{m}\) is the antipotential coefficient.  
The speed regulation method used in this servo motor is a speed closed loop; the speed and current loops can be regarded as proportional links, and the gains at these two places are represented by \(K_{p}\) and \(K_{I}\), respectively. In addition, the control signal is represented by \(U_{r}\), and the motor's rotational speed constant is represented by \(K_{\omega}\).  
The relationship between the servo motor speed and the input voltage can be obtained by performing the Rasch transformation of Equations (7), (8) and (10) in combination with the motor speed control method:  
\[G(s)=\frac{N_{r}(s)}{U_{r}(s)}=\frac{K_{I}K_{p}K_{t}K_{b}}{\big{(}L_{q}s+R_{s}+ K_{i}\big{)}(L_{I}s+D)+K_{m}n_{p}K_{t}+K_{Q}} \tag{11}\]  
where \(K_{Q}=K_{I}K_{p}K_{t}K_{b}K_{\omega}\).
## 4 Materials and Methods
### Methodology
The accuracy is defined as the sum of the True Positives (TP)s and True Negatives (TN)s divided by the number of all samples. To show the relevance of False Positives (FP) and False Negatives (FN) in the final results, a confusion matrix is also provided. For verification and authentication, the adopted metrics are the Receiver Operative Characteristics (ROC) and the Equal Error Rate (EER), which is the point on the ROC where false positive and false negative rates are equal. The value of the X axis is used to determine the EER in this paper. 9. Impact of noise. Additive White Gaussian Noise (AWGN) is added to the original data sample to simulate the presence of noise in the environment. This is a common practice in the literature [26; 27] to evaluate the performance of the classification algorithm in terms of identification accuracy for different values of Signal Noise Ratio (SNR).
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 4 Simulation Model
### Application Layer Traffic Characterization  
In order to evaluate the performance of uplink communications in our simulation model, the application layer on each device generates data messages destined to the coordinator. The time intervals between generated messages follow an exponential probability distribution. Each message is encapsulated in the MAC Service Data Unit (MSDU) of one data frame. All devices in all scenarios use the same application layer settings. This way, in a scenario with \(N\) devices employing an application message size of \(L\) bits and average time between messages of \(t_{s}\) seconds, total offered load \(T_{L}\) normalized to the physical layer transmission rate \(R_{b}\) is:  
\[T_{L}=N\cdot\frac{L}{t_{s}\cdot R_{B}}(\%) \tag{9}\]
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 2 Mixed Signal Dereverberation  
The reverberant component always exists in a speech signal recorded in a real environment. However, the reverberant component can lead to the degradation of signal quality which makes it difficult to separate the original sound source signal from the reverberant mixed signal and leads to a poor performance of the separation algorithm. Therefore, it is very important to restrain the effect of reverberation on sound source separation.  
In this paper, a multi-source separation method for the reverberant environment, which consists of a dereverberation module proposed in [21] and a separation module, is proposed. The dereverberation module is used to remove the reverberation component from the recorded signal and maintain the direct component of the source. The dereverberation module is implemented in the TF domain, which also consists of two stages. The first stage is to learn the spectrum structure and obtain the dictionary matrix; in the second stage, a non-negative matrix factorization (NMF) model is used to represent the signal spectrum and obtain the optimal estimation of the sound source signal.  
Figure 1: Schematic diagram for the proposed method for multi-source separation.
## 4 Experiment and Result Analysis
### Error Cases  
Although our model achieves competitive results, there are still some errors that leave room for further research. As shown in Table 5, there are a total of three common errors, respectively:  
1. Incorrect spans: A common error is the prediction of a slightly incorrect entity span, usually with one more or one less word than the ground truth. Here, \"interferon alfa\" should be marked as an entity but we marke \"interferon\" as one entity. This error occurs particularly often in domain-specific ADE and SciERC datasets.
2. Logical: Sometimes, the relationship between entities is not explicitly described in the sentence, but can be logically inferred from the context. In the case described, the \"_Work-For_\" relationship between \"[PERSON]\" and \"DOE\" needs to be inferred from some information (\"[PERSON], chief of waste disposal for the commission\" and \"the commission refers to DOE\").  
Figure 4: Visualization of joint entity and relation extraction results of different models on Conll04 dataset.  
3. Missing annotation: There are some cases where a correct prediction is missing in the ground truth. Here, in addition to the correct prediction (Shoshone-Bannock, _Located-In_, Idaho), EIENT also outputs (Hatcher, _Live-In_, Onondaga territory), (Hatcher, _Live-In_, Shoshone-Bannock) and (Hatcher, _Live-In_, Idaho), which are correct but unmarked.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 3 Proposed Time Classification Algorithm  
Figure 1 shows the overall procedure of the proposed time classification algorithm based on the sky information. In the training step, the dataset including the sky is trained using Mask R-CNN to detect the sky region. A reference color histogram representing the time period (dawn, day, and night) is created based on the sky information. In the evaluation step, the sky region is extracted from test image through the trained Mask R-CNN and a color histogram of the sky area is generated. Finally, the test image is classified into dawn, day, and night in the comparison process between the color histogram of the test image and the reference color histogram.  
Figure 1: The flow chart of the proposed algorithm.
## 2 Experiments
### Compressive Strength and Direct Tensile Test Setups  
The compressive strength test was performed with a 50 mm\({}^{3}\) cubic specimen. A universal testing machine (UTM) with a maximum load capacity of 25 t was used to apply a uniaxial and monotonic load to the specimen. The measurements of three samples for each mix proportion were recorded and averaged to ensure the accuracy of the test.  
The uniaxial direct tensile test was performed following the recommendations of the Japan Society of Civil Engineers (JSCE) [23]. Figure 2 illustrates the test setup and details of the test specimens. The tensile test was conducted using another UTM with a loading capacity of 25 t. Two linear variable displacement transducers (LVDTs) were installed, as shown in Figure 2a, to measure the displacement only between the gauge region of the specimen. The measured tensile load and displacement in the gauge region were converted to the tensile stress and strain of the hatched region with a cross-sectional area of 30 mm \(\times\) 13 mm and a height of 80 mm, as shown in Figure 2b. Additionally, a uniaxial  
Figure 1: Cumulative volume percentage and particle size distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Diameter, \(d_{f}\)** & **Length, \(I_{f}\)** & **Aspect Ratio** & **Density** & **Tensile** & **Elastic** & **Modulus,** & **Elongation** \\  & **(\(\mu\)m)** & **(mm)** & **(\(I_{f}\)/\(d_{f}\))** & **(g/cm\({}^{3}\))** & **(MPa)** & **(MPa)** & **(\(E_{f}\) (GPa))** & **(\%)** \\ \hline UHMWPE fiber & 30–32 & 18.0 & 562.5–600.0 & 0.97 & 3000 & 100 & 3–4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometrical and physical properties of ultra-high-molecular-weight polyethylene (UHMWPE) fiber.  
load was applied based on a displacement control with a loading rate of 0.8 mm/min. Five specimens for each variable were tested, and their results were averaged.
## 4 Discussion
### Effect of Various Factors on the SRV of the Interlayer/Reservoir  
When the fractures are close to each other, there is strong interference between them, causing mutual compression and leading to the fractures tending to expand vertically. As a result, the stimulated reservoir volume (SRV) in the interlayer is larger than that in the reservoir. When the cluster spacing is 20 m, the mutual compression between fractures suddenly decreases, resulting in an increase in the SRV in the reservoir and a decrease in the SRV in the interlayer. With increasing spacing, the total SRV gradually stabilizes (Figure 18a).With an increase in the number of clusters, the SRV in both the reservoir and interlayer increases simultaneously (Figure 18b). A larger displacement leads to a larger SRV. As the displacement increases, the volume of fracturing fluid lost into the formation increases, which has a greater impact on the mid fracture. The degree of fracture rupture  
Figure 17: Illustration of the relationship between fracture width and half-length. (**a**) Relationship between width and half-length of the edge fracture. (**b**) Relationship between width and half-length of the mid fracture.  
in the mid fracture in the interlayer is less than that in the edge fracture. However, the volume of fractures expanding in the interlayer for the edge fracture is larger than that for the mid fracture. Therefore, with an increase in displacement, the SRV in the interlayer exceeds that in the reservoir (Figure 18c). A larger elastic modulus indicates a stronger resistance to deformation in the rock, resulting in a decrease in the total SRV. However, as the ratio of elastic modulus between the interlayer and reservoir increases, the SRV in the reservoir slightly increases, while the decrease in the SRV in the interlayer is more significant, which is beneficial for reservoir exploitation (Figure 18d). As the ratio of Poisson's ratio and permeability between the interlayer and reservoir increases, the SRV in the reservoir slightly decreases, while the SRV in the interlayer slightly increases, with minimal impact on actual exploitation, which can be essentially ignored (Figure 18e,f).  
Figure 18: Shows the impact curves of various factors on the SRV (stimulated reservoir volume) of the interlayer/reservoir. Panels (**a–f**) represent the effects of different cluster spacing, cluster count, displacement, elastic modulus, Poisson’s ratio, and permeability on the SRV of the reservoir and interlayer.
## 6 Conclusions  
According to the direction and texture information of surface defects of the continuous casting slabs with complex backgrounds, a new feature extraction approach DNST-GLCM-KSR is proposed, which combines multi-scale and multi-directional DNST features with GLCM texture features and uses KSR technology to reduce dimensionality. The experimental results are as follows.  
1. The DNST feature obtained the highest average accuracy and the best accuracy. It can better characterize defects of continuous casting slabs than that of Contourlet, DST, wavelet, and GLCM  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Type** & **Positive Sample** & **Negative Sample** & **Correct Number** & **Sample Total** & **Accuracy** \\ \hline Positive sample & 106 & **5** & 106 & 111 & 95.50\% \\ Negative sample & **4** & 133 & 133 & 137 & 97.08\% \\ total & 110 & 138 & 239 & 248 & 96.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The confusion matrix of discrete nonseparable shearlet transform gray-level co-occurrence matrix kernel spectral regression (DNST-GLCM-KSR) scheme.  
Figure 6: Visualization results.  
2. The accuracy of the training set and test set of the DNST-GLCM feature were 96.77% and 90.73%, respectively. Both results were higher than those of DNST feature. The recognition accuracy of continuous casting slabs can be improved by combining the features of DNST and GLCM.
3. The recognition accuracy of the DNST-GLCM-KSR scheme is 5.64% higher than that of DNST-GLCM, and the classification time of DNST-GLCM-KSR was shorter than that of DNST-GLCM. Using KSR technology can improve recognition accuracy and shorten classification time.
4. The proposed scheme can extract more discriminative features of defects and make the recognition accuracy of crack defect up to 95.50% and the total accuracy up to 96.37%. The new scheme provides a new method for the surface defect recognition of continuous casting slabs.
5. Future work should collect more defect samples, establish a complete sample database, and improve the recognition accuracy of crack defect.  
[PERSON] contributed to the methodology of the study and investigation. [PERSON] contributed significantly to formal analysis and manuscript writing. [PERSON] and [PERSON] helped perform the analysis with constructive discussions.  
This research was funded by the National Natural Science Foundation of China (grant number 51674031 and 51874022) and the Fundamental Research Finds for the Central Universities under Grant FRF-TP-18-009A2.  
The authors declare there is no conflicts of interest regarding the publication of this paper.