esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Phase-Locked Loops
### Discrete Model  
A block diagram for a DPLL with unity gain is shown in Figure 2. In the figure, \(F(z)\) represents the transfer function of an arbitrary loop filter, and \(N(z)\) is the transfer function of the NCO, where both are digital equivalents of \(F(s)\) and \(V(s)\), respectively, and can be affected by the characteristics of the particular digital implementation of the analog  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Loop** & **Loop Filter Transfer** & **Closed-Loop Transfer** & **Typical Filter** & **Sensitive to** & **Application Examples** \\
**Order** & **Function, \(F_{i}\)(s)** & **Function, \(H_{i}\)(s)** & **Values** & **Sensitive to** & **Application Examples** \\ \hline First & \(F_{1}(s)=\omega_{0}\) & \(H_{1}(s)=\frac{a_{0}}{s^{2}+a_{0}}\) & \(\omega_{0}=4B\) & Velocity stress & Aided code tracking loops \\ Second & \(F_{2}(s)=\frac{a_{2}a_{0}s+a_{0}^{2}}{s}\) & \(H_{2}(s)=\frac{a_{2}a_{0}s+a_{0}^{2}}{s^{2}+a_{2}a_{0}s+a_{0}^{2}}\) & \(\omega_{0}=1.89B\) & Acceleration stress & Aided or low-dynamics phase-locked loop, unaided frequency-locked loop \\ Third & \(F_{3}(s)=\frac{b_{3}a_{0}s^{2}+a_{0}s^{2}+a_{0}^{3}}{s^{2}}\) & \(H_{3}(s)=\frac{b_{3}a_{0}s^{2}+a_{0}s^{2}s+a_{0}^{3}}{s^{3}+b_{3}a_{0}s^{2}+a_{ 0}s^{2}s+a_{0}^{3}}\) & \(\omega_{0}=1.27B\) & \(\omega_{0}=1.1\) & \(\omega_{0}=1.27B\) \\  & \(b_{3}\) & \(\approx\) & 2.4 & & loop \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of loop filters.  
Figure 1: Linearized phase-locked loop (PLL) model in the continuous-time domain.  
integrator. Additionally, the computational delay is inserted between the loop filter and the NCO to model the effect of the inevitable delay due to the calculation processes of the discriminator and loop filter. The ideal case that does not have such a delay can be represented by substituting \(t_{\text{D}}=0\). Finally, the computational delay is combined with \(N(z)\) to form the delay-included NCO transfer function \(N_{\text{D}}(z)\) later.  
Without considering the computational delay, the closed-loop transfer function of the DPLL \(H(z)\) is given by:  
\[H(z)=\frac{N(z)F(z)}{1+N(z)F(z)} \tag{4}\]  
which can be rewritten in polynomial form as:  
\[H(z)=\frac{\beta_{m}z^{m}+\beta_{m-1}z^{m-1}+\cdots+\beta_{0}}{\alpha_{n}z^{n} +\alpha_{n-1}z^{n-1}+\cdots+\alpha_{0}}=\frac{\sum_{j=0}^{m}\beta_{j}z^{j}}{ \sum\limits_{i=0}^{n}\alpha_{i}z^{i}} \tag{5}\]  
where \(\alpha_{i}\) and \(\beta_{j}\) are filter coefficients to the \(n\)-th order polynomial in the denominator and the \(m\)-th polynomial in the numerator, respectively, and \(m\) and \(n\) are the number of zeros and poles, respectively (\(n\geq m\); \(n\) = the order of the loop).  
There are three primary categories of methods for obtaining a discrete equivalent of a continuous transfer function because of the discrete nature of the system, as described in the following sections [13].
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 4 Discussion  
The proposed method outperforms most of the previous methods on EEG-based emotion recognition tasks in three classification scenarios. Compared with SVM, 1D-CNN, 2D-CNN, EEGNet [35], ShallowNet [31], CapsNet [11], and EmoPercept [50], the proposed method has a better recognition performance for two reasons. Firstly, the multi-band EEG topology maps that combine the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals are a spatial representation that is more helpful in recognizing emotional states. Secondly, the proposed emotion recognition network ERENet is more capable of mining the features related to specific emotional states. However, ERENet has a lower recognition accuracy compared with 3D-CNN [51] because 3D-CNN [51] can simultaneously explore spatial and temporal information and simultaneously extract multi-scale features which can effectively detect attention states. Although the recognition performance of 3D-CNN [51] is better than that of ERENet, ERENet has a simpler structure and fewer parameters, which is more suitable for situations with limited computing resources, as shown in Table 8.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 3 Results
### Effects of Porosity and Inclination on Wind Speed  
Figure 7 shows the variations in average wind velocity (m/s) with respect to distance (cm) at the windward and leeward sides of the wind barriers for three different cases: Case A (porosity of 0% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))), Case B (porosity of 25% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))), and Case C (porosity of 50% with inclinations (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\))). In Case A, the average wind velocity (m/s) decreased significantly at a distance of 50 cm behind the wind barriers and gradually increased as the distance from the wind barrier increased. In contrast, Cases B and C exhibited a gradual decrease up to a distance of 300 cm, followed by a slight increase. These results suggest that changes in wind flow behind wind barriers are influenced by both porosity and inclination. Notably, Case C outperformed Cases A and B, demonstrating a significantly higher wind speed reduction. Specifically, a wind barrier with a porosity of 50% and an inclination of 70\({}^{\circ}\) exhibited better performance, providing an ample wind protection range.  
Figure 6: Visualization of the spatial distribution of the average wind velocity according to distance for Case C with a wind barrier porosity of 50% and an inclination of (**a**) 90\({}^{\circ}\), (**b**) 80\({}^{\circ}\), and (**c**) 70\({}^{\circ}\).
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Flow-Based Attack Detection  
Flow data contains packets grouped in a period, which is the most widespread data source for IDSs. The KDD99 and the NSL-KDD datasets are both flow data. Detecting attacks with flow has two benefits: (1) Flow represents the whole network environment, which can detect most attacks, especially DOS and Probe. (2) Without packet parsing or session restructuring, flow preprocessing is simple. However, flow ignores the content of packets; thus, its detection effect for U2R and R2L is unsatisfactory. When extracting flow features, packets must be cached packets; thus, it involves some hysteresis. Flow-based attack detection mainly includes feature engineering and deep learning methods. In addition, the strong heterogeneity of flow may cause poor detection effects. Traffic grouping is the usual solution to this problem.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Dynamic Motion Model
#### 3.4.2 Multi-Trajectory Hypothesis  
In scenarios of prolonged occlusion (continuous loss of three or more frames), a single motion model often fails to accurately predict the target's reappearance location. To address this, we devised a multi-modal trajectory hypothesis strategy, simultaneously generating and maintaining three complementary motion model predictions as follows:  
Constant Velocity Model (CV): \(\mathbf{\hat{s}}_{\text{CV}}=\mathbf{A}_{\text{CV}}\mathbf{s}^{\text{f}_{ \text{tot}}}\)  
Constant Acceleration Model (CA): \(\mathbf{\hat{s}}_{\text{CA}}=\mathbf{A}_{\text{CA}}\mathbf{s}^{\text{f}_{ \text{tot}}}\)  
Curved Motion Model (CT): \(\mathbf{\hat{s}}_{\text{CT}}=\mathbf{A}_{\text{CT}}\mathbf{s}^{\text{f}_{ \text{tot}}}+\mathbf{w}_{\text{curv}}\) Here, \(\mathbf{w}_{\text{curv}}\) is a curvature compensation term estimated based on the target's recent turning trend. The system prioritizes searching for matches in these three hypothesis regions in subsequent frames and selects the best hypothesis based on matching confidence until either the trajectory is successfully recovered or the tracking exceeds the preset time window (typically 30 frames).
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 4 Results and Discussion
### Practical Scenarios  
**A. Label noise problem:** In practical scenarios, the dataset is full of error labels because of the expensive and time-consuming labeling process, which could seriously affect the performance of the model. This phenomenon is known as the label noise problem. We set up experiments with noisy datasets to see how well Feature Mining performed for different types and amounts of label noise.  
Following [27, 41], we corrupted these datasets manually on CIFAR-100. Two types of noisy labels were considered: (1) symmetry flipping: each label was set to an incorrect value uniformly with a certain probability; (2) pair flipping: labelers may only make mistakes within very similar classes. We use \(\epsilon\) to denote the noise rate.  
Table 8 compares the results with and without Feature Mining using ResNet-110. Our method made huge improvements on different types and amounts of noise compared with the baseline (which only uses cross-entropy to classify). One possible implication  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Validation Accuracy (\%)** \\ \hline ResNet-50 & 66.73 \(\pm\) 0.34 \\ ResNet-50+BYOT & 74.60 \(\pm\) 0.41 \\ ResNet-50+CS-KD & 74.81 \(\pm\) 0.23 \\ ResNet-50+FM & 76.26 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance comparison with self-distillation methods on the CUB-200-2011 dataset.  
Figure 3: Validation accuracy comparison among the baseline, BYOT and Feature Mining on CUB-200-2011 with ResNet-50.  
of the improvement is that our method can make the convolutional layers' training more sufficient, and the robust network has stronger anti-noise ability. Together, these results provide strong proofs that Feature Mining helps raise the internal noise tolerance of the network.  
**B. Small sample size problem:** In some real industrial scenarios, the size of the dataset may be small, which would bring difficulty to the network training. Besides, Reference [42] mentioned that reducing the size of the training dataset can measure the generalization performance of the models. Therefore, we evaluated the effectiveness of Feature Mining on the small sample size problem along with varying the size of the training data. Due to the limitation of the computing resources, we chose ResNet-56 as the baseline model.  
We constructed a series of sub-datasets of CIFAR-100 via randomly choosing samples for each class. Figure 4 compares the results obtained from ResNet-56 trained with and without our strategy. Feature Mining was used on the last two layers of the network. From the graph, we can see that our method yielded much higher accuracies compared to the vanilla setting on all of the sub-datasets, in accordance with the expectations. The performance of Feature Mining proves that our method can effectively improve the generalization of CNNs.
# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 4 Proposed Method  
Since physical activities are performed sequentially rather than concurrently, a clear activity pattern can be identified by observing the acceleration signal. Figure 3a shows the raw data collected from a tri-axial accelerometer during 10 repetitions of abduction, where each part enclosed within the dotted rectangle represents a single repetition.  
Figure 3: Acceleration signal for 10 repetitions of (**a**) Abduction, (**b**) Internal rotation (IR), and (**c**) Extension.  
Figure 2: Axis orientation.  
For all seven activities, each peak along the Y-axis corresponds to a single activity, except for IR and extension, where two consecutive peaks represent the starting and ending points of the activity. Figure 3b,c shows 10 repetitions of IR and extension, respectively, where all data points enclosed within the dotted rectangles belong to a single repetition. The underlying reason for this difference is the movement direction of the activity and the hand position while moving.  
The proposed segmentation method consisted of three main steps. The first step involved the selection of peaks in the Y-axis acceleration signal since it best represents the start and end of all types of activities under study when applying the algorithm. Peaks were selected based on a threshold and a distance, which represent the minimum value of a peak and the minimum distance between peaks, respectively. The second step was to select valleys using a second threshold that represented the highest value of a valley. Finally, the signal's characteristics were analyzed for each peak to identify suitable segmentation boundaries. The method is explained in more detail in the rest of this section.
# Effects of Oxidized Pyrenes on the Biological Responses in the Human Bronchial Epithelial Cells
## 3 Results
### Effects of Pyrene and Its Quinone Forms on Airway Epithelial Cell Apoptosis and Necrosis
At 3 \(\upmu\)M, compared to that in pyrene-treated cells, IL-8 production decreased in the 4,5-PyQ group (\(p<0.01\)), whereas that in the 1,8-PyQ + 1,6-PyQ group showed no significant difference. IL-8 levels were higher in the 1,8-PyQ + 1,6-PyQ group than in the 4,5-PyQ-treated group (\(p<0.01\)). At 10 \(\upmu\)M, IL-8 levels in the 4,5-PyQ and 1,8-PyQ + 1,6-PyQ groups were lower than those in the pyrene group (\(p<0.01\)). No significant differences were observed between the 4,5-PyQ and 1,8-PyQ + 1,6-PyQ groups (Figure 3B). No changes in the sICAM-1 levels were observed after treatment with 1 \(\upmu\)M pyrene, 4,5-PyQ, and 1,8-PyQ + 1,6-PyQ compared with those in the control. At 3 \(\upmu\)M, there was no significant difference in sICAM-1 levels between the pyrene-treated cells and the control, whereas lower sICAM-1 levels were observed in the 4,5-PyQ and 1,8-PyQ + 1,6-PyQ groups (\(p<0.01\) and \(p<0.05\), respectively). At 10 \(\upmu\)M, there was no significant difference in sICAM-1 production upon pyrene exposure compared with that in the control; however, sICAM-1 production in 4,5-PyQ-exposed cells decreased (\(p<0.01\) vs. control) and that in 1,8-PyQ + 1,6-PyQ-exposed cells increased (\(p<0.01\) vs. control). When sICAM-1 levels between groups were compared, no significant difference was found across all groups at 1 \(\upmu\)M. At 3 \(\upmu\)M, sICAM-1 levels were lower in the 4,5-PyQ group than in the pyrene exposure group (\(p<0.01\)), but no significant difference was observed for the 1,8-PyQ + 1,6-PyQ group. Furthermore, there was no significant difference in sICAM-1 production between the 4,5-PyQ and 1,8-PyQ + 1,6-PyQ groups. At 10 \(\upmu\)M, sICAM-1 levels were not significantly different between the pyrene- and 4,5-PyQ-treated cells (although the value in the 4,5-PyQ group was lower); nonetheless, sICAM-1 levels increased in 1,8-PyQ + 1,6-PyQ-treated cells (\(p<0.01\)). When sICAM-1 levels were compared between cells treated with 4,5-PyQ or 1,8-PyQ + 1,6-PyQ, higher levels were found in the 1,8-PyQ + 1,6-PyQ group (\(p<0.01\); Figure 3C). Overall, although the levels of inflammatory mediators such as IL-6, IL-8, and sICAM-1 changed after treatment with certain concentrations of 4,5-PyQ and 1,8-PyQ + 1,6-PyQ, they did not follow a specific, dose-dependent pattern.
## 2 Material and Methods
### Bath Equilibrium Test  
To determine the removal efficiency of Cu(II) and phosphate, a series of batch tests were carried out using 55 mL of conical tubes. The stock solutions of Cu(II) (2000 mg/L) and phosphate (2000 mg/L) were prepared with CuCl\({}_{2}\)-2H\({}_{2}\)O and KHPO\({}_{4}\), respectively. The initial concentration of Cu(II) and phosphate was set to 5 mg/L. The different concentrations of Fe were prepared by adding a desired volume of FHO-A solution, and then a desired volume of Cu(II) and phosphate from the stock solution was added. The conical tubes were rotated at 30 rpm for 24 h without pH adjustment. Similar batch tests were conducted except for controlling for pH during the experiment. The initial pH of mixture was set to 3, 4, 5, 6, 7, and 9 and during rotation, 0.1 NaOH or 0.1 HCl was added at intermittent intervals.  
To determine the Cu(II) and phosphate removal uptake, a series of batch equilibrium tests were carried out for Cu(II) and phosphate following a similar procedure. The following conditions were initiated: The initial concentration of Cu(II) and phosphate were 10 and 15 mg/L, the Fe concentration as Fe was at 1.0 and 2.0 mM. The Langmuir and Freundlich equilibrium model were applied to find the sorption process. Each data was fitted band calculated by a sigma plot program.
## 7 Research Challenges, Open Issues, and Future Directions  
### Privacy and Security Issues  
VANETs are prone to remote attacks that can lead to vehicles malfunctioning. Some examples are attackers gaining access to the vehicles, leading to steering control, down the engines, applying brakes, etc. Even though FL and blockchain can control privacy leakages, the attackers can reconstruct the attributes from the model updates sent to the central server from the individual vehicles (poison attacks). To address the privacy and security issues, advanced encryption algorithms can be used to encrypt the model updates that are sent from the individual devices to the cloud and also when the vehicles are communicating with each other [166].
## 4 Numerical Results
### The Influence of the Used/End-of-Life Quality on the System Profitability  
In this sub-section, we study the influence of the used/end-of-life quality on system profitability; in other words, the influence of the percentage of remanufactured parts. The number of remanufactured items is usually uncertain, and it will not be known until the end of the disassembly process. In fact, we cannot detect the quality of disassembled items from the arrival of the returned product. This uncertainty may have caused profit-loss risk compared to the expected cost of manufacturing a product from new items. In Figure 10, a sensitivity study is illustrated by the expected total cost \(EC[[T_{PR}^{*},X^{*}]]\) as a function of the percentage of remanufactured items. The percentage of remanufactured items is adjusted to see their influence on the total expected cost. To a percentage found, a profit-loss risk will be discussed.  
As shown in Figure 10, increasing the percentage of remanufacturing items from 0 to 90% _under_ 10% of remanufactured parts, the expected total cost exceeds the expected cost with new items. Therefore, the proposed system with remanufacturing parts becomes unprofitable compared to the manufactured system with the purchase of new items in level 2. As a result, it is crucial to put requirements and standards for the quality of the used end-of-life product. Indeed, the remanufacturing process requires us to add used purchase cost of an end-of-life product, remanufacturing cost of disassembled parts, cost of storage of disassembled components, system maintenance cost, etc. Therefore, it is the handling of the used/end-of-life product with poor quality that causes a very low percentage of remanufactured items. The integration of a remanufacturing process becomes unprofitable and can even engender profit losses.
## 3 A Post-Processing Algorithm to Enforce Traction Continuity along the Interface
### Traction Continuity Conditions  
The condition for continuity of normal traction across the interface is given by  
\[\sigma_{22(0)}{}^{0+}+\Delta\sigma_{22}{}^{0+}=\sigma_{22(0)}{}^{0-}+\Delta \sigma_{22}{}^{0-}, \tag{6}\]  
Figure 2: Full-field maps of normal (left) and shear (right) stresses measured for a super-shear crack-like rupture during a test with \(\alpha=29^{o}\), \(P=23\) MPa (\(\sigma_{0}=17.6\) MPa and \(\tau_{0}=\) 9.75 MPa), and a field of view of 19 x 12 mm. **(a,b)** Full-field maps obtained using Vic-2D extrapolation near the interface. **(c,d)** Fulfilled maps obtained with the stress continuity constraints, using a good set of parameters \(n_{x1}\), \(n_{x2,inter}\), and \(n_{x2,cent}\). **(e–h)** Enforcement of stress continuity with extreme values of \(n_{x2,inter}\) and \(n_{x2,cent}\) leads to discontinuities and spurious oscillations in the stress fields. Note that the full-field maps are cropped and their size is slightly smaller than the reported field of view size.  
where the superscripts (\({}^{0+}\)) and (\({}^{0-}\)) denote the values immediately above and below the interface, respectively, and \(\sigma_{22(0)}{}^{0+}=\sigma_{22(0)}{}^{0-}\) are the initial stresses. Assuming a linear elastic response for the materials above and below the interface, the condition in Equation (6) can be written in terms of strains as  
\[\frac{E^{+}}{1-\
u^{+2}}\Big{(}\varepsilon_{22}{}^{0+}+\
u^{+}\varepsilon_{11} {}^{0+}\Big{)}=\frac{E^{-}}{1-\
u^{-2}}\Big{(}\varepsilon_{22}{}^{0-}+\
u^{-} \varepsilon_{11}{}^{0-}\Big{)}, \tag{7}\]  
where the superscripts (\({}^{+}\)) and (\({}^{-}\)) denote the material properties above and below the interface, respectively. For small strains the condition can be expressed in terms of displacement gradients as  
\[\frac{E^{+}}{1-\
u^{+2}}\Bigg{(}\frac{\partial u_{2}}{\partial x_{2}}{}^{0+}+ \
u^{+}\frac{\partial u_{1}}{\partial x_{1}}{}^{0+}\Bigg{)}=\frac{E^{-}}{1- \
u^{-2}}\Bigg{(}\frac{\partial u_{2}}{\partial x_{2}}{}^{0-}+\
u^{-}\frac{ \partial u_{1}}{\partial x_{1}}{}^{0-}\Bigg{)}. \tag{8}\]  
Similarly, the continuity of shear traction is expressed via  
\[\sigma_{12(0)}{}^{0+}+\Delta\sigma_{12}{}^{0+}=\sigma_{12(0)}{}^{0-}+\Delta \sigma_{12}{}^{0-}, \tag{9}\]  
and  
\[\mu^{+}\Bigg{(}\frac{\partial u_{1}}{\partial x_{2}}{}^{0+}+\frac{\partial u _{2}}{\partial x_{1}}{}^{0+}\Bigg{)}=\mu^{-}\Bigg{(}\frac{\partial u_{1}}{ \partial x_{2}}{}^{0-}+\frac{\partial u_{2}}{\partial x_{1}}{}^{0-}\Bigg{)}, \tag{10}\]  
where \(\mu\) is the shear modulus.