esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pretreatment of Petrochemical Wastewater by Fenton Oxidation
#### 3.1.2 Effect of H\({}_{2}\)O\({}_{2}\) Concentration  
Figure 2 shows the effect of H\({}_{2}\)O\({}_{2}\) concentration on COD removal (Figure 2a), B/C (Figure 2b), and BOD\({}_{5}\) (Figure 2c) during Fenton oxidation. At 120 mg/L Fe\({}^{2+}\) and a reaction time of 60 min, the H\({}_{2}\)O\({}_{2}\) concentration increased from 100 mg/L to 500 mg/L, and COD decreased significantly from 978.58 mg/L to 250.79 mg/L, mainly due to the oxidation of Fe\({}^{2+}\) by H\({}_{2}\)O\({}_{2}\) in the wastewater to produce \({}^{\bullet}\)OH (Equation (1)) [24]. However, when H\({}_{2}\)O\({}_{2}\) was increased to 1000 mg/L, COD increased to 356.52 mg/L (Figure 2a), which was due to the consumption of the generated \({}^{\bullet}\)OH by the excess H\({}_{2}\)O\({}_{2}\) (Equation (3)) [25]. At 500 mg/L of H\({}_{2}\)O\({}_{2}\), the B/C value was 0.62, which was greater than 0.3 and higher than the B/C values of the other experimental groups (Figure 2b), and the BOD\({}_{5}\) of the reacted wastewater was the  
Figure 1: Effect of Fe\({}^{2+}\) concentration on (**a**) COD removal; (**b**) B/C values; (**c**) BOD\({}_{5}\) variation during Fenton oxidation. Conditions are as follows: H\({}_{2}\)O\({}_{2}\) concentration 300 mg/L; 250 mL wastewater; pH = 3.0.  
smallest at 155.49 mg/L (Figure 2c). Therefore, 500 mg/L H\({}_{2}\)O\({}_{2}\) was selected as the condition for the treatment of petrochemical wastewater by Fenton oxidation.  
\[\mathrm{H_{2}O_{2}+\ ^{\bullet}OH\to H_{2}O+\ ^{\bullet}O_{2}H} \tag{3}\]
# Unrepeated 240-km 64-QAM Transmission Using Distributed Raman Amplification over SMF Fiber +
## 1 Introduction  
In fiber-optic communications, quadrature amplitude modulation (QAM) with coherent detection is widely deployed due to its good balance between robustness against optical signal-to-noise ratio (OSNR) degradation and spectral efficiency. To maximize the transmission distance while using high-order modulation formats, it is essential to maintain an acceptable OSNR through the system, which is critical while using advanced modulation formats with a large number of constellation points that consequently increase the number of bits transmitted per sample. Distributed Raman amplification (DRA) reduces signal decay in the fiber span by distributing the power over whole length of a transmission span, which results in higher received OSNR, allowing longer reach in long-haul transmission or longer total distance in unrepeated systems [1; 2; 3; 4; 5]. Ultra-long Raman fiber laser (URFL)-based second-order amplification can reduce the signal power variation by pushing an amplification even further into the fiber. A similar method was shown by [PERSON] et al. [6], where the authors demonstrated cascaded sixth-order distributed Raman amplification using several FBGs, improving the receiver's sensitivity by 10 dB. These methods are proven advantageous incomparison with systems based only on erbium-doped fiber amplifier (EDFA) [7; 8; 9] in both long-haul and unrepeetered transmission systems.  
In this paper, we experimentally investigate the performance of a single-channel 28-GBaud 64-QAM transmission with coherent detection over different Raman amplification schemes and confirm the results with numerical simulations. Our results illustrate the potential improvement attainable just through the use of digital back propagation (DBP) adequately combined with careful distributed amplifier design in an unrepeetered standard single-mode fiber (SMF-28) span without the need for a remote optically pumped amplifier (ROPA), large effective area, or ultra-low-loss fiber that surely could extend the transmission distance even further. Here, we can refer to Reference [10] where authors presented unrepeetered transmission up to 370 km using combinations of different low-loss/effective-area fibers, with forward and backward ROPA combined with amplification map optimization. By employing only low-loss fibers (0.169 dB/km) in our system, theoretically, we could extend our achievable distance from 200 km (41 dB) up to 242 km, which gives almost 25% total improvement, without forward and backward ROPA, using a simple design of distributed Raman amplifiers [9] instead. These are first-order counter-pumped, hybrid dual-order (both first and second order), and URFL Raman-based amplification with second-order counter-pumping.
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 3 Stackelberg Game Model and Problem Statement
### Utility Functions
Given that leaders can make priority decisions, we need to first analyze the non-cooperative decision making process of femtocells. As for given parameters, each femtocell chooses service objects according to the descending order of signal strength of macro users to be serviced. Each femtocell chooses the parameters that will maximize its utility function. This can be expressed as follows:  
\[ \begin{array}{l}\max\!U_{j}=S_{F_{j}}\log(\frac{r_{j,j}}{r_{0}})+S_{F_{j}} \log(\frac{r_{j,j}^{c}}{r_{0}})-(1-\alpha)\omega W_{j}\\ s.t.\;\;\;\beta_{j}\geq 1\;,\;j=1,2,\ldots,N\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; 0\leq W_{j}\leq W\end{array}  \tag{8}\]  
where \(S_{F_{j}}^{c}\) represents the macro users served by a femtocell, which can be expressed as \(\left\lceil(\beta_{j}-1)\cdot S_{F_{j}}\right\rceil\) since \(\beta\) is not an integer. \(\omega\) is a positive constant. From the above expressions, it can be seen that the load taken by a femtocell for the macro base station becomes the revenue of the former. Assuming that each femtocell cannot get the information from one other, and the Equation (8) is an independent process of non-cooperation, the optimal strategy for \(F_{j}\) can be defined as follows:  
\[ \begin{array}{l}\beta_{j}^{*}=\underset{\beta_{j}}{\operatorname{argmax}}U_ {F_{j}}\\ s.t.\;\;\;0\leq W_{j}\leq W\end{array}  \tag{9}\]  
Meanwhile, the strategy set of femtocells can be defined as \(\beta=[\beta_{1},\beta_{2},\ldots\beta_{N}]\). For the macro base station, although it sacrifices partial spectrum, users receiving its service still enjoy sound performance with uplifted system gain. The revenue function of macro base station can be defined as follows:  
\[\begin{array}{l}\max\!U_{m}=S_{m}^{*}\log(\frac{r_{m_{i}}}{r_{0}})\\ s.t.\;\;0\leq\alpha\leq 1\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;
## 4 Architectures and Settings  
Most prior research uses CNN-based models for SER [37]. Among such models, the notable ones include AlexNet [38], VGG [39, 40], and ResNet50 [41, 42]. This section provides a short overview of the models. Our proposed model, VGGE, is a variant of VGG.  
* **AlexNet** is one of the famous CNN models used in applications such as image classification and recognition and is widely employed for SER classification [43]. It achieved an outstanding result at the ImageNet competition in 2012 [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Duration (s)** & **ASED** & **EMO-DB** & **RAVDESS** & **URDU** \\ \hline
1–2.0 & & 126 & & \\
2–3.0 & 850 & 224 & & 200 \\
3.0–4 & 1624 & 136 & 1440 & 200 \\
4.0–5 & & 24 & & \\
5.0–6 & & 20 & & \\
6.0–7 & & 3 & & \\
7.0–8 & & 1 & & \\
8.0–9 & & 1 & & \\ \hline
**STD** & 0.444 & 1.067 & 0 & 0.5 \\ \hline
**Mean** & 2.967 & 2.267 & 3 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of original clip lengths in seconds for all datasets. In the table, 1–2.0 means 1 s \(\leq\) d \(<\) 2 s).  
* **VGG**[39] appeared in 2014, created by the Oxford Robotics Institute. It is well known that the early CNN layers capture the general features of sounds such as wavelength, amplitude, etc., and later layers capture more specific features such as the spectrum and the cepstral coefficients of waves. This makes a VGG-style model suitable for the SER task. After some experimentation, we found that a model based on VGG but using four layers gave the best performance. We call this proposed model VGGE and use it for our experiments. Figure 1 shows the settings for VGGE.
* **ResNet**[42] was launched in late 2015. This was the first time that networks with more than a hundred layers were trained. Subsequently, it has been applied to SER classification [41].  
Concerning the experimental setup, the standard code for AlexNet and ResNet50 was downloaded and used for the experiments. For VGGE, the network configuration was altered, as shown in Figure 1. For the other models, the standard network configuration and parameters were used.  
In all experiments, the librosa v0.7.2 library [45] was used to extract MFCC features.  
We used the Keras deep learning library, version 2.0, with a Tensorflow 1.6.0 backend to build the classification models. The models were trained using a machine with an NVIDIA GeForce GTX 1050. Our model employed the Adam optimization algorithm with categorical cross-entropy as the loss function; training was terminated after 100 epochs, and the batch size was set to 32.
## 2 Previous Models
### Flame Submodel
#### 2.6.1 Flame Height Submodel  
Flame angle and flame length are determining factors in the radiative heat transfer of a flame. Flame geometry plays a key role in determining the rate of spread of fires where radiation is the dominant heat transfer mechanism to the unburned fuel [33]. The joint effects of wind speed and slope percentage affect the flame geometry. We focused on the flame height \(F\), understanding that this is the height of the flame above the vertical, relating the flame length \(L_{f}\) and flame angle \(\theta_{f}\), \(F=L_{f}\sin\theta_{f}\). The flame height, and not the flame length, is one of the input parameters determining the non-local radiation calculation in our model. Data from [34] and the sensitivity analysis of the model performed in [35] led us to propose the following flame height sub-model:  
\[F=(F_{H}+F_{v}|v|^{1/2})(1+F_{s}s^{2}) \tag{25}\]  
where \(F_{H}\) is a flame-height-independent parameter, \(F_{v}\) is a wind correction factor, \(F_{s}\) is a slope correction factor, \(|v|\) is the wind strength, and \(s\) represents the slope at each point on the surface. The first factor in Equation (25) represents the correction of the flame height due to the wind. This expression is derived from the observation of the experimental curves for different fuels gathered in [34], where the increase in the height of the flame due to the wind responds to such a function. \(F_{H}\) stands for zero wind, and the correction coefficient \(F_{v}\) was added to experimentally adjust the different behaviors for each type of fuel by a least-squares method. The second factor in Equation (25) represents the correction of the flame height according to surface slope. When there is no slope or wind, the flame is vertical, and its height is equal to its length; as deduced from [36], a relationship can be derived between the flame height and the vertical gas velocity inside it. However, when there is a slope, the following proportionality factor must be considered:  
\[\frac{1}{\cos^{2}\alpha}=1+\left(\frac{\partial h}{\partial x}\right)^{2}+ \left(\frac{\partial h}{\partial y}\right)^{2}=1+s^{2}\]  
where \(\alpha\) is the angle between the horizontal plane and tangential plane to the surface \(S\) on each point and \(h=h(x,y)\) is the surface height, so \(s\) is the slope at each point on the surface \(S\). Again, a correction factor \(F_{s}\) was added to adjust data from [34] in the sense of least-squares.
# Second Harmonic Revisited: An Analytic Quantum Approach
## 2 Results
### Quantum Regime  
The quantum dynamics of second-harmonic generation can be described through the Hamiltonian:  
\[\hat{H}=\hbar\gamma\hat{a}_{\rm F}\hat{a}_{\rm F}\hat{a}_{\rm SH}^{\dagger}+h.c. \tag{25}\]  
where \(\hat{a}_{j}\) and \(\hat{a}_{j}^{\dagger}\) are the annihilation and creation boson operators for the input (\(j=\) F) and second-harmonic field (\(j=\) SH) with the usual commutation rules \(\left[\hat{a}_{j},\hat{a}_{k}^{\dagger}\right]=\delta_{j,k}\).  
No exact solution can be found for the output state since it is not possible to identify a finite-dimensional Lie algebra for this Hamiltonian [17]. As mentioned in the Introduction, here we exploit a perturbative approach. A comparison with the classical regime will help to test the consistency of our results.  
The Heisenberg equation for the number operator \(\hat{n}_{\rm SH}\equiv\hat{a}_{\rm SH}^{\dagger}\hat{a}_{\rm SH}\), with the Hamiltonian \(\hat{H}\) in Equation (25),  
\[i\hbar\frac{d\hat{n}_{\rm SH}}{dt}=[\hat{n}_{\rm SH},\hat{H}] \tag{26}\]has no exact solution, as mentioned above. Nevertheless, it allows one to retrieve the coefficients of the Taylor expansion for \(\hat{n}_{\text{SH}}\),  
\[\hat{n}_{\text{SH}}(t)=\hat{n}_{0}+\sum_{k}\frac{d^{k}\hat{n}_{0}}{dt^{k}}\frac{ \hat{n}_{0}^{k}}{k!}. \tag{27}\]  
being the derivatives of all orders provided by the iteration of Equation (26) and \(\hat{n}_{0}\equiv\hat{n}_{\text{SH}}(0)\).  
Equivalently, one can study the evolution of the states. The most general input state for an SHG process can be expanded over the basis of the number states as:  
\[\hat{\rho}_{0}=\sum_{n,m}c_{n}c_{m}^{*}|n\rangle\langle m|\otimes|0\rangle \langle 0| \tag{28}\]  
where \(c_{k}\) are suitable coefficients such that \(\sum_{k}|c_{k}|^{2}=1\). Given the evolution operator \(\hat{U}=\exp\left(\frac{i}{\hbar}\hat{H}t\right)\), we get the output state from:  
\[\hat{\rho}_{0}\rightarrow\hat{\rho}(\gamma t)=\hat{U}^{\dagger}\hat{\rho}_{0} \hat{U}, \tag{29}\]  
by suitably expanding \(\hat{U}\). In particular, we focus on the evolution of Fock states and then generalize to any input state by expanding it like in Equation (28), i.e.,  
\[\hat{U}^{\dagger}\hat{\rho}_{0}\hat{U}=\sum_{n,m}c_{n}c_{m}^{*}\hat{U}^{ \dagger}|n\rangle\langle m|\otimes|0\rangle\langle 0|\hat{U}. \tag{30}\]  
Thus, we just need to compute \(|\psi(\gamma t)\rangle=\hat{U}^{\dagger}|\psi(0)\rangle\rangle\), where \(|\psi(0)\rangle\rangle\equiv|N,0\rangle\). For instance, up to the second order in \(\gamma\), the output state reads:  
\[|\psi(\gamma t)\rangle\rangle\sim\left(1-\frac{1}{2}(\gamma t)^{2}N(N-1)+o(( \gamma t)^{3})\right)|N,0\rangle-\left(i\gamma t\sqrt{N(N-1)}+o((\gamma t)^{3} )\right)|N-2,1\rangle. \tag{31}\]  
Both the Heisenberg (Equation (27)) and the Schrodinger (Equation (29)) pictures will be exploited in the following. In particular, in Section 2.2.1, we need the expansion of the number operator to retrieve the first moment of the SH photon-number distribution. On the contrary, the conversion probabilities in Section 2.2.2 are more easily computed by evolving the input state.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Classifier Models
#### 2.5.5 Discriminant Analysis  
Linear and quadratic discriminant analysis were used. Linear discriminant analysis (LDA) is a generalization of the [PERSON]'s linear discriminant that finds a linear combination of features characterizing or separating two or more classes. In turn, quadratic discriminant analysis separates input features into two or more classes of objects by a quadratic surface, becoming a general version of the linear version.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 5 Conclusions  
This study presents an enhanced YOLOv8 model to improve the efficiency of high-resolution image processing, preserve image details, and adapt better to low-quality samples in fabric defect detection. Detection accuracy and speed are significantly improved by the following innovations: first, the RG-C2f module optimizes model efficiency by reducing parameters and computation and improves the inference speed while maintaining the efficiency of high-resolution image processing; second, the DySample upsampling operator replaces the traditional nearest neighbor interpolation method and efficiently retains more image details by learning the optimal sampling pattern, thus improving the detection ability of small-size defects; and finally, the designed inner-WIoU loss function introduces dynamic sample weights, which enhances the generalization ability of the model  
Figure 9: Comparison of results of Tianchi dataset testing, where (**a**) denotes the knot, (**b**) denotes the three threads, (**c**) denotes the stains, (**d**) denotes the marks.  
in edge regression, especially in dealing with different defect types and large differences in sample quality.  
Compared to the original YOLOv8, the enhanced model demonstrates a substantial increase in mAP across multiple public datasets. The frame rate of the model is 110.2 FPS, which is able to meet the real-time detection requirements in industrial real-world production. Therefore, the improved model not only outperforms multiple existing single-stage detection methods in terms of accuracy but also achieves higher inference speed, making it suitable for industrial online defect detection.  
Although the improved model significantly enhances accuracy and efficiency, it still faces some limitations. False detections may occur in complex backgrounds and for defects with high similarity. Additionally, the model shows limited adaptability to fabrics under extreme color or lighting changes. The model's adaptability to various industrial environments and hardware platforms, especially in terms of resource consumption and inference speed for real-time inspection systems, also requires further verification. Future work can focus on improving the model's robustness and adaptability for broader application scenarios.  
Conceptualization, X.L.; methodology, X.L.; software, K.N.; validation, K.N.; formal analysis, S.W.; investigation, S.W.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.L.; writing--review and editing, Y.J.; visualization, Z.Z.; supervision, Y.J.; project administration, T.W.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shanxi Province, grant number 202103021224218.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Optimization Model of Express-Local Train Schedules Under Cross-Line Operation of Suburban Railway
## 7 Computational Results
### Sensitivity Analysis
(4) Maximum passenger waiting time  
The maximum passenger waiting time \(\Delta t\) has a close relationship with whether passengers can travel successfully, i.e., its value directly affects the number of unsuccessful passenger trips. Therefore, to further evaluate the impact of the maximum waiting time parameter on the optimization results of train schedules under the combination of cross-line operation and express-local train in this chapter, the maximum waiting time of passengers is set to be 3 min, 6 min, 10 min, and 14 min for the comparison of the solutions, and the value of \(\Delta t=10\)min is taken as the benchmark value. As shown in Table 5, the objective function value is highest at \(\Delta t=3\)min, which is 12% higher than the \(\Delta t=10\)min scenario, indicating that when passenger tolerance is low, the system runs more trains to avoid the penalty cost increase, and therefore the overall cost increases. When \(\Delta t=10\)min, continuing to increase the maximum passenger waiting time also causes the overall cost to increase, indicating that the system balances between service capability and cost at \(\Delta t=10\)min. Combined with Figure 10, it can be seen that as the maximum passenger waiting time increases, the number of unsuccessful trips significantly decreases and then flattens out, and the per capita waiting time rises slightly, suggesting that significantly alleviating the problem of successful passenger trips may result in a slight compromise in the waiting experience. This indicates that setting a reasonable passenger maximum waiting time threshold can avoid resource waste while safeguarding the service level to determine the optimal service strategy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\Delta t}\) & \(\mathbf{\Delta t=3}\) & \(\mathbf{\Delta t=6}\) & \(\mathbf{\Delta t=10}\) & \(\mathbf{\Delta t=14}\) \\ \hline Objective function value & 3,725,174 & 3,315,659 & 3,289,185 & 3,222,923 \\ Ratio to baseline & 1.12 & 0.99 & 1 & 1.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for different maximum passenger waiting time. Figure 9: Comparison of metrics for different passenger cost per unit waiting time.
## 2 Correlation Model of Monitoring Data
### Correlation Modeling Using Partial Least Squares and Multiple Regression  
#### 2.2.1 Reconstructed Variable and Response Variables  
The correlation coefficients of the normal measurement of fault sensor and the measurements of the other sensors are calculated, which are also called the correlation coefficients of the reconstructed variable and response variables. The correlation matrix can be denoted as \(\mathbf{P}\):  
\[\mathbf{P}=\left[\begin{array}{cccc}\rho_{1}&\rho_{2}&\cdots&\rho_{m}\end{array}\right] \tag{7}\]  
The correlation coefficients are sorted from larger value to small value based on their absolute values, while the updated correlation coefficient's sorting matrix can be denoted as \(\mathbf{D}_{P}\):  
\[\mathbf{D}_{P}=\left\{\left(\rho\right)_{1}&\cdots\left(\rho\right)_{i}& \cdots&\left(\rho\right)_{m}\end{array}\right\} \tag{8}\]  
\((\rho)_{i}\) represents the \(i^{\text{th}}\) correlation coefficient absolute value, while \((\rho)_{1}\) is the largest correlation coefficient absolute value and \((\rho)_{m}\) is the smallest correlation coefficient absolute value, respectively. The corresponding measurement to \((\rho)_{i}\) is denoted as \((x)_{i}\), while the updated structural response sorting matrix can be denoted as \(\mathbf{D}_{X}\):  
\[\mathbf{D}_{X}=\left\{\left(\mathbf{x}\right)_{1}&\cdots\left(\mathbf{x} \right)_{i}&\cdots&\left(\mathbf{x}\right)_{m}\end{array}\right\} \tag{9}\]  
The faulty sensor information is considered as the reconstructed variable \(\mathbf{Y}_{P}\), and \(p\) measurements are selected as response variables \(\mathbf{X}_{P}\):  
\[\mathbf{X}_{P}=\left\{\left(\mathbf{x}\right)_{1}&\left(\mathbf{x}\right)_{2 }&\cdots&\left(\mathbf{x}\right)_{p}\end{array}\right\} \tag{10}\]  
\[\mathbf{Y}_{P}=\left\{\mathbf{y}\right\} \tag{11}\]