esa-sceva/satcom-chunk-collection huggingface dataset:
# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 6 Conclusions  
EV charging loads were coordinated via the proposed centralized control algorithm using unbalanced optimal power flow calculations. The non-iterative unbalanced power flow calculations were implemented to formulate the objective function. EV charging loads were reallocated according to the maximized charging power of EVs, while maintaining voltage magnitudes and voltage unbalances within their limits as well as avoiding overloading the distribution transformer and cable. Daily profiles of EV charging loads were modeled based on real datasets acquired from trials at the real project. Two scenarios were investigated to monitor the performance of the centralized control algorithm with uncontrolled and controlled EV charging loads. The results showed that network components at the MV level can cope with uncoordinated EV charging loads. However, high EV uptakes of uncoordinated charging loads can lead to the following issues at the LV level:  
* Deviating from the normal value of voltage magnitude and voltage unbalance.
* Overloading the main distribution line and the distribution transformer.  
These issues were mitigated by reallocating the EV charging loads via the centralized control algorithm with the non-linear programming (see Scenario II).  
This control algorithm can be implemented to control other smart appliances. The centralized control algorithm may be adopted by distribution network operators to defer upgrading needs for network infrastructure (underground cables and distribution transformers) as can be easily integrated into existing power system control paradigms.  
**Acknowledgments:** [PERSON] would like to gratefully acknowledge the financial support of the Ministry of Higher Education and Scientific Research of Iraq and University of Kufa for his Ph.D. study at Cardiff University.  
**Author Contributions:** [PERSON] contributed to the paper by the overall framework and the research idea. The other author contributed to the paper by providing the network, the source for data used, and the overall supervision of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Scenario II UKGDN daily profiles (**a**) phase currents of the underground cable between node 17 and node 18; (**b**) loading power of the transformer T8.
## 3 Proposed Approach
### Discussion of Simple Heuristic Approaches that seem Obvious
In fact, since there are many more routes in a real network, and they intensively intersect with one another, a change in one channel load unpredictably increases the load in almost the entire network. In practice, buffer sizes often start with 100-1000 packets. Figure 4a shows a graph of the relationship between changes in the intensity of information flows in channels between nodes 4 and 6 and 6 and 7 on the load of the channel between nodes 4 and 6. Figure 4b shows a similar graph, but with a buffer size of 1000 packets and a load of up to 99%. It can be seen that the amount of emerging additional information flows is no more than 2.5% of the main increase in \(\Delta x\). Figure 3: Example of a network with eight nodes and three information routes (colored arrows). Therefore, the value of the derivative of the total information losses in the network with respect to the information flow in a particular channel only slightly exceeds the value of the derivative of the information losses in the channel with respect to the information flow in the same channel. In other words, one can obtain the value of the derivative of the objective function by the information flow in the channel using the \(Loss(Q)\) algorithm. Let the derivative value be marked as \(\frac{dL_{loss}}{dx_{ij}}\) for the corresponding channel from the \(i\)th node to the \(j\)th one. Moreover, some modifications of [PERSON]'s algorithm (see Algorithm 4) make it possible to find a route with the best derivative value for each target request. ```
1. Required: the matrix of partial derivatives \(dL=\left\{\frac{\partial L_{loss}}{\partial x_{ij}}\right\}\), the loss probability matrix \(p=\{p_{ij}\}\). 2. Assign \(dL_{ij}=\frac{dL_{ij}}{1-p_{ij}}\)\(\forall i,j\). 3. For \(i=\overline{1,n}\): (a) For \(u=\overline{1,n}\): i. For \(v=\overline{1,n}\): A. Assign \(d_{new}=\frac{dL[u][i]}{(1-p[u][i])}+dL[i][v]\). // new distance B. if \(d_{new}<dL[u][v]\) then: // if the distance is better remember the new route \(\bullet\) Set \(dL[u][v]=d_{new}\), \(\bullet\)\(p[u][v]=p[u][i]+p[i][v]-p[u][i]\times p[i][v]\), \(\bullet\)\(next[u][v]=next[u][i]\). ```
**Algorithm 4** Modified [PERSON] algorithm

The result of executing [PERSON]'s algorithm is the matrix, which stores information about the shortest routes. Algorithm 5 allows obtaining route vectors in the form of a sequence of vertices. ```
1. Initialize \(\{u_{1},u_{2},u_{3},u_{4}\}\): (a) For \(u_{1},u_{2},u_{3},u_{4}\): (b) For \(u_{1},u_{2},u_{3},u_{4}\): (c) For \(u_{1},u_{2},u_{3},u_{4}\): (d) For \(u_{1},u_{  
```
1 Set the starting vertex \(u\). 2 Set the ending vertex \(v\). 3 Obtain the matrix of the shortest distances \(dL\) by [PERSON]'s algorithm. 4 Obtain the matrix of loss probabilities \(p\) by [PERSON]'s algorithm. 5 If \(p[u][v]=1\) then STOP. // the route does not exist, stop the algorithm
6 Assign \(c=u\). 7 Until \(c\
eq v\): (a) Add vertex \(c\) to the route. (b) \(c=next[c][v]\). 8 Add vertex \(v\) to the route. 9 STOP. ```  
**Algorithm 5** The shortest routes
# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used  
### Deep Learning  
Deep learning has lately received a lot of interest in the world of machine learning. Convolutional networks, deep auto encoders, and deep belief networks are just a few examples of the hierarchical learning structures used in this method [44; 45; 46; 47; 48; 49]. For tasks such as pattern categorization and representation learning, these designs have several input processing layers. One popular approach for training neural network weights is through the use of a backpropagation algorithm. This algorithm involves propagating the error from the output layer back through the network, adjusting the weights in each layer based on the error gradient with respect to the activation of the previous layer; using these hierarchical architectures aims to lower the total inaccuracy of the network [21; 22; 23; 33]. In place of the traditional backpropagation approach, this study suggests using a modified particle swarm (PS) optimization technique to train deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima [27; 28; 29]. To address these issues, we applied the modified particle swarm optimization algorithm to a deep neural network architecture for classification tasks. In the modified particle swarm optimization algorithm, we replaced exploitation phase PS with the cuckoo search (CS) algorithm, named the PSCS algorithm. PSCS combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters.
# A New General Type-2 Fuzzy Predictive Scheme for PID Tuning
## 5 Obtain PID Parameters with Predictive Control  
The PID controller is usually continuous and its overall structure is described by Equation (13).  
\[\frac{U(s)}{e_{s}(s)}=k_{c}+\frac{1}{T_{I}s}+T_{D}s \tag{13}\]  
The general structure of the discontinuous PID control can also be described as Equation (14). In this relation, \(r_{2}\), \(r_{1}\), \(r_{0}\) are the control parameters and S also determines the response speed.  
\[\frac{u_{PID}(k)}{e_{s}(k)}=\frac{r_{0}\ +r_{1}z^{-1}+r_{2}z^{-2}}{(1-z^{-1})( 1+sz^{-1})} \tag{14}\]  
\[e_{s}(k)=y_{s}(k)-y(k) \tag{15}\]Equation (15) also indicates the error between the system output value and the desired value at moment \(k\). By calculating Equation (14), the output of the control is obtained in accordance with Equation (16). Using this relation, the output of the controller can be obtained at moment \(k\). We also consider the parameter \(y(k)\) to consider the delay between system input and output.  
\[\begin{array}{l}u_{PID}(k)=(1-s)u(k-1)+su(k-2)\\ +(r_{2}+r_{1}+r_{0})y_{s}(k)-r_{0}y(k)\\ -r_{1}y(k-1)-r_{2}y(k-2)\end{array} \tag{16}\]  
If we consider the output of the controller at moment k as \(U_{GPC}(k)\), we can consider an objective function such as equation (17) for it.  
\[l_{i}\prec u_{PID}(k+i)\prec L_{i} \tag{17}\]  
In above equation, \(L_{i}\) is the maximum and \(l_{i}\) is the minimum value that the system can consider as input at the moment \(k+p\). Also, \(U_{GPC}\) can be obtained by minimizing Equation (11). The value of \(u_{PID}\) is also calculated according to Equation (18). In this equation, we can use the predicted value instead of the system output value.  
\[\begin{array}{l}u_{PID}(k)=(1-s)u_{PID}(k-1)+su_{PID}(k-2)\\ +(r_{0}+r_{1}+r_{2})y_{s}(k)-r_{0}y(k)\\ -r_{1}y(k-1)-r_{2}y(k-2)\end{array}\]  
\[\begin{array}{l}u_{PID}(k+1)=(1-s)u_{PID}(k)+su_{PID}(k-1)\\ +(r_{0}+r_{1}+r_{2})y_{s}(k+1)\\ -r_{0}\hat{y}(k+1)-r_{1}y(k)-r_{2}y(k-1)\end{array}\]  
\[\begin{array}{l}u_{PID}(k+i)=(1-s)u_{PID}(k+i-1)\\ +su_{PID}(k+i-2)\\ +(r_{0}+r_{1}+r_{2})y_{s}(k+i)\\ -r_{0}\hat{y}(k+i)-r_{1}\hat{y}(k+i-1)\\ -r_{2}\hat{y}(k+i-2)\end{array} \tag{18}\]
## 1 Introduction
### Outline and Scope of the Paper  
In this paper, we propose an analytic solution of state-constrained optimal tracking control problems for CT-LTI systems that are based on the model-based prediction, the quadratic penalty method, and the variational approach in Section 2. The model-based prediction is a concept taken from MPC, and this is essential to change the direction of the calculation for the solution from backward to forward. The quadratic penalty method plays an important role in deriving the analytic solution since it can transform the problem into a form that does not have inequality constraints. For computational convenience, we also propose a sub-optimal controller derived from the steady-state approximation of the analytic solution, and show that the proposed controller satisfies the Lyapunov stability in Section 3. Finally, the simulation results for a DC motor servo system are shown and compared with the results of the direct multi-shooting method and implicit MPC to verify the effectiveness of the proposed controller in Section 4.
# Study on the Ablation Behavior of High-Intensity Lasers in Vacuum
## 3 Results and Discussion  
### Ablation Depth  
Firstly, we maintain constant single-pulse energy and vary the defocusing distance by adjusting the lens position, which changes the spot size. This allows us to study the effects of different defocusing distances and pulse numbers on the ablation depth, as shown in Figure 3a.  
The defocusing distance primarily affects the diameter of the ablation hole and the distribution of laser energy. As observed in Figure 3a, the ablation depth gradually increases with the number of pulses. However, the ablation depth increases rapidly when the laser  
Figure 3: (**a**) Effect of different defocusing amounts on ablation depth; (**b**) effect of different defocusing amounts on average remove per pulse. The pulse energy used is 660 mJ. The different curves correspond to different distances of lens–target defocusing as indicated.  
Figure 2: (**a**) Schematic diagram of ablation hole diameter; (**b**) the change in spot diameter with laser energy.  
is focused, exceeding 4600 \(\upmu\)m at 500 pulses. We calculated the average material removal per pulse by dividing the ablation depth by the number of pulses, as shown in Figure 3b. It was found that when the defocusing distance exceeds the focal depth, the average material removal per pulse did not exceed 1 \(\upmu\)m/pulse. When the laser was focused, our results indicated that the average removal depth increased progressively with the number of pulses, from 2.24 \(\upmu\)m/pulse (50 pulses) to 9.26 \(\upmu\)m/pulse (500 pulses), an increase of approximately 4.13 times. This shows that the average removal per pulse is influenced by both the defocusing distance and the pulse number.  
To further investigate this phenomenon, the laser drilling capability near the focal point was examined. By fixing the focusing lens and varying the single-pulse energy of the laser, the variation in ablation depth near the focal point with respect to the number of pulses and single-pulse energy was obtained, as shown in Figure 4a. It can be observed that the ablation depth increases with the number of pulses, and the slope gradually increases. When the single-pulse energy of the laser is 150 mJ (energy density is 4.13 J/cm\({}^{2}\)), there is almost no nonlinear increase in ablation depth. However, when the single-pulse energy reaches 310 mJ (energy density is 8.54 J/cm\({}^{2}\)), a clear nonlinear increase in ablation depth is evident. The nonlinear increase in ablation depth becomes more pronounced with increasing energy density.  
The average material removal per pulse was calculated, as shown in Figure 4b. It can be seen that at the focal position, when the laser energy is below 150 mJ, the average material removal per pulse hardly increases with the number of pulses. However, when the laser energy exceeds 310 mJ, the average material removal per pulse increases with the number of pulses.
## 5 Conclusions and Future Work
### Contributions of this Study  
In summary, this research makes substantial contributions to the field of vulgar remark detection in the Chittagonian dialect, as follows:  
1. Gathered a dataset of 2500 comments and posts from publicly accessible Facebook accounts.
2. Ensured dataset reliability through rigorous manual annotation and validated the annotations using [PERSON]'s Kappa statistics and [PERSON]'s alpha.
3. Introduced a keyword-matching-based baseline method using a hand-crafted vulgar word lexicon.
4. Developed an automated method for augmenting the vulgar word lexicon, ensuring adaptability to evolving language.
5. Introduced various sentence-level vulgar remark detection methods, from lexicon matching to advanced techniques.  
Conducted comprehensive comparisons between keyword-matching and machine learning (ML) and deep learning (DL) models to achieve high detection accuracy.
* Achieved over 90% accuracy in detecting vulgar remarks in Chittagonian social media posts, demonstrating a performance acceptable for real-world applications.
## 5 Simulation Studies
### Simulation of MLSMC-Based Satellite Attitude Fault-Tolerant Stabilization Control  
Related parameters are chosen and computed in Table 2. Based on the MLSMC design, simulation studies on MLSMC-based satellite attitude fault-tolerant stabilization control are performed, and related simulation results are demonstrated in Figures 7-10. Additionally, a comparison between the proposed MLSMC approach and the SMC-based approach [31]  
Figure 5: The reconstruction signals of actuator fault in the \(x\) axis under the MLO and the ILSMO [26].  
Figure 6: The reconstruction signals of actuator fault in the \(z\) axis under the MLO and the ILSMO [26].  
is also provided. Under both approaches, the curves of satellite attitude angular velocity are illustrated in Figure 7, while the curves of the satellite attitude quaternion are illustrated in Figure 10. The stabilization control errors of satellite attitude angular velocity and of attitude quaternion under both approaches are computed and given in Tables 3 and 4. From Figures 7-10, it can be noted that the MLSMC and the SMC can guarantee the actual satellite attitude angular velocity and that the satellite attitude quaternion can converge to small neighborhoods of zero. However, from Tables 3 and 4, it can be found that, compared with the SMC-based approach proposed in [31], it has higher stabilization control accuracy on the satellite attitude angular velocity and the satellite attitude quaternion. This is mainly because the actuator faults are accurately reconstructed using the MLO, and the mixed learning algorithm in the MLSMC strategy is adopted to compensate for the composite disturbance. In addition, the curves of the sliding mode surface under both approaches are demonstrated in Figure 9, while the curves of the compensation term for the composite disturbance are illustrated in Figure 10. Therefore, we can conclude that the proposed attitude stabilization control approach has actuator fault-tolerant capability and is effective and applicable for rigid spacecraft with actuator faults.  
Figure 8: The curves of the satellite attitude quaternion with the MLSMC and the SMC [31].  
Figure 9: The curves of the sliding mode surface with the MLSMC and the SMC [31].
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 5 Numerical Experiments
### Blow-Up
#### 5.2.3 Effect of Refining the Space Discretization  
Let us then analyze what happens when the space grid is refined; in this regard, the results obtained with \(N=250\) and \(\Delta t=10^{-3}\) are reported in Table 5.  
We notice that \(t_{f}\) slightly decreases, while the maximum and the minimum of \(\mathbf{u}\) in \(t_{f}\) are about the same as previously computed for \(N=100\). Indeed, being the grid finer, the point nearest to the blow-up point is closer (in space) to the blow-up point than in the case \(N=100\). Thus, the solution gets larger earlier.  
To better see this, we can again consider the short example proposed earlier and analyze what happens for \(N=250\). Let us again consider \(u^{*}=u_{2}^{*}\) with \(T=1.00\) in the point \((1,0)\). The solution at \(t=t_{f}=0.981\) at the inner point closest to \((1,0)\), which is \((x_{n},y_{1})=(250/251,1/251)\) is:  
\[\frac{1}{x(y+T-t)+1-x}\bigg{|}_{(x_{n},y_{1})}=\frac{1}{x_{n}(y_{1}+T-0.981)+ 1-x_{n}}\sim 37.207,\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(\mathbf{u^{*}}\) & \(t_{f}\) & \(\mathbf{res_{0}}\) & \(\mathbf{res}\) & \(\mathbf{err_{h}}\) & \(\mathbf{err_{2}}\)**rel.** & \(\mathbf{\max u}\) & \(\mathbf{\min u}\) & \(\mathbf{\max u^{*}}\) & \(\mathbf{\min u^{*}}\) \\ \hline \(u_{2}^{*}\) & 0.981 & \(2.93\times 10^{6}\) & \(1.11\times 10^{-5}\) & \(9.68\times 10^{-4}\) & \(4.97\times 10^{-4}\) & 37.207 & 0.985 & 37.207 & 0.985 \\ \(u_{3}^{*}\) & 0.965 & \(9.86\times 10^{9}\) & \(1.95\times 10^{-5}\) & \(1.04\times 10^{-2}\) & \(1.34\times 10^{-3}\) & 51.201 & 0.974 & 51.201 & 0.974 \\ \(u_{4}^{*}\) & 0.783 & 544,443 & \(8.42\times 10^{-6}\) & \(1.53\times 10^{-1}\) & \(3.09\times 10^{-2}\) & 30.287 & 0.953 & 30.290 & 0.953 \\ \(u_{5}^{*}\) & 0.795 & 14,438 & \(1.20\times 10^{-5}\) & \(1.82\times 10^{-2}\) & \(1.42\times 10^{-2}\) & 24.430 & 0.496 & 24.546 & 0.486 \\ \hline \end{tabular}
\end{table}
Table 5: Error and data of the lagged diffusivity procedure for the last computable solution for the problems in Figure 4 for \(\Delta t=10^{-3}\) and \(N=250\).  
as computed. Analogous results can be easily verified with regard to the derivatives. We also notice that global errors decrease due to the finer space grid, while \(res_{0}\) is of the same order of magnitude as above.  
Lastly, we verify that further reducing \(\Delta t\) allows us to better approach the blow-up time, as remarked at the end of the previous subsection. Thus, in Table 6 we report the results obtained by setting \(\Delta t=10^{-5}\) and by using as \(t_{0}\) the last computed time step \(t_{f}\) of Table 5.  
In all cases we are able to get closer to the blow-up time, as expected. Moreover, global errors decrease.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 6 Methods to Improve WER Performance  
The WER gap between TH-OSD and the original OSD algorithm is caused by the cases where OSD gives the correct decoding result while TH-OSD does not. This happens when a candidate codeword has an \(f\) value smaller than \(f_{th}\) and is encountered before the correct codeword during the flipping process. To reduce the probability of these events, we have two possible solutions: list or CRC. The list method (TH-OSD list) is to use a list to store codewords with \(f\) values smaller than \(f_{th}\). Once the number of codewords in the list reaches the list capacity or the stopping criterion is met, we output the codeword with the smallest \(f\) value as the decoding result. To reduce the total number of tested codewords, the list size should be small. In short codeword length scenarios, a small list is usually sufficient for TH-OSD to achieve the WER performance of MLD. We can confirm this by simulation. The CRC method (CRC-aided-THOSD, CA-THOSD) is to put some CRC bits into the codewords and using them to avoid incorrect codewords during decoding process. CA-THOSD performs the same process of TH-OSD, but with different stopping criterion. We stop the decoding process when we find a codeword that has a discrepancy less than \(f_{th}\) and passes the CRC test. We find by simulation that CA-THOSD outperforms MLD.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 4 Results
### Assessment of GPP at Different Temporal Scales  
A comparison of GPP estimates from the SA-LGBM model with other GPP products was conducted within a footprint near the flux sites. The overall correlation of SA-LGBM with eddy covariance flux tower data is notably higher, as illustrated in Figure 6. In all time scales, SA-LGBM exhibits superior performance, boasting higher \(R^{2}\) and lower RMSE. Averaging over all time scales, the \(R^{2}\) of SA-LGBM is 0.21 higher than that of MOD17 and 0.17 higher than that of GOSIF. These results imply that the SA-LGBM has the ability to not only capture daily fluctuations in GPP but also variations in GPP across seasonal and interannual timeframes. Overall, SA-LGBM partially overcomes the shortcomings of the existing model of \"high observation and low estimation\" and achieves better results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Test Set**} & \multicolumn{2}{c}{**Test Set**} \\  & **(Without Spatial Factors)** & **(With Spatial Factors)** & **(With Spatial Factors)** \\ \cline{2-5}  & \(R^{2}\) & \(RMSE\) & \(R^{2}\) & \(RMSE\) \\ \hline PyTorch Neural Net model & 0.80 & 1.70 & 0.86 & 1.46 \\ \hline XGBost & 0.80 & 1.69 & 0.86 & 1.44 \\ \hline CatBoost & 0.81 & 1.66 & 0.88 & 1.35 \\ \hline ExtraTrees & 0.80 & 1.69 & 0.86 & 1.45 \\ \hline Random Forest & 0.81 & 1.67 & 0.87 & 1.43 \\ \hline LightGBM & 0.82 & 1.63 & 0.89 & 1.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison of different machine learning methods incorporating spatial factors.  
Figure 6: Comparison of the accuracy of GPP estimation across three temporal scales. Every-8 th-day comparison: (**a**) MOD17, (**b**) GOSIF, and (**c**) SA-LGBM. Monthly comparison: (**d**) MOD17, (**e**) GOSIF, and (**f**) SA-LGBM. Yearly comparison: (**g**) MOD17, (**h**) GOSIF, and (**i**) SA-LGBM.