esa-sceva/satcom-chunk-collection huggingface dataset:
# State Feedback Control Based Seamless Switch Control for Microgrid Inverter
## 3 Proposed Control
### Effect of Islanding Detection Time  
The performance of the switching control is highly affected by the islanding detection time. Since during the islanding detection when the grid has been unintentionally disconnected, the system is still in current-controlled mode where the output current tracks the reference. In this condition, the voltage at PCC depends on the output current reference and the load. If the load and the output of the power source are matched, which means that the power in the MG is balanced and the PCC power flowed into the grid is very small, then the PCC voltage will not change too much after islanding even though the control is still in current-controlled mode. However, if the power in MG is not balanced and the PCC power is not that small, then after islanding (the control has not been switched), the PCC voltage will experience a severe variation due to the unbalanced power. Consequently, the islanding detection time will be very important, because it is necessary to switch to  
Figure 9: Bode diagram of the \(G_{FLL}\).  
voltage control as soon as possible to stabilize the voltage before the protection is triggered. In this case, the maximum borne time will be the protection time.  
Furthermore, supposing that the load is resistive, which means \(\mathbf{w}_{p}=\mathbf{i}_{ba}=\mathbf{v}_{c_{i}}\mathbf{k}_{load}\), then the model during the islanding detection becomes  
\[\left\{\begin{array}{cc}\dot{\mathbf{x}}\;=\;\mathbf{A}\mathbf{x}\;+\;\mathbf{B}\mathbf{v}_{c}\\ \mathbf{i}\;=\;\mathbf{C}_{i}\mathbf{x}\end{array}\right. \tag{17}\]  
\[\mathbf{A}\;=\;\left[\begin{array}{cc}-\mathbf{R}/L&-\mathbf{1}/L\\ \\ \mathbf{1}/\mathbf{C}&-\mathbf{1}/\mathbf{R}_{load}\mathbf{C}\end{array}\right] \tag{18}\]  
\[\mathbf{B}\;=\;\left[\begin{array}{cc}\mathbf{1}/L&\mathbf{0}\end{array}\right]^{T} \tag{19}\]  
\[\mathbf{C}_{i}\;=\;\left[\begin{array}{cc}\mathbf{1}&\mathbf{0}\end{array}\right] \tag{20}\]  
which can be used to evaluate the stability of the system during the islanding detection. In other word, as long as the designed current control parameters can ensure the stability of the system (17), the system will be stable during the islanding detection.
## 4 Results and Discussion
### Comparison of the Flow Evolution: Experimental Visualization versus Numerical Computation
#### 4.1.1 Water Case \(Re_{0}\sim O(10^{5})\)  
For the water, three different stages in the flow evolution can be distinguished. We denote as \(t_{1}\) and \(t_{2}\) the instants when the first and the second stage finish and \(t_{fi}\) and \(t_{li}\) as the instants where the first and last ceiling impacts are observed.  
* As displayed in Figure 5, during the first instants, a ripple generated from the free surface deformation due to the initial meniscus travels horizontally through the free surface from the lateral walls toward the tank center; see a snapshot of this traveling wave in Figure 5a (\(t=0.075\) s). This initial stage can be interpreted as a classical evolution of a Rayleigh-Taylor instability. This initial stage finishes with the first fluid-wall impact; see Figure 5b (\(t_{1}\equiv t_{fi}\sim 0.1\) s). In this instant, a four-jet symmetrical image is observed, where two central jets impact the tank ceiling while the other two after traveling up the lateral walls impact at the lateral-top corners.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Liquid** & \(\rho\) **[kg/m\({}^{3}\)]** & \(\mu\)**[Pa-s]** & \(Re=\rho\omega_{0}y_{0}H/\mu\)** & \(\sigma\) **[mN/m]** \\ \hline Water & 998 & 8.9\(\cdot 10^{-4}\) & 1.6\(\cdot 10^{5}\) & 72.7 \\ \hline Glycerine & 1262 & 1.49 & 130 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties and characteristic Reynolds number of the two fluids, water and glycerine, used in the experiments.  
* A chaotic and turbulent regime dominates during the longest part of the experiment. At this stage, the liquid and the gas are chaotically mixed up, making the two fluids difficult to distinguish. The free surface is very fragmented, and air bubbles of different sizes merge and collapse. This second stage finishes at time \(t_{2}\sim 2.84\). A typical snapshot of this regime can be seen in Figure 5c (\(t=1.43\) s).
* The last stage shows up after \(t_{2}=2.84\) s, where a mostly compact fluid is observed. The flow transitions to a standing-wave-like behavior that displays some similarities to the rotary sloshing phenomenon observed in [45,46], with very small vertical sloshing force variations. The last ceiling impact at time \(t_{li}\sim 3.56\) s is part of this stage. A typical snapshot of this regime can be observed in Figure 5d (\(t=3.31\) s).  
It can be observed that the 2D computational simulation is able to reproduce the free surface behavior when corresponding snapshots are compared; see Figure 5. Details such as the double traveling wave that initiates the free surface deformation, the four jets impacting structure and the final standing-wave are well captured by the numerical model.
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
## Methodology
### Analytic Solution  
According to References [23; 24], the solution to Equation (1) can be expressed as Fourier series:  
\[u(x,y,t)=u_{\infty}+\sum_{m=0}^{\infty}\sum_{n=0}^{\infty}\theta_{mn}(t)\sin( \alpha_{m}x)\sin(\beta_{n}y), \tag{3}\]  
with \(\alpha_{m}=(m+1)\pi/a\) and \(\beta_{n}=(n+1)\pi/b\). Each Fourier coefficient \(\theta_{mn}(t)\) is defined as:  
\[\theta_{mn}(t)=\frac{4}{ab\rho c_{p}\Delta z}\int_{0}^{t}\int_{0}^{b}\int_{0} ^{a}f(x,y,\tau)\sin(\alpha_{m}x)\sin(\beta_{n}y)e^{-\omega_{mn}(t-\tau)}dxdyd\tau, \tag{4}\]  
with Laplace eigenvalues \(\omega_{mn}\):  
\[\omega_{mn}=\frac{\kappa}{\rho c_{p}}(\alpha_{m}^{2}+\beta_{n}^{2})+\frac{h} {\rho c_{p}\Delta z}. \tag{5}\]  
Figure 1: Scheme for the laser heating problem on thin metal plates.  
Let \(\vec{C}_{1}(t),\vec{C}_{2}(t),\ldots\) be a sequence of piecewise linear sub-trajectories that discretize the complete laser trajectory (see Figure 2), that is, \(\vec{x}_{0}(t)\approx\vec{C}_{1}(t),\vec{C}_{2}(t),\ldots\). Each sub-trajectory \(\vec{C}_{i}\) (\(i>0\)) is defined as a parameterized line segment:  
\[\vec{C}_{i}(t)=\vec{x}_{0}(t_{i})\frac{t-t_{i-1}}{t_{i}-t_{i-1}}+\vec{x}_{0}(t_ {i-1})\frac{t_{i}-t}{t_{i}-t_{i-1}},\qquad t_{i-1}\leq t<t_{i}, \tag{6}\]  
where the original laser trajectory \(\vec{x}_{0}\) is sampled at \(t=t_{0},t_{1},t_{2},\ldots,T_{f}\).  
The analytic solution of the Equation (4) for the given piecewise linear discretization is presented in References [23,24].
## 4 Service Scheduling Algorithms  
This section primarily introduces the service scheduling selection algorithms involved in the aforementioned architecture. Section 4.1 provides a detailed explanation of the selection algorithm, focusing on the method of weight assignment and the evaluation approach for candidate solutions. Section 4.2 analyzes the proposed algorithm in terms of time complexity and feasibility.
## 1 Introduction
### Expanding the Bandwidth Beyond 8 kHz  
Ototoxic monitoring for patients in the clinic has traditionally been completed at conventional frequency regions (0.25-8 kHz) with higher frequencies (\(>\)8 kHz) explored when hardware and time permits. This expanded frequency bandwidth permits broader characterization of cochlear health which is particularly valuable for ototoxicity, which often affects the base of the cochlea first (corresponding to higher frequencies of human hearing). Consequently, high-frequency audiometry permits for earlier detection of damage from ototoxic agents than conventional audiometry [6]. In this paper, we leverage modern techniques of data recording, calibration, and analysis to characterize DPOAE characteristics up to 20 kHz utilizing patient case illustrations to demonstrate applications for high-frequency DPOAE assessment. The advancement of calibration techniques enabling accuratesignal delivery at all frequencies (e.g., [18; 19; 20; 21; 22]) allow the extension of these measures to the upper limit of human hearing.
## 4 Research Method
### Scientific Databases and Search Strategy  
Four online academic search engines were used to conduct the search and find the relevant papers:  
1. IEEE Xplore digital library;
2. Science direct;
3. Springer link;
4. Scopus.  
To make an automatic search on the chosen libraries, the search string consisted of the following sections:  
**Wireless, sensor, network, (deployment, deployment algorithm, or deployment optimization)**, and **smart city**.  
In the screening phase, the papers that were initially collected were filtered until only papers that answer the research questions remained. In this work, relevant papers were selected using the following inclusion and exclusion criterion.  
**Inclusion criterion:**  
* Publications related directly to the deployment of WSN sensor nodes using optimization algorithms.
* Publications dealing with enhancing or maximizing the coverage and connectivity of WSNs.
* Publications in the field of WSN deployment in smart city applications.  
Figure 10: Systematic mapping process.  
**Exclusion criterion**:  
* Papers published before 2015.
* Publications not written in the English language.
* Publications related to other types of WSN deployment methods, such as using geometric or classical deployment methods.
* Publications related to other WSN problems, such as localization, routing, data gathering, etc.  
Starting with the general search string \"WSN deployment\", the number of publications collected from IEEE Xplore digital library was 253, Science Direct was 354, SpringerLink was 621, and Scopus was 586 documents. After using inclusion and exclusion criterion and removing duplications, the total number of relevant studies was 68 documents that related directly to the deployment problem of WSN using optimization algorithms during this period.
## 3 Results and Discussion  
### FTIR Determination  
The FTIR analysis of lignin was performed to investigate the changes in chemical structure and functional groups of lignin molecules before and after ozone treatment (Figure 1). The calculation of the relative signal intensity for each group was performed by referring to the intensity in the 1426 cm\({}^{-1}\) band, as shown in Table 1. No distinct shift in the positions of the absorption peaks of lignin before and after pretreatment was observed, indicating that the ozone pretreatment did not change the main molecular structure of lignin. The characteristic absorption peaks at 1426, 1514, and 1606 cm\({}^{-1}\) are ascribed to the aromatic ring skeleton of lignin, the signal peak at 1705 cm\({}^{-1}\) is associated with the unconjugated carbonyl group (C=O), the peak at 2932 cm\({}^{-1}\) is designated as C-H stretching vibration in methoxy (-OCH\({}_{3}\)), the peaks at 1113 cm\({}^{-1}\) represent the C-H deformation inside the aromatic ring on the syringyl aromatic ring [23], and the peak at 3451 cm\({}^{-1}\) is attributed to the stretching vibration absorption of -OH including aliphatic and phenolic hydroxyl groups [24; 25; 26]. The infrared spectra showed different signal intensities for the three MWL samples, indicating that the monomer units of MWL were altered after ozone treatment. The intensity of the hydroxyl absorption peak increased with the increase in ozone pretreatment time. This could be attributed to the ozone oxidation process, which breaks ether bonds and results in the formation of additional hydroxyl groups. The small reduction in the intensity of the methoxy absorption peak may be due to the demethylation reaction of the lignin molecular chain induced by ozone, which may lead to a weakened hydrophobicity of lignin. This result presented in FTIR spectrograms needs to be further demonstrated by \({}^{31}\)P-NMR and HSQC-NMR analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Assignment** & **Wavenumber cm\({}^{-1}\)** & **MWL** & **OL60** & **OL120** \\ \hline Hydroxyl group & 3451 & 1.08 & 1.15 & 1.18 \\ C-H stretching & 2932 & 1.03 & 1.01 & 0.98 \\ Carbonyl group & 1705 & 1.06 & 1.11 & 1.12 \\ Aromatic ring & 1606 & 0.97 & 1.02 & 1.05 \\ Aromatic ring & 1514 & 0.98 & 0.97 & 0.98 \\ Aromatic ring & 1426 & 1.00 & 1.00 & 1.00 \\ Aromatic C-H & 1113 & 0.81 & 0.77 & 0.76 \\ deformation in syringyl & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proportion of typical lignin absorption-peak intensity based on the 1426 cm\({}^{-1}\) band.  
Figure 1: The FTIR spectra of lignin before and after ozone pretreatment.
## 2 Materials and Methods
### Method Validation
#### 2.7.4 Accuracy and Precision  
Accuracy and precision were determined by analyzing spiked samples (quality controls for food: 100 ng/mL, serum: 16 and 40 ng/mL and urine: 200 ng/mL) on three different days in independent batches of blank samples. The relative error (%) corresponded to the accuracy, which is calculated for each analyte, comparing the calculated mean result to the nominal concentration of the analyte. Precision was divided into within-run (intra-assay) and between-run (inter-assay). Precision and accuracy at each concentration level should not exceed 15% of the coefficient of variation (CV).
# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 5 Results and Discussion
### The Hybrid ML+DL with Sampling:
The hybrid stacking ML+DL models integrated with resampling techniques show significant advancements in cyber fraud detection by addressing class imbalance and false  
Figure 11: Performance of sampling with the hybrid ML+DL model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{With sampling} & Hybrid & 94.90\% & 99.95\% & 89.85\% & 94.63\% \\  & ML+DL+Bordersmote & 93.36\% & 99.96\% & 86.75\% & 92.89\% \\ \cline{2-6}  & Hybrid ML+DL+Tomek & 99.95\% & 1.0 & 71.43\% & 83.33\% \\ \cline{2-6}  & Hybrid ML+DL+ENN & 99.96\% & 98.70\% & 77.55\% & 86.86\% \\ \cline{2-6}  & Hybrid ML+DL+SMOTEEEN & 94.24\% & 99.92\% & 88.56\% & 93.90\% \\ \hline Without sampling & Hybrid ML+DL & 99.97\% & 97.62\% & 83.67\% & 90.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of resampling techniques. Figure 10: F1 score of sampling with the novel hybrid ML+DL model. positives while achieving strong accuracy and F1 scores. Each model balances strengths and trade-offs to suit different scenarios. The Borderline-SMOTE model achieved an F1 score of 94.63% with high precision (99.95%) and recall (89.85%), balancing fraud detection and minimising false positives. ROS yielded an F1 score of 92.89%, precision of 99.96%, and recall of 86.75%, providing robust performance with slightly lower recall than Borderline-SMOTE. Tomek Links ensured no false positives with 100% precision and 99.95% accuracy but had a lower recall (71.43%), leading to an F1 score of 83.33%. ENN delivered an F1 score of 86.86% with 98.70% precision and 77.55% recall, reflecting reliable performance but with room to improve recall. SMOTEENN achieved an F1 score of 93.90% with precision of 99.93% and recall of 88.56%, offering a strong balance between precision and recall. Overall, Borderline-SMOTE and SMOTEENN excel, highlighting the effectiveness of advanced resampling techniques in enhancing detection accuracy and robustness.
# Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics
## 2 Dots and Boxes  
To carry out this work, we have chosen as a case study a perfect-information game. Perfect-information games are characterized by the fact that every player has access to all information about the game, such as the position of the board or any configuration depending on the game. Examples of these games are Chess and Tic-tac-toe. A player who follows a strategy better than the others is more likely to win. In addition, there are no random variables which may influence the result [26].  
We focus on the perfect-information game Dots and Boxes. It is a combinatorial strategy game for two players popularized between children and adults. The first reference we can find about this game comes from the 19 th century in _L'Aritmetique amusante_ written by the mathematician [PERSON], who called the game \"la pipopipette\" [27]. The aim is to reach the end of the game with more closed boxes than the other player.  
The Dots and Boxes game is a combinatorial game that represents the group of problems to which MCTS has been most often applied. Combinatorial games have the following properties:  
* Two players. There are typically only considered two players, although may not be the case.
* Zero-sum. The gain or loss of a player is exactly balanced by the loss or gain of the other player.
* Perfect information. The state of the game is fully observable to all players.
* Deterministic. There is no randomness in the development of future states.
* Sequential. Players move sequentially in turns.
* Finite. The number of movements must be always finite.  
The game board consists of a set of dots displayed as a \(m\times n\) size matrix. In each turn, the player must link two consecutive dots with a horizontal or vertical line. The strategy to follow is trying to close boxes and trying to avoid the opponent to close his own boxes. To close a box, it must have already three sides, with only one empty side left where the player will place the corresponding line. The turns are alternate, but after closing one box, the player must link another two dots. Movements can be concatenated until there is no possibility of closing any box. When a box is closed, a different mark for each player is written inside. At the end of the game, the score is computed by adding the marks, being the winner the player with the maximum score.  
Figure 1 shows the trace of a game played on a \(3\times 3\) board. \(A\) corresponds to the boxes closed by P1 (Player 1) and \(B\) to the ones closed by P2 (Player 2). \(M\)\(X\) corresponds to move number _Move X_. Each square represents the set of moves made by a player before change turn. We can observe that the main strategy followed is to avoid leaving boxes with three sides that could be closed in the next turn by the opponent. Moreover, when there is no other option, the player must minimize the number of closed boxes that the opponent could concatenate in her turn.
## 3 Results  
The detectability rates of the four lesion types differed independently of the radiation dose (Figure 2). Nearly 71% of lesions were correctly detected (LD = 70.8%; SD = 70.9%).  
The PL was detected correctly by the examiners in 62% of the cases (LD = 62.7%; SD = 61.3%); EP, 60% (LD = 60.4%; SD = 59.4%); BL, 79% (LD = 79.2%; SD = 78.8%); and SF, 83% (LD = 80.8%; SD = 84.2%). These results constitute descriptive values derived from a total of 480 observations per lesion type.  
As calculated with the estimated marginal means method, predictive values for each of the four lesion types did not differ significantly between lesion detectability with the low-dose or standard-dose protocols (\(p=0.9393\)). Differences in predictive values between the detection of the BL, SF, PL, and EP differed significantly (\(p\leq 0.0001\); Table 2).  
Figure 2: Detectability rates of the four different lesions (percentage).  
The dose-modes also differed little in terms of visibility (Figure 3): PL featured average low-dose and standard-dose visibilities of 6.9 and 7.0, respectively; EP, 7.4 and 7.5; BL, 8.5 and 8.8; and SF, 9.1 for both.
## 4 Experimental Results and Discussion
### Jitter  
The comparison of jitter for the proposed technique and existing approaches is shown in Figure 11. The E-CERP algorithm obtained a low jitter value of 0.15 ms for 100 nodes. In contrast, the conventional algorithms, cross-layer-based adaptive threshold and cross-layer fuzzy logic approach, attained 0.25 ms and 0.39 ms, respectively. For 200 nodes, the jitter value of the proposed method was 0.12 ms; however, the conventional algorithms attained 0.20 ms and 0.34 ms, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{5}{c}{**PDR**} & \multicolumn{5}{c}{**PLR**} \\ \hline
**Number of Nodes** & **100** & **200** & **300** & **400** & **500** & **100** & **200** & **300** & **400** & **500** \\ \hline Proposed CERP & 100 & 100 & 98 & 96 & 96 & 0 & 0 & 2.03 & 3.51 & 4 \\ \hline CLAT & 97 & 96 & 96 & 95 & 94 & 2.57 & 3.87 & 4.44 & 5.26 & 5.83 \\ \hline CLFL & 95 & 91 & 90 & 90 & 88 & 4.53 & 8.78 & 9.76 & 10.42 & 11.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of PDR and PLR.  
Figure 10: Comparison of the packet loss ratio.  
For 300 nodes, the jitter value obtained by the E-CERP algorithm was 0.10 ms, and the conventional algorithms attained 0.17 ms and 0.24 ms, respectively. For 500 nodes, the jitter value of the suggested approach was 0.05 ms. However, the existing approaches, cross-layer-based adaptive threshold and cross-layer fuzzy logic technique, attained 0.10 ms and 0.15 ms, respectively. As a result, by evaluating the performance of cross-layer-based adaptive threshold, cross-layer fuzzy logic, K-means, and Fuzzy C-means approaches, the introduced E-CERP algorithm performances are resolved based on the analysis of QoS parameters. Based on the simulation results, the power consumed by the network is low and the network lifetime is high for the proposed method.
## 2 Materials and Methods
### Simple Linear Regression  
Expressing the relationship between a single independent variable \(x\) and the dependent variable \(Y\) with a linear function is defined as simple linear regression analysis [43].  
\[Y_{i}=a+bx_{i}+\varepsilon_{i} \tag{3}\]  
The value of \(a\) represents the dependent variable \(Y\) when \(x=0\), which can also be understood as the point where the vertical axis intersects with \(Y\). On the other hand, \(b\) denotes the regression coefficient in the model. The error \((\varepsilon)\) is the difference between the dependent variable's actual value \(Y\) and its predicted value \(Y\) based on the model.  
\[\varepsilon=Y_{i}-(a+bx_{i})=Y_{i}-\hat{Y}_{i} \tag{4}\]  
Figure 3: Power–frequency variation for GNSS stations with different ellipsoidal heights.  
In regression analysis, we aim to estimate the independent variable \(Y\) by considering the dependent \(x\) variables. However, the results obtained from these estimations often require further refinement or correction. The key question we need to address is the extent of inaccuracy in our estimates. In other words, we must determine the gap between actual and predicted values. The following concepts are used to evaluate the regression model [44].
# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
###### Abstract  
Mode 4 of 3 GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (\(P_{rk}\)) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed \(P_{rk}\) value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal \(P_{rk}\) value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable \(P_{rk}\). The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.  
C-V2X; mode 4; PDR; resource keep probability; CSI; Fuzzy Inference System +
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