esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology  
### Geographical Setting  
The Caribbean is defined using the area 10\({}^{\circ}\) N-25\({}^{\circ}\) N and 59\({}^{\circ}\) W-85\({}^{\circ}\) W (Figure 1). There is large variability in the depth of the Caribbean Sea within the basin. For example, there are relatively shallow areas around islands such as the Jamaica Ridge [34] versus the Cayman trench, which is beyond 7600 m deep, located in the north Caribbean [35]. There are no studies we are aware of that quantify the impact of vertical land motion on sea level in the wider Caribbean Basin [36]; however, some studies have indicated an impact of vertical land motion on relative sea level rise in the Colombia Caribbean area in the southern Caribbean basin [36; 37].  
Jamaica is located at 18.11\({}^{\circ}\) N, 77.30\({}^{\circ}\) W in the northwestern Caribbean. The study area surrounding Kingston and Portmore is on the southeast coast of Jamaica. While there is significant evidence of past uplift in Jamaica (especially on the north coast, which is closer to the edge of the Caribbean plate [38; 39; 40]); the vertical land motion in Jamaica is on the order of less than 1 m/ka over the last 4000 years and, as such, is considered insignificant when considering sea level variability [23; 41].
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Security Requirements
#### 2.2.3 Unbinding Problem  
This problem occurs whenever a watermarking protocol does not implement mechanisms to uniquely bind a given watermark identifying a buyer to both the purchased digital content and the corresponding purchase transaction. In this case, a dishonest content provider can frame the buyer by transplanting his/her watermark into a copy of higher priced digital content that the buyer never bought in order to obtain a compensatory payment. A similar situation may also occur when a content provider can, more simply, know the watermark embedded in a digital content purchased by a buyer. This fact enables the content provider to insert the same watermark into further copies of the content bought by the buyer so as to unjustly accuse him/her of illegal distribution [8].
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 3 Results and Discussion
### Displacements  
Figure 20 shows the deformed mesh of the analysis at the end of shaking. An enlargement around the quay wall is scaled up by five times to easily identify displacements. It can be seen that the top of the quay wall moved horizontally approximately 20 cm toward the ocean and settled nearly 3 cm from its starting point, which is in agreement with the observations where the wall displacements are reported to be 5-15 cm horizontally and less than 10 cm vertically [11]. It follows from this figure that the deformation of the foundation (i.e., riprap layer) leads to displacements of the quay wall. The slippage in the interface beneath the quay wall may not be the reason for the quay wall displacements, and it is mainly due to deformation in the foundation. A detail displacement description of the observed nodes (set in Figure 15) is shown in Figure 21. It can be seen that the horizontal displacements of nodes N1 and N2 are quite similar, which are around 20 cm. However, the settlement of node N2 is slightly higher than that of node N1. The settlement predictions of nodes N3 and N4 are approximately 8 and 4 cm, respectively. When nodes N1, N2, and N3 move horizontally toward the ocean (approximately 20 cm for nodes N1 and N2 and 8 cm for node N3), node N4 deforms to the right side of the model by around 2 cm. It is noticed that the displacements of soil behind the steel pipe piles (represented by node N4) are reduced compared to those of the soil in front of the row of steel piles (represented by nodes N1, N2, and N3).  
Figure 19: Excess pore pressure ratio generated during shaking (\(R_{\text{u,seis}}\)).  
Figure 18: Maximum excess pore water pressure ratio (\(R_{\text{u,seis}}=1.0-\sigma^{\prime}y/\sigma^{\prime}y_{\text{,i}}\)) contours at the end of shaking (dimension unit in meters).
# Two-Stage Configuration of User-Side Hybrid Energy Storage Based on Fuzzy Optimization
## 4 Case Study
### Optimal Allocation of SC  
1. Input the PV output data, load data, and SC price data, calculate the proportion of PV output in the total load output and SC charging and discharging behavior after MF, and obtain the minimum investment cost C01 of SC, the maximum investment cost \(f1 max\) of SC, and the high-frequency component strength after filtering.
2. Input the PV output data, load data and SC price data; calculate the PV output spectrum after MF and the original spectrum of PV output; and obtain the minimum allowable high-frequency ratio C02 of PV output, the maximum high-frequency ratio \(f2 max\) of PV output, and the corresponding SC investment.
3. According to \(D01=f1 max-C01\) and \(D02=f2 max-C02\), substitute \(D01\) and \(D02\) into formulas (25) and (26) to obtain the membership functions, and the multi-objective optimization problems of users and power grids can be transformed into single-objective optimization. Satisfaction can be obtained using \(GUROB1\) as Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Index** & **Battery** & **Super Capacity** \\ \hline Power Cost Coefficient (CNY/kW) & 1500 & 1500 \\ Capacity Cost Coefficient (CNY/kWh) & 1000 & 27000 \\ Operation Cost Coefficient (CNY/kW) & 0.05 & 0.05 \\ Charging and discharging efficiency & 0.85 & 0.95 \\ Range of SOC & \((0.2,0.8)\) & \((0.1,0.9)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative parameters of the energy storage system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification Parameter** & **Numerical Value** \\ \hline Capacity (F) & 10 \\ Rated voltage (V) & 5 \\ Rated current (A) & 40 \\ Rated power (kW) & 0.2 \\ Rated capacity (kWh) & 0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected supercapacitor parameters.  
Because the non-working day load has no peak and does not require energy storage regulation, the number of energy storage charging and discharging cycles throughout the year is greatly reduced. With that load characteristic, the average energy storage only needs 377 cycles in 2017, which achieves the target of minimizing the number of charge and discharge cycles so that the energy storage can be used for 10.61 years in total.
## 4 Results
### PID--Simulated Results
The initial controller coefficients \(K_{p}\), \(K_{i}\), and \(K_{d}\) were manually adjusted to obtain a stable response and define a reasonable starting point for the optimization phase. These preliminary values were then refined using the _particleswarm_ function in _MATLAB R2022b, MathWorks, Natick, MA, USA_, which was employed to simultaneously minimize the final position errors across the four different scenarios with respect to their respective targets. Specifically, the objective function was defined as the total IAE (Integral Absolute Error), computed by summing the IAEs (each one discretized as \(\sum_{i=1}^{n}(e_{x_{i}/y_{i}/z_{i}})\)) over all four scenarios [25; 26]. Following preliminary tests, the _particleswarm_ method was preferred over _fmincon_[25] due to its higher efficiency in finding globally optimal solutions, as also discussed in [27] for another smart handling system. To ensure feasibility in terms of computation time, the optimization was performed with a swarm size of 20 and 20 iterations. The parameter bounds were restricted within reasonable physical limits, with each of the controller gains \(K_{p}\), \(K_{i}\), and \(K_{d}\) limited to values between 0 and 100. Furthermore, simplified control strategies were tested by zeroing out specific gain matrices. Setting \(K_{i}=0\) led to a PD controller, while setting \(K_{d}=0\) resulted in a PI controller. These configurations produced inferior performance in terms of final error and stability compared to the full PID controller, which was ultimately selected and reported in this analysis. The optimized PID gains used in the simulations (with a maximum duration of 7.5 s) are as follows:  
\[K_{p}= \begin{bmatrix}0.2957&0&0\\ 0&0.3950&0\\ 0&0&0.1827\end{bmatrix} \quad K_{i}= \begin{bmatrix}0.0059&0&0\\ 0&0.0088&0\\ 0&0&0.0094\end{bmatrix} \quad K_{d}= \begin{bmatrix}0.0532&0&0\\ 0&0.0502&0\\ 0&0&0.1278\end{bmatrix} \]  
Although some oscillatory behavior remains due to the inherent under-actuation and instability of the system (see Section 3.2), the results are acceptable for the intended applications and robust across different layouts, materials, and inputs. Future studies may further refine these parameters for specific operating conditions. In summary, the control system effectively finished the tasks in compliance with the company's and industry's requirements. Consequently, the solution will be taken into consideration for a future real implementation. \begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{St_{x}}\) **[s]** & \(\mathbf{St_{y}}\) **[s]** & \(\mathbf{St_{\theta}}\) **[s]** \\ \hline \(P_{1}1\) & 7.176 & 7.134 & 6.931 \\ \hline \(P_{1}2\) & 7.228 & 7.194 & 7.255 \\ \hline \(P_{1}3\) & 7.135 & 7.086 & 7.133 \\ \hline \(P_{1}4\) & 7.080 & 7.079 & 6.842 \\ \hline \end{tabular}
\end{table} 
Table 3: Settling time (\(St\)) with 2% threshold for the two displacements \(x\), \(y\) and the orientation \(\theta\) in the four outputs cases of PATH1. Figure 12: PID control PATH 1 simulation outcome [22].
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 3 Proposed Binary Competitive Swarm Optimizer
### Proposed Binary CSO  
The original CSO algorithm has been applied to many practical real-valued optimization problems. However, there are also numerous large-scale discrete problems remaining to be solved such as UC. In this section, a novel binary CSO in proposed. Inspired by evolutionary logic and discrete PSO algorithm, the binary decision variables are determined by a transfer function from the updated velocity. To improve the efficiency of the evolution, a V-shape transfer function (10) is adopted. The detailed transfer function is defined as follows:  
\[S(V_{i,j})=2\cdot|\frac{1}{(1+e^{(-V_{i,j})})}-0.5| \tag{10}\]where the result of this function \(S(V_{i,j})\) is a proportional value, it decides the value of \(X_{i}(t)\) according to (11). The velocity \(V_{i,j}\) will be limited to a certain range after the initialization. In this paper, the velocity is limited to [4; 5; 6].  
\[x_{i,j}=\left\{\begin{array}{ll}1,&\quad if\quad rand<S(V_{i,j})\\ 0,&\quad otherwise\end{array}\right\} \tag{11}\]  
In Equation (11) _rand_ is a number generated random, the range of it is \([0,1]\). If the \(S(V_{i,j})\) greater than _rand_, the particle is 0. The novel BCSO method will be adopted in determining the binary status of UC problem and the detailed procedure will be illustrated in the next section.
## 4 Results and Discussion
### First Part: Factorial Design Multivariate Statistical Analysis
#### 4.1.3 Interaction Plot Analysis in Terms of Air Bubble Area  
Aside from the Pareto chart and regression analysis of the factor used, the interaction between these factors helps to explain the output response better. The interaction plot (Figure 5) illustrates the interaction effect of these factors. The interaction effect analysis plot of 'back pressure versus screw speed' on the air bubble area reveals that air bubble area enlargement is highly reliant on the screw speed and the back pressure level. The plot shows that a higher screw speed level in a low back pressure range produces more air bubbles and a higher back pressure level can substantially suppress the air bubble area. Therefore, back pressure with a low screw speed can be used as one of the plasticization parameters to regulate the air bubble area.  
The'melt temperature and back pressure' interaction plot shows that when the temperature is high, the air bubble area formed is significantly dependent on the back pressure level, where a low back pressure level produces more air bubble area, but when high back pressure is imposed at a low temperature, the air bubble area would seem to be reduced. Similarly, when the melt temperature versus screw speed interaction is analyzed, it also seems that at high temperatures, high screw speed levels have some effect on the formation of the air bubble area, but when the temperature is reduced, the screw speed level has no effect on the generation of air bubble area. Thus, the interaction chart illustrates that the melt temperature plays a significant role in controlling the air bubble area and that it is possible to regulate the formation of the air bubble area at low temperature with low screw speed and high back pressure.  
Figure 5: Interaction plot analysis in terms of air bubble area.
# Method for Volume of Irregular Shape Pellets Estimation Using 2D Imaging Measurement
## 3 Results
### Evaluation of Shape of Pellets  
Depending on the chemical composition of fertilizers, they can be characterized by anisotropic shape. Therefore, ordinary methods of pellet volume by approximation using different geometric shapes introduces discrepancies. For this reason, with any method of pellet volume evaluation, the product's distribution according to roundness should be known. The distribution of roundness of monoammonium phosphate pellets according to ratio between diagonals of area limited by pellet is provided in Figure 5. During the measurement, 100 different samples taken during the production were analyzed. The graph shows the average of the measurements, and standard squared deviation of measurement \(<4\%\).  
Figure 4: The ellipse model.  
After evaluating the results of measuring of roundness of the studied pellets, the assumption can be made that irregularly shaped, elongated pellets are most common throughout all production. Since techniques of processing two-dimensional images cannot take the depth of the image into account, the diameters of pellets, perpendicular to their longitudinal axis, must be thoroughly evaluated. During the examination of shape evaluation, \(\sim\)1000 pellets were analyzed. A quantity of 10-20 pellets were randomly chosen from each roundness interval of different samples. Every pellet was scanned twice--an image of the largest area limited by the pellet, and an image of the pellet rotated by 90\({}^{\circ}\) (principle of pellet evaluation is presented in Figure 6). Results of pellet measuring are provided in Figure 7.  
Results received from analysis of monoammonium phosphate pellets indicate that discrepancy of shape of pellets increases with increasing size of a pellet. However, \(\leq\) 95% of all pellets of the material with which we are working are distributed in the interval from 0.2 mm to 1.0 mm. Within these boundaries, roundness of pellets according to ration of diagonals is distributed within boundaries from 0.4 to 0.8. The discrepancy among areas limited by different contours produced according to longitudinal axis that is in the mentioned intervals is \(<\) 3%. Because of the contamination of equipment, pellets tend to become smaller during production and it is sufficient to evaluate their shape from one position.  
Figure 5: Distribution of monoammonium pellets according to roundness (ratio of diagonals of area limited by pellet’s silhouette).  
Figure 6: (**a**) The pellet is scanned using a 90\({}^{\circ}\) angle between the camera’s field of view and the longitudinal axis of the pellet, when we receive the largest area limited by the pellet and image rotated by 90\({}^{\circ}\); (**b**) evaluation of discrepancy of pellet overlapping area according to averages of areas.
## 4 Discussions
### ElectrochemicalResults  
#### 4.6.1 Polarizations  
The values reported in Table 3 showed results in agreement with what was obtained by the weight loss method: Corrosion current densities decrease when the concentration of the inhibitor increases. Maximum value of inhibition efficiency of 91% was obtained for inhibitor concentration of 300 mg L\({}^{-1}\).  
The obtained polarization curves indicate that the addition of the bio-copolymer influences the kinetics (decrease in reaction rate) but does not modify the mechanism of the cathodic process [22].  
From a thermodynamic point of view, at the acidic pH (1 MHCI) and E\({}_{\text{corr}}\) (ca. \(-450\) mV vs. SCE) of the investigated system, both the oxygen reduction reaction and hydrogen evolution reaction could potentially take place as the iron Pourbaix diagram states [23].  
As for the kinetic aspects related to the cathodic Tafel slope calculation, it is apparent that slope values are markedly different from the theoretical ones expected in the case of pure hydrogen evolution reaction. Tafel slope is in fact known to be 118, 39, or 29.5 mV/decade when the discharge reaction, electrochemical desorption reaction, or recombination reaction is rate-determining, respectively [44]. This might mean that more than one reaction is taking place in the considered potential range.  
However, it is shown in Table 3 that Tafel slopes do not remarkably change with addition of different concentrations of inhibitor, thus confirming the hypothesis that the inhibitor does not significantly change the cathodic reaction mechanisms. Therefore, the use of the polarization curves as a tool for deriving quantitative parameters, such as inhibition efficiency, can be justified since it is based on relative calculations (using current densities values in the presence and absence of inhibitor) and does not imply an absolute evaluation.  
Considering the anodic reaction, C-Mn steel corrodes in acid medium according to an electrochemical process by anodic dissolution of iron. This phenomenon might be accentuated if other parameters intervene, such as microbiological activity or mechanical stresses. The almost constant value of \(E_{corr}\) at a first sight might give an indication that the inhibitor is of mixed type acting by adsorption mechanism. However, the cathodic branches trend, due to inhibitor concentration increase, and the overlap of all the anodic branches points to the inhibitor being a cathodic one. This is also in agreement with results obtained at different temperatures. In fact, data showed that the corrosion inhibition mechanism might be more complex than a simple physisorption process on the steel surface.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 5 Evaluation Metrics  
The character-error rate [34] and word-error rate [35] are the metrics used to evaluate the accuracy of OCR systems. The intuition of how they are calculated is simply based on Levenshtein Distance, which calculates the minimum required number of edits for two strings to be the same, whether as words or as characters. It considers three types of error: (1) insertion errors: the mistaken addition of characters or words; (2) deletion errors: the mistaken absence of characters or words; (3) substitution errors: characters or words with errors in their spelling.  
The character-error rate can be computed as \(\text{CER}=(\text{S}+\text{D}+\text{I})/\text{N}\), which is equivalent to \((\text{S}+\text{D}+\text{I})/(\text{S}+\text{D}+\text{C})\), where I is the number of insertions, D is the number of deletions, S is the number of substitutions, C is the number of correct characters, and N is the number of characters in the reference \((\text{N}=\text{S}+\text{D}+\text{C})\).  
The word-error rate can be computed as \(\text{WER}=(\text{S}+\text{D}+\text{I})/\text{N}\), which is equivalent to \((\text{S}+\text{D}+\text{I})/(\text{S}+\text{D}+\text{C})\), where I is the number of insertions, D is the number of deletions, S is the number of substitutions, C is the number of correct words, and N is the number of words in the reference \((\text{N}=\text{S}+\text{D}+\text{C})\).
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 7 Conclusions  
In this paper, we formulate the beam training process as a finite-space search problem. To solve the repeated search problem in the previous research caused by round to the near integer, a search algorithm based on tabu table is proposed in order to overcome the shortcomings of existing search algorithms. In addition, we also discuss the boundary problem of the beam index and the neighborhood structure of the tabu algorithm. As the proposed algorithm is a combination of the RA and SA, and further enhanced by TS, so it can implicitly approach the function gradient, jump out of the local optimum, and avoid the performance of repeated searches. Simulations prove that it can improve the probability of successful search. In the future, we will address the impact of the initial value on the search performance, which is important to beam training.  
**Author Contributions:** Conceptualization, X.L.; Data curation, X.L.; Formal analysis, X.L.; Resources, C.S.; Supervision, C.S. and F.J.; Visualization, X.L.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 61801382, 61871321), National Science and Technology Major Project of the Ministry of Science and Technology of China (project number: ZX201703001012-005). Key Project of Natural Science Foundation of Shaanxi Province (project number: 2019Z-06). Key Industrial Chain Project of Shaanxi Province (project number: 2019 ZDLGY07-06).  
**Acknowledgments:** The authors thank the fund of National Natural Science Foundation of China (No.61801382, 61871321) and the fund of National Science and Technology Major Project of the Ministry of Science and Technology of China (project number: ZX201703001012-005) for covering the costs to publish in open access and the costs when writing this study. Besides, the authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Search performance of the Rosenbrock algorithm (RA) and the new numerical algorithm (RA + SA + TS).