esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of Numerical Model
### Boundary Conditions  
To accurately reflect pipeline leakage, this study sets the inlet as a velocity inlet oriented perpendicular to the upper boundary, and the outlet as a pressure outlet oriented perpendicular to the lower boundary. The upper boundary is treated as a free boundary, while the lower boundary is configured as a composite boundary featuring a rectangular aperture measuring 2 m in length and 0.9 m in width, flanked by solid walls on its left and right sides. Additionally, the front, back, left, and right boundaries of the model are all defined as solid walls to constrain the fluid and solid regions.  
The upper boundary is utilized to simulate the surface environment, while the lower boundary is designed to investigate the impact of underground pipeline damage on ground subsidence. Since this work primarily focuses on the effects of pipeline breakage without  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Types** & **Effective** & **Stiffness** & **Coefficient** & **Tensile** & **Compressive** & **Particle** & **Particle** & **Volume** \\  & **Modulus** & **Ratio** & **of Friction** & **Strength** & **Strength** & **Number** & **Fraction** \\ \hline silty clay & \(3.5\times 10^{6}\) & 1.2 & 0.3 & \(3.0\times 10^{3}\) & \(2.2\times 10^{3}\) & 19,860 & 96.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mesoscopic calibration results of the model.  
considering pipeline deformation, the lower boundary is modeled with solid walls to represent a typical pipeline boundary. The rectangular aperture in the lower boundary is employed to simulate a pressure-less environment within the pipeline.  
This configuration will have certain impacts on the results, primarily manifested in the following aspects:  
(1) Constrained Flow Field Distribution: The rectangular aperture condition at the lower boundary simulates a pressure-less environment within the pipeline, yet it constrains the distribution of the flow field, preventing a complete representation of the fluid's flow characteristics near the damaged pipeline, especially in proximity to the boundaries.  
(2) Pipeline Deformation Not Considered: This omission leads to the simulation results of soil erosion and sediment transport not including the effects caused by pipeline deformation, which is a significant factor in real-world scenarios.  
(3) Boundary Reflection Effects: Setting the front, back, left, and right boundaries as solid walls serves to confine the fluid and solid regions, but it also introduces boundary reflection effects, impacting the evolution and distribution of the flow field.  
Despite these influences, the research outcomes still contribute significantly to the understanding of soil deformation settlement and the development of soil particle migration processes.
## 6 Discussion  
### The Rationality of the Full Observation  
This study introduces an innovative spatio-temporal variance rescaling strategy (DRL-AST) grounded in a deep reinforcement learning framework, aiming to enhance the applicability and performance of background error variances within VarDA systems. To highlight the pivotal role of background error variances in assimilation analysis, numerical experiments were conducted under idealized full observation conditions, where all variables were observed at every location and time step. In this configuration, background error variances were shown to exert a decisive influence on the quality of the analysis state while effectively mitigating the impact of inter-variable correlations on the results.  
However, full observation is often impractical in real-world applications, where observational data are typically sparse and noisy. Consequently, future research should prioritize optimizing the background error covariance matrix (**B**-matrix) under sparse observation scenarios. This endeavor requires not only the adaptive optimization of variances but also the careful consideration of inter-variable correlations. For instance, the iterative update of correlation functions employed in the CUTE-ST method offers a promising approach for comprehensive adjustments of the **B**-matrix.  
Achieving these optimization goals necessitates the development of more efficient algorithms and thorough validation via numerical experiments. Such advancements are essential for extending the generality and robustness of deep-reinforcement-learning-based assimilation methods, facilitating their application in operational settings.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 4 Proposed Framework for Predictive Maintenance
### Sequence Prediction Model
This modification ensures the accuracy and coherence of the predicted outputs, further exemplifying the overall effectiveness of transformer architecture in processing sequences. Summarizing the above process, this paper transformed the detailed process of sequence prediction into a mathematical form, as shown in Equation (15). \[O_{sqm}=\text{Decoder}[\text{Encoder}(I_{enc});I_{dec}] \tag{15}\]  
where \(\text{Encoder}()\) and \(\text{Decoder}()\) are structured as shown in Figure 3, and their details and included functions were described in the state awareness model. The input to the encoder is indicated by \(I_{enc}\), while the output to the decoder is indicated by \(O_{sqm}\). **(3) Linear Layer**  
Since the dimensionality of the decoder's output does not match the dimensionality of the sequence data, the authors need to add a linear layer to reduce the dimensionality and transform the dimensionality to \(d_{seq}\), as shown in Equation (9).
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 4 Experimental Results and Analyses
### Objective Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Detection Method**} & \multicolumn{3}{c}{**QCIF**} & \multicolumn{3}{c}{**CIF**} & \multicolumn{3}{c}{**720P**} & \multicolumn{3}{c}{**1080P**} & \multicolumn{3}{c}{**Total**} \\ \cline{2-13}  & **NS** & **PS** & \(\Delta\) & **NS** & **PS** & \(\Delta\) & **NS** & **PS** & \(\Delta\) & **NS** & **PS** & \(\Delta\) & **NS** & **PS** & \(\Delta\) \\ \hline Residual detection & 6.76 & 17.24 & 0.61 & 7.67 & 19.88 & 0.61 & 10.04 & 29.58 & 0.66 & 11.73 & 23.88 & 0.51 & 8.63 & 21.98 & 0.61 \\ Similarity detection & 7.36 & 21.91 & 0.66 & 7.63 & 24.31 & 0.69 & 10.17 & 29.63 & 0.66 & 11.90 & 26.30 & 0.55 & 8.79 & 25.23 & 0.65 \\ Noise-level detection & 2.42 & 27.32 & 0.91 & 2.45 & 30.17 & 0.92 & 2.93 & 28.32 & 0.89 & 2.92 & 27.95 & 0.889 & 2.62 & 28.85 & 0.91 \\ \hline \hline \end{tabular}  
* Denote \(R_{1}\) by the average PMR on NS and \(R_{2}\) by the average PMR on PS, and the relative difference between \(R_{1}\) and \(R_{2}\) is \(\Delta=1-R_{1}/R_{2}\).  
\end{table} 
Table 1: Average peak-mean ratio (PMR) values of amplitude spectrums for different detection methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Detection Method**} & \multicolumn{3}{c}{**QCIF**} & \multicolumn{3}{c}{**CIF**} & \multicolumn{3}{c}{**720P**} & \multicolumn{3}{c}{**1080P**} & \multicolumn{3}{c}{**Total**} \\ \cline{2-13}  & **FNR** & **FFR** & **DA** & **FNR** & **FPR** & **DA** & **FNR** & **FFR** & **DA** & **FNR** & **FFR** & **DA** & **FNR** & **FFR** & **DA** \\ \hline Residual detection & 0.70 & 0.10 & 0.60 & 0.71 & 0.00 & 0.64 & 0.90 & 0.00 & 0.55 & 1.00 & 0.00 & 0.50 & 0.80 & 0.02 & 0.59 \\ Similarity detection & 0.70 & 0.00 & 0.65 & 0.67 & 0.00 & 0.67 & 0.80 & 0.00 & 0.60 & 1.00 & 0.00 & 0.50 & 0.76 & 0.00 & 0.62 \\ Noise-level detection & 0.00 & 0.00 & 1.00 & 0.00 & 0.00 & 1.00 & 0.00 & 0.00 & 1.00 & 0.25 & 0.00 & 0.88 & 0.04 & 0.00 & 0.98 \\ \hline \hline \end{tabular}  
\end{table} 
Table 2: FNR, FPR and DA of different detection methods.
## 3 Proposed Method
### Multimodal Decision-Level Fusion Approach
#### 3.3.3 Model Fusion Strategies Based on Evidence Theory  
Fingermark liveness detection accuracy is influenced by various factors, such as the raw material type of the fingermarks and the liveness detection methods employed. Additionally, the accuracy can be affected by the use of models with different classification strategies. This paper argues that network models with different classification strategies have their own strengths and that there is a degree of complementarity between them. The accuracy of forgery detection can be further improved through the method of model fusion. Evidence theory provides a powerful tool for characterizing and fusing uncertain information for decision making. Model fusion strategies based on evidence theory are designed and used to coordinate the complementarities between different models to achieve optimal results. The experiments in this paper only address the fusion of two models.  
For a problem, all possible outcomes constitute a discriminative framework denoted by \(\theta\), where \(\theta\) is a non-empty set. The problem in this paper is fingermark liveness discrimination, so \(\theta=\{A,B\}\), where \(A\) is true and \(B\) is false. Its power set is \(2^{\theta}=\{\emptyset,\{A\},\{B\},\{A,B\}\}\).  
Another important concept in the theory of evidence is the basic probability assignment (BPA), whose mass function \(m\) satisfies the following condition, as shown in Equation (7):  
\[\left\{\begin{aligned} & m(A)\geq 0,\text{for all }A\subseteq 2^{ \theta}\\ & m(\varnothing)=m(A,B)=0\\ &\sum\limits_{A\subseteq 2^{\theta}}m(A)=1\end{aligned}\right. \tag{7}\]  
where \(m(A)\) denotes the extent to which the evidence supports Proposition A. There are two mass functions, \(m1\) and \(m2\), in this paper. If \(m(A)!=0\), then \(A\) is said to be a focal element. Assuming that the focal elements are \(\{A_{1},A_{2},\ldots,A_{n}\}\) and \(\{B_{1},B_{2},\ldots,B_{n}\}\), in each decision, the two models are two sources of evidence, and the confidence output of their dichotomization is the evidence. In this paper, uncertainty weights are introduced to synthesize \(m_{1}\) and \(m_{2}\) into a new probability assignment function \(m_{12}\) according to the Dempster-Shafer combination rule, whose synthesis rule is shown in Equation (8):  
\[\begin{aligned} & m_{1}\oplus m_{2}=m_{12}(A)=\\ &\frac{1}{1-K}\sum\limits_{A_{i}\cap B_{i}=A}\alpha\cdot m_{1}(A_ {i})\cdot\beta\cdot m_{2}(B_{i});\\ & A\
eq\varnothing,K=m_{1}(A)m_{2}(A)+m_{1}(B)m_{2}(B)\end{aligned} \tag{8}\]where \(\alpha\) and \(\beta\) represent the uncertainty weights of the two models, while K is the conflict coefficient, which represents the degree of conflict between the evidence, and the size of K is positively correlated with the degree of conflict. After the BPA calculation, the joint support \(\text{m}_{12}(\text{A})\) and \(\text{m}_{12}(\text{B})\) of the two models for the classification results can be obtained. If \(\text{m}_{12}(\text{A})>\text{m}_{12}(\text{B})\), the input image decision can then be categorized as a real fingermark, or otherwise a fake one.
## 3. Numerical Examples  
### Building Models  
In this paper, a numerical example is considered, as shown in Figure 4, with three two-dimensional steel moment resisting frames (SMRFs) to estimate the accuracy of the simplified method. The SMRFs are designed to satisfy the current seismic requirements in Japan in terms of both strength and drift.  
Figure 4a shows the SMRF with three ways of 6.0 m and story heights of 4.0 m. Steel sections of H-350\(\times\)350\(\times\)12\(\times\)19 and H-450\(\times\)200\(\times\)9\(\times\)14 are used for all the columns and all the beams, respectively. The yield strength of the steel material and Young's modulus are assumed to be 235 N/mm\({}^{2}\) and 205,000 N/mm\({}^{2}\), respectively. The mass of each story is 70 tons. According to EVA, the fundamental natural period corresponds to 0.758 s. Additionally, NTHA is performed using the nonlinear dynamic  
Figure 4: Analysis model: (**a**) a three-story steel moment resisting frame with dampers; (**b**) a six-story steel moment resisting frame with dampers; and (**c**) a twelve-story steel moment resisting frame with dampers.  
analysis program SNAP ver.7 [29]. In SNAP ver.7, the beams and columns are modeled using the beam element with elasto-plastic characteristics. The nonlinear behaviors of the beams and columns are represented using the concentrated plasticity concept with rotational springs. The rotational behavior of the plastic regions follows a trilinear hysteretic response based on ST3, which is the steel hysteretic material model. The plastic moments at the ends of the columns and beams assume that Mp = 592 kN\(\cdot\)m for the columns and Mp = 387 kN\(\cdot\)m for the beams. The beam and column elements have three degrees of freedom in each node. Tangent stiffness-proportional damping is considered with a 2% damping ratio for the first mode.  
Figure 4b shows the SMRF with five bays and six stories. Steel sections of H-400\(\times\)400\(\times\)18\(\times\)28 and H-500\(\times\)200\(\times\)10\(\times\)16 are used for all the columns and all the beams, respectively. The yield strength of the steel material is assumed to be 325 N/mm\({}^{2}\). The mass of each story is 130 tons. The plastic moments at the ends of the columns and beams assume that Mp = 1634 kN\(\cdot\)m for the columns and Mp = 692 kN\(\cdot\)m for the beams. According to EVA, the fundamental natural period is 1.23 s.  
Figure 4c shows the SMRF with six bays and twelve stories. Table 1 also lists the plastic moments at the ends of the columns and beams, and the steel sections. The yield strength of steel is assumed to be 325 N/mm\({}^{2}\). The mass of each story is 205 tons. Based on EVA, the fundamental natural period corresponds to 2.17 s.
# Adaptive Harmony Search for Tuned Liquid Damper Optimization under Seismic Excitation
## 3 Numerical Examples  
In this study, a model was created by placing a water-based TLD device based on single-story and ten-story structures. Twenty-two earthquake records, defined as far-field records in FEMA P-695 [34], were directed in simulations. The list of FEMA earthquake records applied to single and ten-story structure models are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Earthquake** & **Date** & **Earthquake Name** & **Component 1** & **Component 2** \\
**Number** & & & & \\ \hline
1 & 1994 & Northridge & NORTHR/MUL009 & NORTHR/MUL279 \\
2 & 1994 & Northridge & NORTHR/LOS000 & NORTHR/LA270 \\
3 & 1999 & Duzce, Turkey & DUZCE/BOL000 & DUZCE/BOL090 \\
4 & 1999 & Hector Mine & HECTOR/HE000 & HECTOR/HE0090 \\
5 & 1979 & Imperial Valley & IMPVALL/H-DLT262 & IMPVALL/H-DLT352 \\
6 & 1979 & Imperial Valley & IMPVAL/H-EL1140 & IMPVAL/H-EL1230 \\
7 & 1995 & Kobe, Japan & KOBE/N1000 & KOBE/N1800 \\
8 & 1995 & Kobe, Japan & KOBE/SH000 & KOBE/SH090 \\
9 & 1999 & Kocaeli, Turkey & KOCAELI/DZC180 & KOCAEIL/DZC270 \\
10 & 1999 & Kocaeli, Turkey & KOCAEIL/ARC000 & KOCAEIL/ARC090 \\
11 & 1992 & Landers & LANDERS/PLACE270 & LANDERS/YER360 \\
12 & 1992 & Landers & LANDERS/CLW-LN & LANDERS/CLW-TR \\
13 & 1989 & Loma Prieta & LOMAP/CAP000 & LOMAP/CAP090 \\
14 & 1989 & Loma Prieta & LOMAP/GO000 & LOMAP/G0300 \\
15 & 1990 & Manjil, Iran & MANJIL/ABAR-L & MANJIL/ABAR-T \\
16 & 1987 & Superstition Hills & SUPERBT/B-ICCC000 & SUPERBT/B-ICCC090 \\
17 & 1987 & Superstition Hills & SUPERST/B-POE270 & SUPERST/B-POE360 \\
18 & 1992 & Cape Mardolino & CAPEREM/DK0270 & CAPEREM/DK0360 \\
19 & 1999 & Chi-Chi, Taiwan & CHCHCHCH/CHY101-E & CHCHCH/CHY101-N \\
20 & 1999 & Chi-Chi, Taiwan & CHCHCH/TCU045-E & CHCHCH/TCU045-N \\
21 & 1971 & San Fernando & SFERN/PEL090 & SFERN/PEL180 \\
22 & 1976 & Friuli, Italy & FRULI/A-TMZ000 & FRULI/A-TMZ270 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEMA earthquake records list.  
The cylindrical tank geometry used in the model was optimized, considering a mass ratio of 5%. The TLD tank radius and height were optimized between 0.1m and 10m, with a damper period of 0.5 to 1.5 times the uncontrolled structure period, and a damping ratio between 1% and 50%.  
In the optimization process on MATLAB, the number of iterations was 1000, and the pn value was 10. Algorithm parameters are shown in Table 2.
## 4 Simulation Experiment and Performance Analysis
### Simulation Analysis of Positioning Accuracy of Typical Placement Topology Structure  
Based on the previous analysis of solution accuracy on TOA, TDOA, and TOA/TDOA hybrid positioning, we give the simulation results and comparative analysis on positioning error spatial distribution of CRLB and least square algorithms with three kinds of solution accuracy. During the simulation, we assume the variance of measurement and position \(\sigma^{2}=1\); therefore, the positioning error is equivalent to the value of GDOP. The spatial distribution of GDOP is calculated according to the line trajectory, and three-dimensional spatial station placement is adopted, as shown in Figure 3. We consider seven stations when calculating the TOA/TDOA hybrid positioning errors, that is, TOA stations are marked as \(S_{4}\)(1 \(\times\) 10\({}^{4}\) 0 0), \(S_{5}\)(0 1 \(\times\) 10\({}^{4}\) 0), and \(S_{6}\)(0 0 1 \(\times\) 10\({}^{4}\)), the TDOA stations are marked as \(D_{0}\)(0 0 0), \(D_{1}\)(1 \(\times\) 10\({}^{4}\) 0 0), \(D_{2}\)(0 1 \(\times\) 10\({}^{4}\) 0), and \(D_{3}\)(0 0 1 \(\times\) 10\({}^{4}\)).  
For the three-dimensional spatial station deployment, we carried out the GDOP distribution calculation for the line trajectory parallel to the Z-axis and varying in height from 1000 m to 10,000 m, as shown in Figure 4. It can be seen from the figure that the difference between the LS positioning solution error and the theoretical value of CRLB is basically the same; only the LS error is slightly larger than CRLB when the hybrid source localization solution is applied, and the LS error is also marginally higher than CRLB when the stationing placement scheme is altered, but in general, the LS positioning calculation  
Figure 2: TOA/TDOA hybrid positioning simulation curve based on Gauss–Newton method: (**a**) Simulation results for 1-TOA sensor and 4-TDOA sensors; (**b**) Simulation results for 2-TOA sensor and 4-TDOA sensors; (**c**) Simulation results for 3-TOA sensor and 4-TDOA sensors; (**d**) Placement scheme for 3-TOA sensor and 4-TDOA sensors.  
error is close to CRLB, and it is robust and does not require a priori knowledge, such as noise distribution, to obtain.  
The GDOP of TDOA is more uniformly distributed with little performance deterioration, while the GDOP of TOA suddenly deteriorates sharply at a certain position in the low altitude, and the deterioration region is caused by the inverse of the sick matrix due to the rank of the matrix \(\mathbf{G}\) dropping to 2 due to the coplanarity of \(S_{4}\), \(S_{5}\), and \(S_{6}\). The GDOP accuracy of the hybrid source localization is better than the separate localization accuracy of TOA or TDOA alone, which is consistent with the results of the previous localization solution in Figure 2.
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 2 Materials and Methods
### Renewable Energy in Colombia  
Colombia is one of the most privileged countries in Latin America thanks to its geographical location. It provides special characteristics like zones where wind speeds are twice the world average and there is also sunlight most days of the year [23]. As a consequence, a great research scenario is open for new ideas to propose and develop different methodologies to take advantage of these characteristics and then to focus on the optimal use of renewable resources, specifically, in off-grid zones.  
In 2020, 29 off-grid zones supplied their own energy demand using renewable energies; however, it is a small number in front of 1798 off-grid localities that are distributed in almost 51% of national territory [24]. In most of the cases, off-grid energy demand is supplied with diesel-based generators as a single option and only 31.3% of them have electric service available 24 h a day. One of the reasons that could explain this limited use of renewable options to supply energy in those off-grid zones is that their installation cost used to be substantially high and made this solution financially impractical. However, their prices have come down and the renewable generator industry is more competitive than before [25].  
In the same way, in recent years, the Colombian government has developed some laws, such as \"Ley 1715\" [26], that have encouraged the development of projects that promote the generation of electrical energy solutions in off-grid areas [27]. However, these laws do not present a clear framework on the benefits that the use of renewable generation sources can bring in specific contexts such as isolated areas. In this sense, and given the economic difficulties represented by the implementation of projects based on renewable energies in these contexts, it is necessary to develop alternatives that allow for the intelligent management of resources and that ensure their optimal operation.  
The model we propose seeks to manage the energy microgrids considering uncertainty from different sources (environmental variables that affect the generation and demand of energy) at the same time as it implements specific strategies on some components of the microgrid, such as the battery system. This management is carried out through an optimization process by scenarios, which allows for adaptation to various situations whose conditions may affect the operation of the microgrid.
# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 5 Modelling Countermeasures
### Educational Bias  
Education is considered the main long-term measure to counter the spread of hate speech. Indeed, advocates of free speech favour this measure over automatic filtering [40], which they strongly criticise. Education is aimed to develop critical thinking [10], enabling individuals to open discussions about any topic. Structured and argumented debates ought then lead to opinion forming and foster thinking [41]. Although education does not always result in tolerance (programmes fail to mitigate prejudice if they overlook factors such as values or ego defense [42]), from the perspective of the so called media literacy,  
Figure 6: Threshold for accepting foreign opinion subject to the user’s hate score.  
education develops the skills to recognise hate speech itself [11]. Therefore, people need to be instructed in the usage and interpretation of modern digital media, and this is especially the case for youngsters.  
Additionally, education can introduce an initial bias into the view of the population, e.g., by teaching democratic values and human rights [9]. However, rather than modelling how this positive bias is actually introduced (i.e., how education is implemented in the society), we can simply model its effect by skewing the hate score distribution used in the creation of the society. The mean value of the whole distribution should then move into the direction of non-hateful persons, hence decreasing the tendency towards the hatred. However, we assume that, despite the educational bias on the majority of the population, the group of very hateful persons will still be present in the population with the same proportion. So, the parameters of the Gamma probability distribution \(\Gamma(\alpha,\lambda)\) are adjusted in such a way that the fraction of hateful persons stays invariably ca. 1% but their mean values \(\mu\) are decreased. Figure 5 shows the baseline distribution \(\Gamma(10,25)\) and four further distributions with their corresponding mean values which vary from \(\mu=0.40\) down to \(\mu=0.23\). In this manner, we do not model how the positive bias is introduced into the population but take it for granted and simply apply the bias (i.e., the positively-skewed alternative distributions in Figure 5) to the population during the network construction phase (see Section 4.1).