esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions, Policy Implications, and Future Research
### Conclusions  
On the basis of the simulation results, the evolutionary game model constructed in this study exhibits good accuracy and accurately reflects the game behavior and evolutionary process of both participants in the model. In this regard, we were able to draw the following conclusions:  
(1) The total benefits, such as medical service support, emotional support, information support, and community rewards, obtained by users disclosing private personal informa  
Figure 5: Numerical simulation of Scenarios 1 to 9. (**a**) Simulation of Scenario 1; (**b**) Simulation of Scenario 2; (**c**) Simulation of Scenario 3; (**d**) Simulation of Scenario 4; (**e**) Simulation of Scenario 5; (**f**) Simulation of Scenario 6; (**g**) Simulation of Scenario 7; (**h**) Simulation of Scenario 8; (**i**) Simulation of Scenario 9.  
tion in the communities are the central factors that affect whether users choose to disclose personal information. If the total benefits obtained by users are greater than the expected loss caused by privacy leakage in the worst case, even if the communities choose to implement negative privacy protection policies, users will still actively and firmly choose to disclose their private personal information.  
(2) Although the active disclosure of private personal information by users can help to form a good atmosphere within the community, thereby enhancing the credibility and popularity of the community and bringing certain benefits to the development of the community, this benefit is not the decisive factor for the community to choose which privacy protection strategy to implement. The relationship between the extra cost that the community needs to pay to implement the \"positive protection\" strategy and the expected loss caused by the difference in privacy leakage risk between the \"positive protection\" and \"negative protection\" strategy is what affects the community's choice of privacy protection strategy.
# Analytical Modeling and Empirical Analysis of Binary Options Strategies
## 2 Materials and Methods
### Modeling Binary Options
#### 2.5.4 Feasibility Condition  
For a binary option to be feasible, the expected return should be more than the investment. In other words, if the amount of a single investment in binary options is denoted by \(I\), the expected return is  
\[E(return)=pI+prI+(1-p)\times 0\times I=pI+prI=p(1+r)I \tag{1}\]  
In the first line of this expected return equation, \(pI+prI\) refers to the return for a correct prediction, which is the probability \(p\) of keeping the initial investment \(I\) and receiving a return of \(rI\). In the first line, \((1-p)\times 0\times I\) refers to the return for an incorrect prediction. For the investment/trade to be feasible, one should have expected a return higher than the investment, \(E(return)=pI(1+r)>I\), which translates into  
\[p>1/(1+r)\]  
This inequality is referred to as the feasibility condition. This condition, which can be found in similar forms in [53; 54], can be an important guiding principle for investors. Importantly, note that the payout percentage \(r\) in this inequality is the net value, which is the payout percentage mentioned in the user interface of the trading platform minus any other fees/costs associated with trading on the platform.  
As an example of the feasibility condition, if the payout percentage (including the effects of any trading and other fees/costs) is \(r=0.75\), the prediction accuracy should be at least \(p>1/(1+0.75)\Rightarrow\ p>0.5715\).
## 3 Mathematical Formulation and Methodology
### Small-Scale Fading  
We used the maximum likelihood estimation (MLE) method to estimate the parameters of the Rayleigh, Rice, Nakagami, and Weibull distributions [43]. Then, the theoretical cumulative probability functions (CDFs) were generated and compared with the empirical CDFs obtained from the small-scale fading samples. Henceforth, it is possible to verify which distribution properly describes the channel based on the KS test with a confidence level equal to or greater than 95%. This procedure served to define the size of the filtering window.
## 4 Results and Discussion
### Leakage Prediction
#### 4.1.4 Empirical Equations for the Prediction of the Leakages  
2500 conditions with \(r_{\rm G}\) in the range of 0.005-0.05 m, \(T_{\rm G}\) in the range of 0.001-0.0025 m, \(k_{\rm G}\) in the range of 1 \(\times\) 10\({}^{-9}\) m/s\({}^{-1}\)\(\times\) 10\({}^{-11}\) m/s and \(H_{\rm G}\) in the range of 0.6 m-10 m are analyzed. Empirical equations to predict leakages for different shapes of holes are all shown in Table 2. It should be noted that in the empirical equations, the parameter \(r_{\rm G}\) represents the radius of circular holes with the same area for different hole shapes. It can be concluded that \(R^{2}\) for all equations are larger than 0.91. It indicates that the proposed empirical equations are suitable for evaluating the leakage in a CGCW and aquifer system.
## 6 Analysis and Results
### EMCR Evaluation  
In this section, the experiments assess the impact of average vehicle speeds, vehicle density, and cache time on the performance of the EMCR protocol. We set up 10 random communication connections that generate Constant Bit Rate (CBR) traffic between nodes, which generate packets with 512 bytes every 2 s. The maximum buffer size for the SCF mechanism is set to 50 packets. Furthermore, UDP packets of 1500 bytes are used to generate the background traffic at a rate of one packet per second. We evaluate the performance of EMCR over the EMHR and CLWPR protocols. EMCR is a hybrid routing protocol that is aware of hybrid network energy, mobility, and connectivity using the SCF mechanism to hold the packets for a specific time until a better route is found. EMHR [23] is a hybrid routing protocol that is only aware of hybrid network energy and mobility. The EMHR is a hybrid geographic routing protocol that considers energy balancing for sensors to reduce energy consumption and mobility information for vehicles to reduce the end-to-end delay. Moreover, EMHR aims to increase the packet delivery ratio (PDR) and throughput while routing packets in the hybrid network of VANET-WSN. CLWPR [9] is a delay tolerant network for VANETs that is aware of network mobility and connectivity using a carry-and-forward mechanism. In the CLWPR, only vehicles employ a carry-and-forward mechanism by carrying the packets for 2 s.
## 3 The Design of _HeaLow_ and Implementation
### Implementation Details
#### 3.3.2 Work Queue Management  
The most important component of a worker in _HeaLow_ is the work queue. As implied by its name, a workload at the head of the work queue is processed by the worker thread. Of course, a workload that has passed its deadline is consistently removed from the queue, and this event is considered as a failure. The availability checker determines if a workload can be processed by the worker thread. The amount that cannot be processed is thrown into the dump queue, and workloads in the dump queue are sent to other devices or considered as failure. The queue length of _HeaLow_ is perceived in time domain, as deadline is one of the key design issues of _HeaLow_. The worker thread reports the processing time of the last workload to the work queue, where the amount of work in the work queue is divided by the processing rate to get the queue length in time domain. The queue length is then compared to the new workload's deadline. If the queue length is not smaller than the deadline, the workload cannot be added to the queue. The detailed explanation about the offloading decision process in the availability checker is given in Section 3.4.  
The above method is logical and straightforward, but the processing time brings a practical issue. As shown in Figure 2, the processing time often slows down to an order of magnitude for a short period. Although we could not pinpoint the cause of the delay, our best guess is that the android OS is not performing operations for only _HeaLow_, and this created a competition for available resources. This results in a phenomenon we named buffer hazard, and we implemented an additional technique to deal with this phenomenon. A buffer hazard occurs when the processing time slows down rapidly, multiplying the perceived queue length, rejecting all incoming workload for a period of time while unable to finish the workload already in the work queue either. When a buffer hazard occurs, we can anticipate its processing rate will be slow for a short period. Therefore, when it happens, the device enters the hazard state and offloads a portion of incoming workload without checking availability until the queue empties and exits the hazard state. We named this method _hazard state management_.  
If workloads are offloaded without the _hazard state management_ approach, the queue length is perceived to be available to accept more workloads. This brings an extremely unstable behavior to the queue, as workloads accepted will create another hazard state. The workloads that are sent to the dump queue will have a scrambled order. Although all queues are consistently sorted, accesses to the queue may create an unpredicted behavior. The _hazard state management_ effectively handles the sudden increase in processing time, especially when the processing time increases over a few workloads.
## 3 Performance of the Laplace Inversion Algorithm
### Model 1: Homogeneous Sea Half-Space  
The two algorithms were first applied to a homogeneous sea half-space. The seawater conductivity was set to 4 S/m. To simplify the analysis, it was assumed that a central-loop source was placed on the sea surface with a transmitting square loop of \(2\times 2\) m, and the equivalent area of the receiving loop was \(1\) m\({}^{2}\)[16]. The current source carried a trapezoidal waveform, where \(t_{\mathrm{s}}=8\) ms, and \(t_{d}=1\) ms, as shown in Equation (1). The pulse width \(T_{0}\) was assumed to be 10 ms. The current amplitude \(I_{0}\) was assumed to be 100 A. These parameters remained constant in the following study.  
In Equations (7) and (9), the number of terms \(N\) in the summation was selected from 8 to 14. The voltage responses derived from the G-S and Talbot algorithms were compared with the analytic solution [43]. As shown in Figure 2,b, the response obtained by the G-S algorithm deviated from the analytic solution for all values of \(N\), and the relative errors were high for most observation times, especially at late times. However, as shown in Figure 2,d, the best results were derived using the Talbot algorithm, which agreed well with the analytic solution and had the lowest relative errors (below 1% for most of the observation period). Compared with the results obtained by the G-S algorithm, the response for the Talbot algorithm was less sensitive to the value of \(N\). Even when \(N\) had a smaller value, the Talbot algorithm had high accuracy for computing the inverse Laplace transform.  
Figure 2: Numerical results of a trapezoidal wave source for homogeneous sea half-space. (**a**) Voltage response of the G-S algorithm; (**b**) errors of the G-S algorithm relative to the analytic solution, where the dashed line indicates a relative error of 1%; (**c**) voltage response of the Talbot algorithm; (**d**) errors of the Talbot algorithm relative to the analytic solution, where the dashed line indicates a relative error of 1%.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### Nezha-UniLM-CC: An Abstractive Summarization Model
#### 4.2.3. Decoding Optimization Strategies  
(1) Copy Mechanism  
The copy mechanism [70] enables the decoder to dynamically choose between generating new words from the vocabulary or copying words directly from the source text. The probability distribution for generating target word \(w\) at time step \(t\) is defined in Equation (8).  
\[p(w)=p_{gen}p_{vocab}(w)+\big{(}1-p_{gen}\big{)}\sum_{i:w_{i}=w}a_{i}^{t} \tag{8}\]where \(p_{gen}\in[0,1]\) controls the probability of generating \(w\) from the vocabulary, \(p_{vocab}(w)\) is the probability distribution over the vocabulary (enhanced with the maritime judgment lexicon from Section 3.2), \(a_{i}^{t}\) denotes the attention weight on source word \(w_{i}\) at step \(t\), and \(\sum\limits_{i\approx w_{i}=w}a_{i}^{t}\) sums the attention weights for all occurrences of w in the source text. If \(w\) does not appear in the vocabulary, \(p_{vocab}(w)=0\), forcing \(w\) to be copied from the source text. Conversely, if \(w\) does not appear in the source text, \(\sum\limits_{i:w_{i}=w}a_{i}^{t}=0\), forcing \(w\) to be generated from the vocabulary.  
(2) Coverage Mechanism  
To suppress repetitive generation, the coverage mechanism [70] introduces a coverage vector \(\mathbf{c}^{t}\), which accumulates historical attention distributions to guide the decoding focus, as shown in Equation (9).  
\[\mathbf{c}^{t}=\sum\limits_{t^{\prime}=0}^{t-1}\mathbf{a}^{t^{\prime}} \tag{9}\]  
where \(\mathbf{a}^{t^{\prime}}\) represents the attention distribution over source tokens at decoding step \(t^{\prime}\). Initially (\(t=0\)), \(\mathbf{c}^{0}\) is a zero vector, as no source tokens have been covered. During decoding, \(\mathbf{c}^{t}\) is iteratively updated to track previously attended source positions. By incorporating \(\mathbf{c}^{t}\) into the current attention calculation, this mechanism penalizes focus on already covered positions, thereby reducing redundancy. This dynamic prioritization ensures that the decoder progressively shifts focus to uncovered content, enhancing summary coherence.  
(3) Beam Search Optimization  
To balance summary quality and computational efficiency, the decoding process employs a beam search algorithm [71] with beam width \(K\). At each time step, the algorithm retains the top-\(K\) candidate sequences based on their cumulative log probabilities. The final summary is selected as the sequence with the highest global probability, ensuring coherence and relevance. This approach efficiently explores the search space while maintaining high-quality output.
## 3 Results  
The methodology has been proven on a fleet of more than 100 wind turbines, from four different manufacturers, located in very different geographical locations ranging from hot climates, such as south of Spain, to colder ones such as Poland and the United Kingdom. While adjustments were required due to the different variables and characteristics of the turbines, the overall methodology was not modified.
## 2 Methodology  
### Overview  
The DEM is essentially suitable for analyzing the failures and deformations of jointed rock slopes, but the discrete element calculation is an iterative process that needs to choose an appropriate time to terminate according to the specified situation; the DEM requires that the number of iterative steps to be performed in the calculation process needs to be determined before the discrete element calculation. Currently, the specified maximum number of steps is the most commonly used method. Although this method is simple, it is not suitable in some simple and complex cases, because too few iterative calculation steps would bring about inaccurate numerical calculation results, and too many iterative calculation steps would entail unnecessary calculations. In this paper, an enhanced discrete element modeling method is proposed to investigate the deformations and failures of jointed rock slopes through the statistic of the energy correlation coefficient. In this method, the convergence of the energy correlation coefficient is used to determine when the iterative process could be terminated. The proposed method is a generalization of the displacement-based method. The proposed method considers the mass or weight and displacement of the block. Meanwhile, the proposed method in this paper considers the temporal and spatial correlations between all rock blocks. In short, the basic idea of the proposed discrete element modeling method is to provide a method based on energy analysis to determine when to terminate the DEM iterative process to investigate the deformations and failures of jointed rock slopes. As shown in Figure 1, the proposed discrete element modeling method is roughly composed of the following three steps:  
1. According to the specific situation of the study area, the geological model and calculation model are established, and the appropriate mechanical parameters are determined;
2. The deformation of the jointed rock slope under gravity is calculated by the DEM, and the location and volume of all rock blocks are obtained;
3. Correlation analysis is performed to properly terminate the iterative process of discrete element calculation. Specifically, the energy of each rock block under gravity is calculated according to the position and volume of all rock blocks obtained by the second step. Then, the correlation coefficient between the energy at each time and the energy at the initial time is calculated. Finally, the convergence of the energy correlation coefficient sequence is analyzed. If the sequence converges, the discrete element calculation process would be terminated.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 4 Channel Estimation for the Core Network: Link eNB-to-Relay Node
### Interpolation Methods
#### 4.1.1 BLI Method  
In this subsection, the 1D Linear Interpolation (LI) Method is presented, through which the BLI algorithm is derived. LI is the most common and usually employed interpolation method. Taking into account [29; 30] and expression (3), the LI method can be written as  
\[\mathbf{\hat{H}}_{cp,s_{p}^{i}}^{i,j}=\mathbf{\hat{H}}_{cp,s_{p}^{i}}^{i,j}+( \mathbf{\hat{H}}_{cp+1,s_{p+1}^{i}}^{i,j}-\mathbf{\hat{H}}_{cp,s_{p}^{i}}^{i,j })(\frac{\beta}{L_{p}}), \tag{6}\]  
where \(\mathbf{\hat{H}}_{cp,s_{p}^{i}}^{i,j}\) is the estimated channel frequency responses at pilot symbol positions, \(i\in\{1,2,...,N_{Tx}\}\), \(j\in\{1,2,...,N_{Rx}\}\), and \((c_{p}^{i},s_{p}^{i})\) indexes the subcarrier position of the transmitted pilot symbols, with \((c_{p}^{i},s_{p}^{i})\in\{1:L_{p}:N\}\) and where \(N\) is the system subcarrier number. \(\beta\) is  
Figure 3: Channel Estimation and Equalization Blocks.  
given by \((c_{d}^{i},s_{d}^{i})-(c_{p}^{i},s_{p}^{i})\). Further, \(L_{p}\) denotes the data length between two sequential pilot symbol positions, \(p\in\{0,1,2,...,P-1\}\) (where \(P\) is the pilot symbol total), which is estimated by the LS estimator. On the other hand, \(\hat{\mathbf{H}}_{c_{d}^{i}s_{d}^{i}}^{ij}\) describes the estimated channel frequency responses at all data symbol positions and \((c_{d}^{i},s_{d}^{i})\) represents the subcarrier position of data symbols, such that \((c_{p}^{i},s_{p}^{i})\leq(c_{d}^{i},s_{d}^{i})\leq(c_{p+1}^{i},s_{p+1}^{i})\), from which the pilot symbols are located.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion  
This section presents the performance of the proposed model. The model results are analyzed and compared with the various methods. Comparison of the results is made in two scenarios. In the first, only the performances in the first phase (cultivation) are considered. Here, several algorithms that have been used have been successful in solving the refinement problem: Grey Wolf Optimization (GWO) [59; 60], Moth-Flame Optimization  
Figure 3: The working mechanism of the RACO.  
(MFO) [61], and Particle Swarm Optimization (PSO) [62]. In the other scenario, comparisons were made to cover all phases. Since the second and third phases are integrated, it would not be correct to analyze these phases separately. The methods used for comparison are a variant of Firefly Algorithm (FA) [63], Genetic Algorithm (GA) [64], Cuckoo Search (CS) [65], and Glowworm Swarm Optimization (GSO) [66] algorithms. In addition, the MAP-ACO [67] algorithm, which is based on ACO, was also used for comparison. In summary, a comparison and analysis covering all phases was performed to preserve the integrity of the results. The implantation and analysis presentation were performed in MATLAB. The algorithms proposed in this study were performed on a Core i7-5500 U 2.4 processor with 16 GB of RAM.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 4 Proposed Filters Planning Procedure  
Figure 6 shows the flowchart that describes the planning procedure of the STF. Where, the proposed filter planning procedure for harmonic mitigation has the following steps:  
Figure 5: Impact of filter parameters on its characteristics curve; (**a**) effect of \(t_{f}\) and (**b**) effect of \(Q_{c}\).  
Step 1: Randomly initialize the initial set of raindrops that has a size of \(N_{pop}\).  
\[RP=\left\{\begin{array}{c}1\\ \vdots\\ N_{pop}\end{array}\right.\left[\begin{array}{ccccc}filter_{1}^{place}&filter_{n}^{ place}&filter_{1}^{f_{t}}&filter_{n}^{f_{t}}&filter_{1}^{Q_{c}}&filter_{n}^{Q_{c}}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\end{array}\right] \tag{16}\]  
Step 2: Checking the following constraints:  
\[f_{t}\in\left[\begin{array}{ccccc}3&5&7&\cdots&25\end{array}\right] \tag{17}\]  
Figure 6: Flowchart of the planning procedure.  
where \(f_{t}\) is the tuned harmonic order.  
\[Bus\_2\leq filter^{place}\leq N \tag{18}\]  
\[Q_{c}^{\min}\leq Q_{c}\leq Q_{c}^{\max} \tag{19}\]  
Step 3: Evaluate the fitness of each raindrop by the following steps:  
Calculating filter parameters which consist of (series of R, L, and C)  
\[X_{C}=\frac{(V_{base})^{2}}{Q_{C_{F}}} \tag{20}\]  
where \(X_{c}\) is the capacitive reactance and \(Q_{C_{F}}\) is the capacitor reactive power rating in kVAr.  
\[X_{L}=\frac{X_{C}}{\left(f_{t}\right)^{2}} \tag{21}\]  
where \(X_{L}\) is the inductive reactance.  
\[R=\frac{\sqrt{L/C}}{Q_{F}} \tag{22}\]  
\[L=\frac{X_{L}}{2\pi\text{f}_{0}} \tag{23}\]  
\[C=\frac{1}{2\pi\text{f}_{0}X_{C}} \tag{24}\]  
where \(Q_{F}\) is the quality factor of the filter, and f\({}_{0}\) is the fundamental harmonic frequency.  
1. Carrying out harmonic power flow calculation based on the backward/forward sweep power flow for balanced radial distribution feeders [35], Power flow analyses are carried before and after adding the filters into the system.
2. Calculating fitness function using the harmonic power flow results.
3. Checking the buses' voltage, THD, and IHD constraints. If any solution doesn't satisfy any of the constraints, putting the value of its fitness function equals infinity. So, it will not be selected as a minimum solution.  
Step 4: Getting the personal best data and storing its controlled variables data as:  
\[P_{best}=\min(fitness\ function\ matrix) \tag{25}\]  
Step 5: Applying the WCA to generate a new set of solutions.  
Step 6: Repeating steps from 2 to 5 till reaching the maximum iterations number.