esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Single Objective Algorithms Overview
### Learner Performance-Based Behavior
In the case where individuals from perfect Population have not been enough, the individuals are brought from bad Population. 2. non-dominated solutions are stored in external archive. 3. crowding distance is found for non-dominated solutions in external archive. 1. In all cases where the external archive is full, crowding distance method is utilized for the removal of dominated solutions (the ones with low values of crowding distance) in the archive and non-dominated solutions are stored. 4. In the case of meeting the stop condition, this algorithm will stop, or else, go to step 3
7:End
```  
**Algorithm 9**_Cont_. A Comprehensive Analysis of the Benefits, Drawbacks, and Practical Uses of Multi-Objective Algorithms  
Single- and multi-objective algorithms are essential instruments in the quest for optimized solutions across several areas. Single-objective algorithms focus exclusively on a given optimization target, while multi-objective algorithms aim to optimize many competing objectives in a simultaneous manner, which results in a range of Pareto-optimal solutions. Knowing the delicate advantages, limitations, and practical uses of these algorithms is paramount to handle complex problems with skill and create efficient solutions. The Table 3 below gives a detailed analysis of the benefits, constraints, and practical uses of MEO, MODE, MOPSO, MOLPB, and MOFDO algorithms. By making clear their usefulness and applicability in various optimization situations, it offers an essential understanding of how well they work for real problem-solving efforts. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Multi-Objective Algorithms** & **Advantage** & **Disadvantage** & **Methods** & **Applications** \\ \hline \multirow{3}{*}{MOEO} & - & Useful in the solution of & \multirow{3}{*}{-} & \multirow{3}{*}{Memory-less} & - & Multiple & - & Spacecraft trajectory \\  & & real-world problems & & & Pareto-optimal & - & Still to be applied to a \\  & & & & & solutions & & wide range of science \\  & & & & & & and engineering areas \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The advantages, disadvantages, and applications for the multi-objective algorithms.
Sensors_**2009**, \(9\), 6385-6410; doi:10.3390/s90806385  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**High-Resolution Images with Minimum Energy Dissipation and Maximum Field-of-View in Camera-Based Wireless Multimedia Sensor Networks**  
**[PERSON], [PERSON], [PERSON] and [PERSON]***  
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran;  
E-Mails: [PERSON]ADDRESS] (H.S.A.); [PERSON]DRESS] (P.B.);  
[PERSON]MAIL_ADDRESS] (M.E.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +98-21-2990-2268; Fax: +98-21-2397-9896  
_Received: 15 June 2009; in revised form: 17 July 2009 / Accepted: 17 August 2009 / Published: 19 August 2009_  
**Abstract:** High-resolution images with wide field of view are important in realizing many applications of wireless multimedia sensor networks. Previous works that generally use multi-tier topology and provide such images by increasing the capabilities of camera sensor nodes lead to an increase in network cost. On the other hand, the resulting energy consumption is a considerable issue that has not been seriously considered in previous works. In this paper, high-resolution images with wide field of view are generated without increasing the total cost of network and with minimum energy dissipation. This is achieved by using image stitching in WMSNs, designing a two-tier network topology with new structure, and proposing a camera selection algorithm. In the proposed two-tier structure, low cost camera sensor nodes are used only in the lower-tier and sensor nodes without camera are considered in the upper-tier which decreases total network cost as much as possible. Also, since a simplified image stitching method is implemented and a new algorithm for selecting active nodes is utilized, energy dissipation in the network is decreased by applying the proposed methods. The results of simulations supported the preceding statements.  
**Keywords:** camera-based sensor networks; high-resolution and wide images; two-tier network topology; image stitching; camera selection problem; minimum wasted energy
## 4 Objective Function and Its Solution  
### Objective Function for Controller Design  
In order to restore the essential frequency in control-areas, a suitable objective function is significant for the optimization technique to explore the controller parameters [4]. In this article, ITAE is deemed as an objective function \((J)\) to regulate the controller's gains.  
\[J\ =\ ITAE=\ \int\limits_{0}^{T_{\textit{S}_{\textit{S}_{\textit{S}_{\textit{I}}}}} }t\cdot\left[|\Delta F_{1}|+|\Delta F_{2}|+|\Delta P_{\textit{tie},12}|\right] \cdot dt \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ACE**} & \multicolumn{8}{c}{_ACE_} \\ \cline{2-9}  & **NL** & **NM** & **NS** & **Z** & **PS** & **PM** & **PL** \\ \hline NL & PL & PL & PL & PM & PM & PS & Z \\ NM & PL & PM & PM & PS & Z & NS & NM \\ NS & PL & PM & PS & Z & NS & NM & NM \\ Z & PM & PM & PS & Z & NS & NM & NM \\ PS & PM & PS & Z & NS & NM & NM & LN \\ PM & PS & Z & NS & NM & NM & NM & LN \\ PL & Z & NS & NM & NM & LN & LN & LN \\ \hline \hline \end{tabular} Abbreviations: NL = Negative large; NM = Negative medium; NS = Negative small; Z = Zero; PS = Positive small; PM = Positive medium and PL = Positive large.  
\end{table}
Table 2: Fuzzy rule base.  
Figure 3: Membership function of FLC: (**a**) input signal \((e(t))\) (**b**) input signal \(D^{\delta}(e(t))\), and (**c**) output signal \(u_{\textit{FL}}\).  
An optimization problem is carried out by using the ALO technique as in Reference [24] to attain the optimal SFs incorporated into the following constraints: \(K_{p}^{\min}\leq K_{p}\leq K_{p}^{\max}\), \(K_{i}^{\min}\leq K_{i}\leq K_{i}^{\max}\), \(K_{d}^{\min}\leq K_{d}\leq K_{d}^{\max}\), \(K_{e1}^{\min}\leq K_{e1}\leq K_{e1}^{\max}\), \(K_{e2}^{\min}\leq K_{e2}\leq K_{e2}^{\max}\), \(K_{pi}^{\min}\leq K_{pi}\leq K_{pd}^{\max}\), \(\lambda^{\min}\leq\lambda\leq\lambda^{\max}\), \(\mu^{\min}\leq\mu\leq\mu^{\max}\), \(\eta^{\min}\leq\eta\leq\eta^{\max}\), \(\phi^{\min}\leq\delta\leq\delta^{\max}\). The proposed topology of the designed FOFP-iPID controller based on ant lion optimizer (ALO) is highlighted in Figure 4.
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
## 2 Materials and Methods
### Methodology  
#### 2.4.1 Homogenization of Precipitation Series  
Our work begins with the gap-filling and homogenization of the collected precipitation series. This step is based on the Climatol procedure cited in the work of [PERSON] [24]. The method used is based on orthogonal regression, widely known as reduced major axis (RMA). It examines the linear relationship between two continuous variables: a response (Y) and a predictor (X). In orthogonal regression, both the response and the predictor are assumed to a contain measurement error, which is not the case in simple linear regression (least squares regression) [40]. Climatol uses RMA for its simplicity, and to allow the use of neighboring series even if they do not have a common observation period with the problem series, which would prevent the fitting of linear regression models [41].  
Assuming the linear regression model \(y=\alpha+\beta x+\varepsilon\), where \(y\) and \(x\) are dependent and independent variables, respectively; \(\alpha\) and \(\beta\) are ordinary least squares (OLS) regression coefficients; and \(\varepsilon\) is a random error term, the RMA slope coefficient is \(\beta_{\text{RMA}}=\beta/\operatorname{r_{yx}}\mid\), where \(r_{yx}\) is the Pearson correlation coefficient between \(y\) and \(x\). The standard error (SE) of \(\beta_{\text{RMA}}\) is equal to the SE of \(\beta\). The RMA intercept coefficient is \(\alpha_{\text{RMA}}=y-\beta_{\text{RMA}}x\), and the SE of \(\alpha_{\text{RMA}}\) is equal to the SE of \(\alpha\)[42; 43]. The Climatol program first aims to complete the missing data in each series, using the provisional averages and standard deviations, and recalculates them again with the completed series. Then, the originally missing data is recalculated using the new parameters, which will lead to new means and standard deviations; the process is iterated until there are no mean changes to within the initial number of decimal places of the data. Once the means are stabilized, all data (existing or filled-in, in all series) are normalized [24].  
The algorithm organizes stations into a tree structure, where clusters that show high correlations are grouped in the same branch. Outlier and artificial break detection are addressed through quality control of anomalies, using a predefined threshold and relative inhomogeneity detection using the Standard Normal Homogeneity Test (SNHT) [44]. In addition, this program builds a tree of clusters of stations with similar rainfall variabilities and monthly regimes. This operation is repeated before and after the homogenization of the data, which allows us to map groups of stations with similar precipitation patterns.
## 4 Methodological Conception and Execution
### Privacy Preservation Mechanisms  
To ensure robust privacy protection throughout the training process, our model integrates several state-of-the-art privacy-preserving techniques:  
1. Local Differential Privacy (LDP)  
In order to prevent overfitting, each institution adds noise to the gradients with Laplace or Gaussian distribution before sending them to the regional hub. This leads to the fact that the obtained model updates for the local model are sufficiently masked, and potential attackers cannot extract patient data from the gradients transmitted over the network.  
\[\Delta\text{M}=\
abla\text{ L(M;D)}+\eta \tag{3}\]  
where \(\eta\) is the added noise, sampled from a distribution with variance governed by \(\varepsilon\).  
Figure 2 illustrates a federated learning scenario in which two hospitals (Institute 1 and Institute 2) mutually exchange information with a regional node through SMPC. The data is then stored at the central point with the help of General Purpose Data (GPD) and the data is processed with the help of Homomorphic Encryption so that the data remains secure during the whole process.  
Global Differential Privacy (GDP)  
However, there is noise added at the central server after the local updates have been pooled together when computing the global model by the regional hub. These two-level Differential Privacy mechanisms make sure that any residual privacy risks are dealt with during the last stage of model aggregation.  
\[w_{k}+1=\frac{1}{N}\sum w_{k}^{r}+n_{g} \tag{4}\]  
3. Secure Multi-Party Computation (SMPC)  
In order to prevent the exposure of the individual updates during the aggregation process, we use SMPC. Every local update is divided into numerous random shares which are stored at different auxiliary servers. The central server cannot receive any individual updates but is able to rebuild the aggregated result from shares of multiple servers.  
\[\mathrm{M}_{i,j}=\sum_{j=1}^{n}s_{i,j} \tag{5}\]  
where si,j represents the share of the model update i sent to the j-th auxiliary server.  
4. Homomorphic Encryption (HE)  
To further enhance privacy, we applied HE to the local model updates. This allows computation on encrypted data, ensuring that the central server can perform aggregation without needing access to the raw data.
## 4 Conclusions and Policy Implications  
This study examined the relation between economic growth and the SEZ in Songkhla province, including evaluating the accuracy of the prediction approach, forecasting the economic performance of the SEZ, and examining the causal influences among GPP, FDI and TRADE using Vine-COPAR based Granger causality. The main finding can be drawn as follows:  
1. The appropriate specification for a forecasting method using a Vine-COPAR model provides better results than a single time series, since evaluating more dependence structures leads to more accurate predictions. Moreover, the Vine-COPAR based Granger causality can accommodate high-order moment causality and this approach thus provides effective long-run performance.
2. For five-year forecast (2017-2021), the FDI and TRADE appeared to be the important contributions towards the SEZ. However, GPP and FDI displayed sharp fluctuations, and TRADE behaved constantly. Therefore, the government should encourage their competitiveness and maintain continuity of foreign investment and trade policies.
3. Granger causality and bidirectional causality existed among GPP, FDI and TRADE in all sectors.  
This study has some policy implications. The government should promote FDI-friendly policies and trade promotion in the SEZ since these policies can play a crucial role in boosting regional economic growth. The core of the development should focus on the favorable privileges towards FDI; in addition, the free trade regime should be supported by eliminating several non-tariff barriers, particularly bearing in mind that the FDI based-enterprises within SEZ are ordinarily accorded more liberal operating conditions.  
Consequently, the findings from the provincial level suggest that Songkhla province should be promoted as one of the nine industrial centers in Thailand since it has great potential for a considerable acceleration of its economic growth based on the SEZ policy, which could contribute towards the enhancement of the regional economy.  
[PERSON] wrote the paper, conducted the research findings, and revised the manuscript. [PERSON] constructed the model analysis and source coding for the R software. [PERSON] and [PERSON] performed field interviews, collected data and analyzed the results. [PERSON] designed the overall research and revised the policy recommendations. All authors revised and approved the final manuscript.  
This work was supported by the Office of the Higher Education Commission (OHEC), Thailand; the Research and Development Office, Prince of Songkla University; and the Faculty of Economics, Prince of Songkla University, Thailand under research grant ECO580922S.  
The authors declare no conflict of interest.
# A Hybrid Model for Freight Train Air Brake Condition Monitoring
###### Abstract  
The Digital Freight Train is expected to revolutionise the rail freight industry. A critical aspect of this transformation is real-time condition monitoring of air brake systems, which are among the leading causes of train malfunctions. To achieve this goal, advanced algorithms for air brake modelling are required. This paper introduces a computationally efficient air brake model tailored for real-time diagnostic applications. A hybrid approach, integrating both empirical data and simplified fluid-dynamic equations, has been adopted. Compared to other air brake models found in the literature, the innovative contributions of the presented model are the reduction of the number of required parameters and the estimation of the brake cylinder pressure directly from the main brake pipe pressure using a feed-forward approach. Moreover, a new approach in the evaluation of the first braking phase and the brake cylinder pressure build-up as the saturation of the brake mode is presented. The model input includes the main brake pipe pressure, the weighing valve pressure, and the brake mode, and the output includes the pressure at the brake cylinder. The air brake model has been validated using data from a previous experimental campaign. The model's accuracy in replicating the air brake system mechanism makes it well-suited for future development of model-based algorithms designed for air brake fault detection.  
air brake system; hybrid model; brake cylinder; condition monitoring; freight train Article
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Function** & **Error Condition** & **Description** \\ \hline transfer & Insufficient balance of sender & The sender account does not have enough balance to complete the transfer. \\ \cline{2-3}  & recipient is address(0) & Transfers to the zero address are not allowed, preventing token loss. \\ \hline approve & spender is address(0) & Approval to the zero address is disallowed to avoid confusion regarding allowance ownership. \\ \(\vdots\) & & \(\vdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of error conditions in the ERC-20 standard. \begin{table}
\begin{tabular}{l l} \hline \hline
**Returned Data to User** & **ErrorExplainer-Extracted Error Context** \\ \hline (binary) 0x08c379a \(\cdots\) 002645524332303a207472616e \(\cdots\) 0000 & Message: “ERC20: transfer amount exceeds balance” \\ Origin: “_transfer” \\ Error condition: “_senderBalance >= amount” \\ Code snippet: “_require_(senderBalance >= amount,...);\" \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Returned data to users vs. ErrorExplainer’s output on the failed operation of insufficient balance of the sender. ErrorExplainer extracts error-related information from the source code of smart contracts through static analysis and builds a database of error contexts. When a user encounters an error, ErrorExplainer matches the runtime data against the error contexts and provides the user with an \"error description\". Each error description is expressive enough for users to recognize the reason for the error. We can summarize the contributions of this paper as follows:  
* I identify the practical challenges of non-standardized error messages that make it difficult for users to understand errors in Web3 services. * I propose ErrorExplainer, an automated approach that analyzes smart contract code to generate rich, structured error descriptions matched to the user's execution context. * I demonstrate the effectiveness of ErrorExplainer on real-world smart contracts by evaluating its identification accuracy, information completeness, and matching fitness.
## 3 Results
### Spatiotemporal Distribution Trends of GPP Under CMIP6 Climate Change Scenarios  
Based on the response mechanisms of GPP to climate change, this study further investigates the spatiotemporal distribution characteristics of GPP under the CMIP6 global climate change scenarios. To identify the most suitable future climate change model for the desert steppe region in NFYS, a fitting analysis was performed between historical monthly T\({}_{\text{max}}\), T\({}_{\text{min}}\), and PP data from 2001 to 2014, obtained from six CMIP6 climate models (CanESM5, FGOALS-g3, GFCL-CM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0), and observed datasets. The results, as depicted in Figure 4, reveal that the correlation coefficients for both T\({}_{\text{max}}\) and T\({}_{\text{min}}\) between each climate model and the observed data surpassed 0.95. This indicates a remarkable level of consistency and analogous variability between the model simulations and the actual observed records. Nonetheless, in the realm of precipitation data, the IPSL-CM6A-LR model stood out as the sole model exhibiting a robust linear relationship with the observed data. In light of these validation results across different meteorological variables, this research has opted to utilize the daily maximum temperature, minimum temperature, and precipitation data from the IPSL-CM6A-LR model as the cornerstone dataset for projecting future Extreme Climate Indices (ECI) and GPP.  
Figure 4: CMIP6 climate data models validation.  
Utilizing the IPSL-CM6A-LR climate model, this study investigates the trends in normal climate conditions and Extreme Climate Indices (ECI) in the desert steppe of the NFYS under two representative scenarios: SSP2-RCP4.5 (moderate radiative forcing) and SSP5-RCP8.5 (high radiative forcing). As illustrated in Figure 5, the regional normal climate conditions reveal a pronounced trend toward warming and increased humidity in the study area from 2025 to 2035. Specifically, the number of warm nights (T\({}_{\rm N90p}\)) demonstrates a significantly increasing trend, while warm days (T\({}_{\rm N90p}\)), frost days (ID\({}_{\rm 0}\)), and heavy precipitation days (R\({}_{\rm 25}\)) all show declining trends. Notably, under the SSP2-RCP4.5 scenario, the number of consecutive dry days (CDD) is projected to increase, whereas under the SSP5-RCP8.5 scenario, this extreme index is expected to decrease.  
Based on the path coefficients and weights derived from the partial least squares regression model without considering lag effects (Figure 6), it is evident that the GPP of the desert steppe in the NFYS is significantly higher under the SSP5-RCP8.5 scenario (198.87 gC/m\({}^{2}\)yr) compared to the SSP2-RCP4.5 scenario (184.69 gC/m\({}^{2}\)yr). Under the influence of both normal and extreme climate conditions, the rates of change in GPP under the SSP2-RCP4.5 and SSP5-RCP8.5 scenarios are 0.009 gC/m\({}^{2}\)yr and 0.021 gC/m\({}^{2}\)yr, respectively.  
Figure 5: Temporal trends of climate index from 2025 to 2035.
# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 3 Preparation for the Experiments
### Model Parameters Setting  
The STEAD training dataset preprocessed above is input into the automatic seismic phase recognition model based on Bi-LSTM network as training data. After repeated training and debugging, the error of model training is reduced. The final network parameters are shown in Table 2.  
In this paper, seven pieces of label information of seismic waveform data are used as input data, the input tensor size is (batch_size, n_input, n_steps), and the time window step length is 100. Since the length of single seismic waveform data is 30 s and the sampling rate is 100 Hz, the length of single seismic waveform data is 3000 points. Therefore, the input data tensor is \(30\times 100\), the size of batch_size is 100, the size of n_input is 70, the step size of n_steps is 100, and, finally, the input tensor shape of Bi-LSTM network is \(100\times 30\times 100\). The label dimension is set as 3, which represents P-wave, S-wave, and neither P-wave nor S-wave, respectively. The Bi-LSTM-based automatic phase recognition model training uses the cross-entropy loss function, and uses the Adam algorithm to optimize the calculation process of the loss function. The Adam algorithm has the advantages of high computational efficiency and self-adaptive learning rate, which can accelerate the convergence speed of the model.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Influence of CNN Network Hyperparameters on Modulation Recognition Rate  
A large part of the reason why this model can achieve such a good effect is that it selects the appropriate hyperparameters. Next, we will discuss the influence of hyperparameters on modulation recognition in detail. The hyperparameters of a neural network refer to the types of neuron activation functions, the number of layers, the number of neurons in each hidden layer, the size of convolution kernel, etc. These hyperparameters will not participate in the training of the sample. However, the difference of hyperparameters will affect the learning speed of the neural network and the final classification structure. In other words, it affects how fast the cost function drops on the training set and the classification accuracy on the verification set. However, there is no complete theoretical method and basis for how to select the hyperparameters. Therefore, this section mainly studies the problem of hyperparameter selection of the neural network model for radio modulation recognition. Here we use the model designed in Figure 8 as a benchmark. When one hyperparameter is discussed, the other hyperparameters remain unchanged.