esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Active Anomaly Detection with Switching Latency
## 2 System Model and Problem Formulation
### System Model
Thus, the expected cumulative cost incurred by all the abnormal processes during the entire detection procedure is given by  
\[E(C|\Gamma)=E\Bigg{\{}\sum_{m\in\mathcal{H}_{1}}c_{m}\tau_{m}|\Gamma\Bigg{\}}, \tag{1}\]  
where \(\Gamma=(\mathbf{\tau},\delta,\mathbf{\phi})\) denotes an admissible policy for the sequential anomaly detection problem based on the active hypothesis testing model. The objective is to find a policy \(\Gamma\) that minimizes the expected cumulative cost subject to the error constraints for each process:  
\[ \begin{split}\inf_{\Gamma}& E(C|\Gamma),\\ \text{subject to}& P_{\varepsilon}^{m}\leq\alpha \quad\forall m=1,2,\cdots,M.\end{split}  \tag{2}\]  
It should be noted that \(P_{\varepsilon}^{m}=\max(P_{m}^{FA},P_{m}^{MD})\), where \(P_{m}^{FA}\) and \(P_{m}^{MD}\) denote the false-alarm and miss-detection error probabilities for process \(m\) respectively. Besides, let \(C^{*}\) denote the infimum achievable by any policy,  
\[C^{*}=\inf_{\Gamma}E(C|\Gamma). \tag{3}\]  
Further, analyze to simplify the objective function of the expected cumulative cost. During the entire detection procedure, in addition to spending time observing the processes, it also takes time to switch across processes. Let \(N_{\tau_{m}}^{d}\) and \(N_{\tau_{m}}^{s}\) denote the total sample size and the total switching times by the stopping time \(\tau_{m}\), respectively. We set each observation to 1 unit time and each switching to \(s\) unit time. Then, the stopping time \(\tau_{m}\) for process \(m\) can be denoted by  
\[\tau_{m}=N_{\tau_{m}}^{d}+sN_{\tau_{m}}^{s}.\]  
Thus, the objective function can be denoted by  
\[ \begin{split}\inf_{\Gamma}& E\Bigg{\{}\sum_{m\in \mathcal{H}_{1}}c\left(N_{\tau_{m}}^{d}+sN_{\tau_{m}}^{s}\right)|\Gamma\Bigg{\}},\\ \text{subject to}& P_{\varepsilon}^{m}\leq\alpha \quad\forall m=1,2,\cdots,M.\end{split}  \tag{4}\]
## 4 Discussion
### Influence of Environmental Noise on the Proposed Method  
The bridge structure is in a tough external environment, and the structural response will inevitably be disturbed by environmental factors. Therefore, it is necessary to discuss the anti-noise performance of the proposed CNN-LSTM-based damage identification method under different noise levels. This paper adds noise to the obtained acceleration time-history response according to Equation (16), and the noise level is measured using the signal-to-noise ratio [36].  
\[\hat{x}=x_{signal}+x_{noise}=x_{signal}+\varepsilon N\big{(}\mu,\sigma^{2 }\big{)}\] \[\varepsilon=sqrt\left(\Sigma_{i}^{n}\,x_{i}^{2}/\left(n\times 10^{ SNR/10}\right)\right)\ \, \tag{16}\]  
where \(\hat{x}\) and \(x_{signal}\) are acceleration samples with and without noise, respectively, SNR is the signal-to-noise ratio, \(N\big{(}\mu,\sigma^{2}\big{)}\) is Gaussian noise obeying \(\mu=0,\sigma^{2}=1\), and \(n\) is the length of the acceleration sample data.  
Noise was added to the test samples to obtain five new test sets with signal-to-noise ratios of 20, 10, 6.67, 5, and 3.33, respectively; moreover, the trained CNN-LSTM model (i.e., first training) in Section 3.4 was used for testing. Figure 11 shows the identification results of the damage localization and severity prediction of hangers under different noise levels.  
Figure 10: ARIE of CNN-LSTM model about damage severity prediction.  
It can be seen from Figure 11 that the damage recognition effect of the CNN-LSTM model on the hangers is greatly affected by environmental noise. In terms of the overall trend, the accuracy of damage localization and the ARIE of the damage severity decreases and increases with the increase in noise level, respectively. When the SNR is 10, the damage localization accuracy can reach approximately 75%, but the ARIE of the severity prediction increases significantly. When the SNR is 3.33, the recognition accuracy is only 46.40%, which is 47.60% lower than the test result without noise, and the ARIE is close to 50%.  
The CNN-LSTM model was retrained with a new training set composed of samples with Gaussian noise (SNR: 10, 5, and 3.33) and noise-free samples, and the test set with different noise levels was fed into the retrained model. The test results are shown in Figure 11. It can be seen from Figure 11 that the anti-noise performance of the CNN-LSTM model after retraining with noisy training samples was greatly improved. When the SNR is 5 and 3.33, the accuracy of the damage localization of the hangers is increased by 17.40 and 20.66%, and the ARIE of the severity prediction is reduced by 11.67 and 19.00%, respectively. The results show that using the acceleration response dataset containing noise for the second training of the model can effectively improve the anti-noise performance of the model against higher noise levels. Simultaneously, it also indicates that the proposed model can quickly learn the damage characteristics in the acceleration response with noise and has a strong learning ability.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Simulation Scenario  
In our simulation, VanetMobiSim (1.1, Institut Eurecom, Sophia Antipolis, Valbonne, France, 2007) [43] is used to generate the vehicular mobility traces in an area of 3000 \(\times\) 2700 m\({}^{2}\). This area contains 18 intersections and 30 bidirectional multilane roads in which vehicular nodes mobility is simulated. At the start of the simulation, the vehicular nodes are randomly distributed over the multilane roads and move in both directions. The movements of the vehicles on the roads are based on car following model or intelligent driving model [43]. The vehicles speed depends on the kind of the vehicles (like car, bus, truck, etc.) and nature of the roads. The number of vehicles is varied from 100 to 350. The simulation results are averaged over 10 runs. The rest of the parameters are summarized in Table 2.
## 3 Results and Discussion
The type of base material, such as particleboard or medium-density fiberboard (MDF), can also affect the strength of the corner joints. The density and gradation of the materials used in furniture can significantly affect the joint strength. Acrylonitrile butadiene styrene (ABS), polyvinyl chloride (PVC), melamine, and wood veneers are commonly used for edge banding. ABS is known for their impact resistance, durability, and overall appearance. PVC edge banding is manufactured using high-impact modifiers that provide excellent machinability and impact resistance. Melamine-impregnated paper is absorbed with resin and covered with a lacquer surface, resulting in a high strength and resistance to impacts, scratches, and abrasion. Wood veneer edge banding is performed on slices of real wood, allowing it to be painted or varnished to match other furniture parts [3; 4; 5; 6]. Each material has unique properties and advantages, making it suitable for different applications in edge banding (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Advantages** & **Disadvantages** & **Use Cases** & **Key Insights** \\ \hline \multirow{4}{*}{PVC} & \multirow{4}{*}{\(\blacksquare\)} & \multirow{4}{*}{Environmental concerns due to harmful chemicals} & \multirow{4}{*}{Kitchens and bathrooms} & \multirow{4}{*}{\(\blacksquare\)} & \multirow{4}{*}{\(\blacksquare\)} \\  & & & & & \\  & & Cost-effective and widely available & & & & \\  & & Water and & & released during production and & & \\  & & & & & \\  & & Omes in a variety of & & disposal & & \\  & & colors and textures & \(\blacksquare\) & Can appear less & & \\  & & Easy to process and & & premium than & & \\  & & apply & & natural materials & & \\  & & & \(\blacksquare\) & Limited heat & & \\  & & & resistance & & & \\  & & High impact & & & & \\  & resistance & & & & & \\  & & Durable and & & & & \\  & scratch-resistant & & \(\blacksquare\) & Lower heat resistance & & \\  & & More & & than some other & & \\  & environmentally & & materials & & & \\  & friendly than PVC & & \(\blacksquare\) & Limited flexibility for complex shapes & & \\  & & UV resistant & & & & \\  & & Lightweight & & & & \\  & & Provides a natural, & & Sensitive to humidity & \(\blacksquare\) & High-quality furniture & Wood veneer is preferred for high-end \\  & & & & & & \\  & & Laurentious, premium & \(\blacksquare\) & Requires careful & \(\blacksquare\) & Furniture for living rooms, dining & furniture due to its natural beauty but requires careful handling and \\ Wood & & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 2: Overview of edge banding materials.
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 2 Principles  
### Principles of the Sample Moire Method  
Figure 1 shows the process of sampling moire, primarily including sparse sampling, interpolation, and digital phase shifting. For the fringe image captured by the camera (Figure 1a), its intensity distribution is shown by the red curves in Figure 1a, which can be expressed as [24]:  
\[I(x)=I_{b}+I_{a}\cos(\frac{2\pi x}{p_{g}}+\phi_{0}) \tag{1}\]  
where \(I\) is the fringe intensity, and \(I_{b}\) and \(I_{a}\) are the background intensity and the amplitude of the fringe intensity, respectively. The \(p_{g}\) is the fringe pitch, and \(\phi_{0}\) is the initial phase. The captured fringe image is sampled with an integer pixel (sampling pitch \(p_{s}\)). Under the fixed sampling pitch, a series of sampling moire can be obtained by changing the position of the initial sampling points. Then, the sampling moire patterns with the same size as the original image can be obtained by using the spline difference (shown in Figure 1b). As the sampling position moves, the sampling moire patterns also have the phase shift shown in Figure 1c. The sampling moire spacing \(p_{m}\) is obtained by [24]:  
\[p_{m}=\frac{p_{g}*p_{s}}{|p_{g}-p_{s}|}=\frac{p_{g}}{\left|\frac{p_{g}}{p_{s}} -1\right|} \tag{2}\]  
where \(p_{m}\) represents the sampling moire spacing, and the phase shift step of the sampled moire is \(2\pi/p_{s}\). The intensity of the sampling moire patterns under different phase shifting steps can be expressed as [24]:  
\[\begin{array}{rl}I_{m}(x,i)&=I_{b}+I_{a}\cos(2\pi x(\frac{1}{p_{g}}-\frac{1} {p_{s}})+\phi_{0}+i\frac{2\pi}{p_{s}})\\ &=I_{b}+I_{a}\cos(\phi_{m}+i\frac{2\pi}{p_{s}})(i=0,1,2,\cdots,p_{s}-1)\end{array} \tag{3}\]where \(I_{m}\) represents the sampling moire intensity and \(\phi_{m}\) is the phase of the initial sampling moire patterns, which can be obtained by the discrete Fourier transform algorithm using Equation (4), as follows [6]:  
\[\phi_{m}=-\tan^{-1}\frac{\sum_{i=0}^{p_{i}-1}I_{m}(x,i)\sin(i\frac{2\pi}{p_{i}}) }{\sum_{i=0}^{p_{i}-1}I_{m}(x,i)\cos(i\frac{2\pi}{p_{i}})} \tag{4}\]
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 5 Results and Discussion
### Summary of the Results  
Selected performance indicators of the proposed algorithm, investigated in Sections 5.1-5.3, are summarised in Table 2. In addition to the results related to the evolution of the Pareto set size and the Pareto front quality (measured by the \(HV\) metric), the table--in its last five columns--shows the significance of the local search, measured as a relative improvement in each of the performance indicators.  
In summary, the proposed algorithm shows a strong ability to find high-quality, diversified solutions to the multi-objective ship route optimisation problem in a non-stationary environment. The key element in achieving this proved to be the dynamic programming-based local search algorithm, whose application--thanks to its strong exploitation capabilities--improved the final result significantly: the Pareto set size increased more than five times, and the Pareto front quality just about doubled.
# Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
## 4 Analysis and Results
### Barriers and Recommended Solutions for Local Governments  
Local governments face numerous barriers to maintaining strong cybersecurity measures. In fact, this systematic review identified a total of 16 barriers to cybersecurity for local governments. The lack of sufficient funds is the most significant barrier, as mentioned in several articles [4; 7; 8; 11; 15; 19; 53; 61]. Other key barriers include the absence of cybersecurity policies and regulations, inadequate training provisions [61], and a lack of expertise [11; 58]. The lack of collaboration among departments and partnerships with external entities is also a major barrier to ensuring effective cybersecurity in local governments [58]. Figure 7 presents the barriers identified in this systematic review. For better comprehension, we categorized the barriers into four broader groups: (a) resource and infrastructure challenges; (b) technical and operational challenges; (c) policy and regulatory challenges; and (d) accountability and behavioral challenges.  
To counter the barriers or challenges, we identified a total of 23 recommendations during our systematic review. Most of the articles emphasized the need for more financial resources to develop appropriate strategies and implement them. These included recruiting skilled personnel, adopting advanced technologies, and ensuring system monitoring and updates [7; 10; 14; 80]. Other recommendations included establishing well-defined cyber-security policies, standards, and educational initiatives, as well as expanding technical proficiencies through AI [7; 15; 53; 80; 82]. Some of the reviewed articles also emphasized the importance of human factors in cybersecurity, such as enhancing the knowledge of government personnel [7; 49], conducting background checks [14], and ensuring comprehensive training and accountability for Information Technology (IT) infrastructure users [15; 53]. [PERSON], [PERSON], and [PERSON] [10] and [PERSON], [PERSON], and [PERSON] [13] recommended strategic alliances with businesses and research institutions to understand and reduce cyberthreats in local governments. Figure 7 lists all the recommended solutions  
Figure 7: Barriers to cybersecurity and recommended solutions for local governments.  
along with the barriers. The barriers and recommended solutions are further discussed in the Discussion Section (Section 5.2.4) of this paper.
## 6 Conclusions  
In this paper, one of the soft computing methods, Support Vector Regression (SVR), was used to design a triple-band antenna with the desired resonance frequency and gain. The SVR model was utilized to obtain the optimal values for the geometric parameters of an antenna that provides the desired antenna variables. A microstrip patch antenna with triple slots and arc-truncation (MPATSAT) was simulated using these values via High-Frequency Structural Simulator (HFSS), and simulation and measurement results were compared. The performance characteristics of MPATSAT were compared with those obtained for several antennas given in the literature. Then, the antenna was redesigned by using the optimization tools in HFSS with the same desired antenna parameter values. The SVR approach and HFSS optimization method were compared with respect to accuracy and required computation time. These comparison results justify the superiority of the proposed design approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SVR Model** & **HFSS Optimization** & **HFSS Optimization** \\  & **Approach** & **Tool (GA)** & **Tool (PS)** \\ \hline \multirow{2}{*}{computation time} & 2 h and & 9 h and & 7 h and \\  & 36 min & 46 min & 32 min \\ \hline _TAE\({}_{f}\)_ (GHz) (24) & 0.17 & 0.34 & 0.31 \\ \hline \multirow{2}{*}{desired resonance frequencies (GHz)} & 2.4 & 2.4 & 2.4 \\  & 4.7 & 4.7 & 4.7 \\  & 5.3 & 5.3 & 5.3 \\ \hline \multirow{2}{*}{computed resonance frequencies (GHz)} & 2.45 & 2.37 & 2.33 \\  & 4.73 & 4.52 & 4.54 \\ \hline \multirow{2}{*}{computed maximum realized gains at the resonance frequencies (dB)} & 5.21 & 5.17 & 5.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results of SVR model and HFSS optimization tool.  
**Author Contributions:** Conceptualization, T.G. and M.E.Y.; methodology, T.G. and M.E.Y.; software, G.O.G. and M.E.Y.; validation, T.G., M.E.A. and M.E.Y.; investigation, M.E.Y. and M.E.A.; resources, T.G., G.O.G. and M.E.A.; writing--original draft preparation, M.E.Y.; writing--review and editing, G.O.G., T.G., M.E.A. and M.E.Y.; visualization, T.G. and M.E.Y.; supervision, T.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem
## 3 Mathematic Formulation of the Problem  
In this section, we present the problem under consideration as a mixed integer linear programming (MILP) model. First, the problem parameters and decision variables are given, and then the model is provided. Table 2 shows the problem indices.  
Table 3 shows the problem decision variables.  
The research problem can be denoted as \(P\big{|}s_{j},p-batch,K_{i}\big{|}C_{\max}\). The mathematical formulation of the research problem \(P\big{|}s_{j},p-batch,K_{i}\big{|}C_{\max}\) is shown as follows:  
\[MinimizeC_{\max}, \tag{1}\]  
which is subject to  
\[\sum_{i=1}^{m}\sum_{l=1}^{n}x_{jil}=1,\quad j=1,2,\ldots,n; \tag{2}\]  
\[\sum_{j=1}^{n}s_{j}x_{jil}\leq K_{i},i=1,2,\ldots,m;\ l=1,2,\ldots,n; \tag{3}\]  
\[y_{il}\geq p_{j}x_{jil},\ j=1,2,\ldots,n;\ i=1,2,\ldots,m;\ l=1,2,\ldots,n; \tag{4}\]  
\[C_{\max}\geq\sum_{l=1}^{n}y_{il},\ i=1,2,\ldots,m; \tag{5}\]  
\[x_{jil}\in\{0,1\},\ j=1,2,\ldots,n;\ i=1,2,\ldots,m;\ l=1,2,\ldots,n. \tag{6}\]  
The Objective Function (1) shows that our aim is to find a schedule to minimize the makespan \(C_{\max}\). Constraint (2) is to make sure that each job is assigned exactly to one machine. Constraint (3) guarantees that all batches are feasible; in other words, the total size of all jobs assigned to the batch does not exceed the capacity of machine where the batch is scheduled. Constraint (4) indicates that the processing time of a batch is not less than the processing time of the jobs in the batch. Constraint (5) guarantees that the makespan of the schedule is not less than maximum load of all the machines. In Constraint (6), the 0-1 variable \(x_{jil}\) indicates whether the \(j\)th job is assigned into the \(l\)th batch on machine \(M_{i}\) (\(x_{jil}=1\)) or not (\(x_{jil}=0\)).
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
###### Abstract  
A stain defect is difficult to detect with the human eye because of its characteristic of having a very minimal difference in brightness with the local area of the surface. Recently, with the development of Deep learning, the Convolutional Neural Network (CNN) based stain defect classification method has been proposed. This paper proposes a Dual-stream CNN for stain defect classification using a Gabor filter image. Using Dual-stream structure CNN, Gabor filter images and Gray image (Original) preserve their respective features. The experiment based on the Magnetic Tile (MT) stain data set and the Compact Camera Module (CCM) stain dataset confirms that the proposed method has an improved performance based on the precision, recall, and F1-score in comparison to the Single-stream extraction-based method. Gabor filter images have an advantage in image texture analysis and can be used as an input to the CNNs. The Dual-stream structure better extracts the features needed for classification.  
Keywords:dual-stream feature extraction; Gabor filter image; stain defect classification +
Footnote †: journal: Applied sciences
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 2 Related Work
### Entity Relation Extraction Based on Traditional Machine Learning  
Most traditional machine learning models mainly focus on extracting features from sample data. These models usually use classical models such as SVM (Support Vector Machine), the Markov chain, and logistic regression [14]. In 2005, [PERSON] et al. [15] used the SVM machine learning model for entity relationship extraction, and the F1 value on the ACE database reached 55.5%. In 2006, [PERSON], [PERSON] and others applied the Markov chain model to the task of entity relationship extraction [16]. In 2009, [PERSON] [17] and others processed the data input layer of SVM and made some achievements in relationship extraction in the biomedical field by using word bag features, part-of-speech features, and dependency features as model inputs.  
Due to the defects of traditional machine learning-based entity relationship extraction methods and the propagation of feature extraction errors, it is often difficult to obtain a good recall rate in small sample entity relationship extraction.
## 3 Results
### Changes during Storage  
#### 3.2.1 Post-Acidification and Titratable Acidity  
Post-acidification and titratable acidity of all fermented samples after day 1, 7, 14, and 21 were evaluated to understand changes in acidity during storage. Results are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Culture Combination**} & \multicolumn{3}{c}{**Base**} \\ \cline{2-4}  & **Soy** & **Oat** & **Coconut** \\ \hline YF-L01 & 7.09 \(\pm\) 0.10 \({}^{Ac}\) & 8.57 \(\pm\) 0.07 \({}^{Bd}\) & 5.32 \(\pm\) 1.48 \({}^{C}\) \\ YF-L02 & 6.64 \(\pm\) 0.10 \({}^{Ab}\) & 7.38 \(\pm\) 0.03 \({}^{Bbc}\) & 5.11 \(\pm\) 1.26 \({}^{C}\) \\ BY-01 & 6.91 \(\pm\) 0.10 \({}^{Abc}\) & 7.94 \(\pm\) 0.05 \({}^{Bc}\) & 5.70 \(\pm\) 1.16 \({}^{C}\) \\ YF-L01+LGG® & 6.27 \(\pm\) 0.07 \({}^{Aa}\) & 7.27 \(\pm\) 0.05 \({}^{Bb}\) & 5.33 \(\pm\) 1.40 \({}^{C}\) \\ YF-L02+LGG® & 6.02 \(\pm\) 0.10 \({}^{Aa}\) & 6.65 \(\pm\) 0.13 \({}^{Ba}\) & 5.43 \(\pm\) 1.18 \({}^{C}\) \\ BY-01+LGG® & 6.05 \(\pm\) 0.23 \({}^{Aa}\) & 6.80 \(\pm\) 0.09 \({}^{Bab}\) & 5.93 \(\pm\) 1.16 \({}^{C}\) \\ \hline \hline \end{tabular} \({}^{ABC}\) Means with different uppercase superscripts indicate significant differences between different columns (\(p\) < 0.05). \({}^{abc}\) Means in the same column with different lowercase superscripts indicate significant differences between different culture combinations (\(p\) < 0.05).  
\end{table}
Table 5: Fermentation time in hours required to reach pH 4.5 in all fermented bases.  
comparison to other _Lactobacillus_ strains in a plant-based blend. TA of fermented oat in this study was higher than the one reported by [PERSON] et al. (2018). The highest value was 0.84% in samples fermented by BY-01+LGG(r) after 21 days of storage. In fermented soy samples, the presence of LGG(r) showed no significant differences in lactic acid production (Graph A, Figure 1). The highest value was 0.52% in samples fermented with YF-L01 after seven days of storage. Similar TA values were reported by [PERSON] et.al. in 2019 (2019) in fermented soy blends and by [PERSON] et al. in fermented soy peanut blends (2019). In coconut samples (Graph C, Figure 1), LGG(r) only had an effect in day 14 and 21, where the samples showed significantly higher lactic acid content. The highest value was 0.48%, found in samples fermented by BY-01+LGG(r) after 14 days of storage.