esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 General-Configuration Aircraft Optimization Approach
### A Sample Average Approximate Approach  
Analytical methods require the use of an assumed Probability Distribution Function (PDF) of stochastic variables for the conversion from stochastic to deterministic variables.  
Some researchers have assumed that random variables follow known PDFs such as normal distribution and Weibull distribution. These previously assumed PDFs are overly dependent on the subjective judgment of researchers, and errors between the assumed and actual PDFs result in inaccurate solutions.  
It is difficult to solve the stochastic optimization model for GCA using analytical methods. On the one hand, the number of random variables is too large to characterize. On the other hand, the distribution of the high-dimension random binary variable cannot easily be generated from historical orders.  
Alternatively, instead of assuming a PDF, simulation methods do not rely on the PDFs of stochastic variables and are entirely data-driven, making the process of solving the stochastic optimization model of GCA simpler and easier.  
It is worth noticing that random variables are not included in the constraints. Therefore, chance constraints are not included, and we can use Monte Carlo (MC)-based methods to improve the approximation accuracy by using many MC samples. Thus, the objective can be approximated by the sample average function.  
In other words, the SAA approach is an exterior sampling approach that repeatedly generates several scenarios and solves several related optimization problems, in which the expected value of the objective Function (1) is approximated by the sample average function:  
\[\min_{\mathbf{x}\in X}f(\mathbf{x})=C^{AS}(\mathbf{x})+\frac{1}{S}\sum_{s\in S}[C^{RD}(\mathbf{ x},\mathbf{o}^{s})+C^{RA}(\mathbf{x},\mathbf{o}^{s})] \tag{10}\]  
where \(S\) is the total number of scenarios, and where \(\mathbf{o}^{s}\) is the realization of uncertain customer orders in the scenario \(s\). A scenario is a possible production situation with a combination of orders from customers, including certain orders and the realization of uncertain orders.  
The stochastic optimization model is then converted into a deterministic model with the objective of the average production time in all scenarios. The procedure of the approach is shown as Figure 3 and Algorithm 1:  
Figure 3: The flowchart of the MC-based SAA approach.
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 4 Present Digital Twin Manipulations in Marine Industry
### Dockyard and Port Facility Augmentation  
Not only the sea units, but also the shore establishments are predisposed to DT with advances in Building Information Modeling (BIM) [71] with uplifted interoperability, planning, construction, and maintenance. A vast array of sensors from the building will feed generic data input to the facade DT of the structure, which will be processed for intended output to control the operation, site environmental control and other ergonomics. Already implementations are performed in Smart Home concepts [72], which are highly successful in achieving the intended outcome. Further, Intelligent Manufacturing uses a knowledge-driven autonomous platform, which functions with dynamic knowledge bases along with a DT model [73]. For example, the \"smartBRIDGE\" project initiated by Hamburg Port Authority, Germany, has implemented BIM-based DT to monitor the functions of the infrastructure and the process of extending to quay walls, locks, and other port infrastructural assets [74]. These smart technologies will promote self-judgment, self-implementation, and self-development in autonomous manufacturing in the shipping industry.  
Similarly, the factory design framework [75] is also possible with an increase in productivity along with optimized floor utilization with modular-based DT. In such an operation, it can be developed into a Warehouse Management System (WMS) with the ability to customize the process as a Decision-Support Tool (DST) [76]. It can connect the different value chains with eased-up system integration from the design, and production into the later stages of the lifecycle of a component. In the maritime section, dockyards around the world will be the main beneficiaries of such a DT-based solution.  
Further, the DT solution will allow critical operations of a vessel operating at a far location to be handled by the experts in a shore base in an Augmented Reality-induced environment [77]. This can be a routine operation to critical maintenance or fabrication task, which might require the intervention of an expert operator from a distant location.  
The Simulation and Remote Control Centre (SRCC) will perform a task beyond remote monitoring such as crane operation in various sea conditions [78, 79].
## 6 Experimental Study
### Satisfaction  
The mean satisfaction in the network achieved by both algorithms for a variety of network sizes can be found in Figure 8, along with the values of minimum and maximum satisfaction in the network. Note that satisfaction is slightly lower in the case of BA networks, due to differences in topology (_i.e._, minimum satisfaction is lower, due to the large amount of low degree nodes), but it follows the same behavior as in the ER case. It is easy to see that the algorithms achieve consistently high satisfaction values, which are also increasing as network sizes increase. Of particular interest is that: (a) the minimum satisfaction  
Figure 7: Reconvergence speed for preference change and churn (Erdős–Rényi).  
in the network is being increased, also, meaning that individual nodes enjoy high levels of satisfaction, too, implying asymptotically improved _fairness_ properties, as well; and (b) the minimum satisfaction does not significantly affect the mean satisfaction, which implies that the number of nodes having low satisfaction is consistently very low compared to the size of the network.  
Even though the reconvergence results showed that the AdaptiveLID algorithm can efficiently repair its solution once churn stops, it is interesting to see the levels of achieved satisfaction while churn is in progress. The relative satisfaction for ER networks under churn (to the one achieved before churn starts) can be found in Figure 9: the different graphs from top to bottom correspond to the relative satisfaction when churn affects 5% to 50% of the network's nodes (in steps of 5%), for a network of 100 nodes. It is obvious that the amount of satisfaction achieved remains fairly constant during churn and depends greatly on the amount of churn. However, even though churn is an intense operation, it is possible to retain a significant percentage of the original satisfaction, even for churn as high as 50% (_i.e._, when half of the network is changing at every round).  
On the other hand, the satisfaction drop is significant compared to the one caused by preference change. Figure 10 shows the relative satisfaction for both preference change and churn on ER networks,  
Figure 8: Satisfaction per network size.  
Figure 9: Satisfaction while churn is in progress, affecting 5% to 50% of the network’s nodes (in steps of 5%, top to bottom).  
right after the change happens, for various amounts of affected nodes, supporting our argument in favor of improving connections instead of rebuilding them from the beginning.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
###### Abstract  
Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability. In this paper, we propose Anomaly Detection with Drift-Aware Ensemble-based Incremental Learning (ADDAEIL), an unsupervised anomaly detection framework that incrementally adapts to concept drift in non-stationary streaming time series data. ADDAEIL integrates a hybrid drift detection mechanism that combines statistical distribution tests with structural-based performance evaluation of base detectors in Isolation Forest. This design enables unsupervised detection and continuous adaptation to evolving data patterns. Based on the estimated drift intensity, an adaptive update strategy selectively replaces degraded base detectors. This allows the anomaly detection model to incorporate new information while preserving useful historical behavior. Experiments on both real-world and synthetic datasets show that ADDAEIL consistently outperforms existing state-of-the-art methods and maintains robust long-term performance in non-stationary data streams.  
Keywords:time series anomaly detection; concept drift adaptation; isolation forest; incremental learning; unsupervised method; edge computing _Article_
## 5 Discussion and Conclusions
### Application of the GWO-LSTM Modeling  
In this study, the Grey Wolf Optimization (GWO) algorithm was used to enhance the performance of the Long Short-Term Memory Network (LSTM) in predicting surface settlement in urban areas of Kunming.  
GWO is a meta-heuristic algorithm; it simulates the hunting behavior of grey wolves and enhances the prediction model through the following aspects.  
1. Strong social cooperation mechanism: GWO relies on the social division of labor and cooperation strategies in grey wolf packs, which makes the algorithm more efficient in tracking the global optimal solution. This enhances the convergence speed and accuracy of LSTM in surface subsidence prediction.
2. Flexible Approximation Mechanism: In GWO, individual grey wolves update their positions based on the current optimal candidate position. The parameters of LSTM have effectively been optimized. This allows the model to maintain a balanced exploration and exploitation capability at all stages, which enhances the understanding of time series data.
3. Adaptive behavior: GWO introduces adaptive behavior, which enables the algorithm to dynamically adjust the search strategy and behavior. It improves the model's accuracy in predicting various types of subsidence trends.  
It makes up for the shortcomings of traditional LSTM in the following aspects:  
1. Long-term dependency modeling problem: GWO optimizes the parameters of LSTM, such as the number of hidden layers and the number of units, which enhances the model's ability to deal with long-term dependencies. By the optimal combination of parameters, GWO improves the LSTM model's ability to capture and maintain long-term contextual information.
2. Insufficient Data Problem: GWO selects the most appropriate interpolation or filling techniques by the missing data optimization in the data preprocessing stage. Meanwhile, by optimizing the hyperparameters of the LSTM model, GWO can improve the performance of the model on small sample data and enhance the robustness to incomplete data.
3. Overfitting problem: GWO optimizes the training process of LSTM models through cross-validation and early-stopping techniques to reduce the risk. GWO can find the optimal regularization parameters and dropout ratios, which prevents the model from overfitting the training data and improves the generalization ability.
4. Parameter selection problem: GWO can find the optimal combination of LSTM parameters in the parameter space through its global search capability. The parameters optimized by GWO include the network structure, the learning rate, the number of iterations, and so on. Hence, it improves the performance of the model, which ensures the accuracy and stability of the prediction results.
# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 3 Material and Methods  
### GameEMO Dataset  
In this study, the GAMEEMO dataset is employed [19]. The data set contains EEG signals of 28 subjects. The length of the EEG signals in this data set is 38,252. The age range of the subjects is between 20 and 27 years old. This dataset includes computer games-based EEG signals. They are collected from 28 different subjects with wearable and portable EEG device called 14 channel Emotiv Epoc+. Subjects played 4 emotionally different computer games (boring, calm, horror, and funny) for 5 min, providing a total of 20 min worth of EEG data available for each subject. Unlike other data sets in the literature, computer games were used as stimuli in this data set. In addition, the data were obtained with a portable EEG device. The device contains 14 EEG channels including AF3, AF4, F3, F4, F7, F8, FC5, FC6, O1, O2, P7, P8, T7, and T8. Since the data were obtained with a portable EEG device, connections with the subjects were made via Wi-Fi. While generating the dataset, both discrete emotion model and dimensional emotion model were taken into consideration. In the study, a total of 4 games were played to capture emotions and these games were labeled as G1, G2, G3, and G4. In the discrete emotion model structure, G1 and G3 express negative emotions, while G2 and G4 express positive emotions. For the arousal-valence dimensional model, G1 refers to LANV, G2 refers to LAPV, G3 refers to HANV and G4 refers to HAPV. Table 1 explains the details of GAMEEMO data set. The sampling rate of the signals is 128 Hz. The dataset contains both raw EEG data and pre-processed EEG data. More detailed and technical information about the data set can be found in the study [19].  
In this study, pre-processed EEG data were used. The data acquisition stage is as follows:  
* In the first stage, four different computer games were played by the subjects. The subjects played each game for 5 min. In total, EEG data of 20 min was obtained for each subject.
* The subjects filled out the SAM (Self-Assessment Manikin) form after playing each game. The main purpose of filling out this form is to label each game played.
## 3 Results  
### Study Selection  
Three hundred sixteen references were retrieved in the initial searches in databases (PubMed \(n\) = 52, SCOPUS \(n\) = 127, WOS \(n\) = 114; CINAHL \(n\) = 16, and PEDro \(n\) = 7), and 3 additional records were identified through other sources. After deleting 134 references as duplicates, 182 studies were screened by title and abstract, and 99 were deleted for not being relevant to the topic. Later, 83 studies were assessed in detail for eligibility,and 71 studies were removed for not meeting the inclusion criteria (reasons in Figure 1). Finally, 12 RCTs [58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69] were included in our systematic review with meta-analysis, and all provided data were meta-analyzed. Figure 1 shows the PRISMA flow chart of the study selection process.
## 3 Problem Statement and Model Construction
### Model Construction
#### 3.3.1 Design of Casualty Risk Functions  
In the study of casualty risks from sudden accidents, it has been found that the longer the rescue forces take to arrive at the disaster site, the higher the mortality rate of the injured [31; 32]. Additionally, in the face of disasters, the public is prone to panic, and the spread of this panic can create pressure and even affect the progress of rescue efforts. A study by [PERSON] et al. [33] found that disasters can cause significant psychological stress and panic among personnel, negatively impacting rescue and evacuation operations. Therefore, in CER situations, coordinating the rescue waiting time (RWT) at multiple disaster sites is important to reduce casualties and alleviate victims' psychological panic.  
Inspired by the aforementioned studies and field research with freeway management departments, this paper hypothesizes that higher-priority disaster sites correspond to more severe injuries among victims. Based on the impact of the rescue waiting time on mortality rates for different injury severities, a graph (Figure 4) illustrating the relationships between RWT and the risk of casualties was created. This graph is used to design the casualty risk function. Victims of high-priority events (\(P_{i}=1\)) are more vulnerable, and the risk of casualties rises sharply when the RWT exceeds a certain threshold. Therefore, a monotonically increasing function is used as the basis, while an exponential function segmentation is used at the threshold position to reflect the sharp rise in risk. Meanwhile, victims of low-priority (\(P_{i}=0\)) events can theoretically tolerate a longer rescue time, so there is no need for a sharp increase at the threshold position.  
Figure 3: Illustration of the scheduling scheme.  
Formula for the risk of casualties at a single disaster site:  
\[f\left(\overline{t_{i}},P_{i}\right)=\begin{cases}k_{H}\overline{t_{i}}+\left(a \left(e^{\overline{t_{i}}}-1\right)+\tau\right)_{\left\{\overline{t_{i}}\geq t _{H}\right\}},&P_{i}=1\\ k_{L}\overline{t_{i}},&P_{i}=0\end{cases} \tag{7}\]  
where \(k_{H}\) and \(k_{L}\) are the slopes of the function for high and low priorities, satisfying \(0<k_{L}<k_{H}\); \(a\) and \(b\) are constants; \(t_{H}\) represents the psychological threshold time; and \(\tau\) denotes the surge risk. \(\overline{t_{i}}\) denotes the average time for all EVs to arrive at disaster site \(i\), calculated by Equation (8):  
\[\overline{t_{i}}=\frac{\sum_{j\in M}t_{ji}\cdot x_{ji}}{\sum_{j\in M}x_{ji}} \tag{8}\]  
The casualty risk function for the scheduling scheme is as shown in Equation (9):  
\[f_{risk}=\sum_{i\in N}f\left(\overline{t_{i}},P_{i}\right) \tag{9}\]  
It is noteworthy that this function already encompasses the casualty risk. Researchers can fine-tune the parameters based on the actual scenario. Moreover, since it represents the baseline parameters, our model remains adaptable to diverse research requirements by addressing this concern.
## 4 Discussion
This study suggests a possible pathway through which DII may influence cataract prevalence via metabolic diseases, indicating that improving overall dietary habits may be a more effective strategy for preventing and managing both metabolic diseases and cataracts than focusing solely on individual conditions. This study has several limitations. First, since the 24 h recall method was used to assess dietary intake, it may not accurately reflect usual eating habits or intake levels. Second, this study was conducted as a cross-sectional study using data from the KNHANES, which limits the ability to clearly establish a causal relationship between the DII and cataract prevalence because it was not possible to identify the time of the metabolic disease dietary survey and the time of cataract development. Therefore, a prospective cohort study with a follow-up period of at least 2 to 5 years is needed to evaluate the long-term effects of dietary patterns on the development of metabolic diseases and cataracts. Such studies may help clarify how changes in the DII affect the prevalence of cataracts. Third, this study analyzed people aged 40 and older; however, cataracts are generally an age-related eye disease with a sharp increase in prevalence after the age of 60. Future studies should consider age-stratified analyses to investigate potential variations in the relationship between DII, metabolic diseases, and cataract prevalence across different age groups. Despite these limitations, this study is significant as it is the first to analyze the mediating effect of metabolic diseases in the association between DII and cataract prevalence using large-scale national data. This study comprehensively examines their interrelationships, providing new insights into dietary inflammation and cataract risk.
## 4 Discussion  
The experimental results show that clamping the workpiece with the screw's tightening torque set to 50 Nm provides the best results from the point of view of vibration level reduction. At the same time, for the case when screws were tightened with a torque of 90 Nm, the vibration level was the highest. This means that the proposed method is able to accurately predict both the best and the worst clamping conditions. This is especially true for the middle part of the workpiece. For the external zones of the workpiece, the results are only apparently inconsistent. For zones around sensors A1 and A5, vibration levels are very similar for each screw setting. For A1, the relative difference between the mean value and the RMS results for each screw setting is less than 2%, and for A5, it is less than 6%. For comparison, in the middle of the workpiece, for sensor A3, it is 35%. This means that for external workpiece zones vibration levels are eventually almost the same for any clamping screw settings. This confirms that in the case of the considered workpiece, it is important to focus on reducing vibrations in its middle part. Reduction in the vibrations in external zones is less important.  
Comparison of the calculated times shows that the implementation of the EAOV method results in approximately 2.5 times greater time consumption, with respect to standard processing. For this reason, it may seem unattractive from the point of view of its application. However, it should be remembered that standard machining requires a much larger number (six) of actual milling passes, usually impossible due to the limited machining allowance. The EAOV method significantly reduces this number of passes only to two, i.e., the standard and the best one. Drastically reducing the number of passes during machining significantly limits the wear of cutting tools and reduces the amount of material removed.
## 4 Proposed SFO Estimation Scheme
### Estimation Algorithm
For subcarrier pairs having the same \(F_{m}\), the block-wise partial correlation can be obtained as  
\[\Omega_{m}=\sum_{k\in\mathcal{S}_{m}}\tilde{C}_{l}(k),\ m=1,2,\cdots,N_{s} \tag{22}\]  
where the number of subcarriers assigned in \(\mathcal{S}_{m}\) is denoted by \(N_{m}\). Eventually, an estimate of \(\Delta_{s}\) is derived by finding the argument of \(\Omega_{m}\) over all possible \(m\)'s, which takes expression  
\[\hat{\Delta}_{s}=\frac{1}{N_{s}}\sum_{m=1}^{N_{s}}\frac{N}{2\pi F_{m}DN_{u}} \arg\left\{\Omega_{m}\right\} \tag{23}\]  
or equivalently \[\hat{\Delta}_{s}=\frac{1}{N_{s}}\sum_{m=1}^{N_{u}}\frac{N}{2\pi F_{m}DN_{u}}\tan^{ -1}\left\{\frac{\Im\left\{\Omega_{m}\right\}}{\Re\left\{\Omega_{m}\right\}}\right\} \tag{24}\]  
where \(\tan^{-1}\{\cdot\}\) is the arctangent function, and \(\Re\{x\}\) and \(\Im\{x\}\) are the real and imaginary parts of a complex number \(x\), respectively. Note that the parameters \(F_{m}\) and \(N_{s}\) depend on the transmit spectrum mask that defines the set of tones.
# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
## Materials and Methods
### Reagents and Honey Samples
#### 2.1.2 Honey Samples  
All honey samples were from Brazil and obtained after routine analysis from the Laboratory of Inspection of Animal Products and Food Quality Control of the Faculdade de Medicina Veteriaria e Zootecenia. Six samples were authentic honey (TH): eucalyptus (TH 1 and TH 6), orange (TH 2 and TH 3), and wild plants (TH 4 and TH 5). Eleven samples were adulterated honey (AH): eucalyptus (AH 1), orange (AH 2, AH 3, AH 4, AH 5, AH 9, AH 10, and AH 11), and wild plants (AH 6, AH 7, and AH 8).  
The determination of which honey was adulterated was performed using official methods according to the Brazilian normative. Honey samples whose values disagreed with at least one of the following parameters were considered adulterated: humidity (\(\geq\)20%), acidity (\(\leq\)50 mEq\(\cdot\)kg\({}^{-1}\)), the presence of diastase enzyme, the presence of albuminoid substances (Lund reaction, ranging from 0.6 to 3.0 mL), the presence of 5-HMF (Fiehe test), and the quantification of 5-HMF (Winckler's method, \(\leq\)50 mg\(\cdot\)kg\({}^{-1}\)) [17].