esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm
## 4 The Proposed Algorithm
### The Proposed Algorithms Steps:  
The steps of the suggested approach are summarized and discussed in the steps below:  
1. Sampling initial random design points. Creating a small number of randomly distributed sample points in the design space. The sample procedure is carried out using LHD [12].
2. The black box function (expensive function) is used to detect the current frontier points after evaluating the generated sample points.
3. Calculating the built surrogates' root mean square error (RMSE). In this step, three mixed surrogates are introduced to imitate and replace the expensive function: the response surface function (RSF), radial basis function (RBF), and Kriging (KRG) surrogate model. After these substitutes are fitted to the previously acquired sample points, RMSE is calculated. The surrogate with a higher RMSE value (near to 1) is automatically adjusted by the algorithm, and it is given a higher weight factor than the other surrogates with lower RMSE values (close to 0). The surrogate with the lowest RMSE values receives a weight percentage of 10%, the surrogate with the highest RMSE values receives a weight percentage of 60%, and the surrogate with all other RMSE values receives a weight percentage of 20%. The subsequent stages should make use of the selected mixed surrogate.
4. Creating a lot of cheap points in a short amount of time. They are used to assess the enormous number of sample points produced by LHD after the development of the mixed surrogate (cheap function).
5. Combining the sample points. The preliminary approximated Pareto border is identified or moved towards in this stage by combining all generated sample points (expensive and cheap locations).
6. Identifying the candidate points out of all the ones that already exist (points obtained in the previous step). These points are very likely to become Pareto set points.
7. For fresh sample points, evaluate fitness functions. The expensive black-box functions were used to evaluate the points collected in step 5.
8. To identify the new frontier set, new sample points are combined with expensive frontier points.
9. If convergence criteria (termination criteria) are met, the method ends; otherwise, return to step 4.
## 4 Materials and Methods
### Discovery Proteomics  
For MS analysis, 10 ug of retinal protein was used and evaporated to dryness in the SpeedVac (Eppendorf, Darmstadt, Germany) for 30 min at 30 \({}^{\circ}\)C. The proteins were then digested in-solution by trypsin. Prior to MS analysis, peptide purification was performed via SOLA(tm) spin plates (Thermo Fisher Scientific, Rockford, IL, USA) as described inprevious publications [120]. The liquid chromatography-mass spectrometry (LC-MS) measurements were performed with a hybrid linear ion trap Orbitrap MS system (LTQ Orbitrap XL, Thermo Fisher Scientific, Rockford, IL, USA) coupled online to the EASY-nLC 1200 system (Thermo Fisher Scientific, Rockford, IL, USA). Purified and tryptic digested samples were dissolved in 80 \(\upmu\)L of a 0.1% formic acid (FA), and 2 \(\upmu\)L of each sample (0.125 \(\upmu\)g/\(\upmu\)L) was injected into the system for each run. Solvent A consisted of 0.1% FA in water, and solvent B consisted of 0.1% FA in 80% ACN. Peptides were separated using a PepMap C\({}_{18}\) column system (75 \(\upmu\)m \(\times\) 500 mm; Thermo Fisher Scientific, Rockford, IL, USA). Peptide elution was performed in a gradient over 200 min: 5-30% B (0-160 min), 30-100% B (160-180 min), and 100% B (180-200 min). As described in previous publications, the LTQ Orbitrap operated in positive ionization mode and data-dependent acquisition (DDA) mode [120]. The automatic gain control was set to 1 \(\times\) 10\({}^{6}\) ions. For internal calibration, the lock mass was set to 445.120025 \(m/z\) (poly-dimethyl cyclosiloxane). The dynamic exclusion mode was enabled with the following settings: repeat count = 1, repeat duration = 30 s, exclusion list size = 100, exclusion duration = 300 s, and exclusion mass width = \(\pm\)20 ppm. The five most intense precursor ions were selected for collision-induced dissociation (CID) fragmentation in the ion trap with a normalized collision energy of 35%. Raw LC-MS data were analyzed using MaxQuant v. 1.6.17 (Max Planck Institute for Biochemistry, Martinsried, Germany) bioinformatics software for protein identification and quantification [121]. Tandem MS spectra were searched against the SwissProt database with the taxonomy _Rattus norvegicus_ (date: 7 July 2021, entries: 8131 sequences). The settings were chosen as previously described. All protein identifications were filtered with a false discovery rate (FDR) \(<\) 1% (Supplementary Table S1).
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 5 Challenges in Evolutionary Reinforcement Learning
### Issues Encountered by Reinforcement Learning
#### 5.1.5 Policy Search  
In reinforcement learning, the policy search problem revolves around determining the optimal policy to maximize the cumulative reward for an agent interacting with its environment. A proficient policy search capability is pivotal for efficient learning and preventing potentially sub-optimal or erroneous behavior. As highlighted in [40], conventional reinforcement learning approaches face challenges when confronted with intricate and high-dimensional state spaces. These difficulties can impede the learning process, resulting in subpar strategy quality.  
Therefore, ref. [40] proposed a GPRL method which is able to autonomously learn a policy equation. In this method, genetic programming is used to generate the basic algebraic equations that form the reinforcement learning strategy from the pre-existing default state-action trajectory sample. The key to GPRL is that it learns interpretable and moderately complex policy representations from the data in the form of basic algebraic equations.  
In a similar vein, genetic programming was applied in a two-stage process in another study [82]. Initially, programs were generated in a simulated environment using genetic programming, serving as candidate solutions for various tasks. Subsequently, these actions, derived from genetic programming, were adapted to the operational characteristics of specific real robots through reinforcement learning, particularly Q-learning. The pivotal aspect of this approach is that programs created by genetic programming provide an effective starting point for reinforcement learning, thereby accelerating and enhancing the process of policy search and adaptation.  
Expanding on this concept, another study [86] introduced a genetic programming-based method aimed at automating feature discovery in reinforcement learning. Central to this approach is the use of genetic programming to generate a set of features that significantly enhance the efficiency of reinforcement learning algorithms regarding learning strategies. The key lies in utilizing genetic programming to automatically unearth useful features from an agent's observations, capturing the intricate non-linear mappings between states and actions. This, in turn, improves both the efficiency and effectiveness of the policy search process [86].
## 1 Introduction
To guarantee the security of the proposed method, they used the Arnold transform twice during the watermark embedding process. To distinguish the image block type, [PERSON] and [PERSON] [13] introduced the concept of smoothness; based on the smoothness value, different watermark embedding, tamper detection and recovery strategies were applied. However, in terms of recovery capability, this method is highly applicable only to images with low variation. [PERSON] et al. [14] introduced a self-embedding method for digital images in the JPEG domain that applies source coding to recover the lost content using hierarchical trees, by which set partitioning is performed, and applies low-density parity check coding algorithms. Later, [PERSON] and [PERSON] [15] noted that the scheme in Reference [14] is ineffective when the tampering involves the channel parity bits; in such cases the method will use the wrong channel parity bits to perform channel decoding. They also indicated that the scheme had no ability to detect tampering. [PERSON] and [PERSON] [16] introduced a novel multipurpose watermarking scheme that simultaneously embeds both robust and fragile watermarks. However, their approach requires further exploration to eliminate the need to store and retrieve the mapping file and the hidden watermarks. [PERSON] and [PERSON] Reference [17] proposed an effective dual watermark scheme that detects tampered regions hierarchically. A secret key and a public mixing algorithm are used for tamper recovery. However, this method does not perform as well when the tampered area is trivial as when it is large. [PERSON] et al. [18] proposed a novel fragile watermark scheme that uses a dual domain watermark for authentication, tamper detection and image recovery. Later, in Reference [19], [PERSON] and [PERSON] introduced a method that improves the visual quality of the recovered image and can detect the tampered area accurately. In addition, according to the objective index PSNR, its recovery capability outperforms the method in Reference [18]. To enhance the recovery capability of restored images, this paper presents an improved method. Based on the good performance of SVD and edge detection, variable watermark information can be generated corresponding to block classifications. During image recovery, a median filter is applied to smooth and remove noise, followed by three operations on the original restored image that produce a satisfactory effect. Therefore, in the proposed method, SVD and edge detection are used for tamper detection; then, median filtering is used during image recovery. Finally, the corresponding pixels of the original restored image are replaced with the best results among the three operations. The remainder of this paper is organized as follows. Section 2 provides a brief introduction to the method in Reference [7]. In Section 3, the proposed method is discussed in detail. Section 4 reports the experimental results and provides analyses. Finally, the paper is concluded in Section 5.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology
### Temporal Sparse Attention (TSA)
#### 3.1.2 Multi-Head Sparse Attention  
A set of attention processes running in parallel (in parallel termed _heads_) to capture different sorts of relations in the data.  
Query, Key, and Value Computation  
Figure 1: Architecture of the TSA-GRU module.  
The embedded sequence linearly projects to three vectors:  
\[Q=W_{Q}\,x_{\text{embed}},\quad K=W_{K}\,x_{\text{embed}},\quad V=W_{V}\,x_{ \text{embed}} \tag{2}\]  
where \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are learned weight matrices.  
Scaled Dot-Product Attention  
A method where attention scores between query and key are obtained via a dot product and scaled by the square root of the dimension \(d_{K}\). This scaling helps maintain numerical stability.  
Scaled Dot-Product  
\[\text{scores}=\frac{Q_{h}K_{h}^{\top}}{\sqrt{d_{K}}} \tag{3}\]  
Here, \(Q_{h}\) and \(K_{h}\) are the query and key matrices for each head \(h\), with \(d_{K}=H/n\) where \(n\) refers to the number of heads.  
Sparse Query-Key Selection  
Sparsity in this case implies that only relevant connections with attention scores higher than a threshold are retained, and the remaining lateral connections are masked.  
For each query position \(i\), the maximum attention score is computed:  
\[\text{max\_score}_{i}=\max_{j}\text{scores}(i,j) \tag{4}\]  
A threshold of 50% of this maximum is applied:  
\[\text{threshold}_{i}=0.5\times\text{max\_score}_{i} \tag{5}\]  
Scores that fall below the threshold (all but the maximum) are masked out:  
\[\widetilde{\text{scores}}(i,j)=\begin{cases}\text{scores}(i,j),&\text{if scores}(i,j)\geq\text{threshold}_{i}\text{ or }j=\text{arg\,max\,scores}(i,j)\\ -\infty,&\text{otherwise}\end{cases} \tag{6}\]  
The softmax function is applied over these masked scores to determine the attention weights:  
\[\alpha(i,j)=\frac{\exp(\widetilde{\text{scores}}(i,j))}{\sum_{j^{\prime}}\exp (\widetilde{\text{scores}}(i,j^{\prime}))} \tag{7}\]
## 6 Simulation Set-up and Configuration
### Description  
The grid topology for the WSN geographical extent was chosen to facilitate analysis of actual and predicted results. Six sets of inter-nodal distances: 5, 10,15,20,25, and 30 meters; and four positions for the jammer: two inside and two outside the grid were selected for the simulation. Three sets of total number (quantity) of nodes: 25, 50, and 100 were considered. Thus a total of 720 simulations (6 inter-nodal distances X 10 types of jammers X 4 jammer locations X 3 types of node quantity), with corresponding aforesaid parameters, were done using the NS2, MATLAB, and Simulink simulator. Figure 6 shows the schematic diagram of one of the 720 simulation set-ups, where the inter-nodal distance is 20m, the jammer is a constant jammer with high output power, the jammer is located inside the WSN grid at coordinates (30,45) with the sink node located at coordinates (85,85), and the WSN has a total of 25 nodes.
## 5 Simulation and Analysis
### Performance of the Proposed ACE Method with Different Demodulation Thresholds  
This section simulates and analyzes the RACH success probability and power consumption for NB-IoT terminals in different SINRs with the parameters in 3 GPP Release 17 and optimized parameters. To validate the accuracy of the results obtained through numerical methods, we conducted Monte Carlo simulations to obtain the performance and performed a comparison with a theoretical analysis. Figure 7 indicates a consistent alignment between the simulation curve and the results from the theoretical analysis. Meanwhile, Figure 7 shows the RACH success probability and power consumption comparison of the 3 GPP Release 17 parameters and optimized parameters. It is evident that at the same SINR threshold, the RACH success probability with the optimized parameters is higher than that with the original 3 GPP parameter configuration. This improvement is attributed to the smaller repetitions and the optimized back-off window size tailored for the long delay of the LEO satellite. The proposed ACE method prevents NB-IoT terminals from having excessive repetitions within a random access occasion and ensures that other terminals can acquire access successfully. This ACE method mitigates the avalanche effect of random access failures, leading to a significant improvement in RACH success probability. Additionally, it reduces the power consumption of NB-IoT terminals. In addition, it is essential to evaluate the performance of the ACE method throughout the entire lifespan of NB-IoT terminals. This article uses the NB-IoT terminal battery standard published by 3 GPP for calculations. As shown in Figure 8, if we directly use the CE parameters of the terrestrial network in the LEO satellite-based network, it will result in a significant decrease in battery life.
###### Abstract  
In this study, numerical simulations using the Environmental Fluid Dynamics Code model were conducted to elucidate the effects of flow structures in the recirculation zone on solute storage based on the junction angle. Numerical simulations were performed at a junction angle of 30\({}^{\circ}\) to 90\({}^{\circ}\) with a momentum flux ratio of 1.62. The simulation results revealed that an increase in the junction angle caused the recirculation zone length and width to increase and strengthened the development of helical motion. The helical motion increased the vertical gradient of the mixing layer and the mixing metric of the dosage curves. The recirculation zone accumulated the solute as a storage zone, which formed a long tail in the concentration curves. The interaction between the helical motion and recirculation zone affected the transverse mixing, such that the transverse dispersion had a positive relationship with the helical motion intensity and a negative relationship with the recirculation zone size. Transverse mixing exhibited an inverse relationship with the mass exchange rate of the recirculation zone. These results indicate that the transverse dispersion is replaced by mixing due to strongly developed storage zones.  
river confluence; junction angle; recirculation zone; helical motion; storage zone; transverse dispersion Article
## 3 Numerical Modeling of HDS
### System Performance Indices  
The dehumidification capacity (\(D_{c}\)) reflecting the ability of the dehumidification plant can be calculated by Equation (23).  
\[D_{c}=3600m_{a}(Y_{i}-Y_{o}) \tag{23}\]  
where \(Y_{i}\) and \(Y_{o}\) represent the air moisture content of the inlet and outlet, respectively.  
The total energy consumption (\(W_{tot}\)) of HDS can be calculated by Equation (24).  
\[W_{tot}=W_{com}+W_{fan}+W_{et} \tag{24}\]  
where \(W_{com}\) is the compressor's energy consumption. \(W_{fan}\) is the fan's energy consumption. \(W_{et}\) is the heater's energy consumption.  
The specific moisture extraction rate (\(SMER\)) is a comprehensive index to evaluate the dehumidification ability and energy consumption of the dehumidification system and can be calculated by Equation (25).  
\[SMER=\frac{D_{c}}{W_{tot}} \tag{25}\]
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 4 Experimental Design  
In this section, five different datasets are analyzed. They have been collected from different automatic machinery during tests simulating nominal and improper working conditions of one or more components. The components under analysis, the collected signals, the health conditions, the corresponding durations, and the sampling frequency of raw signals for each dataset are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Environment** & **Component/s** & **Signals** & \
\begin{tabular}{c} **Health** \\ **Conditions** \\ \end{tabular} & **Dataset Duration** &
\begin{tabular}{c} **Raw Data** \\ **Sampling** \\ **Frequency (Hz)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Dataset 1} & \multirow{2}{*}{Laboratory} & \multirow{2}{*}{Electric Motor} & \multirow{2}{*}{Vibration} & Nominal 1 & 82.8 min & \multirow{2}{*}{12,800} \\  & & & Nominal 2 & 184.2 min & & \\  & & & Nominal 3 & 77.4 min & & \\  & & & & & Fault 1 & 25.8 min & \\  & & & & Nominal & 15 s & \\  & & & & Fault 1 & 15 s & \\  & & & & Fault 2 & 15 s & 500 \\  & & & & Fault 3 & 15 s & \\  & & & & Fault 4 & 7.05 s & \\  & & & & Nominal & 15.99 s & \\  & & & & Fault s1 & 16.5 s & \\  & & & & Fault s2 & 3.52 s & \\  & & & & Nominal 1 & 67.5 (M1)–8196.5 (M2) h & \\  & & & & Nominal 2 & 796 (M1)–326 (M2) h & \\  & & & & Nominal & 266.4 s & \\  & & & & Fault s1 & 160 s & \\  & & & & Fault s2 & 152 s & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the available datasets.
## 3 Results
### XAI Evaluation
#### 3.8.2 Quantitative
(2024) the ZNCC score quantitatively assessed the feature separation ability of the activation maximization generated images in a benign-malignant (i.e., binary) classification problem [50]. * The pointing game metric is a QT evaluation method used to assess how well the areas identified by saliency maps align with relevant regions in medical images. It evaluates whether the most significant activation points in the saliency map correspond to specific anatomical or pathological features in the analyzed images. [PERSON] et al. (2022) used the pointing game to verify if the CAM saliency maps highlighted significant regions for accurate diagnosis, i.e., breast mass region, peritumoral region, or region below the breast mass [39]; see Figure 13.
* The resemblance votes metric was used in [PERSON] et al. (2021) for QT evaluation of how well the ROE identified by the AI aligns with the regions considered important by physicians for making diagnostic decisions [40]. The metric categorizes the ROE into three resemblance levels as perceived by clinicians: High resemblance (HR), where the ROE closely matches the features used by physicians; medium resemblance (MR), where the ROE partially matches; and low resemblance (LR), where there is little to no match. The contribution of clinicians in scoring the resemblance of AI predictions can aid in validating and improving the model's explainability and utility in clinical practice. Figure 13: US images presenting benign (**left**) and malignant (**right**) breast masses and the corresponding CAM-generated saliency maps pointing out the three predetermined regions in the US images. The white cross indicates the extreme activation value of CAM responsible for the particular pointing game result. Adapted from [PERSON] et al. (2022) [39]; an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (CC BY-NC-ND). Regarding the additional QT metrics identified, the ZNCC score primarily evaluates the AI model's performance in terms of image similarity and feature activation. The evaluation contributes to understanding model behavior by illustrating AI model responses to variations in input; however, it does not directly assess the explainability. The pointing game specifically evaluates the explainability, as it measures the alignment between the model's focus (saliency maps) and the clinically relevant areas in the images. Similarly, the resemblance votes method evaluates the explainability as it quantifies the correspondence between the ROE and the diagnostic areas identified by physicians, rather than the accuracy of the underlying AI system. Comparably, the Shapley values quantify the contribution of individual clinical features to the output. These explanations of which features the model uses to make a specific decision are as important as the decision itself.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Limitations of the Black-Scholes Model  
Despite its mathematical elegance, the Black-Scholes model fails to account for key market phenomena such as stochastic volatility, jumps in asset prices, and transaction costs. These limitations have motivated the development of alternative approaches, including numerical methods such as Monte Carlo simulations and finite difference methods. However, these techniques often require substantial computational resources, making them impractical for real-time applications.  
To address these challenges, machine learning models, particularly deep neural networks (DNNs), have emerged as powerful tools for option pricing. Unlike traditional methods, DNNs do not require strong assumptions about market behavior and can learn complex nonlinear relationships directly from data.