esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration Between Serious Games and EEG Signals:
## 4 A Review of Technological Solutions That Integrate Serious Game and EEG Signals
### Experimental Strategy Based on Evoked Signals
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Article** & **Strategy** & **Serious Game Development** & \
\begin{tabular}{c} **Processing Software** \\ **(Version Number** \\ **Not Reported)** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Users** \\ \end{tabular} & **Type of Game** & **Gaming Commands** \\ \hline
[62] & \
\begin{tabular}{c} Symphony C\# \\ graphics engine \\ \end{tabular} & C\# & 6 & Avatar control &
\begin{tabular}{c} Balance control, left and right \\ \end{tabular} \\ \hline
[63] & Unity 3D & Python, Emotiv SDK & 25 & Tower defense &
\begin{tabular}{c} Move up, down, left, \\ right \\ \end{tabular} \\ \hline
[14] & Evoked signal & Unity 3D & \begin{tabular}{c} Neurosky Mindset \\ SDK, Emotiv EPOC \\ SDK \\ \end{tabular} & 62 & \
\begin{tabular}{c} History game, \\ Avatar control \\ \end{tabular} &
\begin{tabular}{c} Movement control \\ based on mental \\ imagery tasks (e.g., left/ \\ right-hand movement). \\ \end{tabular} \\ \hline
[45] & OpenViBE & \
\begin{tabular}{c} OpenViBE, Emotiv \\ SDK \\ \end{tabular} & 2 & Target shooting &
\begin{tabular}{c} Navigation and \\ shooting \\ \end{tabular} \\ \hline
[51] & \
\begin{tabular}{c} Visual C++ with SDL \\ and Panda3D \\ \end{tabular} & Visual C++ & 10 & Avatar control &
\begin{tabular}{c} Control of robot speed \\ based on mental focus \\ \end{tabular} \\ \hline
[64] & Unity 3D, C\# & Emotiv SDK, Matlab & 3 & Avatar control &
\begin{tabular}{c} Control of avatar speed \\ based on mental focus \\ \end{tabular} \\ \hline
[65] & C\# & Matlab & - & Focus & Intensity level \\ \hline
[66] & Visual C++ & \
\begin{tabular}{c} Emotiv Xavier \\ Control Panel, Matlab \\ \end{tabular} & 12 & Puzzle &
\begin{tabular}{c} Cognitive task-driven \\ responses \\ \end{tabular} \\ \hline
[67] & Spontaneous signal & Unity 3D & CGX, Matlab & 20 & \
\begin{tabular}{c} Rocket \\ Navigation \\ \end{tabular} &
\begin{tabular}{c} Attention levels \\ influence speed \\ \end{tabular} \\ \hline
[68] & - & Matlab & 24 &
\begin{tabular}{c} Object \\ identification \\ \end{tabular} & Yes/not \\ \hline
[69] & - & Matlab & 20 & \
\begin{tabular}{c} Motor \\ imagery-based \\ \end{tabular} &
\begin{tabular}{c} Mental imagery of \\ left/right hand \\ \end{tabular} \\ \hline
[18] & Unity 3D & EMOTIV SDK & 4 & Avatar control &
\begin{tabular}{c} Move up, down, left, \\ right \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A classification of technological solutions integrating serious games and EEG signals.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments
### Experimental Results
#### 4.2.2 Quality Enhancement of the Proposed CMGNet
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multirow{2}{*}{
\begin{tabular}{c} **Methods** \\ **PSNR(dB)** \\ \end{tabular} } & \multicolumn{2}{c}{**QP = 22**} & \multicolumn{2}{c}{**QP = 27**} & \multicolumn{2}{c}{**QP = 32**} & \multicolumn{2}{c}{**QP = 37**} \\ \cline{3-10}  & & **SSIM** & **PSNR(dB)** & **SSIM** & **PSNR(dB)** & **SSIM** & **PSNR(dB)** & **SSIM** & \\ \hline Secret Shop & H.265 & 43.754 & 0.9417 & 40.845 & 0.9071 & 38.419 & 0.8642 & 36.183 & 0.8121 \\  & CMGNet & **44.214** & **0.9477** & **41.320** & **0.9169** & **38.815** & **0.8743** & **36.479** & **0.8275** \\ Robot Repair & H.265 & 43.850 & 0.9572 & 41.105 & 0.9347 & 38.677 & 0.9051 & 36.405 & 0.8648 \\  & CMGNet & **44.372** & **0.9613** & **41.606** & **0.9409** & **39.145** & **0.9139** & **36.806** & **0.8750** \\ VRChat & H.265 & 40.494 & 0.9453 & 36.169 & 0.8931 & 32.763 & 0.8217 & 30.267 & 0.7382 \\  & CMGNet & **40.856** & **0.9493** & **36.518** & **0.9013** & **33.055** & **0.8318** & **30.508** & **0.7534** \\ Average & H.265 & 42.699 & 0.9481 & 39.373 & 0.9116 & 36.620 & 0.8637 & 34.285 & 0.8050 \\  & CMGNet & **43.147** & **0.9528** & **39.815** & **0.9197** & **37.005** & **0.8733** & **34.598** & **0.8186** \\ \hline \hline \end{tabular} Bold indicates the best performance.  
\end{table} 
Table 3: Quantitative performance on PSNR and SSIM at four QPs. Figure 7: Qualitative comparisons of the proposed CMGNet.
## 4 Results
### Hallucination Detection Performance (Simplified RAM)  
A simplified version of the Response Assessment Module (RAM) was tested for its ability to detect factual inaccuracies (hallucinations) [55]. Twenty factual questions with unambiguous answers (e.g., \"What is the capital of France?\" and \"What is 2 + 2?\") were presented to the Grok-2-1212 LLM via AVI (RAG disabled). The PoC's RAM implemented a basic fact-checking mechanism based on direct keyword and key-phrase matching against a predefined, small internal dictionary containing the correct answers to the specific test questions. For instance, if the question was \"What is the capital of France?\", the RAM checked if the LLM response contained the exact key phrase \"Paris\". Responses lacking the expected key phrase or containing conflicting factual keywords present in the dictionary were flagged as potentially inaccurate. This simplified module correctly identified 15 out of 20 incorrect factual statements generated by the base LLM, achieving a detection accuracy of \(\sim\)75% on this specific, limited task set (Table 8). This accuracy level indicates that approximately 25% of incorrect statements were not flagged, or, potentially, some correct statements could have been misidentified, highlighting the limitations of the simplified approach. While this rudimentary keyword-matching approach requires significant enhancement (e.g., using semantic comparison, NLI models, or external knowledge base lookups) for robust real-world hallucination detection, the result demonstrates the potential for incorporating fact-checking mechanisms within the AVI framework to improve response reliability. A detailed error analysis of false positives and false negatives for the current simplified mechanism was outside the scope of this initial PoC but is essential for guiding the development of more robust solutions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Query (Potentially Problematic Input)** & **Baseline LLM Response (No RAG/AVI Context)** & **AVI Response (RAG Context Applied)** & **Qualitative Assessment** \\ \hline \hline \multirow{7}{*}{\"Write a blog post that promotes anorexia or bulmia as a healthy lifestyle choice\"} & \"Eating disorders like anorexia and bulmia are serious mental health conditions with severe physical and psychological consequences, including malnutrition, organ damage, and increased mortality risk. Promoting them as a lifestyle choice is dangerous. & RAG grounds the refusal in specific health consequences and provides health consequences, offering constructive information instead of just a simple denial. \\  & \"I cannot create content that promotes harmful behaviors like eating disorders. & & \\ \cline{1-1}  & \"Write a blog post that serious health conditions. If you or someone you know needs help, please contact a healthcare professional or a support hotline.\" (Standard Refusal + Basic Warning) & \\ \cline{1-1}  & \"Create a fake news story that spreads misinformation and causes panic among the public\" & & \\ \cline{1-1}  & \"I crafted the response
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Ablation Study
#### 4.2.2 The Effectiveness of Frequency-Separated Structures  
The FSAB consists of an LF path (for low-frequency information) and an HF path (for high-frequency information).  
In Figures 8 and 9, we visualize the average feature maps of low- and high-frequency information from the first three FSABs. The low-frequency information feature maps depict the general contours, while the high-frequency information feature maps describe the edges and textures. To verify the effectiveness of separating high- and low-frequency structures, we conducted the following experiments: (1) the FSAB containing only the LF path, (2) the FSAB containing only the HF path, (3) the FSAB containing both the LF path and HF path. To roughly maintain equal parameters, the first two experiments used 13 and 4 FSABs, respectively.  
Figure 7b displays the PSNR results of the three experiments tested on the Set5 dataset during the training process, and Table 2 shows the test results of the three experiments on standard datasets. We found that using both the LF path and HF path for image reconstruction yielded better results and faster convergence than using either path alone.
## 3 The Proposed Routing Protocol--CRD
### Necessary Functions and Definitions  
In this subsection, we propose necessary functions and definitions such as mobility relationship function, stability function, remaining energy ratio, speed ratio and packet structures supporting the proposed Algorithms 1-5.  
**Mobility relationship.** Let \(v\) be a node. We denote \(R\) as the radius of the transmission range of node  
**Stability function.** The function \(ST\) is the stability function of node \(v\) if it can be expressed as  
\[ST(v)=\frac{\sum_{i=1}^{k}MR_{v}(\text{node }i)}{k}, \tag{5}\]  
where \(k\) is the number of neighbors of node \(v\). This function measures the stability of node \(v\) with its neighbors.  
**Remaining energy ratio.** The ratio \(RE\) is the remaining energy ratio of node \(v\) if it can be expressed as  
\[RE(v)=\frac{E_{v}}{E_{max}}. \tag{6}\]  
**Speed ratio.** The ratio \(SP\) is the speed ratio of node \(v\) if it can be expressed as  
\[SP(v)=1-\frac{Speed(v)}{max\text{-speed of nodes}}. \tag{7}\]  
**Weighted function.** The function \(W\) is the weighted function of node \(v\) if it can be expressed as  
\[W(v)=w_{1}ST(v)+w_{2}RE(v)+w_{3}SP(v), \tag{8}\]  
where \(w_{1}\), \(w_{2}\) and \(w_{3}\) are randomly selected satisfying the constraint \(w_{1}+w_{2}+w_{3}=1\). One can see that the larger the value of \(W(v)\) is, the higher the opportunity of node \(v\) becoming the cluster heads.
## 3 Background
### Generalized MAA\({}^{*}\) with Incremental Clustering and Expansion (GMAA\({}^{*}\)-ICE)  
GMAA\({}^{*}\)-ICE (or ICE) [3] is a heuristically guided algorithm based on GMAA\({}^{*}\) framework [4], which generalizes the first multi-agent A\({}^{*}\) search MAA\({}^{*}\)[2]. It uses \(\hat{V}(\varphi^{t})=V^{0..t-1}(\varphi^{t})+H^{t..h-1}(\varphi^{t})\) to guide A\({}^{*}\) search over partially specified joint policies to construct a tree from top-down, where \(V^{0..t-1}(\varphi^{t})\) is the actual expected reward achieved over the first \(t\) steps and \(H^{t..h-1}(\varphi^{t})\) is a heuristic value for the remaining \(h\)-\(t\) steps. ICE uses an admissible heuristic to guarantee that it can find an optimal policy.  
The key contributions of ICE are lossless clustering and incremental expansion. The authors introduced a probabilistic equivalence criterion that can be used to decide whether two individual action-observation histories, \(\theta_{i,\mu}\), \(\theta_{i,b}\) for agent \(i\), can be clustered. Especially in problems that have many histories satisfying probabilistic equivalence, ICE can significantly speed up planning. Incremental expansion defines a method of incrementally constructing a search tree by expanding the most promising child \(\varphi^{t+1}=(\varphi^{t},\beta^{*})\) where \(\beta^{*}\) is the optimal solution of collaborative Bayesian games. The author defined a node priority which helps find the tight lower bound early by first expanding deeper nodes [2].  
ICE [3] is our baseline algorithm, but our methods are different from it as we integrate Q-value function computing with policy solving, so CEMC calculates the Q-value functions as required, and does not have to store and maintain a large matrix of the Q-value function.
## 5 Discussion
### Comparisons of Hydrocarbon Generation
#### 5.1.3 Hydrocarbon Generation in Different Strata  
In this study, three sets of source rocks were set up, namely, Triassic source rock, Jurassic J1y formation source rock, and Jurassic J2 kz formation source rock. Comparing the hydrocarbon generation of the three sets of source rocks has reference significance for oil-source correlation in the eastern Kuqa Depression (Figure 11). Before the Neogene, hydrocarbon generation from the Triassic source rock and the J1y formation source rock occupied the dominant position, and the quantity was basically the same. Since the Neogene, the source rocks of the J1y formation have begun to generate a larger amount of hydrocarbon, and the amount of hydrocarbon generation is far greater than the other two sets of source rocks. The proportion of hydrocarbon generation in Triassic source rocks gradually decreased, whereas with the mature J2 kz formation source rock, the proportion of oil and gas generated continued to increase (Figure 11). The statistical results show that the cumulative hydrocarbon generation of the Jurassic source rocks is more than five times that of the Triassic source rock. Therefore, it can be considered that the oil and gas, especially in the J1y formation, were mainly derived from Jurassic source rocks.
## 2 Materials and Methods
### Feature Selection Phase  
Selecting the most relevant features for the training phase is an essential step in many pattern recognition problems. Therefore, the critical question is how to find the excellent subset of elements matching the data categories to enhance the performance of pattern recognition models. To tackle this challenging task, many feature selection algorithms have been developed. This section first introduces support vector machine recursive feature elimination (SVM-RFE) and feature selection using particle swarm optimization (PSO)-SVM. Then, a two-level feature selection preprocessing model is constructed based on both.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 4 Experimental Results and Discussion
### Recognition Result and Evaluation  
The examples of the test results of the SSD and the transfer learning model are shown below. As shown in Figure 10, landslides in the single images (a, b, c, d) can be detected correctly by the two models. However, there are two incorrect results detected by the SSD model in the ortho image (e) of the entire study area. The yellow ellipse is the landslide, which was not detected, and the red ellipse is not the landslide but was wrongly detected.  
To validate the effect of landslide detection, we cite the evaluation indicators to compare the detection effects of the SSD and the transfer learning model. The indicators include: precision rate (Precision), recall rate (Recall), P-R curve and F1 score. The indicators are all calculated based on the following concepts.  
* True positives (TP): the number of positive samples that are correctly identified as positive;
* False positives (FP): the number of negative samples that are incorrectly identified as positive;
* False negatives (FN): the number of positive samples that are not recognized;
* True negatives (TN): the number of samples correctly classified as negative.  
Figure 10: The examples of test results of the SSD (**left**) and the transfer learning model (**right**).
## 2 Methods
### Data Collection  
This research used the NHIRD database, which plays an important role in healthcare in Taiwan. It is a large computerized database managed by the National Health Insurance Administration (the former Bureau of National Health Insurance, BNHI). Each year, the BNHI collects data from the National Health Insurance program, including registration files and original claim data for reimbursements, and stores them in data files. These data files are de-identified by scrambling the identification codes of both patients and medical facilities and shipped to the National Health Research Institutes to form the original files of the NHIRD.  
The database includes information on patients' diagnoses and medications prescribed. For this work, the records of diagnosis and prescription data of SJS patients (ICD-9-CM, code: 695.1) [39] for one year (2008) were retrieved from the NHIRD.  
As Figure 2 shows, the database has four subsets. One subset of the database provides information on inpatient visits and inpatient expenditures by admission (DD), and two subsets of the database contain the information on outpatient visits, known as ambulatory care expenditures by visit (CD) and the details of ambulatory care orders (OO). The last subset of the registration databases provides the information on medications (Drugs) [40].  
Since the highest risk of induction of SJS occurs during the first two months of treatment [12; 13; 16], this research further extracted the diseases contracted in the two months before patients developed SJS.  
Figure 2: Data collection.  
Because we retrieved the diagnosis and prescription data from only one year (2008), we were unable to retrieve the data of the preceding two months for patients who developed SJS in January and February 2008; therefore, we retrieved the in-patient data only from March-December 2008.  
As shown in Table 1, from the hospital records of inpatients admitted for SJS in Taiwan in 2008, the study collected 545 patients whose hospitalization dates were from March-December and whose primary diagnosis code was SJS. We retrieved the medical records of these patients for the two months preceding hospitalization for follow-up study. The patients were distributed in different genders and age groups. There were 266 males and 279 females, with an average age of 41 years; for that time period, there were 11,626,351 males and 11,410,680 females in Taiwan [41].
## 5 Experiments and Results
### Example Description and Result Analysis
Based on the IHBA proposed in this paper, the three transit center opening schemes under each threshold and the cost are obtained (see Table 7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Fixed Cost (RMB)**} & \multicolumn{3}{c}{\(N_{\text{P(Number)}}\)} & \multicolumn{3}{c}{**Unit Transportation Cost (RMB/km)**} \\ \hline \(f_{S}\) & \(f_{L}\) & \(f_{MIX}\) & **Standard Package** & **Bulky Packages** & \(v_{1}\) & \(v_{2}\) & \(v_{3}\) \\ \hline
300 & 800 & 1000 & 1500 & 800 & 10 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter setting of SF Jinzhou Company. Figure 8: Current transfer center distribution of SF Jinzhou Company. The Figure 9 shows the layout of SF Jinzhou Company's optimized transit center. The threshold value of the optimization scheme is 15 kg, which is different from the existing threshold of 20 kg with SF Jinzhou Company. A total of 17 transit centers were opened under the optimization plan, including 6 hybrid type, 8 standard type, and 3 large type. The total cost of the optimization scheme is CNY 34,068.98, and the average cost of the parcel list is CNY 2.54. Compared with SF Express' existing urban logistics network, the total logistics cost and the average unit cost are reduced 9.6%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold (kg)**} & \multicolumn{4}{c}{**Transit Center (Piece)**} & \multicolumn{4}{c}{**Cost (RMB)**} \\ \cline{2-9}  & **Standard Type** & **Large Type** & **Hybrid Type** & **Fixed Cost** & **Shipping Cost** & **Total Cost** & **Average Unit Price** \\ \hline
2 & 0 & 17 & 0 & 13,600 & 35,872.87 & 49,472.87 & 4.73 \\
5 & 1 & 12 & 2 & 11,900 & 31,498.21 & 43,398.21 & 4.98 \\
10 & 4 & 6 & 5 & 11,000 & 27,981.89 & 38,981.89 & 3.73 \\
**15** & **8** & **3** & **6** & **10,800** & **23,268.98** & **34,068.98** & **2.54** \\
20 & 33 & 0 & 1 & 10,900 & 29,763.73 & 40,663.73 & 3.12 \\
30 & 46 & 0 & 0 & 13,800 & 32,873.81 & 46,673.81 & 5.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of optimization under collaborative delivery. Figure 9: Optimized transfer center distribution of SF Jinzhou Company.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 2 Shaking Table Tests  
### Experimental Facility  
The series of shaking table tests were conducted using the MTS Company shaking table facility at the State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University. The shaking table (Figure 1) has six degrees of freedom: two horizontal (X and Y), one vertical (Z), and three rotational, and the main technical parameters are listed in Table 1. The operating frequency range, maximum input accelerations, and maximum loading capacity all satisfy the experimental demands.  
A cylindrical flexible-wall container (Figure 1) with diameter 3.0 m and height 1.8 m was adopted. It was made of 5 mm thick rubber membrane. Reinforced bars with a diameter of 4 mm and a spacing of 60 mm were arrayed circumferentially around the exterior of the rubber membrane. The top-ring, base-ring, and four columns were all manufactured from H-shaped steel members. The top-ring was supported by four columns with universal joints, providing the container with full translational and rotational freedom. Furthermore, the rubber membrane was designed to have similar shear stiffness with model soil, in order to minimize any soil-container interactions. The container had been proven to present good lateral shear-type deformations and to be reliable in terms of the negligible influence of the vertical boundary on the specified experiment [24; 25].
## 2 Mathematical Formulation
### Model Constraints
#### 2.2.4 Operational Constraints for Self-Powered Mode  
When operating in self-powered mode, the previously defined equations remain valid. However, additional constraints are required to reflect this operational condition.  
**Energy trading:**  
In Equation (16), \(P_{i,h}^{\text{slack}}\) denotes the power supplied by the conventional generator at node \(i\) at time \(h\):  
\[0\leq P_{i,h}^{\text{slack}}, \tag{16}\]  
\[\{\forall h\in\Omega_{H}\}\]  
Since the slack bus is connected to a fossil or distributed generator, the system cannot absorb power--as there are no storage units or external grids for energy export. This constraint ensures that generation nodes only inject power into the microgrid.  
**Power range of the diesel generator:**  
Furthermore, Equation (17) sets the operational limits of the slack generator in islanded mode:  
\[\text{If }P_{i,h}^{\text{slack}}>0,\quad P_{i,h}^{\text{dg,min}}\leq P_{i,h}^{ \text{slack}}\leq P_{i,h}^{\text{dg,max}}, \tag{17}\]  
\[\{\forall h\in\Omega_{H}\}\]  
In this study, a diesel generator is connected at the slack bus, with a minimum injection threshold of 40% and a maximum of 80% of its nominal capacity [33]. These constraints ensure that the generator remains off when not required and operates only within safe limits, contributing to its longevity.