esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.9 Wide Neural Network  
A Wide Neural Network [56] is a structure in artificial neural network architecture that usually contains a large number of neurons in the input layer. This model is used to learn complex relationships between many features. A Wide Neural Network can represent a large feature space, to capture different combinations of input data. This is especially useful when working with high-dimensional and large-scale datasets, because it can model more complex relationships using more parameters and layers. Thus, it allows for more effective feature extraction and learning of more complex data structures in a larger feature space. This model is especially used in problems where there are non-linear relationships between features, and it is often preferred for representing high-dimensional datasets.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 3 Emotion Analysis AI Model Using EEG
### Fine-Tuned Model Construction  
In the present study, the brainwave data (raw EEG) of the SEED dataset were converted to the JSONL format for fine-tuning learning. The SEED dataset consists of EEG data for emotion recognition, and includes data collected for various emotional states (e.g., happy, sad, neutral). These data were used to perform fine-tuning with a GPT-4o-based model and to analyse emotional states based on brainwave data.  
First, the raw EEG data of the SEED dataset were structured in the JSONL format together with emotion labels. In the JSONL format, each sample is represented by an independent JSON object, and it is the optimal format for training the GPT-4o model. Through this format, the model can understand and learn the temporal order and emotion labels of each of the samples. This data-preparation process allows the model to more accurately learn and predict emotional information based on brainwave data. In the fine-tuning process, the GPT-4o model uses the JSONL-format data to perform learning, and the weight values of the model are updated according to the emotional labels during this process. GPT-4o is a language model that mainly deals with text data. However, in this study, the numerical values of the brainwave data were mapped to a vector space and converted into text input, which allowed GPT-4o to perform emotion analysis. This method allows the model to better understand the users' experiences and emotional states in architectural spaces.  
In addition, the hyperparameter settings provided by OpenAI's GPT-4o API were used to optimise fine-tuning. Currently, the epoch value is limited to three during fine-tuning, and the performance of the model is optimised by repeatedly learning the brainwave data under this limited condition. Owing to this fine-tuning, the model analysed and reflected the users' emotional states in architectural spaces in real time and produced significant results (Figure 1).  
Finally, the fine-tuned GPT-4o model can more effectively classify and understand the emotional states of users by using brainwave data in architectural spaces. This approach can play an important role in reflecting and improving users' experiences in real time when  
Figure 1: Fine-tuning method process for EEG dataset (SEED).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Count** & **Percentage** \\ \hline Sad (\(-1\) negative) & 88,802 & 33.3\% \\ Neutral (0 neutral) & 88,802 & 33.3\% \\ Happy (1 positive) & 88,802 & 33.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Emotion distribution.  
designing and operating architectural spaces. The prototype results learned by the fine-tuned model can be observed on the Streamlit-based web platform. Using the web service shown in Figure 2, emotion information can be derived by operating the GPT-4o-based fine-tuned model and inputting raw data for each channel.
## 4 Case Study
### Working Memory, Inference Engine, and Visual Notifications
For example, terminals 2310 and 2273 were classified as cloud cover state and later they were classified to the disrupted state at similar times. Some terminals exhibit transition phases, when the cloud and disruptions occur simultaneously (Figure 10). Figure 7: Classification decision tree for instances of sub route group SUB_GRP_2. Figure 8: Classification decision tree of two clusters described by SNR_X1. Figure 9: Time series data for various terminal IDs vs. time. Green sections are normal working conditions, amber sections are cloud cover, and red is either disruption or heavy cloud cover affecting signal quality, e.g., 2263. These visual representations and colour-based notifications provided an engineer a high level view of the satellite network from a terminal perspective. As a result of the colour scheme used, the engineer could focus on problematic terminals for further investigation.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 3 Related Works  
The solution proposed by [PERSON] et al. [4] utilizes a fuzzy neural network to predict passenger wait times under various control parameters. The input state space is partitioned using fuzzy logic rules, with the controller making decisions based on the network's predictions, although the specific control algorithm is not explicitly defined.  
[PERSON] et al. [5] developed a system for training neural networks to perform immediate cabin allocations. Their training process involved three phases. The first phase used supervised learning with data collected from a system controlled by existing solutions from Fujitec. Selected network weights were frozen to represent the control system's principles. The remaining network was fine-tuned in the second phase to mimic the lift controller. In the third phase, the entire network was further trained in a system simulator to enhance its performance using a simplified form of reinforcement learning. The network's inputlayer consisted of 25 neurons for each lift, and the output layer had one neuron per cabin. Passenger floor requests were assigned to the lift with the highest activation value of the corresponding output neuron. The system was tested in a 15-floor building simulator with six elevators, resulting in a minor improvement over the existing controller.  
In later years, reinforcement learning saw its implementations in elevator control. [PERSON] and [PERSON] [6] applied the concept of Deep Reinforcement Learning (Deep RL) to a single elevator system in a ten-floor building during peak downward traffic. Passengers selected routes with the lowest floor as their destination, and they appeared based on a distribution tied to the simulation phase. The training process lasted four days, during which the algorithms spent over 60,000 h in a simulated environment. The trained algorithms demonstrated notably better performance compared to well-known heuristic algorithms.  
One of the first reinforcement learning implementations for a lift control system was designed by [PERSON] [17]. The author utilized three RL algorithms, all from the family of Q-Value function estimation methods. The results show a decrease in the average waiting time metric for all trained strategies as compared to a reference heuristic policy.  
A more recent solution was presented in 2020 by [PERSON] et al. [7]. The researchers used a new Deep Reinforcement Learning method, called Asynchronous Advantage Actor-Critic, to train an agent capable of optimal dispatching of elevators in a group control setting. The learning process was conducted within an emulated system designed to mimic real-life building conditions. The results obtained during test simulations in various traffic patterns revealed that the trained agent successfully reduced average waiting times compared to traditional heuristic algorithms.
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 3 Results
### The Potential Impact of Climatic Changes to River Water Temperatures
The relationship between changes in water temperature and changes in water flow can be seen mainly in June, especially in rivers east of the Yenisei River basin, and in July and August in the rivers of the Pechora river basin. Other important climate-related factors that may also play a role are a decrease in the maximum flood runoff in rivers and earlier river breaks and their later freezing. Overall, in spring, the smaller water mass takes longer to warm up, while in autumn, the larger water mass exchanges heat with the atmosphere for a longer period. Thus, at more than 70% of hydrological stations in the Russian Arctic, the changes in river water temperature during the warm season (May-October) are influenced by changes in air temperature, especially during the summer. In other cases, local factors may affect water temperature. This could include systematic violations of measurement rules or errors in choosing measurement locations. During the transitional seasons, the ice and water regimes of rivers also play a specific role in water temperature changes.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion
### Performance of SMA-13 Mixture
#### 3.2.5 Uniaxial Penetration Test  
The effect of FRM on the high-temperature shear strength of asphalt mixture is illustrated in Figure 14. The error tolerance requirements specified for repetitive tests in Standard JTG D50-2017 are met by the parallel experimental results [23].  
Clearly, before oil immersion, the shear strength of asphalt mixture specimens with added FRM increased, and the increase was more pronounced with higher FRM concentrations. The greatest enhancement in shear strength occurred when the FRM concentration was increased from 0% to 1%. This improvement can primarily be attributed to the enhancement of high-temperature rheological properties of asphalt by FRM.  
After oil immersion, the shear strength of asphalt mixture specimens without added FRM significantly decreased, with OR\({}_{\texttt{R}\texttt{T}}\) of only 36.39%, indicating a substantial negative impact of oil erosion on the high-temperature performance of the asphalt mixture. Conversely, specimens with FRM exhibited a noticeable increase in OR\({}_{\texttt{R}\texttt{T}}\), demonstrating that FRM can effectively mitigate the sensitivity of high-temperature performance of asphalt mixture to oil erosion. The most significant increase of OR\({}_{\texttt{R}\texttt{T}}\) occurred when the FRM concentration was increased from 0% to 1%, during which FRM was uniformly distributed in FRMA, resulting in the most pronounced improvement in high-temperature performance after oil erosion. When the FRM concentration was increased from 1% to 5%, the increase in OR\({}_{\texttt{R}\texttt{T}}\) was less pronounced, indicating that the dispersion of FRM in FRMA gradually reached saturation. Beyond a concentration of 5%, OR\({}_{\texttt{R}\texttt{T}}\) remained relatively stable, suggesting that FRM was fully saturated in FRMA, and there may have even been uneven dispersion, leading to a slight decrease in OR\({}_{\texttt{R}\texttt{T}}\).  
Figure 14: Results of uniaxial penetration test.  
Figure 13: Results of the IDT test.
## 3 Design Consideration on Radiation-Grafted AEM  
The radiation-induced graft polymerization technique in fabricating AEMs is favorable because this technique is suitable for scale-up in industrial applications. Radiation has a strong penetrative power, which allows it to initiate the grafting process throughout the entire volume of polymer at relatively lower temperatures than the one with chemical initiators. The grafting penetration can also be adjusted accordingly to only functionalize the surface of polymers with the functional groups -COOH, -OR, -OH, -NH\({}_{2}\), -SO\({}_{3}\)H, -R and their derivatives, modifying the surface properties without affecting the properties of bulk material [74]. In comparison to the conventional grafting method, radiation-induced  
Figure 3: Schematic representation of the PAEM fabrication process using R2R equipment. Reprinted with permission from [72]. Copyright Elsevier, 2019.  
-graft polymerization promotes more controllable graft copolymers with a predetermined molecular weight and polydispersity of grafted chains. Moreover, the radiation-induced grafting method is simple, relatively clean and reprocessable. This technique is also capable of modifying the polymeric surface with a wide range of shapes and properties without requiring the presence of a catalyst or initiators. Particularly, the modified surface likely free from any impurities related to functional moiety [74].  
Due to the versatility of this technique, there are a few key parameters, including the selection of radiation source, polymer backbone, monomer concentration, type of solvents and additives, dose rate, total dose, grafting atmosphere and temperatures, that will influence the properties of the final product of AEMs. One of the important and unique properties of radiation-grafted membrane is the degree of grafting (D.O.G) [62; 75], which is used to determine the amount of functional group that is successfully grafted onto the base polymer. The D.O.G can be estimated from Equation (7), whereby the percentage of mass changes the membrane before being grafted (M\({}_{\text{i}}\)) and after being grafted (M\({}_{\text{g}}\)).  
\[\text{D.O.G }(\%)=\frac{\text{M}_{\text{g}}(\text{g})-\text{M}_{\text{i}} \ (\text{g})}{\text{M}_{\text{i}}\ (\text{g})}\times 100\% \tag{7}\]  
The higher the irradiation dose, the higher the D.O.G with more available sites for the cation head groups to attach with [64]; however, a high irradiation dose also causes damage to the base polymer and reduces mechanical strength [76]. A membrane with low ionic exchange capacity usually results in low ionic conductivity, while if the membrane has a high ionic exchange capacity, it is usually associated with high water uptake, which in turn increases the swelling ratio that contributes to the mechanical stresses that eventually damages the membrane structure integrity [69]. A balance between these properties is essential for commercial adoption.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 2 Data and Methodology
### Groundwater Storage Change Estimation from GRACE
It should be noted that this updated method requires a priori knowledge of GWS changes to obtain the scaling factor. In addition to traditional SH products, in which leakage reduction and signal restore are needed in the post-processing procedure, GRACE mascon solutions are developed to localize the mass changes at the specific mass concentration (mascon) blocks or mass tiles [54; 55]. Correlated errors and signal attenuation can be minimized by applying constraints or regularization during the mascon gravity inversion. A recent comprehensive product intercomparison indicates that mascon solutions are generally consistent with SH products in large-scale basins; however, the variability among different GRACE products, particularly for long-term trends, is also significant especially in small-scale basins, which needs to be evaluated on a case-by-case basis [56; 57].
## 5 Conclusions  
This study evaluated the use of rubber dam isolation during orthodontic bonding and its potential impact on bracket positioning. No significant difference was found between the two groups along the X-axis. However, deviations of 0.1 mm and 0.2 mm were observed on teeth 32 and 33 along the Y-axis, and 0.2 mm and 0.1 mm on teeth 34 and 44 along the Z-axis. Considering that linear deviations greater than 0.5 mm are clinically significant, these findings suggest that the use of the rubber dam does not significantly affect the accuracy of bracket placement and can be used without compromising bracket alignment. The findings suggest that the use of rubber dam does not significantly affect the accuracy of bracket placement, indicating that it can be used without compromising bracket alignment. While the rubber dam procedure led to a slight increase in chair time, with a difference of approximately four minutes between the groups, this time extension may be considered minimal, particularly for orthodontists seeking to reduce the risk of infectious disease transmission. Overall, the use of rubber dam, despite the small increase in time, appears to be a viable option for maintaining accurate bracket placement while providing additional protection against potential contamination.  
K.C.K.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, Writing--original draft; T.S.E., Validation, software, investigation,resources, writing, review; I.M., Investigation, Resources, Data Curation, Review. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Kinkkale University Scientific Research Projects Coordination Unit with the project number 2022/115.  
The Non-Invasive Research Ethics Committee approved this study of Kinkkale University (Decision No: 2022.03.30).  
Informed Consent Statement: Informed consent to participate was obtained from all the participants in this study.  
The datasets analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no competing interests.
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
###### Abstract
The difference between the former and the latter is mainly that in overlay mode, ST uses NOMA technology to superimpose PU and SU packets and send them to PR and SR, while the latter uses OMA technology to forward PU packets to PR. * ARQ technology is used at PR to send received information of the PU's packet and PT and ST switch cooperative modes according to the feedback information. In the non-CR transmission mode, PT sends the PU's packet to PR, ST, and SR, while ACK or NACK feedback is sent from PR. If PR sends ACK feedback, the SU and the PU access spectrum in the underlay OMA mode simultaneously. If NACK feedback is sent from PR, ST acts as relay to help forward the PU's packet to PR in overlay NOMA mode. As a reward, ST can send its own packet simultaneously by using the NOMA principle; or ST only acts as a relay to help forward the PU's packet to PR in overlay OMA mode. * We establish a one-dimensional Markov model to analyze the performance of the system through the study of PU mode and state change. We derive the end-to-end outage probability, and then the throughput of PU and SU are derived by using the outage probability and Markov model. * Finally, we use the MATLAB simulation environment and Monte-Carlo technology to numerically simulate the throughput of PU and SU. Numerical results show that the proposed hybrid cooperative cognitive radio network based on OMA and NOMA is superior to the pure OMA cooperative radio network. The rest of this paper is organized as follows. Section 2 introduces the system model of hybrid cooperative cognitive radio network based on OMA and NOMA. Sections 3 and 4 derivate the throughput of PU and SU in terms of the proposed two cooperative protocols. Numerical results and simulations are provided in Section 5. Finally, we conclude the paper in Section 6.
## 3 Data and Method  
### Seismic Data  
The subsurface investigation was based on the Waka 3D seismic reflection data located in the SE of the Canterbury Basin (Figure 1a). The Waka 3D seismic reflection survey was acquired by a 5100 m array of streamers, and it covers an area of 1150 km\({}^{2}\). The survey has a bin spacing of 12.5 by 25 m, and a 4 ms vertical sampling interval. The Waka 3D seismic reflection survey is the final stack time migration, which was compiled by the New Zealand Petroleum and Minerals department (NZP&M). The dominant frequency in the interested zone (1500-2200 ms) is around 35 Hz. The velocity at the seabed is 1500 m/s and at the Eocene level is 2500 m/s, therefore the average velocity in the zone of interest is about 2000 m/s. The vertical resolution is approximately 14.3 m in the Waka 3D seismic reflection survey after incorporating the values of velocity and frequency (using the quarter-wavelength criterion). The 2D seismic data (Figures 1b and 2a) has a similar dominance frequency (\(\sim\)35 Hz), but an average velocity of 2700 m/s, thus making the vertical resolution around 19.3 m.  
The Waka 3D Seismic reflection survey uses the European or Australian polarity standards. The first hard kick in Figure 3 is the seabed, which depicts an increase in the acoustic impedance represented by white color or peak (positive amplitude value). In addition, this change also correlates with the different properties in the sediments due to density, porosity, and stiffness variations. The Waka 3D seismic reflection survey shows the black-white-black density contrast of the zero-phase data (Figure 3).
## 6 Discussion  
The user study demonstrated a significant benefit of using the PSO algorithm to optimize the placement of the robot's end-effector within a VR environment. The implementation of the PSO algorithm resulted in improvements in all REBA scores. The total and the other REBA scores were significantly enhanced, reaching a value close to the ideal score of \"1\". During the human-robot collaboration, the PSO algorithm assigned proper placement locations to the robot to pass workpieces for assembly. Following the end-effector positions, the user was able to perform the task more ergonomically, reducing the risk of physical strain or injury.  
Our proposed method was validated with people of different heights and handedness, demonstrating its potential for application to a wide range of users in the industry. However, it is important to note that the parameters of the framework might need to be adjusted according to the targeted tasks in each use case in the industry. Some factors need to be taken into consideration, e.g., the robot's movement limitations and the REBA threshold value. By making such adjustments, the placement of the robot's end-effector can be further optimized to provide more ergonomic collaboration between human workers and robots in industrial settings.  
This framework provides several advantages, i.e., improving ergonomics and creating real-time safety measures, making it suitable for tasks, e.g., painting and polishing. The use of a VR platform and a non-intrusive motion capture device (i.e., the Kinect camera) reduces the cost of prototyping, training, and maintenance. This facilitates the process of deployment of human-robot collaborative tasks in real industrial settings. However, a limitation of this approach is that the user cannot feel the weight of the objects being passed by the robot. As a result, there might be differences in the user's posture in physical reality when dealing with heavier objects compared to what it is in VR. However, the differences are predicted to be small since the majority of objects used in assembly tasks are lightweight. Therefore, a direct implementation of our proposed framework from VR to physical reality is applicable to most industrial use cases.  
Figure 5: All REBA scores of participants across two conditions: baseline and PSO. All scores were significantly improved when using PSO optimization compared to the baseline. The scores of the same participant across conditions are connected by a dashed line. Error bars show the 95% confidence interval.