esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Principles of Fuel Cell
### Types of Fuel Cell
#### 4.1.1 Solid Oxide Fuel Cell (SOFC)  
A common stationary power FC is a solid oxide fuel cell [41]. SOFCs conduct ions in a ceramic membrane at high temperatures. In SOFCs, the ceramic usually is a yttrium stabilized zirconia (YSZ) that will conduct oxygen ions (O\({}_{2}\)), but other ceramics conduct hydrogen ions. Solid oxide fuel cells operate at elevated temperature ranges, usually around 650-800 \({}^{\circ}\)C. An advantage to an SOFC is that, due to the elevated temperature ranges and oxidizing ion (O\({}^{-}\)) transport, these fuel cells can handle a variety of fuels [39]. A common use for these fuel cells is as backup power for cell phone towers. The reactions associated with solid oxide fuel cells are presented below [42].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fuel Cell** & **Abbreviation** & **Membrane** \\ \hline Solid Oxide Fuel cells & SOFC & Yttria-stabilized zirconia \\ Direct methanol fuel cell & DMFC & Solid polymer electrolyte (Nafion) \\ Phosphoric Acid fuel cell & PAFC & Phosphoric Acid (H\({}_{3}\)PO\({}_{4}\)) \\ Polymer electrolyte fuel cell & & \\ Or & PEMFC & Solid polymer electrolyte (Nafion) \\ Proton exchange membrane & & \\ Alkaline fuel cell & AFC & Aqueous solution Potassium \\  & & Hydroxide (KOH) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of fuel cell systems based on the employed membrane [40].  
Figure 2: Fuel cell operation diagram [38].  
Full reaction:  
\[\mathrm{H_{2}}+\frac{1}{2}\mathrm{O_{2}}\rightarrow\mathrm{H_{2}O} \tag{1}\]  
Oxidation reactions (anode):  
\[\mathrm{H_{2}}+\mathrm{O^{2-}}\rightarrow\mathrm{H_{2}O}+2\mathrm{e^{-}} \tag{2}\]  
CO and hydrocarbons can also be used in SOFC at high temperatures, thus a water gas shift reaction is possible:  
\[\mathrm{CO+H_{2}O}\rightarrow\mathrm{H_{2}}+\mathrm{CO_{2}} \tag{3}\]  
and the steam reforming reaction is also possible:  
\[\mathrm{CH_{4}+H_{2}O}\rightarrow\mathrm{H_{2}}+\mathrm{CO_{2}}\ (\mathrm{in \ the\ case\ of\ natural\ gas\ separation}). \tag{4}\]  
Reduction reaction (cathode):  
\[\mathrm{H_{2}+O^{2-}}\rightarrow\mathrm{H_{2}O}+2\mathrm{e^{-}} \tag{5}\]
## 5 A Review of Blockchain Projects in Thailand
### Generations of Blockchain and Blockchain Projects in Thailand  
Blockchain has been classified into 4 generations according to their stages of evolution [89; 90; 91]. Blockchain 1.0 refers to digital currency applications, started with bitcoin in 2008. It enables decentralized consensus with the aim to reduce transaction cost. Blockchain 2.0, started around 2013, refers to smart contract applications, enabling additional services beyond digital currency applications. Blockchain 3.0, started around 2015, refers to decentralized applications (DApp). It enables a peer-to-peer network between different parties, lessening organizational boundaries. Blockchain 4.0 is emerging to apply the blockchain applications in wider industries, which started around 2018. It involves decentralized artificial intelligence (AI) to automate decision making. The classification of the four generations is a global aspect of blockchain evolution.  
Figure 16: Top five stakeholders of blockchain projects in Thailand.  
Figure 15: A histogram showing a distribution of stakeholders per blockchain project in Thailand.  
Most blockchain projects in Thailand belong to generation 2.0 and 3.0, and there is currently no project that could be classified as generation 4.0. Examples of generation 2.0 projects include an ICO portal project, Inthanon (Central Bank Digital Currency: CBDC), Lightnet (a cross-border remittance service), etc. Examples of generation 3.0 projects include B.VER (a blockchain solution for academic document verification), TradeLens (a joint project by Maersk and IBM to improve the efficiency and security of the shipping industry in global trade, in which The Customs Department of Thailand has joined), etc. We note that classifying a blockchain project to a blockchain generation between generation 2.0 and 3.0 is sometimes challenging, because the lack of details of the projects implementation, therefore, we are describing some example projects instead of providing categorization of all projects. However, we could summarize that the current generation of blockchain projects in Thailand is at most 3.0. We speculate that we might see some projects of blockchain 4.0 sometime soon, because both blockchain and artificial intelligence technology have been active areas of research and development by many Thai industries.
## 2 Problem Description
### UAV and Payload Model  
Denote UAV set \(V=\{V_{i}\mid i=1,2,\ldots,N\}\), in which any UAV \(V_{i}\) can be described as a seven-element combination \(<X_{i},h_{i},C_{i},P_{i},F_{i},D_{i,max},N_{vi,max}>\). \(X_{i}=(x_{i},y_{i},z_{i},v_{i},\varphi_{i},\psi_{i})\) represents the kinetic parameters of UAV \(V_{i}\), including position, velocity, flight path angle, and flight heading angle; \(h_{i}\in[0,1]\) represents the survivability of UAV \(V_{i}\), and \(h_{i}=0\) indicates \(V_{i}\) is destroyed or has encountered faults and then completely loses its mission capability; \(C_{i}=\{C_{i1},\ C_{i2},\ \ldots,\ C_{inCi}\}\) expresses indices of UAVs that communicate with \(V_{i}\); \(P_{i}=\{P_{i,count},P_{i,attack}\}\) is the executable task type of \(V_{i}\), where \(P_{i,count}\in\{0,1\}\), and \(P_{i,attack}\in\{0,1\}\) means reconnaissance capability and attack capability, respectively. When the capability is available, the value is 1, otherwise 0; \(F_{i}\) is the fuel consumption rate per unit air-range of \(V_{i}\); \(D_{i,\max}\) is the maximum air-range; \(N_{vi,max}\) is the maximum number of executable tasks of \(V_{i}\).  
The condition that a reconnaissance UAV discovers and confirms the threat or target is that it is located in the detection area of the reconnaissance payload, as shown in Figure 2a. Reference [33] gives the typical mathematic model of the detection area. The condition that an attack UAV can strike a target is that the target is located in the available area of the attack payload, as shown in Figure 2b. References [33,34] give the typical mathematic model of the available area.  
Figure 1: The hierarchical communication topology of UAV swarms with a distributed group.
## 3 Quality Grade Survey of Surrounding Rock Mass
### Geomechanical Grading of Rock Mass  
Rock mass geomechanics is graded by comprehensively considering compressive strength, rock quality designation (RQD), weathering and metamorphic characteristics, joint spacing, continuity, strike, and dip. The total score of these indicators is used as the rock mass rating (RMR) to assess the rock mass stability. [PERSON] et al. (2015) analyzed the stability of the slope located on Apollo Ramirez Boulevard, Modesto. The RMR is used to obtain the rock mass quality and the stability of the slope. This method is also applicable to support engineering in underground roadways.  
\[RMR=R_{1}+R_{2}+R_{3}+R_{4}+R_{5}+R_{6}, \tag{1}\]  
\[IRMR=RMR+R_{7}=R_{1}+R_{2}+R_{3}+R_{4}+R_{5}+R_{6}+R_{7}, \tag{2}\]  
where \(R_{1}\) is the grade of tensile strength; \(R_{2}\) is the grade of RQD; \(R_{3}\) is the grade of joint spacing; \(R_{4}\) is the joint condition; \(R_{5}\) is the groundwater condition; \(R_{6}\) is the correction coefficient for the negative impact of joint direction on the process; and \(R_{7}\) is the ground stress correction added by international rock mass rating (IRMR).  
The average joint spacing is determined based on the survey data of rock mass joints within Shilu Iron Deposit. The accuracy of \(R_{3}\) is evaluated using Equation (4). The RQD of broken rock mass is obtained according to the results of on-site drilling and coring (Figure 4).  
The RQDs of No.6-7 transportation roadways in the middle section of Baoxiu-120 are 16.31% and 14.42%, respectively. The RQD scoring values are calculated using Equation (3) according to the RQD at different locations. The RQD can be obtained by the RMR evaluation system based on the experimental data of rock mechanics. Table 2 lists the relevant parameters and scoring values:  
\[R_{2}=0.0012I_{R}^{2}+0.0692I_{R}+1.23, \tag{3}\]  
\[R_{3}=3.5411 InI_{D}+16.6617, \tag{4}\]  
where \(I_{R}\) is the ROD of the rock; \(I_{D}\) is the joint spacing of the rock, m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multirow{2}{*}{**Location**} & **Uniaxial Compressive** & **Tensile Strength \(\sigma_{2}\)** \\  & & **Strength \(\sigma_{1}\) (MPa)** & **(MPa)** \\ \hline \multirow{3}{*}{1} & No.6 transportation roadway & & \\  & in the middle section of the & 26.77 & 1.5 \\  & Baoxiu-120 district & & \\  & No.7 transportation roadway & & \\
2 & in the middle section of the & 15.88 & 1.2 \\  & Baoxiu-120 district & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tensile and compressive strengths of samples.  
The surrounding rocks of No.6 and No.7 transportation roadways in the middle section of the Baoxiu-120 district within Shilu Iron Deposit are classified as poor rock mass, Grade IV, according to the quality evaluation results by IRMR, as well as rock mass grading and evaluation by RMR (Table 4).
## 4 Discussion  
The results obtained from the two approaches to training the MobileNetV2 model highlight important insights into the effectiveness of transfer learning and model fine-tuning in simple image classification tasks, such as beach line detection. The first approach, which involved unfreezing the last 10 layers of the base model from the start, demonstrated significant overfitting. Although the training accuracy rapidly approached 100%, the validation accuracy plateaued at 80%, suggesting that the model was unable to generalize well to unseen data. This phenomenon is common when the model becomes too tailored to the training dataset, leading to poor performance on new examples. These findings are consistent with previous research, which has shown that excessive fine-tuning without proper regularization can result in overfitting, particularly when the dataset is relatively small.  
The second approach, where the base model was initially frozen and only the final decision layer was trained, provided better generalization. Both training and validation accuracies were much more aligned, exceeding 95%, and no overfitting was observed during the initial training phase. After fine-tuning the last 10 layers, the model achieved a slight improvement of 1-2% in accuracy. These results suggest that freezing the base model initially and gradually introducing more complex layers for fine-tuning allow the model to learn the domain-specific features more effectively while retaining the general features learned from the pre-training on larger datasets. This strategy is particularly useful when working with limited or unbalanced data, which is typical in environmental monitoring tasks (e.g., beach line detection) where obtaining labeled data can be challenging.  
Despite this, the model performed well, indicating the robustness of MobileNetV2 in handling real-world environmental variabilities. However, it is worth noting that further improvements could be made by incorporating techniques such as data augmentation, which could help mitigate the effects of image noise and improve model performance in less ideal conditions [52].  
Future research could explore the use of more advanced models, such as EfficientNet, which combines both lightweight architecture and state-of-the-art accuracy, or hybrid approaches that integrate domain-specific knowledge into the learning process. Additionally, further validation of the model on larger and more diverse datasets, especially in poor weather conditions, would be beneficial to assess its scalability and robustness in different environmental contexts.
###### Abstract
This study aims to evaluate the quality, precision, and firmness of electrode placement in the Chameleon-1 helmet, comparing it with other EEG helmets and the standards of the 10-20 system. Additionally, it examines how a more precise and stable placement in positions F3 and F4, ensured by the helmet's secure fit on the head, influences the quality and stability of the EEG signal, as well as its effectiveness in measuring brain activity related to attention, concentration, and relaxation. The specific objectives of this research are outlined below:  
* To assess the quality, precision, and firmness of electrode placement in the Chameleon-1 helmet in comparison to other EEG helmets and the standards of the extended 10-20 system. * To analyze the impact of more precise and firm electrode placement in F3 and F4, ensured by the helmet's secure fit, on the quality and stability of the EEG signal. * To compare the performance of the Chameleon-1 helmet with other EEG helmets in measuring brain activity related to attention, concentration, and relaxation. Based on these objectives, the hypothesis proposes that a more precise and stable placement of electrodes with the Chameleon-1 helmet, which minimizes movement due to the helmet's secure fit, enhances the quality and stability of the EEG signal in positions F3 and F4 compared to other EEG helmets. This improvement leads to a more reliable assessment of brain activity. Although this study focuses on positions F3 and F4, the Chameleon-1 helmet allows for electrode placement in a broader range of positions within the extended 10-20 system, which may be considered in future research. Its design and construction focused on its technological development and innovation, highlighting improvements in electrode placement accuracy and signal quality. Since the helmet targets F3 and F4 and the surrounding electrodes, it could be useful in certain therapies. However, this study only evaluates its technological performance, not its therapeutic effects. For example, the results demonstrated that the proposed helmet allows for better detection of the subject's active and relaxed states compared to other helmets, which could be useful in applications such as neurofeedback, cognitive training, and stress monitoring. This work is divided into the following sections. Section 2 shows the complete design of the innovative Chameleon-1 helmet, which has adaptability and precision at the measurement points for different skull dimensions. Section 3 presents the characteristics of the Chameleon-1 helmet, which is configured based on the international 10-20 system and uses mechanisms and electronics to perform precise sensing and acquire data without noise. Section 4 presents the results and discussion of the study conducted on multiple patients with different skull dimensions and brain signal readings.
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
## 3 Network Coding for 6 LoWPAN-Based Wireless Sensor Networks
### Network Coding for 6 LoWPAN (NC6 LoWPAN) WSNs
If the current native packet is sent to the destination node via a neighbouring node (Step 24), then it has to be copied (Step 26). The copy is stored in the NC buffer (Step 30), while the current native packet is sent immediately to the MAC layer (Step 31). The copy of the current native packet (named _BufferedNativePacket_) can be used later on in case of a decoding necessity (the encoded packets received in the future can contain native packets retained in the NC buffer). Otherwise, the buffered native packet will be deleted from the NC buffer after the timeout _tz_. The value of _tz_ directly influences the decoding possibility. By selecting a too low value of _tz_, the encoded packets cannot be decoded correctly in the node because of lack of the buffered native packets in the NC buffer, and in consequence, the coding gain will be decreased. A too high value of _tz_ engages the node's memory resources to keep native packets that cannot be used for decoding. To control appropriate timers reacting to timeout _tz_, current system time is written in the _BufferedNativePacket_tp_ variable during the current native packet buffering (Step 28). Moreover, _BufferedNativePacket_ is marked by the _BufferedNativePacket_role_ variable, indicating that the current node is a source of the packet (Step 27). A network coding procedure in an intermediate node (_CurrentNodeRole_ = _RelayNode_) starts in Step 33. Additionally, Figure 4 graphically illustrates coding scheme. The NC buffer is searched (Step 34) for buffered native packets that can be used for coding operation (Step 34). The buffered native packets meeting the coding rules are extracted from the NC buffer and are coded with the current native packet (Step 37). The coding rule requires that the buffered native packets, which are stored in the NC buffer, will be sent to their final destination over the node from which the current native packet was received. The encoded packet (Step 38) preceded by a Dispatch and NC layer headers (Figure 5) is sent to the MAC layer and broadcast. In order to simplify the decoding procedure, the identifiers of the recipients (hash of the recipients' MAC addresses) are added to the NC header. ```
1:CurrentNativePacket received from 6 LoWPAN layer, \(tc\)--current system time. 2:OutputPacket prepared for sending to MAC layer.
## 2 Manufacturing the Composite Main Reflector
### Initial Design of the Composite Main Reflector  
In order to initially design the composite main reflector, the laminate pattern and the material properties were selected as design variables, and an analysis of the composite main reflector's design was performed.  
Through a relative comparison of the stiffness according to the laminate pattern of the main reflector, the laminate pattern of \(\left[0/45/\overline{Core}\right]s\) was selected, and as a result of comparing the weight and stiffness according to the honeycomb core design as shown at Figure 1, the model with the low-density honeycomb core was superior to the model with the high-density core, and it was calculated that a weight reduction of approximately 8.8% was possible [17].  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Requirement** \\ \hline Mass & 100 kg class \\ \hline
1 st Freq. @ Stowed Condition & Over 33 Hz \\ \hline
1 st Freq. @ Deployed Condition & Over 2.5 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirements for antenna.  
The results of the analysis of the entire model for the deployable reflector antenna, including the main reflector with the low-density honeycomb core, are shown in Table 2 and Figure 2. As a result of comparing the structural performance according to the design variables, the main reflector with the low-density core showed relatively superior weight and rigidity performance, and the initial design of the composite main reflector was derived by selecting the lamination method and the applicable materials through the analysis process [18].  
Figure 1: Design of composite main reflector under density of honeycomb core.  
Figure 2: Modal analysis results for deployable reflector antenna (low-density core).
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 2 Material and Methods
### Study Area  
The roof of the Faculty of Civil and Geodetic Engineering--University of Ljubljana (UL FGG) building was selected as the location for the experiment (Figure 2). The roof represents generic conditions and is equipped with concrete pillars that enable force centering of GNSS instruments. The iron plates on the top of the pillars may be seen as a ground plane during the observations, which is recommended in the case of patch antennas [27]. On the other hand, the coordinates of the pillars are known, as they were determined from several longer static GNSS surveys in the past. Low-cost antennas are very sensitive to multipath error and additionally, phase center offset and variations are unknown. The latter has an impact on the results, especially in the height component [12]. To mitigate the multipath error, an open-sky environment was selected (roof of the faculty), while the missing antenna calibration issue will be addressed in the future.  
Geodetic receiver Leica GS 15 with integrated antenna LEIGS15 NONE was put on the pillar FGG2 (Figure 3a), while two low-cost receivers were placed on pillars FGG1 and FGG4 (Figure 2). GNSS receiver in point FGG1 (Figure 3b) is used as a rover, where the other two are used as reference stations. Spatial distance defined between FGG2 and FGG1 is 65 m, and in the upcoming tests, we refer to this baseline as the G-baseline. The abbreviation G represents geodetic GNSS receiver Leica GS 15 (base). The second baseline, denoted as L-baseline, is 18 m long and was observed with two low-cost receivers, i.e., the base is also a low-cost receiver.  
Figure 1: SimpleRTK2B boards and other used equipment: (**a**) SimpleRTK2B boards connected to ANN-MB-00 antennae and power bank for power supply; (**b**) Connection of SimpleRTK2B board with OpenLog DEV-13955.
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 3 Materials and Methods
### Network Structure  
We denote the observed object's RGB image, depth image, and point cloud as \(I_{o}\), \(D_{o}\), and \(P_{o}\), respectively, where \(P_{o}\in\mathbb{R}^{N_{o}\times 3}\) and \(I_{o}\in\mathbb{R}^{H\times W\times 3}\). \(P_{p}\in\mathbb{R}^{N_{p}\times 3}\) denotes the category prior point cloud, and \(N_{o}\) represents the number of pixels in the foreground with valid depth values. \(N_{p}\) denotes the number of points in the category prior point cloud \(P_{p}\).  
Given a scene image captured by the camera, we first obtain the target object's mask using Mask R-CNN [32], which corresponds to the object in the image \(I_{o}\). We then extract the corresponding region in the scene's depth image with the same coordinates in the image coordinate system, resulting in the target object's depth image. By applying the camera's intrinsic parameters, the depth image of the target object can be converted into the point cloud \(P_{o}\).  
After obtaining the observed object's \(I_{o}\) and \(P_{o}\), we use PSPNet [33] to extract features from the RGB image, resulting in \(F_{\text{rgb}}\in\mathbb{R}^{N_{o}\times C}\). We also use PointNet++ [34] to extract features from \(P_{o}\), resulting in \(F^{p}_{o}\in\mathbb{R}^{N_{o}\times C}\), and similarly, we use PointNet++ to extract features from \(P_{p}\), resulting in \(F^{p}_{p}\in\mathbb{R}^{N_{p}\times C}\). The features \(F_{\text{rgb}}\), \(F^{p}_{o}\), and \(F^{p}_{p}\) will serve as inputs to the entire model.  
In this section, to facilitate the understanding of the following content, we will first provide an overview of our model structure. Then, we will detail each component of the model, which includes the Prior Guided Observation Network, Deformation Refine Module, Low-rank Transformer, and Feature Fusion Attention Network.