esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Results and Analysis
#### 4.3.2 Affective Interaction-Directed Graph Window Size  
To study the influence of the context window size of the directed graph of the affective interaction on the experimental results, we observed the change of macro-F1 value under Our model and Our model(b) by changing the number of directional graph context window sizes. The experimental results are shown in Figure 4.  
The experimental results show that when the window size is 0, the macro-F1 value results of Our model and Our model(b) are close to the results of the models without the affective interaction encoder, which indicates that the model performance without using the affective interaction information between speakers is poor. When the window size is 0 to 3, the macro-F1 value of the model starts to rise, indicating that the model begins to use the affective interaction information between speakers. As the window size continues to increase, the macro-F1 value begins to shift from stable to downward, which indicates that there is too much affective interaction between speakers and a certain amount of noise. Therefore, we set the window size of the emotional interaction-directed graph as 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Conversation Text** & **Context Semantic** & **Affective Interaction** \\  & **Encoder** & **Encoder** & **Encoder** \\ \hline Our model(a) & \(\surd\) & \(\times\) & \(\times\) \\ Our model(b) & \(\surd\) & \(\times\) & \(\surd\) \\ Our model(c) & \(\surd\) & \(\surd\) & \(\times\) \\ Our model & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hybrid models of different component structures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**F1 Value**} & \multirow{2}{*}{**Macro-F1**} \\ \cline{2-2} \cline{4-6}  & **Very Negative** & **Negative** & **Neutral** & **Positive** & **Very Positive** \\ \hline Our model(a) & 52.3 & 56.6 & 82.5 & 74.3 & 64.8 & 66.1 \\ Our model(b) & 56.5 & 60.1 & 81.2 & 75.8 & 68.4 & 68.4 \\ Our model(c) & 56.8 & 61.4 & 81.9 & 76.6 & 69.7 & 69.28 \\ Our model & **60.3** & **64.6** & **83.5** & **78.9** & **72.5** & **71.96** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental results of different hybrid models.
## 3 Results
### Evaluation of SiNW Photodetector-Integrated Stimulator Circuit  
Figure 10a presents a die photograph of the fabricated stimulator IC, and Figure 10b presents a simplified schematic diagram of the switched capacitor structure. The capacitor was charged using a DC power supply through a SiNW photodetector. The dura  
Figure 9: (**a**) Photo-response to light of different wavelengths and (**b**) photoresponsivity in devices 1 \(\times\) 5 at a bias voltage of 3 V. (**c**) Photo-switching response of the 1 \(\times\) 5 device, with (**d**) the falling and rising edges at a bias voltage of 1 V.  
tion of the P\({}_{\text{init}}\) signal affects the charging period of the capacitor and the gain of the stimulation current.  
Figure 10(a),b depict the relationship between the duration of the P\({}_{\text{init}}\) signal and the voltage across the charging capacitor under a bias voltage of 3 V. When a constant light intensity of 5000 lux illuminated the 1 \(\times\) 5 array SiNW photodetector, an initializing pulse of 1 \(\upmu\)s charged the capacitor to approximately 0.56 V, while extending the pulse duration to 10 \(\upmu\)s increased the charged voltage to approximately 2.98 V. Figure 10(c) illustrates the generation of the biphasic pulses with different numbers of SiNW layers at a bias voltage of 3 V. As the light intensity increased to 100 lux, the stimulation current amplitude was observed to be very sensitive to the light intensity, reaching approximately 4.2 \(\upmu\)A in a 1 \(\times\) 1 device and 5.1 \(\upmu\)A in a 1 \(\times\) 5 device. Additionally, under the same bias voltage of 3 V, the stimulation current could be controlled by adjusting the load resistor connected to the H-bridge. At a constant light intensity of 100 lux and a charge duration of 0.5 ms, changing the load resistor values from 4 k\(\Omega\) to 16 k\(\Omega\) affected the amplitude of the biphasic pulse. When the resistance was reduced from 16 k\(\Omega\) to 4 k\(\Omega\), the stimulus current was observed to increase from 5.7 \(\upmu\)A to 62 \(\upmu\)A, as shown in Figure 10(d). Prior to the in vitro experiment, stimulus currents were measured over a charging time of 0.5 ms in response to varying light intensities, ranging from 1 lux to 610 lux. The experiment was repeated to compare the amplitudes of the stimulus currents of the 1 \(\times\) 1 and 1 \(\times\) 5 devices. In Figure 10(e), the amplitudes of the stimulus currents are plotted as functions of the light intensity. Specifically, the measured stimulus current was observed to be 12 \(\upmu\)A when utilizing a 1 \(\times\) 5 array SiNW photodetector under the illumination of 4 lux. With a pulse duration of 50 \(\upmu\)s and at this stimulus current level, an injection of 1.2 nC per pulse is feasible, which substantially exceeds the threshold required for inducing a neural response [33]. The amplitudes of the stimulus currents are proportional to the incident light power on a logarithmic scale, as shown in Figure 10(f). The sensitivity of the SiNW remained high, despite the low light intensities that were observed.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 3 Problem Formulation  
To effectively address the problem of autonomous path planning for vehicles in complex dynamic environments, this study first needed to precisely model the vehicle's kinematic and dynamic characteristics. This process required not only a comprehensive understanding of the vehicle's motion behavior in different coordinate systems but also the integration of causal relationship models with RL frameworks to construct a suitable mathematical model for solving the problem. This section details the mathematical formalization process for the problem. Firstly, by establishing a vehicle dynamics model, we can describe the motion characteristics of the vehicle within a two-dimensional plane and clarify the specific description of the dynamic path-planning problem. Subsequently, we formalize the problem as a Markov decision process (MDP), laying the theoretical foundation for the subsequent causal RL algorithms. Finally, we construct a structural equation model (SEM) to identify key state features that directly impact vehicle decision making, introducing the concept of a state mask to enhance the interpretability of the model and the effectiveness of decision-making.
# An Overview of Fog Data Analytics for IoT Applications
## 5 Research Challenges
### Programability  
Applications deployed on top of the fog nodes are meant to serve the user's specific interests. The type of data storage and nature of pre-processing and data sharing could differ from one use case to another. Therefore, fog nodes should accommodate programmability needs without restricting the platform and underlying hardware. For example, a computer operating on a Windows operating system could act as a fog node or a smartphone with Android; different characteristics and capabilities of devices are taken into account when deploying an application. A smartphone cannot handle continuous data processing tasks because of battery constraints; therefore, the programmability of these types of devices is limited, and similar considerations should be figured out when designing a fog computing environment. It may be challenging to develop a generic fog node setup that is compatible with many devices.  
If the fog node is equipped with high-end processing resources, it could be leveraged to improve data analytics and storage capabilities, which would help to adjust fog network  
Figure 7: Fog network of heterogeneous devices.  
parameters to maximize the quality of the user experience. Machine learning and deep learning can be employed for that purpose. Many works have proposed such solutions in [62; 63; 64], helping to reduce the energy consumption [56], low-latency delivery, and QoS, as well as bandwidth optimization. However, the complexity of the technology stack and architecture of the fog node will directly affect the effectiveness of the data analytics services deployed on the fog node. Fog nodes have a limited data storage capacity compared to the cloud, so data that could be cached into the fog node for quick access are limited. Therefore, appropriately policy managing the cache in such a way that minimum external contact is needed and no unnecessary data remain present is necessary. The author in [65] proposes a new caching scheme known as the Steiner tree-based optimal resource caching scheme. Fog nodes use this tree to reduce total path costs and minimize resource caching costs. However, if the fog layer is composed of many other fog nodes, it further complicates the policy that provides a high availability, consistency, and coherency.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN
### Data Aggregation Routing Protocols for Energy Harvesting WSN
#### 5.2.2 Mobile Charger  
Today's technology enables dedicated wireless charging equipment (WCE) to recharge the nodes' batteries, prolonging the lifetime of wireless rechargeable sensor networks.  
A model in which, in addition to the sink (base station), a mobile station will navigate through the WSN to collect data and charge sensor nodes is considered by [PERSON] et al. (2015). The paper suggests a joint routing and charging strategy. The joint problem is decomposed into two sub-optimization problems: routing tree optimization and charging path optimization. Heuristic algorithms based on simulated annealing algorithms were applied to solve these sub-optimization problems.  
A joint charging and routing algorithm with WCE-assisted data gathering is also suggested by [PERSON] et al. (2015). The model suggested therein assumes that the BS (sink) can get the information of each node at any time, including node location, residual energy,and energy consumption rate. The suggested approach relates to data routing and energy supplement to undercharged nodes. The data routing algorithm considers several factors: sensor buffer occupancy, load, and energy. To find the route for the mobile recharging unit to traverse, they rely on Du [152] to seek the shortest Hamiltonian cycle between the nodes that urgently need energy replenishment traversed by the mobile recharging unit. Additional nodes along the traversed path can also be charged. Furthermore, the mobile recharging unit can also gather data from sensors with a critical buffer occupancy during its recharging cycle.  
In the following section, we discuss network coding (NC) and, in particular, linear network coding in the context of data gathering in WSN.
# Reinforcement Learning Based Resource Management for Network Slicing
## 3 System Model
### Overall System Architecture
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Meaning** \\ \hline \(n\in\mathbb{N}\) & Set of network slices \\ \(f\in F\) & Set of flows \\ \(i\in I\) & Set of inelastic flows \\ \(e\in E\) & Set of elastic flows \\ \(t\) & Time slot \\ \(T\) & Lifetime of slice \\ \(l\) & Time zone index \\ \(\tau\) & Number of time zones \\ \(h\) & Interval of resource trading \\ \(R_{n,t}\) & Achievable rate of slice \(n\) at \(t\) \\ \(R^{buy}_{n,t}\) & Total purchased bandwidth of slice \(n\) at \(t\) \\ \(R^{st,t}_{n,t}\) & Total selling bandwidth of slice \(n\) at \(t\) \\ \(R^{indate}_{n,t}\) & Total bandwidth that violates QoS of slice \(n\) at \(t\) \\ \(\varphi\) & Flow ratio \\ \(d_{i}\) & Instantaneous bandwidth demand of inelastic flow \(i\) \\ \(d_{e}\) & Average bandwidth demand of elastic flow \(e\) \\ \(D_{n,t}\) & Total bandwidth demand of slice \(n\) at \(t\) \\ \(r^{t}_{t}\) & Data rate consumed by inelastic flow \(i\) at \(t\) \\ \(r^{t}_{t}\) & Data rate consumed by elastic flow \(e\) at \(t\) \\ \(s_{t}\in S\) & Set of states \\ \(a_{t}\in A\) & Set of Actions \\ \(\sigma\) & Resource trading unit \\ \(v(s_{t},a_{t})\) & Reward function \\ \(V^{\pi t}\) & Total discounted reward \\ \(p^{buy}\) & Unit buying price \\ \(p^{sell}\) & Unit selling price \\ \(\rho\) & Price ratio \\ \(\alpha\) & Learning rate \\ \(\gamma\) & Discount factor \\ \(\epsilon\) & \(\epsilon\)-greedy parameter \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters. change, the provider also should change the resource price, which is the buying price. This leads tenants to reduce the amount of resource purchase by raising the price when the resource becomes scarce. We simply assume that the unit buying price is proportional to the total allocated resource amount of all slices as follows:  
\[p^{buy}=\
u\cdot\sum_{n\in\mathbb{N}}R_{n,t}^{buy}, \tag{1}\]  
where \(\
u\) is a constant value. Similarly, the end-users pay a fee of \(p^{sell}\cdot R_{n,t}^{sell}\) for their resource consumption to each tenant, where \(R_{n,t}^{sell}\) denotes the resource amount that each tenant cells to the end-users. It is assumed that each tenant determines a fixed unit selling price when entering into the initial contract with the end-users.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 4 Keyword and Correlation Analysis
### Multimodal Interaction Across Educational Levels
#### 4.3.3 Vocational Training  
Vocational training focuses on practical skills. VR and AR have changed traditional training by removing time and location limits. These technologies also provide immersive remote learning.  
This method increases training safety and reduces costs. It also helps trainees develop hands-on skills. In medical training, VR-based surgical simulations let trainees practice procedures and improve precision before real surgeries. [PERSON] et al. [61] studied how EEG and VR work together in emotion recognition, showing its potential in education and vocational training. Multimodal interaction technology is changing education. It improves learning efficiency and supports personalized instruction in K-12 education, higher education, and vocational training.
## 3 Results and Discussion  
### Density  
Figure 6 presents the density of plain concrete and PA fiber-reinforced concrete with powder glass at 28 days. There is no big difference between specimens with and without PA fibers. The density of all mixtures is almost the same. As shown, the density slightly increased with the addition of 10% G without PA fibers but then began to decrease with the addition of 0.5%, 1%, and 1.5% PA fibers. The primary cause for this slight decrease is the use of PA fibers in cement-based materials, which can result in reduced compaction due to the added volume of fibers in the mix. As fibers occupy space, they may generate more voids within the material, thereby lowering the overall density of the structure [66]. This was mentioned in previous study by [PERSON] et al. [67]. This study held the view that the presence of jute fiber in concrete leads to a decrease in its density when compared to concrete that does not contain reinforced fiber. Another factor contributing to the reduction in compaction when utilizing a higher proportion of PA fibers in concrete is \"balling\", a phenomenon in which the fibers in the mix clump together, creating small balls or clumps of fibers [66]. These fiber clumps occupy space within the mix, creating voids or spaces. These voids can actually reduce compaction within the mixture. Compaction refers to  
Figure 5: Mechanical testing: (**a**) compressive strength; (**b**) split tensile strength; (**c**) flexural strength.  
the process by which the particles within a mixture are pressed together, reducing the amount of space between them. This can lead to a denser and more solid mixture, which may not always be desirable. The voids created by the clumps of PA fibers can help prevent excessive compaction, allowing for a more porous and breathable mixture. Balling can be more severe with higher fiber contents in the mix because more fibers can clump together. Furthermore, the type and size of the fibers used, as well as the mixing and curing conditions, can all influence balling [68].
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Estimation of Time-Dependent Parameters in Non-Linear ODE Models via the Augmented Kalman Smoother
#### 2.2.1 State-Space Model  
The Kalman Filter and its extensions operate in the time-discrete state-space representation of a particular model. It is given by the general form  
\[\begin{split} x_{k+1}&=b(x_{k})+\epsilon_{k}\\ y_{k}&=H\cdot x_{k}+\eta_{k}\,,\end{split} \tag{5}\]  
for discrete time points \(k\) of the \(n\)-dimensional process state vector \(x_{k}\) and the \(d\)-dimensional vector of observables \(y_{k}\), where \(n>d\). The transition of the state vector \(x\), from time point \(k\) to time point \(k+1\), is determined by applying the transition function \(b\). This process incorporates additive Gaussian noise, \(\epsilon_{k}\sim\mathcal{N}(0,Q)\), which follows a normal distribution with a mean of zero and covariance matrix \(Q\), to account for process noise. Since not all states are directly observable, the observation matrix \(H\) establishes the relationship between the state vector \(x\) and the observable vector \(y\). Similarly, the observations \(y_{k}\) are subject to additive Gaussian noise, \(\eta_{k}\sim\mathcal{N}(0,M)\), with a covariance matrix \(M\), representing the measurement noise.  
The structure of a given ODE model, represented as \(\hat{x}=f(x,p)\), where \(x\) denotes the states and \(p\) represents \(m\) parameters, can be transformed into its discrete state-space formulation. In this formulation, the solutions of the ODE correspond to the states \(x\) in the state space and the right-hand side \(f\) of the ODE is translated into time-discrete update rules, which are represented by the transition function \(b\). In addition, the observation matrix \(H\) needs to be specified in the state-space formulation, defining the relationship between the data vector \(y\) and the corresponding states in the state vector \(x\).
## 4 Results and Discussion
### Thermal Annealing Test  
In the first experiment, the samples were heated at 130 \({}^{\circ}\)C and 180 \({}^{\circ}\)C, respectively, for 3 and 4 days. After EDS analysis and cross-sectional SEM, the IMCs formed in the sample heated at 130 \({}^{\circ}\)C were Cu\({}_{6}\)Sn\({}_{5}\) with a thickness of 1.18 \(\upmu\)m, Cu\({}_{3}\)Sn with a thickness of 1.09 \(\upmu\)m, and Cu\({}_{3}\)Sn observed as holes in a small amount. Meanwhile, the IMCs formed in the sample heated at 180 \({}^{\circ}\)C were Cu\({}_{6}\)Sn\({}_{5}\) with a thickness of 1.587 \(\upmu\)m and Cu\({}_{3}\)Sn with a thickness of 1.645 \(\upmu\)m (see Figure 8a). After 3 days, the sample heated at 130 \({}^{\circ}\)C resulted in thicker IMC formation: Cu\({}_{6}\)Sn\({}_{5}\) = 3.577 \(\upmu\)m, Cu\({}_{3}\)Sn = 1.764 \(\upmu\)m, and Cu\({}_{3}\)Sn observed as holes in a small amount. Similarly, after 4 days, the sample heated at 180 \({}^{\circ}\)C had thicker IMC formation: Cu\({}_{6}\)Sn\({}_{5}\) = 4.565 \(\upmu\)m and Cu\({}_{3}\)Sn = 2.658 \(\upmu\)m thick (see Figure 8b). As evident in the results, the higher the temperature of the sample and the longer it is heated, the thicker the IMC formation.  
Figure 7: The EDS results of the sample located at (**a**) the Sn film and (**b**) the Cu film after fabrication.
# Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
## 3 Results and Evaluation of Performance  
Candidate BL networks with varying complexities were trained for network complexity selection by adhering to implementation steps 1 to 4. Figure 3 illustrates the objective function values of these BL networks on the validation dataset. This process resulted in the identification of the optimal hyperparameters: \(N_{C}=160\), \(N_{F}=100\), and \(N_{E}=100\). Using these hyperparameters, three numerical examples and one field data application were conducted to assess the effectiveness of the proposed inversion method.
## 3 Experimental Results
### P300
In the central region, a significant three-way interaction was found between fatigue state, interruption type, and trial type for P300 mean amplitude (\(F(1,222)=6.254,\)\(p=0.044\), \(\eta_{p}^{2}=0.027\)). Further analysis showed that in the non-fatigue condition, P300 mean amplitude in the recovery trials during pause interruption was significantly lower than in the pre-interruption trials (\(F(1,222)=4.589\), \(p=0.022\), \(\eta_{p}^{2}=0.022\)). However, this effect was not observed under fatigue conditions. The heatmaps and waveform plots for different electrode regions under the fatigued state are shown in Figures 3 and 4. This is consistent with previous studies, such as [PERSON]'s [24], which utilized brain imaging techniques to reveal that the fronto-parietal network plays a crucial role in working memory and attention  
and parietal regions, with reduced negative potentials represented by light blue areas. This indicates that simple task interruptions impose a relatively low load on early attention allocation and perceptual processing. In contrast, complex task interruption showed a broader range of negative potential distributions in the same time window, particularly in the prefrontal and central regions. This suggests that under complex task interruptions, the brain requires more cognitive resources to handle task switching, resulting in larger P200 amplitudes. These findings align with the ANOVA results. Time Window: 300-450 ms: In this later window, the P300 distribution for simple task interruptions showed reduced positive potential distribution, primarily concentrated in the frontal region. This reflects relatively lower demands on working memory updating and cognitive control. Conversely, complex task interruptions demonstrated higher positive potential distributions, particularly in the central and parietal regions. This corresponds to a larger P300 amplitude, suggesting that complex task interruptions require more cognitive resources for information processing and decision-making. These findings support the ANOVA results, which highlight the significant increase in cognitive load caused by complex task interruptions. Electrode Region Sensitivity: Under both task interruption conditions, the frontal region (FZ) demonstrated higher sensitivity to complex tasks, particularly within the P300 time window. This aligns with the ANOVA findings, which highlighted significant amplitude changes in the frontal region during complex cognitive tasks. The central region (CZ) and parietal region (PZ) also showed stronger responses under complex task interruption conditions. In particular, the P300 response in the parietal region was significantly enhanced, suggesting that these regions bear a higher cognitive load during information categorization and task recovery. The heatmaps and waveform plots corroborate the ANOVA results, further validating the influence of interruption type and fatigue state on cognitive resource allocation and neural activity in the brain.