esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation of Underwater Electric Field Communication
### CSMA/CA Channel Backoff Algorithm Design and Simulation Analysis
#### 4.2.3 CSMA/CA Channel Backoff Protocol  
To solve the potential collision problem in the AUV multi-node, the previous section of this article introduced the design of the CSMA/CA channel avoidance algorithm. This section aims to integrate the CSMA/CA channel avoidance protocol into the Raspberry Pi platform for physical verification. The CSMA/CA protocol flowchart is shown in Algorithm 2. The underwater channel occupancy is identified by checking the Uart_rec_flag channel flag twice. If the channel is occupied, a random backoff time is generated, with the backoff time unit being the data packet transmission time. Once the backoff period is over, the underwater electric field communication baseband signal and carrier signal are generated through the UART and hardware PWM.  
```
While(1)do ifData_buf=0 then  while(1) else ifUart_rec_flag=0 then  Delay(DIFS) ifUart_rec_flag!=0 then  Uart_rec_flag=0  T=Random()  Delay(t) ifUart_rec_flag!=0 then  Uart_rec_flag=0 else  T=T-1 ifT=0 then  Set_PWM_handuare(18, 12000, 50000)  Uart_send(Data_buf)
```  
**Algorithm 2** An CSMA/CA backoff protocol process  
Figure 10: Underwater electrical field communication data packet information.
## 1 Introduction
In addition, we explore other challenges related to predicate set configuration, such as synonymous predicates. We focus on optimizing predicates that do not involve merging and comparing their performance to demonstrate the effectiveness of our approach. We experimentally compare the performances of the same reasoning model on the original KG and the optimized version obtained through predicate decomposition. We utilize clustering quality metrics to adaptively search for the optimal cluster that leads to optimal predicate decomposition, further enhancing the reasoning model's performance on KGs. Overall, our work addresses crucial issues in predicate set configuration and provides a practical solution to enhance the quality of KGs and improve the performance of reasoning models. We introduce a versatile and practical method for improving KG reasoning performance through predicate decomposition. This approach is applicable to various KGs and reasoning models. We pioneer the implementation of predicate inductive reasoning that does not rely on external information and enables authentic KG completion tasks even when the original predicate set varies. This method also serves as a tool to enhance KG quality. We have optimized common KG benchmarks using this method and made the optimized versions publicly available. The structure of this article is as follows: Section 2 mainly describes related works, such as KGC, inductive KGC, and predicate inductive KGC. Section 3 formalizes the methodology of the PDEC framework and provides preliminary theoretical proof. Section 4 describes the experimental datasets, baselines, and experimental setup details, and presents the experimental results. Section 5 discusses and analyzes the experimental results. Section 6 summarizes the work of the paper and analyzes the current limitations and future work plans.
# Numerical Study on the Evolution Mechanism of the Crater under a Millisecond Laser
## 2 Numerical Simulation
### Heat Transfer Governing Equation and Boundary Conditions
#### 2.1.2 Heat Transfer Boundary Conditions  
Herein, it is assumed that the Neumann boundary conditions (natural convection heat transfer, boundary radiation) exist on all surfaces of the target, which are represented as:  
\[-k\
abla T\ =h(T-T_{a})+\varepsilon\sigma\Big{(}T^{4}-T_{a}^{4}\Big{)}. \tag{7}\]  
Also, the heat source and heat loss due to evaporation and radiation exist on the top surface, shown as:  
\[-k\
abla T\ =Q+h(T-T_{a})+\varepsilon\sigma\Big{(}T^{4}-T_{a}^{4}\Big{)}+Q\
u \tag{8}\]where \(h\) is the convection coefficient between the target and the air, \(\varepsilon\) is the radiation emissivity, \(\sigma\) is the Stefan-Boltzmann constant, \(T_{a}\) is the ambient temperature, \(Q_{V}\) is the evaporation heat loss, and the specific expression is given by  
\[Q_{V}=M_{V}\times L_{V} \tag{9}\]  
here, \(M_{V}\) is the vaporized mass flow rate and \(L_{V}\) is the latent heat of vaporization.  
\[M_{V}=\sqrt{\frac{m}{2\pi RT_{s}}}\times P_{sat}(T\ )\times(1-\beta_{r}) \tag{10}\]  
\[P_{sat}(T\ )=P_{atm}\times exp\bigg{(}\frac{M_{a}L_{v}}{R}\bigg{(}\frac{1}{T_{v} }-\frac{1}{T}\bigg{)}\bigg{)} \tag{11}\]  
In this formula, \(m\) represents the atomic mass, \(P_{sat}(T\ )\) represents the saturated vapor pressure, \(\beta_{r}\) is the retro diffusion coefficient, \(R\) is the Boltzmann constant, \(T_{v}\) is the vaporization point of the material, \(L_{v}\) is the latent heat of vaporization, and \(P_{atm}\) is the standard atmospheric pressure [19].  
Also, \(Q\) is the laser heat source, and a Gaussian surface heat source is used. Its specific expression is as follows:  
\[Q=\ 2\eta l_{laser}e^{-\frac{2\eta^{2}}{r^{2}}} \tag{12}\]  
where \(r\) is the effective spot radius of the pulse laser, \(l_{laser}\) is the laser peak power density, \(\eta\) is the absorption coefficient, and \(x\) is the distance between the transverse direction and the focus.
## 1 Introduction
In summary, through the newly proposed coupled CFD-DEM-MCN methodology described in this research, the transient data of the fluid phase information, particle movements with temperature, and the \(k_{eff}\) eigenvalue of the bed system can be acquired during the dynamic process of the heterogeneous bed configuration's transformation, considering the multiphysics interplay. For automatically conducting the criticality/cooling bed safety assessment, the tool of the coupled CFD-DEM-MCN solver was also newly built in this research by integrating existing codes. The CFD solver, DEM solvers, and the sub-models for evaluating momentum/energy exchange between fluid-particle phases were executed via the commercial code of STAR-CCM+ (Version 2020.3) [32], a general-purpose tool allowing the processing of three-dimensional calculation among multi-phases with high fidelity and flexibility. The MCN solver can be conducted on the in-house code of MVP3, developed by the JAEA [33] and validated via conducting benchmarking on the debris particles system with two other Monte-Carlo-based codes (Serpent and MCU), showing that the three modern codes are capable of performing neutronic analysis for the fuel debris particles system without significant discrepancy and with high accuracy [34]. The interface, which is liable for exchanging information and the workflow control between STAR-CCM+ and MVP3, was originally created in this research to realize the automated multi-physics-concerning function in the coupled CFD-DEM-MCN solver. In order to examine the capability of the coupled CFD-DEM-MCN solver, numerical investigations of the different heterogeneous bed configurations' transformations under the SFR's postulated SA scenarios were carried out, and the effects of these alternative processes on the criticality/cooling bed safety assessments were also quantitatively confirmed in this research. The following content will further explain the detailed structure and the examined results of the CFD-DEM-MCN coupled solver.
## 2 Materials and Methods
### Benchmarking Deep Learning Performance against Random Forest  
Additionally, we benchmarked U-Net formulations against a common modeling approach in the remote sensing community: random forest, a tree-based algorithm. Random forest analysis is a supervised ensemble classifier that generates unpruned classification trees to predict a response. Random forest implements bootstrapping samples of the data and randomized subsets of predictors [33]. Final predictions are ensembled across a forest of trees (\(n=100\)) on the basis of an averaging of the probabilistic prediction of each classifier. No maxima were set for tree depth, number of leaf nodes, or number of features; we did this to favor prediction over interpretability.  
Several studies [34; 35; 36; 37; 38] showed that random forest is a very efficient approach for land cover and vegetation classification from satellite imagery in environmental applications, especially when combined with a gray-level co-occurrence matrix (GLCM) textural analysis [39], a statistical approach to examine image texture based on the value and spatial relationships of pixels.  
Here, we adapted the approach of [35] that combines GLCM with random forest for our segmentation task on aquatic vegetation in the SRB, and compared its prediction performance on the satellite data and on hold-out datasets not incorporated in the training set. With respect to the random forest approach, deep learning exploits feature representations learned exclusively from raw image pixels, without depending on additional statistical features, such as GLCM. However, for the purpose of comparison, we also trained a U-Net model with GLCM layers.  
Six GLCM texture features, i.e., contrast, dissimilarity, homogeneity, energy, correlation, and angular second moment (Figure 6; see [40] for texture details), were calculated for each of the eight WorldView-2 multispectral bands by using Python scikit-image package version 0.15 [41]. The same input size (\(256\times 256\times 8\)) and the same batch of training and validation data of satellite imagery and corresponding masks were used in the random forest test, in comparison with the U-Net model.  
Figure 6: Examples of GLCM texture features: contrast, dissimilarity, energy, and correlation, which are calculated from the raw pixel values of the 8-band multispectral WorldView-2 imagery.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 2 Deployable Mesh Antenna Design
### Feed Antenna  
The feed assembly comprises of backfire helix antennas as feed elements. Here, the design and construction of a single element is presented. Apart from presenting broad radiation pattern and simple fabrication [14], backfire helix antennas have low cross sectional area, turning into great candidates for reflectors feed antennas. As explained in [14], when the ground plane of a helical antenna is decreased, the backward radiation increases and operates in backfire mode. The L-band backfire helix antenna operates in right-hand circular polarization within operational bandwidth, with total length of around \(1\lambda\) and width of 0.3\(\lambda\). It is pin-fed by a 50-\(\Omega\) coaxial cable and male SMA connector, soldered onto the ground plane. The feed point is 6.1 mm above the ground plane by having the SMA pin soldered to the copper wire. Figure 4a illustrate the configuration of the backfigure helical antenna. Impedance matching is realized by feed height control and the use of a rectangular copper wavetrap of dimension 32.1 mm by 16.1 mm soldered at the first quarter loop of the helical antenna. A support frame for the helical antenna was 3D printed in PLA material, as shown in Figure 4b, introducing negligible effects to the radiation pattern. Design parameters of the antenna are resumed in Table 4. The antenna pattern was measured in an anechoic chamber using the E8364C PNA Microwave Network Analyzer, and results are depicted in Figure 5 and summarized in Table 5.  
Figure 4: (**a**) Side and top view of backfire helix antenna; and (**b**) constructed helix and wavetrap used for impedance matching.  
Figure 3: Deployable parabolic reflector in (**a**) profile and (**b**) top view.
## 7 Architectural Challenges in Designing Any Smart Healthcare Application
### IoT Heterogeneity in Smart Healthcare
#### 7.3.2 Technology Advancements in Data Processing and Transmission  
Channel allocation, energy consumption, transmission speed, and network throughput are the primary goals in designing data transmission strategies. WMN is a multi-hop network of less capacity that uses an efficient channel assignment scheme in distributed channel assignment protocols such as AODV [96]. Cooperative channel assignment protocols such as COCA solve channel optimization strategies for data transmission with less propagation delay. For IoT data transfer, TDOCP [97] is the most used protocol for applications that require high throughput.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rotor Angular Speed (rpm)** & **Resonant Mode** & **Reference (m/s2)** & **MEMS (m/s2)** & **MAPE (\%)** \\ \hline \multirow{4}{*}{0} & 1 & 30.6 \(\pm\) 0.4 & 30.4 \(\pm\) 0.3 & 0.47 \\  & 2 & 56.4 \(\pm\) 0.9 & 56.3 \(\pm\) 0.9 & 0.17 \\  & 3 & 16.9 \(\pm\) 0.2 & 16.8 \(\pm\) 0.2 & 0.61 \\  & 4 & 9.7 \(\pm\) 0.1 & 9.7 \(\pm\) 0.1 & 0.28 \\ \hline \multirow{4}{*}{120} & 1 & 25.7 \(\pm\) 0.3 & 25.4 \(\pm\) 0.3 & 0.85 \\  & 2 & 45.8 \(\pm\) 0.8 & 45.8 \(\pm\) 0.8 & 0.14 \\  & 3 & 16.5 \(\pm\) 1.4 & 16.5 \(\pm\) 1.3 & 0.59 \\  & 4 & 9.6 \(\pm\) 0.4 & 9.5 \(\pm\) 0.4 & 1.11 \\ \hline \multirow{4}{*}{180} & 1 & 26.1 \(\pm\) 0.5 & 26.0 \(\pm\) 0.4 & 0.57 \\  & 2 & 39.1 \(\pm\) 0.5 & 39.0 \(\pm\) 0.5 & 0.23 \\  & 3 & 16.2 \(\pm\) 0.8 & 16.5 \(\pm\) 0.7 & 1.95 \\  & 4 & 10.6 \(\pm\) 0.2 & 10.6 \(\pm\) 0.2 & 0.33 \\ \hline \multirow{4}{*}{240} & 1 & 24.1 \(\pm\) 0.2 & 24.0 \(\pm\) 0.2 & 0.67 \\  & 2 & 44.1 \(\pm\) 0.1 & 44.1 \(\pm\) 0.1 & 0.1 \\  & 3 & 15.6 \(\pm\) 0.9 & 15.2 \(\pm\) 0.9 & 3.97 \\  & 4 & 11.2 \(\pm\) 0.3 & 11.1 \(\pm\) 0.2 & 0.57 \\ \hline \multirow{4}{*}{300} & 1 & 21.7 \(\pm\) 0.2 & 21.5 \(\pm\) 0.2 & 0.82 \\  & 2 & 38.8 \(\pm\) 0.1 & 38.7 \(\pm\) 0.1 & 0.38 \\ \cline{1-1}  & 3 & 12.4 \(\pm\) 0.9 & 12.0 \(\pm\) 0.9 & 2.96 \\ \cline{1-1}  & 4 & 11.4 \(\pm\) 0.3 & 11.3 \(\pm\) 0.2 & 1.1 \\ \hline \end{tabular}
\end{table} 
Table 3: Mean acceleration amplitude \(\pm\) standard deviation calculated from both the acceleration measurement systems (Reference and MEMS-based system).
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 3 Sensing System
### Reference Structure Implementation  
To implement the sensing model described in the previous subsection, which segments the object space into discrete sampling cells, we employ two kinds of masks. These masks play the role of reference structure, which modulates the FOV of PIR sensors. The first type of mask, Type I, is a fan shape, as shown in Figure 1c. After applying such mask, the FOV of the PIR sensor is no longer a full cone, but a partial cone shape, called a fan cone. The second type of mask, Type II, is a ring shape, as shown in Figure 1d. The FOV of the PIR sensor after masked is still a full cone, but its cone angle \(\beta\) is less than that of the original cone. These two types of masks provide two degrees of freedom (DOF) spatial partitions.  
In our system implementation, the performance of our system will improve as the number of PIR sensors increases [20]. Because of the hardware constraint of our sensor node, seven PIR sensors with masks are multiplexing to segment the object space into sampling cells, as shown in Figure 1b. Four PIR sensors are masked by Type I mask, and the remaining three PIR sensors are masked by the Type II mask. In such a configuration, the object space is segmented into 17 sampling cells. Referring to Equation (2), \(M=7,L=17\), and the measurement matrix \(V\) is shown in Figure 2.  
Figure 1: Sensing model design: (**a**) Measurement space, object space and the human thermal target; (**b**) The projection of sampling cells on the ground; (**c**) Type I mask; (**d**) Type II mask.
## 3 Results and Discussion
### Satisfaction/Total Value  
Using the Wilcoxon method in SPSS, the four Sig. values were found to be 0.142, 0.327, 0.002, and 0.256, as shown in Table 8.  
Figure 8: Test results of operation process/EQ13-EQ20.  
The results for EQ21 don't suggest that the users perceived the design of the MUI to be worse than that of the single-person interface, still offering convenience and a clear appearance. Furthermore, based on the results for EQ22, the users perceived the content of both user interfaces (UIs) to be equally useful, with no notable distinction. The MUI did not compromise the content's usefulness. Moreover, based on the results for EQ23, the users considered operating an MUI to be more interesting than that of an OUI. It is noteworthy that almost all users were utilizing an MUI recipe app for the first time, which created a sense of novelty. Moreover, the outcomes from EQ24 demonstrate that there was no observable difference in the sense of accomplishment between the two interfaces. The users reported that the operation of the MUI did not present difficulties or excessive challenges.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 3 Method
### Loss Function  
To effectively train our lightweight network for decision map refinement, we propose a combined loss function that leverages two complementary components: Binary Focal Loss (BFL) [50] and Binary Dice Loss (BDL) [51]. The total loss \(\mathcal{L}_{\text{total}}\) is defined as:  
\[\mathcal{L}_{\text{total}}=\lambda_{1}\mathcal{L}_{\text{BFL}}+\lambda_{2} \mathcal{L}_{\text{BDL}} \tag{10}\]  
where \(\lambda_{1}=15\) and \(\lambda_{2}=1\) are the weighting coefficients that balance the contribution of each loss term. In our context, BFL helps the network focus on hard-to-classify regions. Formally, BFL is defined as:  
\[\mathcal{L}_{\text{BFL}}=-\alpha y(1-p)^{\gamma}\log(p)-(1-\alpha)(1-y)p^{ \gamma}\log(1-p) \tag{11}\]  
where \(p\) represents the predicted probability, \(y\) is the ground truth label, \(\alpha\) is the balancing factor (set to 0.25), and \(\gamma\) is the focusing parameter (set to 2.0). The focal loss introduces modulating factors \((1-p)^{\gamma}\) for positive samples and \(p^{\gamma}\) for negative samples. This automatically down-weights the contribution of easy examples and focuses on hard examples during training. This property is particularly beneficial for our task, as it helps the network concentrate on refining the decision boundaries where the focus state is ambiguous.  
To complement the focal loss, we incorporate the Binary Dice Loss, which is particularly effective for handling imbalanced segmentation tasks. BDL measures the overlap between predicted and ground truth regions:  
\[\mathcal{L}_{\text{BDL}}=1-\frac{2\sum_{i=1}^{N}p_{i}y_{i}+\epsilon}{\sum_{i= 1}^{N}p_{i}^{2}+\sum_{i=1}^{N}y_{i}^{2}+\epsilon} \tag{12}\]  
where \(p_{i}\) and \(y_{i}\) are the predicted and ground truth values at position \(i\), \(N\) is the total number of pixels, and \(\epsilon\) is a small constant to ensure numerical stability. The Binary Dice Loss is particularly advantageous for our decision map refinement task, as it naturally optimizes for spatial overlap between predicted and ground truth regions.
# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
## 3 Proposed Multimodal Emotion-Recognition Approach
### Speech Module  
To leverage the benefits of mel spectrogram features, our speech module includes a series of convolutional neural networks preceding a fully connected layer. This design captures the intricate patterns within the mel spectrogram, and for enhanced performance and accuracy, ensures that the convolutional layers effectively extract the spatial characteristics, which are then synthesized in the fully connected layer. Specifically, we drew inspiration from the work outlined in [31], and adapted the architecture by implementing changes tailored to our objectives. This allowed us to build on established ideas while incorporating our unique modifications to better suit the needs of the proposed multimodal emotion-recognition approach. Figure 2 presents an in-depth depiction of the speech module architecture.  
Figure 1: Proposed multimodal emotion-recognition approach.  
Initially, we utilized the \"Librosa\" library [32] to procure the mel spectrogram features from the raw speech data. The extracted features were then channeled into two distinct convolutional layers that ran concurrently. These layers are specifically designed to capture and highlight patterns from both the time and frequency domains, ensuring a comprehensive analysis of the audio signal characteristics. We employed two simultaneous convolutional layers as our initial layers, with kernel sizes of (10, 2) and (2, 8). After padding, the output from each convolutional layer yielded eight channels. The outputs were merged, resulting in a combined 16-channel representation for further processing. Subsequently, four additional convolutional layers were employed to produce an 80-channel representation, which was forwarded to the fully connected layer for further analysis and processing. Following each convolutional layer, we incorporated a batch normalization (BN) layer coupled with a rectified linear unit (ReLU) activation function to enhance the stability and performance of the network. Moreover, convolutional layers 1 and 2 preceded a max-pooling operation with a kernel size of two that reduced data dimensionality and further streamlined processing.  
Throughout these convolutional layers, the kernel size remained consistent at 3 \(\times\) 3 with a stride of one. However, as we progressed from convolutional layers 1 and 4, the number of input channels started at 16 and doubled, subsequently increasing in increments of 16, ultimately reaching 80 output channels in convolutional layer 4. This structured increment in channels suggests a hierarchical feature extraction process, wherein each subsequent layer aims to extract more complex and nuanced features from the input.
## 4 Outage Probability Analysis
### Analysis Using Markov Chain
Note that we would calculate \(\gamma_{i,j}\) as follows:  
\[\gamma_{S\to R}=|h_{S\to R}|^{2}p_{SS}=\frac{|h_{S\to R}|^{2}Q}{2|g_{S \to D}|^{2}}, \tag{25}\]  
\[\gamma_{R\to D}=|h_{S\to R}|^{2}p_{RT}=\frac{|h_{S\to R}|^{2}Q}{2|g_{R \to D}|^{2}}. \tag{26}\]  
The outage probability depends on factors like the target data rate we want to achieve, the signal-to-interference and noise ratio, and the number of links available at any given state.
## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Recommendations and Research Directions  
This review is all about minimizing the sampling rate of ECG for an IoT framework and efficient decision making on users' heart health using CS. In this review, key research components are considered and, in this section, new research directions and recommendations are given. Our review shows that in CS there are so many sensing matrices deployed and the best sensing matrix will be the one that is easy to practically implement and effectively reconstruct the signal. The most commonly sensing matrix used is the Gaussian Random matrix. Our study shows that any transforms such as FFT, DCT can also be used as a sensing matrix by retaining some percentage of the coefficients and forcing others to zero. As the sparsity of the signal is the key requirement of CS, the right selection of sparsifying matrix is very important, and our study shows that any transform that is used in ECG signal compression can be used as a sparsifying operator. Few of such transforms are wavelet, DCT, STFT etc. The novel transforms such as 1D-DAST as a sparsifier in CS are unexplored and future work can lead in this direction. QRS-based signal representation and sparsification is much customized for ECG and dictionary based sparsification is also suitable for ECG. As far as the decision making is concerned, learning directly from CS measurements of ECG, which is called compressive learning, is more effective in terms of computation time. CS reconstruction is computationally intensive task and compressive learning eliminates the need for reconstruction and thus is much suitable for CS framework. The functional requirements of CS are the speed and power of operation and the Table 5 depicted in our review helps to identify hardware implementations that are faster and low power.