esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Experimental Results and Discussion
#### 4.4.3 Ablation Experiment  
To analyze the effect of the flow module and the label-embedding module on the F1 value of our proposed model, ablation experiments are performed on the CCKS2020 dataset. The RoBERTa is employed as the baseline model and compared with adding flow and adding label embedding on the baseline, which are denoted as RoBERTa + flow and RoBERTa + label, respectively.  
The results of the ablation experiments are illustrated in Table 7, and it is obvious that the flow module substantially improves the performance of model, while relatively small enhancement is observed with the label-embedding module. Specifically, the RoBERTa + Flow and RoBERTa + Label models outperform the baseline model by 1.18% and 0.10%, respectively.
## 2 Bidirectional DC-DC Converters
### Classification of Bidirectional DC-DC Converter
#### 2.1.3 Comparison of _Nibdcs_ and _Ibbcs_  
In this section, a comparison between the two main general groups of configurations, isolated and non-isolated topologies, was made in terms of the advantages and disadvantages, as illustrated in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{5}{*}{_NibdDC_} & (1) & Symmetrical structure. \\  & (2) & The ripple current is low on both sides. \\  & (3) & It has short circuit safety. \\ \cline{1-1}  & (4) & It can operate with a wide range of voltages and different voltage levels. \\ \cline{1-1}  & (5) & It uses only two switches to simplify the circuitry of the driver and to reduce the driving power. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pros and cons of _NIBDC_ and _IBDC_ bidirectional _DC-DC_ converters.  
Figure 16: Isolated push–pull bidirectional _DC-DC_ converter [18].  
Figure 15: Isolated bidirectional flyback [72].
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results
### Analysis of Experimental Results
#### 4.2.2 Ablation Experiment  
To validate the effectiveness of the improvements to YOLOv7, the following ablation experiments were designed: maintaining consistent training parameters and data for each model, DySnakeConv convolution, CoordConv, and the dynamic detection head DyHead were gradually incorporated into the YOLOv7 model. Models were then trained individually for comparison, as outlined in Table 2.  
Through the analysis of Table 2, the following conclusions can be drawn:  
1. Adding DySnakeConv, CoordConv, and DyHead to the YOLOv7 model resulted in YOLOv7-Tuna. Compared to the original YOLOv7, the precision increased from 91.1% to 96.3%, marking a 5.2% improvement; The recall rate increased from 95.8% to 98.9%, marking a 3.1% improvement; The [EMAIL_ADDRESS] increased from 98% to 98.5%, representing a 0.5% improvement; The [EMAIL_ADDRESS]:0.95 increased from 58.5% to 68.5%, marking a 10% improvement. Therefore, YOLOv7-Tuna demonstrates improvements across all metrics, validating the effectiveness of the proposed algorithm in this study.
2. The models with DySnakeConv, CoordConv, and DyHead added individually show improvements over the original YOLOv7 in various metrics, including precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95. Furthermore, by comparing the enhancement effects of the three models, it can be observed that the model with DySnakeConv added exhibits the highest improvement in recall rate, increasing from 95.8% to 99%, marking a 3.2% improvement; The model with CoordConv added shows the most notable improvement in precision, increasing from 91.1% to 95.9%, marking a 4.8% improvement. The model with DyHead added exhibits the best performance in terms of [EMAIL_ADDRESS], increasing from 98% to 98.7%, marking a 0.7% improvement. Therefore, the three modules added in this study effectively enhance the performance of YOLOv7, each demonstrating advantages in three different aspects. This confirms the effectiveness of the added modules in this study.  
In summary, the modules added in this study effectively enhance the performance of the YOLOv7 model, with each module exhibiting its own advantages in three distinct aspects. The algorithm proposed in this paper, YOLOv7-Tuna, integrates the advantages of the three modules, resulting in superior overall performance. Compared to the original YOLOv7 model, the precision increased by 5.2%, the recall rate increased by 3.1%, [EMAIL_ADDRESS] increased by 0.5%, and [EMAIL_ADDRESS]:0.95 increased by 10%. Experimental results indicate that YOLOv7-Tuna achieved a significant improvement in accuracy compared to the original model, enabling better tuna identification and thereby enhancing the accuracy of tuna counting.
## 2 Methodology  
### Wav2 vec Speech Features  
Early approaches to speech features involved manually crafted Low-Level Descriptor (LLD) features, such as prosodic features, acoustic features, and spectral features. Mel-Frequency Cepstral Coefficients (MFCCs) are classic audio features known for their simplicity and efficiency. However, MFCCs only consider frequency information and overlook the temporal correlation in audio data. Additionally, MFCCs often require manual parameter settings, such as for the window size and stride. With the advancement of deep learning, researchers have turned to deep learning methods for extracting speech emotion features. Common deep learning methods for speech feature extraction include Convolutional Neural Networks (CNNs) [38, 39], Recurrent Neural Networks (RNNs) [40], Bidirectional Long Short-Term Memory (BiLSTM) [41, 42, 43], etc. The Wav2 vec2.0 version used in this paper is an end-to-end training approach that can learn representative feature descriptions directly from audio data through self-supervised learning, eliminating the need for manual parameter tuning.  
For speech emotion recognition tasks, the Wav2 vec2.0 method effectively captures the emotional information in audio. Furthermore, Wav2 vec2.0 can enhance the model performance through pre-training, such as Wav2 vec2-base-960h, which is pre-trained on diverse audio data for 960 h. This pre-training allows the model to capture more audio patterns and structures, resulting in improved robustness in speech emotion recognition tasks. In practical applications, fine-tuning can be performed based on specific tasks and data to further enhance the model performance. It is important to note that Wav2 vec2-base-960h is a relatively complex model, requiring significant computational resources and longer training times.  
The Wav2 vec2.0 process mainly involves taking an input speech signal X and encoding it using a seven-layer CNN network to obtain the latent variable Z. The latent variable Z is quantized into the quantized variable Q through the Gumbel softmax quantization module. Simultaneously, Z is randomly masked at some positions and put into the Transformer [28] network to obtain the contextual feature vector C. The structure and overall process of Wav2 vec2.0 are illustrated in Figure 1.  
Figure 1: Structure diagram of the Wav2 vec2.0 model.
# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 3 Materials and Methods
### Hybrid Models  
Hybrid models are models that combine two or more models. Those models integrate machine-learning models and other soft-computing, deep-learning, or optimization techniques.  
In this research, we used CNN as a base-feature extractor, and these extracted features were fed into machine-learning models to see how the models performed, as shown in Figure 4. The architecture of the feature extractor was the same as that of the CNN model and the various machine-learning models mentioned above. The following are the hybrid models that were experimented on:  
1. CNN + SVM;
2. CNN + Random Forest;
3. CNN + AdaBoost;
4. CNN + XGBoost;
5. CNN + CatBoost;
6. CNN + Logistic Regression.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**CNN** & & \\ \hline
**Architecture Design** & & \\ \hline
**Layer (type)** & Output Shape & Param \# \\ \hline
**Conv2d\_7** & (None, 26, 26, 32) & 320 \\ \hline
**Max\_pooling2d\_7** & (None, 13, 13, 32) & 0 \\ \hline
**Batch\_normalization\_7** & (None, 13, 13, 32) & 128 \\ \hline
**Conv2d\_8** & (None, 11, 11, 64) & 18,496 \\ \hline
**Max\_pooling2d\_8** & (None, 5, 5, 64) & 0 \\ \hline
**Batch\_normalization\_8** & (None, 5, 5, 64) & 256 \\ \hline
**Conv2d\_9** & (None, 3, 3, 128) & 73,856 \\ \hline
**Max\_pooling2d\_9** & (None, 1, 1, 128) & 0 \\ \hline
**Batch\_normalization\_9** & (None, 1, 1, 128) & 512 \\ \hline
**Flatten\_3** & (None, 128) & 0 \\ \hline
**Dense\_5** & (None, 256) & 33,024 \\ \hline
**Dense\_6** & (None, 10) & 2570 \\ \hline
**Total params: 129,162** & & \\
**Trainable params: 128,714** & & \\
**Non-trainable params: 448** & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The CNN architecture design.  
The models were trained using both the Arabic MNIST dataset and the Arabic character dataset. The CNN's only task is to extract relevant features from the handwritten images, which are its outputs. It then passes the features to the machine-learning models, which use these features to find patterns, thereby classifying the images.  
The proposed approaches for the system have their benefits and drawbacks. A CNN was proposed because this approach has been used for handwritten character recognition with many other languages, so adopting this method could achieve the best performance. The CNN approach involved two steps. The first was the extraction of the required features; the second was classification. The standalone approach was proposed to check the individual impact on the dataset and see whether it could perform the same procedure for handwriting recognition. The hybrid approach was designed because it helped to achieve more reliable results than the standalone approach. It works like the ensemble approach in solving the Arabic handwriting recognition problem. This was decided after reviewing the multiple approaches to handwriting recognition reported in the literature.
## 3 Background
### Relation-Aware Transformer  
The relation-aware transformer is an enhancement of the traditional transformer model that employs relation-aware self-attention [22] by incorporating relation embeddings into the key and value entries to augment the vanilla self-attention [23]. For the input node embeddings \(\mathsf{X}=\left\{x_{i}\right\}_{i=1}^{n}\), the formula to compute the attention scores is as follows:  
\[e_{ij}^{(h)}=\frac{x_{i}\mathbf{W}_{Q}^{(h)}\Big{(}x_{j}\mathbf{W}_{K}^{(h)}+r _{ij,K}\Big{)}^{\top}}{\sqrt{\mathrm{d}_{z}/\mathrm{H}}}, \tag{3}\]  
\[\alpha_{ij}^{(h)}=\mathrm{softmax}_{j}\Big{\{}e_{ij}^{(h)}\Big{\}}, \tag{4}\]  
where \(\mathbf{W}\) is the trainable parameter matrix, \(h\) is the number of heads, \(\alpha_{ij}^{(h)}\) represents the computed attention scores, and the edges from element \(x_{i}\) to element \(x_{j}\) are represented by \(r_{ij,K}\) and \(r_{ij,V}\). The final output \(z_{i}^{(h)}\) is computed as shown below:  
\[z_{i}^{(h)}=\sum_{j=1}^{n}\alpha_{ij}^{(h)}\Big{(}x_{j}\mathbf{W}_{V}^{(h)}+r _{ij,V}\Big{)}, \tag{5}\]  
where \(r_{ij,K}\) and \(r_{ij,V}\) are shared across different attention heads. For each layer of RAT, the update process can be simply represented as follows:  
\[\mathsf{Y}=\mathrm{RAT}(\mathsf{X},\mathcal{R}), \tag{6}\]  
where \(\mathcal{R}=\{\mathrm{R}\}_{i=1,j=1}^{|\mathsf{X}|,|\mathsf{X}|}\) is the relationship matrix between tokens, representing the relationship between the i-th token and the j-th token.
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 3 Results and Discussion
### EBSD Analysis
#### 3.6.2 Grain Boundary  
The distribution of angle misorientation of grain boundary is shown in Figure 18, and the statistics of angle misorientation are shown in Figure 19. As can be seen from Figure 19, before quenching, the high-angle misorientation boundary (15-180\({}^{\circ}\)) accounts for 89.2%, while the middle-angle (5-15\({}^{\circ}\)) and the low-angle (0-5\({}^{\circ}\)) misorientation boundary account for 6.5% and 4.4%, respectively. In addition, most of the grain boundary misorientation in the joint is between 0-5\({}^{\circ}\) and 25-60\({}^{\circ}\). As shown in Figure 18a, before quenching, in the FZ, the grain size is not uniform, and there are many red boundaries (2-5\({}^{\circ}\)) inside the coarse M. While in the FGZ, there are more blue boundaries (15-180\({}^{\circ}\)). The BM has more green boundaries (5-15\({}^{\circ}\)) than FZ and HAZ. According to Figure 19, after quenching, the high-angle misorientation boundary decreases to 84.8%, while the middle-angle and low-angle misorientation boundary increase to 7.3% and 8%, respectively. In addition, more grain boundaries of 0-15\({}^{\circ}\) and 50-60\({}^{\circ}\) are formed after quenching, while grain boundaries of 15-50\({}^{\circ}\) decreased significantly. After quenching, it can be seen from Figure 18b that more blue boundaries (15-180\({}^{\circ}\)) are generated in the FZ and more green boundaries (5-15\({}^{\circ}\)) and red boundaries (2-5\({}^{\circ}\)) are generated in the HAZ and BM. Before quenching, there are massive high-angle misorientation boundaries in the FGZ, while the high-angle misorientation boundary of FZ, CGZ, and BM are relatively few. After quenching, the grain boundaries of the FZ are basically composed of high-angle misorientation boundaries, while more low-angle misorientation boundaries are formed in the HAZ and BM.
## 3 Results and Discussion
### GP and FNNs Models Statistic Comparison  
The estimated errors (MAE, MSE, SEE, and MAPE) for each nonlinear model (GP model and FNNs) in Tests 1 and 2 were integrated and associated with a new group of analyses (box plot and ANOVA). According to results obtained in the ANOVA and the graphic (box plot), it was determined that the FNNs model showed the best performance, considering the error and variance values, as observed in Figure 8. Furthermore, the ANOVA with \(P_{value}=0.0155\) supports these results, given that the established hypothesis (the error FNN < GP) is true with a 1.55% risk (Table 7).  
Figure 6: GP model results in Test 2. (**a**) Data estimated (red) versus energy consumption data measured (blue). (**b**) Absolute error between estimated behavior and measured energy consumption (red).  
Figure 7: FNNs model results in Test 2. (**a**) Data estimated (red) versus energy consumption data measured (blue). (**b**) Absolute error between estimated behavior and measured energy consumption (red).
## 3 Results
### Effect of L. regularis on Glutathione and TBARs Level  
Glutathione (GSH) and TBARS are well-known cellular markers of oxidative stress.  
The levels of antioxidant GSH, and the lipid peroxidation indicator TBARs, were measured in the retinas of _L. regularis_-treated and untreated diabetic rats. In diabetic retinas, the amount of GSH was considerably lower (\(p\) < 0.05) than in controls. However, only a high dose of _L. regularis_ treatments significantly enhanced retinal GSH levels in diabetic rats when compared to untreated diabetic rats (\(p\) < 0.05) (Figure 5A). TBARs were considerably (\(p\) < 0.01) elevated in the diabetic retinas, while _L. regularis_ high-dose (300 mg/kg/day) treatments to diabetic rats ameliorated the increased level of TBARs (\(p\) < 0.01, Figure 5B).  
Figure 3: Retinal protein expression of TrkB, AKT, and p-AKT in _L. regularis_-treated diabetic rats. Densitometry was used to measure the band intensities. Representative immunoblots of TrkB, AKT, p-AKT are shown in Panel (**A**). The bar graph is provided as a percentage of control of TrkB and p-AKT protein band intensities ratios (Panel (**B**)). Values are means \(\pm\) SEM for six determinations. *** \(p\) < 0.01 shows a significant variation from their controls; ## \(p\) < 0.05 suggests a marked difference from diabetic retinas.  
Figure 4: _L. regularis_ ameliorated anti-apoptotic and pro-apoptotic proteins expression in the diabetic rat retina. Densitometry was used to measure the band intensities after Western blotting. Bcl-2, Bax, Cyt C, and \(\beta\)-actin immunoblots are shown in Panel (**A**). Band intensity ratios of Bcl-2 protein bands (Panel (**B**)), Bax protein bands (Panel (**C**)), and Cyt C protein bands displayed as a percentage of control (Panel (**D**)). The values are means \(\pm\) SEM for six determinations. *\(p\) < 0.01 shows a remarkable variation from their controls; #\(p\) < 0.05 shows significantly different from diabetic retinas.  
Figure 5: Antioxidant effects of _L. regularis_ in the retinas of diabetic rats. GSH levels in all treated and untreated groups were compared as shown in panel (**A**). Similarly, the levels of TBARS in the _L. regularis_-treated and untreated groups were compared as shown in panel (**B**). The values are means \(\pm\) SEM (n = 6). (*\(p\) < 0.01, vs. diabetic; #\(p\) < 0.05, vs. controls). (C) stands for control, (D) for diabetic, (LRL) low-dosage _L. regularis_, and (LRH) high-dose _L. regularis_. Experiments were carried out twice.
## 3 The Clustering Protocol
### CHs Election
#### 3.4.1 CHs Election in General Clusters  
In general, if a node in a cluster wants to be the CH of the cluster, the following three conditions must be met:  
1. Compared to most other nodes, its location is closer to the center of the cluster.
2. Compared to most other nodes, its distance from BS is relatively small.
3. Compared to most other nodes, it owns more residual energy.  
On this basis, we calculate the center position \(Cen\) (\(X_{C}\), \(Y_{C}\)) of a cluster by the following formula:  
\[X_{C}=\frac{\sum\limits_{p\in C}X_{p}}{|C|} \tag{26}\]  
\[Y_{C}=\frac{\sum\limits_{p\in C}Y_{p}}{|C|} \tag{27}\]  
where \(|C|\) represents the number of nodes in the cluster \(C\).  
Then we construct an objective function for picking an appropriate node as the CH in cluster \(C\).  
\[G_{CH}\Big{(}S(i)\cdot E,d_{toCen}^{i},d_{toBS}^{i}\Big{)}=\frac{S(i)\cdot E}{ \alpha d_{toCen}^{i}+\beta d_{toBS}^{i}} \tag{28}\]  
In Equation (28), \(S(i).E\) represents the residual energy of node \(i\); \(d^{i}_{toCen}\) represents the distance from node \(i\) to \(Cen\); \(d^{i}_{toBS}\) represents the distance from node \(i\) to the BS; and \(\alpha\) and \(\beta\) are respectively the routing factors of \(d^{i}_{toCen}\) and \(d^{i}_{toBS}\), where \(\alpha+\beta=1\). The larger the value of \(G_{CH}\) of node \(i\), the more likely it is to be selected as the CH. Algorithm 2 shows us the CH election in a general cluster.  
```
Inputs: (1) nodes of cluster C (2) size of cluster C: \(NO\) (3) location of BS: (\(BS\)-\(x\), \(BS\)-\(y\)) Result: CH of Cluster C
1: calculate cluster center position \(Cen\) (\(X_{C}\), \(Y_{C}\));
2:for \(i=1\to NO\)
3: calculate the objective function of node \(i\), \(G^{i}_{CH}\)
4: end for
5: select the node with \(GCH_{max}\) as the CH of cluster C;
```  
**Algorithm 2** CH Election Algorithm (General Cluster)
# Bone Conduction Auditory Navigation Device for Blind People
## 4 Design of the Warning Sound
### Phase 1
#### 4.1.3 Results for Phase 1  
The experimental results obtained by the SD test are shown as profile ratings in Figure 6. These figures show the average values for the scores of all subjects. To identify the factors concerning the subjective impressions of the warning sound, factor analysis based on the Promax rotation was performed. The estimated results are shown in Table 4. As shown in the table, three factors, each of which has an eigenvalue of over 1.0, were extracted by following the Kaiser-Guttman criterion. The factors with high correlation are indicated by red characters. As shown in the table, each of the adjectives is categorized into factors. These results suggest that the participant impressions of the warning sound can be generally described using three factors: risk, discomfort, and timbre. Herein, to consider the important factors that can provide user safety and comfort, each of the warning sounds was evaluated through Factors 1 and 2. The factor scores of Factors 1 (risk) and 2 (discomfort) calculated based on the results of the factor analysis are illustrated in Figure 7.  
As shown in Figure 7, the scores for Factor 1 (risk) are higher at 1.2 kHz and lower at 400 Hz for all of the wave types. The 1.2-kHz square wave had the highest score. On the other hand, the scores for Factor 2 (discomfort) were higher at 800 Hz, except for the sawtooth wave. However, the condition of Squ_1200 has relatively higher scores for Factor 2 as well as Factor 1. Therefore, in the next section, detailed time transient characteristics of the warning sound for a square wave with 1.2 kHz will be discussed.  
Figure 6: Average profile ratings obtained by the semantic differential (SD) test for **(a)** 400 Hz, **(b)** 800 Hz, and **(c)** 1200 Hz.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 5 Architecture and Training
### Training  
The reinforcement learning agent was based on this pre-trained model, as it used the CNN as its backbone. We trained action and value heads of the RL agent using Proximal Policy Optimization [38]. Two variations were trained: one with union-based and one with intersection-based normalization. Table 2 shows the hyperparameters we used for training the reinforcement-learning agents.  
The training was performed using the following loss function:  
\[L_{\Theta}^{act}=\mathbb{E}_{t}[min(r_{t}(\Theta)A_{t},~{}~{}clip(r_{t}( \Theta),1-e,1+e)A_{t}], \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Epochs** & **LR** & **Decay** & **Batch** & \(\beta_{1}\) & \(\beta_{2}\) \\ \hline Value & 50 & \(10^{-3}\) & \(10^{-5}\) & 128 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hyperparameters used for pre-training.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & \(N\) & \(N_{n}\) & \(LR_{Act}\) & \(LR_{Crit}\) & \(\sigma_{a}\) & \(e\) & \(K\) & \(\gamma\) \\ \hline Value & 20,000 & 200 & \(3\times 10^{-4}\) & \(10^{-3}\) & 0.6 & 0.2 & 80 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameters used for training the RL-based agent. \(N\) denotes the number of environment steps, PPO made one update for \(K\) epochs every \(N_{u}\) steps. \(e\) denotes the parameter of the PPO loss function, \(\sigma_{a}\) is the variance of the continuous action, while \(\gamma\) is the discount rate.  
\[r_{t}(\Theta)=\frac{\pi_{\Theta}(a_{t}|s_{t})}{\pi_{\Theta_{\text{O}t}}(a_{t}|s_{t} )}, \tag{14}\]  
\[L=LR_{act}L_{\Theta}^{act}+LR_{Criti}(V_{\Theta}(S_{t})-V_{target})^{2} \tag{15}\]  
where \(A_{t}\) is the advantage function, \(\epsilon\) is the PPO hyperparameter, \(\pi_{\Theta}\) and \(V_{\Theta}\) are the actor and critic networks, respectively, while \(V_{target}\) is the target value function derived from the reward \(R\) (defined in Equation (7)).  
Figure 3 shows the number of steps taken to reach the unoccluded state and the rewards received by the agents. Note that the training was successful, since both RL agents were able to learn the solution of the given problem and avoid occlusion in a relatively low amount of steps.