esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Data Preprocessing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Univariate Imputation**} & \multicolumn{2}{c}{**Multivariate Imputation**} & \multicolumn{2}{c}{**Multiple Imputation**} & \multirow{2}{*}{
\begin{tabular}{c} **KNN** \\ **Imputation** \\ \end{tabular} } \\ \cline{2-3} \cline{5-8}  & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Median**} & \multicolumn{1}{c}{**Bayesian**} & \multirow{2}{*}{**Extra Tree**} & \multicolumn{1}{c}{**Bayesian**} & \multicolumn{1}{c}{**Gaussian**} & \multicolumn{1}{c}{**Forens**} & \multicolumn{1}{c}{**Imputation**} \\  & & & & & & & & **Regression** & \\ \hline Ammonia & 0.314 & 0.314 & 0.314 & 0.314 & 0.314 & 0.314 & 0.314 & 0.314 \\ Hydrogen sulfide & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 & \(-\)0.020 \\ Methyl mercaptan & 0.272 & 0.361 & 0.357 & 0.465 & 0.335 & 0.420 & 0.535 \\ Dimethyl sulfide & \(-\)0.173 & \(-\)0.125 & \(-\)0.217 & \(-\)0.026 & \(-\)0.141 & \(-\)0.116 & \(-\)0.184 \\ Acetic acid & 0.331 & 0.331 & 0.331 & 0.331 & 0.331 & 0.331 & 0.331 \\ Propanoic acid & 0.218 & 0.218 & 0.225 & 0.220 & 0.226 & 0.227 & 0.223 \\ Isobutyric acid & 0.203 & 0.204 & 0.211 & 0.208 & 0.212 & 0.213 & 0.209 \\ Normality butyric acid & 0.174 & 0.177 & 0.181 & 0.180 & 0.181 & 0.184 & 0.180 \\ Isovaleric acid & 0.147 & 0.147 & 0.147 & 0.147 & 0.147 & 0.147 & 0.147 \\ Normality valeric acid & 0.154 & 0.159 & 0.163 & 0.163 & 0.163 & 0.163 & 0.163 \\ Phenol & 0.208 & 0.214 & 0.225 & 0.225 & 0.221 & 0.224 & 0.223 \\ P-Cresol & 0.322 & 0.325 & 0.334 & 0.333 & 0.332 & 0.334 & 0.333 \\ Indole & 0.220 & 0.237 & 0.289 & 0.256 & 0.195 & 0.279 & 0.258 \\ Skatole & 0.150 & 0.175 & 0.204 & 0.200 & 0.171 & 0.225 & 0.178 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correlation results by imputation method. Figure 9: Heatmap complete data by best imputation method. (**left**) Heatmap of data using multivariate imputation using Bayesian ridge. (**right**) Heatmap of data using KNN imputation. In Figure 11, it can be seen that the two principal components account for 71.89% of the total variance (PC1: 60.49%, PC2: 11.45%). While PC1 consists of a positive relationship with all variables, PC2 was found to have a negative relationship with some variables. Figure 11: The loading plot about PC1 and PC2. Figure 10: (**top**) Boxplot of data imputed by multivariate imputation using Bayesian ridge. (**bottom**) Boxplot with standardization applied to top data. Table 7 denotes the loading values of PC1, PC2, PC3, and PC4. PC1 was affected by VOCs variables except for acetic acid, and PC2 showed a positive correlation with the sulfur compound dimethyl sulfide and a negative correlation with ammonia, so it was found to be affected by the fluctuations of these two variables. PC3 showed a strong positive relationship with hydrogen sulfide. Finally, PC4 is confirmed to have a strong positive relationship with the sulfur compound methyl mercaptan and a negative relationship with the VOC acetic acid.
###### Abstract
[21] compared the potential and suitability of four commercially available devices, Vortex Activ USB, Scentee, oPhone DUO, and Aroma Shooter, for vehicle use. They found that integrating odor delivery systems into cars could provide warnings, enhance mood, or contribute to the overall in-vehicle entertainment system. Vortex Activ USB is a multi-purpose odor dispensing system that can be installed in vehicles. However, the device is relatively large, making it unsuitable for all vehicles, and it cannot flexibly adjust the intensity or duration of the scent. Scentee is a device that can be connected to smartphones but is primarily designed for iOS devices, limiting its compatibility, and the scent may be missed when users move their heads. The high cost of the device may also affect its popularity. Therefore, a new system called OSpace was developed [22]. It allows control over odor type, dilution, and the timing and air pressure of odor delivery. However, in practical applications, issues such as odor mixing and cross-contamination may still exist, the timing of odor release does not consider the driver's fatigue state, and the study does not assess user acceptance. In summary, olfactory stimulation has been shown to alleviate driving fatigue, and various olfactory or odor-release devices are already available. However, only some of these devices are specifically designed for in-vehicle use, and the timing of odor release needs to be considered. Moreover, there needs to be more consideration for user subjective experience before developing these devices, especially regarding how different variables influence drivers' willingness to use in-vehicle perfume automatic diffusion devices (VP-ADDs). These factors may significantly impact the acceptance and willingness to use a VP-ADD, but empirical research is minimal. Further research into these potential influencing factors will help us design fatigue warning devices that better meet the needs and preferences of drivers and improve their effectiveness in practical applications. Additionally, it will contribute to further refining traffic control policies and measures. Therefore, this study introduces a theoretical framework of a VP-ADD and conducts a case study based on the Technology Acceptance Model (TAM) to investigate drivers' acceptance and willingness to use the VP-ADD. Since [PERSON] proposed the Technology Acceptance Model (TAM) in 1989, numerous studies have used it to predict and explain the degree of user acceptance of emerging technologies or devices [23; 24; 25; 26; 27]. This study will provide a theoretical basis for subsequent research related to the VP-ADD and the application of the VP-ADD, which is significant for research on alleviating driving fatigue and driving assistance systems.
# Directed Path 3-Arc-Connectivity of Cartesian Product Digraphs
## 2 Cartesian Product of Digraphs  
Consider two digraphs \(G\) and \(H\) with vertex sets \(V(G)=\{u_{i}\mid i\in[n]\}\) and \(V(H)=\{v_{j}\mid j\in[m]\}\). The Cartesian product of \(G\) and \(H\), denoted by \(G\square H\), is a digraph with vertex set  
\[V(G\square H)=V(G)\times V(H)=\{(x,x^{\prime})\mid x\in V(G),x^{\prime}\in V (H)\}.\]  
The arc set of \(G\square H\), denoted by \(A(G\square H)\), is given by \(\{(x,x^{\prime})(y,y^{\prime})\mid xy\in A(G)\), \(x^{\prime}=y^{\prime}\), or \(x=y,x^{\prime}y^{\prime}\in A(H)\}\). It is worth noting that Cartesian product is an associative and commutative operation. Furthermore, \(G\square H\) is strongly connected if and only if both \(G\) and \(H\) are strongly connected, as shown in a recent survey chapter by [PERSON] [13].  
In the rest of the paper, we will use \(u_{i,j}\) to denote \((u_{i},v_{j})\). Additionally, \(G(v_{j})\) will refer to the subgraph of \(G\square H\) induced by the vertex set \(\{u_{i,j}\mid i\in[n]\}\) with \(j\in[m]\), while \(H(u_{i})\) will denote the subgraph of \(G\square H\) induced by the vertex set \(\{u_{i,j}\mid j\in[m]\}\) with \(i\in[n]\). It is evident that \(G(v_{j})\) is isomorphic to \(G\) and \(H(u_{i})\) is isomorphic to \(H\). To illustrate this, refer to Figure 1 (this figure comes from [14]), where it can be observed that \(G(v_{j})\) is isomorphic to \(G\) for \(1\leq j\leq 4\), and \(H(u_{i})\) is isomorphic to \(H\) for \(1\leq i\leq 3\).  
For distinct indices \(j_{1}\) and \(j_{2}\) with \(1\leq j_{1}\
eq j_{2}\leq m\), the vertices \(u_{i,j_{1}}\) and \(u_{i,j_{2}}\) belong to the same digraph \(H(u_{i})\), where \(u_{i}\) is an element of \(V(G)\). \(u_{i,j_{2}}\) is referred to as the vertex corresponding to \(u_{i,j_{1}}\) in \(G(v_{j_{2}})\). Similarly, for distinct indices \(i_{1}\) and \(i_{2}\) with \(1\leq i_{1}\
eq i_{2}\leq n\), \(u_{i_{2},j}\) is the vertex corresponding to \(u_{i_{1},j}\) in \(H(u_{i_{2}})\). Analogously, the subgraph corresponding to a given subgraph can also be defined. For instance, in the digraph \((c)\) depicted in Figure 1,if we label the path 1 as \(P_{1}\) (and the path 2 as \(P_{2}\)) in \(H(u_{1})\) (\(H(u_{2})\)), then \(P_{2}\) is identified as the path that corresponds to \(P_{1}\) in \(H(u_{2})\).  
[PERSON] and [PERSON] proved some results of directed path connectivity, that is, the following lemma.  
Lemma 1 ([9]): _Let \(D\) be a digraph of order \(n\), and let \(k\) be an integer satisfying \(2\leq k\leq n\). The following statements are valid: \((1)\): \(\lambda_{k+1}^{p}(D)\leq\lambda_{k}^{p}(D)\) when \(k\leq n-1\). \((2)\): \(\lambda_{k}^{p}(D)\leq\lambda_{k}^{p}(D)\leq\delta^{0}(D)=\min\{\delta^{+}(D), \delta^{-}(D)\}\)._  
Lemma 2 ([15]): \(\kappa(\overleftrightarrow{K}_{n})=n-1\)_._
## 4 Proposal Development
### Final Nodes  
The final nodes were custom designed with adaptability criteria capable of operating autonomously to facilitate deployment on agricultural fields and enable monitoring and control with LoRa wireless communication technology in the Andean region of Ecuador.  
The electronic processor of the final nodes was a TIGO LoRa32-OLED V1 module that integrates an ESP32 microcontroller, a 0.96-inch OLED display and a LoRa module based on the SEMTECH SX1276 chip with a frequency of 868-915 MHz and a high transmission range that is very reliable [35]. Additionally, it can be programmed by Arduino IDE with some preinstalled libraries OLED, LoRa, etc.  
Figure 3a shows the electronic diagram of the transmitter node 1. For supplying electrical power to node 1, a rechargeable 2 Cell 1300 mAh LiPo TURNIGY battery at 7.4 V, a DC-DC LM2596 module configured to supply 5 V to the MCU, to the DHT21 sensors  
Figure 2: General architecture of the LoRa network system.  
measuring temperature, relative humidity and to the capacitive soil moisture sensor HW390 were available.  
For the synergy of the electronic components, a PCB board was designed, which is also protected by a customized IP65 internal protection grade housing 3D printed with PLA thermoplastic as shown in Figure 3b.  
Figure 4a shows the electronic diagram of the transmitter node 2; while Figure 4b shows the manufactured device. Unlike node 1; node 2 switches to the 3 Cell 1000 mAh LiPo TURNGY battery at 11.1 V, which has an ML8511 analog sensor for UV radiation measurement under 5 V operation.  
Figure 3: (**a**) Electronic schematic and (**b**) manufacture of transmitter node 1.  
Receiver node 3, performs the function of receiving information from the gateway via LoRa wireless communication to control an actuator. Node 3, unlike the preceding nodes, reforms to a rechargeable 3 Cell 11.1 V 1000 mAh LiPo TURNIGY 3 Cell battery and a relay module with a controller interface for connecting AC (alternating current) powered equipment/machines, such as a water pump. The structure is similar to Figure 4.  
Figure 4: (**a**) Electronic schematic and (**b**) manufacturing of transmitter node 2.
## 7 Conclusions  
In this paper, a novel and efficient robust speaker identification network model for air-ground communication scenarios, called Chrono-ECAPA-TDNN (CET), is proposed. This model integrates parallel branch architecture, Bi-LSTM module, multi-head attention mechanism, and Sub-center AAM-Softmax loss function, showing excellent performance in handling complex noise and short utterance issues. The CET model achieves a real-time factor (RTF) of 0.0202 for speaker embedding extraction, meeting real-time recognition standards (RTF < 1). In the model performance comparison experiments, the CET model achieves an EER of 9.81% and an accuracy of 88.62% on the air-ground communication dataset, significantly outperforming ResNet34, ECAPA-TDNN, and MFA-Conformer. The stability of the model was evaluated through random cross-validation experiments. The mean EER across all experiments, including CET data, is 88.23%, with a standard deviation of 1.31%, and the mean accuracy is 9.80%, with a standard deviation of 0.11%. Both standard deviations are below 5%, indicating that the model exhibits relatively stable performance across different data splits. The ablation experiment results show that the Bi-LSTM module, multi-head attention mechanism, and Sub-Center Loss function play key roles in improving the model's performance, while data augmentation significantly enhances the model's generalization ability in complex scenarios. The improvements made to the CET model, particularly the effective fusion of local and global features, offer a new approach for speaker identification tasks in short utterances and high-noise environments in air-ground communication. In future work, we plan to apply the Chrono-ECAPA-TDNN model to real-time air-ground communication speaker identification systems and further optimize the model's deployment on edge computing devices, conducting more speaker identification research in practical application scenarios.  
Conceptualization, W.P. and S.C. ([PERSON]); methodology, S.C. ([PERSON]); software, S.C. ([PERSON]); validation, S.C. ([PERSON]) and W.P.; formal analysis, S.C. ([PERSON]) and Y.W.; investigation, S.C. ([PERSON]); resources, W.P.; data curation, S.C. ([PERSON]); writing--original draft preparation, S.C. ([PERSON]); writing--review and editing, W.P., S.C. ([PERSON]) and X.W.; visualization, S.C. ([PERSON]); supervision, S.C. ([PERSON]) and Y.W.; project administration, S.C. ([PERSON]); funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U2333209), National Key R&D Program of China (No. 2021 YFF0603904), National Natural Science Foundation of China (U2333207), Sichuan Provincial Civil Aviation Flight Technology and Flight Safety Engineering Technology Research Center (GY2024-45E), and the Fundamental Research Funds for the Central Universities Grant Number (24 CAFUC03046).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 4 Experiments
### Experimental Results
#### 4.3.3 GRU Module and Recursive Times  
In order to explore the effect of the time of recursive on RLSENet and RRSENet for speech enhancement, the time of recursive is taken from 1 to 5. Objective evaluations are performed on the seen noise test set and unseen noise test set, respectively.  
Table 5 shows the PESQ and STOI values of RLSENet and RRSENet under seen noise conditions. As the number of recursion increases, RRSENet uses the memory mechanism to further learn the feature information between different stages, thereby promoting noise removal, improving the quality of speech and improving the intelligibility of speech.  
Table 6 shows the PESQ and STOI values of the speech enhanced by the RLSENet and RRSENet modules. The PESQ value is the mean of speech quality under different signal-to-noise ratios in the case of unmatched noise. The STOI value is the mean of speech intelligibility under different signal-to-noise ratios. The results in Table 6 show that when \(t=4\), RRSENet achieves the best results under the two indicators of PESQ and STOI. As the number of recursion increases, the results of RRSENet are better than RLSENet, mainly due to the addition of the GRU module. Combining the two experiments, the higher the number of recursion, the better. In RRSENet, \(t=4\) can obtain better results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & \multicolumn{4}{c}{**PESQ**} & \multicolumn{4}{c}{**STOI**} \\ \hline
**SNR** & \(\mathbf{-5}\)**dB** & \(\mathbf{0}\)**dB** & \(\mathbf{5}\)**dB** & **Avg.** & \(\mathbf{-5}\)**dB** & \(\mathbf{0}\)**dB** & \(\mathbf{5}\)**dB** & **Avg.** \\ \hline Noisy & 1.419 & 1.603 & 1.868 & 1.630 & 65.05 & 72.99 & 82.10 & 73.38 \\ FCMNet & 1.714 & 1.976 & 2.279 & 1.990 & 74.24 & 82.19 & 88.17 & 81.53 \\ FDMNet & 1.903 & 2.183 & 2.499 & 2.195 & 76.79 & 83.66 & 89.18 & 83.20 \\ HDMNet & **1.937** & **2.234** & **2.611** & **2.261** & **77.99** & **85.06** & **90.25** & **84.43** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of different modules under unseen noise conditions for PESQ and STOI. **BOLD** indicates the best result for each case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & \multicolumn{2}{c}{**PESQ**} & \multicolumn{2}{c}{**STOI**} \\ \hline
**Model** & **RLSENet** & **RRSENet** & **RLSENet** & **RRSENet** \\ \hline t1 & 2.351 & 2.326 & 84.94 & 83.45 \\ t2 & 2.502 & 2.581 & 86.96 & 87.11 \\ t3 & 2.591 & 2.623 & 88.64 & 88.50 \\ t4 & **2.627** & 2.688 & **88.78** & **88.82** \\ t5 & 2.625 & **2.704** & 88.75 & 88.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of RLSENet and RRSENet under seen noise conditions. **BOLD** indicates the best result for each case.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 4 Principles and Optimization of the YOLOv5 Model
### Adaptive Spatial Feature Fusion  
In the neck network of YOLOv5, there is a solidified feature-matching mechanism; that is, the large target corresponds to the feature map of the high level, and the small target corresponds to the feature map of the low level. When the target of a feature layer is a positive sample, it means that the corresponding part of the other feature layer will be identified as a negative sample. The inconsistency between different levels of features will affect the gradient calculation in the training process and reduce the effectiveness of feature fusion.  
In order to solve the inconsistency between different feature scales of the neck network, ASFF [19; 20] is used in this paper to perform adaptive spatial feature fusion on the neck network (the principle of ASFF is shown in Figure 9) so that the neck network can filter the conflicting information in space to ensure the correct calculation of the gradient so as to improve the proportional invariance of the features. Since ASFF adopts an additive method to carry out adaptive spatial feature fusion, it is necessary to downsample or upsample the feature maps of different layers and adjust the number of channels to ensure that the feature maps of different feature layers have the same size and number of channels.  
ASFF obtains new fusion features by assigning weights to different feature layers and adding them. The formula is shown in (1):  
\[y_{ij}^{1}=\alpha_{ij}^{1}\cdot x_{ij}^{1\to 1}+\beta_{ij}^{1}\cdot x_{ij}^{2\to 1}+\gamma_{ij}^{1}\cdot x_{ij}^{3 \to 1} \tag{1}\]  
Figure 8: Schematic diagram of CA mechanism.  
Figure 9: ASFF schematic diagram.  
where y is the new feature map after adaptive spatial feature fusion; \(x^{1},x^{2},x^{3}\) are the three feature maps output by the neck network; and \(\alpha\), \(\beta\), \(\gamma\) are the weights of the three feature maps in turn.  
In the process of backpropagation, the weight of each feature layer will be updated, thus weakening the solidified feature-matching mechanism of the neck network and avoiding the interference of feature antagonism in the gradient calculation process so that the features can maintain consistency in different levels.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 4 Results
### Results Obtained without Feature Selection: Data Set 1
#### 4.1.1 1 DCNN  
The confusion matrixes for the fivefold cross-validation of the 1 DCNN are presented in Table 6. The average accuracy rate was 91.44%. Take the confusion matrix with CV = 1 as an example; among the outcomes of the 972 matches, 482 matches were correctly predicted wins, 30 matches were incorrectly predicted losses, 396 matches were correctly predicted losses, and 64 matches were incorrectly predicted wins. A total of 879 matches were predicted correctly and 94 matches were predicted incorrectly, yielding an accuracy rate of 90.33%.
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 3 Proposed Framework  
Figure 2 depicts the paper's model structure, which is divided into three parts: preprocessing, the ENBC network, and scene text recognition. The data synthesis tool generates the dataset needed for the ENBC network in Figure 2a. Figure 2b consists of the encoder and  
decoder. The encoder adopts the relatively lightweight backbone network MobileNetV2, and expands the receptive field by obtaining features of different scales using the ASPP null convolution with different expansion rates, so as to get the contextual character feature information with a larger receptive field and eliminate the character's own noise, while the decoding stage fuses different levels of character feature information by Eliminate the character's own noise, while the decoding step eliminates background noise by fusing multiple levels of character feature information using the multilayer character feature fusion module. A multilayer character feature fusion module, a high-level character feature extraction module, and a recognition improvement module make up Figure 2c. The multilevel character feature fusion module eliminates background noise and produces a reconstructed image; the high-level semantic feature extraction module broadens the receptive field through ASPP null convolution to obtain character feature information with a larger receptive field; and the recognition enhancement module improves contextual modeling capability by taking into account both the forward (left-to-right) and backward (right-to-left) information of the text sequences simultaneously, improving text recognition accuracy and robustness.  
Figure 2: Scene text recognition framework to eliminate background and character noise interference.
## 3 Forecasting Approach
### The Proposed Hybrid Methodology  
A SOM algorithm (step I) was firstly used to divide the whole database into several disjoint clusters with similar statistical properties. Then, the seasonality and the linear patterns were captured using a SARIMA model in each cluster (step II). As a result, a set of predicted values and residuals from each cluster was obtained. Finally, a SVR model was then implemented in the third step over each cluster to generalize the non-linear relationship, obtaining the final prediction (step III). Inputs of the SVR model were the original and predicted data from the second (SARIMA) step. Two hybrid approaches were tested in the last step considering the configuration and the number of inputs.  
The forecasting performance was assessed using different prediction horizons: for the SARIMA step, only one-day (\(ph\) = 1) prediction horizon was considered; and for the final (SVR) step, two prediction horizons were tested, one-day (\(ph\) = 1) and seven-day (\(ph\) = 7) ahead. The prediction is one-step ahead (\(y_{t+ph}\)) in both cases. In the \(ph\) = 1 case, the prediction is \(y_{t+1}\), and for the \(ph\) = 7 case the prediction is \(y_{t+7}\). The estimation can be thereby modelled as a nonlinear function of the \(n\) preceding values of the time series and an error term. This is called the autoregressive window (\(n\)) and its design is presented in Figure 1. The autoregressive window for \(ph\) = 1 (steps II and III) is presented on the top of the figure and for \(ph\) = 7 (step III) is showed on the bottom of the figure.  
For the \(ph\) = 7 case, the autoregressive window is composed of values of the container series periodically sampled every seven days in the past. This is due to the weekly seasonality found in the analysis of the autocorrelation function of the time series. The main assumption here is that best prediction is obtained when using as inputs several samples from the past of the same day of the week (i.e., using several successive Mondays in the past to predict a future Monday). The proposed SOM-SARIMA-SVR procedure is shown graphically in Figure 2.  
Figure 1: Autoregressive window sizes in Steps II and III and their prediction horizons (\(ph\)): one-day prediction horizon (below the timeline) and seven-day prediction horizon (allow the timeline). \(n\) is the size of the autoregressive window.