esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Microseismic monitoring technology detects elastic waves generated by rock mass fracturing and has been demonstrated to be a valuable tool for monitoring rock mass stability [14]. Traditional test methods for stress and deformation can only obtain information for individual points, lines, or surfaces. In contrast, the temporal-spatial evolution laws of MS parameters (e.g., event cluster, energy release, and magnitude) are calculated from geophysical theories and can provide three-dimensional information regarding the changes in stress and deformation inside rock mass. MS monitoring technology has played an important role in safety management in deep metal mines and can be used in many aspects, including early warning of dynamic disasters [15, 16, 17, 18, 19, 20, 21, 22], rock mass fracture mechanism research [23, 24, 25], and fault-slip risk assessment [26, 27, 28]. The quantitative risk assessment for deep tunnel failure can be divided into two categories: the machine learning methods with un/supervised models and comprehensive evaluation methods based on rock mass failure evaluation index criteria. The machine learning methods include the neural networks [29], the support vector machines [30], and the Bayes discriminant analysis [31]. The comprehensive evaluation methods include the Fuzzy Delphi method [32], the principal component analysis method [33, 34], grey correlation analysis [35], Toppsis [36, 37], the fuzzy comprehensive evaluation method [38], the efficacy coefficient method [39, 40], the unaccratained measure theory [41], the set pair analysis [42], the cloud model [43], and the Dempster-Shafer evidence theory [44]. The cloud model has attracted the attention of rock mechanics researchers [45, 46] as it can synthetically describe the randomness and fuzziness of concepts, as well as implement the uncertainty transformation between a qualitative concept and its quantitative instantiations. The depth of the Ashele copper mine has exceeded 1000 m. Due to the complexity of the mining system, tunnel failure is induced by mining disturbances that have both spatial and temporal components. In this paper, three different evaluation indicators are considered in the risk assessment of deep tunnel failure (geological conditions, mining disturbances, and MS parameters). After the weighted evaluation indexes were determined by using the entropy weight method, the normal cloud model theory was applied to evaluate the rock mass failure risk at the Ashele copper mine.
## 4 Experimental and Results  
### Experimental Setup  
#### 4.1.1 Dataset  
The experiment was implemented on an internal expressive Mandarin dataset, which contains 15 h of recordings from a male speaker, including 5 h of emotional data. The whole dataset contains 18,376 sentences. We randomly selected 17,776 sentences for training, 300 sentences for validation, and the remaining 300 sentences for testing.  
The audios were unified to a 16,000 sampling rate and 16-bit PCM. The silence at the beginning and end of each sentence was removed. In addition, phone alignment was obtained by the MFA tool [27]. Subwords in Chinese mandarin correspond to Chinese characters. Using the pronunciation dictionary, the relationships between Chinese characters and phonemes can be determined. Subword-level information was provided from tiny-bert [28]. Additionally, we used the PKUseg word segmentation tool [29] to obtain word boundary information. In addition, we used the configuration of frame length 1024, frameshift 256, and window length 1024 to extract the linear spectrogram. We have made both our demo page and source code publicly available (source-code: [[https://github.com/shang0712/HierTTS](https://github.com/shang0712/HierTTS)]([https://github.com/shang0712/HierTTS](https://github.com/shang0712/HierTTS)) (accessed on 4 January 2023); demo: [[https://shang0712.github.io/HierTTS/](https://shang0712.github.io/HierTTS/)]([https://shang0712.github.io/HierTTS/](https://shang0712.github.io/HierTTS/)) (accessed on 4 January 2023)).
## 4 Experiments
### Results and Analysis
However, due to the defects of the attention mechanism in capturing long-distance dependent information and the complexity of the sentence structure itself, simply using the attention mechanism cannot accurately model the relationship between context and aspect words. The IMAN model is an improvement based on the AEN model. They added a multi-head interactive attention mechanism to the last layer, interacting context information and aspect information, and obtained good results. Our model has achieved better results on all datasets except Lap14, which shows that using sentence structure information as a supplement to the model to determine the polarity of emotions can further improve the performance of the model. However, our model achieved sub-optimal results on the Lap14 dataset, and we suspect that this dataset may be insensitive to syntactic information. The T-MGAN model also uses sentence structure information. They used the tree transformer module to capture phrase grammatical information and obtained phrase-level feature representation. However, they only considered the phrase information in the sentence, not the global structure information. The experimental results of our model are better than the T-MGAN model, which shows that the global structure information of sentences is positively helpful for aspect-based sentiment analysis. Compared with the GCN-based models, our model has a certain improvement on all datasets. Through our analysis, the GCN-based models have a significant improvement in the construction of long-distance multi-word dependence compared with the traditional attention-based neural network model. However, the prerequisite is that the dependency tree must be complete and effective. When the sentence is too complex, the dependency tree cannot accurately establish the relationship between the aspects and the opinion words, which will lead to the degradation of model performance. Secondly, in the process of using dependency tree and graph convolutional network to introduce syntactic information, noise information will also be introduced into it. This problem becomes more obvious when the graph convolutional network layer is deeper. Our model combines the multi-head attention mechanism with the graph convolutional network, adds semantic information on the basis of syntactic information, and interacts with the two parts of information to obtain a more complete feature representation, thereby enhancing the accuracy of the model.
## 4 Multi-Objective Optimization for Edible Oil Transportation Scheduling Method
### Edible Oil Transportation Time Function
By solving Equations (8) and (9) simultaneously, the relationship between volumetric flow rate and transportation time can be derived as follows:  
\[t=V/(A\times v), \tag{10}\]  
Since different edible oils have varying densities, it is necessary to convert mass into volume. The calculation formula is as follows:  
\[V=M/D, \tag{11}\]  
where \(M\) represents mass, and \(D\) denotes density. The cross-sectional area \(A\) is defined as follows:  
\[A=\pi\times r^{2}/4, \tag{12}\]  
Next, substitute Equations (11) and (12) into Equation (10), then solve the system formed by Equations (10) and (7) simultaneously to calculate the transportation time \(T_{task}\) required for the edible oil transport task, as follows:  
\[T_{Task}=\frac{V\times 8\times\eta\times L}{A\times\Delta P\times r^{2}} \tag{13}\]  
The scheduling of transportation time is based on the viscosity coefficients of edible oils, simulating their flow characteristics within the pipeline. For edible oils with different viscosities, the model can predict transport delays and adjust the scheduling sequence accordingly, providing decision-makers with a scientific basis for planning.
# Multiple Object Tracking in Robotic Applications: Trends and Challenges
###### Abstract  
The recent advancement in autonomous robotics is directed toward designing a reliable system that can detect and track multiple objects in the surrounding environment for navigation and guidance purposes. This paper aims to survey the recent development in this area and present the latest trends that tackle the challenges of multiple object tracking, such as heavy occlusion, dynamic background, and illumination changes. Our research includes Multiple Object Tracking (MOT) methods incorporating the multiple inputs that can be perceived from sensors such as cameras and Light Detection and Ranging (LIDAR). In addition, a summary of the tracking techniques, such as data association and occlusion handling, is detailed to define the general framework that the literature employs. We also provide an overview of the metrics and the most common benchmark datasets, including Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), MOTChallenges, and University at Albany DEtection and TRACking (UA-DETRAC), that are used to train and evaluate the performance of MOT. At the end of this paper, we discuss the results gathered from the articles that introduced the methods. Based on our analysis, deep learning has introduced significant value to the MOT techniques in recent research, resulting in high accuracy while maintaining real-time processing.  
multiple object tracking; MOT; self-driving; autonomous vehicle; autonomous navigation; SLAM; KITTI; MOTChallenges; MOT15; MOT16; MOT17; UA_DETRAC
## 3 Improved Multiobjective Particle Swarm Optimization Algorithm
### Algorithm Improvements
#### 3.2.6 The IMOPSO Algorithm Flow  
Based on the analysis of improvement strategies, the process of IMOPSO is described in Algorithm 1 as follows:  
Figure 1: Comparison of static and dynamic archive maintaining. (**a**) A given set of non-dominated solutions; (**b**) The result of static archive maintaining; (**c**) The result of dynamic archive maintaining.  
Figure 2: The process of generating new external archive.  
```
Input:\(r_{1}\), \(r_{2}\), \(c_{1}\), \(c_{2}\), \(w\), \(T_{max}\), the population size \(N\), the maximum archive size \(A_{max}\) Output: The Pareto front solutions Step 1. Particle position and velocity are initialized by random generation, and related parameters \(r_{1}\), \(r_{2}\), \(c_{1}\), \(c_{2}\) and \(w\) of IMOPSO are set. Set the maximum number of iterations \(T_{max}\), population size \(N\), and the maximum size of external archive \(A_{max}\), and set \(T=0\). Step 2. According to the set parameters, the fitness value of each individual in the initial population is calculated, and the optimal position \(p_{best}\) of the initial individual is obtained. Step 3. Update the external archive \(A\). Step 4. For \(T=1\): (1) Use Equations (14) or (15) to update the particle velocity and use Equation (12) to update the particle position to form the middle population. (2) The update procedure of archive \(A\) is executed to gather the new non-dominated solutions with bigger crowding-distance values. (3) Use the method in Section 3.2.2 to perform the evolutionary search process. (4) Update \(A\) by using the external archive update strategy in Section 3.2.4. If \(|A|>A_{max}\), use the method in Section 3.2.3 to maintain the external archive. (5) Use the elitist incremental strategy in Section 3.2.5 to update the middle population and form the new offspring population based on Equation (16). (6) Calculate the fitness value of the new generation population. (7) Update the individual optimal position \(p_{best}\) based on the dominant relationship. (8) If the termination condition is met, the external archive \(A\) is output as the result, and the calculation is terminated. Otherwise, set \(T=T+1\), and turn to Step (1).
```  
**Algorithm 1**IMOPSO
# Assessment of a Patient Dose Monitoring System for Average
###### Abstract  
This study assessed the accuracy of average glandular dose (AGD) calculations for two Selenia Dimensions mammography systems using data from the online dose management DoseWatch software version 3.3.5.1. Mammographic images acquired between January 2021 and December 2022 were retrospectively analyzed. The AGD values displayed by the systems were compared with those independently calculated using the Dance and Boone methods. Additionally, real glandular composition of breast was estimated using LIBRA (Laboratory for Individualized Breast Radiodensity Assessment) software version 1.0.4 for a selected subgroup of patients. Results showed that the AGD values displayed by the systems were generally consistent with those calculated using the Dance method, but discrepancies emerged when applying the Boone method, especially when using estimated glandular composition. Most mammograms fell within acceptable and achievable dose limits according to European guidelines, though a small percentage exceeded these thresholds. The findings suggest that the Dance method, using glandular composition estimated through LIBRA, provides a reliable and accurate AGD calculation, offering a simpler alternative to more complex individualized calculations. The study highlights the importance of accurate glandularity estimation for proper dose management in mammography.  
patient dose; glandularity; LIBRA; DoseWatch; Dance method; Boone method
## 5 Experimental Evaluation
### The Effect of Noise Type and Network Distortion on Prediction Accuracy  
Different noise types have different characteristics. Therefore, we intend to understand the influence of various noise types at different SNR values on the performance of MiniatureVQNet-Noise and MiniatureVQNet-Noise-Network models. Moreover, we intend to understand how network distortion impacts different noise types.  
Noise type affects the model performance differently as shown in Figure 5, where a comparison of street and train station noise is plotted. Train station noise constitutes noise from different sources, such as approaching trains, broadcasting loudspeakers, and conversation from nearby people. Street noise constitutes noise from passing cars, singing birds, and other sources. The performance of both the MiniatureVQNet-Noise-Network and MiniatureVQNet-Noise model depends on the noise type for an SNR greater than 0 dB and less than 10 dB. Moreover, for all models, the performance on the station noise was lower than that of the street noise for all SNR values. Furthermore, the model trained on the noise-network dataset exhibits a higher performance than the model trained on a noisy dataset.  
When network transmission errors further distorted the noise-distorted speech, there was no difference in the performance of the noise-network-trained model and noise-only-trained model on different noise types, as shown in Figure 6. The network distortion on the noisy speech effect does not change the effect of noise type. The DNN model prediction accuracy depends on the noise type. Nevertheless, training the model on the noise-network dataset improves the model accuracy on all SNRs and noise types. MiniatureVQNet-Noise-Network is more robust than MiniatureVQNet-Noise on all SNRs and noise types.  
Figure 4: Model accuracy at different signal-to-noise ratios on noisy distorted speech data.  
Figure 5: Effect of noise type on prediction accuracy.
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 2 Research Background
### Estimation Bias of Q-Learning Algorithms
Since the estimated Q values and the true Q values have error \(Y^{i}\) that follows a uniform distribution, the probability density function and the probability distribution are represented by \(f(\mathrm{x})\) and \(P\big{(}Y^{i}>\mathrm{x}\big{)}\), respectively:  
\[f(x)=\left\{\begin{array}{ll}\frac{1}{2\epsilon},&x\in[-\epsilon,\epsilon] \\ 0,  
Therefore, the expected value of \(Z\) can be calculated as follows:  
\[\mathrm{E}[\mathrm{Z}]=\int_{-\epsilon}^{\epsilon}xf_{Z}(x)dx=\int_{-\epsilon}^{ \epsilon}x\left(\frac{\epsilon-x}{2\epsilon^{2}}\right)dx=-\frac{1}{3}\epsilon \tag{12}\]  
Thus, it has been theoretically proven that the Q network update in the TD3 algorithm will result in an underestimation error for the true Q values.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Experiment Protocol
#### 2.1.3 Closed-Loop AAD Phase  
In the closed-loop AAD phase, the calibrated cognitive-driven gain controller system was used to identify the attended and the unattended speaker and to close the loop  
Figure 2: Overview of the proposed cognitive-driven gain controller system for a scenario with two competing speakers. The EEG responses were acquired using the EEG amplifier. The acquired EEG responses and EEG trigger markers were streamed using the gUSBamp application. Using the gUSBamp application and the LSL software package, the streamed EEG responses were forwarded to the real-time AAD (RAAD) for online decoding, to the Lab Recorder application for recording, and to the OpenViBE software for online EEG visualization. The RAAD was implemented and run using MATLAB (MATLAB 1). The RAAD identified the attended and the unattended speaker and generated their corresponding probabilistic attention measure. The generated probabilistic attention measure of the attended speaker (\(\hat{p}_{a}\)) was forwarded to the AGC using the LSL software package. Based on the probabilistic attention measure, the AGC amplified the attended speaker (\(\hat{\lambda}_{a}\hat{s}_{a}\)) and attenuated the unattended speaker (\(\bar{\lambda}_{a}\hat{s}_{a}\)) as acoustic stimuli. The AGC (together with trigger marker and visual stimuli) was implemented and run using MATLAB (MATLAB 2). The AAD loop was then closed by presenting the acoustic stimuli using the audio interface and two loudspeakers.  
by presenting the amplified attended speaker and the attenuated unattended speaker in an online fashion via loudspeakers. The closed-loop AAD phase consisted of three sessions, each lasting 10 min. During each session, participants were cued by an arrow on a screen every minute to switch attention between the presented competing speakers while recording the ongoing EEG responses. To identify the attended and the unattended speaker, in each session a different AAD algorithm was used, i.e., LW-GLM, LW-SSM, and SW-SSM. These AAD algorithms were randomly assigned to the sessions for each participant. After each session, the participants were asked to fill out a questionnaire consisting of multiple-choice questions about the stories uttered by the speakers, similarly to the open-loop AAD phase. In addition, the participants were asked to rate how much effort it took to follow the attended speaker, to ignore the unattended speaker, to switch attention to the cued speaker, and how well they understood the attended story. For analyzing the experimental results, 24% of the results needed to be excluded, 5% due to a technical hardware problem when saving the results and 19% due to poor attentional performance reported by participants themselves after a few sessions (Some participants reported that they either completely lost concentration to attend to the cued speaker or they completely engaged with the story uttered by the non-cued speaker.).
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.3 Bi-GRU Experimental Results  
The number of cells in the hidden layer (NoC) of Bi-GRU is optimized using random search, as shown in Table 10. The model structure is shown in Figure 12. The model used an Adam optimizer with a learning rate of \(1\times 10^{-3}\), a batch size of 1500, and the cross-entropy loss function. In Figure 12, as we prefer to choose the model with better performance on ship classification, the NoC of Bi-GRU is set to 35. Figure 23 plots the learning curve of Bi-GRU. Figure 24 shows the confusion matrixes of Bi-GRU on the testing set. In Figure 24a, part of the sub-trajectories of passenger ships are misclassified into three other categories, and there is confusion between tankers and cargo ships, and some sub-trajectories of fishing boats are misclassified into cargo ships. In Figure 24b, the classifier confuses passenger ships with fishing boats, as well as tankers with cargo ships, and some fishing boats are mislabeled as cargo ships.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**NoC**} & \multicolumn{2}{c}{**F1 Score**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-5}  & **Evaluated on Samples** & **Evaluated on Ships** & **Evaluated on Samples** & **Evaluated on Ships** \\ \hline
15 & 0.6252 & 0.6158 & 0.6219 & 0.6159 \\
20 & 0.6387 & 0.6328 & 0.6335 & 0.6329 \\
25 & 0.6496 & 0.6435 & 0.6455 & 0.6425 \\
30 & 0.6586 & 0.6370 & 0.6546 & 0.6353 \\
35 & **0.6629** & **0.6681** & **0.6566** & **0.6643** \\
**40** & **0.6726** & **0.6539** & **0.6690** & **0.6521** \\
45 & 0.6605 & 0.6381 & 0.6575 & 0.6327 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Unit number in the hidden layer.  
Figure 22: (a) Convolution kernel of the Conv_1; (b) Results of convolution kernel summation (matrix Q); (c) Sum Q by columns and take the absolute value.
A Monte Carlo simulation approach, namely regional sensitivity analysis (RSA) [24], was used to generate a large number of simulated cases to investigate how the variation in the estimated reliability of different analyses can be attributed to variations of their input factors (e.g., parameters in the prior distributions of the Bayesian reliability analysis). However, the use of RSA does not effectively deal with parameter interactions. Therefore, a classification tree data mining approach (Recursive Partitioning) was utilized to identify and locate the values of the key factors and conditions that lead to a great magnitude of error. (For a review on machine learning methodsfor constructing tree models from data, readers are referred to the work of [25].) There are two main advantages of using the classification tree data mining approach. First, it allows for easy interpretability of results in that decision nodes carry conditions that are easy to understand. Second, the IF-THEN rules allow for knowledge extraction and also enable the reliability practitioner to verify the model learned from the data. Therefore, this analysis will provide a guideline for selecting appropriate Bayesian estimation approaches in evaluating the reliability of a system in which components are correlated. The rest of this paper is structured as follows. In Section 2, the formulation of copula-based Bayesian reliability analysis for systems with dependent components is thoroughly delineated. The Monte Carlo based procedure for generating the pseudo input factors and the regionalized sensitivity analysis are presented in Section 3. The integrated Monte Carlo filtering and classification tree learning are given in Section 4. In Section 5, the conclusions and the managerial implications of this study are given.