esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Results
Figure 9: ID model density on a 2000 m circular road for (a) \(\delta=1\), (b) \(\delta=4\), and (c) \(\delta=100\). 78.0 m and 146 s and then increases to 0.09 at 564.0 m and 200 s. At 199 s, the density is 0.02 between 0 m and 477.0 m and decreases to 0.02 at 1722 m. When \(\tau=1\) s, Figure 10b shows that the density is 0.50 from 0 s to 89.5 s which indicates congestion. After the congestion dissipates, the density is 0.02 at 129 s and 33.0 m, and increases to 0.09 at 199.5 s and 525.0 m. Between 0 m and 462 m, the density is 0.02 at 198 s. It increases to 0.09 at 525.0 m and then decreases to 0.02 at 1779 m. When \(\tau=1.5\) s, Figure 10c shows there is congestion from 0 s to 93 s as the density is 0.50. The density decreases to 0.15 at \(-\)54.0 m and 124 s and then increases to 0.18 at 129.0 m and 199.5 s. At 200 s, the density between 0 m and 66.0 m is 0.15 and decreases to 0.02 at 1773 m. When \(\tau=2\) s, Figure 10d shows there is congestion between 0 s and 96 s as the density is 0.50. At \(-\)96.0 m, it decreases to 0.36 at 118.5 s and 0.20 at 198 s. At 198.5 s, the density is 0.20 at \(-\)42.0 m and decreases to 0.06 at 99.0 m and 0.01 at 1746 m. When \(\tau=2.2\) s, Figure 10e shows there is congestion between 0 s and 103 s as the density of 0.5. It decreases to 0.45 at \(-\)99.0 m and 117 s and 0.19 at \(-\)75.0 m and 199 s. At 199 s, the density is 0.07 at 18.0 m and 0.01 at 1767 m.  
Figure 10: Proposed model density on a 2000 m circular road for (**a**) \(\tau=0.6\) s, (**b**) \(\tau=1\) s, (**c**) \(\tau=1.5\) s, (**d**) \(\tau=2\) s, and (**e**) \(\tau=2.2\) s.  
Figure 11 presents the ID model flow for \(\delta=1,4,\) and 100. When \(\delta=1\), Figure 11a shows that at \(-204.0\) m the flow is zero from 0 s to 100 s. It is 0.54 veh/s at 123 s and 0.55 veh/s at 196.5 s. At \(-12.0\) m, the flow is 0.52 veh/s at 199.5 s, and decreases to 0.37 veh/s at 93.0 m and 0.25 veh/s at 1743 m. When \(\delta=4\), Figure 11b shows that at \(-180.0\) m the flow is zero from 0 s to 90 s. It increases to 2.13 veh/s at 112.5 s and varies between 0.67 veh/s and 6.05 veh/s from 131.5 s to 198.5 s. At 196 s, the flow is 0.24 veh/s at \(-96.0\) m and increases to 0.38 veh/s at 144.0 m where it remains. When \(\delta=100\), Figure 11c shows that at \(-141.0\) m, the flow is zero from 0 s to 84 s. It increases to 4.11 veh/s at 106.5 s and varies between 9.17 veh/s and 3.20 veh/s from 124.5 s to 200 s. At 200 s, the flow is 0.24 veh/s at \(-114.0\) m and increases to 0.37 veh/s at 174.0 m where it remains. Figure 12 presents the flow with the proposed model on a 2000 m circular road for \(\tau=0.6\), 1, 1.5, 2, and 2.2 s. When \(\tau=0.6\) s, Figure 12a shows that at \(-174.0\) m, the flow is zero from 0 s to 118 s. It increases to 1.04 veh/s at 225 s and then decreases to 1.00 veh/s at 199 s. At 198 s, the flow is 0.40 veh/s between 0 m and 276.0 m and increases to 1.01 veh/s at 540.0 m. It then decreases to 0.43 veh/s at 753.0 m and remains constant.
## 2 Literature Review
### Simulanous Approach to TLBP and BAP  
Despite the extensive literature on both the TLBP and the BAP, the simultaneous approach to the combined problems has not been fully addressed. The most relevant approaches are as follows.  
[PERSON] [26] studied the simultaneous optimization of the assembly line balancing problem (ALBP) and BAP in the mixed model assembly line. This problem is characterized by a stochastic task time, parallel stations, and a buffer between the stations. A genetic approach was introduced, and a parametric event-oriented simulator was built to evaluate the solutions considering a single objective function \"normalized design cost\". It contained the investment cost together with a penalty function punishing the infeasible plans.  
[PERSON] et al. [27] combined the BAP with the scalability problem introduced in [28], which made the full problem able to simultaneously reconfigure and rebalance the line and reallocate the capacities of the buffer area. A genetic algorithm was proposed to optimize the single objective function that is a combination of the number of machine tools and the throughput.  
To our knowledge, the first attempt to address TLBP and BAP simultaneously was reported in [PERSON] et al. [29]. A GA-PSO approach was proposed with a single objective function where the production rate was combined with the total investment cost. An industrial case study was introduced to validate the method and to compare it with that of the sequential optimization. Referring to this industrial case study, although sequential optimization may obtain a better value of the objective function of balancing problem, simultaneous optimization leads to a better overall solution. Following this result, the simultaneous optimization of TLBP and BAP with a multi-objective optimization approach was carried out in [4]. The overall problem was thus to make simultaneous decisions on all the following:  
* Line configuration: the number of stations and the number of identical machine tools for each station;
* Station configuration: the selection of fixturing type and machine tool type for each station;
* Operation allocation: the allocation of each operation to a station and its positioning in the machining sequence;
* Buffer allocation: the allocation of the buffer capacities between two stations;
* In order to solve the whole problem, several heuristic principles and evolutionary algorithms were built. A priority-based coding system was designed to make the approach suitable for the application of different kinds of algorithms. Four heuristic principles were proposed to ensure the feasibility of the solutions. In order to obtain the optimal set of solutions, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) were introduced and fine-tuned, where the total investment cost and the throughput from simulation-based evaluation were considered as the two objective functions. Finally, an industrial case study was introduced to demonstrate the validity of the proposed approach, where the comparison results showed the benefit of NSGA-II for solving the problem.
# Overview of Voice Conversion Methods Based on Deep Learning
###### Abstract  
Voice conversion is a process where the essence of a speaker's identity is seamlessly transferred to another speaker, all while preserving the content of their speech. This usage is accomplished using algorithms that blend speech processing techniques, such as speech analysis, speaker classification, and vocoding. The cutting-edge voice conversion technology is characterized by deep neural networks that effectively separate a speaker's voice from their linguistic content. This article offers a comprehensive overview of the development status of this area of science based on the current state-of-the-art voice conversion methods.  
voice conversion; voice disentangling; voice synthesis +
Footnote †: journal: _Sci._  
00-908 Warsaw, Poland  
Footnote †: journal: _Sci._  
000-908 Warsaw, Poland  
Footnote †: journal: _Sci._  
* Speaker identity extraction--this involves extracting information about the speaker's identity from the speech.
* Linguistic content extraction--this involves extracting from statements or appropriately processing other data (e.g., text) to obtain the most time-dependent information in the output. These include information about the content of speech, rhythm, and intonation.
* Encoder--this is responsible for the integration and appropriate representation of the above extractions. As the information fed into the encoder and the information obtained from the extraction of linguistic content latent embeddings are time-dependent, these two tasks are often combined.
* Decoder/vocoder--these are responsible for processing the data obtained from the encoder to produce an appropriately manipulated soundtrack in the output. The input is frequently a spectrogram. However, sometimes, to reduce the number of models or unnecessary intermediate representations [7], the encoder is combined with the vocoder, and there is no intermediate representation between them.  
Some of the presented algorithms represent only a part of the task, e.g., proper separation of sound components; however, these are described because of their potential use in the functions mentioned earlier. Furthermore, to maximize the timeliness of the review, much of the algorithms presented will be from recent work.  
The breakdown shown above is for illustrative purposes; it is not as readily apparent in every algorithm.  
Developers recently created an increasing number of zero-shot voice conversion algorithms. An unsupervised zero-shot voice conversion (VC) aims to change the speaker's characteristics in an utterance to match an unseen target speaker without using parallel training data.
## 4 Discussion  
### Fungal Communities  
Our previously unpublished results from 2016 showed that _Penicillium_ was the main fungus genus in the water sample of lacquer, while results from 2017 in the present article showed that _Fusarium_ was the dominant one (Supplementary Tables S1-S3). Moreover, fungi of these two genera, namely _F. solani_ NK-NH1 and _P. chrysogenum_ NK-NH3, were successfully isolated from the lacquerware surfaces.  
This is the first time that _Fusarium_ species have been reported to lead a putative microbial deterioration of wooden lacquerware. Another famous case concerning _Fusarium_ in the biodeterioration of cultural heritage is the Paleolithic painting of the Lascaux Cave in France [20]. The ground and walls were covered by the dense white hyphae of _F. solani_. They not only destroyed the aesthetics of the paintings, but also changed the material structure of them [21]. In addition, _Fusarium_ sp. was isolated from documentary heritage [22]. _Fusarium_ species are mainly plant pathogens that cause root and stem rots. They live in the soil after the plant has been harvested or decayed [20]. Some members of the genus produce mycotoxins that can affect human and animal health andare often found in wounds [23]. The genus also can be found in food and indoor environments [24]. Thus in lacquerware surfaces and water _Fusarium_ might have been brought from the outside environment by visitors or conservation-restorers.  
It is worthy of mention that besides the _Fusarium_, the genus _Cadophora_ should not be ignored, which accounts for 16.7% in sample NHI935. Most _Cadophora_ members were primarily isolated from plants and soil [25]. For example, isolates related to _C. malorum_ cause wood decay of kiwifruit [26]. _Cadophora_ species, such as _C. malorum_, _C. luteo-olivacea_, and _C. fustigiata_ have also been isolated in extreme environments in Antarctica, causing soft decay of wood huts [25,27,28]. Thus this genus may also be responsible for the deterioration of wooden lacquerware.  
Another fungus, _P. chrysogenum_, is known to be a deteriorative agent and was both isolated from ancient documents and the air of indoor environments [12,29,30]. In addition, sequences of the cellulase and hemicellulose producing strain, _P. chrysogenum_, were detected. Therefore, this fungus is a threat to the wooden lacquerware.  
Figure 5: The inhibition of fungal growth by four biocides. (**A**) _F. solani_ NK-NH1 under the treatment of 0.1%(left) and 0.5%(right) concentration biocides and cultured on PDA plates at 28 \({}^{\circ}\)C for 3 days. (**B**) _P. chrysogenum_ NK-NH3 under the treatment of 0.1% (left) and 0.5% (right) concentration biocides and cultured on PDA plates at 28 \({}^{\circ}\)C for 3 days.
# Study of NVIS Channel for USN Protocol Definition in Antarctica
## 2 Sounding System  
To achieve the proposed goal, our research group installed a sounding system [28] during the Spanish Antarctic Campaign 2018-2019 between two points around the Spanish Antarctic Base Juan Carlos I: Argentina Cove (1.34 km away) and Rockie Glacier (5.7 km away), as shown in Figure 1. Our purpose was to have a near node with both groundwave and skywave propagation, and a distant node with only NVIS propagation.  
To decide the locations of each reception sounding system, the power reception of the groundwave have been studied depending on the distance. At Figure 2 we can see the groundwave electric field and power received depending on the distance from the Spanish Antarctic Base for a 24 W power transmission. As we can see, at Argentina Cove, the expected groundwave power received will be about \(-44.39\) dBm, while at Rockie Glacier, it will be about \(-75.47\) dBm. To avoid the reception of the groundwave at Rockie Glacier, the sensitivity of the sounding system has been set beyond \(-75.47\) dBm.  
Figure 1: Locations of the points for channel sounding: Argentina Cove and Rockie Glacier.  
The expected power and electric field have been calculated taking into account the ITU-R P.368 of groundwater propagation [29]. By the use of the software GRWAVE we simulated the received electric field from which we can calculate the antenna factor (1) assuming an antenna gain of -10 dB and a frequency of 4.3 MHz. Finally, we can calculate the voltage received at the antenna (2) and calculate the power received assuming 50 \(\Omega\).  
\[\mathrm{K(dB/m)} = 20\log(f)-G(dB)-29.78 \tag{1}\] \[\mathrm{V(dBuV)} = \mathrm{E(dBuV/m)-K(dB/m)} \tag{2}\]
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 3 Results
### Data Imputation
The KNN imputation method was implemented and the result is presented in Figure 4. However, the results obtained have the same pattern, but its amplitude is higher. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Timestamp** & **X** & **Y** & **Z** \\ \hline
441 & 1493996702582 & \(-0.526\) & \(-8.322\) & 3.39 \\ \hline
442 & 1493996702593 & \(-0.392\) & \(-8.102\) & 3.208 \\ \hline
443 & 1493996702604 & \(-0.507\) & \(-8.207\) & 3.112 \\ \hline  & & & & \\ \hline
449 & 1493996702663 & \(-0.632\) & \(-8.399\) & 3.61 \\ \hline
450 & 1493996702672 & \(-0.526\) & \(-8.322\) & 3.39 \\ \hline
451 & 1493996702682 & NULL & NULL & NULL \\ \hline
452 & 1493996702692 & NULL & NULL & NULL \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data segmentation before applying the K-Nearest Neighbors (KNN) imputation technique. Figure 3: Accelerometer sample related to moving downstairs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Timestamp** & **X** & **Y** & **Z** \\ \hline
449 & 1493996702663 & \(-0.632\) & \(-8.399\) & 3.61 \\ \hline
450 & 1493996702672 & \(-0.526\) & \(-8.322\) & 3.39 \\ \hline
451 & 1493996702682 & \(-0.329\) & \(-9.866\) & \(-1.05\) \\ \hline
452 & 1493996702692 & \(-0.332\) & \(-9.197\) & 2.405 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Missing samples filled after applying the KNN imputation technique.
# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 4 Experimental Results  
### Performance Comparison  
In this study, to thoroughly assess the effectiveness and robustness of enhanced networks in segmenting headlights and grilles from car front images, comprehensive experiments were carried out using the dataset detailed in Section 3.1. Six deep learning-basedsegmentation networks were selected for comparison: DeeplabV3+ [37], DNLNet [38], SegFormer [39], PSPNet [40], Segmenter [41], and TransUNet [17]. The results, shown in Table 1, indicate that MD-TransUNet achieved mFscore, mIOU, and OA values of 95.81%, 92.08%, and 98.86%, respectively, outperforming all the compared models. Figure 4 displays the visual segmentation maps of the improved network and the other six networks for the headlights and grilles.  
The base model, TransUNet, achieved mFscore, mIOU, and OA values of 95.03%, 90.69%, and 98.61%, respectively. Adding the MSAG module increased the mFscore to 95.30%, mIOU to 91.18%, and OA to 98.70%, indicating that the MSAG module enhances segmentation accuracy and robustness through multi-scale fusion. Incorporating the GCN module raised the mFscore to 95.43%, mIOU to 91.42%, and OA to 98.79%, showing that the GCN module improves segmentation precision by dynamically adjusting convolution kernels to better capture image features. When both the DCGCN and MSAG modules are introduced, the mFscore reached 95.81%, mIOU 92.08%, and OA 98.86%, demonstrating that their combination significantly boosts overall segmentation performance.  
In summary, the enhanced TransUNet network shows improvements in mIoU and mFscore metrics, highlighting its high accuracy and robustness in fine-grained segmentation tasks.
## 3 Numerical Results and Discussion  
For the numerical calculations, we use the following model parameters for EuO: \(J=0.0001\) eV, \(I=0.2\) eV, \(T_{\rm C}=69\) K [2], \(E_{g}=1.1\) eV [1]. For UTe, we use \(J=0.0001\) eV, \(I=-0.2\) eV, \(T_{C}=106\) K [33], \(E_{g}=0.6\) eV [34].
## 4 Experimental Study
### Comparisons with Different Methods  
A comparison of the RUL prediction results with results from the other three methods is presented so as to evaluate the performance of the proposed method comprehensively. The first method [14] relies on time-frequency images and CNN to predict bearing RUL. The second method [10] uses the time domain characteristics of bearing vibration signals and CNN to predict the bearing RUL. The third method [7] uses SVR to predict bearing RUL.  
Figure 8: RUL prediction results of the test sets. (**a**) RUL prediction results of the Bearing1_3 by the proposed method; (**b**) RUL prediction results of the Bearing1_4 by the proposed method.  
Figure 7: Vibration signals of the test sets and their RMS: (**a**) vibration signal of the bearing1_3; (**b**) vibration signal of the bearing1_4; (**c**) RMS of the bearing1_3; and (**d**) RMS of the bearing1_4.  
The commonly used performance comparison indexes are mean absolute error (MAE) and root mean square error (RMSE), which are defined as  
\[\text{MAE}=\frac{1}{m}\sum_{i=1}^{m}|\hat{y}_{i}-y_{i}|, \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{m}{\sum_{i=1}^{m}(\hat{y}_{i}-y_{i})}^{2}} \tag{10}\]  
where \(m\) is the number of samples in the test sets and \(\hat{y}_{i}\) represents the corresponding RUL percentage estimation by the deep neural network, \(y_{i}\) denotes the actual RUL percentage of the \(i\)th sample, the comparison results are shown in Table 1. It can be clearly seen that the MAE and RMSE value performed by the proposed method is smaller than those performed by the other three methods. By taking the MAE value as the standard, the RUL prediction accuracy of the proposed method was improved by at least 3% compared with the other three methods. With the RMSE value as the standard, there is also at least a 3% accuracy improvement of the proposed method compared with the other three methods.  
The comparison with the time-frequency features + CNN method shows that the proposed method have more robustness for different test set [18] and a small increase in prediction accuracy, which proves that learning two domain types of features in parallel is more comprehensive and more robust for RUL prediction. Compared with the time-domain features + CNN method, the prediction accuracy of the proposed method has a quite sufficient increase, which proves that the time-frequency features are important to consider. The last comparison with the SVR method shows the defects of traditional machine learning to learn complex features.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm
### Integrity Algorithm
#### 3.3.3 Algorithm  
Although a more complete integrity structure of elements can be obtained through the POS syntactic rules, the number of rules remains limited. The LSTM-CRF model can greatly improve the element recall rate, although the integrity is not guaranteed with this approach. Therefore, we can combine the advantages of both models and propose the LSTM-CRF model with the integrity algorithm. The method incorporates the advantages of the data-driven method and dependency syntax to raise the integrity and improve the accuracy of elements without losing the element recall rate. The inputs of the LSTM-CRF model with the integrity algorithm include \(\mathbf{LS_{\text{tag}}}\), \(\mathbf{R_{\text{tag}}}\), and the following user specified parameters:  
1. \(\mathbf{LS_{\text{tag}}}\), the result set labeled by the LSTM-CRF model, including {B-Attr (\"B-Attr\" means the beginning of the attribute element), I-Attr (\"I-Attr\" means the reminder of attribute element), O (\"O\" means not belonging to any type of element)];
2. \(\mathbf{R_{\text{tag}}}\), the result set labeled based on the POS syntactic rules, including {B-Attr, I-Attr, O};
3. \(\mathbf{L}\), the tags result set;
4. \(\mathbf{R_{\text{tag,begin}}}\), the subscript of the starting position of each attribute (B-Attr);
5. \(\mathbf{R_{\text{tag,end}}}\), the subscript of the ending position of each attribute (I-Attr);
6. flag, as a flag, its value is true or false;
7. \(i\), as a variable to identify the position of each tag. \begin{table}
\begin{tabular}{c} Con (\(W_{j}\)–\(W_{i}\)–\(W_{k}\)) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: POS syntactic rules based on the contextual rules of attributes.
# Context-Based Model for Browsing the Web Through Voice
## 5 Model Functionalities for Voice-Based Web Browsing
### User Context Generation
#### 5.1.2 User Navigation History  
Another element of the user's context is their preferred websites. During the setup phase, these are collected using the voice survey mentioned previously. Subsequently, the user's context is updated dynamically, adding or removing sites based on the user's frequent visits. This process is managed by the Generator of Navigation History, which continuously monitors the web pages visited by the user, using the data provided by the Operations component. Each time the user accesses a website or web page, the Operations component sends the corresponding URL to the Generator of Navigation History, which stores this information in a table with entries for the URL, the date of the last visit, and the number of visits (see Table 6). When receiving a URL, the Generator of Navigation History checks whether it is already registered. If not, it adds the URL and initializes the \"number of visits\" field to one; if the URL already exists, the Generator of Navigation History increments that field by one. In both cases, the date of the visit is updated.  
To facilitate the management of the navigation history and to avoid overgrowth, every ten days, the Generator of Navigation History analyzes the logged pages transferring to the User Context table, under the category of \"website preferences\", those that have been visited five or more times in that period. Although five visits in ten days might seem insufficient to define a site as \"preferred\", it is crucial to consider that the users targeted by this model do not navigate the web in the same way or with the same frequency as the average. For them, visiting a page several times in a short period may indicate genuine interest.  
The Generator of Navigation History maintains in history the information of web pages that meet the following criteria:  
* In the last ten days, they have received at least five visits;
* The last visit must have occurred within the last month.