esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
###### Abstract  
Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance-speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.  
recurrent neural networks; self-attention; quasi-recurrent neural networks; deep learning; acoustic model; speech synthesis; text-to-speech Article
## 3 Model Construction
### Objective Functions
#### 3.1.2 Correlation Analysis  
The multi-objective optimization problem is a competitive game among objectives in essence. Therefore, when constructing the model, it is necessary to analyze the correlation between every two objectives, judge the competitive relationship between objectives and eliminate the objectives that do not meet the conditions or transform them into constraints. In this paper, the inflow data of the extraordinarily dry year under 95% inflow frequency and dry year under 75% inflow frequency were selected, and the NSGA-II was used to solve the double-objective optimal operation model with water diversion and power generation, water diversion and ecology, power generation and ecology as the objective functions, respectively, so as to explore the competitive relationship between every two objectives. The results are shown in Figures 3 and 4.  
Figure 4: Correlation among objectives in dry year (\(p\) = 75%). (**a**) Water diversion—Power generation, (**b**) Water diversion—Ecology.  
Figure 3: Correlation among objectives in extraordinarily dry year (\(p\) = 95%). (**a**) Water diversion—Power generation, (**b**) Water diversion—Ecology, (**c**) Power generation—Ecology.  
It can be seen that there was an obvious competitive relationship between the water diversion objective and the power generation objective. The relationship between total water diversion and total power generation was approximately nonlinear in extraordinarily dry year: y = \(-0.0205\)x\({}^{2}\) + \(0.1355\)x + \(0.5996\). The relationship between total water diversion and total power generation was approximately nonlinear in dry year: y = \(-0.0133\)x\({}^{2}\) + \(0.1357\)x + \(1.9398\). In addition, the correlation coefficients of total water diversion and total power generation in extraordinarily dry year and dry year were \(-0.968\) and \(-0.989\), respectively, and their absolute values were both greater than the critical correlation coefficient 0.267 under the reliability level of 0.01. Therefore, the water diversion objective has a negative correlation with the power generation objective. Further analysis showed that the total water diversion and the ecological AAPFD value were positively correlated in the extraordinarily year and dry year, and the correlation coefficients were 0.984 and 0.972, respectively. The larger the ecological AAPFD value, the worse the river ecology. Therefore, the water diversion objective and the ecological objective were negatively correlated. Similarly, the power generation objective was positively correlated with the ecological objective.  
Compared with the power generation objective, the ecological objective of Huangjinxia Reservoir is secondary. Therefore, this paper selected the maximum water diversion and the maximum power generation as the objective functions and constructed the multi-objective optimal operation model for Huangjinxia Reservoir.
# Characterization of the Intelligibility of
## 3 Results  
### Accuracy of STOI Estimates  
Tables 1 and 2 show the Pearson and the Spearman correlation coefficients, respectively, computed between mean STOI and mean estimated intelligibility for all the VCV recordings in the corpus. The correlation coefficients for the stimuli ama and ana in Portuguese were not available (n.a.) because these recordings were not used in the original experiment, as reported in Section 2.2. The correlation coefficients for the stimulus aa in Italian and German were n.a. because the distributions of subjective intelligibility were highly skewed towards saturation in the tested range of SNR. In general, a high linear correlation is observed, above 0.8 in most cases, confirming the accuracy of STOI estimates of VCV intelligibility. VCV recordings in English and Portuguese yield the highest values of correlation (e.g., Pearson correlation mean: 0.87 and 0.85; range: 0.74-0.98 across 12 VCVs and 0.75-0.94 across 10 VCVs, respectively). The Spearman correlation coefficients are slightly higher than the Pearson ones, suggesting a monotonous relationship between the STOI and subjective measures that is not necessarily linear.  
Table 3 shows the RMSE computed between the STOI estimates and subjective estimates. The observed values of RMSE suggest VCVs in English are associated with the lowest error and, thus, the best prediction performance of the STOI. The set of VCVs in Portuguese yielded similar prediction errors as the one in English, whereas the poorest performance was observed for VCVs in Italian.
## Appendix C Performance Assessment of Orbit, Clock, and DCB
In addition, the inconsistency caused by the different APC corrections adopted in the generation of PPP-B2b and GBM clock products were also determined with their corresponding PCO values using Equation (A9) [73; 94]:  
\[\Delta_{PCO}=e_{B2b,3}-\left[\frac{f^{2}_{B1I}}{f^{2}_{B1I}-f^{2}_{B3I}}e_{GBM,1}-\frac{f^{2}_{B3I}}{f^{2}_{B1I}-f^{2}_{B3I}}e_{GBM,3}\right]\] (A9)  
where \(e_{GBM,i}\) represents the \(Z\)-components satellite PCO (in metres) adopted in the GBM clock offset generation for measurements at frequencies of \(f_{B1I}\) (\(i=1\)) and \(f_{B3I}\) (\(i=3\)), \(e_{B2b,3}\) is the \(Z\)-component satellite PCO adopted in the PPP-B2b clock offset generation for measurements at the frequency of \(f_{B3I}\). Finally, we calculated the single-difference (SD) clock difference between the PPP-B2b and GBM clock products using Equation (A10). \[\Delta C^{j}_{i}=\overline{dt}^{C}_{B2b,B3I}(i,j)-\Delta_{DCB}+\Delta_{PCO}- \overline{dt}^{C}_{GBM,13}(i,j)\] (A10)  
where \(\Delta C^{j}_{i}\) is the clock difference between the clock offsets from PPP-B2b and the GBM at epoch \(i\) for satellite \(j\). Because of the difference in the underlying realization of the time scale, there was a systematic bias between different clock products. This bias is common to all satellites in the same constellation, but varies from epoch to epoch. This bias was absorbed by the receiver clock parameter and had no effect on the accuracy of the other estimations. Therefore, to eliminate this bias, the Equation (A11) is commonly used to calculate the double-difference (DD) clock time series for the clock performance evaluations. \[\
abla\Delta C^{j}_{i}=\Delta C^{j}_{i}-\frac{\sum\limits_{j=1}^{N}\Delta C^{j }_{i}}{N}\] (A11)  
where \(N\) is the number of the satellites. It should be noted that, for PPP-B2b products, only regional satellites can be received at every epoch and, thus, the number of the satellites may vary at different epochs. This may lead to a discontinuity in the DD clock series, severely affecting the STD and RMSE of the DD clock series \(\
abla\Delta C^{j}_{i}\). In clock evaluation, the STD represents PPP positioning accuracy after convergence, and the RMSE usually reflects the PPP convergence time. Therefore, to overcome this problem, Equations (A12) and (A13), proposed by [PERSON] et.al. [33], were used in this study to re-edit the DD series for all satellites:  
\[\
abla\Delta C_{i}^{j}=\
abla\Delta C_{i}^{j}-\Delta DD_{i,j-1}\] (A12)  
\[\Delta DD_{i,i-1}=\left\{ \begin{array}{ll}\frac{1}{N}\sum_{j=1}^{N}\Delta \
abla\Delta C_{i,i-1}^{j}\,&abs\Big{(}\frac{1}{N}\sum_{j=1}^{N}\Delta\
abla\Delta C_{i,i-1}^{j}\Big{)} \geq 0.1\ ns\\ 0&,&abs\Big{(}\frac{1}{N}\sum_{j=1}^{N}\Delta\
abla\Delta C_{i,i-1}^{j}\Big{)} <0.1\ ns\end{array} \right.\] (A13)  
where \(\Delta\
abla\Delta C_{i,i-1}^{j}=\
abla\Delta C_{i}^{j}-\
abla\Delta C_{i-1}^ {j}\) is the time difference of the DD clock series.
# Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification
## 3 Proposed Framework
### Regularization and Deep Architecture Modules
Let \(x,x\in\mathbb{R}^{T\times F}\) be a Mel-filterbank energy feature extracted from an input audio recording, where \(T\) and \(F\) are the number of frames and Mel-frequency bins, respectively, and \(t\) and \(f\) are indices for \(T\) and \(F\), respectively. To apply time masking, we randomly select \(t_{stt}\) and \(t_{end}\), \(t_{stt}\leq t_{end}\leq t_{T}\), where \(stt\) and \(end\) are indices for the start and end, and then, mask the input feature with 0. To apply frequency masking, we randomly select \(f_{stt}\) and \(f_{end}\), \(f_{stt}\leq f_{end}\leq f_{F}\), and then, mask with 0. In this study, we sequentially apply specAugment and mix-up for better generalization. To increase the model capacity while introducing a small number of additional parameters, we investigate two recent deep architecture modules: SE [20] and CBAM [21]. SE focuses on the relationship between the different channels of a given feature map. SE first _squeezes_ the input feature map via a global average pooling layer to derive a channel descriptor, which includes the global spatial (time and frequency in ASC) context. Then, using a small number of additional parameters, SE recalibrates the channel-wise dependencies via an _excitation_ step. Specifically, the excitation step adopts two fully-connected layers that are given a derived channel descriptor and output a recalibrated channel descriptor. SE transforms the given feature map by multiplying the recalibrated channel descriptor, where each value in the channel descriptor is broadcast to conduct element-wise multiplication with each feature map filter. We apply SE to the output of each residual block. CBAM is a deep architecture module that sequentially applies channel attention and spatial attention. To derive a channel attention map, CBAM applies global max and average pooling operations to the spatial domain and then uses two fully-connected layers. Channel attention is applied using an element-wise multiplication of the input feature map and the channel attention map where the channel attention map value is broadcast to fit the spatial domain. To derive a spatial attention map, CBAM applies two global pooling operations to the channel domain and then adopts a convolution layer. Spatial attention is also applied using an element-wise multiplication of the feature map after channel attention and a derived spatial attention map; we apply the result of this multiplication to teach the residual block's output.
## 3 Results
### Results of Situation Awareness  
#### 3.4.1 SA Under Full eHMI Penetration Condition  
The mean and SD of SA across different eHMI information content conditions under full eHMI penetration condition are presented in Table 8. Based on the results of the Shapiro-Wilk normality test, no significant deviations from normality were observed for any of the conditions, allowing the assumption that the data follow a normal distribution. A one-way ANOVA was conducted to examine the effect of eHMI information content condition on SA. The results revealed no statistically significant differences among the six eHMI information content conditions (F(5, 66) = 0.733, \(p=0.601\)).
## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning
#### 3.3.2 Machine Learning-Based Path Planning
As technology advances, the synergy between ML and UAV path planning is likely to play a crucial role in enhancing the autonomy and performance of UAVs in real-world settings.
## 4 Results  
### Experimental Datasets  
While finding the optimal structure through theory is challenging, experiment validation provides an effective way to compare different coupling structures. The reconstruction performance was assessed using two publicly available datasets containing RIRs measured in various rooms. The first dataset, referred to as the DTU dataset [37], includes RIRs collected in a listening room following Ref. [38]. It is divided into a fitting set with 97 measurements on a spherical surface of 0.5 m radius and a reconstruction set with 703 measurements on a circular plane of 0.7 m radius. The second dataset, referred to as the UTS dataset [39], includes RIRs measured in four different rooms: large, medium, and small meeting rooms, as well as a shoebox room. Each room is further divided into five distinct zones, with 64 RIRs measured in each zone. For the evaluation, the data from four zones within each room were used as the fitting set, while the remaining zone was designated as the reconstruction set. The reconstruction performance was assessed by alternately using each zone as the reconstruction set and calculating the average performance metrics. Configurations for both datasets are provided in Table 2.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 2 Preliminaries
### Extended Motivation and Background
#### 2.2.2 Economical Dimension  
Operational costs, including fuel, maintenance, road tolls, and driver wages, are major expenses for transport companies [38]. These costs only partially depend on the load factor of the truck, leading to inefficiencies and higher freight prices for partially empty runs, which in particular increases pressure on small logistics companies that cannot optimize in-house. Maximizing the average utilization of trucks would thus lower logistics expenses; in turn, this could translate into reduced consumer prices, especially in sectors heavily reliant on freight transport. The savings from cutting empty runs would also help stabilize costs across industries, especially in times of high energy prices.
## 5 Experimental Results
### Limitations of Our Approach  
Our algorithm presents a computational complexity of O(N\({}^{5}\)), in which N is the number of vertices of the dataflow graph. Thus, the grouping of the neural network neurons may be necessary so that the algorithm executes in a feasible time. As our results suggest, the LeNet version that groups more neurons presents a limited impact on the results while the algorithms may execute faster, as the problem size is smaller. Other algorithms such as METIS performs an aggressive grouping and, thus, can execute in a feasible time. However, it is worth noting that, with 30 executions, our algorithm achieves results that are close to the best result that DN\({}^{2}\)PCIoT can achieve for an experiment. On the other hand, we had to execute METIS with many different parameters to achieve valid partitionings and find the best result that METIS can get, adding up to more than 98,000 executions. Thus, METIS execution time is also not negligible.  
Current CNNs such as VGG and ResNet would require more constrained devices and/or devices with a larger amount of memory so that partitioning algorithms can produce valid partitionings. However, as they are also composed of convolution, pooling, and fully connected layers, the partitioning patterns [20] tend to be similar. Additionally, as current CNNs present more neurons, strategies that groups more neurons similar to LeNet 2:1 or in multilevel partitioning algorithms such as METIS may also be required so that the partitioning algorithm executes in a feasible time.  
Other strategies that we can use to reduce our algorithm execution time are to start from partitionings obtained with other tools and to interrupt execution as soon as the partitioning achieves a target value or the improvements are smaller than a specified threshold. Our algorithm can also be combined with other strategies such as the multilevel approach, which automatically groups graph vertices, but without the shortcomings of METIS, which are suboptimal values and invalid partitionings. Even with the limitations of our approach, the results suggest that there is a large space for improvements when we consider constrained devices and compare to well-known approaches.
## 3 Results and Discussion
### Bit Error Ratio
From these, we can clearly see that the CSK-based UM-VLC system has a considerably lower performance in the proposed channel model than in the reference channel model. There are no significant differences between the shadowed and non-shadowed RX positions, which can be explained by the fact that in this simulation, the SNR is the same for shadowed and non-shadowed RX positions, something that is not the case in simulations where the SNR is not the independent variable (e.g., see Figure 7). In the BER simulation, the main difference between the shadowed and non-shadowed RX position is the RMS delay, since it tends to be greater in NLOS-dominant positions (see Figure 9). The fact that having more RMS delay does not affect the BER performance means that this communication system is not too limited by inter-symbol interference, either because the data rate is sufficiently low or because the LED's bandwidth is sufficiently high. The lower performance experienced by the proposed CSK-based UM-VLC channel model compared to the reference model is explained by the fact that the color distortion induced by the reference channel model can, by definition, be represented as a linear transformation, which can be easily reversed by the IEEE 802.15.7 color calibration algorithm in the RX. The color distortion induced by our proposed model cannot be represented as a linear transformation but rather only approximated by one, and thus, the color calibration algorithm cannot revert its color distortion as effectively, causing the BER to increase.
# Numerical and Experimental Investigations of a Micromixer with Chicane Mixing Geometry
## 4 Conclusions  
In the present study, the micromixer with a chicane mixing channel structure was investigated using a CFD simulation for volume flow rates of 0.05 mL/min \(\leq\) Q \(\leq\) 10 mL/min, and through experiments. Experimental results showed positive similarities with the numerical results. Based on the numerical results, the stratified and vertical flow regimes were identified. The stratified flow has strictly parallel streamlines and negligible stagnation zones. The mixing process takes place only in the interface between the species and is driven only by diffusion. The lowest mixing index value (I\({}_{\rm M}\) = 0.15) is observed between Q = 0.5 mL/min and Q = 1 mL/min. It can be assumed then, that the geometric structure of microchannels with chicane mixing geometry is not utilized in the case of  
Figure 11: Comparison of experimental (**left**) and simulation results (**right**): (**a**) Re = 13; (**b**) Re = 26; (**c**) Re = 78; and (**d**) Re = 260.  
stratified flow. Additionally, the vortical flow regime shows significant advantages, such as forming vortex structures: recirculation and swirling, which is shown using streamlines. The convective mixing increases the interfacial area dramatically and therefore there is an essential gain in mixing. The increase of volume flow rate provides the increase of the mixing index. When volume flow rate is Q = 10 mL/min, the mixing index reaches the value \(\rm I_{M}\) = 0.8 after four cells. Furthermore, the pressure drop was investigated using CFD simulations. A square dependence of pressure drop vs volume flow rate was shown, which was due to the vertical structure of the flow.  
Mixing costs for different micromixers at Reynolds number of 50 were calculated. The serpentine micromixers with the T-joint have lower mixing costs than the chicanes micromixer, because of drastically higher pressure losses due to right-angle geometry. The generated saddle-shaped flow structure in the serpentine SAR micromixers promotes chaotic advection and extremely high pressure drop, which provide much lower mixing costs than in the chicanes micromixer.  
Methodology, validation, investigation, visualization V.K.; conceptualization, methodology, validation, administration, writing--review and editing, E.B.; conceptualization, methodology, scientific discussions, supervision, funding acquisition, W.R. This study was supported by the Erasmus Mundus Action 2 Programme of the European Union and by the Ministry of Education and Science of the Russian Federation, grant number No. 10.3444.2017/\(\Pi\)VI. The computations were performed on a Bull HPC Cluster Taurus at the Center for Information Services and High Performance Computing (ZIH) at TU Dresden. The authors thank [PERSON] from the St. Petersburg State Institute of Technology (Technical University) for his helpful discussions. The authors declare no conflict of interest.
## 2 Background  
### Soundscape in Automobile  
Soundscape is defined as \"an environment of sound with an emphasis on the way it is perceived and understood by the individual, or by a society\" by [PERSON] [21]. In recent years, the major concept of soundscape has focused on the emotional approach that affects humans [22; 23]. Soundscape in automobiles is not just about eliminating noise but is also involved in providing people with specific affective experiences [24]. The soundscape in an automobile is a complex environment, relating psychological, physical, and context factors. Therefore, it is important to understand the core factors that determine the characteristics of a soundscape considering the environment of an automobile.  
Affective adjectives or variables delivered through linguistic play an important role in auditory judgment based on the psychological dimension in which sound stimuli are evaluated [25]. Previousstudies have focused on the interpretation of a person's complex perception of sound through Likert's or semantic differential scale [26]. A multidimensional evaluation of the sound of an automobile has been conducted, and it has identified the major components of their subjective feelings through questionnaires or field tests [15].  
Principal component analysis (PCA) and factor analysis (FA) are commonly used to identify relationships between components of soundscape [27]. These two methods can be distinguished by the linear independence of the latent variable from the linear combination. [PERSON] and his colleagues conducted PCA on the power train of 33 automobiles to derive two main components: pleasant and powerful [28]. [PERSON] et al. [24] reported that there are five components of subjective feelings in automobile sounds using FA. [PERSON] et al. [29] confirmed that the subjective dimensions of electric vehicles consist of three factors.  
In order to quantitatively explain the dimension of the derived soundscape in an automobile, previous researchers used psychoacoustic parameters [30]. [PERSON] et al. [31] designed a model to predict the sound quality of the vehicle interior noise through 10 psychoacoustic parameters, such as loudness, sharpness, roughness, etc. [PERSON] and [PERSON] [32] proposed a prediction model of discomfort according to different road conditions and vehicle types based on psychoacoustic parameters.