esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DRTO: Deep Reinforcement Learning for Task Offloading  
To minimize the offloading cost, we design an offloading algorithm \(\pi:\mathbf{h}\to\mathbf{x}^{*}\) that quickly selects the optimal offloading location \(\mathbf{x}^{*}=[x_{1}^{*},x_{2}^{*},\ldots,x_{N}^{*}]\) only based on the current channel state \(\mathbf{h}=[h_{1},h_{2},\ldots,h_{N},h_{TC}]\).  
The diagram of DRTO is shown in Figure 3. First, the DNN takes the current channel gain \(\mathbf{h}\) as input, and generates a relaxed offloading location \(\mathbf{\hat{x}}\). Then, we quantize the relaxed location \(\mathbf{\hat{x}}\) into \(K\) candidate binary offloading locations, namely \(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{K}\). The optimal location \(\mathbf{x}^{*}\) is obtained by solving a series of bandwidth allocation convex problems. Subsequently, the newly obtained channel state-offloading location pair \((\mathbf{h},\mathbf{x}^{*})\) is added into the replay memory. A random batch will be sampled from memory to improve the DNN every \(\delta\) time frames. To further reduce the runtime consumption, we dynamically adjust \(K\) to speed up the learning process. In the following subsections, the details of the above stages are described. The pseudocode of the DRTO algorithm is summarized in Algorithm 1.  
```
1:Input: Current channel gain \(\mathbf{h}\).
2:Output: Optimal offloading location \(\mathbf{x}^{*}\) and corresponding bandwidth allocation \(\mathbf{\alpha}^{*}\).
3:for\(t=1,2,\ldots,T\)do
4: DNN generates a relaxed offloading location \(\mathbf{\hat{x}}\).
5: Quantize \(\mathbf{\hat{x}}\) into \(K_{t}\) candidate binary offloading locations \(\mathbf{x}_{k},k\in{1,2,\ldots,K_{t}}\).
6:for\(k=1,2,\ldots,K_{t}\)do
7: Given binary offloading location \(\mathbf{x}_{k}\), the bandwidth allocation \(\mathbf{\alpha}_{\mathbf{x}_{k}}\) and offloading cost \(F\big{(}\mathbf{x}_{k},\mathbf{\alpha}_{\mathbf{x}_{k}}\big{)}\) are obtained by solving \(\mathcal{P}^{\prime}\).
8:endfor
9: Obtain the optimal offloading location with respect to \(\mathbf{x}^{*}=\arg\min_{\mathbf{x}_{k}\in{1,2,\ldots,K}}F(\mathbf{x}_{k},\mathbf{\alpha}_{ \mathbf{x}_{k}})\).
10: Add newly obtained channel state-offloading location pair \((\mathbf{h}_{t},\mathbf{x}^{*})\) into replay memory.
11:if\(t\) mod \(\delta==0\)then
12: Sample a random batch from memory for training DNN.
13:endif
14:if\(t\) mod \(\Delta==0\)then
15: Adjust \(K_{t}\) using (15).
16:endif
17:endfor
```  
**Algorithm 1** The DRTO algorithm.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 2 Model Establishment and Simulation Test Design
### Design of Dynamic Modal Simulation Tests  
Dynamic modal analysis is a fundamental method for assessing the dynamic characteristics of vehicles. Modal analysis can be categorized into two types: free modal analysis and constrained modal analysis [34]. The free modal analysis involves studying the vibration characteristics of the bus body frame without considering the effects of external loads, where the analysis is focused on the bus's completely unconstrained vibration behavior. On the other hand, the constrained modal analysis refers to the determination of vibration characteristics under actual operating conditions, considering the influence of applied loads on the bus body frame.  
Firstly, due to the complexity of the hydrogen fuel cell bus body frame with numerous components and intricate connections, modal analysis is of paramount importance for the established finite element model. Through modal analysis, the overall connectivity of the bus body frame can be visually assessed. Secondly, during city bus operations, the bus is subjected to various excitations from both internal and external sources, with the most common being road surface excitations caused by different road conditions. Modal analysis helps determine whether the natural frequencies of the bus body coincide with these excitations. Once resonance occurs, it can lead to intense and prolonged vibrations, affecting passenger comfort and potentially causing fatigue or damage to the bus body components.  
For hydrogen fuel cell city buses, the actual operating conditions during daily travel are quite complex, and it is challenging to obtain and apply corresponding loads. Moreover, the vibration characteristics obtained from the free modal analysis are sufficiently close to real-world conditions. Therefore, in this study, the free modal analysis method is selected to investigate the vibration characteristics of the hydrogen fuel cell city bus.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 2 Materials and Methods
### Test Vehicle for Qualification Test  
The test vehicle consists of two tunable filters mounted on a common support, a control electronics for commanding the filters, and an interconnecting DC harness. The test results presented in this publication focus on the 3-pole filter already discussed above, a photograph of which is shown in Figure 8.  
Compared to Figure 6, coaxial to waveguide transitions and coaxial isolators (with gold-plated housings) are included on the ports. The filter is screwed on a baseplate. Green cables are seen, which connect to temperature sensors. On top of the filter housing, there are three gold-plated screws,  
Figure 8: Test vehicle for qualification test.  
which are the conventional tuning screws for the filter resonators (those screws for the irises are at the bottom of the filter and are not visible).  
The control electronics was configured for three center frequencies of the filter: 19,740 MHz, 19,780 MHz, and 19,820 MHz.
## 5 Simulation Analysis  
### Simulation Settings  
In order to evaluate the efficacy of the proposed approach to suppress the frequency fluctuations in a small-scale power system by the adjustable speed diesel engine-driven generator, system behavior under vacillating power from a wind power station in the power system was analyzed. Wind speed data, which was a measured record of the real wind in Hokkaido, Japan, was applied to the simulation analysis. This was because it was important to assess the performance of the proposed approach under a situation of natural wind conditions where the ability of the adjustable speed diesel engine-driven power plant to stabilize the power system frequency could be evaluated. Nine profiles of wind power obtained from the natural wind conditions were used in the simulation analysis (shown in Figure 12) and were referred to as Wind No.1 to 9. The wind speed data used in each profile was for 1 h, with a 1 s interval, and the resultant wind power had various different average levels and different vacillating levels corresponding to standard deviation (shown in Figure 12). Simulation analyses for four cases performed in this paper for the combination of two diesel generators are shown in Table 1. The rated capacity of the wind power station was set to 0.5 MW. This was based on the ideal range of controllable output of the ASG in Case 2 of Table 1. Though the rated output of the ASG in Case 2 was 2.5 MW, the actual output should have been within the range from 1.25 (50%) to 2.5 MW (100%) due to the limited range of the diesel engine output. In this situation, when the ASG output was 1.875 MW (75%), the center of the output range, then the ASG could provide ideal control, i.e., increase or decrease the output within \(\pm\)0.625 MW (\(\pm\)25%). However, since the output correction (shown in Figure 9) was included in the control system, the ASG could hardly output the power near the upper limit (0.625 MW). Therefore, the rated output of the wind power station was set to 80% of 0.625 MW temporarily (thus, it was 0.5 MW). The power consumption of Load A and B were both 3.75 MW (75% of the power system capacity).
## 4 Discussion
### Age  
Typically, adults are considered more intelligible than children due to the expected patterns of speech simplifications and articulatory errors often observed in a developing child's speech. Along with the physical, linguistic, and prosodic differences observed in children's speech [37,43], it is not surprising that the acoustic and intelligibility mismatch between adult and child speech is also considered a challenge in the development of synthetic speech for children. It is well established that characteristics of speech, such as pitch, formant frequencies and phone duration are related to the age of the speaker [50]. These acoustic differences result from children having shorter vocal tracts and smaller vocal folds than adults [84]. Younger children show higher pitch and fundamental frequency in comparison to older children [85]. In [PERSON] and [PERSON]'s study [37], the trend showed that as the age of the child speaker increased, less data adaptation was required. Older children did not experience as great of a mismatch between the adult speech (which was used as training data), while younger children showed that considerably more data were needed to account for the mismatch [37]. Younger children therefore show a considerably higher amount of intra- and inter-speaker variability as compared to older children and adults [49,51]. Younger children have high levels of acoustic complexity, which can be attributed to \"three main factors (i) shifted overall spectral content and formant frequenciesfor children, (ii) high within-subject variability in the spectral content that affects formant locations and (iii) high inter-speaker variability observed across age groups, due to developmental changes, especially vocal tract\" [37] (p. 2). Thus, more parameters were required to accurately capture the increased complexity in their speech [37]. If one were to consider building a young child voice, with its high level of acoustic complexity, it may be prudent to first consider the availability of the data necessary for adaptation. Although there is not a consistent cut-off age for the most suitable levels of intelligibility [76] (i.e., seven-year-old speech vs. four-year-old speech), it appears that as a child's level of intelligibility increases and their acoustic variability decreases, which may be at approximately nine years old, the less challenging it would be to build a synthetic voice for them.
# FrameAugment: A Simple Data Augmentation Method for Encoder-Decoder Speech Recognition
## 4 Results
### Frame Rate  
The proposed method changes the degree of augmentation by changing the speed in the augmentation section. As described above, the method of changing the speed uses linear interpolation to change the number of frames. In addition, we experimented with the maximum frame rate range of 0.5-1.5 in order to transform at an audible speed. Speed \(s\) is rounded to the first digit after the decimal point. Tables 3 and 4 show the performance comparison according to frame rate. Even if the frame rate is randomly selected from a set of the values consisting of the minimum range, 1.0, and the maximum range, there is no significant performance difference. For example, comparing \(s=0.5\)-1.5 and \(s=0.5,\ 1.0,\ 1.5\), we observed no consistent WER changes.  
In the WSJ database, there was no significant difference in performance even by changing the range of the frame rate, but in the case of the LibriSpeech database, changing the range of the frame rate showed significant performance improvement. Different results were obtained for the two databases, and additional confirmation was performed through validation loss in Figure 7. In the WSJ database, there was no difference in the validation loss between 0.9-1.1 and 0.5-1.5 for the frame rate, but in the LibriSpeech database, the larger the range of the frame rate, the lower the validation loss value.  
Optimal parameters of \(s\) and \(n\) were tested through experiments. The best parameters \(s\) were 0.5-1.5 and the best parameter \(N\) was 500 in the WSJ database and 1200 in the LibriSpeech database.  
\begin{table}
\begin{tabular}{c c c} \hline \(s\) & **Dev-Clean** & **Test-Clean** \\ \hline
0.9–1.1 & 6.26 & 6.96 \\
0.7–1.3 & 5.95 & 6.60 \\
0.7, 1.0, 1.3 & 6.15 & 6.62 \\
0.5–1.5 & 6.06 & 6.68 \\
0.5, 1.0, 1.5 & 5.93 & 6.74 \\ \hline \end{tabular}
\end{table}
Table 4: WER (%) results of LibriSpeech database according to frame rate \(s\) when \(n=1200\).  
\begin{table}
\begin{tabular}{c c c} \hline \(s\) & **Dev93** & **Eval92** \\ \hline
0 & 7.88 & 5.02 \\
0.9–1.1 & 7.03 & 4.75 \\
0.7–1.3 & 7.38 & 4.82 \\
0.7, 1.0, 1.3 & 7.07 & 4.87 \\
0.5–1.5 & 7.24 & 4.94 \\
0.5, 1.0, 1.5 & 7.04 & 4.70 \\ \hline \end{tabular}
\end{table}
Table 3: WER (%) results of WSJ database according to frame rate \(s\) when \(n=500\).
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 2 Data and Methods
### Model Settings
#### 2.3.2 Ensemble Method  
Ensemble methods involve the amalgamation of multiple machine learning models', a strategy proven to elevate predictive performance, and the ability to combat overfitting, a prevalent issue in machine learning [41; 42]. Ensemble methods have gained a lot of popularity in recent years for different time series tasks [43; 44]. Amalgamations can iron out inconsistencies and errors inherent to individual models, reducing bias and variance in the final predictions and leading to enhanced generalization capabilities even in the face of noisy data and outliers. Ensembles can be achieved through a variety of techniques, including employing different algorithms [45], training on distinct data subsets [46], and utilizing  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Selected Features** & **Total No. of Features** \\ \hline \multirow{4}{*}{LSTM} & Wind Speed & \\  & Generator Speed & \\  & Rotator Blade Pitch Angle \(\times\) Generator Speed & \\  & Rotator Blade Pitch Angle \(\times\)Wind Speed Turbulences & \\ \hline \multirow{4}{*}{FNN} & Wind Speed & \\  & Generator Speed & \\  & Wind Speed \(\times\) Generator Speed & \\  & Wind Speed Turbulences & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected Features for LSTM and FNN.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Activation Function** & **Loss Error** & **Output Cutoff** & **Learning Rate** & **Network Size** \\ \hline \multirow{2}{*}{LSTM} & \multirow{2}{*}{ReLU} & \multirow{2}{*}{MSE} & \multirow{2}{*}{Hard cutoff} & \multirow{2}{*}{0.001} & Hidden Size = 2 \\  & & & & Common Channel = 24 \\ \hline \multirow{4}{*}{FNN} & \multirow{2}{*}{ELU} & \multirow{2}{*}{MSE} & \multirow{2}{*}{Sigmoid} & \multirow{2}{*}{0.01} & Hidden Size = 1 \\  & & & & Common Channel = 20 \\ \cline{1-1}  & & & & Common Size = 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter Settings for LSTM and FNN.  
varying feature selection strategies [47]. By virtue of their diversity in model composition, ensembles often produce superior, more reliable prediction across various scenarios.
# An Effective Simulation Scheme for Predicting the Aerodynamic Heat of a Scramjet-Propelled Vehicle
## 3 Validation Tests
### Transonic Stage
#### 3.1.3 Simulation Parameters  
Figure 5 demonstrates the distributions of \(m\) under three different Mach numbers of freestream \(M\), where the \(X\)-axis was the ratio between \(x\) and \(c\) and the \(Y\)-axis was the value of \(m\). EXP represents the experimental data, SST and SA represent the simulation results based on SST k-omega and Spalart-Allmaras, respectively. The overall tracking effect of the simulation data on the experiment data was good. At \(M\) = 0.768, the average error rate of SST k-omega was 9.69%, and that of Spalart-Allmaras was 8.34%. The variance of SST k-omega error was 0.000368, and that of Spalart-Allmaras is 0.000122. At \(M\) = 0.817, the average error rate of SST k-omega was 9.52%, and that of Spalart-Allmaras was 8.18%. The variance of SST k-omega error was 0.00035, and that of Spalart-Allmaras was 0.000152. At \(M\) = 0.86, the average error rate of SST k-omega was 10.14%, and that of Spalart-Allmaras was 8.52%. The variance of SST k-omega error was 0.000359, and that of Spalart-Allmaras was 0.000284. The total mean error rates were 9.78% for SST and 8.35% for Spalart-Allmaras. In general, the trend of the simulation results was in good agreement with the experimental data, and of the two models, the Spalart-Allmaras simulation results were better than those of SST k-omega at the transonic stage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Validation Parameters**} \\ \hline \hline \multirow{2}{*}{Solver} & Pressure-based with coupled algorithm & Pressure-based with coupled algorithm \\ \hline \hline Turbulence Models & SST k-omega & Spalart-Allmaras \\ \hline \multirow{3}{*}{Materials} & Density: Ideal-gas & Density: Ideal-gas \\  & Viscosity: 1.7894 \(\times\) 10\({}^{-5}\) Pa-s & Viscosity: 1.7894 \(\times\) 10\({}^{-5}\) Pa-s \\  & C\({}_{p}\) (/kg-k): 1006.43 & C\({}_{p}\) (/kg-k): 1006.43 \\ \hline \multirow{3}{*}{Initial conditions} & Mach number: 0.768/0.817/0.86 & Mach number: 0.768/0.817/0.86 \\  & Static temperature: 300 K & Static temperature: 300 K \\ \hline \multirow{3}{*}{Boundary conditions} & INPUT: pressure far field & INPUT: pressure far field \\  & FARFIELD: pressure far field & FARFIELD: pressure far field \\ \cline{1-1}  & OUTPUT: pressure outlet & OUTPUT: pressure outlet \\ \cline{1-1}  & SYMMETRY: symmetry & SYMMETRY: symmetry \\ \cline{1-1}  & WALL: no-slip, isothermal & WALL: no-slip, isothermal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validation parameters of convergence results at the transonic stage.  
Figure 5: Local Mach number versus x/c for different Mach numbers of freestream.  
In conclusion, the mesh with the value of \(y^{+}\) as 1, = the turbulence model applying Spalart-Allmaras and the pressure-based solver, adopting a coupled algorithm, is preferred at the transonic stage.
# A Blockchain-Based Supervision Data Security Sharing Framework
## 4 Performance Evaluation and Analysis of Proposed Framework
### Analysis of Security Assessment
#### 4.2.1 Data Immutability  
When a user queries data from the IPFS file system through _CID_, the visited node will combine the distributed hash table and routing mechanism based on the anti-tampering mechanism stored in the IPFS, as shown in Figure 2 to obtain the NodeID of the node where the searched file is located. Then, the routing mechanism will route to the node, query the file, and return it to the client. If a user modifies the original file data, the IPFS generates a hash address that uniquely corresponds to the file. Changing the original file will result in a change in its corresponding hash address. Due to the characteristics of the hash algorithm, the hash address uploaded to the blockchain cannot be tampered with, and forged data will not pass verification. Comparing and testing ensures the immutability of supervision data.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Flavonoids
#### 3.1.4 Cyclodextrin Complexation  
The use of cyclodext, particularly \(\beta\)-cyclodextrinrin, facilitates the formation of inclusion complexes, where the hydrophobic segments of flavonoid molecules are encapsulated within the hydrophilic cavity of the cyclodextrin [113]. This strategy has been successfully applied to flavonoids like baicalin, kaempfer, and quercetin, substantially improving their water solubility and stability [113]. Encapsulation not only enhances solubility but also protects these compounds from environmental degradation, thereby improving their bioavailability.
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 1 Introduction
However, ML-PM does not consider the laws on processor performance. Therefore, a multivariate nonlinear regression prediction model (MNL-PM) based on [PERSON]'s law is introduced to improve the accuracy of the PPA estimation. As shown in Figure 2, the proposed method replaces the synthesis and simulation process to reduce the time cost during the parameter design process. An empirical evaluation of the method shows that the PPA prediction for template-based chip design methods can reach 98.60%, 99.19%, and 98.53% accuracy on performance, power, and area, respectively, when compared with a PPA generated via synthesis and simulation flows. To the best of our knowledge, there is no previous work on fast and accurate PPA prediction on HDL design from template-based processor design methods. We believe that the proposed method is a novel and interesting design point in the space of solutions to the agile chip design problem. The main contributions of this work can be summarized as follows:  
* The PPA prediction model, ML-PM is proposed to achieve the PPA prediction via the parameter set, in which there are alters for normal multivariate linear regression model. * The PPA prediction model, MNL-PM based on Amdahl's law is introduced as to improve the accuracy of the PPA estimation. * The iteration process of the parameters set DSE has been improved with the proposed PPA prediction model, which is time thrifty. To the best of our knowledge, there is no previous work on fast and accurate PPA prediction on HDL design from template-based processor design methods. We believe that the proposed method is a novel and interesting design point in the space of solution to the agile chip design problem. Figure 1: The parameter design process. Figure 2: The parameter design process with proposed PPA prediction method.
## 3 Materials and Methods
### Problems  
Despite great efforts made in the last few years and supporting improvement, the MOT solutions have two big issues. One is that these systems are based on plausible but sub-optimal tracking models [44] that result in the infeasibility of overall optimization in an end-to-end manner. It typically involves three main divisions: the detection of objects, the extraction of features, and the association of data. However, dividing the entire task into separate subtasks resulted in getting local optimum and was helpful in performing calculations. Furthermore, the association of data was dependent on the consistency of the objects detected that couldn't deliver accurate and consistent results in different frames as it rejected the adjacent frames having temporal relationships. The other problem is that issues are arising for boosting efficiencies of multiobject tracking; therefore, two important points; reidentification and attention, are found to enhance MOT efficiency. Reidentity extracts robust data association features (or ID verification). Attention tends to concentrate on the model more, avoiding interruption by irrelevant details, like the background. Regardless of productivity, the presence of existing solutions causes an increase in the complexity of the model and machine costs considerably. We have proposed a new method for online tracking, namely the Super Chained-Tracker (SCT), that defines an end-to-end model incorporating feature extraction with data association based on object detection. Our proposed model is convenient, simpler, and more efficient than existing MOT detection techniques. In a single regression model, the joint detection with tracking is carried out simultaneously using the adjacent pairs of frames that show matched bounding boxes as the goal. We have added a joint attention module to increase the efficiency of our SCT using expected confidence maps. It directs the regression branch for paired cases to concentrate on insightful spatial areas with two other branches. Firstly, the branch of object classification that provides the appropriate values for the box in pairs appears first in the detected cases. Scores from this are then used to direct the regression industry to concentrate on the region of interest. The ID verification branch focuses on forecasting the regression branch to highlight the regions that meet the target. In the last stage, the bounding boxes are classified, and the association of generated box pairs takes place using methods such as Intersection over Union (IoU) [45], Kalman filter, and Bipartite Matching from the adjacent framework pairs with their boxes in the common framework. Thus, chaining of all neighboring frame pairs may be used to ensure the tracking operation.