esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To evaluate the effects of charging conditions on the temperature distribution within a 400L-class type IV hydrogen tank, referred to as tube skid, vertically positioned in a hydrogen transport vehicle, a 2D axisymmetric model incorporating the effect of buoyancy was developed. Comprehensive numerical analyses were performed, utilizing property data from NIST. The study examined the influence of five charging parameters (charging time, inlet temperature, ambient temperature, initial temperature, and initial pressure) on the tube skid's maximum temperature, yielding the following insights:  
* The inlet temperature predominantly affected the temperature at the lower section of the tank due to buoyancy, as hydrogen was introduced from the bottom. This condition increased the temperature disparity between the tank's upper and lower sections without significantly altering the maximum temperature at the top of the tank.
* The initial pressure exhibited minimal impact on the maximum temperature, as the initial mass of hydrogen was relatively small within the examined pressure range of 5 to 20 bars.
* Sensitivity analysis through factorial design further confirmed the exclusion of inlet temperature and initial pressure as significant factors. Consequently, three out of the five analyzed factors were identified as closely associated with the tank's maximum temperature. The initial temperature exhibited the strongest correlation, followed by charging time and ambient temperature, highlighting the importance of these three parameters in the hydrogen charging process.
* Utilizing the three identified critical factors, a regression equation for predicting the tank's maximum temperature upon charging completion was formulated via response surface methodology. With the derived equation exhibiting an R\({}^{2}\) value of 0.99 or higher, it facilitated highly accurate predictions of temperature across different regions of the tank using these parameters.  
Thus, the findings from this study are poised to contribute to the development of charging standards for vertically installed tube skids and may inform subsequent research aimed at optimizing charging conditions.  
Conceptualization, Y.L., J.-H.N. and D.-J.H.; Methodology, Y.L. and J.-H.N.; Software, Y.L. and J.-H.N.; Data Analysis, Y.L. and J.-H.N.; Investigation, Y.L.; Writing--Original Draft Preparation, Y.L. and J.-H.N.; Writing--Review and Editing Y.L., J.-H.N. and D.-J.H.; Visualization, Y.L. and J.-H.N.; Supervision, J.-H.N. and D.-J.H.; Project Administration, J.-H.N.; Funding Acquisition, J.-H.N. and D.-J.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (20026413, Development of the BOP and system for PEM water electrolysis) funded By the Ministry of Trade Industry and Energy (MOTIE, Republic of Korea).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### Path Search Algorithms  
5.1.1 Heuristic-Based Algorithms  
Heuristic-based path search algorithms are mostly based on graph search methods, such as Dijkstra, A*, LifeTime Plan A* (LPA), and dynamic A* (D*). These algorithms are fast and suitable for identifying the shortest path between the selected positions. As of 2019, these algorithms are extended for 3D environments, and improvements (mainly of the A* algorithm) can be used for variable step size and variable weights. Summary: large amount of node calculation, poor RT performance, cannot be used in dynamic environment.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 7 Results & Discussion
### Overall Analysis  
In this section, we compare the best approach of each adaptation method proposed in our work in terms of performance as well as in terms of different deployment aspects such as training time and storage costs, as reflected in Table 3. We consider the TS-SAT strategy for the fine-tuning method, as it provided the best recognition rates (see Table 2). Regarding the Adapters-based method, for each speaker we consider the adapter size that  
Figure 5: Performance comparison (WER test set) of different adapter’s sizes with respect to our proposed adaptation strategy based on the use of adapters and depending on the dataset used to estimate the VSR system. The percentage of learnable parameters offered by each adapter size is specified in Table 1. DEV and TRAIN refer to the development and training datasets, respectively.  
provided the best recognition rate, an approach we refer as _Best Adapter_. In both cases, we only consider experiments where the training dataset was used. Hence, we are able to identify why or in which situations one adaptation method might be more suitable.  
First, it should be noted that regardless of the adaptation method, the end-to-end architecture employed in our experiments presented a real-time factor of around 0.75. Regarding the performance of the VSR system, it is true that the Adapter-based method implies a significant deterioration in the quality of recognition rates. However, similar to the findings discussed in [45; 46], this method shows a dramatic training time reduction of about 80% due to the reduced number of learnable parameters. In addition, this method offers valuable properties in terms of storage cost. For our case study, instead of maintaining twenty full speaker-adapted systems (around 4.2 GB), it is only necessary to maintain the original end-to-end architecture adapted to the task and the Adapters modules estimated for each speaker under consideration. All these aspects support that Adapters can be considered a scalable and efficient solution for deployment purposes when adapting end-to-end VSR systems to specific speakers.
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were performed using IBM SPSS Statistics Version 29.0.1.0. We assessed data normality using the Shapiro-Wilk test. We performed a Mann-Whitney test to assess the baseline differences between the groups in age, height, and body mass. For the normally distributed variables, we conducted ANCOVA with age as a covariate to analyze the differences between the groups at pre- and post-intervention in terms of the biomarkers of muscle health, metabolic profile, and oxidative stress. We selected age as a covariate to adjust for its potential confounding effect. The non-parametric equivalent test (Quade ANCOVA) was used for the data that were not normally distributed. We used a paired-sample t-test (for normally distributed variables) and the Wilcoxon test (for non-normally distributed variables) to analyze the differences within the groups over time in terms of the biomarkers of muscle health, metabolic profile, and oxidative stress. We report the results as the mean \(\pm\) standard deviation (SD) for the normally distributed data and the median with interquartile range (Q1 and Q3) for the non-normally distributed data. We calculated the Hedge's \(g\) effect size for the normally distributed variables to assess the magnitude of the differences, and it was interpreted as small (\(g<0.30\)), medium (\(g\): 0.30-0.69), or large (\(g\): \(\geq\)0.70). We conducted post-hoc power analysis using G*Power software (v3.1.9.2, Dusseldorf, Germany) for the normally distributed variables using the statistical test \"ANCOVA: Fixed effects, main effects and interactions\". Power analysis included the effect size (f), an alpha level of 0.05, a sample size of 28, a numerator of 1, two groups, and one covariate. The graphs were created using GraphPad Prism version 8.0.1.
## 5 Conclusions  
To verify the validity of the developed AE source location technique based on the time difference of arrival, pencil lead break tests have been conducted on a cylindrical metal specimen. In addition, the noise effect outside the specimen has been studied by using a hammer hit. The main conclusions that can be drawn include:  
1. The validity of the developed AE source location technique was verified by PLB tests. The existing method was proven to have the least error in terms of distance from the coordinate origin but with some errors along the \(x\), \(y\), and \(z\) axes. When the PLB sources originate in the middle part of the specimen, the calculated result had higher accuracy, as compared to the other positions. Moreover, the calculated AE sources tended to be concentrated on the central part, with some errors.
2. The outside man-made noise, i.e., a hammer hit noise, had virtually no effect on this AE source location technique.  
In this study, the validity of the AE source location technique was verified by PLB tests on the metal specimen. However, in the triaxial tests, the AE sensors would move and even rotate because of shear banding failure or bugling failure. To solve this problem, the correction of the location of sensors is important, as mentioned by [PERSON] et al. [11]. While in their research, it is still considered in a simple manner, the more accurate real-time location of AE sensors during the test is desirable. Another problem is how to determine an AE source (more detailed discussion will be published in the future). In the real case, especially for sandy soils, many AE sources could originate and arrive at the sensors almost at the same time. Thus, it is very difficult to determine an AE source. In this verification test, only one PLB source was made, without considering such a difficult problem. Therefore, the problem needs further study, for example, more verification tests can be conducted by using two, three, or even more PLB sources at the same time.  
Only solving the above-mentioned problems will give a real answer as to the accuracy of the existing AE source location technique on sandy soils. Thus, the current research gives rise to further solutions, which could help to give a better understanding of the existing AE source location technique for sand specimens.  
Conceptualization, X.L. and J.K.; methodology, X.L. and A.N.; writing--original draft preparation, X.L.; writing--review and editing, A.N., Z.M., and J.K.; supervision, J.K. All authors have read and agreed to the published version of the manuscript.  
Financial assistance for this paper was provided by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT, Grant No. 181534) and the China Scholarship Council (Grant No. 201706260011) for the PhD studies of the first author.
# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Machine Learning Algorithms
#### 3.2.1 Supervised Algorithms  
The vast majority of included publications used supervised approaches for addressing problems in chronic pain research. These models rely on labelled data available for training. Most of the algorithms solved classification tasks by assigning a predefined class label to an observation. Common examples included SVMs [77; 79] and DT-based models (DT and RF) [51; 61]. The predictions made by these algorithms are relatively easy to interpret, making them attractive choices for addressing clinical problems.  
Deep learning-based models have already demonstrated their value, especially in analysing unstructured data and images. Despite their superior performance, they are still only employed to a limited extent in medical research. This was confirmed by the papers included in this review, as only four out of 53 included publications used neural net-based models [26; 36; 68; 78].  
Seven of the included publications used various forms of regression algorithms [37; 41; 52; 59; 62; 72; 80]. Instead of assigning a predefined class label, these algorithms output continuous numerical values. Such outputs may be preferred when predicting risk scores or probabilities or solving other problems requiring a continuous value rather than a class label.
## 3 Results
### Characterization of the Synthesized ZnO NPs
#### 3.1.6 DLS and Zeta Potential Analysis  
The hydrodynamic diameters, zeta potentials, and polydispersity indexes (PDIs) of the synthesized ZnO NPs were determined by DLS analysis. The size distribution histogram of DLS showed that the average hydrodynamic diameters of the ZnO NPs were measured to be 116 \(\pm\) 8.0 nm (Figure 6b). DLS measures the diameter of the particles in solution based on the Brownian motion. DLS analysis measures particle size including the hydration layer of water molecules; therefore, the average diameters of the synthesized ZnO NPs were greater than the diameters measured with FESEM and XRD analysis [35]. The ZnO NPs showed zeta potentials of \(-\)44 \(\pm\) 76 mV (Figure 6a). It is known that a high negative charge prevents aggregation of nanoparticles via electrostatic repulsion and increases the stability of particles. PDI values of the synthesized ZnO NPs were calculated as 0.452 \(\pm\) 0.092. A PDI value less than 0.7 indicates monodisperse particle size distribution [36]. It is well known that a nanomaterial's physicochemical properties, such as its size, shape, surface area, zeta potential, and composition, have a substantial impact on the results of its cytotoxicity. Considering that ZnO NPs exhibit size-dependent substantial cytotoxicity, more soluble and smaller NPs offer more useful toxicological information on nanomaterials in this context. The biological activity of ZnO-NP is significantly influenced by their shape, size, and concentration. DLS average sizes are larger than those determined from SEM images; this is likely because ZnO NPs observed in SEM are only viewed on the surface of the agglomerates. In contrast, DLS measures the particles' as three-dimensional, which might include a biomolecular coating. Such discrepancy between SEM and DLS average size has been reported previously and is related to the specifics of the method used. In  
Figure 5: FESEM analysis of bio-synthesized ZnO NPs.  
order to examine the surface charge and stability of the synthesized NPs, a zeta-potential investigation was conducted. The results revealed that the synthesized NPs have good stability, as evidenced by the emergence of a distinct peak at roughly \(-44\)\(\pm\) 76 mV. The effective stability of ZnO NPs and reduction of metal ions may be due to the extract's high protein and flavonoid content.
## 5 Experiments and Analysis
### Evaluation Metrics
#### 5.3.1 Comparison of Experiments with Different Models  
To further evaluate the classification performance of the model and verify its applicability to underwater object classification, the model in this study was compared with several representative models, including the baseline model ResNet_0, VGGNet, UNet, and Light-ResNet. Ten random experiments were conducted on the three networks separately, and the average values of the experimental results were calculated. Table 3 shows the classification accuracy and computational complexity of several models on the dataset in this paper.  
From Table 3, it can be seen that there were differences in the classification effects of the convolutional network models with different structures for underwater objects, and the classification accuracy of the model proposed in this paper was higher than that of the others. The classification accuracy of VGGNet was as high as 94.5%, which is second only to the model proposed in this paper. However, the complexity of VGGNet was high due to its full connection layer with a large number of parameters. It can also be seen from the table that VGGNet had the largest number of parameters and floating point operations. The UNet connected the features from the shallow and deep convolution layers, which could make full use of the feature information with a small number of parameters. However, it had a large computational consumption with an accuracy rate of only 86.0%. The baseline model ResNet_0 was slightly less computationally intensive than the method in this study because the DSDCN module was not added; but similarly, its classification accuracy was reduced by 7.4%. The Light-ResNet was a lightweight residual network obtained by replacing the convolutional layer of the ResNet with a depthwise separable convolution. The number of parameters was reduced, compared to the original model, but the classification accuracy was also reduced, to a large extent. The above analysis shows that the method in this study was more advantageous in underwater object classification.
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 3 Rainfall Similarity Search Based on NDCG-IPSO
### Image Similarity Search Based on NDCG-IPSO  
The precipitation image has different features. Hence, it may lose other feature information and reduce the accuracy of similarity retrieval if it applies a single feature to represent and measure the similarity of the precipitation image. Therefore, how to fuse the distances of the multiple features to represent the comprehensive distance between two precipitation images is becoming a necessary and urgent task. Given two precipitation images \(A\) and \(B\), let \(D_{P}\), \(D_{R}\), and \(D_{C}\) represent the regional precipitation distance, precipitation distance, and precipitation center distance between two images, respectively, the fusion distance \(D\) between two images \(A\) and \(B\) can be calculated as follows:  
\[D=\gamma_{1}D_{P}+\gamma_{2}D_{R}+\gamma_{3}D_{C} \tag{9}\]  
where \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) are undetermined coefficient weights for the distance of regional precipitation, precipitation distribution, and precipitation center.  
There are three kinds of methods namely the subjective weight method, objective weight method, and subjective-objective comprehensive weight method, to determine the undetermined coefficient. Subjective weighting relies on expert's experiential knowledge, leading to subjectivity and variability. Objective weighting depends on the problem domain  
Figure 4: The distance between \(R_{A(i,j)}\) and \(R_{B(i,j)}\) of two precipitation distribution matrixes.  
and sample data, but its results are poorly interpretable with low persuasiveness. Integration weighting methods can combine subjective and objective features, compensating for the shortcomings of both approaches [25].  
The NDCG-IPSO is a new subjective-objective comprehensive weight method proposed to improve the efficiency of precipitation image similarity searches, which uses IPSO to adjust the weight of multiple indicators to make the evaluation results close to the evaluation results by experts based on subjective experience, and then applies the NDCG as indicators to evaluate the image search results weighted by multiple features. The process of the NDCG-IPSO is shown in Figure 5. The method combines the advantages of the objective weighting method and the subjective weighting method and makes the weighting result meet the requirements in the way of fitting and approximation.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Case Design and Performance Metrics  
In experiments, this study designed two cases of simulations, which are the whole day simulation and the traffic accident simulation, respectively. Case 1 is the whole day simulation. This study obtained the peak and off-peak of traffic flow from this case. Through the variation of actual traffic flow, this study analyzed the use of channels and handover occurrence. Case 2 is the traffic accident simulation. This study simulated the vehicle movement and communication behavior before and after traffic accident. In Case 2, this study analyzed the surging amount of traffic flow and discussed the relation of traffic flow and the number of handovers.
## 4 An Integer Programming Formulation
### Elementary Experiments and the Results  
We solve the integer program formulated so far by a solver to obtain a solution and to examine the solution time. We use IBM ILOG CPLEX 12.2 [19] as a solver, which is installed on a single PC of Intel Pentium Dual E2200 processor (2.2 GHz) with 1 GB RAM. Since the computational time spent for solving a fixed formulation by a solver on a same computer is always the same in principle, we adopt the one by a single computation throughout our experiments.  
We performed experiments for challenge numbers 5, 10, 15, 20, 25 and 30. Remind here that we give as an input an appropriate board on which it has a Tantrix solution. Table 1 shows its computational time and the number of loops in a solution for each challenge number. Figure 9 depicts the obtained solution for challenge number 10. We see that we can solve our formulation within 3 seconds up to challenge number 25. For challenge numbers 30 and 35, on the other hand, it requires approximately 100 and 13,000 seconds, respectively, and we can confirm that the computational time increases drastically. We can imagine that it is difficult to obtain solutions more than these challenge numbers under our current formulation.  
As we mentioned, elementary constraints C1-C4 cannot eliminate subloops. In fact, for challenge numbers 20, 25 and 35, their solutions include subloops. We show in Figure 10(a) a board used for challenge number 20, and in Figure 10(b) the obtained solution. Hence, we have to eliminate these subloops to obtain a Tantrix solution.  
**Figure 10. (a) A board of size 20 used for challenge number 20; (b) a solution obtained by solving a formulation using C1-C4; including 2 subloops of its designated color blue, where an orange line is a cut defined by them; and (c) a final solution obtained by solving a formulation by adding C5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \
\begin{tabular}{c} **constraints** \\ **challenge**\# \\ \end{tabular} &
\begin{tabular}{c} **C1, C2, C3, C4** \\ **time** \\ \end{tabular} \\ \hline \(5\) & \(0.02\) & \(1\) \\ \(10\) & \(0.06\) & \(1\) \\ \(15\) & \(0.27\) & \(1\) \\ \(20\) & \(2.23\) & \(2\) \\ \(25\) & \(2.28\) & \(2\) \\ \(30\) & \(98.88\) & \(1\) \\ \(35\) & \(12,944.06\) & \(2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational time (s) of solving IP formulations using constraints C1, C2, C3 and C4, and the number of loops in each solution.  
Figure 9: The solution for challenge number 10, where we use a board in Figure 6.
## 4 Numerical Analysis for Uncertain Travel Time
### Data Analysis

Table 3: Number of goods transportation companies, statistics from December 2022. For our study, we utilized the extensive and up-to-date travel time data available on Google Maps. This allowed us to capture the dynamic nature of traffic patterns in Casablanca. Considering real-time traffic conditions, we were able to analyze travel times between the depot and the customer in four different scenarios, namely, \"Morning peak hour\", \"Inter-peak\", \"Evening peak\", and \"Off-peak\". Additionally, we accounted for two distinct states of traffic: \"recurrent congestion\", representing normal traffic conditions, and \"non-recurrent congestion\", which corresponded to unpredictable traffic fluctuations.