esa-sceva/satcom-chunk-collection huggingface dataset:
# A Preliminary Study of Model-Generated Speech
## 3 Method
### Three Models
#### 3.2.3. Sequitur  
Sequitur is a data-driven translation tool originally developed for grapheme-to-phoneme conversion by [39]. It is based on the joint source-channel model. The joint source-channel model was first introduced by [PERSON] et al. (2004). When given paired sequences \(X\) and \(Y\), where \(x\) and \(y\) are representative of their segment units, the conversion process seeks to find the alignment for the subsequences of the input string \(X\) and the output string \(Y\). This can be represented for an n-gram model as the following:  
\[\begin{array}{rcl}P(X,Y)&=&P(x_{1},x_{2},\ldots,x_{k},y_{1},y_{2},\ldots,y_{ k})\\ &=&P(<x_{1},y_{1}>,\ldots,<x_{k},y_{k}>)\\ &=&\prod\limits_{i=1}^{k}P(<x,y>_{i}|<x,y>_{i-n+1}^{i-1})\end{array} \tag{2}\]where \(k\) is the number of alignment units, and \(P(X,Y)\) is the joint probability of the \(i\)th alignment pair, which depends on \(n\) previous pairs in the sequence.  
In this study, we applied three different models to learn changes in Chaoshan pronunciation. Although these three models are machine learning-based, the design of their architectures is different. For the DirecTL+ model, the alignments between the sources and targets must be completed before training the string transduction model. Thus, the settings of the m2m aligner, such as the maximum number of tokens that can be aligned, affect the performance of the alignments and propagate to the DirecTL+ model. The Sequitur model is different from many translation models, such as DirecTL+, as it is able to train a joint n-gram model from unaligned data. Additionally, the model can be built up cumulatively. The higher order n-grams are trained iteratively from the smaller ones--first, a unigram model is trained, this is then used for a bi-gram model, and so on.  
The seq2 seq model takes another perspective to deal with this problem. Instead of learning the alignment or correspondence between the subsequences of the input and output strings, it adopts a neural network to encode all of the input strings into a vector, which condenses and embeds all its information. Then, another neural network is used to decode this vector to produce the output. This architecture can consider the whole input string, which means that the seq2 seq model can learn more constraints across the entire input sequence, not just the adjacent tokens.
# Forecasting Irregular Seasonal Power Consumption.
## 3 Results
### Application of Discrete Seasonality Models (nHWT-DIMS)  
The application of discrete seasonality carries with it a differentiated strategy. The use of nHWT-DIMS models makes it possible to model localized seasonality at specific instants of time, independently of other seasonality. In Figure 12, we show two different periods for the series. In addition to the seasonal pattern described at the beginning (of one hour), a new pattern can also be observed in Figure 12a, whose length is established at 27 time units (2 h and 42 min). This pattern is framed between two dashed lines including the demand peaks. Figure 12b shows another seasonal pattern that has the same length, but a different behavior. These two patterns will be called DIMS a and DIMS b. The appearance of each discrete seasonality does not occur on a regular basis. This situation causes the recursion required in the Holt-Winters models to be variable. This is indicated in Figure 12 by the lines with arrows. The solid lines indicate the recursion for the DIMS a, and the dashed lines indicate it for the DIMS b. The information regarding the DIMS is organized in Table 4. This table includes the locations of the discrete seasonalities on every appearance (starting and ending time when the DIMS is defined, used in the variable \(t_{h}^{*}\)) and the associated recursivity in minutes, which corresponds to \(s_{h}^{*}\). As an example, the time interval when the second appearance of DIMS a is defined starts at 04:00 pm on the 14 th and ends at 06:42 pm on the 14 th. The recursivity \(s_{h}^{*}\) during this interval is 618 min. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Arguments** & **Parameters** & **Fit RMSE** \\ \hline \multirow{5}{*}{BATS (0.717,0, 5,2, 10)} & \(\lambda\ =\ 0.717,\,\alpha\ =\ 0.120,\,\gamma\ =\ 0.206,\) & \\  & \(\delta\ =\ -\ 0.004,\) & \\ \cline{1-1}  & \(\phi_{1}\)= 0.945, \(\phi_{2}\ =\ -\ 0.625,\,\phi_{3}\ =\ 0.022,\) & 46.77 \\ \cline{1-1}  & \(\phi_{4}\ =\ 0.104,\,\phi_{5}\ =\ -\ 0.500,\) & \\ \cline{1-1}  & \(\theta_{1}\ =\ -\ 0.153,\,\theta_{2}\ =\ 0.515.\) & \\ \hline \multirow{5}{*}{TBATS (0.756,0, 5,2, [10,1])} & \(\lambda\ =\ 0.756,\,\alpha\ =\ 0.862,\gamma\ =\ 0.105,\,\delta_{1}\ =\ -\ 0.0004,\) & \\ \cline{1-1}  & \(\phi_{2}\ =\ 0.0001,\) & \\ \cline{1-1}  & \(\phi_{1}\)= 1.293, \(\phi_{2}\ =\ -\ 0.552,\,\phi_{3}\ =\ -\ 0.359,\,\phi_{4}\ =\ 0.227,\) & 45.44 \\ \cline{1-1}  & \(\phi_{5}\ =\ -\ 0.1968,\) & \\ \cline{1-1}  & \(\theta_{1}\)= \(-\ 1.358,\,\theta_{2}\ =\ 0.783.\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SSM models, their parameters, and fit RMSE for fitted results. The general model described by Equations (21)-(25) now results in the Equations shown in (27)-(32), with one seasonality of length ten time units and two DIMS as described in Table 4.  
\[l_{t}=\alpha\left(\frac{y_{t}}{l_{t-s_{10}}^{(10)}D_{l_{2}^{a}-s_{2}^{a}}^{(a)}D_ {l_{t}^{b}-s_{2}^{b}}^{(b)}}\right)+(1-\alpha)\ (l_{t-1}+\ b_{t-1}), \tag{27}\]  
\[b_{t}=\gamma(l_{t}-l_{t-1})+(1-\gamma)b_{t-1}, \tag{28}\]
###### Abstract
(4) Building upon single-step prediction, multi-step prediction is introduced to enhance the scheduling efficiency of the control frames in bandwidth allocation and to optimize the utilization of network resources.
# Hydrogen vs. Batteries: Comparative Safety Assessments for a High-Speed Passenger Ferry
## 6 Results and Discussion
### Hydrogen Version
#### 6.1.2 Scenario 2: Leakage Diameter 16 mm at the Inlet of the Bottom Right Stack  
The hydrogen release rate (Q\({}_{\text{gas}}\)) for a leakage of 16 mm was calculated on the basis of Equation (2): Q\({}_{\text{gas}}\) = 0.1493 m\({}^{3}\)/s.  
To achieve 30 ACH, the total exhaust flow rate needs to be 0.16 m\({}^{3}\)/s. Leakage was detected at 1.9 s by the middle sensor and stopped 1 s later (Figure 10a). Maximum hydrogen concentration levels reached 7.5%, which was within the flammability range (Figure 10b). Flammable masses were mostly concentrated near the ceiling, due to hydrogen's highly buoyant nature, and released after 30 s with 30 ACH (Figure 10c,d). Since flammable masses were near the top for a few seconds, the fire risk was lower, considering that there were fewer ignition sources near the ceiling and higher ignition probability near the stacks. However, the minimum ignition energy of hydrogen is very low, and therefore in case of ignition, ventilation systems need to be shut off and a gaseous fire suppressant (Novec1230) should be used. The deflagration to detonation transition (DDT) phenomenon was considered unlikely since there was a limited run-up distance in the confined fuel cell room, which had a volume of 19.19 m\({}^{3}\)[20].  
By increasing the ventilation rate from 30 to 60 ACH, flammable masses were released much faster at 18 s, resulting in lower ignition risk (Figure 11). Maximum hydrogen concentration was also reduced from 7.5 to 7%.  
Figure 10: Leakage diameter 16 mm at the inlet of the bottom right stack: (**a**) detection at 1.9 s; (**b**) concentrations at 3.8 s; (**c**) concentrations at 30 s; (**d**) concentrations at 40 s.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 4 Design Analysis
### Stability Analysis  
Stability analysis is conducted by roll stability study. The result of the roll stability test is presented in Figure 5.  
From the obtained result in Figure 5 at the initial condition, a 5 kg load is put on the right side of the USV. The USV will be tilted to the right by 12.66\({}^{\circ}\) then it will be tilted to the left by 0.36\({}^{\circ}\). Afterward, on the second roll, the USV is tilted to the left by 1.97\({}^{\circ}\) and next tilted to the right 0.21\({}^{\circ}\) until it reaches a stable position or at around 0\({}^{\circ}\). The roll reduction between the first roll and the second roll is 86.5%.  
In terms of the graph analysis using the logarithmic decrement method is acquired, with the value of damped period (Td) around 0.5 s, damped natural frequency (\(\omega\)d) is 12.56 rad/s, and natural frequency (\(\omega\)n) is 12.63 rad/s. The calculated damping ratio is 0.11. To conclude, this USV is moderately stable even though the damping ratio is quite small.
## 3 Results and Discussion
### Cr(VI) Adsorption Experiments
#### 3.1.3 Effect of the Temperature  
The temperature at which the process is carried out could improve or hamper the adsorption process. The adsorption percentage as a function of the different temperatures used is shown in Figure 4. As can be seen, an increase of the temperature resulted in a drastic decrease of Cr(VI) removal from the solution. The adsorption efficiency decreased with the temperature due to the probable decrease in ion mobility and its tendency to be adsorbed onto the activated carbon surface, as well as the increase of temperature decreasing the activity of the binding sites on the adsorbent surface [38]. Thus, the calculated adsorption percentages varied between 45% (at 20 \({}^{\circ}\)C) and 3% (at 60 \({}^{\circ}\)C). These results reveal that an increase of the temperature hampered the Cr(VI) adsorption process from the solution onto the activated carbon.
## 4 HMBAC Design and Implementation
### HMBAC Architecture Design
As the data are encrypted with ABE and the decryption process requires partial decryption through the corresponding DBC, the system enforces data access through the HMBAC system. Note that CA management at the stakeholder level is managed outside our system and our main goal is to offer credential interoperability, i.e., credentials issued from independent authorities are mutually trusted, without assuming a globally trusted root authority. In the following subsection, we describe in detail the services provided by each building block.
## 2 Materials and Methods
### Fatigue Protocol and Biochemical Analysis  
The effort was performed on the Cyclus 2 ergometer (RBM elektronik-automation GmbH, Germany) with the athlete's own bike installed (the same on which he trained and competed in the race). The ergometer was calibrated in accordance with the manufacturer's recommendations. Before the effort, the participant performed a 5 min warm-up with a load totalling 1.2\(\%\) of body mass. The pedalling rate during the warm-up equalled 90 rpm. During the final 3-5 s of the second and fourth minutes of the warm-up, the athletes carried out maximal accelerations. Two minutes after the warm-up, the maximal 30 s all-out effort with stationary start was performed. The resistance applied during the test was set at 10\(\%\) body weight [31]. At the \"go\" command, the task of the subject was to obtain the highest-possible pedalling rate as quickly as possible and then maintain it in a seated position until the end of the effort (with strong verbal encouragement). During the effort, peak power, time to obtain peak power, mean power, and minimal power were registered. Fatigue index was calculated in the following manner: ((peak power \(-\) minimal power) \(\div\) (peak power)) \(\times\) 100\(\%\)[32]. After effort, cyclists were pedalling 2 min without load with frequency 50-60 rpm. During the 3 rd and 6 th minutes following the effort, 20 \(\upmu\)L of blood were collected by medical staff from the fingertip for measurement of blood lactate concentration (BL) with the Super GL2 device (Dr. Muller Geratebau GmbH, Freital, Germany), employing the enzymatic amperometric electrochemical technique. The analyser was calibrated before each series of obtaining samples.
## 3 Results and Discussion
### Implications for Water Treatment and Environmental Remediation
However, the immobilization potential of such Me ions by GP has to be evaluated in future studies, with particular focus on near-neutral and acidic aqueous media where Me ion immobilization by GP is crucial. Further studies should also consider the impact of temperature, (ad)sorption kinetics, presence of competing ions in solution and renewability/stability of GP under certain conditions in order to make progress in yet untested geopolymer applications in diverse water treatment scenarios at full-industrial scale. The use of Cu\({}^{2+}\)-substituted geopolymers is increasingly considered as a novel, efficient technology to enhance the antimicrobial properties of building and construction materials [68]. These chemically resistant aluminosilicate structures may even withstand the aggressive conditions frequently met in wastewater systems [33]. They exhibit good antimicrobial effects on Cu\({}^{2+}\)-treated geopolymer surfaces and a sufficient in vitro stability in the long term [69; 70]. [PERSON] et al. [70] have demonstrated that the antimicrobial activity of metakaoin-based geopolymers against fungi hyphae can be increased by the incorporation of Cu\({}^{2+}\) into the geopolymer structure. Our results proved that this chemical substitution can reach CuO/Al\({}_{2}\)O\({}_{3}\) ratios of 0.3 \(\pm\) 0.1 in the GP matrix under the experimental conditions used in this study. The presence of additional CuO/Cu(OH)\({}_{2}\) phases  
Figure 6: Photo documentation of the efficiency of Cu\({}^{2+}\) removal from solution by GP. (**a**) Dark blue Cu\({}^{2+}\)-containing solution at the beginning of experiment Cu:GP\({}_{\text{PH}}\)-1:2; (**b**,**c**) Precipitates obtained at the end of experiments Cu:GP-1:2 and Cu:GP\({}_{\text{PH}}\)-1:2; (**d**) Transparent solution at the end of experiment Cu:GP\({}_{\text{PH}}\)-1:2 with a Cu\({}^{2+}\) concentration below international guideline values. on geopolymer surfaces and within fine pores could further increase their bactericidal activity (Figure 3), as indicated by a biological study using S. aureus and E. coli bacterial strands treated with Cu(OH)\({}_{2}\) nanoparticles and mixed Cu\({}_{1-x}\)Mg\({}_{x}\)(OH)\({}_{2}\) nanorods, i.e., a bacterial cell reduction of >99.99% was observed after 180 min at room temperature [64]. Thus, GP has a high potential to be used for advanced and large-scale water treatment and purification studies after successful testing and calibration against latest wastewater purification technologies [71; 72; 73; 74; 75].
In recent years, scientific research for susceptibility analysis of GE, and work on the statistical relationships between GEVs and the spatial distribution of gullies, have been addressed using various statistical and machine learning methods including bivariate statistics (BS) [1], weights-of-evidence (WoE) [13], index of entropy (IofE) [8], logistic regression (LR) [22; 23; 24; 25; 26], information value (IV) [24; 25], random forest (RF) [27], bivariate statistical models [28; 29], maximum entropy (ME) [30; 31], frequency ratio (FR) [28], analytical hierarchy processes (AHP) [29], artificial neural network (ANN) [12; 31], support vector machine (SVM) [31], and boosted regression trees (BRT) [12]. For this purpose, various GEVs such as topography (e.g., elevation, slope, aspect, plan curvature, profile curvature, slope length), lithology, land use, soil properties (e.g., soil texture, soil type, erosivity, soil water content), land use, climate (rainfall intensity, rainfall period, and spatial distribution of rainfall), infrastructures (road, bridge) and hydrology (e.g., TWI, SPI, drainage density) were used. A comprehensive literature review shows that there are still dimensions that require further research, and that a large number of potentially useful methods have not yet been fully implemented to provide GE susceptibility maps. The main objectives of this study are: (i) To determine the relationship between gully occurrence and conditioning factors using Weights-of-Evidence Bayes theory, (ii) assessing the capability of RF, MARS, and BRT data mining/machine learning models to predict GE susceptibility; and (iii) validation of models using the AUC curve and SCAI methods. Studyof the research background showed that using MARS, BRT, and RF data mining models in GE zonation is very new. It will help managers in future planning to prevent human intervention in sensitive areas.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 2 Related Work
### Skeleton-Based Action Recognition Using Graph Convolutional Networks  
GCNs were initially proposed by [PERSON] et al. [29], whose core concept involves treating graph nodes and edges as local neighborhoods, aggregating features of each node through weighted convolution operations to extract spatial structural information.  
In the scenario of skeleton-based action recognition, joints are treated as graph nodes, while connections between joints correspond to edges derived from the physical topology of human skeletons, resulting in the adjacency matrix. Because GCNs effectively leverage such topological structure information, they can derive highly discriminative node features. Compared to traditional CNN- and RNN-based methods, GCN-based approaches demonstrate superior capabilities in processing high-dimensional structured data.  
[PERSON] et al. [21] first introduced the Spatial-Temporal Graph Convolutional Network (ST-GCN) to model skeleton sequences, significantly improving recognition accuracy. With the advancement of GCNs, several improved methods have emerged. For example, Dynamic GCN integrates dynamic graph structures that adaptively modify joint connections, effectively capturing features specific to different actions [30]. Additionally, attention mechanisms were introduced into GCNs, such as Graph Attention Networks (GATs), which dynamically learn node importance within graph structures, assigning varying attention weights to neighboring nodes and efficiently aggregating information from neighborhoods of variable sizes [22]. Furthermore, Info-GCN integrates an attention mechanism within GCN frameworks, enhancing the model's ability to focus on critical joints, thereby improving fine-grained action recognition performance [14]. AT-Shift-GCN proposes a lightweight graph convolutional network that integrates non-shared topology structures  
Figure 1: Comparison between (**a**) the baseline adjacency matrix and (**b**) the proposed dynamic adjacency matrix.  
with attention-guided multi-scale modeling. This design significantly improves the accuracy of skeleton-based action recognition while maintaining low computational cost [31].
## 4 Results and Analysis  
### Results of Optimal Asphalt Binder Content  
The stability, flow value, gross volume relative density, void ratio (VV), voids in mineral aggregate (VMA) and voids filled with asphalt (VFA) corresponding to five kinds of asphalt-aggregate ratios of the heat-recycled asphalt mixture with 40% RAP content were calculated, and the calculation results are shown in Figure 4.  
According to the asphalt binder content of RAP sections and the mix ratio of RAP sections, it could be calculated that the content of the old asphalt was about 1.7%. Therefore, the optimal content of the new asphalt in the heat-recycled asphalt mixture with 40% RAP content was 2.24%.  
Figure 4: (a) Stability results of 40%HMA; (b) flow value results of 40%HMA; (c) gross volume density results of 40%HMA; (d) void ratio results of 40%HMA; (e) voids in mineral aggregate results of 40%RHMA; (f) voids filled with asphalt results of 40%HMA.  
Next, we calculated the stability, flow value, gross volume relative density, VV, VMA and VFA corresponding to the five asphalt-aggregate ratios of the waste engine oil, recycled asphalt mixture with 40% RAP. The calculation results are shown in Figure 5.  
According to the asphalt binder content of the RAP sections and the mix ratio of the RAP sections, it could be calculated that the content of the old asphalt binder was about 1.7%. Therefore, the optimal content of the new asphalt binder in the waste engine oil, recycled asphalt mixture with 40% RAP was 1.98%.  
Carbon nanotubes have adsorption effects on light oil, so they also affected the optimal asphalt content in the carbon-nanotube-modified, waste engine oil, recycled asphalt mixture. We calculated the stability, flow value, gross volume relative density, VV, VMA and VFA corresponding to five asphalt-aggregate ratios of the carbon-nanotube-modified, waste engine oil, recycled asphalt mixture with 40% RAP. The calculation results are shown in Figure 6.  
According to the mix ratio of the RAP sections and the asphalt binder content of the RAP sections, it could be calculated that the old asphalt binder content was about 1.7%.  
Figure 5: (**a**) Stability results of 40%RHMA; (**b**) flow value results of 40%RHMA; (**c**) the gross volume density results of 40%RHMA; (**d**) void ratio results of 40%RHMA; (**e**) voids in mineral aggregate results of 40%RHMA; (**f**) voids filled with asphalt results of 40%RHMA.  
Therefore, the optimum new asphalt binder content in the carbon-nanotube-modified, waste engine oil, recycled asphalt mixture with 40% RAP was 2.25%. It could be seen that the carbon nanotubes could adsorb part of the oil in the waste engine oil, recycled asphalt binder, resulting in a slight increase in the amount of new asphalt in the recycled asphalt mixture.
## 4 Conclusions  
The following main conclusions can be drawn based on the experimental analysis and evaluation of the results:  
* Comparison of core flood pressure-drop data with rheometer and e-VROC(r) pressure data has provided insightful information about turbulence-dominated excessive pressure drop occurring inside porous media. The existence of this excessive pressure drop in porous media will help to understand pore-scale mechanisms in reservoirs.
* Deep inside the reservoir, flow dynamics of polymer solutions could be predicted by using the approach here presented (linear viscoelastic part). However, around the injection and production wells, elongational/extensional measurements should be incorporated to completely understand the flow regimes in porous media.
* Reservoir semi-harsh conditions, at a high temperature and with an increased amount of divalent cations, significantly counter the designed viscoelastic properties of polymers. Extensional, steady shear viscosity and oscillatory measurement were used to investigate the impact of semi-harsh conditions on polymer elastic properties. The activity of divalent ions at high temperatures makes it a worse condition for polymers to hold good viscoelastic properties. However, low salinity pre-flush as a water-softening technique before polymer flood can help to mitigate this issue.  
Figure 16: Oil recovery and pressure drop versus pore volume (PV) injected for micromodel at 22 \({}^{\circ}\)C. Synthetic seawater (SW) injection is performed as the secondary mode for both flooding experiments.  
Figure 15: Oil recovery of seawater injection reproducibility at 22 \({}^{\circ}\)C. SSW-1/SSW-2 represents the oil initialization, while BF-1/BF-2 represents after 5 PV injected brine [3].  
* Comparison of rheological characterization before core flood and effluent showed that the primary loss of viscoelastic properties occurs at the sand face of injection, well within a radius of a few inches.
* Results of oil recovery factor conclude that brine salinity is a more sensitive parameter, as compared to brine hardness, in focus of shear flow. Higher salinity and lower hardness can challenge the polymer target shear viscosity and influence the sweep efficiency in porous media.  
[PERSON] performed experiments and defined methodology; [PERSON] worked on conceptualization and results validation; and [PERSON] reviewed and performed editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge support given by the Open Access Publishing Fund of Clausthal University of Technology.  
The authors declare no conflict of interest.