esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement Uncertainty  
The value of the expanded measurement uncertainty (U\({}^{\prime}\)) was determined by applying a coverage factor of k = 2 (at a confidence level of 95%) to the combined measurement uncertainty (u\({}^{\prime}\)), as indicated in Equation (2). The combined measurement uncertainty (u\({}^{\prime}\)) occured as a contributionof the uncertainty component for the bias (u\({}^{\prime}\)(bias)) and the uncertainty component for the precision (u\({}^{\prime}\)(precision)), as represented by Equation (3).  
Residue results do not need to be adjusted for method bias if the mean bias is less than 20%, and the default expanded measurement uncertainty of 50% is not exceeded. If the bias exceeds 20%, it is possible to mathematically adjust the result by employing a recovery factor. In this scenario, the initial result acquired from the analysis of the relevant pesticides was multiplied by the recovery factor, which was calculated as 100% divided by the recovery percentage.  
The initial estimation did not account for recovery correction, as outlined in Equation (4), whereas the subsequent estimation integrated recovery correction, as shown in Equation (5). Equation (6) was employed to assess the u\({}^{\prime}\)(precision), which was determined by computing the within-laboratory reproducibility RSD\({}_{\text{wR}}\) of the pesticides.  
\[\textbf{U}^{{}^{\prime}}=\textbf{k}\times\textbf{u}^{{}^{\prime}} \tag{2}\]  
\[\textbf{u}^{{}^{\prime}}=\sqrt{\textbf{u}^{{}^{\prime}}(\textbf{bias})^{2}+ \textbf{u}^{{}^{\prime}}(\textbf{precision})^{2}} \tag{3}\]  
\[\textbf{u}^{{}^{\prime}}(\textbf{bias})=\sqrt{\textbf{mean}_{\textbf{bias}}^ {2}+\textbf{SD}.\textbf{P}_{\textbf{bias}}^{2}} \tag{4}\]  
\[\textbf{u}^{{}^{\prime}}(\textbf{bias})=\frac{\textbf{RSD}_{\text{wR}}}{ \sqrt{\textbf{N}}} \tag{5}\]  
\[\textbf{u}^{{}^{\prime}}(\textbf{precision})=\textbf{RSD}_{\text{wR}} \tag{6}\]  
The mean bias error is the arithmetic mean of the bias, SD.P\({}_{\text{bias}}\) denotes the standard deviation of bias in the population, RSD\({}_{\text{wR}}\) signifies the reproducibility of recovery within the laboratory, and N denotes the number of recovery tests conducted. Typically, the expanded measurement uncertainty (MU) should be lower than the 50% threshold commonly used in the SANTE guidelines.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### World Size and Simulation Time
#### 3.1.2 Movement Models
The simulation space could be divided into small cells to provide the boundaries. A modified version allows for the destination to be anywhere along the path of travel. In the boundless simulation area model originally presented by [PERSON] (1997)[PERSON] (1997), the movements are random, and it considers previous velocity, as well as the associated acceleration. The operation of the model at the borders of the world is novel, that is, when a node \"leaves\" the world at a border, it \"re-appears\" on the other side of the world. This results in a torus-shaped world, which is not realistic in worlds where size and shape are relevant to MANETs. The Gauss-Markov model presented by [PERSON] and [PERSON] (2003) [PERSON] and [PERSON] (2003) assumes that there is some relationship between a node's future movement and its present movement. Thus, the node's location at a given time is determined through a probability function based on its last location and velocity. [PERSON] (2012) [130] present a mobility prediction model based on Gauss-Markov and used learning automata for a boundless simulation area with no obstacles. [PERSON] et al. (2011) [131] develop a Markov trace model to analyze mobility in complex mobility systems (e.g., vehicular-mobile systems) using discrete mathematics. A limited attempt is made to relate Markov models to real-world situations. As previously mentioned, most papers that mention Gauss-Markov compare it with other mobility models.
## 4 Results
### Different Droop Functions in Active Power Curtailment Method
#### 4.1.3 Exponential Droop  
This case has droop-based controllers in PV inverters driven by an exponential droop function. Figure 10 presents the voltage profile of the twelve houses and power curtailment after using APC method using the exponential function. The maximum voltage at houses 9 to 12 is 1.057 p.u., which is less than \(V_{cri2}\). Houses 9 to 12 do not experience overvoltage at any time during the 24 h period. The maximum power curtailment is 1.92 kW for houses 11 and 12, which is slightly less than that in APC method with quadratic droop. The curtailments from house 7 to 12 are the lowest in this method compared to above droop-based APC techniques as the function has the lowest curtailment for the same voltage difference among the three techniques. The energy loss in APC method with exponential droop is 12.40 kWh.
## 3 Numerical Simulations
### External Flows  
Cylindrical structural bodies are common in nature and widely used for engineering purposes. Therefore, many studies on the fundamentals of flow around a single circular cylinder have been conducted because the existence of the cylinder results in complicated flows, vortex shedding, and changes in the hydrodynamic forces acting on it, which are significantly affected by **Re**. Cylindrical structures can be found not only singly but also in groups such as fuel rods in nuclear reactors and risers of offshore plants. It can be expected that the flows are more complex in these cases owing to the interactions among them. The arrangement of two circular cylinders, which is the simplest case of a group or multiple structures, can be classified as tandem, side-by-side, or staggered ones. Previous  
\begin{table}
\begin{tabular}{c c c c} \hline  & **In-House Code** & **icoFoam** & **Proposed** \\ \hline Discretization & FVM & FVM & WMLS \\ Grids & Structured & Unstructured & N/A \\ Variable Arrangement & Staggered & Collocated & Collocated \\ Algorithm & HSMAC & PISO & Fractional Step \\ Elapsed time (1 core) & 49.55 & 63.87 & 49.98 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of computational time (Cavity flow, **Re** = 100).  
Figure 5: Contour maps of \(u\) for **Re** values of (**a**) 100, (**b**) 400, and (**c**) 1000.  
Figure 6: Comparison of velocity profiles (**a**) \(u\) and (**b**) \(v\) along the vertical and horizontal center planes for different Reynolds numbers.  
studies have found that the flow patterns are dependent on the **Re** and ratio of the distance between the centers of the two cylinders with respect to the diameter (D) of the cylinder. It has been reported that the flow characteristics and hydrodynamic forces change suddenly at a critical spacing ratio, as summarized in [33].  
In this section, the simulation results for flows around a circular cylinder under two **Re** values and two circular cylinders in tandem arrangement under a fixed **Re** value with various distances between cylinders are presented. The reason for selecting the tandem arrangement for the two cylinders is that the flows are more complicated, and a sudden drag jump is expected.
# Iteratively Refined Multi-Channel Speech Separation
## 4 Results
### Analysis of Iterative Results
In addition, each additional processing leads to an increase in the real-time factor (RTF) of the model, thereby increasing the data processing time. Considering this, the output of post-separation after the first iteration was used for evaluating the results of our model in comparison with other methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & \multirow{2}{*}{**RTF**} & \multicolumn{2}{c}{**SDR**} & \multicolumn{2}{c}{**SI-SDR**} & \multicolumn{2}{c}{**SIR**} & \multicolumn{2}{c}{**PESQ**} & \multicolumn{2}{c}{**STOI**} \\ \cline{3-10}  & & \(\hat{z}_{q}^{(n)}\) & \(\hat{y}_{q,1}^{(n)}\) & \(\hat{z}_{q}^{(n)}\) & \(\hat{y}_{q,1}^{(n)}\) & \(\hat{z}_{q}^{(n)}\) & \(\hat{y}_{q,1}^{(n)}\) & \(\hat{z}_{q}^{(n)}\) & \(\hat{y}_{q,1}^{(n)}\) & \(\hat{z}_{q}^{(n)}\) & \(\hat{y}_{q,1}^{(n)}\) \\ \hline
0 & 0.024 & - & 14.41 & - & 13.91 & - & 30.67 & - & 4.13 & - & 0.98 \\
1 & 0.049 & 18.79 & 21.84 & 17.08 & 21.13 & 27.21 & 30.67 & 3.93 & 4.13 & 0.99 & 0.98 \\
2 & 0.074 & 22.07 & 24.17 & 20.95 & 23.48 & 33.21 & 33.21 & 3.93 & 4.23 & 0.99 & 0.99 \\
3 & 0.104 & 22.10 & 24.48 & 21.07 & 23.84 & 33.23 & 33.93 & 3.99 & 4.25 & 0.99 & 0.99 \\
4 & 0.125 & 22.21 & 24.91 & 21.20 & 24.26 & 33.80 & 34.36 & 3.98 & 4.26 & 0.99 & 0.99 \\
5 & 0.148 & 22.31 & 24.60 & 21.34 & 23.98 & 34.05 & 34.24 & 4.00 & 4.25 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of speech separation results at different stages.
# Numerical Investigation on NOx Emission of a Hydrogen-Fuelled Dual-Cylinder Free-Piston Engine
###### Abstract  
The free-piston engine is a type of none-crank engine that could be operated under variable compression ratio, and this provides it flexible fuel applicability and low engine emission potential. In this work, several 1-D engine models, including conventional gasoline engines, free-piston gasoline engines and free-piston hydrogen engines, have been established. Both engine performance and emission performance under engine speeds between 5-11 Hz and with different equivalent ratios have been simulated and compared. Results indicated that the free-piston engine has remarkable potential for NOx reduction, and the largest reduction is 57.37% at 6 Hz compared with a conventional gasoline engine. However, the figure of NOx from the hydrogen free-piston engine is slightly higher than that of the gasoline free-piston engine, and the difference increases with the increase of engine speed. In addition, several factors and their relationships related to hydrogen combustion in the free-piston engine have been investigated, and results show that the equivalent ratio \(\varphi=0.88\) is a vital point that affects NOx production, and the ignition advance timing could also affect combustion duration, the highest in-cylinder temperature and NOx production to a large extent.  
free piston engine; hydrogen energy; combustion; NOx emission; ignition time; 1-D simulation 2023
## 3 Analysis of Abutment Pressure Evolution Law Based on Microseismic Monitoring
### Analysis of the Abutment Pressure Distribution Range  
The microseismic monitoring system arranged in the field recorded the vibration response of the working face during the mining process. In order to analyze the causes of these accidents, microseismic events were counted on a daily basis and projected onto the planar graph. The results are shown in Figure 7.  
Figure 7: Display of support pressure distribution patterns based on microseismic data analysis. (**a**) Plane projection. (**b**) Strike profile projection. (**c**) Dip profile projection.  
1. Strike abutment pressure distribution As can be seen in Figure 7, the spatial distribution of microseismic events in the working face stoping process has typical zoning characteristics, and is closely related to the transfer of the mining stress field. The mining influence range is 150 m in front of the working face, 75 m behind the working face, 120 m outside the auxiliary track channeling, and 80 m outside the belt channeling. According to the principle of microseismic event zoning (the correlation between the distribution density of microseismic events and the \"blind area\"), it can be inferred from Figure 7b that the peak position of strike abutment pressure on the working face is 65 m in front.
2. Inclined abutment pressure distribution Combined with Figure 7a,c, the lateral abutment pressure distribution characteristics of the working face can be predicted. As shown in the figure, the mining influence range on both sides of solid coal in front of the working face is wider, and the outer side of the auxiliary track channeling (120 m) is wider than that of the belt channeling (80 m). On both sides of the goaf behind the working face, the microseismic response is not obvious in the belt channeling area, but it is more obvious on the track channeling side, especially in the 80 m coal pillar and 1301 belt channeling, where a large number of microseismic events are distributed. According to the corresponding relationship between the fracture position of the upper strata and the stress of coal, it can be inferred that the peak position of lateral abutment pressure in the goaf is about 65 m away from the goaf.
#### 4.1.3 Results  
From Table 6 can be observed that structural safety is not verified for any of the partial factor approaches applied; moreover, in the case of the TS [2], the results obtained by all of the NSBs do not satisfy the safety requirements. Therefore, a second stage with updated information would be necessary. It can be also observed, from Table 6, that the results obtained for the assessment values of the effects of actions (bending moments), \(M_{E,a,1}\), show a relatively slight variation, while the assessment values of resistance, \(M_{Ra,1}\), show more significant variations. The small variation in the values of \(M_{E,a,1}\) can be explained taking into account that both the characteristic values of actions and their probabilistic models, that were used for the deduction of partial factors, have not been derived from site-specific data but were adopted according to the recommendations of [2,3,8,10,21] or NDPs of CEN member states.  
Regarding \(M_{R,a,1}\), large differences appear between the results obtained by the different CEN member states based on TS [2]; whereas the values resulting from the application of the tools developed in [9] and the two approaches proposed by [10] are quite similar. The large differences between the results for \(M_{Ra,1}\) obtained by the experts of CEN member states are mainly due to the assumptions for the prior information, the procedures that have been used for updating the models for \(f_{y}\) and \(f_{c}\), as well as the characteristic values adopted for these material properties. Particularly, in the case of the NSB3 a comparatively low value for \(M_{Ra,1}\) has been obtained. This is due to the fact that the characteristic values for \(f_{c}\) and \(f_{y}\) have been determined from the sample parameters, according to [12], without taking into account the prior information, available from the former code [20]. The second reason for this low \(M_{Ra,1}\) value was that the corresponding partial factors have not been updated and that the values for new structures, according to [22], have been applied instead. On the contrary, a much higher value of \(M_{Ra,1}\) = 197.0 kNm was obtained by the NSB6, since the test results listed in Tables 1 and 2 were ignored by considering that they are not representative. Instead, data from Table 7 (second stage) were used to determine the characteristic value for concrete compressive strength, and the characteristic value for the yield strength of reinforcement was assumed based on the available specification that was in force at the time of the building construction.
## 3 Multi-Sensors Spatio-Temporal Multi-Correlation Fusion Analysis  
### Spatial Correlation Analysis  
Literature [34] pointed out that when measuring the same signal source, different sensors would affect the consistency of space and time, which resulted in time delay and direction error. Elimination of time deviation can ensure the synchronization of data measured by heterogeneous sensors. Figure 3 shows a schematic diagram of measuring the same signal source with heterogeneous sensors.  
Where \(S(t)\) is the measured signal source, \(X(t)\), \(Y(t)\), \(Z(t)\) are the signals measured, respectively by the three sensors. It can be seen from the figure that the propagation speed is different and there is a time deviation due to the different distances between the sensors and the same signal source. In order to eliminate the influence of time deviation, it is necessary to study the spatial correlation between sensors. The space coordinate system was established with the signal source as the origin, and three s were obtained. The relationship between the signal source and the position parameters of the three sensors is shown in Table 1.  
The established signal source and sensor position distribution diagram is shown in Figure 4. Assume that there is only translation, not rotation, between the three sensors. In the space coordinate system, the position coordinates of the sensors are known. The direction distance of X, Y, Z can be regarded as the X coordinate, Y coordinate and Z coordinate of the three sensors. The spatial coordinates of three sensors are established to find out the spatial position correlation.  
The spatial coordinate matrix of the three sensors is:  
\[\left[\begin{array}{ccc}X_{1}&X_{2}&X_{3}\\ Y_{1}&Y_{2}&Y_{3}\\ Z_{1}&Z_{2}&Z_{3}\end{array}\right]=\left[\begin{array}{ccc}0&a_{2}&a_{3}\\ b_{1}&0&b_{3}\\ c_{1}&c_{2}&c_{3}\end{array}\right] \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sensor 1** & **Sensor 2** & **Sensor 3** \\ \hline X distance & 0 & \(a_{2}\) & \(a_{3}\) \\ Y distance & \(b_{1}\) & 0 & \(b_{3}\) \\ Z distance & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial location parameters of three different sensors.  
Figure 4: Position distribution of signal sources and sensors.  
Figure 3: Different types of sensors measure the same signal source.  
The coordinate transformation relationship between sensor 1 and sensor 2 and between sensor 1 and sensor 3. The position distribution of signal sources and sensors is shown in Figure 4.  
\[\left[\begin{array}{c}X_{1}\\ Y_{1}\\ Z_{1}\end{array}\right]=\left[\begin{array}{c}X_{2}\\ Y_{2}\\ Z_{2}\end{array}\right]+\left[\begin{array}{c}-a_{2}\\ b_{1}\\ c_{1}-c_{2}\end{array}\right],\left[\begin{array}{c}X_{1}\\ Y_{1}\\ Z_{1}\end{array}\right]=\left[\begin{array}{c}X_{3}\\ Y_{3}\\ Z_{3}\end{array}\right]+\left[\begin{array}{c}-a_{3}\\ b_{1}-b_{3}\\ c_{1}-c_{3}\end{array}\right] \tag{8}\]  
Equation (8) is used to unify the spatial information of the three sensors into the coordinate system of sensor 1, which can eliminate the time deviation between different sensors.
## 4 Discussion  
In this study, 15 design alternatives were derived by combining three operation methods and five in-vehicle locations for electronic gearshifts. A total of 40 respondents, varying in age and driving experience, assessed each design alternative using a 7-point Likert scale across nine evaluation measures (accuracy, efficiency, rapidity, learnability, intuitiveness, safety, preference, memorability, and satisfaction). The major findings can be summarized as follows--note that **Major findings A** and **B** pertain to the main effects of the operation method and in-vehicle location, respectively, while **Major finding C** represents the results that did not surface in the main effects but emerged through the interaction effects between the two design variables. **Major finding A. ****M\({}_{\mathbf{DR}}\)** was evaluated as significantly inferior compared to **M\({}_{\mathbf{LS}}\)** and **M\({}_{\mathbf{BP}}\)** (Figures 2 and 4). **Major finding B-1. ****L\({}_{\mathbf{LWS}}\)** and **L\({}_{\mathbf{RWS}}\)** were evaluated as significantly inferior compared to **L\({}_{\mathbf{UCF}}\)**, **L\({}_{\mathbf{LCF}}\)**, and **L\({}_{\mathbf{CC}}\)**. **L\({}_{\mathbf{LWS}}\)** was also evaluated as significantly inferior compared to **L\({}_{\mathbf{RWS}}\)** (Figures 3 and 4). **Major finding B-2. ****L\({}_{\mathbf{UCF}}\)** was evaluated as significantly inferior compared to **L\({}_{\mathbf{LCF}}\)** and **L\({}_{\mathbf{CC}}\)** (Figures 3 and 4). **Major finding C-1. ** When the operation method was **M\({}_{\mathbf{LS}}\)**, **L\({}_{\mathbf{CC}}\)** was evaluated as significantly superior compared to **L\({}_{\mathbf{LCF}}\)**. When the in-vehicle location was **L\({}_{\mathbf{CC}}\)**, **M\({}_{\mathbf{LS}}\)** was evaluated as significantly superior compared to **M\({}_{\mathbf{BP}}\)** (Figure 4). **Major finding C-2. ** When the in-vehicle location was **L\({}_{\mathbf{RWS}}\)** or **L\({}_{\mathbf{UCF}}\)**, **M\({}_{\mathbf{LS}}\)** was evaluated as significantly inferior compared to **M\({}_{\mathbf{BP}}\)** (Figure 4). **Major finding A**: the finding that **M\({}_{\mathbf{DR}}\)** was evaluated as inferior to **M\({}_{\mathbf{LS}}\)** and **M\({}_{\mathbf{BP}}\)** aligns with actual accident cases associated with the use of **M\({}_{\mathbf{DR}}\)**. For example, in 2016, an accident occurred in a vehicle equipped with **M\({}_{\mathbf{DR}}\)** when a driver intended to shift into park (P) but mistakenly engaged reverse (R)--believing the vehicle was stationary, the driver exited the car; however, since it was in R, the vehicle moved backward and tragically
## 3 Results and Discussion
### Shelf-Life Stability of Packaged Selected Pork Sausages  
Factors such as high fat content, comminuted structure, and lack of thermal processing render pork sausages susceptible to quality deterioration and spoilage by lipid oxidation and microbial contamination. In the present study, the sausage seasoning (see Section 2.2) contains antioxidant (E301--sodium ascorbate) and preservative (E223--sodium metabisulphite) agents which serve to enhance sausage quality and shelf-life.  
The shelf-life extension of processed meats such as pork sausages is complex due to the multi-functional properties of the ingredients used in products. For example, salt is used positively as a flavour enhancer and binder and has preservative effects with respect to microbial growth [50]. However, salt can also negatively impact lipid stability in processed meats by accelerating lipid oxidation, resulting in \"off\" odours and flavours [51]. Similarly, dried ground seaweeds contain a range of functional ingredients and health-promoting bioactive compounds [10], which would enhance product quality from a technological perspective, i.e., improve water binding, extend shelf life, and increase consumer appeal by presenting healthier products. Seaweed also contains minerals such as iron and copper [52], which are known to promote lipid oxidation in muscle foods.  
In the current study, several packaging formats were examined to minimise the potential negative effects of ingredient reformulation on pork sausage quality and shelf-life. Based on the sensory analysis results, the control (C25f/2s) and two seaweed-containing sausages (SS10f/0.5s and IW15f/1.0s) were stored in aerobic packaging (AP), 80% O\({}_{2}\): 20% CO\({}_{2}\) modified atmosphere packaging (MAP80/20), 70% N\({}_{2}\): 30% CO\({}_{2}\) (MAP70/30), and vacuum-packaged (VP) conditions. These packaging conditions were selected to determine a suitable packaging condition for reduced-fat and -salt sausages containing brown seaweeds.  
Shelf-life indicators examined included lipid oxidation and microbiological analysis of packaged sausages. AP is the most used packaging method utilised for sausages due to its cost effectiveness and ability to maintain sausage appearance [53]. The high oxygen in MAP80/20 is used to maintain a desirable colour, which is appealing to consumers, while CO\({}_{2}\) inhibits bacteria growth and nitrogen reduces lipid oxidation, which helps prevent rancidity and maintains the quality of meat over time [52]. VP is a packaging condition with the absence of any gas and is not widely used in sausage packaging as it can distort sausage shape, making it less appealing to consumers. However, by removing air from VP, it creates an oxygen-free environment, which is crucial for lipid oxidation reduction and retarding microbiological growth. In the absence of oxygen, lipid oxidation is inhibited, and the proliferation of aerobic bacteria and fungi is significantly slowed down, which helps delay spoilage. However, certain anaerobic bacteria can tolerate these conditions, but their proliferation is much slower compared to aerobic conditions [54; 55].