esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **E (Bq h\({}^{-1}\))** & **E (Bq h\({}^{-1}\))** & \multirow{2}{*}{**u-Test**} \\  & **Method 1** & **Method 2** & \\ \hline Lecce stone & 0.049 \(\pm\) 0.012 & 0.040 \(\pm\) 0.010 & 0.6 \\ \hline Modica stone & 0.011 \(\pm\) 0.003 & 0.007 \(\pm\) 0.002 & 1.1 \\ \hline Ignimbrite & \multirow{2}{*}{0.018 \(\pm\) 0.005} & \multirow{2}{*}{0.007 \(\pm\) 0.002} & \multirow{2}{*}{2.0} \\ Campana & & & \\ \hline Viterbo tuff & 0.072 \(\pm\) 0.018 & 0.057 \(\pm\) 0.014 & 0.7 \\ \hline Comiso stone & 0.008 \(\pm\) 0.002 & 0.005 \(\pm\) 0.001 & 1.3 \\ \hline Mendicino stone & 0.008 \(\pm\) 0.002 & 0.004 \(\pm\) 0.001 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific radon exhalation rate, together with its uncertainty, and corresponding u-test values for the analyzed samples. Figure 5: A comparative bar chart. The simulations were conducted under the conditions reported at the end of the previous section, and assuming the highest radon exhalation rates (as determined by _Method 1_), i.e., the most conservative scenario [68]. Obtained C\({}_{\infty}\) values are reported in Table 3.  
Figure 6: Radon accumulation growth curve calculated by applying the Markkanen room model in the most precautionary scenario of highest radon exhalation rate values, (i.e., as assessed by using _Method 1_) standard-sized room (4 m \(\times\) 5 m \(\times\) 2.8 m), and minimum ventilation rate (0.2 h\({}^{-1}\)) for the six investigated natural stones. It is important to highlight that, for all the investigated samples, the simulated indoor radon activity concentrations, ranged from 4.1 to 37.3 Bq m\({}^{-3}\), remain well below the threshold values established by Legislative Decree No. 101 of 31 July 2020. Specifically, the decree sets a limit of 300 Bq m\({}^{-3}\) (annual average) for existing buildings and workplaces, and a stricter limit of 200 Bq m\({}^{-3}\) for new residential constructions starting from 31 December 2024 [45]. Moreover, simulated indoor concentrations are all below the WHO recommended limit of 100 Bq m\({}^{-3}\) and the EPA action level of 148 Bq m\({}^{-3}\)[41, 69]. Given these results, it can be reasonably concluded that, under the tested conditions, no significant radiological hazard from indoor radon exposure is expected for human health. Finally, it is worth noting that the obtained results take into account limitations such as the small number of stone samples per type and the laboratory conditions that may differ from real-world humidity and temperature; nevertheless, poor ventilation or unusual construction, as well as wall thickness and building age, could raise exposure. To this aim, future investigations will be carried out to include in situ validation and a broader range of materials.
## 4 Results and Discussion
### Effect of the Height of the Retaining Wall  
When analyzing the influence of retaining wall height on the bearing properties of the supporting structure, the retaining wall heights of 2.5 m, 3.0 m, 3.5 m, 4.0 m, 5.0 m, and 6.0 m were selected for analysis and calculation, and the base elevation of the retaining wall was unchanged. The length of the support pile is 13 m, and the pile-wall spacing is 2 m.  
(1) Horizontal displacement of the USCSP  
Figure 17 shows the change curve of horizontal displacement of the USCSP under different retaining wall heights. The figure shows that the horizontal displacement of the pile gradually increases after excavation with the rise in the retaining wall's height; this is because a higher retaining wall leads to stronger soil pressure on the wall's back and higher passive resistance of the soil in front of the wall when the retaining wall remains stable. In addition, the passive resistance will react on the loaded section of the supporting pile. When excavating at 1 m, the wall height rises from 2.5 m to 6.0 m, and the pile top displacement increases from 5.8 mm to 11.0 mm, increasing by 89.7%; when excavating at 2 m, the wall height rises from 2.5 m to 6.0 m, and the pile top displacement increases from 10.9 mm to 29.4 mm, increasing by 169.7%.  
(2) Internal force of the USCSP  
Figure 18 shows the stress distribution curves of the piles corresponding to different retaining wall heights. With the retaining wall's height growing, the load on the loaded section of the USCSP increases, resulting in a gradual increase in pile stress; at 1 m excavation, the wall height rises from 2.5 m to 6.0 m, and the maximum tensile stress in the pile increases from 331.2 kPa to 948.7 kPa, increasing by 186.4%, and the maximum compressive stress in the pile enhances from 514.3 kPa to 1243.3 kPa, increasing by 141.7%. At 2 m excavation, the wall height rises from 2.5 m to 6 m, the maximum tensile stress of the pile increases from 565.1 kPa to 2220.3 kPa, increasing by 292.9%, and the maximum compressive stress of the pile increases from 852.3 kPa to 2640.9 kPa, increasing by 209.9%.  
Figure 16: Soil pressure of the retaining wall with different pile–wall spacing values. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
Figure 17: Pile displacement diagram of different retaining wall heights. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
Figure 18: Stress diagram of pile body with different retaining wall heights. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.
## 2 Materials and Methods
### Study Area Selection and Phenological Evaluation Indicator  
In this study, the diagonal center points in different irrigation areas of preset point 1, preset point 3, and preset point 5 were taken as the regions of interest (ROI), with the irrigation equipment below. Due to the late planting time of the crops in the farmland near the research base, the farmland crops had little impact on the early stage of image acquisition. Secondly, the distance between the near-earth remote sensing equipment and the crops in the crop field is relatively large, and the height difference is relatively large. When vegetation and crops grow vigorously, a small degree of error may occur, but the overall impact will not be too great. Considering this problem after image acquisition, the region of interest at preset point 5 was placed in the center, away from the edge of the crop land, so as to minimize the error resulting from the external environment on the extracted vegetation index while not affecting the overall research results, as indicated in Figure 4. The computer PyCharm compiler and the Python language self-compiled algorithm \"image processing tool\" were used to read images as a time sequence; occasionally, the image quality could be adversely affected by variable light conditions, rain, snow, fog, or condensation frost on the camera housing window. To ensure the objectivity of the results, neither selective editing nor artificial enhancement of any archived images was performed. No smoothness or filtering of the time sequence resulting from the image analysis was conducted. Equation (1) was used to extract the average brightness value (digital number, \(DN\)) of the R, G, and B wave bands in the image ROI. To reduce the color balance changes affected by light, the mean value method was adopted, with 1d as a period, so as to calculate the average daily photo RGR brightness value.  
\[DN_{channel}=(sumDN)_{channel}/N_{channel} \tag{1}\]  
Figure 3: Test equipment and installation site.  
_DN_ represents the value of each wave band, \((sumDN)_{channel}\) indicates the sum of all pixel brightness values of a certain band in the image, \(N_{channel}\) indicates the number of pixels in the ROI.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 2 Materials and Methods
### Refraction in Transmission Method  
In the case of transmission of ultrasound waves through the soft tissue structure of a female breast immersed in water, the refraction phenomenon will occur due to the differential velocity of ultrasound in water and the breast and due to the inhomogeneity of the breast structure. It can be estimated that the refraction of the ultrasound beam at the water/breast interface will not be significant due to the near unity index of refraction \(n\), defined as the ratio of ultrasound velocity in water to ultrasound velocity in the structure [22]. Figure 1a shows the dependence of the refraction angle of the ultrasonic wave at the straight-line interface of the water/breast on the angle of incidence, calculated from the Snellius law. Figure 1b shows the dependence of the amplitude pressure coefficient of the transmission of the ultrasonic wave through a circular breast section immersed in water, calculated sequentially for the water/breast/water boundaries using the formula:  
\[D_{p}=\frac{\cos\alpha-\frac{\rho_{1}c_{1}}{\rho_{2}c_{2}}\sqrt{1-\left(\frac{ c_{2}}{c_{1}}\right)^{2}\sin^{2}\alpha}}{\cos\alpha+\frac{\rho_{1}c_{1}}{\rho_{2}c_{ 2}}\sqrt{1-\left(\frac{c_{2}}{c_{1}}\right)^{2}\sin^{2}\alpha}} \tag{1}\]where \(\alpha\)--the angle of incidence of the ultrasound wave at the boundary of the first and second medium, \(\rho_{1}\)--the density of medium 1, \(c_{1}\)--the ultrasound velocity in medium 1, \(\rho_{2}\)--the density of the medium 2, \(c_{2}\)--the ultrasound velocity in medium 2, \(D_{p}\)--the pressure coefficient of transmission of the ultrasonic wave through the boundary of the two media.  
Calculations show that in almost the entire range of incidence angles, the refraction of ultrasound wave rays when passing through breast tissue in water is negligible, while the transmission coefficient is close to unity in the range of incidence angles 0-80\({}^{\circ}\) (Figure 1). Simulation of the phenomenon of ultrasound wave refraction by means of a mathematical model that takes into account the refraction and elongation of beam rays for a complicated internal structure of a heterogeneous biological medium is a difficult task due to the necessity of considering refraction coefficients for all borders between quasi-homogeneous structures and the necessity of an iterative search for such a beam ray coming from the source, which will reach the detector after multiple refractions in the medium (rays are curvilinear in the case of continuous changes of the ultrasound velocity in the structure) [36; 37; 38; 39]. Moreover, there may be many such rays in the case of \(n>1\), so we should additionally look for a transition in the shortest time and with a measurable amplitude. Such an operation must be performed for each position of the sending and receiving pair of transducers when scanning a biological medium with the transmission method [22]. Problems of critical angles must still be considered; above such angles of incidence, the ultrasound wave disappears in the tissue.
## 3 Results and Discussion  
### The Effects of Pulse Duration on Electrical Characteristics  
Figure 2 shows the typical waveforms of the applied voltages and the corresponding discharge currents in air. The pulse peak voltage and pulse repetition rate are set as 15 kV and 1 kHz, respectively, and the pulse durations of the voltage pulse in Figure 2 are set as 0 ns, 20 ns, 60 ns, and 100 ns, respectively. It is found that there are three obvious discharge current peaks during one voltage pulse. The first discharge current peak occurs during the rising time of the voltage pulse and, correspondingly, the second discharge current peak occurs during the falling time of the voltage pulse. We named these two discharge current peaks as the first peak (FP) and the second peak (SP) of the discharge current, respectively. As the pulse peak voltage during the pulse duration is not ideally maintained as a constant, there is a voltage difference during the pulse duration. Thus, the third discharge current peak appears during the pulse duration. Moreover, the discharge duration becomes longer with the increase of the pulse duration, and the reason will be explained below. It should be pointed out that the shape of the voltage and current waveforms in air are the same as those in pure nitrogen. In Figure 3, the peak values of the FP and SP of the discharge current under air and pure nitrogen are calculated. It shows that the four curves in Figure 3 all present an increasing trend with the increase of pulse duration, and in the studies of [PERSON] et al. [27] and [PERSON] et al. [28], they also found that the discharge current density and the electron density increase drastically with the increase of the pulse duration. The reason is that the discharge can be maintained during the pulse duration due to the crucial  
Figure 1: (**a**) The experiment setup and (**b**) magnified view of the electrode structure.  
role played by the penning ionization between the metastable state molecules [27]. Hence, the discharge current and the discharge duration get longer when increasing the pulse duration. Additionally, the current value of the discharge operated in pure nitrogen is higher than that in air; this is due to the electron attachment by O\({}_{2}\)[29; 30], and the main electron attachment reactions [30] are listed as follows (M stands for the third body in the three-body attachment reactions, it can be N\({}_{2}\) or O\({}_{2}\)).  
\[\mathrm{e}^{-}+\mathrm{O}_{2}\rightarrow\mathrm{O}^{-}+\mathrm{O}\]  
\[\mathrm{e}^{-}+\mathrm{O}_{2}+\mathrm{M}\rightarrow\mathrm{O}_{2}^{-}+ \mathrm{M}\]  
Figure 3: Effects of pulse duration on the peak values of the first and second discharge currents under air and nitrogen.  
Figure 2: Waveforms of the applied voltages and discharge currents under different pulse durations in air.
# Detection of Carrageenanen in Cheese Using
## 3 Results and Discussion
### Lectin Histochemistry  
3.2.1. Lectin Concentration  
Prior to the analysis itself, the correct lectin concentration to be used in the analysis must be determined. [PERSON] et al. [12] recommends first diluting lectin to a concentration of 10 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\), because this concentration is optimal for most lectins in indirect methods of lectin histochemistry and then adjust the concentration for optimal results. In the case of high concentrations of lectins, a false positive reaction can occur [40].  
The _Arachis hypogaea_ lectin concentrations of 10 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\), 2 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\), and 1 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) were tested on a sample of processed cheese from the retail market (Table 1) in this work. These concentrations were selected following previous work in which carrageenanan was determined in meat products [17].  
For the L* and a* parameters, a statistically significant difference was confirmed for the tested concentrations of 1 and 10 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) (\(p<0.05\)). No statistical difference was demonstrated for the b* parameter. The L* parameter confirms that the higher the lectin concentration, the higher the signal intensity (darker histological tissue staining). The lectin concentration of 2 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) was selected as optimal, where the color intensity is comparable to the highest lectin concentration of 10 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\), while a lower concentration allows the cost of a single analysis to be reduced. This concentration was also used for the following analyses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Lectin** & **Color Intensity** & **L*** & **a*** & **b*** \\
**Concentration** & & & & \\ \hline
10 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) & +++ & 81.54 \(\pm\) 9.18 a & \(-\)2.63 \(\pm\) 2.01 a & 18.17 \(\pm\) 5.73 b  \\
2 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) & +++ & 86.05 \(\pm\) 5.68 ab & \(-\)5.08 \(\pm\) 0.94 b & 17.91 \(\pm\) 4.22 a  \\
1 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{\,mL}^{-1}\) & +++ & 89.82 \(\pm\) 3.73 b & \(-\)6.02 \(\pm\) 0.94 b & 11.77 \(\pm\) 3.53 a  \\ \hline \hline \end{tabular} Note: Mean values denoted by different letters at individual column are significantly different (\(p<0.05\)). Data are expressed as mean value \(\pm\) standard deviation. Signal intensity is from + (weak) to +++ (very strong).  
\end{table}
Table 1: Validation of _Arachis hypogaea_ lectin reactivity on a sample of processed cheese.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 3 Results
### Satellite Data Sensitivity to Soil Texture
#### 3.1.2 Optical Indices and Sensitivity Analysis
As shown in the figures, the relationship between soil moisture and clay or sand content is linear in both cases: the mean moisture content clearly increases with increasing clay fraction (R\({}^{2}\) = 0.54), and decreases with increasing sand content (R\({}^{2}\) = 0.43). Figure 7: Brightness index as a function of clay content.
## 4 TSN Integration with Edge Computing for Enhanced Reliability
### Multiple Application Instances Handling End Device Capability  
In this case, the industry end device can handle multiple application instances (e.g., the device is able to process multiple frames that come from different application instances). This option has fewer challenges for TSN-cloud interworking since arbitrary deployment options of the application instances in the edge domain can be handled by the device. However, typically, the application software on the device side has to be adapted to the simultaneous communication towards multiple application instances. In contrast, in the edge domain, the continuous synchronization among the instances should be handled, so backward compatibility is limited in this case.  
Since the device can communicate with multiple application instances, separate TSN streams can be established between a device-application instance pair. The end-to-end redundancy can be realized on a per domain level:  
* In the cloud domain, the different application instances are deployed using different cloud resources (e.g., nodes/pods) ensured by the orchestration system;
* The application instances are connected to different TSN FRER entities by using separate paths in the cloud domain--this can also be managed by the orchestration system. It is important to provide redundant connectivity between the cloud and other (e.g., TSN transport) domains, e.g., the cloud domain can be reached via multiple data center gateways (DC-GW);In the TSN transport domain (that may include a 5 GS virtual TSN bridge as well), the CNC can configure different paths for the replicated frames.  
Figure 8 shows two options for the multiple application handling scenario; in the case of option 1, the TSN FRER is provided by the DC GWs, while in option 2, the TSN FRER entity is virtualized and deployed in the edge cloud domain; in the latter case, the TSN FRER could be realized as a platform as a service component. The blue and red lines between the corresponding TSN FRER entities and the end device show the TSN member streams that carry the duplicated packets of the different application instances by using independent network resources (TSN bridges).  
It is important to note that due to the per-stream redundancy, the required resources are scaling with the number of application instances (as the frames of all application instances are forwarded to the device).  
The multiple application scenario can also provide redundancy without the TSN FRER. In this case--as shown in Figure 9, illustrated by blue and red lines--a single TSN stream is established between each device and application instance pair.  
The centralized network configuration (CNC) can configure independent paths for the TSN streams over the TSN domain. In the uplink direction, the device has to send responses to each application instance.  
In any of the above scenarios, the 5G network can be integrated into the communication path seamlessly as a 5G-TSN bridge.
## 4 Results and Discussion
### Transient Analysis
#### 4.2.2 Fixed Gear Vehicle Speed Transient  
The results in Figure 26 illustrate the required time for increasing the vehicle's speed from 1100 to 3000 rpm. As is expected, an aggressive tip-in leads to a fast vehicle speed transient. The pre-turbine pressure has almost a negligible effect on the required time. The comparison between the e-turbo and the baseline engine for the 2-16 BMEP shows the improvement in transient response for the electrically-assisted model. On the other hand, the comparison for the 2-4 BMEP tip-in shows no difference at all due to operating at the non-boosted area of the engine. Figure 26b demonstrates the amount of energy provided or harvested by the motor during the transient period of interest. It is clear that during the first phase of the tip-in, the motor provides a high amount of power (limited to 5 kW) to the compressor for meeting the BMEP demands. Once the targeted boost pressure is achieved, the e-turbo starts harvesting energy for most of the cases. The overall average energy demand is negative (energy generation) for most of the cases, except the most aggressive one, as shown in Figure 26b, where the BMEP is increasing for the whole duration of the transient as presented in Figure 26c. The Brake-Specific Fuel Consumption (BSFC) of the engine is slightly deteriorated when higher pre-turbine pressures than the baseline model are applied, as shown in Figure 26d.  
Figure 25: Open-loop load tip-in results at 1600 rpm engine speed: (**a**) BMEP vs. time; (**b**) average power vs. time to torque; colour and symbol categorization indicates the amount of power provided during the first section.  
Figure 26: _Cont._
## 2 Materials and Methods
### Transposed Convolutional Layer  
The transposed convolution operates in the opposite direction of a convolution and functions as a decoder, whereas a convolution acts as an encoder [16]. Illustration of 2D transposed convolution is shown in Figure 2 (note that the channels are ignored for the illustration purpose).  
Originally, transposed convolution performs convolution and up-sampling simultaneously. However, when applying transposed convolution to a new signal feature dimension, it expands the number of dimensions and projects the dimensions into a larger dimensional space with features. The number of dimensions must be expanded as a parameter. However, because the projection to the feature dimension is learned from each data point, the feature amount does not need to be selected in advance, as in conventional EEG research.  
Transposed convolution using the convolution kernel \(K[W,R,Ch_{in},Ch_{out}]\) for the signal \(S[Time,Ch_{in}]\), sample time \(t\) and EEG electrodes \(Ch_{in}\) follows (1). Here, \(W\) is the convolution width, \(R\) is the number of new feature dimensions to be expanded, and \(Ch_{out}\) is the number of output channels of the transposed convolution (Figure 3).  
\[(S*K)(t,r,ch)=\sum_{w}^{W}\sum_{c}^{Ch_{in}}S(t-w,c)K(w,r,c,ch) \tag{1}\]  
Figure 1: 2D convolution architecture.