esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 2 Methodology
### UPML Absorbing Boundary Condition
 \tag{53}\]  
In the sub-step of \(n+1/2\)\(\rightarrow\)\(n+1\), the difference scheme of Equation (38) is the same as above, which can be expressed as  
\[D_{z}^{n+1}(i,j)=\frac{bx}{ax}\cdot D_{z}^{n+\frac{1}{2}}(i,j)+\frac{1}{ax} \cdot\Big{\{}\frac{1}{\Delta x}\cdot\left[H_{y}^{n+\frac{1}{2}}\Big{(}i+\frac {1}{2},j\Big{)}-H_{y}^{n+\frac{1}{2}}\Big{(}i-\frac{1}{2},j\Big{)}\right]- \frac{1}{\Delta y}\cdot\left[H_{x}^{n+1}\Big{(}i,j+\frac{1}{2}\Big{)}-H_{x}^{ n+1}\Big{(}i,j-\frac{1}{2}\Big{)}\right]\Big{\}}. \tag{54}\]  
Equation (39) can be discretized as  
\[E_{z}^{n+1}(i,j)=\frac{by}{ay}\cdot E_{z}^{n+\frac{1}{2}}(i,j)+\frac{er}{ay} \cdot\left[D_{z}^{n+1}(i,j)-D_{z}^{n+\frac{1}{2}}(i,j)\right], \tag{55}\]  
where  
\[D_{z}^{n+1}(i,j)=D_{z}^{n+\frac{1}{2}}(i,j)+\frac{ay}{er}\cdot E_{z}^{n+1}(i,j )-\frac{by}{er}\cdot E_{z}^{n+\frac{1}{2}}(i,j). \tag{56}\]  
Equations (40)-(43), respectively, can be discretized as  
\[B_{x}^{n+1}\bigg{(}i,j+\frac{1}{2}\bigg{)}=\frac{by}{ay}\cdot B_{x}^{n+\frac{1 }{2}}\bigg{(}i,j+\frac{1}{2}\bigg{)}-\frac{1}{ay}\cdot\frac{1}{\Delta y}\cdot \left[E_{z}^{n+1}(i,j+1)-E_{z}^{n+1}(i,j)\right], \tag{57}\]  
\[B_{y}^{n+1}\bigg{(}i+\frac{1}{2},j\bigg{)}=\frac{bx}{ax}\cdot B_{y}^{n+\frac{1 }{2}}\bigg{(}i+\frac{1}{2},  
(2) Calculate \(D_{z}^{n+1}\) by Equation (56),  
(3) Calculate \(B_{x}^{n+1}\) and \(B_{y}^{n+1}\) by Equations (57) and (58);  
(4) Calculate \(H_{x}^{n+1}\) and \(H_{y}^{n+1}\) by Equations (59) and (60). We used a simple air model to verify the absorption effect of the UPML boundary conditions. Visual Studio 2010 was used as a development tool, and the CPU was an Intel Core i5-4200H with an NVIDIA GeForce GTX 950m. The computing environment above supported all the computing processes in this paper. The simulation area was a \(2.0\,\mathrm{m}\times 2.0\,\mathrm{m}\) rectangular area; the spatial step was 0.005 m, and the time step was \(\Delta t=0.01\,\mathrm{ns}\). A Ricker wavelet was added at the positive center of the model's area (see Figure 3). Figure 4 shows the
## 4 Results and Discussion
### Lamb Wave and Bearing Estimation
#### 4.4.3 Relationship between the Lamb Waves and Bearing Estimation Error  
At sites S1 and S2, in the excitation frequency band near 400 Hz, which corresponds to the second peak, the error is relatively smaller, within 25\({}^{\circ}\), as shown in Figures 5a and 6a, and the bearing estimation is efficient. At site S3, there is no excited second peak, the bearing estimation errors in the frequency band near 400 Hz are larger, more than 25\({}^{\circ}\), as shown in Figure 7a, and the bearing estimation is bad. According to the previous analysis in this study, the bearing estimation errors are connected with the value of peak displacement, and the excited modes the of Lamb waves are connected with the excited second peak in Figure 8a. Thus, it can be presumed that if some modes of the Lamb waves of the ice plate are excited, the bearing estimation error in the frequency band of the excited peak would be relatively smaller, and the frequency band for the bearing estimation would be efficient. In other words, if some modes of the Lamb waves are excited, the efficient frequency band for the bearing estimation would be widened.  
Figure 9: Reflection coefficient and Lamb wave with ice thickness d = 2: (**a**) reflection coefficient and (**b**) reflection coefficient and some modes of Lamb waves.  
Normally, the thickness of the ice layer cannot be changed. If the ice thickness is constant, the excitation of the Lamb wave is related to the incident angle. Therefore, the bearing estimation error is related to the incident angle. However, we can adjust the incident angle by moving the received distance to excite some modes of the Lamb s wave so that more energy can enter the ice layer and the frequency band with a good bearing estimation effect will be widened.  
If the incident angle is constant and some modes of the Lamb waves are excited, there will be a dip in the reflection coefficient diagram, as shown in Figure 10a. The thinner the ice layer, the higher the frequency of the dip. That is, if the ice layer is thinner, the Lamb waves will be excited at a higher frequency, which means that a relatively higher and wider frequency band of acoustic signals may be obtained for good bearing estimation. Therefore, if we want to bear an acoustic source in a higher frequency band, a thinner ice layer would  
Figure 10: Reflection coefficient at different incident angles: (**a**) incident angle of 35\({}^{\circ}\), corresponding to site S1; (**b**) incident angle of 73.6\({}^{\circ}\), corresponding to site S2; and (**c**) incident angle of 45\({}^{\circ}\), corresponding to site S3.  
be selected, and some modes of the Lamb waves can be excited, letting more energy enter the ice layer, which is conducive to the bearing.
# Improvement Effect of Membrane-Free Stem Cell Extract on
## 3 Results  
### Clinical Severity and Scratching Behavior on Dorsal Skin of HDM-Sensitized NC/Nga Mice  
As shown in Figure 2, the dorsal skin of HDM-sensitized VEH mice visibly expressed AD-like symptoms, including erythema, dry skin, edema/excoriation, erosion, and lichenification, beginning at 2 weeks after HDM sensitization. In contrast, MFSCE application to the dorsal skin inhibited these AD-like symptoms. The clinical severity was scored according to the standard for dermatitis symptoms.  
The skin severity scores for each group are shown in Figure 3A. All groups, except the NOR group, exhibited progression of AD-like symptoms for up to 2 weeks. However, starting at 3 weeks, the dermatitis scores in the MFSCE-treated groups showed a downward tendency, similar to those in the DEX group, while the VEH group deteriorated more seriously. Scratching behavior was recorded to assess the effect of spontaneous scratching frequency on pruritus (Figure 3B). Although all VEH- and MFSCE-treated groups showed the highest scratching frequency at 3 weeks, the level of scratching frequency in the MFSCE-treated groups was lower than that in the VEH group. Although the scratching frequency decreased slightly at 4 weeks, that in the VEH and MFSCE-treated groups was significant. These results prove that MFSCE application recovered AD-like clinical symptoms in HDM-sensitized NC/Nga mice.  
Figure 2: Clinical pictures of AD-like dorsal skin lesions in HDM-sensitized NC/Nga mice. Six groups: NOR group, VEH group (HDM treatment), MFSCE 5 group (HDM treatment + MFSCE 5 uL/head), MFSCE 10 group (HDM treatment + MFSCE 10 uL/head), MFSCE 20 group (HDM treatment + MFSCE 20 uL/head), and DEX group (HDM treatment + DEX 540 ug/head).
## 4 Aggregated Channel Attenuation Model
### Pointing Error  
Misalignment between the transmitter and receiver due to weak earthquakes, dynamic wind loads and building sway caused by thermal expansion will cause vibration of the transmitted beam, which in turn causes aiming errors and further leads to performance degradation of the FSO system, and its expression is as follows [20]:  
\[f_{h_{p}}(h_{p})=\frac{\gamma^{2}}{A_{0}^{\gamma^{2}}}\big{(}h_{p}\big{)}^{ \gamma^{2}-1},0\leq h_{p}\leq A_{0} \tag{12}\]where \(A_{0}=\left[erf(v)\right]^{2}\) represents the received optical power at \(\varsigma=0\) and \(erf(\cdot)\) is the error function. \(v=\sqrt{\pi}r/\sqrt{2}w_{z}\), where \(r\) represents the radius in the transverse plane of the incident wave, \(w_{z}\) denotes the optical beam width. \(w_{zeq}^{2}=w_{z}^{2}\sqrt{\pi}erf(v)/2v\exp\left(-v^{2}\right)\) denotes the equivalent beam width. \(\gamma=w_{zeq}/2\delta_{s}\), where \(\delta_{s}\) denotes the standard deviation of the pointing error displacement at the receiver.
# Aerodynamic Analysis of Simple Grider Bridges under Construction Phase
## 3 Results  
### Single Beam  
Figure 5 shows the drag coefficient and Strouhal number obtained in each simulation with a single beam geometry. Note that the simulation results of \(z_{0}=3\) m do not follow the same path as the other simulations, and obtained aerodynamic coefficients change considerably depending on the wind speed. The influence of the proximity of the ground could explain these strange results (Figure 6). The no-slip condition in the ground is generating a small boundary layer, so the closer to it the section is, the lower the actual wind speed will be.  
Regarding the simulations with higher bridges, some common trends can be observed. By increasing the beam height (\(h\)), the Strouhal number decreases, while the drag coefficient increases, except for H = 3 m, which might be affected by the ground boundary layer. [PERSON] [9] observed that the wider the wake is, the higher the drag coefficient and the lower the Strouhal number are. A considerable change in wake's width was observed in the graphical results (Figure 7), upholding the experiments made by [PERSON] [9]. Since the extracted parameters are non-dimensional and depend only on the shape of the section, it can be thought that the ratio \(b/h\) is the cause of the changes in the width and the aerodynamic coefficients.  
Figure 5: Values of the Strouhal number and drag coefficient extracted from the simulations with one beam. The dotted lines represent the results for a particular wind speed, while the continuous lines are the average values from all the wind speeds (double column, B&W in the printed version).  
Figure 6: Vorticity contour for a particular moment of the simulation with \(h=3\) m, \(b=0.9\) m, \(z_{0}=3\) m, and \(U=5\) m/s (double column, B&W in the printed version).  
Regarding the distance to the ground, both parameters increase when the bridge becomes closer to it. The increase in both parameters is probably caused by a mechanism similar to the blockage effect, which is also observed in wind tunnel tests. Although the model has no limits on the upper side, the ground's proximity also hinders the flow around the lower side of the section. This resistance increases the pressure in the upstream zone, increasing both parameters.  
About the oscillations of the forces, the lift coefficient decreases considerably when the beam height increases (Figure 8). This result was expected, as the reference dimension for calculating the coefficients was the height of the beams. Thus, when the beam height increases, the reference dimension increases, but the flanges' area remains the same. Regarding the drag oscillations, a peak in the intermediate values of \(h\) can be observed (Figure 8).
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
###### Abstract  
The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.  
mobile data offloading; reinforcement learning; delay tolerant +
Footnote †: journal: Sensors
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
###### Abstract  
Reclaimed asphalt pavement (RAP) is preheated to 120 \({}^{\circ}\)C or lower in hot central plant recycling due to specification and equipment limitations. However, the insufficient activation of reclaimed asphalt (RA) caused by low preheating temperature may affect the final properties of reclaimed asphalt mixtures (RAM) and lead to insufficient utilization of RA binder. This study evaluated the influence of preheating temperature and aging of RA binder on binder activation by producing specimens with 100% RAP. The volumetric and mechanical properties of specimens were analyzed to reflect the effect of activation. The results indicate that preheating temperature has a significant impact on the activation of RA binder. Regardless of the source of RAP, RA binder can be highly activated at 180 \({}^{\circ}\)C, while the degree of activation decreases significantly at 120 \({}^{\circ}\)C. By using an artificial RAP with different degrees of aging, the aging of RA binder is found to be harmful to activation only when the preheating temperature is low (such as 120 \({}^{\circ}\)C). Hence, if the equipment is capable of doing so, it is better to raise the preheating temperature of RAP to improve the activation of RA binder, especially for binder with a higher degree of aging.  
RAP; asphalt binder activation; reclaimed binder; preheating temperature; aging Article
## 2 Materials and Methods  
### WorldView-3 Satellite Image  
The high-resolution satellite image used for this study was acquired on 29 July 2023, using the WorldView-3 satellite. The acquired scene covered an area located in Spoleto, a city in the Umbria region (central Italy), an area of approximately 28.54 square kilometers and bounded by the following coordinates (Datum ETRF 2000, EPSG:6708 RDN2008/UTM zone 33N (N-E)): upper left at 4,743,417.3 N, 317,279.4 E and lower right at 4,737,005.7 N, 321,757.2 E (Figure 2).  
Satellite image processing, including orthorectification and pan-sharpening, was performed using the Catalyst software v. 2223.0.3 (by PCI Geomatics Enterprises) [30]. First, the fusion of the panchromatic and multispectral images was performed using the pan-sharpening algorithm, resulting in a multispectral image with a resolution of 0.31 m/pixel (Figure 3).  
The geometric correction and orthorectification of the image were performed using the following methods:  
* the positions of 20 Ground Control Points (GCPs) distributed across the scene were obtained through an NRTK (Network Real Time Kinematic) GNSS (Global Navigation Satellite System) survey. The GPSUMphria network [31] was used to determine the GCP positions using the Virtual Reference Station (VRS) mode (Figure 4);
* a Digital Elevation Model (DEM) was derived from the regional vectorial cartography [32] (scale 1:5000) with a resolution of 2 m.  
Six of the measured points were designated as Check Points (CPs) to properly estimate the planimetric accuracy of the orthorectification process.  
The results of the orthorectification are reported in Table 1.  
The data indicate that the altimetric residuals on the GCPs and CPs are lower than the planimetric ones, although the latter remain acceptable considering the geometric  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Point**} & \multicolumn{2}{c}{**Residual Error—Ground (m)**} & \multicolumn{2}{c}{**Residual Error—Image (px)**} \\ \cline{2-5}  & **East** & **North** & **Elevation** & **X** & **Y** \\ \hline GCPs & 0.099 & 0.103 & 0.025 & 0.33 & 0.36 \\ CPs & 0.168 & 0.145 & 0.035 & 0.56 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Residual errors of the orthorectification.  
Figure 3: Pancromatic (**a**), multispectral (**b**), and pansharpened (**c**) images.  
Figure 2: Location of the study area (**on the left**) and the acquired WorldView-3 satellite image (**on the right**).  
resolution of the WV3 images. This may be attributed to the fact that the points were accurately acquired using a dual-frequency GNSS receiver and corrected with a suitable geoid undulation model (UmbriaGeo). Furthermore, the effect of altitude on the orientation of the images is generally smaller than that of the planimetric coordinates, especially in areas with relatively low elevation changes, such as the one under study.
## 3 Results and Discussion
### Results of Image Processing  
Although image processing methodology from the literature was followed (where paper was the substrate) [21, 40], results obtained in this study showed that settings on the software must be tailored to the specific substrate. Settings used for paper substrate were not suitable for silica substrate. Figure 2 shows the results of the silica plate with color thresholding, completed following procedures by [PERSON] et al. [21]. Contrary to when paper is used, it was observed that this method leads to high background noise and is not suitable for silica substrate (Figure 2b). A better image was obtained from manually adjusting the color contrast (Figure 2c) with much of the background noise eliminated. The best image resulted from setting the color contrast to \"Auto\", i.e., allowing the software to correct the color contrast and then optimizing it (Figure 2d). Using this setting, the background noise was successfully reduced. This was true for both inverted and un-inverted images. The background color intensity was measured against the set concentration of zero as a blank was established. In the inverted images, the typically orange complex appeared as a luminescent blue against a black background.  
While other studies predominantly inverted all images before color intensity readings [9, 40], this work explored both inverted and un-inverted images and measure color intensities as presented in Figure 3. The linearity of the method was determined and observed to not go beyond an Fe amount of 4.5 ng (15 mg/L). As observed in Figure 3, both inverted and un-inverted images yielded linear graphs with a relatively good co-efficient of determination (R\({}^{2}\)), as high as 0.9969 and 0.9984, respectively. Un-inverted images yielded better linearity after being log transformed, while such a transformation made the inverted results worse. For both types of images, mean gray seemed to be a better tool of color intensity measurement, which is consistent with previous reports for similar studies [12, 21]. This is investigated in detail and discussed in Section 3.4 (Color Intensity Measurements). These results showed that images can still give good results without being inverted first.  
Figure 1: Spotting of iron on a silica plate device showing color development of the orange ferroin complex with polyacrylic acid (**top**) and without polyacrylic acid (**bottom**).
## 3 Results
### Univariate Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**50–60 Years**} & \multicolumn{4}{c}{**60–70 Years**} & \multicolumn{4}{c}{**\(\geq\)70 Years**} \\  & \multicolumn{2}{c}{**(n = 83)**} & \multicolumn{2}{c}{**(n = 106)**} & \multicolumn{2}{c}{**(n = 214)**} & \\ \cline{2-10}
**Factors** & \begin{tabular}{c} **Fallers** \\ **(n = 16)** \\ \end{tabular} & \begin{tabular}{c} **Non-Fallers** \\ **(n = 67)** \\ \end{tabular} & \(p\) & \begin{tabular}{c} **Fallers** \\ **(n = 22)** \\ \end{tabular} & \(p\) & \
\begin{tabular}{c} **Non-Fallers** \\ **(n = 84)** \\ \end{tabular} & \(p\) &
\begin{tabular}{c} **Fallers** \\ **(n = 59)** \\ \end{tabular} & \(\mathbf{Non}\)-\(\mathbf{Fallers}\) \\ \hline \multicolumn{2}{c}{Intrinsic} & & & & & & & & \\ \hline \multicolumn{2}{c}{Gender} & & 0.064 & & & & & & & 0.054 \\ Male & 0 (0) & 13 (100) & 4 (17) & 20 (83) & 17 (20) & 67 (80) & 42 (32) & 88 (68) & \\ Female & 16 (23) & 54 (77) & 0.003 & & & & & & & \\ History of Fall & 9 (43) & 12 (57) & 8 (27) & 22 (73) & 30 (40) & 46 (60) & & & & \\ No & 7 (11) & 55 (89) & & 14 (18) & 62 (82) & & 29 (21) & 109 (79) & & \\ Fear of Fall & 9 (36) & 16 (64) & & 11 (22) & 40 (78) & & & & & \\ No & 7 (12) & 51 (88) & & 11 (20) & 44 (80) & & & & & & \\ \hline \multicolumn{2}{c}{Extrinsic} & & & & & & & & & \\ \hline \multicolumn{2}{c}{Medication} & & & & & & & & & & \\ Benzodiazepipes & & & 0.012 & & & 0.038 & & & & & \\ Yes & 7 (44) & 9 (56) & & 8 (38) & 13 (62) & & & 22 (32) & 47 (68) & \\ No & 9 (13) & 58 (87) & & 14 (17) & 71 (83) & & & 37 (25) & 108 (75) & \\ Antidepressants & & & & & & & & & & \\ Yes & 7 (39) & 11 (61) & & 6 (26) & 17 (74) & & & 19 (36) & 34 (64) & \\ No & 9 (14) & 56 (86) & & 16 (19) & 67 (81) & & & 40 (25) & 121 (75) & \\ Antihypertensive & & & & & & & & & & \\ Yes & 6 (23) & 20 (77) & & 0.553 & & & & & & \\ No & 10 (18) & 47 (82) & & 5 (12) & 36 (88) & & & 12 (25) & 36 (75) & \\ Polypharmacy & & & & & & & & & & \\ Yes & 9 (43) & 12 (57) & & 16 (28) & 41 (72) & & & 42 (27) & 112 (73) & \\ No & 7 (11) & 55 (89) & & 6 (12) & 43 (88) & & 17 (28) & 43 (72) & \\ Upper & & & & & & & & & & \\ Extremities & & & & & & & & & \\ Yes & 7 (41) & 10 (59) & & 9 (39) & 14(61) & & & 25 (27) & 67 (73) & \\ No & 9 (14) & 57(86) & & & 13 (16) & 70 (84) & & 34 (28) & 88 (72) & \\ Mobility assistive & & & & & & & & & & \\ Yes & 2 (100) & 0 (0) & 0 & 0 (0) & 3 (100) & & 11 (24) & 35 (76) & \\ No & 14 (17) & 67 (83) & & & 22 (21) & 81 (79) & & & 48 (29) & 120 (71) & \\ Stairs & & & & & & & & & \\ Yes & 14 (20) & 57 (80) & & & 19 (20) & 74 (80) & & & 42 (28) & 106 (72) & \\ No & 2 (17) & 10 (83) & & & 3 (23) & 10 (77) & & 17 (26) & 49 (74) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of individuals by fall occurrence during follow-up, stratified by age groups.
# Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest
## 2 Methods  
### Model Overall Architecture  
Figure 1 demonstrates the overall framework of clover dry matter prediction; the DeepLabv3+ network uses the Atrous Spatial Pyramid Pooling, adept at capturing semantic intricacies within images. Features extracted within the Encoder component undergoes a bifurcation into high-level and low-level variants, providing a versatile means of feature extraction across diverse scales. The pivotal inquiry rests in the capacity of the DeepLabv3+ semantic segmentation model to aptly convey its image features into the dry matter prediction model. In this study's designed dry matter prediction model, the DeepLabv3+ network, augmented with an enhanced ASPP module, first trains on the semantic segmentation dataset to derive image feature representations. Subsequently, a random forest model within the dry matter prediction framework undergoes further training for the purpose of forecasting dry matter content. This comprehensive approach enables the model to establish predictive prowess by internalizing relationships between sample features and the corresponding dry matter content. This orchestrated process leverages input images, feature extraction, and dry matter content prognostication, culminating in a precise prediction of clover's dry matter content. By orchestrating the complete dry matter prediction model, the innate strengths of the DeepLabv3+ network within semantic segmentation tasks are astutely harnessed. This transpositional learning strategy not only truncates training duration but also augments the model's performance in the domain of dry matter prediction.  
Figure 1: A framework for clover dry matter prediction based on semantic segmentation network and RF, where the semantic segmentation network is DeepLabv3+.