esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 GeoSurf Satellite Constellations at Millimeter Wavelengths
Long ago, we called this stochastic process \"process B\" [30]. However, besides the number of occurrences, the duration \(\tau\) itself is also another stochastic variable of great importance because it gives the outage duration. We called this process of fade duration \"process A\" [30]. \begin{table}
\begin{tabular}{l c c c c} \hline
**Site** & **Latitude N (\({}^{\circ}\))** & **Longitude E (\({}^{\circ}\))** & **Altitude \(H_{S}\) (m)** & **Rain Rate Observation Time (Years)** \\ \hline Spino d’Adda (Italy) & 45.4 & 9.5 & 84 & 8 \\ \hline Gera Lario (Italy) & 46.2 & 9.4 & 210 & 5 \\ \hline Fucino (Italy) & 42.0 & 13.6 & 680 & 5 \\ \hline Madrid (Spain) & 40.4 & 356.3 & 630 & 8 \\ \hline Prague (Czech Republic) & 50.0 & 14.5 & 250 & 5 \\ \hline Tampa (Florida) & 28.1 & 277.6 & 50 & 4 \\ \hline Norman (Oklahoma) & 35.2 & 262.6 & 420 & 4 \\ \hline White Sands (New Mexico) & 32.5 & 253.4 & 1463 & 5 \\ \hline Vancouver (British Columbia) & 49.2 & 236.8 & 80 & 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Geographical coordinates, altitude (km), number of years of continuous rain rate time series measurements at the indicated sites. In other words, process B gives the statistics of outages, process A gives the statistics of outage duration. In satellite communications, both are, of course, useful because some services-especially non-real-time services, such as data delivery-can be more disturbed by the number of outages (e.g., in an average year, month, etc.) rather than by their duration, while some others-especially real-time services such as television, video conference etc.-are more disturbed by the duration of the outage. In the next section, we define the probability distribution of the processes A and B; in Section 3, we present worldwide sites investigated with the Synthetic Storm Technique; in Section 4, we show the fade duration experimental results; in Section 5, we study the interdependence of the two processes; in Section 6, we define and study a uniformity index, which compares real cases-fade durations fragmented in many different intervals, with changing \(S\) and site-to the limiting case of all fades lasting the same time; finally, in Section 7, we conclude by summarizing the main results and their impact on system design.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 5 Numerical Examples
### Two-Point Boundary Value Problem  
Consider the following two-point boundary value problem:  
\[\begin{cases}Lu\equiv-(pu^{\prime})^{\prime}+ru^{\prime}+qu=f,&0<x<1\\ u(0)=0,u^{\prime}(1)=0\end{cases} \tag{20}\]  
where \(p=1\), \(q=1\), \(r=10\) and \(f=1\). The errors of nodal displacements \(e^{h}_{i,\text{max}}\), \(\widetilde{e}^{h}_{i,\text{max}}\) and errors of interior displacements \(e^{h}_{\text{max}}\), \(e^{*}_{\text{max}}\), \(\widetilde{e}^{*}_{\text{max}}\) on a series of uniform meshes are presented in Table 1, where their convergence orders are denoted by \(\rho\). From the results, it is observed that the nodal displacements have been improved from \(O(h^{2})\) to \(O(h^{4})\), and the accuracy of EEP displacements has also been improved, as expected, from \(O(h^{2})\) to \(O(h^{3})\).  
To intuitively show the errors of the various solutions, error distributions of the above results are presented in Figure 7 for \(N_{e}=16\). As can be seen from Figure 7, firstly, since both nodal displacements of FEM and EEP are equal, the error distribution curve of the EEP solution is close to that of FEM; secondly, although the nodal displacements of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(N_{e}\)} & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(\widetilde{e}^{h}_{i,\text{max}}\)} & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(\widetilde{e}^{h}_{i,\text{max}}\)} & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(\widetilde{e}^{h}_{i,\text{max}}\)} & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(\widetilde{e}^{*}_{\text{max}}\)} & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(\widetilde{e}^{*}_{\text{max}}\)} & \multirow{2}{*}{\(\rho\)} \\ \cline{5-10}  & & & & & & & & & & & \\ \hline \multirow{4}{*}{1} & 4 & \(1.72\times 10^{-3}\) & - & \(5.99\times 10^{-4}\) & - & \(1.84\times 10^{-3}\) & - & \(2.22\times 10^{-3}\) & - & \(7.79\times 10^{-4}\) & - \\  & 8 & \(4.97\times 10^{-4}\) & 1.80 & \(5.44\times 10^{-5}\) & 3.46 & \(7.99\times 10^{-4}\) & 1.20 & \(5.38\times 10^{-4}\) & 2.04 & \(1.21\times 10^{-4}\) & 2.69 \\  & 16 & \(1.08\times 10^{-4}\) & 2.21 & \(3.31\times 10^{-6}\) & 4.04 & \(2.84\times 10^{-4}\) & 1.49 & \(1.14\times 10^{-4}\) & 2.23 & \(1.80\times 10^{-5}\) & 2.74 \\  & 32 & \(2.68\times 10^{-5}\) & 2.01 & \(1.88\times 10^{-7}\) & 4.14 & \(8.78\times 10^{-5}\) & 1.69 & \(2.75\times 10^{-5}\) & 2.06 & \(2.31\times 10^{-6}\) & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Convergence orders of displacements.  
improved FEM do improve the accuracy of FE nodal displacements, the maximum errors of the interior displacement on each element remain roughly the same; finally, the errors of the improved EEP displacements are much smaller than those of the FEM and EEP solutions, which is numerically verified to be super-convergent.
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 5 Evaluation
### Part 2--Soundscape Curation
#### 5.2.2 Results  
Overall, participants agreed that the application is easy to use and were confident in using it, but there is space for improvement with regards to clarity of features, iconography and the integration of features.  
Specifically, participants found the visual and interactive aspect of PlugSonic most interesting as it gives quick results and a pleasant user experience. They also liked the possibility to test the soundscape and listen to the binaural audio directly on the Create app. What participants did not like about the application is that it first requires some level of understanding of the available features and icons. Furthermore, some tasks where judged as being \"too manual\" and requiring \"too many clicks\" to implement. Additional features were desirable such as being able to save the settings of a single sound and automatically close the source panel or choose the colour for the sources' icons.  
Participants were neutral in whether they would use the system frequently. This is consistent with their rating of a 6 (out of 10) to the question how likely they would use any of the curation tools and add stories to the social platform. Although they see the novelty in the interactivity, the main reason for these scores is the fact that they do not work in the sector specifically or are not used to creating content.  
For the NPS score: among the participants we had 1 detractor (one participant rated a 6), 2 passives (2 participants rated an 8) and 2 promoters (2 participants rated a 9). This results in an overall NPS score of 20, which is good. The list of questions with the average rating is shown in Table 2.  
\begin{table}
\begin{tabular}{l l r} \hline \hline  & **Question** & **SUS Avg (Std)** \\ \hline
1 & I think that I would like to use this system frequently. & 4.4 (1.7) \\
2 & I found the system unnecessarily complex. & 2.8 (1.3) \\
3 & I thought the system was easy to use. & 6.2 (0.8) \\
4 & I think that I would need the support of a technical & \\  & person to be able to use this. & 3.0 (2) \\
5 & I found the various functions in this system were & 5.4 (0.9) \\
6 & I found functionality of features and controls clear. & 5.0 (1.2) \\
7 & I thought there was too much inconsistency & \\  & in this application. & 1.6 (0.6) \\
8 & I would imagine most people would learn to use & \\  & the application very quickly. & 5.2 (1.6) \\
9 & I felt very confident using the system. & 5.8 (0.8) \\
10 & I needed to learn a lot of things before I could & \\  & get going with this application. & 1.6 (0.6) \\
11 & I wanted to share or talk to people about & \\  & my experience with the application & 5.8 (0.8) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average system usability score for part 2 of the evaluation.
## 2 Materials and Methods
### Mineralization-Alizarin Red Staining  
Matrix mineralization was assessed through Alizarin Red S (ARS) staining. Cells cultured on the 3D printed squares for different time period (for 7, 14 and 21 days) were fixed with 4% paraformaldehyde for 15 min at room temperature, then stained with 2% ARS for 30 min. Then, all squares were washed by deionized water 4 times and observed under an Olympus BX41 light microscope. Cells cultured in monolayer were used as control.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 4 Multi-Hop Transmission and Synchronization
### Initialization of Particle Swarm Optimization  
The particle is defined as the different paths of the data transmission, and each particle's dimension \(M\) is the same, which is corresponding to the number of cluster heads in a cell in the upper Voronoi diagram. The position of particle \(i\) is initialized of \(Pos(i,\sigma)\) randomly, \(Pos(i,\sigma)\in Rand[0,1]\) and \(1\leq\sigma\leq M\). The method of selecting the next hop node \(S_{d}\) for node \(S_{S}\) is as follows:  
\[s_{d}=Index(NextHop(s_{s}),n) \tag{15}\]  
where \(Index(NextHop(S_{S}),n)\) is the index function of returning the value of the \(n\)th cell in the set \(NextHop(S_{S})\). \(NextHop(S_{S})\) denotes the candidate nodes for the next-hop, and the value of \(n\) is determined by the formula:  
\[n=[Pos(i,\sigma)\times|NextHop(s_{s})] \tag{16}\]  
In the phase of data communication, the specific steps of using particle swarm optimization algorithm to select the optimal path are as follows:  
1. Initialize the particle \(i\) per dimension with random velocity \(Pos(i,\sigma)\);
2. Mapping particle \(i\) routing tree;
3. According to the routing tree can obtain \(MaxDist\) and \(MaxHops\);
4. Calculate the fitness value \(fitness(Pos(i,\sigma)\) of each particle;
5. Finding the individual extreme value \(P_{best\_i}\) and the global extreme value \(Global_{best}\);
6. Update the speed and position of the particles and make the corresponding adjustment;
7. Repeat Steps 2 to 6 until the threshold number of iterations;
8. According to the individual extreme value and the global extreme value in Step 5, the optimal path from node \(i\) to the relay node can be determined.
## 4 Results and Analysis
### Ablation Experiments  
To validate the performance of this proposed algorithm for each module, we conducted ablation experiments using the NIR dataset. The experimental results are shown in Table 5: (i) When all three modules of this algorithm are used, the tracking algorithm achieves a success rate of 70.3% and an accuracy of 88.4%, running at 40.5 FPS. (ii) Removing the SIP and not predicting the search region results in a 4.6% decrease in success rate, with the tracking operating at 13.8 FPS; this demonstrates the efficacy of the Spectral Information Position Prediction in reducing computational complexity. Due to the fact that the entire capsule network of tracking is complete, few targets are lost, which also suggests that predicting the search area for the next frame is something that is practical for this algorithm. In addition, (iii) uses a normal capsule network to capture the correlation that exists among the object and backdrop without the use of BT, which shows that the importance of the spectral information is very significant, with a reduction of 19% compared to (i) and a tracking run speed of 50.2 FPS. The (iv) uses only the HFBE module and employing a detection-based capsule network framework for the remaining tracking tasks results in a success rate of 50.1% at 36.7 FPS. The (v) algorithm does not use HFBE and the success rate reaches 56.3% at 69.0 FPS, which is 4% lower than (i), indicating that BT is useful for this algorithm. (vi) Our algorithm uses only BT and achieves a success rate of 52.6% at 39.3 FPS, which compares favorably with the algorithm of (iv), in which the importance of using spectacular spectral information to distinguish between different capsule weights can be seen. (vii) demonstrates the case when SIP is used in a detection-based capsule network framework for prediction of the search area, the success rate is only 32.6% at 112.6 FPS. (viii). We replaced the BT routing algorithm with the lightweight architecture MobileNetV4. MobileNetV4 combines efficient design in deep learning models with Neural Architecture Search (NAS), achieving higher performance and computational efficiency. The experimental results show that replacing BT routing algorithm with MobileNetV4 led to a 20.5% decrease in success rate, a 27.4% decrease in Precision, and a 92.3 increase in fps. The decrease in success rate and precision can be attributed to the fact that MobileNetV4 primarily optimizes computational efficiency and memory usage, aiming to provide a lightweight network architecture, while capsule networks excel in modeling spatial relationships, particularly in handling dynamic and deformable objects. However, MobileNetV4 is more advantageous for real-time deployment.
## 3 Results
### Scanning Electron Microscopy (SEM) Observation of Adhesive-Dentin Interface  
The mean thickness of the different adhesive layers ranged between 10 to 17 \(\upmu\)m was observed and measured under SEM for all adhesive systems (Figure 3, Table 3). SU and IB mean values were significantly higher than the mean values of IPM and FB (\(p<0.05\)). Some specimens were created with IB fractured at the resin-dentin interface, probably due to SEM preparations and working pressure (Figure 3f).  
No relation was observed between the SBS values and the thickness or the depth of resin-infiltration into dental tubules (Tables 2 and 3). SU and IB had the same infiltration depth and layer thickness but SU had higher SBS values at 24 h and 6 months. Therefore, thickness and tag length had no impact on the SBS values after the aging periods. One interesting finding was the observation of some voids in two IB six-month-aged samples (Figure 4).  
Figure 3: Representative photos of scanning electron microscopy (A-E, x2000 magnification) demonstrating the adhesive layer thickness (white arrows) and tag depth (black arrows). (**a**) FuturaBond M+—‘FB”; (**b**) Scotchbonl Universal—‘SU”; (**c**) Iperbond Max—‘IPM”; (**d**) Iperbond Ultra—‘IPU”, (**e**) Ibond Universal—‘IB”. (**f**) SEM preparation artefact (x3000 magnification, white arrow).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FB** & **SU** & **IPM** & **IPU** & **IB** \\ \hline Adhesive & & & & \\ thickness & 11 \(\pm\) 4 \({}^{\text{a,c}}\) & 15 \(\pm\) 6 \({}^{\text{a,b}}\) & 10 \(\pm\) 4 \({}^{\text{b,d}}\) & 13 \(\pm\) 7 & 17 \(\pm\) 9 \({}^{\text{c,d}}\) \\ (\(\upmu\)m) & & & & \\ \hline Tag depth & & & & \\ (\(\upmu\)m) & 4.6 \(\pm\) 2.2 & 4.8 \(\pm\) 1.8 & 5.6 \(\pm\) 2.1 & 6.1 \(\pm\) 1.8 & 5.6 \(\pm\) 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adhesive layer thickness and tag depth values (means and standard deviations) of the five adhesives. FuturaBond M+—‘FB”, Scotchbonl Universal—‘SU”, Iperbond Max—‘IPM”, Iperbond Ultra—‘IPU”, Ibond Universal—‘IB”. Superscript letters (a–d) indicate significant differences at 5% significant level (\(p<0.05\)).
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
###### Abstract  
Wireless avionics intra-communication (WAIC) refers to a wireless communication system among electronic components (e.g., sensors and actuators) that are integrated or installed in an aircraft and it is proposed to replace heavy and expensive wired communication cables. Recently, the use of a frequency band (4.2-4.4 GHz) for the WAIC (so-called, WAIC band) has been approved by international telecommunication union (ITU). Accordingly, several existing wireless protocols such as IEEE 802.11 and IEEE 802.15 are being considered as candidate techniques for the intra-avionics sensor network. In this paper, we perform a real field experiment to investigate wireless channel characteristics in intra-avionics sensor networks at the WAIC bands by a software-defined radio platform (universal software radio peripheral, USRP) and self-produced monopole antennas for the WAIC band. Through the experiment, we validated the feasibility of IEEE 802.11 protocol for the intra-avionics sensor network at the WAIC band in real aircraft environments. Furthermore, based on the measurement data, we evaluated the bit error rate (BER) performance of multiple antenna techniques where we considered the maximum ratio combining (MRC) for the multi-antenna receiver and the space-time block coding (STBC) for the multi-antenna transmitter.  
channel measurements; IEEE 802.11; monopole antennas; sensor networks; software-defined radio (SDR); wireless avionics intra-communications (WAIC) IEEEexample:BSTcontrol
## 3 Results and Discussion
### Free Radical DPPH S scavenging Capacity  
Metabolism produces free radicals. When oxidative stress in the body increases and the free radical content is significantly high, damage to the surrounding enzymes, proteins, and the intranuclear DNA occurs, leading to phenomena such as aging and pigmentation [44]. The results showed that the DPPH free radical scavenging capacity of the water extracts of NNGB, FNGB, NGB, and FGB increased with the increase in sample concentration and had a dose-response relationship (\(p\) < 0.05; Table 2). When the sample concentration was 10 mg/mL, the percentages of DPPH free radicals scavenged by the water extracts of NNGB, FNGB, NGB, and FGB were 43.24 \(\pm\) 4.64%, 44.24 \(\pm\) 1.45%, 67.35 \(\pm\) 9.87%, and 66.48 \(\pm\) 2.97%, respectively. The water extract of germination black soy milk significantly increased the capacity of scavenging DPPH free radicals by 22.24-24.11% (\(p\) < 0.05) and had better antioxidant activity, as compared to nongermination black soy milk. When the concentration of the ethanol extracts of NNGB, FNGB, NGB, and FGB was 10 mg/mL, the percentages of DPPH free radicals scavenged were 72.87 \(\pm\) 2.14%, 83.84 \(\pm\) 1.62%, 77.15 \(\pm\) 3.80%, and 78.73 \(\pm\) 2.44%, respectively (Table 2). In addition, ascorbic acid (ASA) was used as a positive control in this study. No significant differences were observed in the DPPH free radical scavenging capacity of the ethanol extracts of FNGB and FGB from the positive control group (\(p\) > 0.05), indicating that the FNGB and FGB ethanol extract had good antioxidant activity.  
Previous studies have suggested that germination increases the production of soluble proteins and biologically active substances such as free amino acids and oligopeptides in edible seeds. In addition, germination soybeans have higher antioxidant activity [45]. Therefore, in the water extract groups, germination black soy milk contained more water-soluble substances and therefore had better antioxidant capacity than nongermination black soy milk. Germination can increase the content of nitrogenous substances, sugars, peptides, free amino acids, and total phenols in the soybean water extract and was associated with the increase of antioxidant capacity [17]. Previous studies have indicated that the ethanol extract of soy milk fermented with TWK10 had a good antioxidant capacity and could significantly improve the chelation of ferrous ions, scavenging of DPPH free radicals, and the total reducing capacity [24]. The results of this study also suggest that the ethanol extracts of NNGB, FNGB, NGB, and FGB had a higher antioxidant activity than water extracts, with the FNGB and FGB ethanol extracts having the strongest antioxidant activities, which was consistent with the literature results. After the assessment of the above experiments, the ethanol extract was used as the main sample for the melanogenesis test of zebrafish embryos.
## 3 Proposed TSR Methodology
### Traffic Sign Classification (TSC) Step  
In order to classify the detected traffic signs, machine learning and deep learning methods were used to train and test different algorithms. Table 6 recapitulates the classification results achieved by applying the TSC methods to the GTSRB dataset. In fact, using deep learning models (CNNs) yields more accurate results than applying machine learning classifiers.  
The classification process consists of two steps, which are training and testing, as shown in Figure 6. In the training process, the images are first preprocessed to feed the deep neural Traffic Sign CNN model later. Once the model is trained after several epochs, the testing step takes place using test images to evaluate the developed CNN model. The CNN model is displayed in the figure below.
## 1 Introduction
In recent years, studies have shown that the ultimate bearing capacity, ductility, and energy consumption of the straight shear wall and frame column using this splicing method are comparable to those of the corresponding cast-in-place components [24; 25]. However, the research on its application performance in actual structural components is still insufficient. At present, the research on prefabricated walls mainly focused on the straight shear walls [10; 14; 15; 16; 17; 18]. However, in order to meet the requirements of building functions, L-shaped shear walls are often used in practical projects. Due to the asymmetric geometry of  
Figure 1: Schematic of grouted sleeve connector: (**a**) grouted splice connector; (**b**) type-I grouted lapping connector; and (**c**) type-II grouted lapping connector. L-shaped shear walls, the wall body is susceptible to eccentric lateral force. It is apparent that L-shaped shear walls have higher requirements for joint connections. Therefore, the reliability of L-shaped shear walls connected by APC connectors urgently needs to be evaluated. Besides, previous studies have focused on the effects of low axial compression, i.e., axial compression ratio less than 0.3, on the seismic performance of prefabricated components [10; 14; 16; 25; 26]. In practical projects, both large structural dead weight and insufficient concrete strength will cause large axial compression ratio, which is a great challenge to the seismic performance of the structure. Furthermore, with the increase of axial compression ratio, the ductility and energy dissipation of the specimen decreased obviously [15; 27]. Therefore, it is necessary to investigate whether the prefabricated specimen can achieve the seismic performance equivalent to the cast-in-place specimen under high axial compression ratio [26]. According to reference [26], the axial compression ratio of 0.5 was selected for this test. To study the seismic performance of a fully prefabricated L-shaped shear wall connected by an APC connector under high axial compression ratio, three full-scale L-shaped shear walls, including a cast-in-place wall and two prefabricated walls, one connected by a type-I connector and the other by a type-II APC connector, were designed for a quasi-static test, and the failure modes, bearing capacity, stiffness, deformation, and energy dissipation of the specimens under the axial compression ratio of 0.5 were comparatively studied.