esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Different InGaN/GaN Electron Emission Layers/Interlayers on Performance of a UV-A LED
## 2 Experimental  
We grew the UV-A LED epi-structures on a 2-in, c-plane (0001) Al\({}_{2}\)O\({}_{3}\) (sapphire) substrate using an Aixtron 2600 G3 HT metal organic chemical vapor deposition (MOCVD) system (Aixtron, Herzogenrath, Aachen, Germany) [11]. The epi-structures consist of a 200-nm thick GaN buffer layer, a 4-um thick unintentionally doped GaN layer, a 3-um thick Si-doped In\({}_{0.01}\)Ga\({}_{0.99}\)N/GaN interlayers (ILs), 3 pairs of 3-12-nm-thick In\({}_{0.01}\)Ga\({}_{0.99}\)N/GaN EELs, 6 periods of InGaN/GaN/AlGaN N MGWs, and 150-nm thick Mg-doped GaN layer. During the growth of layers, various MO sources such as trimethylaluminum (TMAI), TMGa, TEGa, and trimethylindium (TMIn) were used. To investigate the effects of growth conditions of the EELs and ILs on crystallinity of the post grown active layer and light efficiency of UV-A LEDs, the MO Ga precursors were changed for the barrier GaN layer in ILs and EELs as denoted in Figure 1a. We prepared three structure of full epi-structures (p-GaN/MQWs/EELs/ILs/n-GaN/u-GaN on Al\({}_{2}\)O\({}_{3}\) substrate) of UV LEDs consisting of different EELs/ILs with GaN barrier layers with TMGa/TMGa (UVLED I), TEGa/TMGa (UVLED II), and TEGa/TEGa (UVLED III), respectively. In addition, GaN barrier layer-terminated EELs/ILs/n-GaN/u-GaN on Al\({}_{2}\)O\({}_{3}\) substrate were named following the UVLED chips as EI-UVLED I, EI-UVLED II, and EI-UVLED III, respectively. The strain status and structural characteristics of the UVLED wafers were evaluated by high-resolution X-ray diffraction (HR-XRD). The details for UVLED epitaxy structure were investigated by using a high-resolution transmission electron microscopy (HR-TEM) to elucidate the structural characteristics of the InGaN/GaN MQWs. The electroluminescence (EL) of the fabricated UV-A LEDs was investigated using the probe station WPS3100 (Opto system). The photoluminescence (PL) properties of MQWs was measured by a Nd:YAG 266 laser as the excitation source (output power is fixed at 2.0 mW). For LED chip fabrication, general photolithography, patterning, and metallization processes were conducted as follows [12]. The as-grown LED epi-structures/Al\({}_{2}\)O\({}_{3}\) were partially etched by inductively coupled plasma until the n-type GaN layer was opened. Then a 200-nm thick transparent conductive indium tin oxide (ITO) layer was deposited by electron beam evaporation (ULTECH-UEE). Cr/Au electrodes (10/500 nm) were subsequently evaporated for n-and p-type electrodes. Finally, the UVLED wafer was divided into rectangular chips with an area of 485 \(\times\) 425 \(\upmu\)m\({}^{2}\).  
Figure 1: (**a**) Schematic diagrams of a full epi-structure with different growth conditions for electron emitting layers (EELs) and interlayers (ILs); (**b**) a cross-sectional TEM image of the UV-A light emitting diode (LED) structure; and (**c**) magnified cross-sectional TEM image for InGaN/GaN/AlGaN multiple quantum wells (MQWs). (**d**) Schematic band diagram of the UVLED structure.
## 3 Results and Discussion
### Surface Texture of the Binder-Coated Aggregates  
The images to study the surface texture of aggregates coated with GNP-modified asphalt binder were taken both from a digital camera and an SEM. Figure 15 shows the images of a piece of aggregate coated with base binder. Figure 16 shows the images of GNP-modified asphalt binder.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Samples** & **Pull-Off Tensile Strength (POTS) (MPa)** \\ \hline Base Binder & 8.67 \\
1 & 8.67 \\
2 & 8.76 \\
3 & 8.73 \\
2\% GNP-Modified Binder & \\
1 & 11.67 \\
2 & 11.95 \\
3 & 11.67 \\
4\% GNP-Modified Binder & \\
1 & 13.66 \\
2 & 13.38 \\
3 & 13.38 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Pull-Off Tensile Strength (POTS) of the binder. GNPs = Graphene Nano-Platelets.  
Figure 14: The Pneumatic Adhesion Tensile Test Instrument (PATTI) Assembly.  
Figure 15: A piece of aggregate coated with base binder.  
It can be observed from the images that the surface of the aggregate coated with GNP-modified binder is rough in comparison to the one coated with the base binder. This is because nanoparticles add their nano-texture to the surface, which is also the reason that nanoparticles are used to make the pavement surface rough in order to increase its skid resistance [30].
## 2 Materials and Methods
### Batch Experiments  
Laboratory batch experiments were conducted to understand the nutrients' removal efficiencies of the basalt bed, zeolite bed and MZP-PM filter. Batch experiment data was used to obtain batch adsorption isotherm of these materials. Adsorption tests were performed at the initial concentration of N and P of 20 mg/L and 5 mg/L, respectively, and shaken with different doses of zeolite, basalt or MZP-PM in 100 mL solutions. The suspensions were agitated at 180 rpm for 12 h at 25 \(\pm\) 0.5 \({}^{\circ}\)C and pH 6.5. The amount of nutrients adsorption at equilibrium, \(\mathrm{q_{e}}\) (mg/g) was calculated using Equation (1):  
\[\mathrm{q_{e}}=\frac{(\mathrm{C_{0}-C_{e}})\mathrm{V}}{\mathrm{M}} \tag{1}\]  
where, \(\mathrm{C_{0}}\) is the initial concentration of NH4\({}^{*}\)-N and TP (mg/L), \(\mathrm{C_{e}}\) is the equilibrium concentration of NH4\({}^{*}\)-N and TP (mg/L), V is the volume of solution (L), and M is mass of adsorbent (g).
## 5 Strengths, Limitations, and Future Research Directions  
To the best of our knowledge, this is among the first studies to assess demographic determinants of status and access to dental health care among multicultural migrant populations in several EU member states. Other studies [57] have addressed and shown how SES status affects oral health mainly in the general population, but this study has focused specifically on migrants from war-torn or poor countries characterized by specific vulnerabilities, such as feelings of discrimination, when accessing the health system. Another strength includes the breadth of our questionnaire, which investigated numerous demographic factors alongside assessments of physical and mental health. As a result, our study allowed for numerous comparative analyses between demographic and dental health factors. However, this study had also several limitations. Due to the nature of the design, the cross-sectional study provided a snapshot of the respondents' dental condition, and temporal constraints reduced the viability of assessing causal relationships between dental health determinants and outcomes. Furthermore, the self-administered questionnaire depended on self-reported information and assessments of health, which may introduce reporting bias. However, this study did not aim to clinically assess the dental health of newly arrived migrants, but rather to assess demographic factors and health determinants that may have an influence on it, in planning preventive interventions and to potentially predict the dental health status of populations in future migrant waves. In addition, the sample consisted of an overwhelmingly large proportion of male respondents, which may be a result of cultural barriers to female respondents. Finally, self-perception of oral health was not related to clinical epidemiological oral health indicators or dento-oclusal aesthetic indicators, except for untreated decayed teeth. Although the dmft Index, DMFT Index and dmft/DMFT Index have been used intensively in clinical settings to assess dental caries prevalence, as well as dental treatment needs among populations [58], the use of these scores was not part of the study to check the association with self-perceived oral health, as was the case in other studies. [59] Since the proximal consequence of dental decay is pain [60], it is likely that the contribution of decayed untreated teeth to self-reported oral health is viewed by migrants through their subjective measures [61].  
For the basis of prospective studies, our results could be used to further investigate the dental caries prevalence and oral health status in different migrant settings (urban/non-urban) within documented or non-documented people and access among subsets of migrant groups, as divided by the identified demographic determinants.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 1 Introduction
### Model-Driven Prediction Methods  
A model-driven method is a model with a fixed structure based on certain assumptions, the parameters of which can be calculated from empirical data. For example, [PERSON] et al. [3] combined Kalman filters with accelerometers and GPS to predict vehicle deceleration for accident detection purposes in real time, overcoming the limitations of accelerometers. [PERSON] [4] used a fuzzy logic car-following model to predict the driver's acceleration and deceleration rates, and determined the driver's longitudinal acceleration based on the relationship between the preceding vehicle and the subject vehicle. [PERSON] et al. [5] improved the action point model, and the focus of the paper was placed on the deduction of the acceleration equations by considering the car-following situation in congested traffic flow in order to achieve the prediction of acceleration and replicate car-following behavior. Model-driven prediction methods use much less training data than data-driven methods, as their complexity is bounded. However, when faced with large datasets, these models will become very complex and they cannot take advantage of the additional information present in large datasets.
## 1 Introduction
In this study, a check function is used to correct the abnormal data, considering the boundary range of the abnormal data, to ensure maximum relevance between the corrected abnormal data and other parameters. The DST and UDDS datasets were used to test the proposed method. Compared with traditional BP neural networks, CNN and RNN, the proposed method achieved the best effect in battery SOC estimation using the two datasets, with a maximum error of less than 5% in both datasets. In addition, when the battery SOC was less than 20%, the battery SOC estimation error did not increase but rather decreased, demonstrating the advantages of this method.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 1 Introduction
This mechanism of spreading the information in time before transmission and gathering it in the receiver potentially increases the communication system robustness to the combination of time-varying fading and localized noise effects. Wavelet coding was thoroughly evaluated in previous works, which include its property of error correction [19; 20; 15; 21]. The obtained results have shown that such a technique is quite efficient, which makes it suitable for applications in communication systems. Preliminary results evaluating the effects of the addition of wavelet coding on a control system were evaluated in [22]. However, the evaluation of the impacts of this coding technique embedded in control systems has not been presented so far in detail. This paper proposes the design of a control system with adaptive wavelet coding and an evaluation of its performance over flat Rayleigh fading channels. The increase of the robustness in the wireless communication provided by the wavelet coding allows the transmission of auxiliary data, which can be necessary in control system scenarios [23]. The rest of this paper is organized as follows. Section 2 presents the coding and decoding algorithms with matrices of wavelet coefficients. Section 3 presents the control system with adaptive wavelet coding proposed in this paper. The results and discussions are presented in Section 4, and the conclusion are drawn in Section 5.
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Ballot Encoding  
Vote options are expressed in a strictly encoded 32-byte memo field available in every Stellar transaction. We do not want to limit our solution to just one answer per ballot, especially when it is straightforward to achieve multiple options.  
Figure 2: Architecture of our i-Voting system.  
We call \(A_{i}\) the answers for question \(q_{i}\), and \(|A_{i}|\) the number of all possible answers for question \(q_{i}\). Then the first \(\lceil lg|A_{1}|\rceil\) bits of whole 256 bits memo field will be reserved for answer \(q_{1}\), next \(\lceil lg|A_{2}|\rceil\) bits will be reserved for answer for question \(q_{2}\), and so on. We give an example for easier understanding. If the ballot contains three questions \(q_{1}\), \(q_{2}\), \(q_{3}\). For \(q_{1}\) we have 10 possible answers, for \(q_{2}\) eight possible answers and for \(q_{3}\) three possible answers. Then the first \(\lceil lg|10\rceil=4\) bits will be reserved for encoding answer \(A_{1}\), next \(\lceil lg|8|\rceil=3\) bits will be reserved for \(A_{2}\) and next \(\lceil lg|3|\rceil=2\) bits accordingly for \(A_{3}\). Which gives us \(4+3+2=9\) bits reserved for encoding answers.  
Representing vote is just a matter of bytes interpretation, meaning that we can choose any other voting system. For example, we could use Majority Judgment [39], where each voter can give a grade for each candidate. There are six different grades: Reject, Poor, Acceptable, Good, Very Good, or Excellent. The winner is the candidate, whose median grade is the highest. Adding this voting system is just a matter of introducing the third dimension of data that needs to be encoded. The six grades can be encoded using \(\lceil lg|6|\rceil=3\) bits. The example described above could be encoded using Majority Judgment by simply giving each candidate three bits for encoding value from 1 to 6 (Reject to Excellent). The \(q_{1}\) has \(|A_{1}|\) candidates, to encode the grade for each of them we need \(3*|A_{1}|\) bits for encoding \(q_{1}\). The total number of bits needed to encode this example using Majority Judgment is \((|A_{1}|+|A_{2}|+|A_{3}|)*3=(10+8+3)*3=63\) bits.  
Such encoded ballots have to be encrypted to prevent results calculation during an election.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
###### Abstract
However, classifications such as traditional CNN networks or SVMs generally have the following shortcomings:  
Figure 1: Examples of LED chips: (**a**) sketch of chip; (**b**) normal chip; (**c**) chip with scratches; (**d**) chip with crack. * Difficult to locate position of the defect accurately. * Irregular defect shape increases image annotation time. * Poor versatility and portability for different batches and types of products. * Long training time, expensive calculation, and difficulty in collecting training samples; When the defect standard changes, training needs to be carried out from scratch, which cannot meet the production timeliness required by the enterprise. In this paper, a chip segmentation method based on position pre-estimation and a modified NCC matching algorithm, as well as feature enhancement methods are proposed, which can accurately segment LED chips and stably detect various defects. By pre-extracting the coordinates of possible chips, the position pre-estimation algorithm effectively avoids the interference of chaotic chips and improves the robustness of segmentation. By using the modified NCC algorithm, calculation efficiency was highly improved, meanwhile, the precise location result was not affected by the change in brightness. According to the characteristics of defects, feature enhancement schemes were also designed to extract low-contrast defects.
# Fatigue Testing of Shear Reinforcement in Prestressed Concrete T-Beams of Bridges
## 2 Experimental Investigations  
### Test Specimens  
Considering the cross-section geometry, the experimental investigations of prestressed concrete beams with shear reinforcement can be divided into two test series (Figure 1). The first series with I-shaped beams (I-beams) was designed to exhibit shear failure with diagonal cracking in the web independent of flexural cracks. The results are presented in [40; 42; 43]. In order to take into account the influence of flexural cracking on the shear capacity, a further series of T-shaped cross-sections (T-beams) was investigated. With these beams, the shear cracks arise from flexural cracks on the lower surface of the cross-section in the shear-loaded area.  
The T-beams with a web width of \(b_{\mathrm{w}}\) = 0.3 m had a height of 0.7 m and a length of 6.5 m (Figure 2). The shear reinforcement ratios (\(\rho_{\mathrm{w}}=a_{\mathrm{sw}}/b_{\mathrm{w}}\) with \(a_{\mathrm{sw}}\) as the area of shear reinforcement within one meter (cm\({}^{2}\)/m)) in the web of the shear span varied between \(\rho_{\mathrm{w}}\) = 0.22% and \(\rho_{\mathrm{w}}\) = 0.67% using closed stirrups \(\mathcal{O}\)12 with a steel grade B500. Within one beam, both shear spans featured the same shear reinforcement ratio. The steel properties, determined by axial tension tests on single bars taken from the same batch, are shown in Table 1. In the middle part of the beam and at the support, the distance between the stirrups was reduced to avoid a premature failure in these areas. The flexural reinforcement consisted of 5\(\mathcal{O}\)25 (B500) or 5\(\mathcal{O}\)26.5 (high strength steel S900/1100 [44]).  
With an axial distance of 9.5 cm from the bottom side of the beam, two straight tendons (each 3\(\mathcal{O}\)0.6\" (1.4 cm\({}^{2}\))) were arranged for post-tensioning (St 1570/1770,\(f_{\mathrm{p0.1k}}=1500\) N/mm\({}^{2}\)). After the subtraction of all losses, the prestressing force for the beams varied between 330 kN and 500 kN (Table 1). The mean strengths of the ready mixed concrete during the test period were in a range of 32 and 48 N/mm\({}^{2}\) over all tests with a small variation over the test period of each beam (Table 2).  
Figure 1: Experimental test setups with typical crack patterns (**a**) series 1: I-beam; (**b**) series 2: T-beam.  
Figure 2: (**a**) Cross-section with reinforcement and longitudinal section with reinforcement; (**b**) Test setup.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 2 Maritime Scenario and Problem Statement
### Coverage Status in the Baltic Sea  
Currently, a 5G base station operating at 3.5 GHz is located in the harbor of Helsinki, and the signal strength was measured along the ferry line that is depicted in Figure 2. Using the equipment shown in Figure 1, a set of received signal strength measurements were taken. The 5G signal reached 8.11 km from the shore with a strength of between \(-105\) to \(-110\) dBm and then deteriorated rapidly. The performance was better than initially expected, but not enough for covering shore to shore. A table listing the received signal strength values versus the distance can be found in Appendix A.  
Similarly, a 4G base station operating at 700 MHz is located in the Finnish Vuosaari harbor and, in this case, the performed measurements indicated that the signal reached 45.85 km from the shore with a strength of between \(-105\) to \(-110\) dBm and then also deteriorated relatively quickly (see Figure 3). A table listing the received signal strength values versus the distance can be found in Appendix A. However, the signal could reach above 45 km even if using 4G; the technical limitation on 700 MHz 5G new radio (NR) is currently 35 km and, thus, covering shore to shore, even with the coastal base stations in Muuga (Estonia) and Vuosaari (Finland), is not possible.  
From the measurement campaign conducted, a series of conclusions were obtained, as described in the two following sections.  
Figure 1: Equipment used for the received signal strength measurement campaign.
## 4 Computational Results and Discussion
### Pumping Characteristics  
The characteristic feature of peristalsis is pumping. The pumping characteristic can be scrutinized in the form of \(\Delta P_{\lambda}\) versus \(\theta\), illustrated in Figure 3a-d. Pumping action divides the whole region into the four segments: pumping region (adverse pressure gradient \(\Delta P_{\lambda}>0\), Positive pumping \(\theta>0\)), backward/retrograde pumping (\(\Delta P_{\lambda}>0\), \(\theta<0\)), augmented pumping (favorable pressure gradient \(\Delta P_{\lambda}<0\), Positive pumping \(\theta>0\)) and free pumping (\(\Delta P_{\lambda}=0\)). Figure 3a elucidates the \(\Delta P_{\lambda}\) against \(\theta\) fordifferent values of \(W_{e}\). It is seen that pumping decreases as \(W_{e}\) increases in the pumping region (\(\Delta P_{\lambda}>0\)). But in co-pumping (\(\Delta P_{\lambda}<0\)), the pumping increases as \(W_{e}\) increases. For free pumping (\(\Delta P_{\lambda}=0\)), curves coincide i.e., there is no difference between Carreau fluid and Newtonian fluid. Figure 3b portrays the \(\Delta P_{\lambda}\) against \(\theta\) for different values of \(n_{i}\). It reveals that pumping enhances as \(n_{i}\) increases in the pumping region (\(\Delta P_{\lambda}>0\)). But in the case is reverse in co-pumping (\(\Delta P_{\lambda}<0\)) i.e., the pumping decreases as \(n_{i}\) increases. Figure 3c depicts that \(\Delta P_{\lambda}\) decreases as \(m_{e}\) increases. It physically declares that formation of EDL on charged surface opposes the Carreau fluid flow. Furthermore, Figure 3d depicts \(\Delta P_{\lambda}\) decreases as \(U_{HS}\) increases. Here pumping is modulated in presence of electric field. It is noticed that because of \(U_{HS}\), the relation between \(\Delta P_{\lambda}\) against \(\theta\) becomes linear.  
Figure 3: \(Cont\).  
The distribution of pressure gradient is plotted in Figure 4a-e for various values of Weissenberg number \(W_{e}\), fluid index \(n_{i}\), electroosmotic parameter \(m_{e}\), Helmholtz-Smoluchowski velocity \(U_{HS}\) and volume flow rate \(\theta\). Figure 4a highlights that by increasing the \(W_{e}\), magnitude of pressure gradient \(dp/dx\) increase. As Weissenberg number \(W_{e}\), is the inversely proportional to channel width. So, lesser the channel width, large amount of pressure is needed to maintain the flux to pass through it. Figure 4b depicts that by increasing the \(n_{i}\), magnitude of \(dp/dx\) decreases. Figure 4c shows that by raising the values of \(m_{e}\), \(dp/dx\) decreases. Figure 4d illustrates that the magnitude of \(dp/dx\) increases as \(U_{HS}\) increases. Also, Figure 4e presents that by increasing the values of \(\theta\), \(dp/dx\) decreases. Because there is an inverse relationship between \(dp/dx\) and \(\theta\).  
Figure 4: \(Cont\).  
Figure 4: \(Cont.\)
## 4 Method
### Different Datasets Fusion  
Due to the discrepancies in the SH solutions from different institutions, it may lead to inconsistent results [43]. To improve the reliability of our results, we used the GTCH method to evaluate the uncertainties of TWSC results from six GRACE solutions. Then, according to the uncertainty results, we fused these six TWSC results by using least square method. The advantage of this method is that it can estimate relative covariances of different datasets without a prior information [44,45]. The technical detail about the GTCH method can be found in Refs. [46,47].  
We fused the different observation series by using the least square method, the expression is as follows [48]:  
\[Z=\sum\limits_{i=1}^{N}l_{i}\bullet Z_{i}(i=1,2,\cdots,N) \tag{4}\]  
where \(Z_{i}\) is the \(i\)th dataset and \(l_{i}\) is the corresponding weight, and \(Z\) is the fused result. The weights were calculated according to the relative variances.  
\[l_{i}=\frac{1/f_{ii}}{\sum\limits_{n=1}^{6}1/f_{nn}} \tag{5}\]  
where \(f_{ii}(i=1,2,\cdots,6)\) is the relative variance of \(i\)th dataset estimate by the GCTH method. We fused the different datasets on each grid point according to the above method.