esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Extracted Beams
### Mechanical Characteristics of the Beam Materials
#### 2.2.1 Compressive Strength of Concrete
The concrete of the beam DB1 was on average of better quality with respect to the one of the beam DB2 even if the two beams were very close in the structure (Figure 1c) but the concrete of the beam DB1 had a wider scatter of strength values. Furthermore, the compressive strength at the top part of each beam was greater than the one at the bottom part. This is probably due to two main aspects: 1. Concrete segregation after the concrete cast that is typical of old concretes; 2. The adopted load scheme produces compression at the beam extrados and tension at the beam \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DB1** & & & **DB2** \\ \hline Concrete core & R & Steel rebar \(\mathrm{f_{0.2\%}}\) & Concrete core & R & Steel rebar \(\mathrm{f_{0.2\%}}\) \\ ST7 & 16.25 & RT1-1 (\(\mathrm{\char 36}\)) 384.55 & ST2 & 10.72 & RT2-1 (\(\mathrm{\char 36}\)) 344.36 \\ ST8 & 9.77 & RT1-2 (\(\mathrm{\char 36}\)) 393.54 & ST3 & 10.73 & RT2-2 (\(\mathrm{\char 36}\)) 636.51 \\ ST10 & 16.51 & RT1-3 (\(\mathrm{\char 36}\)) 439.33 & ST8 & 10.08 & RT2-3 (\(\mathrm{\char 36}\)) 322.77 \\ ST11 & 20.53 & RT1-4 (\(\mathrm{\char 36}\)) 537.25 & ST10 & 12.27 & RT2-4 (\(\mathrm{\char 36}\)) 304.93 \\ ST11 inf-1 & 13.94 & RT1-5 (\(\mathrm{\char 36}\)) 374.91 & ST11 & 10.65 & RT2-5 (\(\mathrm{\char 36}\)) 313.44 \\ ST11 inf-2 & 7.68 & RT1-6 (\(\mathrm{\char 36}\)) 435.53 & ST13 & 8.86 & RT2-6 (\(\mathrm{\char 36}\)) 418.32 \\ ST14 & 16.02 & & ST22 & 12.91 & RT2-7 (\(\mathrm{\char 36}\)) 415.38 \\ ST16 & 16.93 & & & RT2-8 (\(\mathrm{\char 36}\)) 367.71 \\ \hline \hline \end{tabular} Note: ST11 inf-1 and ST11 inf-2 are two specimens obtained by the same core ST11 inf (Figure 3b).  
\end{table} 
Table 1: Cubic concrete compressive strength (R) by compressive tests on cores (ST) and steel yield stress (\(\mathrm{f_{0.2\%}}\)) by tensile tests on steel rebar pieces (RT); [MPa]. Figure 3: Beams DB1 and DB2: cubic concrete compressive strength values (R) along the beams obtained by (**a**) the corrected non-destructive tests at U test stations or (**b**) by destructive tests on concrete cores (ST). intrados; Even if the concrete cores were extracted where damage was modest, inevitable concrete cracking may affect the concrete mechanical characteristics. Similar compressive strength values were observed for the cores extracted at the beam end and at the beam midspan extrados (ST7, ST10, ST14 for the beam DB1; ST2, ST8 and ST10 for the beam DB2). It seems that eventual preexisting cracking at the beam regions in tension, when the beam was in the structure (extrados at the beam ends and intrados at the beam midspan), did not affect the mechanical properties of concrete. These experimental results should be investigated better by further experimental research on \"real\" existing beams and closely considered to assess the beam strength and deformation capacity [60,61].
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 6 Discussion
### MLE Search Methods  
Regarding the MLE algorithm, it does not improve the target location performance overall when all the devices are in good visibility and propagation conditions (such as Case 1), instead, it slightly worsens the positioning error, as seen in Table 2. This is because MLE assigns a low initial score to the new sources. Nevertheless, the benefits of MLE appear in more realistic scenarios where input data introduces outliers (Cases 2 and 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Nelder-Mead** & **L-BFGS-B** & **TNC** & **COBYLA** & **SLSQP** & **No Weighting** \\ \hline \multirow{3}{*}{Case} & \(\mu\) [m] & 1.14 & 1.46 & 1.43 & 1.14 & 1.14 & 1.07 \\ \cline{2-8}  & \(\sigma\) [m] & 0.77 & 1.2 & 0.99 & 0.77 & 0.77 & 0.67 \\ \cline{2-8}
1 & 80\% cdf error [m] & 1.63 & 1.84 & 1.93 & 1.63 & 1.63 & 1.45 \\ \cline{2-8}  & Time elapsed [s] & 0.103 & 0.065 & 0.169 & 0.200 & 0.067 & 0.070 \\ \hline \multirow{3}{*}{Case} & \(\mu\) [m] & 0.98 & 1.52 & 1.52 & 0.96 & 0.95 & 1.11 \\ \cline{2-8}  & \(\sigma\) [m] & 0.67 & 1.74 & 1.74 & 0.67 & 0.67 & 0.84 \\ \cline{2-8}
2 & 80\% cdf error [m] & 1.3 & 1.83 & 1.83 & 1.25 & 1.23 & 1.46 \\ \cline{2-8}  & Time elapsed [s] & 0.125 & 0.079 & 0.176 & 0.225 & 0.082 & 0.078 \\ \hline \multirow{3}{*}{Case} & \(\mu\) [m] & 18.7 & 19.08 & 18.36 & 18.7 & 18.7 & 18.14 \\ \cline{2-8}  & \(\sigma\) [m] & 9.65 & 10.75 & 10.14 & 9.66 & 9.66 & 10.71 \\ \cline{1-1} \cline{2-8}
3 & 80\% cdf error [m] & 27.84 & 27.36 & 26.88 & 27.84 & 27.84 & 25.71 \\ \cline{1-1} \cline{2-8}  & Time elapsed [s] & 0.109 & 0.059 & 0.194 & 0.254 & 0.052 & 0.050 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the search methods in both scenario.  
In Case 2, the use of MLE, with the methods of Nelder-Mead, COBYLA or SLSPQ, improve the location system performance in all the statistical metrics proposed in the Table 2 and SLSQP has a very similar time resolution without using MLE. In Case 3, it is noticeable that the MLE reduces the standard deviation error, although it increases the mean error. This is expected due to MLE reducing the impact of data with higher variances. Thus, despite reducing the impact of the standard deviation, the offsets introduced by multipath predominate on the location estimation. Again, the SLSQP search method is very similar to the non-weighted method. Hence, L-BSGS-B and TNC both show not to be suitable for positioning. In contrast, SLSQP search method proves better than the rest of the methods for improving the location performance against outliers or bad propagation conditions that are very typical for indoors scenarios.  
The positive results obtained with MLE can be further improved using techniques, such as Kalman filters [24], or complementing the weight calculations with additional contextual information, such as the knowledge of LOS/NLOS conditions (obtained, for instance, with machine learning) [36].
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design
### Main Controller  
The Main Controller begins counting the data when it receives the LiDAR data. First, the header file starts counting from 0 to 41. When the count reaches 41, the flag Flag 1 state is started. When cnt_b starts counting 1, it will notify Flag 1 to move to the state of Flag 2. When the cnt_b count is 2, Flag 2 will be notified to move to the horizontal angle 1 (Az1). When the horizontal angle receives a cnt_b count of 3, it will move to the horizontal angle 2 (Az2). The horizontal angle 2 will start to calculate the horizontal angle information. The horizontal angle output is an 8-bit Az1 signal combined with an 8-bit Az2 signal equal to a 16-bit horizontal angle, as shown in Equation (12).  
\[\text{Azimuth}=\{\text{Az1,Az2}\} \tag{12}\]  
When the horizontal angle 2 (Az2) is calculated and the next step of cnt_b is received, it is moved to the distance 1 (Dist1) for calculation. When cnt_b changes again, cnt_b moves to distance 2 (Dist2) for calculation. After the calculation, the 8-bit distance 1 (Dist1) information is combined with the 8-bit distance 2 (Dist2) information and multiplied by 2 to be the 16-bit distance information, as shown in Equation (13).  
\[\text{Distance}=\{\text{Dist1,Dist2}\}\times 2 \tag{13}\]  
When the operation of distance information is completed, the cnt_b changes again to the Reflect state for calculation. If the cnt_b count value is less than 100, the main controller returns to the distance 1 (Dist1) state. If the cnt_b count value is 100 and the Iblock value is less than 11, then it returns to the flag 1 (Flag1). If the cnt_b count value is 100 and the Iblock value is equal to 11, then it returns to the LiDAR receiving information state until all information is received. Therefore, the main controller needs about 1716 cycles to decode one packet, as shown in Figure 8.  
Figure 7: System architecture.
###### Abstract  
In order to solve the problems in the accuracy and adaptability of the existing methods for blade twist measurement, a high-precision and form-free metrological method of blade twist based on the parameter evaluation of twist angular position and twist angle is proposed in the study, and the theoretical model, the measurement principle and the key technologies of the method are discussed in detail. Three key issues of the twist metrology of a blade are solved based on technologies of calibration, a priori planning and geometric analysis: aeroengine axis matching, high-precision coordinate acquisition of the leading edge and the trailing edge, and extraction of twist angular position of the profile. The measurement path planning, sampling strategy optimization and high-precision coordinate collection are executed automatically without theoretical model data of the measured blade, thus the form-free and high-precision metrology of the blade twist is achieved. The research results show that the metrological method of blade twist presented in this study is effective, and that its measurement uncertainty is less than 0.01\({}^{\circ}\). This method is form-free, efficient and accurate, and can solve the problems of high-precision measurement and evaluation for the twist of aeroengine blade primely.  
aeroengine blade; blade twist; measurement and evaluation; a priori planning; geometric analysis Article
## Nomenclature
### Abbreviations  
\(M\)MC & Modular multilevel converter  
\(SLPWM\) & Shift level pulse width modulation  
\(HVDC\) & High-voltage direct current  
\(SMs\) & Submodules  
\(KVL\) & Kirchhoff's Voltage Law  
**Variables**  
\(i_{k}\) & Output MMC currents  
\(I_{(u,)k}\) & Upper and lower arm currents of the MMC  
\(i_{cirk}\) & Circulating currents of the MMC  
\(i_{dc}\) & MMC dc-link current  
\(v_{dc}\) & MMC dc-link voltage  
\(v_{(u,)k}\) & Submodule voltages of the MMC  
\(v_{k}\) & Output voltages of the MMC  
\(I_{mj}\) & Maximum magnitude of the \(j\)th harmonic of the output current  
\(v_{mj}\) & Maximum magnitude of the \(j\)th harmonic of the output voltage  
\(I_{dc\_ir}\) & dc component of the circulating current  
\(I_{mcirj}\) & Maximum magnitude of the \(j\)th harmonic of the circulating current  
\(\beta_{j}\) & Angle difference between the output current and voltage  
\(\alpha_{cirj}\) & Phase difference of the circulating current  
**Parameters**  
\(L\) & Output inductance of the MMC  
\(R\) & Output resistance of the MMC  
\(L_{t}\) & Arm inductance of the MMC  
\(R_{t}\) & Arm resistance of the MMC  
\(\omega_{1}\) & Fundamental angular frequency of the MMC  
\(\omega_{j}\) & \(j\)th-order angular frequency of the MMC variables  
\(C_{(u,)k}\) & Equivalent SM capacitors of the MMC arms
###### Abstract
Moreover, the system should enable social presence, which was defined as the \"sense of being together with another\" [10]. Social presence was also described as an important quantification factor for 3D remote collaboration systems [11; 12]. The guidelines proposed in [10] were explored in this work to assess social presence. In [13], a social robot, AIDA, is presented that uses the driver's mobile device as its face designed to be a friendly in-car companion. The results of an experimental study where participants were placed in a mock in-car environment show that AIDA users were less stressed. The effects of different types of intelligent virtual agents (IVIAs) on the drivers' perceptions in autonomous driving scenarios was investigated in [14]. The results revealed that drivers were more engaged when conversational agents were present. A recent study highlighted that productivity could be improved by using AR headsets, which can provide large, flexible, virtual workspaces for passengers that work during travel with cars, trains, subways, and planes [15]. Therefore, we can hypothesize that it is important for future in-car companions to be embodied and interactive. In this paper, we explore how subjects that use HoloLens 2 perceive the social interaction while performing two non-driving activities in a Metaverse-based mixed reality environment during autonomous driving. We present the findings of a user study in which participants are engaged in an interpersonal discussion, as well as in a simple, yet competitive tic-tac-toe game. To our knowledge, there are no studies that evaluate social presence in a collaborative, immersive mixed reality environment during autonomous driving.
## 2 Topological Optimization Problem with Stress Constraints under Aperiodic Load
### Topology Optimization Model Design  
The stress level of the structure is an important criterion to measure whether the structure will have strength failure, and it is also a design factor that the structural designer must consider at the early stage of the design stage.  
Based on the BESO method, the discrete topology optimization design variables can solve the stress singular value problem in essence, which has certain advantages for solving the stress-constrained topology optimization problem. With the minimum volume of the structure as the objective, the maximum stress as the constraint, and the presence or absence of materials as the design variable, the optimization model is as follows:  
\[\begin{array}{l}\text{find}:\mathbf{x}=[x_{1},x_{2},\cdots,x_{n}]\\ \text{min}:V=\sum\limits_{i=1}^{n}V_{i}x_{i}\\ s.t.:\mathbf{M}\ddot{\mathbf{U}}+\mathbf{C}\dot{\mathbf{U}}+\mathbf{K} \mathbf{U}=\mathbf{F}\\ \sigma_{vm}^{\text{max}}\leq\sigma_{vm}^{*}\\ x_{i}=0\text{ or }1\end{array} \tag{10}\]  
Where \(x\) is the design variable and \(x_{i}\) represents the design variable value corresponding to the \(i\)th element. The design variables can be understood as the relative density of the element and, more directly, as an indicator of the presence or absence of the element material. When \(x_{i}\) equals 1, it indicates that element \(i\) is a solid element with material. When \(x_{i}\) equals 0, it indicates that cell \(i\) is an empty element without material. \(n\) is the total number of finite elements in the design domain, \(V\) is the overall volume of the structure, \(V_{i}\) is the volume of \(i\)th element, \(\sigma_{vm}^{\text{max}}\) is the maximum Mises stress of the structure, and \(\sigma_{vm}^{*}\) is the artificial allowable stress.  
The above optimization model means that the structure with the smallest volume can be obtained by updating the design variables under the conditions necessary to satisfy the given load, boundary conditions, and stress constraints.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 4 Research on Key Technologies and Equipment of Seed Tape Laying
### Soil Covering Device of Seed Tape Planter  
During the operation of the seed tape planter, the embedding depth of seed tape will affect the crop emergence rate and other indicators. Seed tape sowing requires that the tape covered by the soil should be even, without naked leakage, and the thickness of the soil is generally 2-3 cm.  
In 2000, [PERSON] [97] designed a direct seeding machine with two rows for paddy fields, which is mainly used for direct seeding of rice. The seed tape was first led to the surface of the paddy field. Then the planter pressed seed tape into the paddy field by a pressing wheel fixed on the back of the planter. Later in 2002, [PERSON] [98] optimized the original design. The vertical type of the original rotating blade was optimized for bending, which improved the pressure damage to seed rope during sowing. When the planter works in a dry field, seed tape is usually covered by a soil covering device and suppression device. However, in loose soil conditions, the covering and suppression devices can be omitted because of the fallback properties of the soil [99]. [PERSON] et al. [100] designed a dynamic testing system for the depth of the rope seeding machine to improve the performance of soil covering. The soil covering system was tested under the simulated field conditions. The mathematical model of soil covering depth was established and the soil covering structure parameters were optimized. The thickness of the soil cover of the rope seeder is easily affected by the speed of the machine, the depth of the ditch and the angle of the soil cover plate. [PERSON] et al. [101] determined the primary and secondary relationship of its influencing factors by orthogonal test. [PERSON] [30] made a mechanical analysis of the interaction between soil particles and soil covering plate (Figure 12). The soil covering device was designed and optimized to improve the operation quality of the rope seeding machine.
## 2 Materials and Methods
### Statistics  
All data were reported as means and standard deviations. Prior to inference statistical analyses, all variables were tested for normal distribution (Shapiro-Wilk Test) and the assumption of variance homogeneity (Levene Test for equality of variances). Mean differences between sports were tested using a one-factorial univariate general linear model. Based on the (expected) significant group differences concerning age, weight, and height, we used these variables as covariates to evaluate the \"pure\" differences in postural performance. Bonferroni tests were used for pairwise analysis to analyse differences between the sports (basketball, gymnastics, ski jumping, diving, and recreational athletes).  
Differences between means were considered as statistically significant if \(p\) values were <0.05. To estimate practical relevance and to quantify the postural performance differences between sports, effect size (partial eta squared, \(\eta_{\text{p}}\)\({}^{2}\)) [18] was calculated for the ANOVA main effects (\(\eta_{\text{p}}\)\({}^{2}\)). To evaluate effect size, \(\eta_{\text{p}}\)\({}^{2}\) was classified as \(\eta_{\text{p}}\)\({}^{2}\geq 0.01\), \(\eta_{\text{p}}\)\({}^{2}\geq 0.06\), or \(\eta_{\text{p}}\)\({}^{2}\geq 0.14\) indicating small, medium, or large effects, respectively [19].  
Pearson's product moment correlations were calculated in order to evaluate the relationship between anthropometric/demographic parameters and posturographic parameters to detect dependencies between these parameters. The magnitude of correlations (r) between measures was interpreted as follows: <0.1 = trivial; 0.1-0.3 = small; 0.3-05 = moderate; 0.5-0.7 = large; 0.7-0.9 = very large; and 0.9-1.0 = almost perfect [19].  
All statistical analyses were conducted using the SPSS version 25.0 for Windows (IBM, Armonk, NY, USA).  
Figure 1: (**a**) Subject on stable force plates, (**b**) subject on force plates with foam pads. Source: [PERSON] (person is one of the authors).
## 2 Results
### Factors Associated with Attitude  
Simple linear regression was used to model the correlation between Attitude as the outcome measure and a number of covariates. The selection of covariates was derived from the literature review on AMR and the challenges that have been faced by the community pharmacists when trying to tackle it. The results of the bivariate analysis are presented in Tables 4-7. Table 4 shows the correlation between Attitude and three of the independent variables, i.e., age, gender, and postgraduate education. The Attitude was positively affected--increased--by the age of the participants. Younger community pharmacists increased the attitude score by 0.34 SD and the effect was statistically non-significant (\(p\) = 0.5550). Gender, however, affected the Attitude, negatively. That is being male community pharmacists reduced the attitude score by 2.37 SD, and the effect was statistically significant (\(p\) = 0.0265). Postgraduate education had a relatively large positive effect on the Attitude and the effect was statistically significant (\(\beta\) = 5.16, \(p\) = 0.000664). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{5}{c}{**Attitude**} \\ \cline{2-6}  & \(\beta\)1  & **SE2 ** & **95\% CI ** & **_p-Value_** \\ \hline Age & 0.34 & 0.57 & 0.78–1.46 & 0.5550 \\ \hline Male & \(-\)2.37 & 1.07 & \(-\)4.46—0.28 & 0.0265  \\ \hline Postgraduate education & 5.16 & 1.50 & 2.21–8.11 & 0.000664  \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bivariate analysis of Attitude with socio-demographic factors such as age, gender and postgraduate education. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{5}{c}{**Attitude**} \\ \cline{2-5}  & \(\beta\)1  & **SE2 ** & **95\% CI ** & **_p-Value_** \\ \hline Knowledge & Mean = 82.96 & \(-\)0.01 & 0.03 & \(-\)0.08–0.6 & 0.82 \\ \hline Training experience during pharmacy course & & & & & \\ \hline No & 211 (54.52) & & & & \\ Yes & 97 (25.07) & 1.78 & 1.01 & \(-\)0.21–3.77 & 0.0804 \\ Not sure & 79 (20.41) & & & & \\ \hline Training experience since degree qualified & & & & & \\ \hline Yes & 120 (31.01) & & & & \\ No & 267 (68.99) & 2.07 & 1.09 & \(-\)0.07–4.21 & 0.0593  \\ \hline Sources of knowledge  & & & & \\ \hline Training session & 274 & 3.14 & 1.11 & 0.96–5.31 & 0.00485  \\ Special literature & 245 & 2.63 & 1.04 & 0.58–4.69 & 0.0122  \\ Patient information leaflet & 139 & \(-\)0.12 & 1.06 & \(-\)2.19–1.97 & 0.9140 \\ Sales representative & 106 & 2.38 & 1.13 & 0.14–4.60 & 0.0365  \\ Articles in CCE & 297 & 0.82 & 1.21 & \(-\)1.55–3.18 & 0.4980 \\ Guidelines & 258 & 1.51 & 1.07 & \(-\)0.60–3.62 & 0.1600 \\ Others & 9 & \(-\)2.74 & 3.37 & \(-\)9.36–3.87 & 0.4160 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bivariate analysis of Attitude with antibiotic stewardship training factors. Table 5 shows the correlation between Attitude and antibiotic stewardship training factors. It was found that Attitude was slightly affected--decreased--by the knowledge
## 4 Case Study
### Simulation Results
#### 4.2.3 Case 3 Step-By-Step Case without the Virtual Leader  
This case is used to test the effect of a step-by-step consensus algorithm considering geographic factors. The communication topology of all BESSs is shown in Figure 3e. The consensus controller is activated at 10 s. All BESSs in each cluster will reach consensus at 17 s, and then two clusters are coordinated. In addition, there is a \(1.26+j1.26p.u\) load decrease after 30 s and a \(2.52+j2.52p.u\) load increase after 50 s. It can be observed in Figure 7 that all BESSs can reach consensus in two steps. Firstly, BESS 1,2,3 and 7 in cluster 1 and BESS 4,5 and 6 in cluster 2 reach consensus respectively. Secondly, cluster 1 and cluster 2 reach consensus. Compared with case 1 and 2, the consensus speed is slower. It is noted that the speed of every BESS is negatively correlated with distance.