esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminary Plan for Human-Machine Collaboration in the Metaverse + Industrial Robots  
Human-machine collaboration (HMC) refers to the complementary advantages of humans and machines working together to accomplish tasks, resulting in higher efficiency, better outcomes, and greater flexibility compared with working independently. The goal of HMC is to leverage human intelligence, creativity, and judgment in combination with the computational power, precision, and repeatability of machines, thereby optimizing workflows, enhancing productivity, and creating value.  
The integration of metaverse technology with industrial robotics significantly expands the prospects for HMC applications across various fields. In virtual training and simulation, new employees can quickly acquire operational skills through interaction with virtual devices. During training, the system records user operation data, analyzes and evaluates these data, and provides personalized training programs [30]. In industrial manufacturing, operators can remotely control robotic arms using VR devices to perform precise assembly and adjustments. Multiple operators can collaborate in a virtual environment, optimizing production processes and increasing efficiency [31].  
For system maintenance and repair, experts can remotely guide on-site operators through the maintenance and repair process using the system. They can view the equipment status via VR devices, provide operational suggestions, and monitor the repair process in real time, ensuring accuracy and safety in operations. This approach enhances the efficiency of maintenance and repair tasks [32; 33].
## 3 Results and Discussion
### Limitations  
Several studies have demonstrated that a comprehensive index that incorporates multi-source information and multi-sensor data can effectively monitor agricultural drought and assess its impacts [1; 26; 30; 31; 45]. SIF data, which are more sensitive to changes in water and heat, were used in this study as the basis to build the CDI, which increased the sensitivity of drought monitoring but also restricted the use of the CDI. In comparison to grassland and farmland, the CDI was less reliable for drought monitoring in woodlands and better suited for short-term drought monitoring. The accuracy of the CDI for monitoring agricultural drought is also weakened by the presence of mixed pixels in remote sensing data, which can be strengthened by higher-resolution remote sensing data in the following step. The growing season used in this study was from May to September; however, since drought is a continuous occurrence and the actual sowing and harvesting dates for each grid in the study area vary by region, year, and crop type, more research is needed to determine the relationship between drought hazard and yield loss for specific crops by improving the temporal resolution of the data.
# A Review of the Network Arch Bridge
## 3 Network Arch Bridges of Different Structural Systems around the World
### The West Seven Street Bridge, Forth Worth, Texas  
The West Seven Street Bridge, Figure 10, is the first precast network arch bridge. It was completed in 2013. Even though concrete is considered the material of choice for arches, few concrete arches have been built in modern times due to the necessity of falsework and time required. For the West Seven Street Bridge, an innovative solution was used: the concrete arches with a network hanger system were cast on their sides; after the hardening of the concrete, they were rotated to the vertical position, transported to the site and installed.  
In 2015, [PERSON] et al. published a paper discussing the stresses in the first precast NAB [19]. The bridge consists of 12 arches that were cast on their sides and then transported to the location of the bridge. The arches were prestressed both in the tie and the rib. Each of the arches has a span of 49.8 m and a rise of only 7.16 m. The low rise-to-span ratio was dictated by operating requirements, that is, for the center of gravity to be as low as possible. The low rise together with aesthetics considerations led to no cross-bracings between the arches. The arches' lateral stability is ensured by the frame created between  
Figure 10: Street view of the West Seven Street Bridge in Forth Worth, Texas, from Google street view.  
Figure 9: The network arch bridge at Steinkjer in Norway: street view of the bridge.  
the arches and the crossbeams. The arches were monitored using 224 vibrating wire gauges during the manipulation and posttensioning. The stresses during the two stages of posttensioning were presented. Then, after the bridge was opened, a static live load test was conducted; the conducted test showed that the stresses from the live load were relatively small compared to the construction stresses. Special attention should be given to the construction stresses and the modulus of elasticity, shrinkage of the concrete and creep that affect the stresses. Also, another important conclusion for such a structure resides in considering the uncertainties due to the hangers. Multiple hunger forces should be included in the model to obtain a reliable design. A second paper [20] describes the bridge superstructure, the instrumentation, and the efforts in the bridge from the modeling stage. The hunger arrangement chosen was for hangers parallel to each other, having an angle of 35\({}^{\circ}\) with the vertical plane, 26 hangers in each direction, resulting in 52 hangers for one arch.
# Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging
## 3 Methodology  
### Bayesian Model Averaging  
To more effectively address the uncertainty of parking demand arising from stochastic parking behavior, a BMA approach was applied to depict the characteristics of the parking demand (i.e., parking duration). For a predefined model space denoted as \(M\) consisting of \(K\) components (i.e., candidate models), represented as \(M_{k}\{k=1,2,\cdots,K\}\), the BMA approach weights individual candidate models and integrates the results into a deterministic model. Let \(x\) denote the quantity of interest (i.e., an observation of parking duration) and \(D\) denote an observed dataset; the posterior distribution (i.e., derived probability density function of BMA) can be formulated as  
\[p(x|D)=\sum_{k=1}^{K}p(x|M_{k},D)p(M_{k}|D) \tag{1}\]  
where \(p(x|M_{k},D)\) represents the probability density function of \(x\) within the candidate model \(M_{k}\), and \(p(M_{k}|D)\) is the posterior model probability, which represents the likelihood of the candidate model \(M_{k}\) being a correct model for the given observational dataset \(D\). For a specific model space \(M\), \(\sum_{k=1}^{K}p(M_{k}|D)=1\), and \(p(M_{k}|D)\) can be calculated using Bayes rules:  
\[p(M_{k}|D)=\frac{p(M_{k})p(D|M_{k})}{\sum_{i=1}^{K}p(M_{i})p(D|M_{i})} \tag{2}\]  
where \(p(M_{k})\) denotes the prior probability of the candidate model \(M_{k}\) when it is considered as a \"true\" model, and \(p(D|M_{k})\) represents the corresponding marginal likelihood, which is formulated by  
\[p(D|M_{k})=\int p(D|\theta_{k},M_{k})p(\theta_{k}|M_{k})d\theta_{k} \tag{3}\]  
where \(\theta_{k}\) is a parameter vector of the candidate model \(M_{k}\), \(p(\theta_{k}|M_{k})\) denotes the prior probability distribution of \(\theta_{k}\) under candidate model \(M_{k}\), and \(p(D|\theta_{k},M_{k})\) is the likelihood under the candidate model \(M_{k}\) and parameter \(\theta_{k}\). Then, the posterior mean \(E[x|D]\) and variance \(Var[x|D]\) of the BMA approach are formulated as  
\[E[x|D]=\sum_{k=1}^{K}E(x|D,M_{k})p(M_{k}|D) \tag{4}\]  
\[Var[x|D]=\sum_{k=1}^{K}\left(Var[x|D,M_{k}]+E\Big{[}x\Big{|}D,M_{k}]^{2}\right) p(M_{k}|D)-E\Big{[}x\Big{|}D]^{2} \tag{5}\]  
where \(E(x|D,M_{k})\) and \(Var[x|D,M_{k}]\) denote the mean and variance of candidate model \(M_{k}\) under the given dataset \(D\), respectively.
## 3 Hardware Design
### Power Amplification  
The controller performs PID calculations based on the difference between the actual temperature and the set temperature, and the result of the calculation is converted into an analog signal. This analog signal, as shown in Figure 3, changes the current through the  
Figure 2: (**a**). The circuit of the Wheatstone bridge. (**b**). The circuit of ADS1232.  
heating element via the power amplification circuit, thereby controlling the temperature of the cesium oven in real-time.  
The output of the front-end digital-to-analog conversion is driven by an operational amplifier to control the heating power through the power transistor. The Darlington transistor TIP122 was chosen for its amplification factor of 1000 and a maximum output current of up to 5 A, which meets the amplification requirements. Based on the heating requirements of the cesium oven and the results of preliminary experiments, a 5.1 \(\Omega\) power resistor was selected as the sampling resistor, which can control the maximum heating current to around 500 mA. This maximum heating current is the optimal value obtained after comprehensive consideration of factors such as heating speed, power supply capacity, and cooling conditions.  
Other circuits include power supply, the minimum system of the controller, and serial communication, etc., which are quite common and need not be elaborated. Since this circuit is a module of the entire machine, all controls are implemented through serial port commands, and common human-machine interaction functions such as keyboards and screens have not been designed.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results
### Effect of \(\theta_{S}\)  
Figure 5a,b show the effect of the target utilization \(\theta_{S}\) for the storage resource on the average delay \(\zeta_{D}\) and the average utilization of the storage resource \(\xi_{S}\), respectively. As shown in Figure 5a, the average delay of RNA-OTS decreases with the increase in \(\theta_{S}\). This can be explained as follows: Larger \(\theta_{S}\) means that observation satellites can store their observations for longer time, which implies that observation satellites can have more chance to transmit their observation without going through the relay satellite. This can lead shorter average delay. Meanwhile, since the other comparison schemes operate with the fixed policies, their average delay and average utilization of the storage resource do not change (see Figure 5a,b). This indicates that RNA-OTS can achieve better performance gain (i.e., lower average delay) compared to the other schemes when higher \(\theta_{S}\) is given. Specifically, with \(\theta_{C}=0.5\), the average delay of RNA-OTS is just 13% of that of RELAY.
## 3 Methodology
### Data Collection and Pre-Processing  
Accordingly, the social impact depends on various factors, such as the resident, the economy, urban transportation, business performance [76; 77]. To evaluate how each HSR network has impacted and involved society, this study provides a detailed analysis of the growth of HSR since their operation until 2019 compared with 12 social factors, which were conducted from official and reliable sources such as the World Bank, HSR's financial reports, sustainability reports and publications [70; 71; 72; 73; 74; 75]. Then, the research uses PCC as the main methodology for finding correlations between those factors that lead to future improvements.  
Figure 1: An overview of research methodology.  
These variables have been classified into three categories: quality of life (PD), education (ED), and employment (EC). Those variables represent social impacts from the growth of HSR service. The details of variables are shown as follows:  
PD group: The variables in this group reflect life quality of all ages after the coming of HSR services.  
* Population growth (PD1) [78]--The variable represents a country's resilience in the face of adversity. Significant population growth will deplete agricultural land and intensify food demands, social care and infrastructure.
* Age dependency ratio (young generation) (PD2) [79]--The variable shows the ratio of under 15 years old dependents to the working-age group (15-64 years old).
* Age dependency ratio (elderly generation) (PD3) [80]--The variable shows the ratio of over 64 years old dependents to the working-age group (15-64 years old).  
ED group: The variables in this group reflect educational benefits after the coming of HSR services.  
* School enrolment in three levels (primary (EDI), secondary (ED2), tertiary (ED3)) [81; 82; 83]--The variables are the proportion of mandatory school age students enrolled to the total amount of compulsory school age equivalents. Education is a factor in the UN's and UNESCO's development plans.
* Secondary school advancement (ED4) [84]--The variable represents the quantity of new students accepted in a given year as a percentage of the total student population.
* Completion rate of primary level (ED5) [85]--The variable represents the overall quantity of new students attending primary school in the final grade multiplied by the number of students entering at the final year enrolment age.  
EC group: The variables in this group reflect economic performance after the coming of HSR services.  
* Labour force (EC1) [86]--The variable represents the total number of people aged 15 and older, involved in production and service sectors.
* Employment rate (EC2) [87]--The variable represents the fraction of the employed population compared with the total population.
* Unemployment rate (EC3) [88]--The variable represents the fraction of the unemployed population compared with the total population.
* Women's representation in national parliaments (EC4) [89]--The variable represents the percentage of women elected to parliament, related to employment rate and gender disparities.
## 3 Optical Indices
### Field of View
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Designation** & \begin{tabular}{c} **FOV** \\ **(arcsec)** \\ \end{tabular} & **Comments** \\ \hline CH1 & 3 \(\times\) 20 & \begin{tabular}{c} CH1 requires rectangular FOV with spectral \\ direction of 3\({}^{\prime\prime}\) and spatial direction of 20\({}^{\prime\prime}\). \\ \end{tabular} \\ \hline CH2 & 3.9 \(\times\) 20 & \begin{tabular}{c} CH2 requires rectangular FOV with spectral \\ direction of 3.9\({}^{\prime\prime}\) and spatial direction of 20\({}^{\prime\prime}\). \\ \end{tabular} \\ \hline Pointing system & 16.5 & Pointing system requires a 16.5\({}^{\prime\prime}\) circular FOV. \\ \hline Telescope with & 27.9 & \begin{tabular}{c} Telescope is diffraction-limited within 27.9\({}^{\prime\prime}\) to \\ ensure well-resolved point-spread function (PSF) at the slit center. \\ \end{tabular} \\ \hline Telescope with & 40.5 & \
\begin{tabular}{c} Telescope FOV within 41.5\({}^{\prime\prime}\) allows for capturing \\ a star and locating it in the slit center. \\ \end{tabular} \\ \hline Telescope with & 44 &
\begin{tabular}{c} Edge of telescope FOV is extended to 44\({}^{\prime\prime}\) to \\ capture slit background, demanding to be unvignetted to \\ ensure that photons enter the slit without obstacles. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of field of view (FOV) requirements.
## 4 The Effect of Pre-Treatment Methods for NSLA on the Performance of AN-LAC
### The Shrinkage of AN-LAC and Microstructure Analysis  
The effect of different pre-treatment methods for NSLA on the shrinkage of AN-LAC is shown in Figure 14. It was clear that the shrinkage rate of AN-LAC-1W and AN-LAC-1S at 90 days was 17.7% and 10.3% lower than that of AN-LAC-1N, respectively, indicating that these pre-treatment methods for NSLA could significantly reduce the shrinkage of concrete. In addition, the shrinkage of AN-LAC mainly occurred during the hardening process of concrete. After 28 days of curing, the consecutive hydration reaction generated more hydration products to fill the pores of concrete, in which case the shrinkage of AN-LAC increased slowly.  
Moreover, the characteristics of the ITZ of AN-LAC after different pre-treatment methods for aggregate are exhibited in Figure 15. As shown in Figure 15a, there were a large number of wide cracks around the untreated NSLA. This was attributed to the fact that the untreated NSLA absorbed water from alkali-activated slag paste, resulting in significant pore pressure. Therefore, the cracks spread around the aggregate and the drying shrinkage of AN-LAC-1N was higher. Figure 15b showed that the number and length of cracks around the aggregate of AN-LAC-1S were lower than that of AN-LAC-1N because the hydrophobic membrane was generated on the surface of NSLA modified by silicone hydrophobic agent, which weakened the water absorption capacity of aggregate and then reduced the expansion of cracks. For AN-LAC-1W, only a small number of cracks propagated around NSLA, as exhibited in Figure 15c. This was due to the fact that the water stored in the pre-wetted NSLA could be released to the paste during the hydration process, which promoted the hydration reaction and improved the structural compactness of the concrete. Thus, the drying shrinkage of AN-LAC could be decreased significantly by using pre-wetted NSLA.  
Figure 14: The effect of different pre-treatment methods for NSLA on the shrinkage of AN-LAC.
# A Feedrate Planning Method for the NURBS Curve in CNC
## 2 Feedrate Planning of a NURBS Curve Based on CCC  
NURBS is a general mathematical model for the free-form curve, and its expression [22] is:  
\[C(u)=\frac{\sum_{i=0}^{n}N_{i,p}(u)w_{i}P_{i}}{\sum_{i=0}^{n}N_{i,p}(u)w_{i}},\ \ \ u_{0}\leq u\leq u_{n+p+1} \tag{1}\]  
where \(P_{i}\) is the control point whose quantity is \(n+1\), \(w_{i}\) is the weight, and \(N_{i,p}(u)\) is the \(p\)-order basis function defined by the node vector \(U=\left[u_{0},u_{1},\cdots,u_{n+p+1}\right]\), which is calculated as  
\[\begin{split}& N_{i,0}(u)=\left\{\begin{array}{l}1,u\in\left[u_{i},u _{i+1}\right]\\ 0,u\
otin\left[u_{i},u_{i+1}\right]\end{array}\right.\\ & N_{i,p}(u)=\frac{u-u_{i}}{u_{i+p}-u_{i}}N_{i,p-1}(u)+\frac{u_{i+p+1}-u}{u_{i +p+1}-u_{i+1}}N_{i+1,p-1}(u)\end{array}\,\end{split} \tag{2}\]  
in which, \(0/0=0\) is stipulated.  
In order to ensure the machining accuracy in machining NURBS curves, the chord error and normal acceleration should not exceed the set upper limits, \(\varepsilon\) and \(a_{n}\), therefore, the feedrate should satisfy the constraint described by the following formula [23; 24]:  
\[F_{\varepsilon,n}(\rho)=\min\left[\frac{2\sqrt{2\rho\varepsilon-\varepsilon^{2 }}}{T},\sqrt{a_{n}\rho}\right], \tag{3}\]  
where \(T\) is the interpolation period and \(\rho\) is the radius of curvature.  
It can be seen from Equation (3) that any point on the NURBS curve has its corresponding feedrate-constraint value, therefore, for any NURBS curve, there exists a curve with the arc length as the abscissa value and the feedrate-constraint value as the ordinate value corresponding to it, shown as \(C_{1}(s)\) in each figure in Figure 1 (for the convenience of the problem analysis below, \(C_{1}(s)\) is set as a monotonically decreasing curve). Thus, the essence of feedrate planning for a NURBS curve is to construct a feedrate curve \(C_{2}(s)\) that lies below \(C_{1}(s)\), while satisfying the kinematic constraints during Acc/Dec process, with the goal of minimizing machining time.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 3 Hybrid RF-FSO Systems Techniques and Their Advanced Achievements  
This section discusses advancements in FSO technology used in conjunction with other RF schemes to improve the capabilities of long-distance data transmission systems. The significance of radio-over FSO (Ro-FSO) signaling systems, different techniques of modulating the optical carrier using RF signals, and the strategic approach for a full-duplex Ro-FSO communications system are reviewed. In addition, a design of hybrid antenna for RF and optical signals transmission are discussed. A review on the state-of-the-art (SOTA) advanced achievements and recent implementations of FSO communication techniques with WDM and sub-carrier multiplexing (SCM) schemes, worldwide interoper  
Figure 16: The configuration of a node composed of optical amplifiers, OA-1 and OA 2, in the AR and AF schemes.  
Figure 17: The OBPF implemented in a node-n to minimize signal with ASE, background noise, and noise modulation from an optical propagation transmitted by a previous node.  
Figure 15: The optical relaying networks in FSO composed of nodes at the spacing of Li.  
ability for microwave access (WiMAX), and space optical satellite communications (SOSC) are presented.
## 5 Conclusions  
The Indoor Illuminance, Temperature, and Humidity of a Photovoltaic Greenhouse Can Satisfy the Growth of Crops  
In this paper, the illuminance, temperature, and humidity of a specific photovoltaic greenhouse in a tropical area were studied simultaneously. The result were shown as follows: (a) Illuminance: in winter (5 January 2016, limited rain), the distribution of indoor illuminance was relatively even. The average illuminance between 10:00 and 16:00 was 7.02 klux (the illuminance even reached 9.35 klux during 13:30-15:30), and the average light transmittance was 25.77%; in summer (thunderstower on 28 July 2015, thunderstower to cloudy on 29 July 2015; cloudy and sunny on 31 July 2015), the distribution of indoor illuminance was not even. The maximum and minimum compound average illuminances of the collection sites were 24.52 and 10.09 klux, respectively. The difference was about 59%, which indicated an uneven distribution of illuminance in the photovoltaic greenhouse. The average light intensity and the average light transmittance in summer were 15.47 kLux and 32.35%, respectively. (b) Temperature and humidity: the indoor average temperatures in winter were between 22.2 and 28.4 \({}^{\circ}\)C, and the average relative humidity values were between 68.8% and 95.8%, which were basically the same as the outdoor average temperature and humidity; the indoor average temperatures in summer were between 26.2 and 36.2 \({}^{\circ}\)C, and the average relative humidity values were between 50.9% and 90.3%. The temperatures of all the measurement sites were higher than the outdoor temperature. The average temperature difference was 2.7 \({}^{\circ}\)C, with the maximum difference of 3.9 \({}^{\circ}\)C and the minimum difference of 1.4 \({}^{\circ}\)C.  
The simulations of illuminance, temperature, and humidity in the photovoltaic greenhouse were performed using Design Builder. The simulation trend was basically the same as the measured results. Based on the above research, it was concluded that the illuminance, temperature, and humidity of the photovoltaic greenhouse studied in this paper can basically satisfy the production requirements of shade-enduring and neutral crops.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 3 Methodology and Design
### Point Cloud Data Preprocessing  
In this study, the noise points represent a small amount of rubber tread edge information that has not been calibrated correctly. The noise points are related to the camera resolution and calibration errors. In general, high-resolution cameras have a greater pixel density and are more prone to noise points. The primary steps in point cloud data preprocessing include filtering, alignment, and segmentation. Preprocessing enhances processing efficiency, improves data quality and alignment accuracy, reduces noise interference, and increases the accuracy of analysis and feature extraction. Additionally, it improves visualization and algorithm robustness. These preprocessing steps ensure cleaner and more reliable point cloud data, thereby laying a solid foundation for subsequent analysis, identification, classification, and modeling.