esa-sceva/satcom-chunk-collection huggingface dataset:
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Diagnosis of the Municipality of Mecayapan
#### 3.1.3 Infrastructure  
According to Institute of Statistics and Geography (INEGI, for its acronym in spanish) data for 2015, 64 homes in the municipality of Mecayapan had internet service, which represents 1.4% of the total population. Computers are available in 143 homes in the municipality, which represents 3% of the population. Although there are a good number of homes that have cell phones, it is not specified if they are smartphones, that is, if they have the technology to connect to a WiFi network [49].  
This diagnosis corresponds to the fact that telecommunications companies have privileged maximizing the return on traditional infrastructure investments and delaying the classification of new high-speed broadband access technologies in those localities where their performance would be relative.  
In terms of transportation, there are paved federal trunk roads, paved state feeder roads, and surfaced rural roads, as shown in Table 1, and in terms of means of transportation, the population uses cars, trucks, vans, and motorcycles as its main means of transportation,as shown in Table 2. Regarding housing infrastructure, there are 4704 inhabited private homes, not all of which have access to basic services, and few have access to goods and information and communication technologies, as shown in Table 3.  
Under these conditions, perhaps the impact of the implementation of the antennas is not considered to have a direct impact on the population; however, from telematics, the reproducible antenna model would make it possible to reduce marginalization and to promote an increase in the level of development of the locality.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### Band MMSE Block Equalizer  
In this subsection, we propose an efficient algorithm by exploiting the circulant property of the transform matrix \(\mathbf{\Phi}\) and the band approximation of the Doppler-frequency channel matrix. We find that the equivalent frequency-domain channel matrix can also be approximated as banded in UP-OCDM. With this property, we propose an LDL\({}^{H}\) factorization based algorithm to achieve low complexity equalization for UP-OCDM over doubly selective channels.
## 5 Conclusions  
This study intends to understand how different virtual-reality interactive interfaces (i.e., hand gesture recognition and joystick control) affect users' perceptions and intentions to use the XRSPACE MANOVA system and its interactive interfaces. The results show that the hand gesture recognition performs better in users' perceived enjoyment (PE), satisfaction (SAT), and confirmation (CON), which means that the innovative hand gesture recognition provides users with a certain degree of fun experience and users' expectations of system use are better confirmed. On the other hand, the advantages of using a joystick controller are reflected in users' perceptions of its perceived usefulness (PU), perceived ease-of-use (PEOU), attitude to use (ATU), and perceptions of internal control (POIC) and suggest that, in terms of the operating performance, users prefer the joystick control.  
Meanwhile, this study examines various potential factors (i.e., gender, college major, task completion time) to investigate whether they affect users' subjective evaluations of the system. The results show that gender and college major do not affect the user's experience with the system. However, the length of task completion time influences the subjective assessment of attitude to use (ATU), perceptions of internal control (POIC), and behavioral intention to use (BIU). Some of these findings might be due to the fact that users feel tired with their sore hands, which detracts from their evaluations.  
Specifically, the current research identifies three major issues with hand gesture recognition for XRSPACE MANOVA and provides suggestions for improvement. First, unlike joystick controllers, hand gesture recognition does not have a set of controlled norms. Even with proper guidance, inconsistencies in gestures still exist between users and developers. In practice, users need to participate in more training activities to become more comfortable with hand gesture recognition. Second, users' hands must follow the relative detected area of the helmet's cameras, which can cause users to feel tired and experience overall sensory degradation. We suggest that the recognized area of hand gestures can be expanded to overcome this obstacle. Third, whenever users use a gestural command to aim the virtualbuttons without the vibrating feedback given by the joystic controller, they feel uncertain whether the aiming is accurate. The addition of vibrating feedback to the helmet or voice prompts will solve this issue.  
Conceptualization, S.-C.Y., E.H.-K.W. and Y.-R.L.; methodology, S.-C.Y., E.H.-K.W. and Y.-R.L.; validation, C.-W.C., S.-C.Y. and E.H.-K.W.; formal analysis, Y.-R.L. and R.V.; investigation, Y.-R.L.; resources, S.-C.Y. and E.H.-K.W.; writing--original draft preparation, Y.-R.L.; writing--review and editing, C.-W.C.; visualization, Y.-R.L.; supervision, C.-W.C., S.-C.Y. and E.H.-K.W.; funding acquisition, C.-W.C., S.-C.Y. and E.H.-K.W. All authors have read and agreed to the published version of the manuscript.  
This article was subsidized by the National Taiwan Normal University (NTNU), Taiwan, ROC.  
The authors declare no conflict of interest.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.2 Albedo  
The albedo is the measure of the diffuse reflection of the solar radiation, and it is measured on a scale from 0 to 1. The proportion reflected is not only determined by properties of the surface itself but also by the spectral and angular distribution of solar radiation reaching the Earth's surface. For the Earth, the reflectivity is greater over land areas, and it generally increases as the local height of the Sun decreases and when the cloud coverage increases. Data related to albedo values for orbits with different inclination are available in NASA's data library [22].  
Figure 12: A CubeSat in nadir pointing mode remains oriented towards the center of Earth throughout the orbit [20].  
The heat gained from a surface of the satellite (W) due to the albedo flux is as follows:  
\[Q_{albedo}=aS_{solar}a_{albedo}AF_{1-2}\]  
where  
\(a_{albedo}\) : a dimensionless albedo constant that describes the reflectivity of the celestial body;  
\(F_{1-2}\) : the view factor from the surface of the celestial body to the spacecraft;  
\(A\): the CubeSat's surface area exposed to albedo radiation.  
The view factor is the proportion of the radiation that is directed from one surface to another, and it has to do with the relative orientation between the two surfaces. It is considered that the CubeSat is nadir oriented, so it has its sides either vertical or horizontal to the nadir direction (both 2U and 4U sides are horizontally oriented, and one 8U side is vertically oriented since the other 8U side constantly faces the zenith direction). The respective view factors can be considered as follows [23]:  
\[F_{ver}=\left(\frac{1}{h^{*}}\right)^{2}\text{and }F_{hor}=-\frac{\sqrt{\left(h^{*} \right)^{2}-1}}{\pi\left(h^{*}\right)^{2}}+\frac{1}{\pi}tan^{-1}\left(\frac{1} {\sqrt{\left(h^{*}\right)^{2}-1}}\right)\]  
where \(h^{*}=\frac{R+h}{R}\), \(R\) being the radius of the celestial body radius and \(h\) being the altitude.
## 5 Experimental Analysis
### Result Analysis and Optimizing Uncertainty
See Table 11 for the results from comparing the repeatability uncertainty component estimated in Section 3.4.2, the repeatability uncertainty component of the workpiece daily measurements and the repeatability uncertainty component updated in real time using the Bayesian formula. As can be seen from Table 11, the predicted repeatability based on 10 applied measurements, once determined, will remain static, so it can not reflect the latest information in daily measurements in real time. However, when the sample data of daily measurements is used to evaluate the uncertainty component caused by repeatability, the small sample size can only reflect limited information and is easily affected by accidental factors in the experiment. Using the Bayesian method to fully fuse the \begin{table}
\begin{tabular}{c c c c} \hline
**Measured Value** & **Workpiece A/\({{t_{\text{j}}}}\)** & **Workpiece B/\({{t_{\text{2j}}}}\)** & **Workpiece C/\({{t_{\text{3j}}}}\)** \\ \hline \({{t_{i1}}}\) & 0.0057 mm & 0.0051 mm & 0.0060 mm \\ \({{t_{\text{2}}}}\) & 0.0048 mm & 0.0044 mm & 0.0049 mm \\ \({{t_{\text{3}}}}\) & 0.0051 mm & 0.0054 mm & 0.0053 mm \\ \hline Mean value \({{{{\bar{t}}}_{i}}}\) & 0.0052 mm & 0.0050 mm & 0.0054 mm \\ \hline \({{{n_{i}}}}\) & 3 & 3 & 3 \\ Standard deviation & 0.458 \(\upmu\)m & 0.513 \(\upmu\)m & 0.556 \(\upmu\)m \\ \({{{u_{\text{r}}}}}\) & 0.265 \(\upmu\)m & 0.296 \(\upmu\)m & 0.321 \(\upmu\)m \\ \hline \end{tabular}
\end{table} 
Table 10: The daily measurement data of flatness. historical information and the current information for updating the uncertainty component introduced by repeatability in real time can integrate the latest measurement information into the evaluation results to reflect the latest trend of the random effects in the measurement system in time; moreover, the amount of information is comparatively great in the samples used for fully integrating the current and historical information, so the evaluation results will not easily be influenced by accidental factors and the repeatable uncertainty components tend to be stable and reliable. See Table 12 and Figure 5 for estimations of the uncertainty optimal evaluation for the flatness measurement of the workpieces A, B and C in the same batch:  
As can be seen from Figure 5, when the extended uncertainty of the first evaluation is regarded as the uncertainty evaluation result of the flatness measurement task, the requirements of the target uncertainty \(U_{\text{o}}\) cannot be satisfied; however, the extended uncertainty of the flatness measurement tasks for three workpieces to be measured after optimal evaluation can meet the accuracy requirements of the measurement tasks, but the sacrifice will be relatively high accordingly. Therefore, according to the basic principle of the task-oriented uncertainty optimal evaluation described in this section, the surveyors should make choices according to their own measurement conditions.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 1 Introduction
Based on this, in this paper, for the design of trajectory planning and optimization of the Par4 parallel robot, the Lame curve was calculated for a smooth transition of the pick-and-place path; some comparisons were made between the fifth- and sixth-order polynomial motion laws; and Grey Wolf Optimizer (GWO) was selected for optimizing the Lame curve parameters with minimum mechanical energy consumption.
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### Linear Regression Forecasting Model  
Linear regression is a concept derived from statistics. It is a linear technique for modelling the correlation between a dependent variable and one or more independent variables. If one independent variable is considered, then the process is a simple linear regression. On the contrary, if several independent variables are considered, then the process is a multiple linear regression. Unlike multivariate analysis, which entirely focuses on joint probability, linear regression focuses on conditional probability.  
Linear regression is extensively used for the study of practical applications, since it is easy to fit the models that have linear dependence with its historic data. Hence, linear regression has greater significance in forecasting applications.  
The simple linear regression model is typically formulated as \(y=a+bx\); '\(y\)' is the output, '\(x\)' is the input, '\(b\)' is the input coefficient, and '\(a\)' is a constant. In case of multiple inputs, such as \(x1\), \(x2\), \(x3\), the model representation is \(y=a+b1x1+b2x2+b3x3\).  
Additionally, in this case, 70% of data are randomly considered as train data, and the remaining 30% are considered as test data, using the train_test_split function. Finally, the energy demand with respect to the test data is predicted and compared with the original data. To determine the accuracy of the model, MSE, MAE, and RMSE are calculated. The architecture of linear regression forecasting is presented in Figure 4. The model implementation is done according to the listed steps:  
**Step 1:**: Create testing and training datasets with the train_test_split function. Here, we divided it into 70% training dataset, 30% testing dataset.
**Step 2:**: Build the linear regression model using a linear regression function with appropriate parameters for the training dataset.
**Step 3:**: Forecast the consumption values for the testing dataset.
**Step 4:**: Plot the actual and forecasted values of the testing dataset.
**Step 5:**: Calculate MSE, MAE, and RMSE.  
Figure 3: Architecture of support vector regression.
## 2 Design and Optimization
### Topology Optimization  
The numerical gear model was prepared in Abaqus/CAE, and the resulting input file was transferred to CAESS ProTOP 6.2 [20] for additional configuration and optimization. The gear body was configured as a design domain to allow for the replacement of the full solid body design by a lattice structure, while the gear hub and toothed ring were defined as fixed domains; thus, they remain unchanged during the design and optimization process. According to [PERSON] et al. [35], topology optimization of the lattice structure differs from conventional topology optimization. In the present study, two different types of lattice cell were employed to configure the gear-body structure. Both lattice cells, a cube, and a plane diagonal were designed in a cylindrical coordinate system. A detailed explanation of the lattice design parameters can be found in [36]. The research was primarily focused on the design process of a lattice structure and topology optimization, which was thoroughly addressed, including model preparation by specifying free and fixed design domains. This research focuses on the manufacturing and experimental validation of the lattice-structure gear. Furthermore, a novel device was conceived, designed, and produced for this purpose.  
The initial lattice structure was obtained by designing some potential lattice structures with different design parameters and by examining their initial stresses. The lattice structure with an acceptable stress level was defined as initial structure. This initial structure was then optimized by employing topology optimization. Figure 2 depicts the von Mises stress distribution of the gears used in this study. The initial lattice structure of the gear body and its von Mises stress distribution are shown in color mapping in Figure 2a. The lattice structure was then optimized; the von Mises stress distribution is presented in Figure 2b. The stress distribution for the solid gear body was also analyzed and is presented in Figure 2c.  
The initial gear model had a maximum stress of about 590 MPa at the tooth root, while the peak stress within the lattice structure was approximately 400 MPa, as shown in yellow in Figure 2. In order to reduce stress concentrations, especially within the lattice structure, topology optimization was applied. As a result, the volume of the initial model increased from 41.2% to 49.3%, which drastically reduced the peak stress within the lattice structure to approximately 150 MPa. Furthermore, the stresses in the tooth area also decreased to around 550 MPa.  
The increased thickness of the optimized lattice structure compared to the initial structure results from the higher material volume and the incorporation of additional finite elements during the optimization process. Each optimization cycle involved adding and removing finite elements to and from the structure. An uneven surface accumulates stress, which leads to mechanical deformation, while a smoother surface ensures better stress distribution. Therefore, the lattice structure was subjected to surface smoothing, as shown in Figure 3.
## 2 System Architecture
### Control Design
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Procedure** & **Nr.** & **Actions** & **Type** \\ \hline \multirow{6}{*}{SETUP} & 0.1 & The block sample is placed on the machine supports. & Manual \\ \cline{2-3}  & 0.2 & A marble paste is applied to the electrodes and block surfaces. & Manual \\ \cline{2-3}  & 0.3 & A glycerin gel is applied to the transducers and block surfaces. & Manual \\ \cline{2-3}  & 0.4 & The ”Proceq Pudnit Lab+” equipment is turned on and the block width (\(\gamma\)) is & Manual \\ \cline{2-3}  & 0.4 & introduced by the operator. & Manual \\ \cline{2-3}  & 0.5 & The ”EUTRON ATR700” power supply is turned on. & Manual \\ \hline \multirow{6}{*}{HOME} & 1.0 & To proceed, the RESET button must be pressed. & Manual \\ \cline{2-3}  & 1.1 & The EUTRON power supply applies 0 V. & Automatic \\ \cline{2-3}  & 1.2 & The YA actuator retracts. & Automatic \\ \cline{2-3}  & 1.3 & The YB actuator retracts. & Automatic \\ \cline{2-3}  & 1.4 & The Z actuator goes back to its origin. & Automatic \\ \cline{2-3}  & 1.5 & The X actuator goes back to its origin. & Automatic \\ \hline \multirow{6}{*}{HOME} & 2.0 & Operator introduces the length (X) and the height (Z) of the stone block and the & Manual \\ \cline{2-3}  & & voltage value of the power supply. & Manual \\ \cline{1-1} \cline{2-3}  & 2.1 & To proceed, the PREPARE button must be pressed. & Manual \\ \hline \multirow{6}{*}{HOME} & 2.2 & The X actuator moves to a position where the detection of the stone block surface & Automatic \\ \cline{2-3}  & & is guaranteed. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Machine workflow. According to the actions stated on the machine workflow (Table 3), the prototype hardware requirements can be defined. Figure 9 shows the prototype implementation (wiring diagram). Basically, three main controller components are selected: a PLC, an HMI, and a SCADA system.
## 5 Case Study
### Capacity Curves  
ConsideringTijuana's status as a border city with the United States, typologies with capacity curves and similar characteristics developed in the HAZUS manual [21] were sought, with the closest match being typologies RM2/L and RM2/M, corresponding to masonry shear wall buildings with low (1-3 stories) and medium (4-7 stories) concrete diaphragms, respectively (Table 6). Each HAZUS capacity curve for buildings is a graph that shows the lateral resistance of a building as a function of a characteristic lateral displacement. It is derived from a graph of the equivalent static base shear versus the building's displacement. To make it easier to compare it to earthquake force (i.e., by overlaying the capacity curve with a response spectrum), we transformed the axis representing force into one that shows spectral acceleration and the axis representing displacement into one indicating spectral displacement.  
The capacity curves defined by HAZUS are given by the yield point (Dy, Ay) and the maximum capacity point (Du, Au). The yield capacity represents the true lateral resistance of the building, considering redundancies in design, conservatism in code requirements, and actual material strength. The maximum capacity represents the maximum strength of the building when the overall structural system has reached a fully plastic state. HAZUS provides only the values of these two points, so the curves were reconstructed using a fourth-degree polynomial while ensuring the following conditions are met: (1) the curve passes through the yield point (Dy, Ay); (2) the curve passes through the maximum capacity point (Du, Au); (3) the curve is linear from the origin to the yield point; (4) the curve retains the initial slope at the yield point; and (5) the curve is horizontal at the maximum capacity point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Housing Classification in Tijuana** & **HAZUS Housing Classification** \\ \hline
The capacity curve was transformed into a bilinear capacity curve to obtain an idealized representation, following the ATC-40 procedure [47], where a yield point A with coordinates (dy, ay) and an assumed performance point B with coordinates (Du, Au) are defined, and the area designated as A1 is approximately equal to the area designated as A2 [48].  
Figure 7 shows the idealized bilinear HAZUS capacity curves for buildings with reinforced masonry low-rise (RM2/L) and medium-rise (RM2/M)load-bearing walls, with low (L), moderate (M), and high (H) seismic design code levels.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 4 Results of Maximum Distances  
This section shows the obtained results. Each subsection represents the maximum distance between the two antennae and the average RTT in ms. registered for each distance depending on the working frequency and the water temperature.
## 4 Results and Discussion
### Reactive Scheduling for the Decreasing Demand Scenario  
For the decreasing demand pattern, the results indicated the application of the reactive aggregate production scheduling approach in the green coffee SC. For this demand pattern: ten tardy orders for the initial production schedule, an 88% service level, and nineteen tardy orders from the estimated production results, a 77% service level (Figure 8). This value falls below the expected level. Consequently, Figure 9 shows the updated aggregate production schedule with t\({}_{\text{mod}}\) = 7 and E\({}^{-}\) = 2, resulting in ten tardy orders and an 88% service level.