esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Lens Distortion  
The camera lens distortion and how well it is being simulated impact the accuracy of the 3D reconstruction significantly. The estimated distortion coefficients used for adjusting for image distortion are provided for both cameras in Tables 5 and 6. Figures 5 and 6 show the profiles of the radial and tangential distortions associated with the captured images of both cameras in terms of the distance in pixels from their sensor center. The values of both distortions are zero at the sensor center of both cameras. These values start increasing as the distance from the image center increases until they reach their maximum at the edges. These inferences are further demonstrated in Figure 7, which presents the lens total distortion as discrete vectors across the entire sensor area for both cameras. Each vector is pointed out from the center of its corresponding sensor cell. The vector length represents the total distortion value, both radial and tangential, associated with its corresponding cell. As demonstrated in the distortion profiles and plots in Figures 5-7, the distortion associated with the smartphone data is substantially higher compared with the data of the compact camera. This is mainly attributed to the smaller lens and sensor of the smartphone camera. Nevertheless, the distortions associated with the captured images can be modeled precisely within the bundle adjustment regardless of their magnitude as long as the provided Exif data are accurate. It is worth mentioning that a symmetrical and consistent distortion across the sensor area is an indicator of successful self-calibration and distortion modeling, Figure 7.  
Figure 4: The automatically detected targets utilized to scale the specimens’ 3D data: (**a**) an example of the algorithmically detected targets on an image in the sandpile data set; (**b**) the four targets (12-bit, center point radius = 10 mm) utilized in this study with their associated coded numbers.
# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
## 4 Experiment and Result  
### Experimental Setup  
To verify the performance of the proposed system, the data collection and search performance were verified through experiments using MySQL, a general conventional RDBMS, and MongoDB, which is typically used in NoSQL. The data write performance was tested to verify the ability to collect large amounts of data in real time, and the read performance was tested to verify the search performance required for data analysis. The hardware environment and version of each software used in this experiment were as follows:  
* CPU: Intel Core i9-8950 HK 2.90 GHz
* RAM: 32 GB
* RDBMS--MySQL version: 8.0.22 [28]
* NoSQL--MongoDB version: 4.4.1 [29]  
The structure of the dataset used in this experiment is as follows. Virtual data were created using assumptions regarding the data collected based on the location of each device, given that the data can occur in an actual road pavement environment. Then, through experiments using these data, the levels of performance of the write, read, and delete operations were verified.  
* ID: Integer
* DeviceID: String
* Speed: Integer
* FlatnessL: Double
* FlatnessR: Double
* Longitude: Double
* Latitude: Double  
Figure 8: Virtualization server scale-out.  
A virtual dataset was constructed via random generation of values. Portions of the experimental data used are shown in Figures 9 and 10.  
With regard to the total number of data points in the dataset used in this experiment, 100,000 data points were generated. The average time was calculated after each operation was performed five times. The unit of time is milliseconds (ms). In this experiment, the levels of performance of the write and read operations were tested using 10, 50, 100, 1000, 5000, 10,000, 20,000, and 50,000 data points. On the other hand, for the delete operation, the amounts of time required for deleting 10, 50, 100, 1000, 5000, and 10,000 data points corresponding to random keys were measured.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 1 Introduction
Finally, we propose a direct correction method of segmented mirror aberration for the complicated correction process of WH technology in the traditional mode.
## 4 Current Issues of Last Mile Logistics
### Delivery Technology Innovation  
Along with the studies addressing operational challenges, LML studies related to overcoming technical issues were also abundant in the literature. Newly emerging delivery technologies such as unmanned aerial vehicles (UAVs) or drones, connected autonomous vehicles (CAVs), EVs, automated lockers, real-time data transmission systems, dynamic route planning systems, fleet management solutions, tracking devices, and identification means and devices [4; 12; 82] were discussed to improve LML performance (see Table 6).  
An innovative transition of delivery services from truck- or van-based parcels deliveries to drone-assisted deliveries in urban areas has received attention in logistics [4, 82]. According to the 2016 Material Handling Industry (MHI) Annual Industry Report [85], 59% of survey respondents recognized that emerging technologies, including UAVs or drones, CAVs, EVs, or robots, were having influence on logistics. The report also claimed that adoption rates for such technologies are expected to grow to higher than 50% over the next decade. Indeed, drones have been extensively utilized in the logistics field, deployed for delivery services in LML [86, 87]. However, challenges in the legal restrictions and restricted service areas, labor and logistics costs, battery safety, and cost efficiency of drone applications for LML prevented drones from being prevalently used for urban LML deliveries [1]. For more reliable deliveries by drones in LML, operational challenges for drone-assisted delivery or truck-drone hybrid delivery will need to be tackled [88, 89, 90, 91]. With improved adoption processes, drones can be an essential strategic delivery method to innovate conventional LML delivery services if they can carry heavier goods while hovering [22].  
Traffic flows in urban areas have frequently fluctuated by uncertain traffic factors on the road. Accordingly, home delivery services impose many social costs due to a sudden increase in delivery service vehicles in residential areas [83]. The use of CAVs for future LML delivery services in urban areas has been the kernel of recent studies as traditional logistics delivery services have caused intrinsic drawbacks [84]. Furthermore, autonomous trucks or vans can be integrated with drones or robots, which may significantly improve LML delivery services in urban areas [92]. EVs have also received attention in LML as an alternative to internal combustion engine vehicles, with increasing interest in new forms of delivery vehicles which are more economically and environmentally sustainable.
## 7 Speaker Tilt Angle Variation
### Discussion  
All 10 readings collected at the five different tilt angles at each check point were treated as one sample. For each sample the Most Probable Value (MPV) was calculated. The difference between MPV and true position of the check point as well as its standard deviation can be found in Table 3. Signal detection rates can be found in Table 4. From the average of all 10 check points it appears that  
Figure 4: Five different tilt orientations in which phone was tested. The curved sound bars show direction and orientation of the phone’s main speaker as it gets rotated from downwards to upwards.  
phone over the check point less accurate and the user's body could also interfere with direct line-of-sight of the signal. Accuracies for some check points at 0 \({}^{\circ}\) may appear misleading. For example point 10, which is the farthest point from the centre of the room appears to have good accuracy (24 cm) at tilt angle 0 \({}^{\circ}\), however detection rate for this point and angle is only 30%. Angle 45 \({}^{\circ}\) has a much better detection rate than angles 0 \({}^{\circ}\) and 135 \({}^{\circ}\), and has the greatest spread of accuracies when compared to every other orientation. From a practical point of view, angle 135 \({}^{\circ}\)has little importance, because a user is not likely to hold the phone in such an awkward orientation through normal usage.  
In terms of accuracy, detection rate, and consistency, speaker angle 180 \({}^{\circ}\) (upward) is undoubtedly the most reliable orientation. This suggests that unless some parameters of the system are changed such as increased sound volume, less directional microphones, or lower signal detection thresholds, the most accurate approach would be to instruct users to momentarily flip their phone upside down (_i.e._, point display screen at floor) and employ the phone's accelerometers to detect this flip to trigger the ultrasonic \"beep\" procedure. Although this may not be ideal for user interface interaction, increased positional accuracy and reliability can be a valuable trade off for enhancing the user's navigation experience. However, it should be noted that overall positioning accuracy is still sub-meter in all speaker orientations.
## 7 Coal Burst Control and Verification of 76 Working Face in Wuyang Mine
### Practical Application Effect on Site
#### 7.3.2 Safe Excavation of Working Face  
Both coal pillars on the 76 working face are 10 m wide, and the two lanes, each 5 m wide, have been safely excavated. Most areas remained safe during the excavation process, with only local areas experiencing stress warnings. The early warning was lifted after drilling and pressure relief for the coal, and there was no significant deformation in the roadway. The stress early warning diagram of the 76 working face is shown in Figure 20. On-site stress monitoring and early warnings confirm the effectiveness of the drilling pressure relief in the 76 working face, consistent with the previous theoretical analysis and numerical simulation results.  
During tunneling, the implementation of drilling pressure relief measures in response to coal burst warnings has proven to be highly effective. The surrounding rock of the tunnel remains stable, with no occurrences of tunnel cross-section shrinkage, roof subsidence, or anchor bolt damage, as shown in Figure 21.  
Figure 19: Stress change curve of monitoring points: (**a**) Monitoring point I, (**b**) monitoring point II.  
Figure 20: Coal burst warning diagram of 76 working face before and after pressure relief: (**a**) Before pressure relief, (**b**) after pressure relief.
## 2 Background and Related Work
### Satellite Location  
As previously stated, existing software packages provide a method for calculating satellite visibility at a single location, at any time and this software provides detailed information on the orbital information of GNSS satellites. Another method for survey planning is to design a programme which can track satellite positions at all times, such as those employing a two-line element set (which describes the orbits of earth-orbiting satellites) as proposed by [33] or the Keplerian parameters used in the GRASS module employed by [35]. These methods are heavy in terms of computational performance but will provide the user with real-time information on satellite position. An alternative is to use a simulated GNSS constellation, such as that employed by [31, 36]. The innovative method proposed in this paper utilises freely available planning software such as Trimble Planning, and then reverses the satellite prediction algorithm output. Instead of calculating the number of satellites visible at the receiver, the proposed method calculates a pesudo position for the satellite while simultaneously maintaining its correct azimuth and elevation. This approach is enabled through a basic HTML editor, the output of the Trimble Planning desktop application, and provides savings in terms of processing time.
## 3 Methodology
### Least Square Support Vector Machine (LSSVM)
\begin{bmatrix}0&I^{T}\\ I&\Omega+\gamma^{-1}I\end{bmatrix}\begin{bmatrix}b\\ a\end{bmatrix} = \begin{bmatrix}0\\ y\end{bmatrix}  \tag{10}\]  
where \(a=[a_{1},a_{2},\ldots,a_{N}],\,y=[y_{1},y_{2},\ldots,y_{N}],\Omega_{ki}= \varphi(x_{k})^{T}\varphi(x_{i}),\,k,i=1,2,\ldots,N_{o}\)  
Due to the high dimensionality of the feature space after the non-linear mapping \(\varphi(x_{k})\), directly working with it can be computationally expensive. To address this, LSSVM utilizes the kernel trick. This technique leverages a kernel function \(k(x,x_{k})\) that operates on the original input space \((x)\) to compute the inner product in the high-dimensional space. The Mercer condition ensures that such a kernel function exists. Mathematically, this relationship is expressed as in Equation (11) and the final regression function of LSSVM is obtained in Equation (12). \[k(x_{k},x_{i})=\varphi(x_{k})^{T}\varphi(x_{i}) \tag{11}\]\[y(x)=\sum_{k=1}^{N}a_{k}k(x,x_{k})+b \tag{12}\]  
Within the LSSVM framework for this study, the radial basis function (RBF) kernel is chosen due to its well-established generalization ability and wide convergence domain. The RBF kernel function is defined as Equation (13):  
\[K(x_{k},x_{i})=\exp(-\frac{\left\|x_{k}-x_{i}\right\|^{2}}{2\sigma^{2}}) \tag{13}\]  
where \(\sigma\) (sigma) represents the width of the kernel function. Optimizing the parameters of the RBF kernel, particularly sigma (\(\sigma\)) and gamma (\(\gamma\)), is crucial for achieving optimal performance in the LSSVM model. These parameters significantly influence the model's ability to learn from the training data and generalize well to unseen data [49]. Therefore, after obtaining the ideal of these two values, the next stage is to optimize them using IBFOA. The structure of the LSSVM model can be visualized as depicted in Figure 3. In this structure, the final output is a linear combination of the values from intermediate nodes. Notably, each intermediate node corresponds to a support vector within the LSSVM model [50].
## Appendix A
### Nomenclature  
1-3 Station points through the turbine: stator inlet, rotor inlet, rotor exit A Area (m\({}^{2}\)) b Height, passage or blade (m) c Blade chord length (m) c x Blade axial chord length (m) C\({}_{L}\) Zweifel coefficient, Loss model lift coefficient C\({}_{P}\) Specific heat capacity at constant pressure (J/kgK) D Diameter (m) h Specific enthalpy (J/kg) k Rotor tip clearance (% span) M Mach Number \(\dot{m}\) Mass flow rate (kg/s) N Turbocharger shaft rotational speed (rpm) P Static pressure (Pa) P\({}_{t}\) Total pressure (Pa) R Stage reaction (%) r Radius (m) S Blade pitch (m) s Entropy (kJ/kgK) T Static temperature (K) T\({}_{s}\) Isentropic static temperature (K) T\({}_{t}\) Total temperature (K) TE\({}_{b}\) Trailing edge blockage U Rotor blade linear velocity (m/s) V Absolute flow velocity (m/s) W Relative flow velocity (m/s) Y Pressure loss coefficient Z Number of blades \(\alpha\) Absolute flow angle (\({}^{\circ}\)) \(\beta\) Relative flow angle (\({}^{\circ}\)) \(\gamma\) Ratio of specific heats \(\varepsilon\) Convergence criteria n Isentropic expansion efficiency (%) \(\lambda\) Blade stagger angle (\({}^{\circ}\)), Secondary loss coefficient \(\xi\) Enthalpy loss coefficient p Density (kg/m\({}^{3}\)) \(\sigma\) Axial solidity \(\phi\) Flow coefficient \(\psi\)  Blade loading coefficient Abbreviations bgd BladeGen geometry file bgi BladeGen batch input file HTR Hub to Tip Ratio LE Leading Edge TE Trailing Edge CFD Computational Fluid Dynamics FEA Finite Element Analysis
# Exploring Visual Perceptions of Spatial Information for Wayfinding in Virtual Reality Environments
## 4 Results
### Average Gaze Data for Understanding Spatial Information
The AOI gaze fixation for \"sign\" showed initially a high average gaze fixation rate in both the E-FAS and the D-FAS groups, which then gradually decreased (see Figure 4b). Figure 5 shows the gaze fixation ratio for AOIs over the experimental period for the \"easy\" and the \"difficult\" groups. In the \"easy\" group, the gaze concentration ratio for the \"sign\" (55%) during the first section of the experiment (0 \(\sim\) 10 s) was approximately three times higher than that for the \"path\" (25%) and the \"background\" (19%). In the third section (20 \(\sim\) 30 s), which comprised the middle of the experiment, the \"easy\" group focused on the \"path\" rather than the \"sign\" in the order of \"path\" (52%), \"background\" (30%), and then \"sign\" (18%), unlike at the start of the experiment. After this, their attention was focused on the \"path\" rather than the other AOI areas, which shows that the gaze was fixed in accordance with the purpose of the experiment (see Figure 5a). During the first section of the experiment (0 \(\sim\) 10 s), the gaze fixation in the \"difficult\" group occurred in the order of \"path\" (40%), \"sign\" (36%), and \"background\" (23%), and the difference was less pronounced. In the second section of the experiment (10 \(\sim\) 20 s), the gaze fixation was higher for \"path\" (35%) and \"background\" (39%) than for \"sign\" (26%). In the middle of the experiment, a high gaze fixation ratio was shown for \"path\" (50%). In the final section of the experiment, gaze fixation was also observed in the AOIs rather than the direction for wayfinding in the order of \"path\" (54%), \"sign\" (33%), and \"background\" (12%) (see Figure 5b). In the interview during the final stage of the experiment, the groups were divided according to the difficulty experienced when perceiving space, and the extracted gaze fixation data were analyzed. As a result, differences in the capacity to understand the spatial information appeared according to the ratio of gaze fixation. The cognitive processing of information can be analyzed according to the time intervals of the experiment. In the experiment, the \"easy\" group's understanding of spatial information was obtained by perceiving the \"sign\", and their gaze was fixed on the wayfinding of the space for the purpose of the experiment. However, in the \"difficult\" group, the gaze fixation data appeared scattered in spaces other than the perception of the \"sign\" and the wayfinding.
## 5 Performance Evaluation
### Broadcast  
In the Broadcast experiment, the data type was Integer and the size of the transferred data was 256 MB. Figure 8 graph shows the result of Broadcast communication time. In the experiment graph of Figure 8, C represents MPI communication in C environment, Bcast_B represents MPI  
Figure 7: Four Docker Containers Architecture bound to NUMA node in SNC-4 mode.  
buffer communication in Python environment, and Bcast_O represents MPI object communication in Python environment.  
Figure 8: _Cont._  
Figure 8 shows the result of executing Broadcast. Figure 8a is the Host environment, Figure 8b is the one Docker Container environment, Figure 8c is the two Docker Containers environment, and Figure 8d is the four Docker Containers environment. In all environments, communication using MCDRAM in Flat mode showed the fastest communication time. The case of using DDR4 in Cache mode was second fastest. Finally, the slowest communication time was shown when DDR4 was used in Flat mode. The method proposed in this paper is \(\mathrm{Bcast\_B}\), which performs Python buffer communication when using MCDRAM in Flat mode. When using only DDR4 in Flat mode, \(\mathrm{Bcast\_B}\) showed slower time than C. However, in the experiment environment of Host and one Container, the communication time of \(\mathrm{Bcast\_B}\) and C was similar in MCDRAM of Flat mode. \(\mathrm{Bcast\_B}\) communication in MCDRAM of Flat mode was confirmed to improve performance of up to 487% compared to \(\mathrm{Bcast\_B}\) communication in DDR4 of Flat mode. This best case was shown when 16 processes were used in one Docker Container. In addition, the performances of the Host (Figure 8a) and one Container (Figure 8b) environments were similar. This result confirmed Docker's claim that the performance difference between the main Host and Docker's Container is not significant because Docker directly uses the Host's resources. However, as the number of Containers increased in MCDRAM of Flat mode, \(\mathrm{Bcast\_B}\) showed slower communication performance compared to C. Through this result, we confirmed that multiple Docker Containers are relatively less efficient than communication in the Host environment.  
As mentioned in the background of the previous section, in Cache mode, MCDRAM is used as a cache. MCDRAM cache is treated as a Last Level Cache (LLC), which is located between L2 cache and addressable memory in the memory hierarchy of Knights Landing processors. Therefore, it can be seen that the \(\mathrm{Bcast\_B}\) communication in Cache mode is up to 88% faster than the \(\mathrm{Bcast\_B}\) communication in DDR4 of Flat mode.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 5 Numerical Analysis
### Analytic Model
When the dimension of matrix \(R\) is large, considerable computational errors due to truncation and round-off may be accumulated in the iteration process. An effective method of \(R^{2}B_{r}+RA_{r}+D=0\) is presented in Appendix A[41; 42]. Next, the boundary probability vector \(\widetilde{p}=(p_{0},\cdots,p_{c-1})\) is the unique solution of \(\widetilde{p}T=0\) and \(\widetilde{p}e+p_{c_{1}-1}R(I-R)^{-1}e=1\), where the matrix \(T\) is a generator (\(Te=0\)) given by  
\[T=\left[ \begin{array}{ccccc}A_{0}&D&&&\\ B_{1}&A_{1}&D&&&\\ &\cdot&\cdot&\cdot&&\\ &&\cdot&\cdot&\cdot&\\ &&B_{r-2}&A_{r-2}&D&\\ &&&B_{r-1}&A_{r-1}+RB_{r}\end{array} \right] \tag{5}\]  
The solution is then obtained by a two-step process: First, the rate matrix \(R\) is determined by iterative substitution in \(R^{2}B_{r}+RA_{r}+D=0\). Second, once \(R\) is known, the \(r\cdot c_{1}\) boundary probabilities \(\widetilde{p}\) are determined by solving a system of \(r\cdot c_{1}\) linear equations. If \(r\) and \(c_{1}\) are large, the dimension of the matrix \(T\) for the boundary probabilities also becomes large. Therefore, it is difficult to provide the desired accuracy with the direct solution of the linear system \(\widetilde{p}T=0\) and \(\widetilde{p}e+p_{c_{1}-1}R(I-R)^{-1}e=1\). An alternative technique using decomposition can be applied to reduce the dimension. This procedure is also presented in Appendix A[41; 42]. Once the matrix \(R\) and the boundary probability have been computed, the following performance measures are easily obtained. Figure 5: State transition diagram of proposed dynamic spectrum access strategy.
## 2 Materials and Methods
### Characterization of Sorbent  
Scanning electron microscopy was used for the characterization of the surface of sorbent particles. Observations were performed on a Quanta FEG 200 (FEI, Merignac, France, Thermo Fischer Instruments). The semi-quantitative analysis was performed using an energy dispersive X-ray analyzer (Oxford Instruments France, Saclay, France) with samples collected from the sorbent loaded at pH 5.5, with solutions containing a metal concentration of 0.3 mmol L\({}^{-1}\) and a sorbent dose (SD) of 1 g L\({}^{-1}\). The same conditions were selected for loading the sorbent for FTIR spectrometer analysis (equipped with an ATR tool). The analysis of nitrogen content was carried out using the volumetric titration method reported by [PERSON] et al. [39]. A fixed amount of sorbent (m = 50 mg) was dispersed into 50 mL (V, L) of 0.05 M HCl solution (C\({}_{0}\)), for 15 h. The residual concentration of HCl (C\({}_{1}\)) was titrated by 0.05 M NaOH solution (and phenolphthalein as the indicator). The molar concentration of amine groups in the sorbent (\(n\), mmol g\({}^{-1}\)) was calculated by: \(n\) = (C\({}_{0}\)-C\({}_{1}\)) \(\times\) V/m.  
The pH\({}_{\text{PZC}}\) of the sorbent was determined by the pH-drift method [40]. A fixed amount of sorbent was mixed with a solution at fixed pH\({}_{0}\) values using 0.01 M and 0.1 M NaCl salt as background. The equilibrium pH (pH\({}_{\text{eq}}\)) was measured after 48 h of agitation (at 150 rpm). The pH\({}_{\text{PZC}}\) corresponds to the pH value verifying pH\({}_{0}\) = pH\({}_{\text{eq}}\).