esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Background
### BIM Lightweight Visualization Methods
As for component redundancy removal, since the instance alignment process is not included in the methods proposed by [PERSON] et al. (2020) or [PERSON] et al. (2020), the specific implementation of the software system and local orientations of the instances will influence the display results. Regarding the sparse ICP method ([PERSON] et al., 2020), its matching accuracy and efficiency depend on the initial positions of components. Therefore, lightweight display technology needs to be customized for steel structural BIM models. In this paper, dynamic LOD models adjust the level of detail based on context-specific requirements and constraints, which is particularly valuable in Web-based BIM systems. This study focuses on graphical LOD techniques such as dynamic model loading and edge collapse algorithms, specifically designed for the computer graphics domain. These techniques are intended to reduce geometric complexity efficiently, thereby ensuring rapid rendering and high visual quality, which are essential for large-scale visualization projects. These strategies ensure efficient data transmission and visualization, tailored to the unique characteristics of steel structures. Consequently, the proposed LOD technique enhances the overall performance of the BIM system by improving rendering speed, reducing data redundancy, and maintaining high display accuracy, making it highly suitable for managing large-scale steel structural projects. In this study, the challenges include managing large amounts of complex data, ensuring high-quality steel structure installation, and optimizing the performance of the BIM system for large models. Model view definition (MVD), part of the IFC standard, aims to standardize BIM data representation ([PERSON] et al., 2020). [PERSON] et al. (2020) developed an XML-based MVD library that supports entity-based concept integration. The conceptual framework defines entities, relationships, and attributes, thereby simplifying the access and reuse of existing concepts and facilitating the development of new model views. [PERSON] et al. (2020) proposed MVDLite, a lightweight MVD rule representation method that modifies the XML structure to make it easier to understand. While MVD supports specific data exchange needs and enhances interoperability and data exchange efficiency, it cannot fully address our specific challenges. The high level of detail and specificity required for managing steel structure components, such as detailed records of members, connectors, and welding seam, necessitates more granular data classification and measurement methods than what MVD typically provides. Additionally, the complexity and redundancy in IFC files require advanced data extraction and redundancy removal algorithms beyond MVD'scapabilities. Therefore, a redundancy removal approach by combining the PCA algorithm and the Hausdorff-based comparison algorithm is proposed to identify standardized steel components, and an efficient visualization method on Web3D for redundant instances is also achieved.
## 4 Results and Analysis
The simulation parameters used in this simulation environment are shown in Table 3.  
The results of the proposed scheme in this simulation environment are compared with well-known standards such as first come first serve (FCFS) [29], longest job first (LJF) [30], and shortest job first (SJF) [31] algorithms. The comparative analysis includes transmitted data, number of GTS-requesting nodes entertained, transmission delay, and the mean trust value of GTS-allocating nodes for varying numbers of data-requesting nodes, for varying channel bandwidth, and for varying signal power. Furthermore, the performance of the proposed scheme is compared in different prospects with 0/1 knapsack, FCFS, SJF, and LJF for varying trust threshold values.
## 3 Design and Analysis of the Sliding-Mode Controller
### Switching Frequency  
The main problem of theoretical SMC is the infinite switching frequency required for the implementation. A classical solution to this problem is to introduce a hysteresis band \([-\Delta\Psi,+\Delta\Psi]\) around the sliding surface (\(\Psi=0\)) to limit the switching frequency:  
\[-\Delta\Psi<\Psi<+\Delta\Psi \tag{42}\]  
In that way, since the closed-loop operation imposes \(\Psi=0\), the hysteresis band produces the switching function ripple reported in Equation (43). The ripple magnitudes \(\Delta i_{L1}\) and \(\Delta i_{L2}\) were reported in Equations (21) and (22), respectively, and the ripple magnitude \(\Delta v_{C2}\) was reported in Equation (23). Finally, the SMC ensures that \(i_{L2}\) follows the reference \(i_{R}\) provided by the high-pass filter. Hence, the subtraction between those two signals is \(i_{R}-i_{L2}=-\Delta i_{L2}\).  
\[\Delta\Psi=[k_{c}\cdot\Delta i_{L2}-\Delta i_{L1}]+[-\Delta i_{L2}+k_{v}\cdot \Delta v_{C2}] \tag{43}\]  
In steady-state operation, \(k_{c}\cdot i_{L2}=i_{L1}\), as given in Equation (20), where \(k_{c}=\frac{d}{dt}\); thus, \(i_{L2}=i_{R}+k_{v}\cdot(v_{R}-v_{C2})\). Therefore, the term \([k_{c}\cdot\Delta i_{L2}-\Delta i_{L1}]\) of Equation (43) is equal to zero. Moreover, the analysis reported in Figure 3 shows that the peak value of \(i_{L2}\) occurs when the ripple in \(v_{C2}\) is equal to zero; similarly, the peak value of \(v_{C2}\) occurs when the ripple in \(i_{L2}\) is equal to zero. Therefore, the peak value of the term \([-\Delta i_{L2}+k_{v}\cdot\Delta v_{C2}]\) of Equation (43) occurs at the peak value of \(i_{L2}\) or at the peak value of \(k_{v}\cdot\Delta v_{C2}\), depending on which component has the highest magnitude. This condition is formalized with the following equation for the ripple magnitude of the switching function:  
\[\Delta\Psi=\max(|\Delta i_{L2}|,|k_{v}\cdot\Delta v_{C2}|) \tag{44}\]  
Finally, Equation (44) is used to design the hysteresis band \([-\Delta\Psi,+\Delta\Psi]\) in agreement with the maximum switching frequency \(F_{sw}\) supported by the MOSFETs used to construct the Zeta converter.
## 3 Results and Discussion
### Determination of Catechin Profile and Caffeine (CAF) Content
The other non-esterified catechins, GC, C, and EGC, presented significantly lower values of 0.75-6.80 mg/g DE (2019) and 1.39-2.27 mg/g DE (2020) for GC, 2.72-7.16 mg/g DE (2019) and 3.54-5.12 mg/g DE (2020) for C, and 4.49-14.65 (2019) and 0.58-9.86 mg/g DE (2020) for EGC. [PERSON] et al. [37] reported significantly lower values for the catechins with respect to our results, which may be related to differences in terms of the extraction/analysis methodologies employed, as well as genetic differences, geographic location, processing, and storage conditions. Furthermore, it should be highlighted that comparing data from several studies is difficult, given that different raw materials, extraction protocols, analytical methods, and units of measurement are used. It should also be pointed out that several studies that focused on tea catechins variations among different cultivars reported that geographic location, genetic variation, and agricultural practices have a significant effect on catechin levels [3]. Additionally, the growing environments (including soil types, soil fertility, temperatures, sunlight intensity, water stress, rainfall distribution, and growth altitude) have an effect on tea's uniqueness [4] and thus, offer a possible explanation for the higher values of ECDs observed in green tea from Azorean samples. Furthermore, some authors reported that the levels of total and individual catechins show high seasonal fluctuations in reaction to temperature, irradiance, UV doses, and day length [38; 39]. According to [PERSON] et al. [4], given that tea grows in temperate climates, these influences are difficult to separate from each other but should be considered in the future when seeking to optimize and/or maximize their catechin content.
# Investigation of the Opposite-Electrode Effect on the Planar Solid-State Pulse-Forming Line
## 5 Experimental Results
Figure 8: Images depicting the DC source, load resistor, and high-voltage probe. Figure 10: The thermal characteristic outcomes following the sustained operation of the two types of PFL: (**a**) surface temperature distribution and (**b**) the frequency distribution of temperature. Figure 9: The experimental results for the withstand voltage of (**a**) the full-electrode PFL and (**b**) the opposite-electrode PFL. The test results indicate that following an equal number of consecutive runs, the highest and average temperatures of the opposite-electrode PFL are lower than those of the full-electrode PFL. Specifically, after 10,000 consecutive runs, the average surface temperature of the full-electrode PFL is 31.3 \({}^{\circ}\)C, with a peak local temperature of 35.7 \({}^{\circ}\)C; in comparison, for the opposite-electrode PFL, these values are 30.2 \({}^{\circ}\)C and 33.3 \({}^{\circ}\)C, respectively. After 20,000 consecutive runs, the average surface temperature of the full-electrode PFL rises to 34.3 \({}^{\circ}\)C, with the peak local temperature reaching 40.8 \({}^{\circ}\)C. In contrast, for the opposite-electrode PFL, these figures were recorded as 31.9 \({}^{\circ}\)C and 37.6 \({}^{\circ}\)C, correspondingly. As shown in Table 1, compared to the full-electrode PFL, the withstand voltage of the opposite-electrode PFL increased by 43%, reaching 33 kV and showed a better temperature distribution after continuous operation. The opposite-electrode PFL exhibits a higher withstand voltage compared to the full-electrode PFL, attributed to the reduced electrode edge electric field enhancement effect. Furthermore, both PFL structures exhibit localized heating at the output end. However, compared to the full-electrode structure, heating in the opposite-electrode PFL is more uniform across its structure. There exists a concentrated heating area at the high-voltage electrode edge field enhancement region for the full-electrode PFL, as shown in Figure 10.  
This observation further validates our theoretical analysis presented in Section 2 regarding the relationship between the heating power and electric field: additional field enhancement leads to increased heating, which impacts the PFL's insulation capabilities.
## 4 Result and Discussion
### Performance of LS-PMSM with Presence of Fault
Another issue found during the simulation analysis is an increase of time duration for the transient section when any bar is broken. Figure 13 illustrates the starting time for both the healthy and faulty motor under different levels of load. As is clear in the figure, the starting time trend increased based on increment in the load. A recent study performed on a squirrel-cage induction motor revealed that the startup load is not important in BRB fault detection based on the transient analysis; however, they mentioned that this approach is especially suitable for applications with heavy startup transients [34]. Other researchers also did not consider the effects of starting load for fault detection in induction machines based on the analysis of current in transient state [35; 36]. However, this research indicates that the starting load effects in the transient time should be carefully taken into account for BRB fault detection in LS-PMSM. For LS-PMSM that starts with a squirrel cage, the application of the transient current is even more justified, due to the carrying of significant currents in the squirrel cage during the start-up. This is why the steady-state signal is impractical for BRB fault detection in this motor. The presence of BRB in LS-PMSM can produce changes in the air gap flux and the current distribution between the bars during acceleration from zero to rated speed. This part indicated that the BRBs slightly modified the performance of the motor during startup and is critical in fault detection during transient time. Figure 12: Comparison of the maximum starting torque [2.3 Nm] for (**a**) healthy and (**b**) faulty motor. Figure 13: Comparison of the result of starting time under different loads. The effect of BRB in starting torque is also surveyed experimentally. It was found that the motor with one BRB cannot run at a starting torque value of \"2.3 Nm\", which the motor is designed for, and the starting torque value decreases to near 1.70 Nm. The rate of torque variations for the faulty motor is higher than for the healthy one, so a higher noise accompanied with lower performance are expected from the faulty motor. The time duration for the transient section is also determined through experimental work. The stator current signal for the different load situations is illustrated in Figure 14 for healthy and faulty motors. It is obvious in this figure that instances of broken bars lengthen the starting transient time of the motor. Figure 15 provides a comparison between the starting time for both healthy and faulty motors in different levels of load. The results presented in Figure 15 are based on a mean of 40 samples for each condition. Figure 14: Experimental current signal in four load conditions for healthy and faulty motors. (**a**) No load; (**b**) 0.5 Nm; (**c**) 1 Nm; (**d**) 1.5 Nm. Figure 15: Comparison of the experimental result of starting time under different loads, based on the mean value of 40 samples for each load.
## 2 Materials and Methods
### Objective Functions
#### 2.2.3 Vibration Response Strength  
Many studies have suggested that a sensor should be placed where the vibration signal energy is strong [7; 25]. This arrangement improves the signal-to-noise ratio and provides highly accurate modal parameter recognition results. The concept of the average _MKE_ (_AMKE_) is introduced in this section. The _AMKE_ of each DOF is obtained by averaging the arithmetic mean of each row of the _MKE_ matrix and then summing up all the nodes.  
\[AMKE_{i}=\frac{1}{m}\sum\
olimits_{j=1}^{m}MKE_{i,j}=\frac{1}{m}\sum \
olimits_{j=1}^{m}diag\left(\Phi_{i}^{T}\Phi_{j}\right)\text{,} \tag{6}\]  
where \(m\) denotes the total modes selected to solve the OSP. The higher _AMKE_ is, the better signal quality is. To transform the problem into a minimizing optimization problem, we introduce a third objective function, namely \(f_{3}\), which is defined as the _MKE_ of all nodes divided by that of the sensor-placed nodes. The parameter \(f_{3}\) is expressed as follows:  
\[f_{3}=\frac{\sum\
olimits_{i=1}^{n}AMKE_{i}}{\sum\
olimits_{i=1}^{s}AMKE_{i}} \text{,} \tag{7}\]  
where \(n\) is the total number of possible locations for OSP and \(s\) is the total number of sensors. The numerator is the _MKE_ of all the nodes, which is a constant in this model, and the denominator is the _MKE_ of the sensor-placed nodes; thus, \(f_{3}\) is always greater than or equal to 1. If sensors are appropriately placed to capture vibration signals, the denominator will be larger; hence lower \(f_{3}\) is better.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 4 Mine Operation Using GIS  
The aspects of mine operation can be classified into ore haulage operation optimization, wireless communication system design and management, decision support systems, and mine ventilation/safety/drainage system design and management.
### Motivation  
The motivation of this paper was overcoming the challenges in UWSNs, which was also the key objective of this research. In a two-dimensional UWSN (2D-UWSN), sensor nodes are able to be deployed in the spatial location, whereas, in an ocean environment, it is essential to handle the monitoring at different depths. For this purpose, a 3D-UWSN is launched in which underwater sensors are employed at different depths [23; 24]. The challenging issues created by 3D-UWSNs are the consumption of higher energy and the delay on sensor nodes due to data transmission. The gathering of sensed data is the key task from which the environmental changes are analyzed. As a solution, a cluster is constructed and then inter-cluster routing is performed with or without the participation of an AUV [25; 26]. An AUV moves in and around the network to gather data from sensors. When using a single AUV, it takes quite a long waiting time for sensors to transmit their sensed data, and so multiple AUVs are introduced [27]. The utilization of AUVs enables efficient collection of data from specifically concerned sensor nodes and minimizes energy consumption and delay for the following reasons:  
* The collected sensor information is not transmitted via multiple hops; it is directly sent to the AUV when it arrives near to the sensor. Therefore, the direct transmission of sensed data also reduces the energy consumption of the sensors and the transmission delays;
* Data gathering from CHs also reduces the overall energy consumption of the sensors and the gathering delay, since it is not required for all the sensors to forward their data to the AUV or to wait in order to deliver the data;
* Planning of multiple AUV paths appropriately minimizes the tour length, which reduces the delay in transmitting the data from sensors to sink;
* Allotment of sleep schedules for sensors enables the reduction of a considerable amount of sensing energy.  
The design of the 3D-UWSN was motivated towards the reduction of delay and improvement of network lifetime with the incorporation of clustering, data gathering and supportability of delay-aware event message transmission. Events are instances when emergency information occurs; such information is more sensitive, and, hence, it needs to be transmitted as early as possible to avoid heavier damages. Moving on with this motivation, the major, problematic issues in this research were identified and resolved with reinforcement learning, optimization algorithms and artificial intelligence methods. This enabled us to achieve the objectives of this proposed research work, and the achievement is discussed by comparing the designed system with previous AUV-assisted UWSN designs.
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 3 Methodology and Data Acquisition
### Obtaining Power Consumption Data  
Due to the short execution time of code segments, the power consumption data must be updated at a high frequency, as otherwise some details get lost. As mentioned in Section 2.1, the sampling power consumption data at a high frequency using the built-in DAQ or power meter occupies a notable CPU time, introduces additional overhead, and leads to inaccurate power consumption data. In this case, the power consumption data should be measured and collected outside the embedded system. For devices without a built-in DAQ or power meters, the measurement and collection of the power consumption data from outside the device seems to be the only choice.
# Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
###### Abstract  
In our study a robot was used to deliver objects for measurement into the Equator gauging system. To investigate the robot's manipulation influence on dimensional measurements, the robot's tasks were divided into basic functions. Based on these basic functions, nine different robot-manipulation scenarios were defined, i.e., from zero to full robot manipulation, for two measuring objects (named Magnet and PKR) and six measurement characteristics (rectangular and spherical). The robot's manipulation influence was determined on the basis of the statistical parameters \(C_{p}\), \(R\), and the \(\hat{\sigma}\) obtained from a measurement system analysis (MSA) type-1 study. The results show that the degree of implemented manipulation of the robot affects the scattering of the measurement data. However, the effect is much more pronounced in the case of length measurements than with spherical geometries. Different measuring methods (touch-triggering or scanning measurement mode, number of sampling points) were used, which showed similar measurement data. This directly indicated the influence of the robot's manipulation on \(C_{p}\), R and \(6\sigma\). Increasing the degree of the robot's manipulation decreases the \(C_{p}\) value and increases the R and \(6\sigma\) values for the length measurements. There is no such pronounced course in the spherical geometries, where the values of \(C_{p}\), R and \(6\sigma\) remain approximately the same. The main influential factor for decreasing the \(C_{p}\) value with increasing robot manipulation was the angular misalignment of the object's orientation in the fixture.  
CMM; robot; manipulation; SPC; dimensional measurement; comparative measurements Article Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell  
15 June 2021  
1 Faculty of Electrical Engineering, University of Ljubljana, Trzaska Cesta 25, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS]  
2 Kolektor Orodjarma d.o.o.o., Volkova Ulica 10, 5280 Idrja, Slovenia; [EMAIL_ADDRESS]
## 4 Shear Load-Displacement Curves Model and Analysis
### Curve Model
Calculating the derivative of the strain \(\varepsilon\) at both ends of Equation (5) yields the following:  
\[\frac{d\sigma}{d\varepsilon}=E\cdot\exp\left[-\left(\frac{\varepsilon}{\eta} \right)^{m}\right]\left[1-m\left(\frac{\varepsilon}{\eta}\right)^{m}\right]. \tag{6}\]  
By substituting the boundary conditions into Equation (6):  
\[\mathrm{E}\cdot\exp\left[-\left(\frac{\varepsilon_{\rm vp}}{\eta}\right)^{m} \right]\left[1-m\left(\frac{\varepsilon_{\rm vp}}{\eta}\right)^{m}\right]=0. \tag{7}\]  
Because \(E\
eq 0\), \(\exp\left[-\left(\frac{\varepsilon_{\rm vp}}{\eta}\right)^{m}\right]\
eq 0\) and  
\[1-m\left(\frac{\varepsilon_{\rm vp}}{\eta}\right)^{m}=0. \tag{8}\]  
Equation (8) can be rearranged as follows:  
\[\eta=\frac{\varepsilon_{\rm vp}}{\left(\frac{1}{m}\right)^{\frac{1}{n}}}. \tag{9}\]  
By substituting Equation (9) into Equation (5) and using the boundary conditions, the following can be obtained:  
\[m=\frac{1}{\ln\left(\frac{\varepsilon_{\rm vp}\varepsilon_{\rm vp}}{ \sigma_{\rm vp}}\right)}. \tag{10}\]  
Equation (10) can be substituted into Equation (4), yielding the following:  
\[D=1-\exp\left[-\frac{1}{m}\left(\frac{\varepsilon}{\varepsilon_{\rm vp}} \right)^{m}\right]. \tag{11}\]  
Equation (11) is considered to be the damage evolution equation of the ECC expansion joint composite structure under a shear load. Equations (9) and (10) show that \(D\) is related to the peak stress, peak load strain, and initial modulus of elasticity of the current material. In this study, the shear load-displacement curve was measured instead of the stress-strain curve. Therefore, when calculating the damage, the shear load-displacement curve was normalized. The normalized dimensionless stress value was \(\sigma=F/F_{\rm vp},\ \sigma_{\rm vp}=1.0\), and the normalized strain value was \(\varepsilon=\delta/\delta_{\rm vp},\ \varepsilon_{\rm vp}=1.0\). The initial modulus of elasticity \(E\) was obtained by normalizing the shear load-displacement curve. By substituting Equation (11) into Equation (5), the damage constitutive model can be obtained:  
\[D=1-\exp\left[-\left(\frac{\varepsilon}{\eta}\right)^{m}\right] \tag{4}\]0  
Equation (12) is considered to be the deformation damage constitutive model of the ECC expansion joint composite structure under a shear load. The initial elastic modulus, peak load strain, and \(m\) can be calculated by the tests conducted in this study.