esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and System Modeling
### Energy Storage System Model
#### 2.3.2 Pumped Heat Energy Storage (PHES)  
The PHES stores electricity as sensible heat in two thermal storage system; a hot high pressure and temperature tank (+500 \({}^{\circ}\)C, 12 bars pressure) and a cold low pressure and temperature tank (\(-160\)\({}^{\circ}\)C, 1 Bar). It also consists of a two compressor/expander pair, argon as a working fluid and it uses gravel as the storage medium. The operation strategy is analogous to pumped hydro storage but rather than pumping water, heat pumping is used to create temperature difference. Theory of operation and development is adequately covered in [44; 45; 46]. Figure 2. shows the schematic diagram of the PHES.  
The energy stored in a PHES depends on the temperature differences between the two thermal storage system. The energy stored \(ESS_{\textit{plus}}(t)\) in the reservoirs per unit volume is the difference between the internal energies of the storage medium in the hot and cold stores. The internal energies of the storage medium are the function of the mass (\(M_{r}\)) and specific heat densities of the storage medium (\(SH_{r}\)). The energy stored can be determined by the temperature difference between the hot and cold store [47] as illustrated below:  
\[ESS_{\textit{plus}}(t)=M_{r}\times SH_{r}\times\{(T_{2}(t)-T_{3}(t))-(T_{1}(t) -T_{4}(t))\} \tag{6}\]  
The power output and input \(P_{\textit{plus}}(t)\) of the PHES per unit volume (for charging and discharging instance) is determined by the mass (\(Mg\)) and the specific heat of the argon gas (\(SHg\)), and the temperature difference [48] as follows:  
\[P_{\textit{plus}}(t)=M_{g}\times SH_{g}\times\{(T_{2}(t)-T_{1}(t))-(T_{3}(t)-T _{4}(t))\} \tag{7}\]  
where \((T_{1}(t)\), \(T_{2}(t))\) are the top and \((T_{3}(t)\), \(T_{4}(t))\) are the bottom section temperature of the hot tank and cold tank respectively.
## 5 Results of Health Monitoring
### Vibration Monitoring Results  
The girder vibration data measured in 15 April 2015 and 15 April 2016 were chosen to analyze the changes of girder vibration frequencies during the first operation year. The calculated and measured results are shown in Table 3, the small frequency changes reflect the stable structural stiffness and mass of the main girder.
## 4 Development of Jeju Pavement Condition Index
### Data Calibration  
Of the three distress types that constitute the Jeju Island pavement condition index, the crack rate unavoidably has an error rate because it is calculated via human (manual) analysis of surface images that were captured by automatic pavement condition survey equipment. The reliability of the crack rate analysis values has not been documented; however, in this field, it is generally recognized that an error rate of 5% or less exists [21]. On the other hand, the rut depth and roughness are immediately calculated during the survey using special equipment that is equipped with automatic pavement condition survey devices. Because the values are measured by equipment, although an error rate may exist, it is known to be extremely small. Therefore, the outlier removal rate was set at 5% for the crack rate data (2.5% each for the upper and lower bounds) and 0.5% for the rut depth and roughness (0.25% each for the upper and lower bounds). The outliers are removed in preparation for the database construction. The outlier removal standards are shown in Table 1.  
After the outliers, which were judged to have a large effect on the index development results, were removed, the elements that constituted the final database that was constructed for this study included roads, directions, lanes, starting/ending points, pavement types, presence of earthworks/bridges, crack rates (%), rut depths (mm), and IRIs (m/km).
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 2 State of Technology  
Localization is a subsection of the visual place recognition problem that has been heavily researched in robotics [4]. While visual place recognition concerns itself with the question of whether a place has been seen before, localization also tries to estimate a pose relative to either a previous pose or an arbitrary origin point. This problem is also referred to as global localization, kidnapped robot problem, or wake-up robot problem, where a robot starts from an arbitrary location and has to subsequently determine its location. Generally, the problem of localization can be divided into two categories: localization with and without external sensor input.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 4 IEEE 802.11 Technology for TSN
### Traffic Scheduling  
Traffic scheduling studies of 802.11-based TSN are introduced [8,9]. In [8], SHARP used the time-division multiple access (TDMA) to support traffic scheduling between TSN traffic and non-TSN traffic. TDMA-based traffic scheduling decreases latency and improves reliability for TSN in a wired/wireless network environment. To implement TDMA in 802.11-based WLAN, the transmission period of TSN traffic and that of non-TSN traffic of 802.11 station are separated. To ensure communication only for the TSN node, the AP transmits a clear to send (CTS) signal without a request to send (RTS) signal to prevent channel access of 802.11 stations. When the wireless channel is idle, the TSN nodes preferentially transmits packets after short interframe space (SIFS) without random backoff. In addition, when an 802.11 station sends an RTS, a CTS is transmitted only if transmission is completed within the allotted time. In [9], the delay time for transmission from an AP to the corresponding node can be minimized by adding a new TSN access category that does not perform backoff for enhanced distributed channel access (EDCA) in the WLAN downlink. Therefore, TSN packets are transmitted preferentially.  
802.11 ax proposed the target wake time (TWT) that is a new power saving mechanism [2,10]. In wireless TSN, TWT is used for packet scheduling and the AP schedules packet transmission sequence of stations. As shown in Figure 8, the stations transmit a TWT request to the AP to participate in TWT, and the AP transmits a TWT response to the station. The TWT response includes the transmission time of the first target beacon (TBTT). Stations receiving the target beacon can communicate with the AP during the TWT service period (SP). As shown in Figure 8, if TWT and OFDMA are simultaneously used, because the channel contention is reduced during TWT SP, the reliability is improved and transmission delay decreases.
## 2 Materials and Methods  
### Design Consideration and 3D FDTD Approach  
Figure 1a shows the proposed design of the MRR waveguide. It consists of a ring waveguide coupled to two straight waveguides, thus enabling them to interact with each other within a small distance (gap). Based on MRR's geometric configuration (Figure 1b), the proposed design consists of a ring radius (R), gap (g), waveguide height (h), and waveguide width (W), with values as shown in Table 2.  
A ring radius of 4.50 \(\upmu\)m is considered to have the ability to reduce bending loss, while a waveguide cross-section of \(0.50\times 0.22\)\(\upmu\)m\({}^{2}\) provides low propagation loss. In addition, a gap of 0.05 \(\upmu\)m was chosen to minimize the impact of optical coupling scattering. The silicon waveguides were formed in 1 \(\upmu\)m silica buffer layer and supported by a silicon substrate of 250 \(\upmu\)m. The Silicon-on-Insulator (SOI) configuration of MRR (Figure 1b) was favored due to its ability to confine and, thus, optimize light propagation in waveguides. Based on the proposed design, the FDTD approach has been carried out. The Finite Difference Time-Domain method, or FDTD, is often used to simulate a photonics device because of its ability to provide both the spatial and temporal properties of photonics structures at once in a single calculation. FDTD uses [PERSON]'s equations based on Yee Mesh to derive numerical scattering problems and electromagnetic absorption on the basis of [PERSON]'s equations [26]. In this work, Lumerical FDTD Solutions software has been used to simulate the proposed design of SOI-based MMRs. Throughout this study, the three-dimensional (3D) FDTD method was used to simulate MRR based on SOI because it was able to provide good accuracy, though it was time and power consuming.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MRR Geometry** & **Dimension (\(\upmu\)m)** \\ \hline Ring radius & 4.50 \\ Gap & 0.05 \\ Waveguide width & 0.50 \\ Waveguide height & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dimensions of the proposed microring resonator (MRR).  
Figure 1: Illustration of the proposed design of the microring (MRR) shows (**a**) configuration of add-drop system ports and (**b**) the cross-section of the MRR based on Silicon-on-Insulator (SOI) material.
## 3 Results and Discussion
### UV-Vis Analysis  
Figure 5a shows the characteristic UV-Vis absorption spectra of NiSb\({}_{2}\)O\({}_{6}\) calcined at 700, 800, and 900 \({}^{\circ}\)C in a wavelength range of 200 to 800 nm (1.55 to 6.2 eV). NiSb\({}_{2}\)O\({}_{6}\)'s absorption bands were identified in the range from 200 to 500 nm [16; 28], which are characteristic of semiconductor oxides with a trirutile-type structure [29; 30].  
In the range from 600 to 700 nm, the bands corresponding to the electronic interactions occurring in the oxygen-metal (O-Ni) bond [31; 32; 33] were recorded. To estimate the values of the forbidden bandwidth (\(Eg\)) of the NiSb\({}_{2}\)O\({}_{6}\) powders, [PERSON]'s equation was used [34].  
\[\propto(E)=A\frac{\left(E-Eg\right)^{\frac{1}{2}}}{E}, \tag{2}\]  
where \(E\) is the energy of the incident photon, \(\alpha\) is the optical absorption coefficient, \(Eg\) is the forbidden bandwidth, and \(A\) is the proportionality constant. In our case, a direct transition (\(n=1/2\)) was considered for NiSb\({}_{2}\)O\({}_{6}\)[16; 28]. [PERSON]'s equation was used to elaborate the graph of (\(\alpha\)\(\hbar\)\(v\))\({}^{2}\) vs. \(E\), superimposing a straight line on the spectrum's final part (Figure 5b). With this, the bandgap energy of the calcined material at 700, 800, and 900 \({}^{\circ}\)C was estimated at \(\sim\)3.10 \(\pm\) 0.02, \(\sim\)3.14 \(\pm\) 0.04, and \(\sim\)3.23 \(\pm\) 0.03 eV, respectively. According to Figure 5, the material's crystal size strongly influenced the variation of the forbidden band [25; 28]. In our case, as the calcination temperature increased, the forbidden band value had a slight increase. This is attributed to the fact that, when the calcination temperature rose from  
Figure 5: (a) UV–Vis spectra of NiSb\({}_{2}\)O\({}_{6}\), (b) determination of the forbidden band at the different calcination temperatures.  
700 to 900 \({}^{\circ}\)C, the crystal size increased (see Figure 4a), provoking the energy variations of NiSb\({}_{2}\)O\({}_{6}\).  
Then, the crystal size, whether it increases or decreases, has a very significant effect on the compound's bandgap value. In the literature, bandgap values of ~2.7-2.8 eV at a calcination temperature of 700 \({}^{\circ}\)C and ~2.9 eV at 800 \({}^{\circ}\)C for materials such as our oxide were reported [16]. In reference [28], a bandgap value of ~3.92 eV was estimated for NiSb\({}_{2}\)O\({}_{6}\). In comparison, our results are within the accepted energy range by different studies for the same or similar material to ours (see Figure 5) [16; 25; 29; 35].
## 3 Results and Discussion  
### Test Results of the Matrixes  
In essence, the deformation behavior of a freshly mixed matrix under stress can be explained by its rheological test results. A matrix is a non-Newtonian fluid and the relationship between shear stress and shear strain rate is not constant when subjected to shear stress. It should also be noted that the viscosity of the matrix varies with the test parameters, such as the type of viscometer, the type of spindle, and the shear strain rate, which affect the measured viscosity value. Therefore, unless the test parameters are clearly defined, the viscosity obtained via measurement is customarily referred to as the apparent viscosity to distinguish it from the viscosity of Newtonian fluids.  
In this study, the rotational speed of the spindle was set to an appropriate range, that is, between 5 and 100 RPM, and the change in the rotational speed of the spindle was in a gradually decreasing manner (i.e., 100\(\rightarrow\)50\(\rightarrow\)20\(\rightarrow\)10\(\rightarrow\)5 RPM). The results of the rheological test on the matrixes are shown in Table 8. Among them, the mix proportions of the CM2 matrix were pure cement pastes, while the CM1 and CM3 matrixes were a mixture of ternary cementitious materials. Compared with the CM2 matrix of pure cement paste, the CM1 and CM3 matrices were a mixture of ternary cementitious materials whose apparent viscosity was significantly increased by the incorporation of silica fume and ultra-fine silica powder. This could ensure sufficient stability of the mixture and strengthen the bond between the hardened cement paste and the aggregate. Furthermore, as described above, the apparent viscosity of each matrix was closely related to the rotational speed of the spindle. The test results confirmed that the apparent viscosity increased as the spindle speed decreased, as shown in Figure 1. Especially at low speeds, the apparent viscosity of the CM1 matrix was more than 30,000 cP. On the other hand, there was a clear linear relationship between the torque and rotational speed of the spindle, as shown in Figure 2. This result indicated that these matrixes had appropriate rheological behavior that conformed to the Bingham model.  
Table 9 shows the experimental results of the matrixes. As shown in Table 9, the flow value of each matrix ranged from 70.0 to 116.7 mm. Among them, the flow value of the CM1 matrix was the smallest (70.0 mm) and the flow value of the CM3 matrix was the highest (116.7 mm). In terms of compressive strength, the matrix's 7-day compressive strength ranged from 69.1 to 87.3 MPa, where the CM1 matrix had the lowest compressive strength (69.1 MPa) and the CM2 matrix had the highest compressive strength (87.3 MPa). These results showed that the properties of the matrix will adhere to the surface of the coarse aggregate uniformly, thereby helping to improve the mechanical properties of the subsequently prepared LPC [31].
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 3 Parameter Design and Analysis of Critical Components of Seed Metering Device
### Parameter Design of the Metering Wheel
#### 3.2.2 Hole Design  
The seed length of \"Long Ma 3\" is greater than the width, and the width is greater than the thickness. According to the minimum potential energy principle, the seed is more likely to be encapsulated into the hole in a \"flat\" posture [30; 31]. Considering that \"Long Ma 3\" is elliptical, it is convenient for the seed to fall out of the hole smoothly. The designed hole is cone-shaped, and the cone angle of the hole is 45\({}^{\circ}\). To ensure that the hole of the metering wheel can fill more seeds at one time, the diameter of the hole should be at least two seeds at the same time. The hole diameter should be greater than the sum of the maximum length of the industrial hemp seed and the maximum width of the seed and less than two-thirds of the thickness of the metering wheel. The structural strength of the metering wheel should be ensured to prevent damage to the metering wheel at high speeds and affect the metering performance. The hole depth should be satisfied so that the multi-seeds can fully enter the hole in the overlapping state. Considering the posture of the seed being encapsulated into the hole, the hole height should be twice the maximum thickness of the seed. The hole diameter and depth should meet Formula (3) [32].  
\[\left\{\begin{array}{l}(L_{\text{max}}+H_{\text{max}})\leq d\leq\frac{2}{3}B \\ (L_{\text{max}}+w_{\text{max}})\leq d\leq\frac{2}{3}B\\ 2H_{\text{max}}\leq h\\ h<d\end{array}\right. \tag{3}\]  
where \(L_{\text{max}}\) is the maximum length of industrial hemp seed, mm; \(h_{\text{max}}\) is the maximum seed thickness of industrial hemp, mm; \(w_{\text{max}}\) is the maximum seed width of industrial hemp, mm.  
The maximum length, width, and thickness of the \"Long Ma 3\" seed were 4.1 mm, 3.2 mm, and 2.5 mm, respectively. Comprehensive formula, the hole's diameter, and depth should meet 6.7 mm \(\leq d\leq\) 13.0 mm and \(h\geq\) 5.0 mm.  
\[\Delta p\geq 2L_{\text{max}} \tag{4}\]  
According to the maximum seed length of 4.1 mm, the distance between the holes should be greater than 8.2 mm. The general plant spacing of industrial hemp step seeding is 10.0 mm, and the linear speed of the hole wheel is generally not more than 0.3 m/s. The operating speed of the known seeder is 4.7 km/h, and the number of holes meets the Formulas (5) and (6) [33].  
\[Z=\frac{\pi dv_{b}}{Sv_{n}} \tag{5}\]  
\[v_{\omega}=\omega D \tag{6}\]where \(S\) is the theoretical spacing, mm; \(\omega\) is the angular velocity of the metering wheel, rad/s, \(v_{\omega}\) is the linear velocity of the metering wheel, m/s.  
Combined with the Formulas (2) and (4)-(6), the number of holes in the metering wheel is \(8.8\leq Z\leq 17.9\), and the angle between adjacent holes is taken as integral as possible to facilitate processing. The number of holes in the metering device is designed to be 10.
## 4 Implementation
### Edge Side
#### 4.2.2 DSSIM Generator  
The DSSIM Generator produces a SSIM-MAP with Equation (1) and then converts it to a DSSIM-MAP with Equation (2). Since there is a weakness that much time is required to calculate a SSIM-MAP, its calculation was performed using multi-processing [24]. Additionally, to estimate the proper image size for real-time calculation of an SSIM-MAP, we measured the calculation times of five cases in image resolution: 1920 \(\times\) 1080, 1280 \(\times\) 720, 960 \(\times\) 540, 800 \(\times\) 450, and 640 \(\times\) 360. Table 2 shows the average processing times when two different images are calculated for 100 times on Raspberry 5, respectively.  
As shown in the table, the average time to generate SSIM-MAPs decreases as the resolution of source images becomes smaller. For the original CCTV images with the resolution of 1920 \(\times\) 1080, the average processing time to generate a SSIM-MAP appeared to be 269 ms. This means our system only handles around 3.7 frames per second. It is not satisfactory when considering that the general CCTV video has a rate of 30 frames per second.  
On the other hand, for images with a reduced resolution of 640 \(\times\) 360, the average processing time decreased to 23 ms and around 42.8 frames can be calculated for a sec  
\begin{table}
\begin{tabular}{c c} \hline
**Image Resolution** & **Processing Time (ms)** \\ \hline
1920 \(\times\) 1080 & 269.59 \\
1280 \(\times\) 720 & 112.68 \\
960 \(\times\) 540 & 56.24 \\
800 \(\times\) 450 & 37.12 \\
640 \(\times\) 360 & 23.37 \\ \hline \end{tabular}
\end{table}
Table 2: SSIM-MAP Processing Time by Resized Image Resolution in Raspberry Pi 5.  
Figure 4: Result of the reference image generator operation: moving objects are removed, presenting only the background.  
ond. Thus, in our experimental environment, 640 \(\times\) 360 was selected for the appropriate resolution to generate SSIM-MAPs in real time.  
Accordingly, each SSIM-MAP was transformed to a DSSIM-MAP by Equation (3) described in Section 3. Figure 5 shows an image painted by translating 0.0\(\sim\)1.0 scale values of a sample DSSIM-MAP to 0\(\sim\)255 scale values.  
In Figure 5, a brighter area represents an area with higher DSSIM values. Therefore, bright areas can be interpreted as detected differences from the background, while dark areas correspond to the background and can be considered as having lower value as regions of interest.
## 2 Materials and Methods
### Induction of OA and MEST Application  
MIA-induced OA model is one of the most popular animal models, especially in pain mechanism research [22]. Rats were briefly anesthetized using isoflurane/O2 inhalation and received an intra-articular injection of MIA (Sigma Aldrich, Milwaukee, WI, USA) in the left knee. MIA in a total volume of 30 uL of physiological saline was injected through the intra-patellar ligament using a 26-G needle. As motor behavioral deficits in OA animals were not evident at 2 weeks after the 1 st MIA (1 mg) injection in our experimental protocols, an additional 2 nd MIA injection (3 mg) was administered similarly. MEST (MEST-B2375, OV MEDI, Korea) is a device for intramuscular insertion of specially designed polydioxanone filaments. It comprises a protective cap, needle (23-gauge, 75 mm) with handle, a catheter (18-gauge, 35 mm) with a handle, and flexible PDO filament (outer diameter 0.3 mm) with cogs (1 mm intervals) (Figure 1A-D). The PDO filament, folded and inserted into two lines, is equipped with bi-directional cogs for maximal holding, supporting, and stimulating muscles (Figure 1C,D). Cogs in the opposite direction of filament insertion were covered by a catheter for protection (Figure 1D). Although the needle and catheter of MEST were designed to be fully inserted into the target muscles until the handle of the catheter reached the skin for clinical human use, some of them were inserted into muscles for this animal experiment (Figure 1F). Skin incision and closure were additionally performed for animal experiments because loose skin over the target muscles hindered the correct injection of the MEST needle to the intended points (Figure 1E-H).  
The skin over the thigh muscles of the left hind paw was shaved and opened under brief anesthesia (isoflurane/O2 inhalation) (Figure 1E). The MEST needle was inserted approximately 1/3 of the entire length of the thigh muscles from the knee and advanced toward the knee at an angle of approximately 30\({}^{\circ}\) below the horizontal (Figure 1F). Once the needle of MEST reached the distal femur area, the needle and catheter were withdrawn back sequentially, leaving the PDO filament fixed to the quadriceps muscles by the cogs. Five MESTs were administered at intervals across the lateral, medial, and ventral parts of the thigh muscles (Figure 1G). The skin was closed with 5-0 silk thread after removing the remaining part of the PDO filaments outside of the skin using scissors (Figure 1H). The skin was wiped with a povidence-iodine solution, and the animals were returned to their cages for recovery. Sham seedling of MEST was performed similarly without the PDO filament.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Rate of Failure for DRFTSA Using Regular Dataset by Varying Number of VMs  
In this section, we precisely present the generated rate of failure for DRFTSA for a regular dataset by varying the number of VMs. VMs are varied over tasks linearly assigned to them, i.e., 100, 200, 300, 400, 500, 600 tasks assigned to 10, 20, 30, 40, 50, 60 VMs, respectively. Generated rate of failure for PSO for 100, 200, 300, 400, 500, 600 tasks is 45.17, 50.78, 56.97, 58.36, 59.45, 61.36, respectively. Generated rate of failure for GA for 100, 200, 300, 400, 500, 600 tasks is 47.36, 49.52, 54.57, 60.44, 62.74, 63.19, respectively. Generated rate of failure for ACO for 100, 200, 300, 400, 500, 600 tasks is 51.72, 52.47, 55.73, 59.18, 65.14, 67.74, respectively. Generated rate of failure for DRFTSA for 100, 200, 300, 400, 500, 600 tasks is 30.85, 28.78, 25.62, 24.64, 22.18, 20.04, respectively.  
From Table 13 and Figure 13, it is evident that the rate of failure of DRFTSA compared to various approaches is greatly minimized with the considered dataset and VMs.  
Figure 12: Calculation of rate of failure for regular dataset using fixed number of VMs.  
Figure 13: Calculation of rate of failure for regular dataset by varying number of VMs.