esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This research investigates the grid connected Merredin WA mining sector that has demonstrated the effectiveness of integrating solar PV systems with battery storage to address the energy challenges faced by mining operations in Western Australia. By leveraging advanced RE technologies, this research delivered a reliable, cost-effective, and environmentally sustainable solution, significantly reducing reliance on conventional grid power. This study initially selected the site Merredin WA, planned and designed system using PV and BESS model and optimized energy solution. It considered cost savings, cash flow and long-term cost projection analysis. The hybrid data-driven LSTM-classical optimization method is applied to determine cost savings value and compared with the existing algorithms. Accordingly, the energy consumption from grid system can be reduced  
Figure 14: Long-term cost projection.  
Figure 15: Sensitivity analysis of the Merredin WA mining sector.  
in both summer and winter weekdays that shows yearly 54,196 kWh to 23,781.1 kWh when implemented the proposed solar projection system. Hence, around 57% load demand can be fulfilled by the proposed PV project using the optimization method that saves annually approximately USD $8627.53 for future projection. With this, the cash purchase and finance can be improved up to USD $209 k and USD $213 k over the 25 years correspondingly. The applied optimization method resulted least computation time 1.153 s and high accuracy 99.247% when compared with existing other algorithms. The proposed research is further capable of reducing carbon emissions by 67% to meet net zero carbon emission target.  
Beyond the mining industry, the Merredin WA Solar Project serves as a model for other sectors and regions. Therefore, for future research, the grid stability considering higher penetration of RESs, and AI-driven hybrid control systems will be investigated to enhance system efficiency.  
Conceptualization, M.O.Q. and R.K.; methodology, M.O.Q.; software, M.O.Q. and R.K.; validation, M.O.Q. and R.K.; formal analysis, M.O.Q., R.K. and M.A.; investigation, M.O.Q.; data curation, M.O.Q. and R.K.; writing--original draft preparation, M.O.Q.; writing--review and editing, M.A., S.L. and U.A.; visualization, M.A.; supervision, S.L. and U.A.; project administration, S.L.; funding acquisition, M.O.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Edith Cowan University, Australia and the APC was funded by Applied Sciences, MDPI.  
Not applicable.  
Data available on request.  
The authors declare no conflicts of interest.
# A Hybrid Vibration Isolator Based on Elastomeric and Electromagnetic Restoring Force
## 6 Results and Discussions
### Force Capability of the Electromagnetic Actuator  
The calculation of magnetic force by the model is carried out using the solution procedure for the current (where the transient terms are neglected) and the electrical  
Figure 14: Measured magnetic force (blue) and model magnetic force (red) vs. displacement.  
Figure 12: Values of \(dM_{03}/dx\) obtained with a misalignment of 1 mm (grey lines) and 5 mm (black lines) of the axis of the bottom coils, with a current of 3 A in the coils.  
Figure 13: Measured magnetic force (blue) and model magnetic force (red) vs. time.  
parameter-estimation methods mentioned above. The obtained results demonstrate a good agreement between the magnetic force calculated from the experimental data and the model-based magnetic force. Therefore, the electromagnetic model obtained allows the possibility of predicting the magnetic force generated by the coil, which is a fundamental achievement for the development of an isolator control.  
The possibility of analyzing the force capability of the isolator is given by the obtained electromagnetic model. By fixing the currents in the upper coil of the isolator and considering a variable current in the bottom coils, the maximum values of electromagnetic force for each position along the \(x\)-axis can be obtained. These values are shown in the isovalues maps in Figures 15-17.  
According to the values of the upper coil current, it is possible to reach a force value of about 200 N. However, the most interesting point is the possibility of changing the force value for each position by varying the bottom coil currents. The same considerations can be carried out by considering a fixed current value for the bottom coils and varying the currents in the upper coil. As highlighted in Figure 18, for each value of the relative displacement, it is possible to vary the electromagnetic force according to the variation in the currents in the coils. These results are very important for the real-time control of the system, where the fast dynamic of the electromagnetic system can be exploited to change the isolator's restoring force.  
Figure 16: Force isovalues (N) map for the upper coil current of 3 A and the bottom coils’ currents in the range from \(-3\) A to 3 A.  
Figure 15: Force isovalues (N) map for the upper coil current of 1 A and the bottom coils’ currents in the range from \(-3\) A to 3 A.  
The experimental results show that the adopted electromagnetic topology has a higher number of tunable parameters with respect to those equipped with permanent magnets [18; 19]; furthermore, electromagnetic systems are characterized by a high robustness and efficiency [22].
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 2 Operation Scenario Setting
### Environmental Factors  
1. Terrain According to relevant technical standards in China [18], land heliports and water platforms should have at least one final approach and take-off (FATO) area. When heliopters take off and land with Class 1 performance, the local slope of any area should be less than or equal to 5\({}^{\circ}\). The aircraft selected in this paper is a multi-engine helicopter, and the flight mission is performed in a populated area that meets the helicopter's level 1 performance operation standards. The slope of all grids in the take-off and landing area needs to conform to Formula (1): \[Slope\leq 5^{\circ}\] (1) In addition, this paper also considers the undulation factor to ensure that the take-off and landing site is flat. In the general take-off and landing field, the undulation accuracy is required to be relatively high, which should conform to Formula (2): \[F\leq 0.5\,\mathrm{m}\] (2) In practice, the slope and undulation factors selected in different directions are mostly used as constraint variables to define that the flatness of the site conforms to the specification.
2. Area requirement According to the technical requirements, the smallest circular temporary stop area needs to be not less than 2C (C is the outer clearance of the helicopter landing gear, and C is selected as 4.5 m in this paper); the area of square temporary stop area should be not less than 2C \(\times\) 2C (In this paper, the area is rounded up to 85 m\({}^{2}\)).
3. Barrier buffer Due to the fact that obstacles, such as road networks and river networks, exist that may affect the safe operation of helicopters when a forced stop occurs, this paper sets a safe distance. Theoretically, the safe area should be at least 3D or greater (D is the length of the entire fuselage of the helicopter and is set at 13.46 m in this paper). To ensure safety, the safety distance is appropriately increased for buildings or tall obstacles according to their heights.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 1: The PQ8 Satellite
#### 4.1.1 Initial Relative Motion  
The initial relative motion between satellites was determined by computing the angle between their position vectors. Depending on the drag configuration, this angle either increases or decreases over time. It also exhibits oscillatory behavior due to orbital variations between apogee and perigee. Figure 7 illustrates this behavior across multiple orbital cycles. The first and second derivatives of the angular separation yield the relative angular velocity and angular acceleration. To accurately capture the average angular acceleration, values are extracted from extended simulation periods. As shown in Figure 7d, the average converges to a stable value of 0.0103/day2 after approximately 140 days, despite initial variability.
## 3 Results
### Difference Due to Assistance of Power Support Scissors  
PSD analysis of the sEMG for the 100 s scissors opening/closing task every 5 s revealed an interaction between time and power-assisted device use (F\({}_{\text{[19, 836]}}\) = 2.50, \(p\) < 0.01, \(\eta^{2}\) = 0.01) and a main effect of time (F\({}_{\text{[19, 836]}}\) = 1.70, \(p\) < 0.05, \(\eta^{2}\) = 0.01) in the 45\({}^{\circ}\) dorsiflexed wrist position. In a simple main effect analysis, the MF was significantly lower without the power-assisted device (F\({}_{\text{[19, 836]}}\) = 3.09, \(p\) < 0.01). Additionally, in the power-assisted condition, the MF was lower when the task duration was 50 s (Figure 2). Thus, the muscle activity slowed in the absence of power support. In the neutral position of the wrist, the use of the power-assisted device did not affect the MF (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Survey Items**} & \multirow{2}{*}{**Classifications**} & \multicolumn{3}{c}{**Participant Ratio**} \\ \cline{3-5}  & & \(n\) & \% & \(p\)**-Value a ** \\ \hline Height (cm) & 149\(\leq\) & 1 & 4 & \\  & 150 \(\leq\) 159 & 16 & 70 & \(p\) = 0.002 \\  & 160 \(\leq\) 169 & 6 & 26 & \\ \hline Weight (kg) & 49\(\leq\) & 9 & 39 & \\  & 50 \(\leq\) 59 & 14 & 61 & \(p\) = 0.042 \\ \hline EHI score & 80 \(\leq\) 89 & 11 & 48 & \\  & 90 \(\leq\) 100 & 12 & 52 & \(p\) = 0.001 \\ \hline CTSI-JSSH score & 0\(\leq\) & 23 & 100 & \\ \hline \multicolumn{3}{c}{Body mass index (kg/m\({}^{2}\))} & Mean & SD & \\  & 20.55 & 1.63 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participant characteristics.  
The results of the BA analysis are presented in Figures 11-14. The mean changes in the scores are as follows: at 45\({}^{\circ}\) wrist dorsiflexion, the MF ranged from \(-\)0.82 to \(-\)5.67 (standard deviation [SD]: \(-\)32.96 to 28.28) with the power-assisted device and from \(-\)1.37 to \(-\)12.72 (SD: \(-\)43.10 to 41.27) without it. In the neutral wrist position, the MF ranged from \(-\)3.96 to \(-\)0.63 (SD: \(-\)44.80 to 36.85) with the power-assisted device and from \(-\)7.13 to \(-\)26.65 (SD: \(-\)33.26 to 24.65) without it. The BA plot shows the error between the baseline (0-5 s) and phase 2 (5-10 s), the baseline and phase 3 (10-15 s), and every 5 s thereafter. Finally, we compared the baseline and the 19 th phase (95-100 s), and at each measurement time point, all four outcome measures were verified to be free from systematic bias.  
Figure 3: Comparison of the MFs with and without the power-assisted device at the midpoint of the wrist. The y-axis indicates the MF bandwidth, and the x-axis indicates the task execution time. No significant differences were observed between the groups. Error bars indicate standard errors.  
Figure 2: Comparison of the MFs with and without the power-assisted device at 45\({}^{\circ}\) wrist dorsiflexion. The y-axis indicates the MF bandwidth, and the x-axis indicates the task execution time. Post hoc analysis revealed that the MF was higher in the power-assisted condition 50 s after the beginning of the task. Error bars indicate standard errors. * \(p\) < 0.05.
# Automated DNA Extraction Monitoring System Based on MTConnect Technology
## 1 Introduction
POCT devices must be remotely controlled by a connection to the examination information systems of the medical institution, and the results must be collected, reviewed for quality control purposes, and automatically entered into the patient's electronic medical record. Therefore, it is a very important to ensure the reliability of diagnosis results for a medical institution to be able to remotely monitor the state of POCT equipment at a remote location in real time. There have been few studies on the remote monitoring of medical devices, including POCT equipment [14; 15]. However, these medical device monitoring systems simply monitor the measurement results or simple operation status. Therefore, this study proposes a system that can monitor detailed elements of device components with MTConnect standard technology. In this paper, an MTConnect technology-based monitoring system for automated molecular genetic diagnostic test devices is presented. This automated DNA extraction system consists of a component for gene extraction and gene amplification based on real-time PCR and a Raspberry Pi-based control system that controls a series of extraction procedures. The user can control the system using a web application-based user control interface. In addition, MTConnect, which is an open and standard machine monitoring data interface based on XML [16], was used to enable POCT device monitoring data collection and monitoring of POCT devices as web application-based clients. This paper is organized into five sections. The method for system design, automated DNA extraction system, and MTConnect is in Section 2. The experimental results are in Section 3, and discussions are reported in Section 4, and finally, the conclusion is presented in Section 5.
## 3 Results and Discussion
### Development of Optimal Dust Suppressant Formula
#### 3.1.4 Test for Relative Damage Rate of Plant Seeds  
Plant seeds were cultured with the optimal formula solution, the preliminary formula solution, and the pure water on days 1, 3, 5, 8, 10, and 12, respectively (Figure 6). The sprout number and the germination rate were calculated. The relative damage rates for plant seeds under the optimal formula and the preliminary formula were obtained through further calculation, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Serial** & **Sprout** & **Germination** & **Average** & **Relative Damage** \\  & **Number** & **Number** & **Rate (\%)** & **Values (\%)** & **Rates (\%)** \\ \hline \multirow{3}{*}{Optimal} & 1 & 58 & 58 & \multirow{3}{*}{63} & \multirow{3}{*}{29\%} \\  & 2 & 55 & 55 & & \\ \cline{1-1}  & 3 & 75 & 75 & & \\ \cline{1-1}  & 4 & 72 & 72 & & \\ \cline{1-1}  & 5 & 85 & 85 & 81 & 9\% \\ \cline{1-1}  & 6 & 85 & 85 & & \\ \cline{1-1}  & 7 & 85 & 85 & & \\ \cline{1-1} Pure water & 8 & 93 & 93 & 89 & – \\ \cline{1-1}  & 9 & 90 & 90 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative damage rates of plant seeds.  
Figure 6: (**a–f**) Germination of plant seeds cultured with the optimal formula solution, the preliminary formula solution, and the pure water on days 1, 3, 5, 8, 10, and 12, respectively.  
According to Table 6, the germination rates of plant seeds cultured for 12 days with the optimal formula solution, the preliminary formula solution, and the pure water are 63%, 81%, and 89%, respectively. The relative damage rates of plant seeds under the optimal formula and the preliminary formula are 29% and 9%, respectively, both of which are less than 40% and meet the requirements of the Water-Soluble Road Dust Suppressant Industry Standard (T/CAEPI 7-2017). In combination with the results of moisture retention tests under natural conditions and hygroscopicity tests under different moisture conditions, the optimal formula demonstrated the best comprehensive performance in terms of moisture retention, hygroscopicity, and environmental protection.
## 4 Experiments
### Comparison with Prior Methods
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**FPS**} & \begin{tabular}{c} **\#Params** \\ **(M)** \\ \end{tabular} & \begin{tabular}{c} **mAP** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}_{50}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}_{75}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}_{75}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AP\({}_{M}\)** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **AP\({}_{L}\)** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Search** \\ **Cost** \\ \end{tabular} \\ \hline EfficientDe-D0 [61] & Efficient-B0 & BiFPN & 512 & 3.9 & 33.8 & 52.2 & 35.8 & 12.0 & 38.3 & 51.2 & \\ NAS-FPN [33] & Res50 & Searched & 640 & 60.3 & 39.9 & - & - & - & - & - & 333 * \\ NAS-FCOS@128 [35] & Res50 & Searched & 1333 \(\times\) 800 & 27.8 & 37.9 & - & - & - & - & - & 28 \\ SpinNet-495 [62] & Searched & FPN & 640 & 11.9 & 39.5 & 59.3 & 43.1 & 20.9 & 42.2 & 54.3 & - \\ SM-NASE [26] & Search the combination & 800\(\times\)600 & - & 40.0 & 58.2 & 43.4 & 21.1 & 42.4 & 51.7 & 187 \\ EfficientDet-D1 [61] & Efficient-B1 & BiFPN & 640 & 6.6 & 39.6 & 58.6 & 42.3 & 17.9 & 44.3 & 56.0 & - \\ DetailsX [31] & Searched & FPN & 1333 \(\times\) 800 & - & 42.0 & 63.9 & 45.8 & 24.9 & 45.1 & 56.8 & 44 \\ NAS-FPN [33] & Res50 & Searched & 1024 & 60.3 & 44.2 & - & - & - & - & 333 * \\ Auto-FPN [34] & Res50 & Searched & 800 & 32.6 & 40.5 & 61.5 & 43.8 & 25.6 & 44.9 & 51.0 & 16 \\ NAS-FCOS@2865 [35] & R-101 & Searched & 1333 \(\times\) 800 & 57.3 & 43.0 & - & - & - & 28 \\ SpinNet-49 [62] & Searched & FPN & 640 & 28.5 & 42.8 & 62.3 & 46.1 & 23.7 & 45.2 & 57.3 & - \\ SM-NASE [36] & Search the combination & 800 \(\times\) 600 & - & 42.8 & 61.2 & 46.5 & 23.5 & 45.5 & 55.6 & 187 \\ Hit-Detector [63] & Searched & Searched & 1200 \(\times\) 800 & 27.1 & 41.4 & 62.4 & 45.9 & 25.2 & 45.0 & 54.1 & - \\ OPA-FPN@64 [64] & Res50 & Searched & 1333 \(\times\) 800 & 29.5 & 41.9 & - & - & - & - & - & 4 \\ \hline FastDARTSDet (N = 1) & Searched & Searched & 640 & 5.8 & 36.4 & 53.4 & 38.0 & 18.6 & 39.4 & 45.3 & 42 \\ FastDARTSDet (N = 2) & Searched & Searched & 640 & 6.9 & 40.0 & 59.4 & 41.7 & 20.5 & 41.1 & 54.3 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with prior works on the COCO test-dev. Different blocks indicate models with various inference speeds and prediction performance. “*”: the unit of search cost is TPU-days, while the unit of other methods is GPU-days. Figure 9: Performance of joint and independent search for backbone and FPN on MS-COCO validation set.
## 2 System Description and Problem Statement  
This section includes the off-shore WPP under study in this paper to analyse and design grid-forming controllers.  
Figure 1 illustrates the comprehensive system diagram, featuring a 1.2 GW off-shore WPP distributed across three clusters, each with a 400 MW capacity. The WPP establishes a connection to an on-shore Modular Multi-level Converter (MMC) through a DR-HVdc link. Each WPP cluster consists of 50 8 MW type-4 WTGs, connected to a 66 kV collector. The use of aggregated WTGs allows WPP complexity reduction. For that, an aggregated WTG models each string of WTG, as shown in Figure 1. The used WTG aggregation technique is proposed in [19]. Aggregating each string allows a reasonable computational cost reduction in simulations while keeping the complete dynamics of the DR-HVdc stages. The realistic model considers saturation for all transformer models (WTG and DR).  
The DR-HVdc system is composed of three DR platforms [4]. Each platform includes two 12-pulse DR of 200 MW, with a parallel connection on the ac side and a series connection on the dc side.  
As Figure 1 shows, the clusters WPP-\(i\) (\(i=1,2,3\)) are connected to the DR platforms using the off-shore ac collector grid. Each DR platform includes ac and dc filters. Lumped pi-parameter cable models are used for the off-shore array cable.  
DR stations are connected to the on-shore MMC converter using an export HVdc cable, modelled using a distributed frequency-dependent parameters model. The on-shore MMC operates by controlling the HVdc grid voltage and giving support with reactive power to a high-SCR on-shore grid. The electrical system parameters are listed in Table 1.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 3 Methodology
### Attention Network
The final output of the multi-attention mechanism is the element addition of the channel attention feature and the spatial attention feature. In this way, better feature appearance information can be acquired. As shown in Equation (5). \[P_{b}=N_{b}+O_{b}\ \ \ b=1,\ 2,\ I,\ d \tag{5}\]
## 5 Numerical Experiments
### Experiments with Inventory Control Models
#### 5.2.2 Effect of Ordering Policy \((\alpha,\beta)\) on Inventory Quantity  
Ordering policies are very important in inventory management. In this section, we discuss the effect of the parameters \((\alpha,\ \beta)\) representing the ordering policy on inventory holdings. Figure 25 shows the inventory quantity calculated for five combinations of the adjustment parameters \((\alpha,\ \beta)\) of the ordering policy. When \((\alpha,\ \beta)=(0.5,\ 0.5)\), the inventory quantity sometimes shows negative values. It indicates that the inventory is out of stock. When \((\alpha,\ \beta)=(1,\ 0.5)\), the inventory quantity is relatively low, indicating a possibility of running out of stock. When \((\alpha,\ \beta)=(1,\ 1)\), \((\alpha,\ \beta)=(0.5,\ 1)\), and \((\alpha,\ \beta)=(2,\ 2)\), there is some room in the inventory. This result shows that when \(\alpha\) is small, a large quantity of  
Figure 23: Increase and decrease in order quantity.  
Figure 22: Order quantity and inventory.  
final inventory is left over; a small value of \(\beta\) will result in less excess inventory, but may cause stockout.