esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Early Cracking Resistance of Mortar  
The results measured by the ring method are shown in Figure 7.  
As can be seen from Figure 7, the addition of DE extends the initial cracking time of mortar and significantly reduces the crack width. When the amounts of DE mixed are 1% and 3%, the cracking times of mortar are delayed by 10.7% and 15.7%, respectively. At the same time, with the increase in DE content, the crack width is significantly reduced. The reason why diatomic can delay the initial cracking time of mortar is that, during the  
Figure 7: Mortar initial cracking time and crack width diagram.  
mixing process of mortar, the pre-water-absorbed DE is evenly distributed inside the slurry. As the cement hydration reaction proceeds, the moisture inside the cementitious material slurry gradually decreases, resulting in a humidity gradient difference and capillary pore negative pressure, which cause the diatomite to release the pre-stored water. This water compensates in a timely manner for water loss in the mortar, reducing the internal relative humidity gradient, promoting the hydration of cement, inhibiting the early self-shrinkage and drying shrinkage of the mortar, extending the initial cracking time of the mortar, and reducing the crack width of the mortar.  
In summary, the rational use of DE can improve the internal relative humidity, significantly reduce its self-shrinkage, and enhance the early crack resistance of high-strength low water-binder ratio cement mortar without reducing its 28 d compressive strength. DE is an excellent internal curing material for low water-binder ratio cement mortar, and its introduction of internal curing water achieves good results, showing a good curing effect on low water-binder ratio cement mortar.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Objectives
#### 4.1.2 High Intelligence  
Intelligence in ITS sensing means that transportation systems understand the surrounding environment through intelligent sensing functions, thus providing valuable information for efficient and effective decision-making. Many ITS environments today still have unreliable or unpredictable network connectivity. These could include buses, planes, parking facilities, traffic signal facilities, and general infrastructures under extreme conditions. Edge computing functions can be designed as self-contained, thereby neatly supporting these environments by allowing autonomous or semi-autonomous operation without network connectivity. One existing example could be ADAS functions, which automatically run onboard vehicles. Without Internet connection, it may not serve as a data collection point for other services but is still able to warn and protect drivers in risky scenarios. However, high intelligence often requires high-complexity methods and computation power. Concerns exist in the resource constraint on edge devices, the ability to handle corner cases that the machines never encountered, and other general challenges in AI.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 2 An Overview of MSE Growth of GaN
### MSE System Configuration Based on Liquid Ga Target
Pure Ga does not wet any surface and has a very high surface tension (\(7.35\times 10^{-3}\) N/cm) [34]. However, the formation of native oxide layer due to reaction between Ga and oxygen may also change the wetting properties of liquid Ga [35]. Therefore, the trough material should be selected so that it can be wet by the liquid Ga while also UHV-compatible at the same time. In the literature, stainless steel [8; 36] and molybdenum [37] have been used to contain liquid Ga. In addition to the material selection, the shape of the trough must also be optimized to prevent the formation of air pockets in the target and maintain a flat surface, as shown in Figure 3a-d. From our experiments, we found that a stainless steel trough with concave-shaped surface is the best option to minimize the formation of Ga droplets and voids in the target during the deposition process [21]. As mentioned above, one of the challenges of using a Ga target is the target poisoning, caused by the formation of compound GaN on the target surface, which leads to a reduced sputtering rate. The change of Ga target from fresh Ga metal to partially-poisoned mode is shown in Figure 3e,f respectively. In addition to a poisoning of the target surface, nitrogen ion-bombardment can also cause nucleation of microscopic solid GaN particles inside the liquid target which irreversibly leads to a solid-liquid two-phase bulk target composition, which is not wanted for maintaining a stable process. Another issue related to liquid Ga target for sputtering is the formation of gas bubbles inside the target. The gas bubbles are formed by the Ar and N\({}_{2}\) gas ion bombardments, resulting in trapped gas within the target. During deposition, there is a possibility for these bubbles to \"burst\" out of the target surface, resulting in splashing of Ga droplets within the chamber. From our experiments, the bubble formation can be suppressed by using a lower chamber base pressure and better cooling mechanism of the magnetrons. Figure 2: (**a**) Schematic of an MSE system with liquid Ga target. (**b**) Schematic of a liquid Ga target equipped with a type II unbalanced magnetron. This configuration results in magnetic field lines extending toward the target [21]. Reproduced with permission from Linköping University Electronic Press.
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## 4 Results
### Nose Optimisation  
One of the objectives of the study was to find the nose geometry of X-43A, defined by the design angles \(\alpha_{2}\) and \(\alpha_{3}\), for which the oblique shock waves from stages 2 and 3, see Figure 1, were meeting on the tip of the lower edge of the scramjet and, consequently, the mass flow rate throughout it was maximum. In this case, the aircraft was considered to be flying with the nose optimised configuration. It is important to realise that, if the shock waves did not impact at the scramjet inlet lower surface, it would highly compromise the engine's efficiency and performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{3}{c}{\(\mathbf{N^{\circ}}\)**of**} & **Minimum** & **Average** & **Mach Number** & **Mach Number** & **Mach Number** \\  & **Elements** & \(y^{+}\) & \(y^{+}\) & **of Stage 1** & **of Stage 2** & **of Stage 3** \\ \hline Mesh 1 & \(2.6\times 10^{6}\) & \(9.10\times 10^{-1}\) & 43.87 & 6.58 & 6.51 & 5.51 \\ Mesh 2 & \(3.0\times 10^{6}\) & \(8.34\times 10^{-1}\) & 26.22 & 6.52 & 6.49 & 5.50 \\ Mesh 3 & \(3.4\times 10^{6}\) & \(1.21\times 10^{-1}\) & 18.4 & 6.51 & 6.48 & 5.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of elements, minimum and average \(y^{+}\) values and Mach numbers of stages 1, 2 and 3 of Mesh 1, Mesh 2 and Mesh 3 for \(AoA=0^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Design Angles**} \\ \cline{2-5}  & \(\mathbf{\alpha_{1}\)**(°)} & \(\mathbf{\alpha_{2}\)**(°)} & \(\mathbf{\alpha_{3}\)**(°)} & \(\mathbf{\alpha_{4}\)**(°)} \\ \hline
1 & 2.7 & 3.07 & 11.5 & 13.9 \\
2 & 1 & 3.07 & 10.8 & 10 \\
3 & 0 & 2 & 12.2 & 11.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design angles values of the three CFD cases.  
In the analytical model, the nose optimisation was carried out despite it did not include the scramjet effects for the calculation of the aerodynamic parameters. Given a certain Mach number and angle of attack, the design angles \(\alpha_{2}\) and \(\alpha_{3}\) that allowed the nose optimised configuration could be found by using an iterative process based on the calculation of the shock waves angles \(\beta_{2}\) and \(\beta_{3}\) and the use of basic trigonometry. In fact, the analytical case that will be studied was designed to reach this type of configuration for \(AoA=0^{\circ}\).
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 6 Discussion
### Theoretical Implications  
From a theoretical perspective, this study provides an in-depth analysis of the different trajectory prediction methods, assessing their strengths and weaknesses. It uncovers the inherent trade-off between the transparency of a method and its predictive power, such as the \"black-box\" nature of deep learning methods versus the interpretability of probabilistic methods. Furthermore, the findings highlight the potential of LSTM models and newer variants such as NSP-SFM, though the practicality of such complex models is questioned.  
The study also underlines the need for further research in this area. Despite the strides made in trajectory prediction, it becomes evident that achieving accurate worker trajectory predictions in real warehouse environments remains an ongoing challenge. This research highlights the need for methods that are not only efficient but also robust to real-world dynamics such as noise in the data.
# Self-Anti-Disturbance Control of a Hydraulic System Subjected to Variable Static Loads
## 3 Simulation Analysis of Optimal Control Strategy
### Simulation Analysis of Classical PID Control  
The simple structure and convenient parameter calibration of the classical PID control make it widely used in many fields. In this section, we describe how the proportional coefficient \(K_{p}\) was firstly determined; then, the integral constant and differential constant were set to zero. The proportional gain gradually increased from zero until the response curve oscillated; then, the value decreased slowly until the oscillation stopped, 30-70% of the proportional gain can be used as the system proportional coefficient, and 60% was taken in this study. The proportional coefficient was determined, and the larger value was taken as the initial value of the system integral constant. After several adjustments, the system stopped oscillating, and 150-180% of the integral constant was taken as the final value of the integral constant of the system, which was set as 165%. In the case of the EHS system output force, the differential constant \(K_{d}\) is usually chosen to be 0. The parameters of the classical PID controller were selected as \(K_{p}=11.17\), \(K_{i}=63.11\), and \(K_{d}=0\). The step response curve of the classic PID control of an EHS system simulated by Simulink is shown in Figure 7.  
Figure 6: Step response curve of the EHS system after pole optimal configured control.  
Figure 7: Step response curve of the classic PID control of the EHS system simulated by Simulink.  
As shown in Figure 8, the EHS system had some improved performance through PID control. When a unit step signal was input, the EHS system tended to achieve a stable state within 1.55 s, and its input and output signals had good matching performance; however, the EHS system was also accompanied by a certain amount of overshoot.
## 5 Simulation Results and Analysis
### Simulation Analysis
#### 5.2.3 Effect of Node Selection Algorithm  
This experiment aims to explore the impact of different node selection algorithms on multipath transmission performance. Here, we use the topology of Figure 5. In Figure 5, we use the Agis topology, Sprint topology, and Ans topology in the The Internet Topology Zoo [40] dataset as the internal topologies of AS101, AS102, and AS103, as shown in Figure 9. In Figure 9, the green node represents the access node, and the yellow node represents the exit node. Each AS has only one access node and one exit node. We set the link bandwidth of the AS internal topology to 10 Mbps, the delay is set to 10 ms, the access bandwidth of the data receiver and the data sender is set to 30 Mbps, and the delay is set to 1 ms. In this experiment, we randomly select 3 nodes in each AS to form a multipath service node set, and the number of paths selected by edge nodes is k = 3.  
In order to evaluate the performance of our proposed node selection algorithm, we compare it with three other algorithms: Random Selection Algorithm (RSM) [35; 41], Node Degree Based Selection (SND) [38] and LB-OOMR [38]. The RSM algorithm randomly selects k nodes from the candidate node set. The SND algorithm greedily selects k nodes with more edges. The LB-OOMR algorithm is based on the betweenness centrality of the candidate nodes, and selects the k nodes with the highest betweenness centrality from the candidate nodes. Figure 10 shows the throughput changes under different selection algorithms. From Figure 10, we note that our proposed algorithm has higher throughput, followed by RSM algorithm and LB-OOMR algorithm, while SND algorithm has lower throughput. This is because our proposed algorithm takes into account the path link overlap factor, through the independence between AS to protect the independence between paths. The SND algorithm has more k nodes in the same AS domain, resulting in mutual influence between sub-paths, resulting in a decrease in throughput.  
Figure 10: Throughput under Different Node Selection Strategies.  
Figure 9: Network Topology of Different ASes. (**a**) Agis topology. (**b**) Sprint topology. (**c**) Ans topology.  
Figure 11 shows the packet loss rates of each sub-path and the average packet loss rates under different node selection algorithms. From Figure 11, we notice that Path2 and Path3 of RSM algorithm, SND algorithm and LB-OOMR algorithm have higher packet loss rates, increasing in the average packet loss rates. This is because the two sub-paths are in the same AS domain, and there are overlapping links between the two paths. On the overlapping link, the two sub-paths compete for the common bandwidth resources, increasing in the packet loss rates on their respective paths. However, our proposed algorithm can guarantee the independence between sub-paths, so there is no case where multiple sub-paths compete for the same link resource. This experiment shows that compared with RSM, SND and LB-OOMR, our proposed algorithm can give full play to the advantages of multipath.
## 6 Results and Discussion
### Achievable Sum Rate Performance Evaluation  
In this subsection, the curves for the BF and RZF precoding are obtained numerically based on the random matrix theory method as in [10], while simulated curves are plotted based on Equation (3). The number of incidences of the channel realizations used in the Monte Carlo simulations is given as \(10^{4}\). The results are presented with \(\theta_{k}=\{-57.5^{\circ},-45^{\circ},-41.5^{\circ},-23^{\circ},-7.5^{\circ},7.5^{\circ},23.5^{\circ},41.5^{\circ},45^{\circ},57.5^{\circ}\}\), [30, 57]. The other salient system parameters are \(T=100\) symbols, \(\rho_{\text{d}}=10\) dB and \(K=10\) users. The results in Figure 2 demonstrate that the proposed CGD training design achieves the best rate performances. The results in Figure 2 confirm that significant improvement in the rate performances are obtained for both the BF and RZF precoders when the channels are strongly correlated, i.e., \(\omega=2.5^{\circ},D=1/2\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Number of BS antennas & \(N\) & 1–500 \\ Number of users & \(K\) & 10 \\ Azimuth standard deviation & \(\omega\) & \(2.5^{\circ},25^{\circ}\) \\ Antenna distance spacing & \(D\) & \(\lambda/2,1\) \\ Coherence time & T & 100 symbols \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3 examines the achievable sum rate comparing different training sequence designs under a weak channel correlation, i.e., \(\omega=25^{\circ}\), \(D=1\). Figure 3 shows that, with weak channel correlation, some loss in the rate performances is obtained for both the BF and RZF precoders with the SCMI training sequence design, which uses a Lagrangian multiplier iterative algorithm, in comparison with the SMSE and the proposed CGD training designs. The degradation in the achievable sum rate performance with the SCMI training design can be justified as the Lagrangian-based iterative algorithm does not consider the spatial properties of the parameter space such as a Grassmannian space or Riemannian manifold in the training sequences optimization. In particular, using the smooth parameter spaces in the sequences optimization allows for an efficient precoding design to be achieved, hence maximizing the sum rate performance of the FDD m-MIMO systems.  
Figure 3: Achievable sum rate versus \(N\), comparing different training designs with \(\omega=25^{\circ}\), \(D=1\).  
Figure 2: Achievable sum rate versus \(N\), comparing different training designs with \(\omega=2.5^{\circ}\), \(D=1/2\).
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 3 System Model
Then, we convert the problem in Equation (5) into finding an optimal \(F_{M\times K}(t)\) at the end of each time slot as follows. \[F_{M\times K}^{*}(t)=\arg_{F_{M\times K}(t)}\max Pr(F_{M\times K}(t)|F_{M\times K}(t-1),\dots,F_{M\times K}(t-w_{a}+1)),\] \[s.t.\sum_{k\in\mathcal{K}}a_{m,k}(t)\leq\eta_{m},\ \ \forall m\in \mathcal{M}, \tag{7}\]  
where \(w_{a}\) is the number of the most recent service popularity matrix used for predicting \(F_{M\times K}^{*}(t)\), and the notation \(Pr(X)\) represents the probability of \(X\). Hereafter, we will call \(w_{a}\) as a window size. Once \(F_{M\times K}^{*}(t)=\{f_{1}^{*}(t),\dots,f_{M}^{*}(t)\}\) is determined, the optimal service cache in each MECS \(m\) is constructed by selecting the top \(\eta_{m}\) services with the highest popularity in \(f_{m}^{*}(t)\). To fast resolve the popularity prediction problem in Equation (7) at the beginning of each time slot, we propose a deep learning approach, which will be detailed in Section 4.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 3 Results
### Comparative Experiment  
The performance of the proposed approach is compared with that of several other state-of-the-art methods for small-target detection. In order to accurately illustrate the effectiveness of the proposed method, its detection accuracy is quantitatively compared with that of the other state-of-the-art methods. Table 3 presents the values of the accuracy measures of the six methods on the two datasets. Among them, the best result for a measure is highlighted in bold, and the second place is marked with a horizontal line.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**IRSTD-1k**} & \multicolumn{4}{c}{**SIRST**} \\ \cline{2-9}  & **IoU (\%)** & **nIoU (\%)** & **F1 (\%)** & **AUC (\%)** & **IoU (\%)** & **nIoU (\%)** & **F1 (\%)** & **AUC (\%)** \\ \hline FPN [22] & 48.36 & 48.77 & 62.41 & 76.88 & 42.38 & 43.96 & 60.50 & 69.31 \\ U-Net [23] & 49.98 & 49.35 & 68.95 & 83.22 & 44.81 & 43.26 & 62.93 & 72.67 \\ TBC-Net [24] & 51.48 & 52.33 & 70.25 & 83.87 & 47.23 & 46.58 & 65.31 & 77.34 \\ ALC-Net [25] & 52.63 & 52.21 & 76.88 & 84.21 & 46.85 & 47.60 & 66.38 & 75.36 \\ DNA-Net [26] & 50.66 & 51.74 & 71.22 & 83.56 & 38.12 & 37.55 & 43.20 & 65.03 \\ Proposed & 54.63 & 55.26 & 77.69 & 84.33 & 48.32 & 49.71 & 67.42 & 76.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detection accuracy obtained with each tested method.  
Figure 8: The AGPCNet model compared with the approach proposed in this paper. (**a**) IRSTD-1k, (**b**) SIRST.  
As shown in Table 3, there is minimal difference in performance between the FPN method and the U-Net method on the two datasets. The performance of the U-Net method surpasses that of the FPN method, suggesting that while the introduction of the FPN method marks a milestone for infrared multi-scale small-target detection methods, the U-Net network still maintains a competitive edge in infrared small-target detection due to its encoder-decoder \"U\"-shaped network. The TBC-Net model incorporates semantic constraint information from high-level classification tasks, effectively addressing the imbalance issue between small targets and backgrounds. The ALC-Net model utilizes a bottom-up attention mechanism to effectively preserve the features of small targets. The DNA-Net model exhibited poor performance in this experiment. The method proposed in this paper demonstrates the best performance compared to all other methods. Although its AUC value on the SIRST dataset is not the highest, the method proposed in this paper achieves the best overall performance and is the most effective of all tested methods for infrared multi-scale small-target detection.
## 2 Material and Methods
### Quantification of Absolute Safrole  
The safrole in the PHEO and cowpea beans was quantified through GC/FID model 2014 (Shimadzu, Kyoto, Japan), adapting the methods previously published by our research group [16; 46; 48]. The chromatographic separation was performed in a DB-5 capillary column (Agilent Technologies, Palo Alto, CA, USA), using a stationary phase composed of 5% phenyl and 95% dimethylsiloxane (30 m \(\times\) 0.25 mm i.d., 0.10 \(\upmu\)m film thickness). Nitrogen 99.999% (Air Products, Sao Paulo, Brazil) was used as the carrier gas in a flow rate of 1.82 mL min\({}^{-1}\) and a 1:5 split ratio. The temperatures at the injector and detector were set at 220 and 300 \({}^{\circ}\)C, respectively. The column was initially at 60 \({}^{\circ}\)C, and the temperature increased by 5 \({}^{\circ}\)C min\({}^{-1}\) until reaching 120 \({}^{\circ}\)C and hold in this condition for 1 min. The thermal desorption period was 3.0 min, after which the fiber was removed from the injector. Each chromatographic run took 12 min, and all the analyzes were managed by the software GCsolution (Shimadzu, Kyoto, Japan).  
Five concentrations of the standard safrole solution (250, 500, 1000, 1500, and 2000 \(\upmu\)g mL\({}^{-1}\)) and a PHEO sample of 1000 \(\upmu\)g mL\({}^{-1}\) were diluted in methanol to build the analytical curve and quantify the safrole in the PHEO. These solutions were injected with the aid of the AOC-20i automatic injector and repeated three times. The safrole in the PHEO was determined from the safrole standard analytical curve. The identification of the compound was performed by comparing the retention time of the peak with the retention time of the safrole standard solution in methanol.