esa-sceva/satcom-chunk-collection huggingface dataset:
# Static Bearing Capacity Investigation of a Novel Prefabricated Light-Steel Beam-Column Connection
## 3 Experimental Results
### Effect of the Column Sleeve Tube on the Proposed Beam-Column Connection  
Figure 14a-d shows a comparison of the loading-displacement curves of specimens with and without a sleeve tube to reinforce the column. Displacement in Figure 14a-d is from the maximum loading-displacement curves measured from two loading points. A specimen with a sleeve tube has a much higher static bearing capacity compared to its counterpart without a sleeve tube. This means that a sleeve tube plug-welded to the RHS column can effectively increase the static bearing capacity of the proposed light-steel beam-column connection. For the specimens named JU60, the sleeve tube can increase load-bearing capacities by about 29.2-56.3%, whereas for specimens named JU100, the increment is between 245.5% and 252.9%.  
In addition to increasing the static bearing capacity of the proposed light-steel beam-column connection, the sleeve tube can also reduce the global deflection of the truss beam.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **Channel** & **Sleeve Tube** & **Truss-Beam** & **Maximum Loading/(RN)** & **Maximum Vertical Deformation/(mm)** & **Arm Length/(mm)** & **Maximum Bonding Moment/(kN m)** \\ \hline JU60-1 & Double-channel & No & \begin{tabular}{c} “V”-shaped \\ brace \\ arrangement \\ \end{tabular} & 24 & 24.7 & 800 & 19.2 \\ \hline JU60-2 & Double-channel & Yes & \begin{tabular}{c} “V”-shaped \\ brace \\ arrangement \\ \end{tabular} & 31 & 24.6 & 806 & 25.0 \\ \hline JU60-3 & Double-channel & No & \begin{tabular}{c} “\wedge”-shaped \\ brace \\ arrangement \\ \end{tabular} & 32 & 34.0 & 516 & 16.5 \\ \hline JU60-4 & Double-channel & Yes & \begin{tabular}{c} “\wedge”-shaped \\ brace \\ arrangement \\ \end{tabular} & 50 & 43.2 & 522 & 26.1 \\ \hline JU100-1 & Single-channel & Yes & \begin{tabular}{c} “\wedge”-shaped \\ brace \\ arrangement \\ \end{tabular} & 76 & 29.5 & 585.5 & 44.5 \\ \hline JU100-2 & Single-channel & No & \begin{tabular}{c} “\wedge”-shaped \\ brace \\ arrangement \\ \end{tabular} & 22 & 39.2 & 579.5 & 12.7 \\ \hline JU100-3 & Single-channel & Yes & \
\begin{tabular}{c} “V”-shaped \\ brace \\ arrangement \\ \end{tabular} & 60 & 23.0 & 932.5 & 56.0 \\ \hline JU100-4 & Single-channel & No &
\begin{tabular}{c} “V”-shaped \\ brace \\ arrangement \\ \end{tabular} & 17 & 43.8 & 926.5 & 15.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The maximum loading and vertical deflection at the truss-beam end.
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 2 Related Literature
### Street-Oriented Vehicle Selection
However, the whole approach does not look into junction-area-based video packet forwarding. In order to address the issue of high latency in locating a possible content provider in an information-centric network, a Preference-Aware Fast Interest Forwarding (PaFF) method for video streaming has been suggested [33]. In the PaFF approach, each vehicle forms a Highly Preferred Content Table (HPCT) in order to preserve the content catching status of vehicles that have similar video playback behaviors and mobility parameters. Considering the HPCT, a vehicle forwarder that employs a preference mechanism is explored to choose the next hop of relevant video packets to minimize delay and improve reliability. The selection of a potential forwarder vehicle for the content delivery depends on fundamental mechanisms including the estimation of similar mobility parameters of neighbor vehicles, preference agreement and potential vehicle discovery beyond one-hop neighbor vehicles. However, in the mobility parameters for potential forwarding vehicles, the various points at the junction have not been considered. Therefore, the next subsection discusses video transmission considering the junction areas of roads.
## 4 Computational Results
### Parameters Setting
In these tables, a value of C(X', X\") equal to 1 means that all points in the estimated efficient frontier X\" are strictly dominated by points in the estimated efficient frontier X\". As expected, the exact method dominates both algorithms entirely in terms of the space covered. Regarding the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instance** & \multirow{2}{*}{**n**} & **k** & **Gurobi** & \begin{tabular}{c} **MA-RK** \\ **v1** \\ \end{tabular} & \
\begin{tabular}{c} **MA-RK** \\ **v2** \\ \end{tabular} &
\begin{tabular}{c} **MA-RK** \\ **v2** \\ \end{tabular} \\ \hline FNO1 & 12 & 5 & 17 & 9 & 15 \\ FNO2 & 15 & 8 & 16 & 15 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantity of non-dominated points for Instances FNO1 and FNO2. metaheuristic procedures, for Instance FNO1, the MA-RK v2 was able to generate points that dominate almost 77.78% over the ones generated by the MA-RK v1. For Instance FNO2, the front of the MA-RK v2 dominates 33.33% of the points generated by the MA-RK v1 which, in turn, dominates 25% of the points generated by the MA-RK v2. We observe that, for Instances FNO1 and FNO2, when the minimum value of latency is obtained, the maximum amount of total tardiness rises to 3.2 times the cost of latency, which might translate to a higher level of customer dissatisfaction. On the contrary, when the minimum value of total tardiness is reached, the overall latency of the system rises up to 1.4 times the optimal (minimum) value. In this case, the increment of latency translates into a significant increase in the cost and, therefore, in a reduction of profit. In addition, it is obvious that the prioritization of the customers generates an unbalance in their demand, especially for the case of having routes where relatively few customers can have significantly high amounts of demand compared to the rest. Another aspect to highlight is that the decision-making process can be seen from two perspectives: (1) savings in tardiness costs can represent up to 72% of the total costs; and (2) savings in latency produce savings for up to 28% of the total costs. In other words, according to the objective function, if a preference must be defined a priori, tardiness must be more important than latency.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
###### Abstract
Third, we propose an N-way concurrent architecture of BP-VISAR for parallelization of multiple streams for BP-VISAR frame generation on a single GPU and evaluate its performance. Finally, we propose a multi-GPU based N-way concurrent architecture of BP-VISAR for further acceleration with scalable computational resources. The contribution of this study is three-fold. First, the relationship between the video SAR frame rate and the radar system parameters is analyzed to help design the video SAR system in non-overlapped and overlapped synthetic aperture modes. Second, parallel pipelined architectures are proposed to handle the phase history data and the range profile data on a sub-synthetic-aperture basis and remove redundant computation in overlapped synthetic aperture modes. Finally, the proposed multi-stream BP-VISAR architecture on multiple GPUs is shown to generate video SAR frames in real-time for streaming phase history data that is scalable in the output frame rate with the number of GPU devices for frames of high pixel resolution. This paper is organized as follows. Section 2 analyzes the relationship between the video SAR frame rate and the SAR system parameters. Section 3 presents architectures for backprojection based video SAR for full- and sub-aperture processing and multi-stream and multi-GPU extended architectures. Section 4 presents the experimental results of the proposed BP-VISAR architectures and finally, Section 5 gives our conclusions.
# Quantum Computing Approaches for Mission Covering Optimization
## 1 Introduction
### Quantum Solutions to MCO
#### 1.1.3 Quantum Alternating Operator Ansatz with Constraint Hamiltonian (QAOAH)  
In QAOA, the starting state is in an equal superposition of all computational basis states, and the mixing Hamiltonian is a sum of Pauli-X operators acting on each qubit.  
In this configuration, every possible classical-state solution can be traversed, making it ideal for unconstrained problems where there are no states to be filtered out. However, it is possible to incorporate constraints by altering the default mixing Hamiltonian \(H_{M}\) and the initial quantum state \(|\psi\rangle\)[20]. Since the mixing operator (the exponentiated mixing Hamiltonian) specifies how to explore the solution space, it is possible to specify a mixing operator that determines how to move to another solution within the constraints of the problem.  
Consider an initial quantum state \(|\psi\rangle\), which represents a solution that does not violate the constraints of the optimization problem but is not necessarily the minimizer. Consider the mixing operator \(H_{M}\) that is constructed such that after an application on a state within the constrained space, the output state is also guaranteed to be within the constrained space. The idea is to construct \(H_{M}\) and \(|\psi\rangle\) so that when stopping QAOA at any iteration, the final encoded solution is always within the constrained solution space. If this mixing operator can mix such that it allows the algorithm to reach every possible classical-state in the constrained solution space, it is then possible to obtain the minimizer in that same space [20].
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles  
#### 3.2.1 UV-Vis Spectroscopic Analysis  
Visual inspection was the first method used for the characterization of ZnO-NP synthesis, followed by UV measurements of this change at various wavelengths (200 to 800 nm) to identify the surface plasmon resonance (SPR). Figure 2 shows that the UV-visible spectra of the ZnO-NPs biosynthesized using the seaweed extracts (_U. lactuca_, _U. intestinalis_, and _S. muticum_) demonstrated significant peaks at 374, 375, and 376 nm, respectively, which were mostly distinguished by ZnO-NPs.  
Figure 1: Used seaweeds: _Ulva lactuca_ (**A**), _Ulva intestinalis_ (**B**) (Chlorophyta), and _Sargassum multicum_ (**C**) (Phaeophyceaeae).  
Figure 2: UV spectra of ZnO nanoparticles synthesized using different seaweeds: _Ultra lactuca_ (**A**), _Ultra intestinalis_ (**B**), and _Sargassum muticum_ (**C**).
## 4 Discussion
### Bone Remodeling and Average Mean Stress in PDL  
PDL stress is considered a key trigger for the bone remodeling process and plays a crucial role in regulating orthodontic tooth movement [10; 47]. The present study's FE model demonstrated a consistent relationship between PDL's normal displacement and average mean stress throughout the molar uprighting process, which aligns with previous findings that suggest a linear correlation between tooth movement rate and PDL stress [48]. The optimal PDL stress range that promotes tooth movement has been reported to be between 0.47 KPa and 16 KPa [47]. This is reflected in the presented findings, where the highest bone remodeling velocity observed was for an average mean stress in PDL ranged between 14 and 18 KPa. Overall, the highest PDL stress value produced by the conventional molar uprighting springs was around 28 KPa, which still remains within the acceptable range, as previous studies have suggested that even stresses up to 39 KPa were suitable for some orthodontic tooth movements, such as canine distalization [49].  
The consistent ratio observed between the PDL's normal displacement and mean stress during molar uprighting indicates that for every 1 KPa of mean stress, the PDL undergoes a normal displacement of approximately 0.41 \(\upmu\)m. This observation is in agreement with the tooth displacement and bone remodeling rates predicted by current finite element models, which yielded an average stress-normalized bone remodeling velocity of \(\sim\)0.9 \(\upmu\)m/(KPa-day), closely matching the values reported in the literature [50].  
Previous molar uprighting simulations treated the alveolar bone as a rigid structure [13]. In contrast, to better reflect clinical conditions, we modeled the alveolar bone as an elastic material, since material properties significantly influence FE outcomes, particularly the behavior of the PDL, which plays a critical role in determining the bone remodeling velocity [21, 51, 52].  
The PDL was modeled as a linear elastic material to balance computational efficiency with biomechanical relevance. Although the PDL exhibits nonlinear and viscoelastic properties, linear elasticity is widely used in FE studies as it captures the overall stress distribution trends needed to trigger long-term tooth movement, particularly in iterative analyses. The linear assumption is further supported, as the PDL's stress-strain behavior is approximately linear under moderate orthodontic loading conditions, similar to the characteristics of conventional uprighting springs.
###### Abstract  
We developed, optimized and validated a fast analytical cycle using high throughput bar adsorptive microextraction and microliquid desorption (HT-BA\(\mu\)E-\(\mu\)LD) for the extraction and desorption of ketamine and worketamine in up to 100 urine samples simultaneously, resulting in an assay time of only 0.45 min/sample. The identification and quantification were carried out using large volume injection-gas chromatography-mass spectrometry operating in the selected ion monitoring mode (LVI-GC-MS(SIM)). Several parameters that could influencing HT-BA\(\mu\)E were assayed and optimized in order to maximize the recovery yields of ketamine and worketamine from aqueous media. These included sorbent selectivity, desorption solvent and time, as well as shaking rate, microextraction time, matrix pH, ionic strength and polarity. Under optimized experimental conditions, suitable sensitivity (1.0 ug L\({}^{-1}\)), accuracy (85.5-112.1%), precision (\(\leq\)15%) and recovery yields (84.9-105.0%) were achieved. Compared to existing methods, the herein described analytical cycle is much faster, environmentally friendly and cost-effective for the quantification of ketamine and worketamine in urine samples. To our knowledge, this is the first work that employs a high throughput based microextraction approach for the simultaneous extraction and subsequent desorption of ketamine and worketamine in up to 100 urine samples simultaneously.  
keywords: ketamine; worketamine; high throughput bar adsorptive microextraction; LVI-GC-MS(SIM); urine +
Footnote †: journal: Medical Imaging
## 3 Results and Discussion
### Analysis of Energy Performance of Heat Pump Systems
#### 3.2.1 Comparison of ENERGY Performance of Heat Pumps
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Project** & \(T_{u,s}\)/\(T_{u,r}\) & \(T_{g,o}\)/\(T_{g,i}\) & \(T_{c}\) & \(T_{e}\) & \(T_{e}\) & \(COP_{t}\) & \(DCOP_{s}\) \\  & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & & \(COP_{t}\) & \(DCOP_{s}\) \\ \hline SG-1 & 32.3/30.0 & 7.7/5.5 & 34.3 & 3.5 & 5.15 & 9.98 & 0.52 & 2.83 \\ SG-2 & 44.8/40.7 & 12.1/9.1 & 46.8 & 7.1 & 3.56 & 8.06 & 0.44 & 2.86 \\ SG-3 & 47.6/39.2 & 13.7/10.2 & 49.6 & 8.2 & 3.59 & 7.80 & 0.46 & 3.05 \\ SG-4 & 40.2/38.5 & 8.3/6.7 & 42.2 & 4.7 & 3.46 & 8.41 & 0.41 & 2.36 \\ SG-5 & 43.1/39.3 & 9.6/6.2 & 45.1 & 4.2 & 2.32 & 7.78 & 0.30 & 1.93 \\ SG-6 & 42.8/39.4 & 7.9/5.8 & 44.8 & 3.8 & 3.89 & 7.75 & 0.50 & 3.07 \\ SG-7 & 41.4/37.6 & 11.0/8.2 & 43.4 & 6.2 & 4.02 & 8.51 & 0.47 & 3.07 \\ SG-8 & 43.8/40.3 & 12.2/10.0 & 45.8 & 8.0 & 3.75 & 8.44 & 0.44 & 2.49 \\ SG-9 & 37.7/35.7 & 13.3/11.8 & 39.7 & 9.8 & 3.39 & 10.46 & 0.32 & 2.63 \\ MG-1 & 42.0/38.4 & 26.9/18.9 & 44.0 & 16.9 & 5.64 & 11.70 & 0.48 & 3.81 \\ MG-2 & 39.5/35.7 & 29.8/19.3 & 41.5 & 17.3 & 4.71 & 13.00 & 0.36 & 3.28 \\ MG-3 & 38.3/33.9 & 20.0/9.7 & 40.3 & 7.7 & 4.15 & 9.62 & 0.43 & 3.40 \\ MG-5 & 40.6/36.5 & 23.3/19.3 & 42.6 & 17.3 & 4.75 & 12.48 & 0.38 & 3.48 \\ MG-5 & 38.7/35.3 & 29.1/18.4 & 40.7 & 16.4 & 4.45 & 12.92 & 0.34 & 3.23 \\ MG-6 & 42.2/37.1 & 28.9/20.0 & 44.2 & 18.0 & 6.68 & 12.11 & 0.55 & 4.48 \\ MG-7 & 44.7/37.6 & 34.0/20.2 & 46.7 & 18.2 & 7.80 & 11.22 & 0.70 & 6.46 \\ MG-8 & 41.0/36.5 & 41.0/23.0 & 43.0 & 21.0 & 6.62 & 14.37 & 0.46 & 5.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Field test energy performance of heat pump systems. Figure 8: Comparison of Condensing temperature (\(T_{c}\)) and Evaporating temperature (\(T_{e}\)). Nevertheless, the _COP\({}_{t}\)_ of heat pumps reaches 9.98 and _DCOP_ reaches 0.5 in SG-1 with \(T_{u,s}\) of 32.3 \({}^{\circ}\)C and \(T_{g,i}\) of 5.5 \({}^{\circ}\)C. Thus, the _COP_ of heat pumps in SG-1 reaches 5.15, showing a better energy performance than heat pumps in MG-2, 3, 4 and 5. That is to say, although the MD-GHPs have advantages in a high-temperature heat source, the heat pump suitable to the specific operating condition, as well as the control strategy from user-side to ground-side should also be analyzed and optimized; thus, the high-temperature heat source could be fully utilized. Otherwise, the MD-GHPs would perform even worse than conventional SD-GHPs and waste more energy.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 3 Proposed SDR-Based LTE-LAA Architecture and Its Resource Management Scheme
### Heuristic Solution
```
1:Input: Users \(j\in J\); required data rate \(D_{j}\); user-BS distances \(d\);
2:Output: optimum user-BS association list that maximizes the unlicensed throughput and minimizes the overall power consumption;
3:for time slot, \(t\in 1,2,\ldots,T\)do
4: Set initial user-BS assignment matrix \(\Pi\);
5: add all users to initial group \(J^{\prime}\);
6:while\(J^{\prime}\
eq\emptyset\)do
7: Set \(F=0\);
8: Find \(j^{*}\) from \(j^{*}=argmin_{j\in I}(u_{i})\);
9: Add all BSs to initial group \(I^{\prime}\);
10: Go to process Algorithm 2;
11:if\(F==1\)then
12: Let \(\Delta=\hat{\Delta}\);
13: update \(Z_{total}\); overall utility value;
14:else
15: Move user to final group \(x\);
16:endif
17:endwhile
18:endfor
19:return x;
```  
**Algorithm 1** The TEA Algorithm. Algorithm 3 processes users in a BS based on certain criteria. Initially, it identifies the users in the BS (line 1) and sorts them in ascending order (line 2). It then iterates through each user (line 3). For each user, it checks if the effective delay \(D_{ij}^{eff}\) exceeds a threshold \(D_{th}\) (line 4). If the condition is met, then it proceeds to Algorithm 2 (line 5). Within this process, if a flag \(F\) equals 1 (line 6), then a variable \(\Delta\) is updated to \(\Delta_{i}\) (line 7), and the algorithm exits (line 8). If the flag condition is not met, then the algorithm continues to the next user (lines 9-12). ```
1:Find \(V_{j*}\) for user \(j*\) where \(V_{j*}=i|u_{j*i}>u_{min},i\in I^{\prime}\);
2:Sort \(V_{j*}\) in decreasing order;
3: set \(F==0\);
4:while\(V_{j*}\
eq\emptyset\)do
5: Find optimum BS by \(i=argmax_{i\in V}(u_{j*i})\);
6: Remove \(i\) from \(I^{\prime}\);
7: Set \(\Pi_{j*i}\) = 1;
8: Calculate current \(Z_{new}\) and \(P_{ji}\) based on new assignment;
9:if\(P_{ji}<\)\(P_{max}\)then
10:if\(Z_{new}>\)\(Z_{current}\)then
11: Set \(\Pi=\Pi\);
12: Set \(F==1\);
13:endif
14:else
15: Go to process with Algorithm 3;
16:endif
17:endwhile
```  
**Algorithm 2** TEA Algorithm 2. ```
1:Find users in BS \(i\) using \(J_{i}=\{j|x_{j*i}=1\&j\in J^{\prime}\}\);
2:Sort users in an ascending order;
3:for\(Y=1\): \(|J_{i}|\)do
4:if\(D_{ij}^{eff}>\)\(D_{th}\)then
5: Go to process with Algorithm 2;
6:if\(F==1\)then
7:\(\Delta==\hat{\Delta}\);
8: exit;
9:endif
10:endif
11:\(Y=Y+1\)
12:endfor
```  
**Algorithm 3** TEA Algorithm 3.
# Injection Molding Process Control of Servo-Hydraulic System
## 4 Discussion  
Observing the experimental results presented in Tables 5-10, as the target position increases from 35 mm to 65 mm, the response time increases but still meet the standard. At the sampling frequency of 500 Hz, pressure at the front of the cylinder sometimes exceeds the preset value. However, all the targets are satisfied under the sampling frequency of 1000 Hz. Moreover, the response time under 500 Hz is a lot greater under 1000 Hz, especially at high target pressure. The response time is crucial for the pull-back motion since the long response time has a risk of affecting the following process. When applying a higher target velocity, the PID controller requires larger gain parameters (K\({}_{p}\), K\({}_{i}\), and K\({}_{d}\)) to achieve expected performances. However, the PID controller occurs integral windup as the limitation of the system, the motor driver will finally end up with servo off which be incapable of performing pressure control.  
Due to the analog feedback signal noise of the position transducer, the minimum distance of the pull-back motion is tested and turns out to be 2 mm under a sampling rate of 500 Hz and 1 mm under 1000 Hz. Consequently, the sampling frequency does have an impact on the accuracy of the position control. Also, applying a filter or a proper signal process may improve the precision of the position control. By the way, the pull-back motion is controlled by the open direction of the servo proportional valve. Input a positive voltage to the valve causes the hydraulic cylinder moving backward, since the back pressure in the system, the pull-back speed, and distance depends on the opening size of the servo proportional valve and the PID parameters. The experiment results indicate that the PID parameters have little effect on the backward speed of the cylinder, and the pull-back motion is dominated by the opening size of the servo proportional valve (Figure 4).  
Though the results are satisfying, there is space for improvement: Injection molding machines in industrial using can have the injection speed much higher than the target speed (80 mm/s) in the present study. Due to the limitation of the hydraulic cylinder stroke (170 mm in the study), the velocity control stage lasts for too short which the cylinder reaches the target position almost simultaneously when the speed reaches target value (Figure 6b). To solve the problem, it could be necessary to run the experiments with an injection molding machine. However, experiments with an injection molding machine could be more complicated since the temperature of the polymer melt and material used for the experiment needs to be taken into account.