esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparative Experiment and Result Analysis
#### 4.5.3 Comparisons on the CLCD Dataset  
Table 4 shows the performance comparison of each method on the CLCD dataset. MDNet ranks second in accuracy, F1-score, and IoU metrics, slightly lower than WS-Net++ but significantly better than other comparative methods. It is worth noting that MDNet's recall rate of 76.52% is higher than WS-Net++'s 75.47%, indicating fewer missed detections of real change regions, an advantage verified by the visualization results in Figure 9. As a small-scale dataset, CLCD has variable spatial resolutions ranging from 0.5 m to 2 m. In such scenarios, MDNet's CNN-Transformer hybrid framework effectively reduces the strong dependency of pure Transformer models on training data scale, highlighting the adaptability of its architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P** & **R** & **F1** & **IOU** \\ \hline FC-EF & 71.7 & 47.6 & 57.22 & 40.07 \\ SNUNet & 70.82 & 62.37 & 66.32 & 49.62 \\ BiT & 61.42 & 62.75 & 62.08 & 45.01 \\ AMTNet & 78.64 & 75.06 & 76.81 & 62.35 \\ WS-Net++ & **82.58** & **75.47** & **79.64** & **65.34** \\ MDNet (Ours) & **80.97** & **76.52** & **78.68** & **64.43** \\ \hline \hline \end{tabular}  
* The red bold font indicates the best performance and the black bold font indicates the second best.  
\end{table}
Table 4: Performance evaluation of the CLCD dataset (%).  
Figure 8: Visualization results of different methods on the WHU-CD dataset. In the prediction results, white represents true positive (TP), black represents true negative (TN), red represents false positive (FP), and green represents false negative (FN). In brief, the lower the proportion of red and green, the better the prediction performance of the model. A and B represent two - phase remote sensing images, Label is the ground-truth change annotation, FC-EF, SNUNet, BIT, AMTNet, WS-Net++ are comparative models, and MDNet (Ours) is the proposed model.  
The qualitative visualization results are shown in Figure 9. In the first-row samples of seasonal vegetation-covered areas, FC-EF, SNUNet, and BiT produce dense false positives (FPs, red noise points), while MDNet effectively suppresses misclassifications caused by such background interferences through the differential feature enhancement mechanism of the DPEM; In the second- and third-row samples of scattered farmland change areas, methods such as SNUNet and AMTNet have numerous missed detections (FNs, green areas) and false positives (FPs, red areas). MDNet significantly reduces missed detections through the cross-layer semantic aggregation capability of the CASAM, but the boundaries of the predicted change regions still appear slightly blurred, reflecting that there is still room for optimization in fine-grained edge modeling. Overall, MDNet still demonstrates comprehensive advantages in overall performance in small dataset and multi-resolution scenarios.
# Direct Inversion Method of Brittleness Parameters Based on Reweighted Lp-Norm
## 3 Experiments
### Field Data Experiment  
The field data were collected from Sichuan, China. The angle stack data volume is shown in Figure 27, with incidence angles of 9\({}^{\circ}\), 18\({}^{\circ}\), and 27\({}^{\circ}\) in Figure 27a-c, respectively. The section had 800 traces with a vertical time length of 750 ms and a sampling interval of 2 ms. The two red lines represent the two layers of geological interpretation, and the black vertical line is the well trajectory. The inversion method was applied to the field data, and Figures 28-30 show the inversion results of the reweighted Lp and the traditional method, respectively. Figure 28a,b express the \(E\rho\) inversion results of the two methods. Figure 29a,b show the Poisson's ratio inversion results of the two methods. Figure 30a,b show the density inversion results of the two methods. For the sake of observation, the black rectangular boxes are enlarged in the figures. In these areas, compared with the traditional approach, the reweighted Lp method had better transverse continuity, while the vertical resolution was effectively improved. In addition, the overall effect in all the other regions was greatly improved and could effectively portrayed stratigraphic features.  
For the purpose of verifying the reliability of the inversion method, we compared the logged data with the inversion data of the well location trace. The inversion comparison chart is shown in Figure 31. In order to more obviously reflect the difference between the two methods, we calculated the absolute error values of inversion for the two methods, as shown in Figure 32. As can be seen in the two figures mentioned above, both methods were in good agreement with the logged data. However, when carefully observed, the inversion results of the reweighted Lp method were closer overall to the logged data and were more stable. The black rectangular boxes indicate the reservoir location. In this area, compared with the traditional method, the reweighted Lp method could invert the characteristics of the reservoir more accurately, with less error and higher accuracy. This further authenticated the validity of the method and the significance of the improvement.  
Figure 31: Inversion results of the trace near the well and the logged well.  
Figure 29: Poisson’s ratio inversion results based on (**a**) traditional method and (**b**) reweighted Lp method.
## 5 5G FWA Field Trials
### Lessons Learned
#### 5.5.2 Enhanced Performance  
The existing FWA that is based on 3 GPP radio access technology leverages cutting-edge radio enhancements like CA, LTE air interface, multi-antenna technologies, and advanced modulation schemes. In addition, wireless transport network terminals have relaxed requirements that can help in improving their performance. Furthermore, the availability of different CPE options such as multi-antenna and high-gain outdoor CPEs can also aid in performance improvement. Based on these benefits, FWA can deliver broadband services at fiber-like speeds with low latency. For instance, the 4G LTE technologies leverage radio innovations in order to offer the peak rate of 1 Gbps. The throughput surpasses copper wire transmission speed and is comparable to the achievable rate of fiber solution as well. Furthermore, with CA of a number of carriers (5 \(\times\) 20 MHz, i.e., 100 MHz) and multi-antenna (i.e., 4 \(\times\) 4 MIMO) technologies, the throughput can be enhanced considerably [5; 10].  
5G network is envisaged to leverage massive MIMO technologies and broader bandwidth aim at minimum DL and UL peak data rates of about 20 Gbps and 10 Gbps, respectively, with less than 1 ms latency in up to 1 GHz bandwidth. The anticipated low-latency and high-throughput of 5G can be exploited to enhance the user experience further and for effective support of services like virtual reality and HD video. Based on the expected improvement in the performance, 5G FWA will be able to support additional subscribers per BS with better rates. This can result in 5G FWA radio access technology delivering better performance compared with fiber networks [5].
## 3 System Architecture and Implementation
### Adaptive Resource Allocation Policy
Initially, our Adaptive Resource Controller reads in the CPU and memory specifications held by the host server. Then, it periodically (default is every 3 s) collects the list of running Docker containers along with their detailed resource usage information. If the current status of container resource usages meet the scaling conditions presented in Table 2, dynamic adjustments of computing resources for multiple running containers are performed. For example, if the current usage of CPU in a container exceeds 90% (\"CPU Upper Thresholds\"), we effectively scale-up the allocated CPU by adding 100% CPU time, i.e., vCPU 1 core (\"CPU Scale-Up Ratios\"). Similarly, if the current memory usage is less than 95% and greater than or equal to 90% (\"Memory Upper Thresholds\"), we add 256 MB of memory to the corresponding container (\"Memory Scale-Up Ratios\"). Scaling down of the allocated computing resources are performed similarly based on the lower thresholds (CPU/memory) and the scale-down ratios for CPU and memory, respectively. Note that the scaling metrics of CPU and memory are managed differently from each other (as seen from Table 2). This is because we observed that CPU usage changed rapidly every time a Big Data benchmark job is completed so that the CPU usage could decrease to the minimum usage (around 10%). Therefore, CPU scaling down policies are designed to be more coarse-grained compared to its scaling up in order to reflect rapidly changing CPU usage patterns in the Big Data workloads. Unlike the CPU, the overall memory usages during the course of Big Data workload executions have remained relatively high. To summarize, based on our adaptive resource management scheme (in Figure 1) and resource coordination policies (in Table 2), we can effectively employ the vertical elasticity of Docker containers in a shared physical platform. For reproducibility and interested researchers and practitioners, we set up a GitHub repository for our adaptive resource management scheme in container-based cloud environments that consists of mainly eight classes and hundreds of lines of Java codes [29].
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 1 Introduction
### Contributions  
The key contributions of this study are summarized as follows:  
1. To propose a mechanism of cooperation between the LTE and next-generation wireless networks, such as 5G. Thereby, we create an equilibrium between network performance and EE via a 5G BSs switching off/on strategy driven by the network instantaneous traffic load demand while guaranteeing service coverage for mobile subscribers by the remaining active LTE BSs. The proposed BSs switching on/off decision-making algorithm is presented in Section 3.1.
2. To determine the optimum criteria of the active LTE BSs (transmission power, the total antenna gain, bandwidth/spectrum, and SINR) that achieves the maximum coverage for the entire area during the 5G BS switch-off session.
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 2 Development of the Dynamic Monitoring System for Asphalt Concrete
### _Composition of Digital Monitoring System_  
#### 2.2.1 Communication Technology  
In highway construction projects and due to the discrete distribution of construction equipment, wired broadband networks cannot meet the requisite needs of mobile machinery on the construction site. Therefore, with the aim of ensuring the timeliness and accuracy of monitoring data, the dynamic monitoring system of asphalt concrete pavement quality adopts the 5G (3 GPP) wireless communication network system to collect and transmit data. As a new generation of communication technology, the 5G communication network shows obvious advantages in transmission rate, coverage and low delay [22; 23; 24]. Indeed, the transmission rate of the 5G network can reach 10 Gbit/s, which greatly improves the data transmission rate. At the same time, the communication delay can be reduced to approximately 1 ms, which effectively solves the problem of data transmission delay [25; 26].
###### Abstract  
Although dual-threaded pedicle screws have been developed, the advantages over single-threaded screws remain controversial. We aimed to investigate the biomechanical performance of two types of dual-threaded pedicle screw by comparing their pullout strength with that of a single-threaded screw in relation to bone quality. Four types of pedicle screw with different thread patterns were designed. Type I: single-threaded screw; Type II: double-threaded screw; Type III: dual-threaded screw; Type IV: a newly designed double dual-threaded screw. Five types of polyurethane foams simulating various degrees of bone quality were used. These were: Type A: cancellous bone; Type B: cancellous bone with cortical bone in the upper margin; Type C: osteoporortic cancellous bone; Type D: osteoporortic cancellous bone with cortical bone in the upper margin; and Type E: osteoporortic bone with cortical bone in the upper and lower margins. A comparison of the pullout strength of Type I, II, and III screws in Type A, B, C and D bone specimens was performed. Type C and E bone specimens were used for comparisons among Type I, II, and IV screws. Compared to the single-threaded screw, the dual-threaded pedicle screws exhibited higher pullout strength in normal-quality bone and significantly lower pullout strength in compromised osteoporortic bone. However, the double dual-threaded screw exhibited better pullout biomechanics in osteoporortic bone with bi-cortical bone.  
p edicle screw; dual-threaded; pullout strength; polyurethane foam block
## 4 Case Study Results and Discussion
### Objective Function According to Fault Location  
According to the location of the fault, and based on the protection criteria included in Table 2, three objective functions to minimize can be obtained, each of them will result in a different adjustment, an adaptive adjustment.  
The scenario analyzed is the three-phase short-circuit scenario with a DG1 penetration level of 17%, the results obtained for the other scenarios can be found in Table A3 in the Appendix A.  
Fault 2: OCR\({}_{2}\) with OCR\({}_{1}\)  
\[\text{min}z=\sum_{i=1}^{m}a_{i,k}(\text{TMS})_{i}=2.27\text{ TMS}_{1}+3.33\text{ TMS}_{2} \tag{8}\]  
Fault 7: Recloser with OCR\({}_{2}\)  
\[\text{min}z=\sum_{i=1}^{m}a_{i,k}(\text{TMS})_{i}=3.49\text{ TMS}_{2}+2.21\text{ TMS}_{RC2} \tag{9}\]  
Fault 21: OCR\({}_{3}\) with recloser  
\begin{table}
\begin{tabular}{c|c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**F2**} & \multicolumn{4}{c}{**F7**} & \multicolumn{4}{c}{**F21**} \\ \hline \multicolumn{8}{c}{**Penetration Level DG**} \\ \hline Fault Type & 17\% & 33\% & 50\% & 17\% & 33\% & 50\% & 17\% & 33\% & 50\% \\ \hline \multirow  
\[\text{min}z=\sum_{i=1}^{m}a_{i,k}\text{(TMS)}_{i}=4.57\text{TMS}_{3}+3.23\text{TMS} _{RC} \tag{10}\]  
Constraints:  
* The minimum operating time of each relay is 0.1 s.
* The normal range of TMS from 0.025 to 1.2.
* Typical CTI setting is 0.3 s.  
_Coordination criterion_  
Fault 2: OCR\({}_{2}\) with OCR\({}_{1}\):  
\[2.27\text{TMS}_{1}-3.33\text{TMS}_{2}\geq 0.3 \tag{11}\]  
Fault 7: Recloser with OCR\({}_{2}\):  
\[3.49\text{TMS}_{2}-2.21\text{TMS}_{\text{RC}}\geq 0.3 \tag{12}\]  
Fault 21: OCR\({}_{3}\) with Recloser:  
\[4.57\text{TMS}_{3}-3.23\text{TMS}_{\text{RC}}\geq 0.3 \tag{13}\]  
_Relay operating time limits_  
Fault 2: OCR\({}_{2}\) with OCR\({}_{1}\):  
\[\begin{array}{c}2.27\text{TMS}_{1}\geq 0.1\text{ TMS}_{1}\leq 0.044\\ 3.33\text{TMS}_{2}\geq 0.1\text{ TMS}_{2}\leq 0.030\end{array} \tag{14}\]  
Fault 7: Recloser with OCR\({}_{2}\):  
\[\begin{array}{c}3.49\text{TMS}_{2}\geq 0.1\text{ TMS}_{2}\leq 0.038.\\ 2.21\text{TMS}_{\text{RC}}\geq 0.1\text{ TMS}_{\text{RC}}\leq 0.045\end{array} \tag{15}\]  
Fault 21: OCR\({}_{3}\) with recloser:  
\[\begin{array}{c}4.57\text{TMS}_{3}\geq 0.1\text{ TMS}_{3}\leq 0.021.\\ 3.23\text{TMS}_{\text{RC}}\geq 0.1\text{ TMS}_{\text{RC}}\leq 0.030\end{array} \tag{16}\]  
The normal range of TMS is from 0.025 to 1.2:  
\[\begin{array}{c}\text{TMS}_{1}\geq 0.025\\ \text{TMS}_{2}\geq 0.025\\ \text{TMS}_{3}\geq 0.025\\ \text{TMS}_{\text{RC}}\geq 0.025\end{array} \tag{17}\]
# Interactive Airfoil Optimization Using Parsec Parametrization and Adjoint Method
## 5 Results and Verification
### Xfoil Verification Computations: Compressible and Viscous Flow
The following figures (Figures 7 and 8) further document the optimization results. The optimization settings were the same as those used for the results presented in Table 3 and Figure 6. The nominal AoA was set to zero. In the following charts, the benefit of the optimization across all the AoAs is visible. The Reynolds and Mach numbers were set to \(Re=1,000,000\) and \(Ma=0\) to make them consistent with the available experimental data, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Original Airfoil**} \\ \cline{2-9}  & **MATLAB** & **Xfoil** & **Xfoil** & **Experiment** & **MATLAB** & **Xfoil** & **Xfoil** \\  & **Inviscid** & **Inviscid** & **Viscous** & **[**13,26**]** & **Inviscid vs.** & **Inviscid vs.** & **Viscous vs.** \\  & & & & & & & **Experiment** & **Experiment** \\ \hline \(\alpha\) [\({}^{\circ}\)] & 6.2 & 6.2 & 6.2 & 6.2 & 6.2 & \(\rightarrow\) & \(\rightarrow\) & \(\rightarrow\) \\ \(C_{y}[1]\) & 0.9777 & 0.9869 & 0.8108 & 0.79 & +23.8\% & +24.9\% & +2.6\% \\ \(C_{x}[1]\) & — & — & 0.01347 & 0.0131 & — & — & +2.8\% \\ \(C_{y}/C_{x}[1]\) & — & — & 60.20 & 60.30 & — & — & \(\rightarrow\)0.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different computation methods with experiments using the original airfoil. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Exp. Data** & \multicolumn{4}{c}{**Xfoil Computation Data**} & \multicolumn{4}{c}{**CFD Computation Data**} \\ \cline{2-9}  & **Original** & **Optimized** & **Optimized** & **CST vs.** & **PARSEC vs.** & **Optimized** & **CST vs.** \\  & **Airfoil (OSU)** & **Airfoil (CST)** & **(PARSEC)** & **Experiment** & **Experiment** & **Airfoil (CST)** & **Experiment** \\ \hline \(\alpha\) [\({}^{\circ}\)] & 6.2 & 6.2 & 6.2 & — & — & 6.2 & — \\ \(C_{y}[1]\) & 0.79 & 0.883 & 0.87 & +11.8\% & +10.1\% & 0.985 & +24.6\% \\ \(C_{x}[1]\) & 0.0131 & 0.0134 & 0.0148 & +2.2\% & +12.1\% & 0.0147 & +12.2\% \\ \(C_{y}/C_{x}[1]\) & 60.3 & 65.9 & 58.8 & +9.6\% & \(-2.0\%\) & 67 & +12.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for NREL S809—GA optimization by [PERSON] and [PERSON] [13]. such as those in [26; 27]. The experimental data presented in Figures 7 and 8 were taken from [26]. The computations were carried out using the compressible and viscous Xfoil 6.99 simulation. Figure 8: Characteristics of the original and optimized NREL S809 airfoil. Xfoil 6.99 computation for \(Re\) = 1,000,000, \(Ma\) = 0. Exp. data from [26]. Results beyond (\(-\)15; 15) deg AoA omitted. (**a**) Dependence of Lift-to-drag ratio on AoA, (**b**) dependence of drag coefficient on AoA. Figure 7: Characteristics of the original and optimized NREL S809 airfoil. Xfoil 6.99 computation for \(Re\) = 1,000,000, \(Ma\) = 0. Exp. data from [26]. (**a**) Polar of the airfoil, (**b**) dependence of \(C_{y}\) on AoA.
## 1 Introduction
[16] used a combination of APF and DDPG to process the obstacle avoidance trajectory planning process in phases, designed the robotic arm artificial potential field method to approach the target, and then the obstacle avoidance phase set the DDPG dominant training, which improves the mobile robotic arms in the case of narrow passages and obstacle restraints Obstacle avoidance ability. For the collision avoidance motion planning of robotic arms, the exploration space is large, rewards are sparse, and the learning efficiency of the agent is relatively low. [PERSON] et al. [17] were the first to propose Hindsight Experience Replay (HER), which enables an agent to learn efficiently from samples of sparse and binary rewards by setting up virtual targets. HER is widely used in path planning research, and scholars add HER to their methods to increase the training effect [18, 19]. Further research has been conducted on the basis of HER, and improved algorithms have been proposed with some success. HER is considered to produce target bias in multi-target learning because of changing the likelihood of the sampled transitions and trajectories used in training. So, bias-corrected HER (BHER) was proposed to correct the bias [20]. The Hindsight Goal Ranking (HGR) method has been proposed to further improve the learning efficiency [21]. Although HER works well for the reach-target problem with binary rewards, it is not optimized for the collision avoidance problem. To grapple effectively with these challenges, this paper presents an innovative action-based obstacle avoidance method that accomplishes active robotic arm collision prevention, guided by the potential field forces in tandem with DRL. The main contributions of this paper are as follows. 1. Propose a DRL-guided method for collision avoidance in a simplified robotic arm model. The local minima problem of APF is solved by DRL guidance, while the use of a simplified robotic arm reduces the difficulty of DRL training. 2. Proposed Hindsight Experience Replay for Collision Avoidance (HER-CA) algorithm to improve the training effect. The algorithm allows the agent to learn about collision avoidance from collision experience. 3. Full-body collision avoidance model for rock drilling robotic arm based on artificial potential field. The rest of the paper is structured as follows. In Section 2, a simplified method for the robotic arm is shown first, followed by a Twin Delayed Deep Deterministic Policy Gradient(TD3) and HER-CA based method. In Section 3, a full-body collision avoidance model based on APF is shown. In Section 4, we conducted experiments comparing HER-CA and Hindsight Experience Replay (HER), and simulation of robotic arm collision avoidance. Section 5 summarizes and concludes.
# Experimental Simulation of Thunderstorm Profiles in an Atmospheric Boundary Layer Wind Tunnel
## 4 Results and Discussion  
### Mean Wind Velocity Profile  
The distance \(h\) between the floor of the wind tunnel and the bottom of the TS grid module was varied and different arrangements of the TS grid modules were tested. Figure 4  
Figure 3: Devices used to reproduce a thunderstorm’s vertical profile: (**a**) Traditional devices; (**b**) New passive device to reproduce the nose-shaped mean wind speed profile of a downburst.  
shows the height of the nose \(z_{m}\) obtained as a function of \(h\). The left-hand side of the \(y\)-axis shows the values of \(z_{m}\) in the wind tunnel (\(z_{mWT}\)), while the right-hand side shows the corresponding full-scale values (\(z_{mFS}\)). P## in Figure 4 indicates the Profile no. ##, corresponding to the different arrangements tested during the current WT test campaign. The two different setups correspond to different extensions of the roughness elements on the floor and different heights of the fence (Roughness 1, Figure 4a, Roughness 2, Figure 4b). Measurements are made at the center of the turntable in the environmental test section of the Giovanni Solari Wind Tunnel.  
For both roughness configurations, it can be observed that, by increasing the separation distance \(h\) of the modules from the WT floor, the height of the nose \(z_{m}\) increases, achieving a large range of possible values for this parameter. The same value of \(z_{m}\) can be obtained also for different values of \(h\), depending on the porosity of the TS grid modules closer to the floor. It should be noted that the full-scale height of the tip of the nose, as depicted on the right-hand side of the \(y\)-axis in Figure 4, is evaluated based on the target geometrical scale of 1:400. For larger scales, the adopted setups would correspond to lower heights of the nose tip in full scale. However, the obtained results have shown that the height of the nose is nearly proportional to the distance \(h\) between the wind tunnel floor and the bottom of the lowest grid. Consequently, to achieve heights of the nose tip typical of thunderstorm downbursts for larger geometrical scales, the distance between the wind tunnel floor and the bottom of the lowest grid should be appropriately increased.  
Figure 5 shows the measured profiles that best match the [PERSON] and [PERSON] target profile for three heights of the nose, i.e., \(z_{m}=60\), 80 and 100 m in full-scale; 10% confidence lines with respect to the target profile are also plotted.  
Figure 4: Variation of \(z_{m}\) with \(h\): (**a**) Roughness 1; (**b**) Roughness 2. Legend: P## (P: profile, ##: test number).  
The graphs show a good agreement between the target and measured profiles within the confidence lines; however, it can be seen that, for heights above 150 m, the measured profiles start to differ from the target, providing lower velocities.