esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Terminal Offload Selection  
Task size affects network utility, terminal decision, and resource optimization results. The model offloading reliability index represents the probability that the task is successfully offloaded, and the completion is calculated. This index will affect the terminal device offloading decision. Three types of reliability indexes are designed for simulation analysis as shown in Table 2.  
Arranged from largest to lowest, the above indexes are listed in the order of the terminal computing reliability index, the UAV offloading reliability index, and the G-MEC offloading reliability index in Task 1. For Task 2, the three types of offloading have the same reliability. For Task 3, the reliability index of UAV offloading is slightly higher. Figure 6 shows the simulation results.  
In Figure 6, as the task increases, the decision of the terminal for the offloading node differs. \"0\" represents zero-level offloading (Level 0), in which the terminal performs the task computation processing. \"1\" represents the first-level offloading (Level 1), in which the UAV performs the task offloading. Finally, \"2\" represents the second-level offloading (Level 2), in which the G-MEC performs task offloading. As the task grows, tasks 1 and 2 begin to select Level 0 offloading, then Level 1 offloading, and finally, Level 2 offloading. Task 3 involves tasks in the following order: Levels 1, 0, 1, and 2 offloading. According to the above phenomenon, when the task is small, the terminal computing resource cost is the lowest, and the model will select the terminal for local calculation. When the task increases, a better system utility and resource allocation can be achieved by task offloading at the UAV. When the task is particularly large, the UAV and the terminal will be unable to undertake the task calculation, task offloading to the G-MEC is more reasonable, and the system benefits more.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Task (1)** & **Task (2)** & **Task (3)** \\ \hline \(\mu_{M}\) & 0.4 & 0.5 & 0.5 \\ \hline \(\mu_{A}\) & 0.52 & 0.5 & 0.52 \\ \hline \(\mu_{u}\) & 0.6 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reliability indexes.  
Figure 5: Multiple scenario utility comparison.  
As the reliability index of the UAV offloading is slightly higher than that of the terminal, Task 3 is a special case. When the task is small, the reliability index of the calculation is more important than the cost factor. Later, tasks increase, and the impact of the cost resource factor is greater than the reliability index. Therefore, the offloading level selection shown in Figure 6 will be generated.  
As the task increases, Task 2 initially offloads to the G-MEC, then Task 3 offloads to the G-MEC, and finally, Task 1 offloads to the G-MEC. The reliability index will affect the integrated offloading decision when the reliability index is high, and the task will choose to offload ahead of time (that is, the system utility is less when the offloading mode is selected).
# The Application Status and Trends of Machine Vision in Tea Production
## 2 Pest and Disease Detection  
Tea plants prefer high humidity and high temperature, which provide a suitable living environment for crop diseases and insect pests, resulting in a wide variety of tea diseases. According to statistics, there are about 140 types of tea tree diseases in China, infecting the leaves, stems, roots, and flowers of tea trees [12]. Due to the centralized planting of tea gardens, any disease occurrence should be handled in a timely manner. Otherwise, it leads to irreversible economic losses. Due to the wide planting area and high density of tea gardens, using manual observation and early warning of crop diseases and pests  
Figure 1: Machine vision system.  
is not only time-consuming, but also labor-intensive. In addition, observation results are easily influenced by people's subjective feelings, because individuals have inconsistent definitions of the degree of infection [13]. On the other hand, the initial symptoms of pests and diseases are not obvious until a large area of plants has already been infected with the disease. In recent studies, researchers have used machine vision to monitor the health levels of tea. The schematic diagram of such a system is shown in Figure 2, and the research results are shown in Table 1.  
The process of disease diagnosis through machine vision generally includes (1) image acquisition; (2) image preprocessing; (3) data processing; (4) network training; (5) and hardware deployment and use.
## 2 Experimental Design and Implementation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Observation** & **Time (s)** & **Torque_Joint\_5_A (Nm)** & **Torque_Joint\_6_A (Nm)** \\ \hline
1 & 0 & 0.819 & \(-1.045\) \\
2 & 0.100 & 4.08 & 2.109 \\
3 & 0.200 & 9.007 & 4.323 \\
4 & 0.300 & 10.118 & 6.137 \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of the torque data captured by one robot in one experiment e.g., The column name _Torque_joint_5\(A\) refers to the torque acquired in the 5 th joint of robot \(A\) (faulty robot). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **New (Nm)** & **Faulty (Nm)** & **Increase** \\ \hline Load 1 & 19,567 & 42,830 & 118.88\% \\ Load 2 & 21,224 & 46,451 & 118.86\% \\ Load 3 & 62,235 & 124,703 & 100.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total torque of the 5 th joint of the new and faulty wrists. _Load 1, Load 2_ and _Load 3_ represent the 0%, 15% and 90% of the maximum payload respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **New (Nm)** & **Faulty (Nm)** & **Increase** \\ \hline Load 1 & 17,910 & 19,039 & 6.31\% \\ Load 2 & 20,852 & 19,982 & \(-4.17\%\) \\ Load 3 & 28,671 & 28,970 & 1.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total torque of the 6 th joint of the new and faulty wrists. _Load 1, Load 2_ and _Load 3_ represent the 0%, 15% and 90% of the maximum payload respectively. The increment in the torque is homogeneous, i.e., The torque increases in the whole trajectory and not only in certain movements or positions. The fact that the torque increases in the whole trajectory and not only in certain poses, reveals that the deterioration affects to the entire movement of the joint. The results also show that at the time of the failure, the electric consumption of joint 5 in the faulty wrist was at least twice as high as expected for a healthy joint. Figure 3: Torque values of the 6 th joint in a faulty wrist and in a healthy wrist with different loads and same trajectory. Figure 2: Torque values of the 5 th joint in a faulty wrist and in a healthy wrist with different loads and same trajectory. Although the acquired data effectively detects the wear in the joint, the detected increment is not enough on its own to deduce a root cause of the fault. Thence, we conducted a root cause analysis with an in-depth mechanical inspection in order to identify the cause.
# Vibration Suppression of Collaborative Robot Servo with Dual Encoders
## 8 Experiment
### Step Response of Dual Encoder Speed Control  
The speed loop's proportional parameter V\({}_{\text{kp}}\) is set to 4, the integral parameter V\({}_{\text{ki}}\) is set to 0.001(V\({}_{\text{kp}}\) and V\({}_{\text{ki}}\) are both parameters in Equation (2)), the current loop's proportional parameter C\({}_{\text{kp}}\) is set to 0.42, and the integral parameter C\({}_{\text{ki}}\) is set to 0.2 (C\({}_{\text{kp}}\) and C\({}_{\text{ki}}\) are both parameters in Equation (3)). The control effect is illustrated in Figure 13 for \(\omega\) = k1\(\cdot\omega\)1 + k2\(\cdot\omega\)2 with k1 = 0.5 and k2 = 0.5. The pink curve represents the speed setting, a step signal at 300 revolutions per minute (RPM). The blue curve depicts the speed feedback from the motor side encoder, while the red curve shows the speed feedback from the reducer side encoder. It is evident that the speed response on the motor side is significantly faster than that on the reducer side due to the presence of elastic factors in the reducer.  
Figure 13: The response of speed given as a step signal under different control parameter conditions. (**a**) (k1 = 0.8, k2 = 0.2, V\({}_{\text{kp}}\) = 15, Vki = 0.001, C\({}_{\text{kp}}\) = 0.42, Cki = 0.2); (**b**) (k1 = 0.7, k2 = 0.3, V\({}_{\text{kp}}\) = 15, Vki = 0.001, C\({}_{\text{kp}}\) = 0.42, Cki = 0.2); (**c**) (k1 = 0.6, k2 = 0.4, V\({}_{\text{kp}}\) = 15, Vki = 0.001, C\({}_{\text{kp}}\) = 0.42, Cki = 0.2); and (**d**) (k1 = 0.5, k2 = 0.5, V\({}_{\text{kp}}\) = 15, Vki = 0.001, C\({}_{\text{kp}}\) = 0.42, Cki = 0.2).  
Figure 13 shows the step response of the system under different parameters. It can be seen that the speed response on the reducer side is slower compared to the speed response on the motor side. When the proportion of encoder coefficients on the reducer side is large, it is necessary to increase the speed loop proportional gain V\({}_{\text{KP}}\). By adjusting different encoder coefficients, k1 and k2, the speed fluctuation on the load side under steady state can be reduced. Speed overshoot on the motor side is usually allowed, as collaborative robots are typically position-controlled and need to ensure that the position does not overshoot. The experimental results show that satisfactory results can be obtained when the motor encoder coefficient k1 is in the range of 0.5 to 0.8 and the reducer encoder coefficient k2 is in the range of 0.5 to 0.2. Usually, the ratio of reducer to encoder k2 should not exceed 0.5.  
In Figure 13c, when k1 = 0.7 and k2 = 0.3, after reaching the set speed, the control effect is excellent with minimal speed fluctuations. Particularly, the fluctuations on the reducer side are minimal, which also means that the vibration on the load side is relatively small, achieving the expected goal. Compared to relying solely on a single encoder installed on the motor side, the dual encoder solution captures the speed of the load side, which can better suppress vibrations on the load side.  
The joint with dual encoders is assembled into a complete collaborative robot, as shown in Figure 14.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 2 Related Work
### Network Status Prediction  
There are many studies on network state prediction, such as traffic classification and latency prediction. Network traffic classification techniques include a port-based approach, deep packet inspection, and machine learning. A quality of a service-aware traffic classification system is proposed by leveraging semisupervised learning [20]. A cost-sensitive learning method is proposed to detect elephant flows [21]. Atlas, a mobile application classification, is proposed to identify mobile applications [22]. As for network latency, estimating a subset of network node pairs latencies by probing the round-trip times of an other subset of network node pairs is a widely used method. Matrix completion [23] and tensor completion [24] are two popular mechanisms to predict the latencies between a subset of network node pairs.  
However, the above research works on network state prediction are based on the prior knowledge of network topology, or directly on the architecture of a soft-define network. Inpractice application, we can usually only obtain the information of the source node and the destination node. Round-trip times between source and destination nodes may also be included for a period of time. We know nothing about the routing information, routing policy, and traffic state between the source and destination nodes. All these bring great challenges to the estimation and prediction of network latency.  
Fortunately, time series analysis is often used to solve such problems. A time series analysis method is proposed to predict network throughput, which is important in improving the network protocols in terms of latency, energy, etc. [25]. [PERSON] et al. compares the network throughput prediction performance between machine learning models and time series forecasting [26]. Time series analysis is much simpler and easier with a certain performance. For the data with a single source and simple structure, this method is very suitable for processing and analysis. At the same time, due to the consideration of real-time application requirements, time series analysis has become the first choice.
# 3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View
## 4 Panoramic View Generation  
Once the shape normalization has been done, one panoramic view will be generated. First, a cylinder is generated around a 3D model as shown in Figure 2a. Its center and axis are the center and the first principal axis of the model. Its radius is defined as \(R=2\cdot\max\{||o-q||\}\). Its height is the height of the model. We sample the axis of the cylinder with a sample rate \(F\). Each sample point of the axis is a center of a cross section of the cylinder. For each cross section, \(M\) rays are emanated from each center to the surface of the cylinder. Thus, the degree between each ray is \(2\pi/M\). Each ray may have more than 1 intersection with the surface of the model. The distance \(rd\in[0,R]\) from a center to the furthest intersection of the ray is mapped to a value in the range of \([0,1]\) for representing one pixel in the \(F\times M\) range image. After generating the panoramic view, SIFT descriptors are extracted from the panoramic view and stored as the feature of the model. Figure 2b shows the SIFT descriptors of panoramic view-based range image.  
Figure 2: _Cont._  
The matching procedure of the SIFT descriptors uses the Euclidean distance as in [16]. Suppose the SIFT descriptor of a query model is \(VQ=(\imath q_{1},\imath q_{2},\dots,\imath q_{n})\) and that of a model in database is \(VD=(\imath vd_{1},\imath vd_{2},\dots,\imath vd_{n})\). The distance \(D\) between the two descriptor is given by  
\[D=\sqrt{\sum_{i=1}^{n}(\imath q_{i}-\imath vd_{i})^{2}}, \tag{7}\]  
A keypoint with the least distance value is defined as a matched keypoint. We match the keypoints of the query model to those of the models in the database and obtain the number of matched keypoints. Finally, we identify the model with the maximum number of matched keypoints as the original model of the query model.
## 3 Results and Discussion
### Numerical Results
#### 3.2.3 Effect of Mass Flow Rate  
Figure 15a,b show the predicted maximum temperature difference \(\Delta T\) versus the mass flow rate \(\dot{m}\) and the predicted thermal efficiency \(\eta_{t}\) versus \(\dot{m}\), respectively, for the different CPC collectors on 1 March 2020 with a constant inlet temperature of 27 \({}^{\circ}\)C. It is noted that the experimental mass flow rate was \(\sim\)0.005 kg/s (0.36 L/min). It can be seen in Figure 15a that all CPCs exhibit a similar behavior, that is, there is an increase in the maximum \(\Delta T\) with a decrease in the \(\dot{m}\) as expected because lower \(\dot{m}\) allows higher heat transfer from the absorber to the water; however, a lower \(\dot{m}\) could result in less hot water availability. The largest \(\Delta T\) was observed for the CPC with an absorber with continuous fins for all mass flow rates. Figure 15b shows that for all CPCs, there is a sharp increase in the thermal efficiency \(\eta_{t}\) with an increase in the \(\dot{m}\) from 0.001 to 0.002 kg/s. For the CPC with continuous fins, there is a slight increase in \(\eta_{t}\) when \(\dot{m}\) increases from 0.002 to 0.005 kg/s, and a decrease in \(\eta_{t}\) from 0.005 to 0.01 kg/s. For the CPC with segmented fins, \(\eta_{t}\) remains almost the same from 0.002 to 0.005 kg/s and decreases from 0.005 to 0.01 kg/s. For the CPC without fins, \(\eta_{t}\) decreases from 0.002 to 0.006 kg/s and keeps decreasing at a larger rate from 0.006 to 0.01 kg/s; however, the difference between the values of \(\eta_{t}\) at 0.002 and 0.006 is \(\sim\)1%. The effect of the \(\dot{m}\) on the \(\eta_{t}\) has been previously observed [39] and it is attributed to the rate of heat transfer. The initial increase in \(\eta_{t}\) with the increase in \(\dot{m}\) is attributed to the higher water velocity, which, in turn, increases the rate of heat transfer to the water [40]; however, after an optimal value of \(\dot{m}\) is reached, the \(\eta_{t}\) decreases with further increase in the \(\dot{m}\).
## 4 Analysis and Results
### Performance Results  
This section analyzes the performance and overhead of the proposed authentication scheme in terms of computation cost, storage overhead, and communication overhead. The scheme was implemented using the network simulator environment OMNeT++ 5.6.2 that runs on the Windows 10 operating system with an Intel(R) Core (TM) i7-6500U CPU @ 2.50 GHz and 8 GB of RAM. The proposed authentication scheme is based on hash function and XOR operation. The type of hash used in the scheme is SHA-1, which produces a 160-bit hash value known as a message digest.
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 2 Methods
### CNN Samples  
The CNN samples were from two sources, numerical simulations and vibration experiments, which are described in the following.  
Numerical simulations: four datasets (A, B, C, D) with a single damage in a rod, double damages simultaneously in 2 rods, triple damages simultaneously in 3 rods and mixed partial samples of three above datasets.  
For the single damage dataset, there were 10 scenarios (9 damage locations + 1 intact structure). The acceleration time history signals of the 13 measurement points for these 10 scenarios were used as the inputs of the CNN samples, and for the corresponding CNN output, the intact structure was set to 0, the damage on Rod 1 set to 1, the damage on Rod 2 set to 2, and so on.  
For the dataset of damages simultaneously in 2 rods, any two of the 9 rods were randomly selected, hence, there are 36 (\(C_{9}^{2}\)) scenarios. The acceleration signals of the 13 measurement points for the 36 scenarios were used as the CNN inputs, correspondingly 1, 2,..., 36 were set as the CNN outputs respectively.  
For the dataset of simultaneous damages in 3 rods, any three of the 9 rods were randomly selected. There were 84 scenarios (\(C_{9}^{3}\)), and the acceleration signals of the 13 measurement points for the 84 scenarios were used as the CNN inputs, correspondingly, 1, 2,..., 84 were set as the CNN outputs respectively.  
Dataset D included the intact structure plus 7 damage scenarios, which were (1) damage in Rod 1, (2) damage on Rod 5, (3) damage in Rod 9, (4) damages simultaneously in Rod 1 and Rod 9, (5) damages simultaneously in Rod 1 and Rod 9, (6) damages simultaneously in Rod 5 and Rod 9, and (7) damages  
Figure 3: Experimental devices. (**a**) Intact and damaged rods, (**b**) acquisition instrument, (**c**) accelerometers, (**d**) hammer, (**e**) laptop.
## 3 Results and Discussion
### Associations between Indoor Concentrations and SBS Symptoms/IEQ Comfort Perception  
The variations in concentrations between the occupants belonging to the SBS group and the non-SBS group are shown in Figure 12. By applying the Mann-Whitney U test between groups of reporting SBS versus number of occupants who reported SBS symptoms were exposed to higher values with significant (_p_ < 0.05) difference in NO\({}_{2}\), benzene, toluene, Propionaldehyde, 2-butanone, benzaldehyde, and hexaldehyde.  
As a next step and for exploring indoor concentration variations in greater depth, tests were performed for each SBS symptom subgroup (Table 5). For occupants who reported eye irritation, NO\({}_{2}\) (_p_ < 0.1), benzene and toluene, benzaldehyde and hexaldehyde (_p_ < 0.05), concentrations were higher in their offices than in those of occupants without symptoms. In offices where occupants with reported respiratory symptoms, NO\({}_{2}\) and toluene (_p_ < 0.05), 2-butanone (_p_ < 0.05) were also measured in higher levels. For the heart and skin symptoms groups, fewer compounds were revealed to be significantly higher (NO\({}_{2}\), Hexaldehyde  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Buildings** & \multicolumn{3}{c}{**PM\({}_{1}\)**} & \multicolumn{3}{c}{**PM\({}_{2.5}\)**} & \multicolumn{3}{c}{**PM\({}_{10}\)**} \\ \hline  & **Mean** & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** & **Min** & **Max** \\ \hline B3 & 6.5 & 2.7 & 46.5 & 10.5 & 2.8 & 156.6 & 20.9 & 2.8 & 919.8 \\ B4 & 4.7 & 0.8 & 312.7 & 13.0 & 1.1 & 506.1 & 36.9 & 1.1 & 3952.4 \\ B5 & 6.1 & 2.5 & 16.7 & 7.9 & 2.7 & 20.8 & 11.8 & 2.7 & 96.9 \\ B6 & 11.7 & 0.9 & 48 & 15.1 & 1.2 & 86.6 & 23.4 & 1.2 & 312.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PM indoor concentrations.  
Figure 12: Differences in indoor concentrations (ug/m\({}^{3}\)) between SBS and non-SBS group.  
(\(p<0.05\)) and toluene (\(p<0.1\)), respectively). For offices where occupants reported general symptoms, mean concentration values of NO\({}_{2}\), benzene and hexaldehyde (\(p<0.05\)) were found in higher concentrations.  
Further to the SBS subgrouping, the analysis was expanded by separating occupants into two groups of satisfied and unsatisfied occupants, regarding the perception of IAQ. Figure 13 presents the values for parameters related to IAQ, such as fresh air, odor, and overall perception. In general, the workplace of the IAQ-dissatisfied group appeared to be located in offices with higher pollutants concentrations. NO\({}_{2}\) appeared to be statistically significant (\(p<\)0.05) for all IAQ parameters. In the case of odor or stuffy air, toluene and d-limonene were significantly higher.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Development of Cloud Management Platform  
The cloud management platform is the core of the crowd sensing crack collection system, responsible for account management, crack information management, and has the query function to check and display the recorded crack information. The management platform is divided into front-end and back-end. The front-end is developed using Vue.js and combined with third-party components of the Gaode Map. The back-end is developed using Java.