esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Optimization of Two-Posture Manipulator of Apple Packing Robots
## 3 Materials and Methods
### Path Optimization Method for the Manipulator of Apple Packing Robots
#### 3.2.3 Packing Path Analysis of the Manipulator  
Apple trays are commonly used in automatic apple packing robots in fruit markets. Different types of trays have 9, 12, 14, or 16 grooves, as shown in Figure 5. The number of placing points is equal to the number of grooves of one tray, and the number of grasping points is equal to the channel number of packing robots. In this study, 4 apple grasping points and 14 apple placing points are taken as an example to explain the principle of path optimization. The external size of the plastic tray with 14 grooves is 470 mm \(\times\) 280 mm. Figure 6 shows an example of a packing path. In Figure 6, 4 black solid circles represent 4 grasping points, and 14 white hollow circles represent 14 grooves of the tray, that is, 14 placing points. The positions of all 18 points are fixed in the automatic packing robot. On the premise of continuous supply of apples at every grasping point, the end effector  
Figure 4: Two different postures of the manipulator at one point.  
starts from the initial position, grasps apples at grasping points, and places them in tray grooves. The end effector returns to the initial position when all the grooves of a tray are filled. The initial position is defined as the position of the end effector when the two links of the manipulator naturally extend forward in the front direction of the pedestal, which is labeled as 0 in Figure 6. Taking the datum of the central point of the pedestal as the origin, the coordinate of the initial position is (0, 750) mm.  
The number of random paths of automatic apple packing is \(n!\times(m^{n})\times(2^{2n})\), which is 6.28 \(\times\) 10\({}^{27}\) when the number of apple grasping points is \(m\) (\(m\) is an integer within the working range of the manipulator, which is 4) and the number of apple placing points is \(n\) (\(0\leq n\leq G\), which is an integer with a value of 14). Choosing the optimal path through the traversal method is difficult because the packing path is complex and changeable, and the computation of traversing all paths is extremely large. We proposed a path optimization method for the manipulator using a genetic algorithm and an optimal parameter selection algorithm to accurately determine the optimal path for automatic packing.
# 3D Gravity Inversion on Unstructured Grids
## 2 Methods
### Gravity Forward Modeling Based on Tetrahedral Grids  
In the right-handed Cartesian coordinate system, as shown in Figure 1, based on [PERSON] [3] and [PERSON] [4], by successively imposing thrice coordinate rotations on every edge of the polyhedral body, the gravity anomalies g of a homogeneous polyhedral body can be converted to the sum of the contributions from all edges, which can be expressed as  
\[g=G\rho\frac{n}{i}\Bigg{[}\cos\psi_{i}\frac{m}{j}J_{j}(i)\Bigg{]} \tag{1}\]  
In Equation (1), G is [PERSON]'s gravitational constant, and \(\rho\) is the density of the cell. n is the facet number of the polyhedral body. m is the edge number of the \(i\)th facet. For tetrahedral grids, \(n=4\) and m = 3. \(\psi_{i}\) is a rotation angle of the \(i\)th facet, while \(J_{j}(i)\) is the contribution of \(j\)th edge in the \(i\)th facet, which is expressed as  
\[J_{j}(i)=\Bigg{[}\big{(}X\sin\theta_{j}-Y\cos\theta_{j}\big{)}\ln\big{(}X\cos \theta_{j}+Y\sin\theta_{j}+R\big{)}-2Z\tan^{-1}\frac{\big{(}1+\sin\theta_{j} \big{)}(Y+R)+X\cos\theta_{j}}{Z\cos\theta_{j}}\Bigg{]}^{(X_{j+1},Y_{j+1})} \tag{2}\]  
where  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\left[\begin{array}{ccc}\cos\psi&0&-\sin\psi\\ 0&1&0\\ \sin\psi&0&\cos\psi\end{array}\right]\bullet\left[\begin{array}{ccc}\cos \varphi&\sin\varphi&0\\ -\sin\varphi&\cos\varphi&0\\ 0&0&1\end{array}\right]\bullet\left[\begin{array}{c}x^{\prime}-x_{\rm obs} \\ y^{\prime}-y_{\rm obs}\\ z^{\prime}-z_{\rm obs}\end{array}\right] \tag{3}\]  
\[R=\left(X^{2}+Y^{2}+Z^{2}\right)^{1/2} \tag{4}\]  
In the above equations, \(\varphi\), \(\psi\), and \(\theta\) are respectively three rotation angles for the \(j\)th edge, and can be calculated as follows [4],  
\[\cos\varphi=-\frac{S_{yz}}{\Big{(}S_{yz}^{2}+S_{zx}^{2}\Big{)}^{1/2}} \tag{5}\]  
\[sin\varphi=-\frac{S_{zx}}{\Big{(}S_{yz}^{2}+S_{zx}^{2}\Big{)}^{1/2}} \tag{6}\]  
\[cos\psi=-\frac{S_{xy}}{\Big{(}S_{yz}^{2}+S_{zx}^{2}+S_{xy}^{2}\Big{)}^{1/2}} \tag{7}\]  
Figure 1: A tetrahedral cell in coordinate system.  
\[\sin\psi=\frac{S_{yz}^{2}+S_{zx}^{2}}{\left(S_{yz}^{2}+S_{zx}^{2}+S_{xy}^{2} \right)^{1/2}} \tag{8}\]  
\[\cos\theta=\frac{X_{j+1}-X_{j}}{\left[\left(X_{j+1}-X_{j}\right)^{2}+\left(Y_{j +1}-Y_{j}\right)^{2}\right]^{1/2}} \tag{9}\]  
\[\sin\theta=\frac{Y_{j+1}-Y_{j}}{\left[\left(X_{j+1}-X_{j}\right)^{2}+\left(Y_{ j+1}-Y_{j}\right)^{2}\right]^{1/2}} \tag{10}\]  
In the above Equations (5)-(8), Syz, Sxz, and Sxy are the projected areas of the \(i\)th facet respectively onto the y-z-, z-x-, and x-y- planes. It should be pointed out that the vertices of the facet must be arranged clockwise in the X-Y-plane before Equation (3) is calculated. However, the generation of tetrahedral meshes is automatic and vertices are arranged and stored randomly. Therefore, we introduce a judgment and rearrangement of vertices before the gravity contribution of each facet is calculated. For more details about the rotations and judgment, please see Appendix A.
# Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System
## 3 Evaluation of the Bridge Condition Using SHM Monitoring Data
### Evaluation of the Bridge Girders' Train Responses
The relationship between the maximum dynamic of strain contents in the passage and opposite directions for the train speed of 360 km/h is illustrated in Figure 11a. While Figure 11b shows the prediction of the opposite direction contents of the dynamic strain for 406 km/h, the statistical analysis of cubic and quadratic fitting shows that the correlation coefficient of quadratic is 0.90, so the cubic fitting in this case is better to predict the dynamic behavior of the strain contents. The comparison between the results of acceleration and strain dynamic contents shows that the dynamic evaluation of acceleration is better and effective to assess the vibration of the bridge, but the strain dynamic contents can be used to decrease the monitoring cost. To perform fatigue evaluation, a simplified rain-flow cycle counting algorithm was used first to process strain history data and the spectrum of stress matrix obtained by statistical analysis [17; 24]. The spectra of stress matrix calculated using the strain history data with train speeds of 360 and 406 km/h for the passage (left) and opposite (right) as shown in Figure 12, respectively. In addition, the maximum stress cycles are presented in Figure 12. It is observed that the maximum stress amplitude, obtained from strain history curves under two trains' effects for the passage and opposite directions, is smaller than 2.5 MPa. Therefore, only a small number of stress cycles occur at the higher stress range. Most cycles occur in the region of stress mean and amplitude from \(-\)0.5 to 0.5 MPa and 0 to 0.5 MPa, respectively. Thus, the mean and amplitude values of the stress for the two trains in the two directions are equal. Figure 11: Cross-correlation and prediction of maximum strain dynamic. (**a**) cross-correlation maximum dynamic of strain; (**b**) predicting effect of cross-correlation. Figure 12: _Cont._  
The maximum number of stress cycles at 406 km/h is smaller than that occurring at 360 km/h in the two directions. The results show that the fatigue stress and number of cycles limit are 29 MPa and 2 \(\times\) 10\({}^{6}\), respectively [25], as recommended by Eurocode 3. The value of the equivalent stress amplitude and the number of cycles when the high-speed train passes through bridge is far less than this value for two trains. However, the fatigue behavior of the bridge deck satisfies the requirement of the infinite-fatigue-life design method.
## 1 Introduction
### Mobility in Wireless Sensor Networks  
There are many cases where mobility is an important feature to be taken into account in WSNs. For example, the position of mobile sensors in a WSN must be determined because the performance of event detection and tracking highly depends on the exact location information of the events that must be reported along with the event features [2,3]. Also, mobile sensors are used to assist in the initial deployment of a WSN [4], because they can move to locations that meet sensing coverage requirements. Other interesting application of the mobile sensors is the intrusion detection [5], because mobile sensors can improve barrier coverage against moving intruders. Therefore mobility applied to WSN is an interesting research topic with many fronts, being tackled along the academic and research world. For example, the NASA Glenn Research Center [6] researches about mobile platforms for sensor placement, space exploration, environmental monitoring for situation assessment, mobile networking technology applicable to mobile sensor platforms, _etc._ One important aspect to be researched is the connectivity of mobile sensors with heterogeneous wireless networks, taking into account the provided quality of service: this is the area where we place our work.
# Semantic Scene Graph Generation Using RDF Model and Deep Learning
## 3 Proposed System  
### Semantic Scene Graph  
In this study, we constructed semantic scene graphs using RDF. The RDF was established by the W3C (World Wide Web Consortium) and was designed to be used to describe additional information pertaining to objects to be described, as well as hierarchical and similarity relations between data. In other words, it provides a way to define data and provide a description or relation.  
The RDF is usually described using a triple model with a subject, predicate and object. The subject denotes the resource data to be represented, and the predicate denotes characteristics of the subject and indicates a relation between the subject and the object. The object, then, is the content or value of a description. Each value can be described using a uniform resource identifier (URI). The RDF not only describes actual data but also supports the RDFS, a schema that describes the types of terms used in the data and relations between them.  
If the scene graph is described using the RDF(S) model, it can be utilized as shown in Figure 2. The upper and lower parts of Figure 2 represent the RDF model and RDFS, respectively. Our proposed scene graph generation method creates a representation of relations between objects in an image and can prevent semantically incorrect scene graphs, such as \"[PERSON] wears Banana\" or \"[PERSON] eats shirt,\" from being generated. For instance, RDFS can set \"person\" as the subject and \"fruit\" as the object for the predicate \"eats.\" Likewise, \"person\" could be used as the subject and \"cloth\" as the object of the predicate \"wears,\" togenerate a semantically correct scene graph. Although image annotations were represented using the RDF model in [3; 4], in these works the user manually inputs the subject, predicate and object. The uniqueness of the present study lies in our application of the RDF model to scene graph generation using a machine learning algorithm.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 1 Introduction
### Architecture for KGC  
Based on this, we proposed a Scalable Formal Concepts driven Architecture (SFCA) to extract formal concepts from the KGs to improve the performance of KGE. The SFCA conidia of three modules:  
1. Formal concept extraction module (FCE) extracts the formal concepts in triples and links the formal concepts with the entities.
2. Formal concept sampling module (FCS) sifting streamlined formal concepts from redundant formal concepts.
3. Formal concept-driven link prediction module (FCLP)leverages sampled formal concepts to improve KGC performance. This framework applies to all knowledge graphs and fully benefits from the self-supervision of the data itself. The contributions of the proposed method are summarized as follows:  
1. We propose a scalable KGC architecture based on formal concept analysis to generate formal concepts from KG. In comparison, existing methods either perform poorly or require commonsense concepts of manual extraction. To our knowledge, we are the first to apply formal concepts to KGC.
2. We design a coarser-to-fine formal concept extraction strategy to choose streamlined formal concepts of entities in the KG that can be used to improve the computational efficiency of the model.
3. Comprehensive experiments on four public datasets demonstrate the effectiveness and robustness of the proposed architecture with each module. Furthermore, the results show that SFCA helps generate KG's formal concepts, which greatly supplements the existing methods.
4. Extensive experiments on real data of industrial proving the practicality of the proposed model. The results show that SFCA is effective and robust in various fields of KGC tasks.
## 3 Experiments and Functioning Tests
### Microrobot Confined Space AR Inspection Test  
The purpose of the given experiment is to, first, assign each microrobot a unique QR code selectable via AR headset, second, utilize the AR headset to select the specific microrobot and inspect the confined space for damages, and third, provide a simulcast to identify key objects through a team of AR headset users. The setup for the first two parts attaches a QR code to the wall below a ceiling that shows signs of a leak and then uses the microrobots to conduct an AR inspection of the area to find the source of the leak.  
For enabling the teaming of the AR headset users to simultaneously view the live stream videos, a network consisting of a HEXBUG nano with a mounted ESP32-CAM, HTC Hub hotspot, Raspberry Pi 4 Model B, and two AR headsets is set up. In this network, the microrobot transmits the video to the single-board processor via the HTC Hub, and the single-board processor accesses the data and creates a flask server that allows AR headset users to view the live stream at the same time by scanning the QR code of the IP address corresponding to the single-board processor.
## 1 Introduction
The results indicated that GRT can detect complete voids, external voids, and unhydrated grouting, but it has difficulty in detecting smaller internal voids. [PERSON] [12] established pre-stressed duct models under different working conditions based on ABAQUS software and combined them with the relevant theory of the impact-echo method to study the impact of different pipeline materials, diameters, knocking forces, numbers of steel strands, signal excitations and reception positions, and defect positions on qualitative detection. Water defects, void defects, and slurry water mixing defects were also analyzed, as well as the influence of the number of steel strands and the position of steel bars on positioning detection. The feasibility of utilizing the acoustic emission (AE) technique for mechanical diagnosis and the delamination characterization of fiber-reinforced polymer (FRP)-reinforced concrete structures has been extensively studied. [PERSON], [PERSON] et al. [13] conducted experimental research on this topic, investigating the application of the AE technique in assessing debonding in FRP-reinforced concrete. In the study of the grouting quality of pre-stressed pipelines, scholars at home and abroad have achieved certain application results by using the core drilling method, X-ray imaging method, impact-echo method, ground-penetrating radar method, ultrasonic waves, and other detection methods. However, there are still some problems in detection accuracy and detection efficiency, i.e., the original structure is damaged, the economic cost is expensive, it is unsafe, the test takes a long time, the metal bellows cannot be detected, and the plastic bellows cannot be detected. [PERSON] et al. [14] demonstrated the potential application of the multi-channel ultrasonic transmission method in the quality detection of grouting in pre-stressed metal pipelines through a theoretical analysis and a numerical simulation. In this paper, a method for quickly characterizing the degree of defects using the longitudinal transmission wave method is proposed, and a finite element simulation and full-scale verification tests are carried out. The proposed method combines the advantages of low economic cost, high detection efficiency, simple operation, small impact of the monitored environment, and non destructive testing, and it is of great scientific significance in the grouting quality testing of pre-stressed pipelines. In Section 1, the preparations of the test samples and transmission wave methods are described. In Section 2, the ABAQUS finite element simulation method is described. In Section 3, an analysis and a comparison of the experimental results are presented.
## 3 Results and Discussion
### Variations of Agro-Morphological Traits
#### 3.1.1 Qualitative Agronomical Traits  
The majority of barley accessions (63%) had an intermediate growth angle, followed by erect and prostate growth angles (Table 1). Only Betaone and Saessalbori among the control cultivars had an intermediate growth angle, while the remaining three cultivars had an erect growth angle. The flag leaf angle displayed similar variations, with the barley accessions having semi-erect (42%), erect (39%), or horizontal (19%) angles. Except for Saechalssalbori, all control cultivars had a semi-erect flag leaf angle. The spike erectness angle also exhibited a wide range of variation. Around 29% of barley accessions had a semi-erect spike erectness angle, while 19% had an erect angle. Betaone and Saechalssalbori had an erect spike erectness angle, whereas Heukbori, Heukdahyang, and Saessalbori had a semi-erect angle. Similar distributions were observed for other spike erectness angles, such as horizontal, semi-drooping, and drooping. Growth angle, flag leaf angle, and spike angle are important traits associated with various desirable characteristics of barley genotypes. For instance, an erect leaf angle has long been proposed as a means to increase yield, while a spike angle could reduce fusarium head height, a major disease affecting barley production ([PERSON], 2001; [PERSON], 2002; [PERSON], 2003). On the other hand, [PERSON] et al. ([PERSON] et al., 2019) revealed the impact of culm elongation on grain size and yield. Overall, the different morphological traits observed
## 6 Parameter Selection  
### Parameter Selection for Non-Machine Learning Methods  
For non-machine learning methods, I mainly focus on two commonly used methods [11], IDW and kriging (e.g., OK). For IDW, it is really dependent on the selection of appropriate values for a power parameter and the number of nearest observations, which can be selected based on their resultant predictive accuracy [41; 90]. The smoothness of the estimated surface increases as the value of power parameter increases [91]; however, manipulating the power parameter to smooth the predictions and to produce visually pleasant maps does not warrant the quality of the resultant predictions and is not recommended.  
For kriging methods, a number of parameters, including window size, and isotropy and anisotropy of data, need to be considered, as well as the variogram model and its parameters. Data transformation needs to be considered when the data are skewed and anisotropic. Three methods of data transformation(i.e., logarithms, standardized rank order, and normal scores) can be employed to reduce the skewness [74; 92]. Some other methods, such as Box-Cox transformation [82], arcsine [88], square-root transformation [47], and double square root or square root and log [12], can be used to normalize the data. The selection of these transformation and normalization methods is largely data-dependent and careful examination should be taken. The selection can also be determined according to their effects on the predictive accuracy.  
In addition, for anisotropy, non-stationary methods like KED should be used in cases with a general anisotropy or trend (i.e., drift) [75]. If different types of non-stationarity exist, application of different spatial predictive methods to each type may improve predictive accuracy [93].  
The parameter selection for these methods can be determined according to the predictive accuracy of resultant predictive models. This is demonstrated in Section 11.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 5 Conclusions  
Rapid technological development has encouraged applications and services in various areas to consider utilizing drones. Generally, employing multiple drones is superior to a single drone for complex missions, and strong connectivity among the drones is essential. Most previous studies modified earlier MANET protocols to make them suitable for FANET and provide sufficient connectivity. However, these inevitably inherited fundamental MANET protocol limitations, which were not ideal for FANET. To overcome these limitations, we focused on GCS advantages and proposed GCS-routing, designed specifically for FANET. GCS-routing makes the best use of GCS to predict topology changes and react to them immediately without requiring periodic HELLO messages for neighbor discovery or link cost estimation. Therefore, current routing protocol overheads were significantly reduced using GCS-routing, making it suitable for high mobility drones in FANET. We designed and implemented GCS-routing on real drones and GCS, and conducted experiments and simulations to thoroughly evaluate GCS-routing performance from diverse perspectives [13]. Evaluation results confirmed that GCS-routing provided higher and more stable throughput, with considerably shorter network disconnected and route update times compared with other routing protocols. Thus, GCS-routing provided better network performance in many aspects and is more suitable for drone networks than other protocols. GCS-routing makes it possible to construct reliable and efficient networks for multi-drone control systems.  
There are several directions for future study. GCS incorporates various information that was not considered in the current study. Therefore, we intend to extend GCS-routing to incorporate diverse information to improve routing algorithm efficiency and performance. Furthermore, this study considered multiple drone networks with GCS-routing, so we plan to develop a testing guideline and benchmark for drone swarm networks.  
Concept, W.L., K.K. and H.K.; Methodology, W.L., J.Y.L., K.K. and H.K.; Software, J.Y.L. and J.L.; Validation, J.Y.L., J.L., S.Y. and S.P.; Original draft preparation, W.L. and J.L.; Review and editing, W.L. and H.K.; and Supervision, H.K.  
This research was supported by the Unmanned Vehicles Advanced Core Technology Research and Development Program through the Unmanned Vehicle Advanced Research Center funded by the Ministry of Science, ICT and Future Planning, Republic of Korea (NRF-2016M1B3A1A01937599).  
Acknowledgments: A preliminary version of this paper was presented at the IEEE International Conference on Communications (ICC), IEEE, 2016.