esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 1 Introduction
The algorithm of YOLOv2 obtained the aspect ratio of the image border by introducing the idea of _anchor box_ in Faster R-CNN and applying the _k_-means clustering, replaced the dropout strategy with the batch normalization, and turned the CNN into the darknet-19. Later in 2018, to further raise the performance of YOLOv2, [PERSON] and [PERSON] developed YOLOv3 [17], being involved with the following improvements. Firstly, the darknet-53 with deeper layers is substituted for the darknet-19. Secondly, the classifier is achieved by the _logistic_ regression, instead of the _softmax_ function. Moreover, the Feature Pyramid Network (FPN) is also introduced to realize the multi-scale inspections. Figure 1 proves that YOLOv3 spends only 22 ms to detect objects, and performs more accurate recognition than a large portion of other detection methods. Accordingly, based on the object detection techniques of YOLOv3, this work develops a novel automatic irrigation system to implement the customized and accurate watering for an individual seedling, via the following ways: 1. Orchid seedlings are visually identified and selected by YOLOv3. 2. Orchid seedlings are accurately positioned in  
Figure 1: YOLOv3 competes with other detection methods for comparable performance, under the platform either an M40 or Titan X, with essentially the same GPU [17]. three-dimensional (3D) space. 3. The stem-root junction is deemed the watering target to avoid damp leaves. 4. Intelligent control is introduced to adjust the watering amount. The remaining parts of this work are listed as below. The system design and methodology are detailed in Section 2. The feasibility of the proposed system is verified through a number of experiments, as described in Section 3. Finally, Section 4 concludes this research and provides possible extensions.
# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 4 Experimental Results and Comparison
### Accuracy of Star/Target Detection Based on Table 1 Parameters  
Generate 100 sets of simulation data for algorithm simulation training, and test the other 100 sets of generated data based on Equations (5) and (6). Among the 100 sets of test data, there are a total number of \(N_{(s,\,t)}=123417\) stars, with \(N_{(s,\,d)}=113219\) detected stars, \(N_{(s,\,f)}=10771\) false alarms, and \(N_{(s,\,L)}=20969\) missed stars; debris \(N_{(d,\,t)}=2000\), detected debris \(N_{(s,\,d)}=1764\), false alarms \(N_{(d,\,f)}=223\), and missed debris \(N_{(d,\,L)}=459\). Obtain the precision and recall rates of stars and debris, as shown in Table 3.  
In 100 sets of generated test data, a total of 472 debris targets were occluded by stellar tails, including 108 targets in occlusion one, 229 targets in occlusion two, and 135 targets in occlusion three. Therefore, it can be estimated that in the simulation scenario based on Table 1, the target's occlusion rate is 23.6%, the probability of complete occlusion (case one) is 5.4%, the probability of edge-to-tail contact (case three) is 6.75%, and the probability of occlusion below 1/3 (case two) is 11.45%. Considering that both scenario two and scenario three can detect targets through processing, 77.12% of the occluded targets can have a chance to be detected after processing.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 2 Gesture-Based Human-Robot Interface
### Leap Motion Based Gesture Recognition
#### 2.1.2 Gaussian Mixture Model (GMM) Based Classification  
Gesture recognition is a supervised learning problem; i.e., training a classifier using a labeled dataset and then finding the right label for a test sample. As is shown in Figure 2, gesture samples are taken for offline training to build a gesture library, and then the operator's gesture can be classified online. It is assumed that there are \(M\) hand gestures, and for each gesture, there are \(N\) samples. The commonly-used recognition algorithms for gestures are support vector machines (SVMs), artificial neural networks (ANNs), hidden Markov models (HMMs), the Gaussian mixture model (GMM), etc. [28]. GMM is a mature regression and classification technique and it can achieve a 95% success rate in existing hand gesture recognition research [29]. In this paper, the gesture classification is only a component of the teleoperation system. For simplicity, GMM is used to model the datasets.  
Figure 1: Hand model used in leap motion and feature selection for hand gesture classification.  
The samples of gesture \(m\in M\) are denoted as \(\mathbf{S_{m}}=\left\{\mathbf{x}_{i}^{m}\right\},i=1\cdots N\), where \(\mathbf{x}_{i}^{m}\) is a vector of five finger bending angles. Each dataset can be modeled by a mixture of \(K\) Gaussians with dimensionality \(D=5\). Therefore, totally \(M\) GMM models are derived. GMM\({}_{m}\) is formulated using  
\[p^{m}\left(\mathbf{x}\right)=\sum_{k=1}^{K}\pi_{k}^{m}\mathcal{N}\left(\mathbf{x};\mu_ {k}^{m},\mathbf{\Sigma}_{k}^{m}\right), \tag{1}\]  
where \(\left\{\pi_{k}^{m},\mu_{k}^{m},\mathbf{\Sigma}_{k}^{m}\right\}\) are the parameters of the \(k_{th}\) Gaussian component in GMM\({}_{m}\). \(\pi_{k}^{m}\) is the prior or weight for \(k_{th}\) component and \(\left\{\mu_{k}^{m},\mathbf{\Sigma}_{k}^{m}\right\}\) is the corresponding mean and covariance matrix.  
The expectation-maximization (EM) algorithm is used to solve the maximum likelihood estimation of mixture parameters [30]. It guarantees that the likelihood of the training set can monotonously increase during optimization. \(k\)_-means_ clustering technique is used to provide an initial estimation and to avoid getting trapped into a local minimum. After optimization, the datasets are converted into a very compact probabilistic form. Detailed optimization steps of EM algorithm can refer to the literature [31].  
Given a query sample \(\mathbf{x}^{*}\), the label can be found by maximizing the log-likelihood,  
\[c^{*}=\left\{\begin{array}{cc}\underset{m\in M}{\operatorname{argmax}}\log p ^{m}\left(\mathbf{x}^{*}\right)&\log p^{m^{*}}\left(\mathbf{x}^{*}\right)>p_{0 }\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]  
A threshold \(p_{0}\) is defined to filter out the irrelevant hand gestures and its specific value needs to be adjusted according to the actual situation.
## 3 Harmony Search  
The harmony search (HS), introduced by Prof. [PERSON] [45], has been utilized from its inception in 2001 up to the present. The main inspiration behind HS is derived from music, specifically jazz improvisation. The algorithm incorporates three operations designed to mimic the behavior of a musician, which are mathematically described in Equations (8)-(10).  
\[\text{HMR}\in[0,1] \tag{8}\]  
\[\text{X}_{\text{new}}=\text{X}_{\text{old}}+\text{bp}(2\text{ rand}-1) \tag{9}\]  
\[\text{PArate}=\text{P}_{\text{Lower limit}}+\text{P}_{\text{Range}}\times \text{randWhere}\text{P}_{\text{Range}}=\text{P}_{\text{Upper Limit}}-\text{P}_{ \text{Lower Limit}} \tag{10}\]  
Equation (8) represents the first operation of the algorithm, which is like the playing of a piece that a musician knows by heart, also known as the memory acceptance rate (HMR). Equation (9) indicates the second operation, which resembles playing a piece and adjusting the tones, known as pitch adjustment (PArate). Finally, Equation (10) represents the action of playing a new piece and randomly adjusting the notes, also known as random selection.  
Further details on the behavior of each operator can be found in the following articles [46; 47; 48]. Algorithm 1 consists of the pseudocode for HS and Figure 2 shows the HS flowchart.  
```
1. State the objective function \(\text{f}(\text{x}),\text{x}=\big{(}\text{X}_{1},\dots,\text{X}_{n}\big{)}^{T}\)
2. Initially generate harmonics (matrix of real numbers)
3. Establish the pitch adjustment rate (PArate) and limits of tone
4. Establish harmony memory accepting (HMR)
5. While (t < Maximum number of iterations)
6. Produce a new harmony and accept the best system
7. Setting the tone for new harmonies (solutions)
8. If (rand > HMR)
9. Randomly select an existing harmony
10. Else if (rand > PArate)
11. Setting the tone at random within a bandwidth
12. Else
13. Produce a new harmony through a randomization
14. End if
15. Accepting new harmonies (solutions) best
16. End while
17. Find the best solutions
```  
**Algorithm 1**HS  
The most recent articles found in the literature regarding HS report applications to real problems in various disciplines. The applications are as follows: healthcare systems [49], scheduling problems [50], optimization problems such as a seismic isolator [51], ground motion [52], a pre-stressed concrete bridge girder [53], and Distribution Network Reconfiguration [54]. Furthermore, some of the more recent variants of the algorithm are presented in [55; 56].
# New Materials and Effects in Molecular Nanomagnets
## 6 Theoretical Aspects and Methods
combined DFT using the VASP code with second order perturbation theory to calculate a strain-induced spin change in iron porphyrin embedded in a graphene lattice [145]. [PERSON] et al. used DFT calculations to model different types of magnetic frustration in molecular magnets containing homometallic {Cr9} rings [146]. [PERSON] et al. also used DFT calculations by combining VASP, SIESTA, and TRANSIESTA to describe the spin-filtering capacities of the metallo-organic molecule CoFeCp\({}_{3}\) (with Cp = cyclopentadienyl) for tunneling, as well as in cases of direct contact [147]. [PERSON] et al., however, used ab-initio calculations of Molcas 8.0 combined with SINGLE ANISO and POLY ANISO to understand magnetization blocking in Fe\({}_{2}\)IIIDy\({}_{2}\)III molecular magnets, or, more exactly, [Fe\({}_{2}\)Dy\({}_{2}\)(OH)\({}_{2}\)(teal)\({}_{2}\)(RC\({}_{6}\)H\({}_{4}\)COO)\({}_{6}\)] complexes, with teaH\({}_{3}\) = triethanolamine and R = meta-CN, para-CN, meta-CH\({}_{3}\), para-NO\({}_{2}\), and para-CH\({}_{3}\)[148]. Besides the aforementioned modules, post-Hartree-Fock ab-initio calculations are nowadays often performed with the modules CASSCF (complete active space self-consistent field), RASSI, and SINGLEANISO [149; 150; 151; 152]. Finally, it should be mentioned that several open-source software solutions are available to calculate the magnetic properties of SMMs and SCMs. [PERSON] et al. [153], e.g., report on using NWChem, an open-source solution for large-scale molecular simulations that is well suited for magnetic systems [154]. [PERSON] et al. [155] used Monte Carlo simulationswith the ALPS framework [156] to investigate lanthanide-based SMMs. The same software was used by [PERSON] et al. [157]. Another one is CP2K [158], a software used to calculate molecular dynamics, which was used, e.g., by [PERSON] et al. [159]. [PERSON] et al. most recently introduced 2 Suscep to calculate the magnetism of molecular complexes [160], while [PERSON] et al. reported on the open-source ab-initio electronic structure program PSI4 [161], and [PERSON] et al. presented the open source molecular simulation toolkit GROMACS [162]. As this short overview shows, most problems are theoretically investigated by DFT calculations, while there are several additional theoretical approaches used in combination with DFT or solely to understand the special experimental findings.
## 5 Conclusions and Future Work  
To comply with the development of precision agriculture/viticulture, an approach to detect ripe grape clusters in the vineyard for the harvesting robot was developed in this paper. At the beginning of the approach, the effective color components of H, Cb, b*, R, G and B which could well distinguish the summer black grape from the background were extracted, based on which the corresponding linear classifiers were built. To identify the grape clusters smoothly, the strong and robust classifier was constructed by combining the AdaBoost algorithm and the linear weak classifier. Then, all pixels of images were classified by the strong classifier and the targets of grape clusters were finally extracted by the enclosing rectangle method. Two experiments were implemented by using the images from vineyard scene and the results were quantitatively assessed and compared with other recent classification methods. The main results are as follows:  
1. The strong classifier was able to automatically distinguish the grapes from background, and the accuracy of the classifications can reach up to 96.56%, which was higher than with any weak classifier.
2. The success rate of the proposed detection algorithm was 93.74%, which was superior to other weak classifiers.
3. The interactive performance of the proposed detection algorithm was investigated, and the elapsed time of every image was less than 0.59 s, which can meet the requirements of harvesting robots.  
In conclusion, the developed approach can effectively detect the harvesting targets in the complex vineyard environment. However, the accurate detection of multiple overlapped and adjoining grape clusters is still an issue that needs to be solved, and will require further research. In addition, the developed approach only can be utilized well for the kind of grape which was choosen as the training samples of the proposed classifiers. To solve these tricky problems, in future research we will consider modeling the whole scene by using a depth camera sensor to get depth maps of the scenes from different angles of view, and then combine the color image segmentation and intelligent methods to detect the overlapping or adjoining targets.  
The authors are grateful to executive [PERSON] from Tianjin Chadian Grape Science Park who allowed us to perform experiments in their vineyard. This work was supported by a grant from the National Natural Science Foundation of China (No. 31571568, 51578162), the project of province science and technology of Guangdong (No. 2014A010104011, 2015A020209111, 2014A020208091) and a grant from Tianjin University of Technology and Education (KJ15-04).  
[PERSON] designed the study and wrote the manuscript. [PERSON] performed the experiments and wrote part of the manuscript. [PERSON] supervised the study. [PERSON], [PERSON] and [PERSON] supported the experiments. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Collision Avoidance Algorithm  
Four-wheel differential-drive mobile robots use a simple drive mechanism that provides differential speed difference between the right and left wheels. Wheels on both sides of the mobile robot can be independently driven in either a forward or reverse direction. It is assumed that no slip happens between the mobile robot's wheels and the ground during motion. A schematic illustration of a four-wheel differential-drive mobile robot is shown in Figure 7. The coordinates of points (X1, Y1) define the position of the GPS receiver of the mobile robot in a global frame. In the figure, L is the length of the robot and W is the width.  
In the proposed collision-avoidance algorithm, it is assumed that all mobile robots move from the source point (X1, Y1) to the target point following a straight line. The main idea of the proposed algorithm is illustrated by taking into account the case in which two mobile robots lie in a trajectory that will lead them to collide. The mathematical operations in the proposed algorithm are based on the condition that the mobile robots moving 180 degrees opposite each other would pass tangential to each other (Figure 8).  
The situation shown in Figure 8 is used to calculate the optimum reference distance and angle values to be used in the collision avoidance algorithm. The calculation of optimum reference distance (D\({}_{\text{Ref}}\)) and angle (tan \(\theta_{\text{Ref}}\)) values are performed using the following equations:  
\[\text{D}_{\text{Ref}}=\sqrt{\left(2\text{L}\right)^{2}+\left(2\text{W}\right) ^{2}} \tag{1}\]  
\[\text{tan }\theta_{\text{Ref}}\text{=}\frac{2\text{W}}{2\text{L}} \tag{2}\]  
where L represents the length of the mobile robot, W is the width of the mobile robot, and D is the longest side of the mobile robot. Mobile robots moving in the field instantly monitor the position and heading angle data of the other mobile robots via the developed R2R communication system. Within the R2R communication system, the real distances (D) and heading angles (tan \(\theta\)) of all mobile robots to each other are instantly calculated. The calculation of real distance and heading angle values are performed using the following equations:  
\[\Delta\text{X}=\left(\text{X2}-\text{X1}\right) \tag{3}\]  
\[\Delta\text{Y}=\left(\text{Y2}-\text{Y1}\right) \tag{4}\]  
Figure 7: Schematic illustration of the metrics of a differential-drive mobile robot.  
in its current position. If the tan \(\theta\) is positive, Robot 2 turns 90 degrees to the right at its current position.  
Step 3. The Robot 2 moves forward by the distance equal to W. Afterwards, the Robot 1 continues its movement in its direction of movement.  
Step 4. When the D = D\({}_{\text{Ref}}\), Robot 2 moves back by the distance equal to W.  
Step 5. After Robot 2 moves back by a distance equal to W, it stops.  
Step 6. Finally, Robot 2 turns left or right according to its target direction of movement. And, both robots move towards their target points without collision.
## 4 Result and Discussion
### System Simulation Analysis  
#### 4.3.1 The Impact of Selecting an Initial Change in Strategy on Evolutionary Results  
The evolution strategies of intelligent construction behavior of coal mining enterprises, under no spillover effect, are shown in Figure 6. In order to gain a deeper understanding of the precise influence of the initial corporate strategy on the evolutionary strategy, we selected a series of distinct initial values (\(p=0.001\), \(p=0.01\), \(p=0.1\), \(p=0.2\), \(p=0.3\), \(p=0.4\); \(q=0\)), as illustrated in Figure 6a. From \(t_{p=0.001}=7.9\) and \(t_{p=0.01}=6.8\), it can be observed that the higher the initial stage of the intelligent construction of the coal mining enterprise, the shorter the time required for the system to reach a steady state. Conversely, from \(t_{p=0.2}=4.5\), \(t_{p=0.3}=4.1\) and \(t_{p=0.4}=4.0\), it can be seen that the higher the degree of intelligent construction of the enterprise, the slower the rate of reaching a stable state. This indicates that the higher the starting point of intelligent construction in coal mining enterprises, the slower the rate of subsequent upgrading and reconstruction. Figure 6b shows the evolution of the probability of intelligent construction by coal mining enterprises, under different levels of regulatory measures taken by local governments, when the initial probability of intelligent construction \(p=0.1\). The local government strictly regulates the probability that the initial value \(q\) is 0.1-0.9, and the step size is 0.2. Under the non-spillover penalty system, the intelligent construction process of coal mining enterprises accelerates with the continuous improvement of the regulation probability of local governments. This indicates that the initial state of the enterprise is not a determining factor in the eventual direction of the enterprise towards a fully intelligent construction.  
However, initial probabilities exert a certain influence on the pace of system evolution. This implies that the initial degree of supervision by government departments and the initial stage of enterprise construction may have an impact on the process of achieving comprehensive intelligent construction. In other words, for enterprises at the same level of construction, the stricter the government's initial supervision in a particular district, the faster and more comprehensive the construction process in that district will be. Furthermore, the higher the starting point of intelligent construction for enterprises in this process, the slower subsequent progress will be. For enterprises that are about to complete intelligent construction, the more challenging it will be to resolve the 'last mile' issue.  
Figure 6: Influence of the initial strategy on the evolutionary game. (**a**) Reflects different starting point game trends. (**b**) Reflects the game trend of different supervision intensity.
# Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems
## 2 Book Overview
### Models for Motion Planning and Control  
The method proposed in the previous papers reveal a critical issue that is propaedeutic to perform reliable motion planning and control: the availability of models. Indeed, model-based approaches have often been proved to be the most effective, since the knowledge of the system model is useful to compensate for unwanted behaviors. On the one hand, these models should be accurate. On the other hand, they should be as simple as possible, to allow for simple model manipulation and inversion. This need is exacerbated if real-time calculations are done. These issues are discussed in the book too, with some meaningful examples of models for optimal motion planning and control.  
To handle nonlinearities in flexible multibody systems, a paper [19] proposes a parametric modal analysis approach to obtain an analytical polynomial expression for the eigenpairs (natural frequencies and mode shapes) as a function of the system configuration. The availability of such a result can be very helpful for model-based motion planning and control strategies due to the simple analytical equations it produces. In the theoretical development, the method is applied and validated on a flexible multibody system, modeled through the equivalent rigid link system.  
A general approach for the dynamic modeling and analysis of a passive biped walking robot, with a particular focus on the feet-ground contact interaction, is proposed in [20]. The main purpose of this investigation is to address the supporting foot slippage and viscoelastic dissipative contact forces of the biped walking robot model and to develop its dynamic equations for simple and double supportphases. Due to its accuracy and simple formulation, such a model could be effectively adopted in motion planning and control.