esa-sceva/satcom-chunk-collection huggingface dataset:
## Hybrid Energy Scheduling in a Renewable Micro Grid  
**[PERSON] 1,*, [PERSON] 1,\({}^{\dagger}\) and [PERSON] 2,\({}^{\dagger}\)**  
1 School of Electrical and Electronic Engineering, North China Electric Power University,  
Beijing 102206, China; E-Mail: [EMAIL_ADDRESS]  
2 School of Economics and Management, North China Electric Power University,  
Beijing 102206, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-10-6177-3926.  
Academic Editor: [PERSON]  
_Received: 14 July 2015 / Accepted: 28 August 2015 / Published: 8 September 2015_  
**Abstract:** In this paper, we address the energy scheduling issue in a hybrid energy micro grid, which consists of photovoltaic (PV), wind power, combined heat and power (CHP), energy storage and electric vehicles (EVs). The optimal scheduling model of these power sources is presented with consideration of the demand response. The objective function is minimum total operation costs, including gas cost, electric power purchase from the main grid and storage and EV charging-discharging costs. In the process of optimization, multi-team particle swarm optimization (MTPSO) is proposed, which uses units, groups and swarm information to update the velocity (position) with faster and more stable convergence. With simulation analysis, it is found that the proposed model is effective, and the presented MTPSO has a better global search ability than PSO.  
**Keywords:** micro grid; scheduling; hybrid energy
## 4 Development of Machine Learning Models
### Regression-Based Machine Learning Techniques
#### 4.1.7 Gaussian Process Regression (GPR)  
GPR is a nonparametric kernel-based probabilistic model [56]. The model explains the response by introducing latent variables from the Gaussian process (GP) and explicit basis function \(h\), projecting the inputs into a p-dimensional feature space. In the present study, the rational quadratic kernel function was used, along with the optimized hyperparameters, by minimizing the five-fold cross-validation loss. In the optimized model, the constant basis function and initial value \(\sigma\) of 0.234 for the noise standard deviation were used.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 6 Evaluation
### Simulation Setup  
The simulator was run on a VirtualBox 6.1.22 [41] virtual machine with Debian 10.7.0 64-bit as the guest system. It was given 20 GB of RAM, 8 CPUs, and KVM as the paravirtualization interface. The host operating system was Windows 10 Home, running on an Intel Core i9-10900K at 3.70 GHz with 32 GB of RAM. The CPU has 20 total threads and 10 total cores.  
The distinction of the parameters into the categories _constant_, _fixed_ and _variable_ has already been made in Section 5.5. In our evaluation, we use the parameter values as shown in Listing 2, where size 16 is demonstrated as an example. As fixed parameters, we take a look at network aspects that can typically not be changed, like the graph size and topology. The variable parameters are the core of the analysis: It is unclear what values for each one are the best. Particularly, caching schemes and strategies are of interest here. Furthermore, we also take a look at the already implemented forwarding strategies as well as the data packet size.
```
1 constant
2 INITIAL_ENERGY_J=10000000
3T_CURRENT_MA=380
4 RX_CURRENT_MA=313
5 IDLE_CURRENT_MA=273
6 SLEEP_CURRENT_MA=33
7 CONSUMER_PRODUCE_OVERLAP=FALSE
8 SIMULATION_LENGTH_S=100
9 NUMBER_PACKETS_PER_CONSUMER=100
10 CONTENT_STORE_SIZE=16
11
12 fixed
13 GRAPH_SIZE={16}
14 GRAPH_TOPOLOGY={ RANDM_P, COMPLETE, STAR, CONNECTED_STARS, GRID}
15 TOPOLOLOGY_PROBABILITY={10,25,40}
16 SUBGRAPH_SIZE={4}
17 CONSUMER_SEQUENCE_TYPE={ INCREMENTAL, REPETITIVE, SIMPLE_RANDOM, LINEAR_RANDOM}
18
19 variable
20 CACHING_SCHEME={ NO_CACHING,CEE, PROBABILISTIC}
21 CACHING_PROBABILITY={1,5,10,50}
22 CACHE_REPLACEMENT_STATEGY={ RANDMW, FWF, LRU, LFU, LRFU, PRIORITY_FIFO}
23 FORWARDING_STATEGY={BEST_ROUTE, RANDOM, SELF_LEARNING, ASF, ACCESS}
24 DATA_PACKET_SIZE_BYTES={128,1024,8192}
```  
**Listing 2**. This is a meta config file for the evaluation for graph size 16. The files for the other graph sizes are identical or almost identical. The number of consumers and producers is missing because these values are dependent on the graph size and computed at runtime.  
The meta config files for sizes 32 and 64 are identical to the file for size 16 (except for the graph size). The only difference to the files for sizes 4 and 8 is that size 4 uses topology probabilities 25% and 50%, and size 8 uses probabilities 5%, 10%, and 25%. This distinction is necessary to ensure that small graphs are connected with a reasonable probability. Eight simulation runs have been done for each variable and fixed parameter combination per graph size.  
The initial energy for each node was intentionally set so high that the nodes are never subject to depletion during the simulation. The Wi-Fi parameters (ll. 4-7) werenot changed, and their ns-3 default values were assumed for the sake of this work. For simplicity, the simulation length, number of packets per consumer, and Content Store size are also supposed to be constant. This avoids extra complexity due to even more parameters. Furthermore, the consumers and producers do not overlap.
## 6 Conclusions  
With the torsional vibration of the flexible-joint transmission links considered, an adaptive fuzzy PID controller is designed based on the dynamic modeling and parameter identification of the flexible-joint robot system. The simulation and experimental results show that:  
1. The established electromechanical-coupling dynamics model of the flexible-joint robot can effectively characterize the system dynamic characteristics after the unknown parameters of the model are determined by the parameter identification. The model output speeds of joint I and joint II can effectively track the outputs of the experimental platform. The identification result of the rotational speed of joint I is that the identification error quickly attenuated to 0. Moreover, the main frequency components of the rotational speed of joint II are identified which is of great significance to the torsion control of the flexible-joint robot system.
2. Based on the analysis of the dynamic characteristics of the flexible-joint robot, the control rules of the designed fuzzy PID controller are determined. With the speed difference between joint II and joint I feedbacked, the designed fuzzy PID controller can effectively suppress the joint torsional vibration of the flexible-joint robot with about 0.5 s to the target speed, and achieve the synchronous optimization of the dynamic quality and the stability accuracy in the control effect of the system torsional vibration.
3. Meanwhile, the designed fuzzy PID controller has good anti-interference. Whether it's step noise or white Gaussian noise, the fuzzy PID controller needs only 0.6 s to adjust, while the conventional PID controller needs about 1.8 s to eliminate the step disturbance and about 1.5 s to eliminate the white Gaussian noise. The research results in this paper can provide references for the torsional vibration control of the industrial joint robots.  
Figure 10: Experimental test effect of the designed fuzzy PID controller on the torsional vibration control of the flexible-joint robot system.  
**Author Contributions:** [PERSON] conceived the experiments and wrote the paper; [PERSON] and [PERSON] provided suggestions about the algorithm; [PERSON] contributed the analysis tools.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Granted 51805001, in part by the Anhui Provincial Natural Science Foundation under Granted 1808085 QE137 and in part by the Research Starting Fund Project for Introduced Talents of Anhui Polytechnic University under Granted 2018 YQQ005.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
###### Abstract  
A 1:25 scale physical model test was employed to study the plane gate vibration and holding force under the conditions of the fixed gate opening and closing process, respectively. We paid more attention to the characteristics of the gate vibration, holding force and the failure of gate-closing in closing process. The correlation between gate vibration and holding force was further examined. The results show that vertical vibration is weaker than the lateral and horizontal vibrations in fixed gate opening and is stronger than the lateral and vertical vibrations in closing process. Gate vertical vibration is self-excited vibration with a frequency of 7-14 Hz. Besides, crawl vibration in closing process is related to the upstream water depth. The higher the water level is, the earlier the crawl vibration appears. After the crawl stage, plane gate stops motion at a certain distance from the chamber floor and then the failure of gate-closing happens. Finally, gate vibration in three directions is significantly correlated with the holding force. In closing process, holding force has positive correlation with the vertical vibration and has negative correlation with the lateral and horizontal vibrations. In the crawl stage, the average of correlation coefficient in lateral, vertical and horizontal direction is \(-0.723\), \(0.733\) and \(-0.664\), respectively. Thus, the influence of gate vibration on holding force should be taken into consideration in determining the hoists capacity.  
plane gate; physical model test; flow-induced vibration; crawl vibration; holding force; correlation +
Footnote †: journal: Applied Sciences
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
#### 3.1.2 Flavonoids
Figure 3: Molecular family of flavonoids: monohydroxylavorones (Cluster B: positive ion), dihydoxyflavones (Cluster C: positive ion, and Cluster O: negative ion), _C_-glycosflavone derivatives (Cluster D: positive ion, and Cluster P: negative ion), flavonoid-3-_O_-glycosides (Cluster E: positive ion, and Cluster Q: negative ion), and flavone-_C_,_O_-diglycoside (”self-loop” node: negative ion), extracted from the full molecular networking (MN) of _Christia respertilionis_ leaf. Red dotted circles represent proposed new structures. * Compound previously reported in _Christia respertilionis_. Identical MS/MS features with that of **20** in negative ion mode, i.e., at \(m/z\) 253 for [(M\(-\)H)-crotonyl]\({}^{-}\), 175 for [(M\(-\)H)-crotonyl-C\({}_{6}\)H\({}_{5}\)-H]]\({}^{-}\), and 151 for [(M\(-\)H)-crotonyl-(1,3B cleavage)]\({}^{-}\), were observed in the MS/MS spectrum of another compound that was represented by the node with the precursor ion [M \(-\) H]\({}^{-}\) at \(m/z\) 321.095. This compound had a mass difference of 68 Da (C\({}_{4}\)H\({}_{4}\)O) from that of **20** and was proposed to be a crotonylated derivative of **20**. Furthermore, a protonated molecular ion, [M \(+\) H]\({}^{+}\), at \(m/z\) 341.123, having a mass difference of 86 Da (C\({}_{3}\)H\({}_{2}\)O\({}_{3}\)) from that of **20**, could be suggested as a malonylated derivative of **20**. This compound exhibited similar MS/MS data as **20**, i.e., at \(m/z\) 237 for [(M+H)-malonyl-H\({}_{2}\)O]\({}^{+}\), 177 for [(M+H)-malonyl-C\({}_{6}\)H\({}_{5}\)-H]\({}^{+}\), and 153 for [(M+H)-malonyl-(1,3B cleavage)]\({}^{+}\). The existence of a fragment ion at \(m/z\) 237 for [(M+H)-malonyl-H\({}_{2}\)O]\({}^{+}\) indicated that the most feasible site for the attachment of the malonyl group is to one of the hydroxyl groups on ring-A. On the basis of these results and in accordance with the fact that the attachment of a substituent on ring-A is generally favored at the 7-position due to the involvement of the 5-OH group in intramolecular hydrogen bond formation with the 4-carbonyl group [21], both compounds were characterized as 7-O-crotonylchrysin (**21**) and 7-O-malonylchrysin (**25**). The proposed fragmentation pathways of **21** and **25** can be found in Figure S8 and Figure S9, respectively.
###### Abstract  
This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) by exploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search (CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposed PSO-CS algorithm combines the ability of social thinking in PSO with the local search capability in CS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotor dynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral, and Derivative) controllers are optimized by using the intelligent proposed approaches and the classical method of Reference Model (RM) for quadrotor full control. Finally, simulation results prove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotor control. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from 3D path tracking simulations and even in presence of wind disturbances.  
control; quadrotor; nonlinear; PSO; particle swarm optimization; cuckoo search Article  
Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking  
[PERSON] _et al._ (2019), 9, 1719; doi:10.3390/app9081719
## 1 Introduction
Following this, the corresponding dq- and sequence-domain response data are derived through coordinate transformation. Thereafter, the impedance is calculated using the appropriate MIMO impedance computation methods [24]. Nonetheless, in the context of actual on-site systems, the operational state is subject to continuous fluctuations, precluding the assurance of consistent operating conditions under the two injected harmonic disturbances. Additionally, MIMO impedance measurement necessitates a greater number of data acquisition sensors and involves a more intricate measurement process [23]. Considering these challenges, the single-input and single-output (SISO) impedance measurement approach, which exhibits marginally reduced measurement precision, has garnered increased attention in recent times [25; 26]. This method simplifies the process by requiring only a single injection of the harmonic disturbance at the target frequency. Furthermore, it obviates the need for extensive data collection by requiring the acquisition of response data from just one phase, thereby circumventing the requirement for additional coordinate transformation. Consequently, the SISO method offers a more streamlined and efficacious approach for practical application. This study delves into the oscillation mechanism of the grid-connected FSC-VSPSU utilizing the SISO impedance measurement technique, and it investigates the pertinent strategies for oscillation mitigation. The principal innovations and contributions of this work are summarized as follows. 1. This is the first instance where SISO impedance matching analysis is employed to examine the oscillation mechanism of the grid-connected FSC-VSPSU, yielding insights into the parameter influence rules;2. A novel oscillation suppression strategy based on grid network impedance compensation is introduced for the first time, characterized by its simplicity in principle and its ease of implementation. The organization of the article is as follows. Section 1 provides the introduction. Sections 2-4 delineate the proposed methods. In detail, Section 2 presents the SISO impedance measurement and oscillation analysis methodologies for the FSC-VSPSU. Section 3 delves into the exploration of the oscillation mechanism. Section 4 outlines the proposed oscillation suppression technique. Section 5 presents the simulation results, and, finally, Sections 6 and 7 concludes the article.
## 1 Introduction
### State of the Art
#### 1.1.1 Piezoelectric Models, Approximations and Finite Elements
The work of Reference [6], mentioned in the context of the monolithic piezoelectrics, should be mentioned here as applied to tree-layered shell structures with two outer piezoelectric layers and an electrically neutral core. A similar sandwich plate structures are modeled as two Reissner-Mindlin piezoelectric layers and one neutral layer of the same character [19], with the mid-surface interdependent displacement dofs. The electric fields are of mixed character and are defined by the outer layers' mid-surface electric potentials and independent electric field mid-surface values of the outer layers. In order to remove locking, the reduced integration method is applied. The generalized analytical Reissner-Mindlin model of laminated piezoelectrics was presented in Reference [21]. It allows analyses of the problems where the electric potential is not prescribed through the thickness. The method assumes small thickness and thus allows decomposition of the three-dimensional Reissner-Mindlin description into two-dimensional in-plane and one-dimensional through-the-thickness problems. Subsequently, the already cited work of Reference [10] concerning hierarchical models of single layered piezoelectrics can also be applied to the numerical analysis of hierarchically modeled multilayered structures, with piezoelectric layers included. The recent example [22] of analysis of multilayered shells concerns the first-order model of the functionally graded layers. In turn, the piece-wise second-order finite element model for multilayered structures with delamination and debonding was proposed in Reference [23].
## 3 Results and Discussion
### Effect of the Shock Wave Structure on the Pumping Performance
As displayed in Figure 7, when the primary fluid pressure is between 0.32 MPa and 0.36 MPa, the Mach number values at the subsonic diffusion section is between 1.6 and 2.1. The peak Mach number value is 2.1 at 0.34 MPa. It indicates that when the primary fluid pressure is 0.34 MPa, the normal shock wave with more energy makes the mixing of two streams more completely and the mixed fluid with a higher pressure can effectively prevent the disturbance generated by the back. Simultaneously, the energy loss of the shock wave at the entrance of the subsonic diffuser section is the smallest when the primary pressure is at 0.34 MPa. As the primary fluid pressure is greater than 0.34 MPa, the front area of the normal shock wave becomes wider (in Figure 7), and the intensity of the normal shock wave becomes weak (in Figure 6). The velocity gradient generated at the wall surface will lead to the boundary layer separation. The partial energy and momentum of the mixed fluid will be lost, and the entrainment ratio will be reduced. When the primary fluid pressure is less than 0.34 MPa, the mixing process of the two streams is not enough resulting in a smaller entrainment ratio because of the normal shock wave within less energy. Figure 7: The curve of Mach number along the axis under different primary fluid pressure.
## 4 Results and Discussion
### Ablation Experiments  
Considering that DHGAR has two important components, the dynamic graph and heterogeneous association function, the ablation experiments are conducted from the following perspectives:  
1. W/O DG: we removed the dynamic graph structure and replaced it with a static graph structure based on Pearson coefficients.
2. W/O HA: We removed the heterogeneous association function. In this case, the model only uses dynamic graphs to model variable correlations.
3. W/O GA: We removed all the graph structures. In this case, the model uses only GRU to achieve wind power forecasting.  
Table 4 shows the results of the ablation experiment. Based on the experimental results, the following conclusions can be drawn:  
1. The dynamic graph has a great influence on the prediction results of DHGAR. If only a static graph is used to establish the correlation between wind power and other variables, the model ignores the difference in the correlation between different variables at different time points, which limits the prediction effect.
2. The heterogeneous association function also has a great influence on the prediction results of DHGAR. Specifically, if the heterogeneous association function is deleted, the model can only achieve multi-variate information aggregation simply using weighted fusion. In this case, DHGAR cannot fully capture deeper correlations between heterogeneous variables, which limits its effectiveness.
3. When the graph structure is removed, the model is equivalent to directly modeling the original data using the GRU. In this case, the prediction accuracy of the model  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Training Time (s)** \\ \hline
**DHGAR** & **157.24** \\ DGCRN & 263.14 \\ AGCRN & 134.25 \\ iTransformer & 100.34 \\ Crosformer & 189.46 \\ \hline \end{tabular}
\end{table}
Table 3: The training time of the proposed model and several baselines.  
Figure 7: Experimental results of different models for dataset #3.  
decreases significantly due to the inability to accurately establish the correlation between the different variables.
## 2 Background
### Feature Extraction
#### 2.4.2 TF-IDF  
The term TF-IDF refers to Term Frequency-Inverse Document Frequency. It measures the term importance in a document in a corpus by considering its frequency [30]. If the term appears frequently, that means it does not have special information about a particular document--for example, \"and\", \"a\", \"the\", and \"of\". For a given corpus \(D\), which contains the number of documents \(d\), the TF-IDF numeric value for a term \(t\) is calculated as shown in Equation (2). \(TF(t,d)\) is the term \(t\) frequency in a document \(d\). \(DF(t,D)\) is the number of documents in the corpus \(D\) that contain the term \(t\). The term \(IDF\;(t,D)\) in Equation (2) is calculated using Equation (3).  
\[TFIDF(t,d,D)=\;TF(t,d)*IDF\;(t,D) \tag{2}\]  
\[IDF\;(t,D)=\log\frac{|D|+1}{DF(t,D)+1} \tag{3}\]
## 5 Analysis of Other Non-AWGN Nuisances  
The performance of the algorithms presented so far depends not only on the additive noise but also on other nuisances, whose impact is analyzed in this section. In particular we observe that the methods R\({}_{n}\)-3 and C\({}_{n}\)-3 are based on the two measured vectors \(\mathbf{f}=[\overline{f}_{C},\overline{f}_{A},\overline{f}_{B}]\) and \(\mathbf{\bar{S}}=[S_{C},S_{A},S_{B}]\) used in Equations (27) and (31), obtained by reading the peak cell and the adjacent cells in the same column (marked as C, A and B in Figure 4). In particular, the code delay \(\overline{\tau}_{p}\) is kept constant in these three cells.  
Figure 11: The distribution of the Doppler frequency estimates.  
Figure 10: Averaging method.  
Since the search space is discretized, in general even in the absence of noise \(\overline{f}_{p}\
eq f_{d}\), and \(\overline{\tau}_{p}\
eq\tau\). This can be seen as a quantization error \(\Delta f_{q}=\overline{f}_{p}-f_{d}\) in the frequency domain, and \(\Delta\tau_{q}=\overline{\tau}_{p}-\tau\) in the time domain. We know that Equations (27) and (31) completely eliminate the quantization errors in the absence of noise. However, in the presence of noise, we experience an accuracy degradation due to the noise influence on vector \(\mathbf{\bar{S}}\), as shown in the simulation results of the previous section. The purpose of the following analysis is to state the influence of the frequency and code delay quantization errors on the proposed frequency estimators, in the presence of noise.