esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Considering the research on the cooperative guidance algorithm, there are more studies on the guidance law with impact angle or time constraints [19; 20; 21; 22; 23; 24; 25; 26], and relatively fewer studies on combining the two as terminal constraints [27; 28; 29; 30; 31]. Impact time constraints are the basis of the cooperative guidance algorithm and are indispensable. Impact angle constraints can make the missile hit the target with the best attitude in order to maximize the effectiveness of the warhead to achieve a maximum damage. Hence, considering both impact time and impact angle constraints in the cooperative terminal guidance algorithm is of great significance for improving the effective damage and combat capability of the cruise missile weapon system. Model prediction control (MPC), also known as receding horizon control (RHC), has an online rolling optimization at the core, which is essentially an optimal control problem in the finite time domain. At each optimal control moment, the current optimal control domain can be obtained, and the first part of the domain is used as the optimal control to act on the system. The optimal control sequence is obtained after multiple sampling. MPC is a model-based algorithm that has been widely used in industry, metallurgy, etc. [32]. In recent years, MPC has been continuously improved, and a few improved methods have been for proposed for MPC [33; 34; 35; 36], which achieved better application prospects and obtained better prediction results. In the present study, we proposed a three-dimensional cooperative terminal guidance law that met the constraints of impact angle and impact time simultaneously, on the premise of meeting the requirements of miss distance. First, the nonlinear motion model of the missile and the target was established, the state quantity and the control quantity were normalized, the concept of leader-follower missiles was introduced, and the impact time cooperative strategy of leader and follower missiles was designed. Subsequently, based on the model predictive control method, a three-dimensional cooperative terminal guidance law was proposed that simultaneously controlled multiple missiles to perform attacks on the target with the constraints of impact time and impact angle on the premise of meeting the requirements of miss distance. Finally, by introducing one leader missile and four follower missiles to simulate the multi-missile cooperative attack against stationary targets, linear moving targets, and snake-shaped maneuvering targets, the effectiveness of the multi-constraint cooperative guidance law algorithm was verified.
## 2 Formulation of the Problem
The use of (7) and (8), \(\Phi(x,\alpha)\) can be represented as:  
\[\Phi(x,\alpha)=\Phi_{1}(x,a)+\Psi_{(+)}(x,\alpha),\ -\infty<x<\infty, \tag{9}\]  
where  
\[\Psi_{(+)}(x,\alpha)=\Phi_{+}(x,a)-A\,\frac{e^{-ikx\sin\theta_{0}}}{\alpha-k \cos\theta_{0}},\ A=\frac{1}{(2\pi)^{1/2}i}, \tag{10}\]  
\[\Phi_{1}(x,\alpha)=\sum_{m=1}^{4}\Phi_{m}^{\prime}(x,\alpha)+\Phi_{1}^{0}(x, \alpha)+\Phi_{-}(x,\alpha). \tag{11}\]  
As seen from (10), \(\Psi_{(+)}(x,\alpha)\) is regular in the upper half-plane \(\tau>-k_{2}\) except for a simple pole \(\alpha=k\cos\theta_{0}\). We shall henceforth use the subscript '(+)' for functions with this property. We shall now derive the transformed wave equations by using the boundary conditions and the radiation condition. According to (1) and (3), the scattered field \(\phi(x,z)\) in the vacuum region satisfies  
\[(\partial^{2}/\partial x^{2}+\partial^{2}/\partial z^{2}+k^{2})\phi(x,z)=0. \tag{12}\]  
Furthermore, the total field \(\phi^{t}(x,z)\) satisfies  
\[[\partial^{2}/\partial x^{2}+\partial^{2}/\partial z^{2}+k_{rm}^{2}]\phi^{t}( x,z)=0 \tag{13}\]  
for \(m=1\), 2, 3, 4, and 5 for regions I, II, III, IV, and V, respectively, where \(k_{rm}=(\mu_{rm}\varepsilon_{rm})^{1/2}k\). By taking the Fourier transform of (12) and applying (6) to the region \(|x|\)\(>b\), it can be verified that  
\[(d^{2}/dx^{2}-\gamma^{2})\Phi(x,\alpha)=0 \tag{14}\]  
holds in the strip \(-k_{2}<\tau<k_{2}\cos\theta_{0}\), where  
\[\gamma=(\alpha^{2}-k^{2})^{1/2},{\rm Re}\gamma>0. \tag{15}\]  
The transformed wave equation for \(|x|\)\(>b\) is given by (14). The derivation of the conversion wave equation for the region \(|x|\)\(<b\) is complicated by the presence of multiple dielectric discontinuities on the \(z=-L_{m}\) surfaces at \(m=1\), 2, 3, 4, and 5. We multiply both sides of (12) by \((2\pi)^{-1/2}e^{i\alpha z}\) and integrate with respect to \(z\) over the range \(-L_{5}<z<\infty\).Then by considering (6) and the boundary condition for tangential electromagnetic fields at \(z=-L_{5}\), we derive that  
\[(d^{2}/dx^{2}-\gamma^{2})\Big{[}\Phi_{1}^{0}(x,\alpha)+\Psi_{(+)}(x,\alpha) \Big{]}=e^{-i\alpha L_{5}}\Big{[}\varepsilon_{5}^{-1}f_{5}(x)-i\alpha g_{5}( x)\Big{]} \tag{16}\]for \(\tau>-k_{2}\) with \(\alpha\
eq k\cos\theta_{0}\), where  
\[f_{5}(x)=(2\pi)^{-1/2}\frac{\partial\phi^{t}(x,-L_{5}-0)}{\partial z}, \tag{17}\]  
\[g_{5}(x)=(2\pi)^{-1/2}\phi^{t}(x,-L_{5}-0). \tag{18}\]  
Next, we multiply both sides of (13) by \((2\pi)^{-1/2}e^{i\alpha z}\) and integrate with respect to \(z\) over the ranges \(-\infty<z<-L_{1},-L_{1}<z<-L_{2},\ -L_{2}<z<-L_{3},\ -L_{3}<z<-L_{4}\), and \(-L_{4}<z<-L_{5}\).
_Foods_**2014**, \(3\), 128-148; doi:10.3390/foods3010128  
_foods_  
**ISSN 2304-8158**  
www.mdpi.com/journal/foods  
Characterization of Botanical and Geographical Origin of Corsican \"Spring\" Honeys by Melisospalynological and Volatile Analysis  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Laboratory of Natural Product Chemistry, UMR CNRS 6134, Grimaldi Campus, Corsican University, BP 52, Corte 20250, France; E-Mails: [PERSON]IL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (M.-J.B.); [EMAIL_ADDRESS] (J.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-495-450-187; Fax: +33-495-450-257.  
_Received: 30 September 2013; in revised form: 17 December 2013 / Accepted: 13 January 2014 / Published: 27 January 2014_  
**Abstract:** Pollen spectrum, physicochemical parameters and volatile fraction of Corsican \"spring\" honeys were investigated with the aim of developing a multidisciplinary method for the qualification of honeys in which nectar resources are under-represented in the pollen spectrum. Forty-one Corsican \"spring\" honeys were certified by melisospalynological analysis using directory and biogeographical origin of 50 representative taxa. Two groups of honeys were distinguished according to the botanical origin of samples: \"clementine\" honeys characterized by the association of cultivated species from oriental plain and other \"spring\" honeys dominated by wild herbaceous taxa from the ruderal and/or maquis area. The main compounds of the \"spring\" honey volatile fraction were phenylacetaldehyde, benzaldehyde and methyl-benzene. The volatile composition of \"clementine\" honeys was also characterized by three lilac aldehyde isomers. Statistical analysis of melisospalynological, physicochemical and volatile data showed that the presence of _Citrus_ pollen in \"clementine\" honeys was positively correlated with the amount of linalool derivatives and methyl anthranite. Otherwise, the other \"spring\" honeys were characterized by complex nectariferous species associations and the content of phenylacetaldehyde and methyl syringae.  
**Keywords:** honey; elementine and asphodel; melisospalynological analysis; HS-SPME; GC
# Evaluation of Two Self-Fitting User Interfaces for Bimodal CI-Recipients
## 4 Results
### Blind Preference Test  
Within the blind comparison test, Figure 10 shows how often one setting was preferred over another. All 18 participants performed a total of 15 pairwise comparisons, resulting in a total of 270. In 55 cases, the expert fitting was chosen, in 100 cases the fitting created with the EQ UI, and in 113 cases the fitting created with the 2D Surface UI was preferred. From these choices, the preferred fitting shown in Figure 11 from each participant in each of the five acoustic scenarios was determined. Overall, the participants preferred the clinical fitting 11 times, while the self-adjusted equalizer UI fitting was selected 27 times. The program based on the 2D-Surface UI was selected 33 times. In 19 cases, the participant did not respond consistently within the conditions, also called a cyclic triad, and thus there was no clear result. To assess whether the observed number of cyclic triads were statistically significant, we compared them with a hypothetical number of cyclic triads. This number was determined using the backward cumulative binomial probability function \(F_{re}(k,n,p)\), where \(k\) is the number of cyclic triads, \(n\) is the number of repetitions, and \(p\) is the probability of occurrence. The critical upper bound of possible cyclic triads \(k_{critical}^{upper}\) is defined as  
\[\min_{k}F_{re}(k,n,p)\leq p_{sig} \tag{4}\]  
where \(p_{sig}=0.05\) and \(p=0.25\). For all decisions (\(n=90\)), this resulted in a critical value of \(k_{critical}^{upper}=29\), which was not reached by the data. For each condition, there was a critical number of \(k_{critical}^{upper}=8\), which was also not reached in any sound environment. The maximum allowed number of cyclic triads per participant was \(k_{critical}^{upper}=3\). This number was reached by participants ID12 and ID17, indicating unreliable feedback from these two participants. The data from these two participants was therefore excluded from further analyses. A check of the remaining data for concordance according to [PERSON] [24; 25] resulted in a coefficient of agreement of \(W=0.089\). The range of possible values for \(W\) is from 0 (maximum disagreement, discordance) to 1 (maximum agreement, concordance). This result was significant at the level of 0.01. The null hypothesis of no correlation in decisions could therefore be rejected. In order to better classify the result, scaling was carried out with the Bradley-Terry-Luce model according to [PERSON] and [PERSON] [26]. This resulted in a choice probability of \(p=0.18\) for the clinical setting, \(p=0.37\) for the setting created with the EQ UI, and \(p=0.44\) for the 2D-Surface UI setting. Thus, there was a tendency towards the 2D Surface UI setting. An even clearer result was that, in about 80% of cases, the settings from self-fitting were preferred over the clinical settings.  
Figure 10: **Blind preference decisions:** The table shows how often one setting was preferred over another in the blind comparison test.
## Low-Cost Spin Pit Design
### Rotor Dynamics and Design Considerations
#### 4.1.3 Design Iteration  
In order to overcome the issues described in Section 4.1.2 the design was improved regarding the following aspects:  
1. Solving the rotor dynamics issue Instead of sub-critical operation the concept was changed to super-critical operation. This was achieved by drastically reducing the overall system stiffness by using a thin, flexible quill shaft connecting the intermediate shaft with the burst rotor (indicated in yellow in Figure 8). As a result, the first eigenfrequency shifted to very low speeds (few hundred rpm depending on rotor mass) and low oscillation energy levels, eliminating problems during resonance run-through. At super-critical operation the rotors self-centering characteristics enabled stable operation while accelerating the rotor to burst speed. Since high system stiffness was not required anymore, the intermediate shaft could be down-sized. Another positive aspect was the mechanical decoupling of rotor and intermediate shaft loads. As a result, smaller and much cheaper angular contact ball bearings could be used instead of hybrid spindle bearings to reach rotational speeds sufficiently high for rotor burst (In this case, rotor burst speeds of 20,000 to 40,000 rpm are desired.). Furthermore, the total moment of inertia of all rotating test rig parts decreased, allowing the system to reach burst speed faster. Using a disc-shaped instead of bell-shaped magnetic coupling reduced cantilevered rotor mass, desensitizing the drive unit against deflections caused by rotor dynamics and increasing system robustness.
2. Solving the rotor design issue The burst rotor (i.e., test flywheel) was redesigned to solve the issues, which arose during first commissioning. The new design, which is described in more depth in Section 4.3, is a single mill-turned part with notches for pre-defined fragment geometry. The new test flywheel not only allows more accurate adjustment of the burst speed, but also solved the issues related to the interference fit by introducing a clamp set with small router diameter (14 mm), hence eliminating imbalance forces caused by play between shaft and rotor.
3. Additional considerations Because of the new, flexible quill shaft with little damping, higher deflections were expected during resonance run-through depending on rotor balancing quality. To limit rotor oscillation amplitude, a trunion was added at the bottom of the burst rotor, which runs in a safety gliding bearing. The flexible shaft was attached to the intermediate shaft using a standard collet (concentricity: 2 \(\upmu\)m) to ensure assembly accuracy and pursue consequent low-cost design.  
Figure 7: Initial burst rotor design and relevant properties.  
A detachable coupling (clamp set \"CLAMPEX KTR131\" [42]) was used to connect the burst rotor to the flexible shaft.
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties  
[PERSON]  
1 Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China; hrafanwe163.com  
2 College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[EMAIL_ADDRESS]  
[PERSON]  
3 College of Electrical Engineering, Shenyang University of Technology, Shenyang 110178, China;  
[EMAIL_ADDRESS]  
[PERSON]  
2 College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
3 College of Electrical Engineering, Shenyang University of Technology, Shenyang 110178, China;  
[EMAIL_ADDRESS]  
[PERSON]  
1 Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China; hrafanwe163.com  
across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.  
wind turbine; stability; active and reactive powers DFIG; MPC; Kalman filter; Lyapunov function
## 3 Results
### Experimental Results of the Dual Thruster  
In this section the experimental results of the study of the dual thruster are presented. On this occasion, the whole family of previous graphs will not be repeated since they do not present significant changes. Only the most relevant one and a table with all the results obtained in numerical form will be included.  
Figure 18 shows some slight differences with Figures 12 and 15. It can be observed that when the second asymmetric capacitor is installed, the thrust curves for different distances are much more convergent than in previous cases. The maximum thrust is now of 6.4 g, twice that obtained with the previous configuration and, although between 40 mm and 60 mm distance the thrust variation is very small, the range of distances seems to have been extended. Although this statement is speculative, it seems plausible that this behavior is the result of the higher linearity of the electric field pointed out in Section 3.2.  
On the other hand, it is important to note that, although the maximum thrust may appear in Figure 19 at 60 mm, this does not imply that the maximum energy efficiency in g/W occurs at that point. Table 2 gathers the full set of values. The same convergence of Figure 19 can be found, but the point of maximum performance, as already predicted by the initial tests, is at 40 mm.  
To present the results in a more intuitive yet accurate fashion, Figure 20 has been included. In this surface plot, the efficiency in g/W has been plotted as a function of the applied potential difference and all emitter-collector distances used in the experimental tests.  
Figure 18: Comparison of the experimental results from Figure 13 for a 30 mm distance, the theoretical expression (1) and the linear regression model.
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 2 Related Work
### Routing and Spectrum Algorithms  
To improve spectrum utilization and reduce the bandwidth blocking probability in elastic optical networks (EONs), various routing and spectrum approaches based on reinforcement learning have been widely explored in recent years. Authors in [25] put forward a fragmentation-aware routing and spectrum-assigned algorithm based on the framework of deep reinforcement learning. It selects the routing path for service requests according to the path weight in terms of path length, normalized fragmentation rate, and available spectrum slicer. The algorithm performance is evaluated on the three network topologies NSFNET, USNET, and India Network. To reduce the spectrum fragmentation level and improve spectrum resource efficiency, the authors in [26] designed a heuristic reward in the frame of reinforcement learning, which outperforms simple reward deep reinforcement learning and the heuristic rule-based approaches. The effectiveness was verified in the 14-node NSFNET topology.  
Neural Networks or Convolutional Neural Networks could not fully extract optical network topology information and could not well exploit the path-level feature extraction as well. The authors in [27] introduced deep reinforcement learning to deal with the problem of routing and spectrum assignment for the EON. Authors utilized the Graph Convolutional Neural Network and the Recurrent Neural Network to realize feature extraction. This approach is conducted on the network of NSFnet, Cost239, and USNET.
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 6 Experiment
### Ablation Study  
We verified the effectiveness of the proposed object proposal voting in performing visual tracking. For this experiment, we adjust the parameter \(c\) in (4); a small value of \(c\) causes the voting process to make a smaller contribution to the object proposal results. Table 2 shows the AUC of the proposed OPV using the OTB dataset, which verifies that the proposed voting process considerably enhances the object proposal accuracy,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input:**\(\mathcal{O}_{v}^{t-1}\) & \\
**Output:**\(\mathcal{O}_{v}^{t}\) and \((\hat{O}_{i},\hat{O}_{j})\) \\
1: Obtain \(\mathcal{O}_{v}^{t}\) using (6) at a current frame \(t\). \\
2: **for**\(i=1\) to \(|\mathcal{O}_{v}^{t-1}|\) **do** \\
3: Sample \(O_{i}\) from \(\mathcal{O}_{v}^{t-1}\). \\
4: **for**\(j=1\) to \(|\mathcal{O}_{v}^{t}|\) **do** \\
5: Sample \(O_{j}\) from \(\mathcal{O}_{v}^{t}\). \\
6: Compute \(d(O_{i},O_{j})\) using (8). \\
7: **end for** \\
8: **end for** \\
9: Find \((\hat{O}_{i},\hat{O}_{j})\) using (9). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description on notations.  
and thus improves the visual tracking. We also examined the contributions of the position, appearance, and vote weight terms in (8) to visual tracking using the OTB dataset. For this experiment, we used different combinations of position, appearance, and vote weight terms. As shown in Table 3, using the voting weight to calculate the distance enhances the visual tracking performance.
# A High-Efficiency K-band MMIC Linear Amplifier Using Diode Compensation
## 2 Nonlinear Analysis and Diode Compensation
The C-V characteristics of the selected \(2\times 20\)\(\upmu\)m diode and the input capacitance of the network versus RF power are shown in \[y_{12}=-j\omega\mathcal{C}_{gd} \tag{15}\]3  
The parasitic parameter value of the selected pHEMT and diode is shown in Tables 2 and 3, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\text{r}_{\text{g}}\) (\(\Omega\)) & 1.5 & L\({}_{\text{d}}\) (pH) & 8.5 \\ \(\text{r}_{\text{d}}\) (\(\Omega\)) & 1.2 & L\({}_{\text{s}}\) (pH) & 0.7 \\ \(\text{r}_{\text{s}}\) (\(\Omega\)) & 0.2 & C\({}_{\text{pg}}\) (ff) & 9.5 \\ \(\text{L}_{\text{g}}\) (pH) & 9.5 & C\({}_{\text{pd}}\) (ff) & 17.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parasitic parameter value of the \(8\times 100\)\(\upmu\)m pHEMT  
Figure 6: The influence of the diode size on the MaxGain of the network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\text{r}_{\text{gs}}\) (\(\Omega\)) & 41.5 \\ \(\text{C}_{\text{pg}}\) (ff) & 89.0 \\ \(\text{C}_{\text{ps}}\) (ff) & 33.5 \\ \(\text{L}_{\text{gs}}\) (pH) & 55.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parasitic parameter value of the \(2\times 20\)\(\upmu\)m diode  
Figure 7: (a) C-V characteristics of the \(2\times 20\)\(\upmu\)m diode; (b) input capacitance of the network versus RF power (Behavior of \(2\times 20\)\(\upmu\)m diode and \(8\times 100\)\(\upmu\)m FET). Converting (7) to the S-parameter matrix:  
\[\left[ \begin{array}{c}b_{1}\\ b_{2}\end{array} \right]=\left[ \begin{array}{cc}S_{11}&S_{12}\\ S_{21}&S_{22}\end{array} \right]\left[ \begin{array}{c}a_{1}\\ a_{2}\end{array} \right] \tag{22}\]  
where  
\[S_{11}=\frac{(1-Z_{0}y_{11})(1+Z_{0}y_{22})+y_{12}{y_{21}Z_{0}}^{2}}{\Psi} \tag{23}\]  
\[S_{12}=\frac{-2Y_{12}Z_{0}}{\Psi} \tag{24}\]  
\[S_{21}=\frac{-2Y_{21}Z_{0}}{\Psi} \tag{25}\]  
\[S_{22}=\frac{(1+Z_{0}y_{11})(1-Z_{0}y_{22})+y_{12}{y_{21}Z_{0}}^{2}}{\Psi} \tag{26}\]  
\[\Psi=(1+Z_{0}y_{11})(1+Z_{0}y_{22})-y_{12}{y_{21}Z_{0}}^{2} \tag{27}\]  
Using the S-parameter to derive the stability factor of the network:  
\[k=\frac{1-\left|S_{11}\right|^{2}-\left|S_{22}\right|^{2}+\left|\Delta\right| ^{2}}{2\left|S_{12}\right|S_{21}} \tag{28}\]  
\[\Delta=S_{11}S_{22}-S_{12}S_{21} \tag{29}\]  
The factor \(k>1\) is a necessary and sufficient condition for network stability. Since the diode compensation method generates additional feedback between the gate and drain, the stability of the network is enhanced compared with single FET. The conclusion can be verified by calculation or simulation. The simulation result of the stability factor is shown in Figure 8.  
Consequently, because of the nonlinear effect of the diodes, the input capacitance of the network is compensated for as the input power increases, thus avoiding the degradation of linearity. As the diode is operated in an inverted connection, there is nearly no additional current or power loss in the circuit.