esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Fuzzy Controller for the Hydraulic Manipulator
### Adaptive Fuzzy Logic System
Seven membership functions namely NB (Negative Big), NM (Negative Medium), NS (Negative Small), Z (zero), PS (Positive Small), PM (Positive Medium), PB (Positive Big) are the linguistic input variables. For the output of the fuzzy system, the adjustable scaling gain \(K_{m}\) specified by seven membership functions including Z (Zero), S (Small), LS (Light Small), M (medium), LB (Light Big), B (Big), VB (Very Big) is employed. Both linguistic input and output sets are signified in detail in Figures 7 and 8. The fuzzy rules are defined by IF-THEN rules and its laws are discussed in Table 2. The triangular membership functions are described in Figure 7 for the change of load torque and Figure 8 for the adaptive gains. Figure 6: Membership function of the tuned feedback gains: (**a**) \(K_{i1}\), (**b**) \(K_{i2}\), (**c**) \(K_{i3}\). \[ \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(K_{mi}\)} & \multicolumn{8}{c}{\(\tau_{load\_i}\)} \\ \cline{2-10}  & **NB** & **NM** & **NS** & **Z** & **PS** & **PM** & **PB** \\ \hline \multirow{5}{*}{\(\tau_{load\_j}\)} & **NB** & VB & B & LB & M & LB & B & VB \\  & **NM** & B & LB & M & LM & M & LB & B \\  & **NS** & LB & M & LM & S & LM & M & LB \\ \cline{1-1}  & **Z** & M & LM & S & Z & S & LM & M \\ \cline{1-1}  & **PS** & LB & M & LM & S & LM & M & LB \\ \cline{1-1}  & **PM** & B & LB & M & LM & M & LB & B \\ \cline{1-1}  & **PB** & VB & B & LB & M & LB & B & VB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rules table of the fuzzy logic system. Figure 7: Membership function of the load torque variation: (**a**) \(T_{load\_1}\), (**b**) \(T_{load\_2}\), (**c**) \(T_{load\_3}\). Since the external disturbance cannot be measured, hence according to [15], this estimated parameter is computed by:  
\[\hat{d}=z+p\Big{(}q,\hat{q}\Big{)} \tag{41}\]  
where \(\hat{d}\in R^{3\times 1}\) is an estimated disturbance, \(z\in R^{3\times 1}\) is an auxiliary variable used to cancel out an acceleration measurement in the term \(M(q)\hat{q}\), function \(p\) is chosen as:  
\[p\Big{(}q,\hat{q}\Big{)}=\alpha\left( \begin{array}{ccc}1&0&0\\ 1&1&0\\ 1&1&1\end{array} \right)\hat{q} \tag{42}\]  
where \(\alpha\) is a constant depending on the maximum velocity of the third link. The auxiliary variable \(z\) can be obtained from:  
\[\dot{z}=-L(q,\dot{q})z+L(q,\dot{q})\Big{\{}C\Big{(}q,\dot{q}\Big{)}+G(q)-\tau-p \Big{(}q,\dot{q}\Big{)}\Big{\}} \tag{43}\]  
where  
\[L(q,\dot{q})=\frac{d}{dt}\Big{(}p(q,\dot{q})\Big{)}M(q)^{-1}\dot{q}^{-1}= \alpha\left( \begin{array}{ccc}1&0&0\\ 1&1&0\\ 1&1&1\end{array} \right)M(q)^{-1} \tag{44}\]  
The proof of the stability of the DO can be deduced from [42]. Figure 8: _Cont_. Figure 8: Membership function of the tuned scaling gains: (**a**) \(K_{m1}\), (**b**) \(K_{m2}\), (**c**) \(K_{m3}\).
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 3 Graphene Perfect Absorbers without External Mirror
### 'One-Port Mimicking Absorber' Scheme
The designed device also showed enhanced fabrication error tolerance, which was approximately an order of magnitude larger compared to the scheme based on degenerate critical coupling [80]. Since the proposed GPA structure does not require any structural symmetry, its design is straightforward, and its fabrication will be easier. Figure 9: GPA based on one-port mimicking, which consists of a high-contrast grating, a slab separated by a gap region, and monolayer graphene placed just below the slab. (**a**) Schematic of the GPA. (**b**) Theoretical model for coupled mode theory analysis. (**c**) Comparison of RCWA and CMT results. (**d**) Electric field distribution at optimal condition. Reproduced with permission from [46], © 2023 Springer Nature. In the GPA design, the critical coupling condition is a common and essential requirement. Mostly, the leakage rate of any resonant mode can hardly be adjusted after device fabrication because it is determined by structural parameters. On the other hand, due to the difficulty of the precise control of the quality of synthesized graphene and unwanted doping in graphene transferred to the substrate, the loss rate of graphene is rather unpredictable, so that the perfect absorption is quite difficult to achieve in practice. To solve this problem, in 2021, [PERSON] et al. [49] proposed the GPA with a loss-adaptive \(Q\)-factor control function enabled by quasi-BIC, in which its leakage rate is adapted to the loss rate by a proper choice of the incident angle (Figure 11). The proposed absorber consisting of monolayer graphene placed on an SWG that supports both quasi-BIC and GMR. Similarly to the one-port mimicking scheme in asymmetric resonators of [47; 48], the quasi-BIC (in detail, BIC2 nd) is responsible for absorption, while the GMR (in detail, GMR1 st) works as an internal mirror. Another outstanding feature of the proposed PA is remarkably uncomplicated manufacturing process since absorbing medium (including graphene) is placed on the ridge side of the SWG. Moreover, the proposed device scheme can have an arbitrarily  
Figure 10: GPA based on one-port mimicking, which consists of an asymmetric single resonator supporting two degenerate resonant modes. (**a**) Schematic of the GPA. (**b**) Theoretical model for coupled mode theory analysis. (**c**) Reflection map as a function of FF when only the graphene is removed for optimized graphene perfect absorber. The inset shows the normalized electric field distribution at seven different points marked by open circles. Reproduced with permission from [47], © 2023 Optical Society of America. small leakage rate via adaptive control of the incident angle, and thus, it can be used to implement a PA for any kind of ultrathin absorbing media.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
## 2 Foreground Trajectory Extraction  
The first step in foreground estimation extraction is to select the initial sampling point. Compared with sampling the background region, the sampling points of the foreground salient region often contain more motion information; therefore, selection of key points is very important. For a pixel \(P(x,y)\) in a frame, \(H(x,y)\) is the Hessian matrix [18] of the pixel \(P\), as shown in Formula (1).  
\[H(x,y)=\left[\begin{array}{cc}P_{x}^{2}&P_{x}P_{y}\\ P_{x}P_{y}&P_{y}^{2}\end{array}\right]. \tag{1}\]  
\(P_{x}\) and \(P_{y}\) are the first derivatives of the horizontal and vertical directions of the pixel \(P\), respectively. If the determinant value \(\left|H(x,y)\right|\) is greater than the threshold _Ths_, then \(P\) is considered an alternative key point. But in video, the key points of each frame will change with time. It is not enough to select the key points only by using the Heisen matrix. Therefore, this paper introduces an optical flow magnitude of each frame as an auxiliary measure of the key points.  
Firstly, the dense optical flow field of each frame is calculated and expressed as \(\omega=(u,v)\), where \(u\) and \(v\) represent the horizontal and vertical components of the optical flow field, respectively. For a point \(P(x,y)\) in a frame, the definition of optical flow magnitude is shown in Equation (2).  
\[M(x,y)=\sqrt{\left(u\big{|}_{x,y}\right)^{2}+\left(v\big{|}_{x,y}\right)^{2}}, \tag{2}\]  
where \(u\big{|}_{x,y}\) and \(v\big{|}_{x,y}\) represent the displacement values of points \(P(x,y)\) in horizontal and vertical directions. Generally speaking, the motion amplitude of background points is less than the average motion amplitude of frames because the foreground points are usually less than the background points, and these background points are usually static. However, the contribution of background points to behavior recognition is often relatively small. In view of this, this paper introduces the idea of adaptive amplitude threshold, defined as \(Th_{m}=\overline{M}\), where \(\overline{M}\) is the average motion amplitude of the current frame. To sum up, for a certain pixel \(P(x,y)\), if it satisfies \(Th_{m}>\overline{M}\) and \(\big{|}H(x,y)\big{|}>\)_Ths_, then P is selected as the sampling point of the foreground trajectory  
For a sampling point \(P_{t}(x_{t},y_{t})\) in frame \(t\), the corresponding position of the sampling point in frame \(t+1\) is defined as Equation (3).  
\[P_{t+1}=(x_{t},y_{t})+(K\cdot\omega_{t})\big{|}_{(x_{t},y_{t})}, \tag{3}\]where \(K\) is a \(3\times 3\) median filter, and \(\omega_{t}\) is a two-channel optical flow matrix of the first \(t\) frame. A motion trajectory (\(P_{t}\), \(P_{t+1}\), \(P_{t+2}\),..., \(P_{t+L}\)) is formed by connecting the sampling points in the continuous \(L\) frames in series. At the same time, in order to avoid the drift of sampling points, the sampling step and trajectory length are set to 5 and 15, respectively.
## 4 Discussion  
For these orbit simulations, two different orbital planes were considered. The study first examined the RO sampling conditions from a 3-satellite LEO constellation orbiting in non-sun-synchronous 45\({}^{\circ}\) inclination plane, where each satellite is separated by 2 min. To assess how the RO sampling from this low inclination differs from the sampling conditions of the more common polar (98\({}^{\circ}\) inclination, sun-synchronous) orbit, a 4-satellite LEO constellation was simulated for both inclinations, all other aspects being the same. These simulations showed that:  
1. Over one year, a 3-satellite constellation 45\({}^{\circ}\) inclination plane will collect 400,000 events and 97% of these are complete events (i.e., all three satellites capture a RO from the same GNSS transmitter).
2. Of these complete events, the superposition of GPM IMERG data reveals that 15,500 will have at least one observation crossing detectable precipitation and one crossing un-detectable precipitation.
3. The 98\({}^{\circ}\) orbit resulted in more events meeting the criteria in (2) above than the 45\({}^{\circ}\) inclination orbit, because there is more distance separation between the RO observations. However, the relative inclination with the GPS and GLONASS satellites also makes these observations more likely to have \(\alpha\sim\)90\({}^{\circ}\) deg, and hence smaller \(d_{\mathit{eff}}\) distances. From a scientific standpoint, it is better to have smaller distances to be able to characterize the convection inside and in the nearby environment (too far apart is less desirable). With this rationale, the 98\({}^{\circ}\) orbit is less optimal for the study of the immediate surrounding of precipitation.
4. Collections of RO observations with \(\alpha\) closer to 90\({}^{\circ}\) are more _perpendicular_ to each other, but also suffer a larger tangent point drift. In other words, even though \(d_{\mathit{eff}}\) is small in general for the 45\({}^{\circ}\) case, the tangent point drift will be smaller. Therefore, there is less likelihood of a ray path overlap between adjacent RO soundings.  
Over one year, these event sampling numbers are sufficient for comparing convective transition statistics amongst climate models over tropical oceans, such as comparing the sharp pickup in precipitation as column water vapor exceeds a critical threshold [10], as well as sensitivity to the free-tropospheric moisture [11].
## 4 The QACMOR Routing Method
### Mechanisms of QEAs  
#### 4.1.1 Basic Elements of QEAs  
The memory unit in a classical computer is the bit, which only has two states: \"0\" or \"1\", whereas the smallest information unit in QEAs is defined as the qubit [10; 11]. A qubit could be in the \"0\" state, the \"1\" state, or in a linear superposition of both, which is denoted as \(\alpha|0\rangle+\beta|1\rangle\), where \(|0\rangle\) and \(|1\rangle\) represents the quantum state, and a pair of complex numbers \((\alpha,\beta)\) is defined with \(\left|\alpha\right|_{2}+\left|\beta\right|_{2}=1\), and the value of \(|\alpha|^{2}\) and \(\left|\beta\right|^{2}\) indicates the probability of the \"0\" state and the \"1\" state, respectively.  
A qubit with the size of \(n\) can be represented as the following, which has \(2^{n}\) kinds of states:  
\[\left(\begin{array}{c|c|c}\alpha_{1}&\alpha_{2}\\ \beta_{1}&\beta_{2}\end{array}\right.\left.\ldots\right|\begin{array}{c|c|c} \alpha_{i}\\ \beta_{i}\end{array}\right) \tag{7}\]  
For example, a quantum individual with three qubits is given like this:  
\[\left(\begin{array}{c|c|c}\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}&\frac{1}{ \sqrt{2}}\\ \frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{2}}&\frac{\sqrt{3}}{2}\end{array}\right) \tag{8}\]  
It can also be represented as:  
\[\frac{1}{4}|000\rangle+\frac{\sqrt{3}}{4}|001\rangle-\frac{1}{4}|010\rangle- \frac{\sqrt{3}}{4}|011\rangle+\frac{1}{4}|100\rangle+\fracwhich means that the probabilities of the states \(|000\rangle\), \(|001\rangle\), \(|010\rangle\), \(|011\rangle\), \(|100\rangle\), \(|101\rangle\), \(|110\rangle\), and \(|111\rangle\) are 1/16, 3/16, 1/16, 3/16, 1/16 wh, 3/16, and 1/16, separately.  
Commonly, \(\xi(\xi\subset(-\pi,\pi])\) denotes the phase of the qubit, and the \(i^{th}\) bit phase is \(\xi_{i}=\arctan(\beta_{i}/\alpha_{i})\). The position of \(\xi_{i}\) in coordination is given in Figure 2.
## 3 Active Fault-Tolerant Control Method for Configuration Transformation
### Multi-Objective Optimizer: NSGA-II  
NSGA-II is considered one of the most efficient methods for multi-objective optimization [26]. Compared to its predecessor, NSGA, NSGA-II offers three key advantages: 1 NSGA-II employs a fast non-dominated sorting algorithm based on classification, which reduces computational complexity from \(O(mN^{3})\) to \(O(mN^{2})\). This improvement accelerates the optimization process, making it highly efficient for large-scale problems. 2 NSGA-II introduces the concept of crowding distance, which eliminates the need to specify a sharing radius as required in traditional fitness sharing strategies. The crowding distance comparison operator allows for a more effective selection of diverse solutions, improving the algorithm's robustness. 3 NSGA-II integrates elitist strategies, which ensure that the best individuals from the current generation are always retained in the next generation. This feature helps prevent the loss of high-quality solutions during the optimization process.  
These improvements make NSGA-II highly efficient for solving multi-objective optimization problems, especially in complex engineering applications where multiple conflicting objectives must be balanced. By using NSGA-II, the proposed optimization framework can effectively explore the trade-offs between design objectives while maintaining structural integrity and reliability.
## 3 Results and Analysis  
### Chlorophyll Content and Spectral Characteristics of Quercus aquifolioides at Different Altitudes  
Photosynthesis accumulates and stores important energy and substances for plant growth. Chlorophyll, as the basic pigment in plants, plays an important role in the absorption, transmission and transformation of light energy. As can be seen from Figure 2, the relative values of chlorophyll content of _Quercus aquifolioides_ under different altitude gradients were significantly different. From 2905 m to 3500 m, the relative chlorophyll content of _Quercus aquifolioides_ increased first and then decreased, reaching a peak at 3200 m and decreasing at 3500 m. This showed that _Quercus aquifolioides_ has lower photosynthetic capacity at 2905 m compared with 3200 m. At the same time, under high altitude and strong radiation environment, _Quercus aquifolioides_ at 3500 m reduced its chlorophyll content. This can reduce its absorption of light and weaken the pressure of the light system, thus avoiding its peroxidation. The chlorophyll content, in this study, showed a rising trend first and then falling with the elevation. Similar results have been reported in the study of chlorophyll content in _Acer gimala_ and the study of the forest structure of _Phyllostachys heterocycle_[29; 30]. The increase of chlorophyll content to a certain extent can accumulate more photosynthetic products for the growth of plants, and the synthesis is affected by light intensity. As the light intensity at low altitude is weaker, and the light intensity at middle altitude increases, it is more conducive to chlorophyll synthesis [30; 31]. With the further increase in altitude, environmental factors, such as air humidity reduction, ultraviolet radiation and low temperature at high altitude become more serious. This may, in turn, cause the chlorophyll synthesis to be blocked or the chlorophyll decomposition to be accelerated, causing the chlorophyll content in the leaves to decrease [32; 33]. Therefore, with the increase in altitude, chlorophyll showed first a rising trend and then falling, which also showed that the middle altitude was more conducive to the photosynthesis and growth of plants.  
Figure 2: Relative chlorophyll content of _Quercus aquifolioides_ at different altitudes. ** indicates that the parameters are significantly different at \(p<0.01\) level. CCI (chlorophyll content index).
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 2 Materials and Methods
### CFD Modeling of the Oil Flow
#### 2.4.5 Computational Performance  
A 48 GFLOPs Linux Workstation (INTEL Xeon(r) Gold 6154 CPU, 4 Cores, 3 GHz) was used to run the simulations, which required on average 20 h each for a single planet carrier rotation. The low computation time achieved by parallelizing the computations on only 4 cores can be attributed to the newly implemented remeshing procedure, which is based on the recursive use of the initial mesh. This way, the effort of the remeshing process is drastically reduced.  
Without the mesh-handling strategy explained in Section 2.4.3 (computation of a limited mesh set and application of rigid rotations), the computational domain would have needed to be partitioned and meshed continuously for every gear position, with a significant increase in the computational effort. In Table 6, the implemented GRAMC with a multi-rotation strategy is compared with a general approach that does not consider the repeatability of the tooth positions.  
By the developed procedure, a 95% time gain per planet carrier rotation could be achieved. This is mainly because the grids were previously computed and are immediately available for the mapping process as the simulation continues. Moreover, owing to the proper domain partitioning that was carried out in the pre-processing phase, it was possible to mesh the geometry with a series of extrusions starting from a 2D mesh. The resulting 3D elements are triangular prisms, which exhibit higher quality parameters and require less memory consumption with respect to tetrahedrons, thus resulting in faster calculation times. [PERSON] et al. [2] simulated the same planetary gearbox with a local remeshing approach. Their simulation model, which also considered the roller bearings and a high level of detail, implemented in ANSYS Fluent, required 15-20 h parallelized on 80 cores of a high-performance computing cluster.
## 2 Materials and Methods
### Description of DALEC-Crop
#### 2.2.3 Model-Data Fusion Approach  
DALEC-Crop, including ACM, was integrated within the CARDAMOM MDF system [43] that used Bayesian inference to approximate the joint distribution of the model parameters, evaluated in a series of experiments (see details in Section 2.3). In accordance with [PERSON] theorem, this approximation can be expressed as:  
\[\mathrm{P(x\,|\,c)}\ \alpha\ \mathrm{P(c\,|\,x)}\times\mathrm{P(x)}, \tag{4}\]  
where \(c\) represents the LAI observation data, x are the model parameters, p(x\(\,|\,c\)) is the posterior parameter distribution, p(c\(\,|\,\)x) is the likelihood of x given c and p(x) is the prior distribution of parameters [44].  
Sampling from the prior distribution of model parameters was achieved using the Markov Chain Monte Carlo (MCMC) Metropolis-Hastings (MH) algorithm, which is used for sampling from a probability distribution [29; 42]. A Markov Chain consisting of a sequence of samples of x is generated by the MH algorithm. Each sample of x is either accepted or rejected after comparing the corresponding likelihood to the previously accepted value of x, in accordance with an acceptance ratio (A):  
\[\mathrm{A}=\frac{f(x^{\prime})}{f(x)} \tag{5}\]  
where x\({}^{\prime}\) is the newly sampled parameter vector and \(f\) is a function proportional to P(x\(\,|\,\)c). To be proportional to the likelihood, \(f(\mathrm{x})\) (and \(f(\mathrm{x}^{\prime})\)) should reflect the degree of agreement between the resulting modelled LAI and the corresponding observed LAI, weighted for observational uncertainty. The outcome from CARDAMOM is an ensemble of acceptable parameter sets that define the parameter joint distribution and parameter uncertainty.  
We further refine the accepted parameter hypervolume generated through the DALEC-Crop MDF approach by imposing a series of ecological and dynamic constraints (EDCs). For each iteration of the MDF, the EDCs check that the sampled parameter sets used to the generate model outputs are broadly within the expected ecophysiology based on our understanding of wheat growth dynamics. Past research has demonstrated the successful use of EDCs when calibrating terrestrial ecosystem models [43; 45]. We applied four EDCs, which principally constrained estimates related to the C pools and fluxes (Table 2).
# Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm
## 4 Trajectory Optimization of Joints Based on SMWOA
### Objective Function and Constraint Function  
The paper takes the time optimal, that is, the shortest movement time of the manipulator, as the objective function to optimize path. The mathematical model can be expressed as follows:  
\[T=\min\sum_{i=1}^{n-1}h_{i}=\min\sum_{i=1}^{n-1}\left(t_{i+1}-t_{i}\right) \tag{23}\]  
where \(h_{i}\) is the running time of each trajectory, and \(T\) is total time of the entire trajectory.  
The velocity and acceleration of joints are constrained as follows:  
(1) Constraint of velocity Velocity expression of the manipulator joints can be obtained by taking the derivative of Equation (21).  
\[\dot{\theta}_{i}(u)=\begin{array}{l}\frac{d}{du}\theta_{i}(u)=\frac{1}{2}(-u^{2} +2u-1)V_{i-1}+\frac{1}{2}(3u^{2}-4u)V_{i}\\ \quad\quad\quad\quad\quad\quad+\frac{1}{2}(-3u^{2}+2u+1)V_{i+1}+\frac{1}{2}u^{2} V_{i+2}(0\leq u\leq 1)\end{array} \tag{24}\]  
This can be written with another form as Equation (25).  
\[\dot{\theta}_{i}(u)=\frac{d}{du}\theta_{i}(u)=\left[\frac{dt(u)}{du},\frac{dq (u)}{du}\right]=\left[t^{\prime},q^{\prime}\right]=\frac{q^{\prime}}{t^{\prime}} \tag{25}\]  
(2) Constraint of acceleration  
Acceleration expression \(\ddot{\theta}_{i}(u)\) can be obtained by taking the derivative of Equation (24).  
\[\ddot{\theta}_{i}(u)=\frac{d}{du}\dot{\theta}_{i}(u)=\frac{1}{2}(-u^{2}+2u-1) V_{i-1}+\frac{1}{2}(3u^{2}-4u)V_{i} \tag{26}\]  
This can be written with another form as Equation (27).  
\[\ddot{\theta}_{i}(u)=\frac{d}{du}\dot{\theta}_{i}(u)=\left[\frac{dt^{\prime}(u )}{du},\frac{dq^{\prime}(u)}{du}\right]=\left[t^{\prime\prime},q^{\prime\prime }\right]=\frac{d\dot{\theta}_{i}(u)}{dt}=\frac{d\dot{\theta}_{i}(u)}{du}\frac{ du}{dt}=\frac{q^{\prime\prime}t^{\prime}-q^{\prime}t^{\prime\prime}}{t^{\prime 3}} \tag{27}\]  
The constraint functions of velocity and acceleration can be written as follows:  
\[\left\{\begin{array}{l}\dot{\theta}_{i}(u)\leq\dot{\theta}_{\max}\\ \ddot{\theta}_{i}(u)\leq\ddot{\theta}_{\max}\end{array}\right. \tag{28}\]