esa-sceva/satcom-chunk-collection huggingface dataset:
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 3 Numerical Method
### Structural Equations of Motion  
Figure 6 illustrates the aeroelastic model of two degrees of freedom (Isogai Wing model), originally proposed by [PERSON] [37]. Incorporating both plunging and pitching motions, this model is widely used as a benchmark for two-dimensional transonic aeroelastic studies. It represents a large-aspect-ratio transonic aft-swept wing, where the outboard section undergoes bending and torsion. In this model, the elastic axis \(E\) is located \(ab\) behind the mid-chord, while the center of gravity \(G\) is positioned \(x_{a}b\) at of the elastic axis, where \(b\) represents the semi-chord length. The elastic stiffness coefficients associated with plunging and pitching are given by \(K_{h}\) and \(K_{a}\), respectively. Based on the Lagrangian formulation, the structural equations of motion for this model can be expressed as follows:  
\[m\ddot{h}+S_{a}\ddot{a}+K_{h}h =-L \tag{12}\] \[S_{a}\ddot{h}+I_{a}\ddot{a}+K_{a}\alpha =M\]  
where \(h\) and \(\alpha\) represent the plunging and pitching motions, respectively. \(m\) denotes the mass per unit span, while \(S_{a}=m\)x\({}_{a}b\) is the static moment about the elastic axis. \(I_{a}=m\)\(r_{a}^{2}b^{2}\) is the mass moment of inertia about the elastic axis, with \(r_{a}\) denoting the dimensionless radius of gyration relative to the elastic axis. \(L\) and \(M\) represent the aerodynamic lift and the aerodynamic moment about the elastic axis, respectively.  
By introducing the dimensionless time variable \(\tau=\omega_{a}t\), Equation (12) can be rewritten as  
\[\begin{bmatrix}1&x_{a}\\ x_{a}&r_{a}^{2}\end{bmatrix}\begin{cases}\ddot{h}/b\\ \dot{\alpha}\end{cases}+\begin{bmatrix}\begin{pmatrix}\omega_{b}\\ \omega_{a}\end{pmatrix}^{2}&0\\ 0&r_{a}^{2}\end{bmatrix}\begin{cases}h/b\\ \alpha\end{cases}=\frac{1}{\pi}(V_{f})^{2}\begin{cases}-C_{l}\\ 2C_{m}\end{cases} \tag{13}\]  
Figure 5: Comparative analysis of CFD and experimental data for aerodynamic coefficients in transonic pitching oscillation. (**a**) Lift coefficient vs. angle of attack. (**b**) Moment coefficient vs. angle of attack.  
Figure 6: Two-degree-of-freedom aeroelastic model.  
where the notation \(\langle\rangle\) represents the second derivative with respect to \(\tau\), i.e., \(\langle\rangle=\frac{d^{2}\langle\rangle}{d\tau^{2}}\), and \(\omega_{h}=\sqrt{K_{h}/m}\) and \(\omega_{\alpha}=\sqrt{K_{\alpha}/I_{\alpha}}\) represent the uncoupled natural frequencies of the plunging and pitching motions, respectively. The dimensionless flutter speed is given by \(V_{f}=\frac{V}{\omega_{\alpha}B\sqrt{\eta}}\), while the mass ratio is defined as \(\mu=\frac{m}{\rho\pi\hbar^{2}}\), where \(V\) denotes the free-stream velocity and \(\rho\) represents the free-stream density. Additionally, \(C_{l}=\frac{L}{\rho V2b}\) and \(C_{m}=\frac{M}{2\varrho V^{2}b^{2}}\) denote the lift and moment coefficients, respectively. This system can be efficiently solved using a fourth-order Runge-Kutta method, and more details of the numerical implementation can be found in [35].
## 4 Experiment
### Experiments on Leafsnap  
We further evaluate the performance of our method on a large-scale plant dataset Leafsnap [47]. Leafsnap includes 23147 lab images and 7719 field images from 184 tree species. The image number varies from 10 to 183 for each tree species. As shown in Figure 11, the leaf images from diverse categories have similar shape and visual appearance. Therefore, it is very challenging to distinguish the leaves in Leafsnap correctly. In our experiment, we use the field images; seventy percent of the images in each species are regarded as the training set, the other thirty percent as the testing set. All comparison results on this dataset are tabulated in Table 9. Our method achieves 93.40% recognition rate, which is the average value of 93.55%, 92.80%, 92.80%, 94.49% and 93.36% for the five repeated experiments. Because large variations existed in the leaf images of Leafsnap, the recognition accuracies of all handcrafted methods are less than 75%. Among the neural network-based methods, our proposed method outperforms the second and third best methods by 2.11% and 3.28%. The experiment results can demonstrate the effectiveness of our method evidently.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Methods** & **Accuracy** & **Year** \\ \hline \multirow{6}{*}{Hand} & SC+DP [9] & 92.25 & 2002 TPAMI \\  & IDSC+DP [10] & 87.75 & 2007 TPAMI \\  & MDM [24] & 80.25 & 2012 TIP \\  & MTCD [13] & 88.75 & 2016 ECAI \\  & MFD [14] & 94.50 & 2019 SPIC \\  & MTD+LBP-HF [27] & 97.50 & 2021 PR \\ \hline \multirow{6}{*}{DNN} & AlexNet+relu5 [58] & 96.00 & 2012 NIPS \\  & VGG16+relu5\_2 [54] & 98.20 & 2015 ICLR \\  & Dual-Path CNN [19] & 95.78 & 2017 ICIP \\  & Deep Plant [18] & 94.26 & 2017 PR \\  & HGO-CNN [20] & 95.16 & 2018 TIP \\  & MaskCOV [35] & 96.25 & 2021 PR \\  & IMTD+relu5\_2 [38] & 99.25 & 2023 ESA \\  & Ours & **99.65**(\(\pm\)0.28) & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification accuracy (%) comparison on CVIP100 dataset with identical settings.  
Figure 10: Example images from CVIP100 dataset; four species are shown.
## 4 Proposed Method: Variable Embedding Locations Exploration
### Solution 1: Random Addition of Expansion Blocks  
Since the purpose is to keep the boundaries confidential, a random addition of expansion coefficients is supposed to be an efficient alternative solution. In order to conceal the borderline, we hereby propose a solution to expand additional coefficients for hiding. However, as mentioned above, the random choice of the embedding blocks causes the deterioration of the quality of the stego signal. Therefore, the proposed method (solution 1) only expands high-frequency coefficients, while the number of coefficients for embedding is randomly chosen frame by frame.  
In the previous method, we first segment \(N\) DCT coefficients into \(M\) blocks, \(\mathbf{L}_{1},\ldots,\)\(\mathbf{L}_{M}\), where  
\[\mathbf{L}_{i}=(X_{(i-1)N/M+1},\ldots,X_{iN/M}). \tag{2}\]  
Then, we determine a fixed border index \(i_{e}\) and embed the data into blocks \(\mathbf{L}_{i_{e}},\mathbf{L}_{i_{e}+1},\ldots,\mathbf{L}_{M}\). In solution 1, the border index \(i_{e}\) is determined randomly among several choices, and the blocks from \(i_{e}\)-th to the highest-frequency block become the target of embedding. To extract the data and recover the original signal, we need to know the \(i_{e}\) of a certain frame; to do this, we use the location map [5] that is embedded in the highest frequency part of the frame.  
As preliminary work, we tried to expand 512 coefficients, counting from the highest coefficients out of 2048 coefficients in each frame, and we divided each of the 2048 coefficients into \(M=16\) blocks. This means \(i_{e}=13\), where the highest four blocks are used for embedding. Meanwhile, in solution 1, we randomly choose \(i_{e}=12\) or \(i_{e}=13\) to use four or five blocks for embedding.
## 4 Advanced SensorNC: In-Network Processing
### Effect of Scaling Element Movement on Model Components  
To make the method of scaling element movements more tangible, let us briefly illustrate its effect on the other modeling components. We base this discussion on simple, yet very commonly-used modeling component instances. In particular, we assume token-bucket arrival curves \(\gamma_{r,b}\in\mathcal{F}_{\text{TB}}\) as arrival curves and rate-latency service curves \(\beta_{R,T}\in\mathcal{F}_{\text{RL}}\). As the maximum scaling curve, we also assume a token-bucket function \(\gamma_{r_{S},b_{S}}\), and as minimum scaling curve, we take a rate-latency curve \(\beta_{R_{S},T_{S}}\). This form of maximum scaling curve is sensible as it can capture the fact that a small number of bytes received might result in a high demand on the computational resources, whereas for larger amounts of received data, this effect degrades. For the minimum scaling curve, the reasoning is just the other way around. Now, when we shift the scaling element across a service curve element, we need to compute the new service curve as:  
\[\underline{S}(\beta)=\beta_{R_{S},T_{S}}\circ\beta_{R,T}=\beta_{R_{S},R,T+ \frac{T_{S}}{R}},\]  
where \(\circ\) denotes the concatenation of functions. Hence, the resulting service curve is again of the rate-latency type with a suitably scaled rate and an increased latency.  
If the scaling elements have been shifted to the sources of traffic, i.e., the sensing inputs, their effect on the arrival constraints can be calculated as:  
\[\overline{S}(\alpha)=\gamma_{r_{S},b_{S}}\circ\gamma_{r,b}=\gamma_{r_{S},r,b_ {S}+r_{b}}\]  
Thus, the arrival curves of the scaled traffic flows remain token buckets with again suitably-scaled rate and increased bucket depths.  
Moving scaling elements across multiplexers has been discussed in the previous section and does not alter the multiplexers. That is true for any multiplexing, be it arbitrary, FIFO, priority-based or any other.
## 6 Conclusions  
We demonstrated the arbitrary phase modulation of a GTF, which could be obtained in the first-order fiber comb filter based on the PDLS. The proposed filter was composed of a PBS, two OWS's of an HWP and a QWP, and two PMF segments. Each OWS was located before each PMF segment, and the second PMF segment was butt-coupled to one port of the PBS so that its principal axis should be \(22.5^{\circ}\) away from the TM polarization axis of the PBS. Basically, polarization conditions, which should be satisfied to modify the phase of a GTF as a desired value, were explained using the spectral evolution of the \(\text{SOP}_{\text{in}}\) and \(\text{SOP}_{\text{out}}\) of the second PMF in the narrowband transmittance function (\(t_{n}\)), on the basis of the continuous wavelength tuning mechanism of previously reported PDLS-based first-order narrowband comb filters. Then, we explained a systematic scheme to find four WOA's for the arbitrary phase modulation of a GTF using the effect of each component of the filter, such as a waveplate or PMF segment, on its \(\text{SOP}_{\text{in}}\) or \(\text{SOP}_{\text{out}}\). By exploiting this WOA finding method, we derived WOA sets of the four waveplates, which could give arbitrary phase delays \(\phi\)'s from \(0^{\circ}\) to \(360^{\circ}\) to a GTF chosen here arbitrarily. Finally, we showed phase-modulated GTF's calculated at eight selected WOA sets allowing \(\phi\)'s to be \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), \(225^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\), and then the theoretically predicted results were experimentally verified. It is concluded from the theoretical and experimental demonstrations that the GTF of our filter based on the OWS of a QWP and an HWP can be arbitrarily phase-modulated by appropriately controlling the WOA's of the four waveplates. Our demonstration is expected to be beneficial to fiber-optic applications, which demand frequency-tunable comb filters with specific transmittances, such as microwave photonic signal processing, optical sensor interrogation, and multiwavelength lasing.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and Y.W.L.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and Y.W.L.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and Y.W.L.; visualization, [PERSON] and Y.W.L.; supervision, Y.W.L.; project administration, Y.W.L.; funding acquisition, Y.W.L. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Experimental phase-modulated transmission spectra of GTF (\(t_{GTF2}\)), measured at eight WOA sets (Sets I\({}_{\text{GTF2}}\)-VII\({}_{\text{GTF2}}\)).  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(2019R11I1A3A01046232).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Mirror Facet Alignment Method
### Two-Rotation Alignment Method
\begin{array}{l}\theta_{\text{A}}=\frac{x_{3}\cdot\mathbf{N}_{\text{ t}}(x)+y_{3}\cdot\mathbf{N}_{\text{t}}(y)-(x_{3}y_{2}-x_{2}y_{3})\cdot\theta_{ \text{B}}}{x_{3}y_{1}-x_{1}y_{3}}\\ \theta_{\text{C}}=\frac{x_{1}\cdot\mathbf{N}_{\text{t}}(x)+y_{1}\cdot\mathbf{ N}_{\text{t}}(y)-(x_{1}y_{2}-x_{2}y_{1})\cdot\theta_{\text{B}}}{x_{1}y_{3}-x_{3}y_{1}} \end{array} \right. \tag{26}\]  
In Equation (26), there are three unknown variables (\(\theta_{A}\)\(\sim\)\(\theta_{C}\)) but only two equations, so the solution of Equation (26) is not unique. If only adjusting two bolts and the other bolt is not adjusted, Equation (26) will have only one solution. This is the mathematical basis of the two-rotation alignment method. The specific calculation process is that \(\theta_{A}=0\), \(\theta_{B}=0\), and \(\theta_{C}=0\) are selected in turn and substituted into Equation (26) to calculate three set angles \(\theta_{A}\)\(\sim\)\(\theta_{C}\). Then, the set of angles \(\theta_{A}\)\(\sim\)\(\theta_{C}\) can be selected to make the error angle \(\theta\) smallest (Equation (18)) for the facet alignment parameters of the mirror unit. Finally, the adjustment value \(d_{\text{A}}\)\(\sim\)\(d_{\text{C}}\) of the bolts 1\(\sim\)bolt 3 can be determined by the Equation (23). The two-rotation and three-rotation alignment methods (Section 5.1) have the same principle. The differences is only in solving the rotation angle \(\theta_{A}\)\(\sim\)\(\theta_{C}\) (the red dashed box in Figure 7) but the other steps are the same. It can be shown that the two-rotation alignment method is a special case of the three-rotation alignment method.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 5 Intention Estimation Module
### Estimation of the Robot Velocity  
In order to obtain a practical solution to minimize production time, the proposed algorithm monitors the robot end-effector position, \(p_{R}\), by using a common convention based on the knowledge of the robot joint angles \(q\,[\text{rad}]\). In fact, \(p_{R}\) is computed by retrieving the transform between the robot base frame, \(T_{base}\), and the end-effector frame, \(T_{ee}\), which are updated in real time by the robot controller and drivers.  
On the other hand, the equation implemented to compute the end effector velocity, \(v_{R}\), is based on the change in the end-effector position over time. More in detail, the change in position is calculated between current and previous positions of \(p_{R}\), while the time difference, \(dt\), is calculated between the current and previous timestamps of the transform. The velocity components in the \(x\), \(y\), and \(z\) directions are computed by dividing the position differences by the time difference:  
\[v_{R_{x}}=\frac{p_{R_{x}}^{curr}-p_{R_{y}}^{prev}}{dt},\qquad v_{R_{y}}=\frac {p_{R_{y}}^{curr}-p_{R_{y}}^{prev}}{dt},\qquad v_{R_{z}}=\frac{p_{R_{z}}^{curr }-p_{R_{z}}^{prev}}{dt} \tag{5}\]  
Figure 5 (center and right) shows the robot end-effector positions and velocities during its motion. The yellow arrow represents the speed, calculated as the Euclidean norm (magnitude) of the velocity vector in 3D space. The velocity vector is represented by the three components \(v_{R_{x}}\), \(v_{R_{y}}\), and \(v_{R_{z}}\) in rgb convention.
## Materials and Methods
### Field Data and Experimental Design  
Over the course of a week, water limitation treatments were applied to _Baccharis pilularis_ plots, and coincident spectral, chemical, and physiological measurements were taken to discern responses to desiccation and saturation. In a 25 m \(\times\) 15 m study area (Figure 1), we cut a series of \(\sim\)2.5 m\({}^{2}\) sections of coyote brush, and left the main stems standing in the pre-cut orientation. Each section was cut 48 h apart, creating a total of four treatments, Tx1 was cut first on 19 July and Tx4 cut last on 25 July. The surrounding brush was left alone and used as a control (C), and a section was watered (W) four gallons per bushel every other day during the experiment.  
Field Data Collection  
Water content measurements of roughly 50 leaves per treatment were taken for multiple samples of Tx1, Tx2, Tx3, Tx4, C, and W. Leaf water content samples were collected from shoots located adjacent to leaf water potential sample locations. The fresh mass of each sample was measured to the nearest 0.001 g, and once brought back to the lab, all samples were oven-dried for 48 h at 60 C and measured again for dry mass, measured to the nearest 0.001 g. The fresh and dry mass of each sample were then used to calculate leaf water content as (fresh mass-dry mass)/dry mass. Water content samples were taken on July 25 and July 26 for all treatments.  
Pre-dawn and midday water potentials were measured using a Scholander pressure chamber [52]. Three samples for treatments Tx3, Tx4, C, and W were cut, sealed in plastic bags, placed into a cooler, and then immediately measured. The pressure chamber allows for the addition of compressed nitrogen gas to be added to a cut stem to measure water tension; the amount of pressure that it takes to cause water to appear at the cut surface of the petiole is equal to the tension the leaf is experiencing. A high value of pressure means a high value of tension and a high level of water deficit. Water potential samples were taken on 25 July and 26 July only for treatments that had been removed from their roots less than 2 days from sample collection (Table 2). Treatments that were cut from their roots more than 3 days prior were not sampled, as after three days the values obtained from the samples saturated.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 3 Results
### Experiment Result
#### 3.2.2 Influence of Number of On-Board Missions  
Since LOC cannot guarantee QoS constraints for all on-board missions, comparison experiments are conducted for GACO, PSOCO, GOC, and SSPCO.  
From Figure 7, it can be seen that the total energy consumption of the four methods increases with the increase of the number of on-board missions. The reason is that, when the number of missions increases, both LEO and GEO satellites process more missions, thus increasing the total energy consumption of the four methods. SSPCO achieves the lowest total energy consumption compared with the others because it does not overly pursue the minimization of processing delay, but makes full use of the computing power of LEO and GEO satellites to minimize the total energy consumption instead. Moreover, GOC has the highest total energy consumption due to the fact that all submitted missions are completely offloaded to GEO satellites for processing.  
Furthermore, when the total energy consumption is the same for the computation offloading strategies, it does not mean that their offloading decisions are the same. This is because the number of successfully offloaded missions may be different for different strategies. Therefore, we propose the average energy consumption performance index (AEC), thus adding the number of successfully offloaded missions to the evaluation of energy consumption. Specifically, AEC is defined as shown in Equation (43). As the number of successfully offloaded missions increases and the total energy consumption decreases, the average energy consumption decreases, and this leads to better offloading decisions.  
\[AEC=\frac{\text{total energy consumption}}{\text{the number of successfully offloaded missions}} \tag{43}\]  
From Figure 8, it can be seen that the proposed SSPCO still obtains the lowest average energy consumption as the number of on-board missions increases. This is because SSPCO can help LEO satellites choose the best offloading ratio to process onboard missions under the QoS constraints to ensure the maximum number of successfully offloaded missions and thus achieve the best average energy consumption. Experimental results show that SSPCO can reduce the energy by 19.45% and 7.73% on average compared with GACO and PSOCO, respectively. In addition, it can save 56.43% of energy compared with GOC.
## 2 Accuracy of Predictive Methods and What Is Required
### QSPR Methods using Molecular Descriptors
Thus, we need to reduce the number of descriptors to a value much less than the number of available experimental parameters. The question arising is how to do this and still having a good predictive method. In practice, often Principal Component Analysis (PCA) is applied, which is a statistical procedure to reduce the number of parameters to a level that the remaining parameters still describe a large part of the variability of the data set. However, as argued before, we need very high-quality correlations to arrive at the required accuracy of prediction and then a retention of variability of 90-98%, the normal range attempted, is often not sufficient. Therefore, we do need sufficient experimental data to allow for more descriptors and therefore more parameters to be fitted. That this works in practice is illustrated by the work of [PERSON] et al. on the boiling points of organics [54]. They applied neural network modelling involving a few atomic connection descriptors (maximum was seven) which resulted in very good results. For saturated hydrocarbons average errors below 2K were obtained. The low number of descriptors ensures that no overfitting is involved. This, however, was for saturated hydrocarbons only, for the alkenes the results were worse (average absolute errors up to 6K) but it illustrates what is possible with a judicious choice of descriptors.
###### Abstract  
A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 \(\times\) 10\({}^{-3}\). Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.  
Keywords:digital signal processing; support vector machines; BCSVM; nonlinear equalization; coherent detection +
Footnote †: journal: Applied Sciences