esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 QED Matrix Element for Third Body-Mediated RET between Two Chiral Molecules
Energy eigenstates \(\mid\)m\({}^{\xi}\)\(>\) for a particle \(\xi\) in a state described by one or more quantum numbers \(m^{\xi}\), with energy \(E_{m}^{\xi}\), are employed as base states for matter, while radiation states are specified by \(\mid\)n\((\overset{\rightarrow}{k},\lambda)\)\(>\), which details the number of photons in the field of mode \(\overset{\rightarrow}{k},\lambda\), with \(n\) being the number operator whose eigenvalues are 0, 1, 2,... Because the unperturbed Hamiltonian is separable, the base states employed to monitor the evolution of the system under the influence of the perturbation operator, \(H_{int}=\sum\limits_{\xi}H_{int}(\xi)\), are product molecule-field states of the form \(\mid\)mol\(>\)\(\otimes\)\(\mid\)_field\(>\)\(=\)\(\mid\)mol_;\(field\)\(>\)\(=\)\(\mid\)m\({}^{\xi}\), \(m^{\xi^{\prime}}\),...;\(n(\overset{\rightarrow}{k},\lambda)\), \(n^{\prime}(\overset{\rightarrow}{k},\lambda^{\prime}),\ldots>.\) The state of the composite system at some final time, \(t_{f}\), \(\mid\)f\(>\), given that at the initial time, \(t_{i}\), it was in the initial state \(\mid\)i\(>\)is then obtained using standard techniques of time-dependent perturbation theory, with the perturbation operator understood to cause the transition \(|f>\leftarrow|i>\). Turning to the last term in Equation (1), the perturbation operator, which describes the coupling between light and matter, takes the following explicit form: for the emitter and the absorber, both of which are chiral, the first few terms of the multipolar expansion of the electric polarization and magnetization fields are retained, yielding  
\[H_{int}(\xi)=-\epsilon_{0}^{-1}\mu_{i}(\xi)d_{i}^{\perp}(\overrightarrow{R}_{ \xi})-m_{i}(\xi)b_{i}(\overrightarrow{R}_{\xi})-\epsilon_{0}^{-1}Q_{ij}(\xi) \
abla_{j}d_{i}^{\perp}(\overrightarrow{R}_{\xi}),\ \xi=D,A \tag{3}\]  
where Roman indices denote Cartesian tensor components in the space-fixed frame of reference, and [PERSON]'s summation convention is in force for indices that repeat. In expression (3), \(\mu_{i}(\xi)\), \(m_{i}(\xi)\), and \(Q_{ij}(\xi)\) are the electric dipole moment operator, the magnetic dipole moment operator, and the electric quadrupole moment operator, respectively. The multipole moments couple directly to the electromagnetic fields.
# Online Distributed User Association for Heterogeneous Radio Access Network
## 6 Performance Evaluation
### CDF of Spectrum Efficiency  
One way to give a better experience to the UEs experiencing low spectrum efficiency is to take the resource from strong UEs and assign it to weak UEs. This task of taking the resource from strong UEs is done through load-balancing. Figure 6(a), for uniform UE deployment, shows the CDF of spectrum efficiency in HetNet for different association schemes. Approximately 85% of the UEs, for all the schemes, have a spectrum efficiency lower than 1 bit/s/Hz. This ensures that very few UEs close to the BS have a higher spectrum efficiency. _Prob. scheme 1_, _Prob. scheme 2_, _d-choices 1_, _d-choices 2_, and  
Figure 5: UE and BS deployment in the network.  
Figure 6: UE share for different association schemes in different deployment scenarios.  
_BS-centric_ schemes perform quite well at spectrum efficiency as compared to the traditional _max-SINR_ scheme. When considering the _BS-centric_ one, our proposed schemes give better service to the majority of UEs--approximately 65%--in terms of spectrum efficiency. In all the schemes (except the _max-SINR_) a majority of the UEs have a higher spectrum efficiency. Approximately 80% of UEs in all the schemes have higher spectrum efficiency, as compared to _max-SINR_. This is because the macro BS is highly loaded, and the total resource is divided among the UEs. This, in turn, lowers the spectrum efficiency of the associated UEs. For non-uniform UE deployment, although many UEs are placed around the lower-powered BSs, the outcome is similar to that of uniform deployment. All of our proposed schemes performed better in the mid-segment of the CDF graph, where the spectrum efficiency of the UEs are higher than that of _max-SINR_. Our proposed scheme, _Prob. scheme 1_, continuously performs better as compared to _BS-centric_ for non-uniform deployment, as shown in Figure 6(b).
## 4 Discussion
We assumed the tissue exists in a 0.1 mm radius cylinder surrounding the wire, which is probably the most arbitrary assumption in our calculation. The electric field in the tissue is almost certainly not independent of distance from the wire, as we have assumed in our one-dimensional model. All these assumptions will impact the predicted electric field in the tissue. However, our main conclusion is that the electric field in the tissue is over one thousand times larger for capacitive stimulation than for magnetic stimulation. Our assumptions would need to account for three orders of magnitude of difference, but it is not clear if accounting for these assumptions would make the electric field larger or smaller. The predicted electric field in the tissue is a factor of 60 less than what we would expect for the threshold for neural stimulation. Some combination of our assumptions and an error in the estimation of threshold as 10,000 mV/m might raise our predicted tissue electric field to threshold level. For instance, a hairpin loop may result in the tissue voltage drop occurring over the distance between the two parallel wires (0.1 mm) rather than over the length of the wire (2 mm), thereby raising the electric field in the tissue significantly. The evidence is convincing that [PERSON] et al. were somehow exciting neurons in their experiment [18], but it is difficult to imagine what other mechanisms could be active besides capacitive coupling and magnetic stimulation. Our model makes specific predictions about microcoil stimulation of the brain via capacitive coupling. We hope that our results will motivate experiments to test these predictions.
# An Analytic Transform Kernel Derivation Method for Video Codecs
## 1 Introduction
This method reduces the complexity of the DST-7/DCT-8 transform and reduces the memory usage by storing only the different point DCT-2 transform kernels. Although the number of TAs is reduced compared to [24], the total number of multiplications increases, i.e., total multiplication required is 160, 482, and 1468 for 16-, 32- and 64-point, respectively. This is because additional multiplication with the TAF has to be performed, and the normalization values of TAF are also not identical, thus resulting in the mismatch of the derived transform kernels with the original transform kernels. In this study, an analytical derivation of the transform kernel using a common sparse unified matrix is introduced. The proposed method reduces the total amount of memory for the MTS kernels by storing only 1648 elements instead of 8180 elements with 8-bit precision. The transform kernels used in this study were DCT-2, DST-7, and DCT-8. Using the proposed method, DST-4 and DCT-4 transform kernels can also be achieved without affecting the memory. Similarly, the proposed method supports the implementation of the fast algorithm of DCT-2 [22] and DST-7 [17]. Figure 1 shows the proposed common sparse unified matrix, composed of two parts: Unified DST-3 matrix (**U**) and grouped DST-7 matrix. The **U** matrix is composed of different block sizes of DST-4 and is used to derive different block sizes of DCT-2 transform kernels. It stores 1368 elements with 8-bit precision. Similarly, grouped DST-7 is used to derive different block sizes of DST-7 transform kernels. It comprises **P**, **Q**, **R**, and **S** matrices, which are different selected rows from 32-point, 16-point, 8-point, and 4-point DST-7 transform kernels. It stores 280 elements with 8-bit precision. Hence, the total memory allocation must be appointed for only 1648 elements for the overall common sparse unified matrix. The remainder of this paper is organized as follows. In Section 2, the proposed MTS kernel derivation method is described. In Section 3, the experimental results are provided, and the concluding statements are provided in Section 4.
## 3 Results and Discussion
### MM/PBSA Binding Free Energy Calculation: Screening
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Compound ID** & **Binding Energy (kJ/mol)** \\ \hline
1 & ZINC82121447 & \(-\)114.03 \(\pm\) 10.8 \\
2 & ZINC02170552 & \(-\)83.26 \(\pm\) 20.4 \\
3 & ZINC65274016 & \(-\)75.24 \(\pm\) 8.4 \\
4 & ZINC10763862 & \(-\)73.36 \(\pm\) 8.4 \\
5 & ZINC02412146 & \(-\)67.71 \(\pm\) 9.4 \\
6 & ZINC12780673 & \(-\)67.54 \(\pm\) 3.4 \\
7 & ZINC65384150 & \(-\)66.7 \(\pm\) 20.2 \\
8 & ZINC00807117 & \(-\)66.6 \(\pm\) 9.8 \\
9 & ZINC13031611 & \(-\)64.4 \(\pm\) 10.1 \\
10 & ZINC06764561 & \(-\)63.8 \(\pm\) 17.6 \\
11 & ZINC64755558 & \(-\)63.7 \(\pm\) 31.1 \\
12 & ZINC67726926 & \(-\)63.07 \(\pm\) 28.8 \\
13 & ZINC67981980 & \(-\)38.6 \(\pm\) 8.9 \\
14 & ZINC58341263 & \(-\)18.1 \(\pm\) 26.5 \\
15 & ZINC91526763 & \(-\)9.99 \(\pm\) 72.6 \\ \hline \end{tabular}
\end{table} 
Table 3: MM/PBSA calculated binding free energies of the compounds. The uncertainty is measured with a standard deviation.
## 2 Materials
### Sentinel-1 SAR Data
The identification of bare soil pixels with low vegetation cover was achieved by using a land cover map of France [89] to delineate agricultural regions and subsequently applying a threshold value to the NDVI (NDVI \(<\) 0.3) obtained from the S2 images (Section 2.5). Additionally, the \(\sigma_{\text{G}}^{0}\) values were obtained for both VV and VH polarizations, along with the average SAR incidence angle (\(\theta_{\text{G}}\)) at each grid cell for every SAR image.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 4 Discussion  
Maneuver is inevitable for a satellite in space because of various perturbations. Since the precise orbit is unlikely to be determined, a GNSS satellite experiencing maneuvering is useless or even harmful for real-time users if no early warning is provided. The proposed strategy takes the advantage of the fact that GNSS satellites are all equipped with high-precision atomic clocks that are stable in short time and unaffected by maneuvers. The clock offset of a satellite experiencing maneuvering can be precisely predicted based on the clock estimates during the periods before maneuvering, which can provide useful a priori information. With the constraint of predicted clocks, the traditional RPPP POD method has been significantly improved, which contributes to the rather precise orbit for the maneuvering satellites, especially in the radial direction. As the experimental results have shown, the average RMS of clock-constrained RPPP POD solution is dramatically reduced by a factor of 7-11 in the radial direction, which is of great significance to the real-time positioning users.  
Figure 8: Orbit differences between the forward and backward RPPP solutions for G22, G12, C02 and C04 around maneuvers.
# An Adaptive 24 GHz PSO-Based Optimized VCO in Next-Generation Wireless Sensor Networks
## 3 The Proposed Adaptive PSO Algorithm
### Experiments and Benchmark Test Functions  
In our work, we chose three benchmark functions, as shown in Table 1.  
The performance of the adaptive PSO algorithm can be significantly influenced by the Smax parameter. The value of \(S_{max}\) that resulted in the best minimum for the algorithm was selected and used consistently for that specific benchmark function in subsequent experiments. Furthermore, the scaling factor \"H\" in the adaptive PSO algorithm was optimized through a trial-and-error process, with a constant value of H = 0.05 applied across all benchmark test functions during the experiment. For the adaptive PSO, the maximum velocity values, \(S_{max}\), used in the benchmark test function experiments for \(F_{1}(y)\), \(F_{2}(y)\), and \(F_{3  
Figure 4: Convergence of adaptive and traditional PSO algorithms for \(f_{1}\).  
Figure 5: Convergence records of top solutions of adaptive and traditional PSO algorithms for \(f_{2}\).  
Figure 6: Convergence records of top solutions of adaptive and traditional PSO algorithms for \(f_{3}\).  
\begin{table}
\begin{tabular}{l c c c} \hline  & & **Adaptive PSO** & **Traditional PSO** \\ \hline \(f_{1}\) & \(\frac{\Delta f}{\Delta y}\) & \(1.9829\times 10^{-5}\) & 0.0196 \\ \(f_{2}\) & \(\frac{\Delta f}{\Delta y}\) & 0.0045 & 0.1170 \\ \(f_{3}\) & \(\frac{\Delta f}{\Delta y}\) & \(4.9018\times 10^{-9}\) & \(1.3092\times 10^{-4}\) \\ \(f_{2}\) & \(\frac{\Delta f}{\Delta y}\) & 1.4142 & 1.1414 \\ \(f_{3}\) & \(\frac{\Delta f}{\Delta y}\) & 0.0065 & 3.5863 \\ \(\Delta\) & 0.0090 & 2.4640 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical outcomes of the benchmark test function experiments.  
According to Figures 7 and 8, the average value of the best function (\(F_{2}\) & \(F_{3}\)) of the 50 running trials is shown below, compared with the parameters for the maximum value. These figures show that the best function value for the traditional PSO algorithm varies sharply with respect to \(S_{max}\), whereas for the adaptive PSO algorithm, it varies very gently with \(S_{max}\).
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 4 Phenology of Grasslands
### Matching Sentinel-2 with Phenocam Data in Grasslands
In comparison, the C-band microwave sensors on Sentinel-1 estimated the SOS and EOS with a delay of 10 and 20 days, respectively. Sentinel-2 was also better able to detect management practices, such as moving events on mountainous slopes, primarily because the phenology detected from Sentinel-1 was less accurate due to the limitations of the microwave signal. The lack of stability in the VV backscatter at high elevations and the non-applicability of general concepts to detect phenology of pasture lands that had a flat time series curve of VH backscatter (i.e., no seasonal amplitude could be detected to extract phenological metrics) also limited the applicability of microwave data. A review of the literature revealed that, in contrast to optical data, the signals from both passive and active microwave sensors are highly dependent on the land cover and hence need to be processed separately for each class [121; 122].
# Fully Reconfigurable Photonic Filter for Flexible Payloads
## 3 Proposed Structure Analysis
### Technological Platform  
The proposed filter is supposed to be fabricated in SOI technology. This choice has been dictated by the fact that silicon photonics represents a transformative technology closely tied to the increasingly demanding performance requirements of the datacom and telecom sectors. This technology leverages well-established manufacturing infrastructure and mature platforms that are compatible with CMOS technology, allowing for its transition from research laboratories to industrial manufacturing facilities. Additionally, the high index contrast of silicon (=3.47 @ 1550 nm) compared to its surrounding materials (e.g., SiO\({}_{2}\) with n\({}_{\text{SiO2}}\) = 1.44 @ 1550 nm) ensures compactness, tight bend radii, high-density integration, and low power consumption due to its reduced size.  
Thanks to advances in manufacturing techniques such as micro-transfer printing, hybrid integration, and wafer bonding, it is now possible to integrate high-bitrate germanium detectors, high-performance modulators, filters, routers, and even sources onto a single silicon chip [26].  
Indeed, silicon proves to be exceptionally well-suited for filters, primarily due to its ability to accommodate various tuning mechanisms. These mechanisms include the plasma dispersion effect achieved through carrier injection [27], accumulation [28] or depletion [29], the Pockels effect when combined with piezo-materials [30], the Franz-Keldysh effect when combined with germanium [31], and the Quantum Confined Stark effect when it has been created with Ge-Si-Ge quantum wells [32].
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 4 Case Study, Results, and Discussions
### PCA Assessment for the Best Algorithms  
The last analysis was performed to verify the influence of PCA on the results. The PCA potentially changes the classification based on the Borda count method (Table 2). In this sense, we ran the top 3 classified algorithms following the same premises of Section 4.3, considering the 6 original numerical dimensions, without PCA: K-Means, K-Medoids, and Hierarchical.  
Figure 12 shows the values obtained for the same 6 indexes from Figures 6-11, presenting the corresponding behavior before and after PCA application. On the right, we plot the same curves of the aforementioned figures, considering these 3 algorithms.  
Initially, we highlight that the numerical values in the ordered axis are not comparable with and without PCA, since the number of dimensions is different, so the metrics' values are also distinct.  
For SSE, SSW, and SI, one can observe the same tendency in the curve, revealing that the use of PCA did not change the comparative performance. Considering the SSB, CH, and WB indexes, we noted that the Hierarchical method increased its performance, overcoming the K-Means with PCA, and presenting a close behavior regarding the K-Medoids.  
Figure 12: Shape comparison before and after the application of PCA to each Index.  
In this regard, we elaborate a new ranking based on the Borda count method in Table 3 considering only Hierarchical, K-Means and K-Medoids:  
Table 3 reveals the same tendency as Table 2. The K-Medoids and Hierarchical methods stood out, but the PCA favored the performance of the last. Due to the small number of contender algorithms, the lousy performance of K-Medoids and the outstanding performance of Hierarchical regarding the SI-index are barely visible. Because of this, the high number of wins achieved by the K-Medoids increased its position in the ranking. Therefore, it is likely that the ranking in Table 2 should not be much modified in the top positions with or without PCA, corroborating the findings presented in Section 4.2.  
In general, it can be graphically verified that the influence of the PCA application does not interfere in the shape of the curve for the different indexes and algorithms despite slight ranking changes. Moreover, we noted a decrease in the average running time of K-Means and K-Medoids of around 2% and 8% regarding the Hierarchical algorithm. We advocate using PCA and more than one clustering method in real applications, especially for large datasets.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 2 Materials and Methods
### Methods
#### 2.3.4 Trend Analysis  
The Theil-Sen Median trend analysis is a common trend analysis method in the field of hydrology and meteorology, which has no specific requirements for data distribution,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drought Type** & **TVDI** & **MCI** & **SRHI (\%)** \\ \hline Drought free & [0, 0.4] & (\(-\)0.5, +\(\infty\)) & [60; 100] \\ Light drought & (0.4, 0.6] & (\(-\)1, \(-\)0.5] & [50; 60) \\ Moderate drought & (0.6, 0.8] & (\(-\)1.5, \(-\)1] & [40; 50) \\ Severe drought & (0.8, 1] & (\(-\)2, \(-\)1.5] & [30; 40) \\ Extreme drought & - & (\(-\infty\), \(-\)2] & [0, 30) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification criteria for TVDI, MCI, and SRHI.  
Figure 3: The flowchart of TVDI calculation based on FY-4.  
reducing the influence of missing values and outliers in the calculation process, with the following equation [54; 55].  
\[\beta=\text{Median}(\frac{\text{x}_{\text{j}}-\text{x}_{\text{i}}}{\text{j}- \text{i}}) \tag{5}\]  
where i and j are time series serial numbers, and X is the variable for which the trendiness needs to be calculated. \(\beta\) is the trend value of Sen. When \(\beta>0\), the values show an increasing trend, meaning drought strengthening in TVDI and drought reduction in SRHI. On the contrary, the values show a decreasing trend, meaning drought mitigation in TVDI and drought intensification in SHRI.
# A Refined Shape Sensing Method for Skin Antenna Structure Based on Inverse Finite Element Method
## 1 Introduction
References [16; 17; 18; 19] have some analyses of three-dimensional frame structures under static or damped harmonic excitations based on strains produced numerically and experimentally. Based on the kinematic assumptions of the first-order shear deformation theory (FSDT), [PERSON] et al. developed three-node inverse shell element shorted form iMIN3 [20], which profiles the complex plate and shell structures accurately. [PERSON] et al. formulated a four-node quadrilateral inverse-shell element [21], which takes the influence of hierarchical drilling rotation into account, avoiding the occurrence of the singular value well. However, due to the heterogeneous and anisotropy of composite and sandwich structures, iFEM based on FSDT may result in slightly insufficient estimates for shape sense. Then, based on Zigzag displacement deformation theory, [PERSON] et al. and [PERSON] et al. improved the iFEM, which is suitable for sensing complex structures [22; 23]. As mentioned above, the iFEM has prominent advantages, such as robustness, efficiency, and real-time response. However, the method needs numbers of inverse elements for shape sensing of flexible structures, leading to increasing the quantities of installing sensor. Therefore, the method is not directly suitable for a sophisticated antenna structure. This paper establishes a novel interpolation method to regenerate the structural deformation field based on data from the multi-node inverse finite element method. The technology optimally compromises the number of sensors and the inverse element and simultaneously retains the reconstruction accuracy. The outline of the paper can be summarized as follows. First, the deformation field theory for plate is presented, and the multi-node iFEM formula is derived based on first-order shear deformation theory (FSDT). Second, taking the five-node quadrilateral inverse finite element as numerical implementation to result in the structure deformation. After that, the displacement field of the practical antenna panel is generated by the proposed method by experimental and numerical analysis approaches, respectively. According to some response dates from iFEM, the utilization of the innovative fitting technology restructured the full deformation field of board very well. The results from numerical and experimental analysis verify the correctness of the method proposed. Finally, the conclusions about the superiorities of the refined restructure methodology are emphasized.