esa-sceva/satcom-chunk-collection huggingface dataset:
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Optimal High-Spatial-Resolution Image Generation  
Pansharpening involves the injection of high-frequency information from panchromatic images into multispectral images and can be defined by Equation (1) [21; 22; 23]:  
\[\widehat{\mathbf{MS}}_{n}=\widehat{\mathbf{MS}}_{n}+g_{n}(\mathbf{P}-\mathbf{I} _{L}),\ n=1,\ldots,N \tag{1}\]  
where \(\widehat{\mathbf{MS}}_{n}\) is the sharpened multispectral image of the \(n\)th band, \(\widehat{\mathbf{MS}}_{n}\) is the interpolated image of the multispectral image on the scale of \(\mathbf{P}\), \(g_{n}\) is the vector of injection gains, \(\mathbf{P}\) is the panchromatic image with a high spatial resolution, \(\mathbf{I}_{L}\) is the synthetic intensity image with a low spatial resolution, and \(N\) is the number of spectral bands.  
The quality of the sharpened image varies according to the method used to calculate the injection gain \(g_{n}\), and there is a trade-off relationship between spatial and spectral accuracy. Pansharpening techniques can be divided into CS- and MRA-based techniques based on the method used to produce the low-spatial-resolution image \(\mathbf{I}_{L}\)[24; 25]. However, to sharpen satellite images that do not provide panchromatic images, it is necessary to produce artificial panchromatic images. These images can be produced using the selected and synthesized band schemes proposed by [PERSON] et al. [15].
## 2 Results and Discussion
### Comparison of the Pesticide Concentrations in Water with Regulatory Values  
The number and amount of pesticides dumped into the environment are continuously increasing, such that the concentrations of pesticide residues found in surface water and groundwater present a serious threat to human health and the environment. Various regulatory limits were established by national, regional, and international institutions for pesticide residues in different kinds of water. Table 5 summarizes the maximum admitted levels of the investigated pesticides in surface, ground, or agricultural waters, or in drinking water. It should be noticed that the regulatory values for drinking water are always lower than those admitted for other water qualities. A comparison of these admitted concentrations to the validation parameters obtained by the developed SPME-GC-MS method shows that the maximum levels of pesticides are higher than the corresponding LOD value. Thus, the analytical method proposed in the present study is suitable for the determination of the investigated pesticide residues in irrigation, agricultural, surface, or groundwater waters. The pesticide concentrations found in the examined water samples were also compared to the regulation values. Except for pirimiphos-methyl, which showed a level lower than its corresponding maximum residue limit, the concentrations of the other detected pesticides notably exceeded the maximum allowed limits. These results are  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pesticides**} & \multicolumn{4}{c}{**Present Work**} & \multicolumn{4}{c}{**Previous Studies**} \\ \cline{2-9}  & **LOD** & **Range** & **LOD** & **mg** & **Range** & **Resovery (\%)** & **Detection** & **Ref.** \\  & **(ng mL\({}^{-1}\))** & **(ng mL\({}^{-1}\))** & **Recovery (\%)** & **mL\({}^{-1}\))** & **(ng mL\({}^{-1}\))** & **Recovery (\%)** & **Detection** & **Ref.** \\ \hline Diazinon & 2.1 & 5-1000 & 103–105 & 0.003 & 0.05–1 & 85–115 & MS/MS & [56] \\ Chloropyribos-methyl & 1.4 & 2–5-500 & 96–109 & 0.01 & 0.05–50 & 87–109 & MS/MS & [34] \\ Primimibos-methyl & 2.3 & 2.5–250 & 95–114 & 0.01 & 0.05–50 & 80–99 & MS/MS & [34] \\ Aldrin & 1.9 & 2.5–500 & 78–98 & 1.91 & – & 44–125 & MS & [51] \\
**Malathion** & 2.1 & 2.5–500 & 93–104 & 0.38 & 0.4–30 & 81–92 & MS & [57] \\ Chloropyrifos & 1.1 & 2.5–250 & 105–117 & 0.002 & 0.05–1 & 93–102 & MS/MS & [56] \\
**Forentos** & 0.8 & 1–500 & 82–95 & 0.056 & 0.17–200 & 90–101 & MS & [58] \\
**Efhion** & 0.3 & 0.5–250 & 90–104 & 0.13 & 0.4–35 & MS & [57] \\
**Tebuconazole** & 0.7 & 1–500 & 90–98 & 0.02 & 0.05–5 & 102–112 & MS/MS & [34] \\
**Fenropoathrin** & 2.5 & 2–500 & 99–109 & - & - & - & - \\
**A-Cyhalothrin** & 1.0 & 1–500 & 89–105 & 0.02 & 0.05–5 & 101–105 & MS/MS & [34] \\
**Delfamethrin** & 2.0 & 2.5–500 & 78–116 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the developed method with those of previous studies.  
worrying, and confirm the importance of the analysis of irrigation waters, as well as the reasonable use of pesticides.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 4 Performance of Different Antenna Configurations
### Different Performance of 3 \(\times\) 3 Antenna Configurations  
Unlike the 2 \(\times\) 2 antenna configuration, the 3 \(\times\) 3 antenna array configuration has a more complex channel matrix, which results in an approximate solution. To compare the difference of the 3 \(\times\) 3 antenna configuration between the arc array and rectangular array, MATLAB simulations were carried out at different \(\xi\) with the same SNR of 10 dB. Figure 9 shows the simulation results at different aspect ratios.  
Figure 9 shows the trend of channel capacity for different 3 \(\times\) 3 antenna array configurations. Both configurations will produce peaks at different aspect ratios, and the peaks will move backward as the distance of the antenna elements increases. For the circular arc antenna array, it needs to meet Equation (42), which cannot be satisfied in some cases, as shown by the red line in Figure 9a, which means that the configuration cannot achieve the optimal channel capacity in this case. For rectangular antenna array, there are many peaks in Figure 9b, which means that the channel fluctuates a lot, especially in short distance transmissions. In this case, the rectangular configuration is sensitive to small distance changes within the channel and is not suitable for short-distance transmission. On the contrary, the circular arc array changes relatively gently and it is not sensitive to small distance changes, so it is more suitable for short-distance transmission. Moreover, due to the particularity of the circular arc configuration, the spacing of the antenna array elements can be set to be smaller under the same conditions, especially when the scale of the antenna is large.
## 2 LMI-Based Control and Filter Optimization
### Optimal Output Feedback Control
Its volume is proportional to  
\[\sqrt{\det(\mathbf{\mathcal{M}}_{v})}=\sqrt{\frac{\mathrm{trace}\Big{\{}\mathbf{ \mathcal{G}}_{v}^{T}\mathbf{\mathcal{P}}\mathbf{\mathcal{G}}_{v}\Big{\}}}{\det\bigl{(} -\mathbf{\mathcal{A}}_{v}\bigr{)}}}\enspace. \tag{50}\]  
Generalizing the statements from [26], the minimization of the ellipsoid volume--with a simultaneous maximization of the error domain for which the linear feedback signals are bounded by some positive constant according to [34] after introducing the denominator terms depending on \(\mathbf{Q}\) and \(\mathbf{Q}_{l,i}\)--leads to the cost  
\[\mathcal{J}_{v}=\frac{\mathrm{trace}\Big{\{}\mathbf{\mathcal{G}}_{v}^{T}\mathbf{ \mathcal{P}}\mathbf{\mathcal{G}}_{v}\Big{\}}}{\det\bigl{(}-\mathbf{\mathcal{A}}_{v} \bigr{)}}\cdot\frac{1}{\det(\mathbf{Q})\cdot\prod\limits_{i=1}^{n_{y}}\det( \mathbf{Q}_{l,i})} \tag{51}\]  
to be minimized for each vertex \(v\). Nonlinearities in the argument \(\mathbf{\mathcal{G}}_{v}^{T}\mathbf{\mathcal{P}}\mathbf{\mathcal{G}}_{v}\) of the trace in (51) are removed by a relaxation into the matrix inequality  
\[\mathbf{\mathcal{N}}_{v}\succ\mathbf{\mathcal{G}}_{v}^{T}\mathbf{\mathcal{P}}\mathbf{ \mathcal{G}}_{v}\enspace\text{corresponding to: }\mathbf{\mathcal{N}}_{v}-\mathbf{\mathcal{G}}_{v}^{T}\mathbf{\mathcal{P}}\mathbf{ \mathcal{G}}_{v}\succ 0 \tag{52}\]  
with \(\mathbf{\mathcal{N}}_{v}=\mathbf{\mathcal{N}}_{v}^{T}\succ 0\), which finally leads to  
\[ \begin{bmatrix}\mathbf{\mathcal{N}}_{v}&&\mathbf{\mathcal{G}}_{v}^{T}\\ \mathbf{\mathcal{G}}_{v}&&\begin{bmatrix}\mathbf{Q}&&\mathbf{0}\\ \mathbf{0}&\mathrm{blkdiag}\Big{(}\mathbf{Q}_{l,1},\ldots,\mathbf{Q}_{l,n_{y}} \Big{)}\end{bmatrix}\end{bmatrix} \succ 0 \tag{53}\]by applying the Schur complement formula. Summing up the expressions (51) for all \(v\in\{1,\ldots,n_{y}\}\), as well as replacing the denominator terms depending on the gain values in (34) by their result from the previous iteration step and doing the same with the gains in (53) complete the proof. Figure 4 provides a structure diagram of the complete iteration process for the parameterization of the filter-based control law of Case 3. There, the precision parameters \(\epsilon_{1}>0\) and \(\epsilon_{2}>0\) need to be chosen so that they are much smaller than the norms of the gains \(\mathbf{K}_{y}\) and \(\mathbf{k}_{f,i}\) resulting from the initialization phase that is carried out prior to the while-loop, for example \(\epsilon_{1}=10^{-6}\cdot\left\|\mathbf{K}_{y}\right\|_{2}\) and \(\epsilon_{2}=10^{-6}\cdot\left\|\mathbf{k}_{f,i}\right\|_{2}\). Figure 4: Structure diagram of the iteration procedure for the proposed filter-based control parameterization. **Remark 2**: _For the examples considered in the following section, the while-loop typically terminated after no more than 30 iterations, where each iteration step took less than a second on a standard notebook computer._
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 2 Materials and Methods
### Post Processing  
In this step, a set of OpenCV functions were applied with Python 3.7. For example, in the first function, morphological operators like \"Erode\" and \"Dilate\" were applied to the images, then the \"FindCountours\" function was applied to help extract their contours. The contour can be defined as a curve that joins all the continuous points at the boundary of the PV installation. So, the \"ContourArea\" function was then used to find the area of the previous contour. Following this pattern, the area was compared with 400 others to filter the bigger area and eliminate the little areas belonging to false positives. Then the \"ApproxPolyDP\" function was used to approximate a shape of the contour to another shape with fewer vertices. Subsequently, the \"DrawContour\" function was used to draw the resulting contour [82; 83]. Finally, the \"Erode\" morphological operators were used again, to expand the known area and compensate for the limitations of the mask with regard to the CPP method and some faulty occurrences caused by the false positives of the DL server. The pseudocode of the openCV functions used is shown in Algorithm A1 in the Appendix A.
## 4 Conclusions  
In conclusion, our proposed method provides a valuable tool for accurately measuring the atom number in an OLC system where clock stability is limited by quantum projection noise, photon shot noise, and technical noise. The numerical results indicate that our approach can achieve an estimated uncertainty below 4% for atom numbers ranging from 50 to 6000 (the maximum setting value in this study). Furthermore, this level of uncertainty is achieved with a total simulated time of 6.7 h. It is important to note that by increasing the simulated time, it is possible to further reduce the uncertainty even more. We also investigated how the fluctuation of atom number affects the measurement results. The numerical results indicate that the standard deviation of atom number fluctuation should be controlled to be below 0.14 times the average atom number. Ensuring a stable atom number over an extended period is crucial in our proposed method. This can be achieved by controlling the atom number during the first stage of cooling and trapping, for example, by providing feedback on the duration of the Zeeman slower or the two-dimensional collimation light [29]. This work can advance research in many-body interaction [13; 14; 19], nondestructive detection [30], and entanglement of atoms [31], as the precise measurement of atom number enables more precise control and manipulation of atomic systems.  
Writing--original draft preparation, G.Z. and F.G.; software, G.Z., F.G. and X.L.; writing--review and editing, X.L. and H.C.; supervision, H.C. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant No. 12203057), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB35010202).  
Figure 7: The atom number fluctuation and estimated uncertainty as a function of \(\sigma_{\text{f}}\). (**a**) The fluctuation of atom number at \(\sigma_{\text{f}}\) = 5%, 10% and 20%, respectively. (**b**) Measurements of the atom number (averaging value of 50 simulations) and corresponding relative uncertainty (the standard deviation of 50 simulations) as a function of \(\sigma_{\text{f}}\). The dashed line shows the set averaging number of atom of 2000.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract
Although it does not directly relate to effector configuration of the system, it implements an optimization framework to exploit best control capability given the system redundancy. The described methods in the literature aims to optimize a certain target within the design space of redundant inputs from a control's perspective. In this paper, we propose a different optimization framework which directly take into account performance requirements, including mission profiles, the desired control bandwidth of transient maneuver and the disturbance rejection ability, using the novel concept of Requirement Moment Set (RMS). Similar to an AMS, an RMS is a set in the moment space, often convex, which consists of all the moments required by a system to perform its predefined tasks. In order for a system to be capable of fulfilling its tasks, the AMS should fully enclose the RMS, and any violation of this enclosing means failure to meet the requirements otherwise. The RMS can be calculated from the predefined mission trajectory, the transient maneuver bandwidth as well as the desired ability to reject disturbances. The RMS seeks to aid the description of a system's control capability, i.e., AMS, by referencing its relative amplitude with respect to the mandatory moments driven by the mission requirements instead of purely examining the size of an AMS itself. It also provides a good insight into the requirements whether they are too ambitious to the overall vehicle design. In this paper, we focus on formulation of the optimization problem and address the related algorithm to solve for the geometry coverage problem between the AMS and RMS as two polyhedral. To define the coverage problem, we propose a mathematical definition that represents the margin between the two convex polytopes, and the optimization directly targets to maximize this margin. Calculation of this margin requires finding intersections between rays extending from vertices of one polyhedron with the facets of another polyhedron. The margin needs to be evaluated in each optimization step such is computationally demanding due to the large number of vertices on a precisely calculated RMS and additionally a large number of facets on the AMS of a highly redundant system. To handle this demanding computation, an efficient intersection solver is proposed to reduce the computation burden. The proposed methods are applied to an eVTOL with eight hover propellers to optimize for their installation angles. Derivation of the RMS is detailed in another submitted paper \"Required Moment Sets: Enhanced Controllability Analysis for Nonlinear Aircraft Models\" [8], which is directly used for this work as a priori information. A brief structure of the paper is given here: Section 2 briefs on the theoretical background and approach overview; Section 3 mathematically formulates the optimization problem; Section 4 elaborates on the efficient intersection algorithm; Section 5 discuss the implementation details and the results; Section 6 briefly concludes the paper.
## 2 Materials and Methods  
### Sample Preparation and Reagents  
_Agastache rugosa_ extract (ARE) was produced and provided by the Natural Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB) of the Korea in August 2019 Collected plants were identified and the voucher specimen (D 190724001) was deposited in KRIBB. ARE produced through the extraction process of _A. rugosa_ leaves (air-dried in dark) at 50 \({}^{\circ}\)C-80 \({}^{\circ}\)C using 30-70% ethanol for an optimized amount of time. The ethanol was then collected and reused. ARE was then concentrated by evaporating the ethanol, drying the sample either by freeze-drying or spray drying, pulverizing the dried sample and storing the resulting extract at -4 \({}^{\circ}\)C for posterior use.  
Tilianin was obtained from Ensol Biosciences (Daejeon, Korea). Oil-red-O, acacetin, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) and dexamethasone(DEXA) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Water and acetonitrile (HPLC grade) were purchased from J.T. Baker (Phillipsburg, NJ, USA). Dulbecco's Modified Eagle's medium (DMEM), fetal bovine serum (FBS), bovine calf serum (BCS) and penicillin-streptomycin (P/S) were supplied by Hyclone (Logan, UT, USA). Trypsin-EDTA and insulin were purchased from Gibco. IBMX was obtained from Santa Cruz Biotechnology (Dallas, TX, USA).  
Figure 1: The chemical structure of tilianin. Tilianin is one of the major bioactive compounds in _Agastache rugosa_ extract.
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
## 2 Estimation Algorithm  
A modal approach that does not include integration and differentiation processes is mainly used to estimate SDS. The modal approach, based on the shape superposition method, is a theory that has been used in various fields for the mechanical analysis of structures, and [12] first applied it to the estimation of SDS. [PERSON] et al. defined the relationship between displacement and strain using a modal approach and developed an algorithm that can estimate displacement using strain data [20]. In addition, an estimation algorithm based on the shape-superposition method using displacement data was developed. In this study, the estimation algorithm proposed by [20] was extended based on [12] to additionally utilize slope and strain data.  
In a linear problem, each response of the structure can be expressed as the product of the shape function \(\Phi\) and weight factor \(\alpha\) based on the shape superposition, as shown in Equations (1)-(3). The weight factors for displacement \(u\), slope \(\theta\), and strain \(e\) are the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
same because the slope and strain are the first and second derivatives of displacement, respectively. This is the basic idea of an estimation algorithm that uses multi-response data.  
\[\{\mathfrak{u}\} = [\Phi_{u}]\{\alpha\} \tag{1}\] \[\{\theta\} = [\Phi_{\theta}]\{\alpha\}\] (2) \[\{\epsilon\} = [\Phi_{e}]\{\alpha\}. \tag{3}\]  
The composition system and analysis process of the estimation algorithm developed in this study are presented in Figure 1. The estimation process of the deformed shape consists of three steps: structure shape function composition (step 1), measurement of data (step 2), and deformed shape estimation (step 3). In step 1, an arbitrary superposed shape matrix (ADS) is constructed by using a shape function matrix \(\Phi\) that consists of a structural shape or mode shape and weight factor. In step 2, a measurement data matrix MD is formed by combining the measurement data of displacement, slope, and strain. In step 3, the error function E is constructed by combining ADS and MD, and the weight factor that can minimize E is calculated using the least-squares method. Finally, the deformed shape can be estimated by applying the weight factor calculated in Equation (1).
# Targeted Therapy in Metastatic Bladder Cancer: Present Status and Future Directions
## 4 Targetting ErbB Receptor Family Signaling
HER2 alterations are equally found in TCGA MIBC and breast cancer datasets, although MIBC is characterized by more point mutations and fewer gene amplifications [9]. Putative activating mutations in the extracellular domain of HER2 are described as being present in 40% of the histological variant micropapillary UC compared to 5-9% of non-micropapillary UC [34; 35]. Micropapillary disease is a rare histological subtype present in around 2% of MIBC patients and is associated with a poor prognosis [36]. The association between putative HER2 activating mutations, worse prognosis and a particular histological variant suggests driver effects of putative HER2 activating mutations. On the other hand, HER2 amplifications are most frequently detected in the MIBC subtype identified as \"genomically unstable\" [27]. This subtype is associated with worse prognoses and is characterized by frequent chromosomal aberrations that can lead to copy-number alterations (CNAs). Due to frequent RNAs detected in this subtype, it has been hypothesized that HER2 amplifications, in the presence of many other large chromosomal aberrations, are possibly passenger effects in MIBC [37]. Thus, patient selection based on HER2 amplifications or IHC alone might not be the best method for selecting MIBC patients for HER2-targeted therapy [27; 37]. Currently, the LUX Bladder-1 trial is investigating monotherapy with the ERB1-4 inhibitor afatinib in platinum-relapsed mUC patients with either HER2 or HER3 mutations, HER2 amplifications, or EGFR amplifications (NCT02780687). In summary, HER2 and EGFR inhibitory therapy have been evaluated in MIBC with initially disappointing results, which was potentially due to an inadequate patient selection process that focused on HER2 amplifications and/or overexpression instead of activating mutations. Future studies on the targetability of HER2 and EGFR should not neglect the molecular landscape of MIBC, which suggests that patient selection should be based on activated HER2 mutations (micropapillary variant histology) and HER2 or EGFR regulon activity as interesting candidates for ErbB inhibitory therapy.
# Predicting Fruit's Sweetness Using Artificial Intelligence--Case Study: Orange
## Materials and Methods
### Data Mining and Structure Algorithms
#### 2.6.3 SVMs (Support Vector Machines)  
SVMs are derived from statistical learning theory, in order to classify points by assigning them against one of two separate halved distances. Therefore, an SVM essentially performs binary classification. For linearly separable data, an SVM finds the hyperplane, which increases the distance between the training samples and the class boundary. For cases that are not linearly separable, the samples are mapped to a high-dimensional space where a discrete hyperplane can be found. The allocation is performed by a mechanism that is called a kernel function [52].