esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integration of Sensors and Geometric Processing
### Georeferencing of Pushbroom Scanner Data
A dual antenna tightly coupled GNSS/IMU solution can overcome these issues, but they tend to be heavier and more expensive. Two GNSS antennae at a relatively short baseline, e.g., ~1 m, can offer an absolute heading accuracy of 0.1\({}^{\circ}\). Machine vision data can be used to assist pose estimation and facilitate more accurate georectification through feature image matching and co-registration [103].
# In Silico SwissADME Analysis of Antibacterial NHC-Silver Acetates and Halides Complexes
###### Abstract  
This study investigates the antibacterial \(N\)-heterocyclic carbene (NHC)-silver complexes using the SwissADME platform, a web-based tool developed by the Swiss Institute of Bioinformatics (SIB). NHCs, particularly their silver complexes, have gained significant interest in medicinal chemistry for their potential as antibacterial and anticancer agents. The effectiveness of these complexes is closely linked to their structure, including factors like lipophililicity, which enhance their ability to penetrate bacterial cells and sustain the release of active silver ions. SwissADME provides computational estimates of pharmacokinetic properties, including absorption, distribution, metabolism, and excretion (ADME) characteristics, as well as drug-likeness and toxicity assessments. By evaluating parameters like molecular weight, topological polar surface area, lipophililicity (LogP), and water solubility, SwissADME offers insights into the drug-like potential of compounds. This study is inspired by a comprehensive review of antibacterial NHC-silver complexes published from 2006 to 2023, which identified superior structures with notable biological activity. The primary aim is to determine whether these active complexes exhibit distinct SwissADME parameters compared to others, providing a deeper understanding of the factors that influence their biological efficacy and aiding in the identification of promising drug candidates. Finally, experimental stabilities of exemplary complexes were confronted with absolute LUMO values derived from DFT calculations.  
A 2024 14 8865 1
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 3 Measurements  
### Measurement Samples  
Figure 5 shows the measurement samples in this study. Patterns (a), (b), and (c) are rectangular stubs, and patterns (d) and (e) are rectangular stubs with a series reactance at the attachment point. Pattern (f) is the radial stub for comparison. The substrate used in this experiment is a glass cloth-based, epoxy resin copper clad laminate Sunhayato NZ-30 KR (FR-4), which has a relative dielectric constant \(\varepsilon_{\mathrm{r}}\) = 4.4 and dielectric loss tangent tan \(\delta\) = 0.02 at 10 GHz. The substrate sizes are longitudinal width 75 mm, lateral width 100 mm, and height \(h\) = 1.6 mm, with a copper foil with a thickness \(t\) = 35 \(\upmu\)m. All sample circuit patterns are fabricated by etching. All I/O line widths are set to \(W_{0}\) = 3 mm, so that the characteristic impedance becomes \(Z_{0}\) = 50 \(\Omega\) for the matching to the measurement system.  
In pattern (a), the characteristic impedance of the stub line is investigated on the four types of \(Z_{1}\) = 12.5 \(\Omega\), 25 \(\Omega\), 50 \(\Omega\), and 100 \(\Omega\) for \(W_{1}\) = 19.4 mm, 8.4 mm, 3 mm, and 0.7 mm. In each, the impedance ratio is \(Z_{1}/Z_{0}\) = 1/4, 1/2, 1, and 2. As a method of further reducing the ratio of \(Z_{1}/Z_{0}\), there is a method of increasing the characteristic impedance of the main transmission line. Therefore, as in pattern (b), only the junction width to the stub line is set to \(Z_{0}\) = 100 \(\Omega\), where the impedance ratio is \(Z_{1}/Z_{0}\) = 1/4. In pattern (c), a tapered line impedance transformer consisting of nine steps is used to evaluate the reflection characteristics of the stub only. Pattern (d) is an inductance expressed by a 0.7 mm length line at the stub attachment point, and its line widths are set to two types, \(g\) = 7 mm and 0.7 mm, in order from the smallest inductance. Pattern (e) is made by a longer line length \(n\) in order to further increase the inductance of the pattern (d), and it was set to two types of \(n\) = 4 mm and 8 mm, in order from the smallest inductance. By setting the slit in the input side like (e), \(L\) is increased without changing the size of the resonator. Pattern (f) is a radial stub, and two types of central angles \(\alpha\) = 60\({}^{\circ}\) and 90\({}^{\circ}\) are examined. In the case where the central angle is \(\alpha\) = 60\({}^{\circ}\), the inner radius is \(R_{1}\) = 1.7 mm and the outer radius is \(R_{2}\) = 13.1 mm. And in the case of the central angle is \(\alpha\) = 90\({}^{\circ}\), \(R_{1}\) = 2.1 mm and \(R_{2}\) = 12.9 mm are used for resonating at \(f\) = 2.5 GHz by using the empirical formula [24].  
Figure 4: Calculated results of \(\left|S_{21}\right|\) and \(\left|S_{11}\right|\) for different inductances when \(Z_{1}/Z_{0}\) = 1/2. Four patterns show the case \(L\) = 0 nH, 1 nH, 10 nH, and 100 nH.
## 3 Results and Discussion  
### Yeast Screening for 3-Met Production  
A total of 46 yeasts were isolated from _Daqu_ samples then their ability to produce 3-Met was tested. Several of these yeasts were able to produce 3-Met but the yield of most strains was relatively low, as confirmed in other reports (Table 1), so they had no potential to produce natural 3-Met [2; 6; 16; 26]. In the present study, four yeasts produced 3-Met at concentrations exceeding 1.0 g/L (Table 5), which gave them an advantage over most previous yeasts, such as _S. cerevisiae_ EC-1118, _K. lactis_ KL71, _R. toruloides_ 6740 and _R. acuta_ 7028 (Table 1) [2; 3; 13]. Among the high 3-Met yielding strains evaluated in this study, the 3-Met yield of YHM-G was not significantly higher than strain F3301 but was significantly higher than strain Y1801 and F9403, and the yeast strain YHM-G produced 1.68 g/L of 3-Met among the highest 3-Met yield in natural microorganisms (Table 1) [27]. Strain YHM-G was therefore chosen as the target strain for the optimization of the medium composition and cultivation conditions to improve the low 3-Met productivity in natural strains.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{2}{c}{**Time point of L-Met Addition (h)**} & \multicolumn{2}{c}{**Glucose Concentration (g/L)**} & \multicolumn{2}{c}{**Yeast Concentration (g/L)**} & \multicolumn{2}{c}{**3-Met Concentration (g/L)**} \\ \cline{2-7}  & **A** & **Code A** & **B** & **Code B** & **C** & **Code C** & **Y** \\ \hline
1 & 6 & 0 & 30 & \(-1\) & 1.2 & 1 & 2.03 \\
2 & 0 & \(-1\) & 40 & 0 & 1.2 & 1 & 2.30 \\
3 & 6 & 0 & 40 & 0 & 0.8 & 0 & 2.98 \\
4 & 6 & 0 & 30 & \(-1\) & 0.4 & \(-1\) & 1.04 \\
5 & 6 & 0 & 50 & 1 & 1.2 & 1 & 2.49 \\
6 & 6 & 0 & 40 & 0 & 0.8 & 0 & 2.83 \\
7 & 6 & 0 & 50 & 1 & 0.4 & \(-1\) & 2.03 \\
8 & 12 & 1 & 50 & 1 & 0.8 & 0 & 2.07 \\
9 & 12 & 1 & 40 & 0 & 1.2 & 1 & 1.88 \\
10 & 6 & 0 & 40 & 0 & 0.8 & 0 & 3.04 \\
11 & 12 & 1 & 30 & \(-1\) & 0.8 & 0 & 1.61 \\
12 & 0 & \(-1\) & 30 & \(-1\) & 0.8 & 0 & 2.03 \\
13 & 0 & \(-1\) & 50 & 1 & 0.8 & 0 & 2.34 \\
14 & 0 & \(-1\) & 40 & 0 & 0.4 & \(-1\) & 1.84 \\
15 & 12 & 1 & 40 & 0 & 0.4 & \(-1\) & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The BBD design and the responses of the dependent variables.
## 1 Introduction  
Focal segmental glomerulosclerosis (FSGS) is a complex and heterogeneous glomerular disease that poses significant challenges in diagnosis, treatment, and long-term management. Characterized by scarring of the kidney's filtering units, FSGS can lead to progressive kidney dysfunction and, if left untreated, may result in end-stage renal disease. The disease's multifactorial nature, coupled with its variable clinical presentations, makes it particularly difficult to manage effectively without personalized treatment plans. Traditional clinical decision-making in FSGS often relies on a combination of patient history, laboratory results, and expert judgment, but such approaches can be prone to inconsistencies and delayed interventions.  
To address these challenges, the integration of machine learning (ML) and expert systems offers promising solutions in nephrology. In particular, the development of modular decision support systems can significantly enhance the ability to predict disease progression, recommend tailored treatment strategies, and monitor patient outcomes in real time.  
By leveraging large volumes of clinical data--ranging from laboratory test results to biopsy findings--these systems can assist clinicians in making evidence-based decisions that improve patient care and outcomes.  
This paper focuses on a specific module within a larger modular expert system designed for nephrology: the decision support module for focal segmental glomerulosclerosis. The system was given the working name FSGS Nephro Decision Support System (FNDSS). The module utilizes AI-driven models and expert-guided algorithms to streamline the management of FSGS. By integrating clinical data, patient-specific biomarkers, and dynamic treatment protocols, it enables timely risk stratification, precise treatment recommendations, and continuous monitoring of treatment efficacy. This approach not only helps in managing the disease more effectively but also contributes to a more personalized, patient-centered care model.  
The following sections delve into the design and functionality of the FSGS decision support module, exploring its key components, including risk classification, treatment guidance, and response monitoring. Through a detailed analysis of these elements, we aim to demonstrate how such a system can significantly enhance the clinical management of FSGS, ultimately leading to better patient outcomes and more efficient healthcare delivery.  
The contributions of this work are twofold. First, we present the design and implementation of the FSGS Nephro Decision Support System, highlighting its innovative approach to integrating machine learning models with expert algorithms to support clinical decision-making in FSGS. Second, we provide an in-depth evaluation of the system's performance in predicting disease progression, guiding treatment strategies, and monitoring patient outcomes, demonstrating its potential to improve clinical practice in nephrology. By leveraging advanced ML techniques and personalized data, this work paves the way for more efficient, precise, and individualized care for patients suffering from FSGS.
## 5 Experimental Results and Analysis
Terminal A normalized the received signal frequency distribution histogram and probability density distribution diagram, and terminal B normalized the received signal frequency distribution histogram and probability density distribution diagram. Among them, the green line is the measurement data of terminal A, and the red line is the measurement data of terminal B. The selected data in this group are the probability density histogram drawn by the indoor turbulence emulation device to simulate the atmospheric turbulence conditions and the samples close to the real atmospheric samples. Observing the graphs of normalized received signal changes over time in Figure 8a-c, it is found that the received signal changed over time at the A and B terminals and were very similar. That is, the instantaneous fading characteristics of the signals received by the two terminals had a certain correlation. In order to analyze the correlation between the instantaneous fading of the received signals at both ends, the measurement data represented by Figure 8a-c are calculated, and Equation (1) is used to obtain the reception at both ends of A and B. The instantaneous fading correlation coefficients \(\gamma_{AB}\) of the signal are 0.8739, 0.9062, and 0.9592 in sequence. This shows that the correlation between the instantaneous fading of the received signals at both ends was very strong at this time. At the same time, Equation (2) is used to calculate the light intensity scintillation factors of the three groups of spot signals in Figure 8. The received signal scintillation factors of terminal A were 0.33895, 0.13269, and 0.01125, and the received signal scintillation factors of terminal B were 0.33659, 0.13378, and 0.01113, respectively. Combined with the frequency distribution histogram and probability density distribution diagram of the spot signal in Figure 8a-c, it can be seen that the greater the scintillation factor of the received signal, where the probability density distribution obeys the logarithmic normal distribution, the smaller the scintillation factor of the received signal, and the probability density distribution obeys the normal distribution, which is consistent with the unidirectional laser propagation characteristics in the atmosphere studied by our research group, [PERSON] [22]. At the same time, it can be found that the larger the scintillation factor of the received spot signal, the stronger the turbulence intensity of the atmospheric channel, and the smaller the instantaneous fading correlation coefficient of the received spot signals of the two terminals; that is, as the turbulence intensity increases, the channel reciprocity becomes slightly worse.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions  
### Comparison with Existing 30 m-Resolution Land Cover Products  
The CLC-SW2010 product were compared with other existing 30 m-resolution land cover products in 2010 over Southwestern China, including CLUDs (the third row of Figure 12), FROM-GLC (the fourth row of Figure 12) and GlobeLand30 (the fifth row of Figure 12). It was found that the quality of CLC-SW2010 was superior to these products according to the respective preliminary validation results, the accuracy of FROM-GLC and GlobeLand30 reached 71.54% [5] and 80.33% \(\pm\) 0.2% [11], respectively. The lack of leveraging variability in phenological patterns is an important factor for the quality of GlobeLand30 and FROM-GLC [18]; however, it has been avoided in CLC-SW2010 by introducing multi-temporal satellite images and a variety of geo-knowledge. Although the total area of each land cover class is closer to reality, the boundary of land cover is vague and inaccurate in CLUDs (the third row of Figure 12).  
Four typical mountainous regions that are hard to map (a: Plateau regions host mostly grasslands; b: low mountains mainly consist of croplands; c: Karsi regions with complex land cover and d: steep mountainous areas mostly covered with forests) were taken as examples (Figure 12) to present the comparison results of four land cover products in detail. The CLC-SW2010 product showed more distinctly the vertical stratification of vegetation (Figure 12a) and the fragmentized landscape (Figure 12b,c) in mountainous areas than the other three products, and the proposed HC-MMK approach especially designed for land cover mapping in mountainous areas was the main contributor. There was also many classification errors identified between mountain shadow and water in FROM-GLC (the fourth row of Figure 12), which were eliminated by some simple geographical rules in CLC-SW2010.
## 2 Materials and Methods
### HPLC-RF Separation and Quantification of Tocpherols  
The oil samples (0.5 g) were dissolved in different volumes of hexane (15-25 mL), so that the tocopherol concentrations fell within the ranges of the calibration curves, filtered through 0.2 \(\upmu\)m Nylon filters and injected directly into the HPLC system (ShimadzuLC20 equipped with a Lichrosorb Si60 column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m) and a fluorescence detector RF20A (excitation \(\lambda\) = 290 nm and emission \(\lambda\) = 330 nm). The mobile phase was hexane: 2-propanol (99.5: 0.5, \(v/v\)), isocartically with a flow rate of 1 mL/min. Quantitative analysis was performed by external calibration with authentic standards (\(\alpha\)-tocopherol, \(\beta\)-tocopherol, \(\gamma\)-tocopherol, and \(\delta\)-tocopherol) (Extrasynthese, Genay, France) in the concentration range of 0.25-50 \(\upmu\)g/mL, having correlation coefficients R\({}^{2}\) of 0.9980.
## 3 Results and Discussion
### Chemical Characterization of the Residues
The monomeric composition of the hemicelluloses in this matrix consists mainly of mannose, galactose and, to a lesser extent, arabines residues forming galactomannans and arabinocalatans [36]. Moreover, xylose polymers are absent from this matrix, and this is a particular element that differentiates SCG from the remaining residues [36; 37]. In addition to its relatively high hemicellulose content, three additional parameters significantly differentiated SCG from other residues in terms of composition. These were the lower ash content (20.4 \(\pm\) 2.5 g kg\({}^{-1}\)) and higher crude lipids (139.8 \(\pm\) 5.4 g kg\({}^{-1}\)) and total phenols (49.2 \(\pm\) 2.4 g kg\({}^{-1}\)) contents than the other three residues. Crude lipid contents in SCG are very similar to those reported in various studies conducted on this matrix [18; 38; 39]. Concerning the phenolic content in SGC, some studies report concentrations ranging from (17 to 50) g kg\({}^{-1}\)[18; 40], and the main components of this fraction fall within the family of chlorogenic acids within which the quantitatively most relevant compound is 5-O-caffeoylquinic acid [39]. Among the matrices investigated, the lowest phenolic content was in wheat bran (9.2 \(\pm\) 1.7 g kg\({}^{-1}\)), although it is considered a relevant source of hydroxy-substituted cinnamic acids [41; 42]. However, a significant fraction of these aromatic acids is esterified to the side chains of arabines residues in araboxylans [35] and, therefore, not present in its free form in WB. Regarding other WB composition data, Table 1 shows that its holocellulosic fraction amounted, overall, to around 390 g kg\({}^{-1}\), in agreement with other works [43; 44]. For the same matrix, however, the obtained Klason lignin content (73.2 \(\pm\) 3.9 g kg\({}^{-1}\)) was significantly lower than those reported by [PERSON] et al. [44] and [PERSON] et al. [45], and equal to (108 and 121) g kg\({}^{-1}\), respectively. The Klason lignin contents in RTB and SCG were not significantly different from each other, providing values of about 200 g kg\({}^{-1}\) while, in barley straw, this parameter amounted to 165.2 \(\pm\) 2.4 g kg\({}^{-1}\). This parameter's highest value, equal to 257 g kg\({}^{-1}\), was found in DSF, since stoned olive pomace represents a significant fraction of the feed biomass to the digester from which this matrix derives. In SCG, the data relating to the total nitrogen content (22.8 g kg\({}^{-1}\)) and the C/N ratio (22.3), determined by elemental analysis, are also in agreement with a survey conducted by [PERSON] et al. [18] on a large variety of spent coffee grounds. From this perspective, the C/N ratios of spent coffee grounds and DSF did not significantly differ. This parameter's highest value, equal to about 86.6, was found in barley straw while, in RTB, it was equal to 71.8.
# Deep-Learning Based Positron Range Correction of PET Images
## 1 Introduction
### Positron Range Correction Methods  
Many correction approaches have been proposed to remove the blurring caused by PR on reconstructed PET images. These methods differ not only for the PR model they are based on, but also for how they are applied. Two main approaches for PRC in PET can be identified.  
PRC applied as a postprocessing step to the reconstructed images: In this approach, the reconstructed images before PRC are considered to represent the distribution of the positron annihilations, instead of the distribution of the positron emissions. Therefore, the goal of the postprocessing PRC procedure is to convert the annihilation distribution into the positron emission distribution (the one expected to be seen with PET). Postprocessing PRC has the advantage of being fast, simple, and to some extent independent of the procedures, algorithms, and codes used in the image reconstruction. On the other hand, it has the risk of increasing the noise in the final corrected images [25]. This approach has been applied using Fourier deconvolution techniques [27] with isotropic and tissue-independent kernels, as well as iterative deconvolution methods such as Richardson-Lucy [28], which enables the use of more realistic PR models.  
PRC applied within the tomographic iterative image reconstruction process: PR models can be included in a point-spread-function (PSF) or resolution kernel [13; 14; 25], or within the system response matrix (SRM) used in the iterative reconstruction. This can be argued to be the most common approach used for PRC with \({}^{18}\)F as, in fact, many image-reconstruction methods use SRM from point source measurements [29] obtained from \({}^{18}\)F in water, \({}^{22}\)Na in plastic, or realistic simulations [30] that incorporate PR effects. This approach has some important limitations: On one hand, adapting an existing SRM to other radionuclides could be difficult, unless a SRM is used wherein PR is factored out [31]. Furthermore, fully realistic PR models would require the SRM to be evaluated in each acquisition.  
In some works, a mismatched projector/backprojector pair has been proposed, with PR blurring only applied right before the forward projection operation. It has been shown that using a PR blurring kernel in the backprojector has only the effect of reducing the convergence speed of the iterative algorithm [8]. In this case, the SRM used should not incorporate any PR effects. In many cases, it is difficult to separate the PR from other blurring effects considered in the SRM, and including an isotope-dependent PRC in a reconstruction procedure introduces the risk of overcorrecting the PRC, yielding overshooting and Gibbs artefacts [32].
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
## 3 Results
### Comparison of OARs in Three Techniques on Dosimetry  
Table 2 shows the average dose of the thyroids, the heart, and the lungs. For the 3D-CRT plan, the left- and the right-thyroid doses were 0.10 \(\pm\) 0.01 Gy and 0.18 \(\pm\) 0.02 Gy, respectively; the atrium and the ventricle doses of the heart were 0.69 \(\pm\) 0.03 Gy and 0.70 \(\pm\) 0.01 Gy, respectively; and doses of the left upper lung, the right upper lung, the left lower lung, and the right lower lung were 6.14 \(\pm\) 0.03 Gy, 0.69 \(\pm\) 0.03 Gy, 0.24 \(\pm\) 0.03 Gy, and 0.13 \(\pm\) 0.02 Gy, respectively. Alternatively, for the IMRT plan, the left- and the right-thyroid doses were 0.11 \(\pm\) 0.02 Gy and 0.20 \(\pm\) 0.01 Gy, respectively; the atrium and the ventricle doses of the heart were 0.78 \(\pm\) 0.04 Gy and 0.94 \(\pm\) 0.02 Gy, respectively; and doses of the left upper lung, the right upper lung, the left lower lung, and the right lower lung were 8.22 \(\pm\) 0.01 Gy, 1.02 \(\pm\) 0.02 Gy, 0.52 \(\pm\) 0.01 Gy, and 0.15 \(\pm\) 0.03 Gy, respectively. Lastly, for the VMAT plan, the left- and the right-thyroid doses were 0.34 \(\pm\) 0.03 Gy and 0.38 \(\pm\) 0.01 Gy, respectively; the atrium and the ventricle doses of the heart were 2.88 \(\pm\) 0.02 Gy and 4.05 \(\pm\) 0.04 Gy, respectively; and doses of the left upper lung, the right upper lung, the left lower lung, and the right lower lung were 8.93 \(\pm\) 0.02 Gy, 3.95 \(\pm\) 0.03 Gy, 0.83 \(\pm\) 0.03 Gy, and 0.77 \(\pm\) 0.03 Gy, respectively.  
These results indicated that the three techniques did not show significant differences in the thyroid dose. Due to its superior CI, the VMAT plan had a substantially higher average heart dose than the 3D-CRT and the IMRT plans. In addition, for all the three techniques, the dose of the measurement point located at the ipsilateral lung (No. 97) was considerably higher than the overall lung dose.