esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation
#### 4.1.4 Orb  
As previously stated, ORB methodology has the advantage of speeding up computation compared to most of the feature-based methodologies. As an example is the application of the aerial image mosaicing process based on ORB to remove the mismatch from thousands of putative correspondences by applying locality-preserving matching (LPM), cited in [95]. Another approach based on Bayesian frameworks aims to formulate it as a maximum likelihood problem and solve the geometric algorithm using the expectation maximization (EM) algorithm. To reduce the matching process, principal component analysis (PCA) is used, reducing dimensions and facilitating the feature extraction process without compromising accuracy, as shown in the root mean square errors (RMSE) results [96], improving the time process by using a GPU with CUDA, obtaining a faster matching process compared to SIFT and SURF. Other developments in the ORB methodology may concur in the implementation of techniques to relate the features; in the case of [97], a preprocess phase correlation method is used to obtain the overlapping area between the to-be-stitched image and the reference image, reducing the feature calculation. Then, using Hamming distance, the relation between the image matching points is improved compared to the classical ORB methodology. Similarly, using a mask to register local clustered ORB features and nonmaximal suppression to remove clustered points, only the feature point with the largest response value is retained [98]. Hamming distance is used for the matching step, and finally, PROSAC is applied to eliminate the wrong matches and calculate the transformation matrix between images. The result is an improvement on the correct matching points, slightly less than that of SIFT and almost the same speed as classic ORB. Table 5 presents a summary of the implementations using ORB.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON], [PERSON], [PERSON] [92] & Images are registered and stitched together seamlessly in real time. \\ \hline [PERSON], [PERSON], [PERSON] et al. [93] & By applying phase congruence, the images are stitched evenly with color changes and illumination. \\ \hline [PERSON] [94] & Accuracy of FAST-9 and FAST-12 methodology, compared in terms of completeness and correctness, is improved. \\ \hline \hline \end{tabular}
\end{table}
Table 4: FAST applied on UAVs.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Hierarchical Baseline Stereo-Matching
#### 3.3.1 Coarse Matching  
Coarse matching is executed macro-pixel-wise, rather than pixel-wise, along the corresponding epipolar line between the light field raw image pair. The matching cost indicates how well a macro-pixel \(mp_{i}\) in \(\mathbf{LF}_{left}\) corresponds to the macro-pixel \(mp_{i-d}\) in \(\mathbf{LF}_{right}\). Zero-mean normalized cross-correlation (ZNCC) has been verified as robust to illumination changes and is widely used in window-based local stereo-matching methods.  
\[\mathbf{C}(V_{i}^{L},V_{i-d}^{R})=\frac{\sum_{j=1}^{n}(V_{i}^{L}(j)-\overline{ V}_{i}^{L})\cdot(V_{i-d}^{R}(j)-\overline{V}_{i-d}^{R})}{\sqrt{\sum_{j=1}^{n}(V_{i} ^{L}(j)-\overline{V}_{i}^{L})^{2}\cdot(V_{i-d}^{R}(j)-\overline{V}_{i-d}^{R}) ^{2}}} \tag{3}\]  
where \(V_{i}^{L}\) and \(V_{i-d}^{R}\) are feature vectors of macro-pixels \(mp_{i}\) and \(mp_{i-d}\); \(n\) is the pixel number; \(\overline{V}\) is the average value of each feature vector.  
Then, we filter the cost volume using the guided filter [24]. It should be noted that, although Gaussian convolution is performed during the pyramid's construction, the macro-pixel map \(\mathbf{I}_{MP}(X,Y)\) cannot be used as color guidance due to aliasing effects. Therefore, we generate the center sub-aperture image by taking the central angular pixels from macro-pixels and perform guided filtering with this center sub-aperture image. Based on the filtered cost volume, the disparity for each macro-pixel \(mp_{i}\) is determined with the highest NCC score using the WTA (winner-take-all) strategy.
# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 5 Sensitivity of the Vol to Initial Assumptions  
### Type of Monitoring Technique and Accuracy  
The posterior distributions of the corrosion parameters when Bayesian updating is performed based on the monitoring data depend on the uncertainty of the technique applied, since this is an important parameter in the likelihood function used in the Bayesian updating (see Equations (9)-(11)). For some techniques, this might be hard to estimate accurately. Hence, in this section the influence of this measurement uncertainty is investigated. The error on the measured static strain \(\sigma_{\epsilon}\) is varied from 0.2 to 1.95 \(\mu\epsilon\)[47; 48; 49], the error of the strain mode shape from 0.5 to 1.95 \(\mu\epsilon\)[47; 48; 49], the error on the frequency \(\sigma_{\lambda}\)  
Figure 7: Influence of the number of elements equipped with sensors on the Vol (for the strategies, reference is made to Table 2).  
from 0.001 to 0.01 [50; 51], and the error on the norm of the mode shape from \(\sigma_{\phi}\) 0.001 to 0.01 [50; 51]. The influence on the VoI is visualized in Figure 8. It can be seen that there can be a substantial influence of the measurement error on the VoI. For this specific case and under the assumed cost values, the VoI increases with decreasing measurement error. The influence on the VoI is largest for the error on the mode shapes. The influence of the error on the frequencies is the second largest, followed by the error on the static strains and the error on the strain mode shapes, respectively. Changing these error models can also change the decision on the most optimal monitoring technique.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 11 Conclusions  
1. By tradition bone formation is divided into four phases: 1. Osteoclast differentiation; 2. Osteoclast-mediated bone resorption; 3. Osteoblast-mediated bone formation/mineralization; and 4. Osteoblast to osteocyte transformation and establishment of bone homeostasis.
2. Zero and modeled microgravity adversely affect all four phases of bone formation, increasing the maturation and the resorptive activities of osteoclasts, decreasing the differentiation, maturation, and bone forming abilities of osteoblasts, and causing apoptotic-mediated death of osteocytes with consequent dysregulation of bone homeostasis.
3. Microgravity-associated changes in osteoblast microstructure include extended cell shapes, fragmented and condensed nuclei, shortened microtubules, smaller and fewer focal adhesions, and even complete collapse of the cytoskeleton due to microfilament and/or microtubular failure. Osteocyte death is reflected by increases in lacunar vacancies and volumes and in the production of sclerostin, a mechanosensitive protein that inhibits bone formation and enhances bone resorption.
4. IGF-1 and its receptor IGF1R play critical roles in all phases of bone formation, promoting both radial and linear bone growth. Osteocyte and osteoblast responses to mechanostressors is IGF-1/IGF1R dependent and deletion of IGF-1 or IGF1R genes abates their responses to mechanical unloading and reloading. Treatment with recombinant IGF-1 (rIGF1) reverses these defects when IGF1R is intact, raising the prospect that rIGF-1 may prove useful in preventing bone loss associated with space travel or, more likely, in the microgravity of the Moon. Another potential candidate to prevent bone loss in space or on the Moon is romosozumab, a humanized antibody against sclerostin which has been approved by the FDA for treatment of osteoporosis.  
Figure 5: A careful balancing is required to diminish factors that lead to bone loss with interventions designed to maintain bone health during spaceflight.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author is grateful for the support of the Division of Academic Affairs in the preparation of this article.  
**Conflicts of Interest:** The author declares no conflict of interest.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 4 System Design and Methodology
### Satellite Imaging and Pre-Processing
The raw image data is verified by computing \(T^{\prime}(d)\bmod P(d)\) when receiving the sequence \(T^{\prime}(d)\); if the result is 0, the transmission is successful; otherwise, there is an error in the data. \[S(d)=d^{n}M(d)+U(d), \tag{6}\]  
\[U(d)=d^{n}M(d)\bmod P(d) \tag{7}\]  
After the image is transmitted to the local farm server or cloud server, preprocessing operation is required, aiming to enhance image quality for further analysis and processing. The first step is denoising the image, for which we use a median filtering algorithm, as shown in Equation (8). \[I_{denoise}(u,v)=median\{I_{raw}(u+i,v+j,\lambda)|(i,j)\in W\}, \tag{8}\]  
where \(I_{denoise}(u,v)\) is the denoised image signal, and \(W\) denotes the filtering window. After denoising, we enhance the image using histogram equalization to improve contrast and make details more visible, as shown in Equations (7), (10), and (11). \[h(k)=\sum_{u=0}^{Y-1}\sum_{v=0}^{N-1}\delta(I_{denoise}(u,v)-k), \tag{9}\]  
\[C_{cdf}(k)=\sum_{m=0}^{k}h_{hist}(m), \tag{10}\]  
\[I_{raw}(x,y,\lambda)=R_{sat}(x,y,\lambda)+N^{\prime}(x,y,\lambda), \tag{2}\]0  
where \(h_{hist}(k)\) represents the grayscale histogram, \(k\) is the grayscale level, \(C_{cdf}(k)\) denotes the cumulative distribution function value at \(k\), \(I_{enhanced}(u,v)\) is the enhanced image signal, \(Y\times N\) is the total number of pixels in the image, \(E\) represents the number of grayscale levels, and \(\delta\) is the Dirac delta function, which equals 1 when \(I_{denoise}(u,v)=k\) and 0 otherwise.
## 4 Discussion
Other volatile compounds showed fungistatic activity, stopping fungal growth without causing cell death [47]. [PERSON] et al. [48] showed that VOCs emitted by the bacterium _Bacillus amyloliquefaciens_ had a fungistatic effect on _Monilinia laza_, _Monilinia fructicola_ and _B. cinerea_. VOCs emitted by _A. pullulans_ also exhibited fungistatic activity against _B. cinerea_[49]. This study showed that VOCs emitted by _Naganishia_ sp. and 2-phenylethanol, the most abundant volatile compound emitted by this fungus, had a fungistatic effect against _B. cinerea_. This is the first report on the effect of VOCs produced by _Naganishia_ sp. against _B. cinerea_ in vivo. However, the biocontrol activity of _Naganishia_ species has been reported [50; 51; 52; 53] and has been used as a biological control in postharvest peaches, cherries, tomatoes, citrus, and pome fruits [52; 53]. Furthermore, _Naganishia albida_, the main component of Yieldplus(r), a biological product that was sold until 2011 for the control of _Botrytis_ during cold storage of postharvest strawberries [50]. Volatile compounds emitted by _Naganishia_ species have not previously been described. The six identified VOCs in this work have been reported as VOCs emitted by yeasts and other microorganisms [54; 55]. 2-phenylethanol has been widely reported with antifungal activity against _B. cinerea_, as a volatile compound produced by yeasts, such as _A. pullulans_, _S. cerevisiae_, _Candida intermedia_, _Candida sake_ and filamentous fungi such as _Trichoderma spiralee_, among others [36]. Additionally, 2-phenylethanol showed high antifungal activity against _B. cinerea_ in vitro and in vivo, without adverse effects on fruit quality [56]. On the other hand, 3-methylbutyl inhibited growth of the yeast and bacteria [57]. Also, this volatile compound inhibited the growth of _B. cinerea_[58], 2-heptanone showed antifungal activity against _Fusarium oxysporum_ f. sp. _niveum_[59]. Many works have proposed the use of antagonistic yeasts for biological control of _B. cinerea_[6; 60; 61]. Therefore, VOCs from _Naganishia_ sp. could be an alternative for postharvest control of _B. cinerea_. Interestingly, the most abundant compound emitted by _Naganishia_ sp., 2-phenylethanol, is classified as generally recognized as safe (GRAS) [62]. Additionally, it has been described that _Naganishia_ species are psychrophilic microorganisms, able to grow between \(-3\)\({}^{\circ}\)C and \(27\)\({}^{\circ}\)C [63]. These characteristics suggest that _Naganishia_ sp. could be used to develop a safe biopesticide for cold environments based on VOCs emitted by the fungus.
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
## 5 Results and Discussion
### Finite Element Model Validation of Natural Frequency  
The accuracy of the created FE bell crank model was investigated through a proper process of validation to minimize any modelling errors, including the element and discretization errors (meshing), and the input errors from boundary and loading conditions [31]. Table 2 represents the results obtained from the natural frequency analysis of the FE bell crank model. The mode shapes from the FE model were analyzed to find the one that best represented a sinusoidal wave as observed in the physical test. It was found that, during the vibrometer testing, the first natural frequency was found in the fourth mode shape.  
A similar mode shape result was found in the FE bell crank model (Figure 8), which displayed a sinusoidal motion at the fourth mode with a resonant frequency of 5285.7 Hz.  
When analyzing both experimental and FE results, the difference in the natural frequency at the fourth mode of each case is below 5%; hence, the FE bell crank model achieved a satisfactory validation against the vibrometer experimental test. The validated FE bell crank model was then used for further topology optimization analysis.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 2 Design and Analysis
### The Proposed Push-Push 48-GHz VCO
#### 2.2.2 The Push-Push VCO  
The schematic of the 48-GHz push-push VCO is shown in Figure 7. This VCO is composed of the cross-couple pair of \(M_{\mathrm{N2}}\), \(M_{\mathrm{N4}}\), \(M_{\mathrm{P1}}\), and \(M_{\mathrm{P2}}\), the LC tank of \(L_{1}\) and \(C_{\mathrm{v1}}\), the NMOS current mirror of transistors \(M_{\mathrm{N1}}\) and \(M_{\mathrm{N2}}\) with a channel width ratio of 16:1, and the LC noise-filtering circuit. In the traditional LC architecture, the inductor used as the resonant circuit can usually be replaced by a \(\lambda/2\) transmission line. However, this transmission line is the state of one wavelength, so the oscillation frequency at \(2f_{0}\) is in  
Figure 6: (**a**) The proposed \(\lambda/4\) transmission line in CMOS 90-nm process, (**b**) slow-wave factor (SWF), and (**c**) attenuation results of 50-\(\Omega\)\(\lambda/4\) transmission line.  
phase and multiplies, \(f_{0}\) is the differential signal cancellation, and an oscillation signal of \(2f_{0}\) is generated; this is extracted through the AC coupling capacitor \(C_{\rm B}\) through open-drain buffers \(M_{\rm N7}\) to output a double-frequency oscillation signal, and \(\lambda/4\) of the transmission line in the circuit is in the \(2f_{0}\) state. It functions as an RF Choke in the circuit to isolate RF signals. \(Z_{\rm in}\) represents the input impedance of the transmission line. The load impedance \(Z_{\rm L}\) in Equation (4) is 0, \(Z_{0}\) is the characteristic impedance of the transmission line, and L is \(\lambda/4\). It can be known that the signal under the \(\lambda/4\) transmission line is infinite and is an open circuit state.  
\[Z_{\rm in}=Z_{0}\times\frac{Z_{L}+jZ_{0}\tan\beta L}{Z_{0}+jZ_{L}\tan\beta L} =jZ_{0}\tan\beta L=jZ_{0}\tan\frac{2\pi}{\lambda}\times\frac{\lambda}{4}\approx\infty \tag{4}\]
# Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines
## 1 Introduction
Each stage is matched to optimum load impedance to produce enough power to drive the next stage. The detailed design is discussed in Section 2 with the simulation results. The on-wafer measurement results are provided in Section 3.
## 4 Proposed Antenna Codebook and MAB Approach
### Proposed Nested Two-Stage MAB Approach
#### 4.2.1 Proposed Nested Two-Stage TS Algorithm
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## 3 Results and Discussion
### Physiological Characterization
#### 3.2.2 Properties of Ethanol Tolerance of the Selected Yeasts  
The ethanol tolerance of the tested strains was evaluated under ethanol concentrations ranging from 8% to 16%. The results are shown in Figure 2B. Within the tested ethanol concentration range (8-16%), the growth of all strains was inhibited to varying degrees as the ethanol concentration increased. However, _Pichia kudriavzewii_ GAAS-JG-1 exhibited superior ethanol tolerance across the entire concentration range, particularly at higher ethanol concentrations, where its OD\({}_{600}\) values were significantly higher than those of the control strains. Specifically, at 8% ethanol concentration, the OD\({}_{600}\) value of _Pichia kudriavzewii_ GAAS-JG-1 was 1.42 \(\pm\) 0.01, slightly lower than that of CICC 31838 (1.57 \(\pm\) 0.01) but significantly higher than those of the other control strains (\(p\) < 0.05). As the ethanol concentration increased to 12-16%, the growth advantage of _Pichia kudriavzewii_ GAAS-JG-1 became more pronounced. At 12% ethanol, _Pichia kudriavzewii_ GAAS-JG-1 achieved an OD\({}_{600}\) value of 0.85 \(\pm\) 0.02, while the OD\({}_{600}\) values of the control strains CICC 1004, CICC 31838, and CICC 31399 were 0.59 \(\pm\) 0.02, 0.44 \(\pm\) 0.01, and 0.38 \(\pm\) 0.01, respectively, with significant differences observed (\(p\) < 0.05). Notably, at 16% ethanol concentration, _Pichia kudriavzewii_ GAAS-JG-1 maintained measurable growth with an OD\({}_{600}\) value of 0.49 \(\pm\) 0.03, whereas the OD\({}_{600}\) values of the control strains were all below 0.45, further demonstrating _Pichia kudriavzewii_ GAAS-JG-1's remarkable ethanol tolerance advantage in high-alcohol environments. Such ethanol tolerance demonstrates the promising potential of _Pichia kudriavzewii_ GAAS-JG-1 for applications in high-alcohol fermentation systems. Moreover, this tolerance characteristic may be related to _Pichia kudriavzewii_ GAAS-JG-1's unique metabolic mechanisms and cell membrane structure, as also observed in previous studies of ethanol-tolerant _Pichia kudriavzewii_ strains [11; 13].
## 4 Conclusions  
In this test, sensors were used to determine the temperature, water content, and dry density at different depths and explored the method of obtaining undisturbed large-scale soil column in the model box, which can well maintain the undisturbed structure of soil sample. The CT tomography tests were also carried out on the soil sample in the model test and obtained the full section CT test data above the freezing pipe. By analyzing all the data, we found that:  
1. Seepage has a very significant impact on the thickness of the freezing curtain. In the freezing process, due to the existence of seepage, the freezing curtain on the side near the sand layer below the freezing pipe is 40% thinner than that above. When melting, the seepage brings the upstream cooling capacity to the downstream. The process of drainage consolidation at the upstream is faster than the downstream.
2. The freezing-thawing cycle under the influence of seepage leads to the change of soil moisture content, especially in the area close to the frozen area, the moisture content of soft clay has decreased by 9% to 18%. The decrease of water content before and after freezing-thawing may be due to the fact that pores caused by freezing-thawing cycle promote the settlement of soil layer and then promote the drainage of soil water.
3. The distribution of the CT number along the height has obvious segmentation characteristics, and the CT number in the frozen area is significantly higher than that in the unfrozen area. We combined the CT number with the dry density obtained from the test and proposed a method for calculating the melt settlement according to the CT number and the real settlement of the model test. The calculated value is very close to the measured value, which means that the fitting method is accurate and effective.  
Conceptualization, J.Z. and C.W.; methodology, C.W. and Z.G.; software, Z.L. and H.Z.; validation, Z.G., C.W. and H.Z.; formal analysis, Z.G., C.W. and H.Z.; investigation, Z.G. and C.W.; resources, W.P.; data curation, Z.L. and H.Z.; writing--original draft preparation, C.W. and Z.G.; writing--review and editing, J.Z. and Z.G.; visualization, Z.L.; supervision, J.Z.; project administration, J.Z. and W.P.; funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.  
The research work herein was supported by the National Natural Science Foundation of China (No. 41702299) and the Foundation of State Key Laboratory of Frozen Soil Engineering (No. SKLFSE201916).