esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Biosynthetic Pathways of Transcription Factors, Signaling Molecules and Secondary Metabolites
This experiment suggests that the gene regulation of the alkaloid biosynthesis in _C. roseus_ is modulated by a cascade of transcription factors belonging to different families (AP2/EREBP and bHLH) [95; 98]. Another example of a JA-responsive gene that participates in secondary metabolite production is found in _Artemisia_ annua (Figure 3) [97; 99]. _A. annua_ produces artemisinin, a natural product drug for malaria treatment. It was found that two genes encoding _AaERF1_ and _AaERF2_, which also belong to the AP2/EREBP family of transcription factors, are responsive to JA. Subsequently, those transcription factors were then found to bind to the promoters of genes that encode for key regulator enzymes in the artemisinin biosynthesis pathway, which are known as sesquiterpene synthase amorpha-4,11-diene synthase (ADS) and P450 monooxygenase CYP71 AV [97]. In support of this finding, researchers also observed that overexpression of AaERF1 and AaERF2 in transgenic _A. annua_ plants increased the expression of the biosynthetic enzymes (Figure 3) [97]. However, it is obvious that these facts indicate that the jasmonates as plant phytohormones interact with transcription factors and hence confer great control over the expression of many target genes, especially in secondary metabolite production. The bHLH and MYB TF family plays a significant role in the regulation of many biosynthetic pathways that are involved in secondary metabolite metabolism and production [6; 30; 72]. MYB proteins play roles in controlling phenylpropanol metabolism; for example, in _Petunia_ flowers, where the _AN2_ gene product is required for anthocyanin production, it has been shown to encode an MYB-related product [6; 72; 100; 101]. _AtMYB75_, _AtMYB90_, AtMYB113, AtMYB114, production of anthocyanin pigment 1-4 (PAP1-4) and TT2 (_AtMYB123_) are known to regulate anthocyanin and proanthocyanidin biosynthetic pathways [27; 72; 100; 102]. The anthocyanins, proanthocyanins and flavonoids belong to one of the largest groups of secondary metabolites that are distributed widely in plants; flavonoids. Flavonoids as secondary metabolites confer many protective functions against abiotic stresses such as signaling and antioxidant activity [12]. In a study conducted by [PERSON] [103], the _VvbHLH1_ gene from grapes was overexpressed in other plants such as Arabidopsis for the sake of increasing the content of valuable flavonoids and thus improving the tolerance of the transgenic plants under abiotic stresses such as drought and salinity. It was also found to increase the content of ABA, which is important for the regulation of many important signaling pathways that are involved in the abiotic stress defense mechanism.
## 5 Errors Compensation
### Feed Offset Error Compensation  
In this section, the nonlinear least squares fitting aperture field phase error is used to solve the axial offset of the feed, and the influence of the measurement accuracy caused by the feed offset error is compensated. Firstly, the data distribution of the surface shape measurement error with the feed offset error interference is obtained by holographic measurement or simulation. Then, the phase error is fitted in two dimensions by using the feed offset error model, and the offset of the feed is deduced in reverse. Finally, the measurement error caused by the feed offset is deducted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diameter (m)** & **30** & **50** & **70** & **90** & **110** \\ \hline \(s_{rms}\) & \(4.53\times 10^{-6}\) & \(7.64\times 10^{-6}\) & \(1.12\times 10^{-5}\) & \(1.63\times 10^{-5}\) & \(2.42\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationships between the scanning error RMS coefficient and antenna diameter.  
Figure 13: Antenna surface shape measurement errors due to scanning error.  
The nonlinear least squares fitting is used to solve the actual offset error of the feed, and the main steps of the compensation for the influence of the surface measurement accuracy caused by the feed offset error are as follows:  
1. The feed offset error model is first determined and the initial predicted feed offsets \(\delta_{a}\) and \(\delta_{l}\) are set: \[\varphi=\frac{4\pi r(\delta_{a}r+2\delta_{l}f\cos\phi^{\prime})}{\lambda(4f^{2 }+r^{2})}\] (17) where \(\delta_{a}\) is the feed axial offset error, \(\delta_{l}\) is the feed lateral offset error, \(f\) is the antenna focal length, and \(r\) and \(\phi^{\prime}\) are the antenna aperture field coordinates.
2. Measure or simulate the radiation far-field data with the feed offset error and inverse the phase distribution of the antenna aperture surface, which is defined as \(\varphi\_real\);
3. Establish the objective function, the sum of squares of the difference between the measured phase error and the model prediction: \[F(\delta)=\sum[\varphi\_real-\varphi]^{2}\] (18)
4. The objective function is minimized by finding the feed offset \(\delta\) that can minimize the objective function \(F(\delta)\) to achieve the optimization goal;
5. The optimization process is repeated until the algorithm converges to a certain tolerance range or reaches the maximum number of iterations;
6. The \(\delta_{a}\), \(\delta_{l}\), and \(\varphi\) are calculated, and \(\varphi\) is subtracted from the phase error distribution of the aperture field obtained from the measured or simulated data.
## 3 Structural Deformation Characteristics
### D\({}_{2}\) Deformation  
The phase 2 (D\({}_{2}\)) deformation is marked by the folding of the S\({}_{1}\) shear foliation, accompanied by the development of an axial planar foliation, designated as S\({}_{2}\) (Figure 4). The fold axial planes predominantly strike 351-20\({}^{\circ}\) and dip at 40-70\({}^{\circ}\), while a minor subset strikes 170-192\({}^{\circ}\) with similar dip angles (40-70\({}^{\circ}\)). The hinge lines of these folds plunge westward or northwest-west (NWW). Incompetent lithologies, including mica schist, phyllite, and siltstone, are typified by tight to isoclinal folds (Figure 4k,l) and overturned fold geometries (Figure 4a-c), as well as the localized development of rounded folds (Figure 4i), angular folds (Figure 4d,j), and the intense transposition of foliations (Figure 4e). In competent lithologies such as dacite and andesite, a distinct overprinting relationship between S\({}_{2}\) and S\({}_{1}\) foliations is observed (Figure 4f). Additionally, outcrop-scale, layer-parallel superimposed folds are well developed, attributed to interlayer slip during the folding process (Figure 4g,h). The axial planar cleavage (S\({}_{2}\)) generally trends 0-20\({}^{\circ}\) and dips at angles of 40-70\({}^{\circ}\), with fold limbs dipping either southward or northward. The hinge lines plunge westward or northwest-west (NWW), indicating that the D\({}_{2}\)-phase longitudinal bending folds resulted from near-N-S-oriented compressional stress. This study interprets the pervasive folding observed within the Yeba Group and Duodigou Formation as the result of N-S-directed compression during a collisional orogenic phase, characterized by a predominantly south-verging imbricate fold system (Figure 4m).  
Figure 4: Characteristics of the D\({}_{2}\)-phase folding in the Yeba rock group. (**a**) Inclined fold; (**b**) tight fold; (**c**) tight and overturned folds; (**d**) upright fold; (**e**) chevron fold; (**f**,**g**) intense foliation transposition; (**h**) superimposed fold; (**i**) photomicrograph of rounded fold; (**j**) photomicrograph of chevron fold; (**k**) photomicrographs of tight folds; (**l**) photomicrographs of tight folds; (**m**) schematic diagram showing D\({}_{2}\)-phase fold characteristics.
_Applied_  
_sciences_  
_Article_  
**Enhancement of Exit Flow Uniformity by Modifying the Shape of a Gas Torch to Obtain a Uniform Temperature Distribution on a Steel Plate**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1\({}^{\copyright}\), [PERSON] 2 and [PERSON] 1,*  
Received: 14 October 2018; Accepted: 6 November 2018; Published: 9 November 2018  
**Featured Application: A gas torch can be used for preheating in welding applications. New gas torches were designed by modifying the inner and outer structures, which led to uniform flow at the outlet and enhanced the temperature uniformity of a steel plate during preheating. The torches were examined through a numerical and experimental study.**  
**Abstract: The objective of this study is to improve the exit flow uniformity of a gas torch with multiple exit holes for effective heating of a steel plate. The torch was simulated, and combustion experiments were performed for validation. Based on a basic model, three different revised models were designed and analyzed with the software ANSYS FLUENT 18.2. The flow uniformity (\(\gamma\)) of the velocity distribution at the multiple exit holes was investigated with the pressure drop ranging from 100 to 500 Pa. The basic model had flow uniformity ranging from 0.849 to 0.852, but the three new models had \(\gamma_{1}\) = 0.901-0.912, \(\gamma_{2}\) = 0.902-0.911, and \(\gamma_{3}\) = 0.901-0.914, respectively. The maximum percentage difference of the flow uniformity index between the three new models and the basic model was 7.3%. The basic model with nonuniform flow distribution made a temperature difference of the back side of the steel plate from the center to the edge of around 229 \({}^{\circ}\)C, while the modified model with uniform flow distribution had a smaller temperature difference of 90 \({}^{\circ}\)C. The simulation results showed good agreement with our experimental results for both the basic model and the modified model. The modified gas torch made a wider and more uniform temperature distribution on a preheated steel plate than the basic one. The results revealed that a trade-off between cost and flow uniformity, as well as the new gas torch, could be applied to a steel-plate preheating process before welding.  
**Keywords:** exit flow uniformity; modification of the shape of gas torch; temperature distribution of steel plate in preheating; LPG/air mixture
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
###### Abstract  
Kidney tumors are a significant health concern. Early detection and accurate segmentation of kidney tumors are crucial for timely and effective treatment, which can improve patient outcomes. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown great promise in medical image analysis, including identifying and segmenting kidney tumors. Computed tomography (CT) scans of kidneys aid in tumor assessment and morphology studies, employing semantic segmentation techniques for precise pixel-level identification of kidneys and surrounding anatomical structures. This paper proposes a Squeeze-and-Excitation-ResNet (SE-ResNet) model for segmentation by combining the encoder stage of SE-ResNet with the Feature Pyramid Network (FPN). The performance of the proposed SE-ResNet model is evaluated using the Intersection over Union (IoU) and F1-score metrics. Experimental results demonstrate that the SE-ResNet models achieve impressive IoU scores for background, kidney, and tumor segmentation, with mean IoU scores ranging from 0.988 to 0.981 for Suresnet50 and Suresnet18, respectively. Notably, Suresnet50 exhibits the highest IoU score for kidney segmentation. These findings suggest that SE-ResNet models accurately identify and segment regions of interest in CT images of renal carcinoma, with higher model versions generally exhibiting superior performance. The proposed Suresnet50 model is a good tool for accurate tumor detection and image classification, aiding medical professionals in early diagnosis and timely intervention.  
Feature Pyramid Network; SE-ResNet; kidney tumors; diagnosis; semantic segmentation 2023
## 3 Modelling and Parameterisation
### Multiscale Geographically Weighted Regression (MGWR) Model  
Regression models describe the dependent variable through the quantitative relationship among two or more interdependent variables. They include the ordinary least squares (OLS) model, generalised linear regression (GLR) model, geographically weighted regression (GWR) model, and multiscale geography weighted egression (MGWR) model. This article focuses on the OLS, GWR, and MGWR models.
## 4 Results and Discussion  
The outcomes of the proposed methodology are as follows:  
* Requirement definition: in this step the SE identifies all the requirements and assigns limit values for the product performance and process performance indicators.
* CAD development: in this stage the designer proposes an adequate design solution and generates all the possible assembly sequences.
* Validation and choice of the appropriate assembly sequence: during this stage the SE analyses the designed solution and compares its response to the initially defined requirements to judge whether it is acceptable in order to select the most optimal assembly sequence.  
Figure 11: ASP3 response to system specifications.  
Figure 10: ASP2 response to system specifications.  
Figure 9: ASP1 response to system specifications.  
Designing in the shortest possible time, with the least expense and the best quality remains a constant challenge for designers. By combining product performance and process performance metrics, the proposed methodology allows a good evaluation of the designed solution with an optimal assembly sequence. Based on the results of the Figures 9-11, all the product performance indicators are satisfied. Indeed, the designed solution perfectly meets the SE expectations and the calculated values are in concordance with the system specifications. This implies that the design solution can be required if the process performance requirements are also met.  
Based on the methodology detailed in Figure 8, as the product performance indicators were validated the SE should move on to the analysis of the process indicators. Thus, ASP1 and ASP2 of the treated example does not meet the threshold values of some assemblability indicators as indicated in Figures 9 and 10. As a result, these two assembly sequences are not acceptable. In contrast to ASP3, all the requirements are marked in green (Figure 11), which implies that the requirements are fully met.  
Since the SE is looking to minimize as much as possible the values of the indicators T\({}_{\text{AT}}\), I\({}_{\text{R}}\), H\({}_{\text{R}}\), C\({}_{\text{F}}\) and to have a value of A\({}_{\text{R}}\) between 50 and 100%, then according to Figure 12, ASP3 is the sequence with the smallest surface area compared to ASP1 and ASP2, which reflects the choice of ASP3 as the best assembly sequence.
## 4 Discussion  
The technology for selecting feature wavelengths can effectively eliminate the phenomenon of spectral information redundancy caused by the large amount of soil hyperspectral data, and it can improve the stability and prediction ability of the soil moisture content inversion model [24; 25; 26; 27]. Since the wavelength selection algorithm proposed in this study is based on the soil radiative transfer model and verified by experimental sample data, the wavelengths selected in this study are effective for large-scale prediction of soil moisture content. In addition, compared with the traditional empirical wavelength selection method [28; 29; 30; 31; 32], the wavelength selection algorithm based on soil radiative transfer model improves the applicability and accuracy of wavelength selection results. However, this method also has limitations: the experimental samples with different soil moisture content in this study are black soil. When detecting other specific types of soil, other wavelength combinations may be more effective than the wavelength combinations introduced in this study.  
Figure 6 shows that the RMSEP of soil moisture content decreases first and then increases with the increase of spectral resolution, the best spectral resolution is 34 nm. Therefore, it is not that the higher the spectral resolution, the higher the estimation accuracy of soil moisture content. This is because the spectral characteristics of soil moisture content of wide and sharp absorption peaks are not required. However, when using hyperspectral data for quantitative estimation of soil component content, due to the large correlation between adjacent bands, there are problems of information redundancy and overlap in hyperspectral data, which cannot achieve the expected estimation accuracy [18]. At this time, we need to resample hyperspectral data. It is particularly important to explore the best spectral resolution for quantitative estimation of soil component content.  
As an important performance parameter of spectral remote sensing instruments, SNR reflects the detection ability of spectrometer for weak signals. Only spectral data with sufficiently high SNR can fully play its role in spectral quantitative remote sensing. Therefore, in the development of spectral instruments, it is necessary to trade off among structural parameters, cost and analysis accuracy in order to obtain the best spectral analysis scheme. The applicable SNR ranges from 150 to 350, which shows that it is not necessary to pursue high SNR when designing spectral instruments to monitor soil moisture content in the future.
## 1 Introduction
With the aim of limiting these microstructural distortions inside the joint, other researchers have investigated the possibility of performing some post-welding heat treatments [17; 18; 19; 15]. Indeed, better microstructural control would allow to obtain even better mechanical properties than those that can be already obtained from a traditional FSW process. In view of this, the recent development in this field of research is moving towards the development of friction stir process operations in which a cooling system is present to limit the effect of the thermal flux on the material during the welding process [20]. This type of system allows the control of the microstructure of the joints during the welding process, limiting the need for post-process heat treatments. Several researchers have studied the response of a specific aluminum alloy to the cooling treatment during welding in the last years [21; 22; 23; 24; 25]. [PERSON] et al. [26] examined the mechanical and metallurgical effects of in-process cooling FSW of AA7075-T6 butt joints. [PERSON] et al. [27] studied the FSW of thick AA2219 sheets considering air-cooling and water-cooling systems. [PERSON] et al. [28] observed the effects of a cooling system combined with an FSW process on an extruded AA6082 billet. However, little has been done on the comparison of the effects of a water-cooling approach applied to a different series of aluminum alloys. In the present paper, both precipitation-hardening alloys (AA2024-T3, AA6082-T6 and AA7075-T6) and a work-hardening alloy (AA5754-H111) have been taken into consideration in order to evaluate the difference between the properties of the joints welded with traditional air-cooled FSW process and those obtained by combining a traditional FSW setup with a water-cooling system. The choice of the studied materials fell on these aluminum alloys, as they are the most used in the industrial field. The research aims to investigate the effects of a water-cooling system on the mechanical properties and the microstructural characteristics of the joints for all the alloys studied in order to compare the effects and to identify the optimal working conditions for each material.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 5 Statistical Analysis of Randomness
### Graphical Tests
In the analysis of the TinyJambu sequences, we obtained, on average, the following values:  
Shannon entropy (ideal) = 8 bits per octet. Shannon entropy (minimum accepted) = 7.976 bits per octet. Shannon entropy measured = 7.9986 bits per octet. Min-entropy (ideal) = 7.91 bits per octet. Min-entropy (minimum accepted) = 7.608 bits per octet. Min-entropy measured = 7.8132 bits per octet. In view of the numerical values obtained, we can say that the Shannon entropy and min-entropy tests were passed. In [18] (Section 5, Figure 7a,b), examples of chaos game representations obtained from quadratic generators show fractal structures that reveal a lack of randomness. 6. Autocorrelation  
Autocorrelation (or cross-correlation) is a mathematical technique that looks for repeated samples among the different portions of a sequence when they are compared. This property is very useful for finding periodic or repetitive patterns within a signal or, in this case, within a binary sequence. The sequence is compared with a shifted version of the same sequence. The number of agreements and disagreements among bits for the pair of the original sequence and shifted version are computed. The comparison is carried out for all the possible shifts of the sequence over itself. Figure 7 represents the autocorrelation of a TinyJambu sequence after analyzing 120,000 bits. The first autocorrelation coefficient is always equal to 1, as the sequence coincides with itself (autocorrelation in phase); the remaining coefficients corresponding to the successive shifts must be as small as possible (autocorrelation out of phase). In Figure 7, it is clear that the obtained values in red are close to 0. This means that, as far as this metric is concerned, the analyzed sequence exhibits good randomness characteristics, and consequently, the autocorrelation test was passed. 7. Fast Fourier Transform  
A Fast Fourier Transform (FFT) efficiently computes a Discrete Fourier Transform or transformation of a signal into the frequency domain. In the analysis of binary sequences, a FFT may detect repetitive patterns in the sequence under study. In fact, a random sequence must exhibit all the FFT harmonics with approximately the same amplitude, and no up/down trends should appear. Figure 8 shows the result of this test applied to a TinyJambu sequence of 120,000 bits. It can be seen that all the amplitudes are in the same range; consequently, the test was passed. Figure 7: Example of autocorrelation for a TinyJambu keystream sequence with values of autocorrelation in red out of phase and close to 0. The blue line depicts the decrement of autocorrelation values related to mutual sequence shift.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 4 Discussion
### Advantages of Proposed Method  
The use of multi-wavelength sensors simplifies optical system design, multi-channel sensor design, and optical signal acquisition and processing. The new sensor technology is not affected by usage time and temperature and has extremely high accuracy and stability. The collection of multiple wavelengths enables a milk sample to correspond to multiple wavelength data, enabling the application of machine learning algorithms for sample composition measurement. Machine learning algorithms can flexibly fit the nonlinear relationship between milk wavelength and component content, with high measurement accuracy, fast running speed, and good generalization ability. The application of broadband NIR LED (SFH4737) can support the emission spectrum range of NIR) from 650 nm to 1050 nm, which can replace conventional NIR light sources such as incandescent lamps, miniature tungsten halide lamps, and tritium lamps. The light source has good stability, long lifespan, and high energy, and the small size design also increases the portability of the detection instrument. The adoption of machine learning algorithms by embedded systems has increased the intelligence of devices without the need for powerful computing devices, reduced the demand for cloud computing, and reduced the cost of portable instruments.