esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### Model Results Presentation
#### 4.3.1 Analysis of the Results of the Sustainable Design Parameters  
In the process of building the prediction model, the sustainable designers chose density, hardness, and strength as the design parameters of sustainable design in terms of materials. The density of the material selected during the product design process affects the weight of the product, which in turn affects the product's grip; the hardness determines the product's drop resistance, and the strength of the material determines the product's torsion resistance, which ensures the product's service life. At the same time, in order to ensure that there is no excessive involvement of the user's emotional level, this side of the selection of cell phone product lines with mobile attributes is the main element of analysis (as shown in Figure 6)  
For this analysis, the 3 GS, 4, 5, 6, 6 Plus, 8, and 8 Plus, as well as the full range of 11 th and 12 th generation products, were selected as the targets for analysis. The first step is to determine the product usage environment for this controlled experiment as a mobile scenario. In this usage scenario, according to the hot zone diagram in Figure 6, the user data in terms of sustainable design parameters are positively correlated with product satisfaction. Regarding the analysis of material density and user satisfaction with product weight: material density is positively correlated with the weight of the product. The model's output indicates that user satisfaction with product weight does not always favor lighter products, which shows that the impact of design parameters on the product is not a simple one-to-one mapping. This further illustrates the effectiveness of the BP neural network prediction model. For sustainable design, balancing product strength with thin and light design is a significant challenge. In product design, one cannot simply pursue thinness and lightness at the expense of the product's strength. In the experimental situation with various materials to choose from, take the 6 Plus and 8 Plus for example. Despite having nearly the same size design, the 8 Plus uses a stronger back plate material,  
Figure 5: Plot of model predictions against actual values.  
addressing the bending issue present in the 6 Plus. As a result, the overall user satisfaction is notably higher for the 8 Plus. In the limited material experimental environment, a horizontal comparison of the same material in the 6 and 6 Plus models reveals that the pursuit of a thin and light design in the 6, at the expense of product strength, results in lower user satisfaction with the product's strength compared to the 6 Plus. These issues are reflected across the 11 th and 12 th generation products.
###### Abstract  
The treatment of articular cartilage defects remains a significant clinical challenge. This is partially due to current tissue engineering strategies failing to recapitulate native organization. Articular cartilage is a graded tissue with three layers exhibiting different cell densities: the superficial zone having the highest density and the deep zone having the lowest density. However, the introduction of cell gradients for cartilage tissue engineering, which could promote a more biomimetic environment, has not been widely explored. Here, we aimed to bioprint a scaffold with different zonal cell densities to mimic the organization of articular cartilage. The scaffold was bioprinting an alginate-based biobank containing human articular chondrocytes. The scaffold design included three cell densities, one per zone: \(20\times 10^{6}\) (superficial), \(10\times 10^{6}\) (middle), and \(5\times 10^{6}\) (deep) cells/mL. The scaffold was cultured in a chondrogenic medium for 25 days and analyzed by live/dead assay and histology. The live/dead analysis showed the ability to generate a zonal cell density with high viability. Histological analysis revealed a smooth transition between the zones in terms of cell distribution and a higher sulphated glycosaminoglycan deposition in the highest cell density zone. These findings pave the way toward bioprinting complex zonal cartilage scaffolds as single units, thereby advancing the translation of cartilage tissue engineering into clinical practice.  
bioprinting; biofabrication; tissue engineering; articular cartilage; human chondrocytes; cell density; cell gradient +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Range of Application of the Method  
The use of a phantom in the method has some intrinsic limitations. The lack of an initial deformation or shift between the CT and CBCT (owing to the rigidity of the phantom and not applicable with the real patient CT and CBCT images) was the basis for the reference condition of the validity of the method. Since the clinical reference DVF has the same frame of reference as the patient CT image, care must be taken to align the DICOM coordinatesystems between the patient and HN phantom CT images before generating the warped phantom CT image based on the reference DVF. This was done to align the phantom CT image with the clinical refDVF to produce a more clinically realistic warped phantom CT image. Indeed, it is important that the warped phantom is created, applying as far as possible the deformation magnitude consistent with the spatial deformation of the patient images.  
From a mathematical point of view, the validity of the method is consistent for every size of patient anatomy, because the quality of the registration was assessed as the ability of the clinical software to replicate the reference DVF, artificially introduced into the deformed phantom CT images. However, the rigid \"coupling\" between the phantom anatomy and the reference DVF should be considered carefully to ensure the clinical relevance of the reference DVF. The more the anatomy is similar between the phantom and patient, the more the clinical value of the reference DVF will be maintained when it is applied to the phantom anatomy. The quality of this coupling should be tested visually, by checking the overlap between the patient and phantom structures. A potential method to overcome the mismatch between the patient and phantom anatomy is to introduce a baseline DIR between the patient and the phantom in the coupling phase, instead of a rigid one. However, introducing this step can increase the complexity of the evaluation method, therefore it was decided to avoid this point to avoid over-complicating the workflow. Indeed, it is important to underline that, as a verification method, the refDVF data did not necessarily represent the real, individual deformations of the patients.  
[PERSON] et al. [34] used a physical anthropomorphic pelvic phantom for the CT and CBCT deformable image registration with the aim of validating a DIR evaluation method for prostate cancer cases. Even if their study focused on evaluating the accuracy of the dose calculation as performed directly on the CBCT images, they presented a similar workflow to the current study for their evaluation, in that the pelvic phantom images were digitally deformed using two realistic patient-based deformation fields, after a rigid alignment between the patient and the phantom's system of reference.  
Since the OAR insertion in the HN processed phantom was done in terms of HU, and not in physical density, the processed phantom images were not suited to studies of the dose calculation accuracy, but only for the DIR registration accuracy. Indeed, the dose calculation using this method is only performed on the patient adapted-CT.
## 3 Results and Discussion
### Composition and Functional Properties of White and Black Garlic and Black Garlic Powder
In the Voghiera PDO garlic (Table 1), the total phenolic compounds and total flavonoids were 0.62 \(\pm\) 0.04 mg GAE./g and 0.02 \(\pm\) 0.00 mg CE./g, respectively. Phenolic compounds and flavonoids have strong antioxidant properties, and the white garlic showed an antioxidant capacity of 0.05 mg TE./g. At the end of the process, the total phenols and total flavonoids were 3.95 and 10.97 mg GAE./g dw and 2.12 and 2.11 mg CE./g dw in the black garlic and BGP, respectively, while the antioxidant capacity increased to 1.09 mg TE./g dw in the black garlic and 3.45 mg TE./g dw in the BGP. According to the literature, black garlic's total phenol content, total flavonoid content, and antioxidant capacity are higher than those of white garlic [3; 27]. Recently, [PERSON] and [PERSON] [31; 32] demonstrated an increase in total phenols, total flavonoids, and antioxidant capacity in black garlic powder in comparison with black garlic. [PERSON] et al. [33] demonstrated that furosine, hydroxymethyl furfural, and furfural produced by the Maillard reaction contributed to the value of these effects. The increase in antioxidant activity in the black garlic and BGP samples may be attributed in part to the drying process, which releases phenolic compounds, and in part to the Maillard reaction, which produces phenol-like compounds. This could be an reason for the increase in phenols and antioxidant capacity, but not in total flavonoids, observed in the BGP.
## 2 System Model
### The Problem Optimization Objective  
This section details the optimization objective of the multi-UAV formation network. The goal of this manuscript was to improve the successful data transfer probability of the U2U links and realize the maximization of the system data transfer rate. The optimization objective is modeled as  
\[\begin{split} maxR_{system}=& max\sum_{T}(\sum_{ m=1}^{M}R_{m}^{UL2B}(t)+\sum_{n=1}^{N}R_{n}^{UL2U}(t))\\ s.t.& c1:P_{m}=P_{max}\\ & c2:P_{ntr}\leq P_{max}\\ & c3:\rho_{n,m}\in\{0,1\}\quad\forall n\in N,m\in M,\end{split} \tag{14}\]  
where \(P_{max}\) stands for the maximum transmitter power used by the UAVs, \(P_{ntr}\) and \(P_{m}\) stand for the transmitter power of the \(n\)th U2U and \(m\)th U2B links, respectively. In view of the fact that the distances from the UAVs to the BS are longer than those between each UAV, the maximum power is adopted by the U2B link transmitters to guarantee the quality of the data transfer. For the sake of maximizing the data transfer rate and generating less interference to the system, each U2U link ought to choose the appropriate power and spectrum. Three power levels were set to be used by the U2U links. Then, \(P_{ntr}\) and \(\rho_{n,m}\) are the arguments to be optimized. The optimization function consists of the rate of U2U links, U2B links and the transmission time limit. The optimization function at each time slot \(t\) that was used as the reward function in the proposed DQN mechanism is expressed as follows.  
\[\begin{split} r_{t}&=a\sum_{m=1}^{M}R_{m}^{UL2B}+b \sum_{n=1}^{N}R_{n}^{UL2U}-(1-a-b)(T-E_{t})\\ &=R_{t}+T_{t},\end{split} \tag{15}\]  
where \(a\),\(b\) and \((1-a-b)\) are the percentage of each part. If the data in the U2U links are transmitted successfully within the transmission time \(T\)s, it is classed as a successful data transfer. The value \(E_{t}\) is the remaining time left to finish the data transfer in the U2U links and it is initialized to \(T\). The third section is initialized to zero, which means the transmitters have a lot of time to transmit data to obtain a bigger reward function \(r_{t}\). If the remaining time \(E_{t}\) gets shorter, the reward function \(r_{t}\) can reduce due to the increase in the failure data transfer probability.
## 2 Full Form of (2+1)-Dimensional Fifth-Order KdV Equation and (2+1)-Dimensional Gardner Equation
### (2+1)-Dimensional Fifth-Order KdV Equation  
Assume \(\alpha\approx\gamma\approx\beta^{2}\), as in Section 5 of [10]. Then, retaining terms up to the second order in \(\beta\), in [10], we obtained the following set of the Boussinesq equations  
\[u_{t}+w_{x}-\frac{1}{6}\beta\,w_{3x}+\frac{\gamma}{\beta}\int w_{ yy}\,dx+\alpha\left(uw\right)_{x}+\frac{1}{120}\beta^{2}\,w_{5x}-\frac{1}{3} \gamma\,w_{x2y}=0, \tag{14}\] \[w_{t}+u_{x}-\beta\bigg{(}\frac{1}{2}\,w_{2 xt}+\tau u_{3x}\bigg{)} +\alpha\,ww_{x}+\frac{1}{24}\beta^{2}\,w_{4 xt}-\gamma\bigg{(}\frac{1}{2}w_{2 yt }+\tau u_{x2y}\bigg{)}=0, \tag{15}\]  
where \(w(x,y,t)=\phi_{x}^{(0)}(x,y,t)\).  
To obtain a wave equation for \(\eta\), one needs to eliminate \(w\) from the set of Boussinesq Equations (14) and (15). In other words, find a \(w(\eta,\eta_{x},\eta_{y},\ldots)\), such that, when inserted into (14) and (15), the two equations become compatible (identical to a certain order of approximation).  
We showed in Section 5 of [10] that with  
\[w=\eta+\beta\bigg{(}\frac{2-3\tau}{6}\bigg{)}\eta_{xx}-\frac{ \gamma}{\beta}\frac{1}{2}\bigg{/}\bigg{(}\int\eta_{yy}\,dx\bigg{)}dx+\alpha \frac{1}{4}\,\eta^{2}+\beta^{2}\bigg{(}\frac{12-20\tau-15\tau^{2}}{120}\bigg{)} \eta_{4x}\] \[\qquad+\gamma\bigg{(}\frac{2-3\tau}{12}\bigg{)}\eta_{yy}\ +\frac{\gamma^{2}}{ \beta^{2}}\,\frac{3}{8}\int(\int(\int\eta_{4y}\,dx)\,dx)\,dx)\,dx, \tag{16}\]  
Boussinesq Equations (14) and (15) become compatible to the second order in small parameters. Both receive the same form  
\[\eta_{t}+\eta_{x}+\beta\bigg{(}\frac{1-3\tau}{6}\bigg{)}\eta_{3x} +\frac{\gamma}{2\beta}\int\eta_{yy}\,dx+\frac{3}{2}\alpha\,\eta\eta_{x}+\beta ^{2}\bigg{(}\frac{19-30\tau-45\tau^{2}}{360}\bigg{)}\eta_{5x} \tag{17}\] \[\qquad\qquad+\gamma\bigg{(}\frac{1-3\tau}{4}\bigg{)}\eta_{x2y}\ -\frac{1}{8}\frac{\gamma^{2}}{ \beta^{2}}\bigg{/}(\int(\int\eta_{4y}\,dx)\,dx)\,dx=0.\]  
Strictly speaking, in [10], the last term was missing in \(w\), making the compatibility of Equations (14) and (15) only achievable under the condition \(\eta_{4y}=0\). The inclusion of a correction term of the order of \(\gamma^{2}/\beta^{2}\) has now made it possible to remove this condition and it obtain the full Equation (17), which is the (2+1)-dimensional extension of the fifth-order Korteweg-de Vries equation derived from the Euler equations describing the irrotational motion of an ideal fluid. Moreover, the presence of this term in (17) ensures the existence of solitary wave solutions to this (2+1)-dimensional wave equation, as shown in Section 3. Without this term, there is no solitary wave solution for Equation (17) (precisely, the assumption of the existence of a soliton solution in the form (23) leads to a contradiction).
### Paper Structure  
The remainder of this paper is organized as follows. Section 2 presents the proposed framework for probabilistic stability and performance assessment and employed system operational indicators. The system that was studied, a grid-connected LCL-filtered voltage source converter (VSC), which included all inner and outer control loops, is introduced in Section 3. The small-signal modeling of the power system and operating point calculations are then discussed. After that, the application of the proposed algorithm to probabilistic stability and the performance assessment of a grid-connected VSC is demonstrated in Section 4. This section investigates the impact of grid short circuit ratios (SCRs), different operating point conditions, and control parameters on the system stability and performance, and recommendations are made. Additionally, simulations and experiments have also been provided to validate the analytical conclusions in Sections 4 and 5. After that, a review and comprehensive discussion on the obtained results are presented, and future works are identified in Section 6. Finally, Section 7 concludes the paper.
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
## 1 Introduction
In addition to these problems, feedback delay may still occur even if the receiver immediately notifies the transmitter of a collision. These two problems have been improved by [7; 8]. In this paper, a novel collision detection method using asynchronous MU-MIMO transmission called random access MU-MIMO [13] is proposed. In random access MU-MIMO, the transmission from each user is started immediately after preparing the data packet for a certain user. Hence, asynchronous downlink transmission is employed, and null data is transmitted for the user whose data packet is not prepared. In this study, we utilize this feature on random access MU-MIMO, and idle antennas, which do not transmit any signal, can be prepared instead of transmitting null data. Thus, the collision detection using idle antennas in random access MU-MIMO is realized similar to that in single user MIMO. In random access MU-MIMO, the unused antennas can receive preamble signals, while transmission is employed with multiple antennas that are used in random access MU-MIMO. The signals are mapped to several subcarriers in short preamble signals of IEEE802.11 based OFDM signals, and the signals in the frequency domain are transformed by IFFT at the transmitter. At the receiver, the correlation is calculated, and the initial timing of short preamble signals is detected. Conventionally, the short preamble signals are discarded after timing synchronization. On the other hand, in the proposed method, the short preamble signals are transformed into the frequency domain by FFT processing. The interference from the interfering user terminals (IT) can be detected by subtracting the short preamble signal, which is multiplied by the estimated channel response using the received signal. The effectiveness of the proposed method is shown by computer simulation. In addition, the interfering power from the IT at the AP and desired user terminal (DT) is verifiedby assuming packet collision in an actual indoor environment. By analyzing the interfering signal power from IT to AP and DT, the possibility of collision detection using the proposed method is demonstrated. The remainder of this paper is organized as follows. Section 2 shows the proposed method using random access MU-MIMO. The basic characteristics and effectiveness of the proposed method are demonstrated via computer simulation in Section 3. In order to verify the possibility of using the proposed method at the AP, the characteristics of the interference power in an actual indoor environment is presented in Section 4.
## 4 Natural Additives in the Context of Active Food Packaging
### Plant Essential Oils  
Plant-based active stabilizers can be isolated by extracting the appropriate biomasses by steam distillation using various solvents. Generally, EOs are complex mixtures containing over 300 different polar and non-polar volatile organic compounds, usually of low molecular weight (below 300) at quite different concentrations [257]. Often, two or three major components exist at relatively high concentrations (20-70%) while others are present in trace amounts. The major constituents of EOs are terpenoids and phenylpropanoids which provide the characteristic aroma and biological properties. Both families are comprised of phenolic compounds. The sufficiently high vapor pressure of EOs, in general, at atmospheric pressure and room temperature causes them to be found partially in the vapor state [257; 258; 259]. In comparison to producing plant extracts, a higher amount of plant biomass must be processed to obtain EOs. However, this can prove worthwhile as EOs contain active components of biomasses in particularly high concentrations and are therefore typically highly effective in different applications. This allows manufacturers to obtain great effects with applying only small amounts of oil. Prominent examples of EO applications for active food packaging are listed in Table 6. Results obtained so far show that plant EOs are interesting components for active food packaging. However, the corresponding materials have to be specified regarding water resistance; water vapor permeability; mechanical properties; and enhancement of antimicrobial, antioxidant, photostabilizing, and light-absorbing properties. Particular research interest lies on EOs obtained from food sources such as cinnamon, thyme, and rosemary or food production byproducts as apricot kernels and banana leaves.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 3 Review of Related Literature
### Outlier Feature Selection Methods  
To the author's knowledge, only one research study has been found that conducted on reducing the dimensionality by eliminating the outliers. In other words, they performed the outliers' detection and elimination in the feature set [28]. The concept was based on feature ranking and isolation forest. The one-class SVM, the local outlier factor and IFOR were employed. In addition, several synthetic and real-world datasets were utilized to validate the efficiency of the proposed FS. A comparison study was introduced using several FS and outlier detection methods such as Laplacian, variance, and kurtosis. The results showed that the proposed FS was similar to kurtosis but better than Laplacian and the variance. Many other studies combined FS to outlier detection to strengthen the FS approach. For example, the authors in [29] suggested a novel FS method combined with an outlier detection technique and classification to identify the attacks. Other studies used FS methods to detect outliers. For example, the authors of [30] aimed to develop an outlier detection approach based on FS and association rules. In [31], the authors proposed a new FS method for unsupervised outlier detection. The authors used the inter and intra distance along with recursive backward elimination to extract the outliers.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 3 Experiments and Results
### Experiment Process  
In this paper, the image pyramid is divided into three levels: the shrinkage factor of the first level is 4, the second level is 2, and the third level is 1. Each layer is the standard single-resolution image registration. Firstly, the CT image is transformed according to the initial space transformation parameters. Since this paper is for the human brain model registration, the rigid transformation model is adopted. Then, the DRR image is generated by the projection of the CT image. First, the projection threshold is set to 0 to obtain the DRR image, mainly composed of bone information. Next, the similarity measure between the DRR and reference images is calculated. After taking the similarity measure as the objective function, the Powell Brent optimization algorithm optimizes the space transformation parameters until the iteration stop condition, stop registration, and output space transformation parameters are reached. The flow chart of image registration is shown in Figure 3.  
In CT image projection, the traditional ray casting algorithm is inefficient and not conducive to registration experiments. Therefore, a fast [PERSON] ray tracing algorithm is adopted [48; 49]. The algorithm sums up the iterative formula of ray path according to the geometric law. According to the formula, it then calculates the intersection point of ray and voxel space. The Siddon Jacobs algorithm is at least one order of magnitude faster than the ray tracing algorithm based on linear interpolation. Before using the projection algorithm, it is necessary to establish the projection coordinate system, that is, the coordinate registration  
Figure 3: Image registration flow chart.  
system. In this paper, the coordinate system, as shown in Figure 4, is designed. Since the patient is almost in the X-ray field center when taking X-ray images, the center of initial CT volume data is set at the coordinate system's origin. The coordinate system is established according to the direction of CT volume data. The projection panel is located on both sides of CT volume data. This projection coordinate system is similar to the real shooting scene. The standard LPS (left poster superior) coordinate system can reduce the calculation [50,51]. In practical application, if the CT image is used to register with the real X-ray image, when the X-ray imaging coordinate parameters are unknown, it is necessary to calibrate the X-ray image to determine the relevant parameters of the projection coordinate system.