esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Millimeter-wave (mmWave) antenna arrays are pivotal components in modern wireless communication systems, offering high data rates and improved spectrum efficiency. However, the performance of mmWave antenna arrays can be significantly affected by structural distortions, such as mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. In this paper, we present a comprehensive sensitivity study of mmWave antenna arrays to structural distortion, employing a coupled structural-electromagnetic statistical concept. The proposed model integrates structural analysis techniques with electromagnetic simulations to assess the impact of structural distortions on the performance of mmWave antenna arrays. In addition, the model incorporates random element positioning, making it easy to analyze radiation pattern sensitivity to structural deformation. Demonstrating the applicability of the model, a 10 \(\times\) 10 microstrip patch antenna array is designed to assess the performance of the model with a random position error and saddle shape distortion. The results of the model are then compared against the acceptable results from the HFSS software (version 13.0), where a good agreement is observed between the two results. The results show the gain variation and sidelobe level under various degrees of distortion and random errors, respectively. These results provide a guide for design, deployment, and optimization of mmWave communication networks in real-world environments. In addition, the model provides valuable insights into the trade-offs between antenna performance, structural integrity, and system reliability, paving the way for more efficient and dependable mmWave communication systems in the era of 5G and beyond.  
antenna array; distortion; mmWave; sidelobe level; sensitivity; performance; coupled structure EM +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
The study obtained digitally created 3-dimensional models of maxillary anterior endodontically treated teeth restored with a simulated post, core, and crown designed in computer-aided design (CAD) software called SolidWorks version 2021 (Dassault Systemes, Waltham, MA, USA). The ferrule was designed with a 2 mm vertical height and 2 mm thickness. The models were then exported into the Standard for the Exchange of Product Data (STEP) file format.  
The STEP-formatted models were then imported into the ABAQUS unified FEA simulation software version 6.10 (Dassault Systemes, Waltham, MA, USA), which analyzes the static stress simulation study using FEA methods. Two post and core materials were tested: polyetheretherketone (PEEK) and high noble alloy. Therefore, there are two tested groups. Each group has eight components. These components are the bone, the periodontal ligament (PDL), the root, the post cement, the post and core, the crown cement, and the crown, as shown in Table 1. Table 2 shows each component's size and number of elements and nodes of these components. Figure 1 shows the components of the model. The crown is subjected to an angled force, F, with a magnitude of 100 N on the mid-lingual surface of the central incisor at a 45-degree angle to the long axis of the crown [8; 21]. The bottom and side surfaces of the bone, shown as the boundary condition region in Figure 2, are fixed from movement. The material properties used for each component in the model are shown in Table 1. The model used 265,326 quadratic tetrahedral elements with 405,204 nodes (a total of 1,215,612 degrees of freedom). The performance of both PEEK and the high noble alloy were evaluated in terms of the total displacement of the crown and the generated stresses on the other components.  
\begin{table}
\begin{tabular}{c c c} \hline
**Material** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Bone & 13.7 & 0.3 \\ \hline PDL & 0.0689 & 0.45 \\ \hline Root & 18.6 & 0.31 \\ \hline Post Cement & 18.3 & 0.3 \\ \hline Post (PEEK) & 3.95 & 0.3931 \\ \hline Post (High Noble Alloy) & 100 & 0.31 \\ \hline Gutta-Percha & \(6.9\times 10^{-4}\) & 0.45 \\ \hline Crown Cement & 18.3 & 0.3 \\ \hline Crown (Zirconia) & 250 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties of the components used.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Element Size (mm)** & **No. of Elements** & **Node** \\ \hline \multicolumn{4}{c}{No. Nodes} \\ \hline Crown & 0.5 & 45,246 & 68,222 \\ \hline Bone & 0.5 & 116,642 & 170,358 \\ \hline Crown Cement & 0.4 & 11,728 & 22,881 \\ \hline Post & 0.5 & 19,982 & 31,060 \\ \hline Gutta-Percha & 0.2 & 5607 & 8820 \\ \hline Post Cement & 0.4 & 11,513 & 22,967 \\ \hline Root & 0.5 & 54,607 & 80,895 \\ \hline  & Total & 265,325 & 405,203 \\ \hline \end{tabular}
\end{table}
Table 2: Component’s materials, sizes, and number of elements and nodes.  
Figure 1: Components of the FEA model.
# Physicochemical Properties and Oxidation Status of Pork from Three Rearing Systems
###### Abstract  
The consumer's interest in his/her health and the quality of his/her food has increased as well as in environmental issues such as animal welfare. Consumers believe that organic and similar (traditional) production systems are more advantageous for consumers (providing healthier food) and animals (providing better welfare). The aim of the study was to evaluate the impact of different rearing systems (organic, conventional and traditional) on the physicochemical properties and oxidation states of pork meat. The meat samples were obtained from three different rearing systems of pigs: organic, conventional and traditional. The samples (_M. biceps femoris_) were obtained from producers directly 3 days after slaughtering for analysis. The following physical and chemical parameters were analyzed: color (according to the CIE L * a * b * system), pH, dry matter, protein, collagen, fat and ash. The oxidation state of the meat samples was measured by thiobarbituric acid reactive substances (TBARSs), free fatty acid (FFA) and antioxidant capacity (2,2-diphenyl-1-picrylhydrazyl). The results indicated that the rearing system affects most of the evaluated parameters. A significant difference (\(p<0.05\)) was observed in color parameters L* and b*, where the conventional pork samples were darker and the organic pork yellower. Total protein content in meat of pigs raised in a conventional system was higher (22.23%) than for organic (20.36%) and traditional (21.21%). The fat content in the meat of organically reared pigs was higher (2.81%) than in pork from conventional and traditional systems (1.43% and 0.37%, respectively). Organic pork was more susceptible to oxidation processes due to its higher TBARS (1.24 mg/kg) and FFA (1.15% fat as oleic acid) values and lower antioxidant capacity (26.42% inhibition), which may result in inferior technological properties of meat.  
ecological system; pork composition; TBARS; antioxidant Article
# An Algorithm for Finding Optimal \(k\)-Core in Attribute Networks
## 3 Proposed Approach
### Algorithms for Finding the Optimal \(k\)-Core
#### 3.2.1 BaseLine Algorithm  
The BaseLine algorithm is easy to understand and implement, including the following steps:  
* Step 1: Perform core decomposition on attribute graph \(G\) to obtain the core numbers of all vertices.
* Step 2: For any \(k_{min}\leq k\leq k_{max}\), select all vertices with \(core(v)=k\) to construct all connected subgraphs as the candidate \(C_{k}\) set.
* Step 3: Filter out the subgraphs uncovering the query node set \(Q\) from the candidate \(C_{k}\) set, and obtain the feasible solution \(C_{k}\) set.
* Step 4: Calculate attribute scores for all feasible solution \(C_{k}\), and return the subgraph with the best score as the optimal solution.  
Performing core decomposition on the graph and constructing all \(C_{k}\) based on the number of core vertices requires traversing all vertices and edges multiple times, which takes \(O(k_{max}*(\left|V\right|+\left|E\right|))\) time. In the worst-case, if step 3 does not filter out any subgraphs, then attribute scores need to be calculated for all \(C_{k}\). Let \(O(F_{k})\) be the time to compute the score of \(C_{k}\), then the time complexity of the BaseLine algorithm is \(O(k_{max}*(\left|V\right|+\left|E\right|)+\sum\limits_{k_{min}}^{k_{max}}F_{k})\). Although the BaseLine algorithm is accurate and can finish in polynomial time, it is not user-friendly for large attribute networks.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 2 System Model and Problem Formulation
### System Model
At the end of time slot \(n\), the remaining data in the buffer of UAV \(j\) can be expressed as:  
\[\mathbb{Q}_{j}\left[n\right]=\mathbb{Q}_{j}\left[n-1\right]+R_{j,S}\left[n \right],\forall n,j \tag{12}\]  
Then, the maximum achievable rate from UAV \(k\) to \(D\) in time slot \(n\) can be written as:  
\[R_{k,D}\left[n\right]=a_{k,D}\left[n\right]\log_{2}\left(1+\frac{p_{k}\left[n \right]\left|h_{k,D}\left[n\right]\right|^{2}}{\sigma^{2}}\right) \tag{13}\]  
Therefore, at the end of time slot \(n\), the remaining data in the buffer of UAV \(k\) can be expressed as:  
\[\mathbb{Q}_{k}\left[n\right]=\mathbb{Q}_{k}\left[n-1\right]+R_{k,D}\left[n \right],\forall n,k,k\
eq j \tag{14}\]  
Considering the buffer constraint, \(R_{k,D}\left[n\right]\) can be reformatted as:  
\[R_{k,D}^{{}^{\prime}}\left[n\right]=\min\left(R_{k,D}\left[n\right],\mathbb{Q }_{k}\left[n-1\right]\right) \tag{15}\]  
Since the buffer equipped on the UAV is allowed to store the data collected from \(S\) in time slot \(n\) and forward it to \(D\) in any of the remaining slots, we propose the following information causality constraints:  
\[a_{k,S}\left[n\right]+a_{k,D}\left[n\right]\leq 1,\forall k,n\]0 \[a_{k,S}\left[n\right]+a_{k,D}\left[n\right]\leq 1,\forall k,n\]1 \[a_{k,S}\left[n\right]+a_{k,D}\left[n\right]\leq 1,\forall k,n\]2
## 3 Results and Discussion  
A series of numerical calculations has been accomplished by employing a useful numerical technique called the Keller-box method on system of Equations (12)-(15) with their appropriate boundary settings (15) to achieve insight into the physical situation of flow configuration. These computations are attained for various values of Brownian motion factor \(Nb\), thermophoresis assumed by \(\ Nt\), magnetic factor \(\ M\), buoyancy factor \(\lambda\), solut buoyancy constraint \(\delta\), inclination factor \(\zeta\), Prandtl number \(Pr\), Lewis number \(Le\), radiation effect \(N\), Brinkman parameter \(\beta_{1}\), suction or injection parameter \(S\), and material factor \(K\), which are presented in different tables and figures. The flow pattern of the current study is presented in Figure 1 using the curves, showing the profiles of velocity, temperature and concentration.  
In order to certify the algorithm established in MATLAB software (The MathWorks Inc., Natick, MA, USA) for the current method, the numerical results for the reduced Nusselt number and reduced Sherwood number are compared with the results of [PERSON] and [PERSON] [39] and prepared the Table 1, in the absence of \(\lambda\), \(\delta\), \(M\), \(N\), \(\beta_{1}\), \(K\), and \(S\) and taking factors \(\Pr=Le=10\), with \(\zeta=90^{\circ}\). The current results display agreement, which verifies accuracy of studied problem. The effects on \(-\theta^{\prime}(0)\), \(-\phi^{\prime}(0)\), and \(C_{fx}(0)\) against altered values of involved physical parameters \(Nb\), \(Nt\), \(M\), \(N\), \(\lambda\), \(\delta\), \(\zeta\), \(Pr\), \(Le\), \(\beta_{1}\), \(S\), and \(K\) are displayed in Table 2.  
Table 2 shows \(-\theta^{\prime}(0)\), \(-\phi^{\prime}(0)\), and \(C_{fx}(0)\) with the altered magnitudes of the under concern parameters. It is clear from Table 2 that the heat and mass exchange rate decrease with growth of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nb**} & \multirow{2}{*}{**Nt**} & \multicolumn{2}{c}{**[PERSON] and [PERSON] [39]**} & \multicolumn{2}{c}{**Present Results**} \\ \cline{3-6}  & & \(-\theta^{\prime}(0)\) & \(-\phi^{\prime}(0)\) & \(-\theta^{\prime}(0)\) & \(-\phi^{\prime}(0)\) \\ \hline
0.1 & 0.1 & 0.9524 & 2.1294 & 0.9524 & 2.1294 \\
0.2 & 0.2 &  
Brinkman factor. On the other hand, wall shear stress improves for cumulative impact of Brinkman factor. A similar effect can be seen in the case of inclination change as the heat and mass flux flow reduce. In addition, the energy exchange rate decreases for the higher values of thermophoretic effect whereas an opposite impact is seen in the case of mass exchange rate. It is observed that the skin friction along with heat and mass exchange rate reduce for the growing values of thermal radiation effect. Moreover, the skin friction enhances for improved values of inclination. The energy flux rate and mass flux rate decline with growing magnitude of Brownian motion effect. Moreover, the skin friction increases by increasing the Brownian motion effect.
## 4 Method and Discussion  
The methodology in the current paper includes five main steps, as illustrated in Figure 5. The steps are: (1) Data collection, (2) data preparation, (3) calculating the surface reflectivity, (4) data calibration, and (5) flood detection and validation. Each step is described as follows.
The latest Cal/Val results and their standard uncertainty as delivered by each methodology are given in Section 4 as well as a comparison of the wet tropospheric delays as derived by the MWR measurements and the GNSS data processing. Finally, in Section 5, the main conclusions for the overall performance of Sentinel-3A altimeter and MWR are given along with future plans for improving uncertainty analysis within the context of the Fiducial Reference Measurements for altimetry [6; 7].
## 6 Discussion  
This study aimed to utilize the neural network's deep learning ability in developing a robust decision-making mechanism for selecting suitable requirement elicitation techniques based on elicitation case scenario parameters, further automating the process to reduce human intervention in the selection process. This succeeds in avoiding human error occurrences due to a lack of experience. The deep learning model consisting of 3 hidden layers (each containing 100 neurons) was able to train the dataset containing the technique selection attributes samples for the 14 elicitation techniques. The neural network training was successful in depicting a higher learning curve in contrast to the loss curve. On the other hand, Figure 4 shows that the error rate decreases with every training iteration. Hence, the deep learning model's ability to predict suitable elicitation techniques has improved, reaching an accuracy ratio of 82%. Additionally, we sought to evaluate the results using the confusion matrix and the area under the ROC curve (AUC). As such, Table 3 of the confusion matrix illustrates the deep learning model's prediction ability to return more relevant results than irrelevant ones, something which can be attributed to its precision score of 0.83. It also reveals the model's ability to return most of the relevant results, which is attributed to its recall score of 0.83. Moreover, its cross-validation score is 0.82 (\(\pm\) 0.10), indicating the dataset's integrity for training and assessing model performance. Moreover, the area under the ROC curve (AUC) was implemented to analyze the deep learning model prediction accuracy. Table 4 reveals the deep learning model's One-vs-One ROC AUC score of 0.74 and One-vs-Rest ROC AUC score of 0.75 for each label. Therefore, our results are a confirmation of the model's ability to distinguish between the elicitation techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**One-vs-One ROC AUC Scores** & **One-vs-Rest ROC AUC Scores** \\ \hline
0.745365 (macro) & 0.754880 (macro) \\ \hline
0.750311 (weighted by prevalence) & 0.755098 (weighted by prevalence) \\ \hline \hline \end{tabular}
\end{table}
Table 4: ROC AUC scores.  
Figure 5: Multiclass ROC curve.  
Furthermore, Figure 5 clearly illustrates the distributions pattern of the values falling in between the false positive rate of zero and true positive rate of one, once again confirming the previous results. Finally, these results appear to be consistent with each other. The deep learning model addresses the limitations raised in the literature review section, providing a robust decision-making model which can take advantage of the enhanced dataset. This further reduces the consequences of human error. The proposed deep learning model has the potential to improve the requirement elicitation process and increase the IS project's success rates, thus lowering risks by introducing a transferable solution through neural networks (such as an automated systematic decision-making model that will assist every requirement engineer in the field to select the most suitable elicitation techniques for the given scenario).
## 5 Performance Evaluation of the Charging Circuit  
In the Arctic Ocean or Antarctica, low temperatures are the primary challenge for the power supply of unmanned equipment. Our charging circuit and charging strategy may have been affected by instabilities of the components and modules. Although for the charging circuit components and modules were selected based on their good performance at low temperatures (\(-50\)-\(30\)\({}^{\circ}\)C), the potential temperature dependence of the charging circuit had to be assessed. The temperature dependence of the detection and ADC circuits, which are important for the charging strategy, were evaluated and assessed.
## 2 Design Basis
### Dsm  
Figure 1 shows a schematic diagram of the DSM. Our DSM is a vault-type vertical storage module made of reinforced concrete. The horizontal concrete modules or vertical concrete casks currently in commercial use occupy a larger storage area than the vertical concrete storage module. Moreover, the vertical concrete storage module has the advantage of reducing the storage area by about 30% due to these structural characteristics. It is advantageous for maintenance due to applying a replaceable design to the cylinders installed in the module. In addition, this DSM provides improved seismic safety due to the wide lower cross-sectional area in contact with the ground. The air inlet and outlet of the SDSS are located at the top and bottom of the side wall, respectively, and meet the requirements of the dry storage system through non-powered natural convection cooling with external air. The positions of the air inlet and outlet are arranged so that they intersect with respect to the cylinder's position. This arrangement is advantageous for cooling the canister, because the air introduced at the inlet makes a flow that is discharged after contacting the cylinder as much as possible. The upper part of the DSM has no air inlet/outlet so as to secure operational convenience, and the short side minimizes interference due to multiple arrangement. The dimensions of the DSM were determined by the results of critical, shielding, thermal, and structural analysis, with width and height of 20.5 \(\times\) 10 \(\times\) 7.8 m, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Fuel Specification} & - Max. SNF burnup; & 45 GWd/MTU \\  & - **Initial** enrichment; & 5 wt.\% \\  & - Minimum cooling time; & 10 yrs \\  & - Number of FAs in canister. & 24 \\ \hline Subcriticality & - Effective k-value & 0.95 \\ \hline \multirow{4}{*}{Thermal} & Fuel cladding temperature: & \\  & - Normal; & 400 °C \\  & - Off-normal. & 570 °C \\  & Concrete temperature: & \\  & - Normal bulk (local); & 67 °C (93 °C) \\  & - Off-normal & 176 °C \\ \hline \multirow{2}{*}{Radiation Shielding} & - Surface at module & 0.5 mSv/h \\  & - Surface at pence & 0.01 mSv/h \\ \hline Seismic & - SSE & 0.3 g \\ \hline Mass & - Total handling mass & 113 t \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design requirements for the DSM.  
Figure 1: Schematic diagram of the DSM.
## 4 POMDP Model Formulations
### CSPOMDP Model
#### 4.1.1 State  
The pipeline system corrodes over time in a continuous process. The continuous state formulation tries to emulate this degradation process. Pipeline degradation due to corrosion comprises two distinct steps: formation corrosion and growth or propagation of the corrosion. The formation process can be a Poisson distribution process or negative binomial distribution [30; 31]. The Markov process, Gamma distribution, and Monte Carlo simulation can describe the continuous progression of corrosion. The two steps can also be combined to formulate the complete corrosion degradation process. For this approach, the Markov process was frequently adopted in the literature [5; 32]. This later approach has been extended with an inspection process to determine the latest degradation state of the pipelines.  
The current degradation state of the pipeline was attained from the inspection report. However, the decisions and rewards depend on the succeeding state, which has to be estimated by the POMDP model. The actual degradation of the pipeline was defined as a percentage of the wall thickness. This is measured by an inspection method or a model that predicts the degradation. Different upper and lower limits for the degradation state can be established to recommend the most effective mitigation action. A maximum degradation state should have to be set so that the pipeline will be replaced when reaching this state.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 3 System Implementation and Analysis
### Opl
In this regard, the power penalty of different values of \(B_{n}T_{s}\) was determined to find the smallest phase error, which consequently represents a lower degradation provided by the OPLL. These analyses were carried out with an SNR of 15.5 dB, corresponding to a BER of 1 \(\times\) 10\({}^{-9}\). Furthermore, eleven \(B_{n}\) values were considered, with each \(B_{n}T_{s}\) value at both sampling rates analyzed using the MAF with 5 and 10 coefficients. For each value of \(B_{n}T_{s}\), the OPLL was subjected to 25 iterations to obtain several samples of the standard deviation of the phase error. An average of these 25 standard deviation values was then calculated. Figure 8a,b shows the standard deviation of the phase error for each \(B_{n}T_{s}\) obtained for a sample rate of 10 GSa/s and 625 MSa/s, respectively. From these curves, it is possible to find a value of \(B_{n}\) that induces a desirable limit for the power penalty and keeps the OPLL with fast dynamics. For example, Figure 8b shows a minimum for the phase error at \(B_{n}T_{s}=0.8\times 10^{-3}\) (\(B_{n}=0.5\) MHz), which will induce a lower penalty. In Figure 8a, on the other hand, the phase error is practically flat in all analyzed \(B_{n}\) values, where the power penalty is almost the same. Therefore, these analyses show that low sample rates will induce a higher power penalty than high SRs. Another observation from these analyses is the influence of the number of MAF coefficients. Increasing the number of coefficients reduces the variance of the phase error, and consequently, the power penalty caused by the OPLL. However, as discussed earlier, increasing the number of coefficients increases the system delay. Thus, a trade-off must be chosen between the number of MAF coefficients and the feedback delay. For example, if a power penalty of 0.67 dB is allowed at 625 MSa/s, the number of MAF coefficients can be increased, which allows the feedback delay to increase. However, the additional delay can make the OPLL unstable since it reduces the projected phase margin, which should be compensated using a lead-leag compensator, for example. Finally, from these analyses, the OPLL was defined to work with a \(B_{n}=2\) MHz for an SR of 10 GSa/s (\(B_{n}T_{s}=2\times 10^{-4}\)), with a power penalty of 0.42 dB found for a standard deviation of 2.6\({}^{\circ}\). For an SR of 625 MSa/s, the OPLL was defined to work with a \(B_{n}=500\) kHz (\(B_{n}T_{s}=0.8\times 10^{-3}\)), where a power penalty of 0.66 dB was found for a standard deviation of 4.06\({}^{\circ}\).