esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Static Modelling  
The static model investigated the impact of varying bolt diameter and steel strengths on the maximum shear load and the respective maximum displacement at ultimate failure. The load vs. displacement graph under static conditions indicates the three distinct stages of shear ductile failure, which include elastic failure, plastic yielding and ultimate failure, as summarised below in Figure 24.  
Figure 23: Load cell mass comparison of (**a**) force vs. displacement and (**b**) absorbed energy vs. displacement.  
The maximum load recorded at ultimate failure has been summarised below in Table 14. As the diameter of the bolt increases, the ultimate shear load at failure increases, which is apparent at all steel strengths. Furthermore, as the steel yield and ultimate strength increases, the maximum shear load increases, which is apparent for all bolt diameters. This was expected as the cross-sectional area and steel yield stress are directly proportional to shear capacity; by increasing either, the maximum shear load will increase. It was observed that for the recorded results of steel yield stress of \(f_{y}=633\) MPa, similar results were produced for a steel yield stress of \(f_{y}=847\) MPa. This was most likely due to an input error, and thus should be deemed an outlier for static results.  
The maximum displacement at ultimate failure has been summarised below in Table 15. As the diameter of the bolt increases, the ultimate displacement increases, which is apparent at all steel strengths. Furthermore, as the steel yield and ultimate strength increases, the maximum displacement increases, which is apparent for all bolt diameters. The obtained results align with both experimental data and other double shear test numerical simulation [28,29].  
Figure 24: Stages of shear failure under static loading conditions.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 1 Introduction
However, by considering only discrete scenarios and approximating flow based on the closest scenario, the reality could differ (possibly significantly) from what is predicted. To allow for the systematic incorporation of uncertainties with race-tracking prediction, we consider this inverse problem in the Bayesian framework [14; 15; 16; 17]. In this framework, the solution to the underlying statistical inverse problem is given by the posterior probability density. For nonlinear inverse problems with expensive forward models and high-dimensional parameters (as is the case for RTM inverse problems), fully characterising the posterior is typically not tractable. Consequently, we compute the Laplace approximation of the posterior, which requires only the maximum a posteriori (MAP) estimate, i.e., permeability and race-tracking strength, which maximise the posterior density and the approximate posterior covariance. The idea of incorporating Bayesian inference into this problem has been explored previously [18; 19]. These investigations focus on the more general problem of predicting a complete permeability field throughout the mould. The purpose of this would be to use pressure and flow front data created during filling to predict defects in the final part after it is produced but before any structural testing. They showed that a permeability and porosity field can be predicted using Bayesian inference, including predicting potential defects. However, extracting enough data with multiple pressure sensors and linear flow front sensors could be considered impractical and tedious in real manufacturing cases. The current work uses a novel, lower-dimensional parameterisation compared to that formulation [18; 19], where permeability throughout the domain is characterised by a homogeneous region, along with homogeneous race-tracking regions with a greater permeability. The purpose of this work is to estimate the race tracking that occurs during mould filling. The parameterisation presented here allows for the efficient estimation of race-tracking strength while neglecting other smaller spatially varying permeability effects. The simplification to a lower-dimensional parameterisation also allows for much faster computation, which opens the potential for online calculation in the future. In this paper, we use offline calculations to compute the optimal locations for pressure sensors, a problem that has not yet been considered to the best of the knowledge of the authors. These locations could be used to predict race tracking online in the future. It is the hope that an optimally designed experimental setup will allow practitioners to extract maximum insight into the permeability, and therefore flow pattern, as the mould is being filled. From there, control actions can be taken, as has been investigated before [5; 13; 20; 21], to ensure that the resin flow is controlled and there are minimal dry spots.
## 5 Simulation and Discussion
Through monitoring the water quality of five ponds, the sensors recorded four attributes: pH, Ammonia (NH\({}_{3}\)), temperature, turbidity, and fish. The studied dataset included 12 columns and 4591 rows. In this study, fish is the dependent variable, and the others are the independent ones. There are 11 different fish categories, 86 different pH values, up to 102 different NH\({}_{3}\) values, 46 different temperature values, and 85 different turbidity values. The proposed system is designed as an embedded system with sensors and an alarm coupled with a GPS application. A variety of sensors are utilized to measure parameters such as the pH, temperature, turbidity, and fish taxonomic group. These sensors are linked to a microcontroller board, which also includes an alarm system. The sensors collect data from the water and transfer them to the cluster head (CH) via the digital signal microcontroller (DSP) for processing and storage in the related memory. The proposed filtering-learning system for sensor nodes in the designated underwater network was created in MATLAB ([[https://www2.mathworks.cn/products/matlab.html](https://www2.mathworks.cn/products/matlab.html)]([https://www2.mathworks.cn/products/matlab.html](https://www2.mathworks.cn/products/matlab.html))). The data are employed during deployments for two types of scenarios during deployment rounds. The initial test was conducted on the fish farm's aquatic data, which were obtained from the University of Dhaka in Bangladesh. The second scenario of inspection carried out real trials at one of our government fish farms. To validate the proposed model, data was collected from 270 sensors laid in five ponds of different sizes and settings. Each pond included  
54 sensors distributed in a decentralized manner. The designated network was constructed using a clustering method in which a genetic algorithm (GA) was employed to determine the ideal structure of the designed network after each transfer cycle, based on a periodic reevaluation of its energy. During the optimization procedure, each chromosomal GA represents a map of the cluster head identification. A gene on a chromosome identifies the appropriate node, whether the cluster head or the ordinary node is utilized. The head node is then built based on the closest neighbour rule, and the ability of a chromosome-driven WSN structure is established by all cluster evaluations. The accuracy, the True Positive Alert (TP) rate, and the amount of noise cancellation achieved for the transferred data were measured.
## 2 Results
### Identification of Cis-Acting Elements in the Promoter Region of DoTPS Genes  
To ascertain the potential biological roles of _DoTPS_ genes in _D. offcimale_, a 2000-bp upstream region of the initiation code (ATG) was identified using the PlantCARE tool. The _cis_-acting elements in the promoter regions of _DoTPS_ genes were classified into three categories of _cis_-elements linked to plant growth and development, phytohormone responsiveness, and stress responsiveness (Figure 5). In the plant growth and development category (159/759), 10 _cis_-elements involved in circadian rhythms, endosperm expression (AAGAA-motif and GCN4-motif), flowering (AT-rich element, CCAAT-box, and MRE), shoot and root meristem expression (CAT-box), seed expression (RY-element), shoot expression (As-2 element), and zein metabolism (O\({}_{2}\) site) were found, the highest proportion being the As-2 element (30%). In the stress responsiveness category (221/759), various elements related to anaerobic induction (ARE, 19%), defense and stress (TC-rich repeats, 8%), dehydration (DRE, 6%), drought-inducible (MBS, 16%), low temperature (LTR, 5%), stress (STRE, 27%), and wounding (WRE3 and WUN-motif, 9% and 10%, respectively) responsiveness were detected. Most of _cis_-elements (379/759) were related to the phytohormone responsiveness category, and were responsive to abscisic acid (ABRE), auxin (TGA-element), ethylene (ERE), gibberellin (GARE-motif, P-box, and TATC-box), MeJA (TGACG-motif and MYC), and salicylic acid (TCA-element). Notably, the largest number of _cis_-elements was the TGACG-motif and MYC associated with MeJA-responsiveness, accounting for 12% and 29% of the hormone-related _cis_-elements, respectively (Figure 5). These results suggest that _DoTPS_ genes might be MeJA-induced and/or -repressed genes, and that they respond to multiple abiotic stresses.  
Figure 4: Comparison of DDoxD, R(R)X8W, and NSE/DTE motifs in _D. offcimale_ DoTPS proteins.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 5 Simulation Results
### Simulation Analysis
In order to perform in a real environment and reduce computational complexity, the equal gain algorithm in [8; 25] was applied as a power control algorithm. Among all cases, the CDF performance of the original cell-free massive MIMO (Full CF) outperformed the other cases. This is because there is no limit on the number of users that can be served for each AP; then, the Full CF mode can serve all users in the area. This characteristic of a cell-free model makes the absolute value of the data rate serving per-user large. It is also shown that the proposed user-serving scheme ensures service for the worst-10% of users compared with the other cases except for the Full CF case. The performance of per-user throughput in the bottom of 70 percentage of users is also improved by the proposed scheme. Finally, Figure 8 shows the ratio of unserved users according to the number of users. This simulation considers the \(\mathcal{P}\) as \(\frac{1}{2}L\), and \(L=100\). In the case of the original cell-free massive MIMO, unserved users do not occur because of its property, but the other three schemes have unserved users. In the proposed scheme, if there were more than 140 users, unserved users occured. This is because the candidate AP set in \(\mathcal{P}\) does not include all APs to maintain reasonable computational complexity in the proposed scheme. In addition, in a situation that has a very large number of users, the users served by only one AP should exist in the set \(\mathcal{P}\). This means that as the number of users within an area increases, the number of users served by only one AP also increases. This case is excluded from STEP 1 of the proposed scheme. In the proposed scheme, since the service of existing users should be given priority, even if the proposed scheme is used, it is natural that unserved users would exist in the simulation of Figure 8.  
Figure 6: BER performance for four cases. Figure 8: The number of unserved users for four cases. Figure 7: CDF of per-user throughput for four cases with two antennas.
# Class-Ceramics Processed by Spark Plasma Sintering (SPS) for Optical Applications
## 4 Transparent Ceramics
SPS was developed at 1450 \({}^{\circ}\)C for 2 h. After sintering, the sample was exposed to HIP under Ar atmosphere at 1400 \({}^{\circ}\)C, 190 MPa pressure for 15 h. Direct transmission was 75.8 and 82.7% at 400 nm and 1100 nm wavelength, respectively. Laser action with the slope efficiency of ~31% and optical efficiency of 20% was recorded. Recently, [PERSON] et al. [49] prepared Nd\({}^{3+}\) (i.e., 0.5, 1, 2, 3, and 5 at.%): YAG transparent ceramics by conventional SPS and compared with high-pressure SPS (HPSPS) method also. SPS process parameters were 1400 \({}^{\circ}\)C, 60 MPa, and 2 h time, while HPSPS parameters were 1300 \({}^{\circ}\)C, 300 MPa, and 1.5 h. The transparency of HPSPS-sintered samples was lower than those of conventional SPS-sintered samples due to the difference in the average pore size. The maximum emission intensity increased sharply up to 2 at.% Nd\({}^{3+}\) and then gradually decreased with increasing Nd\({}^{3+}\) concentrations for both sintering processes. Spinel MgAl\({}_{2}\)O\({}_{4}\) transparent ceramics--undoped and doped with rare-earth ions (0.1%-Tb\({}^{3+}\) and 0.1%-Dy\({}^{3+}\) ions)--were synthesized by SPS process at a sintering temperature of 1400 \({}^{\circ}\)C, the pressure of 72 MPa, and 10 min time. XRD patterns showed the absence of shifts in peaks position and absence of additional peaks in the sample, confirming that the dopant (Tb\({}^{3+}\) and 0.1%-Dy\({}^{3+}\) ions) completely incorporated into the polycrystalline structure. The absorption edge for undoped and doped spinel were 250 and 270 nm, respectively. An increase was observed in the band intensity located at 3.63 eV from pulse cathodoluminescence (PCL) spectra on the addition of dopant owing to the formation of intrinsic host defects [50].
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
## 1 Introduction
The introduced approach performed very well in parallel environments. Therefore, they applied a variable-length genotype coupled with local search operators in a parallel evolution strategy environment and conducted a large number of experiments to assess the performance of this approach. The results suggest that the proposed coevolutionary algorithm in a parallel environment can produce high-quality solutions to the MDVRP in low computational time. [PERSON] et al. [7] focused on economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. They formulated an integer linear programming model for solving a set of small-scale instances using the LINGO solver and developed a computationally efficient Ant Colony Optimization based meta-heuristic for solving both small scale and large scale problem instances in a reasonable amount of time. The latter algorithm's performance is improved by integrating it with a variable neighborhood search. Considering the future when everything is connected, product logistics routing requires faster response times. This paper aims to get better solutions quickly to respond to changes in requirements from customer and market demands. Compared to the existing approaches, we address single depot VRP in parallel and inevitably gave up pursuing the best solution. In our study, after assigning waste collection points to waste disposal plants, each depot solves the VRP, assigning customers to vehicles and planning the order in which customers are visited by the vehicles. This process can be parallelized. The sector combination optimization (SCO) algorithm is used to generate multiple initial solutions, with these solutions as the initial population of genetic algorithm or ant colony optimization algorithm, the number of iterations can be reduced to achieve fast convergence. Additionally, because of the abundant initial solutions, the algorithm can avoid trapping in a local optimum. Then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. The paper is structured as follows. In Section 2, we give a detailed description of the sector combination optimization algorithm. Section 3 explains the merge-head and drop-tail strategy. Experimental results on the benchmark instances are reported in Section 4. Finally, conclusions and some potential directions for future research are presented in Section 5.
## 4 Computational Complexity of Combinations
### The Binomial Coefficient Function  
The binomial coefficient denotes the number of ways to choose \(k\) outcomes without considering their order from a total of \(n\) possibilities. This concept is commonly recognized as a combination. Figure 2 shows the binomial coefficient behavior according to \(n\) and \(k\) values. Some important characteristics can be noticed.  
The binomial coefficient curve has several key characteristics. It exhibits symmetry around its peak due to the symmetry in the combinations formula \(C(n,k)=C(n,n-k)\), where choosing \(k\) elements is equivalent to choosing \(n-k\) elements. Consequently, the curve is symmetric around the middle point. The curve starts at 1 when \(k=0\) (choosing 0 elements) and ends at 1 when \(k=n\) (choosing all elements), reflecting the fact that there is only one way to choose 0 elements (no choice) and one way to choose all \(n\) elements (all elements are chosen).  
The peak value of the curve occurs at the middle point, where \(k=\frac{n}{2}\) (rounded up or down depending on whether \(n\) is even or odd). The number of ways to choose \(k\) elements is maximized, leading to the highest binomial coefficient. However, the binomial coefficient gradually decreases as \(k\) deviates from the middle point. This is because choosing fewer elements as it moves away from the middle results in a decrease in the number of combinations.  
The binomial coefficient specifically represents combinations that do not consider the order of elements. In contrast, permutations, where order matters, would result in a different curve behavior. From a computational perspective, factorial computation can be computationally expensive, especially for large values of \(n\) and \(k\), leading to large intermediate values. However, optimizations can be applied to enhance efficiency in computing binomial coefficients.
## 3 The Proposed Approach
### Cause-Effect Graph
#### 3.2.2 Robdd
: _For two different nodes (\(u\) and \(v\)) in the OBDD, if \(u.var=v.var\), \(u.low=v.low\), and \(u.high=v.high\), node \(u\) is deleted, and the parent of node \(u\) is directly connected to node \(v\)._As an example, Figure 5a-d illustrate the simplification process of the OBDD for the Boolean function \(f(x_{1},x_{2},x_{3})=(x_{1}+x_{2})\cdot x_{3}\) under the variable order \(\pi:x_{1}<x_{2}<x_{3}\). In many cases, the original cause-effect graph may have many events that are not related to the outcome or are recurring, so it is important to remove these extraneous factors without affecting the logical relationship among the events. This can be achieved via the deletion rules and merging rules. The ROBDD is a concise graphical representation of the Boolean functions. In the ROBDD, the paths from the root node to the endpoint \"1\" are the paths that must be traveled for normal operation. The nodes on these paths represent the causes associated with the results.
## 3 Simplification and Performance Analysis
First, the rate of change of frequency (ROCOF) is  
\[ROCOF=-K\Delta p\times[A\zeta\omega_{n}e^{-\zeta\omega_{n}t}\sin(\omega_{d}t+ \beta)-A\omega_{d}e^{-\zeta\omega_{n}t}\cos(\omega_{d}t+\beta)]. \tag{12}\]  
Further, when \(t=0\), ROCOF reaches its maximum value, which can be expressed as  
\[ROCOF_{\rm max}=-K\Delta p\times(A\zeta\omega_{n}\sin\beta-A\omega_{d}\cos \beta), \tag{13}\]  
the peak time \(t_{\rm peak}\), or the time when the frequency reaches a nadir, can be expressed as  
\[t_{peak}=\frac{1}{\omega_{d}}(\arctan\frac{\sqrt{1-\zeta^{2}}}{\zeta}-\beta). \tag{14}\]  
Substituting Equation (14) for Equation (11), then the peak frequency \(f_{peak}\), or the frequency nadir, is  
\[f_{peak}=1+K\Delta p\times\frac{z_{1}}{\omega_{n}^{2}}+K\Delta pAe^{-\zeta \omega_{n}t_{\rm peak}}\sqrt{1-\zeta^{2}} \tag{15}\]  
and the quasi-steady-state frequency \(f_{\rm ss}\) is  
\[f_{\rm ss}=1+K\Delta p\times\frac{z_{1}}{\omega_{n}^{2}}=1-\frac{R}{DR+1} \Delta p. \tag{16}\]  
The settling time \(t_{\rm s}\) can be defined as the dynamic response entering the 2% quasi-steady-state error band,  
\[\left|\frac{f(t_{\rm s})-f_{\rm ss}}{1-f_{\rm ss}}\right|=2\%. \tag{17}\]  
Then, the settling time can be expressed as  
\[t_{\rm s}=-\frac{1}{\zeta\omega_{n}}\ln\frac{z_{1}}{50A\omega_{n}^{2}}. \tag{18}\]  
From the expressions of the deduced indices, the performance of IR and PFR provided by VSG-PEIs is determined by three parameters: the inertia constant H, the speed droop \(R\), and the step change of load \(\Delta p\). The performance properties affected by a single parameter are shown in Figures 4-6. Figure 4 shows the influences of the inertia constant \(H\) to the performance indices. In Figure 4, \(H\) increases from 3 to 100. The load change is set to be 0.03 pu, and the parameters of \(R\) and \(D\) remain constant. From Figure 4a,b, the increasing in inertia constant \(H\) leads to the decreased magnitude of the ROCOF and the nadir. However, the increasing \(H\) would extend the duration of the dynamic response and slow down the later frequency control stages, as shown in Figure 4d. Therefore, there is a tradeoff for the determination of \(H\). From Figure 4c, the settling frequency is irrelative to \(H\). Figure 5 shows the influence of the speed droop \(R\) to the performance indices. In Figures 3 and 4, R increases from 0.03 to 0.07. The load change is set to be 0.03 pu, and the parameters of \(H\) and \(D\) remain constant. From Figure 5a, percentage \(R\) does not influence the magnitude of the ROCOF. From Figure 5b, the nadir gets worse as \(R\) increases. From Figure 5d, the changes in percentage \(R\) do not alter the settling time very much. From Figure 5c, the increase of \(R\) would decrease the settling frequency, since  
From Figure 6a-c, with the increasing of load deviation, the magnitude ROCOF, the peak frequency, and the settling frequency gets worse. From Figure 6d, the load deviation does not influence the settling time.
## 4 Results Analysis and Discussion
### Parameter Estimation and Uncertainty Quantification of Traffic Noise Prediction Model at Scenario C  
Scenario C corresponds to Site C, which is located on the road with old pavement from Albert St to Wascana Pkwy, as depicted in Figure 2. Figure 13 presents the initial and final results of Model (20). The initial uncertainty in Model (20) (indicated by the green lines in Figure 13a) would be significantly reduced through the NEF process (presented by the green lines in Figure 13b). The uncertainties in parameters A and B of Model (20) would also decrease, as shown in Figure 14, during the data assimilation process, and the final values of A and B can be compressed within small intervals (presented in Table 1 and Figure 14a,b). Figure 15 shows a comparison between the predicted intervals and observations in scenario C, indicating an acceptable performance of NEF.  
Figure 13: Initial (**a**) and updated (**b**) traffic noise prediction curves in scenario C.  
As can be seen from the green lines and red line in Figure 13b, the parameter values of Model (20) estimated from NEF are quite different to those obtained from MLE. As presented in Table 1, the mean values of A and B obtained through NEF would be 6.6 and 27.6, respectively, while those values generated through MLE would be 9.2 and 10.1, respectively. However, even though there are differences between the parameter values obtained through NEF and MLE, the performance of NEF, with the value of R\({}^{2}\) being 0.8183, is better than MLE (R\({}^{2}\) value of 0.7646).