esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
## 4 The Optimization Results of SCOD  
### The Determination of the Optimization Problem  
To successfully establish an optimization model, it is the priority to determine the optimization problem [37; 62]. In the study of Albak, the thickness of the inner wall, middle wall, outer wall and ribs of nested tubes are selected as the design variables [35]. However, to explore the enhancement of the size of the inside structure, the circumscribed diameter of the middle wall of SCOD, namely \(D_{mild}\), is quantified, as shown in Figure 1b. Therefore, the geometric parameters which include the thickness of the inner wall tint, the thickness  
Figure 10: Schematic diagram of the entire optimization process.  
of the middle wall \(t_{mid}\), the thickness of the outer wall tout and the cross-section size of the middle wall \(D_{mid}\) are determined as design variables. According to previous studies, the \(t_{int}\), \(t_{mid}\), \(t_{out}\), \(t_{rib}\) and \(D_{mid}\) generated a high sensitivity impact on the crashworthiness with the range of 0.5 to 2.0 mm and 25 to 35 mm, respectively. Hence, the same interval is selected as the range of variation in this study.  
In the field of practical engineering, higher \(SEA\) and \(CFE\) values and lower \(IPCF\) values are the orientation commonly pursued [29; 63]. However, the increase in \(SEA\) values typically accompanies an increase in \(IPCF\), and a higher \(IPCF\) is not acceptable. In addition, the \(CFE\) represents the performance of energy absorbing, which is also a significant criterion. Hence, the \(SEA\), \(CFE\) and \(IPCF\) are taken as optimization objectives. The mathematical model of the optimization problem can be represented as the following equation:  
\[\left\{\begin{aligned} & max(-IPCF,\ SEA,\ CFE)\\ &\left\{\begin{aligned} & 0.50\text{mm}\leq t_{int}\leq 0.20\text{mm}\\ & 0.50\text{mm}\leq t_{mid}\leq 0.20\text{mm}\\ & 0.50\text{mm}\leq t_{mid}\leq 0.20\text{mm}\\ & 0.50\text{mm}\leq t_{rib}\leq 0.20\text{mm}\\ & 0.50\text{mm}\leq D_{mid}\leq 0.20\text{mm}\\ \end{aligned}\right.\end{aligned}\right. \tag{19}\]
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 2 Background and Related Work  
### Bit Allocation and \(\lambda\) Decision of URQ and R-\(\lambda\) Rate Control  
1 URQ rate control For the URQ model, the target bit allocation of \(R_{t}\) is calculated via quantization step size \(Q_{s}\), which can be modeled as a quadratic function:  
\[R_{t}=X_{1}\cdot\frac{MAD}{Q_{s}{}^{2}}+X_{2}\cdot\frac{MAD}{Q_{s}} \tag{1}\]  
where \(X_{1}\) and \(X_{2}\) are the model parameters, and \(MAD\) indicates the mean absolute difference, which is used to measure the distortion between the reconstructed pixels and the original pixels. Then, the Lagrange multiplier \(\lambda\) is calculated using  
\[\lambda=a_{1}\cdot 2^{\frac{(\text{QP}-12)}{3}}\cdot\text{max}\bigg{(}2,\text{ min}\bigg{(}4,\frac{(\text{QP}-12)}{6}\bigg{)}\bigg{)} \tag{2}\]  
where \(a_{1}\) is the predefined factor. According to Equation (2), the bit rate is finally controlled by QP.  
* R-\(\lambda\) rate control  
For the R-\(\lambda\) model, the target bit rate is allocated one by one according to the coding level. For the group-of-picture (GoP) level, the bit rate is allocated via  
\[R_{GoP}=\frac{\Big{(}\frac{T}{\text{FR}}\cdot(N_{c}+\text{SW})\Big{)}}{\text{ SW}}\cdot N_{f} \tag{3}\]  
where FR is the frame rate; \(T\) is the target bit rate; \(N_{c}\) and \(R_{c}\) are the number of the encoded frames and the bit cost, respectively; SW is the smooth window; and \(F_{f}\) is the number of frames in a GoP. Then, the frame level bit rate is allocated via  
\[R_{f}=\Big{(}R_{GoP}-R_{f}^{\text{cost}}\Big{)}\cdot\omega_{f} \tag{4}\]  
where \(R_{f}^{\text{cost}}\) is the sum used bit rate of encoded frames in the GoP, and \(\omega_{f}\) is the predefined weight factor of the current frame. At last, the bit allocation of the coding tree unit (CTU), which is modeled in terms of bit per pixel (\(bpp\)), is calculated according to the following formula:  
\[bpp=\frac{\Big{(}R_{f}-R_{\text{CTU}}^{\text{cost}}\Big{)}}{\text{P}ixel_{ \text{CTU}}}\cdot\omega_{\text{CTU}} \tag{5}\]  
where \(R_{\text{CTU}}^{\text{cost}}\) is the sum used bit rate of the encoded CTU in the frame. \(\omega_{\text{CTU}}\) is the weight factor, which can be obtained according to the \(MAD\) of every CTU. The RD characteristic is modeled as a hyperbolic function, which is defined as  
\[D=C\cdot R^{-K} \tag{6}\]  
where \(C\) and \(K\) are the model parameters. Then, \(\lambda\) is derived by taking a derivate with respect to \(R\), which is  
\[\lambda=-\frac{\partial D}{\partial R}=C\cdot K\cdot R^{-K-1}\overset{\Delta}{ \equiv}\alpha\cdot bpp^{\beta} \tag{7}\]  
Therefore, according to Equation (7), the bit rate is finally controlled by \(bpp\) and parameters \(\alpha\) and \(\beta\).
## 4 POMDP Model Formulations
\[V*(b)=\max_{a}((\int_{s^{\prime}\in S}r\big{(}s^{\prime},a\big{)}b(s^{\prime} ))+\gamma\sum_{o\in O}P(o|b,a)V*(b)) \tag{2}\]  
where  
\[r\big{(}s^{\prime},a\big{)}=\sum_{s\in S}\sum_{o\in O}r\big{(}s^{\prime},a,s, o\big{)}P\big{(}s|s^{\prime},a\big{)}P(o|s,a)\]  
As the size of the core states increases, solving the POMDP problems can become computationally difficult. In these cases, the solution approach developed for discrete POMDP will render useless due to the explosion of belief states. Overall, continuous space POMDP (large-size discrete space POMDP) poses computational challenges that emanate from the curse of dimensionality and history [28]. The curse of dimensionality describes  
Figure 2: POMDP model formulation [23]. the challenges that arise from the POMDP model becoming computationally expensive as the size of the model increases, whereas the curse of history originates from the difficulty of tracking actions and observations as these model components grow exponentially. Two solvers were selected to optimize the POMDP formulations: POMCP and DESPOT. The selection criteria mainly focused on the algorithm's capability to optimize continuous-state POMDP problems. POMCP combined the Monte Carlo simulation and tree search methods. It is an extension of the Monte-Carlo-based tree search (MCTS) algorithm that assesses the value of the belief state in each branch of a search tree to identify the most promising chain of states in the search space [24]. POMCP incorporates particle filters (unweighted) to facilitate the search [26]. A generative function was used to select the succeeding state, available action, and observation formulate to the search problem in each iteration, which loosens the requirement for explicit definition of the transition and observation probability. Further extension of POMCP was proposed by [PERSON] and [PERSON] [26], named Monte Carlo planning with observation widening (POMCPOW), where particle filters were included in the observation function so that a single particle cannot dominate the solution search. [PERSON] and [PERSON] [26] also proposed particle filter trees with double progressive widening (PFT-DPW) to improve the limitations of POMCP. Determinized sparse partially observable tree (DESPOT) is a sampled scenario based on an online POMDP solver (adopting an anytime heuristic algorithm) where the evaluation of values for each action guides the decision-making [25]. An anytime heuristic algorithm is a search algorithm designed to reach a solution sequentially with an allocated run time [29]. Implementing the POMDP to corrosion maintenance optimization requires adopting this general formulation model. An appropriate solver will then be implemented to attain the recommended maintenance action. A summary of the two-model formulation (i.e., CSPOMDP and DSPOMDP) is given in Figure 3, while the following sub-sections discuss the details of the formulation.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 2 Results
### Comparison of Rubellin and Chrysophanol Toxicity  
Chrysophanol is an intermediate in the biosynthesis of cladofulvin and is synthesised by _claG_, _claF_, and _claH_. Based on the homology and gene-expression study carried out above, chrysophanol **7** may also be an intermediate in the biosynthesis of rubellins, as _rccG_, _rccF_, and _rccH_ are coregulated and exhibit homology to _claG_, _claF_, and _claH_, respectively. Furthermore, the presence of chrysophanol was also confirmed in the liquid culture of _R. collo-cysgni_ by mass spectrometry (Supplementary information Figure S1).  
Therefore, the biological activity of both chrysophanol **7** and rubellin D **4** was investigated in planta. Rubellins being nonhost-specific toxins [19], leaves of _Arabidopsis thaliana_ were infiltrated with the two compounds, and lesions size was measured after 24 h. Leaves infiltrated with a solution of rubellin D **4** (50 \(\upmu\)M) exhibited significantly (\(p\) value \(<\) 0.001) larger lesions than leaves infiltrated with a solution of chrysophanol **7** at an equal concentration (Figure 5). Chrysophanol **7** at 50 \(\upmu\)M exhibited a similar lesion size to that of the control (10 mM MgCl\({}_{2}\)). Infiltration with a concentration of up to 1 mM of chrysophanol **7** still produced significantly (\(p\)-value \(<\) 0.001) smaller lesions than infiltration with 50 \(\upmu\)M of rubellin D **4**, suggesting that it exhibits much greater biological activity than chrysophanol **7**.  
Figure 4: Expression of genes in the rubellin candidate biosynthetic cluster during disease development in artificially inoculated barley seedlings. Disease development was previously described by [PERSON] et al. [26]. Early symptoms appearance was recorded at 7 dpi, with typical lesions being clearly visible after 10 dpi. Genes represented in grey did not appear to be coregulated based on the RNA-seq data published by [PERSON] et al. [43]. The expression profile of gene _Rcc_01325_ was used to validate the results of the RNA-seq data. Data show mean normalised expression value \(\pm\) standard error of three independent experiments.
## 3 Test Results and Analysis
### Results of the CT  
#### 3.3.1 Processing of CT Images  
The results obtained from the Avizo software indicate that the porosity of the unimmersed specimen was 19.51%, in contrast to the porosity measured using the conventional gas measurement method, which was 11.059%. This discrepancy between the two measurement results is significant, primarily because the CT scan captures both connected and non-connected pores, whereas the conventional gas measurement method only accounts for connected pores. Consequently, the porosity measurements derived from Avizo software were higher than those obtained through ordinary gas measurement methods. By analyzing the differences between these two measurements, it is possible to deduce the porosity of the non-connected pores.
###### Abstract
The most used types of gears are cylindrical gears, level gears, and worm gears [11], while the most popular tooth profile is the involute, due to its unbeatable advantages regarding contact between conjugated teeth profiles [10; 11]. In gear manufacturing, the most particular issue of concern is teeth machining. Here, the generating of involute teeth profiles can be performed by copying the tool axial profile or by applying the so-called \"rolling method\", which is the currently applied solution [12]. The teeth flanks generated by rolling involve, besides common cutting motions (specific to turning/milling/grinding/slotting, depending on the applied process), a feed motion precisely reproducing the relative motion between the centrodes associated with the cutting tool and the machined part--the rolling motion [12]. In all cases, the speed of this motion is set at the beginning of teeth cutting and is kept unchanged throughout the process [13]. Despite the inexistence of a precise formula for calculating the cutting force in a given cutting process, all the existing formulas, proposed by different researchers, accept the direct connection between the cutting force magnitude and the area of the detached chip section [14; 15; 16; 17; 18]. The modeling of the gear teeth-cutting processes [19; 20] shows a significant variation in this area during teeth machining, which further leads to a high time variation in the cutting force. In other words, the teeth machining process is characterized by important non-uniformity from an energetic point of view. This indicates the variation in the electric power absorbed by the driving system of the used machine tool and determines, obviously, the diminishment of energy efficiency. The authors of [21] analyzed the influence of the cutting force on machining process efficiency and found that to increase energy efficiency, the cutting force must be reduced, for example, by reducing the depth of the cut and by increasing the feed rate. However, they have addressed the case when the given machining operation is performed by using an unchanged regime, during which the cutting force is considered quasi-constant. When the cutting force significantly varies, the above-presented situation specific to gear teeth cutting makes the cutting parameter optimization aiming for energy efficiency less applicable according to the current general approach. The present paper proposes an innovative approach leading to an energy efficiency increase in gear teeth cutting characterized by a new strategy: the smoothing of the cutting force variation during the machining process. A dedicated method, which enables the implementation of this strategy, based on playing the rolling motion with a variable and convenient speed was developed accordingly. The present paper is structured as follows: Section 2 deals with describing this method, Section 3 presents method application in the cases of some typical methods for gear teeth cutting, Section 4 describes a technical solution proposed for method implementation, and Section 5 presents the conclusions.
## 3 Results and Discussion
### Support Vector Regression  
Different types of SVR algorithms were investigated, the results of which are presented in Table 5. Corresponding MSE and \(R^{2}\) values were 0.0182 and 0.9997 for the prediction of moisture ratio. A comparison between the predicted and experimental moisture ratios for data sets for the SVR is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Type of Membership** \\ **Function for Each Entry** \\ \end{tabular} & \begin{tabular}{c} **Number of Membership** \\ **Functions for Each Entry** \\ \end{tabular} & \begin{tabular}{c} \(MSE\) \\ \end{tabular} & \begin{tabular}{c} \(\mathbf{Test}\) \\ \end{tabular} & \
\begin{tabular}{c} \(\mathbf{Train}\) \\ \end{tabular} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} gaussmf \\ trimf \\ trapmf \\ \end{tabular} } & 3-3-3 & 0.0036 & 0.9995 & 0.038 & 0.9991 & 0.042 \\  & 3-3-3 & 0.0050 & 0.9992 & 0.0414 & 0.9972 & 0.0468 \\  & 3-5-3 & 0.0064 & 0.9989 & 0.0451 & 0.9978 & 0.0459 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of different ANFIS arrangements for estimating \(MR\) obtained from testing and training data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Type of Algorithm** \\ \end{tabular} & \begin{tabular}{c} \(MSE\) \\ \end{tabular} & \begin{tabular}{c} \(\mathbf{R^{2}}\) \\ \end{tabular} & \begin{tabular}{c} \(MAE\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Linear \\ Quadratic \\ Cubic \\ Fine Gaussian \\ Medium Gaussian \\ Coarse Gaussian \\ \end{tabular} & \begin{tabular}{c} 0.0023 \\ 3-3-3 \\ 3-5-3 \\ 3-5-3 \\ 3-5-3 \\ \end{tabular} & \
\begin{tabular}{c} 0.9611 \\ 0.0004 \\ 0.0050 \\ 0.0064 \\ 0.0064 \\ 0.0064 \\ 0.0064 \\ 0.0064 \\ 0.0065 \\ \end{tabular} &
\begin{tabular}{c} 0.0352 \\ 0.0182 \\ 0.0201 \\ 0.0203 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of various learning algorithms of the support vector regression method for estimating the \(MR\) obtained.  
According to the results, it seems that quadratic, cubic, and Gaussian algorithms have higher average power in estimating \(MR\), respectively.  
In a study conducted by [PERSON] et al. (2018), they reported that the ANN model has a greater ability than the SVR model to predict the moisture content of wheat at harvest time. In another study, [PERSON] et al. (2019) modeled the moisture content for drying the lentil seeds in a microwave fluidized bed dryer using the SVR and ANN. They showed that the ANN model performed better with \(R^{2}\) = 0.999 than the SVR with \(R^{2}\) = 0.995.
## 2 Theory
\(i\) represents \(i^{th}\) pixel. Because of the background's area, such as the ceiling of the house, it is much larger than the LED's area, and the position of the LED (s(t) (x\({}^{\texttt{t}},y^{\texttt{t}}\))) in the image can be approximated as the centroid of L\({}^{\texttt{t}}\). Figure 2: The flow of our proposed visible light positioning (VLP) algorithm. The world's coordinates of the image sensor is defined as \(z_{i}(t)\) (\(x_{i}^{t}\), \(y_{i}^{t}\),\(z_{i}^{t}\)), where dx and dy represent each pixel's size on IS in different coordinate directions, respectively. The height between the LED and lens is known as H, which is assumed as a fixed value and is already known by the algorithm. With all the conditions above, the pixel coordinate (\(x_{0}\),\(y_{0}\)) of the image's centroid can be calculated as follow:  
\[\left[ \begin{array}{c}x_{0}\\ Y_{0}\\ 1\end{array} \right]=\left[ \begin{array}{ccc}1/\mathrm{dx}&0&\mathrm{x}^{t}\\ 0&1/\mathrm{dy}&\mathrm{y}^{t}\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}x_{i}^{t}\\ y_{i}^{t}\\ 1\end{array} \right] \tag{1}\]  
Next, the horizontal coordinate of image sensor (camera) \(X_{C}\) in the camera coordinate can be calculated by the following equation:  
\[\frac{X_{0}}{X_{C}}=\frac{f}{f+H} \tag{2}\]  
\(Y_{C}\) can be calculated by the same method. As can be seen from Figure 5, we can rotate the coordinate by Equation (3) and realize positioning under any azimuth. \(\left(x_{i}^{t},y_{i}^{t}\right)\) is transformed from \(\left(X_{C},Y_{C}\right)\) by the equation below:  
\[\left[ \begin{array}{c}X_{C}\\ Y_{C}\\ 1\end{array} \right]=\left[ \begin{array}{ccc}cos\theta&sin\theta&0\\ -sin\theta&cos\theta&0\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}x_{i}^{t}\\ y_{i}^{t}\\ 1\end{array} \right]+\left[ \begin{array}{c}T_{X}\\ T_{Y}\\ T_{Z}\end{array} \right] \tag{3}\]  
Figure 4: The geometrical relationship between the Light Emitting Diode (LED) and the image sensor. Figure 3: World coordinate system, image coordinate system, and camera coordinate system. In the equation above, \(\left[ \begin{array}{c}\mathrm{T_{X}}\\ \mathrm{T_{Y}}\\ \mathrm{T_{Z}}\end{array} \right]\) represents translation and (\(\mathrm{x_{I}^{t}}\), \(\mathrm{y_{I}^{t}}\)) has been denoted by z\({}_{\mathrm{I}}\)(t). Let z\({}_{\mathrm{I}}\)(t) = h(s(t),D\({}_{\mathrm{I}}\)), where h represents the mapping function and D\({}_{\mathrm{I}}\) stands for the \(i^{th}\) coordinate of the LED in the world's coordinates that we have known.
## 5 Proposed Methodology
### Fitness Function  
In PSO, fitness functions are crucial in the planning of routes related to a particular particle. Depending on the quantity of information acquired, the total distance traveled, and the average risk of striking into obstacles, distinct path sets, acquisitions site sets, and possible groups of crossing into obstacles obtain varying fitness values. The multi-objective situation can be simplified to a single-objective issue to simplify the level of difficulty of multi-objective solution finding. Several optimization goals include gathering as much data as feasible, minimizing the mean chances of hitting obstacles, and traveling the shortest distances.  
The goal of a fitness function is to connect an optimization algorithm for motion planning to the real world [44]. The fitness function assesses the overall performance of a particle's route. The PSO algorithm's convergence behavior and search efficiency might be affected by the swarm's particle population. PSO follows a population-based optimization approach in which each particle in PSO stands for a potential response to the optimization issue. Based on each particle's best-known location and the most effective available location of the swarm, the particles explore across space to find the perfect solution. The search space is typically extensively explored when there are more particles present. This increases the likelihood that the swarm will cover a greater area of the solution space, possibly boosting the likelihood of discovering the global optimal solution. That means a higher number of particles provides higher fitness values. The closer a particle's journey is to its destination, the higher its fitness.
## 1 Introduction
proposed a hybrid optimization algorithm that integrates genetic algorithms and deep learning technologies to improve scheduling robustness in complex environments [20]. Furthermore, blockchain-based scheduling methods have also begun to be utilized in customs settings, enhancing both security and transparency in the scheduling process [21]. Simulations and real-worldapplication indicate that these methods can significantly enhance the scheduling efficiency of customs inspection vehicles [22; 23; 24; 25]. This research is motivated by the identified gaps in current methodologies, particularly the lack of effective automated solutions that can adapt to the dynamic nature of customs operations. While various studies have explored scheduling techniques, few have comprehensively integrated EVI and biometric systems into a cohesive framework. This research aims to address this gap by developing an automatic scheduling method that leverages these technologies to enhance logistics management efficiency. The problem statement for this study is as follows: how can we develop an effective automatic scheduling method for relocating customs inspection vehicles that optimizes resource allocation while significantly reducing vehicle waiting times? The objective is to propose a solution that enhances the operational efficiency in customs management within a rapidly evolving trade environment. To effectively address the challenges of driver and inspection vehicle identification, this study proposes an automatic scheduling method based on EVI and biometric recognition technology, considering factors such as vehicle arrival time, priority, and yard capacity. By employing genetic algorithms for multi-objective optimization, the method aims to tackle complex vehicle scheduling issues, achieve dynamic scheduling and optimal path planning, and ultimately improve scheduling efficiency and reduce vehicle waiting times. The structure of this study is as follows: Section 2 reviews the relevant literature, highlighting existing approaches and contributions. Section 3 outlines the methodology, providing a detailed description of the proposed scheduling algorithm and its implementation. Section 4 presents experimental results, demonstrating the effectiveness of the method and comparing it to state-of-the-art solutions. Finally, Section 5 concludes the study and discusses future research directions.