esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method  
### The Basic Idea  
In nature, ants usually find the shortest route paths from their nest to food efficiently even though obstacles exists in the path. It was found that there is an important medium used to communicate information among individuals regarding paths, and it is called the pheromone. On the one hand, a moving ant lays pheromone on the ground to mark the path, and the pheromone concentration on the path gets reinforced; on the other hand, the path with a greater concentration of pheromones will attract more ants to detect and select it with a greater probability. Based on the intelligence-ability of an ant colony, some scholars propose the ant colony optimization (ACO) algorithm and try to solve combinatorial optimization problems by mimicking the ants' behavior [18].  
ACO simulates the process of ants finding the shortest paths to obtain the close-to-optimal solution for NP-hard issues. The positive feedback mechanism is the drive of the algorithm, the process of which can be explained by two steps: first, each ant selects the path with the maximum pheromone concentration and releases its pheromone to this path while it moves; then, more ants are attracted to select the path. This not only accounts for the rapid discovery of good solutions but facilitates the process of finding the optimal solution.  
However, ACO is slow to converge while problem scale is large, and also it is prone to be trapped in a local optimum in the later evolution. Based on these deficiencies of ACO, we design an improved ant colony optimization IACO for spectrum allocation, which employs DE and VNS for performance improvement. DE is a greedy genetic algorithm with retained thought, which evolves by mutation, crossover, and selection in the population. In this paper, we adopt DE to optimize the moving mode of the ants to improve the global convergence ability of the algorithm and maintain diversity of the population. Besides, VNS is a local search metaheuristic employing a set of neighborhood search methods to find the local optimum in each neighborhood iteratively and finally to reach the global optimum at the end. In this paper, we design a set of neighborhoods for VNS to improve the local searchability of the algorithm.
## 3 Data-Driven Recurrence Quantification Analysis (RQA)
### Data-Driven RQA
#### 3.2.1 Improved RQA Method  
RQA is very sensitive in detecting any dynamic change. However, it can be easily influenced by the value of the setup parameters. One crucial parameter of an RP is the Euclidean distance threshold \(\varepsilon\). Too small value of \(\varepsilon\) may result in quantification of much noise and a very low recurrence rate, which leads to the loss of recurrence information of the underlying system. While, on the other hand, too large values of \(\varepsilon\) may produce false recurrences or saturate the measurement, which leads to many artefacts [1]. Inspired by the DVV method [39] and the DVRQA method [40], an improved RQA method is proposed here to solve the difficulty of adaptive determination of the Euclidean distance threshold. The basic process is as follows.  
1. Set the initial value of recurrence threshold \(\varepsilon_{0}\) and target recurrence rate vector \(RR_{tar}\), the value of \(\varepsilon_{0}\) can be set up based on the equation [39]: \(\varepsilon_{0}=0.5\cdot\sqrt{\sum_{j}\left|v_{k,j}\right|^{2}}/N\), \((k=1,\cdots,N;j=1,\cdots,M)\), where \(v_{k,j}\) is a delay matrix consisting of embedding the dimension and delay time.
2. Set \(\varepsilon_{i}=\varepsilon_{i-1}\) and construct a recurrence matrix \(RP_{i}\) based on Equation (16). Obtain the \(RR_{i}\) value based on Equation (18).
3. If \(RR_{i}\) and \(RR_{tar,i}\) satisfy the set tolerance, record the target threshold value \(\varepsilon_{tar,i}\). If not, the solution process is performed iteratively until the allowable error is satisfied.
4. Repeat step 2 and step 3 until the threshold vector \(\varepsilon_{tar}\) corresponding to \(RR_{tar}\) is obtained.
5. According to every value of \(\varepsilon_{tar}\), the RQA is performed to obtain the RQA measure vector \(RQA\)s.  
The flow chart of the previously mentioned Improved-RQA method is shown in Figure 4.
## 3 Results and Discussion
### Zinc-Chelating Capacity of the Peptide  
Different molecular weight hydrolysate fractions had different physicochemical properties and functional properties [17]. Figure 1 presents the zinc chelation capacity of the peptide fractions. The zinc chelation capacity decreased from 33.25 \(\pm\) 1.03% to 19.44 \(\pm\) 0.89%, as the molecular weight of the peptide fractions increased. The zinc chelation capacity of P1 with higher surface charge, was higher than that of the other two groups. A peptide fraction with higher potential has more negative surface charges, which may provide more zinc-binding sites. It was apparent that the higher potential of peptide which can attract more zinc ions chelate to the anionic surface of the peptide fraction, although they exhibit similar amino acid composition. Furthermore, the molecular weight of peptide fractions is proportional to their dispersion, surface hydrophobicity, and particle size [18; 19; 20]. [PERSON] suggested that the low molecular weight pig skin collagen peptides with higher degree of hydrolysis exhibited higher surface hydrophobicity and charge, which have better stability and dispersion in solution. [21]. These results suggest that the molecular weight can be used as an indicator to predict the zinc chelation capacity pattern of a peptide. Fish collagen peptides have been reported to bind with several metals, such as Fe, Cu, and Ca [11]; however, this study is the first to report their zinc chelation capacity and the influence of molecular weight on the zinc chelation capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Molecular Weight**} & \multicolumn{3}{c}{**Percentage of Peak Area (\%)**} \\ \cline{2-4}
**Range** & **P1** & **P2** & **P3** \\ \hline \(>\)3000 Da & 15.19 & 32.52 & 52.38 \\
1000–3000 Da & 39.14 & 32.30 & 30.66 \\ \(<\)1000 Da & 45.67 & 35.18 & 16.96 \\ Average molecular weight (AMW)/Da & 1653 & 2745 & 4378 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Molecular weight distribution of peptide fractions.
# Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix
## 2 Unbiased Estimators and Statistical Hypothesis Test
### Importance Index  
We assume the importance sample data of the customer's \(i\)th service item is \(\left(Y_{i,1},\cdots,\ Y_{i,j},\cdots,\ Y_{i,n}\right)\), \(i=1,2,\ldots,q\); then, the estimator of importance index for service item \(i\) is expressed as follows:  
\[\hat{\theta}_{i}^{I}=\frac{1}{n}\times\sum_{j=1}^{n}Y_{i,j}. \tag{14}\]The expected value of \(\hat{\theta}_{i}^{I}\) is  
\[\begin{array}{ll}E\big{[}\hat{\theta}_{i}^{I}\big{]}&=\frac{1}{n}\times\sum \limits_{j=1}^{n}E\big{[}Y_{i,j}\big{]}\\ &=\frac{1}{n}\times\sum\limits_{j=1}^{n}\int\limits_{0}^{1}\int\limits_{y} \frac{\Gamma\big{(}a_{i}^{I}+b_{i}^{I}\big{)}}{\Gamma\big{(}a_{i}^{I}\big{)} \Gamma\big{(}b_{i}^{I}\big{)}}y^{I_{i}^{I}}(1-y)^{b_{i}^{I}}d_{y}\\ &=\frac{1}{n}\times\sum\limits_{i=1}^{n}\frac{\Gamma\big{(}a_{i}^{I}+b_{i}^{I} \big{)}\Gamma\big{(}a_{i}^{I}+1\big{)}}{\Gamma\big{(}a_{i}^{I}+b_{i}^{I}+1 \big{)}}\times\int\limits_{0}^{1}Beta\big{(}a_{i}^{I}+1,b_{i}^{I}\big{)}d_{y} \\ &=\frac{1}{n}\times\sum\limits_{i=1}^{n}\left(\frac{a_{i}^{I}}{a_{i}^{I}+b_{i }^{I}}\right)\\ &=\theta_{i}^{I}.\end{array} \tag{15}\]  
Thus, \(\hat{\theta}_{i}^{I}\) is an unbiased estimator of \(\theta_{i}^{I}\).  
\[\theta_{I}=\frac{1}{q}\sum\limits_{i=1}^{q}\hat{\theta}_{i}^{I}. \tag{16}\]  
Then, when the importance index value of service item \(i\) is greater than or equal to the average value \(\theta_{I}\), the priority of improvement is high. When the importance index value of service item \(i\) is less than the average value \(\theta_{I}\), the priority of improvement is low. Based on the above statements, the hypotheses of the statistical test for importance index \(i\) are defined as follows:  
null hypothesis  
\[H_{0}:\ \theta_{i}^{I}\geq\theta_{I} \tag{17}\]  
alternative hypothesis  
\[H_{1}:\ \theta_{i}^{I}<\theta_{I} \tag{18}\]  
We assume the test statistic is the unbiased estimator \(\hat{\theta}_{i}^{I}\) with significance level \(\alpha\); then, the critical region for importance index \(i\) is  
\[CR_{Yi}=\Big{\{}\hat{\theta}_{i}^{I}\Big{|}\hat{\theta}_{i}^{I}<C_{Y0i}\Big{\}}. \tag{19}\]  
Thus, we have  
\[p\Big{(}\hat{\theta}_{i}^{I}<C_{Y0i}\Big{|}\theta_{i}^{I}=\theta_{I}\Big{)}=\alpha. \tag{20}\]  
Equivalently,  
\[p\Big{(}Z_{Yi}<\frac{\sqrt{n}(C_{Y0i}-\theta_{I})}{S_{Yi}}\Big{)}=\alpha, \tag{21}\]  
where  
\[S_{Yi}=\sqrt{\frac{1}{n-1}\times\sum\limits_{j=1}^{n}\left(Y_{i,j}-\overline{ Y}_{i}\right)^{2}} \tag{22}\]  
and  
\[Z_{Yi}=\frac{\sqrt{n}\big{(}\theta_{i}^{I}-\theta_{I}\big{)}}{S_{Yi}} \tag{23}\]  
are approximate values, and they are distributed as standard normal distributions when sample size \(n\) is large. Then,  
\[C_{Y0i}=\theta_{I}-z_{a}\frac{S_{Yi}}{\sqrt{n}}. \tag{24}\]
# Position Sensing with a Compact 0.1 THz Amplitude Modulated Source
## 1 Introduction
FMCW systems usually require voltage-controlled oscillators (VCO), signal mixers and enough processing/computing power to compute the FFT and extract the distance information from the received frequency spectrum. All of these issues can make an FMCW system expensive and unwieldy. In comparison, THz systems using amplitude modulated (AM) sources are smaller, and signal processing software implementation is simpler. Due to the nature of the AM THz system, the signal mixing elements and a frequency synthesizer are not required, making the system more compact. In recent years, many articles have been written on the topic of using an AM method in new ways to obtain comparable results to other FMCW THz systems in terms of distance measurements. One of those examples is using two different amplitude modulation frequencies simultaneously [13]. Due to the aforementioned reasons, this paper proposes an AM THz system, using the triangulation principle for distance measuring. Triangulation is used for many purposes, including surveying, navigation, metrology and astrometry. In industry, the triangulation principle is already used in laser technology measurements [14], and thus it makes sense to use an AM THz system in the same way to show the improvements to the distance and position measurement. In the paper, a measurement setup is introduced that takes advantage of the triangulation principle. Measurements were performed using a metal plate as the object of interest for the position measurement. Additional measurements were also performed using a Styrofoam covering to confirm the advantage of the THz position measurement compared to the laser beam measurement method.
# Performance Development of Fluidic Oscillator Nozzle for Cleaning Autonomous-Driving Sensors
## 1 Introduction
(**a**) Lidar sensor-cleaning device for vehicle [8], (**b**) Lidar sensor-cleaning device for vehicle [9], (**c**) Lidar sensor-cleaning device for vehicle [10], (**d**) Lidar sensor-cleaning device for vehicle [11], and (**e**) Apparatus for cleaning Lidar sensor [12]. in size. In this study, a fluidic oscillator nozzle shown in Figure 2 was used to remove pollutants adhering to the surface of the autonomous-driving sensor during autonomous driving. Figure 2 shows a patent for \"apparatus\" for oscillating fluid injection\" developed in our prior research. It has the advantage of widening the fluid injection direction and injection angle without a special device; thus, it was selected for this study. To study the nozzle for cleaning autonomous-drive sensors, we referred to studies on fluidic oscillators, which have the same internal structure [13; 14; 15]. A fluidic oscillator is a device that generates a jet that vibrates left and right even without an internal driving part. This fluidic oscillator has a feedback passage with a symmetrical structure formed by an internal block, and the flow supplied from the inlet generates a recirculation flow inside and ejects a vibrating jet through the outlet. A systematic design theory to investigate this phenomenon has not yet been reported. Therefore, the property variation of the fluidic oscillator due to internal shape changes needs to be investigated. [PERSON] investigated the effects of fluid oscillator parameters on jet speed ratio and pressure drop [16]. The optimal shape of a fluidic oscillator for high jet speed and low pressure loss was determined using an optimal design technique. [PERSON] experimentally observed the vibration characteristics of a feedback-channel fluidic oscillator operated under supersonic conditions by applying a high-speed schlieren flow visualization technique [17]. Although research has been conducted on performance improvement using parameters like this, it is insufficient to construct specific designs. Therefore, it is necessary to develop a theory that can be applied to all fluidic oscillators [18; 19; 20; 21]. [PERSON] analyzed the results according to the FBC straight length, inlet wedge width, and mixing chamber thickness to analyze design variables that affect the frequency and spray angle [14; 15]. This study aimed to analyze the internal flow and performance of the nozzle for cleaning the autonomous-driving sensor and improve its performance. In addition, we analyzed the influence of the sweeping angle and frequency according to the design variables and used them as the basic fluidic oscillator nozzle design factors.
## 3 Role of Lipids for Membrane Function and Intracellular Transport
### Effect on Endocytosis and/or Retrograde Transport due to Manipulations of the Lipidome
#### 3.7.1 Consequences of Decreasing the Amount of GSLs  
To our knowledge, the first study where measurements of endocytosis and intracellular transport were combined with quantitative lipidomics was performed in HEp-2 cells using two inhibitors of sphingolipid synthesis. Fumonisin B1 (10 uM, inhibitor of Cer synthase) and PDMP (1 uM, inhibitor of Glcer synthase) both reduced the Gb3 level by approximately 50% after 24 h incubation and resulted in a major decrease of Shiga toxin binding/uptake. A further decrease in transport from endosomes to the Golgi apparatus resulted in a large protection against this toxin [164], as summarized in Table 1. There was no change in ricin toxicity in accordance with the reports that retrograde transport of ricin proceeds equally well or is slightly increased in cells lacking glycosphingolipids [165; 166].
## 3 Results and Discussion  
### Optimization of Geometric Variables Using the RSM  
Before proceeding with the optimization process, the suitability of the current constraint ranges was checked by fixing one parameter at the central points \(W\) = 1.3 mm, \(H\) = 0.55625 mm, and \(L\) = 10.75 mm and presenting the response surface plots that illustrated the relationships between the other two parameters and the objective functions \(P\) and Gv in Figure 6. Stationary points were observed in both the \(P\) and the Gv response surface plots.  
For \(P\) (Figure 6a), stationary points were observed for the \(L\) and \(H\) relationship, saddle points were found for the \(L\) and \(W\) relationship, and only a descending ridge was present for the \(W\) and \(H\) relationship. For Gv (Figure 6b), optimal points were found in the middle of the parameter range for \(L\) and \(H\), stationary points were observed for \(L\) and \(W\), and only stationary points were present for \(W\) and \(H\). However, the descending ridge in \(P\) suggests that the optimal point may lie outside the experimental range [22]. The range for \(H\) is limited to 0.3125-0.8 mm due to constraints related to shape fabrication, so the experimental range could not be extended further.  
The optimization of the shape factors for the objective functions--reduced pumping power and Gv--was conducted. The optimized shapes derived using the RSM are shown in Figure 7. For the shape of the reduced pumping power function, at the optimal point, \(L\) decreased by 0.3325 mm, and \(H\) decreased by 0.67205 mm, while \(W\), which had a relatively minor effect, increased by 0.23061 mm. For the shape of the Gv function, at the maximum point, \(L\) increased by 0.903 mm, \(W\) increased by 1.17 mm, and \(H\) decreased by 0.5539 mm (see Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Configuration** & \(L\) **(mm)** & \(W\) **(mm)** & \(H\) **(mm)** \\ \hline Reference & 9.3 & 0.63 & 0.625 \\ Reduced pumping power & 8.9675 & 0.86061 & 0.607955 \\ Gv & 10.203 & 1.8 & 0.7261 \\ \hline \end{tabular}
\end{table}
Table 3: Geometric parameters of the optimal shape obtained for each objective function.  
Figure 6: Response surface contour plots: objective function analysis for two variables. (**a**) Pumping power (\(P\)). (**b**) Volume goodness factor (Gv).  
Figure 7: _Cont._
## 2 Materials and Methods
### Mission Architecture
In set notation, the mission space with the associated \(T\), \(E\), \(B\), and \(S\) may be represented as follows:\[M(S_{i})= \begin{cases}M_{1i}=\left\{T_{11}(t,x,y,z,B,E),T_{12}(t,x,y,z,B,E),...,T_{ 1n}(t,x,y,z,B,E)\right\}\\ M_{2i}=\left\{T_{21}(t,x,y,z,B,E),T_{22}(t,x,y,z,B,E),...,T_{2n}(t,x,y,z,B,E) \right\}\\ \vdots\\ M_{mi}=\left\{T_{m1}(t,x,y,z,B,E),T_{m2}(t,x,y,z,B,E),...,T_{mn}(t,x,y,z,B,E) \right\}\end{cases}  \tag{2}\]  
where \(m\), \(n\), and \(i\) are mission number, task number, and scenario number, respectively. Figure 1 gives an overview of an example Autonomous Surface Vehicle (ASV) seafloor mapping mission with the proposed architecture. By representing a mission with this framework, set operations may then be used to compare various generic missions and tasks with the intention of limiting test redundancy and reducing the number of test missions to that of the most critical scenarios. The main goal here is to alleviate the need to determine the exhaustive list of all possible scenarios and combinations of mission tasks, events, and behaviors for which the performance of the vehicle autonomy must be tested against. Instead, this mission framework provides an analysis platform with which to test/observe platform autonomy and which is fully modular, versatile, and scalable. Another useful representation is expressing the mission as a directed graph such that \(M\) is defined as the graph, \(T\) is the subgraph partitioned by tasks containing a set of vertices, \(V_{G}\), and a set of ordered edges, \(E_{G}\) (Equation (3)). The subscript \(G\) differentiates the graph representation from that of the set notation of Equation (2). Mission \(M\) may then be represented as:  
\[M(T_{1}(V_{G},E_{G}),T_{2}(V_{G},E_{G}),...,T_{n}(V_{G},E_{G})) \tag{3}\]  
Figure 2 shows an example of this graph structure with the same mission as defined in Figure 1. In Figure 2, insight on relationships between tasks and possible events causing transitions between tasks is observed. Here, the vertices correspond to the possible events and the edges are the possible transitions between events. Behaviors, \(B\), represent the underlying status of the vehicle in response to events and are not shown explicitly in Figure 2 and Equation (3). Figure 3 shows a task and event path for an instance of mission failure versus mission success. Figure 1: Example seafloor mapping mission with associated tasks and behaviors.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 3 Installation Position Optimization
### Static Analysis of Mirror Assembly  
The parameterized model of the flexible support structure is established, and static analysis is conducted under different loads with finite element analysis software. Static analysis includes influences of gravity, temperature fluctuation, and non-flatness error  
Figure 9: RMS value changed with distance \(L\).  
applied to the assembly. The performance of the flexible structure is evaluated by calculating the mirror surface peak-to-valley value (PV), root mean square (RMS) value, rigid displacement, and tilt angle. The surface error, rigid displacement, and inclination angle under gravity must be strictly controlled, especially when the assembly is placed vertically or horizontally. Furthermore, parts will deform differently when the temperature fluctuates because of different material thermal expansion coefficients and impacts on the mirror surface. Temperature fluctuation is usually set to 4 \({}^{\circ}\)C. The non-flatness error transfers to the mirror surface through flexible structures. Therefore, according to design requirements, the non-flatness error is usually set to 0.01 mm on one of the flexible support installation surfaces. Finally, the mirror assembly is subjected to the composite load of all above.  
The simulation analysis results showed that the PV value under the composite load is 49.68 nm, the RMS value is 5.6 nm, displacement of the mirror is 0.005 mm, and inclination angle is 2\({}^{\prime\prime}\). The PV value under the parity is 51.6 nm, the RMS value is 5.01 nm, displacement of the mirror is 0.0037 mm, and inclination angle is 1.5\({}^{\prime\prime}\). The PV value under a temperature fluctuation of 4 \({}^{\circ}\)C is 22.1 nm, with an RMS value of 4.16 nm. The PV value under a non-flatness error of 0.01 mm is 21.65 nm, with an RMS value of 5.17 nm. The simulation analysis results are listed in Table 2.  
Figure 10 shows the mirror surface displacement cloud picture under composite load. The mirror surface error cloud picture is presented in Figure 11. A coordinate system with the center of the mirror surface as the origin is established. The ratio of the x coordinate value of any point to the maximum diameter of mirror surface is defined as x/Rmax and the same for y/Rmax. The changed z coordinate value of any point on the mirror surface is defined as color distribution of cloud pictures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & **PV Value** & **RMS Value** & **Displacement** & **Inclination Angle** \\  & **(nm)** & **(nm)** & **(mm)** & **(\({}^{\prime\prime}\))** \\  & **Results** & **Results** & **Results** & **Results** \\ \hline
**Gravity of 1 g** & 51.6 & 5.01 & 3.7 & 1.5 \\
**Temperature fluctuation** & 22.1 & 4.16 & — & — \\
**Non-flatness error** & 21.65 & 5.17 & — & — \\
**Composite load** & 49.68 & 5.6 & 5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation analysis results.  
Figure 10: Mirror surface displacement cloud picture.
## 2 Materials and Methods
### Study Selection  
Duplicate references were removed using the Rayyan software version 1.5.0. (QCRI, Doha, Qatar) [39] prior to the screening process. The titles and abstracts of all identified studies were independently evaluated by both authors to determine their eligibility. After this initial screening, the selected studies were reviewed in detail by both researchers to confirm their inclusion. Any disagreements were resolved through discussion and mutual agreement. Full-text versions of studies deemed potentially relevant were retrieved for further assessment. Moreover, the reference lists of the included studies, as well as citations identified via Google Scholar, were examined to identify additional studies for potential inclusion in the review.