esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results Analysis  
Once the proposed methodology was applied to the three case studies, a quantitative analysis was conducted to evaluate the impact of the optimization on both the utilized workspace and the total cycle time. This analysis is summarized in Table 9, which  
Figure 14: Optimized layout of the RC used for the screwing process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual** & **Current Transfer** & **AG Trajectory** & **AG + MRT** & **AG + ACO** \\
**Trajectory** & **Time (s)** & & **Transfer Time (s)** & **Transfer Time (s)** \\ \hline
1 to 2 & 1.92 & 5 to 3 & 1.78 & 1.47 \\
2 to 3 & 2.46 & 3 to 6 & 1.98 & 1.64 \\
3 to 4 & 3.12 & 6 to 1 & 3.41 & 2.81 \\
4 to 5 & 2.15 & 1 to 2 & 1.85 & 1.53 \\
5 to 6 & 1.98 & 2 to 4 & 2.10 & 1.81 \\ Total Time & 11.63 & Total Time & 9.60 & 9.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Cycle times in the screwing cell: current configuration, GA + MRS, and GA + ACO.  
presents the values obtained for the current configurations and after the application of the GA + ACO.  
In all cases, a reduction in the occupied area was achieved by spatially reorganizing the workstations, which demonstrates a significant improvement in the use of the available space. On average, a 21.19% reduction in area was obtained, which serves as a reference for the potential that the GA can offer in similar scenarios. Although this value should not be considered a universal benchmark for all RC's, it does provide a reasonable estimate of the spatial compaction capacity achievable through optimization.  
Simultaneously, a substantial decrease in the robot's transfer times was observed, directly affecting the total cycle time of each cell, with an average reduction of 21.15%. This translates into greater operational efficiency, reduced bottlenecks, and increased production capacity without requiring changes to the existing hardware.
## 2 SRT Structure and Dynamics Model
### Single-Module Dynamics Model  
The schematic diagram of the DOFs and forces of the \(i^{th}\) module is shown in Figure 2. The longitudinal motion \(x^{i}\), lateral motion \(y^{i}\), and yaw motion \(\psi^{i}\) are considered, and the corresponding speeds are represented as \(v^{i}_{x}\), \(v^{i}_{y^{i}}\), and \(\gamma^{i}\), respectively.  
Figure 1: Structure of the SRT.  
Figure 2: Schematic diagram of the DOFs and forces of the \(i^{th}\) module.  
For the \(i^{th}(i=1,2,3,4)\) module, the dynamics equation with the CG generalized forces and the hinge forces as the input can be written by the Newton-Euler formula:  
\[F_{x}^{i}+F_{hx,f}^{i}+F_{hx,r}^{i}=m^{i}(\dot{v}_{x}^{i}-\gamma^{i}\dot{v}_{y}^{ i}), \tag{1}\]  
\[F_{y}^{i}+F_{hy,f}^{i}+F_{hy,r}^{i}=m^{i}(\dot{v}_{y}^{i}+\gamma^{i}\dot{v}_{x}^{ i}), \tag{2}\]  
\[M_{z}^{i}+l_{f,i}F_{hy,f}^{i}-l_{r,i}F_{hy,r}^{i}+M_{hz,f}^{i}+M_{hz,r}^{i}=l_{ z}^{i}\dot{\gamma}^{i}, \tag{3}\]  
\[\dot{x}^{i}=v_{x}^{i}, \tag{4}\]  
\[\dot{y}^{i}=v_{y}^{i}, \tag{5}\]  
\[\dot{\psi}^{i}=\gamma^{i}, \tag{6}\]  
where the CG generalized forces are \(F_{CG}^{i}=\left[F_{x}^{i}\quad F_{y}^{i}\quad M_{z}^{i}\right]^{T}\), the front hinge generalized forces are \(F_{h,f}^{i}=\left[F_{hx,f}^{i}\quad F_{hy,f}^{i}\quad M_{hz,f}^{i}\right]^{T}\), and the rear hinge generalized forces are \(F_{h,r}^{i}=\left[F_{hx,r}^{i}\quad F_{hy,r}^{i}\quad M_{hz,r}^{i}\right]^{T}\). The module's parameters include module mass (\(m^{i}\)), yaw moment (\(l_{z}^{i}\)), longitudinal distance from the front hinge point to the CG (\(l_{f,i}\)), and longitudinal distance from the rear hinge point to the CG (\(l_{r,i}\)).  
A linearized dynamics model can be obtained by decoupling the longitudinal motion from other DOFs. The state vector is \(X^{i}=\left[v_{x}^{i}\quad v_{y}^{i}\quad\gamma^{i}\quad x^{i}\quad y^{i} \quad\psi^{i}\right]^{T}\). The above equations can be expressed in matrix form:  
\[\dot{X}^{i}=A^{i}X^{i}+B^{i}F_{CG}^{i}+B_{h,f}^{i}F_{h,f}^{i}+B_{h,r}^{i}F_{h,r} ^{i} \tag{7}\]  
\[A^{i}=\begin{bmatrix}0&0&0&0&0\\ 0&0&-v_{x}^{i}&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\\ 0&0&0&0&0&1\end{bmatrix},B^{i}=\begin{bmatrix}1/m^{i}&0&0\\ 0&1/m^{i}&0\\ 0&0&1/l^{i}\\ 0&0&0\\ 0&0&0\\ 0&0&0\end{bmatrix},B^{i}_{h,f}=\begin{bmatrix}1/m^{i}&0&0\\ 0&1/m^{i}&0\\ 0&1/m^{i}&0\\ 0&-l_{r,i}/m^{i}&1/l_{z}^{i}\\ 0&0&0\\ 0&0&0\end{bmatrix},B^{i}_{h,f}=\begin{bmatrix}1/m^{i}&0&0\\ 0&1/m^{i}&0\\ 0&-l_{r,i}/m^{i}&1/l_{z}^{i}\\ 0&0&0\\ 0&0&0\end{bmatrix},B^{i}_{h,f}=\begin{bmatrix}1/m^{i}&0&0\\ 0&1/m^{i}&0\\ 0&-l_{r,i}/m^{i}&1/l_{z}^{i}\\ 0&0&0\\ 0&0&0\end{bmatrix}.\]  
For the first module, \(B^{i}_{h,f}F_{h,f}^{i}\) does not exist. For the fourth module, \(B^{i}_{h,r}F_{h,r}^{i}\) does not exist.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
###### Abstract  
Security has always been an important aspect of wireless communications. Aiming at further improve the security of wireless communication and how to prevent eavesdropping, this paper proposes a constellation encryption design based on chaotic sequence and the RSA algorithm. The core idea of this method is to effectively combine both chaotic sequences and RSA, and then to generate large number of encrypted sequences with high security. The specific method is to first use the asymmetric RSA algorithm to transmit the system parameters, then to use the initial value sensitivity of the chaotic sequence to generate the secret sequence, and finally to use the secret sequence to encrypt the original sequence. Simulations show that the proposed algorithm further improves the security of satellite communications without increasing the complexity of the original system.  
chaotic sequences; RSA; constellation encryption; security +
Footnote †: journal:  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Constellation  
Encrypting Design Based on Chaotic Sequence and the RSA Algorithm.  
_Electronics_**2022**, _11_, 3346. [[https://doi.org/10.3390/electronic311203346](https://doi.org/10.3390/electronic311203346)]([https://doi.org/10.3390/electronic311203346](https://doi.org/10.3390/electronic311203346))  
doi.org/10.3390/electronic311203346  
Academic Editor [PERSON]  
Received: 13 September 2022  
Accepted: 14 October 2022  
Published: 17 October 2022  
**Published:** **Notic:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 2 Research Methodology
### Phase 3. Reporting the Review  
The reporting phase is the final phase, in which we addressed the research questions from original sources. Moreover, the systematic literature review (SLR) depends on the presentation of the results. Hence, the research paper was well-written, well-structured, and well-documented.  
Results  
The results were gathered and computed based on research questions. A total of 102 studies, as mentioned in Figure 5, were included to complete the review. Moreover, Table 6 presents the number of studies per research question. A total of 42 research articles explain the deep learning (DL) techniques that are highly recommended for short-text semantic similarity. However, we discovered 76 journal articles, including conference papers, that present a number of methods that extract the semantics of keywords based on the context of short sentences. Moreover, 34 studies introduce different datasets that are currently available for short-text semantic similarity. Finally, we discovered challenges and suggested improvements from 25 studies.  
Furthermore, Figure 6 depicts the number of studies from the year 2017 to 2023. We found only four studies which were published in the year 2017. The majority of the studies were related to short sentences and short queries. However, the number of research articles increased from the year 2018 to 2022. In the year 2018, the majority of studies used deep learning models for STSS, and 12 papers were extracted from the duplicate's records; however, in 2019, 18 papers were published in recognized journals to extract the similarity  
Figure 5: PRISMA guidelines for the selection of systematic literature review.  
from short snippets. Furthermore, 21 research articles were mentioned in the year of 2020, 25 and 16 articles for the year of 2021 and 2022, respectively, and 4 for 2023.
## 3 Results and Discussions
### Tube Tests
#### 3.1.2 Effect of Cementation Solution Concentration on the Reaction
Data in Figures 3 and 4 illustrate that the lower the cementation solution concentration was, the shorter the duration of the first stage of reaction and the earlier it entered into the weak and stationary stages. This is because the reaction rate of the low cementation solution concentration was too fast, and the reaction ended sooner. The proportion of the calcium ion reaction was the largest from 0-4 h under different initial calcium ion concentrations, accounting for 41-68% of the whole reaction (Figure 4). A total of 65-86% of the reaction was completed within 8 h of the start of the reaction, 70-95% of the reaction was  
Figure 4: Calcium ion ratio that reacted in each sample at different times. Figure 3: Change in residual calcium ion concentration and precipitation mass in different concentrations of cementation solutions. accomplished in 12 h, and 87-98% in 24 h. The end time of each solution was different. The reaction stopped at 48 h and 96 h when the cementation solution concentration was 0.8 M and 1.6 M, respectively. The reaction still proceeded slowly after 168 h at a concentration of 2.4 M and 3.2 M. This indicated that the reaction time increased with increasing initial calcium ion concentration. This may be because the low concentration of initial calcium ions did not inhibit the urease activity, and the precipitation process was completed first. When the concentration of calcium ions was high, the urease activity gradually decreased over time, though there was still a sufficient calcium source, and the calcium ion conversion efficiency continued to decrease, thereby delaying the reaction stop time.
## 3 Modeling of the TENG System
### Problem Statement  
The power supply for general electrical devices and sensors is required to provide a stable DC output voltage. Due to the fundamental principle of triboelectric nanogenerators, achieving a constant output voltage is challenging; therefore, a DC-DC converter is utilized to obtain the desired output voltage. To compensate for the stability of a nonlinear control system that considers the nonlinearity of DCM, a control system based on operator theory is designed. Furthermore, applying right coprime factorization in combination with a low-pass filter ensures accurate tracking of the target value.
## 2 Methodology of Parallel System Modeling  
### Loss in Parallel Systems without Queue  
We considered a workstation with a mean service rate \(\mu\) with a Poisson distribution (service interdeparture time of exponential distribution), with a total capacity of \(k\) units, in a queue of finite capacity \(k-1\), and with an arrival flow rate \(\lambda\). It was represented by the distribution of state probabilities M/M/1/K shown in (1), with \(\rho=\lambda/\mu\) and inventory L [1]:  
\[i=0,\ldots k;\ p_{i}=\left\{\begin{array}{l}\rho^{i}\frac{(1-\rho)}{1-\rho^{i+1 }}\ if\ \rho\
eq 1\\ \frac{1}{k+1}\ if\ \rho=1\end{array}\right\}\ L=\sum_{i=0}^{k}i\cdot p_{i} \tag{1}\]  
If the server has infinite capacity, the departure rate will be equal to the arrival rate with the same distribution ([PERSON], 1956) [36]. Because of the finite capacity of the buffer queue, the effective input rate will be lower than \(\lambda\); therefore, when the system is full with k units, a new incoming unit cannot enter the system and is lost or discarded from the arrival flow. Due to the memoryless property of the arrival rate of exponential time distribution, the loss does not modify the exponential interarrival time distribution, though it has an effective rate \(\lambda_{\mathit{eff}}\) lower than \(\lambda\) due to loss. The effective rate is \(\lambda_{\mathit{eff}}\) = \(\lambda(1-p_{k})\) based on the PASTA property ([2] p. 128; [1] p. 14 and 102; [3] p. 73).  
Next, we considered the model of homogeneous servers M/M/m/m, with the rate of arrivals \(\lambda\) and \(k\) identical parallel servers with the service rate \(\mu\), which all had Poisson distributions (exponential interarrival time) with a \(k\) maximum size or capacity of the system. This is frequently named the Erlang first model and the probability of the full system is given by the well-known Erlang-B formula \(B(m,r)\) (2), where the traffic flow ratio is r = \(\lambda/\mu\) and the utilization \(\rho=\lambda/(m\mu)\). We ran the experimental simulation of the M/M/1/m and the Erlang systems through the discrete event simulation software Arena by Rockwell, with 30 regenerative replications of 1000 h and a previous 1000 h warm-up period. This confirmed the full agreement of the mean results from the simulation and the analytical model, which were always within 1% difference and inside the half-width interval of simulation variability that included the 95% simulation shots in all the regime ranges. The use of [PERSON]'s law based on this effective input rate allowed for the calculation of the waiting times or cycle time inside the system CT (queue and server) from \(L=\lambda_{\mathit{eff}}\cdot\mathit{CT}\).  
\[p_{i}=\frac{\frac{(\lambda/\mu)^{m}}{n!}}{\sum_{i=0}^{m}\frac{(\lambda/\mu)^{ i}}{n!}};\ i=0,\ldots m;\ and\ B(m,r)\equiv p_{m}=\frac{\frac{r^{m}}{m!}}{\sum_{i=0}^{m}\frac{\mu}{n!}};\ r=\lambda/\mu \tag{2}\]
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 4 Experiments and Results
### Baseline Comparison  
For our first set of experiments, we compared the proposed RL agent with two classical planning baselines that are already implicit in our environment:  
* Traffic-unaware (shortest path): When the traffic-delay term is removed from the reward, every edge has a unit cost; the resulting policy is exactly the static shortest hop route returned by [PERSON]'s algorithm, a standard distance-optimal algorithm.
* Bandwidth-unaware (fastest path): When the data-throughput term is omitted, the reward reduces to negative travel time. The baseline therefore behaves like a time-dependent fastest path algorithm that minimizes congestion-aware travel time while ignoring any data-transfer objective.  
Only the reward weights are altered to create these baselines; all other parameters in Figure 5 remain unchanged. To demonstrate robustness across different journeys, we evaluated each agent on short, medium, and long trips.  
Figure 6 shows the learning curves for a representative short-trip scenario. The RL agent converges slightly faster than both classical baselines and attains the lowest mean trip completion time. The performance gap is modest, as expected for short routes with few alternatives, but still demonstrates a consistent advantage over the shortest path and fastest path baselines.  
Figure 6 compares the performance of our trained agent to the baseline agents during a short trip. We observed that our trained agent learns the fastest because it converges slightly faster than the baselines and that our agent also converges at a lower trip completion time, slightly outperforming the compared baselines. In this experiment, the small differences in performance between our trained agent and the baselines are primarily due to the short duration of the trip and the limited variety of available route options the agent can take.  
We repeated the evaluation for a medium-length trip, as shown in Figure 7. Here, the advantage of the RL agent becomes more pronounced: it reaches its asymptotic performance faster than both classical baselines and settles at a lower mean completion time. This gap widens because longer journeys expose more opportunities for the RL policy to trade off bandwidth and congestion, whereas the classical baselines optimize only a single criterion.  
Figure 6: Baseline comparisons for a short trip.  
Finally, in the long-trip scenario shown in Figure 8, the RL agent widens its advantage even further. It converges noticeably sooner than the fastest path baseline and completes the journey in appreciably less time than the shortest path baseline. These observations confirm that as trip length increases, the proposed multi-objective policy continues to outperform the classical single-objective methods--both in learning speed and in steady-state efficiency.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Node Sleep-Scheduling Mechanism Based on Memetic Algorithm
#### 3.2.1 Coding Rule Design  
The coding is a mapping from the problem space to the solution space. The genetic algorithm cannot directly process the solution data of the solution space. It must map the variables of the solution space into the data structure of the evolutionary space, i.e., the chromosome, before searching. In the coverage of UASNs, each chromosome represents a node-deployment scenario in which each gene describes the state of the node. In this study, the nodes in the UASNs are activated or asleep through proper scheduling to extend the network life cycle. For scheduling, we use binary 0 and 1 to represent the working state of the node (where 0 means sleep and 1 means a node is active).  
As shown in Figure 2, there are 11 nodes that monitor 14 events, and some nodes are redundant (marked with solid lines). To achieve energy-saving optimization coverage, the optimal node-scheduling strategy is integrated into the ESACC so that the redundant nodes go to sleep. From the genetic coding point of view, we use the encoding format \"10101010000\" to represent the chromosome. N represents the length of the chromosome, whose size equals the number of nodes (N is 11). The gene \(A_{j}\) represents the state of node \(s_{j}\) in chromosome \(Q\). In addition, because diversity during genetic evolution varies with population size, the selection of the population size is critical for ESACC. To simplify this work, based on the opinion of [PERSON] et al. [29], the population size of each generation is fixed at 50.
## 2 Factors Influencing the Regeneration of Cucumber Plants
### Explant Sources and Seedling Stages  
Explants are in vitro culture materials cut from organs or tissues during tissue culture. Cotyledons, cotyledon nodes, hypocotyls, root, internodes, stem nodes, leaves, and proto-plasts of cucumber have been used as explants to regenerate plants in vitro [1]. [PERSON] [22] demonstrated that using cotyledon nodes as explants can lead to a higher induction rate of adventitious buds compared with true leaves, radicles, and hypocotyls. [PERSON] [23] changed the explants from stem nodes to cotyledons, and the bud induction rate increased from 63.5% to 78.3%. A higher frequency of shoot differentiation was also observed by [PERSON] [24], and shoot induction rate was as high as 96.2% when cotyledon nodes were used as explants. Thus, although hypocotyls and leaves can be used as explants for cucumber regeneration, the cotyledons and cotyledon nodes can often obtain a higher regeneration frequency and are used most frequently [25; 26; 27]. The elongation period of a typical cotyle  
Figure 2: The protocol of in vitro regeneration (**left**) and _Agrobacteriumum_-mediated transformation (**right**).  
don node is short; stem elongation and rooting are easy, but it is sensitive to antibiotics, so higher concentrations of antibiotics are often not added during screening so that the false positive rate is high. In contrast, cotyledons are induced to differentiate earlier and have a strong ability to divide. Such transformed cells are also more resistant to antibiotics, so it is advantageous to add higher concentrations of antibiotics to eliminate false-positive seedlings [28]. When cutting explants, it is necessary to pay attention to the seedling ages; given the different degrees of differentiation of explant cells at different developmental stages, the transformation efficiency is often affected [29]. It is generally believed that young material has a low degree of differentiation and is conducive to the induction of organogenesis. In related reports, 1-7 d was often used as the optimal seedling age. However, there is always a certain difference in the germination rate and potential of cucumber seeds, and the experimental materials and culture conditions in different studies are also different, resulting in different seed growth rates. Therefore, the seedling stage is used instead of seedling age to judge the inoculation period [22; 30]. In many studies, the stage between cotyled cohesion (two cotyledons detached from the seed shell) to two cotyledons flattened was selected as the best seedling stage according to the frequency of regeneration and the number of buds per explant [29; 30; 31]. In addition, [PERSON] et al. [32] speculated that the explant size might affect the level of endogenous hormones in explants, which in turn affects the induction of adventitious buds. [PERSON] and [PERSON] [33] demonstrated that appropriate methods for cutting and inoculating explants facilitate the exposure of some potential meristematic cells to _Agrobacteriumum_.
## 3 Results
### The Linear Thermal Buckling Modal Response  
In terms of axisymmetry of rotating structures, it is set that each constrained blade is basically affected in high-temperature operation, and a single constrained blade can be selected for thermal buckling analysis. The finite element model is established, with the 1479 elements of SHELL131 and the nodes of 3120, as shown in Figure 8[1].  
Figure 8: The finite element model of the single blade [1].  
Figure 7: Temperature distribution on the blade obtained from FEM.  
Therefore, the unit temperature load is applied to all nodes, and the internal stress caused by multi-field action can be obtained by linear static analysis. The linear buckling analysis is carried out firstly to determine the concentration point deformation. The finite element method is adapted to calculate thermal buckling mode, where the first four orders are presented in Figure 9 [1].  
As can be seen in Figure 9, the effect of thermal shock on buckling modes is obvious. For the first mode buckling, the buckling response mainly occurs at the edge of the blade root, presenting the concentric ring deformation. The maximum deformation is located at the center of the concentric ring, and the displacement gradually decreases from the center to the outside. Meanwhile, for the whole blade, the slight bending only appears at the blade tip, and the deformation of other parts is basically close to zero.  
Different from the first mode, the second mode is characterized by the deformation of the concentric ring, appearing in the middle of the blade tip. The maximum displacement is still in the center of concentric ring. Currently, the blade has obvious folding, which is the bending torsion coupling deformation. By the third-order and fourth-order modal buckling, the deformations are very similar. The folds are alternately distributed in the middle of the root and blade tip, while the number and area increase accordingly. Therefore, the blade root and tip are most prone to deformation and instability under thermal shock load. The occurrence of such buckling modes is related to the characteristics of blade mounting structure. Practically, the blade root is constrained by the wheel disc with longer chord length of the blade tip, and then there is insufficient support in the length direction, relatively. The internal force of the structure will accumulate rapidly, and structural stiffness reduces correspondingly. Therefore, the blade root and tip are most prone to emerging deformation and instability under thermal shock load.  
Figure 9: The linear buckling modes of blades [1]. (**a**) The first-order mode; (**b**) the second-order mode; (**c**) the third-order mode; and (**d**) the fourth-order mode.