esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Robust Optimization Design for the Aerodynamic Shape of Hypersonic Vehicle
### Robust Optimization Design of the Aerodynamic Shape  
To alleviate the effect of the uncertainties on the aerodynamic force and heating, the robust optimization design of the hypersonic wing is conducted in this section. For comparison, two optimization design models, namely the traditional deterministic optimization model and the robust optimization model, are proposed.  
(1) The deterministic optimization is to minimize the heat flux on the stagnation point by designing the geometry parameters, while satisfying the constraint that the lift-drag ratio of the optimized aerodynamic shape the does not decrease compared with the initial  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) (*) & \(h\) (km) & \(v\) (m/s) & \(K\) & \(\mathbf{Q}\) (Kw/m\({}^{2}\)) \\ \hline
40 & 71.27 & 6442.36 & 1.022902 & 265.5743 \\
40.0004 & 71.27 & 6442.36 & 1.022825 & 265.5766 \\
39.9996 & 71.27 & 6442.36 & 1.022923 & 265.5720 \\
40 & 71.27071 & 6442.36 & 1.022902 & 265.5608 \\
40 & 71.26929 & 6442.36 & 1.02  
(1) In contrast with the initial design, the heatfluxes on the stagnation point efficiently decrease by 7.45% and 7.32% in DOD and ROD, respectively.  
(2) Compared with the initial design, the fluctuation of lift-to-drag ratio increases from 0.0239 to 0.0247 in DOD, which means that the robustness of DOD is worse than the initial design, although the heat flux on the stagnation point decreases. The reason is that the influences of uncertainties are ignored in DOD, and thus the robustness cannot be guaranteed.  
(3) Compared with the initial design, the fluctuation of the lift-to-drag ratio decreases from 0.0239 to 0.0233 in ROD by considering the robust constraint in the optimization model. Moreover, compared with traditional DOD, the heat flux on the stagnation point with a slight increase of 0.13% (DOD: 245.80 kW/m\({}^{2}\), ROD: 246.13 kW/m\({}^{2}\)), but the fluctuation of lift-to-drag ratio increases by 5.7% (DOD: 0.0247, ROD: 0.0233). Consequently, it can be concluded that the proposed robust optimization method is superior to the traditional deterministic optimization by simultaneously decreasing the target value and enhancing the robustness of the output responses.  
Figure 6: Illustration of the optimization processes. (**a**) Traditional DOD; (**b**) ROD.
## 3 Research Findings
### Algorithm Performance Comparison  
This section presents a detailed comparison of the NSGAII and MOPSO algorithms, focusing on their computational performance, quality of solutions, and diversity of the Pareto front. The results highlight the strengths and weaknesses of each algorithm in addressing the proposed optimization problem.  
Figure 7 illustrates the computational time comparison of NSGA-II and MOPSO across varying problem sizes. MOPSO exhibits faster computation for smaller problems but shows exponential growth in time as problem complexity increases, whereas NSGA-II maintains a linear growth, highlighting its scalability.  
As shown in Figure 7:  
* MOPSO demonstrates faster computational times for smaller problem sizes due to its relatively simpler heuristic mechanism.
* However, as the problem size increases, MOPSO exhibits an exponential growth in computational time, making it less scalable for larger problems.
* In contrast, NSGAII maintains a more linear growth in computational time, demonstrating its robustness for larger problem instances.  
These results suggest that while MOPSO is more efficient for smaller problems, NSGAII is better suited for larger, more complex optimization scenarios.  
Figure 8 shows the diversity and quality of Pareto front solutions for NSGA-II and MOPSO. NSGA-II achieves a more uniform distribution and better exploration of trade-offs, while MOPSO solutions are more clustered.  
The following observations can be made in Figure 8:  
* NSGAII produces a broader and more diverse Pareto front, indicating a better balance between the conflicting objectives of cost minimization and demand coverage maximization.
* The solutions generated by NSGAII are more evenly distributed, suggesting higher exploration capability and diversity.
* MOPSO, while faster for small problems, shows limited diversity in its solutions, with a tendency to converge prematurely to suboptimal regions of the solution space.  
Figure 7: Computational time required by the NSGAII and MOPSO algorithms for varying problem sizes.  
These results highlight the superior multi-objective optimization capabilities of NSGAII, particularly for problems requiring a diverse set of trade-off solutions.  
The comparative analysis demonstrates that NSGAII outperforms MOPSO in terms of solution quality and diversity, especially for larger and more complex problem instances. However, MOPSO's computational efficiency for smaller problems makes it a viable option in scenarios with limited computational resources. Overall, the choice of algorithm depends on the specific requirements of the optimization problem.
# Prompt Tuning for Multi-Label Text Classification: How to Link Exercises to Knowledge Concepts?
## 3 Prompt Tuning Method for Multi-Label Text Classification
### Prompt Tuning Method for Multi-Label Text Classification  
As is shown in Figure 2, our methods adopt a threshold-based strategy [42; 43] to achieve multi-label text classification. Firstly, the relevance scores of exercise content and knowledge concepts are transformed into a masked language model by prompt tuning methods. Specifically, a prompt template is defined as \(V_{prompt}=\text{``}It\)_belongs to \([MASK]\)_\", and combine the exercise text \(x=\{x_{0},x_{1},x_{2},\cdots,x_{n}\}\) to form the final input for prompt tuning input \(e_{prompt}\), which can be shown as Equation (1):  
\[e_{prompt}=\ [CLS]x\text{, }It\text{ }belongs\text{ to }[MASK]. \tag{1}\]  
Suppose that \(M\) is a large corpus of Pre-trained Language Models (PLMs in short), the probability of filling the token [MASK] for each word of concept \(c\) in the knowledge concepts set \(C\) can be denoted as \(P_{M}([MASK]=c\ |\ e_{prompt})\). Here, we need a map function \(Sigmoid()\) to predict the probability of each concept independently. The relevance scores can be represented as (2):  
\[P(c\ |\ e_{Prompt})=Sigmoid(P_{M}([MASK]=c\ |\ e_{prompt})) \tag{2}\]  
Finally, we add an additional threshold mechanism to determine knowledge concepts corresponding to exercises, which can be formulated as (3):  
\[P(e)=\{c\ |\ P(c\ |\ e_{Prompt})>t,c\in C\} \tag{3}\]  
where \(t\) is the threshold.  
To better introduce our method, we take an example shown in Figure 3. The exercise text \"The stack is characterized by first in, last out, and the queue is characterized by first in, first out. (right)\" is wrapped with template as the input. PLM is adopted to predict the predict the probability of filling the token [MASK] with knowledge concepts word set array, stack, queue, linked list. Then, \(Sigmoid()\) function is used to obtain the probability of exercise text linking to labels \(\{array,stack,queue,linkedist\}\). Due to the probability of exercise text linking to stack, queue greater than threshold, exercise text is regarded as linking to \(\{stack,queue\}\).  
Figure 3: An example of our proposed method.  
It has been proven that binary cross-entropy loss (BCE) over sigmoid activation is more suited for multi-label problems and outperforms cross-entropy loss [12]. Therefore, in our paper, the BCE loss function is chosen to learn parameters in the tasks, which can be formulated as (4):  
\[\underset{\Theta}{min}-\frac{1}{K}\sum_{i=1}^{K}\sum_{j=1}^{N}\left[y_{ij}\log( \sigma(\hat{p}_{ij}))+(1-y_{ij})\log(1-\sigma(\hat{p}_{ij}))\right. \tag{4}\]  
where \(\hat{p}_{ij}\) represents the predicted value of exercise \(i\) belongs to concept \(j\). \(y_{ij}\) represents the value of exercise \(i\) belongs to concept \(j\), and \(\sigma\) is the sigmoid function \(\sigma(x)=\frac{1}{1+e^{-x}}\).
# Kinetic Modeling and Analysis of Fuel Element Pneumatic Lifting System in Pebble Bed Reactors
## 3 Analysis of the Motion Characteristics of Fuel Elements
### Analysis of the Motion of Fuel Elements in Atypical Bends  
Figure 9a-d shows the curves of axial velocity \(v_{z}\), transverse velocity \(v_{x}\), transverse displacement x, and rotational angular velocity w of the fuel element in an atypical bend with time, respectively, and Figure 9e shows the effect of the sphere/pipe-diameter ratio and pipe-bending radius on the motion of the fuel element in the deceleration section.  
At the beginning of the fuel element entering the bend, its transverse velocity \(v_{x}\) is large and the collision between the fuel element and the inner-wall surface of the pipe is frequent and violent, which makes its axial velocity \(v_{z}\) drop rapidly. As shown in Figure 9a, \(v_{z}\) drops rapidly from 4.9 m/s to 2 m/s. After that, the collision strength decays rapidly, and the transverse velocity \(v_{x}\) of the fuel element decreases significantly (as shown in Figure 9b,c). Due to the pneumatic thrust \(F_{\text{d}}\), the fuel element will undergo a re-acceleration process. The decay of the lateral motion of the fuel element in the bend is more significant than in the horizontal straight pipe (Figure 7) because of the larger centrifugal force in the bend. After 0.15 s, the collision process ends and the fuel element rolls against the outer wall of the pipe (x = 1 mm wall), and its rotational angular velocity \(w\) in the pure rolling state is determined by \(v_{z}/D_{\text{s}}\), as shown in Figure 9d.  
The effect of sphere/pipe-diameter ratio and pipe-bending radius \(R\) on the motion characteristics of fuel-element deceleration section is shown in Figure 9e. Decreasing the sphere/pipe-diameter ratio \(k\) and pipe-bending radius \(R\) can limit the final velocity of the fuel element during the whole pneumatic lifting process, i.e., reduce the impact velocity \(v_{zc}\) of the fuel element on the core. When the sphere/pipe-diameter ratio is 0.97, the axial velocity of the fuel element only decreases slightly for a short time, and the impact velocity \(v_{zc}\) is greater than 5.5 m/s. When the sphere/pipe-diameter ratio is 0.92 and the pipe-bending radius is 0.4 m, the deceleration effect is obvious, and the impact velocity \(v_{zc}\) of the fuel element on the core drops to 4.3 m/s. In summary, a larger sphere/pipe-diameter ratio will have a greater impact velocity. A smaller bending radius has a more pronounced deceleration during the collision phase, but its recovery rate is faster and the impact velocity is smaller. The choice of a larger sphere/pipe-diameter ratio and a smaller bending radius will help to ensure the integrity of the fuel elements and the safety of the reactor interior.  
Figure 9: Atypical\(-\)bend section parameters: (**a**) axial velocity, (**b**) lateral velocity, (**c**) lateral displacement, (**d**) angular velocity, (**e**) sphere/pipe\(-\)diameter ratio and pipe\(-\)bending radius.
# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 3 Model  
We now characterize the agents participating in the simulations (i.e., the users of the social network) by discussing their roles, parameters, and dynamics. We also introduce the problem of constructing a synthetic network that resembles a real OSN and state our assumptions about the time dynamics involved in our fake news epidemic model.  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref.** & **Summary of the Contributions** & **Missing Aspects** \\ \hline
[9] & Proposes a model for the competitive spreading of a fake news and its debunking. & No bots, homogeneous agents, no time dynamics. \\ \hline
[21] & Uses supervised learning techniques to detect fake news at an early stage based on its content. & Diffusion dynamics are not among the targets of the study. \\ \hline
[27] & Studies the role of the bots in social manipulation and in facilitating the emergence of polarization between communities, so as to exacerbate social conflict online. & Diffusion dynamics are not among the targets of the study. \\ \hline
[38] & Studies the role of network structure in the spreading of fake news, assessing the emergence of segregation. & Same as [9]. \\ \hline
[39] & Tests fact-checking strategies on different network topologies. & Same as [9]. \\ \hline
[40] & Mixed-method study: proposes a questionnaire to capture the personality of users on a social network. In simulations, agents’ parameters are chosen according to the questionnaires. & No spreading of debunking messages, no time dynamics, no bots. \\ \hline
[41] & Studies the influence of online bots on opinion formation on a social network and shows that bots control the formation of consensus. & Opinion dynamics setting, no time dynamics. \\ \hline
[43] & Uses an agent-based model for the spreading of a physical disease and of information (both online and offline) related to it. The two processes are separate, but interact, showing that fake news spreading effects the diffusion of the disease. & No bots. \\ \hline
[44] & Assesses the effects of heterogeneous agents on the competitive spreading of low- and high-quality information. Studies methods to mitigate the spreading of false information. & Simple agent interaction model, no time dynamics. \\ \hline
[36] & Provides a mathematical formalization of a fake news spreading model and studies its dynamical properties. & Homogeneous agents, no competitive spread of the debunking, no bots. \\ \hline
[37] & Proposes a model of the temporal evolution of the engagement of fake news items on Twitter. & Diffusion dynamics are not among the targets of the study. \\ \hline
[42] & Models the formation of clustered communities echoing an opinion, and analyzes the community segregation that results. & Simple agent interaction model, no time dynamics, no bots. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the contributions and missing aspects of the works in the literature that are most related to our agent-based fake news spreading simulation approach.
# Investigation of the Characteristics of NLS-PNA: Influence of NLS Location on Invasion Efficiency
## 4 Conclusions  
In summary, we investigated the characteristics of NLS-PNA by employing a series of NLS-modified PNAs. These NLS-PNA conjugates were found to be superior to unmodified PNAs at elevated salt concentrations. The difference between C-terminally modified and N-terminally modified NLS-PNAs was not very significant, signifying that N-terminally modified PNA-NLS conjugates can be readily used for research purposes using the same method as previously reported C-terminally modified conjugates. This facilitates the development of new functional peptide-modified PNA conjugates, since solid-phase peptide synthesis requires synthesis from the C-terminus to the N-terminus. As an example, for C-terminally modified PNAs, when multiple functional peptides bound to the same PNA need to be screened, each functional peptide must be synthesised separately. In the case of N-terminal modification, the synthesis of peptide-modified PNAs can be simplified by synthesising the individual PNAs only once, separating the resin beads into as many samples as needed and continuing the synthesis of each NLS. In addition to the finding that the position of the NLS has some influence on  
Figure 4: **(a)** Invasion experiments for N-Pro-PNAs to evaluate the effect of an amino acid between NLS and PNA. Invasion conditions: [DNA] = [each PNA] = 50 nM, [HEPES (pH 7.0)] = 5 mM, and [NaCl] = 0–125 mM at 50 \({}^{\circ}\)C for 1 h. **(b)** Effect of salt concentration on invasion efficiency with various PNAs (U-, C-, N-, and N-Pro-PNAs). The value for invasion efficiency was obtained by comparing the band intensities in a microchip electrophotogram.  
invasion, we demonstrated that the amino acids between the NLS and PNA also play an important role. These findings also provide new perspectives for the future design of novel peptide-modified PNAs.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/23/8663/s1](http://www.mdpi.com/2076-3417/10/23/8663/s1)]([http://www.mdpi.com/2076-3417/10/23/8663/s1](http://www.mdpi.com/2076-3417/10/23/8663/s1)), Figure S1: chemical structures of 2,6-diaminopurine (D) and 2-thouracil (U), Figure S2: HPLC charts of purified PNAs, Figure S3: MALDI TOF MS spectra of purified PNAs.  
Investigation, G.U. and M.S.; writing--original draft preparation, Y.A. and G.U.; writing--review and editing, Y.A., G.U., M.S. and O.S.; supervision, Y.A. and O.S.; project administration, Y.A.; funding acquisition, Y.A. and O.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI grant no. 19K05730 to Y.A. from the Ministry of Education, Culture, Sports, Science, and Technology (Japan). This work was also supported in part by the Izumi Science and Technology Foundation to Y.A., Kato Memorial Bioscience Foundation to Y.A., and Integrated Research Consortium on Chemical Sciences to Y.A.  
We thank [PERSON] for checking the English of this manuscript. The authors declare no conflict of interest.
###### Abstract  
With the rapid growth of mass content retrieval on the Internet, Information-Centric Network (ICN) has become one of the hotspots in the field of future network architectures. The in-network cache is an important feature of ICN. For better network performance in ICN, the cache size on each node should be allocated in proportion to its importance. However, in some current studies, the importance of cache nodes is usually determined by their location in the network topology, ignoring their roles in the actual content transmission process. In this paper, we focus on the allocation of cache size for each node within a given total cache space budget. We explore the impact of heterogeneous cache allocation on content dissemination under the same ICN infrastructure and we quantify the importance of nodes from content dissemination and network topology. To this purpose, we implement a hierarchy partitioning method based on content dissemination, then we formulate a set of weight calculation methods for these hierarchies and to provide a per-node cache space allocation to allocate the total cache space budget to each node in the network. The performance of the scheme is evaluated on the Garr topology, and the average hit ratio, latency, and load are compared to show that the proposed scheme has better performance in these aspects than other schemes.  
Information-Centric Networking (ICN); in-network caching; cache size allocation; content dissemination 10.390/13/00131
## 1 Introduction
### Contributions  
Aiming at the shortcomings of the above traditional network communication methods, we propose a novel secure communication method based on the message hash chain, referred to as the Message Hash Chain (MHC) method. The main contributions of the proposed MHC method are summarized as follows:  
1. The MHC method adopts a new chain transmission method to ensure the non-tampering, non-repudiation, and higher reliability requirements of multiple messages. The main idea is to iteratively hash the digest of the transmitted message to form a hash chain about the message sequence. The two communicating parties can ensure the integrity, immutability, and synchronization of the message sequence through the hash chain, thereby effectively guaranteeing the security of message transmission.
2. When performing data signature and authentication, both parties only need to perform signature authentication on messages at certain intervals and do not need to complete it on each message. In this way, the authenticity and non-repudiation of all previously transmitted messages can be ensured, the overhead of signature authentication is reduced, and the efficiency of secure message transmission is greatly improved.
3. Using the sequence number and node value of the message hash chain of the MHC method can provide anti-protection against replays and ensure reliability.
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 3 Results and Discussion
### Static Modeling and Analysis of the Three-Directional Vibration Isolator  
#### 3.2.1 Vertical Static Equilibrium and Restoring Force Analysis  
Owing to the structural symmetry of the three-directional vibration isolator, the principle of superposition of parallel spring forces can be applied to derive its vertical restoring force. By incorporating the static equilibrium condition, the expression for the vertical restoring force (\(F_{2}\)) is given as follows:  
\[F_{z}=4K_{n}\left[\frac{L(L\sin\theta_{0}-z-a)}{\sqrt{\left(L\cos\theta_{0} \right)^{2}+\left(L\sin\theta_{0}-z-a\right)^{2}}}-\left(L\sin\theta_{0}-z-a \right)\right] \tag{4}\]  
To facilitate further analysis, Equation (4) is nondimensionalized, yielding the following:  
\[\widetilde{F}_{z}=4\delta\left[\frac{\left(\sin\theta_{0}-\widetilde{z}- \lambda\right)}{\sqrt{\left(\cos\theta_{0}\right)^{2}+\left(\sin\theta_{0}- \widetilde{z}-\lambda\right)^{2}}}-\left(\sin\theta_{0}-\widetilde{z}- \lambda\right)\right] \tag{5}\]  
where \(\widetilde{z}=\frac{\delta}{L}\), \(\lambda=\frac{a}{L}\), \(\delta=\frac{K_{n}}{\widetilde{K}_{z}}\), \(\widetilde{F}_{z}=\frac{F_{z}}{K_{z}\cdot L}\).  
By differentiating Equation (5) with respect to \(\lambda\), the dimensionless vertical stiffness of the three-directional vibration isolator (\(\widetilde{K}_{z}\)) is obtained as follows:  
\[\widetilde{K}_{z}=4\delta\left[1-\frac{\left(\cos\theta_{0}\right)^{2}}{\left( \left(\cos\theta_{0}\right)^{2}+\left(\sin\theta_{0}-\widetilde{z}-\lambda \right)^{2}\right)^{\frac{3}{2}}}\right] \tag{6}\]  
Figure 4: Schematic diagram of the three-directional isolator’s vertical and lateral orientations.
## 2 Results
### DisA Does Not Compete with RuvAB for the Binding to DNA  
DisA might compete with RuvAB for DNA binding and inhibit its ssDNA-dependent ATPase activity. To test this hypothesis, DisA was replaced by DisA \(\Delta\)C290. This mutant, which lacks the DNA-binding domain but retains its DAC activity [24], interacts with RuvB (Figure 2B). In the presence of ssDNA or HJ DNA, RuvAB-mediated ATP hydrolysis was inhibited by DisA \(\Delta\)C290, to levels comparable to \(wt\) DisA (Figure 4A,B vs. Figure 4C,D, orange, green, and brown vs. dark blue line, Table 1). This suggests that DisA does not inhibit RuvAB-mediated ATPase activity due to competition for DNA binding. Since DisA neither inhibits the DNA-independent RuvB (or RuvAB) ATPase nor competes with RuvAB  
Figure 4: DisA inhibits RuvAB-mediated ATP hydrolysis. (**A,B**) The ATPase activity of the RuvAB complex (15 nM) in buffer B containing 5 mM ATP was measured during 30 min with circular 3199-nt ssDNA (in **A**) or HJ DNA (in **B**) as the effector. As indicated, DisA (6, 12 and 24 nM) was added, or DNA was omitted. (**C–F**) DisA inhibits RuvAB-mediated ATP hydrolysis by a direct interaction. Circular 3199-nt ssDNA (10 \(\upmu\)M in nt) (**C,E**) or HJ DNA (**D,F**) was incubated with RuvAB (15 nM) and DisA \(\Delta\)C290 (**C,D**) or DisA D77N (**E,F**) (6 to 24 nM) in buffer B containing 5 mM ATP, and the ATPase activity was measured (30 min at 37 \({}^{\circ}\)C). All reactions were repeated three or more times with similar results, representative graphs are shown here, and the mean \(K_{\text{cat}}\pm\text{SEM}\) are listed in Table 1.  
for binding to its DNA substrate, we assumed that the inhibition of the RuvAB ATPase by DisA is a genuine and specific activity of the latter. Furthermore, DisA does not affect the ATPase activity of the unrelated PcrA DNA helicase [27].