esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Seismic Dynamic Analyses of Bidirectional Eccentric Structures
#### 3.3.1 Response of Bidirectional Eccentric Structure under Bidirectional Mainshock
Figure 20: Seismic responses of structures under mainshock–aftershock sequences with different mainshock PGAs (case BBM): (**a**) Top floor maximum displacement with mainshock PGA at 0.15 g; (**b**) IDR with mainshock PGA at 0.15 g; (**c**) top floor maximum displacement with mainshock PGA at 0.25 g; (**d**) IDR with mainshock PGA at 0.25 g; (**e**) top floor maximum displacement with mainshock PGA at 0.32 g.  
Figure 21: Ratios of maximum displacement and IDR of structures under mainshock–aftershock sequences with different mainshock PGAs (case BBS): (**a**) Ratio of maximum displacement of aftershock to that of mainshock with mainshock PGA at 0.15 g; (**b**) ratio of IDR of aftershock to that of mainshock with mainshock PGA at 0.15 g; (**c**) ratio of maximum displacement of aftershock to that of mainshock with mainshock PGA at 0.25 g; (**d**) ratio of IDR of aftershock to that of mainshock with mainshock PGA at 0.25 g; (**e**) ratio of maximum displacement of aftershock to that of mainshock with mainshock PGA at 0.32 g; (**f**) ratio of IDR of aftershock to that of mainshock with mainshock PGA at 0.32 g.  
In addition, compared with the unidirectional eccentric structure, the influence of aftershock on the bidirectional structure is obviously greater. It is noted that in Figures 16f and 20f, when the mainshock PGA is 0.32 g, the IDR response decreased at high eccentricities of 15-30%, which was different from the general increasing situations. The reason is that the bidirectional structure has a greater response than the unidirectional eccentric structure in the bidirectional 0.32 g mainshock. The IDR of the structure with an eccentricity of 30% decreases, indicating that the structure is damaged or even collapsed. In this case, it is difficult for the aftershock to exceed the effect of the mainshock, even if the aftershock amplitude is equal to the mainshock amplitude. Consequently, for the bidirectional eccentric structures, with an increase in the mainshock PGA, the influence of aftershocks on the maximum displacement and the IDR of the structures are decreased. When the mainshock PGA is 0.15 g, the effect of aftershock is the most significant. The maximum displacement of the structure is about 1.5 times greater than that of the mainshock, and the IDR is around 1.4 times greater than that of the mainshock. However, when the mainshock PGAs are 0.25 g and 0.32 g, the impact of aftershocks on the structure is weakened or even not present. The reason is that the bidirectional eccentric structures are close to destruction or even collapse under such strong mainshocks. Meanwhile, compared with the unidirectional eccentric structures, it is demonstrated that aftershocks have a greater impact on the bidirectional eccentric structures under the same bidirectional loading.
# Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
## 2 Related Work
### Maximal Marginal Relevance  
Several re-ranking methods have been proposed to improve the precision and diversity of retrieved contexts in RAG systems. Among the most notable are Dartboard, Cross-Encoder Re-Rankers, Expando-Mono-Duo, and Maximal Marginal Relevance (MMR) models. Each introduces different mechanisms to optimize relevance or diversity, often at the cost of computational efficiency or implementation simplicity.  
Dartboard [20] is an information-theoretic re-ranking algorithm that selects documents by maximizing the relevant information gain with respect to the query. It encourages diversity and informativeness through a greedy but globally motivated selection process. While Dartboard excels at reducing redundancy and selecting semantically distinct results, it relies on complex scoring heuristics and approximations, making it harder to scale and integrate into lightweight retrieval pipelines. In contrast, MMR offers a more interpretable and modular re-ranking mechanism that provides explicit control over relevance-diversity trade-offs via a single tunable parameter (\(\lambda\)), and can be easily integrated into existing vector-based retrieval systems.  
Neural re-rankers such as cross-encoder-based models, including MonoBERT or MonoT5 [21], jointly encode the query and document to compute fine-grained, interaction-aware relevance scores. While these models achieve high ranking accuracy, they demand substantial computational resources due to their pairwise processing nature and lack the ability to naturally enforce diversity constraints. In contrast, MMR offers a significantly more efficient and scalable solution, that is particularly well suited for large candidate sets, as it incorporates redundancy reduction by design. This makes MMR more practical for use in resource-constrained environments.  
The Expando-Mono-Duo framework [22] expands queries or documents before retrieval and then applies mono- and duo-encoders to re-rank. While it improves recall and context matching, the multi-stage design increases training latency and complicates deployment. Compared to Expando-Mono-Duo, MMR provides a lightweight, single-stage re-ranking approach with no need for query rewriting or multi-step re-ranking, making it preferable for domains where simplicity, speed, and transparency are critical.  
Overall, while recent methods bring improvements in specialized scenarios, MMR remains a strong baseline due to its efficiency, interpretability, and ease of integration--particularly in road traffic legal and regulatory systems that demand controllable and explainable behavior.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 3 Results and Discussion  
Effect of Incentse Ash Replacement Ratio on Amount of Water Required for Normal Consistency and Setting Time of Cement Paste  
Table 2 presents the water requirement for normal consistency and setting time of cement paste specimens with different incense ash replacement ratios. The water requirement for normal consistency grew with the incense ash replacement ratio. Specifically, every 10% increase in incense ash replacement ratio caused the water requirement to rise by approximately 6%. Therefore, incense ash can be surmised to require more water than cement. The appearance of an incense ash grain is a lump of irregular shape, with small particles forming cotton-like aggregates on the surface (Figure 1). Because the specific weight of incense ash is lower than that of cement, a greater volume of incense ash is required to replace the same weight of cement, increasing the water requirement. Both the initial and final setting time shortened as the replacement ratio increased, suggesting that incense ash becomes hydrated faster than cement. The results of physical property tests revealed that the incense ash had a fineness of 688 m\({}^{2}\)/kg and had a greater specific surface area than cement, making it bind with water more easily. Moreover, because incense ash has a smaller grain diameter and higher surface energy than cement, a rapid increase in surface area also causes the number of surface atoms to increase rapidly, thus hastening and intensifying the hydration reaction [36]. However, the incense ash only moderately affected setting time; even when 30% of cement was replaced by it, the setting time was shortened only by approximately 6 min.
## 4 Experimental Simulations and Results
### Results
#### 4.2.1 Case 1: Algorithm Benchmarking on D1-D2 Section
Figure 5 presents the Pareto fronts generated by the three algorithms for section D1-D2, plotted with energy consumption on the vertical axis and punctuality metrics on the horizontal axis. The colors and sizes of the data points indicate the safety and comfort indices, respectively. Each particle represents a unique set of independent operational control strategies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Comparison** & **Spread** & **Hypervolume (HV)** & **Inverted Generational Distance (IGD)** \\ \hline ESS-NSGA-III compared to NSGA-III & \(-8.15\%\) & \(0.77\%\) & \(-3.66\%\) \\ ESS-NSGA-III compared to NSGA-II & \(-12.89\%\) & \(4.12\%\) & \(-5.79\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage differences in the optimization indicators between ESS-NSGA-III, NSGA-III, and NSGA-II. Figure 5: Pareto fronts generated by the different algorithms for section D1–D2: (**a**) NSGA-II, (**b**) NSGA-III, and (**c**) ESS-NSGA-III. The results indicate that all three algorithms successfully identified points on the Pareto front. However, the NSGA-II algorithm, which uses the crowding distance to select optimal individuals, exhibited skewness in its solution set. The objective points tended to cluster at the two extremes of the distribution, resulting in an insufficient uniformity in the central interval. This uneven distribution highlights the limitations of NSGA-II in managing high-dimensional multi-objective optimization problems. The most punctual solutions extracted from the Pareto optimal sets obtained by the ESS-NSGA-III, NSGA-III, and NSGA-II algorithms for section D1-D2 were compared, and the corresponding metrics are presented in Table 6. Specifically, ESS-NSGA-III achieved the lowest energy consumption (1.3241 \(\times\) 104 J), which represents a significant improvement over NSGA-III (1.3325 \(\times\) 104 J) and NSGA-II (1.3832 \(\times\) 104 J), with energy savings rates of 0.6% and 4.3%, respectively. In terms of punctuality, ESS-NSGA-III exhibited the smallest time deviation of 0.1 s, outperforming NSGA-III (0.3 s) and NSGA-II (0.4 s). Regarding ride comfort, ESS-NSGA-III achieved the optimal value of 0.0197 m/s\({}^{3}\), performing slightly better than NSGA-II (0.0199 m/s\({}^{3}\)) and NSGA-III (0.0201 m/s\({}^{3}\)). Additionally, ESS-NSGA-III maintained a competitive safety index of 0.8201, also performing slightly better than NSGA-III (0.8228) and NSGA-II (0.8251).
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 5 Numerical Results
### Effects of Various Parameters on OPS design
protocol, the OPS ratio obtained by closed-form is very close to that obtained by doing NS. With the increase of \(d_{1}\), the OPS values of both DF and AF protocols are concave function. This is because when \(P_{1}=P_{2}\) and \(d_{1}+d_{2}=10\) m, the system has symmetric nature. In addition, when the relay is deployed in the middle of \(S_{1}\) and \(S_{2}\), the harvested energy is the smallest with the fixed \(\rho\), and the harvested energy increase when the relay getting closer to any of the source node. Thus, to enhance the performance, more energy is needed to be allocated to energy harvester when \((d_{1},d_{2})=(5,5)\), and when \(d_{i},i=1\) or 2 decrease, less energy is needed to be allocate to energy harvester. We also find that the curves with \(P_{t}=30\) dBm are lower than that with \(P_{t}=15\) dBm. This is because with higher \(P_{t}\), the harvested energy is more prone to satisfy the transmission with lower \(\rho\).
## 4 Experiment and Analysis  
In this section, the function and performance of the proposed simulation and verification platform are evaluated from three aspects: scheduling management, communication management, and configuration management. According to the test results, the feasibility of the simulation and verification platform is analyzed, and by the analysis of the equipment composition, there is the main control terminal, two avionics components (flight management system and flight control system, with each component containing a virtual AFDX board), a display server, and an exciter server. These five devices work together to drive the simulation and verification environment. The network is via Ethernet, and each computer has a memory database so that the interaction between terminals is in the same data pool. Through the establishment of a simulation and verification platform, the model selection, parameter configuration, visual simulation, and detailed evaluation of experimental results have been completely realized.  
In the process of simulation and verification, all operations are completed in the main control terminal, the exciter, display processing units, and components are executed by the preset process, and no operator is required to intervene on each machine. Their interaction process is shown in Figure 9. After the main control terminal successfully starts the software of the exciter, flight control components, flight management components, and display processing unit on each machine, the flight control components continuously receive the flight data from the exciter and flight management components. After parsing, the flight instruction set is generated and sent to the exciter to control the action and attitude of the aircraft. The exciter can set flight parameters and package ICD to send to the flight management components. The flight management components receive the data from the exciter and package them to the display processing unit for dynamic display.
## 4 Deep Learning-Based Minimum Timeline Cover
### Application of the Pointer Network  
Once we have constructed the sequence of node embeddings for each node \(v\), represented as \(P_{v}=[P_{v}^{(1)},P_{v}^{(2)},\dots,P_{v}^{(T)}]\), the next step is to apply the PN to determine the optimal activity intervals for each node based on these embeddings.  
The primary goal of the PN in the context of the MinTCover problem is to select, for each node in the temporal graph, the most effective activity interval that covers all relevant interactions within the shortest possible duration. The Pointer Network achieves this by processing sequences of embeddings that encapsulate both the structural and temporal dynamics of the graph, thus identifying the optimal start and end points of activity for each node.  
The PN processes the sequence of embeddings \(P_{v}\) by applying a softmax pointer layer that computes a probability distribution over each timestamp in the sequence \(P_{v}\). Thisdistribution is indicative of the likelihood that a particular timestamp is the starting or ending point of the optimal interval for node \(v\).  
Suppose the output from the Transformer for node \(v\) is a sequence of vectors \(P_{v}=[P_{v}^{(1)},P_{v}^{(2)},\ldots,P_{v}^{(T)}]\). The PN then applies an attention function \(a\) to compute the probabilities:  
\[a(P(t)_{v})=\text{softmax}(W_{a}P(t)_{v}) \tag{11}\]  
where \(W_{a}\) are the learnable parameters of the network, and \(P(t)_{v}\) is the Transformer output at time \(t\).  
The final output of the PN is a set of intervals \(\{s_{v},e_{v}\}\) for each node \(v\), where \(s_{v}\) and \(e_{v}\) are selected based on the highest probabilities from the softmax outputs. These intervals have to be chosen to minimize the overall activity span while ensuring all necessary interactions are covered.
## 2 Preliminaries
### High Degree F-Transform  
This paragraph introduces the concept of higher degree fuzzy transform or F\({}^{\text{F}}\)-transform. One-dimensional square-integrable functions will now be considered. Let A\({}_{\text{h}}\), h = 1,...,n, be the h\(\prime\)h basic function defined on [a,b] and L\({}_{2}\)([x\({}_{\text{h}-1}\),x\({}_{\text{h}+1}\)]) be the Hilbert space of square-integrable functions \(fg\): [x\({}_{\text{h}-1}\),x\({}_{\text{h}+1}\)] \(\longrightarrow\) R with the inner product:  
\[\langle f,g_{h}\rangle=\frac{\int_{x_{\text{h}-1}}^{x_{\text{h}+1}}f(x)g(x)A_ {\text{k}}(x)dx}{\int_{x_{\text{h}-1}}^{x_{\text{h}+1}}A_{\text{h}}(x)dx} \tag{7}\] \begin{table}
\begin{tabular}{l l l} \hline \hline
**Input Variable** & **Node** & **Value** \\ \hline \multirow{3}{*}{First variable} & x\({}_{1}\) & 1.1 \\  & x\({}_{2}\) & 3.0 \\  & x\({}_{3}\) & 4.9 \\ \hline \multirow{3}{*}{Second variable} & y\({}_{1}\) & 0.1 \\  & y\({}_{2}\) & 0.55 \\  & y\({}_{3}\) & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Set of nodes of the two input variables in the example. Figure 1: Data points and nodes in the example. Let \(L^{r}{}_{2}([x_{h-1},x_{h+1}])\), with r positive integer, be a linear subspace of the Hilbert space \(L_{2}([x_{h-1},x_{h+1}])\) with orthogonal basis given by polynomials \(\{P^{0}{}_{h},P^{1}{}_{h},\ldots,P^{r}{}_{h}\}\) obtained applying the Gram-Schmidt orthonormalization to the linear independent system of polynomials \(\{1,x,x^{2},\ldots,x^{r}\}\) defined in the interval \([x_{h-1},x_{h+1}]\). We have:  
\[\left\{ \begin{array}{l}P^{0}_{h}=1\\ P^{s+1}_{h}=x^{s+1}-\sum\limits_{j=1}^{s}\frac{\langle x^{s+1},P^{j}_{h} \rangle}{\langle P^{j}_{h},P^{j}_{h}\rangle}\end{array} \right.\ s=1,\ldots,r-1 \tag{8}\]  
The following Lemma holds (Cfr. [PERSON] et al., 2011 [3], Lemma 1) :  
**Lemma 1. **_Let \(F^{r}_{k}\) be the orthogonal projection of the function \(f\) on \(L^{r}{}_{2}([x_{h-1},x_{h+1}])\). Then:_  
\[F^{r}_{h}(x)=\sum\limits_{s=1}^{r}c_{h,i}P^{s}_{h}(x) \tag{9}\]  
where  
\[c_{h,s}=\frac{\langle f,P^{s}_{h}\rangle_{k}}{\langle P^{s}_{h^{\prime}},P^{ s}_{h}\rangle_{h}}=\frac{\int_{x_{h-1}}^{x_{h+1}}f(x)P^{s}_{h}(x)A_{h}(x)dx}{ \int_{x_{h-1}}^{x_{h+1}}(P^{s}_{h}(x))^{2}A_{h}(x)dx} \tag{10}\]  
\(F^{r}_{h}\) it is the h-th component of the direct F\({}^{r}\)-transform of \(f\) F\({}^{r}\)[f] = (F\({}^{r}_{1}\), F\({}^{r}_{2}\),...,F\({}^{r}_{n}\)). The inverse F\({}^{r}\)-transform of f in a point x \(\in\) [a,b] is:  
\[f^{r}_{F,n}(x)=\sum\limits_{k=1}^{n}F^{r}_{h}A_{k}(x) \tag{11}\]  
For r = 0 we have P\({}^{0}_{h}=1\) and the F\({}^{0}\)-transform is given by the F-transform in one variable (F\({}^{0}_{h}(x)=c_{h,0}\)).
# Analysis of Low Cost Communication Technologies for V2I Applications
## 3 Materials and Methods  
### System Operation  
In any application for vehicular communication, there are two components: Road site unit (RSU) and on-board unit (OBU). Their responsibilities are different due to their different positions on the road.  
Figure 1 represents a real case, where the road infrastructure or RSU has the message with the information available for the vehicle.  
The purpose of the OBU in the vehicle consists of taking the information from the RSU, filtering it in function of the driver's interest, and displaying it on the screen of the vehicle, but with the necessary time for a safe driver reaction. An example of application would be the Figure 2.  
This way is considered the optimal method, avoiding overload of the network with unnecessary messages, because the displayed messages are enough for enhancing safety and efficiency. In addition to this, there will be an RSU, each 100 meters, which will avoid a message being too heavy for the right delivery.  
Figure 1: System components.  
Figure 2: Operation example.  
In contrast with current methods which can recognize traffic signs with cameras, our method solves the main issues of camera usage, which are poor visibility of the traffic signal or bad behaviors in adverse weather conditions. With V2I methods, you have this data in any case and with time enough. It also facilitates really more road data than visual methods.  
Once the system operation has been reviewed, in the following subsections, it is described the different steps to carry it out.
## 5 Challenges and Future Research Directions
### Quantum Computing  
Commercial quantum computation is expected to already be available in the near future [132]. A certain amount of quantum computation, in particular, can be expected to become a reality, the time it takes for a new node to download the entire blockchain. Throughout the lifespan of 6G enabled UAV networks, the arrival of large-scale quantum computing necessitates the replacement of some current public-key primitives with quantum-resistant ones in the blockchain in 6G enabled UAV networks [133]. Due to [PERSON]'s quantum polynomial time integer factoring algorithm [134], factoring and discrete logarithm-based cryptographic primitives, such as the elliptic curve signature algorithm (ECDSA) [135], are rendered vulnerable once large-scale quantum computation becomes a reality. In the post-quantum environment, post-quantum resistant alternatives must be used to replace these security methods. Fortunately, symmetric primitives, such as cryptographic hash functions used in block formation, are not affected in the same way, based on current knowledge.
## Appendix A The Calculation of the Symplectic Eigenvalues  
The covariance matrix \(\gamma_{AB_{1}}\) depends on Alice and the seawater channel, and it has the following form  
\[\gamma_{AB_{1}}=\begin{bmatrix}\gamma_{A}&\sigma_{AB_{1}}\\ \sigma_{AB_{1}}&\gamma_{B_{1}}\end{bmatrix}=\begin{bmatrix}VI&\sqrt{T_{sea}}Z _{N}\sigma_{z}\\ \sqrt{T_{sea}}Z_{N}\sigma_{z}&T_{sea}(V+\chi_{ch})I\end{bmatrix}, \tag{1}\]  
where \(I=diag(1,1)\), \(\sigma_{z}=diag(1,-1)\), \(Z_{N}=2\pi^{2}\sum\limits_{k=0}^{N-1}\sqrt{\frac{\lambda_{1}^{3}-1}{\lambda_ {k}}}\). Therefore, we can calculate the corresponding symplectic eigenvalues  
\[\lambda_{1,2}=\sqrt{\frac{1}{2}(A\pm\sqrt{A^{2}-4B})}, \tag{2}\]  
with  
\[\begin{split} A&=det(\gamma_{AB_{1}}),\\ B&=det\gamma_{A}+det\gamma_{B_{1}}+2 det(\sigma_{AB_{1}}). \end{split} \tag{3}\]  
The quantum state \(\rho_{B_{1}}\) arriving at [PERSON]'s side introduces an electric noise \(v_{el}\) when it was detected by an imperfect detector. This trusted detection noise can be purified by introducing an additionalEPR source with a variance of \(V_{d}\) as well as two modes \(F_{0}\) and \(G\)[43], which can be seen in Figure 4. The corresponding covariance matrix of this EPR state can be noted as  
\[\gamma_{F_{0}G}=\begin{bmatrix}V_{d}I&\sqrt{V_{d}^{2}-1}\sigma_{z}\\ \sqrt{V_{d}^{2}-1}\sigma_{z}&V_{d}I\end{bmatrix}. \tag{10}\]  
Subsequently, one of two modes of the EPR state is coupled to the signal. Then, the whole system can be represented by the quantum state \(\rho_{ABFG}\), and its corresponding covariance matrix can be derived from [32]  
\[\gamma_{ABFG}=\Gamma_{BS}^{T}(\gamma_{AB_{1}}\oplus\gamma_{F_{0}G})\Gamma_{BS}, \tag{11}\]  
where  
\[\Gamma_{BS}=I\oplus\begin{bmatrix}\sqrt{\eta}I&\sqrt{1-\eta}I\\ -\sqrt{1-\eta}I&\sqrt{\eta}I\end{bmatrix}\oplus I. \tag{12}\]  
The conditional covariance matrix \(\gamma_{AFG}^{B}\) can be derived from  
\[\gamma_{AFG}^{B}=\gamma_{AFG}-\sigma_{AFGB}^{T}H\sigma_{AFGB}, \tag{13}\]  
where \(H=H_{hom}=(X^{T}\gamma_{B}X)^{MP}\) for homodyne detection and \(H=H_{het}=(\gamma_{B}+I)^{-1}\) for heterodyne detection. \(X=diag(1,0)\) when the homodyne detector detects the amplitude quadrature, and \(X=diag(0,1)\) when it detects the phase quadrature. \(MP\) denotes the pseudoinverse of the matrix. The matrices \(\gamma_{AFG}\), \(\sigma_{AFGB}\) and \(\gamma_{B}\) are the submatrices of the covariance matrix \(\gamma_{AFGB}\), which can be obtained by rearranging the rows and columns of the matrix \(\gamma_{ABFG}\).  
By computing the symplectic eigenvalues of the matrix \(\gamma_{AFG}^{B}\), we can get  
\[\lambda_{3,4}=\sqrt{\frac{1}{2}(C\pm\sqrt{C^{2}-4D})},\lambda_{5}=1, \tag{14}\]  
where, for the homodyne case,  
\[\begin{split} C_{hom}&=\frac{X_{hom}B+T_{sea}(V+ \chi_{ch})+V\sqrt{A}}{T_{sea}(V+\chi_{thom})},\\ D_{hom}&=\sqrt{A}\frac{\sqrt{A}\chi_{hom}+V}{T_{sea }(V+\chi_{thom})},\end{split} \tag{15}\]  
and, for the heterodyne case,  
\[\begin{split} C_{het}&=\frac{B\chi_{het}^{2}+2\chi_{het }[V\sqrt{A}+T_{sea}(V+\chi_{ch})]+A+1+2T_{sea}(Z_{N})^{2}}{T_{sea}^{2}(V+\chi _{thet})^{2}},\\ D_{het}&=(\frac{V+\sqrt{A}\chi_{het}}{T_{sea}(V+ \chi_{thet})})^{2}.\end{split} \tag{16}\]