esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Performance  
This study compared the effectiveness of AR-based instructions to paper-based methods in terms of task completion time. Notably, completion times varied between maintenance tasks, with a significant increase (50%) from simple to complex tasks across all instruction types. Both AR (24.48 vs. 13.30 min) and paper-based (28.76 vs. 13.34 min) instructions required considerably more time to complete complex tasks than simple ones. However, while completion times for simple tasks were similar with both methods, completion times with AR-based instruction were significantly faster for complex tasks. These results indicate that AR-based instruction during complex activities can greatly shorten maintenance times. It may be claimed that paper-based instructions may be less efficient for challenging tasks because they require the user to browse the paper and extract information from the material. In contrast, AR-based instruction systems offer a variety of features that can be displayed upon request. Therefore, while AR did not enhance performance for simple tasks, it proved significantly more efficient for more challenging tasks.  
This study supports previous findings [21; 75; 76; 77; 78]. [PERSON] et al. [21] observed that paper manuals and AR support were equally effective for simple tasks, but AR support outperformed paper-based support for complex tasks. [PERSON] and [PERSON] [79] discovered that using paper instructions for assembly activities with a modest level of complexity was more time-consuming than following AR instructions. Similarly, [PERSON] et al. [75] reported minor improvements when applying AR to tasks of low complexity compared to traditional approaches, while AR was found to be more effective when applied to more complicated activities, as indicated by [PERSON] et al. [77]. [PERSON] et al. [78] investigated the influence of product difficulty (difficult, medium, and easy) on task completion time with different instruction types (AR, tablet, projection, and paper instructions). They found that compared to paper instructions, using AR glasses resulted in time reductions of 3.16%, 9.51%, and 9% for easy, medium, and difficult tasks, respectively [78].
## 3 Space--Time Dynamic Analysis Method Based on DCM
### Space-Time Discretization and Numerical Scheme of DC Dynamic Analysis
\begin{array}{rcl}\widetilde{\mathbf{K}}&=\frac{EI}{L^{4}}\mathbf{W}_{B_{i},B_{3}}^{( \xi^{2})}\mathbf{W}_{B_{3},B_{3}}^{(\xi)}\mathbf{W}_{B_{3},B_{3}}^{(\xi^{2})}\mathbf{W}_{B_{3 },B_{1}}^{(\xi^{2})}+\frac{\rho}{\Delta T^{2}}\mathbf{W}_{B_{i},B_{12}}^{(\tau)}\mathbf{ W}_{B_{12},B_{1}}^{(\tau)}\mathbf{W}_{B_{12},B_{1}}^{(\tau)}\\ &-\frac{\rho_{\mathbf{\Gamma}}^{\prime}}{\Delta T^{2}L^{2}}\mathbf{W}_{B_{i},B_{N}}^{( \xi^{2})}\mathbf{W}_{B_{i},B_{12}}^{(\tau)}\mathbf{W}_{B_{12},B_{i}}^{(\tau)}\\ \widetilde{\mathbf{F}}&=\mathbf{F}+\Big{(}\frac{\rho_{\mathbf{\Gamma}}^{\prime}}{\Delta T^ {2}L^{2}}\mathbf{W}_{B_{i},B_{N}}^{(\xi^{2})}\mathbf{W}_{B_{N},B_{12}}^{(\tau)}\mathbf{W}_ {B_{12},B_{3}}^{(\tau)}\\ &-\frac{EI}{L^{4}}\mathbf{W}_{B_{i},B_{3}}^{(\xi^{2})}\mathbf{W}_{B_{3},B_{N}}^{(\xi)} \mathbf{W}_{B_{3},B_{3}}^{(\xi)}-\frac{\rho}{\Delta T^{2}}\mathbf{W}_{B_{i},B_{12}}^{( \tau)}\mathbf{W}_{B_{12},B_{3}}^{(\tau)}\mathbf{y}_{0}\\ &+\Big{(}\frac{\rho_{\mathbf{\Gamma}}^{\prime}}{\Delta T^{2}}\mathbf{W}_{B_{i},B_{N}}^ {(\xi^{2})}\mathbf{W}_{B_{N},B_{3}}^{(\tau)}-\frac{\rho}{\Delta T}\mathbf{W}_{B_{i},B_ {3}}^{(\tau)}\Big{)}\dot{\mathbf{y}}_{0}\end{array}  \tag{15}\]  
According to Equation (14),  
\[\mathbf{Y}_{B_{i}}=\widetilde{\mathbf{K}}^{-1}\widetilde{\mathbf{F}} \tag{17}\]  
Based on the DC principle, the unknown velocity vector \(\dot{\mathbf{Y}}_{B_{i}}\) in a time step is the first derivative of displacement to time, namely  
\[\dot{\mathbf{Y}}_{B_{i}}=\frac{1}{\Delta T}\mathbf{W}_{B_{i},B_{i}}^{(\tau)}\widetilde {\mathbf{K}}^{-1}\widetilde{\mathbf{F}}+\frac{1}{\Delta T}\mathbf{W}_{B_{i},B_{3}}^{(\tau) }\mathbf{y}_{0} \tag{18}\]  
According to Equations (15) and (16), the displacement and velocity vectors at the end of the step at the internal point except the boundary at both ends are  
\[\mathbf{Y}_{B_{4}}=\mathbf{\Gamma}\widetilde{\mathbf{K}}^{-1}\widetilde{\mathbf{F}} \tag{19}\]  
\[\dot{\mathbf{Y}}_{B_{4}}=\frac{\mathbf{\Gamma}}{\Delta T}\Big{(}\mathbf{W}_{B_{i},B_{i}}^{ (\tau)}\widetilde{\mathbf{K}}^{-1}\widetilde{\mathbf{F}}+\mathbf{W}_{B_{i},B_{3}}^{(\tau)} \mathbf{y}_{0}\Big{)} \tag{20}\]  
where \(\mathbf{\Gamma}\) represents the transformation matrix used to extract the node displacement at the end of all unknown node displacements. The matrix has \((m-2)\) rows, corresponding to node group \(B_{4}\) which represents nodes at the end of the time step. The matrix has \((\text{N}-2n-m+2)\) columns, corresponding to node group \(B_{i}\) which represents the nodes to be solved. Suppose that \(B_{i}\) denotes the node number from 1 to \((\text{N}-2n-m+2)\) and \(B_{4}\) denotes the node number from 1 to \((m-2)\), then \(\mathbf{\Gamma}\) can be expressed as  
\[\mathbf{\Gamma}=\left[ \begin{array}{cccccc}1&0&\cdots&0&0&0&0\\ 0&1&\cdots&0&0&0&0\\ \vdots&\vdots&\ddots&0&\vdots&\vdots&\vdots\\ 0&0&0&1&0&0&0\end{array}
## 2 Related Issues: State-of-the-Art
### At Which Level Must the Information Received from Each Channel be Fused?  
In general, there are two phases in the emotion detection process: the features extraction phase and the classification phase. Since we consider a multimodal system, it is necessary to determine when to fuse the information received from each channel.  
On the one hand, there is the possibility of having a unique classifier that receives the extracted features by each channel as inputs. Using that information, the classifier makes the decision about the user emotion using some kind of algorithm. In this case, it is said that the fusion is made at the \"features extraction level\".  
On the other hand, it can happen that each channel has a classifier that determines the detected emotion using just the information perceived by this channel. In this case, a decision rule is necessary to determine the final principal emotion. This rule must consider the emotion perceived by each channel and its confidence. In this case, the information fusion is made at the \"decision level\". In this sense, [PERSON] [35, 36] gives the following formula to establish the \"weights\" given to each channel during the emotion detection: sentence meaning (semantic) 7%, intonation 38% and face 55% (the well-known \"7%-38%-55% rule\").  
In Figure 2, both approaches are illustrated using several sensitive channels.  
[PERSON] [37] presents a summary of several experiments by other authors, mainly [PERSON], [PERSON] and [PERSON]. The experiment carried out by [PERSON] [38] showed that the success rate using the audio channel is 75%; the one for the visual channel is 69%, and the success rate obtained for their combination at the feature level (one classifier for both channels) reaches 97%. In the same line, [PERSON] [39] obtained that the following success rates are 45% (audio), 56% (visual) and 89% when the fusion of both channels is also made at the feature extraction level. On the contrary, [PERSON] [23] makes the fusion at the decision level and claims to have a 72% success rate. These works and the others presented in [37] seem to prove the following two points: first, that the fusion of the information from several channels improves the success rate; and second, that the fusion made at the feature extraction level obtains higher successful results.  
Figure 2: Two kinds of fusion levels: decision and feature extraction level. (**a**) A unique classifier (fusion at the feature extraction level); (**b**) one classifier for each channel (fusion at the decision level).  
Nevertheless, [PERSON] comments that these comparisons are not very rigorous, since, among other things, the emotion sets considered in these works are different.
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
## 2 Methodology
### ELA Mechanism  
To optimize the feature integration, the CA module in HS-FPN is replaced with an ELA module. Figure 5 illustrates the attention mechanism structure of ELA. The ELA mechanism fundamentally differs from CA in its directional sensitivity to elongated defect patterns. In DR images of aluminum welding seams, certain defects, such as unpenetrated defects, exhibit an aspect ratio greater than 5, appearing as dark quasi-linear discontinuities. ELA employs axial strip pooling with 1 \(\times\) 7 (horizontal) and 7 \(\times\) 1 (vertical) kernels, creating directional receptive fields that match the typical defect dimensions. This contrasts with CA's isotropic 3 \(\times\) 3 spatial compression, which leads to signal dilution in high-aspect-ratio regions.  
The ELA attention mechanism primarily consists of two steps. In the first step, strip pooling operations are performed on the output of the convolutional module, which is represented by \(\mathbb{R}^{H\times W\times C}\), where \(H\), \(W\), and \(C\) represent the height, width, and channel dimensions, respectively. Before the strip pooling operation, average pooling is performed separately along the horizontal direction \((H,1)\) and the vertical direction \((1,W)\) for each channel in the two spatial dimensions. Then, each output with height \(h\) and width \(w\) is represented as follows:  
\[z_{c}^{h}(h)=\frac{1}{H}\sum\
olimits_{0\leq i<H}x_{C}(h,i) \tag{1}\]  
\[z_{c}^{w}(w)=\frac{1}{W}{\sum\
olimits_{0\leq j<W}x_{C}(j,w)} \tag{2}\]  
\[v=\frac{4}{\pi^{2}}\bigg{(}arctan\frac{w^{gt}}{h^{gt}}-arctan\frac{w}{h} \bigg{)}^{2} \tag{3}\]  
Formulas (1) and (2) yield the \(z_{h}\) and \(z_{w}\) features, which not only capture the global perceptual field but also capture precise position information. To effectively utilize these features, a one-dimensional convolutional method is employed to enhance the position information in both the horizontal and vertical directions. Subsequently, the GroupNorm module processes the enhanced position information, thereby representing the position attention in both directions, as illustrated by Formulas (4) and (5).  
\[y^{h}=\sigma(G_{n}(F_{h}(z_{n}))) \tag{4}\]  
\[y^{w}=\sigma(G_{n}(F_{w}(z_{w}))) \tag{5}\]  
Figure 5: ELA attention mechanism structure diagram.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 2 Unmanned Ship-Wind and Wave Coupling CFD Model
### Grid Model  
Grid division is a key step in numerical simulation. Factors, such as the size and the number of grids, are important as they affect the convergence and accuracy of the calculation. Therefore, the grid quality should be improved under the premise of meeting the calculation accuracy [29].  
Considering the complexity of the surface shape of the unmanned ship, we first used Hypermesh to generate a hybrid surface mesh, with a size of the unit mesh of 2-4 mm. The volume grid was generated by the surface mesh. The initial number of meshes was about 340,000. The total number of grids after mesh refinement was approximately 10.23 million. The mid-section mesh is shown in Figure 3.  
The surface mesh skewness was less than 0.85, and the volume grid skewness was less than 0.95.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ship Hull Parameters** & **Symbol** & **Value** & **Ship Hull Parameters** & **Symbol** & **Value** \\ \hline Length of the ship & \(L\) (m) & 1.03 & Width of the ship & \(B\) (m) & 0.33 \\ Draft & \(d\) (m) & 0.21 & Ship’s displacement & \(m\) (kg) & 186 \\ Block coefficient & \(C_{b}\) & 0.69 & Prismatic coefficient & \(C_{p}\) & 0.76 \\ Wetted area & \(S\) (m\({}^{2}\)) & 0.66 & Draft difference & \(\tau\) (m) & 0 \\ Stern propeller distance & \(l_{1}\) (m) & 0.6 & Bow propeller distance & \(l_{2}\) (m) & 0.6 \\ Propeller diameter & \(D_{p}\) (m) & 0.2 & Number of blades & Z & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Unmanned ship hull parameter.  
Figure 1: Unmanned ship structure.  
Figure 2: Schematic diagram of the computational domain.
## 4 Discussion
### Verbal Moods  
The second finding was that the verbal moods employed in the written prompt had an impact on the three dependent variables observed. Regarding the subjective judgment of the written prompt, the results demonstrate that prompts using a combination of three moods achieve the highest objective performance. Those combining two moods show moderate success, while prompts with only the indicative or subjunctive mood achieve the lowest scores.  
Regarding the subjective judgment of the response given by the AI, the results suggest that responses to prompts combining two or three verbal moods were evaluated most favorably. In contrast, responses from prompts with only the imperative mood were evaluated as not achieved, and responses to prompts employing the indicative or subjunctive were evaluated as moderate achievement.  
Regarding the length of the utterances, the general trend observed in the analysis suggests that a prompt employing a combination of verbal moods leads to better performance and greater achievement. This pattern highlights that greater variety in verbal moods positively impacts both the quality and complexity of the prompts and responses.  
The differential impact of verbal moods on LLM response quality may be explained by the inherent linguistic features of each mood. The indicative mood typically conveys factual and objective information, making it more compatible with the declarative nature of most AI-generated responses. In contrast, the subjunctive mood, often used to express hypothetical or uncertain situations, may introduce ambiguity that challenges the model's interpretative capacity. Linguistic studies have demonstrated that the processing demands associated with subjunctive constructions are generally higher than those of indicative forms, as they require greater contextual inference and pragmatic interpretation [40; 41].  
These findings suggest that the model's tendency is to favor indicative constructions over subjunctive ones. Future research could further investigate how the representation of verbal moods in training data influences the robustness and coherence of AI-generated responses.
###### Abstract  
Orbital Edge Computing (OEC) capability on board satellites in Earth Observation (EO) constellations would surely enable a more effective usage of bandwidth, since the possibility to process images on board enables extracting and sending only useful information to the ground. However, OEC can also help to reduce the amount of energy required to process EO data on Earth. In fact, even though energy is a valuable resource on satellites, the on-board energy is pre-allocated due to the presence of solar panels and batteries and it is always generated and available, regardless of its actual need and use in time. Instead, energy consumption on the ground is strictly dependent on the demand, and it increases with the increase in EO data to be processed by ground stations. In this work, we first define and solve an optimization problem to jointly allocate resources and place processing within a constellation-wide network to leverage in-orbit processing as much as possible. This aims to reduce the amount of data to be processed on the ground, and thus, to maximize the energy saving in ground stations. Given the NP hardness of the proposed optimization problem, we also propose the Ground Station Energy-Saving Heuristic (SGESH) algorithm to evaluate the energy saving we would obtain in ground stations in a real orbital scenario. After validating the SGESH algorithm by means of a comparison with the results of the optimal solution, we have compared it to a benchmark algorithm in a typical scenario and we have verified that the SGESH algorithm allows for energy saving in the ground station up to 40% higher than the one achieved with the benchmark solution.  
earth observation; low earth orbit; energy saving; ground station
# Integrated Information in Relational Quantum Dynamics (RQD)
## Appendix A Computational Scalability and Practical Estimators for \(\Phi\)
### Tensor Network Estimator (Polynomial Memory)  
Even with the above heuristics, an exact evaluation of \(\Phi(\rho)\) becomes impractical for very large \(n\) due to the exponential growth in partitions. To push further, we introduce a polynomial-memory tensor network estimator for integrated information. The key idea is to approximate the full state \(\rho\) by a matrix product state (MPS) of fixed bond dimension \(\chi\), and then compute an integrated information on this compressed representation. Specifically, we define a surrogate measure:  
\[\tilde{\Phi}_{\chi}(\rho)\ :=\ \min_{A|B}\ D_{QJS}\Big{(}\rho_{\chi}\,\Big{\|} \,\rho_{\chi,A}\otimes\rho_{\chi,B}\Big{)}\,,\]  
where \(\rho_{\chi}\) is an MPS approximation to \(\rho\) with bond dimension \(\chi\) (we typically take \(\chi\leq 128\)). By construction \(\rho_{\chi}\) retains the essential correlations of \(\rho\) up to some truncation error. One can show that this estimator is _accurate_: the difference from the true \(\Phi\) is bounded by the trace-norm error in the state. In particular,  
\[\Big{|}\Phi(\rho)\ -\ \tilde{\Phi}_{\chi}(\rho)\Big{|}\ \leq\ 2\,\epsilon(\chi)\,,\]  
with \(\epsilon(\chi)=|\rho-\rho_{\chi}|*1\) being the truncation error. In practice, even a moderate bond dimension (e.g. \(\chi=128\)) can achieve \(\epsilon(\chi)\lesssim 10^{-3}\) for many one-dimensional states, making \(\tilde{\Phi}*\chi\) accurate to within \(10^{-2}\) (a few percent of the full-scale \(\Phi\)).  
Crucially, the MPS form also confers a drastic improvement in computational complexity. Storing the MPS \(\rho_{\chi}\) requires only \(O(n,d,\chi^{2})\) memory (where \(d\) is the local Hilbert space dimension, for example, \(d=2\) for qubits). Evaluating the divergence \(D_{QJS}(\rho_{\chi}|\rho_{\chi,A}\otimes\rho_{\chi,B})\) for a given bipartition can be performed by contracting the MPS tensor network, which scales as \(O(n,d,\chi^{3})\). By caching partial contractions (left and right \"environments\") and iterating over cut locations, one can evaluate all candidate bipartitions' divergences in overall \(O(n,d,\chi^{3})\) time. Thus, for fixed \(\chi\), the runtime and memory cost of computing \(\tilde{\Phi}_{\chi}\) scale polynomially with \(n\). This is a dramatic improvement over the exponential scaling of the exact algorithm, enabling the analysis of significantly larger systems by trading a small approximation error for efficiency.
## 2 Materials and Methods
### Semantic Data Model
#### 2.2.3 Application Level  
After the design of the structures and contents of the domain and mission levels is completed, the application level is divided based on specific work tasks. The division of the application level focuses on various aspects of implementing and managing underground pipeline facilities. Current and historical states are used to record information data in pipeline facilities. In terms of application, engineering planning includes overall, zoning, and detailed planning schemes, as well as planning the statuses of pipeline points and pipelines, realizing a preliminary layout plan for the overall pipeline network from various aspects. Pipeline construction includes environmental assessment and approval to minimize the impact of the project on natural and community environments while complying with regulatory requirements. Attention is given to the preparation of construction materials, focusing on material quality, durability, and environmental performance. Completion acceptance involves confirming that the project meets design standards and safety specifications, involving various performance tests and quality audits. The preliminary construction of underground pipeline projects is completed according to the layout plan. Online monitoring includes monitoring points for gas, drainage, and water supply, as well as monitoring points for various application categories of pipelines. Monitoring data collection includes key parameters such as pressure, flow rate, and temperature. Operation and maintenance include inspectors, inspection records (including pipeline integrity and the integrity of connection points), areas of concern, and repair records. During the daily operation of pipeline facilities, inspectors record data and focus on reporting areas of concern, identifying and recording them, and making repair work more targeted and timely. When underground pipeline facilities reach their design life or need to be retired for other reasons, a comprehensive assessment of the retirement pipeline facilities is conducted, including the health status, residual value, environmental impact, etc. The safety of personnel and the environment is ensured during the dismantling process and damage to surrounding buildings and underground facilities is avoided. Therefore, the design includes two parts: assessing facility conditions and safe dismantling.
# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 5 Process of EACI for Path Planning  
In order to improve ACO efficiency, reduce turning angles, and minimize deadlock ants, this study introduces the EACI algorithm. The steps for applying EACI in path planning are outlined below.  
**Step 1** Initialize the parameters: The parameters, including \(S\), \(E\), \(m\), \(\alpha\), \(\beta\), \(\varepsilon\), \(\lambda\), \(N\), \(Q\), and \(\kappa\), are initialized. Simultaneously, the maximum and current iterations are set to \(Nc_{max}\) and \(Nc\).  
**Step 2** Determine the divided island number based on Equations (13)-(15).  
**Step 3** Calculate \(\tau_{ij}(0)\), which indicates the initial pheromone, and it is calculated according to Equations (18)-(22).  
**Step 4** Path selection: First, the beginning position \(S\) is designated as the first island, while the ending point \(E\) is set as the \(N\)th island. The sequence of all islands can be represented as \([S=L_{1},L_{2}\cdots,L_{z},\cdots L_{N}=E]\). Additionally, the number of deadlock ants isinitialized to 0. In addition, the ant is initially placed on the first island. Using Equations (8), (16), and (17), the next grid \(j\) is calculated. Once the ant passes through the grid \(j\), it is added to the taboo list, and this process is repeated. If the ant successfully reaches the next island, that island becomes the new starting position, and the path planning continues, passing through the intermediate islands in sequence until the target point is reached. The complete path formed by connecting all the nodes--which the ant has passed through--is the path discovered by the ant. If the ant has a deadlock between any two adjacent islands, it halts its route search, and the number of lost ants increases by 1. Finally, another ant is placed at the first island to begin path planning from grids \(S\) to \(E\), following the same process as the previous ant. The iterative cycle continues until all ants have completed their path planning.  
**Step 5** Global update of the pheromones: Once an iterative search is completed, the paths searched by all ants, from \(S\) to \(E\), are recorded. The longest and the shortest path lengths are identified from these records. The adaptive volatilization coefficient \(\rho\) can then be calculated using Equation (25). Following this, the pheromones on shortest route are rewarded, and the pheromones on the longest route are punished, as described in Equation (24). Finally, the global pheromones for the whole map are updated according to Equation (23).  
**Step 6** Search end: If \(Nc\) exceeds \(Nc_{max}\), the outputs include the optimal route length, its sequence, deadlock ant count, and other associated parameters. Otherwise, let \(Nc=Nc+1\) and clear the taboo list. Then, return to Step 4 and continue the path planning process until the stopping condition appears.  
**Step 7** Output the optimal solution.  
The flowchart demonstrating the EACI algorithm for path planning is shown in Figure 10.  
Figure 10: Procedure of the EACI algorithm for route planning.