esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of a Tunable Stiffness Organ-Grasping Device by Design of a Wavy-Shaped Beam Structure
## 3 FEM Study
### Results  
Figure 7 shows the bending stiffness (Figure 7a) and stiffness gain (Figure 7b) given in the four conditions, beams with Homogeneous, PSL-pitch, PSL-thickness, and Pillars designs. Maximum stiffness gain 13.0 was observed in PSL-thickness as shown in Figure 7b. This was greater than Pillars, which gave the stiffness gain 11.0. As shown in Figure 7a, the increased gain was caused by the higher stiffness of PSL-thickness than Pillars in a high-stiffness state. The gain in PSL-pitch was 9.5, which was smaller than Pillars (Figure 7b). This condition showed higher stiffness than Pillars in a high-stiffness state; however, its stiffness gain was smaller because the stiffness in a low-stiffness state was also higher (Figure 7a). Homogeneous materials showed the stiffness gain 4.5 (Figure 7b), which was slightly larger than four of the theoretical predictions because of the second moment of area of the high-stiffness material in a high-stiffness state was increased by the height of friction sheets.  
Figure 8 shows the maximum and minimum principal stress in a high-stiffness state of the four conditions. Positive values in the maximum principal stress are tensile stress, and negative values in the minimum principal stress are compressive stress. PSL-pitch showed similar distribution as the homogeneous material (Figure 8a,b). In the case of Pillars (Figure 8d), stress was concentrated on the surface of the device (Figure 8f). On the other hand, the PSL-thickness distributed the stress in the high-stiffness material (Figure 8c,e).  
Figure 9 shows the maximum and minimum principal stress in a low-stiffness state of the four conditions. In the cases of PSL-pitch and the homogeneous material, stress was concentrated on the top or bottom edge of the high-stiffness material. In the cases of PSL-thickness and Pillars, the stress was concentrated on the friction sheets or the other side of the high-stiffness material.  
Figure 7: Stiffness of the device in high-stiffness and low-stiffness states (**a**); the stiffness gain (**b**) calculated by simulation for the four conditions. Homogeneous, PSL-pitch, PSL-thickness and Pillars.  
Figure 8: Maximum and minimum principal stress in a high-stiffness state in the four conditions. Homogeneous (**a**), PSL-pitch (**b**), PSL-thickness (**c**) and Pillars (**d**). Magnified images of (**c**,**d**) with finer stress scales are shown in (**e**,**f**), respectively. The load size was 0.1 N. The size of maximum vertical deflection in each condition is shown in (**a**–**d**).  
Figure 9: Maximum and minimum principal stress in a low-stiffness state in the four conditions: Homogeneous (**a**), PSL-pitch (**b**), PSL-thickness (**c**) and Pillars (**d**). The load size was 0.01 N. The size of maximum vertical deflection in each condition is shown in (**a**–**d**).
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Deep Attention-Based Feature Fusion  
The deep attention-based feature fusion module combines features from two different sources: an image-encoding branch and a 3D-encoding branch, as discussed earlier. The image-encoding branch focuses on extracting features from the input image, while the 3D-encoding branch processes initial point cloud data to capture high-level geometric information. By merging these two types of data, the module creates a more complete and detailed understanding of the input.  
The fusion process first merges the output of the two branches through a linear layer, reducing the combined dimensionality from 512 to 256. This fused representation is then merged with the output of the image-encoding branch through a second fusion layer, further refining the features into a 128-dimensional vector. Finally, the third fusion layer processes this refined representation to produce the final 512-dimensional fused features. Sequential fusion of these layers is critical for integrating information from both branches, resulting in a comprehensive feature representation. To enhance this process, attention mechanisms are embedded in each fusion layer. This approach helps the model focus on the most important features in the image-encoding branch, ensuring better generalization of the input image and varying inputs. The mathematical formulation of the fusion layers is as follows:\[\begin{split} X_{f1}&=[X_{I},X_{P}].W_{f1}+b_{f1},\\ X_{f2}&=[X_{I},X_{f1}].W_{f2}+b_{f2},\\ X_{fused}&=[X_{I},X_{f2}].W_{f3}+b_{f3},\end{split} \tag{7}\]  
where \(X_{I}\) represents the features from the image-encoding branch (computed in Module-01), and \(X_{P}\) represents the features from the 3D-encoding branch (computed in Module-02). The weight matrices \(W_{f1}\), \(W_{f2}\), and \(W_{f3}\) are responsible for transforming and combining the feature representations at each stage. The dimensions of these matrices are as follows: \(W_{f1}\) has dimensions \((512+512)\times 256\), \(W_{f2}\) has dimensions \((512+256)\times 128\), and \(W_{f3}\) has dimensions \((512+128)\times 512\). Bias terms \(b_{f1},b_{f2}\), and \(b_{f3}\) are applied to each layer to fine-tune their outputs. This fusion method effectively fuses image features with shape parameters, resulting in a robust and unified feature representation that accurately captures the object's shape and geometry.
## 3 Results
### Effects of Organic Amendments on Soil Metals  
The total concentrations of multiple metals, including Ag, Al, As, Ba, Cd, Cr, Cu, Fe, Hg, Mn, Pb, and Se, in soil samples with or without organic amendments, were quantified on days 0, 7, 30, 60, and 90 during the soil incubation study. Results showed that the concentrations of all metals were very low, except for Fe and Al (Table S1 in Supplementary Materials). Due to the relatively high concentrations of Fe and Al in all soils, sequential extraction was carried out to study the geochemical fractionation of these two elements in the soil. The toxicity of metals depends on bioavailability; hence, understanding the geochemical fractions of metals will provide a better understanding of the potential toxicity of these metals in soil [23]. Geochemical fractions of Fe and Al on days 0 and 90 are shown in Figure 5. Results showed that the water-soluble, exchangeable, and carbonate-bound Fe and Al were negligible in gob spoil soil on day 0 (Figure 5A,C). The majority of the Fe present in the unamended soil was in oxides-bound (17.5%), organic-bound (7.5%), and residual silicate-bound (74.9%) forms (Figure 5A), while for Al, the corresponding percentages for these forms were 3.5%, 19.6, and 76.6%, respectively (Figure 5C). Figure 5A shows that the percentages of organic bound Fe in soils amended by compost (5.0%, 5.9%, and 5.8% for 5%, 7.5%, and 10% amendment rates, respectively) were slightly lower than the unamended soil. This is consistent with the slightly lower total OM contents for compost-amended soil than that for the unamended soil (Figure 3B). In comparison, biochar-amended soil showed similar (at amendment rate 5%) or higher (at amendment rates 7.5% and 10%) organic-bound Fe compared to the unamended gob spoil soil, which was also consistent with the trends in total OM results (Figure 3B). The oxides-bound Fe in gob spoil soil amended by 10% biochar was the highest among all treatments and the control. The two major geochemical fractions for Al were residual silicate-bound and organic-bound in gob spoil soil, followed by oxides-bound for all samples. On day 0, the addition of biochar at 10% increased the percentages of oxides-bound and organic-bound Al, while all other organic amendment treatments did not significantly change the percentages of Al geochemical fractions compared to unamended gob spoil soil. By the end of the incubation period, the percentages of organic-bound Fe for all organic amendment treatments increased compared to time zero (Figure 5B). A similar trend was observed for Al in terms of the organic-bound Al fractions (Figure 5D). An insignificant increase in plant availability of Al (i.e., the sum of water-soluble and exchangeable forms of Al) was observed, but the percentages were very low at <2%.  
Figure 5: The impact of organic amendments on geochemical fractions of Fe and Al: **(A)** Fe fractions on day 0, **(B)** Fe fractions on day 90, **(C)** Al fractions on day 0, and **(D)** Al fractions on day 90.
## 5 Findings of the Study
### Intrinsic Evaluation
#### 5.2.5 t-SNE Visualization  
Figures 5 and 6 exhibit the t-distributed Stochastic Neighbor Embedding (t-SNE) visualization conducted on multiclass and one vs. all distributions of _myPersonality_ and _Sentiment140_. The SLDA clustered the _myPersonality_ classes with a clear boundary whereby the features were not overlapping on each other in the multiclass and one vs. all distributions. On the contrary, the plotting for _Sentiment140_ revealed a strong overlapping for both distributions. In the plotting of _Sentiment140_ for one vs. all distributions, the Extraversion (green) and a part of the Neuroticism (blue) instances from the multiclass distribution were merged to form the not-Psychoticism classes, whereas another part of Neuroticism fused with Psychoticism (red) to form the Psychoticism classes. As noted earlier, our pilot study has disclosed that some mutual inter-relationships are present between Extraversion and Neuroticism even though the two traits are rooted in the opposite or inverse sentiment polarities. Thus, the overlapping relationships between Extraversion and Neuroticism illustrated in _Sentiment140_ one vs. all have affirmed the previous finding that the nature of human traits is mutually inclusive [21].  
Furthermore, the presence of some co-existing relationships among the instances illustrates the reality of human nature even though it was not seen in _myPersonality_. Intuitively, the asynchronous association is perhaps because of the smaller size of _myPersonality_ corpus. Our team is also unable to make concrete explanations regarding the formation of coalescing like adjacent soap bubbles in _myPersonality_ multiclass distribution. While the exact mathematical inference for this phenomenon is not fully understood, it also may be due to the effects caused by size of the corpus towards perplexity, and such projection also can be seen in the medical domain [62]. The further experiment is required to derive a concrete interpretation and justification regarding the phenomena illustrated in Figure 5. To summarize, the t-SNE plotting illustrates that larger documents could carry more diversity in nature, and the overlapping relationships among the topics indicate that some statistically correlated words of a category could also be relevant to other categories to a certain extent.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & _myPersonality_ & **Probability** & _Sentiment140_ & **Probability** \\ \multirow{2}{*}{Multiclass} & Amazing & 0.040 & Amazing & 0.054 \\  & Sad & 0.040 & Annoy & 0.039 \\  & Motherfucker & 0.046 & Hell & 0.041 \\ \hline One-vs-all & Asshole & 0.042 & Stupid & 0.052 \\  & Fuck & 0.051 & Asshole & 0.044 \\  & Miss & 0.062 & Hurt & 0.051 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Probability of top three seed words.  
Figure 5: _t-SNE_ visualization for multiclass distribution of _myPersonality_ (**a**) and _Sentiment140_ (**b**).
## 4 Experimental Results and Analysis
### Load-Displacement Relationship  
Figure 12 presents the load-displacement curves for each test specimen retrofitted with the RSL system. All specimens reached their maximum load at a drift ratio of 2.75%, followed by a decrease in load to less than 80% of the maximum at a drift ratio of 4.5%  
Figure 11: RSL system after testing: (**a**) steel plate, (**b**) bolt, and (**c**) after removing the buckled steel link.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Crack Propagation at**} & \multirow{2}{*}{**Observed Damage**} \\  & & **the Bottom of the Column** & \\ \hline  & E130-10 & E130-5 & \\ \hline  & Severe spalling of the concrete & \\  & As the length of the sliding slot lengths, & \\  & The number of flexural and shear cracks & \\  & The number of flexural & \\  & The number of flexural & \\  & The number of flexural & \\  & The number of flexural & \\  &  
due to shear cracks and concrete spalling at the bottom of the column. The maximum load increased as the sliding slot length and eccentric distance decreased. For specimens with a 5 mm sliding slot length, NE-5, E65-5, and E130-5, the maximum loads were 76.8 kN, 62.0 kN, and 55 kN, respectively, while those with a 10 mm sliding slot length, NE-10 and E130-10, had maximum loads of 73.9 kN and 52.4 kN. The maximum load decreased in all specimens as the eccentric distance increased, and the torsion also increased. Regardless of the sliding slot length, the maximum load for specimens with an eccentricity of 130 mm was about 70% of that for specimens without eccentricity. The maximum load for E65-5, with an eccentric distance of 65 mm and a 5 mm sliding slot, was about 81% of that for NE-5, which had no eccentricity. Previous research [26] showed a maximum load of about 60% in reinforced concrete columns subjected to eccentric lateral forces compared to columns without eccentricity. Therefore, it is evident that the RSL system effectively resists torsion. Even with a doubling of the eccentric distance, the reduction in maximum load was only about 10%, indicating that brittle failure was not observed despite increased torsion. Regardless of the eccentric distance, the difference in maximum load due to the length of the sliding slot was about 5%. While there was a tendency for the maximum load to decrease with increasing sliding slot length, the difference was minimal. This phenomenon likely occurred because the experiment was terminated after both the column and steel link fully resisted the load. This suggests that the length of the sliding slot had a minimal impact on the maximum load capacity.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 6 Simulation Results
### Parameter Settings  
The platooning consists of five members, namely \(M\) = 5. The computation resources of MEC server and PMs are \(f_{0}=1500\) Hz, \(f_{1}=200\) Hz, \(f_{2}=650\) Hz, \(f_{3}=250\) Hz, \(f_{4}=500\) Hz, \(f_{5}=850\) Hz, respectively. The average number of CPU cycles consumed by each vehicle to calculate its own task is \(\lambda_{1}=45\) cycles, \(\lambda_{2}=60\) cycles, \(\lambda_{3}=100\) cycles, \(\lambda_{4}=20\) cycles, \(\lambda_{5}=80\) cycles, respectively. The data transmission power of all PMs is \(p_{tr}\) = 0.1 W [39; 40], computation power is \(p_{c}\) = 0.5 W [41], and idle power is \(p_{i}\) = 0.001 W. The distance is the same between adjacent vehicles. Each time interval of simulation is set as 0.1 s, and the total simulation time is 30 s. All simulation parameters are summarized in Table 2.
## 2 Experimental Study and Results
### Determination of Basic Parameters  
#### 2.2.1 Physical Parameters  
The performance indicators of backfill materials, such as specific gravity, bulk density, particle size distribution, and porosity, directly affect slurry transportability, filling quality, and strength development. In this study, tailings were collected from multiple locations within the phosphate tailings pond in Jingmen, Hubei Province. The samples were naturally air dried and then homogenized using the standard quartering method to obtain a representative composite sample for testing. The key physical parameters evaluated included bulk density, true density, compactness, and porosity. The results of these measurements are presented in Table 1.  
The test results in Table 1 indicated that the physical parameter differences among the samples were minimal, suggesting acceptable uniformity for engineering application  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Tailings** & **Sample 1** & **Sample 2** & **Sample 3** & **Sample 4** & **Average** \\ \hline Bulk density (t/m\({}^{3}\)) & 1.730 & 1.720 & 1.720 & 1.740 & 1.727 \\ True density (t/m\({}^{3}\)) & 2.862 & 2.782 & 2.821 & 2.857 & 2.831 \\ compactness & 0.6044 & 0.6183 & 0.6097 & 0.6090 & 0.6100 \\ Porosity & 0.3956 & 0.3817 & 0.3903 & 0.3910 & 0.39 \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameters of phosphorus tailings.  
Figure 1: Particle size distribution curves of the tailings.  
purposes. Overall, the bulk density, true density, compactness, and porosity of the tailings fall within a reasonable range, providing foundational data for investigating their feasibility as backfill material.
## 2 Materials and Methods
### Existing Issues  
While CTDE has proven effective in homogeneous multi-agent systems [22], its application to heterogeneous scenarios faces critical limitations:  
* Partial Observability Constraints: In decentralized execution, agents lack access to global state information. Existing CTDE methods (e.g., QMIX, COMA) assume that agents can implicitly coordinate through shared value functions; however, this fails when the agents exhibit heterogeneous observation spaces or roles.
* Static Interaction Modeling: Traditional value factorization approaches [24] decompose joint Q-values under the assumption of fixed monotonicity, ignoring dynamic agent relationships.
* Heterogeneous Information Fusion: Agents with diverse sensor modalities (e.g., LiDAR vs. cameras) generate multimodal data with varying structures and scales. Simple concatenation or averaging of observations can lose critical relational patterns [26], while manually designed fusion rules lack adaptability.  
To address these challenges, we model heterogeneous multi-agent systems as graphs. GCNs [25] apply fixed spectral filters, assuming homogeneous nodes and static topology. This is inadequate for our dynamic heterogeneous graph setting. In contrast, GAT [16] computes adaptive edge weights via attention mechanisms. These insights motivate our QMIX-GNN framework, which integrates GAT-based relational reasoning with monotonic value factorization, as detailed in Section 2.3.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Transmission Selection  
IEEE 802.1 Qbu TSN sub-standard implements frame preemption to interrupt the ongoing operation of a low-priority (preemptable) queue if a time-sensitive (preempting) queue is selected for transmission. In addition, low-priority frames are split into smaller fragments to further reduce overall latency.  
If a wireless device is transmitting multiple traffic flows, placing the time-sensitive traffic in the highest priority queue may not be enough to mitigate the residual delay caused by large ongoing low-priority transmissions, which may include many aggregated packets and last up to the maximum physical protocol data unit (PPDU) duration (i.e., \(\sim\)5 ms). As shown in Figure 5, a possible solution to that issue could be based on the adaptation of the IEEE 802.1 Qbu frame preemption mechanism, which would also foster the use of packet aggregation for best-effort traffic even in presence of time-sensitive traffic, thus improving overall throughput.  
Just as IEEE 802.3 br does it with respect to Ethernet, integrating frame preemption into WiFi would require several changes in the physical and link layers, such as the format of preemptable frames and the methods to fragment frames while preserving integrity of preemptable traffic. In any case, it seems reasonable to only support this new feature when aggregate MAC protocol data units (A-MPDUs) are transmitted, and so extend the service field used to identify the different MPDUs.  
Despite the fact that frame preemption may well be applied on outgoing transmissions from the same node, its extension to incorporate transmissions from other APs/STAs would require a complex channel access mechanism. In that situation, and in presence of a time-sensitive traffic flow, it would be advisable to simply avoid the use of packet aggregation in the OBSS, even if that implied a severe throughput loss.
## 3 Results
Legend: COMP—Comprehension; CONF—Conflict; STRAT—Strategy; ENJ—enjoyment; PART—Participation; INT—Interaction; COOP—Cooperation; Q-DEV—Q-device; Q-ACT—Q-activity; Q-ADU—Q-adults. Figure 4: Box-and-whisker plots of each variable at T1, T2 and T3. Statistically significant differences among timepoints, as defined by Friedman test, are marked with a star. Figures 5-7 show the impact of activities on each variable; for this analysis, we decided to take all of the activities into consideration (including Musical Puzzle and Canon), for the sake of completeness. As can be seen from Figure 5, the understanding of the activity improves from the beginning of the session to the end, and achieves good absolute ratings in general. The exceptions are some of the music-based activities, Musical Puzzle and Canon, which remain below 2 at the end. Strategic behaviors in the groups of children tend to increase over time, with both increasing practice and familiarity with the task, especially from T1 to T2, reaching high ratings in \"Whack-a-mole\" and Eggs activities. Participation is generally high in all of the activities; it increases from T1 to T2 and remains stable over T3. Enjoyment is high in all the activities and increases over time, with the exception of music-based activities: in Musical Puzzle, Canon and Jam session, enjoyment at T3 is lower than at T2. Variables related to social interaction are represented in Figure 6. Interaction among peers is good, especially in \"Whack-a-mole\" and Eggs, and increases over time; the lowest value is observed in the Canon activity. Conflict is very low overall, and tends to decrease after the first evaluation. Cooperation is high, especially in Sound Environment, \"Whack-a-mole\", Farm and Eggs. Figure 7 represents the requests directed to adults and care-givers: the requests of explanations and clarification are infrequent (between 1 = \"not at all\" and 2 = \"rarely\") concerning both the device and the activity, and direct requests of help are quite uncommon, except for Musical Puzzle, where requests increase during the session. Finally, results of the correlation analysis are reported in Table 7. The understanding of the activity strongly correlates (Spearman's rho > 0.7) with strategic behaviors and participation, and has a moderate correlation with interaction. Furthermore, strategy, enjoyment, and participation moderately correlate with interaction. Enjoyment has a very strong correlation with participation and a moderate correlation with cooperation. Finally, a very strong correlation was found between cooperation and interaction. Figure 5: Game-related variables: (**A**) Understanding of the game, (**B**) Strategic behaviors, (**C**) Participation and (**D**) Enjoyment. Figure 6: Interaction with peers: (**A**) Interaction, (**B**) Conflict and (**C**) Cooperation. Figure 7: Clarification requests: Questions concerning the device, activity and help requests.