esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 5 Experimental Results and Analysis
### Qualitative Analysis
To verify the effectiveness of the proposed model compared with the single granularity model, Figure 5 shows two example results on three models: SgSR model, TgSR model, and MGSCRN model. As observed in Figure 5, the MGSCRN model had a larger number of relevant answers on its list when compared to the top 10 ranking lists of the SgSR model. In the SgSR model, there were four and five relevant answers for each of the two cases. In contrast, our approach boosted the number to six and seven, respectively, which resulted in a higher NDCG score. Note that our model also outperformed the TgSR model in terms of NDCG. Additionally, in both cases, the ground-truth (GT) answer is ranked first by our proposed model. Through the above analysis, compared with a single granularity semantic representation model (SgSR/ TgSR), we validated our model performed better on the visual dialog task due to collaborative reasoning, using two different granularity semantic representations. Figure 4: Example results on the VisDial v1.0 validation set. In the question-aware attention refer (QAAR) module, two types of attention weights are displayed. One is \(P^{s}\) on \(t\) rounds of dialog history, the other is \(P^{token}_{t,j}\) over all tokens in the \(j^{th}\) round of dialog history, which may have dependencies on the \(i^{th}\) token in the question, \(Q_{l}\). A darker blue square indicates a higher attention weight, and a lighter blue square indicates a lower attention weight. In the visual-aware attention alignment (VAAA) module, we visualized three types of attention: Object difference attention (OdA), question-aware attention (QaA), and collaborative reasoning visual object (CRVO), where top three visual objects for each attention are displayed. Visual objects in the red dotted box are the target objects for answering the question, \(Q_{l}\).
## 3 Results and Discussions
### Effect of Thickness of Sand-Tire Shred Mixture on Peak Blast Pressure  
Two additional sand-tire shred mixture layers with thicknesses of 2 and 4 m and the same mixture of 30% shred tire (rubber) and 70% sand are added to the investigation to evaluate the effect of layer thickness on peak blast pressure under different explosion scenarios. Figure 4 shows the results of the simulations for the three mixture layers with varying thicknesses under four surface explosion charges. The results show that the thickness of the sand-tire shred mixture has a significant effect on its behavior under an explosion hazard, especially at lower depths. The overall behavior of the mixture layers indicates the positive impact of thicker layers in reducing peak pressure. This impact is more noticeable up to the interface of the mixture layers with sand. An increase in the  
Figure 3: Effect of sand–tire shred mixture on peak blast pressure under surface explosion with charge weights of (**a**) 100 kg; (**b**) 500 kg; (**c**) 1000 kg; and (**d**) 5000 kg.  
value of peak blast pressure is seen at the interface of the layers, followed by a gradual decrease at a greater depth.  
A similar trend is observed for the simulations under 100, 500, and 1000 kg charges in terms of peak pressure reduction. The most significant peak pressure decrease under a 100 kg charge (Figure 4a) is 81%, which occurs at 2 m depth by increasing the thickness of the sand-shred tire layer from 2 m to either 4 or 6 m. The most significant reduction in peak blast pressure (between 79% to 81%) under 500 kg (Figure 4b) and 1000 kg (Figure 4c) charges occur at a depth of 3 m by increasing the thickness of the mixture layer from 2 m to either 4 or 6 m. The explosion of a 5000 kg charge (Figure 4d) extended the crater to a depth of 8 m, where the thickness of the mixture layer played a less significant role in reducing peak blast pressure. The results show that the mixture layer's thickness is a critical parameter in reducing the peak pressure under all explosion scenarios. The thicker the mixture layer, the more significantly it reduces peak blast pressure. This is more evident near the surface and less prominent in the depths below the interfaces of the mixture layer and sand.
###### Abstract  
Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively.  
FANET; routing protocol; trajectory knowledge; Q-learning 2025
## 3 Numerical Results and Discussion
### BER versus SNR  
To verify whether the proposed dual-driven MIMO detector will outperform the baselines at different noise powers, we conducted the following test by setting the SNR to between 0 dB and 25 dB using a channel estimation MSE of \(-30\) dB and seven layers in the dual-driven network. Figure 4 presents the results of the dual-driven MIMO detector, MMSE MIMO detector, OAMP detector, and OAMP-Net detector using testing data samples generated at various SNRs, with a channel estimation MSE of \(-30\) dB. From the figure, it is evident that the proposed dual-driven learning-based MIMO symbol detection method outperforms the other three MIMO detectors at all SNRs from high-SNR to low-SNR regions. In particular, the dual-driven detector exhibits significant performance advantages over the other three detectors at higher SNRs. The MMSE and OAMP-Net detectors' performances appear to be similar.  
Since the only difference between the dual-driven MIMO detector and the OAMP-Net detector is the inserted data-driven symbol correction network, as shown in Figure 3, we can conclude that the increased performance of the proposed dual-driven MIMO detector is completely caused by detection error correction in the symbol correction network. For example, when the SNR is 20 dB and the channel estimation MSE is \(-30\) dB, the dual-driven MIMO detector can achieve a BER of \(4\times 10^{-4}\), while OAMP-Net can only achieve a BER of \(2.48\times 10^{-3}\). To achieve the target BER of \(1\times 10^{-2}\), the dual-driven MIMO detector requires a BNR of 15.1 dB, while the MMSE and OAMP-Net detectors require SNRs of 17.7 dB and 17.6 dB, respectively. This implies that the dual-driven MIMO detector achieves the same BER, with an SNR reduction of 2-3 dB compared to the other two detectors. Moreover, the proposed dual-driven detector achieves a BER of \(1\times 10^{-3}\) at a BNR of around 22.5 dB, while the other three detectors fail to reach this level. Notably, the OAMP detector exhibits a poor performance and is highly sensitive to the channel matrix structure and noise, leading to performance degradations.  
Considering that the training data samples were generated at an SNR of 20 dB and the testing data samples were generated at different SNRs, the results demonstrate that the data-driven component of the proposed dual-driven detector effectively corrects symbols and exhibits robustness to noise.  
To sum up, this test verifies the hypothesis that the proposed dual-driven MIMO detection outperforms the OAMP-Net, OAMP, and MMSE detectors at different noise powers.
# Local Contrast Learning for One-Shot Learning
## 2 Related Work
### Multi-Column Neural Network Shared Weights  
A Local Contrast Neural Network (LCNN) is a multi-column neural network with shared weights (MNNW) among all columns. MNNW is distinguished from the multi-column neural network [30] that does not share weights among all columns. MNNW consists of multiple columns, which means that it has multiple dataflow paths from input to output, and the columns share weights. There are two representative architectures of MNNW; one is Siamese architecture [31; 32; 33; 34] and another is triplet architecture [35; 36; 37]. MNNW is good at learning an embedding for metric learning [38; 39; 40; 31] by comparing input samples of all columns. In order to train the embedding function, the contrastive loss function is designed in such a way that the minimization will decrease the energy of genuine pairs and increase the energy of impostor pairs. In this paper, we use a similar way of designing the loss function. [PERSON] et al. [41] proposed a masked Siamese network for learning image representations by integrating invariance-based pre-training and mask denoising techniques.  
Unlike Siamese networks with two columns, triplet networks have three columns, feeding three samples: one object and two contrast samples (one genuine, and one impostor). Triplet networks have been used in various tasks, such as remote sensing image retrieval [42], camouflaged object detection [43], and face detection using a Swin transformer [44].  
LCNN does not learn similarity metrics like the approaches above, because we argue that it is difficult to learn a global similarity metric in a one-shot setting. LCL inputs pairs of samples into a branch and performs feature mapping by using one of the samples in the pair as a reference. This approach is distinct from Siamese and triplet networks, which input individual samples into a branch and perform feature extraction in the global feature space. In the global feature space, all samples are mapped to a single low-dimensional space. However, with a reference sample, the mapped low-dimensional space changes with each different reference sample. The purpose of inputting a pair of samples into the network is to construct a local space for feature mapping based on the reference sample. Thus, even for the same sample, the mapped space will change if the reference sample changes because the projection feature plane is associated with the reference sample.
## 4 Results and Discussion
### Effects of Base Motions
#### 4.1.3 Base Yawing Motion  
Figure 16a shows the frequency-response characteristics of deflection at the SFD node under base yawing motion. The results indicate that yawing motion leads to increased vibration amplitude at the SFD node, primarily due to the influence of the inertial moment term \(-\Omega\int_{i}^{p}\omega_{bz}\) introduced by base yawing motion. Comparing Figure 16a with Figure 12a reveals that under identical angular velocities, neglecting gravity effects, yawing and pitching motions have identical impacts on the deflection at the SFD node. The identical response characteristics result from the equivalent magnitudes of inertial moments induced by both motions orthogonal to the rotation axis, only distinguished by their action planes. As observed from the rotor trajectories shown in Figure 16b, under yawing motion, the journal center exhibits a static offset in the negative z direction.
## 3 A Simple Self-Indexing Method
### Using the FM-index  
A problem when partitioning into \(k+1\) pieces is that some may be much more frequent than others. Since any partitioning into \(k+1\) pieces is good, it is natural to look for the partitioning yielding the least number of verifications to carry out. This can be easily found in time \(O(m^{2}k)\) with (a different) dynamic programming [16]. However, we need to know the frequencies in the text of every pattern substring. That is, we need to be able of efficiently _counting_ the number of occurrences of a string.  
The LZ-index is not good at this task: It can only count by essentially locating all of the occurrences. We tried some approximate counting methods with no success (more precisely, the final search time decreased, but the time for counting exceeded the gains by far).  
Suffix-array based indexes, instead, are very efficient at counting. In particular, we used a variant of the FM-index [24; 36], which is a self-index that simulates a suffix array within \(O(uH_{k})\) bits of space. The FM-index can easily count the number of occurrences of all the substrings of \(P\) in \(O(m^{2})\) time. Within the same time, the suffix array intervals corresponding to each of the pieces is found. Once the optimal partition is chosen, a context around each occurrence of each of the \(P_{r}\) pieces is obtained, and DP is run on the context to find the approximate matches.  
For the experiments we used an FM-index variant that favors speed over space, described in [50; Sec. 12.2]. As optimizing the partition did reduce the overall time, we show only this variant in the experiments.
## 1 Introduction
optimized the performances of the response time, size and thermal performance by a theoretical model of solenoid actuator, which employed EMC method. Moreover, it is inaccurate to only consider the static EMC models without dynamic phenomenon, such as the hysteresis and the eddy current in the magnetic materials. Thus, experimental analysis strategy is also essential for the dynamic performance of the LPSs. This study aims to improve the static and dynamic performances of the LPS, which is used in SPV [22]. In order to improve the static performance, a multi-objective optimization strategy is presented in this study. The optimization objectives of the static performance include maximum average push force in the working stroke, minimum standard deviation of the push force in the working stroke and the maximum average push force to moving mass ratio. Since these objectives are mixed with each other, a genetic algorithm (GA) optimization algorithm was used to optimize the shape of the LPS by a high accuracy magnetostatic FEA model. In order to improve the dynamic performance, an experimental analysis of the coil parameters and the LPS component material of was employed in this study. A comparison result between the manufactured conventional actuator and the manufactured optimal actuator could be found through using a high-precision measuring device in the static-push-force test and dynamic direct current-input-signal test. By the static optimization strategy, the average static push-force in the working stroke was improved by 21.8%. Moreover, by the dynamic optimization strategy, the cutoff frequency of the push force response was improved by 74%, 79.6% and 74.3%, respectively, at start, middle and end position in the working stroke. In summary, this study is organized as following chapters: In Section 2, the magnetostatic 3D FEA model of the LPS is introduced and developed. Meanwhile, the static-force optimization process of the LPS is also described. Then, in Section 3, the dynamic performance improvement strategy of the LPS's coil parameters and materials is presented and verified by the high precision test device. Finally,the proposed performance optimization strategy is validated with the test data of the manufactured optimal LPS. The overall description of this performance optimization strategy is shown in Figure 1.
# Compatible Ground Motion Models for South Korea Using Moderate Earthquakes
## 3 Results
### Comparing Residuals
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**GMM** & **2016 M\({}_{W}\) 5.5** & **2017 M\({}_{W}\) 5.5 P** & **M\({}_{W}\) 5.1 F** & **M\({}_{W}\) 4.7 A** & **M\({}_{W}\) 4.5** & **Sum** \\ \hline AS08 & 1.38 & 1.62 & **1.18** & **1.77** & 1.58 & **7.53** \\ BA08 & 3.15 & 2.68 & 2.53 & 4.03 & 3.64 & 16.03 \\ CB08 & 3.91 & 9.06 & 4.71 & 10.87 & 5.04 & 33.59 \\ CY08 & 2.32 & 2.45 & 2.28 & 3.29 & 6.92 & 17.26 \\ I08 & **1.36** & **1.43** & 1.24 & 2.67 & **1.42** & 8.12 \\ \hline ASK14 & **1.56** & 1.86 & **1.16** & 1.75 & 2.48 & 8.81 \\ BSSA14 & 1.65 & 1.85 & 1.75 & **1.40** & 3.80 & 10.45 \\ CB14 & 1.75 & 2.19 & 1.29 & 2.00 & 1.94 & 9.17 \\ CY14 & 2.03 & 2.21 & 1.86 & 3.28 & 5.15 & 14.53 \\ I14 & 1.82 & **1.15** & 1.23 & **1.98** & **1.45** & **7.63** \\ \hline DASG15 & **1.88** & **3.67** & **2.09** & **5.00** & 1.66 & **14.30** \\ PCZT18 & 2.93 & 5.07 & 2.93 & 5.47 & **1.47** & 17.87 \\ \hline SUM & 25.74 & 35.24 & **24.25** & 43.51 & 36.55 & \\ \hline \end{tabular} \({}^{\rm F}\) foreshock. \({}^{\rm A}\) aftershock. \({}^{\rm F}\) possible induced or triggered earthquake.  
\end{table} 
Table 7: _Score_ for each GMM per earthquake event for SA(T = 0.02 s). Numbers in bold are the lowest in the group. Ends of rows and columns are the summation of the previous metrics for comparison. performed poorly and was overtaken by CB08 and BSSA14. Again, both NGA-East GMMs did well, with DASG15 scoring the lowest total across all earthquakes studied.
## 7 A Newton-Type Algorithm for the Parametric Assignment Problem
### The Illustrative Example (Continued)
#### 7.2.2 Solution of the Parametric Assignment Problem by the Newton-Type Algorithm  
Let us solve the standard minimum-weight assignment problem with the matrix \(\mathbf{K}\). We can see that assignment \(I=\{C_{1}=[(1,2),(2,1)];\,C_{2}=(3,3)\}\) is optimal for this standard single-matrix problem, \(\sum_{(i,j)\in I}k_{ij}=k_{1,2}+k_{2,1}+k_{3,3}=3\). This is the minimum number of vehicles required to meet the given flight schedule.  
Calculate the average profit received with the assignment \(I\):  
\[\lambda_{0}=(900+600+300)/(1+1+1)=600.\]  
The value \(\lambda_{0}\) can be taken as the lower bound of the desired maximum average profit \(P^{*}\), \(600\leq P^{*}\).  
Consider the following matrix \(W(\lambda_{0}=600)\):  
\[W(600)\ =\ \begin{pmatrix}600\cdot 1-300&600\cdot 1-900&600\cdot 1-600\\ 600\cdot 1-600&600\cdot 2-600&600\cdot 2-1200\\ 600\cdot 1-900&600\cdot 1-300&600\cdot 1-300\end{pmatrix}\ =\ \begin{pmatrix}300&-300&0\\ 0&600&0\\ -300&300&300\end{pmatrix}\]  
The optimal assignment \(A^{*}(\lambda_{0}=600)\) of the matrix \(W(\lambda_{0}=600)\) is as follows:  
\[A^{*}(\lambda_{0}=600)=\{C=[(1,2),(2,3),(3,1)]\}.\]  
The cost of the assignment \(A^{*}(\lambda_{0}=600)\) at point \(\lambda_{0}=600\) is negative; \(w(A^{*},\lambda_{0})=-300\)\(+\)\(0+(-300)=-600\). Therefore, \(\lambda_{0}=600\) is not an optimal solution to the **PAP** problem considered. This means that the average profit \(\lambda_{1}\) obtained with the assignment \(A^{*}(\lambda_{0})\) is greater than \(\lambda_{0}\): \(\lambda_{1}=(900+1200+900)/(1+2+1)=750\).  
Now, in a similar way, consider the following matrix \(W(\lambda_{1}=750)\):  
\[W(750)\ =\ \begin{pmatrix}750\cdot 1-300&750\cdot 1-900&750\cdot 1-600\\ 750\cdot 1-600&750\cdot 2-600&750\cdot 2-1200\\ 750\cdot 1-900&750\cdot 1-300&750\cdot 1-300\end{pmatrix}\ =\ \begin{pmatrix}450&-150&150\\ 150&900&300\\ -150&450&450\end{pmatrix}\]  
The optimal assignment \(A^{*}(\lambda_{1}=750)\) of the matrix \(W(\lambda_{1}=750)\) is the same as \(A^{*}(\lambda_{0}=600)\). The cost of the assignment \(A^{*}(\lambda_{1}=750)\) at the point \(\lambda_{1}=750\) is zero; \(w(A^{*},\lambda_{1})=-150+300+(-150)=0\). Thus, \(\lambda^{*}=\lambda_{1}=750\) is the optimal solution to our PAP problem, and according to Proposition 4, the maximum average profit per aircraft is \(P^{*}=\lambda^{*}=750\).
## 3 Experiments
### Verification of Kinematic Model of Surgical Robot
#### 3.1.2 Experiment for Decoupling Model for Multi-Joints  
To verify the decoupling model for multi-joints operation, a series of experiments were conducted as follows: shoulder joint 1 was rotated within its specified range while the other joints were monitored for any unintended movement. Similar experiments were performed for shoulder joint 2, the elbow joint, wrist joint 1, and wrist joint 2. As shown in Figure 9, all joints moved independently without affecting each other, confirming the effectiveness of the decoupling model.  
Implementation of Parameter Identification and Experiments of Repetitive Positioning Accuracy of End-Effector
## 2 Materials and Methods
### Production of Deli Meat Products with Natural Antioxidants
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Hexanal (mg/kg Meat)**} \\ \cline{4-9}
**Treatment** & **Antioxidant** & **Level (mg/kg)** & \multicolumn{2}{c}{**Week 1**} & \multicolumn{2}{c}{**Week 7**} & \multicolumn{2}{c}{**Week 13**} \\ \cline{4-9}  & & & **Estimate** & **SE** & **Estimate** & **SE** & **Estimate** & **SE** \\ \hline T1 & Control & 0 & 0.14 a & 0.01 & 0.16 a & 0.02 & 0.16 a & 0.01 \\ T2 & Guardian 09 & 200 & 0.09 b & 0.01 & 0.13 ab & 0.02 & 0.13 ab & 0.01 \\ T3 & Guardian 09 & 400 & 0.08 b & 0.01 & 0.14 ab & 0.02 & 0.10 b & 0.01 \\ T4 & Guardian 09 & 600 & 0.09 b & 0.01 & 0.11 bc & 0.02 & 0.09 bc & 0.01 \\ T5 & StabilEnhance OSR D 2.5 & 200 & 0.08 b & 0.01 & 0.11 bc & 0.02 & 0.10 b & 0.01 \\ T6 & StabilEnhance OSR D 2.5 & 400 & 0.08 b & 0.01 & 0.08 c & 0.02 & 0.06 c & 0.01 \\ T7 & StabilEnhance OSR D 2.5 & 600 & 0.08 b & 0.01 & 0.10 bc & 0.02 & 0.10 bc & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hexanal content (mg/kg meat) in cured deli turkey. Estimate indicates the mean hexanal content for triplicate determinations. Means with different letters within each week indicate significant difference (\(p\leq\) 0.05) between treatments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Antioxidant**} & \multirow{2}{*}{**Level (mg/kg)**} & \multicolumn{4}{c}{**Hexanal (mg/kg Meat)**} \\ \cline{4-9}  & & & **Week 1** & \multicolumn{2}{c}{**Week 7**} \\ \cline{4-9}  & & & **Estimate** & **SE** & **Estimate** & **SE** \\ \hline  & T1 & Control & 0 & 0.07 c & 0.01 & 0.81 bcde & 0.17 \\  & T2 & Guardian 09 & 400 & 0.07 c & 0.01 & 0.23 g & 0.17 \\  & T3 & Guardian 09 & 600 & 0.06 c & 0.01 & 0.35 erg & 0.17 \\
**P** & T4 & Guardian 09 & 800 & 0.07 c & 0.01 & 0.30 fg & 0.17 \\  & T5 & StabilEnhance OSR D 2.5 & 400 & 0.09 c & 0.01 & 0.71 cdef & 0.17 \\  & T6 & StabilEnhance OSR D 2.5 & 600 & 0.09 c & 0.01 & 0.68 cdefg & 0.17 \\  & T7 & StabilEnhance OSR D 2.5 & 800 & 0.08 c & 0.01 & 0.52 defg & 0.17 \\ \hline  & T1 & Control & 0 & 0.27 a & 0.01 & 1.52 a & 0.17 \\  & T2 & Guardian 09 & 400 & 0.17 b & 0.01 & 0.87 bcd & 0.17 \\  & T3 & Guardian 09 & 600 & 0.17 b & 0.01 & 0.90 bcd & 0.17 \\
**nP** & T4 & Guardian 09 & 800 & 0.18 b & 0.01 & 1.01 bc & 0.17 \\  & T5 & StabilEnhance OSR D 2.5 & 400 & 0.25 a & 0.01 & 1.12 abc & 0.17 \\  & T6 & StabilEnhance OSR D 2.5 & 600 & 0.25 a & 0.01 & 1.08 abc & 0.17 \\  & T7 & StabilEnhance OSR D 2.5 & 800 & 0.25 a & 0.01 & 1.25 ab & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hexanal content (mg/kg meat) in chicken fillet with or without phosphate. Estimate indicates the mean hexanal content for triplicate determinations. Means with different letters within each week indicate significant difference (\(p\leq\) 0.05) between treatments. P = samples containing phosphate, nP = samples without phosphate.