esa-sceva/satcom-chunk-collection huggingface dataset:
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
###### Abstract
Nevertheless, most existing studies remain limited to theoretical analysis or idealized simulations, with relatively few works addressing real-world deployment or experimental verification of such systems. To the best of our knowledge, practical platforms that jointly leverage UAV swarm coordination, CF-mMIMO communication, and distributed computation frameworks (e.g., cloud-edge collaboration and FL) are still at an early stage [29; 30; 31]. Existing prototypes typically simplify the wireless front-end (e.g., adopting centralized Wi-Fi or LTE links) or fail to exploit the potential of distributed optimization across communication and computation domains. Furthermore, challenges such as real-time synchronization, delay compensation, and adaptive formation control in decentralized swarm environments remain largely underexplored. To address the aforementioned challenges, this paper proposes a comprehensive UAV swarm communication and computation framework that integrates cloud-edge-end collaboration with CF-mMIMO technologies. The proposed system is designed to enhance the scalability, efficiency, and reliability of computational offloading in multi-UAV scenarios. The main contributions of this paper are summarized as follows:* We design a lightweight dynamic task migration framework that enables flexible workload allocation between onboard UAV processors and remote edge/cloud servers. This synergy significantly alleviates the processing limitations of UAVs, achieving over 30% reduction in onboard computational load, as validated through extensive experimental evaluation. * A CF-mMIMO-based communication architecture tailored for UAV swarms is proposed, featuring over-the-air synchronization and distributed delay compensation mechanisms. These techniques address key challenges such as remote radio unit (RRU) coordination, timing alignment, and channel state information (CSI) acquisition in dynamic and decentralized swarm environments. * To validate the proposed architecture and computation offloading strategy, we implement a prototype system involving a swarm of nine UAVs with onboard sensing and control capabilities. The platform enables real-time object detection and offloading operations in flight, and experimental results demonstrate improved communication reliability and latency performance compared to conventional centralized baselines. The rest of this paper is organized as follows. We introduce the system model in Section 2, and in Section 3, we present key technologies for the proposed system. Prototype system and experimental validation are discussed in Section 4. Finally, we draw our conclusions in Section 5.
## 5 Experiment
### Results of the Tracking of Contact Points  
#### 5.1.1 Ablation Study  
In this paper, a parameter adaptive contact point tracking and positioning method has been proposed, and the results are shown in Figure 7. Through the positioning module, discriminator, and parameter feedback update, the closed-loop control of the train under long-term outdoor operation is ensured, and fast and stable contact point positioning is achieved. For the selection of the L1 discriminator, the discrimination range was set to 12-30 according to the datasets, and the slope of the contact line was made following judgments to improve the operation effect of the L1 discriminator. The hash discriminator was determined according to the 50 \(\times\) 100 tracking area, and a corner patch using the 32 \(\times\) 32 area around the contact point was used as the hash comparison patch. Through both discriminators, the effective feedback update of the algorithm was achieved, which greatly improved the algorithm's performance and stability under long-term train operation. In the following ablation experiments, the advantage of the contact point closed-loop frame is demonstrated.  
The proposed method is compared with the global positioning method that simply relies on template matching (global method), the global method combined with the l1 discriminator (global+l1), and the method that integrates local tracking (local+global+l1). As can be seen from Table 1, taking the image sequence collected by the left camera as an example, the parameters compared were operating efficiency, success rate, arrow error rate, and loss rate, with the threshold of precise positioning and the positioning accuracy of successful positioning set at five. Further, a detection rate with a threshold of 50 was also adopted. It can be seen that the global method can effectively improve the detection rate of the algorithm after adding the discriminator of l1. However, compared with the addition of local tracking, its operating efficiency does not meet the real-time requirements of high-speed trains. Compared with the method that integrates local tracking, our method is slightly slower than using only the l1 discriminator without an update, but the speed  
Figure 6: The tested datasets which contains various complex situation. (**a**) Image in different illumination, (**b**) illustration of sparks in PCS images, and (**c**) an example of complex background.  
of 175 frame per second (FPS) still meets the requirements of most usage scenarios and can achieve a higher positioning success rate and accuracy. The cover accuracy with a threshold of 50 is even better than the effect of global positioning, which provides the dual advantages of positioning stability and efficiency.
## 2 Background
### Collaborative VR  
The application of VR to industrial needs has been investigated for several decades, showing the potential to aid, enhance and transform many of industrial tasks and processes [7; 8]. With a given flexibility to simulate dangerous contexts and enable natural interactions with virtual objects in immersive virtual environments [7; 9; 10; 11], VR has been successfully applied in industry to facilitate training [12; 13], AR-prototyping [14; 15] as well as different phases of product development cycle [7; 16; 17; 18; 19].  
\"Distributed virtual environments\" [20], also widely referred as collaborative VR (CVR), have become especially in demand during the COVID-19 pandemic, when people all over the globe were forced to work remotely. The major advantage of collaborative VR is the possibility to blur geographical barriers and immerse people from diverse locations into shared working spaces [21], addressing the needs of multidisciplinary global collaboration. Due to the increased feel of presence and immersion together with the ability to communicate verbally and non-verbally, collaboration in VR is understood as more efficient and flexible than the collaboration via traditional conferencing tools. Evidence has shown that VR is capable of positively affecting the elements of remote communication, such as the clarity and richness of communicated information, and enhance the quality of discussion and knowledge transfer due to shared context and awareness of others [22; 23; 24].  
A case study by [PERSON] et al. [16], for instance, demonstrated the application of VR to support early design decision making, which resulted in escalation of identified design issues and provided solutions, in addition to increased sense of team engagement and participation in the collaboration process. Furthermore, a recent study demonstrated that real-time collaboration over multi-user VR leads to increased performance in comparison to the traditional approach [25]. The studies by [PERSON] et al. [17; 26], which explored the use of VR to aid the collaborative design review process, concluded that VR technology is a \"useful addition, and not a replacement\", which potentially accelerates the process and ensures inclusion of all professional groups. Other studies reported that collaboration in VR may strengthen lean and agile practices [27; 28; 29; 30], optimizing value creation and team performance, while reducing resource waste and time span. Furthermore, collaboration in VR supports the innovation mindset of employees and overall sustainability [31], whereas VR itself is recognized as motivating and engaging technology by industrial employees [12; 14].
## 3 Regularization
### \(L_{1}\) Regularization  
Another less common type of weight penalty is \(\left\|\cdot\right\|_{1}\) penalty used in \(L_{1}\) regularization, which results in a model with _sparse_ parameters. Incorporating the norm penalty term  
\[penalty=\left\|\theta\right\|_{1}=\sum_{i}\left|\theta_{i}\right| \tag{45}\]  
into cost function \(J\) gives regularized cost function  
\[\bar{J}(\mathbf{\theta};\mathcal{D})=J(\mathbf{\theta};\mathcal{D})+\lambda\left\| \theta\right\|_{1} \tag{46}\]  
with gradient  
\[\
abla_{\theta}\bar{J}(\mathbf{\theta};\mathcal{D})=\
abla_{\theta}J(\mathbf{\theta}; \mathcal{D})+\lambda sign(\mathbf{\theta}), \tag{47}\]  
where \(sign\) function is applied element-wise on parameter vector \(\mathbf{\theta}\). Update in iteration \(t\) is therefore given by  
\[\mathbf{\theta}_{t}=\underbrace{\mathbf{\theta}_{t-1}-\eta\lambda sign(\mathbf{\theta}_{t- 1})}_{\text{decay parameters toward 0}}\underbrace{-\eta\
abla_{\theta}J(\mathbf{\theta}_{t- 1};\mathcal{D})}_{\text{make update}}. \tag{48}\]  
The decay is constant here; if \(i\)th parameter \(\theta_{i}\) is greater than 0, then we subtract \(\eta\lambda>0\) from it; if it is negative, then we add \(\eta\lambda\) pushing it in that way towards 0. Due to the resulting sparsity, \(L_{1}\) regularization is often considered as a feature selection method; we ignore features with 0 coefficients.
## 4 Experiments and Analysis
### Experimental Methods  
After data preprocessing, a series of typical ship trajectories with large turning amplitudes is selected for experiments. In this paper, considering model calculation complexity, calculation time, and prediction performance for the SVM model, the radial basis function \(K(x,y)=\exp(-\big{|}\big{|}x-y\big{|}\big{|}\big{|}_{2}/\sigma^{2})\) is selected as the kernel function, where the values of kernel function parameter \(\sigma\) and penalty coefficient \(C\) are \(\sigma^{2}=3\) and \(C=50\), respectively. For the LSTM network model, 30 hidden-layer neurons are selected, the number of iterations is 1000, and the learning rate is 0.01. The parameter values for the GA [35; 36] are shown in Table 1, where MaxGenerations is the maximum number of iterations before the GA stops optimization and PopulationSize is the size of the initial population. The smaller its value is, the more prone sick populations are to appear. The larger the population size is, the more difficult the algorithm is to converge and the lower robustness it has. CrossoverPop is the crossover probability; if the value is too large, it is easy to miss the optimal individual and the randomness is large, while the crossover probability is too small to effectively update the population. MutationPop is the mutation probability; if its value is too small, the diversity of population decreases too quickly, which easily leads to the loss of effective solutions and is hard to repair. If its value is too large, the probability of the optimal individual being destroyed also increases, which is not conducive to finding the optimal solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MaxGeneration** & **PopulationSize** & **CrossoverPop** & **MutationPop** \\ \hline
100 & 40 & 0.8 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values for GA.  
Figure 5: Cubic spline interpolation results.
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles  
In UM environments, the work of people and machinery to drill, crush and fly rock generates large amounts of suspended dust particles. Therefore, it is necessary to first analyze the interaction of the light beam with the dust particles, then generate a dust particle distribution model to finally model the scattering effect and introduce it as a component in the complete UM-VLC channel model.  
Figure 1: Geometry of the channel components that make up the SISO UM-VLC system.
###### Abstract  
In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both indoor and outdoor environments. Using the window glass as a medium, energy transmission occurs through a coupling mechanism between the planets. The design focuses on large array antenna design, beamforming networks, and coupler design on both sides of the glass. Beamforming networks enable the indoor and outdoor antenna arrays to switch beams in various directions, optimizing high-gain antennas with narrow beamwidths. Through electromagnetic induction and filter couplers, a robust signal transmission channel is established between indoor and outdoor environments. This setup significantly enhances communication efficiency, particularly in non-line-of-sight environments.  
6 2024133581358  
This study extends the practical work based on the smart transfer planer concept proposed in [1], Figure 1, featuring multiple antennas suitable for both indoor and outdoor applications. The planer is symmetrically structured and is installed at both ends of a window, serving as a separator between indoor and outdoor environments. Communication between the planers occurs through the glass window via a coupling mechanism. The primary focus of this design lies in the large array antenna design, beamforming networks, and coupler design on both sides of the glass.  
The beamforming networks enable indoor and outdoor antenna arrays to switch beams in various directions, offering a solution for high-gain antennas with narrow beamwidths. Using centimeter-wave electromagnetic induction and filter couplers, a signal transmission channel is established between indoor and outdoor environments. This configuration improves communication efficiency for signal transmission in non-line-of-sight environments.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems  
Cryptographic techniques employed in blockchain-based data storage systems play a critical role in enhancing the security and efficiency of both on-chain and off-chain storage solutions.  
_Off-chain Data Storage and Security_: Storing large datasets off-chain reduces the computational burden on the blockchain, addressing scalability and cost issues. Systems such as IPFS enable secure distributed storage, which is particularly beneficial for managing large-scale healthcare datasets efficiently and securely [10; 12; 77; 101]. However, off-chain storage introduces potential security vulnerabilities. To mitigate these risks, hybrid verification and encryption techniques are employed to ensure data integrity and confidentiality.  
_On-chain Data Storage and Security_: Storing data on-chain leverages blockchain's mutability and transparency, ensuring secure and verifiable data management. Keeping data directly on the blockchain allows network participants to verify data accuracy and integrity [46; 50; 103]. The on-chain method is particularly ideal for high-security applications, such as financial transactions, smart contract data, and sensitive healthcare records. However, on-chain data storage presents cost and scalability limitations, as it increases blockchain size, making large-scale data management more complex. Despite these challenges, cryptographic techniques such as Merkle trees and blockchain hashing facilitate secure and efficient data storage [58; 109; 115].  
Blockchain systems employ both asymmetric and symmetric encryption techniques to ensure data security. While each method offers distinct advantages, they also come with specific trade-offs in terms of performance, security, and use cases.
## 4 Results and Discussions
### Result Analysis and Performance Comparison
#### 4.2.4 Comprehensive Performance of Ablation Experiments  
In this study, we propose several optimization strategies for the multimodal fusion recommendation model based on consumer preferences. To verify the impact of different modules and features, as well as to evaluate the effectiveness of the model architecture design, we conduct ablation experiments in this section by progressively removing various components of the model. The variations in different metrics enable us to quantify their influence on the recommendation system's performance. The experimental results are presented in Table 3.  
The experimental results demonstrate that price preference features and the multi-head self-attention mechanism significantly contribute to model performance improvement. When price features were excluded, the introduction of the multi-head attention mechanism increased the AUC from 0.786 to 0.802, with 3.2% improvement in NDCG@10, indicating that the attention mechanism enhances cross-modal feature interactions through dynamic allocation of modality weights. Upon incorporating price features, the base model's AUC improved from 0.786 to 0.816, representing 3.0% increase, while NDCG@10 showed 4.3% improvement, confirming the effectiveness of price features in capturing users' explicit preferences and mitigating semantic and demand biases. Notably, when both price features and the multi-head attention mechanism were simultaneously introduced, the model achieved optimal performance, with AUC and NDCG@10 improving by 4.7% and 5.6%, respectively, compared to the base model using only visual and textual features. This synergistic effect suggests that the multi-head self-attention mechanism and price preference feature mining exhibit complementarity in optimizing the rationality of ranking results for clothing outfit recommendations. These findings also validate the effectiveness of the proposed optimization strategies in this study.
## 2 Methods
### Experimental Protocol  
Subjects walked on a force treadmill (Bertec, Columbus, OH, USA), on which the walking grade and walking speed could be adjusted accordingly. Each subject was familiarized with walking on the treadmill for about 5 min with the walking speed set to 1.11 m/s and the walking grade set to 0\({}^{\circ}\). The speed of 1.11 m/s (4 km/h) was selected according to the American College of Sports Medicine [22] and the literature on uphill and downhill walking [23]. Each subject was required to walk for 2 min on the treadmill with three mass backpack loads (10, 20, and 30 kg) during four grades (level (0\({}^{\circ}\)), 3, 5, and 10\({}^{\circ}\) grades). The level and slope walking on the treadmill without loads (0 kg) were also completed by each subject as a reference. Subjects were asked to perform all the load mass tests at one certain grade in order to shorten the experiment time. The load masses were in random order during one grade test, and the grade testes were also in random order. Subjects were asked to walk at one grade for 3 min with one load during one trial. Subjects rested for 3 min between different load mass trials and 30 min between different grade tests. The experimenters adjusted the load mass and the walking grade while the participants rested. The speed of the treadmill was set to 1.11 m/s in every trial. The temporal stride kinematics and surface electromyographic (EMG) during the final 30 s of each trial were recorded.  
The temporal stride kinematics were calculated according to the ground force data recorded by the force platform in the treadmill. Two force platforms were embedded in the treadmill and recorded the ground interactive forces and moments between foot and ground. The software in the optical motion-capture system (Motion, Columbus, OH, USA) [26] calculated the center of force operation based on the forces and moments.  
The EMG signals were collected by the wireless EMG system (Delsys Trigno(tm) Wireless EMG System (Natick, MA, USA)). The muscles selected included the GM, RF, HA, anterior tibialis (AT), and GA, which are the main activated muscles during human lower extremity movements [27]. The pre-amplified single differential electrodes (Trigno, Delsys, Natick, MA, USA) were placed on the muscle bellies after preparing the skin with alcohol (the schematic diagram of human lower limb muscles in this study is shown in Figure 1). The surface EMG (sEMG) signal sensors were fixed with double-sided tapes and bandages to prevent displacement between the sensors and muscles during walking.  
This optical motion capture system (Motion, Columbus, OH, USA) can integrate the Delsys device and the Bertec force platforms by a data acquisition card (DAQ, National Instruments, Austin, TX, USA); thus, the sEMG signal and kinetic data can be collected simultaneously. The sampling frequency was selected as 1200 Hz.
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 4 Edge Matching and Propagation Method
### Edge Depth Propagation  
Since the edge depth map is only a sparse depth map, it is necessary to propagate the sparse depth map to obtain a dense map. Based on the assumption that the depth will not abruptly change in a similar color region, the propagation process mainly utilizes the sub-eye image brightness information to segment the image into regions and then propagates the edge depth to the regions. The pseudo-code is shown in Algorithm 1. The sub-images \(I\), \(I_{i}\), and the edge depth \(D(p)\) are the inputs to Algorithm 1; the output is the dense depth map \(D_{d}(p)\). The target image \(I\) is first over-segmented by the meanshift method [51] in this paper (step 1), then each segmented area \(Am\) and segmented information S are obtained.  
Figure 9: The edge depth map the color bar of the depth map keep the same as Figure 3. From left to right, first row: target sub-image, depth map matched with left-neighboring sub-image, and with top-neighboring sub-image; second row: fused depth map, depth map matched with right-neighboring sub-image, and with bottom-neighboring sub-image  
The depth value and the depth number for each area \(S.area\) are saved (step 2-step 5). The depth of the neighboring area is used (step 9-step 11). The matching cost of area \(S.area\) is then computed according to the candidate depth (step 12-step 14) in Equation (7). The area depth is then selected by WTA.  
```
0: Edge depth map(\(D(p)\)) and color images (\(I,I_{i}\))
0: Dense depth map \(D_{d}(p)\)
1: Image over-segmentation, \([Am,S]=\textit{meaningft\_segmentation}(I)\)
2:for each segmented area \(S.area\)do
3:\(d\_can\leftarrow\) find edge depth values in this area;
4:\(d\_num\leftarrow\) the number of depth in this area;
5:endfor
6:\(S.area\leftarrow\) sort \(S.area\) based on \(d_{n}um\);
7:\(d_{s}=0\)
8:for each segmented area \(S.area\)do
9:for each neighboring area \(Am\)do
10:\(d\_can\leftarrow\) add the estimated depth \(d_{s}\);
11:endfor
12:for each candidate depth \(d\_can\)do
13:\(c(d)\leftarrow\) compute the matching cost;
14:endfor
15:\(d_{s}=\textit{argmin}(c(d))\)
16:\(D_{d}(S.area)=d_{s}\)
```  
**Algorithm 1** Edge Depth Propagation  
\[c(d)= \frac{1}{num}\sum_{num}\frac{\sum\limits_{i}\delta_{i}(p)c^{i}(p,d)}{ \sum\limits_{i}\delta_{i}(p)+\epsilon} \tag{7}\] \[c^{i}(p,d)= (1-\lambda)\cdot M^{i}(p,d)+\lambda\cdot Mg^{i}(p,d)\] \[Mt^{i}(p,d)= min\{|I(p)-I_{i}(p^{\prime}(d))|,\tau_{i}\}\] \[Mg^{i}(p,d)= min\{|G(p)-G_{i}(p^{\prime}(d))|,\tau_{g}\}\]  
where \(num\) is the pixel number of the corresponding area, \(\delta_{i}(p)=1\) if pixel \(p\) in \(I\) has a corresponding edge pixel with depth \(d\) in sub-image \(I_{i}\), \(\epsilon\) is a small constant to avoid dividing by zero. \(Mt^{i}(p,d)\) and \(Mg^{i}(p,d)\) are intensity cost and gradient cost, respectively. \(\lambda\) is weight. \(G(p)\) computes the gradient of \(p\); \(p^{\prime}(d)\) denotes the corresponding pixel in sub-image \(I_{i}\) to have the depth of \(d\), \(\tau_{i}\); and \(\tau_{g}\) are the truncate threshold.