esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Influence of Scaffold Structure on Bone Tissue Repair Scaffolds and Modulation
#### 3.2.1. Aperture Size
The S-0.62 scaffold had a similar trend of WSS distribution as S-0.7, with a concave distribution. Under the inlet velocity of 1 mm/s, 49.4% of the area of WSS was between 0 and 10 mPa, and the cells in this part of the area would be differentiated to osteoblasts; 24.2% of the area of WSS was between 10 and 30 mPa, which would induce the cells to be differentiated to cartilage; 25.2% of the area of WSS was between 30 and 60 mPa, which would induce the cells to be differentiated to fibrous tissues; only 1.1% of the area was unfavorable for cell culture. The inlet velocity was adjusted to 0.5 mm/s for simulation, and the frequency distribution of WSS was counted and is plotted in Figure 15.  
Comparative analysis of Figures 14 and 15 shows that for square crystalline cell-structured scaffolds with different pore diameters, the lower velocity decreases the overall WSS level, but the trend of the WSS distribution remains consistent, which is in keeping with the conclusions of Section 3.1. At an inlet velocity of 0.5 mm/s, 17% of the area of the S-0.4 scaffold was still unsuitable for cell growth and differentiation, indicating that the small pore size of the crystalline cell structure bone tissue repair scaffolds is unsuitable for cell culture, and that the scaffolds should be avoided in designing smaller pore sizes. Figure 14: WSS distribution of S-0.4 and S-0.62 scaffold at inlet velocity of 1 mm/s. Figure 15: WSS distribution of S-0.4 and S-0.62 scaffolds at inlet velocity of 0.5 mm/s. At an inlet velocity of 0.5 mm/s, the S-0.62 scaffold will induce cell differentiation in two directions: 62.5% of the area will be induced to form osteoblasts, and the remaining area will be induced to form chondrocytes. If it is desired that all cells in the region differentiate into osteoblasts, it is necessary to continue to reduce the inlet velocity of the perfusion bioreactor. After simulation experiments, it was found that for the S-0.4-type scaffold, at an inlet velocity of 0.2 mm/s, the WSS were all distributed within 0-10 mPa, which could meet the biostimulation requirements for cell differentiation to osteoblasts.
# Experimental Study of Radiated Emission Due to Secondary Common-Mode Current in Buck Converters
## 2 Configuration of Experimental System  
Figure 2 shows a configuration of the experimental system under evaluation in this article.  
A buck converter based on SiC-MOSFETs (SCT3120 AL, Rohm, Kyoto, Japan) and load resister are housed inside a metal box to block radiated noise from the power converter itself. The metal box is placed on a wooden desk of which the height is about 0.8 m from a ground plane. An aluminum board (1 m \(\times\) 1 m \(\times\) 5 mm), which is placed on the laboratory's floor and is grounded through LISNs, imitates the ground plane. The buck converter and a DC power supply are connected via LISNs (LI-325C, COM-POWER [30]) to stabilize an impedance of the power supply side in the frequency range under evaluation. A 1.5 m three-core cable of which cross section area of conductor is 2.0 mm\({}^{2}\) is used for the connection between the buck converter and LISNs. Note that the metal box is connected to the ground plane via LISNs.  
Figure 1: Radiated emission from power cables of power converters.  
This article defines the following three noise propagation modes in the experimental system. First, differential-mode (DM) of which noise circulates between phases of the power converter. The noise path in which the noise propagates through the ground line E via stray capacitance, and returns through the power lines P and N, is defined as primary common-mode (PCM). Furthermore, there is another propagation path in which the noise propagates through the power lines P and N and ground line E as a whole [27; 28; 29]. This noise propagation path is defined as the secondary common-mode (SCM) in this article.
## 3 Results and Discussion
The highest BAIt values were observed in both tests (7 and 15 days) for Mn (123.9 and 149.7), Cr (16.3 and 18.5), and Ca (14.8 and 16.2) at the Terni industrial site, and the BAIt values for these elements were always significantly higher (\(p\) < 0.05) than those detected at the Perugia urban site and the Monte Martano control site in both tests. The values were often greatest after 15 days of exposure to ambient air. In conclusion, the bioaccumulation indexes calculated in 2021 confirmed that olive pollen is an efficient passive sampler and that Terni, the second-biggest town in the Umbria region (\(\simeq\)110,000 inhabitants) and one of the largest stainless steel production sites in Europe, was the investigated area subjected to greater anthropic pressures. Our results were also in good agreement with those reported by [PERSON] et al. (2013) [36]. The authors investigated the airborne dust over Terni and the morphochemical characterization of dust samples by scanning electron microscopy revealed that the local steel plants supply a regular contribution of calcite, metal particles (mainly Fe, Cr, Mn, Ni, and Zn), metal carbonate, and sulphate compounds to the airborne dust. Since the tests were carried out during the spring-summer period (from June to August), the high bioaccumulation values observed for Mn (61.5 and 46.3, respectively after 7 and 15 days) at the Monte Martano site can be explained by the perturbation of aerosol mass concentrations induced by the increasing height of the boundary layer in the summertime [39]. Although the average bioaccumulation index for fluoride was low and not significantly different (average BFA = 1.2 \(\pm\) 0.8) from the others, the morphological anomalies of the surface of the grains observed at the Terni site could also have been induced by the fluorides emitted from the steel mill. Indeed, fluoride is one of the most harmful air pollutants, affecting agricultural and natural vegetation. Abnormalities and effects on the shapes and sizes of pollen grains exposed to ambient airs affected by fluoride pollution have been evidenced by observations using scanning electron microscopy [46]. In addition to good efficiency, our pollen sampler presents several advantages over the other passive systems reported in the literature, such as those using lichens [32; 33] and spider webs [31; 34]. The deposition chambers are very easy to construct and have low costs for installation and maintenance. The olive pollen can be collected indifferently from both cultivars investigated (San Felice and Leccino), since we did not observe variety-specific bioaccumulation of ions and/or elements. Pollen samplers can be installed in different climatic areas if similar average meteorological conditions are observed at the control and sample sites, and they can be positioned in zones with different altitudes. Finally, after exposure to air, the pollen samples can be used to estimate the effects of their interaction with pollutants on reproductive functions, and on polinosis, to predict the possible increase in allergenic potency.
## 3 Materials and Methods
### Selectivity Studies  
For cross-reactivity tests, 20 mg of polymer was incubated with 1 mL of 0.8 mM analyte **1**, **17**, **18**, **19** or **20** in CHCl\({}_{3}\) for 1 h. For binary competitive selectivity tests, 20 mg of polymer was incubated with 1 mL of 0.8 mM **1** mixed with 0.8 mM of **17**, **18** or **19** in CHCl\({}_{3}\) for 1 h. The supernatant was analysed by either HPLC for **1**, **18**, **19** and **20** or GC-MS for **17**.  
The HPLC method for **1**, **18** and **20** is outlined in Section 3.8.1. Analyte **17** was analysed using a Shimadzu 2010 gas chromatograph coupled to a Shimadzu QP2010 mass spectrometer and a Shimadzu AOC-20s auto sampler. The column was a ZB-5 MS capillary column (30 m \(\times\) 0.25 mm) coated with 0.25 \(\upmu\)m of stationary phase. High-purity helium was used as the carrier gas at 71 kPa, with a column flow rate of 1 mL/min, a total flow rate of 9 mL/min and a split ratio of 15. Samples (1 \(\upmu\)L) were injected and run using the following program: initial column temperature was 100 \({}^{\circ}\)C, which was held for 1 min before increasing to 300 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min. Analyte **19** was analysed using the following HPLC method: The mobile phase consisted of 75% aqueous buffer solution (50 mM K\({}_{2}\)HPO\({}_{4}\) adjusted to pH 3.5 with H\({}_{3}\)PO\({}_{4}\)) and 25% 3:7 H\({}_{2}\)O:CH\({}_{3}\)CN (with 10 mM TEA). A 10 \(\upmu\)L injection volume was used with a run time of 10 min, flow rate of 0.8 mL min\({}^{-1}\) and detection wavelength of 190 nm. A calibration curve was generated using solutions in a range from 0.1 to 1 mM.
# DANet: Temporal Action Localization with Double Attention
###### Abstract
To assess the performance of DANet, we conduct extensive experiments on two TAL task datasets, namely THUMOS14 [22] and ActivityNet1.3 [23] datasets. On the THUMOS14 dataset, DANet achieves a mean average precision (mAP) of 66.56%, a mAP of 78.2% at the time Intersection over Union (tIoU) = 0.4, and a mAP of 58.7% at tIoU = 0.6. Compared with the state-of-the-art method ActionFormer [10], for average mAP and mAP at tIoU of 0.4 and 0.6, respectively, our method outperforms ActionFormer by over one percentage point. In addition, DANet also outperforms all two-stage methods such as AFSD [5], PBRNet [24], and GTAN [8], as well as the other one-stage methods such as RTDNet [25], ContextLoc [26], BMN-CSA [27], and MUSES [6]. In addition, through conducting comprehensive ablation studies, we not only demonstrate the effectiveness of MLSA and MA, but also investigate the impact of some hyper-parameters on the experimental results, such as the number of projection layers, path aggregation, and the adopted normalization method (see Section 4.3). The key contributions of this work are as follows:  
* To enable comprehensive contextual modeling of temporal information, we propose a model named DANet comprising MLSA and MA. MLSA models temporal context by local self-attention. MA aggregates global feature information. * To preserve the salient characteristics of local clip embeddings, MA returns discriminative features by using max-pooling in both channel and temporal dimensions as well as generates attention weights. * Compared with other methods, DANet achieves the best performance on THUMOS14 and ActivityNet1.3 datasets. In addition, ablation studies demonstrate the effectiveness of MLSA and MA as well as the adopted hyper-parameters.
## 6 Calibration in a Stochastic Environment Under Sensor Nonlinearities
### Offset Calibration  
**Theorem 5**.: _Let A2-A7 hold and let (1) hold with \(v_{t}^{i}=0,i=1,\ldots,n\), i.e., the model is linear and the measurement noise is present; let the communication noise be present, so that \(z_{t}^{j}+\eta_{t}^{ij}\). Then, algorithm (5) converges to consensus in the mean square sense._  
Proof.: According to Section 3 and [33; 34; 36; 45; 47], now, we have for \(\delta_{t}^{i}=\delta_{t}\), \(i=1,\ldots,n\) the following vector-matrix recursion:  
\[\hat{f}_{t+1}=(I+\delta_{t}\Gamma)\hat{f}_{t}+\delta_{t}\xi_{t}, \tag{28}\]  
where \(\zeta_{t}=\mu_{t}+v_{t}\), with \(\mu_{t}=[\mu_{t}^{1}\cdots\mu_{t}^{n}]^{T}\), \(v_{t}=[v_{t}^{1}\cdots v_{t}^{n}]^{T}\), \(\mu_{t}^{i}=\sum_{j\in\mathcal{N}_{t}}\gamma_{ij}(\xi_{t}^{j}-\xi_{t}^{i})\) (local measurement noise) and \(v_{t}^{i}=\sum_{j\in\mathcal{N}_{t}^{i}}\gamma_{ij}\eta_{ij}\) (local communication noise), \(i=1,\ldots,n\).  
Let \(\hat{f}_{t}=T^{-1}\hat{f}_{t}\); similarly to Theorem 1, we obtain  
\[\hat{f}_{t+1}^{[1]}=\hat{f}_{t}^{[1]}+\delta_{t}\xi_{t}^{\prime} \tag{29}\] \[\hat{f}_{t+1}^{[2]}=(I+\delta_{t}\Gamma^{*})+\delta_{t}\xi_{t}^{ \prime\prime},\]where \(\zeta^{\prime}_{t}=\pi\zeta_{t}\) and \(\zeta^{\prime\prime}_{t}=S^{*}\zeta_{t}\), \(\dim\{\zeta^{\prime}_{t}\}=1\), \(\dim\{\zeta^{\prime\prime}_{t}\}=n-1\).  
Define the Lyapunov functions \(L_{t}^{[1]}=E\{\tilde{f}_{t}^{[1]}\}\) and \(L_{t}^{[2]}=E\{\tilde{f}_{t}^{[2]}T\tilde{f}_{t}^{[2]}\}\), where \(P>0\) is the solution to the algebraic Lyapunov equation, \(P\Gamma^{*}+\Gamma^{*T}P=-Q^{*}\), for any given \(Q^{*}>0\) (keeping in mind the fact that \(\Gamma^{*}\) is, by assumption, a Hurwitz). It follows from A7 that  
\[L_{t+1}^{[1]}\leq L_{t}^{[1]}+C_{1}\delta_{t}^{2}(1+L_{t}^{[1]}), \tag{30}\]  
\[L_{t+1}^{[2]}\leq(1-\delta_{t}\lambda_{min}(Q^{*}))L_{t}^{[2]}+C_{2}\delta_{t} ^{2} \tag{31}\]  
where \(C_{1}>0\), \(C_{2}>0\) and \(\lambda_{min}(Q^{*})>0\). It is possible to show that \(L_{t}^{[1]}<\infty\) and \(\lim_{t\to\infty}L_{t}^{[2]}=0\). Consequently, \(\tilde{f}_{t}\) tends to a vector \([\tilde{f}_{\infty}^{[1]}0\cdots 0]^{T}\), where \(\tilde{f}_{\infty}^{[1]}\) is a random variable satisfying \(E\{\tilde{f}_{\infty}^{[1]}\}<\infty\). Therefore, one concludes that \(\tilde{f}_{t}\) tends to \(\chi_{\infty}\mathbf{1}\) in the mean square sense, where \(\chi_{\infty}\) is a scalar random variable satisfying \(E\{\chi_{\infty}^{2}\}<\infty\).
## 3 Results
### Beam Behavior--Experimental Results  
#### 3.2.1 Cracking Pattern and Mode of Failure  
Figure 4 shows the cracking pattern of the beams tested at 28 days for the N100, CR50, and CR100 series. These cracks were marked and numbered at the end of the test and their length and thickness dimensions were recorded. All tested beams showed a similar failure pattern and number of cracks; vertical cracks were first generated within the central third, which is associated with flexural bending developing from the lower part of the beam towards the compression zone [41]. During the last part of the test, diagonal cracks associated with shear failure formed, and tended to be more diagonal in shape as they grew further away from the central third [19].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Compressive Strength** & **Standard** & **Modulus of** & **Standard** & **Fleural** & **Standard** \\  & **(MPa)** & **Deviation** & **Elasticity** & **Deviation** & **Strength** & **Deviation** \\ \hline N100 & 30.00 & 2.99\% & 33276 & 0.51\% & 3.79 & 5.57\% \\ CR50 & 29.10 & 1.84\% & 31997 & 2.26\% & 3.47 & 4.64\% \\ CR100 & 26.18 & 5.01\% & 30717 & 5.26\% & 3.16 & 4.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of series.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Efficiency Analysis
#### 4.7.1 Computational Complexity Analysis  
The main processing flow of the proposed method includes: image reading and preprocessing (brightness analysis and contrast enhancement), image denoising (edge detection, morphological processing, and connected domain extraction), and image fusion and result output. The computational complexity of each module is analyzed as follows: In the image preprocessing stage, the main operations include statistical distribution analysis of grayscale images, contrast enhancement, and adaptive gamma correction. These operations are pixel-by-pixel processing, with a time complexity of O(n), where n represents the total number of pixels in the image. In the image denoising stage, CUDA acceleration is implemented through GPU, and Canny edge detection, binarization, morphological processing, and connected domain extraction are performed in sequence. Canny edge detection and binarization are essentially linear scanning processes with a complexity of O(n); morphological opening operations use fixed-size kernels for local sliding window calculations, with a theoretical complexity of, where k represents the size of the structural element (kernel). Connected domain extraction completes region labeling by scanning the image twice, and the complexity is also O(n).  
The time complexity of the overall processing flow of this method is O(n), where n is the number of image pixels. Each module, such as Canny edge detection and morphological processing, is a linear or local window operation, and most operations are completed on the GPU, which significantly improves the operating efficiency.
## 8 Conclusions and Discussion  
We comprehensively surveyed and investigated Intelligence Edge Computing (IEC) characteristics and benefits, challenges, potential use cases and market driver. Every part of this paper started with an overview then was followed by intensive investigations of IEC factors in 5G networks. 5G applications empowered by IEC technology are being enabled by extending some intelligence to the edge of the network. To summarize the main contributions of this paper, some conclusions are as follows:  
* Integration of IEC in 5G technologies enables sufficient and massive support for different services including IoT, smart environments, augmented and virtual reality, and sustainability of energy systems, vehicles connection, network performance, video games, economic services, education technology, ICT markets, and communication.
* IEC's main characteristics and benefits include proximity of end-users, ultra-low latency, location awareness, integrated virtualization, super network performance, flexibility, real-time analytics, and automation.
* IEC's current and up-to-date challenges include privacy and security, latency, distributed resource management, data traffic and bandwidth, heterogeneity, and scalability in addition to some issues related to network openness, multiple services and processes, data management, durability and resilience.
* IEC's potential use cases and applications that have been investigated in this paper include three main categories as customer-oriented services, operator and third-party services, and network performance and QoE improvement. To enhance these categorizations, it is essential to integrate IEC in 5G technologies.
* IEC's Market Drivers vary based on the different use and accessibility of 5G technologies in various sectors. We investigated five main sectors including smart environments, autonomous vehicles, healthcare, gaming in AR and VR and smart energy.
* Intelligence Edge Computing will have a significant role in 6G Network. Some of the examples and methods of using IEC in business have been discussed in addition to the architecture, momentum and market drivers of IEC in 6G Network.  
Conceptualization, A.A.-A. and A.M.; methodology, A.M.A.-A.; software, I.A.E.; validation, A.K., A.M.A.-A. and I.A.E.; formal analysis, A.A.-A.; investigation, A.M.; resources, A.K.; writing--original draft preparation, A.A.-A.; writing--review and editing, I.A.E., A.M. and A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research is based on the Applied Scientific Research under the SPbSUT state assignment 2021.  
The authors declare no conflict of interest.
## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.2.4 Ablation Study  
This ablation study aims to validate the effectiveness of the dynamic weighting strategy in the PPFL-SAGIN method proposed in this paper, particularly its performance in environments with severe data heterogeneity. The experiment was conducted using the MNIST dataset, where the satellite layer primarily contains samples from classes 0-3 with proportions of 50%, 30%, 15%, and 5%, respectively; the aerial layer primarily contains samples from classes 4-6 with proportions of 45%, 35%, and 20%; and the ground layer primarily contains samples from classes 7-9 with proportions of 40%, 35%, and 25%. In terms of data volume distribution, satellite nodes have limited resources, aerial nodes have moderate resources, and ground nodes have abundant resources. Regarding device heterogeneity, the satellite layer has low computing capability with strict bandwidth limitations (1-5 Mbps), the aerial layer has moderate computing capability with moderate bandwidth limitations, and the ground layer has high computing capability with sufficient bandwidth. Table 2 shows the performance metrics of the PPFL-SAGIN (dynamic weighting) and the method based on static weighting strategy after 10 rounds of training.  
Figure 10: Comparison of the impact of different privacy budgets on model accuracy based on the MNIST dataset.  
As shown in Table 2, the dynamic weighting strategy achieved significant improvements across all evaluation metrics, particularly with a 55.32% reduction in average loss, indicating a substantial improvement in model fitting quality. In terms of convergence speed, the PPFL-SAGIN (dynamic weighting) achieved over 90% accuracy by the 6 th round, while the static weighting strategy failed to reach 90% accuracy within 10 rounds.  
By analyzing the accuracy of different methods across various categories, the following results were obtained: The static weighting strategy achieved 67-75% accuracy for satellite layer categories (0-3), 84-88% accuracy for aerial layer categories (4-6), and 92-95% accuracy for ground layer categories (7-9), showing excessive dependence on ground layer nodes with larger data volumes and poorer fitting for satellite layer categories. The PPFL-SAGIN (dynamic weighting) method achieved 91-94% accuracy for satellite layer categories (0-3), 94-96% accuracy for aerial layer categories (4-6), and 95-97% accuracy for ground layer categories (7-9), demonstrating more balanced accuracy across categories and good fitting even for satellite layer categories with fewer samples.  
The experimental results indicate that the dynamic weighting factor design proposed in Equation (1) of this paper is crucial for addressing data heterogeneity issues in space-air-ground integrated networks, effectively handling complex, heterogeneous network environments while ensuring model performance.
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 2 Related Work
### Non-Cooperative Game Models  
Non-cooperative game theory provides a framework analysis of the interaction between selfish users. The Slotted ALOHA mechanism and its variants have been widely studied using non-cooperative models. In [4; 12], we have proposed a stochastic game model of Slotted ALOHA combined with ZigZag Decoding. The model assumes that all users are selfish, and therefore they do not cooperate with each other. Compared with the cooperative model, we found that the selfish behavior of users yields the worst system performance, especially in the case of a large number of users.  
In [13], the authors provided a non-cooperative game model for Slotted ALOHA. To achieve the desired throughput, they proposed to adjust users' transmission probabilities at each iteration of the game. Then, they investigated the equilibrium of the game.  
The authors of [14] proposed a non-cooperative game analysis of a network scenario operating using the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. Each user in the proposed scheme attempts to minimize the age of his information. They studied different profiles of pure and mixed strategies. They found that the collision length affects the dominant strategy of the game.