esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tactile Internet  
There has been continuous evolution of the Internet since its deployment. The development of new innovative applications and advancements in communication technologies push each other to new heights all the time. In the early days, the Internet primarily supported the transmission and reception of text, files, email, and similar applications. At that time, images and videos were treated as bandwidth-hungry information, and due to slower data speeds, it was challenging to share such media over the Internet. With the technological advancements in hardware, software, and communication technologies, the focus shifted toward enhancing speed and reliability [3]. The quality of service (QoS) became a crucial factor as Internet speeds increased, especially for multimedia applications, to guarantee low latency and packet loss with high reliability and a real-time experience for users. From the era of downloading files to streaming video content, the Internet infrastructure improved and grew exponentially, supporting applications, such as live video streaming, high-definition video transmission, real-time communication, virtual reality/augmented reality (AR/VR), multi-party gaming, and many more [4]. Furthermore, the speed and reliability of the Internet have boosted its use in industry, education, healthcare, and others. The Internet is pushing the limits of speed, QoS, and application possibilities with 6G, which promises an even more connected and interactive future.  
Figure 2: Targeted capabilities of IMT-2030 [19].  
Today's Internet enables real-time communication and the exchange of data, graphics, and videos; however, it cannot convey a physical sense of touch or movement, which limits its application in immersive experiences [15]. To support such applications and requirements, a new paradigm for the Internet is emerging called the Tactile Internet, which aims to support haptic interactions, incorporating a sense of touch (tactile) and muscle movement (kinestic) interactions [25].  
In the Tactile Internet, achieving a remote sense of touch requires users to wear highly sensitive devices, such as gloves, styluses, or other haptic interfaces [18]. These devices not only simulate tactile sensations but also transmit sensory feedback in real-time. At the receiving end, actuator systems, such as robotic systems replicate the user's movements while collecting touch feedback through advanced tactile sensors, enabling seamless interaction across distances [26]. It enables real-time remote touch using ultra-low latency communication with haptic feedback, allowing the users to experience sensations, such as touch, softness, weight, temperature, and others, in real time [27; 28].
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Insecure Working Environment  
For some specific fields of application, for example, underwater security monitoring or target tracking, the working environment of UWSNs may be insecure. The underwater sensor nodes may be deployed to monitor hostile objects in high seas or hostile sea regions. These nodes could become  
Figure 2: Cluster UWSNs with autonomous underwater vehicles (AUVs); (**a**) AUV joins Cluster3; (**b**) AUV joins Cluster1.  
highly vulnerable to threats and malicious attacks. These nodes may be physically destroyed and, even worse, the malicious attackers may compromise nodes to get the data and inject the compromised nodes into the network acting as legitimate node to cause continuous and more serious damage.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 3 Agent-Based Model and Simulation
### ABM of Selfish Mining
#### 3.1.2 Block Mining  
All miners have certain computational (hashing) power which they devote to solving the PoW problem. If mining honestly, the more computational power a miner invests, the greater its possibility to mine the next block first. As the agent of our model, each miner is assigned certain computational power share, \(c_{i}\), such that \(\sum_{i=1}^{N}c_{i}=1\). Further, we assume that the selfish miner controls a share of \(\alpha\) of the network's total computational power. Thus, honest miners control \((1-\alpha)\) of the network's hashing power. Bitcoin's hashing power distribution is very heavy-tailed. A single entity controlled as much as 21% of Bitcoin's total mining power [19]. To reflect this heterogeneity of computational resourcesacross miners, we consider various hashing power distributions in the simulations, such as uniform random, power-law, and exponential.  
Blocks are mined continuously and independently at a constant average rate. Thus, the PoW algorithm that determines block creation is a Poisson process. Therefore, the time between events--here an event is the creation of a new block--follows the exponential distribution. In Bitcoin, the network adjusts the difficulty to mine blocks every 2016 blocks (or in expectation roughly every two weeks) such that the average inter-block time \(\tau_{b}\) remains at 10 min, which is denoted as \(\tau_{b}=10^{\prime}\). Given that we assume there is no such difficulty adjustment in our simulation, if there is selfish mining activity in the network, blocks are expected to be mined at a slower rate, because relatively more hashing power is wasted on outdated and orphaned blocks. Again, we justify this simplification with the fact that the defining time scale for selfish mining is much lower than the time it takes to find 2016 blocks. Thus, the probability that miner \(i\) finds a new block in a given small time interval \(dt\) is  
\[p_{i}^{m}(t)\approx\eta_{i}dt=c_{i} \tag{1}\]  
Equation (1) implies that the time it takes miner \(i\) to find a new block follows the exponential distribution with parameter \(\eta_{i}\). Note that since computational power is normalised, \(c_{i}\) is equal to miner \(i\)'s share of the network's total computational power, meaning that the probability of mining a new block is proportional to the miner's share of the network's computational power. Further, given that the block creation process is a Poisson process, the number of blocks mined by all miners in a given interval follows a Poisson distribution, but with parameters \(\sum_{i}\eta_{i}\). Hence, it is immediate to derive that \(\sum_{i=1}^{N}\eta_{i}=\tau_{b}{}^{-1}\).
## 3 Results
### Method Characterization  
A series of experiments was performed under the optimized conditions (Table 2) and the analytical characteristics, such as the linear ranges (LRs), LOD, and relative standard deviations (RSDs), were determined to validate the developed method. Working solutions containing the target analytes at concentrations of 1, 2, 5, 10, 20, 50, 100, and 200 ug L\({}^{-1}\) were prepared to construct the working curves. Good linearities for the nine OCPs were achieved with determination coefficients (R\({}^{2}\)) ranging from 0.9916 to 0.9989. The LOD for all of the analytes were calculated at signal/noise (S/N) ratios of 3. The LODs range from 0.07 to 1.03 ug L\({}^{-1}\). The repeatability of the method was evaluated by performing six replicate analyses of spiked samples with 10 ug L\({}^{-1}\) of each of the OCPs, and the RSDs are in the range 1.0-8.5%. All of these results indicate that the proposed method has high sensitivity and good repeatability.
# Effect of Vaneless Diffuser Shape on Performance of Centrifugal Compressor
## 3 Result and Discussion
### Flow Field Analysis  
#### 3.2.1 Design Condition Meridian Velocity Streamline  
Figure 4 shows the meridional velocity streamlines for the four diffusers design conditions. The position and size of the vortexes inside the vaneless diffusers get greatly affected by the convergence of the width of the vaneless diffusers. The tail vortex area from the radial and axial directions within compressors is suppressed in all three pinch shapes of diffusers. Compared with the parallel wall diffuser, the flow cross-section of the fluid in the diffusers with pinch width is reduced,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative Mass Flow** & **Vaneless Diffuser Shape** & **Press Ratio p/p\({}_{1}\)** & **Press Ratio p/p\({}_{3}\)** \\ \(\varphi\)/\(\varphi_{\text{D}}\) & **Vaneless Diffuser Shape** & **Press Ratio p/p\({}_{1}\)** & **Press Ratio p/p\({}_{3}\)** \\ \hline
1.125 & Parallel & 1.865 & 1.026 \\
1.125 & Convergence from shroud & 1.872 & 1.018 \\
1.125 & Convergence from hub & 1.876 & 1.014 \\
1.125 & Convergence from shroud and hub equally & 1.874 & 1.016 \\
1.0 & Parallel & 1.878 & 1.028 \\
1.0 & Convergence from shroud & 1.915 & 1.029 \\
1.0 & Convergence from hub & 1.913 & 1.026 \\
1.0 & Convergence from shroud and hub equally & 1.915 & 1.027 \\
0.6 & Parallel & 2.252 & 1.025 \\
0.6 & Convergence from shroud & 2.288 & 1.023 \\
0.6 & Convergence from hub & 2.290 & 1.022 \\
0.6 & Convergence from shroud and hub equally & 2.289 & 1.023 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Centrifugal compressor impeller and diffuser pressure ratio.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relative Mass Flow \(\varphi\)/\(\varphi_{\text{D}}\)** & **Vaneless Diffuser Shape** & **Isentropic Efficiency [\%]** \\ \hline
1.125 & Parallel & 81.04 \\
1.125 & Convergence from shroud & 81.28 \\
1.125 & Convergence from hub & 81.36 \\
1.125 & Convergence from shroud and hub equally & 81.35 \\
1.0 & Parallel & 82.12 \\
1.0 & Convergence from shroud & 82.75 \\
1.0 & Convergence from hub & 82.81 \\
1.0 & Convergence from shroud and hub equally & 82.78 \\
0.6 & Parallel & 72.24 \\
0.6 & Convergence from shroud & 72.62 \\
0.6 & Convergence from hub & 72.44 \\
0.6 & Convergence from shroud and hub equally & 72.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Centrifugal compressor isentropic efficiency.  
which decreases the pressure gradient inside the diffusers and makes the flow within compressors relatively uniform. Not only the backflow phenomenon in the diffusers are improved, but also the backflow vortexes at the impeller outlet are also suppressed, which make the compressor internal pressure distribution more uniform. Analysis of Figure 4 shows that the convergence of the diffuser width from the shroud side has the best effect on suppressing the return vortex phenomenon.
## 4 Results
### Loss Function Comparison Experiment  
The imbalanced distribution of target and background poses a significant challenge in the domain of liver and liver tumor segmentation. This imbalance not only diminishes models' accuracy and generalization but also tends to favor high precision at the expense of low recall. To address this issue algorithmically, experiments were conducted to refocus the model on segmenting challenging samples by rebalancing the class distribution. We evaluated multiple common binary loss functions in the segmentation field, including Tversky loss, binary cross-entropy loss (BCE loss), Dice loss [25], and focal loss [26], on the LiTS dataset. The goal was to identify a loss function that was well-suited to our segmentation task and illustrate how it could alleviate the impact of imbalanced sample distribution on model performance, showcasing its superiority in enhancing model effectiveness compared to other loss functions.  
The results are presented in Table 1. When the model employed Tversky loss, it achieved the best performance in Dice, Recall, and VOE, with scores of 0.9433, 0.9451, and 0.1053, respectively. In the case of BCE loss, the RVD exhibited the most favorable effect at 0.0189. However, when focal loss was utilized, the model's accuracy reached its highest point at 0.9662, but this came at the cost of a noticeable trade-off between precision and recall, resulting in a pronounced impact on class distribution. In comparison to the other three sets of loss functions, Tversky loss stood out with the most substantial optimization effect on model performance and a superior ability to balance positive and negative samples within the dataset. The gap between precision and recall steadily narrowed as both metrics improved, so this was selected as the experimental loss function.
## 4 Discussion
### Effect of Maize Litter Quality and Mineralization on Potential Denitrification  
With onset of anoxic incubation conditions on 47 DAO, total NO and N\({}_{2}\)O fluxes increased rapidly, while N\({}_{2}\) fluxes increased more slowly. \(\delta^{18}\)O\({}_{\text{N2O}}\) of N\({}_{2}\)O emitted on 48 DAO falls in the range of heterotrophic bacterial denitrification reported in earlier studies [23], indicating that heterotrophic bacterial denitrification was the main N\({}_{2}\)O-emitting process with low reduction to N\({}_{2}\). Interestingly, the \(\delta^{15}\)N\({}^{\text{bulk}}\) values on 48 DAO strongly deviated from measured \(\delta^{15}\)N\({}^{\text{bulk}}\) values on all other sampling days and were slightly outside the reported endmember values of heterotrophic bacterial denitrification. Under oxic conditions, denitrification mostly took place in anoxic hotspots where ongoing reduction led to a fractionation in the NO\({}_{3}\)\({}^{-}\) pool undergoing denitrification, which is reflected in gradually increasing \(\delta^{15}\)N\({}^{\text{bulk}}\) values. With onset of anoxic conditions, previously unreduced NO\({}_{3}\)\({}^{-}\) contributed to N\({}_{2}\)O formation leading to a shift towards more negative \(\delta^{15}\)N\({}^{\text{bulk}}\) values [29; 58]. When the contribution of pools with different N dynamics changes, shifts in the isotopic signature have been reported [59; 60; 61]. For our study, low \(\delta^{15}\)N\({}^{\text{bulk}}\) values on 48 DAO are consistent with very high N\({}_{2}\)O fluxes and the low measured N\({}_{2}\)O/(N\({}_{2}\)O + N\({}_{2}\)) ratio on 48 DAO leading to strong fractionation effects.  
Analysis of \(\delta^{15}\)N and \(\delta^{18}\)O in soil NO\({}_{3}\)\({}^{-}\) may improve accuracy of the N\({}_{2}\)O mapping approach and estimation of N\({}_{2}\)O formation processes [23]. \(\delta^{15}\)N\({}_{\text{NNO3}}\) was higher at the end of the experiment compared to initial soil NO\({}_{3}\)\({}^{-}\) and added KNO\({}_{3}\), confirming the ongoing fractionation during the reduction of the soil NO\({}_{3}\)\({}^{-}\) pool. Furthermore, \(\delta^{15}\)N\({}_{\text{NNO3}}\) increased with decreasing mineralization, indicating a higher share of added KNO\({}_{3}\)\({}^{-}\) residual NO\({}_{3}\)\({}^{-}\) at the end of the incubation experiment. Higher \(\delta^{18}\)O in Straw and Root may point towards a higher contribution of fungal denitrification, which is consistent with the higher contribution of fungi to decomposition of plant materials rich in celluloses and lignin [16; 27]. Overall, it needs to be taken into account, that estimating N2O formation processes based on N2O isotopomers is subject to large uncertainties. Endmember values, reduction and fractionation factors have been obtained under differing incubation and environmental conditions, and may thus lead to over or underestimation of contributing processes [57; 61; 62].
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 6 Challenges, Opportunities, and Research Directions
### Limitation of Analyzed Designs  
The energy-efficient design models in the analyzed studies rely on ideal assumptions, such as perfect channel estimation and stable network conditions, which may not accurately reflect real-world environments characterized by fading and mobility. The energy models focus on computational and communication energy but exclude hardware inefficiencies and environmental factors. Assumptions of homogeneous devices overlook the real-world heterogeneity in device capabilities, which can impact energy consumption and convergence. Moreover, the results derived from the considered studies lack validation in practical deployments, where issues like hardware limitations and unexpected delays may arise. Additionally, the trade-offs between energy efficiency and other metrics, such as latency and accuracy, were not extensively quantified. Addressing these limitations provides valuable directions for future work to refine energy-efficient FL frameworks.
# Containment Control-Guided Boundary Information for Semantic Segmentation
## 3 Method
### CcNet  
In this work, we propose the Containment Control Network (CcNet) to improve the accuracy of complex semantic segmentation tasks by seamlessly integrating the containment control into the model as shown in Figure 4. CcNet adopts PIDNet as the backbone and is composed of three branches: the detailed branch (D), the contextual branch (C), and the boundary branch (B). The design of CcNet places particular emphasis on the relationships between the boundary features and internal features, leveraging containment control mechanisms. This strategic approach facilitates the improved integration of boundary information from the B branch into the internal features of the D and C branches.  
To achieve a nuanced fusion of internal and boundary feature information, we introduce a Feature-Weighted Fusion (FWF) mechanism. This mechanism ensures that each channel of the internal feature incorporates information from the boundary feature, represented as the weighted sum of all channels of the boundary feature. The features fusion matrix \(\bar{\mathcal{L}}\) is specifically designed to quantify the compression process applied to the boundary feature, tailoring it to provide customized boundary features for the internal features. \(\bar{\mathcal{L}}_{1}\) plays a crucial role in guiding the fusion between the D branch and the B branch, facilitating the amalgamation of detailed feature information and boundary feature information. Similarly, \(\bar{\mathcal{L}}_{2}\) is instrumental in guiding the fusion of the C branch and the D branch, promoting the integration of contextual feature information and boundary feature information. Both feature fusion matrices have corresponding \(Loss_{cc}\) terms dedicated to their updates. These loss terms help ensure the effective utilization of boundary feature information by regulating the fusion process in their respective branches.  
In the FWF mechanism, we first weight the boundary feature channels, i.e.,  
\[\mathcal{B}_{i}(j)=\bar{\mathcal{L}}(i,j)\times\mathcal{B}(j), \tag{6}\]in which \(\mathcal{B}(j)\) denotes the \(j\)-th channel feature map of the boundary feature channel, and \(\mathcal{L}(i,j)\) denotes the value of the \((i,j)\)-th element of the feature fusion matrix. Then, the weighted boundary feature channel is compressed and downsized, and summed with the internal feature channel to obtain the output, i.e.,  
\[out(i)=\sum_{j=1}^{C}\mathcal{B}_{i}(j)+\mathcal{I}(i), \tag{7}\]  
in which \(C\) denotes the number of feature channels. The sum over channels \(\sum_{j=1}^{C}\mathcal{B}_{i}(j)\) is akin to a \(1\times 1\) convolution, which is essentially a weighted sum across the channels. Therefore, the FWF mechanism, with its complexity of \(C\times H\times W\times C\), is a very efficient operation, especially given the substantial functionality it provides in processing feature maps. This efficiency makes it well suited for scenarios where both computational cost and performance are critical.
# Efficacy of Iopamidol for Sealing an Injured Thoracic Duct: Pilot Experiments in a Large Animal
###### Abstract  
Chylothorax can be spontaneously healed by lymphangiography using lipiodol, but pulmonary or systemic embolization is a potential complication. We determined the efficacy of iopamidol for treating chylous leakage in an animal model. Twelve pigs were randomly divided into two groups. After inducing thoracic duct damage, pigs from groups A and B were injected with iopamidol and lipiodol, respectively. At 5, 10, and 30 min after damage induction, the drug effects were monitored by video-assisted thoracoscopy and lymphangiography. In vitro, chyle samples from the pigs were incubated with iopamidol and lipiodol. The damaged thoracic duct was harvested and examined using microscopy. In group A, four and two pigs did not show chylous leakage after 5 and 10 min, respectively. In group B, none showed chylous leakage after 5 min. Nevertheless, the \(p\) value was 0.46, and there was no statistically significant difference between groups A and B. In vitro, both iopamidol- and lipiodol-treated chyle samples adhered after 5 min and solidified at 30 min. Our findings confirmed that the damaged thoracic duct was clogged with an amorphous proteinaceous material (iopamidol). Therefore, iopamidol is potentially a new therapeutic agent for chylous leakage. Thoracic duct embolization failures or systemic embolization risks from lipiodol injection may be avoided by injecting iopamidol via the thoracic duct, and this warrants further investigation.  
Keywords:chylothorax; thoracic duct embolization; iopamidol +
Footnote †: journal: Applied Sciences
## 4 Optimization Mathematical Model
### Formulation of the Stiffness and Topology Optimization Problem  
The static stiffness can be obtained by applying static loading to wing components (for both segments). The deformation value under a static load refers to the displacement mechanical resistance [40]. The stiffness can be calculated based on the displacement, as follows:  
\[K(x)u=F \tag{11}\]  
where \(F\) is the nodal forces vector, and \(u\) is the nodal displacement. The nodal displacement can then be found with the following equation:  
\[u(x)=K(x)^{-1}F \tag{12}\]  
As already mentioned, the main objective of the structural TO of the morphing wing design problem is to maximize the wing's stiffness while minimizing its weight. The density-based approach is coupled with the static linear method [41], so that the TO problem can be formulated mathematically as follows:  
\[\begin{array}{c}\min f(x)=f(x_{1},x_{2},\ldots\ldots,x_{n})\\ g_{j}(x)\leq 0,\quad j=1,\ldots\ldots,m\\ x_{i_{min}}\leq x_{i}\leq x_{i_{max}}\quad i=1,\ldots,n\end{array} \tag{13}\]where \(f(x)\) represents the objective function, \(g(x)\) denotes the constraints, \(x\) are the design variables, which are considered as independent variables of the \(f(x)\), and \(x_{min}\) and \(x_{max}\) are the upper and lower bound constraints, respectively.  
The TO algorithm coordinates the distribution of the material throughout the design domain by optimizing the user-defined objective function under certain constraints. In this study, each wing component's design space for both segments was defined as a separate element in the optimization process.  
The gradient-based optimization method is widely utilized, as it is recognized as a very efficient method. The constraints' screening process was applied to compute the set constraints, thereby minimizing the required gradients.  
The compliance of the static structural stiffness is completely transformed into potential deformation energy [42]. The static structural stiffness can be calculated using Finite Element Analysis (FEA). The TO solver OptiStruct uses algorithms to set up the optimal material distribution inside the design domain in order to achieve the optimal wing geometrical shape. Then, it computes the design structure that offers the optimum solution under given boundary conditions by minimizing the wing components' compliance while respecting the design constraints to decrease their weights. The optimum solution is reached when the solution convergence is obtained in an iterative approach.