esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Thus, Min-delay routing may increase the energy consumption. Above all, in order to prolong the network lifetime, nodes with more residual energy should be chosen as relay nodes even though they are far from the sink node, while in order to minimize the end-to-end delay, nodes near to the sink node should be chosen. Network life and end-to-end delay are both important in UWSNs. Therefore, it is necessary to introduce significant compromises at the routing design stage. To cater for these issues, we propose a Q-learning-based delay-aware routing (QDAR) mechanism to extend the network lifetime for UWSNs. In QDAR, nodes only need to know about their residual energy and the delay from neighbors. As the action-utility function (Q-value) of Q-learning technique takes both direct reward and discounted long-term reward into account, Q-learning-based protocols can determine the global optimal next hop instead of a greedy one. The main contributions of QDAR can be summarized as follows: (1) it defines a data collection phase and designs the packet structure before routing decisions to quickly adapt to the dynamic underwater environment; (2) it takes both delay and residual energy into consideration by defining two kinds of cost functions: delay-related cost and energy-related cost; (3) it uses an adaptive mechanism to ensure a longer network lifetime and a relatively shorter delays: when the residual energy is enough, the end-to-end delay is restricted, while the residual energy of some nodes is lower than the threshold, an adequate path consisting of nodes with longer delays but more remaining energy is determined; (4) QDAR is easily extendible: energy consumption, channel capacity, communication reliability and many other metrics can be integrated into the action-utility functions in future research for different targets. The QDAR algorithm can work adaptively and distributively through trade-offs between the network lifetime and end-to-end delays. The simulation results show that our algorithm achieves nearly the same network lifetime extension as the existing lifetime-extended protocol, and reduces end-to-end delay by 20-25%. The rest of this paper is organized as follows: in Section 2, related works on underwater routing protocols are discussed briefly. In Section 3, the basic Q-learning technique is introduced and adopted into our system model. In Sections 4 and 5, the QDAR algorithm is described in detail. The simulation results are shown and discussed in Section 6. Finally, we conclude this paper in Section 7.
## 3 Edge Computing and Related Paradigms
### Cloudlet Computing  
Cloudlet computing is a critical innovation within the edge computing spectrum. This concept revolves around deploying high-performance computing resources close to end-users, offering enhanced computational and storage services [68]. Imagined as miniature yet potent clusters, cloudlets are equipped with specialized computation and storage facilities. Strategically placed near user-centric locales like commercial buildings and shopping malls, they are engineered to support myriad functions such as data processing, computational offloading, and content caching [69]. These entities act as trusted and resource-dense nodes, optimally situated at the network's edge to ensure seamless internet connectivity [70].  
The essence of cloudlet computing lies in its ability to transcend the inherent limitations associated with traditional cloud computing frameworks, particularly the challenge of their high latency, which becomes acute in internet of things (IoT) applications [71]. Cloudlets are pivotal in empowering mobile devices, which often suffer from resource limitations, enabling them to offload heavy computational loads. This capability is indispensable for applications that demand prompt data processing [59]. Despite their ability to meet the burgeoning demand for computational resources, spurred by the exponential increase in connected devices, cloudlets must also navigate the potential for overburdening due to this rising demand [72].
# Assessment of Laser Joining Quality by Visual Inspection, Computer Simulation, and Deep Learning
## 3 Results
### Computer Simulation  
For the computer simulation, we followed the procedure used in a previous study. The results of the simulation are summarized in Figure 7. The output and welding speed of the laser beam are described in Table 1. The focused spot diameter was set to 5 mm, which is identical to the actual welding condition, and the absorption of the laser heat source was set to 60%, considering the PC transmission (90%) and ABS absorption efficiency (70%). The input heat source was assumed to be a 3D ellipsoidal Gaussian distribution with a skin depth in z-direction, as described as Equation (1).  
To quantitatively analyze the results, the temperature distribution throughout the sample was traced from 0.25 mm underneath the surface at a position offset by 2.5 mm from the direction of travel of the laser (Figure 8). As shown in Figure 8, we predicted good results under the 4 and 5 J/mm conditions, where the temperature was observed to vary from 120 to 180 \({}^{\circ}\)C in all sections. However, as shown in Figure 8c, under low-power  
Figure 6: Results of the welding evaluated by visual inspection.  
Figure 7: Simulation results for Table 1 (max. temp 500 \({}^{\circ}\)C).  
and low-speed conditions (experiment #1-1), 3 Watt-60 mm/min, a lack of fusion was found as the maximum temperature was 100 \({}^{\circ}\)C. At high power and high-speed transfer (experimental condition #3-5), 17 Watt-204 mm/min, the probability of welding failure was predicted to be high, as the maximum temperature would be at least 190 \({}^{\circ}\)C.
# A Comparative Study of the Availability of Electric Buses in the Public Transport System
## 1 Introduction
In Lublin, there are eight main end-route stops at which chargers with two or four charging points with a nominal power of 450 kW each are installed. The length of the route covered by the bus between subsequent charging stations, on a full all-day schedule, does not exceed 40 km, and the energy consumption is 43 kWh. Therefore, the charging time does not exceed 6 min and corresponds to the times of breaks in the schedule at end-route stops. Pantographs are used in the buses for fast charging, and their special design allows them to be connected for charging in a short time. The charging infrastructure was also equipped with stationary chargers installed in the road with a nominal power of 40 kW. This system was created with the aim to meet the assumptions of charging a bus in three hours, so a bus leaving the route at 0:00 am, after performing service activities related to its preparation for running the following day, would be ready to leave at 4:00 am to provide transport tasks. The chargers are also used to charge buses dispatched on a given day to perform tasks on peak schedules. The infrastructure for electric buses in Lublin ensures that the routes can be served by both diesel and electric vehicles, which do not need additional time for charging. The authors of the present study attempted to assess the progress of electromobility in a selected example of a public bus service with regard to the problem of vehicle technical availability. The aim of the study was to evaluate the comparative availability of electric buses in relation to buses of similar capacity with diesel engines. It was assumed that the study will be carried out in real operating conditions with the example of the city of Lublin.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 5 Experimental Results and Analysis
### Ablation Experiment
#### 5.4.2 The Impact of Historical Frame Amount  
To assess the impact of historical frame input on 3D skeletal localization accuracy, we evaluate the mmHSE model using varying numbers of preceding frames. Specifically, we consider five input configurations with one to five consecutive frames, keeping the frame rate constant. The corresponding results, in terms of both Mean Absolute Error and Root Mean Square Error, are presented in Figure 11.  
As shown in Figure (a)a, increasing the number of historical frames from one to four leads to progressively improved localization performance, with MAE decreasing from 5.13 cm (single-frame input) to 2.78 cm. A similar trend is observed for RMSE in Figure (b)b, which drops from 7.74 cm to 4.35 cm. These results indicate that incorporating short-term motion history enhances spatial prediction robustness, particularly under noisy or occluded radar conditions. However, adding a fifth frame slightly degrades performance (MAE = 2.96 cm; RMSE = 4.87 cm), suggesting that excessive temporal input may introduce redundancy or propagate outdated motion cues, thereby diminishing spatial sensitivity. Notably, the sharper increase in RMSE compared to MAE implies that rare but large prediction deviations are more susceptible to long history accumulation. Overall, these findings demonstrate that using four historical frames provides an effective trade-off between temporal context and noise resilience. They highlight the importance of judicious selection of input length in radar-based pose estimation to achieve accurate and reliable skeleton localization.
## 3 Results and Discussion
### Effect of Tea Extracts on Digestive Stability of \(\omega\)-3 FA During In Vitro Digestion  
An in vitro digestion simulation system was employed to measure the content of EPA and DHA from \(\omega\)-3 FA at each phase, salivary, gastric, and small intestinal, as shown in Table 2, in order to evaluate the effect of various tea extracts on their digestive stability. The highest stability of EPA and DHA was observed with GTE65, where the digestive stability of EPA reached 57.59 \(\pm\) 1.84%, 25.59 \(\pm\) 1.00%, and 8.61 \(\pm\) 0.39% in the salivary, gastric, and small intestinal phases, respectively, whereas DHA reached 49.11 \(\pm\) 1.94%, 20.57 \(\pm\) 0.51%, 8.12 \(\pm\) 0.45% in each respective phase. In regards to the digestion of \(\omega\)-3 FAs with GTE35, NFGT100, NFGT30, and AdiphenonTM, EPA and DHA exhibited no significant differences relative to GTE65 (\(p\) < 0.05). Overall, \(\omega\)-3 FAs were relatively stable in the small intestinal phase during digestion. The digestive stability of both EPA and DHA was increased when they were co-treated with various tea extracts compared to \(\omega\)-3 FAs alone in continuous in vitro digestion. All tea treatments specifically showed a significant difference compared to \(\omega\)-3 FAs alone (\(p\) < 0.05) except for NFGT100. Similar findings were observed from a previous study that demonstrated that the bioavailability of \(\omega\)-3 FAs can be enhanced when formulated as complexes with other compounds [29]. For instance, polyphenols prevented the oxidation of \(\omega\)-3 FAs, supporting their intestinal uptake while preserving their bioactivity [30; 31]. The results of the current study confirmed that \(\omega\)-3 FAs become more stable during digestion when mixed with tea extracts, providing greater stability compared to \(\omega\)-3 FAs alone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **EPA** & **DHA** \\ \hline Linearity & & \\ \hline Regression Equation & y = 10,049x + 4907.4 & y = 5739.6x + 3804.9 \\ \hline Correlation coefficient, \(r^{2}\) & 0.9966 & 0.9981 \\ \hline Accuracy and Precision & & \\ \hline Accuracy (\%) & 85.80–101.83 & 91.78–100.31 \\ \hline Precision (\% RSD) & 0.90–1.10 & 0.03–0.50 \\ \hline Sensitivity & & \\ \hline Limit of detection & 0.182 & 0.035 \\ (LOD, \(\upmu\)g/mL) & & \\ \hline Limit of quantification & & \\ (LOQ, \(\upmu\)g/mL) & 0.553 & 0.107 \\ \hline \end{tabular}
\end{table}
Table 1: Bio-analytical methods validation of EPA and DHA using HPLC-UV.
# Digital-Twin-Based Monitoring System for Slab Production Process
## 3 The Proposed System
### Defect Detection Module
#### 3.2.1 Overall Structure
Its mathematical expression is given by f(x) = max (0, x), where the activation function outputs the input value itself when the input is positive, and it is zero when the input is negative. This effectively addresses the issue of vanishing gradients. Concatenate (Concat) is an operation in neural networks that involves connecting two or more tensors along a specific dimension. It is utilized to merge features from different parts or layers, offering an efficient means to integrate information from diverse sources. This process provides a more comprehensive feature representation, aiding the model in better understanding and learning the relationships within the input data. Figure 3: The REPVGG module. As illustrated in Figure 4, the attention unit (AU) functions in the Head part of the network, utilizing attention mechanisms to weight the predictions of different bounding boxes. It extracts fine-grained features that are difficult to detect and observe in the image, aiming to increase attention to small targets, reduce the occurrence of missed detections in the network model when detecting small targets, and enhance overall detection accuracy. The REPVGG module introduces both a residual branch and a \(1\times 1\) convolution branch. This addition is crucial for subsequent reparameterization into a single-path structure, and both the residual branch and the conv \(1\times 1\) contribute to increasing the network's performance. We integrate different modules into an end-to-end model, which can directly generate object detection outputs from raw image inputs without the need for an additional region proposal step. This simplifies the entire object detection process, enhancing both the training and deployment efficiency of the model. The model can detect multiple targets in a single inference when processing a single image, eliminating the need for multiple inferences. The defect recognition model detects features at different levels of the feature map, allowing it to capture target features at different scales and adapt well to targets of varying sizes. The fully convolutional network structure in the model enables the extraction of global contextual information across the entire image. This contributes to an improved understanding and inference of targets, providing the model with enhanced global perceptual capabilities. Compared to other state-of-the-art models, our model structure is relatively simple, making it easy to comprehend and implement.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 5 Example Analysis
In order to explore the applicability of the change of spectrum width parameters to different frequency domain methods, four sets of power spectra with spectrum width parameters of 0.178, 0.4657, 0.7285, and 0.9611 were generated using Matlab by changing the frequency domain range of the load spectrum. Through different frequency domain methods, the fatigue life of the structure is calculated, and the results are compared using the rain flow counting method as a reference. The estimated results and relative errors are shown in the Table 3:  
Combined with the above analysis results, in general, when the spectral width parameter is 0.1-0.4, the error of the WL method is small; when the spectral width parameter is 0.7-1, the TB2, AL, and DI methods are better than other methods. This is basically consistent with the previous conclusion. Through the above comparison, we can find that the 10 common frequency-domain methods analyzed in this paper have their own application range of spectral width parameters, but none of the frequency domain methods can be applied to all spectral width parameters or most spectral width parameters. It is only suitable for a small range, and their applicabilities to the slope of the S-N curve of the materials are also uneven. The same frequency domain method will have a large difference in the degree of application of different \(k\) values. Through the analysis of the applicable range of the  
Figure 12: Response stress power spectral density. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\epsilon\) & \(\mathbf{T_{RF}}\) & \(\mathbf{T_{NB}}\) & \(\mathbf{T_{WL}}\) & \(\mathbf{T_{AL}}\) & \(\mathbf{T_{OC}}\) & \(\mathbf{T_{TB1}}\) & \(\mathbf{T_{TB2}}\) & \(\mathbf{T_{DI}}\) & \(\mathbf{T_{ZB}}\) & \(\mathbf{T_{SM}}\) & \(\mathbf{T_{NK}}\) \\ \hline
0.178 & 28,186 & 27,157 & 26,866 & 25,443 & 25,801 & 25,855 & 25,198 & 25,546 & 26,382 & 25,367 & 33,147 \\  & & 3.65\% & \(-\)4.68\% & \(-\)9.73\% & \(-\)8.46\% & \(-\)8.27\% & \(-\)10.6\% & \(-\)9.94\% & \(-\)6.40\% & \(-\)10.0\% & 17.6\% \\ \hline
0.4657 & 27,379 & 28,543 & 26,864 & 25,765 & 25,610 & 26,143 & 26,112 & 25,489 & 27,765 & 25,454 & 30,654 \\  & & 4.25\% & \(-\)1.88\% & \(-\)7.70\% & \(-\)6.46\% & \(-\)4.51\% & \(-\)4.62\% & \(-\)6.90\% & 1.40\% & \(-\)7.03\% & 11.9\% \\ \hline
0.7285 & 13,678 & 10,108 & 10,983 & 13,191 & 12,700 & 11,544 & 13,090 & 12,924 & 14,895 & 13,533 & 12,631 \\  & & 26.1\% & \(-\)19.7\% & \(-\)3.56\% & \(-\)7.15\% & \(-\)15.6\% & \(-\)4.43\% & \(-\)5.51\% & 8.9\% & \(-\)1.06\% & \(-\)7.65\% \\ \hline
0.9611 & 15,443 & 8721 & 10,543 & 15,365 & 14,578 & 10,689 & 16,465 & 16,377 & 17,878 & 17,820 & 8964 \\  & & 43.5\% & \(-\)31.7\% & \(-\)0.505\% & \(-\)5.60\% & \(-\)30.8\% & 6.61\% & 6.04\% & 15.7\% & 15.4\% & \(-\)42.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Random fatigue life estimation results. spectral width parameters in this paper, it can provide a reference for selecting an appropriate life prediction model when calculating random fatigue damage or life and reduce errors.
## 4 Multimedia-Based Cross-Layer Optimization in VSNs
### Image-Based Cross-Layer Optimization
#### 4.1.1 Progressive Image Coding
The idea is to route more relevant packets through more reliable paths, where the path reliability is a function of the expected error rate and node failure. Instead of achieving higher transmission rates as in video-based multipath routing solutions, multiples paths toward the sink are employed in [23] to increase the probability that the transmitted images are received with quality as good as possible. For that, this work assumes that the source nodes have information about the entire network topology. Besides multipath routing, the authors propose the use of Unequal Error Protection to offer an additional level of reliability. Thus, more relevant data receives stronger protection against bit errors during transmission. An interesting consideration presented in [23] is that higher reliability cannot be achieved just loading more reliable paths with most packets. The success of forward correction codes is based on the distribution of the packets over several paths.
# Understanding Microgrid Sustainability: A Systemic and Comprehensive Review
## 3 Energy Field
### Energy Architectures  
Energy architectures assemble energy elements in ways that suit the specific need of the microgrid. There are three main architectures available in the literature represented in Figure 5 and summarized in Table 3: the AC architecture, the DC architecture, and the hybrid architecture [83; 109].  
The AC architecture has been the most common choice for energy distribution through utilities due to its ease of transformation in different voltages and reduced loss in transport [110].  
The DC architecture is the most used in microgrids because of its high concentration of DC sources and loads, resulting in easier management of the microgrid energy [111].  
The hybrid architecture can be seen in many different microgrids and has more variety than classic AC or DC architectures. The classical way to build this architecture is to connect DC sources and load to DC busses, and AC sources and load to AC busses [112]. However, the architecture can be a bit more optimized and, for example, connect all the sources and loads to an AC bus and all the storage to a DC bus to simplify their management [113]. The DC zonal architecture connects all the loads to DC buses and uses AC buses to connect all the DC buses together [114]. The solid state transformer based microgrid centralizes all the buses in this high-frequency transformer that can manage AC and DC feeders as well as power flow between the main grid and the microgrid [115; 116].  
Figure 5: Representation of the different energy architectures.  
In between these three main types of architectures, many different solutions are more subtle to solve a specific need with a mix of their benefits and downsides [117].  
In the literature, classic microgrids are mainly seen as assembling electrical blocks that can be operated in island mode, grid-connected mode, or both [94]. A clustered microgrid can also be seen as an assembly of smaller grids that are interconnected to form a much bigger entity [118]. Swarm microgrids are another concept where the microgrid evolves in an \"organic\" way resulting in a swarm architecture capable of stability due to energy production decentralization [119].  
In the rural electrification context, DC architectures are the most used since energy sources are easy to implement (solar PV and batteries), and basic loads such as light or electronic device recharge can easily accommodate DC [120].
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 2 Methodologies
### Test Case Descriptions  
In the experiments, the control effects of the actuators on the sound pressure were clarified while changing the freestream velocities, \(U_{0}=10\)-45 m/s, while the simulations were performed at \(U_{0}=30\) m/s. The actuators are shown in Figure 3. The Reynolds number based on the cavity length was Re \(\equiv U_{0}L/\
u=1.3\times 10^{4}\)-\(6.0\times 10^{4}\), and the Mach number was \(M\equiv U_{0}/a=0.029\)-0.131, where \(\
u\) and \(a\) are the dynamic viscosity and the speed of sound in air, respectively. In addition, to clarify the nature of the flow induced by the actuators, flow visualizations and simulations were performed in the absence of a freestream flow.  
Figure 4 shows the measured sound-pressure spectra for freestream velocities \(U_{0}=15\)-45 m/s at the location \(x/L=6.75\) and \(y/L=21.5\) without control. The frequency resolution of the spectral analysis was 4.88 Hz. As shown in this figure, the tonal sound at the fundamental frequency \(f=1500\) Hz becomes most intense at a velocity of 30 m/s, where the existence of an acoustic resonance was confirmed by the phase distributions of the pressure in the cavity [8].  
In both our experiments and simulations, the incoming boundary layer was laminar, and the velocity profile was confirmed to agree with the Blasius profile. The momentum thickness was  
Figure 3: Experimental setup for the measurement of control effects on cavity flow and tone.  
\(\theta/L=0.0071\) for a freestream velocity of \(U_{0}=30\) m/s at the upstream edge of the cavity, where this value was measured and found to be consistent with that predicted for flow over a flat plate without a cavity.