esa-sceva/satcom-chunk-collection huggingface dataset:
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 4 EdgeX over Kubernetes
### Remote Device Service Deployment  
In this section, we discuss the process of deploying a new device service to an EdgeX node using the container orchestration of Kubernetes. A new service can be implemented as a service of pod units, as described earlier, and is deployed to the _Device services_ layer of EdgeX Foundry.  
As shown in Figure 3, the master node commands to deploy (Kind: Deployment) the device service using _kubctl_. The _kube-controller-manager_ creates as many pods as specified in the corresponding _PodSpec_ and _kube-scheduler_ selects an EdgeX node to deploy a pod according to _nodeAffinity_, where _nodeAffinity_ defines the priority and preference for selecting the node to deploy the pod.  
Once the pod is deployed to the selected EdgeX node, _kublet_ starts the pod and transports the status of all device services in the pod to the API server. The _Core Services_  
Figure 3: EdgeX over Kubernetes service deployment procedure.  
Figure 2: EdgeX over Kubernetes architecture.  
layer in the EdgeX node uses _Metadata_ to check whether a device service is already registered or not. If the newly deployed device service does not exist in the EdgeX node, the _Device Services_ layer requests a service registration to _Metadata_, and _Metadata_ adds the device service to the database.  
In summary, in contrast to the existing method of distributing new services locally in EdgeX Foundry, the proposed EoK platform enables remote distribution of new services to multiple EdgeX nodes, increasing the ease of management of application services.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 4 Experimental Results  
To ground the simulations in realistic orbital dynamics, real TLE (Two-Line Element) data were used to model a small-scale LEO network, capturing genuine orbital motion and link availability patterns. For larger-scale evaluations, where comprehensive public TLE data are not available, synthetic constellations were generated. In these synthetic scenarios, each satellite was assigned 3-5 plausible inter-satellite links based on typical orbital spacing and connectivity patterns, preserving the essential dynamics of real LEO deployments. This hybrid approach balances empirical realism with scalability for assessing routing performance across network sizes.
## 2 Materials and Methods
### Reinforcement-Learning-Based Algorithms  
Three state-of-art reinforcement learning algorithms using continuous state and action spaces are applied. Their pseudocode representations are provided below. The Soft Actor-Critic (SAC) algorithm leverages a stochastic policy and an additional entropy component \(\mathbf{H}\), regulated by a parameter \(\alpha\) that determines the weight of entropy in its calculations. By employing a stochastic actor and allowing for adjustment of the entropy weight parameter, SAC can balance exploration (through selecting suboptimal actions) and policy exploitation. Such a balance often leads to faster convergence and a strong performance in high-dimensional, continuous search spaces, such as the marine environment proposed in this study. Moreover, soft updates of the target networks--controlled by the factor \(\tau\)--enable smoother adaptation of the target critics in response to changes in the main critic networks. The pseudocode for SAC, shown as Algorithm 1, is adapted from [61]. Another algorithm employed is Twin Delayed Deep Deterministic Policy Gradient (TD3), an enhancement over the Deep Deterministic Policy Gradient (DDPG) method. Compared to its predecessor, TD3 introduces two critics--helping reduce the overestimation bias in the \(Q\)-function--and delays actor updates to minimize correlations between actor and critic updates. Additionally, TD3 adds exploration noise \(\epsilon\) to the actor's output, encouraging broader exploration of the environment. The pseudocode for TD3 is shown in Algorithm 2 [62]. The Truncated Quantile Critic (TQC) algorithm is an extension of the TD3 method, in which the Q-value is modeled using quantiles. In TD3, the state-action-value function is estimated as a single number; in TQC, the value function is represented by quantiles, enabling more precise modeling of the uncertainty inherent in future rewards. This allows TQC to capture complex environmental dynamics where significant fluctuations in the reward signal may occur. Additionally, TQC permits the use of more than two critics, substantially enhancing the stability of training. The algorithm is outlined below as Algorithm 3 [63]. In this study, the TQC implementation from stable-baselines3 contrib was used [64], which builds upon TD3 by estimating a distribution over future returns via quantiles. Although TQC is often named as a separate agent, conceptually, it is a distributional extension of TD3. This design truncates the highest quantiles to mitigate overestimation. HER and curriculum learning were implemented on top of this TQC base class.
## 8 Simulation Setup and Analysis of Results
### Minimum Path Detection Time for Efficient Route
The designed WSN for BN-MAC protocol is composed of regions. Each region consists of several sensor nodes that are controlled and coordinated by BN. In this experiment, BN broadcasts the control message while setting the paths for data transmission. The broadcasting message process consumes enough energy amount but sensor nodes lack the adequate energy resources. Thus, BN-MAC saves  
Figure 24: Path detection time for different number of hops. energy to use low duty cycle semi synchronous mechanism and AAS model, which also control the idle listening issue at the MAC level. From other side, EAP chooses single efficient path from group of multiple paths to save energy. Figure 25 shows broken routes during entire simulation time. BN-MAC is superior to competing hybrid MAC protocols throughout the entire simulation time. The competing MAC protocols experience the problem due to use of their original routing protocols. As a result, those protocols took enough time for route discovery. The route discovery time could be longer in some critical circumstances. Further, it is also easier to discover the route in WSNs based on single hop discovery process. The single-hop discovery process can handle the scalability and maintain the network mobility efficiently [37]. Path detection time for each hop is varied because it depends on the density of nodes that can be calculated as follows:  
\[\mathrm{F(p)}=\frac{H[Nr]}{2\pi r} \tag{27}\]  
Let us assume that F(p) is the probability density function (a function that defines the comparative probability for the random variable to yield desired value; it is usually associated with absolutely continuous univariate distributions). H [Nr] is the number of hops in network and \(2\pi r\) is the length of network. Thus, the value of \(H[Nr]\) can be calculated as follows:  
\[H[Nr]=\frac{4}{\pi}\arcsin(r) \tag{28}\]  
where: \"\(r\)\", distance from source node \"\(s\)\" to destination node \"\(d\)\". Substituting the value of \(H[Nr]\) and we get as:  
\[F(p)=\frac{4}{\pi}\arcsin(r) \tag{29}\]  
Simplifying (29), we get:  
Figure 25: Broken paths% at different intervals. \[F(p)=\frac{2\arcsin(r)}{\pi^{2}r} \tag{30}\]  
Determining the discovery time for broken links at each path, we need to consider the number of hops, network size and velocity of each node. Where: \(MH(t)\), time for maximum number of hops; N(a ), total network area and \"\(V_{j}\)\" is corresponding velocity of each node. Therefore, consumption time for maximum number of hops can be calculated as follows:  
\[MH(t)=\frac{F(p)+N(a)}{V_{j}} \tag{31}\]  
Substitute the values of F(p) then we can get as follows:  
\[MH(t)=\frac{\frac{4}{\pi}\arcsin(r)}{2\pi r}+N(a)} \tag{32}\]  
\[N(a)=2\pi\int_{r+t/2}^{R}\lambda\,xdx \tag{33}\]  
Substitute the value of N(a) in Equation (32) to get Equation (34):  
Figure 24:0
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
###### Abstract  
Large-scaled flapping wings for high altitude environments have great potential for border patrol and biodiversity exploration due to their high flight efficiency and concealment. In this paper, wind tunnel experimental techniques, neural network models, and flight tests are implemented to optimize and validate the performance of flapping wings. Numerical simulation methods were used to give recommendations for the flight state of the vehicle at high altitudes. From sea level to 4000 m altitude, the Reynolds number was subsequently reduced by 27.98%, and the time-averaged lift, drag, and pitching moment decreased by 33.31%, 33.08%, and 33.33%, respectively. A combination of planform with an increase in the internal area of the wing, six wing ribs, and linen film material was selected for its moderate stiffness to generate at least 1300 g of lift and considerable positive thrust, making it easier to reach a trim state. For high altitude environments, the vehicle needs to increase its flight speed and frequency to compensate for the loss of lift and drag due to reduced air density, but this is at the cost of power consumption, which results in reduced endurance, as verified by flight tests. Finally, this study aims to provide guidance on the design of large-scaled flapping wings for high-altitude environments.  
flapping wing; high altitude; wind tunnel experiment; numerical simulation +
Footnote †: journal: Applied Sciences
## 3 Design of the Measurement System
### Climate Chamber System
#### 3.2.1 Temperature Monitoring  
Due to the long wires needed to connect the measurement instrumentation with the temperature sensors that are opportunely placed in the climate chamber, temperature-to-voltage transducers are to be avoided because of possible voltage drops along the wires. Thus, in this case, AD590 linear temperature transducers were used [36]. They produce an output current proportional to the absolute temperature, with a sensitivity of \(1~{}\mu\)A/K, in a wide temperature range of \(-55<T/(^{\circ}\mathrm{C})<+150\). The AD590 is calibrated in order to provide an output current \(I_{T}=298.2~{}\mu\)A at 298.2 K (\(25~{}^{\circ}\mathrm{C}\)).  
Since commonly used DAQ or data loggers usually provide only the possibility of measuring voltage signals, the output of the AD590 must be properly converted into a voltage signal if one wants to exploit this measurement instrumentation. In particular, in this work, the Pico Data Logger was used to monitor the whole network of sensors.  
The circuit used for this aim is shown in Figure 4. It aims not only to convert the temperature information to a voltage signal but also to adapt this signal to the analog input characteristics of the PicoLog. Thus, the linear current output of the AD590 must be transformed through a linear current-to-voltage converter that allows the setting of the offset and gain. Thus, the \(TM_{1}\) trimmer, which is mounted in series to the \(R_{1}\) resistance, regulates the voltage offset. Then, the signal is passed to the voltage buffer realized with  
Figure 3: Block diagram of the signal preconditioning. The charge pulses in the output from the APD are passed to the preamplification stage to transform these into a voltage signal. Finally, this is passed to a pulse-shaping and voltage amplifier.  
the AD8032A amplifier. Finally, the signal is amplified by the following stage, and the output voltage is:  
\[V_{out}=-(5\:\mathrm{V}-(R_{1}+R_{TM1})I_{out})\frac{R_{3}+R_{TM2}}{R_{2}} \tag{3}\]  
In order to properly regulate the \(TM_{1}\) and \(TM_{2}\) trimmers, all the AD590 sensors are placed in thermal contact with a metal aluminum bar and thermalized in the climate chamber. The \(TM_{1}\) and \(TM_{2}\) regulation aims to best exploit the Picolog input dynamic range. So, the \(V_{out}=0\) V is set for the lowest needed temperature, \(-30\)\({}^{\circ}\)C, acting on the resistance of the TM1 trimmer, and \(V_{out}=2.5\) V (the maximum input voltage of the Picolog) at the maximum temperature of interest, \(T_{max}=70\)\({}^{\circ}\)C.  
The so-realized system was used to monitor temperature drifts on the APD during the measurements.
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 3 Results
### MODIS fPAR vs NDVI  
The NDVI was measured in one corn and soybean field in Story County and compared to MODIS fPAR products. Field-measured daily corn and soybean NDVI ranged from 0.15-0.88 and from 0.16-0.91 (Figure 3A). Corn NDVI was \(<\) 0.4 until DOY 156 and after DOY 256, which corresponds to development stages V6 and R6. Soybean NDVI was \(<\) 0.4 until DOY 163 and after DOY 259, which corresponds to phenological stages V3 and V22/R7. Corn reached NDVI values \(>\) 0.75 early in the season at DOY 166, where plants reached phenological stage V8-9 with an average height of 0.95 m.  
Figure 3: Measurements on the corn (black circles) and soybean (red triangles) field in Story County during the 2016 growing season ; (**A**) measured and interpolated corn and soybean NDVI near the eddy-flux stations; (**B**) linear regression analysis (p \(<\) 0.05) with measured NDVI as independent and MODIS fPAR in the 90% footprint area as dependent variable; (**C**) linear regression analysis (p \(<\) 0.05) with the product of the \(\text{Tmin}_{\text{s}}\), \(\text{VDPs}_{\text{s}}\), NDVI, and IPAR (MJ \(\text{m}^{-2}\) d\({}^{-1}\)) as independent and GPP (g C m\({}^{-2}\) d\({}^{-1}\)) as dependent variable with the slope being \(\text{RUE}_{\text{max}}\). \(\text{R}^{2}\): coefficient of determination, \(\text{Tmin}_{\text{s}}\): temperature scalar, \(\text{VPD}_{\text{s}}\): vapor pressure deficit scalar, IPAR: incoming photosynthetic active radiation, GPP: gross primary production, \(\text{RUE}_{\text{max}}\): maximum radiation use efficiency.  
Soybean NDVI reached values \(>\) 0.75 later than corn at DOY 180, corresponding to plant development stage V8-10/R1-2 and an average height of approximately 0.40 m. There was a significant linear relationship (p \(<\) 0.05) between field-measured NDVI and MODIS fPAR within the 90% footprint area of the EC station, explaining 99% of the fPAR variation with a slope of 1.02 and 0.99 for corn and soybean, respectively (Figure 3B).  
The \(\mathrm{RUE_{max}}\) was then calculated with Equation (3), substituting fPAR with NDVI and using meteorological data and GPP from the EC stations in the studied corn and soybean field in Story County. The \(\mathrm{RUE_{max}}\) was the slope of a linear regression with GPP as dependent and the product of Tmin, VPDmin, IPAR, and NDVI as independent variables. The derived daily corn and soybean \(\mathrm{RUE_{max}}\) was 3.24 and 1.90 g C MJ\({}^{-1}\). The linear relationship was significant at p \(<\) 0.05, explaining 93% and 89% of the GPP variation for corn and soybean, respectively (Figure 3C).
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 2 Related Work
### Super-Resolution Approaches  
Recent advancements in medical image super-resolution have introduced innovative techniques leveraging attention mechanisms and wavelet-based architectures. For instance, multimodal-boost, a multimodal medical image super-resolution using a multi-attention network with wavelet transform, integrates multi-attention mechanisms with wavelet transforms to effectively enhance high-frequency details across diverse modalities [26]. Similarly, multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution utilizes convolutional attention mechanisms with adaptive kernel sizes to improve spatial and frequency domain learning [27].  
Furthermore, wavelet-based methods, such as a super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution and analysis of medical images super-resolution via a wavelet pyramid recursive neural network constrained by wavelet energy entropy, have achieved state-of-the-art performance by employing multi-resolution wavelet analysis and recursive structures [28; 29]. These approaches demonstrate the growing emphasis on domain-specific optimization in medical imaging.  
In comparison, the proposed CS-SISR model builds upon these advancements by incorporating lightweight channel and spatial attention mechanisms. This design ensures computational efficiency while maintaining high diagnostic relevance, particularly for melanoma image enhancement.
# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
## 3 YOLO Algorithm and Point Cloud Algorithm
### YOLO-v4 Network Improvement and Architecture [41]  
The YOLO-v4 algorithm regards target detection as a regression problem and inputs the image containing the target into the model. The algorithm marks the position of the target in the image and outputs it according to the target type. The YOLO-v4 network needs to calculate a lot of parameters in the process of image feature extraction. In order to meet the real-time requirements of mobile devices, the system uses an improved algorithm based on the YOLO-v4 network, Lite-YOLO-v4. The specific improvement measures of the algorithm are as follows:  
1. The backbone feature extraction network, CSPDargent53, of YOLO-v4 is replaced by lightweight Mobilenetv3, and the scale of the feature layer of Mobilenetv3 is modified to change the dimension of the feature to connect with the follow-up network of YOLO-v4. Input the extracted preliminary feature layer into the enhanced feature extraction network for feature fusion.
2. The PANET network in the YOLO-v4 feature fusion network uses a large number of 3 \(\times\) 3 common convolutions. Replace normal convolutions with 3 \(\times\) 3 depth separable convolutions to reduce the amount of calculation.
3. After PANet network integration, three output layers of feature layer are generated for detecting small target, medium target, and large target. Most of the underwater cracks belong to the large target. Only the large output layer needs to be trained, which makes the middle output layer and the small output layer unable to be trained. Therefore, the original three feature output layers are fused into one output layer.  
The improved YOLO-v4 network model, Lite-YOLO-v4, is structured as follows Figure 5.  
In order to verify the damage identification effect of the improved network algorithm, the model is compared with CenterNet, YOLO-v4, YOLO-v4-tiny, MobileNet V3-YOLO-v4, YOLO-v5l, YOLO-v5m, and YOLOv5s algorithms to verify the recognition accuracy and image acquisition performance of the model under small crack conditions. The loss function curves of the eight models are shown in the Figure 6.  
It can be seen that the training effect of the eight models is good, and there is no overfitting phenomenon, which can be used for comparative experiments. The indicators of the above model are evaluated, and the specific evaluation index values are as follows in Table 2.  
Figure 5: Structure of the Lite-YOLO-v4 Crack Detection Algorithm.  
Figure 6: Model loss function plot. (**a**) Comparison curve of the loss function of the training set. (**b**) Validation set loss function contrast curves.  
The results show that the Lite-YOLO-v4 algorithm in this paper improves the detection speed and network performance while reducing the amount of calculation, and can identify cracks in complex underwater environments.
# Preparation Methods and Properties of CNT/CF/G Carbon-Based Nano-Conductive Silicone Rubber
## 2 Experimental Materials and Methods
### Preparation Process and Method of Conductive Silicone Rubber  
1. Preparation of the base solution: LSR is formed by cross-linking and curing of AB glue. AB glue is the silicone rubber (component A) and curing agent (component B). The role of the curing agent is to achieve curing at room temperature. Weigh AB glue at a ratio of 1:1 and mix it with n-heptane at a volume ratio of 1:2. Stir the mixture for 30 min by mechanical stirring and ultrasonically disperse it for 1 h to evenly mix the n-heptane solvent with LSR.
2. Preparation of the mix conductive filler solution: weigh the proper amounts of the carbon-based materials and add them into n-heptane. Mechanically stir the mixture for 30 min, and ultrasonically disperse it for 1 h.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Manufacturer** & **Model** \\ \hline Mechanical stirrer & Suzhou Yinganyang Instrument Co., Ltd., Suzhou, China & JJ-1 \\ Ultrasonic disperser & Ningbo Xinzhi Biotechnology Co., Ltd., Ningbo, China & Scientz-750F \\ Electric blast drying oven & Shanghai Yiheng Technology Co., Ltd., Shanghai, China & BPG-9050 BH \\ Electrometer & Tektronix Technology Co., Ltd., Beaverton, OR, USA & Keithley 6514 \\ Scanning electron microscope & Hitachi, Tokyo, Japan & Hitachi SU8010 \\ X-ray diffractometer & PANalytical Company, Malvern, UK & Empyrean \\ Raman spectrometer & Renishaw company, Wotton-under-Edge, UK & RM1000 \\ Thermogravimetric analyzer & German NETZSCH Instrument Manufacturing Co., Ltd., & TG 209 F1 \\ Instron Electronics Universal Materials & & Instron Corporation, Norwood, MA, USA & 68 TM-10 \\ Testing Machine & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main experimental equipment.  
3. Preparation by the solution blending method: add the LSR solvent into the conductive filler solution, mechanically stir it for 30 min, and ultrasonically vibrate it for 1 min for uniform dispersion.
4. Molding preparation: Vacuum filter the mixture in a vacuum drying oven, pour it into a Petri dish, and let it rest for 24 h. After n-heptane completely volatilizes, cure it in a constant-temperature drying oven at 125 \({}^{\circ}\)C for 30 min to obtain the final sample.
## 4 Experimental Results and Discussion
A PV-connected DC-DC boost converter using a LUENBERGER observer-based fault detection controller was presented in [26]. A deep mathematical approach was investigated in this study and the converter block worked as a part of the PV-based maximum power point tracking system. The converter's disadvantage is not applicable in high switching frequencies and is also proper for high power applications. Therefore, the IGBT is suggested for application instead of the power MOSFET. The authors in [27] presented a time-domain analysis of the state-space observer residual controller technique for interleaved DC-DC converters. The switching frequency was adjusted to 25 kHz. The total cost of the prototype was categorized in the medium to high range. Table 4 presents the general specifications of these controllers. Figure 18 presents the efficiency curves for the presented controllers in Table 4 versus the output power. As can be seen, the performance of the proposed PI controller is considerable, especially in low powers. It can be interpreted based on all facts presented in Table 2. Based on less power MOSFET numbers in the converter's structure and good performance of the proposed PI controller to 350 W, it had the highest efficiency. The performance of the linear observer acts in a similar way to the proposed controller for 350 to 500 power range. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Controller** & \
\begin{tabular}{c} **Switching** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Average Efficiency** \\ **(500 W)** \\ \end{tabular} & **Complexity** \\ \hline
[23] & Capacitor voltage & 20 kHz & 87 & Medium \\
[24] & Inductor current & 15 kHz & 85 & Medium \\
[25] & State estimation & 10-20 kHz & 87 & Medium \\
[26] & LUENBERGER observer & 10 kHz & 85 & High \\
[27] & Linear observer & 25 kHz & 90 & High \\ Proposed & PI & 50 kHz & 91.5 & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different controllers for DC–DC boost converters. Figure 18: Efficiency comparison for different controllers for maximum power point tracking of DC–DC boost converter-based.