esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Estimation of Stray Capacitance of Single-Phase CMIs
### Turn-to-Core Capacitance  
To compute the turn-to-core stray capacitance, \(C_{\rm t-c}\), an assumption is made regarding the electric force line between the wire conductor and the magnetic core, as shown in Figure 9 [20].  
Based on this assumption, the turn-to-core capacitance is determined as a series connection of three types of stray capacitance: \(C_{\rm t-c\dot{i}}\), which is due to the winding's insulation coating; \(C_{\rm t-c\dot{g}}\), resulting from the air gap between the winding and core, and \(C_{\rm t-c\dot{c}}\)  
Figure 8: Winding pitch of the toroidal core-based single-phase CMIs.  
Figure 9: Electric flux line between the winding and the magnetic core.  
originating from the core's insulation. The calculation of the turn-to-core capacitance is, therefore, performed using Equations (16) and (17) [20].  
\[C_{\rm t-c}=\frac{4\epsilon_{0}l_{\rm T}}{\sqrt{\left(\gamma+1\right)^{2}-\,1}} \cdot\tan^{-1}\sqrt{1+\frac{2}{\gamma}} \tag{16}\]  
\[\gamma=\frac{1}{\epsilon_{\rm ri}}\ln\frac{d_{\rm o}}{d_{\rm w}}+\frac{2t_{\rm c }}{\epsilon_{\rm rc}d_{\rm o}} \tag{17}\]  
In these equations, \(t_{\rm c}\) represents the thickness of the magnetic core's insulation, and \(\epsilon_{\rm rc}\) is the relative permittivity of the core's insulation.
## 2 Mathematical Formulation
These parameters, together with the constants \(A\) and \(B\) are given by as follows:  
\[ \begin{array}{c}\lambda=\frac{Ra_{x}}{Pe_{x}},\xi=\left(\frac{2a_{f}}{10a} \right)^{1/2},Rd=\frac{4\sigma T_{0}^{3}}{\kappa\kappa L_{0}a_{f}},S=\frac{-v _{w}}{L_{0}a_{f}},\\ A=\frac{k_{eff}f_{f}}{(1-\varphi)+\varphi\left(\rho C_{f}\right)_{s}/\left( \rho C_{p}\right)_{f}},B=(1-\varphi)^{2.5}\Big{[}(1-\varphi)+\left(\rho\beta \right)_{s}/\left(\rho\beta\right)_{f}\Big{]}.\end{array}  \tag{11}\]  
It is worth to mention that \(\lambda<0\) corresponds to opposing flow or cooled flow, \(\lambda>0\) corresponds to an assisting flow or heated plate, and while \(\lambda=0\) is flow of forced convection. Because of the porous medium characteristics, we can combined Equations (8) and (9) to give a single equation. However, we need to integrate Equation (8) first and applying the boundary conditions (10) to give:  
\[f^{\prime}-B\lambda\theta-1=0. \tag{12}\]  
By substituting Equation (12) into Equation (9), we obtained:  
\[\left[A(1+2\xi\eta)+\frac{8}{3}\xi Rd\right]f^{\prime\prime\prime}+\left(2A \xi+\frac{8}{3}\xi Rd\right)f^{\prime\prime}+2 ff^{\prime\prime}-2\big{(}f^{ \prime}\big{)}^{2}+2f^{\prime}=0, \tag{13}\]  
alongside the new simplified boundary conditions:  
\[ \begin{array}{c}f(0)=S,f^{\prime}(0)=B\lambda+1,\\ f^{\prime}(\eta)\to 1\text{ as }\eta\rightarrow\infty.\end{array}  \tag{14}\]  
The practical interest of physical quantity is the skin friction coefficient \(C_{f}\), in terms of the shear stress of dimensional wall \(f^{\prime\prime}(0)\), which is defined as:  
\[C_{f}=\frac{\tau_{w}}{\rho_{f}u_{e}^{2}}, \tag{15}\]  
where \(\tau_{w}\) is skin friction and given by:  
\[\tau_{w}=\mu_{nf}\left(\frac{\partial u}{\partial r}\right)_{r=a}. \tag{16}\]  
Substituting the dimensionless variables in Equation (7) into Equations (15) and (16), we have:  
\[ \begin{array}{c}(2 Pe_{x})^{1/2}C_{f}=\frac{1}{(1-\varphi)^{2.5}}f^{\prime \prime}(0),\end{array}  \tag{17}\]  
where \(Pe_{x}=u_{e}(x)x/\alpha_{f}\) is local the Peclet number and \(\Pr=\
u_{f}/\alpha_{f}\) is the local Prandtl number for porous medium.
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 5 Numerical Study
### Results Analysis  
#### 5.2.1 Single Objective vs. Multi Objectives  
To compare the performance differences between the single objective function and the integrated multi-objective function, four optimization problems are presented and the results are shown in Table 4. Problems 1-3 optimize their respective objective functions. Problem 4 considers all objective functions with equal weights. It can be clearly seen that there are conflicts and limitations between the various objective functions, and the optimization of one objective is at the expense of the degradation of other objectives. For example, when the goal is to minimize the allocation cost of the gate resource (i.e., min f3), it achieves a minimum value of 4.0, but leads to a larger walking and taxi cost due to the allocation process neglecting the location and the required taxi time of the gate. This phenomenon is alleviated to a certain extent by using a linearly weighted objective function to consider the interests of all parties. Moreover, regardless of the objective function, the running time is within 1 min, so it can be easily extended to the pre-tactical and tactical stages of the gate allocation problem.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Simulation Setup
We thus write the reward as  
\[w_{s,s^{\prime},a}[t;q] :=\left(\mathrm{p}(a_{[0,1]}[t])-\mathrm{p}(a_{[2,3]}[t])\right)\] \[(1-q)+\left(\delta^{b}_{a_{[0]}[t],a_{[3]}[t]}+\delta^{\ell}_{a_{[ 1]}[t],a_{[2]}[t]}\right)q \tag{21}\]  
where \(q=0\). The agent is rewarded the most when it triggers a power control command. The episode is aborted when the constraint imposed on (8) becomes inactive. In this scenario, the agent receives a reward \(w_{s,s^{\prime},a}[t;q]:=w_{\mathrm{min}}\) along with one of the following:  
* A penalty is imposed by awarding the agent with the minimum possible reward \(w_{\mathrm{min}}\). This occurs when the SINR falls below \(\delta_{\mathrm{min}}\). * A maximum reward \(w_{max}\) is imposed when \(\delta_{\mathrm{target}}\) is achieved. In the simulations, the following steps are taken:
* Minibatching is performed with a sample size of \(N_{\mathrm{mb}}=32\) training samples. * The width of the DQN can be found as per [47]. Thus, (22) is written as \[H=\sqrt{|A|+2)N_{\mathrm{mb}}}\] (22) Substituting for \(\mid\)A \(\mid\)=16 and \(N_{mb}\), we obtain H=24. The following performance measures are used to benchmark our algorithms:  
1. _Convergence (\(\zeta\))_: Defined as the episode at which the \(\delta_{target}\) is satisfied over the duration \(T\) for all UAVs. It is expected that, as the no. of UAVs h increases, \(\zeta\) will increase. For several random seeds, an aggregated percentile convergence episode is taken. 2. _Runtime:_ Determining the runtime complexity of the proposed DQL algorithm is difficult due to the absence of guarantees for convergence and stability. Therefore, runtimes from simulation per UAV are considered. 3. _Coverage:_ A complement cumulative distribution function (CCDF) of \(\delta^{\ell}_{eff}\) is built by changing the random seed and performing the simulation in iterations, effectively modifying the way the UAVs drop from the network. The proposed DQL overcomes the shortcomings of FPA by implementing an adaptive power control scheme that can accommodate the changing interfering conditions. Tabular Q-learning relies on the initialised state-action value function, which is fixed. This results in the size of the Q-table becoming quite large and therefore taking longer times to find the optimal solutions, thus resulting in longer convergence time. However, in the proposed DQL, the neural network implementation eliminates the need for a Q-table, instead updating the optimal actions in real time.
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### Bridge Definition and Criticality Models  
In the examined scenario, an in-depth analysis is performed on the infrastructure of a bridge, categorizing it into five primary components, each characterized by a distinct lifecycle and degradation process. These components encompass the roadway surface, superstructure (load-bearing structure), piers and/or abutments, foundation, and coatings. Given the inherent critical nature of this type of asset, namely a bridge, its failure could lead to severe consequences, particularly in terms of safety and the overall quality of service extended to infrastructure users.  
The focus of this analysis centers on the Eduardo Torroja Bridge, situated south of Posadas, Cordoba, spanning the Guadalquivir River. With its inauguration dating back to 1951, the bridge boasts specific dimensions, including a length of 235 m, a width of 11 m, seven cement pillars, and a distinctive appearance featuring eight eyes or inverted bristles supporting the deck.  
In the context of this analysis, the methodology will be exclusively employed for the components of the roadway surface and superstructure. The pertinent data related to the functional location and O&M cost of these specific components are documented and outlined in Table 2.
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 2 Method
### Quality Assessment of Included Studies  
To assess the methodological quality of the included studies, we conducted a qualitative appraisal based on key domains commonly reported in health informatics reviews. These included the clarity of inclusion criteria, sample size adequacy, transparency in sensor data collection, and reporting of algorithm performance metrics (e.g., accuracy, AUC). Given the heterogeneity of study designs and outcomes, we did not perform a formal meta-analysis or apply a standardized risk of bias tool such as ROBINS-I. However, each study was independently reviewed by two authors to identify major methodological flaws or limitations. Discrepancies were resolved through discussion.  
In general, most studies clearly reported their sensor setup and algorithm design but varied in terms of participant demographics, data preprocessing techniques, and validation protocols. We highlight these differences in Table 1 and further elaborate on their implications in Section 4.  
Figure 2: PRISMA 2020 flow diagram illustrating the identification, screening, eligibility assessment, and inclusion process for this systematic review. A total of 856 records were retrieved through systematic searches in PubMed, Scopus, and IEEE Xplore using the following query: (“smartwatch” OR “wearable”) AND (“stress” OR “mental health”). After removing duplicates, 719 records were excluded during title and abstract screening. The remaining 137 full-text articles were assessed against predefined inclusion and exclusion criteria, resulting in the exclusion of 75 articles. Ultimately, 61 studies were included in the qualitative synthesis.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 3 New Energy-Aware Routing Protocols
### Threshold Algorithm  
The Threshold algorithm incorporates a user-defined energy threshold below which the node will conserve its energy. Such a threshold is designed to prevent the node from running out of energy causing shutdown of its processor. The threshold can have a significant impact on the performance of routing protocols. For example, nodes may save energy for later, so they can transmit packets directly to the base station when they come within the transmission range.  
The energy threshold \(\mathrm{E_{th}}\) is defined as a fixed percentage (\(\mathrm{th}_{\mathrm{percentage}}\)) of the harvested energy (\(\mathrm{E_{h}}\)) in the system. Given the harvested energy \(\mathrm{E_{h}}\), we define the energy threshold as follows:  
\[\mathrm{E_{th}}=\mathrm{th}_{\mathrm{percentage}}\times\mathrm{E_{h}} \tag{4}\]  
Specifically, the Threshold algorithm will not forward packets to other nodes or accept forwarding packets when the stored energy falls below \(\mathrm{E_{th}}\). Effectively, the node changes to the Direct Delivery mode to only transmit packets to a base station. The node will also stop collecting data from the sensor (such as GPS), if the energy falls below a fixed threshold [30; 31], as the energy requirements for sensing can also be significant. A threshold of 0% corresponds to no energy awareness, and a threshold of 100% is equivalent to the Direct Delivery algorithm.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 3 System Model  
In a typical communication system, there are at least two main components: the transmitter and the receiver. The transmitter is responsible for sending the original information in the form of a signal after encryption and compression. Conversely, the receiver filters and decompresses the received signal, which is often mixed with interference and noise. From a functional perspective, the autoencoder can be seen as a simplified version of a communication system. Therefore, it is plausible to consider using an autoencoder network to simulate communication processes to fulfill the intelligence requirements that traditional systems cannot achieve.  
To this end, this paper proposes a communication scheme based on deep learning (DL). The system model is shown in Figure 1. In the autoencoder model, the encoder network implements the function of the traditional modulator, and the decoder network implements the function of the traditional demodulator. The symbol string \(I_{S}\) representing the input information is converted into a one-hot code matrix \(\mathbf{O}_{e}\), where \(I_{S}\in\{0,1,2,\ldots,N-1\}\). The encoder network at the sending end uses the trained mapping function \(f_{e}(\mathbf{O}_{e},\theta_{e})\) to compress and map different types of signals into constellation I/Q values, where \(\theta_{e}\) represents the weight parameter set of the encoder part in the autoencoder network. The encoder output \(x\) is input into the Additive White Gaussian Noise (AWGN) channel.  
It is assumed that the signal transmitted by the transmitter passes through an AWGN channel. The noise generated by the channel has a fixed variance \(\beta=(E_{b}/N_{0})^{-1}\), where \(E_{b}/N_{0}\) represents the ratio of the energy per bit transmitted to the noise power spectral density. The received signal input to the decoder network can be expressed as follows:  
\[\mathbf{y}_{\mathbf{R}}=\mathbf{H}x+\mathbf{n} \tag{1}\]  
In the equation above, \(\mathbf{H}\) represents the channel gain matrix, \(\mathbf{n}\) is additive white noise, and the noise samples have a Gaussian distribution and have the same power density at all frequencies. The channel output is restored by the decoder network, and its mapping function is \(O_{d}=f_{d}(y_{R},\theta_{d})\), where the channel output \(\mathbf{y}_{\mathbf{R}}\) is used as the input of the mapping function, and \(\theta_{d}\) is the weight parameter set of the decoder part in the autoencoder network. The output of the receiver is \(\mathbf{O}_{S}=\mathrm{L}(O_{d})\), and the output of the decoder needs to be converted into a one-hot code and then compiled into a symbol string \(\mathbf{O}_{S}\).
## 2 Related Work
### Faster R-CNN  
The R-CNN model was the first to apply CNNs to object detection. Traditional object detection models showed limited improvement in performance through minor modifications and combinations, necessitating a new approach to solve the object detection challenge. R-CNN leveraged the CNN model, which had shown superior performance in image classification, to object detection by splitting the task into classification and localization, which determines the object's coordinates. It employs a selective search for region proposals to identify potential areas containing objects within an image, reducing randomness and proposing regions of interest (ROIs), enabling more efficient processing. With the powerful feature extraction capabilities of CNNs, the model can learn the diverse and complex features of the input images. After undergoing processes like SVM (support vector machine) and bounding box regression for box adjustments, the R-CNN model achieved a 30% higher performance than previous state-of-the-art models based on PASCAL VOC 2012 [18].  
R-CNN introduced a scalable yet straightforward object detection algorithm that breathed new life into the previously stagnant area of object detection. However, it faced significant challenges, including prolonged training times due to having to feed thousands of ROIs one by one into the CNN, and reduced accuracy because the SVM and regression models were trained separately within the architecture. To tackle these issues, fast R-CNN was developed [19]. Fast R-CNN processes the entire original image through the CNN to generate high-resolution feature maps in a single pass, thus eliminating the need to input individual ROIs sequentially. It reduces redundant computations and decreases overall processing time through ROI Pooling. Furthermore, employing multi-task loss to concurrently train the object classifier and the regressor simplifies the training process and enhances computational efficiency. Fast R-CNN is over nine times faster in training than R-CNN and boasts improvements such as shared weights. Nonetheless, it still encountered limitations due to the region proposal method being executed on the CPU, necessitating a new algorithm. This led to the development of faster R-CNN, which markedly increased object detection speeds by integrating RPN with object detection [20]. Unlike previous R-CNN models, which used fixed-size region proposals, faster R-CNN utilizes a region proposal network to dynamically generate proposal regions.  
In this study, we selected the faster R-CNN model for our experiments because it can accurately localize defects within the image while maintaining a reasonable processing speed, using only bounding box labels to generate segmentation results.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers
#### 5.3.7 Hello Flood Attack  
In wireless sensor networks, routing protocols use Hello packets for detection of neighbors. In this type of attack, fake packets are used to camouflage hello packets and to attract the sensor nodes [70]. Attackers with ample radio resources and processing capabilities can generate this type of attack. The victim node will identify false hello packet as normal node.  
Various cyber-attacks concerning different network layers and their respective countermeasures are described in Table 3.
## 5 Conclusions  
In this paper, we propose a novel method for optimizing the geometric dimensions of filters using a new design approach. This method utilizes artificial neural network modeling techniques to replace EM simulation and employs the PPO reinforcement learning algorithm to learn design strategies through trial and error. We validate this method on a hairpin filter and demonstrate its ability to correctly adjust structural parameters to meet design specifications. Compared to other related research directions, our method shows significant advantages in terms of time efficiency. Finally, we believe that this method has the potential to be applied to the design of other microwave circuits.  
Conceptualization, J.W.; methodology, Y.Y., Y.W., J.C., G.S. and J.W.; writing--original draft preparation, Y.Y. and Y.W.; writing--review and editing, J.W. and J.L.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 62206081.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Design Target** & **Design Method** &
\begin{tabular}{c} **EM/ANN Model** \\ **Evaluation Counts** \\ \end{tabular} & **CPU Operation Time** \\ \hline
[39] & coupled-line filter & BO with local sampling & \(1077\) & \(5.32\) h \\ \hline
[40] & SW * filter &
\begin{tabular}{c} narrowband bandpass \\ synthesis algorithm \\ \end{tabular} & \(101\) & \(44.75\) h \\ \hline
[41] & waveguide filter &
\begin{tabular}{c} Homotopy \\ Optimization + ANN \\ \end{tabular} & \(1500\)\(\sim\)\(17,263\) & \(8.3\) min\(\sim\)\(12\) min \\ \hline
[42] & Microstrip filter & MOEA/D-FDTD & \(\approx\)\(2700\) & \(9.5\) day \\ \hline this work & Hairpin Filter & PPO + ANN & \(5\)\(\sim\)\(15\) & in seconds \\ \hline \multicolumn{4}{l}{* substrate integrated waveguide.} \\ \end{tabular}
\end{table}
Table 2: Comparison of optimization results for different methods.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 3 Design Overview  
ErrorExplainer transforms a low-level error message into a high-level _error description_, offering a structured explanation of the following:  
1. Which condition triggered the error (e.g., a balance check in require)?
2. What message is given for the error (e.g., \"ERC20: insufficient balance\")?
3. Where in the code did the failure arise (e.g., inside transfer function)?  
ErrorExplainer's workflow comprises two analysis stages and one generation stage. Figure 1 illustrates the two tracks of analysis to reconstruct error-relevant information from both the user-side and contract-side data. When a user's transaction fails, the transaction is handed over to the execution environment analysis. Through the sequential steps, the execution environment analysis provides the implementation address and the user context to the source code analysis and the error description generation, respectively. ErrorExplainer analyzes source code on a component basis. The inter- and intra-component analyses are performed in an interleaved manner to propagate the results at the contract-wide level. The results from the two analysis tracks are delivered to the error description generation stage. ErrorExplainer synthesizes the information into an error description by matching the results.  
Specifically, ErrorExplainer has the following stages:  
1. **Execution Environment Analysis.** ErrorExplainer infers what was actually executed from user-level information. It first analyzes the user's raw transaction data and then resolves the implementation contract address when a proxy pattern is detected, which is used for upgradability [17]. ErrorExplainer then retrieves the verified source code stored on a blockchain explorer, such as Etherscan [6]. Simultaneously, ErrorExplainer parses the calldata to recover the called function and its ABI-encoded arguments.
2. **Source Code Analysis.** ErrorExplainer performs structural, intra-component, and inter-component analyses to extract every potential revert site together with its guard condition and metadata. Because smart-contract bytecode is immutable, the analysis results can be cached and reused until the proxy's implementation address changes.
3. **Error Description Generation.** ErrorExplainer matches the observed revert data against the pre-computed error contexts, identifies the exact failing statement, nor  
Figure 1: Overview of ErrorExplainer. Two analysis tracks, one over the execution environment and the other over source code, converge to produce an error description showing the failure’s cause.  
malizes heterogeneous formats (strings or custom errors), and synthesizes a human-readable error description.  
We assume smart contracts are written in Solidity and executed on EVM-compatible platforms. This setup is widely used in real-world smart-contract deployments.