esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ECA-DenseNet
#### 2.4.3 ECA Attention Mechanism  
When modeling spectral features, conventional convolutional neural networks tend to overlook nonlinear dependencies between channels, leading to redundant and inefficient features. In contrast, Efficient Channel Attention (ECA) [22] captures channel dependencies without dimensionality reduction, owing to its lightweight cross-channel interaction mechanism.  
The operation process begins with applying a 1D convolution to the channel dimension of the input feature map:  
\[f=\sigma(\mathcal{F}_{\text{convld}}(X)) \tag{8}\]  
\(X\in\mathbb{R}^{C\times H\times W}\) is the input feature, where C represents the number of channels, and H \(\times\) W denotes the spatial dimensions. \(\mathcal{F}_{\text{convld}}\) signifies a 1D convolution with kernel size k (In this study, k = 3), and \(\sigma\) is an activation function such as Sigmoid. This step captures local dependencies between adjacent channels, with the kernel size k controlling the scope of cross-channel interaction.  
Subsequently, the 1D convolution output directly generates channel attention weights \(A\in\mathbb{R}^{C\times 1\times 1}\), where each element \(A_{c}\) represents the importance score of the c-th channel. The formula is:  
\[A_{c}=\frac{1}{k}\sum_{i=-[k/2]}^{[k/2]}X_{c+i}*\omega_{i} \tag{9}\]\(\omega_{i}\) are the weights of the 1D convolution, which adaptively adjust the interaction strength between adjacent channels during training. Finally, the generated channel attention weights are multiplied by the original features, i.e., \(\mathrm{Y}=\mathrm{X}\times\mathrm{A}\), enabling the model to focus on wavelength channels with strong discriminative power in the spectral data, suppress noise and redundant channels, and achieve adaptive enhancement or suppression of channel-wise features. The module enhances modeling of nonlinear inter-channel dependencies and boosts key feature responses via lightweight 1D convolution.
## 2 Results and Discussion
### Antifungal Activity
The volatile components inhibited the growth of _Bacillus subtilis_, and _B. cereus_; the methanol, dichloromethane and hexane extracts inhibited the growth of _B. subtilis_, _Staphylococcus aureus_ (methicillin-oxacillin resistant), _Klebsiella aerogenes_, and _Escherichia coli_ at 0.25 and/or 0.50 mg/disc. In the minimum inhibitory concentration (MIC) testing, hexane extracts showed the best results inhibiting most of the tested bacterial growth (_B. subtilis_, _B. cereus_, _K. aerogenes_, _E. coli_, _E. coli_ (O157:H7), _Pseudomonas aeruginosa_, and _Proteus vulgaris_, at the lowest concentrations (from <50 ug/mL to 1000 ug/mL). The methanol extract showed higher MIC values for gram-positive bacteria _B. subtilis_, _B. cereus_, _S. epidermidis_, and _S. aureus_, and gram-negative bacteria _P. aeruginosa_, and _P. vulgaris_ at 250 to 500 ug/mL. The dichloromethane extracts did not show any activity in MIC test, and the essential oil from algae showed great bactericidal concentrations but only against tested gram-positive strains (_B. subtilis_, _B. cereus_, _S. epidermidis_, and _S. aureus_) at <50 ug/mL. They also tested all extracts and essential oils against fungi _Candida albicans_, but no significant growth inhibition was detected [15]. [PERSON] et al. [52] documented a high antifungal activity of the methanol/toluene (3:1 \(v/v\)) _C. bursa_ extracts on different tested fungi (_C. albicans_ and _Aspergillus niger_).
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 2 Effect of Background Gain/Loss  
### Preliminary Theoretical Considerations  
Theoretically, Equation (1) is a variable coefficient nonlinear Schrodinger equation which has been studied extensively in the literature [30; 31; 32; 33]. Most works focus on the case where the variable coefficients are functions of time (\(t\)) only [30], while some concentrate on the case of spatial dependence (\(x\)) alone [32]. As illustrative example, for the case of constant gain/loss \(\gamma=\alpha=0\), \(\beta=\) constant, in Equation (1), a simple transformation \(B=A\)\(\exp(-\beta t)\) will map Equation (1) to  
\[iB_{t}+B_{xx}+\sigma\exp(2\beta t)|B|^{2}B=0\]  
which can be handled by standard conversion to the constant coefficient case [30]. For this special case, the rate of dissipation or growth of the total intensity is given by  
\[\left(\int_{-\infty}^{\infty}|A|^{2}dx\right)_{t}=2\beta\int_{-\infty}^{\infty }|A|^{2}dx\]  
If gain/loss is absent, it is possible to treat theoretically an NLSE with variable coefficients as general functions of both space and time [33], i.e., (\(g_{n}\), \(n=1,2,3\) as arbitrary functions)  
\[i\
abla_{t}+g_{1}(x,t)\
abla_{xx}+g_{2}(x,t)\
abla^{2}\Psi^{*}=g_{3}(x,t)\Psi\]  
through a sequence of similarity transformations.  
If gain/loss is present, such a similarity transformation for a general variable coefficient case is feasible only if the amplification/attenuation functions take on special forms, e.g., polynomials [34]. Hence, the present computational investigations might serve two purposes. Firstly, stability and robustness of these similarity solutions can in principle be tested. Secondly, more general forms of gain/loss functions can be handled as well.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background
### 3D Path Planning Algorithms  
The performance of UAVs in finding the optimal route is determined by the algorithms used for three-dimensional path planning. These algorithms can be classified into four categories: sampling-based algorithms, node-based algorithms, bio-inspired algorithms, and multiple algorithms [15; 19; 21; 22]. Figure 4 illustrates the classifications of these algorithms.  
Figure 3: Path planning of a network of UAVs for communications [1].
# Point Cloud Repair Method via Convex Set Theory
## 2 Methods
### Hole Definition  
Let \(\varphi_{1}\) be the missing area inside the 3D point cloud, and \(\varphi_{2}\) be an unclosed missing area at the outer boundary. As illustrated in Figure 3, the line segment connecting \(A\) and \(B\) is marked by \(L_{AB}\), where \(A\) and \(B\) are points on the \(\varphi_{2}\) boundary. Any boundary point in \(\varphi_{2}\) is in the closed area defined by line segments \(L_{AB}\) and \(\varphi_{2}\). Assuming that \(0\leq\Delta\leq 0.1\) (in this paper, \(\Delta=0.1\)), the boundary arc length of \(\varphi_{2}\) is \(C_{\varphi}\). If \(L_{AB}<\Delta C_{\varphi}\), \(\varphi_{2}\) is considered a boundary hole. Inner hole \(\varphi_{1}\) and boundary hole \(\varphi_{2}\) are collectively referred to as _hole_.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.9 Airflow  
Airflow [67] is a workflow management platform that offers functionalities for managing and scheduling tasks (depicted as DAGs). Airflow is not limited to Kubernetes like the kubeflow tool; rather it is designed to be integrated into the Python ecosystem. It is not intuitive by design and has a steep learning curve for new users. One can define tasks in python and orchestrate a simple pipeline. It is also quite difficult to integrate Airflow for ML projects which are already under development.
### SDN/NFV-Based Architectures for Industrial OT Networks
Security is a critical benefit of SDN, as it isolates network segments and controls data flows based on real-time analysis. This capability allows the network to quickly respond to security threats or anomalies. For industrial OT networks, such as the described wind farm, this means that sensitive data and critical infrastructure are better protected against cyber-attacks. The deployment of NFV instances within the PDC and enhances the overall security posture, ensuring that robust defense mechanisms are in place. Collectively, these attributes under  
Figure 6: Software-defined IIoT–Edge network for next-generation offshore wind farm data-acquisition system (see Figure 3) connecting wind turbine generator access network (nacelle and tower access switches) to the pico data center ethernet switches. Figure 7: Considerations for adopting software-defined networking for industrial OT networks. score the potential of SDN/NFV-based architectures to significantly optimize industrial OT networks. SDN/NFV-based architectures are still being researched, with most studies reviewing their interdependence and potential application scenarios. Several SDN/NFV-based architectures are designed for cyber-physical energy systems. [PERSON] et al. [110] propose a \"Gridstat\" communication middleware framework based on a publisher-subscriber model to address the wide-area network-level limitations and achieve the timely and robust delivery of updates. [PERSON] et al. [21] proposed a middleware communication framework that deploys a software-defined network/network function virtualization (SDN/NFV)-based framework for the inter- and intra-domain management of wind park communication networks in the EU H2020 project \"VirtuWind\". [PERSON] et al. [111] reviewed SDN controllers for SDN protection and control systems, describing the role that SDN plays in transmitting delay-sensitive electrical messages such as the IEC 61850 GOOSE and SV in digital substations. [PERSON] et al. [19] designed a micro cloud-software-defined network testbed for onshore wind farm network recovery. This section addresses the performance, security, reliability, and scalability challenges that hinder the implementation of software-defined IIoT-Edge networks in offshore wind farms' data acquisition systems.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Measurement of Voltage, Current, and Power of PD
#### 3.1.2 Current  
A vibrating PD can be modelled as a current source or a voltage source [21]. Therefore, the current generated by PD can also be measured in two different ways:  
* When the PD is subjected to excitation, the positive and negative terminal was connected to a current meter/ammeter (Type CD771 was used in this study). This device measures the RMS value of the current (I\({}_{\text{RMS}}\)). The measured current can be converted into peak current (I\({}_{\text{PK}}\)) by using the Equation (5). \[\text{I}_{\text{PK}}\ =\sqrt{2}*\ \text{I}_{\text{RMS}}\] (5)
* The current of the PD can also be calculated using the concept of PD equivalent circuit (Figure 5b).
* If the capacitance of the internal capacitor is C\({}_{\text{P}}\), and the reactance of internal capacitor is X\({}_{\text{C}}\), the peak current flowing through the internal capacitor, I\({}_{\text{PK}}\) is defined as \[\text{I}_{\text{PK}}=\omega\text{C}_{\text{P}}\ \text{V}\text{k}\] (6) where X\({}_{\text{C}}=\frac{1}{\omega\ \text{C}_{\text{P}}}\). In this study, the current obtained using both methods was found to be the same.
## 2 Results
### Untargeted Metabolomics Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metabolites** & **MW** & **RT** & **VIP Score** & **Trend** \\ \hline \multicolumn{5}{c}{**Positive ionization mode**} \\ \hline
2-Acetyl-1-alkyl-sn-glycero-3-phosphocholine [12] & 523.3638 & 17.98 & 1.66212 & \(\uparrow\) \\
3-Dehydroxycarnitinene & 145.1103 & 3.51 & 1.13186 & \(\downarrow\) \\
3-Methylxanthine [13] & 166.0491 & 1.38 & 1.80968 & \(\downarrow\) \\
4-Hydroxycinamic acid & 164.0475 & 4.17 & 1.80279 & \(\downarrow\) \\
5-Hydroxymidoleacetic acid [13] & 191.0582 & 8.42 & 1.17858 & \(\downarrow\) \\ Adenine & 135.0545 & 3.08 & 2.03098 & \(\uparrow\) \\ Benzoic acid [14] & 122.0370 & 4.17 & 1.38913 & \(\downarrow\) \\ Carnosine & 226.1064 & 3.95 & 1.06014 & \(\downarrow\) \\ Epinephrine & 183.0896 & 8.35 & 1.52508 & \(\downarrow\) \\ Hippuric acid [13; 14; 15] & 179.0582 & 7.83 & 2.37849 & \(\downarrow\) \\ Histidine & 155.0695 & 2.14 & 1.30363 & \(\downarrow\) \\ Isoniazid & 137.0589 & 7.97 & 1.43638 & \(\downarrow\) \\ LysoPE(18:1) & 479.3014 & 17.55 & 1.66856 & \(\uparrow\) \\ \(N\)-Acetyl-phenylalanine & 207.0896 & 9.17 & 1.73877 & \(\downarrow\) \\ \(p\)-Aminobenzzoic acid [13] & 137.0477 & 1.44 & 1.45154 & \(\downarrow\) \\ Retinol & 286.2295 & 12.93 & 1.07027 & \(\downarrow\) \\ Theophylline & 180.0648 & 6.76 & 2.21588 & \(\downarrow\) \\ \hline \multicolumn{5}{c}{**Negative ionization mode**} \\ \hline
3-(3-sulfooxyphenyl)propanoic acid & 246.0195 & 7.18 & 1.72228 & \(\downarrow\) \\ Adenosine monophosphate \({}^{*}\) & 347.0631 & 1.18 & 1.60799 & \(\uparrow\) \\ Gluconic acid [16; 17] & 196.0587 & 1.39 & 1.74004 & \(\downarrow\) \\ Hippuric acid [13; 14; 15] & 179.0583 & 7.81 & 2.21443 & \(\downarrow\) \\ Indolelectric acid [13] & 205.0739 & 11.00 & 1.31510 & \(\downarrow\) \\ \hline \end{tabular} \({}^{*}\) Fragmentation spectrum not confirmed with online databases.  
\end{table} 
Table 2: Differential metabolites in positive and negative ionization mode. References indicate BC’s previous clinical studies in which the metabolite was selected as discriminant and/or dysregulated (MW—molecular weight; \(\downarrow\) indicates down-regulation and \(\uparrow\) indicates up-regulation in BC). Figure 2: Box Whisker charts for selected compounds differentiating the studied groups (red (BC)—bladder cancer patients, green (H)—healthy volunteers).
# Defect Prediction for Capacitive Equipment in Power System
## 2 Related Work
While online monitoring enables personnel to make timely and accurate judgments on equipment status based on collected monitoring data, it is impractical to install sensors on all capacitive equipment for real-time monitoring due to limited resources, which would also consume significant manpower and resources. With advancements in technologies such as sensors and artificial intelligence, monitoring techniques for capacitive equipment are undergoing continual evolution. Nevertheless, there remains a scarcity of research concerning the utilization of historical defect data for modeling and analysis, which could enable defect prediction in capacitive equipment [24; 25; 26; 27; 28]. Our proposed methodology diverges from the norm by integrating equipment information, geographic data, substation details, etc., and employing machine learning techniques to investigate the correlation between these variables and defects in capacitive equipment. Subsequently, we construct machine learning models to forecast the likelihood of current equipment developing defects.
## 4 Experimental Results and Analysis
### Stiffness Deterioration  
Due to concrete cracking and material yielding during the cyclic loading, the stiffness of the elements decreased, this phenomenon is known as stiffness degradation. In this study, the stiffness degradation was assessed using the secant stiffness determined from each complete hysteresis loop. The secant stiffness was determined using the slope of a line passing through the peak loads at both directions. It represents the ability to resist deformation. The stiffness degradation ratio versus drift is demonstrated in Figure 12 and Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{8}{c}{**Yielding at**} \\ \cline{2-11}  & **D1** & **D2** & **D3** & **T1** & **T2** & **T3** & **R1** & **R2** & **R3** & **R4** \\ \hline \multirow{2}{*}{SC1} & Drift (\%) & 2.01 & 2.41 & 1.79 & 1.65 & 1.52 & 4.35 & 0.88 & 1.71 & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Joint-bending & \multirow{2}{*}{670} & \multirow{2}{*}{711} & \multirow{2}{*}{631} & \multirow{2}{*}{598} & \multirow{2}{*}{566} & \multirow{2}{*}{774} & \multirow{2}{*}{382} & \multirow{2}{*}{614} & \multirow{2}{*}{} \\  & moment (kN) & & & & & & & & & \\ \hline \multirow{2}{*}{SC2} & Drift (\%) & 3.2 & 1.89 & 1.56 & 2.34 & 3.92 & 3.75 & 1.38 & 2.57 & 1.66 & 3.4 \\  & Joint-bending & \multirow{2}{*}{881} & \multirow{2}{*}{700} & \multirow{2}{*}{611} & \multirow{2}{*}{788} & \multirow{2}{*}{913} & \multirow{2}{*}{908} & \multirow{2}{*}{561} & \multirow{2}{*}{820} & \multirow{2}{*}{640} & \multirow{2}{*}{894} \\  & moment (kN) & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Joint-bending moments and the corresponding drift at several yielding points.  
It was observed that the joint stiffness gradually degraded. There was no sudden drop in stiffness. This means that the local failures did not affect the global behavior of the joint. It can be seen that up to 2% drift, the stiffnesses of the specimens were almost the same. After 2% drift, the stiffness of specimen SC2 degraded less than specimen SC1. This was in agreement with the order of yielding points presented in Section 4.3. At the high drift levels, the stiffness of specimen SC2 was about 16.2% greater than that of specimen SC1. At the end of the test, this value was about 21.2%. This highlights the contribution of the supplementary plates on the joint stiffness.
## 3 Applications of the CDS for the Fiber Optic Link
### Perceptor  
Typically, the perceptor is made up of three main subsystems: (i) Bayesian modeling, (ii) Bayesian filtering, and (iii) entropic state (see Figure 6). However, the perceptor proposed here (see Figure 7) for fiber optic application has more features than those discussed in [7]. The proposed perceptor operates in two modes: (i) prediction mode and (ii) BER improvement mode. If the BER is less than the FEC threshold, the CDS extracts a statistical model of the fiber optic channel and saves it in the model library. Under this condition, the CDS operates in the BER improvement mode. If there is a disturbance in the fiber optic channel, the BER could increase and exceed the FEC threshold. Now, the CDS switches to the prediction mode in which the perceptor selects one of the previous models that is closest to the current condition. The executive, in conjunction with the perceptor, attempts to bring the BER below the threshold and then the CDS will switch back to the BER improvement mode.  
As shown in Figures 6 and 7, the input of the perceptor block is the received symbol \(Y_{n}^{k}\). The index \(k\) denotes the cycle number of PAC. As discussed later, the main sub-blocks of the proposed perceptor are: (i) three layered Bayesian generative model, (ii) previous model selection, and (iii) Bayesian filter.
## 4 Results
### Statistical Analysis of Earthquake Types  
The dataset includes earthquakes from a variety of tectonic settings--namely strike-slip, thrust, and normal faulting regimes--providing a comprehensive representation of the coseismic deformation characteristics associated with each fault type. The statistical distribution of faulting mechanisms is shown in Figure 5c. Strike-slip earthquakes represent the largest proportion of the dataset, comprising approximately 46.77%, which aligns with their global predominance along transform plate boundaries [44]. Thrust earthquakes are moderately represented, accounting for approximately 33.87% of the dataset. In contrast, normal faulting events are less frequent, making up around 19.35% of the dataset. This lower proportion is primarily attributed to their occurrence in extensional environments, such as rift zones and back-arc basins, where slower stress accumulation leads to reduced seismic activity. Additionally, a subset of earthquakes in the dataset exhibit complex rupture mechanisms that involve combinations of multiple fault types. These events serve as valuable training samples for deep learning models designed to interpret coseismic deformation in structurally complex fault systems.
## 6 Results and Analysis
### Routing Overhead  
As shown in Figure 8, a downward trend could be observed as regards the amount of routing control packets in circulation in both networks as the number of selfish nodes in the networks increases. This is most likely due to fewer good nodes participating in the route discovery and route management processes for other nodes. As mentioned earlier, these selfish nodes do not participate in the route discovery processes for other nodes in the network. The number of routing control packets circulating in the network when the proposed reputation model was deployed is less compared to the network without the reputation model. This is expected, because when the selfish nodes are detected in the reputation model, they are deleted from the routing path and are denied network access, such that route requests from these nodes are ignored. This means that fewer route requests, route replies and route error packets will always be in circulation due to the isolation.  
Similarly, it is observed in Figure 9 that there is also a gradual decrease in the number of routing control packets circulating in the networks as the number of selfish nodes increased from two to 12 gradually. One significant difference between Figures 8 and 9 is in the amount of routing control packets in circulation as the number of selfish and deceitful nodes increases in the networks with and without the reputation model, respectively. This difference is due to the un-identical behaviour patterns, such that selfish nodes do not participate in route discovery or the route management process for other nodes. On the other hand, the deceitful nodes periodically participate in route discovery and management processes for other nodes. Hence, for the network with deceitful nodes, more routing control packets are in circulation compared to the network with selfish nodes.  
Figure 8: Routing overhead for networks experiencing an increase in selfish nodes.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 3 Logic of Events
### Protocol Authentication Proof Process  
In the protocol proof process based on LOET, we first abstract the interaction flow between the initiator and responder of the protocol. Subsequently, we prove matching sessions based on the preconditions and postconditions of the protocol states. Finally, we consider the proof of weak authentication properties and strong authentication properties. In addition to proving authentication properties, it is essential to consider the definition of basic sequences in the protocol and analyze the one-sided sequences of the initiator and responder. The proof of protocol authentication can only be established when both sides of the sequence are proven to possess strong authentication properties. The protocol security proof process based on Logic of Events is illustrated in Figure 1. The specific steps are as follows.  
1. Begin by formally describing the protocol, constructing standardized basic sequences for initiators and responders, and defining the strong authentication properties that the protocol needs to verify.
2. Under the assumption of honest entities, analyze the thread messages and define actions on the selected thread as an instance of the basic sequence. Specify the matching sessions that need to be proven and proceed to demonstrate unilateral authentication for initiators or responders.
3. Confirm whether the matching events comply with the current matching session. If they do, proceed with subsequent proofs. If not, continue filtering matching sessions until the current matching event satisfies weak matching.
4. With the weak matching confirmed, analyze the matching length during the protocol interaction process. Prove strong matching sessions based on relevant axioms and rules in Logic of Events.
5. If the strong authentication property for one party is successfully proven, proceed to prove the strong authentication property for the other party. If only one party satisfies the strong authentication property, it indicates that the protocol as a whole still does not meet the strong authentication requirement. Successful proof in both directions is necessary for the overall strong authentication property of the protocol to be established.