esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Routing Protocol Design  
As shown in Figure 2, suppose four vehicles exist in the observed highway scenario. Based on the radio range \(R\), except for the disconnected link between V\({}_{1}\) and V\({}_{4}\), any vehicle pair can communicate. Vehicle V\({}_{1}\), denoted as the SUV, wants to obtain a specific content. Suppose that V\({}_{2}\) and V\({}_{4}\), defined as the SOVs, own the subscribed content. To help the SUV obtain the target content efficiently, a routing scheme is designed as illustrated in Figure 3. The designed content dissemination scheme includes the SOV finding process, the SOV decision process the BVs' selection process and the BVs' re-selection process. First, V\({}_{1}\) broadcasts the _content_request_ message to vehicles V\({}_{2}\), V\({}_{3}\) and V\({}_{4}\). After receiving the requesting message, the vehicles that own the queried content, V\({}_{2}\) and V\({}_{4}\) in this example, transmit the ACK response back to V\({}_{1}\). After comparing the characteristics of vehicles V\({}_{2}\) and V\({}_{4}\) as the SOV, V\({}_{1}\) broadcasts the _SOV_response_ message with the decision of the SOV selection. After receiving the SOV confirmation message, V\({}_{4}\) begins the BV request process. The vehicles that receive the _BV_request_ message will conduct the timer backoff process, and the one who wins is selected until reaching the SUV. Then, V\({}_{1}\) will transmit the BV list back to the selected SOV via the _BV_response_ message. Finally, the SOV will transmit via the selected BV chain until the constructed path breaks. These processes will be described in detail as follows.
# On Transient Stability Problems in DC Microgrids
## 3 Analysis of Control on Transient Stability  
This section focuses on the influence of converter control on transient stability. It derives the equilibrium points, power-absorption limitations, and stability conditions of PI-power-controlled and droop-controlled DC generators. The PI controller was chosen for this analysis due to its simplicity, ease of implementation, robustness, and wide adoption in DC microgrid applications. While PI control served as the baseline strategy, droop control was also included to provide a comparative perspective and highlight its potential benefits for transient stability.  
In stability analysis, several assumptions about the system dynamics to simplify the problem and focus on the key aspects of transient stability are made, which include:  
1. The power converters are modeled as ideal voltage sources with controllable output voltage. This assumption simplifies the analysis by neglecting the internal dynamics and non-idealities of the power converters, such as switching losses and delays.
2. The transmission lines are represented by their equivalent resistance, neglecting the capacitive and inductive effects. This assumption is valid for short distribution lines in DC microgrids, where the line inductance and capacitance are relatively small compared to the resistance.  
Figure 3: Power-transfer curves of AC and DC microgrids.  
3. The fast inner voltage and current control loops have been neglected, assuming them to have unity gain. This is a common assumption in transient stability studies, as the power control loop is the dominant factor determining the stability.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 2 Research Method
### Step 1. Spectral Transformation Methods for Speech Processing
#### 2.1.2 Log-Mel Spectrum Features  
The log-Mel spectrogram is one of the most used features in speech signal processing applications. It represents the energy distribution of a signal on the Mel frequency axis, obtained by mapping linear spectral data onto the Mel scale using a Mel filter bank [32]. For birdsong analysis, the log-Mel spectrogram effectively captures frequency components over time by applying a logarithmic transformation.  
To compute the log-Mel spectrogram, the short-time Fourier transform (STFT) is first applied to birdsong fragments to generate spectrograms. These are then processed using a Mel filter bank with 40 frequency bands to map the data onto the Mel scale. The process emphasizes perceptual features relevant to the auditory system and highlights birdsong-specific characteristics. Figure 4b shows the extracted birdsong features.  
The calculation is shown below in Equation (2), where Mel_bank and STFT represent the Mel filter bank and short-time Fourier transform, respectively. The final log-Mel spectrogram is obtained by combining the Mel scale mapping and a logarithmic transformation. In Equation (2), \(t\) denotes the time index of the spectrogram (in seconds), \(f\) represents the frequency index (in Hz), \(y\) is the input audio signal (typically a birdsong or speech waveform in the time domain), and \(\epsilon\) is a small positive constant added to avoid numerical instability when computing the logarithm. _Mel_bank_ refers to the Mel frequency filter bank, which is a set of filters that transforms the frequency spectrum into the Mel frequency scale.  
\[log-Mel\ spectrum(t,f)=\log(Mel\_bank(|STFT(y)|^{2})+\epsilon) \tag{2}\]
# Addressing ZSM Security Issues with Blockchain Technology
## 7 Assessment of Blockchain and DAG Platforms to Be Used for the ZSM-Solution Implementation
### Candidate Blockchain and DAG Solutions for Our Approach
#### 7.1.4 Iota  
IOTA is a very popular DLT solution, which is based on the Tangle DAG. It is supported by the IOTA Foundation which aims at the development of new DLT-based solutions. On July 2016 the IOTA main net was activated and it is considered a public permissionless network. Some of IOTA's main characteristics are the increased scalability, the increased sentiment of decentralization and the zero transaction fees. In contrast to typical blockchain networks which present scalability issues as the transaction number increases, IOTA becomes more efficient and more powerful when the transaction number grows. Since IOTA does not use miners in the network, a node is at the same time the creator and the validator of a transaction. This means that everyone in the network contributes to the consensus process, which is a fact that highlights the decentralized nature of this particular solution. Moreover, the consensus mechanism implemented in the latest IOTA version is a probabilistic leaderless binary voting protocol called fast probabilistic consensus (FPC). This mechanism is responsible for Tangle's validity by addressing issues such as the double spending problem [44].  
IOTA supports the creation of smart contracts called ISC and hence the development of several applications for various use case scenarios. ISC is agnostic regarding the virtual machine which executes the SC code. IOTA currently supports two types of SCs: the Rust/Wasm-based and Solidity/EVM-based [49]. Furthermore, IOTA allows the use of tokens that can be exchanged among entities in this DAG-based network. A well-known token used as a cryptocurrency in this solution is the MIOTA which can be purchased by a user in order to buy assets in the network. MIOTA is an example of a fungible token; however, the use of NFTs is also supported. Moreover, the use of oracles is supported in this DLT [50]. Oracles bring off-chain data to decentralized applications and smart contracts on the IOTA network. These mechanisms provide blockchains with outside information, typically for use in smart contracts, or provide interoperability between different distributed ledgers.
## 2 Numerical Methods
### Numerical Algorithm  
The procedure is carried out by means of a numerical algorithm. The arch is discretized into blocks, the number of which, i.e., \(n_{Blocks}\), can be defined by the user to increase accuracy, or decrease computation time. Three main sections (Sections 1-3 of Figure 4) are identified, and possible lines of thrust \(\overline{Y}(x)\) are defined by varying the values of \(H\), \(M_{l}\), and \(M_{r}\) in Equation (4). The arch is considered to be verified if there is at least one line of thrust crossing the three sections that, for every \(x\), is contained in \(d(x)\). Figure 4 shows all possible line of thrust that cross the three main sections. Rejected lines of thrust, that are not contained in \(d(x)\) for every \(x\), are depicted in red. In this configuration the arch is assumed to be verified, since at least one line of thrust is accepted (green lines).  
For the line of thrust to cross the main sections, one and three, and for the equilibrium at the base sections to be satisfied, the values of the bending moments \(M_{l}\) and \(M_{r}\), and therefore the values of \(Hy_{0,l}\) and \(Hy_{0,r}\), must necessarily be lower than \(R_{l}s^{\prime}\) and \(R_{r}s^{\prime}\), respectively.  
\[0<H\cdot y_{0,l}\leq R_{r}s^{\prime} \tag{7}\]  
Figure 4: Accepted (green) and rejected (red) lines of thrust.  
\[0<H\cdot y_{0,r}\leq R_{r}\cdot s^{\prime} \tag{8}\]  
As a consequence, the algorithm will employ only the values of \(y_{0,l}\) and \(y_{0,r}\) shown in Equations (9) and (10):  
\[0<y_{0,l}\leq\frac{R_{l}}{H}\cdot s^{\prime} \tag{9}\]  
\[0<y_{0,r}\leq\frac{R_{r}}{H}\cdot s^{\prime} \tag{10}\]  
where \(R_{l}\) and \(R_{r}\) are the vertical reaction forces at the base extremities of the arch, as shown in Figure 4, and \(s^{\prime}=\frac{L}{2}-\sqrt{\frac{L^{2}}{2}+s+2 rs}\) is the base horizontal section of the arch, with the arch internal radius \(r=\frac{L^{2}}{8f}+\frac{f}{2}\). It is important to notice that, as shown in Figure 4, for arches with a rise-to-span ratio \(f/L=1/2\), \(s^{\prime}\) coincides with the depth of the arch \(s\).  
In addition, for equilibrium conditions, and for the line of thrust to cross the main Section 2, the total bending moment in the midspan \(M_{tot}(x=0)\) must have a value between \(H\cdot(f+s)\) and \(H\cdot f\).  
\[H\cdot f\leq M_{tot}(x=0)=M_{\rm F}(0)-\frac{M_{l}}{2}-\frac{M_{r}}{2}\leq H \cdot(f+s) \tag{11}\]  
Therefore, given the hypothesis of Equations (7), (8) and (11), it is necessary to find values of H, \(M_{l}\) and \(M_{r}\) such that:  
\[\frac{M_{\rm F}(0)-\frac{M_{l}}{2}-\frac{M_{r}}{2}}{f+s}\leq H\leq\frac{M_{\rm F }(0)-\frac{M_{l}}{2}-\frac{M_{r}}{2}}{f} \tag{12}\]  
\[0<M_{l}\leq R_{l}\cdot s^{\prime} \tag{13}\]  
\[0<M_{r}\leq R_{r}\cdot s^{\prime} \tag{14}\]
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 6 Conclusions and Future Work  
Vulnerability detection is crucial for blockchain-based smart contracts. Existing studies have provided a number of promising methods through different software-testing techniques, i.e., fuzzing, symbolic execution, formal verification as well as static analysis. In this paper, we proposed two static analysis tools _ASGVulDetector_ and _BASGVulDetector_ for smart contracts in source code and bytecode, respectively. We designed a novel graph representation for smart contracts and utilized the pioneer graph-based deep learning technique GMN for vulnerability detection. The contract graph is able to reflect more syntactic and semantic information, while GMN provides a good performance guarantee of similarity learning. Experimental results reveal that _ASGVulDetector_ improves the best of three source-code only static analysis tools (i.e., _SmartCheck_, _Sliter_, and _DR-GCN_) on F1 score by 12.6% on average, while _BASGVulDetector_ improves that of the three detection tools supporting bytecode (i.e., _ContractFuzzer_, _Oyente_, and _Securify_) on F1 score by 25.6% on average. We also investigated the parameters of GMN as well as its superiority to the GNN model.  
However, there are still limitations to the proposed approaches, which can be summarized as two aspects. On one hand, we consider four types of vulnerabilities in this work, but there exist various types of smart contract vulnerabilities, which contain different syntactic and semantic characteristics [29; 30]. On the other hand, the proposed approaches require sampling contracts of different vulnerabilities to train specific models, thus being limited in unknown vulnerability detection. In the future, we plan to study more types of smart contract vulnerabilities to enhance the scalability of the proposed approaches, and develop new methods to detect unknown vulnerabilities. Generalizing the proposed method  
Figure 10: Impact of different models for _BASGVulDetector_.  
Figure 9: Impact of different models for _ASGVulDetector_.  
to other software testing scenarios, such as vulnerability detection for other programming languages and code clone detection, is another direction for future research.  
The research for this paper was undertaken by Y.Z. and D.L.; conceptualization and investigation, Y.Z. and D.L.; software and validation, D.L.; writing-original draft preparation, Y.Z. and D.L.; writing-review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20190346) and the 2019 Industrial Internet Innovation and Development Project, Ministry of Industry and Information Technology, China (Grant No. 6709010003).  
Not applicable.  
The data supporting this article are from previously reported studies and datasets, which have been cited.  
The authors declare no conflict of interest.
# Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network
## 2 Proposed R-CNN Model  
### Dent Highlight in an Image with Mach Bands  
A special lighting device with light-emitting diodes (LEDs) and a stripe cover, as shown in Figure 1a, were employed to highlight dents in an image using Mach bands [12]. The overall size of the light device used in this study was 20 cm (width) \(\times\) 9 cm (height) \(\times\) 3.4 cm (thickness); the sizes of the bright and dark stripes were 0.5 cm (width) \(\times\) 6 cm (height) and 1 cm (width) \(\times\) 6 cm (height), respectively. The light device intensity could be adjusted to determine the best light intensity for different defect types and ambient light conditions. The LEDs with adjustable intensity (1500 lumens) using a rotary knob emitted light. The stripe cover created Mach bands (dark-bright linear stripes) by blocking light behind the stripes and passing light between the stripes. The Mach bands can create a contrast pattern on the vehicle surface of interest and were distorted around dents because of light diffusion, as illustrated in Figure 1b. For a smooth surface, light beams are reflected back in the same concentration; however, an irregular surface scatters light beams (called diffuse light), as shown in Figure 1c. This diffuse light can distort the Mach bands around dents.
## 1 Introduction
### Limitation of Previous Studies  
It has been seen from the detailed literature review that there are still limitations or gaps in the techno-economic analysis of the PV + ESS systems. These gaps in the literature are listed below.  
1. Some of the studies in the literature analyze system performance based on static meteorological data, fixed load profiles, and energy production values derived from various databases. However, static meteorological data and fixed load profiles fail to capture the dynamic variations in PV system performance, while reliance on generalized database values reduces modeling accuracy by neglecting regional differences. In addition, neglecting sudden production and load changes causes deviations in technical and economic analyses, while studies based on historical data instead of measured field data reduce the reliability of system design.
2. Some studies overlook the annual degradation of PV system performance and battery storage losses, leading to incomplete or inaccurate assessments of system efficiency. This omission results in misleading conclusions in both economic feasibility analyses and system optimization efforts.
3. Some studies focus only on the simple payback period in the economic analysis of PV + ESS systems and ignore critical economic parameters such as interest rate, inflation rate and discount rate. The exclusion of these time-dependent cost and revenue factors prevents an accurate evaluation of long-term financial sustainability.
4. Many studies in the literature are based on fixed rules and traditional optimization methods in the energy management of PV + ESS systems. However, these approaches cannot adequately adapt to the time-varying conditions of the system and ignore dynamic decision-making processes. In particular, ignoring fluctuations in electricity prices, demand variations, and real-time operational conditions prevents a comprehensive assessment of the system's economic and technical efficiency.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 2 MFC Power Supply for Sensors and Biosensors
### Effect of pH
In addition, due to the beneficial role of self-sustaining pH control by SC-MFCs, the hybrid stack demonstrated a stable operation time of around 15 hours, which was almost two times higher than that reached using isolated MFCs.
## 2 Mechanisms of Acidogenic Fermentation
### Technical and Operational Challenges
Their study also highlighted the influence of sludge structure, showing that granular sludge systems were more efficient for VFA production compared to slurry systems. Specifically, reactors seeded with granular sludge exhibited up to three times the relative abundance of _Clostridium_, leading to enhanced VFA production efficiency. Conversely, a higher presence of _Chlorofexi_ was linked to reduced VFA yields, indicating its less effective role in acidogenic fermentation processes. High sludge concentration systems have been shown to enhance hydrolysis and VFA productivity by providing a stable microbial environment. Additionally, the long-term operation of these systems can lead to changes in the dominant microbial species, which in turn affect the VFA spectrum. For instance, [PERSON] et al. [80] observed a shift in their semi-continuous fermenter, where acetic acid was initially dominant, but caproic acid became the primary VFA, accounting for 55% of the total VFAs by day 23 and remaining stable throughout 115 days of operation. This transition was attributed to the adaptation of the microbial community to the substrate mix of primary sewage sludge and organic waste (50/50 COD ratio in the feed). A critical insight from this analysis is that overcoming the challenges associated with VFA production requires a comprehensive approach that transcends the optimization of individual parameters such as substrate concentration, C/N ratio, or microbial community composition. The complexity lies in the multifaceted interactions among these factors within dynamic systems, where each operational adjustment impacts microbial activity, substrate degradation pathways, and VFA composition in a cascading manner. Future research should prioritize the development of integrated strategies that address these complexities holistically. For instance, the co-digestion of substrates with complementary characteristics could mitigate nutrient imbalances and enhance microbial efficiency. Additionally, the application of advanced predictive models to simulate microbial interactions and metabolic outputs under varying operational conditions would facilitate the more precise control of the fermentation process.
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 2 System Model  
### Communication Scenario  
As shown in Figure 1, we consider a wireless covert communication scenario with the assistance of a jammer, involving a covert information transmitter ([PERSON]), a covert information receiver ([PERSON]), a covert communication detector ([PERSON]), and a jammer assisting [PERSON] in covert transmission. [PERSON], [PERSON], and [PERSON] are equipped with a single antenna. We consider a quasi-static block fading model where the channel is static and frequency-flat within each coherent interval containing \(n\) symbols. The signal received by [PERSON] and [PERSON] can be expressed as  
\[\hat{Z}_{b} =h_{b}D+n_{j,b}+\hat{n}_{b}, \tag{1}\] \[\hat{Z}_{w} =h_{w}D+n_{j,w}+\hat{n}_{w}, \tag{2}\]  
respectively, where \(D\in\mathbb{C}\) is the transmitted signal, and \(h_{b}\in\mathbb{C}\) and \(h_{w}\in\mathbb{C}\) are Alice-to-Bob and Alice-to-Willie channel coefficient, which are assumed acceptable to everyone. \(n_{j,b}\) and \(n_{j,w}\) denote the interference signals received from jammer at [PERSON] and [PERSON], both of which are composed of the zero-mean complex-valued Gaussian noise vectors. And \(\hat{n}_{b}\in\mathbb{C}\) and \(\hat{n}_{w}\in\mathbb{C}\) denote the zero-mean complex-valued Gaussian noise vectors with the covariance \(2\delta_{b}^{2}\) and \(2\delta_{w}^{2}\). Instead of adopting the conventional assumption of Gaussian input data, we let \(D\) be the symbol randomly selected from well-known BPSK constellations set with amplitude gain \(\beta\), i.e., \(\{-\beta,+\beta\}\). In practical communication systems, BPSK modulation is favored over Gaussian modulation due to its simplicity and efficiency [5; 8; 30]. In contrast, Gaussian modulation introduces complexity with a continuum of signal states, making it less practical for many communication scenarios. Considering the composition of jammer interference signals, we can equivalently rewrite the channel as  
\[Z_{b} =h_{b}D+n_{b}, \tag{3}\] \[Z_{w} =h_{w}D+n_{w}, \tag{4}\]  
where \(n_{b}\sim\mathcal{CN}(0,\sigma_{b}^{2})\) and \(n_{w}\sim\mathcal{CN}(0,\sigma_{w}^{2})\) denote the equivalent noise after superimposing the interference signals. In the following context, we will analyze and optimize our problem based on equivalent channels (3) and (4).
# Computer Vision System: Measuring Displacement and Bending Angle of Ionic Polymer-Metal Composites
###### Abstract  
A computer vision system for measuring the displacement and bending angle of ionic polymer-metal composites (IPMC) was proposed in this study. The logical progression of measuring IPMC displacement and bending angle was laid out. This study used Python (version 3.10) in conjunction with OpenCV (version 4.5.5.64) for the development of the vision system. The coding functions and the mathematical formulas used were elaborated on. IPMC contour detection was discussed in detail, along with appropriate camera and lighting setups. Measurements generated from the vision system were compared to approximated values via a manual calculation method. Good agreement was found between the results produced by the two methods. The mean absolute error (MAE) and root mean squared error (RMSE) for the displacement values are 0.068080668 and 0.088160652, respectively, and 0.081544205 and 0.103880163, respectively, for the bending angle values. The proposed vision system can accurately approximate the displacement and bending angle of IPMCs.  
ionic polymer-metal composite (IPMC); computer vision; OpenCV; Python; Nafion; displacement measurement; bending angle measurement +
Footnote †: journal: _Applied Sciences_