esa-sceva/satcom-chunk-collection huggingface dataset:
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 6 Discussion  
### Findings  
Our findings regarding RQ1 (vulnerabilities) indicate that a uniform set of smart-contract vulnerability definitions does not exist in research work and bugs pertaining to the same mechanisms sometimes appear with different names. For example, the _mishandled exception_ vulnerability has been referred to as _exception disorder_ in [25], _unchecked-send bug_ in [11], _unhandled exception_ in [101], and _unchecked external calls_ in [43, 102]. This inconsistency makes it difficult to identify, categorize, and analyze vulnerabilities, as well as challenging to compare security-analysis tools. The Smart Contract Weakness Classification Registry [62] gathers smart-contract weaknesses and maps them to Common Weakness Enumeration (CWE) [63]. The community also has Ethereum Improvement Proposal [103], which describes standards for the platform, protocol and contract, and guidelines for Ethereum smart-contract best practices [79] to approach a better security level for smart contracts and their run-time environment. For RQ2 (countermeasures to mitigate), we explained some safeguarding approaches and best practices. However, as technology improves new vulnerabilities may emerge.  
As far as RQ3 (tool support) is concerned, the results indicate that SmartCheck, DefectChecker, contractWard and sFuzz tools are better choices in terms of more coverage of vulnerabilities; however, tools such as NPChecker, MadMax, Osiris, and Sereum target some specific categories of vulnerabilities if required. While contractWard has good accuracy and fast process time, it can only detect specifically pre-defined vulnerabilities. The NPChecker has a slower processing time, but it may find new patterns for each vulnerability.  
Our results complement [35] and only share the SmartCheck tool. However, their data is SolidiFI-eccentric and thus there is no direct comparison between their accuracy and efficiency with ours. We found that there is inconsistency in the definition of various vulnerabilities in different research work. This poses challenges to creating unified metrics or benchmarks for comparisons among security-analysis tools.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Impacts of Parameters
#### 5.1.1 Impacts of \(\alpha\)  
We vary the value of \(\alpha\) from \(0.3\) to \(0.8\) and measure the network lifetime's variation. The results are shown in Table 8. We can see the network lifetime enlarges significantly when \(\alpha\) increases from \(0.3\) to \(0.5\). It dramatically drops when \(\alpha\) reaches \(0.6\) and becomes stable. This phenomenon can be explained as follows. As shown in Equation (4), the new Q-value is calculated from the current Q-value, the reward, and the estimated maximal Q-value. \(\alpha\) is the weight of the last two components, while \(1-\alpha\) is the weight of the first one. Intuitively, the current Q-value reflects the experience the agent has learned so far. Meanwhile, the reward and the estimated maximal Q-value can be seen as the knowledge the agent has just attained through the current action and the future prediction, respectively. When \(\alpha\) is relatively small, e.g., less than 0.5, increasing \(\alpha\) helps exploit the experience and the future forecast in making the decision, thus improving the goodness of the actions. However, when \(\alpha\) is significantly large, the current experience and the future prediction dominate the Q-value. It means that the agent makes a decision primarily based on the current reward and future forecast and ignore all the experiences the agent has learned so far. The Q-learning now converges to the greedy approach. That is why the performance drops severely when \(\alpha\) increases from 0.5 to 0.6 and becomes stable beyond that. From the experiment results, \(\alpha\) should be moderate values around 0.4 and 0.5.
###### Abstract  
This study aimed to design chaos-based synchronized dynamic keys and develop an improved chaos-based advanced encryption standard (AES) algorithm with the proposed synchronized random keys. First, based on sliding mode control (SMC) technology, a rippling control scheme was introduced to guarantee the synchronization between master-slave discrete chaotic systems. Under the synchronization, the same dynamic random chaos signals could be simultaneously obtained at the transmitter and receiver in communication systems. Then, a novel modified AES cryptosystem with dynamic random keys based on chaos synchronization was presented. In a traditional AES cryptosystem, a static key is used, and it must be exchanged in advance and confirmed to be safely kept. However, in the proposed design, by introducing the synchronization technology of chaotic systems, the static key becomes dynamic and random, and it does not need to be kept or transmitted in open channels. Consequently, the disadvantage of key storage could be eliminated and the security of encryption could be improved. Finally, the developed chaos-based AES (CAES) algorithm has been applied to construct a novel image encryption algorithm. The statistical analysis, histogram, information entropy, and correlation indexes have been calculated and analyzed through simulation experiments in order to demonstrate the capability and improvement of this presented CAES cryptosystem.  
advanced encryption standard; rippling sliding mode control; synchronization; dynamic random key; image encryption Article
### Ash Yields and Carbon Contents
#### 3.5.2 Fly Ash  
Smaller sized fuel particles, dust produced by attrition in the bed or soot formed by fuel conversion are entrained from the fluidized bed and are considered fly ash. As described in Section 2.2, this fly ash is in the used research facility captured by a primary cyclone, a secondary cyclone and a ceramic candle filter. The yields of the respective fly ash fractions for the reference run (\(n_{\rm O2}=0.25\), \(\theta=842\), \(n_{\rm SC}=1.05\) mol mol\({}^{-1}\)) are \(Y_{\rm ash,prim.cyclone}=0.051\) kg kg\({}^{-1}\), \(Y_{\rm ash,sec.cyclone}=0.008\) kg kg\({}^{-1}\) and \(Y_{\rm ash,filter}=0.003\) kg kg\({}^{-1}\). It can be seen that the majority of the fly ash is separated by the primary cyclone.  
In Table 7, the fly ash yield and the carbon mass fraction are shown for different runs, wherein all respective fly ash fractions (prim. cyclone, sec. cyclone, filter) have been cumulated. It can be seen that the fly ash yield per fuel mass is for all presented runs between 0.059 kg kg\({}^{-1}\) and 0.072 kg kg\({}^{-1}\). The carbon mass fraction of the fly ash varied between 0.047 kg kg\({}^{-1}\) and 0.110 kg kg\({}^{-1}\) and was found to be dependent on gasification temperature.
# The Cerebral Arterial Wall in the Development and Growth of Intracranial Aneurysms
## 1 Introduction  
Intracranial aneurysms (IAs) are reported in around 3-5% of the general population and are most common in individuals aged between their third and sixth decades of life [1, 2, 3, 4]. The mean age of rupture is between 50-55 years, with some geographic and racial differences [1, 2, 3, 4, 5, 6]. The prevalence is higher in females due to hormonal status [3, 6]. With the advances in imaging modalities, the rate of detecting unruptured intracranial aneurysms is also increasing globally, but the average five-year rupture risk is as low as 0.3-15% [7, 8, 9, 10]. Several scales, including the PHASES score and ISUIA scale, may provide the cumulative risk of rupture of IAs depending on age, race, comorbidity, previous history, and size and location of the aneurysm [11, 12].  
IAs are pathologically characterized by a focal or segmental alteration of the arterial wall [13, 14]. The loss of internal elastic lamina with disruption of the media is the classic pathologic change observed with IAs [13, 14]. Precisely, IAs may be considered as the phenotypic expression of the arterial wall disease, either focal or segmental or of any underlying etiology. Considering the aneurysm just as an outward expansion of the arterial wall would not explain the different etiology of aneurysm formation and growth. The basic pathogenic mechanism of such an alteration in the arterial wall is complex and still not clearly understood. The observations made by [PERSON] in 1997 that the intracranial arteries have a thin tunica media and lack external elastic lamina are not incorrect but do not fit for all types of aneurysms [15]. Moreover, a series of studies havedemonstrated that hemodynamic stress induces endothelial dysfunction and activates cascades of inflammatory cytokines, which amplifies a chronic inflammatory process [1].  
In general, the interaction between vessel wall alteration, inflammation, hormones, genetics, hemodynamics, and environmental factors orchestrates aneurysm formation, growth, and rupture. In this review, we focused on the role of the cerebral arterial wall in aneurysm formation and growth mechanisms.
# High-Speed GaN-Based Superluminescent Diode for 4.57 Gbps Visible Light Communication
## Materials and Methods  
### Blue SLD Structure  
The epitaxial structure of the device is grown on a c-plane GaN substrate using a metal-organic chemical vapor deposition (MOCVD) technique. The SLD is fabricated based on a conventional laser epi-structure [7], consisting of In\({}_{0.15}\)Ga\({}_{0.85}\)N/GaN quantum wells as the active region, together with AlGaN cladding layers, GaN waveguiding layers and an AlGaN electron blocking layer. The top GaN layer is highly Mg-doped in order to guarantee high-quality ohmic contact. The SLD has an etched ridge waveguide structure and an 8.3\({}^{\circ}\) tilted facet, as illustrated in Figure 1a. This angle is controlled for minimizing the direct reflectance from the front facet, and thus the formation of a resonant cavity is suppressed. In particular, anti-reflective (AR) coating is not needed for this structure. The device mesa and 2 um ridge waveguide is defined by UV photolithography and plasma etching. The Pt/Au and Ti/Al/Ti/Au metal contacts are deposited using RF sputtering as \(p\)- and n-contacts [7]. The tilted facet is defined using focus ion beam (FIB) milling technology using FEI Helios System. In order to increase the output optical power, the back facet of the SLD is coated with a highly reflective (HR) mirror with reflectivity over 90%. The top view of the fabricated device is shown in Figure 1b.
# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 2 Modelling
### Path Optimization Model
In summary, a path optimization model can be constructed for the hotspot area of the airport field with the objective of minimizing the total taxiing time of the aircraft by taking into account the constraints of the aircraft's taxiing path, the safety interval, and the conflicts during the taxiing process.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 4 Detection of Various Damage Types Using ABT-ABT on a 3-mm Quasi-Isotropic Composite Plate
### Composite Specim  
A 3-mm-thick in-house quasi-isotropic CFRP composite plate with a stacking sequence of [\(-\)45/90/45/0]\({}_{\rm 3s}\) was investigated. The dimension of the specimen was 500 mm \(\times\) 500 mm \(\times\) 3 mm. There were two different types of damage in the 3-mm quasi-isotropic composite plate, including delamination simulated by the Teflon insert and impact damage. A hot press machine was used to manufacture the composite specimens, as shown in Figure 3. The simulated delamination was created by inserting a circular Teflon film (25 mm in diameter) between plies 20 and 21, which was 0.5 mm away from the bottom surface of the specimen. For the impact damage, a 304 mm \(\times\) 204 mm coupon was cut from the 3-mm quasi-isotropic composite plate and was used to conduct the impact testing using a drop-weight impact tower. The details of the impact test are given in Table 3. This table indicates that the coupon was impacted using a 3.06-kg impactor with a potential energy of 17.56 J. This energy was chosen using engineering judgment to try to obtain a 25-mm-impact damage size in the 3-mm-thick quasi-isotropic composite coupon. The force-time history and the absorbed energy of the [\(-\)45/90/+45/0]\({}_{\rm 3s}\) coupon are given in Figure 15.  
An ultrasonic C-scan was performed on the 3-mm-thick quasi-isotropic composite plate to verify and image the simulated delamination and the impact damage. An ultrasonic immersion tank was used to inspect the specimen. In the experiment, a 10-MHz, 25.4-mm focused transducer was used. First, a full-specimen NDT inspection was conducted, and there were no other manufacturing defects. Then, a small-area scan was performed to detect and image the delaminations simulated by the Teflon inserts and the impact damage. The scan area was 75 mm \(\times\) 75 mm. After an initial scan with the focus on the top surface, the transducer was refocused at the depth of the Teflon film and the maximum impact damage for a clearer C-scan image. Figure 16 shows the C-scan images of the NDT results. The C-scan images show the presence of the 25-mm delamination and the 25-mm impact damage. Therefore, the simulated delamination and impact damage were successfully detected and quantified in the NDT detection.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 2: Self-Constructed Bearing Experimental Dataset
#### 4.3.2 Model Diagnostic Performance Comparison and Analysis  
Table 8 presents the experimental results for the lightweight and diagnostic evaluation metrics of different models. The proposed model achieved 100% in four diagnostic evaluation metrics, accuracy, P, R, and F1, demonstrating the best performance among all the compared models. Although the proposed model was not optimal in terms of parameters and diagnostic time, it improved all of the diagnostic evaluation metrics by 2.33% and reduced the time by 29% compared to the best small model of SqueezeNet. Additionally, there was only a 0.04 ms time difference between the proposed model and ResNet-18, but the number of parameters was only 17% of the number in ResNet-18.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault Type** & **Fault Label** & **Train** & **Test** \\ \hline Normal & 0 & 130 & 70 \\ Cage Fault & 1 & 130 & 70 \\ Inner Ring Fault & 2 & 130 & 70 \\ Outer Ring Fault & 3 & 130 & 70 \\ Rolling Ball Fault & 4 & 130 & 70 \\ Rust Fault & 5 & 130 & 70 \\ \hline \end{tabular}
\end{table}
Table 7: Self-Constructed bearing experimental dataset composition.  
Figure 15: Accuracy and loss curves of HFDF-EffNetV2 during the training process in Case 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Accuracy/\%** & **P/\%** & **R/\%** & **F1/\%** & **Parameters/M** & **Time/ms** \\ \hline EfficientNetV2-S & 99.67 \(\pm\) 0.19 & 99.67 \(\pm\) 0.19 & 99.67 \(\pm\) 0.19 & 99.67 \(\pm\) 0.19 & 20.10 & 28.05 \(\pm\) 0.72 \\ ResNet-18 & 99.95 \(\pm\) 0.10 & 99.95 \(\pm\) 0.09 & 99.95 \(\pm\) 0.10 & 99.95 \(\pm\) 0.10 & 11.18 & 17.52 \(\pm\) 0.62 \\ RepViT & 99.14 \(\pm\) 0.44 & 99.16 \(\pm\) 0.44 & 99.14 \(\pm\) 0.44 & 99.14 \(\pm\) 0.44 & 4.72 & 23.17 \(\pm\) 0.77 \\ SqueezeNet & 97.67 \(\pm\) 0.18 & 97.67 \(\pm\) 0.17 & 97.67 \(\pm\) 0.18 & 97.67 \(\pm\) 0.18 & 0.73 & 24.57 \(\pm\) 0.40 \\ MPNet & 99.63 \(\pm\) 0.24 & 99.63 \(\pm\) 0.24 & 99.63 \(\pm\) 0.24 & 99.63 \(\pm\) 0.24 & 2.50 & 21.00 \(\pm\) 0.51 \\ MobileNetV3 & 95.95 \(\pm\) 0.48 & 95.95 \(\pm\) 0.48 & 95.95 \(\pm\) 0.48 & 95.95 \(\pm\) 0.48 & 3.91 & 18.69 \(\pm\) 0.35 \\ HFDF-EffNetV2 (Ours) & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 100 \(\pm\) 0.00 & 1.85 & 17.56 \(\pm\) 0.41 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of different models’ diagnostic performances in Case 2.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
###### Abstract  
Currently, most digital image watermarking schemes are affected by geometric attacks like rotation, scaling, and translation (RST). In the watermark embedding process, a robust watermarking scheme is proposed against RST attacks. In this paper, three-level discrete wavelet transform (DWT) is applied to the original image. The three-level low frequency sub-band is decomposed by the singular value decomposition (SVD), and its singular values matrix is extracted for watermarking embedding. Before the watermarking extraction, the keypoints are selected by scale-invariant feature transform (SIFT) in the original image and attacked image. By matching the keypoints in two images, the RST attacks can be precisely corrected and the better performance can be obtained. The experimental results show that the proposed scheme achieves good performance of imperceptibility and robustness against common image processing and malicious attacks, especially geometric attacks.  
Keywords:image watermarking; scale-invariant feature transform (SIFT); discrete wavelet transform (DWT); singular value decomposition (SVD); geometric attacks +
Footnote †: journal:
## 4 Experimental Setup
### Evaluation Metrics  
We adopt Mean Reciprocal Rank (MRR) and Hits@\(K\) (\(K=1,3,10\)) [1] as the evaluation metrics. MRR measures the average reciprocal rank of the correct entity in the prediction results, while Hits@\(K\) indicates whether the correct entity appears within the top-K ranked candidates. A higher MRR score reflects better overall ranking performance of the model, whereas a higher Hits@\(K\) value represents a greater probability of covering the correct answer within the top-\(K\) candidates [1]. These metrics jointly evaluate the model's performance in terms of both precision and recall.
## 4 Remote Sensing Platforms Used in Peatland Research
### UAV-Based Remote Sensing
#### 4.3.1 Mapping Carbon Fluxes and Stocks  
An article by [PERSON] et al. (2021) demonstrates the potential of UAV remote sensing for mapping CO\({}_{2}\) fluxes and carbon (C) stocks in peatland ecosystems. Their findings highlight the potential of UAV remote sensing for accurately assessing microform groups and estimating CO\({}_{2}\) fluxes [116]. Those findings are consistent with previous research by [PERSON] et al. (2019), who successfully mapped vegetation communities in rewetted fen sites using UAV-based multisensory data [117]. By exploring the potential use of aboveground vegetation characteristics as indicators for mapping below-ground C stocks in peatlands, they demonstrate that UAV remote sensing combined with structural equation modelling can improve the estimation of below-ground C stocks and our understanding of C dynamics in peatland ecosystems.
## 5 Results and Discussion
### Comparative and Qualitative Analysis
The ConvGRU-L\({}_{\text{S}}\) model significantly deviates, underestimating both the intensity and extent of scintillation and failing to depict the broader context effectively, which may hinder its application in scenarios requiring detailed monitoring. Similar to sunrise and noon, the sunset scintillation in 2020 was less pronounced than in 2015. The intensity and spread were reduced, highlighting the overall decrease in ionospheric activity during the solar minimum. The ConvGRU-L\({}_{\rm C}\) model closely aligns with observed data, accurately capturing primary scintillation zones and gradients, albeit with slight overextensions in less intense areas. This model's high fidelity makes it ideal for applications necessitating precise monitoring. Conversely, the ConvGRU-L\({}_{\rm log-cosh}\) model diffuses scintillation intensity, spreading activity more broadly and less intensely, which may not suffice for specific needs. The ConvGRU-L\({}_{\rm S}\) model significantly underestimates both the intensity and distribution of scintillation, presenting a diluted view of ionospheric dynamics that could be less effective for detailed monitoring. Overall, the ConvGRU-L\({}_{\rm C}\) model stands out for its accurate and detailed replication of observed ionospheric conditions, proving highly effective for operational monitoring and prediction. The ConvGRU-L\({}_{\rm log-cosh}\) model, while useful for broader assessments, lacks the precision necessary for detailed studies. In contrast, the ConvGRU-L\({}_{\rm S}\) model underperforms in capturing the complexity of ionospheric dynamics, especially during periods of intense solar activity.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 3 System Overview
### Complexity Estimation of Function Call Sequence  
To estimate the complexity of the function call sequence, we start with the nodes of the sequence. A function constitutes a node, so we need to analyze the complexity of the function. Specifically, we evaluate the complexity of a function using its control flow graph. A CFG is a directed graph, so we estimate the complexity using two aspects: the nodes of the graph and the structure of the graph.  
For the structural complexity of a CFG, we use the classic cyclomatic [30] complexity to measure it. It is a quantitative measure of the number of linearly independent paths. Given a CFG, the number of edges of the graph \(E\), the number of the nodes of the graph \(N\), and the number of connected graph components \(P\), the cyclomatic complexity \(C_{M}\) is calculated using  
\[C_{M}=E-N+2P \tag{1}\]  
The nodes of the graph are basic blocks, which are composed of assembly instructions. Due to different assembly instructions having their own execution time, we calculate the complexity of a basic block first. Given a basic block \(B\), all the assembly instructions of the basic block \(I_{B}\), and the time required to execute an instruction \(T\), the basic block complexity \(C_{B}\) is calculated using  
\[C_{B}=\sum_{i\in I_{B}}T(i) \tag{2}\]  
Figure 2: The system overview of SEZZ.  
Our design of the complexity of the basic blocks within a function reflects the scale of the function, and the design of structural complexity can be seen as a weighted consideration of the scale of the function. Therefore, we calculate the complexity of a function \(C_{F}\), where \(\mathbb{B}_{F}\) denote all the basic blocks of function \(F\):  
\[C_{F}=\log C_{M}\sum_{B\in\mathbb{B}_{F}}C_{B} \tag{3}\]  
After obtaining the complexity of a function, we can estimate the complexity of the sequence of functions, i.e., the function call sequence. We add up the complexity of each function in the function sequence to get the complexity of the function sequence \(C_{S}\) using  
\[C_{S}=\sum_{F\in\mathbb{F}_{S}}C_{F} \tag{4}\]