esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
###### Abstract  
The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.  
s +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Numerical Simulation
### Obtaining Mesoscopic Parameters
#### 3.3.2 Damage Analysis of Different Freezing and Thawing times  
* Crack damage during the freezing and thawing process:  
During the freezing and thawing process of SRM samples, the volume change caused by the water ice phase change is the main cause of irreversible freeze-thaw damage. Only its macroscopic strength and failure mode can be studied in lab tests. Further microscopic analysis of freeze-thaw fissure damage was performed via the particle flow program.  
Figure 12a demonstrates the number of fissure developments under different freeze-thaw cycles. As the number of freeze-thaw cycles increases, the fissures generated by contact fractures between particles in the SRM also increase. At the same time, the damaging impact generated by the freeze-thaw cycle and the increase in the number of fissures will be reduced. This indicates that while the damage caused by the freeze-thaw cycle on the SRM will reach its peak value in a certain number of cycles, the subsequent increase in the number of freeze-thaw cycles will gradually decrease the impact on the soil-rock mixture. This indicates that the damage of the freeze-thaw cycle on the SRM will reach a peak at a certain number of cycles, and the subsequent increase in the time of freeze-thaw cycles on the SRM will gradually reduce the impact.  
* Stress-strain curve:  
Figure 13 shows the stress-strain curve of the SRM sample after different freeze-thaw cycles under a confining pressure of 100 kPa. From the figure, it can be seen that under different numbers of freeze-thaw cycles, the peak stress and elastic modulus of the curve will be different and decrease as the number of freeze-thaw cycles increases. On the other hand, after the stress reaches the peak value, the curve of cycle 0 almost fluctuates near the peak value. After undergoing freeze-thaw cycles, the curve will show a certain post-peak softening trend.  
Figure 12: Number of cracks in SRM under different numbers of freezing and thawing cycles. (**a**) Number of fractures in the soil–rock mixtures at different numbers of freeze–thaw cycles. (**b**) Relationship of modeled cracks with the number of freeze–thaw cycles.  
In the process of increasing the freeze-thaw cycles, it can be seen that the first freeze-thaw cycle causes the greatest damage to the soil-rock mixture, ranging from never experiencing frost heaving to contact fracture between particles to produce tensile cracks, the peak strength is reduced. As the number of freeze-thaw cycles increases, further damage will be caused, but the degree of damage will gradually decrease. Finally, it will almost remain unchanged when it reaches a certain number. For example, when the number of freeze-thaw cycles increases from 10 to 20, less than 100 tensile cracks are added, and the peak intensity is reduced by about 10 kPa.
## 5 Experimental Results
### Security Analysis  
The proposed lightweight Diffie-Hellman (D-H) protocol is specifically designed to address the unique security challenges of resource-constrained IoT environments. This section presents a comprehensive analysis of the protocol's security properties, with a focus on ensuring confidentiality, authentication, integrity, and resistance to known attacks.
# Link Prediction with Hypergraphs via Network Embedding
## 2 Materials and Methods  
Figure 1 shows the complete flow chart for HNE, the link prediction approach we propose based on hypergraphs with network embedding. First, the heterogeneous networks constructed from library loan records of seven universities are explored, which consists of two types of nodes (_Node I_ represents students, _Node II_ indicates the books borrowed by the  
students from libraries) and their interactions. The hypergraph is constructed according to these interactions; the hyperlinks represent _Node II_ linked with _Node I_. The _Node I_ network is constructed based on hypergraph properties. The incidence matrix denotes the relationships between _Node I_ and hyperlinks. The adjacency matrix describes the links between _Node I_. Second, the embedding vectors of _Node I_ are generated by the network embedding model. Then, the embedding vectors of links are generated by concatenating the vectors of pairwise nodes. Finally, the links vectors are divided into training data and testing data. The training data are put into the random forest classifier to train the model, then the testing data are used to predict potential links.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery
### Content Recommendation
#### 4.2.2 Average File Size Constraint  
\[\begin{split}\text{maximize}&\sum_{u=1}^{U}\sum_{ f=1}^{F}r_{uf}x_{uf}\\ \text{subject to}&\frac{1}{N}\sum_{f=1}^{F}x_{uf} l_{f}\leq\delta*R_{u}*T_{s}\qquad\forall u\\ &\sum_{f=1}^{F}x_{uf}=N.\end{split} \tag{26}\]  
Similarly, this problem can be decoupled and solved with respect to each user. For each user, the problem becomes a two-dimensional cost knapsack problem [38], which could be solved by dynamic programming. Define \(g(f,l,j)\) to be the maximum interest that can be attained with the file size less than or equal to \(l\) and the number of contents less than or equal to \(j\) using contents up to \(f\) (first \(f\) contents). Then, the state transition equation is:  
\[g(f,l,j)=\max\{g(f-1,l,j),g(f-1,l-l_{f},j-j_{f})+r_{uf}\} \tag{27}\]  
where \(l_{f}\) represents the size of content \(f\) and \(j_{f}\) always equals one, which means that one content is recommended. That is, when it is decided whether or not to put the \(f\)-th content, we need to compare the interest in putting the \(f\)-th content and the interest in not putting the \(f\)-th content.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 5 Conclusions  
This study proposes a novel procedure using solvents to purify produced water. Produced water contains high concentrations of salts, primarily sodium chloride, which, in the case of the Rumaila oil field (Iraq), taken as a reference, is approximately 80,000 ppm. It also contains oil droplets in the form of highly stable, complex emulsions that are difficult to break.  
The procedure involves mixing a solvent with produced water containing an initial oil concentration of 150 ppm under agitation. The mixture is then left to rest for 10 min, allowing the oil to separate as a supernatant phase. This oil can be recovered and commercialised.  
A total of 40 solvents from various chemical families were tested: linear aliphatic hydrocarbons, cyclic aliphatic and aromatic hydrocarbons, chlorinated hydrocarbons, alcohols and glycols, low- and high-molecular-weight ethers, ketones, esters, and a miscellaneous group of solvents with special characteristics.  
The most effective solvents, with oil recovery rates exceeding 99%, were tetrachloroethylene (100%), tetrahydrofuran (100%), cyclohexane (99.5%), xylene (99.4%), and dichloromethane (99.4%). Tetrachloroethylene was excluded due to its suspected carcinogenicity [41], and dichloromethane was ruled out because its boiling point (40 \({}^{\circ}\)C) is below the industrial operating temperature of 45 \({}^{\circ}\)C. Therefore, cyclohexane, xylene, and tetrahydrofuran are preferred. These three solvents are relatively non-toxic compared with crude oil, are readily available at an industrial scale, and are cost-effective.  
It was also found that solvents capable of reducing the oil content in produced water to below 20 ppm (oil recovery exceeding 87%) are grouped into three solubility spheres according to the Hansen solubility parameter (HSP) model:  
* A lipophilic sphere with a centre at (16.6, 5.1, 1.6) and a radius of 5.5,
* A lipophilic-hydrophilic sphere with a centre at (16.9, 9.2, 13.7) and a radius of 5.2,
* A hydrophilic sphere with a centre at (14.9, 11.4, 26.0) and a radius of 4.1.  
This grouping is explained by the amphiphilic nature of resins and asphaltenes, meaning that any solvent within these spheres can destabilise oil emulsions and purify produced water.  
In conclusion, the application of solvents is an effective and viable alternative for the purification of produced water.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 3 Methods  
There is a wide variety of fabric defects; common ones include cracks, color differences, and holes. The complexity and diversity of these defects, especially the differences in size, make the inspection task more difficult. The complexity of defects exceeds the capabilities of traditional machine vision methods, which cannot meet the high accuracy and real-time performance required in practical applications. Although the YOLO series models perform well in target detection, they still face challenges such as high computational effort, incomplete detail recovery, and insufficient generalization capability in the application of high-resolution fabric images.  
To address these issues, YOLOv8n is used as the benchmark model, and the YOLOv8 architecture is enhanced to better adapt to fabric defect detection. The improved network structure is shown in Figure 1, which mainly includes the following key parts: the RG-C2f module, the DySample upsampling operator, and the improvement of the WIoU loss function. First, the RG-C2f module is proposed, which combines the RepConv structure and the adjustable scaling factor to significantly enhance the feature extraction capability while reducing the computational and parametric quantities to ensure fast inference for high-resolution images. Second, the DySample upsampling operator replaces the traditional nearest neighbor interpolation method, which effectively preserves the image details and improves the detection accuracy by learning the optimal sampling pattern. In addition, the introduction of the inner-WIoU loss function combines dynamic sample weights with auxiliary frames of different scales, optimizes the border regression process, and boosts the model's capacity to identify complex fabric defects. These improvements enable YOLOv8 to achieve higher detection accuracy and efficiency when dealing with fabric defect detection tasks.
## 4 Results and Discussion
### Evaluating the Performance of the Model Based on Data Updates  
In practical quantitative analyses of storage tank accident probabilities, the emergence of new data may lead to changes in the structure of the DFT, such as the addition of new BEs. In this case, traditional computational methods face a complex recalculation process. In contrast, the ANN-based method proposed in this paper demonstrates remarkable adaptability and efficiency, which can quickly respond to the structural changes in the DFT and provide fast prediction results.  
By deeply analyzing the data in Table 6, the ranking of the 22 BEs in terms of their degree of influence on TE occurrence was obtained, and the results are displayed in Table 9. Assuming that only 21 events were included in the initial incident analysis (removing the least influential event X13), in this case, the MSE of the prediction set obtained by applying the Fuzzy DFT-ANN model proposed in this paper is \(1.96\times 10^{-6}\). However, the MSE of the prediction set is significantly reduced to \(1.23\times 10^{-6}\) when the model is extended to include all 22 BEs. The result not only highlights that the new data play a key role in improving the model prediction accuracy, which can enhance the accuracy of the calculation, but it also confirms the high efficiency and adaptability of the proposed ANN model for storage tank accidents in the quantitative calculation of accident probability.  
The findings in this section show that ANN models can be optimized continuously to provide more accurate probability predictions as new data are continuously integrated. This data-driven approach to model optimization provides an effective tool for dynamic risk management, especially in scenarios that require rapid response to system changes and timely updating of risk assessment results.  
Figure 9: The comparison of different numbers of hidden layer neurons in different training sets. Note: The MSE curve for 100 training sets is referenced on the left-side \(y\)-axis with the same color, while the MSE curves for 400, 800, and 1000 training sets are referenced on the right-side \(y\)-axis with the same color.
# SDToW: A Slowloris Detecting Tool for WMNs
## 5 Results
### Experiment 1: Measuring How the Traffic List Size Affects the Blocking Delay  
We start the experiment using one attacker client on the first AP near the Web server. Therefore, the attack occurs at a one-hop distance from the target. We use the experiment to analyze how much the traffic list size influences the blocking time.  
After starting the attack, the collection module CM running on the Web Server captures the network traffic and creates a traffic list (TL). CM sends the TL to the Concentrator, which through its analysis and filtering module (AFM), applies the pre-established filters to detect the attacker's IP address. Upon identifying the malicious IP, it will be added to a blacklist and later sent to the AP. Once the blacklist is received, the BM compares the received IPs with its ARP table, obtaining the respective MAC address. After that, the blocking module (BM) prevents new traffic from the attacker by blocking its MAC address.  
In this experiment, we varied the amount of HTTP traffic to change the traffic list size. By changing the traffic list size, we analyzed its impact on the total blocking time. The traffic list size directly influences the transfer list time (\(TL_{T}\)) and the traffic list processing time (\(TLP_{T}\)). Therefore, by increasing the time to send and process the traffic list, this consequently raises the total blocking time (\(TB_{T}\)); see Figure 8.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 3 Learning to Solve COPs
### Methods  
#### 3.1.1 Supervised Learning  
Combining with Branch and Bound  
Traditional approaches for COPs have the same challenges: high computational complexity and dependence on expert knowledge. There have been rich works studying how to replace some components of traditional approaches with supervised learning methods to respond to those challenges. Most of them focus on a classical algorithm called branch and bound (B&B) [69], which is broadly applied for ILP-formed COPs. Table 1 summarizes them.  
Figure 2: ML methods for COPs. Recent works are categorized firstly based on the ML approach and then the fundamental problems in COPs.  
[PERSON] et al. [70] proposed to approximate the scoring strategy of strong branching (SB) with supervised learning for solving mixed ILP problems. Similarly, [PERSON] et al. [71] proposed to speed up the traditional B&B method with imitation learning. [PERSON] et al. clearly formulated B&B search as a sequential decision-making process and learned the node selection (prioritizing) policy as well as a node-pruning policy using DAgger. Compared to traditional approaches, this method learns problem-solving patterns from experience, which speeds up the future process. Meanwhile, the sequential decision-making process takes the effects on the future into account when choosing actions. It may be also treated as a simple reinforcement learning approach since it involves the concept of learning from demonstration, but essentially, it directly fits the policy with collected data, which is more like a kind of supervised learning.  
Moreover, [PERSON] et al. [72] proposed to solve the scoring problem of SB as a learning-to-rank problem as opposed to a regression or classification problem, and enabled an on-the-fly solver, where the transition from traditional to ML approaches is seamless. That is, instead of learning a policy offline as [70; 71], [PERSON] et al. proposed a three-phased online method for a given mixed ILP problem. Firstly, SB is used as the branching strategy for a limited number of nodes. Then, the dataset collected in phase is fed into a learning-to-rank algorithm. At last, the learned model replaces SB for branching until termination.  
[PERSON] et al. [73] applied the above idea to the selection of quadratic semi-definite outer approximation of cutting planes. They used NNs to learn cut selection offline, thereby reducing the computational complexity during testing. [PERSON] et al. [74] applied the idea for the container pre-marshaling problem (CPMP) (the Container Pre-Marshaling Problem is concerned with the re-ordering of containers in container terminals during off-peak times so that containers can be quickly retrieved when the port is busy), using DNNs to learn the branching strategy and predict bounding bounds.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Wavelet Signal Decomposition  
The filtered signals underwent wavelet transformation based on the Mallat algorithm [39]. Figure 6 illustrates the decomposition diagram of the employed filter bank. The signal was filtered using high-pass filters (HPF) to calculate detail coefficients (D) and low-pass filters (LPF) to compute approximation coefficients (A). Additionally, to reduce dimensionality at the output of each filter, the signal was downsampled by a factor of 2. In this process, the Daubechies 8 (db-8) wavelet function was chosen due to its effective balance between time and frequency localization.  
The signals passed through the wavelet filter bank, where wavelet coefficients were constructed in the alpha (8-13 Hz) bands, associated with states of conscious relaxation [8], and beta (13-32 Hz) bands, attributed to motor functions [6]. In the alpha band, we utilized coefficients A7 and A11 at decomposition levels 4 and 5, while for the beta band, we used coefficients D9, D11, A6, A8, and A10 at decomposition levels 3 and 4. The number of samples per each 1 s epoch was reduced from 256 to 32 for the beta band and to 16 for the alpha band.  
Delta and theta bands were not employed because these bands typically appear in infants, while in adults they only appear during sleep or indicate the presence of other disorders when present during wakefulness [7]. Moreover, the gamma band, related to cognitive brain functions, is not used in this work as it requires the combination of several wavelet coefficient levels 2, 3, and 4, with level 2 operating at 64 samples per second.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Signal**} & \multicolumn{5}{c}{**SNR**} \\  & & & **(dB)** & & & \\ \hline WGN level & \(-15\) & \(-10\) & \(-5\) & 0 & 5 & 10 & 15 \\ SGF 5–15 & \(-5.3\) & \(-3.63\) & \(-0.75\) & 3.32 & 7.97 & 12.8 & 17.79 \\ SGF 13–31 & \(-4.64\) & \(-3.16\) & \(-0.46\) & 3.47 & 8.07 & 12.87 & 17.85 \\ SGF 22–35 & \(-2.84\) & \(-1.76\) & 0.40 & 3.90 & 8.29 & 13.01 & 17.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results obtained from comparing the SGF 22-35 filter with other SGF filters.  
Figure 5: Comparative spectrograms of the signal with 15 dB noise, before and after SGF filtering. (**a**) Spectrogram of the noisy signal (SNR = 15 dB). (**b**) Spectrogram of the filtered signal with filter SGF 22–35.  
Our focus is on reducing the computational complexity, which is why the alpha band at 16 samples per second with two wavelet coefficients and the beta band at 32 samples per second with five wavelet coefficients better meet this requirement.