esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Discussion
### Implementation  
We have implemented the three strategies, exhaustive, genetic and greedy, as three plug-ins of the ProM process mining framework (available in the ProM nightly-builds, [[http://www.promtools.org/prom6/nightly](http://www.promtools.org/prom6/nightly)]([http://www.promtools.org/prom6/nightly](http://www.promtools.org/prom6/nightly))), within the Configurable Processes package. The genetic evolutionary strategy is implemented using a Genetic Algorithms and Genetic Programming package written in Java (JGAP); this flexible package fits in our genetic evolutionary approach. All experiments have been performed in a laptop with an Intel Core i5 CPU at 2.7 GHz, 8 GB RAM, running OS X El Capitan 64 bits.  
Figure 9: Overview of the greedy strategy to derive a process tree. There are two event log inputs to show different subtree and sub-log scenarios to finally derive a process tree.  
```
1:procedureGreedy(Log \(L\), CPT \(Q^{\alpha}\), Weights \(W\))
2:\(configurableNodeList\gets getPrioritizedConfigurableNodes(Q^{\alpha})\)
3:\(bestConfiguration\leftarrow emptyConfiguration()\)
4:forall\(cn\in configurableNodeList\)do
5:\(sQ^{\alpha}\leftarrow getSubTreeFromConfigurableNode(Q^{\alpha},cn)\)
6:\(activityList\gets getActivityNodes(sQ^{\alpha})\)
7:\(sL\gets L\mid activityList\)
8:\(best\leftarrow null\)
9:forall\(conf\inator\in\alpha(cn)\)do
10:\(m\gets derive(sQ^{\alpha},configrator)\)
11:if\(\mathit{conformance}(m,sL,W)>\mathit{conformance}(\mathit{ derive}(sQ^{\alpha},best),sL,W)\)then
12:\(best\gets configurator\)
13:endif
14:endfor
15:\(bestConfiguration_{cn}\leftarrow best\)
16:endfor
17:return\(bestConfiguration\)
18:endprocedure
19:
20:procedure\(getSubTreeFromConfigurableNode(\textsc{CPT}\ Q^{\alpha}\), ConfigurableNode \(cn\))
21:if\(IsActivityNode(Q^{\alpha},cn)\)then
22:\(nodeRoot\gets getParent(Q^{\alpha},cn)\)
23:elseif\(IsOperatorNode(Q^{\alpha},cn)\)then
24:\(nodeRoot\gets cn\)
25:endif
26:forall\(n\in Ancestors(Q^{\alpha},nodeRoot)\)do
27:if\(IsLoopOperatorNode(Q^{\alpha},n)\)then
28:\(nodeRoot\gets n\)
29:endif
30:endfor
31:\(sQ^{\alpha}\leftarrow getSubTree(Q^{\alpha},nodeRoot)\)
32:forall\(n\in Descendants(sQ^{\alpha},nodeRoot)\)do
33:if\(n\
eq cn\)&&\(IsConfigurableNode(sQ^{\alpha},n)\)then
34:\(n\leftarrow\tau\)
35:endif
36:endfor
37:return\(sQ^{\alpha}\)
38:endprocedure
```  
**Algorithm 3** Obtaining a configuration based on the greedy strategy.
## 3 Leak Detection Experimental Setup
### Field Trials  
In order to fully evaluate the performance of the nodes and sensors in real life conditions they were deployed on a leak testing facility (connected to the main water supply). Each node was connected to two temperature sensors and one FSR based pressure sensor. One of the temperature sensors was attached directly to the pipe to measure the pipe wall temperature while the other temperature sensor was located approximately 30 cm away from the pipe in order to measure the soil temperature surrounding the pipe. Each sensor node measured all three parameters every 1,027 s (approximately 17 min) and transmitted the values to the mother node located in a building close to the nodes. These data were then logged locally and shared via the internet with other devices. Figure 9 shows the nodes installed on the pipe with sensors attached to the pipe and in the surrounding soil.  
Figure 8: Photo of the laboratory test setup for leak detection.  
Figure 9: Sensor node and its sensors attached to the pipe on a leak test facility.
# Detection of DDoS Attacks in Software Defined Networking Using Entropy
## 1 Introduction
There is no effective trust management mechanism between controller and application. Among the confirmed security vulnerabilities, DDoS is still one of the most important security problems at the control plane [9]. In view of the importance of controllers in software-defined networks, DDoS attacks on controllers are very dangerous and protecting controllers from attacks is also a major concern of researchers. DDoS attacks send a large amount of traffic to the network and consume network resources and cause network congestion. Many DDoS attacks are launched from distributed hosts [10]. DDoS attacks have two stages. First, an attacker creates a distributed attack network of thousands of targeted computers, known as zombies, robots or attack hosts. The attack host then sends massive traffic to the victim either on the attacker's command or automatically [11]. To build an attack network, attackers seek out computers that are less secure, such as those that have not been properly patched. A DDoS attack is an aggressive and destructive network attack that causes the system to stop working by depleting system resources. It can destroy the user's available network services, thus seriously threatening the network. When malicious data packets are sent by attackers on the network, normal traffic is processed or even cannot be processed due to the consumption of network resources. As a result, the network and servers become jammed and normal services are interrupted. Attackers who apply DDoS often target SDN mainly because of its unique characteristics. In order to solve the impact of DDoS attacks on the SDN controller, this paper proposes to use fusion entropy for attack detection. Through research, it is found that information entropy has the characteristics of rapid entropy reduction in attack scenarios, but it does not have the function of rapid detection. Although the log energy entropy reduces the entropy value in the attack scenario less than the information entropy, it can detect the attack at the beginning of the attack. Based on this, this paper proposes the fusion entropy, which combines the two entropies by weighting. The fusion entropy can effectively utilize the complementarity between the two entropies, which can quickly detect attacks and significantly reduce the entropy value. The remainder of the paper is organized as follows: Section 2 introduces related work of this study. Section 3 gives a detailed introduction to the proposed method. Section 4 is about the attack detection simulation. Section 5 is the experimental results and discussion. Section 6 presents the conclusion.
## 3 Patulin Control Methods
### During apple harvest processing and storage
One alternative to room storage is the use of packaging materials such as polyethylene, which through their own atmospheric control, have been shown to reduce patulin production in apples [211]. In the same study mentioned previously [206], patulin was not detected in cider from tree-picked apples stored 4 to 6 weeks at 0 to 20 but was detected at levels between 0.97 and 64 \(\mu\)g/Lin stored, tree-picked, unculled fruit. Other room apples stored in a controlled atmosphere and culled showed 0 to 15.1 \(\mu\)g/L patulin while unculled apple and yielded 59.9 to 120.5 \(\mu\)g/Lpatulin. The longer the storage of the apples, the greater the risk of increased patulin content. This is particularly evident to juice producers within the U.K. who see dramatic rises of patulin content in juice produced in June, July, and August- the months just prior to the new harvest season where source apples have been stored for almost a year [48]. Furthermore, even if apples are perfectly processed prior to storage, addition of diphenylamine, which is used to treat'storage scald' in stored apples, can provide an inoculum of patulin-producing fungi in the fruit [44]. Various fungicides and other treatments have been investigated for the ability to reduce apple rot and patulin production at all processing steps. Postharvest treatment of apples with benzimidazole fungicides was used from the 1970s through the early 1990s but has since been largely abandoned due to fungal resistance [44]. Apples inoculated with _P. expansum_ and heated at 38 \({}^{\circ}\)C for 4 days showed no decay lesions after storage at 1 \({}^{\circ}\)C for 6 months. After heat-treatment, inoculation with _P. expansum_ followed by infusion of 2% calcium chloride alone and _Pseudomonas syringae_ treatment alone reduced decay incidence by 25% each. Calcium treatment plus _Pseudomonas syringae_ treatment without and with subsequent heat treatment reduced decay by 89% and 91%, respectively [212]. Finally, other methods used for the control of human pathogens like _Escherichia coli_, such as washing with solutions of peroxyacetic acid, chlorine dioxide, and chlorine [213; 214; 215], may also provide some benefit toward preventing postharvest apple decay.
# A Model to Study the Creep Behavior of Carbon Fiber/Epoxy Resin Composites Under Temperature
## 2 Literature Review
A thorough understanding of the material's characteristics and mechanical constants is necessary to comprehend this phenomenon, as was studied in [44]. [PERSON]'s equation is a fundamental relation for studying the nonlinear viscoelastic creep behavior of carbon fiber-reinforced composites. A traditional, well-known, and effective technique for determining the overall engineering constant is the finite element method (FEM). Because of its exceptional mechanical qualities, which can be used in a variety of applications, carbon fiber-reinforced plastic adoption is a trend in the composites industry. The purpose of the paper [45] was to quantitatively measure the effects of temperature and strain/stress on the initial strain, permanent deformation, and modulus decay behavior of carbon fiber-reinforced plastic. In [46], the creep behavior of a structural epoxy adhesive is studied through an experimental program. For a variety of materials, the model provides a good fit with the experimental creep curves [47]. The results for a carbon fiber-reinforced composite with two fiber orientations and a brittle epoxy are given. When the epoxy matrix of the composite is in the glassy state, a fracture criterion and a creep model--which was effectively applied in a prior paper [47]--are used [48] to predict creep rupture at temperatures ranging from close to T-g to far below T-g. A three-dimensional (3D) local finite element analysis (FEA) of the mechanisms driving composite damage (in the vicinity of a fiber break) was presented in [49]. Viscoelastic and plastic matrix behaviors, both with and without debonding at the fractured fiber/matrix contact, are taken into account in the model. According to finite element analysis, even a basic viscoelastic equation that describes the behavior of epoxy glue causes the load on nearby unbroken fibers to increase with time.
# Edge Computing Approach for Vessel Monitoring System
###### Abstract  
A vessel monitoring system (VMS) is responsible for real-time vessel movement tracking. At sea, most of the tracking systems use satellite communications, which have high associated costs. This leads to a less frequent transmission of data, which reduces the reliability of the vessel location. Our research work involves the creation of an edge computing approach on a local VMS, creating an intelligent process that decides whether the collected data needs to be transmitted or not. Only relevant data that can indicate abnormal behavior is transmitted. The remaining data is stored and transmitted only at ports when communication systems are available at lower prices. In this research, we apply this approach to a fishing control process increasing the data collection process from once every 10 min to once every 30 s, simultaneously decreasing the satellite communication costs, as only relevant data is transmitted in real-time to the competent central authorities. Findings show substantial communication savings from 70% to 90% as only abnormal vessel behavior is transmitted. Even with a data collection process of once every 30 s, findings also show that the use of more stable fishing techniques and fishing areas result in higher savings. The proposed approach is assessed as well in terms of the environmental impact of fishing and potential fraud detection and reduction.  
vessel monitoring system; sea transportation; edge computing; tracking Article
## 4 Experiments and Results
### Evaluation Metric  
The MAE, MSE, and SSIM were used to evaluate the performance of the model. The lower the MAE and MSE, the more accurate the model's predictions, and the higher the SSIM, the greater the visual similarity between the predicted image and the actual image. The metrics are defined as follows.  
The MSE metric, which measures the average squared difference between the estimated and actual values, is mathematically expressed as  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(x_{i}-y_{i})^{2} \tag{10}\]  
The MAE metric, which measures the average magnitude of errors in a set of predictions, without considering their directions, is calculated as  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|x_{i}-y_{i}| \tag{11}\]  
The SSIM metric measures the similarity between two images in terms of luminance, contrast, and structure. The value of SSIM ranges between \(-1\) and \(1\), where \(1\) indicates identical images. Conversely, \(-1\) indicates that the images being compared are structurally completely the opposite. This is the case when one image is the exact shading inversion (negative) of the other. The SSIM is mathematically defined as  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{12}\]  
where \(x\) and \(y\) represent the images to be compared. \(\mu_{x},\mu_{y}\) represent the average intensity values of images \(x\) and \(y\), respectively; \(\sigma_{x}^{2},\sigma_{y}^{2}\) denote their variances; and \(\sigma_{xy}\) is the covariance between images \(x\) and \(y\). Constants \(c_{1}\) and \(c_{2}\) are included to stabilize the division with weak denominators; for example, \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\), where \(L\) is the dynamic range of pixel values, and \(k_{1}=0.01\), \(k_{2}=0.03\) by default. \(x_{i}\) and \(y_{i}\) correspond to the pixel values of the predicted and ground-truth data, respectively, and \(n\) denotes the total number of pixels in each image.
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 3 Proposed CM-LBT Scheme  
The aforementioned existing LB-LBT schemes use fixed contention window size and the fixed CCA threshold at LTE-LAA eNB. Our CM-LBT scheme controls the contention window size and CCA threshold by considering both the total system throughput and the fairness between LTE-LAA and WLAN systems. We assume a scenario in which one LTE-LAA eNB and multiple WLAN nodes, including WLAN access points (APs) and nodes, share the unlicensed spectrum, while LTE-LAA eNB is used as a supplemental downlink for the LTE technology operation using the licensed band [16].  
In this section, we first present an approach to estimating the throughput of LTE-LAA and WLAN systems according to the different contention window sizes and CCA thresholds. We then describe the determination of the contention window size and CCA threshold at LTE-LAA eNB. Table 2 summarizes the notations used in this paper.
## 4 Experimental Results
### Experimental Protocol  
The three datasets proposed in this paper are described in Section 3.1. For each dataset, 30% of the samples were reserved as the test partition while the remaining instances were further divided into training and validation partitions with, respectively, 40% and 30% of the total samples. The total amount of examples for each dataset was summarized in Table 2. The samples' selection for each partition was not random because subsequent videos of a given camera positioning share frames. Considering the fact that 10 different camera positions were used for each device in each dataset, data from different positions were divided into partitions as follows: samples from three positions from each device for the test set; four different positions for the training set; and the remaining three for the validation set. This guarantees that all instances from a given camera position would always be in the same partition, preventing information leaks from the training data to the testing data. Exploiting this strategy, the experimental results were validated using k-fold validation (\(k=3\)), choosing different sets of camera positions for each data partition.  
The external dataset investigated in the second series of experiments was the University of Houston Camera Tampering Detection Dataset (UHCTD) [39], proposed to test camera tampering detection methods. Tampering corresponds to an unauthorized or an accidental change in the view of a surveillance camera. In the UHCTD dataset, this includes covering, defocusing and intentionally moving cameras with malicious intent such as committing theft or property damage. In our work, the tampering instances are grouped to form the anomalous class.  
The dataset consists of 576 instances of tampering induced over 288 h of video captured by two surveillance cameras whose resolutions are 2048 \(\times\) 1536 and 1280 \(\times\) 960, respectively. To maintain the same experimental protocol adopted for the other datasets investigated in this work, videos were sampled at 55 frames, with 224 \(\times\) 224 spatial resolution. However, the data partitioning proposed by the UHCTD authors [39] was maintained, using the traditional hold-out validation strategy. Hence, after sampling, 30,252 samples of normal videos and 10,572 samples of anomalous videos were obtained to compose the training set. For the test set, 60,437 and 21,140 instances composed the normal and abnormal classes, respectively. In both sets, the class distribution is nearly 3:1, with anomalies comprising the minority class.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partition** & **60 frames** & **DenserGlitch** & **1R** & **Percentage** \\ \hline Train & 2496 & 144 & 1248 & 40\% \\ Validation & 1872 & 108 & 936 & 30\% \\ Test & 1872 & 108 & 936 & 30\% \\ \hline Total & 6240 & 360 & 3120 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amount of instances per partition for each of the produced datasets.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LEO & Low-Earth-Orbit \\ ISLs & Inter-Satellite Links \\ FRR & Fast Reroute \\ SR & Segment Routing \\ PLR & Point of Local Repair \\ \end{tabular}
# A Novel Deep Learning Model to Predict Ultimate Strength of Ship Plates under Compression
###### Abstract  
The evaluation of ship strength during the hull structure design stage is essential for structural safety. Additionally, plate is one of the basic structural members of the hull, it is important to analyze and predict the ultimate strength. The curved plate used for shipbuilding must be able to withstand repeated axial loads and complex loads, and stability needs to be confirmed through ultimate strength analysis. In general situations, the magnitude of the transverse compression is smaller than that of the longitudinal and combined loads, but transverse compression causes different physical behaviors from the longitudinal load state, which affects the ultimate strength, so study on the ultimate strength of the curved-plate under transverse compression is essential. Therefore, in this paper, a curved plate under transverse compressive load was selected as a subject, and the ultimate strength of the curved plate under the corresponding compression condition was predicted using a deep learning model. To obtain the training data for the deep-learning model, 4050 cases were selected and analyzed using the ANSYS. The accuracy of the model was verified by comparing the results predicted by the model and empirical equations with those of the FEM analysis. The study shows that it is possible to consider the ultimate strength more efficiently in the initial design stage of the ship.  
ultimate strength; curved plate; deep neural network Article
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion
### Strengths and Limitations  
This review presents several strengths, including a rigorous methodology based on the principles established by the PRISMA statement [24]. This approach ensures that the selection and evaluation of studies were conducted systematically and objectively, providing a _comprehensive overview_ of current malware detection techniques and their applications. Furthermore, the inclusion of _recent studies_ such as those by [PERSON] et al. [30] and [PERSON] et al. [79] ensures that the latest trends in the field, such as the use of _federated learning_ and _Transformers_, have been evaluated. This is particularly relevant since these emerging approaches are designed to better adapt to the dynamic IoT and modern cybersecurity environments.  
However, a notable limitation of this review is the dependence on a restricted set of indexed sources, which may have inadvertently excluded relevant studies published in alternative repositories or emerging venues. This constraint could limit the comprehensiveness of the literature surveyed, particularly in rapidly evolving areas of cybersecurity research.  
In terms of the _results of this review_, the _superiority_ of _machine learning_ and _deep learning_-based approaches over traditional signature-based methods is emphasized. For instance, _optimizable decision trees_ combined with the _AdaBoost_ algorithm, presented by [PERSON] et al. [83], have shown _98.84% accuracy_ in detecting malware in PDF files. These methods compare favorably with signature-based techniques, which often struggle to adapt to new malware variants. This reflects an advancement in the ability to _adapt to evolving threats_, a critical factor in today's cybersecurity landscape.  
When comparing approaches, it is evident that _machine learning_ techniques, such as _decision trees_ and _boosting algorithms_, offer greater accuracy and adaptability in malware detection compared to traditional signature-based methods. Additionally, approaches based on _computer vision_ and _convolutional neural networks (CNNs)_, such as the EfficientNet method presented by [PERSON] et al. [34], have proven particularly effective in detecting malware on _mobile devices_ and _cloud platforms_[35]. These platforms present unique challenges, such as the need to scale quickly and detect threats hidden within decentralized networks and heterogeneous devices.
## 3 Procedure, Data and Methodology
### Automatic Recognition of Lexical Situation Types
#### 3.3.2 Prediction Experiments Based on Embedding Vectors  
The experimental procedure for prediction based on embedding vectors follows a similar framework to the aforementioned approach. We extracted four types of embedding vectors for verbs, thereby constructing four distinct datasets for model training. Using verb embedding vectors as independent variables and situation type labels as the dependent variable, we conducted tenfold cross-validation with three classifiers across the four vector matrices, respectively. Through comparative analysis of experimental results, the model achieving the highest prediction accuracy was selected as the optimized model for automatic situation type identification.