esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis for the LLB Method  
### Analysis Model for the LLB Method  
ANSYS LS-DYNA, a general-purpose finite-element software, was utilized to simulate the cut methods. This software has been broadly used to simulate complex dynamic and non-linear problems. It provides numerous material models and equations of state (EOS) to handle various material states under various conditions. The LLB method was modelled to study the mechanism of rock fragmentation during blasting, as shown in Figure 5. An arbitrary Lagrangian-Eulerian (ALE) solver was used to couple solid (rock) and fluid (explosives) materials [22].  
The computing simulation was focused on the cut area, which has four blast holes (first round) located near a large-diameter uncharged hole, in order to investigate the initial failure behavior. The LLB method was modelled after an actual design pattern at a subway tunnel construction site. The blasting influence range was initially investigated by modelling 3000 \(\times\) 3000 \(\times\) 3000 mm (length, width, and depth, respectively) of rock materials, with 2000 \(\times\) 2000 \(\times\) 2800 mm ultimately used for simulation efficiency. The spacing between blast holes and LLB holes was 400 mm. The diameter and depth of the  
Figure 4: Schematic of the (**a**) V-cut method, (**b**) cylinder-cut method, and (**c**) LLB method.  
Figure 5: Geometry of the numerical simulation model for the LLB method: (**a**) front view and (**b**) side view.  
blast holes were 50 mm and 2200 mm, respectively, and each blast hole has explosives with a depth of 1000 mm and the remaining 1200 mm was filled with stemming materials. In addition, the LLB hole was designed with a diameter of 382 mm from the tunnel face to the end of the rock. The LLB hole was designed with a diameter of 382 mm until the end of the rock. All explosives were set to detonate simultaneously, and a non-reflecting boundary condition was set on all sides of the rock material except for the tunnel excavation face. The total number of meshes in this simulation model was 1,739,052, with a size of 20 mm.
# Malicious Traffic Detection on Tofino Using Graph Attention Model
## 1 Introduction
Through the above detection method, Maltof effectively balances performance and accuracy, realizes online detection of malicious traffic, and ensures that malicious flows can be quickly and effectively identified and processed. In general, the contributions and innovations of this paper include the following:  
(1) A CPU/Tofino collaborative detection architecture is proposed: on the Tofino side, a rule-based RF is used for rapid, parallel pre-filtering, and suspicious traffic is cloned to the CPU side for more refined EGAT inference analysis, balancing real-time performance and accuracy. (2) An efficient real-time malicious traffic detection method is proposed: in the P4-programmable switch, multiple RF rule tables are deployed to quickly identify malicious traffic; meanwhile, multi EGAT models are used to perform deeper feature aggregation and classification of suspicious flows, improving detection accuracy. (3) Through the dynamic feedback and collaborative update mechanism between the CPU and Tofino, once malicious traffic is recognized by EGAT, the switch-side blacklist can be updated immediately to drop subsequent traffic of the same type, greatly reducing the spread latency caused by false positives and false negatives. Experimental results show that this architecture clearly outperforms traditional methods in terms of real-time performance while maintaining high detection accuracy. The rest of this paper is organized as follows: Section 2 provides background and related works, including malicious traffic detection based on deep learning and P4 programmable switches. Section 3 describes the Maltot architecture and its components in detail. Section 4 presents the experimental results and analysis. Finally, Section 5 concludes the paper and discusses future work.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 4 Physical Layer Tests Analysis
### Modulation Tests  
In this subsection we compare the results of modulation tests to find the most suitable configuration. The modulation tests have been divided into two categories: (i) spread spectrum modulations, used because of their better robustness and (ii) multi-carrier modulations, applied because of their higher throughput. In this scenario, the most robust modulation is the one that maintains low BER despite adverse channel conditions; so, the priority is the quality of the data received, and not the total amount of data transmitted. The modulation with higher throughput is the one that reaches maximum bitrate maintaining a low BER; so the priority in that case is the total amount of data transmitted, maintaining a minimum BER value in the receiver.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Proposed Approach: Predictive and Proactive Scheduling  
Traditional scheduling approaches often assume static job assignments, making them vulnerable to unexpected machine failures. To address this issue, our research integrates Predictive and Proactive Scheduling strategies [11]:  
* Predictive Scheduling: We employ deep learning-based fault prediction to estimate machine failures in real-time and anticipate disruptions in scheduling.
* Proactive Scheduling: Instead of reacting to failures after they occur, our approach proactively reallocates jobs to alternative machines before failures impact the system.
# Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
###### Abstract  
It is necessary to determine the location and number of leaks in a pipeline in time to repair it, thus reducing economic losses. A multizone leakage detection method based on the YOLOv5 and OMD-ViBe algorithm is proposed to detect the metal hose's location and leakage rate. The deep learning model of YOLOv5 is used to accurately recognize the zone of the metal hose for the region of interest rectification. The multiframe averaging method is applied to construct the initial background of the video frames. The _OTSU_ algorithm based on the background difference method and the adaptive threshold of the maximum intraclass and interclass variance ratio method is used to improve the recognition rate of bubbles and reduce the influence of illumination change. In a comparison with the existing algorithms, the experimental results showed that OMD-ViBe improves the F-measure by 1.79-16.41% and the percentage of misclassification by 0.003-0.165%. Analysis of the pressure data indicated a comprehensive leakage error reduction of 1.53-25.19%, which can meet the requirements of metal hose leakage detection.  
air tightness detection; metal hose; YOLOv5; OMD-ViBe Article
# Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
## 2 Preliminaries  
### Standard Fuzzy C-Means (FCM) Algorithm  
The FCM algorithm [2] partitions a finite collection of \(N\) elements \(X=\{X_{1},X_{2},\ldots,X_{N}\}\) into a collection of \(C\) clusters with respect to some given criteria. Each element \(X_{i}\in X\), \(i=1,2,\ldots,N\), is a vector with \(d\) dimensions. We define a way to divide \(X\) into \(C\) clusters with cluster centers \(V_{1}\), \(V_{2  
\(\bullet\quad D(i,k)^{2}=\|X_{i}-V_{k}\|^{2}\) is the squared distance between the element \(X_{i}\) and the cluster center \(V_{k}\). \(\bullet\quad m\) is the fuzzification coefficient of the algorithm.  
**Summary of steps for the FCM algorithm:**  
**Input:** the dataset \(X=\{X_{1},X_{2},\ldots,X_{N}\}\), the fuzzification coefficient \(m\).  
**Output:** the partition of \(X\) into \(C\) clusters.  
**Steps:**  
* Step 1: Initialize value for \(V\), let \(l=0\), set \(\varepsilon>0\) and \(m>1\).
* Step 2: At the \(l-th\) loop, update \(U\) according to the formula: \[U(i,k)=\left(\sum\limits_{j=1}^{C}\left(\frac{D(i,k)}{D(i,j)}\right)^{\frac{2 }{m-1}}\right)^{-1}\] (2)
* Step 3: Update \(V\) for the next step \((l+1)\), according to the formula: \[V_{k}=\frac{\sum\limits_{i=1}^{N}U(i,k)^{m}X_{i}}{\sum\limits_{i=1}^{N}U(i,k) ^{m}}\] (3)
* Step 4: If \(\|V^{(l)}-V^{(l+1)}\|<\varepsilon\) then go to Step 5; otherwise, let \(l=l+1\) and return to Step 2.
* Step 5: End.
## 1 Introduction
It should also be noted that during operation, these materials are directly subjected to stresses that negatively affect their durability [9]. Piezoelectric transducers have the highest energy density of the listed transducer types. Despite this, their use in practice is limited to systems with low power consumption, often on the order of micro- and milliwatts [9]. However, this energy can be extracted from a wide range of vibration frequencies with high conversion efficiency [10]. The design of the piezoelectric transducer itself can vary. It is possible to use a layer of piezoelectric material glued to the vibrating element, together with electrodes, which allows energy to be recovered based on the surface deformation of the material of the vibrating element. A very common solution, however, is to use a simple system, which can be represented by a model, consisting of a vibrating mass, a spring and a damper connected in parallel to the base. However, the development of non-classical piezoelectric transducers, offered in the form of thin and flexible piezoelectric films, such as macro fibre composite (MFC) transducers, enables the development of energy recovery systems from mechanical vibrations towards systems in which the piezoelectric transducer is bonded directly to the vibration-deforming surface of the mechanical sub-system or integrated into its structure, as is the case with composite structures. The main aim of this research was to analyse the possibility of creating a method to help engineers design systems for energy harvesting based on the application of MFC piezoelectric transducers. A mathematical model and computer-aided tool were experimentally analyzed to prove their accuracy and to demonstrate that they can be successfully used for designing such systems with a high efficiency of energy generation, taking into account the conditions of their excitation. MFCs were introduced in 1999 and have been studied in numerous scientific papers. However, it is still worthwhile to conduct research that can develop systems based on their applications and increase their popularity. In this case, it is valuable to provide knowledge and tools that can help design practical applications of MFC-based energy harvesting systems. The main goal of this paper is to demonstrate the applicability of a relatively simple mathematical framework for modeling and analyzing the efficiency of an energy harvesting system from mechanical vibrations using MFC piezoelectric transducers. The primary aim of combining CAE, FEM, and laboratory tests with mathematical modeling using the proposed approach was to verify the accuracy of the model and prove that it can be successfully applied to engineering tasks related to the design of energy harvesters based on MFC piezoelectric transducers. The results presented show that the model is a useful tool that can assist engineers in designing energy harvesters with optimal efficiency, taking into account the conditions of their operation. These topics, as well as the details of the proposed mathematical approach, have also been partially presented in [5].
## 2 Method for RUL Prediction
### Problem Formulation  
To enable transfer learning, we employ partially labeled training data with the same distribution as the test data to build an RUL prediction model. These data are called the same-distribution training data. When the size of the training data with the same distribution is insufficient, it becomes very difficult to train a precise battery aging mapping relationship for the test data. The training data whose distribution is different from that of the test data are called diff-distribution training data. Although these data are usually ample, their RUL prediction results on test data are often not ideal due to the different distribution. Specifically, let \(X_{s}\) be the same-distribution sample space, \(X_{d}\) the diff-distribution sample space, and \(S\) the label. The concept of battery aging is a function \(f(\cdot)\) mapped from \(X\) to \(Y\), where \(X=X_{s}\cup X_{d}\), which can be expressed as follows  
\[f(\bullet):X\to Y,\ X=X_{s}\cup X_{d} \tag{2}\]  
The test dataset is represented by \(S=\left\{(x_{i}^{d})\right\}\), where \(x_{i}^{d}\in X_{s}(i=1,\ldots,k)\). \(k\) is the size of the unlabeled testing set \(S\). The training dataset \(T\subseteq\left\{X\times Y\right\}\) is split into two labeled sets, \(T_{source}\) and \(T_{target}\). \(T_{source}\) represents the \(T_{source}=\left\{(x_{i}^{d},c(x_{i}^{d}))\right\}\), where \(x_{i}^{d}\in X_{d}(i=1,\ldots,n)\). \(T_{target}\) is denoted by \(T_{target}=\left\{(x_{j}^{s},c(x_{j}^{s}))\right\}\), where \(x_{j}^{s}\in X_{s}(i=1,\ldots,m)\). \(m\) and \(n\) are the sizes of \(T_{target}\) and \(T_{source}\) separately. \(c(x)\) is the label of the data sample \(x\). The combined training set \(T=\left\{(x_{i},c(x_{i}))\right\}\) is expressed as follows  
\[x_{i}=\begin{cases}x_{i}^{d},&i=1,\ldots,n\\ x_{i}^{s},&i=n+1,\ldots,n+m\end{cases} \tag{3}\]  
\(T_{source}\) corresponds to the source domain battery data, but it is unknown which part \(T_{source}\) is beneficial for training. Thus, utilizing \(T_{target}\) to find out the useful part of \(T_{source}\), which is the running idea of the RT-TrAdaBoost algorithm. Therefore, given a small amount of labeled training data \(T_{target}\) of the same distribution, training data \(T_{source}\) of a different distribution, and unlabeled test data \(S\), the goal of RT-TrAdaBoost is to train a strong learner \(\hat{f}(\bullet)\) such that the unlabeled data. The prediction error on the set \(S\) is minimum, which can be described as follows.  
\[min\Big{|}f(x_{i})-c(x_{i})\Big{|} \tag{4}\] \[s.t.x_{i}\in X_{s}(i=1,\ldots,k)\]
# Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
###### Abstract  
This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a comprehensive view of the surgical site. The implemented algorithm consisted of a weakly supervised neural network, which was trained on 2D US images of different phantoms mimicking the imaging conditions during knee arthroscopy. Image-based classification was performed and the resulting class activation maps were used to localize the arthroscope. The localization performance was evaluated visually by three expert reviewers and by the calculation of objective metrics. Finally, the algorithm was also tested on a human cadaver knee. The algorithm achieved an average classification accuracy of 88.6% on phantom data and 83.3% on cadaver data. The localization of the arthroscope based on the class activation maps was correct in 92-100% of all true positive classifications for both phantom and cadaver data. These results are relevant because they show feasibility of automatic arthroscope localization in 3D US volumes, which is paramount to combining multiple image modalities that are available during knee arthroscopies.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 3 Results
### Comparison of Mesh and DSM Surface-Based Orthomosaics  
To evaluate to which degree the reproducibility of orthomosaics depends on the use of the underlying mesh and DSM surface in the forest environment, both surfaces have been used in otherwise identical computation workflows. Using 5 identical orthomosaic computations, 82% and 85% of the pixels were considered reproducible using the DSM and mesh, respectively. All non-reproducible pixels were found in the forest clearing areas of the images. The surrounding meadow did not differ between the orthomosaics (Figure 5). When only the forested area is considered, the number of reproducible pixels decreased to 69% in the DSM and 74% in the mesh-based processing.  
While being nearly identical in their amounts of reproducible pixels, there is still a large contrast between the orthomosaic reproducibility of the two surfaces. If a pixel in the DSM-based orthomosaics is non-reproducible between two images, there is a high probability that this pixel is non-reproducible in all images (Figure 5). The mesh-based orthomosaics show significantly more pixels that are non-reproducible between only one or two different orthomosaics, but were stable between the other computations.  
Figure 5: Comparison of DSM and mesh-based orthomosaics in terms of their reproducibility. High values (yellow) denote high reproducibility of the RGB values in this pixel over the 5 orthomosaics. (**a**) RGB orthomosaic of the forested area with the mesh as its orthorectification basis. (**b**) and (**c**) show the sum of the 10 correlation layers of the 5 identical computations using the DSM (**b**) or the mesh (**c**).
# Policy-Compliant Maximum Network Flows
## 5 Conclusions and Future Work  
Knowing the maximum throughput between two nodes in a network is key knowledge for a network operator. In practice, network policies severely complicate this task and often leave the question open-ended. We put forward a formal model with which it is possible to model real-life policy constraints, and we analyzed the impact of policies on throughput. As exact solutions are difficult to obtain, we defined a series of conditions and algorithms that allow us to calculate a sequence of increasingly tighter bounds on the exact value. These algorithms are built upon classic algorithms like Ford-Fulkerson to solve the generic maximum flow problem, which are adapted to our needs and augmented with specific functions to ease bookkeeping of additional data like transitions or the specific realization associated with the flow.  
Although the approach to specify the conditions on the augmenting paths in order to enforce their policy-compliance is reasonable in combination with Ford-Fulkerson, it requires complex formulae that express conditions on the augmenting path, end-to-end. Future work in the direction of push-relabel algorithms would solve this problem: on the basis of local conditions, it might be possible to express specific constraints that guarantee path-compliancy upon arrival of the flow.  
All code is freely available and downloadable at [[http://www.dna.idlab.ugent.be](http://www.dna.idlab.ugent.be)]([http://www.dna.idlab.ugent.be](http://www.dna.idlab.ugent.be)).  
Conceptualization and methodology: P.A., D.C. and M.P.; software, validation, formal analysis, resources, visualization, writing: P.A.; investigation, writing--review and editing: P.A., D.C. and M.P.  
This research was funded by Ghent University--imec, IOP 2016 Derudder-Pickavet.  
The authors wish to thank Ghent University--imec for the support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
Figure 4: Runtimes for the first lower bound algorithm, for random networks with up to 256 vertices.
## 4 Challenges Faced in the Application of _E. coli_ O157:H7 Phages
CRISPR-Cas systems play a significant role in limiting phage infection and proliferation as an important bacteriophage resistance mechanism [154]. By using these systems, bacteria can collect and preserve genetic material from earlier phage contacts and use this information to locate and eliminate the phage DNA when there is a contact with the same phage. Restriction-Modification (R-M) Systems: _E. coli_ O157:H7 and other organisms may have R-M systems. These systems are made up of enzymes that protect bacterial cells by breaking down foreign DNA (including phage DNA) that enters the cell. They recognize and cleave phage DNA sequences on the recognition site [155], thereby inhibiting phage genetic material from multiplying. Abortive Infection: The infected _E. coli_ strain employs abortive infection mechanisms, resulting in self-destruction prior to the completion of the phage's replication cycle [156]. This behaviour effectively inhibits the phage epidemic from spreading to neighbouring cells, thus ensuring the safety of the bacterial colony. Understanding the mechanisms of phage resistance is critical in phage therapy. The mechanisms described above are strategies adopted by numerous bacterial populations to protect themselves from phage predation, and they can differ amongst strains of _E. coli_ O157:H7. Due to the pressure exerted by phages, the pathogen can develop resistance to phages over time. As a result, phage cocktails can be used as a method to target multiple mechanisms or in combination with other treatment options to overcome bacterial resistance.
## 4 The Fast Second-Order Blind Identification (SOBI) Method for Diagonalization of Average Matrix
### The Fast SOBI Method Based on Diagonalization of the Average Matrix
#### 4.2.2 Implementation Algorithm Steps of Improved MF-SOBI Method  
The implementation algorithm flow of the improved MF-SOBI method is as follows:  
Step 1: the improved median filtering method in Section 3.2 is used to filter the sampled signal.  
Step 2: estimate the filtered sample covariance matrix \(\widehat{R}(1)\) from the \(T\) sample data. Singular value decomposition of \(\widehat{R}(1)\) is performed to obtain a whitening matrix \(\widehat{W}\). Let \(\lambda_{1},\cdots,\lambda_{n}\) represent the \(n\) largest eigenvalues, and \(h_{1},\cdots,h_{n}\) the corresponding eigenvector.  
Step 3: under the assumption of noise, the estimate of noise variance \(\overline{\sigma}^{2}\) is the average of \(m-n\) minimum eigenvalues of \(\widehat{R}(1)\). In the ideal case of no noise or white noise, the last \(m\)-\(n\) singular values of R are equal to 0.  
Step 4: fix a set of delay \(\tau\in\left\{\tau_{j}\middle|j=1,\cdots,K\right\}\), estimate the sample covariance matrix \(\widehat{R}(\tau)\), and calculate \(\left\{\widehat{R}(\tau_{j})\middle|j=1,\cdots,K\right\}\).  
Step 5: obtain the approximate diagonalizer \(\widehat{U}\) or diagonalization matrix \(\widehat{V}\) of the average matrix \(\frac{1}{K}\sum\limits_{k=1}^{K}\widehat{R}(\tau_{j})\) of the set \(\left\{\widehat{R}(\tau_{j})\middle|j=1,\cdots,K\right\}\).  
Step 6: the estimation of the source signal is \(\widehat{s}\left(t\right)=\widehat{U}^{H}\widehat{W}x(t)\) or \(\widehat{s}\left(t\right)=V^{-1}\widehat{W}x(t)\), and the estimation of the mixed matrix \(A\) is \(A=\widehat{W}^{*}\widehat{V}\).
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Single-Hop Probability of Connectivity
#### 4.2.2 Connectivity with SNR  
The inter-vehicle connectivity probability is plotted against SNR in Figure 7 for both \(\rho=0.01\) (Figure 7a) and \(\rho=0.04\) (Figure 7b). As shown in lognormal distribution plots in Figure 2 for highway environments with dominant multipaths due to shadows and obstructions, these effects are captured by Nakagami-\(m\) and Weibull fading factors, \(m,C\) respectively. Moreover, the randomly induced communication ranges are suitable to sustain connectivity between vehicles as the SNR at the receiving vehicle increases. For vehicle density at \(\rho=0.04\) in Figure 7b, the connectivity improves albeit lowest Weibull-induced connectivity (\(C=3\) due to LOS components) from communication ranges in typical urban highways characterised by many signal multipath directions and NLOS.  
Figure 6: Impact of vehicle density on connectivity probability analysis. (**a**) Connectivity probability at \(\alpha=2\). (**b**) Connectivity probability at \(\alpha=3\).  
Figure 7: Impact of SNR on connectivity probability. (**a**) Connectivity probability at \(\rho=0.01\). (**b**) Connectivity probability at \(\rho=0.04\).