esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 4 Design Philosophy and Challenges  
The ECT reconstruction algorithms are built on matrix multiplication. To enhance the performance of these algorithms, an efficient matrix multiplier is crucial. Commonly, there are two methods for implementing matrix multiplier, either fast processors or dedicated hardware. The implementation using the first one is slow and considered as a bottleneck in most applications. Therefore, the design of the matrix multiplier on the FPGA offers two main advantages, fast computation time and flexibility.  
The FPGA platform contains all fundamental modules such as data acquiring and processing modules as well as all mathematical processing operations needed for the reconstructed algorithm. Furthermore, the FPGA design includes modules for communicating with I/O devices such as the Wireless Base Station (WBS) and the display module. Integrating all modules on one platform realizes a low cost and small size system. Moreover, the system will attain high robustness and fault tolerance since the FPGA resources are dynamically reconfigurable.  
The System-on-Programming-Chip (SoPC) technology is applied in the proposed system. The Qsys is a powerful development tool usually used to build systems based on processors, peripherals, and memories. Utilizing the Qsys tool in defining and creating a complete system significantly reduces the design time compared to traditional and manual integration methods. A comprehensive integration between the hardware programming and software programming has been carried out in order to reduce the complexity of the system. Therefore, some complicated mathematical operations are implemented by a soft processor called the ARM processor defined in a hardware description language. This processor is implemented in Altera's FPGA platform by using the Quartus CAD system. The software package required for building and configuring this software processor is Qsys.  
There are design challenges that have to be identified before implementing the proposed system. First, studying the benefits and difficulties when dealing with a Real Time Operating System (RTOS). The RTOS can afford convenience since all of its drivers are included even they are not used in the current system. However, this occupies a lot of the available resources of the FPGA platform. Therefore, the RTOS is not deployed in the proposed system, which imposes a challenge of designing each desired driver on the chip while reducing the target chip size. Second, careful attention has to be paid to the synchronization between the designed VGA interface and both the processing unit and the memory. Lastly, applying the soft processor requires predefined libraries and modules while all the libraries and modules are designed and implemented in the proposed system since they are not existing in the embedded software.
# Structure-Aware Scheduling Methods for Scientific Workflows in Cloud
## 3 Model and Problem Formulation
### Scientific Workflows  
A workflow can be represented as a Direct acyclic graph (DAG), \(G=<V,E>\), where \(V\) is a set of tasks, and \(E\) is a set of edges that represents the data dependency constraints between the tasks. Additionally, we are given a set of VMs to execute the workflows' tasks \(VM=(vm_{1},vm_{2},\dots,vm_{m})\). For each \(v_{i}\in V\), the execution of this task must wait until receiving all of the required data from its parent tasks. The last parent task to transmit its data to \(v_{i}\) is termed the most influential parent (MIP) for \(v_{i}\). A task starts its execution by using the initial data submitted with the workflow or the intermediate data that are received from the task's parents. The time required to transmit data between any two tasks is referred to as the communication cost. In situations where the two tasks are executed on the same VM, the communication cost between these two tasks is considered zero.
# Internal Interface Diversification as a Security Measure in Sensor Networks +
## 2 Interface Diversification
### Attack Scenarios Mitigated by Diversification
#### 2.2.2 Worm Propagation  
Another potential attack scenario in the IoT environment is self-replicating and spreading malware. An example of such a worm is Mirai. Mirai turned infected devices into a botnet that can be exploited in large-scale network attacks. It scans devices for telnet, uses a hard-coded list of common credentials to access devices and starts running malicious code [4]. In the current software monoculture, this kind of worm is potentially compatible with millions of nodes, which allows it to spread uncontrollably. However, the malicious code cannot run on a diversified system even if the worm gets past the authentication. Consequently, diversification also mitigates the propagation of malicious software.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Knowledge Gap Layer (KGL)  
For the relationships in the evaluation logic where there is insufficient level of understanding (<3), index nodes are introduced at the knowledge gap layer (KGL). In the context of Bayesian networks, an index node refers to a node that serves as a discrete categorization or classification node within the network, helping to organize or reference states in the network. Index nodes, themselves, do not model uncertainty directly but, instead, categorize or partition information in a way that aids in network design. In the KGL, these nodes represent knowledge gaps and assist in modeling possible scenarios illustrating how the corresponding relationship could behave in worst-/best-case scenarios. This allows for a targeted investigation of the influence of extreme scenarios on the achievement of the EVCs. Figure 10 schematically illustrates how knowledge gaps related to individual influences are represented in their extreme scenarios. The figure shows how node I, as an index node in the KGL, can activate fundamentally different probabilities for the states (manifestation scenarios) of node A. Thereby, the causal relationships are altered fundamentally in the best-/worst-case scenario.  
Figure 10: Scenario-based activation of probabilistic assignment in the evaluation logic through index nodes of the gaps in understanding for the mapping of best- and worst-case scenarios at the level of the evaluation logic (letter A, B, C, I as place holders).  
Figure 9: Principle of detailing causal relationships according to the level of understanding. Critical factors potentially impacting the operational sequence and fulfilling a evaluation criterion are detailed until detailing is no longer possible due to knowledge gaps.  
In this way, the local, inadequately describable relationship is modeled in a way that allows the effects of these hypothetical extreme scenarios to propagate through the network. The influence strength of the knowledge gap on the EVCs depends on its position within the evaluation logic and other influences in the chain of effects through which impacts propagate to the EVCs. A knowledge gap with a minor effect on the EVCs indicates its lower significance for further PSS development. Conversely, a significant influence on the EVCs suggests that the relationship is critical for evaluation logic and that improving understanding would be beneficial for a more precise EVC evaluation.
## 2 Systematic Methodology  
This review employs a systematic approach to identify relevant literature on pile integrity testing. The process is based on the fundamental concepts of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) workflow [35]. The article selection flowchart is shown in Figure 2. It specifically includes the following steps:  
1. Problem Formulation: Formulate the research questions for this review and identify relevant keywords and phrases. This paper aims to discuss the application of NDT techniques and AI technologies in pile integrity testing.
2. Literature Search Strategy: A comprehensive literature search was performed across multiple databases, including Web of Science, Scopus, and IEEE Xplore, to ensure broad coverage of relevant studies.
3. Keywords and Search Terms: The search employed a targeted set of keywords, such as \"pile integrity testing,\" \"low-strain detection,\" \"acoustic emission,\" \"thermal integrity profiling,\" \"pile defects,\" \"NDT for piles,\" \"AI-based pile testing,\" \"Fourier transform,\" \"wavelet transform,\", and \"signal processing,\" to optimize literature retrieval accuracy.
4. Inclusion and Exclusion Criteria: Studies were selected based on their relevance to pile integrity testing, encompassing signal processing, NDT methodologies, and AI applications. Irrelevant studies were systematically excluded.
5. Literature Screening: An initial screening phase involved evaluating titles and abstracts to isolate studies focused on pile integrity testing and AI integration.
6. Full-Text Review: The full texts of the selected papers were analyzed. At this stage, the focus was on the methods, principles, signal processing, and AI applications in pile integrity testing.
7. Data Synthesis and Organization: The collected information was analyzed and structured, referencing papers classified according to the application of non-destructive testing technology in structural inspection.
8. Analysis and Evaluation: In this section, an analysis was conducted to assess the advantages and disadvantages of the various methods and technologies discussed in the selected literature. Trends, advancements, and notable findings were also identified and introduced.
9. Identification of Research Gaps: Research gaps in the field were identified during the literature review process to establish promising research directions for the future.
10. Writing and Presentation: The methods, structure, and insights from relevant review papers were applied to various sections of this review. This paper follows a well-structured format, making it highly readable.
11. References and Citations: The paper concludes with a list of references, including all sources reviewed in the paper, cited according to standard citation methods.
12. Review and Revision: This paper has undergone multiple reviews and revisions by the authors to ensure clarity, accuracy, and comprehensiveness.
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 2 Background
### Kernel Representation for Free-Breathing and Ungated Cardiac MRI Recovery  
The kernel representation [19] states that the intensity value \(x_{m}\) at location \(m\) in the image frame \(\mathbf{x}_{i}\) can be compactly represented as the linear combination of kernels:  
\[x_{m}=\sum_{n\in N_{m}}\alpha_{n}\mathcal{K}(x_{m},x_{n}), \tag{1}\]  
where \(N_{m}\) is a user pre-defined neighborhood of \(m\) and the kernel function [27]\(\mathcal{K}(x_{m},x_{n})\) is the inner product of two features:  
\[\mathcal{K}(x_{m},x_{n})=<\varphi_{m},\varphi_{n}>.\]  
The notation \(\varphi\) is used to denote the feature vector, which is obtained at each image location from the image prior \(Y\). \(\alpha_{n}\) in (1) is the kernel coefficients.  
Based on (1), we can use the following matrix form to present the free-breathing cardiac MR images in the time series:  
\[\mathbf{x}_{i}=\mathbf{K}\mathbf{\alpha}_{i},\quad i=1,\cdots,M.\]  
Here \(\mathbf{K}\) is the kernel matrix, and \(\mathbf{\alpha}_{i}\) is the frame-based kernel coefficients.
# Frequency-Separated Attention Network for Image Super-Resolution
###### Abstract
However, the absence of an effective network structure to distinguish between high- and low-frequency information in images results in outputs where parts with complex textures appear smooth. [PERSON] et al. [17] and [PERSON] et al. [18] processed high- and low-frequency features separately, but in deep convolutional networks, some informative features may be overlooked. To address these issues, we propose a Frequency-Separated Attention Network, termed FSANet, for image super-resolution. FSANet employs dense connections to fully integrate multi-level features for sustained memory, separately processes high- and low-frequency features to meet the demands of high-frequency feature processing, and utilizes channel and spatial attention to leverage informative features in both high- and low-frequency domains. In FSANet, we design an FEM for feature extraction and a Non-Linear Mapping Module (NMM) for the non-linear mapping of features from LR to HR. The FEM contains a convolutional layer and two Res ASPP Modules, expanding images into a high-dimensional feature space and increasing the receptive field, respectively. In the NMM, three FSABs are stacked, and the results of these FSABs are adaptively fused to fully leverage global multi-level features. Each FSAB employs an LF path for low-frequency features and an HF path for high-frequency features, with the LF path containing three densely connected Low-Frequency Attention Modules (LFAMs) and the HF path containing three densely connected High-Frequency Attention Modules (HFAMs). Within the HFAM, an HFAB and a Fusion Block are integrated, with the HFAB incorporating a projection error mechanism for processing high-frequency features. Similarly, the LFAM includes an LFB and a Fusion Block for processing low-frequency features. To fully utilize informative features and enhance the details in image restoration, we introduce attention mechanisms in both the LFAB and HFAB. In summary, the main contributions of the proposed image SR method are as follows:  
* We introduce the novel deep convolutional neural network FSANet for image super-resolution tasks, utilizing a densely connected structure to leverage the powerful representational capability of deep CNNs and employing a parallel branching structure to separately process high- and low-frequency features. * To further enhance the quality of the network's output images, we incorporate attention mechanisms in the LFAB and HFAB to fully exploit informative features across both channel and spatial dimensions. * Experimental results demonstrate that our proposed method achieves higher performance compared to state-of-the-art super-resolution methods.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 4 Method
### Reward Function  
The reward function uses the classic negative queue length as the reward signal, which directly reflects the congestion level on the lanes. The optimization objective is to reduce the average travel time by minimizing the queue length. Specifically, a shorter queue length implies a lower degree of congestion on the lanes, allowing vehicles to pass through intersections more smoothly and reducing waiting times.  
\[r_{i}=-\sum q(l),l\in L_{i}^{in} \tag{3}\]  
where \(q(l)\) represents the queue length for each incoming lane \(l\).  
By introducing the queue length as a negative value in the reward function, our method incentivizes the model to take actions that reduce the queue lengths on the lanes, effectively alleviating traffic congestion. This approach not only improves the passage efficiency of individual intersections but also positively impacts the flow of the entire traffic network.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### Assumptions  
As previously noted, the process of estimating the link duration in MANETs considers many factors that make building a mathematical model a complex task. Nevertheless, to simplify this process several assumptions were made. First, in this model we assumed that the distribution of the nodes in a given scenario is uniform, and all nodes move in random directions like the RWP [33], which is considered as the most widely deployed mobility model used to build and analyze simulation scenarios of MANET routing protocols. In this mobility model, the nodes move in straight paths. At the beginning of the script, each node chooses the starting location randomly. Next, it selects the destination point, so that the direction of the target, the followed distance and the node's speed are selected randomly. When the node moves closer to its destination, it keeps moving at constant speed and when it reaches the destination, it stays there for a random period of time and then selects a new destination in a continuous way, so that it will have a new direction, speed and random distance for every movement [34].  
The DSDV protocol builds its routing table using the shortest path concept, i.e., the path with the smallest number of hops. In this mathematical-based model, we assumed that the intermediate nodes along the path between the source and the destination are located on a straight path. We also assumed that the mobility scenario follows medium- to high-density networks, i.e., in such a way that each node has at least one adjacent node.
## 3 Results
### Differential and Common Mode Current Components in the Charging Path
In Figure 21 the common-mode current components through the diodes between the charging path and the generator stages are shown in light-blue colour. \(I_{\text{D1,C}}\) corresponds to the common-mode current component through the diode pair (D3, D4), and \(I_{\text{D7C}}\) to the one through the diode pair (D31, D32). The small common-mode current component through the diode pair (D7, D8) of the second left stage, \(I_{\text{D7D8,C}}\), corresponds to the difference in stored magnetic energy between the core of the leftmost current-compensated choke and the remaining ones and, hence, its different common-mode current components. Thereby, the common-mode current components through the other diodes situated in the centre of  
Figure 21: Common-mode current components through the diodes are shown in light-blue colour. Figure 22: Voltages across the capacitors C1 (V\({}_{\text{C1}}\)) and C4, C7, C10, C13, C16, C19, and C22 (V\({}_{\text{C4}},\dots,C22\)), currents through D3 (I\({}_{\text{D3}}\)) and D7 (I\({}_{\text{D7}}\)) according to Figure 3 together with the generator’s charging current \(I_{\text{ch}}\) (R\({}_{\text{D}}\) = 20 Ohm, on-time of the solid-state switches: \(t_{\text{on}}\) = 22 \(\upmu\)s). the generator, \(\mathrm{I_{D,C}}\), are almost zero. As a consequence, the current through the damping resistor shown in green colour is also almost zero during the charging phase. As soon as charging ends and the charging current falls below the amplitudes of the common-mode current components through the current-compensated chokes, the diodes start to block, and the common-mode currents through the chokes are equalised. Therefore, energy is transferred to the core of the leftmost current-compensated choke, and charging of the stage capacitor C1 continues for that moment, and a voltage drop across the damping resistor \(\mathrm{R_{D}}\) is visible. Now, the common-mode current components in all current-compensated chokes and, hence, the stored energies are equal. After charging, the common-mode component of the current commutates to the damping resistor and the energy is dissipated in \(\mathrm{R_{D}}\). As a result, the common-mode component of the current decays. In the case when the on-time of the switches is large enough so that when opening the switches, the charging current is already larger than the common-mode current component through the current-compensated chokes, the energies stored in the two leftmost cores are almost equal, as shown in Figure 15.
## 2 Materials and Methods
### Data Evaluation  
CBA pixel values were extracted from the HYFOG product for each station location and each time step. As the temporal resolution of the lidar and satellite datasets differ (lidar: 10 min frequency; CBA product: 15 min frequency), and because measurement times were not aligned, the CBA product was subsequently resampled to the temporal frequency of the lidar measurements using linear interpolation. Additionally, as the CBA product only supplies information at 3 km resolution per pixel but the topography can change significantly within one pixel in complex terrain, the height difference between the actual elevation of the lidar site and the elevation of the digital elevation model used in the HYFOG algorithm was added to the cloud base altitude. On average this difference is 47 m. Because the 12 measurement levels of the lidars were not set up to the same heights for all sites, data from fixed height ranges were binned. A height bin width of 15 m with the first bin center at 47.5 m above ground  
Figure 3: Histogram of the elevation of all sites.  
Figure 2: Locations of measurement stations.  
was chosen. Thus, the first bin stretched from 40 m to 55 m above ground, the second one from 55 m to 70 m, and so on. If at any site two measurement levels fell into the same bin, the measurement level with the height closest to the bin center was chosen, thus warranting that only one availability value per bin per site per time step was used in the analysis. Two measurement levels might fall into the same bin if the lidar is set up with a height difference between two levels smaller than the chosen 15 m bin width. This kind of set-up is done to cover the prospective hub height at a site with smaller measurement level increments. As only a few sites covered the height bin of 107.5 m, data of this bin were not representative and therefore left out of the analysis. For each height bin the mean of the 10-min availability was calculated for cloud-free and CBA \(\leq 100\) m situations.  
To get a spatial estimate for the availability, we assumed a commonly used 10-min availability threshold of 80%, which means intervals (data points) with 10-min availability below 80% are considered invalid measurements and thus not available. With the CBA \(\leq 100\) m frequencies for 2006 to 2017 (\(f_{CBA\leq 100\) m) we can calculate yearly and monthly maps of estimated availability for Germany with  
\[Availability=1-f_{CBA\leq 100\,\mathrm{m}}\]  
As can be seen in Figure 4 (marked by the arrow), the threshold is reached at about 130 m a.s.l. All higher measurement levels are also below the threshold, therefore the maps are valid for measurement heights of 130 m a.s.l. and above.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
###### Abstract  
Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall's tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall's tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure.  
prognostic; vibration; bearing; detrended fluctuation analysis; Kendall's tau; scaling exponent +
Footnote †: journal: Applied Sciences