esa-sceva/satcom-chunk-collection huggingface dataset:
# Determination of Fracture Properties of Concrete Using Size and Boundary Effect Models
## 2 Size and Boundary Effect Models
### [PERSON] et al. Boundary Effect Models  
#### 2.1.1 [PERSON]'s Boundary Effect Model  
The boundary effect models can characterize the effect of crack length on the failure load. Figure 1a shows a wedge-splitting (or compact tension) specimen with an initial crack length \(a_{0}\) under its failure load \(P_{max}\), and Figure 1b presents the two nominal stresses in the fracture analysis. The nominal strength \(\sigma_{N}\) of the specimen is defined without considering the existence of the initial crack \(a_{0}\), which can be calculated by the equilibrium conditions of the forces and moments [23]:  
\[\sigma_{N}=\frac{4P_{max}}{Wt}, \tag{1}\]  
where \(W\) is the sample size, \(t\) is the thickness of the specimen. \(\sigma_{n1}\) in Figure 1b represents the stress at the crack tip without considering the stress singularity, and assuming a linear distribution along the ligament, which reads [23]:  
\[\sigma_{n1}=\frac{P_{max}(4W+2a_{0})}{t(W-a_{0})^{2}}, \tag{2}\]  
where \(a_{0}\) is the initial crack length. In [PERSON]'s boundary effect model, the ratio of two nominal stresses \(\sigma_{N}\) and \(\sigma_{n1}\) is a dimensionless factor \(A_{1}(\alpha)\) depending only on the initial crack length to sample size ratio \(\alpha\) (\(\alpha=a/W\)). Figure 1: (a) A wedge-splitting specimen under its peak load and (b) the definition of two nominal stresses in [PERSON]’s boundary effect model. For wedge-splitting (compact tension) specimen, \(A_{1}(\alpha)\) can be calculated by relating Equations (1) and (2). \[A_{1}(\alpha)=\frac{\sigma_{N}}{\sigma_{n1}}=\frac{2(1-\alpha)^{2}}{2+\alpha}, \tag{3}\]  
The stress criterion adopted in [PERSON]'s boundary effect model assumes failure happens when \(\sigma_{n1}\) reaches its critical value, the material tensile strength \(f_{t}\). Thus, one can obtain the nominal strength \(\sigma_{N}\) predicted by the stress criterion by the following expression:  
\[\sigma_{N}=A_{1}(\alpha)f_{t}, \tag{4}\]  
The criterion of LEFM is directly adopted as the energy part of [PERSON]'s boundary effect model. The nominal strength predicted by the LEFM criterion can be written as a function of crack length \(a_{0}\):  
\[\sigma_{N}=\frac{K_{c}}{Y(\alpha)\sqrt{\pi a_{0}}}, \tag{5}\]  
where \(Y(\alpha)\) is a geometrical factor. For wedge-splitting and compact tension specimen, the expression of \(Y(\alpha)\) is identical [35]. This is because the geometry and loading conditions are the same for compact tension and wedge-splitting fracture tests [36]. It is given by:  
\[Y(\alpha)=\frac{(2+\alpha)(0.886+4.64\alpha-13.32\alpha^{2}+14.72\alpha^{3}-5.6\alpha^{4})}{4\sqrt{\pi a}(1-\alpha)^{3/2}}. \tag{6}\]  
To link Equations (4) and (5), a transition (or reference) crack length \(a_{\infty}^{\star}\) needs to be introduced, which reads:  
\[a_{\infty}^{\star}=\frac{1}{1.12^{2}\pi}\left(\frac{K_{c}}{f_{t}}\right)^{2}. \tag{7}\]  
The transition crack length \(a_{\infty}^{\star}\) is a material constant proportional to material's characteristic length \(l_{ch}\)[37].
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Providing Multiple QoS Constraints  
Common QoS metrics used in the QoS enable multicast protocols include available bandwidth, end-to-end delay, probability of packet loss, delay variance (jitter), life time, and link reliability. Multi-constraints QoS multicast routing can be seen as the problem of solving a multi-constraints Steiner tree. It has been shown to be a NP-Complete problem, and heuristic approaches are usually used to solve the problem. This section outlines the existing proposals dealing with the multiple-constraints QoS problem.  
In order to meet requirements (constraints) of applications, routing protocols must use QoS metrics to find QoS satisfied paths. While the number of hops is usually used for selecting routes in none-QoS routing, various metrics reflecting different application requirements are used for QoS routing. Note that QoS metrics used for selecting routes is not necessarily the same as QoS constraints. For example, stability metric can be used for selecting routes to meet the bandwidth requirement of applications. Metrics for selecting QoS paths can be classified into three categories, namely additive, multiplicative, and concave (minimum) properties. With additive metric, QoS of the whole path equals to aggregation QoS metrics of all links along the path. With multiplicative metric, QoS metric of the whole path equals to the product of QoS metrics of all links along the path. With concave metric, QoS metric of the whole path equals to the minimum QoS metrics of all links along the path. Delay, jitter, and cost are examples of additive metrics; packet loss ratio and link reliability are examples of multiplicative metrics; while available bandwidth is an example of concave metric.  
The mechanisms for dealing with multiple QoS constraints can be classified into two categories, the constructed metric technique and independent metric technique. Both techniques will be described in detail in the following subsections.
## 2 Related Work  
With their variable and unpredictable performance, networked communication systems suffer from significant bottlenecks in real-time systems: delay and jitter between nodes. The following research on the analytical study of jitter in an IP network has been reported.  
A simple analytic approximation was derived for the end-to-end delay, jitter incurred by periodic traffic with a constant packet size by [PERSON] et al. [3]. The formula is based on computing the expectation (average value) of a waiting time. The main limit of approximation lies in the assumption of independence with respect to the waiting times of two consecutive packets. This assumption holds only in light of moderate traffic conditions, and hence, the validity range of this formula is limited.  
The estimation of jitter experienced by a periodic traffic [4], Poisson arrival [5], and non-Poisson arrival first-come-first-serve (FCFS) queue with a single flow [6] has been proposed in the literature. Further, [PERSON]. et al. [7] reported a jitter analysis of a tagged stream in the presence of a background stream. They defined jitter as the percentile of the inter-arrival time of successive packets at the destination. However, the solution for jitter was obtained numerically, which introduced an error in the computation. Most of these approaches for jitter estimation are based on steady-state analysis methods (SAMs). However, the significant characteristics of jitter, such as the probability distribution, cannot be obtained by a steady-state analysis.  
In contrast to SAM-based approaches, [PERSON] et al. [8] presented an analytical expression for the end-to-end delay jitter using a transient queuing analysis. Network jitter was derived using the arrival and service process model built on empirical traffic data for a specifically tagged TCP or UDP traffic flow with background traffic. [PERSON] et al. [9] used the transient analysis method to obtain the queue length in a random time interval, which was scaled by the arrival of real-time services. The authors computed jitter values based on a queue length evolution. The length of the time interval generated by real-time services and the number of arrival packets of non-real-time services are stochastic. These random features challenge the queue-length-based jitter analysis.  
Most state-of-the-art jitter estimations and analyses have been based on mathematical hypotheses on networks and traffic, a transient analysis, and used for light to moderate network configurations. On the contrary, we formulate a probabilistic network calculus based on general traffic models with moment-generating functions, which estimates multi-hop performance without requiring assumptions beyond statistical independence and the existence of the respective MGFs.
## Materials and Methods
### Classifier and Evaluation Indicators  
LDA is one of the most popular supervised classification techniques and also a dimensionality reduction technique, which is used to find a linear transformation that will decrease the distance inside the data classes while maximizing the distance between classes [27]. It can be described by Equation (1), where \(S_{b}\) is the between-class scatter matrix, \(S_{w}\) is the within-class scatter matrix, \(w\) is the projection vector when there are only two classes, and \(T\) means the transpose of the matrix. The value of \(w\) is calculated by means of the generalized Rayleigh quotient \(\mathrm{x^{H}Ax/x^{H}Bx}\), where matrices A and B are Hermitian matrices, B is a positive-definite matrix, H is the conjugate transpose, and x is a nonzero vector. We assumed that the spectra data of each class conform to Gaussian distribution. In this way, after the LDA was used for projection, the maximum likelihood estimation could be used to calculate the mean and variance of the projection data of each class, and then the probability density function of the Gaussian distribution of that class could be obtained. When a new spectrum arrived, we could project it and then bring the projected spectrum wavelengths into the Gaussian distribution probability density function of each class to calculate the probability that belongs to this class. The class corresponding to the maximum probability is the prediction class.  
\[argmax\ J(w)=\frac{w^{T}S_{b}w}{w^{T}S_{w}w} \tag{1}\]  
The dataset was randomly shuffled and divided into a training set and a testing set in a 7:3 ratio in this study. The classification results of the testing set were evaluated with sensitivity, specificity, accuracy, and \(f1\) scores depicted by Equations (2)-(5), respectively.  
\[sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
\[specificity=\frac{TN}{FP+TN} \tag{3}\]  
\[accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{4}\]  
\[f1=\frac{2\times TP}{2\times TP+FP+FN} \tag{5}\]  
As Table 1 shows, a true positive (TP) occurs when a positive sample is correctly classified, while a true negative (TN) occurs when a negative sample is correctly classified. On the other hand, a false positive (FP) is when a negative sample is incorrectly classified, and a false negative (FN) is when a positive sample is incorrectly classified.
## 4 Evaluation
### Implementation  
We implemented MANE using PyTorch 0.3.1. Training and prediction were performed on a per-sentence level. We set the sizes of the character, morpheme tag, and NE tag embeddings to 50, 128, and 128, respectively. Subsequently, we randomly initialized and fine-tuned these embeddings. For alphabet embedding, we set the number and sizes of the CNN filters to 100 and 1, 2, 3, respectively. Next, we set the number of attention heads to 4. The training required 100 epochs and was performed by mini-batch stochastic gradient descent, based on the Adam optimizer, with a fixed learning rate of 0.001. Each mini-batch comprised 32 sentences due to our hardware limitation. The length of each sentence was fixed to 200 which was the maximum length of a sentence in the training data. For short sentences, the remainder of the input units were filled with padding.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Numbers** \\ \hline Person & 3416 \\ Location & 2611 \\ Organization & 4010 \\ Date & 2688 \\ Time & 388 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the public NE-tagged corpus.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Numbers** \\ \hline Sentences & 833,386 \\ Morphemes & 21,034,232 \\ Tags & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the 21 st century Sejong corpus.
## 3 Mathematical Formulation  
In the following, the mathematical formulation is provided to the reader. For computing the directivity and axial ratio of the 3D array, a flowchart of the methodology is depicted in Figure 2. In the following, the different blocks listed here are presented:  
* Geometry Definition: A specific 3D geometry is first created by defining the \((x,y,z)^{n}\) positions for each antenna element. Moreover, rotation angles \((\theta_{r},\varphi_{r})^{n}\) are computed for each element.
* Coordinate Transformation: The element pattern \(\underline{\tilde{E}}_{c}(\tilde{\theta},\tilde{\varphi})\) computed through full-wave simulation is rotated for each element according to the rotation angles and positions computed in the previous step.
* Array Performance Calculation: Directivity and axial ratio for the array at specific steering angle \((\theta_{s},\varphi_{s})\) are computed.
## 5 Experiments  
### Experimental Setup  
The devised experiments were carried out to evaluate the performance of the proposed system in terms of query latency, transaction throughput, resource usage and energy consumption. It is worth pointing out that the aim of the tests was not to provide an extensive analysis on every factor that impacts blockchain performance or efficiency [97; 98; 99], but to show the overall performance of the system in a realistic but limited permissioned scenario. In addition, the tests were also aimed at evaluating the performance of the system in resource-constrained devices, where power consumption is crucial [100]. Furthermore, it must be indicated that the results shown in the next sections depend on the underlying hardware and on the existing resource constraints (e.g., limitations in CPU, memory or disk I/O), which can introduce variability in performance and energy measurements. Nonetheless, in the performed experiments, thanks to averaging out the obtained measurements, only small variations were observed, which do not impact the overall tendency for each experiment (e.g., the overall tendency of the difference in performance between PoA and PoW).  
Specifically, the following parameters are evaluated in the following subsections:  
* Latency of read operations, which allow for determining how fast the developed system responds to requests such as AR verifications.
* Throughput of write operations, which enables analyzing the speed of the blockchain implementation.
* CPU usage and CPU power consumption.
* Energy consumption when deploying a node in two different resource-constrained devices.  
For the sake of brevity, the performed experiments were focused on evaluating the power consumption of the consensus protocol, which usually supposes the most energy-hungry subsystem of a DApp.  
The experiments were carried out in a local network, so it was necessary to use a client framework. Such a framework allows for configuring and for executing a node able to access the network. The used framework was Geth, which was selected due to its flexibility and good documentation. However, it is worth noting that there is previous research on other popular alternative frameworks that focus on performance and efficiency [100; 101; 102; 103; 104; 105], which are interesting for real-world applications that make use of resource-constrained devices. For example, Nimbus [106] and Reth [107] are good alternatives for green blockchains, since they provide a consensus and an execution client focused on energy efficiency and Internet of Things (IoT) devices.
# Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
## 4 Basic Idea of PU-CSL Algorithm and Efficiency Analysis
In fact, the correlation degree increases as nodes become closer to each other. However, there is also one point that needs to be clear: the proposed PU-CSL algorithm that integrally explores the correlation between source signal and SUs using CS. The correlation between source signal and SUs is the decisive factor to determine the result of the positioning. Hence we put the main focus on how to integrally explore the correlation between source signal and SUs and achieve higher localization accuracy. Figure 4 describes how the source node is linearly represented by 40 SUs which are randomly distributed in a quadratic sensing area of 100 \(\times\) 100 m\({}^{2}\). The black solid triangles in the figure are the source node, the circles and five-pointed star show the SUs, the circle represents a SU whose correlation coefficient is zero, and the five-pointed star denotes the the SUs whose correlation coefficient is nonzero. There is positive correlation between the size of pentagram and the correlation coefficient of the node. As can be seen from Figure 4, the greater the correlation coefficient of the node is, the closer the geometric position are to the source node. The SU with relatively small and even zero correlation coefficient is always far away from the source node. More importantly, in the sensing area, few SUs have a large correlation coefficient. Most of the nodes' correlation coefficients are zero or close to zero, that is, the vector that constitutes of all the correlation coefficient of SUs is sparse. The conclusion is consistent with the above theoretical analysis. Therefore, the degree of correlation between SUs and source node can be accurately reconstructed using CS theory, thus it is possible to estimate the location of the source node. Figure 3: The node localization flow chart of the primary user localization algorithm based on compressive sensing (PU-CSL).
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
### Appendix A  
The extra computational complexity put forward by ROAD PB compared to conventional pulse blanking is the sum of the computational complexity put forward by the steps to calculate ROAD value. The following steps are used to calculate ROAD value of a center sample in one OFDM symbol:  
1. The received OFDM symbol for each time epoch is considered as a one-dimensional vector. For a one-dimensional vector of size (\(1\times\text{N}\)), the absolute difference between a center sample and a receiving sample for each time epoch \(f_{d}(k)\) are calculated as in (A1) \[f_{d}(k)=|r_{k}-[r_{k-fracN-1/2},...,r_{k+frac(N-1)/2}]|\] (A1) No complex multiplications are introduced in this step. Here, the number of complex additions put forward is \(N-1\) for center sample or for one subcarrier. It is the same as that of \((2\cdot N-1)\) real multiplications. Thus, for \(N\) number of subcarriers, the number of complex additions involved in the calculation of absolute difference from a center sample \(C_{a}^{d}\) is as follows, \[C_{a}^{d}=N\cdot(N-1).\] (A2) It is the same as \(2N\cdot(2\cdot N-1)\) real additions and can be expressed as in (A3) \[R_{a}^{d}=2N\cdot(2N-1).\] (A3)
2. The difference vector (\(f_{d}(k)\)) is sorted in increasing order. \[Q(k)=sort(f_{d}(k)).\] (A4) No complex multiplications or additions are introduced in this step. For a one-dimensional vector of size N, the number of real-time additions put forward by sorting depends on the type of sorting that is used. For instance, if selective sorting is used, it introduces \(\frac{N\cdot(N-1)}{2}\) number of real-time additions for a single center sample. Thus, for \(N\) number of samples, the number of real-time additions involved in selective sorting is as in (A5) \[R_{a}^{s}=\frac{N^{2}\cdot(N-1)}{2}.\] (A5)3. The ROAD value is calculated as the sum of first \((N-1)/2\) values of \(Q(k)\) \[ROAD=\Sigma_{k=1}^{(N-1)/2}Q(k).\] (10) No complex multiplications or additions are introduced in this step. Finally, the number of real-time additions put forward by adding the first half values of the sorted output is \(\frac{(N-1)}{2}\). For \(N\) number of samples, the number of real-time additions involved is as in (11)  
\[R_{a}^{a}=\frac{N\cdot(N-1)}{2}. \tag{11}\]
## 4 Results
### SAM 2 Video Segmentation for Image Volumes  
Figure 12 shows the results of the video segmentation tests described in Section 3.5 in a box plot format. Table 6 shows the exact values of the results. Only the Coronacases and Radiopaedia datasets are shown, as the Montgomery dataset is not structured in volumes. Each Montgomery's sample is an independent X-ray, with no time relation between them. The X-axis compares the results of SAM and SAM 2 for the Jaccard index and the Dice score. While SAM needs one prompt for each slice to achieve these results, with our proposed technique, SAM 2 only needs one prompt for the whole volume. The Coronacases dataset has 10 volumes and 2156 slices with their corresponding mask. With SAM, we would need 2156 prompts to segment the whole dataset. However, with SAM 2, we only need 10 prompts, one for each volume. The Y-axis shows the values of each metric. Each box plot represents the distribution of the results for each dataset and metric. The boxes are paired per metric. The outliers are not shown in the plot. The median value is shown as a  
Figure 11: Hypothesis tests.  
solid green horizontal line inside the box, while the average is dashed. The notch around the median represents the 95% confidence interval. The state-of-the-art results are shown as a horizontal line atop each box. The delta between different model versions is shown as a green vertical dashed line between the boxes. The delta between the model and the state-of-the-art results is shown as a dashed red line at the right of each pair of boxes.  
When comparing the results in the Coronacases dataset, SAM 2's results are slightly worse than SAM's. The average Jaccard index is 0.50% lower, and the average Dice score is 0.50% lower. In both cases, this decrease is below the 0.50% threshold. However, the distribution of values with SAM 2 is more compact than with SAM.  
On the other hand, the Radiopaedia dataset shows a different behavior. Results are slightly better with SAM 2 than with SAM. The average Jaccard index is 1.64% higher, and the average Dice score is 1.31% higher. Again, the distribution of values with SAM 2 is more compact than with SAM.  
The results are shown in Figure 14. When processing the Coroncases dataset, the Jaccard index and the Dice score show an improvement of 0.60% and 0.40%, respectively. The Radiopaedia datasets result in a decrease of 0.18% and 0.16% for the Jaccard index and the Dice score, respectively. In both cases, the results are very similar to the ones obtained when using the seed prompt from the same volume. For the 2156 masks in the Coronacases dataset, using a single seed prompt would reduce the number of prompts needed to segment the volume to one. That is a 99.95% reduction in prompts needed to segment the volume. This previous test shows that the model can successfully segment all the volumes in a dataset using a single, representative seed prompt taken from one of the volumes, drastically reducing the number of prompts needed to segment the dataset (e.g., from 2156 to 10 in the Coronacases dataset).