esa-sceva/satcom-chunk-collection huggingface dataset:
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 3 System Design and Model  
This research work proposes an R-DEAR strategy for SWs in IaaS cloud environments. Figure 1 shows the workings of the proposed R-DEAR strategy. One or more users submit SWs to the workflow manager. The workflow/resource information provider (WRIP) registers the details of resources and SW requirements. It is assumed that for each computing resource, the energy consumption is predefined on the basis of tasks contained in the workflow. The workflow scheduler acquires information about the tasks of each workflow from the workflow manager. The tasks are arranged in ascending order as T1 < t2 < t3< span = \"\">... Tn, according to energy consumption. The WRIP provides information about  
\begin{table}
\begin{tabular}{c c c c c} \hline
**References** & **Strategy** & **Contribution** & **Evaluation Platform** & **Limitations** \\ \hline
[9] & QFWMS &
\begin{tabular}{c} **Provides scheduling solution** \\ based on data awareness \\ \end{tabular} & WorkflowSim & Not an energy efficient approach \\ \hline
[17] & DSB &
\begin{tabular}{c} Bag-of-tasks-based solution \\ to reduce financial cost \\ \end{tabular} & WorkflowSim & Not an energy efficient and fault tolerant approach \\ \hline \hline
[26] & PID controller &
\begin{tabular}{c} **Previous and mitigates data** \\ storage overload and \\ memory overflow \\ \end{tabular} & Activity-based simulator & Not an energy efficient approach \\ \hline
[27] & GA-PSO &
\begin{tabular}{c} Provides a solution based on \\ genetic algorithm to \\ minimize time and cost \\ \end{tabular} & CloudSim & No consideration of fault tolerance \\ \hline \hline
[35] & R-PCP &
\begin{tabular}{c} Provides a replication-based \\ scheduling solution in \\ multi-cloud environment \\ \end{tabular} & WorkflowSim & Not an energy efficient approach \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of related work.  
resources and workflows to the workflow scheduler. The available resources are listed in descending order by energy usage R1 > R2 > R3... Rn. The tasks and resource information are sent to the workflow engine by the workflow scheduler. The workflow engine starts the execution of each workflow by assigning tasks to resources based on sorted lists. The workflow engine distributes workload among available resources and communicates task and resource status to the workflow replica manager. During task execution, the workflow replica manager keeps a copy of each task and checks the resource status. If a task fails, a duplicate of the task will be sent to the resource to finish the execution. The workflow engine will compile the result after successful execution and return it to the end user.
## 2 Methodology
### Image Enhancement  
Usually, image denoising is employed to reduce image noise before image matching. However, the denoised images may suffer from relatively low image contrast when the image has only a single channel or the color change is not obvious [32]. This makes it difficult to extract feature points. To enhance the details of the denoised images and increase the number of extracted feature  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field Name** & **Description** \\ \hline ID\_Img & The ID of pre-earthquake images \\ Ref\_Lon\_Top & Top longitude of a pre-earthquake RS image \\ Ref\_Lat\_Left & Left latitude of a pre-earthquake RS image \\ Ref\_Lon\_Bottom & Bottom longitude of a pre-earthquake RS image \\ Ref\_Lat\_Right & Right latitude of a pre-earthquake RS image \\ Ref\_X & Center x (in projected coordinates) of a pre-earthquake RS image \\ Ref\_Y & Center y (in projected coordinates) of a pre-earthquake RS image \\ Ref\_Pixelsize & The spatial resolution of a pre-earthquake RS image \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information in the coordinate files of the pre-earthquake remote sensing (RS) image database.  
Figure 1: Flowchart of the proposed method.  
points, the proposed method adopted a 2% linear stretch method to enhance both the input and the reference images.  
The 2% linear stretch method is usually performed by defining a transfer function in the following form:  
\[g(x,y)=\begin{cases}c,&0\leq f(x,y)<a\\ \frac{d-c}{b=a}[f(x,y)-a]+c,&a\leq f(x,y)<b\,,\\ d,&b<f(x,y)\end{cases} \tag{1}\]  
where \(g(x,y)\) is the value of pixel \((x,y)\) after image stretching, \(f(x,y)\) is the original value of pixel \((x,y)\), and \(a\) and \(b\) are the values at 2% and 98%, respectively, of the cumulative frequency of the original image. \(c\) and \(d\) are the minimum and the maximum of the stretched image, respectively. In this work, \(c=0\), and \(d=255\).  
To test the advantage of the 2% linear stretch method, an experiment was performed using the GaoFen-1 (GF-1) multispectral image (with an 8-m resolution) and the SIFT algorithm. The original image yielded 16 matched tie points, as shown in Figure 2a. In contrast, the enhanced image obtained 119 matched tie points (Figure 2b). The experimental results show that applying the 2% linear stretching method on the images can facilitate the extraction of feature points and increase the number of matched tie points.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 3 Proposed AIER Mechanism
### AIER Mechanism
#### 3.2.2 ANN Model Training  
After the first stage is complete, we use the BPA algorithm [22] to train the ANN model. The training data require preprocessing before the model is trained. Separating the label data and feature data in the training data set, we consider Field \"C\" as the label data (in red) and the other fields as the feature data (in blue), as shown in Table 2. The feature data of each record are the inputs of a neuron model, whereas the label data of each record are used for error computation with respect to the output of a neuron model. Furthermore, we need to normalize the feature data such that their values range from 0  
Figure 8: Example of the data plane in SDN.  
Figure 7: Pseudo code of the artificial intelligence enabled routing (AIER) mechanism.  
to 1. Next, the training data set is randomly divided into a training data subset and a test data subset on the principle that the former subset is much larger than the latter subset. The training data subset is used to train the ANN model, whereas the test data subset is used to verify the accuracy of the trained ANN model. Generally, accuracy should be at least 0.8.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis
### Multipath Error Analysis Results
The autocorrelation function was distorted due to the limited bandwidth, causing the MPEE to fluctuate, and the MPEE fluctuation gave the higher maximum multipath error. The higher the chip rate, the more severe the multipath performance degradation was due to the limited bandwidth (Figure 5b). When the front-end bandwidth was 16.5 MHz, the maximum multipath error of BPSK(8) was 5.98 m, which was about six times larger than the 0.98 m when the front-end bandwidth was infinite. Figure 5c,d shows the MPEE of the sine-BOC and cosine-BOC candidates when the front-end bandwidth was 16.5 MHz, where it can be seen that the lower the chip rate, the larger the maximum multipath error. BOC\({}_{c}\)(3,3) had the smallest maximum multipath error at 4.8 m, and BOC\({}_{s}\)(3,1) had the largest maximum multipath error at 8.12 m.  
On the other hand, it is not intuitive to use the MPEE to evaluate multipath performance. This is because the MPEE is composed of two envelopes, and in some cases, the envelope heavily fluctuates. Therefore, we used a running average (RA) of MPEE to overcome this problem. The RA of the MPEE can be obtained by using the cumulative sum of the absolute envelope values for the multipath delays [45]. Figure 6 shows the RA of the MPEE corresponding to Figure 5, and Table 9 enumerates the RA values for specific multipath delays reflecting the band-limited effect. According to [45], the typical multipath delays in open and urban environments are 26 m and 51 m, respectively. When the multipath delay was 26 m, BOC\({}_{s}\)(5,2) had the lowest RA value. This means that BOC\({}_{s}\)(5,2) was the most robust to the multipath in the open  
Figure 6: Running average of the multipath error envelope of the candidates in the S-band: (**a**) \(B_{r}=\infty\); (**b–d**) \(B_{r}=16.5\) MHz. environment. In the urban environment, namely, when the multipath delay was 51 m, \(\text{BOC}_{\text{c}}\)(3,3) had the best multipath resistance performance. If the multipath delay was more than 62 m, \(\text{BOC}_{\text{c}}\)(4,4) had the best multipath performance, and its RA value was 0.37 m with a multipath delay of 400 m.
## 3 Visualization and Clustering
\begin{table}
\begin{tabular}{c l} \hline \hline
**Filters** & \multicolumn{1}{c}{**Description**} \\ \hline \multirow{4}{*}{Diacrtics filter} & \multicolumn{1}{c}{Removes all diacritical marks. Diacritical marks are signs that have been attached to a character usually to indicate distinct sound or special pronunciation. Examples of words (terms) containing diacritical marks are naive, jäger, reclame, etc. If the specific language texts are analyzed, the diacritical marks cannot be rejected because it can change the meaning of the word. For example, torn/torn (Swedish), sääari/saari (Finnish). \\ \hline \multirow{2}{*}{Number filter} & Filters all terms that consist of digits, including decimal separators ’, or ’.’ and possible signs ‘+’ or ‘-’. \\ \hline N chars filter & Filters all terms with less than the specified number N characters. \\ \hline Case converter & Converts all words to lower or upper case. \\ \hline \multirow{4}{*}{Punctuation filter} & Removes all punctuation characters of terms. \\ \hline \multirow{4}{*}{Stop words filter} & Removes all words which are contained in the specified stop word list. Often words such as ‘there’, ‘where’, ‘that’, ‘when’, etc. compose the stop word list. Not all of them are important for texts analysis. However, the common word list can depend on the domain of texts. For example, if we analyze scientific papers, the words such as ‘describe’, ‘present’, ‘new’, ‘propose’, ‘method’, etc. also do not characterize the papers and it is not purposeful to include the words into the texts dictionary. Stop words list can be adapted for any language. \\ \hline \multirow{2}{*}{Stemming algorithm} & The stemming algorithm separates the stem from the word [16]. For example, we have four words ‘accepted’, ‘accepted’, ‘acceptation’, ‘acceptance’, and ‘acceptably’. The stem of the words is ‘accept’, so only this word will be analyzed, other words are ignored. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions of filters. compare a few words in texts, and so obtained similarity results are more accurate. The main steps of n-grams usage are the same as presented in the scheme in Figure 2. We further suggest adding sorting (Figure 1) which helps to avoid a problem when the words in different texts are written in a different order. Suppose we have two n-grams 'data mining methods' and'methods of data mining'. After filtering (common words are rejected) and the sorting (ascending) step is completed, we get the same n-gram: 'data methods mining'. In the final results, we get the frequency matrix (1) where each \(x_{pl}\) will be equal to frequency of the \(l\)th n-gram in the \(p\)th text. The proposed approach to find similarity between texts can be used to detect plagiarism (see Figure 1).
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
## 4 Fatigue Assessment
### Assessment of Crack Propagation Life by Fracture Mechanics
#### 4.3.2 Weight Functions Approach  
The idea behind this technique [68; 69; 70] is to split a given random stress distribution \(\sigma(x)\) into differential pairs of unit forces opening the crack. The weight function _m(x, a)_ then describes the effect of each differential force on the crack. The stress intensity factor for a given crack length \(a\) is, thus, an integration over the crack length (see Equations (8) and (9)). This procedure can be applied for the deepest point 'a' and the surface points 'c' of a crack by application of the appropriate weight function. In [69], general forms for the weight functions for semi-elliptical surface cracks in plates with finite thickness are published (see Equations (10) and (11)). Figure 20 provides an overview on the crack geometry parameters and the stress distribution at the weld toe due to axial loading.  
\[K_{a}=\int_{0}^{a}\sigma(x)\cdot m_{a}(x,a)dx\ \ \ \ \ \ \text{for the deepest point 'a' \ of a crack} \tag{8}\]  
\[K_{c}=\int_{0}^{a}\sigma(x)\cdot m_{c}(x,a)dx\ \ \ \ \ \text{for the $ surface$ point 'c' \ of a crack} \tag{9}\]  
\[m_{a}(x,a)=\frac{2}{\sqrt{2\pi\ (a-x)}}\left[1+M_{1a}\left(1-\frac{x}{a} \right)^{\frac{1}{2}}+M_{2a}\left(1-\frac{x}{a}\right)+M_{3a}\left(1-\frac{x} {a}\right)^{\frac{3}{2}}\right] \tag{10}\]  
\[m_{c}(x,a)=\frac{2}{\sqrt{\pi x}}\left[1+M_{1c}\left(\frac{x}{a}\right)^{ \frac{1}{2}}+M_{2c}\left(\frac{x}{a}\right)+M_{3a}\left(\frac{x}{a}\right)^{ \frac{3}{2}}\right] \tag{11}\]  
The determination of the parameters \(M_{1a}\)-\(M_{3a}\) and \(M_{1c}\)-\(M_{3c}\) is covered by many publications for various crack shapes. A sound approach is provided in [69]. However, [71] offers a refined set of formulas enabling a wider range of crack proportions; therefore, these are selected for the present work.  
Figure 20: Sketch of the weld geometry including weld and crack geometry parameters.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Protocol  
In Figure 8 we present the flowchart of the proposed key exchange protocol. The protocol includes two applications that are running in parallel on the two devices involved in the key exchange, i.e., the emitter and the recorder application. Both applications are waiting for the user interaction and then they compute the share to emit between each other, i.e., \(aP\) in the case of the emitter and \(bP\) the in case of the recorder application. The emitter starts to send the share, \(aP\), on the audio channel. The receiver is recording and when the predefined start frequency is detected, the collected frequencies are extracted from the frame (for frame structure see Figure 6) and are stored into an array. The recording is stopped when the expected number of frequencies are received and the values of the acquired frequencies are out of range. Next, the recorder computes the session key based on the received \(aP\) and the calculated \(bP\). The Wi-Fi SSID and password are encrypted using the computed session key. The encrypted information and the value of \(bP\) are embedded in a QR code which is displayed on the screen. The emitter scans the QR code and extracts the share \(bP\) and the encrypted data. Based on the value of \(aP\) and the received value of \(bP\), the session key \(abP\) is computed (this session key will be used to decrypt the Wi-Fi SSID and password which are used as an example in our application).  
As stated before, in the experiments involving the head unit we also used the environment noise to obtain a seed for key generation as can be seen in the flowchart in Figure 8 marked with a dotted line to suggest this as an optional feature. To synchronize the two devices to start recording the noise, the emitter sends a predefined audio signal and both devices start recording. After finishing the recording and performing the FFT, an algorithm to extract the seed is applied. The algorithm consists in selecting only the desired number of frequencies (in our case 4) with the highest amplitudes and these frequencies will form the seed. After this, the flow continues as explained above.  
Figure 7: The audio signal in the time domain (**left**) and frequency domain (**right**) for the recorded key.
## 3 Results and Discussion
### Sensitivity and Selectivity of the ECL Biosensor
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Number** & **Add/fM (\(n=5\))** & **Found/fM** & **Recovery/\%** \\ \hline
1 & 5 & \(4.9\pm 0.13\) & 98.0 \\
2 & 10 & \(10.3\pm 0.21\) & 103.0 \\
3 & 20 & \(19.9\pm 0.36\) & 99.5 \\
4 & 50 & \(51.2\pm 0.28\) & 102.4 \\
5 & 100 & \(96.1\pm 0.62\) & 96.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The recovery of the proposed ECL genosensor in normal human serum. Figure 6: Selectivity and stability of the modified electrodes. (**A**) Selectivity for the target DNA against nDNA and sDNA was investigation: (a) blank solution (0 pM target DNA); (b) 100.0 fM nDNA; (c) 100.0 fM sDNA; and (d) 10.0 fM target DNA, the experiments were repeated 3 times (error bars = SD, \(n=3\)); (**B**) ECL profiles of the sensor for 10.0 fM target DNA with 6 continuous CV cycles.
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### Overall Architecture and Measurement Setup
#### 3.4.5 Setup 5: Redundant Transmission via 5G Mid-Band and 5G mmW Systems  
In this setup, we use two different 5G networks operating in the 3.7-3.8 GHz (5G n78 band) and the 26 GHz (5G n258 band). Please note that compared to Setup 4, we have complete 5G system redundancy with independent core networks. The replicated data sent from Switch 1 are transmitted in parallel via 5G mid-band and 5G mmW systems, as shown in Figure 9. The timestamps of the data were measured after replication and before elimination operations.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.3 SVM in Network Measurement  
SVM is a collection of supervised machine learning models with nonlinear classification (SVC) and correlation kernels for regression (SVR) [40]. This approach, nevertheless, was simple and reliable. The kernel approach is used to perform nonlinear classification.  
As a common kernel learning method, the computational objective of SVM, a popular kernel learning technique, is to create the optimal selection or line restrictions that can divide the n-dimensional space into classes so that we can insert new information points into the appropriate classification without overstretching. The hyperplane is the name given to this optimal choice limit [41]. SVM maps the sample vector to the high-bit space, represents each sample data point in the space to distinguish distinct sample points as much as possible, and identifies the hyperplane that best separates the two types of data to maximize the distance between each classification and the hyperplane. The SVM's classification error decreases as the distance increases. The SVM is frequently employed in binary classification issues because of its strong classification capabilities.  
[PERSON] et al. [42] proposed a throughput estimation tool based on a support vector machine that forecasts end-to-end throughput using several stream-level features as input. The measurement accuracy, when paired with the prior file transfer and basic path attributes, was nearly three times greater than it was with the history-based method. This tool has not been validated in a big flow and scientific traffic environment; instead, it has only been evaluated on an artificial network track, taking only the unique network path into account. SVM differs from conventional statistical methods in that it essentially does not use probability measures or the law of large numbers. As a result, it considerably simplifies typical classification and regression issues. Similarly to this, utilizing SVM to deploy huge training samples is challenging. When the number is large, it takes up a lot of computer memory and processing time, and when solving the multiclassification problem, there is also the issue of classification accuracy.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 3 Proposed Scheme  
We propose a privacy-preserved scheme that supports different privacy-preserved access rights in a single image based on the difficulty of solving the factorization problem and the discrete logarithm problem. As shown in Figure 1, the image manager can formulate the access policy for specific users in the initialization phase. According to the access policy, specific users can read the original part of authorized blocks in a single image. To prevent private content from being exposed to unauthorized access, the image manager first encrypts the original image in the image protection phase before sending it to the cloud server. The protected images with the access policy will be uploaded to the cloud server. When a user performs image retrieval, the cloud server will provide the user with corresponding authorized images and authentication information according to the access policy. In the image management method, the authorized user can compute the decrypted keys to recover the original content of image blocks. The proposed schemes consisting of an image protection method and an image management method are described as follows.
# Mechanical and Energy Evolution Characteristics of Sandstone under True Triaxial Cyclic Loading
## 1 Introduction
Whether the rock damage and failure laws obtained through a conventional triaxial or uniaxial test are applicable to mining rock masses under the true triaxial state remains to be determined. So far, many scholars have gradually begun studying the mechanical rock properties and energy evolution law in the true triaxial state. For example, [PERSON] [17] carried out experimental studies on the failure effects of granite, limestone, and dolomite under different conditions, and found that had a significant effect on brittle materials. [PERSON] et al. [18] studied the failure behavior of sandstone in different types of triaxial compression tests and simulated the internal fracture mode of rock after failure by PFC3D. Finally, they proved that the grain shape had a great influence on micromechanics. Based on uniaxial tension, uniaxial compression, and biaxial compression tests, [PERSON] [19] proposed a new strength criterion for rock under true triaxial stress conditions, which could help reduce the effort required for experimental research and improve the mining efficiency of underground engineering. [PERSON] et al. [20] used the improved original Mogi-type testing apparatus to study the deformation and brittle failure behavior of hard rock and the cause of rockburst. [PERSON] et al. [21] explored the deformation, energy, and damage evolution law of marble during cyclic loading through true triaxial cyclic loading and unloading tests. [PERSON] et al. [22] investigated the relationship between rock properties and cumulative damage and their changing rules via cyclic loading and unloading tests on rocks with different properties. However, only a few studies have been conducted on sandstone's mechanical characteristics and energy evolution under true triaxial cyclic loading, but the influence of intermediate principal stress on rock mechanical behavior in the true triaxial state is particularly important. Therefore, this experimental research examines the mechanical and energy evolution characteristics of sandstone under different intermediate principal stresses, explores the influence of axial circulation on the deformation, energy, and damage of sandstone under different intermediate principal stresses, and reveals the mechanical characteristics, energy evolution, and damage deformation law of sandstone under true triaxial cyclic disturbance.