esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions
## 6 Related Work
### Studies on Reducing Redundant Test Cases for SPL  
The RTS methods [4; 5; 36; 40] for SPL have been proposed to reduce redundancy of test cases for retest of a modified product family. [PERSON] et al. [36] propose an automated code-based RTS method, which reduces unnecessary testing cost by reusing the testing artifacts of one product for the other products and by avoiding the in-depth analysis of source code and test cases. [PERSON] et al. [4] selects regression test cases using a delta modelling technique and an incremental model slicing technique. [PERSON] et al. [5] use the architectural similarity between products of a product family to select and prioritize test cases for a modified product family. [PERSON] et al. [40] propose a goal-oriented test selection approach that selects test cases based on the priorities of features.  
Several other approaches [41; 42; 43; 44] have been proposed to minimize the size of test suite by removing obsolete or redundant test cases. [PERSON] et al. [41] use an integer linear programming to minimize a test suite for a product family. Their method obtains an optimal test suite that minimizes the testing costs and maximizes the testing profits. [PERSON] et al. [44] suggest a weight-based geneticalgorithm that optimizes the total execution time of a test suite and its fault detection capability. [PERSON] et al. [43] propose TITAN, which is a test suite optimization technique for highly configurable systems. Their method optimizes a test suite using both a test minimization technique and a test prioritization technique so as to achieve a high fault detection rate and a low test execution time. Another work of [PERSON] et al. [42] uses the coverage matrix of test cases and the fault detection history to identify redundant test cases that are not likely to detect faults. Their method minimizes a test suite by excluding redundant test cases from a test suite.  
The approaches discussed above reduce the cost of SPL regression testing by selecting test cases for retest or eliminating redundant test cases. However, they do not address how to efficiently execute test cases for retest. So even though these approaches are applied, SPL regression testing still may suffer from redundancy at the level of test execution. On the other hand, CPLTE addresses the challenge of reducing redundancy of test executions for individual SPL test cases.
# Numerical Investigation of Air Entrapment Dynamics for High-Speed Thermal Spraying
###### Abstract  
For thermal spraying, bubble entrapments are highly undesired, as this would lead to pores in the final coating and lower its adhesion quality. This understanding warrants an investigation of the process behind their formation. Nevertheless, the air entrapment process is difficult to study via experimental methods since molten droplets are always opaque and hard to visualize. Most numerical models are focused on air entrapment at the moment of impact, which could only explain the pores observed around the center of the splat. Here, in this paper, the air entrapment of a micron-sized molten nickel droplet impacting on a stainless-steel substrate is numerically studied. The results show that, besides the air entrapped during the high-speed impacting (impacting air bubbles/IM bubbles), bubbles may also be entrapped due to the fallback of the pointed-out finger on the edge during the spreading process (spreading air bubbles/SP bubbles). The number and size of the entrapped SP bubbles are related to the solidification rate and spreading rate. Therefore, both low (50 m/s) and high (200 m/s) impacting speeds could achieve an entrapped bubble ratio that is about 10% lower than that of a medium one (100 m/s). However, the formed coating is thick for low impacting speeds, and the low entrapped bubble ratio is obtained due to the cut-off of the peripherical fingers, which is actually unwanted.  
molten droplet; high-speed impact; entrapped bubbles; thermal spraying; solidification 2022
## 3 Problem Definition and Measurements  
The current measurement models of the digital divide and ICT skills among students were designed firstly by focusing on developed countries. Later, the models were adopted as cross-national and their assessment results have always been integrated into the authentic contexts that reflect the real students' experiences in whatever nation those models are applied in [56]. However, in any assessment of a measurement model, the goal should be to provide the real experience from the participants and the context should reflect the familiar situations that they are accustomed to. So far, it is unclear to what extent the models designed for the developed countries' contexts maintain authenticity when they are implemented in developing (low or middle-income) nations [56]. It is of value to point out that the students in those countries may have different experiences. Similarly, this is a problem that can arise when assessing any group of individuals with a socioeconomic life that is different from the others (e.g., minority groups) within any country. As an example, in 2013, the IEA International Computer and Information Literacy Study (ICILS) conducted an ICT skills assessment, the results revealed that, for Thailand and Turkey, the only countries that were assessed with lower ICT development, high school students in the first year have a very low level of ICT skills compared to all other countries that participated in the assessment [57]. In low-income countries, technology access tools at school are insufficient, making it often impossible for students to rely on school computers. Therefore, cross-national measurement models may lead to inadequate conclusions and there is a need for models that integrate developing countries' local and regional contexts. Such models could achieve cross-group (e.g., gender in socioeconomic groups), cross-country, and over-time digital divide and ICT skills comparisons [56].  
Referring to the previous study, commonly used methods to find the most influential variables and to study the digital divide are Logistic Regression (LR) and Concentration Index (CI), respectively [13; 15; 16; 58]. This paper follows a similar approach to investigate the determinants of technology fluency among the EA undergraduate students by class year (1 st year to 4 th year) using an LR based model [11; 12]. Moreover, it uses CI to model the evolution of digital inequalities [13]. A combination of LR and CI in the presence of binary variables is known to perform a better analysis of key factors and to explicitly explore the variations of inequalities among individuals [59; 60; 61; 62].
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 6 Results and Discussion  
### Experiments  
#### 6.1.1 Experiment 1: Does This Extraction Vulnerability Really Happen?  
To verify that this vulnerability actually occurs depending on the extraction algorithm, the following two experiments were conducted.  
Figure 6 shows an experiment to check if duplicate 3-bytes actually occur frequently when different extraction algorithms are applied to the same bit string by encryption algorithm. The experiment proceeds as follows. In order to continuously obtain 3-bytes of different OTP data, an arbitrary bit sequence (\(2^{25}\)-bit, or \(2^{22}\)-byte) is input to the block cipher AES [26], and a continuous cipher sequence is generated accordingly. Data that does not apply any extraction method to this string  
is called a pure cipher text sequence. In this bit string, each static/dynamic/improved algorithm is applied every 20-byte to extract 3-byte. It repeats this to accumulate 1,000,000-byte per algorithm.  
And this was done by applying NIST's statistical randomness test [27] for each data set. The experimental group is the Improved algorithm sequence, and the control groups are the pure cipher text sequence before OTP data extraction, the static algorithm sequence, and the dynamic algorithm sequence.  
The result is shown in Figure 7. All of the control group passed every statistical random number test, but the experimental group did not. Although this statistical test cannnot entirely a substitute for cryptographic analysis, it can be useful at least as a first step in determining whether a pseudo random number generator is appropriate for a particular cryptographic application. The experimental group did not pass a number of tests such as BlockFrequency, Longest Run, Fourier Transform Test, etc., and did not cross the minimum margin line to determine whether it could be used as a cryptographic pseudo random number. Therefore, it can be judged that this data set is very weak to use as a cryptographic pseudo random number.  
Figure 8 also shows the results of analyzing this data set with the hex viewer. As shown in Figure 8, it can be seen that 256 cases of 3-byte overlap frequently occur from 0x00 to 0 xFF.  
Figure 6: Experiment on whether extraction algorithm affects bit string.  
Figure 7: SP800-22 tested result of Improved extraction data.  
Figure 9 shows the result of 2-byte duplication due to the improved algorithm. Even if the first extraction information and the second one are different, if the second and third ones are the same, the remaining 2-bytes are duplicated. When the LSB 4-bit of the first extraction data satisfies a specific condition (if the extraction informations are the same) as the blue part, the rest appears as the red part.
# Thermal Conductivity and Thermal Behavior of Mortar Containing Wood Shavings and Sawdust
## 3 Results
### Thermal Conductivity Coefficient  
The thermal conductivity coefficient exhibits a linear decrease with increasing volumetric replacement levels, for both wood shavings and wood sawdust (Figure 4). This can be attributed to the fact that wood is less heat conducting compared to conventional aggregates. The irregular shape and low density of wood particles disrupt the continuity of the cement matrix, further limiting heat transfer. This insulating effect becomes more pronounced as the volume of wood replacement increases.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 5 Discussion  
The direct subsurface information of a volcanic area can be acquired from the results of analysis by drilling a core and well-logging. In the research area, we unfortunately did not have adequate drilling or logging data. From the seismic survey data, we inferred a basalt flow with strong amplitude contrast but could not determine the precise depth of the target structure. Various geophysical methods pose challenges to acquiring the basalt depth from indirect survey data. We focused on improving the velocity by using composite seismic data-processing modules. MVA yielded accurate velocity information, and it is one of the most innovative techniques among the tomography-based methods. Another option for successful velocity calculation is the full waveform inversion method [29; 30], which is based on wave equation extrapolation. The selection of a velocity-calculation method might be dependent on the available seismic data and geological interpretation.  
The present seismic data-processing flow showed increased horizontal continuity with regard to expansion phenomena, which leads to a reduced elevation range but a more complicated shape. If more dense, gridded 3D seismic exploration data can be obtained and processed, and more specific basalt eruptions can be observed in seismic images. Even in the case of a 3D survey, the large aspect of the horizontal change will be similar and the vertical depth will be similar, as demonstrated by the present results.  
One challenge that remains unresolved is the tracking of changes in the anisotropic medium. Although the basalt flow shows less anisotropy, it is necessary to accurately consider the anisotropy of the cap rocks and images because it has migrated from the eruption location. To consider the anisotropy, it is recommended to record the seismic reflection signal using a streamer for as long as possible. With large offset data, anisotropic MVA [31; 32] will be possible, enabling the boundary of the basalt flow to be defined more accurately.
## 2 Related Work
After discovering the destination node, Q-learning calculated the Q-value of the path-related parameters to evaluate the efficiency of the entire path. Q-learning enabled the fuzzy system to conserve energy and ensured good communication performance. Being highly dynamical with a variable link quality, a couple of studies [36; 37] proposed improved Q-learning-based routing protocols with adaptive learning rates. Meanwhile, others improved the exploration parameters of Q-learning to adapt to different mobile scenarios. For example, in [38], the simulated annealing (SA) optimization was used to self-adaptively control the exploration rate through the declining temperature rate. The authors of [39] utilized Q-learning to perform a joint optimization of E2E delay and energy consumption and adaptively adjusted the Q-learning parameters based on the link conditions. In addition, an exploration and exploitation mechanism was introduced to evaluate potential optimal routing paths. Since the aforementioned routing protocols based on Q-tables were not scalable and could not be adapted for large-scale networks, researchers proposed deep Q-network-based routing protocols. In [40], the authors used a DQN to understand the relationship between the best routing decisions and the localized information of the forwarding nodes.Furthermore, they utilized a deep value network (DVN) with a feedback mechanism to leverage the system dynamics in order to further increase the adaptability and efficiency of the DQN routing. The authors of [41] assigned the UAVs as relay nodes to compensate for broken links, adopted a DQN model to select the optimal decision in order to adapt to a dynamic topology and variable link conditions, and a priority queue model was designed to improve the quality-of-service (QoS) performance. In [42], the researchers proposed an adaptive and reliable routing protocol using a DQN that considered the link status when making routing decisions. The authors of [43] exploited a DQN to design a vertical routing protocol that considered residual energy and mobility rates.
# Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
## 1 Introduction
Hence, the learning of the Braille code may take too long to accomplish or to feel comfortable with due to older age. On the other hand, communication by concepts is faster than letter by letter wording, as in Braille. Thus, the learning of a small set of concepts is simpler and can always be progressively increased. This paper analyses the feasibility of communication by concepts for deaf-blind individuals through a vibrotactile system and introduces TactileCom, as a communication system of substitution based on an efferent human-machine interface (HMI) implemented as a glove. The term efferent refers to the direction of the HMI interaction: if the interface performs a stimulation function it is an efferent HMI, but if the interface performs an acquisition function it is an afferent HMI [32]. TactileCom delivers information to the user by means of vibrotactile stimulation. This information is coded in an abstract stimulus-meaning relationship [33], similar to the sign language, i.e., each stimulation pattern corresponds to a concept or idea. For instance, some information that can be relayed is \"we are going to the hospital\" or \"you will be left alone.\" This facilitates the learning of the code because deaf-blind individuals are often familiar with that kind of communication. The entire system was developed in a manner taking into account deaf-blind individuals' preferences after discussion with potential users. Two community associations of deaf-blind individuals were consulted: ASOCYL (Association of Deaf-Blind People Castilla y Leon) and ASPAS (Association of Parents and Friends of the Deaf). Although TactileCom is presented as a communication system for individuals with deaf-blindness, unimpaired subjects can also benefit from it. Its application in environments with difficult verbal communication is straightforward, e.g., noisy workplaces where people from different countries collaborate together, as in major infrastructure works or oil rigs, or for multimodal action-specific warnings [34]. The coded work instructions and coordination between the workers would be language independent. The wireless link to the HMI overcomes the noise limitations of the environment and even the lack of visual between sender and receiver.
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 4 Methodology  
### Overview  
To set up and maintain the credentials, the PKI is more efficient than the shared key, mainly for large-scale networks [9]. In a PKI, all devices have a unique pair of keys. Issuing, revoking, and updating the certificates is the responsibility of the CA. The PKI uses digital certificates to link the identities of the sensor nodes to a public key. The sensor node is being authenticated and recommended to the CA by the RA.  
To cope with the problem of availability and scalability of centralized CA in SG environments, we propose to distribute the CA among many sensor nodes elected in the network using a clustering algorithm. Each cluster consists of one CA which is its CH, at least one RA, and many Members Nodes (MN) which are ordinary nodes belonging to the cluster. A CH must be a trustful node due to the sensitivity of its role in the cluster and to prohibit malicious nodes from competing in the CH election. In addition, a CH must have the highest energy level. Moreover, it must have at least two trustful neighbors. The RA must be also a trustful node, its role is to protect the CA from malicious nodes, and in case the CH is no longer CA in its cluster, the RA will replace it. The new clustering algorithm is detailed in the following section.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 4 Results of Numerical Analysis  
### Definition and Influence of Rigid-Flexible Pile  
Under the action of lateral load, different pile foundations have different deformation characteristics (i.e., lateral displacement of pile) and failure behaviors. In foundation engineering, these are divided into rigid pile and flexible pile. There are many experts and scholars in the world who have studied and defined the discrimination methods and standards of rigid piles and flexible piles. [PERSON]. [21] summarized the displacement response at the top of the pile through elastic foundation beam (BEF) and dynamic finite element analysis; thus, the stiffness or flexibility of the pile is not related to the length of the pile, but to the ratio of the Young's modulus of the pile and soil. [PERSON] and [PERSON]. [22], through the analysis of the ratio of pile-soil stiffness and the ratio of soil weight to strength, the comparison of piles of different sizes and different types of soil was carried out, and the judgment method of rigid piles and flexible piles was summarized. [PERSON] and [PERSON] [23] adopted a set of dimensionless ratios between pile bending stiffness and soil stiffness to determine whether the pile is flexible or rigid. This method is also the most widely used and most recognized criteria at present. The method is as follows:  
\[\frac{E_{\text{P}}I_{\text{P}}}{E_{\text{s}}I_{\text{L}}^{4}}>0.208,\text{ Rigid pile}\]  
\[\frac{E_{\text{P}}I_{\text{P}}}{E_{\text{s}}I_{\text{L}}^{4}}<0.0025,\text{ Flexible pile}\]  
where \(E_{\text{P}}I_{\text{P}}\) refers to bending stiffness of the pile; \(E_{\text{s}}\) refers to Young's modulus of soil; \(I_{\text{L}}\) refers to the depth of the pile into the soil. According to the size of the large-diameter piles in the pile-bucket foundation selected in this paper, the above formula is used to calculate the ratio of the flexural stiffness of the pile to the soil stiffness, which is 0.1031, and which is between the rigid piles and flexible piles, belonging to rigid-flexible piles.
## 4 Results
### Model Evaluation
#### 4.1.3 Summary  
Table 6 shows the effectiveness of decision tree and random forest. Although the concept of decision tree is similar to that of random forest, random forest involves the idea of ensemble learning, and therefore random forest usually outperforms decision tree. Thus, we reveal the effectiveness of decision tree and random forest and also show how much improvement random forest can achieve as compared with decision tree, as shown in Table 6. We can observe that random forest can achieve about 4-5% improvement rate in terms of precision. However, the decision rules of random forest are much more complicated than those of decision tree. Since the interpretability is important for applying the learned model in a real-world approach, it is reasonable to trade such slight improvement for the significant interpretability. Furthermore, the comprehensive evaluation yields the following two conclusions:  
**Overall score** The random forest model was superior to the decision tree model across all four indicators, leading us to conclude that the random forest model was the optimal analytical model for eco-driving.  
**Scores for each energy consumption level** The scores of the indicators of low and high energy consumption were better leading to the conclusion that the model clearly discriminated the classification effects of low and high energy consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Indicator** & **Decision Tree** & **Random Forest** & **Improvement Rate** \\ \hline \multirow{4}{*}{Overall} & Accuracy & 0.821 & 0.832 & 1.34\% \\  & Precision & 0.741 & 0.778 & 4.99\% \\  & Recall & 0.719 & 0.743 & 3.34\% \\  & F1 & 0.723 & 0.758 & 4.84\% \\  & Precision & 0.88 & 0.89 & 1.14\% \\  & Recall & 0.94 & 0.94 & 0.00\% \\  & F1 & 0.91 & 0.91 & 0.00\% \\  & Precision & 0.61 & 0.63 & 3.28\% \\  & Recall & 0.5 & 0.53 & 6.00\% \\  & F1 & 0.55 & 0.58 & 5.45\% \\  & Precision & 0.79 & 0.82 & 3.80\% \\  & Recall & 0.75 & 0.76 & 1.33\% \\  & F1 & 0.77 & 0.79 & 2.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the optimal decision tree and random forest models.  
Figure 8: (a) Confusion matrix of the random forest; (b) ROC curves of the random forest.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.6 Distributed Data Analysis  
As IoT devices can send their data samples to local FNs, monitoring and anomaly detection operations can be performed in a distributed way. If unusual events or abnormal behaviors are detected in the data, faster response times can be achieved. This way, malfunctions in IoT devices can be detected and transmissions of incorrect information can be avoided in a timely manner, which can improve the network reliability.